tag:blogger.com,1999:blog-25503860663855820832024-02-20T09:44:31.816-08:00R for Public HealthSlawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.comBlogger31125tag:blogger.com,1999:blog-2550386066385582083.post-55976597154010667192015-10-27T20:10:00.001-07:002016-10-13T08:25:08.812-07:00Mapping with ggplot: Create a nice choropleth map in R<div height="1050">
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e0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v%2BPTmTi3E%2FglC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO%2BYOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr%2BH1jR7km9vcvqN9657vffefkv%2BfKxge1X%2B7RdjYUPIESN7gTvRkB%2FRMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F%2BUX4%2BWKS4iBTP0IWxGtZgOdMaV4KTayqHQF%2FVihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g%2FqRCuy3vmQ3W9Vu8qbVbP%2BSzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB%2BLopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9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%2FTAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAA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%3D%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIGhvcml6LWFkdi14PSIwIiAvPgo8Z2x5cGggaG9yaXotYWR2LXg9IjQwMCIgLz4KPGdseXBoIHVuaWNvZGU9IiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ4OyIgZD0iTTc1MyA2NDFxNSAtMSAxNC41IC00LjV0MzYgLTE1LjV0NTAuNSAtMjYuNXQ1My41IC00MHQ1MC41IC01NC41dDM1LjUgLTcwdDE0LjUgLTg3cTAgLTY3IC0yNy41IC0xMjUuNXQtNzEuNSAtOTcuNXQtOTguNSAtNjYuNXQtMTA4LjUgLTQwLjV0LTEwMiAtMTNoLTUwMHY4OXE0MSA3IDcwLjUgMzIuNXQyOS41IDY1LjV2ODI3cTAgMjQgLTAuNSAzNHQtMy41IDI0dC04LjUgMTkuNXQtMTcgMTMuNXQtMjggMTIuNXQtNDIuNSAxMS41djcxIGw0NzEgLTFxNTcgMCAxMTUuNSAtMjAuNXQxMDggLTU3dDgwLjUgLTk0dDMxIC0xMjQuNXEwIC01MSAtMTUuNSAtOTYuNXQtMzggLTc0LjV0LTQ1IC01MC41dC0zOC41IC0zMC41ek00MDAgNzAwaDEzOXE3OCAwIDEzMC41IDQ4LjV0NTIuNSAxMjIuNXEwIDQxIC04LjUgNzAuNXQtMjkuNSA1NS41dC02Mi41IDM5LjV0LTEwMy41IDEzLjVoLTExOHYtMzUwek00MDAgMjAwaDIxNnE4MCAwIDEyMSA1MC41dDQxIDEzMC41cTAgOTAgLTYyLjUgMTU0LjUgdC0xNTYuNSA2NC41aC0xNTl2LTQwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkxOyIgZD0iTTM5IDU4Mmw1MjIgNDI3cTE2IDEzIDI3LjUgOHQxMS41IC0yNnYtMjkxaDU1MHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC01NTB2LTI5MXEwIC0yMSAtMTEuNSAtMjZ0LTI3LjUgOGwtNTIyIDQyN3EtMTYgMTMgLTE2IDMydDE2IDMyeiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwOTI7IiBkPSJNNjM5IDEwMDlsNTIyIC00MjdxMTYgLTEzIDE2IC0zMnQtMTYgLTMybC01MjIgLTQyN3EtMTYgLTEzIC0yNy41IC04dC0xMS41IDI2djI5MWgtNTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDU1MHYyOTFxMCAyMSAxMS41IDI2dDI3LjUgLTh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MzsiIGQ9Ik02ODIgMTE2MWw0MjcgLTUyMnExMyAtMTYgOCAtMjcuNXQtMjYgLTExLjVoLTI5MXYtNTUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djU1MGgtMjkxcS0yMSAwIC0yNiAxMS41dDggMjcuNWw0MjcgNTIycTEzIDE2IDMyIDE2dDMyIC0xNnoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTAxOyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek03MDQgOTAwaC0yMDhxLTIwIDAgLTMyIC0xNC41dC04IC0zNC41bDU4IC0zMDJxNCAtMjAgMjEuNSAtMzQuNSB0MzcuNSAtMTQuNWg1NHEyMCAwIDM3LjUgMTQuNXQyMS41IDM0LjVsNTggMzAycTQgMjAgLTggMzQuNXQtMzIgMTQuNXpNNjc1IDQwMGgtMTUwcS0xMCAwIC0xNy41IC03LjV0LTcuNSAtMTcuNXYtMTUwcTAgLTEwIDcuNSAtMTcuNXQxNy41IC03LjVoMTUwcTEwIDAgMTcuNSA3LjV0Ny41IDE3LjV2MTUwcTAgMTAgLTcuNSAxNy41dC0xNy41IDcuNXoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTI2OyIgZD0iTTYwMCAxMTAwaDI1MHEyMyAwIDQ1IC0xNi41dDM4IC00MC41bDIzOCAtMzQ0cTI5IC0yOSAyOSAtNzR2LTEwMHEwIC00NCAtMzAgLTg0LjV0LTcwIC00MC41aC0zMjhxMjggLTExOCAyOCAtMTI1di0xNTBxMCAtNDQgLTMwIC04NC41dC03MCAtNDAuNWgtNTBxLTI3IDAgLTUxLjUgMjB0LTM3LjUgNDhsLTk2IDE5OGwtMTQ1IDE5NnEtMjAgMjcgLTIwIDYzdjQwMHEwIDM5IDI3LjUgNTd0NzIuNSAxOGg2MXExMjQgMTAwIDEzOSAxMDB6IE01MCAxMDAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNTAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djUwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTYzNiAxMDAwbC0xMzYgLTEwMGgtMTAwdi0zNzVsMTUwIC0yMTNsMTAwIC0yMTJoNTB2MTc1bC01MCAyMjVoNDUwdjEyNWwtMjUwIDM3NWgtMjE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxMjc7IiBkPSJNMzU2IDg3M2wzNjMgMjMwcTMxIDE2IDUzIC02bDExMCAtMTEycTEzIC0xMyAxMy41IC0zMnQtMTEuNSAtMzRsLTg0IC0xMjFoMzAycTg0IDAgMTM4IC0zOHQ1NCAtMTEwdC01NSAtMTExdC0xMzkgLTM5aC0xMDZsLTEzMSAtMzM5cS02IC0yMSAtMTkuNSAtNDF0LTI4LjUgLTIwaC0zNDJxLTcgMCAtOTAgODF0LTgzIDk0djUyNXEwIDE3IDE0IDM1LjV0MjggMjguNXpNNDAwIDc5MnYtNTAzbDEwMCAtODloMjkzbDEzMSAzMzkgcTYgMjEgMTkuNSA0MXQyOC41IDIwaDIwM3EyMSAwIDMwLjUgMjV0MC41IDUwdC0zMSAyNWgtNDU2aC03aC02aC01LjV0LTYgMC41dC01IDEuNXQtNSAydC00IDIuNXQtNCA0dC0yLjUgNC41cS0xMiAyNSA1IDQ3bDE0NiAxODNsLTg2IDgzek01MCA4MDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NTAwIHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEyODsiIGQ9Ik00NzUgMTEwM2wzNjYgLTIzMHEyIC0xIDYgLTMuNXQxNCAtMTAuNXQxOCAtMTYuNXQxNC41IC0yMHQ2LjUgLTIyLjV2LTUyNXEwIC0xMyAtODYgLTk0dC05MyAtODFoLTM0MnEtMTUgMCAtMjguNSAyMHQtMTkuNSA0MWwtMTMxIDMzOWgtMTA2cS04NSAwIC0xMzkuNSAzOXQtNTQuNSAxMTF0NTQgMTEwdDEzOCAzOGgzMDJsLTg1IDEyMXEtMTEgMTUgLTEwLjUgMzR0MTMuNSAzMmwxMTAgMTEycTIyIDIyIDUzIDZ6TTM3MCA5NDVsMTQ2IC0xODMgcTE3IC0yMiA1IC00N3EtMiAtMiAtMy41IC00LjV0LTQgLTR0LTQgLTIuNXQtNSAtMnQtNSAtMS41dC02IC0wLjVoLTZoLTYuNWgtNmgtNDc1di0xMDBoMjIxcTE1IDAgMjkgLTIwdDIwIC00MWwxMzAgLTMzOWgyOTRsMTA2IDg5djUwM2wtMzQyIDIzNnpNMTA1MCA4MDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjUgdjUwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEyOTsiIGQ9Ik01NTAgMTI5NHE3MiAwIDExMSAtNTV0MzkgLTEzOXYtMTA2bDMzOSAtMTMxcTIxIC02IDQxIC0xOS41dDIwIC0yOC41di0zNDJxMCAtNyAtODEgLTkwdC05NCAtODNoLTUyNXEtMTcgMCAtMzUuNSAxNHQtMjguNSAyOGwtOSAxNGwtMjMwIDM2M3EtMTYgMzEgNiA1M2wxMTIgMTEwcTEzIDEzIDMyIDEzLjV0MzQgLTExLjVsMTIxIC04NHYzMDJxMCA4NCAzOCAxMzh0MTEwIDU0ek02MDAgOTcydjIwM3EwIDIxIC0yNSAzMC41dC01MCAwLjUgdC0yNSAtMzF2LTQ1NnYtN3YtNnYtNS41dC0wLjUgLTZ0LTEuNSAtNXQtMiAtNXQtMi41IC00dC00IC00dC00LjUgLTIuNXEtMjUgLTEyIC00NyA1bC0xODMgMTQ2bC04MyAtODZsMjM2IC0zMzloNTAzbDg5IDEwMHYyOTNsLTMzOSAxMzFxLTIxIDYgLTQxIDE5LjV0LTIwIDI4LjV6TTQ1MCAyMDBoNTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTUwMCBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djEwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzMDsiIGQ9Ik0zNTAgMTEwMGg1MDBxMjEgMCAzNS41IDE0LjV0MTQuNSAzNS41djEwMHEwIDIxIC0xNC41IDM1LjV0LTM1LjUgMTQuNWgtNTAwcS0yMSAwIC0zNS41IC0xNC41dC0xNC41IC0zNS41di0xMDBxMCAtMjEgMTQuNSAtMzUuNXQzNS41IC0xNC41ek02MDAgMzA2di0xMDZxMCAtODQgLTM5IC0xMzl0LTExMSAtNTV0LTExMCA1NHQtMzggMTM4djMwMmwtMTIxIC04NHEtMTUgLTEyIC0zNCAtMTEuNXQtMzIgMTMuNWwtMTEyIDExMCBxLTIyIDIyIC02IDUzbDIzMCAzNjNxMSAyIDMuNSA2dDEwLjUgMTMuNXQxNi41IDE3dDIwIDEzLjV0MjIuNSA2aDUyNXExMyAwIDk0IC04M3Q4MSAtOTB2LTM0MnEwIC0xNSAtMjAgLTI4LjV0LTQxIC0xOS41ek0zMDggOTAwbC0yMzYgLTMzOWw4MyAtODZsMTgzIDE0NnEyMiAxNyA0NyA1cTIgLTEgNC41IC0yLjV0NCAtNHQyLjUgLTR0MiAtNXQxLjUgLTV0MC41IC02di01LjV2LTZ2LTd2LTQ1NnEwIC0yMiAyNSAtMzF0NTAgMC41dDI1IDMwLjUgdjIwM3EwIDE1IDIwIDI4LjV0NDEgMTkuNWwzMzkgMTMxdjI5M2wtODkgMTAwaC01MDN6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzMTsiIGQ9Ik02MDAgMTE3OHExMTggMCAyMjUgLTQ1LjV0MTg0LjUgLTEyM3QxMjMgLTE4NC41dDQ1LjUgLTIyNXQtNDUuNSAtMjI1dC0xMjMgLTE4NC41dC0xODQuNSAtMTIzdC0yMjUgLTQ1LjV0LTIyNSA0NS41dC0xODQuNSAxMjN0LTEyMyAxODQuNXQtNDUuNSAyMjV0NDUuNSAyMjV0MTIzIDE4NC41dDE4NC41IDEyM3QyMjUgNDUuNXpNOTE0IDYzMmwtMjc1IDIyM3EtMTYgMTMgLTI3LjUgOHQtMTEuNSAtMjZ2LTEzN2gtMjc1IHEtMTAgMCAtMTcuNSAtNy41dC03LjUgLTE3LjV2LTE1MHEwIC0xMCA3LjUgLTE3LjV0MTcuNSAtNy41aDI3NXYtMTM3cTAgLTIxIDExLjUgLTI2dDI3LjUgOGwyNzUgMjIzcTE2IDEzIDE2IDMydC0xNiAzMnoiIC8%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTkwOyIgZD0iTTIwMCAxMTAwaDcwMHExMjQgMCAyMTIgLTg4dDg4IC0yMTJ2LTUwMHEwIC0xMjQgLTg4IC0yMTJ0LTIxMiAtODhoLTcwMHEtMTI0IDAgLTIxMiA4OHQtODggMjEydjUwMHEwIDEyNCA4OCAyMTJ0MjEyIDg4ek0xMDAgOTAwdi03MDBoOTAwdjcwMGgtOTAwek05MDAgMzAwaC03MDB2NTAwaDcwMHYtNTAwek04MDAgNzAwaC0xMzBxLTM4IDAgLTY2LjUgLTQzdC0yOC41IC0xMDh0MjcgLTEwN3Q2OCAtNDJoMTMwdjMwMHpNMzAwIDcwMHYtMzAwIGgxMzBxNDEgMCA2OCA0MnQyNyAxMDd0LTI4LjUgMTA4dC02Ni41IDQzaC0xMzB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE5MTsiIGQ9Ik0yMDAgMTEwMGg3MDBxMTI0IDAgMjEyIC04OHQ4OCAtMjEydi01MDBxMCAtMTI0IC04OCAtMjEydC0yMTIgLTg4aC03MDBxLTEyNCAwIC0yMTIgODh0LTg4IDIxMnY1MDBxMCAxMjQgODggMjEydDIxMiA4OHpNMTAwIDkwMHYtNzAwaDkwMHY3MDBoLTkwMHpNNTAwIDcwMGgtMjAwdi0xMDBoMjAwdi0zMDBoLTMwMHYxMDBoMjAwdjEwMGgtMjAwdjMwMGgzMDB2LTEwMHpNOTAwIDMwMGgtMTAwdjQwMGgtMTAwdjEwMGgyMDB2LTUwMHogTTcwMCAzMDBoLTEwMHYxMDBoMTAwdi0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE5MjsiIGQ9Ik0yMDAgMTEwMGg3MDBxMTI0IDAgMjEyIC04OHQ4OCAtMjEydi01MDBxMCAtMTI0IC04OCAtMjEydC0yMTIgLTg4aC03MDBxLTEyNCAwIC0yMTIgODh0LTg4IDIxMnY1MDBxMCAxMjQgODggMjEydDIxMiA4OHpNMTAwIDkwMHYtNzAwaDkwMHY3MDBoLTkwMHpNMzAwIDcwMGgyMDB2LTQwMGgtMzAwdjUwMGgxMDB2LTEwMHpNOTAwIDMwMGgtMTAwdjQwMGgtMTAwdjEwMGgyMDB2LTUwMHpNMzAwIDYwMHYtMjAwaDEwMHYyMDBoLTEwMHogTTcwMCAzMDBoLTEwMHYxMDBoMTAwdi0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE5MzsiIGQ9Ik0yMDAgMTEwMGg3MDBxMTI0IDAgMjEyIC04OHQ4OCAtMjEydi01MDBxMCAtMTI0IC04OCAtMjEydC0yMTIgLTg4aC03MDBxLTEyNCAwIC0yMTIgODh0LTg4IDIxMnY1MDBxMCAxMjQgODggMjEydDIxMiA4OHpNMTAwIDkwMHYtNzAwaDkwMHY3MDBoLTkwMHpNNTAwIDUwMGwtMTk5IC0yMDBoLTEwMHY1MGwxOTkgMjAwdjE1MGgtMjAwdjEwMGgzMDB2LTMwMHpNOTAwIDMwMGgtMTAwdjQwMGgtMTAwdjEwMGgyMDB2LTUwMHpNNzAxIDMwMGgtMTAwIHYxMDBoMTAwdi0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE5NDsiIGQ9Ik02MDAgMTE5MXExMjAgMCAyMjkuNSAtNDd0MTg4LjUgLTEyNnQxMjYgLTE4OC41dDQ3IC0yMjkuNXQtNDcgLTIyOS41dC0xMjYgLTE4OC41dC0xODguNSAtMTI2dC0yMjkuNSAtNDd0LTIyOS41IDQ3dC0xODguNSAxMjZ0LTEyNiAxODguNXQtNDcgMjI5LjV0NDcgMjI5LjV0MTI2IDE4OC41dDE4OC41IDEyNnQyMjkuNSA0N3pNNjAwIDEwMjFxLTExNCAwIC0yMTEgLTU2LjV0LTE1My41IC0xNTMuNXQtNTYuNSAtMjExdDU2LjUgLTIxMSB0MTUzLjUgLTE1My41dDIxMSAtNTYuNXQyMTEgNTYuNXQxNTMuNSAxNTMuNXQ1Ni41IDIxMXQtNTYuNSAyMTF0LTE1My41IDE1My41dC0yMTEgNTYuNXpNODAwIDcwMGgtMzAwdi0yMDBoMzAwdi0xMDBoLTMwMGwtMTAwIDEwMHYyMDBsMTAwIDEwMGgzMDB2LTEwMHoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjQ4OyIgZD0iTTYwMCAxMTAwaDE1MHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xNTB2LTEwMGg0NTBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtOTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY0MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDM1MHYxMDBoLTE1MHEtMjEgMCAtMzUuNSAxNC41IHQtMTQuNSAzNS41djQwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoMTUwdjEwMGgxMDB2LTEwMHpNNDAwIDEwMDB2LTMwMGgzMDB2MzAwaC0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI0OTsiIGQ9Ik0xMjAwIDBoLTEwMHYxMjAwaDEwMHYtMTIwMHpNNTUwIDExMDBoNDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTQwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNjAwIDEwMDB2LTMwMGgzMDB2MzAwaC0zMDB6TTUwIDUwMGg5MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMCBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTkwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTEwMGgtNDAwdjEwMHE0IDAgMTEgMC41dDI0IDN0MzAgN3QyNCAxNXQxMSAyNC41djU1MGgtMTAwcS0yNSAwIC0zNyAtMC41dC0zMSAtMnQtMjguNSAtNS41dC0yM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<script src="data:application/x-javascript;base64,/*!
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<p>I was working on making a map in R recently and after an extensive search online, I found a hundred different ways to do it and yet each way didn’t work quite right for my data and what I wanted to do. Eventually, I found a way to make it work after pulling together code from <a href="http://blog.revolutionanalytics.com/2009/11/choropleth-challenge-result.html">this blog</a>, <a href="http://www.kevjohnson.org/making-maps-in-r/">this blog</a>, and <a href="http://stackoverflow.com/questions/11052544/convert-map-data-to-data-frame-using-fortify-ggplot2-for-spatial-objects-in-r">this stack overflow page</a>. Combining all of those sources, along with a good amount of trial and error, has led to this blog post.</p>
<p>This post shows how to use ggplot to map a choropleth map from shapefiles, as well as change legend attributes and scale, color, and titles, and finally export the map into an image file.</p>
<div id="preparing-the-data" class="section level3">
<h3>Preparing the data</h3>
<p>We need a number of packages to make this work, as you can see below so make sure to install and load those. We load in shapefiles using the <strong>readShapeSpatial()</strong> function from the maptools library. A shapefile is a geospatial vector data storage format for storing map attributes like latitude and longitude. You can download lots of shape files for free! For example, for any country in the world you can download shape files <a href="http://www.diva-gis.org/gdata">here</a>. Or just google it.</p>
<p>My project is to graph some data on India so I have downloaded a shapefile with state boundaries of India and I will now load that into R.</p>
<pre class="r"><code>library(ggplot2)
library(maptools)
library(rgeos)
library(Cairo)
library(ggmap)
library(scales)
library(RColorBrewer)
set.seed(8000)
##set directory to the folder where the shapefile is, then input shapefile
setwd("/Users/srokicki/Dropbox/3ie Replication Project/Analysis/Shapefiles/IND_adm/")
states.shp <- readShapeSpatial("IND_adm1.shp")
class(states.shp)</code></pre>
<pre><code>## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"</code></pre>
<pre class="r"><code>names(states.shp)</code></pre>
<pre><code>## [1] "ID_0" "ISO" "NAME_0" "ID_1" "NAME_1" "TYPE_1"
## [7] "ENGTYPE_1" "NL_NAME_1" "VARNAME_1"</code></pre>
<p>The class of the loaded shapefiles are of a spatial class, and we can see what is in it by checking its names. You can print out these components to see what they are. For example, let’s print out the state names like this:</p>
<pre class="r"><code>print(states.shp$NAME_1)</code></pre>
<pre><code>## [1] Andaman and Nicobar Andhra Pradesh Arunachal Pradesh
## [4] Assam Bihar Chandigarh
## [7] Chhattisgarh Dadra and Nagar Haveli Daman and Diu
## [10] Delhi Goa Gujarat
## [13] Haryana Himachal Pradesh Jammu and Kashmir
## [16] Jharkhand Karnataka Kerala
## [19] Lakshadweep Madhya Pradesh Maharashtra
## [22] Manipur Meghalaya Mizoram
## [25] Nagaland Orissa Puducherry
## [28] Punjab Rajasthan Sikkim
## [31] Tamil Nadu Telangana Tripura
## [34] Uttar Pradesh Uttaranchal West Bengal
## 36 Levels: Andaman and Nicobar Andhra Pradesh Arunachal Pradesh ... West Bengal</code></pre>
<p>Now we’ll get the data we want to plot on the map. Here I’ll just make the data up, but you can import a csv of it or extract it from your model estimates. The important thing is that the id numbers are the same in the shape file as in your data you want to plot, because that will be necessary for them to merge properly (you could also merge by the name itself).</p>
<pre class="r"><code>##create (or input) data to plot on map
num.states<-length(states.shp$NAME_1)
mydata<-data.frame(NAME_1=states.shp$NAME_1, id=states.shp$ID_1, prevalence=rnorm(num.states, 55, 20))
head(mydata)</code></pre>
<pre><code>## NAME_1 id prevalence
## 1 Andaman and Nicobar 1 80.84018
## 2 Andhra Pradesh 2 36.94596
## 3 Arunachal Pradesh 3 40.57686
## 4 Assam 4 67.05668
## 5 Bihar 5 67.23842
## 6 Chandigarh 6 44.20251</code></pre>
<p><strong>Important Note: ID_1</strong> is the ID that uniquely identifies that state in this shapefile so that is what I’m using in my prevalence data and that is what I use when I fortify in the next paragraph. You should look at your shapefiles and determine what ID uniquely determines the data and use that (for example, it may be <strong>ID_2</strong>). This ID must also be the SAME ID as in the mapping data (what is called here mydata). Please do this to check:</p>
<pre class="r"><code>print(mydata$id)</code></pre>
<pre><code>## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30 31 32 33 34 35 36</code></pre>
<pre class="r"><code>print(states.shp$ID_1)</code></pre>
<pre><code>## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30 31 32 33 34 35 36</code></pre>
<p><strong>If those are not exactly the same (and unique), you will encounter an error later on. Please change your mydata$id to match the states.shp ID you are using.</strong></p>
<p>Now we need to merge the shapefile and the dataset together. First, we <strong>fortify()</strong> the shapefile (a function of ggplot) to get it into a dataframe. We need to include a region identifier so that ggplot keeps that id when it turns it into a dataframe; otherwise it will make it up and it will not be possible to merge properly. This should be the same region identifier as what is in your prevalence data (or that data you want to map) so that later this can be merged properly.s</p>
<pre class="r"><code>#fortify shape file to get into dataframe
states.shp.f <- fortify(states.shp, region = "ID_1")
class(states.shp.f)</code></pre>
<pre><code>## [1] "data.frame"</code></pre>
<pre class="r"><code>head(states.shp.f)</code></pre>
<pre><code>## long lat order hole piece id group
## 1 92.89889 12.91583 1 FALSE 1 1 1.1
## 2 92.89917 12.91555 2 FALSE 1 1 1.1
## 3 92.89943 12.91556 3 FALSE 1 1 1.1
## 4 92.90000 12.91500 4 FALSE 1 1 1.1
## 5 92.90028 12.91528 5 FALSE 1 1 1.1
## 6 92.90056 12.91528 6 FALSE 1 1 1.1</code></pre>
<p>We can see that the class is now a common dataframe and each row is a longitude and latitude.</p>
<p>Now we can merge the two dataframes together by the id variable, making sure to keep all the observations in the spatial dataframe. Importantly, we need to order the data by the “order” variable. You can see what happens if you don’t do this and it’s not pretty.</p>
<pre class="r"><code>#merge with coefficients and reorder
merge.shp.coef<-merge(states.shp.f, mydata, by="id", all.x=TRUE)
final.plot<-merge.shp.coef[order(merge.shp.coef$order), ] </code></pre>
</div>
<div id="basic-plot" class="section level3">
<h3>Basic plot</h3>
<p>We’re ready to plot the data using <strong>ggplot()</strong>, along with <strong>geom_polygon()</strong> and <strong>coord_map()</strong>. Note that in the aes statement, <strong>group=group</strong> does NOT change; “group” is a variable in the fortified dataframe so just leave it.</p>
<p>You do need to change “prevalence” to whatever variable you want to plot.</p>
<pre class="r"><code>#basic plot
ggplot() +
geom_polygon(data = final.plot,
aes(x = long, y = lat, group = group, fill = prevalence),
color = "black", size = 0.25) +
coord_map()</code></pre>
<p><img 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" width="288" /></p>
<p><strong>Troubleshooting</strong>: I have had many comments about an error occuring at this step, with usually this error:</p>
<p>Error in seq.default(from = best<span class="math inline">\(lmin, to = best\)</span>lmax, by = best$lstep) : ‘from’ must be of length 1</p>
<p>If you receive this error, that means that the shapefile <strong>did not merge properly</strong> with the mapping data. Go back to the <strong>Important Note</strong> above, and follow the instructions for making sure you have unique and matching IDs across your shapefile and mapping data.</p>
</div>
<div id="making-the-map-pretty" class="section level3">
<h3>Making the map pretty</h3>
<p>This is a great start, but there are a number of options that can make the map look much nicer. First, we can remove the background and gridlines by including <strong>theme_nothing()</strong> from the ggmap package, but indicating that we still want the legend. We can also change the title of the legend and add in a title for the whole map.</p>
<p>Next, we can change the palette to “YlGn” which is Yellow to Green. You can find palette options as well as help on color in R <a href="https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf">here</a>, or type in <strong>display.brewer.all()</strong> to see a plot of them in R.</p>
<p>We can also use <strong>pretty_breaks()</strong> from the scales package to get nice break points (the pretty algorithm creates a sequence of about n+1 equally spaced round values, that are 1, 2, or 5 times a power of 10). If you don’t like the way <strong>pretty_breaks</strong> looks, you can change your scale with <strong>scale_fill_gradient()</strong> and set the limits and colors like this:</p>
<p><strong>scale_fill_gradient(name=“My var”, limits=c(0,100), low=“white”, high=“red”)</strong> I do this in the third map, below.</p>
<p>More about using color gradients in R, <a href="http://docs.ggplot2.org/current/scale_gradient.html">here</a>.</p>
<pre class="r"><code>#nicer plot
ggplot() +
geom_polygon(data = final.plot,
aes(x = long, y = lat, group = group, fill = prevalence),
color = "black", size = 0.25) +
coord_map()+
scale_fill_distiller(name="Percent", palette = "YlGn", breaks = pretty_breaks(n = 5))+
theme_nothing(legend = TRUE)+
labs(title="Prevalence of X in India")</code></pre>
<p><img 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" width="288" /></p>
<p>Other options:</p>
<p>If we wanted to reverse the color to go from 0 being dark instead of light, we can just add in the option <strong>trans=“reverse”</strong> into the <strong>scale_fill_distiller</strong> statement (see the plot below).</p>
<p>If we were plotting a discrete variable instead of a continuous one, we would use <strong>scale_fill_manual</strong> instead of distiller, like this:</p>
<p><strong>scale_fill_manual(name=“My discrete variable”, values=c(“red”, “blue”, “green”, “yellow”))</strong></p>
<p>Additionally, we can put the names of the states on the map. This may or may not be a great idea depending on the size of the areas you’re mapping, but if it’s useful, you can do it easily using <strong>geom_text()</strong>. First we need to identify a longitude and latitude where you want to plot each state name. One way we can do this is using <strong>aggregate()</strong> to calculate the mean of the range of the longitude and latitude for each state.</p>
<pre class="r"><code>#aggregate data to get mean latitude and mean longitude for each state
cnames <- aggregate(cbind(long, lat) ~ NAME_1, data=final.plot, FUN=function(x) mean(range(x)))
#plot using geom_text
ggplot() +
geom_polygon(data = final.plot,
aes(x = long, y = lat, group = group, fill = prevalence),
color = "black", size = 0.25) +
coord_map() +
scale_fill_gradient(name="Percent", limits=c(0,70), low="white", high="red")+
theme_nothing(legend = TRUE)+
labs(title="Prevalence of X in India")+
geom_text(data=cnames, aes(long, lat, label = NAME_1), size=3, fontface="bold")</code></pre>
<p><img 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" width="288" /></p>
<p>This doesn’t look great on this particular map, but it may be useful in other settings. You could also use <strong>geom_text()</strong> to put other text on the map to identify certain features or highlight an area.</p>
</div>
<div id="exporting-map" class="section level3">
<h3>Exporting map</h3>
<p>Finally, one way we can export the map is by using Cairo. For this to work on my mac, I had to install X11, found <a href="http://xquartz.macosforge.org/landing/">here</a>.</p>
<p>We save the plot in an object and then use <strong>ggsave()</strong> to export the object as a png. First save the ggplot statement in an object, and then export with ggsave, indicating the width and height. You can also specify the dpi (default is 300).</p>
<p>Another way to save to pdf is via the pdf function, also shown below.</p>
<pre class="r"><code>#save map
p2 <- ggplot() +
geom_polygon(data = final.plot,
aes(x = long, y = lat, group = group, fill = prevalence),
color = "black", size = 0.25) +
coord_map()+
scale_fill_distiller(name="Percent", palette = "Reds", breaks = pretty_breaks(n = 4))+
theme_nothing(legend = TRUE)+
labs(title="Prevalence of X in India")
ggsave(p2, file = "map1.png", width = 6, height = 4.5, type = "cairo-png")
#another way to save
pdf("mymap.pdf")
print(p2)
dev.off()</code></pre>
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I was working on making a map in R recently and after an extensive search online, I found a hundred different ways to do it and yet each way didn't work quite right for my data and what I wanted to do. Eventually, I found a way to make it work after pulling together code from [this blog](http://blog.revolutionanalytics.com/2009/11/choropleth-challenge-result.html
), [this blog](http://www.kevjohnson.org/making-maps-in-r/
), and [this stack overflow page](http://stackoverflow.com/questions/11052544/convert-map-data-to-data-frame-using-fortify-ggplot2-for-spatial-objects-in-r
). Combining all of those sources, along with a good amount of trial and error, has led to this blog post.
This post shows how to use ggplot to map a chloropleth map from shapefiles, as well as change legend attributes and scale, color, and titles, and finally export the map into an image file.
### Preparing the data
We need a number of packages to make this work, as you can see below so make sure to install and load those. We load in shapefiles using the **readShapeSpatial()** function from the maptools library. A shapefile is a geospatial vector data storage format for storing map attributes like latitude and longitude. You can download lots of shape files for free! For example, for any country in the world you can download shape files [here](http://www.diva-gis.org/gdata). Or just google it.
My project is to graph some data on India so I have downloaded a shapefile with state boundaries of India and I will now load that into R.
library(ggplot2)
library(maptools)
library(rgeos)
library(Cairo)
library(ggmap)
library(scales)
library(RColorBrewer)
set.seed(8000)
##input shapefiles
setwd("/Users/Analysis/Shapefiles/")
The class of the loaded shapefiles are of a spatial class, and we can see what is in it by checking its names. You can print out these components to see what they are. For example, let's print out the state names like this:
print(states.shp$NAME_1)
Now we'll get the data we want to plot on the map. Here I'll just make the data up, but you can import a csv of it or extract it from your model estimates. The important thing is that the id numbers are the same in the shape file as in your data you want to plot, because that will be necessary for them to merge properly (you could also merge by the name itself).
Important Note: ID_1 is the ID that uniquely identifies that state in this shapefile so that is what I'm using in my prevalence data and that is what I use when I fortify in the next paragraph. You should look at your shapefiles and determine what ID uniquely determines the data and use that (for example, it may be ID_2). This ID must also be the SAME ID as in the mapping data (what is called here mydata). Please do this to check:
If those are not exactly the same (and unique), you will encounter an error later on. Please change your id to match the states.shp ID you are using.
##create (or input) data to plot on map
Now we need to merge the shapefile and the dataset together. First, we fortify() the shapefile (a function of ggplot) to get it into a dataframe. We need to include a region identifier so that ggplot keeps that id when it turns it into a dataframe; otherwise it will make it up and it will not be possible to merge properly.
We can see that the class is now a common dataframe and each row is a longitude and latitude.
Now we can merge the two dataframes together by the id variable, making sure to keep all the observations in the spatial dataframe. Importantly, we need to order the data by the "order" variable. You can see what happens if you don't do this and it's not pretty.
Basic plot
We're ready to plot the data using ggplot(), along with geom_polygon() and coord_map(). Note that in the aes statement, group=group does NOT change; "group" is a variable in the fortified dataframe so just leave it.
You do need to change "prevalence" to whatever variable you want to plot.
ggplot() +
geom_polygon(data = final.plot,
aes(x = long, y = lat, group = group, fill = prevalence),
color = "black", size = 0.25) +
coord_map()
Troubleshooting: I have had many comments about an error occuring at this step, with usually this error:
If you receive this error, that means that the shapefile did not merge properly with the mapping data. Go back to the Important Note above, and follow the instructions for making sure you have unique and matching IDs across your shapefile and mapping data.
This is a great start, but there are a number of options that can make the map look much nicer. First, we can remove the background and gridlines by including **theme_nothing()** from the ggmap package, but indicating that we still want the legend. We can also change the title of the legend and add in a title for the whole map.
Next, we can change the palette to "YlGn" which is Yellow to Green. You can find palette options as well as help on color in R [here](https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf), or type in display.brewer.all() to see a plot of them in R.
We can also use **pretty_breaks()** from the scales package to get nice break points (the pretty algorithm creates a sequence of about n+1 equally spaced round values, that are 1, 2, or 5 times a power of 10). If you don't like the way pretty_breaks looks, you can change your scale with scale_fill_gradient() and set the limits and colors like this:
scale_fill_gradient(name="My var", limits=c(0,100), low="white", high="red")
I do this in the third map, below.
More about using color gradients in R, [here](http://docs.ggplot2.org/current/scale_gradient.html).
#nicer plot
ggplot() +
geom_polygon(data = final.plot,
aes(x = long, y = lat, group = group, fill = prevalence),
color = "black", size = 0.25) +
coord_map()+
scale_fill_distiller(name="Percent", palette = "YlGn", breaks = pretty_breaks(n = 5))+
theme_nothing(legend = TRUE)+
labs(title="Prevalence of X in India")
Other options:
If we wanted to reverse the color to go from 0 being dark instead of light, we can just add in the option trans="reverse" into the scale_fill_distiller statement (see the plot below).
If we were plotting a discrete variable instead of a continuous one, we would use scale_fill_manual instead of distiller, like this:
scale_fill_manual(name="My discrete variable", values=c("red", "blue", "green", "yellow"))
Additionally, we can put the names of the states on the map. This may or may not be a great idea depending on the size of the areas you're mapping, but if it's useful, you can do it easily using __geom_text()__. First we need to identify a longitude and latitude where you want to plot each state name. One way we can do this is using **aggregate()** to calculate the mean of the range of the longitude and latitude for each state.
#aggregate data to get mean latitude and mean longitude for each state
ggplot() +
geom_polygon(data = final.plot,
aes(x = long, y = lat, group = group, fill = prevalence),
color = "black", size = 0.25) +
coord_map() +
scale_fill_gradient(name="Percent", limits=c(0,70), low="white", high="red")+
theme_nothing(legend = TRUE)+
labs(title="Prevalence of X in India")+
geom_text(data=cnames, aes(long, lat, label = NAME_1), size=3, fontface="bold")
This doesn't look great on this particular map, but it may be useful in other settings. You could also use geom_text() to put other text on the map to identify certain features or highlight an area.
Exporting map
Finally, one way we can export the map is by using Cairo. For this to work on my mac, I had to install X11, found [here](http://xquartz.macosforge.org/landing/).
We save the plot in an object and then use ggsave() to export the object as a png. First save the ggplot statement in an object, and then export with ggsave, indicating the width and height. You can also specify the dpi (default is 300).
Another way to save to pdf is via the pdf function, also shown below.
#save map
ggsave(p2, file = "map1.png", width = 6, height = 4.5, type = "cairo-png")
#another way to save
pdf("mymap.pdf")
print(p2)
dev.off()
</div>
Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com16tag:blogger.com,1999:blog-2550386066385582083.post-55533364161864558352015-08-25T16:11:00.000-07:002016-10-13T07:59:27.203-07:00How to use lists in R<div height="1050">
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oPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cb08AABFOAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAjPL%2FpQAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAAxADsAUABTACAAMAAwADEALgAwADAAMQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAHa4ADV2ADV8AC1cEs3pishg2FfJaEtxSn94IlU6ciwvljRcm9wVDgxsadLb0%2FGIypoPBp1Fx0xGTbQdxoAU51YoZ9RXGB0bXNPyK3JLMApRwa%2FIMy1PPoJDx39kimekZX1c%2BDSW41tEZBuFiwdwx1dRoPVA2vWPSlsSDqhNkqYfhrqlVUGD0J3HEAgZavmtLnDC5WBriSpD8Uk02KsUkJ9vCFz2CZXAd5viwGZ2xcVYRPa1bEIai51nMYlbL2ERuB2TCzLAbPWWRZ3%2FsZ%2FKBjLk8%2FgAZzK1OaxNw4oGBbsNhx6Reg5HRFVCrwa15kGmJEy5kX1YypVm%2FHo7TjKP3l%2B%2FnuCTOyiOa6S8QEJbuiGYNlCnM7tHChCHRRQHXQh7yPXASlcvc5KrNKol6orb35kbo%2BiEwl8d230cfWPwTy00bFDRYURYGchbwsoDcaR2AJsFGCrbWCzBdQ0qCobWwLfueqAzSkzaHX3yCjDlGYPV0VZqVXlqbr4poRaG5NNPMDv5MCHkLSFyABMRBhMiGSZiDgABYmwsLWDjUZCUnHwvXt0VAUy4%2FqjwVgjfUqp1evzUqutJB7DW99Cq5aEhGePsa4omKUp7LnSQUz%2BZq51pU72ApMxkaVZE9jF3IGKVnF0GlK1E6iqLJXFzy1BEv6Kr0ngCzIgVANRwCkVHYJj86Sv2dzqgVrjYKlg8W94FbPGQghEbTakZ4AoS9h0u39DOoRJTA2iirs9xa5m9Ir0KwkogoqFNVDbTIGO0%2FWuJYfp61Kq0bhgbcbnvbTDj4HBESsXMdvrvpp7Owf%2FS2EbndoEoglO%2FjigGAuLUTUmG07ZGlRCZ4QZLu81tPEgTtAvF99BxfnxNslgVYH5Jc4AfHwsiktDBO4wjgjizFMsIeBTGmIYrhtrK3tvtBu5tYB0PDMXILUkFYPM%2B5yLLGOU9DtoDLYWLQig%2B8wNRZbJZZWeK5JyKNwumLgP7X6pqgiZMFUvfCTl6Urx1gUOjzhcICumUUsiNESZCmTANkgHoh%2BKU0t1fgQYOBkNfp4MRiJysezS1Vz%2BahE7ohM0iJBn4I%2BT9RJN%2FuQk6SjQGwC%2Bom%2FSD7Zq2RxvcSjtdxHfo7NtktA46mEg5gBkTtMKu7oFb1KgqwqBLzQqZI%2Fs4QSRhYF3ani1URTcMD38kDDqlc3DChmYJTCR9WpaWrYFiXeNi7jYa1Oe%2BSP30gxVyeC10DYOakGpWaU1qkQ9OXlujEbM4A6A%2BQ243TA2QnI8mKzNXNo1HPN1CFawST6uC9dkyQTqq5bOKkkuGjz0nkj35Sr%2BQ%2FkLGg2iQMrTKImSGshnX7%2F0Jyh0JziDbTzxp3IrbosXgpchLcBJpLLUBrFbt%2FU6qXqAaNFJoeBItBoqgQlPZkpTq31s5VZsgIkFupAno5G9oKwCGRqv5294xFqOrEpewcjx%2Be06mxDkN85c0ckmWmMzbkFX9YHAf83p0EsnBywjZbcPxZw91kqg7voxasp3VfDhEDAufUx0MTddLO5kCyKkUBwOm%2FMurQH4bJQgK%2Bp14dkBBhgxpBUkTlSbTE8DDu9qKiDSCtvmPY7FRMn0wuS6XR7J%2BnpjjeaDNwiOgIKUWCP9aLb%2BXpc4toQCryp6gjCVq1Cl1Di%2BtEx4WnBp2hmGZpEksZwTSggXuqAWD4B7WAMcir2wmA%2BI43ECxUSeTDT%2BxgR3O4XFPOGxHh09lxpScampLahUbICao0db7%2F%2F%2B4CBMcCokQ7aOQ02MOzh0NEaJGGOvPR1t4sY1I55lNfQ3IkF04onnAomXxiNg2EbBbPTMBBlv%2BggPSmkb4GNFW6Ehd6nDzJlnIXFfzr1bVWQlfylDodFBbhCDf%2FVB13j441dc2rx3h4hfAWIJuZqbH%2Fwbuafg%2FnTQJ9bnMfR3U6TwYgDQJIOPOtmZW4ZHsR2VifZGaC6Pzv4ZzC2nSAaqSe09F9MxuI4UmUVA2RAUqBUIETEIAhB%2BtBhsxhVMXUmv3RPe1grhYWLM%2BPHwwO5W069qIgDXtETXZQ3741DSk%2BvO6y8k%2F7Mg6GFC5fSK02yXZ1XV0NZe8iknoIbSFnDm%2BbMP%2BBCQhoLKOeU3FVNQ%2BgPEntwkHT950cQYEYQc%2FXuL4nxwcRW2bIN%2ByskPaD5Rz6lN8LW5IDLYfuvhxlLcjWXSADeTshWsJk%2FHCuRbwjqLcx66fc6UlF33HipliQ1cvfKlgRpVcsmIsiYnJjzJIovUyqi%2F2OTQcIwt%2B3WW44BAQh%2F2GymOIscSpIt0YUePiwOCJN%2BQ60lqkSRbWi10lotzgWZsqzEI7OGFGeIoFyqli41%2FNn7LfLQ0qtxkaAwbsKZQ0D9s%2FwhBqoxhAlxCVNVF%2FJ6rsUBQ49MLtR3ICtKPLKZ%2FQPEeIRgWUNLbNR3v3tTaL8uiXO%2BOj3tSJc56KJHol9nGPuclbrQpCn4keT05ETSOQ%2FDwwKjCRLgIzBi7x%2BU7ByTZGUWcGLPY0VtXWC7kQ38C5thIGJx%2FxLM3KsoGyRwC5AFUNWPzU0QBCaVXo6z1NCclLuPVA8%2FwWuNhiAG9CPPSSlIOCuh2AQM1d6MyIEIQArIa9YenQ5ug37h9gPNDHVLImcjE91ewI7pz7HgOEVqTD%2FjREYRtEoDIfgxzDePkVvHWJAtcpCJ%2Bg2o4akZpphRUQ0U92lJcKZGJI9mnpl8UVzpMi8SBGXUAPpgcS2cDAusSE6q0hNibhCgZHLReLsA%2BSv8wF6LiDL3TogB1DsO%2FHeMg0ETucRO2hIOvbp1NVkFMclluNmBQbTy%2Bosr2bJ7oWdUtSHi1s%2B2vWxq8njY1eW4BCVr2EqSyIUuFKS9iwVF5FBGeg0hRcHwCKMtqYLBfvVSYXHdGZK3Ug95C2d%2B3t9ZWEYqq6F4TocgcDS4tI8cvEEXK1%2ByB9RofdKpHoA3eP9%2FB%2FEBG%2BIgvmwZ4Iir1SOYFEtUG%2FoVkECQqoPTdHoNpQKpA9K9k7TisAUBsKPtWgBriyeOxs1o%2B4KCIFumN744dH3Yf7X8F426LGtEhFLULhlKFgXF8Wq2D6kFSBkViLad9D9U%2FP6A%2Fr8SE7jsr9s%2Bsb5ODWI5uS6BkZJI5RQ5QDVZ5%2FKOG6Dzc1RLQKAdGkgwLiZLQdEYQ9yhQLtD5kWr4wGHTv4GqloWePyHYyuy3Ek3iQVQkdkAz0MxcsHzccdMLO8KFh7ywo4jXywraIhIicAI4dWdD5wEXBhsMklzbWc8zi8opC3IlPsLXHCWOqOgq4iPU6xyDS2tdSLnlhSFjsCCWvMRt5TAI30EHR4f16NYyzoG%2FoKgCUsW4182Wvj5gpb0bGGLe8YRIYRGVgF2hwIlEfm7eUwwLZWW%2BmmBZPggPnIKI0b6G%2BlHCncx0wkaVPRQpZKWIIrzh4yC4Ar2vTKzhVKSutNi1dSzmM73AGUXSBJrQH5J4Dfb%2BRU25tYrIVdLr4ogiwTE2i2hl39ffJWHNTSWq3PvVJplNqDTJgJcvCHYoQAuHeOew0gZGfmX3p4D0aSr1BhXbXRA1qipfS6LiggtiT1eDg4FTCWwCdAFC9FCOZ52EdrFetMBxUdC0dfnqpr0mELJE2m5FVMvwplyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFsYABEAAAAAoUAAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABgAAAABwAAAAcalXC8EdERUYAAAGcAAAAHgAAACABCAAET1MvMgAAAbwAAABDAAAAYGenS4RjbWFwAAACAAAAARsAAAJySvAJmmN2dCAAAAMcAAAACAAAAAgAKAOHZnBnbQAAAyQAAAGxAAACZVO0L6dnYXNwAAAE2AAAAAgAAAAIAAAAEGdseWYAAATgAABODAAAiTweHjMhaGVhZAAAUuwAAAA0AAAANgJiWP5oaGVhAABTIAAAABwAAAAkCjIED2htdHgAAFM8AAABFAAAAvTBwRGObG9jYQAAVFAAAAGrAAABuDSPVk5tYXhwAABV%2FAAAACAAAAAgAgQBoG5hbWUAAFYcAAABggAAA3zUr5ntcG9zdAAAV6AAAANAAAAIhLlGpmlwcmVwAABa4AAAAC4AAAAusPIrFHdlYmYAAFsQAAAABgAAAAZYr1LmAAAAAQAAAADMPaLPAAAAAM8MFvIAAAAAzwwJLnjaY2BkYGDgA2IJBhBgYmAEwltAzALmMQAADagBDQAAeNpjYGZpZJzAwMrAwszDdIGBgSEKQjMuYTBi2gHkA6Wwg1DvcD8GBwbeRwzMB%2F4LANVJMNQAhRmRlCgwMAIAC2EJ1gB42s2RP0vDYBDG723aSIrSUESsiHcIWqqDXbvFRe0gBJw6tTgUCx2Kk926dusixc0P4OiXaQZzjx2cnNRFhPiagENdHBx84P693P0O7iUihzLbJGM9mb6tTFrnTWhjSAEVyLfZCgnt060U5UDacrdd3vnYNVWvWlJHPa1oTRva1JZ2tKdDHesUHiqooYEjNNFCD0OMcY2bR0qSr10pcc8S6QfRaEF9Fa1roKElnutARzqBgQ9BHQFOEKKDAUaYYJoSTfKWzJMo6epSPI%2Fv44sHJ9qI1malWVEqsi5lWRZXiN%2F5lV%2F4mZ8YfMWX3Ocud7jNLT7jUz7mQw62ouwafyvj0jfW5KzLLTZkX5EpX6B%2FLXfxYfU3U5%2BPg2iWAAAAAI8AKAL4eNpdUbtOW0EQ3Q0PA4HE2CA52hSzmZDGe6EFCcTVjWJkO4XlCGk3cpGLcQEfQIFEDdqvGaChpEibBiEXSHxCPiESM2uIojQ7O7NzzpkzS8qRqnfpa89T5ySQwt0GzTb9Tki1swD3pOvrjYy0gwdabGb0ynX7%2FgsGm9GUO2oA5T1vKQ8ZTTuBWrSn%2FtH8Cob7%2FB%2FzOxi0NNP01DoJ6SEE5ptxS4PvGc26yw%2F6gtXhYjAwpJim4i4%2FplL%2BtzTnasuwtZHRvIMzEfnJNEBTa20Emv7UIdXzcRRLkMumsTaYmLL%2BJBPBhcl0VVO1zPjawV2ys%2BhggyrNgQfYw1Z5DB4ODyYU0rckyiwNEfZiq8QIEZMcCjnl3Mn%2BpED5SBLGvElKO%2BOGtQbGkdfAoDZPs%2F88m01tbx3C%2BFkcwXe%2FGUs6%2BMiG2hgRYjtiKYAJREJGVfmGGs%2B9LAbkUvvPQJSA5fGPf50ItO7YRDyXtXUOMVYIen7b3PLLirtWuc6LQndvqmqo0inN%2B17OvscDnh4Lw0FjwZvP%2B%2F5Kgfo8LK40aA4EQ3o3ev%2BiteqIq7wXPrIn07%2BxWgAAAAABAAH%2F%2FwAPeNq9vQlgG%2BWVADzfzEijWxpJo5FkS7IkS%2FIpxZJlxfGR4Nx3yM2dgIBCOMIVrhAghXK0CYZCKA0tKSyQco6UUNpu2G3pwoq26hnSQrcsvVhajm0hu90m8fC%2F981Ilu0Eyu7%2F%2F3EkzSXNe%2B973%2FvePQzLtDEM%2BQzXwnCMwKRLhMkMlgWefS9bMhr%2BbbDMsbDJlDg8bMDDZcHIHRssEzyeE6NiIifG28gs9e0%2F%2F5lrOfZmG%2FsThjBFpsgv4ZcwMtPKKExGsecUUlWsWaL4M4rnkGLIKu6qImRLAdLJTOvx5KOpgizmxIIsRKWoLKTEuCikCkXCPb%2Fj%2BQq8CKeO1TYPTzqgjtHLAA0G%2FtH7WplFTNnCMJ14c4He3JAtE8bSuW8W4cydRLFlFMshhc0q5qrCZ8tmC54yC%2BbOssWMmxbG3FmyU%2BgCJCrW%2Fsgo6SKj6mb14PiWupmMUpwN%2FBf4Z5gCs4VRshmlrVpuy%2BJPtaXNFI4ohSOSVQwZpTmnGKtKMKtIGcVaLUtWvFByIWTTM0qBQharlkKRLHy6St2kU3FllXRVcWZL%2FaSzFCuIbkXoV7rFsrUl29%2Ffj1Qs5HoL8UKur9CXy8o%2BOd6bZuMxBytEhahRgrcwn8sOs%2FmcUTDGY6k0SRWLlxleL96WWHzV%2Fg82Dxqfyy1ZFPb3z5npIdcX1YNGsgvezdP6Z%2Bek0KIluWciGzc9Xr26dcRGDhcz%2BeLjp%2B544fwLC%2BunBbxdpw4X033FRVeOdDqD09bln7rwqq%2Bmn7yGwTGpkFF%2BCXsA%2BMtNeYGrEoXPlAzayANRK9wtx7YiORlGH8Pavx7Y%2F%2BgD0sW%2FyJ%2FESEyEUbiM4qjCmBHFlynJ8BMlMye6S1axv39aD%2Bf15aLZvt5kPCakSTxmlLyygzguW86%2Bf80TT1yT7up67pIv%2FZIdWUPeX3H50x885dh49S8eCNgcm5Bt4FXkFeAdHuaBhbEzTJ7IBZIQzQYY9QpgcXQF6VIPsuvZ9TD6XUU8qG6u0L3DY4%2Byp6k20jW2F36H%2B%2BjDjz7kX%2BBfYFjGyLgYRkgAexN4Ffp6MyQZE%2Bxk6b%2Bfeog979DaQ%2BfY7Q84W532zf9%2BsnbgdHvK8YDdXqMFwKQwZibHlI3Iz0JV4WAaWTKK6RCQssyZkHM4AzCuicNNkxEY10qJK0aJCNMpHxV5BYE79iaQUhl7fez1YpFN4u87GBPMl2eZFoZJhklhmOTFhJgUHETW93qTgsFBJDhm5FtvX34SID9r0fJ1bvfty%2BetcljGXBYH7H%2F%2BkvagtKGDPXP32F9dcvDyvkJ7QD65wF1msXLf5TyWseWiP8AANeSP3uX%2FhX%2BA8TBNzClM2YEYuTOKXFWaNMZozijkkCJVFcmFskExwBwAdg9Ionsfx7rcrXK%2FYhCB85mS2wFTwNyvyKLi6lea3PsIIxjg%2FLQetwsQkrwC8XmdxBhLEWB31uVr6XMlW2TSDKzWvIx0CcLFJq9JPXj1zspnXyTu735XfZ%2B8h%2BfU31d2Xq0ehJMXCwLpWsZepP75xe%2FCFRp%2FFpn1%2FLP8HCYAHEmUYEZhDiFPOqqlJo2le4fZMJHhjZW8Dk5I88Xpp1599bVd066%2F5qr1fbOvvWXv8PDjt1w7m3PN2rKqm184Z%2B4CvnvVlln91950Y3nNmvKNN10LtProm8x8%2Fj4YewtjA5aKenKeKPGYiYcrLCD%2Ffg97D%2Fmluv1u9SZ1%2Bz33slwLFUV%2FUmcSj%2Foe%2BWf4pHzY%2BBs%2Bxs8odhC%2FVAzxwERypuSnIE%2F4dY8sFORUIZ4Spt5n9r8s%2FM73Fn33naXLjnNH9sqdv%2F3Cjj984ec%2Fn8C7HpjHeZKPJYdIb1%2FWFyJeY1wiEhlNzFT%2BR5mZIKN3EVK8L1fZqihbK7n7iupHdzE4%2F%2Bj3l9B1yco44Xdk4JoIE2dSTCeTgRmhMGUGOYivlvwtICiFjBLLKaaq0ppVrBklmVNsVaUtqzgzSkdOcVWVrqziySjpnOKtKtOyiH84m0NyEKWXjiNf3WeyubytclbhXSii9pntooS7%2FqrSDO8aV4aySkt1X2tb1zQ81eIqReHKRHt3D%2B5y1VIeBZPNCoIp2NTfrzjFUnMIBVQPXS%2Fz8fykF0pBmKVRcpxzvHJ0f7H%2BD8TN2F76OjB%2BkF%2FSeAnKUhBOx7bWjzC6fNPWRifQMMPMZEaYsh2p15UDtJFkoYwySEnTC7wxi9LDRddHP7y7Si2AOBCtvapMz5ZOonwD8qEPJK5PEmFMo7FkgEzcJ59wPgRcIIVCkroZ38e32fUnOpNtOMxe3LAz9sKJzjAgPSfjvpw5i7mUuZG5C9ZqSoXS4EU5pEOpt5hFSpSWbM0hLUpzroT97kzp9C%2FAfmu1tPqWLFBnFKlTciHTmYH52rKUQtNAUs1aAdv91dKC9fjpKm2AY5tvgu0LqqVrbs9mS3dTyuFcGCDabOgksaSY%2F%2Fh9kv%2F%2F9vqQVEGCneCNjP7fzmfre%2BTR420e%2B6%2F%2F6wWwztbkReMYr9V5fJDyeC%2Bd%2FktySnNVmYOjqpyOY6qshhHdMGFE5ROMaGnj8UfP64PdPjichF2jNPk8jgbyPZV9UY36H7f%2FydRmD%2BDO2Bx8P%2F52I83Hv%2F0pKIoyPMTE%2BA%2F4DsbAMLA4pEgqRB7hsgfHvvYj8rJ6OtcLWz%2FG6y5kLuTn8fNATuN1BTORzUQwkwtJUH3rIAmS4EH1LfoGH8MT9w%2FiNbBWTdANUszzE7QDUAviOSVUVaLZcnMI1ZzmJGg8oWbcDEVAv9b1h7YG%2FSEJAiucVRJVpSVbTiTx0kQcvpVM4GayGb6VqGsZ7TDSSdAyyqwPRHa%2FkhAVd78SAK1DdgUbtA7ZDVqHq78UAu1jH2OQ%2FHiuSSx7A6S%2F%2FxN0Dw7keU7KSXEpnv9YPWResQKi%2FhO0EfUUvAile412X6a0izBnHE%2BzapmqWUV1zeo51KyaQx%2BjWz2HulVz%2BBO1Kw6WsY%2FHDLUG8r6O2I5r1VcFH1xlqiH2InG9CIhRXZGR%2BQdAf2iC1X4ug4ajBdDoouuSUC0L1JgTwJhTBFfJBhh5qmWbBw%2FawI5Ck6lkE2CQZH9zHACnmpm7IIE26wVg873JVN7nBg2NjaVZok1g1HfpBJZ%2F9vj5W4%2BQs49sPf%2Fxn522%2B9V3X919Gvl1SCri7CjiAvU8aRt%2BrFC5pnTkSOmaSuGxYfWXz2%2BBq%2BBiYhtfs2DdLTIE5FKprsdMY8o8rjgmWy5HFFdG4REfRMIESFjBTNbQcVZLIpU2BbCwCRjHtb8iGCNdFRj6CkGDhBxWbWh5g6KwAukWZJr4XfwuZj5zMnM2g0ywoqoszih9QL2VlHoLqsoCV2kp3AMWtFVApwUM2lBDwPZLxX1OKdePrNDqLgUyYFiWVgDLlwRQYJTF4j4umpqLZ%2Fvcpc6TkOE93gjrixDBJxdkH2zmsjPZvplE7iukCn2wme%2FNsMkMKSRTQioJm%2FGYkzU6ScooyIIRNg2oFEpeYywZNN7POvzciu7BWw3dPYZkW6wpkzSmM4bPTZ%2B2gvM7yZcMhi8Rl8yt6Br8nKF7mlG7wpDtMtw6kF7BBezs%2FUZy%2FrbyNvjPnhJtTxp7ugyfG0ifzAXhlMFwP2sPcid3DX3O0NWDX27uThpzHYbPzciczAUc2s87AtzJmRmfM2TSxuS0wPpt29afsm0byEKBKX70Ea8YvKD3j2ugBeYOphzFudYK1v603iwsFalMuSPblwN1oakKKiYqFY4uOJ7JULsVzPvgIVx1Wl2lBM7DrNKGmmk50UYlFAMM3eZCeaRMAy3VVeqBre6s0ltV7Nlybw9e1OuBi3pdqGeCUESHAHWi4CJSX0kalhMPqJ3aFrKSB16obnr07aLTQjwD7aSrfYB4LE6H9ehbVkeRu2WgfWxO%2B0CRyhjkM03a8CRrcRbbBwba4XtZq8Nx7BJkvvbBwXb2wNgc9gBYxkf3a5uavQR%2FBon%2FNdggXUzZTPV14HvQz82HUFc3UT0bqQRMXzKYQPAQOlnNJF5zurCj7C0V9SD%2Bsaewt4xtHduLcoRdj%2FyOAvBtkBMORmRiTBmMpU6CXA%2FSzlgtGwkSzGgCieDRZhPVSesUISIpOqzkV2TUYT32jNXBriddAX6b1aHaxs4A9Dg34o4ylmd4%2Fin%2BKZjDHuCB7Qza4M6q4kUhW5a9eBtZgnHx4JBTwxAgsMOouUpeQHD8ogBcJLtwcsO8R6Ox5LWDnLJwDg%2FMrJIow46Zd6Kty5S8TtgTGLsLT3nwlJGINl0Qu1tbeLeL5Vta3boA9qCEkPg9xE3mEPeePer76gH1fd%2BHZO2HH6pPzAeR8Y3GE3v2sGepT3yIp8dUIOlBaiuwHz3IMAYv0BTlVVq3soQcYgwWlTNDHQ9O6nggZmRhlGO6tIqTHAd%2FJMrFOU%2BOixfJsz%2BVHvb%2BhDw79lb7B209bzY9wSvoQDm6gq4FhzW%2FD6vr6No9FzFlK95TuxuYICQ7LirLvIC35hlN9NuBjhYqK0sCD5KKNYOksosgX6lXDL0g6KcEiKjtRf6kvoHyU30Dtn7z7LPUjwciFP14lSKMM%2FAr2PfPAhQyE2bO0TVID53LBhjYCB1YsaqI2kIKymSzqyTBFiiOLbikiqJ7v513y1Q3aBaVcL8iuffbDB5fiI6p7IE1l5jMTFBfUXuH2WyYpUsRqY0kR4U9R87a88bhN%2FacpX2c%2FgFZ%2FcEH6lMrdlV2HSENJ%2BCDZdWnPsDzKp2uwLQTeTbIbKzxrM6iTQ0sipiAjeuv82UzYgJ8uR%2F50ouY%2BEVF6lfgCDKnj2LicSImRoGhetDxWZJrgokcF0%2FEk8uPHNl1Yq5Uz9ZwoUzC1n0KiE%2B7Lk2AR1xUsWcBJW8GRwKMbzOocjxxUJ0sL3qiTTVZmJOjhRwXrXAt%2F0pA1m23OiqVLOnKVjaOPRwgv0LJpSZg4pO3YJk9PIGGEuNn5jXMe6RhgNIQYLDVKRcEyok2IAxvtnA%2Bf30aU0L55CmE8uj6SAvMbMOJyPQhmUHaJ5OJ7IcRf%2BpJOH8nubsCnFvTmZqZVQy6mWFsxYwSADhDFE6A2%2BkqWTReDQOcFoTLwHu8cgCH2CyWJB%2Bu%2BSKOPGHMXgkPB0SFAu0Ns%2BhAjnuNLUkXOpmFqCjAiu4gsq4k7TqifhU0otsvuYeM3v2Vl0EjYn%2F0rqYWbQFVCTSqk%2FHMdaftZjQdj6Hwupgocx1TFnE8YX75qmVfBOe4Lwhz3I7zrmw34AE7gy7ymOacgIHXGDdYLcUBGReqMaK7HwF%2BXjBYLaCbRBH%2BoLtkNiFavggsL1EqHsx4nQGd6JrLDrApiNFCEt3GnBgVQJHpy%2BdgTsZjKcRuy8tfuVvd%2FMXNt5WOLCSj9HOXdphdj4rfdXh2xZESLk34SY8xsOoVdZ49nh9rBaOYMoo%2FhyIumMWgRHMOBy2URWnXkkMxE80ir7XmUHdPZKmJYzpUtrm8qGxYq2WH2wdbqDCAtMOVDZf7BBgaqYaXBxb8IRKVEnntpfmMuFuOvVlzEqEMHH%2Bh1wnd8tryr%2F0f21us7zbIa9SHQF7LOHYtmo7TCiN2CEalbAjioBlQUgdduNQpDgC4CQ86QIdBBYgpteCqxjsMQToz0Kc0MG4w5xq8SaDcaOo5KB9bVpKulVuoInJsK2guIMWp3Vpk17cPVFZu2bKyAgrMXjjP%2FmEX1cR1XYH6gTiYy7K2rmEkAbmJq9J4ApAQ5K5nmITZYVIQHSTNGQVAe%2FC2a7ZefmGxve36W0Zvu%2FJUL9KPjA5Os0ebDMtPJodPnmtpa7PMPRnJwuly6n7g6h7mJOZ8ppxB6gznlGRV6aPjHAIyjei2DK5iMaBNZ1XpdJWysDVQVQZcJRdVG0qzgbWznUAjmzXUTKVJMgOzU3B5vBxytw3WOifKObFuxQyTljCRxvfTbMzBSh5Rc0sgQdEtkZi077CCQrfj98T4%2Bx1085xH3nj7jUfOqdhMe0w2%2BsauH98mHpCSVtJ1408vv%2FynN6oHtb0r4QvwvSvHXiU%2FxAvVXnxv2NbleIVfz73NGEBnY8SoQTRESQHjIzJYBSkwczWpzyo7dsyv%2FSejILMr6pMNh2q%2FxW2mv%2BVk3AxVExyHUL6JehAEfz1RABtDSIGYrf%2F2gbmXXjh7rfar%2Ffnbnv36rX2X3HlPbfy%2BzZ%2FN%2FZX%2BZogZpHEr9BKH9d9G35BHEzwRGB0PrDb7XD5ZW94Jgwqcw6Pxc4BEE2KC1O8PVpIEhrNcQMuIaNCQeygsZwrXXy2cbNw5aiR3A1zH3qyQVwCwJ24tXHLn3VXT%2Fd99YZdpxFT%2B3VtlU90%2FroBUEYGbAwBnlNokM5nZYPstZpYTonF4adocMEbc1XJ7z1yUGcFMOdW5RDNQyonupXgslimxRjgSB4U8C2iWHAtgL1ot9S5Ef%2BcKNFpKhjD6RqslDqOSIc07ZkfTJldV8vDuKqWtnUoHdbnMypbT1M%2BSjpk7y8TqxLskXKUCfGfOAtgeqJaSi%2FDTVVoClBzJKsur5f6heSjLToaLpoVgZfJK%2FkDvTFR9CwlY0Nvap2OosxRrAgp7vNOpnRoXy%2FyMAfTZhN2zzAZfoK8wODRrNiV%2BT1SM59HdkstH0ZmuCUcCLw6EIofqYB5UQ%2BqRwYu0oyAAorirXc2BuCQoQuFFDqPdTUaLRdVGtSw0xFF4dmkWueajh%2B3K2F78wEuobNVlbBcIDYwhHsB3lKfsAfwlelGxAltF7m1kzgqK4814EQpo7hYQvnM0P765Pu5T15PNmvVT5r3NOLr2akkKZbP0GNVcW2nQUPMwoGyxZVGtRX0QTBQryhq0S%2FywBZZrk%2Bbxj1dRSJdcGMwQjP39pSaQ1jScgf5%2FNCGBPCmgmIwrDXxK8JnXP3Gfol2pryKVCvf20RWwxb19zF857bTjfk7Q9QRN1xN0jxZPtTxUZmCKU13PLQBrMEYr1fVED64cZsJ4ZV%2B20NebSsYEAhbu6%2BwBsOh%2BGQqvCofwDYbgVxYn6Ho27nerQ%2BFwCN%2Fo3Cdg%2B%2Fxav28A5Ld2Zzmn3VzxZmu2nVmL%2BNH774f7%2BwKU55rIVAgSOfSeElA4dUB%2Bod0Q3lhrEY9kyYsIktUxCSQF9%2BFkLfao0cQCs1y3yjS910qXEktVsUwwpy0MejQFE501Au4Q3kDplBMLoLuBnhMVi89yXymOuYvs%2Bzx59pgfWLBIhwBj93S9LMH9GMBA7E3COmyUKuRF8mJIOvamFCIH1Ln8Nm847G1Y91A%2Fl0G7OYUpJxBGsJAs1Ga0cLXUCkXIljnLBAuyjaIA3MlmkUFNmm9E0FyzJiuuf85UTPPkAYOhGg%2BTOyp5YItLE4RM4ArDBPWGCPFRlaEC6gCw3LE3823siuEzWZdFHba42JEk67GQwxYPm2StlrG9FhwDmLNzKhX2ta1byam4lh29%2BUsWh8OCbxPyA0JMgukGvriUZiOAqGvPKN2gpmVgMhFM%2FAAsItVyhPonIxnALuIqpejyXsoBKhEYBiXQX%2BpMAUrR1kQLXTS622EvEoujRahExVK6BwSax73fGghmpmmKkZbdoPko0zws2A7WSbMcGHSheY0x1uvD7IcYKn8D7dzbqBzt4p25wqqOSvvKGWmrbRdoSsXRyuhoxTi8fnh4PQHq4FWoNJma5uY7YUfyDrSDKQzXjJIWvGj4TKrv5enYPgs2cQxeGvfZc0q4WmZtKN2JEs%2BUWnUtNA%2BW7EwSlWEYpGg%2BmUoTWOviYAA5iJMQj%2BzJk4vbh1s9p5PbVro68%2BRrsTZ32GhUbzxDvczfbOtwOsmmcvrKEV9f15%2Fe6F43MkI6PGmHnXvrmHtaky0oCORfyStfUL8HPIf%2Bn%2B8Bz7WAlOhhzmXKLQhZtKp9Ejo%2BHVWlJ6NZusBhdJT4Q3ANLlZRWLYwvQa065ZDIjArHaVEGkbJBwPTHoWN5n6lQ4RdpQed%2F4BgniZHpLlU3oDeSxyJMA%2BfJJ5CuwGHKK9txEFHZc2%2B1gj5xTXflqMxqw0I3du55bHlla%2BdctMNZz70%2BSUX7d19ipBr4waa5ZDdKcwnyhcKZxTaTAJny520Zd7qu5dWNq4%2B9ebi9UtXbqzPS%2B5Sqnu36CNBaJgWxIADo94oovR0BAS14OnDIUjlo7KDcxLO6JOlytIrTE9b2uYbzQbydTY6IxYwGG6yTJvfL8zOcCdPb%2FUQjvT3m%2BKphM127F97B439DPvRc7pfyAxzYBtTbtLstTJr8OGCYwOut1GudwHXW4AjklRQahouWp0wW0AHCFFfQ9lPAzn%2BIAZy%2FDSQ0wRf82trk7OKc6ZkAdlaMlDbLdKkxSRsosLDEKRg9hfkuJgTPGIuCjswGp1ElIHdCiKYpJy0ePHiG26A15Fd7IFdWZeUjsYqRXVzsRKLZrwiqH2PP37szce5c3CZDaUkM3vsmWyxmOVWsmYpFaJrUPSjb%2FB3gfxDfO9gQO0CfNkqNVEb0bUA3E4NXcuhT49kCHSbfazJTFDRcfnRGgJRRsUCawL8GRDZSkQE2R2ksnsmASRlAWQfdwLUo49r2BU11L2ZcdTTkit3g0YZ7sd11Me2IOrsnTXU9XyBxngqowsgjHLSZeDT7ockfglaYkf34zvXgu%2B4hsD223TbTyOY62kEcy%2BoLris0IhL5WO3%2Fh5YxUn7nk84PxHW8e3KCcOtdXDI4fqmuvl4R48Db1L3MoJ%2B0Z3B4EAog4kYQxmlt4q5GHq%2BxRCZGCf25D%2FdfiMmxUayVxrHozJhELSQFwDPXlTfVN8%2B3tGPx0vDSMPuk%2FASP3W8%2FPijdaLtcaTIrE%2FYRJRMx80twPyRlTp%2BS2h%2BwRyaQ3M6zaFZTfMLLqL5BcUsYrw1hzGcK0FM3IQ6QtnVMi1Lsw3K%2FrbpaPXc%2FL%2FKDDle5sf%2FjZa1IOfHv6G%2FZcIc%2FsTtT0H1Y6OfZoSMH%2B3Wx8cImmqQ6WBmMItgZUSrPUmz3dJA9cUZNDQ185yuIBiOBylK42CfXrRNSPg495XHHkPzBmcOznOcOTDnu3BbPTg%2B%2F1VbDWoUBWAWohxWbfySo2%2F9vcIO%2FhmpjSI18ONswFdmyrOoP2txRpl1SJldpehO6yn8H3HD0LNHpulo5NPhx7UUo49XKn%2BvHD%2F6FjrV2OQNi4uLdR%2BRwr1NvTrdNHMRphhafx6qYoP15YW108zQfAymxNt15y%2B6XCammYHl0hVh3490dUXG3JEu7hYw1z3snXR%2FC7yTopa4yzXMcxkoe4o%2Bu2G5r4VyaR7FBN9zqIquED38gxkVoQBAJPYrkljyuKniApCVXR4JXRRNouLtn5o4N5MYUsQjRt26Jh908%2Bs9gYDn6F53sPhX0rGIJN495r9o2eZlyzZ3cYfdwaD7mA3e3%2FvGZx8kw%2Bqb5LD6Izy3DOh270cf8s38C4wXtPQhHYOQBnuEwq7lgKAz1qQFrJokIJ2dR1hDCKvA%2BKme4ekFayobJl6qtaf5VBJ5iZoYxnuv%2Fel11%2F%2Fk2oUL%2F7G%2F3xI754wrOme%2BeP%2BFm%2B6%2F%2F9Au9o9bf3Hztlf%2F%2B77L%2F3tkxBzbdOmexZ%2FfRc%2FcjzrVeHxNYhaOR9e8WnTN1xBd82hEldHDJuogYojCgJG9ktcDihJG0Y4bQ8uJsYmhs6VHFWVyxMysfvUItxZguorhuX83OBkf6HkMoM0VwkbU6oU0WwgTuZBmU8m%2BmcRBrppz8RVXhKWFy9ctnZlYtu3Lyy%2F%2F3k3bHKed5hJ8aYuTNZtPL5BdZ37za195acP8u7ZcfcVVn5t7ygPFAd545je3rbwoeLbRt6ht4Y7e3CW76vbyS%2FxnmGYmCmvlOqYcRGo4qxgVwAFL0QHToktIDV%2B11IbUsGEcOBRG01HxiaVIDAliwlCxFNJCHry4ryUWT%2BguSOA2XhYmKDoFSWO9RIGkzIBihrzzKLkk8m31FTQIyeH2gco7j6oHH32H7P8aefBydRP522WXSaejo%2F3Rd4ymb4ORaMMrb6j85tF33rkwSx68DK75n8suW9M8Hs%2B9D%2BZSM5NmFuiYwbSJZZRUVTFrs9mlGAHLDMXSXkWHIlOKuYAFpVArsGDJHIS5lOgv8Ub4TNantmwU4uO45GuokGEuQiQzkWIOAyBjYG9dc%2FdkZB4Pn3%2F%2BWeEQ%2BYz6gOBfMHvt7H4NoxUt62sYPZAHhEg02esxE%2FJTMocM%2F5J1yLnZF4%2FjtZf6AeK4%2FjfpeJm1EWvVcRkP%2B1P%2FGY3wC%2BEI%2Bi8wqyoK42UGRPf5wObXxqvUEm0QXicYLTObIinArWtopKw%2BMhm5taSJLdyhrrxHw0ku1VCaW%2Fk64rRD%2FT2eP%2FmLWn62lpckM3OZshux8FHrEewbs1ao4z2kcFnEzUQLdUouL0xCI5VpPswJssswH1mxxNio541IsEaYSRw%2BzCQPH2bMZ5bQGwoCN%2FU96g2tfE99Dd4r7Gmk7WXtyMvqL9WDL1eoM%2FXlcfv2FqCxB6isRQWtOer202gL8Ej1ZA4tgkHgT1OAKpp6cli10Y2spuWqi8l%2FHN1P3lYX1XIba7IeJX1ZQj%2B53YkZWX56F7GGNVMLSdXuMn4n%2FUY0e2DKzdSAfkN%2F%2FaaEub2up3j1eg7MhREy6O3CnBft129ne%2FE32SSbHP%2BVBn%2FcEuRCoC4tnMGrKF9WmCJ3C10vIwxTmJTQNjnBrXL85N1dx9fe9HsX%2BSX095nCuCtKcwg2fmkXLugN30M6t9Vwpr4KQyPOwDciLNDkMNs79kN%2BCSKMFSrk7SljRGMZINlzNebksyg0AvWfIZPiWXk8BsMzrrSiqoV3gNdz4yYn%2BQYOEtwPswy4t2t8R2F1aHznphVZ1qrOd9Hj3aur8U70PhPuocUfQafh26hO49Z1GlRoUFQgO0%2FWzaNkgpnGLxl7%2FUhdCydOthdo9C1G4P7Gf53m6GLqbepb7Io%2Fqw%2BRjX9m140pfyYbYQtxc330M34lfyVaMwkzKRBZkOAg2ag%2B9Bd2UCJjyov00rGXfISMlTRZxzEc%2FzT%2FNPXnjtDMB0cVS9YQav%2FUzIeAnvmwDzMfqP9CwjRZzHuYmvWQAzmBSmVcinN7iAfkrGfPHvU99YD63h0V%2Bo%2BmO9QP7tlDTqaHJ8DlBB1iPC8J4XI1wGXX4MLcIPsEuGwWjGcwsMTwsGqWiLF%2FMnjRfHQKWFh4cRygptIqS2HS9VV%2F3QmmUUggcEebC8Uoj2DYpSk3lwtyIVVICSlBngzEqld37Hh15054P7hjCixttTPwPhEmcSJM7gaYPHWYHOMwOafABJwlpCZDs4l4ye5n3z1vCiS3qO%2BQ3c%2B8e54WTx2HYzozyFxEIemvKoNUF4hS9bpXG70hOtfCVSWsuWUBvmGAL4zrTbQX1pu0uN%2FMt80YoMOYGtSHUYmKSgdmKpdtzjQq2r1iyUim4JDLood82DCD5hV4jYKDl3PDfL43zWGlpAf4cTKGr9zFOwUja2DNvIP3cB6fSTY4UyE%2FqRjc3bHmeM%2BcnqbsyXOTt0%2FlVzbAGm0WwcCxxOvy2VyElyNDSc7Snl2fzS1p8xv9%2BQ1qujiBPhjJHahxtEQXZDdN4vFVMdeIH3ftltxSjYd9qB1PRbcQxYgnJpwfD7H3yKgWeJwK%2BB1gEBUrcL4xDucHS%2FMCpuzV43ARWhDFZMqOQDRHi6jKtng3jQNMQ%2BlcNjS3o3%2FDUy0L4RT6N3pQcwUVYl8sns7Q4XPDniL1lxhMywn1K0Qsh9vatThePjdMCvmcFGZlQMBBBCmeT5NUDtRxONpXiOdzsAsH4VRO%2Bl7kts9X%2BrZscv%2FgB4HnT6l84fbwBv%2Bliyv39b5SCZy7qLL4Ev9PyeHKDfcFKxXvis%2FOrZy3N%2FrSS03337DwYv%2BPfjTtq5VFF%2Fp%2F%2BAP5ogUVjV8b8%2BYi4zlfnroJWM%2Bb0yUfGn6YM0VzvrzBiclxNC%2FgBMlxDVkPJ0r9WnjbbRfX%2Fp84ffNXDVcxU%2FNVJ%2BMQnIpD0wQc%2FJNwCHwMDoABORH0m3as%2B%2BFO9bETZ%2FjFd6yr7lQfnwJzkw6zj7I%2FXy8O1SR7QIOZFofWcu2cXgqz6NNhdjQdJ9dOJpji5aHpXb2pKVAPs9OLVz75YXXj5ic%2FHJiab3dFkS0Mfzj2wcCHT27eWKtXPof6KYxMkObcYThW125KrAEYm6HKMc1kxuLhsb1snLxEs5l%2FPuX7ep5J7ft1XxXWoIOWpH2fpqx2qQPqLNKtwfBDmKOLYI4aUU8yUC%2BQQHOw%2BCr%2BUIk3NKRVIyA%2FJC%2BSA%2BohrZx5bC%2FWFOLvfAYG4a3a7xhrv0PGfweWCcXYrxdug3ryGdIGsHSoM2u%2FU6PJv1K9DauowZhG0B2ccd4d%2F%2FEgXnWW8g%2FbK7e8%2BvT5JuLUsGBb51zVKGuM8E2xlp%2FlyJScuo4nE%2FjzwEuMyjeT5E719Zvf2UmSN6ubbyaj%2BgHOjEdgF34vxsT5nfxO%2BnsMGLpREWxd7VdiJIUXqa%2BRvTeT1E71tZsVOKJu3q6%2Bxubxx1I71Ne2Y20Nzal8gOrVcWY51dpaqkqc%2BrNDmVpmBWl0Cpk1qy%2FgwuxKm52PxrUs0DjIaTMuVyER6Nk%2F2W9BPGGCajrG61BNR7Fdy6zUPsjaGXzc2%2BeN8zN4v%2FfHkn8aSO%2Ffjp%2BHD3ae%2BjNvKOQlaXgfewlkuKFO1yTTwUxjemGF6WXKKZTkPTkMjqLUhsW4h3Zm6KWdGYYA%2Ft4e0b3favPkZ2hhdzEnRWlyDMDV15vMEKyyd8JUg02cVU7iIF6JlrN6YlgEQrNtCmHMAh%2FF9eRSwrEx61CqJfSfPrdHej8aSg1Z4ix7aRFzwYtczON%2B9Vewedf3DY4WN8u1aBZikVg9pqdS08RRu89vG3WnkyWT10Jo14DDG6Zl%2Fhm31M3cLS%2FZ2tf1Ix999Cf%2BWVo7lB3P88uBtoEpsKVeyk1SvpDGHL9hQ8HLOvg0Gxf6fIW%2BxDAmEaSSMS1WzQu%2FNJs4aXo6c8G5X77j7c90LfrMZZ%2B95vrTVznPcadmF8j0eRs3ndbu402CNyrGvzI0pN51iu%2Bu7%2BYHbzp7%2B8Dgmlx3uD%2F8gvrDn%2BxekzMbPc7CjfZVI3uTM8%2B6aXXWazaEArkrWlpe%2BiHOmUsZGz%2BH%2Fw9mAXAZURZSjppbVea6SoMwFIvgNTgX2GdGH029cXtlWAl9GrBhArCCLhMmiA9f6GNTPtmXSqbSbAE0nDDn4AUjbpNLDeZ58yIjM6a3OVsc3uVrRUPEZzE7Oc7kCLT6%2B%2Bdsmj3LmXz0H3xSar7TvXyVIWfvOH9R2snaeBMhVlfQnZwhC47emeRbI%2BHpN3ZLlsTwUHjkvdSKvcXowi6Pt8Upm6yEN3qbh2ZfMvspsurirjUPGVgh%2BrnX7jNFzrv48VW2gabepjY54OIt6aVrW9KXYJ3695gb%2BPf4HBNlZjLzmdkMLk%2FtVWWGlk66IKMMHlIK1dJCIEIBVL5ZFnMgaGpvyw%2FTPC%2BmZJ4hup9nxNZ0fnhkPhX6QBdjNmyIAFmMsbQhVQgbskCZNJ%2FSio3yvQWj7JMLmJgwFDQ2z1t%2BxZYvjn5xyxXL5zUbg5MP7BZmd5%2B2%2Brqbrl55StcCKymEZvSHe5T%2FUdLn3dpzwQWJJR4vu77N0rZu9Yb5mcz8DavXtWE26MR9y4ahM6d3tuVOH%2FiMgyxMzJ0RWHQKlryvWbjuiq2zL%2FBvaG3FaQq0YIAWCuOjGQ0nMV9jys6aJTqSwWxQpS%2F47aH%2F%2FM9fM1KnRXGmHYrtO4aSg%2FzNodi%2Fozhd%2B6xOm6dzn4u%2BB%2Bh7kL630vcEvpfhbMudLXfGjWCg9SuBfiXYr7T2K4l%2BxdrPPG%2B12V2BYGsirf8jsyxwyOGccDCdVmYFCaPRGtOncySOBC64kaw6gWcSmvoBVxSSMAgsDghfJ%2FiCocHEverL9yZWPnb5BSnL7p89n9h0FyVqRg7PcDpIW9LXnYlN82TIxs7U7JvT9mBQSJx67hfHyS3uPLriLunc86%2Bd7mxT3%2Brb4X78Eo2imXXNK5uCY4%2Ff7UxmkzP9s9gbp93Td9csb1ubrXfT2efR%2FghfBeHopvqGG3PRk9StkQKQBbSkQX2WxCaSj1%2FalDA4Ek3S2DbvdO%2FYtv8iL5AX1Mcx4DNnzvwWviVmaDl6qMj%2BfKy7iIIMu4PU8rUkKnfbYVZj8ihyc0dGSVA568OOOGUfzZL0%2Bc2d5YSvVqNa6gRO9yVguofCaJOZw7DpS1CnfL63r4DeB%2BqPChP0FaQSIvUZZEhcMGKfhYJoMCaLPifZtH7rerLJ6fOL6gPFoOfMN870BIvqA6KfGNu9PQ4yl6waXr9%2BWH1a%2FUdHj7fda7f9Tf3bab5ZXtG8bJlZ9M7ynUaEv9lwNa7le5sndWEowGpSzzllVjHr6tH3PJaZabkwmAUEhj5G3huz0j%2FltuYLObqfXxKSdmHi5C4pVPzYfzT2xL5fuzAkHVtUS2GH9ebv2tL89JzhGv4ZxsK4QJc%2BieppfhpZ9NCkGxNtL8Flx40zQmsRfLDUW8V9gt1N6yhcYKU5cBCHCPZ6QC8pcBmY%2BlFSkLV9WCDX%2FwUXvYfG9qpPEu66Hc%2BpT7KP7bjuITz4l0qFXf8yelBfQpXtup37x%2B7beb12oNFf5gZdUqsBdoEhmZvoZZoYHiNiPJasZYuNPaX%2BpIjZ9JgQpuXPIzcPMFqeFR3%2FBHBzN7OBKUfw983VspmWapgxj0%2FKYg5MRw7N%2Bk6gRpr6UpPYFAMbcXCukpvQDGEZSx5LGeByDlVckDxusRSOALlkTI7TekJJXhAgoKgPG1O9WLzgdRhAlNfaMYGNahTXpPKJgfaIaAUbG%2Fb9qaXnfuU7Xzl3acpvxPZMpItdXzlyWcvpAavV2xxv60r7RdORSsfKbRdetiKXW3HZhdtWkrVaIi9W%2BWG%2Fod8y3%2BQ%2Fw72BWhMno6Ms%2FDuygWz87dg%2Fkd2%2FQ3fZ79jZSOt5zOv8D3mmVhtvJvPYOezIb9WH1D1caOwFdvbv0Amn1dPNYIqGLaDjm2AetTGoU%2BtOGIGq1uiB4QVNRdc2HLiE0QpIGTQoeKFjXXyZdJKuH5Cbf3HsTXLvL8gNuN9ZYQ%2BQJvUPtBUR1rVtVv9AmlCdBxjdMGZPwJhlAdpyDMcrkFO6gPYuJallB2Pwha8im6BsAhWpFbMXMWWxWYtegLJUkpsBpG46KskC8A7oHOj7RM8n52A7CV1R6QjRzzgur3hZNJZMuQ1OR4C7KWloEs%2FEydj3CmsKuyOhjpB6EN6yNI1GnjZ2W0u3gXvS7%2FY8LEpw%2FNhM3u6jszervYe9UqjS4WMacoOxjsPZmAHHozau2LMUl3pFInZdIfmczvRRCQulK3rB62EsXuVu0Qpatd%2FW8n%2Fxt10wVvXfdtL8QYP222IGGbnBPsuRKBgW4%2FeIEmy1pB5suNMxP9aW1aYWOhoISQOPbacyNapVs9HOTtQzhz2cNLccb6YaH8lpTJCDr738MukgHS%2B%2FrB5CDteSyXX%2Bhf8ZsCe3w2820%2BoitEctGdoSajydjIZroihxotL4T8An%2FVH1Fy%2B91JADpgCVtfqChVqGuRKsYmeFEC1RMlDu8VZpWVZTzXcXoVZQOUKbqUWwmRpWaJkjoj69J6f8NBEtc1f2YNAIhGG8JpOO%2BdsHIk8ZWr3sHk%2FC8JR6V1FLtKdDBm9kDfs7X6pbHmul3iy6ru%2FWc3WtIKsKYPXQsmsGC4IVo6vUAbBladl0qcOIfs8gJTAHTDuTSGlezg9yw4YBkhATcY%2FPAfyMHv4US9vZ6HIzljw3GwjwjljQ9wphWd4g8BX2kbEzkhnWYnMauFXeEF%2Fg3g5JyLzYMCDrz9rtR1hB8kXXHAF92sILnIElR449U1HftVlJs0ROVn%2FLraS%2B%2FmdoXgvLLAL6nwf074R1Nq9XEeVoRmskhzLVny2305zq9gTWxfVmtNZD7TnAqqtHi%2BH3DZCCB%2ByCVJIGLASjFEYbuu4ABVTQkDOE0fA0xhY1ed1XzXRet8ptdHvP9ML7quucM7eI3qag2xQuXvjQ8hv%2BMmOGO0get02fN9067SJyphR6msxfus3b4glKnlbiuXGp%2Bq2nAfM2gbOGJHe71yu0%2F1vP0FCPEflTAp3oQf5B1Ldoqyn8S6G3uyCgwzslC7L0x2Vvdu%2Fe3f3msj%2Ft3%2F%2Bn2vYf95EX6Mc%2BevrBrt8s%2B%2BP%2B%2FX9c9puuB7U1uqjXRcSYNKx%2FNK7cdghj5lio4msD1vPWqhaId4BgRAwsbS3xpZ7v4mRBRxFnkpwkRCUunsLgZXFoq%2FNIOvF%2BaCilfrN5KJVtmubYcLe3uKsIhvOLRw7djm2ZPlxc7CfrI53FrpO3CLu6Tm5rfvlfggn1DnL1qwef%2Fc0N6h3FxtqwJTqcm5lyM45qgnp7mRw2A4BVU8CSZ2x2YanHx2G4Yf3kaU2GEtfam2gVu9grQNSwTMZFd9nU1oUucK1et92tdMKqkkYR3kE9STmxIPYOEOQG2aNPwHr6WIZNFiIwBVPRPBcX4jDTPgn3Ioq0sb03kFEggLp3MgEOvgrIk6vpvIT1r2iQ%2BDUU95OYaxl0pbbSFiXtNEPbi2iXe2idfA8t9Rw5Lu7dWnJtN82o7R4E6YJFct2A%2FH6b2OSdSY3CplZAOQ4UEBV%2Ff8nbA5SRB0%2Bi3uWCSIkA0z0BMzpCaElWtLaiEdoqQY566DJHk7ijHSzmv%2BTB8k0ZTJVis8NBLnRYRdvFDusakl6z6eLVm7YLTU71SeExsAESnzU2OX%2FgcrtdaoFYeTMn8DxrsHxxrfo07eC1mLWLCYv5LaNts8Prdly7ZHSZ%2BrQv9XDTKWSV1BaWvCFCWM7A2UwO%2B%2FOr3tVl2rnAN2uZMI2hXEs7omiZorBeT8f6CmVGtjydioPp%2FUi7IbqUYC0CbYIQ6dJqEZBxgNBeF0pAZVALsCQjWFJoa%2BbbKe1CrTThQomICtNf4qZj5Uiqi9KOBSFo0EiBJcCGMFvLYkpx8VgqhzI8WhATAha6CQ5DB0ElMyeeexESyuq42O6yOsjFgjf6lEl90tVk3L5pteUNZ5PxsySxSf36urstBmIAgpl5K1FbuT9W2NmjS8i1Lpdkv8xmfIu3p8SxD%2Bxfa5PIqmK7V336FPLf7636ptVpNXMYaSGqDSmszbWNqLMaHGCx9DEzwEIpF3CuTYc5NZBRpENKAfuZldslKkJnAHlycMCFein280O%2FSzsGWdx9%2Ff0lR4bmdTFY4RNma%2FHvVCKP6VCSmEoancSITVAKw4ZBrA7ow0nlkw2iEMUaso0UdNZuf7hNUp9eNrrkWmfCsdl32qrnfQEN8mwWkTpFNZvY9eyP1fuMnshjAlmLpLlw9SWb1qg%2FW20j9kt81rX3uO11GpEKutK%2Bj4RUXwe7paLHMFjGwr1n8MO63QscM5dR8loPmH6apJ7PKp3VcictwemcDphHs1h8mtKy0mcA5p0YB7SBSl5ypgBzO1X%2FjBEwOWeCrJCw1mSIJLQmkZgF4CScJyqLBgc1SZN5cRBWlVTSSVCTZ12%2Bzfak81qydHQZWeVtf9g%2B9iHMAMuX3yV%2FPUV9ytuWzXKC0e%2F75j2A%2BsKI7xI7sa7JVdOrL7pk9YU4mcha4bFmh1PdDqOuvr7d2Oz8vs%2Ft9pGKVXCZOLv7HvYyijiL%2BjjMkycZB6w0M3R9xUX1Nz2%2FjKbvac1DXTZ0yLq1Hn2Mh1aEl0ze%2FnpNuB42YEEFF8xEMpy159fXPVwPlb27hyRYUJ%2FIPeol7Jrrfr3nrD3qu3oA1EMsr7xIkqTjJV1ffRJgcsGqN123zGAK%2BjSYtGwGi5bKUPJi7yU35nWVLYzYr1Wpo0tLpBBhV0kKErqzC9E8QHRt450r5B4EST2oAUQB1gB6VH39xVdemkKjYZ1GYp06Wvadezz7TkRj1ubsp1aaQcIsPHfJ6tW6zk6gU4J2rSBT6QTraKUGUwORHkXLi4xOhmmpDpO3cdC0vMUGsKQJYLl0sESaHGjDdZBx9U8dyoSZ9qM4Doi0irNyHBg3oyWp6dd8HU4XczvzJebLzFZ9PDuqyvKM8oUMUXZTcDURu17rTvIg5oAx2sCuF2c5HAZPNN46uGjNNbfcese96BuwuGdZ7LHktIFTz9%2FywJepEF7eIbqf903rG5y3YM06vOYL4iyzhfGuv%2BjWHTvvpW5Oz0Se8Bh9gtcnZNE1LIfZgg9dcF4HQaHEFmA6wn%2FsCGwEZRCjqX1pgtuFvkKYlcMkAko5nCkMc4VkAUPlhTSX6kvhjIcrU8aUAyYptocV4D4OnPwFXxbevD4pzRVylKTXUuJpJP2bqckYIzaf3%2BRf7RvuD5nSXEe3kRg%2Bc1U4Fuccabtjnmgeakm7si6eGNt51hQIyh6P1egytjUZbW0Oh4fnE7zBIvh9Rpcp4pbNlvb4TJvVHOmzWYXMSqfH7ewMDpucQ05pmOM8hOshHBfkLKLVLcTMXc2tD5Mp84CcEr262TwvYPXwtrApE%2Bbdi7zzzUaPxcZdEWodjpiJILishLVa4zKbYe0mzpd0hwKh5rDLSIhg8STMJm6hJHdaHB3egNnt4cxWOSW1CCmDnTPwrXGfjeNsbqOFALWElNMqC7HLLrO1ChabyPvXENbI286qxclY%2Fln%2BVsbLMIOkQID0fbJBLoCSniLGWIY42fXRZRv%2BYac6dvdR%2Bz9cv33sKWen89K7Ol3sunNf6Tv93Dvf3vbs6fMyY0%2B5XJcwtAfiBuDNp%2FS8cM03t4LBfgO9VaVArePmDPaVAkMfphPol9OruBQmqjT%2Bkz9U6ukf72lZzlPrLY%2FWG3ZGxJjQ%2F6Jag2CzKdpwaqJRd6JtsEIPqzatdrpS%2BZdxQ%2B9bx9tUH0BHIHugON7fAW0ArNjS6rX0kKrWMVmzq5kSY9INd1ycDKCfCGKRe%2FvYIu4W9eEKv6SIBd7qQWwAwzTaFminJ5gN1INDaFVrKIeagj%2BG1e1aBWgyozgPKTHaddAZQwKCagJg0Ebeca0nIRaGxWlhGHYhZLO47jIlB6y4SkRzD0qNHVPrJR7Ua0fd0rQzbI12pGugvdI%2BgH4GIBz9Rw1jMto%2BoBEKSzppHTozwa7HuKgXtKNaZp47h1F2jKpr4VbMTCA0Fwk9lNgfhStgEFW4ubKzglFX7YN9Td%2BrYEi1gi84wLXQj2MPaCfZt7RrGL1X515%2BL813iYCNPsIsYX6md2x3VtFTviinBKrK3CyWBffTvgS92XIbpVnbSebOWvudpQ0pAbAk6IXrUWsndtpIaqb%2BcFUZdtG4yYLqvsKC2aZOpYdaGwXaB0%2FPfliGSjD2FbA4aFuN0jBI31L3IOg%2FBTAdnjM7PXxGyzoKaz25bKLWG2dfR%2FfgMG62aUk4SlwsZQZw4fGT43X58ekd7%2BKxPupPwC0ZbBGWes20%2FZSghakSWkEA2Ogn6gt0cSTd0iL7lsKrpcUnr2yPXEfM2o7DCXbOghwJt6QjcKqFlE%2FQPug27fzvu6It6ZZvt%2FQYthGHw4c7lrt9odZcbqWvBW9D%2FaO%2FZAb4bwPvLAftCvMdUlW04kCa9MGoVbFXBPZvYEopzDmY2a%2B0iPutzmBTD1JIcpeyOWpEGGG5yWVxVeorEIy3pjCALBDgN71%2FvYCNJeGwfgk26AnDwgNqZoTgN1C5jMdAwpKm4LT4lnkzp0dntafaRHJn3O9sOtVrTp8UV68X5pJrvR65Kel0tY79undh4VSzwTmtNen3ktN7hzb1BeSztpiE046NCXNZw%2Bzpouu85XNXbTq1bb7KkEP%2FtKx%2FVrOrp6OzC3%2F17Mw6kY3G1SuF2eQqn0tuTeEvjsyMD89qbZXw9wp5Xtx99rmn%2Fe0jRhgkH83ef8a6z7VGC374Mc23uILp47%2FHr4cZl2eGGJTC3dSPI2QJkg%2B0Bq2bWIemNWCzjI5mbH0uOLV8HC4HRDVofh0gCHVHYZ0CdlRMsylYhb24jPtkMG2HWbRcY05KJ%2BOKl2656%2Bzz77r5xfi62fNf2iC6Oi%2BfNzJ%2F9rr4E0PDs3xnbFx5mW3W7OEN%2FYtmbLk8t2DwHM712Zduvvmlz6ZPvWj%2BvH%2F%2BrCzPuHneSfPmX3RqumntScO%2BUy89%2FVLb8Lz1zfM3rnrymRXnaPj1f%2FQBfzX%2FT4wf1h7G4wVAYIwJbXqfErADPoyzYKQnQKgQkC7wCSZEL80fgBHvH9kx57QzRkjTyMgOk%2FWCg%2Br1fz3LnQ4VRg6O7BBsFxwkt8J%2BJhyKF0Z2jJxx1iz1DyNk41mzdo7gV749ssNswu%2F8z1nu7umFEcKfAd%2By0G9tEHtyofiFnXBkZET9wyxNrlf03oHYQSWEefA00hqgVrWbemSMWtcXGBnYd2tVGLSDBhU1Gdr%2BpckNyqbR4NMew1AQtZSOmlVciIpRt16ZXxk878EnvlycoYVWimio%2FsvQ2qGhtUXuW1I2HQ6ns9Kx%2BSCx%2F8IeOPYanhia0EvdP54VE8hgozEU0wTjR6lkb6EvKxOfV0DFgYCMJpkf%2FwxUWmvc6XR0OEgT%2FWhV%2F3jwJ2TrTw4SuRV2nU71LSd%2BxNX%2FUg%2F%2B7McMR1KMG%2BzK3zA9wKGzmfsZrGPsoPWNw9lyN089LuhJGKBOSHe17B7AY24Puhzm0AY5sNZlq7hAZl2lLtJZ5t0zMBUQDE5vZJhuuUqzgJAgOpqx5r6pWpqLTY4wu4jtV7pExYyBkG7Ybe9XBsRvMI6m1Awa61fcmDGI3nDQccU0iaf5fK%2BeKcjLcRAKooyV%2BEbMF8Sid483zAGXiQ7ioU1fSerzhnWLjB42PbIsE12%2FfXkxOXflYCf3kKlv0azY4IpCW3l38Utr2gJ7XWKHt1nghxb%2B6Yn1K0lp6TlOspwYHYFM%2F%2FrC6bfNFpYt5z1dMy8YWbDYrlYdgqdr8Nzhzz9hXbpMXNe2iQ2HuvySUQCz3m0aGOt03zprfkCPCZ%2FH3cs%2FDbTdweAqBHYDqChhGk1spo3srNWyhz4QxOPQaSocUvppD%2FrWbNlPux%2F6w%2BbOskD9XwKjVZQ7Na8FUtKPjW%2FZ6SOUYk7xOWukpaMnhzsOt9IFtO3owcUNjUtGLHUNaoHlHNILaEmlSryXPkJDAEsfKJ2T4j7YFoZJrg%2FntIx90ZI0QyY1bMAclOJjfq%2FFzGW6Nz528%2FcfmLOiNbHa2x6Vw395ySRJ7TPiZ0vRL0SXDGbbl6c65J9lU2tlf8EoWUSbaJ5ma2XXF3sKwYG1Z7atL1%2Fdu7g5JHX1N6%2FqyBWz20MDfVYHiYW%2FGJC5EZ4P2qx38w6j3SpaCnfNB5kD1jX%2FjK7LRGr9Px0ZxYbNRJAoJSsYg4qp3lQ0LppZfGdpDAXsZf2TRgFpSQUNhxzbyt2C0T1WlxFG%2BgwF7H7xmN4TxpgDIa1tmmhipbdaYnhgbzvNk6eeuXKIRkdDMrZAwz5LOIosbZah5dXhgwYwtc4uwxct%2BiNfLLSisNSE7chdoLIGstl9bpdkopVyHpRN2Gmjlo6nmERYE8DoNdFaSMUuKiIOqGaLR2nPaQz0aI2QOFFrilQAFZJ9uYIxpNEirb45p3gOOruLBFv2ICFo01c8q7U4OowNiiqVCTRx0DrDGLNHyxcsCRbsPER9koCrifpdQHK6qJO3mYpU31QCCICMUwJkgX9FJIDDReNfoRaNKB4gzr6gxWrSNLwMNt5qJICgESAWwWoD3oDVBqVmF5zzYx2mEmigho49N5kqDdRAGhTrJDlnnBoaBdgD41Q5thVLcigPLtF5xA1csqnWvxRNAnRfACEoU5TtDtrGUMSJLWcUEX1R%2Bzwu0UQf7SLQRyV5aB4hdmSyUuzxIR20A85%2BhuWMtEuEA6tdDTydt7o%2FgyLGYXBDREtL527kZRgzbUxrDA4aOiDCvQ1GDj4oosJ8HA4Chd6NmJTdtL%2B2G9t7YLFYleJgRd%2FVPg8dHTvFU6A4WDQcRMrCFAfPRBx8k3FIUOg1Bk014kBbEBfPacShSIHHP8qVuERSvtTtGgtwJmKxktERsNZqRFz6VKWwOw%2BVBasHlyRgUmw7Sbva%2BixmfAfpq8FtNaE45YxaFUuN4LQvWO2Vk2rihNaD0da2mLrYQHTatrv%2B%2Bt%2FBawV4nRReEDAgfCx0nbC4EV6L7xPgzUlcA8x1eCsakHoz3hODW%2B%2F5qNWdd4zX7jZPLY2W6wXRzNSC7Mn1Y1jQ%2BPD77z2CRYwVraRRq1zU6hi1E%2B%2BcVCt3HGivxdFqsMTQizkJmvhUaFBkyCAWaKklU2r24GbseCXjkyH0dJP3H0ZYsNLykfcmAFkhS9V9COkj72l1mI2QqrYjR8bjfv9reGPj8Eb%2FHnjlJiznrUM0Ad7ikSMatO8%2FPBnaIiCy9P932kY5hLU2zhNhJYePaEyAiACPzGwg7WaAlfKkwUvrC4PMIFNfjqnbBysH3FqnY1AHZNrVGESpQI2oWvaGIooKp%2BXYY0KEJ18HLl%2BDWnvUW6UGFxlFeBCuivqw%2BjA%2BBmxCIS7V814DQXeI%2FyoTxYwfh%2BYbgiWxmUakMRnC16wlQ5Q86PF34kwFwoAtF0HLuI%2FJ9YHhZuhzF%2FoyoImBrsUIxtdMMknvNhjMZrPDZmWdxGE2yerPyZdZVjCbnRb%2BgPrhYhcreNRFXslqctrZCzyiSHjyrM1pM7uMP1d%2FutZTn8uUbn4Y45lMmdPpZsKabCw1CtK8DhCL3vrUtmoTuhTk9MxExSUqzVrnURjSQj6KhZDUY4VZn7R5BKUpjm8mr9Uik8OOyhlnPPoOrkxU6AAxnd%2FS6PctXmDZd2hQQKNsQ01AiGllVutwOumiGgTAEg22mVXz5CRR5QOT7Dl%2FIBxpaUWtF%2FSDOFYtYzWLyRyOxenRoEi9bfUi65Sge920gt9CCvMVE7RqQJ9Ny66rVVQ%2FuGVR8dF3yJ%2FJxvvI4fvUJzSWOHvh9ffWGPS6Ze88aiZr71Nt96kPTaltObVWj9NEH9Lhaaxw0apysKUEWKKBuoeq9iC053mzxeF0e7RKRrkJjhCjYLO7ROaENTqs3ljiRFU68uV7Lr98z4mLdNx4%2BvIGOYa%2BUBfofmm9C7eod3CgD3mUaBGfCfQF%2BnQ6NzqQfbpPsSYLzCxt7Q%2BrvMPK%2B62uY37yAnkRMxmxWyY2l3Va1Pe4FuxGjGwytiXLHOf%2BnZPu%2F%2FfdXEshNE%2B4excWqauzyZnjd2cP0PDU4aNvTbj3jXBv7LtZw11boQ1aY0rQ6GxZvL1Iby%2Fi7aUpuIMZOuHmPezqL6vzdrG3Ntz8u%2BqP2dW71Xn3j83I1p%2FfhXijz9Zfx1yqYgDRoPUi91HMffTWPrx1cDLmciHFJTxYWdoIwMaDGzez3%2BKO3HfwPvKjBvL%2F29mvbjz2JmH%2F8T%2FhjGo%2FHhzpE8Dhyf4doHhkQS7I1gZQth48%2B1UO7%2FY3ck4DMYoHNx4k7wB8977HfuvoH7OaDJPxmRz8s6BJmbDOg6e2n5lWPhmr2HFOK6rHmh8zkfkH1IT6%2FeXsnrEN5CHOdfQV4lKvJnuq3BNj02sysUhxwyrMpQyuHkYYSCMdSHzmhiNbNor0gRI87Jmytef%2FgfwxUhHkpO4hWkrnNIrustkna14hTaTgcwhkWjGOOm4siXiHpCPA8ZWwd9fYnF2YKVYs7uLe9oapEDz2DBboq5tD3PkVyn%2FDtP%2Beh%2BbjzWJQPwQInRRCJxgVZaNzAnjh2kJtpJ2KmZIRRGDZKvv1xvh1oCSQfhSkRAHLo7hxwNR3r9mmfg4hm3v9NWTj9WriN40Qvq6%2Bd82NAGAJTm64Xm3lnqjU5orR4KCwykycPiuxtQGYRENnBY0qQorUaeJpKIvUwTj%2FCfWQ%2BrxOoOITT9xY%2Bz8OivohvYgS6%2Bir9ORNX8f3Cb1J3ZR2F9E1GWjnoLRzoEljdKHKHcRy5BJvytbJ5wQKM%2BOBHLpAOl3Iz9j9x5alTsAgtijxYGk5rOYlJLB2yDKRzvhATkpmTSnnaFa7RufNqo2ip4WhKw00BjsCsHqSCiPaPrhSYY6D02nHwel42EyAvxF4JSRinkcJi0LKVlsA%2FUOwSE5GQR8oLdZvJuMoVID6l1AcaLRfPdiIwyUVOjY6Fvj01BoOFZoPZ6acMrl2QNMQwVzR%2BrcdXcErev86MnqE5g4ni7RrxZib%2FiDGls6D33tGt6m0ztg3Y6Si5AvmsL1JSW6mD4ssWZ05fFxkyebSqSPUWuhaqSsNvRA%2BugpTIlktaFUZNOPVh40NeM7p0mNAbq8kah0btZJPUDlKxAv0MyFhtXRb%2BKNuB%2FqiehHuDmDWv6Zf0h7Ro3rTaLoJBtrL7LdpRA%2Fzcg%2B9TON79V72tbrsGVpdNi29ZmpOFdmKS9M%2Bs0s2Yf9njNWYqcZusVJHYY8mHkX9ycvam1DvZ5HwYrXOeA957UmwxWxW%2F08bhtBGgVYL3W4Nqu8GEuwB2h4ba2Bq75t%2FoJ5Ee2D8czGUj%2FqNBvUK2o9vZcQjXinOjTT2Nl7C5DBDhkYjbTklVy2zVgfau22Zsr85gZXmKXxWwniyaxsmuxpbaFPjaB4hp8%2F2kbAHLZenSX7APjRwiksPboc51OqdBCi8OEAOBxaTUZrJmw0E1M2LA6otsFjdXDsy3ZYkd6pbkrbp04nBLDm4a3wBwk6Hw%2BoWcmf98LHb8bAe29RsJz%2FTzLSAnZxmskzZr2sKvgz1mdNSH7cX3UCZjNb4yO2iLBVoplqcOMVKKuQS2K%2B5bsNHZSkB6KSiJJ%2BQtCxz0E4PotE0WrkAdJl1RfWOYq24AQswUxfQY6hQaVdrBs3YnMrZ6qFi3dwfrZx9dqU4Nu%2BRWx%2BBcXExLv4h%2FiF8RrA7zMlhLjeT9GHBJxAwTVKFYYLpw07iIpHhe847%2F6f3Xx%2BJPCIKnu94el1bdh47d4todHze0e1%2BmPt95x13HTzv%2FHuGouRhd9pxu4MXt5x7bOcWscf7oscoPjwxtx%2FXt4zeXwmf45qheXsnZUoj9AEvTWhfZmCCtYn7ku3ZWZRomJpQIPneYS6LzCvp9VE0K8Hrm0mGyRDpa5ElL3aIBo0%2FzcdjDt5JJAe5mPUlV25emfQRQi52SChkvOGQ9%2Fve0A4yvINuGIPFra9vO%2F2xq4ozu8zm7EFvzkEyWd7eEpC8Tc0WS1b9qSPnxXZ47JNSgI%2FyMekev%2F8eKQabAaliCpzU0y5F29qiVkt9%2FmIecKAefycTO%2FdGpUQ0hT1dJa2LPX2JMn16x0fcN26gj%2BPQus5Xdh2hT6TGf%2Brmou5zqN3DTDvatOoRdm%2BOSjvLIepN1vpUaQuB9gSReuO%2FWoubXH2riKkGtEKIfhT1Hc0cr6VwsAc0rqImJj5LSu%2Bph70UsGpsge5PcNXtnq4Gf4Ko%2BRPw%2BX2iiz4DoCXe1qF1I6D7wZZkW7tW5DmpjR6G2jzoPMxpXf0NOXQogjqBfeiVrewBrLccm7NVoZ2TKMNjLyqgm%2F6UM3IYr1Bt8I7VGns1IUa930XNoff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AxNRE0NjMhMhYVERQGIyEiJhkBNDYzITIWFREUBiMhIiYBETQ2MyEyFhURFAYjISImGQE0NjMhMhYVERQGIyEiJh0VAZAVHR0V%2FnAVHR0VAZAVHR0V%2FnAVHQJYHRUBkBUdHRX%2BcBUdHRUBkBUdHRX%2BcBUdMgGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHf29AZAVHR0V%2FnAVHR0CbQGQFR0dFf5wFR0dAAAJAAAAAARMBEwADwAfAC8APwBPAF8AbwB%2FAI8AdgCyDQAAK7E8bDMzsATNsTRkMjKwHS%2BxTHwzM7AUzbFEdDIysC0vsVyMMzOwJM2xVIQyMgGwkC%2BwANaxECAyMrAJzbEYKDIysAkQsTABK7FAUDIysDnNsUhYMjKwORCxYAErsXCAMjKwac2xeIgyMrGRASsAMDE9ATQ2OwEyFh0BFAYrASImETU0NjsBMhYdARQGKwEiJhE1NDY7ATIWHQEUBisBIiYBNTQ2OwEyFh0BFAYrASImETU0NjsBMhYdARQGKwEiJhE1NDY7ATIWHQEUBisBIiYBNTQ2OwEyFh0BFAYrASImETU0NjsBMhYdARQGKwEiJhE1NDY7ATIWHQEUBisBIiYdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0BkB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHQGQHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdMsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAFYAsg0AACuwPDOwBM2wNDKwEy%2BwTDOwHM2wRDKwLS%2BwXDOwJM2wVDIBsGAvsADWsRAgMjKwCc2xFygyMrAJELEwASuxQFAyMrA5zbFIWDIysWEBKwAwMT0BNDY7ATIWHQEUBisBIiYRFBY7ATI2PQE0JisBIgYVPQE0NjsBMhYdARQGKwEiJgE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHQGQHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdMsgVHR0VyBUdHQGlFR0dFcgVHR0VyMgVHR0VyBUdHfz1yBUdHRXIFR0dAaXIFR0dFcgVHR0BpcgVHR0VyBUdHQAAAAEAHQAiBPIEKgAFAAATCQEnAScdAaMDMtT9oc4Bxv5cAzTU%2FaHPAAABAGoAagRGBEYACwAAEwkBNwkBFwkBBwkBagEa%2FubUARoBGtT%2B5gEa1P7m%2FuYBPgEaARrU%2FuYBGtT%2B5v7m1AEa%2FuYAAAMAF%2F%2FsBMQEmQATABsAJwC3ALIOAAArsBIvsBfNsBwvsCMzsB3NsCEyshwdCiuzQBwmCSuyHRwKK7NAHR8JK7AbL7ADzQGwKC%2BwAdawFc2wFRCxJgErsB4ysCXNsCAysiUmCiuzQCUjCSuyJiUKK7NAJhwJK7AlELEZASuwBc2xKQErsSYVERKyAhYbOTk5sCURsBI5sBkSswMXEBokFzmwBRGwBzkAsRcSERKwEDmxHQ4RErMABRUYJBc5sBsRshQZATk5OTAxEhAAIAAVFAcBFhQPAQYiJwEGIyICEBYgNhAmIAM1MzUzFTMVIxUjNRcBHAGQARxOASwHB20IFAj%2B1HeOyIPCARLBwf7uRmTIZGTIAe0BkAEc%2FuTIjnf%2B1AgUCG0HBwEsTgJs%2FvDCwQESwf5ZyGRkyGRkAAADABf%2F7ATEBJoAEwAbAB8AXQCyDgAAK7ASL7AXzbAbL7ADzQGwIC%2BwAdawFc2wFRCxGQErsAXNsSEBK7EZFREStQMCEBIcHSQXObAFEbAHOQCxEg4RErAJObAXEbAQObAbErUBAAcFHB4kFzkwMRIQACAAFRQHARYUDwEGIicBBiMiAhAWIDYQJiADITUhFwEcAZABHE4BLAcHbQgUCP7Ud47Ig8IBEsHB%2Fu5GAZD%2BcAHuAZABHP7kyI15%2FtUHFgdtCAgBLE4CbP7wwsIBEML%2BWcgAAAIAFwAXBJkEsAAbACsARQCwGC%2BwCs0BsCwvsADWsAfNsgcACiuzQAcDCSuwBxCxDAErsBPNsgwTCiuzQAwQCSuxLQErsQwHERKzFxgcIyQXOQAwMRM0EjcVDgEVFBYgNjU0Jic1FhIVFA4CIi4CARQWOwEyNjURNCYrASIGFRfSp2d8%2BgFi%2Bnxnp9Jbm9Xs1ZtbAd0dFWQVHR0VZBUdAli3ASg%2BpjfIeLH6%2BrF4yDemPv7Yt3bVm1tbm9UBDBUdHRUBkBUdHRUABABkAAEEsASxAAMABwALAA8AMACyBAAAK7EIDDMzAbAQL7AE1rAHzbAHELEIASuwC82wCxCxDAErsA%2FNsREBKwAwMTczESMBETMRMxEzETMRMxFkyMgBLMhkyGTIAQEs%2FtQB9P4MAyD84ASw%2B1AAAAIAGgAbBJYElgBHAFEAYgCwEi%2BwUM2wSy%2BwNs0BsFIvsADWsEjNsEgQsU0BK7AkzbFTASuxSAARErELPTk5sE0Rsw8VMzkkFzmwJBKxGS85OQCxUBIRErEHHTk5sEsRswMhJ0UkFzmwNhKxK0E5OTAxExQfAhYfAQcWFzcXFh8CFjMyPwI2PwEXNjcnNzY%2FAjY1NC8CJi8BNyYnBycmLwImIyIPAgYPAScGBxcHBg8CBgU0NjIWFRQGIiYaBpcCDhgDUC08hQUtMQUmKCIbLyYGLi8FhjgwUAMYDwGYBQWYARAXA1AsPIYFLTAGJigiGy8mBTIsBYU7LlADGQ0ClwYBZ36yfn6yfgJZISkmBjEsBYY7LlEDGg0ClwUFlwINGgNRLD2GBSwxBiYoIhwtJgYzKgWGOi9RAxkOAZgFBZgBDhkDUS86hgUvLgYmMBlYfn5YWX5%2BAAAABwBk%2F%2F8EsAUUABkAIwAnACsALwAzADcAiQCyIQAAK7AkzbIoMDQyMjKwJy%2ByKjI2MzMzsBvNsBcvsATNsQ4sMjKwLy%2BwCc0BsDgvsBrWsCTNsCQQsSUBK7AFMrAozbAsMrIlKAors0AlAAkrsCgQsSkBK7AwzbAwELExASuwLTKwNM2wDTKyNDEKK7NANBMJK7A0ELE1ASuwHc2xOQErADAxEzU0NjMhNTQ2MyEyFh0BITIWHQEUBiMhIiYTESERFAYjISImNzMRIxMzESMRITUhEzMRIxMzESNkDwoBEzspASwpOwETCg8OC%2FvmCw5kA4Q7Kf1EKTtkZGTIZGQBLP7UyGRkyGRkBAEyCg9kKTs7KWQPCjILDg78bgMg%2FOApPDwpArz9RAK8ASxk%2B7QCvP1EArwAAAAAAQABAAEFFQTdAAoALACyCQAAK7AEMwGwCy%2BwCdawCM2wCBCxBQErsATNsQwBK7EFCBESsAE5ADAxEwkBIxEhESERIREBApAChMj%2B1P7U%2FtQCWQKE%2FXz9qAGQ%2FnACWAAAAgBkAAAD6ASwAA4AEQAiALIMAAArAbASL7AB1rAGzbIGAQors0AGCAkrsRMBKwAwMTcRNDYzIREhERQGIyEiJgERAWQOCwHbAZAOC%2FyuCw4CWAEsGQR%2BCw7%2BDP1dCw4OAxIBLP7UAAADAAQABASsBKwACwATABkAggCwCi%2BwD82wFC%2BwF82yFxQKK7NAFxUJK7ATL7AEzQGwGi%2BwAdawDc2wDRCxFAErsBfNshcUCiuzQBcZCSuwFxCxEQErsAfNsRsBK7EUDRESswoDDhMkFzmxERcRErMJBA8SJBc5ALEUDxESswcADRAkFzmxExcRErMGAREMJBc5MDESEBIkIAQSEAIEICQSEBYgNhAmIBMRMxEzFQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqkdkyAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP9%2FgGQ%2FtRkAAAAAAL%2FnAAABRQEsAALAA8ALgCyAAAAK7AHM7AKL7AMzbAPL7ADzbIDDwors0ADAQkrsAUyAbAQL7ERASsAMDEjATMDMwMzASEDIwMTMwMjZAGv0RWiFNABr%2F3mKfIoMeAbqgSw%2FtQBLPtQAZD%2BcAH0ASwAAAAAAgAAAAAETASwAAsADwBKALILAAArsAzNsA8vsAnNsAEyAbAQL7AE1rAHzbIEBwors0AEAAkrsAcQsQ0BK7AKzbERASuxBwQRErECCTk5sA0RsQgMOTkAMDExESEBMxEhETMBIRElMzUjAer%2B3sgBLMj%2B3gHq%2FuGvrwGQASwB9P4M%2FtT%2BcMhkAAMAAQABBK8ErwAPABcAHgBjALINAAArsBPNsBcvsAXNAbAfL7AB1rARzbARELEZASuwHM2wHBCxFQErsAnNsSABK7EZEREStA0EEhcYJBc5sBwRsB45sBUStAwFExYdJBc5ALEXExEStQEICQAaHiQXOTAxEjQ%2BAjIeAhQOAiIuARIQFiA2ECYgAzMRMxEzAwFfoN703qBfX6De9N6gXPIBVPLy%2FqxQlsiW%2BgHe9N6gX1%2Bg3vTeoF9foAIC%2Fqzy8gFU8v5kASz%2B1P7UAAMABAAEBKwErAALABMAGgBhALAKL7APzbATL7AEzQGwGy%2BwAdawDc2wDRCxGQErsBjNsBgQsREBK7AHzbEcASuxGQ0RErQKAw4TFCQXObAYEbAVObARErQJBA8SFiQXOQCxEw8RErUBBgcAFRgkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAxsBIxEjEQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2Bqk%2F6%2BpbIAbYBRAESoKD%2B7v68%2Fu6goAJf%2Fqrz8wFW8%2F5iASz%2B1P7UASwAAAACAAAAAASwBLAACwATACkAsgkAACuwDM2wEDKwEy%2BwAs0BsBQvsRUBKwCxDAkRErIEAQ45OTkwMTUREyEbARQGIyEiJhMzFyE3MwMhyAMgxwEOC%2FuCCw7IyDIBLDLIYf2iGQHbArz9RP4lCw4OAebIyAH0AAMABAAEBKwErAALABUAGABGALAKL7APzbAUL7AEzQGwGS%2BwAdawDM2wDBCxEQErsAfNsRoBK7ERDBEStQQJCgMWGCQXOQCxFA8RErUBBgcAFhckFzkwMRIQEiQgBBIQAgQgJBMUFiA2NTQmIAYBEQUEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzAToBKQG2AUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FP%2BigGRyAABABcAFwSZBLAAHABTALAFL7ANzbARL7AZzQGwHS%2BwAdawD82wDxCxCgErsAnNsR4BK7EKDxEStQUEExQXGSQXObAJEbEVFjk5ALERDREStAEACRQVJBc5sBkRsBc5MDESFB4CMj4CNSMUBiAmEDYzMhcHIREHJiMiDgEXW5vV7NWbW5b6%2Fp76%2BrGIbpIBkJGdxnbVmwLO7NWbW1ub1Xax%2BvoBYvpRkgGQkXpbmwAAAAIAFwAABJkEsAAQACEAegCyEQAAK7AfL7AWzbIWHwors0AWGgkrsA0vsAXNsg0FCiuzQA0ACSsBsCIvsADWsBDNsBAQsRkBK7AazbEjASuxEAARErEREjk5sBkRtwcFCwoTFB8hJBc5sBoSsQkIOTkAsRYfERKwITmwDRGxCRI5ObAFErAHOTAxEzQ%2BAjMyFzcRITcmIyIGFQMRIQcWMzI2NTMUDgIjIicXW5vVdsadkf5wk3CHsfpJAZCTcIex%2BpZbm9V2xp0CWHbVm1t6kf5wk1D6sf2oAZCTUPqxdtWbW3oAAAoAZAAABLAEsAADAAcACwAPABMAFwAbAB8AIwAnAFAAsAgvsBgzsAnNsBoysAwvsBwzsA3NsB0ysBAvsCAzsBHNsCEysBQvsCQzsBXNsCUyAbAoL7AI1rIMEBQyMjKwC82yDhIWMjIysSkBKwAwMTMhESETESERJTUzFSc1MxUnNTMVJzUzFRMhNSE9ASEVJTUhFSU1IRVkBEz7tGQDhPzgZGRkZGRkZGQB9P4MAfT%2BDAH0%2FgwB9ASw%2B7QDhPx8ZGRkyGRkyGRkyGRk%2FahkZGRkyGRkyGRkAAIAAAAABEwEsAAZACMASgCyFwAAK7AgL7AJzQGwJC%2BwBdawGs2yBRoKK7NABQAJK7AaELEbASuwDs2yDhsKK7NADhMJK7ElASsAsSAXERKzBA4FGiQXOTAxNRE0NjsBNTQ2MyEyFh0BMzIWFREUBiMhIiYBITU0JisBIgYVOylkdlIBLFJ2ZCk7Oyn8fCk7AZABLB0VyBUdZAJYKTvIUnZ2Usg7Kf2oKTs7AuWWFR0dFQAAAAIAZAAABEwETAADABUAFwCyAAAAKwGwFi%2BwANawA82xFwErADAxMxEzERM%2BAR4CPgE3EQ4BLgMGB2RkZDyHeHxyamQpKHuEkId0WhQETPu0AZA8MA0hGwVPUQH0UUUKKCgKRVEAAAAAAwAAAAAEsASXACEAMQBBAGcAsi8AACuwPjOwJs2wNjKwDC%2BwHc0BsEIvsADWsAfNsAcQsSIBK7ArzbArELEyASuwO82wOxCxEAErsBfNsUMBK7EyKxESswwLHRwkFzkAsSYvERK0BxATFAMkFzmwDBGxCA85OTAxERQWOwEyNjURND4BIB4BFREUFjsBMjY1ETQuAiIOAhUTETQ2OwEyFhURFAYrASImJRE0NjsBMhYVERQGKwEiJg4LMgsOjeQBBuSNDgsyCw5jo97o3qNjyAwIoAgMDAigCAwCWAwIoAgMDAigCAwBEwsODgsBLH%2FRcnLRf%2F7UCw4OCwEsdN6jY2Oj3nT91QHMCAwMCP40CAwMCAHMCAwMCP40CAwMAAAAAgAAAMgEWAPoAAUAEQAAESEFEQUhATcnNxc3FwcXBycHASwBLP7U%2FtQCsI2NR42NR42NR42NAZDIAyDI%2FquNjUeNjUeNjUeNjQAAAAIAAADIA3AD6AAFAA8AEgABsBAvsA7WsAnNsREBKwAwMREhBREFISU3FhUUByc2NTQBLAEs%2FtT%2B1AK8RW9qQ1YBkMgDIMg5NYevqYU2boqSAAAAAAMAAAC6BGID9wAFAA8AHQA8ALAAL7ABzQGwHi%2BwDtawCc2wCRCxEwErsBrNsR8BK7ETCRESsxAWFx0kFzkAsQEAERKzCQ4TGiQXOTAxGQEhJRElATcWFRQHJzY1NDcXFhUUDwEXNzY1NC8BASwBLP7UAZJFb2pDVl4He3cHUQaOkAYBkQGQyPzgyAHJNYevqoU3boqRzQiXwb2WCEIIsuPmsggADQAAAAAEsASwAAcAEQAVABkAHQAhAC8AMwA%2FAEMARwBLAE8BAQCyAAAAK7EwRDMzsBLNsikxRTIyMrAaL7InK0wzMzOwG82yJS1NMjIysCIvsQIGMzOwI82wCDKwHi%2BxDkgzM7AhzbE0STIyAbBQL7Aa1rEFHjIysB3NsQMfMjKwHRCxMAErsQ0sMjKwM82wNTKwMxCxLgsrsCoysCXNsEAysi4lCiuzQC4iCSuyAQsPMjIysCUQsTcBK7FESDIysDvNsSZKMjKwOxCxTAErsEIysE%2FNsjk9RjIyMrFRASuxMB0RErUUFRgZND8kFzmxNyURErIoKTg5OTkAsSIbERKzExQ4OSQXObAjEbIEOjs5OTmwHhJACQUWGTY3PD1AQyQXOTAxMSERIzUjFSM1MzUhNTM1IxEhExEhEQERIREDNTMVAzM1IwE1IREzFSMVIzUjNTM1AzUzFQMzETMRITUjNTMRIRMRIREDNSEVATUzFRM1MxUB9MhkyGQBkGRk%2FgxkASz%2B1AEsyGRkZGQBLAEsyGTIZGRkZGRkyAEsyMj9qMgBLMgBLP7UZGRkAfRkZGRkZGQBLPu0ASz%2B1AK8ASz%2B1P2oZGQCvGT%2BDGT%2B1GRkZGTI%2FgxkZAPo%2FtT%2B1MhkAfT%2BcAEs%2FtT84GRkA4RkZP1EZGQAAAAACQAAAAAEsASwAAMABwALAA8AEwAXABsAHwAjAHAAsgwAACuyBBQcMzMzsA3NsRUdMjKyDAAAK7AFzQGwJC%2BwCNawC82wCxCxEAErsAwysBPNsA%2FNsBMQsRQLK7AXzbAXELEYCyuwG82wGxCxIAErsCPNsSUBK7EQCxESsQcGOTmxGxcRErEcHTk5ADAxNTMRIxM1IRUnETMRFzUzFScRMxEVNTMVNREzERU1MxUnETMRZGRkASzIZMhkZMhkZMhkyMgD6PtQZGTIA%2Bj8GMhbW8gD6PwYyFtbyAPo%2FBjIW1vIA%2Bj8GAAAAAIAAAAABLAEsAAHABMAKQCyBwAAK7ASL7AEzQGwFC%2BwAdawCc2xFQErALESBxESsgAGCzk5OTAxERM0NjMhCQEAFBcWMjc2NCcmIgcBDwoB2gK8%2Fgz92B0eUx4dHR5THgK8AdsKD%2F1E%2FgwD41QdHh4dVB0eHgAAAAMAAQAABd0EsAAHABMAGQAxALIHAAArsBczsBIvsATNsBQyAbAaL7AB1rAJzbEbASsAsRIHERK0AAYLFhkkFzkwMRsBNDYzIQkBABQXFjI3NjQnJiIHJTMJAScBAQEOCwHaArz%2BDP3XHh1UHR4eHVQdAgtkArz%2BDDIBwgK8AdsLDv1E%2FgwD41QdHh4dVB0eHrD9RP4MMgHCAAAAAAEAZAAABLAEsAAKAD8AsgAAACuwBy%2BwAs0BsAsvsADWsArNsAoQsQUBK7AEzbEMASuxCgARErICBwg5OTkAsQcAERKyAQQFOTk5MDEzETchEQcRIQchEWSvA51k%2FRJkAu4EAa%2F8GGQD6GT8GAAAAAABAMgAAARMBLEACgAAMwkBETQmIyEiBhXIAcIBwh0V%2FOAVHQG8%2FkUEfhQeHhQAAAADAAAAAASwBLAACwAXACcAWQCyJQAAK7AczbAKL7ADzbIKAwors0AKAAkrsAcysgMKCiuzQAMBCSuwBTIBsCgvsADWsAvNsAIysAsQsQgBK7AFMrAHzbEpASuxCAsRErMMDyciJBc5ADAxNREzFyE3MxEjNSEVExchNwMuASMhIgYHAzc%2BATMhMhYfARYGIyEiJshkAlhkyMj84DUoAlA%2BXgIQCv4%2BChACQiYCEwoB9AoTAiYCCwr9qAoLZAK8yMj9RMjIAtl8fAFaCw4OC%2FuBmAoODgqYCg4OAAAABAAAAGQEsARMAB0AJQAtADEAbwCwAy%2BwJc2wKS%2BwLc2wIS%2BwE80BsDIvsADWsB%2FNsh8ACiuzQB8vCSuwHxCxJwErsCvNsTMBK7EfABESsBk5sSsnERKzISQlICQXOQCxLSkRErMfIiMeJBc5sCERsS4xOTmwExK0CxkaLzAkFzkwMTUUFjMhMjY1ETQmKwEuBCsBIg4CDwEjIgYVADQ2MhYUBiICFBYyNjQmIiU1MxU7KQPoKTs7KZYEDzM3UyrIKVI6LgsMlik7AWSQyJCQyAY%2BWD4%2BWAFYZMgpOzspAlgpOwgbRTUrKTs7FRQ7Kf5wyJCQyJABIFg%2BPlg%2BXmRkAAIANQAABLAErwAeACIAHgCyAAAAK7ANM7AezbICDA8yMjIBsCMvsSQBKwAwMTMhNSIuAT8BIRcWBiMVITUmJy4BLwEBIwEGBw4BDwEBExcTNQFtKT4kE1wBh1IQKzUBoSIoEh4GBv5%2FXf5xGBwMKg8PAWuyLnRCFjYs6t4tV0JCASoTLg4NA%2Bb8EjAbDBoHBwHHAcmM%2FsMAAwBkAAADwwSwACAAKQAxAGUAsiAAACuwIc2yIAAAK7ABzbApL7AqzbAxL7ANzbANELALzQGwMi%2BwBNawIc2wKjKwIRCxLgErsBDNsCUg1hGwHM2xMwErsSUuERKwFjkAsSkhERKwHDmwKhGwFjmwMRKwEDkwMTM1PgE1ETQuAyc1BTIWFRQOAg8BHgQVFA4BIyczMjY1NCYrATUzMjY1NCYjZCk7AgkWJB8B13i6FyEiCwwIG0U0K3amT8ihWYB9Xp%2BLTGyom1kHMygDOxwXHQ0PB0cBsIw3XTcoCAcDDDNBdkZUkU3IYVRagWR7TVJhAAAAAAEAyAAAA28EsAAZACAAsgAAACuwAc2wGDKwCy%2BwDjOwDM0BsBovsRsBKwAwMTM1PgE3EzYmJy4BJzUhFw4DDwEDBhYXFchNcwitCihHBgkFAakCIToiGQUFgAowRzkHQy8DUTgkEwEDATk5CCMnJQwM%2FMc0PAY5AAL%2FtQAABRQEsAAJACUAfgCyGwAAK7AfL7ICBRYzMzOwDM2yHwwKK7NAHxAJK7AKMgGwJi%2BwAdawB82wBxCxCgErsCXNsCUQsR0BK7AYzbIYHQors0AYGgkrsh0YCiuzQB0bCSuwGBCxEAErsA%2FNsScBK7EKBxESsQUIOTkAsR8bERKwCTmwDBGwBDkwMSczESM3FyMRMwcTETMhMxEjNC4DKwERFxUhNTcRIyIOAxVLS0t9fUtLffqWAryWMhAVLiEiyGT%2BcGTIIiEvFBHIAyCnp%2FzgpwNjASz%2B1B0nFQkC%2FK4yZGQyA1ICCRUnHQAAAAIAIf%2B2BI8EsQAJACUAiQCyCAAAK7ACzbAfL7AWM7AMzbIfDAors0AfCgkrsA8yAbAmL7AK1rAlzbAlELEdASuwGM2yGB0KK7NAGBoJK7IdGAors0AdGwkrsBgQsRABK7APzbEnASuxHSURErMCCAkBJBc5sRAYERKzBAYHAyQXOQCxAggRErEABTk5sB8RsgEEGjk5OTAxPwEVITUXBzUhFQMRMyEzESM0LgMrAREXFSE1NxEjIg4DFSGnAyCnp%2FzgZJYCvJYyEBQvISLIZP5wZMgiIS4VEDN9S0t9fUtLA88BLP7UHScVCQL9djJkZDICigIJFScdAAAAAAQAAAAABLAETAAPAB8ALwA%2FAAA1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVHRUETBUdHRX7tBUdHRUDIBUdHRX84BUdHRUD6BUdHRX8GBUdHRUCWBUdHRX9qBUdMhQeHhRkFR0dFcgUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFQAEAAAAAASwBEwADwAfAC8APwAANRQWMyEyNj0BNCYjISIGFREUFjMhMjY9ATQmIyEiBhUTFBYzITI2PQE0JiMhIgYVERQWMyEyNj0BNCYjISIGFR0VBEwVHR0V%2B7QVHR0VBEwVHR0V%2B7QVHcgdFQK8FR0dFf1EFR0dFQK8FR0dFf1EFR0yFB4eFGQVHR0VAfQUHh4UZBUdHRX%2BcBQeHhRkFR0dFQH0FB4eFGQVHR0VAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wLS%2BwJM2wHS%2BwFM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYTNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJhM1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR1kHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHcgdFQJYFR0dFf2oFR0yZBUdHRVkFB4eAmxkFR0dFWQUHh7%2B6GQVHR0VZBQeHgJsZBUdHRVkFB4eAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wHS%2BwFM2wLS%2BwJM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0yZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAAAAAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AUgCyDQAAK7BMM7AEzbBEMrAdL7BcM7AUzbBUMrAtL7BsM7AkzbBkMrA9L7B8M7A0zbB0MgGwgC%2BwANayECAwMjIysAnNshgoODIyMrGBASsAMDE9ATQ2OwEyFh0BFAYrASImETU0NjsBMhYdARQGKwEiJhE1NDY7ATIWHQEUBisBIiYRNTQ2OwEyFh0BFAYrASImATU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0BLB0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHTJkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh78kGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgAABv%2BbAAAEsARMAAYACgAaACoAOgBKACAAsAAvsCYzsAHNsC4yAbBLL7FMASsAsQEAERKwBDkwMQM1MzUXBzUTMxEjExQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWOwEyNj0BNCYrASIGFWXJpqbIZGTIHRUCWBQeHhT9qBUdHRUBLBQeHhT%2B1BUdHRUB9BQeHhT%2BDBUdHRVkFB4eFGQVHQH0ZEt9fUv%2BDARM%2B%2BYUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRUAAAAABgABAAAFFQRMAA8AHwAvAD8AQwBKABcAskAAACsBsEsvsEDWsEPNsUwBKwAwMTcUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjsBMjY9ATQmKwEiBhUBETMRExc1MzUjNQEdFQJYFB4eFP2oFR0dFQEsFB4eFP7UFR0dFQH0FB4eFP4MFR0dFWQUHh4UZBUdAyBkIafIyDIUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRX75gRM%2B7QCJn1LZEsAAgAAAMgEsAPoAA8AEgAtALANL7AEzbAEzQGwEy%2BwANawCc2xFAErsQkAERKwEDkAsQQNERKxERI5OTAxGQE0NjMhMhYVERQGIyEiJgkBESwfAu4fLCwf%2FRIfLAOEASwBEwKKHywsH%2F12HywsAWQBLP2oAAADAAAAAASwBEwADwAXAB8AWQCyDQAAK7AfL7AbzbAXL7AEzQGwIC%2BwANawEM2wEBCxGQErsB3NsB0QsRUBK7AJzbEhASuxHRkRErARObAVEbETEjk5ALEfDRESshATFTk5ObAbEbAUOTAxNRE0NjMhMhYVERQGIyEiJj8BBScBExEhEjQ2MhYUBiIaEgRYExkZE%2FuoEhpk9wEqSgEl7PwYbE5wTk5wLAP0EhoaEvwMEhoa7baDnAE%2B%2FuAB9P7OcE5OcE4AAgCU%2F%2FMEHAS9ABQAHgA9ALINAAArsB0vsATNAbAfL7AA1rAVzbAVELEbASuwCM2xIAErsRsVERKxDQQ5OQCxHQ0RErIHABg5OTkwMRM0PgEzMh4BFAcOAQ8BLgQnJjcUFjMyNjQmIgaUedF6e9B5SUm7OTkKImNdcys%2Fwpdqa5eX1pYC6XzXgX7V9pVy9kJCCSJrb6BLi5Zrl5fWlpcAAAIAAQABBK8ErwAPABUASQCyDQAAK7ATzbAUL7AFzQGwFi%2BwAdawEc2wERCxEwErsAnNsRcBK7ETERESsQ0EOTmwCRGxBQw5OQCxFBMRErMBCAkAJBc5MDESND4CMh4CFA4CIi4BEhAWMxEiAV%2Bg3vTeoF9foN703qBN%2B7CwAd703qBfX6De9N6gX1%2BgAgn%2BnvoDVgACAHUABAPfBQ8AFgAlAAATND4DNx4GFRQOAgcuAjceARc3LgInJjY%2FAQ4BdURtc3MeFUlPV00%2FJU5%2Bmk9yw4B%2BDltbEAcWLgoPAgkJXDcBll64oZ3FYEePdndzdYZFWZlkOwQGXrh%2BUmwaYgYWSihJjTQzbpYAAAADAAAAAATFBGgAHAAhACYAVwCyGgAAK7APzbAIL7AEzQGwJy%2BwANawDM2wDBCxEwErsBbNsSgBK7ETDBESswYdHiAkFzmwFhGxHyI5OQCxCA8RErMVHR8hJBc5sAQRsyAiIyUkFzkwMRkBNDYzBBcHISIGFREUFjMhMjY9ATcVFAYjISImJTcBJwkBFzcvAeulAW4fuv7JKTs7KQH0KTvI66X%2B1KXrAbShAZxy%2FmsB%2BXFxFVwBkAEspesGCLo7Kf4MKTs7KX3I4aXr62oyAZxx%2FmsB%2BHFxVRwAAAAAAgAAAAAElQRMABwALgBIALIaAAArsBDNsCIvsCfNsAkvsATNsAQQsAbNAbAvL7AA1rANzbEwASsAsSIQERKyFR0kOTk5sQkaERKwJTmxBAYRErAmOTAxGQE0NjMhFwYHIyIGFREUFjMhMjY1NxUUBiMhIiYBPgMfARUJARUiDgXrpQEFAoVVkSk7OykB9Ck7yOul%2FtSl6wGnHmdnXx4dAWj%2BmQcYSENWQzkBkAEspetQIFg7Kf4MKTs7KZk1pevrASEmNBMJAQHRAUQBPtgCDhczQ20AAAAAAgAAAAAEqARMAB0AIwBSALIbAAArsBDNsAkvsATNAbAkL7AA1rANzbANELEUASuwF82xJQErsRQNERKzBx4fIiQXObAXEbAhOQCxCRARErMWHyIjJBc5sAQRsSAhOTkwMRkBNDYzITIXByEiBhURFBYzITI2PQE3FRQGIyEiJgkCJwEn66UBLD1Csv6jKTs7KQH0KTvI66X%2B1KXrAVYBGwI3if5SkgGQASyl6xexOyn%2BDCk7OylFyKml6%2BsBjf7kAjeJ%2FlGTAAABAAAAAQSwBLEAFwBFALISAAArsBYvsA4zsALNsAkyAbAYL7AU1rADMrAQzbAIMrEZASuxEBQRErEGEjk5ALEWEhESsQ0XOTmwAhGxAAw5OTAxEQEVMzUjCQEjFTM1CQE1IxUzCQEzNSMVASzIyAEsASfDyAEs%2FtTIw%2F7Z%2FtTIyAJbASjGyAEs%2FtTIxv7Y%2FtTGyP7UASzIxgAAAAABAMgAAAOEBEwAEwAdALIRAAArsAszAbAUL7AA1rANzbAIMrEVASsAMDE3ETQ2OwEyFhURAREBERQGKwEiJsgdFWQVHQH0%2FgwdFWQVHTID6BUdHRX%2BSwHn%2B7QB6P5KFR0dAAAAAQAAAAAEsARMABcAHwCyFQAAK7ENDzMzAbAYL7AA1rARzbAIMrEZASsAMDE1ETQ2OwEyFhURAREBEQERAREUBisBIiYdFWQVHQH0AfT%2BDP4MHRVkFR0yA%2BgVHR0V%2FksB5%2F4ZAef7tAHo%2FhgB6P5KFR0dAAABAIgAAASwBEwABgAUALIGAAArsAQzAbAHL7EIASsAMDETAREBEQERiAI0AfT%2BDAImAib%2BGQHn%2B7QB6P4YAAAAAQDIAAAETARMAAIAADMJAcgDhPx8AiYCJgAAAAIAyABkA4QD6AAPAB8AADcUFjsBMjY1ETQmKwEiBhUBFBY7ATI2NRE0JisBIgYVyB0VyBUdHRXIFR0BkB0VyBUdHRXIFR2WFR0dFQMgFR0dFfzgFR0dFQMgFR0dFQAAAAEAyABkBEwD6AAPAAA3FBYzITI2NRE0JiMhIgYVyB0VAyAVHR0V%2FOAVHZYUHh4UAyAVHR0VAAAAAQAAAAAEKARMAAYAFACyAAAAK7AFMwGwBy%2BxCAErADAxMREBEQkBEQH0AjT9zARM%2FhkB5%2F3a%2FdoB6AAAAQAAAAAEsARMABcAHwCyAAAAK7EQFjMzAbAYL7AU1rAEMrANzbEZASsAMDExEQERARE0NjsBMhYVERQGKwEiJjURAREB9AH0HRVkFR0dFWQVHf4MBEz%2BGQHn%2FhkBtRUdHRX8GBUdHRUBtv4YAegAAAEBLAAAA%2BgETAATAB0AsgAAACuwDjMBsBQvsBLWsAIysAvNsRUBKwAwMSERARE0NjsBMhYVERQGKwEiJjURASwB9B0VZBUdHRVkFR0ETP4ZAbUVHR0V%2FBgVHR0VAbYAAAIAZADIBLAEKAAPABIAEgCwDS%2BwBM0BsBMvsRQBKwAwMTc1NDYzITIWHQEUBiMhIiYRIQFkHRUD6BUdHRX8GBUdBEz92vpkFR0dFWQVHR0BDwI0AAEAuQAHA%2FkEqQAFAAATATcJASe5AlDw%2Fp8BYfACV%2F2w8AFhAWHwAAABARD%2F0gRSBHQACAAAJQkBNwEXBxUBARABYf6f8QI8FQH9sMIBYQFh8P3FFgEB%2FbEAAAAAAgADAAMErQStAAsAFwBCALAKL7AOzbAVL7AEzQGwGC%2BwAdawDM2wDBCxEQErsAfNsRkBK7ERDBESswQJCgMkFzkAsRUOERKzAQYHACQXOTAxEhASJCAEEhACBCAkEzMVMzUzNSM1IxUjA6ABEwFEAROgoP7t%2Frz%2B7YnIyMjIyMgBtgFEAROgoP7t%2Frz%2B7aCgAVHIyMjIyAACAAMAAwStBK0ACwAPAEkAsAovsAzNsA8vsATNAbAQL7AB1rAMzbAMELENASuwB82xEQErsQ0MERKzBAkKAyQXOQCxDAoRErEHADk5sQQPERKxAQY5OTAxEhASJCAEEhACBCAkEyE1IQOgARMBRAEToKD%2B7f68%2Fu2JAlj9qAG2AUQBE6Cg%2Fu3%2BvP7toKABUcgAAAAAAgADAAMErQStAAsAFwAyALAKL7AEzbAEzQGwGC%2BwAdawB82wB82xGQErsQcBERKxDBA5OQCxBAoRErENFTk5MDESEBIkIAQSEAIEICQTFzcXNyc3JwcnBxcDoAETAUQBE6Cg%2Fu3%2BvP7tU9WNjdWOjtWNjdSNAbYBRAEToKD%2B7f68%2Fu2goAEo1Y6O1Y2N1I2O1Y0AAAIAAwADBK0ErQALABEAMgCwCi%2BwBM2wBM0BsBIvsAHWsAfNsAfNsRMBK7EHARESsQwOOTkAsQQKERKxDQ85OTAxEhASJCAEEhACBCAkEwkBJwcnA6ABEwFEAROgoP7t%2Frz%2B7WsBFAGbr%2BxmAbYBRAEToKD%2B7f68%2Fu2goAGE%2FusBm67sZgAAAAADAAMAAwStBK0ACwA4ADwAbACwCi%2BwOc2wPC%2BwJ82wIS%2BwG82wNC%2BwBM0BsD0vsBLWsB7NsB4QsS4BK7AHzbE%2BASuxHhIRErEhNDk5sC4RtAkEKDo7JBc5ALEnPBESsQcAOTmwIRGwKjmwGxKyDA8uOTk5sDQRsQYBOTkwMRIQEiQgBBIQAgQgJBMzMhYyNjQ%2BBToBMzIWFRQGBw4EFzM%2BBDU0LgMjIg4CEzM1IwOgARMBRAEToKD%2B7f68%2Fu3JjwQPBwYCBQIJBA4EEwMTFggXBQ8nHRgByAUSLSIcIzFEMRsyUUUmiMjIAbYBRAEToKD%2B7f68%2Fu2goAIZAgYMCgcFAwIBFBAWDBABBBcfPSYDCikyWDIzTCgYBhg1YP4uZAADAAMAAwStBK0ACwAVABkAOwCwCi%2BwDM2wFS%2BwDjOwEs2wES%2BwFs2wGS%2BwBM0BsBovsRsBKwCxEhURErEHADk5sRYRERKxBgE5OTAxEhASJCAEEhACBCAkNyE1IxEhFTMVIxMzNSMDoAETAUQBE6Cg%2Fu3%2BvP7t7QGQZP7UZGRkyMgBtgFEAROgoP7t%2Frz%2B7aCgiWQBLGTIAZBkAAACAAAAAASwBLAAGAAvAGkAshQAACuwEi%2BwH82wHDKwAC%2ByDhkhMzMzsAHNsgwjLTIyMgGwMC%2BwFNayBRwpMjIysBPNsgceJzIyMrExASuxExQRErMiIy4vJBc5ALESFBESsBU5sQAfERKwHTmwARGyHigpOTk5MDERNTM%2BATc1MxUeAhczFSMOAQcVIzUuASczHgEXNTMVNjcjNTMuAScVIzUOAQczFcMfh4vINnZrEsvLGbdZyIyIHmAYb0vIlTTJyBllSshLbhjRAfTIfZUayMgUUINFyGaoIcXFG5d9SW0Yzs4wnshKahjMyxdsSMgAAAAAAwAEAAQErASsAAsAEwAfAEYAsAovsA%2FNsBMvsATNAbAgL7AB1rANzbANELERASuwB82xIQErsRENERK1BAkKAxQaJBc5ALETDxEStQEGBwAXHSQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiADNyc3FzcXBxcHJwcEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqpJh4dth4dth4dth4cBtgFEARKgoP7u%2Frz%2B7qCgAl%2F%2BqvPzAVbz%2FduHh22Hh22Hh22HhwAAAAMABAAEBKwErAALABMAGQBGALAKL7APzbATL7AEzQGwGi%2BwAdawDc2wDRCxEQErsAfNsRsBK7ERDREStQQJCgMUGCQXOQCxEw8RErUBBgcAFxkkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAzcXNxcBBKABEgFEARKgoP7u%2Frz%2B7hbzAVbz8%2F6qa41XzI7%2BpgG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BI41XzY7%2BpwAAAAMABAAEBKwErAALABMAGwBGALAKL7AWzbARL7AEzQGwHC%2BwAdawDM2wDBCxGQErsAfNsR0BK7EZDBEStQQJCgMPFCQXOQCxERYRErUBBgcADhskFzkwMRIQEiQgBBIQAgQgJBMUFwEmIyIGExYzMjY1NCcEoAESAUQBEqCg%2Fu7%2BvP7uFj4COGR0q%2FPNYXCr8zsBtgFEARKgoP7u%2Frz%2B7qCgAbRzZAI3PvP98jvzq3BhAAAAAAEAAABjBLAD6AAGABoAsAUvsALNAbAHL7EIASsAsQIFERKwADkwMREBESERIRECWAJY%2FagCIwHF%2FtT%2B1P7TAAABAAAAYwSwA%2BgABgAaALAAL7ABzQGwBy%2BxCAErALEBABESsAQ5MDEZASERCQERAlgCWP2oAZABLAEs%2Fjv%2BQAEtAAAAAAEAzAAABEoEsAAGAB8AsgUAACsBsAcvsAXWsATNsQgBK7EEBRESsAE5ADAxEwkBIREhEcwBwgG8%2Ftb%2B1AJYAlj9qP2oAlgAAAEAaAAAA%2BYEsAAGAB8AsgYAACsBsAcvsAHWsATNsQgBK7EEARESsAY5ADAxEyERIREhAWgBKAEsASr%2BPwJYAlj9qP2oAAAAAAEAAADHBLAETAANAAA1PgM3EQkBEQ4DBkaJ55wCWP2oX7CkgsiE1a1nCAEP%2Fjv%2BQAEtAiREdQAAAgAAAAAEsASwAAYADQARALIAAAArAbAOL7EPASsAMDExERcBFwEXExcBFxEhF4EBJo7%2B2oHrjgEmgf5wgQGQgQEmjv7agQMJjgEmgQGQgQACACIAIwSOBI4ABgANAAA3ASchEScJAREXARcBFyIBJ4EBkIH%2B2QGogQEnjv7ZgbABJ4H%2BcIL%2B2QI1AZCBASeN%2FtmCAAMAFwAXBJkEmQAPAB8AIwBPALANL7AgzbAjL7AUzbAdL7AFzQGwJC%2BwAdawEM2wEBCxGQErsAnNsSUBK7EZEBEStQUMDQQhIyQXOQCxFCMRErEJADk5sB0RsQgBOTkwMRI0PgIyHgIUDgIiLgEBEx4BOwEyNjcTNiYrASIGEzM1Ixdbm9Xs1ZtbW5vV7NWbAVY6BCMUNhQjBDoEGBTPFRgwyMgB4uzVm1tbm9Xs1ZtbW5sCRv7SFB0dFAEuFB0d%2FcVkAAAFAAAAAASwBLAAJgAqADAANAA7ADQAsicAACuwMTOwKs2wMjIBsDwvsDHWsAUysDTNsAcysT0BK7E0MREStAsMEzU6JBc5ADAxETMVIREzESE1MzUjNjQmLwEuASMiDwEGByYvASYjIgYPAQ4BFBUjEyERIRMiNj8BFxMRIREBNx4DI2QBkMgBkGRvAQMCIgs9JyAd7xYSExXuIR0nPQojAgJvZAGQ%2FnBkAyITEtXbAZD%2Bj8oFDiASAgMgyAEs%2FtTIZAELEwisJzARkA0WGAyQEi4msQgUCgH8fAGQAfRgMC%2B%2F%2FHwBkP5wA4TFDClXOQACAAD%2F6gSvBLAAGwAyABcAsgAAACsBsDMvsCfWsA%2FNsTQBKwAwMRU1Ny4CPgE3PgU3FAIOBC4CIwc2Fjc2JT4DNz4BJyYiBgcOAQ8BBAfYCQgDFTguL2llmonoaCxKaHGDeHtcUw9jEidDNwE4RmFrWykWBAgHFCERI509Pf6PWRaPwTU8gGKCOzxVMy0eOR69%2FszQm1UzCQYTDzd%2FDVNCqCY%2FX4BUMhQJBR0ZM3MgIMXMAAABAG8ADAREBOcASAAjAAGwSS%2BwAdawRc2wRRCxPAErsUoBK7E8RRESsTo2OTkAMDESFBceARcWPgM3PgEnHgEHDgEHDgQeAT4BNz4ENzYCJxYXFicmJy4CNw4EFx4DDgQHBi4CNw4BbwUJRkYfQjo4KA8gDhRPVhEFHxYKCQ8DAwgOGSQYOURrQ0APJqWkFhUnRw8ST1MFMw0qZ0ouDwIMBAgBAQsQGhImOhcHDjQ%2FAblCHjh%2FLRUKJT49HkLtJ1CoZCFJLBMUIA8XCAsBBAYUHD1DbkOsAVNtLFWfBQIHIYbZlQgfZm2nUww7GzQbKBcZEAQKLk1WIC5uAAAD%2F8MAfQTtBDMAIQA%2FAEcAQwCwGi%2BwKc2wOi%2BwCc0BsEgvsDzWsDfNsUkBK7E3PBESQAoJGRoIKSg1PkBDJBc5ALEJOhEStwARJC41PkJHJBc5MDEDNz4GMh4FHwEHDgYiLgUnNx4FMj4ENy4EJxYVFAYiJjU0NwYXFhc3LgEvAT0aBhxGT3N2k5CTdnNPRhwGGhoGHEZPc3aTkJN2c09GHAabB0MtW1R6gHdSWSxICwE3HTo5HjGw%2BLAuZoUxaWklTBMUAlgoCihXVGBHLy9HYFRXKAooKAooV1RgRy8vR2BUVygKKApgPV44KygzXDtoDgFJJUU6GUpZfLCwfFVJV3N8Q2kYYCQkAAAABP%2FDAAAE7QSwABYAIAApAEEAoQCyDwAAK7AOMwGwQi%2BxQwErsDYauj3v790AFSsKsA8uDrAMwAWxDgH5DrANwLAPELMLDwwTK7MQDwwTK7MZDwwTK7MaDwwTK7MkDwwTK7MlDwwTK7IQDwwgiiCKIwYOERI5sBk5sBo5sCQ5sCU5sAs5ALcLDA0QGRokJS4uLi4uLi4uAUAKCwwNDg8QGRokJS4uLi4uLi4uLi6wQBoBADAxAzc%2BBjMyFzczASM3LgQnNxIXNy4BNTQ3BhcWFz8BLgEvAQE3PgY3Jic3HgIfAQcOBD0aBhxGT3N2k0g9PCWU%2FsaUJVKmcmknCpvStyVrjy5mhTFpLxceOg8OASgmFi0vIjATLwFhKydDgS4NGhoHJVplkwJYKAooV1RgRy8RjvtQjxVlZ3k4Dyj%2B5jaNEqduVUlXc3xDL1ccUhsa%2FaeRDyYyJj8YQAJ%2FMJI2j0AUKCgMNGtiZgAAAAP%2FngAABRIErAALABIAFwAAJhYzITI2JwEuAQcBNwkBITUjFREbATUjbxslBQ4lGxX9fhQ4FP1%2B9QG9Ab3%2Bp8hkZMhEREcgBCAiBSD71mQC0%2F0tZGQBkP7UASxkAAAAAAEAZAAVBLAEsAApAEgAsB4vsAnNAbAqL7Al1rAFMrAWzbALMrIWJQors0AWGAkrsiUWCiuzQCUjCSuxKwErsRYlERKxHR45OQCxCR4RErEWJTk5MDETNTQ2NwERNDYyFhURAR4BHQEUBiclERYdARQGLwEjBwYmPQE0NxEFBiZkFg8Ba1h8WAFrDxYYEf6ZZBoTXt5eExpk%2FpkRGAEGKRQxDgFFAVM%2BWFg%2B%2Fq3%2Buw4xFCkUDQz5%2FvlbFkAUEQlOTgkRFEAWWwEH%2BQwNABEAAAAABEwEsAAJABsAHwAjACcAKwAvADMANwA7AD8AQwBHAEsATwBTAFcAADUUFjMhMjY1ESE1ITU0JisBNSMVITUjFSMiBhUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUdFQPoFR37tARMHRWWZP4MZJYVHWRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZDIUHh4UAu5klhUdZGRkZB0V%2FEpkZMhkZMhkZP5wZGTIZGTIZGT%2BcGRkyGRkyGRk%2FnBkZMhkZMhkZP5wZGTIZGTIZGQAAAMAAAADBXgErgAKABAAGQBBALAAL7AYM7ABzbATMrALL7AIM7AQzbADMgGwGi%2BxGwErALEBABESsREWOTmwCxGzBw0SFSQXObAQErEGDjk5MDE9ASEBMzUJATUjCQEhFzcnIQE3FzM1CQE1IwEDAljxASz%2B1J%2F9qP6rAQN6jbX%2BqwKmjXqfASz%2B1PHIyAJYxv7Z%2FtTF%2FagCWHqOtP2VjnvG%2Ftn%2B1MUAAAEAAAAABLAETAASABoAsg4AACuwEC%2BwDDOwBM0BsBMvsRQBKwAwMRkBNDYzITIWFREUBiMhAREjIiY7KQPoKTs7Kf2s%2FtBkKTsBkAJYKTs7Kf2oKTv%2B1AEsOwAAAwBkAAAETASwACUAKQAtAGAAsh8AACuwCc2yCR8KK7NACQEJK7AVMrAmL7AqM7AnzbArMgGwLi%2BwANawJjKwA82wKDKwAxCxEgErsCoysBfNsCwysS8BK7EDABESsCQ5sBIRsR4fOTmwFxKwGTkAMDETNSEVFBcWFxYzMj4GJzQ9ASEVFA4FIi4FGQEhESERIRFkASwGEVUnNSU7KR8RCwMCAQEsBhgnTWWdwJ1lTScYBgEsAZABLAJYyPpxIFwZCwsUHCMoLC4YEQj6yCpSfmpxUDMzUHFqflIBVgEs%2FtQBLP7UAAAAAAH%2F4gC4BGgD3gAFAAADFwkBNwEe4wFgAWHi%2Fb4Bm%2BMBYf6f4wJDAAABAEYA2gTMBAAABQAAEwkBJwkBRgJEAkLi%2Fp%2F%2BoAMd%2Fb0CQ%2BP%2BnwFhAAAAAAL%2FOgBkBXYD6AAIABEAKACwBy%2BwBM0BsBIvsAfWsATNsRMBK7EEBxESsAE5ALEEBxESsA45MDEDCQEjESEXIREBFyERIwkBIxHGASsBLMsBgdf84AGU1wF9xgErASvIArwBG%2F7l%2FnDIAlgBLMj%2BcP7lARsCWAAAAAEAEgAABKoEsAAyAEYAsiIAACuwGTOwLM2wLBCwJs2xFR0yMrAvL7AEzbAQL7AJzQGwMy%2BwJNawH82wHxCxHAErsBfNsTQBK7EXHBESsC05ADAxEyY3NjMhNz4BOwEyFhQGKwEDDgIrARUUBiImPQEhFRQGIiY9ASMiJjU0NjMhNyEiJicSBQ8OGQOAJgUbEV4UHh4UNskCCB4SHx0qHf7UHSodMhUdHRUCFzD9hyAtBQOrGBITohEVHSod%2FD8EDRYyFB4eFDIyFB4eFDIeFBUdyCoWAAAAAAIAAAAABLAETAADAA8AIACyAAAAK7ABzbAEL7AFzbANMrAJzQGwEC%2BxEQErADAxMREhEQE1MzQ2MyEyFhUhFQSw%2B1DIOykBLCk7AfQDIPzgA4RkKTs7KWQAAAAAAgABAAAF3QRMAAMAEAAoALIAAAArsAHNsA8vsA3NsAUysAnNAbARL7ESASsAsQEAERKwBDkwMTMBIQkBETM0NjMhMhYVIRUhAQEsBLD%2B1PtQyDspASwpOwH0%2FBgCvP1EAZACWCk7OynIAAAAAQEuAAADggSwAAkAIQCyCQAAKwGwCi%2BwAdawB82xCwErsQcBERKxBAk5OQAwMQEzESMJASMRMwEBLsbGASoBKsbG%2FtYBLAJYASz%2B1P2o%2FtQAAAAAAQAAAS8EsAOCAAkAHACwCC%2BwAs0BsAovsQsBKwCxAggRErEABTk5MDERARUhNQkBNSEVASwCWAEs%2FtT9qAJYASrGxv7W%2FtfFxQAAAAQAAAAABLAEsAAPABkAHQAhAEkAsgwAACuwGs2wHjKwHS%2BwIDOwBc2wEC%2BwFM0BsCIvsBvWsB7NsB4QsR8BK7AJzbIfCQors0AfAAkrsSMBK7EJHxESsBk5ADAxPQE0NjMhMhYdARQGIyEiJhsBPgEzITIWFxMBMzUjFzM1IzspA%2BgpOzsp%2FBgpOx%2BsBSQUAqATJQWs%2Fo9kZMhkZGRkKTs7KWQpOzsBVQLjFictF%2F0k%2FtRkZGQAAAAD%2F5sAZASwBEwACwApADcAJgABsDgvsADWsAbNsAYQsSoBK7AyzbE5ASuxKgYRErEMGjk5ADAxAzU0Nj8BFS4EFzU8Az4FOwElESUjExYOASMiKwEiJicCARE0NjMyFhURFAYjIiZlMhkZBA4iGhbJAQICBAUHBMgCo%2F1dJi8CCgwPBQNTFB0ENwPoHRUUHh4UFR0CWDIYMg0N%2BgIHFRYhVfoCDAQKBAcDBQIB%2BvyuyP7sDAsBHBUBUf7iA1IVHR0V%2FK4UHh4AAAIASgAABGYEsAArADMANQCyLwAAK7AzzbApL7AfM7ADzbAYMrIpAwors0ApJQkrAbA0L7E1ASsAsSkzERKxLDE5OTAxEzQ2OwE3Ez4BNycmNjsBMhYHBhUeARcTFzMyFhUUBgcOBCMiJi8BLgEFHgEyNjcGIkobFBJ1Pw96UxIGEhReFBIGElN6Dz92ERQbGhIIHmRqn0998To6EhoBpww4RjgLMGwBXhUdrQFHTX4UIBMaFRMkARN%2FTf65rR0VFCgHAwsdFRIpFBQHKdwxPT0xBgAAAQAVABUEnAScABcAABMXBzcXNxc3Fyc3JzcnNwcnBycHJxcHFxXpTuAtm5st4E7qtLTqTuAtm5st4E7pswG9LeBO6bOz6U7gLZucLOFO6bS06U7hLJwAAAMAAABkBLAEsAADACIALgAaAAGwLy%2BwKNawFs2xMAErsRYoERKwFDkAMDE1MxEjARQ7ARY7ATI3EzY9ATQmIyE2PQE0JisBIgYPAgYVExE%2FATMVByEVAyMnyMgBLGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiMgCWP3zS2Q5AVgfK2QsUXYHlixRKBzGxBol%2FokBd9XUr%2BF9%2FolkAAAAAAMAAAAABLAETAADACIALgBwALIcAAArsCXNsBUvsAAzsCjNsC4vsAfNsAEysCwvsArNAbAvL7AA1rADzbADELEEASuwI82wIxCxJgErsBjNsBgQsSkBK7ARzbEwASuxJiMRErIIKCw5OTmwGBGwFTmwKRKwKzkAsS4cERKwKjkwMRkBMxE3ETQ7ATY7ATIXExYdARQGIyEWHQEUBisBIiYvAiY3HwEzNSchNQMjByPIZGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiGQBkAJY%2Fah9AZBLZDn%2BqB8rZCxRdgeWLFEoHMbEGiXU1a%2FhfQF3ZAAAAAMACABkBRUEVQADACIAQQB5ALAgL7AkzbAbL7ApzbAxL7AUzbABMrIxFAors0AxAAkrAbBCL7AA1rADzbADELEEASuwI82wIxCxLQErsBjNsi0YCiuzQC08CSuxQwErsS0jERK0DBEbFD8kFzkAsRskERKwIzmwKRGwGDmxFDERErIXPEE5OTkwMTcRMxE3ETQ2PwElNjMyHwEWFRQPASEyFhQGKwEDDgEjISImNxchEz4BOwEyNjU0JiMhKgIuBCcmNTQ%2FAScFCMhkHA4OAWoOCxEMbQ4LVQEuVWttVGuCBxsP%2FqsHpmRkASWDBhsPyxASEhD%2BNwELBAkDBwQEAgUKk1b%2BrcgCWP2oSwINESUKCeYGDHAOFBIOeUyQTv6tFieiG1kBUxUoHhUUHQEBAgMFAwwIDg23U%2BwAAAAD%2F5sAZQSwBFYAHgA4ADwAeQCwGC%2BwJM2wHS%2BwH82wOC%2BwA82wOjKyOAMKK7NAODkJKwGwPS%2BwAdawH82yHwEKK7NAHywJK7AfELEmASuwFM2wFBCxOQErsDzNsT4BK7EmHxEStAcMHAQpJBc5ALEdJBESsCY5sB8RsAA5sQM4ERKyAScsOTk5MDECNDYzBScmNTQ%2FATYzMhcFHgIVERQGIyEiJicDIyInMzIWFxMhNxElBxcWFRQHDgUqASMhAREzEWVsVQEuVQsObQ0QCw4BbQcUIawI%2FqsQGwaCa1QK3g8bBoMBJWv%2BqVeRCgUBBQMHBAkECwH%2BJAPpyAJDkEwBeRAQFQ1xDAbmBA0nEf3yDaEoFQFTZCkU%2Fq1ZAfbtU7gLDwsJAwUDAgEB%2FgwCWP2oAAAAAAMAYQAABEwFDgAbADYAOgBHALI3AAArsDjNAbA7L7AV1rA3MrApzbIpFQors0ApOgkrsDMysCkQsS8BK7AOzbE8ASuxKRURErESNjk5sQ4vERKwETkAMDEbAR4CMyEyNjURNCYnJTU0JiIGFREnJgYPAQYXNxcWNz4FPAE1ETQ2Fh0BFBYXBREHIQM1IRVh5gQNJxECDQ2iKBX%2BrU6QTHkPJQ5wFltTtxYZAwUDAgEBMjIoFQFTWf4JCAJYAs%2F%2BlQYTH6YHAVYPGwaDalRua1X%2B0lQMAQ1uFgtWkhINAQUDBwQJBAsBAcgWEhMVyhAbBoL%2B2mT%2BcMjIAAAAAAMAAgAKA%2B0FGAAdADQAOABFALA1L7A2zQGwOS%2BwCtawNTKwK82yKwoKK7NAKzgJK7AhMrArELEnASuwD82xOgErsSsKERKxDB85ObEPJxESsA05ADAxEwYfAR4BPwEUBhUUFjI2PQElPgE1ETQmIyEiBg8BAxMhFxEFDgEdARQGJjURPAEuAScmDwETNSEVAhAWcA0mD3kBTZBOAVMUKaIN%2FfMRJQoKmuwB91n%2BrBQoMjIDBwYYFriSAlgCSR8Wbg0BC1UzzS5UbG5UaoMGGw8BVgemHA4P%2FoIBU2T%2B2oIGHA%2FKFhISFgHICwcQCAMNEpICccjIAAAAAgAFAAAEsASrAA4AFQA6ALIMAAArsBDNsA8vsAXNAbAWL7AA1rAQzbEXASsAsRAMERKxCRI5ObAPEbEAEzk5sAUSsQgUOTkwMRM0PgIzMgQSEAIEICQCARchBwkBFQVfoN16ogEToKD%2B7f68%2FuygASUCASwCAZL%2BbgJVet2gX6D%2B7P68%2Fu2goAETAQrJwgEmASrFAAIAAAAABKsEqwAQABcAOACyDgAAK7AUzbAWL7AFzQGwGC%2BwFNawCs2xGQErALEUDhESsBI5sBYRsgoAETk5ObAFErAXOTAxETQ%2BAjMyHgIVFAIEICQCNwEnITchNV%2Bg3Xl63aBfoP7s%2Frz%2B7aDIAZICASwC%2FtQCVXrdoF9foN16ov7toKABE6X%2B2sLJxQAAAAIABQAABLAEqwAOABUAPgCyDAAAK7ARzQGwFi%2BwANawEc2wERCxEgErsAnNsRcBK7ERABESsQwPOTmwEhGxBRU5ObAJErELFDk5ADAxEzQ%2BAjMyBBIQAgQgJAIlMxEzETMBBV%2Bg3XqiAROgoP7t%2Frz%2B7KABJ8jIyP7UAlV63aBfoP7s%2Frz%2B7aCgAROl%2FtQBLAGQAAACAAUAAASwBKsADgAVAE0AsgwAACuwFC%2BwBc0BsBYvsADWsBXNsBUQsRIBK7AJzbEXASuxFQARErEMDzk5sBIRsQUQOTmwCRKxCxE5OQCxFAwRErIIABA5OTkwMRM0PgIzMgQSEAIEICQCJQkBIxEjEQVfoN16ogEToKD%2B7f68%2FuygAScBLAEsyMgCVXrdoF%2Bg%2Fuz%2BvP7toKABE6X%2BcAGQASz%2B1AAAAAAEAAUAAASwBKsAEACIAJgAmgB8ALIOAAArsCrNsE8vsIwvAbCbL7AA1rAUzbAUELFYASuwCs2xnAErsRQAERKwEjmwWBFADg4FEyEjJDxKVXiEhYmUJBc5sAoSQAoNIiYwO1pndpmaJBc5ALFPKhEStxYwNjxGSFVXJBc5sIwRQAkKABRYbIWNjpQkFzkwMRM0PgIzMh4CFRQCBCAkAhMGFgcUFgcyHgEXFhceAjcWBhcWFxQOARcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQcGJyY3NjceAhceAR8BNDYnJjY3PgM3JjcyFjI2Ny4DJzYnHgE%2FATYuAScGJw4DBwYmBw4BBwYWBw4BJT4BNxYyPgE3FBYVLgM3MwVfoN16ed2gX6D%2B7f68%2Fuyg%2BQgbBiIBDBYYCBhUFj45HQguAyotBgEFaHUeIiMDDi4NDkYRCT0gLhAyEAQBBikEAggZGhcTEwsGEAYoGwYMKA4OEwQEJQQFCgcYFgYQCB8SFwkKKSM%2FDAsJHzYMCwcvUg8TEg8rGj4IDz0PFT4DAxMBAzEBAwMaAwoRCxIHIgksHCSiAQJVet2gX1%2Bg3Xqi%2Fu2goAETAVkhdxwJRhkLEwQMHggvHgQSShRHCQYTCgwDcx0kPh8JAQcHEAsBAgsLIxcCLwINCAMWJhIdGR0cHhAGAQEHChMlCQgDSRUXKwoOKhQZCRITAwkLFycVIAcnBQ0DBQQkIxYMAwMMEgYKAQMHBgcnDwsXByJxcQwkBwoMEQQYVQECBgQMXwAAAAABAAAAAgSvBIUAFAAAPAE3ASY2NzYXBRc3FgcGJwEGIi8BDwJYIU5gpI7%2B%2FZH7DaR7gv2sDysPb48rEAJXZck2XGWK6H6vXEYv%2FawQEG4AAAYAAABgBLAErAAPAB8ALwAzADcAOwBQALAML7A0zbA3L7AFzbAcL7AwzbAzL7AVzbAsL7A4zbA7L7AlzQGwPC%2BwNdaxMTkyMrAJzbEYKDIysjUJCiuzQDUACSuxECAyMrE9ASsAMDE9ATQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYBITUhEyE1IRMzNSM7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTsCWAH0%2FgzIASz%2B1GTIyMRkKTs7KWQpOzsBuWQpOzspZCk7OwG5ZCk7OylkKTs7%2Fplk%2FgxkArxkAAACAGQAAARMBLAAAwAJACUAsggAACuwAC%2BwAc0BsAovsAjWsAfNsQsBKwCxAAgRErAEOTAxEzUhFQUhAREHEWQD6PxKA4T%2BosgETGRkZP4M%2FtTIAfQAAAAAAwAAAGQEsASwAAkAIQAlAGAAsAcvsAHNsAovsB0zsA7NsRgiMjKwJS%2BwE80BsCYvsA%2FWsCLNsCAysg8iCiuzQA8LCSuwADKwIhCxIwErsB4ysBjNshgjCiuzQBgcCSuwAjKxJwErALEOChESsB85MDE9ASEVFAYjISImGQE0NjMhNTQ2OwEyFh0BITIWFREhNSMVETM1IwSwOyn8GCk7OykBLDspyCk7ASwpO%2F4MyMjIyMjIKTs7AVUBkCk7ZCk7OylkOyn%2BcGRkAfRkAAAAAAQAAAAABLAEsAAGAA0AFAAbABQAsgAAACuwEjMBsBwvsR0BKwAwMTERFzcXBxcBNxc3JzchATcXNxEhNwM3JyERJweByI7Igf5wgciOyIH%2BcALZjsiB%2FnCByMiBAZCByAGQgciOyIEDIIHIjsiB%2FJmOyIH%2BcIEC5siB%2FnCByAAABgAAAAAEqASoAAsAFQAfACkAQgBMANIAsgoAACuwD82wHi%2BwSjOwGc2wRTKwKC%2BwOTOwI82wNDKyKCMKK7NAKEEJK7AUL7AEzQGwTS%2BwAdawDM2wDBCxFwErsBvNsBsQsSELK7AmzbAmELEqASuwPs2wPhCxQwErsEjNszdIQwgrsDHNsDEvsDfNsEgQsREBK7AHzbFOASuxJhsRErMKDhQDJBc5sT4qERKxLTw5ObE3MREStQkPEwQvOyQXOQCxHg8RErMHAAwRJBc5sBkRsyotPD4kFzmwKBKxBgE5ObAjEbEvOzk5MDEYARIkIAQSEAIEICQTFBYgNjU0JiAGFjQ2MhYVFAYjIjY0NjMyFhQGIyIXNDY%2FAiY1NDYzMhYUBiMiJwcWFRQGIiYlNDYyFhQGIyImoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzbR8uICAXFk0gFxYgIBYXUikfegEJIBcWICAWDg83ETNIMwEeIC4fIBYXIAGyAUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FOHLh8gFhcg5CwhIC4guiAxBX4BDg4WISAuIAqRFh0kMzNSFiAfLiAgAAAAAf%2FYADsEugSwAE8AOgCwBS%2BwJ82wIC%2BwFc2wNi%2BwSs0BsFAvsVEBKwCxJwURErA%2FObAgEbQLDxobMSQXObAVErEyMzk5MDECBhceATMyNz4CNzY3AT4BJyYnJiMiBgcBBxcBNjc2MzIXFgcBBiMiJicmPgI3NjcBPgIzMhceAQcGDwEDHwEBPgEnLgEnJiMiBwYHARsaMCN2Rj84IUApJygRAYojGA8bWhQJLkEj%2FnsHRQF5FBMXGyYPECT93TRJN1oJBQ8wJCYYFAFcND1rNhkXX3YIB1v8%2FQdFAgVDOBEQZk9FU2taKEf%2BAAHWvk45QBwQMSorLBEBiiNiL1cRAiEj%2FnQHQwF1FhAXJCck%2Fd00Qj8jPkAkJBUUAVw0NzUEEZtiZVv5%2FwAHPAH%2FQ7RdV4YkITcYR%2F4AAAAAAAIAUAA2BMMEWAAbADUAPQCwMy%2BwLTOwA82wBzIBsDYvsADWsBzNsBwQsSoBK7AKzbE3ASuxKhwRErMDBw8YJBc5ALEDMxESsAU5MDETNDYzMhc2MzIWFRQOAgcGDwEnLgInLgM3FB4BHwEWFzY%2FAT4CNTQmIyIPAScmIyIGUMWEj2Jnj4HCI1dDR8VgERArckZCR0NXI6o9PkAWXWFScQxAQz5gOUo6dnIzSDxjAxCDxYGBxYMuWmxHRr%2BDFxc6gUZBRkdsWi4bVkE%2BFlpvXG8MPkZYHEdhU6uuUGMAAAAAAgA5%2F%2FIEdwS%2BABgAMwAAExQfARYzMjcBNjQvASYnBxcBJzcnJicHBhMUHwEWFzcnARcHFxYXNz4BNTQvASYjIgcBBjlCjURbXUIBG0JCjQgLadT%2Be%2FdfEi4dN0LUQo0HDGnUAYX3XxIvHh0jN0KNQl1fQP7lQgFhX0COQkIBG0K6Qo0JCGnU%2Fnv4XxItODdCAQRdQo0HCmnUAYX3YBExMx0jaitdQo1CQv7lQAAAAAADAMgAAAPoBLAAEQAVAB0ARQCyDwAAK7AZzbAdL7ASzbAVL7AGzQGwHi%2BwANawEs2wEhCxEwErsAvNsAsQsBvNsBsvsR8BK7EbEhESsgYFFjk5OQAwMTcRND4CMh4CFREUBiMhIiY3IREhEhQWMjY0JiLIPGacqppkOjsp%2FagpO2QCWP2oxD1WPT1WZAO5FTIuHh4uMhX8Ryk7O%2FECvPzZVj09Vj0AAAABAAAAAASwBLAAGAARALIAAAArAbAZL7EaASsAMDExATcnIQEnJjQ3NjIXARYUBwYiLwEBEScHAS%2FP0gEsAQsjDw8OKg4BGw8PDioOJP7p1NABfNDUARckDioODw%2F%2B5g8qDg8PI%2F71%2FtTSzwADAScAEgQJBOEAMQA9AEMAlwCwLS%2BwKjOwBM2wPjKyLQQKK7NALSwJK7A7L7AfM7ASzbISOwors0ASEwkrAbBEL7AO1rAAMrAyzbABzbAyELEsASuyBBI6MjIysCvNshQfPjIyMrArELFAASuwJ82wHCDWEbAbzbFFASuxMgERErACObAsEbAJOQCxBC0RErApObA7EbYADhsnOkBDJBc5sBISsBU5MDEBMx4BFxEuASMuBDU0PgE3NTMVHgQXIy4BJxEXHgQVFAYHFSM1JicuARMUHgMXFhcRDgETNjU0JicBJ4sFV0oGEwIuQk4vIViCT2QmRVI8KwOfCDZKQCI8UDcosptkmFUoGagQESoUHAcEPUnqqlhSAbFNYw8BTwEGDhkvOVg3XIdDB05PBBMsP2lCSEsN%2Fs0OBxMsPGU%2Bi6oLTU4RVyhrAh4dLBgVBgcCAQESCDv9KxKFQEcZAAAAAQBkAGYDlAStAEMAjQCwMS%2BwKs2wAC%2BwHjOwAc2wHDKwEy%2BwC82yEwsKK7NAEw4JKwGwRC%2BwB9awOTKwGM2wJDKyGAcKK7NAGB4JK7IHGAors0AHAAkrsBgQsQ8BK7AOzbFFASuxGAcRErMCOEJDJBc5sA8RtQsfICoxMyQXObAOErAsOQCxKjERErEtOTk5sAARsSw8OTkwMRM1MyYnLgE%2BATc2MzIWFSM0LgEjIgYHBhUUHgEXMxUjFgYHBgc%2BATM2FjMyNxcOAiMiJgcOAQ8BJz4FNz4BJ2SmGBQKCQMvLWGmgcqZRFAkJVQUKSEXHvHFCBUVKTojYhUhjCFMPDIpTycqF9IyJ1YXGDcGFQoRDBEJMAwkAlhkMTcaO1ZeKFiydzRLHB0VLDkcUyozZDKCHTs2Cw4BIh6TGRcDQgQEGgwLkQQOBg0LEQo3j0cAAgACAAAErgSwAAYADQAfALIMAAArAbAOL7AM1rALzbEPASuxCwwRErAIOQAwMRMJASMRIxEJAiMRIxECASoBKsbIAZIBKgEqxsgBLP7UASwDhPx8AlgBLP7U%2FHwDhAAABQACAAAD6ASwAAYADAAWAB4AIgCmALIHAAArsAYzsArNsgcAACuwCM2wEy%2BwFM2xAAQyMrANL7AOzbAdL7AfzbIdHwors0AdFwkrsBoysCIvsBjNsAIyAbAjL7AB1rAEzbAEELEIASuxDRcyMrAKzbEdHzIysAoQsRUBK7EbIDIysBDNsQsZMjKzEhAVCCuwE82wEy%2BwEs2xJAErsQQBERKwBjmwCBGwBTkAsRQIERKwEDmwDRGwETkwMRMzETMRMwEhNTMVMxUBNSEVIxUjNTM1AxEhESM1IxU3MzUjAsbIxv7WAZBkyP7UASxjZGPIASxkZAFkZAEsA4T8fP7UyGRkAZBkyGRkZAEsAfT%2BDGRkyMgABQACAAAD6ASwAAYADgAUAB4AIgCgALIGAAArsQcKMzOwDS%2BwH82wIi%2BwCM2wDy%2BwEs2wEM2wGy%2BwHM2wFS%2BwFs2wAjIBsCMvsAHWsATNsAQQsQcBK7EPFTIysA7NsREfMjKwDhCxCwErsR0gMjKwCs2xExcyMrMaCgsIK7AbzbAbL7AazbEkASuxBAERErAGObAHEbAFOQCxIh8RErMBBAUAJBc5sRwQERKwGDmwFRGwGTkwMRMzETMRMwEhESERIzUjFQM1MxUzFQE1IRUjFSM1MzUDMzUjAsbIxv7WAZABLGRkZGTI%2FtQBLGNkY2NkZAEsA4T8fP7UAfT%2BDGRkArzIZGQBkGTIZGRk%2FHzIAAAEAAIAAARMBLAABgAMABIAFgBrALILAAArsAwvsBPNsBYvsAjNsA0vsA7Nsg0OCiuzQA0RCSsBsBcvsBHWsBDNsxMQEQgrsAfNsAcvsA0zsBPNsBAQsRQLK7ALMrAKzbEYASsAsQgLERK0AAIDBgEkFzmxDg0RErEFBDk5MDETCQEjESMRBREhESM1AzUzESMREzM1IwIBKgEqxsgCWAEsZMjIZAFkZAEs%2FtQBLAOE%2FHzIAZD%2BDGQD6GT%2BDAGQ%2FHzIAAAABAACAAAETASwAAYADAASABYAawCyCwAAK7AHL7AIzbASL7ATzbISEwors0ASEAkrsBYvsA7NAbAXL7AL1rAKzbMTCgsIK7ANzbANL7AHM7ATzbAKELEUCyuwETKwEM2xGAErALETCxEStAACAwYBJBc5sQ4WERKxBQQ5OTAxEwkBIxEjESU1MxEjEQMRIREjNSczNSMCASoBKsbIAljIZGQBLGRjZGQBLP7UASwDhPx8ZGT%2BDAGQAZABkP4MZGTIAAAAAAUAAgAABLAEsAAGAAoADgASABYAUgCwBy%2BwCM2wCy%2BwDM2wDy%2BwEM2wEy%2BwFM0BsBcvsA%2FWsgcLEzIyMrASzbAWzbIWDwors0AWCgkrs0AWDgkrsRgBKwCxCwgRErMCAwYAJBc5MDETCQEjESMRBTUhFQE1IRUBNSEVATUzFQIBKgEqxsgB9AH0%2FgwBkP5wASz%2B1MgBLP7UASwDhPx8yMjIASzIyAEsyMgBLMjIAAAABQACAAAEsASwAAYACgAOABIAFgBSALAHL7AIzbALL7AMzbAPL7AQzbATL7AUzQGwFy%2BwC9ayBw8TMjIysA7NsArNsgoLCiuzQAoSCSuzQAoWCSuxGAErALELCBESswIDBgAkFzkwMRMJASMRIxEFNTMVAzUhFQE1IRUBNSEVAgEqASrGyAH0yMgBLP7UAZD%2BcAH0ASz%2B1AEsA4T8fMjIyAEsyMgBLMjIASzIyAAAAAACAAAAAARMBEwADwAfACoAsg0AACuwE82wHC%2BwBM0BsCAvsADWsBDNsBAQsRcBK7AJzbEhASsAMDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFeulASyi7u2j%2FtSl68g7KQH0KTs7Kf4MKTsBkAEspevto%2F7UpevrQSk7OykB9Ck7OykAAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMtAe6iASyl6%2Bul%2FtSj7cg7KQH0KTs7Kf4MKTvIAU3%2BswGQASyj7eul%2FtSl6%2BtBKTs7KQH0KTs7Kf4M%2BvoAAAAAAwAAAAAETARMAA8AHwAiAD4Asg0AACuwE82wHC%2BwBM0BsCMvsADWsBDNsBAQsRcBK7AJzbEkASuxFxARErEgIjk5ALEcExESsSAhOTkwMRkBNDYzITIWFREUBiMhIiY3FBYzITI2NRE0JiMhIgYVFxsB66UBLKPt66X%2B1KXryDspAfQpOzsp%2FgwpO2T6%2BgGQASyj7e6i%2FtSl6%2BtBKTs7KQH0KTs7KWT%2BswFNAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMhA%2BulASyl6%2B2j%2FtSl68g7KQH0KTs7Kf4MKTtkAfT6AZABLKXr66X%2B1KLu7T8pOzspAfQpOzsp%2FnABTQACAAAAAAUUBEwABgAaADwAsgcAACuwCM2wAC%2BwAc2wES%2BwEs0BsBsvsAzWsBfNsRwBKwCxCAcRErAFObEBABESsAQ5sBERsAM5MDEZASE1CQE1EzUhMjY1ETQmIyE1ITIWFREUBiMBLAGQ%2FnDIAfQpOzsp%2FgwBkKXr66UBkAEsyP6i%2FqLI%2FnDIOykB9Ck7yOul%2FtSl6wAAAAEA2QACA9YEngAhACgAsA4vsBPNAbAiL7Ac1rAWzbEjASuxFhwRErAZOQCxEw4RErAPOTAxExYzIQIHBh8BMzI3NgAzNicmIwU2Ejc2LwEjIgcOAQAHBtkIFwEumwUFCQkJDgwLAg8BDgkIF%2F7TAZoCAgcJCQ8KBLz%2B80sQAgcT%2FkoUFQsIDw0CaRMREQEEAa8MDwwJEAbT%2FtFWEwAAAAACAAAAAAUUBEwAGAAfAD8AsgMAACuwCM2wCBCwBs2wGS%2BwGs2wEC%2BwEs2wFM0BsCAvsADWsAvNsSEBKwCxGgMRErEdHjk5sBARsBw5MDERFBYzITI3NSEiJjURNDYzITUuAS8BIgYVAREhNQkBNeulASwvNf4MKTs7KQH0DshdXaXrAlgBLAGQ%2FnABkKXrD7k7KQH0KTu5BAcCAuul%2FtQBLMj%2Bov6iyAACAAAAAASwBLAAHQAkAFQAsgMAACuwD82wFi%2BwGs0BsCUvsADWsBLNsBIQsQsBK7AHzbEmASuxCxIRErUYGR4fICQkFzmwBxGwIzkAsRYPERK1CAkeIiMkJBc5sBoRsB85MDERFBYzITI2PQEnBxUUBiMhIiY1ETQ2OwE3JyMiBhUlASchEScB66UBLKPtTno7Kf4MKTs7KZx2SmSl6wHwAWGVAfSV%2FqoBkKXr66ViSXuUKTs7KQH0KTt6TuulCQFWlf4Mlf6fAAMABAAEBKwErAALABMAGwBaALAKL7APzbAbL7AXzbATL7AEzQGwHC%2BwAdawDc2wDRCxFQErsBnNsBkQsREBK7AHzbEdASuxGRURErcECQoODxITAyQXOQCxFxsRErcBBgcMDRARACQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiACNDYyFhQGIgSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqhdyoHJyoAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BEqBycqByAAAAAwAAAAAETASwAAkAEAAUAC4AsgkAACuwEc2wFC%2BwBc2wCzIBsBUvsBLWsAjNshIICiuzQBIACSuxFgErADAxMRE0NjMhMhYVEQkCIREhEQEzNSMOCwQYCxD8GAG9AcL%2B2f7UAfRkZAETCw4PCv7tAyD%2BDAH0AZD%2BcP12MgAAAAADAAAAAARMBLAACQAQABQAKwCyCQAAK7ARzbAUL7AFzQGwFS%2BwEtawCM2yEggKK7NAEgAJK7EWASsAMDExETQ2MyEyFhURASERIREhCQEzNSMOCwQYCxD8GAEsASwBJ%2F5DAV5kZAETCw4PCv7tArz%2B1AEsAfT75jIAAAAAAwAAAAAETAR%2FAAkADwATAC4AsgkAACuwEM2wEy%2BwBc0BsBQvsBHWsAwysAjNshEICiuzQBEACSuxFQErADAxMRE0NjMhMhYVEQkCJwEnATM1Iw4LBBgLEPwYATECVJr%2BRpYChWRkARMLDg8K%2Fu0Cwf7PAlSb%2FkaX%2FToyAAQAAAAABEwEsAAJAA0AFAAYACsAsgkAACuwFc2wGC%2BwBc0BsBkvsBbWsAjNshYICiuzQBYACSuxGgErADAxMRE0NjMhMhYVEQEXNycDJRMHJwcXATM1Iw4LBBgLEPwYYdRhcAK5A%2FqV1JUBzmRkARMLDg8K%2Fu0D3GLVYfzgAQK775XUlf4NMgAEAAAAAARMBLAACQANABQAGAAuALIJAAArsBXNsBgvsAXNAbAZL7AW1rATMrAIzbIWCAors0AWAAkrsRoBKwAwMTERNDYzITIWFREBFzcnExcHFzcXCwEzNSMOCwQYCxD8fNRi1QPvldSV%2BQFjZGQBEwsODwr%2B7QJk1GHUAev6ldSU7QK5%2B%2BkyAAAAAAIAF%2F%2F%2FBLAErwAFAAgAFwCyBAAAKwGwCS%2BwBdawBs2xCgErADAxEwERCQERFwkBFwSZ%2FiX%2Byk8CoP1gAZ8DEPvJARD%2BdwGgzQOq%2FTgAAAAAAgAAAGQETASwABUAGQBNALARL7AGzbIRBgors0AREwkrsA4ysgYRCiuzQAYECSuwCDIBsBovsADWsBLNsAbNsBIQsQ8BK7ALzbEbASuxDwYRErIJFhc5OTkAMDE1ETQ2OwERIREzFxEUBisBESERIyImATM1Ix0V%2BgH0ZMgeFJb9RJYVHQJYZGSWA%2BgUHv7UASzI%2FK4VHQGQ%2FnAdA2fIAAMAAAA%2BBRQEsAATABkAHQBAALAPL7AGzbIPBgors0APEQkrsgYPCiuzQAYECSuwCDIBsB4vsADWsBDNsAbNsR8BKwCxBg8RErILFxg5OTkwMTURNDY7AREhETMXFQEnByERIyImJTcXARcBAzM1Ix0V%2BgH0ZMj%2B7Hh%2B%2FoaWFR0CRXt4AWF7%2FiXhZGSWA%2BgUHv7UASzI2v7teH%2F%2BcB2xe3gBYHv%2BJAOqyAAAAAADAAAABgUOBLAAEwAXACMAFQABsCQvsADWsBDNsAbNsSUBKwAwMTURNDY7AREhETMXEQcnASERIyImATM1IxM3JzcXNxcHFwcnBx0V%2BgH0ZMhnqv7W%2FreWFR0CWGRkZKqqf6qqf6qqf6qqlgPoFB7%2B1AEsyP7zZ6r%2B1v5wHQNnyPvVqqp%2FqqqAqap%2FqqoAAAADAAAAAASwBLAAEgAZAB0AbACwDi%2BwBs2yDgYKK7NADhAJK7AGELAMzbAaL7AbzbEECDIyAbAeL7AA1rAPzbIPAAors0APCwkrsAAQsAbNsA8QsRoBK7AdzbAMMrEfASuxGgYRErATOQCxDA4RErEXGDk5sRoGERKwCjkwMTURNDY7AREhETMXESEVIREjIiYlCQEjESMRAzUzFR0V%2BgH0ZMj%2BcP4MlhUdAlgBLAEsyMjIZJYD6BQe%2FtQBLMj%2B1Mj%2BcB2r%2FtQBLAEs%2FtQCvMjIAAAAAAMAAAAABLAEsAASABkAHQBbALAOL7AGzbIOBgors0AOEAkrsBovsBvNsQQIMjIBsB4vsADWsA%2FNsAbNsA8QsRoBK7AdzbEfASuxGgYRErATObAdEbANOQCxBg4RErILDBk5OTmwGhGwCjkwMTURNDY7AREhETMXEScBIREjIiYlMxEzETMJATUzFR0V%2BgH0ZMjI%2Ftb%2BbpYVHQJYyMjI%2FtT%2B1GSWA%2BgUHv7UASzI%2Fm7I%2Ftb%2BcB2r%2FtQBLAEsAZDIyAAAAAADAAAAyASwBEwACQATABcAADUUFjMhMjY1ESE1ITU0JiMhIgYVEzUhFR0VBEwVHftQBLAdFfu0FR1kAZD6FR0dFQImZJYVHR0V%2FRLIyAAAAAYAAABmBLAErgAGAAoADgAVABkAHQCBALAFL7EWGjMzsALNsRccMjKwBy%2BxCw8zM7AIzbEMEDIyAbAeL7AH1rAKzbAKELELASuwDs2wDhCxFgErsBnNsR8BK7ELChESswIFBgEkFzmxFg4RErMEAw8QJBc5sBkRsxIUFREkFzkAsQIFERKwADmwBxGxARQ5ObAIErATOTAxEQEVIRUhFQM1MxUzNTMVMzUhNQkBNQM1MxU7ATUjASwBkP5wyGRkZGQBkAEs%2FtRkZGRkZAGQASrGyMYCusjIyMjIxv7W%2FtbG%2FgzIyMgAAAACAGQAAASwBLAAGAAvADoAshQAACsBsDAvsADWsATNsAQQsRcLK7AQzbMJEBcIK7AFzbAFL7AJzbAQELEKCyuwDs2xMQErADAxExE3FxEzETcXETMRNxcRBxEUBisBIiY1ESUUHgIfAREUFjsBMjY1ETQmBwUOARVkMjJkMjJkMjJkHRXIFR0CWBUdHQsKHRXIFR0kGv7sGSUCvAGQZGT%2B1AEsZGT%2B1AEsZGT%2BcMv%2BQRUdHRUBv2QdNSEYBgX%2BcxUdHRUEUh8TEXQRRR8AAAAAAQBkAAAEsARMADMAOACyAAAAK7AMM7AzzbICCw4yMjKwKC%2ByGBwlMzMzsCfNsBoyAbA0L7E1ASsAsSgzERKxBiA5OTAxMyE1IiY1ESERFAYjFSE1Ii4DNRE0Nj8BNSEVMhYVESERNDYzNSEVMh4DFREUBg8BZAGQSxkB9BlLAZAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGTgMJgGK%2FnYmDDg4AQUJFQ4DeBYZAQI4OAwm%2FnYBiiYMODgBBQkVDvyIFhkBAgAGAAAAAARMBEwADwAYABwAIAAqAC4AMgCyGAAAK7AQzbAWL7ASzbISFgors0ASFAkrAbAvL7EwASsAsRIQERKzBAMZHCQXOTAxERQWMyEyNjURNCYjISIGFRMhNzMlESEHIQM1IRUBNSEVASEXFSUzNSEnIQE1JRU7KQEsKTs7Kf7UKTtkAZDIaQEn%2Fldk%2FolkASz%2B1AEs%2FtQBkMgBJ2n%2BV2T%2BiQH0AZABLCk7OykB9Ck7Oyn9RMhi%2FtZkASzIyAEsyMgBkMhiYshk%2FUajhaMAAQAQABAEnwSfACAAABIeAxceAzM%2FATYmLwEmBg8BLgEnNz4BLwEuAQ8BEAEfPpJmZ9GXex8foxEHE8ATNBB2jvxldhEGDosOLRKiA%2BQriY%2FUZmeSPSEBohIuDogOBBF2ZfyOdhExFMITBhGiAAAAAAIAAAAABLAETAAdAEAALwCyGwAAK7AMzbAoL7A4zQGwQS%2BxQgErALEMGxESsSAvOTmwKBGzJikyQCQXOTAxPQE0NjcBNTQ%2BAzIeAh8BFQEeAR0BFAYjISImERQWPwE%2BAT0BNiAXFRQWHwEWNj0BLgQjIg4EDwEVDgFtAhYmUnBSJhYBAQFtDhUdFfu0FB4dFMoUHY0BPo0dFMoUHQYaZHzaflymdWQ%2FLAkJMtQUMw4BLzIEDSAZFRQbHAoKMv7RDjMU1BUdHQKrFRkEIQQiFZIYGJIVIgQhBBkVyAgZQTEpFSEoKCELCgACAGQAAASwBEwAAwAZABQAsgAAACuwAc0BsBovsRsBKwAwMTM1IRUlISc1NxEjFSM1IxUjNSMVIzUjERcVZARM%2B%2F8Dtn1kZGTIZMhkZGRkZMiW%2BmQBkMjIyMjIyP5wZPoAAAAAAwBkAAAEsARMAAkAEwAdACQAsgoAACuwFDMBsB4vsArWsBPNsBMQsRQBK7AdzbEfASsAMDEzIRE0JisBIgYVARE0NjsBMhYVETMRNDY7ATIWFRFkASw7KWQpOwGQOylkKTtkOylkKTsBkCk7Oyn%2BcAPoKTs7KfwYArwpOzsp%2FUQAAAAABf%2BcAAAEsARMAA8AEwAfACcAKwBIALINAAArsBDNsBMvsATNAbAsL7AA1rAQzbAQELERASuwCc2xLQErsREQERK1FBUgIygqJBc5ALETEBEStRQaICYoKSQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUzESM1IxMRMxFksHwCvHywsHz9RHywyAOE%2FHxkASzIyP7UyMgBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qAEsZGT%2B1GRkZAEsZP5wASz%2B1AAAAAAF%2F5wAAASwBEwADwATAB8AJwArAEgAsg0AACuwEM2wEy%2BwBM0BsCwvsADWsBDNsBAQsREBK7AJzbEtASuxERARErUUGSAjKCokFzkAsRMQERK1FBogJigpJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMzNTMVMxEjFSM1IwEzNTMRIzUjExEzEWSwfAK8fLCwfP1EfLDIA4T8fGRkZGRkZGQBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qMjIAfTIyP4MZAEsZP5wASz%2B1AAABP%2BcAAAEsARMAA8AEwAbACMARACyDQAAK7AQzbATL7AEzQGwJC%2BwANawEM2wEBCxEQErsAnNsSUBK7EREBESsxQVHB0kFzkAsRMQERKzFBocIiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETITUjETM1IQEhNSMRMzUhZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1AGQASzIyP7UASwB9HywsHz%2BDHywsBgCvP2oZAEsZP4MZAEsZAAAAAAE%2F5wAAASwBEwADwATABYAGQBEALINAAArsBDNsBMvsATNAbAaL7AA1rAQzbAQELERASuwCc2xGwErsREQERKzFBUXGCQXOQCxExARErMVFhcZJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMFERMtAWSwfAK8fLCwfP1EfLDIA4T8fGQBLGQBLP7UASwB9HywsHz%2BDHywsBgCvP6ilgEs%2FtSWlgAAAAAF%2F5wAAASwBEwADwATABcAHwAnAFoAsg0AACuwEM2wFC%2BwGM2wIzKwHy%2BwJTOwFc2wEy%2BwBM0BsCgvsADWsBDNsBAQsRQBK7AYzbAYELEcASuwIc2wIRCxJAErsBfNsBcQsREBK7AJzbEpASsAMDEDETQ2MyEyFhURFAYjISImNyERIRMRIRElMzI2NCYrAQQUFjsBESMiZLB8Arx8sLB8%2FUR8sMgDhPx8ZAK8%2FaiCKTY5JoIBEzYpgoImASwB9HywsHz%2BDHywsBgCvP2oAfT%2BDGRUglZWglQBLAAABf%2BcAAAEsARMAA8AEwAfACMAKQBIALINAAArsBDNsBMvsATNAbAqL7AA1rAQzbAQELERASuwCc2xKwErsREQERK1FBUgISQnJBc5ALETEBEStRQaICImKCQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUjEzMRMxEjZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1MjIAZFkZGNkZMgBLAH0fLCwfP4MfLCwGAK8%2FagBLGRk%2FtRkZGQBLP5wAfQABv%2BcAAAEsARMAA8AEwAZAB0AIQAnAEwAsg0AACuwEM2wEy%2BwBM0BsCgvsADWsBDNsBAQsREBK7AJzbEpASuxERARErcUFRocHh8iJSQXOQCxExARErcUGBobHiAkJiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNSMTNTMVFzM1IxMzETMRI2SwfAK8fLCwfP1EfLDIA4T8fGQBLMhkZWTIZGRjZGTIASwB9HywsHz%2BDHywsBgCvP2oAZBk%2FnDIyGRkASz%2BcAH0AAAAAAb%2FnAAABLAETAAPABMAHQAhACUAKwCbALINAAArsBDNsB4vsSIpMzOwH82wIzKwGi%2BwG82wFC%2BwJjOwFc2wJzKwEy%2BwBM0BsCwvsADWsBDNsBAQsR4BK7AUMrAhzbAhELEcASuwF82zGRccCCuwGs2wGi%2BwGc2wFxCxIgErsCXNsCUQsSoBK7ApzbApELAmzbAmL7ApELERASuwCc2xLQErALEbHxESsBc5sBQRsBg5MDEDETQ2MyEyFhURFAYjISImNyERIRc1IREjFSM1MzUDNTMVITUzFQM1MxEjEWSwfAK8fLCwfP1EfLDIA4T8fGQBLGNkY8dkASxkAchkASwB9HywsHz%2BDHywsBgCvMhk%2FtRkZMj%2BcGRkZGQBkGT%2BDAGQAAADAAQABASsBKwACwATAB0AeQCwCi%2BwD82wHS%2BwGs2wGS%2BwFs2wEy%2BwBM0BsB4vsAHWsA3NsA0QsRQBK7AazbAaELERASuwB82xHwErsRoUERK1Cg4TAxYdJBc5sBERtQkPBBIXGyQXOQCxGh0RErQHDRAAFCQXObAZEbAVObAWErMGDBEBJBc5MDESEBIkIAQSEAIEICQSEBYgNhAmIAM1NyEVIRUhFSEEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqodZAEs%2FtQBLP7UAbYBRAESoKD%2B7v68%2Fu6goAJf%2Fqrz8wFW8%2F3%2ByGRkyGQAAAQAAAAEBKgErAALABMAIAAkAKAAsAovsA%2FNsCEvsBQzsCLNsBsvsBXNsBMvsATNAbAlL7AB1rANzbANELEUASuwIM2wGzKyIBQKK7NAIB4JK7AgELEhASuwGTKwJM2wFzKwJBCxEQErsAfNsSYBK7EgFBESswoOEwMkFzmwIRGwFjmwJBKzCQ8SBCQXOQCxIiERErQHDRAAHiQXObAbEbIXGB85OTmwFRKzBgwRASQXOTAxGAESJCAEEhACBCAkEhAWIDYQJiADESEXFSM1IxUzFSMVMzUzFaABEgFEARKgoP7u%2Frz%2B7hbzAVbz8%2F6qGQEsZGTIyMjIZAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP9mgGQZGRkZGRkZGQA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%2F4WwAY0AS7AIUFixAQGOWbFGBitYIbAQWUuwFFJYIbCAWR2wBitcWFmwFCsAAAABUuZYrgAA%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIC8%2BCjxnbHlwaCAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZDsiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDQiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzNCIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI2IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTciIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYzIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMjEyOyIgZD0iTTIwMCA0MDBoOTAwdjMwMGgtOTAwdi0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjVmYzsiIGhvcml6LWFkdi14PSI1MDAiIGQ9Ik0wIDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjYwMTsiIGQ9Ik0tMTQgNDk0cTAgLTgwIDU2LjUgLTEzN3QxMzUuNSAtNTdoNzUwcTEyMCAwIDIwNSA4Ni41dDg1IDIwNy41dC04NSAyMDd0LTIwNSA4NnEtNDYgMCAtOTAgLTE0cS00NCA5NyAtMTM0LjUgMTU2LjV0LTIwMC41IDU5LjVxLTE1MiAwIC0yNjAgLTEwNy41dC0xMDggLTI2MC41cTAgLTI1IDIgLTM3cS02NiAtMTQgLTEwOC41IC02Ny41dC00Mi41IC0xMjIuNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDEzOyIgZD0iTTI5IDQ1NGw0MTkgLTQyMGw4MTggODIwbC0yMTIgMjEybC02MDcgLTYwN2wtMjA2IDIwN3oiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI3OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzEgMTIxLjUgLTI5Mi41dDI5Mi41IC0xMjEuNXQyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNXQtMTIxLjUgMjkyLjV0LTI5Mi41IDEyMS41dC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNMzUwIDYwMGwyNTAgMzAwIGwyNTAgLTMwMGgtMTUwdi0zMDBoLTIwMHYzMDBoLTE1MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI4OyIgZD0iTTAgMjV2NDc1bDIwMCA3MDBoODAwbDE5OSAtNzAwbDEgLTQ3NXEwIC0xMSAtNyAtMTh0LTE4IC03aC0xMTUwcS0xMSAwIC0xOCA3dC03IDE4ek0yMDAgNTAwaDIwMGw1MCAtMjAwaDMwMGw1MCAyMDBoMjAwbC05NyA1MDBoLTYwNnoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI5OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzIgMTIxLjUgLTI5M3QyOTIuNSAtMTIxdDI5Mi41IDEyMXQxMjEuNSAyOTNxMCAxNzEgLTEyMS41IDI5Mi41dC0yOTIuNSAxMjEuNXQtMjkyLjUgLTEyMS41dC0xMjEuNSAtMjkyLjV6TTUwMCAzOTd2NDAxIGwyOTcgLTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMwOyIgZD0iTTIzIDYwMHEwIC0xMTggNDUuNSAtMjI0LjV0MTIzIC0xODR0MTg0IC0xMjN0MjI0LjUgLTQ1LjV0MjI0LjUgNDUuNXQxODQgMTIzdDEyMyAxODR0NDUuNSAyMjQuNWgtMTUwcTAgLTE3NyAtMTI1IC0zMDJ0LTMwMiAtMTI1dC0zMDIgMTI1dC0xMjUgMzAydDEyNSAzMDJ0MzAyIDEyNXExMzYgMCAyNDYgLTgxbC0xNDYgLTE0Nmg0MDB2NDAwbC0xNDUgLTE0NXEtMTU3IDEyMiAtMzU1IDEyMnEtMTE4IDAgLTIyNC41IC00NS41dC0xODQgLTEyMyB0LTEyMyAtMTg0dC00NS41IC0yMjQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMxOyIgZD0iTTIzIDYwMHEwIDExOCA0NS41IDIyNC41dDEyMyAxODR0MTg0IDEyM3QyMjQuNSA0NS41cTE5OCAwIDM1NSAtMTIybDE0NSAxNDV2LTQwMGgtNDAwbDE0NyAxNDdxLTExMiA4MCAtMjQ3IDgwcS0xNzcgMCAtMzAyIC0xMjV0LTEyNSAtMzAyaC0xNTB6TTEwMCAwdjQwMGg0MDBsLTE0NyAtMTQ3cTExMiAtODAgMjQ3IC04MHExNzcgMCAzMDIgMTI1dDEyNSAzMDJoMTUwcTAgLTExOCAtNDUuNSAtMjI0LjV0LTEyMyAtMTg0dC0xODQgLTEyMyB0LTIyNC41IC00NS41cS0xOTggMCAtMzU1IDEyMnoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDM0OyIgZD0iTTEwMCAwdjExMDBoMTAwdi0xMTAwaC0xMDB6TTMwMCA0MDBxNjAgNjAgMTI3LjUgODR0MTI3LjUgMTcuNXQxMjIgLTIzdDExOSAtMzB0MTEwIC0xMXQxMDMgNDJ0OTEgMTIwLjV2NTAwcS00MCAtODEgLTEwMS41IC0xMTUuNXQtMTI3LjUgLTI5LjV0LTEzOCAyNXQtMTM5LjUgNDB0LTEyNS41IDI1dC0xMDMgLTI5LjV0LTY1IC0xMTUuNXYtNTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzU7IiBkPSJNMCAyNzVxMCAtMTEgNyAtMTh0MTggLTdoNTBxMTEgMCAxOCA3dDcgMTh2MzAwcTAgMTI3IDcwLjUgMjMxLjV0MTg0LjUgMTYxLjV0MjQ1IDU3dDI0NSAtNTd0MTg0LjUgLTE2MS41dDcwLjUgLTIzMS41di0zMDBxMCAtMTEgNyAtMTh0MTggLTdoNTBxMTEgMCAxOCA3dDcgMTh2MzAwcTAgMTE2IC00OS41IDIyN3QtMTMxIDE5Mi41dC0xOTIuNSAxMzF0LTIyNyA0OS41dC0yMjcgLTQ5LjV0LTE5Mi41IC0xMzF0LTEzMSAtMTkyLjUgdC00OS41IC0yMjd2LTMwMHpNMjAwIDIwdjQ2MHEwIDggNiAxNHQxNCA2aDE2MHE4IDAgMTQgLTZ0NiAtMTR2LTQ2MHEwIC04IC02IC0xNHQtMTQgLTZoLTE2MHEtOCAwIC0xNCA2dC02IDE0ek04MDAgMjB2NDYwcTAgOCA2IDE0dDE0IDZoMTYwcTggMCAxNCAtNnQ2IC0xNHYtNDYwcTAgLTggLTYgLTE0dC0xNCAtNmgtMTYwcS04IDAgLTE0IDZ0LTYgMTR6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNjsiIGQ9Ik0wIDQwMGgzMDBsMzAwIC0yMDB2ODAwbC0zMDAgLTIwMGgtMzAwdi00MDB6TTY4OCA0NTlsMTQxIDE0MWwtMTQxIDE0MWw3MSA3MWwxNDEgLTE0MWwxNDEgMTQxbDcxIC03MWwtMTQxIC0xNDFsMTQxIC0xNDFsLTcxIC03MWwtMTQxIDE0MWwtMTQxIC0xNDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNzsiIGQ9Ik0wIDQwMGgzMDBsMzAwIC0yMDB2ODAwbC0zMDAgLTIwMGgtMzAwdi00MDB6TTcwMCA4NTdsNjkgNTNxMTExIC0xMzUgMTExIC0zMTBxMCAtMTY5IC0xMDYgLTMwMmwtNjcgNTRxODYgMTEwIDg2IDI0OHEwIDE0NiAtOTMgMjU3eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzg7IiBkPSJNMCA0MDF2NDAwaDMwMGwzMDAgMjAwdi04MDBsLTMwMCAyMDBoLTMwMHpNNzAyIDg1OGw2OSA1M3ExMTEgLTEzNSAxMTEgLTMxMHEwIC0xNzAgLTEwNiAtMzAzbC02NyA1NXE4NiAxMTAgODYgMjQ4cTAgMTQ1IC05MyAyNTd6TTg4OSA5NTFsNyAtOHExMjMgLTE1MSAxMjMgLTM0NHEwIC0xODkgLTExOSAtMzM5bC03IC04bDgxIC02Nmw2IDhxMTQyIDE3OCAxNDIgNDA1cTAgMjMwIC0xNDQgNDA4bC02IDh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzOTsiIGQ9Ik0wIDBoNTAwdjUwMGgtMjAwdjEwMGgtMTAwdi0xMDBoLTIwMHYtNTAwek0wIDYwMGgxMDB2MTAwaDQwMHYxMDBoMTAwdjEwMGgtMTAwdjMwMGgtNTAwdi02MDB6TTEwMCAxMDB2MzAwaDMwMHYtMzAwaC0zMDB6TTEwMCA4MDB2MzAwaDMwMHYtMzAwaC0zMDB6TTIwMCAyMDB2MTAwaDEwMHYtMTAwaC0xMDB6TTIwMCA5MDBoMTAwdjEwMGgtMTAwdi0xMDB6TTUwMCA1MDB2MTAwaDMwMHYtMzAwaDIwMHYtMTAwaC0xMDB2LTEwMGgtMjAwdjEwMCBoLTEwMHYxMDBoMTAwdjIwMGgtMjAwek02MDAgMHYxMDBoMTAwdi0xMDBoLTEwMHpNNjAwIDEwMDBoMTAwdi0zMDBoMjAwdi0zMDBoMzAwdjIwMGgtMjAwdjEwMGgyMDB2NTAwaC02MDB2LTIwMHpNODAwIDgwMHYzMDBoMzAwdi0zMDBoLTMwMHpNOTAwIDB2MTAwaDMwMHYtMTAwaC0zMDB6TTkwMCA5MDB2MTAwaDEwMHYtMTAwaC0xMDB6TTExMDAgMjAwdjEwMGgxMDB2LTEwMGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwNDA7IiBkPSJNMCAyMDBoMTAwdjEwMDBoLTEwMHYtMTAwMHpNMTAwIDB2MTAwaDMwMHYtMTAwaC0zMDB6TTIwMCAyMDB2MTAwMGgxMDB2LTEwMDBoLTEwMHpNNTAwIDB2OTFoMTAwdi05MWgtMTAwek01MDAgMjAwdjEwMDBoMjAwdi0xMDAwaC0yMDB6TTcwMCAwdjkxaDEwMHYtOTFoLTEwMHpNODAwIDIwMHYxMDAwaDEwMHYtMTAwMGgtMTAwek05MDAgMHY5MWgyMDB2LTkxaC0yMDB6TTEwMDAgMjAwdjEwMDBoMjAwdi0xMDAwaC0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0MTsiIGQ9Ik0wIDcwMGwxIDQ3NXEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDQ3NGw3MDAgLTcwMGwtNTAwIC01MDB6TTE0OCA5NTNxMCAtNDIgMjkgLTcxcTMwIC0zMCA3MS41IC0zMHQ3MS41IDMwcTI5IDI5IDI5IDcxdC0yOSA3MXEtMzAgMzAgLTcxLjUgMzB0LTcxLjUgLTMwcS0yOSAtMjkgLTI5IC03MXoiIC8%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDU5OyIgZD0iTTAgMjc1djY1MHEwIDMxIDIyIDUzdDUzIDIyaDc1MHEzMSAwIDUzIC0yMnQyMiAtNTN2LTY1MHEwIC0zMSAtMjIgLTUzdC01MyAtMjJoLTc1MHEtMzEgMCAtNTMgMjJ0LTIyIDUzek05MDAgNjAwbDMwMCAzMDB2LTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDYwOyIgZD0iTTAgNDR2MTAxMnEwIDE4IDEzIDMxdDMxIDEzaDExMTJxMTkgMCAzMS41IC0xM3QxMi41IC0zMXYtMTAxMnEwIC0xOCAtMTIuNSAtMzF0LTMxLjUgLTEzaC0xMTEycS0xOCAwIC0zMSAxM3QtMTMgMzF6TTEwMCAyNjNsMjQ3IDE4MmwyOTggLTEzMWwtNzQgMTU2bDI5MyAzMThsMjM2IC0yODh2NTAwaC0xMDAwdi03Mzd6TTIwOCA3NTBxMCA1NiAzOSA5NXQ5NSAzOXQ5NSAtMzl0MzkgLTk1dC0zOSAtOTV0LTk1IC0zOXQtOTUgMzl0LTM5IDk1eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEzNiA1NTBsNTY0IDU1MHYtNDg3bDUwMCA0ODd2LTExMDBsLTUwMCA0ODh2LTQ4OHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcyOyIgZD0iTTIwMCAwbDkwMCA1NTBsLTkwMCA1NTB2LTExMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MzsiIGQ9Ik0yMDAgMTUwcTAgLTIxIDE0LjUgLTM1LjV0MzUuNSAtMTQuNWgyMDBxMjEgMCAzNS41IDE0LjV0MTQuNSAzNS41djgwMHEwIDIxIC0xNC41IDM1LjV0LTM1LjUgMTQuNWgtMjAwcS0yMSAwIC0zNS41IC0xNC41dC0xNC41IC0zNS41di04MDB6TTYwMCAxNTBxMCAtMjEgMTQuNSAtMzUuNXQzNS41IC0xNC41aDIwMHEyMSAwIDM1LjUgMTQuNXQxNC41IDM1LjV2ODAwcTAgMjEgLTE0LjUgMzUuNXQtMzUuNSAxNC41aC0yMDAgcS0yMSAwIC0zNS41IC0xNC41dC0xNC41IC0zNS41di04MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3NDsiIGQ9Ik0yMDAgMTUwcTAgLTIwIDE0LjUgLTM1dDM1LjUgLTE1aDgwMHEyMSAwIDM1LjUgMTV0MTQuNSAzNXY4MDBxMCAyMSAtMTQuNSAzNS41dC0zNS41IDE0LjVoLTgwMHEtMjEgMCAtMzUuNSAtMTQuNXQtMTQuNSAtMzUuNXYtODAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwNzU7IiBkPSJNMCAwdjExMDBsNTAwIC00ODd2NDg3bDU2NCAtNTUwbC01NjQgLTU1MHY0ODh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3NjsiIGQ9Ik0wIDB2MTEwMGw1MDAgLTQ4N3Y0ODdsNTAwIC00ODd2NDM3cTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgxMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTEwMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDM4bC01MDAgLTQ4OHY0ODh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3NzsiIGQ9Ik0zMDAgMHYxMTAwbDUwMCAtNDg3djQzN3EwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djQzOHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc4OyIgZD0iTTEwMCAyNTB2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek0xMDAgNTAwaDExMDBsLTU1MCA1NjR6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3OTsiIGQ9Ik0xODUgNTk5bDU5MiAtNTkybDI0MCAyNDBsLTM1MyAzNTNsMzUzIDM1M2wtMjQwIDI0MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDgwOyIgZD0iTTI3MiAxOTRsMzUzIDM1M2wtMzUzIDM1M2wyNDEgMjQwbDU3MiAtNTcxbDIxIC0yMmwtMSAtMXYtMWwtNTkyIC01OTF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4MTsiIGQ9Ik0zIDYwMHEwIDE2MiA4MCAyOTkuNXQyMTcuNSAyMTcuNXQyOTkuNSA4MHQyOTkuNSAtODB0MjE3LjUgLTIxNy41dDgwIC0yOTkuNXQtODAgLTI5OS41dC0yMTcuNSAtMjE3LjV0LTI5OS41IC04MHQtMjk5LjUgODB0LTIxNy41IDIxNy41dC04MCAyOTkuNXpNMzAwIDUwMGgyMDB2LTIwMGgyMDB2MjAwaDIwMHYyMDBoLTIwMHYyMDBoLTIwMHYtMjAwaC0yMDB2LTIwMHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkxOyIgZD0iTTAgNTQ3bDYwMCA0NTN2LTMwMGg2MDB2LTMwMGgtNjAwdi0zMDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MjsiIGQ9Ik0wIDQwMHYzMDBoNjAwdjMwMGw2MDAgLTQ1M2wtNjAwIC00NDh2MzAxaC02MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MzsiIGQ9Ik0yMDQgNjAwbDQ1MCA2MDBsNDQ0IC02MDBoLTI5OHYtNjAwaC0zMDB2NjAwaC0yOTZ6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NDsiIGQ9Ik0xMDQgNjAwaDI5NnY2MDBoMzAwdi02MDBoMjk4bC00NDkgLTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk1OyIgZD0iTTAgMjAwcTYgMTMyIDQxIDIzOC41dDEwMy41IDE5M3QxODQgMTM4dDI3MS41IDU5LjV2MjcxbDYwMCAtNDUzbC02MDAgLTQ0OHYzMDFxLTk1IC0yIC0xODMgLTIwdC0xNzAgLTUydC0xNDcgLTkyLjV0LTEwMCAtMTM1LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NjsiIGQ9Ik0wIDB2NDAwbDEyOSAtMTI5bDI5NCAyOTRsMTQyIC0xNDJsLTI5NCAtMjk0bDEyOSAtMTI5aC00MDB6TTYzNSA3NzdsMTQyIC0xNDJsMjk0IDI5NGwxMjkgLTEyOXY0MDBoLTQwMGwxMjkgLTEyOXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk3OyIgZD0iTTM0IDE3NmwyOTUgMjk1bC0xMjkgMTI5aDQwMHYtNDAwbC0xMjkgMTMwbC0yOTUgLTI5NXpNNjAwIDYwMHY0MDBsMTI5IC0xMjlsMjk1IDI5NWwxNDIgLTE0MWwtMjk1IC0yOTVsMTI5IC0xMzBoLTQwMHoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTMzOyIgZD0iTTUgNTk3cTAgMTIyIDQ3LjUgMjMyLjV0MTI3LjUgMTkwLjV0MTkwLjUgMTI3LjV0MjMyLjUgNDcuNXExNjIgMCAyOTkuNSAtODB0MjE3LjUgLTIxOHQ4MCAtMzAwdC04MCAtMjk5LjV0LTIxNy41IC0yMTcuNXQtMjk5LjUgLTgwdC0zMDAgODB0LTIxOCAyMTcuNXQtODAgMjk5LjV6TTMwMCA2MDBoMjAwdi0zMDBoMjAwdjMwMGgyMDBsLTMwMCA0MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzNDsiIGQ9Ik01IDU5N3EwIDEyMiA0Ny41IDIzMi41dDEyNy41IDE5MC41dDE5MC41IDEyNy41dDIzMi41IDQ3LjVxMTYyIDAgMjk5LjUgLTgwdDIxNy41IC0yMTh0ODAgLTMwMHQtODAgLTI5OS41dC0yMTcuNSAtMjE3LjV0LTI5OS41IC04MHQtMzAwIDgwdC0yMTggMjE3LjV0LTgwIDI5OS41ek0zMDAgNjAwbDMwMCAtNDAwbDMwMCA0MDBoLTIwMHYzMDBoLTIwMHYtMzAwaC0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzNTsiIGQ9Ik01IDU5N3EwIDEyMiA0Ny41IDIzMi41dDEyNy41IDE5MC41dDE5MC41IDEyNy41dDIzMi41IDQ3LjVxMTIxIDAgMjMxLjUgLTQ3LjV0MTkwLjUgLTEyNy41dDEyNy41IC0xOTAuNXQ0Ny41IC0yMzIuNXEwIC0xNjIgLTgwIC0yOTkuNXQtMjE3LjUgLTIxNy41dC0yOTkuNSAtODB0LTMwMCA4MHQtMjE4IDIxNy41dC04MCAyOTkuNXpNMjU0IDc4MHEtOCAtMzMgNS41IC05Mi41dDcuNSAtODcuNXEwIC05IDE3IC00NHQxNiAtNjAgcTEyIDAgMjMgLTUuNXQyMyAtMTV0MjAgLTEzLjVxMjQgLTEyIDEwOCAtNDJxMjIgLTggNTMgLTMxLjV0NTkuNSAtMzguNXQ1Ny41IC0xMXE4IC0xOCAtMTUgLTU1dC0yMCAtNTdxNDIgLTcxIDg3IC04MHEwIC02IC0zIC0xNS41dC0zLjUgLTE0LjV0NC41IC0xN3ExMDQgLTMgMjIxIDExMnEzMCAyOSA0NyA0N3QzNC41IDQ5dDIwLjUgNjJxLTE0IDkgLTM3IDkuNXQtMzYgNy41cS0xNCA3IC00OSAxNXQtNTIgMTlxLTkgMCAtMzkuNSAtMC41IHQtNDYuNSAtMS41dC0zOSAtNi41dC0zOSAtMTYuNXEtNTAgLTM1IC02NiAtMTJxLTQgMiAtMy41IDI1LjV0MC41IDI1LjVxLTYgMTMgLTI2LjUgMTd0LTI0LjUgN3EyIDIyIC0yIDQxdC0xNi41IDI4dC0zOC41IC0yMHEtMjMgLTI1IC00MiA0cS0xOSAyOCAtOCA1OHE2IDE2IDIyIDIycTYgLTEgMjYgLTEuNXQzMy41IC00dDE5LjUgLTEzLjVxMTIgLTE5IDMyIC0zNy41dDM0IC0yNy41bDE0IC04cTAgMyA5LjUgMzkuNXQ1LjUgNTcuNSBxLTQgMjMgMTQuNSA0NC41dDIyLjUgMzEuNXE1IDE0IDEwIDM1dDguNSAzMXQxNS41IDIyLjV0MzQgMjEuNXEtNiAxOCAxMCAzN3E4IDAgMjMuNSAtMS41dDI0LjUgLTEuNXQyMC41IDQuNXQyMC41IDE1LjVxLTEwIDIzIC0zMC41IDQyLjV0LTM4IDMwdC00OSAyNi41dC00My41IDIzcTExIDM5IDIgNDRxMzEgLTEzIDU4IC0xNC41dDM5IDMuNWwxMSA0cTcgMzYgLTE2LjUgNTMuNXQtNjQuNSAyOC41dC01NiAyM3EtMTkgLTMgLTM3IDAgcS0xNSAtMTIgLTM2LjUgLTIxdC0zNC41IC0xMnQtNDQgLTh0LTM5IC02cS0xNSAtMyAtNDUuNSAwLjV0LTQ1LjUgLTIuNXEtMjEgLTcgLTUyIC0yNi41dC0zNCAtMzQuNXEtMyAtMTEgNi41IC0yMi41dDguNSAtMTguNXEtMyAtMzQgLTI3LjUgLTkwLjV0LTI5LjUgLTc5LjV6TTUxOCA5MTZxMyAxMiAxNiAzMHQxNiAyNXExMCAtMTAgMTguNSAtMTB0MTQgNnQxNC41IDE0LjV0MTYgMTIuNXEwIC0yNCAxNyAtNjYuNXQxNyAtNDMuNSBxLTkgMiAtMzEgNXQtMzYgNXQtMzIgOHQtMzAgMTR6TTY5MiAxMDAzaDFoLTF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzNjsiIGQ9Ik0wIDE2NC41cTAgMjEuNSAxNSAzNy41bDYwMCA1OTlxLTMzIDEwMSA2IDIwMS41dDEzNSAxNTQuNXExNjQgOTIgMzA2IC05bC0yNTkgLTEzOGwxNDUgLTIzMmwyNTEgMTI2cTEzIC0xNzUgLTE1MSAtMjY3cS0xMjMgLTcwIC0yNTMgLTIzbC01OTYgLTU5NnEtMTUgLTE2IC0zNi41IC0xNnQtMzYuNSAxNmwtMTExIDExMHEtMTUgMTUgLTE1IDM2LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzNzsiIGhvcml6LWFkdi14PSIxMjIwIiBkPSJNMCAxOTZ2MTAwcTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNWgxMDAwcTQxIDAgNzAuNSAtMjkuNXQyOS41IC03MC41di0xMDBxMCAtNDEgLTI5LjUgLTcwLjV0LTcwLjUgLTI5LjVoLTEwMDBxLTQxIDAgLTcwLjUgMjkuNXQtMjkuNSA3MC41ek0wIDU5NnYxMDBxMCA0MSAyOS41IDcwLjV0NzAuNSAyOS41aDEwMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTEwMHEwIC00MSAtMjkuNSAtNzAuNXQtNzAuNSAtMjkuNWgtMTAwMCBxLTQxIDAgLTcwLjUgMjkuNXQtMjkuNSA3MC41ek0wIDk5NnYxMDBxMCA0MSAyOS41IDcwLjV0NzAuNSAyOS41aDEwMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTEwMHEwIC00MSAtMjkuNSAtNzAuNXQtNzAuNSAtMjkuNWgtMTAwMHEtNDEgMCAtNzAuNSAyOS41dC0yOS41IDcwLjV6TTYwMCA1OTZoNTAwdjEwMGgtNTAwdi0xMDB6TTgwMCAxOTZoMzAwdjEwMGgtMzAwdi0xMDB6TTkwMCA5OTZoMjAwdjEwMGgtMjAwdi0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzODsiIGQ9Ik0xMDAgMTEwMHYxMDBoMTAwMHYtMTAwaC0xMDAwek0xNTAgMTAwMGg5MDBsLTM1MCAtNTAwdi0zMDBsLTIwMCAtMjAwdjUwMHoiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUwOyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTYwMiA5MDBsMjk4IDMwMGwyOTggLTMwMGgtMTk4di05MDBoLTIwMHY5MDBoLTE5OHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUxOyIgZD0iTTIgMzAwaDE5OHY5MDBoMjAwdi05MDBoMTk4bC0yOTggLTMwMHpNNzAwIDB2MjAwaDEwMHYtMTAwaDIwMHYtMTAwaC0zMDB6TTcwMCA0MDB2MTAwaDMwMHYtMjAwaC05OXYtMTAwaC0xMDB2MTAwaDk5djEwMGgtMjAwek03MDAgNzAwdjUwMGgzMDB2LTUwMGgtMTAwdjEwMGgtMTAwdi0xMDBoLTEwMHpNODAxIDkwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTU0OyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTgwMCA0MDB2MTAwaDIwMHYtNTAwaC0xMDB2NDAwaC0xMDB6TTgwMCA4MDB2NDAwaDMwMHYtNTAwaC0xMDB2MTAwaC0yMDB6TTkwMSA5MDBoMTAwdjIwMGgtMTAwdi0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1NTsiIGQ9Ik0yIDMwMGwyOTggLTMwMGwyOTggMzAwaC0xOTh2OTAwaC0yMDB2LTkwMGgtMTk4ek03MDAgMTAwdjIwMGg1MDB2LTIwMGgtNTAwek03MDAgNDAwdjIwMGg0MDB2LTIwMGgtNDAwek03MDAgNzAwdjIwMGgzMDB2LTIwMGgtMzAwek03MDAgMTAwMHYyMDBoMjAwdi0yMDBoLTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTU2OyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTcwMCAxMDB2MjAwaDIwMHYtMjAwaC0yMDB6TTcwMCA0MDB2MjAwaDMwMHYtMjAwaC0zMDB6TTcwMCA3MDB2MjAwaDQwMHYtMjAwaC00MDB6TTcwMCAxMDAwdjIwMGg1MDB2LTIwMGgtNTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTc7IiBkPSJNMCA0MDB2MzAwcTAgMTY1IDExNy41IDI4Mi41dDI4Mi41IDExNy41aDMwMHExNjIgMCAyODEgLTExOC41dDExOSAtMjgxLjV2LTMwMHEwIC0xNjUgLTExOC41IC0yODIuNXQtMjgxLjUgLTExNy41aC0zMDBxLTE2NSAwIC0yODIuNSAxMTcuNXQtMTE3LjUgMjgyLjV6TTIwMCAzMDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDUwMHE0MSAwIDcwLjUgMjkuNXQyOS41IDcwLjV2NTAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41IGgtNTAwcS00MSAwIC03MC41IC0yOS41dC0yOS41IC03MC41di01MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1ODsiIGQ9Ik0wIDQwMHYzMDBxMCAxNjMgMTE5IDI4MS41dDI4MSAxMTguNWgzMDBxMTY1IDAgMjgyLjUgLTExNy41dDExNy41IC0yODIuNXYtMzAwcTAgLTE2NSAtMTE3LjUgLTI4Mi41dC0yODIuNSAtMTE3LjVoLTMwMHEtMTYzIDAgLTI4MS41IDExNy41dC0xMTguNSAyODIuNXpNMjAwIDMwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoNTAwcTQxIDAgNzAuNSAyOS41dDI5LjUgNzAuNXY1MDBxMCA0MSAtMjkuNSA3MC41dC03MC41IDI5LjUgaC01MDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHpNNDAwIDMwMGwzMzMgMjUwbC0zMzMgMjUwdi01MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1OTsiIGQ9Ik0wIDQwMHYzMDBxMCAxNjMgMTE3LjUgMjgxLjV0MjgyLjUgMTE4LjVoMzAwcTE2MyAwIDI4MS41IC0xMTl0MTE4LjUgLTI4MXYtMzAwcTAgLTE2NSAtMTE3LjUgLTI4Mi41dC0yODIuNSAtMTE3LjVoLTMwMHEtMTY1IDAgLTI4Mi41IDExNy41dC0xMTcuNSAyODIuNXpNMjAwIDMwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoNTAwcTQxIDAgNzAuNSAyOS41dDI5LjUgNzAuNXY1MDBxMCA0MSAtMjkuNSA3MC41dC03MC41IDI5LjUgaC01MDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHpNMzAwIDcwMGwyNTAgLTMzM2wyNTAgMzMzaC01MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE2MDsiIGQ9Ik0wIDQwMHYzMDBxMCAxNjUgMTE3LjUgMjgyLjV0MjgyLjUgMTE3LjVoMzAwcTE2NSAwIDI4Mi41IC0xMTcuNXQxMTcuNSAtMjgyLjV2LTMwMHEwIC0xNjIgLTExOC41IC0yODF0LTI4MS41IC0xMTloLTMwMHEtMTY1IDAgLTI4Mi41IDExOC41dC0xMTcuNSAyODEuNXpNMjAwIDMwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoNTAwcTQxIDAgNzAuNSAyOS41dDI5LjUgNzAuNXY1MDBxMCA0MSAtMjkuNSA3MC41dC03MC41IDI5LjUgaC01MDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHpNMzAwIDQwMGg1MDBsLTI1MCAzMzN6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE2MTsiIGQ9Ik0wIDQwMHYzMDBoMzAwdjIwMGw0MDAgLTM1MGwtNDAwIC0zNTB2MjAwaC0zMDB6TTUwMCAwdjIwMGg1MDBxNDEgMCA3MC41IDI5LjV0MjkuNSA3MC41djUwMHEwIDQxIC0yOS41IDcwLjV0LTcwLjUgMjkuNWgtNTAwdjIwMGg0MDBxMTY1IDAgMjgyLjUgLTExNy41dDExNy41IC0yODIuNXYtMzAwcTAgLTE2NSAtMTE3LjUgLTI4Mi41dC0yODIuNSAtMTE3LjVoLTQwMHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY3OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzAwaDMwMHYtMzAwaDMwMHYzMDBoMjk1bC00NDUgNTAwek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY4OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzA1bDMwNSAtMzA1bDU5NiA1OTZsLTE1NCAxNTVsLTQ0MiAtNDQybC0xNTAgMTUxek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY5OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgOTg4bDk3IC05OGwyMTIgMjEzbC05NyA5N3pNMjAwIDQwMGw2OTcgMWwzIDY5OWwtMjUwIC0yMzlsLTE0OSAxNDlsLTIxMiAtMjEybDE0OSAtMTQ5ek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTczOyIgZD0iTTAgMTUwdjEwMDBxMCAyMCAxNC41IDM1dDM1LjUgMTVoMjUwdi0zMDBoNTAwdjMwMGgxMDBsMjAwIC0yMDB2LTIxOGwtMjc2IC0yNzVsLTEyMCAxMjBsLTEyNiAtMTI3aC0zNzh2LTQwMGgtMTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNTgxIDMwNmwxMjMgMTIzbDEyMCAtMTIwbDM1MyAzNTJsMTIzIC0xMjNsLTQ3NSAtNDc2ek02MDAgMTAwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTc0OyIgZD0iTTAgMTUwdjEwMDBxMCAyMCAxNC41IDM1dDM1LjUgMTVoMjUwdi0zMDBoNTAwdjMwMGgxMDBsMjAwIC0yMDB2LTI2OWwtMTAzIC0xMDNsLTE3MCAxNzBsLTI5OCAtMjk4aC0zMjl2LTQwMGgtMTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNjAwIDEwMDBoMTAwdjIwMGgtMTAwdi0yMDB6TTcwMCAxMzNsMTcwIDE3MGwtMTcwIDE3MGwxMjcgMTI3bDE3MCAtMTcwbDE3MCAxNzBsMTI3IC0xMjhsLTE3MCAtMTY5bDE3MCAtMTcwIGwtMTI3IC0xMjdsLTE3MCAxNzBsLTE3MCAtMTcweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNzU7IiBkPSJNMCAxNTB2MTAwMHEwIDIwIDE0LjUgMzV0MzUuNSAxNWgyNTB2LTMwMGg1MDB2MzAwaDEwMGwyMDAgLTIwMHYtMzAwaC00MDB2LTIwMGgtNTAwdi00MDBoLTE1MHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV6TTYwMCAzMDBsMzAwIC0zMDBsMzAwIDMwMGgtMjAwdjMwMGgtMjAwdi0zMDBoLTIwMHpNNjAwIDEwMDB2MjAwaDEwMHYtMjAwaC0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE3NjsiIGQ9Ik0wIDE1MHYxMDAwcTAgMjAgMTQuNSAzNXQzNS41IDE1aDI1MHYtMzAwaDUwMHYzMDBoMTAwbDIwMCAtMjAwdi00MDJsLTIwMCAyMDBsLTI5OCAtMjk4aC00MDJ2LTQwMGgtMTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNjAwIDMwMGgyMDB2LTMwMGgyMDB2MzAwaDIwMGwtMzAwIDMwMHpNNjAwIDEwMDB2MjAwaDEwMHYtMjAwaC0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE3NzsiIGQ9Ik0wIDI1MHEwIC0yMSAxNC41IC0zNS41dDM1LjUgLTE0LjVoMTEwMHEyMSAwIDM1LjUgMTQuNXQxNC41IDM1LjV2NTUwaC0xMjAwdi01NTB6TTAgOTAwaDEyMDB2MTUwcTAgMjEgLTE0LjUgMzUuNXQtMzUuNSAxNC41aC0xMTAwcS0yMSAwIC0zNS41IC0xNC41dC0xNC41IC0zNS41di0xNTB6TTEwMCAzMDB2MjAwaDQwMHYtMjAwaC00MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE3ODsiIGQ9Ik0wIDQwMGwzMDAgMjk4di0xOThoNDAwdi0yMDBoLTQwMHYtMTk4ek0xMDAgODAwdjIwMGgxMDB2LTIwMGgtMTAwek0zMDAgODAwdjIwMGgxMDB2LTIwMGgtMTAwek01MDAgODAwdjIwMGg0MDB2MTk4bDMwMCAtMjk4bC0zMDAgLTI5OHYxOThoLTQwMHpNODAwIDMwMHYyMDBoMTAwdi0yMDBoLTEwMHpNMTAwMCAzMDBoMTAwdjIwMGgtMTAwdi0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE3OTsiIGQ9Ik0xMDAgNzAwdjQwMGw1MCAxMDBsNTAgLTEwMHYtMzAwaDEwMHYzMDBsNTAgMTAwbDUwIC0xMDB2LTMwMGgxMDB2MzAwbDUwIDEwMGw1MCAtMTAwdi00MDBsLTEwMCAtMjAzdi00NDdxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDQ3ek04MDAgNTk3cTAgLTI5IDEwLjUgLTU1LjV0MjUgLTQzdDI5IC0yOC41dDI1LjUgLTE4bDEwIC01di0zOTdxMCAtMjEgMTQuNSAtMzUuNSB0MzUuNSAtMTQuNWgyMDBxMjEgMCAzNS41IDE0LjV0MTQuNSAzNS41djExMDZxMCAzMSAtMTggNDAuNXQtNDQgLTcuNWwtMjc2IC0xMTZxLTI1IC0xNyAtNDMuNSAtNTEuNXQtMTguNSAtNjUuNXYtMzU5eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxODA7IiBkPSJNMTAwIDBoNDAwdjU2cS03NSAwIC04Ny41IDZ0LTEyLjUgNDR2Mzk0aDUwMHYtMzk0cTAgLTM4IC0xMi41IC00NHQtODcuNSAtNnYtNTZoNDAwdjU2cS00IDAgLTExIDAuNXQtMjQgM3QtMzAgN3QtMjQgMTV0LTExIDI0LjV2ODg4cTAgMjIgMjUgMzQuNXQ1MCAxMy41bDI1IDJ2NTZoLTQwMHYtNTZxNzUgMCA4Ny41IC02dDEyLjUgLTQ0di0zOTRoLTUwMHYzOTRxMCAzOCAxMi41IDQ0dDg3LjUgNnY1NmgtNDAwdi01NnE0IDAgMTEgLTAuNSB0MjQgLTN0MzAgLTd0MjQgLTE1dDExIC0yNC41di04ODhxMCAtMjIgLTI1IC0zNC41dC01MCAtMTMuNWwtMjUgLTJ2LTU2eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxODE7IiBkPSJNMCAzMDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDMwMHE0MSAwIDcwLjUgMjkuNXQyOS41IDcwLjV2NTAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41aC0zMDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHpNMTAwIDEwMGg0MDBsMjAwIDIwMGgxMDVsMjk1IDk4di0yOThoLTQyNWwtMTAwIC0xMDBoLTM3NXpNMTAwIDMwMHYyMDBoMzAwdi0yMDBoLTMwMHpNMTAwIDYwMHYyMDBoMzAwdi0yMDBoLTMwMHogTTEwMCAxMDAwaDQwMGwyMDAgLTIwMHYtOThsMjk1IDk4aDEwNXYyMDBoLTQyNWwtMTAwIDEwMGgtMzc1ek03MDAgNDAydjE2M2w0MDAgMTMzdi0xNjN6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE4MjsiIGQ9Ik0xNi41IDk3NC41cTAuNSAtMjEuNSAxNiAtOTB0NDYuNSAtMTQwdDEwNCAtMTc3LjV0MTc1IC0yMDhxMTAzIC0xMDMgMjA3LjUgLTE3NnQxODAgLTEwMy41dDEzNyAtNDd0OTIuNSAtMTYuNWwzMSAxbDE2MyAxNjJxMTcgMTggMTMuNSA0MXQtMjIuNSAzN2wtMTkyIDEzNnEtMTkgMTQgLTQ1IDEydC00MiAtMTlsLTExOCAtMTE4cS0xNDIgMTAxIC0yNjggMjI3dC0yMjcgMjY4bDExOCAxMThxMTcgMTcgMjAgNDEuNXQtMTEgNDQuNSBsLTEzOSAxOTRxLTE0IDE5IC0zNi41IDIydC00MC41IC0xNGwtMTYyIC0xNjJxLTEgLTExIC0wLjUgLTMyLjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE4MzsiIGQ9Ik0wIDUwdjIxMnEwIDIwIDEwLjUgNDUuNXQyNC41IDM5LjVsMzY1IDMwM3Y1MHEwIDQgMSAxMC41dDEyIDIyLjV0MzAgMjguNXQ2MCAyM3Q5NyAxMC41dDk3IC0xMHQ2MCAtMjMuNXQzMCAtMjcuNXQxMiAtMjRsMSAtMTB2LTUwbDM2NSAtMzAzcTE0IC0xNCAyNC41IC0zOS41dDEwLjUgLTQ1LjV2LTIxMnEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTEwMHEtMjAgMCAtMzUgMTQuNXQtMTUgMzUuNXpNMCA3MTIgcTAgLTIxIDE0LjUgLTMzLjV0MzQuNSAtOC41bDIwMiAzM3EyMCA0IDM0LjUgMjF0MTQuNSAzOHYxNDZxMTQxIDI0IDMwMCAyNHQzMDAgLTI0di0xNDZxMCAtMjEgMTQuNSAtMzh0MzQuNSAtMjFsMjAyIC0zM3EyMCAtNCAzNC41IDguNXQxNC41IDMzLjV2MjAwcS02IDggLTE5IDIwLjV0LTYzIDQ1dC0xMTIgNTd0LTE3MSA0NXQtMjM1IDIwLjVxLTkyIDAgLTE3NSAtMTAuNXQtMTQxLjUgLTI3dC0xMDguNSAtMzYuNXQtODEuNSAtNDAgdC01My41IC0zNi41dC0zMSAtMjcuNWwtOSAtMTB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg0OyIgZD0iTTEwMCAwdjEwMGgxMTAwdi0xMDBoLTExMDB6TTE3NSAyMDBoOTUwbC0xMjUgMTUwdjI1MGwxMDAgMTAwdjQwMGgtMTAwdi0yMDBoLTEwMHYyMDBoLTIwMHYtMjAwaC0xMDB2MjAwaC0yMDB2LTIwMGgtMTAwdjIwMGgtMTAwdi00MDBsMTAwIC0xMDB2LTI1MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg4OyIgZD0iTS0xMDAgMzAwdjUwMHEwIDEyNCA4OCAyMTJ0MjEyIDg4aDcwMHExMjQgMCAyMTIgLTg4dDg4IC0yMTJ2LTUwMHEwIC0xMjQgLTg4IC0yMTJ0LTIxMiAtODhoLTcwMHEtMTI0IDAgLTIxMiA4OHQtODggMjEyek0xMDAgMjAwaDkwMHY3MDBoLTkwMHYtNzAwek0yMDAgMzAwaDMwMHYxMDBoLTIwMHYzMDBoMjAwdjEwMGgtMzAwdi01MDB6TTYwMCAzMDBoMzAwdjEwMGgtMjAwdjMwMGgyMDB2MTAwaC0zMDB2LTUwMHoiIC8%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%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<p>In the <a href="http://rforpublichealth.blogspot.com/2015/03/basics-of-lists.html">last post</a>, I went over the basics of lists, including constructing, manipulating, and converting lists to other classes.</p>
