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<title>Analyzing RNA-seq data with DESeq2</title>
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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%2Ffont%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%2BLopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3t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AF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%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%2BcBdwFFAAAAAgAAg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3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNzA5OyIgZD0iTTI1IDExMDBoMTE1MHExMCAwIDEyLjUgLTV0LTUuNSAtMTNsLTU2NCAtNTY3cS04IC04IC0xOCAtOHQtMTggOGwtNTY0IDU2N3EtOCA4IC01LjUgMTN0MTIuNSA1ek0xOCA4ODJsMjY0IC0yNjRxOCAtOCA4IC0xOHQtOCAtMThsLTI2NCAtMjY0cS04IC04IC0xMyAtNS41dC01IDEyLjV2NTUwcTAgMTAgNSAxMi41dDEzIC01LjV6TTkxOCA2MThsMjY0IDI2NHE4IDggMTMgNS41dDUgLTEyLjV2LTU1MHEwIC0xMCAtNSAtMTIuNXQtMTMgNS41IGwtMjY0IDI2NHEtOCA4IC04IDE4dDggMTh6TTgxOCA0ODJsMzY0IC0zNjRxOCAtOCA1LjUgLTEzdC0xMi41IC01aC0xMTUwcS0xMCAwIC0xMi41IDV0NS41IDEzbDM2NCAzNjRxOCA4IDE4IDh0MTggLThsMTY0IC0xNjRxOCAtOCAxOCAtOHQxOCA4bDE2NCAxNjRxOCA4IDE4IDh0MTggLTh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjcwZjsiIGQ9Ik0xMDExIDEyMTBxMTkgMCAzMyAtMTNsMTUzIC0xNTNxMTMgLTE0IDEzIC0zM3QtMTMgLTMzbC05OSAtOTJsLTIxNCAyMTRsOTUgOTZxMTMgMTQgMzIgMTR6TTEwMTMgODAwbC02MTUgLTYxNGwtMjE0IDIxNGw2MTQgNjE0ek0zMTcgOTZsLTMzMyAtMTEybDExMCAzMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAwMTsiIGQ9Ik03MDAgNjUwdi01NTBoMjUwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtODAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgyNTB2NTUwbC01MDAgNTUwaDEyMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAwMjsiIGQ9Ik0zNjggMTAxN2w2NDUgMTYzcTM5IDE1IDYzIDB0MjQgLTQ5di04MzFxMCAtNTUgLTQxLjUgLTk1LjV0LTExMS41IC02My41cS03OSAtMjUgLTE0NyAtNC41dC04NiA3NXQyNS41IDExMS41dDEyMi41IDgycTcyIDI0IDEzOCA4djUyMWwtNjAwIC0xNTV2LTYwNnEwIC00MiAtNDQgLTkwdC0xMDkgLTY5cS03OSAtMjYgLTE0NyAtNS41dC04NiA3NS41dDI1LjUgMTExLjV0MTIyLjUgODIuNXE3MiAyNCAxMzggN3Y2MzlxMCAzOCAxNC41IDU5IHQ1My41IDM0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMDM7IiBkPSJNNTAwIDExOTFxMTAwIDAgMTkxIC0zOXQxNTYuNSAtMTA0LjV0MTA0LjUgLTE1Ni41dDM5IC0xOTFsLTEgLTJsMSAtNXEwIC0xNDEgLTc4IC0yNjJsMjc1IC0yNzRxMjMgLTI2IDIyLjUgLTQ0LjV0LTIyLjUgLTQyLjVsLTU5IC01OHEtMjYgLTIwIC00Ni41IC0yMHQtMzkuNSAyMGwtMjc1IDI3NHEtMTE5IC03NyAtMjYxIC03N2wtNSAxbC0yIC0xcS0xMDAgMCAtMTkxIDM5dC0xNTYuNSAxMDQuNXQtMTA0LjUgMTU2LjV0LTM5IDE5MSB0MzkgMTkxdDEwNC41IDE1Ni41dDE1Ni41IDEwNC41dDE5MSAzOXpNNTAwIDEwMjJxLTg4IDAgLTE2MiAtNDN0LTExNyAtMTE3dC00MyAtMTYydDQzIC0xNjJ0MTE3IC0xMTd0MTYyIC00M3QxNjIgNDN0MTE3IDExN3Q0MyAxNjJ0LTQzIDE2MnQtMTE3IDExN3QtMTYyIDQzeiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMDU7IiBkPSJNNjQ5IDk0OXE0OCA2OCAxMDkuNSAxMDR0MTIxLjUgMzguNXQxMTguNSAtMjB0MTAyLjUgLTY0dDcxIC0xMDAuNXQyNyAtMTIzcTAgLTU3IC0zMy41IC0xMTcuNXQtOTQgLTEyNC41dC0xMjYuNSAtMTI3LjV0LTE1MCAtMTUyLjV0LTE0NiAtMTc0cS02MiA4NSAtMTQ1LjUgMTc0dC0xNTAgMTUyLjV0LTEyNi41IDEyNy41dC05My41IDEyNC41dC0zMy41IDExNy41cTAgNjQgMjggMTIzdDczIDEwMC41dDEwNCA2NHQxMTkgMjAgdDEyMC41IC0zOC41dDEwNC41IC0xMDR6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAwNjsiIGQ9Ik00MDcgODAwbDEzMSAzNTNxNyAxOSAxNy41IDE5dDE3LjUgLTE5bDEyOSAtMzUzaDQyMXEyMSAwIDI0IC04LjV0LTE0IC0yMC41bC0zNDIgLTI0OWwxMzAgLTQwMXE3IC0yMCAtMC41IC0yNS41dC0yNC41IDYuNWwtMzQzIDI0NmwtMzQyIC0yNDdxLTE3IC0xMiAtMjQuNSAtNi41dC0wLjUgMjUuNWwxMzAgNDAwbC0zNDcgMjUxcS0xNyAxMiAtMTQgMjAuNXQyMyA4LjVoNDI5eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMDc7IiBkPSJNNDA3IDgwMGwxMzEgMzUzcTcgMTkgMTcuNSAxOXQxNy41IC0xOWwxMjkgLTM1M2g0MjFxMjEgMCAyNCAtOC41dC0xNCAtMjAuNWwtMzQyIC0yNDlsMTMwIC00MDFxNyAtMjAgLTAuNSAtMjUuNXQtMjQuNSA2LjVsLTM0MyAyNDZsLTM0MiAtMjQ3cS0xNyAtMTIgLTI0LjUgLTYuNXQtMC41IDI1LjVsMTMwIDQwMGwtMzQ3IDI1MXEtMTcgMTIgLTE0IDIwLjV0MjMgOC41aDQyOXpNNDc3IDcwMGgtMjQwbDE5NyAtMTQybC03NCAtMjI2IGwxOTMgMTM5bDE5NSAtMTQwbC03NCAyMjlsMTkyIDE0MGgtMjM0bC03OCAyMTF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAwODsiIGQ9Ik02MDAgMTIwMHExMjQgMCAyMTIgLTg4dDg4IC0yMTJ2LTI1MHEwIC00NiAtMzEgLTk4dC02OSAtNTJ2LTc1cTAgLTEwIDYgLTIxLjV0MTUgLTE3LjVsMzU4IC0yMzBxOSAtNSAxNSAtMTYuNXQ2IC0yMS41di05M3EwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTExNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5M3EwIDEwIDYgMjEuNXQxNSAxNi41bDM1OCAyMzBxOSA2IDE1IDE3LjV0NiAyMS41djc1cS0zOCAwIC02OSA1MiB0LTMxIDk4djI1MHEwIDEyNCA4OCAyMTJ0MjEyIDg4eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMDk7IiBkPSJNMjUgMTEwMGgxMTUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMTA1MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTExNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYxMDUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTEwMCAxMDAwdi0xMDBoMTAwdjEwMGgtMTAwek04NzUgMTAwMGgtNTUwcS0xMCAwIC0xNy41IC03LjV0LTcuNSAtMTcuNXYtMzUwcTAgLTEwIDcuNSAtMTcuNXQxNy41IC03LjVoNTUwIHExMCAwIDE3LjUgNy41dDcuNSAxNy41djM1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjV6TTEwMDAgMTAwMHYtMTAwaDEwMHYxMDBoLTEwMHpNMTAwIDgwMHYtMTAwaDEwMHYxMDBoLTEwMHpNMTAwMCA4MDB2LTEwMGgxMDB2MTAwaC0xMDB6TTEwMCA2MDB2LTEwMGgxMDB2MTAwaC0xMDB6TTEwMDAgNjAwdi0xMDBoMTAwdjEwMGgtMTAwek04NzUgNTAwaC01NTBxLTEwIDAgLTE3LjUgLTcuNXQtNy41IC0xNy41di0zNTBxMCAtMTAgNy41IC0xNy41IHQxNy41IC03LjVoNTUwcTEwIDAgMTcuNSA3LjV0Ny41IDE3LjV2MzUwcTAgMTAgLTcuNSAxNy41dC0xNy41IDcuNXpNMTAwIDQwMHYtMTAwaDEwMHYxMDBoLTEwMHpNMTAwMCA0MDB2LTEwMGgxMDB2MTAwaC0xMDB6TTEwMCAyMDB2LTEwMGgxMDB2MTAwaC0xMDB6TTEwMDAgMjAwdi0xMDBoMTAwdjEwMGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMTA7IiBkPSJNNTAgMTEwMGg0MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNDAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY0MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek02NTAgMTEwMGg0MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNDAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY0MDAgcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNTAgNTAwaDQwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djQwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTY1MCA1MDBoNDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTQwMCBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djQwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAxMTsiIGQ9Ik01MCAxMTAwaDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTQ1MCAxMTAwaDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMCBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek04NTAgMTEwMGgyMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTIwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMjAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek01MCA3MDBoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMCBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTQ1MCA3MDBoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MjAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNODUwIDcwMGgyMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTIwMCBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MjAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNTAgMzAwaDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTQ1MCAzMDBoMjAwIHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTg1MCAzMDBoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MjAwcTAgMjEgMTQuNSAzNS41IHQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAxMjsiIGQ9Ik01MCAxMTAwaDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTQ1MCAxMTAwaDcwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC03MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMCBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek01MCA3MDBoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MjAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNDUwIDcwMGg3MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTIwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNzAwIHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MjAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNTAgMzAwaDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTQ1MCAzMDBoNzAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDAgcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC03MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAxMzsiIGQ9Ik00NjUgNDc3bDU3MSA1NzFxOCA4IDE4IDh0MTcgLThsMTc3IC0xNzdxOCAtNyA4IC0xN3QtOCAtMThsLTc4MyAtNzg0cS03IC04IC0xNy41IC04dC0xNy41IDhsLTM4NCAzODRxLTggOCAtOCAxOHQ4IDE3bDE3NyAxNzdxNyA4IDE3IDh0MTggLThsMTcxIC0xNzFxNyAtNyAxOCAtN3QxOCA3eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMTQ7IiBkPSJNOTA0IDEwODNsMTc4IC0xNzlxOCAtOCA4IC0xOC41dC04IC0xNy41bC0yNjcgLTI2OGwyNjcgLTI2OHE4IC03IDggLTE3LjV0LTggLTE4LjVsLTE3OCAtMTc4cS04IC04IC0xOC41IC04dC0xNy41IDhsLTI2OCAyNjdsLTI2OCAtMjY3cS03IC04IC0xNy41IC04dC0xOC41IDhsLTE3OCAxNzhxLTggOCAtOCAxOC41dDggMTcuNWwyNjcgMjY4bC0yNjcgMjY4cS04IDcgLTggMTcuNXQ4IDE4LjVsMTc4IDE3OHE4IDggMTguNSA4dDE3LjUgLTggbDI2OCAtMjY3bDI2OCAyNjhxNyA3IDE3LjUgN3QxOC41IC03eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMTU7IiBkPSJNNTA3IDExNzdxOTggMCAxODcuNSAtMzguNXQxNTQuNSAtMTAzLjV0MTAzLjUgLTE1NC41dDM4LjUgLTE4Ny41cTAgLTE0MSAtNzggLTI2MmwzMDAgLTI5OXE4IC04IDggLTE4LjV0LTggLTE4LjVsLTEwOSAtMTA4cS03IC04IC0xNy41IC04dC0xOC41IDhsLTMwMCAyOTlxLTExOSAtNzcgLTI2MSAtNzdxLTk4IDAgLTE4OCAzOC41dC0xNTQuNSAxMDN0LTEwMyAxNTQuNXQtMzguNSAxODh0MzguNSAxODcuNXQxMDMgMTU0LjUgdDE1NC41IDEwMy41dDE4OCAzOC41ek01MDYuNSAxMDIzcS04OS41IDAgLTE2NS41IC00NHQtMTIwIC0xMjAuNXQtNDQgLTE2NnQ0NCAtMTY1LjV0MTIwIC0xMjB0MTY1LjUgLTQ0dDE2NiA0NHQxMjAuNSAxMjB0NDQgMTY1LjV0LTQ0IDE2NnQtMTIwLjUgMTIwLjV0LTE2NiA0NHpNNDI1IDkwMGgxNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di03NWg3NXExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTE1MHEwIC0xMCAtNy41IC0xNy41IHQtMTcuNSAtNy41aC03NXYtNzVxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0xNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY3NWgtNzVxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYxNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg3NXY3NXEwIDEwIDcuNSAxNy41dDE3LjUgNy41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMTY7IiBkPSJNNTA3IDExNzdxOTggMCAxODcuNSAtMzguNXQxNTQuNSAtMTAzLjV0MTAzLjUgLTE1NC41dDM4LjUgLTE4Ny41cTAgLTE0MSAtNzggLTI2MmwzMDAgLTI5OXE4IC04IDggLTE4LjV0LTggLTE4LjVsLTEwOSAtMTA4cS03IC04IC0xNy41IC04dC0xOC41IDhsLTMwMCAyOTlxLTExOSAtNzcgLTI2MSAtNzdxLTk4IDAgLTE4OCAzOC41dC0xNTQuNSAxMDN0LTEwMyAxNTQuNXQtMzguNSAxODh0MzguNSAxODcuNXQxMDMgMTU0LjUgdDE1NC41IDEwMy41dDE4OCAzOC41ek01MDYuNSAxMDIzcS04OS41IDAgLTE2NS41IC00NHQtMTIwIC0xMjAuNXQtNDQgLTE2NnQ0NCAtMTY1LjV0MTIwIC0xMjB0MTY1LjUgLTQ0dDE2NiA0NHQxMjAuNSAxMjB0NDQgMTY1LjV0LTQ0IDE2NnQtMTIwLjUgMTIwLjV0LTE2NiA0NHpNMzI1IDgwMGgzNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0zNTBxLTEwIDAgLTE3LjUgNy41IHQtNy41IDE3LjV2MTUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAxNzsiIGQ9Ik01NTAgMTIwMGgxMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY0MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek04MDAgOTc1djE2NnExNjcgLTYyIDI3MiAtMjA5LjV0MTA1IC0zMzEuNXEwIC0xMTcgLTQ1LjUgLTIyNHQtMTIzIC0xODQuNXQtMTg0LjUgLTEyM3QtMjI0IC00NS41dC0yMjQgNDUuNSB0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHEwIDE4NCAxMDUgMzMxLjV0MjcyIDIwOS41di0xNjZxLTEwMyAtNTUgLTE2NSAtMTU1dC02MiAtMjIwcTAgLTExNiA1NyAtMjE0LjV0MTU1LjUgLTE1NS41dDIxNC41IC01N3QyMTQuNSA1N3QxNTUuNSAxNTUuNXQ1NyAyMTQuNXEwIDEyMCAtNjIgMjIwdC0xNjUgMTU1eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMTg7IiBkPSJNMTAyNSAxMjAwaDE1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTExNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0xNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYxMTUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTcyNSA4MDBoMTUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtNzUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMTUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2NzUwIHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek00MjUgNTAwaDE1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTQ1MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTE1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djQ1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek0xMjUgMzAwaDE1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTI1MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTE1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41IHYyNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXoiIC8%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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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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMyOyIgZD0iTTEyNSAxMjAwaDEwNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xMTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMTA1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djExNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNMTA3NSAxMDAwaC04NTBxLTEwIDAgLTE3LjUgLTcuNXQtNy41IC0xNy41di04NTBxMCAtMTAgNy41IC0xNy41dDE3LjUgLTcuNWg4NTBxMTAgMCAxNy41IDcuNXQ3LjUgMTcuNXY4NTAgcTAgMTAgLTcuNSAxNy41dC0xNy41IDcuNXpNMzI1IDkwMGg1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek01MjUgOTAwaDQ1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNDUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2NTAgcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTMyNSA3MDBoNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di01MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2NTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNNTI1IDcwMGg0NTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di01MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTQ1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djUwIHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek0zMjUgNTAwaDUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC01MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTUyNSA1MDBoNDUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC00NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1MCBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNMzI1IDMwMGg1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek01MjUgMzAwaDQ1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNDUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2NTAgcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzMzsiIGQ9Ik05MDAgODAwdjIwMHEwIDgzIC01OC41IDE0MS41dC0xNDEuNSA1OC41aC0zMDBxLTgyIDAgLTE0MSAtNTl0LTU5IC0xNDF2LTIwMGgtMTAwcS00MSAwIC03MC41IC0yOS41dC0yOS41IC03MC41di02MDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDkwMHE0MSAwIDcwLjUgMjkuNXQyOS41IDcwLjV2NjAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41aC0xMDB6TTQwMCA4MDB2MTUwcTAgMjEgMTUgMzUuNXQzNSAxNC41aDIwMCBxMjAgMCAzNSAtMTQuNXQxNSAtMzUuNXYtMTUwaC0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNDsiIGQ9Ik0xMjUgMTEwMGg1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTEwNzVoLTEwMHYxMDc1cTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTEwNzUgMTA1MnE0IDAgOSAtMnExNiAtNiAxNiAtMjN2LTQyMXEwIC02IC0zIC0xMnEtMzMgLTU5IC02Ni41IC05OXQtNjUuNSAtNTh0LTU2LjUgLTI0LjV0LTUyLjUgLTYuNXEtMjYgMCAtNTcuNSA2LjV0LTUyLjUgMTMuNXQtNjAgMjFxLTQxIDE1IC02MyAyMi41dC01Ny41IDE1dC02NS41IDcuNSBxLTg1IDAgLTE2MCAtNTdxLTcgLTUgLTE1IC01cS02IDAgLTExIDNxLTE0IDcgLTE0IDIydjQzOHEyMiA1NSA4MiA5OC41dDExOSA0Ni41cTIzIDIgNDMgMC41dDQzIC03dDMyLjUgLTguNXQzOCAtMTN0MzIuNSAtMTFxNDEgLTE0IDYzLjUgLTIxdDU3IC0xNHQ2My41IC03cTEwMyAwIDE4MyA4N3E3IDggMTggOHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%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%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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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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDgzOyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek03NDEuNSA5MTNxLTEyLjUgMCAtMjEuNSAtOWwtMTIwIC0xMjBsLTEyMCAxMjBxLTkgOSAtMjEuNSA5IHQtMjEuNSAtOWwtMTQxIC0xNDFxLTkgLTkgLTkgLTIxLjV0OSAtMjEuNWwxMjAgLTEyMGwtMTIwIC0xMjBxLTkgLTkgLTkgLTIxLjV0OSAtMjEuNWwxNDEgLTE0MXE5IC05IDIxLjUgLTl0MjEuNSA5bDEyMCAxMjBsMTIwIC0xMjBxOSAtOSAyMS41IC05dDIxLjUgOWwxNDEgMTQxcTkgOSA5IDIxLjV0LTkgMjEuNWwtMTIwIDEyMGwxMjAgMTIwcTkgOSA5IDIxLjV0LTkgMjEuNWwtMTQxIDE0MXEtOSA5IC0yMS41IDl6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4NDsiIGQ9Ik02MDAgMTE3N3ExMTcgMCAyMjQgLTQ1LjV0MTg0LjUgLTEyM3QxMjMgLTE4NC41dDQ1LjUgLTIyNHQtNDUuNSAtMjI0dC0xMjMgLTE4NC41dC0xODQuNSAtMTIzdC0yMjQgLTQ1LjV0LTIyNCA0NS41dC0xODQuNSAxMjN0LTEyMyAxODQuNXQtNDUuNSAyMjR0NDUuNSAyMjR0MTIzIDE4NC41dDE4NC41IDEyM3QyMjQgNDUuNXpNNTQ2IDYyM2wtODQgODVxLTcgNyAtMTcuNSA3dC0xOC41IC03bC0xMzkgLTEzOXEtNyAtOCAtNyAtMTh0NyAtMTggbDI0MiAtMjQxcTcgLTggMTcuNSAtOHQxNy41IDhsMzc1IDM3NXE3IDcgNyAxNy41dC03IDE4LjVsLTEzOSAxMzlxLTcgNyAtMTcuNSA3dC0xNy41IC03eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODU7IiBkPSJNNjAwIDExNzdxMTE3IDAgMjI0IC00NS41dDE4NC41IC0xMjN0MTIzIC0xODQuNXQ0NS41IC0yMjR0LTQ1LjUgLTIyNHQtMTIzIC0xODQuNXQtMTg0LjUgLTEyM3QtMjI0IC00NS41dC0yMjQgNDUuNXQtMTg0LjUgMTIzdC0xMjMgMTg0LjV0LTQ1LjUgMjI0dDQ1LjUgMjI0dDEyMyAxODQuNXQxODQuNSAxMjN0MjI0IDQ1LjV6TTU4OCA5NDFxLTI5IDAgLTU5IC01LjV0LTYzIC0yMC41dC01OCAtMzguNXQtNDEuNSAtNjN0LTE2LjUgLTg5LjUgcTAgLTI1IDIwIC0yNWgxMzFxMzAgLTUgMzUgMTFxNiAyMCAyMC41IDI4dDQ1LjUgOHEyMCAwIDMxLjUgLTEwLjV0MTEuNSAtMjguNXEwIC0yMyAtNyAtMzR0LTI2IC0xOHEtMSAwIC0xMy41IC00dC0xOS41IC03LjV0LTIwIC0xMC41dC0yMiAtMTd0LTE4LjUgLTI0dC0xNS41IC0zNXQtOCAtNDZxLTEgLTggNS41IC0xNi41dDIwLjUgLTguNWgxNzNxNyAwIDIyIDh0MzUgMjh0MzcuNSA0OHQyOS41IDc0dDEyIDEwMHEwIDQ3IC0xNyA4MyB0LTQyLjUgNTd0LTU5LjUgMzQuNXQtNjQgMTh0LTU5IDQuNXpNNjc1IDQwMGgtMTUwcS0xMCAwIC0xNy41IC03LjV0LTcuNSAtMTcuNXYtMTUwcTAgLTEwIDcuNSAtMTcuNXQxNy41IC03LjVoMTUwcTEwIDAgMTcuNSA3LjV0Ny41IDE3LjV2MTUwcTAgMTAgLTcuNSAxNy41dC0xNy41IDcuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDg4OyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek02MDAgMTAyN3EtMTE2IDAgLTIxNC41IC01N3QtMTU1LjUgLTE1NS41dC01NyAtMjE0LjV0NTcgLTIxNC41IHQxNTUuNSAtMTU1LjV0MjE0LjUgLTU3dDIxNC41IDU3dDE1NS41IDE1NS41dDU3IDIxNC41dC01NyAyMTQuNXQtMTU1LjUgMTU1LjV0LTIxNC41IDU3ek03NTkgODIzbDY0IC02NHE3IC03IDcgLTE3LjV0LTcgLTE3LjVsLTEyNCAtMTI0bDEyNCAtMTI0cTcgLTcgNyAtMTcuNXQtNyAtMTcuNWwtNjQgLTY0cS03IC03IC0xNy41IC03dC0xNy41IDdsLTEyNCAxMjRsLTEyNCAtMTI0cS03IC03IC0xNy41IC03dC0xNy41IDdsLTY0IDY0IHEtNyA3IC03IDE3LjV0NyAxNy41bDEyNCAxMjRsLTEyNCAxMjRxLTcgNyAtNyAxNy41dDcgMTcuNWw2NCA2NHE3IDcgMTcuNSA3dDE3LjUgLTdsMTI0IC0xMjRsMTI0IDEyNHE3IDcgMTcuNSA3dDE3LjUgLTd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4OTsiIGQ9Ik02MDAgMTE3N3ExMTcgMCAyMjQgLTQ1LjV0MTg0LjUgLTEyM3QxMjMgLTE4NC41dDQ1LjUgLTIyNHQtNDUuNSAtMjI0dC0xMjMgLTE4NC41dC0xODQuNSAtMTIzdC0yMjQgLTQ1LjV0LTIyNCA0NS41dC0xODQuNSAxMjN0LTEyMyAxODQuNXQtNDUuNSAyMjR0NDUuNSAyMjR0MTIzIDE4NC41dDE4NC41IDEyM3QyMjQgNDUuNXpNNjAwIDEwMjdxLTExNiAwIC0yMTQuNSAtNTd0LTE1NS41IC0xNTUuNXQtNTcgLTIxNC41dDU3IC0yMTQuNSB0MTU1LjUgLTE1NS41dDIxNC41IC01N3QyMTQuNSA1N3QxNTUuNSAxNTUuNXQ1NyAyMTQuNXQtNTcgMjE0LjV0LTE1NS41IDE1NS41dC0yMTQuNSA1N3pNNzgyIDc4OGwxMDYgLTEwNnE3IC03IDcgLTE3LjV0LTcgLTE3LjVsLTMyMCAtMzIxcS04IC03IC0xOCAtN3QtMTggN2wtMjAyIDIwM3EtOCA3IC04IDE3LjV0OCAxNy41bDEwNiAxMDZxNyA4IDE3LjUgOHQxNy41IC04bDc5IC03OWwxOTcgMTk3cTcgNyAxNy41IDd0MTcuNSAtN3oiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkwOyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek02MDAgMTAyN3EtMTE2IDAgLTIxNC41IC01N3QtMTU1LjUgLTE1NS41dC01NyAtMjE0LjVxMCAtMTIwIDY1IC0yMjUgbDU4NyA1ODdxLTEwNSA2NSAtMjI1IDY1ek05NjUgODE5bC01ODQgLTU4NHExMDQgLTYyIDIxOSAtNjJxMTE2IDAgMjE0LjUgNTd0MTU1LjUgMTU1LjV0NTcgMjE0LjVxMCAxMTUgLTYyIDIxOXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTAyOyIgZD0iTTI2MCAxMjAwcTkgMCAxOSAtMnQxNSAtNGw1IC0ycTIyIC0xMCA0NCAtMjNsMTk2IC0xMThxMjEgLTEzIDM2IC0yNHEyOSAtMjEgMzcgLTEycTExIDEzIDQ5IDM1bDE5NiAxMThxMjIgMTMgNDUgMjNxMTcgNyAzOCA3cTIzIDAgNDcgLTE2LjV0MzcgLTMzLjVsMTMgLTE2cTE0IC0yMSAxOCAtNDVsMjUgLTEyM2w4IC00NHExIC05IDguNSAtMTQuNXQxNy41IC01LjVoNjFxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di01MCBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC01MHEtMTAgMCAtMTcuNSAtNy41dC03LjUgLTE3LjV2LTE3NWgtNDAwdjMwMGgtMjAwdi0zMDBoLTQwMHYxNzVxMCAxMCAtNy41IDE3LjV0LTE3LjUgNy41aC01MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjVoNjFxMTEgMCAxOCAzdDcgOHEwIDQgOSA1MmwyNSAxMjhxNSAyNSAxOSA0NXEyIDMgNSA3dDEzLjUgMTV0MjEuNSAxOS41dDI2LjUgMTUuNSB0MjkuNSA3ek05MTUgMTA3OWwtMTY2IC0xNjJxLTcgLTcgLTUgLTEydDEyIC01aDIxOXExMCAwIDE1IDd0MiAxN2wtNTEgMTQ5cS0zIDEwIC0xMSAxMnQtMTUgLTZ6TTQ2MyA5MTdsLTE3NyAxNTdxLTggNyAtMTYgNXQtMTEgLTEybC01MSAtMTQzcS0zIC0xMCAyIC0xN3QxNSAtN2gyMzFxMTEgMCAxMi41IDV0LTUuNSAxMnpNNTAwIDBoLTM3NXEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djM3NWg0MDB2LTQwMHpNMTEwMCA0MDB2LTM3NSBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0zNzV2NDAwaDQwMHoiIC8%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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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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUzOyIgZD0iTTQwMCAzMDBoMTUwcTIxIDAgMjUgLTExdC0xMCAtMjVsLTIzMCAtMjUwcS0xNCAtMTUgLTM1IC0xNXQtMzUgMTVsLTIzMCAyNTBxLTE0IDE0IC0xMCAyNXQyNSAxMWgxNTB2OTAwaDIwMHYtOTAwek0xMDAwIDcwMGgtMTAwdjQwMGgtMTAwdjEwMGgyMDB2LTUwMHpNMTEwMCAwaC0xMDB2MTAwaC0yMDB2NDAwaDMwMHYtNTAwek05MDEgNDAwdi0yMDBoMTAwdjIwMGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTQ7IiBkPSJNNDAwIDMwMGgxNTBxMjEgMCAyNSAtMTF0LTEwIC0yNWwtMjMwIC0yNTBxLTE0IC0xNSAtMzUgLTE1dC0zNSAxNWwtMjMwIDI1MHEtMTQgMTQgLTEwIDI1dDI1IDExaDE1MHY5MDBoMjAwdi05MDB6TTExMDAgNzAwaC0xMDB2MTAwaC0yMDB2NDAwaDMwMHYtNTAwek05MDEgMTEwMHYtMjAwaDEwMHYyMDBoLTEwMHpNMTAwMCAwaC0xMDB2NDAwaC0xMDB2MTAwaDIwMHYtNTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTU7IiBkPSJNNDAwIDMwMGgxNTBxMjEgMCAyNSAtMTF0LTEwIC0yNWwtMjMwIC0yNTBxLTE0IC0xNSAtMzUgLTE1dC0zNSAxNWwtMjMwIDI1MHEtMTQgMTQgLTEwIDI1dDI1IDExaDE1MHY5MDBoMjAwdi05MDB6TTkwMCAxMDAwaC0yMDB2MjAwaDIwMHYtMjAwek0xMDAwIDcwMGgtMzAwdjIwMGgzMDB2LTIwMHpNMTEwMCA0MDBoLTQwMHYyMDBoNDAwdi0yMDB6TTEyMDAgMTAwaC01MDB2MjAwaDUwMHYtMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTY7IiBkPSJNNDAwIDMwMGgxNTBxMjEgMCAyNSAtMTF0LTEwIC0yNWwtMjMwIC0yNTBxLTE0IC0xNSAtMzUgLTE1dC0zNSAxNWwtMjMwIDI1MHEtMTQgMTQgLTEwIDI1dDI1IDExaDE1MHY5MDBoMjAwdi05MDB6TTEyMDAgMTAwMGgtNTAwdjIwMGg1MDB2LTIwMHpNMTEwMCA3MDBoLTQwMHYyMDBoNDAwdi0yMDB6TTEwMDAgNDAwaC0zMDB2MjAwaDMwMHYtMjAwek05MDAgMTAwaC0yMDB2MjAwaDIwMHYtMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTc7IiBkPSJNMzUwIDExMDBoNDAwcTE2MiAwIDI1NiAtOTMuNXQ5NCAtMjU2LjV2LTQwMHEwIC0xNjUgLTkzLjUgLTI1Ny41dC0yNTYuNSAtOTIuNWgtNDAwcS0xNjUgMCAtMjU3LjUgOTIuNXQtOTIuNSAyNTcuNXY0MDBxMCAxNjUgOTIuNSAyNTcuNXQyNTcuNSA5Mi41ek04MDAgOTAwaC01MDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoNTAwcTQxIDAgNzAuNSAyOS41dDI5LjUgNzAuNSB2NTAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTg7IiBkPSJNMzUwIDExMDBoNDAwcTE2NSAwIDI1Ny41IC05Mi41dDkyLjUgLTI1Ny41di00MDBxMCAtMTY1IC05Mi41IC0yNTcuNXQtMjU3LjUgLTkyLjVoLTQwMHEtMTYzIDAgLTI1Ni41IDkyLjV0LTkzLjUgMjU3LjV2NDAwcTAgMTYzIDk0IDI1Ni41dDI1NiA5My41ek04MDAgOTAwaC01MDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoNTAwcTQxIDAgNzAuNSAyOS41dDI5LjUgNzAuNSB2NTAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41ek00NDAgNzcwbDI1MyAtMTkwcTE3IC0xMiAxNyAtMzB0LTE3IC0zMGwtMjUzIC0xOTBxLTE2IC0xMiAtMjggLTYuNXQtMTIgMjYuNXY0MDBxMCAyMSAxMiAyNi41dDI4IC02LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1OTsiIGQ9Ik0zNTAgMTEwMGg0MDBxMTYzIDAgMjU2LjUgLTk0dDkzLjUgLTI1NnYtNDAwcTAgLTE2NSAtOTIuNSAtMjU3LjV0LTI1Ny41IC05Mi41aC00MDBxLTE2NSAwIC0yNTcuNSA5Mi41dC05Mi41IDI1Ny41djQwMHEwIDE2MyA5Mi41IDI1Ni41dDI1Ny41IDkzLjV6TTgwMCA5MDBoLTUwMHEtNDEgMCAtNzAuNSAtMjkuNXQtMjkuNSAtNzAuNXYtNTAwcTAgLTQxIDI5LjUgLTcwLjV0NzAuNSAtMjkuNWg1MDBxNDEgMCA3MC41IDI5LjV0MjkuNSA3MC41IHY1MDBxMCA0MSAtMjkuNSA3MC41dC03MC41IDI5LjV6TTM1MCA3MDBoNDAwcTIxIDAgMjYuNSAtMTJ0LTYuNSAtMjhsLTE5MCAtMjUzcS0xMiAtMTcgLTMwIC0xN3QtMzAgMTdsLTE5MCAyNTNxLTEyIDE2IC02LjUgMjh0MjYuNSAxMnoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY1OyIgZD0iTTYwMCAxMTg2cTExOSAwIDIyNy41IC00Ni41dDE4NyAtMTI1dDEyNSAtMTg3dDQ2LjUgLTIyNy41dC00Ni41IC0yMjcuNXQtMTI1IC0xODd0LTE4NyAtMTI1dC0yMjcuNSAtNDYuNXQtMjI3LjUgNDYuNXQtMTg3IDEyNXQtMTI1IDE4N3QtNDYuNSAyMjcuNXQ0Ni41IDIyNy41dDEyNSAxODd0MTg3IDEyNXQyMjcuNSA0Ni41ek02MDAgMTAyMnEtMTE1IDAgLTIxMiAtNTYuNXQtMTUzLjUgLTE1My41dC01Ni41IC0yMTJ0NTYuNSAtMjEyIHQxNTMuNSAtMTUzLjV0MjEyIC01Ni41dDIxMiA1Ni41dDE1My41IDE1My41dDU2LjUgMjEydC01Ni41IDIxMnQtMTUzLjUgMTUzLjV0LTIxMiA1Ni41ek02MDAgNzk0cTgwIDAgMTM3IC01N3Q1NyAtMTM3dC01NyAtMTM3dC0xMzcgLTU3dC0xMzcgNTd0LTU3IDEzN3Q1NyAxMzd0MTM3IDU3eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNjY7IiBkPSJNNDUwIDEyMDBoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0zNTBoMjQ1cTIwIDAgMjUgLTExdC05IC0yNmwtMzgzIC00MjZxLTE0IC0xNSAtMzMuNSAtMTV0LTMyLjUgMTVsLTM3OSA0MjZxLTEzIDE1IC04LjUgMjZ0MjUuNSAxMWgyNTB2MzUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNTAgMzAwaDEwMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTI1MGgtMTEwMHYyNTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41eiBNOTAwIDIwMHYtNTBoMTAwdjUwaC0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE2NzsiIGQ9Ik01ODMgMTE4MmwzNzggLTQzNXExNCAtMTUgOSAtMzF0LTI2IC0xNmgtMjQ0di0yNTBxMCAtMjAgLTE3IC0zNXQtMzkgLTE1aC0yMDBxLTIwIDAgLTMyIDE0LjV0LTEyIDM1LjV2MjUwaC0yNTBxLTIwIDAgLTI1LjUgMTYuNXQ4LjUgMzEuNWwzODMgNDMxcTE0IDE2IDMzLjUgMTd0MzMuNSAtMTR6TTUwIDMwMGgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yNTBoLTExMDB2MjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXogTTkwMCAyMDB2LTUwaDEwMHY1MGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNjg7IiBkPSJNMzk2IDcyM2wzNjkgMzY5cTcgNyAxNy41IDd0MTcuNSAtN2wxMzkgLTEzOXE3IC04IDcgLTE4LjV0LTcgLTE3LjVsLTUyNSAtNTI1cS03IC04IC0xNy41IC04dC0xNy41IDhsLTI5MiAyOTFxLTcgOCAtNyAxOHQ3IDE4bDEzOSAxMzlxOCA3IDE4LjUgN3QxNy41IC03ek01MCAzMDBoMTAwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjUwaC0xMTAwdjI1MHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTkwMCAyMDB2LTUwaDEwMHY1MCBoLTEwMHoiIC8%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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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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTk5OyIgZD0iTTcwMCAxMDB2MTAwaDQwMGwtMjcwIDMwMGgxNzBsLTI3MCAzMDBoMTcwbC0zMDAgMzMzbC0zMDAgLTMzM2gxNzBsLTI3MCAtMzAwaDE3MGwtMjcwIC0zMDBoNDAwdi0xMDBoLTUwcS0yMSAwIC0zNS41IC0xNC41dC0xNC41IC0zNS41di01MGg0MDB2NTBxMCAyMSAtMTQuNSAzNS41dC0zNS41IDE0LjVoLTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMDA7IiBkPSJNNjAwIDExNzlxOTQgMCAxNjcuNSAtNTYuNXQ5OS41IC0xNDUuNXE4OSAtNiAxNTAuNSAtNzEuNXQ2MS41IC0xNTUuNXEwIC02MSAtMjkuNSAtMTEyLjV0LTc5LjUgLTgyLjVxOSAtMjkgOSAtNTVxMCAtNzQgLTUyLjUgLTEyNi41dC0xMjYuNSAtNTIuNXEtNTUgMCAtMTAwIDMwdi0yNTFxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTUwaC0zMDB2NTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41djI1MXEtNDUgLTMwIC0xMDAgLTMwIHEtNzQgMCAtMTI2LjUgNTIuNXQtNTIuNSAxMjYuNXEwIDE4IDQgMzhxLTQ3IDIxIC03NS41IDY1dC0yOC41IDk3cTAgNzQgNTIuNSAxMjYuNXQxMjYuNSA1Mi41cTUgMCAyMyAtMnEwIDIgLTEgMTB0LTEgMTNxMCAxMTYgODEuNSAxOTcuNXQxOTcuNSA4MS41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMDE7IiBkPSJNMTAxMCAxMDEwcTExMSAtMTExIDE1MC41IC0yNjAuNXQwIC0yOTl0LTE1MC41IC0yNjAuNXEtODMgLTgzIC0xOTEuNSAtMTI2LjV0LTIxOC41IC00My41dC0yMTguNSA0My41dC0xOTEuNSAxMjYuNXEtMTExIDExMSAtMTUwLjUgMjYwLjV0MCAyOTl0MTUwLjUgMjYwLjVxODMgODMgMTkxLjUgMTI2LjV0MjE4LjUgNDMuNXQyMTguNSAtNDMuNXQxOTEuNSAtMTI2LjV6TTQ3NiAxMDY1cS00IDAgLTggLTFxLTEyMSAtMzQgLTIwOS41IC0xMjIuNSB0LTEyMi41IC0yMDkuNXEtNCAtMTIgMi41IC0yM3QxOC41IC0xNGwzNiAtOXEzIC0xIDcgLTFxMjMgMCAyOSAyMnEyNyA5NiA5OCAxNjZxNzAgNzEgMTY2IDk4cTExIDMgMTcuNSAxMy41dDMuNSAyMi41bC05IDM1cS0zIDEzIC0xNCAxOXEtNyA0IC0xNSA0ek01MTIgOTIwcS00IDAgLTkgLTJxLTgwIC0yNCAtMTM4LjUgLTgyLjV0LTgyLjUgLTEzOC41cS00IC0xMyAyIC0yNHQxOSAtMTRsMzQgLTlxNCAtMSA4IC0xcTIyIDAgMjggMjEgcTE4IDU4IDU4LjUgOTguNXQ5Ny41IDU4LjVxMTIgMyAxOCAxMy41dDMgMjEuNWwtOSAzNXEtMyAxMiAtMTQgMTlxLTcgNCAtMTUgNHpNNzE5LjUgNzE5LjVxLTQ5LjUgNDkuNSAtMTE5LjUgNDkuNXQtMTE5LjUgLTQ5LjV0LTQ5LjUgLTExOS41dDQ5LjUgLTExOS41dDExOS41IC00OS41dDExOS41IDQ5LjV0NDkuNSAxMTkuNXQtNDkuNSAxMTkuNXpNODU1IDU1MXEtMjIgMCAtMjggLTIxcS0xOCAtNTggLTU4LjUgLTk4LjV0LTk4LjUgLTU3LjUgcS0xMSAtNCAtMTcgLTE0LjV0LTMgLTIxLjVsOSAtMzVxMyAtMTIgMTQgLTE5cTcgLTQgMTUgLTRxNCAwIDkgMnE4MCAyNCAxMzguNSA4Mi41dDgyLjUgMTM4LjVxNCAxMyAtMi41IDI0dC0xOC41IDE0bC0zNCA5cS00IDEgLTggMXpNMTAwMCA1MTVxLTIzIDAgLTI5IC0yMnEtMjcgLTk2IC05OCAtMTY2cS03MCAtNzEgLTE2NiAtOThxLTExIC0zIC0xNy41IC0xMy41dC0zLjUgLTIyLjVsOSAtMzVxMyAtMTMgMTQgLTE5cTcgLTQgMTUgLTQgcTQgMCA4IDFxMTIxIDM0IDIwOS41IDEyMi41dDEyMi41IDIwOS41cTQgMTIgLTIuNSAyM3QtMTguNSAxNGwtMzYgOXEtMyAxIC03IDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIwMjsiIGQ9Ik03MDAgODAwaDMwMHYtMzgwaC0xODB2MjAwaC0zNDB2LTIwMGgtMzgwdjc1NXEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDU3NXYtNDAwek0xMDAwIDkwMGgtMjAwdjIwMHpNNzAwIDMwMGgxNjJsLTIxMiAtMjEybC0yMTIgMjEyaDE2MnYyMDBoMTAwdi0yMDB6TTUyMCAwaC0zOTVxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzOTV6TTEwMDAgMjIwdi0xOTVxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0xOTV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIwMzsiIGQ9Ik03MDAgODAwaDMwMHYtNTIwbC0zNTAgMzUwbC01NTAgLTU1MHYxMDk1cTAgMTAgNy41IDE3LjV0MTcuNSA3LjVoNTc1di00MDB6TTEwMDAgOTAwaC0yMDB2MjAwek04NjIgMjAwaC0xNjJ2LTIwMGgtMTAwdjIwMGgtMTYybDIxMiAyMTJ6TTQ4MCAwaC0zNTVxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1NWgzODB2LTgwek0xMDAwIDgwdi01NXEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTE1NXY4MGgxODB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIwNDsiIGQ9Ik0xMTYyIDgwMGgtMTYydi0yMDBoMTAwbDEwMCAtMTAwaC0zMDB2MzAwaC0xNjJsMjEyIDIxMnpNMjAwIDgwMGgyMDBxMjcgMCA0MCAtMnQyOS41IC0xMC41dDIzLjUgLTMwdDcgLTU3LjVoMzAwdi0xMDBoLTYwMGwtMjAwIC0zNTB2NDUwaDEwMHEwIDM2IDcgNTcuNXQyMy41IDMwdDI5LjUgMTAuNXQ0MCAyek04MDAgNDAwaDI0MGwtMjQwIC00MDBoLTgwMGwzMDAgNTAwaDUwMHYtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMDU7IiBkPSJNNjUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGg1MHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMTAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djEwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoNTB2NTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek0xMDAwIDg1MHYxNTBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTgwMCBxMCAtNDEgLTI5LjUgLTcwLjV0LTcwLjUgLTI5LjVoLTYwMHEtMSAwIC0yMCA0bDI0NiAyNDZsLTMyNiAzMjZ2MzI0cTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNXYtMTUwcTAgLTYyIDQ0IC0xMDZ0MTA2IC00NGgzMDBxNjIgMCAxMDYgNDR0NDQgMTA2ek00MTIgMjUwbC0yMTIgLTIxMnYxNjJoLTIwMHYxMDBoMjAwdjE2MnoiIC8%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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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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTEwMGgtNDAwdjEwMHE0IDAgMTEgMC41dDI0IDN0MzAgN3QyNCAxNXQxMSAyNC41djU1MGgtMTAwcS0yNSAwIC0zNyAtMC41dC0zMSAtMnQtMjguNSAtNS41dC0yMiAtM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<script src="data:application/x-javascript;base64,/*!
