O tempo necessa¡rio para sequenciar molanãculas-chave pode ser reduzido de anos para minutos
Usando um nanoporo, demonstrou-se o potencial de reduzir o tempo necessa¡rio para o sequenciamento de um glicosaminoglicano - uma classe de moléculas de açúcar ligadas em cadeia longa tão importante para nossa biologia quanto o DNA
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75nvmGxj0Ph1uLVAk4zjLv6nh9PyODmGNL/AOKffvPQnpjp8KIBNTU1CIRHEvTi3QeA+L7wdcdPcuHYYY8xPi/ruEwmO0+doYsE+4yPTwYysh8aJqBZqampeNXeKi33aTGmQfpTw01NdYfhl+TWIsEHUwxpkLtfGem+3fd6esHYYY8x/wC568oWjNK6zc1lQamsxTAZublqJat2CRGVlI8FSQCampqamYnj9OeExzMh9le8mY+LZkHK44mOZV/OuLBB1MMaW/byYWP8WYlbxsNhGpsWaM4wrruwf6A7DDHmIN39fcnItK8VmgUKJlVcSjwNOU3NwtM478CLyYCATXZYnNNaPhwjq9ml1ugTB9u35M9p4AETKt93I1Kz9dcWDsMMaXH4g8WNT71utAiMI4jDTdGXtwj+wDNzc3CYxjmYaca+ox6hAAO27G1A+iHnOF41kyAfBWugIO7Mr0fDjnVtl8Ztnpubm4qs5qwXMFaotgmVlvb1YalD7VTAZubm4TCY5mS/wB48On2aYYY8uHz1ZezCb6AZubm4TGaVp71njetHhw0n6ODDWZAUPZV7lXdWNsIO9lDLYhrfvMVHcpgWGfo0WW0hetONZdKcCquaAhjR438+tZ9tkaAzc3NwmM0sfQY828WBT7l3Qwx5eROQgYTRM9toaNw47Q1uJU/turzlOU5RniI9xRBWvn3ytWZtHE9tI+BCQobNUQ5lpn6u+DNuETPErtSzpkU+6v28GLV7VBEYS1fgIzPj4CiAaHQxpadDtSzgUsgacpyhePbqHlaVxSYMRZ+lSNiR63TvxKvZp6GZGWtce2yyampqBmWV5MGiNTUZAZx4yvm7+zfBjXGJiIIPj8GqLNAjKxjS3YkstFQd2sPaNg05pEBDDJo5d+Bj+7bDGlkxNBB2GNMptJ2kbgYrBfP1AnviNcZXWbIiagWampxjLLsftw6+d+5ubmVmb8FVrVFGWxdQiERhMW/3R+GPpsWCEBlysQ0non8uQRWYu3govalgwZcjH5T7dmPjvkMiLWsMaWTG/gDNzc3CYxmS227mG+ypebIsAgE1NTUIjCZFfZgjjVubmbkeLHt9p5qEQibKPU4sT8LKXjchgMB6ZGCDGVkav+VzbPiw7uDmW1LZHpdJ8zFxhYyBUXoY0sMqOlDTc3NwtLH0CeR72G+uMv0qIIOwxpYIw03SpwEDy27hX4jMN+dMMMYTDt4MCD+FbWLEBKsrQGbm5YqWCzFCT7nx0v7lRhjma4BH5LubhMYytPetsoeqLZOc5wvNs5ycYrijwsOlH8Vgg7DGjy7+cqClExkNOVW2PLLOcHjwG1bDDGl52w2ImVfXKPUEab3+BfSLR9VZDzlOULy5/oHjMwj9BjGWmY786aGnKFozT6namsVJHoreHDWfo4uJWIqhQyhldDXZ4W++OfoWCDsMaWS4/uTDXndHUOt9Xs3+PE/uehlh4r9+zGynoNdi2p53RXDYk/T3T2bzBiOZbSleMPGZhwmO0b5mK2rFOrPchsiU22Sqpah4PUMfmvhSp7jjkqUMBgM3NzcJjGWt8E7OtT0ofu9PUB/3PHhjeT0MyW+e3EyDRYPn8RhyXXE+IzG+K2aMd9Kvi2uiy6wYtMWtE8mZhEHvox3vNda1JdjCwodRTAZubm4THaZFm4BqGek9fVF1b4/T13ZDGOgx5N2men5P4vqFXt5Hi+83xUnfWsE21J7dfmyMFLZdRZS25ubldVlhpwVE+AIJfRzgbRDzlOULRnllvUz0kfV09UtBI8eJX7dHTJbS99fxZQ/uVfh5dPv0/Y+Gv79npy8sr8C+r3U4rsUUmLXUs3NzcEHSylLI2NYsYtXPeEN0Zye30pf25Y4rrZi7/bxY1XvWdbm5Wd/2OEfr/EzsT3PCBvtM9Nq4U/g3ULbG51EPOc5xTBB2WqLEurNT9n36YtftY89TbWOOn28FaNY1VYqTpc3FPAZi/wBz+Lk4aXS2i2mbm5ub6Be7Fp9+0DQ/CIBDYtZn6MyzGZErMUwGbm5uEy9BYh2pm5rfTAo927p6tB0+8NZ6bm5ublWM9krRal65LbbwKNviDd/4x+ZZh0PD6Ykupel9d9VT3PRStFf49iezYrQGbm5uExmmR9TcZodKqmuspqWmvp6sPjqp1NK8/ToZ+kEGIkSqtJubm5uExjybwY4+rDTVX5NtSWrfgWJPtN9Nz7ynAseVVJSv5DoHWyt6SrzlOULxnll0303KMWy800pSnX1FOWMOwHUV4DNzc3Nzc3L3+jwfeY9PL816q7I3p1Bn/G1bX0+gRKq6/wA18VDDjWiexfsYtplmAWh9Pvn6DIiemvKsGmvuYBldDVZ2bgeB5ynKcpyheXE78GLjs5rrWtf9M9Rx+aju3OZnuRGaxxgiJj1JLqUuS/DtpO+zcrqstNHp6rAAo/07Nw9eBFaxsTFGOvZbiU2xvTIfTbYPTGlfp9KxVCj/AFHJwktluLdVNzc3BtjTgWvKaK6V/wBeaip4cHGMGHjrFRV//Ab/xAAsEQACAgEDAwMEAwADAQAAAAAAAQIDERASIQQgMRMiMDJBUFEUQEIjM2Bh/9oACAEDAQE/Af8A1MYSkegx0yX4+qrPLIwNo4llSZJYePxkFlkcIldCBLq5fZH8iz9n8iX3J/8AJ4PRkOqS/Exi5MrpSNg6ydP61qh7TaOBZUmSjtePw9NeEJGDBKJbHEtKl7UYMEkdRDjP4atZkQFqzqPOnTvMFqy3x+Gr+ogLVnUP3adJLhrVl0sL+0oSZ6Mhwa+OqfAmZMkpEnuedKZ7ZmTJKRdZl4FFs2S/pJNipkOqS1qp+7FA2jgW0/dfBCDkyFKRsNuDdgdhbdng3G7SF3HI70TucuEV0fsUDYTqTPTgvI6oPwLp2fxiVDXgxj4q4biFaRtJ4isssmpeELhlWJLKEjA0SRZX7uBxa893TLgSMGC+xVolNvtb0ohnkijBgaOo4iVV4Qom0cS2rPxVz2PJHDWUTkoRyyc3N5ZjSuxweSuamsrRkjbmZOtMsr2vt6R+UIbSWWO5v6SVW55Y+nROtx7qF7SItGdXPnaRQkYGiSL44fxdJdh7GdQ3ZPavCI0pGwdaJ1Y8FdjreUQsU1lDJMqWZZGi2GV21T2TTEyUt7FE2m0lAthtfbQ/aR1tmoRyyT3PLKJboJi0ZI6jVVSY65LnuisvghESMDRKJZHDITcHlELlNEpFcNsRlg/PbVburwQRO6FfkfXP7IXWy+6IXwsL48dtdmxkJpoTMnU3b3heNOkn/kTMjZJl8svSiCccigOvKwTg4PD7aF7iItZFy40zg6e2H+vJkZ1Mvb3U/UWW+nHgbz2epvry+6qzYxWpl1+fbHWMnF5RXYpLKNw5DefBOh+RrB0fKaMGDq4ezPbQ/cRFrIufGnktq2orvnAj1MZHUTzx3ReHkveWu1SwsfJCbh4P5LKf+VZFDA4ltWTouJta9Vf/AIj2xeHkrnkTMmSTLZZYlnhFVGzl+S1cfE3n5cGH29F9DMDRJEPbctOq6jb7I90IObwiPTbY8M3NcM3jmTt/RXVKzwVUxrGWdsY7ngXSzH02Pv8AHkzpgro/YqzYTpTJQcdeh/69GSJvFkS6/wBOPHk893R14W7RpPyWdOn9LwejJvDZX08F5EZGy+fHcuoWOSd7fj5FHSlZkRiYMDRbDK16TisyNkmWyzIsnulnuSy8EOFgyZGyx/chambzeWWpEpbnn5n2QWsHtlkjqyZasS0p4gkbhyLbcFazItjh90Xh5K7UxSNxKxItt3eBPB6siuEZR3HUdL/qHzvsXZ01mVt1ZMt+rSN7R/IJXSelVeETpU44JwcHh92cCumj1pjbfns6SS9Pk3R/Z1EKpcxfP9GBCDkRpR6KJUL7HureSu1TRkbLZ4XdFZeCKEjqKPUXHk8fFGLk+CFCR6Y60Tqx40wY+XBWskIiRgaJwyc1y4I3pk7kic3Lu6aOZZIi06