<p>Knowing the basics, in this post, we’ll use the <strong>apply()</strong> functions to see just how powerful working with lists can be. I’ve done two posts on <strong>apply()</strong> for dataframes and matrics, <a href="http://rforpublichealth.blogspot.com/2012/09/the-infamous-apply-function.html">here</a> and <a href="http://rforpublichealth.blogspot.com/2013/10/loops-revisited-how-to-rethink-macros.html">here</a>, so give those a read if you need a refresher.</p>
<div id="intro-to-apply-based-functions-for-lists" class="section level4">
<h4>Intro to apply-based functions for lists</h4>
<p>There are a variety of <strong>apply()</strong>-based functions that can be used depending on what you want to do. The table below shows the function, what it inputs, and what it outputs:</p>
<table>
<thead>
<tr class="header">
<th align="left">Function</th>
<th align="left">Input</th>
<th align="left">Output</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">apply</td>
<td align="left">matrix</td>
<td align="left">vector or matrix</td>
</tr>
<tr class="even">
<td align="left">sapply</td>
<td align="left">vector or list</td>
<td align="left">vector or matrix</td>
</tr>
<tr class="odd">
<td align="left">lapply</td>
<td align="left">vector or list</td>
<td align="left">list</td>
</tr>
</tbody>
</table>
<p>For example, if you have a list and you want to produce a vector (of the same length), use <strong>sapply()</strong>. If you have a vector and want to produce a list of the same length, use <strong>lapply()</strong>. Let’s try an example.</p>
<p>The syntax of <strong>lapply()</strong> is:</p>
<p><strong>lapply(INPUT, function(x) (Some function here))</strong></p>
<p>where INPUT, as we see from the table above, must be a vector or a list, and function(x) is any kind of function that takes <strong>each element of the INPUT</strong> and applies the function to it. The function can be something that already exists in R, or it can be a new function that you’ve written up.</p>
<p>For example, let’s construct a list of 3 vectors like so:</p>
<pre class="r"><code>mylist<-list(x=c(1,5,7), y=c(4,2,6), z=c(0,3,4))
mylist</code></pre>
<pre><code>## $x
## [1] 1 5 7
##
## $y
## [1] 4 2 6
##
## $z
## [1] 0 3 4</code></pre>
<p>and now we can use <strong>lapply()</strong> to find the mean of each element of the list (mean of each of the vectors x, y, and z), and output to a new list:</p>
<pre class="r"><code>lapply(mylist, function(x) mean(x))</code></pre>
<pre><code>## $x
## [1] 4.333333
##
## $y
## [1] 4
##
## $z
## [1] 2.333333</code></pre>
<p>But let’s say we wanted the result in a vector, not in a list, for whatever reason. Instead of doing the above and then converting the list into a vector (using unlist() or ldply() or whatever), we can do this directly using <strong>sapply()</strong> instead of <strong>lapply()</strong>. That’s because, as you can see in table, <strong>sapply()</strong> can take in a list as the input, and it will return a vector (or matrix). Let’s try it:</p>
<pre class="r"><code>sapply(mylist, function(x) mean(x))</code></pre>
<pre><code>## x y z
## 4.333333 4.000000 2.333333</code></pre>
<p>This is really great! Anytime you want to do the same thing over and over again, put all those things in a list and then use one of the apply functions. This reduces the need to run a loop, which can take a lot longer.</p>
<p>Let’s do another example where we write our own function this time:</p>
<pre class="r"><code>#write function to find the span of numbers in a vector and check if it's larger than 5
span.fun<-function(x) {(max(x)-min(x))>=5}
#apply that function to the list
sapply(mylist, span.fun)</code></pre>
<pre><code>## x y z
## TRUE FALSE FALSE</code></pre>
</div>
<div id="creating-a-list-using-lapply" class="section level4">
<h4>Creating a list using lapply()</h4>
<p>You don’t need to have a list already created to use <strong>lapply()</strong> - in fact, <strong>lapply()</strong> can be used to <em>make</em> a list. This is because the key about <strong>lapply()</strong> is that it <em>returns</em> a list of the same length as whatever you input.</p>
<p>For example, let’s initialize a list to have 2 empty matrices that are size 2x3. We’ll use <strong>lapply()</strong>: our input is just a vector containing 1 and 2, and the function we specify uses the <strong>matrix()</strong> function to construct a 2x3 matrix of empty cells for each element of this vector, so it returns a list of two such matrices.</p>
<p>If instead of empty matrices we wanted to fill these matrices with random numbers, we could do that too. Check out both possibilities below.</p>
<pre class="r"><code>#initialize list to to 2 empty matrices of 2 by 3
list2<-lapply(1:2, function(x) matrix(NA, nrow=2, ncol=3))
list2</code></pre>
<pre><code>## [[1]]
## [,1] [,2] [,3]
## [1,] NA NA NA
## [2,] NA NA NA
##
## [[2]]
## [,1] [,2] [,3]
## [1,] NA NA NA
## [2,] NA NA NA</code></pre>
<pre class="r"><code>#initialize list to 2 matrices with random numbers from normal distribution
list2<-lapply(1:2, function(x) matrix(rnorm(6, 10, 1), nrow=2, ncol=3))
list2</code></pre>
<pre><code>## [[1]]
## [,1] [,2] [,3]
## [1,] 9.467982 9.794397 10.52168
## [2,] 10.022561 10.179758 10.47954
##
## [[2]]
## [,1] [,2] [,3]
## [1,] 7.990455 10.95596 11.94031
## [2,] 8.952418 10.97080 11.24791</code></pre>
<p>Again, we can use <strong>lapply()</strong> or <strong>sapply()</strong> on this newly created list to get the sum of each column of each matrix:</p>
<pre class="r"><code>#input list, output column sums of each matrix into a new list
lapply(list2, colSums)</code></pre>
<pre><code>## [[1]]
## [1] 19.49054 19.97416 21.00121
##
## [[2]]
## [1] 16.94287 21.92676 23.18822</code></pre>
<pre class="r"><code>#input list, output column sums into a **vector** (which binds them into a matrix)
sapply(list2, colSums)</code></pre>
<pre><code>## [,1] [,2]
## [1,] 19.49054 16.94287
## [2,] 19.97416 21.92676
## [3,] 21.00121 23.18822</code></pre>
<pre class="r"><code>#instead of binding, we can stack these column sums by using tranpose function t():
t(sapply(list2, colSums))</code></pre>
<pre><code>## [,1] [,2] [,3]
## [1,] 19.49054 19.97416 21.00121
## [2,] 16.94287 21.92676 23.18822</code></pre>
</div>
<div id="practical-uses-of-lists-using-lapply" class="section level4">
<h4>Practical uses of lists using lapply()</h4>
<p>Finally, what are lists good for? Often, I find a lists are great when I want to store multi-dimensional objects into one object, for example group a bunch of data.frames into a list, or store all my model results into one list. Here’s an example, where I run four linear models for four different outcomes. I want to store all my models into one object.</p>
<p>There are two ways to do this:</p>
<ul>
<li>Use a for() loop and insert the results of each iteration into the list</li>
<li>Use lapply! Faster and less code</li>
</ul>
<pre class="r"><code>#create some data
set.seed(2000)
x=rbinom(1000,1,.6)
mydata<-data.frame(trt=x,
out1=x*3+rnorm(1000,0,3),
out2=x*5+rnorm(1000,0,3),
out3=rnorm(1000,5,3),
out4=x*1+rnorm(1000,0,8))
head(mydata)</code></pre>
<pre><code>## trt out1 out2 out3 out4
## 1 1 1.496148 5.2140842 7.8220283 12.7108382
## 2 0 -1.243485 0.5332667 2.8407921 4.6709677
## 3 1 11.070722 4.6477594 4.6725192 0.4216170
## 4 1 2.681000 1.8717883 0.3333281 0.4401036
## 5 0 -3.459300 0.8945582 3.1010555 -0.2620342
## 6 1 -2.266221 9.1754452 6.4914437 3.0443185</code></pre>
<p>Now I want to run each of the four outcomes on the trt variable using linear regression and save the results. I’ll do this first as a loop, then using <strong>lapply()</strong>:</p>
<pre class="r"><code>#1. Use a loop
#first, initialize the results list
results<-vector("list", 4)
#now use a loop for each outcome
for(i in 1:4){
results[[i]]<-lm(mydata[,i+1]~trt, data = mydata)
}
#2.Or, use lapply in one statement!
results<-lapply(2:5, function(x) lm(mydata[,x]~trt, data = mydata))</code></pre>
<p>In the second case, we are taking the vector c(2,3,4,5) and for each component of this vector, we’re running the model that we describe in the function. We can always name the components of the list as below, and I’ll print out the first two elements:</p>
<pre class="r"><code>names(results)<-names(mydata)[2:5]
print(results, max=2)</code></pre>
<pre><code>## $out1
##
## Call:
## lm(formula = mydata[, x] ~ trt, data = mydata)
##
## Coefficients:
## (Intercept) trt
## 0.1905 2.7707
##
##
## $out2
##
## Call:
## lm(formula = mydata[, x] ~ trt, data = mydata)
##
## Coefficients:
## (Intercept) trt
## -0.01892 4.73405
##
##
## [ reached getOption("max.print") -- omitted 2 entries ]</code></pre>
<p>Why is this a great way to store data? Well, we can <em>keep</em> using the <strong>apply()</strong> functions, for example to put together all of the treatment effects for each outcome into one matrix:</p>
<pre class="r"><code>#extract coefficient and std error for each outcome and store in a matrix
sapply(results, function(x) summary(x)$coefficients[2,1:2])</code></pre>
<pre><code>## out1 out2 out3 out4
## Estimate 2.7707490 4.7340543 -0.1344969 1.3293520
## Std. Error 0.1915748 0.1876549 0.1912755 0.5324664</code></pre>
<p>You can also easily use other functions like <strong>stargazer()</strong> (previous post on this function <a href="http://rforpublichealth.blogspot.ie/2013/08/exporting-results-of-linear-regression_24.html">here</a>) to create a quick table of results like so (in latex code):</p>
<pre class="r"><code>require(stargazer)
stargazer(results,
column.labels=names(results),
keep.stat=c("rsq","n"),
dep.var.labels="")</code></pre>
<p><img 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" title alt width="384" /></p>
<p>Or easily create a graph of the model estimates and 95% confidence intervals:</p>
<pre class="r"><code>#extract coefficients from the list
coefs<-as.data.frame(t(sapply(results, function(x) summary(x)$coefficients[2,1:2])))
coefs</code></pre>
<pre><code>## Estimate Std. Error
## out1 2.7707490 0.1915748
## out2 4.7340543 0.1876549
## out3 -0.1344969 0.1912755
## out4 1.3293520 0.5324664</code></pre>
<pre class="r"><code>#add outcome columnn and change name of SE column
coefs$Outcome<-rownames(coefs)
names(coefs)[2]<-"SE"
#use ggplot to plot all the estimates
require(ggplot2)
ggplot(coefs, aes(Outcome,Estimate)) +
geom_point(size=4) +
theme(legend.position="none")+
labs(title="Treatment effect on outcomes", x="", y="Estimate and 95% CI")+
geom_errorbar(aes(ymin=Estimate-1.96*SE,ymax=Estimate+1.96*SE),width=0.1)+
geom_hline(yintercept = 0, color="red")+
coord_flip()</code></pre>
<p><img 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" title alt width="384" /></p>
<p>I hope that was useful! There are many great ways to use lists and the <strong>apply()</strong> functions to make your programming more efficient and less prone to errors.</p>
<p>For another great resource on using the <strong>apply()</strong> functions with lists, definitely check out <a href="http://stackoverflow.com/questions/3505701/r-grouping-functions-sapply-vs-lapply-vs-apply-vs-tapply-vs-by-vs-aggrega">this StackOverflow page</a>.</p>
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In the [last post](http://rforpublichealth.blogspot.com/2015/03/basics-of-lists.html), I went over the basics of lists, including constructing, manipulating, and converting lists to other classes.
Knowing the basics, in this post, we'll use the **apply()** functions to see just how powerful working with lists can be.
I've done two posts on apply for dataframes and matrics, [here](http://rforpublichealth.blogspot.com/2012/09/the-infamous-apply-function.html) and [here](http://rforpublichealth.blogspot.com/2013/10/loops-revisited-how-to-rethink-macros.html), so give those a read if you need a refresher.
Intro to apply-based functions for lists
There are a variety of apply functions that can be used depending on what you want to do. The table below shows the function, what it inputs, and what it outputs:
For example, if we have a list and you want to produce a vector (of the same length), we use **sapply()**. If we have a vector and want to produce a list of the same length, we use **lapply()**. Let's try an example.
The syntax of lapply is:
lapply(INPUT, function(x) (Some function here))
where INPUT, as we see from the table above, must be a vector or a list, and function(x) is any kind of function that takes **each element of the INPUT** and applies the function to it. The function can be something that already exists in R, or it can be a new function that you've written up.
For example, let's construct a list of 3 vectors like so:
mylist<-list(x=c(1,5,7), y=c(4,2,6), z=c(0,3,4))
and now we can use lapply to find the mean of each element of the list (each of the vectors x, y, and z), and output to a new list:
lapply(mylist, function(x) mean(x))
But let's say we wanted the result in a vector, not in a list, for whatever reason. Instead of doing the above and then converting the list into a vector (using unlist() or ldply() or whatever), we can do this directly using **sapply()** instead of **lapply()**. That's because as you can see in table, **sapply()** can take in a list as the input, and it will return a vector (or matrix). Let's try it:
sapply(mylist, function(x) mean(x))
This is really great! Anytime you want to do the same thing over and over again, put all those things in a list and then use one of the apply functions. This reduces the need to run a loop, which can take a lot longer.
Let's do another example where you write your own function this time:
#write function to find the span of numbers in a vector and check if it's larger than 5
span.fun<-function(x) {(max(x)-min(x))>=5}
#apply that function to the list
sapply(mylist, span.fun)
Creating a list using lapply
You don't need to have a list already created to use lapply() - in fact, lapply can be used to _make_ a list. This is because the key about **lapply()** is that it *returns* a list of the same length as whatever you input.
For example, let's initialize a list to have 2 empty matrices that are size 2x3. We'll use lapply(): our input is just a vector containing 1 and 2, and the function we specify uses the matrix() function to construct a 2x3 matrix of empty cells for each element of this vector, so it returns a list of two such matrices.
If instead of empty matrices you wanted to fill these matrices with random numbers, you could do so too. Check out both possibilities below.
#initialize list to to 2 empty matrices of 2 by 3
list2<-lapply(1:2, function(x) matrix(NA, nrow=2, ncol=3))
list2
#initialize list to 2 matrices with random numbers from standard normal distribution
list2<-lapply(1:2, function(x) matrix(rnorm(6, 10, 1), nrow=2, ncol=3))
Again, we can use **lapply()** or **sapply()** on this newly created list to get the sum of each column of each matrix:
#input list, output column sums of each matrix into a new list
lapply(list2, colSums)
#input list, output column sums into a **vector** (which binds them into a matrix)
sapply(list2, colSums)
#instead of binding, we can stack these column sums by using tranpose function t():
t(sapply(list2, colSums))
Practical uses of lists using lapply
Finally, what are lists good for? Often, I find a lists are great when I want to store multi-dimensional objects into one object, for example group a bunch of data.frames into a list, or store all my model results into one list. Here's an example, where I run four linear models for four different outcomes. I want to store all my models into one object.
There are two ways to do this:
Use a for() loop and insert the results of each iteration into the list
Use lapply! Faster and less code
#create some data
set.seed(2000)
x=rbinom(1000,1,.6)
mydata<-data.frame(trt=x,out1=x*3+rnorm(1000,0,3),out2=x*5+rnorm(1000,0,3),out3=rnorm(1000,5,3),out4=x*1+rnorm(1000,0,8))
head(mydata)
Now I want to run each of the four outcomes on the trt variable using linear regression and save the results. I'll do this first as a loop, then using **lapply()**:
#1. Use a loop
#first, initialize the results list
results<-vector("list", 4)
#now use a loop for each outcome
for(i in 1:4){
results[[i]]<-lm(mydata[,i+1]~trt, data = mydata) }
#2.Use lapply in one statement!
results<-lapply(2:5, function(x) lm(mydata[,x]~trt, data = mydata))
In the second case, we are taking the vector c(2,3,4,5) and for each component of this vector, we're running the model that we describe in the function. We can always name the components of the list as below, and I'll print out the first two elements:
names(results)<-names(mydata)[2:5]
print(results, max=2)
Why is this a great way to store data? Well, we can __keep__ using the **apply()** functions, for example to put together all of the treatment effects for each outcome into one matrix:
#extract coefficient and std error for each outcome and store in a matrix
sapply(results, function(x) summary(x)$coefficients[2,1:2])
You can also easily use other functions like **stargazer()** (previous post on this function [here](http://rforpublichealth.blogspot.ie/2013/08/exporting-results-of-linear-regression_24.html)) to create a quick table of results like so (in latex code):
require(stargazer)
stargazer(results, column.labels=names(results),keep.stat=c("rsq","n"),dep.var.labels="")
library(png)
library(grid)
img <- readPNG("~/Dropbox/Harvard Doctoral/Rforpublichealth/post30/sgtable2.png")
grid.raster(img)
Or easily create a graph of the model estimates and 95 confidence intervals:
#extract coefficients from the list
coefs<-as.data.frame(t(sapply(results, function(x) summary(x)$coefficients[2,1:2])))
#add outcome columnn and change name of SE column
coefs$Outcome<-rownames(coefs)
names(coefs)[2]<-"SE"
#use ggplot to plot all the estimates
require(ggplot2)
ggplot(coefs, aes(Outcome,Estimate)) + geom_point(size=4) + theme(legend.position="none")+ labs(title="Treatment effect on outcomes", x="", y="Estimate and 95% CI")+ geom_errorbar(aes(ymin=Estimate-1.96*SE,ymax=Estimate+1.96*SE),width=0.1)+ geom_hline(yintercept = 0, color="red")+ coord_flip()
I hope that was useful! There are many great ways to use lists and the **apply()** functions to make your programming more efficient and less prone to errors.
For another resource on using the **apply()** functions with lists, definitely check out [this StackOverflow page](http://stackoverflow.com/questions/3505701/r-grouping-functions-sapply-vs-lapply-vs-apply-vs-tapply-vs-by-vs-aggrega).
</div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com5tag:blogger.com,1999:blog-2550386066385582083.post-43247903272986531292015-03-09T20:12:00.000-07:002016-10-13T07:58:37.295-07:00Basics of Lists<div height="1050">
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<p>Lists are a data type in R that are perhaps a bit daunting at first, but soon become amazingly useful. They are especially wonderful once you combine them with the powers of the apply() functions. This post will be part 1 of a two-part series on the uses of lists. In this post, we will discuss the basics - how to create lists, manipulate them, describe them, and convert them. In part 2, we’ll see how using lapply() and sapply() on lists can really improve your R coding.</p>
<div id="constructing-a-list" class="section level4">
<h4>Constructing a list</h4>
<p>Let’s start with what lists are and how to construct them. A list is a data structure that can hold any number of any types of other data structures. If you have vector, a dataframe, and a character object, you can put all of those into one list object like so:</p>
<pre class="r"><code># create three different classes of objects
vec <- 1:4
df <- data.frame(y = c(1:3), x = c("m", "m", "f"))
char <- "Hello!"
# add all three objects to one list using list() function
list1 <- list(vec, df, char)
# print list
list1</code></pre>
<pre><code>## [[1]]
## [1] 1 2 3 4
##
## [[2]]
## y x
## 1 1 m
## 2 2 m
## 3 3 f
##
## [[3]]
## [1] "Hello!"</code></pre>
<p>We can also turn an object into a list by using the <strong>as.list()</strong> function. Notice how every element of the vector becomes a different component of the list.</p>
<pre class="r"><code># coerce vector into a list
as.list(vec)</code></pre>
<pre><code>## [[1]]
## [1] 1
##
## [[2]]
## [1] 2
##
## [[3]]
## [1] 3
##
## [[4]]
## [1] 4</code></pre>
</div>
<div id="manipulating-a-list" class="section level4">
<h4><strong>Manipulating a list</strong></h4>
<ul>
<li>We can put <strong>names on the components of a list</strong> using the names() function. This is useful for extracting components. We could have also named the components when we created the list. See below:</li>
</ul>
<pre class="r"><code># name the components of the list
names(list1) <- c("Numbers", "Some.data", "Letters")
list1</code></pre>
<pre><code>## $Numbers
## [1] 1 2 3 4
##
## $Some.data
## y x
## 1 1 m
## 2 2 m
## 3 3 f
##
## $Letters
## [1] "Hello!"</code></pre>
<pre class="r"><code># could have named them when we created list
another.list <- list(Numbers = vec, Letters = char)</code></pre>
<ul>
<li><strong>Extract components from a list</strong> (many ways): the first is using the [[ ]] operator (notice two brackets, not just one). Note that we can use the single [ ] operator on a list, but it will return a list rather than the data structure that is the component of the list, which is normally not what we would want to do. See what I mean here:</li>
</ul>
<pre class="r"><code># extract 3rd component using [[]] -> this returns a *string*
list1[[3]]</code></pre>
<pre><code>## [1] "Hello!"</code></pre>
<pre class="r"><code># print a list containing the 3rd component -> this returns a *list*
list1[3]</code></pre>
<pre><code>## $Letters
## [1] "Hello!"</code></pre>
<p>It is also possible to extract components using the component’s name as we see below. Again, be careful about the [ ] vs [[ ]] operator in the second way. You need the [[ ]] to return the data structure of the component.</p>
<pre class="r"><code># extract 3rd component using $
list1$Letters</code></pre>
<pre><code>## [1] "Hello!"</code></pre>
<pre class="r"><code># extract 3rd component using [[ ]] and the name of the component
list1[["Letters"]]</code></pre>
<pre><code>## [1] "Hello!"</code></pre>
<ul>
<li><strong>Subsetting a list</strong> - use the single [ ] operator and c() to choose the components</li>
</ul>
<pre class="r"><code># subset the first and third components
list1[c(1, 3)]</code></pre>
<pre><code>## $Numbers
## [1] 1 2 3 4
##
## $Letters
## [1] "Hello!"</code></pre>
<ul>
<li>We can also <strong>add a new component to the list</strong> or <strong>replace a component</strong> using the $ or [[ ]] operators. This time I’ll add a linear model to the list (remember we can put anything into a list).</li>
</ul>
<pre class="r"><code># add new component to existing list using $
list1$newthing <- lm(y ~ x, data = df)
# add a new component to existing list using [[ ]]
list1[[5]] <- "new component"</code></pre>
<ul>
<li>Finally, we can <strong>delete a component of a list</strong> by setting it equal to NULL.</li>
</ul>
<pre class="r"><code># delete a component of existing list
list1$Letters <- NULL
list1</code></pre>
<pre><code>## $Numbers
## [1] 1 2 3 4
##
## $Some.data
## y x
## 1 1 m
## 2 2 m
## 3 3 f
##
## $newthing
##
## Call:
## lm(formula = y ~ x, data = df)
##
## Coefficients:
## (Intercept) xm
## 3.0 -1.5
##
##
## [[4]]
## [1] "new component"</code></pre>
<p>The Letters component is gone, so there are now only 4. Notice how the 4th component doesn’t have a name because we didn’t assign it one when we added it in.</p>
<p><strong>More extracting:</strong> If we want to extract the dataframe we have in the list, and just look at it’s first row, we would do <strong>list1[[2]][1,]</strong>. This code would take the second component in the list using the [[ ]] operator (which is the dataframe) and once it has the dataframe, it subsets the first row and all columns using only the [ ] operator since that is what is used to subset dataframes (or matrices).</p>
<p>For help on subsetting matrices and dataframes, check out <a href="http://rforpublichealth.blogspot.com/2012/10/quick-and-easy-subsetting.html">this post</a>.</p>
<pre class="r"><code># extract first row of dataframe that is in a list
list1[[2]][1, ]</code></pre>
<pre><code>## y x
## 1 1 m</code></pre>
</div>
<div id="describing-a-list" class="section level4">
<h4><strong>Describing a list</strong></h4>
<p>To describe a list, we may want to know the following:</p>
<ul>
<li>the <strong>class</strong> of the list (which is a list class!) and the class of the first component of the list.</li>
</ul>
<pre class="r"><code># describe class of the whole list
class(list1)</code></pre>
<pre><code>## [1] "list"</code></pre>
<pre class="r"><code># describe the class of the first component of the list
class(list1[[1]])</code></pre>
<pre><code>## [1] "integer"</code></pre>
<ul>
<li>the <strong>number of components in the list</strong> - use the length function()</li>
</ul>
<pre class="r"><code># find out how many components are in the list
length(list1)</code></pre>
<pre><code>## [1] 4</code></pre>
<ul>
<li>a <strong>short summary of each component in the list</strong> - use str(). (I take out the model because the output is really long)</li>
</ul>
<pre class="r"><code># take out the model from list and then show summary of what's in the list
list1$newthing <- NULL
str(list1)</code></pre>
<pre><code>## List of 3
## $ Numbers : int [1:4] 1 2 3 4
## $ Some.data:'data.frame': 3 obs. of 2 variables:
## ..$ y: int [1:3] 1 2 3
## ..$ x: Factor w/ 2 levels "f","m": 2 2 1
## $ : chr "new component"</code></pre>
<ul>
<li>Now we can combine these functions to <strong>append a component to the end of the list</strong>, by assigning it to the length of the list plus 1:</li>
</ul>
<pre class="r"><code># construct new list of two components
new.list <- list(vec, char)
# notice that it has two components
length(new.list)</code></pre>
<pre><code>## [1] 2</code></pre>
<pre class="r"><code># append a component to the end and print
new.list[[length(new.list) + 1]] <- "Appended"
new.list</code></pre>
<pre><code>## [[1]]
## [1] 1 2 3 4
##
## [[2]]
## [1] "Hello!"
##
## [[3]]
## [1] "Appended"</code></pre>
<p>Notice you could keep doing this as the length is now 3. You could also use the $ operator to name a new component and that would append it at the end, as we saw above.</p>
</div>
<div id="initializing-a-list" class="section level4">
<h4>Initializing a list</h4>
<p>To <strong>initialize a list</strong> to a certain number of null components, we use the vector function like this:</p>
<pre class="r"><code># initialize list to have 3 null components and print
list2 <- vector("list", 3)
list2</code></pre>
<pre><code>## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULL</code></pre>
</div>
<div id="converting-a-list-into-matrix-or-dataframe" class="section level4">
<h4>Converting a list into matrix or dataframe</h4>
<p>Finally, we can <strong>convert a list into a matrix, dataframe, or vector</strong> in a number of different ways. The first, most basic way is to use unlist(), which just turns the whole list into one long vector:</p>
<pre class="r"><code># convert to one long string - use unlist
unlist(list1)</code></pre>
<pre><code>## Numbers1 Numbers2 Numbers3 Numbers4
## "1" "2" "3" "4"
## Some.data.y1 Some.data.y2 Some.data.y3 Some.data.x1
## "1" "2" "3" "2"
## Some.data.x2 Some.data.x3
## "2" "1" "new component"</code></pre>
<p>But often we have matrices or dataframes as components of a list and we would like to combind them or stack them into one dataframe. The following shows the two good ways I’ve found to do this (from <a href="http://stackoverflow.com/questions/4227223/r-list-to-data-frame">this StackOverflow</a> page) using <strong>ldply()</strong> from the plyr package or <strong>rbind()</strong>.</p>
<p>First, we create a list of matrices and then convert the list of matrices into a dataframe.</p>
<pre class="r"><code>#create list of matrices and print
mat.list <- list(mat1=matrix(c(1,2,3,4), nrow=2), mat2=matrix(c(5,6,7,8), nrow=2))
mat.list</code></pre>
<pre><code>## $mat1
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
##
## $mat2
## [,1] [,2]
## [1,] 5 7
## [2,] 6 8</code></pre>
<pre class="r"><code>#convert to data frame
#1. use ldply
require(plyr)
ldply(mat.list, data.frame)</code></pre>
<pre><code>## .id X1 X2
## 1 mat1 1 3
## 2 mat1 2 4
## 3 mat2 5 7
## 4 mat2 6 8</code></pre>
<pre class="r"><code>#2. use rbind
do.call(rbind.data.frame, mat.list)</code></pre>
<pre><code>## V1 V2
## mat1.1 1 3
## mat1.2 2 4
## mat2.1 5 7
## mat2.2 6 8</code></pre>
<p>Get ready for part 2 next time, when we’ll see what we can use lists for and why we should use them at all.</p>
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Lists are a data type in R that are perhaps a bit daunting at first, but soon become amazingly useful. They are especially wonderful once you combine them with the powers of the apply() functions. This post will be part 1 of a two-part series on the uses of lists. In this post, we will discuss the basics - how to create lists, manipulate them, describe them, and convert them. In part 2, we'll see how using lapply() and sapply() on lists can really improve your R coding.
Creating a list
Let's start with some basics: what lists are and how to create them. A list is a data structure that can hold any number of any types of other data structures. If you have vector, a dataframe, and a character object, you can put all of those into one list object like so:
create three different classes of objects
add all three objects to one list using list() function
print list
list1
We can also turn an object into a list by using the as.list() function. Notice how every element of the vector becomes a different component of the list.
coerce vector into a list
Manipulating a list
We can put names on the components of a list using the names() function. This is useful for extracting components. We could have also named the components when we created the list. See below:
name the components of the list
list1
could have named them when we created list
Extract components from a list (many ways): the first is using the [[ ]] operator (notice two brackets, not just one). Note that we can use the single [ ] operator on a list, but it will return a list rather than the data structure that is the component of the list, which is normally not what we would want to do. See what I mean here:
extract 3rd component using -> this returns a *string*
list1[[3]]
print a list containing the 3rd component -> this returns a *list*
list1[3]
It is also possible to extract components using the operator or the components name. Again, be careful about the [ ] vs [[ ]] operator in the second way. You need the [[ ]] to return the data structure of the component.
extract 3rd component using
extract 3rd component using [[ ]] and the name of the component
Subsetting a list - use the single [ ] operator and c() to choose the components
subset the first and third components
list1[c(1,3)]
We can also add a new component to the list or replace a component using the $ or [[ ]] operators. This time I'll add a linear model to the list (remember we can put anything into a list).
add new component to existing list using
add a new component to existing list using
Finally, we can delete a component of a list by setting it equal to NULL.
delete a component of existing list
list1
Notice how the 5th component doesn't have a name because we didn't assign it one when we added it in. Now if we want to extract the dataframe we have in the list, and just look at it's first row, we would do list1[[2]][1,]. This code would take the second component in the list using the [[ ]] operator (which is the dataframe) and once it has the dataframe, it subsets the first row and all columns using only the [ ] operator since that is what is used for dataframes (or matrices).
For help on subsetting matrices and dataframes, check out [this post](http://rforpublichealth.blogspot.com/2012/10/quick-and-easy-subsetting.html).
extract first row of dataframe that is in a list
list1[[2]][1,]
Describing a list
To describe a list, we may want to know the following:
the class of the list (which is a list class!) and the class of the first component of the list.
describe class of the whole list
class(list1)
describe the class of the first component of the list
class(list1[[1]])
the number of components in the list - use the length function()
find out how many components are in the list
length(list1)
a short summary of each component in the list - use str(). (I take out the model because the output is really long)
take out the model from list and then show summary of what's in the list
str(list1)
Now we can combine these functions to append a component to the end of the list, by assigning it to the length of the list plus 1:
Initializing a list
To initialize a list to a certain number of null components, we use the vector function like this:
initialize list to have 3 null components and print
list2
Converting list into matrix or dataframe
Finally, we can convert a list into a matrix, dataframe, or vector in a number of different ways. The first, most basic way is to use unlist(), which just turns the whole list into one long vector:
convert to one long string - use unlist
unlist(list1)
But often we have matrices or dataframes as components of a list and we would like to combind them or stack them into one dataframe. The following shows the two good ways I've found to do this (from [this StackOverflow](http://stackoverflow.com/questions/4227223/r-list-to-data-frame) page) using ldply() from the plyr package or rbind().
Here, we first create a list of matrices and then convert the list of matrices into a dataframe.
create list of matrices and print
mat.list
convert to data frame
1. use ldply
require(plyr)
ldply(mat.list, data.frame)
2. use rbind
do.call(rbind.data.frame, mat.list)
Get ready for part 2 next time, when we'll get to what we can use lists for and why we should use them at all.</div>
Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com11tag:blogger.com,1999:blog-2550386066385582083.post-47319767068510735772014-12-26T07:46:00.001-08:002016-10-13T07:57:41.909-07:00Animations and GIFs using ggplot2<table border="0">
<tbody>
<tr>
<td><img atl="Gif" height="240" src="https://dl.dropboxusercontent.com/u/15296944/Rforpublichealth_public/spline.gif" />
</td>
<td><img atl="Gif" height="240" src="https://dl.dropboxusercontent.com/u/15296944/Rforpublichealth_public/dist1.gif" />
</td>
<td><img atl="Gif" height="240" src="https://dl.dropboxusercontent.com/u/15296944/Rforpublichealth_public/happy2015.gif" />
</td>
</tr>
<tr>
<th align="center">Tracing a regression line</th>
<th align="center">Diverging density plots</th>
<th align="center">Happy New Year plot</th>
</tr>
<tr>
</tr>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<p>Happy New Year everyone! For the last post of the year, I thought I’d have a little fun with the new animation package in R. It’s actually really easy to use. I recently had some fun with it when I presented my research at an electronic poster session, and had an animated movie embedded into the powerpoint.</p>
<p>All of the GIFs above use <strong>ggplot</strong> and the <strong>animation</strong> packages. The main idea is to iterate the same plot over and over again, changing incrementally whatever it is that you want to move in the graph, and then save all those plots together into one GIF.</p>
<div id="tracing-a-regression-line" class="section level4">
<h4>1. Tracing a regression line</h4>
<p>Let’s start with first plot that traces a regression line over the scatterplot of the points. We’ll make up some data and fit a loess of two degrees (default). You could easily do this with any kind of regression.</p>
<pre class="r"><code>#make up some data
tracedat<-data.frame(x=rnorm(1000,0,1))
tracedat$y<-abs(tracedat$x)*2+rnorm(1000,0,3)
#predict a spline fit and add predicted values to the dataframe
loess_fit <- loess(y ~ x, tracedat)
tracedat$predict_y<-predict(loess_fit)</code></pre>
<p>Now let’s make the completed scatterplot and loess line using <strong>ggplot</strong>. If you need help on how to plot a scatterplot in ggplot, see my post here: <a href="http://rforpublichealth.blogspot.com/2013/11/ggplot2-cheatsheet-for-scatterplots.html">ggplot2: Cheatsheet for Scatterplots</a>.</p>
<p>It is possible to use <strong>stat_smooth()</strong> within <strong>ggplot</strong> to get the loess fit without predicting the values and using <strong>geom_line()</strong>, but the predicted values are going to make it easier to make the animation.</p>
<pre class="r"><code>#plot finished scatterplot with loess fit
ggplot(tracedat, aes(x,y)) +
geom_point() +
geom_line(data=tracedat, aes(x,predict_y), color="red", size=1.3) +
scale_x_continuous(limits=c(-3, 3)) +
scale_y_continuous(limits=c(-10, 10))</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-3" alt="plot of chunk unnamed-chunk-3" width="288" /></p>
<p>Now what we need is for the loess fit to appear bit by bit, so to do this we’ll cut off the dataframe for <strong>geom_line</strong> for only those x-values up to a certain cutoff x-value (by subsetting the dataframe called tracedat in the <strong>geom_line</strong> statement). Then we’ll just keep moving that cutoff forward as we iterate over the range of all x-values.</p>
<p>First, we will build a function that takes the cutoff value as an argument. Then we can pass whatever value of x we want and it will only graph the line up to that cutoff. Notice how the scatterplot itself, however, is for the full data.</p>
<p>For more on how to write functions, see my post about them, <a href="http://rforpublichealth.blogspot.com/2014/06/how-to-write-and-debug-r-function.html">here</a>.</p>
<pre class="r"><code>#function to draw the scatterplot, but the curve fit only up to whatever index we set it at
draw.curve<-function(cutoff){
a<-ggplot(tracedat, aes(x,y)) +
geom_point() +
geom_line(data=tracedat[tracedat$x<cutoff,], aes(x,predict_y), color="red", size=1.3) +
scale_x_continuous(limits=c(-3, 3)) +
scale_y_continuous(limits=c(-10, 10))
print(a)
}
#try it out: draw curve up to cutoff x-value of -2
draw.curve(cutoff=-2)</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" width="288" /></p>
<p>Almost done! Now we just need to iterate the <strong>draw.curve()</strong> function we just created for the full range of the values of x. So we’ll use <strong>lapply()</strong> to iterate the <strong>draw.curve()</strong> function over the sequence of i=-3 to 3 (incrementing by .2) and we call the <strong>draw.curve()</strong> function for each value of i. Finally, we’ll use the <strong>saveGIF()</strong> function from the animation package to stick all the images together successively into one GIF.</p>
<p>The interval argument tells you how fast the GIF will move from one image to the next, and you can give it a name. If you don’t want the GIF to loop back to the start again, you would add an argument “loop=FALSE” into the function call.</p>
<pre class="r"><code>#function to iterate over the full span of x-values
trace.animate <- function() {
lapply(seq(-3,3,.2), function(i) {
draw.curve(i)
})
}
#save all iterations into one GIF
saveGIF(trace.animate(), interval = .2, movie.name="trace.gif")</code></pre>
</div>
<div id="diverging-density-plots" class="section level4">
<h4>2. Diverging density plots</h4>
<p>The same idea used above is used to make the diverging density plots (plot 2 above). Here we are showing the distribution of scores before some intervention and after the intervention. We need to create the code for a <strong>ggplot</strong> density plot, and turn it into a function that can take as an argument the variable we want to plot (that way we can use the same code for the “before” intervention plot and the “after” plot. Then to animate, we’ll iterate between them.</p>
<p>We’ll make up some data and plot the “before” plot to start. Notice that we’ll use <strong>aes_string</strong> rather than simply <strong>aes</strong> in the ggplot statement in order to be able to pass the data in as an argument when we turn this into a function. More on how to plot distributions using <strong>ggplot</strong> in my post <a href="http://rforpublichealth.blogspot.ie/2014/02/ggplot2-cheatsheet-for-visualizing.html">ggplot2: Cheatsheet for Visualizing Distributions</a>.</p>
<p>It’s important to set the scale for x and y axes so that when we iterate over the two plots, we have the same dimensions each time. The alpha argument in <strong>geom_density</strong> makes the colors more transparent.</p>
<pre class="r"><code>dist.data<-data.frame(base=rnorm(5000, 0, 3),
follow=rnorm(5000, 0, 3),
type=c(rep("Type 1",2500),rep("Type 2",2500)))
dist.data$follow<-ifelse(dist.data$type=="Type 2", dist.data$follow+12, dist.data$follow)
#plot one to make sure it's working. Use aes_string rather than aes
p<-ggplot(dist.data, aes_string("base", fill="type")) +
geom_density(alpha=0.5) +
theme(legend.position="bottom") +
scale_x_continuous(limits=c(-10, 20)) +
scale_y_continuous(limits=c(0, 0.20)) +
scale_fill_manual("", labels=c("Type 1", "Type 2"), values = c("orange","purple")) +
labs(x="Score",y="Density", title="title")</code></pre>
<p>Again, we write two functions: one that draws a density plot based on the arguments passed to it (<strong>plot.dens()</strong>), and one that iterates over the two different plots (called <strong>distdiverge.animate()</strong>). In the <strong>dist.diverge.animate()</strong> function, we pass the plot.item (which is a character class that aes_string will understand as the name of the column in dist.data to plot), and the title, which is also a character class.</p>
<pre class="r"><code>#function that plots a density plot with arguments for the variable to plot and the title
plot.dens<-function(plot.item, title.item){
p<-ggplot(dist.data, aes_string(plot.item, fill="type"))+
geom_density(alpha=0.5) +
theme(legend.position="bottom") +
scale_x_continuous(limits=c(-10, 20)) +
scale_y_continuous(limits=c(0, 0.20)) +
scale_fill_manual("", labels=c("Type 1", "Type 2"), values = c("orange","purple"))+
labs(x="Score",y="Density", title=title.item)
print(p)
}
#try it out - plot it for the follow data with the title "After Intervention"
plot.dens(plot.item="follow", title.item="After Intervention")</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-7" alt="plot of chunk unnamed-chunk-7" width="288" /></p>
<pre class="r"><code>#function that iterates over the two different plots
distdiverge.animate <- function() {
items<-c("base", "follow")
titles<-c("Before Intervention","After Intervention")
lapply(seq(1:2), function(i) {
plot.dens(items[i], titles[i])
})
}</code></pre>
<p>We’ll make the interval slower so there’s more time to view each plot before the GIF moves to the next one.</p>
<pre class="r"><code>#save in a GIF
saveGIF(distdiverge.animate(), interval = .65, ,movie.name="dist.gif")</code></pre>
</div>
<div id="happy-new-year-plot" class="section level4">
<h4>3. Happy New Year plot</h4>
<p>Finally, to make the fun “Happy 2015” plot, we just make a black background plot, create new random data at every iteration for the snow, and then iterate over the letters of the sign.</p>
<p>Let’s start with the first plot for the letter “H”. We create some data and the objects that will hold the letters of the sign that scrolls through, the colors (I use the colorspace package to pull some colors for me for this), and the x- and y-coordinates for where we want the letters to go. You can think about randomizing the coordinates too. Then we just plot a scatterplot and use annotate to add the letter to it.</p>
<pre class="r"><code>#create dataset
happy2015<-data.frame(x=rnorm(500, 0, 1.5), y=rnorm(500, 0, 1.5), z=rnorm(500,0,1.5))
#create objects to hold the letters, colors, and x and y coordinates that we will scroll through
sign<-c("H","A","P","P","Y","2","0","1","5","!!")
colors <- rainbow_hcl(10, c=300)
xcoord<-rep(c(-2, -1, 0, 1, 2),2)
ycoord<-c(2, 1.7, 2.1, 1.5, 2, -.5, 0, -1, -.8, -.7)</code></pre>
<p>We set up the ggplot theme and test the first plot.</p>
<pre class="r"><code>#set up the theme in an object (get rid of axes, grids, and legend)
theme.both<- theme(legend.position="none",
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
plot.background = element_rect(fill = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#plot the first letter (set index=1 to get the first element of color, letter, and coordinates)
index<-1
ggplot(happy2015, aes(x, y, alpha = z, color=z)) +
geom_point(alpha=0.2) + labs(title="", x="", y="") +
theme.both +
scale_colour_gradient(low = "white", high="lightblue")+
annotate("text", x=xcoord[index], y=ycoord[index], size=15, label=sign[index], color=colors[index])</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-10" alt="plot of chunk unnamed-chunk-10" width="288" /></p>
<p>Finally, we again go through the structure of two functions - one to draw a plot based on the “index” we give it as an argument, and one to iterate through all the letters using lapply(). Notice we put the dataframe statement in the first function - this will make the scatterplot different every time, rather than stay static, which is more festive (more or less like falling snow). Again, we save it in a GIF, with a slow interval in order to give time to read it.</p>
<pre class="r"><code>#set up function to create a new dataset, plot it, and annotate it by an index argument
draw.a.plot<- function(index){
#make up a new dataframe
happy2015<-data.frame(x=rnorm(500, 0, 1.5), y=rnorm(500, 0, 1.5), z=rnorm(500,0,1.5))
#plot according to the index passed
g<-ggplot(happy2015, aes(x, y, alpha = z, color=z)) +
geom_point(alpha=0.2) + labs(title="", x="", y="") +
theme.both +
scale_colour_gradient(low = "white", high="lightblue")+
annotate("text", x=xcoord[index], y=ycoord[index], size=15, label=sign[index], color=colors[index])
#print out the plot
print(g)
}
#set up function to loop through the draw.a.plot() function
loop.animate <- function() {
lapply(1:length(sign), function(i) {
draw.a.plot(i)
})
}
#save the images into a GIF
saveGIF(loop.animate(), interval = .5, movie.name="happy2015.gif")</code></pre>
<p>Other great sources for learning animations:</p>
<ul>
<li><p><a href="http://alstatr.blogspot.com/2014/02/r-animating-2d-and-3d-plots.html">Analysis with Programming</a></p></li>
<li><p><a href="http://trinkerrstuff.wordpress.com/2013/05/11/animations-understood-5/">TRinker’s R Blog</a></p></li>
</ul>
</div>
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Happy New Year everyone! For the last post of the year, I thought I'd have a little fun with the new animation package in R. It's actually really easy to use. I recently had some fun with it when I presented my research at an electronic poster session, and had an animated movie embedded into the powerpoint.
All of the GIFs above use ggplot and the animation packages. The main idea is to iterate the same plot over and over again, changing incrementally whatever it is that you want to move in the graph, and then save all those plots together into one GIF.
Let's start with first plot that traces a regression line over the scatterplot of the points. We'll make up some data and fit a loess of two degrees (default). You could easily do this with any kind of regression.
#make up some data
tracedat<-data.frame(x=rnorm(1000,0,1))
tracedat$y<-abs(tracedat$x)*2+rnorm(1000,0,3)
#predict a spline fit and add predicted values to the dataframe
loess_fit <- loess(y ~ x, tracedat)
tracedat$predict_y<-predict(loess_fit)
Now let's make the completed scatterplot and loess line using ggplot. If you need help on how to plot a scatterplot in ggplot, see my post here: [ggplot2: Cheatsheet for Scatterplots](http://rforpublichealth.blogspot.com/2013/11/ggplot2-cheatsheet-for-scatterplots.html).
It is possible to use **stat_smooth()** within **ggplot** to get the loess fit without predicting the values and using **geom_line()**, but the predicted values are going to make it easier to make the animation.
#plot finished scatterplot with loess fit
ggplot(tracedat, aes(x,y)) +
geom_point() +
geom_line(data=tracedat, aes(x,predict_y), color="red", size=1.3) +
scale_x_continuous(limits=c(-3, 3)) +
scale_y_continuous(limits=c(-10, 10))
Now what we need is for the loess fit to appear bit by bit, so to do this we'll cut off the dataframe for **geom_line** for only those x-values up to a certain cutoff x-value (by subsetting the dataframe called tracedat in the **geom_line** statement). Then we'll just keep moving that cutoff forward as we iterate over the range of all x-values.
First, we will build a function that takes the cutoff value as an argument. Then we can pass whatever value of x we want and it will only graph the line up to that cutoff. Notice how the scatterplot itself, however, is for the full data.
For more on how to write functions, see my post about them, [here](http://rforpublichealth.blogspot.com/2014/06/how-to-write-and-debug-r-function.html).
#function to draw the scatterplot, but the curve fit only up to whatever index we set it at
#try it out: draw curve up to cutoff x-value of -2
draw.curve(cutoff=-2)
Almost done! Now we just need to iterate the **draw.curve()** function we just created for the full range of the values of x. So we'll use **lapply()** to iterate the **draw.curve()** function over the sequence of i=-3 to 3 (incrementing by .2) and we call the **draw.curve()** function for each value of i. Finally, we'll use the saveGIF() function from the animation package to stick all the images together successively into one GIF.
The interval argument tells you how fast the GIF will move from one image to the next, and you can give it a name. If you don't want the GIF to loop back to the start again, you would add an argument "loop=FALSE" into the function call.
#function to iterate over the full span of x-values
trace.animate <- function() {
lapply(seq(-3,3,.2), function(i) {
draw.curve(i)
})
}
#save all iterations into one GIF
saveGIF(trace.animate(), interval = .2, movie.name="trace.gif")
####2. Diverging density plots
The same idea used above is used to make the diverging density plots (plot 2 above). Here we are showing the distribution of scores before some intervention and after the intervention. We need to create the code for a ggplot density plot, and turn it into a function that can take as an argument the variable we want to plot (that way we can use the same code for the "before" intervention plot and the "after" plot.
Then to animate, we'll iterate between them.
We'll make up some data and plot the "before" plot to start. Notice that we'll use **aes_string** rather than simply **aes** in the ggplot statement in order to be able to pass the data in as an argument when we turn this into a function. More on how to plot distributions using **ggplot** in my post [ggplot2: Cheatsheet for Visualizing Distributions](http://rforpublichealth.blogspot.ie/2014/02/ggplot2-cheatsheet-for-visualizing.html).
It's important to set the scale for x and y axes so that when we iterate over the two plots, we have the same dimensions each time. The alpha argument in **geom_density** makes the colors more transparent.
dist.data<-data.frame(base=rnorm(5000, 0, 3),
follow=rnorm(5000, 0, 3),
type=c(rep("Type 1",2500),rep("Type 2",2500)))
dist.data$follow<-ifelse(dist.data$type=="Type 2", dist.data$follow+12, dist.data$follow)
#plot one to make sure it's working. Use aes_string rather than aes
p<-ggplot(dist.data, aes_string("base", fill="type")) +
geom_density(alpha=0.5) +
theme(legend.position="bottom") +
scale_x_continuous(limits=c(-10, 20)) +
scale_y_continuous(limits=c(0, 0.20)) +
scale_fill_manual("", labels=c("Type 1", "Type 2"), values = c("orange","purple")) +
labs(x="Score",y="Density", title="title")
Again, we write two functions: one that draws a density plot based on the arguments passed to it (**plot.dens()**), and one that iterates over the two different plots (called **distdiverge.animate()**). In the **dist.diverge.animate()** function, we pass the plot.item (which is a character class that aes_string will understand as the name of the column in dist.data to plot), and the title, which is also a character class.
#function that plots a density plot with arguments for the variable to plot and the title
plot.dens<-function(plot.item, title.item){
p<-ggplot(dist.data, aes_string(plot.item, fill="type"))+
geom_density(alpha=0.5) +
theme(legend.position="bottom") +
scale_x_continuous(limits=c(-10, 20)) +
scale_y_continuous(limits=c(0, 0.20)) +
scale_fill_manual("", labels=c("Type 1", "Type 2"), values = c("orange","purple"))+
labs(x="Score",y="Density", title=title.item)
print(p)}
#try it out - plot it for the follow data with the title "After Intervention"
plot.dens(plot.item="follow", title.item="After Intervention")
#function that iterates over the two different plots
distdiverge.animate <- function() {
items<-c("base", "follow")
titles<-c("Before Intervention","After Intervention")
lapply(seq(1:2), function(i) {
plot.dens(items[i], titles[i])
})}
We'll make the interval slower so there's more time to view each plot before the GIF moves to the next one.
#save in a GIF
saveGIF(distdiverge.animate(), interval = .65, ,movie.name="dist.gif")
####3. Happy New Year plot
Finally, to make the fun "Happy 2015" plot, we just make a black background plot, create new random data at every iteration for the snow, and then iterate over the letters of the sign.
Let's start with the first plot for the letter "H". We create some data and the objects that will hold the letters of the sign that scrolls through, the colors (I use the colorspace package to pull some colors for me for this), and the x- and y-coordinates for where we want the letters to go. You can think about randomizing the coordinates too. Then we just plot a scatterplot and use annotate to add the letter to it.
#create dataset
happy2015<-data.frame(x=rnorm(500, 0, 1.5), y=rnorm(500, 0, 1.5), z=rnorm(500,0,1.5))
#create objects to hold the letters, colors, and x and y coordinates that we will scroll through
sign<-c("H","A","P","P","Y","2","0","1","5","!!")
colors <- rainbow_hcl(10, c=300)
xcoord<-rep(c(-2, -1, 0, 1, 2),2)
ycoord<-c(2, 1.7, 2.1, 1.5, 2, -.5, 0, -1, -.8, -.7)
We set up the ggplot theme and test the first plot.
#set up the theme in an object (get rid of axes, grids, and legend)
theme.both<- theme(legend.position="none",
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
plot.background = element_rect(fill = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#plot the first letter (set index=1 to get the first element of color, letter, and coordinates)
index<-1
ggplot(happy2015, aes(x, y, alpha = z, color=z)) +
geom_point(alpha=0.2) + labs(title="", x="", y="") +
theme.both +
scale_colour_gradient(low = "white", high="lightblue")+
annotate("text", x=xcoord[index], y=ycoord[index], size=15, label=sign[index], color=colors[index])
Finally, we again go through the structure of two functions - one to draw a plot based on the "index" we give it as an argument, and one to iterate through all the letters using lapply(). Notice we put the dataframe statement in the first function - this will make the scatterplot different every time, rather than stay static, which is more festive (more or less like falling snow). Again, we save it in a GIF, with a slow interval in order to give time to read it.
#set up function to create a new dataset, plot it, and annotate it by an index argument
draw.a.plot<- function(index){
#make up a new dataframe
happy2015<-data.frame(x=rnorm(500, 0, 1.5), y=rnorm(500, 0, 1.5), z=rnorm(500,0,1.5))
#plot according to the index passed
g<-ggplot(happy2015, aes(x, y, alpha = z, color=z)) +
geom_point(alpha=0.2) + labs(title="", x="", y="") +
theme.both +
scale_colour_gradient(low = "white", high="lightblue")+
annotate("text", x=xcoord[index], y=ycoord[index], size=15, label=sign[index], color=colors[index])
#print out the plot
print(g)}
#set up function to loop through the draw.a.plot() function
loop.animate <- function() {
lapply(1:length(sign), function(i) {
draw.a.plot(i)
})}
#save the images into a GIF
saveGIF(loop.animate(), interval = .5, movie.name="happy2015.gif")
</div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com5tag:blogger.com,1999:blog-2550386066385582083.post-86717904790077069532014-10-20T21:27:00.000-07:002016-10-13T07:56:45.615-07:00Easy Clustered Standard Errors in R<div height="1050">
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<p>Public health data can often be hierarchical in nature; for example, individuals are grouped in hospitals which are grouped in counties. When units are not independent, then regular OLS standard errors are biased.</p>
<p>One way to correct for this is using clustered standard errors. This post will show you how you can easily put together a function to calculate clustered SEs and get everything else you need, including confidence intervals, F-tests, and linear hypothesis testing.</p>
<p>Under standard OLS assumptions, with independent errors,</p>
<p><span class="math">\[V_{OLS} = \sigma^2(X'X)^{-1}\]</span></p>
<p>We can estimate <span class="math">\(\sigma^2\)</span> with <span class="math">\(s^2\)</span>:</p>
<p><span class="math">\[s^2 = \frac{1}{N-K}\sum_{i=1}^N e_i^2\]</span></p>
<p>where N is the number of observations, K is the rank (number of variables in the regression), and <span class="math">\(e_i\)</span> are the residuals from the regression.</p>
<p>But if the errors are not independent because the observations are clustered within groups, then confidence intervals obtained will not have <span class="math">\(1-\alpha\)</span> coverage probability. To fix this, we can apply a sandwich estimator, like this:</p>
<p><span class="math">\[V_{Cluster} = (X'X)^{-1} \sum_{j=1}^{n_c} (u_j'*u_j) (X'X)^{-1}\]</span></p>
<p>where <span class="math">\(n_c\)</span> is the total number of clusters and <span class="math">\(u_j = \sum_{j_{cluster}}e_i*x_i\)</span>.</p>
<p><span class="math">\(x_i\)</span> is the row vector of predictors including the constant. Programs like Stata also use a degree of freedom adjustment (small sample size adjustment), like so:</p>
<p><span class="math">\[\frac{M}{M-1}*\frac{N-1}{N-K} * V_{Cluster}\]</span></p>
<p>where M is the number of clusters, N is the sample size, and K is the rank.</p>
<p>In this example, we’ll use the Crime dataset from the plm package. It includes yearly data on crime rates in counties across the United States, with some characteristics of those counties. Let’s load in the libraries we need and the Crime data:</p>
<pre class="r"><code>library(plm)
library(lmtest)
data(Crime)</code></pre>
<p>We would like to see the effect of percentage males aged 15-24 (pctymle) on crime rate, adjusting for police per capita (polpc), region, and year. However, there are multiple observations from the same county, so we will cluster by county.</p>
<p>In Stata the commands would look like this.</p>
<pre class="r"><code>reg crmrte pctymle polpc i.region year, cluster(county)</code></pre>
<p>In R, we can first run our basic ols model using lm() and save the results in an object called m1. Unfortunately, there’s no ‘cluster’ option in the lm() function. But there are many ways to get the same result.</p>
<pre class="r"><code>#basic linear model with standard variance estimate
Crime$region<-factor(Crime$region)
m1<-lm(crmrte~pctymle+polpc+region+year, data=Crime)</code></pre>
<div id="get-the-cluster-adjusted-variance-covariance-matrix" class="section level4">
<h4>>>> Get the cluster-adjusted variance-covariance matrix</h4>
<p>First, I’ll show how to write a function to obtain clustered standard errors. If you are unsure about how user-written functions work, please see my posts about them, <a href="http://rforpublichealth.blogspot.com/2014/06/how-to-write-and-debug-r-function.html">here</a> (How to write and debug an R function) and <a href="http://rforpublichealth.blogspot.com/2014/07/3-ways-that-functions-can-improve-your.html">here</a> (3 ways that functions can improve your R code).</p>
<p>There are many sources to help us write a function to calculate clustered SEs. Check out these helpful links: Mahmood Arai’s paper <a href="http://people.su.se/~ma/clustering.pdf">found here</a> and DiffusePrioR’s blogpost <a href="http://diffuseprior.wordpress.com/2012/06/15/standard-robust-and-clustered-standard-errors-computed-in-r/">found here</a>.</p>
<p>I’ll base my function on the first source. It uses functions from the <strong>sandwich</strong> and the <strong>lmtest</strong> packages so make sure to install those packages. However, instead of returning the coefficients and standard errors, I am going to modify Arai’s function to return the variance-covariance matrix, so I can work with that later. The function will input the lm model object and the cluster vector.</p>
<p>After that, I’ll do it the super easy way with the new <strong>multiwayvcov</strong> package which has a <strong>cluster.vcov()</strong> function. Help on this package <a href="http://cran.r-project.org/web/packages/multiwayvcov/multiwayvcov.pdf">found here</a>. The function also needs the model and the cluster as inputs.</p>
<p>Here are both ways:</p>
<pre class="r"><code>#write your own function to return variance covariance matrix under clustered SEs
get_CL_vcov<-function(model, cluster){
require(sandwich, quietly = TRUE)
require(lmtest, quietly = TRUE)
#calculate degree of freedom adjustment
M <- length(unique(cluster))
N <- length(cluster)
K <- model$rank
dfc <- (M/(M-1))*((N-1)/(N-K))
#calculate the uj's
uj <- apply(estfun(model),2, function(x) tapply(x, cluster, sum))
#use sandwich to get the var-covar matrix
vcovCL <- dfc*sandwich(model, meat=crossprod(uj)/N)
return(vcovCL)
}
#call our new function and save the var-cov matrix output in an object
m1.vcovCL<-get_CL_vcov(m1, Crime$county)
#equivalent way: use the cluster.vcov function to get variance-covariance matrix
library(multiwayvcov)
m1.vcovCL.2<-cluster.vcov(m1, Crime$county)</code></pre>
<p>Now, in order to obtain the coefficients and SEs, we can use the <strong>coeftest()</strong> function in the lmtest library, which allows us to input our own var-covar matrix.</p>
<p>Let’s compare our standard OLS SEs to the clustered SEs.</p>
<pre class="r"><code>#the regular OLS standard errors
coeftest(m1)</code></pre>
<pre><code>##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00262057 0.02757101 -0.0950 0.9243
## pctymle 0.16568860 0.02688128 6.1637 1.276e-09 ***
## polpc 1.40604239 0.23482618 5.9876 3.595e-09 ***
## regionwest -0.01424935 0.00168946 -8.4343 2.317e-16 ***
## regioncentral 0.00227384 0.00147881 1.5376 0.1246
## year 0.00022901 0.00032286 0.7093 0.4784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>#the clustered standard errors by indicating the correct var-covar matrix
coeftest(m1, m1.vcovCL)</code></pre>
<pre><code>##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00262057 0.01909911 -0.1372 0.89091
## pctymle 0.16568860 0.06511197 2.5447 0.01118 *
## polpc 1.40604239 0.88833006 1.5828 0.11398
## regionwest -0.01424935 0.00329691 -4.3220 1.799e-05 ***
## regioncentral 0.00227384 0.00391121 0.5814 0.56120
## year 0.00022901 0.00019074 1.2006 0.23035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<p>We can see that the SEs generally increased, due to the clustering. Now, let’s obtain the <strong>F-statistic</strong> and the <strong>confidence intervals</strong>.</p>
</div>
<div id="getting-the-f-statistic-and-confidence-intervals-for-clustered-ses" class="section level4">
<h4>>>>Getting the F-statistic and confidence intervals for clustered SEs</h4>
<p>To obtain the F-statistic, we can use the <strong>waldtest()</strong> function from the <strong>lmtest</strong> library with test=“F” indicated for the F-test.</p>
<p>For the 95% CIs, we can write our own function that takes in the model and the variance-covariance matrix and produces the 95% CIs. You can modify this function to make it better and more versatile, but I’m going to keep it simple.</p>
<pre class="r"><code>#function to return confidence intervals
get_confint<-function(model, vcovCL){
t<-qt(.975, model$df.residual)
ct<-coeftest(model, vcovCL)
est<-cbind(ct[,1], ct[,1]-t*ct[,2], ct[,1]+t*ct[,2])
colnames(est)<-c("Estimate","LowerCI","UpperCI")
return(est)
}</code></pre>
<p>Now, we can get the F-stat and the confidence intervals:</p>
<pre class="r"><code>#do a wald test to get F-statistic
waldtest(m1, vcov = m1.vcovCL, test = "F")</code></pre>
<pre><code>## Wald test
##
## Model 1: crmrte ~ pctymle + polpc + region + year
## Model 2: crmrte ~ 1
## Res.Df Df F Pr(>F)
## 1 624
## 2 629 -5 7.3616 1.018e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<p>Note that now the F-statistic is calculated based on a Wald test (using the cluster-robustly esimtated var-covar matrix) rather than on sums of squares and degrees of freedom. The degrees of freedom listed here are for the model, but the var-covar matrix has been corrected for the fact that there are only 90 independent observations.</p>
<p>If you want to save the F-statistic itself, save the waldtest function call in an object and extract:</p>
<pre class="r"><code>#save waldtest in an object
w<-waldtest(m1, vcov = m1.vcovCL, test = "F")
#check out what is saved in w
names(w)</code></pre>
<pre><code>## [1] "Res.Df" "Df" "F" "Pr(>F)"</code></pre>
<pre class="r"><code>#use $F to get the F-statistic
w$F[2]</code></pre>
<pre><code>## [1] 7.361599</code></pre>
<p>For confidence intervals, we can use the function we wrote:</p>
<pre class="r"><code>#obtain the confidence interval using our function
get_confint(m1, m1.vcovCL)</code></pre>
<pre><code>## Estimate LowerCI UpperCI
## (Intercept) -0.0026205744 -0.040126885 0.0348857363
## pctymle 0.1656885998 0.037823465 0.2935537342
## polpc 1.4060423854 -0.338436157 3.1505209280
## regionwest -0.0142493530 -0.020723744 -0.0077749620
## regioncentral 0.0022738418 -0.005406889 0.0099545724
## year 0.0002290114 -0.000145564 0.0006035867</code></pre>
<p>As an aside, to get the R-squared value, you can extract that from the original model m1, since that won’t change if the errors are clustered. Again, remember that the R-squared is calculated via sums of squares, which are technically no longer relevant because of the corrected variance-covariance matrix. But it can still be used as a measure of goodness-of-fit.</p>
<pre class="r"><code>#r-squared
summary(m1)$r.squared</code></pre>
<pre><code>## [1] 0.2198695</code></pre>
</div>
<div id="one-function-to-do-everything" class="section level4">
<h4>>>>One function to do everything</h4>
<p>Notice, that you could wrap all of these 3 components (F-test, coefficients/SEs, and CIs) in a function that saved them all in a list, for example like this:</p>
<pre class="r"><code>super.cluster.fun<-function(model, cluster)
{
require(multiwayvcov)
require(lmtest)
vcovCL<-cluster.vcov(model, cluster)
coef<-coeftest(model, vcovCL)
w<-waldtest(model, vcov = vcovCL, test = "F")
ci<-get_confint(model, vcovCL)
return(list(coef, w, ci))
}
super.cluster.fun(m1, Crime$county)</code></pre>
<pre><code>## [[1]]
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00262057 0.01909911 -0.1372 0.89091
## pctymle 0.16568860 0.06511197 2.5447 0.01118 *
## polpc 1.40604239 0.88833006 1.5828 0.11398
## regionwest -0.01424935 0.00329691 -4.3220 1.799e-05 ***
## regioncentral 0.00227384 0.00391121 0.5814 0.56120
## year 0.00022901 0.00019074 1.2006 0.23035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## [[2]]
## Wald test
##
## Model 1: crmrte ~ pctymle + polpc + region + year
## Model 2: crmrte ~ 1
## Res.Df Df F Pr(>F)
## 1 624
## 2 629 -5 7.3616 1.018e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## [[3]]
## Estimate LowerCI UpperCI
## (Intercept) -0.0026205744 -0.040126885 0.0348857363
## pctymle 0.1656885998 0.037823465 0.2935537342
## polpc 1.4060423854 -0.338436157 3.1505209280
## regionwest -0.0142493530 -0.020723744 -0.0077749620
## regioncentral 0.0022738418 -0.005406889 0.0099545724
## year 0.0002290114 -0.000145564 0.0006035867</code></pre>
<p>Then you could extract each component with the [[]] operator. Check out <a href="http://rforpublichealth.blogspot.com/2014/06/how-to-write-and-debug-r-function.html">this post</a>(“Returning a list of objects”) if you’re unsure.</p>
</div>
<div id="hypothesis-testing-when-errors-are-clustered" class="section level4">
<h4>>>>Hypothesis testing when errors are clustered</h4>
<p>Now what if we wanted to test whether the west region coefficient was different from the central region? Again, we need to incorporate the right var-cov matrix into our calculation. Fortunately the <strong>car</strong> package has a <strong>linearHypothesis()</strong> function that allows for specification of a var-covar matrix. The inputs are the model, the var-cov matrix, and the coefficients you want to test. Check out the help file of the function to see the wide range of tests you can do.</p>
<pre class="r"><code>library(car)
linearHypothesis(m1, vcov=m1.vcovCL, "regionwest = regioncentral")</code></pre>
<pre><code>## Linear hypothesis test
##
## Hypothesis:
## regionwest - regioncentral = 0
##
## Model 1: restricted model
## Model 2: crmrte ~ pctymle + polpc + region + year
##
## Note: Coefficient covariance matrix supplied.
##
## Res.Df Df F Pr(>F)
## 1 625
## 2 624 1 18.522 1.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="troubleshooting-and-error-messages" class="section level4">
<h4>>>>Troubleshooting and error messages</h4>
<p>One reason to opt for the <strong>cluster.vcov()</strong> function from the <strong>multiwayvcov</strong> package is that it can handle missing values without any problems. When doing the variance-covariance matrix using the user-written function <strong>get_CL_vcov</strong> above, an error message can often come up:</p>
<pre class="r"><code>#error messages
get_CL_vcov(m1, Crime$couunty)</code></pre>
<pre><code>## Error in tapply(x, cluster, sum): arguments must have same length</code></pre>
<p>There are two common reasons for this. One is just that you spelled the name of the cluster variable incorrectly (as above). Make sure to check that. The second is that you have <strong>missing values</strong> in your outcome or explanatory variables. In this case, the length of the cluster will be different from the length of the outcome or covariates and tapply() will not work.</p>
<p>One possible solutions is to remove the missing values by subsetting the cluster to include only those values where the outcome is not missing. Another option is to run <strong>na.omit()</strong> on the entire dataset to remove all missing vaues. Here’s an example:</p>
<pre class="r"><code>#force in missing value in outcome
Crime2<-Crime
Crime2$crmrte[1]<-NA
#rerun model
m2<-lm(crmrte~pctymle+polpc+region+year, data=Crime2)
#this produces errors
v1<-get_CL_vcov(m2, Crime2$county)</code></pre>
<pre><code>## Error in tapply(x, cluster, sum): arguments must have same length</code></pre>
<pre class="r"><code>#but we can remove the observations in county for which crmrte is missing
v2<-get_CL_vcov(m2, Crime2$county[!is.na(Crime2$crmrte)])
#or can use na.omit
Crime3<-na.omit(Crime2)
m3<-lm(crmrte~pctymle+polpc+region+year, data=Crime3)
v3<-get_CL_vcov(m3, Crime3$county)</code></pre>
<p>However, if you’re running a number of regressions with different covariates, each with a different missing pattern, it may be annoying to create multiple datasets and run na.omit() on them to deal with this. To avoid this, you can use the <strong>cluster.vcov()</strong> function, which handles missing values within its own function code, so you don’t have to.</p>
</div>
<div id="using-plm" class="section level4">
<h4>>>>Using plm</h4>
<p>Finally, you can also use the <strong>plm()</strong> and <strong>vcovHC()</strong> functions from the <strong>plm</strong> package. You still need to do your own small sample size correction though.</p>
<pre class="r"><code>#use plm function to define formula, dataset, that it's a pooling model, and the cluster variable
p1 <- plm(crmrte~pctymle+polpc+region+year, Crime, model='pooling', index=c('county'))
#calculate small sample size adjustment
G <- length(unique(Crime$county))
N <- length(Crime$county)
dfa <- (G/(G - 1)) * (N - 1)/p1$df.residual
#use coeftest and the vcovHC functions, specifying HC0 type and identifying cluster as 'group'
coeftest(p1, vcov=function(x) dfa*vcovHC(x, cluster="group", type="HC0"))</code></pre>
<pre><code>##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00262057 0.01909911 -0.1372 0.89091
## pctymle 0.16568860 0.06511197 2.5447 0.01118 *
## polpc 1.40604239 0.88833006 1.5828 0.11398
## regionwest -0.01424935 0.00329691 -4.3220 1.799e-05 ***
## regioncentral 0.00227384 0.00391121 0.5814 0.56120
## year 0.00022901 0.00019074 1.2006 0.23035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<p>A website that goes further into this function <a href="http://landroni.wordpress.com/2012/06/02/fama-macbeth-and-cluster-robust-by-firm-and-time-standard-errors-in-r/">is here</a>.</p>
<p>Update: A reader pointed out to me that another package that can do clustering is the <strong>rms</strong> package, so definitely <a href="http://cran.r-project.org/web/packages/rms/rms.pdf">check that out</a> as well.</p>
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Public health data can often be hierarchical in nature; for example, individuals are grouped in hospitals which are grouped in counties. When units are not independent, then regular OLS standard errors are biased.
One way to correct for this is using clustered standard errors. This post will show you how you can easily put together a function to calculate clustered SEs and get everything else you need, including confidence intervals, F-tests, and linear hypothesis testing.
Under standard OLS assumptions, with independent errors,
$$V_{OLS} = \sigma^2(X'X)^{-1}$$
We can estimate $\sigma^2$ with $s^2$:
$$s^2 = \frac{1}{N-K}\sum_{i=1}^N e_i^2$$
where N is the number of observations, K is the rank (number of variables in the regression), and $e_i$ are the residuals from the regression.
But if the errors are not independent because the observations are clustered within groups, then confidence intervals obtained will not have $1-\alpha$ coverage probability. To fix this, we can apply a sandwich estimator, like this:
$$V_{Cluster} = (X'X)^{-1} \sum_{j=1}^{n_c} (u_j'*u_j) (X'X)^{-1}$$
where $n_c$ is the total number of clusters and $u_j = \sum_{j_{cluster}}e_i*x_i$.
$x_i$ is the row vector of predictors including the constant. Programs like Stata also use a degree of freedom adjustment (small sample size adjustment), like so:
$$\frac{M}{M-1}*\frac{N-1}{N-K} * V_{Cluster}$$
where M is the number of clusters, N is the sample size, and K is the rank.
In this example, we'll use the Crime dataset from the plm package. It includes yearly data on crime rates in counties across the United States, with some characteristics of those counties. Let's load in the libraries we need and the Crime data:
library(plm)
library(lmtest)
data(Crime)
We would like to see the effect of percentage males aged 15-24 (pctymle) on crime rate, adjusting for police per capita (polpc), region, and year. However, there are multiple observations from the same county, so we will cluster by county.
In Stata the commands would look like this.
reg crmrte pctymle polpc i.region year, cluster(county)
In R, we can first run our basic ols model using lm() and save the results in an object called m1. Unfortunately, there's no 'cluster' option in the lm() function. But there are many ways to get the same result
#basic linear model with standard variance estimate
Crime$region<-factor(Crime$region)
m1<-lm(crmrte~pctymle+polpc+region+year, data=Crime)
Get the cluster-adjusted variance-covariance matrix
First, I'll show how to write a function to obtain clustered standard errors. If you are unsure about how user-written functions work, please see my posts about them, [here](http://rforpublichealth.blogspot.com/2014/06/how-to-write-and-debug-r-function.html) (How to write and debug an R function) and [here](http://rforpublichealth.blogspot.com/2014/07/3-ways-that-functions-can-improve-your.html) (3 ways that functions can improve your R code).
There are many sources to help us write a function to calculate clustered SEs. Check out these helpful links: Mahmood Arai's paper [found here](http://people.su.se/~ma/clustering.pdf) and DiffusePrioR's blogpost [found here](http://diffuseprior.wordpress.com/2012/06/15/standard-robust-and-clustered-standard-errors-computed-in-r/).
I'll base my function on the first source. It uses functions from the sandwich and the lmtest packages so make sure to install those packages. However, instead of returning the coefficients and standard errors, I am going to modify Arai's function to return the variance-covariance matrix, so I can work with that later. The function will input the lm model object and the cluster vector.
After that, I'll do it the super easy way with the new multiwayvcov package which has a cluster.vcov() function. Help on this package [found here](http://cran.r-project.org/web/packages/multiwayvcov/multiwayvcov.pdf). The function also needs the model and the cluster as inputs.
Here are both ways:
#write your own function to return variance covariance matrix under clustered SEs
get_CL_vcov<-function(model, cluster){
require(sandwich, quietly = TRUE)
require(lmtest, quietly = TRUE)
#calculate degree of freedom adjustment
M <- length(unique(cluster))
N <- length(cluster)
K <- model$rank
dfc <- (M/(M-1))*((N-1)/(N-K))
#calculate the uj's
uj <- apply(estfun(model),2, function(x) tapply(x, cluster, sum)
#use sandwich to get the var-covar matrix
vcovCL <- dfc*sandwich(model, meat=crossprod(uj)/N)
return(vcovCL)}
#call our new function and save the var-cov matrix output in an object
m1.vcovCL<-get_CL_vcov(m1, Crime$county)
#equivalent way: use the cluster.vcov function to get variance-covariance matrix
library(multiwayvcov)
m1.vcovCL.2<-cluster.vcov(m1, Crime$county)
Now, in order to obtain the coefficients and SEs, we can use the coeftest() function in the lmtest library, which allows us to input our own var-covar matrix.
Let's compare our standard OLS SEs to the clustered SEs.
#the regular OLS standard errors
coeftest(m1)
#the clustered standard errors by indicating the correct var-covar matrix
coeftest(m1, m1.vcovCL)
We can see that the SEs generally increased, due to the clustering. Now, let's obtain the F-statistic and the confidence intervals.
Getting the F-statistic and confidence intervals for clustered SEs
To obtain the F-statistic, we can use the waldtest() function from the lmtest library with test="F" indicated for the F-test.
For the 95% CIs, we can write our own function that takes in the model and the variance-covariance matrix and produces the 95% CIs. You can modify this function to make it better and more versatile, but I'm going to keep it simple.
#function to return confidence intervals
get_confint<-function(model, vcovCL){
t<-qt(.975, model$df.residual)
ct<-coeftest(model, vcovCL)
est<-cbind(ct[,1], ct[,1]-t*ct[,2], ct[,1]+t*ct[,2])
colnames(est)<-c("Estimate","LowerCI","UpperCI")
return(est)}
Now, we can get the F-stat and the confidence intervals:
#do a wald test to get F-statistic
waldtest(m1, vcov = m1.vcovCL, test = "F")
Note that now the F-statistic is calculated based on a Wald test (using the cluster-robustly esimtated var-covar matrix) rather than on sums of squares and degrees of freedom. The degrees of freedom listed here are for the model, but the var-covar matrix has been corrected for the fact that there are only 90 independent observations.
If you want to save the F-statistic itself, save the waldtest function call in an object and extract:
#save waldtest in an object
w<-waldtest(m1, vcov = m1.vcovCL, test = "F")
#check out what is saved in w
names(w)
#use $F to get the F-statistic
w$F[2]
For confidence intervals, we can use the function we wrote:
#obtain the confidence interval using our function
get_confint(m1, m1.vcovCL)
As an aside, to get the R-squared value, you can extract that from the original model m1, since that won't change if the errors are clustered. Again, remember that the R-squared is calculated via sums of squares, which are technically no longer relevant because of the corrected variance-covariance matrix. But it can still be used as a measure of goodness-of-fit.
#r-squared
summary(m1)$r.squared
One function to do everything
Notice, that you could wrap all of these 3 components (F-test, coefficients/SEs, and CIs) in a function that saved them all in a list, for example like this:
super.cluster.fun<-function(model, cluster){
require(multiwayvcov)
require(lmtest)
vcovCL<-cluster.vcov(model, cluster)
coef<-coeftest(model, vcovCL)
w<-waldtest(model, vcov = vcovCL, test = "F")
ci<-get_confint(model, vcovCL)
return(list(coef, w, ci))}
super.cluster.fun(m1, Crime$county)
Then you could extract each component with the [[]] operator. Check out [this post](http://rforpublichealth.blogspot.com/2014/06/how-to-write-and-debug-r-function.html)("Returning a list of objects") if you're unsure.
Hypothesis testing when errors are clustered
Now what if we wanted to test whether the west region coefficient was different from the central region? Again, we need to incorporate the right var-cov matrix into our calculation. Fortunately the car package has a __linearHypothesis()__ function that allows for specification of a var-covar matrix. The inputs are the model, the var-cov matrix, and the coefficients you want to test. Check out the help file of the function to see the wide range of tests you can do.
library(car)
linearHypothesis(m1, vcov=m1.vcovCL, "regionwest = regioncentral")
Troubleshooting and error messages
One reason to opt for the cluster.vcov() function from the multiwayvcov package is that it can handle missing values without any problems. When doing the variance-covariance matrix using the user-written function get_CL_vcov above, an error message can often come up:
#error messages
get_CL_vcov(m1, Crime$couunty)
There are two common reasons for this. One is just that you spelled the name of the cluster variable incorrectly (as above). Make sure to check that. The second is that you have __missing values__ in your outcome or explanatory variables. In this case, the length of the cluster will be different from the length of the outcome or covariates and tapply() will not work.
One possible solutions is to remove the missing values by subsetting the cluster to include only those values where the outcome is not missing. Another option is to run na.omit() on the entire dataset to remove all missing vaues. Here's an example:
#force in missing value in outcome
Crime2<-Crime
Crime2$crmrte[1]<-NA
#rerun model
m2<-lm(crmrte~pctymle+polpc+region+year, data=Crime2)
#this produces errors
v1<-get_CL_vcov(m2, Crime2$county)
#but we can remove the observations in county for which crmrte is missing
v2<-get_CL_vcov(m2, Crime2$county[!is.na(Crime2$crmrte)])
#or can use na.omit
Crime3<-na.omit(Crime2)
m3<-lm(crmrte~pctymle+polpc+region+year, data=Crime3)
v3<-get_CL_vcov(m3, Crime3$county)
However, if you're running a number of regressions with different covariates, each with a different missing pattern, it may be annoying to create multiple datasets and run na.omit() on them to deal with this. To avoid this, you can use the cluster.vcov() function, which handles missing values within its own function code, so you don't have to.
Using plm
Finally, you can also use the plm() and vcovHC() functions from the plm package. You still need to do your own small sample size correction though.
#use plm function to define formula, dataset, that it's a pooling model, and the cluster variable
p1 <- plm(crmrte~pctymle+polpc+region+year, Crime, model='pooling', index=c('county'))
#calculate small sample size adjustment
G <- length(unique(Crime$county))
N <- length(Crime$county)
dfa <- (G/(G - 1)) * (N - 1)/p1$df.residual
#use coeftest and the vcovHC functions, specifying HC0 type and identifying cluster as 'group'
coeftest(p1, vcov=function(x) dfa*vcovHC(x, cluster="group", type="HC0"))
A website that goes further into this function [is here](http://landroni.wordpress.com/2012/06/02/fama-macbeth-and-cluster-robust-by-firm-and-time-standard-errors-in-r/).
Update: A reader pointed out to me that another package that can do clustering is the rms package, so definitely [check that out](http://cran.r-project.org/web/packages/rms/rms.pdf) as well.
</div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com3tag:blogger.com,1999:blog-2550386066385582083.post-27408584587653896902014-07-07T06:49:00.001-07:002016-10-13T07:55:58.203-07:003 ways that functions can improve your R code<div height="1050">
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<p>My <a href="http://rforpublichealth.blogspot.com/2014/06/how-to-write-and-debug-r-function.html">previous blog post</a> went over the basics of the syntax and debugging of user-written functions. In this post, I’ll show you examples of useful functions that you can write to make your life easier when using R.</p>
<p>Here is the data we’ll be using for this post:</p>
<pre class="r"><code>set.seed(10)
bpdata<-data.frame(bp=rnorm(1000,140,20),
age=rnorm(1000,50,3),
sex=rbinom(1000,1,.5),
race=as.factor(c(rep(1,500),rep(2,500))),
out=rbinom(1000,1,.8))
bpdata[c(100,200,400),2]<-NA
bpdata[c(300),1]<-400</code></pre>
<div id="functions-to-help-you-avoid-repeating-yourself" class="section level3">
<h3>>> Functions to help you avoid repeating yourself</h3>
<p>One of the main reasons to use functions is that you can re-use them over and over again, without repeating the same code again and again. Copying and pasting code that you change just a little bit every time leads to errors. With a function, if you decide to change the way you do something, you just change the one function and it will now automatically update your code everywhere you use that function.</p>
<p>As an example, in a previous blog post I wrote about <a href="http://rforpublichealth.blogspot.com/2013/08/exporting-results-of-linear-regression_24.html">calculating robust standard errors and exporting</a> them.</p>
<p>The calculation went like this:</p>
<pre class="r"><code>library(sandwich)
cov.fit1 <- vcovHC(fit1, type = "HC")
rob.std.err <- sqrt(diag(cov.fit1))</code></pre>
<p>Well, if you are going to be using robust standard errors more than once in your analysis, it makes more sense to make a function. That way you don’t need to copy and paste this code and change the object fit1 everytime.</p>
<p>To do this, I just take the exact same code, but I change the fit1 to a more general modelfit that is passed in as an argument to a function I call <strong>robust.SE()</strong>. Also, instead of loading the library sandwich, I can just require it for that function (note that you still have to have the package installed for R to load it using require). Then you can pass in the model fit as the argument and return the robust standard errors. Like this:</p>
<pre class="r"><code>#function to calculate robust standard errors for a model
robust.SE<-function(modelfit){
require(sandwich, quietly=TRUE)
cov.fit1 <- vcovHC(modelfit, type = "HC")
rob.std.err <- sqrt(diag(cov.fit1))
return(rob.std.err)
}
#get robust SEs for model 1
model1<-lm(out~age + sex, data=bpdata)
robust.SE(model1)</code></pre>
<pre><code>## (Intercept) age sex
## 0.203340 0.004071 0.025304</code></pre>
<pre class="r"><code>#get robust SEs for model2
model2<-lm(bp~age+sex, data=bpdata)
robust.SE(model2)</code></pre>
<pre><code>## (Intercept) age sex
## 10.7907 0.2141 1.3653</code></pre>
<p>Instead of copying and pasting the code for robust standard errors twice, I just wrap it up in a function that allows me to evaluate it for any model fit I want to throw at it.</p>
</div>
<div id="functions-to-customize-your-output" class="section level3">
<h3>>> Functions to customize your output</h3>
<p>Another reason to use functions is to make it easier and faster to do the things you always need to do, in the exact way you want. For example, it’s important when you start with a new dataset to look at it descriptively and look at whether there are strange values or missing values. There are many ways to do this. You can of course do <strong>summary(data)</strong> which is a good start:</p>
<pre class="r"><code>#use the basic summary built-in function
summary(bpdata)</code></pre>
<pre><code>## bp age sex race out
## Min. : 79.8 Min. :40.3 Min. :0.000 1:500 Min. :0.0
## 1st Qu.:126.5 1st Qu.:48.0 1st Qu.:0.000 2:500 1st Qu.:1.0
## Median :139.9 Median :50.0 Median :0.000 Median :1.0
## Mean :140.5 Mean :50.0 Mean :0.492 Mean :0.8
## 3rd Qu.:154.5 3rd Qu.:52.2 3rd Qu.:1.000 3rd Qu.:1.0
## Max. :400.0 Max. :61.4 Max. :1.000 Max. :1.0
## NA's :3</code></pre>
<p>But this doesn’t give me all the information I want and it’s not in the format I want. If I had 20 variables, which is not out of the ordinary, it would be hard to read this and it takes up too much space in a report. I could also use the describe() function in the psych package, but it doesn’t deal well with factor variables.</p>
<p>I want to create a table with the variables down the rows and some specific stats going across the columns, namely mean, standard deviation, min, max, and the number missing. I want to make sure to treat factors as dummies instead of numeric variables. Here I create a new function that takes a dataset and produces this kind of table for all the variables within the dataset. I call the function <strong>summarize.vars</strong>.</p>
<p>The only argument I pass through is a dataset. I use the package dummies to create dummy variables of all the factors so that I have only numeric variables in my data (it ignores all character variables if I had any). Then I use the <strong>apply()</strong> function to do my summarizing. Check out my previous post on how apply works, <a href="http://rforpublichealth.blogspot.com/2012/09/the-infamous-apply-function.html">here</a>.</p>
<pre class="r"><code>#function to summarize the variables in the data
summarize.vars<-function(data){
#use dummies package to turn all factors into dummies
require(dummies, quietly=TRUE)
dat.d<-dummy.data.frame(data, dummy.class="factor")
#use apply to calculate statistics for each variable
mat<-t(apply(dat.d, 2, function(x) c(length(x),
round(mean(x, na.rm=TRUE),2),
round(sd(x, na.rm=TRUE),2),
round(min(x, na.rm=TRUE),2),
round(max(x, na.rm=TRUE),2),
length(x)-length(x[!is.na(x)]))))
#assign column names and rownames to output table
colnames(mat)<-c("N","Mean","SD","Min","Max","Num Missing")
rownames(mat)<-colnames(dat.d)
return(mat)
}
summarize.vars(bpdata)</code></pre>
<pre><code>## N Mean SD Min Max Num Missing
## bp 1000 140.46 21.46 79.76 400.00 0
## age 1000 50.05 3.14 40.31 61.44 3
## sex 1000 0.49 0.50 0.00 1.00 0
## race1 1000 0.50 0.50 0.00 1.00 0
## race2 1000 0.50 0.50 0.00 1.00 0
## out 1000 0.80 0.40 0.00 1.00 0</code></pre>
<p>I can also use this to summarize only part of my data, for example by subsetting my data like so:</p>
<pre class="r"><code>summarize.vars(bpdata[bpdata$race==1,])</code></pre>
<p>You can think about making this function better. One way would be to have the N for the factors count within the level, since that is more useful information. You can customize it in whatever way makes sense to get the kind of information that is most useful.</p>
<p>Let’s do another example of a function to summarize information. This time we’ll put together a function that runs a linear model, succinctly summarizes the results, and produces some diagnostic plots at the same time. This is useful if you run linear models often.</p>
<p>Remember that you can pass anything through a function, including a formula. In this function, I pass in a formula for the model and the dataframe and I don’t return any values; instead, I print directly from inside the function. You could change this so that it returned the output and you could store it in an object outside the function.</p>
<pre class="r"><code>#linear model function
lm.model.diagnostics<-function(formula, dataset){
#run model and print specific output
model1<-lm(formula=formula, data=dataset)
stats<-round(c(summary(model1)$fstatistic[c(1,3)],
summary(model1)$sigma,
summary(model1)$r.squared,
summary(model1)$adj.r.squared),3)
names(stats)<-c("F","DF", "Sigma","Rsq","AdjRsq")
l1<-list(round(summary(model1)$coefficients,3), stats)
names(l1)<-c("Coefficients","Stats")
print(l1)
#run specific diagnostic tests
par(mfrow=c(1,3))
hist(model1$residuals, main="Histogram of residuals", xlab="")
plot(model1, 1)
plot(model1, 2)
}
#run function for model of blood pressure on age and sex
lm.model.diagnostics(bp~age+sex, bpdata)</code></pre>
<pre><code>## $Coefficients
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 122.111 10.909 11.193 0.000
## age 0.359 0.217 1.658 0.098
## sex 0.693 1.359 0.510 0.610
##
## $Stats
## F DF Sigma Rsq AdjRsq
## 1.480 994.000 21.439 0.003 0.001</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-7" alt="plot of chunk unnamed-chunk-7" width="672" /></p>
<p>So we can see that there is a funny outlier in observation 300. That observation has a blood pressure of 400, which we think is an incorrect value. We can see what happens when we take out that outlier:</p>
<pre class="r"><code>#take out the outlier
lm.model.diagnostics(bp~age+sex, bpdata[c(-300),])</code></pre>
<pre><code>## $Coefficients
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 125.961 10.089 12.485 0.000
## age 0.283 0.200 1.410 0.159
## sex 0.152 1.257 0.121 0.904
##
## $Stats
## F DF Sigma Rsq AdjRsq
## 0.996 993.000 19.818 0.002 0.000</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-8" alt="plot of chunk unnamed-chunk-8" width="672" /></p>
<p>Note that if your formula is long and you use it more than once, you can avoid copying and pasting it as well by just saving it in an object like this:</p>
<pre class="r"><code>form1<-bp~age+sex
lm.model.diagnostics(form1, bpdata)</code></pre>
</div>
<div id="functions-to-aid-in-your-analysis" class="section level3">
<h3>>> Functions to aid in your analysis</h3>
<p>Another useful way to use functions is for improving your analysis. For example, R summary output of a model fit doesn’t provide confidence intervals. It’s useful to have a function to calculate the confidence intervals and put them in a nice table. As a bonus, you can make the function versatile so that it can provide nice output of logit or poisson models, by exponentiating the results to get odds ratios or incidence rate ratios, respectively.</p>
<p>Notice how in this function I set up the defaults for the parameters that I pass in. Unless I indicate otherwise, exponent=FALSE, alpha=.05, and digits=2. That way the function runs even without specifying those parameters. If I want to change them, I can do so the way I do in the second example.</p>
<pre class="r"><code>#function to get confidence intervals for glm output, can get exponentiated output for logit or poisson
glmCI <- function(glmfit, exponent=FALSE, alpha=0.05, digits=2)
{
#get SE from model fit
se <- sqrt(diag(summary(glmfit)$cov.scaled))
#calculuate CI for linear case
mat <- cbind(coef(glmfit),
coef(glmfit) - qnorm(1-alpha/2)*se,
coef(glmfit) + qnorm(1-alpha/2)*se)
colnames(mat) <- c("Beta", "LowerCI", "UpperCI")
#if exponent=TRUE, exponeniate the coefficients and CIs
if(exponent == TRUE)
{
mat <- exp(mat)
if(summary(glmfit)$family$link=="logit") colnames(mat)[1] <- "OR"
if(summary(glmfit)$family$link=="log") colnames(mat)[1] <- "IRR"
}
#return a rounded matrix of results
return(round(mat, digits=digits))
}
#1. use glm with identity link on continuous response data (default family is gaussian)
g.glm<-glm(bp~age+sex, data=bpdata)
glmCI(g.glm)</code></pre>
<pre><code>## Beta LowerCI UpperCI
## (Intercept) 122.11 100.73 143.49
## age 0.36 -0.07 0.78
## sex 0.69 -1.97 3.36</code></pre>
<pre class="r"><code>#2. use glm with logit link on binary response data
b.glm<-glm(out~age+sex+bp, family=binomial(link="logit"), data=bpdata)
glmCI(b.glm, exponent=TRUE, digits=3)</code></pre>
<pre><code>## OR LowerCI UpperCI
## (Intercept) 5.906 0.414 84.210
## age 0.990 0.942 1.040
## sex 0.831 0.609 1.133
## bp 1.001 0.994 1.009</code></pre>
<p>There are tons of other possibilities for functions, but hopefully this post has convinced you that functions can improve your coding by reducing repetition, increasing customizability, and improving your analysis and reports.</p>
</div>
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My [previous blog post](http://rforpublichealth.blogspot.com/2014/06/how-to-write-and-debug-r-function.html) went over the basics of the syntax and debugging of user-written functions. In this post, I'll show you examples of useful functions that you can write to make your life easier when using R.
Here is the data we'll be using for this post:
set.seed(10)
bpdata<-data.frame(bp=rnorm(1000,140,20),
age=rnorm(1000,50,3),
sex=rbinom(1000,1,.5),
race=as.factor(c(rep(1,500),rep(2,500))),
out=rbinom(1000,1,.8))
bpdata[c(100,200,400),2]<-NA
bpdata[c(300),1]<-400
Functions to help you avoid repeating yourself
One of the main reasons to use functions is that you can re-use them over and over again, without repeating the same code again and again. Copying and pasting code that you change just a little bit every time leads to errors. With a function, if you decide to change the way you do something, you just change the one function and it will now automatically update your code everywhere you use that function.
As an example, in a previous blog post I wrote about [calculating robust standard errors and exporting](http://rforpublichealth.blogspot.com/2013/08/exporting-results-of-linear-regression_24.html) them.
The calculation went like this:
library(sandwich)
cov.fit1 <- vcovHC(fit1, type = "HC")
rob.std.err <- sqrt(diag(cov.fit1))
Well, if you are going to be using robust standard errors more than once in your analysis, it makes more sense to make a function. That way you don't need to copy and paste this code and change the object fit1 everytime.
To do this, I just take the exact same code, but I change the fit1 to a more general modelfit that is passed in as an argument to a function I call **robust.SE()**. Also, instead of loading the library sandwich, I can just require it for that function (note that you still have to have the package installed for R to load it using require). Then you can pass in the model fit as the argument and return the robust standard errors. Like this:
#function to calculate robust standard errors for a model
robust.SE<-function(modelfit){
require(sandwich, quietly=TRUE)
cov.fit1 <- vcovHC(modelfit, type = "HC")
rob.std.err <- sqrt(diag(cov.fit1))
return(rob.std.err)}
#get robust SEs for model 1
model1<-lm(out~age + sex, data=bpdata)
robust.SE(model1)
#get robust SEs for model2
model2<-lm(bp~age+sex, data=bpdata)
robust.SE(model2)
Instead of copying and pasting the code for robust standard errors twice, I just wrap it up in a function that allows me to evaluate it for any model fit I want to throw at it.
Functions to customize your output
Another reason to use functions is to make it easier and faster to do the things you always need to do, in the exact way you want. For example, it's important when you start with a new dataset to look at it descriptively and look at whether there are strange values or missing values. There are many ways to do this. You can of course do **summary(data)** which is a good start:
#use the basic summary built-in function
summary(bpdata)
But this doesn't give me all the information I want and it's not in the format I want. If I had 20 variables, which is not out of the ordinary, it would be hard to read this and it takes up too much space in a report. I could also use the describe() function in the psych package, but it doesn't deal well with factor variables.
I want to create a table with the variables down the rows and some specific stats going across the columns, namely mean, standard deviation, min, max, and the number missing. I want to make sure to treat factors as dummies instead of numeric variables. Here I create a new function that takes a dataset and produces this kind of table for all the variables within the dataset. I call the function **summarize.vars**.
The only argument I pass through is a dataset. I use the package dummies to create dummy variables of all the factors so that I have only numeric variables in my data (it ignores all character variables if I had any). Then I use the **apply()** function to do my summarizing. Check out my previous post on how apply works, [here](http://rforpublichealth.blogspot.com/2012/09/the-infamous-apply-function.html).
#function to summarize the variables in the data
summarize.vars<-function(data){
#use dummies package to turn all factors into dummies
require(dummies, quietly=TRUE)
dat.d<-dummy.data.frame(data, dummy.class="factor")
#use apply to calculate statistics for each variable
mat<-t(apply(dat.d, 2, function(x) c(length(x),
round(mean(x, na.rm=TRUE),2),
round(sd(x, na.rm=TRUE),2),
round(min(x, na.rm=TRUE),2),
round(max(x, na.rm=TRUE),2),
length(x)-length(x[!is.na(x)]))))
#assign column names and rownames to output table
colnames(mat)<-c("N","Mean","SD","Min","Max","Num Missing")
rownames(mat)<-colnames(dat.d)
return(mat)}
summarize.vars(bpdata)
I can also use this to summarize only part of my data, for example by subsetting my data like so:
summarize.vars(bpdata[bpdata$race==1,])
You can think about making this function better. One way would be to have the N for the factors count within the level, since that is more useful information. You can customize it in whatever way makes sense to get the kind of information that is most useful.
Let's do another example of a function to summarize information. This time we'll put together a function that runs a linear model, succinctly summarizes the results, and produces some diagnostic plots at the same time. This is useful if you run linear models often.
Remember that you can pass anything through a function, including a formula. In this function, I pass in a formula for the model and the dataframe and I don't return any values; instead, I print directly from inside the function. You could change this so that it returned the output and you could store it in an object outside the function.
#linear model function
lm.model.diagnostics<-function(formula, dataset){
#run model and print specific output
model1<-lm(formula=formula, data=dataset)
stats<-round(c(summary(model1)$fstatistic[c(1,3)],
summary(model1)$sigma,
summary(model1)$r.squared,
summary(model1)$adj.r.squared),3)
names(stats)<-c("F","DF", "Sigma","Rsq","AdjRsq")
l1<-list(round(summary(model1)$coefficients,3), stats)
names(l1)<-c("Coefficients","Stats")
print(l1)
#run specific diagnostic tests
par(mfrow=c(1,3))
hist(model1$residuals, main="Histogram of residuals", xlab="")
plot(model1, 1)
plot(model1, 2)}
#run function for model of blood pressure on age and sex
lm.model.diagnostics(bp~age+sex, bpdata)
So we can see that there is a funny outlier in observation 300. That observation has a blood pressure of 400, which we think is an incorrect value. We can see what happens when we take out that outlier:
#take out the outlier
lm.model.diagnostics(bp~age+sex, bpdata[c(-300),])
Note that if your formula is long and you use it more than once, you can avoid copying and pasting it as well by just saving it in an object like this:
form1<-bp~age+sex
lm.model.diagnostics(form1, bpdata)
Functions to aid in your analysis
Another useful way to use functions is for improving your analysis. For example, R summary output of a model fit doesn't provide confidence intervals. It's useful to have a function to calculate the confidence intervals and put them in a nice table. As a bonus, you can make the function versatile so that it can provide nice output of logit or poisson models, by exponentiating the results to get odds ratios or incidence rate ratios, respectively.
Notice how in this function I set up the defaults for the parameters that I pass in. Unless I indicate otherwise, exponent=FALSE, alpha=.05, and digits=2. That way the function runs even without specifying those parameters. If I want to change them, I can do so the way I do in the second example.
#function to get confidence intervals for glm output, can get exponentiated output for logit or poisson
glmCI <- function(glmfit, exponent=FALSE, alpha=0.05, digits=2){
#get SE from model fit
se <- sqrt(diag(summary(glmfit)$cov.scaled))
#calculuate CI for linear case
mat <- cbind(coef(glmfit),
coef(glmfit) - qnorm(1-alpha/2)*se,
coef(glmfit) + qnorm(1-alpha/2)*se)
colnames(mat) <- c("Beta", "LowerCI", "UpperCI")
#if exponent=TRUE, exponeniate the coefficients and CIs
if(exponent == TRUE) {
mat <- exp(mat)
if(summary(glmfit)$family$link=="logit") colnames(mat)[1] <- "OR"
if(summary(glmfit)$family$link=="log") colnames(mat)[1] <- "IRR"}
#return a rounded matrix of results
return(round(mat, digits=digits))}
#1. use glm with identity link on continuous response data (default family is gaussian)
g.glm<-glm(bp~age+sex, data=bpdata)
glmCI(g.glm)
#2. use glm with logit link on binary response data
b.glm<-glm(out~age+sex+bp, family=binomial(link="logit"), data=bpdata)
glmCI(b.glm, exponent=TRUE, digits=3)
There are tons of other possibilities for functions, but hopefully this post has convinced you that functions can improve your coding by reducing repetition, increasing customizability, and improving your analysis and reports.</div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com5tag:blogger.com,1999:blog-2550386066385582083.post-29652961669385305642014-06-08T17:43:00.000-07:002016-10-13T07:55:16.196-07:00How to write and debug an R function<div height="1050">
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<p>I've been asked on a few occasions what is the deal with R user-written functions. First of all, how does the syntax work? And second of all, why would you ever want to do this? </p>
<p>In Stata, we don't write functions; we execute built-in commands like <strong>browse</strong> or <strong>gen</strong> or <strong>logit</strong>. You can write your own programs that create new commands (like ado files) but it's less common for users to do so.</p>
<p>In R, there are built-in functions like <strong>summary()</strong> or <strong>glm()</strong> or <strong>median()</strong>, but you can also write your own functions. You can write a quick, one-line function or long elaborate functions. I use functions all the time to make my code cleaner and less repetitive.</p>
<p>In this post I'll go over the basics of how to write functions. In the next post, I'll explain what kinds of functions I have used commonly in public health research that have improved my data cleaning and analyses.</p>
<h2>Basic syntax of a function</h2>
<p>A function needs to have a name, probably at least one argument (although it doesn't have to), and a body of code that does something. At the end it usually should (although doesn't have to) return an object out of the function. The important idea behind functions is that objects that are created within the function are local to the environment of the function - they don't exist outside of the function. But you can “return” the value of the object from the function, meaning pass the value of it into the global environment. I'll go over this in more detail.</p>
<p>Functions need to have curly braces around the statements, like so:</p>
<pre><code class="r">name.of.function <- function(argument1, argument2) {
statements
return(something)
}
</code></pre>
<p>The argument can be any type of object (like a scalar, a matrix, a dataframe, a vector, a logical, etc), and it's not necessary to define what it is in any way. As a very simple example, we can write a function that squares an incoming argument. The function below takes the argument x and multiplies it by itself. It saves this value into the object called square, and then it returns the value of the object square.</p>
<h3>>>>Writing and calling a function</h3>
<pre><code class="r">square.it <- function(x) {
square <- x * x
return(square)
}
</code></pre>
<p>I can now call the function by passing in a scalar or a vector or matrix as its argument, because all of those objects will square nicely. But it won't work if I input a character as its argument because although it will pass “hi” into the function, R can't multiply “hi”.</p>
<pre><code class="r"># square a number
square.it(5)
</code></pre>
<pre><code>## [1] 25
</code></pre>
<pre><code class="r"># square a vector
square.it(c(1, 4, 2))
</code></pre>
<pre><code>## [1] 1 16 4
</code></pre>
<pre><code class="r"># square a character (not going to happen)
square.it("hi")
</code></pre>
<pre><code>## Error: non-numeric argument to binary operator
</code></pre>
<p>I can also pass in an object that I already have saved. For example, here I have a matrix called matrix1, so I pass that into the <strong>square.it() function</strong>. R takes this matrix1 into the function as x. That is, in the local function environment it is now called x, where it is squared, and returned.</p>
<pre><code class="r">matrix1 <- cbind(c(3, 10), c(4, 5))
square.it(matrix1)
</code></pre>
<pre><code>## [,1] [,2]
## [1,] 9 16
## [2,] 100 25
</code></pre>
<h3>>>>Local vs global environment</h3>
<p>Now, it's not necessarily the case that you must use <strong>return()</strong> at the end of your function. The reason you return an object is if you've saved the value of your statements into an object inside the function - in this case, the objects in the function are in a local environment and won't appear in your global environment. See how it works in the following two examples:</p>
<pre><code class="r">fun1 <- function(x) {
3 * x - 1
}
fun1(5)
</code></pre>
<pre><code>## [1] 14
</code></pre>
<pre><code class="r">fun2 <- function(x) {
y <- 3 * x - 1
}
fun2(5)
</code></pre>
<p>In the first function, I just evaluate the statement 3*x-1 without saving it anywhere inside the function. So when I run fun1(5), the result comes popping out of the function. However, when I call fun2(5), nothing happens. That's because the object y that I saved my result into doesn't exist outside the function and I haven't used <strong>return(y)</strong> to pass the value of y outside the function. When I try to print y, it doesn't exist because it was created in the local environment of the function.</p>
<pre><code class="r">print(y)
</code></pre>
<pre><code>## Error: object 'y' not found
</code></pre>
<p>I can return the <em>value</em> of y using the <strong>return(y)</strong> at the end of the function fun2, but I can't return the object itself; it's stuck inside the function.</p>
<h2>Getting more complex</h2>
<p>Obviously writing a whole function to square something when you could just use the ^ operator is silly. But you can do much more complicated things in functions, once you get the hang of them. </p>
<h3>>>>Calling other functions and passing multiple arguments</h3>
<p>First, you can pass multiple arguments into a function and you can call other functions within your function. Here's an example. I'm passing in 3 arguments which I want to be a matrix, a vector, and a scalar. In the function body, I first call my previous function <strong>square.it()</strong> and use it to square the scalar. Then I multiply the matrix by the vector. Then I multiply those two results together and return the final object.</p>
<pre><code class="r">my.fun <- function(X.matrix, y.vec, z.scalar) {
# use my previous function square.it() to square the scalar and save result
sq.scalar <- square.it(z.scalar)
# multiply the matrix by the vector using %*% operator
mult <- X.matrix %*% y.vec
# multiply the two resulting objects together to get a final object
final <- mult * sq.scalar
# return the result
return(final)
}
</code></pre>
<p>When you have multiple arguments in a function that you call, R will just evaluate them in order of how you've written the function (the first argument will correspond to X.matrix, the second y.vec, and so on), but for clarity I would name the arguments in the function call. In this example below, I already have two saved objects, my.mat and my.vec that I pass through as the X.matrix and y.vec arguments, and then I just assign the z.scalar argument the number 9.</p>
<pre><code class="r"># save a matrix and a vector object
my.mat <- cbind(c(1, 3, 4), c(5, 4, 3))
my.vec <- c(4, 3)
# pass my.mat and my.vec into the my.fun function
my.fun(X.matrix = my.mat, y.vec = my.vec, z.scalar = 9)
</code></pre>
<pre><code>## [,1]
## [1,] 1539
## [2,] 1944
## [3,] 2025
</code></pre>
<pre><code class="r"># this is the same as
my.fun(my.mat, my.vec, 9)
</code></pre>
<pre><code>## [,1]
## [1,] 1539
## [2,] 1944
## [3,] 2025
</code></pre>
<h3>>>>Returning a list of objects</h3>
<p>Also, if you need to return multiple objects from a function, you can use <strong>list()</strong> to list them together. An example of this is my <a href="http://rforpublichealth.blogspot.com/2013/06/sample-size-calculations-equivalent-to.html">blog post on sample size functions.</a></p>
<p>For example:</p>
<pre><code class="r">another.fun <- function(sq.matrix, vector) {
# transpose matrix and square the vector
step1 <- t(sq.matrix)
step2 <- vector * vector
# save both results in a list and return
final <- list(step1, step2)
return(final)
}
# call the function and save result in object called outcome
outcome <- another.fun(sq.matrix = cbind(c(1, 2), c(3, 4)), vector = c(2, 3))
# print the outcome list
print(outcome)
</code></pre>
<pre><code>## [[1]]
## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
##
## [[2]]
## [1] 4 9
</code></pre>
<p>Now to separate those objects for use in your further code, you can extract them using the [[]] operator:</p>
<pre><code class="r">### extract first in list
outcome[[1]]
</code></pre>
<pre><code>## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
</code></pre>
<pre><code class="r">
## extract second in list
outcome[[2]]
</code></pre>
<pre><code>## [1] 4 9
</code></pre>
<h2> Tricks for troubleshooting and debugging</h2>
<p>When you execute multiple statements in a function, sometimes things go wrong. What's nice about functions is that R evalutes every statement until it reaches an error. So in the last function, the dimensions of the objects really matter. You can't multiply matrices of incomptabile dimensions. Like this:</p>
<pre><code class="r">my.fun(X.matrix = my.mat, y.vec = c(2, 3, 6, 4, 1), z.scalar = 9)
</code></pre>
<pre><code>## Error: non-conformable arguments
</code></pre>
<h3>>>>Using the Debug() function</h3>
<p>When you have an error, one thing you can do is use R's built-in debugger <strong>debug()</strong> to find at what point the error occurs. You indicate which function you want to debug, then run your statement calling the function, and R shows you at what point the function stops because of errors:</p>
<pre><code class="r">debug(my.fun)
my.fun(X.matrix = my.mat, y.vec = c(2, 3, 6, 4, 1), z.scalar = 9)
</code></pre>
<pre><code>## debugging in: my.fun(X.matrix = my.mat, y.vec = c(2, 3, 6, 4, 1), z.scalar = 9)
## debug at <text>#1: {
## sq.scalar <- square.it(z.scalar)
## mult <- X.matrix %*% y.vec
## final <- mult * sq.scalar
## return(final)
## }
## debug at <text>#4: sq.scalar <- square.it(z.scalar)
## debug at <text>#7: mult <- X.matrix %*% y.vec
</code></pre>
<pre><code>## Error: non-conformable arguments
</code></pre>
<p>We see that the first line calling the <strong>square.it()</strong> function was fine, but then an error occurred in the line defining mult. This debugging is useful especially if you had many more statements in your function that multiplied matrices and you weren't sure which one was causing the issues. So now we know the problem is that X.matrix and y.vec won't multiply. But we still need to know why they won't multiply. </p>
<p>More on debugging <a href="http://www.stats.uwo.ca/faculty/murdoch/software/debuggingR/debug.shtml">can be found here.</a></p>
<h3>>>>Printing out what's happening (sanity checks)</h3>
<p>At this point, a good way to troubleshoot this is to print out the dimensions or lengths of the objects or even the objects themselves that are going into the statement causing errors. The great part about functions is that they evaluate all the way until there's an error. So you can see what is happening inside your function before the error.</p>
<p>If the object is too long, you can <strong>print(head(object))</strong>. This helps to see if you're doing what you think you're doing. Note that you have to use the function <strong>print()</strong> to actually print out anything from within a function.</p>
<pre><code class="r">my.fun <- function(X.matrix, y.vec, z.scalar) {
print("xmatrix")
print(X.matrix)
print("yvec")
print(y.vec)
print("Dimensions")
print(dim(X.matrix))
print(length(y.vec))
# use my previous function square.it() to square the scalar and save result
sq.scalar <- square.it(z.scalar)
print(paste("sq.scalar=", sq.scalar))
# multiply the matrix by the vector using %*% operator
mult <- X.matrix %*% y.vec
# multiply the two resulting objects together to get a final object
final <- mult * sq.scalar
# return the result
return(final)
}
my.fun(X.matrix = my.mat, y.vec = c(2, 3, 6, 4, 1), z.scalar = 9)
</code></pre>
<pre><code>## [1] "xmatrix"
## [,1] [,2]
## [1,] 1 5
## [2,] 3 4
## [3,] 4 3
## [1] "yvec"
## [1] 2 3 6 4 1
## [1] "Dimensions"
## [1] 3 2
## [1] 5
## [1] "sq.scalar= 81"
</code></pre>
<pre><code>## Error: non-conformable arguments
</code></pre>
<p>Now we can see the actual dimensions of the objects and fix them accordingly. This example is really simple, but you can imagine that if you've written a long function that uses many arguments, you could easily lose track of them and not be sure where the issue in your function was. You can also throw in these statements along the way as sanity checks to make sure that things are proceeding as you think they should, even if there isn't any error.</p>
<h3>>>>Using the stop() and stopifnot() functions to write your own error messages</h3>
<p>One other trick you can use is writing your own error messages using the <strong>stop()</strong> and <strong>stopifnot()</strong> functions. In this example, if I know I need dimensions to be the right size, I can check them and print out a message that says they are incorrect. That way I know what the issue is immediately. Here's an example:</p>
<pre><code class="r">my.second.fun <- function(matrix, vector) {
if (dim(matrix)[2] != length(vector)) {
stop("Can't multiply matrix%*%vector because the dimensions are wrong")
}
product <- matrix %*% vector
return(product)
}
# function works when dimensions are right
my.second.fun(my.mat, c(6, 5))
</code></pre>
<pre><code>## [,1]
## [1,] 31
## [2,] 38
## [3,] 39
</code></pre>
<pre><code class="r"># function call triggered error
my.second.fun(my.mat, c(6, 5, 7))
</code></pre>
<pre><code>## Error: Can't multiply matrix%*%vector because the dimensions are wrong
</code></pre>
<p>You can do these kinds of error messages for yourself as checks so you know exactly what triggered the error. You can think about putting in a check for if the value of an object is 0 if you are dividing by it as another example. </p>
<h2>Good function writing practices</h2>
<p>Based on my experience, there are a few good practices that I would recommend keeping in mind when writing function.</p>
<ol>
<li><p>Keep your functions short. Remember you can use them to call other functions!</p>
<ul>
<li>If things start to get very long, you can probably split up your function into more manageable chunks that call other functions. This makes your code cleaner and easily testable.</li>
<li>It also makes your code easy to update. You only have to change one function and every other function that uses that function will also be automatically updated.</li>
</ul></li>
<li><p>Put in comments on what are the inputs to the function, what the function does, and what is the output. </p></li>
<li><p>Check for errors along the way.</p>
<ul>
<li>Try out your function with simple examples to make sure it's working properly</li>
<li>Use debugging and error messages, as well as sanity checks as you build your function.</li>
</ul></li>
</ol>
<p>The next post will go over examples of useful functions that you can use in your day to day R coding.</p>
</body>
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<div style="visibility: hidden; font-size: 5pt">
I've been asked on a few occasions what is the deal with R user-written functions. First of all, how does the syntax work? And second of all, why would you ever want to do this?
In Stata, we don't write functions; we execute built-in commands like **browse** or **gen** or **logit**. You can write your own programs that create new commands (like ado files) but it's less common for users to do so.
In R, there are built-in functions like **summary()** or **glm()** or **median()**, but you can also write your own functions. You can write a quick, one-line function or long elaborate functions. I use functions all the time to make my code cleaner and less repetitive.
In this post I'll go over the basics of how to write functions. In the next post, I'll explain what kinds of functions I have used commonly in public health research that have improved my data cleaning and analyses.
Basic syntax of a function
A function needs to have a name, probably at least one argument (although it doesn't have to), and a body of code that does something. At the end it usually should (although doesn't have to) return an object out of the function. The important idea behind functions is that objects that are created within the function are local to the environment of the function - they don't exist outside of the function. But you can "return" the value of the object from the function, meaning pass the value of it into the global environment. I'll go over this in more detail.
Functions need to have curly braces around the statements, like so:
name.of.function <- function(argument1, argument2){
statements
return(something)
}
The argument can be any type of object (like a scalar, a matrix, a dataframe, a vector, a logical, etc), and it's not necessary to define what it is in any way. As a very simple example, we can write a function that squares an incoming argument. The function below takes the argument x and multiplies it by itself. It saves this value into the object called square, and then it returns the value of the object square.
Writing and calling a function
square.it<-function(x){
square<-x*x
return(square)
}
I can now call the function by passing in a scalar or a vector or matrix as its argument, because all of those objects will square nicely. But it won't work if I input a character as its argument because although it will pass "hi" into the function, R can't multiply "hi".
#square a number
square.it(5)
#square a vector
square.it(c(1,4,2))
#square a character (not going to happen)
square.it("hi")
I can also pass in an object that I already have saved. For example, here I have a matrix called matrix1, so I pass that into the **square.it() function**. R takes this matrix1 into the function as x. That is, in the local function environment it is now called x, where it is squared, and returned.
matrix1<-cbind(c(3,10),c(4,5))
square.it(matrix1)
Local vs global environment
Now, it's not necessarily the case that you must use **return()** at the end of your function. The reason you return an object is if you've saved the value of your statements into an object inside the function - in this case, the objects in the function are in a local environment and won't appear in your global environment. See how it works in the following two examples:
fun1<-function(x){
3*x-1
}
fun1(5)
fun2<-function(x){
y <- 3*x-1
}
fun2(5)
In the first function, I just evaluate the statement 3*x-1 without saving it anywhere inside the function. So when I run fun1(5), the result comes popping out of the function. However, when I call fun2(5), nothing happens. That's because the object y that I saved my result into doesn't exist outside the function and I haven't used __return(y)__ to pass the value of y outside the function. When I try to print y, it doesn't exist because it was created in the local environment of the function.
print(y)
I can return the *value* of y using the **return(y)** at the end of the function fun2, but I can't return the object itself; it's stuck inside the function.
Getting more complex
Obviously writing a whole function to square something when you could just use the ^ operator is silly. But you can do much more complicated things in functions, once you get the hang of them.
Calling other functions and passing multiple arguments
First, you can pass multiple arguments into a function and you can call other functions within your function. Here's an example. I'm passing in 3 arguments which I want to be a matrix, a vector, and a scalar. In the function body, I first call my previous function **square.it()** and use it to square the scalar. Then I multiply the matrix by the vector. Then I multiply those two results together and return the final object.
my.fun <- function(X.matrix, y.vec, z.scalar){
#use my previous function square.it() to square the scalar and save result
sq.scalar<-square.it(z.scalar)
#multiply the matrix by the vector using %*% operator
mult<-X.matrix%*%y.vec
#multiply the two resulting objects together to get a final object
final<-mult*sq.scalar
#return the result
return(final)
}
When you have multiple arguments in a function that you call, R will just evaluate them in order of how you've written the function (the first argument will correspond to X.matrix, the second y.vec, and so on), but for clarity I would name the arguments in the function call. In this example below, I already have two saved objects, my.mat and my.vec that I pass through as the X.matrix and y.vec arguments, and then I just assign the z.scalar argument the number 9.
#save a matrix and a vector object
my.mat<-cbind(c(1,3,4),c(5,4,3))
my.vec<-c(4,3)
#pass my.mat and my.vec into the my.fun function
my.fun(X.matrix=my.mat, y.vec=my.vec, z.scalar=9)
#this is the same as
my.fun(my.mat, my.vec, 9)
Returning a list of objects
Also, if you need to return multiple objects from a function, you can use **list()** to list them together. An example of this is my [blog post on sample size functions.](http://rforpublichealth.blogspot.com/2013/06/sample-size-calculations-equivalent-to.html)
For example:
another.fun<-function(sq.matrix, vector){
#transpose matrix and square the vector
step1<-t(sq.matrix)
step2<-vector*vector
#save both results in a list and return
final<-list(step1, step2)
return(final)
}
#call the function and save result in object called outcome
outcome<-another.fun(sq.matrix=cbind(c(1,2),c(3,4)), vector=c(2,3))
#print the outcome list
print(outcome)
Now to separate those objects for use in your further code, you can extract them using the [[]] operator:
###extract first in list
outcome[[1]]
##extract second in list
outcome[[2]]
Tricks for troubleshooting and debugging
When you execute multiple statements in a function, sometimes things go wrong. What's nice about functions is that R evalutes every statement until it reaches an error. So in the last function, the dimensions of the objects really matter. You can't multiply matrices of incomptabile dimensions. Like this:
my.fun(X.matrix=my.mat, y.vec=c(2,3,6,4,1), z.scalar=9)
Using the Debug() function
When you have an error, one thing you can do is use R's built-in debugger **debug()** to find at what point the error occurs. You indicate which function you want to debug, then run your statement calling the function, and R shows you at what point the function stops because of errors:
debug(my.fun)
my.fun(X.matrix=my.mat, y.vec=c(2,3,6,4,1), z.scalar=9)
We see that the first line calling the **square.it()** function was fine, but then an error occurred in the line defining mult. This debugging is useful especially if you had many more statements in your function that multiplied matrices and you weren't sure which one was causing the issues. So now we know the problem is that X.matrix and y.vec won't multiply. But we still need to know why they won't multiply.
More on debugging [can be found here.](http://www.stats.uwo.ca/faculty/murdoch/software/debuggingR/debug.shtml)
Printing out what's happening (sanity checks)
At this point, a good way to troubleshoot this is to print out the dimensions or lengths of the objects or even the objects themselves that are going into the statement causing errors. The great part about functions is that they evaluate all the way until there's an error. So you can see what is happening inside your function before the error.
If the object is too long, you can **print(head(object))**. This helps to see if you're doing what you think you're doing. Note that you have to use the function **print()** to actually print out anything from within a function.
my.fun <- function(X.matrix, y.vec, z.scalar){
print("xmatrix")
print(X.matrix)
print("yvec")
print(y.vec)
print("Dimensions")
print(dim(X.matrix))
print(length(y.vec))
#use my previous function square.it() to square the scalar and save result
sq.scalar<-square.it(z.scalar)
print(paste("sq.scalar=", sq.scalar))
#multiply the matrix by the vector using %*% operator
mult<-X.matrix%*%y.vec
#multiply the two resulting objects together to get a final object
final<-mult*sq.scalar
#return the result
return(final)
}
my.fun(X.matrix=my.mat, y.vec=c(2,3,6,4,1), z.scalar=9)
Now we can see the actual dimensions of the objects and fix them accordingly. This example is really simple, but you can imagine that if you've written a long function that uses many arguments, you could easily lose track of them and not be sure where the issue in your function was. You can also throw in these statements along the way as sanity checks to make sure that things are proceeding as you think they should, even if there isn't any error.
Using the stop() and stopifnot() functions to write your own error messages
One other trick you can use is writing your own error messages using the **stop()** and **stopifnot()** functions. In this example, if I know I need dimensions to be the right size, I can check them and print out a message that says they are incorrect. That way I know what the issue is immediately. Here's an example:
my.second.fun<-function(matrix, vector){
if(dim(matrix)[2]!=length(vector)){
stop("Can't multiply matrix%*%vector because the dimensions are wrong")
}
product<-matrix%*%vector
return(product)
}
#function works when dimensions are right
my.second.fun(my.mat, c(6,5))
#function call triggered error
my.second.fun(my.mat, c(6,5,7))
You can do these kinds of error messages for yourself as checks so you know exactly what triggered the error. You can think about putting in a check for if the value of an object is 0 if you are dividing by it as another example.
Good function writing practices
Based on my experience, there are a few good practices that I would recommend keeping in mind when writing function.
1. Keep your functions short. Remember you can use them to call other functions!
* If things start to get very long, you can probably split up your function into more manageable chunks that call other functions. This makes your code cleaner and easily testable.
* It also makes your code easy to update. You only have to change one function and every other function that uses that function will also be automatically updated.
2. Put in comments on what are the inputs to the function, what the function does, and what is the output.
3. Check for errors along the way.
* Try out your function with simple examples to make sure it's working properly
* Use debugging and error messages, as well as sanity checks as you build your function.