 * Bootstrap v3.3.5 (http://getbootstrap.com)
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<h1 class="title toc-ignore">Analyzing RNA-seq data with DESeq2</h1>
<h4 class="author"><em>Michael I. Love, Simon Anders, and Wolfgang Huber</em></h4>
<h4 class="date"><em>11 November 2017</em></h4>
<div class="abstract">
<p class="abstract">Abstract</p>
<p>A basic task in the analysis of count data from RNA-seq is the detection of differentially expressed genes. The count data are presented as a table which reports, for each sample, the number of sequence fragments that have been assigned to each gene. Analogous data also arise for other assay types, including comparative ChIP-Seq, HiC, shRNA screening, mass spectrometry. An important analysis question is the quantification and statistical inference of systematic changes between conditions, as compared to within-condition variability. The package DESeq2 provides methods to test for differential expression by use of negative binomial generalized linear models; the estimates of dispersion and logarithmic fold changes incorporate data-driven prior distributions This vignette explains the use of the package and demonstrates typical workflows. <a href="http://www.bioconductor.org/help/workflows/rnaseqGene/">An RNA-seq workflow</a> on the Bioconductor website covers similar material to this vignette but at a slower pace, including the generation of count matrices from FASTQ files. DESeq2 package version: 1.18.1</p>
</div>
</div>
<div id="TOC">
<ul>
<li><a href="#standard-workflow">Standard workflow</a><ul>
<li><a href="#quick-start">Quick start</a></li>
<li><a href="#how-to-get-help-for-deseq2">How to get help for DESeq2</a></li>
<li><a href="#input-data">Input data</a><ul>
<li><a href="#why-un-normalized-counts">Why un-normalized counts?</a></li>
<li><a href="#the-deseqdataset">The DESeqDataSet</a></li>
<li><a href="#transcript-abundance-files-and-tximport-input">Transcript abundance files and <em>tximport</em> input</a></li>
<li><a href="#count-matrix-input">Count matrix input</a></li>
<li><a href="#htseq-count-input"><em>htseq-count</em> input</a></li>
<li><a href="#summarizedexperiment-input"><em>SummarizedExperiment</em> input</a></li>
<li><a href="#pre-filtering">Pre-filtering</a></li>
<li><a href="#note-on-factor-levels">Note on factor levels</a></li>
<li><a href="#collapsing-technical-replicates">Collapsing technical replicates</a></li>
<li><a href="#about-the-pasilla-dataset">About the pasilla dataset</a></li>
</ul></li>
<li><a href="#differential-expression-analysis">Differential expression analysis</a></li>
<li><a href="#exploring-and-exporting-results">Exploring and exporting results</a><ul>
<li><a href="#ma-plot">MA-plot</a></li>
<li><a href="#alternative-shrinkage-estimators">Alternative shrinkage estimators</a></li>
<li><a href="#plot-counts">Plot counts</a></li>
<li><a href="#more-information-on-results-columns">More information on results columns</a></li>
<li><a href="#rich-visualization-and-reporting-of-results">Rich visualization and reporting of results</a></li>
<li><a href="#exporting-results-to-csv-files">Exporting results to CSV files</a></li>
</ul></li>
<li><a href="#multi-factor-designs">Multi-factor designs</a></li>
</ul></li>
<li><a href="#data-transformations-and-visualization">Data transformations and visualization</a><ul>
<li><a href="#count-data-transformations">Count data transformations</a><ul>
<li><a href="#blind-dispersion-estimation">Blind dispersion estimation</a></li>
<li><a href="#extracting-transformed-values">Extracting transformed values</a></li>
<li><a href="#variance-stabilizing-transformation">Variance stabilizing transformation</a></li>
<li><a href="#regularized-log-transformation">Regularized log transformation</a></li>
<li><a href="#effects-of-transformations-on-the-variance">Effects of transformations on the variance</a></li>
</ul></li>
<li><a href="#data-quality-assessment-by-sample-clustering-and-visualization">Data quality assessment by sample clustering and visualization</a><ul>
<li><a href="#heatmap-of-the-count-matrix">Heatmap of the count matrix</a></li>
<li><a href="#heatmap-of-the-sample-to-sample-distances">Heatmap of the sample-to-sample distances</a></li>
<li><a href="#principal-component-plot-of-the-samples">Principal component plot of the samples</a></li>
</ul></li>
</ul></li>
<li><a href="#variations-to-the-standard-workflow">Variations to the standard workflow</a><ul>
<li><a href="#wald-test-individual-steps">Wald test individual steps</a></li>
<li><a href="#contrasts">Contrasts</a></li>
<li><a href="#interactions">Interactions</a></li>
<li><a href="#time-series-experiments">Time-series experiments</a></li>
<li><a href="#likelihood-ratio-test">Likelihood ratio test</a></li>
<li><a href="#approach-to-count-outliers">Approach to count outliers</a></li>
<li><a href="#dispersion-plot-and-fitting-alternatives">Dispersion plot and fitting alternatives</a><ul>
<li><a href="#local-or-mean-dispersion-fit">Local or mean dispersion fit</a></li>
<li><a href="#supply-a-custom-dispersion-fit">Supply a custom dispersion fit</a></li>
</ul></li>
<li><a href="#independent-filtering-of-results">Independent filtering of results</a></li>
<li><a href="#tests-of-log2-fold-change-above-or-below-a-threshold">Tests of log2 fold change above or below a threshold</a></li>
<li><a href="#access-to-all-calculated-values">Access to all calculated values</a></li>
<li><a href="#sample-gene-dependent-normalization-factors">Sample-/gene-dependent normalization factors</a></li>
<li><a href="#model-matrix-not-full-rank">“Model matrix not full rank”</a><ul>
<li><a href="#linear-combinations">Linear combinations</a></li>
<li><a href="#group-specific-condition-effects-individuals-nested-within-groups">Group-specific condition effects, individuals nested within groups</a></li>
<li><a href="#levels-without-samples">Levels without samples</a></li>
</ul></li>
</ul></li>
<li><a href="#theory-behind-deseq2">Theory behind DESeq2</a><ul>
<li><a href="#the-deseq2-model">The DESeq2 model</a></li>
<li><a href="#changes-compared-to-deseq">Changes compared to DESeq</a></li>
<li><a href="#methods-changes-since-the-2014-deseq2-paper">Methods changes since the 2014 DESeq2 paper</a></li>
<li><a href="#count-outlier-detection">Count outlier detection</a></li>
<li><a href="#contrasts-1">Contrasts</a></li>
<li><a href="#expanded-model-matrices">Expanded model matrices</a></li>
<li><a href="#independent-filtering-and-multiple-testing">Independent filtering and multiple testing</a><ul>
<li><a href="#filtering-criteria">Filtering criteria</a></li>
<li><a href="#why-does-it-work">Why does it work?</a></li>
</ul></li>
</ul></li>
<li><a href="#frequently-asked-questions">Frequently asked questions</a><ul>
<li><a href="#how-can-i-get-support-for-deseq2">How can I get support for DESeq2?</a></li>
<li><a href="#why-are-some-p-values-set-to-na">Why are some <em>p</em> values set to NA?</a></li>
<li><a href="#how-can-i-get-unfiltered-deseq2-results">How can I get unfiltered DESeq2 results?</a></li>
<li><a href="#how-do-i-use-vst-or-rlog-data-for-differential-testing">How do I use VST or rlog data for differential testing?</a></li>
<li><a href="#can-i-use-deseq2-to-analyze-paired-samples">Can I use DESeq2 to analyze paired samples?</a></li>
<li><a href="#if-i-have-multiple-groups-should-i-run-all-together-or-split-into-pairs-of-groups">If I have multiple groups, should I run all together or split into pairs of groups?</a></li>
<li><a href="#can-i-run-deseq2-to-contrast-the-levels-of-many-groups">Can I run DESeq2 to contrast the levels of many groups?</a></li>
<li><a href="#can-i-use-deseq2-to-analyze-a-dataset-without-replicates">Can I use DESeq2 to analyze a dataset without replicates?</a></li>
<li><a href="#how-can-i-include-a-continuous-covariate-in-the-design-formula">How can I include a continuous covariate in the design formula?</a></li>
<li><a href="#i-ran-a-likelihood-ratio-test-but-results-only-gives-me-one-comparison.">I ran a likelihood ratio test, but results() only gives me one comparison.</a></li>
<li><a href="#what-are-the-exact-steps-performed-by-deseq">What are the exact steps performed by DESeq()?</a></li>
<li><a href="#is-there-an-official-galaxy-tool-for-deseq2">Is there an official Galaxy tool for DESeq2?</a></li>
<li><a href="#i-want-to-benchmark-deseq2-comparing-to-other-de-tools.">I want to benchmark DESeq2 comparing to other DE tools.</a></li>
<li><a href="#i-have-trouble-installing-deseq2-on-ubuntulinux">I have trouble installing DESeq2 on Ubuntu/Linux…</a></li>
</ul></li>
<li><a href="#acknowledgments">Acknowledgments</a></li>
<li><a href="#session-info">Session info</a></li>
<li><a href="#references">References</a></li>
</ul>
</div>
<div id="standard-workflow" class="section level1">
<h1>Standard workflow</h1>
<p><strong>Note:</strong> if you use DESeq2 in published research, please cite:</p>
<blockquote>
<p>Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. <em>Genome Biology</em>, <strong>15</strong>:550. <a href="http://dx.doi.org/10.1186/s13059-014-0550-8">10.1186/s13059-014-0550-8</a></p>
</blockquote>
<p>Other Bioconductor packages with similar aims are <a href="http://bioconductor.org/packages/edgeR">edgeR</a>, <a href="http://bioconductor.org/packages/limma">limma</a>, <a href="http://bioconductor.org/packages/DSS">DSS</a>, <a href="http://bioconductor.org/packages/EBSeq">EBSeq</a>, and <a href="http://bioconductor.org/packages/baySeq">baySeq</a>.</p>
<div id="quick-start" class="section level2">
<h2>Quick start</h2>
<p>Here we show the most basic steps for a differential expression analysis. There are a variety of steps upstream of DESeq2 that result in the generation of counts or estimated counts for each sample, which we will discuss in the sections below. This code chunk assumes that you have a count matrix called <code>cts</code> and a table of sample information called <code>coldata</code>. The <code>design</code> indicates how to model the samples, here, that we want to measure the effect of the condition, controlling for batch differences. The two factor variables <code>batch</code> and <code>condition</code> should be columns of <code>coldata</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds <-<span class="st"> </span><span class="kw">DESeqDataSetFromMatrix</span>(<span class="dt">countData =</span> cts,
<span class="dt">colData =</span> coldata,
<span class="dt">design=</span> ~<span class="st"> </span>batch +<span class="st"> </span>condition)
dds <-<span class="st"> </span><span class="kw">DESeq</span>(dds)
res <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">contrast=</span><span class="kw">c</span>(<span class="st">"condition"</span>,<span class="st">"treat"</span>,<span class="st">"ctrl"</span>))
<span class="kw">resultsNames</span>(dds)
res <-<span class="st"> </span><span class="kw">lfcShrink</span>(dds, <span class="dt">coef=</span><span class="dv">2</span>)</code></pre></div>
<p>The following starting functions will be explained below:</p>
<ul>
<li>If you have transcript quantification files, as produced by <em>Salmon</em>, <em>Sailfish</em>, or <em>kallisto</em>, you would use <em>DESeqDataSetFromTximport</em>.</li>
<li>If you have <em>htseq-count</em> files, the first line would use <em>DESeqDataSetFromHTSeq</em>.</li>
<li>If you have a <em>RangedSummarizedExperiment</em>, the first line would use <em>DESeqDataSet</em>.</li>
</ul>
</div>
<div id="how-to-get-help-for-deseq2" class="section level2">
<h2>How to get help for DESeq2</h2>
<p>Any and all DESeq2 questions should be posted to the <strong>Bioconductor support site</strong>, which serves as a searchable knowledge base of questions and answers:</p>
<p><a href="https://support.bioconductor.org" class="uri">https://support.bioconductor.org</a></p>
<p>Posting a question and tagging with “DESeq2” will automatically send an alert to the package authors to respond on the support site. See the first question in the list of <a href="#FAQ">Frequently Asked Questions</a> (FAQ) for information about how to construct an informative post.</p>
<p>You should <strong>not</strong> email your question to the package authors, as we will just reply that the question should be posted to the <strong>Bioconductor support site</strong>.</p>
</div>
<div id="input-data" class="section level2">
<h2>Input data</h2>
<div id="why-un-normalized-counts" class="section level3">
<h3>Why un-normalized counts?</h3>
<p>As input, the DESeq2 package expects count data as obtained, e.g., from RNA-seq or another high-throughput sequencing experiment, in the form of a matrix of integer values. The value in the <em>i</em>-th row and the <em>j</em>-th column of the matrix tells how many reads can be assigned to gene <em>i</em> in sample <em>j</em>. Analogously, for other types of assays, the rows of the matrix might correspond e.g. to binding regions (with ChIP-Seq) or peptide sequences (with quantitative mass spectrometry). We will list method for obtaining count matrices in sections below.</p>
<p>The values in the matrix should be un-normalized counts or estimated counts of sequencing reads (for single-end RNA-seq) or fragments (for paired-end RNA-seq). The <a href="http://www.bioconductor.org/help/workflows/rnaseqGene/">RNA-seq workflow</a> describes multiple techniques for preparing such count matrices. It is important to provide count matrices as input for DESeq2’s statistical model <span class="citation">(Love, Huber, and Anders 2014)</span> to hold, as only the count values allow assessing the measurement precision correctly. The DESeq2 model internally corrects for library size, so transformed or normalized values such as counts scaled by library size should not be used as input.</p>
</div>
<div id="the-deseqdataset" class="section level3">
<h3>The DESeqDataSet</h3>
<p>The object class used by the DESeq2 package to store the read counts and the intermediate estimated quantities during statistical analysis is the <em>DESeqDataSet</em>, which will usually be represented in the code here as an object <code>dds</code>.</p>
<p>A technical detail is that the <em>DESeqDataSet</em> class extends the <em>RangedSummarizedExperiment</em> class of the <a href="http://bioconductor.org/packages/SummarizedExperiment">SummarizedExperiment</a> package. The “Ranged” part refers to the fact that the rows of the assay data (here, the counts) can be associated with genomic ranges (the exons of genes). This association facilitates downstream exploration of results, making use of other Bioconductor packages’ range-based functionality (e.g. find the closest ChIP-seq peaks to the differentially expressed genes).</p>
<p>A <em>DESeqDataSet</em> object must have an associated <em>design formula</em>. The design formula expresses the variables which will be used in modeling. The formula should be a tilde (~) followed by the variables with plus signs between them (it will be coerced into an <em>formula</em> if it is not already). The design can be changed later, however then all differential analysis steps should be repeated, as the design formula is used to estimate the dispersions and to estimate the log2 fold changes of the model.</p>
<p><em>Note</em>: In order to benefit from the default settings of the package, you should put the variable of interest at the end of the formula and make sure the control level is the first level.</p>
<p>We will now show 4 ways of constructing a <em>DESeqDataSet</em>, depending on what pipeline was used upstream of DESeq2 to generated counts or estimated counts:</p>
<ol style="list-style-type: decimal">
<li>From <a href="#tximport">transcript abundance files and tximport</a></li>
<li>From a <a href="#countmat">count matrix</a></li>
<li>From <a href="#htseq">htseq-count files</a></li>
<li>From a <a href="#se">SummarizedExperiment</a> object</li>
</ol>
<p><a name="tximport"></a></p>
</div>
<div id="transcript-abundance-files-and-tximport-input" class="section level3">
<h3>Transcript abundance files and <em>tximport</em> input</h3>
<p>A newer and recommended pipeline is to use fast transcript abundance quantifiers upstream of DESeq2, and then to create gene-level count matrices for use with DESeq2 by importing the quantification data using the <a href="http://bioconductor.org/packages/tximport">tximport</a> package. This workflow allows users to import transcript abundance estimates from a variety of external software, including the following methods:</p>
<ul>
<li><a href="http://combine-lab.github.io/salmon/">Salmon</a> <span class="citation">(Patro et al. 2017)</span></li>
<li><a href="http://www.cs.cmu.edu/~ckingsf/software/sailfish/">Sailfish</a> <span class="citation">(Patro, Mount, and Kingsford 2014)</span></li>
<li><a href="https://pachterlab.github.io/kallisto/about.html">kallisto</a> <span class="citation">(Bray et al. 2016)</span></li>
<li><a href="http://deweylab.github.io/RSEM/">RSEM</a> <span class="citation">(Li and Dewey 2011)</span></li>
</ul>
<p>Some advantages of using the above methods for transcript abundance estimation are: (i) this approach corrects for potential changes in gene length across samples (e.g. from differential isoform usage) <span class="citation">(Trapnell et al. 2013)</span>, (ii) some of these methods (<em>Salmon</em>, <em>Sailfish</em>, <em>kallisto</em>) are substantially faster and require less memory and disk usage compared to alignment-based methods that require creation and storage of BAM files, and (iii) it is possible to avoid discarding those fragments that can align to multiple genes with homologous sequence, thus increasing sensitivity <span class="citation">(Robert and Watson 2015)</span>.</p>
<p>Full details on the motivation and methods for importing transcript level abundance and count estimates, summarizing to gene-level count matrices and producing an offset which corrects for potential changes in average transcript length across samples are described in <span class="citation">(Soneson, Love, and Robinson 2015)</span>. Note that the tximport-to-DESeq2 approach uses <em>estimated</em> gene counts from the transcript abundance quantifiers, but not <em>normalized</em> counts.</p>
<p>A tutorial on how to use the <em>Salmon</em> software for quantifying transcript abundance can be found <a href="https://combine-lab.github.io/salmon/getting_started/">here</a>. We recommend using the <code>--gcBias</code> <a href="http://salmon.readthedocs.io/en/latest/salmon.html#gcbias">flag</a> which estimates a correction factor for systematic biases commonly present in RNA-seq data <span class="citation">(Love, Hogenesch, and Irizarry 2016; Patro et al. 2017)</span>, unless you are certain that your data do not contain such bias.</p>
<p>Here, we demonstrate how to import transcript abundances and construct of a gene-level <em>DESeqDataSet</em> object from <em>Salmon</em> <code>quant.sf</code> files, which are stored in the <a href="http://bioconductor.org/packages/tximportData">tximportData</a> package. You do not need the <code>tximportData</code> package for your analysis, it is only used here for demonstration.</p>
<p>Note that, instead of locating <code>dir</code> using <em>system.file</em>, a user would typically just provide a path, e.g. <code>/path/to/quant/files</code>. For a typical use, the <code>condition</code> information should already be present as a column of the sample table <code>samples</code>, while here we construct artificial condition labels for demonstration.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"tximport"</span>)
<span class="kw">library</span>(<span class="st">"readr"</span>)
<span class="kw">library</span>(<span class="st">"tximportData"</span>)
dir <-<span class="st"> </span><span class="kw">system.file</span>(<span class="st">"extdata"</span>, <span class="dt">package=</span><span class="st">"tximportData"</span>)
samples <-<span class="st"> </span><span class="kw">read.table</span>(<span class="kw">file.path</span>(dir,<span class="st">"samples.txt"</span>), <span class="dt">header=</span><span class="ot">TRUE</span>)
samples$condition <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">"A"</span>,<span class="st">"B"</span>),<span class="dt">each=</span><span class="dv">3</span>))
<span class="kw">rownames</span>(samples) <-<span class="st"> </span>samples$run
samples[,<span class="kw">c</span>(<span class="st">"pop"</span>,<span class="st">"center"</span>,<span class="st">"run"</span>,<span class="st">"condition"</span>)]</code></pre></div>
<pre><code>## pop center run condition
## ERR188297 TSI UNIGE ERR188297 A
## ERR188088 TSI UNIGE ERR188088 A
## ERR188329 TSI UNIGE ERR188329 A
## ERR188288 TSI UNIGE ERR188288 B
## ERR188021 TSI UNIGE ERR188021 B
## ERR188356 TSI UNIGE ERR188356 B</code></pre>
<p>Next we specify the path to the files using the appropriate columns of <code>samples</code>, and we read in a table that links transcripts to genes for this dataset.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">files <-<span class="st"> </span><span class="kw">file.path</span>(dir,<span class="st">"salmon"</span>, samples$run, <span class="st">"quant.sf"</span>)
<span class="kw">names</span>(files) <-<span class="st"> </span>samples$run
tx2gene <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="kw">file.path</span>(dir, <span class="st">"tx2gene.csv"</span>))</code></pre></div>
<p>We import the necessary quantification data for DESeq2 using the <em>tximport</em> function. For further details on use of <em>tximport</em>, including the construction of the <code>tx2gene</code> table for linking transcripts to genes in your dataset, please refer to the <a href="http://bioconductor.org/packages/tximport">tximport</a> package vignette.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">txi <-<span class="st"> </span><span class="kw">tximport</span>(files, <span class="dt">type=</span><span class="st">"salmon"</span>, <span class="dt">tx2gene=</span>tx2gene)</code></pre></div>
<p>Finally, we can construct a <em>DESeqDataSet</em> from the <code>txi</code> object and sample information in <code>samples</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"DESeq2"</span>)
ddsTxi <-<span class="st"> </span><span class="kw">DESeqDataSetFromTximport</span>(txi,
<span class="dt">colData =</span> samples,
<span class="dt">design =</span> ~<span class="st"> </span>condition)</code></pre></div>
<p>The <code>ddsTxi</code> object here can then be used as <code>dds</code> in the following analysis steps.</p>
<p><a name="countmat"></a></p>
</div>
<div id="count-matrix-input" class="section level3">
<h3>Count matrix input</h3>
<p>Alternatively, the function <em>DESeqDataSetFromMatrix</em> can be used if you already have a matrix of read counts prepared from another source. Another method for quickly producing count matrices from alignment files is the <em>featureCounts</em> function <span class="citation">(Liao, Smyth, and Shi 2013)</span> in the <a href="http://bioconductor.org/packages/Rsubread">Rsubread</a> package. To use <em>DESeqDataSetFromMatrix</em>, the user should provide the counts matrix, the information about the samples (the columns of the count matrix) as a <em>DataFrame</em> or <em>data.frame</em>, and the design formula.</p>
<p>To demonstate the use of <em>DESeqDataSetFromMatrix</em>, we will read in count data from the <a href="http://bioconductor.org/packages/pasilla">pasilla</a> package. We read in a count matrix, which we will name <code>cts</code>, and the sample information table, which we will name <code>coldata</code>. Further below we describe how to extract these objects from, e.g. <em>featureCounts</em> output.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"pasilla"</span>)
pasCts <-<span class="st"> </span><span class="kw">system.file</span>(<span class="st">"extdata"</span>,
<span class="st">"pasilla_gene_counts.tsv"</span>,
<span class="dt">package=</span><span class="st">"pasilla"</span>, <span class="dt">mustWork=</span><span class="ot">TRUE</span>)
pasAnno <-<span class="st"> </span><span class="kw">system.file</span>(<span class="st">"extdata"</span>,
<span class="st">"pasilla_sample_annotation.csv"</span>,
<span class="dt">package=</span><span class="st">"pasilla"</span>, <span class="dt">mustWork=</span><span class="ot">TRUE</span>)
cts <-<span class="st"> </span><span class="kw">as.matrix</span>(<span class="kw">read.csv</span>(pasCts,<span class="dt">sep=</span><span class="st">"</span><span class="ch">\t</span><span class="st">"</span>,<span class="dt">row.names=</span><span class="st">"gene_id"</span>))
coldata <-<span class="st"> </span><span class="kw">read.csv</span>(pasAnno, <span class="dt">row.names=</span><span class="dv">1</span>)
coldata <-<span class="st"> </span>coldata[,<span class="kw">c</span>(<span class="st">"condition"</span>,<span class="st">"type"</span>)]</code></pre></div>
<p>We examine the count matrix and column data to see if they are consistent in terms of sample order.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(cts,<span class="dv">2</span>)</code></pre></div>
<pre><code>## untreated1 untreated2 untreated3 untreated4 treated1 treated2
## FBgn0000003 0 0 0 0 0 0
## FBgn0000008 92 161 76 70 140 88
## treated3
## FBgn0000003 1
## FBgn0000008 70</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">coldata</code></pre></div>
<pre><code>## condition type
## treated1fb treated single-read
## treated2fb treated paired-end
## treated3fb treated paired-end
## untreated1fb untreated single-read
## untreated2fb untreated single-read
## untreated3fb untreated paired-end
## untreated4fb untreated paired-end</code></pre>
<p>Note that these are not in the same order with respect to samples!</p>
<p>It is absolutely critical that the columns of the count matrix and the rows of the column data (information about samples) are in the same order. DESeq2 will not make guesses as to which column of the count matrix belongs to which row of the column data, these must be provided to DESeq2 already in consistent order.</p>
<p>As they are not in the correct order as given, we need to re-arrange one or the other so that they are consistent in terms of sample order (if we do not, later functions would produce an error). We additionally need to chop off the <code>"fb"</code> of the row names of <code>coldata</code>, so the naming is consistent.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rownames</span>(coldata) <-<span class="st"> </span><span class="kw">sub</span>(<span class="st">"fb"</span>, <span class="st">""</span>, <span class="kw">rownames</span>(coldata))
<span class="kw">all</span>(<span class="kw">rownames</span>(coldata) %in%<span class="st"> </span><span class="kw">colnames</span>(cts))</code></pre></div>
<pre><code>## [1] TRUE</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">all</span>(<span class="kw">rownames</span>(coldata) ==<span class="st"> </span><span class="kw">colnames</span>(cts))</code></pre></div>
<pre><code>## [1] FALSE</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cts <-<span class="st"> </span>cts[, <span class="kw">rownames</span>(coldata)]
<span class="kw">all</span>(<span class="kw">rownames</span>(coldata) ==<span class="st"> </span><span class="kw">colnames</span>(cts))</code></pre></div>
<pre><code>## [1] TRUE</code></pre>
<p>If you have used the <em>featureCounts</em> function <span class="citation">(Liao, Smyth, and Shi 2013)</span> in the <a href="http://bioconductor.org/packages/Rsubread">Rsubread</a> package, the matrix of read counts can be directly provided from the <code>"counts"</code> element in the list output. The count matrix and column data can typically be read into R from flat files using base R functions such as <em>read.csv</em> or <em>read.delim</em>. For <em>htseq-count</em> files, see the dedicated input function below.</p>
<p>With the count matrix, <code>cts</code>, and the sample information, <code>coldata</code>, we can construct a <em>DESeqDataSet</em>:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"DESeq2"</span>)
dds <-<span class="st"> </span><span class="kw">DESeqDataSetFromMatrix</span>(<span class="dt">countData =</span> cts,
<span class="dt">colData =</span> coldata,
<span class="dt">design =</span> ~<span class="st"> </span>condition)
dds</code></pre></div>
<pre><code>## class: DESeqDataSet
## dim: 14599 7
## metadata(1): version
## assays(1): counts
## rownames(14599): FBgn0000003 FBgn0000008 ... FBgn0261574
## FBgn0261575
## rowData names(0):
## colnames(7): treated1 treated2 ... untreated3 untreated4
## colData names(2): condition type</code></pre>
<p>If you have additional feature data, it can be added to the <em>DESeqDataSet</em> by adding to the metadata columns of a newly constructed object. (Here we add redundant data just for demonstration, as the gene names are already the rownames of the <code>dds</code>.)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">featureData <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">gene=</span><span class="kw">rownames</span>(cts))
<span class="kw">mcols</span>(dds) <-<span class="st"> </span><span class="kw">DataFrame</span>(<span class="kw">mcols</span>(dds), featureData)
<span class="kw">mcols</span>(dds)</code></pre></div>
<pre><code>## DataFrame with 14599 rows and 1 column
## gene
## <factor>
## 1 FBgn0000003
## 2 FBgn0000008
## 3 FBgn0000014
## 4 FBgn0000015
## 5 FBgn0000017
## ... ...
## 14595 FBgn0261571
## 14596 FBgn0261572
## 14597 FBgn0261573
## 14598 FBgn0261574
## 14599 FBgn0261575</code></pre>
<p><a name="htseq"></a></p>
</div>
<div id="htseq-count-input" class="section level3">
<h3><em>htseq-count</em> input</h3>
<p>You can use the function <em>DESeqDataSetFromHTSeqCount</em> if you have used <em>htseq-count</em> from the <a href="http://www-huber.embl.de/users/anders/HTSeq">HTSeq</a> python package <span class="citation">(Anders, Pyl, and Huber 2014)</span>. For an example of using the python scripts, see the <a href="http://bioconductor.org/packages/pasilla">pasilla</a> data package. First you will want to specify a variable which points to the directory in which the <em>htseq-count</em> output files are located.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">directory <-<span class="st"> "/path/to/your/files/"</span></code></pre></div>
<p>However, for demonstration purposes only, the following line of code points to the directory for the demo <em>htseq-count</em> output files packages for the <a href="http://bioconductor.org/packages/pasilla">pasilla</a> package.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">directory <-<span class="st"> </span><span class="kw">system.file</span>(<span class="st">"extdata"</span>, <span class="dt">package=</span><span class="st">"pasilla"</span>,
<span class="dt">mustWork=</span><span class="ot">TRUE</span>)</code></pre></div>
<p>We specify which files to read in using <em>list.files</em>, and select those files which contain the string <code>"treated"</code> using <em>grep</em>. The <em>sub</em> function is used to chop up the sample filename to obtain the condition status, or you might alternatively read in a phenotypic table using <em>read.table</em>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sampleFiles <-<span class="st"> </span><span class="kw">grep</span>(<span class="st">"treated"</span>,<span class="kw">list.files</span>(directory),<span class="dt">value=</span><span class="ot">TRUE</span>)
sampleCondition <-<span class="st"> </span><span class="kw">sub</span>(<span class="st">"(.*treated).*"</span>,<span class="st">"</span><span class="ch">\\</span><span class="st">1"</span>,sampleFiles)
sampleTable <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">sampleName =</span> sampleFiles,
<span class="dt">fileName =</span> sampleFiles,
<span class="dt">condition =</span> sampleCondition)</code></pre></div>
<p>Then we build the <em>DESeqDataSet</em> using the following function:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"DESeq2"</span>)
ddsHTSeq <-<span class="st"> </span><span class="kw">DESeqDataSetFromHTSeqCount</span>(<span class="dt">sampleTable =</span> sampleTable,
<span class="dt">directory =</span> directory,
<span class="dt">design=</span> ~<span class="st"> </span>condition)
ddsHTSeq</code></pre></div>
<pre><code>## class: DESeqDataSet
## dim: 70463 7
## metadata(1): version
## assays(1): counts
## rownames(70463): FBgn0000003:001 FBgn0000008:001 ...
## FBgn0261575:001 FBgn0261575:002
## rowData names(0):
## colnames(7): treated1fb.txt treated2fb.txt ... untreated3fb.txt
## untreated4fb.txt
## colData names(1): condition</code></pre>
<p><a name="se"></a></p>
</div>
<div id="summarizedexperiment-input" class="section level3">
<h3><em>SummarizedExperiment</em> input</h3>
<p>An example of the steps to produce a <em>RangedSummarizedExperiment</em> can be found in the <a href="http://www.bioconductor.org/help/workflows/rnaseqGene/">RNA-seq workflow</a> and in the vignette for the data package <a href="http://bioconductor.org/packages/airway">airway</a>. Here we load the <em>RangedSummarizedExperiment</em> from that package in order to build a <em>DESeqDataSet</em>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"airway"</span>)
<span class="kw">data</span>(<span class="st">"airway"</span>)
se <-<span class="st"> </span>airway</code></pre></div>
<p>The constructor function below shows the generation of a <em>DESeqDataSet</em> from a <em>RangedSummarizedExperiment</em> <code>se</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"DESeq2"</span>)
ddsSE <-<span class="st"> </span><span class="kw">DESeqDataSet</span>(se, <span class="dt">design =</span> ~<span class="st"> </span>cell +<span class="st"> </span>dex)
ddsSE</code></pre></div>
<pre><code>## class: DESeqDataSet
## dim: 64102 8
## metadata(2): '' version
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample</code></pre>
</div>
<div id="pre-filtering" class="section level3">
<h3>Pre-filtering</h3>
<p>While it is not necessary to pre-filter low count genes before running the DESeq2 functions, there are two reasons which make pre-filtering useful: by removing rows in which there are very few reads, we reduce the memory size of the <code>dds</code> data object, and we increase the speed of the transformation and testing functions within DESeq2. Here we perform a minimal pre-filtering to keep only rows that have at least 10 reads total. Note that more strict filtering to increase power is <em>automatically</em> applied via <a href="#indfilt">independent filtering</a> on the mean of normalized counts within the <em>results</em> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">keep <-<span class="st"> </span><span class="kw">rowSums</span>(<span class="kw">counts</span>(dds)) >=<span class="st"> </span><span class="dv">10</span>
dds <-<span class="st"> </span>dds[keep,]</code></pre></div>
</div>
<div id="note-on-factor-levels" class="section level3">
<h3>Note on factor levels</h3>
<p>By default, R will choose a <em>reference level</em> for factors based on alphabetical order. Then, if you never tell the DESeq2 functions which level you want to compare against (e.g. which level represents the control group), the comparisons will be based on the alphabetical order of the levels. There are two solutions: you can either explicitly tell <em>results</em> which comparison to make using the <code>contrast</code> argument (this will be shown later), or you can explicitly set the factors levels. You should only change the factor levels of variables in the design <strong>before</strong> running the DESeq2 analysis, not during or afterward. Setting the factor levels can be done in two ways, either using factor:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds$condition <-<span class="st"> </span><span class="kw">factor</span>(dds$condition, <span class="dt">levels =</span> <span class="kw">c</span>(<span class="st">"untreated"</span>,<span class="st">"treated"</span>))</code></pre></div>
<p>…or using <em>relevel</em>, just specifying the reference level:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds$condition <-<span class="st"> </span><span class="kw">relevel</span>(dds$condition, <span class="dt">ref =</span> <span class="st">"untreated"</span>)</code></pre></div>
<p>If you need to subset the columns of a <em>DESeqDataSet</em>, i.e., when removing certain samples from the analysis, it is possible that all the samples for one or more levels of a variable in the design formula would be removed. In this case, the <em>droplevels</em> function can be used to remove those levels which do not have samples in the current <em>DESeqDataSet</em>:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds$condition <-<span class="st"> </span><span class="kw">droplevels</span>(dds$condition)</code></pre></div>
</div>
<div id="collapsing-technical-replicates" class="section level3">
<h3>Collapsing technical replicates</h3>
<p>DESeq2 provides a function <em>collapseReplicates</em> which can assist in combining the counts from technical replicates into single columns of the count matrix. The term <em>technical replicate</em> implies multiple sequencing runs of the same library. You should not collapse biological replicates using this function. See the manual page for an example of the use of <em>collapseReplicates</em>.</p>
</div>
<div id="about-the-pasilla-dataset" class="section level3">
<h3>About the pasilla dataset</h3>
<p>We continue with the <a href="http://bioconductor.org/packages/pasilla">pasilla</a> data constructed from the count matrix method above. This data set is from an experiment on <em>Drosophila melanogaster</em> cell cultures and investigated the effect of RNAi knock-down of the splicing factor <em>pasilla</em> <span class="citation">(Brooks et al. 2011)</span>. The detailed transcript of the production of the <a href="http://bioconductor.org/packages/pasilla">pasilla</a> data is provided in the vignette of the data package <a href="http://bioconductor.org/packages/pasilla">pasilla</a>.</p>
<p><a name="de"></a></p>
</div>
</div>
<div id="differential-expression-analysis" class="section level2">
<h2>Differential expression analysis</h2>
<p>The standard differential expression analysis steps are wrapped into a single function, <em>DESeq</em>. The estimation steps performed by this function are described <a href="#theory">below</a>, in the manual page for <code>?DESeq</code> and in the Methods section of the DESeq2 publication <span class="citation">(Love, Huber, and Anders 2014)</span>.</p>
<p>Results tables are generated using the function <em>results</em>, which extracts a results table with log2 fold changes, <em>p</em> values and adjusted <em>p</em> values. With no additional arguments to <em>results</em>, the log2 fold change and Wald test <em>p</em> value will be for the last variable in the design formula, and if this is a factor, the comparison will be the last level of this variable over the first level. However, the order of the variables of the design do not matter so long as the user specifies the comparison using the <code>name</code> or <code>contrast</code> arguments of <em>results</em> (described later and in <code>?results</code>).</p>
<p>Details about the comparison are printed to the console, above the results table. The text, <code>condition treated vs untreated</code>, tells you that the estimates are of the logarithmic fold change log2(treated/untreated).</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds <-<span class="st"> </span><span class="kw">DESeq</span>(dds)
res <-<span class="st"> </span><span class="kw">results</span>(dds)
res</code></pre></div>
<pre><code>## log2 fold change (MLE): condition treated vs untreated
## Wald test p-value: condition treated vs untreated
## DataFrame with 9921 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## FBgn0000008 95.144292 0.002276428 0.2237292 0.01017493 0.99188172