The next post will go over examples of useful functions that you can use in your day to day R coding. </div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com4tag:blogger.com,1999:blog-2550386066385582083.post-45892073663873223952014-02-17T19:21:00.000-08:002016-10-13T07:54:19.341-07:00ggplot2: Cheatsheet for Visualizing Distributions<div height="1050">
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<p>In the third and last of the ggplot series, this post will go over interesting ways to visualize the distribution of your data. I will make up some data, and make sure to set the seed.</p>
<pre><code class="r">library(ggplot2)
library(gridExtra)
set.seed(10005)
xvar <- c(rnorm(1500, mean = -1), rnorm(1500, mean = 1.5))
yvar <- c(rnorm(1500, mean = 1), rnorm(1500, mean = 1.5))
zvar <- as.factor(c(rep(1, 1500), rep(2, 1500)))
xy <- data.frame(xvar, yvar, zvar)
</code></pre>
<h3>>> Histograms</h3>
<p>I've already done a <a href="http://rforpublichealth.blogspot.com/2012/12/basics-of-histograms.html">post on histograms</a> using base R, so I won't spend too much time on them. Here are the basics of doing them in ggplot. <a href="http://docs.ggplot2.org/current/geom_histogram.html">More on all options for histograms here.</a></p>
<p>The R cookbook has a nice page about it too: <strong><a href="http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/">http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/</a></strong></p>
<p>Also, I found <a href="http://sape.inf.usi.ch/quick-reference/ggplot2/geom">this really great aggregation</a> of all of the possible geom layers and options you can add to a plot. In general the site is a great reference for all things ggplot.</p>
<pre><code class="r">#counts on y-axis
g1<-ggplot(xy, aes(xvar)) + geom_histogram() #horribly ugly default
g2<-ggplot(xy, aes(xvar)) + geom_histogram(binwidth=1) #change binwidth
g3<-ggplot(xy, aes(xvar)) + geom_histogram(fill=NA, color="black") + theme_bw() #nicer looking
#density on y-axis
g4<-ggplot(xy, aes(x=xvar)) + geom_histogram(aes(y = ..density..), color="black", fill=NA) + theme_bw()
grid.arrange(g1, g2, g3, g4, nrow=1)
</code></pre>
<pre><code>## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust
## this. stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to
## adjust this. stat_bin: binwidth defaulted to range/30. Use 'binwidth = x'
## to adjust this.
</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-2" alt="plot of chunk unnamed-chunk-2" style="display: block; margin: auto auto auto 0;" /></p>
<p>Notice the warnings about the default binwidth that always is reported unless you specify it yourself. I will remove the warnings from all plots that follow to conserve space.</p>
<h3>>> Density plots</h3>
<p>We can do basic density plots as well. Note that the default for the smoothing kernel is gaussian, and you can change it to a number of different options, including <strong>kernel=“epanechnikov”</strong> and <strong>kernel=“rectangular”</strong> or whatever you want. You can <a href="http://docs.ggplot2.org/current/stat_density.html">find all of those options here</a>.</p>
<pre><code class="r">#basic density
p1<-ggplot(xy, aes(xvar)) + geom_density()
#histogram with density line overlaid
p2<-ggplot(xy, aes(x=xvar)) +
geom_histogram(aes(y = ..density..), color="black", fill=NA) +
geom_density(color="blue")
#split and color by third variable, alpha fades the color a bit
p3<-ggplot(xy, aes(xvar, fill = zvar)) + geom_density(alpha = 0.2)
grid.arrange(p1, p2, p3, nrow=1)
</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-3" alt="plot of chunk unnamed-chunk-3" style="display: block; margin: auto auto auto 0;" /></p>
<h3>>> Boxplots and more</h3>
<p>We can also look at other ways to visualize our distributions. Boxplots are probably the most useful in order to describe the statistics of a distribution, but sometimes other visualizations are nice. I show a jitter plot and a violin plot. <a href="http://docs.ggplot2.org/0.9.3.1/geom_boxplot.html">More on boxplots here.</a> Note that I removed the legend from each one because it is redundant.</p>
<pre><code class="r">#boxplot
b1<-ggplot(xy, aes(zvar, xvar)) +
geom_boxplot(aes(fill = zvar)) +
theme(legend.position = "none")
#jitter plot
b2<-ggplot(xy, aes(zvar, xvar)) +
geom_jitter(alpha=I(1/4), aes(color=zvar)) +
theme(legend.position = "none")
#violin plot
b3<-ggplot(xy, aes(x = xvar)) +
stat_density(aes(ymax = ..density.., ymin = -..density..,
fill = zvar, color = zvar),
geom = "ribbon", position = "identity") +
facet_grid(. ~ zvar) +
coord_flip() +
theme(legend.position = "none")
grid.arrange(b1, b2, b3, nrow=1)
</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" style="display: block; margin: auto auto auto 0;" /></p>
<h3>>> Putting multiple plots together</h3>
<p>Finally, it's nice to put different plots together to get a real sense of the data. We can make a scatterplot of the data, and add marginal density plots to each side. Most of the code below I adapted from this <a href="http://stackoverflow.com/questions/8545035/scatterplot-with-marginal-histograms-in-ggplot2">StackOverflow page</a>.</p>
<p>One way to do this is to add distribution information to a scatterplot as a “rug plot”. It adds a little tick mark for every point in your data projected onto the axis.</p>
<pre><code class="r">#rug plot
ggplot(xy,aes(xvar,yvar)) + geom_point() + geom_rug(col="darkred",alpha=.1)
</code></pre>
<p><img 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QwXgCy7evrpzafqA6Zm4l18kLbCfCK0iBxYZ63g8AJdUlaG5En6qZ/6qS1Pg4ewhg8ylNA+drP8/M//fEdWAyEd+MY3vvHKintGWSETyb0JQYLjuUwNWXadnE2UczfcY7AAdHX5LI9vgrVkL3BAyFIgE2KBcoF3tARSd876tzRnFtf2i170oskf/uEfdhzQve99b4d3rdzmNreZ/Mmf/MmuLd8iBaNtzfoM+SL57dW0g7VVTiiBZieXJUJbf3shBkjjzbzOdcqnhk5PGt93fMd3dKQykGFSfPd3f/eWNsr08mXNhzzkIZ1Z4zMv2YTbkvgY/vEv//Iv3RKwzFlu+u1+esjAiIQGpp/+6Z/e8Vrnn3/+MVzD0x99sEuyXnbppZO3lyU8W3JCGX2mrQutc2+3obXuO+0YEwD5ecEFF0xLtuXcG97whs5sQGoyx77oi75oy/lpP37nd36nA6BIw2PErNmEWDsaAIaJuM57Cm4899xzu6kYAgsXnXz6BV/wBZP/+Z//OVpEJuvP/MzPbAmWPHqyscPEurS0RV8MIUxm5cjy9V//9ZNnPvOZ+dC293eiDRsQvVPPm0WdL2tqDtYEu6KsTXvBW9+a6+Ho/v4S6HZ68ar0CU1g0+7o0D4WuS8OJz6J0/csfcff//73bznl9yL33nLxgj8A7Sbq2DMJLYjYHstw/Nqv/drcpXVdXU/4skOHDk2+9Vu/dS4Xff2sraVAgNOq634T9VtXZP2s9fllfg8WgE4q5sh1iorbEp/lCeK1dd7Lm3a+dc12j1HNvcBN3fee97xn5/q2iLgRipm2qXsLIvS827kfbxXzCAi3plR4H5aIDfDxm8bI3KGBCDJkVvFIteTCEsjqD9DwINai7N7XLKmflakr6DE0XXX/8Ic/fFt10SrDTrRhU2po4/V7pTUuK4MFIGbWaT2TUedpOMtW2FCuE9SH03jLW94y+YzP+Iyj5PVOlP/d7353N/8KKD3sYQ+bKFst5ooxfXSsBzzgAZ3JoqGfXAJOaYGf9VmfVV/SubfNgg8t5q53vWuXx+te97ourWkd4ouYWDoPTyKXv3lkQEr+3/RN3zR57nOf2xH+ghWZNryJuQ0961nPmjznOc/p5o0pY3wy+VoFKgecU++8YKaciNE6rSycN0tQApYQYebgj44VGSwH9NEStv/6l7yk+Z4OFO3oRkdiS1oJeCjyyNlKs+pjQ/8szyL1UX+Wh1taUCQBCK95zWu2EOJMFJ0uvhCl88e+a4BWXwCl4ErAAYhoM/7ETvWJ6RsALS/VAZAAhXAGppIRHVj6QKJgT3mG3O52t9tiFq/iszyAULS0gEwiSJLzYJrsRBseP8uT3sgFRUV/95EF09PhbvekQvhOA6A6/fh7PTXARHrsYx97FHzcRcc35yvCCQDNu8qcvgw4ed81ZxZPXxbn5QPoaAzf8z3f052mPdk3/aSORo7rf+u3futaawcBHhIENtNMyMJbC8eYPY/SMPMs+cELuR0JrRBZ74sjAT7y5ECYBUDbufduunawJtgnFDX+xsWD0ZKTist6lJ2vATPPcTJZrC0NfACETmziKZOMlxBfRcQ0mZYBSMwJE00cosOeddZZnZYjNAHpzJMVIr3vzNOaWsJEevSjHz253/3udxSk/P6Lv/iLo8lxQsCHAKNa2zCDfrsAZBpI3MO0E+AZE3DV0bEigwWgS0vDuKRn8fFregIUj5WXuhueU2fKS6RGmYz8tAjuai50wjy7y13u0vE8OBjhAqZotOSVr3zlUROLS/hpT3tat9ZQTmupD3yP4E3mFA0KuN3iFrfowMe9s4b0tre9rTO1wpWMd8oigvwVr3jF0UO3utWtju4vs8P8D/BxPTPPva0OYFkRPNOxIoMFoAOFxxGE2JIT5iD9WtcN9Rj1XafBg/QRnjou7kNwHO1AR1+nGNGtUGilwlp4uN70pjdtOQysmEezpC437aQWHJJpK8COZkVTCjFlpS6TiaoBPtLRuF71qld1ZDJuyJIaYnlElSPDzznnnMhuqa0yA1xcWAgQIszRTfOTUYad2A4WgE4o7s1Te2J9nDtWhLaARA1B8loCQqcKMas8z/xmVpic2RLemLqT53S0ld/93d/twI4ZMy2tezCl/vzP//xop0dkMpN0uAxC97///fNtevcPHjzYAcQLX/jCrgwAQpksN1JLy5ShfWXBIfFwZfFMvgZCS4r4Hd4yf6sSAKkekOi1eD/HigwWgC4v6vdF//Vfzfd0QnF/Xq+o27tVTLb0kb+a4Fy0vP9Vnj+Dj+uRs0996lOPrjToWJg69gkOowYgEycf/OAHd6sU8kiJZQmi+GNXTbrYFt6a4Gq4+E0gnSaW3vAnLun8wgeZEc8jaOoITcB3vHx/zCd1slgt0Z/o78zx0G5+7ud+rrsGqP7SL/1St4qi+W5ihhYVmk/9nJFHgE/8nmfLezbPBxw9eysdULrDHe4wz632RJp9Q32Kq8pIfXmxmVt/V5TOtFvlKU95yuTzPu/zur965F20zEyLFleiE2TJJojjrWkoPDvxGWaaSZ55H3lZOCzAx7G//uu/jlMzt0DNEhjAhwASJpoYmxp8kLJWOfQp6Pvc5z5bTJW40Utf+tKjpC039p/92Z/FqanbWuPIpld9ofAA5LTnruUfyzQgmiBzkigDzxXN8+53v/sWN399bfz2occAIYu0eSbmIfP1WJHBakC0nFN6AhHFAe1GMZudh4YACctD6GjRCBcts/VyLOruT+StOTpG8x/4gR/YkhU3L7MigKe1LIcOlKX+7RzyldYWJsIic9O4zq1HZOQXSzNN8FVZBPbhTLLUgYn170jLayawUJDfF37hF26Z4CsNsrolwAcwvvOd7+xO44FM+/ActhFrJMwAQS34EelNANbTn/70mWQyronGoz5bJmSX2R7/N1gAKj14crga6eNd+WLGTgsPDa+GxoWnIFT67G71myawHcncBF4FKOkkJmjG4lu3vOUtO55DfAkOhnu8Fm5l5hyPEF5EFC+SNHd8efMgGfkB3Xd913fV2fT+RnyHq/uBD3zglomy9UU1mNAYaxEtrW6ZlzpxrUVFelHOoWkyAT27BcIQ9z47nYMM4xpbABPg47c8AI/7Bfg4DqgFQtbmdD1hU9qWqMc+E7CVfq8dGywA7S9E84l9XrDKjbrJl8abo3NYsTDEKMv7ogP7OgQ3K9OJBrRKdTtcuXganY0Ai5e//OUdPzKNIzGDnQlASwEsTCN/T3ziEzstjVudZkKjEKSH4KXNzBIgwXsU4CM9b5do3xbHAjSDV6IZWmy+BS6u9QmeWVKHAgiOFHDIq/XjP/7jnZu+zoM2GXO54lx8haNVZlNdvviLv7h7xwh/AZJ9cUiR37j9WA0MFoAuKUTuh1Mnzy/UekA3OKJ15OOb2PeZmQw+7kk11/AtmYp85T0CPMuaXvEcvD86M14FIWsUfvKTn3wUfKTj0jUvqeURinxiq0yI5TDVHOfBYibytuVP2OCVeNsyqBn1gYeJoDQzGgaCuQYBnZjWkAMMowzI5Oj8zNRwT8f52OroxPN99md/9hYw5H5nQtGk3AMI00iBPq0uODL3qV3/nt/EXcAppEFQpEBJa/wQ2pjzBhTPoW4AvvvRIE3fcM0072CXUfVvlvexSr5nfg4WgK4ujf3Swqm05HBxn+6URDRrvr+OHeSr4y3iOKefZ5/XSkcwrYEwKQBCq7O3TJi+e9QTIW9729t2c6Qy+LhWh7bGjXuGCAqM37xbZoHn8uiwXNtA40d+5Ec6jRBIZWEiZql/O0e7AIwhwFWME+DDydgSphlTFEdDqzMIZBFpTbjmke4AnEkFfIg6pr3W6/v89E//dOdFpAGKvQpzy4By5plndtfO+880ENNHEPpMdd5HJvCxIoOl2y/lhi+znFt/F/cA0yZeKpMhZksbBTUmplbfkhKtMtECELXcsdm7Y3Q1dUEnxk8E+MgjXO3iacJMYGrgLubRfuQBGNyDNvXVX/3VXcCdyZuCHFtLvuKJsuToXseBVJZa47PcRi3MO1yLMiNmdUgufCEHhOaTwccxGg+gOatM0QjwcZw3DXgAkhp8nPe8vtahzuRpCka9cH+fl0zdrsJ8tgpAeBMNIszeY0kGqwFZ8+dA1QHixZ3YczzOr3MLfM4777wtEy4XuZ8RETcRXiizwI3CRvhHPvKRHcHMlOM2px3Ean7iawATjuZJT3pSx0MwjxCcOtosobkxJyJQj+mSvWkC83BXTBTkOvOGFhPivrxkOn1IfIAwfmfTTuf1XbBagCePk3wAClEmWgeNi5aCuEe0Z2H+RJ3l48BcHdJWWvXAtMpaqwXFkPZMPwS8Olmn1GXOdbTO++6WvIcLQCecMDmhgFBLjiuNeCdF55rXs6EBWohdB8ab2OZGqdPgLMQPRafj5gVIFq3HbzDvxNTQXAIALMYVHwCcpy5MAQjwkR5xDHBilAcuwZcoR/be6TS8STmyWR6t+BnAZsTXuXE3yku7ypHbrq25n+CFRHofOnSoC7ZUNwEqoSG5Noty4qsELDIRgQ0QDdCJbb7GeYS7uWPrFgCnrmmPtOSY2b/u++6W/He2p26jFj7KxV2ifltycrVmbSvNbjnGLRwTHZkAphgw40IVp4Uw5YI4jXJT13nbrPVMaEIBPn4DKB6seTxVLynrKoV3KJamMGcMf4PoxiHlYMYMPoDBvKkafJShFnFD8aUPJC6vW/AtZqQDOR4l2pZZ7kw2z+0ZsleJiYb4xffQcLKIecpucmDJKYBr8mzquAU6OQ/7AHATAGTwYGaraxptmM91efbq78ECUEfkVmr40ZdUNJAhCM0hwEd5aSBMAKaWTqYDBgmLG3nGM55x9LG4fbNw8TNPokMbTbMnBk8iJkYawIXD4LHCEUV0M23HioHuicBlItHIaCi0tJoPoREJ8qs7dF5aI5ex5mGirNIAS2CDbwpS13Hub2AqTIAAEMusumcGxe5k+QcsMwABS+YZt399/7im3jJxaVSeWR20TMX6GuBGiwHcrXLV6f3G4ak/2ieHAi22T7QVUdLKBXzr99933W4/PtgVEd9qtnLPsgWnlfk9X/r4x/fWfR8f0HeBCGYj1Xa8V67XGeLLn3GvvFqgDq7ztO6jc9KKeJeUR4NHzuaVAhHR3PCWoNDhaBSe1T11ouCLEMymOAgIrMU1PF7OiUAO0VGED4Qoj47a4iwcd8/sAXMdtzZCeBEBSJ4x+CCeIi7vPrGcRUyP6EuTj8ufp485yJxl0nlPYe5Ki2/ri/pmaiO+Tbq1NKyBgHZHk5sl4piEI4TY7/uKKrMzTGDvVzvYtLfMwGSwygOEsnuv9eAUzzRrOwxVofEUFxfz68IyGrT+/q+MKKsQpK6GbzKlMH6fOl618PLgfgTG8ci0wMc9aUPMNZoNDxgwEXOUza473elOXSeSDyDRUcXvZPCRl2kJra83OGc0BiBMvCyhicUxYNgCH+dxP7ia2uulDgGx48oH5PqeN+6jsSOKEdBiimrwyZHazLjgrOL6WVv58yzyogWflMHH9TFHrpUXrdRk2ViXGvAGULTS52MRLBrHpgFnhAxIiyPMfF1cP8TtYE2wk619UxC5JacUTWIVwjyKDs7zY5a5zr1KMWLLt090EK5aHQ9hWU+mpA31iUXZTZ2oBRBYwB2A5bWRpYvRjYYV4QQmkdKaCDMGR8Vd3ydGxAgLkAZBDRwR6UEmm/LBtGPihTAPaWz1MzrvWcJUjPS2tBfzroiYHGnid3dwjn8GmmmCV2oJEGAu10ILmkey15HW1TcoyMtzRvCl3/PeQ9rdLMPVgIq6e3FZS6X1d2HPSomLvoh6NK1/L5pfK70OwzvDzdxqgIL5uKV1XjwQjYwKTkTo9k2mdL42gcQWIbXNMsepiH0xWvdxFvgUkcPZ3Y6rqcGnJk5NRciiA+MtMljq9HWcEDMDJxXTHiIP9d4CH+eZTTgnfyTfozvQ+LfIe/TsBiL3qbkuM+JrEfpgWZN5JJujtC6TivvKD9Sz5Kkt+fjQ9gerAV2/dL6+6RYnzzkCzXpZPCdMI5yI0TrzLbOunfd8LKIuPW8I3iU6tAafV81jHgGd80qcEXUdoNAY+qTmCGgbpoIE4OAueMA07hoMaCPc+rWEhpSP5w5NC2EK8Vq5n2BCPJc0iO98H8tW8FDRLnEftCsmjKkrOqQRnyYFLDMI5nvX4Q64Ghpjn+B6aGQAxfOrv9YzxfUGh9DSDBB5/hlgzXyR+CEkuWOzBAH9+Iqn9L77ylKbwPXvWffbrecHqwHtL67p4wqX0PwrMUKrEFyF+BF8iNgVvMUqBIGrY+FRsm1Ps8gmkU7LKxWic4sQBobMqGngQ2NgtmTBzSBUdW6ubh2RiaZj4ZaYScoElPBEMYs/56ED1hHQOW6JdqYjAhfcFjAAcEb27JWTJxOQBsYdD1CVibbnm/bywQ8x/3zsr1UW9eAZmcn4IUSwOuKJ6hOghlCXv+fo6/CuByQBPn4jfrNoD7Qj9way2koLfNxDPQCn4M3kW3NNHAPKb1qIMopHCslcGSCnDe8FGawG9JHiVv5gekH5ZZxSPBOrlLrDbSdvpgcXex3yL0+mB/I2C0DQIHEyNJK6E+e0eV+jb3EbAT5MqSw6pA4yS6wlpOw6XHjV8jW8IbTGMJmUW/mtYlgHFwI+AZNc/sENyQv48bjxCOrQOhwODCBnM8h9aFqANTqz38pYTwjOZbRfk+z1eVoosM0akMEBcW41SdwdMWjEdBnaKvCuPWCeBdFNaNNAlwbjXQYgOce8pj0GbydmikasTuOYdOrUwOUdDF0GqwF9uIyYHy0vv/X3kSOLSO3Gl2N6Qgt8dMKYYZ3LrfFZwsN8MubXLKFZGamZLTU/pFNZ9yfHyUR+OWqaCUQbYhZF4CAgpIWY8mHCaQYf/ItyukZnC/CRt46Ha6oDBuO+NAFaRDbjmG1RRsASnq8MPjQC5QAkAT7yZM61gDfuN++WdnleMXUBgnAH5UOOA4Q8OVWMUghN0LMoj+k0NCQgGeAjHZCK56HBhQBaZm981dVx5moszlZrVmcuOOk17rPbtoPVgK5bwuTP6FG1T65I0N1U6TVBq2xMPWBhwTAuf2C0qGj8GnuM/DqxUVPoQBCbRly8B+5C5wphutC+aCE6gejqIEh1JLxJfCpG5zm3uKyzBOAYkXnLmBHhlj548GDHdWTwyNfGvvO8QoCMh6xPdFpl1CGziRLpAa/1j7IwB4ngPUR31jpyurzv+YE5k41pmIEml493ylSKEKDj/sFD4bWUNUCSZ5G2SZDOvGjej/AEPJe6FjQaQnPUNvKzai+0rMgz0g5xO1gAuryQlZeV0a4lu1mtAww6AbNEp6NlaKC0HCLeRVwJkhQvwtXLs1KbZvVz84oE+DhnCgUuJXc24IFIF/BGa9E5mBU0lPCI4R4CfORDQ8HBZKENMIuYAllMI8FR4XbEziC7RfjWkcTBPeVrcVo8dAAsR0jnNO4rXQaDfN6+a2sNiFYG2JlLuT7qa/0GUjE73W91YapJltzxmWnih5heCHRaWR20mNMrg2eQjsZI62UyAp0AngxYyGrtoAZwPGLWGnP5hrS/m/vq1Hq8qnAcV/b8XbZgtO3UG23zpAbH7MqdQoc24tESjGx1A2ei4UF0Rp2SNw5YZJEfAlagIRLXqFgLbqLuzIIYdQAcBCDM5oHAOCNuLRp7iPsAMWYa/iYLLcz0DgQp75QJpkwlWlAWnJoyu5cOzMwRkPl93/d91yqv63jzxB4BhlaMkDQ+RkhyPXcHyr+ImbGdxqHRBAFD7WwArFloWSG0GYBqGgazlImlbqYJMxG3RIBLJvEdA0AhNCZakTp3HOiIcjcA7AXZqAakQ5jLYvTVYPKLVJkaYZCRGoFguT6xHEffbPj9pdHvBmECaSxGL6OiOUnEnKwIKov6qBthzJCX3qgtDogpEIKTMe2CyJfGhJ/B/wAJf3WecS3NRfCjKGIai5Gb4D1aHTius6WxmYtloiavj47nWdwvPEbyCLe5qOqsUcnDb54yf1nquBodzr1yXA1QzhpKXF97qOI4AAxSmDfORwGYlTUhLj1vmo8OmrFPEwxhIvFQqWM8GrOLKRuTVcVbZQ0lNJm4vrVVLiaYQUTdZZCnOVqHST3h7ICmL24wUQkTdK/IxjQgI7jR2EgmfoXKn0Vn0RGM6P5qAjWntW89aOsBtf761oqu81j3b6ZINEzkYhCrGlwWGk4mJHW44Aki3Rvf+MbY7bb1tBBgRLPBWzh3ZkVS1pMXgY0GDkBCvIMAxjhWb2lzOosOiHPigdIpudxb4rxRPkTn8Xw6dTZNdMSoq0gLdDP4OE6rYsbWUl8b54EiTsuUCbPnAVoLfKTnOdQuAUPu5N4XHgkgAleAr30CY0DORG6FCUQZ6q13DeS0D3WZwcc9mM5CCQBcLof9/LvOd4i/N6YB8eCEFwf6B3EZlWYUgfxMD+ZBPTo6H9qRa95ROtmHjiy8HnnE9uQCZCYZ9on7e9HrFqaD4MEQI7BycafjbPA7RmcdA3Eb3hEmCm2AphSi0fLA6CQ6vtgTv0MAjz9eK/fM/IDGjBPKYoRVn1lLqjuxjl5fl/MIbUkegGYeMQgZyZkhTCJaMXC1iFotPH/MH5qwAEplwefkDhvXxDyu+J23tQmVz7X25SUuSlnVI22OeZUB03XeF/ORJo8DOq8Q+7RUbXiaaPu53nNa95BHDAziwORdD1qbasO5bOqiZcJupy9tDIACEDR6nSTUyXhAo7GH09F0JJ1IJwuxfGfmQU4s6feVl9WSa8qoF6H5rfMqsu5srXTbPYZQNlIiEt0TGQtYY+F2+SOBPbuRPZc5ByRKRzsUg0JMV+CeBegIWb9DAvDy8yGn6wZMG+My7hMjLZPDcqWztCJ55MGhL884DnyIZ6Lh1JxLpKOpMK38qSMdvQU+kX6V2/gkj3qkwVnCRPxPTby7p+BJAAqwZ4GP9PJQ/9pC9m45RwJ87NNmaW3aPxMbKNOOcXfal0EOPxQ8l2taYgA3sOU21ko37RhNF/dUB8DOIvan5jnt5KrPaVDQnfoZgVxxDypsqLHOMSkyABkFMyf0F4cOTf4nLq62h0ujyXEq1emuAqeNmHX6eX7TBkQZ43SQsxqjkRP4kAAEDU6DCW1HY+QRi68uxL2Q11nybx4mQK0B43BmCc3LX64TjXeacKkbqYFcBiAEOrI6zAOkq0a97OxswYjzBNQJnNTxDFLbafDTnrnvHM0LN4Qzo53TXpU7JOp1Hu4nrgFwLfCJ87HV6WMCNBAyMJhXh0ONNkT7ZML2CQ+rtklrMuewdh70XVcf9+zaBC08i7a1rGxMA1JwJHMsOF4XGKIb5cTA4Ac87DTZV+zy4wvQtORw0TY2LUZAHhTiZTOzsomUy1NHVsescyq/zm29ZY1EPhoq7bF+6dKKhsXFzBL16m8RYRoxXUJzjWu5jMODE8d8YjgDkJgiQBxiQOHhEjRI08NxBCALgIyvxUoPUIGbETuHFTjHbGFO1seda8mqNF3mcJQX38PsYhapo0VFmdRPmK/TrtcOsjYkrfbh+QN8HKv5QMdCpNWOCD5MO10WgCLPVW43BkDIPWp6mAsCznRSAVtUcNrPuSXATYVxT7P1p8kVRXO4ulRoS45PbuP6vI6s4QCBZQL+6vzidxDMfrtHX6g/YI3GLC3vmGfVgW0BhQ6r83NVqw9mFc6mJYBoHRIaomfxrgwgZxZiGzGMhEXU4q1IzQtk8DGC09ZED4tlwl8ZxcNkY25ns4oWgdxVFzxoh4qmm+tSfcwruZ7nvaaVLueDhK+9dfU1PGbAo9ZqpZMXEPGHO8H5xJYmqV3SVKyC4Bwvaoh0tB95q9PQwqZpkPW7qX9H3ju13RgACUjzV0vmgrgaqdfzVBKe5+PRKVtzvaqQiC0xAjD/gktCbBrNViE8UKFSs5HlraHm2d/SiOvRQEOYVkyqeHbHcQ6CBeUBnP3Jy3QG2gHQCa4nd17Xurc61JBnaT2AxTKglueYJuoMUOJBiIbvmM5EI8jgW+cDbGJBL7yWETjAR9psWsa15kv5i8DEDECRZqe2GYz6yiDoESjg+kJTEfelLjI4AxjmOLPWgIN2APSEcwKvw+wMwhoHGN94M4gavL1DU0X6hHbNC0jLZCrhkHaTDHZJ1peXOI1XF3u2JSeWl32/QvLVYuH0/JlfDaL1bar6unl+aySmUmhgRimxIvgfnYcZ5T5cuTiVPGIZ7Wl8wSPEvaQD2IBJoweeAcxATShDqzMIWkNMA4hZHRcxjeCsl4WIMtjGDHnmZMT25PP2W1HRRmsgaKTOZiKtpvaYqQNlpS3V/A5tQCeMjux+oTHYX1bmMc902AziOj+P3TTzCVFO2zdA5AGX1kL7o0FmUUfMUh5gfFoIYIqJtsw9gARoPDsx0ISWGtdM2yqz9x3XT0vbd24aBwQIl5HNkyXLlLJxzYHCxu8rL6/5d+KJjSsmnWs1OrEErXiS5oVzHNRJjFDUbuBDzPvCoQhqM1+Ix0eUMyDSaTUqI1LLs6IBIwzlh2z2F14wjZWKHsJVS333PMhaz5iX8Yh0dfQs7QkB3icaK/5Ap+LNO7OYYC2pPWzSIG0BfAYfx+tIZnXElPPl0nptH+kBL/ABBsx0WhhQWlZoIqQF3nWeGXycU69WWwT+/gwStejo3rXlc7PgcoCPARD4hXg2HE5dL6bjEIBtvhjeZjvgAdy3c32Ud9XbwWpALymmzF+XQK6WnFA693eXl9oSo4wAMC/kUOEXVglCrfshbLOJg2QNbwmuh0dFWAIXr06hkWctQDnziKuzAiJqO3XdNUbXWniushexPu834KhNOI2UaUBMqaAhUfWBJC1vmrelu+jIv1be+Xzs5/vFsVlbpmyYdbPS5vO0BtonjWIZAcAAzGCgXgCsAFMDRR0o2pc/YBKMKxo7IuOlZbaZ0iEfYI/snyaLakA5L+attk8McLPaSZew/FuHBjRYAHpZ6bDnVepsVNQJZbT8rsKrtETn17HwGMwDa+DM48qOvHRs3AQ+46yzztqyNEOkyVsdJfNc+Zx9Uy4AkU+uyDOv+zKrc1J7hTUwYWoBLEbRCAPI54GcEby1LIh0NCWeLRyE0TcAsFWeeUyZfO9p+zXY9qWtQbovXT5Ok2Ce4tbiefJ5z6YjiuMJHiafr/eZUwYvbYh2c25xoMwjYoaUn4mVxSCSZwdop94tbc+qmbV2uCwAGdy8+zDfaOgcIDT4WTICUKqhPy6jyOt6zIf9xUR5cDV1IS5lovjoXQgQERDHu2FKBBdxy6SI9Ajk3HHxLV7oNDmvRMhqeBHcFmmNoOYfAaggsOOcLWASO5X5j3zevpES90RD0XF0iBBuenOJai0Hr8O0wYnV5+JaHisgPY+pEtfYbgeQBNoJx2iZpPkey+yr6z6Pofetnnj81HnMsZv3PtoNjiw0x3wdczk7IgAdLUqbq4VmwuHgnQhPCIIeeNZa27IApCxBEbi/8mgHuKhZsg4A+rgxOuvuu+z8/vISNfbWn5G0T+qgKRoNm93LNorNCtuvGzHytNXw4v5GMuCWvR/OefGCAeXXAh9pjEpG7VkCxICKdX6o90BDtDFgdI667bx7Eq5vo14f+EhDG2uBT+QhTUt46ZYV5kcNPvVSHpF3eIPi96ztNBBXD0CcKVyTxLPydZ4XsH7u29zmNl3sU03wayst8JFPgIC2GODjONI+x1k5tqwARG0kBNEd941jm9wOFoB8D0wHaf1dXlC+T5CdGrXOLQ4oYlkivVF/mpgom9VVbnyeqhYIGW14LrhAa7VfetMpjCr14vFxfxG4EXMCaPtEkF90XJySEfkOd7hD57KVB+KW5tcqY86zDjrM52JfHjgQwXQtEaczTz6ta1umT14hMF8zj/cSWEa9TQPbnO+sOspp837txaRZm7mPM5s2IMYAqs4Esrp/K8ARP5Qj0vO97Z9f4oo4O3CO9SBZp2VWG5jwoX2ezfqadf3ub9XruuOK8j1uSocsxFbvXYwAJnmy9b1wnZM6S3h7ZkWJWjPGiJfn3lgBr3Yty0+D6Zt57byGAsyUB7GaRfxG1kDyfk7nObKK75zGSIz6ng+v1OKCukTpHxOklpY5ijAFnghVfEZL6uO4jFZerWvzsVaZnJ8HUHjYlgWUXIa+/XgeZaw1LBHbOrd2UQ8+kZ8Z+kI2vFt5mHKBBxIFXos86nWjIo17WI0Aj8jhYZCcJQaQiMCflXad5wcLQCcVINlXGnXr7/hCovYJTcGXFnQinAP39XmFo2F+6VSzCGnucWmzK9hIG+7dfF/xHbiHkKzqAj2BhkReuBtzjajEiOl5GlHkO2vL1S9gcB7NRBmzmVV3dECp7oCe6OZYflRIQQhXcwTPxTGdrM4rzq1yS1OIAQX4LwNAtZneV77W8xjgLLSGZzGlaJoYeLS9LExn8UOtd5W5m7hGWxHWkbXHzFFGut267SdLdmuJj5TrhoWYO6V4u1qyv2gyfSKaOMwaIw0eBumYTTGaEa3BKIY/iXWcNQ4mVbxs4AU8TJPIwMVtrRFqyEZBEbGuyaMkLwQy3JQGHTbUZpwSL10rjL/vmWYdF89i8iJAnGW6GNVxYjgR2xpI1B3PnmcTHQ2AeM1y6ADyu+74rc46q9zLnDeIhIdnmetdU8f/LJIPzs4ARwwmEXnM3BfhHtqp83UEubgiYKJd0XbwPghiZrQ6Zl7RkEzPCJD1nmoxsA5FBgtA/1Ea2kcbngQVv6/neHeuMt2CI4gXhqDlsSLIYSZSeEV03gAf52k99Qhm1IvJlshGMSIZeFwXwiWsEwf4OA7k8qLwkXaZLU0GEOj8GjGhdptnJdYEL1QvH6osGr95aDX4uD7MCZ0U+JDgn7of5V9ME4nf82yneanq66e569XfNNG58zuclnaeczg8XiqciroGvjhGA5i5jrxZNB1Lm9BqnNPmmOeI/hBueRpxmK4GRIOGPzwYLx3hJdQmvTvCWxkDaneg/JtFI0S63bAdrAlWCIUJn07rb1rFsrljhDDi1JM8s/dBPvk3F2lWjZk1tdBgMl/TBz5xnQ4RXEIc4zVbRmowrbUQeXoeIzT+IebE1feyyt92tIA6v3l+ZxDO6WOkj2PeGRJ1UZEPrW2V4BMfc6Qlm1MXWh7NGugQJhmTHccDHAGUKHHAEoJPFN4R4BPHYxuDR/zOE3IttaEcWVp8ZD5f79NeDYa0q9x263Tr+D1YALpO9QG/XDnHl5GmTzREwYdUW7Eb4j6yGKHihdIgNKwQoxLzSmOxmHpoSnFeI6sDzOJcbDO/YroEjxXVWufI5yL9ItvW9IvW9aZfGKEjIruVZtFjzNVZol5rsJ11TfY4Sku70gGzE2BWHs4zy/pAlfal/GFqz5MfkwjQiGZGANdAGdyfNpEHAmUwAAClEGZbi0OM89pIPm+eX4gARW0xS4sryufzvrJZIkceTEYBqJuUwQLQB0sjtBpQ68/XMmaJDt8SL5oJhWi1GiCvVxZRx2Z0I4lpQ0YP8SO4HuH52evFVKgn6eXG6JwRB2mYSe24X2hq8Tu27qtRZlGuWfxOpG+5eePcMluggsc6s0xV6BOaHj6kBnzpa5DJebQ0I5+yacXS9GkQOb/WvnsAtXk8hXF9Njtdh+PhRPAs3g0zl3jegwcPdvv5X9Y0cEE0cetltcxeDgrhFXhFpLO6xiXxemk7eXUB9+gLXcj3j32DUDbhAKog1U3JYDmg6xUN6GNhddeuqn1H3OrXPjP7iLB6jUEDoS4DEYDUEufZ7X1mloYYK9C1eBFgZF5X3zKewv3Fl9QTOgUcCtPHURmJTZAEKsHPtMqaj9UTH/O5ZfaZHgL4kNx9wvQRg9WSVqdrpZt2jCYwy+s07frtnBO+Qfv0yeWW8BDWXGGdzvvzJw6t1d5wcrg5Wpd3TouiXZF6Lld8FLK+R/wG3toO7kq9ZV6MidsaDOPaVW8HqwFdUEhV0T6tv2t61gOap/KYZnl0MlNZFHPLXEFOt8CHdiUoTEwGjSovKhVlwNdY67gPfKTDOdWjm+NGLKCEyGS78+xFY3Q+BP8wSzTmPslhA31p8nFmDo1xUTMr57Hs/k6Bj/LidnwqqM/7VmumNF9zv9RTXVctFzpuBs+E6MbP+Uptft80uBwxD8hoSy0BPLQ1pDbNzABHk7Lv3U1bHaGV33aPDRaADhdVd1HB/dAeeCb6lrEU55KJZg3EC2MOeflZ+kYKIx4VPEwCHiezn0UQB1EM5LLrOueLC5IW2dqKoqXpyJO9XpcJ6CiXMtQBivkesZ9NiTgWW+BkVKcRGiVnCaA2SgcZOyv9XjrP4yX6XAgHTgiXyFwi9SDl/WiL2lRdV+b2IZG9e54uGlHtLKjfGc6HUyELDTkPpHEOOR7tgibEnKW9ASGrRKx7dYgoR2wHC0AXlyCzRYTZAQS8UKMAV3dLxPNoOGa8M5+45UN8iC+PPDgfblCAEaqsl9kafdxP42o1isg/tkBQGWk4eWoI8AIEdYOO62yp0L5d3jdVIqedtU+rOKtMKTDyTrtnzqcPVHOa7e6r82XEezLC9/F/y+SZrwEMtBMaD5PbO1eHdVAp01z8WW2WIbJNyxELBKDEidFUat7M83NcGBgRx/JnBmYuTT4x2OUy1tN+6t857Sb2B8sBnVHiWd5bTJGWtFCVKZNHm0wW13nobLxE1FMvMUCDZhRaj2MW65LWxEOdngpu9GoRpLwM8XWD+n75Nw9bH0ejLC1SNl9P69Lo+8wBjRagzCt4KmbpKsSEzXpFgJxvaJ4Z5PP52M/xM3Fsnq36Y870ecPmyWNWmkzg0lS1Bdqq9qS9MJVMj2mFEnhn+Yus7oVsrvlDJp9oaWtChXjvNGJ8IK25b44XzpH5jr80IEZ8UeSz6W2rr266DEvd75qyOBYSuvV3dSNHBKmGEFLH/8RxW94ajRxgARpaBw1HRHOYRJZO0KAAm1nc0eERqq0Z9a1jOkTtejcKAiHnanEsQgTiHI4p8wjKYtGt1ueLXbMI+MQ9VrUFPkbqPgE8s8Cn79o4zhNXu8TjnPdp+ZRVi/vFu8xeTl+jZcZaSkPYAO8pc8pxg0xfOXP5aCgI6Cz1V26dA3w0JlqYQaqlhUunPEBKNLv2HDSBczshg9WAPlLWUe6fctquSt9TN5ojAeuOnK+ozQgvjIs1y7SI21pldp3lROtV/EKzyvmKD6GCa7S1d0NjzGS4Z+AVyaLsdfnz+Z3en1ZvqygbjWGVAlha7ynfw+CjY9fcjEEgQikMEuapcRoQGrhVFPBGAhP7gFd9ZY2YJ5FZXAtNK4cRaCfyDK2yTr9bfl97mN0tJZtRjquK6dMntKKW0DYQdvXaLXVatnt4gNjrVNVaxGLQimoBCoIXLcOAcwoOicpcazv1tfFb4zRlIrStOJ7Bx7EafBybx/Ml3XakpZ1tJ7/dfG2Y3LPKWINPnZ72VXu4AJclO5C/4rha3FQdWpHnLOZ7GJw4WEJ4tHY7+CjrYDWg4ws30Sf9Z/qu2HqctsNGNqr0EZ5IxJbr16iGIBRURgQpUqGBUVbPt97x4794r2hLGlDd+D6eqn9PBC7wyp4TQGYUnzWS9+e69QzTYVaH23rFzv8CJMyzOqZqVslWqU1aIpd5RFNh1keUPV7On/dDa0IB1B8kjHJO0/B4snBctK081SOu3Y3b4QJQUXn7pE8D6kvfOk6l9iKBRktzqe1yc8yABv6FdhTCNjdjuaViyzeDktGQp83ExWWJVtfV1y4DZFH+1nZo4OMZvIdFwaf17IseA9a0Fp5NAYY8pzyc4oDq0AaaJS/qwYMHewFIu+wTXrDc9vrS7abjgwWg/VPigFYBQAg6nBGNgkZTc0C0lOjoXry1ccyMJtKG50gDbJlK0mXwYTrF+kAt044Ws2ogUYbtCAAN1+8qNYXtlGmT13r2OopbdDHCWd1YTJ5ZVJuspvm0hJlmKRbhF1lwlsxy3ta8nGpOI9KcB5aXbdF5cjmfTe8PFoCsiNgHNNs1wcw+jsAuZthjH/vYLsAuvxweDYDDVc+tGuAjDXeoOBzuUyOSiOdYuiLnkfcFh1mcHkclqK2W3QY+ygdAj0Xg8ey8j8xsmm8W32vDIeJ3AILARPOreB8DYIAI8107AyrBN/ISZvChgXPLm3yqrvs8Vq5TDh5ZAxmXf+tTTbmcu2V/sAB0eXE39gFNHzDNW+n1qJZjOyIPGgm7XeRoHfRnxDOSEe5QUam1uRX52OZz3OiZv8np1r3Pe+dZa42NCTFrftEyZcvPvcz1O3mNzg4YmFGxxAf+zkATIRk8WAanOK+8AhVpxRaiY5abaGpel1UWDGLAKII+aVPzxOkIWlQeYiDDBQEiAyA+UP6LLuLfZbaBf4MFoANzfEZE/RmhrZlizhXVV3SzBjBNqLFicXAx1Gx2exYkopcqStkohVxkuyOgsxeKGYasniZGNdHLeIEQc3l2QqwVxLSsAQj4mL5imsV2BAms07mHIDrTFZi6ODLawdCEVgJczNljqnO518GmGXzi+bjigxPUhuJjj8DDFBygoR3Vy2zE9fW2jmY2Gz5/hUVoh3JFSEB9/U7+HiwAHV80kHlEsKDJmoQqbJTqixLN+TGxmFJIv1CR4zxPQ0yR0HEsp+DPi9eZgiisieq4Pm8BZICPRixKNzeenHbd+7wwNLCWiGHCTbU8f630rWM8OOqShwbIm1TrXaindWhYrTLEMZ1W3bcAItLMuw2NtY7zssZQXjws8vPs9QTVOKc9BSDFsWlbdYofMgAaOJDd2mctNLPdCECDjQO6ophg80gO4pJeB6KissNNtMxEcM5PzI0GFaptPtfnmreSoYmIwYu04jpyPvU+F+2qXOV13nVMUX1+1m8jNk1yu8K8sGgbj99ZJaDOkiOLLAS23fvH9ep53d48Gl8tgkw9P/OzFqECPrE0r+AnrVEujsjkanXKYUKzrGWRRcrqa9f5eykACvVxnQWblfeBQgL2iVfLzPJnUbEI/6dhmGwqJscCT4cOHeomnkba2NJIvEyT/Gy5b+OcLXc7olEDqwPVpKVFSMd+nxZx3Vf+OB73jN/LbkXbMq8e/OAHXyuL8GJd60R1QIdhelo9b5ViBJ9H+1HXqxRaQ4vbW+Qe6mTaICP41AchCVObiW66BN4n2mTcj7fMes/xVQ9xQAYz7047xEtGe7A1oCCs8wCJ6HYOIJlJb+qRQFr7Yo7y9cvs4za1l/raeIZltvPZMVXO7FQq3bQFqKpLVv7zxDJaTJMgAr1s3AWPhHL7SGAWLz1P6nMOIRjXA1tcENMti7lk/ow2ebKgl4S0ZaLJQz25N1PDVxHmFRyAzq4xbVdoZvgFDacWDXseMpimaAlapKvpALTIVUmeQtCXJ6DabaJO+ia20jgQzQYk5DJHBE3PMi00a46L/NwWkvfOtRleLZph8GKxSmFuC96lNsncD00O2EW75VH1FxJaefxeZktr1F7iHpHHNBCONH3bpTQgWkTEufRlvO7jF5bJfH1imdYsNB9fnxAfQaPJUv92rp7LZfTlMs0u0sijNvGo19zu8alkXg/7tK6W8KS1olYBRv098Nb18xzTSPEtghxb0meGttLiTFYJPq17LHJMWxQns9sEXxaBj0I1mF7a4Cte8YoOtPBG0nBicOkbnHyKibMElxjgE89lIKs1NtfHGuWWj6nXKI9rd/N2KQ3ICM8FqQL8hViWs9WZ4vwqt8eVzPy1pO+4tBqBjk1dRdi13JO0ntAWjERMBBHOhHsdqBCfts0mGDVaDA9CMBoQ74NjrvPFjFoDcz0PE5OwFt6Q/FWO+jygRDzWDTOno5HhXWhTiwBNzmM379fz43ZLWefxGAahz4yKdsFjRXOqRXuiGdXxPTSt3crv1M/Q+r2UBiSkHEpbuEswVvyFvdu60aqPXVk8GOKA+v6m3Y9dLWCsBT60HK5haqY/I1NeplLMBa2HOso2Dxucbcx7YUQL8FIG5o1jBNDUQrt6zGMeUx/ufpuP1ifKjkSfxeEEqR3bvvz22vHMsdAwNi3A3rtfRpjv3m09mbRviZVl7rFbrlkKgDR+2kP9F+7nTTzcDUrszDqkXi7CbyZcCI2Fzc0WzouD+R12tlijcN1riDwbtJSWCSRS1rUt6Yt+BnBGPVNFImitdf1uPEYjW4XM6tymQYgOli4GiVXcd948kMTLapz6lek8MVk17rnp5VLjvuvcLtUafP5WBQkjF1QmroFZZpGuTcllU5bjmLcMOrhQd54CwYY0HkGFMVeH5nL22Wd3rk2aChLRMZqMILG8wJnAxRh1EdN5agYSGMl43nnnXatotWaiw8zqXMpJVV+WI9I5Zt3jWgVdwUksGOEAAEAASURBVAEEthX5ViHTOjet0BIqtNlp6aaVY7v1M80snnZfnrNof3gdIHS7292um8iKL9xrclx5QayYhYT3i3sbkQaAsOAWu2aWrWqEm1WgZxUy+V2F0OuTh5Uw9D6hQfCq0EzyN5FMKAU47G0kO34rx/zwlrUC9bhYcTiZvAZI9fovwCu4oShb65h4m/imeKRb9dagwYvneVchzIXdEJ7hWTgbNOta88nTHKY9M4Du0z6nXbeKc8xx61WhOYCYtiDqvhZt2EC0STGockLU71n/X9YRsLAG5MUqhIpBODMfcCG0iFYE5roq6MoShNUn8yBqDT7yitntQMGok8HHeQ2zJUyveqnTxz/+8Z2GmNMDH+S9We8x870GJC+zz7Wb89ruPu1gVeCjLHWjjPLVS07Ecdts2ubjeX8ZF68AvRp8xGPNa65mDi/KsoqQE+2KZsUjFvxOmOruY1EyzgqaD3Kd80M0/rq4H04Rn46q22488ya2CwOQClRpQEiFxXetmB+bnEJwQlGzAU3rr34ocTg4AdHPFo8nKr+WPi4m0hmVhPCrAxpEFuZoFmH4gspqATxc9S3TSxyI0TuW7ayvHdpv01mmmTLZhO17tlWBcT2/re9+jpsEGuZ0pIt2E7/zFmAZsGYJAGIliN8RowNcMigCaxxjfmaaGA9mfOJn1j0oCO5hatA040afEOgocvqsEpEe05Vm5b/q8+0hfcZddGQkKLcgU4bd6jvr9VyYGdls6/SBKQsz5Yy9QBUs8pUAHmaX9ZbrheJf9KIXdQ0AmLQWhude94w0Hh1LYBhNgorsXBYNtv7CgfPu69rae6WxiP3xR6SZ1oC6RLv836xVIOv63+Tj0KyYOOE4iHsDCRzfvHFu0nObt961PLNpitth9obZjzfNQovULmimNJMsAGiexcbQCFGvFIU73vGOXSR0HZGPj8zaN163Xlok339d+0sBkBHcdAQmiYKbgImU3SRLf/kU+zebYFTZAB+VyMwy6mg03PHW+gl731bUsz9pWlMXAEOo6FTlPsH/RL51GsBSN/xWmvrY0H7PAtCWFjjrmlXVAdd8xOHkPHVK4RQRXZzPtfalF4aS21ik01a0JYBAuxUFzckRwsTiUeakAIgx+127EmCZJ02L4q9FgKP8mbKm/QDUAB9ptWHmlQDHHEriXD1gzqPBuW7VshQA8WQAIEFRiFemyabFy50lQKBe3EtAoZEBT0ANZvJYs8fLylHNrmUu2WqsllxYRIw8CPm6ky2Sx1DTGpgASR5h53mWaeBD0+AEEJdl+sx2xcDUt8BXHYox6159M9u1tTpQUMePr/LqO75MyjzkYc18EK4QJWClACZhnqRq8DKQsjyCt/TxA4voMR3r8gOqWgTN+trLy172so5KMb1mJ2QpL5hKg/pveMMbukpQ+FZQ3zof6FfKRLt3lvD0PuEFM+IERyWdxaGAJU+DkQNzL5ZG2dnEVNwADKMRz16AkjV7qMU119N3f8eZa1zlrvUd+T6iVudatLNOu+9OnJvXZBS+IYJ8WZn3PovkL9p91SEkBiD0BPJaYKs5icTAR8NCXwCZ1lSgaWVnvlEAcgya9Ew9XmlAxAFiMmtQBdqeWfPblXV4wZYCoHgQyK2T6szMDdGbm1LlfquMhv925NvbUZ7Y0o0AEK4qT3EwE5mqmyfpAZ8g4Lxc6YUWANiaBzBKAZXsDTO6GHVqTifKEltzyTQ43hnX1+ZZ61hcu+6tuCVTB6ZpINPKMA0UkKo5JoaLmbOifv5p+a/7nPiaVXqCahe5gc3gZdKyKRfuFVqKgY4XOQQPaa6dzm6grLkbcUEI5loAu/4XwkUvkp4pN+szVHHNrO06AKh2GM0qw5bzVESNi9agA24qBkghNGBA0/qLQuKq2M46AXVVqECtaeTfRi3hBVT9PMM98jN72QhGPLvGYG4ZEGOqTRN8kxER+deaGrBoh8wgOO2+s87JB/AuCz7yn3ZtBh9pjfyLPqvrQmYBfaRbZNtnQsljmqnfeo8RI5fvb5BSR2LnfJk1wEca/GmI9sEzpjwGwkc96lHXipKvzcaDBw9234dnTmUBgkBpVeCT817l/lIAJArX6moINeSbCZdUv01Oijs8hQMCSkTls6Gpo6ZHaExsX3EYBDEIpFrSCv7ySdyYfOt7XzESBWGdwayVp2O8Zi3Csi99fdwzmMe2nU6c85RPrc7n87ttf1aoxDLlDadC69pp4FqDAXDkDYtPMkd+gIUp35o4S0MJsexGUACOif/B/zChQrRXS3loBzQ3WjXP2rR4q7h2N26XMsHE1Nz1rnftOjLtYifk6QX83t0zWRMAzYqENsuc96BuRPEsGoK1mWk9yEEv3DGN1bOz3TWsLMCu5a2QxmgpDxqQ+It5RQM1ORG4mWuHwwJ4646Unrd8Q0/HrNCJW/P0Zj2btk+D7OP28vU8xN5fmPV4PwM4wHLOdtoyJ5bjEFsWgruMVRnOPPPMbqCdBqRx3Xa26zDBlvKC6QBGTSTXTgHQGTe5yeQ9PbU5j1oXmkxPFp05GZqSNHijiHNiytXgI40RrA+AnCexRszHfk06jQx5WEfuxnkjvobKVcs13LeuUKSPrfdSmz9xbie3tE5aRQ62W1V5APw0jaW+j6DSc4s2L2ByGVG/3N+Ze+nLBweYAcSAYvWICF3J7nN51JxgrfllwNQWEfvzBHb2lW+njs/TV69VNpGZvDuQ96wS5GdUX+TFXyvDJQ5cPSUOqF6QbInst1xCfQ7wcULsU8vrJ9Yiz8+hQjNVpY1GGhxS3IAZ1wc+0ri3NDnfuHbadt3gA+DqtWmmlSfOCbSkcayKw4p8bVttkKbRuhfznFmu/S5LQAvhqEniXJ7aLKoXqM8rIuapHky5bGLzcNUAVP+eRwvLZdst+0sBEBPEx89UKE7FCALdBTwtEvK+nUo4vTSgoqY0//bPGSU97/15ufJkOwuFUZfPOeeco2v9yIs5p7EDC65Q4QnsfqCDXBRzIcrW9UOQadotgMOt6eCLCq0jd7B5rlf/NJxFRQRxa2oLMlycVytGpnUP967N9XCp18ddz7zPkcu1g0Y7ESYS8tSnPrVLb8B65CMfGYe7LXDhRmfih+QvXDC9hjpTfikTLCqB6sekAERGAqMbr5CRJbu6I/0qt1cVc+S4wsk0pe94M/HsgxqLTsOVqsMBE1t/CEeBZTorO15a6bjxs+CPTDQ0mtGUpBMkOW/ErbyEEOi4i2pDuRytfRwXs7qWebQopoTYKAAxzyqA9T3ybx25L0IcF7YoeS/+SgApbYHJ3Yp8zvdv7SOB1Y223ipb1mLy9dIyyUMyuewY6yHTACY+azchylqvFSVcxLMIYMQB8Xy5P4uk5ZGLvHbzdikSWkWJqWE6CO47q6ixsWg7ddZ8FF6xdcoflaU0/r5njePC9k0eUgIL+8SIsd1OjIhHDBKdjxkakax1/FFdDgS29YGsrsh2D+8ZQGNGaKx5DpHraZjircwnqhtznf8yv3XWVujBMnktew3tEMBaU7slrchy2qmJwf7UD24pmzC0Ce+Ka5u3qOWJat0rjnkP7tsC6EjT2gIX/WMaiCtX/dHLnJf5lXn9JPxZzBVkdZivuIjQ9gx4s1zz2iDNUXvgDQ6eah0k9FImGPWVa1BAGW9MgI/KMGLUMQmLVNK8aX0XjP7T+tu3Yg2oVaYcYW1kzvZ9jjrVeGuvl0bwsIc9rNMYAnzcwz7Pm2DO2qYXwi+idh3g495iRtYlMf+pNkPq+wFgsTI6ZTZfIl3r2cXUGEx4NYPYpiVI609H1dGtTAB8gBNzCrDMI95DBh9cDdCbJTSYaeDjes6HaSJAFK2B5jBoBfi4xmL0iwhtSj4cKOKLpgkFw3w0W+9hnbTKUgCkYNYCajUo2gAzbN2yv6jrx5XO3for9tBab4+QzgBB/c0grG5iprNOkDkIHWCaMNNMcakF57ZdE6fOM/82x6r+xG8+v+w+04lmgrg3hwuIttqN/GmRJgAzX313jVY2j6ivaS7o/EUJ4GSEz+9vnntIg9czuGQgcJxmYgXGPuGdapXPMeBowDHXq6WVA2SzDeqJz6GVuCdNS/32aXeel8JgS0ylmvaxg9zODJbT1ibvMtzGv6UAaBv3W9mllx1ZGD5Gury9+siSFqu4mXw1AGYlXssIoiFm0cDyetjs/5hDltPZ1wFqV3xOMy0osC/PfP2y+7S4TIoum099XUwzoPYDaks+IK95CE01CKIbKAUx7TnNz2Kmmv4yS2g0VqoMrSZ3zlnXTjufQyoMHGKwxN5kzca9TQL13a8+HoZFACSBbxbXAkdra/EqP+c5z8mnt+zjsqzgAKxwboIRaX9ixEx6xSdqm63Fy9wHfxiirqOu4ljeZg8vrnKR+Y85n3n2t0VCz3ODdaU5vXAi+3u0iX2psrd7f1wXUn2a1F8DQaaKIcokZL5+WSCJESznFftMXxNqlxUa3POe97xlL29ep5PgrHRcIykA13l1Jk6LzJEA+izMqRwBnM/V+0zX4K+AhvdVm731NfVvnbIug8FCvRgw7NfiGoQwDxZTiekIoPJUC6EK4SWL9XbwfuqlngIyiyRnRvKE0VAENGpfjgURrozeIcdILVZ8UD7mpGU+WCp9wkRjZiLelXlVgN6632AByJKs1zQaRfeQxW5fRLxAXAEV1vyuPA2jb44Xt6fORPVmcuoweZSx0pxR/NGPfnSv94XWpGNmMVoBL6OiUV0nnUfqmJN5rslpzFFatdAEhSpER2OKAlEualNZkJrbFUQsEy8EX0Hbwidlfi3O07gAI41PHRPp7UdHjrSWuvA++sSzBPAJSARg9fusNQ0dOoAoFiaL/HP7iWN5K/8cs4SLDXCLdH1xSTi+lnYU1+WtPPPyH/ncqvcHa4LheY4rGlDrb/+CHNChQ4e6GfHARofJJlKOt4jKN3oY6ajMtA4gRAsS5xNidMQF8ez0SYto1agtDUHdbrl9W3npVExEnW43iQW38tK3ocExM5GoOrx6yjKtw+d0sY/7oE2EiO1hDrXARxqDBr4LCIa0OrJz6r/moZSXN+2Zz3zmUU5FWgNQDT6Oc04AyNaSrvX76uMHrbUudAO45tUmcHa0Sl5XIGoS9aaAw7OtQgarAZ1eRs99hcRryb5CCi4i2YMVo1qQyoIJqaOAxss3Opg1T+MwgsU6MghEoJHD4Y2yRqTQdNjTTCURtDqNGfr4kFigKspspKNhiQ3ROWaJyOLzCoHb1+lmXT/rPELds0zLX720AFP4gGesRRBfa+5TgFSdHpndIlmZLeJgdDwdkpeKkySL+vY+g0zFNdUkbJ9Z7B15djE41vTxjpkkMRE57gNMgFod6Q6YwtUOHGnEIQh37w0QA5DWGuKehTZNLAEMzDgkmFtnlfAXRLZQgzp4Me6x27eDBaBTihZyagGBlhwoDW4RoRLH5E6A4cNwWYws/mqpPRv5t0YlYlzjJchYtjmykBdFR2P2hLYFnIKElV5nplnxsBj5rEBJ42p1UJzCopyHe8wrNJlZ0gIf1zBRmFo54phb/swSiDevCPzs49PkIW6IeaH+Dx482MXH8PQgaB2jEfFcBgDV90Ue42rC3KZRMp1or0w8Hih5+bNeDyDKc/KYToAJtxVrS8WnlvO9uMKRzkBFWIV7KjswpGkFIZ+vyetZMRPNKQNinqvlNcvXDmF/sABkLtgVPVzPNaUzLyJAAdOvIfAkZPV8Wj4WLuNG1bB4aw4dOnQ0uZEtwMdBjVsnCBdutuWd1/gyADmG/wkXPrDq85IAKAGhVHUNdBXieWpOZJl8jdQZfOQB4PFsee1l5PH5hfRsdSqd8yEPeUjzayLyy1wKMKaNCeJj9rgWiAMH2iSNNe7BvAbc55Yod1owYKQp0SxoObQXGkf+3BQtpH5PtC+DDQGWhNerFVCpjVlzSrwXAXR5Hlh3MP2zxI0lXAgtq/4aS0o6yN3Feuo2HzHYex3cyJI1BlnPOp9vf1nhEa7sAaDjDxzISZv71PnHPe5xHUEMRGgYETDXvKDnIPUa/1KPXrXnQMOuP1uURzEdRB550msENOJRxMW0tB/FAhYib80xWwSAcA44kZyvzstLAvBWAUB1telE3Mbydu+Qt7zlLd3XGxzzoYAs+BagZS0c3Fsub04X+8h7pHBenwrwM9dEqzN/ma3euUjjWCJDmbxP7w6fg6MLkjnybpmh6iwL0GqBT6SJQSh+T9vSdpiQeCvmIFNtL8nGAMiLNAL55IgoYqpo9jbNOl9X+nFFy9nXAzTHlUY+S4BGqNxGMBpJnwch5yV6lSrOnAoArcFHenwRdzBvjwYN7CIYT+Nm9nl+s+SZAAAGV6QD4EzwAcwJ5QKQ0zodTUvw3rRGn58h9luuZfep45wifWwRsbXLOs7N2urAQhu4kzMAuY47u2Xq0gD8MX2m1UPcO/NwcUzetCNiqzN7b1lLdQ7Y+1POGnxoabii7GZ3Da1IwGAQ6HnSqPNZmNre9bwCMIfK78zzjBsDICOzDxkaAZG4JtNlmXVeg8+N/sTyYkRDt2TfHLPhzy/qfgiVmkY0C4CARZhZ7HfmUYBQ5GVLZXdOGmaTERJHgkuIsH62PXDiHgVOEcioMWfBLbSAIqexz4xQfir+dmTWvWhNrTTeLaK+Nk9aZRF0l99lpKGBtIjpOA/4szBfWhHN6kJ5chBjDTSABKdiSgyzSbm9L+/XfoBJvl9854wmlnkx+8rhHfLwtTxeiHwmJ8dCLlfO/5jcLyPKRqQw94dLA+3uVUaXwyVwa8t9Z50vnf9wcX8e/fvZH/mRw4+93vWaf08488wtebd+lIZ29KOqRVU/XHiBVrItx8oIePSa0lgOF6DZct6PMgm3rBZ73NF0xZzp0hTu4egx1+a/MhIfLqbFtfIqavfhAnBb0pb5PIcLiB8uI+mW46XzHC6a1pZj+R7b2Xe/4mk6XLSz3vyL+Xy4gEfzfAGKw4WobZ7L5Sqc2sw0Ob39oj02rynhFFvqU/kKQHRpiyZ1uPBC3X6J+zpcAh4PF2308Kz7Fw3+aJ5Fgz1637POOuvo8aI9Hj2ufGUt8MOFdD5ceLCjafbaTgHdpR9pYxqQ0So8JVRvxGCWWecFCObZv/9QRrm/7Il4PlA4JjZzn9BISqPpiEm8gEl/NVHqWqq4UVmgoZHfKIarCKHN1fdhWpa3EUk6c1OalpkWidQHNZvXK3+Pnv0fhKm0OB5fVlUXVHnmbGgcLVd35G8r0JI5lwlVx3mIaE0iebO4DzPRqO7PvVpucNfQAr2/7GLOeXkG3iEaJK7KnLA69ED6eJZ87az9vkBNdZ7fDTLa8+NnaCLBw5hm468l2gVekmgvTM/IU2Qxc5E2IwYsjteaFm+XdOog0rTuteix0KoXvW476bV/Gl6tdTIT6/487302BkDiOHBAXgjbujZ3Zp3X2f2FnFEa8qnlryUnF9IOCPjT2HUQHqsszuUpFBk0pBP4FVMw8DmWzsCNmFbAfBPTIcalvg6I6IzxkvAR0uB/slDx62s1UvyEDoJ4rAPbAIX7E/yRfSbDLBE4aa2iKFNOD/haYfl4Kx2QeIYafPBWPIeeFVfFo1RzOnEfzwrgI55FORC8Of5KWuBu/hcTrWXmRX71FmiLWg5uSmdAWuPOAIQQCB4tJtw80zvkB3xxg57JewLC+X15Js8MUD1PnBOTJE5HebRX7TzOxWBSm+2cEzyZ+kZNaNfPmn9HvvnYuvfdc5X33RgAaawamI4NRblViYhYHqC+830V+pFid3+04gQi7ZWFbyEiVsPd6X6zliGI623xCCFcqqYTnFliV4CPkUwsB28KYlLwGdcqohQwiFlBRJonhFfAd9TeC6OGThGio9CyziojbeZHWkDlmrhHXG/bSsvjCEyCcM/p7dPw/EXnjfPiWBC1OqPOUYuJlUIWiKUiavDR+cJj5Hl8ycMz69hA05wlx2hCkU7ApwC/FvgAFfXVavw0Y6R9BIzi15D5kdbaNri3uuMre/3cjgk+9Ty4HG1zEfEeA+g9F83TewWqsUaWNkMjjnfNIaKO3Ium2CrnImUYUtqlFiTbzgNqXF5In8w6H9f91VOeMnnZE58YP7dsTymj85cUAMnTKLxwHiymixcco9GWC9MPHcVC8EQjpb7r9EjhEK5cqjjwyRM5RUkjOKMTGgUBFhAQh+IbYTSCV7ziFZFVFwuiw0Rg4tETC+zQiMS/1FG6C2SxJSkAogUa1fM8IhoPspgZo/PQ7gJEZBDPvyWz9ANYGPUzKNIyzY4XzxTmUbqkc6kD9AhIDLAFTI45570qi/L2SVznPHe/QYVmpt6ds2YOM5p4fxaLz+787kT6FxpQDCbMSNHuLRBNl3W72hXTMIdO8BAKU5gl87ThWXksen7XLEi2aMFz+mngI92s85HX4fLy9pUG0vqzRpBOkNVZIxPwmVcAjcYoSAzIMBHrAMWwe2vvDPAh0SlpEDqdOCMaGa8XT2AW5tYs8NFB+kRjVs7Xve51fUmmTv50fS1GY8ALhNQfoWUAXII/Mv8tnlMezJ762brE6R8TJYOPU7QBJojpCa3nVD+AggZt+Qv39WUIgAN8QmbxLKKbfY1U2IJ7HixgymPJg4fzyxNCPVceJOIeBi/3bS0opo0x3UJqqiGO29J6mHZZ8ppM8meS19plTj/0/Wu3uoE80amlQ5xYzIvWHxIaOFiHhflhRGqFxk97VNchJ6nzeAlC40HOAjYcA2KWWEcnSObYdieqfzGyOmzhMfcgOnUNYt2J9M9IjXPqE40ZyR9mR53OiKnsfeL6WozmJjrqcDgvwMNkjnLUhCuA9pHK3InrPFu/kevmvgGfpxTNVqdkymYgigXw8FGeQxmsNkADycIcjIEhH499pjRuK/itOB7bmiusf6sL0dOi590fINbC9D/vvPO6tgPksuRnov0YNLQlpi6KILQt4RjypxGacOpdcJhox8j8cOjkvIUWuJ+BU8DmEGTjJtiqKuW1ZfQ7r9jXLQFK905fEKjTrEN9RRBTpY1WAs2YEfV9TNkwAlPPeWPEj2h8OnKE8Ndl9dsIr+Hn2JNWulnHjM7LeJrqfIGADkJjY6bR+ABvHqlpsrUZQkMCkAGSQAYZTfMRLxUkdX0/JLl5XLnzSoM/Ygo6T+sQqKiemE3Tgv1oPa5rCa2HGcT89q50/iw0wgwqNGTmaZhgOa19wM6BQcOSn/eNK6QV+90H1vhFgBOivTB3wxmg7uqlj52PuWiui/YWeWx3uw4TbH6bZLulX/H11yuNjberJSeWBrlpMYr7I8hGnZI6LdiQWcSup0khrHE1zBBi0uS5ha+aBg7Mj9wYuwt7/gECGlULrDL4aPgxF8r3yfMa1z1ZHz3M/EFCm8bAREDW0o5yAB7PE+0g8znCGfAz6gQpb+kTz41v00H7BEAL/qMthIhGpnlGYCdvZyxwJmCQN7LvmQIAI6+8xfvQ+vqkZVKZukJbaZmeQDeHj8gXB+hvmtCI4p0rkzYQ4OO6lmlYz+ivf0+7306dG6wG9NZC4L68LIjfkpOKefZV1fKXOV2tmeRz69rntjaCAyCjYAYIBLaRl3njvIaXXdT1bHKubw04dzB5R8dizuCsItq3NQtcxzAS01TMnTKVILireeuA+cPbhx9iqjI9AuTUMaAVGxTlwhmF5yzuAZzN25pHdDpAI5bJPQFZSH5+oOaZcChAMj+XMtMMatMq8unbegYz4cVbyZ85zdsV8/sAv3LV3s6+/FrH3YO3jHbEK8zc4qEUrU3rFDUf00DsM8WyMPt4e2metGbev5q3zOkX3R81oFRjpxUt57qFF2nJyaVx7mYxozkACJhw1dNaaEVBInNZ4wjwV3iLHCjJ1KA1iDWJ+UrRyT23Y7QqS4zo9C3RmILw57LOnbSVvnUMUIojYkKZhxbgIy1TiBljUisTRcxVjruK/DxLBg/xT0C4JUAlz4vC9YSGlZ9fOWhjQNr9kdYEyAqpYK55/icWL6o6nkdMHQoPqHrjTKD5BAAxPz3vdgAIbxOcEoAFuAauEAMU0xGo0B5rYVoCIaEivIra1m6XwZpgF5ZR4oLS0Vpy6REvVOvcbjgWjVZZdJwgrrNGw7SgPQCrICaj7AI6mTzc7cw6v7PgB0jWEPJ5nQ6JishEqGc3cE6H3wB84rZCatOGN8hfS4CLOVn+WoIfia810Cqk0wmZM5lkRUrjyDIIyw/AMOvCM5c5J/kRzxqLwdMqY6a9TspU06nnEaZmiPtYtUBdREQ3UGoBbFwzz5YWGULzAWiZLPc+xBWRPi1enfsbigzWC3ZGMTGuV15I6+8TS6DXJkRDRD63PEh999eQM2DQFMJWZ5plQaRqiOF1yudwAjpkdts6D7SQkTx006YpADAdHXmaG3ncg4ovj0yuAiv5Mw+miXTCDnAfwWO00gMuJgehtdBoeAaBEg2ANiR+yno/9uUbWptrLFfC7V8HL/KY0RxrCa0zjteAFsfzlmaIHM/aHfPVlyNMLhYKwEQC9jkcIOcxbR+pzSPKA5i1J89JM97rMlgN6PjCMZzSY2qd2DNHbJUvk4qPUNXBNHYj6TT3b9y7nnOFiwgzgIoPkAQTEqQj8+HQoUNd1HDWRFyns3K54jp0EJHF3OU0mogAl082cfwOwJTOM4h/qQXoCfCLtM7TShDQwUPkayz0zhzikRKDhS8Jol0HRSLXUgOCEV8egNEfoSEKf3COiBY2wuNEEPvyrue36cxE2YEFAfKeJwdJ0gINIupP+WlS6t8zepdMGvUZ9waKwNrcrlgYjEcMF5SBurth+qd+eeaEUuC7QjtjakaYg+SeTdlxQOb7IaL3ugwWgC4q/Md7i2u2JZZrvdWcxGbr+nmOcUPH6M6Dw/2pMc6S7KqW1vyy6CR4H40+AMh5ajkuIIMPrUej5nXJ3A2gITiCiML2GyEN+HBJOlNeN8ixliBqmRi10EiYPvgn4p68UUwknVssC89eFnWlI9dzzphBWTyjcobJ5BxzNQDAb2DhD9eBE6rBR3kAFgClnQIVGgytKsh4Jh3in8bhCyTCCfAl5uDxtMmXcMHne9NGaT3hCaPN0r5ohAGY3YXpn0FEecKk9DuWcxX7BfBCPGusMR7H9vp2sAB0QekAF5WRoiVXH5kL1jq3qmPMgSz173wu7zN9NGINj4lS8yPIZSN1zBsKkjXnESZbBh/nmQP4H+EAopcF9RnJkdHAStyJRq9jzYoYlh/OQcfMAZQ0EnkBXyaeETvc1jSBWF3Q9SEANptOcTwTrHEMkPM06dBIYzOws9YS6XgN63t5B9HRw4voeZmNwCiE+Yp340WK6HNl5zHLPJIIaWQu4prQVJHXRKwQEjuENtOaAoIjijJJm6e0qF/vPwYBptixJoPlgK5TGuZpRRVv/Z26ARKOJyXUcGp5REvPakD4CaMqXiMIxXyN0VngWnxRNJ+bta8TMcNwG0ZdozOOgetZeZkczuFNZgkzARjSCmgP4VHR8ZmFeCymGI2IZqDjSk87qQXxSyuTTmeOTk5jRCLXQrtDRqsDZCvPj20t6pxZRGzjfdTpEPqWXw2h+dCQwkSM41Gu+M3EpQkKL+DepjUBU+ZurJQQabNWGcds6xnu+XnVMW3RwCEWC1AeazJYDcj3wE4onbUlfcdbaZc9xj7Hs2i0rdFdvjgOo3fdeXTm6NB995+2PrXRmymnM2SORl46r5EUwRveNTyJmB/CDGEOzRJaUswE19F56Li0a6HJ4b90rLiHNJ4Z0DHPmF5GfgAovZGfpiMYUqcGTMy9bI7IQ6cGAICCpsBEAXpnFi5FvngSZaPFxJrbQE3gIjMJl6IO8E+mb1hGl5nnGu+sxWW5L0HCMweR33VUtXLW9a5MLWH+GlBwQEjqWkuitfWZb6389tqxwWpAJxbwOaXY962/k0qj2ZS0wIeZwoPEq8VcoYXMI1Rxc38sB8E8EevRCtUHPgIA+4LpmBsRT6Sj5AhlJh2tpRaaV5a8koDj8dWHnCb2aXM6fACe40Z+5Q/ehyYVAKM8mbuhvTgPZHIeANDEXcu4mLxqeoJ91wIfJDozDcCayoGbkg6HA/DUO8A13SM0OnVrUCDyCAlex28ePOZZH6h4J4ApxECgDH0ijIIjwb3z8/WlP5aOD1YDOqM0+BsU+7wlJ6fgrdb5ZY5p2DqaURQvME2kDS8U9Z93i+cEZ0KTsB5MdMzIh1qPg6ANEB2NCQXAjPZZdFh8xMFCWAdhms/btwi7TiVfgNiSrEG5t44l0phJaVJnFr/NRzKLHN+hLkJ0dOYSzQshDzyR1NzInju8e5HetuZ/gE0Q2bgX3JXyB//iGvu0FsQysXxHnHcPvJa60dFdz7M1TRDm6hCXZovXApKtsIc6H6aT6/E7Ir7r56nTt37TxpDmynqsymAB6IrSsS8rDaYlVxcNYZXCnGE+EHyIjmg07pN6lKMliU7mNtaBdHRmS9ZgdIIAH/niaoAXwNOxAZp8XcOkoLrr9DxiNAG/80L/Oia+iSlRmwtRbu5sphNNjVkSXrQ4H1sghvsR2h+ENI3C8RCA5M/Ki0DsvOKlIkwt4JWDLx2PeXP2mW4mtdLOaCeuwb+oE/xIeNwADE2MZ1CHz/Uln/C+WUY1BgCcFLc60M88kPQkx14xl+YVJp9y4tdqNzxPlzYDgHP+OW+eQR43dQ7AgOaxKIM1wS4u/MoHi+3f+rugqPirlOh08qRNaFzThClAa6FhAAgNDNcRnIOYEb+R0ToU/oNL1ygcorMALp4yHIL76oyWuwjg4mpnRtAEmDu1Rw3w1N4u8TNhbunYOqdjfeAjnocJ4Z7RqZUxg0+U2Zb3jpcpCyCtQZlr/NwyXQSwAfTw9gGdIIeBEVMzazI8gEH4hkcq38s+QKNt8oQJVmRyMpHcT7kR6NkzVV+/nd/q1DtlcuHRPFstnhX4eKfeEWfEuspT33u3/R6sBmS+16nFE9aSVc8F07FjdHW/empEqww0AS53mgk5v4p5AUwWxgrNgFbDPANKgIu2BBwidkUntvyCwEMaj04IHJgcvCcITiOyvzxfyvQAk1NxQrQp875y+ZkBOKMgnJWVCSlWReegiQV3kyOrkeg6EW1ENHOYeUBUXIw85M1cRQDzgnneCNjD4/jzuwYSrvcQIELbiXpynHZkUOjj1njD8EwAM3u2lBW34124J3BYJno5ytba0vziGZ0XUlHPhvd+/QFfYr9vAOgS7OF/gwWg/YU0Pe1ITEb9fk5aMQekARmhgAFydl6vRYCP8iFLeaiACl5H/IxYkhAjs4abyd7QVCKN32JP8nXO6Wy0IKDFXMPR8FgxExwX4JiFaYZnIVzSCFVubyCCBKe91QGTcT3zUTmBko4DFKUXPGl6AnMLKIrRAR6IXOmUDbjyamUXNo3JBExpgRZtMMhhQXlc4IS2BySZKrbCCrKE1w1oAlQmXUtiIACs+CwDxSqFqZelFRrgfQhNEIQKeJSh5czI+ezV/cEC0JU6wRHCtn45q+aANBLuXX8EGDFtjJ4ZZLqTPf+YIMwO4BAiZieifplcRnbCG0aFp/UAPBMf3Qs53IoV0eEAGjNMkCNXr/sBtdr0kT/NJbukdfiI3zFi1+ATUdVAJNahAVY8TwCzNdMdUHGP46UAE0BQFsGQnjlMOBwV8MCt1RLzxBynLbhXfK46azbOc5UDK2ZuPQ/MdTQxeeRPX9cAL5/tCvPbs9BOcUC0vizML1wZrVSdaFutd5Sv2cv7gwWgk4on5LSeuTInJhfpql8egpGWIcJWLBB+JrtwF7kfVz3g0Sgzf4NcDpMD0PGG6Vwt8Mn3Ax4AiEeLNkF0YuRxFgSyQEXi3gE+foudyQJg3d9z42cCgKSZNleJeRQR0jgdgAMYxQCZuMn8oh1wkfcJbUK6kKxN8EDxmgEiGh0NQscGPjyMsVwsDxPgdx43BYCYukBxnoDMuPc8W9oXbQ+h768WpLlIdW3HO0BEA/VjWQYLQJcXTeKi0ilaclXpVOsSRKYGRBCOIlgz57LofUPrieuCd4nfOphOE4ASx1tbpgmtKadlCgElc55ajV0HxQmFO582EpyPe7g/klrnAR5MH9yK/WnPnTUN+TBfIwgP2GbAdb4lAIw2pmxA5mAi6ZnBtAhuf5opfodYxOuZz3xmZ6rSeJQxzBsaDw7IcVrdqkQ7YIYCcnWF9G5pNcA02g5A1nZa72RV5RpCPoMFIDPeT05kZa7sVXNAOW+mRZb6dz63zL4RVKRyuNSZXQCCqh5Eb52vER/RSyPALwCHHHzIHPCHO8I/1TErQFXH5L4OIIp76Ejh7panIL2sBUW6esvUEpsTAiwWFdqXpUX6RN0LN8iASQvEKbWitiOfVYKPPJmUMS+N186zq6daao2x/l2nPxZ+H1+4hkNDfNArCwn6gWKenFC8MfXfgTKqnzElCK2OYVnk+WkZyGSmCsKUtyhG2Gn50CyASO4sfenlC3gE8XEfAwyeoZg4mV227o1MxeO4B7HsKc2B14rZFKJzijh2TV48i0dLvApCWABkiIA/8U6x6qLjyi9+KLvGIz1y2rwt2g9tBc8EHPFTTA9eMWWwXSTmJvJvbWl8Qag7T9Pri71pXb/dYwCfhzTXm+duBaueeWS6hlAC8VfCM2ZNyekr33bacF+es44DfG0v+LtIrz0ty6cNdk3oq4sK+x+NiY8qxbfCTqg8P1FZtrWZkc/Nu08bASizxAvj7tVxecKWbXBxH/FCOjTTiLcJvyIqOgvwQcbiYVpmEi0L0V1rb0wI69UALXly6buPiZg0o9DAPDcNozajeAvD/EPAMtXci+CikORMTGI5UyT8KiQ+n6TT49XmdQxs597MLvFgNErvFKemzvFU3rewg3XKKtrwouUTuqBtCLzMoh3Vkf35/LT9wQLQR4oG8o9HXMn1AwKfm01R+Tf58nAm4c0x6otfCU2lLve8vxHCYoHMt6obuk7BFKNl0HK4rZGz+IbwbjGrAFANhjQgfAqyNiJ9Y5oAs5CJFkLzA4QhgLZ2QSNZkeyAS2wQbSWEV++8I9HSjrmeOzpCHeRvlF9Epr1XZqM6U56+2B/vhklHg0RS9wkeByeFwCfKSgMQFe3aVZt4rXJMe9ZW+lUcWwcAfWx4WkXpNpzH4TKSXlkadOvvqgqhN1y0LbfLnQxwZG5mS8IFfgAynEoNPjQOoAB8CEChwfCeWRMI4Gi4ZnnX4CM97iSimJl7AZzO5cmptBoBkVloHbVZJrgS6NB6arW9Tst0Ex0uHsh+DvzM91lm38L86kX8EROt9vTJk2eOVwyBTZuJOWat+9EsA3ycly9zCmme33fr2vHY1hoYLABdUtTdC8ryDK2/C3u8Y1sffTO/xIWE4FRqcynOrWKLTA4zKfKL0RgpyoxClpoi0JKYJR7nqNYBHDQjC5xx8TMx6lgbIBOu77heJxXwpwzAKExWnr867gc4ZxHbtCrJrnw8VV4ULO6RgUMdxnSPOB9b5kfmxBwPs9J108jvyGPcfrwGFtNxP37dju/dsHAMt2p4GhRsXxmNd4twyfJoabi4lDBp1lG+ekoD8ENkczuTWSaNYD5xRwhVXAYPFA6JaUQrEAQYgYB1+QFVmHhxDmGPeAUmzJ4wP1veHwCJc8KVAb5WHE3ku+iWF9EEWQIEa/B0XF3l5VDzwOF8CJM2eC4aKM0wXzfPTPrIa9yW91FQu71Wwy6vnSsKT/HmYmK0ZF/hOK5TlnLok52wn3myNP4cCd1XvmWPWwDNXC9cB6AQ/xPzt7LrnRcGee2Yjl978ZhhCObQfphXtKc6XV1OnE+43nnxTJ5lBlpJADghKpl1ea5XzgO3QkNDJpsrt6j0vVfgbxVH5h3SWJPHj2VPFS0GmY0bE5uDK6qFaVsT7+eXeWU0T8/lHHJ9EyR437PWZV7l73VwQIMFoMsLt3B+6RQtsVri6T1R0tLvlAvTvZkAmxLcD/6DCWTGtWA5ndH0hzB5LKMKKLIwnXixYmxSXzghjX6WABH8kuhwQIa8FhsTgn9adQRy5D3rvdJcaFbAxmBgSZRF45O4z4EOsaSI9avxbOp1kzLrWddRFhQCTo/DIIuyLOsFG6wJdlVR1f+vmAstERd0oLio+2Sdo4eR3hwpPAEthKeKuKdGP08cUF+5+44jTC1ApiEwm5hPACa+isoE46nBP0kT4CM/k2PrMvGSyUeAnTLjfZC3rjNZtZ7flMtlYqvwAPUg39osdKy+X75+O/uz3qsYpMzXmKIybenbVlloOwAbOHP5yy+etZV+XcdmPes67utZgW39/upwjkXuPVgAOrEEG/bNht8/JQZokcpZJq2JnjFbnScH71F7fJbJt+8ajR9AxFpDRmRznZhLwCO0GOYUDUinQzZHgCI+hPcLUAgWjLgdMTpiguRh35QHYvb7wRJkGMDaV644zgwEXMIDNGCaGHd1H5cU1y2z9YzuwwQSzFlLzf3Eukp1umm/cVne8SirqYHBAtBFRdX/nyPEYl0V+wuJ+WklTmMnJM+BMlpYgmKdAIRTCvDxvALkmAM4FFqP5TsChIxcwIC7m6tbFC/uIsqM58kTXoM3qnmrAK956hdRC7AAcQiXdwuAPAdA7eOI4vq+Le0svqkGSPE8uB8aGfG8gEk9mLPVt2RHX/7j8dXXwGDd8AdKwz6jmBStv9OLbb5TkslTWgjSc53ifjlGx5ywiPFB/tKOQsIDxztlFEcUxzwvaZhjLRFMGZMrdWpTDaYJAKQx4QqYPTmeyHX1BFzHmDbyNo0irxfk3DwCeAN8pKdt4XwQ40ToAC8k7dCggMsJba9L0PgnrEAAJ61qlPXUwGA1oH3FNLi6h/jjBdsJ0QFyYwUAq5rzNO15zAUDHvidenb1oUOHOkIZL8XDgxSmfYjpMWmVuRKkYu3hiXtaTweQ8p6Jdo7Yojift5YRMSUDGU3zyACHmEb68hRlwVFZEA1oECahcmYwz+lb+whSXERN8kfAYI7zcR9xPtNMMB4vJqk4JgKERZiPstoaGKwGdGmZaPnh4o1o/X1khUFsi1R3xJrENfW3z+P4qre0E8tTmB5Qg4PfzjGvQgTLiffBzwAfnJDJrPlLn5E2tngbHEqdf5yPrVn1wIdk8PFbh3eP0MQcIzSR7OKvf38s1fT/tD5alJgfBC0BgKEB1protKkWrvUuA3z8rrU4x0bZfg0MVgO6TiFNb9pjChxf5uXshOA6hPKHWJZhNwgth3cquCJcCDI6AupwOuYy1VM7ouw4JB4xWhYtiVbV5/logUtoNvWnfiJ/RDcNyCJjeDPb4J8izTxbpqFlR+Rh4XmEcZijNCpALc4I/zPL+yWgkEYZsVB5MbR5yjKmma8GBgtAx5WR3bIbLTEbficEhyHAz4x1kbSLxpisusxiNpDK3PQ6nc7H3NHBmWNZkL99ItI3gFUwH22oNcve9Tq65UpwLeaLcVVbUZFmMq0+BP7R4IDVLC2rr5xxnDZVT4ylVSGp5xUxPpYuwQExHeuvo86bz5hueg0MFoBMRr2muF2bUkbUnRKqfq3u71RZrA6I+yFAARjEAl/MIWaFAEOdLUyVVlnreVk5jqhOT4ti4gG0mBTbN62hvpYmtF3wqfPczm8a7G7RYrfzHLv52sFyQCqVadD6Kwd3c51vrGz1pElz0nz9k+BHTNKkqVhmYxpZjoyNTxEzY+rPzGzsgcYb7bkaGKwG1I2WpTO0ZF+x3UeZdIvnZ9c688tSFEwowixpTQyt6w44WU/HukK4EGYSgraOcq6vG3+PNTCrBgarAe0vdr4PELb+TixenVE+tjSraOYQi4KJAVpGuLnxSHgRZDt+J6Z6LJPfstcAP2YlV34Oblw2v/G6na2BwU5Gxf+8/cj0gGtVYRnZTdXok52YR8OrQ2uro4r7yriq455VDAwPmMDDcFEvkz/tKQchygu3VPM2zLvMAS1zr75rfNjwcY973NHTAgzDo7UT75U2KfCyjj86WsA17ezEs65jNvxgbZWriqtVLFBL9hfTbBoAta7Zy8dWtUg7lzawYcoR7vwafNZdj5b3yCJeJwAoHx/3h1EDgzXBDpS4leuWOI/W32lT1gIaxmvZnaVERNM+eLrM19qJSZnZlY/Dak063Z21N5aqVQOD1YBaDzMe+3gNCMbL0cUfP7O9Pcu59i3pur2c57uaB445K45JYKP4olGGWwOD1YCGW+XrL7moYtMmBEa21j9efwnWewdBiwIhZ02nWG8pxtxXUQODBaBrCg/R+iKGYxYrO1bFes7ifUwhMJvbkhzEFAXrGZtpvunV+47VdzE+9+waGK4JVoINr6qWhozHPV580JEJiXHsWNkyvbL4/frXv76beOo4jxhw8hmZUcYa2OkaGKwGZCrG4aIFtf56p2jsdG1v4P44kfiihEmogMYXT7PUv/O5cX+sgU3WwGA1oOMPlO9RH4no3WSF7YZ7IWBpMX1zrHxzS6CemelickwgzWvlWN95lLEGdkMNDBaAdkPl7UQZLIVhzR3im19Pe9rTmsXI0yRMpfDVVNMpfO2i/qppM4Px4FgDG6iBwUZCX11mW7+9ROG2xHIclmztk52IIl1FJLTFw0yliPV1PJ9vqcfcrtbz7sSzrjMSuvWMcWwnnnWMhJ502vUx91keILO/Z+ExUx72oojrEQwYKw1aRKxeAGwvPvf4THu3BgZLQh9XomD3F5K19Ycf2osCWH2TihZkPWf7Rv1RxhoYag2MHNDA3px5Xflb5AMr/ljcsQa21MBgNaAtTzH+GGtgrIFB1sAIQIN8bWOhxxrYGzUwAtDeeI+DeAqrKJ511lmTu9zlLh1/NYhCj4Vcaw0MlgMSCd03FaNbrnWPEtFrbQ1rzvwJT3jC5NWvfnV3F8GSJsuOa/msudJ3efaDBSCTUa+65JJm9XLR71VPWPOBB3Iwf+hPkevfA3mMsZgrrIHBApAJpyeVmJhRhlMDZuObGGtFxVvc4hbdErHDKf1Y0nXUwEYByHQA3w73KWAfobPQeRbfOL/gyDKrYl18k3yUvVMDd7vb3brPM2sD5rHFV0v3zhOOT7JoDWwMgMzA9qG6hz70oZM3v/nN3Tep8hcbLBthwfZzzjmnewbLbY6yuRowzePFL35x9zli35L3/a91yM1udrOJv1HGGlADGwOgm9/85hN/ROP2PfIsvrhgWoEP5ZlIWX9a10f0AFeIFf94U5YR4JbnUy2Tx6LX+M44sUTGJmXeZ/XJnde+9rVd0UxafcELXrB0MT2rCG1TRTYp8z7rKsukLZsiY7WBTcpOPGtMBWLBZDF4LSsbA6BQt5lYGne9rrDPmuicN77xjSe+fOCrnlT2EJ8Szl/vPKO88PeVZSZa4rvxJ1fmXU6nIuuFu/L5dexHA93051vmeVZkcICPZxdpbUDw1YtlxECC57mkx0mwTJ7zXDPPs86TzyJpdEYd8LINr8K5E89qHqJ3WgPOdrTltQKQtYkV1gfx7nnPe3ZLhPqY3b3uda/Jp5YvWmQBMP6Ic9y0GYCATwYgn2R+1zvekbM4un+4zJmatuyoazfdYICr8IBp5Tr6ACvcmedZAY3ZzJZwJerfCLtsWWk+vgu27PXLPv48z7ps3n3XGViOlWcNsK3f63Y+zbRWALJweAiTC8l83/vedwuQxHnfd6Id+OyK75fn9WwiTd7qzOMnmHONLL/PZLLG0NOf/vSOA3rEIx6xfGbjlWMNLFADawWgXA4cDvPLZ3WJEfdBD3rQ5PnPf343u5v2c+6553ar973//e+f3O9+98uXj/trrgFeR4vZjzLWwCZrYNctSMZkm5eoPf/885eqq51YuGoVC5It87A78azjgmTLvKnFrtmJ97qOTzPvOl/3vOCz2OsaU481MNbAbqyBXQdAu7GSxjKNNTDWwHpqYASg9dTrmOtYA2MNzFEDIwDNUUljkrEGxhpYTw2MALSeeh1zHWtgrIE5amAEoDkqaUwy1sBYA+upgRGA1lOvY65HauDlL3/55N73vvfk4Q9/+OTd7373WC9jDWypgY0FIm656wp+CLu/usygb4lP9oxR0q2a2eyx973vfZOzzz67m6rgzlY7EHg6ylgDUQODBSBfRr3iooviObZsTUY96brX3XJs/LH5GqDxmCcV8s53vjN2x+1YA10NDBaA9pcZ7Sdf//rja9zFNXDb2962+4Di2972tq6UTLFRxhrINTBYAMoPMe7vzhoQ1f7CF75w8spXvrKb+3fnO995dxZ0LNWO1cCeByBfzrimmAHXXHXV5EBZdsLXNK4sa9V8sCzlcfpNb9qdK6uTTQ6XV7DfZ44Lt7T/5JMnHy38xZVljRfX+NL84XJNmSo+sfTAZWVm/4Gy5s2VxQT8UJmPdtoNb9hxTseX86eWxdSuLGumXPie90xwUVdffPHk4g98YHJ6MQkvK/vvfetbJyeVPK8u5ZmUvxPL8ePL8hWXlAm4l5XJumfc5CaTiz/4wckJp58+ufrSSyeXl1UkLymrAxwoZbvKc5Q/+Z98oxtNTijHLizLmyr/gbIsxPHl9xUlj6vKdRbtP1zKc6D8XVLuf3Ip1zWFMztcrj2xXOv+eDT1sq+sLOD+5Qkn+y2wVfK6TqmbK6z98qEPdeks9H9S0TivLCsWqJfTyrpNF7zlLZMDN7jBZH85d5pyl7JcUsr7yUXzubCsJ9StCVSW+DhQnu+i8jx/+f/bO7cYm64wjq+ZyaAUraTaapveCA8Sl4giIu5CJAgShMQDcYkEwRMRCQ8EES8eXBIvHgRxDy9uIZHICHFrE5pWJ23i1lEzZgwzs/v/rXRPz6F6nDnnmL33+b5k5px91mV/3//71rfXWnutb5044crllJTgqhTvCX5rlK9EPHT8/ntXons36p414qWzwq/wOzyX675gC36ddJ9Ht265EuEAv8jwoeSpUX2f/fCDe1ZZ6aQkV6/5pueq5+M+fVw73RNMCDFSq9+RGazRbVvpkeB0jarH86aypWAinsuES4N45fjvjtJxterDTj6QLKX6rU51fTdggGtQ+VrVB971so1OCmeC3trIjtBjk2ywQdeB7t9W16/Eyyd9+7oX0vOfsgfsh/hUXRSwL8zPNEK1ZGmj/GXisw24iR90/5HiaZcof63uhZ7RV6PugUztFAcrADPkEkbUE6aLdU9lqgs+yyUjUxn+U2XS5k6FB23Ht4mwYAE+k++AaDBSGo0XZeBgmnT9WEYcOiSUhxG21w59HA0navx1/75v+B1l8PyGojFEDK9GhtEeo1fjeaAg651lEKGjKlWjqpPDeFBR4RsCDuGpnFQ7GclL8fJIBlcug2r4J4AVBoPhV8thccpHexljPY1DjgnDfSEjx+HhGOAxkBw0oLYKDkXcI/LiVEvEc7kagHe41K00HFMgmQMa1N27rkmy03AxTmSinK/zH/l4JUoDo7E/UvRJ7tMonrlPmRoPDaFJdVO2SnLSyEr0WaL8GDnXGH+15EVW3wDAW+VxUmW6L05A/3zj005on4d7PtV8EY7Qn3YiuZ/BLwavPOVqyNwDx9dODbKWuEXizctHWaW/VFqtGjSOAKIe8tTKgZYLm1eqE/2jexwjZXmw4PQgwruQjpMoBSf98cDBMdFgcTo4L/jh4cODqF4y1krWEqXToDkogc8OsocGpXHdVgHLsK9Xcp6U5fqlHBANv0YyP7xxwz+00NELHlQKV4u+cQZVshXsgMMXkJvfqJ+HVpPqfqaHJBjz0MMOqlVfp2++8TriAYLd8+BAN9ibf9ioXKnwqBNWyItjRr5GpXMdvryBP3gutAOy1/De/OyfIWAItAYC5oBaA3W7pyFgCHgEzAGZIRgChkCrIWAOqNWgtxsbAoaAOSCzAUPAEGg1BMwBtRr0dmNDwBCIXEzopKq2BxNrAAAGwklEQVSEY6lZs8Kpo0mnAwcOuE+0Pmj48OFJF9Xt1UEKBPQfOHBg4mXdsWOHP7WGQ0HzRYlfB5QvoHKtp0HrPHBAxUDs//JrbYpAWGRlLVUxEIsl8y2rDcGKwXJMRkMgogiYA4qoYowtQ6AYELA5oPekZc5fhzKd+Pqe2CnobX7XthLOLmceKOn0m/a/ddTWDM5NTzoRToUDRTtpW0i+yBxQvpC0egwBQyBrBGwIljVkVsAQMATyhYA5oHwhafUYAoZA1gjYa/isIXu3AtWKFXTy5En3VGESunXr5iZMmKDoDv/CTfrOnTubKxs3bpzr3bt383VcvvyosB3nz59X5IsGN3369DfmuDKlx0VO+GQtF3M+nRVSA311USiVVNqzZ4/XN7+xNmj8+PGpybH6nkmWc+fOuZs3b/r5oBkzZjjOqm8RaW2KUQEQOHLkSFBRUeFrPnr0aHDlypW0u9y5cyc4dOiQwue89H9aN5OWHoeLurq6YNOmTcHz58+DysrKYNu2bWlsZ0pPyxzxi9u3bwcKqO+5RHf79u1L47i+vj7YvHlzsz7lkNPS43SRSRZNRge7d+9WCKOGQA+f4NixYy0Wz4ZgLXLbmQuxCrivot5B5QoCRU8olQjYTo+I3sMjBTDzgbpSM8Tg+wNFCPxKga/aK7Lel4pgKIfje0Ih65nSw3xx+PxW0QonTZrkWUWffykwWCr9oeB0vA27dOmSu3fvXiz1GcqTSZafFU20jyJNEpRtgCJChjG/w/LZfJoDygatLPLSTUdBdxXZ7/r1627o0KFppVlVShQ+hmccVUO+uBFOFecT0geKTKjeUHjpne7/pTdnjMEXZOMPmQ8ePOiH1Kls1ygCIjGwP1eoWvV83alTp1KTY/U9kyypen9d59kK+u+kRLYlLX8aApyBtWvXLv/bxIkTXf/+/Z266u6E4iAvWrTIG29qgdQ9Yeq/eqPt0aNHapbIf2fcr+56M59sS+igsK0hZUoP88Xl87FCwWro4aZNm+a+VtznVGL+LpzDI23r1q1vOKnU/FH+nkkWnE6od3ROz6+lZD2gliL3WjmefGvXrvV/OJ+fFLCdU0GXLFniPlIM39eJ0yLuK+40xFCFnlDcCJlZdIgDZfjFZ+pEe6b0OMnLkIuJ2dmzZ7vu3bu/wfrVq1fdhQsX/O8MqeO84DSTLAy3f1UsbOgXHQaQi+3aQkQPY/7/bdiwwc+HhG8HcEpjx45169evd8uXL/enhGryzjfYFwoIPn/+/LThTP45KkyNFy9edLcU4J+3esyR9OzZ0w8peQvEPMF/pReGk8LWun//fj+UZmgNsSJ43rx5zbL26tXL74zHAfNAmTNnjp8fKyxXhamd3s1e7fJ/XZbQdhlWE/GAodgzHUSwYMGCFq+ONgdUGB2+c604n9BJvXOhiGXkFTyT6G+bSM+UHjFxcmKHxstcUBIokyzMY7LlJhcyB5QLelbWEDAEckLA5oBygs8KGwKGQC4ImAPKBT0rawgYAjkhYA4oJ/issCFgCOSCgDmgXNCzsm8gwKv4qVOnOuIHQ+yd6tevn39Txlsx3pyEtHjxYsdyhFodIbxw4UL3hY7BZn8Ve8p4qwaNGDHCbdy40X2qo7BPnz4dFrXPhCBgDighioyKGKzuXrFihVu9erV/Hc0iTDZlsriN9SLHjx/3rOJ0tJ/KDRs2zG3fvt2xvP/atWvu8uXL7obOS+e1N8S2hrNnz/o1ODgyo2QhYCuhk6XPSEgzZMgQN3PmTDdmzBgfnJ5eDsRv2oDr18iws3zw4MF+Pc2sWbPc3LlzXdeuXf2CRlaEhxEkKbds2bLYriqGf6O3I2A9oLdjYyk5ILBy5UofroFFauG6mMmTJ/vVwuw1Yj8VYRwg9swtXbrUD7PYosK+uNRTNdjwapRMBMwBJVOvrS7VunXr3KhRo/z8TVVVleeHWMIjR450Ck/izpw546ZMmeJ/Z/6HuR/iy7CqmjO2mEsKCQdllEwEzAElU6+tKhWTxTiYw4cPu9GjRzt6QyHR62HPHNEBwm0NT5488dcMwQhTwh46NjkaJR8Bc0DJ1/F7lZBwHPRoFJzM75LesmWLd0RMJENECnj48GHz8IvfVq1a5dasWeMGDRrk36DRM4pjeBJkMcoOAduKkR1elrtACDDkoifEJk+j4kHAHFDx6NokNQQih4ANwSKnEmPIECgeBMwBFY+uTVJDIHIImAOKnEqMIUOgeBAwB1Q8ujZJDYHIIWAOKHIqMYYMgeJBwBxQ8ejaJDUEIoeAOaDIqcQYMgSKBwFzQMWja5PUEIgcAn8Des/5zVESaBwAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-5"/> </p>
<p>Another way to do this is to add histograms or density plots or boxplots to the sides of a scatterplot. I followed the stackoverflow page, but let me know if you have suggestions on a better way to do this, especially without the use of the empty plot as a place-holder.</p>
<p>I do the density plots by the zvar variable to highlight the differences in the two groups. </p>
<pre><code class="r">#placeholder plot - prints nothing at all
empty <- ggplot()+geom_point(aes(1,1), colour="white") +
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank()
)
#scatterplot of x and y variables
scatter <- ggplot(xy,aes(xvar, yvar)) +
geom_point(aes(color=zvar)) +
scale_color_manual(values = c("orange", "purple")) +
theme(legend.position=c(1,1),legend.justification=c(1,1))
#marginal density of x - plot on top
plot_top <- ggplot(xy, aes(xvar, fill=zvar)) +
geom_density(alpha=.5) +
scale_fill_manual(values = c("orange", "purple")) +
theme(legend.position = "none")
#marginal density of y - plot on the right
plot_right <- ggplot(xy, aes(yvar, fill=zvar)) +
geom_density(alpha=.5) +
coord_flip() +
scale_fill_manual(values = c("orange", "purple")) +
theme(legend.position = "none")
#arrange the plots together, with appropriate height and width for each row and column
grid.arrange(plot_top, empty, scatter, plot_right, ncol=2, nrow=2, widths=c(4, 1), heights=c(1, 4))
</code></pre>
<p><img 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iSg3Sh+OZLhOlscAZqgtOjtt7B3z+/4Ym5v9G5vXSvvOApL/L09pSseaROC6Qtfxd+H/8LCN982+Dg1i2tYHQpEXcrLPlyOH9dtwOyeH6FT3X46XG15Uan8S7224Z3VE3E94joWLX4HNOSBAxNgAkzA3ATYgmjuFuD8tSIQGRmJoYMfRWzkKfy9erzViUNlCD3ahOLvz8YjLvI0hg0ZBKo7B4hd76NGjMZfO48JXcp/SF4cKtq0lm9DLB+5ExeORmDihKdgjnHWirLwNxNgAkxAQcCoApFm0m3YsAErV65U22WWkZGB9evX49NPP8WePXsUZeJvJqBC4LfffsNjw4ZgbPdgbF40BP7epnN2rFIQE+74CXWkuo5+uKZYd2Ig50CroQzoNxD1HDtgybCtqO5pGUsXGqpNvFyqYfGQTXBIqYaRjz0OGl/JgQkwASZgTgJGFYgHDhwQHQHPnDkTcXFxCA8PV6nrn3/+iSZNmuD555/H9evXcfv2bZXzvCNvAtSduHjRO1j01uv48a3BmP14e9Asa7kEqusLozuIdX/37dfx5sLXkZeXJ5fqi/VMSkrCzOdn4e3XF2Nen8/xVJfXYW/nYJUMHOyd8HKfz9DcuweGDx0hPhOtsqJcKSbABCRBwKiDXahrbNy4caIT4NatW+PatWsICwsrATNo0CBxm34EyHEvTeNXDqdOnQJZGRUhICAA/v7+il2TfZMzVvrIKZBDZXMG8tM3+aknkZEYjSNfPIlADUvlVbWc5AqFfANaaujRti6OfjkRTy7agfFjR2PN9z/ovQKLk5OTKLLN3b7asN64cSNef20hOoT2xldP/g13Zy9tLlOJQ+1KY/osxQ+iSuEq2Jn+yCIEnArC4yNH46dNG9C2bdsKYpY/TC8V9Bwl349yCHJ6NpPPR7qXpfC3K4d7Tw51NKpApB95cuxJgb6VxZ4CbmpqqtjNTDc9LWmnHKKjo1Uc+tKDj5w4mzpYuoAwBg8SEuYKZE0eNWIYOjaqhs/eGq/1iij6lpd+VC3dMhkU4I0/VozD/NUH0LtnD6z78Sd07NhR5yrTvUx1NWf7Vlbo48ePY/7LryEu+gEWDPgabev0qOySCs9TXaUkDhUVebzjDPi4+Yvdzet+XIuePXsqTmn8pvqSkJBinTVWrIKTcno2kzikFx5L/tutoJn4sEQJ6CUQx48fj759+2LEiBFwd3evsOq0NFBOTo5ofaOVSNTFpZUdZsyYgYMHD4qfRx99tCS9oUOHlmzTBi0xpIsXeZWLq7Ajt7WYqZ3MwZmaiFbEeGbaFMwY3krwD9geBfm5yBQ+xgz04JXKyhPvTO6CZrW9hS7IwVjwxpsYPXq0Tmgs2VH2hQsXsHL5xzh58iQebzMLgx9/WuxOrsrqSSQgTLmMok6NUUnkh+oOhgOcBd+YT2D5ig/Rr1/lM7bpmUs9MlK5nytBUOlpOT2bFY6ydXk2q/vNrRQqR2AC/xHQq1/tiSeeAA0ar127NiZNmiQKO3VvrGTto2XQKNy8eRNl14XcvHlzybhD6hLhm1lEJdv/yPff5Kcn4sNnHxbFoWxBVFLxx3s1wa8fjMCq5Uvw9lsLJSuAqJr03CCfhuNGP4HxYyagZm5LfD3hKIa3ecZqxxpW0rwqpzvU7YM3Bq7Byy++gl9++UXlHO8wASbABIxJoEpL7VEX8qZNm7BlyxZxQPWTTz6JqVOngtYDpUBvsnSOBtbTWJHJkyeL3VuLFi3CnDlzxO7j7du3i13L1DUyduzYct3MypUnC6I5XEDI6S2V2iE0NLRE2CvzN+b2mjVr8L+Vy7Fu4SB0bm7aYQRSsiAqt0FcYgaeeGcnXHyC8OnqL0DW+MqCpVgQ09PTxWfDd9+sRU5mPgY3nYy+TcfCxVF1HHJl9ansvJQtiMp1uxp7Bm/teALzF8wTn5PK55S3eak9ZRrWta2wINIqUtoGMuJwYAL6EqiSQDx79qw4fnDr1q2iKKQxUWQVXLJkicpDjLqXaVxMRaGy84rrWCAqSBjv2xwC8YMl72H7L5vx87vDEBbqa7zKVZCyVAUiVScnNx+zVu7HqcgUfP3t96hTR7PzcHMLRBpX/K2wasjmzT+jYY2WeLTp0+hQpw9oZRFjBGsRiMQmMv4iFv42DrNfnCH23KjjxQJRHRXrOMYC0TraUUq10OupvGrVKjRr1gw0C5mE3+7du3H48GEsW7YMa9euxUcffaTCQJM4pIiVnVdJjHeshgCNk5r74hz8+cd2/CGsRWwOcSh1mE6O9vhiXj+Mf6Q2Hhs+FEeOHLHIKt24cQMvzJyDvr374d6ZHHz42HYsGrxRdHZtLHFokSCqUKh6/s3w/tCf8b+PPsOXX3xZhZT4UibABJhA5QT0mqRy6dIlLF++HH36CG/+/7kHIbM3va03bdoUH3/8ceU5cwxZE6CJB88/+wwyE2/j92Uj4e2hOoNd1nD0qPycMR3QIMQHzz4zFfPmvwaaSGYJgQbUL//wI9BQkgHNJuCrCUfE2bmWUDYploFWXXl/+M9YsHo0cnPzMGPm81KsBpeZCTABCRDQyYJIa+HS5+jRo+jevbvwgMoV9+nHftasWaDVHvz8/NC5c2cJVJ2LaC4CJBrGjh4F14J4bHt/OItDAzXEoIca4LelI/DZx8vw1ptvmHXyCk0++eGHdejZozcSrhTi8/GH8PRDb7A4NEBbB/vUF1aT2YK1367HyhUrDZAiJ8EEmAATKE9AJ4FILmjIjcLFixfFb9qmD80+/ueff9ClS5fyOfARJqBEgJynjxg+BO3qOGPt64/CWegi5WA4As3rBeDAx2Nw8cRfmPTkeNEBveFS1y6lmJgYjBk1Ft9++gPefHQtXuj5EXzda2h3McfSikCgd218MPwXbFy3FR8sWarVNRyJCTABJqALAZ0mqZBVgHyK0WxlGmuoCNTNrOhqVhwzxjdNUjHHUmNSnsSgazvQJJXq1asbZS1YeomYOuVpvDi6rbBsXgddi2a0+HTvWtvKE8WTV/bhaHgCvvt+HRo1aiTyo4HuVF9jeQOgNdVnzXgBfZuMxcTOr8LBruLJaUZrUKWE6X5W54JLKYqkN++nxmD+L49h4LC+eGfR26DVpug5KVXfj7o2hpyezeRNQ+HnUltONWrwi5m2rDheeQI6CcTyl5v2CM9iNj5vY81iXr9uHZZ+8D4+ebE3qCvUkoI1/8h8tvU0lq4/jg8+XIb+/fvDWLOYSYR9tHwFfhDE6Iu9/od2tR+xiCa2plnMFQFNzIjDgu2Po2vPDvj+h+/Flzt2lF0RLeke51nM0m07qZZc6y7mY8eOoVWrVmI9afxhw4YNy31oNjMHJqBMgMapvvbqK1j9yUfi+DhLE4fKZbXG7ecea4O1bwzE66++jKVLlxjFskTjkp995jns2rIPK0b9bjHi0BrbU12dqrlVx5KhW3H8r3N48omJsllFRR0LPsYEmIDhCGhtQaQuKVojt3Xr1jh//rzart66devCx8fHcKUrkxJbEMsAMcKuIS2INBbt+WenwdM+C9/M7w8/7+J1uY1Q7Colac0WRAWY6PupmPDuLnj5h2LN9z8YbEhIYmIinnpyMpxzfDGvz2qDO7pWlF/fbzlYEBVs0nNSsPiPp+Bf21NwnP4/WazZK6dFDNiCqLjT+dtUBLS2IHp4eIjikArWokUL+Pr6om3btuKYiP3794sPI2OKQ1MB4XwMQ2Dv3r0Y9OgAPNLUE1sXD7NYcWiY2lp+KsEBntgtuBOqXy0XXbt0Ete8rmqp6QVgxLBRqGnXBK8PWGNx4rCq9ZPa9e5OXlg+ejvS7hRg/NgJSE1NlVoVuLxMgAlYEAGtBaJymefOnSt68s/JyUHPnj3FGcw0w/nOnTvK0XhbhgTonnhz4et47ZWX8OXLfbDwqYcE/5h63WYypGfcKpNT7f+92BeLp3bBlKcn4bNPP9V7Aoc4G33YSHSqORgzH/kQdrZ2xi08p64VAWcHV2Ht5u/gnRuKx4aOAIl4DkyACTABfQjo9cu9ceNG0echWQ7Javjrr7+Kjnn37dunUgYaKL1hwwasXLkSu3btUjlHO9Rt/dNPP+Hzzz8XHenKZWB1ORBWcuDKlSsYMmgAbl/5F4c/G4/e7TUv+2Yl1ZZcNcb2bY69K0dj59Z1mDhhPOLj43WqAznKHzViNAY3mYYJnebpdC1HNj4BezsHvNh7FdpVH4hhQx7DuXPnjJ8p58AEmIDVEdBZINJsRRJy5PuQVkcYMWKECIUmI1A3tHI4cOAAaJr9zJkzERcXh/DwcOXToPMNGjTA9OnTxeNnzpxROc870iBA98TXX32Fx0eNwMRetbHl3aGoXs1NGoWXaSkbhFTDvlWjhS7nbAzs3xeHDh3SisTp06cxbsx4TGg/H8NbP6PVNRzJPASe7PQKnmg7X+xupkUMODABJsAEdCGgs5dimsTQqVMnDB48GH///bfYvfzNN9+IfhEXLFigkjd1Q40bN05cgo8mt1y7dg1hYWElcXr06CEKTTrg4OCA5OTkknO0kZGRoTLrknwgmsLfokohhB3K0xz5li2HKfapfSloW18S/i++MAsJsbewW1hPuWkdf1MU06B5UJ0V9TZowhaYmKKe9O3i5ICPZvZCz9YRmDXzOYx8fAzmvTK/wrXR6e/9manP4vnuS9C90TALrF35IsmpbRW1V65z36ZjEORdB28umIoL5y9ivuBRgCZlWUuQ27OZ2lbbZ7O1tDHXw3wEtJ7FrFxEWlpv06ZNaNasGdq1a4fff/8dzZs3R3BwsHI0vP/++6DxiiT+SByeOnUKY8eOVYlDOxEREfj555/xwgsviJNeFBE++OADFYfNNM6xV69eitP8bSQC9BDSxrkwDRuYNHECxvZugg+e7wUa48ZBmgSi76dgwtu/IjXPGRs2bi5xrK2oDQ0jIRcqCwZ9ja6NBioO87dECJBD7bd+nQD3AEds/HkDgoKCJFJyLmZVCNCznAMT0JeAXgJR28xo7OGUKVNEKyEtz3fz5k3R8qh8/eXLl7Fjxw5MmzYN3t7eyqfKbbObm3JIDH6AHiihoaG4detWhWnTKg3Lli3Fpg0/4lPB8XW/TvUqjCuFE3Jwc6NoBycnJ9ECkZWVpThU8l1YWISVm05g1eZTeGX+a+K4YjpJL29vv7kIr/X/Gi1DHiqJL4UNObm5ofbw9PREenq62pWB8gvy8N3R9/BnxGYsWfo++vXrJ4Um1FhGdnOjEQ9q166tOQKfZQIaCOhl8qGupjfeeEO07ikvUbZq1SpxtQZFfmRRJKFBlkYSh2XfWmlMIrlDmTFjBlxdLdNHnqIu/F1MICkpCTOeewZZyfdw8JOxIPcpHKyDgK2tDV4c0wE9WtfC1KUrcfDAPjRu2gI/rFmHd4f8hAbVW1pHRWVaC5q8MqXrm2gV3A0L5r2IP3bvxVtvLxRFpUyRcLWZABPQQEAvC2L9+vUxefJk0BhC5fEsdFzZFyKJiS1btohOtambma4hC9WiRYswZ84ccXYzTXiht0AKbdq0Qd++fSssLlsQK0RjsBOaLIg0FGDyUxPRvXkAls14BI4O1uHahC2I5W+ftMxc9Ju9ETfvZGLeI1+iU/Pu5SNJ4AhbENU3UmpWIr74+3VcjD2Ktxe9iYEDpTlsgC2I6ttXcZQtiAoS/K0PAZ0FIo1Nq169ujgrWdvxDTTD2dHRUZ/yqVzDAlEFh1F2KhKIR48exXPTp+Hlce3x7PA2RsnbXImyQFQln5mdh6mLf0fkzVw8XW8Wjp5JRP2GjfDww93E8cSqsS17jwWi5vY5fmMvVh9+DWFNG+KtRQtRp460XFOxQNTcviwQNfPhs5oJ2Go+Xf4sCQgau/Ltt9+ChJ82wRDiUJt8OI5xCOzcuVOYvToZ/3uhp9WJQ+MQk26qMfFp6D97E1LjPLG81xK0q18XTw9qgYzEu1i/7gfcu3dPupXjkpcj0KFuH6wedwg18pph0MDBWPzue7wCSzlKfIAJyJOAzgKRMJGDa5p8QsvtkdsaxWf37t3ypGjFtd4oODJ//bVXsPGdIXj0oQZWXFOu2rFLMXjkuR/RzL0T3uz6Glzsi4d+uLk4YuQjjdCxsT+2/bIVR/75R8X9FJOTNgFnBxdM7PwqPh69B+H/xKBb1+74+utvQKsicWACTEC+BHTuYiZU5JmfrIc0fpBmzJHTbOqmKzsG0dBYuYvZ0ETLp6fcxfzD2rVYteJDbBHWUm5Rv3r5yFZyhLuYgW9+O4u3v/4HM9pOR+86FY83TE7Lxs6jN5BTYIe+/frD39+y/V5yF7Puf6SX757A98ffQ3zmHcx5aba4GAL9jVhi4C5mza3CXcya+fBZzQT0siB6eXnhiy++wNChQ/Hvv/9i/fr1UCy7pzk7PisVAiQOP165DL9+MMKqxaFU2sNY5aTxhs+8vxsrfjiHZT3f0ygOqQzeHs4Y16cxmod6YPPmTTh27JhalyrGKi+na3wCTWq2xwfDfsFzXZbim4/XoWeP3ti6dStbjY2PnnNgAhZFQC8LYs+ePdG7d29Uq1ZNXMd14sSJGDRokOgvrWHDhkarIFkQaTUVUwc5WZjIgkhOkV97ZS52LhuFJhJcGUXX+4NWJlB216Tr9VKKT94EqI2pB+Da7QSMf2M7fO2C8Uqnl+Dh5K5TVRJSMrHjSCQK4ID+Ax61SGsi1VUbp+86VdyCI7u5uYEWMjBknf+J2In1J5ahwD4bc195EcOHD7eY1Tzk9Gwma6mLiwvIO4i2gZa65cAE9CWgs0CkBw/ddNHR0aLl8M6dO6JPxMWLF4tjEhXrKutbIE3XcRezJjqGObdt2zYsEnyj/fLeMDSvF2CYRC08FTn9yCgcZa/dcQpz//cnRjceKXwe03upwSLBufbxK/dw5GKM4KaqLdq3by8urWkpTc5dzIZpCXru/3N9Jzac+gg2TvmYM3e2aBQgAW7OwF3MmunXZkfZmgHxWY0EdO5ipgcCjTm8cOFCScJkfaHVUAIDA0uO8Yb0CNAko7cWLsCuj8bJRhxKr5WqVuKsnDxMf38nXv/sKN55+A2MaTJCb3FIJbERnGt3bFoTE/s3w50bV/DThvW4f/9+1QrJV1scAXrud20wCJ+M3o/Hm76EZe9+jL69+uOPP/6wuLJygZgAEzAMAZ0tiJQtOb+mbmXymUVdVvSD0KRJE3FNZnpjN1ZgC6KxyAJ//fUXZj7/LDa8PQQDH26G5ORk42VmYSnLxYIYcScRk97ZBS8E4uUOc+DlbNhVcMjKdCo8Fn+fj0bzFi3RqVMnFUf65mh2tiAah3phUSEOhv8iWBSXw8ffE/NenYtu3boZJzMNqbIFUQMc4RRbEDXz4bOaCeglECnJq1evgvzj0ZjAxx57DA0aGN8FCgtEzY2p71maaDRN8HP4zfz+6N2+jrgmNgtEfWla5nWb918Wu5THNhmFcc1GIV9YT9tYISU9G7//exOpWYXo1bsPaMlNcwUWiMYlX1CYjz2XfsJPp1agXoM6mL9gHlq1amXcTJVSZ4GoBEPNJgtENVD4kNYE9BaIWudgwIgsEA0I87+kTp8+jacmPolPXuyFQf/5OfT29mYLouFRmyXF7Nx8zBPGGv5x5DZe6/IyWgc2F7uU8wQXVcYO56/H4c/Td8SXx67CKiw0/tHUgQWiaYjn5mdj+7lv8fPpT9Hloc545dWXTbIqCwtEze3LAlEzHz6rmYDWAvHkyZOYNm2axtSWLl0qzm7WGKkKJ1kgVgGemkvPnDkjiMMJwrrKPTCiR1hJDBaIJSgkvXEjJglPvr0TbgUB4ixlb2cv2AtDQGg8mSkEIsHLyMrFvpNRuHM/Hd17PAJjejlQ11gsENVRMd6xtOxkbD79CX6/sFbsWXrhxdni5EVj5cgCUTNZFoia+fBZzQTs3hKC5ijFZ8l9Qps2bTBgwABxua0CoYuKBOP48ePFqfenTp3Ciy++CE/P0nFN5Eh748aNoo/ExMTECruht2/fLnZVBwRonjWblZWl9fJ+2tRJ2zjWOEaNLIdPT3oSS5/thpE9G6ugkNNDlypujW5ufj18DWNf347ewf3xQvvn4SKslqGoKwlEU7n1cXSwQ1ioL3w9nfHn0bOIirqNwJo1QfeYKQK1rSFdvpiizFXJg6y05MLIXHV2ElbfaR3SDT0aPoYjpw9i8YqFoPGKLVq0MMp4VGt8NlfU/rRkLX0yMjIqilLuOL3sc2AC+hLQ2oKonAG9lZDAID+IijBnzhy0bNkSkyZNUhzCnj17xEksNHh5reB4uXPnzuKyfIoItJQTCUgazzhq1KhKx66wBVFBrmrfR44cwbPPTMXymY+oWA4VqbIFUUFCet+5eQV4/fND2PpnBF7tPBeta7RQqYSpLYjKmeflF+Cf8zE4ExGHtm3bCp92RhENynmyBVGZhum3I+9fwJp/F+FuWqTgQ/El0apIot1QQU4vs2SkoY8uXgLYgmioO02e6ei1fhI9dGNjY1UEIrm96dWrlwrFyMhIjBs3TvSL1rp1a1y7dk1FINKbULt27eDj46NynWKHRCWJQkWgWZGUjqmDNVmYaAb6c88+g28XDMaQbqXdymWZkisjuQSyqJnL4mJIxjfvJmHMa1thk+WFbwZ/Bl/X0hc4RT6i1zqhvo4G/JFWpF3Zt2D8QN9O9dG6UaAwieUawq9cEZfrM8UEt8rKZi3n6V52dXW1mOq0dO+MlXV34d/re/H5ijew5uvvseTD99CnTx+DlNGans2VAaG60m8vO7+ujBSfNxQBvQTiq6++iu7du6Nr166oKXQX7d27F7Vq1So3/pBmwioeVvRd1jROFkj6kJBUF8jiqLxgPKWRkpKiLqpRj5FZn7ptpB5WrVqJzz/7BJveGYouLUKQnZ1drkr0A0PdNurOlYtsJQeswcq07a9wzFy2F0MbPIoJHcfCztZOXCu9bBNRXYVuA6POYi6bZ9l9Hw8nYbm+Jrh8Mx67dmyHn38AejzS0yhj1ehHlcS/NbwAlOWobp8sTPSsMtUQAnVlUHesVfDD+GTMAey5+BMmPzkNDcPqY+Hbb4i9Turia3vMWp7N2tSXrKX00eU30FRDObQpP8eRHgG9BOKUKVPE8YgHDhwAjS187733MGzYsHLdRbQsEAk88pVIDy1drVJlLQvm6mKmHxcpC6a0tDS8Mu8lRFw+hz0rHke9IB+14kH59qXxo3IKUq0vraU8/9OD2H0kCgs6v4I2gcVdyhUJBFvhBUCYpWIRAoLGJtYL8sa/l+5i/bof0LhxE3QSXgrpuWGoYA3iXxcW9Kyie7mi9tclLWPE7d34cTzcYDB+Pfs1RgwbhW7du2HuvBf1nvEs9WezLozpZYd+S2ksPgcmYAoCeglEKhhNWKGPpkD+z27duoVmzZrh5s2bCAoK0hSdzxmBAM0+f2nOLLSu54P9q0bDw1Xo5+NgFQTOXIvF1MW7Ud2xFj7vvwo0S1lqwcHeDg+3DEHL+gE4dPYOvluzBu2E5fpoKAlZsjlYHwEnexc83m4mBjR7AptPfYJBAwdj8ODBmPXCTLFHyvpqzDViAtIkYLjRwmrqT2MSyQnz6tWrERcXVzJ+cNGiRUhPT1dzBR8yFAHqhlj4xuuY8vREzB3VHGteG8Di0FBwzZwOTfZY8v0RDJ27BYNqjcCibgslKQ6VMXq6OQl+OOtjTO8w3Im8jO+/+xaXLl2STdewMgu5bHs4++Dph97A6vF/Ie2aPXr37IM333hLp0kYcmHF9WQC5iCg1yxmXQtK3cs0VqSqwVxdzFKaKUfdDz/88ANWC2MNe7QOwbtThXGifh46oedZzDrhMmnk01djMePDfXDI88JLHWYj2LOmTvmbcxazLgUlH44Hz0Sj0MZecLzcFfXq1dPl8pK4cutiJjdj9PJtqV3MJQ2jZiM25bawIstK/B3xG0Y/PhrTn3sG1atXVxOz9JCUns2lpdZvi2cx68eNr9KfgEkEov7FU72SBaIqD+U9mlW+4cf1gjhci+Z1/bDgyY5o31g38aBIjwWigoTlfNPydYu+PYLN+8Mxsfl4DGk4ALY2uncASEUgEnkaX3b55gMcPhcNV3dPPNT1YZ2X7WOBaDn3sLYluZd8CxtPf4y/r/2G4cOH4RnB6wJNglQXWCCqo1J6rLbgko4DE9CXAAtELchZ6kOILAX79u3Dr7/8jGPHTmBAlwZ4dlhLtA0L1KJWFUdhgVgxG1OfyS8oxNpd5/Hed/+iZUBzPNNqCvxcffUuhpQEoqKSBQKDs4LvxCMX74Kc6Xd56GHxW3Fe0zcLRE10LPtcXOodbD27Gvsvb8Yjwiz3Z56dKjrcVi61pT6blctoqG22IBqKJKejLQEWiFqQsqSHUHR0NA4ePIgDe3fjqCAKWzQIxMju9fCYsFReNU/DzP5kgajFTWHkKIWFRdh6MFwQhsfgXOSJqS2fRsvqzaqcqxQFoqLS5AT8ZPg9HL98D6GhocKM5y4qvlgV8ZS/WSAq05DmdnLmA+w4vwa7Ln2PunXr4qkpE9G/f39xRq8lPZuNTZcForEJc/plCUhOIJrD3Qy5FsjLyyvLziT7NNnk6NGjOHzoL+FzUHBQfh/d29ZBv/a10L9TPdTwNaxDa/KD6OHhgdTUVJPUzxIysSQRQW5rNu67hFU/nYZtvjOeaDoe3Wp1FtdPNgQrEojk5kaqbn2IQXZOnugah8Zj0trOJBSVl/hU5iQ3P4jkSiwzM1OSYxCV203ddk5+Fv4M/0UQimuQmBmLUaNH4YkJ4yGXblRy/0S+gJUXj1DHSfkYew5RpsHbuhKQnECkh5+pgymdsZJz8RMnTgizv4/i3yOHEX41Ei0a1kT3ljXxSJta6NAkCLS+rbECCUT6sdXFGauxymKqdC1BIJ4TulDX7b6ETfuuoI5PLYxoMBxdgjsYTBgqWNoJrmNoNRUpC0RFXdKzcnFU6HY+H3lfXKGpY8dO4suN4jx9U9vShA0azyiHQC93tCCBFCep6NI+V2PPYM+VH3H42nZBINbF8JFDMHDgQKt2pUbikD4PHjzQGlVISIjWcTkiEyhLQHICkZw+mzoYsxsjKioKZ86cwamTx3Hy+DFcvxGFpvUD0bVZoOAfLhidmgWBXICYMnAXs/Fp09jCk1fu4o9/b2L7oUgkp+XgkdBu6F+3L+r6hBqtAFLuYq4ISmpGjigULwkrszQSLIrt2ncA3cMULEH8V1RuYxyX8ixmfXgUFOXhyPXdOHzjV5y88Sfq1amPvgN6o0ePHmjevDnIgmwtgbuYraUlpVMPFohatJUhBCK90d++fRtXhPVnLwv+3S5eOINz5y8KXde5wqSSYHQI80enpjXRvklNuLtU3SWQFtWqMAoLxArR6H3i7oM0XIyMB3WL/nshThhLFw1vF090CGyHzkEd0ap6c3F5PL0z0PJCaxSIiqqTUDx2+S4uCJzrCLM327RtJ1qUCgoKFFGs/ltuApGcqSus4dl5WThz+y+cuL0XJ6P+RH5hDjp17IyHunVGx44dQStzUQ+JVAMLRKm2nHTLzQJRi7bTViDSg4rczcTExIAmk5AgvHUjAjeEtaYjb94CrRrRtG4NwQ1NNbSq749WDWugYUg14S3Xsh5aLBC1uCnURElOy8bNe8m4eTcZt4TviNupwicJETEPQBMs6vkFo75XA4T5hqFFQFPUcA9Qk4pxD1mzQFSQo3GcJMTPXIuDl2BJbNGytSgO5LAyi5wFoqL9Fd9RCVdx7s4/uBgrfGKOoQiFaNO6Ldp1LF4FjCyMui7/qkjbHN8sEM1BXd55skDUov1JICYlJeHu3bvih0Sg+Ll3F/fj7gkfYT8uHvEJSXB1cUJwdW+E1vBC7eruqFfTC/WDfdCwli+C/HVzWK1F0YwShQVixVjJSnU9Okn4JBZ/CyIwMjoZUXFJSM/ORg1PoZ09q6OGa03UdAsSHVnX8gpGoHt1vfwWVlwS/c7IQSAqyJB7nPCoBJyOuI+E5EzUFyxIjRqFib4UranrUVFf+maBqEyjdJvGoN5Juo4rd0/iarzwuX8ad+5HIjSkDlq2aokWrZqhadOmwnrgjS1WNLJALG1P3jINARaISpxzcnIQSdY+4XPjxg3cFKx/t4W1pO9Ex+BBYjL8fDwQFOCNID93BPq6ILCaiziLOFCYSUyziUkAmnq8oFLxDbbJAhFISMnEtduJCL+dgPBbwveNJFy98wAPUtMR6OWLEGEFkyC3ENR0F4SgRyCCPGqguluASbqJq9LQchKIxMlW6FIsFMQBteclwel2uGDRzRIsjOR4uVZobbELmu53KXc9Kt8PLBCVaWjezsxNx/X75xERdw43Ei/gevx53H0QhcDqwcKLRCOENW2A+vXro06dOuLHy8u8a52zQNTcnnzW8ASMKhCpy3Xz5s3iOszkjoJmmSmHys4rx6VtQ6+kQoLwk//9D+FXLiLi2jXcuRuHAF9PwdrnhwZBnqgf5IU6Nb0FC6CvsFydG5wd7csWySr35SIQE1OzcCcuFdHx6Yi4k4Drd1IQEZWM60KXcFpWNoJ9qiPUKwQhbqHiN23T0naOduYdI1qVm06uAlGZGYnFGzFk9U1DTHyq0PVoAz/faqjm6wcvL29xJjTNFqWeAycnJ1DXNH3I6kgfEpOWKihZICq3tO7b2XmZuJUQjqgHV3E76Srupl1HdGIkYhOj4eYqGAhqBgsvFcJLYXAgAmvWEB22+/v7w8/PD76+vvDx8THIsrLqSs4CUR0VPmZMAkYViHv27BGdmXbr1g1r165F586dRXcUigpVdl4RT/FtaIEYHh6OUSOG471nuiGstp84HlCdBVB5ILSiLNb8bYkCkRxH5+TlCz7whI8wno/G9OXk5gvHhO//9rNpX/hkCXFoHBp9yBVKcnoOklJykZiSg/ikTNxPzkBccipoJnGAhw9qUpewS2BJl3CIZ5BgEQy0eGugPvcgC8Qy1ATrYpIwdjRe6IJOSMkS7pVc4eUgDxnCJzs3T7yf8vIL1LvJEYYOC3JRHENMwpFmTNPHwcFefO6ReywnRyc4kdB0dhEFJ4lO8mdHH8W2QoSWKZleuywQ9cJW6UX5BXmIT4sBre5yPy0aD9LvITErFklZcUjKjEdyhvBJTxSeRdlwc3GHt6ePOJOenqXePvTxhJePl/AC4iUOA6B2og+5JaJxkPRNIlPT0AcWiJU2E0cwMAGjmsSoq3bcuHHiQ7N169a4JljpwsLCSqpQ2fnjx4+LTl8VF9DC7bTUlqECWQlIeOw9cUv8VJQuWQvk4keNGChcg2j0HPefXzkxjvBf8XeR+E2niBd17RV/Q+RMrAsKSj/5wrb4yS8UZiIWIk8QbHm0Lcw6pR/lPPoWP/koEGaB6xvsbRzgai/4ELN3g5u9B6o51EAdX8FS5OBJP++CNUgobxYQm5WF2AeROIlIfbOy+OsUli853c86Nwq5Gf3P/zxNHyN7Md3ZivuavikQQ9rO/++ceH8L93hhUSEKcoV7O6tAvL/pnqb7mf4eKgrUFa6wVJKItxMmtNnZ2Yt/i6LlUjhGa28rWzDFthStmYLvc+EfBVshXlFlfyvFUSsqiqSOU72pbcwR7ISbxI8+LvUAWsTKD8gtyEZGbgoy89OQ+SAVt+/F42reDWTlpwufNPHeqKisz0ybjsXvvVvRadEySfcIiUkOTMAUBIwqEMnpM4kwCvRNDlyVQ2Xn4+LiQHEUgf4w6G3bUIHcHrRo1QoXo+M0J0kPVPM8gzSXy0hnaVY1/dhVORQV/xL999NVMUOK4CD8uAkfal3DtXD5GhQhD6lIED8lZ0khyqSBSwSiTOor6iYD3MrivVJ8I5fcNobeyNElQfrbUlMvm0LbygWiLvlYelzx7U4NCHOWm14w6CM8yIRHmvBxhqfwERWk8H9FoVv3hzX+vimGORjyN7CisvBxJkAEjCoQqRuFxvnRUnW5ubnlZodVdn7w4MEqrURdzLp4kVe5uIKdDRt+quBM6WHqCjLHEn+lJTDdFgkIWuf21q1bpsvUzDnJqX2pi4ssxImJiWambprsqZuXlsmUi8WUVs64d+9eiW9A01A2Xy7W9rer6fdN0cWsKU7ZlpCSG5+yZed98xMwqpv54ODgEqFx8+bNcssgVXbe/Hi4BEyACTABJsAEmAATkB8Bo05SId+BW7ZsEd/gyYo4efJkcfbfokWLMGfOHPG4uvMVNUN6ujCOQxgnZupAVjW5WCBoxZeIiAjRzYOpOZsrPzm1L1kOyXuAIcfymqvdON/yBK5fvy72ANDzVg5BTn+7tMxsampqOUOLpnamGdYcmIC+BIwqEBWFou5l6uqpKFR2vqLr+LjhCVBX+ssvv4xVq1ZpnFFn+Jw5RVMQ2L17N+iHZtSoUabIjvMwMYH58+fjpZdeAgsDE4M3QXYnT57EiRMn8Oyzz5ogN86CCZCXBhMETeKQsq/svAmKyFkwASbABJgAE2ACTIAJ/EfAJAKRaTMBJsAEmAATYAJMgAlIh4BRZzFLBwOXVEGAxvSQOyH65mB9BMhFBs3q5WCdBOhvV5OzZeustTxqReNKaSYzByZgKgImGYNoqspwPkyACTABJsAEmAATYAJVJ8BdzFVnyCkwASbABJgAE2ACTMCqCLBAtKrm5MowASbABJgAE2ACTKDqBHgMYtUZSj6FU6dO4fTp0+IKG926dUP9+vVV6rRnzx5cuHBBPEYLzE+dOlXlPO9YJoErV67g4MGDot9DcmtTo0YNlYJWdl4lMu9YFAFyVbRz505xKdKaNWti4MCB4lrOikLS+S+//FKxi379+qFZs2Yl+7xh2QTob3PXrl0lhZwyZQpoFSRFoNVUtm3bJi5f2717d7QSlozlwAQMTYAFoqGJSiw9Wh+bRMTs2bPFZRE//fRT0Q+i8iSVS5cuYfr06aI7IuXjEquqrIpL/ix/++03zJgxQ1xW76effsILL7xQwqCy8yURecMiCRw4cAC0lnzbtm2xfft2nDlzBu3bty8pa3R0NOrUqQPFcqW0vCIH6RCgxQr69OmDxo0bi4Uu6/h806ZNYttWq1YNn3zyCRo2bAhXV1fpVJBLKgkC3MUsiWYyXiHJB+W0adNE6wMtBp+SkqKyagytrEIi8uLFiyBLI+1zsHwCcXFxoHV56UeDlrSkFYhoBRVFqOy8Ih5/WyaBHj16lFiNSDwkJyerFJQEIv0908tffHw8z2xWoWP5O9R+mZmZOHz4sNrVw2hFFfr7plnNdevWRVRUlOVXiksoOQIsECXXZIYtMP24kGsMEg8//PAD+vbtq/JjQl1VtOA7fWipw88//9ywBeDUjEKABIOyRcHFxUUU+orMKjuviMfflkmAuhvJKkiWprNnz6Jr164qBaXVqcjaT93PGzZsEOOpROAdiyagcFVEQ3pWrFgh9u4oCkzCkcS/ItDfOb3Ec2AChiZQepcZOmVOz2IJUHfjtWvXQA8f6nakH5Nvv/1W7Kag8SzKgX6IFF2TTZs2FccqkpVReTyMcnzetgwCzs7OKj8q5PtQ2YdaZectoxZcCk0ELl++jB07dohLr9ELgHJ49NFHS3ZpHXlapo26pDlIgwAN6VGEO3fuiGPA27VrJx4iX6b0zFYE2qYXeA5MwNAE2IJoaKISSG/MmDFYuHChKPyoy/irr74SxzL17NmzXOlpMPTatWvF42RlpIcRWRw5WDaBwMBAxMTEiMMFqHuZRIKy1aGy85ZdOy5deHg49u7dK44x9fb2Lgdk69atJd2ONJyALIkcpEGAnsmrV68uEYFl248sxzQ0iKyG9Hd9+/btchPQpFFTLqWlE2BH2ZbeQkYuH81eXrduHQICAkpymjVrFn799Vc0adIELVu2xJYtW5CUlCSOZXrkkUfQqVOnkri8YbkEaPwSjR2lYQJDhw5Fo0aNxO5GRbuqO2+5teGSKRN49913xWEhZAmm0KZNG3F4yKJFizBnzhzx75Umr9BLAU1IIs8DykMOlNPibcsjcOzYMXHMN1n+q1evDnqpP378OMiaOGLECHHIAHmXoJd2+numCS0cmIChCbBANDRRK02PHlQ0LoZnQ0qrgekHhNpNMaapbOkrO182Pu9LiwCJQ4WIlFbJubRkSaTnLnUpVxTofNkZzhXF5eNMQFcCLBB1JcbxmQATYAJMgAkwASZg5QR4DKKVNzBXjwkwASbABJgAE2ACuhJggagrMY7PBJgAE2ACTIAJMAErJ8AC0cobmKvHBJgAE2ACTIAJMAFdCbBA1JUYx2cCMiBw8+ZNNG/eHNevXxdru2bNGtB6zrT+69NPP11CgFxttG7dWpwpTX75aJY7+cgMDQ0VHfxSxHPnzmHSpEniesFhYWHs1LeEHm8wASbABCyXAAtEy20bLhkTMBsBWseXXGeQy5S7d++K63O/8sor6NatGzZv3iwuyUiF27VrF/z9/UXfmE888YQoAik+rf7w8ssvi+tAkx9GcqXUoUMHLFu2TMVht9kqyBkzASbABJiARgI8i1kjHj7JBORLgKyDtHoOrbhDK3O8//77IoxBgwZh7NixGD9+PEaPHo1+/fqJVkVarYOsieS89/z586BVeegY+dDs3bu3uFSjfGlyzZkAE2AC0iLAFkRptReXlgmYjAAtzffss8+KzrZnzJhRki+JQ3KeTpbBffv2Yfjw4eK5+Ph4PPzww6LT9blz56KgoADky41CcHCw+M3/MQEmwASYgDQIsECURjtxKZmAyQkkJydj1apVoCUY58+fX5I/rcpy6NAhcbWdhx56CD4+PmJXMq3w8NJLL4ld0vv37xctiWRNpMAO1kvw8QYTYAJMQBIEWCBKopm4kEzA9ARI7PXv31+0FpKlcPfu3WIh3N3dRdG4YMECcQkwOpieni6eo65kWrljw4YN4hJvtNIDBybABJgAE5AeAXvpFZlLzASYgLEJHDhwQLQQhoeHi7OSly9fjunTp4vdzSQQaW3YHTt2YMiQIWJRatWqhYkTJ4prd/v6+orrw9Ka3deuXUNISIixi8vpMwEmwASYgIEJ8CQVAwPl5JiAnAnQxBYbGxu4urrKGQPXnQkwASYgeQIsECXfhFwBJsAEmAATYAJMgAkYlgCPQTQsT06NCTABJsAEmAATYAKSJ8ACUfJNyBVgAkyACTABJsAEmIBhCbBANCxPTo0JMAEmwASYABNgApInwAJR8k3IFWACTIAJMAEmwASYgGEJsEA0LE9OjQkwASbABJgAE2ACkifAAlHyTcgVYAJMgAkwASbABJiAYQmwQDQsT06NCTABJsAEmAATYAKSJ8ACUfJNyBVgAkyACTABJsAEmIBhCbBANCxPTo0JMAEmwASYABNgApInwAJR8k3IFWACTIAJMAEmwASYgGEJsEA0LE9OjQkwASbABJgAE2ACkifAAlHyTcgVYAJMgAkwASbABJiAYQmwQDQsT06NCTABJsAEmAATYAKSJ8ACUfJNyBVgAkyACTABJsAEmIBhCbBANCxPTo0JMAEmwASYABNgApInwAJR8k3IFWACTIAJMAEmwASYgGEJsEA0LE9OjQkwASbABJgAE2ACkifAAlHyTcgVYAJMgAkwASbABJiAYQmwQDQsT06NCTABJsAEmAATYAKSJ8ACUfJNyBVgAkyACTABJsAEmIBhCbBANCxPTo0JMAEmwASYABNgApInwAJR8k3IFWACTIAJMAEmwASYgGEJsEA0LE9OjQkwASbABJgAE2ACkidgb8oafPPNN0hOThazbNKkCQYMGKCS/Z9//okLFy7A09MTY8aMgbOzs8r5sju3bt0qe8jo+1Sm7Oxso+djCRl4eXnBzs4OiYmJllAco5dBTm1bvXp1pKWlITMz0+hczZ2BjY0NHBwckJuba+6imCT/2rVrIyoqCkVFRSbJz5yZULsWFBSgsLDQnMUwSd70fPLx8cG9e/e0zo/uBUsPV65cwa5du0qKOWXKFNBvjyI8ePAA27ZtQ0ZGBrp3745WrVopTqn9VugCd3d3uLi4ID4+Xm08Tc/70NBQ3LlzR+19pem6atWqifdjSkpKuTw1PYfoPqZncnR0dLnr6ICmPGvUqAHKLysrq9y19vb24rV+fn7lzmlzwGQCkR7OSUlJmD17tlguW1tV4+XNmzdx48YNPP/88/j777+xd+9eDB48WJs6cBwmwASYABNgAkxAggQiIiLQp08fNG7cWCw9iSXlsGnTJlELkPj65JNP0LBhQ7i6uipH4W0jETCZQLx79y48PDxE8UeKNywsTKVKkZGRaNmypWixateuHT7//HOV89evX8f9+/dLjgUEBMDb27tk31QbdPOSKpdDcHR0BAl5ehOTQ5BT25JlmN5Ky76oWWM705s71ZfuZ7kE+puVgwWR2pWsh3KoKz2frPF5TFYz+uqUHZgAAEAASURBVD0/fPgw6Le/rEBMTU1FSEiI+Kdbt25d0TquEJPUC0K9jsqhfv36IicnJyfxt7qi36/Knvdubm5q7ytN19E5uifV5anpOUTX0Hl111HdNOVJ19KznL7Lhqo+302mdNLT00ENFhgYiBMnToAshgMHDiypD3U916xZU9wnszCZk5VDTEwM6E1DEfLz88W0FPum+qZGkItApJuSblpqDzkEObUt1ZUEk7qHijW2NT0o6X6WS6AfDDkEalcSh3IQiFRX+ljb85jqRIGGlq1YsQLz588XtQIdoyEwyr+3ZDlU1gZ0/vLlyxS1JDRr1kx8rtF19HyriFdlz/uK/oY0XUd5aroXqa7qnkP0O6vpt1ZTnpSmQgyXQPhvg9KsytAakwlEajT6UKD+/eXLl6sIRGrEnJwc8XxeXp5obRR3/vuPxh7QRzkoxhooHzP2Nt00PAbR2JTNk76c2pbHIJrnHjNFrmT5oHFbmn6oTFEOU+RBP7ZyG4NY0Zg6dbzpXrD0MH369JIi0rg/sgiSJZECCR9lgUPbylY2eo7RmEXloNAFFI90RUW8ND3vSaMkJCSYfAyiPmXVZgyiMh9dtlUHAupypY5xT506hb/++ku8iiBQpZRDcHAwFA1L1kWFNVE5Dm8zASbABJgAE2AC1kGAhgesXr26RATGxcWp/PaT5Yx6OshqSC88t2/fLqcdrIOEZdbCpBbE7777DjSWkG6CCRMmiEQ2bNgAmtHcvHlzhIeH44svvgCNOXjmmWcskxiXigkwASbABJgAE6gyAeoebdOmDb7++mtQzyFZBMk4dPz4cXEW8YgRIzB06FCQdqBhZaQVzDH3oMoVlWgCJhOIZCom0UfdyLStCGPHjlVsYvTo0eKbhJwGk5dUnjeYABNgAkyACciMQMeOHdG+fXtRICq0QYcOHUAfCg0aNBA/JCDVjd+TGS6TVtdkXcyKWiluAMV+2W8Wh2WJ8D4TYAJMgAkwAesloJhooamGLA410THOOZMLRONUg1NlAkyACTABJsAEmAATMBQBk3UxG6rAnA4TYAJMgAkwAXMQqG0BK5NQGRQTOs3BgPOUDwG2IMqnrbmmTIAJMAEmwASYABPQigALRK0wcSQmwASYABNgAkyACciHAAtE+bQ115QJMAEmwASYABNgAloRYIGoFSaOxASYABNgAkyACTAB+RBggSiftuaaMgEmwASYABNgAkxAKwIsELXCxJGYABNgAkyACVSdAC0bVzbQetL04cAELIkAC0RLag0uCxNgAkyACUiGwNWrV8W1gmmBB/r4+Pjg0KFDcHZ2FtcNpoq0a9cOr7/+Ov7++2+EhobC19dX/KalZdevX4+QkBCEhYWBlpXjwAQsiQALREtqDS4LE2ACTIAJSIZAvXr1cOPGDWzcuFEs89y5c9GtWzdRAK5btw7nzp3D6dOn8dRTTyErKwurVq0SfRhmZmZi7969KCwsRHR0NJYtW4aPP/5YMvXmgsqDADvKlkc7cy2ZABNgAhZNIF/oeT3zpjMSz9ghsFc+ms/PgY2FmzDs7e2Rk5ODadOmYcKECViwYIHImPa/+uorJCQkoGfPniAheeXKFbz77ruga6g7OT09Hd7e3uL6wkOHDrXotuHCyZOAhf/5ybNRuNZMgAkwAbkRuLraEbe3OCD9hi0ivhK2t1m+/YIE4IABA1CrVi288MILoG7j/Px8TJo0SbQUkkicOnWq2JSvvvoqevXqhYMHD4rd0UVFReJxJycnuTW1SeobGxsLsuJy0J8AC0T92fGVTIAJMAEmYCACGTGqP0eZZfYNlI1Bk9m3bx8iIiLEbuQWLVqgcePGuH//vjjOcOTIkeJYxOHDh4t5koVx9erVaNu2Lfz9/UUBadDCcGIqBBYvXozp06eLFl6VE7yjNQHLf0XTuiockQkwASbABKRKoO7YPMT8bo/CHBs4eheh1rA8i6/K6NGjQR91oaz1at68eZgxY4YoGm1tS8UwCUcOhiegmBVOFl220urHlwWiftz4KibABJgAEzAgAb8OBei3PwPJV2zh26YQTtWKu2ANmIXZk3J1dTV7GeRWAIVQlFu9DVFfFoiGoMhpMAEmwASYQJUJuAYVwTWI/QFWGSQnUEKABWIJCp03WCDqjIwvYAJMgAkwASYA0EQTxWSTqvBQ7nKuSjp8bXkC1MXMQT8CLBD148ZXMQGjE7DPiIJDWgRyqrVDoaO30fPjDJgAE9CdALm5IX+G+gYHBwdxVrO+1/N1mglUpW00p2z9Z1kgWn8bcw0lSMD5/l/wPzkTNkX5KHD0RezDW1DgHCC5muSlAxfed0JKuB1CBueh/iTLn3ggOchcYLMSIAtVVbox7ezszFp+a8+cLYj6t3DpVCr90+ArmQATMDAB96hNojikZO1yE+By7w8D52Ca5C5/5ISbPzoi8bQdzr3tjPv/8I+hachzLkyACRCBqoh3uRNkgSj3O4Drb5EE8l2DVcpV4Bqksi+VnbRI1UdMmuAEmQMTYAJMwFQEWCDqT5q7mPVnx1cyAaMRSGn4POzyUuCQchmZgf2RVb2n0fIyZsKhI/MQd4ishjZw8CpCYG8eMG5M3pw2E2ACqgS4i1mVhy57LBB1ocVxmYCJCBQ5eCKh1RIT5Wa8bEIG58O9TiZSr9oi4OECuARYn28749HjlOVK4LfffsOxY8fEtZvlysBQ9c7L43HP+rLk/h59yfF1TIAJaEXAp1khQkfkszjUihZHshoChXlwvPQpXP6ZAfto7ccQL126FLNnz0ZKSorVoDBnRXJzc82ZvaTzZoEo6ebjwjMBJsAEmIAlEnC6uArOZxfD4dZWuPz1FGwfnNGqmL6+vvjss8+0isuRKifAArFyRhXFYIFYERk+zgSYABNgAkxATwJ2D06XXGkjbNkllO6XnFCzMXnyZNjb8+gvNWj0OkR+KjnoR4AFon7c+ComwASYABNgAhUSyAvqXXKuyNYRBTW6lezzhukIZGdnmy4zK8uJX1OsrEG5OkyACTABJmB+AnmNnkaRayBsky4jP2QACr0amL9QMixBVlaWDGttmCqzQDQMR06FCTABJsAEmIAKARKGoA8HsxFggag/ehaI+rPjK5mA4QkU5sItejtsCrKRETwURQ4ehs+DU2QCTMCiCfTu3Rv04VB1ApmZmVVPRKYpsECUacNztS2TgN/pl+Aat18snHv0VsR23Sz4mObl6SyztbhUTIAJWDqBjIwMSy+ixZaPJ6lYbNNwwWRHoEhwJB33Z0m1HVPDYZ8ZXbLPG0yACTABJqAbgfT0dN0u4NglBFgglqDgDSZgZgKCpTDPs1FJIQocfZHvXKNknzeYABNgAkxANwKpKWm6XcCxSwhwF3MJCt5gAuYnEN/uE3hFfCaMQcxBar0pgvM0J/MXikvABJhAhQRcXFxQVKT/EpK2tmynqRCuAU6kJKUaIBV5JsECUZ7tzrW2UAIFLoFIbLHIQkvHxWICTKAsARKHVRGIZdPjfcMSSEnmJQv1JcoCUV9yfB0TYAJMgAnIngA5Yi4oKNCbg7OzM+zseCKa3gA1XGgHByQlJ2uIwac0EWDbtiY6fI4JMAEmwASYABOQJAF7OCElhQWivo3HAlFfcnwdE2ACkiJw/l0n7GjnhkPjXJAcTqvjcmACTMCaCTjAGckpSdZcRaPWjQWiUfFy4kyACVgCgZh9toj4xhE5CbaIP2qP/QPcsae3K7JiWShaQvtwGZiAMQg4wAUZWenIy8szRvJWnyYLRKtvYq4gE2ACOQ/KM0iLtMO1rxzLn+AjTIAJWAUBGoPo6OCExMREq6iPqSvBAtHUxDk/JsAETE4g5NFCeNTTfyKByQvMGVoFgYQLRYj4qRCZcdq7wXnrrbcwaNAgTJs2DVFRUVbBwZyV8HL1QUJCgjmLINm8WSBKtum44EyACWhLgJa07rUzEw99lwGvxsVC0aN+ARpOzdU2CY7HBHQicOOXQmzrVohDzxZha+dCZMRULhJ3796NuLg47NixAyNHjsS7776rU54cuTwBT1dfFojlsWh1hN3caIWJIzEBJmDpBIoKgaw4G7hUL4KNmldf8jleo3uh8MlErjCx0dHb0mvE5ZMygYgfSwVhjjBPImpXEZpM1TzmtXPnzujSpYtYbXLAHRMTI2UEFlF2T6dqePBAzRgTiyidZReCBaJltw+XjgkwAS0IZN23waGxrki/YQv3uoXotiETLgGlP9Blk2BxWJYI7xuagFd9G0TvK70HvRpoFoeUv5eXl1gMEoazZs3Cl19+aehiyS49D8dqbEHUs9VZIOoJji9jAkzAcghErnEQxSGViEQi7Td7hbuPLaeF5FeSNq/ZID8bSLxYhLqP2SCoR+UCkSjdvHkTjz32GFatWoX27dvLD5yBa+zh4Iv4eLYg6oOVBaI+1PgaJsAEKiRA3bwJJ+zg3aIA7rVKLSgVXmCAE7ZlJiPbOBggUU6CCVSBgKOHDbqu0E4UKrK5e/euOPbwu+++Q8uWLRWH+bsKBDwcfXA/9mYVUpDvpWpG6sgXBtecCTABVQIJp21xeIIL/pnsgpTwyh8XqZG22NPLDcdmumBvHzc8OG6aJcTqTcpFtVbFk0/ou/5TxrMeZt6zwcFRLtjW1B0n5zmjiCdHq940vKc3AZqUEh0djUmTJqF169aiWNQ7Mb5QJEACMT4unmnoQYAtiHpA40uYgBwIFOQARya7ChM6iq0gaRG26H8oQ2PV7/xqj/yM4viFuTa49bMD/DoYX0E5+QCP/JKJ/CzA3kVjEat88vJHTkg4WfzojNrsgOoP5yNkcH6V0+UEmMBnn30G+nAwHAEagxgfwwJRH6IsEPWhxtcwARkQyEm0KRGHVN2MaBuQaKTZwBUF91rCVGKl4FZmX+mUUTaNKQ7v/2Mn1j83RbXoeam6dSOqXs17TIAJGJOAO3UxP4gzZhZWmzYLRKttWq4YE6gaAdfAIgQ8lI/7/xQ/JshKpkkcUm61HstH2o0cxB60h29b6/EzeOpVJ9z6qXigY7U2+bD3KEJ+mg08GhQI1kPpLuNFgv/6Gkek37QR286/o/GtvVW7K/lqJqAbAQ+HakhOTUJOTg6cnDS83eqWrCxis0CURTNzJZmAfgQeWpOFu3vsYetYhMBelYsH8j/YbF6u+KkoR5u8VBQ5eFZ02uKOFwrDGW9tKp31knjaHr13pwtjD23g2aAQtqWnLK7slRXo4lInXP+2WPhG/eKAPn9kwKOOaSYWVVY2qZy3s7NDUZH+zGxs2AJtzLam5fbcXTxFB+S1atUyZlZWlzYLRKtrUq4QEzAcARI/wY8aZnydXdY9BByfCof0G8jxaYX77b8UhKK74QprpJRohjRZUzNjin/I7d2L4CbMzrZ30V8UGKmoOif7QJhtrghFeTZIOmcnCETDtLciXWv/JofWHCyXQGFhIQK8a4JmiLNA1K2dKp+WqFt6HJsJSJqAU8IJ+B+bAr9TL8A+I0rSdbG0wntGfi2KQyqXU9JZuN/ZbGlFrLA8nT7Pgn/nfKHbPB+dv8gy+kSYCgti4BM1epSKQTtB8Pq1r9xKbOAiSDo5sv4Z4iNpCBZe+IKCAgR4BImzwy28qBZXPLYgWlyTcIHMRcAmLw1+J5+DbX7xTF37zNuIfXiruYpjhfmWsbjR2ngSCT7NhNVZfhSmSFtZaPJCrmANLRTGINoiZEg+XIPKtJGV1ZerIz8CZEH0d6vFAlGPpmeBqAc0vsTyCAjaDulRtuKYsMomUlRUetusuBJxSHEc0iMhDC6CYCKo6BI+rgOB1LqT4Rx/FA6ZUcjxaob0Wo/rcDVHNQYBGjNae2SpFdEYeXCaTMCcBMiCGOhZBzeunzJnMSSZNwtESTYbF1qZQPJlWxwa54q8FBvRGtJjSyac/XS3hBR41EGOdws4JZ8Xk88IGsLiUBl0FbcLXINwr8cu2OYmodCpWhVT48uZABNgApUToAlEQT51cOTqz5VH5hgqBFggquDgHXMSsMuMFqx2N5FTrQ2K7N20LkrEN46iOKQLMm7bIkpwztxouh4radjY4X6n7+ASu1fMPyvgEa3LwBG1JCBYY1kcasmKo1kcgVu3bpmtTM7OzvDx8cG9e/fMVgYpZkxdzME+9XEjSugR4qATARaIOuHiyMYi4BJ3QJwYYlOUj3yXIMR23YxCR2+tsnP0UrUWlt3XKpH/IhXZOSMzaLAul3BcJsAEmAATsFACJBCre4agsKBQnMlcs2ZNCy2p5RWLZzFbXpvIskQet9aDxCEF+6wYwYq3T2sOYc/nwr+L4MTZtUgYaJ+H0JHSdVysdaU5IhNgAkyACVRKgLqYaaZ57eqNEB4eXml8jlBKgC2IpSx4y4wE8p1rqORe4BKosq9px8m3CN3WW98MU0115nNMgAkwASZQOQGFE/M61Zri8uXL6NmzZ+UXcQyRAAtEvhEsgkBy2BzY5qXAIe06MoQu3mz/hyyiXFwIJsAEmAATkC4B6mKmULdaM5w/c1Dc5v+0I8ACUTtOHMvIBAqd/PCg3SdGzqXi5O3TIuESfQKF7k2QK8xk5sAEqkIgNxniGtYe9QrhFWaZ/h4LhREdMb/bg9x+pguTuzLv2KL2qDxU78bOsqvS9nytZRFQCMQG1Vtiy/7/WVbhLLw0LBAtvIG4eMYn4JByBTX+GSOMgcwDLfz2oN2nyKrOM5iNT946c8hJsMG+ga7Ivi8M8bYpQoeV2aITakur7fGZzojZrbqQNAnG3n9kwlMQthyYgDUQUHQx1/ZrjISkB4iPj4e/v781VM3odZCsQKS3Apr2b+pgb29vlnxNXU/Kj+pKC9Gbg7Mp6+t246AoDilPcontESe4uQkdYMoimDwvW1tbODg4WG3b5mcWI7V3Lf6m+5jqbIoQc8imWBxSZkU2uLXBCfcPCp9jNgjsIQjGJQWgNa6NGehvVvHDqC6fgmzBeri7/OO/qMAGmdecENBU1TOAujQs4Ri1KT2nNNXVEsppiDI4OjqK97C1P4+rykrBhyamFAdh3XThHqFPg5rNcOXKFYSEhIj7irjq8nRyclJ7X1E6FV1H5yjfis5X9Byq7DpNeSqe5er+Bqr6zCv/hFBHygKPUcWzs4WnnIkDNbw58jVxNcXsFH8g1l5fG9cGUPa6mOVW3+rbmF6w8vLyrLKe1752wIX3nUCrhLRcmIP6T+aLYjg3Vw/fmHr84TkG2glXlT5ac9OKcP9fOgZE/mgDj0a5qPekcWfa09+suh8MsRD//edex01YYk/xI1p80N6jCF5tsoX7QhoCkV5yaKUMRTeicv2scZvqae3P46q2m4KP4v7PzxNcp+UXe8io79cKx/49hm7duokiThFXXZ45OTlq7ytNGsDV1VW8H9WlS8KR7ld1zyE6TuVVdx2VTVOemp7lmoSlujqXPVb6FCt7hveZgEwIZNXojcSmC+D24DCyPFsgrc6TMqm59VUzLx2iOEShDWip5/PvOqHOaBKIpqurf6cCtHgjG7e3OsCjQSFs7IqQfKlYIFIpsu+rijLTlUw1p85fZeHyR44ozLWBb5t8gZcNgh/Ng0t1aYhD1drwHhNQT6CgsHRMbaOAtjh0Yp36iHy0HAEWiOWQ8AE5EkivPR75YZMrfIOTIxOp1pl6lswtcRo8nQf6UKClIO/+4YD8dBs4+hQiVJgIYgmBxhl2+tT0vTCWUHcug3wIFPxnPaQah9Vog08PzlNrGZQPEe1rappBOdqXh2MyASbABPQm4CDMMmr+Wg5s7Itg61iElm/mwM6pfHI5iTa4uNQR595xQka0cS163k0K0f9gBrr+kIl+f2bAPdTc8rU8Dz7CBKyVQL4wlEYRanjVgqODMyIiIhSH+FsDAbYgaoDDp5gAEzAugULh2R21RRiXkwLUHpkPcnpe1UCWu7rjhIQF3VcsDssLwKPTnJFwqvjxd3ePvSjc9J04kpcK2AoiVJ0QVdSF6lW9a2lXl+I4fzMBJmBcAjTWWjmEBbbG2bNn0bJlS+XDvK2GAFsQ1UDhQ0yACRQTyEkCsqo4Zq5QmBtyeZVgrVvkhFxBTCmHk3OdcfpVZ1xc4ow/RwgDvHOUz+q/LSypXaFgI1GacKp0TGBmjOAD8G55EalN7heWOGJ7S3fxE73DsO/bxC3iWweRW0o4P6q1aQ+OwwSUCdDEkNxc1YdK/WqtcO7MeeVovF0BAcM+0SrIhA8zASYgPQLXvysWJzTho+H0HDR/Rb9ZwHsHuCL9RrEgu7XZAUPOposzjInI3X2lj6CMKFukRtjCp5lxffCRpZAmksT/W5y3e51CuAaVWi5vbnAQhZlLYBFq9s0TJ3EED8yHS43SOFT29CgbXPuiuP+6UPgNOvu2E4IHFc+WpPNVDZTezR8dxWRubXQQrZzO/qplqGoeprqeBDi51XELKUTNPmxJNRV3uedjZyu4nBJmIysHcpi98ewfyod4uwICpU/nCiLwYSbABORHoEj4Db+wRBA/gjikcO1zJ3HSha4Chax16TdKrV/5aTa4f9QW1R8qFoHVWgpC7WjxY8jBq0gYn2dccahoyc5fZiHyB0eQP0AfoQx/jXYF1bnuhFycfo1Enw2EVR9x/3Bx2a6uLkTfvRlw9FakIMQorZZ4kCbHGDIouFCa+Rk2SDpvi8Be0hNX5Dh8/yBX5CYVA2vyYg4az9TvZcOQfDkt6ydgb0fu8FQFYv2AFri2M1x082X9BKpWwzKPuKolxlczASZgJQSEJ4PKmDpbYdKHg+7WK7LW2ai4mCmCp+D6RRE6fJyNepNyUWt4Hrqtz4SDh+KMcb8pn7DnctFkjiAI5zsj8bQdks7Z4exCcr5fXunlPLDFg+Oq79NuIUUImyH8+Airpdi5FqHVO6o/RLrUICvWBjSWUTn4dy61RtKkmxvrHZF0QXqP7HjBB6RCHFL9aLUWDkzAFARIIGYJFkSFT0TK09vVD16uPjxRRYsGkN7TRotKcRQmwAR0J0Bj3iLXOeDKx47IjrNBmyXZcPAUxI+LIH7eylGxnumSesdPskT3LvZuhWj2ag5cAkqvdvYT0hZmGrf/KBveTUuFY2kM424VZAE5D0oFYUGm4Mi6ocJKVyqISaB5NlIcLy1T05dyMfRiuthtHti7VNCVxqh869SrTtjV2R2/tXPH7W2l4omcfLd4PRuO1QpRlG+D2D/t8feTrsgXymyJgbqR1Y2V9AorEH1BKspsjnZW5M3f8iJgJwhEW+GFLytL9Y+mbkATXLp0SV4w9Kht6dNIj4v5EibABKyHwMlXnHFnW7G5j8bh9d2fgcHCeEEIus2mdE6HzhUO6luAoL4ZOl9X2QXCwgPIuCMsayXMELZXXgqnsguVztNSfKGj8hEljI2kECQ4im6/PFsYn2gnpFmEmF0OyBTc4NQZL6zTXYF7GmLz77MuuHfAThS51H3tKoxf1CakXrPFrZ+KxxkW5dmITr5rDSsWmmTBrT8pr9jx93+J5SYLP3b3BBFbV7v0tSmDIeLQi8XZhUKBhaUFgwbkodNnpf4VPeoVgZjc+skBbrWK0Hi2/pZWQ5SV05AXATdXJ6Snp4NWOVGEUO/G4pJ7gwcPVhzibzUEWCCqgcKHmIAcCZCFShGyYm1Fa5C9C3DvoD2qtShAwEPlLWiK+DTbmbpnUwSn0MGD89HkBeOOMSsUNNQ/T7ng/t/CGquCkOv8RZbG8inKqe673dJshD6WJ45B9O8iWLsEg2KN7sV19WtXuZiJ+tkB9/YXs0u+aIfwTxzRZnHl11FZbJ1UhZ5Kt75wnsRn0IB8RO8oFrDezQrgXlv1GnV1MvWxKysFkSuIQwoxvzsIzsFzQf4fFSGwZwHow4EJmJqAp5sT0tLSEBBQ2nVRq1oYTl341dRFkVx+pb8Ikis6F5gJMAFDEvBtW4DYA8WPBOpaphWq9g8RJm8I3ZsU2q/IgsK6VTbfYzNcEH+k+Norq+zgIwhKYwqC2IN2ojikctAEjssrHAWBqNqNVLaMmvZpVrO+oaxrnoLs0i7rytIkq2STOTkI/9RRGH9ZhNbvllreFNe2X5EtTE4R1pMVur9rDc0rNzlGEc+c347CBKOchP9KIIzJpPuHAxOwBAJegkBMTVUd4FvbtxF+PBNuCcWz6DKwQLTo5uHCMQHTESABeG21I2iVkXoT8xArWA4V4pBKQQ6l1QlEWv84/qhqH3RGNA1v1l90VVZrsmwqB7sy+8rnjLWdeFZYQk9gQm5yPIVxdqnhdnD2L0SjZ3SznjaelYtGwoQZshaqmwltKzyl1XE3Vr30SbetYIU9Oc9FmIwCoQs5F27BxQLx/+xdBXxcVfY+4xP3pO5G3QVaKFKKu7MssLC4u7PALr64L7DoH4fFKVCktLSUKvWmLmkb90lG/993X97Mm8kkTdI0pO2cXzPz5N777jtv+t73jnyHca3MhqfLPhtE4YNvqhWeT0xiGmgrDaQm2qSsrDTscF3S+0h+0XZlWUxKaqPMuLAZ7Bkrsf+qe8Z1is0ypoHdrgF7sshgA9dhbVE4wEsbEnIZGiejLGZ17kVtO/gDW5iwYRy3sWW6u3ue5ZYN7yOuDWBkCJJf2lJYX/mnU0iNo1kLh/2jRjpMdimuxEg3cVPmtaeDpoxRfpmKmNVIWfumTdb8V4uxLFthUTRGvRDPGZOYBtpKA2lJcZK3BW8uBnHgDbNTRjeVyTxy5EjDntiiUQMxgGjURmw5poFIDSATwlEyX/yWePGkDIzcu1ev50zyIbvYpaxkaUP90vfv0S1jzETuc767DggEpP/lbonvtPtdjIzzGwFqmV1JoGnpBcyfZQmCQ46xY4ZV+sDquq+LG4aaBbc5Qcdjkc5HeOtVxqncECPO2Nd/I219/mnJ+D2WbK932B6ZA2XlypUSA4j1VBPcEAOIQVXEFvYWDTjzZ4jVtU2qO0wRvyNdTJ4KCTDNNZLZuAknnLngaonf/r1qWd77AikdcH0Teu09Tbqd6AVH4c7pW0jJ0vuvcJPijqK7F9tCC38GOOR5ZYymdZUgWLMgOjKiW1fZdl+SFU85VJIKzzn3ZbsMur5GJeL4a02KLol8lzGJaaAtNZCRHCdlyGL2er1itYYgT4/UgbJ82Yq2nMoed6yQtva4qccmHNNAfQ0kr35OUnOfUTuS17wgtanDJGH7t+Kzp0vBqKfFnT6ifqcGtliqtwTBIZskrX9DSvtfo6WWNtCnLTYHgEVqCkDvkh2IGrPWFnOIdoz2mF0bbZ6tsY1WUx0ccjwtQadt3dytcR6tPQYJv43C2NDDv6+S4oUWYRJUW1iWjcePLcc04LCjxKPTIaWlpZKZmRlUSJ/sIfLx4unB9dhCfQ3E7P31dRLbsgdrICHvy+DsrTU7FDjkBou7WFJXPhbc15QFvy0FleZCZUD8fNrtgsnKxLpuJO/bBSHv37TJCfLV+ESZfjTKl5XtwmB7UVcC5t+ucsqPJ8bLxo92/3uvzx0OhCIzmfci1TbrVHqd4wlS9ziz/YryiBblrqA+ioHDZqky1rgVNZCRGi9FRXqavTZwvw4jZOXq5bGSe43oOQYQG1FObNeepwFPcr/gpFEcLrisLTQPnAVQj83vyAiOYUYtNFoVWyJpS/4hXb4ZJZ2nHyT24oUtGUL1Wf2SHeTQ2nkx6H/9/2kJAC0esJ10ZB3kXZFFdztky+c2KV5kkXk3gI9xVeS135XR6/et2gSAiPKDmtSV3KvfbJ/bkj3BJ1N/rJKJr1er2tVxsHLHJKaBP1sDWSnOegAxIzFH0hIzZenSpX/29Nrt8XfvXbTdnnZsYruiAVv5SrGVr96VIXZb3+LBd0tFt9PElTVRCkc9KVUdj1TH8tlSET94XbOPawqE4u9oMyJIbK44iudL0qb3Ea2GsnW1hZK24qHmDhFszxg/o7AE3J4sRQvM8vXEBPnfoERZ+kjLwW7FeuOtzCSVG43rra+hdW9grn7dimiCYVlfbv1jteaINGCTZHx3CqvI5BzoAxfi7jxKbOyYBpqugcyUOCkqzK/XYXDn8TJnzpx622MbNA3s3rtoTMt7nQbSltwrHX85CX8nSOryB9vd+fntqVIy5B9SMPYlcXU4VIpG/lu2HD5bth42A/GHzaczKOt7OWCd9vCvzjlUPMn7Nf+cI8xjRtDZ3MHIsUfOPUrmWK/0PHPPDvpn9ZXqrWZhEsOq5xAnBPqYlkjP00N6iO/il+wJuxcFxXUKT0qJ7xi+3pJz2N19tk23yBcjE+VTgPFV4LuMSUwD+4oGsuhiLgx3MfPch3WaJDN+nLmvqKHZ5xlhj2h2/1iHfUgDZk8ZLGHvBs84af2bUtbvKmQIh2pcBne2owXGErZUKrufLq7sA5Xl0JPcv0XD1KaPgSXzCEnY9o34kU1d2r/5lkz9wHEdAjLl62pUD5EW1x/Wx2oP376I4iesFtISYc3i9GE+IUF3h4O8qErSklGa3mfQ9W5xg1C8FG7+rsd6UOlkF33kOLQbxukVTzrElWeSXn/xNKl0YG2RSVb/xyaFcy2S3M/fKB/kgtudwlrOlKUP26UrqrLE4gKVOmIfe7kGMgEQmcns8XjEZgvFlY/qfrA88+PNMcLsBq5/DCA2oJjY5voa8JvjFB+g2VetdgasiRJoCStw/aHb9RZfXEfhX4sF5TGKRj4mJTW3ip9+t1bQGVl79gYZeINbfr/WqSyInY/wqEzXlp5X+gi/8K81hK5YWtnyf7FI5jif7HelVulEH9uRHpDxz9cvi6fvb8n3QgA4vebyNpQ8PBzE041RBhX8ZpGZ5+L/JKyvFD20tV/oHS5sGno7baNJ/CGja1i72EpMA3ubBuw2iyTFO6W4uFhycnKCp5canyn9Ow2T77//Xk488cTg9tiCpoGW+XNi2ts3NWCxS9GIh8UT3008Cd2lcMSju5TVu68p0e/MahVw2NZ6K19rlgW3O+SP+x2qDF9rHr/LkV45em6lTP25UgGuaKXmWvN4TR2LmdDLHmGJOKuy6q17O2R1aOoYzW1XujRUrtCPLOny3MZvz2tesQXBoX6sinUN9xlyWw14KrWY1d7nulHVZM+OX9XPOfYd00BTNJCVmlAvUYX9JvU6QT7+4H9NGWKfaxOzIO5zl3zXTtiVc4jwb28RW0UuqGf8iC1smft4l/RAQkNKCwi8tY67/5PMPDPOiJPaQg14lPxhloPejfAL7+I0WOLPnty+wEr5qhBY4+nt7qxoHoMW1FUvOLgojky/ZIxs3G1tz6ivsx6nNGwW7HGqVzpNrRSWRoxlFys1xz72IQ1kpjiiAsQD+x0vr752n2zdulU6d+68D2lk56fa8OvmzvvGWuypGoD/jFnIFvAEol6YJGz6QFLAEajA0p56Ti2Yd+qKR6XjjOORcHOiMPmmLSVh8yfS5dtx+Bsv8Vs+a8tDN+tYVZvMQXDIjkULUGKuPi5p1phsTOC5/Em7zIV7ecfMcDDW7MF2Q4cuR3vEZNNO1GTRePx2w2HChhx0k1vGPeuSIbfXyKFfVIt9J6Gzg65zS/ZExFum+iVrf69M/rhKepzWeHIOwXgMHIapPbayj2iAmczFRQX1zjbJmSoH9D1a3n7r7Xr79vUNMQvivvYLwNM9c/6VEr/jB2TnmqUmc4LEFc5SWkja8H+ybfKX4nOGYjT2RvVYqzZJ2rL7xFmgnTfPkck3NUNvxFLcbj9lEmanL/2HmOqCwNKX3C3VnY4Ah077yyxN7OGXhO5+qaqjjekw2dsq1VuWPOiQta9r57vlC6tM+aZKknq3AvJspauXPtwvh31VLYWI82MFkJQBodhGVG5EYohdmCDSG8kkKfuF9u3K4ele73JU4wDPOL4zKyCT3mxda65x/NhyTAN7kwYyCBCXwigSRY4bcqHc+dbpcsWVV0h8fPtOuowy/d22KQYQd5tq2+fA9rJlChxydghTF2fRb8GJMvnEXrJIXB2nBrftjQvpi28TZ8mCsFNjlRSVje1pA5BC2hsDGR2BognbGjpy/NbPJXnNfxRpd/Gg28VevkrM7hKp7nyMkNanNYWWvSUPOaQEhNMdp3hlwGVumfxBtax/1ya2xECr0eqw9JouAa9JSpYgiLx308GR3nd3fif38Qv/ImXejU7Jm6bFJDKp5IgZlTu19kWOwfXtP1kk7zuryr7uDvdve4m/jDbX2LaYBvZ0DaSjJnN5lJrMPC+W3euTPVTeeustueiii/b0U221+ccAYqupcs8YyA/+DwIRnUyEtCsW0NdQArBguVMGq+U98cNR9Luio3FlT2rUGmer3hR2el5HlpQMvlNLIPG0bmZq2IHqVgLQeVm/KyRl9dNqS1l/UAWxjF8UsbjyJAOAlgBSKtdIzpzzVNlANk3a8LZsOxDB1c3Iit7+s0W2fmWVLmM80v3s+gdc9YJd1r6mWfZYlSSxJ8qlIZGEWbwUWs/Wv2cTa3xAup3gFbOGk+oPtJMtHQ7xSskfGki0JsCqjUzhPUWK5oXAraccySSrkek8pnnzL/zdIrPO5zU3oRqOiNdVI33ObTh+cE/RTWyeMQ20Vw2wJnN8lJrM+nzPGHmdPPDchXL22VFujHqjfew7BhD3sQvuRfZx6X43wSL1IqxP6VI05B5xlC4SK0rIVXU5UXzxe2aQbuqKRyR53X/V1axNGyE7JryBZ2/oQW68zBXdzpDU3GfUJnfKQNm+/zsAlDZxGhvt5uXyvpdKZbdTcRTYcQ3l/CIPa6kp0MBh3Q5aDnWxVW+ENXGluNOG6Zsa/S5ZYtZAScAkG973SEWhSfpcEt6lAhnLRqk0ZMUSo/58OvjEwP1H2f6Dp8VUL/td5ZakXn5h1m3XY8DHh+obzRUWtVmDiiYB4KpuJ3kURUwtuAl7/QVj92x8PFZwoRUwZaBPuh3fuOWSoHgprKplq80KFOcc5JNNH2t6sib6Jalv88Ahz5MUNaHXNFGu7L0RINbgN1aLv+T+/piFtLk/8Fj7VtcArYilpaWSmZlZb+xBncdKn6yh8vJ/XpbHHn+s3v59cUMMIO6DV72i13nCP13cGaP1xT32O3Hje8G5O0oWIuFmTYOZyeX9LlOxlxZ3EUryTVLgMNi5DRf8jvo3qcjDu1MGSW3acHHA9U/xxncRW/Vmtey3xAsBf1OlaD5ACcChLvm/1QeI3U8G0IKFkWXkaNnrfGTIqlW50RQEhxyD7lGCxgZwuH6YqN90p3Y9tnFgFrWjYeOsC1E+63ftFpb7ih3k4dq5bf7UKlN/qoJL3NAYizUFJlnzqk0IWjZ+DNNnXam82qIa6fu30HmG9xJZ9qhD1r2tWVV5vImvVcuOGRaVvOOtNMsf9zplzGPRLc+bMBdaSjvBXZ81PgQksw/wyvLHMGbd9cieGNoXefw9dZ2xpXOvcwLAmyR7klcm/tfVot/Knnr+sXm3Pw2kJyF8piT0kh05w3PH3ia3/OckufEmxqPHJAYQY7+BP00DJk+lmL0Vu0ZCXTd7AiV7+Qq15jc7kGiT3eh5udNHNLq/3ew0W2XH+NcQKzpXfLD4+uAOT8l9Fm7mEinveV6zYhCz9/cpHjzG/FE6H1LfytbxgDI545/XSsmKOOk0aLlUZ/9bvNJDtWfVDUeGH8kZmvWMyRktAYdqsAY+aBVsSg1fXy0yquvAIYfSwSGXOb8KcAhGkmb/cg5ikCLoa9iehNjOTGQqHxcdsJavCbeqFsLFrNP+sP/Wb6wAiFwKl7Vv2WTRnZpdes1/bXLwx9XC5BdKBgi9D/w/l2z73ippqACzq2A5/MjtY41Z6gSHlPxfrMpqyt9gTGIa+LM0kAaAWFDaMEDsmTVQJvY5Vm6+8Vb51wP3/VnTbDfHDb/ztZtpxSayt2sgbtt30vn7SdL5h0MlY+Guv60VjnhEWQNrU4dK4cgnAJzS2qUKW0QRg9jQmqyJ4oE7nGTbrDVdOOpJ1JZuHshlKbbJH1ZL/0tqZQoyiPe7uD5ATNz8kXRM/koGjvtIUhNXSPbcvwf1aAHWmfiGS7qgtFyP090y4cXWy6BVfItnxslnw5Lkm8kJQmtlY8Kwy7ThIbBhQUykLuQQ5LkapRbPhGjgkG0I9uZeHQdSbLp960t3uK91sacy9hLlugy8jQnd/LLlS6sQtBqFVViCAkth/uzw93FaFIfeUbtXgkOetz0ldE2irQd1E1uIaaCNNJCW5JTSkuJGj/bXcbfI5599LvPmzWu03b6wMwYQ94Wr3A7PMRW8i2a/9kRNyPtS7KV/NDhLZ/7PoOa5BlyNj4vJq5X5i2zsTewlBWNflB0HgK4m56DI3X/6OoylqizaJ/0ThZYsWsr+DCFdS8V6s/xwkVu+PQkch4jZC5O6a6Jvs7q2iskw2dSBfhn3VI2MerC2Vev4bvzIpqqW8Lik1Fn5tEYYrc8j2vcBL7sU2O17gVsO/bxKBt9UK/1wXgTBkaUIHXhfYLyhLgR18Z3DQSRjNKNJ95O9cvAnVTLqYZccNk2j45n4RrV0Qexkcn+fAp6/XREnP58Rr1zu+hhGlzJ8yZI2OLqFUm+/t30Pv7dWxWdakwKy39W1kjooXN972/nGzqf9a4AAsaysrNGJpsRnyIUH3i233ny7eL371v/ZSMWEv9JG7o2txzSwmzQQWcM5ALdwNLFWrJWseVcEEzXMACslQ+6O1rRdbyPn344Z2n+3/JlWyX3VLgOv0TKD23LipKvRKVp2zDIp9+rQ22vFj6mwtJu301mSanoG+tZujD5YYllze3dLpGU13PYU/egOVBIZfHNIh/0vDS1XbkC84X/tKtu638Ww+CHigJyBa99A7CEG742M4W0/WGT+jVr2OEmxO0wOAcjII9I1rLuHuS99GIDy0zXyv0Eh3ZAaqByJLIlIvqE+e5/nkarNmAf5HhHvOPPceAGJgAy/t2aniTGRx98T1/kycfi30V/o9sTzic15z9dAKgBiVXWNeDwesVobhj/Hj/qbfPnHa/LqK6/KRRdftOefeAvPIPorcwsHi3WLaaCpGigBnx/pZQImm5T1/nuDCSXM0lUUL3UDx2/7Bg/4psCH8JmYkQ2cPedv0vnb/SVtKWJLWjBG+IjNW6MF0SjGmDnj9tZeJhf38ifsMvO8OGFMnKci3GLIeblLRb47MkGmH5MgX03uIMuTP5Ga9DFSkzFeCsY8jyyU3X+boBs3Y7QGSmnZG3BZhL+2GYopXmyWH0+KBxi0q9J1cy7V4gAd6QEFygde6xYup4/wiT1ds2rRZR15jZpyyCTQAOlicQakaKEZbvJE9bf8cTtc5XAz1yXDMCGFtDjzb3bWc0frY8S+YxqIaWD3acBmtUhCHBgJdmJFNOOed/mBD8tTTz4tW7Zs2X0Taucj7/47fztXQGx6ra8BVgpRZfwaGbo2Y4zkHfazbD5ivpQNuLbBljUZY/FcDcVykbPRWTS7wfYN7Uhd/RT6zQHnY6kkbXxH4rZ/21DT3bK99zmgcqlzacZ19MOCFbJ27ZYD1g2a+7JdVjzpkB0/W1XCRDyOTfcqhZU4mL3LjF6dzoYAZvl7gyR/wuuSP/5VcSOm0yjVebA6gitx/btW8TdscDN2adKyFcULJn/gkmPmVYJ4Gm7cXs1/CeCBCIZ/PCFB3CWhWxuTSiLfB7wIn/wJINJdrLXzVuK8H49uxW7oBIoBBsWMspWIR0we4JPxL7hk6cNO8dcChAMM0k1uTwkBSH0cWhcJ3GMS00BMA22vgbQkjepmZ0funT1Yjhh8jtx2yx07a7rX7m/YxrrXnnLsxHanBpyFvyJe8GpkJ1dJdc4hSKZ4qnELFLJ0GxMmZbiT+oujfHmoWQuwg7k2PDCZWcBtKcwAPnx6lYqvY+m6ZnBb79I0S1eEgBIHqt5qlsO/qxJ7WZaYs6rEjWo6G94LvwYNhHmKG6E7048DqKrLYl58bwCZuKHM3BVP22XzZ1ZVlm7EfTWysyIvBEn5v1qQGc34PA1IcXlXhCXwIiVnErK3ww2nUok4TE95uG6aQ/pNwDn7kjipydfGCATMisYm/DgA4Be7UXcaCSq/WsVTpk1iwBXuehQ8kXOOrcc0ENPA7tEAM5kbo7oxHvWsMdfLFe8eIp9++qkcf/zxxl37xHL4HXKfOOXYSe5ODaSsfFKBQx6D9Z6dhc239kXOr3TQreKzaSXlqjodpTgMI9vsbJ28jyynR/GAEqe605E767JL+xkDt2OmBeAgNAxBIbNr2woc8shdjqbbVgNdZntA8fEhKVoyBpsRn6fNbQtoWoxSvtIjqcsfFJb4M0rRAksQHHK7z2WSpY9oVjeWjVv+mEMq1lgUYbW+3djfuMxKgzOQtTzrvHj54dgEWflcfWBnbN/U5biccIBpifPLsH/Ud1eTpNuZE7Lu0T08+Mb67YzHpet64Z0OWf0SXPVIMiKvoi5euO5pfWV8IceiZXE/xJim9g/I+Odq5LhFlXLkr/ibVSmDrmsb67E+t9h3TAMxDYQ0kJ7s2Gkms97aaYuDq/khuefu+xTBtr59X/kOfzLsK2cdO8/dpgGW6zNK5LpxX1OXa9NHydYpvwB4VorfltLUbmHtlEv74O9QMWazuJMHNKs8XdhAja0Q9SB2ZdNndvkdBMGMPWOVjUP+h6zaRuq/0yWftux+kGEvVFQ9pftd3yL2aTOCCRPhPic5YUX3MyWAjIjOU70yGfx7rH2cc6Avam1hgwdfnZ3ds12S17+hlothNqzs+Re1nMJqGNaA6DyKRlXQMmkUfd0NILX0QYeQS7DHKaDHOY2AVaR0qVmK5oduP+QJZN3npgrB2dq3rADc4C9UIFjrOeZxF7LEQSBeF2vpc5llx0+od9wnfGS6ebuf7EbikE1Sh/hkGOhmGrtG1VtNMgNZyrQGUqq24HxO9aAijfZ773S4R7nsyWfYeSp+pzjNyPFaUi0mfNaxtZgGYhrYVQ1kpMTJqryiJg8zovuBMrLLZPnnvf+SRx97pMn99oaG4Xf1veGMYufwp2qgdOBN4nV2gM0KZMXdThMCs1YRoJiWgkP9+H5HulaWrpkmPNZ4ZqUWS02+PlS97yQAqq7TRkuX7/aXdeQHrEtMqMi1yLJ/h4PmyM5JKBGYuPlDsVWuBTB7TRK2oL5yCyR7zvmSivrOqaueAH/hRcERyEfHcmcFcOdGi30bdX+t0LpIS6PZ5pVDz7gt2NdoAaabfOLrLonrUOcORoKHbnXrdLhXnNnadpMlIL3O1sDekgccsv4duyK1nn9zHICqdstxZgeAY0PWvvjOoeXgwRtYqMG9/euJsDo+5ZS5oJeZe7VmxWRzZhd3PzE8wC++S8hSyDas/vLTqfGy6jlwoi21yAbMb93biMOE1Zf7KOQ0/ON+h/x4crysfBbzX0xrcMhiWIhSeaMeqpVJcLFPfL06rOQg35EiwaE2Ko4dPhW1uXSZWcV1MlZyTxe63qm3r/ZPkFkXwAWP311rCbkxSb698UNU8Imix9Y6TmycvVsDmSnxUlzcvBCjCw74h3z/3XSZM2fO3q2ciLMLvcJH7IitxjTQEg24U4dI3qE/4AkLgGBpHBi1ZHy9jwkxjpRAJOGd3qCVvhM3vivpS+9Vo/kAvLYd+JkiqzYOz/jG1OUPoaoyQA9MUymOhVIkhwabkG6FtCqJ3aKDIKsrL9iWC9bqrWHrTVlhlra9YlWwqaN0MTgjq6SmIgEZvQnB+LeyVWYZ+a9wV2r2AT45YXmlEHglmf+Qjr/NCo4TCfBZCeOo2YhdRDwiS9np1kcmvExBbCOBU1If1Ceuq4VcEVGFhLWeVYUTqKLjYV5l2SQtzIj7wucUnECUhbVv2kEjEwIeW78BdY2E+g+8rhaZ2SZh/CVd7J0OJeoLvQsTCJavDgdjS2DlXHK/U1L288lB71ULj5FbF89YDNe6ch2DjNtXrR1XL42XPcGnwGTxIrMkdA0oK2KUKatNfFFgjCSrtox9yiUZI/0q6/mnU2BeVi8UAdkf3I4d1XwbGqX528nvuBLxoWSSom70a9P8kXbeg+UXdb25tplVOcGReAHZVaktMskPxxt/x24hPVNMYhporgZSEx3i9/ulvLxc4uMbce0YBk6JS5fzxt8ut918h/z0C55v+4iE7pr7yAnHTrONNLAbwWHSuteky7Rx0uXb8ZK4/q3dekLxW78Ijs8M6riCmcF1fcEU8ChwqK8fdOp9iDM0gEFktFZvbvi/WmWXk4TlASl+cA5WdT5WH6rJ36zrzNhKXZjYQ/DMOsB6cgT36VyMejv9m0AvDlyB3syhIBx/GdbfM6RoyL1S0fNcvUnYtx2efh0c6jvsyYIYR18YACHJtC4s05cDrkHSyfx4YrziY2SShyrhlxZQ8X2zL3E2WNFEHycVGcNGsSYadI0dnNvYJ2sUB9/AqzVLprE9rZW2iCofusW3bIVFZXUTyBqFLvI0uKJpPe2FDPQht2rgZMlDdvnfwET56eQEZdVkjeZoUgawuvIZPJiQ4Uz3+6K7NOqdbd/iHV2nwcGvSAO70UZo2TZaQklxlPedTcWGzr5Ii8Nt2Wg771WzIwTc2VpP4tl5z8ZbFC1AUlFdkg9bbgeHZUxiGmiRBpBJlpWeJPn5DXuEoo07ZeAZkiDp8vzzL0TbvVduC78L7pWnuAedFAKX6MpMgZvQUr1lD5p4202VVrHUFY/iUYp4OPgD01YgJiSyxtkuTke3TnIYT1Lf4GiEIZ6kPsF1fcHnzJHyXheoVVLy+A84WwYYgElSH59kjKoDNfDB2UuXirVqg95dlczbNvlLKUDGd97kr8Sb2DO4rykLpBRKQIm80gHXS3mPc1Cj+VzJH/uS6poA96rZAFYzx4aDK7oESV3DmDldarL2Bxn5XVLV7RSgwPAHPiuvlAM8sV80IaciXYB62bqeZ3gQA4kqJI+gCsnX1cp6RhBWUxC69TDBZd6NTln3ll2BxlnnwzVpSADhcegaL0HcIo/f+QifKvfHeEg7gOX45w2ZQNEmFbGNJQOZfZ023Iv+SFapc5nrzbg/axwUgkQTCpNO6J4v/M0qru1m2fieTRAOK0xaWf0CgH0dwKNVczVohSj8Sc6/xSHTDkmQRXc7hLGYRvHWWSJTh4T7StOGhl8fY5/mLlesNUnet0gsqqPyYX8CX8Zf7i6hxVanc+Lvrvd5rXMwkm4bf8fpo8L1trvOJzbu3qmBDulxsmP79madnAn3wksm3i9PgxsxLy/c69Osgfagxnh9jUl70UD6svskcdMHajp0beZN/hqJBjDLxMSgAQAWghYdoCj8oj4MbVq2aKncKB1nnie2qo3iypwgBaOfU6CLpjJb1Xqp7Hw8eAGHRB2ciSXlvc4FqLCpWMkBqOqRNdYrNXkeyTnIC5ChWQgzF1wt8du/V2OUANBV9NaApS+uk7jw11whOOww4wTwO2rlo4qG/lOqup6khqEb+Je/xmu8fNjScYpHRvwzBKZqAea+Pz5eylda1EN90tvVkthdV2z9mWz5yiq/XwuePwChDgd7lTvUyKHtqQANDsi29QQVlr5jdZOMEX71p4+Y3M+nuAOZ9UvJHO1TrmZ9P2P9CGScWRpYUpyFcMOWLbcosDbhJZcq98f2tEa2JMqAgIOVbBiDSFC29CFkNsLK1wlu76L5AIEfEOgFVDm9wTfXyndTE/TpqVhEuk/pxo6UugI0qlLOhvc0sFi53i6Jvf0Ath5lIWS856AbNAtkl6O84sY12f6zRTLH+BC7GR4/GTl+U9bdlQH59SIHwCFv7yboJyA6MXvnI72IN23KKC1rQwLyKd9WSTGqyjBTPK5Dw7+n5hyBVt9Jb7nAv2mTBPCJ9gN9UExiGmipBjplJMrSzc03wvTMGihTBp0h/7znX/Lci8+29PB7TL/Qa/weM+W9d6LO/F+CJ0eePkfZ0uB6bEHTQADR/yUDb1EVWAIAY6zI0lqU63ggAABAAElEQVSxjvErn1HgkEeKAz1P4tZPAdATpWTwHZI/7hWp7nJco5eBbl5jIk3PlBdkvHmY9Jw1Vln4rBVrguCQA6WsebHR8RrbWboc2dL/Q7D+yrlBcMj2CQZqGrqTXXmh/+I1O0BtY/Aw5r4JayDAIYWgbvVLjSMHVgbRY/+2/2iVwrlaXzUAPriug0Nu2/hR9PdPuoAPfKdaep7plgFX1MroR2uksyETmRYooyUtb5pVgUOOSfC46nm7ioFkIsRnQ5Lk08G4Rg3UUWafaLLhfaui2Fn2qEMlU4x5rEZOWl0pQ5HNrIFD9jKhHJ8V4DkgPU8PAbf04cgGB10RYzf1Siz6MVIHaaDWhaxno7i2mZSlc+pPlXL03EoFRGk55bwJCvd/qUb6/R2hCqHLZezerOUvTq8AOGRcpjYHgsN+F9fC7e5SrvdmDdaCxkzQYawqXwDyvkOIA0B8awhJ3mthWd70qQ3WZp5fTGIaaJkGumQnSd62HSoWsbkjnDPuJpn965x9ImEl+h28uRqLtW8VDdSmjxRr3ldqLL8FhMSII9sXxVa2XBylf4DvcLx4E3rUU0Flj7Olsusp2I4HYGvGOuoprPoRI9f17U34NgPgpyCpRT2iERCdtuwBWIS/VFVh9NKBPntGE0aq32TLl1b57Ur4QRHbuDj1VDn3hsclMVVzlxhd4Ik9wt1wiYaycBw1khh6Z5YlJqUYxZYUbh1S49MtW+dyTe4bfnxjX1rwjMkLw+6qlUy44ela7nqcNywLuBhxlEaxpwZk/q1OoRWPwsSR+Tc5lQvb2K6xZcbk6RLwmJQFj6CP4IbZ1QGfBq5siG+kwZrzo9WUoIfJNTAqq2vL7b9fo6Fu9ut6vOar736aR9bDFU1KIBMshsn9fTLrb3FiQaLLwGtq4VKPE9ZupgyCpZUW59YQuv43fh8Cs9qYAdALeVpcnaYl82KYAcMGqCUSwx/6eZWqQ92SsfQ+i+9BRaC6euZLHnAqi2vOWH1v7DumgaZrICXRKU6HVXbs2CEdO3Zseke0THSmyNljbpR77/6nfPH1Z2I2t8JbXbNm0HaN994zazsdttqRiofcI2V9LwX4OVmVOvM7WgYgWm1Cf8JAzvyfpcPMU1XmcEe4Tm3lq6LPglQ1rQkOcZTq/pcrih4esDZtOJJFjo9+7CZv1UCG3pwWxmIkfpAGyI3YxqLhD+i7mvW94X2AG4BDirvUJosLn0Ld5LHgPjxLSvtfExwrDfFtIx8Ax+IwH4CLR4bdqbk19QZ9zwlI1gQN0KQM9En/SxoHKT3Own6TBgotcfUTPVgeb9xTNZKJ+L1uJ3tkeDMykwnCuhzjlT7ne1RlFX2OjN1b92YIzBGEDb4FWcrIajUK3dDNEaOFkv3ShmpglpVcmFFNEBrXyS+jHwm55FmRhTF2RpakbgCEE9+sloHX18ohnyKusa4iTMVqxGkCHFICcMkvuDVOaHXd+qVNfr0QVvA6cMj9a18LnR/Xd0WUHg8MjUd9Dbsbmcu4Nm0hvA4bAA5XICFHg9DgjNwILkqUetxVqUb8p1FcsIjHJKaBlmqgR8cU2bRpU4u6Tx18trjKPPLxxx+3qP+e0mnX/9fuKWe6B8yTWadl/a7cA2bayBRBUJaw+WO4ajdIVaejxZOyXyON6+9KyPsaNgftYUbKmHjUTC5LbhtLqi+5t+Qd8p2QtoYl/nZF/PY0VWM6ZeUTMNVZNVc4zE5VXU9Uf7syNmlhdswIjWAfNQx1k18LbTAsMUmEf9HEhrC6A//PpRIqjKAnWltuY5KGDkxZRWXTJzbZ78pwUEkAxb/WEmb96kCLY5owBWY9E5DNOBMWOForAVqbS3lCMMyx8lHtphqk1wtvd2BMt3RBjF7PM6Ez/NEaR7d5yR/IYK4DkNHOK2eiT/hnlLIIGh2/gUOxajOBI3/j4SBX788EF74E0FpJyx8phJojx76fKNPv2qFcu32QJNJYXGlzxt1ZW3ITzjgT4BeckZESyUUZub8p633/5pbfr4dVEtecBPQ5B/J3FgLDTRkj1iamAV0DvTulyuyVa2TcuHH6piZ/W8wWOX/8nfLIQzfKMcccI04nreV7n8QA4t53Tf/UM2IGth5bl7jhbdl20Bfii+/c5Dm5kSUcSgeAhSypX5P77mpDSzliBAvh2s4YJ3FIJLHDze3Knows45EtGrq894XKqhcgEmlFa+cgcNmxhB+zgbse5wGfnk9ISF0Oq1W3kzzCah6sp8xqLgWoAZy1v1cYY0f3aTRpCjhkvzhDaTq1HpH9G23sXd1GcNTnfLeQS5KgauC1IPWGOrPG+uWoWVVSAN7F7ANA0p3ZvCPRvU637oZ3EqSqjoKICTjZ+1cqmhyO9tPJJPbWbpGM4RtySzgYbuyIGSO9ssYCFzNc1bTi0d1fsVYDTiTzZmUbXXSCcX2d123rVxrw2QA3Nfklm3qNOIYzzSyDb3AD4DYPWOrHb+k3iazDwCGAOzkxe53lkfThDYcbNPV43U7wwhqOGu+Il2ViE7PNYxLTQEs10Ktzmnw+K1cqKyslMTEifqYJg47ucYh0+qO3vPrqf+Wyyy5tQo89r0kMIO5516xdz9hZMCs4P7O/VhzF86W6GQCRvHtmoBtVdi77IHF1nBocb3cuxO34UdLnXQmbjl/VbDb7NJ9l8tpXZMcB7yB7eWijh2eZu+Tc58VSWygVPf6iqGvYgUk1rS2ooCejHgi5ixff66gDUOCHQ+JDQrdqyZ9lVZQxPHbeNJusec3frFJ20ebMjGSCKYKbjod6pftJTbMUVqzXrI1M9mC5PcbvNUcY50eQSFCXkFYkcZt/QmxqN5EOo4Uu3pYKLV6u/JAVj9ZKd4kJADEgyx6zBcEhx899xY6qMW6QiZuQ+GOS1EGgXdEwXL3Dk+JnzpWISwQ4ZMbyuGddysLI2FGEFqOvT74/IkHVsmZnxlwaZTsSY3ShvpnRzZjN9i7MWCa/pM5XmIzSjFNAbdSawox1Zns3BzC35vFjY+09GrDbLNKzU5rk5ubKiBEjWnRi5427Xe587kw5++yzJCUF2Xd7mYTuRHvZicVO58/RACtv6NnXAZNNK23XnKngqVvW/6rm9GiVtuQRJDik6OCQy3R3E/TuDCCm/3GHxO/4gV0kDnGUeQdPk7aKIWVGc0hMyrJIoGOUyHXjvqYskyeRVVHoyo3L0SxTNFBVbTEpbsOGrDkstfbjCaiAUUdpU45KLgR8zRVWKSEI7zDjRLHWFqjuxQNvRZ3ocxodihm0tQCBaVEM0cwY7nehG6X0GC+n0QAl9gioRJlVz2vb9MGJ8/O+15KDmNTCGMaD3q8OAyos15cLHsRa6r4u/pBZ3xs/ApKErrqdiOSWukvFpA2SYichkaczyhQaJR0WYVp+KcySTkSSx54gzJBnpjQThmjBzpkUfl67cg4E879dDssqdGZFctSE510qi3xXxoz1jWlgUI8M+XX50hYDxL45w2REt0ny3LPPy6233bLXKTQGEPe6S9r2J2St2iTxoFfxxXWUUsRQ+hzZigi6qvNxsPR0b/sJteCI3vhuDfaqTR8VfR+ynBM3vS+2ilyxw1Kqi9lXjbrK66S2jZKMmNyh4gMxAT48GZuVNV4UDU5toVkcmXDz1dVG1ufIb8eGDyVh4+fiThkoZb0vbtANzpi4n8/QEitITs0qJdl4+H83JUFInUMCY3IoZkYhL2btZR0c8pjbkawwzFAWj9uaKs6iOUFwyD6JqFndGECkJe/Xv8eB1NoEl6RfJr7lDgN0HKPXXz1CfsfK9RbUqwa3IZJidvxiCYt7ZDu6tkmvQ3BIYYUanoszy6/4BpncsuRfBJXhwJxt8wBq+EceShVPCLDYDzGQAy6P7rIe93SNOpYHc+nzN0+LeB553NoSkYLZVmRQ+yUZPIwtEb4YbP/RosITSOuzM9nyhS1YPSX3Pw7pgMo5pLyJJlu/saqxM+Au7n4KwHN91QW75YOoXK8y460wydKHHSopKNggthDTQAs00KdLmkz7bb0UFBRIVlbL4s6Z0Xztm0fJBRf+TdLT01swi/bbJQYQ2++1adczYzWTxE3viTeus1hQS9jC0hIQW8VqKQVP4Z4mZf0uF1vAJY7Nn8OCqLnFYLNC1vE/pBYZwtEkOfdFSc19Ru0CGUqwicpSBuhqK2GMF61MtPB1nIJKFkjioBzxU5WU55qFdDORZNKmbb9I0u83qHa0eBLYlPW7Qq1HfhAw6Vm3TBghUNr6tVWBQ7ala3bhbU6ZMq2+OzEVWb2sREL+Qkqwooxa0z7WvmFTNYoZ4zjywVpJRtwaK6ds/swKS5SWqEGXoiehJw1xQU0bKX304QiqYLhWfI8rn7ErcMh9hfPMCsgxPtMorBtMcEih65zVXDqA2NyYRMIYxz7neYIUK6oxPshN/ttl8UFKHH17Q9/bUKdYn33+TKtMxfXRLYrGPiSbjky6obXWjSoyBKJNEbrOvz8qXoFeVoRhtZlIS+XOxuExZ6FMH8MVKHzJGPHPxq2/1RH8j5Hr+jFJDD7nUpgcIRveh7YRVNtQMhXbRLrzGdYbk5gGdlUDFotZhvTOksWLF8lhh01p0XBd0vrIAX2PQYWVZ+TZ57XnQYsGaoedjL6pdji92JTaowYcRb9L8rpXEStYJXYAQh0ccq5x+TPa45R3OidmkFeMegC1kEMpMnQ5exN7NNjXWTw3uI+u6MrOJ0jJgBtk+wHvqTrIwZ27cWHjx1b5bGiizL4kTlXMSOgSAhA8FSYHGE4pOBMzknGMYitbYVwNWyZgMQrXaZ0yCmsVb/oUFrsL42TmuUzu0G4t8R1hXURZux6nuZUVbvg9yK4xCMv2sRQdM4mL5ltlwW0OoTtx+jHxMu+GOFWz+KvxcFHj/cODbPai4Q9LTfoYRQVVAhezUZY/aZfPhifKZ8MSZcMHVqlYF357s4KDMFIiqS65zvhC3UraG1nA4+HOpAy9swb61MixSfDNSio6XyL325L18fmtL3OPLqGXCMYW1kZQ9eitIr+Z9Tzt4AT5YnSi/IAa1qxYszOhdY4WUSXI+t3wTgMBk40MVLXJFASHbKZ4HaMbA4Oj9PoLkD25MCGkCSJnZDQp+j08ELUwYj2yT9Y4n/Q4Q6NZYlLP0DvCf0eR7WPrMQ00VQMj++fIqlWrxOXS/p83tZ+x3Zmjr5OPPvqoxbQ5xrHa03LsPaw9XY09ZC5mmmkMQusZARKlNq1lwb6G4f7Uxaoux0vK2pfVHDyJvaQ2ZUiD86lBOT5nkQYSmalcjrJ53qTeDbaP3GFGQkt83jfCWs6uDofBuBQCEMa2rEFMupXsCT5J2S/kKmRN3QWw3NGCR1l4pxMJD5VNyu70dztcAgv+JSb6jyHVHQ9X38YPE6w6yWtekkxZL0UX3Cir/9dXGAs47O4a5UZUcXJ1fIykHpl/S5zodC6sEHL0nCpV1k0rtRfd8sTKGLpVjcem63bOpU5kZIcAhLsU1j9UU+l+sleqOx+j/ozz5DIJtlc8wUxn/BKBURbd5QxaLbmfpN4dDqmPbvoiBnEb+AmrNpihW5S6+4vm9mXiSGTySFLPgBz8SchKSr4/o6Wx70UsK+hTiSk/nxp60WCbHLhaaxGPydJ+FMYw6vQ1ru1IjEFN6YYSL1a9YFdcguxHSy6BWr8LcZKNSELX0O+EzUhW3Zjkz7JIJXTQCfGQ+rxorTSW6eO131mCEUsHpvSvkor1ZmSas6Ri9KNmgxZo5bO8Z2i/XY2yJnpbbuVvnZbw3udqtE0pcJvHJKaB1tAASbOZrLJ40SIZP2FCi4bMSe4ihw48VR564GF56pknWzRGe+wUA4jt8aq08zm5sicpImlHySIJoPwG4w5tlWsRg9gJ9Yj/1q5nb0J2MpNnFFdKlJmWDbhOuZQttUVSnXMo4vLCkxWMXcp7X4R4yywVg1jd8YhmgUMTTGIdZp4m1prtasjyXhcI6zlHCh/cv5wDVxyAGOlSDnqvGm5a7eHoB97Ra/+yH92yjBkLAMPQekZXbUPJI4GUvlI65SuU3ZimYhCjudFTVj4uyRveVFM6dMS3Muiqj2DFG6DWk3r5ZPg9tQqIqQ08vsGo4y6BhQzJGnpCi94m8psu54xRrH+sgS2CXa1MXAg8sI8jU3sBiewfXI/E1uHGQ1UuLxJ/03o552Kn0FLWCXWSxz1TI6A3a5LQQslkDAIcZirvd7VGv7P4PofSO4nHWTtaExMyvj3S8RCvZoXD5WPpPl6rXy+IU65rZv/u/x+XytDd2QSiuaUj+3Q82KcqtGz53KpeKgaCGqkhWf0fmyy5X+OMWf4EMo9RS9mRBlAN5o/xL7gU8LbgJzj0tobHMI6d1Bv0Nr3rg3Fjm6zxPliWXbINiT/VSHTa+KFNAXpjSUNj+0WoorL+//gCwBKONpk6vSoIZI3tYssxDbREAxMGdZT3pi+UkaNGid2u/c6aO85pI6+SS94+UK657mrJyNg7ilxE3Eabq5JY+31SAwCFOya8KdsmfSxbD/1RKmA5Kx52v4ph2x20Lq2hY2Yid/zxSOnyzSjpMk3jOWxo3JqsiaiicqzKRk5a918x12hZs/XaA3FUdT0JMZc3Nztb21G6OAgOOW78tm/qDc8NTKDQyanpztz6dchVyKzRQSB31qqbkCPQLf61ufLd/m754dgE+XaSlmUcdWBs9MHaWdHrvAZjLPVsdPanhdiOEohGYexjZwArShw4EbNRaUQXxu3tDBzSbcz6znFwQ3c7mZa7EMojJY4VZe7MNlDjwD3d4aDQ2PoxjN9OAMjBN4H8GiCaMY8j/4XaxuAu5LoDiSSj7qvv6lz2iF3jJoT1k0kk695u2vtyGTKxF9yugUPOgZnKzBJn+TfWtmaihjExh22q4EKnq78vkk5o/SNVEes866XjSA2z/LHoD6b+F7uDNZ8TuvkVeTbH3JmQ5/Gwr6plzL9rxN6AJY9jbEZtY13oli6oizksW4kEI7jRJwC4TnrTFWa91ts35ZsWUkSj1BNaxAmu+ULAmMwFt4C3E4lF0USPg+Q+Jqk0t/Z2tDFj22Ia0DXQISNROmUlycKFC/VNzf7OSOwgRw/7qzx4/0PN7tteOzTtjtheZx+b15+nAfiadGtSQ5Mwwwpnq1qvElnom/I5sxtqulu3M4km6/fLgKM0MGPyu1Qpv6106zYgqcsfClrPkta9rgi/A5HFiBvoSxLrzV+A8w7GR7rcornl6L4OIPLeRLMfxN1AxZlI/jtapoxCbkJW22BCAUFS7sUbpbpspGpSXZgkG1/bLgPvMLo7jb0bX66GfshHSWFtcNbGNgrPi8kPnkqQcPMQsIyRCoZq7jS1PiDT+9KrzfJrK5Hssvl/GjghELQj29pdFzdH1y8TQxoSJs6QHJzWtKF31CJrG6UCoQvyJXJeelLDoOvAn2iHC9cOyiXNexwcsgzE4kZZ+4Zd+vy14XnrbVe/GMpm1rcVGYivuc2E8+Efs55pHex6TP1z4TkbpRZJKPw56HPX91Gn7mJtrlWbULbuF6uq+KLv39Xv5AF+KV1WB8xIbg1X7mZYHudeDRAMyzUztQ8F0OTvq7ny21VO2fK5TYF2xnXShW0UAmqjlK2AaxrXMlJYErJqowag6fZmGcmYxDTQmho4cGhneXf6fBk2bFiLOQ3PGHuNnPOfUXLJZRdL7969W3N6f8pYMYD4p6h97z8oCbKz5l6kOAX5WKFtqLLTsVI8ou3frmyV64PgMKh5IqoIse34RUyuMlRPOQgl/r4P7rXW5oPbERVWMvcPbmtogcPOODteihdoD76t0zwyHq5Lo5gQw8nYPgUSxaKsj8ESixggftvXYq3eLNWdjkLJt66gXjFJISwrpJYhl16kGDNbnXFFYbvtCfA3h9WmCdvd6EpFr/PFbe8uGz6Kl3LPUOk6JEG5aiM7BXEzTrlzI8CQ/aq3meTnU+NVNQxa93TxA0gNvRV6wnM/oXugUVcr49HmXA76GliSKHMuc8ox87SM4EiXOozdwdBO9mNmtB+Z2Cz9VlWXvWycg77c2DctlEbpcqwHlU/Cb6WsnDLxvy7FSUkal2jgihQwrJG9+VP2Nam4S5aSYz1ro5QGXdXa1rJlZgBEY4udLzOxhYlEjOMLXq+6bsOQfBPwBqRogRWUNF7UbfYLydd1y3VNgVm5glm1Z9mjDiHvJssp9j6nPug1zqRogVmBQ25jFjsBJzkeB99UK6wmQ6Hutv+k6Y7ArwMI2KPJ8H+gnnRPP34/ZulxavPLD0YbM7YtpgGjBnJgRWR95t/n/ibHHHuccVeTl9MSsuTY4efLY48+gYzmp5vcr702DL+rtddZxua1x2kgaf2bQcJp3XGYmPe5lPf5O2L1+tQ7H2vVBlWFpDZ1eIPxgfU6NXFDbdpwIfWMHu8HJyRqI98a1jt98W2KV48bazLGq7g8a8021cZvdoo7sW9Y+4ZWGE+lg0O2IXCItAqlLX8Yx/pYDUGoUTz0XvHbU/EUdUv6krslceunal8y3Nt5KFU44NJMkUvVpp1+9Lw0SfLX/CBbcsdLl2HLpcdF2rzpFuVcWPoskuqlsUF/ffZI2fCuZrnJfc8vU3+oUu7Rxvo0tm/pw3YFDtnGmAFsTwU4AGCKzJiONpYH7kodHHI/3bssfKNnazMLOlqc3mzQquhVSpwdIq1UAUWaHe14kdsGXOFWyTQESh0P9cnI+2ug2/BSXSSJZg3kxO7RAQ/HZEwka0IbXbx539a/JXc50iObPuJ2xKGChzLSChc5v8j1inUA5aeB8qZI48Sc/GG1mluwHeZBqyStlBveI6IGxyQAbsGvwRZqfcVT9mDFHvJuJgKwRdagDvWoH75LkMjEptkXmeUoJDDx/FmNh5V/aDnscDCWDVn4kWP1u6hxQGpsH1uOaaAlGjhoeFf575d/yMRJB4rZHO5haOp4p425Uv768iiVGd2/f/+mdmuX7erfjdrlNGOTao8aYGxe2opHxIJEC5aXcxkyYX2O9KhTtsLdGwkQSTadtuQePJdAn4Is6B3jX6vvZ4s6WtM2ksJm+8QPVZyfD2mVNXCdBgymJlZBSQDpsi4kZN428QO4xjsBtBZIZfczxeraKmnzUYoPZqjS/tdITfaBevOwb2c2XKVpcJUiSYOSBILiSJehrXJNsA+ekYpUm4TiObPOFLthH7PFOa8K1HRuqnj7HyVjPiyQ8Z6VsFD2UyiENCk/nhAfzOqt2lwrOfc0bUQdULE149PoiozmAmzKaCwDqLuU9fadpnqQ0QvQCutUU8Ah+zGBgjWnN32suad7nqmRSZNbb94NqOIBYmwmZfT7ewhQeAEgjedSsz3ctZnY07dTi5g+Z/JMHvpFtUoyYV1oyuBbauuIsjWuxz7nNgwMtR7aJ7ONCY5JmaMEbwyMwzPqmCB08sfVSOSxKECWApdwc2Td26juAnBI4TXkujHhpHQJrNh1Lmy22QGL3mFfV6nfi6rvfaJHzWf1S9qLAttQuK8xgEi6oL5/d0vuq7hOiJ/VpQYcjaQa1QE96yrzLyYxDfzZGkhNcsqwvjny7bRv5Igjj2rRdFLi0uWYIbAiPvK4vPjyCy0ao710ahlEbi+zj82j1TVgJvtvE4XWroS8L8RZPE8yF16P6imbgj3L+l4hrqwD4aXSHuLc4bOlICFiTLCNvpC09lUFDrnOmDc97k3fv7Nve9kyyZl5ukpCYUWXaOIHYK3scZa4QJNiBIf24gWSuPljGk2CQi5Eb0IPKYWVsWjkY2rOmQuuFUfpH2IvXymZC64TE2uJRRHGHU58zaX437og7mzCS/W5tVhhRhefPUO5ruNQpo/gsKKkoxTv6KXvlhS4ok1NIb4L9oCX1pklniRYDmmigRTMRvxnHVE117f/oIEjZkEXLzZLNWoLNyRGYmsmjdBF2VLJMxBFcwzy5I16uEbViG7IctTQsZh4cdAH1TL5oypY8LTsWiY5EADxXJfcD25FuLN1YUJPXGfj3MPdxGIOtdX77OxbB4fMRqYLeQjKEE6dUSmTP3RFtWBGG48gafTj/I1o82HCy/ybtYxiY3tSBTG5pbngkGNEAu/IdY7Ja6sLXeI0aFPHh35ODksNaHcDUNTnyYo9TbFkEoieuLxSVZHRxyd9kA4O9W2x75gG2osGDhjSWTZu3CCbN29u8ZROHHGx/PrrbFm+fHmLx2gPHWMWxPZwFdrBHCzVWyUbMYNMKqFLNn/sfzCrlEZnxtg+XUxgGLZWbwKw6qY2EZAVjMXbE7YnbPkMdXSLhRyDAfJlRIiiikFfCh9Tluo8mGHUapM+Mhbdomh22Dhj8e3KRUyQ1BTR6y/rbZmMUTDq6XCia1gNLeAs1IWVVmjd87FAbxRh9ZAR/6wRWhPrMFpYKwJVAjhr1UZx5UxGzeZ0uJgzZP70i2TGJ3egrUkGTXhHDj/7JpCRV+KabEQt6MFqDDfw+9rXkSQBrMMYMGP8YdhBDCtpjPcieTGydSmkyfEj5uy7k82SPzsBSR0BBdTo7qPwJcFvRZot/LSjHqpRrkTGofU+x92k46lBonwweccoQ2B1ayy71tg22nKk1cnrMoI8WKkijhfmduarsZ+/NvYJqOziaMdoyjZaLXU3Ma2ah3yG3wfurKtetCmXLDOyCYQbKneXgP3aPLSjRWZAN2UOjbXpA7LvUnBp5qPsHrPLe58bnq1DC+akN6pl/fs2lXneD1Y/SsVak8w6P15I6p2D6jL742Xn4P/RHQxL5oGhij3GY7Mk3gbwNNIy2v8ytyrRxzjQCUhm2v6TRyUQ5RwYsxYadRZbbl8acNitcujonvLTj9PlrLPPaZGrOcmZKscNuxBWxCfk5f++1L5OsBmziQHEZihrb26avO4VBQ55juQ3TNz0AZDE1Y2eclXXEyR11ZOqjSe+K4Cllj0b1gkppVVdTwzbFLlSBheqc958tZmP65R1L0s1xm6qWGryg01NIAa0uEuUFS24sZGF2vTRoLQ5ThK2fiZ+cI8UjHxCaiOydZEGq9zMSRveUiNVd5gCzscOUUetQgziL0hSYbZpSr9amfQuQFVaXVOk7zqRvONFzWpaUo3WVFfG/vLrVwejoQZyls0+U8Yd8ZQk5ZQBiAKs1cnMc1ETebFmAcz7SuTQacja1brgQU5QZKpn5SOpMK2am5AxzBjEfhe4JW+mH4BB68hYwFXP2qX7CTWSOf8qic//SbzgdywY8zwuxkAZfGM4oNDn0pxvVg3Z9EnImsy+BLmtKQScC+9AJRacT8+z3EhqIPAKCWMVgwKwPO65ailfZVGu0txX7OAtNMuwu8BnGO5JDXYxLtANvOVL1DmG9Y0Z67oQPJGDEj9DWfqgZgms2SEy7zqnqlmt3N/XuMOsb7TK0sW++TPoB1nEDXEWstrIfFhJaeQnpVGvs0MudP340b5prWO2eTRhtvKCW0G2jqGYPNL3gtCYy59wKHDIfjtQd5oJPqyZrCeYRI5XCT7JmSjNp9erJnn5qIc0624Nrn8WajLTkhuTmAbauwZG9Osg81bkoQTfYhkxYkSLpnvC8L/LBW+Ml2XLlsmgQYNaNMaf3Sl0Z2uDmXz11VeqFE1KSopMnTq1XmHrV155RUpLS9VMBg4cKEceeWQbzCp2CKWB8GcpNtXbUE9R5X0uVjGDFtc2WMIOhtUtukWtXseIDZHWPhMDxpoh5b3/FgSqrqxJmnu1Gf2Lhj8oJagfbU/MkFp3fesGaXJI0VPa93IhoIxGKq0fLvdlVL0AOKSUrXbI1qfnSa+7hqJiSQ1iDM9QpQlhV1SJKVVdT9a7KZekNQW1h+uwrsmMKhR2WKK8FZLz2/mybfJX4q52BsGhGj/XIZ4t28XeNV3VR2YCCAFmd2R5jobFyig54CjMOQAP6zq/qC3DAJbQ0I6qGXE7pitwyH5WxF6mrnpKswIbB2rhspHPUR9i23cW6Xa8ZrXUtzXnm1nJSx50SMFvFskYDbclwEcPxCOSlJou2UihZWz544gBgHSY7AWHo09cyIrVuSUr19mVtdQIkiLH4DpjHVmjWAfz1gSUZKwK6XPW+XH1rJdMwmCmNoXUL0fPqVRuXLUBH6SAGXClGxnGuA4dov/fm3cjSL03ar+thXc5UEPaBJ5MgH5kHdPSS0tlc4QAnXyO+tz/+JdDxXbqLzTMyDfKzgA941N1cMh+xXUvMnOv0SyslriAjHsa4ReIqYxJTAO6BjZt2iQ//PCD1NbWytChQ2VCRDWTFStWCLGDLhdeeGGLaWj0MXb2bcJb95QxPUCePVv69esnCQl4y2qmJDiS5bihFygr4iuv0SO350mbAUT64svLy+WSSy4RXvBvvvlGzjrrrKDG3CApKykpkauv1qxWLc0gCg4YW2iWBlgmzlk4W2zVG6U2dShq3Z4qjXDrBsduDCwFG+1kwQMrVVWnoxHP+KWKWSwdcM1OeoTvJlB15RyiXKPKion/3NaKNUigeRTADC6xvpfCKjghvJNhjXWIHSULJNDxAJhPexj2AE+5S1XFEwtc5BRWiqnNHBfWxrhiMRHchkxQ8SUz4crcTxyFcxQ4ZFsm4zDL2wgQuX30ozXKmsPwxklH3S3xSdoxmX1trVwnAegps/s6KdzYi80lLWeNpAZmSpX/ZGGGqQ5YNn5gk/2uqg3LCE1FMlHS+rdQ+SVDCkf8GzxyR8iIO/2yHEZCApIR99HSEwI5HJ+yY6ZFVqDGMbH/UMTYJferD7y0ltE/aUVagySFyjpgE9YKdx/WdNYBSdi+RlZWPG1HooVNkSxXb9asqaGqJSL5yMg9/LsqRcK9AwTMrOM74nZUO7kKlrspXtQyNgFQ+pTllTWgjUJCa1rVaPFkrByvxR+ojkIXez+U0ut8hFfxL4bpKnwIceVFbMABjOCK1WI4Pl27RmHlm8YkmMjCRrCA5r6igV2C3GmHJKhyenwx6NGjsVEM+3B4WjqDAs7DACiAAkCGtErvB8BaNM8iNTvMkjkOpQcB5kl67Qapd7RSd5ljWFovECQJ74Q6zEXzoc86Im4fQgCWPOQAQIRSDULSa1L55EyE27qZINcwTGxxD9UAaxifd955CvQ988wzwszf9PRQkmNubq5MmTJF9ttvP3WGNlu4J2J3nXanzEQZ0D1dfvllhhxxRMuMVcfDivi318cpK+LgwVqY0O6a7+4Yt80AYs+ePYV/FF7gsjL4SQySl5cnSUlJMnPmTOnQoYMMGDDAsFfku+++U2nj+kb+WPbff399tc2+CVz9/sZv5G02mVY9UI74e8zHWxxAiTNDsjG21WrFgwJEv23wH9Lc/2S4bRPF33UKLCInSsip2sSTzMkJa2ibdaKYS1epbQ64Td1nLAVuA+QtXyfW3+4UE9zQvqHXwOoZJ7ZZp8Kz55cAHlJJRwOkZocSacybF8NlrQE1DkaqHsfkR8KOZVxJvKVcSr5fLgVbBkqnXnNl6MEfianD3WKyD4SfMdTSktJNciLmnHMKQBj+KNZvt6CMirYcQDxlek/MCQR2Jzz6nCx6KQmAwywjD35F7N1fl6TsHHGkVkv1dq09jYSdemThMmqAz1S4SOygzKEQbGavfkRMw46TA25Plgm3+qV8g18WPQlLnONUGTNqliRVfCABuNB9Q+6VOWPig1Uw5uXb5MwFtJwBHhXMF+vcu8A34xLv6Lsk0Hmy2h758c5RLilZoQGhOISFeoFD6c4kLc2WT+1SNMcup/8WJ/HZAB7lAWHijDNNm3fkWFzf/psPFUcIZhsWWtm2vU+rquYuZZZudh+7DLvKCp2H9xt5iV82fkCyb620nFTEydyrNAvX+tdNANkm2QZ3PGXulXFy9nK4igM8fgjcWR24J8Bgy/NqSLoeapFN32rjdD0M8ZwTstT/LbYvWOiXeQ95FLH6uLtsktK7PsBku3F3eGTmTTgIDp09xiT5v4fmwOSWilyL/HFnooxEdEZ2Nv8HNyyFS32y5Fmv5IxGJZNZmBf+9T7JLNOPSlTWzwn/ssngv9ukz5qA1BSh0g+uz7KX42TGNSBlR9teJ1rkiLc1gBo8CnR72my/rPnAJ8k9TdLnlDicG+cYsmY74nkNQhdh7Sde+eFsLYSB/IynzXFKSq/o5x88jmGB9ycKQe3eLnz28F5s1N/ecM4Eh2lpWhwOrycNSUaAuGXLFvV7/uWXX2T06NFhz6Nt27YJAaZROJ7FYlHtOJ69oecXfzuN/G6o68PG9pLnPponxcXF0rVrV3UYjtnQ743H5T4+Oym0PJ485lJ54dkX5f2P3lPxjNHwA8fk9W3o2jaGOzjP1NRUSU7G8y1COC4tsy0V7Sxa2rsZ/eLitAcLXcgffvihnHnmmWG9KysrxeHA22XHjvL777/L+vXr5aijjgq26du3r2RmZgbXWeuwoqIiuN5WC6zTSGvn3iuwRNVlzcbHx6sfLa9NQ2IpWSpmgEpP9gSY21r2ZufY+Ikkzb1WHcKy9j0p88N1mjMpeEhr4e9iK5wv7pyJ4ktrwlsY/oNmlK8P9jehzld14TpVWi7lh7+LpXiR2meafp7UdD1W7HXmHSba+FZ/KFVxoZcTs72bpMGyRqsfxQRqn6pty8Sf2E2t1/tI8cnJL6I8X+40scWbpHL0A+KurAI47SHO4XdL3Jo3xJfQRSqH3i3+Rn6/plGPSVwiM5jLpabveXho4/g1FWKacLGMTnxYLLCQ1na/Qcrj+iOboEL2fwqE0TealcVrxB2w4tgr9cso1vIig02TQAbz8cG96nJJTbVb/jfVrOIXeS5bhv9bjv78DgkgHrMcsXTGEmll6wAmyzAHPL/TvkPwNoPrILZpp0rJEdPFn9RTresfrgIBONQsfNzG9dNX++Tnv5ll+0ztwe7CECuRiczqKvPuhJaBzYbfgozga6M/8Is2cKTQmBpQ08biHkpKX5+U5wHFSAhobF/olqr7alRt6r7nBGTLNJPs+FWk40EBOQ7fRQsRcotQoy8P4XG18co3gHapnONo68xW3rGiSnqcZJJFD4bGrikOyPEARqXLkJjymkm2zwjtQ2fJHh+Qif/xyJbp2P8y0qIQZ7j+x0rJGoPLgPP+/Hiz1BRqxyhY7JXjfuEx60vPv4pkHYz/nuXkKRT5+kizlK3S+umtXUVaX/6fjfYQI4j9+UIQWH/DHlrftEEBOewDv/xvAn43sA5SZiBOMmcKEq2YLIZbN3+qv92H2Mq6RKd1n/hk45wKSR+kmgc/zLhF97tUW+VtI66vyICLEOOK84bxWkbe45OC9RWS/xt+Q+i7/C3qSjsmQfpKZKYPvCz6tQ8exLDABzEfuNEeuoZme8UigUBiYmKznnv6c7c9K0AHh9OnTxfOt0ePHmHTJTiiEAA9/vjjcssttyisoG874IAD1H79o6qKHJuIx8Y9jv8H+B1NTBg30ICxh8dkP4fVLJNHdJOvv/pC/nru+ep5SBDY0JjEBzymxxN6W6Sb+dxXRilcQyui12s022sz0wFtQ5imMdxBcMh7eTRcwnGpi5ZKmwFETrCwsFBefvllOeWUU6R79+5hc6bidBMs9/373/8OA4j80fDPKBs2bDCutskyb0Q1NaE34jY5aFsdBD/slNVPSxyqiDBr1jv0Solf8qgkVW6T8p7ng+dwSthMknNfkNTVQCaQmrRRkj/hNdzrjQ9vtWunH868GWFtTFt/kuqkUWob6V9S512hluPhitqx/9tNqnvs6HKSJG16V/WrSR8tFWZYLaqrJb1iXfBYJn8t/qOHP4yq43qhmdEFliYp4Pxg4guF/9XcpZvFzSehQeiKdhbMlARQ5sSBR1HwP6s2DjosWQ9L5ovii++M/wCLpRzzquCTnvQ/YccJDUZAmzn/arj856gYzyofkgiCbZH0M+DWUOO67SkAG1N/qrdZ2xA/SOwdDkd1mG+FpN8l/a6WZNz8eEMpXbYB4LB3sGMRsHN5FUqjOQCmOghci3FCUmQK6U5cNUA0IPTOMCYGwfxkX/KEFA9/IDiOWkjATb0/qrAgEYSSOtgnXlu1xHV1YA0vInViza6RuRcguQHJJZRFD4p0PaNK7Cl1DQxfaeMxzqB4rTQcsq9t8bgZw2WsS6/B38qwJ5KlvHaIrHpV435kybvtvwZk7Xvagyb3/wAgQRxOyX0TCRwvuqRzXQm4lIFxUjNDO1/GzHU/xS2rX+J8cS4DfBLfH7Qv/USWvZCAOsXaGBkjkHyR7ZJMGO28+P1vnwF/fJ0wWWbkv2qlFrjtj39z3lqf/Dkgi55dCRe7CeAQprM6KctFxaFSxJ6G1KPvUt9mgCwCLT5+Dv4UwBYVe5hgpGoVI1N9wJW4PhD+hrf/rLluO8KtrmdQb/qfFeAQiM8gJQC2HqnGy0BoHqyisuINj/S/OPSgsyWj3GKBdi0JzH02F44T+v+D24cUIZHG7AioRBYX+A6XPuyQWoQZjH/RrWIPq7ea5NMDEhTvI69LZ5CAG38Lzh41GDP0QCfJN7OoM8dGT3AhaOLDmvdlHn8XnoUGjbTPRafTKXxpD78/tc+5NndWjDHcvn27nHfeefW6MixNF1LPLFmyRFkSuY0WusgkEh0X6C8NvoZAIPrrbfTx9W+aOtRvCt9DemfJwtx8mTdvnjoWAWA0kMe++guLESA6LQly1JC/yiMP/Vte/M/zUYEcf8cct6Fry7k0hDsInGklJEiMFM6Hv5uWSpsBRLqUmYTyl7/8Rbp06VJvvvPnzxe+9R500EFSUFCg3Mz1GsU27FYNxAFApKx5QR2DnHz+4rliBuUMfySZpddLXuo0uIE7BueQtAFP1zpxlswXe9mKIB2Lvr0p3zUZY1FZ5JNgU1Yy0YVgVRdS0hAwutOG6Zuifsdv+wZg6DtF1VKJRBAV00izF6Sy6ymSvO5VteyJ7wYKHs09EcCDvbrvhchoPl7tM34wxjF1+YMKHBJsutO0pBMCOT+e1maAxw4zTlCJHaHHJR7kZUvVH8fidh3GkEambMB1xkOELSdufEfiCmHWglCvSXAPN9Y+rHPEyiaUcVv7mh2cgy/IiBs2irNroqLw0Z0RHWpelvQO50vxdqAeSMfhmwAO09UyH7aT3nDJNhAnWwHEgqTIFljRU4coXkjVEB9Gbkl9G78nveUCLQ9qTkP9vc/VwMbgmwHM4RIlSCO3Hkmhje8VXOaxGb/I5I60YeDlqwOL5Dc/GKTRxX9YZPULNtk2nbdyXQIy5thXxdft35Jq88sUxCGSXDouJyAzzgyBNh0c6r1YxlAHiKPB/bfiCWStb4B7uVtAZd4yZo5zYaWPCpSr24KsZVb1YLayHXyArAGtC6vBDLmtRiWPpKJutk5ITfBSnqv9BtmWoJZl40ikng6AWVxXy5mlChsCh/ox9G9ybmZP8EF/LOmHuSCmkSTelHXvaNnJXF6O8yHxNbO6mdgTKaSdIfsUOS/1lwG2qQYwY2Y8M7bTh/tl5IO1Mv8mcE2iZnRkjCvb/3aFExVltOvRF5nyPN8ddWCbYxzxcxXiO1ETu44UnAktTNzp8zeNhofxndSfLps+sQpLDxKsJvX2ySGfVjfIn7j+XZswyYbhFSNQ2abLkfWtNPq4se/2pQGCQz77CQ51a6E+QwKjF198US644ALUU7fLjh07ZNy4cfruNvmmBe7wMd3l/R9mq/jIaK7cnU3k+GEXyYVvTBCC106dOu2sebvZ32YAkUkpdC+/9dZb6uTpLmY20jvvvCPMWKb18LXXXpM1a9aoH8E555zTbpS0108ET40EcKY4d/wUdqqmmsLgOuljWE3ECBBVpZE6y1rAZEWmb1awfXMWqsGPWAj3NOs3Mwu5JusArTssVY7ihWFDuVMGq3V76RKJx5xZz1gle9QBQD4B0xfdJmYGhUESN76rKp+oFXz47OActCLWEdQxXmQm2+r4F+leDtgRBxPFBEGLnwt1mBmLyCSYuPyfJWPhjaqUYGWXE1VWM7N+KToIVCuGD+P2yHMyNFOL5ggCv8j1yPYNrdP68jvoVRT/4SK4RVw95IBXI94yLVY57ZpT5I+Z54jFWivdLkhFxNi5wSEJVnTwFNyIhfyxL0mHWacrjkZvXGepQCZ5NCGB9KDrw1EJwd4YJOQYZQTcvwvvdCoX8xCQK1esNaua1kxscGT4Ff+eTqbNOZED8fdV4W/GQ0/6QUxn3Aj3eLKqckJqFtIOpfSD5Q/ASScE50/FmDRCTj9dON/e53lk+tHxsMqZZP07dgCjGul5OgAhsoZ/PCkeP7G6qwmLHQFe/wiXKCu49P6rRyXSrHjGrvom9gig7rAXJfM0AEVrKmsL8+e239W1CsTx2DyWUejWZmII56//xI37ucwxUgfCRGmQrd+Ebu1MimF1lKSeHjWH9e8BkMLyyNrHvcBtOfBq7foMusEtM85ADBUtuTi31CE++e7wBEWdRK7Mia+7VKlFw2GCi9StDg65cQ1eCowcndRZ5QYzYjrD55nUww96nfDfhz7oGrzYEBxSKtbSMmuNCvwYEbPwTtAbebW2JBrvPLWyQX3p4zfluwT8kb9f68Q1MEvfC0O6akrfWJuda4CGo2nTpimD0MMPP6w6nHDCCSoOkdbCk08+WUaOHKk8j7TKMUbvzwBYHTOTpG/XNJk18xc58qijd35iES1S4zPlsIGnyXPPPC//vP++iL3tdzV0F9nNczz99NOFf5FijEW8+GLQpsBUyljEmLSOBkivkrb8gTrwNVFK97sRTxSYaAySBRduHNyjlADitoLk0Xw61Yk7qZ/UpgzVV9V38bD7JW3pP5UFjVY2nzMnbH9zVqo7HSX8M0r8tq9VVrW+zZ3UX7m5Wbc5Z/ZfUPZOs0hZEQtX1u8K1Yzb6DrWxQSgaAogsB4uTWvFWklb+ajaRQJqcHLozdS3N6lXcN0KEnAC1tr0keIFCGV5QF0bqciONjPLAkLLZyT/oyeui3hSBoitdKnYkBRCMVoQ65XpAxBW8Zt14LSi2+kSv/VLde5eZ0e4pFv2skTXnE6OzTnwAR0ppAjKKv5dcS7SOlow4PnIJlHXCcK2Tf4acYgFyooa7Wnsya9A7FmSeFwOZSWK5CU0Dtz9ZGbJ4poAPxAAknya4JDCMnEkoGYWslE6HOKVdW+gMcSR6Zfud48VT51plJVU1vxX27fuTYCbl93y6+WwXqGknB5Hx34ssWe0WnHbjp8tIRCI9bxpVgXyaF0LgkM2RDweE0NmnBUvh39rDEsAfcxtzmApwA0goJ76Y5WiHuqIOcPwLF2O9qr/hgSwsy+KC477B54dzGanKE5NjM3rSAvage+6VMUWtTPKB+ttkysx8UQvstT9OI9Qo1SsU0gFxFrMLgA6Zxb+t2sqUvvSh1MXXlWXmdVVWEpPr8BD0Egey+wDQhY+1anuw56CCudOuJ3rKvYQHHY7wSOrX9Tu5aztzPHNWC1fXSusOU0C9wGXh19TDkcr5zyAPNa6Nkp8x3Bwqe9jex0cchv/+zOWtSFArfdryvcivLRUrNHulyvAC8ks+Egw3pRxYm2ia4CUd08++WTUnWPHjlXbaTEcM2aMiuv7M7EBYxFf+nSRMDEmK6v5xpATh18il79ziFx97VUt6h9VSbt5Y5sBxKaex5/5A2jqHPekdknr39BIrzFpG6hSPIm9parbqcFToJtUB4fcGASHdcvufudLeeIQVTZP4Fo0CquB5E943bipdZcjkl5Yr5hC4KaDQ66TnkcHiKy7XN7nIrjKX+QuKe99UbAqSr0ygnhaVXRH/WOU6iP5tafTYWItzpXk1c+BOPtzpQtaRguHPwS38oggOTa3GcUN+pniwXepusmehJ5SOvBmVEZJlQRYL9OX3qtZFRFz6IF73lKLhBEcj3rnXOm6TtrwNtqnKfoZkmeTF3LbQZ+jMs0W8cZ30hCT8YBNXCbtSCI48irhCqX0PCP8YZyw6F5J2/CR4o3cetBX4mPmQzMlksNS756MEoHfXba/bMmdoDYRENDFaCiBrTcNftM9qIsNgMMocR3CwQFBAauDKFGxd26UFAQ9DZI9WOKQXIW6kOdv0+fh9Yb1fXSdRkpkOcG0oRooKvydxzNCfa1nFYA336WWPYpYwF9RrWR/LwBQ6PgEpaThoSudblQmpix/zCElf8Ci1h0JFrpFEsMVLwqBojWvglOTIB9CC9q6t2wyEMkj0aRwLoGqFse5+J4ymfQmAWhAuZ47gm7GYid4Q3oPjK58D4lGJ7MZ8Ym6y74ELnwb3OdGobW0ISEZ91hwHC57BG5egMDhd9eoij2MHSRFEGMN9WtPi3KkVdk47gbQNBlrdvO3MODyWuXmNrbTlxHlIf0uqUXIAQ6MBKBB14Ps3Bh5oDdswTcTgozS2lVujGPHlhvWAF3PfzY2iHfahGX4fvxhupx62un4f6S9wDY86/A9OcldZXzvw+X1196QG268PnxnO10z3JLb6Qxj09olDdAtbBQrSuoZhSCFVVBs1ZvVZh/WLTRx1Im354lSbe2jr4a+4aOzVa4Fr16WAkOhHdGXSKZNkwkJp5sq1UisqM45VBE4ezHH0n5Xqq602BGk0e1NYQyjUcr6X61iDVXJOD496sSNEoKurIkKENNSWtb3MrinT9R3Sxx8VTmzzoIruSi4jcfIQp1pPipLETdY0ftCKUGN5swF16tye7TueVL2U3+V3c8I9uNCXP4vGjjEMq2YujubySLu5P5CS2IyADyFpfzSABa3T9JiIhlM5W0BYFOD1X2Qu/DA96pk6xegQYC7MBMl9nQxb/5WHLmvqlULammn5j4jRSM066reZmffGz+0yiZU/yAn3sBraxVXIvuYkI6avPJJyVt7VXCImnzEs20yN5lHkSUFjUL+PBgtFcDidpZ0K15Qd/uCJY9cjQRilJXP2RVQVCv4YNJEpPWS5NasN2x2gpYF7lC6mdmGvIwkj1YC4NkdMZK0chUt0KyYOLu6YUNAkSBo/f/ZgtYy0upoQFJrarIi+QsJLoypW3gXLKO0cNWRZhfOhVUPrl6drJoJJUzu4HiliCs0CoGXLkULzSpukXGhBJkkDNeTfPjfgoCcVWFoVZxxZpy4S8wAhX7UrkaSEGIyown5DY1iT0dVF4Ct7T9alUufcYUU6n7xPQ6AYpMMubVGOh2mAWh+dzos3JLa8RDu0/arzk340GMU9aa02tFt35gMudmt3Pp8yaBltLVkPwByxkHyevE3wmsdk31XA6MGdJBFa5Yoyr1IKr6maOWk4ZfKHW+eLldcefkuJY805Vit0SYGEFtDi+14jEpkzcazFjL8Loy9q+pyXL3ZFox5QVJyn8VTC26l3n9XAMpWvlKk9wngBAT4AgdUmMCNm/0biLUBLPx4ahWOfFxqciaHNTGupKx8QlLWvqRAFsEb3dFNEtztC0c/DbJrV1gNZ29iT9kx/jXETX6hLKIB+MnS/7hTxS+6Oh6uho7LnyFpy/6lrIDliCEsRaUU+px4rjw3P2IRjeXyWC3FUTI7DBwa58hHZ+rqZ6Sy2xkq7o4Wy+qOR8K9irjFKMLkmriCn6Ps0TZZXHA907VslDqXuXGTvkyL2XrUuKWFjJVS9Hg8fX+0b4Ia1tKl1SNtqFcOet+luPZUW5LbGYSJNkYxw9KZvuRuRe5d1ekYKesfAntsVwgC5Xk3atmwwMEqpm/YnUA+FLxZm8wW6T7wZ1m/9DC1KaEbrJlwM+rCYjmNlV2rhdXJKAR/jAXLOahS9bOnhYMAYyZzJFE1LYCZowJCl/T2H5C0AqDk2mpRrt4N72ioiyDyUNRQpos2SJ4N4En3NmXuVTxXI4AyoVyeR5JQJq//JajQAotguITakoicYH0+S9ohHjBSev7FjWQXWPUQn0cL43dTEoKWQ514mkTVvdHu/9m7DvA2qqx7bUm23O04thMnOIj/fgAAQABJREFULonTe+8hpJGEEsrSe2fpAXZZ2tLLLizLwk9deu89IYGEkEZCeu+9OnFvsi25/ee80ZNGstyCCbD4fp8taebNmyLZc3TvPecwDsGBhmVpHg+ZwO1PrPSQXNQA/KIXMmPHGyirAxwySg8GK59k/1K9WolfqShN73oHGWdkRJk97IKeu7g+1dL9Bu/nlGSbpTfaPWB8yTVh6KFEb55f+V/PeTSP/Hzvft+mjpfn3+ky7/7rmy+8re9nor6xjV2Xgr7RhOEOJWxOkfgmJo0au5uWcb+TK8BM5viBaTJz0QLJyMjw0WRszCl0TuorKa26KO3GCy64oDGb/KpjWgBiEy9/6H4IJeduADiYhKxRjyZufeyHk/FLmzaWNZ2xfQN6FBNwmbNHzIgx2B/iLZR5j50laYJDBoFnzPYX6gSIzOJFAxwyeGuMAcgq6nApUmb+N1SOCBw1pFj6hQu9gfyJ2P+pxAMcMiLxPAsAsBxZwtgtTypwyOXM0hV3uMQg2AAk+r9v7PeLX3sHsnwgqvCmq6As8Js1Rlnd6bI7JWLaLpiqxKY5L51fKLsTKGJgU0fxbR3FyVMlPOdHDwCNOPCFOk7tIFONc2SGsq5gT9teN8FhF7JLk+Y6cBOva7SxfPMz0JMEOGTkr7PKmvtDZeBjBoirTj9ZKtc+A43EbQrkF3e8wtjI/Tt2638kHIxxBpntTnyOyhPHuNeidO+WidELWC7Vwax0fs+7ZMqV02T9wgukKHqKtL+2k+p3Y0Zr8ZVhAGFB0g7AZugz6BEN8CHLuMSlMlXm3jL2t1WVwiUH8jP0A+4+zSk737IpAkcwSqg6o8jSqs6m8Zhyl1vl+zNg8/ZsuQx/oUxIHNnyf747JXBbfLUdsja+mSp7ktGLR3BlDmZkh7+Emq072G/H94Xz8FgIYOlAwugIYMePAnvjvEEwEyQWsMM7QD4oqqMBbtj3p8vKHEsgfermYrG6k5pcRns9DVaZ2SJD2Bz8XKx90C57PrEh46U7Z40R/u4t5u1CkTGcMMuheg8jkZVkxtY/+EXFJ8PH7C3sC5lFJgO7vqCX88Z/hYoFGVM695BoFCjCEmsUA71oS7BEoqxNfftfM0ge4k9LtFwBXoFOsDZtveWwrFy5QoYNG97kizK111Xy2iuPy+8BIPr+12vyqf6xNqDcSMyym5ENexkMzvOU/dnv4QqQeVzWZkJAcHg0x1/th0z8X3NOZv3YX9d6+fV44f2Y1bBOFggRHM2BYJvQXAOo6s3Zn8j91ZgAKMvJar96kN9j9K5XFTjkYoJDVX6G7uOh8XPgmfwA9N5ipBKl9DKUhOlEoiM0f7USCdevzY8Em+ZwdLhQ9TLqZcEoOcdtfhzA/Ak5OH6eHJy4qE6QzW3MPXXOHEPfjstJEqF2ZfTOV1RfI5fpqKn0vakdnA5AolsM8B4WTJwuh0e8L4fGzQFYHaE3U48Wtwi2Xmgx6R5yWdIYgAH0e+lgH545WG4/csoCSXz8z9Lp3gxP2W/jEyHuDJ3Bej00O/B3VJYmCYLbnegFbNQUNDNjmW1jFrJgA/rlACJSAe7ajgfofL5MlQMJ1LwRBIBtlS0vhAC4ATSjfOwfjr0W2fQknFKwXWhCtZqLzF5m9lgG1hGRZkiu6Nd8ZKaN9n6DnyqTE+Y4ZOJ3xvPj3i9VWoKq/84HxwVJt5ucMvFbyM+4wSHniUitFnui94sFQZQZHHJMbE/fz5a57MlytlvnXvU9cr8sjfL42ZuZDjZ2fUH2OJ1oTN9tfIbzz6oTWN7+oftc/Zfr1yzdLwfxiOC3aItFlsOfub7g+9oKftq/Njis7xhb1v1xr8C4gamyatUqJc/T1KswrOMkKSl0yI8//tjUTY/5+MD/nY/5Yfw+duhD5sAN3p6zVErAcP2jhRNs16KOl0FC5kOQKNorUob/NaCItu6v47pqSzhAWojk974XN+fm+9iVJ46WyINfqt3zFq4lcvJgE8eyMxnNBV1vQSnY0PZTA/1+VYV4+xS5ihlDZ8IwRSKhhI72TG617j6fLamd6Gtm611NBxQdPK6q0NbQ+PI9bwv6Q+nR7Gw9TA+t85FA4eAsA4Sx/BfTBQCtGlZpSy7y9DaGgqyTPfRVzxzU51MCyjgjRrj9oCTPmyy5IN1I0mV4H9C3V4emZHHaBerzzd5JyhmVgcRjDt7AtQQJl5cDtLpwyj439ABMgeDiQxidxk1UBBcdwGObgCLHESk1Muy5cujpIQOH043p7gVO3HjjkwCb7sweZVyGPlemyq1c1+6EMjk8z6JK7HzNoN/v4R+YfQMQQ4bMnlgl+ZD/oRafOUgaIQhj2Zm9jSSGjHi1DD2GIULCRK+/OAOWGvndhxlS6v9RW3ALPKNLAIjSUTJNphC36fDDwMjtdp2rViKdgO64D0oVA5vakyxf+wcBGhne1HgkIO54IeYHEYW6jJSb2fuxl0zG94nyNI0JAmiyx/kXQFLQ+G8cAX2y2UoQkVKlspT8DFjxeTQD+UD7YiZb911yPb2oWa5uKdkGuloty37rVyAhNlx6pLdWsjeTmujTbEH7zZQel8ibr70l/i4wv7Xz9r1j/daO7jd2POUgR5Axy2ApkmW3P2pQLkdJ5tRxAUhgMUdh5+tB8AAocQeBEcEby8E/J6NIaZxs3FWpLUgCitNNWClrM14OJo3D3ngX8s1u6WPQj/k97hT7gpPdMMpYyqyhfwQ7s3wWMUsYs/15gN77fZbzRU1llRRkp0lU3CHoC1J6B36/0d3gONMf4ter1XjCEpaaGwMQBz5eLlGdoKaPHsQ+x70nHZfcj52gJ4rn5w5+YTH0PYxUVeJIaMzd4YTws1XC7Nky4by/qUwpM+HS3/te6O31I5OfW38cLxsOL5FO48G4zujhYYJzDLNL1NmjtAyzmYz4gZW+4FAtrf1ryGmvyaxNN0lZSbykdl0oGX3WyPp3rpINIKFQJmXgP8vFIDZ4t/VnFes15vIzlx1ZSFHsasWeJfiIgTZgvwfLlUxNLLpBtr3s/XfHLBl/6oqsRdADdOvqkVxDkJUw1BdoUTz7wEwLNAbhPAMwV4Ayu2O/ce23vYxMtBt47n4vxHB+cWv6cZ8d4T/MUi2BEiVl+BHNWxssbAsgQYeC3ASBaBv26QHktvye0f1GX+CYMhUAFEGCS8nmMMndUKWcazIu9h2nBtXxi2LTBIcM6v6RnJJ2hjGvWmj61enSSrC1S5UzTALEutlnyc/FjtdtQkY1AXLHC7yZxsg0A0RqrcQuAL4t4NB0QVue/u6uwOi+7eXlr9YoB5g2bdo06fgn9ThPLnvzqaPatkk7+pmDvf8xf+ZEf4TNSa6whMGZNw+yKAAm/r1s/yvXwFa4CT15dypnkIqe14kkX9jkU3O0O0W5nvB2w+whAZuOuPUPwAbvQ/WSoC4boss/J8oABPlTK9QdyLjh1VpnWkD9w8qIdJBP9qilLDHX0nTE3S8su3ZJgOLZ/sGb/jsPz5SCQ+0lMvaQnHXz2UJSCvfB0qsGiNyOyxoTrOpTGoTzJM9FZhQbVbjsknuom8Ql7ZDQMNi2AXz6g21apfU6a4W0XXKeZzfVzGZ6XnmfROz7GKXqV2XuW3fKpnknY0WYbH8vQfWDub3n1eCtKNOyl0wFyswUEK6L+GAMMn5TZmX/rknSZcBX0qnvLEnpukS2l38ma+7FXABPlSVBsmxaGEqYLkXWaEhvrgd6ENn7SCIJS6t7PghRP73vLhdqD1KjMKoj9QPLJbqdVY5A+JpyMwxmArXvsHF0xm/Ow3IxhaR1UCzZP7hs/jmw8kNPZKDQ4FCvC4lh+tA7Jy32ZsJyjr2SiaMqpe/fy2Xemciyu0Gp3m7Tk6GSMrVCIuHq0phg/96l62Nl+9q96hy5DcEyez5D0UenMr8BJiI/yo5tzcFs6Dc4xlD0U1LAO66XKQWKgSRt8EcHiSXrHjZKx4e+s8H9hmVtL8BkD2j+WhfK3dgW5J6WaLkCv+crQNmbEb3ayfx5P8g553r/vzbmnGLC42VE58nywfsfyLRbpjVmk19lTO3/fL/KYfxOdoqv7mWdL5e8vo94Spm/kyNv0mFSuy+keDsEsAskdPWjYsPzpkYZSDxHRn4kuSj1Zo75CkAo1ZgCZdHI/Z94pmPZ3tMXx6VIX8Wvvl3azxokSYvOVsfgGfwLPjGXmVlijjhglK09u0SKp8IPzFUHhynyix5DJnD4wemy97V8BQ65vKQgWVbOvcoDPkvBCC5ANtUJR5ji9AtVqV5v35hH9nYSkpQUtJE3Hpwv7//ra3n1gWWyx3KXYnzrOUJzl0vbHyZLu+9GSGjxJuUmU2lvI+WtBisCiR6nHyk+3mr9/apcvXuFt/GaJdxCP8Hi7CVeoENgR0a1DZkuHQSCZFDj4+MTlI9Z+coIWbvgMvn8hXflvw9tkRl/GqjAoR5YCQu6Lf8XqpxMDsys//srgdyJSxzK2s4MrCiQTXDIKN5lkR2vGWVlZgE7ng9yBBjBJI749igaR8BS9sjXSlXfobHEK62jX/NxD7JttcGhL8DS40NaVcvAx52SPAnsc/QYdr0OQtGz4L3q9p7OWmSVZTdDSsUPHOrtAzGf9bq6HinhwyCpZOEFYTLruEj5Zmikyu4Za7y/CXa/GRGhnFDoWsPyenfIFm1/OQSC2uh33WhRFnveLQI/y4dXujnyldyPdwm/r1F3sgUceq9Jy7Pf9xWg7E15WYls2bKlyScyqftF8v57Hyov8SZvfIw2qP8/8DE6iJbd/LauQLCfCJ1ZYDoIHfBxm/4J0LhVZVGLO15W58G7YnvV8mYOwh2rGuVbnXljdpEi0TrY10j5Gga9jElyyRr5rl79iz1WRqSA6guCiztiwUJ2gfVN32Vb8Q6Aww6SM/A/YEf/G8zmEnFAo7Gs7WRPb2OwM0/aLjxN6RnGZl6FWe7VUymBYE+GE3fJoi7Xqx/PgEY+YV9j/Jq/qdEbFp+rwCdfOEuiZPX8K2TgZKdnpvh1dyuhbS6I2/AICDdzlYC4Z4DfEwvILrpcnZS6TvZsGqtGUC8wEvqADJYvCQ7jU/egt7GTWkaiCmVRmIFiyyFL4D+cHq6IKNbIGng5lyqyAQebgWV1pVVKj9T37ydIgTB66rJsSW1DZsAGoQRNYKiD+6SHsDk4DqpFnvBwonAa+6eHKGY3XVE6XeaUaLBvmQ3VpfJYZMloBTjui1LZBx9rkmIodeIfYQEkVZLGVCHLViVVuBYHZxgyLSybD3u+XGkPDn8RyNkdO9/09glyURmyzr7Bax4kHc5z/SxAdeh7qxIQ59yUAloAsMis32B4TkO7XYFn6hrq82c2tvddZWqfZCfrYDm9oWCf5R5IMakyNUhA1HRsiZYr8L98BZTszYBUmbXQkL0JC0PTbyOjd7thYg+OkHnz5snkyZMbudWxHVbff+hjeyQte/vNXIHCzn+GdMw9ql+tsu3xPlZysduegb3cZ+pYQyGdUwH7O38GbJ0nAt2/pMXnecBhZWiS5PV7BP1t4Z5NQvLXep7zSWjBGkn46XLV71gR3QVLkKVgKqKZg0QWe8FaMNN3q5mZRYzc+76EQv6GVn5VtljlGpMz6NmAe6Y/M8WuGX1HvSO7d5wiBzb0k9Y9cqX9/WNRsvaC4IATNGJhFMAzQTPDHpHvs0VorAHi9MJgE0mG5xJEa0ExRMpJRpj+aCl62YKl30NWRaBwQkTcGdMb86+XyRffLD/NnCaljjaSfMdICW0VLCX7gmTuyRBVB8iQoHTpOuAL2brqVJX9Y8aOxJket7hkH9w4tI4gS8bb4QYy9P8MYESbtr2f+GaZ9PHykRk9s6sIey5L9gSBDEGggn49XF5qCU6Y4SvGTAAY3blKAVgKR/e6o1yW3hCOcqZFYpAN63w5+/BsSg5Gy/5wf9kAiW0nOCGTU02+jtB5pvedBsgmMOx8OZBeHdEJc5bshq7mUouhXwhwRH9lZsgIWklsIbs6En7LgSRS2LOoxbG5C5aQ87JNO8McCYMrPMfDNUi+y4EZ8DxHWZtlZ5ZqGwoLiDbmYJaSZfZVd4cq4XINDM1jePyxPasV+/nIAt4i4GQCQk19UbwTLQLomewJAg8JSxQYNwP5+rb9Pa9jv+6m/8BBB1nghGGVEBZHZrrlrvp7fkubfOwZ7eMkaXuWLF26VCZOnNik7U/oeoG8+/b7LQCxSVftNz6YZT5b4WbVP1YfO/Y3fhp1Hl5p+1NBnBgu0SFoJI/vhcyat1Zodez12U699pNI8RlgehEKAMbStSeANcpbj/C85BNHu5ORQaTNnRHMaoXl/iQhP12mSC01IKSwxK8ychSa9rP/c2/W5Adaxjm63yAxy70WSBbUSAkOGZaKAonc8x4IKfcFnNtcfraFlsmUB1+VnF6P4WZBcGNkYugFzR7Beh1S0OvILGUYNAjpHpPf7TapVMAY05iAsaMoQULCy1QfIi3c2PSvA0YmMnf+21KxfY/0RvY17rgE5SfN9VxHkAVyMiJIsVZPWVMCkoQNmo5vC/sQI/d/LiOu+kzpMjrjDUDHjJhHjLrGKnlZGWp7zsLIXW2UdP119syyNP0fLleOIgRVmUrexv0uIwtJt5LWQ6tkBVwruJ+kIfkydsRVkv8DQe0Lah/8RaFwc/AGTV1FDXRyloOtnSzQKCxTy+nnu+Kvdhn9MkgbPapw+b0aiXxNwWkN1Ao3o1QOoEsP5IaEyNnLpz2TKVy99AbDT5nlWXodcz0BZ11BdvK6h4x+vcj0KhBzIHa90oQsqG+42IbSM0veBsBefqtdDnwN9Iag5iKznJ7saB07ajO2StL+VCF7P8PcJpIMr5e+ZsamBpBkFrAdsrYkzYx8vUwRZ/ie+jvRmHfH8//pWsPmTy9nRjm6q7MWS1uv/195pHj9lmeNv+88MOLtcKkJJAP0v3K+LecR+AqMh+zNG9+skQEDBkhcXOOTAeO6nyVvv/G4IqskJSUFnvxXXFr31/lf8aB+y7sOgsNE2/lTpc2SCyX5hxMkJG/VMT1c9gXGr7lD2iw6U+iz/EsFLfGq43oCQxgfEQpt05GF9ndkcDOqUBo2k08aOhZK4ph9jAMRNGogaOcLAYxZgyuLlCi3BcLblJsh0SVl1gBJnnO8+GcdGzqOutY7U06Twk5/FnorF6dfIKUmYWhuQ3/lusLVqr8UdrwC18a4Xnb0VtoqDnuGx218VJIXnCLJ80+UmC1PeZabn0QAmCUuuViid72mehbDsheqsjWXM4pTz1ZZvu1rpsiyb28WVyluysgIUf6FGbDSg8aVW/13u6x/b4BsWX6GfPbCx7I/6V+e3bCfzUygqAT2YN+dCkxSmHiBzJ7/tXzyIjKEP3h7EZkJM0eb9NVgZ3tL2pRboYUciQpGgOzRF71sJrYtNfQ6X1EhI/5bjmybaT4AF5Y9mf07aalDTttUImdcMlUiy5dJ+9YzpF2nnzy7JrmD/Y06KK1jBjq09GOylGLYzOAJhJwz51pl1wfBEpVRo+Rq6AvcFd69FOPW4JDzUSZmxpBImTU6Un68nIBH78V4LN6F8vmfwjEmQs2v126FB7DOfLJfj/trKJidHIXyezBK0CV7LMrDOGEU7BhjTdcFk1CmiPtkFpXZQx08t5K9gf5S9Ajjkd8pCGQp6+ONGsmAOwn1HXUQGJ6K607xb50Bc+ahHxTgZ8F54SCfwMrPNxmpNiUQXPeo1+ZPz7f73RCZMyVCMbL1soYemfRuTCm7oXmO5Xp//Ud+hlrij3cF4qLDZFD3tvLdtzObdPIxYa3gzzxRPvjgwyZtd6wGt3yam3il7fu+Eu1vHFxV6qP118Spjmo4s0sRB7+CM8om5d1LMsIvHdHox0tadJa0RrmV+z8y9DXJgW9v5nFf1+mtTMFqgtg2ANP2I/PUIVKwOx+WdxVh7RRZIrfPw7UOvSKqkyrn+q8w3wqDcN3Jgmbp1ArpmbjN//QffnSvcTelpdzhUZ+AyHG3lKSfLw7a6UFrhCLZ1H6sL1hi5jExrPBzjnDrMxLUR+15x7MpmcLMQpsj/NA3KOvfDYaz7xcOnjdFsBkEz0dGfSj74h9Xr/Wv/DUANiAgzBwFYHNZmI/tWpUTJBM4UuhQpVO3py5Lh72v3ieR2V+DDJSvhmz8l022v2qUzFbCSo+WegyKUtOXNwZl1K5TN8iY0x+W8/96sgw/8Uk54fZnpNMlFUpORruZMDtJmzRzBlFN5P5FX2Bz+IwDEtFC3UHoZWvfaYlnKMHtbrCVddDpsC00AHUkjKxAn1247PEAVWNNjhtUtkGfIHsCe8G7N7ZHjcQP8m5LZrMGjJR4OQKpG3OsfQDZa7Cby2EDSHZxHq47g8QOc9CRpDFB15RquMMYAeC7zCp97vH2KXI5gSf3yTK++RqxHE8HFYI2AvOfrrPL590iZf65cKnJ8j0ezmMG0XxvkkYg0wlfZrrR9AGDesjT5bUYzpueCoE1oVU5wvAzsR89mf4gkcsdALiBgiX4/e6MZ6D15mV7PrLK1wMjFUA3WgrMa3+7z1nqp14mg7aHdNRpiT/mFRgORnNhfp5s2rSpSRdgUvcL5b2335Pq6tp/t02a6BcY7P1K+gtM/r84ZbXdV1S5KrRVrdNkpo3sXCes4EpSz8F/Dn0TqDW0yQuspft8tuFrZ/xgn2XN+SI4Z5WyWtNnwFKrDft0pJ5V725ar7rF05PXevVtkgUBZ0tZJtxVHoeJhUtsFInOXyOl4e185qFN25GR76vsaBDqoS70xlViDDNy0QBZFKcuwb7NItyKIeEzSzO9QMord8CTdU5mLdmlwBsFwCmBRPkYc2hmNMvitOkLhhaiCqRowg/NMnyx3TVCXou6oirU9zPXbiocQf5brTx2KcvihJ4e2b+MwxCMDjFlocigbdXfNxXW5x6nDLk1Wqq2vy9tD9wkgl3TKYZ6kI75HTELmMXuKNwc7LFE4+EXA9RUZHWQrIxe0i5jubRutwVtAadILsZbYIFnDlriBQp+qRlx/g6Zn3WuFO2LlHRYzSUCsHgCfy/FHS5W/t1cFpbu22zHnkFzDH+xTEjG4BXY+bbNyByaB+A5wap/MDk++p0yyZxjhd5gjWwFESbH7b3MsSYzHrUpCTjm2IIsZS7AW3g7ZEt7V4oDjO8ElMnJ5mV2TpepmRWju4nZMYXz0MbPHASDK//CflwAZJSoeczaro/j4mDvV1NVLUfmQ9wbY1ffjbK2Exk/4OWDM43MLck3Sx8FW/sW88wiXa6BXzRALYOe1PSQ5r+lHvBRriv8s3n0wl7/WI0q37PXkkEpHHOYS/hcTomcxsS6R7wsboJh9njq69eY7X+tMeyzpBtOLjy8W4EsVV8p/tc6xpb9HpsrYLUEy5RhHeXL7+dIu3a4Z9mNFpKG9t6n/UjcG0Lk22+/ld69ezc0/Jiu9/3rPqa7/n3uzNn+ZJT6zkV5tRWEmUdLYecbfE6EvWPMtJGJS7mYCJOki8/Ao3xRknKmt8QLQFKWOPYoZ2rcZtYdH6gbr3l0la2BHgswlXVGitsFI1vWZvH5kgCgSHCoIzxzln7q81gZkSb5vf4uef3/iSzeefAAPk4K4O17cDws6SYsBGHldpS6J6htyIIuBMGkriCIox1dc0Tk7ncgHTNFEpdeocgsfIyE0HXUvo8kYfl1UgTZmrL4oco1hSDW0f40tVsCxNx+j0mlPUkBXDK5mS2krIwOfpbMwTI+YII4o7qp9zj8EEoXrOchwpNrYOdWKsPgK0x7N38ShKvA+LOO7mpYwjHL5h8xHQD8yt7wLK4qKJPyr74BA9f3OyMzeAwCxS3PGaXU0qwImfPZ0zg+SNmApFTQ5UaU+dfIwOCx0n3op/hCBCYwgGkyso7+EXb4e0n66RJJKX5ILriip5z/7j+k700H/IdJYbdpyOR+LEeGvSFxt5wj7U+pkPCUauWV3OVq72eIGxJjt0OJlGVSV6EviDMmBqv5cBBErI11zLiRFc3exDz0TrY/qVKYWSRwZmaTIIdC1uztNAf7BrmOEZ5WJZnf2xRIZ7m3NDNYATgCtfWP2iHTE6FIM7vetcmMYRHy3fhIYQ+hOVJOrkLp13cfxnp4TiPB3ONW9PCBzKIC7wMdU4y+Ru85khluJt5wrLOoNigjyaQzMsetBlRJ4kj0GHqnMOYP8Ju9dDo7ZqwOEpbwV9/jPY8UvC9ksjOY1VSC4iMgmo6+RUoJsZ+xMeErOYRPlu/HsDFT/GpjKAKediZAt5vt/6sdSMuOf/Ur0LFdnKS3jZaFC+Y3+liC8Mc4pcfF8twzLzR6m2M18Hf0Z3isLkkD+0HagXZxyjIuwFDeKM3BzFBD2Tbz+Iael4LEURHVWWwAPuUgktTXF9fQXHWuJxAhlRFRHdfdZ1h1kFVi4f1b4soRij4HDKQ0itPOR8bvbbWaYEdLqJjHu+AsYg5r6X4FLF3QCNS9j1xP4kRY1gIlBF3c8RK1LmfgMyhDZkEyJxq+y94blnm++NV/AVD/RgEZvl8l6OE72ghFBrPVpkfV5rbSvQB393kILFxI2Z+2C/8EMksegGAb1ctovstRF5Li2wTKOgj6CPhpd0fLwCNDX5eove9J+OHZ7utVpRjfcVueUJuwzJ09+EX1nGVMln0Zve9yyk9/NmzWzEQEZsiYhbGW7AHpZY763JSb+iqrojqKLXeV5B/pIB8+9blyNwm2+IIv2scx2KtoDldQO9l/4loDnWFFwqppsmdpL9m28iQkwAAc80RWABCFJcKBBNnBvZ+A2AIw1rnDIUlINmYiRond8YLKDGeO/lSq0KNqDldMT9UHuOiiMCXVEhQMgJg+Q0KDukuVuCdxb+Dckyt7n9wmsRBkLLIOBePXgr7GKkV4KQN4o+NJ3rpwmfSDQ7m2MEvFIOta+yHH9a6WKYscHskeZsco28JMH8ES5W7iBzoU2GSfpzlYwj0y3/t9m6CN4I12gOyDZOz73CapfwKTHw43DLLJ6dISOIJkwz9ClYUdvwCknlopmSh70/qPGUZceLVZApjhKRCjpjA45YYIzAfhujv8JiWpheVgBkvWLFdzzvqCWd3JCxzIGqK8/IWxLcfTSlAHXW/GfVmKcrtFAU9mSRNHAt02IdjrSOB6ZB7+S+DUeqKVgc4sLdFyBX6PV2Di4A7y0herZN++fZKamtqoU5jY41y59PV/yd69eyUtLa1R2xyLQd6/9GOxtz/APpjtMv9rKzPdkJvr9Cuiu0KDEL1xbtKEPXsxCCRfCkuyPyvw37nVmjslZWZ/lSkLLtgqlZ0vkqIOlyjQQ6mXYDAaQkq2q+womdx1RUHPO+XwyA/lyPC3kVFL8AwjSaUsfrgCUEUZV3mWU1Km7Q+TpM2P50rCsqtxDzRuomGH50g8wFj4ke8lbsu/wCR+37MNiTR1gUNb0TYFDjmYfYEx6KP0D2YWG5tdjEPvpTm43aEC2PwdNEBulb2tAoccYy0/LJHIKvoHSTnVJm0S9rC2gewPM5MMZ+uhKitt3o49ljoIktnP6B90uCCwOWlZidDjV0famRUQIT+AXtA/4dr9WxKXX4vr965eLY4+6LNElnP1ilsUOOSK6qoQCbYY5JNYlDTbn2iACJYU259sZDAttgoZeNk8jDZACrcrPBAvM159SaoqzcApSA59B4IIsmgrbw8DgSVEvnv0Otm/bRg38URwZbGEZ37neU29RQIrZvkOo5yavcT4HltTHSzr3+oNqaQL0cPpRaxOtE/OPSNa1k8/QfasGiFxrbfLxA+2yLivSqUs03uMFP2mVzE9jHWQ5JO/zvuay/ndiG44884KV71/G/5hRznXOC9rRI0CWtqRRc9T+7FGYkEeMik4qSEeBxq82gxBcDOg95/DlR8s+z4JUR7QZFZvfDwE4JXnAws+HMegJ8sk4+IKBfYIcifMdMiUhQ5p3av2935/sfOGj984GjqzJB3nCyQpvm3+V0NgnXFRRa0Suv/51PV66Y12yYTzCsvmZJFHplcj4xou300IV+9/Xdu1LG+5Ar/FKxABh5UJA9Pk+znfSUWF8T+zoeOMssfKxN7nyOuvvtHQ0GO6vgUgNvPldrYaBFD0jgJAWUNehpjyCc28B9/pYrY+LYnLrkRZ+06Aq3N8bpy+Ixt+ZYeWXySIFQRUzJLZ1zyKu6VVCnr8TQktV4W18ZlEk3V8FppeuGJ7ow9zoOrjY7awAqVjkluyh72qyCAeiRpKu6A3Ud/Kw3IWS0jBejUT2dPmML8OOzhD2iw4DWLa10qQSdybrGaW9s1AnUCSYFOLfkdve17afT9G/URvbzi1TyFsc/zwxRPy4T0vyTuPzZaZs7+WyjDfjFb0jv9K+2+HKGkcvR0BfdaQV+DHPEAvUo+R+1GWdQeBvyvayNqSHFNpKudXhoJZjoxpXWFHC+T4GQ6wVstkzIelSseP9oAs8etgdlJHDWrPeX0flcpeJ+pF6rHr9U6Z+J1Dxn0OuZZwYxVLkqMfWi6XPTBCrnm0rwyIvwR9om96tlux5gFg+trApBJY8/BcXwB2cMewWkSkysgOai5q/c0/O1yxZzc+EQpvYm/migOo/0gAztYBBpnb342LlNJ8by9w7mHYDybsUj2E1F7UEdmxWvULtjneC3jYN9l6iHeMHpuzAmxod18nl5EQwlgPxq72E1YL1K8aSLr4zYHhURnV0vlqA2zrseaMYUiA/jz6Wxth+vSC5b3nY2b09V8I/ixBjIhIgXbiauNfOPsQ6enM98tVXCNrHwmRxVfbhRI0DJVtdpeCWSanq0ugYN/hckgNkeyiWdPtpxjyQHo8S/rrHgtVouh6WaDH/V9ZZe5p4bLkGruHYR9onBmwu/KCZfktcCiCA07xTsgH3Yg+S+/bFWjzRi9jDyn1PFui5Qr80legR8cEaR0dKosWLWz0rs4edL18/PHHUlBQOwnQ6EmaeWDt/+jNvIM/4nQukFP4cywiAj1wOmyO3QpYHS1pJaja92ZmztJwH8Vp56nSKv/FUvePJe7GBEHi4dGf1Tk07MgPSsJGD+CtUburlCWNVUSQoBrjBlzm7j205a+X1mv+qm6ZIcXbxIbSbebxM5TkTRKe65I2ySE1YBtYYK2RsPIm1Tt6eMR7ErP9Oc/tNmbbs+KM7Ye76Eh9CLUeHSlnKLIOVzjLImXTV94s2JYv+8m4UdtoXewp/nH/FKdutfFhlMfnozT8PO7vFlVOzu91r7SB64q+VVW4wRHnViQdlJrDD3+nSursV4xBSZ9RiF4/c+ldLfT7xX7DtD9576iqTIsxel+qfO+3Db2PCzZC9HkxxH7Rn9blz8hQhWmg4h0cmrdCYuP3exaQbKKddILbp3qWm5+4IEtDRrA3UNrstlARdHhuZOOXtpkohWFjZSWABMEC3Tx0MLtH8eWdLzklKmafDBz/kqxcdINYkzsJraf3fYk+QJB0zJHUcYNUpxp/f8NAYKHkSo3LImnnGhIu3SFiTdkXsmxTplaqnk7z9mvuD5WdbxKQ8ZNozB0/yPj8FWMbnyDoAoBjbyBJJbnLjXPtdi37FQXZtyoF5rRdnrmsOwC6kMv/YgcRJQh+1hVK0JvdEnwvNj9rgx2fGxxjH8Wmsi73zxYDAmlGh/NdMuAR79/uD7c4POXkTJB3+t5fLulnVMpYAP5csNIThlMTsvb7y7lW/z0UbHSeu0gOiBcx3R0qM0jm9+r7AApxrLQI3PNeCLKbhhYjZZb8gxI8y0BqYXk9H720FNEe/Zb3i4p5PDUgNcmGPaDM9OpAcln1YwZH6SVH97gb1oir7zHkeJhZH/SENwN9dDO2bNVyBeq/ApOHdpBXp6+Tzp27SPv27esfjLWprbtI/7TR8vprb8gtt05rcPyxGGD+z30s9teyj2a+AhWRGZ5euBp4Z1Fc+WiDYKw8fojYc5ehFBol5b1v85mKvZSu2D5K5qccZAyCmWYJjxCfMVtlREePdzP3x1K1HVlFMpqZoWUQQJlhgWZ3h2UvwHJv9oW9fSQO6bG0+GOpVtURWStTgTLasiukIqabHB7+LgAlkJ5fFMIeL3rny8pdxhpSLiF2MKzLjbuWLbRErCHGzU/vx7w59QztWYukPGmMWkxHmLy+j8Gp5QNDDLvH7Z7hJPe0gaSQFYC2BrXO7EHPSQ7B5VEG7Q5zBvxHkabYu1qYcXWtmZidKoXnbvIJcO6421lL7gS1P4DU5xST3NxPav6C0Bm6erwJl5lu7hGp6CtbyH8x3qsy+ZKbJL5HgRxKuEosCfGeY9n6jxCUow1g4lmIJwRmFNHufjWu97fL5OtpLwKgAHl9WKOEnMNNJXVul9gvWwa/kCDr/x2reh7bQJ+x65+hL4ipXS7jc8FsaBoAU6AgGcdsg2dFyZN2efsBRKlxl4YeQg0C0W+hABDn2fVOiEz4xgHJGRBL8PGJgYUfYyuJPdCe1EEmsg66lUycWapfqkdmy1bdZfeUvZmZ7HuvU/Ueuk1+1LjiHbgG7tiN/sI+fN/c2d6s1aZzA0Bbe2+Y7Hy9GiV3h7TqWxvM6Xn4WLzDC84I7giiozqi4xMEoAiQhOac6P2bZ0l4I6RwqGvpH8pNx917yXUO9EfWFYOfKleEIBf6NjucU6HOdRdAPaMjSte2nwkOOc8GlOe19/VeAFuyuqPh1NMSLVfgl7oCkeH4nzAoTWZ/N0suuPBiCQlxf+GrZ4fnDJgmd792jlxx5eUSHV13taieKZp1Vd1/tc26m/+xydCrZyva6tFq+zXPLg9ago7kkxSwyx74tPiXgZt0bKhTZSF7dXDsbLCF50lV4hDP5jY4oETveAmZuMPKxaTZwCH2QFeUstZG9o59jrlwSqGYdPL3Y1FCPgM34QopzrjSAw6pC0jQ5YWBzGh2UMeqMoGeozbEvHX2US+mk0lu7weUviGZwvr2bSvcAjA5Tw/zfUQ6iELfDIulUiZfcatEplWqn8lX3aWWcV0VfKYDhpv0o9c54FZDOZ/c/o/7yOOEZ34r5ZnVELk+TfIPpfroJ+ptm/rINgeSelQG0k+7haXTtfcDkIBksOejEPS5uVkppp1E7X4bkjMvS2jRZgW+S8G4zun7DzDML/SM4neFtmNNwARrKIrtI3MCQLXb8qC8fOsP8sWQdFl5h7GvSmAkZqvM0WoAgN11TugVOpUt3Pp/x8umVRcY4JADkbU7MN0mKSBadL7SpfrWUk6tkGFv2WX3Z7Gy9YVQlIWtsh7yKSSoBAqympfdbJfFV4XBCcb4V0iShDkIJp3QPWQUrCdjOBikDIcM/neZREDexhxbXgxROooaHHJdpR9bpMKhP23mLb3PqY1o7ol07A+WiNRq5RltT4AgOkrLna8EGARw1UFP5Y2QsKGUDqPLWbXfQwI9A6wbY+r6bc4+M5tHdxsdLJl7S+DupSYQqMfxkf7YUbA/1NHh/MAlba7nR5K9lN1vcIk9oUb6P+yUcV87ZPx0h/R/0JsZ1XM19rF4N6SAkDWkkLfvdz4Qt+qQYGrs3C3jWq5AY65AT5SaE2PtjWY1ZyT2lj7tR8gLz7/YmOl/8TEtGcSmXmLcQVqvuEHCs34AQAmWvN73g6V8ZlNnOfrx2D+zTNrij4Awt/8TRz+f/5a4I1b5aROy34ui10EAaoy8XveBwXyO/5a1XocUrENPpNMAdrzT1hXMlA19WRFG2KdH0WgCEhWwumuz5CI5NPY7BX7DAKAo1q2D+n1O1Bpz0UvHYMaQfY5RO1+TkKJNiixSZYkQS5Vxp6bfcHnCKDW2tN1Uab3yZhBg5qjX/OWvOehZgSc5A56W2M3QcURPX9SlZ8qkvxhZQ2vx5WCQWqUa+ymDdWAE5HuqcdcLh6SLxZmt5G7M2TbznP7PC7I7yMyH56rsZFBwpUz6+ysSMdh/VPO9JnAwB0uoFDaP2/SYAoMUNrcV7zAPQXY3TUrbTwUpqhhe2eshddNZaFXYFVIq1AUkIYKlVeob0iGFGTH2CjLjtPU1r+bOng9DkMmrEGrgmUGRPbFahj1XrggLs8ZEeErO0d28gIMHFNMdvXE4/F63O+F9DNDkTm4RZJnD/7VeR3s4Op8wcpZZZMqPJeh91N/y8XeGCin9dX0ym8Bl1L7jD3sIl90UZhA2CFi/suHHqgSoy5CRpdA0vn8ojUX6UvP4UwFiGYfnWcCGDkZvYAUkYdQiJUqeuzLYpyTNUvjcqeFKToejOgEM97kbTOjRVbLmgVChkwfFw3fA87oCEj90TRl6B/r2EnPhzgJnnC1ecBwBwNdQdEVpnLqZpSDFtJ1YKSCFe4J9juO+LpWF54erzGIoAGuPW7wAjoAsB6CcjGSC5HFflIJkYgUjuVri+ze8b8+O8ITEq58TlDLicfIaM+jVLTgOZil5zNSubImWK3AsroAqNX+9VjI6dZaMjIwGd3nx0DvkljdPlIsuhltbsm9ve4MbN/OAFoDYxAtqzV+nwCE3U+zY7c8fM4BIGZiEpVeCQLIfPXN9lPh0c2byzJeC9m4RSz+XmpgMCbckecAhx4QfmoG+sfFGnyAbrQJE7KZ/Qsz6TbWGGn/ZQ16qPQqAM3rXGwrI0cKPBA2Gzc/vOYjMaYDNMoBhyvuYw4mSuD9ALk0ar+wINSQlW7gAepVVMOmlqLMnAFrzUd4Npvi3Y4+UdzgHouPerKlnnPsJS8PZQ1/xXyyVURn4ovAAssrbDCDtLpnvsD8pa2ZPBXAJlu5JrnqlO8hOZkl5x9LRAIdGBoikj43LL5Qhl3pvZjb07EUe+BykmPZSDI1IpZLsPiKy2HkeBGxa5Tl/Q7CwBEkJFLJDWVqjbpsOupCEPlntBmE1kn6WU/V2BrtTX/Grb4eDzKV6uHpkyd7ZepjScaR7DPs8s3hd2g6QCTNKWZH2HBa1BEmYoYVdoNj471AfcCggUEyEtmMIqis73rJ6wCG3ZZmSrh9HADpYembW6cBMq/KTJg+HOoW9bnehr7BCSJBglpFC4sx84Yh8du8Am7nA5DBDWZpMkGl0Lxz/usMBKPohg1W8M1gRJghIO16k2xIIHln+dsLGz4Zj038HAGtv2OTgLJvH0pAZwLFwLSFJhvp+GwDctj5vvMeb/gOHFPQTsu9O6xkySxjXr1L5ZROkuvL13ACW7ClEOZkAvOdfXLL0Om87RD56F3Ukj2efYZmsvjdUHHuD0adYoUCtXl/fo7/+o3lsOORnJs11CKVpQuA8o//82Tf5wxnh6ksASTCj0G9ImZz2jdRBNO+jOZ7zC4kGh5yvCO/hGdug8oBLVN931ebYd8scLVfAfAXCwWqehH7E2bO/A+C7tMFSc/u4TjKhx9ny8IOPyvMvPmue6pg/bwGITbzkNSExqrSpwUd9rNK6pqZECkkSFoAC2rdRB68xEb3jZQUOOTYUgInsVzpOHE1Eb38RAO4NJd6ci3JhRUx3zzQEIa0g5KzOMWcFykqjPOv4hGSF9nOOU4LQWUP+KxV+eoakHZqt5VgOJqmkIq63zzwsFVNTkcHSahZSFOUAniyZcx2BIYNOJexFZJRCT5DrmMVjBlcDvsg97ykPY0rq5EFkm44renvexYoBcmrQpEWgaQez1wkmMWVlqL2XNfwtNTeV721Z61SvYQ0EACnvQ3BJ+7/GAHF7zhLPPitddplz53gpK8YdHlGwIQilydq9WlxHCZ7oHSgpQAIoO/wFLJnMxSpik/bhMUU9p/1c0uILPIQe9l1SUJxByaHEpZdDaqcQ4LEd+jbfl+LsBJl3Jm7a7h647CUAGABXk+d5654s6Q15plxW/s0uLPW6cpCVa+tdz/OvDvH2Cqp9oT+y9Yqb1BckvqZDTBRkinLdxCxmmsxB0WTq85Gh6h8UmSa5Q4OjGGQcCQ4zf0Dp+z4v+OF2ZB/Tw5g/OtaCUFJVavw1sqycfm6FUJtv0L/LlQ4jZWyW3gAnG0elUAbzIGR3yKre9wXqsiBbeKPGx5KQyylSHZZEwFqKYw9ScjLe8aIIJgdnYB6/IIA1+12TGcxMKn2jf7oGmo5uRjQ3o1MK3laf4PtFMfTabGl+RGrk+5PDFdGEpXXzdfUHY9QSDNQf6LOzo3zhbyd4YDoy6MgQM9jrR4Du445zlPvRm/F9o4MMGedmoo9e7/8YkQq5HPiH6wx5q35VcgiOOUmjKz29mv7btLxuuQK/1BXokhov2w8UQPpmtkw58aQGd3PBkL/KNe+OlgULFshxxx3X4PhfakALQGzila1C/xqdPNiPVx0arzJHTZwCGZrbQQRZqjaz5/woh8bNgXxJHb1rpslrWE8zBQkDRxPsn4zd9ozaNKSiSGkash9OB7NQ5pmZYSvodK0SNeYYvY7ZI5IXcgYZIE9vr9IkTC24mcdcHlK4oRZAJMg1R+vVf5XMsTNVRu7QGJSZAaK5s+L0iz29lZWRHeEB/ZW6fmTkMqtnRfd+HNjCPC6SO2jtV9B1mgE+IaFT0PMOAxzmrZakJdDQUxAf0iXoy9OsaHUcqEtRMojnxaCXMvsXaZl3ZMS7HuKMWhnglxMkGubmeBxF+ckAh15gxVImDqUWCVl9WSA4ZAAQ90+8WipOvlF2rjtB2qSvllG9H5UcBzKGEehH3P6SBxxyuD3nJz6oiIIoOcEhg/JDkcgA79l4rQccqhX4xdJn+De3SdAZtwFVpkkwznXD46koKRqfrbWPREnkjU/LgTmtJCI6S/pdv0NcyLpG7X3XJ3vL7Lk5yNimSLxiXCMTWtD1VvXecAzbL0e+WoaSK1ik2CxnJaziwGplxMLbmX2GJHMQkJGxzDiywPdfEz2TB/8nAMD2JkPVdo69QbL+4VA5spifP2MffEc2P48e0kowWd16hmqwzy8wc6HTyIzgtlcM3+P+Dxr7Y8bJ7IOsN8ta5HuMenk1ABLt7OhjzOhylZF1ZBmYfZG+oT8xvkvJ/vaP0MQqsJmNbCI/T7QcHD+9FEDSqiRvSCSpLyiheQCANrQ1ZG4wtrGZtEpkZ9kn6Pfvx2dXBMDmiIaVX3MFy/EE1ozd7/HfSlmdJCO9T2Zqx3xUCu1Nm7I+zJxtg5i8FdlkCHujTG71/e6hN2t5bLkCv9gVIGHltRnrZcuWLdKtW7d69xMZGiNXjXxQ7rz9bvnu+1mNtu2rd9KjWOn/3+oopvjjbcJsFH+ONijtoYOlPKtjHzJkvtk1vd78SGFpMoxVOTRuoFB65WhCawHqbYMBEnUwSxkGUWpa2DF7xHCknK76CGPgelHrtkUgGCBIZIjAPDoqYnuqp5TOYUaQdxuWgikBo4OZKJYvSyCnUxXRXvJgT8cIzVspbeefomRjCrvcpI7HEW6ceyhAEgG3+bhYoiYAph4lmbva0SQsax7GeREF91WGYwjJXw0AGSnB4bEecMj9anKLxZWLXsYP8cXgr1xcZzATnD3oBfQefithUXslvu0Wyc00/hGkDt+O6xkFvcZy1a/nmQTXzwwReHxDJz+jfvQYq2OPAoj2/JV6kXrUpBm+8P+CUY1Mdzx6wViu1BlEjotvs1USgmeJzF4tkaUWWfD8Y5K/OZ2r3BEki184HVjVuKK59koZNRp+xaM+VV8kWN5mUPCc/Z+28kz1mu+jHe9TMIAig60A7BtlHFloUcxU9gj2/btTETcIwngTp9sJs20jXjH6OdUG+MVM0c439CuDxECQ4h9973MaJWZguS4oMa+DzV2Rqe9Oj8d3OdX3p18HeiTxg2XbHrcYEjWBxnAZAVMwPva8vlrCJygI51GDhbAZ7PVXlxKOJvGGws9aUsak7a2mDgPAY0aLLigKzLolcyx4z5h90wLh+jicWb5/ayx9h7cFaQVkoIaC+557aoTHuaUTWOdkRzcUq+4OVSCLGUNK3QTSjOQcKadVwm7QaWT5IPnD8n9zhTnjyjkJsutioZv3SQea7je6lOi2Xk5txZylFmSj0ZPYEi1X4BhegdAQq5w8IkM++2Gu8mqOioqqd+/Hdz1NFu36EqXmR+Spp7199/Vu1MwrLfcjmnnOYzbdryEoabXixlhZ/zf1hi4AS4MhYIQyKsLTwC693gNi6tu2BiLJJekXoPR5kTj8+s/q285/HYktzPbY4LLBGz2JCMzEkRxC/2gral5BNRUAaSniHP5vKUg8UWVLCUJIWGD5NhiaiSxl5vZ50EOYMe+nPOE4BRZY1uX50eKNfYkJK65H+fld7K+HlCVPQml0owK8elv6PNNVhTZ6PA6WhOMhAs7nBNMEIiWp56iMILdJWnKJmN1G9DwsL9dAH0MTUrg8GDXUiMyZegjmOUtYao/b+m/4KX+AUipqm6gD6gyiZyCekGntbIS2JRnSZSiTV7TqIwOSr5bIyP2SMXix9L4hU9qsuFJidr2qSEZ03GHUIJXBczKfA0vnGshWQW6IwuFhIEXx2lDAXEdOPzCgw5JADAmWRY8cL5t/PEnikiAQ3b0vrvl1ID9YJGkUMkXAFeEJDunV42kZd849YgsFWsC1XDP7Aln/48WYzguvY3pWqbKn3gelManRRzRXlnS8csUplQyZM/0FWf3RieJ0RMA9BWSInDSJjdvhyUqRwFIE5nlFCcSSTycwsaheuJzFAFB/q0I/XJWse8gOQGGDDZ8NloAQSzdp8zEDxd5Blp87IwOXCr3CQMHMFdf3n/yu9I78syx663JcVF8gFQyZmgmfVIizoEayfvR+JyYZhmV1nj+9gIe/XKZcX/Z9Bns9CFazzOsfdHdZCOu/bf8NASDCu6ScTTjKyMAOuHaFZFzfSl0H2hyyhK+D55OJUinL6bZYltzRBwlmNMEaS6JFW43jZomWotft0UtJWzxdutXzGI816McMTLaIjY2VwkIjm6y34Tzaao/L6MzS5er6QRwJM2v+zlQbvaGDhGSfDucF3obZSGpBpp9VqcAtP3PNFriEtCnUwV5TzRS3WPAFC8Q9/tQVbKtgD64KgPBu14NdD8D7ewvee8LCwqSkxPgS1pjj52fhjxYaF8yePVsyd2TLKd3GB7wE9ECu63PDa11VFfhLRH3bWYKNz1l1HZ/HuCjYYJaDmLd5h3Tr3gP/J4z/vXykDI4TMlnm6NNulDzz+b3SuWuGpKenm1d5nkdGRqrtAuGSYBwPzyU83K2B5dmqcU+8/y0bN75lVDNcATKfqSNIYgK9lTWhoLFTEygebXCfFKYu6niFyoixfEoGKsNWstNnWotjv4SuQRZv9NdqeUmHC4U/jCAAjPr68mpAf9T9cRzPsrYmrbAUGgdf48wx0yWXIAfMYBvErtk/SCcXRhj6+QIFs3rBAB8s7zNo01ZXEHybg8AtF7JAdvgqk/lc3moIAPFDniERW16QQ2O+VmXzyEPGOfM2wn5FekvXCpSkYyGyHVK4HtnQCcrrudX6e1H6XQKyy2DJm/SuJB9/WPUwxs2doEA356DfcjGuI633GHSqSfzpcrW+GnocZfhshCADVxbeDaQJMKNL4qT74M/FlgDHEABvK5jR9JXmNYhdeZfMuPwRKS9Cek56ypdZ78tJ1zg85UBa5LXqx386QZKwfB2AJuqMiBpkaF1OX+JI+vHbpM+zbeX7kwDo9hr/6FJOMwEC3PXpv73uXQhJf8pGw7aStftuNR9/ZQxbJFMvPE+9LkUfKT/X7L/TPYJcUbzbJrOPB7DpGK4kYdRg/Nr/tU3SzvQFgZFg8LJHsRqHwP+37v+lehPPY2jlPknac59sWzEVA91AwLNWZPATTgBQACIASWbn8jFnu0mVqszKkjfLuSwhL5tmV3qH3PQAyrbs1Tw02yo73yKArZEetzoVM5jXksBZTPqGene52f2kgwCABwhm+0743iHFe4Nk7skRqlePw+gL3R0sbPyVeLai/3Pny9BviZ9VdzGLZ27sBGi+2gVA5i3j8vocWWCRqvIgSbmcn1rfYDJCIt4AAEAASURBVD+eOZtsBuO+I72vqt1ZZL0ErcXqfeDrut4LPbY5H8nYHvlGqWo7YAaT711Toj8EydeC8U2tz06XtOgfNuXatYxt/iswpl+KvDFzg6xZs0b69+9f7w5iw1vLLeP/I9Nuulm+mTVd2rZtW+/45l7ZAhCb+4o2Zj7caBUwbMzYZhxDUEe5GvbpMQq63ixFna7x7KG07WQlD8PsoI7gop0+YNBSlqnAHkWra9+G9FYBHk39iGpttfHtjEAyD1lIRqu1XrChFgT45Wg7RZil01EAd5G4Tf/ALbtGSpJPQRYwFqXOFfA0HqlK0XqcfmRZXpfmKRdUAxE5ZksZfKQXtAukFB38fmfPX6WIMYVdb9KL1SOleKKREWSo0j9AbuTBr9RrK7Kx7I0sx3HQdUaV1dUa3GTxGLP5KQlx7FJl9kK8D4dBlKHXNK0EI1AKZ8x75XrZAFcYxpr5l8nFd0+QsHK+d8GKKBSx72OpOXwQ4ND7hcGZAzYsEgxMhvpHbq/7AUQvQxZyDxB4gvSaOEu2rDhNinJTJTI2U8Yef5mU1rwIMk26YvMyw0bCh3/oxn//5Tt/GiUH7ntOaR+WJR6vVlfArs6CMnVVufGvhmXYor0x+PHdmgxhHQQi2/5rk01gOGtx430v7JDepy6U+KvHeL7Q6PF8H/k+/fDJAz52f4nHVYBFWyUppxBQ4H1GVqsTAFcNECfLlvnrghWzV/cXZi3ypr1Ylt/zmVU2PWFXu6HST5XxMdG7DfgYGu8FbYEGsB8zugNyxLgcnj8zYNpUuHvkLrNKIbJ0SWOqpNOl3p0xq0uxboI/9tANe6lMojN8//pWQVOSOpaMQx8XyeBXfPfOTObI18qUHA61DHveZvRF+o7yfUUwZjDCbUqqh57MX/fDlwrsmu4susybtQTs72+RcUE2lm0EKadUoPxe/3Xw3VPDr9rgmvDnaIKZYIpxU+j8WItjU5ty5e12JWHE4x/wKNx8zFj/aE6oZZvf9RWwWILlFJSa3529WFJTUyU+3kh21HVSg9LHycTu58p119wgH336AUT/vV8k69qmuZa3AMTmupK/g3nIPtbgkIcbceBLH4DIfr1MZNCYzbKVHVBnVNlmlCdTyO0Tll6lyssUtD486uNamol1XYaKmB5SkvInxbxmlqygR+1+Psrc0DqQYI+3P970zeEAgM0d8KR5ETKaF6GkOxF6i6UAjh191plfWNGXGA8SDPs3SwASC1BWp51fLvocW629B+dkZH0sIOQIlYf9Imrn6yjb3uiTOqF4uDn85XkoLM0f7jOv5z2GNWCVS/VFRhyZrTaNQda2IrqrOm/28Jlj+5opnpeFOemSfbC7tE1fg2VVcEaZgWuEUnQ4Mk39Zsj2NcjYIdqdBF29AOCQ68haDyE4RATlrBLrkLvkkr+Pl8LsFIlpvVesNpdEASCXJk+WkHP+jkFewKQ2cv9KP6tCScLQaQMwBz9G1s4Wg5Jtxlj0Q3pHr7nPDmATeB46kdBNJBHWb91v9gIW3lDNJUXOlr2rk8x9CvIP1f8nMdMu9e4Az9i/S/3JIPT/mYP2c8z84UQ8i1mOX3o9srRufby0s10y8B9OlREjeDMHhbTN4cw12MhF2wKcD6RdyIre9lIowJQo0Wdm9Tb+K0T1KbJfsc89cFkB3uRlZVZrFQgzJOr3+qtTll0X7pH7yZwjsgIs+AGPlKvjXHMvNnILUsfBCcUfHBJQ7/3Ue/D7vq+Unighh6tz954B+xoTR/j2enrX1n7GLOGQp8uVziRdcra+4HUjobZl+5NKhM4zCy9AGdpDCKKrjE0mwCHmWIOx2mdgLCneGQRv6XDhlyf2fR7/Sanq3axrfHMu3/ZyiJI84px7P8UXkr7oz4Q7TEv8sa9AYqsIGdErWWbN/EbOPe98YSm4vrhsxN3yt8/OkIceeFgefPiB+oY267r6j6pZd9Uy2a99BVjSZP+gDtr0+QdlXw4f9zkAzd1S1u8eqQltJUmwfqOMTCQyVjq7SCAVcdAow/rPEeg1S9vUcSSoqcFdkqLS/sF+QYJO7ju/x91SDiIOS/ElKFfmd7sVYtj/8N9EvWZPZX3gkINiN/9LQsGkJokievdbyJS+qrYtTT4RGcXT1HP9qyjjcqlAf6VvVKE38j2fRY7kkxWQ5UKC3sJOV6NPMNZnDF+Egj0ds/O/IKmUA6qgrw6ZRnNYwDr2L+9zfVLqes8wqw3pGdNNmCtcbnmhky6/Vk6+7S5kiEpkKCRr6gp/r232IVqt5SDTbFfgkNtZKovQj/mRhB+cUdc0qjQ74ZtSmXDt4zL1msslueNyadthpRz/j8WKsWze0AXxZt8gYHMHQE9E2D4ZdcYTEhqUpZeqbJTnhd+Tw2vaqd4/ahbu/8qigGoNysrZkFsacG8eJEwAEtlndqMGh94JSvYFybxzvOLJXLMXWbeZIyNk++s2ZO6AtEzB7KPXDaRGMi6Eow+IIQED4FDHQWgzMvZ9blV6h4WbLehtDFF9i+x5XPtQqAJ0/e4rl1M3lAA0VHvAIberKAyWAyi5b3kOzjZPIt1kcishIGPkrQ0G6SRcvh0fgWyvwWJWK/ArJKa2JI9edzSP1Krc8qw3m8s5qHWJRKxBpPH7XJLgRCLIbyV2vIneLoBDBln8ZDYfq3DmeD8X3Kf/62N1HC37+e1dgSE9ksVuqZIlSxY3eHAWlBz+NvElmTn9O/nkk08aHN9cA1oyiM11JZsyT00VCCEzFVmhFBIiLIs2d9izFqr+PJIrCMgYdMDI6Q9CBoBOZVhbKeh2W8DdsreQZJjITfeLba9RMg1F6bOk3ak+4zlHoKB0SuTejxT5ggxg9ufRws5Wtk8Nt6Ak2AqyNMxW+gczjQSudvgll7Q/A9m33dCLzENP31Cgl6Ovzfgzt+O2PKn2Uw7iRUHXW4ApqlTJt6QtiBcoC3O5DWQaHcFI9bTa+IhiC+v2gLK2E2GX95EqC7OkTXB9aOwsCQUBiOVyK86dQQFsOo7oMDKktPiDhA4AJUEqCTQxWu7GPfCES/4ic9fPk8MLQIg4EC4fPPmVDJ7yvIw66TFxRXYSalCGQSaJ29qnnCxtoJHHCM1dDmb8XkUqqQ5t7Z4N/X8dL8N7skBlkatju0p5p0vEcngxSvLLVW/ZttUnS0lBG+k68Ct1zT0b+j1hZowkgYQxq9T+M3p/r0YcGfKWeJsTjI3ocLLir8yU8UbJ4/O9YcYELVfnHXFoOvo/pyMZGSL0K85Zavxrou6fZlRzxh1rJsqmsRE+zGxmTYc9Wy5JZ7aTU051KOACblStoNaj+PXVcVBZZrCse9AOEXGnpCE7WrQ9WNJOh7A0sptkVxMU0laPhJhd79qQNdPgp/b5cD7tt+zY5wsmS/B6479CVc8hx1EmJyoDxCv6SSObagaCXE9XGo/vMxcgWC5nxnD5LSArwPWGsfw2u4x+q1S2vghNSHw/GPfPVlIdXuzpF1SDfsYvusz4R/ebXMplpTWcZgBJ8eN9X/meabYzBatzVljgAY0vWOm8Xr9coCU4YDs3Bb3NQeLTsYqO+FJBS0i2WrC0z89XS7RcAV4BElPIan79m3XSsWNGg9I3cREJcufkl+Xee8+Xzp07S9++fX/xC9kCEH/xS1x7B63W369KrVwThcxc5nFfBP7PVnvTepeQCEJwEoJSZSRuuIxKe1sFBl2xvaQQ2oAENfxpTAQX7/IZRmIHwU1IwVopSxyLPspTfNbzRRDASptF54B1nOlZF73nLc9z/YRyNwEDJVj2SYb4EWZYDj8y/E1xNYJJrOc1uz1QIoiZPIIyHWQPEwiS7UwnFAplB+/4SJIWwlYQWmuuyM7qPLR0C7ejRJECiGA3sMRciewlr60OEojIUM4eAnb0hkdx47cpAKnX85GA8ciQV5RuobPVQFXqZn+jK6oLHGDg7MK6I8KWmizdxgGUvOf9M10x+1pJf3i4VMemqbn93wNmegm+GVVb4/HZ+tLDMq8MT5FDx89SHuKt09FDWuKQ7MEvSAQE11c+00s2fHGc2m7l3Gtlwuwy2YtSITNBzKylg0BCuRqWfytgG9cThA3rmXeIBcdLVj7JK/x8hMFecNlTfWT3rHSJSEPv15PlcuJikD3A3l19j/FFhTsJAiBq3/lHGXnK42qfVjDqrSBF0ZVm6P+Vy+ZnII6O7COJJWsfIlAyti0vjFLjzb8oVl16RzlKqjhnVFBZ4vUPfKwkb3VtoGMed3ieFR7L5YpNve0Vm0wfxCy34cYSC+u3of9Xppxolk8js5fhBUXGa/6uQVk4SL7sFanKiZYI9A7CfznIVoNrWKHOyzsWvaUP2FV5Ow3r8qAPWbwHoI/ZSFwfEkuA9T3B/kOVVfwPSr3AlDrIpAa/S0a9YZSPU9LBDN+r1/78x8SRkOKBLSKD4GrMxw6J6WKALFrojXq7DGVU9CC6rz1BLFnohVvhrnJ6uGJA8/yPe6fMAxx//lF5Z0AiXBZdAQHyJVa0LFTJCPRahiV6QSBZ7vlgixPoso+SmeBjFSQDTV6AUjwY6rE9qiB7dKz23LKf38MViI4MlQmD0lWpOT09vcFD7tZmAPQR75err7hGps/8WhIS8If/C4b3zvML7qRlat8rEH54tmcBZUtCird6nEI8K5r4xJ69WBKWXY1bVrXK0+jNCdT4Q6JFDbQN/YkWelygR1enC8QCJxXeBivtSQCWJ4gj7exAQ9UylpGZ+TODQz2Y/645D4MC3/nd/2K88PsdCq0/f3DIIQR28WvvgJD2d35b1H7J0tfiK8MU65FEi5Gvl+Gf82iQYe6X+HXorXMHiTbmCDmyUGKxD3MUp5wJMP+JOnaeQ41iF0DKA9fanvuTIrnk9v8nHF4mmzeDDd2DKGkbWUOWn3XQlo6Z3MQV12GbKcjyjdOrIPszWbKhXxiN8jf7Iwu63SK2ahTlTVqGoWFFErP/LSm2nG+U1f2yqgTSOpi9pRC7D4jE8SuvbbfqMWWI2Me5a7m35O8oSJQNz7pkJ0pzDGZALKFlsv4foZ7ePZZJ203pJJWjP9W7k9bLr5ev77heDu3qqJYxabrqnlA5/sMySYXn8rZXQ8Thznr1hH/y6L5P4RoeUWP5+SKAZZBQ0f8hby4yKsPmA5TUIL9f30+NkFhkHrMWUAi6WvX4UWybp9ntugo5PAdsaBPrmHZwJC8YfYbGJ5MyM9+fFA4CBtnKodiDsZwZzPw1FpX9o25hfRGeUq2kezgme7FV2oyvUCLkBC78STm10qNvyEwbZW4YJMx0vcElW1HKVYGyMsF5SBzkcPLRawqA5YAdXzWIKv5hBQgNh89yNsaTyVxzikuCu/uPOrrXlEhlBlAFrlnP28s84FDPmDSqCnJK3i9eevn+Lwka3dcQIHYvSD86s6jHNOYRyX3ZiS8rLK+3P6lS+VCbt+MXKK0ZSQHxbS+G+Gg8sidXg2fzdsfqOQtECUNrX59jtf+W/fy2r0DPjgmy81ChfPPNDJkwoeHkzcSe58quvA1yzZV/lg8+fv8XPbkWgPiLXt7Ak7ugAUhwwahGObcyPDXwwCYspYYhwSGj9i3EmMhWssN40sjfFR0BBuN7Qx5iLdi4I2qJMZunCTs8Bw4mf4FOsAsAkDp+phQHBvKY2NfHUjH18cxMZPM8VShbm8GkeZ0FFoWNCfZkaSeO8qxg5eAxAhp3DpBkkJpRMjTlkKFxtPctmYdtfbHW9NReZL+kpQrSMVhL72j6YOv3j8zn6G3P+wJEXANK3+igLSC9oBl8DyIyZ6nnBJ68rmZw6Ww9XLLxo4N/oIP/VS4bHqoUW02OjDv7bok6sBA/n8DhJU713vF6c97wzNnY7wa9qbqOgfpMOSCoYKuEHFwu5ZH9AOiPSEJ6jOw92ENtGxxSJQULD+F5unrNX7vg51yJzKEn0HfGfjpmskp2gxARly9Z82FntmuoZwiflEErkCzX5bfYxVUQpEg0naH7Fz+wWnJc/5EoXE8KsmdHXyqWoBB1jVkqZAaRpdyIVJThATLC4WfMudqMrZTiHcFSsss3G0gQlbUAaBDBfrNlN4d5ACGFldtN8P08WsJqZOznpUoPcCsAxSF4JxsRpMAImabsszMHy4T0ro4ACHTsN/ZlXh+RbjBtd77pPTZ6J/PTT/BJoe8BjzrB8q6WfegxzFsRDMCtxwYpkWnzfI69Ftj8lSjtxoThlfL9KV4Qbx5XiQzlD+hHpE4h97X1+WKIWkPKpxl8kCk/VAJxaRXIbNL+MGEwCCgot9cXFBPX9ol63NGWmLeAHLPpSQM4U1R83FelEoeMro5qjxalscT/fdPj+EjZpQ2P44vOkSAwxV0BWfrm8fU9J3DdCj3MXAiiM8ve6RJ8M22JlitwFFeAXs2vTV/XKJcVTn8lsoj3fHWOPHDfA/Lq668exR4bt0kLQGzcdWrWUTn9/gkbuedRYoQXc4dLmqUHkX7B5nBG9wQWgpMDys4kljBr52g31TykUc+rW/WSUkn2GRuSv1ZJuIRlz4fg91YpbXOC6jckOGQQHLoiOqr+QZakdZQhy1bQ/Tb9MuAjiTR5Pe9Vki8s81IeRhNjDIAXcDPfhSYco1aAtKCDrjD88Q9KAFlK9vkvhkRNmoTXeJGCEuBGFtAcyuMZPZoxW/8PJw/xY8jWEAhrO8WKiA5S1PkarIOX7AoDKOrtg12F+mmdj2nj90nXrOvk8K5+YBt7j1H1cq69S5W6/U+5Er2HdH5hT6d/2FFat/E4yuxScHCgRLfZKCdOrZKfbNOkwDlABg5+VFbPu1xyTQCRjiC9kPVb/XfcqAEOU09zIWtVA3ZoGG6QVsibREiPISf67apGOsE3maVleg4zWA7ueZtT9QkWH4yT8nbTZDHKg5yDDNNRb5XJvk9B7oCvshHef1EDnyhTpW7K+Kx/NFSxgzUb2T3Y82DOFhJQ7gY4Ze6an05GZUmwAoIdzgHxBADXvC4yBZmyafCnvhPZXpQv2RtojawRCjQjwQuGrkOB3oMz6Omsj1MgqYNSPLygaX1Xng3QjmtGUKmDZcZykBZW/DWsFngKh1B4CsTAC9Z7zzesbZUsIPs2N1iiOlVJt5udstmU2dTz8tj9mdUslzcHQPTv16Ps0B4Im/e5y5vh9R6H8YwZfHqAF24ygGVoAs7tZAAofDE4miAA8wQ+e7nIaJoBYsfzK2TvF5BPwvVlJpUl5bpiFd7TTAXakeGF1NGkuQ43072uLepezqzmRoBNBh117NDSbI/zbImWK9DUK2CHy8rpY7rJh3PmSnJyskRH1yFF4Z5YkVZOeEmmfTxJ3nnnHTnllFOaustGjff+N2rU8D/2oCDemdCn9nODwtT5ve8LOE1IwQZInnymXEqK0y9qNDGjGKQSWubRlq4scTQICVcosGKB5iGFm13R3QOChYAHUcdC9g0mLr4Q0i2bfEaQoVsZluyzjKQQMzikLVth5z/7jKnrhSP9XDjFnGusRvMYXURohUdNQR0Ej1G73xFr0Tal60eSSWHn65S3crvJldJ2TJFkzo+Gg0iJ9LyF2QbvjVrPoR8pnN165TSPtaBezsdg3O2otRi75d9qhiK8J6549A0CJGppHKl0QoT6Wo8Di61wixSnngO3mrVqDHv0kudOhLzOmRAovxzvx09qX8zuOUBQaSjyFuTL3EdmSTX0YyxgHZ9722mSmLJRbWYrhk5lgAlcEPdmTyB1D8keL0aPYDEytwz6NBfnJsnbj84WVzn/EUE6JR79f65w6Qmf65QuP0lYRIHs2TwW2n84T7iMdEf5kx6+oa3BCEUWJvN7m8w9LRi2dsa/EGaxMvcPFytK0ZVOltRrJH5wpXSGnt/2l4xSNffNKIEI96KL7FKKkmlIKzqKGOCRDNMt/4fewzxvdsjYwvidA51A9kJCOlNl4qrKQYBBT+SR+fQlrhErZHZyl0Lv0I/YwteB2KPMVP5wBljNIKl4o0banVyheiZJNlEuJwDCBB5an55af8kTqtRP2/HVYFNbJaZ7tXS9Dtlz4INUEFwIBitdNZK7RGcmIUF0YgWIKZZa4JBi5P0fKBeY5qiSOJ1lItNxDfDGHvzG2L54hwVM8wolC8QyKiMYZecQOIJo8O09ByT+BxolzdzVwZKL3kaWgOnO4h8kHOE7TZ3Bku72VypRAvfeKngt6grOV7Ah2AMOOY6ZtsbY+tU1J/sGdUWA72UCZJHMwRIuWfXMMNuTkE/3Hqp5mHrOvkgd7N0k0Seive98en1Dj/p90OMKNqIEfrJ+1fLYcgWadgXS2sZKv86JMmvWN3LmmWc3KH1DEe3bJ74ot067WLp27SopKSlN22EjRtfzp9SIrf8gQ6xwtUhc/mfcaA+IM+k4KR/wDP47+970muNSWMoOS+KSiz3Aw1p6EEDy3sZNDeJDoP7CKgC3RmfeGthT5J53aoFDvYkztq+PxiKZ2TVgH2uQWNp+qkdPUW/DTBaBF4kctPtzthqkMpNBqE9VxHQz7pbosStrO0lv4nkkS1iXavVClrgPjp+HgqvIWedDUPvkagmxF0tJ1bmSL3Vfx5itzwQEh5zXiT5FOrCUQtKG5WT2yZGc4gGHGMOeSRRcOVyF1XlE4rbjM+IOgldr+WGJ3f6sFOBuTKBL28J8ZFNJkGko9sxMVOCQ46oq7bJ57fmSkHKP+hKh9SrNc1Rh7qJOVynWtc5ixuE6O+OHqF5XZkXXL+zvBofcEkAvN01NsXzGFZLeaRYIJD/J5feNlq2W5yXiuO4KgG0COcKJzBiDmbEKNzhUC/ArZ2cqmLP6XwoyPcttcnh+hco8rgCDmDdkgqdssHcJDhkaHKoX+EVP455dngYh5m96kefRv/+P5PxUgKsDX0NgugjzQUssEmXekj2mjBO2ZgtoLcMd9NNloUfRFxxyV0Gy8Z9wgEFrAoPEhik/OjzgUC00/WLGyJw1Wg59QFr1GQG0ZIr4fhS85gIu96IyZrI0+OyMjCt/GKvv9WYn1QL8GvNxqex40ybFYFp3QOYsDIBo9iTYHZYa88XCwnDwjdESPaEEIN6ienG5rw0AVmM/RWm2jwHushaj7H8rCEew/euOzGTXawKXR9nDOeajMln7YKjyQCZhhRm7QLH9NWTUUAq2onxvBulRHesGlJwn8wcYB+DzwC929E/2D3pNk9PG65R+doXHZs88jsfJjHZDwc8LJXsYzNpSn/Joo90UZC4/5ucdJCR8npKb6PBytPtt2e5/9wqM7tte3v52kyxfvkyGDh3W4In2SB4s5w+9Va5GP+KXX3+u7Poa3KgJA/R/8yZs8scbGrPtOQUOeeahRxZApPgb9K+d1uwXgv1jZuBB+ZHfUvj3AOrbXBXLmT1uBwAMV4xYBX563avEqyOgp1cBZmpRh8t8ToXZyNarbvMAs9arboWMzunwKn5ZjSPTN2fAf0DiGOuzXTD6EAnWtZe1eSVL3MHOXAVUaeenuSFhyBDmmwf6PdcyQHrxyu+vkg1LzpXYpH3Sq3uSMkCj1qIOOqOwbEwJHgaPMRjtAo15v2JgzadBM8+RdoQhYHMoez4wtAmS/SMpco7skKs9i2PSihUQrgaqSIaFn0dOh8eSMAZi4k+ojKsFlnzm0O8f3XNsaavNq3ye78+aKtETOyn/7IQEAHUFaACE4+q7AdMPt/a/EzKHe0wDM31siWI/U7ia4tHmiEB/ISVhItKqpd/5iyQj61lpddMKCIP3EJJyDjkgsD55oLQ7wQvC9fYHVe+gG2yhFOzfG2gNh0XeLU7lysIeSgIXK0q/A9ELuPRGhdb0VOqRBJfyLPd8WEKCCkvUYYm1gQRFt5ffGqayk11Qfg6HXE3mHPM18M5DIkkkgBK/D5BMYy4lM5MZKLoha0urwbw1QD8orZIkRE7SPmQYi9EXuO8Lmwx6ohzlVsgCAcjaIOfS955y6XNWomIxH5hJoGocA8+DTPK4Pkb5laVWnXncgHmZKTRExWsfieqdhOg4vi7VXulewp6+dQ8DeOE4CVZJriHrmp7Hve+uvR2lenYAULJcXbzdAPRbnq2Wid85agm9Z/0IghC0GFnqZz9pRGopZHPqB511HSgdZJhdZWtCu8kVyvmlrrENLW87tkrGflaq5IgoQs4scku0XIGfcwUomD11ZIa8OXMlXFbSGmWtd96wafLTe9/K0/95Rv56+19+zu5rbWt8Ta61uGWB7xXwuzGaNSZ8B/6sV8zCVYNprIPuEL+JwB2MpWr6/1axDoag4HZRxysla9DzSs+wCizUvD4PSeboLwGeMlCyvUmBntz+jxtuLX5s2yB4UJGcoCPYmQcCyCv6Jcgu0KEzMY71ClrbBQKHXF8GcgcFsykgbb6O5Si31hf0QmaJnO/yoV0DZQHM0fMOd5FdayfI5ruyam8KYEc/bb0PK9xJCiGjU1foTw8zexoccizbAUJylkoS3Etk0RdS+srrYt3+ndBCj6QfLWTXY8p8GTzxOYhRr5BhJ/5b+vb8p8RufUpJI2UNf1tc9nQpzgeAxQ00HH2h1DpkFHW4FOdk/Im7orpKaexItZxAvveTAONd9ZHpR7Va9v7UVbUrmEv6XNMB/XW0XiPAIuDxDS8Y8ixH72fbcQaoYxlQA5DOV8JHeAiWY33CiEoZP90hU9cVK+/jiC6oHyNY5h4w9jXJOHGvdHqsr3QYsAQe2j/imvjehLXmoN5naxNblCLXJy4pkS6XV8rpa1zIBJbI6dtKZOoqh8pWJR3nC/raneRSxBU9l/FItxffcXo9eyvpWc3yOkkUK/7i21toi61BT6JL2sN6bvTbpSCngI37ls0HHOKEJLxNjUwfHCEr/xaqejP1/JRq6XA+AB1Alwq8vxvQg0hwqF8zs8fSO4EgBbYXnA+yyl7jeCmrYo4Y6DrqIMHIGwao875u+jNK3HiOk0/Rq3j8x2Uy/KVyiUz1/6yIbH/VJusfs3vAIfdIwOrTb8iFiP3TcX44dwaBrhYiVwuO4leb46vUZ5mfyZ8brfqj5xEZzhZw+HOvZMv2+grERYfJuIGpSvrG5TK+0Ol1gR6ppzgNfs1vvvGmbN++PdCQo15m/rp71JP8r29Y1Pla6AuuVmxPV+uh6Bs7qVlOmQQSWt+Vtx4GCRqwLtGbeGTk++hB/FKVD0tSz2r0fhTgQke9OdMVlvmdoYmHxqD8nncqQeZGT+geGJy9Utp9f77S7CtvNRi6etOROdulSsDMFPoHxZ7tBavUYjqWlAPkUhfQP3iu7L9jNpZBHT2edxDYwjosLmhs+AWBozkI/orTL1SlWl5HNlRx7qxhb8D55SPI87RBP+al5k1qPXfF9ITA9bfS/lswrPPa+6wvyQ58F+HcGuCGoORsQxtCefwwxW7m7ZC3s+ogK97HFPUeV0Ckjz2Y8Rvuh7C1cY4hcFSh3M2ONZNlxmvPA/tYJfqjA3LB3yaLPbxQeL2pp1jc5zYZdvbVYnX+w3NsZK07ce55EWfJ7HvflLxDHUFg2S1n3nSu0qLkQOo1uuL6SuGqEllw50BVTmWJjjqBxeuc0n5SjQz/r0O5ciy7yfvFpNIVgb7OfZ596SfMJNF6jeFESnbL8yGy620b2MIGCNXj9CMlTXRJUy/jI2/MYyB9wyySf78Y34v87n8T548/ydy375XcQxlgSyO7GNVTplx2vZSGbZSibtcquRQrSscdL0DZH2And4VVadxRmDhzthUlcPb8VaLcaeyZj+xTNMew58sgLm1TmThm9pInwsIQGc7edzoBXnCyeBN7wgaPxJRAYSag+K9PHrRb+jyV4AHFXE8dSd8MI7KJAG26l41eynRV0SVcsr9XQybIHGRfE5DqICg0BwHU7pnoVZwC32mwaunfTFIH2d8k0eigvR/t8gjkUv8EJn4DrGS9XV2PZCh3OM8FlxKkOKHhSEITg24yKvOJZC3tBVsPRvYUvXo5ZuKJe1Jmd6M6eUGse3GtkjJF2lui5Qr8L1+BPp2SZOfBQpn3w1w5YdLkBk81OTZdTu93jTxw70PyzvtvNTi+sQNaAGIjrhQ9ig+NmwPnkwLcLNrhq65xk2zEpnUOidz7AYSUH1RAgmSFw6M+UiCR+yqoQyOwrsloixa/7i6VdStpf7rk9X0E6KQCuoF3eUAMe/ZKk8arrFNd8wRaHrLuSQUOuY4lVHveMgCPugGy8jI2TRQM15S6IrffEwCG5ynhaBekYyqQxWu15V+e4TVW35sjVxR11G4gByEs3VlyBj0bkAVO8WoHGs9oDxiz4wXY4P25Vg8k3UZIdqmgZR0yosWDHpdU56PKl7gwJw3LQDw4Ywf22sVzTPoJAb05/p+96wBsqzq7n7UseW87doaz904ggyxIgBQoBAKUUXahlLKhrLIpFAqUllFGmWWVvUcCSQgJCdl772U73lOyNfyfc5+u9CTb2W3pX31gvXXvfe9dKXpH3zin2Z4IKcAHUKV8TShPk4TXjoatqpmjfrNkL7tZqrtcoiq/qYBCIwXOsu8uUuCQ2zXl7WXziuOl74h31Xxng1tw74iXIH/4geTNPg0g0QgbWxBOJ+H60qUjAQ5x/bDqss6yeOavpds5P1PbfGHO5KKnXODiM7xO6gGujgak+EeLbHg5EUTHDZI1zAO+OxSkoAhm+Al/kc+ef0p2/yoJ+/1y1F/dLUBSfEqjZPZukk2NmaFzRa9QDWXupU6EQUHtglAjzYo83vS1jyAVoAJFM5eGUghY3EBQxgrgtL5XQE7vSqkKhh7Zr74mVz59/kVpdGu0BjABMmYqnaj8uWAOXeVKi8ppY24eyZuZq1dwAnj6hnOUSCPgJYCqD+YskgA7sX29ynMj6KzdaoXXD0TXwDok7bY6URSCmp6qNRZ4C0HbU0mgxvsKAzbjDM0y4NRvMNYvQiekp2/Tyzq0bvQhIKKKigaIbKyLaYz8wTBo1wMNuq9RVtwPmhZTYQ09uj5TtXT2QBTCoAM+0uCAbN0DUXgWCrkm1iPsj6KlVjx8+nwHsySFT88rm1RqB3MJ+ftO555ynAXXOQX/TFQaAHP2QgZPMnMB2Tepk2l/sAG1izn/9C7mgIeyw2lhoBsa4wBX8BWOUDg8l1DHITk5f2DELDYDP8UZOHFEZ3kR1DebNm3ar8oKr3/K4CvlV6+/JvPnz5cRI+AsOQIWA4gHOon4tg3Et/0wPNBhdDsqXujHCnV448sWQNVjnD58UMs0yMZpz1rSLlSogviY3IoEP9qow6s4CvlUPBiLCg2TdoZqGakbn1b8gPRMmqlUWKXrqFiCXMpGBeCaUvvhKYwnbGvnhbePSiI0FWLGnBiPTuMC6wF2o03L2Vk9e8UP72BbJZis3M1Z8GtVWMIxLJ4yqRj0UGi4lE3PI0z7hNpuTO6DAp+rpSn/GKk/9nmZkvmI7F2WLfYB/cQ21gBbzG3MWHUvClQ2K/5EVkwz5GkFCbmqRoZEX/bSG0LgMHSiqBUW1nCONEDk4cQ8zM/GcMPk9N2hDfItxjVWKYLuYijutJt5IvSS4R6DkU7I1bASawZAVPtc8AohhBwyIK9APUFC6//U6QUjZUwlct1ozc3QDK6/T3Z8h7mFsXp0A7je+t0cBhp8/30fvyp1s85GizAI4kOfFatmwFQ8A6HEP4oMe8TDA1CKuVWRtnM9fslK2T3hG9k1J082veKQ0rnGNbJgoWUIG+ApBA7ZG7mBAJA/XuVSnsPO5wBEALBRuYNeMbNteK5ZTvjKK4ndzXuNdTM44x5K2Pl9qAwuMuZj1yd2WfskOBhRYU1AN+Qhj/KSheX2Wo7Ja9tTNlm64cO8FfyR9JYRrIbNuD5SyPS+tgkE1/A0431wgg6GoIWmqVjCfUQ6wdOnvICYY4It3ifD6GPeaJC1f4Ec406rKgLKH5nZqpIKARv5JTlvLHApQFEFpNYPycjORP7B6KKSRFAEaWPOJwuTtHkB8poqjG3ze0SPbY9ftQRqzCaoQBU2AxV9ME9HwvgjZPv7djVUBcjPU3uDgH9IzCN5JOY2NsaRnQFXvF1OGtlFPv1muqpQjo/f97PbaXfJlIFXyLPPPB8DiEf2rfj3j6bULILE1fxKJUH0oVpzNPjCgCmb/47ikO5QadmghqW35kAqZqOvoXHQbdJcvlbJqTXkTwagGyIFM45TYJNtc384D4D0QqnucRVQmF1V6NLbSu8cCzLyZ58CveFUKR329D5l8jKX34YQ/p7Q6b3OfFQ23xba1issbiGdT2vzZUXVN8FLfNVSFcZl1bG2eBOBNPPYUjb+TR+S+No1CPVeqcLzzQI6EfduSe+M96QpS3YHjsN9wasFEJ7AvEAYgWV8+Y+43iK1zSrm5J3vogJ7m9re14sPZOEkxk7a/hbC0puRL9lXejzYRep/Xyx16xrAJfiudOg5PzQEFUYKZmK+AfZrO18EPsXfwAP3sDrui8+RYUPull0/dkLu5FGS03GFdP1tugLZqgG8yDkLrpBx4xzy4arXsSv8sA6dACtuFGXQk0Zr9lukdGW2WtcvfLCbzf3JF/L+H9/DLgNExdmgEtKvWUnkVYLi5MffwLuq8+bQqmFXuL+9fltoKP6oKf++XuZdbgK0waMsXKFyhr5mq51Jc1bQ7kR+SbKIg3+khiHlixl46BM1I39tx6cW6X0DCiNesSvSb3qqBt3nkfYIQ+/9PgzeootcOAbBIY3hW4LpA8ldK1ufK7Z/+iPkBdUgpheCJeoEnzirXuk/M9eRFD60DFQ8b33LWNev+J2gjFXTiZ2YGmBTVdwMn3c4mQUhkLhDqLh0BWhhoNDCPE/8kwwZ8xOrgxQ5BP5D/uiWzme37Y0j2KcXMto2I61g+b3xaq57/qYx4seDuS1D+oVnNQlD57R8VCmTBzPaCKDrtlkkb7xPhfl5nB5l8mOSz5HWD1KKbVVaqwYH+FIbVPLRzeu2WGIAUU9GbPmTm4HO+enSq1OGTJv2FbgOT93v9U3qc468/vKjUlJSIrm5ufttv78G4W/G/bWMHT+iM1CB6tX0VSDgBRipA4ehN6VlGPNAT1iBiuGsZbcAOFWjWOJy6Du/Jkm7PlLdA1Cn2DviRYAzw1N3oGPqds1pPSFv9yXREkBXjaRsei4EDtmGnkLyIAaQqFWL8K/aBwBHOUFXUC2GFcVpAIsMlbZllsayyEMg+Y72DtJjl7X4WhU2b8ibpKqcdRtyDubMv0hdjx7IrOjSkDdR78a4FhXOR/JceB/WSDNkNiuuyQqqHr8zR+UR1lXlyUJoIQf8Nhk28VlxZYVbx0PqsAFShKmbX1A7Cfo1LOJ6wJqM92AgQO8dijNRywkG4MJxtM+Q0y4+WeUx6hFZoESKImfJLBWG5v6Ura/InnGfo1L5GHVd6WseRpXvGjn7hjPAueeU6qF3KEqe1NUPKXlAvyND7Ph8FfYWGYrrXfzNlWr4zGEABV6blC8XRU7da/Q0Wbxtsj61elBT9YTAKD4zIF3BZUhT2tOBBPnqufuxFUYOzT4rQqM+pcXbBFJnMzjERkQYrw5qNqUfbpd5n90EL6FfXMO6qbGjX0hKndNxlTh/+KukJq+XuI5d5a17/oFwcHRLY5u0NH7PPjxBeDMYfl4O7WMa0kYBfuJl5LMe5EI2y7I7uV+/Y6qJ6SX8bvK3mDWR5zFAo6lRxCozEMqXhOeIB6m0wmptXXDB81EjmCA1WootjjgKuXzhtiBjBsHz8vscknesX+b9Cp5OgPqtb9gVIXcZiMZpecdCVnAG3HviEr7PY99yqzxPhsw1OFQN8bLjQ3sIIDLfkTJ9TClgPiY9lNQy73F5E3S3w947gkaCZA3E1z8TD0USr5JH1OOal0MfblRFHJSLpHdxKQAxeSMNVR6DHqZougEat77pAFF6g5LS448DDQ453oZn448IQGQOrlEIY3hs81CJHLPYDPyUZ2DCkI4INa+UtWvXSu/e+DLfhyU702Rw4TEyffp0Of/88/fR8sAOxQDigc3TEW/Fh3/Z8GeOyLiUZ9s9cTYeJnjww9vVbpYRFuXgFqiAEKAdtuHZSW+hHaHb1sxeu0ntdpZ8J9mLf4vQbuQXL0PT+7Ia5AhmLblWPaKp+lLX4UwFRlmo0QSvJY2Vu7owhACUXrxGFqbAkkAurhVX1A68MATrg2e2qvvV0hAlq1fZ93aEO3+nzqfbc9mYOgAayivULg8oZwgOabuar5BZj/eR2mARy/a14+Ri8ATG4SFO+JBQOluaQadTlztJrZuvhbDD6q8VT/pQANP4kEYzx3WVfg9C7RXwPm7hZsj8jixUhd8n+d8eG9qnVhCW9yFn1SddQYg+FvmMa9RuirukYn4yV94Zam8BONQ29rQHpfDY7bK38C4FRvLaoeq8qFYcO15WutG2iutl25rxkjXUKz2u7S29r26SGtC7JHcOKCqQjGW3SdLuj0GePVbqKt7Qw4aWDbtsMv34ROl75iLsCyf8UZuYFCg6y6C03fXy+SsJkLEzwJOrDmALOWhmUGkFNU0+KG2szl7S3P/PUgugTo97Wl/oEW8LnTJyBWN4yYUYsjCoI9AqnBKQinXm4/BsIpS9A2CFOWmtgUOC40wApgQAmy2oPmbkfvAfPErGL3SaNlbyxnulcrkZIDYroETi6p0f6H8LCP/jn2xrtvpRINFg5W74eBzyGOOlfCnkB4Me3+p15nMARAY9buzDwh2C4kxU2vLaozkimde36mGHOOHpW36XAZwZb6Cijc5xXPekQYETKgzBh5leSfISKsPckrB7X2YugBkCuhz+kbqHFEjkYyz6xgCIHINch7ljDHocI2XBAO1UYzkSRqL11J4NKgeRVew6N/ZIjB0bIzYD/4oZsNusKtT84XezpFMnKHsltIy4mM87qN14mTv7hxhANE9KbB0zAHBIc+eMFzu8TTSGdxvTBqn1Q32hd84BKTkzOCTNDU6ocvzoqdOFK9QXNoNDPjqYN1jV6/p9nt7dbpIUjf9S5S82g+ona+n1QbCIh8ZRzyN0fQwqLvRTKTgUnlS2uq1KtpDerdbM5i4CqETVMICV2VjhS/CXtI2h3k0qh7Op02lSXngpNI2/wrYvpHKyE0TMC66dFAFiqkEsXePuJonpVeAhNApHCEiTSqabTxOxnr7hLyqPlaFhW+NedawpqRvobha1AKpJez5B1flkVJ/fASB7ixFi7nqJUIpQW3WP34ofCjWJu95HCsBuXEeFPhRaUnLPioSxbdtPlnUl10l6Jrw2QZzEcCZJvQlyR530uPprhE54ifVNlBqjojaod8t8ToJDmiux5TlCJ8NdrP+4j0w46zZZ8O3vpL48XeXFrbgfXH4rrHL0Ex7IxmGWguCQ/eilGvOmW9Y+7JOyZaRQipMUFJ+Efk8AUKt0DBxJBLFxm2YKaRtt4iQ+J6Aql1mo0RywgnPPJgSfBqk0QCh0lRfCU2YGp3r8jMF+GftmQ5DYWqT/LcjhxUeeH6OMgX6lj6zbmpfpA32q2IIUONSEDptRZJEEwB22OFUFvgFa0PGQaRt0jwfcgQbYIn9jW9ZCktwEsJkybAZvzuzwOCS8nnMBPM1BgvMagEv+KYBuOll0SoEplVl9doY8CBlCVECjTkr634oq7zRT5wNczRgUQBgdRPagAjIDxBwovtBcoP4ZAjDOCmg6a6mbfKQsvX9A+Bez2Az8t8wAVVa6tU+T72d/JyecGI72tHb93XL7y5c/vNjaoYPeF/MgHvSU/fQ6MN+PAKExY7AKnbIKmiFra8MeeM5OASjJaPWiXSUzxbn3e9WvoeCUVts4lv1R8lY+0eIYZdzo4YovXwh1joGhELkvsWNE27LBj4s7/8SIfW1t+KDwwb80hEg1nOPSVTxDSH9iVg3xOzJVgUu7mSeEgCsfhbpfW+cw76eyCP+0OSmjiAr16LnYgjBeNIjIbr9KUhI2Stx+nlvmMDfPw7BwE/SR+RdACLgaFEo2gNvWrp3gjdyTu46fp8L6zdR4MxuQHsFlfLXhRTQf4jq9thUDH5Tt646ROY8wuQ2z86qo3LPca4zWJHxPgmSh1Y9yVhhlFNt/PUwBcuY7sho8YE3E7VsV8M9F2HfEaU/Lgi9/gwh9y9kmRc4Pn4Kmxh2JGkjvQm9WE4oxMob6pGKx8dVDKbv2PRfLil383KBEGEbPW930RZI9uAHXMcZAZdhvkGKrJgf00oiwM3H40rvjxQdvmS+oNmKHJN84hF7pIY1+Xzlwah+fjH/PHQLS3Bf87cVVGXC3R2kkqxxC5D2a1WBIbs0K2UhwqLopEBitKkIZPH0N5FGc8GGDakwv29zLXKbq5PAnxL2bP85wTQjbkhInFXyHVAdhWgDBHa/VmRtQBOEcf97lAKCoKiYAzRyO3M51qnv4JQpcU/pv+7ugL2qKg0qMVwG5cGNoTiMHsuBndUzljZgXc5sDXe84BXmSTrci8aaEI6vYk7ui+AockPW78HmpMkD2Mng4s4Y1HDYdz4FeV6xdbAZ+ajPAUPMLHy+X3bt3S0FBQZuXl5vSQYpK9rR5/GAOxADiwczWT7At+fAylzJUGlDqHoouB0Bif0ovzrIfJHsRCktgyTvelnIUDNR3OL3FHdrXvdRiH3ewEpfeLLNHi/tZ3WuBx4qhT+YJ7gscsuAkA2TYVBHxZB6tiLZZgduU1p9DhYxgKm3tnyJyH1VVNvgEzV5NM1zRgMaLohCSRLdlpF3JWP0HVCPvFXfPy8DZ2PLXWXJ2uZRJOOG3/+jX5ZhT/xjtlIw4BcPkvP+6gp9LLkLu2ujNJF2QF1XmRROeUbtJ/G2+du70SL5s23qKuCxx8JzZFRUQXTbU6ibXJVMUaI7a9WoZ/eK3pUrxmPch6ZciWx/dgcN9Q002v+qQEUGASEBf2/Vi5Ig+KQ21GVJV1lG2rx0vxduGSKe+0yX33ix1PjeAWsLeWWqM4Se9JNm3nSULb06SyiU+8boBJvxGiJINosEh99GJO+PnYYDb/uQmFXKlFylh3bfgfZwi1dKJTZU5l70i3z50nVRVQlLuIjvATpOkgCOPxNRhC4MmczgyfNxY85goYbiH3IFOABBnLrxVJu+b7kepPmDvNm0dKoE1KCRIzDraJ54ySChCnePH37rAOeiN6Esglz3CLwSPVKOp3dwou6FqkoRCnD3TwuHVelMxDzkkExGer15jgMHWfvowhbYQ3IOp3UG/g9w+XezDsDWLfEga/vV4gPtgVfdi6FazaprclZHjQW0GeYYs0MmCfnbhmT7FY0gZPk1uHnFD7I352dccRbff1zaLU5bdGQ/vsgWeWdAIoZCGCiVUh9HGe2BOpzlcrY/FlrEZ+F+YAVY1U4rvu1kz5Zxzz8PzJ/qpYcyCk89QbyMKvfj9eHgWA4iHN3//8d7JW/8BcIGf8jBKv7mgb9wAAur9GT1/ZosvX9ASIMJF0OzMlDgUnZitGRn03tTIZFnyHRK8NGF/BfgAD8RI9ZO453PVNLHoS3ghe0J15XJ48E6WCoA/AzgepSqSddGNHpd8j/SosWCDUnpmUxJ2qIDOAM8kizRy518kJaNeV+Ozupse1/r2p8M7NQr8kXcpcmv2ty+8WerH9wXo7ag4L50lM0CQvkImjpkjs3ZRXaWb9BnxnipQUV414gvziYPrBmxBUn7xNDU2C4WYC2o2hm1VjA5ePm9yD3gQEfpVfkRw7nkS5c0/fyLVu3MQ1myWo55Ete3x9ZI37zyE+tcob175oEdUCLoe9DrUeKb57JnwOE7BuZrBLwnVm4R85CX+RRJsA3E0DBAJJkIG0Mk8z23rJsinzz0nPq8rdGjbmgnSpWGbJA+3S3YOQaZhDI8nW5bK6FdHqrzX9Y+Uy6rnCvXhNpaRM7UHhQlH/dUoEqJ3+Niz75CvXntC6qtRmT3pb7L8u4ukePtgNdbavwoAFjSALwQlzALcZ63+2oqTvO6QKRzaUzqejkIJePNYYbv1bUer3k19YfS4sdqaHITae6ePcckKYoZpSeic1Lk5Ik+NQDDak1kNXkQ4WUME1nsg/+fIQP4rCK1TQENzzGtu8CkaZyiZY4WWMt5zePoadhs0NVpqriuIrbWVgdw6yKeud7WyjAO9TbyMeMrTotqY4XDKyWlwyM70yDG/b/x7DbJ3ng3cggDL8NBRU7gdCl/MRr5HB2h4/h3GXFCCQ230NPPZloNrNVRi4Kl0NCsgrtvElrEZ+F+cgUHdc2XxhhJZv359m9yIXjh7rCjybAtAHsy86W/ag+kTa/sTmgGlnFK1PHRFVA45EKPqRyoqkrV5UOgSbQmzLxVL7Va1229PU97CZsSvWK1MaT1tNnAD5s05E0UiqAi1xAOMvdkCQOq25iUrhM1Gb14cnooEfnWdzlZ/PJ6+8h5zM7XuDlYllw59SoFMEo/HV69UQKuy100AqytCfQigM5ci7AnlkaQ9H6v9CVCZKRr7MULzAGpBYztKCgZQgtpuzhnCKmZlePiffNmvdTNFdm2DxzFxz2ehfXqF8nu6kIb7rADXBH/RRhCsE+0ISAn0kooMsLxl1USAQ2N++YDf+dgaGZL6gAKHHIc5npQcZI5iTQ+EgQGwqMjTgDzOAHIOzcZ7OOaUR2Tb6vHibUwGHaUXIVIDrNr3/iDt514BAF4r/5z5RQQ41GNs+bxQBJe1t/ff5BdXnaB28weCSgVY+6jwB0pml0zZlf22VJV2FbsD5MsIM+/PEtphVoLT0pB3vCSdWCJnHXU/7qU/KJpeks0PRaYlzAd1DsONVBLRZnN4ZOA9XvnhLuQXAhRSy5l5g/SG0dtEDkN65XTVquqHfEt65eZelKDAN/tEeiVBxwLA9BU8b0qrGO1JTt01SKhMrxbpbszmrUUFdVP4uvizQfP9Va2yCXWPjWppkflXIGwcDHX70M/WzSejXmoQJ3IQteoMaYfm/NKoUjafp7V1/buD8nEMB++CLF1SgRU5k/C49oSSDfIldbGMBqCZQ/HvYWjkD5bWxv537UsqhG5zN7/UbjI8hswN5Wej11XQt84LqFQAakUnA6zHLDYD/8szEIeI0lh4EWfO+0F69Ogh1G6OtqqGUklPzYzefUjbMYB4SNP20+lEWbI4cFgor1jHqaGK3/1dYSMkA/ce9QJyEGejz2DFzWfuY6vfIfbd00K7CHpKRr6KnLfFLfgUM1Y/oMAhGyvaG3i0ysB7uD+rw/UmgtjbCu8j8+WSUeDCYojyAX9QXkTd34tCDj4azI9lehcbM4cDgTng+Zyi/lioEgC4ZPiVPIMSpPrhOHb3TgWi9JhxVDlBaLchZ7ykbH9D7SaQo2cwoeSbMDjUHYLLenAYViPHM3VdZF4mQ7DMJySQbgfFE7PHUHsG9VBeVFaXD3xIb0rCrk8kMQgOeZ/x7eJDx7iSlLob5NKLIvYF4sJt9kWwTgnG1Oxtctn9I6Sk+CjxT71dbO2MHxEJK/+kwCEHTnRsx2tkaN98wqK1faQi7SyJlyJZMPdOqfw0UwZ23CkpA0BAnVgsF955LFRgOgpJvl+4a6G4a8xfUMgpwym9AEbNfoOrMKGjX+kOp+x5CxyTT6jrqM8/WXmQmUs78PRp8s1TfVHla0M4OCCksaH53XHIo/MpapeOpzfLjh/7ImRrAAsCvU0vOWTA7xulHQAT/2gEiyXfgogbU6g9UtxP8E3KmvRBPqlcZnwVsoiFVD0KHLIRKomX3RUva//skIQCEFtfT29dS77FgDpV9KeUA0DfG+FkD/LrrAg1a3BoHME1AGy2A9UKw8L0mhEY1WwgZY/50976uPRS9kK1OY3h3qPhaR76R5GufTrJmtm1qM6Olyx4Xrtf2qgUSjQA1ef+Ty55rwTbVngGqWc8/t0GFBGBsge/LUgITuNcdELVccxiMxCbgfAM9OiQIXNX7pENGza06kXcXblFOnboGO5wGGuzIwYPAABAAElEQVQxgHgYk3ckujIXMB7KI+6s0QqoHeyY9CCWHhX2BB5Mf0/2aIRZR7faxY/SxGZ4A5nrRyPtSvbCK6EA8oPapmRcMyUO4HWzRHkCmVt4IMb8xdKhf4UX6nWEY79WXUieTCk25WELDkK5PPPjkrs9kOaLNhKDa2tCNW60Nw9kK/owgGQSilyGKcCrdxLIMdTN0HO08RFNbsKKAferQ8zbc4B8m4CZofGqbleKN32QOrYXQDpr8dWoEAY9CwpR/I500byH9L5V9scYfKKDW5IAN3kb0wQM4zLplNHSx9MoO18rlYycTTJ2inFOtsBs478A9K6XSjpyJ1nl3JaRm5FhbpozoUba9/hR/IumiLc9KsKPf9F4Agc7jzn1Qamp7SIlm5E6ECpaYOqCAczScrdKWt0X8t3s52XZm0Z6wXZ5Uc65+RTJ67QctxOQNABRhscjwSHOnVglUz5dJGu/Hhsijt472y6zTyyWC24I3xs9ssyd5fwnX3uUnPgLD8KkcbJ3jk0U7UvwWqliQh694pk2hEcjAYSmXPHWi8yDt45Se6RtIYjUx4LDqEUCJPlGPuuWza/bQehtyK8xZBxhAImsvoaYjvxwWQLk6xpl3ZPhnEvVFkCv++WNSmXFV4c9oTk01unJzBzcLIVngzj6nyg1hjFsOuCORtnwgl1W/YncggjrIvzf5/pGpariKTWug7Qy+GcRYYmd/TLpS1RZh38nqOP4faSK/Wed5QqB6uxR4EN8wx3R/z+98eNvnShIwY3BuiJ1YNA9UFO5LOom/9MXGTt/bAZ+ijOAX05H98mTHxcvbBUgbipdLv0H9TsiVx4DiEdkGg9tEBeUObIXG9UCKRufkb0jX1Og5dBGO7K9mlHc0DD6aXGu+JP44K2ifF/W0ptDJ0nZ8gr9bWrbFx8OaxNIVXf7TaidKnUkGGrF7DXrkB94MUKmkQ/5FpyJdK+YjAUofoBLVjtTHaYGSi4RKjHIq8tZcHlEqJfd2a+m88VKio7hWdLc+JztTCPj4YrtJmgz1xZMgTfzwwjglgBwbMX1lkA3OwBwXDb0L5L3/RlQblmB/MBzlcoJr8Wb3A3a3bOUDJ/fma1CwglFXwN+gg4IHkiGdHPnnK1C4sz/I/g2m9+VK72vaZKjTv7AKM4xHSQ41Ja07Q2pa3eSeDMMYEoUwVC7DaH6+vyTUAB0l24qZUU95JPn/i6k5+k19CM5rtNb4u5yvtjLl6g2qVk7ZerVU+WZG1YroGJ0tEjPYR8hbNwgRx3/FK67TiqW8B3WBpC2bZDkdF6v5tLesANh5o76YGiZmrkDhTmLofwxPrSPK+VbOihA6XACzQXNj8po5gcy7Eg9ZfWX1yQbX7QrKT1E8IMky+gAENZQBDWStIBR7YqCk4T2xvXNucCFazW+3rT3EYgYsm3IDaQnEx8pB8K6LCApmgmib3j4mBNYCn3q+u1Wsaeg6CuCV1FfYJyUfEdgw/NoWG8c64Aw6IDbmqAKghDxxS5wNlqNAwhTE6iSj3AwqpPTkeNosTVDBcWvaH5mn4O8zyCgpFwgybyP/Rge+9k25D8GZPGtUWAUozJEHw0OjZPh399mfwgcch+VSrR3UrfRS81Pqbf/HUuCfg0Oeb4tAOeU22vrfo7UNbU1B0dq/Ng4sRn4d81Ar06ZMmPxDikqKpLu3cOOEZ5/RdH3cu0lvz4ilxIDiEdkGg9tEBdUMrTxUcMCE3q1firm6zBZ3IUoGKmokIQd7+ORGC6k0OCQ12prLJaygX9Ekcw2cDCOk6Z0FEUArGSBiJrSd00pvRWYUvmSpptzIbxtBod85DLES5WZkKFQpqHgZ+KA9rM2vz0dwPKCUBiY+Yb1oOmxgxORwI+VuwRh0cbcveStr6JKGpQ5kLmr6nUjFF8Mz6Vuy2rp9HWomAaCIH9kHCuyG7bqwwB1q0KePyc8dARFNL5/qQD5BPpcZ6iZyjF+S4GCEuaq8pQNLytwyH6saK7uepm44d201e9UxSVNQe8ocz09uIa8eRcgBGlRRNbxCdXSvtsCdlXnyVrxe4DByUo5hftsnhIuFFC0mKQG53x8q1SXdVbH1i06XaovKZa0ozLlmL5PS5JtM3A8ch0rz5EOp/pkxweGZ6fzyUUy+qoVihdSV4t36feN7Fg3Vo1DcOW86GLZDT5G0ixlrLpHqjf1VMf0i8XileN/eaPUuO4NOiPDwMqZXCYbl01G9XS2uOtSZPP6KVK1Jx8fAoA+3Odpd90tlsm/lDVv9wM4NH5kINMhwuLhxK4MUqEQZFE3mqHcaI1l3YnULVofuAkFJ3u+suDPuF+2YTg4GvjpvnpZBQ/jgLsaZe0T8QCR3Mt3HAUyrzlk6J88ALfQfp7RIJ8MTDJoanA/5EV0elfID/cMCOYwItxch1A48u00ONTjq9C3s1nxKXLf7ml+qKOEv6od6QEZ/ljbXvq07lYAZfy7gUeUxsIUhmujbcnt8SjosauK7pF/c6tQb3QbbvP3GaXwyENpbYlVW+uyz312eEnDfJRIqQBQ/1eCQ/6IoPwj37d86E8PfxxyneHp3Oe1xg7GZuCnOAPMPezfNVtWrVoZARAr6vfK5uK1MmpUyyjYodxH7J/JoczaEerTCCBFBRBth0torcc5oks8HVgJqyT2ggMzbMqqZYZkacozBo+VUB4vaIm7PwWwMMAXi0cInsibaDZy7JmNz7D6jmcKcw6ZO8cQbtaSG5SEIIGjIrxGG1Ypax1k9neWzQ9VMifDs1k07lMFSpljGG0ETc7KZUAVy6BgsjyCW5FtLdAo2bVhhHz/0e147jfL6NMflY5dwgBRFZyQjRjmg7pHtOnnsNVbBe/fI1J69EvRTSLoeniwGUUxJANvzbzIxyvrc498e1k/Kdpi/HgYNvEZGXPaQ6o5C3vSTOBZj2EGh9wX8BnXrI8XLc+TItQ21Uz6RE684XGZdu8vZef8rupwnzEfSNez68RxEqlnrle0Q1lLb8R1e6X36SvFc1ytVK93SAEetgm9ctSngLybLE6yDQVQ/toPHWV8FjB/o67+QvxT7pDvb0AOJDxitDh4z0hEbQV59bTX/6z2Rb80NaTKB3c9KGM3PSV+W+TnRI9BcEWZN7N6CKuMF90Mj1yEhUGpmfQ5okloQ7+D2IFcQwlSxIQOY4X0NN0v9qq/2efivUNFMEHi9vft0v4Ur6qgXnIHuBdNv1EC3jhZ/FB7U4FLnKx5wqGuv/ul9JCG35+Cn3lDhNk8L8Hb9vdZdRwnSV0C6lyKLmcrFIcA3LJBicPQsjZbfJxMeJ9hcxu0ncGVeH7L0G3pj1YVDmcfD5RuVvwhXnE/6jFqt0DG7y0HxjWk8eq2WsGlGJAxr7uhRMJriDT8jjtg2htqRx/9lFtWI6zO8PnAu9oGu5FnObSttaAlqlhmfDft+tQueWN9sdzGQ5vKWK+f0Az065Il//hqtfh84QjbnI2fyagRoyU5OfmIXGkMIB6RaTy0Qeo7TFWeKuaxkRDYnXfcoQ10BHux0MPw+vXCk/5UiZ9/s9g3vxVxBr9S56gw7QsAPHgA3MJPKRbOmC0u2vWDgx7k9NXnnRDKP2R7R+USKdj6iipaIRm2lgmMA5EeC0QSoXJi9xSZh1a5hlboRNNYSZ2w50tVTR2GBRHNQxvUwY62gN8qn7zwvDQ2pKtDnz/3lFzx0GBUi7V8KHrBz1jR725U3YI6p5Wx4nyt530xDE2ScvbxQjavruPZ0ZcRsV3kPhPgMDy3K+b8UgFE6i9//d7TsmvlIMntuEJO+dVlYrUZeZbwyYAXs6OiPiLkGXTeDCl6GLKFdYjRmqxyTZLsdNwEcJgU2rth/mQ5/iwAvopClRfLivHdx86Ax7ZUUfIUYMCCE5tC7c0r9ASR7JkFCC5IuKV0PVY8eCMoqaaNhRkD4YGbcUqa3tXq0teUIDNfukEmfNyI6lwjR1A35Bg9oRPMAofe1yGP7zmQQYPmhpQp+zYTAGy1YfhTQy+TUXyCzyVCvQm4H+YIMm9QG4GR2RiyXXyzE55FfR5jvAHH/EM2LDnZ3FRs8BLSWFTT44omaSgmWLPIxr875LPhidBJ9krfm5qkEnQ7zFHUXlT2UbJ523Gv8JqS83Dwg25x4JmgC1EYmjdrKLOP2YD1I4wAVhu9orPOBJdaReRckvx7wwsOGf5oJKAj0F0PNRgnyLiH/9kDLkXjM8jxGE4u/s4mqT38ER5KFua0mxD5HaHPf6SXhu5zeFQv6IdiFpuB//YZyExNkJREp2zbtk3at2+vbmfmpnfk6lsvP2K3FgOIR2wqD2EgxH3qOp2j/g6ht9EFhQ70rAVQCHG4Zqvbpuhq4qihBWusmi+2KHDI/fXQNfZWrw3RvNR3OENR0/CYNrZJ2vkuiJw3qiKNmi6X6kMRywBCsWZzBulpeA3WaCCIwhKzNQFc8TxWd0m4Ehl5hvReOuo2mpsqL1f0Y6GZAsYBKGagpfGoxj03onglCA45gKc+A/QwCRLvqlPjUUuZMTd6UAPIL1R0PFA7yZ85KaJKmvmOVX1uUX2iXygdtweyglaEg/2oaK5ZC9qabc9JXvI0hOjHSnWPa+COCV8tH/YM52rlktSs7WrI+V9dIxt+OF6tb109UT782+ty+jUXoILai3sClx8AYunwZ/EjxCt56/4sV9w/QOUIfvD024pvkB2peUxgxYIJhl9pianF6vQhmh/sC8Rnqj/VYD8vnCKGNWmp6x5HEc6bUtDjTdm1Bu5FmAvUJZTTo2KGrkBWB0IvYZDWHLDJ3o82I1cPXmVo/urwMHMJkzEGrQD3wDw75hb66oP5iKGxuBKeS27lHedFPqBfSubapGa9RZEvd4Dnj7yFqb2w/3s7vGHNAJ24kaB5oQAz9p/1UjYPQNc0HAm85/0KYBzHc+GZyoVUXOTvgjiZ/Jf3pZf1dvG4k2TD4tMwojFAACC3Apx/VF4hGE0f4Je1j9ul7Efja3nd0/GSPdov658BMXcwxK6vh2Te2up3WGTO+cYPiC7nNUnh6/pI28ucUX68915F1G2Dl7HvzWHQW73e2gIc6pGiJQBZcU0uRhpzOJei4pvFM2obod1vTkwMgeVhj7ml0+lhb4dq9G94IfhmoROVZlIAVDtOiUTHLBLahYKZ9L6YE3jFq6HZzfcxtXcU+v83XGvsFLEZOJgZ6NY+VTZuWK8A4saS5bK3dpdMmjTpYIbYZ9sYQGxleixNVRIPT1YTCIypI/xTNSdyFrOW3KiKMUgcXTHwD4d1qc6yuRHhT/uuL83PQgWiygc8JA0dThULeADJicjwKHP5oo1FI8XHUCN4hwJBbBdhBKGgtok4QUSDyEN1KBqp6/QLhOQ/Ul5XNqUWsbvdCSonMIDCDlvdFuQi/lyFpaOGanEaetg8oPrR6iB8ZDN30YXct17DPhDm6dF6H/Ue8uHgHQVmYRsLKrTbTzsaS7c0grOPeYbkbSw+5j0VRo8vnaequlmoQvDrLJ0r9Mq6c49VBNxqUL5g7ljosuLmKtn4fifsuAkE3AnKM+hFAU4DgK+2eORoMSRHOTVHil+OOmeZrN52m2ze9QvdRC2Ld+O6THmHjsqlAIkd1DF6qa32RsnK3yjn3vwzWV3/mVg65EjOCdAYhqNoOHSS1z7sFZdskWPP+r0q1PHkjIkY/2A3GMJPhXeVdspFF8iC+XdLddppCKs2Sfr2p+W0S+bJG3dB7cUf/hpytgOwxHPZoJmJk85D5srql0Zg/g1ApClmqIay7ikHtICbZP5VLqnbani7LPDKtZvoVYCRbaLN4gjAmxWQDj/3gWePoBBvBcKc/Ciy0pjh6vxJPqW3vPtL8CgCfNFyANRmnpoQ5OprVsUmlLnLhlrJSQvrVLEJC0doPeDZXP80BlSfmGaZ+eBpUjcVXs7FU9Rx/UJy6JkmdRkV0jaG0E0g2WdI54V26BUAWOZqRhtlIZv+FjVIVCOGj/cCHFPZhRyP1EM25wCy6IdpANF8j/yRwj5miwoUKAJwVmPj95HKmwx7UkV2fGj/jwBEaopPnluncjIZoicY18YCpZUPGomVlQhDK1lNvG+rcP9Uc4lpNuuZii1/ijPQBRrNXyzYLBOOPU4+W/WS/OIcFD/Gh3/YHu41m/6pHO5Q/z/606uT9/1UcPOVI3pjhwfmb63SnvwU7jYdRMWalDkJfIJ1yN9zQf3DBVDiQbFLVW8ANz75DtDstZsiWgYALiye0tA+Po4UBQziU7konNAFGqyaDVHt4OlgB1BjhTABkC+pS6g/V+xVKxWAY3jVl9BBhWjJacixGlGc4UkfKikoJDEXwdAbV9H/bkUJUw5t4dQNKASBdjDvn/mHlVBNqUGhB1EcKWMCthSEpqsizstwddngRwDinlXUNAFIW3DcSMNZbcly4oXXSr9RbyupvPQJuVKU8K7kfx9+uBMc0phbmbztdXD3XQEPbpqqps6Ht4zXbkPxiW3h5bivXapt6oanpGjMhyHAZgEFTupXlwEcfquO82Xxt1fIiJ/9GcVKs1HtPFndrz6YPxHejYkNwhDmjFPPgedLXzvBgAEUGNojUbrNg1glzIJYYdbi63Dfj0JCcCw8q5+o/dTpHXRWjtQi9tsQjPK1B11MSg+EhR1JEpd4oxSzUOYgPjtq4KgXc6EQaXZGn/CA7D5+spr/tBUoOkKBSWJqkdRWdAj19BTp+xIZfO06GZp1nfx9ycLQcbMyCMOdBCxmiboA+AMZsmVOHiuft7zhMOkZA1M1WWT931Cc8U+CFa/K/WPhBatoVz2CvEHQ59CG/smtFEe2vAluvmTK8gVkwW91eD4OBSl2pYPMtgRXGhw27EHuHvro94RLT4lVln7ZMo3A7BXmONH5jiQFb3ccCKLhaa1BFTQ9dM5sVGqDj5ESe0tY3YwQs9nwG0egtCWCDJAmhIp3fmRcf/tT4PXGtz29YzNOSzC80cwRfcGNcwDRmWzZPc4W4JCHkzoZGsmmpiDiDkjBSV7Z/TnvuVnckDWcNjFRzV0ypBHNRq/xf8o4J63J9NVtif4RYcwnwfGe6TYAxEhA/J+6/th5YzPQ2gzkZydJXV2D7CjZLHM2fi73PR9+nrTW/mD3xQBi1Iwxf43gkMbQXNKOf/5kAWI0wHGWfBfy2LBAgyCttuulUXfY9qar9PuIg56+10jCgptDIJGaxgyr0lOnwSE70EtGz1lj+mDJ/fFi0L4sR16gC5XLT0bMncVbDWB5YYhUm5WxySC0LkK4VYVs4zOUNrMZHHL8htyJkj/rRKzBswGXj9WkQZaM94dKJHEI+5IcW4Mj9jMbPawseNE5jQyfUn1EF78QZtX1uUFqM0aBKPtbiT+6izRDxk9lW2FschmS0Dva0lDAQ48zw8n8cWG+doa+tRHI58+ajJzDLpiXJ5SMoKt5u9js7pCCicNZixzCJsXFSABdMvJ1gLTIf6LM7QuDQ4BU5H1RhSKtTwA5Z14pkdckZ/6FKk+Tj7oE8CBWvfeDfPfK3eJw3CjDr4B84OgxkmVHuN5Tqy8PXjiDly7O2lWGPFQg3cctkKYP3wUJ9i5pGjRVVs48UzEWdb2gSbRsXKhzGyteV6T33eKrxlsIoEJCwaBNOvcm+eCpt7EVCXR42G9JlaT0Ehk8/kVZOguf4zgADAWIgm3hQVtyu1Pl6m153fghlDPah5w8Q6WFaiJJnZrRBggO/cxeMebXbfy78UubeH/Z3Uab4GXJTqiwFIKkufslTbLyj/GyHSTOBED6OtvSKN71OUKZUeFgjtkAneNwf6NwpGS2FR7KMCBmO7N1+LlXgT2+t5Pn1EsT3q6938NrDzxXcKJP6rfCU/mscQ8MEycUBBQ4tljxKUQUdSaAIItLaEvvbJa0fkhlgPdUpypwTnaiaCMaIJKSJ9psSQEZeHc4DK2PMxOCUn/zMTe7PzfeAxbOrHiIuYqN8LR6MJfIQURhS58bWvbX4/ynlgwpr/1rczAMHn5/eT1p8DzGLDYDP+UZYDVzQU6qvD3/STlh0gnSrl27I3q5kU+fIzr0f+dgPqhwmC1623zsP71e2edWgJ7rlaeIuYyswDWbo2a9UHO5Ka0/QsHwNuzHKJ9G0EQj+Ayk9ZSGU+eIdcZlkK4D6EMOIKXpfHjw++JzQG+zV7UlKMpdcCm8f0MUOOROetlSNj0bARCZ48giErOxMpYJbwGAQ4KHRJP6CdvVQjOZ6iqkqGnLGD5NgOe0NWPFNfWdre7iEDjU7Qj4qrv/GvdToIox7GmdoGDhAefjBbqJsQRIKx32jKRt+KsKZxO8mYEoPZ4Mf3Oe6QUlLyKNdDXOysXGGHjlPDnqNoFP8iapKfwlwGGjTL7wGvnuwzsxBX6ZcOZdoWIYjuGoWaPGCA2AFYIATIYCPNzPPKkxrxkeTXpQM+ffHlHE04i8t+/unCAs+ECgVGY+lCWTJhsAy1b6oySVrpA9ZSeAl64zh1PqIqsfg17KiymyZ/1T3AN1lD1SW2kAkT1f22TiF6biAlRp8DPBHyPRaQT8MWEmK6fCDQEvvZnkmnSAB7O52extU1eAlzixwWvX7rRkqYi7X0YlvyZ9pi6XBfPuko1vZLFRyHZ9apPT1tYpwmyGOyk5R9BSuzlOahF2bg/vVvYIeFbL7bL+lThV7MLOcci5bA7mXKrBFPAMDRuq1GVO3c6PIr3wKmfvd5GfY90zsQNBRkszF4EQ1A++Hx7LR1Hc8XTbAJEeN6Y27PjQJpUrkNMKfsjyxcZXds4Yn3rf6RmkV5kci2bb+o4tBA65n/mVzG2s3RzZjl7BaKOSiQaeBNUksh5wR5Oa1+i2etsXKsox9pRDS5rGMDz//t1WuzVONr3sgDJLMwjM2/5RQ6A/aXq9lM61qpzWEgDwisVWyZvgU7mt/+7rjp0vNgMHOwNZGVZ5df2H8vGTHx5s1/22/68FiM345mxNh3C/d7yfBo35J0BR4tfiAkWLN7Wf1Pb8bcR5KID9rzjvfi6r1cPenFFSdMI8hFsbkQfnEhsAYRJCnARnBHgJqPilOgVz2vaOeQdk0og/7cMqBz6gyKeZX1jX9WKE1jqJtWo1Qtbfq15WgB2qd1BqTheymIdzIm/TbDaAMnrt6rpcAAA2RPypvSJCoKSrqe15VXg+EdL0IefTXr9dDaPk90w0QOax9ToBiA+5otIGQPQldRYfzhsNTNmfIC993WMAiO2kZPxneAC2/d76MkE3M/JlSVn/V3EixB1tcVY7sE+8lI5+HR7V+eJHUUc8QsVmgKj7EBjxvCQfL0yYJe2PvU6FXQkgtTXHIUcR1xX9WUvthnzBx0CL8pINOrXQCb63SVzlAHogzabMoLNikR5CLd11WUFwaOyu2wMNYkEIdsZFkrbtU2Nn+btYfoM/AzgyJ2/P+sHGMeyrrSwIriNECfqaEiiYbEc+mTPDK2NGXCyp/rkqnaD06BegJjMg1JZlteXD/qrmjOCxuu8txv1YEtTn0VG1SsqbI3+Q0WvT41Ivcv5IlB0Hfsip4u40VY0ZtzwSqHGnEwU8S+9wIo8O+aKQnbM5LLLzc6v8+Fsok8DDSD7ASV94JLEnQox9miS5Cyud46ToG3j6Woke2lOa4ZH0Sd8bvQCQlmDoNHxLXGMYuvxHFDX0Mn6QVSy3gG4mTvLG4UcMaHHMnsrInsYWwSLf1z5X+TCOTcqCYCq6LUPTW99A4cedBjg3H6cnkZKAGf303kiv3473CbxbGgs1zMYU1ejP2IDbfJj/ZiUPSCojOpvRytytxXrH043iHn2A+ZWgEoh2gOvDR2zJa+ezwGwUcpr9i7CSTMVym4x/u3VAz36Jefg7w/i3lzmA76n+ob3vezaf89+xrt8nvfx3nPO/8RzR8xP5iY+8o30d4y+iuKjPlu693366YSvL1vrqfXrZSrfgt3PLI2s98yTR5ZI+ffq0OBg9Fy0a7GfHfy1A5H3ZbP+ay2/od5Pwjxb9+x5a2SAEvk+cuz4HgOwt1cMfR9g1R7U96BcAOwdk9ljVyorTQzZ78MGZ0VfKJn6txkzY/A8sDe8VCayT9s4IPWjbPE9KJ6ke+WzocDy+fC31u0LbXOFYqVtebpHjx2PMOXS3P9kg1QZAtbl3qT/mRe6dOF2aEwukYsIH4tr2HgAscqq6/BIDJor5Xaw++hlJXvUwQC+8YvAostCiLSPorDrqSbFXrpDmTRb8A4LHhY0hEYhkM+QFpqo5yJ53kSLv9iJEbq9d32I4m7tIcuacI7XjXoNXL9JDZW5M6p7kDeH5MR5LUODoeqHEpRQG78MmvoJjVTcXKnjbMivkCf3pfaRs0N2qibVmIz5TXyJUz0KhRqnrcYVYkgtafSx3QzpbN4AYmsW9V7KmXREC7G53BkLJNWK1GsdTs7ZJRt4GqSgGiIZZHVCsKFkn1iA4XLfwNFm7cIq07z5fSnb0k4SUSul3c4LMvS4z5KW0OpqgCmh8xpI7B2QewtHURMa/QPEtvkxO+/VcxVWZuuk5qRr5nDqPfvHnj5dK/GmLAyhb/YRVype6pP2JI6TreQFp935AimZZVK7fkLv9kjuKY0f/yxPpeVEANCuc9eDXKDypXoC17UFi79pNVpnwlle2/MOuwCHPSbLokhkO6X4O8vdAUTPoFgDK+yyKU5DHI61Zul3oF1d2nHw1LgGqK6jsNtG/mNs6kizq+2fT6xZZeIsBxlht7qnAtCF/TRvl/TgGq3+VDjOueRAAGL+7bKkikz724Vp8sneeRbZ/DHD7BQAP+ltRbNN5arOs/ov5X4ceFcAYXsiEbPyIgHOOEfvE9uFjXItPC1+D+UhyYbPUbAofS+uOa2vle7S98RFG19bPbx6T613OEFl4ffi9IYhu3GNXgDy67ZHc1g9AveTYdfgBEFbPgWLPIgv+Pdj26QE9ktf0rxrLarWqH7GtvV//qnP+N46r54c/+Gl6GX0v6miwTfQx1U+9hP+t6Db77BcElW2es43z6fZ6qc+ll22d0+11y4zdX0sjnnf8N2D+d8C+/Mwcjh3Yv/7DOcO/qC8nsqk1F8C/6Hx6WNeeGZKw9U21GY8CiYRVj4X0eXWbA1kSAOXOPQc0MBsAahA6GvQwFENOOpCu+25jy0K4r5PEEySZrNGadtDz5feDgJeitiaDloKS3jPtUiFaesxYzdyYOVzK+9whBdOPQROjL72NGXMuAIH1J0A0aeLpcpnRnc+TqPewydVZGkDNQkvc+b5kBgGi8qjBtWUlcAyapXareKzp8GpC+STofVP/kACwaOQh1P+8WVFb3vf3krn6gWDvyIUd70Pa18fL7gkAsqjAbtUCCEsiVK85HY2xm8WOUG1rn0UPqpwJ+GjhR2d45NQlt0n8zi+UykwT3jN3t18D+eFPW9Tc6N3mpbNiXQgcTnvjEVk9D58pV72cePnN0rX7p+qhaLV5Ql287kQpmlYr+RkiRZDJ+/LVJ0PHhkx4QcadcZ+UD3xIPHdPknV/RxU2vIR9brXI3m/qoCpTL2mjUmX+b1JCfUq2DwytNwPQe901oXSGVHhb6cWmkk5l/7vUD4i1qDxeEwQ9xbMt4shpkFEvgwMQD3VWazMToq3brkEBBKBP6HzMoQPDU8jKlhrfCc58tuOfYY5cr1RusMqeOQCYUFrRFc/6eHgZJ2uf5Fei8W65QSDdmjeQvIP5J3vUdW56k6F7w9jebAR5Jy+uQ56fTZbcZqR4sFgiY2QjrsUiZQhl5iJnkl69ynWofv51k/S+sVkqllolc6gfoK9ZskaQgkV/yYNXEjQ8Ce1E6TUXz4+DNrRLUeww93L0y25Vkc1r6HebW+p2OdW98quAYeiOp3pV1feyeyGdCWqdwqleSR3obXO+zfdyIOsZgxCeDRJSs6jHlt140GOziIbUOdmg4SEI3p/Z7Xbh91QgEPa+O+HwTuxgk3qV94nEClAueb2tuIv3N/hP7Dgf/vSWtvZd8xO71P/o5ej50Z7lQBteQAswRFvHeAPs39oncF/9rOyDv7bG5bdSa8f0N0drx3gtbZ3zo/VfyLA++bJsS5GsW7dOunXrxuYhI1g+HJDIb8OYHcQMWBrhIjAZiysOxVjYQXBII7hJ3vrKEQGIKZtekLT1f1bj+uHRI6ChDB3zvg7FmpkbaDLmKdYgb4/5cY7q1eDtG48q2cfomjK1gucmb5KwgESbvWG7EUaFy4P5Z8xPizYWe5C6h9RCjRlDhfyKfuQ68lzxe38QVyVk6co6SF11ruQVLgNUrRQqibBwpTXTSi88RgWUxsyjhdyJDsjstWas+k3c87nKJ2ztOJ++5aASylh5b4QH1Y6CIFYL13Y+P0Iq0Qbpv30ZvxQYvicQris8r0VTFr0k7P4cNEF5RlVzK78+G9MGImyfK6Xr2ilwyEGaAALnvnONdL3jU6nLO15Ss1FlvmtAaPwc6xcSyBoipXN6hfZxpXR3H/WF2DT9SylAoc7RNy+WTRn/kK3vJElO4D0hgGy095Hl7T5EtaoBWjqNXKvSGZguQKnI/G8nKO5FkmqnIgd1PcihF39zhcTn1UjBFRnI54v8yqleZ1VFEiTVroUXmJyMBIr8Tq9YRq8iQsPBatjkzthponchFyKPa0k50tPQ+t/aqIAjK387nYGiGHjxvjjWDm9b5GdUNQ6CQWNdv+qva3xujCH1AbXMHAYt5WAENwke1crlGsAB/A3xhTSgyb9HQFiMkDzBLI25gItApF2+wJiH1XG4puAxEk4f90kDgFz4pFRBoRIISar5TeHeY5VjP6xTQJoazuRfpJG2puhbmype4XY9PKcswMgd55PMIeCbxNw6s5EDi1D80X8N/2Bg2yNlI593o0LcgTxeke6XgcfUhOX3dw5Wi1MthuCY80Ficua60it7sMbi+3HvNahqcuaLdjkPbtaYxWbg/9kMeHyN8t76j+TDx6bK3S/OkbVr17YAiId7y5Hf1oc72v9Af0+HU8S58SUVamX+W22Xiw7prv3xkQCJQOhIWDLCv9pYsVt09IsIhbfMTdBt9rf050+Q2o7kH/xA5TJWIf+QBQlVPa9D4Qpk13LHtwCHHLMSesrOMmoV71SnYD5h1uJrUYFcqwoX9o54WRV1qIN4obRe3pypoSIZFuCwWIRcfJ6sEdIB1cJrfjwD0myPATwgibxwiZx13VRU6U6X6p7Xovr5LuSL+JD25EC+oeEt4O+/JgAoCyhvarpcAkC+vk1wqK+DXIhtGfnjKuuhTw0iUl4rcwlpfESzWtiJnMM9E75W4NdevQaAaaY6rtuENqJW4vBEZTpA8tZ/KG9gQ8HJ4kYFdd6cM0N60zXI16vtciEAMz43JC4MWjMSxIqPeVfqGxfrXRFLJ6h4JpyxHD9b40CSXSgDjnld2uXOEO/JKyQja5HYPkUBR70BcArHrpWFC+6Vua9dosZIz90kNRVIf/C6ZKPcIjXl7WXiObfKyFvnyZIXRgLIBaTLHwZLuft+yVpxu+rDz1zqpr+JO2s0+ubLl688iXAvvmZ24P+FeNgHwZBqzBBxLXbB+UNPmAJS2Jc+EJ5r0GYaEnYi7eH5YrEBi01Iy7LwOiiV1GIOMNbAe9xSu9EqlJ4jbQ2NnqcRfwuDoCXIUWw1VIxzjXiuQWkqV68JfxVqrkWcAKMZAEwNjBdS01ALef5vnUpqcCAodYjb6ZUshPJJLWhTkDUCg2cZ+Yw00tSYTYNDtc80HyQB5xyk9jI+vzxOgNpYHr4G8gpSTpDexagggbo2zucnZ9bKxvcwgbB1TzokfZAfypI2dZzgkOTorVn5YguIrpE+AKqgfiDOZqV0a0alFWo400nb+Rd4b1SOIuYdFDxUyDlY24EfDcwjNRurzFmc0/PXhwbuXDnN0ue68Dyax46tx2bg/8MMfLrxK+nbJVNGDego/Tuny5o1a+SUU045orcW/lY8osP+/x2sGXltRWM+AjhYC3qVTor/7lDutgk6zFW9bgA1y9sozOgolX2NB+yhjGXuQ2+T5gBUlcgI/R6W4enH8CABH43ervRVf0BhxDy13bzxKUUS7U3urrZDLwAxpUf/HXrEjypaHKBIgLMN6jApX5J2vCMVqPrV5kShha6g5r7EnR+Eq4nhrmnG03DR9CsVOOTx4m1DZOfGkZIwsgtA5DgAqrFC3r2M5b8PFYZY4K2s63C60ndmn4RdH3MRYcYjHKEbnKOx42nw1J0YcVxvrINnZDV48mjUyh35xKuouP4YnIxPKtDL/SyEsddvlWZUguf+cB6gRSQwYBttrAi3+OpAxt4dodcUyZuHfMygkby7qgfANKh4tJFvMWXrK0bB0YhXFEhnjiarAAKQPnQcdwKk2xrF5zGukZQ5NKrFlBZ3lvwuC0HC/aCkZW+T7c13SHIxPEr9+8rEL92qYCMFNCQ5o86Wb0bV6VNKZUm30DpXdm06WjzQSP7+3uHiVaDSKotuipNJD8FNZjKek/MYqF1kgEN9zASG1C5sK7USAA0FDrkT+whmzLbrY8Ndx8yOHr9uNMAhGhAsrbgvXk78DuXL+zACuhYGcNgPnsaCSTgGOqsfr7KGcgfJtdhxSpN0Pscr3/8yyBkYHIA5f5teNOaY3H9j3myQ4Y8bYNRTCtqd24JoCe8+eRW7XgDN5l+hqAjyeVqppsW1mHYQHNMaEZgg8LWDtJqeOHrlaOkDoX8NHkRa35saUZlsVYDOihxH7p9/pQveRDMwCs8n6W3ovRzbvkHRIqlBTC8/Xu1SPIbctQAg/OQFdQC5pgbB1dnnJaCi2vhRsWeaTca/E077aNl6/3t0WDq6JeeMHlQNQKOPx7ZjM/C/OgON8B6+u+59+ce9P1NT0Ldzlrwzf+URn47Ib+IjPvz/0wERTiXAO1wjubMieD7cgUz9mT/GIhqGa6tB4OxH+LE1Y3g4Y/nt4O4rBQ3Mr+Cduri1ZuF9AIr0Tqav/VN4H9ZIU8OwcAuAiGMkwuZ+evmizedEIpXJCLYZBtZhYfIFamMumwV5hQkpZVJe3FPvFv/AkxU45A7S5PDPi4plc+Ww+brIZRjtE6otPB+E4r9TQMvphBcDNDetGVU7tO2GLFfNjSnS3OV8eEi3gyz7DXXIb8f4ALIsyokGhzyvJ2M4cLJT8UVSj9nWVKbyN7MWXa2HDi1TNz4dca30jtJYJJS8+RUst8BDO1f9SCkb8JBYpn0EcPhIqH/RtqGycNqVyEeskxn/fFDt/+GLmyXvqHLZ9X17OCE9MvyhOOlwFgozLja8NOQATclAoVJR8L0B76Dd3iDeJgP0dOo1G6CxC8ChAZA4aPkSq9IQr88/SWlgMxeV3JIKvF54n6R/XI38v1TjugDKIjyIxl7ZAOLqAzXmqJmNSifU+3WhojvaSHvD0CXpYaKN1CYpXY0+S25DxbOpsIRtkwqR/zc8IGPfapBZZwAAa3AbdZo1jzkEhd340eCTjqdB4tAKvzUAJo3AjlzsfoSVWwOHpPKhp44FHZWQ3GNImBJ4XCcI8yF8bIPnlB49bfRG0qNILynDxvqYHzKDaxGiNmte6z7mJUH1jJ8nSKezvDII3j7mfNLIImX2VBJMNqFt1RqLrHw4Hp9ng0ycIXUNDtmvfKFNSPcJISNl9EJuRwV1Iuhzul3kPaAwM8nAN7/KHwE8CyfYuF9WQhM8UvYuZrEZiM1AeAY+2/i19CrMkJH926ud/brkyH2vLgg3OEJrMYB4hCbyXzEM1TYIONosmmjlpN6UnlIyygAsrRwO7cpYCeLk2o1qm6DPDS9ctOpJqHFwxVn2Y/Qutd2U2rfV/dzpKpkZcYx5kQw/1na5KGI/gRwLdZK2g/gaVd1VvW9Wxx2VyxSfIjeOPesOmfbGo1LVMFC6XQLFlkknR4zBDYa+CTSZD8ens6tklvD66J3MWn5H8NFjdGtK7Az1mbMUODQPRL5GUsaY8yTjkRPV0GB4TVi4oD0rVHEJxMXDu8fK7krJ++EccUMNJtr4yHNBXcWTOULlcPK4pa5SMjY9r3gsW7QPAkLuZxGTGXAmQuPaGqzOoGpN7rzzxI9kfVfSTeKuyzGGaoY+7ye3S2a79aGhfY0uBQ7VmHCcLfsjAOKZfkWNFI9ioISiaTLprHyZ+e79oLbJlyEgqM7rvASh/TMBHHdK/1FvydY140PjccXBMCrmuXwwfjggtJ+IHwNJuz9ELuensvvYb2UcKnOLv3Or/Dc/QAdzEJlfuP1dDQjMw4XBgd4b76qSRjcS54LW9edF8JB1CGkyE0w0VcbJtncABFeT99CnZPQYap12fKJ44Ck1LDw2NZ010TWvJTr8nDnUB3BjeOHIL+hANXOIHkYDRQ4KwKt5CcuQU5hUCIWaextl5UPxKkdxyB89KvxMz172SF8oZB6PXMAsFKH0uwX0VHwf7nai6hY/jYIR3c2vhtVfmLNoth0fOJRkHZVfmKtpNoI8Zh9w2dLC908Au+0tBx2nMuxPRkiYQLb7pSDeDoJ1eskJumec4grd++zzXPg+wufWpNvN83w1Lkl5UdP7+2X2ufC4Bvkl3Zj7QSDXLp1vlTV/digwyvzQaI1j6naPgxeyFDrXJXPwWQLopPHfGQFpzGIzEJuB8AzQe/gOvIev3BOOdvXslCl7yyqkpqZGUlJacfuHux/UWgwgHtR0HbnGCXu+AOXKJoTiThCCumhLW/OICimyerei310Ik06NbhKxzRy+JIRlmetX32FKqJI0opFpw9IEVQuTRW+bDoVWmQvoKp2ttgnCKOdXjxBuY9bIUBvzSvLml9U96n3swxw1klq7y5DLl3ecPqSW1CumZ5MVxZSb8yFcnvPjZehlWEbeZjnr5l/IruPnG+6ZiN7GBnWRSVidgpAsLXXz84ruhkBVK+QYLQFuEA5mnl8xJPB8SYVqd8ayWwFwPlGgrLLf742CFaCISb+6T+Y8e440eZJl8CUL4MGZiLzBpZjzd8VVTI9h+GEdzQepz8els3y+8u6SnyRn/sWKs5L7m5K6qfeOaQHMG4y0yIekBoe6jQXIwhIPbeFhn0Bx5DK9Wy0dTiSMhUxfozGjDnyPpACgpiFMri01cxdoay5WeaKN6YNQWLRTxpz6kD4sHm/Quxjco4i7g+t8z7TR48mweHWv61Wunt6fM9JAL11/6Q15yfQxEkiz8KRuswHEuX/ChY+IM26X7Fg3Rgp6YN5PulMG1jbKsnvgngNY6zgFMm8g7177F2zD9kwDUM5rABciCLlD4JBH8NlDGJYeuaEPeUKeLeYPDgDYIiE2vYjdAJKYV2i2jEEBaDQDGZmMIDMDuX0azPAQPXrdweHI0DQ/tBxb2zGvuNV14vceJBN9oTTSWWe5QmMs+p1Tso6qhzpO5Pud1tcH0m+EkuElVIb7Xv1ovJy8pE6y3zOApzMnAA8m2iEHknQ/wYZYBPvw8iOHlVJwMMJvaDTFa7/fNUn7k0G8jYhxBgpbmGsYAsY4rr2sHMZc3U1vI0Fxv981hsAhB6XqD724P1zuCskd1m23yImz6nk4wigTyb/O58bJN5MTVFEOz7cGpO06hB/RIbYRm4H/0Rn4fNM06d4xTUYP6BCaAafDJoUFWbJhwwYZNmxYaP/hrsQA4uHO4CH0T9mM3LwgR17KlpekaOwnSi5OD2Vt2K3AIbf5oKWHjxW9EU8c3ZhLAJjc+RepvEhuMsRJ0uJ9WXX3K43CDjw13NljDihkXouwKLkOmX/JKmVS2rRlybivtHWP6scTAIcrBIaYq8dK62iAmAqlkpStr6khXWXzpCFnPPprUIPnG+JhFQMeaBMc6mux127Wq2ppr0IVdBt8igxdE/TWAiBaazYpcMhO9NilojCGCik2hHU7Zr4kXe54SY0XwFO+qOELgNdLVOhb7TS9mK/ZtNvoq8KvTZjD9aH54AHOyR7SAAE48r00X68JZ0QPF7Htrs+I2LZYvDJ88guyaDGqwv3ZKh+OYcQ1f44XZ7oFeZR+AMBFEX30Bue+YsijqBLfIznzEEoP6jtnn14oKXP8oCKxKk8SK3W18bNhCarrcB/zK7XhtsQWKIcW9tPIrawQBwqQOp0xQja/YgA7tmtE8UVjhflum6W+2znSNeNq6TD0eVWMVI80Aeb1sdDCC+9aCkKucy8xCjL0uTY8D+oTgJZo4zVYgJ0UH6HpIAEd9YQJdFhoEW2pffwAiJFflSwooeJI+UKCMYSCUS2riz8IsBgKz06fJ9nFf1XeaHrEU3v3BNG4Rd2jpnBp2G0CnigkopZxz980KaDHit52x/ogV4eqbACtT4cmhUAagSYLeca+6VZFK/Xb42TW2QiFB8Pb7QBCmc+44TmHCnMPutcDcnOb7P2OnlvDckYEXZbBbVLhcG6orkJw60HltDlkrvtxqcGi3sfKYVZ3E4RrIJs71qfulaFybQ278C8Lp41SkNSHEW7n5yA8J5TpGwbnND2cMYvNwP/6DDT5vcp7+OKdx7eYip4dM2Xjxo0xgNhiZv7LdrDaVRs5AuPLF0QARMan+JjSX6ssoGgTHKKdFeFTgjZtzE0DszEOhPPm9DG9pOfPkzUK3qxKeDB77XN83YfXQO9kvUxRuyjjpyqZUXHrTekRakaQk4a8QX39PMAwLLLFQm0YOieFi7N0DoAmPZFnwHsYvgc2VPcd6gHak373SEP+z0x7IlcJrDNW3Ss2AER64ijPx/Asry2u6PNQ4xYhWxQKWZCLaYfycsS8o0qd1+dFERE9s1o9hoVAJNwmuNRG7yiuOOKeuUWwuGXVsbLg66vFmVgl46bcp7yie0e+hrBwqvKocgyCdBr5MUtGvCp536NKOljU401AfibO76jbqNroF589AzyVFXpTeg//QNYvPg1hSxCmWhvleOgcf/nKX8TbYACoQuSdMS+Mf7m5uVJbWyueVWOEYJymr5friUVfSEPxSeLJmyDF4/k+zUU+ax68s/3kWFCxMK+PFCvrn3XIuqfioDzSKPbhT0vOwiuVh7Qh9zhxoxobqbDyw6/gJYNaSLveu+SUX06X+V9dKzs3tJPK0sjPJ71XRTNsCgzxGvgv4Pub+svm46fDs+SVFRfCywfvWf/bUYk7EYTW6t2iLrE3XOSCXkXfEgTxnTS/m9gCqGO+4qKbnJBSQwGGridBS3pTjfZcho38jKRuac2oOeyCUgvz+ggs90y3KZA46/QEqJCgCChxqJx5bYNk5S+VHT90lulP/oH/EEA10yzHfV4vCaD26QYZu5UPOdXwDD2vfNihPJCjno/MhbXg+gbf1ygr/mCEr6lxzHAyjWBz/bO45yA45D7mAvI8I59zI7xtyA+yKnnp75sVJQ5D3kMeDn9+GVCYOSVRVWOTv3HUS25UY6NwxzQmx422ONAO2XEeAtCEds0y4QN4b5FGkNipWQrPJCIHDyE4GknDQ+sE7sW2wCGPUxmH1d+cU1pSF4DVGDhUcxF7ic3AV5u+kcL8ZBkzqGOLyehRkCxbt2xpsf9wdkT+LD6ckWJ9D3gGGtMHhyTRCFao2Ws2RSMD0mlq/1KirKL/PebDLdbZnlx4NvDm0agYsi9wqAcg0OFfnBcPS4RbmQfYlmYzOfnSVz2gdHfpVWPBRzZ0oGnN65+Q4tH/VMoyxg4EoZhcFTQ+pmt6/AbXV6SoXNiXofWsZUaeIbWW+SRR6immPgnF3yqg5wNAqgMwbQCf476MMoAuADptVSCddudPxrVmqFCzJciXWJ9/IopzypATaCT1OuAhdGx5UXXz29NU4Q3ngTmgOQsuV/0r+qLyFzKGzE1kxbkfMnhmgMdiFyZ/pWx/U59egcOG2gz57O/Pi99neMoaarLk3N/hPtC25Jh3lPa0D8Crvt3PEHK+RIWgKTdIbsnkTX9X1DzMD63qcZW0Qzjc3rBDjd+E97h4zPsKoFO/OmnXh1LY5zs5/9bjZXvNhdJu4E7Zseq0EDhkp52oBi48E+FNzIP//XOldiF0lrs3S9nAB1XRSzK8txoE8/GctONtBRD5GXTnTVTn5QuLLzKHBuTLYxJBcG2glHlXuMC3N0yqNyyWDtAH7n876F/QduNLDoBD42umaG1v+fq1J2T7uvEcJtIANAbc5YGmMihPfh+v8goJEGkMG++dB9WWoCdqwbUuOQXhVc2zV3iWD7x/HlC6GEDLGNjoa6zzNQwWmSNHT5kdXr/WrGiGVXkzCagUzyBAaWu2AeBYX2NdjciK+8HMubJJgUO299Sny/LZF8hxv7hD1s2coMAh9zNsu/srm3S/xCs9LqesoF9W/hEFJnNQSQ9gSWBmAbl51tF+qJM4FXE0w8cD7mxUVDrgf0eFMwA6vG0a5GpKHY5Pa6pEsch7oIoBWJv0db1SNOF8DX6AoBDFKcbHUbXly67P7CECcRa+sIKYtD1mY1FNSnd/iOcxczj0oF8FOTemXYfTU1ENn3pL2KvM/iTwLgJ9jxUUQblj/eYhW6zTKzr65Qa8l/FidTWrSu0WjWI7YjPwPzgDPrje31n/njx5y/hW775zfpp8vnJTq8cOdechAUS32y0uaP/F7NBmoBoPe1KbqBxEeFkivG/BIWu7XmrQvPDns3YVtHU6eBj3Hv2SpGx+UXndarpd3lbLFvvtNRsATC5UnixWFpeMfsugUIlqSXCYAPJkGqukWWiijWFw195ZYYCIpw9VVdJUxTNIertcKnUgkaaxiIQeUtLfmI1hcU0czv3GI5lAE4AEwFIVteApRPJpektZEWwuImEfaiubzQtvl65iLhnxmsoXZFU3+RVTNj4XAojmPqQI2jP+C4RC/yaJuz9VhyiLx7/isR+am2Ku/gkv6KcKLNa3PxXXtj0CILIxiz00OOR2VWlnlW8YYLVvIBnAcDJyLfMlGcCS+Yk0G8i/Gd52gUeSZt/2GoA7wtpjPwYQ/AggDlJ87eHFxeeC1fTMrdSk5Fn56yW1+wtSBK+fIw2fnb+oIdQLqWxo8d89Jq/f+IDUV4MSye6WU667UeyX3ISw9rLQDxe2i/7hwn3a4PiWhqIwcGIxRQn0gWkEF8wnY8iVYVuzueszzZvhdXjWph2XJGNeawD4q5cvRiYqbx8bMGzJ0KM2hjAZxtUgZxMqYCPBoW5pXoavo/M50K8GT15rRg8dr5+24Tm/HAey5vYnexWAYlEKia+ZV8fQr+GpjByFXkqz2UE3VF7cTRK6AJGtCB9hGFdbWl8SWZvdZHGy9Z92FPdAp1nlCYJa52UH1EV8QgDGqmp6KO3JAXHiPliJ3Pu6RsVzGD3fDAWTyDu5iw9g3S4rH8R14BIJ4nPH+KQ9lvToMf/TbCyAKcD71/8Oj6JBSoYnrwY5luR7JAVQB5B5s+KawQ1WXVPuMAkKM6Sl4T7a9g9sKjeR1zf4AY+hVW0c2ucrib0JKmP23zUDMVzwr32/vt06W7Iz4+W4YZ1bPVHH3BTZ/eXyVo8d6s5DAoi9evWS6dOnS48ePQ71vP/b/RAuJADcr+lv2v02xEMrqbNUDHzgAFpGNkne9o9QmJMePvIc1iA/MdrMHIU85gf3ntmaUvqEN5ET2QiZuaoevwUYZNHM6eFjwXsiETTzL7WRB1F7r7iPjyvzo5bhbFfR1wBC72J/AJ68ZEUQ7TNpWDMv0g7yavZjf4h94dUwAkVyJdIzSI9YLZRLSL9DzylzCnW42OcqUNQz9MiajeHVaOO5awD2tdEbGx2+zi5YK3mdlkrx9sGqWY8TF8neES8hLaAY1cfnq+rq6D5sGF00xFA+EVFdp7P16ZSHM2HPV7hZQ4NazxfD0bQs5IQN/ZNbeQ4JLPpcD6/Uomtk7eddFDhkG5Jgr/7mZzJx0A34kfGiZC0BATq0rTmJSTvfM+aq6yVsGmE8BUPW2942zkXSbNKSaKvZHCfZAHVdUQ1MbxnpaBI6+KXd5ICUvQzg6A2/N7oPPYQbX3TIY2hccAAAQABJREFU0U96ZMQzbhVO9cPb1w98f8wBJEiidT4XOZwobK7bHocKWRsAXRCR6IFaWVrtHjnuK543ToGsVpqoXfS4aavdQo+gVV1P72ualFY0ARlz9ZjLOHOqRapwXJsrqUxGnfm6zLNepULl5F9cNudyKMlcpYo6LAjdOgG8GC5vd1ykJy25hx+AODx/rERuisjHNLZJ20NwSCNZOKu1aYtvdsm4txtk0ysO8RY5pXiBMT69cFlHQWoO78WKBwAOg97QHR/aVTX0jo+8kgk6HwJWvlc7P0aVOboyX7GpyqlUV3pc5pX50N/WBTk7PnRIx9OpUIMfCQjBf3dWQohuh/J2JMtugkd18a34txYEzItuBK/ionrjYmOv/y9n4Pbbb1epK7feeuv/y/v7T94U5fveWf+u3Pub4W1eRl5mkpTsLW3z+KEcCH8bHkTv9u3by8qVK2MAMWrOrJ69ADHTFP+fJ3dc1NHD27TV78DD+3pFOt3Q/ucqHy8U1zmMoQMIqZpNbSOH0EoJwdQgfx0a1CKsTGocghAvOAupdEJQSo1jd+4EKKqE75fXSWURbckIVZJcvJnul6A1oiK6ZMQrKreNoCxj1T36kFpSQi6eoAgeVKqh5KIgxGwkxabOr9lbGgfXDq+PxmXGGmgKw7NZAzCesfKuUK4dlWFI/l007lN4J/eq4hdWPceD07CeFDUAXDVdL1fgLR7314Dqau5rN2syPL9pCPnfbeRtqjOFX3h/DDWnwOOnzWL1y5nXnSlbkYdozUoW2+V3IA3NIukr7w8RgxPwmo3gtwaqKVnLbsF9EOqi8AjexUxUWFf0vVN5DBkKZ56izVQUosfQWtHcLpzqU39cZ9U1vcBJaeH3gfuT0oolvhqgEDmrZcOfCYW6eSwdhUYeVKl7U3tzUxnBKumHjr59JDxRPcluA8QkMg/VqvRg2VxNIBV3yronLHL0I+Vy/LfNUoP0yQVXJ8jy5wfizQlI3uhKKZ6bHhwxvGDeHC19QEB5qUhdg+9GxauXWAgd4vyA9L62SRV7zJiiSayNPsYokFY73y1bSePiD3+9DTv3K0npNFr9QDDatf6a0iMgpcEiCaqmaE9fSvfwe0QPZhzC0wPv9MA7B6WUFU8pEvGCbvMlr/o5SSq4GB7PFPwRPBoAkp48ONpRhAJNZFRem23LG3apXh8GmjzGMG+fa0GEjUpgrid38yP87lO8jua+ep25nsmQJDzqCY906pQr3z+xB4UuoDGCh7AKhTFLbocbzzxNwY4ls21SMtv4V0MvZCd8Xja+YADxoul2zKNf8WSaORLZVW+Tp1BzMXL/XlDV0OhR1uCQ215wPe6rOIVtYvbfPQOdOnWShQsXKm3sw9H//e+ehX/N1c/d+SO+nn1y8ujubZ4gM8UlVTXBX4xttjq4A+Fv0IPoxwT3qVOnSnZ2tvrTXR9//HE54YQT9Ob/1JLFHnnfn65Ckbzxqh7XhPjujsREsOpZF6IkwYvGvDTSwtASd7ynKpcZbqxBOHefWeBRF0PwRFqZ+MolasxGcAYWfAswg7ClL2ekeCa+rXrUdzxThRwZ/iTdDelkzOBMD0ttZDM45H72cVSvQjHKUbqZWnKbf/HgV9TATjdwVi2HZ88lRQjj5s+dqndHLOnBS97yivJEqjxMm6o0CLUhibV9x/ag5N+O0H56xjzZo8VetxlFL5Ol2cE8zGpxsmgGf6mbnpPqHleDl/ERgBkk+1csU1yDBljbLpkgGC8e80FoPPNKXeG5yFVELl9wJ6X/JClFcs7Ixefht2Kt3y7ZKOSwu3ebu0WsEzA7wG+owaE+mAj6HRe8nhbEV1khbAaHfPbrc3pTwmBO92X6ZfmiPEmA/F2PIZ/CozlINi2bLLkdV8iIyU8ABB+nmyrPZGgDK/wsaEjjLPlOshddqc7VHGeXOKQkaCnHE7+rl72flcmiBwpVd3+TA14ri1yQeabUT+sjtZsfN4YFmV7zzu2SmGeX+uIk7DPCpBlD/EJPHW3982Hlmm0It+q7q99mRa4fprQQXuRQ+Bq8l+2oM4yiD3jBcl2fyZbXz1Lj8IVUP6OGXy2+mdmyd+Q/IgvCcJwqJTs+wr0gD3IIKHDWPhGvABB5AaOrmheiwGXH/7H3HeBRlen3byYzyaQXUoBQE5CONAEpAip20LX7s69lLaur6FrW7tp77xXFta5rQ1EEBaT33kInIb1OJjOZSf7nfHe+mTuTCQQM/HE37/Nkyr3f/e6dO5O5Z973PedACNrsCS1yK2aJlO3rxiIz207WzeHnUL8beGiKBpTSCa5I6GCQ1cyey9DxLNGzN/Gk2Q6p2YmsZ2/DUYXZx3UvRPuZwmoS3DA7OQ9kIHpaJz1WD9tBD4A1RLuBa385O97opdSDg+4Dx5k/3SrUMjQHgR3jiKvdyieZ/ZuJPdA+4PO9Th0I5jL2rUEiy84Mvj6W8gnUGb1ucO/P15LapvXmj3UG2Hb27bffKh2+jh07igaJxATEBq1x4GfgM/Qe3nDeQLFYAv+vobPF2m3i8aBaUAf3IejitkQcEEC8++675bbbbmu0/06dOjVa9r+wwIqLeTQMWNmnpkNlt7pfo5/+7vsIcxMWZiOxhGEv+EXaIDvGoEUbL9pVYUqCakCYm3rU6oqGvu5fk7YYIAaAgGEtnCfW7V+LJBvggXqN4TQb1VjHNlUWZXbRzPjlOpZwPXFd+DAoSEpJX/AXgNPF/jKvGehY6tHrCiKJBz7VNucu/7YsyVKmh9qB7ZdOUstpSxcZ1cY/xvyAxA7zvHT6SF9ygxpCaZ38ER8pAorehmSfNivvRrl6g3K6yVh0VRBYI1u6qbC4SoKgQT0IMnnHzVDD7UVzAQ7/otjVTW3P5e6EbiihEzg1DoJDBkXONVObz0l8asA2HpBn6E1tDmaXfp4AQsmuHjLLOldOu/ovcsylH8iAZ2HzV5wrDuskYQ+lDrY/pCpRcZSNQaCq1cAeaIMscf0VFQGl5dg906XC5/UdAwZq215bMU0XPZVUF2fKR5NeUb2X/oV4EJdUKL1Hz5aytleiXxHEhp5AMqYoQeYsEHqPxhJnbgUASlxgNR55qlBCn1SnsqXx8xZDNPxYiIanqTGde81S91ZXEUTBp8iGsrsBuizSDiCHDOA5l8X4e/0y0Jc3enL4/reSZSB9EBwyAPQCETjWbTsmhqwLjOKjpF7eIJu7mjzOE5iLZJMe17ghBG8AZR6flsTh9nUguGgZGT7XUV8La8v5SOMivlheKcf/bKyhtA97JpsMgGL9WqjrmAOGO9nYlJqhGwq9lhntjvWBVcjUpPStZ+eICjKxx35e43dPyQaA1THoERdeB91UIEzekf+BjYPHtuEVtKHg/eiCfWmdzMYjW5cc7mfgpJNOkiOPRIUgJOLigv9XQ1a3Pt3HGVhbtEHyqvPlvONP2etIDR7r64O/S/e60T5WHhBALCsrkwceeEB4z1+pXi+YbaWl8sorr8hZZ521j13+d61us+zvcI34DgCE+Z6AVZw7TBbn97zyStjmkURAH2NXUl9xthuvpouqWBs0bVRl8POglft6gveSpJWggFD3voLs1+S1j6nLHOVaio98TOkcWtAgVYcydAV6EZnhCw1m/+wAhwwSL5gVcyUPlISdn/qHsncwosG4WHIhQVHRUa8jAzgC8kCL/OP29UBfgl14X6ys8/kiAqAnE+zh0GwdV8cWzlQkEM1+1ttQs5BWhpGcBx3+mghjrdkJotBbiunMbXhJrMz+s94MDO73G4FDvh4vgC2BvQXuKA2YLz7vW/FCVoZC5Czhq08W6pMEyuasYUX365CZXaPkZ3iOCfapI7jjA6vEROfLoPZXSZR7pyxe/yTAIYALot4TKQtWvSYdnkpEVqlKahKH+I9PP3CAOGUrXwnSzELDHpD1YwTPdygRyB0i8m4f2lsGn/qBLPmOpCTjrJcXZavteWOJrJV2XZfLrk3DZMuqE4SC06PAhA0NaugFtAeRPbO6QfZBmRRntc/46ZI24VTxrJwnKyYPUH2UddVWWXJ7pORN80h69lVy1o3ny9r550hMQpkMHPO2f/oV/x4vi9+JVc/tz8NKD17KmgjChYUg2mz92ApghHqwKSgDs4xl2rDBd9p4rZlwb4nfDfFu+DSHC7zdQZyzdGTcWD42SCqw3oPYdPaFAZBlnmPnN1bFaI5Bmd2ZZ7wn5vX6ccUWSO/geKGipIg8PZG9Y1aUYe4VtWfWK1/xojlWSRvuFbqzkEV8MrKWq5+Kljz0ji680Q45nFoQUBoUsSccuYckm+Q+AckcfRy8T0TZe2+x4oFouOAYaHPX91Y5YbpD7Wtv27SuOzzPAIHgU089pbT4iAkIVGphXTp8+HD56KOPDs+D/gMc1ZebvpKrzjhSKIa9t3DXGdn7lsoecl9732MTR3PttdfKJZdcImvXrpW+fftKfHy8TJkyRf70pz81scV/52IbsjgEhwxexL0ou7rArqUHMS/YLRkUpd593Ex1gVY+xbzSINj/x5IoszmMmrYnqPsDuaGnsjlTVw+DVU9nSLKUV+51usTNb/ouj+iVK5qNDNaNkj9u2l634cpGgtbIxLqSB0idvT2EmfMUIGKJ1oo+QR3UNnT5fLBdqYOQ3RoGILMAZx8MS5TBkza/poeGvaeEDEWqzRFJFeIwwT5INzQiA5d/YxD9p1ORYaQjDMMLPUIH+kIJ4KPLl6tlBH4FYJa7TWLiocQe6isWDHtX2qGEbgZ+nIC2fW7oMO46BU1uaN4ieKRWY/qSv6Fkv0McWaejJWCc8j+mRqEVZJvCXk/IzDM7iWM7wUNXqRp2tZx48S2S7v4czw2AyLntYXyLuVxHPFoWErf/Sz2NQhme5JtqkHpCJZDIvnZCrsgc7MPs/MQJsn5eiThKjQyeeX19fZTEZVbL7s1ALwj2qa19JkoyPgsGiTmX1gHMQLNvWqlsm0omuAHOsvv9KB3/D245ngoZPuYBWT3lUwUQ1WTI6uVPt0m+dJe6P38gQ++bis/TceJas17yF6Ds0iVdNs4ZpYbyprYAmWj0/rE8TYFqHUvvsqO8W42sl15iEC4q1gcyhQSqBihskE4QzKaOIm3hKLpNVvAuaDMuQM9laNDHmCVtMnsZBGTUlSyaC4s5CGPn/QgHmKII6Xk9SrImjLkeWoxrnjBAXgSs7rpe5Ma2DWBSW6UaZBpzdBhrVSQe9m4yeqNns/OZEAIH5uX+1iNjRwlPlp9z3zXmrAFBhmVgzlm50SKbQRZiUMZoxf12GQldxOYGSS6rHotWtnnpkOrpD8kjnXEMnYM9jDr4WahYGwmAGAzO9frW+8P7DDz77LOwI62Rq666SpWar7zySnnooYeE5JXWOLAzUFxTIvN3LZE3J169zwnKqmolMSEOZejAd9k+N9rHgP0GiMwYVlRUyF133SWTJ0+WrVu3yo033ih79uyRH374QU455ZR97PK/ZzU18cxZw3pkgmjPFg0yAEkeHltCi75YejLXhcxJ8sCe0Z8rQWdazLmQdTrQsISUsb1JR6gM2b7m80KyRZeleT5IoGhOkNARC0Fmnd0j+EuAdZ0OgqZ0sGq9kASKpO8Xglk19j8ySDCphLtLRberkIVLEw8EsdkDyQxcUxEBAfFQUogeS3IIyS86KCXjgV9zQwREsk1ZTApUa3DIsZEQq07c+p7KAuptDZFu4yKrl1XB95kWi2RMe61JyK5mS9avJwext/VY3vNcGoErPY7bg9Iz5WsUzRQANGPeJehV3KyGUOza9uldAIdTfNtAGmXFyQogdu0zUwZPnCLrllwgyd0dMnLCMxK5AD132ZdjbONSNsGmjnpvpOR+liZFDVHSeWIPie10Ad6jz5G1TJfiQc/oYf57gpBfL04DOPRd+EFIiU0okprKTGMM+g/z94zzj+cD6iSufzlKgSISXhbeZFdlzmSUMjM68sdBln983tbhAMs/SNb0Y3De6mToiS/JnK8aX4CK16crn20yjn945G0Ie+N4IFNDdxF/oLxKUgpFoX8+BWBO6x0CaK582A7WN7KW2H4n+hMJ3MyRiJJ4D/TlUTooGf2B5iCzOG1YvfRBNpBaiQSE2p84bajHDw71NgRtBGsbXjbAGokjm6Ef2XYcxKzRE0lu1x5oM+poQB9gKnoFqf/Y/x9gHn8bCWIJ+hKB4bJO8cj4R9HWUIi+AlOYS7wU3Gb8OB4T+4IM8hLoL2ad6BFngf7cGStDn+ttmrrf+rHNLxVUCVBNO8YjrgqfFaXjC115GDZ4XlduipBd31HI3KP6KJvax+G6fOu/bIpIxPI8fahDe1gP1+NuiePKzc2VW265RYYNGyavv/664ink5OTI008/Le+++25L7OJ/bo7vNk+TU47uJhmpcegv3PsPp12FldK+beNK3e85acHfes2YKQJadEwlEySy3+CDDz5QW6WmpsqOHTuaMcMfYwjBB8t7dI6gKHK48CKDUt77dkna+LLSNazMuQpM19OUbRpdQAqGvSfu1IHhNm3RZSxx6jLn75mYYsiUp4mGFzAzcrxqRdTkY0rjwtXU3KVHPqxs+1h2rez+F/HGBi7oehsbSuHsV3OjX02LLrtTB0jx4BeVXqIWgNbj9b0W/6YodR3AmtL/w0qWOzMWojcQiAIUB8JSqYtBYzRAJf2rqc0YGh4A+Jr2J0ps0a+hqxQYY8bUgrKrDjJ+05dNCgKHXFfR7UpJhY5jI6Dpy+Lu3Hi0TJ38itQ628iRFy2RUSNvULJAHmRGtZxOJDJg7BndW1TmXAkh7NXoW7xG9bc6wSYuOupVgHYDeFpYRzRFatJyiYqrFbfDSE9ldkL20RejTnlEujx7irT/5SSxVuaJrEH5L2+OOEZ8oof479k3qWPWf+6WZTPPVk+3Itt06b3oORx8FeR9wmfIy1YaQs96e3sKSkwPbJMZN/gAIlbQHYMkE7OOIOVlmDXbDvmV3d8bqbMyZJciI7rpqdS9NcECC8dnFTjkgqPGvyodx+TJ5vw/y+Jn0Ifpc/4g8KiFdR8dXxQ45GAAwLpyr/QeN12q9yRKtzO2wqrPyKwSWJnLwgXQIMybZkrhcXtTdD3PDTZy48/YlikQ+r4X5x8gs+uFbpm4olqV/bd8iBYCTJeNzF+4qFgXAIBcT4HunV9DWxDnihnANoPh++wTHCdBJuXIACjteJpXOp4WyILbYoIBXrj9cRmJL1p/kYztlL4GeM4c7cU6r8rmcV9HXBX+mJual5JG5jAy2uYlgcd9bnUr2SH2INL8ae0zxmeXwt1RKTXSbpwJ0Ac2OywfkXG/9B/8ruTnDu8nftcNfxm/Dv5HIisrS2GA4447Ttxut5SUlMh/Gy44lG8lhbGn5k6TKQ+d2qzdrt9eIjk53Zs1trmD9hsgcuJrrrlG+vfvL1u2bJFt27bJeeedJzNmzJD58+c3d7+H9TiWjjN/u0D1+9XDJqDw6PcBFPuFPWaKLvOPkbzuaQUO+ZjZjTg4hBwKgMj9/Z4g6SJtGcSS8bqdGWNVXxvBj63gN4mYd4tEd75c9dUpS74wO3KD+UxXD2r7URw7dtfXOCcX+8uPnLftb+f7QVtp33ug53eBmol+zHXIjLWdczayd+zbs8AVph1KzLuD9hQJMBSJ8q0dfzVg8hLAa91EgkOGzbkzaBv9hMQNd0JP5R1tqXOorCPLsvwi1/Z1qkWAmUq1lNd3XJyRKeP7aA4vsow10CKMRpnacIAJrOVlubzbdfL1nbeIu8oAccveHiJHZoEdmrEc/WDBrymwpbqWSG1Sf4mh3AyCQuQNtjj0cj6nwCGXMUtIkK3tBtmDmAaSDo+dEZXglQm3/UNWfT1G7LHlip2sVuCmpt0JOGcFfmkdLreVrVKZScrbkFzD9YqExBqhL7avHaMfirs2UfZsGyg5ia/g/fs/gPEi9EyCgBTfxT+GRBWzf29sp2hJOa23dF/hFrKR4+GdzJIj9QjprqEJElUQYS5EmZVsa3PY29slvsQp1Tti0LuHrNw9VuXrzTEbl50iq367UKx9e8ueual+cBiVWq8yWCsftIMUEgzikpM2y4lnXaV2wRLsup+7iKc9JJVScNxWsH4hR8PQrFz1pNENtQUDAM28ejnKsfo1bZ0SpcBVfOcGPzvbPNb8mKxgZlF1plGvqy00jqfPJGg/oipPsepOp9cpUMW3qWob9CXbQvETSXW+Hvx+bxTMgi69E99jcyJVr+HAf9bKgutjlP2dBeVqMo97/tWFTJ/xOWL5m5Z59IJmvyP7D0ODcjZL/m6H3iFsFm8GGeXywP8JZXxyAYhJpiHwpL1eU8Hj7TjReI+YOTYHfyAcTIBYMBs6lysjVbYyNAtsPo7mPq5WwDjwBuwNGDd3zj/SuCuuuEKOPvpo6datm0ycOFFOOw1WmwCK55xzzh/pZRw2x0ppm4w2MTK0d/tmHdPiDYXSd9ApzRrb3EEHBBDJYCZjyWq1ys8//6yyiDfffDPQa05z93tYj4vb9ZUChzxIEhLiYGPWFEA0vxAyeM3hMT0nY5eZLl6MD7dI3vAseucMYBKX/33Q4UUWzJPMvBlqGQGJsq3b9IoiRJT0fwgkjgH+8Upr0Gd1Fw2ZmjwARy96/sjeNWf0YtDrqAEiN6auHsu7ZO7WZBzrJ6nUIxNoCZMJ3FfmjXPykuYF0CSZhdqBbVbcKSnrnuAqsJ+7K5kaysaQrayDFCP99W5BNpAX3NCIwK86emeXwp6OANV8vghud5Se7weHeluPGzVEBiRzXLDIi4aPc2hwv9GOXNkzbDKyljcDDP4m9tm/oQeyV9BQ9jbqYP8f+xxTIIHEdgdGJ+9n0unyz/QQqep0nmo7oJwPg8xvXZZ2Q8aIn0e62CigidfjwvtZ0vc+5cpDQlRWN7zWgu5qWytcVzI78nMCJis0JnX/bS0Y1OXQxeT/CEHGkKdqlX8xewgH/BPoBNH/Lpf642Oyqgl8SEQp+MXGRQiUFr+1KmcQkifYH0ig1+M6tyQAVJYuq5FY+B7HdWiQspJ7JGLqqzL1nVfwHuF8rDdm0Lfu0kAGq2KdVXk1l68FcaedV445/zU1jO/t16+/A5/skXqzpu9RmvaXn9UoeJIDDISCCr4uDTD1ZPg9IiSXVG2xwJHFg4xlMLAs/C1SNrwOn21I9Ix4B4QZCH9vhA6hlvBhPyKDSWOWXTtMACMY54Ds31/Pi1WZKoph82OBjxdEqmulyy1678Z97geGKDaf7foGlpYYp72Rle0gStO0TjQHXWrSQVxpKpbebvfbLK74Z7S0R2majGYGGekkm9AykCV9Hm9zoj2yvrSDVIHMZduxweC+OXM0dwwz1otuNv4v1z4XJcNfdaIP1KrK3N3BIA/p5GnWtJmjPOhbrVeMbG7AftT/pejdu7ds2LBBydsQKL722muSnJws55577v/SaWix1/rdlqlyxZnhE1PhdjJr+U45/7rh4VYd8LIDAoj8pUCA2KNHD4iydhbK3vw3hSekROqJ6dCsl+eAXzCZrLy4s1eObh2MhNx3FKuX38ylfe+Fs8hZzZrvUA0i29gcZNXqnkKzP3LCJpJR4OVLqAtJn7Rlt4KQ0wHkiWKlJ2iDDZ4O9uDZkfGyoxQcgfl5idDgi5IsLI8mbn5LLGACx0FUWwNI9rdxW0Y4cKhWhNwoPT4COizX++B90ZCXDIFnoIFYuLDoYNbQ6tiGTA+04rAFXw8JF57E7tA8XKKH+efyL8ADSu+kw40kDySckoFPKn1HG95zBrOuEYVb8Kires6b6NgKSe+wTh0bWxCok5gx/3JFrPEP8j1gBjWqemPg3GM5j60ezi+Ut+GxUm+zDbKGNhy/A4SkpK3v4VxWqhlCL8MEeg6T8woHFR4Ny8Edn0CasY1UdsDnEGW9xM1v+LOxJNrY0Me5Z8TH0n72RBl71v2SmLpTKks7SO+RX0p8aolU5FwjyZtfVfv0QoyaPbeZyOTlj/kPsonZQt9g/oUL+hzPv84Q1I7PDgYg7PPTbixtx9XJMJTn2J/HMIMVErZ2odyuwKGxOqzNnG+VZIwCMHy7XqoL3TLr5rulHDqWWdnzAQ7H6yFN3tN/mDZxqx+PAnPYAOeUoskAGAgNsobt6QC3PqcTgj6KTWsG8UZoO57wk8MPpMg4n3tljD9bSVcSSsNs9BFEOD+3d+x0yeonowHuCJ7gWX0P2Mb4PaBL516n/tQjUwim9Yhrgz8JLpTazRGapdWWheYx+3qsPKr1IJTu6W1t/CwzFlIHMfbUxudIbxLungDalozMJYBl5hiPpJrK6OHG/55l5p5SgvpFf4vxvQZYByKrOPJtoOb9DILK4751gJxjlbhO9UoOaD+n+EMPf/nllxVJ5eKLL1bJo7/+NXwbyh/6RR6ig9+FNqANkCA759jjmrXH5Rv3iNuL7wZUdlsyAj+192PWE044Qd58802hGOYNN9wgS5cu3Y+tD/+h1R3PkcouF6tsCx1ESKZoVuBnfEXPm2EB97nKqPBnP4FQ8vqnFehhuTJl9cMKmDRrvkM0iL1u9WDMMsjazQfppXjA4+IY/Y4CJfow0MGlAIt+TpkXSqFEObZI8qaXlQafXlcX20WSN76ILNO3quePZV5H1kQp63W7EvOmK0jilrclniQVc5bQBw71PKH39aYMml5XB5CaP/JTVe7UyyiyTXs8gvP0BVdgcXCGJLpoHvyWX/a/HloJ1mWOVBlSPUdT92SM84cAAb8bPZvmyDjKIe3GGnVS2ruddPHflPNM/pjvFGmCoubM0pX2/ocUDXw66PyuRFvDtEkT5Ncv7pU6l4GMPNHpChxyHwSLKeh9jIODTBQsBVOg4ajBofkY+NgblRJscYg6YwSQAaVwKJnk7Xc9KK1AGQj6gpujHrVMlo2Z1bVG1YII8rIce/HD4rnqFdl54iLli80e2wU/3CAvTdogr92xUhb/dIV8P76tfN0/DhkwIwvE3x2b30XTPpxCWOZk0F1FZ8fIvmVPHr2Oe97g8oNDjiuCB7EGh3weGiyN4oPjX3zE1S5kf4LfY65MP9rjd5KhwPSuXzKluqydbFjyJ/4C8W8fADfmZWgbyXFIdtm1fnDIDYjHZ0yMA1MXmT4TBmK5dAw0AbugP7HzOW4Z95VD9rCM7guWXIsXGCCTixwQwDaXslnSnX5SnGIY621Yrme21QCHXArWNyRrPE3gF5J8vO7g19DlPLCsYfvHIBGEfX9dzgVZC3OTTNHvDt+bo0Y076YP7A/1+WNJeV9yNs2bVSQTYJ5i6QcTHPJYgshKeG4AXOMoi+YH3iNjSfNv+a/UAf2s1Ir8X4vRo0fLxo0blbLJhAkT5N///rcqMf+vnYeWeL0kp5w5tqckxDav4vjpjHUgCIP/0IIMZr6OwLfXfrwq9hzyLy8vT8nbkNZOhs37778vAwYESo77MeVhMZQXfWZo3NAZLO9zZ8scU7g6ZcvM3GKzGBI6M1R/Wh1KkNTpqMmagIsJUiIEET52szkPwUsQAYs5CPQKhr+v+gOpj9d+1kT/amoPFvSYJPUxGarXze4rRfsH+B4QjFPbjyVvLd1DgkoFQA0t9xqQ7khZ9YDYQaTRQaeWBAggawIIlyvJGwCoBGTLQoOZuDbrHg1azFdCGz2+/6FRkzpUrHXlEKfeqFaxlUC3HLB/UvIDW8SWzpGxz2WI/T+3SExcqUTHVOOCTcDVFT8OHpSE7R+rwQRwe+ASo8/hzo3D5ed/GSXwPXKV1NuTZehV01EiPh8Ae3ZgB3sB0Hx/vHCTcSf3gczSjXgfDaAWu/sbRSJiSbwS4u10iTEHbRMjl9+GrOpOWCoaJWk6pjjbDEev5Rr146G89234LBg/IhpQlt6e/pzM/dZ4f2sdUTL3m9uhe2bsb9Uj0ZI1tlQWXwff5c0d1K6YsaHeod3Dc9jfv/uOJ1dJ2gj4BwNM5k7GfYXxKaNo894iF57D5rJvfBeA5/5e6BgGfvP2uqkWBA/8qPE15ulyrZ43CmCJgIplwZp8/PxR++b+jU83xxUvj5d1CWOhw1jrl9rhJ796a4RsfD1aldRzLg6UEtmvN/ixAOCi00jZCgNwEJAlw0JQR3KveuVKUqlt9kwsamNMg3Q8A2LkFJkmmPWJc0chg7nuhcCFw3Ay4RaGPI492SJlc8AW/yVS2sCdhj2GY7+okXUvop0AXtIxKOEPftyFzCjkZ4y3TB9Ss++p1UihcX41JGTzfB0esefXSNVvSeY4gS+Z3uGi+xXGe8b3JhOl7LXPRKu2Bo7NHBl+m3DztC4LnAFmr5g4evHFF+Xrr79WeIAKJ3feeadcfz1+kLZGs86AG2ytH7f+LF9djx+xzQiX2yMf/bRW3v/gnmaM3r8hBwQQ9S7YgEpbFwpiRkUhW9bC6FXv51Dcx0P3LWX1Q+qC7UwbAXeRN/B9G7jYHOgxMGPDrGLS+ucwH5qi+/xDAbADne9gbdeAn751IZkk7svT9UyxbZoctFsKYTMLljkPpQQQFXSQWEGwqQLAmLZ90QB7RqAHa8ZYMI2zFIDUZWSuY7avaOhrKnvnSchRwy1wRlH6jnCMqcq+HOSJI8RatVn1EtKX2hzsF7QXzjIvQnn7N5Rrc4OWUYeQ2TIb9BZDg6SLhuhkP2Azr48pXWS4zeA10bGlBmxvrQlI15igAIBjFja5ezp0EY3j1BnoGNMxUgYpA5lND7KcFJ8uye8ZNE2+408oYZ+kllV0u0ZlW702ZAWRhU3c8haOE+8NyuIO9Gwmbf/Qv60XTOyiYW/7n/NByppH/OA5adOrwgw5LoNiK5wrsRU7lWUjs946YvaAve1zmuGywiGvopdzlF6t7h2pxwY9r683Z11wdL98DHA4yT+G0i3MIkZHFvqX4Z2X2LgCPO+oer4oWr0J5VUCNzKaQaKXIpAhwIORtCEBwMh5QqVX1j0PN44dPAYD3LUZ7JHucPEwB23qCFQ1EcRdZlGrKVa9/QszUjJAqt62YMeRMuK0J2U2GN0EYeYoWRIpxYsixRZvEFFiQjQmCVJIgClEVoq9eXxtDAKYrSDtGL8fjWM2z2s8hug5jqvjhDoZBDC3+ml8x+Ilshdxy4eBzx3nOHkusta4Zx9g/sI6+fl0ZKB9gHLoS05ZD0Cp5WSK5lnl2K9qDhgc6uNUotkZ+tnhcb/4Frs/E009y/YnVCtNyNCjo1Zlj7/w82F8RgiimemmnmXOpfjV0BoHfAYokk1swHvyFPjXGs0/AzO3zZGcDinSv1tmszaaMm2lZHfNVpnbZm2wH4MO6J1777335O2331bp5AsvvFDpIfbrF1xq249jOCyGqj4sdXEhW3SuIk7Q27jJ4LcygImWHGlyHFawhFtF1i4AogYWoeMp28KePdqaueB1fLiEa9hj4sTLTIC+HkGdo93JUgNRaGb5CJZ01GSMQXbqWv0UrzVCCoe/B4LPV6qUS0DECOdBzL4+Ej8qjwj8yqwHmCvrc1dgPjxKXfOQCXAGrUKGLzjzR59lc/ASzHC0P1WJaYeyk50AQBEAr7Upg+DustSUQyIkQD/V7m+ldMBjxiS8RW0xecPzCoiSuMOeTAK2quw/q/e5cPg7WDdflXR1tpG9lyxl62DvIo+rBszxrOHbxDbVJXUOoCFEJ2SOWBJuQOmfwuMVPDf4/JQut8i6j24C3alGesE5JKkPABU8pylQTrAamh1Uk4Xe4L2JXPq4JC03MpbUYyRA1J9NgmtG0e6eUlaQIylpS4EnxwTNwqxRziVulfUzVhBs8dVESLsBWyQr7UeJT75AqsvbqdVJ2VVSvtYi29aOM4arW4ts+ipb7PMBESfgfEJ2ZdijYMJveFasm3fJNy8/K7tmZ6mRvGgPuB/IELH8/mip2qQBaYMkQMuwyqelx/3Tzm7s50YNln19lIuJa2eRDe9qcGgcp5oMNzwuWyLdR/gasH0fr1TnQnal1gCDnXrPBYDvodbpbXjPMjd7Kj1VBtCkVM24L8EeMYXq78M0xQBl+PeW3VOtMuhh9DSip9CfOTSN1+cwsChCFvw1Vk6ZXy1bP7UJmb0GOAy8Bpbsq3C8LM8ytnyHf1gfOORzkn80OORz9tjRqdMWz2d/7KjYiM9yfISgE0PJg2rvaL4q9hZ60J/JXtB9BYk0/W5vBYb7Ok97W79kyRKhWPZ3330nY8eOlauvvlrpIrcCxL2dtcbrvs79Wm65PFBlaTwisKS+vkGe+XiR3H7Pw4GFLfjogADi3LlzZdKkSYrG3pK2Li34uvZ7qnqIPaPGoLbj10k9SoBNBcujbZbdBlmWagX+eAHfV2hx50bjkCZJhPtH8saX1Co6gVR1PBcgcSikSZBBaoEsZqN97s8C7L+8zx0AHn/F662EJmR7tbW2gNNTWVirCzlWvmaSMhK2fQgAFQzg9Hb6PjbvB8jinKD0As3nXgFnSNK42gxpVP4lo5eglV7P5vIy5zTneTQRhYCWfZE1sAOMBnPagrIxx5FQxH1mTBuDZVVwcEmXmszjJHHHx/rwlBSP0oXE6ySQit/xqcrq6QHlKOlWQrCbIE4Fane1GaONx+g9pc0etF0UsYQ/QLQoOffPc1R71Cg5flgd7OUakGmqkx4RN0vstGnIMCIjOORlkG16c3P57YoYMVi6SVJ6bb2cNnO3AqZOlMGru1zg15g0dozDAd21rM/dKDGD+QuBbsVEx5yelV/K0mnXi6fOLkceM1lpfrrShqnNKLa+8zO3TJuMrDfOUOw3NXLsNC8yYXpW437AAy5oGdJtQ2ezMDahQPKXZ8v767+S4865TTYunSC1rjZyxAMdZe3TyTiFGthh5qh69Cja1WS5r1TIRQ+ehV45UH0K8qWyJMsPDjmA2R0yoVkSLV5o/toCONpkADTjqNhPZjwiq3jG6bH+jJJez9cUAGL4dGDzmp2B46qAnt3RbziVcHQC3FE6nf4nWfsU9rkgMEO78W7Judgjcy4xyu5cU4bt+LvRV9H2D975deB4yc6ee2VgG/8g3wN6F1esh68yGLU6PNUR0Ia0KnCol4Ewj8+T/1lQNrDd0MC2HJE2FFZ+OEdVucZrTAYANoND2giufhygG0zrLufUSeez/hgl1gU32AF+IfiO8jvZ8vSB7n2TWxGK+B53/T+3kCzTGofmDCxcuFAGDRokzzzzjGRkHGap5UNzCn73XlYUrJYyV6mcfswRzZrr85nrxGpPUECctoYtHcHfJM2c3eGAllxlpbhcaAy34Rv7vyBK+j0IyZN7lB5cZber4aDRqclXxVJ0JMAFg4COZT+zFlyTG4ZZ0Qa9X3Fw1zAHvYj5V4WsYlm/+8yr/r89bsAVxWu6qtQiy0migs7EWZwF0m7GeFjO/QmZwOuCjpNldTKeFZuZxB0AlXqfOwkv1QyCz3azTldzFg94UoHFaGTgMhf82RiQ+4bSLyRTmr2OzPQVD34e2o2bAZCyJGPxtSgdB7KGBI3UWIzwovyMjFiEyU5P9/SxBF7a/2EF/tqA2arZ3Cyb18e2l7Ke6COEJA8zf27I02T9NApjKnykpeALDzOIfnBoHLG6jSpdCpLSMyoryQXcqgoEqMRtH6j1lJ1xJ/ZWjykRw94ulnhjl0xTyygUzkxl8eDnxLrmF4DDc9Vy3jjzIiRh0cOSUP6dWmYvWwZpIQi7g4VPncj0hX9B3+QGnKvBsm3YTKnYGCsJGSijYfQ3r7wku1Yb+92wdKIcD6KIDuosLls+Dk+Nd6emKBZOHs6wwCErZ6Fs2h0oP9dUGWURT220LFpwv5RuTFbzFF6B/jufELPeD3X9dIeCszJJZrxzq9RBEmjHes5nQTnVLfVeA3xSX1H3y2WO9sBeToNSDNW9e76JHdsjoZtoZNU0K1rvM3BvvLYYAAiWh4MjQub9JQY9jfXQ90MmMjpBuuM3YNWeOqUNyBIvs5kEg4lHeP3ZObqykG8FCc3g0B/y4KVBz2j3d9SzYMjDgYVtpj+MifPLyFggY8NeRmoKaoJPAgS0CRydILB0Pb8O22EjX3Q92SY9/+aSbXD1sMbhvKPXcfQUpyIMEQwThJqDrjHbPzMOmiSaxB41zSZZlCyFtNO/kaHtgnN1CWwGTW+LeR8t/bhinUWBQ87bgGzp2mejFEDscY1bleTZhnA49Ua29Os/HOcjHqiC0QDxQWsc2Bn4bMMXcu1ZA8RmDfxgbWomei8/8sFCeer5V/191k2NPdDlBwQQL7roInnnnXeUrQ7FMC+77DIZM2bMQTvIA31x+7MdMzQUe25OaIFmPTb0uV6+r/sINCLReq2poDTL4QIQQ4+RciaUQomFaHUMxJu1tl7SppdQnl8JO7xYRRRxQY6E/Wu7xgOkMcuI0iwBUNrKf/in9KKMqnsZCTiTUGKkiwldRMzBrFvesYZINh1ZCMhczPwi8kd9ocreUSCvqD7B9qepvsW4nV9grp/M0/gfsz+SZWGmkLzoFTUH7QIdnc6Wqpwr1OK2v070/ygguKv1gTquZCbTAVJPaPA8sE9TE1G4nljB6tyNku5n6KnMVdnVDtNHIWuZhozjWCMLyeY7cwAxZMy7DOX1VdK5V7psXzdOraX7R6x7pX8kST3UNKyGSHlC7tsKHHKlOzdffrwxWZwl8ej188jJD74GcBgo55cXZktVabXEQ9JFR0x3pAsDU4PtGli3e5pVtsFOjQzYUWc8J2W5KVK8u4/e1H/vrAYC9IFMlmvp3FGzC77HeyKUJiCTrTu/YiqM0SC5K5ExN0W91wZBaPgDx1uk/91GeZmr+ZhZwu2fNY1GuD4JNnhmUEV3EDKCXcV6nyqpq0SeDdkTE5ID6CSBYeHf7DL8FbRA4HQMezH4FzpHD3/NaTCPYX9XsSZSqAmorex4rLt+iET2zvxlz/No7IcSOuyt7DixTukIatY2zwtL1QQ9rpII6X4lWMI5Deo41sOOjyXT/nfXSkJX+L/jtJhlatgPOXVYGYAjz42xn7lXxcipKFH3uwP/f2EiOAMLYA0x7qZYuATFhRCYZgaT5J5ZF8T6ezrpZ33kPYH3Kcyu/ItKVwBY/gfC6fgMZV9k+Ff7VzbjAc8dPzP6NZrbp7XgdzOmaR3Sgmfg1FNPVRZ7o0aNku7duytccPbZZ0t8/H9BL0MLnqemptoIWZu1RRvk49OuaGpI0PI3vlomGW07CBnjdLY7GHFAAPHkk08W/pWXl8unn34qjz76qFAb8ZJLLlFG3e3b46L7XxzlvW4FWeIfCvCQLUsCxYEES5Us2eq+tAaSyi3Q5WMtEeGGz3Jzw1JbpMgOSliZHdgHOWLyf1R+xC6II0di3zp4SYotmqWe0vUjb/R/xJuQje9x9l/G4GqG3ifHNj1c3TfQu9mU4bPCkSXKsT1oDJ+4kDX0ojzKv9BgVpEMZwf8jnUvHccwk2YOillTr5DhTjgCbiXxQukZMtjJLFYsZgh0s/fPHGZSDZfbK9f6V/N16T5D/0I8iMmfjn3xIhYc9HGm1AzZ19EVBgi2ovxN5xa2L+Qd8604049BX+EsdUzVHc9Wln+c5fRr/qyAVF1KD+kwcpc4bWPEhhK+DkVCgTi2+XjXzDtPgUOOqauyypbPUqV99mLJ23KU2oxeucmxqyWypMbw8kaNdPA1v4m1KF7K8jpKx/+LB5AxzlklSpXzr0NZ2NfjtjP+Q2Rng0EBM3+WSJT+XUY/pT62rCE75aTjrpWGslIpTr5MilMuB6O2QapXFUvevMbfGQRKYz5FlSJRz2Dc8+N9BAgGzFxpaz28YCJ1/8DC2VYllZINuzuSV5LxL9r9GqcSiFbHr8aCWIKSJDUWqbtX8Gto6g/lbAhXU9Jm/DSHJnErN5P1L8GfG8cX1xmvE+BQhyFAHTgf5lKxMcYYS73Eo6G1V74qEsLh+JSEVHXpesJzTQ/leJS5Ge2O9eIPyNcUZnDIxaseRea8MACcuExlGuGv3FRGrdNZyIyit5ERnVYvtNprKhbeaJR1uT51sCfotZslfJranssp70ORb50NrcnDDwC46+xPsGeQepCULiLxZ/BjweB9f+ZqHdsyZ4Cg8KmnnpLHH39cpk+fLp988oncc889Qus9uq8NH3749Na3zCtu2VneXTlZrjlzoCTGBX9vhttLcXmNPI3ew/c/+Cjc6hZb9ruQxLZt22TTpk2yefNmIShkepkK6o899phccMEFLXaQh9tELME5Qcpgf1e9Hd3RvyMo5qy8ZXGFKKf8CHsS0a9GsMK+v+aEysjRMxjA0oUSY8HRHwSnFZozyX6MYek3felNagu6kdAdJLKq8S8YgrZ2c86SYvTQuZL7g+jykQJgCVtxfL5AB5iU9rsXjN/l6NObrMCfzbFVr1b3FIRmFpJC5KFBIe+YvB+VS0okiCr0YWbpuKzfAwq4OzqcjizdRybxaZTwABIJukj8iM37Xtqsujd4Whx3ypp/KvFv3VBW1uvvYPbeqMrbHGy+BNejrsZssAamEWAAZCy8Ukn1BE8ceJa29GalsxlYYjwio9nq2oO+wxdRzq4E6zpJ/RAh+5rPIyPBzh2IknLDt7Lxm9OkvH6E9O3RRuISStQEbH2IdBVAaxJi3CCukAgTlR78bx6bUCyjJj4qi2dPkvLMC2XQ6Jel/cJn1PbsvSzvcZO023iZdD7XAApV+BFUJg+o9ZUbkDoyESDc1ewhxB8AWkL7CunQZaZsWnSieNyx4sEhRcZCfqafV7LgtNE3/Q6QkdbJohnXypyvmcGMkC7nu2X4Bwny43Euqd7u+2JETxnLyew5DAWHjl0gP0GImO9AR1jOESSqLJIJHGKBsu1b9QiPDRlblJCPethwx1j/CjJr/rERkH0xwNdRT1OMugFgEp+N7RaU7/E6fVGzCwL3AFAs8xK0zbs6xi+Jgp87GBX4NKQPC0Z6ndHTtwVOJuoY9YS4r0VmcPk9dr8EDlnUx35doz5uFTjHi8DG5XGWLDaynDp7yeTy5vdsqj+y/XEQJA/xg2aGLzTI6KYMUFORgwwes62U7mkL72M64IQL9nSy509H6RKrGBI7Buhl6b85Qb1HDQ45vggWiwcSZKn3/gt+W4ItSyWN1jg8zkBpaakisFITkQSVNm3ayE033SRdunSRjz/++PA4yMPsKNYUrkP2cK18eNblzTqyB9+bK8cfP16OPPLIZo0/0EHBV45mzvL8888rvSNmEC+99FL54YcfVEqZmzPdeeutt/5XA0S+ThIwmiSecEAzg/7GRUNfDxpdnDoo6Pm+niTmwuHEl3VkRioR5cXQPsB9zbE/6wnmzOGFLmAliA3R8EkmkKFnsA72GyZvoBVcQlj3EIKqWpBGajOOUWxdbpe+8BqVPeNj9v+VoP8uXLCnse1v5yIjud2/mqDUDpu/Nuh53DPma5WhzRs7Vdr/fKxEeo3eGGYQWRImuI8qM9VR/bPgco7zqUrn6Oujq4wXfY510DKMqtqkRvGSSNFuC8q/VvQfZsy9EH2rXRSgI6tZWxdysCcqDWOK1Xb6hlnjSPxpoo1evjn3TKlc8Zv0zHlVIjt2UQQVEmgKj3pd9buytkrZnZmfPyDLf/2z2mxl+sly2R0jxBaNfjlE8vrnpWTQU5I/9ntVbs842iHddi+RXQuyJStnvvQf9YFUlHSSgZcsldKj/iTtf3hRbccblvYpYWTOQCbs+AzPG9Cv+aBQEoblPU8Vz4ApAGbsnaAZ+FswiKecC/UPKQez+NMJ0gUlxXnf34wNje23fRwlvW5wy5jP4N87GQl0YMQciGfDdVGVOkkyYVn4iKvdSt5l+qlxyuPXtGfTQwOoMQtmdvpgBi1vukWgZKQkZ0wbKP/jzGNQQgYo0vqF7AGcdVGMyh5yLMvUtLObNi7OBAzNs8DhxtYgvW+BJ/Flwf19lLhhOdZMKOGWzD5qfUQ+pysKGdcsj5atwgZ+EIvzAIayjtz3bbLynwbw3f0d+klTamBJF8j49bvdJQuuixNXGf2iPdIVPa0E5+w93FtQo3HDa3ZZ+TC0FCFafgTK2qHB94TlbWY8GdFt6mUk3ttdZInjfe0KQe7mRCq0Gc2l/4yRgeMnYYa9qa3xxzsDs2fPVskh3rP17L777lPZQ8rfEcBnZWUJk0oEiq0ROAMN+G59fcXbctcVo5uVPVyyPl++nr1Zps94MzDJQXp0QABxy5Yt8vTTT8v48eMbaR/26dNHXnjhhYN0uK3ThjsDtJozBwEjmbHUYDwYwWxeA1jXulTrzBwLcsoZxq7wYU+EVzOdVXQwqxddYqKA+lbwcl6NcqjO0unxRYNfUA4s7NavyTpNL250zxK2GRyaB1hrdvufNsADq8EWh1SDARC5wuKtVuvDnSMeV23qYGRJ/6bG6BsyinUwC0lwqCO6ch1Epdepp3WxHfVidU+/6sgClpuDQz33zcF9zp75tCz54lw1aGnKRLnkruORyXwYbPaTlfcxV5A4Q6mgTctPUeN4U12UJtTq69B9vloWuwc2gA2PKxRCh5z2cA3p+KcyEWC3bWuPkQ8e/VFcNSmSkJIn499fCOZ4ur/NQZF7ANiTNr4MMlGgbMfyd2X2ZagrZsuAB2tlsc/HVu1Q3TRAs7Dx1wn9aAtmRSLrxpL9JbJILobotKmciMyjFVlGflT73BLcI7f0TmTYIMnCWPFgNMSs0SuqLN3UopAbeEAf/6Jk95sp3rPvlZXvgoC0JDAkuTfPMLyK76uVX86OFa2BWLbCKqtgo8dSd/sTPKqvj+Ct/XiUXWH5xjetFwATexTJQG4qotMbfLp6wSOqt2EbU8Y1wgr3kk7I1sFFxhx2ZDLrHBHyw+hYRU6hhI4uXXc+OwC82P9pjh3wLjYDRJbLry1Ikdw1O5ol76Ln4rmu3mq8vlUPw1sZItjxnY1zpsewtE92N20HeV6o8ZjSB5ZyfUzvpx68l3uWh8d+ViM7vkQPIsgt1KeshR3gnEtihFJBZFmPmgzQnhq8/71M2brqMDgDzBgyQfTRRx9JEk0WTEGQ+O677yqQaFrc+hBn4MctM6U2olyuO3uoOGsC16hwJ4eyNre+/Kv87eZJkp6eHm5Iiy4L/rZp5tTMIDYVaWlpwr/WODRnQLmKlJmuhNgts3bMToUDPy1xVO5klLFHTFEMX/beOTPHBaaNiFB6hpGgp8Yj88Qsqyv1KBThLMjsLVfj6pGKIOEE1xhVdq6GnWEQaxxOHY6OZwbmbOKRdy/lfUfWqUFbMQNoNfUjaoFrT1wwmOMlqabtiSjN7gjank/M/YwaHDcaxHF47cx8RiP7SGFwZlb5Wjk378MFl+fOGupfVVXWQXbnHiVd7N8jkznDD9bItM+DbV9KL5c4DDyoynyJWYX+balrqFJUAOtkdmuJoRmf/lNWzLrMP66qrL3sfG+GRN/9tCGmDYY2y+4e9NQWjPxYEWwsnio1nsfewPQeotPpHmj6uZW4tB3OHLUFXnRGBP9IUQNx0+Fkj+yEDl8gYC/nNeZRy5Apo1YdpUo2wZaPPXzMUrF86DKq5r5NoXWX2nQZ0RJZJ/1gt7h93RiR5fXSe5IBWliu7TTRI+lDIyDeazBb6c286xsDDHHy3HeN41n3QoPqNSSredWjdqWjx/Wb340S7bzB50Y0SJujvArQ2JBRHfx4AEzrEbxvM9iregg1+Or5V7f0vM4t3w6Jh2Vf4NOQMtAji2+y+5nLBIe0w+tybh3mCLxuklbMsRNED5Z5Bz8aAGmRNpyrFPwewqK86RAqBpOZx5j3kxXl/noF/rZBU5FzEXxSXNvtc7Ax5kZpGxnfCItX9UCa95eG16w1Js3L9/WYuosr7rdL6UqLZAGI98aPgZR+gWPe/I5NnUvOUw6yDzOlvW8O/sGwr33s+g7kKbCxyfDm+28SXdjXpq3rW+AMXAEewt7ipJNO2tvq/8l1FarT9AoAAEAASURBVK5KeXPFu/Lefaco5rJRA2r6VLz33QpxR8Soym3To1pujfmbu+VmbZ3pkJwBC5qCwlnJ0cmjLqH7QTsGgk9Kt7BXsBol3SCA6NtrWb/7xVa5CaBwmdILpNRKRc7VkLqplJiS+QEdQDCb7YWzwby9cL+Pl5lMWvDFb/sX5qsC+CKMIRCDeHEcQJIpmHljDyHlaFhedsFGjlGTebzSQbSXLvYDuDhk4LxRBjvaNMVeH3ow3opSNKO2zTBIzXQUJ6SAoirWSWzhL2o5L+0EygomwhIjlP3eJmujlBd1UWMtFvh2Z24xXpMpk0e5HhJrBrwUL1HPuYX2cSxr1vR4GOLSr6MPMkbpPGb9OEKd4yqQdugg48iPCgKHaie4iUr2KIJNfTTK4mBJE4ByDifcYooHPSP0zI6g3ieAI+VzGPgN4Ldqg6CMfDfIJs5q/aMwGAbnf+uUNgPsYD0j6+QL9hUCi6oguKF13KwLY4NKrnReGXDJHFn61kgiU2RH50r/iUVg7J4IZrBVnCjHOpHRw8cP5V34CY/4UP715DdSi8yofIpM4b21qoeROzGs9gwAmzc9UpWZWR6lBI65XM7sJMkttF6jyLIOii/TM3rrZ1ZxbNWZvwhkApEJ+9TIROuxofdkJh/7H4cCanRZoWMHg5I2c6/ASt9PhvxpPL7APjmGrG8zOGQXiS7vcr0OMsrJHvY5IarF7FVkmbxkkf6KD7wvqYM8yI4ay7d8aJMTZjgUGFv6DwBlZDtZol98C44N2d0BD7pQ8seOTVGyxCLL7gXBDueLnswdTt137+H6l2HP6HOrWQ9WN11lOpxm2s741/XvBb9t9isodk5dRJbm4RKpnGoGPRIAoPs1Wevg1jNwiM7A68velvFDO8vYQZ33uUcSUx6aPF/eegd2tpH6e2ifm/2uAfrb43dN0rrxwTsDCVveV1IolGNhD5gH/X466nEhr7fY/dklPi/r+XdeEdHDNxtEmnHqsR7fUvfJ654SO/rgGEm5bymwVZs+Imh6soEJDnXYkeWsgJB0+tJJKO8GMzEpByNyoR4adE+h7IQt7wkBDMWo6bBCGz4LXEaS1z8tVugiRkJD0RzIUUnK+ifQM9hFajPHIpu6BxI1FZIPRjV7Hgmw/IFsZSH8o+Oxj9T1TwYWA+xRq9HSEMhiEIDyuhV8GcdFG4C8cMjrErfne1yfrDiu58QC/UQGrfTMUdr/Ibi5nKzYy0nQNzR6EfdgO5sMehBC5B+6xQ0XkSFDHpLktB1KBNwBUlTCri/VNNVkaSMry565gRAH1uGWI6X4qFdwZXRLhx+HqSwy1yWCGFQKoWyr40t8LJDpa9BfLNCtGz5H2l0/RKIAjqn3yCBoTdrwggKItekjZff42bjoAtSw7hoSJJKAYyvH3vCy/Pz8XwxwFnJ2Nn/cRv48/BRx3/SBbJ+aJOltl0v/wa/Ctu4ecVuypM/tdTzkIHDI3ZDE0LbdUrn8vjvEWdVGMjuvkMrq6yBJc6zyXP7lbLQM+KK+3iIrfP2YetnOb2xKvkY/5z3FtpfdDRCBiEDP4DEfOyR/uk02vaXBq6HVSHFliixv/YjL0VsIpjO6FKTfbW6Zfy1BnREpIbqOern5ni4mpSiTs2TL7JsOMoVJlA/8K4R+qkQ2vRkFEkqdsn/jdntmgoBlkujRc0WhFIuPdVCQcBIAh1wVmJ/9jjpqiyCEj4wd+wfbAhgXL7BA2gcHxgDYIls7FCAu/Bt+hOw2MrCLJtklYxTs7IIrisb2plsSfczhCHneDlnFbQCQLhxPYk+vdLs0GJSatw33OLRvs4TtAa3RegYO4zMwd+dCWVq4VBY8ekmzjvK+t39DW98JMnjw4GaNb4lBrQCxJc7iQZrDhuxTyrrH1ezUzqNAd9GwNwN7g8ZFycAnFIOXWbOyXrfDKeQ5ECk2qjFVKN2W9b03ML6FHkUG1/7ATA6qBaq9kH1LUKslfNiXlwg2cSg45OAgEAZWA11KyNh1ZI6X9EXXYBsj8W51bMO+KpTmIkvEoRm40JfH8irL7BnzL1eAiQSaPSM/Ad6BhR229zu2AFDr7Jh5DvNxBZbzX8aU+cCzKngbxxZCf7AjnEAAdnV/I7ch2UWDeJUUoRYk3jcXehwLj57MIUYtkCwCIK7Bj6M0Vl4IW8FCcdX3kvKet/rBOIfGFMzEskkAuSHaL1yJsDm2+MGhsURwPj+XKOt6GXfu3TLnqzvFEh8jx70RL6kjR0pNDTKSFcFZMGpYmsMGvUYrSu610LQMR8yyX3m1ZC0vk9zvkb0LjYh6gNN8yQWTt6aElm9D8HyCXHzLaCV8Xt77diU4HY+yYDWcPHSwJy9+dDbA6g4FlAmgnW2PVaudewLj1AJvAPzo7Wm358bvhrXPondxh0W6X9wgu74LjGuoi1DEkD4glpCAUrkRpegzwF5HCZYx6GEXyspupTGodfUI8gY+VAu3GzAzUTrOuXjvIMYMSDe9ZVPahsD2KN9a0NNok8RueM0Q9GbWDh93ROD4+IwgjKCQJX0Gs63mYOk4IQfZU0jEhJJQWPongzxczyb7/rT1HjOoFPtmsA8zZQBff+BnkJnRzJL15nei0AsaOE6WwpnV1B7TaqIwNxTzZv8kz3tUCrOHgXNHZvqci8F6Rw8mg4DUvN8w0zVaVBdUIseZbMWHjc5R64LD5wyUOcvl2UUvyat3jJeUxMCPzqaOcP6aXfL9/C0gprzT1JCDsrwVIB6U09oyk0a6CoMmCn3OlSwF8o9hq1jrB4d8Hrf7m/0HiE1kizifjsrsPxt9dQA/dAJxZh6rRK2ZEWP2kjIpFB6n5A7L0QQ+BGM2H4lDz8N7kigq4Syig84yJJ8wFFD0gUM+jypf48+W7gsc1uNK7Gw7HjaGb/gBE0EnBaetkIGhvA4dXqq7XMipVc8gs39kQTMIuJmJDA1mSvmatDC415oobSCJw4jf8YkUDn0LjjNJKmPJZZT3iS2cyYfq8p8MIXFHZ4OIohbyxiRmR/9lSuQw48lI2gw2s7YbwXMKiqeuvE9J/pD5HRoeAHEvXnsk5mHwWFgiZhw5+kMwmD9ExvRtEAHOgCyVWgy9zT4o/1/lkxnKCPLAjtv5JfZ3lzp2gnzl2cx0milsYHbn9FsCgHidaanxsPdQkFvQT1lTEgC029aOVSujYHfIYMl69GQIQ7+A3tPtFpVpywb48maMlYI2HylGuDNthHgSuqnxzHQldIN93GaigACY4UqSOwgOyGJejjLoTpA4GPm/gAwDIGgOZsdIjmjKg1frBjqhIUhnFWapqEU47CWn+S0zTxn0uICC0jqQjaNm3x54NxufBGNFTNt6Ofm3atn5TSSymzFKs1BvwnuKUevIQNaRskB0PaE+IgW8k+CSEi74Fg17MbiMzXF9/1ErXdHXuPF1vnaL8tM2WyjGo2xOC0WWhA1md6C3ctUj0SbvbWOvWSfXNSKzhDseMpVPmO6QivWRShzcTEAp+NXqB4fclr2EdBTan2AW1Rx0tWmN1jNwOJ4BspYfX/CMnDG2m5wwLHufh6iIKS/9IjdPgv4yJIMOZbQCxEN5tvdzX7Xok6NjSBSAHwFNVdfL9joDRbdZZtYZt/3pQ4yCdE3a0luULEpV10ukvNctTe6rNnOM5I2DowmYwgQXUEVGpu9av9agrXIDMptPouT6ozEHgKRZ9oVf5W6Ax/Jet6EMnK3KxwRd3H8UJGp00BGlzt5ObLX5alEdAIIWltZjwt2z745C1N6YdoZTimkQwSGD5WL2UVZ3vgAAqkbZHTq7nCsR5WuR9fOKHf14OupiO4O4cpwCujXIapIcQ39pLAjyY46qXK90HgtGfKREr73RGcpeMAYAUedcSNCxVW4EOWcKwFsKvLyvQDYwALgo2K3BIfdPNxmyxM1sbfZI8q+k/yMKPFIgnLqOzPwxw1d49BRJQOmfSIkZyLi8b/2Z6LqkngDDg/VL89+v23mr5P96h7SBBEnnJMii+NbEb//Y/5iaijEQ8q5BiVwHz13GgiulXadySfrbTBBrhkh1ZYYUVY+TTkf8IsNH3iNVMceg1xEZvXIDMGXlLFKbM6PL8r83pq0Uzos0hK/R+0dNvhhkwBhkgPOPUQm5lw3QMbSACTz8VSccWZCJQ0/hlvcDpBfN/N2FEnO0T+NQbYwsnc5Qqee4cZdbZONr0Uoou+2Yxj8G9DgCSS1Jkw/Sx/bPbU0CmK0fgyjxCRxgwO5NHeCVPNVbyJkgE7ScmU99Zo3Z+Rqg1oQsoRe9fNXYD44JpWVqMdIXuc2gANAhkCYZpc/fXeq4N7yG8u8l7qA+RWNW45Zg9oi/uAEGjfPT7XK3ZAKo5U6OUgSbduOQzQ4TOZfUYd7GAK1ooQnwYrvet9RKz+sbjwszpVpE8BnfyfgBZh6TDIcbM9BPRu/l/gaJSLunelTGNSHbK71hNdgarWfgcDwD/1r7udRYCuXha89v1uG9N3WleCxxciEc7A51tALEQ33G92d/uPDvAdiILl2mLqLm/sNw07CcWjTkFQVaePEt73FzuGFhl1FOxeoDYolb3gYIAOMsaUTYsVzotWeoPz4mWYZagTqY5WImrKngJZLlXxf1Hn21sWT0vUWbwCG3JVGkULGlf8HjVMWGTl90NUDiGvT99RIX/hJ2/du/G4JjgtuKI27EMgNgONFHlwTJHU1g8Q/mCJSZecE2i1qTTONsd1wwQEzIURqHqasfQEbvdXFAdqZk0NNqKnvxHHU8fMJsHUEpgVo5hLUZHaYNC4IEFci+Zsy/1A8CbWgHUL2DajQyXwDMHsrO+LKGnKus9z9UBpYuKwRpOpLXPopMIdAFwo4ewqJhb6vHdPYpReuBjqrsS0GcOUqxsF0gzgj6LsUJ55bF90pc1Q7ZXHSDzLthtBq+7RNkdetq/QCIxxKAX2ABb3kP/tcbpaL7tVK5xS5Vi52SAlZyPGRqKLOjpXbccV2kYNTnsssyUQHrYz6ula1vOyTF8bUMHfeM2hfJO/bZS1Vv6KpHcvzEEAIYghmzuDMT23MuBnkGUjcMsmxPmA5NSADJbf9CuwBKnaHBUqqr0Biv+vSgZ6i3N48NZQab1/ExWdbmiNo8VWTKQpn7zc34cdNGATZa0xXMscjSO41yUSkI+1mn1snAh2tV5pESOuuei4aXtnkm4/GiSTEy4k0ngC9Zz3BYGRzI2jUeDZI2+ih3f29kRskQJ2uXGTN6RHc+KxiA0V6v8zlYBsxVuSlCfp4Qq0ranHcQ3EdCtQsJVhffahda9mUe41XsbJ3gbovnlcgAMli+7nI2fkgEnxq1bn9vUgfWy9AXapX1XhLK3bTem433mqCc543HyXOzt+C/8sh3nIqcEtqPubftWte1noFDeQaW5q+Qz9d/KT+/fIHYo/bxocaBlVfVyiMgprz93geHjJhiPh/7PkLz6NbHh/4M4JvPlTas2fvl2KJmjo8qXarKuAQ1FmSCzEGHkuYGgWkNCDG6lEo3DvoKE0jF5RvEDV261XMyk6bBIZeR3WyOWkjjlPa7TwFjyuDoKICHMZ1KSDRh1tMMEDmO5W8F+EoWAhQNBYjsFxYc1qOnrbT/PwFuS4OymyTTMHvKnjd6GzNs1VuUyLcWj+Zrqqz4M8rofcD0fQ6agS+p3srKnCsVONTHyvt6XK0CUjEQxwGgMGcIKYXDYHZs5T8BICpi5egxJ0qP7h+q5WRdR1VtUGXfzNlnqWX6RoNDPrdTnJz9jQr06hGBe5b86dJC55uadieKdd5jErntKzWg6nv+EDAAIhcULYiUnPMqjPOIc8zX4I1MFJu7EGB4lfrbuaSXTL/3TPRzdpGlMb/IhbefqHoF1YS4iXJsUzI57PlkMCs44AmAp4ZzpGHet2ClLFHLWfYnQSYyOkc9N27A9SZ2NwXZ2mZwV5ULpjo+ogRGx31bg8xRJHrbGmTlQ3YlH0OW8pAna5XYds3OSOk8ARlDF1xNJqMXEqBx9zdWAGHYz3X3KhBi2lWjhz0g0l3wK3y/UZJN6bhTBvS8Xz54+GepqUpXYymlc8o8BzKLwQddjKzo8JcMsOeE2DcBHIXDyYqmK4m3xgCve2ZYlfNLJuR3QqN6WwRII/jhs9WiSsP0oS4FcPIHMqMstfNv91QbXjtA34P+tepBIvoUq3Ixz40BcMgVzIaGAkQSdtgfyaBPduqRIIxcbvwf9L3NpXQLHeibpIMLWdktFR0neIR/jOX3R/vccuC3DeJK2nCPAqPN2VcrOGzOWWod8//jDBQ4iuThuU/Ki7eOl5yslGYdwqMfzJeRo0Yrm8La2r3/cGzWhPs5qBUg7ucJ+28ZztJexoIr/P15BGQs81JDsQa9eyxD2lFqtW6eLCl7lgNUnLRXoFo8+Hlc6H9Rp0fJ3iC1wCxbmesuuKjEYd0sBQRoC0jR6tJ+gasYM5D0V9bEE3okF4GRG44QwR0oFjL7H1F6LB6AUjZKqNymElmthNy3DbCEcXR0iSBF1hQcx9dKnUL2SNbDB5q9dTozVxfXVc27B0A0ZfWDYi9bivLuNtMMxsM2K+4C4eVjRW4pHfAowOoKiUKPpRcAkKV+HQShHEt5mooefwPjexgyhBnIEBaqIXSRYSy6ya5IE3w8bc0D0vHBqRKbYKSbCMCj4JATDaDYVNQj2xqNnj6SX8JF8ronJWLJD7J4+jV4zfMk4bQCSfL993fu8assmPo3bGakgzKP8Ujcrv+IdsyxANXwvAgAoo7VU/oAHBrj3c54WbH7aRk0+EsxStJGiZDng1JIQa0OyBizbYJAXIcbkkzMtDFzRbHoPmANU5vPHAQjKQArutSbAVs3SsgwErsjg4Y/Rvvx1arPjWVLJNFR0vQiy1UvNpsNMkQN0vd24/PgvD1C2erRBlBnyNQEYW4IQk+e45CaPEgKbblA6otBIPGBQw6ngDbbPNn/aA66izAIDqefFBDnpmuJPnd6/A7oGYYDiCvR96fFwje9HaUkeNof72nUC6jn0d7P618lO9umQNyQp2oV6A/dJ0vgoVG22gCtenkZGM/OPR5V8uZ4Cp8z8O+Hkj16YgNkcr3J7753lwWnJUOf/+4dtE7QegYO8RlweVzywJyH5bIJfWXCqO7N2vuGHSXy0U9r5MefXmjW+IMxqBUgHoyzeojnZCaNYsjOtJFG+bAZ+2efIMGgDopA7z5+NtjDFX5Gb/SaF8S+CpI2GETR6z2jv4C/cQ+9SfA9akAky9CTmF7LBH3V8PDV7GBnu/Eo3Y4HU+Kh4O1wpcmETZ32X3amDjWY2tRQCRM87jToGZIdTfcWAs0akyh2KEvaXPrmdG4cf8KOjyH/MkUVoRsgE+SJ7QAyST+pT+krpZ1R/q3ZBVCDkieznKZgH6guVZMp3v7XCSiPfqGAb9ry29VIZkbzR3/pf90kklAqxuIqhabgbZIECRwXbAl57OxlrAYDmsGeMx3euigpdw2QmISZwGywsCuaq6RxzPvXY/U9X2fGvEuktO/9IMEYc+p1vI/d/a18+NKnUlbQTS3euWGMXHY7tCmLO0pcVq0c80GJ5M9KVH7DFLeW7cEXaQqNN6AtgOSgud/dLPlre5qnB/W0P2SVSgDOf/P3S5KAFCrzw40qetyotqVtoQNOOW60GhAcpRyJMi0YwrnQ5iOI6nQmbBPTAqDrmCk1sg39fyw3mt1FzAcCLK16Cs3Lwj1maTomozFACjeWywgiE7qiRF13miTXvizZfX+SLavxeUa0P7FOyeCUrzVl9rCcItcMZh+1cwufhwI1LiuE20y4CNU9JFv3yPvgYAKwvOHVKJU5NG+XPsIjRas8cDsxGgMoOLDs7mjpD51E7Bl/xvuaMdKj+hPN2/KxBt16ubNA5IcxcaqEn5DjVUxsOuZQ3oYA8YirIVB+Z+B7RG/3e+7JHid7m0QjknQ643PQGq1n4I98Bp5Z+KJ07GiVe2Gn10CR0mbEXW/MkSuuuFLatw8kHJqxWYsOaQWILXo6D/1kLG8mwdqOQcZswdEfIpWx77fVnXIkeuaSUe5ErQvhBPGEmTmvSSPQWjhfreMNnUOiUZJuEiD6RrZZcafE5k9Tz+Kg3Uc/4FDZFN9QdWd17PCDQy5gxk7AJm4qqMGos33xO7+A0DXK2Zlj/cOr258GMPSdytB5wZz2wgfZnAGkZIsuFfNSGQERas1Iruo0UTKRVaWkUB39pbtcCnA2S11S6SXtQSleSwhxhxxHpjizozroEEMpmuquF+lF6j5h63vIaM5Vj2PQt8hMn2ZQc2H2hSATgDDBoFdtervVKKXjgg7GW9KGZxWQomQR+w4tLCWHCYLX1NX3ofTdC0DdqTKpBKNkXldFDPaDQ25atjNDvpsxXzb+OwsnoUFJpfSHXIoOAli+j3aU6nkuynv/XcobJuF8zJOVD16qh/nvo9d9JRnxt/mf8wFFwXmuAxDPWM3McHmfO4PG7vjaqsAhFzrzLMrJZMPr9XL890afIZdbY70ydMBdOK4fxLWwt8xb86aUbU4GuaNO2o3bO9hDMhw9bgAdcC/peEZAW5DZvbLlkUpbsTklUwqGu5F9HtF7j2SuKZGGKFgZHmcAGDMzl8erpVooZ7PPCPw+CBra83o3GNSRShcypb9X2iF7iCSsAk2U5aGNYcUmi1BnMBXZ0E7oQXRuCz7jrjKWitHbiD7HXd+CQAMJIDrVhPuaINt4FzyeddSCRKP7O1nWpxvLmqej/YSfjW9EKceXhJzgfertD+Q+pX+9nDS7Wln/kaVtzvCufS5K6ALDvku61yR1OZA9tG7TegYO3Rn415rPZJtzg/z81PmwJoY2xt6/qtSBTV+0VdZsK5MX37v20B1omD01fSUOM7h10eF3BljS00GmcFTlWjhj9NeLmrxn2XLPqE8VwGEPoiNrYqOxnnbHiLVgjlpOCRgSHUIjGmVclb3MHKccOOxFv/mHkGhhq9681+OhywfJKDrTR+u+pjrflbwMGN3moDtKGQgh1V0vVsChDTJ51B6sQ5nXhixjpHuxYna7E3oq4g37CaNxjsKFfceXCvRxHVnDLAMXoIeOWUu6trDPL2XtY/6eQo4jcCS7msLkOoJKqr6FFnqNmSIx9w1kEU9XPZZcTKkVeuoyQ9R++G6xzin1j1ZAFvI71Z3PU9nNJJCIdHiRpYsMqC0rMEvR77j8qb5cEXoK4W3tGHmnpHXbLsWbO6tN2/SPMMAhn0GCZc1T0dIdvWaUiFGLQPgpHP6eYmGbWdbVcV0kPgdlx0C1WY1PiTA+J8bWxm0k/K6pCxlOjsc8jo/NziV6nbvUovrRXOg/JGg46qKvJd4nGL7i49GyYmq6Grrj39AlBKimXiEZvvT6DY0Fk8Au/tx4cdRkPH5qjQIgM89ClhM9gdRCHPNJjV8HMXR783MlCo9dt+/KpYHsVudzXHBi4T6MLN1WkGcio92qdM7yMyVt0uGkUrHeAlAa/NWrweTuH60QyLaJHd7O1Dcku/qUuQ70X0ZIIvo4zaCOQJGfmc3vR6ks5TaU0SOstTLqr1ZJPxqZRGT6mInteb0B/Nsfj35L/JkDsqKyFU4s5C11gVZhFwhm86PKbTNGwXEF4JnAUAePKxTxM7PdaKHe4ADvKbydqjQZAxMUzY+Udc8bP6LYj8ry++g3gl9PYPQf6xGllFyQXCIg5vvaGv8dZ+C3nQvki43/kZ9ePF8S44zP7r5emddbL/e89ZvcetsdEhsbu6/hB3V98LfUQd1V6+QH4wywPEoiA4Mgzhvi3LG3fXqxLfv2mgpXr+tQm8wUd/4yRWzQOnR6PJ1MEtHzx6CbCHvy2N+nySrM4NXF5+jhwffIjFFfL27XV8oKjiQXb2x7ZccXPDDwLHXF3ch4lgUW4BHJLylrH1dZnVT2+rE5CkFwqIOyP5XdrwZQGaNK37YqkE7KDKkVPYbnzgJmrzkacDUmYOUfWd4J26ao1V40t7G3sabdCaq87fQer5qy7CUL8PpBjAE5JjSqkFGM3/lvHK+R/eMxxeZNBfAciZ5Asp/bSsbgKsU0pp6kmdTjgDc09Q0Z1V3+T+J3f6XkiJSGZLfrJHnDM7hEB7JU2udaHwMznCz/H/0FwMAUAywMuCxJpvRz+sudVrBSlQug3sh3bwaHetXgJ2rhqxstxUvw9YH3sdMZbskZNgusCz3CuKdAeB0+F82JjqfXgeRhBTs5+Cspz8fWLVmMj6InW9r6Tm3eVlOvJQAutytBWyNL9cf+p6bRLnd+G7jqktBRiawbNRIJDhleMJWZmTL7A3O5c0+EKndSTNpse8d1obHpdTsWGfNxXfFiZPc22GGHaOzbAcHuYz5ySgycWpbdGy1b/4VMHUgmjM5nwS4RIHDBX+H0A/IMg+VljmdmMjQ7qQbghjI/LMszmFhe+g+7HHMTtvsQ7OnVsM0DoKMzjA6WpZn1s2caJJ4lt9ulYp0BAPN/tsroD51yxFV16o/b1OTDirEU/uqbLeoY6QBD8Lnj38wyguSD80ISzKGIWmR7zRH63LzuYDzm+7l7qlVlYDu3EIObx7njK6v6EcEfSWwRGD3Z6f+hdjBeR+uch+YM5JZtlacWPC+T7z+12aQUHtn7369Cq0eSnHnmmYfmQPeyl+Bv470MbF11eJ6Bkr4PwFbuKQWcKHZM+Zn9CZZ4CUgIKiq7XR1MckD/m6fLGVKeMj7slHEAPDqoAUjf4ZIBj0sdyqnsQawCmAlHNKFcC/vq4kGEYDDT6EGptzptlKQvvk5tU9b7jkbgggza8FEvKavu9+s/ho5pQD8jSRYpK+9XGU1KyZgBYh2yYkUg2bSb9aegTZ3pxxjP6xzK71mvjATzds/w95GtXS/p868AmENmB+4s7KnjnwWvheQUc3iwzyoIZCcq/URjDUu4fO9Yiq3MvlwSdn6mHGQI/MyhejexQDHLkeLLGzMV53oNtotASfzPQeCQr5Nlc3OQkMOIShTpca1bMcZTN9ws4y7sIHO+uFkssdEy6NHaphK35qnUY2boRrwVvI+S0hdVfyUzSUqOCeeEGc/mfh7Z+zb2cyd66iJkAwBMFfx6o8BE3vMzgYgRpYW9lDA7WwI6950r29eN1av89+VrAkDQvxAPUvo2ALAZAMMG9xD2tsV2CAY2FGhm7xszdx1OQakW4PCnk+JUVpdzDX7CCaKKBb7SNsXmHQSgHA/Bah1VYKKbw4MeugZvYD0BFVnMzBANesglPfFe5CFjSBcZZgqLF0b6wSHnqTI5y5jnNT/Wlnd6me/3kQIYzLqag24xq58wshjUpFxyRwAcclzhb/iBid8P5pIuwSVBow62TxkSO8a5rNoYKY6d7BU0v049umXv26GUT9Y5PxsR0MLsfiV/DAaDxpbdY2C2inUWmXV+jP8HlavEJT2uCd/qEdiqeY/Wvxjlz6CTZEQVAe3Z3bwZWkcdbmegrLZc7p31kNx75QgZM7Bzsw+v0uGSxz5cIC++AoMHS/D3SbMnacGBrQCxBU/moZ6K/XjKpxhCyxSmZhl0fyNt6c2KfcvtKLmSP+oL1ZPHvrO4RQ8J+xBtSf0Uq5gSLeZoiCSF0cjoEazQWo99jBVH/FVl1Jhxs215F+XrCf6sGsEotQRDwwZBaB4LwRWDotn5Y78LGkbgRA9mHbq0VdH9ep9wtV6DTBLIDw3ssXTmA6hcgPkm+UvL7HOkSDWzkQRndH7xgElbl3aURBUvUJOQaWxHz6ANFnPx2z8JAmH10GBhr6I5yxfYM9wvAHxL+97XqBe04ogbsM9KBe5qkwdAogcpPQSzf4lb3/fPR6INXxt7Cvk+EKjaC36RtGW3KBDsaHuC1CYPhA/2G0HHxUs0yT6a8MPXwPfC0RHAF5m+WLC9WWJX3tTonTwS2bj+R70qZGGz3P17QlkHjvigySlYHmT/GF38+t3hUvI04QYndGuQIU8YWc6qLRGq1Kns4tAn2fGsBkWUcizbLbts2ZJ1ipF1K1sW+BqjEU4oyOF+Rr5WJyuepKVdBIgVhrcyGbkEYSwLM/NIIgm4XoqA0XZsteRDfsZs4ZaLUi59ixkUuP5xXJxyHdGuH7St89YGA5a0ofWYn0AxAuVbN6zxAkCKJXEtIcM5STxhplLb4GmSC9eFC/4r0MKOdoLGcTaoLF+4sVwWSngJZQcTdJnBYbh5CG4JsJlxZbCEHY7JTNUsgklTS3O46fZrGfdz3Dc1Uros0gD4OH/Yw37NcaCDKRJuJhcVzo0EQDzQ2YK3i0oOfCa4JvR58OjWZ4f7Gajz1oGx/IicMrqzXDFhwH4d7rOfLJaBg4YoWZv92vAgDQ58sx6kHbROe3DOAO3NEre8oyanrh5LoCSE7FcANLBHUAeziO1m/0lp+tWz5MqrLYKZu0T0tYW6q1h8BBeOIZghA7oezFxG0kZklOjmgSBgyh/9H4CwHNXzqBaG3Jg1/biKBJAImOk2MO3li5r2pyggxaeUxMlHf2A95HHYT8nSMgETwwVAWwqXEX/DFogS6SF9h074CjvBfqarCUkPdG2pGP6qRK0H6AJKiGN2b8Nzar7QGzqthLPh0+MUc9fcLOZbwXItnVNiCmaIB32X8QCIfjjBq6kpmFGkJA6BF0k+dH3RDjl0qPG71Ji28c/lW8b3h+esDcBxDACmBt8ExTqQJFZuLPr5wbgnkJl7VcBGzrE9Qk74GQhiH0GruxN+dCiNwGQ4k9Bhw11hk5+v6OsHbtHpwefNggRZOAJ8bDtkAB8zgKfeLd+iAWAEL7svOqjPrt5luK6E2rVFmxjVnIOAYTm2zRhZJ+tfYnk5OCJskNVBT2TM08iq4uWSBIOPovKIzgfZgwCRpBAdBGfj/l2jgCl7/dKHB9bpMbxnKZnWf/SEppTOmI8NC7v4rnBvARmlqaB/NOWBCmcbvYk5l7pl9WOB47bDi7k5MeTpWgiCg8WM19T3Nnej8jdZ6CseiFYAse/f3S2WaeOx8Rw1dV6ac+wHOibtKLwXFpwfX0tA+tHh35sDmX/Agy6UmA2dz25gcB+Ik8yB7Ld1m4NzBp5d9LIkpbnl8b+O3a8dbM8vl7e+WS5ff/Pdfm13MAe3AsSDeXYP5ty80phCM3NNi/b9EOjA0X4CgIpRKjY7eGhw6J+EV6WQoLsJQSWDoKOBwnO+sJeiIcwXPLbo8uUKINbFdwP4M/oDdZZMj6NGoWYJU04la8Y4ob6izoyW95yEXr12wrI4M14ek+RORc+b0GM4SpW2ndQWbATQCJ8CF0B3ch91pU3e+LzafRxAYmU0HFAgwcJsXSJ8lXU0oKxrPr97A4fchhaC4cIKBnXbOWf7+yQd0Ja0F89XLiksH1PIWocLAJb9if4Ih3r8K5E1QzY3EmXuoMAPgARkJvnazMGMpdac5HvODG9LBTO2KWseUa48VV0uwvt0BnrwALh8vX7cTzV68Ro8yFZ5SyAcnha0axyy7Eapl+zijrBPI4jqco7xQ4UDy1ZF+sEhn5v4OXgGYgckV5jlam4Ql7NkbI6sU92KJGJP90rOZS5VdiZA6HO7S2ZfFCtVG4wsIrchSFz9FJxNTMzfdAg7k3FMRxBdeqXl3ooHgW74MUTWzVttHGTvW13SC0xlHcySacFovYz37AEkeYXdB/GdUHr39R3Su3r9K9EyDE4k+woSkEa951QkGfY0ErASDNN32ZbYAAC97zm4D0oSnTw75LPm2zm/Jij4rklHq5+Ikuz/Q8Y28NWwr8NssfX1+NisfDgaBB6QmIZ4ke2tbSTj09ydEbQdMwUscPQgJoNIQjJPSwXnJmmqNf74Z+CLdV/LasjOzXz5fLFG7scXEV76na/NlHPOOUeys7MPmxPRChAPm7cicCDsLyNjVok5g5FMoeWYgp8VWaJ44NPKjYPN//QcTtjxqZAMUNYEKAnMGv5Raf8HldcvS7cR8NlKXYvMmy9YUrZASM0DDTxa2DGol2hD7x2Z0qVHPgyiyT3IHFZB2+4G1W8Ws2c6ANYMsSLDqYM9gC6QVxgl6M0juYV9hzVgPifmvqOAEeVqigc+hSzgm37ZHmbMkja+rBxR2qy8V9nJ8ZywJEpihwpcBTLQh0fPYrKJC2k3F1InS1n9kMpwGhsAVIB57IDrShLs/cxhK10h0u4MJezMY9aEFydK9xSLJglFB+fg+fEggxljWs73QvcM6rH6PgYagXpOLqN7ze4T5qrVMchYpi27DUC0TmoJDtOG683UfRlkYVgmt7AGChRlJqV4rYloMXhCEje9DiC+zJ+VtGCuhNz3gubhEwLeuhM/F6ejSirt3VVbQKNBB7ggddUDYHTPUluTNETppbjO2ZI2zCPFC4yvm65nVEr7Oacp+SF3Qg+wpd9VWWBuRHCx+d0otf3md+pl/DSHUcIkkgPyK/wtAM44iKzP7le4/HZ25gway4BLbrMjYxch/f8OkeeL1bRBNwSTBKHVW4nc8J7Ajm/wIy4pQNl52T12IQBjUJOPtnXHfVUjq8CeJROa7G9aApatNMaogbjpfE5dkHYfHV9IStGgyUCJxujNb9uCAKKeg/2C6+ABzd853a9yyW+XxvjJLuyfNEdI8tm8qtFjvl6doUKbMDKnFtVP2RGSOeGEuhtNsK8FPI3GqTRGhj7f1/b7sZ6kHgfIRikQ8Ma/QKPY/plNct8zPkvVcKLheet1QwCMN9pgHwuYufz/kb3cx2G1rj5MzsDyPavl3ZUfwkbvQklJRFP1fsTCtbvlp4VbZMbMd/8fe1cB2NZ1dj8LzAyxHXSYGdokbaApMzOtW3Hryv3bldut3dauK/PKsDJshTXQJk3SMDTMDpkZZMkW/Ofc5yc9yZYtO3bqtPpaWQ/uve++K0U6+uCcNvTq/KZhgNj5a9ymK5D0OnPxxV4AUN3nIqUUwkHISZe0/QUlu8b98pH3K3UODyhJPO3VmMI3BitclQFsWW27UUACnj6EaV1j/0/iVtwhpqLlkrrhIanqe5lkLP+9ClWSQ5E0OfkzvtL64m904Xwh7Uyg1WVMR+FCb3WYIeiyMX/1NqnB/VFlRC9mYRV0I/TT2iO8yupfSsTRqN/rROi6Ysgtaj913X0SXYYSV1gU1EbS1v5JSiY8rfb1P7EA10ZjAYkZDMAkjzaao7u2DiT3Lhn1V4Dv9xorwzOlGvyKDGWzsIS5kDUA5/Ry2tOnyupPTpFdS8ZJt17rZPSfqo1D+m1TucVoJOdWxTooXGExR/6RrHIG1Q2ALsEQcyDJ6ch9euP2z5qvcjuZK5r6Eyq6ERanHrUTnle+ZsW4755zZwK4+LwbZpdvPtSqrkMhUE3OBZKcPRWgp1o8to71XFgMWtFMO7DY9gsLdFiZmYcqWVZLD05/VKy5uWopKCMYn/uuVA26TtGrkA9RNxZg0GPYP+1ZSdz+klr/lbn/weksvYmijelxvFN6HO895N1gha5exLHqPmgLHx3hV9GrNzzsmToFTJ22CBl2kwM5jBEqJE7vmm6k3GEeZc+TnCrvcMgf6lWuIwt2cj/wVV9Hd0PBCahnjMZMjcZsDePhoNsMDvxwKQAhFFpoez7lmvjmUrsvQtImOqV0uUVVI7cX9Kx/NEr2fqF5Tzf8HVyQI1zCKuUDMTq6xzzgkDX3aCFmFvwgs6LDjVrR1GvmaxSVapWZn9c2oTei8o3RAveN58Lb4RU4kBUosZVCRu/v8vwdJ8jwft3E4fBPZ2lpbA9+8N/54g9y9z33SXJycktND/o536fxQb90+ILNrQDz04zeocgKeLUMRu1ao7lBJdNhBndF+fC7VDUq8/k8oGHR50KqFBPI7/Q8NhJskyTaSJMTXba82alQMSSoIcytg8MIeDHpPXWggIPPzrheCNf+n/KeGvvH7vsCtDrDkF93glihaGI0S22ucVdtE+zq1DsEdySPVl5MqI/oVgWgWp89C7ppdolCoUr6T3cqAKjOly4BSP0YgPgjKR92p0TDS5q24c/qVNEXBbL0w1e17b0jxflhvozpvU15IXmQ1dwkzmZIl1yJjuTR4E7EHJBfWJt9onRfcLL3OmZ7sZRMfFaNxdzIjKVXedffBMI6htFJWEe97fyjZqucz+7zjpYYhO9pLKZhoRBBfqA56Kmb/JbyFvLLdcHtdqy7SYbdampSMMK32JYXcR3wsvW/rMHrcQocs7l9Vi6TK5JGiqNK9yR4iDHtaBGl0ILjEagI9TOA4bI1Jll4WawqItHPmaM9kpy1W5Kh6EMzIQQ/cvhjkjv7UbiBI5S0HXWB2TeQM4/tjWFtevsIAAHVeMrP6IVkwUpNLrxMCN8SgBnBIRuzGCNxiM9zp/gAG0fJOdcpSUNqVcFL5jSkUyB8azQCJJKfl61q+nFLupRAs5dGeMGhds4f6GQf5UIVeZ0K3fNaDB3TGJ6nasrnn5VKwoAYmfR0HcLR/nPJn2dWnlwqqdTu9X8dtP0DA4icR9FCEHs3UvX4vQY82UG2812r9zVygIaHlEUkFTda7zMalCQhC5P4XgrUnTa2DW+HV6C9K+CEY+XPi/8mFxw3WM6aOUzcbt/nRChjfjRvk1Sg6Ovaa6+VwsLCULoctDZNP7EO2qXDF2puBeoDvEy27ONUCJdf+i6EVenF60wz26HRC3BI08Gh93oBodtAzkV72uHewhm9D71W1YjtmQB+9AIW/ZzfM77dun9/gvKK8TipZ/KnwVsEAOkGa248QukWzI3G8HTa6luhvzxQgckogEkavwqr+1yoto1/GAp3wvNqRricnjh3VCq/8Y1NVAV39PwLpKLv5RpfIZOpDKbyKEuXK4UUpfbSeK6yOMfQCvNeswZ0OVeBz/F3KEj5nWT9cKYKoRsbkRi8eMLzuOZKLzjkedLm6MYfBsb1j6LCTIDR22ys6mZI2dQIDuuxflSQ0aEFuRlZYc5w5+LfxsB7yA+xCKncHiPHzav1G5mayHmzNc8SKU2OX1CjdItJTExVjeHImwtGw8FUBEfKOKQiFMj8R46HhyoGeXMeOQxghcCGVoU1pueU3kOGoIsiL5ZFoBDhF7luSdBSHg15uNgUoNVGs1WnyQ9vX6+BQ4SCCeL2fhapaFeOnQ0PUgDVCufJ0C4LC3LODs7Xt+EfkbLlWbSDbX05UmZ9WeulU+ExgsZR99hb5PsjpUzySLcitMb3RZMU2MOescsPF8YAhJolvq9L0ie5UHADFZd1JpCUR8rQG+q9xTWUAUwbDw/hSv+P57QJTpWfqOe/GUEq55k/1yy73tVCqtRv5us15QWg80bb/z+LLLlGC32RC3HIH0DszZA91oeez+7HNgWret9Qn2t2Ryi1Fr09eReHIKyLf8YdarHZ/l/CMQH7vBiLnPje5g8IFjmFopTToZMMD/arWIFX174pscl2uf/Kk9t8vzZ7g9z/2o/yyKNPKL34Ng/QyR38P4E6+WK/puHpyUpZj1AmgFF1v99oVCMhLEAdyJfLEDqOLoK3CRWs/MKtzrlEVRs7Y3t3aL5Yc9NhfpqxeETfJmVKKfIVWZlMYELS6dqep/oNwSKR+tgcibTleo/XIwSateBM5XmsgVqLMbzsbYSNmPxvveCQxwluzFAycSHHj4/8Iz+XbGgfW+qLVTeGLxN2v4vHv9U+50nvXi3Cp4HG0HAFeBWpxJKy6VExbS5VhRkEXaR9YT6hrq6SUbZaalC4E2gswnGkTVCH7fD+xcG7Shs49itZ+r/rxWFLwZegS0Yc/r46TgJxJ2T+jIUn6gT+UDXGDJDLPE56/PSiHXpEdVOADl5HhrFpLFixQkWGxOh6MRAVWzgvI5DU+9tQeEK+R3p56cmrGniNOuWAd4r5dLrxC53cecYamNJVPvDMtlXgnVt2A3IroV5B+xEg4+RlNcorqI9jfCZpeu2PlQoc8jhpatb9NQoAUQtn84dCwbRPkfNao97Pa69HniBCuEYjFQ3zvRpkGAqpTlZpFj8tvFQqCvuoZkYPH7c557he/gCH9DPZxzjFVRshqYNBeu6P+dU4LKDZBrCkGz1elN5jNXHeNxaJhIdOB7Z6m+ae6XWdf0GsVEKPOaa7W458y6YAit42MgmKJghLE7H3PsOpCJeX/kEDayVLEHoHdczgq32pAUe8VSc7kedIIOcoNakxJz5ubwKC9fH5rOdL6sfKQQdjNMryGY0e1WO+tUk1CmBYWEP1kgM1Fruwelsn+6ZCTEeDQ85x8HUgqsf7ka8V1WEoO9ic8frZsw7cK9rc2OFj4RVYtn+lzM6dKwtevLjNRSlcvac+XCEDBg2Vo48+uksuZhggdtLLksqiisbcOOaLOVLHaQTCIVyP+W18eA1hxYakYd7dljZI2ULvjB0eIxu4ANtqrCpltXAyCzgYzjzsb1IekY08sr4qz5FE2C1ZAfLoMpf+BiFiFI2gYtlcu88blo5HHmENikPqU8Y0GSKQU9ANvhLK8CmDS4YE2jo45DECVjPy23QjYIxwB8/7iCxfK5ThI68hLQr7eTORT4j4X/fvjtOHUYCMXluzU+MrJPE0gbmt+/Fe4u7a3ueokHHStuclUfbIZXcfJfu2TpH0nhslLWu7GovzSd30d++4xg2V3wlgyG/O4nFPIW/yDolAwQqvpRvJpqlMw5xHFgkl7HoTr8mTCE/HSdHEF6Ue7yeGnQnAFVURwrQ6DQ5VYeq6zVCazHW4F6PFdmeiPWTYAEpoA2ZtELMpE7NFDLjRmEO3+2MNsNGzFN/PJZQC040E0OTUC+aRoR535K69aH683kUBUGJdFlTo6bL0aNIYyjYavWe9TvZ94bMAp2LQ9fC29TA2826bowAQMj+Fl/rwJl5qeuNaAii5HyFU2RgOVQOCbzEZuXicmjH8S0oXUruQzLob8vTG/dmOELd3CrILKiwEhzRqSW9FiH7837T3Iz2K889FhXwjh2LpCq1K2tcbXlVVGe0DiCQOH3RVgwyAZrIiokYhjfF6xr76NoGp0fCW8DN6LXe96zuUfpjmVaUSCoHyns/NkgQ5vyRDKN3XOrStKGS8THjULhuQ32iJ88hYEIJ3hvH1mfRPu/K6uFwuhPU64yrhMcMrEHwFKuyV8tiyJ+XpW4+RHhnII2mj7Suqkuc+XS2ffPp5G3sevOZhgNhJa617hTg8wQKrf5XCRCddj8OygpiVrrT4vR9JCa5sA9dfW60aXH0MCyclpYrZCnBaVhb6EJZo6Be/pyVE4Zu55zea100fgBQyDSiqILef0WwolHEgr5CePAKc0lF/wcJpX7ikyDGGdVWBDIAo7zEWeX26BdOg5jUJMI0whNXClrp94oiZAE/oGQiN/0sNo8LW6ZDLgzdUN3oeWfxiXb1TatC2cshNYgPNDouGCDjjEktk8IQv8Cr7W6Bnj94+evNYFa2qFiKsAH3/VMTk7Jm67l78kBirijq4z0r1SjwYXqd6CI0hZXonSwCyM5Zfi3B7kTpOupri8c+oQp26bkd6waw6yT9AZ8kbQay+9305+/LusnvEGLGaa6TviLlSs/FcVfCkt6WqSupYl9gB3HJQkRudJuo5930NEWUf7WwCDgliVtymVf2OHj9YJp/wpYyZ/qqsXXCZRKawcMMtX4yOV97K4beAGw8qIroNvLJeeQAZvqZSxlRQsRgzAOjhXHDVIKHKBHMBCTI5P4ZE3ds2ypj+d0lm6Xpx/ZAq+dP/g5SE0PNy6dkzWo/joNaB0GSgbftXpLegY/eHJoTcNe1cgqzR9zr8PLDsa/TI0kOng0Oe2/MpiirgoWSep/KEkgT8VB84ZBvdWMWc0LfpfPTzxmd6XLnWJPym9TSAbO73Pg1I1V2nim26gaqmR2NImdXAc0+K1fphLoc9a/fmi7JfW43XUddqa8eA9kyHICBn9XFzr0lA8/BueAUO6go8ueJZOXFqXzlxyoB2Xff+VxcrOb2BAwe2q//B6BQGiJ20ygwLp2zSvG2OpOHIyxrdSVfyDRvV6LHUj3C/PQBR9ae7wqQBNH28Nj03um0a4HnUcwTZP3nHS0purnjCs6rYwjsm8hsLURXN0DxDynrhCs/Tkwg/EP7Tviid8Tkqj7Cq3xUAk5BkQ+iV1DKOtIne4Ywb9MIZwSHP1cO76UgapZqRAJzUMlGeKqlKPRL3bXANoQUVawgGaaThYYEJQ8RKaxgayXrlLgtnLDW7xIwv4eaMgDGyBnJ827fBjRalpA0JUnXj/VnAI8iqXz8L4DFxUwsa/JM6OGRb5mU2JA0VR8Zkv67cSdr8hALAOpdjtHu3DB6z29suqsw/v5HgRlcH0RuRZJq8hARrzdGhkJ5Gp7FZ8uVN0nvQDzLznPtkyjnPSMHRX8kXE5Eq0Kgysv7RSEUFEw3i6fzvIB2I3Lihf3RI+kRIJkJNJCDVFW0gPwZwSCPvYCpy8WZ+qK1x1g93eJWAqElOXkljqF6ff7DnnLMbMG8z8vcsCnSO/UvzHq9A3V/9Xqt3AMCgkrn/5fWSP8eCAg1Nq3ewoWCCnj1605wIddNIwM3CmKO/rlX3lQqaFubIHajRe3fUFwgZz89Aqke5UN860Bje5sNolPvTQSWLeUgPQzWPjcjN5D8FViKnjDjw+Rmv2do2ydXnnQbOSawvSvtlIsi5A+fd2hjh8+EV6KwV+C73B9kJKre3r72kXZdYumG/zFu5W757/O129T9YncIAsZNWurrfZSrMSyoS5qwFgo7OuKw9fQqURt70Ds39kA0eJhaBMJzZJqZhwwWs0GJWhRAATbqVj7gHnq7rkHfn80JSSo7KLwXTv9Cbac+IiTUBRzjjjMuR8hH3QlbwVYBF0OSMvF8VvVBSri5zBsLFqyV272cqRNtgIM/WBydwkv0+N34VwHslCLEZQtfNnoG1io4WD6qYA42vodHikfcYWzhHHYJv2As+6f2sHHAN1jBbGmJ6S3z+f7XcSqePakYfh1XaNBbVpG58RG3Te2lHhXWgJezyfYhQ5o+SfSS/JlzWgW8dchSZa0mjog1BMYF1A8LjetGROtn4x4O1poY0zZ45vfFo0ydWYCduewGh+ESxjLxDkZ03bYUxiv3jmaXWU0Clkyb0Ros1Dp4rQy9OHI/SVSZZfAViqY13MfJOuyreMLRUmxZUoBrNanA+1+P1jqzapE7zR4SiCDI2bmWbAGjSk01f88BurHLeg0pZhtdNmI/bIKln2x+hCJiPRN5gczJ/FAOa8q862fKcRkg9HOoqNHoGE/o2BXGB125tn+FvFqVQ05lgdfhdsbJ7d7Gqam6tL8/H5xhfHBTlwGNHpRbeK23JNSY5YSEQ20G0ggUWn7oNQOt28BmGAeJBfAHClwq6AlWOanlu1cvy8t3HSkKs7zskaIeAExqtzUK54aZbuhytTcBUJQwQA1ekA/cJTA784z/0Cdm7TVP5aSTZZg6iHeApFKPCB8mm6YWimknR5Dd8RNSGAWLyZytgVA+PaHXORfhe93kYqaNMUMKvciqJVPe7XPVk2Hf/0fNRsPOw4hXUhzOG4PVjLT3XIp+SYCUalDNpyOm0Vm6B8pUD1/N5FqPLlsm+Y35U3jnjWJxrBIiuWZRCXeOanAuNp0HqvUOFcU3xAHaZJyhZO9LM0PvKSmR6KqNYVQz3GT2PZnu+t3+gqgrD48xRpDkroR4Db6AdHlFS96RALg9kK+qcLesY9VyDUD6LkaJKV2Bt5yJ/8wroSJ+qFGNIlh3hggpF7U7Vln9MAPIeANt4gEYdHPJ4de/z+aTuM3Phed5cy+YKblhZ7jrsYWmoAMC2ZqFg51QQopfgNQfAZEyz0UyOMqQs3OQtlDGvuV1pIevnjc8ME7OQhWTQSUNdEn/52VIac7ZqwnmOvhfybHfzvKiKXVbgBvL7lYDXj3l3gcacv74X1EsuPFusVKZaCq1ys0kWPPyYuIr+JONP/0Ayz+3ppRcKHONA90kuffz3lLShDPegAABAAElEQVQzKcoUUvKwoIX0KSTG1i3Q+6kfz0AoOuOw5j3LepvAZ+YukraG3s000OSMR26fQXlSNWfRET1t5HskyTeVUnJyAkdqeT9rugteQruqPmb1eN9L6hU9jN6LIWhbAeiEUAhE72fiAH9AqbfryOdQqpQ78nrhscIrEOoKvLLmdTlqYi85anxOqF382n0wd5PUNJjl4osv9jveFXd83wZdcXbhObV5BezIP+OjLUZNZ4JDGqXu4vZ+6gV4+jhRoHjJWHWD2mVlLGONyjuEIwRYBIc0goGkbc/59weQJCk0iae9xm+/NljCrre8Ch1RFT95e+phZx6g2gnDvU4STRsN168acJU6Ql7CjCUAfPA6ku6mdOQDUDC5TahnTYva+b6XRzBy8xagvDoUhIyVvBmosoY+NGmIkqHRrIfN6UF0YRwV4oWnkzyAtFhUObOohBaFB/MJC6Z9jOr0BWoMchnqxgIkEozroJlzozXE9cOjj1YNXa95MUm2zUIiV2x3aNZtV+0IyrkmvG9L7W4vOOTJaORo1kDuLh562Cz8IcE45e+69ewv61+tkT2zG6Rf5uvSLfZbWTP/WvHkjJBeFyZICihbrMhv1KuoOZalscCH24HW80QndIBrFVBpLkxMrsCep6BqGS87eQFpDLsqV2Ij1I2F56o5Y7bCOKibjH0IMnq+3ySy/OZoqdzEA9ky59k/yokX1qLURvM27kcFMh8Eqw1VrHa1SLdp/pXCzV2rpWPkHNTpfUifUooq4ZSRrmbJt1saJ9Rzu1FAo+d95n0LxRN4CEf+X71f973/sXjJwJnPuP01q0zUfiv4tWttZ9CVDcKHblkznUpikPssGJp3Mrg7UU1NPeLJz9s7hBJHv1Zzz+S1HHmXXdH2xPdxqzzP5tqFj4VX4GCuwNbSHTJ/z0JZ/sDl7bosaW0eeG2xorWxWLo+/Or6M2zXyxDu1JYV8AS4PTwAEoFGD5rRokB1owdOGX400q24mikSCKxcNuYYGsdtdhuhUF3zudnzjQc5B4ajW7JE8CHGwAtJMyHfL2MZlGEMfl4dpOljJG9/Xm3W9DpbKEtIqwBhNUGhtSYXYOs0lV+qcgcZnm/0wDEX0Wik7alCCJnUNE0MXkJzXUGTw/Qcbv9utOTtvEn6Dp8n/UbOhQdUA/9lwxG6r71aItFGgXLkRjI/kt5BejH1qnALCM1JtbTvKJB1o/RTX/cdnzll0R8YFoaahlwtZsvl4nLidQc23fkpCp7AKzftvcGSAk5DvUCIFegtGSXrYnu4gjZhZa7RqrYS3HH2jaZhO32vybMRHPKkrpLCbdKqsBKXnkkWNiy5VrsYCZR1K1ocrcAccykP1Hgdvcgj2FiUy2MuXQrATmNKbrCmzR5nkZDRHIYwPrn9Nv4zSkgSbbRAbkTjubZsT3m5TgpAi8N8VMrx6Yor5EzcBe3qjuBMbG0+g34H0IpH2MIr0FVW4MW1r8htFx8u3VKQNtMOI63NwMHDZMaMGe3offC7hAHiwV/zLnfFqgFXKw8UcwjpfazpdWaTOdZBUi4p4kklBceTdeBB1E3J5428H7rJTwOEJCBH8AH9lPeZhSXM/UvMhZoHvnWoLRyKJW59DlW7AD4gCXdFoqoauYys2CXtjgUAjVq+yqNoghoHr2sIkQaOTxDWpCDDAA7ZvgGSgFaAu8AK5DgoqVAHuWLwDcpTV43Qs9ECK9SZD+hBlTKrpVlIwzBuUEPIuLbXGUqtxdhm/eLzZfa7j6pD5AA86/rzJTv2v1JF3WvIAXr1qA2dSHXDMDg9qaZG2h9qQFsQFne7kpTGN8P+BUv8vcwKHBrGIefh9tejJf6eV+G5XYhrJSIU3jQ/0tBFbcbD00tpRHIwlg//E7ydaYFNvPvpfXNlhwzy7lPqLRRjzh1zB6mDvOlJ7ccMtZ7L1oKvEZyK7haGIWg7GLbxiUjv3Hqc0CCHP9d6niPnBSe4OMo1ScA+ZzUoLkR7kUks8SiEuVTzHsJ5L4tAdq5zR9ITy/MsdBl2k7+HMZR7JRAt/hEUN6C3ic50y9r7o1F1bZIe8AoPA4F3oExgHDx6YQuvwK9tBX7ct1xKHAVy7ZkntevWC0pr5OmPVshHH+MX+CFiYYDYES8UP7HpCWkBnHTEZTprDFYNU0ZOlaga+TkMF2Q+ZeGUd1UeYH3yCBSHHGU4C5Le3merh9/BgJ0KgEKCUepGB9LcBDRVuwxdJ297Rm2bEO6sB0VMxcBrJQYk4i4AueLxTwGQogIgBCNgoqoJaWLoqPL3u/gGsMNDVzIOrPaojE4D7YwOshjKZo4g1T/yZn7r62DYslZtAbjcocLXCbnvqDOk5Cma9II0AJQ1a/COkp7IGZUpZUP/hHubh/5rFIjeu90fxO3dOlkyjvABHFdcT7gztXC0cWzqOtMr7AuD462JfMLMJZepPEref924L2WtjMSWthKxiUViq+pmHAaFF2gJJBb4Wvs1atxh0U3M/q8kaddr6oi1cqsCyGVj/9pcc3XPh6f/UaLPukL2bj1Skk8YKL3PTPW1xbo0VyzFCmiqc5AlaQKIo4+CBi85GaPh8ZxzHH7Vo6BBGSpfA7dNkfD6AfS012pAbUOOQ+YDEqAGM0rekQdRN6rR1OQ6UAyCEy0YVU1IQs7cxuxZTpn8Yp0cO7dWUeQkDnSLLuHH+9XBIYejl/KkZbXiglyXFYTbbTE/ihuEj8mBWTBP+1qghzd5OOiEQEQ9/HaH7EM4Owm5mMOhVx228Ar8mlaAhSVvbnhb7rz8MImKtIjT2fbPkUfeWiqnnnKqDB48+JBZujBAPMCXihx1yRsewShQYIDHhEDpkLUg4FC/n3rkEfJxIKZk7kIcgNXORjNBeSN149+08GkxsAs8eqVjNQ8b25GvkOHx5rxWBGG6LF0wcMiCF3opCSbrUTiiV/ka52Cx7VOk1oEAN3b/lyDivh0jgKcPQWvdqFltBiVNsEBZ2po7lEqI3t777LZLt6n1snmZ9wjy3+r9vLMVg29UOY3Mn1TXRBzTiQpyFgnR7ZO29i6l2sJCG1b6ssiGxvvv3e01Ofazx2TPHKeqHB5afJZsXnisbFp2lpTmDZb0sdUy8Eot4c+MUDwLhOgVdKBwJtB4Ln3FH9S989z6H8+T7z+6H/guQkY/YmqWE485qzgt42a+qh4VA/8gVRHXqer0jJXXKy1ueqlLxj7urTan6suW5zSPIUHR2vui5MQfsQErXoK56uAQ+9EgyO57fr0kDnZJXHeESTdbJWWio918ers/sSieR4ZYCZpmfGxrQsejJoI/DCdTwYPVzTQqi1gD+BbViYA/Gx6L8upHszCFFEAEZyxwMRrDyJnTnFK4QPv4JuhdcXu07P/SKlQyOf0zvM79jD2Cb1Mtxktxg3sr/8n33mUvm9JrdskQ8FbyEbbwCvwaV2DxvmVSH1Ej580a1q7b35hbIp8u2CILfni1Xf1/rk4HFSB+9913sm7dOklMTJTzzz8frCLRfvfd2nm/xl1gh5WxKev/osKInE7qhoeUhFvQb44uMOdDYQoEcyTBbojtI7bMo1XlNMO1VDNJ3PWG9xZ07WIr+KhSQWRNjxlBHoF6DausDUZw05w1xPRUoIs5lrFlSyRlCwAJzJ5CgNgU1tkyZynvZ1TZStC/PAdPXYxSnuF89YIZY3ia4XQWm9AioMfGYhsTwF91n4sUDQ2LWYLZ4Gnfi6P/SdDkNQuLBqKOvEaMUMEFOh16M6Mq18G7OlADxqCv0Y1ydrpxTY3mSR0mmX1BtTJa+9I3L82WCbNeUg+XxEj+0d+q8UjwTs+rXsRTNvyuJmsbn/uu994bHDEy998PI8yredBW3emRnifW+BFHcx6U44vL+9I7JQLYjKW/w5q7tJQBnIktnIfimk+U+g4b0qloNOM+vXosjilHmBktZfA1yOsEllx+I3Iy0W/sPa52g0Nec9sruB8AKBpJr4sWmluUcJv0ZJ2svidacR9SE5ocha0Zq5CNtvEfUQCI/j+S9PNTXqkT8hci3RTeVI8svBQuVRj1rH+40yZHvK+3bPk5LoDihgTkBfPwTgbfZFS6+4A8ri1fOXw2vALaCpSUlMhnn30mtbW1Mn36dBkzxl9pq7XzB2Md39/8odx80QQxm32fr2257gMgxb7it7+TzMxMSH4eOj+0DhpA3LVrl+zcuVN+//vfy8KFC2X27NlyyimneNe4tfPehl1pA19m+CT1zQjfRAQH/h/zvtMHvMViBnikGEI82EaAQRk/VuNW9b0M7jtf8n9HzsVSvV3SV96o1pFlBlQuyZvxjcqB43UIKnReQiWJh7zCzEXnqQpmnidIS978T4CKC7Dj+8dM7ejyIbeCS/F1lcfI18ltipGS8U8oGUOG0JO3PsUhlEWXr/TmPPIAi0NYkEJewYylVyq6HZ14mgUopKqR0qVaZ/y1dZuh7oGE6a6YLHU8A142XX6RHrS86V8pdR3yOerG9w5hCCuOa0CX03MMqn9PCh7OYM5n6YZEWQIKGVtFJDRqnTLoWn0033Nd1tFSNuxOhLC/hxd4tESPuA55AUh4a7TS0Y/g/p9GKLpUVaDrXtjo4kVecMim1KDWwTcLcUgGH4X7riztIXk7Jkl8Sh7Aoe+9AVaeJsCubK1J9u35nQzoCYDaABcpvJ0M3zdnEcip1I28gQN/Wyfb/hUD8NcgY+6m91DzcjLkO+MDmxSD9Jrew4S+bvl8FJRboM5CW/2AWXoitbZR3U8da8sfjokUXa9xvyVLGw8i7K+aB3dKIQTzjAVlD2XudOsHehmSdmvvANL4mKE3rOUj6m30Z/7z098Xynuqn8BzW/jts2dCP5zh468skgyKm1F3O5SCDsPL6ZPwoyQEYGu4dHgzvAJtXoEPPvhAYYHU1FR55plnZNCgQRIbq/3g4WCtnW/zBdvYYV3hRinGD+XzZvnwSluGWLh2r6zeVixPvHpNW7p1ibYHDSDu2LFDRo8eDQRulgkTJsgLL7zgtwCtnXc4HH5xf5aIm1CYcLAtAvEj73UjE6Ry6M2StOkfahqVUOSIACmw9pXUsTOLLF0p6ZBWM8ELRSm10omoru3knEfeKx/R8FClg4ZF3RcAAvPyWChhBqVK/K53FHir6Xe5qpI90LuOqt2hgJU+Dr2E7oQcfVcKp30C8mmQQEPL2J41S+W9kd7GaMxxNJmbvrXNzkroOWt0MWxvguJJxsobpGzMX6QeKiwERQRINBJyU9klFoCUAK+u+4k4ityw/01WIF01avxDapmiKW8pShgrAK6t56lSA6J03WKg7pGw5TkUyKzQDyGMvQ80MvlSOuk5SVlzpzB/0QV96Yrh0GUGyCVgpYcw8B0eAf3l1NW3qzzFOhBcV4O+Z8E9CVKxr7sae93fRbofZwNXXVMAY+t/mfBBi0FhTESEQ6x1eyVt+fXIndwJ3scTpQzzIbDWr+tMGqx+8Ojv6YakId73fzpC6sw9nPfBg5DU+40al39yRsyT3PUzFc4ZcVuDWGP00US2v2GR1fdqYeL1vf8ox3xVJ54PH5MNn18OycIimXzi4xKVXK/SAajlbet9ltTuMivglzbeJbOOu1KOHrxaLFaHuFMGSlHEh7iONjsTAhLZ07X7dteb/H67cV72AiimrNOoaZIGNV0ftglm4x6qRxg3Qmr3RciASxG+H82WvvsK1i/wOEPj806JUZXBcJdCIaRecs7SfgBEeDievtLYgqxgbAY/b3zHAsfjfuYUjyaF+KEFXj+RaY/G4gdGFYiyW75H6mtvew2fo8Ck094CT2gqRzNJVJJIkgKubb8/jnAwze/z+GBe+Ge4lv69oz//DFPolEtWVVVJr1691Nj9+vUDyftuGTp0qPdaLZ2nDrc9QNwgcH1a+tfT0jl+rkTg39Cn2z+Xq04frXIP9UnxfRfM9O9N/fx9r/6oSLETEhLU92ng/NhOP6Y/633155be5/r1muvb0jz1sVt6bvot2lLrAzhXUVEh3btrX2IxMTHKnWwcrrXz/BWxfPlyb5fDDz9cLrgAXqKf23o9IJ4pN6lZpEQlS2f94PasvJLxI3UdFmn0dG+QiD4nt+vuPbZCke+vFynbKDLoAomYdFeL48SBCMVoiXVbJDEL1anvzICrrECdSrJvk4iTPzE2a9e2J/0MEaiswI2h+kcOu8D74aENiA+SgfDWNZon8ViRNQz/NbrtEfI1H/1iQB+tsWfNTr2b99kCbsNuP/5GBOFVOf4dkRV/VYDDfNgD0iMT1xk8ydvWw/WCBzfQIrCGPQcgLDLgXXWK8Ed/H3jq8Zp98wf0q/Hvlpgj2YNAhE2QP3SROkffUTf/Vk32PIvxw6povjoet+8LiUtIFUftVX7tUgBou/dq/p+2p2a/yLcAiaXrJG3AOYj1VkrhhihZt+ghSYD3b9zDayRyxGm+8fDB7Yl+XWQz1iZlsMRPulsS8MOI5gG4LC/K8QOHPG6zHCVX5yUrB25sN3+Q8f1HlWihed1r90DTeEmGfPe3u8Vp592LVJX3kdO/H4EtFFwk9YP+tcjs46sEznPFgXgWJOx6DdL+HUjleumJhfbE9gTI8f/ALv7JCYDY2A6jZU+1yHenx0oDABody2d8GS85x+F9E6rhbTd0caiNg7fb+nE9wGHjewE5k3veiZUjbwQigyWf6kKeY6WqZOb+kIuipE9//DsLwXq9j7fYq6hmhuOFXxQeD9yzLZjH7ZHXj6mU8q2aB7N8aYxcuFSbRwvdwqe6wAroYKoLTOWAp2Cz2cTIB0jPIUPNurV2nmDyiSee0Jur58cff1w5oqKiohQg43N7LCoyUgprimX5/jXywfk3ITUuLuRh9Gt+NG+DVDlMcvvtt4vVam21f3tf24yMjKBj19QEfPcEbdn0RPPfIk3bHfARgkJ6AWkNDQ1CNG201s5fcsklwofRcnNzjbsHZZt5k4G/WHwX5pdf51hGPTPDfFZUUi52027fgTZspaJ4IX7fl1qPFQ9LoSlH7BlHNBkhKSlJ/UOrMg+RbMS09CKHssTJ4tg0X7IbwSE7uvfNl7Ifn1fFEMwbrG+H9jQrbZPAO2hOGikN2X1UQYQ9bZrgJ2WTufkOmCTysFcVETQLNFT4OwJf/AF9IuC6SbT2lea/AuFpmXulIp/On/yekLZH6JQMGCO6eLUfgCNJdsmEp+DRndGkrT6/uN3vS5oBHFLirrrv5VKdc7G49gKstdFSS+C1NfSpdJjlsJOflzlvP4hQrkV6Ttou9chzCZi6twdzNeMLftT2N70m5e6p8uETH0pDvfbhV+jaLmOeC1jvSIDkUY1AOb8MffkAoOl5tkTkzVHbxj8xqbtBB1GtHQocqgfcfD/pH5QeKSstBTj0vbPzC46QFV83CKlssmbulw3I/XPVax/wzObYuPZSAERt/qQ7+vb/zLLtVWgwo3Bj0lN1qDDWAM8O8h96cK1Gq9rTAHCogVXmJC5/vlwihvh7nvW2bXmu2m6Szc9Cs5g4/48OpfTSUv9aK+fg+6Ixp9vxWvl+dMz8xARJP4uqeM45pxrnSlsartlzOTk5smfPnhY9iHVQRinf6nsnFSx3yY5NuxXAbHbQLnqQX7r0Irnd2uveRafZIdPid09KSork5+eHPB7fC13ZCKSMOXncjo/3vS9bO0+P41NPPeV3izouIN6gF93eiDv8GmGHP6SCvW+iMS/2/3TTf+WkyQMl0uSUykrt+52ANlgVM3EMx1QRT5db7nx+ntx0+72Sl5enwCrfr8b71efE48xP3Ldvn37I77kl3JGVlaXmVlfnS8fRO3Ou7BuIt/TzrT0fNIDYs2dPlYM4duxYYb6h7k3UJ9jaeb3dr/W5YsjNYl2Zi0rdfIQCTwGgO7LdS2G2a14/fYDmSJr1c3ymXFzRhOeQgzgb150qddnHKEk3J+hxqN+s2sT1giLJrWqbeX4FRwB0IF+xLZa25v+EnH00tyVOhU9D6V+fOk7K8NDNirC0pXaP2NMnK15GbmcuvkhV9LoRh2SRSHNmtuUpoFk14MrmTiu5PBf4GPWCDSWzx8rgYOEGIJEU5EMarabH6agy1tbJeLy17SiEqVkIw7xJtyUBIdhqcUZnCaX6Um+3y7knvCK19X0l8ojJykPW7Hj4sDTXaa+Xfj6v/BQvOOSxgg05+Nv0g4bnAo33EQV5x/Gr35OVn5+HZfBIdt+VMvGmPCnfeIwiru422Z8SZswDmhpKLapj+11Ur/LoNj3l9pJeR6aY5IeL4QaDdZvqlF6nY30NFjl5IvJSz1Th/N3OP8qWFzRwyYrhNfdGy6z/AlnC0ie5VIiWxRa0tLEeqTGAVaqSHKhRDGjhJTFSV6ABz7LVJjl2jnb9io3wjuIeMyY7hYoo9SC07ntRA9Rm3DLqHiiEvAPZwD4eGX2fP2VM0lAoiAxt9IYf6ARb6K/yNPu74M3UPLepY1yHHDhs4fbCpw6RFWDKWSQ8dfQa0nvIHzYzZ870zr61896GnbDhAqHq/3bNljcuO65do7/59TqJT0yTk046qV39u0KngwYQR44cKZs3b5YXX3xRmFNw9dVXq/t/7733ZNiwYRLsfFdYpK4wB8qx5R01RwEz5tgdiLHIIBrSeVTbcEL9gwUMLVksQplpP92lKkyttlzw4s1UXIaFk9+GhvH7yEPbKJHla71DsHgjGoCmrQAxEiFD3UhJw9y+ehBOt8UoE5jKuaKTE3mKBUd+rHSWzfWaJ4bg0BmZ7peLaBy/ORUY/TyVSMgJGL/vU/0Qik6WAzAf69332wB4jADFjdEqAfRDNgA6SvOZ6/b56H3QmVrNLMJpSBykqqjVeIdfpCT9go0d0VANHsTLlYeXld7Mc3QmDpSoCbPE+g+3kqNj38xpbQNODmh+D3xwq0w8drpEOfeKPftIWbzoRVn3Vw3kpQB4sHhEr2mip++wp/0B+szPbFDqwC9dUMMsu8X33i5aZJExf7bL8NscoHRh0YRLci6PlbyqvyiPoX2FBm70ezbUsyjS52nv1WnFFwBdAy+MkPiBTsmfbRFW6rLK+UDNAYJpHRxyLIItFqDs/cwqq/4UhSNYZ1DcUOWFtu11q5y0uFYGXtGgHurgz/SHYXauz443EBnAt8CAyw98PX6mWwlf9hBfgdNOO01ef/115ZUjFkhOTpZly5bJ3r175ayzzpLmzh+MW16et1oS4ixy+IiW0zWam0stJPX+9s4yeeLp55XnsLk2h8KxgwYQ6c4977zzlHuVvxh0M+YRNndeb/dLfjY5SuARsqGitXert3mg4JAXIMjJm/GVqOrblLFe+bVgF0/a/qIChzwfXboMxRYrEf49TKl51CcNh8TbK35dmRrvwLhtNQLVeCiW0OidVOAI1WP0WDIJLS7/K8Aak6K7CcZUHJ/7jgKHHIOyedFFC1GNnMJdr9XkXCiRJUtBa7NUHWuIy1GVzY7so1WlsrchNhjqJK1JApL2SVTshIKJ0agwE9TAs1LV/7dKAo9tqiFV1xYeyNSf7vYDo/p1SAnT1hA+QS370QgOXb2Ol4pJT0mk3SkzP7XJ7o+tkNcDd+B5/h47/ZotPROoFp/wJcAwSMgBorff4AN55WvMUoZH+kTEh4NYXOxeGXv0YmhUD5eNvSdJzS4NUFEhJDrdI0OuAwffdchPhArK/2bEKVCWNtEpR7xRJ8kjnFKxXvsYg3MXISWfQ5fX5EMlcQOhauMcOBDa8ZYVldSREpPlBhB1qWpj3hq5CSknuBPeQa4yTQeH3HYhxF26CpRFIKPuCkawPvzWA1+PrnAv4TkcuiswcOBA4YOpZ3qe3qRJk4QPWnPnD8bdfrt7jlxy4tB2Xeq5j1fK0GHDZcqUKe3q31U6HTSAqN+wERzqx4zPrZ03tv0lbMfv/je4FP+MrxM3JO7Ogt7vQwfltlxI7OcjFCPAMlKxUEZONyu8fEZjXl7p6IfbDGA4RtnI+5WcG+lTEna8JroOcm33k0FtU6TAKdvVoZK6GOokgZYIoBpZtdHvsBP3WJd1lKq2jS75UejxckBfmPJ9NIaMiya9pNaCuRpIMPX2ryuKUIUNlJ2jlNmxj34hvV3PqPMEqqUj7hN75gy1z0jzfpAOA3tJjxOcXo9Z5ZCbADpPV95ahrC7wYvnikxDmPkWVYntvVjgBuKXcfu/kLqaZNm66lSJSSiRgWO+UtHsUJRNOJwJGsyp6x9QFcpNeCBjM9GAP9Scih9wwmVz4Xm9R0zz6sDreJOXezBwWkH3EWYnOKTFdncL10wZ1DkIpAKNHH6Uc+s5ZY8MKz8NNEWoZkajY26+TRb9+1oUakTI0Bvr/WhpNj0NVZfGcG7pcotQZ9lR0ngd9C1bbQZYNEnKyKbXC7x+c/tGcNnceR6r2mZCKFvzDtbuNgGAOmXMg3aVg9j7TA1cx/V2qx8VTccArQ3k7LqSuRDh3gOPJ9+/fc5oEGR2hC28Aj/LCujgMNjFWzsfrF97jlfaq2TJ3lXy/NFXtLl7UXmtPPPxKnn/Q83Z0eYBulCHgw4Qu9C9+08FHioLcvMIKIIncfl36Yi95C1PKHDIseg9q+r3W+T85XTE0B02RvmIe6HKcaeYkW9Y3e9yhDYHe8cmF2HS1mcUAOJB5uXp4VxvI8NG8uZ/KGocV2x3KRnzqOIBpFaxYjXGM7kG4/Z8CPilfdmya2zefxv9MdpAMcULxFrxk798HQAVtaA1v43WrnzQjV6gWjpOI8DmGRbpEJDQmE/IauCqQXBRBdgeeNV0oEP5s21vJ8vgxsJ5AvrUjX+RhL0fgpj7LvnulsOl4Dvtn1Pmx5p3Sw1Hz+f+/0hk2WqJLl/h9cRyjYoOfz3gioZdVD3YLf3kvcdelcqSHHVi6GkrZMLtO8SWfYKhYfDNlE2PKrJptlDe4qQRINVeD/1qhKZH36LArN6bKQQ6v6Trfx9KfvfTJH1aDJQ59BahP4//m13W3IdiruIIGXQV7h98f0bb+a5VVt+leRk3P9tPMm/vIxk9NqvXLifiUcn84xL8AHjR2EVt821iNNLAUDkEVJiNpmkS63uhPpNHcPnN0Uqyb8j1LSuG2PGjQfcOcnw7AGr/S3zvVR4bfT9QF265BlXa1C0umIP3BboN/WO9xGb5rwXb/5y25NoY7/uWuZL0JgdLqW3rPAm46/JAk5WJn1Phb5q2Ll+4/c+4AnN2zpepI3tLVpqvYCbU6fz59UVy3HHH+VH1hNq3q7UL/7PFK0Jy5sylV6gvSH55Fk5+E96QdnwztuPVdZvjFLchuzI3zMMYVRCLzftaUjY8jC8bTS3Eln18kJbaYVN9heLea0gYACCW02Lblk4yhMhcvubMGddHqccY8/KCEXkzTzFxx7/UMCbw7nXDmpuQG8dvD3oP6Wmj1QPI8GtUB3t8dkLT2GKgmMladAFk9h5DuBlgCTmP5BV0m2PF7K7kEFBds4DQuRHNqSO+P9SeNlqw+VJJwmjRqf5AwARqHQKuhEX340t2jrcpJdAc5ZBbQ2Q7aceLqMxu6u0k72Brti3qeS84ZNudi8bJsO4+cN5afxY0Ga0aBS2FWciXROg7Mx5rUF0tRYvNUOQwS41plgwb/75sXHqW/O8tFtaAoLmHW479GHrYtm+Vqo0dvIvBjPx+VHxh8UdCf48c+bYGwJtrr2v98pzHZZbdm6YrgKi3jSn+Ae+LSvBrImZsMFYJM1xNzx3Dub1OpffTLctvAUMCikCG/B5SeiDTDtXwm0LK15mUTJ3+Q2DD36Og/NIg8X08sumpSPA2opikp0cm/MMObkm3ClkbFVsG/rZpiDYGJNqHP+/zRIfimQx1zh3Zjt7DAsj56UYVmjoSc3dvuoaVm02y8Z9aatCwm+pVjifTL2p2mSQB60LVGqORkWv+BbFSudGM1AU33g+2A1KyMY4d3g6vQGevwOzc2XLj5UPafJlNucXy/pwNMnvO023u2xU7hAEiXhWCFt17Elm9FZ68T+Epu+ygvF4MKbOowoRihopBf4RKij948U4Cnijl+WqswOW2DbmEwXLxGF7MXniWui966ErGg44FxSVB7QC+xdwW368shl7rE4c1exneo9EIAhQIBNBKAfehA/Q2ESDhZkFObY/TJH7/56q5A8ofZSPuUSHQKEMeXTw8jU5w/nVbdpUiVnYBbFNLmYCxYuhtqoLZeD19m3mBBJRR5atBUTNdanudoU5Zi5eJBfyAPMbwe+8znMgZq9cKG8a4pd9dg8S9KkoRhetj8TnGtEt9CeogIzrTLZGN2MZaudnY1LvNHwbRxYtRFR48RyV6eA8xRyNfEOFWWvIwf8DqHSzIBgEy75HezgZQAPG+jN/kxctFfriIP0giZIc8JvW2KGg/n6n2OaRtv0nqXn5J+o79N3ehRHOzVEPXOdBApQjy5zhVtRthATh6rk66HxM8zy4VhNfUGtYt/qihqM62yo+f3QQd5/MlOWufjBqZKIEpuQn9PHL897WKUlJXRKFiCY+11QiO5p8fK8yRxBvGr7vLFiFlayMAiDTUUw9Wn7X3RynQqyu2lKBAhp4xoxKK3yCGnWAeOYJzVXyDPMnsWcHXyzBUh24S1BHcVW/XQGJ0N7cq/tEvsvc/Fsn9AAA5x63+DdgLtXB++U9mOeItmywAAGSIPwYpBTM+skksclh124V+BIc0/rvY8kKUTPi7DzTr7cLP4RXoaiuwt2q/5JbvkxOnnNTmqd3x/PdyzbXXCalnfgnm+5T+JdxNO+8hsPDDE/hzuJ3jhtKNACFv1nf4jsKHa7BvEgxEXeAIAz0LdaDJS+hReWRNrxRbMNsLellVzGpjBRCxnbjzNVVoosnHnS7pq25G1THy8yAXVzL+n008N01H9z9CHWPdCEa4bwyTU4M4fs8HaAICX4BVXaJOgz1azwiAxO4LTlE7taDxKR3zN/AFXoJ7KAVdzWEKCFcMvkkyl1+lXwpe0V6Ssfz3ChzyoNlVKzXINyxD35bM7CiGhwqShVBOcSSPxOKiCGXn65CN+7vq5gY1Tf60zxHb7iHjHwGS4ENZihRO/bckomiHCiJW2151lGH3aU9vl41PRIKCJlGG3AaC1sbUOOpHxxbh9YW5wc/ojAcVTfUW9N2NuV+rioVcAYUvqjH+sEBj6muQlnvVqrbpuWnNqJ1MfWf+0GAommDdgms5Uic0KUYqWMRXwPcqbC2DostQuNV2+a6SkrzFuxOb/22zADHvfxYFDtnQ4wTYfCMSADG4B3HQlZCMjPRI+XqzyteMPfZY+WnOCFn27WB1LVs1eCj/0iCTX2weUOjg0DuxdmwULTI3gkN25hrg3x+eSatDqpmC7zVww7M0eih14z+5blMODNARHP5wUawacuuLgsruOul5Mtb+INvUV+tk05NRKgdxMIqB9Gpz0vQs+2M0ZoP7Xug/KeaBbns50pv/WZdnkl3vWWX4zb73J4R6/Iyvd9jCK3AorMCcXd/LmTOHSUyUtUUu0cB7mbN8l/y0o1T+9cGNQXkSA/t09f0wQMQrVDXgaoQK14kVYqt28LpR//egWwvgkHPxIHu8qv+V3opYbvNYMAustiWdDS0exM3Jm7V8vBgockSVrxHm9NGiS5dAVeRygIrBak0kaYw63tofe9rhAIU/qWYefMMEVjBnLvmNt4K2IaYXqov34WvHg1y4wWKt3qZCzB6lY61diTl79KbSk+gN6iIemLxNKxBhq/qEgVIx4DqVr6j10v/6vsj1I8ZnStplLTwHIFX7Mo7EvN3wOjI3UjeTywbN4vkIUV+oH9Ke4cVNWf8QcglXqXB2dW9U5aPylo+hi4+XYecDsOPOitNfAM/2kaoPOSsLIZlHKqA6vLcICnUj6Kc0XzCAyHYZh7vUQ+/T0jOpbLIWnu39YVCF8Dc9qUawbuyfOZVf2how4nGrY4+MvMmOLNKRqmK4+9Riyeyjva48T/k/Wj7CkiXLzJJ5pEsBpZiAvLrAfdXJ8If5aAN/y1fW++qKzd7b0MIfkPmd6KAdVqQbLR3rzNzJ+N7acQLAtPHwIK+0gAZGq6Q2tj/Q7cIf/AFoAdISfg6AyPzQCY81BeKsGDf+eLAkoIK/Wvu3xfA+K6CNxlxQo+Wc2yB5yL0s+sECwA1aIYDPsIVX4FBYge/2fC9vXH1ym6bqBCn2n15aKPc/+GeJi4vzkmq3aZAu2DgMEPGiUGu34IiP4OIBaOC3Vxc1VsTW9D5Hza61CmQ7wokVg66HN+lrxUdYOfiPqh+BitHIsWc0erf4IE9i7elL4Zbz/yIzttW3K6HL7IoBaXZNLki8T/Kj62GoW6dXYXtq/+ZN+w+28IWD3EhFjYJ8we7fH4+q20I1pCcCv9wCwC/H0EEoG5kxrhvXtCGnLq7gW9XPDZm9eoSjqafMgpqY4oUIXcILAo9wXeYM5VVL2P2uFxyyE7/yklHMoXs11UD4YyzE0Y/FFs5V4JD7BJEJ8MrSHInDlTeX2wS+9ODZoZetG713UaDVyVgK76eh0oLV4ZH4YcK10Kuh9T7teSbY11Ml2J9eZALEYNZtoshUUMWsuCFCHBUxkrtgmLgh49j9vP2oyO4jW7ZnSdWmZXLKLbeLK6G3VA68TvZ9bZGl18WoIbe+ICq3LHOaS4be4JA9n1olcZBbRvyf7nENduWmx7NmocgIQKJykxlZEx5V3NK0VccdSUXKwLCbHbLjTav6J8/cxkXgWBz/tzrkGbpV5sb09+vg5UQIFQA4BuHkjjTS73D9dEufdPC9h/q1m3vudoRLIlPdIPjWXOHM/6RHm9bzRPCnAlOWQ9da/VAAYOx/sQ/ssw2pWo98s04Yyj+IARleOmzhFWj3Cmwo3iyuiHqZMS5H2iJR9/IXayQ+OUPOPx9UZr8gVZ+ui4ba/RIfQMcuDA71u2oNGOrt+Fw18Fr1MB6zgVMwbt8nChgx3FvV70qxbHwYXr39xmao6M6XCAAtsWb7HW92B+OQuLk5I/k1i0cIqGj0/LkR2o3PBVDL/wbcgBeKJypOhZRT1z8IwKjRrBipdNiPqiEEjvS60UikzaKX0nH/gBz0PHhCV0nCrjcldcNDkrzlnxgH1ZgId+vmQrV4AcLGpJgxGr/yImu2Gw+pXD2G2wPNTaHbZswIgHmaRT00U305QtdvSHQh5me4BgFlHQBkIql8tj2n2pYPuwMh9UvVdnv/sBiJKQcM19McKPZpzRIHQhaqwpdDumfzkVL6QiXj4apr/rJussX0nKQDvNEK5/v/YCiYb4HiiUuGgZKGj/Yaa7OO+symAFlsj44HZM3NaygqlrMBTOeepHniyb84/9w4peAy+eU6xWlI5ZPOsOyjXHLYM3UqBzENYDHn7PYBxNq9EUK5v3TkdXZkXR3B4NFf21TuIQuPqIhjNFLZkoeyNWsLOGRhFzlHk4e7VIFXa2OHz4dXoKNXYN7u7+XsowYpGb5Qxy6usMnf31kqr78JDt5WIoGhjtlV2oUBYld5JQ7SPOhZLJzynipesKcfrryLeZDPY04cK6SjS5eqmbAwxBPT7YBmZYW3MmPFH5RXjQMRsLBYJgNFJaz+pcUUzAFw+0zxE+ZP/6861twf6iOXD75RUjc/qk4TKKZs+LMUQtKPRNrkDKT3jhZYDMNjpLOJRnVsVb8rVFg7CgUiZnedAso8T1OVz73PBQ/gzdqBgL/2DKQfwINLtRYWwujXowea2soxUD1xpIxR2+yasfw6lY8ZMIySJzTVV4nJ4/O0cR0OFCC6EMoumviiJOx+TxXv8AeCMsw1AQTifD3qQEtkLFZixW1ctyqpLdKq9tOyN4snpbvUFuqz9uDL2geS6PnKfV8/B0k78ACGZMixTd74V5Cdf40UhqHgyvwLfiikq64otkcOG/SMo0DUfQG4+GJCGrFDGjU0hk2Ng1HBhcUZAy7TfozkzzWjojlKUeqMutshSYN962Hs19btnic5ldRgW/vp7fPnmeXHq2NU3ie5Jo/6j83r5dPbHMgz3xv9IA94MIz8kt+fHQtFnwi1zjM+tqmq8c64Nr2aez4B9yPeun3AXRkQrOiMS4bHPARWgNJ6C/Yskk+v19g0Qp3yg68tlmOPPU7GjAktJSvUcbtCuzBA7AqvwkGeA1U4/JQ4kFHeAM9e8cTn4V1EcQbyAWt7nioaZGj/5BgS1kEUR2HYmBXPOjjkMVaNJ2x/WWk8M+ewJWtIGeV3muozujlRpdua8R5J5lwy4WlJ3Pa8JIM30WiOHsdL+Yi7FfgjCGSYmgUz3hxB/DosG/kAKqrvU0U9SVuegtRdlFQM+z+Vh8hiFa8hX9FYvOM9jg1zfQnC0+8aDzXxbJocZfCIvqFC1wSOBKGhGBVu+DBa4o5XlVeVxwikC6fg2pnHqSaMeE/F7s5HN4rFUyajL1gipq0LZU7Vw1JT20cG/p4qMr7wap+znEjcrpOSpRZFNdNStbJxDgTAiblvqUOksUnG2pWNepBvNZl/XixyHjXPZMH3FhWaNPbtzG0C3qyjnGKk3uH1SleYpPynaEkb55S1D0WL26F5VJchHH/MN773XWfOrbWxd74VqcAh27FwhETtgaHe1sboKudZ5EJwSONz7r+tQjAeaPSWshiMwQHySqajxqyt9uNVMfDcal99VBDqSO7Hts4l3L7rrMDKgrWSmhgtowZkhjyplZvz5T8Lt8vc714Juc+h1DAMEA+lV6uT58pq7po+53XYVeihNIY867rNUACtPnEI8hI3q+sQeqQgJOxBCLgYiiZ2eDN1Y9FFEiqK6dVsgNepYuitoPaZBfLnuRjXKhWNeZXk+yOXpQsVxB6EgWsgaeeKylDeSVYrwzWlCLjrk30Ak8VInq3PAsBq3qDarOOkdvzfKCwiKfB00eNGS8h9W/JnfKnC5N4ST5Qoc57Guepz9j4DdNuRexhdtsJ7yLihfRX6jnipdirWqwppXl8PfccgFE9wTelAFs5UDLnF1zGELYbfdeN1SX0jogFEHk/oHyGjXwDA9vSQrG+vFFOPGrn4juPgj42Q/ccgj1NS2MxrDIe2NSRKUKybrTpNVr45Xey9IyX76AYvOOT5IhRvMEIepDhfHyL4M4A5biTkxDdWm095pU4KoPW86g6QewNokf9x338BQmD0NBmtFuTXXcVIL2M0KtgcqkZqKKMF7vMciR4WXR6j6Je4T+WcU5e1La0B5A9ecMgxFPdjAbgfDRQ9PB62X98KzN+7QM6aNTDkG/fgDXn78wvkhhtvkvR0LRoScudDpGEYIB4iL1SXmyYodpK3PClRKGYhVU/loD/4FWBwviTnLpj6PoolvlXqHQxv0ihtlwjvGPvqhSfe4g4DQExfdaPEgH6HFlm7Syy1uVJ45Ed43o3K42QvHQ85FGNLFqp24qwS0vew4lYHXdoJ/7+sPCZlTTSqlQkcWVQSbUFmPbLvYwq/8zY2gwSPnJOsanaBrLsWcoi1vc70K8TxNg7YKJ7wLHIQX0PI932EuMuRR5mtcjv1ZuSMJECtBR2NHXNOXfN/4H78j37a+2yFwo9u5OwkSK5HKDtUY/U0509jGN2ePkWaz6bENzBdeo2mvL8IT7fXSJZMTw2l50xDTlBUQpbavfLhkx9KWYH2QZwP9RkSKescksmQyTNH1ADi+fIiQ70+qZT4XiA+LB92O8D0RSF1ZdpQ9nSXnLiwViivuPHJSNn9vgYQOQDDt7rEX6BqSkgX6KRGw291KPUXFvb0OqVBmNd4qNqAy/FDYZtZikH/k4EK8v6XNg1tO6s1bk79HvmaMG8R/yxDNhbPJAxw+bgfAUwDK7JDHizc8BezAg2uBlm0Z6k8cPv5Id/Tu99ukJoGi1x62WUh9znUGoYB4qH2inWR+TLPLXHnq2o2pAgijU5t77ObzI7VwJUGaT42YO4ZvWCs9o1afau3TwNUbIwWVervfVNeRwAYqrcYzWLbb9xVnjadkoUnzI3nvaHixtb1ySMADkf49eUO8y/praO54X2MRZEJzQJ5vKQdBLevA/j+W+VvqhNB/nisCVIFLycfQs8WvIoJO17BGK/Aw5kupSMfUkCTRTucY3PgkEMbvbDcV+oz3AjRWEDkikxXOtV1AJfNVWiroRBvrgCwojY4YpegOrrGmycY4qW8zSiQM/dkjTybB8c8aBbPhZ+Jc+tWLzjkcRJVz/qqRhguNZvr5IiJv5OUb5fgNRilfkiErGhECiKAQ3KD0ugFpjKPXg2/6yOTFC6BggyAYI/j4CZuxhhupyeJQAuY3mvj/mrncgheTkmf1HVAGJV6Dn8WLrFfgLGYZWIzdDvGW2MRTuZ0JwqltK+tjCmg20nDP602viSK+xE5pcxBHAL6nUOgNtG4DOHtTliBFflrpEdGkgzqjTdUCFZtq5cHX/9RHn/yWbFYfrkw6pd7ZyG8yOEm7V8BUtoYzQoPX1vN1v1EKScdDahVHKnjUNzh7/FxpE/2cjRybKUEojNQGy7Gfqnr7oU3DvlL8f3gIZvsPcs8QV3qrhJ8l5WDb/CeC7bBPENqcjMHkd7FVBTDGI1VwrF53wD4DjEebnkb4JBGJZIVc65VocsEVBCPfcAhUSjO8ICiR/co6gNR9rEOajlUQUlb/4CqTibnJO8vumSxquJmqJze0NaMhTx80Oi5NdXDlZI0oUm3GhTp1HY/SQGtwEryJo1bOFC02EeezWbMMet/SYyYBo+W+L5uJdHG40moWEX9kYx72IEilscAvpfwsPIs80cIOUpDM7gN8b/XGI9stNwPLbLiNqva2/Ueci5fs0nWjOCoosexTpn6us3L9UguykPFWPDzyUlVsm9hnKrQJrcjsjF+MTblpTrZ+198bcHR3fMUAv2235zifnz0lwGsfzEv7M98I4v2L5bTZ/YPeRb/eG+ZjB4zTo444oiQ+xyKDcMA8VB81brAnFm8Eb/3Y0U7Qy8bQUV7rLrfbyBr+Jtmu5aAwoYFFpHImWMYO1iVLz2XLLqhF86RNknlIXJAcixS9US3xO0vgWz8d16vkn488JmFLOScVAagQQLxWBRZGI1AtFWDiyK6bJkKTesFOMVLzLL+rwBnMBZmsGJ3Ar6s3FGpKIC5D16wR7CmLlRDj5aScY97PXh2yCSaHCUgve4nschJTG/0vDKPsnDqewCyoWXrk+ibwIuWmDlNbOOfx0IRWvuMXjcfvPIdb8tWPMLKGmLTxiYopBHfT3vXBnWYSISVqQVslS8nxkNXuUGOuUproxryjyHc7T2GDVahUgM4PgfhaG0pVc4hw8r0HDJZjfmquvewGKTeRqOKSUsAkW2z4Gnk41Czzc9Fya6v6EWNULyUaRNc0u/CpuHaQ+2+9PkyN7XPmQSGYQuvQMesAKuXF+9bJncfcU5IA+4uqJR//Xet/Oe/X4XU/lBuFAaIh/Kr9zPOvT51rORP/wLgbY2S6GuVnxFet1SAE0r6sXijHGCIXrNgFtFQoxRfqLRi63Fyq8UzrFDmQxmAhca/CKAD8KpLFHKbXIrBzFy1VVK2vq6UVaoAWhXAAHgiNY8ZuXMEVlResWccgTmdEmwY7ThAXrelV3iLVCrguaQ3rHafPxgjj51utaDQ4cOM8HYKyLvTV94E8Hy51GXN0nIukXdJM+ZIMoeR+YUtAURyQlprdkoDQvPUr9YtsnABQun74C3tpR/qsGfK1dF7tfOdSJWDOPpeX0UqiadH/ckhc06MFWetdv97v7DKrrOvkeFxC8SKXFMWMjXHrWnbHyHfnxOrchaZGzj9A5vQI0RjzmGtUkGCRxYgV7eERnCq73elQhN9Tq0947eOrLkvGoUZJsk+xikjbq/3yjka+9aX+95PPB64b2z7c22TnJxa3KljXTLk9z55v59rPuHr/rpXYF3RJkmOj5ZhOaEVmjzw6iI599xzpW/fvr/4hQsDxF/8S9x5N2hiPAueCuOXcbCrxe/+QHkceT4eVCvUJG4pfJiMquaEPf9Ww0WXrwT46488x56qApf6yUEBKcBhxvJrlIoKYQMBg16gUT78TyoPUA0a8Id60Snzz0folfeEwBUk+UiHw0QlpdsM3Wfm54VqzJc0VjCTxJv3S2JmVp9SvxZEiM3SkqShKEbno4xa9ZPkzfwGNDfZ3ksTDMblfendZ75eMDPb9knm4ovEAv1p5j06Ud1tBQk6jRXfLsgMdpblnItqZzyCGepl/MwTlyr5Y7/Ea1Dq9Zz6NcDOjret3oIWFilQ99lIh8J7CjRdAUQ/7ij1B1H68VCf6cHc9a5V6isjpC/kFVuTFgx13JbabX4OhTOgZKFVv2iWpCFu6X1607UdcHm9FMyFTnI5yMbxPutzTtfyHu7/1qKALu+DBUz0AA++pm2VyOwbtvAKdNQKLMlbJidODQ3s/bS9UL5dtlO+f/SNjrp8lx4n4CO6S881PLkutAKsGE1dd7+aEUEHVUpaylmzOAr9Zs/8vpaMHi+jRZcsUVWwVGRh0UbhYa8JvZiBRilBSuzRCAPIuUeqltbMWr3DCw7ZNgr0NCSW7gawSfk65v0VgScyVP4VgjFWDOuazyTWzlx4niLaPubrs6V4iUXi+7slEY9As9Ts8h4iIbgFIM8IEFWuJsKokRVrVV4m8xBpSZufAI/lpwDf/RURNfuwgprgkMb7qOt+MkK0RRJpQiXnkBtCAveqcyf8GQ2euyXXRYujLEKFQdPGcS3gE8XaBTNE//2MGsGtGZVeLHGQdmz0VnYPUqTS2jj6+eU3R8v+rzSwlvuhVY6bW+sLdeuNOvjZtt+fXidwX79c8nC3/G5nsmz8br8kDsF9B3fS610O6nPlJv/7CNw/qJMJXyy8AliB5QXL5cnLjgxpLR56Y6lcc+11kpLiT/0VUudDsJH/v9ZD8AbCU/55ViBu7yfeCxOARDeCMu/BgI2anmchFy9JHXUj/MdiiJastudp3tPsR8+eLtfHIpH4/Z95zxs3qG9M/j7dCNRCMcrjuVgt0WgMIychv0/XNmZoPDb/f/rpVp9d0ZkAaQ9LffwAeOlSQHNTBYLwdaqYJtazUbqjEKI5cMiBSaOjG/uzqtrPUG5b3e8yyAw+LrbGdWLRCius1WuBnMnkTY+pLu4A/TV6bosOf10aTvlWGrI0YOk39oHuoJo4de2fpPucGZK2+naJcNUFHZEVwSctr5XTNtTI2D/7QtBBO+BE/8vqkT8Iry70mjOnOWXgb1r3PlG67/hvGhCWdcjkl2wy6HcH5lUjmbdu9ARTBaSzjZ5KKs3QIpM9Kmcz2DWjk00g+HZ3OXDI+bIAiK+dZp4DUpJpHCT8FF6Bdq9AXnWBlNSWy+QRPVodY/mmPFm1tVCuuurqVtv+Uhr4Pul+KXcUvo+DsgINcX29HIa8IDkPWzJnfA4Ip79WJNDMLyO1i2K+DSiQ0McgQGwAmLGCAJu8hnqYWD/Pyt7mjFQ25SPuUcUpboRPS0c91Fwzv2P02Flse6X8iLfFmvsJPKEpUt37PG8hiLcxeVAajTrQZgBjkmUHy6Vk7iQfWQvPhnpKuepJ6Gq17QFP4zB9KL9n5lwSXJaM+ovyVtZlzggaFqdXMg68iea6AhB5+7uKdGBLbyPzJlXuZ9phQQt9/CZxADtMC4jfp4F3S95/VWpAS6kEfPnb4uWiB3Hqa8FBZ+DUWT2dPw960RNFBl0FaUXfSxjYNOT9dBR+6EockaluVZUdcud2NmQl9bFzaoUet7TxbolK1UFWOwf8mboxN3XWVzZFiJ462oU8RHqNwxZegZ9nBVbkr5YjRvURq6X1D4a/v7tCrrzyaomLixO7/ddRBR8GiD/P+/KQvSo9eWlr71TFKQ2xvRWYqmEVcTN8goE3yRA0ARWrcbstvlgBTFLXlIyFt4tEaAFGihld/YSk19SLji5epIpiqnMuDWjt26WSCh+hWCwAVtqaO5hJuvlZtAAAQABJREFUqfIi86d+4A27VoK/MLJqk+JEtIFuxpZ1rBoycccrqoCGO6SiIRl4c/PXr1/T6xxJrXxA7TpjuksdiKqbM9LPsLCFVcwMTxdOfss7l+baJ2/8u1e+jiTeDQDpLPBgIQ6LbGjMyVO5lM0N0AnHzHYtnK0PHbivHz8Yz3mzoY7yJ63MOR9F6B6zVQb+9sC8h5z3pKfqZOuLkSoHkQTPgWHvjro3MvUYfz/F9YRudk9XRw3fqeNAalyF4aPS3FDLgW45fxk1Gj3nwbznepvwc3gFDsYKrC5eLaec2LPVSzH3cMWmfHniX8G/d1od5BBsEAaIh+CL1tyUWflKMmSSMXemJe542evNMzdUSiloY6gu0hZLhPZydKP8G2Xz4hGursm5oOUh4PpREnNtlJlreVCoKkDSTimGoCE9iTFFC8TW/QTVjVXReUfNUXQ5xkKc+N1a8QwbUUs6ipXcAfrH0YXfSxL0nhniLYdWcwGApwWewzqEroORP8fmf63AIcdl7mIsC1HAC0NQqTyuPGGw2KLvvHtmSAqWjvqzeCITVVUyvZBtMVWcA5JptyHM3pb+etua3mdJ3N4PVUjdbUlotfpc79fcs8kBYnLQFJkQnq/qe5lSx2muXbBjFev9vQLl67h/4AAxMklUFXGw63bE8Q2PR8q2lyMluptHJj1Rd0h52ljE890ZSCPZqYXeB1xRL6PvCS2FoCPWLjxGeAVCWQE3ChrX5K+Xx8a27kx46qPVcvGll0p8fEASdCgXOoTbdH7yzCG8OIfE1JHz1e3Hy6THvKOlx9zpijy5M+dtcpT5DU8puraankuo9zO5avVNv2fy/WV/fyKKO85VYVK/k0F2qN8chYIWgotQzFj8wfYEY4FmBIeqDTynupHcOqpspVKF0Xn7TAgnZ0AmkDmHMSWLJH3N7crDSmJwE/hK4hCCZSg50Lw0PY0nYgvmStai8/DazoIk4ILA5n65iW5TtDTAi+sAhVAo4HDXf5zyw7URsvVZhKk3vyo9Zh8hPfH+0XMXm1wsxAMxBfMUoKso7iOfvfKWzP7NKMmb4w/UQhwKIf5blBZ2HKrelWeVXC+tWATEdpM3/0Myll0jfUfNkwiLLxTb44SmVb+tDOc9jbRXKVlubkJT5G3QgRsVG02y+ekocdkjhJQ8q0Fv83NaozhNyFMgENfBITvt/SLshwh58cIND9oKbC/bKbHRiCr0apnJYX9xtXz943a57LLLD9rcusqFwv9yu8or0c55xBbMBp3KctWbHr2kbc9J8aQX2zla691q+l4M6bm5ADo10BbOkhrFO9d6P2MLeoNi4GEzg86EYdGaZjyQKpQNbWJdOo0FD/mgezEauRJJaq0bvahZi85XhSVucyyKMVDp3AqBdPmwOxTzshX6zvZ+FyjCbX28YM/Ma0zZ8LBSWjGjIjh52zOqaQ2AadmoB3G8SKme6P0ttXvUJtVnmI+oKrEBLIsnPgsN6Ol6M1W4Y4K4rCLXRv5kHAAyjUU5DGvr1cp6h7KR9yuJQwu0mpkz6Yrupp9q8blsjUm+O4/FHfx9mCxxG1wy+SQtF4zyiSQkD3WswAtRtYbRxG/efFLyd41Xp5f+3iwnQOe4rZq3kRXrvMOTz69kXrmYe8bLJsik2QtNCBfXS8+T/UFf0tZnvBKQg2WBuN/+VPavHSmZkyIkaYzWliDSw2TEECVG6BH7/qxYqdhgRg6jRyb+0w7tY//reieKDWQISOFCkKDHQppvYtOQMPm/d75jldKVZsj6OVFw4j+WXm2tjxm4rx/v7GfqUv94VYyUrzWhIMglh79QF1K+aHwfaG+joMbt0OLKSYO191Znzzc8fngF2rICawrWI/+wZ6tdXvvyJzn+uGMlIyOj1ba/tAZhgHiIv6KkfDFa4L7xXEdsU9Ytb+a3KhxLTd/meOdau44zYQBCt7OhfAKSZuoq44uaZM7xez8C72CDooIhQNTBIccj6NKLWkwAwhnLrlY5jPUJg5VmL0OjVErRizMIwuJBbF3WCkBUBSETn1NTjo6GpyaE5GMWwpQA3EVWrAcg9VVj0+NJgNiA+7OnjEcYfaUaV8+HjC34n68SGwTXcfu+8AOIrKCoGnSdVMl1yLVc7AWIHISFM4FGz2blkBsDD7e6Xwb9Y6Pl547z7tIjyvzH9hp5Ffnjobyon3cId32E2PIi2gwQKTNIvsfSggHy/uOfi8OWqACax6UBj2U3RkvKaHhkG4myeUErckaNlpm5SuKvHiJWK3gLgYkJtJO2PKnyNMtGPaAIz8vXwUt3DzywVREy9AaH9D7NH7BR+5fgkMZrb30pskWAuOi3MV694AGosjaShHMMgsM192pewb2fW8Wa6C/9R/WT7GMaJH+2VVX8Dr859PAswazT7vOa8nrtta0vRAIcavfNohwW/Ay8ovUQPX8ITP1XnWx7JVKi0jwy/LbQ59/euYb7hVegrSuwvvQnOeuIHi12a3C65M1vNshL/3qjxXa/1JN0IYTtEF6BuqyjpDb7BEXt0hDbR6jY0dnGYhNyELYHHOpz84Ahl0BR9+IwhEiZtBSEBzOX/EYBR1u3mXpzReuiZ7rH577nraCOrN4C/d7XoeP7VxBrf+Btzw2lmsLYYCeZE+TZbrNPsaMBlDTKAPQqB1wjLuTg0VPlaSzAaYjzgSa2I+VMMKO0IAtNSPHjSBkj5UNvC9a0zce7Henyo3PsN2KuktZzR0SpfEnK/rXXSkc/oqh9hoMXU7fEoS7JA0HyhscixV6igTv9XEvPpAkqHfkgksP/ocAh2+rgUN+u3ef/EWbDvwXd6EWua+SI5DGmRyRt/qfK8zS57UrZh2kBy2+KVkCI8n0rbo0We7H/HKMz/D1gLXlCa3ZHeMEhr0lib3oUjVa6yh+gB+6zoGPKS3Y5dm6NnLikJmQqGALPL0bFy9NJZbLjzfaDfH2ucLT6mavRI+h3MMgOuSdZbT7hMbvEZHYMYA1yqfDh8Aq0eQU8qAD7qWCTTBnZsgfxy8XbpVtmlowd25Rzt80XPQQ7HPinyCF407+oKQOAlEKzuNT1SKcXqHTWullAUh0FKhbdWDnM0G3VgKtUuJV5eyS8rup/pQZKGaMzGryOur6wfpj60AkolmAOYOHkN0E03V0/1WHPboA3UvFEVa5XYyrybyZswSOauukRMTur1XEWq9Rmnyh12ceg0OZmiUFuIdVPqAvdklUAFPJBo0c1Jh/chdBiVsC6sSM9ryzicMVkNR5p/YkVpGf/EC3bX10t3SOelf6jvpHyIXehUOh8xLP9wUvro/m34A+HAkgw5iByHrfEpkiwNz8TKVugEUzLm2ORY76xKYUaBfhbuh7WkTrb5kHNyyMmDnKB788ffbE9pQOtKB6yZ86A4k4PAysm3zcGsNL4Pqor8oFMjzNCzdkIAknFMvxWh+wE2IuDxvTo+wKQk7oz7Q9BFfMeOQ4tNhusnFjS3Z9YsN6RSnWl2xFO2ftZ4z1FeBSvo9bb/29CP8Nc/U812cv/zgwvKNbYo113zYNR0vtskKzHNGka8oGBV0KV5TuLULEmYaALqjGd92Mr5EmFG4ZXoANWYFfFHkQVkKbSSv7hW99ulvMuuKQDrnhoDhEGiIfm69Z01p1cvdz0gh13xChJx1HdCJuT4DoF2s0EhzQqpMTmfaUAQ03OhcLcS3oPmcNY3e8KRWKtK4YwRGpya2EtC/ISE+BxrBh6ixqnQ/8AYBDM6maBhB35FJ0AccxzMxpz4wgkHanjpeiwf7XJ+8qCm6wfzlQk2CQB5w8CW/bxqnAlfdXNKmxtyzpGSsY9AXTi7/0yziEGRN+xhfPEkQSpvsNukqgHB4mt9i+yz/MQEGjHfxSQu6++UmTp730opWoLcvPWfCpZ+Q8qMFo24l6QfZ9qnGaT7f6XNUgl+hUvNkvGFJf0AfCph/pK1kxns+xCrCgPrCrnoFRoqRz4B+TpPksUJ+XD78SzSQYBCG18XAOwJOFOHBTwAwR9qRnMR0uGlFhZeEmsFxxGpbvlsGfrpCY3QlbchpCyOwKhaqS84q0x9XWblMGTmDndqUitWxq3tXPMFVxyDda4ERyq9sSWoePLZi+R0BcE4/NrxVYQIXEgG28Jyzc7QPhgeAW66AqsL94ohw1r2XuYV1ItS9btkX++fFoXvYvOn1bHfyt0/pzDV/iFrUB90lD1XaZDGxvk4Pht5EGI0Gh6SJsh7oJpnyo+RXdkGtqigGHcPyV148Pq25eh3riCb71d3agWYGWxG1yBLQEob4fGDRJQx4BKhqTgjvTDA08rcOFIGectEqLkoB5PpKeQfJHMo6S3MA75hzTL/v1Cku+qQb9vOl6QIzGF33nl8kjJE7/nQwUQqVetV4QTMEehWMmRNqnZUVjZnbHqJnWO5NrOeACKPvhlDICkHs32OvCDpIRJQoi5cpPmmUwc1CDZefd66XxS1z8IWqETWwSojNBP/Ic/4G7vzJjjWd0X9w1ArBOcD72+Xrof41Q5iMz/awFjt3hZ3qO92OCNBM5MGeGWokW4d4BD3Wr3miRruks99GMH8szQOPM8jTbijnpVJGM81p5tpjjH9z5ApNmeC4f7hFegE1dgU9lGmT4ru8UrfDB3k8ycMUOSkvAh9is136fZr3QBwrf9868AybBLQZbNnMPK/ldBCeVuMYFwuR45igRT9Jq5LInCULTRlGZv47d5feo4Fca1gsswFuCQJN7M/7OnTgLI+156zp4KypwTlIfPOEawbV4/64czVJ5aJvIj43PfabZpyfgnkW94tdghh0cPZvcFp6l8SIKefUcvlP1HzYWO8xS/vqw6bos5Y/1/6er7gcUkVHcJZuRqNFpE0Qrjbqvb9FLt+rdVUb202jigwZFv1cH75pDB1zlk+mtFXhDNZtSaBnoK6NG5ux4rckMDlGeShriF0n/Ey+01hrytiT4wpVcwE3RSF1m3fhe37InU24X6nNDfN7bep3dAZbR+PPwcXoHwCohsKN4sh7cir/f+vG1yxlnn/KqX6wA+Dn/V6xa++Q5eAQIqVgazKpdchtkAWinwkFkRsqXXzOyskuTtzyOU/E2zV6bmb/IWFCAoOAlPHSqk9x2zWFjEE9VIl0KJO5J0h2LMXSRtkG6spm3O6M1kQYqeh8g2ibveVB5LAhHyLNb2PN2nQw2vaGs61IHXofeyfMitAMyDpRbe1YrBmidQyfAZGscCCAczakuzQlk3d69j9M1Wn1m0Mef4WFl1Z7TMPzdWdr7bfE5gsIFUJeut9TLitnqJ7J6owrxaBNSk7suvYgaD/H975wHfdnXt8WPLkve248RJnL13SEhCAgmEPcosAVpGgQJtofBaWqDllfKAR4FSyoMWaKGFDqCMMlsII0BZARIIEJKQvR3H25ZtybKkd861/7Ik/yVLf8uav5uPov+483vlv47Ovecc8fko8Zzztz/GsqOvRXGgNuLhumhLlz7ZTmPO4+Xoq+w0765uradoQI/8ZzsteqiDjnqhjZe0RSiOXMpi//jFPS58pNaymenKwXbkWkBNIJA8BBo6GqnW2kizJwwNOKgvthygmgYrLV26NGCeVLiBJeZUmOUgY0zjPX7mlq/Z8GEcL8HGhyo9u/Y9Fs6adHut+RT0v6mEH1H/aCajsmGKjRxE++iTQlw/lBB6IsRopUU4k2RhTVw+C4DidqZ5wg94Xxtb/PYsV6axexdJbmmXjWS01JVb1R2HmgVVFYfaQLSS1nEXk7y8k71kvvcp79vk5fYASUIh1ix+XPmflCXvwgkriFq7jWgCFPFcPvCOiTobe4XL3c9n0NjzjAs5sswr+0jdPF/+UWUszV9R+SffU4K+dEA+n5I/UZLEGp57W/f+V+8+i7FI5bGDJ+we/pcOdp9jUR//pTcMpYOtLcorlHcfcAwCIEC0sW4zzRgzjCxmE3V16f9NPvnGV3TiSScp91ipzAwCYgrPvuyxq2DH0hlsMSwh4WoW/DnscGYh4+OIL/m7n1SuRiR2s9Nv2dS7HgcLq95JhDzRDEroNjHO0E2spmmY9t/KgTWLaGyA8DNeRsziMIBnKAMWCe2nop5wvGMzG7w4CqfqVqNdlPv1s+9SUU/EcljiMosl8ZCPLvXs+xNhtXYBayRZGK2fzS56vvwftbdOQutp+yW1+kTTaBtyuHYakXd76Xylvczd8yy7BWJjnSqey7ad6livAe/Y1uH8FCgY77sEXDDB91yvrf6uCQ+9JAK4zLWWMnt8SWrn/b2L8Cr+97LYtcrsX9p8fCT2VzaR77OymiZd0b10nV3Cwnxosn8iDxl9BwFDBDbWfU0LZgQPRfrkGxvozt8kzg9TQyBCKAQBMQRIyZoll2Mgi3AoSVyl5O96nB093zIowy398hckIdMk5bFAs3/ZKz5RULwbVXsS2Qde7t4XlBsZ64gzeamZI52wxixYrGBxcSLLuSr1WOWKM+nGGTepJes03utmZgGq5MtfUs2Sp7yb9D1mH1kSbzmrfrVaoraOOlfdN7PwohmFyAXRdmlJnDrLK9pJBFd5icV3xceXquZlyVuuhZxkGbeHl16ZktkumverJtr9TxPlscuZ6T81rj3Uq9/7mq10IWsW2XG62pvIfsu9Is1459M7Fh+En/y421qYeGrWdmTREY936GXFNRAAgRQl8HXTRrpyyviAo1+zqZo6+ZF46KH6Bn8BCybhDQiI8T6p4qttIDvng4zP3yGyRMEYrJTFy8ZakhB7ltZNKm6wds3/XQQ9j7DHNx1F0/yz6J/rCDoi/Hon/3Pve3IsgmzJV92CslgHi8Pl9hGnkqNgMvvYG6H2N0o+8WsY7ST7M4VfFztF18xtxQG0tx/Igm1/ZM3i5cx4s9rDJ74JdRM7ES9bcxUvO69SY6udd5+uv0iJNb246Ao6/II2XiKfQjXZf2UdX45ulQO9KFsdDix+QrkxcvAyf3vlCSFX2b6fNWfe1sIcx1hLnfwRaNqWTqL99As+pGXBOwiAQJITcPH36abaHTRvcuDVnOfe2ULfPHsFpaezdwDXwFdLEhkpBMQ4nj2J95u36x8cb7eS3bj8JuLLv1ZefpUlvWw2CrCzFXDLuEt0aYgBSP6Ov/ESaz0bWKwIGgFEtwK+aC+Zp6yL5b5EB3HkTwiUNeLX7cVzqIONNGRvo1g2t0z4HlkaPmX/iH/nPYSlvJfw+7ynsHfJ0zsGsHRGDFBEQJQl6wOHPcnh317mMRRxmLaTIt7XYBVm1bxNyu8hRwGR8dTOf0Atb0u/JMSixGyWJFrT4o13KY2wnLcNP4WXwO+QQ5+Uues5FVdbLoo/R3HoradBFsMejz9KzpfD/hTbRp7uU1egExFoZc9mOjsNt47+lnJiHSivdl2W95t7tgDk7HtZlZfY0I1Tb1COr7V8/u/iNNvbpc7Y87t5NHAs4XfPt7AbG3aePtZJy9hgRAxKkCJLoHG9COhsMDMztb9UI0sVtUWSwK7mPZRtyaCRFQW61UqEleff3UJPP3eP7v1UuwgBMU5nPJP3yuXv/JvqnSyvFm+8gw4ufDSyvWWhooH3zvWXSj6/keMCv6KyiQ+9/cte5eVh3vQURqqfdRtb4U5k7VcdyZJtVA1iWANbO/8hjq7xNQuCJco4ovKt43i5uHv5UWJC1/XEY5YhyVJxHi+3i4GK7IZr91o6Fq1r65gLJJvakyjCkjj1VsvLIRrAqMIG/ivafB87AO+2jBVhN4t9G9rKF6v9jhKWrmjDnWqpuGHq9VS+9hpPCzJnEqpPuQXyXGXZ0s+Zt/+5ltVlztMO1bsrw/fc56bfiQi0YhEuKWf/q1R9JH92WKANJckez9J11/M8sMDBBuXyQ6WWnYwHSpq1sMQNzqpwUcmsbkFl8x/MysehlGvdbuKlcjON/07wZXLROLL7zGAr74G6kZLX192UyeH9uuPCj+aIK4fc3tdQJyXBYNBxRWBz/TaaMzGw/8OPN+xXf/SLFi2ivXv3xlXfY9EZCIixoB5Cm/Jl6J0kpFqskuzF05KJHU7L0qVEBAkniVYrFGvUdHud0miKYUgnxyCOWGLhTZaIJYnwrQmH6rwnVJ66yf9JHOSaRX9XYf5spQt0+yHzU/H+CuWGR8q1jLmQmtg4JVIpo20XG+Xk+gh1cu6dZOlbS+ImSDmclgv8K1g0oqbOBnVb8rl1hDr7KNYgb3tSaQ+d7HBcQhnqpaZJ17Dxyy4WsLey1vRk3pe5XC+b7rWsho8912W/qwh9joKJnmvBDkwd+7qFw55M4vKov1TznolaeSlZQsNpyd8431zYawSj5dHeGR2t4X2Mu58zk+Rb9GAHSUSYZEyt2zjCy7XZ1LY3jcZd4CBxGG4kSbCjbX8xe4rufNJCM66zk5dS3nMPByAQSwJbGrfQ3LnsFypAeuG9rXT8iSfx7h3Nf0WAjClyuXeTTooMOFGGKZqhjrLFqrsudurbPPGqmHVdDAe05GT3LqIJ9E8i2JV/cgVVvrmcijbd7X87pHOpY9h/TqPSL26koR+cR7kcMWQwUicvX3Zl9f6KlDB1/kn27bXwPr5AQqqlab1HOJSyEgYwUqlk3Q1UyU69h7+xjKOmPOWptpE1gw7eeyjLySKQBtxbyA83iSwj+wU788bz8d26WjvRAh9Y8rTSCItD70CCm1ic13C+vSd81r0E3bMnVoQ92b+Y7uUv0tPZngNb6SLPJWHuyOW9kyEmiVIj/ddSa9XZ2qHu+5Y/menD7+bQ+jsz6c2Tc5XgIxmnXtNJ5QtcZMp2U9UZDhoZxIl03UesYWThUJKjOY2+/FWvuyJ1MYn+W/fLLGpYZyJ7XboKNShL8UaSKITZCYInZeS6IxLFxVMhDkAgQgS2Nm+h2RMrdGuT5eUX39tGJ510iu79VLwIDWK8zjrvlas99A9sELGbRLvj9lvmi2a3G2bdShIOz8TGENZRZ+suLxd9fa/S/Em/CrY9QrbiQ8hWsSysbir/hz1aLykoVsxtVd8Mq45QMos288CSf7AxCu8l5D2I7ZXh7yWUGNAu9nWoxXx2FHT7SQyl/WB5JFpM3r4XVBZZWi3c9FuPY23Zm1d95CusIeSl034Ml8QFzoHDnw3WVPc9rkf8NIabsmre4WXsq9jauEstsVcveVbXwlyEU9nrKT4iraPOId3gyYEa5zXjGp6nrIPvkexB7CyeFSinQrLnhd7HmbM9jUPcZdCYFQ7KHuKmo//J8Zs7jWnI/BsVjdvBdzOoaJozoffb2et9tST+5/7jDnTOjypacH8HfX4LC9P80ZzxczsMgQLBwvWYERADla11u2nGuKN0+yDWy2zCxhrGubr3U/Fi7xM1FUcf72NmTVBXGBqXwRqOhCVrHXdp0OpNtoM+9zX3OT4X+zmRZWXv1OV37n1POxa/f4Wb71cxj8W9i4MF2VCS7MdrHXtRKFl184i7HYn8IgYYTo7B3DQxDLcyPTWKAUbBjkepK5MNMNhvo2jqxH+i5vdRsokw2yf1Ixz2yT8IF/J2/0MJh1K1iTW/Et5QjFD8k9paMP4y/8shn8tnrz9r8S7ejfHueTnU+AVLKlpKc3Mc5PCXhssWOJWWUfYpWorcNON637101p1pSjvZ1da9Q3XB72w04kR9Z7taV+L1fdL3OumTH7GBU1calcx2UsWS8HlpY6s43EnHvha7bTBaP/AOAoEI7GutpkxzBo0Y4qXu9sr8Ii8vH3f8CVhe9mICAdELBg6NE2gdc77yG5jmcihXMO1Djw27MuX/kP0wdvs/HEtNk3/Utw5eBshhowtL6xZ2mn0sGzFcp3wbSsZMtkzet3wVbzLu3Q/Vt4LIXZEYy/5xlkOtPcO6XfVdnEJbaIPaE3lw4Z9VaD7Zy1jIBimujAKq9/NLKYJ4IYcgNNnrWcC90HD7ofYzUD4nW9Z7J/9z73vhHO9bmUHr/juTXCyrzPqFnapO7V/42vuvDLVUqrWTVe6i2bfaWcPH6qwgSRSxXz9godrVJhqy2EkTL+9UnoPm322j2TfZSGRz0Y55p/2vZ1C3cChX02gPO+ZOVAFx5CldVDqvjWwH0qhoButO8G3gPdU4TjICOxp30dRRQwKO6qX3t9Mdv9H5zglYIvlv4JGQ/HMclRFKrN/9y1aqSB4i6PlHEgm1E20jz2QXKmcGzJ6//REq3vQbdT9/x2OsYusVIMQ/oAhOYlWsvtljvNFYXLxk1X/M++5Gq1CG3oMq2PYnFi96jSXMLPBqSaykW0efz2PwXQKU+yWf38BWwR+qrJlc9/4jX9Nd2tXqGqz35klXqb2HlpZN1MbRbToqjhxwU+Kve821WdRl7R732uuyODydlSRMXbCU4WcULVrA4SGEtdvxhJm++nX3HsOD72VQZpmLRp/V/Xny3lPn3XbBRF+h0//cO28iHOcM4x9c/EICgWQnsLNpN00dV6I7zC+21lCbvQvOsf3oQED0A4JT4wSc2UNZAxY4ALrxmntLZtd+4DmR/W+O7OFkZmtXSeLvMJsdWxeJAMn71+pn/A8vT4avyfQ0MIADCcs39N0zKcNWrZaM6+fc1WtlzPX6G3aI4YxP0hEO5b5oTrUk+x/NvEfVbiC2s1aH0XdxUyRjimQSWd/beN9l512YssLbj4A4/IQuGnGKg0STKGEBp/3Yd1k4UB+bN/oaZTRvFHVh7w8OvXJDlzppFofw2/dKBi9hu2jylZHZ16jXFq6BAAhEjsAe9sRw0phS3Qpfen8rHXPMsco5tm6GFL3o+4RMUQgYdvQJpHF0kzL21SdWz+IQXFkZhNANGzvc9k4iHLaOOEMtxdbN+TUVb7hDGY5ItJSSL2/ieoNoRzg+tLllcx9/gN71Gz3OPvi2Eg6lvGgKJXSfd/KOECKh5ZrYT2Eoqc3LoEYsgu2F00Mp1n8e5iSOuHPYMChWLpXEGtZb4Jp4WWdIrlKUkcT/2eiMzVY6ZmU75Y0OMudeJEaczMJgenfeNJObl4odXncDH46/0EFLn+ygmTfaKUSXjoErwx0QAIGoENjdsocmjtTXIL78wU7ef3hiVPqRSI1Ag5hIs9XTV1PHAd6n95xy+izRUKK15y6SqAq3POiJrCIOwe1FM9jHXl/3AiLAFWx9kKWsDHb1832OgiIh5DZx2Tc83cno2E+NI25Wy8vKqXLPHdkPyRIiv/ou1QrDig+/TVK2i41MDi76C1FWZCyRpfku1mx6JwnR553EZ2Fj+34ex0oSX4uO3DHetwMeN035KYmFsoTXkz2YYVkFB6yVo198dZsn8konG94cWMwCbZT2cnp3S1zSjDrLoX4v5FWFJuhp5f33C2rXA72Lf8PlL7VT3Scm5euwcJLv8nGgcql+nXdOKPc/LZvTqeq0Lhr7rdAE61TnhvHHjoC4sNnTVEPjRxT36cT2fY2092AzHXbYYX3upfoFCIgJ9glIY6+0Fe+fQ5qVsKXpc2rgKBqJlky2Gp8u+1tBq5u8KW3Ix5cqK1k5t7BD6+pl/2YfgBf5CIjZ7Mh7+JtH0kGOltIy9hIq4H2KElKvacqPWTbUV5KLFa4Ih5Iy7LXKFUtH6f+o80j8J0Jc4+RrWZB/nsMKjmeDm2t9qrU0fUFFX/9Gia6ZzV+xn8KckByJy75EFbXFp7aBn+T2uNaRmiTsnuyJFLc6sUi5I8ITDIP1UTmcl5AoAVLRVBfJCyl0ArJvc8fjFlWgfg0v63N867JDjVtAh94ycoKAMQK17bw/nWMrDy3tGwXqldXbaNnSI8hi6f5MG2shOUtBQEyweRWjAE04lK5LHOVETNbR57GT5bdU6DjR4EmEDv/UbXRS57lsbtupwq11ctxoiU2dv+3PHCf5S3VfXK0Ubvk91c27jwXIC1izZgkazs8/XJz/uafRARy0jruY3QNd7FsDazUz2vepWNDees3MpnU++SyNn3PsaI6RXTSLbEOO8Lk3GCeOvHGUyUKrJHHMHimr5MHoayh1imBYvuZKDkf4ITnY2Xbj4kf4l0B5KEWTLo+E6G7Zkk45I11k0ffwEdaYW7b6/uiScwiIYSFE5igTqLYeoKryvtpD6cYrH+2mb170wyj3KDGag4CYGPPk6aV8kbsy8im9i9d5ONkjGY7O08rgH0iovv1HriSLdQvvo2OrZx1H4M6sCmV4ktn4mepQx5ClrGnLpkz+0hcH0mII4pN6tIXio7C/JPGgsxrWsADxAcm+RnEZI78fJQyfmUMJSlvilzCSSbSmFR+ezwLiXrWs7eZlczG0kSTtaUm0ixUffIu1i92arbrZd+guv2v5I/Eu+zfFuEeMZyTknoTqS+SUu+c5JRzKGMzWrZT39QNkm/aLRB6Sob5LTOm3z8yh1q0mysh305JH26l0bvfnylCFXGjUmQ6q/UAMetKUr8hhy7s/w0brQzkQGGwCB6wHqWpo319HTa02WrNhD/1u2bLB7kJC1g8BMcGmTb64a9hfXv7Ox9mdSwl/mV+aYCPo7a4IcrZ+hLmDCx5mv4f/4v1wGdTG+/Ykla67Xi0Ly7HmVLqL/fI1T7xSLoWUxA1P7fwHfPJm7fgHFXx6g7rm2nQPVR/xAguJvnsJfQqEeZLHEUVEOJQky9rW4d9Q9XfmT/ZxBi2RQzThUPJms/GI3v5MuRepJMJwPWtlkyVpgrdnPF7ukDzXUuBgz4tmJRzKULta02jzQxZa9JBtQCMfdUYX5Y9rV1rJiiOcKlLNgCpEYRAYZAIH2+qoamTf5eU31+6k2TOnU1FRYv8gHix8EBAHi+wg1it7wyT8XSok0Ri2VZ3VO1S3k7VcrBbpSWIhvP+IF9nP4HjtkuH3rL0siPakdGe7WuIVTWOkkozFO4nwqRdj2z/+c6Jqib3HGu1jK1u2i0N12d8pMaDb2LgpFZOl0Hc/J3snikgqmeUieSGBQCIQqOcf5HOH9I1K9fonu2nZ8hMSYQgx6aPvZpKYdAGNgkAYBNj4pGXC9zwFrCNOi4hwKBU6Cid76pWDzvxuq2ZZ8i3Y/Hu1HO2TIcyT1tHfVhbLovW0lcwncYitl2zlh5Es+bZVnkwNU3+mG8JOrxyu9RKQLQs1i5/iyDpvUfXy18mZH5qVeG8NyXEkUV5GfdNBphw3lc7vomk/Cs1HZHKMHqMAgW4CDRxAwd9ARSybV7EGcenS3u094OVLABpEXx44SwACLRzbt52jd6SxF2VH/oSI9bht6n+R0+nmPYhfs3B2EokxjBiL+OwHnHM3O7w29ovTbc4nCadHrAXtE8PNbxTiBkdeWjI3b1B76uzFc7lfc7TL4b2Lr8P6D7t9Qw45LbyyPbnFR6LRKDmGGhxIIbb4ln2saQGcjg+k6kQpK65/5t1p41ei9Bj9BIHIE2jsaKSKkrE+FX+x9SA7ucigKVOm+FzHSS8BCIi9LHCUQAS6cqsi31uO6aZc43jVnH3wHd/9gAd5P6BBAdFTrXxrh5G8hVRZMKydxwYXFeH/6i3lMH25+15ULTsPvkw0/96QeyECaskXv6DMlg1sFTyOXQo9GNH9mSF3BBlBAARAIEwCjR0tVF6U41PqrU930uIli1P6B6QPEJ0TLDHrQEn1S2Z2pZNT/Sql+VsJpyAYcTPjnewcZzraKbvmTY+QKq5xcjicYLhJ/GdqwqGUNe3kiCnsbDuUlL/9MRr23llKOJT8Zus2KhTn5UhxQ0D+VNv2eDtOipuuoSMgEHMCTR2tVFKY7dOPt9ftZwEx/B/aPpUk+Qk0iEk+weEOL5dDrZWwpkm+alybKqn95DfCrSL+87NjuLzdz5D4WRRjhmCWyqKpq5t1u/I3KcYiAzJa4XYLtv6RDSfWU/vQY6hNouCEkPwdVncWTguhlG8WMZBxWkrVmOWOm63hZcmbnP1Hwcjfzsvi/omdmBtKvMwtzr6RIkdg9wsZtOYnWeR2pKmY1KO7lcSRawA1gUACE+jkkKo2h4NKCnoFxC6ni1Z/sYtuvnthAo9s8LsOAXHwGSdUC3m7n1LCoXQ6nUPBZexbRVR0VEKNob/OlnxxE+X1RA7J2/007V/2Cu+r62vhptXTPuJUktdAU8G2R6hoy+9UNbJ07cweSray/sM7yX7LhumNJO5vxH+kddSK8LvCPiJr591PRRt/TWKhnZGZS4Vvn0Ousd/t1xG3M2uIj3P2LksZyT7QcFJW7fv8w+Nn7L+zjZon/TCggU44dUYrr4SUk/ByJXNcgQLzhNSV1m1p1LDOpJxK545kQTlC6au7MpVwKNXtfclMNWtZeC/rrpxxqy2v5r4u4CLUOqoBgfgm0GK3UqY5g7IsveLO51tqqLi4kEaMGBHfnY9x77DEHOMJiLfmHTm+e/tc+aPjrYuG+5NZ9xENfecbvNT6kqcOicAioeWikSy8j887yb6+UJNoLuvm/647MkuA8IH91dVZPEsJZ2Jokl67hsz1a6ls7dV9HY77VdQw6zblsNyRM4oaORb0/qPfoq68MX65gp+WfHGj8v0owmnRhjtIN7Ri8Cpicnfj/RZ6/bhcevusXPrwsmwSBaiRVPuRiV4/PpfWXJtNrx2TS00bIvfoFQtl75SR262h3fY3M704K49enJ1Hm36PMGLejHCcOgTaOtuoILtXeygjX/3VPlqwENrD/j4FkXtK9dcS7icEgaapP+224GVfix3zbiVX+byE6He/neRv9rLPfsyRW7Z69vNJGYlKI6HYopHahx3nacadbuHoKUd6zqNxIBq8itUXkrl9l6e5dLYEN9kOeM71DsRSvOawv1P1ka9wxJmL+rXA1qsjnUPfaUl8V6oYydqFOH4Xx9Jaqn4zg51OG3tk7n4+g9xd3YKby57Gmr5ebYZWv9H3ObfYKXuYi9Iz3TT1v+xUOtlEHNGRvriVNYtObtOdRl/92kKdzUZbQDkQSFwCVkcb5edk+gzg4021NG8+BEQfKDonkXtK6VSOS4lHwGUppvo5d6mOFxYWUnj2tnE8Xv7GlDBy3qlt+CnUMvY7UQsrJ/Gma9jtioWdN3dwfOWuvLHe3RnU4zSHlfL2Pt+nDTHCiaSroD4N9FxomnQNFa+/VQnn1pFnUVfuqEBZ4+p61hA3Wa3dgl1ahluFljPSwfyxLp9ieeN8z31uhnlSvsBJJ37QRm6u0qNc5i77bPX0Pw+zDWQHgUQl0O7o6CMgrtm4n37ws7mJOqSo9RsCYtRQo6GYEjBZeN/bhVSw/U+qG9bhp1LD7Nuj3iV76XySV7STm134ODmMhqlHSJY9l63sHK+paLEhjWC4/beOOkcZ5ojmMNIxrsPtSzj55/+mgz67MYv3IKbR1GvslFXuu5wbal3jL3SQvS6d6j42UcVSdl7N4eoGkpw2opp3TZRd4abimd3Cpkc45IrZvRvNvsWm+i6C44zr7YR9iAMhjrKJSqBDCYi9KwEHG9uovslKU6dOZb+37JMWKSABCIgB0eBGshFomnIttbEWL421iZ1FM+JmeBnteyj7wBvkyB1ryL9hSANh34t1h/wfG6mIdpiXeA+7jTrzZhO1t4dUPBKZXJmlkagmqnVIOLnlLw2cEe8ooBk3RCaKiZOrWXV6DrVs6tbvz/i5jSZe2tcaffRZXVR1qlVpFk2+K2xRZYjGQCCWBNq7Oigv2+zpwufsIHvyxHFkNpshIHqo6B9AQNTngqtJSsBR4BtOL9bDNHUcoKHvnqGse6UvjVOu431+Fw5Kt0RzWbPkKVV3RUUFUSub5gZLspEtvffBGiwr7kWPQP0aDjfZIxxKq9v/atEVEOUepk8oIKUygQ6HjfJyezWIX207SFOmxY+CIJ7nxtiO63geEfoGAglEIKvuQ49wKN3OOfBaXPS+eP0tNPLVuVS56miyNH0ZF31CJ7oJZFfKZsPepe7ckd1LzOADAiDQl0CHaBBzenVh63c10eQp4fuS7Vtz8l/ppZZgY3W5XJSZGf11E5PJFJN2YzE9MtZUG284nylLzXtkat1K9mFHkyvXoD+tspmy4Mv/ur/wnSUzo/L5Sk9Pp4yMDN22zHWfUP6uJ9RHLqNjP5Vsuosal/4jFh/BiLUpn+NIxWTuqOFwhx+nU/EMF8XCC1QmK8EX3eukTX9IJ3alSfNu6/sslM+x26hPnohRH/yKtGdUKoxVlkTlMxzOM2rwZyD+WtD4aH/vti4bleVZ1HeZ9HbT7gY6Z9q0fr/bLBaL7t9QsO9EuSdJ64M66flP+iPPXa1f3vfkWRxsboO1KXXKZ0NkIv8k9waSElZAFJh2e2T29IQDMFbthtPHSOXNyspSVcWCc6TGEGo94hswb/uDlN60mfcpnkLtw08KWjRv5+NU/NWtKo9rwz1UfcSLyvF10EI6N+3Z48h1yL0kEWwcbNXcMuEKckfhcy0Pk66uLt2/oTSb1aenbv4FnsifAfmblQdoZ2enz7iMnLTuSKNVp+ZSFxutpJnddPhfOqh8YfQ3uleeQiQvLfl/ZGS+UkVoEkMDvS9HjU2yvMvnWOY0kf8WozEXGh/t89/Bz/acrAy139DlctPWXQdp9OjR6lzLq9cveV7ofa6CyQDyWZSXXr3BnkPSTrC5DdamlHVwpBi9NkXw1IRWvTH2dy2hBcT+Bof7IBAqgaKvf0u5O/+msmfV/ofdsFQFNWSRWNVaSu+yUlbd+xw670ztUljvHUOPJnnFS7KVLaT2Icso5+Db5DLlsHPtq+OlazHvx54XzUo4lI5IaLudT5ljIiDGHAQ6AAIJQsDm6qDcHiOVXQeaWbtnofLy8gTpfWy7mbACYmyxofWEISAxg8XnRz/J3LLJk0O83plbvg4qIIqxS1bDGlVGFocd+ZM85SN5YGn8nDI69lFH+RKOnRyleGnsL6Vu/u/J1L6PfUQWcxjCnEgOKaHryhvlu4yTW+V7ntCDQ+dBIAkJqD2I2d3Pzq37GmnsmFFJOMrBGVL/35yD0y5qjSGB3D3/JImNay+Zy3F9z/PzqBvDjkWw6Yy23RxG7ioyt26j9mHHUv3sO4MKiu2VJ3gEPpcpWy355tS8SU2Tf6TrSFocP0s0FDNHZmkb/g0WJqdHsPfdVckydknPMnZXdiVVH/4cC4n5EW8nUIXOnOHqVjqHIzS37aLOwmnkNnVvOwhUJtmvjzy1i6Op2Kl6VQbHZnbSxMsHvmyd7MwwPhCIJQFZYs7L6Q5Ovp0FxDFjohegIJbjjkTbEBAjQTGB6hB/e6UcF1dSbvUryg+GtersBBpBaF0t3HwfWVq3qMy5vBzcUbE86L5CiXWcVjyR3LVfUtHWByirca0qm2HdziHmVvZpVLRq4ldxMFPenmc81YuxSFbdauoYdoznWjQOMus/pvKPr6B0l40kTnfN4iejFnkmGuMLtw3eBkbTru1Ur3DLIj8IgED0CXRHUul2c7N9fwuNGjMz+p1I0BYHZuKSoINO5W5nNn3uM3xL4xc+58lykiahJrxSGgs4/SXHkMXUUXkcu53p9Q+Y0b6XyBmGlog3kWdXr6T87X8m8XE4kOTIHe0pLsvYsi8y2il/+2NKOJR2ze27KUd+VCRwsjcSNW1IJ9l5gAQCIJD8BERALMjpERCrW2nUKCwxhzrr0CCGSipJ8nWw8UH+tj953Kp0VByZJCPzHUbL+Msps2Eth5ZrInvRTGqvPNE3Q4AzZ/YwspUupKz61SpHeyVbM3OYvlBTIRu7FG77o8pesO0Rql76smGNW+O0n6kweBJpxVq1ghwFg7PPMdjYXJklPredllKf80Q6OfCOiVZfwSEHbWlUPMtJs26yUe3qDCrlpeJYWCInEjv0FQQSlYC1s90Ti3lHdRMExDAmEgJiGLCSIau95BCqOexxXq78gOzFc8letiAZhtVnDLIncP/yVWSyHaSuHPZR6B2otk9u3wsHD32IZP+h7DEUgTqclHPgdU92U2cDZTZ+xsvbxoRwV2YZ1c+R0HixS00Tf6g0oRY24pF9mh1Do7vEHcmRf/2ARQmHUmfj5yZ6+yw2vnGJSRLRwgc6aPjxgdWKbvZks/0JM+8/TKeR33BQ6VwYpyhw+A8E4pyA1d5BhXmZ7LLGTXuq66mqKvorMXGOKGD3ICAGRJO8NzqLZ5G8kj2JQYWhZVmOT9Y+7HhDeDoLp7NBx05V1s31dOZPNFRPvBRyZZVT7YJujajRPqXbatnY5hbKYEMX2e9qHf0to1UNqJylQBbqvVKPcChX9q3MCCogbrjXQpvu63bMv/1xMx3zShvlj/Orz6tqHIIACMSegJsDELTZbVSQm0n761qVP9Sysm6Dldj3Lv57gD2I8T9HEeuhpfkrGvLB+Rz790zKOvhOxOpN+orcLnb5wnsRXf3vRWyY/t/UMuYiah96LNXOf4A0S+BEYZRh3UFDPryAKt86lvJ2/j0i3S7ecDuHEHxDGQ2VfHVbzEL3Tb/BTkUznJSR56ahyx0+YyuZGdzZde2H3RESpJD4P6xf23vuU9EAT2QZfNtfzdRxsFuzOcDqUBwEUppAJ+9FFyfT+bwHccf+JqoaWZnSPMIdPDSI4RJL4Pxln/4XKaMLHkPZpz+ifcvfYbcpeQk8osHveprDShWrLyRLy0Zy8pJvzYI/UVf++IANixuapqk/DXg/Xm6kb3mSij+5mQrZpU/D9F96thqUfPlLj7ufYhbmbKULgo43lPFonzktr/h17CyaoZ1G7T1/jJuWv9juaW/HP5x0YJWJSg9x0rgLfAVGT6aegyGHOal+TffjUiKolM4LLlD6lw/lXJbA19/ZraXc+H8uOmZlO2WWQEsZCjvkAQE9AjY2TizIzlJCotp/WAUDFT1Oga5BgxiITLJd501UJgki25PSnR1swMEmnUhBCeTufU4Jh5LJxP4AC7c9HDR/ItxM72yijPeuVvszxb9h6efXe7ptsh/0HIsOy2Sv9ZwbPRAXQpqYI65yOsqWGK0qouXGrHDQoodsNPEyB6X1oxCc8sNOmnOrjcZ/p5OW/YM3vY/VRhS5Lu15qff3ur0unby1lpFrBTWBQOoQsPP3XGFuthrwjv3N7OIm8I/71KES+kh7n0ihl0HORCTA34Cto86hgp1/Vb3vKD+CjTdGJuJIottnU7dGR2tUDFf0UlbNO5S778XueMrjLmXJyrecXplYXUvj0IBp7l6DjPTOZl43ZaMLNuRpGXsxiRaRo74q628xahpoaht5unIkLs7LJYyfOyN3oFVGvbwIkGO/FVzLONBOFU51UfPGHkk13U0Fk2AIM1CmKJ/aBOwcZq8or3vP4bZqKy2aNTq1gYQ5egiIYQJL5OxN025g4wv288f7MmxJar0c6fmxDj+NsmveUns2Jbxe84Qr+jRhbtlM5Wt+wEJV9xe6+FFsmtqrletTIMYXnGzV7Rx3Npm2PaV60iJj6rHybqv6JkfYmc+aw4Ns5T5HOVKPRHcd+RN0I9JEou5kqWPWL2xkzuVN9bvTafQ5DioYDwExWeYW44gNgU4X7ztmC2ZJssR87ujR6hj/hUYAAmJonJImVyeH10MKgwD7QBRjE2WgEkB7aGle7xEOpebMpi/DaKCfrOx4W6KZULqJBbd5/WQO/XbX0gdYW/gd6mCDC39L76680SQvpOgSsHC42Nk326PbKFoDgSQnUFKYpUa4fW8dfCCGOdcQEMMEhuwpSiCAcCg0ZNnUZcphzWy3AUR7xVERg1T62bXdIRG5RuvIM6lh5i0Rq9tZNIW62nuNNiJWMSoCARAAgTghUFxgoQP1VuUHcdiwYXHSq8ToBgTExJgn9DJKBEzt+0isbO1F7CcyxH2EzuxKOrDkaXbl8ho5csdGLF5yur3BIxzK8HP3PEuNU2/gPXzs4BkJBEAABECgXwIlhZndLm5GVFJ6Ouxy+wXmlQECohcMHKY2gcy9/6byj69mAw4ndeaNp5rFT4YsjHXljSEJ7xfJ5GIXRK6MPI4NbVXVujjMnZvd0iCBAAiAAAiERqC4IJu27Wuk0WNGh1YAuTwEIE57UOAg1QnkbHlYCYfCwWLdSrm7n44tEl7Wrj3kXrIXTlMWxXLMDr1i2ye0DgIgAAIJRECMVLbDxY2hGYMG0RA2FEpGAs6sCjJ7Dax44x3Kitc6+jyvq9E9tJctohpevkYCARAAARAIn0Ahh9nbfsBKh84YE37hFC8BDWKKfwAw/F4C1pk3kr1gqueC6OrydzzmOccBCIAACIBAYhEoys9iFzfNVFVVlVgdj4PeQkCMg0lAF+KDgCt3ONXPucsT9UN6JVpFJBAAARAAgcQkUMAaxF376yEgGpg+CIgGoKFI8hIQY5PG6TeRI2cU2dhhdMOMm5N3sBgZCIAACCQ7AfYla223U2VlZbKPNOLjwx7EiCNFhYlOwDpqBckrnpPJVkNOS0nEIp3E81jRNxAAARAwSqCpzU5Dh5SS2ey9w9xobalVDgJias03RpvoBJydNOSTyyiLo6uIgFg7/0EV5zjRh2Wo/6wZIHZJROl4jBnih0IgkAIE6ps7aMRwaA+NTDWWmI1QQxkQiBEBccYtwqEkU2cDFW75XYx6Ettms2reoeGvL6aRK+dR/raHY9sZtA4CIBCXBMTQsLaxnYZVjojL/sV7pyAgxvsMoX8g4E3AT1vmTktN7VnJlzeRydFEaa5OKtr0GzJ1VHtTwjEIgAAIkMlkon21rRAQDX4WICAaBIdiIBALAu0VR1P70GOUpbUjZyQ1Tbo6Ft2IeZtpLrunD6IlEEERCQRAAAS8CWSkm6i60UZDKuCNwptLqMepqX4IlQ7ygUC8EWANYh1HVElz2jjsXla89S5q/Wmaci2VfHkzR77potZR51JX7qiotY2GQAAEEoOAxF4+0NBOS4cMSYwOx1kvISDG2YSgOyAQCoFUFg6FT9vIM6lj6NFKUIavylA+McgDAqlHwJSepvYglpeXp97gIzBiCIgRgIgqQAAEok/AZS4kkhcSCIAACOgQEA1iXZOVSktLde7iUn8EsAexP0K4DwIgAAIgAAIgkHAEWIFIDc1WKilhn7FIYROAgBg2MhQAARAAARAAARCIdwIu9pUq7lILC7HSYGSuICAaoYYyIAACIAACIAACcU3A6XJRQV4uyVIzUvgEQC18ZiiRBAQyG9bSsLdPZGfLSyhv15NJMCIMAQRAAARAwJuA08kCYkGe9yUch0EAAmIYsJA1eQiUfH4jmdt2qmgkxetvJYltjAQCIAACIJA8BLpYQMzPg4BodEYhIBolh3IJTSCdo3BoKY1clO5o0U593zn2cXb1a5RZt9r3Os5AAARAAATimoASEPPz47qP8dw5CIjxPDvo26ARaJ7wfY5GIjE42KfesOPJkT+hb1tuJ1WsvoDKP72GKj66mIo23Nk3D66AAAiAAAjEJQE2UaE8aBANzw38IBpGh4KJTMA65nzqqDiK0rus5CiYpDsUc8tmymz6wnMvb8/T1DT1p55zHIAACIAACMQzATflQkA0PEEQEA2jQ8FEJ+DMGU7OIINwZg8lV3ompffE/UU4tyCwcAsEQAAE4pBAdk5uHPYqMbqEJebEmCf0MgYEXJZijnv8W7IXz6GOIUupbvavY9ALNAkCIAACIGCUQA4ERKPoCBpEw+hQMBUI2FgwlBcSCIAACIBA4hGABtH4nEGDaJwdSoIACIAACIAACMQxgaysrDjuXXx3DRrE+J4f9E6HQM7eFymz+Us2MjmSbGWH6eTAJRAAARAAARAgys7OBgaDBCAgGgSHYrEhkLfrCSpZf4tqPG/n36lm0d+ps2RObDqDVkEABEAABOKaQGZmZlz3L547hyXmeJ4d9K0Pgay6jzzXxIthVn3vuecGDkAABEAABECACWCJ2fjHAAKicXYoGQMCtrIFnlbdfGQr7T333MABCIAACIAACDABi8UCDgYJYInZIDgUiw0Ba9U55DLlsANr2YN4FJaXYzMNaBUEQAAEEoIAlpiNTxMEROPsUIRSon8AABkcSURBVDIWBNLSqH3EqeoVi+bRJgiAAAiAQOIQgAbR+Fxhidk4O5QEARAAARAAARCIYwJmszmOexffXYOAGN/zg96BAAiAAAiAAAgYJAAB0SA4LgYB0Tg7lAQBEAABEAABEIhjAhAQjU8OBETj7FASBEAABEAABEAgjglkZMDUwuj0QEA0Sg7lQAAEQAAEQAAE4poABETj0wMB0Tg7lAQBEAABEAABEIhjAiaTKY57F99dg4AY3/OD3oEACIAACIAACBgkAA2iQXBcDAKicXYoCQIgAAIgAAIgEMcE0tMh5hidHpAzSg7lQAAEQAAEQAAE4poAlpiNTw8EROPsUBIEUouA20kln/+MRrwylyo+OI9MHQdSa/wYLQiAQMIRgIBofMogIBpnh5IgkFIEcqpXUt7e5yndZaPMxnVUuPm+lBo/BgsCIJB4BNI4PCuSMQIQEI1xQykQSDkC6Y4WnzH7n/vcxAkIgAAIxAEB7EE0PgkQEI2zQ0kQSCkCbZUnUWfeeDVmV0YetYy7NKXGj8GCAAgkHgEsMRufM7gYN84OJUEgpQi4zfl04PBnyWzdRl3Zw0nOkUAABEAgnglgidn47EBANM4OJUEg9Qikm8lRMDn1xo0RgwAIJCQBLDEbnzYsMRtnh5IgAAIgAAIgAAJxTAACovHJgYBonB1KggAIgAAIgAAIxDEBCIjGJyeqS8z//ve/affu3VRYWEjHHXcclZSU+PT8kUceoaamJnVt6tSpdMIJJ/jcxwkIgAAIgAAIgEDyEBCZYNWqVWS322nmzJm0aNEin8Ft3LiRRHbQ0qWXXqpkCO28v3fsQeyPUOD7URMQN2zYQC0tLXTFFVeQTPirr75K5513nqdnnZ2d1NjYSFdffbW6BqnfgwYHIAACIAACIJCUBJ599lm66KKLlNB3//3306RJk3yUR1u2bKFjjjmGpkyZosZvNpvD4gAr5rBw+WSOmoA4ZswYkpckmeDm5mafjuzfv5/y8/Ppvffeo6FDh9Lkyb4b4b/++muqqanxlJE8paWlnvNoHUjg73A/oNHqW6TbyczMJBHUZV5SIQ3a3DrtZGr+mlx5o8ltKYgLlDLW7OxsSpWHp4xTPs+pkuRv1u12J/1wZV5dLldKjFW+d2S8yfY8FuGwuLhYfVZF2yeKJO/Vxb1799KQIUPo3XffpXnz5vl8/0redevW+XzORZCU7y3t2VZQUEA5OTk+eeSkv+d9Xl6e7ucqWDmLxaI+j4HmKNBzSK7L2AOVC9amlJVnueTxT8JhIM+BvjX6txChcxmAJFlCfuaZZ+jcc8/1qdlqtaoH+LBhw+iTTz6hHTt20IknnujJU1tbS9u3b/ecC5TKykrPebQOpF15pUKSccoHLFW+WAdlbm0NlPXqCZTeuoPdwhSQ/dhnyFU2O+YfH3kYyQNF3pM9yRjlJYJEqiT5okqFpH0BDuRLMFE4yfNJPsfJ9jzWhMM333xTCTqjR4/2mRKZY0ki6N1zzz10/fXXexjYbDYfuUDyzZo1S31Ha882EQ71mPX3vA/0NxSsXLA5CvYckjEGm9tgbUpZ+fGgcRIGWpJrsjprNKXxH9ag/cy844471L6CGTNm0Omnn051dXX08MMP01lnnUXjx48P2OeOjg66++676cYbbwyYR27s3Lkz6P3BuJmVlUXyoUyFJHtF5YPZ0NCQCsOlwZjb/O2PUfHGOzz82oadQPVz7/acx+qgoqKCWltbqb29PVZdiFq78uCVB+hAHpRR62wEGpIv2F27dg1IcxCBbkSlCplXp9OZEsK/PJ9EmKqurg6Zrb+wFXLBQcy4evVqeu2111QLV155pdIWyh7DAwcO0AUXXKCrCdO689xzz9HIkSOVJlG75v+uyQU//elP6emnnyZZotbTrgV73o8aNYr27Nmj+7kKVk40n/J59F8hlT4Gew7J51ieyaIt1UvB2pTVVGlP5Cb/JOOWsmVlZf63QjofVA3idddd5+mEDECMUL797W/TiBEjPNe1g7Vr15JoEZcuXUqiLZRBI4FAohNwWQp9huB/7nMTJyAAAiCQ5AQWLlxI8tKSCIfy3X8RLzX7a8FE6//QQw/RJZdcQqLRk21mCxYs0IqG9C5KDiRjBAZVQPTukhilyPLy3/72N3VZJFqxRnriiSdILJanT59Ojz76KG3dulV9CM4//3zv4jgGgYQk0FZ5MmXWfUQ5NW9QZ+F0ap7w/YQcBzoNAiAAApEmIIqjlStXKoXQnXfeqao/7bTT1D5E0eCdeeaZNHfuXLXy6HA4lJYt3K1lorlDMkYgagLiihUrSF7+yXsv4uWXX66WpPX2C/iXwzkIJASB9AxqmH07NdDtCdFddBIEQAAEokVAtjHde++9us0deuih6rpoDOfPn08iIIYrG0A41EUb8sW4c5Qd7gcg5JEiIwiAAAiAAAiAQMIRkKVnI7IBlIcDm+q4ExAHNhyUBgEQAAEQAAEQAIFuwxBwME4AAqJxdigJAiAAAiAAAiAQpwTYwVWc9iwxugUBMTHmCb0EARAAARAAARAIgwD2IIYBSycrBEQdKLgEAiAAAiAAAiCQ2AQgIA5s/iAgDowfSoMACIAACIAACMQjAawwD2hWICAOCB8KgwAIgAAIgAAIxCMB7EEc2KxAQBwYP5QGARAAARAAARCIQwJYYh7YpEBAHBg/lAYBEAABEAABEACBpCMAATHpphQDAgEQAAEQAAEQgJebgX0GICAOjB9KgwAIgAAIgAAIgEDSEYCAmHRTigGBAAiAAAiAAAjASGVgnwEIiAPjh9IgAAIgAAIgAAIgkHQEICAm3ZRiQCAAAiAAAiAAAiAwMAIQEAfGD6VBAARAAARAAARAIOkIQEBMuinFgEAABEAABEAABEBgYAQgIA6MH0qDAAiAAAiAAAiAQNIRgICYdFOKAYEACIAACIAACIDAwAhAQBwYP5QGARAAARAAARCIRwJp8dipxOkTBMTEmSv0FASiQiDN2UGZ9Z+QyXYwKu2hERAAARAAgfgjkBF/XUKPQAAEYkUg3dFMFe+tIHP7bnKnW6h23u/IVr44Vt1BuyAAAiAAAjEiAA1ijMCjWRCIRwLZ1a8p4VD6lubqpPwdf4nHbqJPIAACIBCUQG5uLmVkQAcWFFI/NyEg9gMIt0EglQi4Mst8huv0O/e5iRMQAAEQiFMCP//5z+m5556P094lRrcgICbGPKGXIBAVAh0VR1LL2IupK3MIdZQvoaZJ10SlXTQCAiAAApEkUFRURAsXLoxklSlXF/SvKTflGDAIBCfQNOVakhcSCIAACIBA6hKABjF15x4jBwEQAAEQAAEQAAFdAhAQdbHgIgiAAAiAAAiAAAikLgEIiKk79xg5CIAACIAACIAACOgSgICoiwUXQQAEQAAEQAAEQCB1CUBATN25x8hBAARAAARAAARAQJcABERdLLgIAiAAAiAAAiAAAqlLAAJi6s49Rg4CIAACIAACIAACugQgIOpiwUUQAAEQAAEQAAEQSF0CEBBTd+4xchAAARAAARAAARDQJQABURcLLoIACIAACIAACIBA6hKAgJi6c4+RgwAIgAAIgAAIgIAuAQiIulhwEQRAAARAAARAAARSlwAExNSde4wcBEAABEAABEAABHQJQEDUxYKLIAACIAACIAACIJC6BCAgpu7cY+QgAAIgAAIgAAIgoEsAAqIuFlwEARAAARAAARAAgdQlAAExdeceIwcBEAABEAABEAABXQIQEHWx4CIIgAAIgAAIgAAIpC6BNDen1B0+Rh6MwJtvvknNzc10xhlnBMuGewlI4MEHH6QlS5bQ9OnTE7D36HIwAtdeey3deuutlJWVFSwb7iUYgc2bN9Mrr7xCV199dYL1PDbd/eijj0iYnX/++WF34Cc/+QndfPPNlJOTE1bZf/7zn1RYWEjLly8Pq1x1dTU98sgjdOONN4ZVTjLff//9qr0pU6aEXba/AtAg9kcohe93dXWRvJCSj0BnZyc5nc7kG1iKj0h+79vtdsLv/uT7ILhcLpK/W6TQCMh3l8PhCC2zXy6jf0NGvzO1v1u/boR0Kp8J+WwMRoKAOBhUUScIgAAIgAAIgAAIJDABCIgJPHnoOgiAAAiAAAiAAAgMBgHsQRwMqklSZ21trVLRV1ZWJsmIMAyNwI4dO6i0tJQKCgq0S3hPEgJffvklTZ06lUwmU5KMCMMQAlarlWpqamjcuHEAEgKBhoYGxayqqiqE3L5Z1q9fT7KnL9y/of3795PZbKby8nLfCvs5s9lstHPnTpo8eXI/Ofve3rZtGw0ZMoTy8/P73hzgFQiIAwSI4iAAAiAAAiAAAiCQbASwxJxsM4rxgAAIgAAIgAAIgMAACUBAHCBAFAcBEAABEAABEACBZCOQkWwDwniME2htbaV//etf1NTURLLv8MQTT6SMjN6PiNz/wx/+4GnguOOOgx89D434PXjrrbdI9qXJfsNzzjmnj3+8/u7H78hSu2f4e03e+d+4cSP9+9//9gzw0ksvVf71tAt1dXX0/PPPU1tbGy1dupRmz56t3UrJd+H19ttvK7ds3/zmN2no0KE+HALdF7c0Tz/9tNrbOXHiRPWd51Ow5+TFF1+ksWPH+nzfBapTK7927Vr69NNP1T7GI444gsaPH69u9Td3u3fvpjfeeIM6OjrU3Hr7qg3lWd3S0kJ//OMf6cc//rHWFcPv0CAaRpd8BVetWkUTJkygK664Qg3us88+8xnk3r17acyYMfTDH/5QvWQjPFJ8ExBjlO3bt9MPfvADNXevv/66T4f7u++TGSdxRQB/r3E1HRHtzJYtW+iYY47xPGvF+bJ3euqpp0h+oIvguHLlSmpvb/e+nVLHYuDx0ksv0YUXXkinn346Pfnkkz7jD3Zf/oZEmLzqqquUkLhp0yafsuIP8S9/+QuJ021vn8DB6pQKRHAXgfU73/kOrVixgsSBtuabtL+5e+GFF9Q4vvvd79Izzzzj8XEY6rP62WefJTHQiUSCgBgJiklSx7Jlyzy/RMUSSzSJ3kkERNEoygdfLJzT0/Hx8eYTj8di4TZr1iz1K3bevHkqsoB3P/u7750Xx/FFAH+v8TUfkeyNPGtF6Hv33XeVJsm/btESjRw5knJzc5Vma9euXf5ZUuZcLLuFhUQ9GTFihOLlLcwFuy/Pv7lz56rn45w5c/o8H0XQk+fmwoULfXgGq1MyWiwWuuyyy9T3pXxnSkQyTUDsb+5EWC0uLqY9e/aoerTv2VCe1fJ5EU1odna2T3+NnuAb3ii5JCwnv1LFrF9+va5bt06FYvMepnhsT0tLU8vPTzzxhMrnfR/H8UdAhHwtXJQ8NOSB5536u++dF8fxRQB/r/E1H5HsjSYUyLaQe+65R0XH0eoXwdF764/8ffv/XWt5U+Hd+xkm4/V/zgW7731Pj2NJSYlyGeXP0bucXpuiYBG3MyKo/vWvf6Vjjz1WKVRCnTvRZIrWcdKkSR4Noneb/mOUPojQKt/dixcv9u+u4fPeDWaGq0DBRCZwxx13qIfPjBkzlFp7w4YN9PLLL9P3vve9Pr9CTjrpJM9Q5dfQmjVr1JK05yIO4o6APEhkmUSShJ3y95XV3/24GxA65EMAf68+OJLmRNvmIwMSTZLsIRZNlqTMzEyfkHvywz0vL0/dS8X/JOa49oyT8ctzTjSrWgp2X3v+iUAXDsdgdWrtSn1/+tOflEZP9olKCnXuxB+ivGQvoQh9IihqfZV69J7lorSRLWL/+c9/lPb5448/pkMPPVSyG07QIBpGlxwFr7vuOvrFL36hhEP51SJ71K688koqKirqM0D5RaMtZcivFTjQ7oMo7i7Ikos4YJUke1j856y/+6og/otLAvh7jctpGXCnJK7uAw884BEC/Z+1ssojS5iiNZQf6mLU4G+UMeBOJFAFw4YNo3379ikWYtghTLw1rMHu+z//hg8fHtLIg9UpFcgcinB3yCGH0FFHHeWps7+5k3L333+/Z+7lXPtR799X/2e5tFNRUaFWjGSlT4TYgSbTLzkNtBKUTw4CDz74oPrlIdZX7733njoWr/233HKL+qCLul02A4vxiniMP+WUU5TX+OQYfXKOQjzsi/bh/fffV+9ixSy/YrU5lYeO3v3kpJFco9L7e5W9S/fdd59aZhKtEv5eE2/O5ctdBAPxKPHBBx9QWVkZLVq0iEQjtHr1arXkKZE6xBjhww8/VOfTpk1LvIFGqMfyPJOlXLH6lj14p556qmKm8ZK9hXr3pXkR9EQpIkYoIlyK547GxkbP35DWxc2bNysrck0QD9SmaPFk7qqrq+mdd96hgwcPqu9S+T6VvY5alBX/uROjEvm7XbJkidrGJXMv27ykPU1z3N+zXPZhitAoL/lciDX3QBMiqQyUYAqWFwuuSPw6SUF0MRuyLHeI1iFQ6u9+oHK4Hv8E8Pca/3Ok10MRNGQpUYSRQEnui9CBREoIlL2b2v5NfyYiJAa6b/T5F6xO//b9z4PNncy90+nUnVujffVvP5RzCIihUEIeEAABEAABEAABEEghAtiDmEKTjaGCAAiAAAiAAAiAQCgEICCGQgl5QAAEQAAEQAAEUoqA7GmUvYORTrLPXyyTJYnPS9l/GI8JAmI8zgr6BAIgAAIgAAIgkJQExK3c2xxwQpIIoa+99po6jrf/ICDG24ygPyAQJwTEXcSZZ55Jv//971WPxJ2GWASuX79eRWcRx61a+v73v68cu4ojWPHhJu4ixOpdLOkkZrCkI488kn71q18pVwyvvvqqVhTvIAACIBA3BMSdmwhwo0ePpueee87TL3ke3nrrrSpaizzfbrvtNk90lOXLl9Ojjz6q4i2LZbT2zBQjlosvvli5jRs1ahSJ32FJ4ttQwvCJNxCJmSzC4re//W360Y9+RA8//LCnTbl+wQUXeM6jfsCDRgIBEAABXQLsHsfNPjHdBw4ccLMLCPcNN9yg8h1//PFujlGqjtkfm5sjPrg5/KL7f//3f91HH320m323udlPn5vDPrnZH5jKxy513Bxf1s2uV1R9ug3iIgiAAAjEiACHs3OXlpa6WTB0f/HFF+6ZM2e6+Uex6s1jjz3m5mVh96effupmFzpudi3kZrdD6h4Lf+rZtnHjRvV8Y48Rbv4B7ea40O7DDz/cXVdX52an9m72aehm4dDNQSbUs5EtldXzkeNqu/mHtMrPkVA8o+d4zO7f/va3nvNoH0CDGHWRHA2CQOIQOOyww+jcc88lFuxoJzvcvummm1Tn5Zr48pIk/sfET5v4azvvvPNUcHvx2SWukMSzPwuXKp/8d80119DJJ5+stIieizgAARAAgTggID4RxafkaaedprSIouXTEguISusnvoFl/6BoBsXPqJZ+8pOfqOgn8nyTMJiiHRQXRBIJR/xZjh07lvhHtNIyamXE7Y5EfZF84rdUfAuzYKr8KIqbmxdeeCEi/gy19sJ9h4AYLjHkB4EUI3DttdcqZ9qXX365xyebPEDFEazVaqVnnnmGxAG3JIkUcPXVVysBUEIzylKKPOi0JM5ckUAABEAgHgls3bpVBYXQ+rZgwQLtUEVrueuuu5RwKAKiHEvQCC1JFBMtidAnfg7lOSk/mi+55BL1TBQh0jssoJZfe5d40PLcfP755+mtt95SAqd/xBQtbzTeISBGgzLaAIEEJiDBlmSPjewflCgDknhJWYWQkl+4b775pgrVKNdl/6HsPZToLLJXUWKB8rKI3FJJBEgkEAABEIhHAlVVVSTxzbUk4Um1NH/+fLr99tuVdk8ipciP38cff1y7rSKgeE56DkQYlD2Gok2UvKJxlL2KwZL82BYBUV4rVqwIlnXQ70FAHHTEaAAEEpeAGJOIACibtXlvIYk2UUvyIJM43hIeSpZUJNXX16tzWWIW9w2yZCO/pJFAAARAIN4JLF26VIUzlNB6EoHoqaee8nRZQvj9+c9/Vj+S5UevGJXcc889nvt6B7wHkc4++2wlPJ5wwgke1zbeeUXb2Nzc7LnE+7vp888/J3n2nnXWWZ7rsTiAgBgL6mgTBBKAABufKI2gPAQlYPyvf/1rJSiuWrVK9V722kisUW15WS7KEsqNN95ICxcuVBbQp59+uvqlnQDDRRdBAARSnAAbpShLZdl7PX78eJ+QshKnWWIji3Wz7K2WrTPXXXddUGLnn3++2mMo+xZFOyl7DmXJ2TtJm6K1nD17trosoRVFmBwzZgzJD+1YJoTaiyV9tA0CSUhAfl2LJlGMVpBAAARAINEIyKqHaBDlh7F/kh/OkkTzF2qSuiSGsmzN0UsSe1mWo7Ozs9VtcW1z7LHHKi2lXv5oXYOAGC3SaAcEQAAEQAAEQAAEAhBYt26d8oMoW3PEmlm0ibFMWGKOJX20DQIgAAIgAAIgAAJMgH0wqqXolStXxlw4lAmBBhEfSxAAARAAARAAARAAAR8C0CD64MAJCIAACIAACIAACIAABER8BkAABEAABEAABEAABHwIQED0wYETEAABEAABEAABEAABCIj4DIAACIAACIAACIAACPgQgIDogwMnIAACIAACIAACIAACEBDxGQABEAABEAABEAABEPAh8P/cn4mVbfICEQAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-6"/> </p>
<p>It's really nice that grid.arrange() clips the plots together so that the scales are automatically the same. You could get rid of the redundant axis labels by adding in <strong>theme(axis.title.x = element_blank())</strong> in the density plot code. I think it comes out looking very nice, with not a ton of effort. You could also add linear regression lines and confidence intervals to the scatterplot. Check out my first <a href="http://rforpublichealth.blogspot.com/2013/11/ggplot2-cheatsheet-for-scatterplots.html">ggplot2 cheatsheet for scatterplots</a> if you need a refresher.</p>
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In the third and last of the ggplot series, this post will go over interesting ways to visualize the distribution of your data. I will make up some data, and make sure to set the seed.