## FBgn0000014 1.056523 -0.495113878 2.1431096 -0.23102593 0.81729466
## FBgn0000017 4352.553569 -0.239918945 0.1263378 -1.89902705 0.05756092
## FBgn0000018 418.610484 -0.104673913 0.1484903 -0.70492106 0.48085936
## FBgn0000024 6.406200 0.210848562 0.6895923 0.30575830 0.75978868
## ... ... ... ... ... ...
## FBgn0261570 3208.388610 0.29553289 0.1273514 2.32061001 0.0203079
## FBgn0261572 6.197188 -0.95882276 0.7753130 -1.23669125 0.2162017
## FBgn0261573 2240.979511 0.01271946 0.1133028 0.11226079 0.9106166
## FBgn0261574 4857.680373 0.01539243 0.1925619 0.07993497 0.9362890
## FBgn0261575 10.682520 0.16356865 0.9308661 0.17571663 0.8605166
## padj
## <numeric>
## FBgn0000008 0.9972093
## FBgn0000014 NA
## FBgn0000017 0.2880108
## FBgn0000018 0.8268644
## FBgn0000024 0.9435005
## ... ...
## FBgn0261570 0.1442486
## FBgn0261572 0.6078453
## FBgn0261573 0.9826550
## FBgn0261574 0.9881787
## FBgn0261575 0.9679223</code></pre>
<p><a name="lfcShrink"></a></p>
<p>In previous versions of DESeq2, the <em>DESeq</em> function by default would produce moderated, or shrunken, log2 fold changes through the use of the <code>betaPrior</code> argument. In version 1.16 and higher, we have split the moderation of log2 fold changes into a separate function, <em>lfcShrink</em>, for reasons described in the <a href="#changes">changes section</a> below.</p>
<p>Here we provide the <code>dds</code> object and the number of the coefficient we want to moderate. It is also possible to specify a <code>contrast</code>, instead of <code>coef</code>, which works the same as the <code>contrast</code> argument of the <em>results</em> function. If a results object is provided, the <code>log2FoldChange</code> column will be swapped out, otherwise <em>lfcShrink</em> returns a vector of shrunken log2 fold changes.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">resultsNames</span>(dds)</code></pre></div>
<pre><code>## [1] "Intercept" "condition_treated_vs_untreated"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">resLFC <-<span class="st"> </span><span class="kw">lfcShrink</span>(dds, <span class="dt">coef=</span><span class="dv">2</span>)
resLFC</code></pre></div>
<pre><code>## log2 fold change (MAP): condition treated vs untreated
## Wald test p-value: condition treated vs untreated
## DataFrame with 9921 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## FBgn0000008 95.144292 0.00155932 0.15353974 0.01017493 0.99188172
## FBgn0000014 1.056523 -0.01200958 0.05031619 -0.23102593 0.81729466
## FBgn0000017 4352.553569 -0.20935007 0.11026137 -1.89902705 0.05756092
## FBgn0000018 418.610484 -0.08715582 0.12358964 -0.70492106 0.48085936
## FBgn0000024 6.406200 0.03956173 0.12886543 0.30575830 0.75978868
## ... ... ... ... ... ...
## FBgn0261570 3208.388610 0.25744403 0.1109237 2.32061001 0.0203079
## FBgn0261572 6.197188 -0.14674317 0.1220335 -1.23669125 0.2162017
## FBgn0261573 2240.979511 0.01138380 0.1014129 0.11226079 0.9106166
## FBgn0261574 4857.680373 0.01150000 0.1438479 0.07993497 0.9362890
## FBgn0261575 10.682520 0.01898573 0.1043720 0.17571663 0.8605166
## padj
## <numeric>
## FBgn0000008 0.9972093
## FBgn0000014 NA
## FBgn0000017 0.2880108
## FBgn0000018 0.8268644
## FBgn0000024 0.9435005
## ... ...
## FBgn0261570 0.1442486
## FBgn0261572 0.6078453
## FBgn0261573 0.9826550
## FBgn0261574 0.9881787
## FBgn0261575 0.9679223</code></pre>
<p><a name="parallel"></a></p>
<p>The above steps should take less than 30 seconds for most analyses. For experiments with many samples (e.g. 100 samples), one can take advantage of parallelized computation. Parallelizing <code>DESeq</code>, <code>results</code>, and <code>lfcShrink</code> can be easily accomplished by loading the BiocParallel package, and then setting the following arguments: <code>parallel=TRUE</code> and <code>BPPARAM=MulticoreParam(4)</code>, for example, splitting the job over 4 cores. Note that <code>results</code> for coefficients or contrasts listed in <code>resultsNames(dds)</code> is fast and will not need parallelization. As an alternative to <code>BPPARAM</code>, one can <code>register</code> cores at the beginning of an analysis, and then just specify <code>parallel=TRUE</code> to the functions when called.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"BiocParallel"</span>)
<span class="kw">register</span>(<span class="kw">MulticoreParam</span>(<span class="dv">4</span>))</code></pre></div>
<p>We can order our results table by the smallest <em>p</em> value:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">resOrdered <-<span class="st"> </span>res[<span class="kw">order</span>(res$pvalue),]</code></pre></div>
<p>We can summarize some basic tallies using the <em>summary</em> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(res)</code></pre></div>
<pre><code>##
## out of 9921 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 518, 5.2%
## LFC < 0 (down) : 536, 5.4%
## outliers [1] : 1, 0.01%
## low counts [2] : 1539, 16%
## (mean count < 6)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results</code></pre>
<p>How many adjusted p-values were less than 0.1?</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">sum</span>(res$padj <<span class="st"> </span><span class="fl">0.1</span>, <span class="dt">na.rm=</span><span class="ot">TRUE</span>)</code></pre></div>
<pre><code>## [1] 1054</code></pre>
<p>The <em>results</em> function contains a number of arguments to customize the results table which is generated. You can read about these arguments by looking up <code>?results</code>. Note that the <em>results</em> function automatically performs independent filtering based on the mean of normalized counts for each gene, optimizing the number of genes which will have an adjusted <em>p</em> value below a given FDR cutoff, <code>alpha</code>. Independent filtering is further discussed <a href="#indfilt">below</a>. By default the argument <code>alpha</code> is set to <span class="math inline">\(0.1\)</span>. If the adjusted <em>p</em> value cutoff will be a value other than <span class="math inline">\(0.1\)</span>, <code>alpha</code> should be set to that value:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">res05 <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">alpha=</span><span class="fl">0.05</span>)
<span class="kw">summary</span>(res05)</code></pre></div>
<pre><code>##
## out of 9921 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 407, 4.1%
## LFC < 0 (down) : 431, 4.3%
## outliers [1] : 1, 0.01%
## low counts [2] : 1347, 14%
## (mean count < 5)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">sum</span>(res05$padj <<span class="st"> </span><span class="fl">0.05</span>, <span class="dt">na.rm=</span><span class="ot">TRUE</span>)</code></pre></div>
<pre><code>## [1] 838</code></pre>
<p><a name="IHW"></a></p>
<p>A generalization of the idea of <em>p</em> value filtering is to <em>weight</em> hypotheses to optimize power. A Bioconductor package, <a href="http://bioconductor.org/packages/IHW">IHW</a>, is available that implements the method of <em>Independent Hypothesis Weighting</em> <span class="citation">(Ignatiadis et al. 2016)</span>. Here we show the use of <em>IHW</em> for <em>p</em> value adjustment of DESeq2 results. For more details, please see the vignette of the <a href="http://bioconductor.org/packages/IHW">IHW</a> package. The <em>IHW</em> result object is stored in the metadata.</p>
<p><strong>Note:</strong> If the results of independent hypothesis weighting are used in published research, please cite:</p>
<blockquote>
<p>Ignatiadis, N., Klaus, B., Zaugg, J.B., Huber, W. (2016) Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. <em>Nature Methods</em>, <strong>13</strong>:7. <a href="http://dx.doi.org/10.1038/nmeth.3885">10.1038/nmeth.3885</a></p>
</blockquote>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"IHW"</span>)
resIHW <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">filterFun=</span>ihw)
<span class="kw">summary</span>(resIHW)</code></pre></div>
<pre><code>##
## out of 9921 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 504, 5.1%
## LFC < 0 (down) : 540, 5.4%
## outliers [1] : 1, 0.01%
## [1] see 'cooksCutoff' argument of ?results
## [2] see metadata(res)$ihwResult on hypothesis weighting</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">sum</span>(resIHW$padj <<span class="st"> </span><span class="fl">0.1</span>, <span class="dt">na.rm=</span><span class="ot">TRUE</span>)</code></pre></div>
<pre><code>## [1] 1044</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">metadata</span>(resIHW)$ihwResult</code></pre></div>
<pre><code>## ihwResult object with 9921 hypothesis tests
## Nominal FDR control level: 0.1
## Split into 6 bins, based on an ordinal covariate</code></pre>
<p>If a multi-factor design is used, or if the variable in the design formula has more than two levels, the <code>contrast</code> argument of <em>results</em> can be used to extract different comparisons from the <em>DESeqDataSet</em> returned by <em>DESeq</em>. The use of the <code>contrast</code> argument is further discussed <a href="#contrasts">below</a>.</p>
<p>For advanced users, note that all the values calculated by the DESeq2 package are stored in the <em>DESeqDataSet</em> object, and access to these values is discussed <a href="#access">below</a>.</p>
</div>
<div id="exploring-and-exporting-results" class="section level2">
<h2>Exploring and exporting results</h2>
<div id="ma-plot" class="section level3">
<h3>MA-plot</h3>
<p>In DESeq2, the function <em>plotMA</em> shows the log2 fold changes attributable to a given variable over the mean of normalized counts for all the samples in the <em>DESeqDataSet</em>. Points will be colored red if the adjusted <em>p</em> value is less than 0.1. Points which fall out of the window are plotted as open triangles pointing either up or down.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotMA</span>(res, <span class="dt">ylim=</span><span class="kw">c</span>(-<span class="dv">2</span>,<span class="dv">2</span>))</code></pre></div>
<p><img src="data:image/png;base64,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" width="480" /></p>
<p>It is more useful visualize the MA-plot for the shrunken log2 fold changes, which remove the noise associated with log2 fold changes from low count genes without requiring arbitrary filtering thresholds.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotMA</span>(resLFC, <span class="dt">ylim=</span><span class="kw">c</span>(-<span class="dv">2</span>,<span class="dv">2</span>))</code></pre></div>
<p><img src="data:image/png;base64,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" width="480" /></p>
<p>After calling <em>plotMA</em>, one can use the function <em>identify</em> to interactively detect the row number of individual genes by clicking on the plot. One can then recover the gene identifiers by saving the resulting indices:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">idx <-<span class="st"> </span><span class="kw">identify</span>(res$baseMean, res$log2FoldChange)
<span class="kw">rownames</span>(res)[idx]</code></pre></div>
</div>
<div id="alternative-shrinkage-estimators" class="section level3">
<h3>Alternative shrinkage estimators</h3>
<p>The moderated log fold changes proposed by <span class="citation">Love, Huber, and Anders (2014)</span> use a normal prior distribution, centered on zero and with a scale that is fit to the data. The shrunken log fold changes are useful for ranking and visualization, without the need for arbitrary filters on low count genes. The normal prior can sometimes produce too strong of shrinkage for certain datasets. In DESeq2 version 1.18, we include two additional adaptive shrinkage estimators, available via the <code>type</code> argument of <code>lfcShrink</code>. For more details, see <code>?lfcShrink</code></p>
<p>The options for <code>type</code> are:</p>
<ul>
<li><code>normal</code> is the the original DESeq2 shrinkage estimator, an adaptive normal prior</li>
<li><code>apeglm</code> is the adaptive t prior shrinkage estimator from the <a href="http://bioconductor.org/packages/apeglm">apeglm</a> package</li>
<li><code>ashr</code> is the adaptive shrinkage estimator from the <a href="https://github.com/stephens999/ashr">ashr</a> package <span class="citation">(Stephens 2016)</span>. Here DESeq2 uses the ashr option to fit a mixture of normal distributions to form the prior, with <code>method="shrinkage"</code></li>
</ul>
<p><strong>Note:</strong> if the shrinkage estimator <code>type="ashr"</code> is used in published research, please cite:</p>
<blockquote>
<p>Stephens, M. (2016) False discovery rates: a new deal. <em>Biostatistics</em>, <strong>18</strong>:2. <a href="https://doi.org/10.1093/biostatistics/kxw041">10.1093/biostatistics/kxw041</a></p>
</blockquote>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">resApe <-<span class="st"> </span><span class="kw">lfcShrink</span>(dds, <span class="dt">coef=</span><span class="dv">2</span>, <span class="dt">type=</span><span class="st">"apeglm"</span>)
resAsh <-<span class="st"> </span><span class="kw">lfcShrink</span>(dds, <span class="dt">coef=</span><span class="dv">2</span>, <span class="dt">type=</span><span class="st">"ashr"</span>)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mfrow=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">3</span>), <span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">4</span>,<span class="dv">4</span>,<span class="dv">2</span>,<span class="dv">1</span>))
xlim <-<span class="st"> </span><span class="kw">c</span>(<span class="dv">1</span>,<span class="fl">1e5</span>); ylim <-<span class="st"> </span><span class="kw">c</span>(-<span class="dv">3</span>,<span class="dv">3</span>)
<span class="kw">plotMA</span>(resLFC, <span class="dt">xlim=</span>xlim, <span class="dt">ylim=</span>ylim, <span class="dt">main=</span><span class="st">"normal"</span>)
<span class="kw">plotMA</span>(resApe, <span class="dt">xlim=</span>xlim, <span class="dt">ylim=</span>ylim, <span class="dt">main=</span><span class="st">"apeglm"</span>)
<span class="kw">plotMA</span>(resAsh, <span class="dt">xlim=</span>xlim, <span class="dt">ylim=</span>ylim, <span class="dt">main=</span><span class="st">"ashr"</span>)</code></pre></div>
<p><img 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width="768" /></p>
<p><strong>Note:</strong> due to the nature of the statistical model and optimization approach, <code>apeglm</code> is usually a factor of ~5 slower than <code>normal</code>. For example, with 10,000 genes and 10 samples, <code>normal</code> may take ~3 seconds, while <code>apeglm</code> takes ~15 seconds (on a laptop). However, <code>apeglm</code> can be more than an order of magnitude slower when there are many coefficients, e.g. 10 or more coefficients in <code>resultsNames(dds)</code>. The method <code>ashr</code> is fairly fast and does not depend on the number of coefficients, as it uses only the estimated MLE coefficients and their standard errors. A solution for speeding up <code>normal</code> and <code>apeglm</code> is to use multiple cores. This can be easily accomplished by loading the BiocParallel package, and then setting the following arguments of <code>lfcShrink</code>: <code>parallel=TRUE</code> and <code>BPPARAM=MulticoreParam(4)</code>, for example, splitting the job over 4 cores. This approach can also be used with <code>DESeq</code> and <code>results</code>, as mentioned <a href="#parallel">above</a>.</p>
<p><strong>Note:</strong> If there is unwanted variation present in the data (e.g. batch effects) it is always recommend to correct for this, which can be accommodated in DESeq2 by including in the design any known batch variables or by using functions/packages such as <code>svaseq</code> in <a href="http://bioconductor.org/packages/sva">sva</a> <span class="citation">(Leek 2014)</span> or the <code>RUV</code> functions in <a href="http://bioconductor.org/packages/RUVSeq">RUVSeq</a> <span class="citation">(Risso et al. 2014)</span> to estimate variables that capture the unwanted variation. In addition, the ashr developers have a <a href="https://github.com/dcgerard/vicar">specific method</a> for accounting for unwanted variation in combination with ashr <span class="citation">(Gerard and Stephens 2017)</span>.</p>
</div>
<div id="plot-counts" class="section level3">
<h3>Plot counts</h3>
<p>It can also be useful to examine the counts of reads for a single gene across the groups. A simple function for making this plot is <em>plotCounts</em>, which normalizes counts by sequencing depth and adds a pseudocount of 1/2 to allow for log scale plotting. The counts are grouped by the variables in <code>intgroup</code>, where more than one variable can be specified. Here we specify the gene which had the smallest <em>p</em> value from the results table created above. You can select the gene to plot by rowname or by numeric index.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotCounts</span>(dds, <span class="dt">gene=</span><span class="kw">which.min</span>(res$padj), <span class="dt">intgroup=</span><span class="st">"condition"</span>)</code></pre></div>
<p><img 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" width="480" /></p>
<p>For customized plotting, an argument <code>returnData</code> specifies that the function should only return a <em>data.frame</em> for plotting with <em>ggplot</em>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d <-<span class="st"> </span><span class="kw">plotCounts</span>(dds, <span class="dt">gene=</span><span class="kw">which.min</span>(res$padj), <span class="dt">intgroup=</span><span class="st">"condition"</span>,
<span class="dt">returnData=</span><span class="ot">TRUE</span>)
<span class="kw">library</span>(<span class="st">"ggplot2"</span>)
<span class="kw">ggplot</span>(d, <span class="kw">aes</span>(<span class="dt">x=</span>condition, <span class="dt">y=</span>count)) +<span class="st"> </span>
<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">position=</span><span class="kw">position_jitter</span>(<span class="dt">w=</span><span class="fl">0.1</span>,<span class="dt">h=</span><span class="dv">0</span>)) +<span class="st"> </span>
<span class="st"> </span><span class="kw">scale_y_log10</span>(<span class="dt">breaks=</span><span class="kw">c</span>(<span class="dv">25</span>,<span class="dv">100</span>,<span class="dv">400</span>))</code></pre></div>
<p><img 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" width="480" /></p>
</div>
<div id="more-information-on-results-columns" class="section level3">
<h3>More information on results columns</h3>
<p>Information about which variables and tests were used can be found by calling the function <em>mcols</em> on the results object.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mcols</span>(res)$description</code></pre></div>
<pre><code>## [1] "mean of normalized counts for all samples"
## [2] "log2 fold change (MLE): condition treated vs untreated"
## [3] "standard error: condition treated vs untreated"
## [4] "Wald statistic: condition treated vs untreated"
## [5] "Wald test p-value: condition treated vs untreated"
## [6] "BH adjusted p-values"</code></pre>
<p>For a particular gene, a log2 fold change of -1 for <code>condition treated vs untreated</code> means that the treatment induces a multiplicative change in observed gene expression level of <span class="math inline">\(2^{-1} = 0.5\)</span> compared to the untreated condition. If the variable of interest is continuous-valued, then the reported log2 fold change is per unit of change of that variable.</p>
<p><a name="pvaluesNA"></a></p>
<p><strong>Note on p-values set to NA</strong>: some values in the results table can be set to <code>NA</code> for one of the following reasons:</p>
<ul>
<li>If within a row, all samples have zero counts, the <code>baseMean</code> column will be zero, and the log2 fold change estimates, <em>p</em> value and adjusted <em>p</em> value will all be set to <code>NA</code>.</li>
<li>If a row contains a sample with an extreme count outlier then the <em>p</em> value and adjusted <em>p</em> value will be set to <code>NA</code>. These outlier counts are detected by Cook’s distance. Customization of this outlier filtering and description of functionality for replacement of outlier counts and refitting is described <a href="#outlier">below</a></li>
<li>If a row is filtered by automatic independent filtering, for having a low mean normalized count, then only the adjusted <em>p</em> value will be set to <code>NA</code>. Description and customization of independent filtering is described <a href="#indfilt">below</a></li>
</ul>
</div>
<div id="rich-visualization-and-reporting-of-results" class="section level3">
<h3>Rich visualization and reporting of results</h3>
<p><strong>ReportingTools.</strong> An HTML report of the results with plots and sortable/filterable columns can be generated using the <a href="http://bioconductor.org/packages/ReportingTools">ReportingTools</a> package on a <em>DESeqDataSet</em> that has been processed by the <em>DESeq</em> function. For a code example, see the <em>RNA-seq differential expression</em> vignette at the <a href="http://bioconductor.org/packages/ReportingTools">ReportingTools</a> page, or the manual page for the <em>publish</em> method for the <em>DESeqDataSet</em> class.</p>
<p><strong>regionReport.</strong> An HTML and PDF summary of the results with plots can also be generated using the <a href="http://bioconductor.org/packages/regionReport">regionReport</a> package. The <em>DESeq2Report</em> function should be run on a <em>DESeqDataSet</em> that has been processed by the <em>DESeq</em> function. For more details see the manual page for <em>DESeq2Report</em> and an example vignette in the <a href="http://bioconductor.org/packages/regionReport">regionReport</a> package.</p>
<p><strong>Glimma.</strong> Interactive visualization of DESeq2 output, including MA-plots (also called MD-plot) can be generated using the <a href="http://bioconductor.org/packages/Glimma">Glimma</a> package. See the manual page for <em>glMDPlot.DESeqResults</em>.</p>
<p><strong>pcaExplorer.</strong> Interactive visualization of DESeq2 output, including PCA plots, boxplots of counts and other useful summaries can be generated using the <a href="http://bioconductor.org/packages/pcaExplorer">pcaExplorer</a> package. See the <em>Launching the application</em> section of the package vignette.</p>
</div>
<div id="exporting-results-to-csv-files" class="section level3">
<h3>Exporting results to CSV files</h3>
<p>A plain-text file of the results can be exported using the base R functions <em>write.csv</em> or <em>write.delim</em>. We suggest using a descriptive file name indicating the variable and levels which were tested.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">write.csv</span>(<span class="kw">as.data.frame</span>(resOrdered),
<span class="dt">file=</span><span class="st">"condition_treated_results.csv"</span>)</code></pre></div>
<p>Exporting only the results which pass an adjusted <em>p</em> value threshold can be accomplished with the <em>subset</em> function, followed by the <em>write.csv</em> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">resSig <-<span class="st"> </span><span class="kw">subset</span>(resOrdered, padj <<span class="st"> </span><span class="fl">0.1</span>)
resSig</code></pre></div>
<pre><code>## log2 fold change (MLE): condition treated vs untreated
## Wald test p-value: condition treated vs untreated
## DataFrame with 1054 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## FBgn0039155 730.5677 -4.618741 0.16912665 -27.30936 3.283533e-164
## FBgn0025111 1501.4479 2.899946 0.12735926 22.76981 9.134947e-115
## FBgn0029167 3706.0240 -2.196912 0.09792037 -22.43570 1.765125e-111
## FBgn0003360 4342.8321 -3.179541 0.14356712 -22.14672 1.121885e-108
## FBgn0035085 638.2193 -2.560242 0.13781525 -18.57735 4.901323e-77
## ... ... ... ... ... ...
## FBgn0037073 973.10163 -0.2521459 0.10099319 -2.496662 0.01253684
## FBgn0029976 2312.58850 -0.2211265 0.08858313 -2.496259 0.01255108
## FBgn0030938 24.80638 0.9576449 0.38364585 2.496169 0.01255428
## FBgn0034753 7775.27113 0.3935148 0.15767268 2.495770 0.01256840
## FBgn0039260 1088.27659 -0.2592536 0.10387902 -2.495727 0.01256994
## padj
## <numeric>
## FBgn0039155 2.751929e-160
## FBgn0025111 3.827999e-111
## FBgn0029167 4.931170e-108
## FBgn0003360 2.350629e-105
## FBgn0035085 8.215598e-74
## ... ...
## FBgn0037073 0.0999513
## FBgn0029976 0.0999513
## FBgn0030938 0.0999513
## FBgn0034753 0.0999513
## FBgn0039260 0.0999513</code></pre>
</div>
</div>
<div id="multi-factor-designs" class="section level2">
<h2>Multi-factor designs</h2>
<p>Experiments with more than one factor influencing the counts can be analyzed using design formula that include the additional variables. In fact, DESeq2 can analyze any possible experimental design that can be expressed with fixed effects terms (multiple factors, designs with interactions, designs with continuous variables, splines, and so on are all possible).</p>
<p>By adding variables to the design, one can control for additional variation in the counts. For example, if the condition samples are balanced across experimental batches, by including the <code>batch</code> factor to the design, one can increase the sensitivity for finding differences due to <code>condition</code>. There are multiple ways to analyze experiments when the additional variables are of interest and not just controlling factors (see <a href="#interactions">section on interactions</a>).</p>
<p>The data in the <a href="http://bioconductor.org/packages/pasilla">pasilla</a> package have a condition of interest (the column <code>condition</code>), as well as information on the type of sequencing which was performed (the column <code>type</code>), as we can see below:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">colData</span>(dds)</code></pre></div>
<pre><code>## DataFrame with 7 rows and 3 columns
## condition type sizeFactor
## <factor> <factor> <numeric>
## treated1 treated single-read 1.6355014
## treated2 treated paired-end 0.7612159
## treated3 treated paired-end 0.8326603
## untreated1 untreated single-read 1.1383376
## untreated2 untreated single-read 1.7935406
## untreated3 untreated paired-end 0.6494828
## untreated4 untreated paired-end 0.7516005</code></pre>
<p>We create a copy of the <em>DESeqDataSet</em>, so that we can rerun the analysis using a multi-factor design.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ddsMF <-<span class="st"> </span>dds</code></pre></div>
<p>We change the levels of <code>type</code> so it only contains letters (numbers, underscore and period are also allowed in design factor levels). Be careful when changing level names to use the same order as the current levels.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">levels</span>(ddsMF$type)</code></pre></div>
<pre><code>## [1] "paired-end" "single-read"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">levels</span>(ddsMF$type) <-<span class="st"> </span><span class="kw">sub</span>(<span class="st">"-.*"</span>, <span class="st">""</span>, <span class="kw">levels</span>(ddsMF$type))
<span class="kw">levels</span>(ddsMF$type)</code></pre></div>
<pre><code>## [1] "paired" "single"</code></pre>
<p>We can account for the different types of sequencing, and get a clearer picture of the differences attributable to the treatment. As <code>condition</code> is the variable of interest, we put it at the end of the formula. Thus the <em>results</em> function will by default pull the <code>condition</code> results unless <code>contrast</code> or <code>name</code> arguments are specified.</p>
<p>Then we can re-run <em>DESeq</em>:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">design</span>(ddsMF) <-<span class="st"> </span><span class="kw">formula</span>(~<span class="st"> </span>type +<span class="st"> </span>condition)
ddsMF <-<span class="st"> </span><span class="kw">DESeq</span>(ddsMF)</code></pre></div>
<p>Again, we access the results using the <em>results</em> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">resMF <-<span class="st"> </span><span class="kw">results</span>(ddsMF)
<span class="kw">head</span>(resMF)</code></pre></div>
<pre><code>## log2 fold change (MLE): condition treated vs untreated
## Wald test p-value: condition treated vs untreated
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## FBgn0000008 95.144292 -0.04055736 0.2200633 -0.18429862 0.85377920
## FBgn0000014 1.056523 -0.08351882 2.0760816 -0.04022906 0.96791051
## FBgn0000017 4352.553569 -0.25605701 0.1122166 -2.28181137 0.02250048
## FBgn0000018 418.610484 -0.06461523 0.1313488 -0.49193622 0.62276444
## FBgn0000024 6.406200 0.30898382 0.7560075 0.40870468 0.68275640
## FBgn0000032 989.720217 -0.04837925 0.1208420 -0.40035128 0.68889781
## padj
## <numeric>
## FBgn0000008 0.9494634
## FBgn0000014 NA
## FBgn0000017 0.1302627
## FBgn0000018 0.8593904
## FBgn0000024 0.8877697
## FBgn0000032 0.8902013</code></pre>
<p>It is also possible to retrieve the log2 fold changes, <em>p</em> values and adjusted <em>p</em> values of the <code>type</code> variable. The <code>contrast</code> argument of the function <em>results</em> takes a character vector of length three: the name of the variable, the name of the factor level for the numerator of the log2 ratio, and the name of the factor level for the denominator. The <code>contrast</code> argument can also take other forms, as described in the help page for <em>results</em> and <a href="#contrasts">below</a></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">resMFType <-<span class="st"> </span><span class="kw">results</span>(ddsMF,
<span class="dt">contrast=</span><span class="kw">c</span>(<span class="st">"type"</span>, <span class="st">"single"</span>, <span class="st">"paired"</span>))
<span class="kw">head</span>(resMFType)</code></pre></div>
<pre><code>## log2 fold change (MLE): type single vs paired
## Wald test p-value: type single vs paired
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## FBgn0000008 95.144292 -0.2623745 0.2185279 -1.2006453 0.22988882
## FBgn0000014 1.056523 3.2898915 2.0531720 1.6023457 0.10907917
## FBgn0000017 4352.553569 -0.1000200 0.1120782 -0.8924127 0.37217178
## FBgn0000018 418.610484 0.2290491 0.1302603 1.7583952 0.07868028
## FBgn0000024 6.406200 0.3060704 0.7514061 0.4073303 0.68376543
## FBgn0000032 989.720217 0.2374130 0.1202745 1.9739259 0.04839017
## padj
## <numeric>
## FBgn0000008 0.5361332
## FBgn0000014 NA
## FBgn0000017 0.6831308
## FBgn0000018 0.2917133
## FBgn0000024 0.8804532
## FBgn0000032 0.2175655</code></pre>
<p>If the variable is continuous or an interaction term (see <a href="#interactions">section on interactions</a>) then the results can be extracted using the <code>name</code> argument to <em>results</em>, where the name is one of elements returned by <code>resultsNames(dds)</code>.</p>
<p><a name="transform"></a></p>
</div>
</div>
<div id="data-transformations-and-visualization" class="section level1">
<h1>Data transformations and visualization</h1>
<div id="count-data-transformations" class="section level2">
<h2>Count data transformations</h2>
<p>In order to test for differential expression, we operate on raw counts and use discrete distributions as described in the previous section on differential expression. However for other downstream analyses – e.g. for visualization or clustering – it might be useful to work with transformed versions of the count data.</p>
<p>Maybe the most obvious choice of transformation is the logarithm. Since count values for a gene can be zero in some conditions (and non-zero in others), some advocate the use of <em>pseudocounts</em>, i.e. transformations of the form:</p>
<p><span class="math display">\[ y = \log_2(n + n_0) \]</span></p>
<p>where <em>n</em> represents the count values and <span class="math inline">\(n_0\)</span> is a positive constant.</p>
<p>In this section, we discuss two alternative approaches that offer more theoretical justification and a rational way of choosing parameters equivalent to <span class="math inline">\(n_0\)</span> above. One makes use of the concept of variance stabilizing transformations (VST) <span class="citation">(Tibshirani 1988; Huber et al. 2003; Anders and Huber 2010)</span>, and the other is the <em>regularized logarithm</em> or <em>rlog</em>, which incorporates a prior on the sample differences <span class="citation">(Love, Huber, and Anders 2014)</span>. Both transformations produce transformed data on the log2 scale which has been normalized with respect to library size or other normalization factors.</p>
<p>The point of these two transformations, the VST and the <em>rlog</em>, is to remove the dependence of the variance on the mean, particularly the high variance of the logarithm of count data when the mean is low. Both VST and <em>rlog</em> use the experiment-wide trend of variance over mean, in order to transform the data to remove the experiment-wide trend. Note that we do not require or desire that all the genes have <em>exactly</em> the same variance after transformation. Indeed, in a figure below, you will see that after the transformations the genes with the same mean do not have exactly the same standard deviations, but that the experiment-wide trend has flattened. It is those genes with row variance above the trend which will allow us to cluster samples into interesting groups.</p>
<p><strong>Note on running time:</strong> if you have many samples (e.g. 100s), the <em>rlog</em> function might take too long, and so the <em>vst</em> function will be a faster choice. The rlog and VST have similar properties, but the rlog requires fitting a shrinkage term for each sample and each gene which takes time. See the DESeq2 paper for more discussion on the differences <span class="citation">(Love, Huber, and Anders 2014)</span>.</p>
<div id="blind-dispersion-estimation" class="section level3">
<h3>Blind dispersion estimation</h3>
<p>The two functions, <em>vst</em> and <em>rlog</em> have an argument <code>blind</code>, for whether the transformation should be blind to the sample information specified by the design formula. When <code>blind</code> equals <code>TRUE</code> (the default), the functions will re-estimate the dispersions using only an intercept. This setting should be used in order to compare samples in a manner wholly unbiased by the information about experimental groups, for example to perform sample QA (quality assurance) as demonstrated below.</p>
<p>However, blind dispersion estimation is not the appropriate choice if one expects that many or the majority of genes (rows) will have large differences in counts which are explainable by the experimental design, and one wishes to transform the data for downstream analysis. In this case, using blind dispersion estimation will lead to large estimates of dispersion, as it attributes differences due to experimental design as unwanted <em>noise</em>, and will result in overly shrinking the transformed values towards each other. By setting <code>blind</code> to <code>FALSE</code>, the dispersions already estimated will be used to perform transformations, or if not present, they will be estimated using the current design formula. Note that only the fitted dispersion estimates from mean-dispersion trend line are used in the transformation (the global dependence of dispersion on mean for the entire experiment). So setting <code>blind</code> to <code>FALSE</code> is still for the most part not using the information about which samples were in which experimental group in applying the transformation.</p>
</div>
<div id="extracting-transformed-values" class="section level3">
<h3>Extracting transformed values</h3>
<p>These transformation functions return an object of class <em>DESeqTransform</em> which is a subclass of <em>RangedSummarizedExperiment</em>. For ~20 samples, running on a newly created <code>DESeqDataSet</code>, <em>rlog</em> may take 30 seconds, while <em>vst</em> takes less than 1 second. The running times are shorter when using <code>blind=FALSE</code> and if the function <em>DESeq</em> has already been run, because then it is not necessary to re-estimate the dispersion values. The <em>assay</em> function is used to extract the matrix of normalized values.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">vsd <-<span class="st"> </span><span class="kw">vst</span>(dds, <span class="dt">blind=</span><span class="ot">FALSE</span>)
rld <-<span class="st"> </span><span class="kw">rlog</span>(dds, <span class="dt">blind=</span><span class="ot">FALSE</span>)
<span class="kw">head</span>(<span class="kw">assay</span>(vsd), <span class="dv">3</span>)</code></pre></div>
<pre><code>## treated1 treated2 treated3 untreated1 untreated2 untreated3
## FBgn0000008 7.607799 7.834807 7.594933 7.567177 7.642058 7.844499
## FBgn0000014 6.318607 6.040987 6.040987 6.412578 6.173698 6.040987
## FBgn0000017 11.938303 12.024550 12.013558 12.045714 12.284641 12.455933
## untreated4
## FBgn0000008 7.669033
## FBgn0000014 6.040987
## FBgn0000017 12.077397</code></pre>
</div>
<div id="variance-stabilizing-transformation" class="section level3">
<h3>Variance stabilizing transformation</h3>
<p>Above, we used a parametric fit for the dispersion. In this case, the closed-form expression for the variance stabilizing transformation is used by the <em>vst</em> function. If a local fit is used (option <code>fitType="locfit"</code> to <em>estimateDispersions</em>) a numerical integration is used instead. The transformed data should be approximated variance stabilized and also includes correction for size factors or normalization factors. The transformed data is on the log2 scale for large counts.</p>
</div>
<div id="regularized-log-transformation" class="section level3">
<h3>Regularized log transformation</h3>
<p>The function <em>rlog</em>, stands for <em>regularized log</em>, transforming the original count data to the log2 scale by fitting a model with a term for each sample and a prior distribution on the coefficients which is estimated from the data. This is the same kind of shrinkage (sometimes referred to as regularization, or moderation) of log fold changes used by the <em>DESeq</em> and <em>nbinomWaldTest</em>. The resulting data contains elements defined as:</p>
<p><span class="math display">\[ \log_2(q_{ij}) = \beta_{i0} + \beta_{ij} \]</span></p>
<p>where <span class="math inline">\(q_{ij}\)</span> is a parameter proportional to the expected true concentration of fragments for gene <em>i</em> and sample <em>j</em> (see formula <a href="#theory">below</a>), <span class="math inline">\(\beta_{i0}\)</span> is an intercept which does not undergo shrinkage, and <span class="math inline">\(\beta_{ij}\)</span> is the sample-specific effect which is shrunk toward zero based on the dispersion-mean trend over the entire dataset. The trend typically captures high dispersions for low counts, and therefore these genes exhibit higher shrinkage from the <em>rlog</em>.</p>
<p>Note that, as <span class="math inline">\(q_{ij}\)</span> represents the part of the mean value <span class="math inline">\(\mu_{ij}\)</span> after the size factor <span class="math inline">\(s_j\)</span> has been divided out, it is clear that the rlog transformation inherently accounts for differences in sequencing depth. Without priors, this design matrix would lead to a non-unique solution, however the addition of a prior on non-intercept betas allows for a unique solution to be found.</p>
</div>
<div id="effects-of-transformations-on-the-variance" class="section level3">
<h3>Effects of transformations on the variance</h3>
<p>The figure below plots the standard deviation of the transformed data, across samples, against the mean, using the shifted logarithm transformation, the regularized log transformation and the variance stabilizing transformation. The shifted logarithm has elevated standard deviation in the lower count range, and the regularized log to a lesser extent, while for the variance stabilized data the standard deviation is roughly constant along the whole dynamic range.</p>
<p>Note that the vertical axis in such plots is the square root of the variance over all samples, so including the variance due to the experimental conditions. While a flat curve of the square root of variance over the mean may seem like the goal of such transformations, this may be unreasonable in the case of datasets with many true differences due to the experimental conditions.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># this gives log2(n + 1)</span>
ntd <-<span class="st"> </span><span class="kw">normTransform</span>(dds)
<span class="kw">library</span>(<span class="st">"vsn"</span>)
<span class="kw">meanSdPlot</span>(<span class="kw">assay</span>(ntd))</code></pre></div>
<p><img src="data:image/png;base64,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" width="480" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">meanSdPlot</span>(<span class="kw">assay</span>(vsd))</code></pre></div>
<p><img src="data:image/png;base64,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" width="480" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">meanSdPlot</span>(<span class="kw">assay</span>(rld))</code></pre></div>
<p><img src="data:image/png;base64,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" width="480" /></p>
</div>
</div>
<div id="data-quality-assessment-by-sample-clustering-and-visualization" class="section level2">
<h2>Data quality assessment by sample clustering and visualization</h2>
<p>Data quality assessment and quality control (i.e. the removal of insufficiently good data) are essential steps of any data analysis. These steps should typically be performed very early in the analysis of a new data set, preceding or in parallel to the differential expression testing.</p>
<p>We define the term <em>quality</em> as <em>fitness for purpose</em>. Our purpose is the detection of differentially expressed genes, and we are looking in particular for samples whose experimental treatment suffered from an anormality that renders the data points obtained from these particular samples detrimental to our purpose.</p>
<div id="heatmap-of-the-count-matrix" class="section level3">
<h3>Heatmap of the count matrix</h3>
<p>To explore a count matrix, it is often instructive to look at it as a heatmap. Below we show how to produce such a heatmap for various transformations of the data.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"pheatmap"</span>)
select <-<span class="st"> </span><span class="kw">order</span>(<span class="kw">rowMeans</span>(<span class="kw">counts</span>(dds,<span class="dt">normalized=</span><span class="ot">TRUE</span>)),
<span class="dt">decreasing=</span><span class="ot">TRUE</span>)[<span class="dv">1</span>:<span class="dv">20</span>]
df <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">colData</span>(dds)[,<span class="kw">c</span>(<span class="st">"condition"</span>,<span class="st">"type"</span>)])
<span class="kw">pheatmap</span>(<span class="kw">assay</span>(ntd)[select,], <span class="dt">cluster_rows=</span><span class="ot">FALSE</span>, <span class="dt">show_rownames=</span><span class="ot">FALSE</span>,
<span class="dt">cluster_cols=</span><span class="ot">FALSE</span>, <span class="dt">annotation_col=</span>df)</code></pre></div>
<p><img 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" width="480" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">pheatmap</span>(<span class="kw">assay</span>(vsd)[select,], <span class="dt">cluster_rows=</span><span class="ot">FALSE</span>, <span class="dt">show_rownames=</span><span class="ot">FALSE</span>,
<span class="dt">cluster_cols=</span><span class="ot">FALSE</span>, <span class="dt">annotation_col=</span>df)</code></pre></div>
<p><img 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" width="480" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">pheatmap</span>(<span class="kw">assay</span>(rld)[select,], <span class="dt">cluster_rows=</span><span class="ot">FALSE</span>, <span class="dt">show_rownames=</span><span class="ot">FALSE</span>,
<span class="dt">cluster_cols=</span><span class="ot">FALSE</span>, <span class="dt">annotation_col=</span>df)</code></pre></div>
<p><img 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" width="480" /></p>
</div>
<div id="heatmap-of-the-sample-to-sample-distances" class="section level3">
<h3>Heatmap of the sample-to-sample distances</h3>
<p>Another use of the transformed data is sample clustering. Here, we apply the <em>dist</em> function to the transpose of the transformed count matrix to get sample-to-sample distances.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sampleDists <-<span class="st"> </span><span class="kw">dist</span>(<span class="kw">t</span>(<span class="kw">assay</span>(vsd)))</code></pre></div>
<p>A heatmap of this distance matrix gives us an overview over similarities and dissimilarities between samples. We have to provide a hierarchical clustering <code>hc</code> to the heatmap function based on the sample distances, or else the heatmap function would calculate a clustering based on the distances between the rows/columns of the distance matrix.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"RColorBrewer"</span>)
sampleDistMatrix <-<span class="st"> </span><span class="kw">as.matrix</span>(sampleDists)
<span class="kw">rownames</span>(sampleDistMatrix) <-<span class="st"> </span><span class="kw">paste</span>(vsd$condition, vsd$type, <span class="dt">sep=</span><span class="st">"-"</span>)
<span class="kw">colnames</span>(sampleDistMatrix) <-<span class="st"> </span><span class="ot">NULL</span>
colors <-<span class="st"> </span><span class="kw">colorRampPalette</span>( <span class="kw">rev</span>(<span class="kw">brewer.pal</span>(<span class="dv">9</span>, <span class="st">"Blues"</span>)) )(<span class="dv">255</span>)
<span class="kw">pheatmap</span>(sampleDistMatrix,
<span class="dt">clustering_distance_rows=</span>sampleDists,
<span class="dt">clustering_distance_cols=</span>sampleDists,
<span class="dt">col=</span>colors)</code></pre></div>
<p><img 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" width="576" /></p>
</div>
<div id="principal-component-plot-of-the-samples" class="section level3">
<h3>Principal component plot of the samples</h3>
<p>Related to the distance matrix is the PCA plot, which shows the samples in the 2D plane spanned by their first two principal components. This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotPCA</span>(vsd, <span class="dt">intgroup=</span><span class="kw">c</span>(<span class="st">"condition"</span>, <span class="st">"type"</span>))</code></pre></div>
<p><img src="data:image/png;base64,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" width="480" /></p>
<p>It is also possible to customize the PCA plot using the <em>ggplot</em> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pcaData <-<span class="st"> </span><span class="kw">plotPCA</span>(vsd, <span class="dt">intgroup=</span><span class="kw">c</span>(<span class="st">"condition"</span>, <span class="st">"type"</span>), <span class="dt">returnData=</span><span class="ot">TRUE</span>)
percentVar <-<span class="st"> </span><span class="kw">round</span>(<span class="dv">100</span> *<span class="st"> </span><span class="kw">attr</span>(pcaData, <span class="st">"percentVar"</span>))
<span class="kw">ggplot</span>(pcaData, <span class="kw">aes</span>(PC1, PC2, <span class="dt">color=</span>condition, <span class="dt">shape=</span>type)) +
<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">size=</span><span class="dv">3</span>) +
<span class="st"> </span><span class="kw">xlab</span>(<span class="kw">paste0</span>(<span class="st">"PC1: "</span>,percentVar[<span class="dv">1</span>],<span class="st">"% variance"</span>)) +
<span class="st"> </span><span class="kw">ylab</span>(<span class="kw">paste0</span>(<span class="st">"PC2: "</span>,percentVar[<span class="dv">2</span>],<span class="st">"% variance"</span>)) +<span class="st"> </span>
<span class="st"> </span><span class="kw">coord_fixed</span>()</code></pre></div>
<p><img 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" width="480" /></p>
</div>
</div>
</div>
<div id="variations-to-the-standard-workflow" class="section level1">
<h1>Variations to the standard workflow</h1>
<div id="wald-test-individual-steps" class="section level2">
<h2>Wald test individual steps</h2>
<p>The function <em>DESeq</em> runs the following functions in order:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds <-<span class="st"> </span><span class="kw">estimateSizeFactors</span>(dds)
dds <-<span class="st"> </span><span class="kw">estimateDispersions</span>(dds)
dds <-<span class="st"> </span><span class="kw">nbinomWaldTest</span>(dds)</code></pre></div>
<p><a name="contrasts"></a></p>
</div>
<div id="contrasts" class="section level2">
<h2>Contrasts</h2>
<p>A contrast is a linear combination of estimated log2 fold changes, which can be used to test if differences between groups are equal to zero. The simplest use case for contrasts is an experimental design containing a factor with three levels, say A, B and C. Contrasts enable the user to generate results for all 3 possible differences: log2 fold change of B vs A, of C vs A, and of C vs B. The <code>contrast</code> argument of <em>results</em> function is used to extract test results of log2 fold changes of interest, for example:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">results</span>(dds, <span class="dt">contrast=</span><span class="kw">c</span>(<span class="st">"condition"</span>,<span class="st">"C"</span>,<span class="st">"B"</span>))</code></pre></div>
<p>Log2 fold changes can also be added and subtracted by providing a <code>list</code> to the <code>contrast</code> argument which has two elements: the names of the log2 fold changes to add, and the names of the log2 fold changes to subtract. The names used in the list should come from <code>resultsNames(dds)</code>.</p>
<p>Alternatively, a numeric vector of the length of <code>resultsNames(dds)</code> can be provided, for manually specifying the linear combination of terms. Demonstrations of the use of contrasts for various designs can be found in the examples section of the help page for the <em>results</em> function. The mathematical formula that is used to generate the contrasts can be found <a href="#theory">below</a>.</p>
<p><a name="interactions"></a></p>
</div>
<div id="interactions" class="section level2">
<h2>Interactions</h2>
<p>Interaction terms can be added to the design formula, in order to test, for example, if the log2 fold change attributable to a given condition is <em>different</em> based on another factor, for example if the condition effect differs across genotype.</p>
<p>Many users begin to add interaction terms to the design formula, when in fact a much simpler approach would give all the results tables that are desired. We will explain this approach first, because it is much simpler to perform. If the comparisons of interest are, for example, the effect of a condition for different sets of samples, a simpler approach than adding interaction terms explicitly to the design formula is to perform the following steps:</p>
<ul>
<li>combine the factors of interest into a single factor with all combinations of the original factors</li>
<li>change the design to include just this factor, e.g. ~ group</li>
</ul>
<p>Using this design is similar to adding an interaction term, in that it models multiple condition effects which can be easily extracted with <em>results</em>. Suppose we have two factors <code>genotype</code> (with values I, II, and III) and <code>condition</code> (with values A and B), and we want to extract the condition effect specifically for each genotype. We could use the following approach to obtain, e.g. the condition effect for genotype I:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds$group <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">paste0</span>(dds$genotype, dds$condition))
<span class="kw">design</span>(dds) <-<span class="st"> </span><span class="er">~</span><span class="st"> </span>group
dds <-<span class="st"> </span><span class="kw">DESeq</span>(dds)
<span class="kw">resultsNames</span>(dds)
<span class="kw">results</span>(dds, <span class="dt">contrast=</span><span class="kw">c</span>(<span class="st">"group"</span>, <span class="st">"IB"</span>, <span class="st">"IA"</span>))</code></pre></div>
<p>The following two plots diagram hypothetical genotype-specific condition effects, which could be modeled with interaction terms by using a design of <code>~genotype + condition + genotype:condition</code>.</p>
<p>In the first plot (Gene 1), note that the condition effect is consistent across genotypes. Although condition A has a different baseline for I,II, and III, the condition effect is a log2 fold change of about 2 for each genotype. Using a model with an interaction term <code>genotype:condition</code>, the interaction terms for genotype II and genotype III will be nearly 0.</p>
<p>Here, the y-axis represents log2(n+1), and each group has 20 samples (black dots). A red line connects the mean of the groups within each genotype.</p>
<p><img 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" width="480" /></p>