library(ggplot2)
library(gridExtra)
set.seed(10005)
xvar<-c(rnorm(1500, mean=-1), rnorm(1500, mean=1.5))
yvar<-c(rnorm(1500,mean=1), rnorm(1500, mean=1.5))
zvar<-as.factor(c(rep(1,1500),rep(2,1500)))
xy<-data.frame(xvar,yvar,zvar)
Histograms
I've already done a [post on histograms](http://rforpublichealth.blogspot.com/2012/12/basics-of-histograms.html) using base R, so I won't spend too much time on them. Here are the basics of doing them in ggplot. [More on all options for histograms here.](http://docs.ggplot2.org/current/geom_histogram.html)
The R cookbook has a nice page about it too: http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/
Also, I found [this really great aggregation](http://sape.inf.usi.ch/quick-reference/ggplot2/geom) of all of the possible geom layers and options you can add to a plot. In general the site is a great reference for all things ggplot.
#counts on y-axis
g1<-ggplot(xy, aes(xvar)) + geom_histogram() #horribly ugly default
g2<-ggplot(xy, aes(xvar)) + geom_histogram(binwidth=1) #change binwidth
g3<-ggplot(xy, aes(xvar)) + geom_histogram(fill=NA, color="black") + theme_bw() #nicer looking
#density on y-axis
g4<-ggplot(xy, aes(x=xvar)) + geom_histogram(aes(y = ..density..), color="black", fill=NA) + theme_bw()
grid.arrange(g1, g2, g3, g4, nrow=1)
Notice the warnings about the default binwidth that always is reported unless you specify it yourself. I will remove the warnings from all plots that follow to conserve space.
Density plots
We can do basic density plots as well. Note that the default for the smoothing kernel is gaussian, and you can change it to a number of different options, including __kernel="epanechnikov"__ and __kernel="rectangular"__ or whatever you want. You can [find all of those options here](http://docs.ggplot2.org/current/stat_density.html).
#basic density
p1<-ggplot(xy, aes(xvar)) + geom_density()
#histogram with density line overlaid
p2<-ggplot(xy, aes(x=xvar)) +
geom_histogram(aes(y = ..density..), color="black", fill=NA) +
geom_density(color="blue")
#split and color by third variable, alpha fades the color a bit
p3<-ggplot(xy, aes(xvar, fill = zvar)) + geom_density(alpha = 0.2)
grid.arrange(p1, p2, p3, nrow=1)
Boxplots and more
We can also look at other ways to visualize our distributions. Boxplots are probably the most useful in order to describe the statistics of a distribution, but sometimes other visualizations are nice. I show a jitter plot and a violin plot. [More on boxplots here.](http://docs.ggplot2.org/0.9.3.1/geom_boxplot.html) Note that I removed the legend from each one because it is redundant.
#boxplot
b1<-ggplot(xy, aes(zvar, xvar)) +
geom_boxplot(aes(fill = zvar)) +
theme(legend.position = "none")
#jitter plot
b2<-ggplot(xy, aes(zvar, xvar)) +
geom_jitter(alpha=I(1/4), aes(color=zvar)) +
theme(legend.position = "none")
#violin plot
b3<-ggplot(xy, aes(x = xvar)) +
stat_density(aes(ymax = ..density.., ymin = -..density..,
fill = zvar, color = zvar),
geom = "ribbon", position = "identity") +
facet_grid(. ~ zvar) +
coord_flip() +
theme(legend.position = "none")
grid.arrange(b1, b2, b3, nrow=1)
Putting multiple plots together
Finally, it's nice to put different plots together to get a real sense of the data. We can make a scatterplot of the data, and add marginal density plots to each side. Most of the code below I adapted from this [StackOverflow page](http://stackoverflow.com/questions/8545035/scatterplot-with-marginal-histograms-in-ggplot2).
One way to do this is to add distribution information to a scatterplot as a "rug plot". It adds a little tick mark for every point in your data projected onto the axis.
#rug plot
ggplot(xy,aes(xvar,yvar)) + geom_point() + geom_rug(col="darkred",alpha=.1)
Another way to do this is to add histograms or density plots or boxplots to the sides of a scatterplot. I followed the stackoverflow page, but let me know if you have suggestions on a better way to do this, especially without the use of the empty plot as a place-holder.
I do the density plots by the zvar variable to highlight the differences in the two groups.
#placeholder plot - prints nothing at all
empty <- ggplot()+geom_point(aes(1,1), colour="white") +
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank() )
#scatterplot of x and y variables
scatter <- ggplot(xy,aes(xvar, yvar)) +
geom_point(aes(color=zvar)) +
scale_color_manual(values = c("orange", "purple")) +
theme(legend.position=c(1,1),legend.justification=c(1,1))
#marginal density of x - plot on top
plot_top <- ggplot(xy, aes(xvar, fill=zvar)) +
geom_density(alpha=.5) +
scale_fill_manual(values = c("orange", "purple")) +
theme(legend.position = "none")
#marginal density of y - plot on the right
plot_right <- ggplot(xy, aes(yvar, fill=zvar)) +
geom_density(alpha=.5) +
coord_flip() +
scale_fill_manual(values = c("orange", "purple")) +
theme(legend.position = "none")
#arrange the plots together, with appropriate height and width for each row and column
grid.arrange(plot_top, empty, scatter, plot_right, ncol=2, nrow=2, widths=c(4, 1), heights=c(1, 4))
It's really nice that grid.arrange() clips the plots together so that the scales are automatically the same. You could get rid of the redundant axis labels by adding in __theme(axis.title.x = element_blank())__ in the density plot code. I think it comes out looking very nice, with not a ton of effort. You could also add linear regression lines and confidence intervals to the scatterplot. Check out my first [ggplot2 cheatsheet for scatterplots](http://rforpublichealth.blogspot.com/2013/11/ggplot2-cheatsheet-for-scatterplots.html) if you need a refresher.</div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com8tag:blogger.com,1999:blog-2550386066385582083.post-10409761705364078922014-01-09T03:47:00.000-08:002016-10-13T07:53:42.697-07:00ggplot2: Cheatsheet for Barplots<div height="1050">
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<p>In the second of the series, this post will go over barplots in ggplot2. Our data is from mtcars as before.</p>
<pre><code class="r">library(ggplot2)
library(gridExtra)
mtc <- mtcars
</code></pre>
<pre><code class="r"># preview data
head(mtc)
</code></pre>
<pre><code>## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
</code></pre>
<p>To introduce the barplot, I show the basic default bargraph that you would get if you indicate an x-variable and use the default <strong>geom_bar</strong> layer, which is <strong>geom_bar(stat=“bin”)</strong>. You could just write <strong>geom_bar()</strong> and it would also work. Remember that in ggplot we add layers to make plots, so first we specify the data we want to use and then we specify that we want to plot it as a bar graph (instead of points or lines). The basic plot gives a count of the number in each group of the x-variable (gears).</p>
<pre><code class="r">ggplot(mtc, aes(x = factor(gear))) + geom_bar(stat = "bin")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-3"/> </p>
<h3>>> Aggregate data for barplot</h3>
<p>Instead of this, we would like to graphically show the mean weight of the cars by the number of gears. There are a number of ways to make this graph. The first way is that we summarize the data beforehand, and use the summarized data in the ggplot statement. I show two ways to summarize here, with two different results of how the data looks when summarized, using aggregate and tapply. Using the tapply() inside of a data.frame() statement, we can put together a new dataframe of the mean weight by gear.</p>
<pre><code class="r">#using aggregate
ag.mtc<-aggregate(mtc$wt, by=list(mtc$gear), FUN=mean)
ag.mtc
</code></pre>
<pre><code>## Group.1 x
## 1 3 3.893
## 2 4 2.617
## 3 5 2.633
</code></pre>
<pre><code class="r">#using tapply
summary.mtc <- data.frame(
gear=levels(as.factor(mtc$gear)),
meanwt=tapply(mtc$wt, mtc$gear, mean))
summary.mtc
</code></pre>
<pre><code>## gear meanwt
## 3 3 3.893
## 4 4 2.617
## 5 5 2.633
</code></pre>
<p>Now we can use the summarized dataframe in a ggplot statement and use the <strong>geom_bar</strong> layer to plot it. </p>
<p>In the first argument we indicate that the dataframe is summary.mtc, next we indicate in the aes() statement that the x-axis is gear and the y-axis is meanwt, and finally we add the <strong>geom_bar()</strong> layer. We use the <strong>geom_bar(stat=“identity”)</strong> to indicate that we want the y-values to be exactly the values in the dataset. Remember, by default the stat is set to <strong>stat=“bin”</strong> which is a count of the x-axis variable, so it's important to change it when we have summarized our data.</p>
<pre><code class="r">ggplot(summary.mtc, aes(x = factor(gear), y = meanwt)) + geom_bar(stat = "identity")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p>
<p>Another option for quick graphing is to use the built-in <strong>stat_summary()</strong> layer. Instead of summarizing data, we use the original dataset and indicate that the x-axis is gear and the y-axis is just weight. However, we use <strong>stat_summary()</strong> to calculate the mean of the y for each x with the following command:</p>
<pre><code class="r">ggplot(mtc,aes(x=factor(gear), y=wt)) + stat_summary(fun.y=mean, geom="bar")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-6"/> </p>
<p>There are reasons why we would want to use the first or second method. For the first, summarizing our data the way we want it gives us validity that we are sure that we are doing what we want to be doing and gives us more flexibility in case we want to use that summarized data in a later portion of our analysis (like in a table). Using the <strong>stat_summary()</strong> layer is faster and less code to write. </p>
<p>For now, we continue with the second method, but later on we'll come back to the summarizing method.</p>
<h3>>> Horizontal bars, colors, width of bars</h3>
<p>We can make these plots look more presentable with a variety of options. First, we rotate the bars so they are horizontal. Second, we change the colors of the bars. Finally, we change the width of the bars.</p>
<pre><code class="r">#1. horizontal bars
p1<-ggplot(mtc,aes(x=factor(gear),y=wt)) + stat_summary(fun.y=mean,geom="bar") +
coord_flip()
#2. change colors of bars
p2<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(gear))) + stat_summary(fun.y=mean,geom="bar") +
scale_fill_manual(values=c("purple", "blue", "darkgreen"))
#3. change width of bars
p3<-ggplot(mtc,aes(x=factor(gear),y=wt)) + stat_summary(fun.y=mean,geom="bar", aes(width=0.5))
grid.arrange(p1, p2, p3, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-7"/> </p>
<p>For the colors, I color the bars by the gear variable so it's a different color for each bar, and then indicate manually the colors I want. You could color them all the same way using <strong>fill=“blue”</strong> for example, or you can keep the default colors when you fill by gear by leaving off <strong>scale_fill_manual</strong> altogether. </p>
<p>You can also use <strong>scale_fill_brewer()</strong> to fill the bars with a scale of one color (default is blue). This R cookbook site is particularly useful for understanding how to get the exact colors you want:<strong><a href="http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/">http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/</a></strong></p>
<p>Note that if you are summarizing the data yourself, you change the width this way (graphs not shown since they look the same):</p>
<pre><code class="r">ggplot(summary.mtc, aes(x = factor(gear), y = meanwt)) + geom_bar(stat = "identity", width=0.2)
</code></pre>
<h3>>> Split and color by another variable</h3>
<p>Now, let's make the graph more complicated by adding a third variable. We can do this in three ways: bars next to each other, bars stacked, or using 'faceting' which is making multiple graphs at once. We would like to know the mean weight by both gear and engine type (vs). Stacking is a particularly bad idea in this example, but I'll show it for completeness.</p>
<pre><code class="r">#1. next to each other
p1<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean,position=position_dodge(),geom="bar")
#2. stacked
p2<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean,position="stack",geom="bar")
#3. with facets
p3<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean, geom="bar") +
facet_wrap(~vs)
grid.arrange(p1, p2, p3, nrow=1)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/> </p>
<p>You can also indicate the width of the spread between the bars in the first plot using
<strong>position=position_dodge(width=.5)</strong> and play around with the width number.</p>
<p>You can change the order of the stacking by re-ordering the levels of the fill variable. Here is a prior blog post I had about <a href="http://rforpublichealth.blogspot.com/2012/11/data-types-part-3-factors.html">how to reorder factors</a>. </p>
<pre><code class="r">mtc$vs2<-factor(mtc$vs, levels = c(1,0))
ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs2)), color=factor(vs2)) +
stat_summary(fun.y=mean,position="stack",geom="bar")
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAADYCAYAAABLNT5AAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGFVd9v21QUPolvUqQWPyBYR4eKxa9VU1u5GxqtxgZJk6XtShal6dgqJOQ6N4mpGwfb6baqT3uBNwb8AUDZAw9IPCENBmJ72fbAtElThyqqSUh76MQPISbtBVXhu3ZiJ1PEXPX6yznfOec7517bRD1fabWaGVWIlquunc8klZOnFpSeTYrSs9RLA9Sr6U4tkcvNEi7BFffO6+EdigjL7ZHu/k72I796i9zRiSJPwG4VHX0Z+AxRzNRrtksUvwf7+Gm3BtzzHPDTNgQCqwKXfZwSeNHHJz1OIT8JjtAq6xWtCLwGPLzYZi+3YV8DGMiT4VVuG7oiZpGzrZJhcs/hL49xtzH/Dy6bdfTsXYNY+5yluWO4D4neK/ZUvok/17X0HPBLsF+vuUlhfwX4j/rSfAJ4H1H0qZJ9dN7nR19frRTeBt4Fe9FwpwtN+2p1MXscGLHR9SXrmMgjONd1ZxKzpBeA71b4tNhj6JGoyFNp4GHgwUp9qplfmnFW5oTdy7NamcwCI49kv6fN5IAHgD+0rbyoBc3SOjczohbyS1drbq6pQdqumllRC/0ymTtej8gpbbuVwpQfyw66dqEZyxZKxtHpJn+tZnpnEdrYBbueF9qQn93S7HQGGHnYP7w6L+YGHNtd1FJitqPAR+hERCNOFi1i1alKO6RQnjKUxL1GNjwlMsiEhcPLYTEiT9ISbN15OY/jx4SMshe9LaJRpTvHr3C/ybFYP1PZAfwfYrPsMBtnE6SwN9ib7AhLwTrBDgUKcm06FSrTfSj187xPdVQWOk5Q8vxAfSiIUc7Z7xr6zY/+hpqwSyv0I0/QMTRb7RMgBxNodTfSPqdraz/sDjzKBrv4zu2+a2t0/HHzjd2Lbcc2sG7GtsL42K+xLfxtUgI7YHqKlqHK8HbCCXgjHT1cAdMlDetv4FnQ2lLasaOl6vmB0CMmwT/IPszSueHQqv6i/qluqF+oF9TfO2qEGTumJH0qfSv9KH0nfS/9TIp0Wboi/SRdlb6RLgU5u++9nyXYe69fYRPdil1o1WufNSdTTsp75BfllPy8/LI8G7AUuV8ek6fkvfDsCfbNDP0dvRh0CrNqTbV7LfEEGDQPJQadBtfGVMWEq3QWWdufk6ZSNsjG2PQjp3ZcnOWWing6noonSInvi0/Ex+IzAreevPhe+CawpgP1/pMTMDo64G0sTCXIM+KdOnFWRfQKdJvQzV1+Bt8OokmrdtY2yhVX2a+qrykJfMq4Ml3VR4cVzTQVz+UoNne4vcKLoyS+gyKO6EHe+75Fdt0Mbe5bRIf/wjvrVmhbqBN97RD1vxrahvBOfOYzoosH9bq94uejSOQGkVM6sN/7HelL4t10t9F4gPdVzydEOx83Gv+uNxo7XyL/FtFl8z9ZAHF4bBsrEwAAG1NJREFUeAHtnQeQFMXbxt9Dcs45S5QkCBQ5SBSUWH4qaKECCkVQVIogZYkUIApaEgqQKGApGRQQJFiIKFEQEI4cCsnh4Mhpvnn676x73N5xu0zcebpqb2dnZ/rt9zezz3X3dL8do+lJmEiABEjAAQKpHLBJkyRAAiSgCFCAeCOQAAk4RoAC5Bh6GiYBEqAA8R4gARJwjAAFyDH0NEwCJEAB4j1AAiTgGAEKkGPoaZgESIACxHuABEjAMQIUIMfQ0zAJkAAFiPcACZCAYwQoQI6hp2ESIAEKEO8BEiABxwhQgBxDT8MkQAKp3YTg2rVrcuvWLVuLlCpVKsHr3r17tto121jatGnl7t274uXgBrgOTzzxhPLDbD7J5Qe7OXPmTO4QfmcRAVcJ0O3btwUiZGfKnDmzpEuXTuLi4uw0a7qtQoUKKXZ37twxPW+7MsyYMaNkyJBBLl++bJdJZQeiRwGyFXnAGJtgARTcIAESsJsABchu4rRHAiQQIEABCqDgBgmQgN0EKEB2E6c9EiCBAAEKUAAFN0iABOwmQAGymzjtkQAJBAi46jF8oFRJbNTaE5vEN+7cvaliOXcWjKUiAZcQYA3IJReCxSABPxJwXQ0Ig8KiJdnpS0xMjBrRbadNs68TRiQj2e2DYddsf5jfowm4ToDwQ4qWZLcvsGe3TTOvlVF+u32w256ZzLyel+sEyOtzsoJvCDt9wRyw+/fve3pOG8oPP+zkhutld40r+B7x+zb7gPx+B9B/EnCQAAXIQfg0TQJ+J0AB8vsdQP9JwEECFCAH4dM0CfidAAXI73cA/ScBBwlQgByET9Mk4HcCFCC/3wH0nwQcJEABchA+TZOA3wlQgPx+B9B/EnCQAAXIQfg0TQJ+J0AB8vsdQP9JwEECFCAH4dM0CfidAAXI73cA/ScBBwlQgByET9Mk4HcCFCC/3wH0nwQcJEABchA+TZOA3wlYGpAMgaU2bdoksbGxki1bNunQoQODP/n9jqP/JBBEwNIa0IYNG+TmzZvSrVs3yZ49uxw4cCDINDdJgAT8TsDSGtCuXbukffv2qhZUs2ZNVQsKBv7zzz9LfHx8YFepUqWkePHigc9e38iRI4dtLiCsaJYsWeTBgwe22TTbUJo0aSR16tRiJzf4gDCwTM4QsLQGdPXqVYHIINbvpEmT5NSpUwm8TJ8+vWTIkCHwwo8IN0NSrwQne+BDUn5YtR9IrMrbrnyd8MHLou2Bn0GyRbS0BoT/aC1atJAiRYpIxowZZevWrdK2bdtAgRo0aBDYxsbFixclLi4uwT4vf7DTl0yZMqna5J07dzyLDPcIlsixkxtg4R9fzpw5PcvNywW3tAYE4UEtCOnKlSuCGg8TCZAACRgELK0BtWrVSlatWiWbN29W/527d+9u2OU7CZAACYilAoTOxJdfflnQLEibNi1xkwAJkEACApY2wQxLFB+DBN9JgASCCdgiQMEGuU0CJEACBgEKkEGC7yRAArYToADZjpwGSYAEDAIUIIME30mABGwnQAGyHTkNkgAJGAQoQAYJvpMACdhOgAJkO3IaJAESMAhQgAwSfCcBErCdgKUjoW33xgMGa+2JtaaUVuWrl3ZTxXLWlJm5+p4Aa0C+vwUIgAScI0ABco49LZOA7wlQgHx/CxAACThHgALkHHtaJgHfE6AA+f4WIAAScI6A656CIYxrtKRo8cUuPxCQPiYmRuyyZ9xnsMnkDAHXCRAC2EdLihZf7PIDweER/N4ue8Z9hjjUTM4QcJ0ARdMKBdHii11+GHaMd7t+EqwB2UU6sR1Kf2Im3EMCJGATAQqQTaBphgRIIDEBClBiJtxDAiRgEwEKkE2gaYYESCAxAQpQYibcQwIkYBMBCpBNoGkmOglgyAAW4MyWLZt8//33KXYST/omT56sFuxM8Un6gUOGDJExY8aEc4ps2rRJLYleu3Zt+frrr9W5H3/8sXz++edh5WPFwTH6uAvNiowjyRNrw8fHxyd5qmWhLJK0+HhfhApj4TUfQCCUH49HJvTZWBs+S5Yscvbs2dAHWLQXa8NjGfFI0rFjx6REiRKyZs0aqV+/vlqAEwtxYmwRBlYGp+AFOtevXy+NGjUS3PPB69IHH2Oca4yLunDhgpQpU0b27dsnBQsWNL5O9H779m1Jly5dYH+1atUEr7p160rXrl1l//796rs6derIkSNHFPPAwTZvsAZkM3Caiy4CjRs3Vg61bNlSTpw4IZ07d5Y8efJIpkyZpF+/fuo7iA1+7BDXZs2aya1bt6RHjx7qu2LFiqn36dOnS968eSVfvnzSp08fuX79unz77bdKGMuVKycdO3aU5cuXS6VKlZT4NG3aVN5++2117g8//KBqYBCo5557TkqWLCn58+eXiRMnCmpa06ZNk9GjR0vz5s3VQE+Us3Tp0iqfJUuWqDyc+kMBcoo87UYFgYULFyo/fv/9d8mVK5c8/fTTqlYxcOBAmTVrlvruo48+kpo1awoEonDhwrJ7924lCPjyr7/+kuPHj0u3bt0Ex23btk0WLVokc+fOVeJx8uRJdezYsWNl165d8uSTT6o833zzTdXku3nzpsyYMUM1sVCbWbdunYwbN06dj5oZamJVq1ZV01uwTDpqQqh5ISEvlMXJRAFykj5te54AajpImTNnVs0vNI/Q1/LTTz/JtWvXBM2hjRs3qh89akAQixo1aqjjcV727NmVCKEn5NVXX1WigKbcihUr8LUSjrZt20rRokXl0KFDqmaD/agRYclz1JxQM3rrrbeUyKEGhhf6pdauXYtD5e7du9KhQwc5f/68LFu2TNDkREItCbUhJ1PCRqqTJaFtTxHwWl+WHf1YK1eulNmzZ8ulS5dk6tSpsnPnTtUXU6tWLcF3DRs2lBdeeEEGDBggOXLkUNf76tWrUrlyZbWNTmwIx2+//SbDhg1T+4L7ctBci4uLC+zv0qWLDBo0SEqVKiX16tVTNSQ0/9CHNmXKFBk8eLDKp1evXkoE58yZI+izwmTf3LlzK4GMtO9LFcKEP6wBmQCRWZAACEBg0OxB/wpqQKh5nDp1SoYOHaqaVujfQUc7+oEgGnhyhmMLFCggEyZMUGKCPp42bdrISy+9lAgqvouN/S+mOGo9eGjTvXt3dSzy3LFjh8rzq6++Uk26y5cvy8yZM1WfUvv27VVf1KpVq9Txe/fulSpVqiSyY+cOPgWzkHao/7peqzkATzT4EcoH49I/zlMwI4/gdzS90CR7OKG/JkOGDIHdaJ6hk9jYh208BUufPn3gmOAN1KzKli0rR48eDZm/cSzsoOaU3Cx/PH1Dzevw4cNJ2jPys/KdNSAr6TJvXxIIJT4AYQiNAQUiEbwPgpGU+OAcPK7HY3Q8HUsuIc/kxAfnoi+qd+/eydpLzoZZ37EPyCySzIcEbCAwcuRIVWt6XFMYIvAokXpcGyk5nwKUEko8hgRcQgCxi4ynWI9TJDPyeBz7xrm2CBBGXm7fvl06depk2OU7CUQFgWtdOj+2H5lmzlGhaB87Iw9mYHkf0I0bN2Tp0qXJTrHwIDcWmQRMI+DniIyW14AWLFighoBv3rw50QWbN2+e4DGhkTBKs0KFCsbHEO//PYIM8aXrdmFofeLkLR9Q/mjwI7QP/7s6xlyrxNeKe6wmYKkAbdmyRQoVKpTkxLny5csLakhGwqhQzIGJlhQtvkSDH4/yAaOUmewnYJkAQVgw7LtJkyaydetWNesXfUEYx2AkDKwKTo+aDR98rBe2H3XTe8EHlDEa/EjOB7d0yHrlfjCznJYJEC4qRnQi3bt3T/XcY+4KEwmQAAkYBCLqhEZN5eF07tw5Vcsx9mOQVfXq1dULNR00rzBMnYkESCB5AhgN3aJFiwT9o8mf4d1vwxIgxDHBC9P5jW28o7nVt29f+fHHH0OSQAegEbsk5AHcSQIkoAgcPHhQzRVDtwWmZkR7CkuAWrdurYaO79mzR71jyDdeGHqOkAMIusREAiQQOYEzZ87IpEmT5Kmnnoo8Ew+dGVYfEMJO4pElQjsi2JIRHAlDut0wrNtD3FlUEghJALGA/JTCEiAMmEKcW4SDRNwShBZo166dej3zzDN+4kZfSYAETCAQVhPMsDd8+HA1jR8DCbNmzari2yJi2x9//GEcwncSIAESeCSBsGpARm7opcfIZsSfxQsxbdH/AzFiIgESIIGUEohIgNBBhrCPPXv2lFGjRqkYtxzMlVLkPI4EHk0AYVn9kCISIETdRyBsjHTGkiMY7Yx+IXROB8ew9QNA+kgCJBA5gYj6gLD20Pjx4wUxZfHIEHFoIUJYn4iJBEiABFJKIKIaEJZ6RdBtBLdG/w8WZVu8eLEavZlSwzyOBKKCgD7iX2Ii+j8eFe4/rhMRCRDWp0boDKxRjTWQOAbocS8Dz/cqgVJt/u+xi35aXxPMrzGBIhIgDEhkIgESMIeAX8UH9Fh3NOceYi4kQAIREKAARQCNp5AACZhDgAJkDkfmQgIkEAEBClAE0HgKCZCAOQQi6oQ2x3ToXJIbyHho/uzQJ7l0b/wzo11asvCKldw1CS8n545Ozgc3dQIfOXJE+vfvr4L7IcZWhw4dnINmg2XXCRDWy04qeS1seHK+JOWjG/dHgx/J+eCmaUS9evWSESNGCCZ3N2vWTJ599lkVTdSN94UZZWITzAyKzIMETCJw+vRpqVq1quTKlUvq1asnWFkmmhMFKJqvLn3zFAGskZc+ffpAmXPkyCEXLlwIfI7GDQpQNF5V+uRJAlibLHidPCwllNyCip508qFCU4AeAsKPJOAUAUQbRYx1rDqj6dMztm/fLli8M5qT6zqhoxk2fSOBRxH47LPPpHPnzmrVGYQ9xsrC0ZwoQNF8demb5wg0bNhQ8MJyV8H9QZ5zJIUFZhMshaB4GAnYScAP4gOerAHZeVfptrw2mBJ4LlQcbjMlmvMLAdaA/HKl6ScJuJAABciFF4VF8hcBPPHya2ITzK9Xnn6bQuDM8y1NycevmbAG5NcrT79JwAUEKEAuuAgsAgn4lQAFyK9Xnn6TgAsIUIBccBFYBBLwKwEKkF+vPP0mARcQoAC54CKwCCTgVwKWPoaPj49Xa8jHxcVJwYIFBZPrMOOXiQRIgARAwNIa0Lp166R06dLSo0cPRRtryDORAAmQgEHA0upIo0aNJHPmzMpWmjRpBDWh4DR58uQEEd8wC7hmzZrBhyTYvpPgk/s/oNb3cPKaDyh/KD9kT+zDrrn6c0gf/i3xvXv3XF32aC6cpQKULVs2xe7gwYOyc+dOeffddxOwRJPszp3/fpJoniEYU1LJa0HpQ/niNR9wLUL5kdQ1cuv+5HzAqhgZM2Z0a9GjulyWChDI7d27V5YtWyY9e/ZU0d6CaRYpUiT4o7rR0W+UVPLajzfUSgxe8wHXIpQfSV0jt+5Pzgc3rYrhVn5WlctSAYqNjZXVq1dL7969+R/GqivIfEnAwwQsFaAFCxYI2tdjx45ViKpVqybNmzf3MC4WnQRIwEwClgrQkCFDzCwr8yIBEogyApY+ho8yVnSHBEjAZAIUIJOBMjsSIIGUE6AApZwVjyQBEjCZAAXIZKDMjgRIIOUEKEApZ8UjSYAETCZg6VMwk8vK7FxEwGvLC3FpIRfdPEFFYQ0oCAY3SYAE7CVAAbKXN62RAAkEEaAABcHgJgmQgL0EKED28qY1EiCBIAIUoCAY3CQBErCXAAXIXt60RgIkEESAAhQEg5skQAL2EqAA2cub1kiABIIIUICCYHCTBEjAXgKuGwmdPn16ewlYaC1afIkGP6LBBwtvVceydp0A3bp1K0kY/1tfI8mvXfdFKF+85gOgRoMfoXwwbhjGhDZI2P/OJpj9zGmRBEjgXwIUIN4KJEACjhGgADmGnoZJgAQoQLwHSIAEHCNAAXIMPQ2TAAlQgHgPkAAJOEaAAuQYehomARKgAPEeIAEScIwABcgx9DRMAiRAAeI9QAIk4BgBCpBj6GmYBEiAAsR7gARIwDECFCDH0NMwCZAABYj3AAmQgGMEKECOoadhEiABChDvARIgAccIUIAcQ0/DJEAClkZEvHfvnsyfP1/Onj0rZcqUkVatWpE4CZAACQQIWFoDWrduneTPn1/69OmjRCg2NjZgmBskQAIkYGkN6PDhw9KpUydBzN2qVavKgQMHpFy5cgHqo0aNUsJk7GjdurU0btzY+Jjo/UaiPe7eUbRo0UQF9JoPcCAa/Ajlg3Fx7t69a2zy3WYCMZqerLI5cuRI+eCDDyRNmjRKfLZv3y6vvPJKwNy1a9fk/v37gc83btwQNNvsTJkyZRKsmHDx4kU7zZpuq0CBAnLhwgXx8o8pQ4YMgusBP+xMqVKlkkKFCtlpkrb+JWBpDQg31O3bt5UA3blzRzJnTrgmxMOfIT443s704MEDgQYHC6Gd9s2yBR/gi5f9QPmRvOyDWdfTL/lY2gdUuHBhOXbsmGJ59OhR/pfxy11FP0kghQQsrQE1adJEFi5cKBs2bFC1oOeffz6FxeJhJEACfiBgqQDlyJFDunXrJmh+pU2b1g886SMJkEAYBCxtghnloPgYJPhOAiQQTMAWAQo2yG0SIAESMAhY+hjeMOLm902bNsnBgwfltddec3MxH1m2ESNGSJcuXTzd0b9z507ZunWrdO/e/ZH+8oDoIOD7GhAe+Xp57IxxG6KfzXiMbezz2juuBfxg8g8B3wuQfy41PSUB9xGgALnvmrBEJOAbAr7vA7p06ZJgSkhyc4W8cDfs379f+YDR515NV65ckcuXL0vx4sW96gLLHSYB3wtQmLx4OAmQgIkE2AQzESazIgESCI8ABSg8XjyaBEjARAKWTsUwsZyWZIXwINu2bVOz4Tt06CB58+a1xI4dmaIPCP4g/pIX0759+2TFihWBomMKT7Zs2QKfuRGdBHwrQIg99Ouvv8o777wjx48fl0WLFkmPHj08eZXhy9KlSz39g8Vg0GbNmkn58uXVNUAMKaboJ+DbJljGjBmlX79+qvaDUCHZs2f37NVesGCBNG/e3LPlR8FPnjwpEFJETrh586anfWHhU07AtwJkIEItCDc9guZ7MW3ZskVNvyhYsKAXix8oM6ISImXNmlW+/PJL2wPTBQrCDVsJ+F6AEIP6ww8/lCVLlnhuGgBqDMuWLZPUqVOrOVQIK4u+IC8mNH9r1aol1atXl8qVK8vu3bu96AbLHCYB3woQ4g7PnDkzgAtxoRE830sJ5W3Tpo2Ko2yU34uhTzCHbeLEiYF/AFjGyes1Oi/dR06W1bed0Llz51adttOmTVM3fosWLTwnQOnSpVM1BtxA586dk0OHDkmJEiWcvJ8iso3mV7Vq1WTq1KlqYnC+fPkoQBGR9N5Jvh8JjdnXeOISExPjvasXZSVGTQiRCSCsTP4g4HsB8sdlppck4E4Cvu0DsvJyoFaFJpET6cyZM7aYxSRexu6xBXVUG6EAmXx5ly9fLujDwCqvkSyyuGrVKsGS1pGkjRs3yvjx4yM5Nexz9u7dK5988knY5/EEEggmQAEKpmHCNkYkYzVYhBbF4/Fw0+TJk+Wff/4J9zQVDfHjjz+WQYMGhX1uJCfUq1dPjhw5ol6RnM9zSAAEKEAm3geffvqpzJs3T8aOHSsDBgxQI3sxvgXL/ubMmVNefPFFiY+PVxYR+wafMf8M66UhHvL06dNl9erV6tw5c+YoUZkwYYJUrVpV5TF06NBA2NVRo0bJsGHDBIs/YjrJrFmzVDwgLG2MdOLECVULy5Url7zxxhvSsWNH2bNnj/pu/fr1UqVKFTX6G3PggpdC/uabb9R0CKxaiydTEFKkh+1hH84dOHAgNplIIDICWBueyRwC+sBATZ8Mquk/Vu369euaHihea9q0qaaPa9FiY2M1fbS1NmXKFGWsbdu2mh4IXzt9+rSmN5u0OnXqaLdu3dJ0MdL0MTFqe9y4cVqFChW0zZs3a3rzSitdunTg/Pfee0/TxUVbvHix+l4XGU0XoYAjeg1F69u3r6bXptS7fndouphoet+UliVLFm327NnaqVOntNdff11DXkgHDhzQdAHT/vzzT03v49H04PCaPsVDffewPezUhUvTm5vqe/4hgUgIYC4Uk4kE8IOGcCDpy1KrH7mxrfcLaXqtRbt9+7amN880fQa4Ok5//KytXLlS0/uMtPbt2weEpHbt2po+LUEdgz/Dhw/XGjRooD5DEGDLSBCwtWvXqo96R7Smj61RIoIdepRBTR+0qARo0qRJmj7iWNNrYOqlTwLVypUrp87DPr2WpLbj4uKU7UqVKqnPD9tTO/U/+gBIDccykUAkBMLvpIisouXLszBSGc0jNHny5MmjxrjUqFFDMPkVoVP1H77igjFIGAj5cMIsfV2EAruxPWPGjMBnNL+MpItdIJwI+mYQ1hQr0yJhom2pUqXUNiZ9YppD2bJl1WfjD/qdMPp47ty56nX16lV1TvBKG8H2jPPgF2yjScdEAuESYB9QuMTCOB79P+j7wQ8e/S81a9ZUs+8hDOgL0ptfgdzQ/2P0Dxk7MVr777//Nj6qfEqWLBn4HDx1BDGtjfwgBtjWaybqWMS8Pnz4sNpGGfTakvoex+CFOEIQH/QjYWb9woUL1f7+/fur8hoGg+1hH8QJww28Hk/b8I/v9hOgAFnIHJND8bQIHc2oeaCDGSN98RkTLvV+GPUDx2z8L774QtDxi05kQzhQK/ruu+8EHdYYdzN//nypW7duyBIjP8TUQUKoEdgdPXq0oCYzZsyYwJAAvU9K9D4l2bFjhzoWnd0tW7ZUYgIbej+TVKxYUZULc+WSWzMNHd3wxahpqQz5hwTCIEABCgNWuIeiBjFkyBA1yxtPofT+nYBIYA6a3h8jqNH06tVLCRCaYvXr15f3339fPeEaPHiwmpaA+V1orhUrVkywL1RCrQdjc4yEJ3Fr1qxRc8PQJENTCRNV0fTDKqqwg2YYRArlQO0G0RQhlMhL7/xW88xQQ8Ks+1AJ9iB8TCQQMYFIOo54TsoJoIP5/PnzSZ4Q6js9IJfqkDZOQucwnrAll/B0SxczTW9uabCp1140vfaiTsE+vVaknsYZeaDDG0+xQiXs11cpDfVVgn3t2rXT9HFPCfbxAwmEQ4BzwSKWbvediGYcJtb26dNHrXWPkdio6SDcLGpPqHWZldDc6927t2DkNhMJREqAAhQpOReeh/4adCR37dpV9DFFKuY1BjiiSYV4y0bUQTOK/ssvv0iBAgUCT/LMyJN5+I8ABch/15wek4BrCLAT2jWXggUhAf8RoAD575rTYxJwDQEKkGsuBQtCAv4jQAHy3zWnxyTgGgIUINdcChaEBPxHgALkv2tOj0nANQT+HzICWDwZg6q3AAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-10"/> </p>
<p>Note that if you are using summarized data, just indicate the position in the <strong>geom_bar()</strong> statement. </p>
<p>Faceting is a really nice feature in ggplot2 and deserves more space on this blog, but for now more information on how faceting works can be found here: <strong><a href="http://www.cookbook-r.com/Graphs/Facets_(ggplot2)/">http://www.cookbook-r.com/Graphs/Facets_(ggplot2)/</a></strong></p>
<h3>>> Add text to the bars, label axes, and label legend</h3>
<p>Next, I would like to add the value in text to the top of each bar. This is a case in which you definitely want to summarize the data first - it is much easier and cleaner that way. I use the aggregate() function to summarize the data by both gear and type of engine.</p>
<pre><code class="r">ag.mtc<-aggregate(mtc$wt, by=list(mtc$gear,mtc$vs), FUN=mean)
colnames(ag.mtc)<-c("gear","vs","meanwt")
ag.mtc
</code></pre>
<pre><code>## gear vs meanwt
## 1 3 0 4.104
## 2 4 0 2.748
## 3 5 0 2.913
## 4 3 1 3.047
## 5 4 1 2.591
## 6 5 1 1.513
</code></pre>
<p>Now, I use the <strong>geom_bar()</strong> layer as in the first example, and the <strong>geom_text()</strong> layer to add the text. In order to move the text to the top of each bar, I use the position_dodge and vjust options to move the text around.</p>
<p>The first plot shows the basic output, but we see that the first number is cutoff by the top of the y-axis and we need to round the text. We can fix it by adjusting the range of the y-axis exactly how we did in a scatterplot, by adding a <strong>scale_y_continuous</strong> layer to the plot. I also change the x-axis label using <strong>scale_x_discrete</strong>, change the text to be black so it's readable, and label the legend. Notice here, it is the <strong>scale_fill_discrete</strong> layer.</p>
<p>Go back to the <a href="http://rforpublichealth.blogspot.com/2013/11/ggplot2-cheatsheet-for-scatterplots.html">cheatsheet for scatterplots</a> if you want to go over how to customize axes and legends.</p>
<pre><code class="r">#1. basic
g1<-ggplot(ag.mtc, aes(x = factor(gear), y = meanwt, fill=factor(vs),color=factor(vs))) +
geom_bar(stat = "identity", position=position_dodge()) +
geom_text(aes(y=meanwt, ymax=meanwt, label=meanwt),position= position_dodge(width=0.9), vjust=-.5)
#2. fixing the yaxis problem, changing the color of text, legend labels, and rounding to 2 decimals
g2<-ggplot(ag.mtc, aes(x = factor(gear), y = meanwt, fill=factor(vs))) +
geom_bar(stat = "identity", position=position_dodge()) +
geom_text(aes(y=meanwt, ymax=meanwt, label=round(meanwt,2)), position= position_dodge(width=0.9), vjust=-.5, color="black") +
scale_y_continuous("Mean Weight",limits=c(0,4.5),breaks=seq(0, 4.5, .5)) +
scale_x_discrete("Number of Gears") +
scale_fill_discrete(name ="Engine", labels=c("V-engine", "Straight engine"))
grid.arrange(g1, g2, nrow=1)
</code></pre>
<p><img 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gFXxerup7djRdIyeDxgkEou7lcNg3bUTrHcW93XOucbFQznXIva7EmNKBi1eYI8NgmQAAmQQGwScHqhNgwUUTTN6f2s6V8P0gPDtZpc3Mlj4I44RKfXwXC6AuROld/CRSDsdTDC1XHulwRIgARIgARIgARIgARIwHkEqGA475qwRyRAAiRAAiRAAiRAAiQQsQSoYETspWPHSYAESIAESIAESIAESMB5BBiD4bxrwh6RAAmQQMwSWLZsmSxatMikfrVDgN/59OnTBQXwunTpIsOGDbOvrtLnxhPvqNL2Vdr42eertDk3JgESIAEnEqAFw4lXhX0iARIggRgkgAKts2bNkr1791Y4+7lz55rK8+PHjzdKBmpSUEiABEiABJxJgAqGM68Le0UCJEACMUfg7bffliFDhng9bxS169Onjyna2rt3b1m+fLlbO2RjQvYh64WMOxQSIAESIIHaIUAXqdrhzqOSAAmQAAnYCCxcuFBatWolLVu2tC09+DE7O1vS0tLMArzn5eUdXKmf7r//fsnKynItGzFihE9lxdWo/EOu54Ia/J6amirt27evwSNGzqEaNmwYOZ2toZ62bt26ho7Ew5BA1QhQwagaP25NAiRAAiRQRQJwjXr//fdl8ODB8sMPP8jOnTsFsRhdu3Z17blOnTqmLgIKjaEWQEZGhmsdPtx8882mOJu1cM+ePbJu3Trrq9/3Rn7XhndlQUGBcfkK71Eia+9QLBBzk5OTE1kdD3NvoVxAiXZ6HYx27dqFmQR3HwkEqGBEwlViH0mABEggigmgcvPIkSPNGWLwhO+eVZwxuFq7dq306NFD1qxZY6wddiSe7a1q0PY2Tv1Md66KVwZMyIVcKhLgkkghwBiMSLlS7CcJkAAJRCmBlJQU6du3r3n17NlT6tevLx06dDBnO2nSJMnNzTXWjQULFsjkyZPNjD/iMCgkQAIkQALOJEALhjOvC3tFAiRAAjFJoGnTpnLVVVe5zn3ChAmuz1dccYVxj/K0Vrga8AMJkAAJkIAjCDhOwYB/bbCSkJggccE2roV2oZxLLXQvog4ZH19mbCPT6rtscXFlfz3RwhRZhKzfSfVR4p6cRoDKhdOuCPtDAiRAAhUJOE7BQIrBYCVBBxRIROhUJSOUcwn2nGO1neWLS6bV9wuwBuPRwtRSmKqPEPdEAiRAAiRAAiRQGQKOUzAwCxmsFJSUyj713W2qGUWCkezkZEnQbTKLDvhsXqDBhbt0ny01q4mn7E1MkgM6k96wcL/nKvN9S500abS/QJLLz8HzXLYdOCANEhIlKd67SuRvfZEGvO04UCTNk71beKq6Pk8VuwN6jPqJ3n8Sgdb76zvgeFtfgiA+ryRFEspn1z1XezL1XM/voROIFqZUMEK/9tyCBEiABEiABMJBwPtoMhxHqsZ9FuoA/v7NWfJNQqqUDB4urfNyZfK386StvnuTXckpct2A4+TXho2kKC5eTtqyUR5eOF9SvCgzt/UdKEvr1ZcPP3nftascddu6+ahj5OtmLVV5KJbDd+2USYsWSLvy4y1u2Fj3f6zkJCWbbU7fsFbu+eUn1/ZL9uXLhA2bJbfcOnNK/bpyS8vmQa9/bcdOmbJ1h6SoYlJfFZQrmjaWIboPS6qyfq/26Z6NW+T73DxJ0kH9YXVS5VbtW+uUsnMJtL4q5wYmn+dUrNjbUF3fPujW2To9vpMACZBApQhEgjsVFONI6GelLkAlN4J11VsmsUruLqo2g0urZX126olh0srpfXQqu2jqV0QqGO/sypal+QXybnGBNP1ojvzr8D5y7YDjZebnH3i9Nvf07id1iovksw9nSZL+8C8cdIo8dWhPueH3X9zav9emveDVOSfbbfmkI/rJ9tQ68v3s6Wb5bf0GyuPde8kjqqTkq8Vj7LEnykUrl8k1f/wqazLryuhBQ+SErVnSS1sX6PFuXLdR/tKogVzWpJGsV2vL31evl4EZ6XJc3cyA6+fvzTXKxeQObeVQHfy/t3uP/GdLlhybmSFpCfFS1fWPbtkqOzUt5IfdOplzu3dTlkzdtkPualNW7Mrf+qqe2zXNm8olTQ9moM8vLmM1okF9N/78QgIkQAKVIXBArcZOF7h/RkI/a5IjBqh4kUtF6kjj7PQ6GLQmV7xusbgkIhUMuNVgcNpo1R5JVYvCkE0b5B8D2qt1Ik4S9WZtlx0pqfJ+2w7ywcfvSWN1X4JctvxPeVCVEruCsVndm+47oq9cuGq5LGzS1LWLbapYvKfbv/fp+y7Xqtt+WSTfN2lm2sxof4g0LsiX8apcwPHpkL058n/ffyOWI9McVQga6Yz85apc4I+uvbpf3aOD90T9DAm0/rUdu+Qi3fawtDqm/ciG9XU/Irl63lAwqrJ+n97AP87OkVc7dZB0VZQg1yrXn/PK3MPgkuVvfaC+B1rfwrh7WaREHlarVEtdNlYtNBQSIAESqCoBK3arqvsJ9/aR0s9wc7DvH0zIxU6k7HMkcKGCUfG6xeKSiFQwRjdu6LpWWaoAvNjlUDlu6+YKygUabUjPkEQdSHexWSUOy94lO3W7vWpqzNQZLkR9wAXqqqVLtG2pm4IBdyls30SViDc6dpbNaWp5yNosZ69bbfqwpEFD6bF7l6yoW18+bdVaLSSlMnL9amleeEB2aIulWqW1q1oeVu8vlK/UHQiKxVB1b2pani0r4Hq11Ixu1NBsC3ektuq6dFr9ei4FBZacyq5foNYR9Kehxl3MVKtQlrIYoJaVYQ3qmXNbqX33tz5g3wOcuzlI+X+L1EVrtipjr3Rq7zo3+3p+JgESIAESIAESIAESiAwCEV1ob2pcohw/dKSsyqwn92pMhDfZqApGPY+g7HrlQeGwTkCmdu0u8TpbcsmKpRV2sVUtG/UOFMoF6vb0Uau28lOjxubzK4d0NW2z6qQbJWaMul2t0X68dkgXGT5khKzOyDTrt6kVYLMqG+PWrJd1etwZOpAfs3KNrNtfFijubz0Cq2FleGvXLnk8a5sGShfpLP9W+cfaDYIA6aqu366m1rpqufjbmnUyNydHflMF5mrt59s7d5u+B1rvr+/YQaD15iDl/z2/faecqC5jsPBEpeh1jNfrKOVxOIHOMU6TDMTl5flvpr+h+D3u7nzWBnGq3MXrNfUqsPKVv8wMIT5TSIAESIAESIAESKCaCESkBcM697NLi2TY3M/kpQ6dZOQpp8usT+dIi3z37E/FOkPvPWeTGMvEH/UbyFS1gMz87AOv7fZp5igoGVcu/V3+urJMAXlS4zfgYnXu2pWSp7P/i1Xp+HLOu+bYsIaMPGW4PHrY4TJRP+frwBKWh5ldDzFWCygGF69aK89q0Pa/27byu/6GFmVuWIVqFZneuaNxsbpEB5WjVqyReWoNOTwtzaCo7HooL1AixqhL2PlqJYE8r/EXT6gyc7paMQKtr8q54dwt2aDWnZ/ULWuKxplEnej1yvj4A0le+qeo46w5vbyhw2T/kX29nmpcfr5kzpguycuXSYkqW8V6bfaee76UNGjg3l6vXd1pr0qJ/n5zzzzbtS5x4wZJ+2KuJG5cL3Gq0BY3bqzbj5LiZmW/peRlf0rd1191tcdfS/3GTSR7/HWuZfxAAiRAAiRAAiRAAlUhENEKBobE6RrzcNdPC+UTtS581rKNXLRqmRuPZuratEfT09oF3+N0oN9yX55cfMIpgjawYkCW6oBte2qa3KOB3WOX/S5NC3QIpm3PULcnS4ZtXCf/1+MIWadWimY6IOy/batLsYFJ6DQd3M1s19E0b6wKSJ/0NJdLVLwqPCfpTP1HGvsA8be+gcZuYH+nqkuU5dOIGf5O+lpRsF9O0P1UZX3n1DJrwan1ylyi0J+T62XKc6pkbNDBaZPylLW+1vvrO/YVaD3aQGbvzhb05XDlFG2S+tMiHexvkOwrrpYS5Zy66AfJeG+mFLVurYP+g5nErPNO/3COJGzfJtlX/k2KmjaTtC+/UIXgFckee7VoqhnTLD5nj2TMninJa1ZLQe8jrU3Ne/pHH0hRy1aSc94oiVNrScbMdyRz+huSfc0/zPqELVvkQNt2kjt8pGb5iJPmzZvL5m3b3PbBLyRQGwTy1GI3c+ZMyc7W+0HnzjJkyBC3buzdu1emTJniWjZ06FDp0aOH6zs/kAAJkAAJOIcAxqcRJXDpuEWzMn2pM/iWwEKRoMtzy+MarOV4b68KyAFNTbui7sFB9B/1G0pztXQk6TaDtmySfjqg8yUddXtEVWdrqltLNmkcBhSUVvpA7Lh3jyowB9ehzcb0dGlTnsK2vcZM5Hi4xWzRWIdW5fUs/K1HLYjWOqjcY9se9S62aR0PBENXdT3iOSD2/mUVFhlLTktlGWi9v75jv4HWow3ka40FObnewbS7ZUuj4/+kdWslf8AxUtJQ1WF1Ryvo119KU1MlacP6iieo1zbljyVS0KevFLVqLaLXIP+4EyRB0yInryhTnOE2Vf/Jx6Q0PkEKO3d120e8xgLBNWqfbiOqhJaqhaug/0BJ1N83LCOQRCgY7dpLsSoWxc1bSHybtmrlaOK2H34hgdog8MUXX8hhhx0m48aNk5UrV8r69e5/Ixs3bpQOHTrItddea15oSyEBEiABEnAmgYizYGAmv4sGTaMuxBHKtJkO2p7rfKjkqivTkE1lD6RVmir226Yt5ILVy41l4eTNG+UJdVn6r2Z3ghLyfJfD5PLlf5grcpVaKeyC2IrdhyTLnYt/MIvhcjVg2xa5r1dfeWzB12b7F3T7Y7ZuMVmlRmvWqefU+jFNA8BHrV4hP6u7FKwptyxZbLY/S7M+vaKZoN7dtVvO1PSrSzQoG8rRuGZNg1o/qnEDc67HZ2ZIG1UIpum+itRl/ihVYiBVWd9MlZQj1Wrwf1lb5d42rTSmo0Re37lL+mWkmaxSnZStv/VVPTf0HwoTXKQ6lVtTsCyaZO+o0W6nk6DWgngd7BdrcoAKAhcqVT6Lm5b9NrC+VBVMKCSJWVlS2L2nqNlB9o66UA50PEQyZrzttosS3adlqbBWQMEpqVPH7APLErO2SFGLFpI29zOjuBw4qr9Im3ZGiba24TsJ1AaB008/3Rx29+7dsmfPHkkvv8dZfYGCkahW1Xnz5kn37t2lZcuyVNrW+l9++UX22QqkNmrUSBpCsXe4oN5DRkaGw3tZs92zaj2Qizt3jH/SdOIoWoqjup8dv0UbgYhTMHABRmlNiV06GBuls7UFp55pgrif/naeSRGL9T83aiJ39znKxEgk6uz/nYsXyriBJ0jfM84zdTBO3rxBLvYS0I1tvckj38+XG/sfK/3O+ItaLkR67dohT3/7pWkK96pn5n8h/+x3tInLKNX5/4s1VuP8tatNFqkmqtA8rPEGqC/xZNZ2U7kaNTHOUMUDEmj9GaqUZGmQ+F81bgMZnepqatqHdH9QDiBVXX9X6xYyUQvtnfrnCrO/7poO9wFbfIS/9YH6Hmg9DgjloljfO+qMe9SL/mbTP3hPCtt3MApChfPV3wqsCSmLF0th10PNoD9p7RqJz82VuPIUy6WqLEC5CEYS1HUv9fvvZN9JJ5t9wbqRkL1bUpb8Kvt79TbKSuHLL0n6EX0k7/SRweySbUggrARyNDHBa6+9JpmZmZKqirVdCtVtEwMsKBbTpk2TkSNHGlcqqw2sHlBOLOnVq5e0anUw1sta7u1db+u1JihIVkf/ru2yTSciwMBzub2N/TOUL7g7QgHzFM9Ur5a7rWc7J32H0sVCbRWvCK4d/i48r2nFllxCArVPQD19dArZIbJz506Bn22wEr/4Z8n//FNpqT7pvgK57ftCTYxMzQjlrYK3vZ2vz6joDamrs8zeZIsGgyOdranFkZAoO+68263ZNt0OKWGhKHgTf+sx0w+lykpv67l9VdejYjckU2/sOj0i8eoHjZgBuPVA3NabJfqftrOkQt/1oWkXrG+i7mZxmvbX7BM/O18/Pfu25W3wsGndpo2sX7cuImfbMbjPfPN1iVfXuT0XXyalPmYsk1YuN0HYxRrUjQDvxC2bpVR/V/sPPUzyTzjRjrTMgqFc7EHeVoPETRul7msvy/4ePSXvNJ0Zxm9Or0HSmlVS1La9sWiAafNlf8r+V1+W3dffrAHjZUqvtY9Ie2/fvn2kdZn99UEAVgrEZAwfPtxriyVLlshvv/0mF1xwgdf1WBjK86TxxDt87ifcKxKffV6y1EIJwXnde++98ueff0qB3jOgRP373/9Wb8mD9YLs/QGnSZMmyY4dO0zxtQkTJsioUaNcTa6//noT1+JaoB/gYoblThZYn1BkDwon5SCBNvoM3KJurk4vtMd78cFrFsufKk53RBAN3HLr64Beh05BiVVoL6jGXhr5Uiyspp4ZrKzl1rsv5SCY9VBK/G1f1fVGsUDGozmzvWY8Muutjpa/N7p7gou9vTRefv8BkjdshFvr1itXSN03XpPdGmyMgXPyH79L3bemubWxvmRfNlaKNE4gWjIexakFot7LLxp3pz2XjlWFwX220jpvvB/o1EWy/3aNJP/5h2mHYOy6qph4damyb2j7nLRClRTdJv+Y42TfiYMPrtFByoEu3Q5+108JR/aVOFUwEnbuiHgFw+3E+CXiCEyfPl369+8vbdu2NS4gnu4xM2bMkCOPPFLatWsnW7dureAiFXEn7KXDd955pxxxxBHy8ssvGwXpjDPOkOeff16uvlqTPHgIguFvuukm+fvf/y4XX3yx/Pzzz3LhhRfK4YcfbmJZ0PyPP/4wysQpp5zi2rqxZpajkAAJkEC4CUS0ghFuOLG2/1AzHmX/bbwbIrjzpGtKVuN+Y1sTp1YpZE6yy4FDOsluj+3TP/9ELSe7ywKctXE0ZDxCxqd6Lz4vRZoxau85fzGB23YOnp+hWMB6kX/8ILMKwdmwYpTojF4wkvzn75L5znTJVauFZyrcBB2U4fog7S0CwCElsAjhvW50Btmbk+N/EUFg4MCBMnv2bOMCAlcQyzqBGXrMuPfr18+shxsQZvfHjh0bEecVbCd3aZ0cZMW65hqdYEhOlhYaKwWFC5YabzJr1iypp1bmyy67zKwGn+OOO07efPNNufvuu2W/ThitWrVKBg8eLIceqi6XFBIgARKoQQJhVTACpR2swfOs8UPt1Ydgtro0OU2QecqbNQL9dMt4pN+R8QjBwMh45C2lKjIRuUT9o1GXYZ+68ZgMSK4V6nalqVL3H36E1PluvmspApft2ydpytWk1as0HevfNBK57Gdpz3hUqu488WoeLtaaIJEkGbNnSanGzeQfd7xJP2v1vUSzmsFNKmH7dnPeBX37GdcxpKitM/9rybnoEt0uwSgEhWrV8GRq7cf+bhS5d9/R1LV9TCB3wuZNrtXFmvK2uEkTU5gvbd7nknfKqRKPDGsfzinLKqVWJQoJ1CaB1pq6GbPxiLXAANsSuP1AYNFAhikoF57xGVbbSH5HQPo999xjTgGey4sWLZJPPvlE7rvvPq+ntWHDBhPsbl+JzFqLNYYLsnz5cvO+efNmYxFBXMP5558viE2hVC8BuHLl62RQs/J6Q/72DsVvu9738Xv3JytWrHCLMfLXlutIwIkEwqpgWGkHe/fuLU8//bR069bNmL+dCKI6+5StD8d+I87Vohorq3O31bIvDM9f79RBOnrJ2hRSxiOP3qR/8pGUaNYXa+bdWo0gYxR826fuOnYFw1pv3nU96jXsG3SSm9IR6RmP4tUv2kovW3/KZLdTzjt5iElBm6jKW4YGfkMpMGlsjxogiaoYNPjvQyZuwtSsOPMct219fUn96UeJ14dXnYXfm5e93a5/3GhS5eaecbakfTVPGv7nAXNd4rr3kJyz9LdKIQGHELArF966FI3Khed5IvYEcRhwbTrnHO9//1AwYOWwS32No8LgFfL7779LscbWvfPOO9KnTx+jrGBfL730khx77LH2zfi5kgSgzP3f//2fcU9DBrOOHTvK448/Ll27uqcQt3b/zDPPyKOPPmpianCtbr75ZoEbnKe88sor8uCDD5qYHM91/E4CkUIgrApGoLSDSCsIrd8SzOCEklYwXmfBfdfptvZa8+8FOlOEuhxFai1wmtRRS0CcKhee/s0V+qnWlyR1pylWV6aUw3tJir9z0dmblEU/yIGLL5UMm6tN3NYsSdbBbOG1N0h6eZYopNjzDHBOmP+NxOusZIJmO8qwsknp7wIZj+poXYjivkdpJqW9goxH9dWqUgRXo0gQnXEteOQxrz1F6LxJTHn8CVKgL1eSSt2m9IqrpFAVBROYrcpqWUJiL7u5+BKz0LXt8BFSoC9vUuYQpWsO6y4l+irUh2GcWooydICSrskVKCQQLgIItkbQrl2QJQkz6p7L7W1i+fPUqVMFM9gYZF555ZXy3HPPVcDhK1UpuELgLoVYDrxDsB9YMB555BEqGIZI1f+D+x5iXp566iljebvllltk/PjxRpnz3PvcuXPN9cQ1Pfvss2XhwoUmtgaB21AAIQjehhICpSUWFGlPRvweXQTCqmAAlb+0gzDhIlDNEgS3BZtWENuUIlWr88bw1uk48h24UjSblr/0h6VQ+l6cqj5S+j7+ekks99f3dUKlqkSoM7Akq5tPnCowkFK9Ucrrr4qcebakqim4tDw7GI4d5xHkXLpwgcjAY6SOLYsR3INEg53jO3WSBM2iBEn69hvZ8s7bkjn0VMkMwqXHM0GaZ3rGbM1SAo+rhuUpf81BbP8d0CxZOzRFcAsv1h7PfWMzz/3bdhX6Rw9Goe8gwBa2/fv7LQTYC1eTgE8CcGWCDBo0SH744QdXOwyMkcno1FNPlUsuucS1nB8OEkAqXrxggbj88suNVaKJujjapanWy0G9ELvgeYoBKwTPUs/nKaqfY/BKqToBWJCQ9RJufajZgtdf//pXueiii8x1QXyMXT744AMTfH/eeeeZxUcffbQMGDBAoEzCwwOCfSEw/6qrrpL//e9/Zhn/I4FIJRB2BaOuzmgjaG2eptPDy5528C9/cZ+JxkyXZd4NBmiKFqxLV0tB2ZA2mC3YBgNj5IrfXl5XwZOIPeNRzkWXSilm0stN7p5tzXfdX4OvvjTVp/NtM+Gp3y+QdK3DULBC/YD1FXegSFJ1g4JZM6RQU6eaOg/6He5A9TZukN3qplPieRytNC25eeY1Y1e2vJeQKhuHjJC9S5bJqfW2yO2tmktyuULj2be7NmyWj/a4pzi8rEkjubJZE1O5/O6Nm2X+3jxJ1+07aAHDe9roAz35oN/3U1nbZNbubJNJt35igjzcrrVWJk8xhynRcz7xj+WyX9/t4sv1zN7GSZ+RphYWpVD+5pzUf8++eBZm81zP7zVLAPd6zNpC7EosFHEMfK14g3D2KhJmgfF3iH4uXbrUuMy8+uqrJngbXKzfNGa2Pc8FbjioGWJfDreqzp07m2UYoK5evdoEfFuMly1bZtyq7NtY65z0DisMnlVO7ic4f/XVV27YkMkLrk9Q/jwnnKCM4JrZzwlxGNiHtQzZwOC+9vXXXxsFw1puHQT7TNHnkLd6J1YbJ7xjEgG/a0psEwirghEo7WBso3fe2Yea8QhngKDkhN27TL0F+xkVN2pYFldgX+jlc/KypVLcoqWUeKROtGc82qJV2h/ZnCUvNqonA//3pvx41Ti5ak+uvKxVza9o6j3l4nKdPR2r646v63IgMjVI0IVHt2yVNQWF8uIh7VS5SJEXtu2Qm9dtkuf1e6reFOep4vpRdo480q6N9NCq8U9s3S636vo3OncwD431GjMC5WJqx3aSEn/QhNbKhyUE9UJQwVtKD9YN8YKixhfhAVCiAxsKCYSDwGeffWZm4DFogquOJfjd1dTgw7KiWMf29X7wLuGrRfiWYzCGfiL9Liz+cKG59dZbTSpe+OsjKBtF9NDm888/N/egk046yfjuP/DAA/LWW2+Zehnff/+94IWgeLTt2bOn3HbbbYKZcrhJffnll4JZdPj9B8slfGftf89QrKBUOb2f9rOARwZc2W688UaTwcu+Dp+RyQvWCrgHYuIVyQxwPWClss7Tio3BOoi13HzR/6B0IUjc6XUwaurv2+LCd2cSCKuC4SvtoDNRsFehZjwCMSgYpVpfoaRBQzeAqOeAlyVxWjQLQcgIAkcdDEuwfZGXzBv2jEcLtEr6SeqmdOzcT2W/1sfooK4DJ5VmyeK8fdZu3N4L9YG9TiuEH5uZIZ09BtC4QX+xZ69cqsrHoeVuQn9Vy8YbO3erRSNXBterK2+o4jKiQT3pqVXNIZfq+um6/pd9+XJEeposz98vrVWZ6FG+3u3gXr4kL18mmVoDxMqO5aVJrS3Kh4+YR0HIWusMDxxVBDDbipnW119/ParOK1wnA15QGJ544gk56qijjPsNnqFwn7FmwxH8i8EbFAy44MDdCQoD0tJiUH7XXXcZRQV9REYpKBso1Ldp0yYzkIULDlx4KisYDGNfCC73VfwP99hA8SE4Ptp5inWensud/h3xpHBlQ2wFFGpvgnTCSC0MJeLEE0+Ub7/91iiBKJxHIYFoJBBWBcNX2sFoBBnp51SZjEc4Z6RZLYLCoA/Hyghm9vcjg5Kn6EPUynh02dSn5XKd0bFnPFqsQcpH+IgNWa3KBWSrxli8vWu3JGigzkhVGA5ThaBQH2oF+oJblCVpeqwMTSW7smC/KhgimzTu4oLGB2f2kda3eVKirNX9GgVDZwdbqlI1U922flAXrja6r9GNG0pdbedV9KEMJSy+fFbKaxsuJIEoJYBgVhSQW7t2rdvMKwbSp512WpSedeVOCxkXX3jhBePGCjchzHTbBRmg7DJkyBA5+eSTjbXDM6MU2l166aXmhcKEKLBnBYDb9xHMZ6ScR4XxTz/91My8Qzn417/+ZYLGPbf/8MMPTTphz+X4DmsL6nXAuuVZx+SQQw4xy71t5+RlsAyhECLiJq677jqfXcW1fP/9901ldbgpX3HFFSY2CVYnCglEI4GwKhgWsEBpB612fK89AnBR2nH3fX47sL/PkYKXXfIHnSR4BZJSnV3ztv/sa/7hc9OituoyMOavgmJzyHjURgO+S7UwHCwMWaoEXNTO3Wpi7WhZfoEU65cPsvdIT7VSfKmWibGr18mj7dvIURnpGkuRLB+qC9RxauHAjNlPagnZVVQsucUlckBn9Heoab6eh7IA5WEnAtdV4H71q1ozOqv7VFvd1/u798gnur9XOrWXdI/tzAb8jwRimABmdJEeFbPs9llvpPSkeCfQQIttBiuwaHhTLuzbB1Ofwd7e8zOK9yElK6qpI/h82rRpcvvttxv3LaSftwtm6OH6Y5f//Oc/snHjRpNxCcsRyAxFA78JSyJxnPDxxx8bpQKWI2To8ieIsYGSjWrrljz22GPSSZ9rFBKIRgI1omBEIzieU80RKFUlwcpO9er2nTJVYybub6sZUmxB2fbe9Fcl4jFVJvAOuVBdnK5WBeNZjaWAgnFd86Zy8/pNMmrFGumQmizL1OWpm7pSNdBg7lLzz763g58Ty600VzZtIkj3e0h5dqlzGjaQEctWykwNCr+wsXs6zoNb8xMJxCYBVKjG7Hekur/E5lVzP2tkAYOLT9u2bc2KMWPGyH//+1/56aefTH0re2vM1NstLwsWLJBvvvnGzNwjQBmCGh19+/aN6ArjSI5x0003CZLVdO/e3a1mRZcuXUyxSHvMDJTrf/zjH4YD4jE++ugjgWvV/fffb8fHzyQQNQSoYETNpYzuEzEm+d//lDdVwYDy0EtjIXxJc42PwMsug+pmynOqmEAGqOXi5UPay1dq2chU16ibWzSXf27YpHEVySYrVV1dlqNuTXbB9xb6gIB4xl40VvcpLEPcB4UESMCdAIrFzZ4922tBMXtLuOHMnDnTpC5Hhh64/9gFga1IHAJ3Hwzghg0bZl/Nz2EkMHnyZLe9WynmLYXDbaXtC4rPYRAO1yEMqi2BBQODcgSxI9PV8ccfL+eee25EKaGw6uTm5griYvCyC9ymwMYeMwMXMHCA2xoCtZFJDefvmX7Yvh9+JoFIJkAFI5KvXjj6joxHO3Ug7iUALxyHC2afUC4m7c2Xn9R96TnN3NTBS10K+35maNzFeh3sX9eimWsx3JqaqiIA+VKzRCGGAsHdkL2qPMCtqk1KmQLRTmfZlms8xvGqlFjrt2g8R0tVWtCXiRu3aKxGppxQvh5uVWu0vWUxMRvxPxKIYQLwK0dNAAiKqSLQG6k77a4/CFBGLQxLvvjiCxOYjDgEBDbD9cY+gEXKW2RTQh0BZKWCy4mne461r0h6b//ZvFrt7oIe7i5OgTqDwTFcgvr37y/HHHOM3+ZwqUJmLOu3gMb4Dnep9957zwRFIxEA4jl+/fVXQeE6f4KA6MzMzICFYnGf9hRPCxpqhiAYPZTivvZ9Iv0+Xv7kpZdeclv9t7/9zcRqIAbDX5HJwYMHu1lE3HbCLyQQIQSoYETIhaqpbiYv/VMy35qm0ds+ApZrqiO248xo20E+6H2U/N+P38n+Cy6UpaoMQFI1RSxqUxTpwwQB1/0y0gTKAeIuHt681Qz4Mej/TgOx5+Xkyt+1BgYEysCrGsfxf1rbIjkuXh7bsk0Gajsrq9Q5DevLU1nbZXj9eia4G2lsu+s+u2vMBR5SSEf7jLpbwa0KweFIl4vg8dO1PYUESKAsg5HnrK4nF0/f89NPP900weALBeSsGhDWdqtWrZLRo0ebQGUoIZhFtysYqB+BWkqWIBMT2gUjZdFVwbSMvjZQ2oIVKAdwlcLMPQKWA8V2vP322yarkj3eBtmvoHggfa5VjA4WkhtuuMFkvYJSCaUDNSBQpwfy7LPPmhSvKG6H38YFF1wg2MZyubL3H0oDAtqh2Npl0aJFRoHF7wsZnxCMDlcuWFZQMwRpgmtKEMdSGUG8DSwe3hSoyuyP25BAOAlQwQgn3Ujct7ohOC3j0YuHdJFivbGOP0pny1atdVHtqpaM/3XqIAd0cP8frW0xoVULo2Ag+Pq6Fk3lCS2Wd4cW+CvW9bBWQHGA/KVRA1mqFo0zl61ShUGklz7E/qXbWnKKpqpdokHc561YLcgwhaDwO3S9NQM2Smt87Nag8Eu0L/n6MEMhvsfVbcvTLcvaH99JINYIYJYZQbyhCgawKB6H7T2LjGHG2Rpw4h0uVXaBQmGvGwAFxbPStb29/XNZtJZ9Sex8DpYRYg4QyAz277zzjrk+/rZFfAFesEZ5toNyAbGWI20rBIHkUDpw/ZESFy5WsHbAzQpKANLzwnKFdLCI68FyT1mxYoVRLubMmeNW4BEWAxwPcRCIAUEMBArfwU3pzDPPNIHp1u/Lc59O+Q7lAgX7wMbJ4vm36+S+sm/hI0AFI3xsuedqIvD+p3PK9qSD/R0TK5rQEXDtaeY/X5UAvLara1NDnQ1LKA/Qxo6Q6emBtq1ln2aNUiOIKa5n7yra3tSyuVyjweAFRoFw/zOppwrFP7WK+E0tm0m2KhqIwaCQAAl4J4DBpGeuf8zEouIx0tSioje+QzCjDLeTefPmmReqgVuCauBwz0GwLAqRZWRkWKvMO+Iy7AJrBgZjwUgsKxieM/3eeGVlZRmrASxGcG1DxqdA2yGTFOIsMFtvb4tq4vfdd588/vjj5jeA4yFYHAJ3JbTFQP+A3rvx+ZNPPjGudPgdQYFs3769+Y46Evb9mh3of6imDWsE6oDYBbP+UFhgecFvDMoFBOlyn3nmGaNg2H9v9m2d8hnnAAZOL7TnFF7sR+0SKLur124feHQSCBuBJjoYsSsX9gOlqXsTKnf7Eqyrr8qJL0FWKSoXvuhE1vJ1mv4YgyHMcq5duzbozsNtB7O5oQgGvgj4tKr1BrMtBtyWC1Ew7Z3UBn76qCqNAeUbb7xh/PFRjwHZczAYRCE4CAK4169fbz7DzcVTgUBdJevarFmzxgTJmsb8L+wEkJIWbkuo9wALwZIlS8wLVg0I/g4QF2Mf+KKd3YXN6iQC+FGsD4oKBssI8oYLFGI6PN3msA3coVAzxS6oreLLpQnWiTZt2phUulAkkCLXspTgbw5Kif04sHRBsYVlhFL7BEaOHCkdOnRweyE+K1RBPM+oUaNC3Yztq5GA79FTNR6EuyIBEiABJxPAAwxKBl5W0CdccDzjADCDiEGUVc8BxbIweEadB0swiPGW0x+z7/AZx8AaGZK8tbH2gXcMvixXgxNOOMH4sn/33XeC6s6RJFAqMCC1/O0xkIQ/PCwSTz75pFE4EOSL80K2KZwz3BExsIRgoHD99dcLAl+hzH399deGf6QqXJF07dBXKADWAA+uRHa55ZZbBIHLsEBMnDjRpGyFIgKBgoHMUJ4Ca9WDDz4oTz31lFEq8HeG2hlwVQpGUIhw8+bNRtnx1h4Zqqx4CwxUEQeC3xUsKlBaody/++67guxm+J0hIcGOHTuCtnY1nniHt8PWyDITVXLfgzVyrNo6CJRPTPag6rwlPXr0sD6adyiJuH/YxfO+i/uD5YpntYNrGV72ey/u59Zv1mrH9+ohUCkFAzNwnhkQtmlFZsxKeS6vnm5yLyQg8kaHTrJdA6qdJrBkjNBK4f6sIU7rM/tzkAAGUFAU8OA58sgjBcHCKIaFdKgIYkUxLczEYpb1oYceMoMRuFUgjz/SUX711VeC2XXsAwMqfMcgesKECcaVA64YyKAEl44ff/zRVDPGIAntoGggWBmBrdgfsiodccQR8s9//tMMeNqrOwiOD3eis846ywS6RpqCAYUBgwZLwYCShsGn5WZjLQdDDCo8BwrgaAkYe6631vE9PAQQoA2LkT9BLQi87ALXJl+C3/qLL75oLAsY3Hkq8r62w98gitMhwNueYczeHsooXKwsF6iLLrpIBgwYYCwa+LvF7+nKK680FdBhTYHyiwEsxy52irX7Gb8HpPG1BEH7SGGNaum4R6MSvHX/te7fuLeiqj3ul7hHIJYLFlJMTGAb3F9wD0aCAtzjMSmEyR5YVpHoAK6aI0aMsA7J92ogEJKCgRk1yKBBg0yJe+v4MGdfe+215uF4ySWXWIv5TgLVRmBnSqr8SzNJJWv2JqdJiRbnQ8D54X5qczitz+zPQQIYxMONAq46v/32m6AwGAYxGJTAOvHpp58alwsMXPBggq8/FA0EpmKGDL7euP+NGzdOMNGCwdiUKVPMDCsefpbFAw87POSQCQezqJhdhZsH9gnfb8wCI7f++PHjjZKBgRBSd8JvHAoGtkHbSJNbb73VPDMwY4w0teCJcwdbZHm64w73GWH77KK3cw203ts2XOZMApZyGah3UEoxAIQFC65YGFz6Es/MYVDusQxuXBBYAxEADgUIx4eFDK5fvlyufB2Hy8NHANd51qxZrgMgwQN+A7i/3nbbbaaK/HHHHSd//vmnqTWCeyzW4T6M2B0IJoywHIJ3jF8RC4ZJHCgfmKiB5RSJCKBkwg2QCobBVW3/haRg4CGIXOQQu3kKZkZom7gBUEggHASQ1RzREkgH6zSpg0hxSsQSgMsG7me4jyF7DVyZkKEGM6t4SGHGC65JMMsjiw0G+xjkQOAqhRl6vKBAYBAN/29YQLAPuGtAMIt2+OGHGwsGvlupQTGT+txzz5n0qtg3lAg86JAKFJYMbNOrVy8TKIttrBgF7CNSBFYHnAsGdGA7bdo06dOnj8kChdlGKHgUEvBFAAPLm2++2Sj+SG9rj5/w3AZtUcwOYxWrUCNms2Exw4AUgt+hZS3Dd2QuwwCTv0PQcIbAUgEXOm+CiR8oExDck+EyiWW4d+Me+pJH7RFrH1BKcU/H5AYyk1lV1i+++GJTDwVKjDfvHGt7vodOAGO2oAVmKWiHSAeIgCh8xgvaIWblPLN4BL1jNiQBEiABhxDADBkUAgRWY7Ycgxa4PEGJQAYaWBUw+457HtxCMTOGQQyUATy0kHEHlggMWOBLDMG2EGuWFA8zCCy+CFyGkgH3DQh8wuGPjhlX7Bs+5BAoOlBeIkFwDlAqIJgxRv0KPPihXCDwEs8KDAzCPaiD8hjMKxKYhquPwfCp6TZQ9vHCcREvAfcYWLqgoCKIGy9YB7EeHhSouQLLIf4e8TcGiyT+DmH9gwsM/j6RYhftV65cKXfeeadZh7ELJgJgjcTvNZjzDNd1CHa/wfSxttvgnlkVQa0SjDGtF75bgkkdnJ8lSIcNqygsEbi/+BLL8mltC6sWfjuwZkydOtVYPzB5RKk+AiFZMPAHDw0Qfm7Dhg0zfo4I+sILPm7VIdaDOJh9JSYnCeeOgyFla6PXEH9ovjgbptqGEjwBfRRKsgbv+mKaoDdE/O04VXz126n99dUvPCysh4evNsEsh882Bh1QJpB3HgoAZscefvhhY55HRhpYHjDYx6wYAlYRq4F3+PXCmov7IfL/e1537A8uG3hwtmjRwvh9I24Ds6qIsYBgOQJnYT3BMWDNgMAaAmtGJAjcxuBjD0EGIMttwd53e/E1+/Lq/IwBJ8U/AX+M6t1+i/+Nw7g2/vGnjcLw/PPPm78Fe9AvDou4CdSygNIBhQFKORRX/G1iQIogXygYSNqASvJWzAb+nmCxgHsj/j4xQP3vf/9rjhXG06m2XeMe5++aVduBqrAjKBie975QdgdFwa4s4B5ij8mw7wuuq/PnzzfWYyiKwbLB7+fSSy811lTEfMCVM9ht7cfnZ98E4vSHUGlVE/5veAi/9dZbJiASs3ZVCUAMJW85Till8c+SPme2xOvshJMkS90tBg07W4psWrZT+ldHb6hPdmgr3dPcMzBY/Uv+9RfJeG+m45ju0BiMo0ecKyUOHKjDReqxdm18xmAk/75EMma+4zim1jXfcfd91seIfm+vFoPqEgxMoHh5KiwY9ONlzYbheLBG2Ge+sK2/gl3w+4VAifEnnlmsMOOG4HBPH3N/+3DSOswWW6wwURVuCeV5UpuZgTr95aJwo/C7f88aQvbGtcml8NEnjPuSvT+hfMbvbdeuXUah97Yd/r7w92139/bWznNZbTJBX7I1ixTOzclSnffiQOcJd1VYr1B0EamPEf+GbIDBCtyscD+yMgMGux3bBSZw0M4UuK2rBcyNMG9DsYCCgYsJn1rkkqaQAAmQQKQTgILgqVzgnDDDZVcusMyuXOC7P+UC6+EjDrenQIMEe2YdZEixglWxj0gSWIAQh4GCa8j+g/N/4IEHIukU2NcIJIBBI/5mfAn+vkJVLnzti8trjwAmXJDND9caVmNYkkMR/AaoXIRCLPi2lZpGgvkbKRxhxsfFhImRpqXgobMlCZBA7BJAykW4aIQyi48JnA8//DAiocElBW61SAKCwmyonQD3lbPPPptxexF5RdlpEnAOAcTDWcmHnNMr9gQEKqVgwB8Oad5gvYDmiIBIPEBQsRWFpCgkQAIkECkESvP3SYlmFamKlKrLQkK3Q4P2Ow5FuUC/vFlTqtLfmtoWHrgIyEW2KARTQuALj4BbDAqYGKSmrgSPQwIkQAI1S6BSCsZpp50meEGQmhFpFVGwBC5TnsV2avZ0eDQSIAESCI1AwQsaRPrbr5pztlK3Q9FUJEjxJEnnXSApw08P7eBR3hqBnqiejPoiliAYH5NTKCZol71795qJK8RpwJ0KiUTsihjWw7/akqFDh5pAX+s736OLwGHfLqy1E/IXl1JrnYqBA2NCIu+SMVU+04z/lU1mVHlH3EGVCFTqiYpCVDDXY1YK8RcoXIJUcrjhU0iABEggoghAQVArhuRXrddxmtWOUpEAihIef/zxJv88fJ1RjRlutnCTsgssGqisjAxcs2fPNpNXcL+1BLnrkcPeKoZFt1yLDN9JgARIwHkEKqVgIAsKfIIfeeQRkzUqUs33zrsc7BEJkAAJRD4BZNKC9QEB8Ujdi5SQcKtFulrEXkCR8JRBgwYZaweWQxGxaoVY7aBgYJ/ztD4J6ovAymEXpA9Ghi9LkL6USohFw/c7GVVk42Qm6FsVkn9WPFkuIYEwEaiUgoGCexQSIAESIAES8EZg4cKFxkKBInuoqIzXDTfc4K2pa1m9evXMZ1RdXrx4sck25VqpH5C9EC5XUCyQI3/kyJFuigqKFSL5iCWwrCMvfjCi9quYFV/1BQAkVrk4mQlq7lBIIBIIVErB+Oabb2TChAmmUib8aS157LHHjLuU9Z3vJEACJEACsUcA1ghUPEcSkK+++krwbEBaXkvZQFIQFEDzFBQTRHwGMhR6phAdPny4qzlmcJG6124JQR58u6AOxvr16+2LfH5u7HNN9K/wxyhWuTiZyebNmwOmuK7tX21N1sGo7XPl8X0TqJSCcckll8jll18ueIjYg/A6derk+0hcQwIkQAIkEDMEUKkbL1TLhcDFCcoGKnyPHj1aZs2aJXalAdXNP/30U7nmmmu81hKZMWOGic9o166dsVR4ukjFDFieKAmQAAlEAIMvzO0AADTKSURBVIGQFQzMHOXk5JgMIFUpBR8BbNhFEiABEqgUAVSURaBy8+bNTVBzZmZmpfYTDRstW7bMJAX54osvjNUBgdoTJ06sUJHcKj74+OOPm9NGnB8sHpMmTZLrr7/e1FsCU0xqFRQUyNixY6MBD8+BBEggTATOOussk+G0W7duriOcd955ZtkhhxziWlbVD8iIhwyqSFBBOUggZAUDSgWyRb3wwgty0UUXVahqe3DX/EQCJEACsUfgu+++k/nz58snn3wizzzzjKlafd9998UUCLhx3Hvvva7igHhmwPL98ssvixVr4QkEyUO8CdxxIUh3O27cOKNcpKamemvKZSRAAiTgInDSSSfJq6++au5FWAjXN7x8KRdIQuFZ1RvLMO61e+t4trvtttvcrK75+fkVXDw9t3F1Moo/xFfm3JCP/IorrpBGjRoJNEPr9dFHH1Vmd9yGBEiABKKGAGLUMHOGBxJcgTBzH2sCqwVqViDIG/WRnn76acPEl3IRCh8qF6HQYlsSiF0CY8aMkXfeeceVdeuVV16Ryy67rAKQVatWCdqOGjXKuHRijIvCoKjFg3TaqBb+wQcfmO0QH4YEEtgPsuNBbrnlFmOdvfLKK00R0XPPPdesgzKD5BSYGLngggtMBr0lS5aYbWLhv5AtGIBy9913yx133FGBD2MwKiDhAhIggRgjgFgDPJAg9evXFwQbx5pAsYAlB5NO1113neABjoxOsGTg1aJFi1hDwvMlARKoYQINGjQwrpjffvutHHPMMUbZQOIJT5k8ebJAOUC9nueff964t1oJjFDv7euvvxa0gavrpk2b5PPPP5e1a9fK4YcfbnaF9Nhoj3e4SUHhgGsn7n+IFUPmr6eeesrcBx944AFBxrtYkEopGL169fLKBn6xFBIgARKIZQKYpc/Vyt4Q1INo0qRJzOFAbSQUycMLLk67d+82Adx4WMN1Kj09XaZOnWrWxxwcnjAJkECNEUBCIrhJ4Z7Uu3dvoyQgWx2szBAoAii9AKsrartBUYCigfi5o48+2rSBtw7q6mCixIqzQKaspk2bmvX2/zy3wb6///57WbRokWlWt25de/Oo/lwpBQPaG6wYeGgg6Bta265du4wZHEWVLIGZCcWVUDAJWhzMTXY/Nqsd30mABEggWgjgIYYZM5jJMYtvzXJFy/mFeh54YMNVYMeOHeYhDR/nlJQU49cc6r5CbU93qsDEyKgiIyczwd+O08dR+Jt3SgFmxGEgRgJjVbj2Qw477DCjUFhXHhYKKBxoi0x3mCxHTR3PgouwwmIf2Nfy5ctl27Zt1i5c757bHHfccQKl4p577pEtW7YYK4qrcZR/qJSCAR+0iy++WKAFwgcNwXfwV7M0QovZ3LlzTZ5yaHzI/vHzzz9zxsqCw3cSIIGoJIACcEi3ivesrCxz74vKE/VzUnjwIgYD8ShQsjDTh9oXeEYgS5S3Ghh+dlfpVcFa1TMqfYTI39Afo1jl4mQmmElHTRkni1OUCzBCgPaIESMEaa6RdMObIEYCbv/PPvusmSyHxQNJOjwFKbJxD0NcBrLhNW4cuFIMJtZR2wfZqzARf/vtt3vuNmq/h6xgQHPbs2ePuRjICLJmzRq59tprzYMU/maAaQnqZED5gGDWCpYMuyCVo10DPOWUU4xpyt7G3+eiDevkQFyl4tT97Taq18WpmRD+gG3ql1XN9TzZok0b5YC2oQRPIF5/g03BtEF9rxsVZ22RQgczbdOmjdd+c2HlCGAGC0HN3jKJVG6PkbfVunXr5PfffzepG/HQxoOZQgIkQAK1QQBumlY2Om/HR6IiBIPb79nIkmoJLB5YDyssXF7hmQMLB4qJQuDuCUGchyV///vfrY+m9o99364VUf4hZAUD2iD8Z6FkIBYDUfkQzEjBDG4XK2PIihUrZPHixSbYz74eF9CuiSONF0xIwUpS9h5JKy2RuGA3YDs17ZWYP5It+fu80khSJTCtpJRMvdLxvhBK986dO2RLQb7XBknZu6WOMnWq2hbK35zXE3TIwtatWzukJ2Xd8KxE7ajOhbkziL2YNm1amI/C3ZMACZBA9REIdM+GxQIxFRj35uXlSSjpxwPtu/rOwjl7ClnBQNevvvpq41e8evVqE0l//vnnC9yhFixYUOHM4EYF8xDcqjwBI3DGLsi2griNYCVBYz9KtTEVjGCJaTsFhpgZu2Jn3zreMKWCYWcS6HOpQi0u8se0pAx8oB3V0npfv4Va6g4PSwIkQAIkQAKOJACLLCU4ApVSMJCCC3mAIdOnTzc+xqi06lm8ZOnSpcYX+ZprrnErQhJc19iKBEigtgisKdgvHVJTAh4e1hu7wMLpKfuKS2SvKq7NkpM8V8l+DQbM1VcjrRlRW1JauF8ktU5tHZ7HJQESIAESsAjwXmyRiPj3Sj3Vkf8XgXoI3B4/frxs375d5s2bJwMGDHAD8vbbb5uZcrSF9OnTR4YMGeLWhl9IgAScReDtnbvl6a3bZe5hXfx27K4Nm+WjPTlubS5r0kiubHYwLWuxKiC3rN8ozTUG61+tD9Y+KFSl4v7NWfJNTq4U6x5a6voH27WSVsnJbvuriS//Oe4keaPnkZKeUKnbockoskUDLx9v10EO5tCriZ5H1zECZR2EpQ0TWvB97tKli1u8X3SR4NmQQOwS6DTi3CqffFaV98AdVAeBSj1RUcEQGUGQdgvKBSwaiKo/++yzzY3f6ti//vUv6yPfSYAEHE6gSJWBV7bvlKnbdkhKEEHpyzWV39imjeX4ugdzzTS0WSK2aUzVA5uy5Me8fXK6R1KBd3Zly9L8AnmjS0dJ12Pdr+3uWL9ZXurUvsYprYtPkF2FB2SXHKjSsbdXUkGp0kEjYGNkkkKAJTJqIX2lJY899pjLEo5lgbIOYj1y0yMbCxKMwEKO4EwKCZAACZCA8wiErGDAJQLZQVAwCalpIW3btjXl0fEAwMwShQRIIPII3LZ+k6xU16gxaoWYrlYMfwILxLr9hXJsZoZ0Tk2t0HS3zjZfsGKN9ElPk6Mz0iush2PVNc2bulyjTlAlZYJaRKDkJHpxs6qwAwcuSApCKXNgt8PepUsuuURQ7GrQoEFu+fs7derkdmys95d1EEWuRo8ebXLTo9YI8tDbFQwoMvYYPjyXUH+J4p+AlYzFf6vYWutkJqgmbVfUY+vK8GwjiUDICgZ8rPEQ+O2331zniR87Arn/+c9/upbxAwmQQGQROLNhfemrCsEPuftkuvhXMFarcgHZqlaKt3ftlgRNtTCyQT05LK0slgFKwoNtW0lfVS7u3ri5AojRjRu6lsHS8aYqNP21baQqF66T4Qc3ApiQysnJMc8Gb/E59sbWoM5X1kGkOU9LSzOb4B1ZXOyCqulWBXUsR1ZCz6JX9vb8XEaAjCr+EpzMBH0L9LdU8Yy4hARqnkDICga6+NBDD5l6FSg0gvoWiKpHnmC4SVFIgAQik8Axao0IVpapexNiJz7QVNE969SRL/fmytjV6+TR9m3kKFUUMvUhCOUikLyo7ljP6qtuQry82qlDoOZcH2EEMBAaOnSovPDCC4K05MkBYmz8ZR1EFkIUGcMzp7Cw0GXtsJB4xvchKyEKWwUjgctlBbOXyGzjj1GscnEyEyjanpn/oGyjdEAgi51nUg78Yr0pK0hdDkuJZVEM9ZeNytUUEqhUav5zzjlHFi1aJJdeeqlxjfriiy9M1UMna/281CRAAtVHANaGx1SZeKBta7lQXaqmdGwnPdV68awGh4ciZ6jV5HVVLE6omykXr1wrsGZQoosA3JauuOIKQVpyuDRZLxRmtYs962D9+hWLVqLOCRKMQFDgtVWrVuYz/yOBWCaAtPNXXnmloHCxP0H9ho4dO7q9UNzYU/C3hUkBjPEoJFAVApVSMHBAVIM++uijjSUD1Q2/++67oGeLqtJhbksCJFD7BJprylkoGXYZpErC2nLXKftyf58RFN5R0+He1KKZlGg9ka80q1S0yHvvvSdMdCFy9913y8KFC00QNwpUWa/+/fu7XWpkHdy9e7fJUPjAAw+YSSs0mDRpknF9Gjx4sKm1NHnyZJNJCnEYFBKIZQJInADl/dtvvw2IAdZBFMD84IMPXC/Pmg6IYxozZoxbLFPAHTu8wYMPPig9evSQnj17yoUXXmhcNtFl3J+3bdsWVO/h/u9L4Xr33Xfl6aefrrCfUPZfYWMfC/z1w8cmtbq4Ui5Sd955pzz11FNGybBbLR555BGmoa3Vy8mDk0DNEJihcRfrVZm4ThUDS5BVqmlS4FsKzPS3akD5cI3ZgOUCEq8xHHjl2bIMWfuNxHe4kT7zzDMyfPjwSOx+tfa5V69eXvdXoL8Xu/hSxpCByhIMpuAeFcjVymrPdxKIVgJwAYRbIMoDnHjiiQFPE8l5+vbtK4ceeqjXthgoYwB70003yX/+8x+vbSJt4a+//mrig3/++WeTYGLixInGxf/ee+8VTFRA6bDEfl/BZzynUlLKakHddtttrvgvtEd8F1w1IYj7gnsaYpGxjTUm9ty/aVz+n317azmWwV0t0ZaJEa5w9pgbb/3w3AYWrXhNOOLN9c06Vk29V8qC8fzzz5sMHtCIEextvTx9YGvqJHgcEiCB8BJAdifUx1inPvAQxF0gMPs7jb0o0XXz9X2eWh/OadggYEdw4+tSJ1WmbN1hXKLy9cb84vYdsk/fkU0qGgTuQN5mtaLh3EI9h88//9xYuvEwx0wiBjiwgM+ZMyfUXZn2VC4qhY0bRRkBDHAxiTFlyhRp0CDwfRfjtVTN+Pfoo4+a+mWoKWOPyUChZGQCHTt2bNSQQhwJ6uZ89dVXRinAJMbNN98s33//vSxevFhuuOEGk/IaGewOP/xw+fPPP42V4/zzz5djjjlGbr31VsMCpRh+/PFH+eWXXwSWU1h5zjrrLEGqbcgnn3wif/vb30wsMjKs2vdvGpT/h0x42HbUqFEmxADuo8jGOmzYMBPDjHskLEyQF1980XgJweqCvkBJsvrhaxu4yaHvI0eOlBkzZpQftfbeKqVgtGjRwi1bR+11n0cmARKoCQIHVIn4z5atsmRf2axzZ1UQrmvRVJ7I2iYn/7lCbleLxBjNDHWOxlQEI6MaNZBe6XXkrxp3cZpu/97uPXK/Zp1qXz5jFMw+nNwGaVntM1FO7mu4+4YHLyaf8PBEmll8b9++vXlAh/vY3D8JRCsBBFLDTT0YQSa3jRs3GrcgKBm4N2GwDW8USzDAjra4JiQiwoQ4FDF8xuAeAfFwzzziiCPkv//9r7E8tGnTxtTVadq0qanxBmvOhx9+KG+++abBA6sALBRQSLA/LEecGJZDwA3uZphUev311932bxqU/werBuJl3nnnHTn22GNNsWor5TAUEyiLr776qrHS4vrA9Q3KBILuoQxa/fC2DdxL0We4ms6aNcsRE1yB/RnsdMo/P/nkkwbgaaedJo0bH8wzgSwh+JFSSIAEIpfAsWpF8KziXUdNrgt6uBc1O79RQ8Fru5p2EUuRoJYJbzKxdcVaBBmaZeqWls3lBnWx2nmgyLhWOcGk663/XFZ5Angown3gjjvuMDOFCCC99tprTdE9BHlj5o5CAiQQXgKw+mFgDBcpK8MTBth33XWXXH311VGnWFg0kRQCtdlgrYELEgp0XnzxxYKAd7tYbmOweMBS8cYbbwgm0q2BvNV2/fr1rjHuUUcdJfn5+WaVtT0SUUCZ8yU47rJlywThBNj38ccfb4qHWooiLN/IlLd582bTb2uSCrEznuK5zZdffmmSX5xxxhmmKSxciNFBcdLakkopGNDiunfvblKiwdfLEsv3zPrOdxIggegn0KTcF7UyZ4q6F800YJwSnQSgNKanpxslA7EYCPCGNGzYUPCwDrdgtpbinwAZVeTjZCaIC7AGnlbPMfbCy1e/sdxTmYcbDRQMWDbgHmUX/N1CKfG1P3tbb58xeLaPDb21qYllsABgIuN///ufiZk44YQT5KWXXjKHBi9LgbDGrqjnBqUB1gRYDTwnzI877jiT0AjvcLuyBv7eztW+f+tcsR1cq0466SSzPeLQ4MJlHd9q114tvEiVjNo+UBSQRMlTPLcZOHCgURRhvcB53XfffSZzn+d2Nfm9UgoGCiFt2rSJgXY1eaV4LBIgARKIQAKYIcWDevXq1YIZRfgIw9d7wYIFYT8bz0ByXweMjsgfX2fnf7k/RrHKxclMMMPtWQfDctXx1W/MmmPA+fjjjxvXHvwi4NMPway553awPCLQ2XO52SCI/7wNuIPYrNqb/OUvf5H58+cb98wmTZqY7FhW5ixYcLAe9ydLoAA8/PDDAhdXWDwQ22Iv6Hn77bebrHiIvYAi4k8Bs/YPJccKFh83bpyx5qIPUCDgDoX4DW8C9y3EasAVC/1AHSB/grg2pB0+88wzjSsV4kqsQHR/24VzXaUUDKQLhKnHUyMOZ0e5bxIggeonsDW1jmwKMbVs9fei4h4T1duqZYCibBW3ctaSk08+2fjzOqtXNd8bBCaeeuqpZtYVAd+wYlx//fUVZk1rvmc8IglELwEEFGNwjbinzp07m0lhBAEjUxRccDDIRSxCp06dohYCBtjIeIoZfbgu2evrIP014hyswT8gQAmBtQBtLVcyLJ86dSrejKsVguQRGgBXT1h+UDPEkq5duwriNyDe9o8aQIi/gHJiKQwILbAEBauxHrJ8+XITowFlr0+fPiaGxOoHgr4tsW8DBchSPj2tXFb7mnyvlIIBrRbpF6H52mMw8OPFg6QqEkqGEAD07vVdlR5E+bZq+sQfnS/OZUxJNZRfQZz+CgMyVe5Ok1z9+zn29HMkaeUap3VNEFT+UtdO0jM9Lei+OcUsH3SHY6ghLBh48MFdCgMcJzz8Ygg/TzUGCfz000+CtKyYpcdgFvUgMNiGUoFZeQQZY7AcCwKLil25sM7ZrlxYy/BuVy7sy2EluOSSS0zANQK7MdnuT3zt31Iu/G0LqxTc2DDevuyyy/xaS+z7cdK9tVIKBgonIWjPU6pDE7a0L899e/seX1Kspbm01L23lVzmg0BZJgJfnMuYlpKpD3reFpfqrxA3A19M43Sdjpcdx/SA3nTx91OIzjlM0rRv+Wqi9sXUW3cZJO6NSu0vg1sU8s7Pnj3bpMeEzzGCIaFoUEiABKpOAEHDngLFAi9LEOCN1KdIuoBBKJR9XwKXKkpFAgjKxqsm5JprrhG8IlkqpWD4KpxUHSCsoJtg9lVa4ryBUTD9rtU2igyMfXEuIdNKXR5/TGHiFDOUr9SuY3QjVduUm6/fqTcolVUwih2oYHk7v0hdhtk3uIvdc889sn37dpPL/fTTT5ezzz7bZEqJ1PNiv0kgEgnUq1cvErvNPkcggUopGBF4nuwyCZAACXglsFODGZHNqpG6DlZWSUEhwiaa3YXiTgBKIioIIysL8rlD2rZt6wr0RgpJT4Glo2PHjqYon30dilIhT7wl8H1G4T4KCZAACZCA8whQwXDeNWGPSIAEapDAG/37ysrcPJ91PILtSve6mcE2jZl2UNgyMjLkt99+c50zrFJIB+npIoXsOChgBfcMKCGegnSaKJY1YsQIs8ozTaNne34nARKILAK4X2SdXrU43sg64+juLRWM6L6+PDsSIIEABNLVH7lXfboNBMBU6dUPPfSQ8VuGcoBkCMheg8wncJOyCwJP4SeOlIzeBAoGfMfnzZvnqsNkb4cqudu2bXMtOvHEEwW54YOR/cE0itI2/qo3xyoXJzPpu3hJrf0SNwz1H9Rcax3jgR1JgAqGIy8LO0UCJEAC0UHgnHPOMa5Mc+bMMbnlEXuBtJmeguJ7eCG9pjdBXn7McLZs2VKmTZtmMqzY94P878hdbwnaIuYjGKkbTKMobeOPUaxyIRPvP3Z/XOxb4G+UQgJUMPgbIAESIAESqFYCGOjv3r3btU9YJcaMGeP6DksDgk19pXF0NbR9QGp0SxDb8eOPP7opKi1atLBWm/edO3eawlpuC/mlAgEobhR3AmTizsP6Ri4WCb4HQyA+mEZsQwIkQAIkQALBEvjmm28EOeP9vRDMHYrMmDFD1q1bZzZBqlvOkoZCj21JgARIoGYJ0IJRs7x5NBIgARKIegLIAoUYC1SjhUvUBRdcID179nQ7b7gwBSOoiIvK3/369TO1NBCHgeJTY8eODWZztiEBEiABEqgFAlQwagE6D0kCJEAC0UygXbt28t577xk3KVgebrzxRhMPcd5558moUaNMGlpf529libLWT5gwwXxENqpx48YZ5SI1NdVazXcSIAESIAEHEqCLlAMvCrtEAiRAAtFAALEXl19+uXz66aemFkbdunXl0ksvlQEDBsgvv/xSqVOkclEpbNyIBEiABGqUABWMGsXNg5EACZBAbBLYvHmz4LVlyxZBzYvi4uLYBMGzJgESIIEYIEAXqRi4yDxFEiABEqgNAosXL5bp06fLW2+9ZTJGnX/++cZ1qmvXrjXSHcRrUPwTIKOKfMikIhMsCZYLsrwFG2Pl/UhcGg0EePeNhqvIcyABEiABBxH4448/BHUpMMiAUjFz5kxTHM9BXWRXSIAESIAEwkiACkYY4XLXJEACJBCLBJBGdsWKFZKQkCD//ve/zcuTwxtvvCHnnnuu5+Jq/V5UVFSt+4vGnZFRxatKJhWZYEmwXGi98M4v1pbWiIKBfOdIW9ijR49Y48vzJQESIIGYI3D00UfLxo0b/Z43qnZTSIAESIAEopNAWBUMBPK9+eabsmzZMmnbtm10EuRZkQAJkAAJuBFAhe5WrVq5LeMXEiABEiCB2CEQVgUjLy9P+vbtK0hV6E0WLlwo+/btc61C1demTZu6vgf6EF8nVeL0HyUEAuoTnZaWJpmZGV43iq9Th8FZXsn4XojfYFo6mGZ6bWR+p0EWFfO6g5hcaP1OvTONSSQ8aRIgARIgARKIEAJhVTBgAsdr1apVXnFkZWXJnj17XOuQIx0zX0FLYpJQvwialmkIdSw5Odk3Z2ZdCQ0oWivUpCR/TPV3yh9qSFzhw5uUlOT7dxrS3tg4kgj4cqmF/zcyUiG+o0uXLjJs2LBIOi32lQRIgARiikBYFYxAJEeOHOnWZOfOnbJjxw63Zf6+pOzdK+maDo3FPPxRcl+H9HHZ2dmyo3C/+4ryb8nKNINMvbLxtRBM94DpgUKvTcqYlvB36pWO94WlpSVm8mFH0QHvDbwsRaVnSuQSCORSO3fuXGnevLmgGvjLL78sS5culW7dukXuCbPnJEACJBDFBGpVwYhirjw1EiABEiCBEAgEcqmFJXz06NEmM1Xv3r1l+fLlbgoGgsoLCw8q+chgFZJFPIS+RlNTMqp4NcmkIhMsIRfvXLjUOwEqGN65cCkJkAAJkEANEgjkUgvLK+LHIHiHQmKXOXPmuFnAjz/+eOnfv7+9ic/PB9USn02idkWjRo18nlusciET7z8Jf1y8b8GlsUygRhSMESNGxDJjnjsJkAAJkEAVCdTRBBRwo0JsDiwVni5xV111ldsR4HK7efNmt2W+vjT2tSIGlvtjFKtcyMT7D98fF/sW7du3t3/l5xglwPCFGL3wPG0SIAESiCQCrVu3lrVr15our1mzhmlwI+nisa8kQAIxR4AKRsxdcp4wCZAACUQOgUmTJklubq4MHjxYFixYIJMnTzaZpBCHQSEBEiABEnAmgRpxkXLmqbNXJEACJEACTiPg6VI7YcIEVxevuOIK4x6FVNsUEiABEiAB5xKgBcO514Y9IwESIAES8CBA5cIDCL+SAAmQgAMJUMFw4EVhl0iABEiABEiABEiABEggUglQwYjUK8d+kwAJkAAJkAAJkAAJkIADCTAGw4EXhV0iARIgARKoOoHU1NSq7yTK90BGFS8wmVRkgiXBcikpKZH4eM5fe6cYO0upYMTOteaZkgAJkEBMESgoKAjqfDOCahWdjfwxilUuZOL9t+6Pi30LKhd2GrH7mSpm7F57njkJkAAJkAAJkAAJkAAJVDsBKhjVjpQ7JAESIAESIAESIAESIIHYJUAFI3avPc+cBEiABEiABEiABEiABKqdABWMakfKHZIACZAACZAACZAACZBA7BJgkHfsXnueOQmQAAk4hkBRUZFMnz5dtm7dKl26dJFhw4a59W3v3r0yZcoU17KhQ4dKjx49XN/5gQRIgARIwDkEHKdgBJsGDQgTk5MkzjksI6MncXGCSri+OBum2oYSPIE4/RUmp6T4ZJqQpL9TMg0eqGnp/3fqbWdMjeiNSuQsmzt3rjRv3lzOO+88efnll2Xp0qXSrVs31wls3LhROnToICNGjDDLEhISXOv4gQRIgARIwFkEHKdgBJsGDRhTCg9Isr5zOBzCj6q0VAoLC6WgwPvDORlMtQ2ZBs+0VJTp/v1SkOiD6YEDkkKmwQM1La3faXBpRrEJUyOGiNhhzVetWiWjR48WKA69e/eW5cuXV1AwEhMTZd68edK9e3dp2bKl2xl8+OGHsmfPHteyrl27SqdOnVzf+cE7gUaNGnlfEcNLycT7xScX71y41DsBxykY3rvJpSRAAiRAAtFMIDs7W9LS0swp4j0vL8/tdDExAksgFItp06bJyJEjpXPnzq429evXFygglqSoVfGAKvfBCCaqYlX8MYpVLmTi/a/BHxfvW3BpLBM4eDeOZQo8dxIgARIggVolUKdOHdmvlsAkdSmEMpGR4V7mbfjw4a7+lapF8Mcff3RTMAYOHOhajw87d+6UnJwct2W+vjT2tSIGlvtjFKtcyMT7D98fF/sWDRs2tH/l5xglwCxSMXrhedokQAIk4CQCrVu3lrVr15ourVmzRlq1auXWvRkzZsi6devMMgSCe7pIuTXmFxIgARIggVolQAWjVvHz4CRAAiRAAiAwePBgWbBggUyePNlkkkIcBmTSpEmSm5sr/fr1k/fff1+effZZWbJkifluGvA/EiABEiABxxGgi5TjLgk7RAIkQAKxR6BBgwZyxRVXGPcoZLqzZMKECeYjXKbGjRunCSoKfGZss7bhOwmQAAmQQO0SoAWjdvnz6CRAAiRAAjYCduXCttj10VeKbVcDfiABEiABEqh1AlQwav0SsAMkQAIkQAIkQAIkQAIkED0EqGBEz7XkmZAACZAACZAACZAACZBArRNgDEatXwJ2gARIgARIIBwE6E4VmCoZVWREJhWZYEmwXEpKSlj41DvCmFpKBSOmLjdPlgRIgARihwACwoMR94obwWwRPW38MYpVLmTi/fftj4t9i/h4OsfYecTqZ/4KYvXK87xJgARIgARIgARIgARIIAwEqGCEASp3SQIkQAIkQAIkQAIkQAKxSoAKRqxeeZ43CZAACZAACZAACZAACYSBABWMMEDlLkmABEiABEiABEiABEggVgmENci7qKhIpk+fLlu3bpUuXbrIsGHDYpUzz5sESIAESMAPgUDPi0Dr/eyaq0iABEiABGqYQFgtGHPnzpXmzZvL+PHjjZKxdOnSGj49Ho4ESIAESCASCAR6XgRaHwnnyD6SAAmQQKwQCKsFY9WqVTJ69GhJSEiQ3r17y/Lly6Vbt24utg8++KBRPKwFw4cPlxNPPNH6GvC9aNtWKSwsFE24HLBtTTZIKS2V4rg4SdCX0yRf+9ahVUtpm5nptWtFO3c4lmmp9tiRTEtKpT2Y1q3rlWnx7l2y34G/02QFir+cOAf+TveBaYsW0rZ+Pa9MuTD6CAR6XgRa/9BDD0lWVpYLDCzmJ510kuu7vw/7/K2M8nVt27b1eYaxyoVMvP8k/HHxvgWXxjKBsCoY2dnZkpaWZvjiPS8vz431uHHjpLi42LVs3759smnTJtf3gB9atJS4f94hooNmJ0lifIIsUcvN+l27nNQt05dEHUxm5uTIJn15labNHMk0Pi5efm/ZQtbtdCbT+nv3yiZ9eZXGTRzMtKWs3bnTa7drcyEUyQZ5ubJJX8FKmzZtgm3Kdg4kEOh5EWj93//+98o/T+59oFaIoHDZOp2Y2LZtW60cHwf1+8ytJS4NGjSQpepivdfXPTXMtJzIBKfcsmVL+Vl/K3AXrA3xy8XWId6LbTBi+GNYFYw6derI/v37JSkpycyKZ2S4l+3x/I4/GrQPSeqUKTAhbRPmxqVqsWmmD429qmA5UexKndf+OZApCvc0qFtPcnaTqddrVomFYFpff6cNdu+uxNbh3yTg7zT8XeARapBAoOdFoPXV8jypwfPFoVDxuBQWb9tEWw13wZGHAxOwIZeKlwdMyKUiFy5xHoGw+ha1bt1a1q5da856zZo10qpVK+cRYI9IgARIgARqnUCg50Wg9bV+AuwACZAACZCAi0BYFYzBgwfLggULZPLkySbWAnEYFBIgARIgARLwJODreTFp0iTJzc0VX+s998PvJEACJEACtU8gTk2RYQ9gQCB2cnJywLPdqb7gteVzGbBzITRAUDusNevXrw9hKzb1RwDuPPDrXLdunb9mXBcCgWhj2r59+xDOnk2dSiDQ8yLQeuu8IuF5ArevevXquQWnW/2P5fdGjRrJgQMHJMdXrGCMwsEzcMuWLbUWgxEsdt6LgyUV3e3CasGw0AWjXFht+U4CJEACJBC7BAI9LwKtj11yPHMSIAEScA6BGlEwnHO67AkJkAAJkAAJkAAJkAAJkEA4CdSIi1Q4T8CJ+96zZ4+gxse///1vJ3YvIvsE17n77rtPHnigdtJJRiS0AJ1GWuiJEyfKww8/HKAlV5MACYSDwJ9//imff/65XHPNNeHYfcTuc/r06dKkSRMZNGhQxJ5DODqO+zV+K2BDIQGnE6AFIwxXCGEtIafbDUM/ommXZFr9V5NMq58p90gCoRBAulHEGlDcCYAJU7G6M8E3jCtqIGy24oG5hAQqQYAKRiWgcRMSIAESIAESIAESIAESIAHvBKhgeOfCpSRAAiRAAiRAAiRAAiRAApUgwBiMSkALtAnMu8uXL5fu3bsHasr1QRJAlfdly5aRaZC8gmkGFwT4gPfo0SOY5mxDAiRQzQQQW7Zt2zY55JBDqnnPkb27TZs2mdT2jDVwv464X3fs2FFSUlLcV/AbCTiQABUMB14UdokESIAESIAESIAESIAEIpUAXaQi9cqx3yRAAiRAAiRAAiRAAiTgQAJUMBx4UdglEiABEiABEiABEiABEohUAomR2nGn9jsvL09mzpwp2dnZ0rlzZxkyZIhTuxpx/UIMxqJFi2T06NER13endRi+31OmTHF1a+jQoYzFcNHgBxIIP4H169fLZ599Jvn5+XLCCSfw78+GvKSkRKZOnSojRoyQFi1a2NbE5kfer2Pzukf6WVPBqOYr+MUXX8hhhx0mvXv3lqefflq6desmbdu2reajxN7uUBRu1qxZUq9evdg7+TCc8caNG6VDhw7mAY7dJyQkhOEo3CUJkIAvArifjRkzRtLT001RVjw34uPpVABec+fOlbVr1wqSe1BEeL/mryASCfBuVs1X7fTTTzfKxe7duwUVvfHwoFSdwNtvv01rUNUxuvaAB1ZiYqLMmzdPtm/fzoGNiww/kEDNEBg/frw0aNBANmzYYDImUbko4457U1ZWlnTp0qVmLkQEHIX36wi4SOxiBQJUMCogqfqCnJwcee211yQzM1NSU1OrvsMY38PChQulVatW0rJlyxgnUX2nX1hYKHFxcYbptGnTZMWKFdW3c+6JBEggKAJLly6VGTNmSNeuXQVuQbEuSPEOy87ZZ58d6yjczp/3azcc/BIhBBLuUomQvkZMN5Gj+qijjhK49WDgxpmYyl86MHzhhRekU6dOxmS+Zs0aadq0qTRu3LjyO+WW5jeJQQ1Y1q1bVxYvXiw9e/YkGRIggRokgPvYMcccI/PnzzcTUrF+X/voo48Eg+mCggJT9wgKR/v27Y21tQYvi+MOhTEE79eOuyzsUAACtGAEABTq6unTpwuC9yCYkcrIyAh1F2xvI4DYgJEjRxpXM1iD8D05OdnWgh8rQwCzpuvWrTObbt26ldahykDkNiRQSQJ4Njz55JNmMI1d4Dss3rEuiEPBREdaWppRKnDPh6U11oX361j/BUTm+TPIu5qv28CBA2X27NnGNQo3xgsuuKCajxBbu4M1qG/fvuakUfF25cqVJjg5tihU/9n269fP/E4Rh4HZwrFjx1b/QbhHEiABrwQQb9G/f395/vnnzUAamZLoAirm3o7kE5AlS5aYhCmsWi3C+7XXPyMudDgBVvIO0wWCmZcz7WGCy91WGwEoF4wTqjac3BEJhEQAlovi4mJJSkoKaTs2jk0CvF/H5nWP1LOmghGpV479JgESIAESIAESIAESIAEHEmAMhgMvSiR0CRYauCzVhiCFYU1ITR2nJs6FxyABEiCBaCIQzDMIhW+RKIRCAiRQ8wSoYNQ884g/4pw5c6RZs2YyfPjwShVC+vjjj00hpcqAQLYVBEfWhDz00EPy008/1cSheAwSIAESCAuBp556ygSQW8lHrIMMHjxYkKK6OgSpxBGgXVMS6BmEc0aMC2IXEDSO16efflpT3eNxSIAElAAVDP4MQiaAPOU33XST/PDDD5VKH/jss8/Kpk2bQj4u/JWRVfm2224LedvKbHDnnXfK7bffLqWlpZXZnNuQAAmQgCMIoCL2/7d3ZqE6f10c3++lCJFyYR6KyFiSKWQuIdyYp2QmMhQ3MmemkCFjColS3LhBGS6kiEKkiAtjuJGL/a7P+rd//d7nPc8Z3nM6vec531XPeZ7ftPf+fZ/nrPXb67vW2osWLfq/GEtNDKI8G4SNYGHW69evhxcvXoQ3b96E48ePh6lTp2aV82piDGpDCAiB8hHQBKN8fHS0AIGdO3eGy5cvh0OHDoX169c7/YzhYiG8Zs2auRL/9euXX8VK5ih11lpghXPWWmBNCzxJXHvhwgUvz4i3qXfv3t7G5s2bswWnhg0bFugPtoT66OfOnQtt2rTJVkfHIweL0rx58zB37twwefJkrzxC53fu3Ak9e/YMTZs29UWbvnz54mOCLi823sL+uLZJkyZurPxi/RECQkAI1EEExo4dGyhHzQKwZcmQIUMCawwlGTRokD+MP3v2LMyePTssWbLE9eyYMWPC27dvw9ChQ0Pr1q3D/v370yVechc9zOrkI0eOzJxIOGi2bt0aWrVq5Tp+27ZtmdOmUOdmjdkHHEpl2YZCG5S/hokUzPOJEycClbmSUN0Re0OZc+Tz589uF9Dx2Im7d++mU8PZs2dD165dvcR8nz593JHGwV27doUtW7b4faxcuTK8evUq9O/f39khznvw4EHWhj4IASFgCNg/v0QIVBoBe0CP06ZNi6Zso8W3xu3bt8cRI0ZEM17RVqWNtiBQNOXu7U2YMCHOnDkzfvr0KVpYUxwwYEC0KhjRJhvx6NGj/vnw4cOxW7du8dGjR9HCn2Lnzp2z680gRTNU8caNG9HyIaIZr2iTjGysZgTjihUrorEh/s7P2ViVaLkh0WrKx/Pnz8ePHz/GOXPmxNWrV/t15Y23sD8uOHLkSFy2bFnWpz4IASEgBOoSAujeKVOmRAtjiubsifZw7cMfPnx4vHjxon+2h3HX3+m+zKkT7QHar7Fy667v3717F3v16hVbtGgRb9++HZ88eRLNqeR6HP2N/l2zZo3r41mzZkWbjHhz9sAebZG4aOGm3h76/uHDh36sLJ2bxlDMNhTaoHQ+74zJnEL5XWV+xgZhF7BN5vSKHTp08PO454YNG/pYv337Fq18dxw1apQfw4aYMyteu3bN7ZU5tKJNliLjMYdbNCdZmX1ppxCorwiIwdA0s0oINGjQwMvvshASL5tsOLMAS0G5U5sgBJKjScAjTpYQo5YtW7oHjJAj1l2gJKMp8UB9czNwYcGCBb7yuU1Agin9YBODbEyrVq1y9gMW4+XLl+4B4yDeuPv373vIFPXjYT6Sd4pFicyIZQv0bdy4Mdy8edPbLDbe1GG+P/bhdaNfiRAQAkKgLiNAPoJNNAI6riqCzl+3bl1o27ZtIG8Drz3vNtkIrDz+9OlTbw69TngS+njDhg3OOv/+/dsZAZiNjh07+mrU8+bNC+Y0yoZQqHPTgWK2odAGpfN5f//+vTMtaR9J3qyj1LdvX3/BftjEwe0B94QNg/lmzNwHdsYmS86os1YJeSU2CUnNhfHjx4eJEye6vcKWPX782O3D0qVL/brsRH0QAkIgaKE9/QiqhQAP9dDFhCSZZyv8/fvXE+ug2zEEXbp08fZZdHD06NH/1RerSUNfJ+Hz6dOn06bT8GnDPGgebsU2NH27du2cjmcbqrtTp058DB8+fAhQ++Y18+30h7yPYuNN50D754WJE/1KhIAQEAJ1HQHCk9DJt27dqvSt5BcA5IE86XQaYK0n1vHgYRynTqNGjbxdQoxwIhmD7KFSu3fvDgcOHMj6JKQoSaHOTfsrsg3pvPw74U7YHiY2jAUnFg4mhBApJiDYB+yRMTj5S91hRTL4pUuX/PXz50+3KYRqJcHhlGTfvn0ebtuvXz9fIBAH2vTp09NhvQuBeo+AGIx6/xOoHgDkM5B7wQM9K6+ibI0O9Ad/cjHy3h/yL1J+RuoVD9jz58/TprdjdHW2nVgJdpB/kdrDkPD5x48ffi4GhWQ+hDHAhnA8vfA0YSiLjdcvtD/5/tiHgcRzJxECQkAI1HUEcMSkB+O8Lkbv/fnzx2+PCQNe/iSFOjHtL3xHVybBKQN7AHsNc7Jjx45MF79+/dqZ63RusfYrsg3p+vw7NgKnEAneCCzDpEmT/GXhu76PCRK5ddisZB9gL2BWyPMjQfzq1at+bO3atVm+CBfnx0rbnAdjj12xsLDw9etX70N/hIAQUBUp/QaqiQAKlYRAlDqeIRK4YTHY7tGjh4c7MeG4d++eGza8Sni20sQAVoNSiSSEY9SuXLkSBg4cWOaoaA/jhOBJo989e/YEPE179+7NSuZaTojT1RaP6+fiuSI5EU9UsfH6iWX8oT/6lQgBISAESgEBwkRhe6kCmITQIMuL8E0esNHhVRXWRaKMOPoeFhr93Lhx42C5eL79/ft3PzZjxoz/SA4v1k9VbEO+DRxZixcv9kkMtgHnE/eEHWBiAetCiBcJ5NgEJgiEQlkOodsgwny7d+/uYz1z5kxRLAjnPXnypDvYYC5gS7h3iRAQAv8gIAZDv4RqIYCHZ9OmTR6XSywr3qI0CTh16lQ4duxYgJEgRhXPGdT04MGDgyUDekUOcjRQzO3bt3fqHbaAfWUJrAVlB5NQycqSDf1aQqYI0cJ4EJplydzeD2FSTEIYB96n8sab2s2/0x/9SoSAEBACpYKAFdlwXZnuh9LflsTsuhQnDw/YVRUe0ilfTq4FTiN0LjJu3DhnMghp5eEdhoQqghVJVWxDvi36w/YcPHjQcypgbci3IFSKEDGE+6WiFnYHtptx40hi8oWjDJ1PyBf5G7AcVB8sFCpKUXKdcDBe5AHCukiEgBD4B4F/2YxbU279GqqFAD8hmIFiypUSsYXHrJqUJ3snyhlPE0mCTA6KCaUFSTCEzobBgM7GcwRVDR0Pa0L8Le8IhgymhDK2ealovOlcriU5kMX28HxJhIAQEAKligCsBd5+ysxWR9DTOHsKBR2NwGBXRSpjG4q1x8SA+yqmvxkrtgnHV16wZ+BAbklFAjNjVQv/pzWhKmpbx4VAXUZAE4y6/O3Vw7HDgjARWb58ebASuB4WBSNC5SjYDzxXNSXEDTM5WbhwYU01qXaEgBAQAkJACAgBIVDyCGiCUfJfcWndIN4omIv58+cHWBAWSGIBPyhtFneqjMepsoiw+iv9JJalstfpPCEgBISAEBACQkAI1GcENMGoz9++7l0ICAEhIASEgBAQAkJACNQwAhUHGNZwh2pOCAgBISAEhIAQEAJCQAgIgdJFQBOM0v1udWdCQAgIASEgBISAEBACQqDWEdAEo9YhV4dCQAgIASEgBISAEBACQqB0EdAEo3S/W92ZEBACQkAICAEhIASEgBCodQQ0wah1yNWhEBACQkAICAEhIASEgBAoXQQ0wSjd71Z3JgSEgBAQAkJACAgBISAEah2BfwOg4vATjqV1BQAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-12"/> </p>
<h3>>> Add error bars or best fit line</h3>
<p>Again there are two ways to do this, but I prefer summarizing the data first and then adding in error bars. I use tapply to get the mean and SD of the weight by gear, then I add a <strong>geom_bar</strong> layer and a <strong>geom_errorbar</strong> layer, where I indicate the range of the error bar using ymin and ymax in the aes() statement.</p>
<pre><code class="r">summary.mtc2 <- data.frame(
gear=levels(as.factor(mtc$gear)),
meanwt=tapply(mtc$wt, mtc$gear, mean),
sd=tapply(mtc$wt, mtc$gear, sd))
summary.mtc2
</code></pre>
<pre><code>## gear meanwt sd
## 3 3 3.893 0.8330
## 4 4 2.617 0.6327
## 5 5 2.633 0.8189
</code></pre>
<pre><code class="r">ggplot(summary.mtc2, aes(x = factor(gear), y = meanwt)) +
geom_bar(stat = "identity", position="dodge", fill="lightblue") +
geom_errorbar(aes(ymin=meanwt-sd, ymax=meanwt+sd), width=.3, color="darkblue")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-13"/> </p>
<p>And if you were really cool and wanted to add a linear fit to the barplot, you can do it in two ways. You can evaluate the linear model yourself, and then use <strong>geom_abline()</strong> with an intercept and slope indicated. Or you can take advantage of the <strong>stat_summary()</strong> layer to summarize the data and the <strong>geom_smooth()</strong> layer to add a linear model instantly. </p>
<pre><code class="r">#summarize data
summary.mtc3 <- data.frame(
hp=levels(as.factor(mtc$hp)),
meanmpg=tapply(mtc$mpg, mtc$hp, mean))
#run a model
l<-summary(lm(meanmpg~as.numeric(hp), data=summary.mtc3))
#manually entering the intercept and slope
f1<-ggplot(summary.mtc3, aes(x = factor(hp), y = meanmpg)) +
geom_bar(stat = "identity", fill="darkblue")+
geom_abline(aes(intercept=l$coef[1,1], slope=l$coef[2,1]), color="red", size=1.5)
#using stat_smooth to fit the line for you
f2<-ggplot(summary.mtc3, aes(x = factor(hp), y = meanmpg)) +
geom_bar(stat = "identity", fill="darkblue")+
stat_smooth(aes(group=1),method="lm", se=FALSE, color="orange", size=1.5)
grid.arrange(f1, f2, nrow=1)
</code></pre>
<p><img 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8H1q2pYfVz8s75woRaTSXcvZls2UsXO+iIVmdTnWV/sziZdGWV9Ef4dstjri7zOIvXjH/9YVq5caUoZXm5ww+FBtWbNGvOShhcuvKzh4UdHAiRAAiRQvgRYX5Rv3jPlJEACQ49AXt/sZ8+eLQ8++GCi7/mpp55q9mfNmiW33nqr+RI9f/78oUeVKSIBEiABEghFgPVFKFz0TAIkQAJFTSCvBsaUKVPks5/9rKD7ELodWDdnzhxBZYLuMvjRkQAJkAAJlDcB1hflnf9MPQmQwNAikFcDw6LyGxf2HLtFWRLckgAJkAAJWAKsLywJbkmABEigdAmw+aB0846akwAJkAAJkAAJkAAJkEDREaCBUXRZQoVIgARIgARIgARIgARIoHQJ0MAo3byj5iRAAiRAAiRAAiRAAiRQdARoYBRdllAhEiABEiABEiABEiABEihdAjQwSjfvqDkJkAAJkAAJkAAJkAAJFB0BGhhFlyVUiARIgARIgARIgARIgARKlwANjNLNO2pOAiRAAiRAAiRAAiRAAkVHgAZG0WUJFSIBEiABEiABEiABEiCB0iVAA6N0846akwAJkAAJkAAJkAAJkEDREaCBUXRZQoVIgARIgARIgARIgARIoHQJ0MAo3byj5iRAAiRAAiRAAiRAAiRQdARoYBRdllAhEiABEiABEiABEiABEihdAtWlqzo1JwESIAF3AtOm3eYkvHz5Iic5CpEACZAACZQmAdYX4fONLRjhmVGCBEiABEiABEiABEiABEggBQEaGCnA8DQJkAAJkAAJkAAJkAAJkEB4AkXfRcrzPKmoqMiYMvjJxl9yQFbGbpOvZ3PsGnemsNPpZK/Zbaawgq5H0dtV1uprt0F6ZTrnGjfCjSJr9XLR3crYrQ0rzDaqbBT5VHqmC9Nes9tUYbiejxJuvmURvkscVsZuw7CxMnYbRnao+GV9EZyTtkzYbbCv9Gch6yrvKmvjs9v0GgZfdY0boUWRtdq46G5l7NaGFWYbVTaKfCo904Vpr9ltqjBcz0cJN9+yCN8lDitjt2HYWBm7DSObrd+iNzCQkNra2rTpqaqqMpmTyV9QIDU1Nc6yCA+ZgzDy4dKlB2mGS+cnnU6VlZVSXV3tJA85yLvEDblC6o38ctHbsnSVRbyuvFG+XHn79e7r67OHOdum4xGlnGSjYLq4M8nnUzbKMwH3tWteR3kOZuJVStcz5W0UTqwv0tfFQeUkynOA9UV43qwvgkqh+7sSQsv0TAmOMXY2k+xQri+K3sAA/K6urnT5J7t27TK/TP6CAsEXL/xcZBEeZLu7u80vKPwo59LphIc2XDo/6eLGy2ZPT4+TfF1dnTGsXOK2FYaLLNITRW88ePFzjRvxI6+R52Ed9O7t7XWKGy9EkI+iN2QRRq5dOp1w70bVO52+6eJOJ4dr+ZSN8kyor68X5LeLfngmpHuWVezcKZVvb5SaqVNExo7NhKgkr7O+CM421hfp3yGCqLG+YH1hy4XL8zhb2WKtL6z+uA9cXdEbGK4JoxwJkAAJDHkCauxWtrVJ5cZNUru5VTw1gJuXvSpVGzdKlRoT2MKoqFq/QSrVwIDruOt2kenThzwaJpAESIAESMBHwOuTyu5WqercKLW9rSIjj/JdzP0uDYzcM2WIJFBWBDh9Xx6yWw2FytYtaiS8HTcUNsUMBWM0vG0MCmNAbHhbKrQF1+9G+g8C9iu2bQs4y1MkQAIkkH8CrC/ywHhXl1R1bdKffkzCVg2I2H58Gz+u7GoV7VeQUMAbfo9IzcGJ41zv0MDINVGGRwIkQAIpCFR0dEjlps3asqCVAIwFNSBMCwOOtbWhRq9VqtEwZfPmFCFEP12xI9aSET0khkACJEACJJAvAhU92wIMhn4DoqZH6ws1Hpq7t7qpsGO9yAg30WykaGBkQ4l+SIAESCAVAdtNyRgMG0V6eqXxzTelYe26RFcldGHyd1NKFVSU833Dh0nlpEnSOXKk9I0fJ7vsb9xYPR4fO544QZomTowSDWVJgARIgARcCaCbUpd+ZIq3NlS090rTxjekSV/27TnbAlHR1+0aS1o5jCLtqx0llQ7jSdMGnHSRBkYSEB6SAAmQAAjUyC4ZLzukRbbLBP1hi9/Iy9b2Gw5xoyK5m1JzjhB6GOA/alTCWDCGw7iY8ZAwInA8YbxUNzZKS0uLbFq1KkexMxgSIAESIIFsCNRV98rE4VpfjNB6Yvh23def7o9+YfWALkvJ3ZQQ9rBsIsjCj1ehtVbdGP1pnVCvv7qxZr/P7Pcfw091bYOpLySP9QUNjCwyjV5IgASGDoEm6ZaqFW/J+2W1TIwbDRPihgQMiInSob8dMk5SdCW699nIMLy6WtllDAVtWTAtDPrw1xaH2j33lIqWibK1od4c92G2J52Ni44ESIAESGDwCYxo6JLqjjdl7r6rEsbDBJ8hYQ2KkY0pWhtW/Tmy0n1VjT6jIWYowGioHbmHVDS1SFuP1hdqVPTVaH+nIqovaGBEznoGQAIkUGgCFeLJaOk0LQzWQIgZC7FWB9sCMVmNh0bpFfnATfLbPCjdN3y4VExqkZ4xo6VXjQPTTUkNCGxjLQ5qUGhrg9cc3MYxYsQIM01tT6vO8EFHAiRAAiSQcwKVFX0yrnlnwmCwLQ9odYDBYI7j+/U1OonG726Vxz+XWzUS3ZSaJ0l39WjpqUXLg7/FIbYPw8GrbgyMvNjrCxoYgdnGkyRAAsVAAN2U0LqAlgbb2mD3x3zqeXlGXoobFdtFV4HIi8q9Ou/GRjVL1kmTrNffESe9Ww2G+JgGGA5xAwLndEUmmTx5srTpgG2smUJHAiRAAiQwOATQTSnZQDBdlbTFYdyfnpO/XLLMXIdxUVWZn/qiu7dS1m/TumKb1hm6PfKE98a7KfUbDKb7khoUUllt6ostQ7S+cDIwDj30UFm3bt2AEoMF1Ebq4MJjjz1WrrzySrMa7QAPPCABEiCBOAF0U6pevsLMoLRQXh1gQKDlwbY4jNVWiZTuMZH3pryY+YJWMWo0NKvR0GgMB2tAwIiw+9i+rde13TkR4PKvLzL7sekW23X/zcS1bHaWL18k5TRVI+uLbEoF/ZAACaQiYLoptb9hBkGfOuuV2BiHETreIT7WIWM3JQSs828cPDVVDJnPt3fWyLqtWjf4jIf1OE4617q9YUBgy7/kry+26LXXBlzPdFDK9YWTgXHYYYfJq6++Kp/61KdkvH61+9WvfiX33nuvfO1rX5Prr79err76avnyl7+ciRuvkwAJDCEC6KY0Rsct+A0E200JYxpiLRAdMkkNCNNN6cibTOrvzSEDfJNqk7oBBgIMBr/RgP1fv3iB7HXQD3MYM4NKRYD1RSoyPE8C5UsguZtSzEjYIRPiXZNgPNjWCNNN6clbDawfnpk7ZphEaVNHgzEa1m2NtTjAYNigRgQMCXvuiT+cL3vte3fuIi6TkJwMjPvvv19efPFFQf8vuEMOOUT++te/SkNDg9x4441yxhln0MAokwLEZA59ArU6ZmF83HDA+IaYARGbXWnMOX+XP8jLie5LVXnqptQjlfoBSisCNQ426A/bhefPkUu/+6oxHqwBsVbP92rLRCbnNTVl8sLrOSLA+iJHIBkMCZQAAdtNKda6EJtVyY5tGOxuSuu0mxK6K8FoOOWTR8glVyxLdF0yhkS7Dp7uq8xINdUYiIyCZe7BycCor6+XNWvWJAwMT83A119/Xbsf18rOnTsT58ucLZNPAkVNAIu+YbE3LPSGbkq2tcFu7ZiHMem6KT0uMitCKvv0WbKic2CLwwYz3qE50QqBLkzJ3ZQQ5dEXLpLbvntbhNgpOhgEWF8MBmXGQQL5JWAWfYuvCH2adlOKTcMa66YUa2nQFgdtdUg5mxLUi9hNCbMp/XNtbbx1QVum1UDwd1OC0YBzyd2UEPVx1yyS7z/D+gIsBss5GRiXXHKJzJ07Vz70oQ/JKJ2j/dFHH9U+xdPkne98pxx88MFy2WWXDZb+jIcESMBPoE8X8WndYlaKrtRVorE6dF93jwz/5xtmhWisHG1WkNbVoiu6uhKSue6mtMV0U8L4BnRP6h8gjTEPMQOiSX6j3ZTQkrDfND70ExkxBHdYXwzBTGWShgYBb5cu+tYaX+Bto1mvwdvYIyPf/qdZIdou+FbVtUn8i77dfWbuko9uShu1m1KspaG/m1JsoLSvm9KzWl9UNcg7WF/kDn6eQ3IyMM455xx517veJb/5zW+kS19S7rvvPnn3u98t27dvl1//+tey11575VltBk8CZUZA77OqNbrAG1oc1GiA4WBbH4zBgJWi1XjAitEVamT4HcYlBE+K6veVeR/dlN7WbkoxoyHWTemkCw6XS294JdHagK5L+LGbUmae5eKD9UW55DTTWTQEenUKix3+Bd60fsDK0fEWCBgMOMaK0hg7l+xyUV/Y2ZTQTcm0LGhXJdtNyd91aUO23ZTUuKArLQJOBgaSOGvWLNl7771l27ZtsqcuDgXXpF8j8StGV06zthQjf+oUTKCiHd2UYoaBNRoSRkR8lWicr2zbql2Y8uPQTWm5r5uS34Dwj3nAGAj/bErQ5pjPazelG9gCkZ+cCQ61FJ9lrC+C85JnSSAMgcqerdqyEDcWEgZD0nH3Zqns2SaTwwQcwm9fdZP8c43OqBQf24DthrgR4T+3ZUf9bqGym9JuSPJ+opD1hZOBsXz5cjnllFPkL3/5i6B/LQZ333TTTXLyySfnHVahIihkJhUqzYzXkYDtpmRbG9AtacdOaVi9RmpNKwS6LmmlkNRNyTG2QDF8k+rTaaOxuFvN1CmyY8Rws2K0Wb8B6zboKtJm4beJE8RrbJT92ewcyJEnoxNgfZE9Q0xJSVdmBPzdlOItDNVrd0hj+xqpb9dW63hrQ3I3pVxS8rQdo692lFmvAatDb/eapTe+6BvWbOjz7bObUi7JD+2wnAyMs846S0488UT55S9/KcN15dpnnnlGPvaxj5kxGAcccMDQJsbUlS0Br7NTqtRIwNgGdEUyXZNgPBhDwh5ra8Om3bspAZrTzZZE26uulj5dJTpwobdxscXf7MJvUlNjpNHC2LZqlfQldZ1KCpqHJJAXAqwv8oKVgRY5AS/eTalyp9YRaG0wP60nEt2UcG6TpOqmFHt6R0ukV6FLlcI4UCMBK0LHfv3Hfea8HscXfUNsqC+2sL6IBp7ShoDTOw++SF188cVSURFbfOqII44wU9M++eST4jcw2tvb5eGHH5a2tjaZNGmSHHfccVKtL0jLli2TpUuXSm9vryxcuFAmTpzI7CCBghGo2NYeG9OArkiJbkn64I8bD3aMw66tW3U61vy4Pm0FNC0K2uKA1oVdWCE6vo8WiPppe8nGqirpGz1Keyn1L/qWH20YKgnkjgDri9yxZEiFJxDrphQzDoLGNVgDwuvFmj/5ceimlDAWYDgkDIixUjNiqjSMmSZvd6BVYmR+FGCoJJAFAScDAwO877jjDmNUwGDYsGGDac34wQ9+MCDKJ554Qvbdd195z3veIw8++KA899xzppVjyZIlct5550lra6tgjvTFixcPkOMBCUQmsEtnxzCzKWlFEO+qVKfjGKo3t8rot1YmBklj/ENFV3fk6IICMN2URmk3JTUYqqZMlt6xui716NjxACNiwnjTTSkoDJxr1C5M9cOGSZ/eZ3QkUGoEWF+UWo6Vob4B3ZTqvW1S07NZxrZpfWFbH3Rb0deTF0D+bkpVw7W+0FaFzsqRMUMibkCYrkq6j25KqVyivuhmfZGKEc8PDgEnA6O7u1swM8iFF14oU6dOlZdfftmMxTjppJOM1nPmzJHvfe97Zirb5ubYfAQ12l0DLRkwRiCDmwA/rJuBlgwYKnCbtHsJftaZm0XHeaRzVfplF60pGA+Saxc1zCjy6WSRZrh0ftKxqKysNOuWpPOT6hryCvG7xI144VxkIVepZa923XqpXL3aTLtaqeMYTJclGBF2H2MbNunsGCm6BDUioAjO07LcN3aM9Klh0Kcr2XtobYjvx7ZofdBrek4LtolppI6H6NGZoLq1vFsHEvhluglx74CbKzPEB9l8dJGKqhN0c3WFijtKvEhrFPlcyNqWZ1fuYeVYX2RPLBf5GxRb2dYXfV1S16XjGLZr11btqlTZiZ/WFeYXO0aLQ0WK2ZTAMtPzOYi3/5xXWSt99WN1HIPWCw3ajbVe6wbz02M1FmL7eq5ujFYI/fVFt9YXXawvEiij3BsIJIp8oWSLQe8o9YXTvXPNNdfIlVdemcj45B27wrfdYhG+v//976al4rXXXjOGhZXBAHFMb2v94vrTTz9tLxtjZMGCBYnjoB378MRCf7l2WOcjiosin04WmY6XznR+0ukNZjD+sEhiWAdZxO8aN+LbTVa7H8l6/eKixkPF+vUiaiRgH+fMMa5hX43UXEyhF5Rms7qzDnoWDHxGtz3st+h2Ao4nSO2ee0j36NEialzAWQMhKKzkc1GNMjDfjVlyJGmO7f2VxovTpSg6RZGFslHkCyVbDHp36liiwXSsL7Knna9yOeTqi642kR1aV+zQemGn1gvY7nas53U2pVyMZQjKQa9mmDYvY5IMrSca4tvGieLpPs6jq1J3tdYX9f3vENnWGawvdice5d5AaFHkCyVbDHpHqS+cDIyZM2ea1ohXXnllwAvqfvvtJ6PxAuZzaN146KGH5NxzzzWzTcESxNoZ1vX09AyY2vbQQw8V/PxuxYoV/sPd9hHnLu0SsxUvqTl269atixRiFPl0sngAtbS0SDo/6RSH7JYtW8Sl8GBgP76qb968OV0Uopmi3ZR0ER+Ma9BB0WhpqNZttc6o1PXm8v5B0jr2Ie/dlLRFAcZCpY4F2j682XRbsgOlzaDoDN2UkNC9dH2X9W+9JZ5DmRin3aTQWtehq2eHdWjFG6ZdpND65+IwaA+y+WjBcC1/SEcU2ajyUeKOIlsMettWZegyGI71RfaUo5StdLIlUV8EdFOq7t6kdc1O6dn45qB3U5ImrS+atb7whmk3pd0HRqfrpoQc32tMvL7wwr9DsL7Y/Z5JV7539737mSjyhZJFKgoVt403Sn3hZGBcfvnlZlraCfpl17YeAMS1114r8+bNw65xMECwyjfGW+AlCQ4vtph9Cl/O8XKLLR5+dCVEQF+UK9auk1ptbTLjG+IGhFnPAfvWoMBsSgEtJGgzyUU3pV3opuQbFJ2YehXTsKKLkrk2NtFNCTcKWsy2qUFDRwIkMDgEWF8MDueijUVnU6ro1Cm6W183syalGhhd2d0auOgb0hW5vqjEbEqYRUnrBjubkm9gdGw2Jb1eqx9I492UbH2xlfVF0RYtKlbcBJze7G+//XZBV6YxY2JdRVIl8Sc/+YkZX3HDDTcYL1jtGwYIFl269dZbBbNMzZ8/P5U4zw8ygQpdNNEYB2ogmNWirbGQdFyp+QaX+xEvunaDLtS4Sw0E/4xKGCSNWZXwG/mO/aVD/WxvyEfsgwyc0ZFAGRBgfTE0M7l/NiX9qGSmXtUpWO10rOZYz2MaVp1NCS6qkRBEsX82Ja0fjMEw0IAYNWl/ae/T+qI3992ng/ThORIggX4CTgYGWiHQ1SOTgfHlL3+5PybfHgaBz54924whsIN+fZe5m0sC6KakXZnMwm4wFIzRoIOgtZvSsJWrZJiOc4i1OOi57jzNjqHjNfp0NqXY1KvjpUG7Gm1ttNOy6qA30+Kgg93QTUlbGNI6jIeAgbNjR1pvvEgCJFAcBFhfFEc+ZKWFdlOq0gHPlVjcLWEkaL3xpo6T3LpaRug4B7vwW35nU9Juz9ragJaFep1ydWtPwwADwrY4eFXpPzR5o+L1RS/ri6zyn55IIIcEnAyMG2+8UQ455BA59thjZaxOvWnd6aefLjNmzLCHabfsFpUWT+aLO7V72fIVUvv887FuSjq2IdH6gPUb4scwLoK6KSGCusyxpPXRJVWyQb9LrTe/ZlknTbrfJBdcdUyixcF0VdKuTHY2JRiUmEVsm45loCMBEhj6BFhfFEEe79KB/duWazelF+KtDAMNCGM0qEGRrptSbN5C97R09VTJ+vZGWbe1yfywv173L/jScabbEmZainVf0vqiIhabrS+2sr5wB09JEigQAScDA9PTHnjggWbxPH8LhH88RoHSU/LRjpROaZHtMkF/DQ88mDAaYoOkMb4BxoNWBO2xZmcdupxzZ7opaXekZ5b3qMHQHDcgtFJQ4wG/DfFtqwS3Npx92sk514kBkgAJlCYB1hf5y7dRjVpfDN8uE/XXuFrrC9tFKbkFone7UULnxMu566tuNq0NTz/XI+u2xYyHDdhuixsT8XNtO4NbG86+5aM514kBkgAJFJ6Ak4GBaWfXrNFBW3mYFrbwSHKvQaX0yXjZYQyHiWo4wICYqMcTpSNxDsc4Xye7+hX4/E/79yPueeimpKtAm25KOqYBU6526toMXXrOP8YBYx9sN6UPTrstYqwUJwESKHcCrC/ClYCqSq0vhml9oEZDywgYD7H9ibrvPzdR/dTV+OqL538eLqI0vmOLvmk3pfi4hrpRe8jOCq0vqrUOqdPxDvHuS9i33ZSOOov1RRqkvEQCZUfAycA46qij5LHHHpPjjjuu7ID5E9wgPWokxAyGCQMMiI64AaHX3nuX7BTt0+oXzOG+7aZkuyfZFoYLrj62v5uSGhSYilWn/ErEjH7ROxynqU0Ewh0SIAESyECA9UUMUEON1hcwFgYYCnEDIn5u8qN3S+d1rTo+MQNUx8vopoSWhfXxVgW0OGzQY9tNCQaFWS0ai77FuykhKtQX21lfOFKnGAmUJwEnAwPTyx5//PFmkLd/DMZ1110nxxxzTN5ITnP8or58+aJQOo3SbkowHPBr/PmDZv2Gb8jvTAtDrPUhdm2EdGcOt39R8sx+fT62Sa00TJ8sz7yJbkqxrkn9W4x7wLlm2ZJiLqezTz3JFxp3SYAESKAwBIZ8fRHvpgTDwXZT+taJTya6LsUMih0yvCGL+gJeHIyLrTtrpXHsZHn6ue5ENyUYEevZTakwhZ6xkgAJiJOBccUVV8hll122G7599tlnt3PFcqIqqZuSbXmwW4x5sF2XBnRTujDWTenzOUhIn4axMTEoOmY0wEjAQGl/CwTOdWrWLH98kbCbUg7AMwgSIIGCESjJ+iKpm1KLtjxgnIO/m5I9V1uNJ3vcxbspff5Ie8J926fBbuzQuiHJSECLgx3rYFoi1JDo7NH6Qj+ksZuSO29KkgAJ5JaAk4GBlVmLxaGbkr9VIWi/ZdYPtAOTNjvnSWl0U8JMStZIQOuCbW246vaPyvFnPxkfHN2oZk6+tMhT4hgsCZAACUQgUEz1BZJRV90rs/bYkOiuBMPBP9Yh392UOjGbUtxosAYCZlOCIXHVdz4qxy3U+kKP327Xqbw91hcRih5FSYAECkjAycD4/e9/L1/5yldk/fr10ofPLHF3/fXX57yLlNfVJY0/+4VZ+O2b8W5KGBxtB0UPz6ab0marYbjtVu2m1KjdlDDV6gPPtg0wIKwxgW1bim5KiO0rR86Vv8mr4SKmbxIgARIYIgQGs77IBtkEHRz95IU/Se01YjcljGP42SNbjRHhH+tgjYpUsylBoa9MOEKeW/VKat14hQRIgARKhICTgXHmmWfK2WefLXPnztXlDfqDyEsXKV3Qb/QXLjE4F+cAqu2m5DcQ/C0OsX20RvR3U0K0pzuO/8iBygyCBEiABEqWwKDWF1lQwvoL2Tp8P3tbuylZ48DfNcl/DoaE7aaEsE8/lTMqZcuY/kiABIYmgX7rIMv0eZ4n27Ztky9+8YtSoVOf5t3V1GQVRafpphQb19A/piG2+NvVt39Ejot3U3pbuzKxm1JWSOmJBEiABCIRGPT6Igttu3urZW1bk3Tvqowt+mYGQ2u3VttN6TrtpvRRdlPKAiW9kAAJkEBKAqENDBgVRx99tNxxxx2ClbvzvhZGXZ30TJ9muin9PN5NyS70Zlsh0OqQrpvS5UfOlefYTSllIeAFEiABEsgHgUGvL7JMxNQvn5PS51fGs5tSSji8QAIkQAJZEghtYCDc9vZ2Oeecc2Tx4sUyefLkRFT5mKa2Qg2MDY//ysTxCXZTSrDmDgmQAAmUAoHBrC9KgQd1JAESIIFyIOBkYKSadnDKlCnlwIxpzJKAy7olYdcsyVIVeiMBEigQAdYXBQJfYtGyviixDKO6JJCBgJOBsWnTJkGlsUVX9kQf2127dklra6vcfPPN8pGPfCRDlLxMAiRAAiRQLgRYX5RLTjOdJEACJNBPwMnAOPfcc+UTn/iEvPzyy3LQQQdJc3Oz3HPPPbJgwYL+kLlHAiQQioDLFzxEwFafUJjpeZAJsL4YZOCMriwIsL4oi2wu6USGXsUHLRZbt241K3ljsHd3d7dccMEFctRRR8kjjzxS0jCoPAmQAAmQQO4IsL7IHUuGRAIkQAKlRCB0CwZmBWlqajJGBlZovfvuu016R48eLStXriyltJeFrvzKURbZzESSQFESYH1RlNmSUinWFynR8AIJkEBIAqENDIT/mc98RmbMmCFvvvmmrFixQk4++WR54okn5Nlnnw0ZfWbv+AJWX1+f2WMaH1Hko8hCpSjyhZItZr0rKyulRtdGcWEDuaqqKidZW7zqdFYzF4d4XfXOFJ9l0dJyfSavu11ft+5zu50Lc8LGHUbG+o0iizCiyBdKtpT1tvkWdsv6IntihSqXUeIt5jLN+mL3smfzmvXF7mxSnbHMUl1Pdz6KLMKNIl8oWcvDycC4+OKL5ZhjjjGreD/++OOmFePzn/+87L333jbcnG3xBayzszNSeFHko8hC6SjyhZItZr37dGndnp4eJ65YdR6/KFy7urrMxAZhCyQmQnDVO1NcUdITRbaQ5aSQcZcqM6s3xswNpmN9kT1tm0fZS/T7LJQsNChU3JniZX3RXz7sXiZm1l/QNooswosiH0W2kHGXut5R6gsnAwOZhRaM3t5e010Kq3rjxY2OBIYCAZduAhxoPRRynmnIFwHWF/kiy3ALTYD1RaFzgPEXK4HQg7yREHSLwkJ7kyZNkltuucUsuHfNNdcUaxqpFwmQAAmQQIEIsL4oEHhGSwIkQAIFJOBkYHzyk5+U6dOny5VXXmlURxP4vffeK6+99loBk8KoSYAESIAEio0A64tiyxHqQwIkQAL5JxDawMCg65deekkuuuiixOCTPfbYIzHQO/8qMwYSIAESIIFSIMD6ohRyiTqSAAmQQO4JhB44gUHXGPTxj3/8I6ENBlI99NBDgrEYdCRQzgRc+uOCF8dwlHOpGbppZ30xdPOWKYtOgPVFdIYMoXgJhDYwkJRvfOMbcvjhh8u0adPM1Ju33XabHHDAAXLCCScUb0qpGQmQAAmQwKATYH0x6MgZIQmQAAkUnICTgfGRj3xEDjroIHn44YfN1Jsnnnii7LvvvgVPDBUgARIgARIoLgKsL4orP6gNCZAACQwGAScDA4pNmDBBDj30ULMmwKZNmwS//fbbT7CiNx0JkAAJkAAJWAKsLywJbkmABEigPAg4GRiXX3653HTTTcbIwArF1l177bUyb948e8gtCZAACZBAmRNgfVHmBYDJJwESKEsCTgbG7bffbqakHTNmTFlCY6JJgARIgASyI8D6IjtO9EUCJEACQ4lA6GlqkfiWlhbp6OgYShyYFhIgARIggTwQYH2RB6gMkgRIgASKnIBTC8aNN94ohxxyiBx77LEyduzYRBJPP/10mTFjRuKYOyRAAiRAAuVNgPVFeec/U08CJFCeBJwMjAsvvFAOPPBAmTRpklRW9jeC+MdjlCdOppoESIAESMBPgPWFnwb3SYAESKA8CDgZGK+//rqsWbNGamtry4MSU0kCJEACJOBEgPWFEzYKkQAJkEBJE+hvfgiRjKOOOkoee+yxEBL0SgIkQAIkUI4EWF+UY64zzSRAAuVOwMnA6OzslOOPP96Mv9h///3F/h555JFy58n0kwAJkAAJ+AiwvvDB4C4JkAAJlAkBpy5SV1xxhVx22WW7Idpnn312O8cTJEACJEAC5UuA9UX55j1TTgIkUL4EnAyMmTNnhiL24IMPyvTp0+Wggw4ycsuWLZOlS5dKb2+vLFy4UCZOnBgqPHomARIgARIoDQKsL0ojn6glCZAACeSSgFMXqWwV6Orqkrvuukv++Mc/GmMCcmguX7JkiZxxxhmyYMECuf/++7MNjv5IgARIgASGKAHWF0M0Y5ksEiCBsiTg1IKRLant27fLrFmzZNSoUQmRDRs2yNSpU6WxsdH8du7caYyP6uqYKjjGz7qamhqJOv1tFPkoskhDFPlCyRaD3nvscbMtAqG2K1d+VqLIhorM5zlKXiGYKPKFki2k3oWMOwrvUtYbuufTsb7gcyBs+bL3YpRnfhTZsPpa/1Zvexx2G0W+ULJIYznGHSXNhWQWVW/onlcDY/To0YLfG2+8gbiMa2trM4aFPW5oaBBULCNGjDCnnnrqKXn00UftZdO1atGiRYljl53Jkye7iBmZKLIIIIp8oWSpt8n6UH9R8oq8Q6FOeI7CvFCyxZDXHR0dCYbFtMP6gvVF2PIY5T4uhnsxbHqt/yjpLpRsIXkXMu4ovItB7yj1RV4NDHsz+Lf19fWCpnDrenp6pKmpyR7KvHnzzC9xQndWrFjhPwy9v3LlytAyViCKLMKIIl8oWeptcz/7bZS8Iu/sOft9RmFeKNliyOvm5mY/xqLeZ32RffaUc5nOntJAn4ViFiVepCCKfKFkC6l3IeOOwrsY9I5SX+R1DAbgJLuWlhazSJ/neaYrFLa2e1SyXx6TAAmQAAmULwHWF+Wb90w5CZBAaRMY9BaM4cOHm3EZt956q7S3t8v8+fNLmyC1JwESIAESyAsB1hd5wcpASYAESCDvBAbFwPjwhz88ICFz5syR2bNnS2VlpfkNuMgDEiABEiCBsiXA+qJss54JJwESGEIEBsXACOLFblFBVHiOBEiABEggmQDri2QiPCYBEiCB4iYw6GMwihsHtSMBEiABEiABEiABEiABEohCgAZGFHqUJQESIAESIAESIAESIAESGECABsYAHDwgARIgARIgARIgARIgARKIQoAGRhR6lCUBEiABEiABEiABEiABEhhAgAbGABw8IAESIAESIAESIAESIAESiEKABkYUepQlARIgARIgARIgARIgARIYQIAGxgAcPCABEiABEiABEiABEiABEohCgAZGFHqUJQESIAESIAESIAESIAESGECABsYAHDwgARIgARIgARIgARIgARKIQoAGRhR6lCUBEiABEiABEiABEiABEhhAgAbGABw8IAESIAESIAESIAESIAESiEKABkYUepQlARIgARIgARIgARIgARIYQIAGxgAcPCABEiABEiABEiABEiABEohCgAZGFHqUJQESIAESIAESIAESIAESGECABsYAHDwgARIgARIgARIgARIgARKIQoAGRhR6lCUBEiABEiABEiABEiABEhhAoHrAUREeeJ4n9fX1kTSLIh9FFkpHkS+ULPUOX9yi5BV5h+dNZuGZRS2j4WMcfAnWF27Mo5aNKPKFkgWpQsUdJV7qPfhlPEp+RZEt5byG7kVvYFRUVEhnZ6dbiYpLRZGPIovoo8gXSpZ6xwtOiE2UvCLvEKB9XqMwL5RsMeR1c3Ozj+LQ2mV94ZafUe6HYijTbqlm/RyWW6mWE5bRsDndf29EqS/YRSo8d0qQAAmQAAmQAAmQAAmQAAmkIEADIwUYniYBEiABEiABEiABEiABEghPgAZGeGaUIAESIAESIAESIAESIAESSEGABkYKMDxNAiRAAiRAAiRAAiRAAiQQngANjPDMKEECJEACJEACJEACJEACJJCCAA2MFGB4mgRIgARIgARIgARIgARIIDwBGhjhmVGCBEiABEiABEiABEiABEggBQEaGCnA8DQJkAAJkAAJkAAJkAAJkEB4AjQwwjOjBAmQAAmQAAmQAAmQAAmQQAoCNDBSgOFpEiABEiABEiABEiABEiCB8ARoYIRnRgkSIAESIAESIAESIAESIIEUBGhgpADD0yRAAiRAAiRAAiRAAiRAAuEJ0MAIz4wSJEACJEACJEACJEACJEACKQjQwEgBhqdJgARIgARIgARIgARIgATCE6CBEZ4ZJUiABEiABEiABEiABEiABFIQoIGRAgxPkwAJkAAJkAAJkAAJkAAJhCdAAyM8M0qQAAmQAAmQAAmQAAmQAAmkIEADIwUYniYBEiABEiABEiABEiABEghPoDq8SHSJZcuWydKlS6W3t1cWLlwoEydOjB4oQyABEiABEhhyBFhfDLksZYJIgATKgMCgt2B0dnbKkiVL5IwzzpAFCxbI/fffXwaYmUQSIAESIIGwBFhfhCVG/yRAAiRQHAQGvQVjw4YNMnXqVGlsbDS/nTt3mpaM6uqYKr/+9a8FP+umT58un/3sZ+2h03aPPfZwkoNQFNmo8lHijiJLvUEgnCPvcLzgm8wGl5nl3dHRET7iAkmwvsgevM3f7CX6fUaRRShR5AslS7378z/bvSh5VUjehYy7VJlZvaPUFxWeumwLVy78Pf/887JixQqZP3++Ce66666Ts846S0aMGGGOu7q6BF+trIPh0draag8DtyNHjpS+vj7Ztm1b4PV0J2tra2X06NGyfv36dN5SXkP3rs2bN0tPT09KP6kuDB8+XCorK6WtrS2Vl5TnwWX8+PGydu3alH7SXZgwYYKJF7zDuubmZqmpqZEtW7aEFTXpnTRpkqxevTq0LATGjRsnKPAwTMO6pqYmqa+vN/kVVhb+YRivWrXKRVTGjBljyvX27dtDyzc0NAiYb9y4MbQsBKZMmWLKCe6RsG7UqFGmbLs8ZOrq6gT3Jl4SXRzKydtvv20+QISVj/JMQNlGfkV5JuCZ1d3dHVZtGYxnAsrT2LFjQ+tWCAHWF/3UB6Ns9Mc2cI/1xUAe2RyxvsiGUr8f1hdDr74Y9BYMvOD5X2rxYo4XP+tQyPDzu0wvVrCR8PK0a9cuv1hW+1bGbrMSSvLkGncUvSsqKowWrnpHiRuy+LnEDTk4F1nIRdEb+eSqN+KGs2HEjrL/L7Te4A3dwzro7crMsnLNa+gKWRf5KHFXVVUZTC7xWr5R9Ma97RJ31GeC1b2Ytqwv+nMjyjMkatmIEneUZwhk4VzuB8hF0TvKMwRxw9kwYkfZ/xdab/CG7mFdlLy2rFzzGrpC1kU+StysL1KXkkEfg9HS0iJr1qwxNz6+QqNA2u5RqdXkFRIgARIggXIjwPqi3HKc6SUBEhgqBAa9BQPNvLNmzZJbb71V2tvbE12lhgpQpoMESIAESCA3BFhf5IYjQyEBEiCBwSYw6AYGEjhnzhyZPXu26Y+PMQh0JEACJEACJBBEgPVFEBWeIwESIIHiJlAQAwNI2C2quAsGtSMBEiCBYiHA+qJYcoJ6kAAJkEB2BNh8kB0n+iIBEiABEiABEiABEiABEsiGgA6yLnn3wAMPeLp2hlM63nrrLe+aa65xkoXQ1772NW/lypVO8o888oj3i1/8wklWp+/0/uu//stJFkLf+c53vNdee81J/ne/+533f//3f06yOpWwd8kllzjJQkjH7ngvvPCCk/wf//hH784773SS1dnOvC984QueTj3qJI94Eb+LQ3pvueUWF1EjA97g7uKQz0uXLnURNeUL5czVoXzrFLdO4rivcH+5ONzPuK9dHZ4neK64ODzH8DxzcTrbnnf55Ze7iJaVDOuL8NnN+iIcM9YX4XjBN95HWF+E41bs9cWQaMHAVLe9vb3Z2FO7+cH0ZP5pc3fzkOEEZLVIZPAVfBk6u+qNOKPojTn6XaahQ0qgs8sc/5DNhd4u09AhbsihrLi4qHoj3kLojbRGKaNR9Eb5ci0nUfUu5L0F3lHurUKVUZf7ohRlwNf1ucv6InyOs74IzyzKczdKPQdNWV+Ey6+o7wZDub4YEgZGuOJA3yRAAiRAAiRAAiRAAiRAAvkiQAMjX2QZLgmQAAmQAAmQAAmQAAmUIYGqr6or9XSPGjVKpkyZMmBF8GzTVFtbK3vssYeMGTMmW5EB/rAQ1KRJk6SmpmbA+WwORo4cKZMnT5Zhw4Zl432AH8yqAr3HjRs34Hy2BxMmTDB6I/1hHfQFb8xRH9Zh1cs999xTxo8fH1bU+IcceGOF37CuublZpk6dKiNGjAgrKlgJF7zBza6KGyaQsWPHmrxuaGgII2b8NjY2Gr1Rzl0c0oxy6jIl9OjRo43e0CGsQx4hboTh4nBvQG+XGYRwb0V5JkBv5JmLg874uTwTUDaht8szAfFFeSa4pLUUZVhfhM811hfhmLG+YH2RbYkZyvVFhfYfcxtAkC09+iMBEiABEiABEiABEiABEigbAuwiVTZZzYSSAAmQAAmQAAmQAAmQQP4J0MDIP2PGQAIkQAIkQAIkQAIkQAJlQ6BgK3m7En7wwQdl+vTpctBBB5kgli1bJjpPv5l2cOHChTJx4kTBuV/+8peJKM455xzT737z5s3yk5/8RBYtWmSubdq0SXROdNm+fbscccQR8q53vUva29tF5xYWnX9f9t9/fznqqKOMX0y1d9ttt8lZZ50l/v7o//jHP2TFihXy4Q9/OKU+uJDsD+eS9dH59+Wxxx6TnTt3Gn1sGoP8PfHEE2Y6uRkzZsjs2bMRnDz88MOCMNB/Gyzq6uoMl2S9b7/9dmlrazMyBxxwgBx77LGGWTJHePDr/cYbb8jPf/5zI2f/zj77bNNH/n/+53/sKTn66KNN/iTrjenYdH0CaW1tNXk4b948I/Pb3/7WxIMxHaeccoqZShZpgY4Yb3HccceZOJL9oY//b37zGyOLgCD/qU99yuQ9OKA/O3SxYwCS9UFeJ+sNHf/2t78Jxoocfvjhss8++5jygXICffbdd18Jqzd003Uo5Hvf+57oWho4DCyjTz/9tMk/v97Q8amnnjLnkVfQybpXX31V/vrXv8ppp51mrieXHZTrIL2D8t+GGXTvYOxGUH5YGbvFPfLss8/KK6+8YtifeOKJhiN0TGZqZfzbtWvXChigfMydO1f222+/QE5BY2iCOKXSxx8n9lPd87iWXGZwLtk9//zz8oc//MGMufi3f/s3My4q1b2cLLt69Wpzz6PczZkzR5DHcOnyyIaBKVMff/xx0fnjTRn/6Ec/aspB0D3qOn7HxlWqW9YXYsoE64tTzLg91hesL/AsY30xWgalvsAYjFJwnZ2d3g9+8APv0ksv9Z577jmjsr6Ie1//+tc9fZHyVq1alVikBYtsaaVvFkWzC6O99NJLxu9VV12VSO5NN91kFsnr6Ogwi+0hnCVLlnj6Qudp5e3dc889nr5gm8W+sADMRRdd5MGvdfqwMovd/ehHP0qrT7I/eA7S54YbbvD05crTlw0Trs5nHejv29/+tvGH69dff72nL0GevhgmFr/D4iv6shmoN8L+5je/mWCjL2FeKo7JeiM+8MQP+t98880m3S+//LL305/+NHEtld5YoE9fiIzMHXfc4S1fvtx78803ve9///se9FADx9MXArPQ2F/+8hfjD3n5pz/9KdAfPIAF8gQ66dzhRq/77rvPyEIv5CFcEO9kvbEY3be+9S0TDsJE2UI5QJnQF2QTDsoMFlHLVm8jpH9IL8qudcllFPoF6a1GiaeGnQem//u//+vpg9EEgbIK/bDwIFxQ2QnSOyj/TQDxv2S9cBoLkyXnh1/G7utLjIcyA4ct+IJjEFMrY7fIP6QH/pE2lAm4IH2sjH8bxClIH7+M3Q+653EtqMxYGbvFvYNF+Xbs2OHpBwtzb+FaUH5YGf8WzxU1rMw9D05bt241+8n3qF/G7qshZxaORNnAIo4/+9nPTDkJuketTLlsWV/013OsL2L1Cso+6wvWFygHrC8Gp74omS5S+tIhs2bNkve9732Jj2loZcBsL2hRwMwr+PKPr5b4KqgVvvnyi3Nw+EL4mc98JiGLHXxVhnxTU5P5oq4vjvLPf/7TfH3HLBD4Wq0vksYfvqxjtL/fQe6kk05KnEqlT7I/CATpc/755wu+NKqxJJjdCV+Og/ydeeaZxh+uQ0+kA60t0AXpXbNmjWAWHZxP1htfidHC8fvf/96kFWFkqzf8YqYafDnVlzL52Mc+ZtIO3pjpBy0guppwSr0xgxJ001XIjW6QQavIzJkzzZdu5C++xs7Vr9doTYJDfGg5CPIHPVAuXnzxRfMlH8fTpk2T+fPnJ2T1hc3sB3FM1hstIp/+9KdNWqAbZPVZJCeccIIcfPDBsmXLFnMO+RmkT5DeiBwtEP/yL/8i/hmkkstokN5ID/IGDNACgJYKzIQFh5Y425KC46CyE6R3UP5D3rpkvXA+VbqsjN3qigJ9BYMAAA5wSURBVOOm1QGtGP/6r/8q73jHO0w5DmJqZewWrYCYAUmNTvP75Cc/aS4F6WNl7DYVpyB9rIx/G3TP43pQmfHLYR/PDOiNvMVMdHju6At+YH4ky2IxLTWozHMF9zueL7g/MuWRDQd6o8UD9yXKF55Vqe5RK1MuW9YX/TnN+iJWr7C+iPUYQMlgfbF7vcr6ov+drv/pEW2vZAwMdHOx3QdskvHi6e+uhEoeFQsqWTh0mdEvhOZFAS+IeDG0Di8C/ukvEQ5k0S3p0UcfNUbKn//8Z/NCiW4y6HqV7A499NBEXLiWSp9kf/CbrA/OwaF7iX6JNC9qeCAG+bPdHdA9Amnea6+9jCz+tCXBvKRA3yC98UKDrlN4mdGv0vKrX/0qlN6IA1zw8minqcULFQwddGfSr/Dy+uuvB+qN6WnxAnX//fcbgwIvZH5mNv/QBQZdlBDO3//+d3n/+98f6A/NnHjhxg/p0q/5hgfCQbh4CUf3Krggjsl64yUXxheM1Lvvvtu8wNuyBGNNW0PMdRgi2eoNAwHpOOyww4we9s+Ga8sojpP1RvpsUy7Sd+2115rVwLVFx0wdC95+l1x2cC1Z76D894eRrBdesoPywy9j9xGXtlwYHZEXeFGGcZSKqZXDFsactniY8oEX5zvvvNNcDtLHXPD9peIUpI9PLLEbdM/jYlCZSQjFd9BdEy/20Bn5AiMU8cIF5UdczGzABveAtj4YgxVd3sAhUx7ZMKD3M888Y55V6AIDDtYl36P2fLlsWV/05zTri9h7Ae4P1hf95SLo+cT6YmD91U+rf4/1RT+LTHslNwbDnyC86OEFyDp8EYQR4W+pQGsAxhHg67jf4SUbL5jWYR8PH3xNR59x9KnGV3T0wc7WpdInW3n4Q0sEfuivjxdT9EMPchhjsn79esHXKb/7+Mc/bl44fvzjH8vixYv9l8w+bg784PDCj5fWvffeO5Cj8RTwBzYYe2Hd8ccfb3fNF38YLmj9SXYwnNCiAoPoySefFLwU4aXa5iHyDy+jcHjZfOihh+Tcc89NGA3J/vDia9N44IEHmq/8eEFDONrFxvQxRBpTuSC94V+7M5kvwhiXYx0MgfPOO8+00qClJlu9Mf4FLJBeGLV4CcXX/aAyCi5+vdEahRaUk08+2RhcaPnBVxZwwdggvESifOLFFOUkqOwk6400J+e/NcKQ1iC9cO8k54fl4t/ihRljXmyrIvRDaxLurSCmfll8wce6F5CH0+5SpjUulT7JssmcwCSVPn5Z7COfXe95xIExXnjRxxotMDjsGJGg/EiOG/crZJG3MGjQ8gi5dHlkw0BZBS+MW3nnO99pWj7tteR71J4v522q53M2ZYz1BesL1hex9wXWF6wvsq1HSqYFIyhBtksBXi7syxi+eN5yyy0J4wFfkJO/9CIsfCHHSw1aLSCPQZn46q/jO8wLMAZJYxA4uj9k64L08beSpAsHrRU33nhjQm8c25ftZDkYF/jKCePCho+Xerz4wiE9MJaCHF6kdCyEuYTuTEhzGL3t11k7cBoBwXBAVxG4VLxxDZU0XszhwB4vZ+jahpYDOHSPQV7hywpakfBCjxcuuCB/yJ+77rrLXEerA15kwQ0DZPHihhacdC5Zb3CAYfee9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alt="plot of chunk unnamed-chunk-14"/> </p>
<p>And as before, check out <a href="http://www.cookbook-r.com/Graphs">The R cookbook</a> and the <a href="http://docs.ggplot2.org/0.9.3.1/geom_bar.html">ggplot2 documentation</a> for more help on getting the bargraph of your dreams.</p>
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In the second of the series, this post will go over barplots in ggplot2. Our data is from mtcars as before.
library(ggplot2)
library(gridExtra)
mtc<-mtcars
#preview data
head(mtc)
To introduce the barplot, I show the basic default bargraph that you would get if you indicate an x-variable and use the default geom_bar layer, which is geom_bar(stat="bin"). You could just write geom_bar() and it would also work. Remember that in ggplot we add layers to make plots, so first we specify the data we want to use and then we specify that we want to plot it as a bar graph (instead of points or lines). The basic plot gives a count of the number in each group of the x-variable (gears).
ggplot(mtc, aes(x = factor(gear))) + geom_bar(stat="bin")
Aggregate data for barplot
Instead of this, we would like to graphically show the mean weight of the cars by the number of gears. There are a number of ways to make this graph. The first way is that we summarize the data beforehand, and use the summarized data in the ggplot statement. I show two ways to summarize here, with two different results of how the data looks when summarized, using aggregate and tapply. Using the tapply() inside of a data.frame() statement, we can put together a new dataframe of the mean weight by gear.
#using aggregate
ag.mtc<-aggregate(mtc$wt, by=list(mtc$gear), FUN=mean)
ag.mtc
#using tapply
summary.mtc <- data.frame(
gear=levels(as.factor(mtc$gear)),
meanwt=tapply(mtc$wt, mtc$gear, mean))
summary.mtc
Now we can use the summarized dataframe in a ggplot statement and use the geom_bar layer to plot it.
In the first argument we indicate that the dataframe is summary.mtc, next we indicate in the aes() statement that the x-axis is gear and the y-axis is meanwt, and finally we add the geom_bar() layer. We use the geom_bar(stat="identity") to indicate that we want the y-values to be exactly the values in the dataset. Remember, by default the stat is set to stat="bin" which is a count of the x-axis variable, so it's important to change it when we have summarized our data.
ggplot(summary.mtc, aes(x = factor(gear), y = meanwt)) + geom_bar(stat = "identity")
Another option for quick graphing is to use the built-in __stat_summary()__ layer. Instead of summarizing data, we use the original dataset and indicate that the x-axis is gear and the y-axis is just weight. However, we use __stat_summary()__ to calculate the mean of the y for each x with the following command:
ggplot(mtc,aes(x=factor(gear), y=wt)) + stat_summary(fun.y=mean, geom="bar")
There are reasons why we would want to use the first or second method. For the first, summarizing our data the way we want it gives us validity that we are sure that we are doing what we want to be doing and gives us more flexibility in case we want to use that summarized data in a later portion of our analysis (like in a table). Using the stat_summary() layer is faster and less code to write.
For now, we continue with the second method, but later on we'll come back to the summarizing method.
Horizontal bars, colors, width of bars
We can make these plots look more presentable with a variety of options. First, we rotate the bars so they are horizontal. Second, we change the colors of the bars. Finally, we change the width of the bars.
#1. horizontal bars
p1<-ggplot(mtc,aes(x=factor(gear),y=wt)) + stat_summary(fun.y=mean,geom="bar") +
coord_flip()
#2. change colors of bars
p2<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(gear))) + stat_summary(fun.y=mean,geom="bar") +
scale_fill_manual(values=c("purple", "blue", "darkgreen"))
#3. change width of bars
p3<-ggplot(mtc,aes(x=factor(gear),y=wt)) + stat_summary(fun.y=mean,geom="bar", aes(width=0.5))
grid.arrange(p1, p2, p3, nrow=1)
For the colors, I color the bars by the gear variable so it's a different color for each bar, and then indicate manually the colors I want. You could color them all the same way using fill="blue" for example, or you can keep the default colors when you fill by gear by leaving off scale_fill_manual altogether.
You can also use scale_fill_brewer() to fill the bars with a scale of one color (default is blue). This R cookbook site is particularly useful for understanding how to get the exact colors you want:http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
Note that if you are summarizing the data yourself, you change the width this way (graphs not shown since they look the same):
ggplot(summary.mtc, aes(x = factor(gear), y = meanwt)) + geom_bar(stat = "identity", width=0.2)
Split and color by another variable
Now, let's make the graph more complicated by adding a third variable. We can do this in three ways: bars next to each other, bars stacked, or using 'faceting' which is making multiple graphs at once. We would like to know the mean weight by both gear and engine type (vs). Stacking is a particularly bad idea in this example, but I'll show it for completeness.
#1. next to each other
p1<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean,position=position_dodge(),geom="bar")
#2. stacked
p2<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean,position="stack",geom="bar")
#3. with facets
p3<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean, geom="bar") +
facet_wrap(~vs)
grid.arrange(p1, p2, p3, nrow=1)
You can also indicate the width of the spread between the bars in the first plot using
position=position_dodge(width=.5) and play around with the width number.
You can change the order of the stacking by re-ordering the levels of the fill variable. Here is a prior blog post I had about [how to reorder factors](http://rforpublichealth.blogspot.com/2012/11/data-types-part-3-factors.html).
mtc$vs2<-factor(mtc$vs, levels = c(1,0))
ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs2)), color=factor(vs2)) +
stat_summary(fun.y=mean,position="stack",geom="bar")
Note that if you are using summarized data, just indicate the position in the geom_bar() statement.
Faceting is a really nice feature in ggplot2 and deserves more space on this blog, but for now more information on how faceting works can be found here: http://www.cookbook-r.com/Graphs/Facets_(ggplot2)/
Add text to the bars, label axes, and label legend
Next, I would like to add the value in text to the top of each bar. This is a case in which you definitely want to summarize the data first - it is much easier and cleaner that way. I use the aggregate() function to summarize the data by both gear and type of engine.
ag.mtc<-aggregate(mtc$wt, by=list(mtc$gear,mtc$vs), FUN=mean)
colnames(ag.mtc)<-c("gear","vs","meanwt")
ag.mtc
Now, I use the geom_bar() layer as in the first example, and the geom_text() layer to add the text. In order to move the text to the top of each bar, I use the position_dodge and vjust options to move the text around.
The first plot shows the basic output, but we see that the first number is cutoff by the top of the y-axis and we need to round the text. We can fix it by adjusting the range of the y-axis exactly how we did in a scatterplot, by adding a scale_y_continuous layer to the plot. I also change the x-axis label using scale_x_discrete, change the text to be black so it's readable, and label the legend. Notice here, it is the scale_fill_discrete layer.
Go back to the [cheatsheet for scatterplots](http://rforpublichealth.blogspot.com/2013/11/ggplot2-cheatsheet-for-scatterplots.html) if you want to go over how to customize axes and legends.
#1. basic
g1<-ggplot(ag.mtc, aes(x = factor(gear), y = meanwt, fill=factor(vs),color=factor(vs))) +
geom_bar(stat = "identity", position=position_dodge()) +
geom_text(aes(y=meanwt, ymax=meanwt, label=meanwt),position= position_dodge(width=0.9), vjust=-.5)
#2. fixing the yaxis problem, changing the color of text, legend labels, and rounding to 2 decimals
g2<-ggplot(ag.mtc, aes(x = factor(gear), y = meanwt, fill=factor(vs))) +
geom_bar(stat = "identity", position=position_dodge()) +
geom_text(aes(y=meanwt, ymax=meanwt, label=round(meanwt,2)), position= position_dodge(width=0.9), vjust=-.5, color="black") +
scale_y_continuous("Mean Weight",limits=c(0,4.5),breaks=seq(0, 4.5, .5)) +
scale_x_discrete("Number of Gears") +
scale_fill_discrete(name ="Engine", labels=c("V-engine", "Straight engine"))
grid.arrange(g1, g2, nrow=1)
Add error bars or best fit line
Again there are two ways to do this, but I prefer summarizing the data first and then adding in error bars. I use tapply to get the mean and SD of the weight by gear, then I add a geom_bar layer and a geom_errorbar layer, where I indicate the range of the error bar using ymin and ymax in the aes() statement.
summary.mtc2 <- data.frame(
gear=levels(as.factor(mtc$gear)),
meanwt=tapply(mtc$wt, mtc$gear, mean),
sd=tapply(mtc$wt, mtc$gear, sd))
summary.mtc2
ggplot(summary.mtc2, aes(x = factor(gear), y = meanwt)) +
geom_bar(stat = "identity", position="dodge", fill="lightblue") +
geom_errorbar(aes(ymin=meanwt-sd, ymax=meanwt+sd), width=.3, color="darkblue")
And if you were really cool and wanted to add a linear fit to the barplot, you can do it in two ways. You can evaluate the linear model yourself, and then use geom_abline() with an intercept and slope indicated. Or you can take advantage of the stat_summary() layer to summarize the data and the geom_smooth() layer to add a linear model instantly.
#summarize data
summary.mtc3 <- data.frame(
hp=levels(as.factor(mtc$hp)),
meanmpg=tapply(mtc$mpg, mtc$hp, mean))
#run a model
l<-summary(lm(meanmpg~as.numeric(hp), data=summary.mtc3))
#manually entering the intercept and slope
f1<-ggplot(summary.mtc3, aes(x = factor(hp), y = meanmpg)) +
geom_bar(stat = "identity", fill="darkblue")+
geom_abline(aes(intercept=l$coef[1,1], slope=l$coef[2,1]), color="red", size=1.5)
#using stat_smooth to fit the line for you
f2<-ggplot(summary.mtc3, aes(x = factor(hp), y = meanmpg)) +
geom_bar(stat = "identity", fill="darkblue")+
stat_smooth(aes(group=1),method="lm", se=FALSE, color="orange", size=1.5)
grid.arrange(f1, f2, nrow=1)
And as before, check out [The R cookbook](http://www.cookbook-r.com/Graphs) and the [ggplot2 documentation](http://docs.ggplot2.org/0.9.3.1/geom_bar.html) for more help on getting the bargraph of your dreams. </div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com1tag:blogger.com,1999:blog-2550386066385582083.post-35312292937769950092013-11-13T11:56:00.000-08:002016-10-13T07:52:59.382-07:00ggplot2: Cheatsheet for Scatterplots<div height="1050">
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<p>The graphics package ggplot2 is powerful, aesthetically pleasing, and (after a short learning curve to understand the syntax) easy to use. I have made some pretty cool plots with it, but on the whole I find myself making a lot of the same ones, since doing something over and over again is generally how research goes. Since I constantly forget the options that I need to customize my plots, this next series of posts will serve as cheatsheets for scatterplots, barplots, and density plots. We start with scatterplots.</p>
<h3>Quick Intro to ggplot2</h3>
<p>The way ggplot2 works is by layering components of your plot on top of each other. You start with the basic of the data you want your plot to include (x and y variables), and then layer on top the kind of plotting colors/symbols you want, the look of the x- and y-axes, the background color, etc. You can also easily add regression lines and summary statistics.</p>
<p>For great reference guides, use the <a href="http://docs.ggplot2.org/0.9.2.1/index.html">ggplot2 documentation</a> or the <a href="http://www.cookbook-r.com/Graphs">R Graphs Cookbook</a>.</p>
<p>In this post, we focus only on scatterplots with a continuous x and continuous y. We are going to use the mtcars data that is available through R.</p>
<pre><code class="r">library(ggplot2)
library(gridExtra)
mtc <- mtcars
</code></pre>
<p>Here's the basic syntax of a scatterplot. We give it a dataframe, mtc, and then in the <strong>aes()</strong> statement, we give it an x-variable and a y-variable to plot. I save it as a ggplot object called p1, because we are going to use this as the base and then layer everything else on top:</p>
<pre><code class="r"># Basic scatterplot
p1 <- ggplot(mtc, aes(x = hp, y = mpg))
</code></pre>
<p>Now for the plot to print, we need to specify the next layer, which is how the symbols should look - do we want points or lines, what color, how big. Let's start with points:</p>
<pre><code class="r"># Print plot with default points
p1 + geom_point()
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-3"/> </p>
<p>That's the bare bones of it. Now we have fun with adding layers. For each of the examples, I'm going to use the <em>grid.arrange()</em> function in the <strong>gridExtra</strong> package to create multiple graphs in one panel to save space. </p>
<h3>>> Change color of points</h3>
<p>We start with options for colors just by adding how we want to color our points in the geom_point() layer:</p>
<pre><code class="r">p2 <- p1 + geom_point(color="red") #set one color for all points
p3 <- p1 + geom_point(aes(color = wt)) #set color scale by a continuous variable
p4 <- p1 + geom_point(aes(color=factor(am))) #set color scale by a factor variable
grid.arrange(p2, p3, p4, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>We can also change the default colors that are given by ggplot2 like this:</p>
<pre><code class="r">#Change default colors in color scale
p1 + geom_point(aes(color=factor(am))) + scale_color_manual(values = c("orange", "purple"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-5"/> </p>
<h3>>> Change shape or size of points</h3>
<p>We're sticking with the basic p1 plot, but now changing the shape and size of the points:</p>
<pre><code class="r">p2 <- p1 + geom_point(size = 5) #increase all points to size 5
p3 <- p1 + geom_point(aes(size = wt)) #set point size by continuous variable
p4 <- p1 + geom_point(aes(shape = factor(am))) #set point shape by factor variable
grid.arrange(p2, p3, p4, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-6"/> </p>
<p>Again, if we want to change the default shapes we can:</p>
<pre><code class="r">p1 + geom_point(aes(shape = factor(am))) + scale_shape_manual(values=c(0,2))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-7"/> </p>
<ul>
<li>More options for <a href="http://docs.ggplot2.org/0.9.3.1/scale_manual.html">color and shape manual changes are here</a></li>
<li>All shape and line types can be found here: <strong><a href="http://www.cookbook-r.com/Graphs/Shapes_and_line_types">http://www.cookbook-r.com/Graphs/Shapes_and_line_types</a></strong></li>
</ul>
<h3>>> Add lines to scatterplot</h3>
<pre><code class="r">p2 <- p1 + geom_point(color="blue") + geom_line() #connect points with line
p3 <- p1 + geom_point(color="red") + geom_smooth(method = "lm", se = TRUE) #add regression line
p4 <- p1 + geom_point() + geom_vline(xintercept = 100, color="red") #add vertical line
grid.arrange(p2, p3, p4, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-8"/> </p>
<p>You can also take out the points, and just create a line plot, and change size and color as before:</p>
<pre><code class="r">ggplot(mtc, aes(x = wt, y = qsec)) + geom_line(size=2, aes(color=factor(vs)))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-9"/> </p>
<ul>
<li>More help on scatterplots can be found here: <a href="http://www.cookbook-r.com/Graphs/Scatterplots_(ggplot2)">http://www.cookbook-r.com/Graphs/Scatterplots_(ggplot2)</a></li>
</ul>
<h3>>> Change axis labels</h3>
<p>There are a few ways to do this. If you only want to quickly add labels you can use the <em>labs()</em> layer. If you want to change the font size and style of the label, then you need to use the <em>theme()</em> layer. More on this at the end of this post. If you want to change around the limits of the axis, and exactly where the breaks are, you use the <em>scale_x_continuous</em> (and <em>scale_y_continuous</em> for the y-axis).</p>
<pre><code class="r">p2 <- ggplot(mtc, aes(x = hp, y = mpg)) + geom_point()
p3 <- p2 + labs(x="Horsepower",
y = "Miles per Gallon") #label all axes at once
p4 <- p2 + theme(axis.title.x = element_text(face="bold", size=20)) +
labs(x="Horsepower") #label and change font size
p5 <- p2 + scale_x_continuous("Horsepower",
limits=c(0,400),
breaks=seq(0, 400, 50)) #adjust axis limits and breaks
grid.arrange(p3, p4, p5, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-10"/> </p>
<ul>
<li>More axis options can be found here: <a href="http://www.cookbook-r.com/Graphs/Axes_(ggplot2)">http://www.cookbook-r.com/Graphs/Axes_(ggplot2)</a></li>
</ul>
<h3>>> Change legend options</h3>
<p>We start off by creating a new ggplot base object, g1, which colors the points by a factor variable. Then we show three basic options to modify the legend.</p>
<pre><code class="r">g1<-ggplot(mtc, aes(x = hp, y = mpg)) + geom_point(aes(color=factor(vs)))
g2 <- g1 + theme(legend.position=c(1,1),legend.justification=c(1,1)) #move legend inside
g3 <- g1 + theme(legend.position = "bottom") #move legend bottom
g4 <- g1 + scale_color_discrete(name ="Engine",
labels=c("V-engine", "Straight engine")) #change labels
grid.arrange(g2, g3, g4, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-11"/> </p>
<p>If we had changed the shape of the points, we would use <em>scale_shape_discrete()</em> with the same options. We can also remove the entire legend altogether by using <strong>theme(legend.position=“none”)</strong> </p>
<p>Next we customize a legend when the scale is continuous: </p>
<pre><code class="r">g5<-ggplot(mtc, aes(x = hp, y = mpg)) + geom_point(size=2, aes(color = wt))
g5 + scale_color_continuous(name="Weight", #name of legend
breaks = with(mtc, c(min(wt), mean(wt), max(wt))), #choose breaks of variable
labels = c("Light", "Medium", "Heavy"), #label
low = "pink", #color of lowest value
high = "red") #color of highest value
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-12"/> </p>
<ul>
<li>More legend options can be found here: <a href="http://www.cookbook-r.com/Graphs/Legends_(ggplot2)">http://www.cookbook-r.com/Graphs/Legends_(ggplot2)</a></li>
</ul>
<h3>>> Change background color and style</h3>
<p>The look of the plot in terms of the background colors and style is the <strong>theme()</strong>. I personally don't like the look of the default gray so here are some quick ways to change it. I often the theme_bw() layer, which gets rid of the gray. </p>
<ul>
<li>All of the theme options <a href="http://docs.ggplot2.org/0.9.3/theme.html">can be found here</a>.</li>
</ul>
<pre><code class="r">g2<- ggplot(mtc, aes(x = hp, y = mpg)) + geom_point()
#Completely clear all lines except axis lines and make background white
t1<-theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(size=.4)
)
#Use theme to change axis label style
t2<-theme(
axis.title.x = element_text(face="bold", color="black", size=10),
axis.title.y = element_text(face="bold", color="black", size=10),
plot.title = element_text(face="bold", color = "black", size=12)
)
g3 <- g2 + t1
g4 <- g2 + theme_bw()
g5 <- g2 + theme_bw() + t2 + labs(x="Horsepower", y = "Miles per Gallon", title= "MPG vs Horsepower")
grid.arrange(g2, g3, g4, g5, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-13"/> </p>
<p>Finally, here's a nice graph using a combination of options:</p>
<pre><code class="r">g2<- ggplot(mtc, aes(x = hp, y = mpg)) +
geom_point(size=2, aes(color=factor(vs), shape=factor(vs))) +
geom_smooth(aes(color=factor(vs)),method = "lm", se = TRUE) +
scale_color_manual(name ="Engine",
labels=c("V-engine", "Straight engine"),
values=c("red","blue")) +
scale_shape_manual(name ="Engine",
labels=c("V-engine", "Straight engine"),
values=c(0,2)) +
theme_bw() +
theme(
axis.title.x = element_text(face="bold", color="black", size=12),
axis.title.y = element_text(face="bold", color="black", size=12),
plot.title = element_text(face="bold", color = "black", size=12),
legend.position=c(1,1),
legend.justification=c(1,1)) +
labs(x="Horsepower",
y = "Miles per Gallon",
title= "Linear Regression (95% CI) of MPG vs Horsepower by Engine type")
g2
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-14"/> </p>
<h3>>> Reader request: Display Regression Line Equation on Scatterplot</h3>
<p>I received a request asking how to overlay the regression equation itself on a plot, so I've decided to update this post with that information.</p>
<p>There are two ways to put text on a ggplot: <strong>annotate</strong> or <strong>geom_text()</strong>. I was finding that the <strong>geom_text()</strong> layer did not look very nice on my screen so I checked up on it and it seems others have this issue as well. I'll show you how the two behave, at least in my version of everything I use on my mac.</p>
<p>We'll go back to the example where I add a regression line to the plot using <strong>geom_smooth()</strong>. To add text, you need to run the regression outside of ggplot, extract the coefficients, and then paste them together into some text that you can layer onto the plot.</p>
<p>We're plotting MPG against horsepower so we create an object m that stores the linear model, and then extract the coefficients using the <strong>coef()</strong> function. We envelope the <strong>coef()</strong> function with <strong>signif()</strong> in order to round the coefficients to two significant digits. I then paste the regression equation text together, using <strong>sep=“”</strong> in order to eliminate spaces.</p>
<pre><code class="r">m <- lm(mtc$mpg ~ mtc$hp)
a <- signif(coef(m)[1], digits = 2)
b <- signif(coef(m)[2], digits = 2)
textlab <- paste("y = ",b,"x + ",a, sep="")
print(textlab)
</code></pre>
<pre><code>## [1] "y = -0.068x + 30"
</code></pre>
<p>Next, I take the original p1 ggplot object, add points and a linear model to it, and then add a layer of text. I will show the two ways here, first using <strong>geom_smooth</strong> and then using <strong>annotate</strong>. </p>
<p>With both methods, you must specify the x- and y-coordinates for where the text should be centered. In the <strong>geom_text</strong> code, notice that that <strong>label=textlab</strong> is included in the aes statement, while this is not the case for annotate. If there were mathematical or formatting symbols in the text, I would indicate <strong>parse=TRUE</strong> instead of FALSE, as we will see in the next example.</p>
<pre><code class="r">##basic ggplot with points and linear model
p3 <- p1 + geom_point(color="red") + geom_smooth(method = "lm", se = TRUE)
##add regression text using geom_text
r1 <- p3 + geom_text(aes(x = 245, y = 30, label = textlab), color="black", size=5, parse = FALSE)
##add regression text using annotate
r2 <- p3 + annotate("text", x = 245, y = 30, label = textlab, color="black", size = 5, parse=FALSE)
grid.arrange(r1, r2, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-16"/> </p>
<p>In a fancier way that I got from <a href="http://stackoverflow.com/questions/7549694/ggplot2-adding-regression-line-equation-and-r2-on-graph">this StackOverflow page</a>, you can use a function to piece together your text (which would be useful if you were doing this a lot). It also shows you how you can put in mathematical symbols and formattting changes, like making your variables italic by using <strong>substitute()</strong>, and adding in a dot for the multiplication symbol.</p>
<p>The function <strong>lm_eqn()</strong> takes the arguments x, y, and a dataframe and evaluates the same linear model as before. Then it uses the <strong>substitute()</strong> function to piece together the regression equation using an expression, which is an R object of class “call”. </p>
<p>Finally, the function returns the expression, and is used exactly the same way in the two ggplot statements, EXCEPT that since we now have these formatting changes, we must use <strong>parse=TRUE</strong> in order to properly display the expressions.</p>
<pre><code class="r">##function to create equation expression
lm_eqn = function(x, y, df){
m <- lm(y ~ x, df);
eq <- substitute(italic(y) == b %.% italic(x) + a,
list(a = format(coef(m)[1], digits = 2),
b = format(coef(m)[2], digits = 2)))
as.character(as.expression(eq));
}
##add regression equation using geom_text
r3 <- p3 + geom_text(aes(x = 245, y = 30, label = lm_eqn(mtc$hp, mtc$mpg, mtc)), color="black", size=5, parse = TRUE)
##add regression equation using annotate
r4 <- p3 + annotate("text", x = 245, y = 30, label = lm_eqn(mtc$hp, mtc$mpg, mtc), color="black", size = 5, parse=TRUE)
grid.arrange(r3, r4, nrow=1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-17"/> </p>
<p>Of course, you can change the font and do more formatting stuff on the text itself - <a href="http://docs.ggplot2.org/0.9.3.1/geom_text.html">find that information here.</a></p>
<p>Lastly, I will go over functions in a post that I plan on doing very soon so be on the lookout for that if the function used here is confusing or you'd like to know more. </p>
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The graphics package ggplot2 is powerful, aesthetically pleasing, and (after a short learning curve to understand the syntax) easy to use. I have made some pretty cool plots with it, but on the whole I find myself making a lot of the same ones, since doing something over and over again is generally how research goes. Since I constantly forget the options that I need to customize my plots, this next series of posts will serve as cheatsheets for scatterplots, barplots, and density plots. We start with scatterplots.
### Quick Intro to ggplot2
The way ggplot2 works is by layering components of your plot on top of each other. You start with the basic of the data you want your plot to include (x and y variables), and then layer on top the kind of plotting colors/symbols you want, the look of the x- and y-axes, the background color, etc. You can also easily add regression lines and summary statistics.
For great reference guides, use the [ggplot2 documentation](http://docs.ggplot2.org/0.9.2.1/index.html) or the [R Graphs Cookbook](http://www.cookbook-r.com/Graphs).
In this post, we focus only on scatterplots with a continuous x and continuous y. We are going to use the mtcars data that is available through R.
library(ggplot2)
library(gridExtra)
mtc<-mtcars
Here's the basic syntax of a scatterplot. We give it a dataframe, mtc, and then in the **aes()** statement, we give it an x-variable and a y-variable to plot. I save it as a ggplot object called p1, because we are going to use this as the base and then layer everything else on top:
#Basic scatterplot
p1 <- ggplot(mtc, aes(x = hp, y = mpg))
Now for the plot to print, we need to specify the next layer, which is how the symbols should look - do we want points or lines, what color, how big. Let's start with points:
#Print plot with default points
p1+geom_point()
That's the bare bones of it. Now we have fun with adding layers. For each of the examples, I'm going to use the *grid.arrange()* function in the **gridExtra** package to create multiple graphs in one panel to save space.
Change color of points
We start with options for colors just by adding how we want to color our points in the geom_point() layer:
p2 <- p1 + geom_point(color="red") #set one color for all points
p3 <- p1 + geom_point(aes(color = wt)) #set color scale by a continuous variable
p4 <- p1 + geom_point(aes(color=factor(am))) #set color scale by a factor variable
grid.arrange(p2, p3, p4, nrow=1)
We can also change the default colors that are given by ggplot2 like this:
#Change default colors in color scale
p1 + geom_point(aes(color=factor(am))) + scale_color_manual(values = c("orange", "purple"))
Change shape or size of points
We're sticking with the basic p1 plot, but now changing the shape and size of the points:
p2 <- p1 + geom_point(size = 5) #increase all points to size 5
p3 <- p1 + geom_point(aes(size = wt)) #set point size by continuous variable
p4 <- p1 + geom_point(aes(shape = factor(am))) #set point shape by factor variable
grid.arrange(p2, p3, p4, nrow=1)
Again, if we want to change the default shapes we can:
p1 + geom_point(aes(shape = factor(am))) + scale_shape_manual(values=c(0,2))
* More options for [color and shape manual changes are here](http://docs.ggplot2.org/0.9.3.1/scale_manual.html)
* All shape and line types can be found here: http://www.cookbook-r.com/Graphs/Shapes_and_line_types
Add lines to scatterplot
p2 <- p1 + geom_point(color="blue") + geom_line() #connect points with line
p3 <- p1 + geom_point(color="red") + geom_smooth(method = "lm", se = TRUE) #add regression line
p4 <- p1 + geom_point() + geom_vline(xintercept = 100, color="red") #add vertical line
grid.arrange(p2, p3, p4, nrow=1)
You can also take out the points, and just create a line plot, and change size and color as before:
ggplot(mtc, aes(x = wt, y = qsec)) + geom_line(size=2, aes(color=factor(vs)))
* More help on scatterplots can be found here: http://www.cookbook-r.com/Graphs/Scatterplots_(ggplot2)
Change axis labels
There are a few ways to do this. If you only want to quickly add labels you can use the *labs()* layer. If you want to change the font size and style of the label, then you need to use the *theme()* layer. More on this at the end of this post. If you want to change around the limits of the axis, and exactly where the breaks are, you use the *scale_x_continuous* (and *scale_y_continuous* for the y-axis).
p2 <- ggplot(mtc, aes(x = hp, y = mpg)) + geom_point()
p3 <- p2 + labs(x="Horsepower",
y = "Miles per Gallon") #label all axes at once
p4 <- p2 + theme(axis.title.x = element_text(face="bold", size=20)) +
labs(x="Horsepower") #label and change font size
p5 <- p2 + scale_x_continuous("Horsepower",
limits=c(0,400),
breaks=seq(0, 400, 50)) #adjust axis limits and breaks
grid.arrange(p3, p4, p5, nrow=1)
* More axis options can be found here: http://www.cookbook-r.com/Graphs/Axes_(ggplot2)
Change legend options
We start off by creating a new ggplot base object, g1, which colors the points by a factor variable. Then we show three basic options to modify the legend.
g1<-ggplot(mtc, aes(x = hp, y = mpg)) + geom_point(aes(color=factor(vs)))
g2 <- g1 + theme(legend.position=c(1,1),legend.justification=c(1,1)) #move legend inside
g3 <- g1 + theme(legend.position = "bottom") #move legend bottom
g4 <- g1 + scale_color_discrete(name ="Engine",
labels=c("V-engine", "Straight engine")) #change labels
grid.arrange(g2, g3, g4, nrow=1)
If we had changed the shape of the points, we would use *scale_shape_discrete()* with the same options. We can also remove the entire legend altogether by using **theme(legend.position="none")**
Next we customize a legend when the scale is continuous:
g5<-ggplot(mtc, aes(x = hp, y = mpg)) + geom_point(size=2, aes(color = wt))
g5 + scale_color_continuous(name="Weight", #name of legend
breaks = with(mtc, c(min(wt), mean(wt), max(wt))), #choose breaks of variable
labels = c("Light", "Medium", "Heavy"), #label
low = "pink", #color of lowest value
high = "red") #color of highest value
* More legend options can be found here: http://www.cookbook-r.com/Graphs/Legends_(ggplot2)
Change background color and style
The look of the plot in terms of the background colors and style is the **theme()**. I personally don't like the look of the default gray so here are some quick ways to change it. I often the theme_bw() layer, which gets rid of the gray.
* All of the theme options [can be found here](http://docs.ggplot2.org/0.9.3/theme.html).
g2<- ggplot(mtc, aes(x = hp, y = mpg)) + geom_point()
#Completely clear all lines except axis lines and make background white
t1<-theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(size=.4)
)
#Use theme to change axis label style
t2<-theme(
axis.title.x = element_text(face="bold", color="black", size=10),
axis.title.y = element_text(face="bold", color="black", size=10),
plot.title = element_text(face="bold", color = "black", size=12)
)
g3 <- g2 + t1
g4 <- g2 + theme_bw()
g5 <- g2 + theme_bw() + t2 + labs(x="Horsepower", y = "Miles per Gallon", title= "MPG vs Horsepower")
grid.arrange(g2, g3, g4, g5, nrow=1)
Finally, here's a nice graph using a combination of options:
g2<- ggplot(mtc, aes(x = hp, y = mpg)) +
geom_point(size=2, aes(color=factor(vs), shape=factor(vs))) +
geom_smooth(aes(color=factor(vs)),method = "lm", se = TRUE) +
scale_color_manual(name ="Engine",
labels=c("V-engine", "Straight engine"),
values=c("red","blue")) +
scale_shape_manual(name ="Engine",
labels=c("V-engine", "Straight engine"),
values=c(0,2)) +
theme_bw() +
theme(
axis.title.x = element_text(face="bold", color="black", size=12),
axis.title.y = element_text(face="bold", color="black", size=12),
plot.title = element_text(face="bold", color = "black", size=12),
legend.position=c(1,1),
legend.justification=c(1,1)) +
labs(x="Horsepower",
y = "Miles per Gallon",
title= "Linear Regression (95% CI) of MPG vs Horsepower by Engine type")
g2
Reader request: Display Regression Line Equation on Scatterplot
I received a request asking how to overlay the regression equation itself on a plot, so I've decided to update this post with that information.
There are two ways to put text on a ggplot: annotate or geom_text(). I was finding that the geom_text() layer did not look very nice on my screen so I checked up on it and it seems others have this issue as well. I'll show you how the two behave, at least in my version of everything I use on my mac.
We'll go back to the example where I add a regression line to the plot using geom_smooth(). To add text, you need to run the regression outside of ggplot, extract the coefficients, and then paste them together into some text that you can layer onto the plot.
We're plotting MPG against horsepower so we create an object m that stores the linear model, and then extract the coefficients using the coef() function. We envelope the coef() function with signif() in order to round the coefficients to two significant digits. I then paste the regression equation text together, using sep="" in order to eliminate spaces.
m <- lm(mtc$mpg ~ mtc$hp)
a <- signif(coef(m)[1], digits = 2)
b <- signif(coef(m)[2], digits = 2)
textlab <- paste("y = ",b,"x + ",a, sep="")
print(textlab)
Next, I take the original p1 ggplot object, add points and a linear model to it, and then add a layer of text. I will show the two ways here, first using geom_smooth and then using annotate.
With both methods, you must specify the x- and y-coordinates for where the text should be centered. In the geom_text code, notice that that label=textlab is included in the aes statement, while this is not the case for annotate. If there were mathematical or formatting symbols in the text, I would indicate parse=TRUE instead of FALSE, as we will see in the next example.
##basic ggplot with points and linear model
p3 <- p1 + geom_point(color="red") + geom_smooth(method = "lm", se = TRUE)
##add regression text using geom_text
r1 <- p3 + geom_text(aes(x = 245, y = 30, label = textlab), color="black", size=5, parse = FALSE)
##add regression text using annotate
r2 <- p3 + annotate("text", x = 245, y = 30, label = textlab, color="black", size = 5, parse=FALSE)
grid.arrange(r1, r2, nrow=1)
In a fancier way that I got from [this StackOverflow page](http://stackoverflow.com/questions/7549694/ggplot2-adding-regression-line-equation-and-r2-on-graph), you can use a function to piece together your text (which would be useful if you were doing this a lot). It also shows you how you can put in mathematical symbols and formattting changes, like making your variables italic by using substitute(), and adding in a dot for the multiplication symbol.
The function lm_eqn() takes the arguments x, y, and a dataframe and evaluates the same linear model as before. Then it uses the substitute() function to piece together the regression equation using an expression, which is an R object of class "call".
Finally, the function returns the expression, and is used exactly the same way in the two ggplot statements, EXCEPT that since we now have these formatting changes, we must use parse=TRUE in order to properly display the expressions.
##function to create equation expression
lm_eqn = function(x, y, df){
m <- lm(y ~ x, df);
eq <- substitute(italic(y) == b %.% italic(x) + a,
list(a = format(coef(m)[1], digits = 2),
b = format(coef(m)[2], digits = 2)))
as.character(as.expression(eq)); }
##add regression equation using geom_text
r3 <- p3 + geom_text(aes(x = 245, y = 30, label = lm_eqn(mtc$hp, mtc$mpg, mtc)), color="black", size=5, parse = TRUE)
##add regression equation using annotate
r4 <- p3 + annotate("text", x = 245, y = 30, label = lm_eqn(mtc$hp, mtc$mpg, mtc), color="black", size = 5, parse=TRUE)
grid.arrange(r3, r4, nrow=1)
Of course, you can change the font and do more formatting stuff on the text itself - [find that information here.](http://docs.ggplot2.org/0.9.3.1/geom_text.html)
Lastly, I will go over functions in a post that I plan on doing very soon so be on the lookout for that if the function used here is confusing or you'd like to know more. </div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com13tag:blogger.com,1999:blog-2550386066385582083.post-9817437564054417322013-10-04T14:32:00.000-07:002016-10-13T07:51:56.419-07:00Loops revisited: How to rethink macros when using R<div height="1050">
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<p>If you're a Stata user (or SAS for that matter), you are most likely a big fan of macros. They're very helpful when you're repeating the same actions over and over again. In R, we don't have macros. Instead we have functions and loops, and even better than loops are the apply functions. I already had one post on the apply() function about a year ago, so as this is the one year anniversary of my blog (yay!), I revisit apply() and show even more examples of how incredibly versatile and useful this function is, once you get used to the syntax. I also show where loops can be useful. </p>
<p>This blog post is inspired by <a href="http://www.ssc.wisc.edu/sscc/pubs/stata_prog1.htm">this great U-W Madison site</a> for computing that I found when I was searching for a way to do a loop in Stata. They go through all of the ways you may want to loop using macros in Stata. So in this blog post, I show how to do all of these problems in R. </p>
<h3>The R perspective</h3>
<p>The first thing to do when you're trying to think about how to solve a problem in R that you've done in Stata using macros, is to stop thinking 'macro' and start thinking 'objects'. The idea is that when you use R, you have a space in which to store many different objects - vectors, dataframes, matrices, lists, etc. I went over all of these in a <a href="http://rforpublichealth.blogspot.com/2012/11/data-types-part-1-ways-to-store.html">series of blog posts</a> called “Data types” in November of last year.</p>
<p>You can use the power of objects to change the way you're thinking about your programming problem. Let's start with U-W's first example: running multiple regressions of various types using a fixed set of control variables. We'll run a linear regression and a logit. In Stata you do:</p>
<pre><code>local controlVars age sex occupation location maritalStatus hasChildren
reg income education `controlVars'
logit employed education `controlVars'
</code></pre>
<p>In R, we can take those local controlVars and put them into a new object, for example a matrix. Then we use that same matrix in all of our regressions. Here is an example. We create some data:</p>
<pre><code class="r">set.seed(10)
x <- rnorm(100, 5, 2)
z <- rnorm(100, 6, 5)
w <- rnorm(100, 3, 2)
y <- x * 2 + w * 0.5 + rnorm(100, 0, 1)
ybin <- as.numeric(y < 10)
mydata <- as.data.frame(cbind(x, z, w, y, ybin))
</code></pre>
<p>And now if we have two models to run, a linear and a logit, we can create a matrix of explanatory variables that we put on the right hand side each time. You don't need the “data=mydata” part since ybin is also a vector in our workspace, but generally if you were to import this data as a dataframe, then you would need to include it or you would need to create a separate ybin vector object from the dataframe you imported.</p>
<pre><code class="r">xvars <- cbind(x, z, w)
summary(lm(ybin ~ xvars, data = mydata))
</code></pre>
<pre><code>##
## Call:
## lm(formula = ybin ~ xvars, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6179 -0.2446 0.0175 0.2177 0.6699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.44087 0.10888 13.23 <2e-16 ***
## xvarsx -0.20002 0.01673 -11.95 <2e-16 ***
## xvarsz -0.00179 0.00656 -0.27 0.785
## xvarsw -0.03123 0.01631 -1.91 0.058 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.313 on 96 degrees of freedom
## Multiple R-squared: 0.605, Adjusted R-squared: 0.593
## F-statistic: 49 on 3 and 96 DF, p-value: <2e-16
</code></pre>
<pre><code class="r">summary(glm(ybin ~ xvars, family = binomial(logit), data = mydata))
</code></pre>
<pre><code>##
## Call:
## glm(formula = ybin ~ xvars, family = binomial(logit), data = mydata)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3283 -0.0962 -0.0030 0.0720 2.1836
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 20.0872 5.3972 3.72 0.00020 ***
## xvarsx -4.0466 1.0590 -3.82 0.00013 ***
## xvarsz -0.0136 0.1197 -0.11 0.90967
## xvarsw -1.1068 0.4088 -2.71 0.00679 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 133.750 on 99 degrees of freedom
## Residual deviance: 29.772 on 96 degrees of freedom
## AIC: 37.77
##
## Number of Fisher Scoring iterations: 8
</code></pre>
<p>Next, we want to run the regression if the data meet some requirement. In Stata we would do:</p>
<pre><code>local blackWoman race==1 & female
reg income education `controlVars' if `blackWoman'
</code></pre>
<p>Again in R, think objects. We can subset our original dataframe “mydata” to another matrix “data.sub”. I go over subsetting in a blogpost <a href="http://rforpublichealth.blogspot.com/2012/10/quick-and-easy-subsetting.html">here</a>. I take the subset of my data based on the conditions I want, and to stick with the above example, I do a xvars matrix as well so I can combine the two methods like so. Notice the degrees of freedom have been reduced since we're only using data in which x>2 and z<3:</p>
<pre><code class="r">data.sub <- as.data.frame(mydata[x > 2 & z < 3, c("x", "z", "ybin")])
xvars.sub <- as.matrix(data.sub[, c("x", "z")])
summary(lm(ybin ~ xvars.sub, data = data.sub))
</code></pre>
<pre><code>##
## Call:
## lm(formula = ybin ~ xvars.sub, data = data.sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4706 -0.1793 -0.0171 0.1504 0.6144
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4185 0.1611 8.81 1.5e-09 ***
## xvars.subx -0.2113 0.0287 -7.38 5.0e-08 ***
## xvars.subz -0.0434 0.0238 -1.83 0.079 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.274 on 28 degrees of freedom
## Multiple R-squared: 0.672, Adjusted R-squared: 0.649
## F-statistic: 28.7 on 2 and 28 DF, p-value: 1.67e-07
</code></pre>
<p>Well now that we have the basics down, let's get to some of the more interesting problems of loops. </p>
<h3>Looping over Variables</h3>
<p>In Stata, if we want to run regressions for three different outcome variables, we can do it this way, via a <strong>foreach</strong> loop:</p>
<pre><code>foreach yvar in mpg price displacement {
reg `yvar' foreign weight
}
</code></pre>
<p>In R, we can use apply(). The syntax of apply() is three arguments: </p>
<p>apply(dataset, margin, function)</p>
<ul>
<li>the first argument is the dataframe or matrix that you want to apply your function to, </li>
<li>the second argument is the margin, meaning are you doing it over rows (margin=1) or over columns (margin=2) of the data, </li>
<li>and the third argument is the function that you want to apply. </li>
</ul>
<p>In this case, we can use the apply() function as follows. I will take the two columns of mydata (y and ybin), and for each of those columns (since margin=2) I apply the function that I create in the third argument. The function I create is to take an argument (outcome) which refers to each of those two columns and to run a linear model on each of those columns with x and z as the explanatory variables: </p>
<pre><code class="r">apply(mydata[,c("y","ybin")], 2, function(outcome){summary(lm(outcome~x+z))})
</code></pre>
<pre><code>## $y
##
## Call:
## lm(formula = outcome ~ x + z)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.863 -0.887 -0.035 0.899 3.570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7342 0.4299 4.03 0.00011 ***
## x 2.0224 0.0764 26.46 < 2e-16 ***
## z -0.0204 0.0297 -0.69 0.49450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.43 on 97 degrees of freedom
## Multiple R-squared: 0.879, Adjusted R-squared: 0.877
## F-statistic: 352 on 2 and 97 DF, p-value: <2e-16
##
##
## $ybin
##
## Call:
## lm(formula = outcome ~ x + z)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6057 -0.2183 0.0068 0.1967 0.7292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.335967 0.095381 14.01 <2e-16 ***
## x -0.200010 0.016962 -11.79 <2e-16 ***
## z -0.000098 0.006587 -0.01 0.99
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.317 on 97 degrees of freedom
## Multiple R-squared: 0.59, Adjusted R-squared: 0.581
## F-statistic: 69.7 on 2 and 97 DF, p-value: <2e-16
</code></pre>
<p>And out come the two summaries of the models, the first with y and the second with ybin.</p>
<h3>Looping over parts of variable names</h3>
<p>In this Stata example, which I feel comes up often, we want to take each of our monthly income variables and create an indicator of whether there was positive income in that month. We want to create 12 new variables with the original name of the variable like “Jan”, now in an indicator form like “hadIncJan”. The Stata code is as follows:</p>
<pre><code>foreach month in Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec {
gen hadInc`month'=(inc`month'>0) if inc`month'<.
}
</code></pre>
<p>In R, this is one of those times that a for() loop actually works very nicely. I actually found this solution on <a href="http://stackoverflow.com/questions/14324562/creating-new-named-variable-in-dataframe-using-loop-and-naming-convention">Stackoverflow</a>, which if you're not familiar, can be a lifesaver for your programming struggles.</p>
<p>I create some month data. In this example, we'll use the names() function in R, which is very useful. I had a <a href="http://rforpublichealth.blogspot.com/2013/03/extracting-information-from-objects.html">blog post about the names()</a> function if you want to read up on its other uses.</p>
<p>We also use the paste0() function, which is a way to concatenate character strings and numbers, along with a new way to index, the “[[” operator. You can read about this operator in the help file. The idea is to refer a single element of the dataframe (a vector) by inserting the column name that is being referred to via an index from the loop. I think reading the code makes things more clear. We make our data:</p>
<pre><code class="r">jan <- rnorm(100, 3, 5)
feb <- rnorm(100, 4, 8)
march <- rnorm(100, 2, 5)
months <- as.data.frame(cbind(jan, feb, march))
names(months)
</code></pre>
<pre><code>## [1] "jan" "feb" "march"
</code></pre>
<pre><code class="r">head(months)
</code></pre>
<pre><code>## jan feb march
## 1 2.6027 10.956 -2.510
## 2 8.9088 -1.440 1.726
## 3 13.9307 5.386 -4.784
## 4 5.0309 2.724 3.586
## 5 -0.6918 10.348 8.979
## 6 -6.7824 17.555 -4.056
</code></pre>
<p>And now we run our loop:</p>
<pre><code class="r">for (n in names(months)) {
months[[paste0("HadInc_", n)]] <- as.numeric(months[[n]] > 0)
}
head(months)
</code></pre>
<pre><code>## jan feb march HadInc_jan HadInc_feb HadInc_march
## 1 2.6027 10.956 -2.510 1 1 0
## 2 8.9088 -1.440 1.726 1 0 1
## 3 13.9307 5.386 -4.784 1 1 0
## 4 5.0309 2.724 3.586 1 1 1
## 5 -0.6918 10.348 8.979 0 1 1
## 6 -6.7824 17.555 -4.056 0 1 0
</code></pre>
<p>The for() loop takes each column name in months, and creates a new variable by concatenating the string “HadInc_” with that column name, and assigns to it a binary indicator of whether the original variable had monthly income greater than 0. If this is confusing, I suggest breaking it down to parts. You can run it this way to see what is actually happening (output not shown here, but you can run it on your own to understand it)</p>
<pre><code class="r">for (n in names(months)) {
print(n)
print(months[[n]])
print(paste0("HadInc_", n))
}
</code></pre>
<h3>Looping over Varlists</h3>
<p>In Stata in order to do something to all or a lot of variables (for example, to rename them to have all lower case or upper case letters), you use a <strong>foreach</strong> loop like this:</p>
<pre><code>foreach oldname of varlist * {
local newname=lower("`oldname'")
rename `oldname' `newname'
}
</code></pre>
<p>In R, for this exact situation, you can use the toupper() function on the names of the data, or a subset of the data if you only wanted to do some of the column names.</p>
<pre><code class="r">names(months) <- toupper(names(months))
head(months)
</code></pre>
<pre><code>## JAN FEB MARCH HADINC_JAN HADINC_FEB HADINC_MARCH
## 1 2.6027 10.956 -2.510 1 1 0
## 2 8.9088 -1.440 1.726 1 0 1
## 3 13.9307 5.386 -4.784 1 1 0
## 4 5.0309 2.724 3.586 1 1 1
## 5 -0.6918 10.348 8.979 0 1 1
## 6 -6.7824 17.555 -4.056 0 1 0
</code></pre>
<p>For other situations, like replacing indicators of missing values to NA for a bunch of variables at a time, check out <a href="http://rforpublichealth.blogspot.com/2012/09/the-infamous-apply-function.html">my previous blog post</a> on using apply() in these situations.</p>
<h3>Looping over numbers</h3>
<p>We revisit the same problem we had with the monthly income, except now we want an indicator by year. We have variables of the yearly income. In Stata this would be the code, using now the <strong>forvalues</strong> loop with a macro:</p>
<pre><code>forvalues year=1990/2010 {
gen hadInc`year'=(inc`year'>0) if inc`year'<.
}
</code></pre>
<p>In R we can use a for() loop along with the “[[” operator that we used before, but this time we make use of the seq(along=x) syntax that will let us go along a sequence of numbers. Our dataframe called “Income” includes columns for each year of income. We make a new vector called “years” that just contains numbers from 1990 to 1992. Then for each value along that vector, we make a new column in our Income dataframe with a name that concatenates “hadInc_” with the number in the sequence, and this variable is just a binary indicator of whether that year's income was positive.</p>
<pre><code class="r">Inc1990 <- rnorm(100, 5, 6)
Inc1991 <- rnorm(100, 3, 8)
Inc1992 <- rnorm(100, 4, 4)
Income <- as.data.frame(cbind(Inc1990, Inc1991, Inc1992))
years <- c(1990:1992)
head(Income)
</code></pre>
<pre><code>## Inc1990 Inc1991 Inc1992
## 1 3.6391 -14.001 6.169
## 2 0.8242 7.169 8.095
## 3 8.0399 2.629 7.251
## 4 8.6974 -6.196 9.204
## 5 -5.0013 8.035 6.272
## 6 2.9825 -2.812 3.520
</code></pre>
<pre><code class="r">for (i in seq(along = years)) {
Income[[paste0("hadInc_", years[i])]] <- as.numeric(Income[[i]] > 0)
}
head(Income)
</code></pre>
<pre><code>## Inc1990 Inc1991 Inc1992 hadInc_1990 hadInc_1991 hadInc_1992
## 1 3.6391 -14.001 6.169 1 0 1
## 2 0.8242 7.169 8.095 1 1 1
## 3 8.0399 2.629 7.251 1 1 1
## 4 8.6974 -6.196 9.204 1 0 1
## 5 -5.0013 8.035 6.272 0 1 1
## 6 2.9825 -2.812 3.520 1 0 1
</code></pre>
<p>Again, run just parts of it to understand it if you're having trouble with the syntax. </p>
<h3>Looping over Values and Levelsof</h3>
<p>Finally, we may want to run the same functions over values or levels of a variable. Here's are two situations in Stata, the first can use the <strong>by</strong> statement, and the second uses <strong>forvalues</strong> for a survey function. </p>
<pre><code>by race: regress income age i.education
forvalues race=1/3 {
svy, subpop(if race==`race'): reg income age i.education
}
</code></pre>
<p>In R, there is no “by” option for linear regression but we can use the lapply() function instead. The function lapply() is the same idea as apply() except it can be used to apply some function over a list. We create some data similar to the Stata example: </p>
<pre><code class="r">race <- c(rep(1, 30), rep(2, 30), rep(3, 40))
age <- rnorm(100, 25, 3)
y <- age * 10 + ifelse(race == 1, 100, ifelse(race == 2, 2000, 0)) + rnorm(100,
0, 1)
racedata <- as.data.frame(cbind(race, age, y))
racedata$race <- as.factor(racedata$race)
</code></pre>
<p>Now we use lapply() to run the summary of lm for a subset of the racedata where we subset by each value of the list. </p>
<pre><code class="r">lapply(1:3, function(index) summary(lm(y~age, data=racedata[racedata$race==index,])))
</code></pre>
<pre><code>## [[1]]
##
## Call:
## lm(formula = y ~ age, data = racedata[racedata$race == index,
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0021 -0.8978 -0.0686 0.8602 2.0371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103.0121 2.2522 45.7 <2e-16 ***
## age 9.8836 0.0895 110.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 28 degrees of freedom
## Multiple R-squared: 0.998, Adjusted R-squared: 0.998
## F-statistic: 1.22e+04 on 1 and 28 DF, p-value: <2e-16
##
##
## [[2]]
##
## Call:
## lm(formula = y ~ age, data = racedata[racedata$race == index,
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4785 -0.4682 -0.0427 0.6685 1.3434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.00e+03 1.34e+00 1491 <2e-16 ***
## age 9.96e+00 5.31e-02 188 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.881 on 28 degrees of freedom
## Multiple R-squared: 0.999, Adjusted R-squared: 0.999
## F-statistic: 3.52e+04 on 1 and 28 DF, p-value: <2e-16
##
##
## [[3]]
##
## Call:
## lm(formula = y ~ age, data = racedata[racedata$race == index,
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1961 -0.3204 0.0736 0.5295 2.1861
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.0024 1.0816 -1.85 0.072 .
## age 10.0814 0.0442 228.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.998 on 38 degrees of freedom
## Multiple R-squared: 0.999, Adjusted R-squared: 0.999
## F-statistic: 5.21e+04 on 1 and 38 DF, p-value: <2e-16
</code></pre>
<p>To make this even better, we take the levels of race, which is a factor, and run the lapply() function of those instead of the number 1-3 so that if those levels change, we won't have to change our code. </p>
<pre><code class="r">lapply(as.numeric(levels(race)), function(index) summary(lm(y~age, data=racedata[racedata$race==index,])))
</code></pre>
<p>This idea can also be applied to any function that you want to evaluate by different values. </p>
<p>Of course there may be more efficient ways to do what I've shown here. If you have comments on improvement over these solutions, let me know!</p>
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If you're a Stata user (or SAS for that matter), you are most likely a big fan of macros. They're very helpful when you're repeating the same actions over and over again. In R, we don't have macros. Instead we have functions and loops, and even better than loops are the apply functions. I already had one post on the apply() function about a year ago, so as this is the one year anniversary of my blog (yay!), I revisit apply() and show even more examples of how incredibly versatile and useful this function is, once you get used to the syntax. I also show where loops can be useful.
This blog post is inspired by [this great U-W Madison site](http://www.ssc.wisc.edu/sscc/pubs/stata_prog1.htm) for computing that I found when I was searching for a way to do a loop in Stata. They go through all of the ways you may want to loop using macros in Stata. So in this blog post, I show how to do all of these problems in R.
The R perspective
The first thing to do when you're trying to think about how to solve a problem in R that you've done in Stata using macros, is to stop thinking 'macro' and start thinking 'objects'. The idea is that when you use R, you have a space in which to store many different objects - vectors, dataframes, matrices, lists, etc. I went over all of these in a [series of blog posts](http://rforpublichealth.blogspot.com/2012/11/data-types-part-1-ways-to-store.html) called "Data types" in November of last year.
You can use the power of objects to change the way you're thinking about your programming problem. Let's start with U-W's first example: running multiple regressions of various types using a fixed set of control variables. We'll run a linear regression and a logit. In Stata you do:
local controlVars age sex occupation location maritalStatus hasChildren
reg income education `controlVars'
logit employed education `controlVars'
In R, we can take those local controlVars and put them into a new object, for example a matrix. Then we use that same matrix in all of our regressions. Here is an example. We create some data:
set.seed(10)
x<-rnorm(100,5,2)
z<-rnorm(100,6,5)
w<-rnorm(100,3,2)
y<-x*2+w*.5+rnorm(100,0,1)
ybin<-as.numeric(y<10)
mydata<-as.data.frame(cbind(x,z,w,y,ybin))
And now if we have two models to run, a linear and a logit, we can create a matrix of explanatory variables that we put on the right hand side each time. You don't need the "data=mydata" part since ybin is also a vector in our workspace, but generally if you were to import this data as a dataframe, then you would need to include it or you would need to create a separate ybin vector object from the dataframe you imported.
xvars<-cbind(x,z,w)
summary(lm(ybin~xvars, data=mydata))
summary(glm(ybin~xvars, family=binomial(logit), data=mydata))
Next, we want to run the regression if the data meet some requirement. In Stata we would do:
local blackWoman race==1 & female
reg income education `controlVars' if `blackWoman'
Again in R, think objects. We can subset our original dataframe "mydata" to another matrix "data.sub". I go over subsetting in a blogpost [here](http://rforpublichealth.blogspot.com/2012/10/quick-and-easy-subsetting.html). I take the subset of my data based on the conditions I want, and to stick with the above example, I do a xvars matrix as well so I can combine the two methods like so. Notice the degrees of freedom have been reduced since we're only using data in which x>2 and z<3:
data.sub<-as.data.frame(mydata[x>2 & z<3,c("x","z","ybin")])
xvars.sub<-as.matrix(data.sub[,c("x","z")])
summary(lm(ybin~xvars.sub, data=data.sub))
Well now that we have the basics down, let's get to some of the more interesting problems of loops.
Looping over Variables
In Stata, if we want to run regressions for three different outcome variables, we can do it this way, via a **foreach** loop:
foreach yvar in mpg price displacement {
reg `yvar' foreign weight
}
In R, we can use apply(). The syntax of apply() is three arguments:
apply(dataset, margin, function)
* the first argument is the dataframe or matrix that you want to apply your function to,
* the second argument is the margin, meaning are you doing it over rows (margin=1) or over columns (margin=2) of the data,
* and the third argument is the function that you want to apply.
In this case, we can use the apply() function as follows. I will take the two columns of mydata (y and ybin), and for each of those columns (since margin=2) I apply the function that I create in the third argument. The function I create is to take an argument (outcome) which refers to each of those two columns and to run a linear model on each of those columns with x and z as the explanatory variables:
apply(mydata[,c("y","ybin")], 2, function(outcome){summary(lm(outcome~x+z))})
And out come the two summaries of the models, the first with y and the second with ybin.
### Looping over parts of variable names
In this Stata example, which I feel comes up often, we want to take each of our monthly income variables and create an indicator of whether there was positive income in that month. We want to create 12 new variables with the original name of the variable like "Jan", now in an indicator form like "hadIncJan". The Stata code is as follows:
foreach month in Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec {
gen hadInc`month'=(inc`month'>0) if inc`month'<.
}
In R, this is one of those times that a for() loop actually works very nicely. I actually found this solution on [Stackoverflow](http://stackoverflow.com/questions/14324562/creating-new-named-variable-in-dataframe-using-loop-and-naming-convention), which if you're not familiar, can be a lifesaver for your programming struggles.
I create some month data. In this example, we'll use the names() function in R, which is very useful. I had a [blog post about the names()](http://rforpublichealth.blogspot.com/2013/03/extracting-information-from-objects.html) function if you want to read up on its other uses.
We also use the paste0() function, which is a way to concatenate character strings and numbers, along with a new way to index, the "[[" operator. You can read about this operator in the help file. The idea is to refer a single element of the dataframe (a vector) by inserting the column name that is being referred to via an index from the loop. I think reading the code makes things more clear. We make our data:
jan<-rnorm(100,3,5)
feb<-rnorm(100,4,8)
march<-rnorm(100,2,5)
months<-as.data.frame(cbind(jan,feb,march))
names(months)
head(months)
And now we run our loop:
for (n in names(months)){
months[[paste0("HadInc_",n)]] <- as.numeric(months[[n]]>0)
}
head(months)
The for() loop takes each column name in months, and creates a new variable by concatenating the string "HadInc_" with that column name, and assigns to it a binary indicator of whether the original variable had monthly income greater than 0. If this is confusing, I suggest breaking it down to parts. You can run it this way to see what is actually happening (output not shown here, but you can run it on your own to understand it)
for (n in names(months)){
print(n)
print(months[[n]])
print(paste0("HadInc_",n)) }
Looping over Varlists
In Stata in order to do something to all or a lot of variables (for example, to rename them to have all lower case or upper case letters), you use a **foreach** loop like this:
foreach oldname of varlist * {
local newname=lower("`oldname'")
rename `oldname' `newname' }
In R, for this exact situation, you can use the toupper() function on the names of the data, or a subset of the data if you only wanted to do some of the column names.
names(months)<-toupper(names(months))
head(months)
For other situations, like replacing indicators of missing values to NA for a bunch of variables at a time, check out [my previous blog post](http://rforpublichealth.blogspot.com/2012/09/the-infamous-apply-function.html) on using apply() in these situations.
Looping over numbers
We revisit the same problem we had with the monthly income, except now we want an indicator by year. We have variables of the yearly income. In Stata this would be the code, using now the **forvalues** loop with a macro:
forvalues year=1990/2010 {
gen hadInc`year'=(inc`year'>0) if inc`year'<. }
In R we can use a for() loop along with the "[[" operator that we used before, but this time we make use of the seq(along=x) syntax that will let us go along a sequence of numbers. Our dataframe called "Income" includes columns for each year of income. We make a new vector called "years" that just contains numbers from 1990 to 1992. Then for each value along that vector, we make a new column in our Income dataframe with a name that concatenates "hadInc_" with the number in the sequence, and this variable is just a binary indicator of whether that year's income was positive.
Inc1990<-rnorm(100,5,6)
Inc1991<-rnorm(100,3,8)
Inc1992<-rnorm(100,4,4)
Income<-as.data.frame(cbind(Inc1990, Inc1991, Inc1992))
years<-c(1990:1992)
head(Income)
for (i in seq(along=years)){
Income[[paste0("hadInc_",years[i])]] <- as.numeric(Income[[i]]>0) }
head(Income)
Again, run just parts of it to understand it if you're having trouble with the syntax.
Looping over Values and Levelsof
Finally, we may want to run the same functions over values or levels of a variable. Here's are two situations in Stata, the first can use the **by** statement, and the second uses **forvalues** for a survey function.
by race: regress income age i.education
forvalues race=1/3 {
svy, subpop(if race==`race'): reg income age i.education
}
In R, there is no "by" option for linear regression but we can use the lapply() function instead. The function lapply() is the same idea as apply() except it can be used to apply some function over a list. We create some data similar to the Stata example:
race<-c(rep(1,30),rep(2,30),rep(3,40))
age<-rnorm(100,25,3)
y<-age*10+ifelse(race==1,100, ifelse(race==2, 2000, 0))+rnorm(100,0,1)
racedata<-as.data.frame(cbind(race,age,y))
racedata$race<-as.factor(racedata$race)
Now we use lapply() to run the summary of lm for a subset of the racedata where we subset by each value of the list.
lapply(1:3, function(index) summary(lm(y~age, data=racedata[racedata$race==index,])))
To make this even better, we take the levels of race, which is a factor, and run the lapply() function of those instead of the number 1-3 so that if those levels change, we won't have to change our code.
lapply(as.numeric(levels(race)), function(index) summary(lm(y~age, data=racedata[racedata$race==index,])))
This idea can also be applied to any function that you want to evaluate by different values.
Of course there may be more efficient ways to do what I've shown here. If you have comments on improvement over these solutions, let me know! </div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com3tag:blogger.com,1999:blog-2550386066385582083.post-51974857267299707422013-08-24T06:43:00.000-07:002016-10-13T07:50:59.879-07:00Exporting Results of Linear Regression with Robust Standard Errors<div height="1050">
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<h1></h1>
<h3>Motivation</h3>
<p>Basic linear regression in R is super easy. Just use the lm() function, summarize the model with summary() and you're done. But as researchers we need more than that. What if our model is more complicated, like if our errors are heteroskedastic? And once we have run our regressions with our adjusted standard errors, how do we export the results for presentation in word or latex documents? This post will go over exactly these things, with the help of the stargazer package created by my fellow Harvard grad student Marek Hlavac. More posts about stargazer can be found <a href="http://www.r-statistics.com/2013/07/tailor-your-tables-with-stargazer-new-features-for-latex-and-text-output/">here on this blog.</a></p>
<p>Today we are going to analyze how temperature affects ice cream sales. We just make ourselves a little dataset like so: </p>
<pre><code class="r">set.seed(5)
temp <- rnorm(500, mean=80, sd=12)
sales <- 2+temp*3
for(i in 1:length(sales)){
if(temp[i]<75 | temp[i]>95) sales[i] <- sales[i]+rnorm(1,0,25 )
else sales[i] <- sales[i]+rnorm(1,0,8 )
}
female <- rnorm(500, mean=.5, sd=.01)
icecream <- as.data.frame(cbind(temp, sales, female))
head(icecream)
</code></pre>
<pre><code>## temp sales female
## 1 69.91 209.6 0.4854
## 2 96.61 296.8 0.5124
## 3 64.93 227.5 0.4957
## 4 80.84 244.6 0.5001
## 5 100.54 271.2 0.5012
## 6 72.77 210.4 0.4959
</code></pre>
<p>So there we have the temperature of 500 cities, sales per 100,000 people, and gender ratio of each of these cities. Let's plot out the temperature and sales.</p>
<pre><code class="r">plot(icecream$temp, icecream$sales, xlab = "Temperature", ylab = "Sales/100,000", main = "I scream for icecream")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-3"/> </p>
<p>Here we definitely see a relationship between the temperature in a city and the sales of icecream. However, we notice that our errors are not exactly homoskedastic. We see that for both lower temperatures and very high temperatures, the variance is larger than the middle temperatures. So we will have to deal with that.</p>
<p>Let's start by running a linear regression using the lm() function and summarizing the results:</p>
<pre><code class="r">fit1 <- lm(sales ~ temp + female, data = icecream)
summary(fit1)
</code></pre>
<pre><code>##
## Call:
## lm(formula = sales ~ temp + female, data = icecream)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.34 -8.02 0.34 8.19 84.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.9164 41.1872 0.58 0.56
## temp 2.9716 0.0671 44.30 <2e-16 ***
## female -37.8194 81.0642 -0.47 0.64
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.1 on 497 degrees of freedom
## Multiple R-squared: 0.799, Adjusted R-squared: 0.798
## F-statistic: 985 on 2 and 497 DF, p-value: <2e-16
</code></pre>
<pre><code class="r">names(summary(fit1))
</code></pre>
<pre><code>## [1] "call" "terms" "residuals" "coefficients" "aliased" "sigma" "df" "r.squared" "adj.r.squared"
## [10] "fstatistic" "cov.unscaled"
</code></pre>
<p>We see the strong association between temperature and icecream sales, but no relationship between the gender ratio and icecream. Don't forget that you can extract many things from the summary of the lm object (read up on extracting information using names() in <a href="http://rforpublichealth.blogspot.com/2013/03/extracting-information-from-objects.html">this post.</a>) Now let's deal with the heteroskedasticity. We know that under the constant variance assumption,</p>
<p>\[ V[\hat\beta] = (X'X)^{-1}X'\Sigma X(X'X)^{-1} \]</p>
<p>where</p>
<p>\[ \Sigma = \sigma^2I \]</p>
<p>and we can find an unbiased estimate of \( \sigma^2 \) by summing the squared the residuals. However, in this case, we see that the variance is not constant, so the naive covariance of the OLS estimator is biased. To fix this we employ the White Huber, or sandwich, method of robust standard errors. The sandwich method assumes that</p>
<p>\[ \Sigma = diag(\sigma_1^2, ... , \sigma_n^2) \]</p>
<p>and again the \( \hat\sigma_i^2 \) are estimated via the residuals \( \hat u_i^2 \). In R, we can use the sandwich package to estimate robust standard errors this way:</p>
<pre><code class="r">library(sandwich)
cov.fit1 <- vcovHC(fit1, type = "HC")
rob.std.err <- sqrt(diag(cov.fit1))
</code></pre>
<p>The vcovHC() function returns the variance-covariance matrix under the assumption of “HC” (Heteroskedasticity-consistent) estimation. We then take the diagonal of this matrix and square root it to calculate the robust standard errors. You could do this in one line of course, without creating the cov.fit1 object.</p>
<p>Now, we can put the estimates, the naive standard errors, and the robust standard errors together in a nice little table. We save the naive standard errors into a vector and then column bind the three columns together like so:</p>
<pre><code class="r">naive.std.err<-summary(fit1)$coefficients[,2]
estimate.table <- cbind("Estimate" = coef(fit1),
"Naive SE"= naive.std.err,
"Robust SE" = rob.std.err)
estimate.table
</code></pre>
<pre><code>## Estimate Naive SE Robust SE
## (Intercept) 23.916 41.18715 40.40784
## temp 2.972 0.06707 0.08379
## female -37.819 81.06423 78.92591
</code></pre>
<p>That's a good start. But of course we want p-values and, even better, upper and lower 95% confidence intervals, so we can add those to the table:</p>
<pre><code class="r">better.table <- cbind("Estimate" = coef(fit1),
"Naive SE" = naive.std.err,
"Pr(>|z|)" = 2 * pt(abs(coef(fit1)/naive.std.err), df=nrow(icecream)-2,
lower.tail = FALSE),
"LL" = coef(fit1) - 1.96 * naive.std.err,
"UL" = coef(fit1) + 1.96 * naive.std.err,
"Robust SE" = rob.std.err,
"Pr(>|z|)" = 2 * pt(abs(coef(fit1)/rob.std.err), df=nrow(icecream)-2,
lower.tail = FALSE),
"LL" = coef(fit1) - 1.96 * rob.std.err,
"UL" = coef(fit1) + 1.96 * rob.std.err)
rownames(better.table)<-c("Constant", "Temperature", "Gender Ratio")
better.table
</code></pre>
<pre><code>## Estimate Naive SE Pr(>|z|) LL UL Robust SE Pr(>|z|) LL UL
## Constant 23.916 41.18715 5.617e-01 -56.81 104.643 40.40784 5.542e-01 -55.283 103.116
## Temperature 2.972 0.06707 6.750e-175 2.84 3.103 0.08379 2.324e-138 2.807 3.136
## Gender Ratio -37.819 81.06423 6.410e-01 -196.71 121.066 78.92591 6.320e-01 -192.514 116.875
</code></pre>
<p>A bit wide, but there we are. We are finished with our linear regression analysis. But now we would like to export this into a pdf or word document so we can send it to our advisor who will be very excited about this cutting edge icecream research. There are two options that I'll go over, xtable() and stargazer().</p>
<h3>xtable</h3>
<p>The first option is xtable. You can put in just about any object (dataframe, table, matrix, lm, glm, etc) and it will create latex code for you that you can copy and paste into latex. This is how the resulting table will look in latex:</p>
<pre><code class="r">xtable(fit1)
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAB2CAYAAAAk5YFjAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGFVd9v21QUPolvUqQWPyBYR4eKxa9VU1u5GxqtxgZJk6XtShal6dgqJOQ6N4mpGwfb6baqT3uBNwb8AUDZAw9IPCENBmJ72fbAtElThyqqSUh76MQPISbtBVXhu3ZiJ1PEXPX6yznfOec7517bRD1fabWaGVWIlquunc8klZOnFpSeTYrSs9RLA9Sr6U4tkcvNEi7BFffO6+EdigjL7ZHu/k72I796i9zRiSJPwG4VHX0Z+AxRzNRrtksUvwf7+Gm3BtzzHPDTNgQCqwKXfZwSeNHHJz1OIT8JjtAq6xWtCLwGPLzYZi+3YV8DGMiT4VVuG7oiZpGzrZJhcs/hL49xtzH/Dy6bdfTsXYNY+5yluWO4D4neK/ZUvok/17X0HPBLsF+vuUlhfwX4j/rSfAJ4H1H0qZJ9dN7nR19frRTeBt4Fe9FwpwtN+2p1MXscGLHR9SXrmMgjONd1ZxKzpBeA71b4tNhj6JGoyFNp4GHgwUp9qplfmnFW5oTdy7NamcwCI49kv6fN5IAHgD+0rbyoBc3SOjczohbyS1drbq6pQdqumllRC/0ymTtej8gpbbuVwpQfyw66dqEZyxZKxtHpJn+tZnpnEdrYBbueF9qQn93S7HQGGHnYP7w6L+YGHNtd1FJitqPAR+hERCNOFi1i1alKO6RQnjKUxL1GNjwlMsiEhcPLYTEiT9ISbN15OY/jx4SMshe9LaJRpTvHr3C/ybFYP1PZAfwfYrPsMBtnE6SwN9ib7AhLwTrBDgUKcm06FSrTfSj187xPdVQWOk5Q8vxAfSiIUc7Z7xr6zY/+hpqwSyv0I0/QMTRb7RMgBxNodTfSPqdraz/sDjzKBrv4zu2+a2t0/HHzjd2Lbcc2sG7GtsL42K+xLfxtUgI7YHqKlqHK8HbCCXgjHT1cAdMlDetv4FnQ2lLasaOl6vmB0CMmwT/IPszSueHQqv6i/qluqF+oF9TfO2qEGTumJH0qfSv9KH0nfS/9TIp0Wboi/SRdlb6RLgU5u++9nyXYe69fYRPdil1o1WufNSdTTsp75BfllPy8/LI8G7AUuV8ek6fkvfDsCfbNDP0dvRh0CrNqTbV7LfEEGDQPJQadBtfGVMWEq3QWWdufk6ZSNsjG2PQjp3ZcnOWWing6noonSInvi0/Ex+IzAreevPhe+CawpgP1/pMTMDo64G0sTCXIM+KdOnFWRfQKdJvQzV1+Bt8OokmrdtY2yhVX2a+qrykJfMq4Ml3VR4cVzTQVz+UoNne4vcKLoyS+gyKO6EHe+75Fdt0Mbe5bRIf/wjvrVmhbqBN97RD1vxrahvBOfOYzoosH9bq94uejSOQGkVM6sN/7HelL4t10t9F4gPdVzydEOx83Gv+uNxo7XyL/FtFl8z9ZAHF4bBsrEwAAQABJREFUeAHsXQVgVMfT/8XdPYFggQgECBDcpbi7e9EWK8X5A6VYodDSYsXd3d2CQxICIYEIcXe7y+Vuv9kXLlaCtAlQvrctuServ923szszO6PCKEAMIgIiAiICIgIiAiICXxUCql9Va8TGiAiICIgIiAiICIgICAiIBF4cCCICIgIiAiICIgJfIQIigf8KO1VskoiAiICIgIiAiIBI4MUxICIgIiAiICIgIvAVIiAS+K+wU8UmiQiICIgIiAiICIgEXhwDIgIiAiICIgIiAl8hAiKB/wo7VWySiICIgIiAiICIgEjgxTEgIiAiICIgIiAi8BUiIBL4r7BTxSaJCIgIiAiICIgIiAReHAMiAiICIgIiAiICXyECIoH/CjtVbJKIgIiAiICIgIiASODFMSAiICIgIiAiICLwFSIgEvivsFPFJokIiAiICIgIiAiIBF4cAyICIgIiAiICIgJfIQIigf8KO1VskoiAiICIgIiAiIBI4MUxICIgIiAiICIgIvAVIiAS+K+wU8UmiQiICIgIiAiICIgEXhwDIgIiAiICIgIiAl8hAiKB/wo7VWySiICIgIiAiICIgEjgxTEgIiAiICIgIiAi8BUiIBL4r7BTxSaJCIgIiAiICIgIiAReHAMiAiICIgIiAiICXyECIoH/CjtVbJKIgIiAiICIgIiAughB6SAgk8mQlZVVOpmLuYoIiAiICHylCKipqUFXVxcqKipfaQs/XbNEAl9KWHt6euLMmTPIyckppRLEbEUERAREBP4tAipQVVWBQqH4qIw48eXkV8FY8ekojir9E/IWrim+4h3x3+RkbW2NESNGQE9Pr/i8xTcfhIAKo/BBMcVIH4WAXC4H38WLQURAREBE4EtFQKHIQVRkIuzKWH54FYlkZGelIitHFUaGBsWkY8iRpCMlMwemJsaQpqciNUcNlib6xcTPf8wXD1paWvkPxKt/jIC4g//H0L07IWcz8X9iKBkEZJnJePDkKe0YVKFGmiMSaTb42pSvTjnOhiamqOpaDdqqH6NWIkdsTByMzaygqf7fYgdmpSdg746NePE6EbYOzmjfvA5evo5Cl7bthJ3VO1HPTsX1a1cRmSxD49btYG9W3CRdOBcmycTDe/chUdOAhpoKMrMkQgShD1TVoKVnCjc3V+ho/LewLNzKD79jtOsNfuWFx16BMClbBa0b1vjwxP8iJmMKRIQGw8c3ADo6WsiRZSNHznfgChgY2cC9vhs0P6ALpFnp2Dh3EXIcmmP6+A4fXCM5lffg2jHciTTBzO/6vjWdIkcGv4eXceBmPJYsmoCE0DAsXLobI2bPQqMqVm9NIz4seQQ+ZjYs+dLFHEUEPhABmSwdvk8f0kQ0EgOHfI/b9x/D56kPnj/1xIblC9Fv0Ej4h6fSAqC4DOXw9/NBujSfqxL86DQa1K+PLcdvF5foHz9PiQjH88DIf5z+XQll6TEYO2gQ7gYq0KNXD5ipJWNInwFYd+BmHnGPCQpCUFTSW7NhTIbQgEeYM/V7XPEMeGuctz2UZ0vxyscTiyd/i279huP6Qy+hD174PMWejSvQu0sf3PePe1vSz/qME8SYiCCERseUaD14vqmJMTiweTWW/r5VWGyWaAHFZMZ5rulpibh7/TQGdO+H3/46htfBwXjh440VC6ajfceh8HwVW0zq3Mc5MinO7f0Tj2IZvh3Z5p1x3/YyWyJDZob8zSuGzNR4+L7yLxQ1J1uG9PQckqWrwrpCNfTv5IwVCxcjJbvYj7RQevHm3yMgEvh/j6GYwydAQNfQDkNHTkST8nbQ0LTF+NEjMW78OIwd/x22HjyINq6OCAyJL17Gp0jHprV/0a5VkjcRG1uWRR03N1SwL/kdxbMbJ7H71K1SQcbr+lHc9Zfi15+no16dehgwZCJG920OhUw54QLXjm7H+XuFJ1xlZVS0TNFv9Aw46GlB+hFiJHVDY/T5dgI6t6gGmZoeJk6ZKPTBt+PG4/ethzCkfXW8eh2kLOaL+c3JzsaVI3tx4faLEq2Tqpo6XGo2xoB2NaAizyzRvN+VmSpxqao418R3Y4dCn2hltbptMWLkSEz4bjI2rFuOWN9z+H7aUkiLpaMM6QnBWL/7LHqOHwsjLQ0oSKT4yus+vJ4Hv6voQu+Iky4EnjbE2wNbt54s9J7fKOOoa2qjXpueMM8MwvpdV/8WT3xQOgiIBL50cBVzLWkEaKbQJLkcVwhSIRKtQddaWmp4/PgpGBGsFm0aIj05kdj2CqSlJCI4OAhxiUnIyMhlIz++dAD37viT0g/JBt8oPhqXqYFtu/egdW0HUELIsiXIkmQLNU+Ii0ZyRlZeK6KjopCZ/XeFydioUASHhufF4xc5WSnYfOQQMolZwMuSy/NnWnl2hlC3xNT8vAsl/oCb5LgYxCeHwC9aIognNDS10L1LL5R7w2pPSwrFtjPnaHZVJ/Zt4fJJMIrIyCh6RRgSgfq4oCLgrkLseEHFSsH7QAu+9+9BoqJLdWiOlJQEyKnN/AQJb3V2Vgay3uzYsonQZtE/ApviZRQqOjYqnHAJJSazMlB/FDiJkpaaipx8GJWRPug3JT4Ihw6co37QFupWsD/yM8gV9+Te0zWVVVA9iV/HREUihFjNBavBiTy/z1U5A2TEmpZQ27kODg8yYmdzUUZOgcozBXFQQl4jKu7tHBYh4Xv+qFIfqNFYF2gsVUBTUxOa2jowtKqMcnbGCHnuDb7BfhuGcsLV+8Z5KIwqoF29ikJJTCFH2DNPzJ48DGMnz8Dtxx++GMqWJGPf9p2IjlUI+Oa8aXvRJujoGqB7p7q4em4/xE18UXRK5/5jv/DSqYWYq4jAByLAJ1KFPBuxCQlIDgvD2rX7seT3avimxwCaZbXgffk4dp66C/sKNgh4dhU5Nm0xub0DFv60Gr6vU/HT7Bmo6NoAP47rjy0rZ2PHoRsYM2cTerqrYNx3PyAwWhutOrrTJJ2JoGfP0ah9f6iGPEeCQoJnL3wxYsZy9GhaHUyRhVXLFiAgVQ2mWXF4GaaKn9b+CmcrYOWiBThx6THKx+/A9Jh76DV0Ehq5lsfzyycx/bftqFnHDS+8H6PzwO8womerD2x5frQaDZpDX/oTerVphfadWqNF69ZoXq8mfp5fA5KUKMydOgse955BYforgu7bY9SEGXAqY4Lbp7bj921X4VC1HJJjY/EyPgka6v9kjc81o3MQFxdPRDABv/+5BpN+rYWa7fqjkkwd+/9chrW7jsGlbjOwmFfwDk7Dn+sWYu3inxGWoYGWDV1x8uBxdPt+PuZP6IUFM75HeIYOyptrwisgBrMXLkXtynpYNHkyztz1xTftWsPzynmol6mJg4c2Q+8jqixNicaKmT/glu9TpO5Yi8BnVTGEuBCuZY3yASXinRz1CovnLEZQYirKVa2LuXOn4fL+tTh46g4quzSGvUEcnkVJYaQhQ1CcDPMXLUTVctTZbwJnQ2eTUtmKmdOIc+KD0Qv/QL/61pgxZTIevEjE/37djLb17BH57BFm/LwSBvaVIY0JRjm31pg/ZUQuoVZm9qG/b1YaqhoaQgq+uE0KeYbAwDg4t+wOjewEzJkyBde9gtC8eWN43bgGE6cG2Lp+CW7ceIQKTo2hS7oUPKhpaKJpn2FwbtIKxw/vxIr5k/G7pQOGDh2BDs1rF1s/mVSCXauWYOeZa9A0S8C02clo3mUQutSvIuRb8I86LYZq1m+ClA2LERibQd+KqCVfEJ9SuaaVqRhEBP4zCMzs9g2zsCzPZs6ay/p0bcHsXduz0OR0Rsd1KCjYuO5N2LKt51hKWgYL8TrFxk7/lSXExrDTmxYyh0r1mIePP4uKTSL9KMbiooNYPVcX9r/fzjKZNIvdPbeF6ekasjELN7P0rEy2e/FkZqBtztbsvcIyMzPZ3EEtmXub8UJJaSGPmbWWAft570OWlZnButZ1Zf0mraMq5LCYqDDWunIFNmTOBhYeGc6ypNksLdKPNahcnv2w9hij3S27f2Qtc3RyZ/4JUiG/j/mjkOewC3t/Yc6VyjFDAz2mq6vHHKrVZWc9g0i8LmVR/o+Zq6UNW7zjIouIimBSmZz53trLHOwqsg0n7gnle1/YxSyM9dnmi48+pmgh7rqZg5iWsSWbNG8h+7Zre1amfEX2PD6/HUlx0WxQoxrMysGNXT53jHXv3Jl5h8ezO4fWMwM1bTb39x1s2eQhbNHGPWztjNGsRssuLDIlU6jX73NGMteGHVlUuoxFBT1hLqbGrHnf8ez45hWs99BxLE32cdVVyKUswPMmc6vixJZsOM0iImOYhPAoGuSSTOZ9/TRztLFmE5fuYvIcGfO9fZJ17ziA3bp5iblUsGFz/zrLEuJi2ZDW9digqcuELGTSTHZ45VjWutdoJlfI2Ws/T9amjiNbuO0KbYql7LnHceZU3pHtu+DHMlJi2cAWdVmPsQtZZkY6C/O5wuq4VGenH70uWp3331NZCQEPWGUjY9agVXc2a9Ys9uP0H1jPTu3YoFGTWEBEApUvY6+8rjFnC3PWbsgkdmD9UjZi/A8shcoeO6A9W7T7xlvKUbBsSRZLjH3Ntq/7hXVu14517zOCHTl3iymhz8mWsku717L5y/bQd6Rg4cGBbHD7RqxD39ksPCKS8pcwUsRjD0/vYN9N+bVAGQqWHv+K9Wjsyi76RBR4Ll6WFgLiDr5Ulk1ipqWFABFQGJs4YuLkSdCXD8CkqRshzVQF006Bj38krMraYN2i73HzRHU0atwUnTt9A0NTcxgb6RE7Wwu2NjawIlY237eYW9lBX19PkNtzGWGFShWhb2SMoQO7QY/YnZaGhjAkOX2/bs1IW1kNtvZlkOWbKDRN39wBvyxfTGx9fxw+9hLZGqqIDXpEXIRxsLSygA7tjI1MzGBrbSsY7LjucQaPA9LwTVYyTp0+g6ywRMRHvhaUoarUK/NRcOUQG7hV7+9xv90Q+D5/Do/rV/Hbqt8wY8JsNLu1D9a2ZtCknZmpuSVsrGyE8vds2wqFjQOGd3CnEwOqqNagAbVVl1jJ+UzxD62EXJ4DXSNrfDdpOizV0qA+cSIkXJxhCjzy8Eatxu4w09aFY92maN66Exo0bQNtHV2EWJnTaQdrfNOlO+pa9Sb5RSwaV/seLSesg42hjlB8v0EDsPC3vjh7KwjD25aHIRk8adKxJzr2a4w2A+TQ/cgZS0VVE5bWloI4wtiE8KBrpVy4YHtVtXRQuU4T9GjtgguXz0E2tTd8OQen73C4122IOQsWIANJOHXqKHI0cxD4KrRgcuGa7+It7CrCzkRTYO+rkIjE1q4stDXUoUpHP5IifHDR8zU6OGvg7NnzyEqNI45LPG4/CUTH2uX+lt/7H5AoQaGOqjUbYfDgDqRJLyctemNYmpnReNWhdjJY2ZWBqYExmnXshR7d3dEph/qb9FFS4+PgopuLeeFyuBhGGyYW5TBg1Pfo1n8krp49jD+mjyEujBs2bt6ECia8E3LZB/xIGx9nuiQiyNQ2pO/LWhhvXIv+rYFEC0xFjuj49Le+Fh+WLAIfwewq2YLF3EQE/gkCfFrhEymfwIysHTBl2kjYmGoiNsATew9dQpehUzFldG9kJ0Vjy+olWLlqPdLoSJ1yUleji5CAEGLxc6WoXPaksh6cxamhZgkzA0PhkYLJiTDpQUvjzXFHRgIC0gHgIYO0+s/euoA9+0ixiCY2c1NDmmAzcuW2NLG+mf6gIk3H68BAJGWkQ91ED3a2xtDS1IBR+RrYuncfWjp/xPljoWTA48ROHD3rBwMiWO4NmmLi9LnYsGwaEkLuI4qLtt80VoWUsXJSYxEWGor4+GgYGusLxF3IhojPv7EUJvQBLYIMjC1J2W4GypvpQRIfjB27DuVmTwgYEJFXU1cnq2R6gsETAgcaROz0qf1alFY9Jwtp6Vkw1jN/0zJAR1sPGqR5HRebIvSzKmihRH2gTixkXerzfxKUvczbK01PxrNn0W/Nhtep3/DhSH75AGdvP8Ujn2h0aFUX0pQY3L9yAgePnoBM1YjGmx6x43Ny+7poTioKImCg9uZOrQLGVC4fD9mSDMg1VVGmgjXhoAF9Mxss2bAZIzu5Fc3lg++5PoQ5fQeOjo6o6uKCcmXsCG9O3HOzUKExzZXyjGihpK6h9QZDwl9Hk8ZJ7igtrjA1Shf0/CFuXL8MhbEVajdsRPnkigMKpaGyhJz4L+kXPHsaUkCXolBMulEBDQlo8z9iKHUERAJf6hCLBZQkAly5SJWIkzDNqGjA1a0q9EjZ7vb1a9A2UcX+zZvResgPOHL6PM4fXku7JD/EZtFkDNI4oknXgOaVi6eP4GlQMlVLg87Uk7KSeq5RDS3SJuYTsib98qDFd15E0OlHCBo0OfOyebi0cTlpZb/EH7u3oW/ndrTb1yBFJw0c/OsI4lOySE+AQU1TB3JShjuw7wQcnWpAU5oGy8p10KFDB3Ts3BEmSEBEMUfZhEKK+ZMWH4rD104Jb1XJBgBXPnSsWoV2UYbQpnkbVDbfmWvrGSD22T2cu3gb1V1qISo0Gsp9E1c2JImGoJjFM8rJTMWVi5cQFp8q5PuuP5qapGRHWBI9FIIjnUQw0VPH4yuXkaJiQySZCBztWFWLTOIaHF/6j+8QeVDTtSLiZATvV57CPf8TFRaENEN1VKlmR3fcEhoE4p4X4R9c8L4nFijhpI3Y4Ec4cPbeW3NRpfP9lWp9g5rl9TBv3kwwuxqoZK6Faye34YhHCOl6bELf7tTXvOF0Bv785WMIJLj0tGkcUVsFmkqUTkoKa2lvOCNyWSak2dQXWpowsywHc2qQqq4lOnTsgA7t2sLFTheh/+Q4JRWmQVwHXiYvmxNx/u9vizZqszqVqUZtUwYV+m409YzIBkSK8lGhXxJP4Ob5YxgzeABmL10Lq5ptseXgISyaMhLm+vn5FEwkJ86aKn1HUlky9hw4yb+2XDwKRqJrRjL7tAxVmJn9s8VakezE2/cgIBL49wAkvv4yEJCkJ+HcyYO45eePuISXOHT8JI4eOYIjh/fjxynfYtri7bC2cUaoz20sJiWmlwGB8PQPh7FpZRgTQbIlpSZFVjT2nTwPr9cBsLLSxe2LBxFEBOX+7VN46utLhPgQEpOCcODQIQSHvMQpj0eIiyZieuwGgv0e4voNb0RH++DKXS/omJpATmfqH3n74O7lw7h22w8xoS9x+cFjUmnWRiUHKzy8dhwHjp5GppoVnOp0wISujbBgygTcfPAUV07twaqj12Bk+mFGZgr2gqYaw5X9GzD953V49jKAzqbfwayFq9Go/zjY8rWKtjHKWOvh4sn9OHz+Jpi+PQYR+76Kfgp+nLsMvtTW5T/9jMiwOJzdsQ8BUSmIfOGB/j26Y8M7bALIs9Nw/dwRnL70kHAiLPcexrGjR3D06CEsnTcBQyb9D3Y1KsP38S3cfx2Bl7T7O3fjDvjZgyxS/jt44hJxMlJwZO9uhNIOHZomWLJ4HvwvbsaGvSfg9eAmJs1YjRZDJ6NDdQtcOHYUwalpArF5REZd/mlQ1TWCJa3szp07RcqPV2BVKZ9jUDRPLR1DDOzdnjhCAWjRqQ24Dpq2hh6YJAu+nl546HERh++8QmLYc1y+dB+v/bxw9IoXAp4+xZlrD4nIqsPCwhr3z52Bl7c3lvy6nuwRhOHk8f2IlFth9oQ+OL5pBfafv4WHty5hwcY9UDUqoPBXtEJvueecpuhAf+Ie7Uc8cYZu3jqNC9cfQJqruJ+XIkeaivPHTyAoKRk3L52Cl3+w8I7vzF0sbRD6jMZqgZCVkYzTh7ajX6+eWLlhL+p06IMdO3Zj6qhBqGBjJixS+IKiaOALCwcSjfl6X8cJGg85NKT5MqAof4Br6seG+SMKdnCwLb4PiuYv3v9zBNQWUPjnycWUIgKfBgEZsTc97j2CVUUn1KtXA4x2GSkpZP6Sjk8xVW3Szm2Orl1aw5pYiTYWhnhGRj8SszUwbtwEVLA2hrFtJVSy0serwCDUa9EVDatXwgua4Mo614GjjRFMrEyRkCJB48aNYaipTrJrcyRkyNC0WSOoaBvBSp+OOmlZom7d6tAmuX2HHoNQxUoHL0izPjZLFeN/mAYrIry1SbO9tnNl1GlcD6lRxJrPNsGwkQNgYqCNRh26wkJHCk8vL8SnKzBu7HeobGuSx079UCRTkhJQs0l7WDNa9Fw8j3ueT1C5fg/Sxh5MrE8SJ6hpo26T2gj184WWmSMGDuhI1vrM0al9C0S+8qbFTADMXdxRv3Z1GOiqwb5yDThWtoeeqTXaftNGYEG/rS4qcgkePbgFHStHNKvvDpXsLKSnpiCFiLBMoYaqteth8IBuiAt9BX07FzR0K0/MBE24VnWClOTN3gGJaNO+NdRkabCv4gIL0oso41ADzd2d8fTRE7wMDkfd1n2xcPIg0mHIwb37PqjZqCnKmmnCwMwalcravq1a732mrq6H2jWcERMZCB1LJwzs0Y6s7b19b8OJla19OapXVbRv2RBatDsuW9kVVcqawt/3OZLJ9OrIcRNR0VIL5eu0hKMxyHCLDuq614S6gRlqulRCDbe6yCRxRcDrUFSu0QANarsQl0kV5Zzc0Oqbb1C1gim8Hj9BVEIKuvceSmOxAnEq3kY6i2kaEfikiBD4h8SiTfeucCljRmIBPVRzrYQ3wiQhIT+S6ekTSN9GQ9iSXoCRuTUq2HEZOS1aVJKw6+BldO3XH5zrzq3hndizAZsP3kWPgcMxc8Zk1K1RFYb6xNonLlHB2nFC/fr5Y4Sm6qJFY1diuavDuXoNqGfG07egjv79B8CcxntU4DM6QZGF9m0bCPUh5Tyc20O2KOQ2+HZA+0J5FtNS8fG/REC0Rf8vARSTfyoEyP41naEmTmsxgVjrpOjD2YucHSs4taCZTIOeEYdSCEqTnuok/+STFr8XLN9RnmpEGJVnl3lkdbrnZ8iF2ZDY0fw0Wa45UM7pVyW2PT9jzk2EMkEun3svI9a0upCW+N/EmuVnvlWFeinnb2UdOCuVn19X1k2o4Af+4W0UTkDTRKusEy+Xy/aVgU/CUrIkxmW0ec8JF44hV6vjog6QjgH3McIx4uZ/syk+x+ZdxEZGFtB4muICz4uRspdcGYnaqcmPcREe2dyOAPUF70Iug1aWo6C6yjjW9IKLHLicngcZ72/hiuXiSu/+aeB48PZRRvl4FJcZ4SSjs/y8jsrAFRtzcuQCC5w/5/cqtFvnO3zBrgLvYBonmrzulD6373NxZoQzPRJEDWrU4VxJkbeXjwGON99Rf2zg7RHyoLR8vOeVXSSj4jBMT4zE6AEDUH/MT5jUvYmQKo0WayrEyteiPlT2QZHshFtuqvbawY24FW6GhTPoeKoQaGyRrgsfGpo0riGX4cmFfdh5hY5R/jqFnpK1u4QQDOs9Gj3n/Y6+LZxzk4l/SxWB3C+pVIsQMxcRKAkEOAF/vwMKPmEWF7iilnqB1/y+YCjqO0CTZPsFA3H6C4Wi+XFZeF6gyV7rjaw57xldFE1T8N2HXqtRI3Kr8kYX4S0JOWHX1i5SYU5sC9YRhT9/vkB6X9AgRa33Blr8FCk5lwCRHPptgS82uNGiooEvFkoqCAudongUlznhVJC482jqhDn/pwwFx6Jm0YUHpS/sLKUwzmq0GOP//k3g7Sk6Pt+WX3EY6hpZYObMsZj3x1a0qu+KajbGMDD8MFGBsOgiHYMcWmTkh8JjixN6vtBTGpWSkihh7x9rYVK7A7o3dcxPJl6VKgIfv3Qs1eqImYsIiAiICIgIlDYCXKHQpX5XjB/YAqev3f2o4jjnxaxMGVSpWPwJEM6dMLGxhbNzGWLeyBEX4Is4iRkWLxhPRzhFsvNRgP+LyCKL/l+A966kycnJpJD19uM470onvhMREBEQEfhUCHCRATelW5jj8P7SyYgO7eC5+KV4TgRZC0I2ibC4iIiLleTE19GmI6fv8iHPxQ38CGzZsmUFM8zvr4kY410IFN8770olvnsvAiEhIbhy5QrJK98hsKRcOKuOn5Itzn7zewv6D0TgSjhc8/efGFX5DzRPrKKIwH8aAb7bFuT4H9MKSsNVW96djuJwnQuuI0CxueEd4fId5fC4lpaW6NOnj0Do3xFVfPUBCIg7+A8A6Z9E4QpbSvnTu9InRwYgSqqNKuVs/5Gyzbvy/lLeeT30RMUapLHNz09/KZUS6yEiICLwRSLAFxwfog/yRVb+C6uUuIMvpQ7hCltFlbaKFpUU6oN5S35B79EzoR/0FJdvPqZjRWQJi1ax+mQhrGuPHjDRyVfsKZr+v3EvR/yrx/h9/wWsWjoT1gYfoKRVqg2Tw+fRfcSSq63adeqTFbX34/vw3m0Yl3VFZbtcJSQmScPdB4/Imp0a3Oq4w9yogNGOnEzcv/8IWWRC1I1cuRqRiVtlkKUm4M5jT/KJY4rGDWsJBmGU7z7fL+Hx5BEiE9JQybEaHOytC1XF59EdRCdK4epeF9Zkie9dIT4sAN5+gbCs4AxXB/u8qDI6+/6AjsFlQwu16roTJvmYR4X4wefFa8EkcL1aVQstAEP8feAfGgULm4pwq+aQl9/nuOAcqMhgf/gHRcPK3gFVq5R9ZzU45y7wVSDsKlakY2j5Y4Ankmcl4f6jp1BoGKB27Vp0ZO+dWZXMS5pTUuIj4eXjDx0jK9SpXbXY8cfr/trvKYKjEsj0swOqVSlHdWBkyyAOT3xCBeNSZPNJOOHAibEqsd41ydgPDwqFFAEBYahQ2QEab1bzcmkGQiJiYV3GXjieGB8ZhjSZJiqWLzzWhAzEPyWKgKjtUKJwfkRmLAvL5i2ErWtPNKruQCY/LWCuK8PSufNw9MIT2JYtV+xZ3YKl3Lt4Fa/IIpVw3Kvgi890LUtPwJXrV5BNx4xygxq+GTgK5RUBWPfXSUjIOMznCpLEYEwZMxS/bj8Cb49T6Na3L6JSc93JFlenpFBPjBo2BHf8ooQoydHBmP7jVBw7dxHH9qxDiyZNsf8K2aCnIE0OxfAePXH4ykP43LuAQb27wy8iSXgX4HUNHTv1wLmH/ji7Zy36DZoBck3/WQMnNP8bPhq7jl5FUJAX+nbthNnLdgkTN5PG48dRPcku/G7yyncXg6hdt/3Ci63v6W3L0H/sdLwgA0Mzxg/EH/suCHHjQx/hh+njcMv7BbyvnkbffiPx6FWM8O763i2YMe0X3Lp7H3MnDUPr3hMRkcLHB8P2RXOxaPkWhEeEYsr4IegzdA6dNxf0t4utQ2m9yKGjgad3rca3U35CgL8/Vs6fglkbck3yFi6TjoIlx2D/js2YNHYwBg/7AQlkI0EZuLLZK5+7GNxnIA5duovzW9Zi8IhZIMu8pRq45z/f+6cwaPgEItD+OLFlFQb/+DNS31JuNp2d37ZuJZat34WAoJf47adpWLxmp+AvPuTJFQzq1ha13Gqgeo0aqOlWE26162LSyoNIiwvD9o3r8O3APhj73c+Q5rWIQZIcSc87ooZrbVrYNkLbLkPxwCcuL4Z4UYoI0G5RDJ8BAd8bO1iTZl1YUHSy4AmNzgKz7KQAVsPKjo2duZWRje4PqFUOmzl+DLv8NJLReewvIsQ+u8nGjP6RZWQUrn+I5wXWqFF75hsWL3hy+/SVlbLpQ9qyVp3Hsdi4RPb4+FpmY2XJHoQkvqMqWWzqwNZMS1OP7b0eIMRbPWcUa9Z5Gsskj3CZGSmsXysXZutcnyVJFWzd/4azWu0G0jsJWeTMYmN7NmftR8xjiuwM1tndkbUcMptlSaQsKymIvJtVZIu2XHxH2aX/KtL7DDPU0GA/777NpFIJ+2lsR6ZlXZWFZCrYrsXjmbVtNeYfnUltkbBFgzuxOh1GMXr1t+B1aStzsHdg5x6EUT4prIWTJRsyc7kQb9WswWz+X8eYhNpNftLZxgWTWN9Ja5lCEsmaOTuxreceC17kAh4dZiY6emzckn20WU5iVe1MWLvRKyg/KTu/ZR5T1zFhe26Rt7xPHei7jAm8w+q5ubKtZ58wCXkOfOFxkLm61mTXfWKL1Ia8pSVGsaOHDrJV875jDpUbs7AUJWAKlhIdzNrWrc2+nb+OJScnsT/mjmLlarRlURlFsinh24zUCNartTsbt2grk1A/x0W9YM3cXdjqvR6FSyJPiBG+t1jXNsNYFHlP5GM4Jvgx69bmG/YkNJGd27yMdenWl635/Xf222+/saXUlzVquLFzD8NYatxrdmD/fjZnfF/mWrMry2+SnKWE+7JOLVqzIcNGsnHjp7OzV58waXZO4bLFu1JBIH95WYqLCDHrvyNw8tQ1VGnUDtbE3uWcLG7bW/il3Qu3nc0Nr2SkJCLg5QsEkw3xtOR43L19i6x9RRAbjHYyimxcPb4DJ27eR1JcDCIjomnXnKvQJ89KgceNq7h06QYS07mxFSAmIgzPnr9AUloK/J8R+y0kPk+bNTU+DNfIjvj1W48gIWMeypASG4Krly/ipodnrhEYKjM0JBj+ZC42JTkJj+7fxf3Hz/KU5zJTYvHn2pV45heN0LAwxCcm5ynVlK3ehOx6Z+HcHT8yIpJfhrKs0v4NeXIJR677Y/DYbwm7HFRs3AcXL19B9TJkiqyYcG73ZsjMyYqZmQmyWe52JzbAC/fuXEKcVAM6uobo1r4ZosIjEBgVhYtnr6N6VfI8p60l2D3v0KQmnnpcQWDAc3i/eIm6DWsK5j61jcuT0xk9HDlKjmo+YzCyrYwRY75FbUc7wcaANCWJ7O+T1TgS3J265gFVq6qoTNb6+Nn5ho2dEHjvDAKiuZOeAoFJsGLFb7Cu2RUtXE1AvmOw5cRV/Dx5hBApMSaWTM4eQUS6jBzMaEOVdrHqdIY7OysVT8m078Vr1+m8vjYqurWBi7kGnnvdpI9BB0NGDkOHFuT5js7Cy9PJrC2xjY3IOtqnDnLiRHnevYWYLA00dK9OTlp0YEdWAPVVs3DxhkeR6qhAz8QKHcnOfD03JzKCk69xwo3D3D25E35ROZhAlg25EaU+3y3Amf3rYKlbJJsSvOVGdhLIsqDXy3g0adqYNNq1oGdoB5cKlrh8/gzNNfmBK8ApyGKkv+99HDtzV+h3PQ1tMsnMjTmR7wHyoDh9wUqMGzMGw4cNhVqaDL0nLEBrNzuyNGhPnhs7oLpD2VzFOmW2QgFSONXrgA3r1mLVykVo05T8MhQRWyiji78li4Aogy9ZPD8sNwVNbp6+aDpljOAoIi8R/8KUgS79Hl7DvMUrES1VQxN3VzKfao1rl2eh24TF6F7TBDt3nEBkeBgO7VqPZ06VMWHCRGgkBeK7aT/AtGJtWMgTsWr9Zqz5YzVu7N6EP7YeRKWadaES9xov42Q4RV6iUn1OYt7Pf6F6k7aQxfrity1m2PLnUoTcP4v5K9aifpvOiHh8G1uOumPVrEH4Y8k8HDt/G7ZVa6Fpg1pkw/wulqvaYc3K5fC/dAQXb3shJNUYa/9Yj9qtumFQp0bkwYyU61R14Fq5Irxu30RO5zqf/AN/fv8qLV6ycPv4JoTfN4T/q2j0HjMV1YoxJZcQ+BiHrnlh5uypuL7/QK7Akfpm9OzfUad/Mi3McuWqT548I5ezRjCEhAh5Ihq1yZfHm1saIZsWXwlJ6WThi0+SSllsrhOV2MAQED1Efgpl53+aX12zSlj6y0phIXlp73ocvx2FGbNWwZiEp1I61kSDM08mrkZOdqSSFITEJMHVJp8iyVOj8Jzk7vqOZbDs5yXITCIXqAYuWLJoktCI9l26YMuwGWjTPACDuraEn1cY5vy5AFpGBti99yDMqtQV4nFPdEEks2hKntHIzQ8mzV4CWVYmPG+ewcpNZ9Fn2FS0cv30MltuZS80MIDs6ZPxG6H7SBucTN+qa5FXwrCIt3RUrnW6ojIzbgXvxsMnyKQx8BctgvWJ9sfEx2P03IXFysLfkvlHPyJ2GeLCyYEPHWvTIhPMPAieAA0NaFMQLjiFURIBTsSNy1VFNScdQcxy7XxfmOqko8aAsXC1NQLr0IUs3VFs8hj3+OoB+Gca4tchHcgqZO5ChhsHom3o3+tIYyfA6ypmzI2BhjwTMk1bLJw3lcw3l5who78XKj7hCIg7+M8xDmiHnRyngQpEsPk51OJCzeZdMKlfS0TTkbvGfSbguynT0aCyEQ7tOAk9Kwcs+GEkyphaYuSUeZj+/Xew0FPDklk/4FW2LeZOn4bpi2iyTgvGHyT77kvyUQc9TYSnKDD/5/no068H1OndlGkLYFu/P36YPBFNnK3wiBxgBJEy0YyZ82HdZDAmjB6DVasW4umFA7jsnYp5s6ZCiya9+l2HYNqUKfj999+Q6nsV3y1eg3pdBqJX+yYoS4uLBfPn5hF3ZfucyxtBkh5Kk8C7jw4q45fkb2x4MpISM6Fj44IJU2ej7zcVaCc1CF4htDssGhQS/PHbWrTuNVnYwfJzu1yViIfy1eqiS8c24FNTqPd57L30DGOnzSY795rkDpSse7ECOgbClkgOi/LOtGMqA6+7fkIeipRw4oSEQ508jb1FDCrE+SR/iGukra2OOzev4dSlC0hVMUBFZweBqH/TuDYkUWRnP5cBBI8nvsjMJCJPttgLhqy0BKSmyBEZl4YhE2dg9vwfaKe6Cj8u3ylEq99xCLo2qobkyGdYvnIVwpgeTezkjURFE23JNr97lTJCvN8X/oRsbUdMmzhUuOdWAF88fYCjp04hKFEOB1eyef6Ob6VgnUr6mhFnjA7FCGaJc/PmXLaPO1qmoDGfTBw5iSQWLs07kxW5KbDXjMfEEeMQl1EY05KvP5nKJa5f9hsPd3n5c7+2hYIKdAytMGnqeDrxkoPTezdg34HrsLe0EEzy5prVVaFvOA5bNx9FG/IVb6ilXB4Uyij/hrgYGpp6qF69OoaP+Q4/TvseYQ8OY+L8Xz/v2M+v4Vd9JRL4z9C9WZnJiCIDEBq0Yn5X4CZJ9QzJ33PF6mhQvTz0yKezpqYBcee5HWyyj05bCs7Y16J42sQWRlYMHpDHK5XMRGzftg3rN25AoiSdfIEHCuw2bVrBuzVsA8eaTTDzh0lIffkY/uEytOvcBgbkQKVZ/x9w9uRRGCX74lVYONJCn2Hzpk3YsOcYJLIkRMVFkotKTVL+0yOnGs7Q09GFnnkl9CSnHI/OXUMmEQx1qpeqippwhpXv3AsGbtIzLol8Yr9tlV8wYilca2nLYWSoh179e5JJTkM0b9sdOdxT3Om/uw69tH894oxroncbF7KvTXbfaZIi6/ZkejN3F87P9afHBGHEuDn4ZtQcLPquF1SlUmHSVyMMlEGFdjacRSrXscLq1WtoIXQJc1asxtz5vyA2k3Ai06Cffw+jjnbdemP5n3swvGN5jB3UBx6BMRj+41J0q29BCm4TsXL5T7jz9AV0adupS4vEgkGDjJioqWShSae+KG9pCCPLymhGznZO7NwL8gqAjfOnIt6kJjw8bmHisB7wvXEIIybNp3ecQZDrjvfS7uXYeT4YO48cQm0Hs7zsazVsjTmLVuPPRUOwYvZYbDx1O+/dp7xQUN/zTzV/gcHPdBNx/CjNVhUoaPwbWVRD37a1YUAcjG6ksBYVcA83n4aVanP42OWrNvLKkFcOrz9fuBYMXAkw/KUHFv9+Av9bdwSHt/0KR1M5eUAciQteoUJURU42fG6dgE84Q1O3igWTF3NNrmr1LDB5zlzUcLCFqaUdujSvBo/TBxEmKFQWk0x8XCIIvGf5VSJliJkUQUBTSw/GxKrju4D3BiIqJoZESPPMOxb8KHOv1UhOGfH0LlJJTsz9mzhWa4jWrVsLLLkOHTsRQTOBjnA6jRYDlI9gl5t8c2eQb2aZnDtDyR0GhsQNqErH84Lu3CWRpzYaN/0GTauXFVhwnbsNIH/WtshJCaDJgmpOq//c2tOkr6lLrlglfBpRnp0RmnXhzH3UaegGM/JkJbyidMYGnCB8QLuFFCX3x9jCEqq02+AEiZeupaNPnAjyyhWfVKSQHBy6eBMhKfqY/P1E2q3EIzwjDTtX/w/q6eMwsHMzwiASs0eNRcOe4zFr4mAE+j+FmbU5uaDlLO38Tyo1NpOOQunRQk6BSs27klvV2nhBLjstenTAk4cXkUJHkD7nocFLJ7djy75L+GnNOuJUGKFD83ZYtuEULns8R6MhLfDHtoPwfe4DiZoe7NVjcM/vDCqXMS2El7quMbQN9GgBSbgK3aoCUxrf0uQEOpYVjB23/LBm6wk4Ohhh8erNaOBqj+kbLiAqZRnsjYD7F/fip91PsG7vcTSqbQLPJz4w0U3D7IW/Y9iUOWhXzxWNW3eCcepCnCUdhwmdGxcqv7RvOCE0sbKhtj0nbtubBatcihxa+Bkav/vYYMG6ceaDOflgV1XP1a9Roe9aW99QYGknpaYXjFqi17z+RmY29M3RZkAp96ZpQ5KeRp4E7Qr5DJCRt7dLO7fBtnJ9DO/QCKqsHmrUaETucwfj3Ok7aFvTHtkyGU6ePAu5mRH5hs9fMBRXaUlWBuZPGoTboVo4d+oA9AgHfdJjyJEmkL4GLfOMPsUZweJq9/U/f38Pff0YfPIWqpH7USPNTMSmJhVaRasToearajVVGvTCZEm7BpJ5cZU05Wegxgm0QOxpBU6TBPfapaOvDp+7txGUoIZqzhXw/JUfKlWqBCcnJ2hnRuHo2Zv0YdKOkfJRLeBgxZFcpVprSXHjygMyysNLUcHBbX8iVGIHK1MteL6OJr/mDkI+AQ+v46ZXIDncUKG45KqVlPVy5W1pOHXPA3butWFIOxQZ7QKYsIsln+XE7k+V0f0bhEOjMkkeb0SlvGncm+ef4qd6o29gQPa3Q+nYGldSTIsPRTJNfs41HKl4Oc7s2YxN+87QIkUdK9Zsxra1SzFvPok+vhtIMlc19Bk1Bd1a1YM8OwVzJw+BpIIbiSMaI9DvKTasXoc0VXPUalgN/gHP8prz0OcZrJ2royzJ6+dMHIaZSw7SoqkpyqhF48WrJHw7fEhe3M9x8eDKaRw5cRYZNI54iKMjXlKmhjJlrOFzfjM69BkCE5cGqF+rBo6c90CDziNRkcZFcngAVixbgWehCVDTt4O7UxUEvgjMa4JvdAjMaOzp0YIqh5Q8ZYzGLBcHEMenRdOWJFayJg4P8JKU9sYv3Y3B40bD2jQTFw7sxoHzd2lX64kjR44gNDFNyDMrLQ78ysKy8OIir8BSvOBcBje32tDPTkVgWCItYBWQkFJqUrIcDciOAjfBeuPUXmyiRYxMOdCpPlpETDkjX1Mnd6xzrk+rZvWgRhjHCzqDckS85otldThVsim1FvDFvFVFR5SjhbXvs2ChHDktUAJeRqF67cYkTpfh6lGq/65jAsucPl/StZCRt0Sy40GOhczsK6GOcxWYGugLaRXybIS8eglj4kAo56SCldckt4ucMaDUK+HSuKy4HNhbu8CQEnDvh0+eB0HL2Bl2pvm6HAXzEK9LDgHRH3zJYfnhOanp4NGdM4iQl0ELd0ci4qp0PvYO/iDFtNuez5BChkEUKuROUxaHPzZuw2PvQCg0jaGaGYqde/chMDQShtY0sVZzxMXzxxAUFQv/5yFo2qk3OreuhSsn9uM+nVnOICK2//BB1KrfFi/uncOB05cQlZQEPSNDMtRRkfx/28COtIF37tpDZ4wVePbgIm68TEafvv3gVolk/Ts3k3yQDHZ438JV0qRv36UrTNQysZXq9Co0gpSOFNi9fh3uRzL8snoZqtiaIycpAkdPX0BaRixS1bTQqWUDsj/Nd7UM27ZsgJlbd7Sq5yS0+cMB+/cxDSzsII14jL37rsHCTAPr1qyGVoX6+PnH0VQ/GaYTsT3lFY7RI/rCQFsX+iQnjgn1xeYNm+DpFwwJcTqcnZ1wdc8qLFhzAAGv/LFn107s2L4dURJdzJg0CtXKWWDXuk2Qm5dFctBdrN11GtMWroJbZVvsX0eKjiFpqGqvSwuHRXBsOgazx3cswPb992382BzMtFXx0O81TE1MkRzxHAuXroF9rZ5Y/OMwxPrdxJoNR+BK551P/bUUDyKADRuWC36+/e6cxvCJs1GucWfUdywL58pm2EU4pauZI8TzBPZdeIzFv6xCDSL8khBPbNy5iww3mSDY3xMrVu9As85j0KKqLvp27oP7Pq9w//IpbCcc9x46iXp9J6Jvm1rkd/4JtAwswMjH+NLFi5CkURGrV/4MG2Ml6fjY1v6z+HxhomtqRYuOhzhx9iGsLLSw86/fkGVTB/O+H0QUS4Jf/zcB+876Y+ioAVCVxGPLlr+w5/hJvAwMRnhCIokjdFG1sj3MbCvgicdZXLznC23VDPy+eguqdxyD8b1blCJXi7RHNHSpvETs2X2CjPRY4NbJrbgbkYOff5oNQ3UFlk4bj0N0umX4yMGwtzfEvu078DQ4A2okljt7bDv8MjUxZcoomOhpkcgqA/vonLzUwgnD+3R5AypDKp2DX7dpI4m8LiCYlPcikmlJpm0KR3tLWBjqwOuBF2Q0Dzy8chh7zz3A9MVL0MCl/GdY6v+zcfBfTSWaqv1MPXd5xxIsOxKAQ/v+pA9HB5Fk0euZXyj0DAxpzsgkdrIRytjokbZ3CO0GaOmrqg97G028ikiAJq269S1sUbu6CyKCX+DOw6dkcYpYmQ2qEcEgi1VxEbh++w7SSenLqWZ9uJIlqueeD2jhkEO7fgkRdiu416hGlvZUiVUmxasXXvB+9hKqOgZo0rwVrM1Izk+ytgg63nWXtP0VarqoRwZdylmZQRLhg1YteqPPvCWw1sgiboIBGtKu1M7KmMrmbqAleHj9KgLJ+E79lm1QwcZc8HmeEemDrj3JPeVfB9DYhViDn0FhSkrKjeeOHUNARAwMTO3Rs083Ili5BMP/mSeSFAaoT0aHlCEi5CW8fQOJAJqAzi2jimtNsJQo+AVH0y5FEEjQDoxY0hYV0bCuM23ucvDq0T1cuO8p7PQca7VEi/pVaTekSscUX+H4iXNIzZDC2oHv/pvScbrC8mxluZ/qV0HOQF77eePClRtIJRGLqW0V9OjeEWb6OtSPKTh/4gRZbouFOrGWe/TuBTsLE4ENn52ZBI+Hz1HVzR2Whlqkm5ADf+87OH3hDo1bLdSn0xN1q5UTFnHZkizcu3EGns9CIKNFa9X6jdGqTnViDUtw/cpN4hiQhPiNLJvvNmvUbYwyZnqIjwjF6XNnEJuUCg0DM3Tr2gflyKXpZxg21B0M6SnxuHD6FCIS02mxYkWL3Y6wMdKnPlcg9KUP4rPVUat6VdoRZ+KWx106VqlOC0V14nSlw6ysE9ydy1E2DPGxoTh6+BiS6LRh+Sou6Ny2OXQ/wTiQZqbD49p5PAuMgJqWAVq2bQ/HcsQ5oHEc4u+LGKk66tV0EjTkI0MCcPbMFaRJsmFEisDtO3SCjZmhQIy50Z8z+7ZAal4VfTo0yxuqEtIrunXnIVTpGJ4OzStJ6emwd6wJV7JWl5MtwfPH14mj95zmCy24N21FxwirCFyCvAzEi1JBQCTwpQLr+zOVkhy3b9+B6DFtNfq3rJ5LHIl9pUraPMqjJqo0m3EHLYJCD2XJ3TRyBTWB6UfXakJc0twm9jr3qc0Jdm4g5zV0zpY4ZbmKTDwdVwMWAj2lXQlPqwz8KFCuIxjuBztfhsyJmJJ1r07PuapZ/Ms7aNZqAL7feByDW7gIH2nBNDxPOU34fNLmWrdKZvz2xVNxOdwQ63+dRbK/zyd5zqHzyHKqGycmBT1h8YmaI8sxVwbefo4L7xNuvlOVRBD0wQjp+aQvBIrOlQqV6biiEj/nz9mz6iQSyOsTSiejhRkXwaiS2IWzQL+EwNstIzYzVUsYQwX7kotilOOP+xXPR4b3McmSCQ9l4O3OpjFHg0vwpa7Eg7/n4yF3HPHxSH7V34xT/pwHXjYPfJwLGPObAnjxXbRSd4K/+lwhh/qPi8S4gqsmX3S/CXyc5OKX+03xdgljidrDxw2PX/B7yyZFGf6N87ZqEOv+UwVeLzkfm1QvDRLVKYc6rz9fY+XVkerGZe28jm/Dnn9DjMZ80THMfW/wPqT/he+fz0nKccDnGJkwPkjsSGV/jgX+p8L5SypHJPCfrTcYHl/eh1VbL2D2omVwITmc8oP7bFV6T8GSxDAsW/ALLj96jDJO7pg1ZxZcK1q9t97Prx3B4t+O48dfV6JGhffHf081xNciAiICIgIiAh+AgEjgPwCk0orCfSrfPnsQMhMXNGtQI29nU1rl/dt8GVeuS0mHGmnd8xU5t+SmqZHPCXh7/nSedtMOmNRugro1yAEF5+OLQURAREBEQESg1BEQCXypQ/zuArgWLmd3KdmW747933wrI5akKrFmRbbcf7P/xFqLCIgI/DcREAn8f7PfxFqLCIgIiAiICIgIvBMBkV/6TnjEl18bAoyONSUlcyc4bzS73tnAHDJvW9QQTm4CuSwLSSmphVLnEKfijR5Roedvu0lJzXjb4y/u2dvaWVwlM1JTkJH5xrbtWyKlJCWSdnn+C6WSXYEnBczB8qdyJCYmKtUZ86N9kisZ0tL+3keMzoEnJb/FvHGROilonCWnvD3eh+ZRJMt/fcvHfDIdk31z/uOd+XHFu+Sk5LfG4fVPLgYDGRm2SUnJtV9QNLEsO7PYd0Xjivclg8CnU+EsmfqKuZQ0AvTRR4RFwszOBtp5WvglXciXkB+Dx5VjOHneCzp09t8vNByD6Cx3p0aub62c96WjWH/gAmzoKFFEWAy+I9v8rhVzDZKc3/kHjt17CWsLAzJaIsO8hYvI+Yw2PA6swcz1F+BAJjkFtWTSIlZX10SPkTPRuVGlvHKukmnWNdeTcXLz0rxnX+LF+QMbcOTyC5SlY2vhMjLWs/B/KEdGlf4WctKwat58RJIte3VJAkzKuQl2AZRa9wnBPli4eBU0rMohOyIOHcZPRvt6Vchs7UHMWnMY1au5gcli4P9SgZUbV6KitS6eXz2P3SevQNtCF48f+6FT928xenCrQpr8f6tHCT3IlmZh+/JZ8E6tgD9XThJy5ZrjZ/dswjE66mVvZUoLFVP8sGAyzLTyTxLwiDzelX2baXz40Zl5I6RlyjF19hyUMdETjOJcO7EDu056oKy1BfmB18I0MqZU1qh0j0vyEw7BT25j9abdMClbBgnRMeg5eCxa1q/+N8T40ckX5C1x3c5jMLe3RXxMPIaNmwb3qhXo6KQMN4/twoFrj8lqI9lOSJViyozZKE9HZLkVvGOb1uBGQDxMDVUhVzEix0U/woiOCnJMTu3eQMdHg2FlooMUDUP8OGs6bHUKY/e3yogP/j0CtKoTw/9nBHKS2ZzJM8jvdwaZWv96Q2qED6vjWJn98MtBFh8Xz+YN68xsKtRhwcl0ErtISIl4yhrUcmJrDl5jaRlp7LcZQ1n9tv0FX+hBD46wqtWqke/zlywtNYlN7NOSdR/7PyGHtd8PYpUdXViLFi1Zi5YtWaO6VZiZWXl28VlUXgkpVA9newtWu9PYvGdf4kXQ4+PMpaorO33vFbUzkX0/oA3rOm7RW8fItqVjWe2W3VlYfCqLfu3D6lV3YmsP3xKalRrmyxq712TTl/7FklPjWZPK9qzh4J+Ed3cO/sIszCxZxYoVWa06LdmGnZdZDh+EmXHkK74S6z/pV5aensZWzhzCDMzLsyvPY0odKlJ8Zc/vHmIVzC1Yr+G5Pu1l5EN9/++LWK16LdiNh8/Y3RMbmLFpBXbkYWih+ghp7xxh7nXd2cm7fiwuJpJNHtKR9R47n0wFZDPP87tZtcqObMvxOyw2MpSN6tSS9Rw3n8kK5VLSNwqWmhDCerSpy35YuZMlJSWwYxsXM/cmbVlIQr7X9txSFSyJfLe3bVaL/fTXKZaYEM+2r/iBNWrbi8VlyFiw93lWv14ttu+aN4uLjWZzv+3BOg+dymQyKaBsAg8AAC7KSURBVPM4/gerVb8Je/AyjEVHBrJBHZuw8Qs2CO1+eHEbc3NvyK49fU3tDmGje7Vmw2f9Rv5vxFDaCIgs+n+/RvpP5+D/8Abu+L8koyv85G7hwFmuWdnK8/OF39F2hNjR+fxW7u/6Xaw/iURSJINPe8vIdr4hmUo1MJBDn5y8VK5UAfGvA5GY+vd63Tq7G+Hk8nZQtybQ19XHgBFDEO51A7f9E3Bk306om9VE69oOZO3OGAP7dce9c4cRkpiKDP2yOHCGPLOdOolTJ0+ied36mPjTcrQgL31CIN/pG8l9rwUZStEkFuiXHPau30g77lpo616J2mmC4QO74P5psuCXUIQFL0vElr3n0azdINiRgSQrexe0dLUjZ0e7hOZt/2UhIjLNsWjKUPLnboZxs2Zg4sC2wruMbBVMnr8dnp6euH7tJIb3a06KmPxVDoysDcloij709PTJfn0lpKXEISz+7axfIbMS+cOQmRqDnVvPwdSe7M+TDQRuCCYt6iW5XN6JdkOmoZGbIxzrNsPsOVPhVs6sUKlcmfT0oQNQM3RGe/fKMDU1Q+/uHcjl7Wn4x6bjML1L0SLnTB3rwtTcEv27kpOm80fwIqL02sV35IGel+EdLMVAcrRkTBYFm3XrQXaJA3H42tNC9adFCDxvHUdgrCbZn28HEzLw1L5nbyQFeuHcgwBcO3UIUo0K6N6kGrXNFL36dCULmZfhGRqPwwePwKpCQ9RysIOZmS26fNMQ108dRFyWFEfI8ZBJhfpoUtWe2m2Fvl2bk7OZQwhLzbWDUKgS4k2JIvAWfluJ5i9m9gUj8JLsy0+cugiegbFYtmg+HJ1qYtTQflDNSsKa5f/D63RygRoXh2rtB2Bi34b4deFi3Hjij5btWyArKhIhkZGoXK8tapXTw7W7T/A6KAxNuw/HyO4tEOJzE0vIiUlMkgZqkkvQdFkGXcswcvxUtKxb5ZOfGjC0dcbhK1ehrWeAnKx4nPJ4iJqd+8PJxvBvPfToJlnkYgbQf+Ocw5zM3BohE15koe7pA1/o63XKMzFbrjyx7ZOj8JRM/A6fOIkck1iScxng+oHVCEm3xrqR5Jb3jYGDa4c2IE7fCX2aJ2DHiwJuZf9Wg8/9IAd3XwRB14ZMCr+pu7m9IySZsWTFLwZVzMrmVTAtJhChr+PRwcQsd4FIRmmqVjLFjp2+yJCl4cC5O7Bz7IZdm7ciJiEe1Zu0RvsmtYT0anRkMiTYk0yovkJ6ujq6kIlkx3KmZMjcijzLXYa6tj5yMlNwYP9ZODo3QBs3+7xyS+NCJs3G0W1bYVC9JZpkROI1sba5nsDzh9cQkJiDDtmxWLNiLdVPG32HDkfZIrbUuaGXpw+fQcvwG2F8M4Uq7OysoEKLkxf+UUgg/w0qmlYktiHWNClraJBB/uy0MARFJsDVzqA0mkSWK4nlTn7osyXqMOEOAKiXNHVMYKObA99HvkDP+nnlciM4XmSNjmVrwlCXSAM5h9I1MoMpibS87nlCQfG11VwEh1Xcso81OeFRy0ggx0nP8cLnNQzqdhRM7irIsE+FclbIjI1EcEgsvAPDoFujqXCKRkG+DszKVUR6WjQCwpNg72KRV754UfIIiDv4ksf0P5OjJfkpH9S+FcyMzNGxa0+0b9OM7LIryD77RJx6lob58+djwfQh2LN6Mc7ei0bnHl2Q4Ef23I/fQpcR4zFtRCdsXj4bq4/cw4gx36NfEwf8/vMCBCdkwqaiK+pWMscDWuGbOjXCnPkL0MJRB2MHD8ElsvnOrcl90kCEx4R2HfcvH8eUieNoN06LmF8XgM9jRUNqLLl+pcldub/g7mJVaCcny8xAWiJZLVPJ52oIVga5NTjyNGJpnUvcJUnBZMDoHEbNnEZ+tXMLSIn0xK5jTzF+zFCo0qT7ZQeyYEg7r+wCfaRChJ73mJR8FhQMOZlZkKRxh6r5mKiSm1wyhYbMlCQkytLJI90V6JR3Reu6jpgzfgT+PHpXyEJNIUOg7ws416wH7awA9OnVDc8juWIaeUAj+/jPH1whWf5o7LsRgXkrf4UNOUwprSDsdL1v4varTOJWdIScvMXxQCxURMRFI0maiFue3mjashkkAXfRd+AohCVlFa4OeXNMI9/1jH7zAmeLUR4gYl+pvB0U5MqZv+X5JpAZ3qwsNSQllZ43ORq2SE3IIh0HsiaY10fc3RNZu8yU5lWTX3Cf8elxZG2RxvMb/0PcvCB/AzmZuk2jRY6C3B8rv1zyISRwObIzMpBJzncox7z8+HhRITsf0owsweWyPC9DniVZ66R6cYuPYihdBEQCX7r4ftG5GxObsFJZM/Ilr4eqrtVQqVxZZIY/xYlL11HR0Rn+Ps8Rlk7mNiWRuH7vPipUdYdtGSM06ToUVcqVg135ymS4RpdYdYNRzs4aTo6OUMtKJ7vmOcLK36mSPSzIZnXfns1gZW5BfsBnwkYjFrsPHCRC8Xl2sLUatMC8RcvR170MRvfvhZexf2eP8vmYT2xv+zj4O9rY5Ac+//Ep700afnd531bEpdugYRVLfkuTeyY2/bkdrUdOQDlylsJ9xquQudovN3CzvdyvXj4CfMLmgZvgLRgErOjBm9fCK4Em0BVfJGWRPwSXOp3JB3o9uLfuis61zbDul5+RQdk06jkBR49sRJN6dTBm5o/QjPHFqvUH87J3rdUQU/63HOMG1sf0iSNx7UVY3ruSvSDWPO2yd+06j34TvocJub7lsgI13kfUQDnZX9cixbCRoyegNtnRHzNlHJJ8b2Db0euFqiEgQ3+od/OfC/SRzBSraKL/sO9QRjcGy5f9iTs3L2PrgePkTlgbGkUU9fITl9AV1YkL4ArUioa3UNu/FaDglJde5cUV2pMbjS8A+BvlO97Pudc0KoR4yjf0XHlJz/mYUaUxnxeUL3l2YihVBAqgXqrliJl/iQjQhyZ8tPShqdByPDUuEglxUZBmKaBJ7MqAgFfk/jSM2OrTMbBjE5rkcmiHr4GKxHYUDPOQPXJ1FR1YGZK8VMiLGlnAT3zupELsSDJyw4OmgTlsLA0RGhYm7BKEh5/oT2LYcxw7fgrpqnrCwmTkt/3h99gDOw55/K0GhmZkc57bl3/zRrCjLSfb/9o6MDDmE5zyDUmLc/huh55ovnlGcvbNh47BilysaryZ5PwenMa2856Ie/kAG9avx5mn/ogJ88XGbXuRSA6AvrygDnViQ9MhgLzANahppUILOuXMnftKQ0cLWuTkm8hhXlwpEXWFqjo0dfUE2/MmZSsKdsu5HfpyFcohNjYKcbQrnjLxWxy+4S3YtNfUMoU9ibQDX3kiIz0Jhw8fIvktiMVdHhMnTYDk1WNs2n4kr4ySvOD6I3fPbcYtP2I13zyBvzZugPfLQLx+eR8HTl6FipYhNLQNqC6Wgv14bWNr6JEvdH/yFlcwqJDrV30jGgf0qwxy4njw8cEJqq2jO44f2g9LRSLue79Em9ZNyTc6YGNuooxe8r/UXYYm5BOCu3+lMZ0byOWtlJYhRRYWnO4amqoL/hPyFmy0SONqOCpaetA3IZfTyjxozuD+BcgLBo0VHega8O8iv93ZUuIYEDteQ0831+69smgi9gruF0OFFpDEyhdD6SIgIly6+H7xuQusM1pd6xANvrR9C4JTNMlXtybMneqgb//+GDRoCPp1bYsc+tBB3sDUiP2qqc2dxRDB4w43KK2qZq5TGU2SWXOWtZbSgYaw3SVf9srdQkYMXkcnony5CoIDl08JzrnNv5Ir2G9x5l6gIB7QIWLNCbeEjvrwEBUahIDQKOG6ZiM3mpQz8s5sp0ZHIkWuCVf3mqhaqzLSySe8MkSSfFGua4HKDrmKdPLUKLwKiICtXb5s0aqiO+ZMHQtLUxPiaFhASg5EVbR0YEdHpbQ082Y+ZZZfwK86alcsi6z4/HPQ8cEBNCGT+88K1iSjzYQP+bpPleTAwLYSrMuYkLJiPia+fvEwd6wCY31zuNmYIo2OYSpDcnwsjS9dqBDL+8j+CwgPi37zSoLQWAXMLSvj6a0DGDp0GA5fvS2MJ20tXdKJUCCLxAGlEbjzn6r1O2D88MHkOdCYvAOaQ0FsZw0dQ5iZm6OakyOMyId6SkLuuXiWnUHiGinMzYwFVjv3fPfqdZiwUHGuXQVZ0lwsuCOfmPAo5GibkH6LFem1eWHXicvoPeFHjB01HFI6Z29q6w63N2OnNNqmRoTdqXZNctaTg3RB7EAENiMJ0ekMjrVchPrHUf1fBocLjqmqN3QjrkIOsvh6jr5faWo8EiUaqF7fFdXI21yWPFVQpuU7/cSIKEg0DFCjtiscXOyRRro7PHAWf1hQNDTNLFHRnvCjo6ZZ8fmYxAcGQkvLHJXsSd9CDKWKgEjgSxXeLz9zMxtrSNNj8ODxQ9rBRMPGsS4mjOqFM9tW4Cb5po8gN7YrlyxCaGgyPB964HlIAu5dv47Q8Ag8efQQiWnxeHjPA5FEBD0eP0V8agI8Hj2BlFb3msTmTEgOxPbDlxEW8RrL5k1HmqY5xg3pCW3OBv2EwcGtJqyMLWGpr0BqCim5bdlFrjDLo0ePhlQLCSb37Y4ewyeDk5Dm3YbBXDUCm3ZfQUZGKjb+tQ6Wrg3RzNkCPWnBkxZ0G6fu+iMtPQEb9+xD3bY94GSWe5Y5KzkGqWQgRE8//2yzsUV59OnTG7169oSrU3mocAKhUIeVrR00i+yIPyEk7yxq0IRvkRLkgWM3c9u5fuc+1O7QB1XMteBz7RjatGqNnVe8iDtjhpH9WuH8/7V3FnBZXu0f/xHSCIKIIKmAlAKKhYU6GxVs3WbHnPXOwM3EmduMuenc1Nk1A7sQAwsDMAAFBAuL7nqI87/OTVp79w5B9H/uzwee57nj3Od8z7nvU9e5fjvXIzohFS+iAnGSpIeH0coDPlg95uuReBB0GFfCovHifgB2nr6H7oNHwNjQFF2bt0Od6jrk/CQV+39fiScF6hgzvA9N95hDv5Ye2UzoIZUaDhvX/YY0akT17+32t3H+twe5sqBBXSf06dcfHu7usDTTQyaVX3kaguFGcnUdXdGqoSE2bVyPeDI63b9lE1JVTDGoZwca7crAgilD0LfvVGTKK6PXwIHIfBGAg5dCyUlPLLbt2wcH126wofIRFngOy5avRAjJDQdfOYW/zobim9nf0ZTAq6Mi/zYdb7tOnhQNzZ07wUa/ABvW7UBiUjyO7d+K3JrG6Ne+MRlOZmD26C/Rf9gEpBRUo7j2hYlaMjZsOkCObsg6ftdWaNS1Q7fmVmjn0R9ICMXuU7fIbiAOm3ftgjVNeTU210O/ge54fPs0/O8+RUJ8NJ1zBq5u/VFLTQV9hw3A89DzOH3jEeISnmHbwRNo1q0vrf8v7fG/Le5iX/kJKHjRVv5gRAgfK4GaphZQlkvFlcsBsHHugnYu9nBq0R4mGjk4c+YigoKC0aBTH7h3sMOh/QegqWcCRfJkxXsvIfdCaVmUGeQz00iPXg/XAiJhYWmGlOwcODdxRly4P3wuPYeNgSr8/a8gmZxfLPhhORpZmlS6X3qDeg6oa6iMk6eO46zfWZDMOZaQlX9bB4o/Dalm0rCwvnVTdCTnH6qa+mhmYUya2IcRePMqHserYdEPS2BK0ws1DOvDSlMR3seOk/WwH7JVbfHjkpmooc5HNajTU5CNq1cC0apnf+qZGUn7+KiGJK2al4qtW7dBTccctpb6eBEbD2daSlc8ui+dXEX+1TCgdBKvwwcO4HrAdeSqm2Lp4nnQJatERWqUvEyWoVv3HqTdroGGzdoih5zZHL3gD/9LF9Co4whMHd5DGuo1s3OGOhLw1/5j8L9yDY7t+2Oh5wia6lFBU2dTHN63Fad9LyGQ5tenzv0J3VrZoXotMzSsZwifg/tx4eI5WuKViOleyzGwk3OFlRspj2hcOi32AfZ5H4SehSPMa6sgJlcZLk0awaWFE66Tnrovrb54+CKD4rOYyg6NRFHdnC9LRU0za3Ro3QQ1a9eFhboCDh49jlu3r0GmZo0FXjNQs7oqtPV0kfTyCcIDg3H9djAGfTUNg9xaUpoqsp9FI2qk/97UwRaXfY/jxu0ghD1Kw6RpXnC2qSPNoefmZUDLtAE6tWpMDdMacLYwxzmfowigcx/GMMyY+z3IdwO0dIypIauOw8eP4NatAKTJG2P+/NmoXUMTBmb20M9Pg/dJH9wOvAotk9bwmvUV1Gm0T8/YBrU1SXSKlo8GUphqBg0wb9Y30CYnOGKrWALCF33F8v0oQpflZEvD1nyOtFgjnXufKrZy5frNXPs5h86T7Gxo/o3rhvMlRPw3739Uo+F6rncubXyYXlkZl3cuxZiFZ+DntwsaGup0It9Pc7sV12H5W96Facqj+cccSg9NRaiRwVvRy5V74iLFdxoyLxxZKKAlTzm0rpmfq6yiQoaIPN6FEedztjlklZxN84yqKkpQofnqkiTR0GVmZiYZTqlKzF6NEA15Unh85kLaKDwV4lRVN64HLyNjyBwa2lWhuXbOgKeTuzHNJhsNJSXlkgpXRn4OZJyhfDWJYTFHnjbOlpzF0MSEonRMRblodIOHQ43BrKwsibESlY1i0aV84i8jzllZ2YXH6F6v649XCDeKUw6lrTCLyIqEbAmktFCmZWZlUlpkNNytjLJx5eWKL5lXLkoXLx/ZNHHNnwe+r7R8MOoxZ1G5ovLH01NZaeJ5xg0eiSVnqqSkQvFXLmHN40+mJFQWC/OF0Rx5FuVnDo8/xZGX/2KhKC6Oxct9Ll3Dn3GpTBQV/jxKVw6tpeevAZ5uVWkqrzCX+D34M8M14SUmRWWpQvJQBFpCQFTwJSjEl/dJ4HlkMJbM/RbeFyIxZuosTP56ELQreVj+faZHhCUICAKCwMdGQFTwH1uOfSTxTYl7iqCQ+5KRlFyBKpq2diZDvqpoUPaRABXRFAQEAUHgfyQgKvj/EZg4/R8SoCHNfLKmLd6kOejiH+JTEBAEBAFBoMIJiAq+whGLGwgCgoAgIAgIApVPoCLNNys/NeKO7yDAEB4aiufxSaUGXu84s7y7ueGdTMaNeaqiA5fypk5cLwgIAoLAx0NArFP4ePLqX8VURs4n5s2cjpthkYh6kIstu/9CM4c6JVax/yrQv7nId+M6rDt4ACaNemPJrJFkZVu6HvxvLqv4Q1kJWLliLfQt7VCDlixxbx3cCYhK9dpoTU48Sqzgy8Tk0b1rtNzpHECKck1adkJLRyvpaMKje9hz+BSyaQmBiSWtn+7uKu0PvXQIe849Qotm1tzbB+0jJ0BkpW/dsBnun9+JwJckvGNrRJ686BitvZYj96WOTZpJGtlSAJX8LzflKRYvXQu7Fi7QIIc73HKcexfT1DNAc6cGiH0Sgv1HziIjLQe1LMjlMCmjveb8rEyMGW5eOo27L+Xxed/PyuwHgkmPwNc/mPy758O+TWd0cWnwynH+I/TKQRzzT4bn1GGFx0io5qC3N6JIqUxBSR1u/QfBwkCr8NgH+J+bmYAD5KHwWXwGNHUN4NbPA7XV3+7Lga8yiIm+jxOng9BrcH/oFOue07RV/IsH2L/vKLLJ0ry2VQO492iPylhHwa3fL5w8hFvkj0BJVROuvXrDzuhtjmbI0p/WuO/Zuw8vUrKhZ2CJQQN6SI6wuCV+1N1AHDrqgzTugrhRc3j07AROgfvyv3XlDE75XUMO+Xhwce2ITm2a0BEGWWoc9h08Dn1Te1JSVMDd27dRw8QZnV3tP0BO/j+7ZUXr0YrwPyyBK/uXM4eWXdiVS0eYRy8Pdu5uNGluV5wSc2ZyEps2ohf7rNd00vLO+rCJL3P3tCc3mbOJLjM0NGBGRkbMxNSY6airs+b9Zr1Vj/v60bWsBelb/7bjMNu05GtmXt+eRacylhUbyrq2bMLm/7qD3bjmx3q4NmWL/vCW7rTt25FMQ0OTGRkb0z2MmbGRPlNXN2A7rjxgc3q1JA1xXWZMx/hfHQMdpmNgxy5EJpaJZeV+jY04y2rpaTMDQ6MiJiYUX3XWb9xClhITzMaO8mCb959iNy76sZED+7FJ3298qx58iP8JNmXCBNbUrh7l+4xXEnH9zCY2fMww5nPRn104so917tCF/Xnkxivn5GU9Y10aWrDWXaYU7i/IYb8uGsM8F/7Ert+4wf5cPp+5dvmcRcZnvnJdZf3IzkphsycNZu5DpjL/y5fZnIlfMrfRVL5lr8WggPTUn0ew+TOnM4+u7ZiVtSuLTil+1uhYTCTr3d2VTV/4J7ty7izr36sTm7Jy+2uBvP+ftAyW7f59LmvTpQ87S/mwfeVc5tKtHwuLSX/tZqQdHxfNvujRkfUYNoX5XbzIhvbsxEZNW0kr5/LYvYAzbPiIkWzLXm+2/fflzMrMjI30XMJo6R3zO7qdDRn9NfM+fJCt/H4aM6tbjy34/S8Kv4ClvQhjzvXr0LOhS8+GKWve0oNdDHz62r3Fz4ogQEO2YvuUCZxc+w3r1H8US0lLowo3vUIr92KOS74dzTq6e1apCv627y721YRZ7HpAIAsJCaVK6yhr3aYlu3DrzRdNVmIka2Zlwjx/OMBIFIf57/6Jtf+sM4vLYWzzknHMqlk3lpkjYyQPyg79MZuZ2rdiyfTba+IotmnfCRYSGsrCwsLZjzOHM4/h0+kFmMxGDPyCnbh4jYVKx0LZ1593ZlMWbKiU/CjOl9c/L+76mY2csZTdDg6heN1jl49tZc2admS3nqay81vns15jPSnuuYzWpLOksPOsaeuBLPH1So0CTU2IYVEPn7BJ/duzNl2nvnKbeePc2ewNx0nMLI/RWmh2Yt0C1n3owlfO2bF8NjMy1GPt3aZL+/MSoljHjq1ZcHw2Mc5nOdnZbGiP7mz1oTuvXFcZPwryc1nUtQPMztae+d55yWiNO4uJuMgcbazZDt/wN6Igy0pn9yPC2ZFNS1l9q1YlFXxuThY7uMaT2TftyhKzc5gsO4Nd2L2Y2TRsyaLiqWBV2FbAkmMjWFtHa7Zgk69UZpMSYpl7K1s25Ye9r9w1T5bNTq6fw4xMzNiFiAQmk2Wxmz6bmZmVE7sa8ZL9MvNLZt+oP4tJyWXpqUlsxYw+TNukPvMPe8q+7tuGtek+QyovyYlxbExPJ2bq0I69yCCnz89D2LBB49i+Q0fYKZ+LLD6J3kN5xQ2fV6IgfrxnAmKI/hMesXkSFoQz18PxPDoXR475oIVrW5iTAIjfsZ3kUvIJueBSQR/y+V1PRw4HvA/jRWI6XFo1Q/hVfzzNyEFX94HksjUJR46fI1/syugzoD/M9LS4vxo8jgrCsZN+SE7MhGOrrjQc51jiOIMPy5V1ZhP7MBRbvY+TJnU2rJu1Rw/X5pXjtKRM3mrXscHYcS6wq29Ew9D5WDF9FdyGz4NLA8MyZxV+vXh4GyKS5bCorQWukNc7bTt3HD44DmpKDBfOnCevZB2hWuQQx9HRHjkv/kRgeCw6egxBQxcXqJNruriI87ganIPF6+aiGg1tDh41Hq4uzpKznGCfLXjJbLCOpHiLHYi8EYlK2GFg1QxTnM1hXa8WiQ1lYcqqBRg+7Ts0MNTEdXVN+O1fg7lWtpg3YTAunTmL2iYmUH/LG0OTXMqSAzRK95tD1iq065elnrA0qI7BHZvC71IQbBsOLkndvUtHcPmxPAZ0aY7rTwsdJSmQumFydCQmjPgKq35eCjOFGDwjCeIRVrVLrqusL1wjPTAogNy4qqCukR7kScNeTb8eNNQYrty4isEdCqdtiuNTjeJubmaKmGCu71468cOFWc76B0Fdvy65plUixzMKMCN1xoy09bgVHo26LvWKg3ivn3xYPfZROKJIMnZifXPJo6KKihpMSWsgkDwxMvQtiWU+ObgJu3WHP72oRR4KFRUUoF1LH2kpsbh08wEMNZXI0c9DJJKjeh0dFXLpa0U++QMQm5oJXVV58gL4EFlMkRz7qJEevAkSbz9FhgzQInllbSMj2NlaITMti56/Qk2L95pQEdhbCQgju7di+TR2pqekSHrMXNs7NjGZvM7J4bd5E/DN4vVo2rItdLKiMGDAMNx/kYLYB6FYNHMaxv7nOyjp14Fq6j306tIWX3mtRi1LWzyhOdIBwyYhmby0pUffQc9un+O5Yj20aWKOOSO/wOq9VyGjl1jxVuyt7dmd83B3G4hYFSN85toCy2d8jVVbfCrdCM/E2oHmv01Qjfzj++1di+spWpg+rONbK9jwm0HIzU7Cmp+WkfJdFKaOGoT5v+6WXoRPo9KpgVP62ChRDSaXk0m+ufPg0q41NGiSWo6lwXPWcniQPK51bQ2S0dNChw7N6V7ykCVFYeaKXZi5kNy+fmDHP/VoDtWWhE64h74DVLnHydlhbL92UuOsSdehGNSS3NPSnLiDUyP8sP0c1qyeB6XSOqs4q+mTq6VxhfE3t9FTZsFcLg5Du3dA48aN8UTeEIun9pVOzMt4iWW/7MGIaVOgJc8FaotCUNPH4pkTcPPEZrRp1gitP+uDAVNnoo11qYDPm3eqmD18Pj3x2TN6dih+xYWay+xROUpMKBXYKXt3zuJ1a1ZJnCU2TvKWyFPJT5Enz31c8CgpuVDEpmwY7+s7V4tMiX1OinB51KgoCpXurUCNjOS01BKFeH6Ex1tHjxomVLx5Jc//c237fNJ0j33wAoNm/IGga6dgra9KFX0u9pJPeb3axnC0Nse8P31w+eRmVKcGXW5OAo743ICtbUMYktkEV5ULvnAYK3/bhmPe29Claw9cuhNVFBnxUZEESt9UFXkXEfYHIWDbrB3c2jvBxKo+Rg8dBP2cMKzevBduo2egmWMjDPnPZOQ+v4UT1x5h5AxP6qHook3v8ejezQ39evdGbkYmen0xHt3atyOf2R2Q/jACSVwelSzkjbVqoYEp+Qxv2QNOjrVx/rSP5IayNKGFNcHKxYuRoGwBzy97wonERfo4mmDz9p2Sq9PScyvxW0EKSbZ6o20Xj5Key+t3T4njLjcVMPq7+RgyfByWzRmMtUvnkGDKE6iQYR6TK7NCgL80Kan8hV28BR3fg9BHgHv7N43JvP9YQwpcViTQoV18+gf/zE+PxW/bTsH9889L4iIvp4zaBmZo7+qC9LhHCAgNwe+7zhZXwSXn/bcv1RSpR1vPkqRRGyEyLBi+5y/ggF+4dNmfS1fAvOtgNDbWIvEdzrD0daSmoQdn58akRKaB4PuR2Ejl9mkKdQc/wCbFi2rlkgqe4sCzW67sMNV/iZd0PqWPpulLzyz6LjUISve+32+8bEo1thwpJJYJmr4Xu14u3stdUrftMwI6FK+de86Sa9sM7F63m1xWZ9O5PIMUoKamhnxyR3vxyAb4RaRi6c+/wIS0GbiypCrJxspysrBx+XzEVDOi0ZfFUKU0q+mSz/oflmH1igWY/p0XWhnLMHHyXGTwZ0dsFUqg9Imq0NuIwD8UAe5Pmvv15m/mlw/vIpPMd0N9vTFv3hzMXbQG5jb1YUy9TPmsVKhWJ+EQO0tpGI/7GldVrgU7E32pl5tPFbsC77lQUNXrNsKkmaNxZKMXvpu3EHcjn5JVcDwlscwbpKjGC42JR54sEYvmeWHWzFkISVdBY1J2UyiWlK0AMOe3LUXzpk3g4OAg/S3ffZ5GDApHF+5ePoabMelwblj/nXfWqslQo6YuGjUwlc6pZ0vD7ilpuHQllLSvSUebvTYUTcku7tzRvAfWb9oEHXuyTC9T6fOAGInNbCALauvWnd957w9x4MbJLYiMK0CLpoXp5XH4xWs8ribWxOlzl3HtygW4t7DEspljEEiqgv94IxGTr0eNgnHHsfA5fQWnj+6BoXwMvp0xFTevHoF3UDg6OZkiJOQOXsbFIysjHvfuP0LUrdOYNP93LNp6DJev3cTqBf9B6JmtWLx2xz++9b85kVuanzm0vqTcNCUhoLU7/KBCou28Mq9WphUnlfTSTP9Ht2Mktcx97ZcWi8JvNO36j67/tydJDRF60yvR/ctufHSi7MZV9YxsWmPX5p8R7rcJ0z2/Q5RMHUrVtWhaoqZ0KreWDz1zEHN+2oHv1+zEgA5OJUFwX/Q+m1Zh6+lIbNi5D81oSoVrv0eE3ES6gpqkFq9IDeSmztaIj/LHw/iKkf8tiZD4IjEXGD5hArx3ILXU6dlWUdemRrgy+o6Zhn4tLellQzvzc5GYkQsFxKEavcRUqeLlr4HiXkXxq6f4txKteru8dyVG/GcFpizegAmDP4PX5Pu4Rc9qaOBdOLZw5FeXXK9Jw9KGFk2wYNECmrdUkUgnkYpaRcrFWrdwwywtW8mTHn931ne0JQW0wrbs1YsXkUH7dLTfvXzP0Nyaei1RIMlveivSH70IefOgmpo26tuTClx8YWOBJybheRJyFDVhaFy05CgrDpdvRqDRsBH88Ctb6pNgBD+KRVerWq/s/9A/fPfTlIliTWiVIMmFz+1H6D11gzRQa2ThhF3HjqJti+YIjSQFPJO3jz7wMlK2F56TcB/BaapYMOoLKYkunfrh2D4FdPvqR8QmZ8JUXxvb/1wrHbt6KxxJijJs33caNtrR0HTohuZFOunjZ6+EXOZjHAqPqFBU8jTnbO/kCi+vmlL55TrxNrYOeHwtBEoFV1FAPVipEUtTMjm0dNDEuM474yNPzxZnwT/5xpdLmtYxQeQzaiHTxiv1rPgYKNCctZGxrrSvIv7x+9YixUgNko3NLyiMCx+2T6PpAn09uzcqAEZKkdCri/UbNtIxhkeBe3HC5zxaNrGRBGue3T2H5T9vxKI/tsPVsR7OHvsLahauaFpPB7fP7MSu4zew88ARmOsqY+9f29CkvRsWeH4D/xh1BARdRHV69nLSabqA0q3wWoOjItL//z1M0YP/hEsAmT8jNT2b5viSkUG1lVHDDmhiXwe/rf4FaaRqxnXQl8yYhBP+YUjPzqQ5uSwkJqfQcCmpfJFONHdYk0EKWlxVLI0+s/NIFSxbhgDf85BTq4uhvVtCiXrngZduU4WYgyP7TiEhPYOuTSPlrVSak8/DyEE9EX3jMC7cjaYXDK2V9tuDb39YS/fj96+YrbaFPXr07Al30vb28HCHrXmtkuHI55F3aWRCDkVtjaII5GLGl73Re/S3oNcbXLsPRk1ic/7iXen4Vb9DkNWsAde2DujU0w2P7/uREWLhpafOHIa+rTMczLhRFZCZGI20xHjo13xzzXbM4zDkZafSscJzC0P48P/vJCZRi6cm1Arf/xQhRVhoqsP3sHdJ5JKePqMGYF042OgjOuA0rfVvgb1XwqTjvJzJqNJLJJuPlPQEGiUidTXquSlp6UM7g4w0fS6VhPMkiioWXRu07jIA6zZuwS+//ir92dXRgGG9llj03Wg4WFgg5uYl6uFlFl2Xh5dPFdHIvrS3WBLge/zCK2R9E0sqMx5S2enZww2WdfXRsLkLqpOk8qlzQeBpfRxyGfEyFXRu35YMR7OwbMYIuPefjgxed1PFLSPFuaTERHp2UhGfRPPc9BxwtUU3t/ZkfBmE4GdZyKORtXPnfMjozhpNLAzfYypeC4oaJTWM6qMhTXUcO3iKRxB5GY8RGBGHTt16kJ59FhZPHA6P4f9BOqXtwe0z6ODaBTt9SUeCnoaVP66FU6uBaG2rh2dRNzFy+CgkatZCwOlDWDB3Hlau2gZlDSXcCzyBMdx+x9gM+zeugde307Br1wloqinDydIZvToNhS6fn8/LhLffdZg6dEBd3cIG/2sxFj/fI4G32MS+x9BFUB+UwLnjO7DF9x7k0uQwe84CzF64CH9s2Y6Z0z0xfsJkaFXLIwcmDujsqIu5c2dTL04be35bhtoFA3Dq8F7o1FLG+mWLkT3EDX8euQZNqpfmLfsD34ychLMRizD+mxmk6VwAy9adEBhwCxmdbXD1+FZcIgt9WfJjLF+3D7Mne2JxKrBiznT42JgiiSbeRkyeSsZoleHe4038KiqaqE7WwAplhlv5WemZ6UjMzZZGL3Qsm2PlgrFYMGcCHlxyxjlfPyz6ZT2cDEjytt9kjD5+GxNHj0MTO00cv5aONWvWl1SONJALDRop0VAnZzqvbQo0esKPqVcV5z9F8VPXVIOWrLqU9sJdcpj700+YMHk8hox+BLPaOgi6E4qBU+bB0UCTnM/IpIYgqchK2wNyCOT14xo8SSG5XbmHGDpmPMZOnYPPHEywcvm3+G6xJx7daAfF/HSEPEiE19IlUCu6d05SNPWYZ+GJohGU825i/MLVWDVrLMb1uY6vhgyAC021PHwYBnmthlgxrn/RVZX4QZW+jrEzFs8dj2Wr5yL2XjPcuX4BX3jOh6udLnKoYZydxRvQfOSLnMSkxWDu/IW4HXEfRkYqmDV1HNr3/BzffN4NTp2GY4RHCGZOHIfGZItylWxfvH5eRY5wSlpWFZIwbs+wZPlPmDhjOWbNf4GYqNuwd/sSYwe4gFHDLJPm2pNSlWgaQgF6htZwaGCOCz7bEOD9EHE1WmL9qtlc8xenD+xCWAyJKsecRcBFX2o0y8PKqT2sdJSxdOEWxGZUg+/B3ThJDXk++uHaYwR0qSU9zHMKFs3+HhMnRSE7IRzp6rbY8seiSnHwUyFAP6JAhS/6jyiz/teo5lIPnMkpksa7gqQFraJCc4lF75KXz6lHLa8KQ31d2kca5lkyqHEjGVrKJkfzhNwgR4XG42XUwpfj2u80vKdC3s4yqeetRlrOfDL+xdMXUKT1UTW1NKgnkAElWoLH6EWQL1eNqrl86vEXSHrpPN6ZKUmIT0lFLQMTqFSr2Bfa33FKinmKxGx5mJsYlPTq33V+DC3Vinj0EnVtHFDntV531D2aNyZPX3YNnaCtVmZOnqyOI8Pvo6axObTVX+2h5MsyERH1BGaWljQVwod7q8YW9/wxMmjJpJmx/msRyiOvY3eQlC4jBnYw0Hn7yAN3T5xNvXY1dTWpkssg40wlWopVrWgINi8rBbeogZAnp4KGjZygplia/4wV6pSrUtmRI3aZ2XlUDgu5xUQ/QOTj59CqaQh767qvxa2Sf1LPPDH2CcIfPIe+UT3UNa71jgiQbjxpvivSs6OkqEiVfyYYDY8XL6tkNDr2OOoeXtCyPwtiqqdV3NR5R3DvbTejaYUk3LkbDhUaWWnwNzz5KFPw7VAUqNUgOxTrkoYf14kvoCH/Qi+NhRGT7HIoO/mIDc1DlB6j72Ub0fm5qbgZFEorBzTRkJaWip7le8vYvw1IVPB/i0ccFAQEAUFAEBAEPk4C1OQSmyAgCAgCgoAgIAh8agREBf+p5ahIjyAgCAgCgoAgQATEVEgFFYPAwEAcPXq0cG6qgu4hghUEBAFB4FMiwJcPGhoaYujQoZJTnU8pbR8iLWIOvoKop6bSEpn4eGm9awXdQgQrCAgCgsAnR0CJDBR5Ja9AfgnEVj4CooIvHz9xtSAgCAgCgoAgUCUJiDn4KpktIlKCgCAgCAgCgkD5CIgKvnz8xNWCgCAgCAgCgkCVJCAq+CqZLSJSgoAgIAgIAoJA+QiICr58/MTVgoAgIAgIAoJAlSQgKvgqmS0iUoKAICAICAKCQPkIiAq+fPzE1YKAICAICAKCQJUkICr4KpktIlKCgCAgCAgCgkD5CIgKvnz8xNWCgCAgCAgCgkCVJCAq+CqZLSJSgoAgIAgIAoJA+QiICr58/MTVgoAgIAgIAoJAlSQgKvgqmS0iUoKAICAICAKCQPkIiAq+fPzE1YKAICAICAKCQJUkICr4KpktIlKCgCAgCAgCgkD5CIgKvnz8xNWCgCAgCAgCgkCVJCAq+CqZLSJSgoAgIAgIAoJA+QiICr58/MTVgoAgIAgIAoJAlSQgKvgqmS0iUoKAICAICAKCQPkIiAq+fPzE1YKAICAICAKCQJUkICr4KpktIlKCgCAgCAgCgkD5CIgKvnz8xNWCgCAgCAgCgkCVJCAq+CqZLSJSgoAgIAgIAoJA+QiICr58/MTVgoAgIAgIAoJAlSQgKvgqmS0iUoKAICAICAKCQPkIiAq+fPzE1YKAICAICAKCQJUk8H90SHmOqFd22gAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-9"/> </p>
<p>But now what we really want is to compare the naive to the robust in one table. If we want to use xtable, we will have to put those vectors together ourselves, the way we have done above. You can't input multiple models into the xtable function. </p>
<pre><code class="r">xtable(better.table, digits = 3, caption = "Comparison of Naive and Robust SEs")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-11"/> </p>
<p>This table is not all that attractive though - it's too wide for the amount of information it's imparting and it's not how we are used to seeing regression results. We can move on to another way of exporting tables that is better. </p>
<h3>stargazer</h3>
<p>The package I encourage you to try is called stargazer. It can make the task of creating tables and reporting results easier and more attractive. The basic function is just stargazer(fit1), which prints out latex output into the R console. You can copy the latex code and paste it into a latex document. Here is the output for the basic stargazer table produced just from the model itself with all of the function defaults:</p>
<pre><code class="r">library(stargazer)
stargazer(fit1)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-13"/> </p>
<p>If you want a text file instead to put into a word document, there is an option for that, </p>
<pre><code class="r">stargazer(fit1, type = "text", out = "reg_results.txt")
</code></pre>
<p>and it will export a text file of the same table.</p>
<p>This is a nice start, but the great part about this function is that if we have multiple models like we do with our icecream example, we can juxtapose them without having to create a table ourselves using cbind. We just put in as many model objects as we want and it will create that many columns. Then we can adjust the standard errors of the models. Finally, we can change a lot of the physical appearance with stargazer's many customizable options.</p>
<p>Here is the code: </p>
<pre><code class="r">stargazer(fit1, fit1,
se = list(naive.std.err,rob.std.err),
title="Regression Results",
align=TRUE,
dep.var.labels=c("Icecream sales"),
covariate.labels=c("Temperature","Gender ratio"),
no.space=TRUE,
omit.stat=c("LL","ser","f", "rsq"),
column.labels=c("Naive SE", "Robust SE"),
dep.var.caption="",
model.numbers=FALSE)
</code></pre>
<p>This is a lot of code in one bit but all of the options are very self-explanatory (there's also descriptions of every option in the help file). So in this table we are going to:</p>
<ul>
<li>Juxtapose two models - the linear model with naive SEs and the linear model with robust SEs - so we put in <strong>fit1, fit1</strong> which takes the same coefficients for both columns of the table. </li>
<li>Then specify the two different standard errors which we list together: <strong>se=list(naive.std.err, rob.std.err)</strong>. This is not necessary if you just go with the model produced standard errors.</li>
<li>Then we add a title for the whole thing </li>
<li>Add a dependent variable label and some covariate labels</li>
<li>Remove all the spaces</li>
<li>Take out some of the default stats that show up</li>
<li>Label the columns</li>
<li>Take out the dependent variable caption</li>
<li>and take out the model numbers. </li>
</ul>
<p><img 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alt="plot of chunk unnamed-chunk-16"/> </p>
<p>Looks pretty great! So there you go, in this way you can conduct linear regression with robust standard errors and then report your results in a physically attractive and easy way. </p>
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library(sandwich)
library(stargazer)
library(pander)
library(xtable)
options(width=150)
Motivation
Basic linear regression in R is super easy. Just use the lm() function, summarize the model with summary() and you're done. But as researchers we need more than that. What if our model is more complicated, like if our errors are heteroskedastic? And once we have run our regressions with our adjusted standard errors, how do we export the results for presentation in word or latex documents? This post will go over exactly these things, with the help of the stargazer package created by my fellow Harvard grad student Marek Hlavac. More posts about stargazer can be found [here on this blog.](http://www.r-statistics.com/2013/07/tailor-your-tables-with-stargazer-new-features-for-latex-and-text-output/)
Today we are going to analyze how temperature affects ice cream sales. We just make ourselves a little dataset like so:
set.seed(5)
temp <- rnorm(500, mean=80, sd=12)
sales <- 2+temp*3
for(i in 1:length(sales)){
if(temp[i]<75 | temp[i]>95) sales[i] <- sales[i]+rnorm(1,0,25 )
else sales[i] <- sales[i]+rnorm(1,0,8 ) }
female <- rnorm(500, mean=.5, sd=.01)
icecream <- as.data.frame(cbind(temp, sales, female))
head(icecream)
So there we have the temperature of 500 cities, sales per 100,000 people, and gender ratio of each of these cities. Let's plot out the temperature and sales.
plot(icecream$temp, icecream$sales, xlab="Temperature", ylab="Sales/100,000", main="I scream for icecream")
Here we definitely see a relationship between the temperature in a city and the sales of icecream. However, we notice that our errors are not exactly homoskedastic. We see that for both lower temperatures and very high temperatures, the variance is larger than the middle temperatures. So we will have to deal with that.
Let's start by running a linear regression using the lm() function and summarizing the results:
fit1<-lm(sales~temp+female, data=icecream)
summary(fit1)
names(summary(fit1))
We see the strong association between temperature and icecream sales, but no relationship between the gender ratio and icecream. Don't forget that you can extract many things from the summary of the lm object (read up on extracting information using names() in [this post.](http://rforpublichealth.blogspot.com/2013/03/extracting-information-from-objects.html)) Now let's deal with the heteroskedasticity. We know that under the constant variance assumption,
$$V[\hat\beta] = (X'X)^{-1}X'\Sigma X(X'X)^{-1}$$
where
$$\Sigma = \sigma^2I$$
and we can find an unbiased estimate of $\sigma^2$ by summing the squared the residuals. However, in this case, we see that the variance is not constant, so the naive covariance of the OLS estimator is biased. To fix this we employ the White Huber, or sandwich, method of robust standard errors. The sandwich method assumes that
$$\Sigma = diag(\sigma_1^2, ... , \sigma_n^2)$$
and again the $\hat\sigma_i^2$ are estimated via the residuals $\hat u_i^2$. In R, we can use the sandwich package to estimate robust standard errors this way:
library(sandwich)
cov.fit1 <- vcovHC(fit1, type="HC")
rob.std.err <- sqrt(diag(cov.fit1))
The vcovHC() function returns the variance-covariance matrix under the assumption of "HC" (Heteroskedasticity-consistent) estimation. We then take the diagonal of this matrix and square root it to calculate the robust standard errors. You could do this in one line of course, without creating the cov.fit1 object.
Now, we can put the estimates, the naive standard errors, and the robust standard errors together in a nice little table. We save the naive standard errors into a vector and then column bind the three columns together like so:
naive.std.err<-summary(fit1)$coefficients[,2]
estimate.table <- cbind("Estimate" = coef(fit1),
"Naive SE"= naive.std.err,
"Robust SE" = rob.std.err)
estimate.table
That's a good start. But of course we want p-values and, even better, upper and lower 95% confidence intervals, so we can add those to the table:
better.table <- cbind("Estimate" = coef(fit1),
"Naive SE" = naive.std.err,
"Pr(>|z|)" = 2 * pt(abs(coef(fit1)/naive.std.err), df=nrow(icecream)-2,
lower.tail = FALSE),
"LL" = coef(fit1) - 1.96 * naive.std.err,
"UL" = coef(fit1) + 1.96 * naive.std.err,
"Robust SE" = rob.std.err,
"Pr(>|z|)" = 2 * pt(abs(coef(fit1)/rob.std.err), df=nrow(icecream)-2,
lower.tail = FALSE),
"LL" = coef(fit1) - 1.96 * rob.std.err,
"UL" = coef(fit1) + 1.96 * rob.std.err)
rownames(better.table)<-c("Constant", "Temperature", "Gender Ratio")
better.table
A bit wide, but there we are. We are finished with our linear regression analysis. But now we would like to export this into a pdf or word document so we can send it to our advisor who will be very excited about this cutting edge icecream research. There are two options that I'll go over, xtable() and stargazer().
xtable
print(xtable(fit1), type="html")
The first option is xtable. You can put in just about any object (dataframe, table, matrix, lm, glm, etc) and it will create latex code for you that you can copy and paste into latex. This is how the resulting table will look in latex:
xtable(fit1)
library(png)
library(grid)
img <- readPNG("figure/basicxtable.png")
grid.raster(img)
But now what we really want is to compare the naive to the robust in one table. If we want to use xtable, we will have to put those vectors together ourselves, the way we have done above. You can't input multiple models into the xtable function.
xtable(better.table, digits=3, caption="Comparison of Naive and Robust SEs")
library(png)
library(grid)
img <- readPNG("figure/xtable.png")
grid.raster(img)
This table is not all that attractive though - it's too wide for the amount of information it's imparting and it's not how we are used to seeing regression results. We can move on to another way of exporting tables that is better.
stargazer
The package I encourage you to try is called stargazer. It can make the task of creating tables and reporting results easier and more attractive. The basic function is just stargazer(fit1), which prints out latex output into the R console. You can copy the latex code and paste it into a latex document. Here is the output for the basic stargazer table produced just from the model itself with all of the function defaults:
library(stargazer)
stargazer(fit1)
library(png)
library(grid)
img <- readPNG("figure/basic_sg.png")
grid.raster(img)
If you want a text file instead to put into a word document, there is an option for that,
stargazer(fit1, type="text", out="reg_results.txt")
and it will export a text file of the same table.
This is a nice start, but the great part about this function is that if we have multiple models like we do with our icecream example, we can juxtapose them without having to create a table ourselves using cbind. We just put in as many model objects as we want and it will create that many columns. Then we can adjust the standard errors of the models. Finally, we can change a lot of the physical appearance with stargazer's many customizable options.
Here is the code:
stargazer(fit1, fit1,
se = list(naive.std.err,rob.std.err),
title="Regression Results",
align=TRUE,
dep.var.labels=c("Icecream sales"),
covariate.labels=c("Temperature","Gender ratio"),
no.space=TRUE,
omit.stat=c("LL","ser","f", "rsq"),
column.labels=c("Naive SE", "Robust SE"),
dep.var.caption="",
model.numbers=FALSE)
This is a lot of code in one bit but all of the options are very self-explanatory (there's also descriptions of every option in the help file). So in this table we are going to:
* Juxtapose two models - the linear model with naive SEs and the linear model with robust SEs - so we put in fit1, fit1 which takes the same coefficients for both columns of the table.
* Then specify the two different standard errors which we list together: se=list(naive.std.err, rob.std.err). This is not necessary if you just go with the model produced standard errors.
* Then we add a title for the whole thing
* Add a dependent variable label and some covariate labels
* Remove all the spaces
* Take out some of the default stats that show up
* Label the columns
* Take out the dependent variable caption
* and take out the model numbers.
library(png)
library(grid)
img <- readPNG("figure/sg33a.png")
grid.raster(img)
Looks pretty great! So there you go, in this way you can conduct linear regression with robust standard errors and then report your results in a physically attractive and easy way. </div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com14tag:blogger.com,1999:blog-2550386066385582083.post-20037210300734311582013-07-11T08:28:00.000-07:002016-10-13T07:48:35.122-07:00Let's Make a Date: Date and Time classes in R<!DOCTYPE html>
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<h1></h1>
<p>Of all the frustrating data manipulations to deal with in any programming language, dates and times are the worst in my opinion. In R, there are many different packages that use various functions to deal with dates, which lead to different classes of dates that are not always compatible. Depending on how your data is organized, there are different solutions to your date and time problems. Here, I'll show the way that I think is easiest to deal with dates depending on the organization of your data.</p>
<p>Ok so I find in public health data that birthdays and death days are the most common dates to be dealing with. In a dataset like the DHS, dates can either be found as three separate integer variables (month, day, year) or they can be in one character variable like “05/03/2009”. So let's start with the first situation, numeric dates. </p>
<h1>Numeric dates</h1>
<pre><code class="r">#Create some data
dates<-as.data.frame(cbind(c(1,3,6,11,4,12,5,3),
c(30,14,NA,NA,16,NA,20,31),
c(1980, 1980, 1980, 1983,1983, 1983, 1986, 1980),
c(2, NA, NA, NA, NA, 12, 4, NA),
c(2, NA, NA, NA, NA, NA, 29, NA),
c(1980, NA, NA, 1985, NA, 1983, 1987, NA)))
colnames(dates)<-c("birth_month", "birth_day", "birth_year", "death_month", "death_day", "death_year")
dates
</code></pre>
<pre><code>## birth_month birth_day birth_year death_month death_day death_year
## 1 1 30 1980 2 2 1980
## 2 3 14 1980 NA NA NA
## 3 6 NA 1980 NA NA NA
## 4 11 NA 1983 NA NA 1985
## 5 4 16 1983 NA NA NA
## 6 12 NA 1983 12 NA 1983
## 7 5 20 1986 4 29 1987
## 8 3 31 1980 NA NA NA
</code></pre>
<p>I've included a lot of missing cells, even in birth date, because that's the most common problem that I have - birth and death data, especially from developing countries, is full of missing birth months or days. </p>
<p>Ok, so what I would like to do here is to figure out if I have any infant mortalities in my sample size of 8. I find the easiest way to create a date object from three integer month/day/year variables is by using <strong>ISOdate()</strong>, which is just in base R. ISOdate() follows the following syntax: </p>
<p><strong>ISOdate(year, month, day, hour = 12, min = 0, sec = 0, tz = “GMT”)</strong></p>
<p>and if you had hours and minutes you could easily throw those in too into the ISOdatetime() function which is the same syntax. So let's create a birth date:</p>
<pre><code class="r">dates$DOB <- ISOdate(dates$birth_year, dates$birth_month, dates$birth_day)
dates
</code></pre>
<pre><code>## birth_month birth_day birth_year death_month death_day death_year DOB
## 1 1 30 1980 2 2 1980 1980-01-30 12:00:00
## 2 3 14 1980 NA NA NA 1980-03-14 12:00:00
## 3 6 NA 1980 NA NA NA <NA>
## 4 11 NA 1983 NA NA 1985 <NA>
## 5 4 16 1983 NA NA NA 1983-04-16 12:00:00
## 6 12 NA 1983 12 NA 1983 <NA>
## 7 5 20 1986 4 29 1987 1986-05-20 12:00:00
## 8 3 31 1980 NA NA NA 1980-03-31 12:00:00
</code></pre>
<p>This is super easy. The column DOB that we have added to the dataframe is of class POSIXlt, which is a class of calendar date and time. Since I don't have any time variables, I don't want it crowding up my dataset so I can use the function <strong>strptime()</strong> to get rid of it. Here's the syntax:</p>
<p><strong>strptime(x, format, tz = “”)</strong></p>
<p>where x is your POSIXlt object, and format is whatever the format is of that object (see below). The <a href="http://astrostatistics.psu.edu/su07/R/html/base/html/strptime.html">strptime help file</a> is a good place to understand the formats.</p>
<pre><code class="r">dates$DOB <- strptime(dates$DOB, format = "%Y-%m-%d")
dates$DOD <- strptime(ISOdate(dates$death_year, dates$death_month, dates$death_day), format = "%Y-%m-%d")
</code></pre>
<p>You can, of course, do this in one step, which I've done for death date. Now we can look at our dataset:</p>
<pre><code class="r">dates
</code></pre>
<pre><code>## birth_month birth_day birth_year death_month death_day death_year DOB DOD
## 1 1 30 1980 2 2 1980 1980-01-30 1980-02-02
## 2 3 14 1980 NA NA NA 1980-03-14 <NA>
## 3 6 NA 1980 NA NA NA <NA> <NA>
## 4 11 NA 1983 NA NA 1985 <NA> <NA>
## 5 4 16 1983 NA NA NA 1983-04-16 <NA>
## 6 12 NA 1983 12 NA 1983 <NA> <NA>
## 7 5 20 1986 4 29 1987 1986-05-20 1987-04-29
## 8 3 31 1980 NA NA NA 1980-03-31 <NA>
</code></pre>
<p>Now if we want age at death, we can use the <strong>difftime()</strong> function that follows the following syntax and produces a difftime object (which you can convert to numeric using as.numeric() if you want to, which I highly recommend):</p>
<p><strong>difftime(time1, time2, tz,units = c(“auto”, “secs”, “mins”, “hours”,“days”, “weeks”))</strong></p>
<pre><code class="r">dates$Age.atdeath <- difftime(dates$DOD, dates$DOB, unit = "days")
dates$Age.atdeath
</code></pre>
<pre><code>## Time differences in days
## [1] 3 NA NA NA NA NA 344 NA
## attr(,"tzone")
## [1] ""
</code></pre>
<pre><code class="r">class(dates$Age.atdeath)
</code></pre>
<pre><code>## [1] "difftime"
</code></pre>
<pre><code class="r"># check if there were an infant mortalities
dates$Age.atdeath < 365
</code></pre>
<pre><code>## [1] TRUE NA NA NA NA NA TRUE NA
</code></pre>
<p>Ok, I found two infant mortalities, but I see that there's a problem. I see that I'm missing two birthdays that come up as NA, and this is because I'm missing the birth day for those two people. However, I do have birth month and birth year, and this is information I don't want to lose. There are many ways to deal with this, including imputing birth days or assigning them randomly from a uniform distribution or whatever. </p>
<p>In this case, what I will do is very simple - just replace the missing birth day with 1 if it's missing (and similarly replace a missing birth month with 1 if it's missing) and replace missing death month and day with 12 and 30, respectively. That way I have the maximum possible age at death and I don't lose potentially important information. There are a number of ways to accomplish what I want to do, but I love using the <strong>ifelse()</strong> function because I find it extremely intuitive so I will do that:</p>
<pre><code class="r">dates$DOB2<-strptime(ISOdate(year=dates$birth_year,
month=ifelse(is.na(dates$birth_month), 1, dates$birth_month),
day=ifelse(is.na(dates$birth_day),1, dates$birth_day)),
format="%Y-%m-%d")
dates$DOD2<-strptime(ISOdate(year=dates$death_year,
month=ifelse(is.na(dates$death_month),12,dates$death_month),
day=ifelse(is.na(dates$death_day),30, dates$death_day)),
format="%Y-%m-%d")
dates$Ageatdeath_2<-as.numeric(difftime(dates$DOD2,dates$DOB2,unit="days"))
dates[,c(1:6,10:12)]
</code></pre>
<pre><code>## birth_month birth_day birth_year death_month death_day death_year DOB2 DOD2 Ageatdeath_2
## 1 1 30 1980 2 2 1980 1980-01-30 1980-02-02 3
## 2 3 14 1980 NA NA NA 1980-03-14 <NA> NA
## 3 6 NA 1980 NA NA NA 1980-06-01 <NA> NA
## 4 11 NA 1983 NA NA 1985 1983-11-01 1985-12-30 790
## 5 4 16 1983 NA NA NA 1983-04-16 <NA> NA
## 6 12 NA 1983 12 NA 1983 1983-12-01 1983-12-30 29
## 7 5 20 1986 4 29 1987 1986-05-20 1987-04-29 344
## 8 3 31 1980 NA NA NA 1980-03-31 <NA> NA
</code></pre>
<p>So now we see above that I have all birthdays completed, and I was able to reveal that I have another infant mortality in my data, and another death more generally. </p>
<h1>Character Dates</h1>
<p>Ok so onto the second type of dataset you may encounter, which is that somebody inputed the dates into Excel or whatever like this:</p>
<pre><code class="r">dates2<-as.data.frame(cbind(c(1:5),
c("8/31/70", "NA", "10/12/60", "1/1/66", "12/31/80"),
c("8/31/56", "12-31-1977", "12Aug55", "July 31 1965" ,"30jan1952")))
colnames(dates2)<-c("ID", "date_factor", "date_horrible")
dates2
</code></pre>
<pre><code>## ID date_factor date_horrible
## 1 1 8/31/70 8/31/56
## 2 2 NA 12-31-1977
## 3 3 10/12/60 12Aug55
## 4 4 1/1/66 July 31 1965
## 5 5 12/31/80 30jan1952
</code></pre>
<p>In the first column at least all of the dates have the same format, but in the second (which happens really often!), every date is a different format which seems, at first, like a total nightmare. But fortunately R is here to rescue us. </p>
<p>Ok, let's start with the easy one. The tricky part with this kind of data is that R often immediately converts the dates to factors, like so:</p>
<pre><code class="r">class(dates2$date_factor)
</code></pre>
<pre><code>## [1] "factor"
</code></pre>
<p>This happens if you are reading in a csv file as well. You can either stop R from doing that by using the as.is option like so (assuming your dates were the second and third columns): </p>
<pre><code class="r">df <- read.table("data_with_dates.txt", header = TRUE, as.is = 2:3)
</code></pre>
<p>or we just have to remember to use the <strong>as.character()</strong> function before we do anything. Ok, so the point here is that even though these look like dates, they are <em>not</em> dates. They are factors or maybe characters, but not manipulatable like you would want. For example, this gives you the following error:</p>
<pre><code class="r"># NO: this gives an error, you can't do this with characters, need the date format
dates2$age <- difftime("02/27/13", as.character(dates2$date_factor), unit = "days")
</code></pre>
<pre><code>## Error: character string is not in a standard unambiguous format
</code></pre>
<p>So we need to get these character dates into actual date formats. If all of our formats are the same, we can use the chron package and the <strong>chron()</strong> function really easily like so. </p>
<p><strong>chron(dates., times., format = c(dates = “m/d/y”, times = “h:m:s”),out.format, origin.)</strong></p>
<p>where dates is the vector of <strong>character</strong> dates, and the format is whatever the format is of the data you have. Note that it yields warning messages because of the missing value, but that is ok. </p>
<pre><code class="r">library(chron)
dates2$date.fmt <- chron(as.character(dates2$date_factor), format = "m/d/y")
</code></pre>
<pre><code>## Warning: wrong number of fields in entry(ies) 2
</code></pre>
<pre><code>## Warning: NAs introduced by coercion
</code></pre>
<pre><code>## Warning: NAs introduced by coercion
</code></pre>
<pre><code>## Warning: NAs introduced by coercion
</code></pre>
<pre><code class="r">class(dates2$date.fmt)
</code></pre>
<pre><code>## [1] "dates" "times"
</code></pre>
<pre><code class="r">dates2[, c(1, 2, 4)]
</code></pre>
<pre><code>## ID date_factor date.fmt
## 1 1 8/31/70 08/31/70
## 2 2 NA <NA>
## 3 3 10/12/60 10/12/60
## 4 4 1/1/66 01/01/66
## 5 5 12/31/80 12/31/80
</code></pre>
<p>Note that it looks very similar to the date_factor column but it is not, because it is now a dates and times class as I show above. We can also change how we want the outgoing format to look:</p>
<pre><code class="r">dates2$date.fmt <- chron(as.character(dates2$date_factor), format = "m/d/y", out.format = "month day year")
dates2[, c(1, 2, 4)]
</code></pre>
<pre><code>## ID date_factor date.fmt
## 1 1 8/31/70 August 31 1970
## 2 2 NA <NA>
## 3 3 10/12/60 October 12 1960
## 4 4 1/1/66 January 01 1966
## 5 5 12/31/80 December 31 1980
</code></pre>
<p>And now we can find the age of these people using <strong>difftime()</strong> as before. Let's say I interviewed everyone on March 1st of this year and I want their age in years at interview:</p>
<pre><code class="r">dates2$age <- as.numeric(floor(difftime(chron("03/01/2013"), dates2$date.fmt, unit = "days")/360))
dates2[c(1, 2, 4, 5)]
</code></pre>
<pre><code>## ID date_factor date.fmt age
## 1 1 8/31/70 August 31 1970 43
## 2 2 NA <NA> NA
## 3 3 10/12/60 October 12 1960 53
## 4 4 1/1/66 January 01 1966 47
## 5 5 12/31/80 December 31 1980 32
</code></pre>
<p>I can also do things like add a day to everybody's date if I had some reason to do that, and I can compare dates to see which one came first, which can be useful:</p>
<pre><code class="r"># Add a day to everyone's date for some reason
dates2$date.fmt + 1
</code></pre>
<pre><code>## [1] September 01 1970 <NA> October 13 1960 January 02 1966 January 01 1981
</code></pre>
<pre><code class="r"># Compare the date to some other date to see which came first using < operator
dates2$date.fmt < chron("04/02/62")
</code></pre>
<pre><code>## [1] FALSE NA TRUE FALSE FALSE
</code></pre>
<p>Ok finally what if your data is as horrible as what we see in our second column?</p>
<pre><code class="r">dates2[, c(1, 3)]
</code></pre>
<pre><code>## ID date_horrible
## 1 1 8/31/56
## 2 2 12-31-1977
## 3 3 12Aug55
## 4 4 July 31 1965
## 5 5 30jan1952
</code></pre>
<p>Chron won't help us here, as it needs one format for everyone:</p>
<pre><code class="r"># NO: chron needs the same format
chron(as.character(dates2$date_horrible))
</code></pre>
<pre><code>## [1] 08/31/56 <NA> <NA> <NA> <NA>
</code></pre>
<p>It just gives us NAs (and warnings, which I've hid). However, there is a package called <strong>date</strong> that will take any type of date and figure it out for us. Watch how amazing it is, the only argument to the function you need is the vector of <strong>character</strong> dates:</p>
<pre><code class="r">library(date)
# as.date (lower case) will correctly convert dates in vector
as.date(as.character(dates2$date_horrible))
</code></pre>
<pre><code>## [1] 31Aug56 31Dec77 12Aug55 31Jul65 30Jan52
</code></pre>
<p>Extraordinary! :) It just knows. However, I find that it stubbornly reformats itself into the number of days since 1960 when adding it as a column to the dataframe: </p>
<pre><code class="r">dates2$date_autofmt <- as.date(as.character(dates2$date_horrible))
dates2[, c(1, 3, 6)]
</code></pre>
<pre><code>## ID date_horrible date_autofmt
## 1 1 8/31/56 -1218
## 2 2 12-31-1977 6574
## 3 3 12Aug55 -1603
## 4 4 July 31 1965 2038
## 5 5 30jan1952 -2893
</code></pre>
<p>It's also not super easy to work with because it's a date object, not a Date object (VERY case-sensitive stuff here!). Feel free to chime in here if you have a better solution, but my simple fix on that is just to envelop it in an <strong>as.Date()</strong> function (from base R) like so: </p>
<pre><code class="r">dates2$date_amazing <- as.Date(as.date(as.character(dates2$date_horrible)))
dates2[, c(1, 3, 7)]
</code></pre>
<pre><code>## ID date_horrible date_amazing
## 1 1 8/31/56 1956-08-31
## 2 2 12-31-1977 1977-12-31
## 3 3 12Aug55 1955-08-12
## 4 4 July 31 1965 1965-07-31
## 5 5 30jan1952 1952-01-30
</code></pre>
<p>Now it works great. That's pretty incredible that one short line of code can transform your awful dates column into perfectly coordinated and workable dates. You can use <strong>difftime()</strong> the same way as before. Hope that this was useful for those pulling their hair out over date and time objects in R.</p>
<p>Also, if you made it all the way to the end, please enjoy <a href="http://www.youtube.com/watch?v=PD3aHKeFlSI">this hilarious episode</a> of Let's Make a Date on “Whose Line Is It Anyway?'' featuring none other than Stephen Colbert. :D</p>
</body>
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library(date)
library(chron)
options(width=150)
Of all the frustrating data manipulations to deal with in any programming language, dates and times are the worst in my opinion. In R, there are many different packages that use various functions to deal with dates, which lead to different classes of dates that are not always compatible. Depending on how your data is organized, there are different solutions to your date and time problems. Here, I'll show the way that I think is easiest to deal with dates depending on the organization of your data.