<p>In the second plot (Gene 2), we can see that the condition effect is not consistent across genotype. Here the main condition effect (the effect for the reference genotype I) is again 2. However, this time the interaction terms will be around 1 for genotype II and -4 for genotype III. This is because the condition effect is higher by 1 for genotype II compared to genotype I, and lower by 4 for genotype III compared to genotype I. The condition effect for genotype II (or III) is obtained by adding the main condition effect and the interaction term for that genotype. Such a plot can be made using the <em>plotCounts</em> function as shown above.</p>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA8AAAAJACAIAAADNc5igAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdZ0BT1/sH8OcmIRASpoB71j3qqEqr1oK2tm7Uuifaun6Ke+/aWvceuK3bWhX3wlm1dS/cFhUVoSgQCASy7v9F/o1h55KEG5Lv5xX35OaeJzHj68255zAsyxIAAAAAAJhGwHcBAAAAAACFCQI0AAAAAAAHCNAAAAAAABwgQAMAAAAAcIAADQAAAADAAQI0AAAAAAAHCNAAAAAAABwgQAMAAAAAcIAADQAAAADAAQI0AAAAAAAHCNAAAAAAABwgQAMAAAAAcIAADQAAAADAgYjvAgo9uVx+48aNd+/eRUdHCwQCLy+vqlWr1qtXTyKR8F0aAAAAAFgeAnQ+sSy7d+/e33777fTp02q1OtOtTk5O7du3Hz58eNOmTXkpDwAAAACshGFZlu8aCp+IiIihQ4f++eefee7Zs2fPFStWeHl5FUBVtkMul2/atOn06dP37t378OGDQCAoWrRoo0aNvv/++3bt2gkEGDgEAAAAhRgCNGcXLlxo06aNQqHQbwqFwmbNmtWrV6948eJyufzZs2f37t27d++eYf86deqcOnXK19eXp3oLWlhYWHBwcGJiYra31qlTZ+fOndWqVSvgqgAAAAAsBQGamz///LNFixZpaWlEJBAIhg8fPnXqVB8fH+N9WJYNDw+fPn3633//rW8JDAwMDw93hDOvBw4c6Nixo2FTKpVWr149JSXlyZMnWq1W3+jm5nbp0qVPP/2UpxoBAAAAzIIAzUFSUtKnn3766tUrIpLJZEeOHPnqq69y2lmj0XTt2nX//v36zVWrVg0dOrSACuVJQkJC+fLl5XI5Efn6+q5bt84wYEOpVC5atOjnn39OT08nogoVKjx8+NDZ2ZnnigEAAAC4s/9zohY0ceJEfXoWCAS5p2ciEolEO3fubNSokX5z/vz5Go2mIKrkT2hoqD49i0SiS5cuBQUFGU66SySSqVOnHjhwQL8ZGRm5efNm3goFAAAAMAMCtKni4uI2bdqk/3vs2LG5p2c9Z2fnqVOn6v9+9eqVKRcdFmphYWH6PwYPHly5cuWsO7Rs2bJXr176v/ft21dwlQEAAABYDqaxM9W6dev0ww9kMtmUKVNMvFeLFi2aNGkSFxdHRA8ePAgMDMx2N5ZlHzx4cOPGjdjYWJlMVr58+dq1a5csWdL08lQq1ZkzZ168eKFQKD755JNq1apVr149z3uZ36+BUqm8efOm/u/u3bvntNu33367fft2Inrw4EE+egEAAADgHwumadCggf4ZGzp0qAUPq9Ppdu/eXalSpUz/LgKBoGPHjlevXs32XhMmTNDvdv/+fZ1Ot2bNmkwXMhLRN998c+fOHcv2m4t//vnHcJCEhIScdvvrr7/0+7i5uXHtAgAAAMAWYAiHSVJSUm7duqX/u1WrVpY6rFqt7tOnT7du3Z49e5bpJp1Ot3///iZNmmzZsiX3g4waNWrIkCHv37/P1H769OmAgICXL19aqd+sd+zUqVOnTp2Cg4M9PT1z2u3Fixf6PypWrMjp+AAAAAA2AkM4THLr1i3DLGxffPGFpQ4bEhKiH89ARM2bN2/btm316tUTEhJu3bq1fv36+Ph4tVodHBwslUo7d+6c7RG2bNmybNkyZ2fn8ePHBwQEVK5c+cWLF1u2bNEP105MTBw6dOixY8cs3m9WFStW/OOPP/LczZDL69SpY+KRAQAAAGwL36fAC4e9e/fqny4PDw9LHfP06dP6Y4pEot27d+t0OuNbY2Njv/vuO/0OxYsXT0xMNL7VMISDiIoVK3bjxo1MB589e7b+VplMptVqLdWvmX777TdD2Xfv3rXgkQEAAAAKDIZwmOTDhw/6P7y9vXPZrXPnzqVylmliinnz5un/mDNnTteuXRmGMb7Vz89v9+7dZcuWJaJ3795t27Ytp04XLFjw2WefZWocP368WCwmIoVCERUVZY1+OWFZNjQ0dMCAAfrNXr16YSEVAAAAKKQQoE2iX3qQiIRCYS67xcXFvc1ZdHS0Yc+YmJjw8HAi8vLyGj16dLZH8/DwmDhxov7vc+fOZbtPlSpVevTokbVdLBZXrVpV/7fx/NOW6peTp0+ftmrVasiQIfpKvvrqq/Xr15t/WAAAAABeIECbpEiRIvo/EhISLHLAS5cu6f8IDAzMJZR//vnn+j+uXbuW0w45rRAukUis16+J4uPjR44cWaNGjRMnTuhbOnTocOjQIRcXF3MOCwAAAMAjXERoEuMArVQqs82mRHT+/Pmc7h4fH2/c8vjxY/0f+/fvzzSIIluJiYnZtmedhy53luo3TyzL7tixY8SIEYYHXqRIkRUrVnTr1s2UfgEAAABsFs5Am8QwfFmn012/fp3TfbVabdYYmilP50mhUBimATHm5+fH6TiW6jd3MTExHTp06N27t747FxeXiRMnPn/+vHv37kjPAAAAUNjhDLRJKlSoUKpUqTdv3hBReHh406ZNTb9vVFSUTqfL1GgYl9y0adOclifMRKvVZh10wTWPWqrfXJw5c6Zz586GsS7BwcE//fRTqVKlONUJAAAAYLMQoE3CMEyzZs22bt1KRBs3bpw2bZqTk5OJ9z158mTWRsNsHuXKlZs5c6aFysybtfsNDw9v06aNfs3zihUrbty4kdN/NgAAAABsH4ZwmGrIkCH6P6Kjo9euXWvivbRa7apVq7K26+eJI6JHjx5ZpDwTWbXfd+/edevWTZ+eW7ZseefOHaRnAAAAsD8I0Kb6/PPPmzRpov97woQJJgbQZcuWRUREZG1v3Lix/o/79+/HxMTkdPewsLCgoKCgoCCuC2vnxKr9rl69Wj9hdtOmTQ8ePCiVSs0rFgAAAMAWIUBzsHLlSv3qJKmpqYGBgffu3ct9/x07dowbNy7bmypVqlSjRg0iSktLmzRpUrb7aDSaCRMmHDx48ODBg1xn28iJ9fpNT09ft26d/u9169aZPsQFAAAAoHBBgOagdu3aixYt0v8dGxvr7+8/ffp0uVyedc+oqKgBAwb06tVLp9OVLl3ay8sr0w4Mw4wZM0b/95YtWxYsWJBpsgutVtuvX7+nT58SUcWKFRs1amSRh2C9fo8fP/7vv/8SUfXq1YsWLZqYl+TkZIs8IgAAAIAChosIufnf//6XkpKiX6gvLS1t9uzZc+fObdasWd26dYsWLcowTGxs7OXLly9fvqwPppUrVz59+vTw4cMPHTqU6VB9+/bds2eP/hLD8ePH79u3r0OHDrVq1SKix48fr127Vp9iXVxctm3bZsHZ36zUr2GG6YcPH2b9D0NWtWvXvnPnTj4fAwAAAAB/EKC5YRhmwoQJ5cqVGzFiRGxsLBGp1eqTJ09mO9VG586dV65c6efn16hRo6wBWiAQ7N69Oygo6MKFC0R09erVq1evZtrHyclp+/bthnUBLcJK/UZGRlqwSAAAAACbhSEc+dG1a9dnz57NmDHDsMCKMScnp+++++7UqVO///67fqGTnAZCeHp6njp1asmSJUWLFs10E8MwPXv2fPz4cadOnSxevzX6RYAGAAAAB8GwLMt3DYUYy7KPHz9+8ODBu3fvkpOT/fz8ihcv3rhxY09PT07H0Wq1N27cuH//flxcnI+PT+XKlatWrZo13VocX/0CAAAAFF4I0AAAAAAAHGAIBwAAAAAABwjQAAAAAAAcIEADAAAAAHCAAA0AAAAAwAECNAAAAAAABwjQAAAAAAAcIEADAAAAAHCAAA0AAAAAwAECNAAAAAAABwjQAAAAAAAcIEADAAAAAHCAAA0AAAAAwAECNAAAAAAABwjQAAAAAAAcIEADAAAAAHCAAA0AAAAAwIGI7wIKgbi4OIVCwXcVAIVG+fLlTd/5xYsX1qsEwP6Y/v6Kj4+Xy+VWLQbAnnD68kKAzptWq1Wr1XxXAWCf8OYCsBJ8eQFYD4ZwAAAAAABwgAANAAAAAMABAjQAAAAAAAcI0AAAAAAAHCBAAwAAAABwgAANAAAAAMABAjQAAAAAAAcI0AAAAAAAHCBAAwAAAABwgAANAAAAAMABAjQAAAAAAAcI0AAAAAAAHCBAAwAAAABwgAANhdJPP/0UGBi4bt06vgsBsBPZvqfwRgPIN9PfU3ijFUYI0AAAAAAAHCBAAwAAAABwgAANAAAAAMABAjQAAAAAAAcI0AAAAAAAHCBAAwAAAABwgAANAAAAAMABAjQAAAAAAAcI0AAAAAAAHCBAAwAAAABwgAANAAAAAMABAjQAAAAAAAcI0AAAAAAAHCBAAwAAAABwgAANAAAAAMABAjQAAAAAAAcI0AAAAAAAHDAsy/Jdg62LiYlJSkriuwqAQqNy5cqm7/z06VPrVQJgf0x/f8XFxSUkJFi1GAB7wunLC2egAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4YFiW5bsGW5eWlqbRaPiuwipEIpFQKCQitVqt0+n4LschOMJzLpPJTN9ZoVBYrxIeMQwjFouJiGVZlUrFdzmOwtnZmez9OTf9/ZWenq5Wq61aDF8c4YPU1giFQpFIREQajUar1fJdjlVw+vISWa8Ou6HRaNLS0viuwipkMpmTkxMRpaam2uvnrK2RSqX651ypVNrrdzynzyB7fXMxDCOVSolIrVbb62O0QfrXnlartePn3PT3lx1/eTnCB6mtkUgk+uc8PT3dXl9XnL68MIQDAAAAAIADBGgAAAAAAA4QoAEAAAAAOECABgAAAADgAAEaAAAAAIADBGgAAAAAAA4QoAEAAAAAOECABgAAAADgAAEaAAAAAIADBGgAAAAAAA4QoAEAAAAAOECABgAAAADgAAEaAAAAAIADBGgAAAAAAA4QoAEAAAAAOGBYluW7BlunUqkYhuG7CqsQCoUCgYCINBoNXgkFwxGecycnJ9N3VqvV1quEX/rngWVZjUbDdy2Owu6fc61W6+LiYuLO6enp+k8b+2P4INVqtTqdju9yHIJAIBAKhWTXzzmnLy+R9eqwGyzL2mvQMXy2sixrr+8HW4PnPBO7fxLwD80Le33OuX4Z2evzYPgg1el09voYbY3hTCI+0/QQoPOmVqvT0tL4rsIqZDKZ/j+Uqampdnwi0KZIpVKJREJESqVSpVLxXY5VODs7m75zcnKy9SrhEcMw+udBq9Xa62O0QfrnXKfT2fFzrv8AMYVGo1EqlVYthi+O8EFqayQSiUgkIqK0tDR7DUWcvrzs88cdAAAAAAArQYAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMEaAAAAAAADhCgAQAAAAA4QIAGAAAAAOAAARoAAAAAgAMR3wUAAABwo9FoTpw4cenSpbi4OIlEUqdOnQ4dOhQpUoTvugDAUSBAAwBAYXLnzp1BgwZFRkYaWvbs2TNr1qxp06ZNnjzZ9ONoNJozZ8789ddfiYmJbm5u9evXb9mypVgstkLJAGBvEKABAKDQiIiI6NChg0KhyNSelpY2ZcoUgUAwceJEU45z/fr1oUOHvnz50rixaNGiixcvbtGihaWqBQB7hTHQAABQCCiVygcPHvz4449Z07PB9OnTnz17luehrl692rFjx0zpmYhiY2P79Olz5MgRM0sFALuHAA0AADYtMjJywIABFStWDAgIeP78eS57qtXqLVu25H40tVo9cuTItLS0bG/VarVjxoxJSkrKd7UA4AgQoAEAwHZdvHixWbNmhw4dUqlUpux/9erV3He4dOlS7ik8Pj7+8OHDHEoEAMeDAA0AADYqLi4uODg4JSXF9LvEx8fnvsOJEyfyPMi1a9dM7xEAHBAuIgQAABsVGhrKdTSFr68vEWk0mvDw8L///jspKcnNza1BgwYtWrQQi8UpKSm///57ngfJM4UDgINDgAYAABt16tQprndp0qTJpUuX+vbt++rVK+P2EiVKLF26NCIiIpdrEA28vLy49gsADqXQB+jw8PDly5evXbu2ePHiWW99//79zp07//nnn3fv3hUpUqRChQqdO3cuV65cgZcJAACcvX79mutdLly48Msvv6Snp2dqj46O7t69e4kSJUw5SIMGDbj2CwAOpXAHaJZlL1y4kNOtt2/fnjt3rlKpJCIPD4/o6Oi3b99euXLlhx9+aN26dQGWCQAAplKr1bt3796zZ8/9+/dTU1O53v3MmTM53aTVak1J5G5ubm3btuXaLwA4lEIcoHU63aFDh+7evZvtrWq1es2aNUqlslGjRkOHDnV3d09NTf3999/379+/YcOGGjVq4Dw0AICt+fDhQ8+ePW/evMljDf379/f09OSxAACwfYUyQN+4cePSpUsRERH//vtvTvtcuHAhJibGx8dnzJgxTk5OROTq6tqvX7/4+Pjz58/v3bt33LhxBVgyAADkQafT9e7dm9/0TESdO3fmtwAAsH2Fchq7v/766+zZs7mkZyKKjIwkoubNm+vTs0Hz5s0NtwIAgO04dOjQ9evX+a2hfPnyVapU4bcGALB9hfIMdLdu3QyDmCdPnpztILm3b98SUZkyZTK163+Yi4uLs3KNAADAzcGDB/kugSZNmsR3CQBQCBTKAO3r66uf6ZOIhEJhtvsEBwf36NGjdOnSmdqfPXtGREWLFrVqhQAAwFXuCwQWgNGjR3fo0IHfGgCgUCiUAdoU2V4jqFAo9FPoN27cONt7DR48OOuSV8HBwU2bNrV0gTZBIPj/MTwymYxlWX6LcRCG51wqlbq6uvJbjC2w+6u1RCKR3T9GS9FoNLz0KxAI/P39p0yZ8u233/JSgOl0Op3pOzs5OTk7O1uvGB7hg7TgGZ5zV1dXFxcXfouxBXYboLOKjo6eP39+TEyMt7d3mzZtst3n6dOnWVe9SkpKEons/InK6UQ+WA+ecz27f3MxDGP3j9FSypYtW8AnoaVS6cWLF8uXL19YVk7RarWm7+wIrz18kBY8gUBgCNOOzCGeArVa/fvvv4eEhERGRrq5uU2fPt3d3Z3vogAAIIOWLVuac/cQosyD9vISFBRUr169wpKeAcB2MIX9h/uePXsmJyfntBIhEb169WrBggVRUVFEVKtWrVGjRvn4+OR0tOTk5KxPiE6n4/Sf/kJEJpPpf+CTy+V8/XjqaKRSqf7Hr+TkZJVKxXc5VlGkSBHTd/7w4YP1KuERwzDe3t5EpNFo5HI53+UUDsnJyf7+/rnPsJSTIKIDRAqhcKBWu8u0u0il0gsXLhS6NQFMf3+lpKSkpaVZtRi+uLq6SiQSsusPUlsjkUj0o2UUCkXWlT7tA6cvLzv/cefkyZPr1q1Tq9VeXl7BwcFfffUVwzC57O/m5pa1UaFQ2Gu4NP7fQmH/r1RhYXieWZbFc04O8MLDP7TpZDLZb7/91qVLl+TkZE53LEm0QX8ErXYnUTOiEUS5r2Ho4eGxYcOGsmXL2ve/jn0/OsL7qwAhMGRiz0M4Lly4sGrVKrVa/cUXX6xZsyYgICD39AwAAPyqX7/+uXPn2rZta/rgXQHRb0TGJ44aEeX+9V6uXLndu3cHBATkt0wAcHR2G6DfvXu3fPlyIuratevEiRNxlS4AQKFQtmzZTZs2PX36NCwszJRJJMYTNTfaTCfqQaTM9S4vX77s2bPno0ePzCwVAByW3QbokydPqtXq5s2b9+zZEyeeAQAKFzc3Nx8fnzyHWn5GNCtjy1iiuyYcPz4+fsCAAWq1Ot8VAoAjs9sx0JcuXSIif3//hISErLcKBAIPD48CLwoAAPL2+PHjS5cu3b59O/fdpEQ7iMRGLSeIVpncy7Nnz44fP96uXbv8FQkAjsw+A7RardZfxz1nzpxsd/Dz89uwYUPBFgUAAHmIjo4ePXr0mTNnTNl5BVEVo81/iYLzGv2cyblz5xCgASAf7DNAx8XF8V0CAABw8/bt21atWkVHR5uycyeiYKNNlqg/UQzHHk3sCwAgk0IfoHfs2JG1sUSJEocOHSr4YgAAIN/Gjx9vYqItRbQuY8sSojMuLsRx2mNcXw4A+WO3FxECAEAhEhUVderUKVP2FBBtJfI2aokgmkLUokULrp1WrVqV610AAAgBGgAAbMGVK1dM3HMyUaDRZhpRD6IvAgNnz57NaSExgUDQvn17LjUCAPw/BGgAAODf+/fvTdmtAdH0jC0jid75+MyfP79EiRLHjx8vVaqUiT0OGDAAZ6ABIH8QoAEAgH/u7u557iMj2kHkZNRyjOh63bqHDx8uV64cETVo0CAiImLOnDkNGjTIcxGW+vXrm1MwADgyBGgAAOBf7dq189xnNVElo833YrFu8+YTJ09WrFjR0Ojh4TF+/Pi9e/fmebTdu3fnp1AAAARoAACwBZ9++mmNGjVy2aEzUW+jTR1RD7Va6+WV7Vqzjx8/znMVw7t3TVmyEAAgGwjQAADAP4ZhFi1a5OLiku2tpYnWZmxZSHSaZZcuXZrt/klJSXn2KJfLWZbTuisAAP8PARoAAGzCZ599tnv37mLFimVqFxHtJvIyarlFNI2IiC5fvqxSqbIeysfHJ8/ufHx8sj17DQCQJwRoAACwFY0bN75+/frSpUs7derUoEEDfeNUokZG+6QQ9SDSp2a1Wh0bG5v1ONWqVfPy8srabqxRo0a57wAAkBMEaAAAsCEuLi49e/YMDQ3dsmULETUmmppxhxFET4w2s51tQyQSBQcHZ203YBjmxx9/NL9aAHBMCNAAAGCLfHx8ynl5bScSGjXuJ9potOnt7Z3TaI1Ro0YZzmFnNXLkyFxuBQDIHQI0AADYIoFAsMvLq5xRyxuigRn3CQoKEgiy/yJzcXHZu3dv7969RSKRcbu3t/eCBQsmT55s4XIBwJGI8t7FNFqt9uHDh9HR0TExMTExMTqdrnjx4sWKFStZsmT16tWFQmHehwAAAPiP8+7dn0dGGjZ1RH2JPhjt4OPjM2bMmFyOIJVKFy9ePGbMmPPnz0dFRYlEourVqwcEBEilUqtVDQAOwdwAHRkZefTo0bNnz547d04ul2e7j6enZ0BAQLNmzVq3bl2hQgUzewQAALsnfPVKlvEk8a9EZ402S5UqtXXrVj8/vzwPVbJkyZ49e1q6QABwaPkM0Dqd7vTp0ytWrDh27FimeTSFQqGXlxfDMPHx8VqtlogSExPDwsLCwsJGjBjRqlWrkJCQr7/+Oqcf3QAAwNFpNG5DhjDJyYaGt8WLHypWzO35cyKqXLlyy5Yt+/fv7+bmxl+JAODQ8hOgjx49OmbMmCdPnhCRWCyuX7++v7+/v79/3bp1/fz83N3d9eGYZVm5XP7vv//euXPn6tWrV69evXHjxtGjR48ePVq1atVFixa1atXKwo8GAAAKP9d580TXrxs2WanU9cCBo598wmNJAADGuAXot2/fjhw58o8//iCigICAXr16derUydPTM9udGYbx9PT09PSsXLlyly5diEgul+/bt2/btm0XLlxo3bp1586dly5dWqJECfMfBgAA2Aenv/92XbHCuEUxb54W6RkAbAm3AF2tWjWlUjlw4MBJkyaVK1eOa2ceHh79+/fv37//q1ev5syZs2nTphMnTpiy4CoAADgCRi53GzqUtFpDS3rbtuldu/JYEgBAVtwGIgcFBT1+/Hjt2rX5SM/GypYtu3bt2sePHwcFBZlzHAAAsCeysWMFr18bNnUlSyoWL+axHgCAbHE7A71161YL9v3JJ59Y9oAAAFB4uWzf7hwW9nFbIEhevZrNYZQgAACPMBUGAADwT/jihXTaNOOW1DFj1I0a8VUPAEAuEKABAIBvarXbkCGMQmFo0NSrlzpqFI8VAQDkAgEaAAB4Jp0zR3TzpmGTdXdPXr+enJx4LAkAIBcI0AAAwCenK1cka9YYtyjmz9eWKcNXPQAAeUKABgAA3jCJiZnnrevaNb1TJx5LAgDIE7dZOLRa7aNHj8zpr2bNmubcHQAA7IlbSIjg7VvDprZsWcWvv/JYDwCAKbgFaKVSWatWLXP6Y1nWnLsDAIDdcNmyRXz8+MdtkSh5zRrWzY2/igAATMJtCIeTk9M333wjEGDgBwAAmEX49Kl0+nTjltTx4zUNGvBVDwCA6bidgXZ2dj516lRsbOzQoUP379+vb1y5cqVQKLRCbQAAYJ8Ylcpt0CBGqTS0qD//PDUkhMeSAABMxy1A6xUtWvSPP/748ssvL1++TESDBg0SifJzHAAAcEyus2eLIiIMm6yHR/Lq1YRzMQBQSORzMAbDMMOGDbNsKQAA4Aiczp2TrF1r3KJYsEBXujRf9QAAcJX/0cxffPGFBesAAABHIPjwwW34cDK6oDytR4/0Dh14LAkAgKv8B+gyZcrIZDILlgIAAHaOZWUjRghiYw0N2nLlUn75hceKAADyIf9jlxmGSUhIICJcQQgAAKaQbNwoPnny47ZIlBwayuJcDAAUNmZd/IdrBwEAwETCx49dZ80ybkmZNEnz2Wd81QMAkG+Y0RkAAKyOUancBg9m0tIMLepGjZT/+x+PJQEA5BsCNAAAWJ10xgzRgweGTdbTM3nVKsxbBwCFlFXGYDx//pyIPvnkE4ZhrHF8AAAoRMRnz7ps3FhNx/UAACAASURBVGjcoli4UFeqFF/1AACYySoBulKlSkSkVqsxSBoAwMEJ3r+XZZq3rm/f9PbteSwJAMBMGMIBAABWw7KykBDBv/8aGrQVKqRkvJQQAKDQQYAGAABrkYSGik+fNmyyYnHyhg2sVMpjSQAA5kOABgAAqxA+euSacZGU1KlTNbVq8VUPAIClIEADAIDlMenpboMGMenphhZ1QIBy8GAeSwIAsBQLXOT34MGD9evXZ20fPXq0QJAhoC9dutT87gAAwPZJp0wRPXpk2NQVKZK8ciVhaiYAsAsWCNAvXrxYtmxZ1vYVK1ZkakGABgBwBOLwcJetWz9uM4xi6VJd0aL8VQQAYEkWCNDVq1dfsmSJccuoUaOIaOHChUJMkg8A4GAEMTGy//0vw7x1AwaovvuOx5IAACyLYY0+4yx2UIYhO5oHWqFQpBktP2tPZDKZi4sLEcnlcrVazXc5DkEqlUokEiJKSkpSqVR8l2MVPj4+pu/8/v1761XCI4ZhihQpQkRqtVoul/NdTgHS6Ty6dHG6cMHQoK1SJTE8nHVxKYDO9a89rVabkJBQAN3xwvT3V0pKilKptGoxfHGED1JbI5FIpFIp2XUo4vTlZQ8B19qEQqFLgXz0FzzDTwRisRg/FxQMw/8qnZycMl0k4Jjs9c1lWIdVIBDY62PMlnjpUuP0TM7O6Zs3O3t6FmQNDMPY63PO6ZyXHb/2jL+88EFaMJycnDL94eBwBjpvarUaLxcAgLzdukVffEHGZwSXL6fhw/kryN5otVrTT3bgywvAeuwh4FqbSqVKSkriuwqrkMlkzs7ORCSXyzUaDd/lOASpVKo/J5ScnGyvvzzqhy6Y6MOHD9arhEcMw3h7exORRqNxkCEcTGqqR9euQqNXtbp586Tu3akA/4n1rz2tVpuYmFhgnRYw099fdvzl5erqqh/CYccfpLZGIpG4uroSkUKhSDeantKecPryQoDOG8uy1jhPbwuMH5e9PkZbY3ie7fh1xYndPwmO8w8tnTRJ+Py5YVPn45O8fDlLRHw8fAd5zvNk98+D47y/eIfAkIlVAjSeWQAAhyI+csRlx46P2wyjWLZM5+fHX0UAAFaEofcAAGAWQXS025gxxi3KgQNVLVrwVQ8AgLUhQAMAgBl0Ordhw5j4eEODtlq11KlTeawIAMDarDgGOiEh4dy5c7GxsfXr169Tpw6uBQYAsD+uy5Y5/fmnYZN1dk5eu7ZgZn0GAOCLZQL0kSNHDhw44OPjM2/ePH3LzZs327VrFx0drd+sW7fukSNHSpQoYZHuAADAFoju3HFdsMC4JWX2bE21anzVAwBQMCwwhGPUqFFt27bdtGnTkydP9C0qlapPnz6G9ExEt2/fDgwMxERpAAB2g0lJcRs8mIwWMVV9/XVav378VQQAUEDMDdAXL15cunQpETk5OZUvX17fePLkyYcPHxLRTz/99OjRo+nTpxPR06dP9+/fb2Z3AABgI2QTJgj/+cewqfP1VSxfTv8twQgAYMfMDdCLFy8mIj8/v4iIiCVLlugbDxw4QESlS5eePHly1apVZ82a9e233xLRtm3bzOwOAABsgfPhw8579nzcFgiS16zR+fryVxEAQMExN0A/fvyYiIYMGVK5cmVDY3h4OBH17dvXsOJo27ZtiSgyMtLM7gAAgHeCt29lo0cbtyiHDlV/9RVf9QAAFDBzA3RUVBQRVTO6ZCQqKur169dE1KxZM0NjyZIliejVq1dmdgcAADzT6dyGDmWM1srWfPppyqRJPFYEAFDAzA3Qnp6eRKRUKg0tJ06cICInJyd/f39DY2pqKhGJxWIzuwMAAH65LlzodOWKYZN1dU0ODSV8vAOAIzE3QFeoUIGILl++bGjZuHEjETVr1szV1dXQePv2bSIqU6aMmd0BAACPRLduuS5datyS8ssv2kqV+KoHAIAX5gbo5s2bE9HWrVv379+v0Wj27t177do1IgoKCjLsc+PGjQ0bNhBR1apVzewOAAD4wiQluf34Y4Z561q3TuvVi8eSAAB4YW6AHjFihLu7u0ql6tSpk4uLS5cuXYioaNGi3bt3JyKFQhEYGPj5558nJiYS0ciRI82vGAAAeCEbP14YFWXY1BUvnrx4MY/1AADwxdwA7e3tffjwYR8fHyLSarVEJBaLV69e7eHhQURpaWnnz5/Xtw8bNuzzzz83u2AAAOCB8549zvv2fdwWCJJXrWK9vfmrCACANxZYyrtp06b37t07fvz47du3/fz8OnbsWKNGDcOtJUuWrFu3bp8+fTp37mx+XwAAUPCEr17JMs6zkRoSov7yS77qAQDglwUCNBEVL168f//+Wdt9fHzevHljkS4AAIAfGo3bkCFMcvLHhtq1U8eN47EiAAB+mRugFy5cSESjR48WCHIbDRIVFfX777/7+vr27dvXzB4BAKAguc6fL7p+3bDJuromr12LeesAwJGZG6DHjRtHRCNHjsw9QL9//37cuHElS5ZEgAYAKESc/v7bdfly4xbFvHnaTz7hqx4AAFtg7kWEptBqtRcuXCCiuLi4AugOAAAsgpHL3YYOJa3W0JLetm16t248lgQAYAvycwZaP+eGsaJFizIMk9P+SqVSvxKhfkFvAAAoFGTjxglevzZs6kqUUCxaxGM9AAA2Ij8B+sOHD5la4uPjTbnjsGHD8tEdAAAUPJedO50PHPi4LRAkr17NennxVxEAgK3IT4CeMWOG4e9Zs2YR0bRp03IfA+3k5NSwYcNvvvkmH90BAEABE758KZ0yxbgldfRodePGfNUDAGBTGJZlzbo/wxCRWq0WiSwzI54NUigUaWlpfFdhFTKZzMXFhYjkcrnaaHlesB6pVCqRSIgoKSlJpVLxXY5VZB3llYv3799brxIeMQxTpEgRIlKr1XK5nO9yONJoPNu0Ed28+bGhbt3Eo0fJyYnHokxhWNUrISGB71qsxfT3V0pKilKptGoxfHGED1JbI5FIpFIp2XUo4vTlZW7qHTRoEP0XowEAwA5If/3VOD2zUmlyaKjtp2cAgAJjboAODQ21SB0AAGALnP76S7JqlXGLYuFCbYUKfNUDAGCDLDPugmXZFy9ePHv2LM8BId99951FegQAAItjEhMzz1vXpUv699/zWBIAgA2yQIC+cuVKr169Xrx4YcrOZg65BgAA65GNHSt488awqS1TRjF3Lo/1AADYJnMDdGRkZGBgIIbwAwAUdi6//eZ88ODHbZEoOTSUdXPjryIAABtlboCeNWuWPj23a9du0KBB5cqVy30+OwAAsEHCyEip0RSlRJQ6bpymQQO+6gEAsGXmBuirV68SUevWrcPCwjAXBwBAYcSoVG4//MCkpBha1P7+qSNG8FgSAIAtM/ds8cuXL4noxx9/RHoGyIe0tLSoqCh7nQsZCgvXn38W3b9v2GQ9PJLXrCGhkMeSAABsmblnoL28vGJiYvTrBQA4iPj4+A0bNhw/fvzFixcCgaBy5cpt2rTp16+fTCYz/SAnT55csmTJ9evXNRoNEZUoUaJr167Dhw93w5BTKFhO589LMk5Iqpg/X1e6NF/1AFjE8+fPL1y4EBMT4+rqWqtWraZNm4rFYr6LAvthboD29/c/ePDgrVu3mjRpYpGCAGzctWvX+vXrFxcXZ2i5efPmzZs3N2/evH379mrVquV5BJZlR40atWzZMuPG6OjoJUuWHDhwYP/+/aWRXaCgCD58cBs2jIzmR0rr3j29Y0ceSwIwU1xc3JgxY06cOGE88Vfx4sXnzJnTpk0bHgsDe2LuEI7Ro0czDLN8+XI7XjcVwCAqKqp79+7G6dn4pi5dupjyRli1alWm9Gzw8uXLnj17Yll1KCAsKxs5UhAba2jQliuXMmcOjxUBmCkuLq5Vq1bHjx/PNG3uu3fvgoODt23bxldhYGfMDdBNmzZdsmTJP//807p16zt37likJgCbNXfu3KSkpJxujYmJWb58ee5HUKlUM2fOzGWHR48e7du3L3/lAXDisnGj+MSJj9v6eeu4jEQCsDVTp07VX52VrUmTJr1+/boAywG7Ze4QjrCwsLJlyw4aNGjt2rV169Zt3LhxlSpVSpcuLRJlf+SpU6ea2SMAX1Qq1bFjx3Lf58CBAzMyzgWWyZUrVz58+JD7QY4fP96tWzfO9QFwIXzyRDprlnFLysSJms8+46seAPPFxcWFhYXlskN6evrWrVunTJlSYCWBvTI3QHfo0MF48/Lly5cvX85lfwRoKLzevn2bYjTPVy77SKXSbG9VKpULFy7Ms6Pnz59rNJqc/hcKYD5GpXIbNIhJSzO0qL/4QjlsGI8lAZjv6tWrOp0u932uXLlSMMWAfTP3GzqnoABgf9LT003cLdv3RVJSUlBQ0H2jycJy8vTp0/Llyzdq1GjYsGFffvkl50IB8uI6Y4bowQPDJuvpmbx6Neatg8LOlClBMW0oWIS5AVqhUFikDgDbV7x4cYFAkPvpDVdXVy8vr2xvmjx5sinpWS8tLe3s2bPnzp0LCQnB7zZgWeKzZyUbNxq3KBYu1JUqxVc9AKa7c+fOtWvXUlJS/Pz8GjduXLlyZSJSq9UnTpy4cuXK7du38zyCh4eH9csE+4ffiAFM5eHh8dlnn12/fj2XfZo1a5btokLR0dF79+7l2iPLssuWLStdunTfvn253hcgW4L372XDh2eYt65Pn/T27XksCcAUjx49Gjly5K1bt4wbS5Ys2aZNm+PHj0dFRZl4nDp16lihOnA45s7CAeBQRo8encutAoFg5MiR2d508eLFPEfm5WTOnDkqlSp/9wXIgGVlI0YI/v3X0KAtXz7lp594rAjAFHfu3GnVqlWm9ExEb9++Xbt2renpmYiw9BtYhLlnoJ8/f85p/4oVK5rZIwCPvv766zFjxixatCjrTQKBYO7cubVr1872ju/evct3p/Hx8X///XfTpk3zfQQAPcm6deJTpz5uOzklh4ayuJQFbFtqamr37t0tNWR00aJFFStW7NSpk0WOBg7L3ABdqVIlTvtnmtgcoNCZOHFizZo1f/3116dPnxoaa9euPX36dOOMm5aWJhaLBYL//5HHxcXFnE4jIyMRoMFMwkePXH/+2bglZepUTb16fNUDYIrExMQWLVpY8Mo/lmUnT57cokULNzc3Sx0THBDGQANw1qZNmzZt2jx//vzFixcMw+jnPtff9Pjx45UrV4aHh3/48IFhmKpVq3bq1OmHH36oUqWKOT3me/gHgB6Tnp553rqAAOXgwTyWBGCKwYMHv3jxwrLHjI+PP3ny5Pfff2/Zw4JDMTdAHz58ONt2jUbz+vXrJ0+e7Nq1Kz4+vnXr1hs2bDDzJByATalYsWKmIUk7duwYP368Ybwyy7KPHj36+eefd+7cuX37dj8/v3+Nxp5yUq5cOTOrBQcnnTZN9OiRYVPn7Z28YgUJcBkM2Ipjx45t3br11q1bCQkJRYsW9ff3HzBgwKFDh86cOWON7u7du4cADeZgrD2mIikpacyYMRs2bGjZsuWRI0cEhfDzWqFQpBmdtrEnMplM/78auVyuVqv5LqdwO3/+fNeuXXM6VVyhQoXRo0cPHz48H+84Z2fn58+fF6L/f/r4+Ji+s73OycowjP5aJbVaLZfL+S1GfOaMe/fuxjNvJG3dqmrZkseSrET/2tNqtQkJCXzXYi2mv79SUlKUSqVVi7EIlUo1ePDgnM7HWUmPHj2WLVtWkD0WdhKJRL/EgR2HIk5fXlaPs+7u7mvWrPnqq6+OHz++adMmix8/PDy8Xbt2OV2hJZfLQ0ND+/fv37Fjxx9//HHTpk2pqakWrwGAiH766adcBlpERkYmJibOmTNHLBZzPbJGo1m4cKFGozGvQHBQgri4zPPWDRhgl+kZCqlx48YVcHomLAMHZiuI88Eikahfv35EtHnzZssemWXZCxcu5HTrhw8fRo0adezYsffv34tEotjY2LCwsLFjx2LxF7C4ly9f5rlIyvr163fv3q3PwUKhUCwWFytWrH379kFBQbnfUavVLlu2LCQkxGLlguPQ6dyGDBHExRkatFWqpMycyV9BABk8fPhw165dBd/vzp07jS8EB+CqgAZU1KhRg4geP35swWPqdLqDBw/evXs3px3WrVv3/v37ChUqrFq1as+ePUuWLPHz83vz5s3WrVstWAYAEUVGRua5z6tXr+7evas/S63ValUq1fv37xs2bBgaGtqrV6887753797jx49boFZwJJLVq52MzjKwYnHy2rVs4RkOBHbv0KFDvEzPlZKS0rp1a4tfngiOo4AC9MuXL4koPT3dIke7cePG0qVLBw4cmMuYELlcfu3aNbFYPGnSpNKlSzMM88knn0yePJmILl68iGUpwLLyN4Jco9FMmTKlf//+9evX79mzZ577W/w3HLBvonv3pL/+atySOnOmpkYNvuoByMpKp4GdnJzy3CcxMXHChAnW6B0cQUFMY5eWljZ//nwi0q9Zb76//vrr7Nmzue/z559/arXaunXrFi1a1NBYoUKFChUqREZG3rhxo1GjRhYpBoCIDNPY5cOxY8eOHTtmyp5Xr17Ndy/gaJjUVLfBg8noZIEqMFD5ww88lgSQSUxMzMWLFy14wLp1637xxRf16tVbsmTJgwcP8tz/3LlzUVFRZcqUsWAN4CDMDdC7d+/O5VaWZaOion777bdHjx4R0ddff21md3rdunVr3bq1/u/Jkydne12gfkBq3bp1M7XXq1cvMjLy7t27CNBgjtevX+/Zs+fGjRsKhcLHx6dBgwbFixfPc7nBskTdiebmt9PU1NT09HRnZ+f8HgAciHTKFOGzZ4ZNXZEiipUriWF4LAnAmFqt7tatmzlz1IiIhhKtJjJcYf3o0aPdu3d7e3ubPmnBjRs3EKAhH8wN0N27dzdxTz8/v4kTJ5rZnZ6vr6+vr6/+b6FQmO0++jmMSpQokam9ePHiRJSYmGiRSsAxrVix4tdffzUetnH06NE871WF6DRRaSIXopn56lckEiE9gynER4+6bN/+cZthFMuX6/z8+KsIILOdO3eacpI4JwKi34h6EH1N1JlIP0I0LS3t9OnTXbt2/fTTT69cuWLKceLj4/NdAziyghjCIZPJAgMD16xZ4+3tXQDd6en/U5t1nhqZTGa4NasjR45kHR5ds2bNrEHcPhj++yEWi3P6rwhksnjx4p9++onrvWoQnSYqTkREM4hSiBZw77pYsWKFaDZoE9nfI9Jj/jvXKxAICvgxMtHRrqNHG7eohwwRtGtnn090dhiGsdfXFafr7Qr+tcfJwYMH831fhmgNUQ8iImpLdICoE5F+yuuXL1+6uLj07t173bp1pqzhapefq9ZgGFZuyvhyR2BugM7zN2uGYXx9fQt+/RR9RNbHZWO5B+jFixcnJSVlapw6daqlRm/bLIlEwncJhcOrV6/mzJnD9V6NiI4SeRq1zCM6SXSP43HatWuX9SVd2NnfI8pEKBQW6GPU6WjIEDI+qVajhtPChU6O9B4XCAT2+rrSarWm7ywSiWz5s/2Z0RCjnDBM9su9DSMaaLTZkugwUXuiFCKGYWQyWbFixRo1anTp0qU8uwgMDLTXV4uVODs747dQMj9AFytWzCJ1WFzu/03HqnuQP7t27eI6mUwA0SEiN6MWlmg09/RMROPHj+d+J3Awc+eS8TXWLi60cyfZcIoCh2XKKok5fZWvI/qaqJ1RS3Oik0StiMqUKTNu3LglS5aY+J8N/YqhAFwVxBAOXnh6eiqVypSUlEzt+lVUvLy8sr3X6NGjsw7hqF69ur2uveLs7Kz/LUapVHI6seGwrl27xmn/tkS/Exn/Oqgl+pEoH9PRBQYGFilSpFC8FDmdzikUjygfGIbRDyHTarUFtpyy4PZt11mzjFvS58xRV6hAdvokZ6V/7el0OntddJZlWTc3t7z3IyIijUZjqdljraFkyZL5voIwnagz0U6iTkaNjYnOEi0/cWKrCRelGLx9+7ZkyZL5K8OhiMVi/Uq66enp9noWktOXl8UCtFwu37Fjx7lz5+7fv5+QkKDRaLy9vWvUqNG8efPevXt7enrmfQiL8vT0fPfuXdYArW/JKUC3adMma6MdL/suEon0AVqlUtnr+8GyOF1+2oNoC5HxYDEVUQ+ifdz79fb2XrBgQWF5HXL6DCosD4orQ4DW6XQF8xiZ1FTP4OAM89Y1b57cqxfZ6TOcLf1rj2VZe31dEZHpAbrAXnv5ExAQ8PDhw3zfXUXUlWgTUR+jxs+IJhw9epooj9Gl/9GP9rHlZ8l2MAyjD9BqtdpenzFOX14WGJrMsuy6detKlSr1v//9748//njy5Mm///4bHx///PnzgwcPhoSElC5dev369QW81JA+smcdoh0bG2u4FYArHx8fE/ccTLQtY3pOJWqbr/RMREqlcteuXfhPDuRCNmGC8J9/DJs6X1/FihWYtw5s0+vXr02cJSMXWqL+RJnmq6tOdI7IxFPKdevWxXBeyB8LBOi5c+cOGjRI/zusu7t7/fr1v//++27duvn7+3t4eBCRQqEYOHDg4sWLze/LdLVq1SKirAt937lzh4hq1qxZkMWA3WjcuLEpu00gWpPx3ZVI1ILoVH77VSqVixYt6tu3LzI0ZMv58GFn41n59fPW/TfdJ4BNiY2Nbdeunf7r2Exaoh+IlmVsrEJ0iaiCCXf/AUsLQX6ZG6Dv378/depUIipSpMiyZcvevXt3/fr1vXv37tq16++//3737t2yZcv0I/QnTJjw5MkTC5Rsmi+//FIoFN69e9d4iFVMTMzTp09dXV0///zzAqsE7En79u2N17bM1swsS6X8SxRIdNns3k+fPr1+/XqzDwP2RhAdLRszxrhFOXSoykILVwFY3KxZs968eWOpo7FEI4l+zthYjugcUaVc79ihQ4dOnTrlugtAjswN0CtXrtTpdE5OTocOHQoJCXF1dTW+VSKRhISEhIWFiUQirVa7fPlyM7sznYeHR8OGDZVK5cKFC/VXk8jl8rlz57IsGxgYqB/HA8CVTCZbuXJlTq8fhmgp0YyMje+ImhFZ4EwLERGtWbPGlJlNwYHodG5DhzIJCYYGTa1aKZMn81gRQC6SkpLMmQE6J9OIMi3VVoboIlGt7HZ2dnYeNWrUqlWrGIxxgvwyN0CfO3eOiHr37p3LythNmjTp27cvEYWHh5vZHScDBw4sUqTI3bt3e/bsOWzYsD59+kRGRpYpU6Z3794FWQbYmYCAgLCwsGrVqmVqFxJtIhqRsfEFUROi/K+1lUVMTIwpk6eC43BdvNjp8sefN1iJJHntWsI5ArBV9+7dyzrblUXMI5qQsaUY0RmiOkYtIpFo0aJFd+7cmTx5MhYEAXOYG6Dfvn1LRP7+/rnvph8yYcGfbExRpEiRpUuXtmzZ0tPT8+3bt76+vh07dlywYEGm0+QAXDVo0ODChQtHjx7t16+f/oJUZ6Lfifpl3O0hUROiSEv3rr8QFoCIRLdvu2a8vCTll1+0lXL/4RqAT5zmMuJqPtFQIuMf6XyJzhEZRm326tWrT58+pl8ODpATc6ex08+tkecMG9b7lWTHjh253Orh4TFkyJAhQ4ZYqXdwWAzDNGzYsGHDhr169Wr/9dcHiL7JuMMNou+IPuR8hICAgPPnz+ejayyaBXpMSorb4MFkdF2pqnXrNPzCBrYtp2lkLeX5N98MOn16rdEJQk+i00Ttid5UrjxlyhSr9g6Ow9wz0Prpx69evZr7bn///TcRlSpVyszuAGxN7bJlL7m6ZkrPF4ia5Zqef/jhh127dpUvX55rd2KxuBLOLwIREcnGjhVGfvyFQ1e8eHLBTnYEkA+1a9c2c+a4XE7JtWjRYtu2beV/+WWAi4vGqF1GdEwoDJ8wAZPYgqWYG6CbN29ORNu2bbt48WJO+1y+fPm3334jombNmpnZHYBNEcTFeQYF1cm45tkRopZEyVl2dnZ2rlixYt++fS9fvrx+/XpXV9cNGza4u7tz6rFly5amL6MAdsz599+d//jj47ZAkLxyJevtzV9FACaRyWSdO3c25wgsywYFBWWK0QKBIDg4eOPGjUKhcODAgVMjIk4OGqQRCg07OGu1pYYMER87Zk7XAAaMmeubPHz4sFatWjqdzsvLa9q0aQMHDtQvvqWXmpq6fv362bNnf/jwQSAQ3L9/v3r16mbXXNDseCVCmUzm4uJCRHK5HBMMcyV488bj+++Nl64gol1EfYn0T6WLi8uXX375ySef+Pr6NmnSpF69ekQklUolEgkRJSUlqVSqyMjIiRMn6i/G1WOYHN+V3t7e4eHhpUuXttpjsgxO4wvfv39vvUp4xDCMfgZPtVqd7/WKcyKMivIMCGCSP/43TTlyZAp+m/7vtafVahOMpiWxM6a/v1JSUgpsGXlO4uPjW7ZsGRmZzytEvLy83r59++zZs4MHDz569Ihl2cqVK7dp0ybrb3ri8HC34GDG+BtcLE4ODU1v2zbfxTssiUSiD3h2HIo4fXmZG6CJaOHChePGjdP/7ebmVqVKlbJlyxLRq1evnjx5kvzfR/y8efPGjx9vZl+8sOPXCgJ0vglfvXLv2FEYFWXc+CQgYGGFCrFxcVKptG7dukFBQd5ZzghmCtD6xtevX9+8eTMpKcnX17dixYqTJ0/OOjy6bNmymzZt+vTTT631kCwHAZqsGqA1Gs927UTXr39sqF078dgxzLxBCNAZ2WyAJqLY2NiQkJCzZ88aN7q6uo4dO3bv3r2PHj3K5b4dOnTYsWNH1g/SbDldueLeoweTkvKxSShMXro0vVs3sx6A40GAzsQCAZqINm/ePGLEiOTkrL9aExG5ubktXrx4wIABhXTCRTt+rSBA54/w8WOPzp0FMTHGjcqQkJSpU/NcOTnbAJ0Jy7Lh4eFhYWEPHjzQarVlypT59ttvu3Tpov/Hsn0I0GTNAO3666/GM2+wrq6JZ85oK1a0Vigr0AAAIABJREFUYBeFFwK0MVsO0Hp37949e/bs69evnZ2da9So0apVK29v73379g0ePDinuwiFwvDwcH9/fxMDNBE5Xb3q3r278S82xDCKX39NGzDAQo/DISBAZ2KZAE1ESUlJO3bsOHv2bEREhP6Ty8vLq2bNmoGBgT179tSv6V1I2fFrBQE6H0S3b3t068bExxs3KkNCUqZNM+XupgTowg4BmqwWoJ2uXvVo3560WkOLYsWKNJxL+w8CtDHbD9A5GT9+/ObNm7O2Mwwzb9684OBgrh+kort3Pbp0yfC5zTAps2crBw2yXNV2DgE6E4sFaDtmx68VBGiunC5fdu/Vi1EoPjZx/BRGgM4EAZrDMeVyr8BAwevXhpb0Nm2Ss8sZDgsB2ljhDdBEtGPHjgULFujXmtCrXr36zJkzAwMDKV8fpKKICPfvvxd8yDA9UuqECaljx1q0cLuFAJ2JufNAAzgO8alTbv37M+npH5uEQsWSJWndu/NXFDgQ2fjxxulZV6KEAvPWgZ3q2bNnt27dHj58+OLFC5FIVKlSJTNn8NTUrCk/fNijUyfBu3eGRtd58xil0sTfDwGMWSZAazSaI0eOXLx4sVKlSsarlgwePDg6Ovqbb74ZNGiQGBe4QGHmvG+f2/DhxotW4GpuKEguu3Y579//cVsgSF61irXymhQAPBIKhbVq1apVq5alDqitVEl+5Ih7hw7G139Lli8nrTZlxow8r2ABMGbuPNBEFBUVFRgY2KFDhyVLlvyTcUqvxMTEw4cPh4SENGrU6NmzZ+b3BcALly1b3IYONU7PrESStH070jMUDOHLl9LJk41bUkeNUjdpwlc9AIWUtkwZ+cGD2owT3klWrZKNH086XU73AsjK3ACtUqmaN29+6dIlInJ3d6+Y8Urwb7/9Vj/x882bNzt27KjRaLI/CoANkyxfLhs3zvizlfXwSPrjD1VgII9VgQPRaNwGDzYeea+pWzd1zBgeKwIovHSlSsmPHNFWq2bc6LJli9uwYYSUAiYzN0CvWbPm+fPnRDR69Oi4uLhMU88EBwdHRESsXr2aiCIiItauXWtmdwAFzHX+fOns2cYtOh8feViYumFDvkoCRyOdO1d086Zhk5VKk0NDycmJx5IACjWdn598/35NjRrGjc5797oNGUK4nh5MY26APnDgABE1a9Zs4cKF2Y5yZhhmyJAhXbt2JaIjR46Y2R1AwWFZ6ZQprgsWGLfpihaV79+vqVmTr6LA0Tj99Zdk5UrjFsWCBdoKFfiqB8A+6Hx85AcPaurXN250Dgtz79cvw5XiADkwN0DrRzZ36tQp90VSWrRoQUS5ry0EYEO0WllIiGTdugxtZcpk/eEPwHqYxES3oUONZ31Ob9cuvXNnHksCsBush4f8jz8yXUsgPnXKrW9fxk6naQMLMjdA6+dwdXd3z303/XyNMRlXbgOwUSqV+w8/uOzebdymrVJFfvSotlw5nmoCRyQbO1bw5o1hU1umjGLJEh7rAbAzrFSatHOnOiDAuFF85ox7ly4Z5vsHyMLcAF28eHEiunXrVu673bx5k4iKFi1qZncA1sakpnr06iXOONxIU6eO/NAhXbFifFUFDshl61bngwc/botEitBQNq+zFQDACSuRJO3YoWrZ0rjR6a+/3Lt2ZZKS+KoKbJ+5AbpZs2ZEtH379hcvXuS0z+vXr7du3UpEX375pZndAVgVI5e7f/+907lzxo3qRo3k+/frvL35qgockPDFC+n06cYtqWPGqBs04KseADvGisVJGzakt2lj3Oh07ZpHhw4C49W/AYyYG6CHDx/OMExcXFzLli1Pnz6ddWHwy5cvt2nTJi4ujogGYdF5sGGC9+89goKcrl83blR9803Snj2smxtfVYEjUqvdBg9mUlI+NjRsmDpqFI8VAdg5sTh5w4b0bt2M20T37nm0ayeIjeWrKLBl5q5EWLdu3ZkzZ86YMePJkyctWrSoXr16nTp1ypQp4+zsHBUVFRERcf2/ODJ06FCcgQabJXjzxuP774UZVwJK79AhedUqzBcGBUz6888io3FxrLu7IjSUhEIeSwKwf0Jh8rJlrFDosmPHx7YnTzyCguT79ulKlOCxNLBBFljKe9q0ae7u7hMnTkxPT3/48OHDhw8z7cAwzNixY3/99Vfz+wKwBmFUlHvHjsJXr4wb0/r2VcyfTwILrNYJYDqn8+cloaHGLYoFC7SlS/NVD4ADEQgUS5awUqnxFEzC58892rRJOnBAW7Ysj6WBrbFAOGAYZuTIkf/888+MGTP8/f0Ns0EzDFO1atVhw4bdv39//vz5Qpw+AZskfPzYo3XrTOlZ+cMPigULkJ6hgAni492GDTNe9jKtW7f0jh15LAnAsTBMyi+/ZFrpU/j6tUdQkDAykq+iwAYxWUctm4ll2eTkZI1G4+HhYR+hWaFQpNnplJAymczFxYWI5HK52iGXXxLdvu3RrRuT8TIRZUhIyrRpVupRKpXqZ3VMSkpSqVRW6oVfPj4+pu+snwrT/jAMU6RIESJSq9VyudzEe7n36SM+ftywqS1bNvHcOQzBN53+tafVahMSEviuxVpMf3+lpKQolUqrFsOXAvgglSxfnnkZWl/fpH37NI66FIBEIpFKpWTXoYjTl5flT7AxDOPu7u7t7W0f6RnsmNPlyx4dO2ZIzwyTMnu29dIzQC5cNm40Ts8kEiWHhiI9A/BCGRKSMmOGcYsgLs6jfXvR7dt8lQQ2hVuAznaejXxjWfbUqVOWOhoAJ+JTp9y7ds0wVb5QqFi6VDl4MH9FgeMSPnkinTnTuCVlwoRM6wwDQEFSDhuWaSwfk5Dg0bmz6MYNHqsCG8EtQLdo0SIwMPDy5cvmd3zx4sWvvvrq22+/Nf9QAFw579/v3q8fk57+sUksTl6/Pq1HD/6KAsfFqFRugwYZrx6s/vxz5fDhPJYEAESU1q+fYtGiDBlaLvfo1Mnpzz95rApsAbcAvW7durt37zZp0iQgIGDz5s2mD+wzSExM3LhxY9OmTb/66quIiIj169dzPQKAmVy2bHEbMoSMxnyzEknS9u3pbdvyWBU4MteZM0UPHhg2WQ+P5DVrMG8dgC1I69UrOTSURB9nLWNSU9179sy05BY4Gs4XEcbGxo4aNWrXrl1E5Ozs3LZt26+//trf379mzZoiUfaT4qnV6oiIiKtXr4aHhx85ciQ9PZ2IevTosWTJEj8/P/Mfg7XZ8Xh5B7yIULJ8ufTnn8noZc+6uyft2qVu2LBgCsBFhJnY60WE6v9r774Dmjj/P4B/LoFACAEF3OBARLRWvzjROuqotrVuXFWLVSqOSrXV2rrqap2tItaF1omK1F2tta6Ks1q1auseqKhUUUOYWff7435NQ8BAQpJLLu/XX+G55+4+CXmST+6eoVazLOvp6SmXy01fa5AcOeLTt6/he1KZkJDfvbvtYxQgDCI0hEGEViT55Rf5kCGM4bkkksyEBNW779rh7I4AgwiNWDgLx9mzZxcvXrx161b9G9fLy+v1118vX768n58fN/Y8IyMjIyPjn3/+uXz5sr4NSySSvn37xsbGNnaevn0Cfq+4WgJdeFQ1W7asYssWTcOGdosBCbQR4SXQZ8+ejYuLO3bsGPe5V7ly5V69en388cd+Ra0GL8rIKNO6teiff/QleYMGZX33nf3CFRYk0IaQQFuX5NAh+eDBhl2tyN1duWKFi9y9RAJtpFTT2KWnp69atWrPnj3nzp3TarUmaorF4iZNmnTp0iU6OtoprjobEvB7xYUSaJaVTZkiXbHCsExXoYIiOVlr3zmJkEAbEVgCvWzZsunTpxf+PKxUqVJSUlIdozcby/oMHCgxGEutrVHj5eHDrLe3HUIVJCTQhpBAW537yZM+AwYUHn2eV3ANcEFCAm3EOvNAKxSKY8eOnTx5Mi0t7cmTJ+np6URUsWLFChUqVKlS5Y033mjdurWPj0/pT8QLAb9XXCWB1mq9x4713Ly5QFnVqpnbtmmrV7dzLEigjQgpgf7555+joqJe9aFapUqVEydOcN9AHOnKlbJJk/6r4e7+8qef7Hk/RHiQQBtCAm0L7mfO+PTvzyiV/xUxTNY33+RFR9szDPtDAm3ECkt5E5Gvr2+XLl26uMZdDHAyKpU8Jsbjp58My7S1ayuSk3WVKvEVFAjS119/beKSRFpa2urVq2NjY7k/xdeueRXsUJQ9aRKyZwAHp27WTLFjh2+fPv+tIcCy3hMnMlptbkwMr6GBXWGlYhAyJjfXd+BAo+xZ87//KXbvRvYM1nXr1q3r16+brrNv3z7uAZOfbzxvXYsWuSNG2DA+ALASTYMGL3fv1lWo8F8Ry8omT/ZasIC/oMDekECDYDEKhU9kpNFMQ+oWLRTbt+uKGs4FUBr37t0rts7du3e5B7KpU93+/ltfrvPzU65YYTjXLAA4Mm3t2oodO4wuxHjNnWs0Th0EzMLPa5Zlk5OT+/XrV6tWLT8/v4iIiLlz5yoNuwQVNHPmzLCwsLCwMEvjBDCP6Nkz3+7d3X//3bBQ9dZbmUlJWBsZbKEk40m4OpJDhzzXrDEsfzRjhsbZRlcDuDhtrVqKvXu11aoZFkoXL5ZNm0bWW7MZHJYlCfTLly/79+/fp0+fpKSkW7duvXjx4syZM1988UWtWrXOnz9f5C7p6enXr18v9v4mgFWI0tN9e/Rwu3LFsDC/R4/MdetYT0++ogJhCwoKKrZOtWrVRE+feo8ebfj9uowo6OOPw8LCYmNjr169assYAcCatEFBip07tcHBhoXS77/3Hj+edDq+ogL7sCSBHj16dFJSEhG5ubmFh4e/8847lStXJqL09PQ333zzHNaIB16J79/37dxZfO2aYWHeBx8oly8nd3e+ogLBePr06Z49e9auXbt169Zbt27py68VfMsV6Z233/aOjRU9faov+ZtoHBERvXjxYvPmza1bt65cuXLPnj1/KthxHwAcky4wULFnj9F0qJ7r1slHjSKNhq+owA7MnoXj6NGjGzduJKKIiIjExMTg4GAi0mg0CQkJsbGxSqVywIABFy5c8PLysn6wAMURX7/uGxkpevLEsDA3Ojr7m2+IYfiKCoTh+fPnU6ZM2b59u8bgezEiImLevHl16tRJTEw0vTvDMGO0WsnBg/qSfKIBRDkFq6nV6pSUlJSUlK5duy5fvtwdv/oAHJuufHnF9u0+kZFuf/2lL/T48UfSaJRLl+LCjVCZfQV6zZo1RBQQELBnz57gf29buLm5jRgxYtOmTUR048aNadOmWTVIgBJxu3ixTNeuxtlzbGz27NnInqGU/vnnn7fffnvr1q2agleVTp8+3alTpzNnzly4cMH0Ef7HsgELFxqWfEF08dX1d+/e/eWXX1oeMQDYiy4gQLFrl6bgEsseO3f6DB7M5OfzFRXYlNkJ9JUrV4ho6NChhaeb7t27d1RUFBHFxcWVZEA6gBW5nzzp26PHfxNzEhHDZM+cmT1lCn9BgXCMGzdOP4eGkdzc3GHDhmVmZprY3YtoE5HIIPk+QBRX3EnXrVt30OCKNQA4LNbXV/Hjj+qWLQ0LJQcOyD/4gBHosiMuzuwEmuvn9/rrrxe5dc6cOd7e3iqVagqyFrAjyYEDPn37Fl5eNXf4cP6CAuG4c+fOzz//bKLCo0ePTPdbW0RkOAnRU6LBRCUZqD9s2DAhLdYIIGCsTJa5aZP6zTcNCyWHD/v06VPg6wkEwewEmrvw/PLlyyK3VqxYkbvnmJiY+HvBGcQAbMRj+3afwYML/MSXSJQJCXnvv89fUCAoKSkpxdbx9fV91aYeRB8Z/MkSDSV6XLJTK5XKb7/9tmR1AYBnrFSamZioeucdw0L3U6d8e/ZkhLu8vGsyO4EODQ0lomPHjr2qwqefflq7dm2WZYcOHZqPrj9gY57r1slHjCC1Wl/CSqWZGzbkY2F5sJ709PRi65QpU6bI8ipECQVL4on2mHP2pKQkHabEAnASrESSuWqV0XeQ24ULvpGRIsNOhuDkzE6gGzRoQERbt27lhgwW5unpuXbtWpFIdOXKlaFDh+JzH2xHunix0XSbrI9PZnKyql07HqMC4SnJtEJVqlQZOnSoUaGIaB2Rv0HJX0RfmHl2pVJ58+ZNM3cCAP5IJMqEhPx+/QzL3C5d8i00zB2cl9kJ9IgRIyQSCRENGDDg3Xff3bx586VLl1QqlWGdiIiI8ePHE1FiYmLnzp0vX75ckjW6AMwiXbxYNnOm4YIUbNmyiuRkdbNmPEYFgvTaa6+VpM4333wzefJkw2x7AlF7gzp5RO8T5ZofwI4dO8zfCQD4IxYr4+LyBgwoUHb9um+PHqJHj/gKCqyIsSC13bBhw+DBgw0vLe/Zs+e9994zrKPVamNjY5cuXcr9KRaLtVotlWy1W0eTlZWVJ9AhtN7e3p6enkSkUCjUBr0gHB3LyqZMka5YYVimq1BBkZxsNJu9A5LJZFKplIgyMzONfnkKRuEpekxwihFyKpWqSZMmj179tScWi48cOVKnTh0iev78+YEDB65evRr84sUnSUluBh+VHxN9b1EA77zzzvr16y3a1bVw7z2tVvtCuP1NS96+srOzc3Mt+L3mBJzmg7SobyttUFDm9u3a6tV5islCUqlUJpORoJMis768LFmJcNCgQUeOHGnevLmJOmKxeMmSJfPmzeMG1nDZM4AVaLXeY8YYfx5Vrar46SfHz57BSUkkkm+++UYkeuUH5rBhw+r8+/bz8/Pr16/fzC+++PjUKcPs+WeipZYGIOB0EEDIGCZ71qyczz4zLBM/eODbvbv4zh2+ggKrsCSBJqLWrVufPHnyn3/+OXr06LJly2rVqlW4DsMw48ePf/jw4fLly3v16hUeHm5ilDpAiahU8o8+8izY/14bGqr46Sen+zUPzqVz585xcXHcRS9DDMMMGzas8OpR4qtX1Wlp+j8fE0WVbN66Ir1qhCIAOL6cL74wWpFAlJbm+957blev8hUSlJ7ZS3kbKleuXJs2bdq0aWOijre3d0xMTExMTGlOxC+GYRiBrmNn+Lwc/zkyubnyqCj3w4cNCzUNGmRu3cr6+zt69P/Sv84Cfl+ZxfFfhBs3bty5c4dl2fDw8N9//33dunXHjx9PT0/39fUNDw8fNGhQ/fr1C++VERLSgWXXELUhYomiiZ6WIobw8HDHf6EcCl4ujuBfB2f5IM375BNGLPYy+KUtevrUt2vXzORkTXg4f3GZwbkSBjuwpA+0q1Gr1e5Yy553L1/Se+/RiRMFClu3pj17yMeHp5hA4LjFtP/++299SWho6Ndffx0ZGVnsvmfPnm3atKmY6AsiH6IJpQjD3d39+vXrNWrUKMUxQCC0Wq1YLC5hZXx5OZzly2nUKMOZo6hMGdq3j0z2iQXHVNoEesuWLSWpJpfLK1SoULFixfLly3OTeDiR3Nxcofbh9vDw4D5eHfw5Mk+fSrt3F126ZFio7dQpd8MGKnRL3cE5y2teGt7e3iWvnOWoC3QtXLhw6tSpRW764osvJk2aZHr306dPv/XWW1aJZNasWZ988olVDiV43HtPp9Pl5OTwHYtNsCwrl8tLWFnAHzISiYTLJfLy8jQaDd/hmMFtyxbPESPIIGbWyytv61atyZv5jkD/mufn5zvTrAPmMOvLq7QJtAWX8YODg3v27Pnpp59WqlSpNKe2GwEPOHWKWThE6em+vXuLC/YVy+/eXbl0KTnhxRWnGTxeCgKYheP06dNdu3Y18fG4devWtm3bmjjCw4cPw0t9Z1YsFk+ZMmXUqFGlPI7rwCwchjALh2Py2LFDPmqU8fpf69apTX6k8A6zcBixcBChnr+/v7+/f7E5u1wu18+NeufOnQULFtSuXXv//v2lPDsInvj+fd/33jPOnvv2VS5b5ozZMziLRYsWmb64sHDhQtNHCAoKqlChgrnnLVOmjFwul8vlVatWHTJkyJ9//onsGUBg8nv0yFyzhjW4G8/k5voOHCjZt4/HqMBcpRpESETPnj27evVqt27dbt686efnFx0dHR4eXrVqVTc3t9TU1L/++mvFihVPnjypW7fuvn37iCg1NXXjxo3Lli1TKpU9e/a8ceNGYGCgNZ4ICJD4+nXfyEijdZvyoqOzvvmGMIIBbEaj0aSkpJiu8/vvv2dnZ3PXY4qUn59vbu+ULl26rFq1ysRkeQAgDKpOnZTr18sHD2b0l3JVKp/oaOWKFUZrgIPDKu0n9bNnzzp27Hjz5s2BAwc+ePBg7ty5/fr1a9GiRdOmTXv37j1t2rR79+4NHz78zJkzXbt2lcvl4eHh33777fnz56VSaW5u7qJFi6zyNEB43C5eLLzqaW5sbNbs2ciewaYyMjKKvSms1WqfmFyS98SJE9nZ2SU/acWKFX/44QdkzwAuQtW+fWZSEmt4A1+tln/0kWfJhpYB70r7YT1nzpyHDx82btx49erVhgvY6nl4eMTHx7do0eLEiRPff///i3CFhYVNmTKFiA4dOlTKAECQ3E+e9O3ZU/T8+X9FDJM9Y4bRVJoAtuDh4VGSatz4gVe5d++eWSdNT0///PPPna43JwBYTN2iRWZSEms4KlSr9Y6N9Vy1ir+goKRKm0Bv376diLp162Zibg03N7du3boR0dq1a/WFrVq1IvO/Y8AVSH791advX0ap/K9ILM5auDB3xAj+ggIX4uvrW2z35TJlypiuY+74bJZl16xZMwJvcgBXom7aVLFjh87P778ilvWeOFG6fDl/QUGJlDaBTktLI6LqxS0CV7VqVSK6efOmvqRatWpEZNYtTnAFHjt2+ERFMYYjfCUS5cqVeQMG8BcUuBaGYbp37266TteuXd3cTI0h4T7izLV79+69e/dasCMAOClNgwaK3bt1hj/IWVY2ZYrX/Pn8BQXFK20CzS0we/HiRdPVzp8/T0SGq+D+888/RFSlSpVSBgBC4rl+vXz4cOPJfTZsyO/alceowAV9+umnJi4w+/v7jx8/3vQRWrZs6WPREj8rVqw4dOjQ8ePHMzIyLNgdAJyOtnZtxc6duoJz+3rNmyebOZOvkKBYpU2gIyIiiCgxMfHhw4evqvP48eONGzcSUcOGDfWFu3btIiKsrQV60lWrvMeNM1yiiZXJMhMTVe3a8RgVuCY/P7/k5OQiryIHBgZu3bq1YsWKpo/g6ek5efJkC0596tSpfv369ejRo06dOn369LlacA5HABAkbUiIYu9ebcHPHOnixd5ffEFYMdohlTaBHjlyJBE9efKkc+fOp0+fLlzh7NmznTt3fvz4MRENHz6ciLRabVJS0ty5c4moJCvigiuQLl4s+/JLw48JtmxZxbZt6lateIwKXFmdOnV+++23GTNmNG3a1N/f38/Pr0mTJlOnTj127Fj9+vVLcoS+ffv6+/tbHADLskeOHHnrrbcOHz5s8UEAwFlog4IUO3dqg4MNCz1Xr/YeP77A6t/gGEq7EiERxcTErFy5knvcvHnzBg0aVKtWjWGY1NTUS5cunThxgtvUv3//xMREhmEGDhyYmJhIRIGBgTdv3jQ9kt0RCHjRHYdYiZBlZVOnGg2Y0JUvn/njj5o6dfgJyZacegGtEhLASoSlp1KpOnToYJXrx3K5/MyZM+XKlSv9oQQPKxEawkqEzkj09KlPr15uRsuHRUYq4+PJ5LgLW8NKhEas8M9YtmxZuXLl5syZo9VqT506derUqcJ1Ro4cuWjRIm7db+51Dw0N3bt3r+Nnz2BbWq33p596btpUoCwoKHPbNi2694AzW716tbV6XyiVypUrV06aNMkqRwMAR6YrVy5zxw6fyEi3K1f0hR4//khqNZbgdShWmLRfJBLNmjXrr7/+GjNmTO3atQ03BQUFDRky5Ny5c99//737v//1zp07//jjj3/88UdISEjpzw5OTKWSf/SRcfYcGqrYuxfZMzi7HTt2WPFomDIfwHXo/P0VO3dqGjc2LPTYtctn8GAmP5+vqMCIFbpwGMnOzs7IyNBoNH5+ftwcHc5OwHcreOzCweTmyqOiJEeOGBZqGjTI3Lq1wIyYgiPgO4966MJBRNWqVcvJybHW0fz8/K5fv26towkYunAYQhcOp8ZkZ/sMGuSekmJYqGrXTrluHcvH3Xt04TBi/WVjZTJZ1apVg4ODhZE9gy0wmZk+kZFG2bO6eXPj+eQBnBPLstb9Ui/h4ogAIBjcPFTqN980LJQcPuzTpw+TlcVTUPAfqyXQCoVi6dKlvXv3DgsLq1Chgr+/f61atbp37x4fH//y5UtrnQUEgHnxwrd3b/fffzcsVHXokLl1a4EVTQGcFsMwxc5zR0Rjx4796KOPBg4cWGzN0NBQa8QFAM6ElUozExNV775rWOh+6pRvz56McO+xOAsrDCJkWTYhIeGzzz7LKviT6Pnz57du3dq1a9fEiRO/++676OhobhAhuDJRerpv795io/HF3bsrly7F2AgQkrZt227YsMFEhapVq06cOJF7fOXKFdPLUXXr1s2awQGAk2AlksyEBPnw4R579ugL3S5c8I2MzExOxj1bHlnhCvScOXNiYmK47NnHx6dx48aRkZH9+vVr1qyZr68vEWVlZQ0bNuy7774r/bnAqYnv3/d97z3j7LlPH4wsBuEZNWqUu8l39dixY/WPZ8yYYWJh8Pr16/fv39+awQGAE5FIlAkJ+f36GZa5Xbrk26WL6MkTvoKC0ibQly9f5lbb8vf3j4uLe/z48dmzZ5OTkzdv3nz69OnHjx/HxcVxSwlMmDABg2Bcmfj6dd/33hPfu2dYmBcdrVyyhN+5LQFsISQkJCEhQSwWF7m1f//+AwYM0P/ZvHnzpUuXcoOijDRo0GDTpk0m0msAED6xWLl4cd7QoQXKbtzw7dFD9OgRX0G5uNIm0EuWLNHpdO7u7rt3746NjfXy8jLcKpVKY2Njd+7c6ebmptVqFy9eXMrTgZNyu3jRt2tX0ePHhoXg4QbcAAAgAElEQVS5sbFZs2cTOvaAQEVFRR06dKhhw4aGhZUqVVqwYEFcXJxRl7YePXocP348Ojo6ODhYIpHI5fKIiIhvv/123759FSpUsG/gAOB4GCZr9uzcmBjDMvGtW2UKXZkC+yjtVY0jR44Q0aBBg1q0aPGqOi1btoyKilq9evXBgwdLeTpwRu4nT/oMHMgolf8VMUz29Om5I0bwFxSAPbRp0+b06dMXL168du2aRqOpWrVqvXr1XnVZumrVqrNnz7ZzhADgNBgme9YsVi73WrBAXyZ68MC3e3fF9u1Ga4CDrZU2gU5LSyOiZs2ama4WERGxevXqhw8flvJ04HQkv/4qHzKEMZwzUizO+vbbPIP71wDCVqNGjRpYGwgArCFnwgRWKpXNnKkvEaWl+b73Xua2bZo6dXgMzNWUtgsHtw5LsauxYP4N1+SxY4dPVFSB7FkiUa5ciewZAADAMrmxsdnTphl2gBQ9ferbtavb+fM8RuVqSptAV6lShYjOnDljutrp06eJKDAwsJSnAx49fPjwzz//vH37tk6nK0l9z/Xr5cOHk8ECh6xUmrlhQ37XrjaLEQAAQPhyR43KmjePRP9lcczLl759+ridPctjVC6ltAl0+/btiWjDhg3Hjh17VZ0TJ06sW7eOiNq1a1fK04H9abXalStXNmrUKDw8vEOHDhEREWFhYdOmTVMa9mkuRLpqlfe4cWSQanOLKqnwHgAAACi1vMGDld9/bziNFaNQ+EZGur86HwMrKm0CHRsbKxKJNBpN9+7dFy5cmJ2dbbg1JycnLi6uW7duarVaJBKNHj26lKcDO8vPz+/fv/+kSZPu37+vL3zx4sX333/fvn37R6+YPUe6eLHsyy/JoGMPW6aMYts2datWNo8YAADANeRHRhotpMDk5PgMHOh+5AiPUbmI0ibQdevWnTt3LhG9ePHi008/rVSpUpMmTSIjIyMjI5s0aVKxYsUxY8ZkZGQQ0ezZs+vWrWuFkMGOvvrqqyOvaId3794dMmSIcXcOlpVNmWI4uIGIdOXLK3bv1jRqZLs4AQAAXFB+9+6Za9eyHh76EiY313fgQMnevTxG5QqssBLhuHHjfvjhB7lcTkRKpfLcuXPbtm3btm3buXPnuLv8crk8ISFh/PjxpT8X2FN6ejrX9+ZV/vjjjwJTE2q13mPGSJcvN6yjDQpS/PQThgYDAADYgqpjR+W6daynp0GRyic62iM5mb+ghM86q1t9+OGHvXr1SkxMPHz48JUrV168eEFEZcuWrVevXtu2bQcMGMCt6Q2O7+XLlwcPHrxx44ZOp0tPT9doNKbr//LLLx07diQiUqnkw4d77NljuFUbGqpITtZVrmy7gAEAAFycqn37zK1bfd5/n8nK+v8ijUY+ejSj0eT1789raIJlteVhfXx8RowYMQJLYzgtnU4XHx9fuCO7aQ8ePCAiJjdXPniw5PBhw02aBg0yk5J0/v5WDhQAAAAKUjdvnpmU5NO/P5OZ+f9FWq33J58w2dm50dG8hiZMVujCAcLw+eefz5o1y6zsmYhEIhGTmenTu7dR9qyOiFDs2IHsGQAAwD7UTZsqduzQ+fn9V8SysokTjbpWglUggQYiol9//dV0d+dXqR8Y6Nu7t3vBicBV7dtnJiezcrmVogMAAIDiaerXV+zeratQ4b8ilpVNmeI1fz5/QQmTeV04lixZUsrzffzxx6U8AljL06dPv/vuu19//fX+/fuZ+js+5qhENP3oUbfUVMNC1TvvKFetYiUSK4UJAAAAJaWtXVuxc6dvr14ig6lmvebNY/LysqdM4TEwgTEvgS79RM5IoB3EwYMHR4wY8fLlS4uPUIPohJeXvGD2nN+njzIuznBedwAAALAnbUiI4qeffHr0EBt8R0sXL2ays7NmzzZcAxwshi4crujChQtRUVGlyZ7rEJ2RSCrl5BgW5g0dqoyPR/YMAADAL21QkGLnTm1wsGGh5+rVRosEg8XMS6DZUrPR0wCzTJ06VaVSmbuXRCIhIrFY/H7t2n/IZOUKHiE3NjZrzhwS4ScZAAAA/3SBgYXXYfBcv14+ciQVN0ctFAvpjstJS0s7U3DMX0n4+vrevXv35s2b//z448ZHj6SGk3UwTPb06ehZBQAA4FB05copdu3ShIcbFnps2yYfPpzUalufXaPRGC9XLCC42+5ybty4YcGtgN69e0skEu9jx+Qffsjk5f23QSzOWrAgb+BAa4YIAAAA1sCWLatITvbt18/t3Dl9oceuXUxOjnLNGtbDIysrKzk5+fjx48+fP5fJZA0bNuzTp09gYKDFZ3z48OHSpUv379//4MEDhmFCQkK6dOkSExPjZzi/nvNj0K2iWFlZWXmGKaOT27dvX1RUlFm71KhR45dffqn422/ykSML/GaVSJRLl+Z362blEAVNJpNJpVIiyszMtKAjjVMICAgoeeVnz57ZLhIeMQzj7+9PRGq1WqFQ8B2Oq+Dee1qtllsQV5BK3r6ys7Nzc3NtGgxfXOGD1LqYnByfgQPdU1IMC1Xt2u2Kiho+duzz588Ny93d3SdMmPDJJ58YFkqlUplMRsUlRYcOHRo2bFjhqb0qVqy4cePGBg0alPaZ2JJZX164Ai0Qz58/P3jw4N27d4koODi4ffv2r/qpV6lSJbOO3LRp05UrV1Y8cEA+ZoxhrylWIlEmJKjefbc0YQMAAICtsV5emYmJPlFR7keO6Aslhw9XPHJEXehCqlqtnjVrlk6nGzt2rFlnuXbt2uDBg4tMr588edK3b98TJ074C2WFNSTQTk+j0cyfP3/p0qWGb1lPT88RI0aMHz/e3d3dqL67u7tYLNZqtSaOWa1atZCQkOrVq3fq1OnNN9+Url7tPXEiGbQxVibLXL9e3bq1dZ8LAAAA2AIrlSo2bvT56CPJvn36wlYse5ioE9HzQvUXLFjQo0eP6tWrmzhmdnb2xYsXMzIyAgICGjRoMGfOHBMXpzMyMuLi4mbMmFGaZ+E4kEA7N51OFxMTs3v3bqPyvLy8hQsX3r59OyEhQWQwM0ZqamqvXr1MZ89E9P7773/66afcY+nixbKZMw23smXKKDZv1jRubI1nAAAAAHYhkWSuWiWPifHYs0df1pjoIFFHIqPudCqVKjk5efz48UUe6fnz5zNmzEhOTtZ3oZFIJJriJvfYvXu3YBJozMLh3LZu3Vo4e9bbvXv31q1bDUumTZtm1NWpSHPmzNm1axexrGzqVKPsWVe+vGL3bmTPAAAAzsfdXZmQ8Oy99wzLwomOEVUuVPfPP/8s8hhpaWkdOnRITEw07ICuUqmKnXMjLS1NMP3ykUA7t1WrVpmukJCQoH/84sWL/fv3l+SwLMt+NXmy1yefSJctMyzXBgUVnlQSAAAAnMX6xMTAAweWFCysQ3SEyGjqjVcNgB4+fPiDBw8sO7va9tPn2QcSaCeWnZ196dIl03UuX76clZXFPb569Wqxt1c4EqKFT554bd5sWPjQ23vFwIEvzRmjCgAAAI5j9+7d48aNy1epYokWFdwUSnScqKZBSfny5Qsf4eTJk6dPn7bs7GXKlPHx8bFsX0eDBNqJZWRkFDsLIcuy+mnClEplSQ7rRbSLqHfBwvNEDbOyRs2e3bhx4xJexgYAAADHoVarJ0+ezGUOLNFYIqP+yNWIUojq/vtnixYtCh/k4MGDFgfw1ltvWbyvo0EC7cRK+DNOX60kExx6E+0hertgYQpRO6KnRET0/PnzDz/88IjBPDgAAADg+E6cOPH48WPDkq+IvihYpxLRYaL6RH5+fr169Sp8kNu3b1t2dolEMmbMGMv2dUCYhcOJlSlTplq1aqmpqSbqBAUFHThw4Pfff1coFGXKlJFKpSb67/sR7SNqVrDwMFE3oiyDEo1GM27cuNOnTxeeIw8AAAAc0/Xr1wsXziXKIoonYv4tqUB0lOj3MWPKlClTuL5lCTTDMPHx8aGhoRbs65gEnkC/ePFi06ZN165dS09Pr1y58muvvda/f39vb2++47KaAQMGfPPNNyYqPHv2bPTo0SU5VEWiA0SvFyzcRdSXKL9Q5fv37x8/frxt27bmBAsAAAC8edUVtO+JNERLDbollCXquGCBonFjTZMmRpVLMpdXYVKptGfPnhbs6LCE3IXj2rVrI0eO/OWXX1JTU93d3e/cubNnz56RI0daPHTUAY0YMaJevXomKpRwvpjqRCmFsueNRJFFZc+cV81uAwAAAA6oYsWKr9q0gugDIsN5BpjMTO8ePdyPHTOqadna6VWqVLFgL0cm2ARapVLFxcVlZ2e3atVq48aNiYmJa9asadiw4cuXLxcvXlzsVIXOwtPTMzk5ucgrwab7VzCM/l4N1SE6ThRSsML3RFEF25KRwivdAwAAgMNq3bq14be/kUSigUSGk8y55ed79ukj+vVXw2qW3cZvLbiliwXbhePKlStpaWnly5cfM2YMl0r6+/uPGzcuOjr6+vXrqampNWrU4DtG6wgICNi6devx48f37t3L9UyqWbOmp6fnkiVLTOzFsuxHH33k7u6evm/f8nv3jEYX7ggN/fjGDdPnLVeuXGlDBwAAAHupXLlys2bNTExCl0SUTZRM5PlviYdWKx44MOeHHygyUqVSHT16VCKRmHtekUg0bNgwS6N2UIJNoO/evUtEdevWNbwQ6+3tHRIScunSpbt37womgea0bNmyZcuW+j8/++yzYne5cuXK9jFjKm7cWKCUYbK/+uqejw/9u5T3q6xdu1YqlQ4aNEgsFlsUMgAAANjPvn37fv/9d9N1fiLqQbSdSPpviZtOJx8y5MSpU/1++iktLc2C87IsW+ysu05HyF04iKhwVw3uX2hZDx4nwv1+MO3UqVOxgwbpsgwm2BCLs779NnfUqJJMkHfnzp3x48f37t1bMMtyAgAACJVKpfryyy9L0oV1P9HbRIYrRzA6XfMVKzpalD0TEcuye/futWxfhyXYBDokJISILl++bJjevXjx4ubNm/qtQnX48OETJ06UpGayShWl07Fcjyh3d+WKFTkDBmzbtm3kyJElPFdKSsqnxV2rBgAAAH799ttvjx49KmHlY0TtiQyn2xATrSYq0axeRbF49miHJdguHI0aNapfv/6lS5dmzZo1dOjQypUr37t3b+XKlXl5ea1atapZs2aReymVysJ3GXQ6nYlO944mMzNz5MiRJR8luZEowN39O4Z5Fh+/NS9vXpMm9+/fN+uM27Zti42NrVu3bvFVwWD4JsMwTvS+sh2hvgj4R/MLrzlH8K8D2lfJXblyxaz6Z4k6Ef1C5PdvCUMUR3Sd6ID5Z9doNAL7Twk2gRaJRJMmTZo7d+758+cNV75p167dqFGjXvVf7NatW+HJJSZPnty9e3cbxmpVW7ZsycjIMGuXRSrVUX//G2PG5OTkWHBGlmWPHj3aqlUrC/Z1ZXK5nO8QHIK/vz/fIdiWm5ub4J+joxGLxUJ9zbVabckrSyQSmUxmu2AcAT5IS06jMTGxVtHOEbUi+pWo8r8l24gOWXT2sLAwgbVKwXbhIKKTJ09eu3aNiGQyWbVq1Tw9PYnowoULFy5c4Ds0G7Jske2LGRmWZc8c4d2aAQAAEJIKFSpYsNffRO2IHhIR0U6i/kRm/IYz0LlzZ4v2c1yCvQKdkpISFxcnk8m+/PLLiIgIhmFYlj18+PDSpUu/+eab6dOnN2jQoPBeoaGh2dnZRoU+Pj4W/G7ji9Ey9/ah0+mc6CXil0gkEolERKTVaoU3Kpnj5mbGB4uA3znc68CyrFlXDaE0BP+a63S6kk98xLKsUNuXK3yQWp3FMzFfJ3qT6HOi0SZXhzChc+fODRs2dPx3o1lfXsJMoFmWXb9+PRENGzasefPmXCHDMO3bt1coFGvXrt24cWORCfTy5csLF2ZlZb18+dKmAVuRVCotvpK1Va5c2YleIn7JZDLuf5SdnS3U2WACAgKKr/Qvob5zGIbh7ldqNBqFQsF3OK6Ce+/pdDqhvq/InPalVquFOkuSK3yQWl316tVbtGhx8uRJC/a9TRRj6XkZhpk0aZJTNEmzvryE2YUjKysrPT2diBo3bmy0iSu5deuWYBYjNPL6668XX8na3n77bfufFAAAAEpu8eLF5cuXL3KTWCz28PCwxUlZlj1z5owtjswvYSbQHh4e+ps7Rpu4OwhSqVRgo0H1evXqZeeVTbp161avXj17nhEAAADMVa1atV9++aVDhw6FN2m12vz8fBudl5tBWGCEmUBLJJKqVasSUeHpkLmSmjVrCjWBDgsLK/kszqXXtGnTRYsW2e10AAAAYLHAwMDNmzefPn36u+++6927N3e10dYE2c1GmAk0EfXs2ZOI1qxZ8/PPP3P/uZycnB9//PHHH38koh49evAcny1Nnjw5NjbW1q1CKpXOnDlz165d3t7eNj0RAAAAWFHNmjUjIyNTUlLM7c7q4+PTunXrJk2adO7cueS3uwMDA82P0dEJcxAhEb355ps3b97cs2fPsmXLli9fLpfLuQmeGYZ5//33GzZsyHeANiQSiaZMmcIwTFxcnO3OMmfOnPfff992xwcAAAArysvL27hx4/bt269evZqfn69Wq809wujRo7/44ou8vDwiGjRo0P79+0uyV/v27c2O1eEJNoEmoo8++uiNN97YtWtXamrqs2fPgoKCqlev3qtXr+DgYL5Ds4dDhyyb7LxEmjZt2rdvX9sdHwAAAKzowYMHAwYMuHr1qsVHqFGjxueff849Zlm2UaNGBw4cKPYadpcuXcLCwiw+qcMScgJNRHXr1nXZJaZLvuT9q1SpUiUrK6vwDFxt2rRJSEiw81BFAAAAsEx+fn6fPn1u3bpl8RHq1Kmze/duHx+frKwstVodHR29b9++YveqV6/ewoULLT6pIxN4Au3KSrKOgEQiCQ4OjoiIeOedd86dO3f06NG0tDQvL6/XX3+9b9++7du3VyqVGzduPHTo0P379yUSSZ06dXr27NmpUyf7DDsAAACA0luzZo0F2bNMJvP19Q0NDe3SpcvgwYP9/Py48lmzZhWbPfv6+n744YefffYZtw608DBYwqdYWVlZXHcf59K4cePU1FTTdYYPH75s2TIiUigUFvSFAgvo5//PzMwU5MBkMnMu+mfPntkuEh7pF1JRq9VYSMVuuPeeVqt98eIF37HYSsnbV3Z2tuAXUhHwB6l1cZfJzN0rISGhe/fu3GOpVCqTyYjozp07derUMf2yz50794MPPjBrYT9HgIVUgIiobdu2xdb54IMP7BAJAAAA8MiCmZgDAwOLXCXt119/LfZHy/Xr150uezYXEmjBGjVqlOmOFjVr1gwPD7dbPAAAAMALcxdJkUgk8fHxRfa+uHfvXrG7l6SOs0MCLVjVq1efMmXKq7ZKpdK1a9faMRwAAADgR5UqVUpeuWbNmjt27GjZsmWRW0uyDp1Q16ozJPAL7C7u448/lslkU6dONerDLRaLo6KiatWqxVdgAAAAYDft2rW7ffu2iQq+vr6RkZFly5Zt2rRp69atTUy0VaNGjWJPV5I6zg4JtMCFhYUV/iGo1WqXL19++/btbdu2CXV4LAAAAHBGjhy5ceNGE4NKp0+fPmDAgJIcqmPHjhKJxHQ36HfffdfsEJ0NunAImUKhGDx48KsazK+//jp79mw7hwQAAAB2FhgYuGzZMolEUuTWqKioki8t7O/vP2rUKBMV2rVr16pVK7NDdDZIoIVs/fr1z58/N1EhPj5eqJMcAQAAgF7nzp337dvXqlUrw/vS1apVi4+PX7BggVm9lidMmBAZGVnkpvDw8BUrVpQ2VmeALhxCdvjwYdMVsrKyUlJSOnbsaJ94AAAAgC8NGjTYvn3706dPr169mp+fX61atdDQUAuOIxaLly1b9vbbb69YseL8+fPcwm2hoaGDBg0aMmTIq65zCwwSaCEryWreDx48sEMkAAAA4AjKlStXrly50h+nW7du3bp1y83NffbsWZkyZeRyeemP6USQQAuZh4eHVeoAAAAAFCaVSoOCgviOggdIoIUsJCTk6tWrpuvUrVvXPsEAAACA4N2/f3/jxo1nz55VKpW+vr7NmjUbMGCAWRNROwUk0ELWs2fPPXv2mKhQq1YtLEYIAAAAVrF06dJZs2ap1Wp9ybFjxxYvXjxjxowhQ4bwGJjVYRYOIevcufMbb7zxqq0ikWj+/PmusFwQAAAA2FpCQsJXX31lmD1z8vPzJ0yYsGnTJl6ishEk0ELGMMyaNWuaN29eeJOHh8eiRYs6depk/6gAAABAYJ49e/b111+bqDB16lSFQmG3eGwNCbTAlS1bdseOHUuXLm3Tpo2fn59EIgkODh46dGhKSkr//v35jg4AAACEYM+ePdnZ2SYqKBSK/fv32y0eW0MfaOETi8W9e/fu3bs334EAAACAMP3111/F1rly5Urfvn3tEIwd4Ao0AAAAAJRKVlZWsXWUSqUdIrEPJNAAAAAAUColWZylQoUKdojEPpBAAwAAAECptGzZ0ip1nAUSaAAAAAAolfbt24eGhpqo0KBBAxNT6zodJNAAAAAAUCpubm4rVqyQy+VFbvXz81u6dKlIJJy0UzjPBAAAAAD4Uq9evQMHDrRo0cKovG3btgcOHDB9fdrpYBo7AAAAALCCkJCQXbt2Xb9+/ezZs8+fPw8ICGjatGlISAjfcVkfEmgAAAAAsJratWvXrl2b7yhsC104AAAAAADMgAQaAAAAAMAMSKABAAAAAMyABBoAAAAAwAxIoAEAAAAAzIAEGgAAAADADEigAQAAAADMgAQaAAAAAMAMSKABAAAAAMyABBoAAAAAwAxIoAEAAAAAzIAEGgAAAADADEigAQAAAADMgAQaAAAAAMAMSKABAAAAAMyABBoAAAAAwAxIoAEAAAAAzIAEGgAAAADADEigAQAAAADMgAQaAAAAAMAMSKABAAAAAMyABBoAAAAAwAwMy7J8x+Do8vPz+Q7BVtzc3MRiMRGp1WqdTsd3OC7BFV5zDw+PklcWcPviXgedTqdWq/mOxVVwrznLsiqViu9YbEKn00ml0hJWFnDjcoUPUkcjFovd3NyISKPRaLVavsOxCbO+vNxsF4dgMAzDMAzfUdiE/nkxDCMS4XaEPeA1NyL4FwH/aF4I9TU395qXUF8H/QepUJ+gA8KXlxEk0MVTqVR5eXl8R2ET3t7enp6eRJSdnY2LZPYhk8m4C0g5OTlCvUgWEBBQ8soKhcJ2kfCIYRh/f38i0mg0Qn2ODoh77+l0OgG/5tyHdkloNJrc3FybBsMX/Qdpdna2UD9IHY1UKpXJZESUm5sr1KTIrC8v/IYAAAAAADADEmgAAAAAADMggQYAAAAAMAMSaAAAAAAAMyCBBgAAAAAwAxJoAAAAAAAzIIEGAAAAADADEmgAAAAAADMggQYAAAAAMAMSaAAAAAAAMyCBBgAAAAAwAxJoAAAAAAAzIIEGAAAAADADEmgAAAAAADMggQYAAAAAMIMb3wEAn86dO5eWlkZETZo08fHx4Tscl/Dnn3+mpqYSUXh4uJ+fH9/hgK2o1eqkpCQiKlOmTKNGjfgOxyWwLMu95t7e3s2aNeM7HLChy5cv3717l4gaNGgQEBDAdzgu4caNGzdu3CCi1157rWLFinyHwz8k0MXz9vb29vbmOwqbOHLkyK5du4ho9erVwcHBfIfjEjZs2JCYmEhEcXFxoaGhfIfDP6F++WVmZs6fP5+I6tev36lTJ77DcQk6nY57zYODgzt37sx3OPyTyWQymYzvKGwiKSlpzZo1RDR//vywsDC+w3EJP/3005IlS4joq6++qlevHt/h8A9dOAAAAAAAzIAEGgAAAADADEigAQAAAADMgAQaAAAAAMAMSKABAAAAAMyAWThcmqenJzd7nVgs5jsWV+Hh4cG95m5uaH0Cx/2jhToNgmPiXnOhzpsEehKJhPtfu7u78x2Lq9C/5hKJhO9YHALDsizfMQAAAAAAOA104QAAAAAAMAMSaAAAAAAAMyCBBgAAAAAwAxJoAAAAAAAzIIEGAAAAADADJtJyaXPmzDl58iQRDRw4sE+fPnyHI0yPHz+OiYkpXB4QEFC1atV33nmnWbNm9o8KbA2Nyw7QuFwW2pcdoH2ZhivQris3N/fcuXPc4xMnTvAbjCtgGEb0LyJ69uzZ+fPnv/766yVLlvAdGlgZGpedoXG5FLQvO0P7KhKuQLuus2fPqlQqLy+vnJycu3fvpqWlValShe+ghCw5OVk//7xarU5PT9+7d+/evXsPHDjQuHHjiIgIfsMDK0LjsjM0LpeC9mVnaF9FwhVo15WSkkJE7dq1q169OuF3vH25u7sHBgbGxMS0aNGC/v1fgGCgcfEIjUvw0L54hPalhwTaReXk5Pzxxx9E1LJlS+7n4/Hjx/kOyhU1bNiQiO7fv893IGA1aFwOAo1LkNC+HATaFxJoF3XmzBmNRuPn51enTh1uHMC9e/fS0tL4jsvlsCxLRJ6ennwHAlaDxuUg0LgECe3LQaB9IYF2UdxtlzfeeINhmODg4ICAAMLveD6cP3+eiOrXr893IGA1aFwOAo1LkNC+HATaFxJoV5SVlXXhwgUiatmyJRExDMP9jkdPMrtRqVT3799ftmzZqVOnypcv361bN74jAutA4+IdGpeAoX3xDu1LD7NwuKLTp09rtVo/P7+wsDCuJCIiYu/evffu3Xv48GFgYCC/4QlVZGRk4cLKlSvPmjXLx8fH/vGALaBx8QKNy0WgffEC7atIuALtiri7XdwtMK6kXr16MpmM8Dveltzc3NwNcC/+o0ePvv3222fPnvEdHVgHGhcv0LhcBNoXL9C+ioQr0C4nMzPz4sWL9O8tMI5YLG7SpMnRo0ePHz/et29f/qITsi1btuin0iQijUaTmpr6ww8/XL58eeLEiUuWLDHcCs4IjYsvaFyuAO2LL2hfRUIC7XJOnbMGoHIAAAnbSURBVDql0+mIaMKECYW3pqam4kaYfbi5udWsWXPy5MkxMTFPnjw5duxYhw4d+A4KSgWNy0GgcQkS2peDQPvioAuHy+FugXl5eZUvhFulEzfC7EkqlXKjmO/evct3LFBaaFwOBY1LYNC+HAraF65Au5aXL19eunSJiMaOHcsNXjY0b96848eP40aYnXH9yVzzFpiQoHE5IDQuwUD7ckAu3r5wBdq1nDp1imVZmUzGrSFkpFWrVkSUmpr64MEDu4fmonJzc7lvhWrVqvEdC5QKGpejQeMSErQvR4P2hQTatXBT0Lds2dLd3b3w1kaNGkmlUsKNMLtQq9W3bt2aNWvWixcv/P39mzZtyndEUCpoXI4DjUt40L4cB9oXB104XMjz58//+usvImrdunWRFSQSSfPmzQ8fPnzixIl+/frZNzrh6927t37qJSLiRsMQkaen5+jRo728vHiKC6wAjYtfaFzChvbFL7SvIuEKtAs5efIky7L+/v716tV7VR3cCLMdlmV1BkQiUVBQULt27ZYsWVLkTUlwImhc/ELjEja0L36hfRWJYVmW7xgAAAAAAJwGrkADAAAAAJgBCTQAAAAAgBmQQAMAAAAAmAEJNAAAAACAGZBAAwAAAACYAQk0AAAAAIAZkEADAAAAAJgBCTQAAAAAgBmQQAMAAAAAmAEJNAAAAACAGZBAAwAAAACYAQk0AAAAAIAZkEADAAAAAJgBCTQAABQhICCAYZjJkyfzHQgAgMNBAg0AAMU7fvw4wzAMw/zvf/+zbmUAAKeDBBoAACw3bdo0hmECAgL4DgQAwH6QQAMAAAAAmMGN7wAAAMAJNG3a9PHjx0Tk5lb8F4dZlQEAnA4+2gAAoHgSiaRixYq2qAwA4HTQhQMAwN4uXrwYHR0dHBzs6ekZFBTUvn37+Ph4lUpVZOVr166NGjUqLCxMJpN5e3uHhYWNHDnyr7/+KlzT29ubYZidO3cSUUpKSo8ePSpXriyVSuvWrTtkyJB79+4V3oVl2S1btrz99tsVKlTw8PCoXr36sGHDrl27VmQk3PFnzZrF/Tl8+HCGYaZPn05EGRkZ3KjBBQsWFFnZbs8IAMAeWAAAsBedTjdjxgyRqIiLF7Vq1bp//75R/W+//VYsFheuLBKJ5syZo9PpDCvLZDIi2rFjR1xcHMMwRrt4eHicPHnSsL5SqezUqVPhg3t6em7fvt3f35+IJk2aZHT8mTNncn/GxMQU3nf+/PlFVrbPMwIAsA8k0AAA9rNo0SIu+QsICPjkk0/Wr18fHx/ftm1brrBVq1ZqtVpfec2aNVy5m5tbVFTU8uXLly9fPnjwYHd3d6585cqVhgfn0s3hw4cTUXBw8MKFC1NSUnbu3Nm5c2eufo0aNfQZqk6n69atG1fu6+sbExOzatWqmTNnNm/enMuhubOYSKA1Go1arZ4yZQoR+fn5qdVqtVqt1WqLrGyHZwQAYDdIoAEA7CQtLc3T05OIQkNDb9++rS/X6XRjx47lMsIdO3ZwhQqFwsfHh4jKli179OhRw+OkpKRwl4flcvnLly/15Vy6SUTNmzfPyMgwPH7Xrl25Tfrz7tmzhysJDQ29ceOGvrJWqx03bpz+Kq+JBJrz1VdfEZG/v7/Rky1c2dbPCADAbtAHGgDAThISEvLy8ogoPj4+ODhYX84wzOzZs+VyOREdO3aMK9y0aVNmZiYRTZ48uU2bNobHadmyJXfdV6lUbtq0qfCJvv/+ez8/P8Pj67tb3Lx5k3uwbNky7sG6detq1aqlrywSiebNm9ewYcNSPtnCbP2MAADsBgk0AICd7N+/n4jq1Knz1ltvGW3y8PCYN2/eZ599ps9lDx06RERyubzIrsbR0dFcwn3kyBGjTW+88UZ4eLhRYfXq1bkHLMtyDy5cuEBEHTp0iIiIMKrMMMznn39u1lMrCVs/IwAAu8E0dgAA9sCy7Pnz54koPDy88Hg4+renr96VK1eIKDQ0VN+NwZBMJqtdu/a5c+cuXbpktKl27dqF6xsNW8zKyuLmaW7WrFmR0TZt2tTEc7GMTZ8RAIA94QMIAMAelEolN1Fd1apVS1L/xYsXZHCdtTBuE1fNUFBQULEHv337NvegZs2aRVYICgoqcq6M0rDpMwIAsCck0AAA9qDRaLgHHh4eVjkgt8if/rBG5aZxYxlNYBjG6gl0sUrzjAAA7AkJNACAPfj6+nIP7t+/X5L6ZcuWJaLU1NRXVbh79y4RGQ6tK7nq1atz3Uj0l6KNPHr06FULu1jMps8IAMCekEADANiDWCwOCwujf7sCF7Z27dp+/frpe0K/9tprRHTjxo2cnJzClXNzc7n1Arlq5vLw8AgMDCSiM2fOFFmB665tXTZ9RgAA9oQEGgDATjp06EBEZ8+ePXnypNEmlmXnz5+flJT06NEjrqRdu3ZElJmZuWrVqsKHSkhIUCgU+moWaNSoEREdPHiwcA7Nsuy8efMsO6wJtn5GAAB2gwQaAMBOhg8fznWcGDFixMOHDw03rVix4u+//yYi/Qx377//vre3NxHNmDHj1KlThpVPnjw5Y8YMIpLJZIMGDbIsGP1cclFRUYZTKbMsO3369MIpvmm5ublardZ0HVs/IwAAu8HIDAAAO3nttdc+++yzBQsWXLp0qVGjRlFRUeHh4Xl5eQcOHNiyZQsRNWzYUJ/XlilT5rvvvhs2bFhGRkabNm0+/PDDZs2asSx75syZtWvXqtVqIlq4cCHXsdgCnTp16tmz5/bt269fv96kSZP+/fs3btz46dOn+/fv/+233zw9PUNCQl7V28QQtwp3Tk5OfHx869atAwICXjXNiK2fEQCA3SCBBgCwnzlz5iiVyhUrVvzzzz/z58833PTaa69t2bJFIpHoS6Kjo58/fz5x4kS1Wr1y5cqVK1fqN4lEojlz5kRHR1scCcMw69evz87O/uWXXxQKxfLly/WbPD09N23alJSUVJIEukmTJtwDbjXy+fPnG64EbsSmzwgAwG7QhQMAwH7EYvHy5cuPHj3av3//KlWqSCSSqlWrduzYcfHixRcuXDBcUpuIGIaZMGHCn3/+GRMTExISIpVKpVJpSEhITEzM5cuXx48fX+SCLCUnk8l+/vnnzZs3d+rUqVy5cu7u7pUrV/7ggw/OnTvXo0ePEh6kY8eOCxYsqFatmkQiqVy5ckBAgInKtn5GAAD2wWARVAAAAACAksMVaAAAAAAAMyCBBgAAAAAwAxJoAAAAAAAzIIEGAAAAADADEmgAAAAAADMggQYAAAAAMAMSaAAAAAAAMyCBBgAAAAAwAxJoAAAAAAAzIIEGAAAAADADEmgAAAAAADMggQYAAAAAMAMSaAAAAAAAMyCBBgAAAAAww/8BM5SXDy+FvFUAAAAASUVORK5CYII=" width="480" /></p>
<p>Now we will continue to explain the use of interactions in order to test for <em>differences</em> in condition effects. We continue with the example of condition effects across three genotypes (I, II, and III).</p>
<p>The key point to remember about designs with interaction terms is that, unlike for a design <code>~genotype + condition</code>, where the condition effect represents the <em>overall</em> effect controlling for differences due to genotype, by adding <code>genotype:condition</code>, the main condition effect only represents the effect of condition for the <em>reference level</em> of genotype (I, or whichever level was defined by the user as the reference level). The interaction terms <code>genotypeII.conditionB</code> and <code>genotypeIII.conditionB</code> give the <em>difference</em> between the condition effect for a given genotype and the condition effect for the reference genotype.</p>
<p>This genotype-condition interaction example is examined in further detail in Example 3 in the help page for <em>results</em>, which can be found by typing <code>?results</code>. In particular, we show how to test for differences in the condition effect across genotype, and we show how to obtain the condition effect for non-reference genotypes.</p>
</div>
<div id="time-series-experiments" class="section level2">
<h2>Time-series experiments</h2>
<p>There are a number of ways to analyze time-series experiments, depending on the biological question of interest. In order to test for any differences over multiple time points, once can use a design including the time factor, and then test using the likelihood ratio test as described in the following section, where the time factor is removed in the reduced formula. For a control and treatment time series, one can use a design formula containing the condition factor, the time factor, and the interaction of the two. In this case, using the likelihood ratio test with a reduced model which does not contain the interaction terms will test whether the condition induces a change in gene expression at any time point after the reference level time point (time 0). An example of the later analysis is provided in our <a href="http://www.bioconductor.org/help/workflows/rnaseqGene">RNA-seq workflow</a>.</p>
</div>
<div id="likelihood-ratio-test" class="section level2">
<h2>Likelihood ratio test</h2>
<p>DESeq2 offers two kinds of hypothesis tests: the Wald test, where we use the estimated standard error of a log2 fold change to test if it is equal to zero, and the likelihood ratio test (LRT). The LRT examines two models for the counts, a <em>full</em> model with a certain number of terms and a <em>reduced</em> model, in which some of the terms of the <em>full</em> model are removed. The test determines if the increased likelihood of the data using the extra terms in the <em>full</em> model is more than expected if those extra terms are truly zero.</p>
<p>The LRT is therefore useful for testing multiple terms at once, for example testing 3 or more levels of a factor at once, or all interactions between two variables. The LRT for count data is conceptually similar to an analysis of variance (ANOVA) calculation in linear regression, except that in the case of the Negative Binomial GLM, we use an analysis of deviance (ANODEV), where the <em>deviance</em> captures the difference in likelihood between a full and a reduced model.</p>
<p>The likelihood ratio test can be performed by specifying <code>test="LRT"</code> when using the <em>DESeq</em> function, and providing a reduced design formula, e.g. one in which a number of terms from <code>design(dds)</code> are removed. The degrees of freedom for the test is obtained from the difference between the number of parameters in the two models. A simple likelihood ratio test, if the full design was <code>~condition</code> would look like:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds <-<span class="st"> </span><span class="kw">DESeq</span>(dds, <span class="dt">test=</span><span class="st">"LRT"</span>, <span class="dt">reduced=</span>~<span class="dv">1</span>)
res <-<span class="st"> </span><span class="kw">results</span>(dds)</code></pre></div>
<p>If the full design contained other variables, such as a batch variable, e.g. <code>~batch + condition</code> then the likelihood ratio test would look like:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds <-<span class="st"> </span><span class="kw">DESeq</span>(dds, <span class="dt">test=</span><span class="st">"LRT"</span>, <span class="dt">reduced=</span>~batch)
res <-<span class="st"> </span><span class="kw">results</span>(dds)</code></pre></div>
<p><a name="outlier"></a></p>
</div>
<div id="approach-to-count-outliers" class="section level2">
<h2>Approach to count outliers</h2>
<p>RNA-seq data sometimes contain isolated instances of very large counts that are apparently unrelated to the experimental or study design, and which may be considered outliers. There are many reasons why outliers can arise, including rare technical or experimental artifacts, read mapping problems in the case of genetically differing samples, and genuine, but rare biological events. In many cases, users appear primarily interested in genes that show a consistent behavior, and this is the reason why by default, genes that are affected by such outliers are set aside by DESeq2, or if there are sufficient samples, outlier counts are replaced for model fitting. These two behaviors are described below.</p>
<p>The <em>DESeq</em> function calculates, for every gene and for every sample, a diagnostic test for outliers called <em>Cook’s distance</em>. Cook’s distance is a measure of how much a single sample is influencing the fitted coefficients for a gene, and a large value of Cook’s distance is intended to indicate an outlier count. The Cook’s distances are stored as a matrix available in <code>assays(dds)[["cooks"]]</code>.</p>
<p>The <em>results</em> function automatically flags genes which contain a Cook’s distance above a cutoff for samples which have 3 or more replicates. The <em>p</em> values and adjusted <em>p</em> values for these genes are set to <code>NA</code>. At least 3 replicates are required for flagging, as it is difficult to judge which sample might be an outlier with only 2 replicates. This filtering can be turned off with <code>results(dds, cooksCutoff=FALSE)</code>.</p>
<p>With many degrees of freedom – i.,e., many more samples than number of parameters to be estimated – it is undesirable to remove entire genes from the analysis just because their data include a single count outlier. When there are 7 or more replicates for a given sample, the <em>DESeq</em> function will automatically replace counts with large Cook’s distance with the trimmed mean over all samples, scaled up by the size factor or normalization factor for that sample. This approach is conservative, it will not lead to false positives, as it replaces the outlier value with the value predicted by the null hypothesis. This outlier replacement only occurs when there are 7 or more replicates, and can be turned off with <code>DESeq(dds, minReplicatesForReplace=Inf)</code>.</p>
<p>The default Cook’s distance cutoff for the two behaviors described above depends on the sample size and number of parameters to be estimated. The default is to use the 99% quantile of the F(p,m-p) distribution (with <em>p</em> the number of parameters including the intercept and <em>m</em> number of samples). The default for gene flagging can be modified using the <code>cooksCutoff</code> argument to the <em>results</em> function. For outlier replacement, <em>DESeq</em> preserves the original counts in <code>counts(dds)</code> saving the replacement counts as a matrix named <code>replaceCounts</code> in <code>assays(dds)</code>. Note that with continuous variables in the design, outlier detection and replacement is not automatically performed, as our current methods involve a robust estimation of within-group variance which does not extend easily to continuous covariates. However, users can examine the Cook’s distances in <code>assays(dds)[["cooks"]]</code>, in order to perform manual visualization and filtering if necessary.</p>