Ok so I find in public health data that birthdays and death days are the most common dates to be dealing with. In a dataset like the DHS, dates can either be found as three separate integer variables (month, day, year) or they can be in one character variable like "05/03/2009". So let's start with the first situation, numeric dates.
Numeric dates
#Create some data
dates<-as.data.frame(cbind(c(1,3,6,11,4,12,5,3),
c(30,14,NA,NA,16,NA,20,31),
c(1980, 1980, 1980, 1983,1983, 1983, 1986, 1980),
c(2, NA, NA, NA, NA, 12, 4, NA),
c(2, NA, NA, NA, NA, NA, 29, NA),
c(1980, NA, NA, 1985, NA, 1983, 1987, NA)))
colnames(dates)<-c("birth_month", "birth_day", "birth_year", "death_month", "death_day", "death_year")
dates
I've included a lot of missing cells, even in birth date, because that's the most common problem that I have - birth and death data, especially from developing countries, is full of missing birth months or days.
Ok, so what I would like to do here is to figure out if I have any infant mortalities in my sample size of 8. I find the easiest way to create a date object from three integer month/day/year variables is by using **ISOdate()**, which is just in base R. ISOdate() follows the following syntax:
ISOdate(year, month, day, hour = 12, min = 0, sec = 0, tz = "GMT")
and if you had hours and minutes you could easily throw those in too into the ISOdatetime() function which is the same syntax. So let's create a birth date:
dates$DOB<-ISOdate(dates$birth_year, dates$birth_month, dates$birth_day)
dates
This is super easy. The column DOB that we have added to the dataframe is of class POSIXlt, which is a class of calendar date and time. Since I don't have any time variables, I don't want it crowding up my dataset so I can use the function strptime() to get rid of it. Here's the syntax:
strptime(x, format, tz = "")
where x is your POSIXlt object, and format is whatever the format is of that object (see below). The [strptime help file](http://astrostatistics.psu.edu/su07/R/html/base/html/strptime.html) is a good place to understand the formats.
dates$DOB<-strptime(dates$DOB, format="%Y-%m-%d")
dates$DOD<-strptime(ISOdate(dates$death_year, dates$death_month, dates$death_day), format="%Y-%m-%d")
You can, of course, do this in one step, which I've done for death date. Now we can look at our dataset:
dates
Now if we want age at death, we can use the difftime() function that follows the following syntax and produces a difftime object (which you can convert to numeric using as.numeric() if you want to, which I highly recommend):
difftime(time1, time2, tz,units = c("auto", "secs", "mins", "hours","days", "weeks"))
dates$Age.atdeath<-difftime(dates$DOD, dates$DOB, unit="days")
dates$Age.atdeath
class(dates$Age.atdeath)
#check if there were an infant mortalities
dates$Age.atdeath<365
Ok, I found two infant mortalities, but I see that there's a problem. I see that I'm missing two birthdays that come up as NA, and this is because I'm missing the birth day for those two people. However, I do have birth month and birth year, and this is information I don't want to lose. There are many ways to deal with this, including imputing birth days or assigning them randomly from a uniform distribution or whatever.
In this case, what I will do is very simple - just replace the missing birth day with 1 if it's missing (and similarly replace a missing birth month with 1 if it's missing) and replace missing death month and day with 12 and 30, respectively. That way I have the maximum possible age at death and I don't lose potentially important information. There are a number of ways to accomplish what I want to do, but I love using the ifelse() function because I find it extremely intuitive so I will do that:
dates$DOB2<-strptime(ISOdate(year=dates$birth_year,
month=ifelse(is.na(dates$birth_month), 1, dates$birth_month),
day=ifelse(is.na(dates$birth_day),1, dates$birth_day)),
format="%Y-%m-%d")
dates$DOD2<-strptime(ISOdate(year=dates$death_year,
month=ifelse(is.na(dates$death_month),12,dates$death_month),
day=ifelse(is.na(dates$death_day),30, dates$death_day)),
format="%Y-%m-%d")
dates$Ageatdeath_2<-as.numeric(difftime(dates$DOD2,dates$DOB2,unit="days"))
dates[,c(1:6,10:12)]
So now we see above that I have all birthdays completed, and I was able to reveal that I have another infant mortality in my data, and another death more generally.
Character Dates
Ok so onto the second type of dataset you may encounter, which is that somebody inputed the dates into Excel or whatever like this:
dates2<-as.data.frame(cbind(c(1:5),
c("8/31/70", "NA", "10/12/60", "1/1/66", "12/31/80"),
c("8/31/56", "12-31-1977", "12Aug55", "July 31 1965" ,"30jan1952")))
colnames(dates2)<-c("ID", "date_factor", "date_horrible")
dates2
In the first column at least all of the dates have the same format, but in the second (which happens really often!), every date is a different format which seems, at first, like a total nightmare. But fortunately R is here to rescue us.
Ok, let's start with the easy one. The tricky part with this kind of data is that R often immediately converts the dates to factors, like so:
class(dates2$date_factor)
This happens if you are reading in a csv file as well. You can either stop R from doing that by using the as.is option like so (assuming your dates were the second and third columns):
df <- read.table("data_with_dates.txt", header = TRUE, as.is = 2:3)
or we just have to remember to use the **as.character()** function before we do anything. Ok, so the point here is that even though these look like dates, they are *not* dates. They are factors or maybe characters, but not manipulatable like you would want. For example, this gives you the following error:
#NO: this gives an error, you can't do this with characters, need the date format
dates2$age<-difftime("02/27/13", as.character(dates2$date_factor), unit="days")
So we need to get these character dates into actual date formats. If all of our formats are the same, we can use the chron package and the **chron()** function really easily like so.
chron(dates., times., format = c(dates = "m/d/y", times = "h:m:s"),out.format, origin.)
where dates is the vector of **character** dates, and the format is whatever the format is of the data you have. Note that it yields warning messages because of the missing value, but that is ok.
library(chron)
dates2$date.fmt<-chron(as.character(dates2$date_factor), format="m/d/y")
class(dates2$date.fmt)
dates2[,c(1,2,4)]
Note that it looks very similar to the date_factor column but it is not, because it is now a dates and times class as I show above. We can also change how we want the outgoing format to look:
dates2$date.fmt<-chron(as.character(dates2$date_factor), format="m/d/y", out.format="month day year")
dates2[,c(1,2,4)]
And now we can find the age of these people using difftime() as before. Let's say I interviewed everyone on March 1st of this year and I want their age in years at interview:
dates2$age<-as.numeric(floor(difftime(chron("03/01/2013"), dates2$date.fmt, unit="days")/360))
dates2[c(1,2,4,5)]
I can also do things like add a day to everybody's date if I had some reason to do that, and I can compare dates to see which one came first, which can be useful:
#Add a day to everyone's date for some reason
dates2$date.fmt+1
#Compare the date to some other date to see which came first using < operator
Ok finally what if your data is as horrible as what we see in our second column?
dates2[,c(1,3)]
Chron won't help us here, as it needs one format for everyone:
#NO: chron needs the same format
chron(as.character(dates2$date_horrible))
It just gives us NAs (and warnings, which I've hid). However, there is a package called date that will take any type of date and figure it out for us. Watch how amazing it is, the only argument to the function you need is the vector of character dates:
library(date)
#as.date (lower case) will correctly convert dates in vector
as.date(as.character(dates2$date_horrible))
Extraordinary! :) It just knows. However, I find that it stubbornly reformats itself into the number of days since 1960 when adding it as a column to the dataframe:
dates2$date_autofmt<-as.date(as.character(dates2$date_horrible))
dates2[,c(1,3,6)]
It's also not super easy to work with because it's a date object, not a Date object (VERY case-sensitive stuff here!). Feel free to chime in here if you have a better solution, but my simple fix on that is just to envelop it in an **as.Date()** function (from base R) like so:
dates2$date_amazing<-as.Date(as.date(as.character(dates2$date_horrible)))
dates2[,c(1,3,7)]
Now it works great. That's pretty incredible that one short line of code can transform your awful dates column into perfectly coordinated and workable dates. You can use difftime() the same way as before. Hope that this was useful for those pulling their hair out over date and time objects in R.
Also, if you made it all the way to the end, please enjoy [this hilarious episode] (http://www.youtube.com/watch?v=PD3aHKeFlSI) of Let's Make a Date on ``Whose Line Is It Anyway?'' featuring none other than Stephen Colbert. :D </div>Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com5tag:blogger.com,1999:blog-2550386066385582083.post-5877741605479968622013-06-25T17:34:00.000-07:002016-10-13T08:35:57.987-07:00Sample size calculations equivalent to Stata functions<!DOCTYPE html>
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e0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v%2BPTmTi3E%2FglC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO%2BYOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr%2BH1jR7km9vcvqN9657vffefkv%2BfKxge1X%2B7RdjYUPIESN7gTvRkB%2FRMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F%2BUX4%2BWKS4iBTP0IWxGtZgOdMaV4KTayqHQF%2FVihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g%2FqRCuy3vmQ3W9Vu8qbVbP%2BSzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB%2BLopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9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%2FTAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAA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%3D%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIGhvcml6LWFkdi14PSIwIiAvPgo8Z2x5cGggaG9yaXotYWR2LXg9IjQwMCIgLz4KPGdseXBoIHVuaWNvZGU9IiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDM1OyIgZD0iTTYwMCAxMTc1cTExNiAwIDIyNyAtNDkuNXQxOTIuNSAtMTMxdDEzMSAtMTkyLjV0NDkuNSAtMjI3di0zMDBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC01MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djMwMHEwIDEyNyAtNzAuNSAyMzEuNXQtMTg0LjUgMTYxLjV0LTI0NSA1N3QtMjQ1IC01N3QtMTg0LjUgLTE2MS41dC03MC41IC0yMzEuNXYtMzAwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNTAgcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MzAwcTAgMTE2IDQ5LjUgMjI3dDEzMSAxOTIuNXQxOTIuNSAxMzF0MjI3IDQ5LjV6TTIyMCA1MDBoMTYwcTggMCAxNCAtNnQ2IC0xNHYtNDYwcTAgLTggLTYgLTE0dC0xNCAtNmgtMTYwcS04IDAgLTE0IDZ0LTYgMTR2NDYwcTAgOCA2IDE0dDE0IDZ6TTgyMCA1MDBoMTYwcTggMCAxNCAtNnQ2IC0xNHYtNDYwcTAgLTggLTYgLTE0dC0xNCAtNmgtMTYwcS04IDAgLTE0IDZ0LTYgMTR2NDYwIHEwIDggNiAxNHQxNCA2eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzY7IiBkPSJNMzIxIDgxNGwyNTggMTcycTkgNiAxNSAyLjV0NiAtMTMuNXYtNzUwcTAgLTEwIC02IC0xMy41dC0xNSAyLjVsLTI1OCAxNzJxLTIxIDE0IC00NiAxNGgtMjUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MzUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjVoMjUwcTI1IDAgNDYgMTR6TTkwMCA2NjhsMTIwIDEyMHE3IDcgMTcgN3QxNyAtN2wzNCAtMzRxNyAtNyA3IC0xN3QtNyAtMTdsLTEyMCAtMTIwbDEyMCAtMTIwcTcgLTcgNyAtMTcgdC03IC0xN2wtMzQgLTM0cS03IC03IC0xNyAtN3QtMTcgN2wtMTIwIDExOWwtMTIwIC0xMTlxLTcgLTcgLTE3IC03dC0xNyA3bC0zNCAzNHEtNyA3IC03IDE3dDcgMTdsMTE5IDEyMGwtMTE5IDEyMHEtNyA3IC03IDE3dDcgMTdsMzQgMzRxNyA4IDE3IDh0MTcgLTh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNzsiIGQ9Ik0zMjEgODE0bDI1OCAxNzJxOSA2IDE1IDIuNXQ2IC0xMy41di03NTBxMCAtMTAgLTYgLTEzLjV0LTE1IDIuNWwtMjU4IDE3MnEtMjEgMTQgLTQ2IDE0aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgyNTBxMjUgMCA0NiAxNHpNNzY2IDkwMGg0cTEwIC0xIDE2IC0xMHE5NiAtMTI5IDk2IC0yOTBxMCAtMTU0IC05MCAtMjgxcS02IC05IC0xNyAtMTBsLTMgLTFxLTkgMCAtMTYgNiBsLTI5IDIzcS03IDcgLTguNSAxNi41dDQuNSAxNy41cTcyIDEwMyA3MiAyMjlxMCAxMzIgLTc4IDIzOHEtNiA4IC00LjUgMTh0OS41IDE3bDI5IDIycTcgNSAxNSA1eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzg7IiBkPSJNOTY3IDEwMDRoM3ExMSAtMSAxNyAtMTBxMTM1IC0xNzkgMTM1IC0zOTZxMCAtMTA1IC0zNCAtMjA2LjV0LTk4IC0xODUuNXEtNyAtOSAtMTcgLTEwaC0zcS05IDAgLTE2IDZsLTQyIDM0cS04IDYgLTkgMTZ0NSAxOHExMTEgMTUwIDExMSAzMjhxMCA5MCAtMjkuNSAxNzZ0LTg0LjUgMTU3cS02IDkgLTUgMTl0MTAgMTZsNDIgMzNxNyA1IDE1IDV6TTMyMSA4MTRsMjU4IDE3MnE5IDYgMTUgMi41dDYgLTEzLjV2LTc1MHEwIC0xMCAtNiAtMTMuNSB0LTE1IDIuNWwtMjU4IDE3MnEtMjEgMTQgLTQ2IDE0aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgyNTBxMjUgMCA0NiAxNHpNNzY2IDkwMGg0cTEwIC0xIDE2IC0xMHE5NiAtMTI5IDk2IC0yOTBxMCAtMTU0IC05MCAtMjgxcS02IC05IC0xNyAtMTBsLTMgLTFxLTkgMCAtMTYgNmwtMjkgMjNxLTcgNyAtOC41IDE2LjV0NC41IDE3LjVxNzIgMTAzIDcyIDIyOXEwIDEzMiAtNzggMjM4IHEtNiA4IC00LjUgMTguNXQ5LjUgMTYuNWwyOSAyMnE3IDUgMTUgNXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ0OyIgZD0iTTI1MCAxMjAwaDgwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMTE1MGwtNDUwIDQ0NGwtNDUwIC00NDV2MTE1MXEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0NTsiIGQ9Ik04MjIgMTIwMGgtNDQ0cS0xMSAwIC0xOSAtNy41dC05IC0xNy41bC03OCAtMzAxcS03IC0yNCA3IC00NWw1NyAtMTA4cTYgLTkgMTcuNSAtMTV0MjEuNSAtNmg0NTBxMTAgMCAyMS41IDZ0MTcuNSAxNWw2MiAxMDhxMTQgMjEgNyA0NWwtODMgMzAxcS0xIDEwIC05IDE3LjV0LTE5IDcuNXpNMTE3NSA4MDBoLTE1MHEtMTAgMCAtMjEgLTYuNXQtMTUgLTE1LjVsLTc4IC0xNTZxLTQgLTkgLTE1IC0xNS41dC0yMSAtNi41aC01NTAgcS0xMCAwIC0yMSA2LjV0LTE1IDE1LjVsLTc4IDE1NnEtNCA5IC0xNSAxNS41dC0yMSA2LjVoLTE1MHEtMTAgMCAtMTcuNSAtNy41dC03LjUgLTE3LjV2LTY1MHEwIC0xMCA3LjUgLTE3LjV0MTcuNSAtNy41aDE1MHExMCAwIDE3LjUgNy41dDcuNSAxNy41djE1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDc1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTE1MHEwIC0xMCA3LjUgLTE3LjV0MTcuNSAtNy41aDE1MHExMCAwIDE3LjUgNy41IHQ3LjUgMTcuNXY2NTBxMCAxMCAtNy41IDE3LjV0LTE3LjUgNy41ek04NTAgMjAwaC01MDBxLTEwIDAgLTE5LjUgLTd0LTExLjUgLTE3bC0zOCAtMTUycS0yIC0xMCAzLjUgLTE3dDE1LjUgLTdoNjAwcTEwIDAgMTUuNSA3dDMuNSAxN2wtMzggMTUycS0yIDEwIC0xMS41IDE3dC0xOS41IDd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0NjsiIGQ9Ik01MDAgMTEwMGgyMDBxNTYgMCAxMDIuNSAtMjAuNXQ3Mi41IC01MHQ0NCAtNTl0MjUgLTUwLjVsNiAtMjBoMTUwcTQxIDAgNzAuNSAtMjkuNXQyOS41IC03MC41di02MDBxMCAtNDEgLTI5LjUgLTcwLjV0LTcwLjUgLTI5LjVoLTEwMDBxLTQxIDAgLTcwLjUgMjkuNXQtMjkuNSA3MC41djYwMHEwIDQxIDI5LjUgNzAuNXQ3MC41IDI5LjVoMTUwcTIgOCA2LjUgMjEuNXQyNCA0OHQ0NSA2MXQ3MiA0OHQxMDIuNSAyMS41ek05MDAgODAwdi0xMDAgaDEwMHYxMDBoLTEwMHpNNjAwIDczMHEtOTUgMCAtMTYyLjUgLTY3LjV0LTY3LjUgLTE2Mi41dDY3LjUgLTE2Mi41dDE2Mi41IC02Ny41dDE2Mi41IDY3LjV0NjcuNSAxNjIuNXQtNjcuNSAxNjIuNXQtMTYyLjUgNjcuNXpNNjAwIDYwM3E0MyAwIDczIC0zMHQzMCAtNzN0LTMwIC03M3QtNzMgLTMwdC03MyAzMHQtMzAgNzN0MzAgNzN0NzMgMzB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0NzsiIGQ9Ik02ODEgMTE5OWwzODUgLTk5OHEyMCAtNTAgNjAgLTkycTE4IC0xOSAzNi41IC0yOS41dDI3LjUgLTExLjVsMTAgLTJ2LTY2aC00MTd2NjZxNTMgMCA3NSA0My41dDUgODguNWwtODIgMjIyaC0zOTFxLTU4IC0xNDUgLTkyIC0yMzRxLTExIC0zNCAtNi41IC01N3QyNS41IC0zN3Q0NiAtMjB0NTUgLTZ2LTY2aC0zNjV2NjZxNTYgMjQgODQgNTJxMTIgMTIgMjUgMzAuNXQyMCAzMS41bDcgMTNsMzk5IDEwMDZoOTN6TTQxNiA1MjFoMzQwIGwtMTYyIDQ1N3oiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYwOyIgZD0iTTM1MCAxMTAwaDQwMHExNjUgMCAyNTcuNSAtOTIuNXQ5Mi41IC0yNTcuNXYtNDAwcTAgLTE2MyAtOTIuNSAtMjU2LjV0LTI1Ny41IC05My41aC00MDBxLTE2MyAwIC0yNTYuNSA5NHQtOTMuNSAyNTZ2NDAwcTAgMTY1IDkyLjUgMjU3LjV0MjU3LjUgOTIuNXpNODAwIDkwMGgtNTAwcS00MSAwIC03MC41IC0yOS41dC0yOS41IC03MC41di01MDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDUwMHE0MSAwIDcwLjUgMjkuNXQyOS41IDcwLjUgdjUwMHEwIDQxIC0yOS41IDcwLjV0LTcwLjUgMjkuNXpNNTgwIDY5M2wxOTAgLTI1M3ExMiAtMTYgNi41IC0yOHQtMjYuNSAtMTJoLTQwMHEtMjEgMCAtMjYuNSAxMnQ2LjUgMjhsMTkwIDI1M3ExMiAxNyAzMCAxN3QzMCAtMTd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE2MTsiIGQ9Ik01NTAgMTEwMGg0MDBxMTY1IDAgMjU3LjUgLTkyLjV0OTIuNSAtMjU3LjV2LTQwMHEwIC0xNjUgLTkyLjUgLTI1Ny41dC0yNTcuNSAtOTIuNWgtNDAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDQ1MHE0MSAwIDcwLjUgMjkuNXQyOS41IDcwLjV2NTAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41aC00NTBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djEwMCBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek0zMzggODY3bDMyNCAtMjg0cTE2IC0xNCAxNiAtMzN0LTE2IC0zM2wtMzI0IC0yODRxLTE2IC0xNCAtMjcgLTl0LTExIDI2djE1MGgtMjUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDI1MHYxNTBxMCAyMSAxMSAyNnQyNyAtOXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTgyOyIgZD0iTTE3IDEwMDdsMTYyIDE2MnExNyAxNyA0MCAxNHQzNyAtMjJsMTM5IC0xOTRxMTQgLTIwIDExIC00NC41dC0yMCAtNDEuNWwtMTE5IC0xMThxMTAyIC0xNDIgMjI4IC0yNjh0MjY3IC0yMjdsMTE5IDExOHExNyAxNyA0Mi41IDE5dDQ0LjUgLTEybDE5MiAtMTM2cTE5IC0xNCAyMi41IC0zNy41dC0xMy41IC00MC41bC0xNjMgLTE2MnEtMyAtMSAtOS41IC0xdC0yOS41IDJ0LTQ3LjUgNnQtNjIuNSAxNC41dC03Ny41IDI2LjV0LTkwIDQyLjUgdC0xMDEuNSA2MHQtMTExIDgzdC0xMTkgMTA4LjVxLTc0IDc0IC0xMzMuNSAxNTAuNXQtOTQuNSAxMzguNXQtNjAgMTE5LjV0LTM0LjUgMTAwdC0xNSA3NC41dC00LjUgNDh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE4MzsiIGQ9Ik02MDAgMTEwMHE5MiAwIDE3NSAtMTAuNXQxNDEuNSAtMjd0MTA4LjUgLTM2LjV0ODEuNSAtNDB0NTMuNSAtMzd0MzEgLTI3bDkgLTEwdi0yMDBxMCAtMjEgLTE0LjUgLTMzdC0zNC41IC05bC0yMDIgMzRxLTIwIDMgLTM0LjUgMjB0LTE0LjUgMzh2MTQ2cS0xNDEgMjQgLTMwMCAyNHQtMzAwIC0yNHYtMTQ2cTAgLTIxIC0xNC41IC0zOHQtMzQuNSAtMjBsLTIwMiAtMzRxLTIwIC0zIC0zNC41IDl0LTE0LjUgMzN2MjAwcTMgNCA5LjUgMTAuNSB0MzEgMjZ0NTQgMzcuNXQ4MC41IDM5LjV0MTA5IDM3LjV0MTQxIDI2LjV0MTc1IDEwLjV6TTYwMCA3OTVxNTYgMCA5NyAtOS41dDYwIC0yMy41dDMwIC0yOHQxMiAtMjRsMSAtMTB2LTUwbDM2NSAtMzAzcTE0IC0xNSAyNC41IC00MHQxMC41IC00NXYtMjEycTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYyMTJxMCAyMCAxMC41IDQ1dDI0LjUgNDBsMzY1IDMwM3Y1MCBxMCA0IDEgMTAuNXQxMiAyM3QzMCAyOXQ2MCAyMi41dDk3IDEweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxODQ7IiBkPSJNMTEwMCA3MDBsLTIwMCAtMjAwaC02MDBsLTIwMCAyMDB2NTAwaDIwMHYtMjAwaDIwMHYyMDBoMjAwdi0yMDBoMjAwdjIwMGgyMDB2LTUwMHpNMjUwIDQwMGg3MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV0LTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEybDEzNyAtMTAwaC05NTBsMTM3IDEwMGgtMTJxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41dDE0LjUgMzUuNXQzNS41IDE0LjV6TTUwIDEwMGgxMTAwcTIxIDAgMzUuNSAtMTQuNSB0MTQuNSAtMzUuNXYtNTBoLTEyMDB2NTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxODU7IiBkPSJNNzAwIDExMDBoLTEwMHEtNDEgMCAtNzAuNSAtMjkuNXQtMjkuNSAtNzAuNXYtMTAwMGgzMDB2MTAwMHEwIDQxIC0yOS41IDcwLjV0LTcwLjUgMjkuNXpNMTEwMCA4MDBoLTEwMHEtNDEgMCAtNzAuNSAtMjkuNXQtMjkuNSAtNzAuNXYtNzAwaDMwMHY3MDBxMCA0MSAtMjkuNSA3MC41dC03MC41IDI5LjV6TTQwMCAwaC0zMDB2NDAwcTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNWgxMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTQwMHogIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE4NjsiIGQ9Ik0yMDAgMTEwMGg3MDBxMTI0IDAgMjEyIC04OHQ4OCAtMjEydi01MDBxMCAtMTI0IC04OCAtMjEydC0yMTIgLTg4aC03MDBxLTEyNCAwIC0yMTIgODh0LTg4IDIxMnY1MDBxMCAxMjQgODggMjEydDIxMiA4OHpNMTAwIDkwMHYtNzAwaDkwMHY3MDBoLTkwMHpNNTAwIDcwMGgtMjAwdi0xMDBoMjAwdi0zMDBoLTMwMHYxMDBoMjAwdjEwMGgtMjAwdjMwMGgzMDB2LTEwMHpNOTAwIDcwMHYtMzAwbC0xMDAgLTEwMGgtMjAwdjUwMGgyMDB6IE03MDAgNzAwdi0zMDBoMTAwdjMwMGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxODc7IiBkPSJNMjAwIDExMDBoNzAwcTEyNCAwIDIxMiAtODh0ODggLTIxMnYtNTAwcTAgLTEyNCAtODggLTIxMnQtMjEyIC04OGgtNzAwcS0xMjQgMCAtMjEyIDg4dC04OCAyMTJ2NTAwcTAgMTI0IDg4IDIxMnQyMTIgODh6TTEwMCA5MDB2LTcwMGg5MDB2NzAwaC05MDB6TTUwMCAzMDBoLTEwMHYyMDBoLTEwMHYtMjAwaC0xMDB2NTAwaDEwMHYtMjAwaDEwMHYyMDBoMTAwdi01MDB6TTkwMCA3MDB2LTMwMGwtMTAwIC0xMDBoLTIwMHY1MDBoMjAweiBNNzAwIDcwMHYtMzAwaDEwMHYzMDBoLTEwMHoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjEwOyIgZD0iTTQyNSAxMTAwaDI1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTE1MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTI1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djE1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek00MjUgODAwaDI1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTE1MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTI1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djE1MHEwIDEwIDcuNSAxNy41IHQxNy41IDcuNXpNODI1IDgwMGgyNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYxNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNMjUgNTAwaDI1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTE1MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTI1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djE1MCBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNNDI1IDUwMGgyNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYxNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNODI1IDUwMGgyNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNSB2MTUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTI1IDIwMGgyNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYxNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNNDI1IDIwMGgyNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0yNTBxLTEwIDAgLTE3LjUgNy41IHQtNy41IDE3LjV2MTUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTgyNSAyMDBoMjUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMjUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MTUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIxMTsiIGQ9Ik03MDAgMTIwMGgxMDB2LTIwMGgtMTAwdi0xMDBoMzUwcTYyIDAgODYuNSAtMzkuNXQtMy41IC05NC41bC02NiAtMTMycS00MSAtODMgLTgxIC0xMzRoLTc3MnEtNDAgNTEgLTgxIDEzNGwtNjYgMTMycS0yOCA1NSAtMy41IDk0LjV0ODYuNSAzOS41aDM1MHYxMDBoLTEwMHYyMDBoMTAwdjEwMGgyMDB2LTEwMHpNMjUwIDQwMGg3MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV0LTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEybDEzNyAtMTAwIGgtOTUwbDEzOCAxMDBoLTEzcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXQxNC41IDM1LjV0MzUuNSAxNC41ek01MCAxMDBoMTEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNTBoLTEyMDB2NTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMTI7IiBkPSJNNjAwIDEzMDBxNDAgMCA2OC41IC0yOS41dDI4LjUgLTcwLjVoLTE5NHEwIDQxIDI4LjUgNzAuNXQ2OC41IDI5LjV6TTQ0MyAxMTAwaDMxNHExOCAtMzcgMTggLTc1cTAgLTggLTMgLTI1aDMyOHE0MSAwIDQ0LjUgLTE2LjV0LTMwLjUgLTM4LjVsLTE3NSAtMTQ1aC02NzhsLTE3OCAxNDVxLTM0IDIyIC0yOSAzOC41dDQ2IDE2LjVoMzI4cS0zIDE3IC0zIDI1cTAgMzggMTggNzV6TTI1MCA3MDBoNzAwcTIxIDAgMzUuNSAtMTQuNSB0MTQuNSAtMzUuNXQtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTUwdi0yMDBsMjc1IC0yMDBoLTk1MGwyNzUgMjAwdjIwMGgtMTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXQxNC41IDM1LjV0MzUuNSAxNC41ek01MCAxMDBoMTEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNTBoLTEyMDB2NTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMTM7IiBkPSJNNjAwIDExODFxNzUgMCAxMjggLTUzdDUzIC0xMjh0LTUzIC0xMjh0LTEyOCAtNTN0LTEyOCA1M3QtNTMgMTI4dDUzIDEyOHQxMjggNTN6TTYwMiA3OThoNDZxMzQgMCA1NS41IC0yOC41dDIxLjUgLTg2LjVxMCAtNzYgMzkgLTE4M2gtMzI0cTM5IDEwNyAzOSAxODNxMCA1OCAyMS41IDg2LjV0NTYuNSAyOC41aDQ1ek0yNTAgNDAwaDcwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXQtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTMgbDEzOCAtMTAwaC05NTBsMTM3IDEwMGgtMTJxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41dDE0LjUgMzUuNXQzNS41IDE0LjV6TTUwIDEwMGgxMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtMTIwMHY1MHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIxNDsiIGQ9Ik02MDAgMTMwMHE0NyAwIDkyLjUgLTUzLjV0NzEgLTEyM3QyNS41IC0xMjMuNXEwIC03OCAtNTUuNSAtMTMzLjV0LTEzMy41IC01NS41dC0xMzMuNSA1NS41dC01NS41IDEzMy41cTAgNjIgMzQgMTQzbDE0NCAtMTQzbDExMSAxMTFsLTE2MyAxNjNxMzQgMjYgNjMgMjZ6TTYwMiA3OThoNDZxMzQgMCA1NS41IC0yOC41dDIxLjUgLTg2LjVxMCAtNzYgMzkgLTE4M2gtMzI0cTM5IDEwNyAzOSAxODNxMCA1OCAyMS41IDg2LjV0NTYuNSAyOC41aDQ1IHpNMjUwIDQwMGg3MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV0LTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEzbDEzOCAtMTAwaC05NTBsMTM3IDEwMGgtMTJxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41dDE0LjUgMzUuNXQzNS41IDE0LjV6TTUwIDEwMGgxMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtMTIwMHY1MHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIxNTsiIGQ9Ik02MDAgMTIwMGwzMDAgLTE2MXYtMTM5aC0zMDBxMCAtNTcgMTguNSAtMTA4dDUwIC05MS41dDYzIC03MnQ3MCAtNjcuNXQ1Ny41IC02MWgtNTMwcS02MCA4MyAtOTAuNSAxNzcuNXQtMzAuNSAxNzguNXQzMyAxNjQuNXQ4Ny41IDEzOS41dDEyNiA5Ni41dDE0NS41IDQxLjV2LTk4ek0yNTAgNDAwaDcwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXQtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTNsMTM4IC0xMDBoLTk1MGwxMzcgMTAwIGgtMTJxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41dDE0LjUgMzUuNXQzNS41IDE0LjV6TTUwIDEwMGgxMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtMTIwMHY1MHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIxNjsiIGQ9Ik02MDAgMTMwMHE0MSAwIDcwLjUgLTI5LjV0MjkuNSAtNzAuNXYtNzhxNDYgLTI2IDczIC03MnQyNyAtMTAwdi01MGgtNDAwdjUwcTAgNTQgMjcgMTAwdDczIDcydjc4cTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNXpNNDAwIDgwMGg0MDBxNTQgMCAxMDAgLTI3dDcyIC03M2gtMTcydi0xMDBoMjAwdi0xMDBoLTIwMHYtMTAwaDIwMHYtMTAwaC0yMDB2LTEwMGgyMDBxMCAtODMgLTU4LjUgLTE0MS41dC0xNDEuNSAtNTguNWgtNDAwIHEtODMgMCAtMTQxLjUgNTguNXQtNTguNSAxNDEuNXY0MDBxMCA4MyA1OC41IDE0MS41dDE0MS41IDU4LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIxODsiIGQ9Ik0xNTAgMTEwMGg5MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTUwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtOTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY1MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek0xMjUgNDAwaDk1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMjgzbDIyNCAtMjI0cTEzIC0xMyAxMyAtMzEuNXQtMTMgLTMyIHQtMzEuNSAtMTMuNXQtMzEuNSAxM2wtODggODhoLTUyNGwtODcgLTg4cS0xMyAtMTMgLTMyIC0xM3QtMzIgMTMuNXQtMTMgMzJ0MTMgMzEuNWwyMjQgMjI0aC0yODlxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek01NDEgMzAwbC0xMDAgLTEwMGgzMjRsLTEwMCAxMDBoLTEyNHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjQ2OyIgZD0iTTUwIDEwMDBoNDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0zNTBoMTAwdjE1MHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoNDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xNTBoMTAwdi0xMDBoLTEwMHYtMTUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djE1MGgtMTAwdi0zNTBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTQwMCBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djgwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTcwMCA3MDB2LTMwMGgzMDB2MzAwaC0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI0NzsiIGQ9Ik0xMDAgMGgtMTAwdjEyMDBoMTAwdi0xMjAwek0yNTAgMTEwMGg0MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNDAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY0MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek0zMDAgMTAwMHYtMzAwaDMwMHYzMDBoLTMwMHpNMjUwIDUwMGg5MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMCBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTkwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTEwMGgtNDAwdjEwMHE0IDAgMTEgMC41dDI0IDN0MzAgN3QyNCAxNXQxMSAyNC41djU1MGgtMTAwcS0yNSAwIC0zNyAtMC41dC0zMSAtMnQtMjguNSAtNS41dC0yM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<script src="data:application/x-javascript;base64,/*!
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<p>========================================================</p>
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<h1></h1>
<p>Hi everyone, I’m trying out R knitr for my blog posts now; let me know what you think!</p>
<p>Recently, I was looking for sample size calculations in R and found that R really doesn’t have good built in funtions for sample size and power. For example, for finding the sample size necessary to find a difference in two proportions, one can use the power.prop.test() function and get the following output:</p>
<pre class="r"><code>power.prop.test(n=NULL, .10, .25, .05, .90, alternative="two.sided")</code></pre>
<pre><code>##
## Two-sample comparison of proportions power calculation
##
## n = 132.7557
## p1 = 0.1
## p2 = 0.25
## sig.level = 0.05
## power = 0.9
## alternative = two.sided
##
## NOTE: n is number in *each* group</code></pre>
<p>But there are very few options in this function; for example, there is no option for a continuity correction for the normal approximation to binary data, you can’t compare a one sample of a null vs alternative value instead of two sample proportion, and you can’t change the ratio of the size of the two samples. Even more importantly, there’s no way to find effective sample size when data is clustered. There are other functions in R but I found them complicated and not user friendly. The sampsi and sampclus functions in Stata are intuitive and useful, and have all of those important options. I decided to build my own R functions that had the same capabilities, and I’ve posted them here for others to use and comment on. Eventually I would like to make an R package that includes all of these functions for ease of use.</p>
<p>The first function, sampsi.prop(), calculates sample size for one and two sample tests of proportions. The required n for both are as follows: * Two-sample test of equality of proportions, with continuity correction:</p>
<p><span class="math display">\[n_1 = \frac{n'}{4}\left(1+\left\{1+\frac{2(r+1)}{n'r|p_1-p_2|}\right\}^{1/2}\right)^2\]</span></p>
<p>and <span class="math inline">\(n_2 = rn_1\)</span></p>
<p><span class="math display">\[n' = \frac{\left(z_{1-\alpha/2}\left\{(r+1)\bar{p}\bar{q}\right\}^{1/2}+z_{1-\beta}(rp_1q_1+p_2q_2)^{1/2}\right)^2}{r(p_1-p_2)^2}\]</span></p>
<p>and <span class="math inline">\(\bar{p} = (p_1+rp_2)/(r+1)\)</span> and <span class="math inline">\(\bar{q}=1-\bar{p}\)</span></p>
<p>so that <span class="math inline">\(n_1=n'\)</span> and <span class="math inline">\(n_2=rn_1\)</span></p>
<ul>
<li>One-sample test of proportions where null is <span class="math inline">\(p=p_0\)</span> and alternative is <span class="math inline">\(p=p_A\)</span>:</li>
</ul>
<p><span class="math display">\[n = \left(\frac{z_{1-\alpha/2}\left\{p_0(1-p_0)\right\}^{1/2} + z_{1-\beta}\left\{p_A(1-p_A)\right\}^{1/2}}{p_A-p_0}\right)^2\]</span></p>
<p>The function sampsi.prop() takes arguments p1 and p2, which are either the null and alternative proportions, respectively, for the one-sample test, or two proportions for the two-sample test. These arguments are required for the function to run. The rest of the arguments have preset default values so do not need to be indicated, and include the ratio of smaller group to larger group (default=1), power (default of <span class="math inline">\(\beta=.80\)</span>), significance level (default of <span class="math inline">\(\alpha=.05\)</span>), a logical argument for whether to include the continuity correction (default is true), whether to perform a two-sided test or one-sided (default is two), and whether to perform the two-sample or one-sample test (defaut is two-sample test). The output is a list object.</p>
<pre class="r"><code>sampsi.prop<-function(p1, p2, ratio=1, power=.90, alpha=.05, cont.corr=TRUE, two.sided=TRUE, one.sample=FALSE){
effect.size<-abs(p2-p1)
avg.p<-(p1+ratio*p2)/(ratio+1)
sd=ifelse(one.sample==FALSE, sqrt(ratio*p1*(1-p1)+p2*(1-p2)), sqrt(p2*(1-p2)))
z.pow<-qt(1-power, df=Inf, lower.tail=FALSE)
z.alph<-ifelse(two.sided==TRUE, qt(alpha/2, df=Inf, lower.tail=FALSE), qt(alpha, df=Inf, lower.tail=FALSE))
ct<-(z.pow+z.alph)
n1<-(z.alph*sqrt((ratio+1)*avg.p*(1-avg.p))+z.pow*sd)^2/(effect.size^2*ratio)
n1.cont<-ifelse(cont.corr==FALSE, n1, (n1/4)*(1+sqrt(1+(2*(ratio+1))/(n1*ratio*effect.size)))^2)
n<-(((z.alph*sqrt(p1*(1-p1)))+z.pow*sd)/effect.size)^2
if(one.sample==FALSE){
col1<-c("alpha", "power", "p1", "p2", "effect size", "n2/n1", "n1", "n2")
col2<-c(alpha, power, p1, p2, effect.size, ratio, ceiling(n1.cont), ceiling(n1.cont*ratio))
}
else{
col1<-c("alpha", "power", "p", "alternative", "n")
col2<-c(alpha, power, p1, p2, ceiling(n))
}
ret<-as.data.frame(cbind(col1, col2))
ret$col2<-as.numeric(as.character(ret$col2))
colnames(ret)<-c("Assumptions", "Value")
description<-paste(ifelse(one.sample==FALSE, "Two-sample", "One-sample"), ifelse(two.sided==TRUE, "two-sided", "one-sided"), "test of proportions", ifelse(cont.corr==FALSE, "without", "with"), "continuity correction")
retlist<-list(description, ret)
return(retlist)
}</code></pre>
<p>Now we can do the following sample size calculations that match up perfectly to the corresponding commands in Stata:</p>
<pre class="r"><code>sampsi.prop(.10, .25)</code></pre>
<pre><code>## [[1]]
## [1] "Two-sample two-sided test of proportions with continuity correction"
##
## [[2]]
## Assumptions Value
## 1 alpha 0.05
## 2 power 0.90
## 3 p1 0.10
## 4 p2 0.25
## 5 effect size 0.15
## 6 n2/n1 1.00
## 7 n1 146.00
## 8 n2 146.00</code></pre>
<p>Notice that the results from the calculation above are corrected for continuity, so do not match up with the power.prop.test output from above. To get those results, specify cont.corr=FALSE like so:</p>
<pre class="r"><code>sampsi.prop(.10, .25, cont.corr=FALSE)</code></pre>
<pre><code>## [[1]]
## [1] "Two-sample two-sided test of proportions without continuity correction"
##
## [[2]]
## Assumptions Value
## 1 alpha 0.05
## 2 power 0.90
## 3 p1 0.10
## 4 p2 0.25
## 5 effect size 0.15
## 6 n2/n1 1.00
## 7 n1 133.00
## 8 n2 133.00</code></pre>
<p>Here are a couple more examples with some of the other defaults changed:</p>
<pre class="r"><code>sampsi.prop(.5, .55, power=.8, one.sample=TRUE)</code></pre>
<pre><code>## [[1]]
## [1] "One-sample two-sided test of proportions with continuity correction"
##
## [[2]]
## Assumptions Value
## 1 alpha 0.05
## 2 power 0.80
## 3 p 0.50
## 4 alternative 0.55
## 5 n 783.00</code></pre>
<pre class="r"><code>sampsi.prop(.5, .55, ratio=2, two.sided=FALSE)</code></pre>
<pre><code>## [[1]]
## [1] "Two-sample one-sided test of proportions with continuity correction"
##
## [[2]]
## Assumptions Value
## 1 alpha 0.05
## 2 power 0.90
## 3 p1 0.50
## 4 p2 0.55
## 5 effect size 0.05
## 6 n2/n1 2.00
## 7 n1 1310.00
## 8 n2 2619.00</code></pre>
<p>Next we can calculate sample size for a comparison of means. The required n for the one sample and two sample tests are as follows:</p>
<ul>
<li><p>One sample test of mean where null hypothesis is <span class="math inline">\(\mu=\mu_0\)</span> and the alternative is <span class="math inline">\(\mu=\mu_A\)</span>: <span class="math display">\[n = \left\{\frac{(z_{1-\alpha/2} + z_{1-\beta})\sigma}{\mu_A-\mu_0}\right\}^2\]</span></p></li>
<li><p>Two-sample test of equality of means:</p></li>
</ul>
<p><span class="math display">\[n_1 = \frac{(\sigma_1^2+\sigma_2^2/r)(z_{1-\alpha/2}+z_{1-\beta})^2}{(\mu_1-\mu_2)^2}\]</span> <span class="math display">\[n_2=rn_1\]</span></p>
<p>The sampsi.means() function again works the same way as in Stata where you must input as arguments either a null mean and alternative mean for a one-sample test or two means for a two-sample test. At least one standard deviation of the mean is also required. It is possible to have different standard deviations for the two-sample test, but if only the first standard deviation value is assigned and a two-sample test is designated, then the function assumes both standard deviations to be the same. The other arguments and their defaults are similar to the sampsi.prop() function from above.</p>
<pre class="r"><code>sampsi.means<-function(m1, m2, sd1, sd2=NA, ratio=1, power=.90, alpha=.05, two.sided=TRUE, one.sample=FALSE){
effect.size<-abs(m2-m1)
sd2<-ifelse(!is.na(sd2), sd2, sd1)
z.pow<-qt(1-power, df=Inf, lower.tail=FALSE)
z.alph<-ifelse(two.sided==TRUE, qt(alpha/2, df=Inf, lower.tail=FALSE), qt(alpha, df=Inf, lower.tail=FALSE))
ct<-(z.pow+z.alph)
n1<-(sd1^2+(sd2^2)/ratio)*(ct)^2/(effect.size^2)
n<-(ct*sd1/effect.size)^2
if(one.sample==FALSE){
col1<-c("alpha", "power", "m1", "m2", "sd1", "sd2", "effect size", "n2/n1", "n1", "n2")
col2<-c(alpha, power, m1, m2, sd1, sd2, effect.size, ratio, ceiling(n1), ceiling(n1*ratio))
}
else{
col1<-c("alpha", "power", "null", "alternative", "n")
col2<-c(alpha, power, m1, m2, ceiling(n))
}
ret<-as.data.frame(cbind(col1, col2))
ret$col2<-as.numeric(as.character(ret$col2))
colnames(ret)<-c("Assumptions", "Value")
description<-paste(ifelse(one.sample==FALSE, "Two-sample", "One-sample"), ifelse(two.sided==TRUE, "two-sided", "one-sided"), "test of means")
retlist<-list(description, ret)
return(retlist)
}</code></pre>
<p>And here are the examples:</p>
<pre class="r"><code>sampsi.means(0, 10, sd1=15, power=.8)</code></pre>
<pre><code>## [[1]]
## [1] "Two-sample two-sided test of means"
##
## [[2]]
## Assumptions Value
## 1 alpha 0.05
## 2 power 0.80
## 3 m1 0.00
## 4 m2 10.00
## 5 sd1 15.00
## 6 sd2 15.00
## 7 effect size 10.00
## 8 n2/n1 1.00
## 9 n1 36.00
## 10 n2 36.00</code></pre>
<pre class="r"><code>sampsi.means(10, 30, sd1=15, sd2=20, alpha=.1, ratio=1)</code></pre>
<pre><code>## [[1]]
## [1] "Two-sample two-sided test of means"
##
## [[2]]
## Assumptions Value
## 1 alpha 0.1
## 2 power 0.9
## 3 m1 10.0
## 4 m2 30.0
## 5 sd1 15.0
## 6 sd2 20.0
## 7 effect size 20.0
## 8 n2/n1 1.0
## 9 n1 14.0
## 10 n2 14.0</code></pre>
<p>Finally, we often work with clustered data and would like to calculate sample sizes for clustered observations. Here I created a samp.clus() function that works the same way as the sampclus function in Stata; that is, it takes a sampsi.prop() or sampsi.means() object, along with an interclass correlation coefficient (<span class="math inline">\(\rho\)</span>) and either the number of observations per cluster or the number of clusters, and calculates the corresponding effective sample size.</p>
<pre class="r"><code>samp.clus<-function(sampsi.object, rho, num.clus=NA, obs.clus=NA){
if(is.na(num.clus)&is.na(obs.clus)) print("Either num.clus or obs.clus must be identified")
else{
so<-sampsi.object[[2]]
n1<-as.numeric(so[so$Assumptions=="n1",2])
n2<-as.numeric(so[so$Assumptions=="n2",2])
if(!is.na(obs.clus)){
deff<-1+(obs.clus-1)*rho
n1.clus<-n1*deff
n2.clus<-n2*deff
num.clus<-ceiling((n1.clus+n2.clus)/obs.clus)
}
else if(!is.na(num.clus)){
tot<-(n1*(1-rho)+n2*(1-rho))/(1-(n1*rho/num.clus) - (n2*rho/num.clus))
if(tot<=0) stop("Number of clusters is too small")
else{
obs.clus<-ceiling(tot/num.clus)
deff<-1+(obs.clus-1)*rho
n1.clus<-n1*deff
n2.clus<-n2*deff
}
}
col1<-c("n1 uncorrected", "n2 uncorrected", "ICC", "Avg obs/cluster", "Min num clusters", "n1 corrected", "n2 corrected")
col2<-c(n1, n2, rho, obs.clus, num.clus, ceiling(n1.clus), ceiling(n2.clus))
ret<-as.data.frame(cbind(col1, col2))
colnames(ret)<-c("Assumptions", "Value")
return(ret)
}
}</code></pre>
<p>Here are a couple of examples of how it works (the same function samp.clus() works for both a sampsi.prop object or a sampsi.means object:</p>
<pre class="r"><code>ss<-sampsi.prop(.10, .25, power=.8, two.sided=FALSE)
samp.clus(ss, rho=.05, obs.clus=15)</code></pre>
<pre><code>## Assumptions Value
## 1 n1 uncorrected 92
## 2 n2 uncorrected 92
## 3 ICC 0.05
## 4 Avg obs/cluster 15
## 5 Min num clusters 21
## 6 n1 corrected 157
## 7 n2 corrected 157</code></pre>
<pre class="r"><code>samp.clus(ss, rho=.05, num.clus=150)</code></pre>
<pre><code>## Assumptions Value
## 1 n1 uncorrected 92
## 2 n2 uncorrected 92
## 3 ICC 0.05
## 4 Avg obs/cluster 2
## 5 Min num clusters 150
## 6 n1 corrected 97
## 7 n2 corrected 97</code></pre>
<pre class="r"><code>ss2<-sampsi.means(10, 15, sd1=15, power=.8)
samp.clus(ss2, rho=.05, obs.clus=15)</code></pre>
<pre><code>## Assumptions Value
## 1 n1 uncorrected 142
## 2 n2 uncorrected 142
## 3 ICC 0.05
## 4 Avg obs/cluster 15
## 5 Min num clusters 33
## 6 n1 corrected 242
## 7 n2 corrected 242</code></pre>
<pre class="r"><code>samp.clus(ss2, rho=.05, num.clus=5) </code></pre>
<pre><code>## Error in samp.clus(ss2, rho = 0.05, num.clus = 5): Number of clusters is too small</code></pre>
<pre class="r"><code>##Note: the above won't work because not enough clusters given as input; this error message will occur to warn you. You must increase the number of clusters to avoid the error.</code></pre>
<p>Finally, I add one functionality that Stata doesn’t have with their basic sample size calculations, which is graphing sample size as a function of power. Here, I created two functions for these purposes, for either proprotions or means. The input is the starting point and ending point for the power, as well as the same inputs and defaults from the corresponding sampsi.prop or sampsi.means functions:</p>
<pre class="r"><code>graph.power.prop<-function(from.power, to.power,p1, p2, ratio=1, power=.90, alpha=.05, cont.corr=TRUE, two.sided=TRUE, one.sample=FALSE){
seq.p<-seq(from.power, to.power, by=.01)
n<-rep(NA, length(seq.p))
for(i in 1:length(seq.p)){
ob<-sampsi.prop(p1=p1, p2=p2, power=seq.p[i], alpha=alpha, ratio=ratio, cont.corr=cont.corr, two.sided=two.sided, one.sample=one.sample)[[2]]
n[i]<-as.numeric(ob[7,2])
}
plot(n, seq.p, ylab="Power", xlab="n (in smaller arm)", type="l", main=paste("Power graph for p1=", p1, "and p2=", p2))
}
graph.power.means<-function(from.power, to.power, m1, m2, sd1, sd2=NA, ratio=1, alpha=.05, cont.corr=TRUE, two.sided=TRUE, one.sample=FALSE){
seq.p<-seq(from.power, to.power, by=.01)
n<-rep(NA, length(seq.p))
for(i in 1:length(seq.p)){
ob<-sampsi.means(m1=m1, m2=m2, sd1=sd1, sd2=sd2, power=seq.p[i], alpha=alpha, ratio=ratio,, one.sample=one.sample, two.sided=two.sided)[[2]]
n[i]<-as.numeric(ob[9,2])
}
plot(n, seq.p, ylab="Power", xlab="n (in smaller arm)", type="l", main=paste("Power graph for m1=", m1, "and m2=", m2))
}</code></pre>
<p>And this is what it looks like. This example will graph power as a function of sample size. We restrict the graph from <span class="math inline">\(\beta=0.6\)</span> to <span class="math inline">\(\beta=1\)</span>.</p>
<pre class="r"><code>graph.power.prop(.6, 1, p1=.20, p2=.35)</code></pre>
<p><img 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" 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Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com11tag:blogger.com,1999:blog-2550386066385582083.post-83390931429960855262013-04-07T11:37:00.000-07:002013-04-07T11:37:08.826-07:00Mastering MatricesR has many ways to store information. Most of the time, our data comes in the form of a dataset, which we bring into R as a data.frame object. However, there are times when we want to use matrices as well. This post will show you <b>how matrices can be useful and how to manipulate them easily</b>.<br />
<br />
First of all, the big difference between matrices and dataframes is that all of the rows and columns of a matrix must have the same class (numeric, character, etc). In a dataframe, you can have some of each. See my initial post about objects, <a href="http://rforpublichealth.blogspot.com/2012/11/data-types-part-1-ways-to-store.html" target="_blank">here</a>.<br />
<br />
You can <b>convert from one to the other using as.data.frame() or as.matrix()</b>. Be careful though, that if you convert a dataframe with different classes of columns, then your matrix will just be all character:<br />
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<br />
In order to have a numeric matrix, I'm going to just take the first 6 columns of the <span style="font-family: Courier New, Courier, monospace;">mydata</span> dataframe. I can <b>delete columns of a matrix or dataframe</b> in two ways:
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<pre class="r geshifilter-R" style="font-family: monospace;">mydata.mat<-<a href="http://inside-r.org/r-doc/base/as.matrix"><span style="color: #003399; font-weight: bold;">as.matrix</span></a><span style="color: #009900;">(</span>mydata<span style="color: #009900;">[</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">1</span>:<span style="color: #cc66cc;">6</span><span style="color: #009900;">]</span><span style="color: #009900;">)</span>
mydata.mat<-<a href="http://inside-r.org/r-doc/base/as.matrix"><span style="color: #003399; font-weight: bold;">as.matrix</span></a><span style="color: #009900;">(</span>mydata<span style="color: #009900;">[</span><span style="color: #339933;">,</span>-<span style="color: #cc66cc;">7</span><span style="color: #009900;">]</span><span style="color: #009900;">)</span></pre>
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These two lines are doing the exact same thing. In the first, I am subsetting the dataframe <span style="font-family: Courier New, Courier, monospace;">mydata</span> by taking all rows and the first 6 columns of the dataframe, then I'm converting that subset to a matrix. In the second, I'm taking all rows and all columns <i>except</i> the 7th column. Note that if I wanted to drop even more columns, I would just use the <span style="font-family: Courier New, Courier, monospace;">c()</span> function like this:
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<pre class="r geshifilter-R" style="font-family: monospace;">mydata.mat<-<a href="http://inside-r.org/r-doc/base/as.matrix"><span style="color: #003399; font-weight: bold;">as.matrix</span></a><span style="color: #009900;">(</span>mydata<span style="color: #009900;">[</span><span style="color: #339933;">,</span><a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span>-<span style="color: #cc66cc;">3</span><span style="color: #339933;">,</span>-<span style="color: #cc66cc;">7</span><span style="color: #009900;">)</span><span style="color: #009900;">]</span><span style="color: #009900;">)</span></pre>
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Note now that since I have taken out the one character column in my dataframe before I convert it to a matrix, I will get a numeric matrix instead of a character matrix:<br />
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This kind of operation for deleting columns works the same way in both matrices and dataframes. However, <b>to add a column to a dataframe or matrix is different</b>. In a dataframe, you can use the $ operator to identify columns, like <span style="font-family: Courier New, Courier, monospace;">mydata$Married</span> is the vector corresponding to the Married column. However, you can't use the $ operator on matrices. You will get the following error that the "$ operator is invalid for atomic vectors", which I see all the time when I'm converting back and forth from dataframes to matrices and make a mistake:<br />
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All this message means is that the object you're using is a matrix (<span style="font-family: Courier New, Courier, monospace;">mydata.mat</span>) and you can't use the $ operator on a matrix. If you get this message, you can either use <span style="font-family: Courier New, Courier, monospace;">as.data.frame()</span> to convert your matrix to a dataframe, or you can adjust what you are doing to accomodate the rules of matrices. <b>For adding columns to a matrix, you use <span style="font-family: Courier New, Courier, monospace;">cbind()</span></b>, and likewise for rows, <span style="font-family: Courier New, Courier, monospace;">rbind()</span>.<br />
<br />
So let's say I want to add an age squared column. In the dataframe, I do:
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<pre class="r geshifilter-R" style="font-family: monospace;">mydata$agesq<-mydata$Age^<span style="color: #cc66cc;">2</span></pre>
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which instantly names the new column "agesq". Now for a matrix, there are two ways to do this, via indexing by number or by name of the original column:
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<pre class="r geshifilter-R" style="font-family: monospace;">mydata.mat<-<a href="http://inside-r.org/r-doc/base/cbind"><span style="color: #003399; font-weight: bold;">cbind</span></a><span style="color: #009900;">(</span>mydata.mat<span style="color: #339933;">,</span> mydata.mat<span style="color: #009900;">[</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">2</span><span style="color: #009900;">]</span>^<span style="color: #cc66cc;">2</span><span style="color: #009900;">)</span>
mydata.mat<-<a href="http://inside-r.org/r-doc/base/cbind"><span style="color: #003399; font-weight: bold;">cbind</span></a><span style="color: #009900;">(</span>mydata.mat<span style="color: #339933;">,</span> mydata.mat<span style="color: #009900;">[</span><span style="color: #339933;">,</span><span style="color: blue;">"Age"</span><span style="color: #009900;">]</span>^<span style="color: #cc66cc;">2</span><span style="color: #009900;">)</span></pre>
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In the first line, I'm taking all rows and the second column of the <span style="font-family: Courier New, Courier, monospace;">mydata.mat</span> matrix and squaring it, then I'm column binding it to the original matrix. In the second line, I'm doing the exact same thing, except that instead of indexing with a number, I can use the name of the column "Age". I get the following after running both statements:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh96hko3QwLCyyfAk1GTqwJOMTcbHLX0JX5BeeSTgG0GlcIPrIdW8T04yvW7sQAAJiORhyphenhyphenNVGFqQ6iANYT2s1zRmbSToRaLbdGeSn075kbqrhZH8XJR8ObbzlJZcrskiUy-_Lu45_qIfPw/s1600/agesq.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="105" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh96hko3QwLCyyfAk1GTqwJOMTcbHLX0JX5BeeSTgG0GlcIPrIdW8T04yvW7sQAAJiORhyphenhyphenNVGFqQ6iANYT2s1zRmbSToRaLbdGeSn075kbqrhZH8XJR8ObbzlJZcrskiUy-_Lu45_qIfPw/s320/agesq.png" width="320" /></a></div>
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<br />
Notice that the last two columns of this matrix do not have names, which can be rectified, by using the <span style="font-family: Courier New, Courier, monospace;">colnames()</span> function:
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<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/base/colnames"><span style="color: #003399; font-weight: bold;">colnames</span></a><span style="color: #009900;">(</span>mydata.mat<span style="color: #009900;">)</span><span style="color: #009900;">[</span><span style="color: #cc66cc;">7</span>:<span style="color: #cc66cc;">8</span><span style="color: #009900;">]</span><-<a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: blue;">"AgeSq"</span><span style="color: #339933;">,</span> <span style="color: blue;">"AgeSqAgain"</span><span style="color: #009900;">)</span></pre>
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I don't want to rename everything, so I take the 7th and 8th columns and name those appropriately.<br />
<br />
Finally, what can matrices do for us? One important aspect of matrices is of course matrix multiplication, which is how we do any multivariable regression analysis. I'll do a post soon on regression analysis by hand in R. But another reason is that <b>matrices are great way to store values that you return during the course of running a loop</b>.<br />
<br />
For example, say I want to show how great the central limit theorem is. I'll generate deviates from some other distribution, say the Poisson, and take the mean of the draws each time. I'll do this 1000 times and then show what the histogram looks like. In a problem like this, I'll use a loop. I'll also use a matrix to store the mean each time.<br />
<br />
Ok, we start out by initializing a matrix. We'll create a matrix of all NAs with 1000 rows and 3 columns using the <span style="font-family: Courier New, Courier, monospace;">matrix()</span> function:
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<pre class="r geshifilter-R" style="font-family: monospace;">mat1<-<a href="http://inside-r.org/r-doc/base/matrix"><span style="color: #003399; font-weight: bold;">matrix</span></a><span style="color: #009900;">(</span><span style="color: black; font-weight: bold;">NA</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/nrow"><span style="color: #003399; font-weight: bold;">nrow</span></a>=<span style="color: #cc66cc;">1000</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/ncol"><span style="color: #003399; font-weight: bold;">ncol</span></a>=<span style="color: #cc66cc;">3</span><span style="color: #009900;">)</span></pre>
<pre class="r geshifilter-R" style="font-family: monospace;"></pre>
<pre class="r geshifilter-R" style="font-family: monospace;"><span style="font-family: Times; white-space: normal;">
</span></pre>
<pre class="r geshifilter-R" style="font-family: monospace;"><span style="font-family: Times; white-space: normal;"></span></pre>
Next, we'll set up the <span style="font-family: Courier New, Courier, monospace;">for()</span> loop. Let's look at it first and then go through the logic:
<br />
<pre class="r geshifilter-R" style="font-family: monospace;"><span style="color: black; font-weight: bold;">for</span><span style="color: #009900;">(</span>i <span style="color: black; font-weight: bold;">in</span> <span style="color: #cc66cc;">1</span>:<a href="http://inside-r.org/r-doc/base/nrow"><span style="color: #003399; font-weight: bold;">nrow</span></a><span style="color: #009900;">(</span>mat1<span style="color: #009900;">)</span><span style="color: #009900;">)</span><span style="color: #009900;">{</span></pre>
<pre class="r geshifilter-R" style="font-family: monospace;"> vec1<-<a href="http://inside-r.org/r-doc/stats/rpois"><span style="color: #003399; font-weight: bold;">rpois</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">1</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">)</span>
vec2<-<a href="http://inside-r.org/r-doc/stats/rpois"><span style="color: #003399; font-weight: bold;">rpois</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">10</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">)</span>
vec3<-<a href="http://inside-r.org/r-doc/stats/rpois"><span style="color: #003399; font-weight: bold;">rpois</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">100</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">)</span>
mat1<span style="color: #009900;">[</span>i<span style="color: #339933;">,</span><span style="color: #009900;">]</span><-<a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/base/mean"><span style="color: #003399; font-weight: bold;">mean</span></a><span style="color: #009900;">(</span>vec1<span style="color: #009900;">)</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/mean"><span style="color: #003399; font-weight: bold;">mean</span></a><span style="color: #009900;">(</span>vec2<span style="color: #009900;">)</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/mean"><span style="color: #003399; font-weight: bold;">mean</span></a><span style="color: #009900;">(</span>vec3<span style="color: #009900;">)</span><span style="color: #009900;">)</span>
<span style="color: #009900;">}</span></pre>
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<br />
So in the first line, we're saying for each value of i going from <span style="font-family: Courier New, Courier, monospace;">i=1</span> to <span style="font-family: Courier New, Courier, monospace;">i=nrow(mat1)</span>, do the stuff in the loop. We could have written 1:1000, but it's nice to leave it as <span style="font-family: Courier New, Courier, monospace;">nrow(mat1)</span> since we may want to change the size of <span style="font-family: Courier New, Courier, monospace;">mat1</span> later and this way the loop will still be fine.<br />
<br />
Next, we draw from a Poisson distribution three times, each time a larger number of draws (first 1 draw, then 10, then 100), and each time with a lambda of 1.<br />
<br />
Finally, and this is where the matrix comes in, we'll take the mean of each one of those vectors and store it. We will store the three values in the ith row of the mat1 matrix, filling in all three columns. In a longer way, I could have done:
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<pre class="r geshifilter-R" style="font-family: monospace;">mat1<span style="color: #009900;">[</span>i<span style="color: #339933;">,</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">]</span><-<a href="http://inside-r.org/r-doc/base/mean"><span style="color: #003399; font-weight: bold;">mean</span></a><span style="color: #009900;">(</span>vec1<span style="color: #009900;">)</span>
mat1<span style="color: #009900;">[</span>i<span style="color: #339933;">,</span><span style="color: #cc66cc;">2</span><span style="color: #009900;">]</span><-<a href="http://inside-r.org/r-doc/base/mean"><span style="color: #003399; font-weight: bold;">mean</span></a><span style="color: #009900;">(</span>vec2<span style="color: #009900;">)</span>
mat1<span style="color: #009900;">[</span>i<span style="color: #339933;">,</span><span style="color: #cc66cc;">3</span><span style="color: #009900;">]</span><-<a href="http://inside-r.org/r-doc/base/mean"><span style="color: #003399; font-weight: bold;">mean</span></a><span style="color: #009900;">(</span>vec3<span style="color: #009900;">)</span></pre>
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and it would have come out the same, but the first way is nicer since it's more compact. <b>Remember that matrices are just columns and rows of vectors, so you can always assign a vector to a row</b>, <b>as long as it's the same length</b>. When you concatenate numbers (using the <span style="font-family: Courier New, Courier, monospace;">c()</span> function), you make a vector, which is why it works.<br />
<br />
Now, let's see how the old CLT is working by plotting some histograms:
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<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/graphics/par"><span style="color: #003399; font-weight: bold;">par</span></a><span style="color: #009900;">(</span>mfrow=<a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">1</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">3</span><span style="color: #009900;">)</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/graphics/hist"><span style="color: #003399; font-weight: bold;">hist</span></a><span style="color: #009900;">(</span>mat1<span style="color: #009900;">[</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">]</span><span style="color: #339933;">,</span> main=<span style="color: blue;">"n=1"</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/graphics/hist"><span style="color: #003399; font-weight: bold;">hist</span></a><span style="color: #009900;">(</span>mat1<span style="color: #009900;">[</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">2</span><span style="color: #009900;">]</span><span style="color: #339933;">,</span> main=<span style="color: blue;">"n=10"</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/graphics/hist"><span style="color: #003399; font-weight: bold;">hist</span></a><span style="color: #009900;">(</span>mat1<span style="color: #009900;">[</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">3</span><span style="color: #009900;">]</span><span style="color: #339933;">,</span> main=<span style="color: blue;">"n=100"</span><span style="color: #009900;">)</span></pre>
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<br />
Again, with the histograms, I can plot each column at a time by subsetting the <span style="font-family: Courier New, Courier, monospace;">mat1</span> matrix:<br />
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<br />
Pretty nice! Other very helpful places to better understand matrices:<br />
<ul>
<li><a href="http://www.ats.ucla.edu/stat/r/library/matrix_alg.htm" target="_blank">UCLA IDRE</a></li>
<li><a href="http://www.statmethods.net/advstats/matrix.html" target="_blank">Quick-R</a></li>
</ul>
<br />
<br />
<br /></div>
Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com0tag:blogger.com,1999:blog-2550386066385582083.post-65319387332728700892013-03-17T12:06:00.001-07:002013-07-11T08:13:40.772-07:00Extracting Information From Objects Using Names()One of the big differences between a language like Stata compared to R is the ability in R to handle many different types of objects at once, and combine them together or pull them apart. <a href="http://rforpublichealth.blogspot.com/2012/11/data-types-part-1-ways-to-store.html" target="_blank">I had a post about objects last year</a>, but I thought I'd show in this post how to extract information from objects you create in R.<br />
<br />
For this example, I'll go back to a dataset I've used in the past called mydata.Rdata and it's in the <a href="https://sites.google.com/site/rforpublichealthcode/downloads" target="_blank">Code and Data Download site</a>.<br />
<br />
One function that is extremely useful to know is <span style="color: #003399; font-weight: bold;">names()</span>. The <b>names() function will show you everything that is stored in R under that object name</b>. So, for example, if you do<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIKNTIO1I1QtGfqMsyrXxxWwyW3qVmA6bJS6kNcbcE_Ji7roy960pX9A6rx3i_RNg_aI7bzbWIPZADTPmhjA3caLsQ4fKUNjnPaMe6YOnV22DepDNr7I1Mx5zie19WvQ_ASHLqgsxEnVo/s1600/names1.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIKNTIO1I1QtGfqMsyrXxxWwyW3qVmA6bJS6kNcbcE_Ji7roy960pX9A6rx3i_RNg_aI7bzbWIPZADTPmhjA3caLsQ4fKUNjnPaMe6YOnV22DepDNr7I1Mx5zie19WvQ_ASHLqgsxEnVo/s1600/names1.png" /></a></div>
<br />
<br />
<br />
<br />
where <span style="font-family: Courier New, Courier, monospace;">mydata</span> is a dataframe object, you will get the names of the columns, which are the vectors that comprise the dataframe. Note that <span style="color: #003399; font-weight: bold;">names(</span><span style="font-family: Courier New, Courier, monospace;">mydata</span><span style="color: #003399; font-weight: bold;">)</span> is an object itself (because everything is an object in R) - it is a character vector of length 7. You can save this vector and print out the class to verify this.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgt-tRG0XJhY1nvHlIRs7JrzfHAarRRfEEUS3iDlqyFuiDKNFQ8MctkdD68aK-TfKCPcmqmxEYJDiF0pOcw2bSiK0UKxVOAmbldY9OhYKh28NJyGsEmQAkJunXEts3izDct9f4gy8g5SyU/s1600/colnames.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgt-tRG0XJhY1nvHlIRs7JrzfHAarRRfEEUS3iDlqyFuiDKNFQ8MctkdD68aK-TfKCPcmqmxEYJDiF0pOcw2bSiK0UKxVOAmbldY9OhYKh28NJyGsEmQAkJunXEts3izDct9f4gy8g5SyU/s1600/colnames.png" /></a></div>
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<br />
<br />
<br />
<br />
<br />
<br />
But <span style="color: #003399; font-weight: bold;">names()</span> can be useful for much more than just column names, as we'll see in a moment.<br />
<br />
But before we go on, let's take a moment <a href="http://rforpublichealth.blogspot.com/2012/10/quick-and-easy-subsetting.html" target="_blank">to remember how subsetting works</a>. In subsetting, you use square brackets to pull out exactly the element of an object that you want. So if I want to subset a dataframe, I can say
<br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;">mydata.subset<-mydata<span style="color: #009900;">[</span><span style="color: #339933;">,</span><a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">1</span>:<span style="color: #cc66cc;">2</span><span style="color: #009900;">)</span><span style="color: #009900;">]</span></pre>
</div>
</div>
<br />
which is saving into the new object <span style="font-family: Courier New, Courier, monospace;">mydata.subset</span>, all the rows and only the first two columns of the <span style="font-family: Courier New, Courier, monospace;">mydata</span> dataframe.<br />
<br />
Now, let's combine the concept of using the <span style="color: #003399; font-weight: bold;">names()</span> function with the concept of subsetting to change one of the column names of our dataset:<br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/base/names"><span style="color: #003399; font-weight: bold;">names</span></a><span style="color: #009900;">(</span>mydata<span style="color: #009900;">)</span><span style="color: #009900;">[</span><span style="color: #cc66cc;">4</span><span style="color: #009900;">]</span><-<span style="color: blue;">"Weight_lbs"</span></pre>
</div>
</div>
<br />
Here we are saying, of the <span style="color: #003399; font-weight: bold;">names(</span>mydata<span style="color: #003399; font-weight: bold;">)</span> object, take the fourth component and make it "Weight_lbs". Now, if you run the <span style="color: #003399; font-weight: bold;">names()</span> on our dataframe, we find the change has been made:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtBrmHpTsiaB-gneGJTctTGiiyArl1g6rJzOlPoCGEFQ5FPlV6QfUltZ7a-AnbBwRCE6REH9KFtIMeey5hzOgkISpiZ7U1EgikfLJ34-SahQUiVRzN-duVXQ_67NcWuEkq1rm8Z32dC2U/s1600/names2.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtBrmHpTsiaB-gneGJTctTGiiyArl1g6rJzOlPoCGEFQ5FPlV6QfUltZ7a-AnbBwRCE6REH9KFtIMeey5hzOgkISpiZ7U1EgikfLJ34-SahQUiVRzN-duVXQ_67NcWuEkq1rm8Z32dC2U/s1600/names2.png" /></a><br />
<br />
<br />
Ok, so now we'll see how the <span style="color: #003399; font-weight: bold;">names()</span> function can be used in other applications.<br />
<br />
<span style="background-color: orange;">1. Summary objects</span><br />
<span style="background-color: orange;"><br /></span><span style="background-color: white;"><b>There are two ways to extract information from objects in R, using subsetting and using the "$" operator. </b></span><br />
<span style="background-color: white;"><br /></span>
<span style="background-color: white;">Below, we summarize the Age vector and store the results in <span style="font-family: Courier New, Courier, monospace;">sum.vec</span>. We print out the <span style="font-family: 'Courier New', Courier, monospace;">sum.vec</span> object and the print out the corresponding names. Now we can <b>extract the 1st element of the summary vector</b> of Age in the following way using the [ ] operator.</span><br />
<span style="background-color: white;"><br /></span>
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg4uit9aEj02XFofbT1AFkhgY2iejDGClSsZ4AZH5nfORZxfr_GqsAQGuIx17220hgPoplYgfnqVPq_A7KVBGXRPfxYGJh5PSeule732izmnhOR7NsXuruJScOFlVPgOeGuBjLtzu-eQq4/s1600/sum3.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg4uit9aEj02XFofbT1AFkhgY2iejDGClSsZ4AZH5nfORZxfr_GqsAQGuIx17220hgPoplYgfnqVPq_A7KVBGXRPfxYGJh5PSeule732izmnhOR7NsXuruJScOFlVPgOeGuBjLtzu-eQq4/s1600/sum3.png" /></a></div>
<span style="background-color: white;"><br /></span>
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<br />
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><br /></span>
<span style="background-color: white;">This gives us the first element, which is the minimum. We could also do:</span><br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;">sum.vec<span style="color: #009900;">[</span><a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">2</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">3</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">5</span><span style="color: #009900;">)</span><span style="color: #009900;">]</span> </pre>
</div>
</div>
<br />
<span style="background-color: white;">for the 25th, 50th, and 75th percentiles.</span><br />
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><br /></span>
<span style="background-color: white;"><b>The other way to extract is by using "$". </b> For example, the <span style="font-family: Courier New, Courier, monospace;">summary()</span> function on a table object gives you a Chi squared test:</span><br />
<span style="background-color: white;"><br /></span>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjx2QNwgS-xdbnMdXh7VGnhJlPRFbzRjvxXq1_zaKeyRJ-H9syAqYHJfVs-B5shVasGuquCI1YyjKwYU84mNxR_S5oBMdzteBsLPyBtc0wzBSc5E59bTqXCOCvvl775lbyHuC7tW9yHT-A/s1600/sum2.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjx2QNwgS-xdbnMdXh7VGnhJlPRFbzRjvxXq1_zaKeyRJ-H9syAqYHJfVs-B5shVasGuquCI1YyjKwYU84mNxR_S5oBMdzteBsLPyBtc0wzBSc5E59bTqXCOCvvl775lbyHuC7tW9yHT-A/s1600/sum2.png" /></a></div>
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<br />
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<br />
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<br />
<br />
<br />
<span style="background-color: orange;"><span style="background-color: white;"><br /></span></span>
<span style="background-color: orange;"><span style="background-color: white;">Here, you can extract any of the pieces of information that came out in the test, including the number of cases, the number of variables, the test statistic, etc. We can extract the pvalue of the test statistic by using the "$" operator, like this:</span></span><br />
<span style="background-color: orange;"><span style="background-color: white;"><br /></span></span>
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5KImpLE89iAUpPbJ53cAuS2vh9Wo01MqT9s_OIexgg33JglHgDxIv0mFrE6dBLtrj_YyGbvdg4M8ysw3nwRQPHEHeRDr6lNfRbyceR0-tt2SWS7QidhdwrbipRces_34p5Iq2Yb9fhFY/s1600/pval.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5KImpLE89iAUpPbJ53cAuS2vh9Wo01MqT9s_OIexgg33JglHgDxIv0mFrE6dBLtrj_YyGbvdg4M8ysw3nwRQPHEHeRDr6lNfRbyceR0-tt2SWS7QidhdwrbipRces_34p5Iq2Yb9fhFY/s1600/pval.png" /></a></div>
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<br />
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<br />
Let's see how this can be useful in the next example.
<span style="background-color: orange;"><br /></span>
<span style="background-color: orange;"><br /></span>
<span style="background-color: orange;">2. Regressions and statistical tests</span><br />
<br />
The standard linear regression that we run in R is using <span style="font-family: Courier New, Courier, monospace;">lm()</span>. It looks like this:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh13I1DMfQqGCHdTSrsGLEVqEGxqB5MBCFwtnK975KjYdufyy3KKdebkGV9D_a3jYT1qI84Hu6L-u3pRpMsuegd6HPcfswdHGy-O1809V1KwzgNCw8PGESXSqOEVVdnjjlh62RewY5ImKg/s1600/reg1.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh13I1DMfQqGCHdTSrsGLEVqEGxqB5MBCFwtnK975KjYdufyy3KKdebkGV9D_a3jYT1qI84Hu6L-u3pRpMsuegd6HPcfswdHGy-O1809V1KwzgNCw8PGESXSqOEVVdnjjlh62RewY5ImKg/s1600/reg1.png" /></a></div>
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<span style="text-align: center;"><br /></span>
<span style="text-align: center;"><br /></span><br />
<span style="text-align: center;"><br /></span>
<span style="text-align: center;"><br /></span>
<span style="text-align: center;"><br /></span>
<span style="text-align: center;"><br /></span>
<span style="text-align: center;"><br /></span>
<span style="text-align: center;"><br /></span>
<span style="text-align: center;">But there's a lot more that R has calculated that is not shown here. We can see this by saving this linear regression as an object and running </span><span style="color: #003399; font-weight: bold;">names()</span><span style="text-align: center;"> on it:</span><br />
<span style="text-align: center;"><br /></span>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgd4SYFk5m9Nu2euGFSKhYaJNs7nIAuUD4oCuhA5x64u0BQdy-VDcxvOZevu29nEdvJ7BcJwj08LvqevMNNlSdGtE8rQo1m7HaoJDiFGyX7IXSJvgyWclgpf8BYCsprpwak4yLKhQgAVQw/s1600/reg2.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgd4SYFk5m9Nu2euGFSKhYaJNs7nIAuUD4oCuhA5x64u0BQdy-VDcxvOZevu29nEdvJ7BcJwj08LvqevMNNlSdGtE8rQo1m7HaoJDiFGyX7IXSJvgyWclgpf8BYCsprpwak4yLKhQgAVQw/s1600/reg2.png" /></a></div>
<span style="text-align: center;"><br /></span>
<br />
<div class="" style="clear: both; text-align: left;">
So we see that saved under the reg.object are the coefficients, the residuals, fitted values, degrees of freedom, and a lot more. To find out everything that <span style="color: #003399; font-weight: bold;">names()</span> provides for a given object, look it up by doing <span style="font-family: Courier New, Courier, monospace;">?lm</span>. Now, to extract any of these components, like the residuals, use the "$" operator like this:</div>
<div class="" style="clear: both; text-align: left;">
<br /></div>
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;">reg.object$residuals</pre>
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</div>
<a a="" href="http://www.inside-r.org/pretty-r"></a><br />
<div class="" style="clear: both; text-align: left;">
You can make use of this extraction by taking the mean of the residuals<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBvMBlOgPC7SrvS5JKH_6r1R0XpC52aF0gWfjbRXBGSplSAd3pIBbZrUz3Oj5SIC2QBew_OYRLSpAv_EJKF4b-LYjeMrdmTIJAr5xozIuglpR6ezpGbBTyUekOJWBKp-HVaAyAKTJjIXs/s1600/meanresid.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBvMBlOgPC7SrvS5JKH_6r1R0XpC52aF0gWfjbRXBGSplSAd3pIBbZrUz3Oj5SIC2QBew_OYRLSpAv_EJKF4b-LYjeMrdmTIJAr5xozIuglpR6ezpGbBTyUekOJWBKp-HVaAyAKTJjIXs/s1600/meanresid.png" /></a></div>
<br />
<br />
<a a="" href="http://www.inside-r.org/pretty-r"></a>
<br />
<br />
or plotting their distribution:</div>
<div class="" style="clear: both; text-align: left;">
<br /></div>
<div class="" style="clear: both; text-align: left;">
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/graphics/hist"><span style="color: #003399; font-weight: bold;">hist</span></a><span style="color: #009900;">(</span>reg.object$residuals<span style="color: #339933;">,</span> main=<span style="color: blue;">"Distribution of Residuals"</span> <span style="color: #339933;">,</span>xlab=<span style="color: blue;">"Residuals"</span><span style="color: #009900;">)</span></pre>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh12iOxz9H7gKghkmx4Sdo8cy7YhzXHQCBtTTqekhhxs1kRUbp8Fpf_lLu262HHvtszRnpciSBFC7vRyt9e9EpaGg6AF69VB8Ylxk0Gn6p_s0ydWkRyo4IEjYczwFOcwAJcci0BvhmBM6A/s1600/plot_resid.jpeg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh12iOxz9H7gKghkmx4Sdo8cy7YhzXHQCBtTTqekhhxs1kRUbp8Fpf_lLu262HHvtszRnpciSBFC7vRyt9e9EpaGg6AF69VB8Ylxk0Gn6p_s0ydWkRyo4IEjYczwFOcwAJcci0BvhmBM6A/s200/plot_resid.jpeg" width="165" /></a></div>
<div class="" style="clear: both;">
<br /></div>
<div>
Don't forget that you can summarize regression objects using <span style="color: #003399; font-weight: bold;">summary()</span>, and get the <span style="color: #003399; font-weight: bold;">names()</span> of that summary too, like this:</div>
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/base/summary"><span style="color: #003399; font-weight: bold;">summary</span></a><span style="color: #009900;">(</span>reg.object<span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/base/names"><span style="color: #003399; font-weight: bold;">names</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/base/summary"><span style="color: #003399; font-weight: bold;">summary</span></a><span style="color: #009900;">(</span>reg.object<span style="color: #009900;">)</span><span style="color: #009900;">)</span></pre>
</div>
</div>
<br />
which will give you more objects you can extract from your regression. <b>You can use the names() function on any statistical model or function</b> such as<span style="font-family: Courier New, Courier, monospace;"> aov()</span>, <span style="font-family: Courier New, Courier, monospace;">t.test()</span>, <span style="font-family: Courier New, Courier, monospace;">chisq.test()</span>, etc.<br />
<br />
<span style="background-color: orange;">3. Histograms and boxplots</span><br />
<br />
Finally, let's go back to that histogram and save that into an object. There are objects under <span style="color: #003399; font-weight: bold;">names()</span> of the histogram object now:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxcrLTmSQ2t0HgtA5BKVfLI4s8wFjjNnmuFW2_eqBQ6QxEb6KTiVJFVqu8R5sp3JJJJAGg-u_yhY_oZcgPMViK0atGnrV5HhGJKq1lEkuugsV0419bIJAmXZXfTChJ8T9hPSmj0cHqi2M/s1600/histnames.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxcrLTmSQ2t0HgtA5BKVfLI4s8wFjjNnmuFW2_eqBQ6QxEb6KTiVJFVqu8R5sp3JJJJAGg-u_yhY_oZcgPMViK0atGnrV5HhGJKq1lEkuugsV0419bIJAmXZXfTChJ8T9hPSmj0cHqi2M/s1600/histnames.png" /></a></div>
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I showed how you can manipulate those in my <a href="http://rforpublichealth.blogspot.com/2012/12/basics-of-histograms.html" target="_blank">post on histograms</a>.<br />
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Similarly, for boxplot:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiiC9FkwJ2-WLRfnk2uVYfz3h1_CdvV8J_eYj_cqCZUm2oHmMLeeyAF4nnGWKGWGFzXGfnbSZ8rIGXdRU4UQdpipECZINUJUScAzq50RZ4qJFTz3N-KY2s8Kx65iXJz6pGymgxrkcdRim8/s1600/boxplonames.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiiC9FkwJ2-WLRfnk2uVYfz3h1_CdvV8J_eYj_cqCZUm2oHmMLeeyAF4nnGWKGWGFzXGfnbSZ8rIGXdRU4UQdpipECZINUJUScAzq50RZ4qJFTz3N-KY2s8Kx65iXJz6pGymgxrkcdRim8/s1600/boxplonames.png" /></a><br />
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Here I've extracted the stats object which gives you the lower whisker, the lower hinge, the median, the upper hinge, and the upper whisker for each group, which you can see below.<br />
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUYnpLWhehYce3F2ttv6-IXKBA1I8enZ18RhBYg3cCgDrNb8IXDjg5c26-hTRLf2LuXGqjz-2eELliA4z9aUzWImSxrIyJxm1GXD93JFQI_S7tJ-jbBmYf0yyCbzS_f_w9ZAnS2e8qeXg/s1600/boxplots.jpeg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="263" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUYnpLWhehYce3F2ttv6-IXKBA1I8enZ18RhBYg3cCgDrNb8IXDjg5c26-hTRLf2LuXGqjz-2eELliA4z9aUzWImSxrIyJxm1GXD93JFQI_S7tJ-jbBmYf0yyCbzS_f_w9ZAnS2e8qeXg/s320/boxplots.jpeg" width="320" /></a></div>
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<br />Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com5tag:blogger.com,1999:blog-2550386066385582083.post-10022868003797509492013-02-25T17:02:00.000-08:002013-02-25T17:07:12.840-08:00Normal distribution functionsAh, the Central Limit Theorem. The basis of much of statistical inference and how we get those 95% confidence intervals. It's just so beautiful! Lately, I have found myself looking up the normal distribution functions in R. They can be difficult to keep straight, so this post will give a succinct overview and show you how they can be useful in your data analysis.<br />
<br />
To start, here is a table with all four normal distribution functions and their purpose, syntax, and an example:<br />
<br />
<table border="1" cellpadding="5">
<tbody>
<tr>
<td></td>
<th>Purpose</th>
<th>Syntax</th>
<th>Example</th>
</tr>
<tr>
<th>rnorm</th>
<td><span style="background-color: white;">Generates random numbers </span><br />
<span style="background-color: white;">from </span><span style="background-color: white;">normal distribution</span></td>
<td>rnorm(n, mean, sd)</td>
<td>rnorm(1000, 3, .25)<br />
Generates 1000 numbers<br />
from a normal with mean 3<br />
and sd=.25</td>
</tr>
<tr>
<th>dnorm</th>
<td>Probability Density Function<br />
(PDF)</td>
<td>dnorm(x, mean, sd)</td>
<td>dnorm(0, 0, .5)<br />
Gives the density (height of the<br />
PDF) of the normal<br />
with mean=0 and sd=.5. </td>
</tr>
<tr><th>pnorm</th>
<td><span style="background-color: white;">Cumulative Distribution </span><span style="background-color: white;">Function</span><br />
<span style="background-color: white;">(CDF)</span></td>
<td>pnorm(q, mean, sd)</td>
<td>pnorm(1.96, 0, 1)<br />
Gives the area under the<br />
standard normal curve to<br />
the left of 1.96,<br />
i.e. ~0.975</td>
</tr>
<tr><th>qnorm</th>
<td>Quantile Function - inverse of<br />
pnorm</td>
<td>qnorm(p, mean, sd)</td>
<td>qnorm(0.975, 0, 1)<br />
Gives the value at which the<br />
CDF of the standard normal<br />
is .975, i.e. ~1.96</td>
</tr>
</tbody></table>
<br />
Note that for all functions, leaving out the mean and standard deviation would result in default values of mean=0 and sd=1, a standard normal distribution.<br />
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Another important note for the <i>pnorn()</i> function is the ability to get the right hand probability using the lower.tail=FALSE option. For example,<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEQ3ISB64soKuw3uer0HWYIeXeomPzY3WNW9fgAFd0uGz9iOjcvoJGno7IvXbwpJ5uuQBNSHV_MhfPfHPjP0DNpgT_TvO2x-F99reqLS89-1l8TDTDj8qAjJKSEloKA0UYodw3jcJ4YmA/s1600/lowertailtrue.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEQ3ISB64soKuw3uer0HWYIeXeomPzY3WNW9fgAFd0uGz9iOjcvoJGno7IvXbwpJ5uuQBNSHV_MhfPfHPjP0DNpgT_TvO2x-F99reqLS89-1l8TDTDj8qAjJKSEloKA0UYodw3jcJ4YmA/s1600/lowertailtrue.png" /></a></div>
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In the first line, we are calculating the area to the left of 1.96, while in the second line we are calculating the area to the right of 1.96.<br />
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With these functions, I can do some fun plotting. I create a sequence of values from -4 to 4, and then calculate both the standard normal PDF and the CDF of each of those values. I also generate 1000 random draws from the standard normal distribution. I then plot these next to each other. Whenever you use probability functions, you should, as a habit, <b>remember to set the seed</b>. Setting the seed means locking in the sequence of "random" (they are pseudorandom) numbers that R gives you, so you can reproduce your work later on.<br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/base/set.seed"><span style="color: #003399; font-weight: bold;">set.seed</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">3000</span><span style="color: #009900;">)</span>
xseq<-<a href="http://inside-r.org/r-doc/base/seq"><span style="color: #003399; font-weight: bold;">seq</span></a><span style="color: #009900;">(</span>-<span style="color: #cc66cc;">4</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">4</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">.01</span><span style="color: #009900;">)</span>
densities<-<a href="http://inside-r.org/r-doc/stats/dnorm"><span style="color: #003399; font-weight: bold;">dnorm</span></a><span style="color: #009900;">(</span>xseq<span style="color: #339933;">,</span> <span style="color: #cc66cc;">0</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">)</span>
cumulative<-<a href="http://inside-r.org/r-doc/stats/pnorm"><span style="color: #003399; font-weight: bold;">pnorm</span></a><span style="color: #009900;">(</span>xseq<span style="color: #339933;">,</span> <span style="color: #cc66cc;">0</span><span style="color: #339933;">,</span> <span style="color: #cc66cc;">1</span><span style="color: #009900;">)</span>
randomdeviates<-<a href="http://inside-r.org/r-doc/stats/rnorm"><span style="color: #003399; font-weight: bold;">rnorm</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">1000</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">0</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/graphics/par"><span style="color: #003399; font-weight: bold;">par</span></a><span style="color: #009900;">(</span>mfrow=<a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">1</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">3</span><span style="color: #009900;">)</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/packages/cran/mAr">mar</a>=<a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">3</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">4</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">4</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">2</span><span style="color: #009900;">)</span><span style="color: #009900;">)</span>
<span style="color: #003399; font-weight: bold;">
</span></pre>
<pre class="r geshifilter-R" style="font-family: monospace;"><span style="color: #003399; font-weight: bold;"><a href="http://inside-r.org/r-doc/graphics/plot">plot</a></span><span style="color: #009900;">(</span>xseq<span style="color: #339933;">,</span> densities<span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/col"><span style="color: #003399; font-weight: bold;">col</span></a>=<span style="color: blue;">"darkgreen"</span><span style="color: #339933;">,</span>xlab=<span style="color: blue;">""</span><span style="color: #339933;">,</span> ylab=<span style="color: blue;">"Density"</span><span style="color: #339933;">,</span> type=<span style="color: blue;">"l"</span><span style="color: #339933;">,</span>lwd=<span style="color: #cc66cc;">2</span><span style="color: #339933;">,</span> cex=<span style="color: #cc66cc;">2</span><span style="color: #339933;">,</span> main=<span style="color: blue;">"PDF of Standard Normal"</span><span style="color: #339933;">,</span> cex.axis=<span style="color: #cc66cc;">.8</span><span style="color: #009900;">)</span>
<span style="color: #003399; font-weight: bold;">
</span></pre>
<pre class="r geshifilter-R" style="font-family: monospace;"><span style="color: #003399; font-weight: bold;"><a href="http://inside-r.org/r-doc/graphics/plot">plot</a></span><span style="color: #009900;">(</span>xseq<span style="color: #339933;">,</span> cumulative<span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/col"><span style="color: #003399; font-weight: bold;">col</span></a>=<span style="color: blue;">"darkorange"</span><span style="color: #339933;">,</span> xlab=<span style="color: blue;">""</span><span style="color: #339933;">,</span> ylab=<span style="color: blue;">"Cumulative Probability"</span><span style="color: #339933;">,</span>type=<span style="color: blue;">"l"</span><span style="color: #339933;">,</span>lwd=<span style="color: #cc66cc;">2</span><span style="color: #339933;">,</span> cex=<span style="color: #cc66cc;">2</span><span style="color: #339933;">,</span> main=<span style="color: blue;">"CDF of Standard Normal"</span><span style="color: #339933;">,</span> cex.axis=<span style="color: #cc66cc;">.8</span><span style="color: #009900;">)</span>
<span style="color: #003399; font-weight: bold;">
</span></pre>
<pre class="r geshifilter-R" style="font-family: monospace;"><span style="color: #003399; font-weight: bold;"><a href="http://inside-r.org/r-doc/graphics/hist">hist</a></span><span style="color: #009900;">(</span>randomdeviates<span style="color: #339933;">,</span> main=<span style="color: blue;">"Random draws from Std Normal"</span><span style="color: #339933;">,</span> cex.axis=<span style="color: #cc66cc;">.8</span><span style="color: #339933;">,</span> xlim=<a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span>-<span style="color: #cc66cc;">4</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">4</span><span style="color: #009900;">)</span><span style="color: #009900;">)</span></pre>
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</div>
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The <i>par()</i> parameters set up a plotting area of 1 row and 3 columns (mfrow), and move the three plots closer to each other (mar). <a href="http://research.stowers-institute.org/efg/R/Graphics/Basics/mar-oma/index.htm" target="_blank">Here is a good explanation of the plotting area.</a> The output is below:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMlLHoPeawmsbfiGQx89p1albl0uflauDxhplsMYQr1Rdlu6mYehYcvFnH30llVtk6fBIjz-9pscz5L2x8aHgLcDRS4EFA3iurJYrD44YWH41Fh7S5k9qJ83z-PYtCf7aZBUZ_3Kh2vU0/s1600/stdnorms1by3.jpeg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="260" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMlLHoPeawmsbfiGQx89p1albl0uflauDxhplsMYQr1Rdlu6mYehYcvFnH30llVtk6fBIjz-9pscz5L2x8aHgLcDRS4EFA3iurJYrD44YWH41Fh7S5k9qJ83z-PYtCf7aZBUZ_3Kh2vU0/s640/stdnorms1by3.jpeg" width="640" /></a></div>
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Now, when we have our actual data, we can do a visual check of the normality of our outcome variable, which, if we assume a linear relationship with normally distributed errors, should also be normal. Let's make up some data, where I add noise by using <i>rnorm()</i> - here I'm generating the same amount of random numbers as is the length of the xseq vector, with a mean of 0 and a standard deviation of 5.5. <br />
<br />
<pre class="r geshifilter-R">xseq<-<a href="http://inside-r.org/r-doc/base/seq"><span style="color: #003399; font-weight: bold;">seq</span></a><span style="color: #009900;">(</span>-<span style="color: #cc66cc;">4</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">4</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">.01</span><span style="color: #009900;">)</span></pre>
<pre class="r geshifilter-R">y<-<span style="color: #cc66cc;">2</span>*xseq + <a href="http://inside-r.org/r-doc/stats/rnorm"><span style="color: #003399; font-weight: bold;">rnorm</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/base/length"><span style="color: #003399; font-weight: bold;">length</span></a><span style="color: #009900;">(</span>xseq<span style="color: #009900;">)</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">0</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">5.5</span><span style="color: #009900;">)</span></pre>
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And now I can plot a histogram of y (<a href="http://rforpublichealth.blogspot.com/2012/12/basics-of-histograms.html" target="_blank">check out my post on histograms</a> if you want more detail) and add a <i>curve()</i> function to the plot using the mean and standard deviation of y as the parameters:<br />
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<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/graphics/hist"><span style="color: #003399; font-weight: bold;">hist</span></a><span style="color: #009900;">(</span>y<span style="color: #339933;">,</span> <a href="http://inside-r.org/packages/cran/prob">prob</a>=<span style="color: black; font-weight: bold;">TRUE</span><span style="color: #339933;">,</span> ylim=<a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">0</span><span style="color: #339933;">,</span><span style="color: #cc66cc;">.06</span><span style="color: #009900;">)</span><span style="color: #339933;">,</span> breaks=<span style="color: #cc66cc;">20</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/graphics/curve"><span style="color: #003399; font-weight: bold;">curve</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/stats/dnorm"><span style="color: #003399; font-weight: bold;">dnorm</span></a><span style="color: #009900;">(</span>x<span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/mean"><span style="color: #003399; font-weight: bold;">mean</span></a><span style="color: #009900;">(</span>y<span style="color: #009900;">)</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/stats/sd"><span style="color: #003399; font-weight: bold;">sd</span></a><span style="color: #009900;">(</span>y<span style="color: #009900;">)</span><span style="color: #009900;">)</span><span style="color: #339933;">,</span> add=<span style="color: black; font-weight: bold;">TRUE</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/col"><span style="color: #003399; font-weight: bold;">col</span></a>=<span style="color: blue;">"darkblue"</span><span style="color: #339933;">,</span> lwd=<span style="color: #cc66cc;">2</span><span style="color: #009900;">)</span></pre>
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Here, the <i>curve()</i> function takes as its first parameter a function itself (or an expression) that must be written as some function of x. Our function here is <i>dnorm()</i>. The x in the <i>dnorm()</i> function is not an object we have created; rather, it's indicating that there's a variable that is being evaluated, and the evaluation is the normal density at the mean of y and standard deviation of y. Make sure to include add=<span style="color: black; font-weight: bold;">TRUE</span> so that the curve is plotted on the same plot as the histogram. Here is what we get:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjHKOj7wx5lrKvB0rJh1uIfe_mifwNWDL7hikBea87-4bi1qxu9bKDsKnENlbTdyDw54oZBehNEIoy0i8Qzxp9am3tKlv5Vv7eIk4AKAGw38ytK4kvawzhkUhyvo0LtOFrv4-vh79VEpV0/s1600/histwithnormal.jpeg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjHKOj7wx5lrKvB0rJh1uIfe_mifwNWDL7hikBea87-4bi1qxu9bKDsKnENlbTdyDw54oZBehNEIoy0i8Qzxp9am3tKlv5Vv7eIk4AKAGw38ytK4kvawzhkUhyvo0LtOFrv4-vh79VEpV0/s320/histwithnormal.jpeg" width="308" /></a></div>
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Here are some other good sources on the topic of probability distribution functions:<br />
<ul>
<li><a href="http://www.statmethods.net/advgraphs/probability.html" target="_blank">Quick-R</a></li>
<li><a href="http://msenux.redwoods.edu/math/R/normal.php" target="_blank">Overview of Normal Distribution in R</a></li>
<li><a href="http://www.r-bloggers.com/using-r-for-introductory-statistics-chapter-5-probability-distributions/" target="_blank">R-Bloggers Introduction</a></li>
<li><a href="http://www.stat.umn.edu/geyer/old/5101/rlook.html" target="_blank">Probability Distributions in R, UMN</a></li>
<li><a href="http://www.r-tutor.com/elementary-statistics/probability-distributions" target="_blank">R Tutor</a></li>
</ul>
Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com5tag:blogger.com,1999:blog-2550386066385582083.post-2713657569321183452013-02-01T05:39:00.001-08:002013-02-01T05:40:42.263-08:00Converting a dataset from wide to long<br />
I recently had to convert a dataset that I was working with from a wide format to a long format for my analysis. I struggled with this a bit, but finally found the right sources and the right package to do it, so I thought I'd share my practical example of reshaping data in R. <b>This post is specifically helpful for those using Demographic and Health Survey (DHS) data.</b><br />
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The DHS dataset includes one observation for each woman. For each observation, there are 20 columns for each birth she could have had for 16 different characteristics. If no birth happened then the cell is left missing. The characteristics include birth month, birth year, sex of the child, death month, death year, who the child lives with, current age of child, and so on.<br />
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For clarity, I've shortened the data to just 7 observations and two characteristics of each birth (b2 and b4) for 3 possible births:<br />
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Here v012 is the mother's age, all the b2 variables are year of births, and the b4 variables are the sex of the child.<br />
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So the first subject, aged 30, has had two births - one in 2000 and one in 2005, both boys. Since she did not have a third birth yet, her values for b2_03 and b4_03 are missing.<br />
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I would like to <b>convert this dataset to one where I have one observation per child born</b> <b>- i.e. from the wide format to the long format</b>. Specifically, I would like the columns to just be the caseid of the mother, age of the mother, year of birth of the child, and sex of the child.<br />
<br />
There are a number of ways of doing this in R, including <i>melt()</i> and <i>cast()</i>, <i>plyr()</i>, <i>aggregate()</i>, and others. However, after a good deal of struggle and looking things up, <b>I found that the <i>reshape()</i> function is the most intuitive and user-friendly for the needs of this problem</b>. You don't need to use melt and cast at all, which are difficult to manipulate in my opinion.<br />
<br />
The <i>reshape()</i> function takes in a number of important parameters that will be necessary for our transformation (there are more parameters than this, but I've boiled it down to the ones that are crucial):<br />
<br />
<pre><span style="font-family: Courier New, Courier, monospace;">reshape(data, </span><span style="font-family: 'Courier New', Courier, monospace;">varying = NULL,</span><span style="font-family: 'Courier New', Courier, monospace;"> </span><span style="font-family: 'Courier New', Courier, monospace;">timevar = "time", </span><span style="font-family: 'Courier New', Courier, monospace;">idvar = "id", </span><span style="font-family: 'Courier New', Courier, monospace;">direction, </span><span style="font-family: 'Courier New', Courier, monospace;">sep = ""</span><span style="font-family: 'Courier New', Courier, monospace;">)</span></pre>
<div style="font-family: sans-serif;">
<br /></div>
where<br />
<span style="font-family: Courier New, Courier, monospace;">data</span> = dataframe you want to convert<br />
<span style="font-family: Courier New, Courier, monospace;">varying</span> = columns in the wide format that correspond to a single column in the long format<br />
<span style="font-family: Courier New, Courier, monospace;">timevar</span> = name of new variable that differentiates multiple observations from the same individual<br />
<span style="font-family: Courier New, Courier, monospace;">idvar</span> = variable in your dataset that identifies multiple records from the same individual<br />
<span style="font-family: Courier New, Courier, monospace;">direction</span> = "wide" if you're going from long to wide and "long" if you're going from wide to long<br />
<span style="font-family: Courier New, Courier, monospace;">sep</span> = the symbol that separates the name of a varying column from its number<br />
<br />
Ok, so <b>the most important thing about the reshape function is that you have to give it variable names that it can understand</b>. Specifically, the columns that I want to turn into one column should all follow the same structure in the naming convention. It can be anything with a pattern like this:<br />
<br />
Birthyear_1, Birthyear_2, Birthyear_3<br />
b1, b2, b3<br />
year.1, year.2, year.3<br />
<br />
etc. As long as they follow the same text and number pattern. If the dataset is not in this format, you will have a lot of problems and I suggest changing your variable names before trying to convert. For the first example, you would use sep="_", the second would be sep="", and the third is sep="." and all of these are valid.<br />
<br />
Ok, so let's try it out. All variables that we want to convert into one column we can put into the varying parameter and R will sort them out based on the naming patterns. We specify a long direction, that our id variable is caseid, and, importantly, that the separating symbol between the name of the variable and its order number is a "_".<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">births.long1<-reshape(births.wide, varying=c("b2_01","b2_02","b2_03", "b4_01", "b4_02", "b4_03"), direction="long", idvar="caseid", sep="_")</span><br />
<br />
This gives us the following result (truncated picture):<br />
<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgfq5ax-znjfcM23xz69dnqP36_jx_OzXZmv5g8h1-p_whwN_O_PsPv4Q9tuczTvYmm8EBelVQxIptBDPxDwYxSu4pu_D5DntjgOL03q_vJVPaAicXAMu2xlrae3GGUil69Y3uByOO3ljc/s1600/long1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgfq5ax-znjfcM23xz69dnqP36_jx_OzXZmv5g8h1-p_whwN_O_PsPv4Q9tuczTvYmm8EBelVQxIptBDPxDwYxSu4pu_D5DntjgOL03q_vJVPaAicXAMu2xlrae3GGUil69Y3uByOO3ljc/s1600/long1.png" /></a></div>
<br />
<br />
Which is what we wanted! Each observation is now a birth, with corresponding mother caseid and age, and the year (b2) and sex of the child (b4) for each observation correctly lined up. R has added in the time variable which is just the number after the "_" for the varying variables. Notice that R automatically orders the dataset by this time variable, so you will have to re-order if you want it by caseid.<br />
<br />
If you wanted to convert all of the 16 characteristics of each birth, <b>you do not need to write out each individual variable</b>. You can easily do it like this, indicating the number of the columns that you want:<br />
<br />
<div>
<span style="font-family: Courier New, Courier, monospace;">births.long2<-reshape(births.wide, varying=c(3:8), direction="long", idvar="caseid", sep="_", timevar="order")</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;"><br /></span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">births.long2<-births.long2[order(births.long2$caseid),]</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;"><br /></span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">names(births.long2)<-c("subject","age","order", "birthyear", "childsex")</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;"><br /></span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">births.long2<-na.omit(births.long2)</span></div>
<div>
<br /></div>
<div>
Here, I've specified that all variables from column 3 to column 8 are varying variables, and I've also indicated that the new variable that R creates should be called "order" instead of the default "time". Then I reorder my dataset by caseid, name the new columns, and take out all of the missing observations, which are just non-existent births. I get the following result: </div>
<div>
<br /></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhawAa6JpUGdPp0mxp6NxI98Sadai49STHGqUlamQMokrmN9ao1q1JS-8xjWVNM0fGrFkWf1qQbha2duV3ONhO9NwEJtr7UE1o8-S2HZ8F8Rtxla0P1fEbj5ba0opUtN1M40a86_VVjngM/s1600/long2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhawAa6JpUGdPp0mxp6NxI98Sadai49STHGqUlamQMokrmN9ao1q1JS-8xjWVNM0fGrFkWf1qQbha2duV3ONhO9NwEJtr7UE1o8-S2HZ8F8Rtxla0P1fEbj5ba0opUtN1M40a86_VVjngM/s1600/long2.png" /></a></div>
<div>
<br /></div>
<br />
Notice how subject 6 is completely gone because she did not have any children and <b>this is now a dataset of children ever born</b>.<br />
<br />
I've made this example of reshape very specific to DHS data, but there are also many great sources on how to reshape data in R. Here are a couple that I found especially helpful:<br />
<br />
<ul>
<li><a href="http://trinkerrstuff.wordpress.com/2012/05/03/reshape-from-base-explained-part-i/" target="_blank">TRinker's R Blog</a></li>
<li><a href="http://www.ats.ucla.edu/stat/r/faq/reshape.htm" target="_blank">UCLA R Stats </a></li>
</ul>
Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com12tag:blogger.com,1999:blog-2550386066385582083.post-60712829701891025272013-01-20T13:43:00.000-08:002013-01-24T08:04:22.884-08:00Translating Weird R Errors<br />
I love R. I think it's intuitive and clever and overall a great language. But I do get really annoyed sometimes at the completely ridiculous, cryptic error messages it often gives me. This post will go over some of those seemingly nonsensical errors so you don't have to go crazy trying to find the bug in your code.<br />
<br />
<span style="background-color: orange;">1. all arguments must have the same length</span><br />
<span style="background-color: orange;"><br /></span>
To start with, I just make up some quick data:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">prob1<-as.data.frame(cbind(c(1,2,3),c(5,4,3)))</span><br />
<span style="font-family: Courier New, Courier, monospace;">colnames(prob1)<-c("Education","Ethnicity")</span><br />
<br />
And now I just want to do a simple table but I get this error:<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjDzOYWKcJtwGGj9TyFejPFPa5HhDVGfbYQgIUcNQ6K8lUi9U-F0cPaaDvhxMUKihhO8k3ceDYNfLil_WxinVj56xLjjDBxpK9fkGvlPkiuXH0a6e4QmR_KlDaxLs2DlVNGkKSVf6DYeAc/s1600/prob1.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="55" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjDzOYWKcJtwGGj9TyFejPFPa5HhDVGfbYQgIUcNQ6K8lUi9U-F0cPaaDvhxMUKihhO8k3ceDYNfLil_WxinVj56xLjjDBxpK9fkGvlPkiuXH0a6e4QmR_KlDaxLs2DlVNGkKSVf6DYeAc/s400/prob1.png" width="400" /></a></div>
<br />
<br />
<br />
<br />
<br />
What the heck. I look back at my dataset and make sure that both those variables are the same length, which they are. <b>The problem here is that I misspelled "Education"</b>. There's a missing "a" in there and instead of telling me that I referenced a variable that doesn't exist, R bizarrely tells me to check the length of my variables. <b>Remember: Anytime you get an error, check to make sure you've spelled everything right. </b><br />
<br />
If I do this, everything works out great:<br />
<span style="font-family: Courier New, Courier, monospace;">table(prob1$Education, prob1$Ethnicity)</span><br />
<br />
<br />
<span style="background-color: orange;">2. replacement has 0 rows, data has 3</span><br />
<span style="background-color: orange;"><br /></span>
A very similar problem, with a very different error message. Let's say I forgot what columns were in my prob1 data and I thought I had a Sex indicator in there. So I try to recode it like this:<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjjjZUl3KKqeLZpWUe5tlLQhsN01N2PmDYVU-40PAVelhQDgcvt6QZ1BCC-FC10MoC1PUK3YiVrxJn1eeBPnq_dvoDYHyiWGCDi6ymIq0DIxSP5wLggFzRcFiDUA3yBlYimw4pV8Y8emE0/s1600/prob3.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="64" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjjjZUl3KKqeLZpWUe5tlLQhsN01N2PmDYVU-40PAVelhQDgcvt6QZ1BCC-FC10MoC1PUK3YiVrxJn1eeBPnq_dvoDYHyiWGCDi6ymIq0DIxSP5wLggFzRcFiDUA3yBlYimw4pV8Y8emE0/s640/prob3.png" width="640" /></a></div>
<div class="separator" style="clear: both; text-align: left;">
This error message is also pretty unhelpful. The syntax is totally correct; the problem is that I just don't have a variable named Sex in my dataset. If I do this instead to recode education, a variable that exists, everything is fine:</div>
<br />
<span style="font-family: Courier New, Courier, monospace;">prob1$Educ_recode<-as.numeric(prob1$Education==2)</span><br />
<br />
<br />
<span style="background-color: orange;">3. undefined columns selected</span><br />
<br />
Ironically, the error we so badly wanted before comes up but for a completely different reason. See if you can find the problem here. I'll take that same little dataset and I just want to know how many rows there are in which Education is not equal to 1. <br />
<br />
So, if I want to know the number of rows of the dataframe prob1, I do:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">nrow(prob1)</span><br />
<br />
and if I want to know how many have a value of Education not equal to 1, I do the following (incorrectly) and get an error:<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5msluwkYXUgpwixt3XQa9kkUT4Bq6kRByuHmFi-laKFqICvwYMHMsp1DBP7aynnlb5SYFl1HnQ3qetii-HdDg6fK3DDc-GpeIbBAGCLeXFFJzKJ51o8K50O8UsTIpcjE3_gM7IbKxVRw/s1600/prob2.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="51" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5msluwkYXUgpwixt3XQa9kkUT4Bq6kRByuHmFi-laKFqICvwYMHMsp1DBP7aynnlb5SYFl1HnQ3qetii-HdDg6fK3DDc-GpeIbBAGCLeXFFJzKJ51o8K50O8UsTIpcjE3_gM7IbKxVRw/s400/prob2.png" width="400" /></a></div>
<br />
<br />
<br />
<br />
<br />
Now I check my variable name and I've definitely spelled Education right this time. The problem, actually, is not that I have referenced a column that doesn't exist but I've messed up the syntax to the <span style="font-family: Courier New, Courier, monospace;">nrow()</span> function, in that <b>I haven't defined what columns I want to subset</b>. When I do,<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">prob1[prob1$Education!=1]</span><br />
<br />
this doesn't make any sense, because I'm saying to subset prob1 but to do this I have to <b>specify which rows I want and which columns I want</b>. This just lists one condition in the brackets and it's unclear whether it's for the rows or columns. <a href="http://rforpublichealth.blogspot.com/2012/10/quick-and-easy-subsetting.html" target="_blank">See my post on subsetting</a> for more details on this.<br />
<br />
If I do it the following way, all is good since I'm saying to subset prob1 with only rows with education !=1 and all columns:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">nrow(prob1[prob1$Education!=1,])</span><br />
<br />
So this error message does make sense in a way, but it's still a bit cryptic in my opinion.<br />
<br />
<br />Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com11tag:blogger.com,1999:blog-2550386066385582083.post-59437199226483809542013-01-14T06:35:00.000-08:002013-01-14T06:35:43.443-08:00For loops (and how to avoid them)My experience when starting out in R was trying to clean and recode data using <i>for()</i> loops, usually with a few <i>if()</i> statements in the loop as well, and finding the whole thing complicated and frustrating.<br />
<br />
In this post, I'll go over how you can avoid <i>for()</i> loops for both improving the quality and speed of your programming, as well as your sanity.<br />
<br />
So here we have our classic dataset called mydata.Rdata<i> </i>(you can download this if you want, link at the right):<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj03LYpCNeJ8GFohR9gDbcecJfyfpsy_xDvL9AIARJZAq7L0swWpyH5DYS6c2Eyi3oNQqgUv874aIUUrZ2kSL0RcuFqaeTp28zk1aP1WYnKRAvFwu6E9P2Q08PwaAaL9zykxBp0fY6LKDA/s1600/mydata.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="188" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj03LYpCNeJ8GFohR9gDbcecJfyfpsy_xDvL9AIARJZAq7L0swWpyH5DYS6c2Eyi3oNQqgUv874aIUUrZ2kSL0RcuFqaeTp28zk1aP1WYnKRAvFwu6E9P2Q08PwaAaL9zykxBp0fY6LKDA/s320/mydata.png" width="320" /></a></div>
<br />
<br />
And if I were in Stata and wanted to create an age group variable, I could just do:<br />
<br />
<span style="font-family: Verdana, sans-serif;">gen Agegroup=1</span><br />
<span style="font-family: Verdana, sans-serif;">replace Agegroup=2 if Age>10 & Age<20</span><br />
<span style="font-family: Verdana, sans-serif;">replace Agegroup=3 if Age>=20</span><br />
<br />
But when I try this in R, it fails:<br />
<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiNLkkTyxhfzgjx15jew9BkOiPzQmYSDzsBHWrmrVHeRDNAG_mXGM4YHn4368h9-NTDSM1VzoUM_eKqFORfLTF5vX3pdpW_sjhuWgpKlbdjssKp5-gwORhJk5R2lGLdh2RGM1ioeP4l4Gs/s1600/failcode.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="88" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiNLkkTyxhfzgjx15jew9BkOiPzQmYSDzsBHWrmrVHeRDNAG_mXGM4YHn4368h9-NTDSM1VzoUM_eKqFORfLTF5vX3pdpW_sjhuWgpKlbdjssKp5-gwORhJk5R2lGLdh2RGM1ioeP4l4Gs/s640/failcode.png" width="640" /></a><br />
<br />
<br />
<br />
<br />
<br />
Why does it fail? It fails because Age is a <i>vector</i> so the condition <span style="font-family: Courier New, Courier, monospace;">if(mydata$Age<10)</span> is asking "is the vector Age less than 10", which is not what we want to know. We want to ask, row by row is each element of Age<10, so we need to specify the element of the vector we're referring to. We don't specify the element and thus we get the warning (really, error), "only the first element will be used." So when this fails, the first way people try to solve this problem is with a crazy <i>for()</i> loop like this:<br />
<br />
<span style="background-color: #f9cb9c; font-family: Courier New, Courier, monospace;">###########Unnecessarily long and ugly code below#######</span><br />
<span style="font-family: Courier New, Courier, monospace;">mydata$Agegroup1<-0</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">for (i in 1:10){</span><br />
<span style="font-family: Courier New, Courier, monospace;"> if(mydata$Age[i]>10 & mydata$Age[i]<20){</span><br />
<span style="font-family: Courier New, Courier, monospace;"> mydata$Agegroup1[i]<-1</span><br />
<span style="font-family: Courier New, Courier, monospace;"> }</span><br />
<span style="font-family: Courier New, Courier, monospace;"> if(mydata$Age[i]>=20){</span><br />
<span style="font-family: Courier New, Courier, monospace;"> mydata$Agegroup1[i]<-2</span><br />
<span style="font-family: Courier New, Courier, monospace;"> }</span><br />
<span style="font-family: Courier New, Courier, monospace;">}</span><br />
<br />
Here we tell R to go down the rows from i=1 to i=10, and for each of those rows indexed by i, check to see what value of Age it is, and then assign Agegroup a value of 1 or 2. <b>This works, but at a high cost</b> - you can easily make a mistake with all those indexed vectors, and also <b><i>for()</i> loops take a lot of computing time</b>, which would be a big deal if this dataset were 10000 observations instead of 10.<br />
<br />
So how can we avoid doing this?<br />
<br />
One of the most useful functions I have found is one that I have referred to a number of times in my blog so far - the <span style="font-family: Courier New, Courier, monospace;"><a href="http://stat.ethz.ch/R-manual/R-patched/library/base/html/ifelse.html" target="_blank">ifelse()</a></span> function. The <span style="font-family: Courier New, Courier, monospace;">ifelse()</span> function evaluates a condition, and then assigns a value if it's true and a value if it's false. The great part about it is that it can read in a vector and check each element of the vector one by one so <b>you don't need indices or a loop</b>. You don't even need to initialize some new variable before you run the statement. Like this:<br />
<br />
<span style="font-family: 'Courier New', Courier, monospace;">mydata$newvariable<-ifelse(<span style="color: blue;">Condition of some variable</span>,</span><br />
<span style="font-family: 'Courier New', Courier, monospace;"><span style="color: #38761d;"> Value of </span></span><span style="color: #38761d; font-family: 'Courier New', Courier, monospace;">new variable if condition is true</span><span style="font-family: 'Courier New', Courier, monospace;">, </span><br />
<span style="color: #990000; font-family: 'Courier New', Courier, monospace;"> Value of new variable if condition is false</span><span style="font-family: 'Courier New', Courier, monospace;">)</span><br />
<span style="font-family: 'Courier New', Courier, monospace;"><br /></span>
so for example:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">mydata$Old<-ifelse(<span style="color: blue;">mydata$Age>40</span>,<span style="color: #38761d;">1</span>,<span style="color: #990000;">0</span>)</span><br />
<br />
This says, check to see if the elements of the vector mydata$Age are greater than 40: if an element is greater than 40, it assigns the value of 1 to mydata$Old, and if it's not greater than 40, it assigns the value of 0 to mydata$Old.<br />
<br />
But we wanted to assign values 0, 1, and 2 to an Agegroup variable. To do this, we can use <b>nested <span style="font-family: Courier New, Courier, monospace;">ifelse()</span> statements</b>:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">mydata$Agegroup2<-ifelse(mydata$Age>10 & mydata$Age<20,1, </span><br />
<span style="font-family: 'Courier New', Courier, monospace;"> ifelse(mydata$Age>20, 2,0))</span><br />
<div>
<br /></div>
<div>
Now this says, first check whether each element of the Age vector is >10 and <20. If it is, assign 1 to Agegroup2. If it's not, then evaluate the next <i>ifelse()</i> statement, whether Age>20. If it is, assign Agegroup2 a value of 2. If it's not any of those, then assign it 0. We can see that both the loop and the <i>ifelse()</i> statements give us the same result:</div>
<div>
<br /></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEho7MZUTxcaMDsdUETNPxYPbNMyinj2sdOhb4PfVeKM20XI4ZkHWZ2hJwe2y6eakFW1ZF28pYzBtXit7Hdt5RZv3IZVpBvfpsMMW8bOYLFbE7Qf3MMzqEgc91D58PE68SlEcmlqWtaaVz4/s1600/resultsagegroup.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="166" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEho7MZUTxcaMDsdUETNPxYPbNMyinj2sdOhb4PfVeKM20XI4ZkHWZ2hJwe2y6eakFW1ZF28pYzBtXit7Hdt5RZv3IZVpBvfpsMMW8bOYLFbE7Qf3MMzqEgc91D58PE68SlEcmlqWtaaVz4/s400/resultsagegroup.png" width="400" /></a></div>
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You can nest <i>ifelse()</i> statement as much as you like. <b>Just be careful about your final category</b> - it assigns the last value to whatever values are left over that didn't meet any condition (including if a value is NA!) so make sure you want that to happen.<br />
<br />
<br />
<u>Other examples of ways to use the <i>ifelse()</i> function:</u><br />
<ul>
<li>If you want to <b>add a column with the mean of Weight by sex for each individual,</b> you can do this with <i>ifelse()</i> like this:</li>
</ul>
<div>
<div>
<span style="font-family: Courier New, Courier, monospace;">mydata$meanweight.bysex<-ifelse(mydata$Sex==0, </span></div>
<div>
<span style="font-family: 'Courier New', Courier, monospace;"> mean(mydata$Weight[mydata$Sex==0], na.rm=TRUE), </span></div>
<div>
<span style="font-family: 'Courier New', Courier, monospace;"> mean(mydata$Weight[mydata$Sex==1], na.rm=TRUE))</span></div>
</div>
<div>
<br /></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjBDrAYgGGHmqwqVWirz4CVEBsXzRlH5ZM3w3aQ9m_BPkW7q5YAvqdDVG-fXQJgQcKeT0r4nBC5PBRLvtU1Ulqt7qZ0H4dzoKWrJWZMHJ6Wv9NTAOCpFhUDAliRfQBvy35dkVvcx-9f6B4/s1600/meanweightsex.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjBDrAYgGGHmqwqVWirz4CVEBsXzRlH5ZM3w3aQ9m_BPkW7q5YAvqdDVG-fXQJgQcKeT0r4nBC5PBRLvtU1Ulqt7qZ0H4dzoKWrJWZMHJ6Wv9NTAOCpFhUDAliRfQBvy35dkVvcx-9f6B4/s1600/meanweightsex.png" /></a></div>
<br />
<ul>
<li>If you want to <b>recode missing values</b>:</li>
</ul>
<span style="font-family: Courier New, Courier, monospace;">mydata$Height.recode<-ifelse(is.na(mydata$Height),</span><br />
<span style="font-family: Courier New, Courier, monospace;"> 9999, </span><br />
<span style="font-family: 'Courier New', Courier, monospace;"> mydata$Height)</span><br />
<div>
<br /></div>
<div>
<ul>
<li>If you want to <b>combine two variables together into a new one</b>, such as to create a new ID variable based on year (which I added to this dataframe) and ID:</li>
</ul>
<div>
<div>
<span style="font-family: Courier New, Courier, monospace;">mydata$ID.long<-ifelse(mydata$ID<10, </span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;"> paste(mydata$year, "-0",mydata$ID,sep=""), </span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;"> paste(mydata$year, "-", mydata$ID, sep=""))</span></div>
</div>
</div>
<div>
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<div>
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<div>
<span style="background-color: white;"><u>Other ways to avoid the for loop:</u></span></div>
<div>
<br />
<ul>
<li><b>The apply functions</b>: If you think you have to use a loop because you have to apply some sort of function to each observation in your data, think again! Use the <i>apply()</i> functions instead. For example:</li>
<ul>
<li>If you have a lot of missing values and want to recode them all at once, or want to sum up the number of times you see a certain value in a row, <a href="http://rforpublichealth.blogspot.com/2012/09/the-infamous-apply-function.html" target="_blank">check out my post on the apply function here</a>.</li>
</ul>
</ul>
<ul>
<li>You can also <b>use other functions</b> such as <i>cut()</i> to do the age grouping above. <a href="http://rforpublichealth.blogspot.com/2012/09/from-continuous-to-categorical.html" target="_blank">Here's the post on how this function works</a>, so I won't go over it again, except to say if you convert from a factor to a numeric, *always* convert to a character before converting it to numeric:</li>
</ul>
</div>
<span style="font-family: Courier New, Courier, monospace;">mydata$Agegroup3<-as.numeric(as.character(cut(mydata$Age, c(0,10,20,100),labels=0:2)))</span><br />
<br />
<br />
Basically, <b>any time you think you have to do a loop, think about how you can do it with another function</b>. It will save you a lot of time and mistakes in your code.<br />
<br />
<br />Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com15tag:blogger.com,1999:blog-2550386066385582083.post-31974358523522783502012-12-22T09:20:00.001-08:002012-12-22T09:20:39.423-08:00Basics of Histograms<br />
Histograms are used very often in public health to show the distributions of your independent and dependent variables. Although the basic command for histograms (<span style="font-family: Courier New, Courier, monospace;"><a href="http://stat.ethz.ch/R-manual/R-patched/library/graphics/html/hist.html" target="_blank">hist()</a></span>) in R is simple, getting your histogram to look exactly like you want takes getting to know a few options of the plot. Here I present ways to customize your histogram for your needs.<br />
<br />
First, I want to point out that ggplot2 is a package in R that does some amazing graphics, including histograms. I will do a post on ggplot2 in the coming year. However, the <span style="font-family: Courier New, Courier, monospace;">hist()</span> function in base R is really easy and fast, and does the job for most of your histogram-ing needs. However, if you want to do complicated histograms, I would recommend reading up on ggplot2.<br />
<br />
Okay so for our purposes today, instead of importing data, I'll create some normally distributed data myself. In R, you can generate normal data this way using the <span style="font-family: Courier New, Courier, monospace;">rnorm()</span> function:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">BMI<-rnorm(n=1000, m=24.2, sd=2.2) </span><br />
<br />
So now we have some BMI data, and the basic histogram plot that comes out of R looks like this:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">hist(BMI)</span><br />
<br />
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<br />
Which is actually pretty nice. There are a number of things that R does by default in creating this histogram, and I think it's useful to print out that information to understand the parameters of this histogram. You can do this by saving the histogram as an object and then printing it like this:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">histinfo<-hist(BMI)</span><br />
<span style="font-family: Courier New, Courier, monospace;">histinfo</span><br />
<br />
And you get the output below:<br />
<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQs1JFOa38cl-RjhVQHvQC_tw5i_m7CnmgQRmL1if7J9WlVd3ymvKapZtvpBlKe7BLPkwW1Oxo6TUJhFbHyAh6bloWhdfbLMnvTeyuZrqKLIuGNiXYvZHU5xjk5YkGoGwJ37hZK0oeEsY/s1600/histinfo.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="404" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQs1JFOa38cl-RjhVQHvQC_tw5i_m7CnmgQRmL1if7J9WlVd3ymvKapZtvpBlKe7BLPkwW1Oxo6TUJhFbHyAh6bloWhdfbLMnvTeyuZrqKLIuGNiXYvZHU5xjk5YkGoGwJ37hZK0oeEsY/s640/histinfo.png" width="640" /></a></div>
<br />
<br />
This is helpful because you can see how R has decided to break up your data by default. It shows the breaks, which are the cutoff points for the bins. It shows the counts, intensity/density for each bin (same thing but two different names for R version compatibility), the midpoints of each bin, and then the name of the variable, whether the bins are equidistant, and the class of the object. You can of course take any one of these outputs by itself, i.e. <span style="font-family: Courier New, Courier, monospace;">histinfo$counts</span> would give you just the vector of counts.<br />
<br />
Now we can manipulate this information to customize our plot.<br />
<br />
<span style="background-color: orange;">1. Number of bins</span><br />
<br />
R chooses how to bin your data for you by default using an algorithm, but <b>if you want coarser or finer groups, there are a number of ways to do this</b>. We can see that right now from the output above that the breaks go from 17 to 32 by 1. You can use the <i>breaks()</i> option to change this in a number of ways. An easy way is just to give it one number that gives the number of cells for the histogram:<br />
<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">hist(BMI, breaks=20, main="Breaks=20")</span><br />
<span style="font-family: Courier New, Courier, monospace;">hist(BMI, breaks=5, main="Breaks=5")</span><br />
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<br /></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhy3TCLiSEuzKlU4t9LNTKgIHj3OFvkwoKd8mzQOsxxKzFppGBK5wpnIRg7rlez3v55ulNaMma0NhUHk_u-E1U3f0G1cHfgDixOz14p0fQ2_As_uQelQ7w0I89U5x401rS6M3zaA0wtSF0/s1600/breaks20_5.jpeg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="202" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhy3TCLiSEuzKlU4t9LNTKgIHj3OFvkwoKd8mzQOsxxKzFppGBK5wpnIRg7rlez3v55ulNaMma0NhUHk_u-E1U3f0G1cHfgDixOz14p0fQ2_As_uQelQ7w0I89U5x401rS6M3zaA0wtSF0/s400/breaks20_5.jpeg" width="400" /></a></div>
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The bins don't correspond to exactly the number you put in, because of the way R runs its algorithm to break up the data but it gives you generally what you want. If you want more control over exactly the breakpoints between bins, you can be more precise with the <i>breaks()</i> option and give it a vector of breakpoints, like this:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">hist(BMI, breaks=c(17,20,23,26,29,32), main="Breaks is vector of breakpoints")</span><br />
<br />
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<br />
This dictates <b>exactly the start and end point of each bin</b>. Of course, you could give the breaks vector as a sequence like this to cut down on the messiness of the code:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">hist(BMI, breaks=seq(17,32,by=3), main="Breaks is vector of breakpoints")</span><br />
<br />
Note that when giving breakpoints, the default for R is that the histogram cells are right-closed (left open) intervals of the form (a,b]. You can change this with the <span style="font-family: Courier New, Courier, monospace;">right=FALSE</span> option, which would change the intervals to be of the form [a,b). This is important if you have a lot of points exactly at the breakpoint.<br />
<br />
<br />
<span style="background-color: orange;">2. Frequency vs Density </span><br />
<br />
Often, we are more interested in density than frequency, since frequency is relative to your sample size. <b>Instead of counting the number of datapoints per bin, R can give the probability densities</b> using the <span style="font-family: Courier New, Courier, monospace;">freq=FALSE </span>option:<br />
<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">hist(BMI, freq=FALSE, main="Density plot")</span><br />
<div>
<br /></div>
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<div>
<br /></div>
<div>
Notice the y-axis now. If the breaks are equidistant, with difference between breaks=1, then <span style="font-family: inherit;"><span style="-webkit-text-decorations-in-effect: initial;">the <i>height</i> of each rectangle is proportional to the number of points falling into the cell, and thus</span></span> the sum of the probability densities adds up to 1. Here I specify <span style="font-family: Courier New, Courier, monospace;">plot=FALSE</span> because I just want the histogram output, not the plot, and show how the sum of all of the densities is 1:</div>
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<br /></div>
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<div>
<br /></div>
<div>
However, if you choose to make bins that are not all separated by 1 (like <span style="font-family: Courier New, Courier, monospace;">breaks=c(17,25,26, 32)</span> or something like that), then the plot still has an area of 1, but the <i>area</i> of the rectangles is the fraction of data points falling into the cells. The densities are calculated like this as <span style="font-family: Courier New, Courier, monospace;">counts/(n*diff(breaks)</span>. Thus, this adds up to 1 if add up the areas of the rectangles, i.e. you multiply each density by the difference in the breaks like this:</div>
<br />
<br />
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<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<span style="background-color: orange;">3. Plot aesthetics </span><br />
<br />
Finally, we can make the histogram better looking by adjusting the x-axis, y-axis, axis labels, title, and color like this:<br />
<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">hist(BMI, freq=FALSE, </span><span style="font-family: 'Courier New', Courier, monospace;">xlab="Body Mass Index",</span><span style="font-family: 'Courier New', Courier, monospace;"> </span><span style="font-family: 'Courier New', Courier, monospace;">main="Distribution of Body Mass Index", col="lightgreen", xlim=c(15,35), ylim=c(0, .20))</span><br />
<br />
Here along with our frequency option, I changed the x-axis label, changed the main title, made the color light green, and provided limits for both the x-axis and y-axis. Note that defining the look of you axis using <span style="font-family: Courier New, Courier, monospace;">xlim()</span> will not have any impact on the bins - this option is only for the aesthetics of the plot itself.<br />
<br />
Finally, I can <b>add a nice normal distribution curve</b> to this plot using the curve() function, in which I specify a normal density function with mean and standard deviation that is equal to the mean and standard deviation of my data, and I add this to my previous plot with a dark blue color and a line width of 2. You can play around with these options to get the kind of line you want:<br />
<br />
<span style="font-family: Courier New, Courier, monospace;">curve(dnorm(x, mean=mean(BMI), sd=sd(BMI)), add=TRUE, col="darkblue", lwd=2) </span><br />
<div>
<br /></div>
<br />
And we get this!<br />
<br />
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<br />Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com7tag:blogger.com,1999:blog-2550386066385582083.post-62863443303616173682012-11-30T08:20:00.000-08:002012-11-30T09:10:13.355-08:00Data types part 4: Logical class<br />
First, an update: A commentator has asked me to post my code so that it is easier to practice the examples I show here. It will take me a little bit of time to get all of my code for past posts well-documented and readable, but I have uploaded the code and data for the last 4 posts, including this one, here:<br />
<br />
<a href="https://sites.google.com/site/rforpublichealthcode/downloads" target="_blank">Code and Data download site</a><br />
<br />
Unfortunately, I could not find a way to attach it to blogger, so sorry for the extra step.<br />
_________________________________________________________________________<br />
<br />
Ok, now on to Data types part 4: Logical<br />
<br />
I started this series of posts on data types by saying that when you have a dataframe like this called <span style="font-family: Courier New, Courier, monospace;">mydata</span>:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhl9DOKx3j0GcUhVuOHYaMcIUD5IKOxl2F8SKmxAih6rl3_TyPsNm013GtMToeApfkX4g9q5iK0SNSAZmoasbuonNo1gaXVPmNOORSu0CMkv4QHLgZVjyhtVJ98nBoe4_cJ1VsoU7RF-k4/s1600/mydata.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" height="188" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhl9DOKx3j0GcUhVuOHYaMcIUD5IKOxl2F8SKmxAih6rl3_TyPsNm013GtMToeApfkX4g9q5iK0SNSAZmoasbuonNo1gaXVPmNOORSu0CMkv4QHLgZVjyhtVJ98nBoe4_cJ1VsoU7RF-k4/s320/mydata.png" width="320" /></a><br />
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you can't do this in R:<br />
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<span style="font-family: Courier New, Courier, monospace;">Age<25</span><br />
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Because Age does not exist as an object in R, and you get the error below:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtqzHRndlh5SmEiiVGEK7YhIptRfD7Qhdz_ak-LZBDTf98rGClwvIOU8CBp8xA8MrGZeOqvxM8vjXYojb0IHPtBDGVSszZ8CoxKNQT-PslENR64c5douab_RBtFAtci3zRnJGydNUtXW4/s1600/errorage.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtqzHRndlh5SmEiiVGEK7YhIptRfD7Qhdz_ak-LZBDTf98rGClwvIOU8CBp8xA8MrGZeOqvxM8vjXYojb0IHPtBDGVSszZ8CoxKNQT-PslENR64c5douab_RBtFAtci3zRnJGydNUtXW4/s1600/errorage.png" /></a></div>
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But then what happens when I do,<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$Age<25</span><br />
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This is perfectly legal to do in R, but it's not going to drop observations. With this kind of statement, you are asking R to <b>evaluate the logical question</b> "Is it true that <span style="font-family: Courier New, Courier, monospace;">mydata$Age</span> is less than 25?". Well, that depends on which element of the Age vector, of course. Which is why this is what you get when you run that code:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg-oV5VQyuekCOj04b_VWorCn4eVOj2nIpZi-bnatYnsmJaFUwGJzMzPEtTxyEG55nLUNwuqtSwun2bVuHcgoia6UFtKtfsnNFUuYcMAm-2Uy6Vgb2w2MKXCILs0LVESaQsa0qC4_v5Rr8/s1600/truefalse.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" height="30" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg-oV5VQyuekCOj04b_VWorCn4eVOj2nIpZi-bnatYnsmJaFUwGJzMzPEtTxyEG55nLUNwuqtSwun2bVuHcgoia6UFtKtfsnNFUuYcMAm-2Uy6Vgb2w2MKXCILs0LVESaQsa0qC4_v5Rr8/s400/truefalse.png" width="400" /></a><br />
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On first glance, this looks like a character vector. There is a string of entries using character letters after all. But it's not character class, it's the <b>logical</b> class. If you save this string of TRUE and FALSE entries into an object and print its class, this is what you get:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgt2jSUuBuZRCRDB8RTfd2zrPCxhyE8qbVUXI_kWNZnkymzT1cs7PAYpn9PHQiysLPEuxd6MtDryvT_pf3BNvPAQ-2c99-XSQWfZC_VQTNcu5qu8DXkfUUlzpzpNkoa66o-aOGIWyfIv4o/s1600/classlogical.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgt2jSUuBuZRCRDB8RTfd2zrPCxhyE8qbVUXI_kWNZnkymzT1cs7PAYpn9PHQiysLPEuxd6MtDryvT_pf3BNvPAQ-2c99-XSQWfZC_VQTNcu5qu8DXkfUUlzpzpNkoa66o-aOGIWyfIv4o/s1600/classlogical.png" /></a><br />
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<b>The logical class can only take on two values, TRUE or FALSE. </b> We've seen evaluations of logical operations already, first in subsetting, like this:<br />
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<span style="font-family: Courier New, Courier, monospace;">mysubset<-mydata[mydata$Age<40,]</span><br />
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<a href="http://rforpublichealth.blogspot.com/2012/10/quick-and-easy-subsetting.html" target="_blank">Check out my post on subsetting</a> if this syntax is confusing. In a nutshell, R evaluates all rows and keeps only those that meet the criteria, which is only rows where Age has a value of under 40 and all columns.<br />
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Or here, in <i>ifelse()</i> statements<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$Young<-ifelse(mydata$Age<25,1,0)</span><br />
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<a href="http://rforpublichealth.blogspot.com/2012/10/what-nice-looking-scatterplot.html" target="_blank">More on ifelse() statements here</a>. The <i>ifelse()</i> function is really useful, but is actually overkill when you're just creating a binary variable. This can be done faster by taking advantage of the fact that <b>logical values of TRUE always have a numeric value of 1, while logical values of FALSE always have a numeric value of 0</b>. <br />
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That means all I need to do to create a binary variable of under age 25 is to <b>convert my logical </b><span style="font-family: 'Courier New', Courier, monospace;">mydata$Ageunder25</span> <b>vector into numeric</b>. This is very easy with R's <i>as.numeric()</i> function. I do it like this:<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$Ageunder25_num<-as.numeric(mydata$Ageunder25)</span><br />
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or directly without that intermediate step like this:<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$Ageunder25_num<-as.numeric(mydata$Age<25)</span><br />
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Let's check out the relevant columns in our dataframe:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgV0cqcJ3xyn79VRTNZPiH9WK6TOWMdYtCfiFI9tBRF3wndADAGsVVq70jMcDTtSX3H3cm2obGBBcJ9rQzYYFp7RmF_3uQiYqa1GPS3JftzVak4iK4wO1bGInp69fviX1se8jsL-xmymt4/s1600/mydataagevars.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgV0cqcJ3xyn79VRTNZPiH9WK6TOWMdYtCfiFI9tBRF3wndADAGsVVq70jMcDTtSX3H3cm2obGBBcJ9rQzYYFp7RmF_3uQiYqa1GPS3JftzVak4iK4wO1bGInp69fviX1se8jsL-xmymt4/s1600/mydataagevars.png" /></a></div>
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We can see that the Ageunder25_num variable is an indicator of whether the Age variable is under 25.<br />
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Now the really, really useful part of this is that you can use this feature to <b>turn on and off a variable depending on its value</b>. For example, say you got your data and realized that some of the height values were in inches and some were in centimeters, like this:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiUlRwNHxAn5U-XPpNN2ehKRcHgFgVJEUOotSZuDPTn6c4t4XxUVNhfLqvE0eS6UFJI3wVPsWsE0pEUGFNvT_pW9T9v5QKW3sQ0BAX8WeaYel5cqZEdGFQ3wL7nnSLDu_hESBKh3tufZvM/s1600/heightwrong.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiUlRwNHxAn5U-XPpNN2ehKRcHgFgVJEUOotSZuDPTn6c4t4XxUVNhfLqvE0eS6UFJI3wVPsWsE0pEUGFNvT_pW9T9v5QKW3sQ0BAX8WeaYel5cqZEdGFQ3wL7nnSLDu_hESBKh3tufZvM/s1600/heightwrong.png" /></a><br />
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Those heights of 152 and 170 are in centimeters while everything else is inches. There are various ways to fix it, but one way is to check which values are less than, say 90, which is probably a safe cutoff and create a new column that keeps those values under 90 but converts the values over 90. We can do this in this way:<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$Height_fixed_in<- <span style="color: #990000;">as.numeric(mydata$Height_wrong<90)*mydata$Height_wrong</span> </span><br />
<span style="color: blue; font-family: Courier New, Courier, monospace;">+ as.numeric(mydata$Height_wrong>=90)*mydata$Height_wrong/2.54</span><br />
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So the first half of the calculation (in <span style="color: #990000;">red</span>) is "turned on" when Height_wrong is less than 90, because the value of the logical statement is a numeric TRUE, i.e. a 1, and this value of 1 is multiplied by the original Height column. The second part of the statement (in <span style="color: blue;">blue</span>) is FALSE and so is just 0 times something so it's 0. If the Height_wrong column is greater than 90, then the first half is just 0 and the second half is turned on and thus the Height_wrong variable is divided by 2.54 cm, converting it into inches. We get the result below:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEsVSj3awAqs7Ae_Jz1lNXt81hWNfEwD-lOi5vkNmZl5LWL1VCm3H8YytQ_PDF_NtwaArC46i74X5eXcmbRy4gddr9g3HMokrLTW_v1zNh9AWYz_Eof4ov6UtJjHNmY5U1T_EOM73oEuM/s1600/heightfixed.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEsVSj3awAqs7Ae_Jz1lNXt81hWNfEwD-lOi5vkNmZl5LWL1VCm3H8YytQ_PDF_NtwaArC46i74X5eXcmbRy4gddr9g3HMokrLTW_v1zNh9AWYz_Eof4ov6UtJjHNmY5U1T_EOM73oEuM/s1600/heightfixed.png" /></a></div>
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Another useful way to use the <i>as.numeric()</i> and logical classes to your advantage is a situation like this:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4LarkuHUVljA8ie-PDnL9zSBLmPOBUh2dRMWOjxKvablYEt2s5jbIJDtxg4Txcg-9nnN00nPwxzOT3mSVPsCyBaKG8YikoJDIxIomzppfm4boXZOB2o4MwyLoP7ybpKnkMtuVJkXKNaQ/s1600/numkids1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4LarkuHUVljA8ie-PDnL9zSBLmPOBUh2dRMWOjxKvablYEt2s5jbIJDtxg4Txcg-9nnN00nPwxzOT3mSVPsCyBaKG8YikoJDIxIomzppfm4boXZOB2o4MwyLoP7ybpKnkMtuVJkXKNaQ/s1600/numkids1.png" /></a></div>
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I have in my dataset the age of the last child born (and probably other characteristics of this child not shown), and then just the number of other children for each woman. I want to get a total number of children variable. I can do it simply in the following way. </div>
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First, a note about the <i>is.na()</i> function. If you want to check if a variable is missing in R, you <b>don't</b> use syntax like "if variable==NA" or "if variable==.". This is not going to indicate a missing value. What you want to use instead is <b>is.na(variable)</b> like this:</div>
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<span style="font-family: Courier New, Courier, monospace;">is.na(newdata$Child1age)</span></div>
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<span style="font-family: Courier New, Courier, monospace;"><br /></span></div>
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Which gives you a logical vector that looks like this:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTQhowiM8rN38hC8Ih8JMAgoVQfVg41Ucwn-5y41BuElaIBI4Iqg35gtwJ7d6Gkq6EG31saTte-q7ZPAgRw0CVGhV6cSqFQKbOokTCQmrZ0y_6mSa_8hPYwcoW_Ix12zji-sC4ICarcRo/s1600/isnakids.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em; text-align: center;"><img border="0" height="28" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTQhowiM8rN38hC8Ih8JMAgoVQfVg41Ucwn-5y41BuElaIBI4Iqg35gtwJ7d6Gkq6EG31saTte-q7ZPAgRw0CVGhV6cSqFQKbOokTCQmrZ0y_6mSa_8hPYwcoW_Ix12zji-sC4ICarcRo/s400/isnakids.png" width="400" /></a></div>
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If you want to check if a variable is not missing, you use the ! sign (meaning "Not") in front and check it like this:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEintiB__2Msw8tXbMkfcyh2io0mpzlLHpIQfA87rkLz9-0jA9i2h5whHH5iIWFjlfdsEDIWk3ZQpSLQoQPlZyjODq9w1u53x3OBFHqzKZfCGfir5jM0TuCbmEr7yvuZjDcfW1zdmAFjN2U/s1600/notmissing.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="30" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEintiB__2Msw8tXbMkfcyh2io0mpzlLHpIQfA87rkLz9-0jA9i2h5whHH5iIWFjlfdsEDIWk3ZQpSLQoQPlZyjODq9w1u53x3OBFHqzKZfCGfir5jM0TuCbmEr7yvuZjDcfW1zdmAFjN2U/s400/notmissing.png" width="400" /></a></div>
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We've seen this kind of thing before! Now we can translate this logical vector into numeric and add it to the number of other children, like this:</div>
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<span style="font-family: Courier New, Courier, monospace;">newdata$Totalnumchildren<-as.numeric(!is.na(newdata$Child1age))+newdata$Numotherchildren</span></div>
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We get the following:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiumIt8yA9I4Fgb9alY3ZLR2bReWgRsIp125H0jomVfk2fXSnS8aeto6PHF46_4ukftDNwxLyfkHX5BLJkQ5ZznsFFjA2obzMb1C03zF1FurXt0pBvpOTLwmI7P8mgX-HT3BOS0WV7Ervk/s1600/numkids2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" height="191" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiumIt8yA9I4Fgb9alY3ZLR2bReWgRsIp125H0jomVfk2fXSnS8aeto6PHF46_4ukftDNwxLyfkHX5BLJkQ5ZznsFFjA2obzMb1C03zF1FurXt0pBvpOTLwmI7P8mgX-HT3BOS0WV7Ervk/s320/numkids2.png" width="320" /></a></div>
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If we want to get those NAs to be 0, we can again use the <i>is.na()</i> function and replace whereever Totalnumchildren is missing with a 0 like this:</div>
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<span style="font-family: Courier New, Courier, monospace;">newdata$Totalnumchildren[is.na(newdata$Totalnumchildren)]<-0</span></div>
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<br />Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com3tag:blogger.com,1999:blog-2550386066385582083.post-6879897509549347122012-11-21T11:46:00.000-08:002012-11-22T06:43:09.417-08:00Data types, part 3: Factors!<br />
In this third part of the data types series, I'll go an important class that I skipped over so far: factors.<br />
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<b>Factors</b> are <b>categorical</b> variables that are super useful in summary statistics, plots, and regressions. They basically act like dummy variables that R codes for you. So, let's start off with some data:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg-oJvX1BLAVEC4IZJGaW1Ck_hqEs6DMcO_MucftwbY7SJ7VVBslPWWkstbIGESTZg4Fg4uzrmE-la72borhlv9G6Pj2HziTeV-H2K5pZy9ZHz9Iwy8J8N_vtynATqakLF7jc1uDeGsuIE/s1600/mydata.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="188" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg-oJvX1BLAVEC4IZJGaW1Ck_hqEs6DMcO_MucftwbY7SJ7VVBslPWWkstbIGESTZg4Fg4uzrmE-la72borhlv9G6Pj2HziTeV-H2K5pZy9ZHz9Iwy8J8N_vtynATqakLF7jc1uDeGsuIE/s320/mydata.png" width="320" /></a></div>
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and let's check out what kinds of variables we have:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvtMbAcJkxeB9KiMlasHj_bD81t6YvDMhmYaBIeBDo53ZktjsWie2C1_esQ1PLgCJrQh0KyOPNQpLzJJPQdpt53iV8UFW1R2-AiyZ0qrMMICXXOGP4u0iqBtKbigyxkbOV2Cz8K7VwaXs/s1600/lsstr.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="113" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvtMbAcJkxeB9KiMlasHj_bD81t6YvDMhmYaBIeBDo53ZktjsWie2C1_esQ1PLgCJrQh0KyOPNQpLzJJPQdpt53iV8UFW1R2-AiyZ0qrMMICXXOGP4u0iqBtKbigyxkbOV2Cz8K7VwaXs/s400/lsstr.png" width="400" /></a></div>
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so we see that <b>Race is a factor variable with three levels</b>. I can see all the levels this way:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJ1UKySRk950AiiSc2SGdWggZEZLo8eQJLH1so2EpyRtG-UyyrhXqMQe-EyCejMR3q1EctwTn1iVgVxgsthH8Yz4tWvmZC9pvRJZh85zch-X2OepQyJsdBC67ndjAlEcfa0nnG-9VyHrI/s1600/levels.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJ1UKySRk950AiiSc2SGdWggZEZLo8eQJLH1so2EpyRtG-UyyrhXqMQe-EyCejMR3q1EctwTn1iVgVxgsthH8Yz4tWvmZC9pvRJZh85zch-X2OepQyJsdBC67ndjAlEcfa0nnG-9VyHrI/s1600/levels.png" /></a><br />
So what his means that R groups statistics by these levels. Internally, <b>R stores the integer values 1, 2, and 3, and maps the character strings</b> (in alphabetical order, unless I reorder) to these values, i.e. 1=Black, 2=Hispanic, and 3=White. Now if I were to do a summary of this variable, it shows me the counts for each category, as below. R won't let me do a mean or any other statistic of a factor variable other than a count, so keep that in mind. But you can always change your factor to be numeric, which I'll go over next week.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5yH_ahMytiVTC9Puxpbm0KQU_79kI9gHXFW7RBzpr4Hlc1DMmON2g3mSNTQhzsAvXQYlPVidRVPOdj5_j1AalfxDjnaCpNk4AjEvznXAJQpN3SlPbcEf19S9H9p8llhAMGosYEE7D5bE/s1600/summaryrace.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5yH_ahMytiVTC9Puxpbm0KQU_79kI9gHXFW7RBzpr4Hlc1DMmON2g3mSNTQhzsAvXQYlPVidRVPOdj5_j1AalfxDjnaCpNk4AjEvznXAJQpN3SlPbcEf19S9H9p8llhAMGosYEE7D5bE/s1600/summaryrace.png" /></a></div>
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If I do a plot of age on race, I get a <b>boxplot </b>from the normal plot command since that is what makes sense for a categorical variable:<br />
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<span style="font-family: Courier New, Courier, monospace;">plot(mydata$Age~mydata$Race, xlab="Race", ylab="Age", main="Boxplots of Age by Race")</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0vN9kCma2btNxSCCyQafiZ5n8iXxZhwleYvbapWRTosrjy2qbA20Kxl21ZqWW8t_MfvnmeZZjcEuTcvqf2bQwTUheM9KYW_npXp89vUlfunyAbDo9Vb-O_pmubAhBw3oANmRCdfpyfaI/s1600/boxplots_j.jpeg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="305" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0vN9kCma2btNxSCCyQafiZ5n8iXxZhwleYvbapWRTosrjy2qbA20Kxl21ZqWW8t_MfvnmeZZjcEuTcvqf2bQwTUheM9KYW_npXp89vUlfunyAbDo9Vb-O_pmubAhBw3oANmRCdfpyfaI/s320/boxplots_j.jpeg" width="320" /></a></div>
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Finally, if I do a <b>regression</b> of age on race, notice how I instantly get<b> dummy variables</b>:<br />
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<span style="font-family: 'Courier New', Courier, monospace;">summary(lm(Age~Race, data=mydata))</span><br />
<span style="font-family: 'Courier New', Courier, monospace;"><br /></span>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiL5nGoiD3rFsIOrTpjzW3KstGLcw2c64YLdvWIpclbYHRJSFAfuBPyyx61sccXJ_AH7GNaiSH-pH4fKoK2s745Dy0TqZhLtjsbIfEppWAzhV7aBCTmJYUFqmdKxwe6FIqvkw0bgHGYGWE/s1600/lm.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="305" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiL5nGoiD3rFsIOrTpjzW3KstGLcw2c64YLdvWIpclbYHRJSFAfuBPyyx61sccXJ_AH7GNaiSH-pH4fKoK2s745Dy0TqZhLtjsbIfEppWAzhV7aBCTmJYUFqmdKxwe6FIqvkw0bgHGYGWE/s400/lm.png" width="400" /></a><br />
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<span style="background-color: #f6b26b; color: #444444;">Here Black is the reference category since it's the first level by alphabetical order.</span><br />
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What if I want to run the same regression as before, but I want to use Hispanic as my reference group? Very easily, I just <b>relevel the factor </b>like this and get the resulting regression output:<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$Race2<-relevel(mydata$Race, "Hispanic")</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">summary(lm(Age~Race2, data=mydata))</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi02XAyVu8AT12KGbTZn3T8yw7XDGY0hw-uGPYbGUbrOiFreE-KbfhrtHBrwPsCDF_JNtDNeC_BCuoXzU1p6hMwSwcmU0R4qhTXIH5miz-H_sBCvDIdXNEkZ9XWBR_4SKb7AAndXpF6MW8/s1600/reg2.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="250" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi02XAyVu8AT12KGbTZn3T8yw7XDGY0hw-uGPYbGUbrOiFreE-KbfhrtHBrwPsCDF_JNtDNeC_BCuoXzU1p6hMwSwcmU0R4qhTXIH5miz-H_sBCvDIdXNEkZ9XWBR_4SKb7AAndXpF6MW8/s400/reg2.png" width="400" /></a></div>
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<span style="background-color: #f6b26b;">Notice how now the Hispanic category is the reference.</span><br />
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So that is great stuff. But it's really important to know how to manipulate the factor variables. First, <b>I can create factors</b> using <a href="http://stat.ethz.ch/R-manual/R-patched/library/base/html/factor.html" target="_blank">the <i>factor()</i> function</a>. I notice from viewing my dataset above (or from running <span style="font-family: Courier New, Courier, monospace;">class(mydata$Marriage)</span>) that marriage is numeric and coded as 0, 1, or 2. I find out in my codebook that those values correspond to Single, Married, and Divorced/Widowed. We can fix that this way:<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$Married.cat<-factor(mydata$Married, labels=c("Single", "Married", "Divorced/Widowed"))</span><br />
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This will create an unordered factor where 1=Single, 2=Married, and 3=Divorced/Widowed. <br />
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Now let's say I want to create a variable that describes whether someone's weight is "Low", "Medium", and "High". In this case, <a href="http://rforpublichealth.blogspot.com/2012/09/from-continuous-to-categorical.html" target="_blank">I'll use the <i>cut()</i> function</a>, which instantly creates a factor that I can label, then I use the <i>ordered()</i> function around the cut function to order the levels. I show it in different colors for ease of viewing the two functions that I'm nesting:<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$Weight.type<-<span style="color: #6aa84f;">ordered(</span><span style="color: #e06666;">cut(mydata$Weight, c(0,135,165,200), labels=c("Low", "Medium", "High"))</span><span style="color: #6aa84f;">)</span></span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguzBZ3vhCkR6Nu_ghTmVRGbeC9CE96Kfy6diEAPTlWPBLX5nOeECCi4nx_vlb6VncXQ9W5mufRXDGmLB5f24CF1GN1T7Vdo_oQOVsUbQ5NNHg7lm2ZM-CZ1KIb3MQljrFcKoEj9McNWgg/s1600/ordered.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="65" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguzBZ3vhCkR6Nu_ghTmVRGbeC9CE96Kfy6diEAPTlWPBLX5nOeECCi4nx_vlb6VncXQ9W5mufRXDGmLB5f24CF1GN1T7Vdo_oQOVsUbQ5NNHg7lm2ZM-CZ1KIb3MQljrFcKoEj9McNWgg/s400/ordered.png" width="400" /></a>If I print out the class and the contents of the variable (left) , I notice that <b>it's an ordered factor</b> and it tells me that Low<Medium< High, which is what I want.<br />
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We can see the new additions to my dataset (I'm showing just the relevant columns):<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjqNeRYUUkTTNCKV-h6tSrTcdMvPdBTaMnr_MY3Ms1LPPIRkePn7jSa3LbVb-ylOmTRL-31XEryU3-ufsy043_JPsUAhMo-GqPNk3cjgr76sIYGm506qEq-qTcECKMClD0eCkLqYcU41k8/s1600/newvars.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="189" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjqNeRYUUkTTNCKV-h6tSrTcdMvPdBTaMnr_MY3Ms1LPPIRkePn7jSa3LbVb-ylOmTRL-31XEryU3-ufsy043_JPsUAhMo-GqPNk3cjgr76sIYGm506qEq-qTcECKMClD0eCkLqYcU41k8/s320/newvars.png" width="320" /></a></div>
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<b>One caveat with factors</b> - if you start off with a level and then you drop the only observations with that level, R still holds on to the level as a stored value and this can mess up your later analysis. For example, I subset my data to the first 6 rows so that I eliminate all Hispanic subjects from my data, but R keeps Hispanic as a possible level:<br />
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<span style="font-family: Courier New, Courier, monospace;">mynewdata<-mydata[1:6,]</span><br />
<span style="font-family: Courier New, Courier, monospace;">summary(mynewdata$Race)</span><br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjKTPGIWO15LbkMujr2g5_0qU-kYjF0WAOv_PiS2o2mYGKxTKzjUJZEvItYy5kDXrhU0AIu0IpOaUEqHUiHQyKIhxkD4ZZUoj-9x7lfk4xj7ZY_2QjxMrlzOT7XR7uULtQ62ghjiCqgfSI/s1600/fac1.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjKTPGIWO15LbkMujr2g5_0qU-kYjF0WAOv_PiS2o2mYGKxTKzjUJZEvItYy5kDXrhU0AIu0IpOaUEqHUiHQyKIhxkD4ZZUoj-9x7lfk4xj7ZY_2QjxMrlzOT7XR7uULtQ62ghjiCqgfSI/s1600/fac1.png" /></a><span style="font-family: Courier New, Courier, monospace;"><br /></span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
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<span style="font-family: Courier New, Courier, monospace;"><br /></span><br />
So here Hispanic just has a 0 count, but is still a category. This can be really annoying, like when you're making a barplot and the category is still showing up in the plot.<br />
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One quick way to get rid of this is to use the <a href="http://stat.ethz.ch/R-manual/R-patched/library/base/html/droplevels.html" target="_blank"><i>droplevels()</i> function</a> to drop all unused levels from your dataset. A commentator let me know that this function<span style="background-color: white;"> was introduced in R 2.12.0. Before, it was necessary to use a separate package called gdata. The function takes the whole dataframe as the argument, and you can use the except argument to list the indices of columns that you do not want subject to the dropping:</span><br />
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<span style="font-family: Courier New, Courier, monospace;">finaldata<-droplevels(mynewdata)</span><br />
<span style="font-family: Courier New, Courier, monospace;">summary(finaldata$Race)</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEht_OXkjKaykWeeQLtZO21XZdXJaZAzJSMPEwPF6tnoELKGI2qXZtaGO1tz3jbk33C5Ozde1HEocXtHFFP5I4J8WfZUUxdSWIGtvQen1nISUJRNPSQZYkW443gDGzOm72JB3octn1SPlDg/s1600/fac2.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEht_OXkjKaykWeeQLtZO21XZdXJaZAzJSMPEwPF6tnoELKGI2qXZtaGO1tz3jbk33C5Ozde1HEocXtHFFP5I4J8WfZUUxdSWIGtvQen1nISUJRNPSQZYkW443gDGzOm72JB3octn1SPlDg/s1600/fac2.png" /></a><br />
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You could do all this very efficiently in one step like this without changing the name of your dataframe:<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata<-droplevels(mydata[1:6,])</span><br />
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which is why we love R!<br />
<br />Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com3tag:blogger.com,1999:blog-2550386066385582083.post-41572889378089349302012-11-08T18:19:00.000-08:002012-11-08T19:21:42.100-08:00Data types part 2: Using classes to your advantage<br />
Last week I talked about objects including scalars, vectors, matrices, dataframes, and lists. This post will show you how to use the objects (and their corresponding classes) you create in R to your advantage.<br />
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First off, it's important to remember that columns of dataframes are vectors. That is, if I have a dataframe called <span style="font-family: Courier New, Courier, monospace;">mydata</span>, the columns <span style="font-family: Courier New, Courier, monospace;">mydata$Height</span> and <span style="font-family: Courier New, Courier, monospace;">mydata$Weight</span> are vectors. Numeric vectors can be multiplied or added together, squared, added or multiplied by a constant, etc. Operations on vectors are done element by element, meaning here row by row.<br />
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First, I read in a file of data, called <span style="font-family: Courier New, Courier, monospace;">mydata</span>, <a href="http://rforpublichealth.blogspot.com/2012/10/getting-data-in-and-out-of-r.html" target="_blank">using the <i>read.csv() </i>function</a>. I get the dataframe below:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiY8k7by6VK7M2di5O1FTlzm9W6UPFgX5-e7nv8e6TGg6EzRjQqVmVW1PcbL5rUeRsWUKDfx9SRO_itJfiF5_0ZgaqurtAvG3pwCRSadH5n8NG3hFr6F_Q5X8QqwhKxkYG4Hr6qz3Xrd9E/s1600/mydata.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiY8k7by6VK7M2di5O1FTlzm9W6UPFgX5-e7nv8e6TGg6EzRjQqVmVW1PcbL5rUeRsWUKDfx9SRO_itJfiF5_0ZgaqurtAvG3pwCRSadH5n8NG3hFr6F_Q5X8QqwhKxkYG4Hr6qz3Xrd9E/s1600/mydata.png" /></a></div>
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I check the classes of my objects using <i>class(), </i>or all at the same time with <i>ls.str()</i>.<br />
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<span style="font-family: Courier New, Courier, monospace;">class(mydata$Weight)</span><br />
<span style="font-family: Courier New, Courier, monospace;">class(mydata$Height)</span><br />
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or<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiXJ07Wxja9uz-Q3XIqAd13xhH97TpNaLodSdTq56nr2HprqI5YnHfgeYSokvt1g0N-jTaHTcfGQvc7bRC3dPSZJMQheuKvWK6W8NiaoENxppfomE4EnwVOyzbr3tYKt8oRiV4Rom_bKdM/s1600/lsstr.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="135" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiXJ07Wxja9uz-Q3XIqAd13xhH97TpNaLodSdTq56nr2HprqI5YnHfgeYSokvt1g0N-jTaHTcfGQvc7bRC3dPSZJMQheuKvWK6W8NiaoENxppfomE4EnwVOyzbr3tYKt8oRiV4Rom_bKdM/s400/lsstr.png" width="400" /></a></div>
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So I see that mydata is a dataframe and all my columns are numeric (num). Now, if I want to <b>create a new column in my dataset</b> which calculates BMI, I can do some vector operations:<br />
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<span style="font-family: Courier New, Courier, monospace;">mydata$BMI<-mydata$Weight/(mydata$Height)^2 * 703</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhLePapYzOJ4x-uXOr2ynrgDdUiulDSPNhe5z6WwTroIJky062qcyIYlBKOylk382ibywA0gIa7jL9ffh-s5k6mB3xxebRO_jn75Q8i_GuQCtSVNfIOYC3pj-vmgaAe4ReVytdvf9TTkfk/s1600/mydatawbmi.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="204" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhLePapYzOJ4x-uXOr2ynrgDdUiulDSPNhe5z6WwTroIJky062qcyIYlBKOylk382ibywA0gIa7jL9ffh-s5k6mB3xxebRO_jn75Q8i_GuQCtSVNfIOYC3pj-vmgaAe4ReVytdvf9TTkfk/s320/mydatawbmi.png" width="320" /></a></div>
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Which is the formula for BMI from weight in pounds and height in inches. Notice how if any component of the calculation is a missing (NA) value, R calculates the BMI as NA as well.<br />
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Now I can do summary statistics on my data and store those as a matrix. For example, I start with summary statistics on my Age vector:<br />
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<span style="font-family: 'Courier New', Courier, monospace;">summary(mydata$Age)</span><br />
<span style="font-family: 'Courier New', Courier, monospace;"><br /></span>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVqSi6w5bNEYUrjClFmUAgF5rP6lfKI492DuLsr6XLB8FSJ4n1w6Rnike3aWzT05e2NfrioPNF0xxMZksy79tyVHNdJSI5m95lAAU8_nKiRY2kGj9XDWDsrE6WQg4E9m_iyTEvFUDeBAM/s1600/summary1.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="48" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVqSi6w5bNEYUrjClFmUAgF5rP6lfKI492DuLsr6XLB8FSJ4n1w6Rnike3aWzT05e2NfrioPNF0xxMZksy79tyVHNdJSI5m95lAAU8_nKiRY2kGj9XDWDsrE6WQg4E9m_iyTEvFUDeBAM/s320/summary1.png" width="320" /></a><span style="font-family: 'Courier New', Courier, monospace;"><br /></span><br />
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If I want to extract an element of this summary table, say the minimum, I can do<br />
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<span style="font-family: 'Courier New', Courier, monospace;">summary(mydata$Age)[1]</span><br />
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which extracts the first element (of 6) of the summary table.<br />
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But what I really want is a <b>summary matrix of a bunch of variables</b>: Age, Sex, and BMI. To do this I can rowbind the summary statistics of those three variables together using the <i>rbind()</i> function, but only take the 1st, 4th, and 6th elements of the summary table, which as you can see correspond to the Min, Mean, and Max. This creates a matrix, which I call <span style="font-family: Courier New, Courier, monospace;">summary.matrix</span>:<br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">summary.matrix<-rbind(summary(mydata$Age)[c(1,4,6)], summary(mydata$BMI)[c(1,4,6)], summary(mydata$Sex)[c(1,4,6)])</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
Rowbinding is basically stacking rows on top of each other. I add rownames and then print the class of my summary matrix and the results.<br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">rownames(summary.matrix)<-c("Age", "BMI", "Sex")</span><br />
<span style="font-family: Courier New, Courier, monospace;">class(summary.matrix)</span><br />
<span style="font-family: Courier New, Courier, monospace;">summary.matrix</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiiHV1-UpZe1sC-4T1oc68jg9lggpNdIJUMib7FCQ5GknllLrq5YOeCwFjX5vpSZqEB2RCKP1kQ70hbG1FKVGcbl_yKgP66av2ho02M7QaYkc_dfvaz2gqBGHEEJcjrNsHeD5oH4lHifDg/s1600/summary2.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiiHV1-UpZe1sC-4T1oc68jg9lggpNdIJUMib7FCQ5GknllLrq5YOeCwFjX5vpSZqEB2RCKP1kQ70hbG1FKVGcbl_yKgP66av2ho02M7QaYkc_dfvaz2gqBGHEEJcjrNsHeD5oH4lHifDg/s1600/summary2.png" /></a><span style="font-family: Courier New, Courier, monospace;"><br /></span><br />
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There is also a much more efficient way of doing this using the <i>apply()</i> function. Previously I had another <a href="http://rforpublichealth.blogspot.com/2012/09/the-infamous-apply-function.html" target="_blank">post on the apply function</a>, but I find that it takes a lot of examples to get comfortable with so here is another application. <br />
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<i>Apply()</i> is a great example of classes because it takes in a dataframe as the first argument (<span style="font-family: Courier New, Courier, monospace;">mydata</span>, all rows, but I choose only columns 2, 3, and 7). I then apply it to the numeric vector columns (MARGIN=2) of this subsetted dataframe, and then for each of those columns I perform the mean and standard deviation, removing the NA's from consideration. I save this in a matrix I call <span style="font-family: Courier New, Courier, monospace;">summary.matrix2</span>.<br />
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<span style="font-family: Courier New, Courier, monospace;">summary.matrix2<-apply(mydata[,c(2,3,7)], MARGIN=2, FUN=function(x) c(mean(x,na.rm=TRUE), sd(x, na.rm=TRUE)))</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
I then rename the rows of the this matrix and print the results, rounded to two decimal places. Notice how the format of the final matrix is different here. Above the rows were the variables and the columns the summary statistics, while here it is reversed. I could have column binded (<i>cbind()</i> instead of the <i>rbind()</i>) in the first case and I would have gotten the matrix transposed to be like this one.<br />
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<span style="font-family: Courier New, Courier, monospace;">rownames(summary.matrix2)<-c("Mean", "Stdev")</span><br />
<span style="font-family: Courier New, Courier, monospace;">round(summary.matrix2, 2)</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjRDzAdzFo8TxwMNgwpf2BDdqKDoVOcTgsYDdnDv-05uWdFSs8hDsP3-nMQghCnJbDmCUTZmPBZPucA5l-M2W4SDZS419Bjd8Jy4saVEtc4Wk4FjAFlo8hWLSO3ap_smXDZOnBk-7Zf_RA/s1600/summary3.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="66" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjRDzAdzFo8TxwMNgwpf2BDdqKDoVOcTgsYDdnDv-05uWdFSs8hDsP3-nMQghCnJbDmCUTZmPBZPucA5l-M2W4SDZS419Bjd8Jy4saVEtc4Wk4FjAFlo8hWLSO3ap_smXDZOnBk-7Zf_RA/s200/summary3.png" width="200" /></a></div>
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Finally, I want to demonstrate how you can take advantage of scalars and vectors when graphing. Creating scalar and vectors objects is really helpful when you are doing the same task multiple times. I give the example of creating a bunch of scatterplots. <br />
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I want to make a scatterplot for each of three variables (Height, Weight, and BMI) against age. Since all three scatterplots are going to be very similar, I want to <b>standardize all of my plotting arguments </b>including the range of ages, the plot symbols and the plot colors. I want to include a vertical line for the mean age and a title for each plot. The code is below:<br />
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<br />
<span style="font-family: Courier New, Courier, monospace;">##Assign numeric vector for the range of x-axis</span><br />
<span style="font-family: Courier New, Courier, monospace;">agelimit<-c(20,80)</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">##Assign numeric single scalar to plotsymbols and meanage</span><br />
<span style="font-family: Courier New, Courier, monospace;">plotsymbols<-2</span><br />
<span style="font-family: 'Courier New', Courier, monospace;">meanage<-mean(mydata$Age)</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">##Assign single character words to plottype and plotcolor </span><br />
<span style="font-family: Courier New, Courier, monospace;">plottype<-"p"</span><br />
<span style="font-family: Courier New, Courier, monospace;">plotcolor<-"darkgreen"</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">##Assign a vector of characters to titletext</span><br />
<span style="font-family: Courier New, Courier, monospace;">titletext<-c("Scatterplot", "vs Age")</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
Ok, so now that I have all those assigned, I can plot the three plots all together using the following code. Notice how all the highlighted code is the same in each plot (except for the main title) and I'm using the assigned objects I just created. The great part about this is that if I decide I actually want to plot color to be red, I can change it in just one place. You can think about how this would be useful in other situations (data cleaning, regressions, etc) when you do the same thing multiple times and then decide to change one little parameter. If you're not sure about the code below, I posted on the <a href="http://rforpublichealth.blogspot.com/2012/10/what-nice-looking-scatterplot.html" target="_blank">basics of plotting here</a>.<br />
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<span style="font-family: Courier New, Courier, monospace;">##Plot area is 1 row, 3 columns</span><br />
<span style="font-family: Courier New, Courier, monospace;">par(mfrow=c(1,3))</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">##Plot all three plots using the assigned objects</span><br />
<span style="font-family: Courier New, Courier, monospace;">plot(mydata$Age, mydata$Height, xlab="Age", ylab="Height", <span style="background-color: yellow;">xlim=agelimit,pch=plotsymbols, type=plottype, col=plotcolor, main=paste(titletext[1], "Height", titletext[2])</span>)</span><br />
<span style="font-family: Courier New, Courier, monospace;">abline(v=meanage)</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">plot(mydata$Age, mydata$Weight, xlab="Age", ylab="Weight", <span style="background-color: yellow;">xlim=agelimit,pch=plotsymbols, type=plottype, col=plotcolor, main=paste(titletext[1], "Weight", titletext[2])</span>)</span><br />
<span style="font-family: Courier New, Courier, monospace;">abline(v=meanage)</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">plot(mydata$Age, mydata$BMI, xlab="Age", ylab="BMI", <span style="background-color: yellow;">xlim=agelimit,pch=plotsymbols, type=plottype, col=plotcolor, main=paste(titletext[1], "BMI", titletext[2])</span>)</span><br />
<span style="font-family: Courier New, Courier, monospace;">abline(v=meanage)</span><br />
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<br />
Notice how I do the main title with the paste statement. <i>Paste()</i> is useful for combining words and elements of another variable together into one phrase. The output looks like this, below. Pretty nice!<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJiDHLfmX6b8uVAmMdVXI_lVeu64T-WTHEYsp7MsdbhBeFqWpd3Y4dJdYSTWQO6tlErUiqm2sDZ15bGBPih4fy1iwTXKSnuMdv-vfWPyTiEznyhwNi4fN4ud89mdj70kLPxfXN35AM7dU/s1600/Rplot_scatterplots.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="257" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJiDHLfmX6b8uVAmMdVXI_lVeu64T-WTHEYsp7MsdbhBeFqWpd3Y4dJdYSTWQO6tlErUiqm2sDZ15bGBPih4fy1iwTXKSnuMdv-vfWPyTiEznyhwNi4fN4ud89mdj70kLPxfXN35AM7dU/s640/Rplot_scatterplots.png" width="640" /></a></div>
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Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com4tag:blogger.com,1999:blog-2550386066385582083.post-78656253855997347932012-11-01T21:16:00.000-07:002012-11-01T21:18:10.648-07:00Data types, part 1: Ways to store variables<br />
I've been alluding to different R data types, or classes, in various posts, so I want to go over them in more detail. This is <b>part 1 of a 3 part series on data types</b>. In this post, I'll describe and give a general overview of useful data types. In parts 2 and 3, I'll show you in more detailed examples how you can use these data types to your advantage when you're programming.<br />
<br />
When you program in R, you must always refer to various objects that you have created. This is in contrast to say, Stata, where you open up a dataset and any variables you refer to are columns of that dataset (with the exception of local macro variables and so on). So for example, if I have a dataset like the one below:<br />
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjnnv6RkkLwJ_4dWqx0k-ZtPo6eZqH7IXQQU-5AkX39A4bK4wF8RMnKsfR-tlV9U-Wn26uljv6IQLNsr8pYwwdqJ1hBYttWasLzfyaUUjO_RS0syl34089hT9azHVnaIh2S4rZR9NUVgAg/s1600/data.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="173" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjnnv6RkkLwJ_4dWqx0k-ZtPo6eZqH7IXQQU-5AkX39A4bK4wF8RMnKsfR-tlV9U-Wn26uljv6IQLNsr8pYwwdqJ1hBYttWasLzfyaUUjO_RS0syl34089hT9azHVnaIh2S4rZR9NUVgAg/s320/data.png" width="320" /></a></div>
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I can just say in Stata<br />
<br />
<span style="font-family: Verdana, sans-serif;">keep if Age>25</span><br />
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and Stata knows that I am talking about the column Age of this dataset.<br />
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But in R, I can't do that because I get this error:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg-qi7yASpZVoqo2SIN6eLeYQVTc0NnSv2MKhmGbCCFUVc3wRQ7OFDDk1osW0PYGX2f8kIiGCuR09KQT_skttuNLMg57loCRenjcz6fWdoBK97dc1e8LF3qPa76Z_9pnqZbtWtCijJI9aw/s1600/agewarning.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg-qi7yASpZVoqo2SIN6eLeYQVTc0NnSv2MKhmGbCCFUVc3wRQ7OFDDk1osW0PYGX2f8kIiGCuR09KQT_skttuNLMg57loCRenjcz6fWdoBK97dc1e8LF3qPa76Z_9pnqZbtWtCijJI9aw/s1600/agewarning.png" /></a><br />
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As the error indicates, 'Age' is not an object that I have created. This is because 'Age' is part of the dataframe that is called "mydata". A dataframe, as we will see below, is an object (and in this case also a class) with certain properties. How do I know it's a dataframe? I can check with the<i> class()</i> statement:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhqpyrFTEvFPc27HVdPvrf90W7eMAZuhPlNLh7pX_FdDNqgT6y7GYBpx2qA9Z4B8uv7UY-j6KN3ggnz4o6jzz4qHfnHIME8R5oamPBP8IiO0daQcUdLgT4v_T_05J9Q5WhUkPJYcDvoWPs/s1600/classmydata.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhqpyrFTEvFPc27HVdPvrf90W7eMAZuhPlNLh7pX_FdDNqgT6y7GYBpx2qA9Z4B8uv7UY-j6KN3ggnz4o6jzz4qHfnHIME8R5oamPBP8IiO0daQcUdLgT4v_T_05J9Q5WhUkPJYcDvoWPs/s1600/classmydata.png" /></a><br />
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What does it mean for "mydata" to be a dataframe? Well, there are many different ways to store variables in R (i.e. objects), which have corresponding classes. I enumerate the most common and useful subset of these objects below along with their description and class:<br />
<br />
<table border="1">
<tbody>
<tr>
<th>Object</th>
<th>Description</th>
<th>Class</th>
</tr>
<tr>
<td>Single Number or <br />
letter/word</td>
<td>Just a single number or <br />
character/word/phrase in quotes</td>
<td>Either numeric or character</td>
</tr>
<tr>
<td>Vector</td>
<td>A vector of either all numbers or all <br />
characters strung together</td>
<td>Either all numeric <br />
or all character</td>
</tr>
<tr>
<td>Matrix</td>
<td>Has columns and rows - <br />
all entries are of the same class</td>
<td>Either all numeric <br />
or all character</td>
</tr>
<tr>
<td>Dataframe</td>
<td>Like a matrix but columns can<br />
be different classes</td>
<td>data.frame</td>
</tr>
<tr>
<td>List</td>
<td>A bunch of different objects all <br />
grouped together under one name</td>
<td>list</td>
</tr>
</tbody></table>
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<b><br /></b>
There are other classes including <b>factors</b>, which are so useful that they will be a separate post in this blog, so for now I'll leave those aside. You can also make your own classes, but that's definitely beyond the scope of this introduction to objects and classes.<br />
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Ok, so here are some examples of different ways of assigning names to these objects and printing the contents on the screen. I chose to name my variables descriptively of what they are (like numeric.var or matrix.var), but of course you can name them anything you want with any mix of periods and underscores, lowercase and uppercase letters, i.e. id_number, Height.cm, BIRTH.YEAR.MONTH, firstname_lastname_middlename, etc. I would only guard against naming variables by calling them things like mean or median, since those are established functions in R and might lead to some weird things happening.<br />
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<span style="font-family: inherit;">1. <b>Single number or character/word/phrase in quotation marks</b>: just assign one number or one thing in quotes to the variable name</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">numeric.var<-10</span><br />
<span style="font-family: Courier New, Courier, monospace;">character.var<-"Hello!"</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhnWY5SG7rC5BEbQwsXlHsCEf5lbbeMTalubtOIDw8PI1-JxZGpVMsgpHodpumT8fFhpPaezX0RPu4ejJLz2j-mXpfCMAIFIveuALVHEwfxWHD78JFUVLLQXKCrZDL21q4FwbICSrGXJvs/s1600/scalars.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhnWY5SG7rC5BEbQwsXlHsCEf5lbbeMTalubtOIDw8PI1-JxZGpVMsgpHodpumT8fFhpPaezX0RPu4ejJLz2j-mXpfCMAIFIveuALVHEwfxWHD78JFUVLLQXKCrZDL21q4FwbICSrGXJvs/s1600/scalars.png" /></a></div>
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<span style="font-family: inherit;">2. <b>Vector</b>: use the <i>c()</i> operator or a function like <i><a href="http://stat.ethz.ch/R-manual/R-devel/library/base/html/seq.html" target="_blank">seq()</a></i> or <i><a href="http://stat.ethz.ch/R-manual/R-patched/library/base/html/rep.html" target="_blank">rep()</a></i> to combine several numbers into one vector.</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">vector.numeric<-c(1,2,3,10)</span><br />
<span style="font-family: Courier New, Courier, monospace;">vector.char<-rep("abc",3)</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhpQC_7N0tHUm3mVShNRrAJE_zCcpDHi4N2lyI5zyB1lcWFOekcgR8aZKplk0Kk0JuTcxpb1eFREWLEkNDi2FZfjBsDUU8kE3bbnUbd-MAMdpP_LRZcP9So7-BJLFJfG2ZPibWsWG9KL2o/s1600/vectors.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhpQC_7N0tHUm3mVShNRrAJE_zCcpDHi4N2lyI5zyB1lcWFOekcgR8aZKplk0Kk0JuTcxpb1eFREWLEkNDi2FZfjBsDUU8kE3bbnUbd-MAMdpP_LRZcP9So7-BJLFJfG2ZPibWsWG9KL2o/s1600/vectors.png" /></a><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span><span style="font-family: inherit;">3. <b>Matrix</b>: use the <i>matrix()</i> function to specify the entries, then the number of rows, and the number of columns in the matrix. Matrices can only be indexed using matrix notation, like [1,2] for row 1, column 2. More about indexing in <a href="http://rforpublichealth.blogspot.com/2012/10/quick-and-easy-subsetting.html" target="_blank">my previous post on subsetting</a>.</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<span style="font-family: Courier New, Courier, monospace;">matrix.numeric<-matrix(data=c(1:6),nrow=3,ncol=2)</span><br />
<span style="font-family: Courier New, Courier, monospace;">matrix.character<-matrix(data=c("a","b","c","d"), nrow=2, ncol=2)</span><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiNpzIBx94ZHvdCeEevHQTMiYVX9SYhv8PO7SzjfcLXL9hH9cl83YdNfQKX60fLg1cODqBtF8RaSGOH9VzelI_A1OfdOPFzw0FKM9l34U1v0RMldoFHQLMKciWHcFKzJpEK25pw9kBLG-I/s1600/matrices.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiNpzIBx94ZHvdCeEevHQTMiYVX9SYhv8PO7SzjfcLXL9hH9cl83YdNfQKX60fLg1cODqBtF8RaSGOH9VzelI_A1OfdOPFzw0FKM9l34U1v0RMldoFHQLMKciWHcFKzJpEK25pw9kBLG-I/s1600/matrices.png" /></a><br />
<span style="font-family: Courier New, Courier, monospace;"><br /></span><span style="font-family: inherit;">4. <b>Dataframe</b>: use the <i>data.frame()</i> function to combine variables together. Here you must use the <i>cbind()</i> function to "column bind" the variables. Notice how I can mix numeric columns with character columns, which is also not possible in matrices. If I want to refer to a specific column, I use the $ operator, like dataframe.var$ID for the second column.</span><br />
<span style="font-family: 'Courier New', Courier, monospace;"><br /></span>
<span style="font-family: 'Courier New', Courier, monospace;">dataframe.var<-data.frame(cbind(School=1, ID=1:5, Test=c("math","read","math","geo","hist")))</span><br />
<span style="font-family: 'Courier New', Courier, monospace;"><br /></span>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjXZluPcFlntEiUYrc0-vkYaD0bvxUY7Hp9_aCByFZQxaAsBtNPmPFnyYhZBsHcK9NNKB4ysuAHXqqL0GiAjfivOPNNwiafQ60qw6XlmslCcx30X_Mr2AqnMj4KJVHYP01vuWJBhK3Orlk/s1600/dataframe.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjXZluPcFlntEiUYrc0-vkYaD0bvxUY7Hp9_aCByFZQxaAsBtNPmPFnyYhZBsHcK9NNKB4ysuAHXqqL0GiAjfivOPNNwiafQ60qw6XlmslCcx30X_Mr2AqnMj4KJVHYP01vuWJBhK3Orlk/s1600/dataframe.png" /></a><br />
<span style="font-family: 'Courier New', Courier, monospace;"><br /></span>
<span style="font-family: inherit;">Alternatively, <b>any dataset you pull into R</b> using the <i>read.csv()</i>, <i>read.dta()</i>, or <i>read.xport()</i> functions (see <a href="http://rforpublichealth.blogspot.com/2012/10/getting-data-in-and-out-of-r.html" target="_blank">my blog post about this here</a>), <b>will automatically be a dataframe</b>.</span><br />
<span style="font-family: inherit;"><br /></span>
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<span style="font-family: inherit;">What's important to note about dataframes is that the variables in your dataframe also have classes. So for example, the class of the whole dataframe is "data.frame", but the class of the ID column is a "factor." </span><br />
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Again, I'll go into factors in another post and how to change back and forth between factors and numeric or character classes.<br />
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<span style="font-family: inherit;">5. <b>List</b>: use the <i>list()</i> function and list all of the objects you want to include. </span>The list combines all the objects together and has a specific indexing convention, the double square bracket like so: [[1]]. I will go into lists in another post.<br />
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<span style="font-family: Courier New, Courier, monospace;">list.var<-list(numeric.var, vector.char, matrix.numeric, dataframe.var)</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhYjzwWTdIZ9ZdSA1sEi6Ng3phkPPF99NqN9w7OHtF3ZuKbizDoBWXKvO7HLVPG28K9jBuzF41v39voW_HC68qQWVXEBJCy7Yc804xm9s8FxfZYtLN7coppKq-TQeLF-qHRkuYWArfpCmk/s1600/list.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhYjzwWTdIZ9ZdSA1sEi6Ng3phkPPF99NqN9w7OHtF3ZuKbizDoBWXKvO7HLVPG28K9jBuzF41v39voW_HC68qQWVXEBJCy7Yc804xm9s8FxfZYtLN7coppKq-TQeLF-qHRkuYWArfpCmk/s320/list.png" width="142" /></a><br />
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To know what kinds of objects you have created and thus what is in your local memory, use the <i>ls()</i> function like so:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-k4kf40ryDWtTOJ-CHOKYyZJ8SNGB_zWGRfpVKadRsaQjGaXx15KtAFoffZLtj0it8lmIDAeGMTbxcThzofLyM6pxOSBOyHpQn2lA29G2xeR3YjUPssq4ownYBAxEtwSU42_V37pUJBQ/s1600/ls.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="46" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-k4kf40ryDWtTOJ-CHOKYyZJ8SNGB_zWGRfpVKadRsaQjGaXx15KtAFoffZLtj0it8lmIDAeGMTbxcThzofLyM6pxOSBOyHpQn2lA29G2xeR3YjUPssq4ownYBAxEtwSU42_V37pUJBQ/s640/ls.png" width="640" /></a></div>
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To remove an object, you do:<br />
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<span style="font-family: Courier New, Courier, monospace;">rm(character.var)</span><br />
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and to remove all objects, you can do:<br />
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<span style="font-family: Courier New, Courier, monospace;">rm(list=ls())</span><br />
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So that was a brief introduction to objects and classes. Next week, I'll go into how these are useful for easier and more efficient programming.</div>
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Slawa Rokickihttp://www.blogger.com/profile/17017311098539878072noreply@blogger.com0