<p><strong>Note on many outliers:</strong> if there are very many outliers (e.g. many hundreds or thousands) reported by <code>summary(res)</code>, one might consider further exploration to see if a single sample or a few samples should be removed due to low quality. The automatic outlier filtering/replacement is most useful in situations which the number of outliers is limited. When there are thousands of reported outliers, it might make more sense to turn off the outlier filtering/replacement (<em>DESeq</em> with <code>minReplicatesForReplace=Inf</code> and <em>results</em> with <code>cooksCutoff=FALSE</code>) and perform manual inspection: First it would be advantageous to make a PCA plot as described above to spot individual sample outliers; Second, one can make a boxplot of the Cook’s distances to see if one sample is consistently higher than others (here this is not the case):</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">8</span>,<span class="dv">5</span>,<span class="dv">2</span>,<span class="dv">2</span>))
<span class="kw">boxplot</span>(<span class="kw">log10</span>(<span class="kw">assays</span>(dds)[[<span class="st">"cooks"</span>]]), <span class="dt">range=</span><span class="dv">0</span>, <span class="dt">las=</span><span class="dv">2</span>)</code></pre></div>
<p><img 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" width="480" /></p>
</div>
<div id="dispersion-plot-and-fitting-alternatives" class="section level2">
<h2>Dispersion plot and fitting alternatives</h2>
<p>Plotting the dispersion estimates is a useful diagnostic. The dispersion plot below is typical, with the final estimates shrunk from the gene-wise estimates towards the fitted estimates. Some gene-wise estimates are flagged as outliers and not shrunk towards the fitted value, (this outlier detection is described in the manual page for <em>estimateDispersionsMAP</em>). The amount of shrinkage can be more or less than seen here, depending on the sample size, the number of coefficients, the row mean and the variability of the gene-wise estimates.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotDispEsts</span>(dds)</code></pre></div>
<p><img 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" width="480" /></p>
<div id="local-or-mean-dispersion-fit" class="section level3">
<h3>Local or mean dispersion fit</h3>
<p>A local smoothed dispersion fit is automatically substitited in the case that the parametric curve doesn’t fit the observed dispersion mean relationship. This can be prespecified by providing the argument <code>fitType="local"</code> to either <em>DESeq</em> or <em>estimateDispersions</em>. Additionally, using the mean of gene-wise disperion estimates as the fitted value can be specified by providing the argument <code>fitType="mean"</code>.</p>
</div>
<div id="supply-a-custom-dispersion-fit" class="section level3">
<h3>Supply a custom dispersion fit</h3>
<p>Any fitted values can be provided during dispersion estimation, using the lower-level functions described in the manual page for <em>estimateDispersionsGeneEst</em>. In the code chunk below, we store the gene-wise estimates which were already calculated and saved in the metadata column <code>dispGeneEst</code>. Then we calculate the median value of the dispersion estimates above a threshold, and save these values as the fitted dispersions, using the replacement function for <em>dispersionFunction</em>. In the last line, the function <em>estimateDispersionsMAP</em>, uses the fitted dispersions to generate maximum <em>a posteriori</em> (MAP) estimates of dispersion.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ddsCustom <-<span class="st"> </span>dds
useForMedian <-<span class="st"> </span><span class="kw">mcols</span>(ddsCustom)$dispGeneEst ><span class="st"> </span><span class="fl">1e-7</span>
medianDisp <-<span class="st"> </span><span class="kw">median</span>(<span class="kw">mcols</span>(ddsCustom)$dispGeneEst[useForMedian],
<span class="dt">na.rm=</span><span class="ot">TRUE</span>)
<span class="kw">dispersionFunction</span>(ddsCustom) <-<span class="st"> </span>function(mu) medianDisp
ddsCustom <-<span class="st"> </span><span class="kw">estimateDispersionsMAP</span>(ddsCustom)</code></pre></div>
<p><a name="indfilt"></a></p>
</div>
</div>
<div id="independent-filtering-of-results" class="section level2">
<h2>Independent filtering of results</h2>
<p>The <em>results</em> function of the DESeq2 package performs independent filtering by default using the mean of normalized counts as a filter statistic. A threshold on the filter statistic is found which optimizes the number of adjusted <em>p</em> values lower than a significance level <code>alpha</code> (we use the standard variable name for significance level, though it is unrelated to the dispersion parameter <span class="math inline">\(\alpha\)</span>). The theory behind independent filtering is discussed in greater detail <a href="#indfilttheory">below</a>. The adjusted <em>p</em> values for the genes which do not pass the filter threshold are set to <code>NA</code>.</p>
<p>The default independent filtering is performed using the <em>filtered_p</em> function of the <a href="http://bioconductor.org/packages/genefilter">genefilter</a> package, and all of the arguments of <em>filtered_p</em> can be passed to the <em>results</em> function. The filter threshold value and the number of rejections at each quantile of the filter statistic are available as metadata of the object returned by <em>results</em>.</p>
<p>For example, we can visualize the optimization by plotting the <code>filterNumRej</code> attribute of the results object. The <em>results</em> function maximizes the number of rejections (adjusted <em>p</em> value less than a significance level), over the quantiles of a filter statistic (the mean of normalized counts). The threshold chosen (vertical line) is the lowest quantile of the filter for which the number of rejections is within 1 residual standard deviation to the peak of a curve fit to the number of rejections over the filter quantiles:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">metadata</span>(res)$alpha</code></pre></div>
<pre><code>## [1] 0.1</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">metadata</span>(res)$filterThreshold</code></pre></div>
<pre><code>## 15.5102%
## 6.150425</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(<span class="kw">metadata</span>(res)$filterNumRej,
<span class="dt">type=</span><span class="st">"b"</span>, <span class="dt">ylab=</span><span class="st">"number of rejections"</span>,
<span class="dt">xlab=</span><span class="st">"quantiles of filter"</span>)
<span class="kw">lines</span>(<span class="kw">metadata</span>(res)$lo.fit, <span class="dt">col=</span><span class="st">"red"</span>)
<span class="kw">abline</span>(<span class="dt">v=</span><span class="kw">metadata</span>(res)$filterTheta)</code></pre></div>
<p><img 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" width="480" /></p>
<p>Independent filtering can be turned off by setting <code>independentFiltering</code> to <code>FALSE</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">resNoFilt <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">independentFiltering=</span><span class="ot">FALSE</span>)
<span class="kw">addmargins</span>(<span class="kw">table</span>(<span class="dt">filtering=</span>(res$padj <<span class="st"> </span>.<span class="dv">1</span>),
<span class="dt">noFiltering=</span>(resNoFilt$padj <<span class="st"> </span>.<span class="dv">1</span>)))</code></pre></div>
<pre><code>## noFiltering
## filtering FALSE TRUE Sum
## FALSE 7327 0 7327
## TRUE 74 980 1054
## Sum 7401 980 8381</code></pre>
</div>
<div id="tests-of-log2-fold-change-above-or-below-a-threshold" class="section level2">
<h2>Tests of log2 fold change above or below a threshold</h2>
<p>It is also possible to provide thresholds for constructing Wald tests of significance. Two arguments to the <em>results</em> function allow for threshold-based Wald tests: <code>lfcThreshold</code>, which takes a numeric of a non-negative threshold value, and <code>altHypothesis</code>, which specifies the kind of test. Note that the <em>alternative hypothesis</em> is specified by the user, i.e. those genes which the user is interested in finding, and the test provides <em>p</em> values for the null hypothesis, the complement of the set defined by the alternative. The <code>altHypothesis</code> argument can take one of the following four values, where <span class="math inline">\(\beta\)</span> is the log2 fold change specified by the <code>name</code> argument, and <span class="math inline">\(x\)</span> is the <code>lfcThreshold</code>.</p>
<ul>
<li><code>greaterAbs</code> - <span class="math inline">\(|\beta| > x\)</span> - tests are two-tailed</li>
<li><code>lessAbs</code> - <span class="math inline">\(|\beta| < x\)</span> - <em>p</em> values are the maximum of the upper and lower tests</li>
<li><code>greater</code> - <span class="math inline">\(\beta > x\)</span></li>
<li><code>less</code> - <span class="math inline">\(\beta < -x\)</span></li>
</ul>
<p>The four possible values of <code>altHypothesis</code> are demonstrated in the following code and visually by MA-plots in the following figures.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mfrow=</span><span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">2</span>),<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">2</span>,<span class="dv">1</span>,<span class="dv">1</span>))
ylim <-<span class="st"> </span><span class="kw">c</span>(-<span class="fl">2.5</span>,<span class="fl">2.5</span>)
resGA <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">lfcThreshold=</span>.<span class="dv">5</span>, <span class="dt">altHypothesis=</span><span class="st">"greaterAbs"</span>)
resLA <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">lfcThreshold=</span>.<span class="dv">5</span>, <span class="dt">altHypothesis=</span><span class="st">"lessAbs"</span>)
resG <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">lfcThreshold=</span>.<span class="dv">5</span>, <span class="dt">altHypothesis=</span><span class="st">"greater"</span>)
resL <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">lfcThreshold=</span>.<span class="dv">5</span>, <span class="dt">altHypothesis=</span><span class="st">"less"</span>)
drawLines <-<span class="st"> </span>function() <span class="kw">abline</span>(<span class="dt">h=</span><span class="kw">c</span>(-.<span class="dv">5</span>,.<span class="dv">5</span>),<span class="dt">col=</span><span class="st">"dodgerblue"</span>,<span class="dt">lwd=</span><span class="dv">2</span>)
<span class="kw">plotMA</span>(resGA, <span class="dt">ylim=</span>ylim); <span class="kw">drawLines</span>()
<span class="kw">plotMA</span>(resLA, <span class="dt">ylim=</span>ylim); <span class="kw">drawLines</span>()
<span class="kw">plotMA</span>(resG, <span class="dt">ylim=</span>ylim); <span class="kw">drawLines</span>()
<span class="kw">plotMA</span>(resL, <span class="dt">ylim=</span>ylim); <span class="kw">drawLines</span>()</code></pre></div>
<p><img 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" width="480" /></p>
<p><a name="access"></a></p>
</div>
<div id="access-to-all-calculated-values" class="section level2">
<h2>Access to all calculated values</h2>
<p>All row-wise calculated values (intermediate dispersion calculations, coefficients, standard errors, etc.) are stored in the <em>DESeqDataSet</em> object, e.g. <code>dds</code> in this vignette. These values are accessible by calling <em>mcols</em> on <code>dds</code>. Descriptions of the columns are accessible by two calls to <em>mcols</em>. Note that the call to <code>substr</code> below is only for display purposes.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mcols</span>(dds,<span class="dt">use.names=</span><span class="ot">TRUE</span>)[<span class="dv">1</span>:<span class="dv">4</span>,<span class="dv">1</span>:<span class="dv">4</span>]</code></pre></div>
<pre><code>## DataFrame with 4 rows and 4 columns
## gene baseMean baseVar allZero
## <factor> <numeric> <numeric> <logical>
## FBgn0000008 FBgn0000008 95.144292 2.248206e+02 FALSE
## FBgn0000014 FBgn0000014 1.056523 2.961952e+00 FALSE
## FBgn0000017 FBgn0000017 4352.553569 3.615380e+05 FALSE
## FBgn0000018 FBgn0000018 418.610484 2.349027e+03 FALSE</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">substr</span>(<span class="kw">names</span>(<span class="kw">mcols</span>(dds)),<span class="dv">1</span>,<span class="dv">10</span>) </code></pre></div>
<pre><code>## [1] "gene" "baseMean" "baseVar" "allZero" "dispGeneEs"
## [6] "dispFit" "dispersion" "dispIter" "dispOutlie" "dispMAP"
## [11] "Intercept" "condition_" "SE_Interce" "SE_conditi" "WaldStatis"
## [16] "WaldStatis" "WaldPvalue" "WaldPvalue" "betaConv" "betaIter"
## [21] "deviance" "maxCooks"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mcols</span>(<span class="kw">mcols</span>(dds), <span class="dt">use.names=</span><span class="ot">TRUE</span>)[<span class="dv">1</span>:<span class="dv">4</span>,]</code></pre></div>
<pre><code>## DataFrame with 4 rows and 2 columns
## type description
## <character> <character>
## gene input
## baseMean intermediate mean of normalized counts for all samples
## baseVar intermediate variance of normalized counts for all samples
## allZero intermediate all counts for a gene are zero</code></pre>
<p>The mean values <span class="math inline">\(\mu_{ij} = s_j q_{ij}\)</span> and the Cook’s distances for each gene and sample are stored as matrices in the assays slot:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(<span class="kw">assays</span>(dds)[[<span class="st">"mu"</span>]])</code></pre></div>
<pre><code>## treated1 treated2 treated3 untreated1 untreated2
## FBgn0000008 154.39604 71.8609681 78.6055335 107.292913 169.048447
## FBgn0000014 1.50181 0.6989914 0.7645958 1.473259 2.321236
## FBgn0000017 6450.25959 3002.1618954 3283.9320689 5301.761109 8353.342790
## FBgn0000018 658.34912 306.4172250 335.1762452 492.704366 776.294589
## FBgn0000024 11.44974 5.3290832 5.8292484 6.885635 10.848861
## FBgn0000032 1561.83053 726.9270379 795.1533243 1158.470643 1825.261870
## untreated3 untreated4
## FBgn0000008 61.2163768 70.8413790
## FBgn0000014 0.8405737 0.9727364
## FBgn0000017 3024.9398185 3500.5486968
## FBgn0000018 281.1143362 325.3137193
## FBgn0000024 3.9286252 4.5463198
## FBgn0000032 660.9697995 764.8935546</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(<span class="kw">assays</span>(dds)[[<span class="st">"cooks"</span>]])</code></pre></div>
<pre><code>## treated1 treated2 treated3 untreated1 untreated2
## FBgn0000008 0.08830678 0.303871771 0.07781046 0.09824099 0.0137763910
## FBgn0000014 1.88673190 0.218246742 0.25186426 1.88310396 0.1847650641
## FBgn0000017 0.01372829 0.004978868 0.00214944 0.08044192 0.0104725895
## FBgn0000018 0.09518416 0.004710886 0.05477260 0.18460854 0.0023367713
## FBgn0000024 0.06631472 0.131131695 0.03122767 0.27064873 0.0004706695
## FBgn0000032 0.07377787 0.015891436 0.02053258 0.34090628 0.0217426932
## untreated3 untreated4
## FBgn0000008 0.18921869 0.0005147318
## FBgn0000014 0.15347774 0.1887766572
## FBgn0000017 0.17278836 0.0549333693
## FBgn0000018 0.07634172 0.0108036598
## FBgn0000024 0.03100295 0.0813891462
## FBgn0000032 0.02482481 0.0774936225</code></pre>
<p>The dispersions <span class="math inline">\(\alpha_i\)</span> can be accessed with the <em>dispersions</em> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(<span class="kw">dispersions</span>(dds))</code></pre></div>
<pre><code>## [1] 0.03035168 2.80571497 0.01289667 0.01565291 0.23770103 0.01691016</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(<span class="kw">mcols</span>(dds)$dispersion)</code></pre></div>
<pre><code>## [1] 0.03035168 2.80571497 0.01289667 0.01565291 0.23770103 0.01691016</code></pre>
<p>The size factors <span class="math inline">\(s_j\)</span> are accessible via <em>sizeFactors</em>:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">sizeFactors</span>(dds)</code></pre></div>
<pre><code>## treated1 treated2 treated3 untreated1 untreated2 untreated3
## 1.6355014 0.7612159 0.8326603 1.1383376 1.7935406 0.6494828
## untreated4
## 0.7516005</code></pre>
<p>For advanced users, we also include a convenience function <em>coef</em> for extracting the matrix <span class="math inline">\([\beta_{ir}]\)</span> for all genes <em>i</em> and model coefficients <span class="math inline">\(r\)</span>. This function can also return a matrix of standard errors, see <code>?coef</code>. The columns of this matrix correspond to the effects returned by <em>resultsNames</em>. Note that the <em>results</em> function is best for building results tables with <em>p</em> values and adjusted <em>p</em> values.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(<span class="kw">coef</span>(dds))</code></pre></div>
<pre><code>## Intercept condition_treated_vs_untreated
## FBgn0000008 6.5584825 0.002276428
## FBgn0000014 0.3720829 -0.495113878
## FBgn0000017 12.1853275 -0.239918945
## FBgn0000018 8.7576500 -0.104673913
## FBgn0000024 2.5966613 0.210848562
## FBgn0000032 9.9910773 -0.091788071</code></pre>
<p>The beta prior variance <span class="math inline">\(\sigma_r^2\)</span> is stored as an attribute of the <em>DESeqDataSet</em>:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">attr</span>(dds, <span class="st">"betaPriorVar"</span>)</code></pre></div>
<pre><code>## [1] 1e+06 1e+06</code></pre>
<p>General information about the prior used for log fold change shrinkage is also stored in a slot of the <em>DESeqResults</em> object. This would also contain information about what other packages were used for log2 fold change shrinkage.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">priorInfo</span>(resLFC)</code></pre></div>
<pre><code>## $type
## [1] "normal"
##
## $package
## [1] "DESeq2"
##
## $version
## [1] '1.18.1'
##
## $betaPriorVar
## Intercept conditiontreated
## 1.000000e+06 1.094939e-01</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">priorInfo</span>(resApe)</code></pre></div>
<pre><code>## $type
## [1] "apeglm"
##
## $package
## [1] "apeglm"
##
## $version
## [1] '1.0.0'
##
## $prior.control
## $prior.control$no.shrink
## [1] 1
##
## $prior.control$prior.mean
## [1] 0
##
## $prior.control$prior.scale
## [1] 0.4045597
##
## $prior.control$prior.df
## [1] 1
##
## $prior.control$prior.no.shrink.mean
## [1] 0
##
## $prior.control$prior.no.shrink.scale
## [1] 15
##
## $prior.control$prior.var
## [1] 0.04091713</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">priorInfo</span>(resAsh)</code></pre></div>
<pre><code>## $type
## [1] "ashr"
##
## $package
## [1] "ashr"
##
## $version
## [1] '2.0.5'
##
## $fitted_g
## $pi
## [1] 1.502747e-01 1.758840e-05 2.349157e-05 4.109161e-05 1.166040e-04
## [6] 7.118828e-04 1.045939e-02 1.664217e-01 2.877691e-01 3.076675e-02
## [11] 9.989217e-02 8.123288e-02 3.932428e-02 1.038946e-01 3.968250e-10
## [16] 4.355989e-03 2.469746e-02 0.000000e+00 0.000000e+00 0.000000e+00
## [21] 0.000000e+00 3.092738e-07 0.000000e+00
##
## $mean
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## $sd
## [1] 0.005779228 0.008173063 0.011558456 0.016346125 0.023116912
## [6] 0.032692251 0.046233824 0.065384502 0.092467649 0.130769003
## [11] 0.184935298 0.261538006 0.369870596 0.523076013 0.739741191
## [16] 1.046152026 1.479482383 2.092304051 2.958964766 4.184608102
## [21] 5.917929531 8.369216205 11.835859063
##
## attr(,"row.names")
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## attr(,"class")
## [1] "normalmix"</code></pre>
<p>The dispersion prior variance <span class="math inline">\(\sigma_d^2\)</span> is stored as an attribute of the dispersion function:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">dispersionFunction</span>(dds)</code></pre></div>
<pre><code>## function (q)
## coefs[1] + coefs[2]/q
## <environment: 0x126e8ad8>
## attr(,"coefficients")
## asymptDisp extraPois
## 0.0140678 2.6551441
## attr(,"fitType")
## [1] "parametric"
## attr(,"varLogDispEsts")
## [1] 0.974076
## attr(,"dispPriorVar")
## [1] 0.4837182</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">attr</span>(<span class="kw">dispersionFunction</span>(dds), <span class="st">"dispPriorVar"</span>)</code></pre></div>
<pre><code>## [1] 0.4837182</code></pre>
<p>The version of DESeq2 which was used to construct the <em>DESeqDataSet</em> object, or the version used when <em>DESeq</em> was run, is stored here:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">metadata</span>(dds)[[<span class="st">"version"</span>]]</code></pre></div>
<pre><code>## [1] '1.18.1'</code></pre>
</div>
<div id="sample-gene-dependent-normalization-factors" class="section level2">
<h2>Sample-/gene-dependent normalization factors</h2>
<p>In some experiments, there might be gene-dependent dependencies which vary across samples. For instance, GC-content bias or length bias might vary across samples coming from different labs or processed at different times. We use the terms <em>normalization factors</em> for a gene x sample matrix, and <em>size factors</em> for a single number per sample. Incorporating normalization factors, the mean parameter <span class="math inline">\(\mu_{ij}\)</span> becomes:</p>
<p><span class="math display">\[ \mu_{ij} = NF_{ij} q_{ij} \]</span></p>
<p>with normalization factor matrix <em>NF</em> having the same dimensions as the counts matrix <em>K</em>. This matrix can be incorporated as shown below. We recommend providing a matrix with row-wise geometric means of 1, so that the mean of normalized counts for a gene is close to the mean of the unnormalized counts. This can be accomplished by dividing out the current row geometric means.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">normFactors <-<span class="st"> </span>normFactors /<span class="st"> </span><span class="kw">exp</span>(<span class="kw">rowMeans</span>(<span class="kw">log</span>(normFactors)))
<span class="kw">normalizationFactors</span>(dds) <-<span class="st"> </span>normFactors</code></pre></div>
<p>These steps then replace <em>estimateSizeFactors</em> which occurs within the <em>DESeq</em> function. The <em>DESeq</em> function will look for pre-existing normalization factors and use these in the place of size factors (and a message will be printed confirming this).</p>
<p>The methods provided by the <a href="http://bioconductor.org/packages/cqn">cqn</a> or <a href="http://bioconductor.org/packages/EDASeq">EDASeq</a> packages can help correct for GC or length biases. They both describe in their vignettes how to create matrices which can be used by DESeq2. From the formula above, we see that normalization factors should be on the scale of the counts, like size factors, and unlike offsets which are typically on the scale of the predictors (i.e. the logarithmic scale for the negative binomial GLM). At the time of writing, the transformation from the matrices provided by these packages should be:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cqnOffset <-<span class="st"> </span>cqnObject$glm.offset
cqnNormFactors <-<span class="st"> </span><span class="kw">exp</span>(cqnOffset)
EDASeqNormFactors <-<span class="st"> </span><span class="kw">exp</span>(-<span class="dv">1</span> *<span class="st"> </span>EDASeqOffset)</code></pre></div>
</div>
<div id="model-matrix-not-full-rank" class="section level2">
<h2>“Model matrix not full rank”</h2>
<p>While most experimental designs run easily using design formula, some design formulas can cause problems and result in the <em>DESeq</em> function returning an error with the text: “the model matrix is not full rank, so the model cannot be fit as specified.” There are two main reasons for this problem: either one or more columns in the model matrix are linear combinations of other columns, or there are levels of factors or combinations of levels of multiple factors which are missing samples. We address these two problems below and discuss possible solutions:</p>
<div id="linear-combinations" class="section level3">
<h3>Linear combinations</h3>
<p>The simplest case is the linear combination, or linear dependency problem, when two variables contain exactly the same information, such as in the following sample table. The software cannot fit an effect for <code>batch</code> and <code>condition</code>, because they produce identical columns in the model matrix. This is also referred to as <em>perfect confounding</em>. A unique solution of coefficients (the <span class="math inline">\(\beta_i\)</span> in the formula <a href="#theory">below</a>) is not possible.</p>
<pre><code>## DataFrame with 4 rows and 2 columns
## batch condition
## <factor> <factor>
## 1 1 A
## 2 1 A
## 3 2 B
## 4 2 B</code></pre>
<p>Another situation which will cause problems is when the variables are not identical, but one variable can be formed by the combination of other factor levels. In the following example, the effect of batch 2 vs 1 cannot be fit because it is identical to a column in the model matrix which represents the condition C vs A effect.</p>
<pre><code>## DataFrame with 6 rows and 2 columns
## batch condition
## <factor> <factor>
## 1 1 A
## 2 1 A
## 3 1 B
## 4 1 B
## 5 2 C
## 6 2 C</code></pre>
<p>In both of these cases above, the batch effect cannot be fit and must be removed from the model formula. There is just no way to tell apart the condition effects and the batch effects. The options are either to assume there is no batch effect (which we know is highly unlikely given the literature on batch effects in sequencing datasets) or to repeat the experiment and properly balance the conditions across batches. A balanced design would look like:</p>
<pre><code>## DataFrame with 6 rows and 2 columns
## batch condition
## <factor> <factor>
## 1 1 A
## 2 1 B
## 3 1 C
## 4 2 A
## 5 2 B
## 6 2 C</code></pre>
<p><a name="nested-indiv"></a></p>
</div>
<div id="group-specific-condition-effects-individuals-nested-within-groups" class="section level3">
<h3>Group-specific condition effects, individuals nested within groups</h3>
<p>Finally, there is a case where we <em>can</em> in fact perform inference, but we may need to re-arrange terms to do so. Consider an experiment with grouped individuals, where we seek to test the group-specific effect of a condition or treatment, while controlling for individual effects. The individuals are nested within the groups: an individual can only be in one of the groups, although each individual has one or more observations across condition.</p>
<p>An example of such an experiment is below:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">coldata <-<span class="st"> </span><span class="kw">DataFrame</span>(<span class="dt">grp=</span><span class="kw">factor</span>(<span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">"X"</span>,<span class="st">"Y"</span>),<span class="dt">each=</span><span class="dv">6</span>)),
<span class="dt">ind=</span><span class="kw">factor</span>(<span class="kw">rep</span>(<span class="dv">1</span>:<span class="dv">6</span>,<span class="dt">each=</span><span class="dv">2</span>)),
<span class="dt">cnd=</span><span class="kw">factor</span>(<span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">"A"</span>,<span class="st">"B"</span>),<span class="dv">6</span>)))
coldata</code></pre></div>
<pre><code>## DataFrame with 12 rows and 3 columns
## grp ind cnd
## <factor> <factor> <factor>
## 1 X 1 A
## 2 X 1 B
## 3 X 2 A
## 4 X 2 B
## 5 X 3 A
## ... ... ... ...
## 8 Y 4 B
## 9 Y 5 A
## 10 Y 5 B
## 11 Y 6 A
## 12 Y 6 B</code></pre>
<p>Note that individual (<code>ind</code>) is a <em>factor</em> not a numeric. This is very important.</p>
<p>To make R display all the rows, we can do:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">as.data.frame</span>(coldata)</code></pre></div>
<pre><code>## grp ind cnd
## 1 X 1 A
## 2 X 1 B
## 3 X 2 A
## 4 X 2 B
## 5 X 3 A
## 6 X 3 B
## 7 Y 4 A
## 8 Y 4 B
## 9 Y 5 A
## 10 Y 5 B
## 11 Y 6 A
## 12 Y 6 B</code></pre>
<p>We have two groups of samples X and Y, each with three distinct individuals (labeled here 1-6). For each individual, we have conditions A and B (for example, this could be control and treated).</p>
<p>This design can be analyzed by DESeq2 but requires a bit of refactoring in order to fit the model terms. Here we will use a trick described in the <a href="http://bioconductor.org/packages/edgeR">edgeR</a> user guide, from the section <em>Comparisons Both Between and Within Subjects</em>. If we try to analyze with a formula such as, <code>~ ind + grp*cnd</code>, we will obtain an error, because the effect for group is a linear combination of the individuals.</p>
<p>However, the following steps allow for an analysis of group-specific condition effects, while controlling for differences in individual. For object construction, you can use a simple design, such as <code>~ ind + cnd</code>, as long as you remember to replace it before running <em>DESeq</em>. Then add a column <code>ind.n</code> which distinguishes the individuals nested within a group. Here, we add this column to coldata, but in practice you would add this column to <code>dds</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">coldata$ind.n <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">rep</span>(<span class="kw">rep</span>(<span class="dv">1</span>:<span class="dv">3</span>,<span class="dt">each=</span><span class="dv">2</span>),<span class="dv">2</span>))
<span class="kw">as.data.frame</span>(coldata)</code></pre></div>
<pre><code>## grp ind cnd ind.n
## 1 X 1 A 1
## 2 X 1 B 1
## 3 X 2 A 2
## 4 X 2 B 2
## 5 X 3 A 3
## 6 X 3 B 3
## 7 Y 4 A 1
## 8 Y 4 B 1
## 9 Y 5 A 2
## 10 Y 5 B 2
## 11 Y 6 A 3
## 12 Y 6 B 3</code></pre>
<p>Now we can reassign our <em>DESeqDataSet</em> a design of <code>~ grp + grp:ind.n + grp:cnd</code>, before we call <em>DESeq</em>. This new design will result in the following model matrix:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">model.matrix</span>(~<span class="st"> </span>grp +<span class="st"> </span>grp:ind.n +<span class="st"> </span>grp:cnd, coldata)</code></pre></div>
<pre><code>## (Intercept) grpY grpX:ind.n2 grpY:ind.n2 grpX:ind.n3 grpY:ind.n3
## 1 1 0 0 0 0 0
## 2 1 0 0 0 0 0
## 3 1 0 1 0 0 0
## 4 1 0 1 0 0 0
## 5 1 0 0 0 1 0
## 6 1 0 0 0 1 0
## 7 1 1 0 0 0 0
## 8 1 1 0 0 0 0
## 9 1 1 0 1 0 0
## 10 1 1 0 1 0 0
## 11 1 1 0 0 0 1
## 12 1 1 0 0 0 1
## grpX:cndB grpY:cndB
## 1 0 0
## 2 1 0
## 3 0 0
## 4 1 0
## 5 0 0
## 6 1 0
## 7 0 0
## 8 0 1
## 9 0 0
## 10 0 1
## 11 0 0
## 12 0 1
## attr(,"assign")
## [1] 0 1 2 2 2 2 3 3
## attr(,"contrasts")
## attr(,"contrasts")$grp
## [1] "contr.treatment"
##
## attr(,"contrasts")$ind.n
## [1] "contr.treatment"
##
## attr(,"contrasts")$cnd
## [1] "contr.treatment"</code></pre>
<p>Note that, if you have unbalanced numbers of individuals in the two groups, you will have zeros for some of the interactions between <code>grp</code> and <code>ind.n</code>. You can remove these columns manually from the model matrix and pass the corrected model matrix to the <code>full</code> argument of the <em>DESeq</em> function. See example code in the next section.</p>
<p>Above, the terms <code>grpX.cndB</code> and <code>grpY.cndB</code> give the group-specific condition effects, in other words, the condition B vs A effect for group X samples, and likewise for group Y samples. These terms control for all of the six individual effects. These group-specific condition effects can be extracted using <em>results</em> with the <code>name</code> argument.</p>
<p>Furthermore, <code>grpX.cndB</code> and <code>grpY.cndB</code> can be contrasted using the <code>contrast</code> argument, in order to test if the condition effect is different across group:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">results</span>(dds, <span class="dt">contrast=</span><span class="kw">list</span>(<span class="st">"grpY.cndB"</span>,<span class="st">"grpX.cndB"</span>))</code></pre></div>
</div>
<div id="levels-without-samples" class="section level3">
<h3>Levels without samples</h3>
<p>The base R function for creating model matrices will produce a column of zeros if a level is missing from a factor or a combination of levels is missing from an interaction of factors. The solution to the first case is to call <em>droplevels</em> on the column, which will remove levels without samples. This was shown in the beginning of this vignette.</p>
<p>The second case is also solvable, by manually editing the model matrix, and then providing this to <em>DESeq</em>. Here we construct an example dataset to illustrate:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">group <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">rep</span>(<span class="dv">1</span>:<span class="dv">3</span>,<span class="dt">each=</span><span class="dv">6</span>))
condition <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">rep</span>(<span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">"A"</span>,<span class="st">"B"</span>,<span class="st">"C"</span>),<span class="dt">each=</span><span class="dv">2</span>),<span class="dv">3</span>))
d <-<span class="st"> </span><span class="kw">DataFrame</span>(group, condition)[-<span class="kw">c</span>(<span class="dv">17</span>,<span class="dv">18</span>),]
<span class="kw">as.data.frame</span>(d)</code></pre></div>
<pre><code>## group condition
## 1 1 A
## 2 1 A
## 3 1 B
## 4 1 B
## 5 1 C
## 6 1 C
## 7 2 A
## 8 2 A
## 9 2 B
## 10 2 B
## 11 2 C
## 12 2 C
## 13 3 A
## 14 3 A
## 15 3 B
## 16 3 B</code></pre>
<p>Note that if we try to estimate all interaction terms, we introduce a column with all zeros, as there are no condition C samples for group 3. (Here, <em>unname</em> is used to display the matrix concisely.)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m1 <-<span class="st"> </span><span class="kw">model.matrix</span>(~<span class="st"> </span>condition*group, d)
<span class="kw">colnames</span>(m1)</code></pre></div>
<pre><code>## [1] "(Intercept)" "conditionB" "conditionC"
## [4] "group2" "group3" "conditionB:group2"
## [7] "conditionC:group2" "conditionB:group3" "conditionC:group3"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">unname</span>(m1)</code></pre></div>
<pre><code>## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 0 0 0 0 0 0 0 0
## [2,] 1 0 0 0 0 0 0 0 0
## [3,] 1 1 0 0 0 0 0 0 0
## [4,] 1 1 0 0 0 0 0 0 0
## [5,] 1 0 1 0 0 0 0 0 0
## [6,] 1 0 1 0 0 0 0 0 0
## [7,] 1 0 0 1 0 0 0 0 0
## [8,] 1 0 0 1 0 0 0 0 0
## [9,] 1 1 0 1 0 1 0 0 0
## [10,] 1 1 0 1 0 1 0 0 0
## [11,] 1 0 1 1 0 0 1 0 0
## [12,] 1 0 1 1 0 0 1 0 0
## [13,] 1 0 0 0 1 0 0 0 0
## [14,] 1 0 0 0 1 0 0 0 0
## [15,] 1 1 0 0 1 0 0 1 0
## [16,] 1 1 0 0 1 0 0 1 0
## attr(,"assign")
## [1] 0 1 1 2 2 3 3 3 3
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
##
## attr(,"contrasts")$group
## [1] "contr.treatment"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">all.zero <-<span class="st"> </span><span class="kw">apply</span>(m1, <span class="dv">2</span>, function(x) <span class="kw">all</span>(x==<span class="dv">0</span>))
all.zero</code></pre></div>
<pre><code>## (Intercept) conditionB conditionC group2
## FALSE FALSE FALSE FALSE
## group3 conditionB:group2 conditionC:group2 conditionB:group3
## FALSE FALSE FALSE FALSE
## conditionC:group3
## TRUE</code></pre>
<p>We can remove this column like so:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">idx <-<span class="st"> </span><span class="kw">which</span>(all.zero)
m1 <-<span class="st"> </span>m1[,-idx]
<span class="kw">unname</span>(m1)</code></pre></div>
<pre><code>## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 0 0 0 0 0 0 0
## [2,] 1 0 0 0 0 0 0 0
## [3,] 1 1 0 0 0 0 0 0
## [4,] 1 1 0 0 0 0 0 0
## [5,] 1 0 1 0 0 0 0 0
## [6,] 1 0 1 0 0 0 0 0
## [7,] 1 0 0 1 0 0 0 0
## [8,] 1 0 0 1 0 0 0 0
## [9,] 1 1 0 1 0 1 0 0
## [10,] 1 1 0 1 0 1 0 0
## [11,] 1 0 1 1 0 0 1 0
## [12,] 1 0 1 1 0 0 1 0
## [13,] 1 0 0 0 1 0 0 0
## [14,] 1 0 0 0 1 0 0 0
## [15,] 1 1 0 0 1 0 0 1
## [16,] 1 1 0 0 1 0 0 1</code></pre>
<p>Now this matrix <code>m1</code> can be provided to the <code>full</code> argument of <em>DESeq</em>. For a likelihood ratio test of interactions, a model matrix using a reduced design such as <code>~ condition + group</code> can be given to the <code>reduced</code> argument. Wald tests can also be generated instead of the likelihood ratio test, but for user-supplied model matrices, the argument <code>betaPrior</code> must be set to <code>FALSE</code>.</p>
<p><a name="theory"></a></p>
</div>
</div>
</div>
<div id="theory-behind-deseq2" class="section level1">
<h1>Theory behind DESeq2</h1>
<div id="the-deseq2-model" class="section level2">
<h2>The DESeq2 model</h2>
<p>The DESeq2 model and all the steps taken in the software are described in detail in our publication <span class="citation">(Love, Huber, and Anders 2014)</span>, and we include the formula and descriptions in this section as well. The differential expression analysis in DESeq2 uses a generalized linear model of the form:</p>
<p><span class="math display">\[ K_{ij} \sim \textrm{NB}(\mu_{ij}, \alpha_i) \]</span></p>
<p><span class="math display">\[ \mu_{ij} = s_j q_{ij} \]</span></p>
<p><span class="math display">\[ \log_2(q_{ij}) = x_{j.} \beta_i \]</span></p>
<p>where counts <span class="math inline">\(K_{ij}\)</span> for gene <em>i</em>, sample <em>j</em> are modeled using a negative binomial distribution with fitted mean <span class="math inline">\(\mu_{ij}\)</span> and a gene-specific dispersion parameter <span class="math inline">\(\alpha_i\)</span>. The fitted mean is composed of a sample-specific size factor <span class="math inline">\(s_j\)</span> and a parameter <span class="math inline">\(q_{ij}\)</span> proportional to the expected true concentration of fragments for sample <em>j</em>. The coefficients <span class="math inline">\(\beta_i\)</span> give the log2 fold changes for gene <em>i</em> for each column of the model matrix <span class="math inline">\(X\)</span>. Note that the model can be generalized to use sample- and gene-dependent normalization factors <span class="math inline">\(s_{ij}\)</span>.</p>
<p>The dispersion parameter <span class="math inline">\(\alpha_i\)</span> defines the relationship between the variance of the observed count and its mean value. In other words, how far do we expected the observed count will be from the mean value, which depends both on the size factor <span class="math inline">\(s_j\)</span> and the covariate-dependent part <span class="math inline">\(q_{ij}\)</span> as defined above.</p>
<p><span class="math display">\[ \textrm{Var}(K_{ij}) = E[ (K_{ij} - \mu_{ij})^2 ] = \mu_{ij} + \alpha_i \mu_{ij}^2 \]</span></p>
<p>An option in DESeq2 is to provide maximum <em>a posteriori</em> estimates of the log2 fold changes in <span class="math inline">\(\beta_i\)</span> after incorporating a zero-centered Normal prior (<code>betaPrior</code>). While previously, these moderated, or shrunken, estimates were generated by <em>DESeq</em> or <em>nbinomWaldTest</em> functions, they are now produced by the <em>lfcShrink</em> function. Dispersions are estimated using expected mean values from the maximum likelihood estimate of log2 fold changes, and optimizing the Cox-Reid adjusted profile likelihood, as first implemented for RNA-seq data in <a href="http://bioconductor.org/packages/edgeR">edgeR</a> <span class="citation">(Cox and Reid 1987,edgeR_GLM)</span>. The steps performed by the <em>DESeq</em> function are documented in its manual page <code>?DESeq</code>; briefly, they are:</p>
<ol style="list-style-type: decimal">
<li>estimation of size factors <span class="math inline">\(s_j\)</span> by <em>estimateSizeFactors</em></li>
<li>estimation of dispersion <span class="math inline">\(\alpha_i\)</span> by <em>estimateDispersions</em></li>
<li>negative binomial GLM fitting for <span class="math inline">\(\beta_i\)</span> and Wald statistics by <em>nbinomWaldTest</em></li>
</ol>
<p>For access to all the values calculated during these steps, see the section <a href="#access">above</a>.</p>
</div>
<div id="changes-compared-to-deseq" class="section level2">
<h2>Changes compared to DESeq</h2>
<p>The main changes in the package <em>DESeq2</em>, compared to the (older) version <em>DESeq</em>, are as follows:</p>
<ul>
<li><em>RangedSummarizedExperiment</em> is used as the superclass for storage of input data, intermediate calculations and results.</li>
<li>Optional, maximum <em>a posteriori</em> estimation of GLM coefficients incorporating a zero-centered Normal prior with variance estimated from data (equivalent to Tikhonov/ridge regularization). This adjustment has little effect on genes with high counts, yet it helps to moderate the otherwise large variance in log2 fold change estimates for genes with low counts or highly variable counts. These estimates are now provided by the <em>lfcShrink</em> function.</li>
<li>Maximum <em>a posteriori</em> estimation of dispersion replaces the <code>sharingMode</code> options <code>fit-only</code> or <code>maximum</code> of the previous version of the package. This is similar to the dispersion estimation methods of DSS <span class="citation">(H. Wu, Wang, and Wu 2012)</span>.</li>
<li>All estimation and inference is based on the generalized linear model, which includes the two condition case (previously the <em>exact test</em> was used).</li>
<li>The Wald test for significance of GLM coefficients is provided as the default inference method, with the likelihood ratio test of the previous version still available.</li>
<li>It is possible to provide a matrix of sample-/gene-dependent normalization factors.</li>
<li>Automatic independent filtering on the mean of normalized counts.</li>
<li>Automatic outlier detection and handling.</li>
</ul>
<p><a name="changes"></a></p>
</div>
<div id="methods-changes-since-the-2014-deseq2-paper" class="section level2">
<h2>Methods changes since the 2014 DESeq2 paper</h2>
<ul>
<li>In version 1.18 (November 2017), we add two <a href="#alternative-shrinkage-estimators">alternative shrinkage estimators</a>, which can be used via <code>lfcShrink</code>: an estimator using a t prior from the apeglm packages, and an estimator with a fitted mixture of normals prior from the ashr package.</li>
<li>In version 1.16 (November 2016), the log2 fold change shrinkage is no longer default for the <em>DESeq</em> and <em>nbinomWaldTest</em> functions, by setting the defaults of these to <code>betaPrior=FALSE</code>, and by introducing a separate function <em>lfcShrink</em>, which performs log2 fold change shrinkage for visualization and ranking of genes. While for the majority of bulk RNA-seq experiments, the LFC shrinkage did not affect statistical testing, DESeq2 has become used as an inference engine by a wider community, and certain sequencing datasets show better performance with the testing separated from the use of the LFC prior. Also, the separation of LFC shrinkage to a separate function <code>lfcShrink</code> allows for easier methods development of alternative effect size estimators.</li>
<li>A small change to the independent filtering routine: instead of taking the quantile of the filter (the mean of normalized counts) which directly <em>maximizes</em> the number of rejections, the threshold chosen is the lowest quantile of the filter for which the number of rejections is close to the peak of a curve fit to the number of rejections over the filter quantiles. ``Close to’’ is defined as within 1 residual standard deviation. This change was introduced in version 1.10 (October 2015).</li>
<li>For the calculation of the beta prior variance, instead of matching the empirical quantile to the quantile of a Normal distribution, DESeq2 now uses the weighted quantile function of the Hmisc package. The weighting is described in the manual page for <em>nbinomWaldTest</em>. The weights are the inverse of the expected variance of log counts (as used in the diagonals of the matrix <span class="math inline">\(W\)</span> in the GLM). The effect of the change is that the estimated prior variance is robust against noisy estimates of log fold change from genes with very small counts. This change was introduced in version 1.6 (October 2014).</li>
</ul>
<p>For a list of all changes since version 1.0.0, see the <code>NEWS</code> file included in the package.</p>
</div>
<div id="count-outlier-detection" class="section level2">
<h2>Count outlier detection</h2>
<p>DESeq2 relies on the negative binomial distribution to make estimates and perform statistical inference on differences. While the negative binomial is versatile in having a mean and dispersion parameter, extreme counts in individual samples might not fit well to the negative binomial. For this reason, we perform automatic detection of count outliers. We use Cook’s distance, which is a measure of how much the fitted coefficients would change if an individual sample were removed <span class="citation">(Cook 1977)</span>. For more on the implementation of Cook’s distance see the manual page for the <em>results</em> function. Below we plot the maximum value of Cook’s distance for each row over the rank of the test statistic to justify its use as a filtering criterion.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">W <-<span class="st"> </span>res$stat
maxCooks <-<span class="st"> </span><span class="kw">apply</span>(<span class="kw">assays</span>(dds)[[<span class="st">"cooks"</span>]],<span class="dv">1</span>,max)
idx <-<span class="st"> </span>!<span class="kw">is.na</span>(W)
<span class="kw">plot</span>(<span class="kw">rank</span>(W[idx]), maxCooks[idx], <span class="dt">xlab=</span><span class="st">"rank of Wald statistic"</span>,
<span class="dt">ylab=</span><span class="st">"maximum Cook's distance per gene"</span>,
<span class="dt">ylim=</span><span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">5</span>), <span class="dt">cex=</span>.<span class="dv">4</span>, <span class="dt">col=</span><span class="kw">rgb</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,.<span class="dv">3</span>))
m <-<span class="st"> </span><span class="kw">ncol</span>(dds)
p <-<span class="st"> </span><span class="dv">3</span>
<span class="kw">abline</span>(<span class="dt">h=</span><span class="kw">qf</span>(.<span class="dv">99</span>, p, m -<span class="st"> </span>p))</code></pre></div>
<p><img src="data:image/png;base64,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" width="480" /></p>
</div>
<div id="contrasts-1" class="section level2">
<h2>Contrasts</h2>
<p>Contrasts can be calculated for a <em>DESeqDataSet</em> object for which the GLM coefficients have already been fit using the Wald test steps (<em>DESeq</em> with <code>test="Wald"</code> or using <em>nbinomWaldTest</em>). The vector of coefficients <span class="math inline">\(\beta\)</span> is left multiplied by the contrast vector <span class="math inline">\(c\)</span> to form the numerator of the test statistic. The denominator is formed by multiplying the covariance matrix <span class="math inline">\(\Sigma\)</span> for the coefficients on either side by the contrast vector <span class="math inline">\(c\)</span>. The square root of this product is an estimate of the standard error for the contrast. The contrast statistic is then compared to a normal distribution as are the Wald statistics for the DESeq2 package.</p>
<p><span class="math display">\[ W = \frac{c^t \beta}{\sqrt{c^t \Sigma c}} \]</span></p>
</div>
<div id="expanded-model-matrices" class="section level2">
<h2>Expanded model matrices</h2>
<p>For the specific combination of <code>lfcShrink</code> with the type <code>normal</code> and using <code>contrast</code>, DESeq2 uses <em>expanded model matrices</em> to produce shrunken log2 fold change estimates where the shrinkage is independent of the choice of reference level. In all other cases, DESeq2 uses standard model matrices, as produced by <code>model.matrix</code>. The expanded model matrices differ from the standard model matrices, in that they have an indicator column (and therefore a coefficient) for each level of factors in the design formula in addition to an intercept. This is described in the DESeq2 paper, but the DESeq2 software package has moved away from this approach, with more support for shrinkage of individual coefficients (although the expanded model matrix approach is still supported using the above combination of functions and arguments).</p>
<p><a name="indfilttheory"></a></p>
</div>
<div id="independent-filtering-and-multiple-testing" class="section level2">
<h2>Independent filtering and multiple testing</h2>
<div id="filtering-criteria" class="section level3">
<h3>Filtering criteria</h3>
<p>The goal of independent filtering is to filter out those tests from the procedure that have no, or little chance of showing significant evidence, without even looking at their test statistic. Typically, this results in increased detection power at the same experiment-wide type I error. Here, we measure experiment-wide type I error in terms of the false discovery rate.</p>
<p>A good choice for a filtering criterion is one that</p>
<ol style="list-style-type: decimal">
<li>is statistically independent from the test statistic under the null hypothesis,</li>
<li>is correlated with the test statistic under the alternative, and</li>
<li>does not notably change the dependence structure – if there is any – between the tests that pass the filter, compared to the dependence structure between the tests before filtering.</li>
</ol>
<p>The benefit from filtering relies on property (2), and we will explore it further below. Its statistical validity relies on property (1) – which is simple to formally prove for many combinations of filter criteria with test statistics – and (3), which is less easy to theoretically imply from first principles, but rarely a problem in practice. We refer to <span class="citation">(Bourgon, Gentleman, and Huber 2010)</span> for further discussion of this topic.</p>
<p>A simple filtering criterion readily available in the results object is the mean of normalized counts irrespective of biological condition, and so this is the criterion which is used automatically by the <em>results</em> function to perform independent filtering. Genes with very low counts are not likely to see significant differences typically due to high dispersion. For example, we can plot the <span class="math inline">\(-\log_{10}\)</span> <em>p</em> values from all genes over the normalized mean counts:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(res$baseMean<span class="dv">+1</span>, -<span class="kw">log10</span>(res$pvalue),
<span class="dt">log=</span><span class="st">"x"</span>, <span class="dt">xlab=</span><span class="st">"mean of normalized counts"</span>,
<span class="dt">ylab=</span><span class="kw">expression</span>(-log[<span class="dv">10</span>](pvalue)),
<span class="dt">ylim=</span><span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">30</span>),
<span class="dt">cex=</span>.<span class="dv">4</span>, <span class="dt">col=</span><span class="kw">rgb</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,.<span class="dv">3</span>))</code></pre></div>
<p><img 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" width="480" /></p>
</div>
<div id="why-does-it-work" class="section level3">
<h3>Why does it work?</h3>
<p>Consider the <em>p</em> value histogram below It shows how the filtering ameliorates the multiple testing problem – and thus the severity of a multiple testing adjustment – by removing a background set of hypotheses whose <em>p</em> values are distributed more or less uniformly in [0,1].</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">use <-<span class="st"> </span>res$baseMean ><span class="st"> </span><span class="kw">metadata</span>(res)$filterThreshold
h1 <-<span class="st"> </span><span class="kw">hist</span>(res$pvalue[!use], <span class="dt">breaks=</span><span class="dv">0</span>:<span class="dv">50</span>/<span class="dv">50</span>, <span class="dt">plot=</span><span class="ot">FALSE</span>)
h2 <-<span class="st"> </span><span class="kw">hist</span>(res$pvalue[use], <span class="dt">breaks=</span><span class="dv">0</span>:<span class="dv">50</span>/<span class="dv">50</span>, <span class="dt">plot=</span><span class="ot">FALSE</span>)
colori <-<span class="st"> </span><span class="kw">c</span>(<span class="st">`</span><span class="dt">do not pass</span><span class="st">`</span>=<span class="st">"khaki"</span>, <span class="st">`</span><span class="dt">pass</span><span class="st">`</span>=<span class="st">"powderblue"</span>)</code></pre></div>
<p>Histogram of p values for all tests. The area shaded in blue indicates the subset of those that pass the filtering, the area in khaki those that do not pass:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">barplot</span>(<span class="dt">height =</span> <span class="kw">rbind</span>(h1$counts, h2$counts), <span class="dt">beside =</span> <span class="ot">FALSE</span>,
<span class="dt">col =</span> colori, <span class="dt">space =</span> <span class="dv">0</span>, <span class="dt">main =</span> <span class="st">""</span>, <span class="dt">ylab=</span><span class="st">"frequency"</span>)
<span class="kw">text</span>(<span class="dt">x =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="kw">length</span>(h1$counts)), <span class="dt">y =</span> <span class="dv">0</span>, <span class="dt">label =</span> <span class="kw">paste</span>(<span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">1</span>)),
<span class="dt">adj =</span> <span class="kw">c</span>(<span class="fl">0.5</span>,<span class="fl">1.7</span>), <span class="dt">xpd=</span><span class="ot">NA</span>)
<span class="kw">legend</span>(<span class="st">"topright"</span>, <span class="dt">fill=</span><span class="kw">rev</span>(colori), <span class="dt">legend=</span><span class="kw">rev</span>(<span class="kw">names</span>(colori)))</code></pre></div>
<p><img 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" width="480" /></p>
<p><a name="FAQ"></a></p>
</div>
</div>
</div>
<div id="frequently-asked-questions" class="section level1">
<h1>Frequently asked questions</h1>
<div id="how-can-i-get-support-for-deseq2" class="section level2">
<h2>How can I get support for DESeq2?</h2>
<p>We welcome questions about our software, and want to ensure that we eliminate issues if and when they appear. We have a few requests to optimize the process:</p>
<ul>
<li>all questions should take place on the Bioconductor support site: <a href="https://support.bioconductor.org" class="uri">https://support.bioconductor.org</a>, which serves as a repository of questions and answers. This helps to save the developers’ time in responding to similar questions. Make sure to tag your post with <code>deseq2</code>. It is often very helpful in addition to describe the aim of your experiment.</li>
<li>before posting, first search the Bioconductor support site mentioned above for past threads which might have answered your question.</li>
<li>if you have a question about the behavior of a function, read the sections of the manual page for this function by typing a question mark and the function name, e.g. <code>?results</code>. We spend a lot of time documenting individual functions and the exact steps that the software is performing.</li>
<li>include all of your R code, especially the creation of the <em>DESeqDataSet</em> and the design formula. Include complete warning or error messages, and conclude your message with the full output of <code>sessionInfo()</code>.</li>
<li>if possible, include the output of <code>as.data.frame(colData(dds))</code>, so that we can have a sense of the experimental setup. If this contains confidential information, you can replace the levels of those factors using <em>levels()</em>.</li>
</ul>
</div>
<div id="why-are-some-p-values-set-to-na" class="section level2">
<h2>Why are some <em>p</em> values set to NA?</h2>
<p>See the details <a href="#pvaluesNA">above</a>.</p>
</div>
<div id="how-can-i-get-unfiltered-deseq2-results" class="section level2">
<h2>How can I get unfiltered DESeq2 results?</h2>
<p>Users can obtain unfiltered GLM results, i.e. without outlier removal or independent filtering with the following call:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dds <-<span class="st"> </span><span class="kw">DESeq</span>(dds, <span class="dt">minReplicatesForReplace=</span><span class="ot">Inf</span>)
res <-<span class="st"> </span><span class="kw">results</span>(dds, <span class="dt">cooksCutoff=</span><span class="ot">FALSE</span>, <span class="dt">independentFiltering=</span><span class="ot">FALSE</span>)</code></pre></div>
<p>In this case, the only <em>p</em> values set to <code>NA</code> are those from genes with all counts equal to zero.</p>
</div>
<div id="how-do-i-use-vst-or-rlog-data-for-differential-testing" class="section level2">
<h2>How do I use VST or rlog data for differential testing?</h2>
<p>The variance stabilizing and rlog transformations are provided for applications other than differential testing, for example clustering of samples or other machine learning applications. For differential testing we recommend the <em>DESeq</em> function applied to raw counts as outlined <a href="#de">above</a>.</p>
</div>
<div id="can-i-use-deseq2-to-analyze-paired-samples" class="section level2">
<h2>Can I use DESeq2 to analyze paired samples?</h2>
<p>Yes, you should use a multi-factor design which includes the sample information as a term in the design formula. This will account for differences between the samples while estimating the effect due to the condition. The condition of interest should go at the end of the design formula, e.g. <code>~ subject + condition</code>.</p>
</div>
<div id="if-i-have-multiple-groups-should-i-run-all-together-or-split-into-pairs-of-groups" class="section level2">
<h2>If I have multiple groups, should I run all together or split into pairs of groups?</h2>
<p>Typically, we recommend users to run samples from all groups together, and then use the <code>contrast</code> argument of the <em>results</em> function to extract comparisons of interest after fitting the model using <em>DESeq</em>.</p>
<p>The model fit by <em>DESeq</em> estimates a single dispersion parameter for each gene, which defines how far we expect the observed count for a sample will be from the mean value from the model given its size factor and its condition group. See the section <a href="#theory">above</a> and the DESeq2 paper for full details. Having a single dispersion parameter for each gene is usually sufficient for analyzing multi-group data, as the final dispersion value will incorporate the within-group variability across all groups.</p>
<p>However, for some datasets, exploratory data analysis (EDA) plots could reveal that one or more groups has much higher within-group variability than the others. A simulated example of such a set of samples is shown below. This is case where, by comparing groups A and B separately – subsetting a <em>DESeqDataSet</em> to only samples from those two groups and then running <em>DESeq</em> on this subset – will be more sensitive than a model including all samples together. It should be noted that such an extreme range of within-group variability is not common, although it could arise if certain treatments produce an extreme reaction (e.g. cell death). Again, this can be easily detected from the EDA plots such as PCA described in this vignette.</p>
<p>Here we diagram an extreme range of within-group variability with a simulated dataset. Typically, it is recommended to run <em>DESeq</em> across samples from all groups, for datasets with multiple groups. However, this simulated dataset shows a case where it would be preferable to compare groups A and B by creating a smaller dataset without the C samples. Group C has much higher within-group variability, which would inflate the per-gene dispersion estimate for groups A and B as well:</p>
<p><img 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" width="480" /></p>
</div>
<div id="can-i-run-deseq2-to-contrast-the-levels-of-many-groups" class="section level2">
<h2>Can I run DESeq2 to contrast the levels of many groups?</h2>
<p>DESeq2 will work with any kind of design specified using the R formula. We enourage users to consider exploratory data analysis such as principal components analysis rather than performing statistical testing of all pairs of many groups of samples. Statistical testing is one of many ways of describing differences between samples.</p>
<p>Regarding multiple test correction, if a user is planning to contrast all pairs of many levels, and then selectively reporting the results of only a <em>subset</em> of those pairs, one needs to perform multiple testing across <em>contrasts</em> as well as genes to control for this additional form of multiple testing. This can be done by using the <code>p.adjust</code> function across a long vector of <em>p</em> values from all pairs of contasts, then re-assigning these adjusted <em>p</em> values to the appropriate results table.</p>
<p>As a speed concern with fitting very large models, note that each additional level of a factor in the design formula adds another parameter to the GLM which is fit by DESeq2. Users might consider first removing genes with very few reads, e.g. genes with row sum of 1, as this will speed up the fitting procedure.</p>
</div>
<div id="can-i-use-deseq2-to-analyze-a-dataset-without-replicates" class="section level2">
<h2>Can I use DESeq2 to analyze a dataset without replicates?</h2>
<p>If a <em>DESeqDataSet</em> is provided with an experimental design without replicates, a warning is printed, that the samples are treated as replicates for estimation of dispersion. This kind of analysis is only useful for exploring the data, but will not provide the kind of proper statistical inference on differences between groups. Without biological replicates, it is not possible to estimate the biological variability of each gene. More details can be found in the manual page for <code>?DESeq</code>.</p>
</div>
<div id="how-can-i-include-a-continuous-covariate-in-the-design-formula" class="section level2">
<h2>How can I include a continuous covariate in the design formula?</h2>
<p>Continuous covariates can be included in the design formula in exactly the same manner as factorial covariates, and then <em>results</em> for the continuous covariate can be extracted by specifying <code>name</code>. Continuous covariates might make sense in certain experiments, where a constant fold change might be expected for each unit of the covariate. However, in many cases, more meaningful results can be obtained by cutting continuous covariates into a factor defined over a small number of bins (e.g. 3-5). In this way, the average effect of each group is controlled for, regardless of the trend over the continuous covariates. In R, <em>numeric</em> vectors can be converted into <em>factors</em> using the function <em>cut</em>.</p>
</div>
<div id="i-ran-a-likelihood-ratio-test-but-results-only-gives-me-one-comparison." class="section level2">
<h2>I ran a likelihood ratio test, but results() only gives me one comparison.</h2>
<p>“… How do I get the <em>p</em> values for all of the variables/levels that were removed in the reduced design?”</p>
<p>This is explained in the help page for <code>?results</code> in the section about likelihood ratio test p-values, but we will restate the answer here. When one performs a likelihood ratio test, the <em>p</em> values and the test statistic (the <code>stat</code> column) are values for the test that removes all of the variables which are present in the full design and not in the reduced design. This tests the null hypothesis that all the coefficients from these variables and levels of these factors are equal to zero.</p>
<p>The likelihood ratio test <em>p</em> values therefore represent a test of <em>all the variables and all the levels of factors</em> which are among these variables. However, the results table only has space for one column of log fold change, so a single variable and a single comparison is shown (among the potentially multiple log fold changes which were tested in the likelihood ratio test). This is indicated at the top of the results table with the text, e.g., log2 fold change (MLE): condition C vs A, followed by, LRT p-value: ‘~ batch + condition’ vs ‘~ batch’. This indicates that the <em>p</em> value is for the likelihood ratio test of <em>all the variables and all the levels</em>, while the log fold change is a single comparison from among those variables and levels. See the help page for <em>results</em> for more details.</p>
</div>
<div id="what-are-the-exact-steps-performed-by-deseq" class="section level2">
<h2>What are the exact steps performed by DESeq()?</h2>
<p>See the manual page for <em>DESeq</em>, which links to the subfunctions which are called in order, where complete details are listed. Also you can read the three steps listed in the <a href="#theory">DESeq2 model</a> in this document.</p>
</div>
<div id="is-there-an-official-galaxy-tool-for-deseq2" class="section level2">
<h2>Is there an official Galaxy tool for DESeq2?</h2>
<p>Yes. The repository for the DESeq2 tool is</p>
<p><a href="https://github.com/galaxyproject/tools-iuc/tree/master/tools/deseq2" class="uri">https://github.com/galaxyproject/tools-iuc/tree/master/tools/deseq2</a></p>
<p>and a link to its location in the Tool Shed is</p>
<p><a href="https://toolshed.g2.bx.psu.edu/view/iuc/deseq2/d983d19fbbab" class="uri">https://toolshed.g2.bx.psu.edu/view/iuc/deseq2/d983d19fbbab</a>.</p>
</div>
<div id="i-want-to-benchmark-deseq2-comparing-to-other-de-tools." class="section level2">
<h2>I want to benchmark DESeq2 comparing to other DE tools.</h2>
<p>One aspect which can cause problems for comparison is that, by default, DESeq2 outputs <code>NA</code> values for adjusted <em>p</em> values based on independent filtering of genes which have low counts. This is a way for the DESeq2 to give extra information on why the adjusted <em>p</em> value for this gene is not small. Additionally, <em>p</em> values can be set to <code>NA</code> based on extreme count outlier detection. These <code>NA</code> values should be considered <em>negatives</em> for purposes of estimating sensitivity and specificity. The easiest way to work with the adjusted <em>p</em> values in a benchmarking context is probably to convert these <code>NA</code> values to 1:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">res$padj <-<span class="st"> </span><span class="kw">ifelse</span>(<span class="kw">is.na</span>(res$padj), <span class="dv">1</span>, res$padj)</code></pre></div>
</div>
<div id="i-have-trouble-installing-deseq2-on-ubuntulinux" class="section level2">
<h2>I have trouble installing DESeq2 on Ubuntu/Linux…</h2>
<p>“<em>I try to install DESeq2 using biocLite(), but I get an error trying to install the R packages XML and/or RCurl:</em>”</p>
<p><code>ERROR: configuration failed for package XML</code></p>
<p><code>ERROR: configuration failed for package RCurl</code></p>
<p>You need to install the following devel versions of packages using your standard package manager, e.g. <code>sudo apt-get install</code> or <code>sudo apt install</code></p>
<ul>
<li>libxml2-dev</li>
<li>libcurl4-openssl-dev</li>
</ul>
</div>
</div>
<div id="acknowledgments" class="section level1">
<h1>Acknowledgments</h1>
<p>We have benefited in the development of DESeq2 from the help and feedback of many individuals, including but not limited to:</p>
<p>The Bionconductor Core Team, Alejandro Reyes, Andrzej Oles, Aleksandra Pekowska, Felix Klein, Nikolaos Ignatiadis (IHW), Anqi Zhu (apeglm), Joseph Ibrahim (apeglm), Vince Carey, Owen Solberg, Ruping Sun, Devon Ryan, Steve Lianoglou, Jessica Larson, Christina Chaivorapol, Pan Du, Richard Bourgon, Willem Talloen, Elin Videvall, Hanneke van Deutekom, Todd Burwell, Jesse Rowley, Igor Dolgalev, Stephen Turner, Ryan C Thompson, Tyr Wiesner-Hanks, Konrad Rudolph, David Robinson, Mingxiang Teng, Mathias Lesche, Sonali Arora, Jordan Ramilowski, Ian Dworkin, Bjorn Gruning, Ryan McMinds, Paul Gordon, Leonardo Collado Torres, Enrico Ferrero, Peter Langfelder.</p>
</div>
<div id="session-info" class="section level1">
<h1>Session info</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">sessionInfo</span>()</code></pre></div>
<pre><code>## R version 3.4.2 (2017-09-28)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] RColorBrewer_1.1-2 pheatmap_1.0.8
## [3] hexbin_1.27.1 vsn_3.46.0
## [5] ggplot2_2.2.1 IHW_1.6.0
## [7] airway_0.112.0 pasilla_1.6.0
## [9] DESeq2_1.18.1 SummarizedExperiment_1.8.0
## [11] DelayedArray_0.4.1 matrixStats_0.52.2
## [13] Biobase_2.38.0 GenomicRanges_1.30.0
## [15] GenomeInfoDb_1.14.0 IRanges_2.12.0
## [17] S4Vectors_0.16.0 BiocGenerics_0.24.0
## [19] tximportData_1.6.0 readr_1.1.1
## [21] tximport_1.6.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 bit64_0.9-7
## [3] doParallel_1.0.11 rprojroot_1.2
## [5] numDeriv_2016.8-1 tools_3.4.2
## [7] backports_1.1.1 affyio_1.48.0
## [9] R6_2.2.2 rpart_4.1-11
## [11] Hmisc_4.0-3 DBI_0.7
## [13] lazyeval_0.2.1 colorspace_1.3-2
## [15] nnet_7.3-12 apeglm_1.0.0
## [17] gridExtra_2.3 preprocessCore_1.40.0
## [19] bit_1.1-12 compiler_3.4.2
## [21] fdrtool_1.2.15 htmlTable_1.9
## [23] labeling_0.3 slam_0.1-40
## [25] scales_0.5.0 checkmate_1.8.5
## [27] SQUAREM_2017.10-1 affy_1.56.0
## [29] genefilter_1.60.0 stringr_1.2.0
## [31] digest_0.6.12 foreign_0.8-69
## [33] rmarkdown_1.7 XVector_0.18.0
## [35] pscl_1.5.2 base64enc_0.1-3
## [37] htmltools_0.3.6 lpsymphony_1.6.0
## [39] limma_3.34.1 bbmle_1.0.20
## [41] htmlwidgets_0.9 rlang_0.1.4
## [43] RSQLite_2.0 BiocInstaller_1.28.0
## [45] BiocParallel_1.12.0 acepack_1.4.1
## [47] RCurl_1.95-4.8 magrittr_1.5
## [49] GenomeInfoDbData_0.99.1 Formula_1.2-2
## [51] Matrix_1.2-11 Rcpp_0.12.13
## [53] munsell_0.4.3 stringi_1.1.5
## [55] yaml_2.1.14 MASS_7.3-47
## [57] zlibbioc_1.24.0 plyr_1.8.4
## [59] grid_3.4.2 blob_1.1.0
## [61] lattice_0.20-35 splines_3.4.2
## [63] annotate_1.56.0 hms_0.3
## [65] locfit_1.5-9.1 knitr_1.17
## [67] rjson_0.2.15 geneplotter_1.56.0
## [69] codetools_0.2-15 XML_3.98-1.9
## [71] evaluate_0.10.1 latticeExtra_0.6-28
## [73] data.table_1.10.4-3 foreach_1.4.3
## [75] gtable_0.2.0 assertthat_0.2.0
## [77] ashr_2.0.5 emdbook_1.3.9
## [79] xtable_1.8-2 coda_0.19-1
## [81] survival_2.41-3 truncnorm_1.0-7
## [83] tibble_1.3.4 iterators_1.0.8
## [85] AnnotationDbi_1.40.0 memoise_1.1.0
## [87] cluster_2.0.6 BiocStyle_2.6.0</code></pre>
</div>
<div id="references" class="section level1 unnumbered">
<h1>References</h1>
<div id="refs" class="references">
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<p>Anders, Simon, and Wolfgang Huber. 2010. “Differential Expression Analysis for Sequence Count Data.” <em>Genome Biology</em> 11: R106. <a href="http://genomebiology.com/2010/11/10/R106" class="uri">http://genomebiology.com/2010/11/10/R106</a>.</p>
</div>
<div id="ref-Anders:2014:htseq">
<p>Anders, Simon, Paul Theodor Pyl, and Wolfgang Huber. 2014. “HTSeq – A Python framework to work with high-throughput sequencing data.” <em>Bioinformatics</em>. <a href="http://dx.doi.org/10.1093/bioinformatics/btu638" class="uri">http://dx.doi.org/10.1093/bioinformatics/btu638</a>.</p>
</div>
<div id="ref-Bourgon:2010:PNAS">
<p>Bourgon, Richard, Robert Gentleman, and Wolfgang Huber. 2010. “Independent Filtering Increases Detection Power for High-Throughput Experiments.” <em>PNAS</em> 107 (21): 9546–51. <a href="http://www.pnas.org/content/107/21/9546.long" class="uri">http://www.pnas.org/content/107/21/9546.long</a>.</p>
</div>
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</div>
<div id="ref-Brooks2010">
<p>Brooks, A. N., L. Yang, M. O. Duff, K. D. Hansen, J. W. Park, S. Dudoit, S. E. Brenner, and B. R. Graveley. 2011. “Conservation of an RNA regulatory map between Drosophila and mammals.” <em>Genome Research</em>, 193–202. doi:<a href="https://doi.org/10.1101/gr.108662.110">10.1101/gr.108662.110</a>.</p>
</div>
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<p>Cook, R. Dennis. 1977. “Detection of Influential Observation in Linear Regression.” <em>Technometrics</em>, February.</p>
</div>
<div id="ref-CR">
<p>Cox, D. R., and N. Reid. 1987. “Parameter orthogonality and approximate conditional inference.” <em>Journal of the Royal Statistical Society, Series B</em> 49 (1): 1–39. <a href="http://www.jstor.org/stable/2345476" class="uri">http://www.jstor.org/stable/2345476</a>.</p>
</div>
<div id="ref-Gerard2017">
<p>Gerard, David, and Matthew Stephens. 2017. “Empirical Bayes Shrinkage and False Discovery Rate Estimation, Allowing For Unwanted Variation.” <em>ArXiv</em>. <a href="https://arxiv.org/abs/1709.10066" class="uri">https://arxiv.org/abs/1709.10066</a>.</p>
</div>
<div id="ref-sagmb2003">
<p>Huber, Wolfgang, Anja von Heydebreck, Holger Sültmann, Annemarie Poustka, and Martin Vingron. 2003. “Parameter Estimation for the Calibration and Variance Stabilization of Microarray Data.” <em>Statistical Applications in Genetics and Molecular Biology</em> 2 (1): Article 3.</p>
</div>
<div id="ref-Ignatiadis2016">
<p>Ignatiadis, Nikolaos, Bernd Klaus, Judith Zaugg, and Wolfgang Huber. 2016. “Data-Driven Hypothesis Weighting Increases Detection Power in Genome-Scale Multiple Testing.” <em>Nature Methods</em>. <a href="http://dx.doi.org/10.1038/nmeth.3885" class="uri">http://dx.doi.org/10.1038/nmeth.3885</a>.</p>
</div>
<div id="ref-Leek2014">
<p>Leek, Jeffrey T. 2014. “svaseq: removing batch effects and other unwanted noise from sequencing data.” <em>Nucleic Acids Research</em> 42 (21). <a href="http://dx.doi.org/10.1093/nar/gku864" class="uri">http://dx.doi.org/10.1093/nar/gku864</a>.</p>
</div>
<div id="ref-Li2011RSEM">
<p>Li, Bo, and Colin N. Dewey. 2011. “RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.” <em>BMC Bioinformatics</em> 12: 323+. doi:<a href="https://doi.org/10.1186/1471-2105-12-3231">10.1186/1471-2105-12-3231</a>.</p>
</div>
<div id="ref-Liao2013feature">
<p>Liao, Y., G. K. Smyth, and W. Shi. 2013. “featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.” <em>Bioinformatics</em>, November.</p>
</div>
<div id="ref-Love2016Modeling">
<p>Love, Michael I., John B. Hogenesch, and Rafael A. Irizarry. 2016. “Modeling of Rna-Seq Fragment Sequence Bias Reduces Systematic Errors in Transcript Abundance Estimation.” <em>Nature Biotechnology</em> 34 (12): 1287–91. <a href="http://dx.doi.org/10.1038/nbt.3682" class="uri">http://dx.doi.org/10.1038/nbt.3682</a>.</p>
</div>
<div id="ref-Love2014">
<p>Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” <em>Genome Biology</em> 15 (12): 550. <a href="http://dx.doi.org/10.1186/s13059-014-0550-8" class="uri">http://dx.doi.org/10.1186/s13059-014-0550-8</a>.</p>
</div>
<div id="ref-Patro2017Salmon">
<p>Patro, Rob, Geet Duggal, Michael I. Love, Rafael A. Irizarry, and Carl Kingsford. 2017. “Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression.” <em>Nature Methods</em>. <a href="http://dx.doi.org/10.1038/nmeth.4197" class="uri">http://dx.doi.org/10.1038/nmeth.4197</a>.</p>
</div>
<div id="ref-Patro2014Sailfish">
<p>Patro, Rob, Stephen M. Mount, and Carl Kingsford. 2014. “Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms.” <em>Nature Biotechnology</em> 32: 462–64. <a href="http://dx.doi.org/10.1038/nbt.2862" class="uri">http://dx.doi.org/10.1038/nbt.2862</a>.</p>
</div>
<div id="ref-Risso2014">
<p>Risso, Davide, John Ngai, Terence P Speed, and Sandrine Dudoit. 2014. “Normalization of RNA-seq data using factor analysis of control genes or samples.” <em>Nature Biotechnology</em> 32 (9). <a href="http://dx.doi.org/10.1038/nbt.2931" class="uri">http://dx.doi.org/10.1038/nbt.2931</a>.</p>
</div>
<div id="ref-Robert2015Errors">
<p>Robert, Christelle, and Mick Watson. 2015. “Errors in RNA-Seq quantification affect genes of relevance to human disease.” <em>Genome Biology</em>. doi:<a href="https://doi.org/10.1186/s13059-015-0734-x">10.1186/s13059-015-0734-x</a>.</p>
</div>
<div id="ref-Soneson2015">
<p>Soneson, Charlotte, Michael I. Love, and Mark Robinson. 2015. “Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.” <em>F1000Research</em> 4 (1521). <a href="http://dx.doi.org/10.12688/f1000research.7563.1" class="uri">http://dx.doi.org/10.12688/f1000research.7563.1</a>.</p>
</div>
<div id="ref-Stephens2016">
<p>Stephens, Matthew. 2016. “False Discovery Rates: A New Deal.” <em>Biostatistics</em> 18 (2). <a href="https://doi.org/10.1093/biostatistics/kxw041" class="uri">https://doi.org/10.1093/biostatistics/kxw041</a>.</p>
</div>
<div id="ref-Tibshirani1988">
<p>Tibshirani, Robert. 1988. “Estimating Transformations for Regression via Additivity and Variance Stabilization.” <em>Journal of the American Statistical Association</em> 83: 394–405.</p>
</div>
<div id="ref-Trapnell2013Differential">
<p>Trapnell, Cole, David G Hendrickson, Martin Sauvageau, Loyal Goff, John L Rinn, and Lior Pachter. 2013. “Differential analysis of gene regulation at transcript resolution with RNA-seq.” <em>Nature Biotechnology</em>. doi:<a href="https://doi.org/10.1038/nbt.2450">10.1038/nbt.2450</a>.</p>
</div>
<div id="ref-Wu2012New">
<p>Wu, Hao, Chi Wang, and Zhijin Wu. 2012. “A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data.” <em>Biostatistics</em>, September. Oxford University Press. doi:<a href="https://doi.org/10.1093/biostatistics/kxs033">10.1093/biostatistics/kxs033</a>.</p>
</div>
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