rp93uj5+KqvCEjBglEtjjn+h065Ii0ZIvWji8Z7+nl9iLEzI2dTWvqXwVYbIIWrLlxpjIqklgnQmTg4/H075Ii0ZIvfBCDm8ItilHHenh5K7cikbhyLrV4+Cp+4gLVlz406aG6WTA0WQyhrDx8UJbWQkJmRslI9GVr/wDhGEYLCL3x8CeCN7Xk/kkrpPtzpCO54J/UUxyyDEzJkky2WeNKIbYaMkXr3fHXbt8kbEzeOZGG7mRkky+eePnekYOXCFFVR06dcDhnlEbDeOZO39aUx3TwLRkjqPPyZI2NeSuyP2N47Cy/9f0IxcnhEaILyOcYIss3adO+CJdRv90fI3JcMy9elWORMyNk5E5bnn598htv+jGbj4PVkN51qntZGQmWVxs8k+mkvA4SXkyVXJcCmbyVmCy3dwvxFd23yRtTN5K5IssctVJo9WQ23+Ly/wD13//EAC0RAAICAQMEAQMEAQUAAAAAAAABAhEDEBIgBCEwMUETQFAiMkJRBRQjUmBw/9oACAECAQE/Af8AtNm43fj5SLLLFL8axj7DyH1RZTHKzcjcvxLY5FlmTFuVxLFIx/tRZYpfiJPj1C2zLIP9K4Rf4Zj49Z7WmF3BcI/hn6Hx6t/qrTpX2rhH7qzci/G1w9GSW+V6YJbZcIrS/s9yNy1ciyyxS8DdDkXrROMsvaPoXQx+WS6H/ixwcXTMctyNoojkWWKRbNzNxvN3jbobLLHmo/1DFK+/BCfbnPhFWbU/fDJjU0YsVd3pJ8YeyTL0sjLxNWMsnMqX9DteyGRxYpKStcL7CYnfHJrtN1G8TvlLjjQ+CI+LJH5Nu4UVH0We/Zl6b5gQm4sjJSVrV6RfGStaeuCYnfGXBK9Je+MNbRfjR1WOv1ohNxfYhJSEN3ouTj3GKLZ9M+mOLRHi1Y1rCNaZF88ER0kyyxO+Mh8cyvGxEOx3avWHvlL0JW+NU+TVlEY6sarStFLTJrjffjIfHK6xsRBEP6HFM2URXJkfsWrNhLsXpFmT1rCPzya49TP+JGJCNC+9ye9UPvHSEPnk3Q53pRRPKo+hQciMUtFxbo3o3+CGOxY0bR40yWOhtI3xHIssUhO9cnvgvRGN88j+NbY4N/J9NLhFctgo84RtkUUUM6jq7dR9DmYZXLjF65PeqIrsJVzfBDRRQoi82Fdha/5HqNv+2hyLMc9sr4oXrSXvSiMRkXzaKKEhRrTahtohP4fnwsWmSahFyZlyOcnJjZZZ0+Tcq4Ij602mw26SYpUJ3z2o2rjkXfSOZL2LJF/PljLayMrLP8lkqCj/AGQwufdiw418H04f0T6WD9DhPDKzHNTV6xXJj0hKvG2ORZZPDf7S6IZWjHk3eSE3EjkTOsW/LXFpSVMcZYZ9iGWMhISrlP1whKu3ik+PUw/mJkJURluV+WffuPj1EbhZFGBUucuMH8c9tSZIfHIrgxECPZCkJ345j45f2EIEfA0VpRGPKGPb3ZP2S9D45XWNiMEe96p+Nj4ImrER8O02CjwojikyONQMk6QiTHx6uf8AARjjtjqiPjcRooovWK8+LJsNxPIkSm5vSZZ7KKMudQ9ezu3bMULlwRHy0NFFCj9j9WXwd2JVpPSSHOa+SU5v2zaKJhhSvgheevsqNq4SQ9GrHiPosXT/ANmykUUJCj+IcTaUKIlWtFL/AMa//8QAOBAAAQIDBQYEBgICAQUAAAAAAQACESAhEiIwMVEDEDJAQYETYXGRM0JSYKGxI2JQgpJygJDh8f/aAAgBAQAGPwL/AL/oRtO0CuwYFee73UYmKu7R3ur4DgrpgdD9uRKsbEwbrrgWdte0KrT1UR9s+CzIZ4Vh3ZRYYKBo7T7PhmdMAnqaDDiEHaqIVeIZ/Zvh7PPqcEM+kYhbZjVdAotdVcUfUL+TZ+yuO+yIvPZWWXG4NnZ33fhF7szJRXqhRbKGzRBgVY2//L7Ernort0K09zioko4B2ezozXXA8uqiJDgeDtP9T9hWGcX6UTnusfTurkZ/BZ/thFh+WR2CD8wofsAuUTmd0UXa77Dz6GVzz0RcczhDzpIcGz0d9gBum9/tLB9WyNb9Rw2+snbBY7Q/YDz572t1OCweWG31kacEfYD/AF37MeRwWHyw2DzkPlgsbqfsA+e8f9O6gVSs1rIx+hw46SQUMC10Z/k4F0ToFc2Y7riA7LiB7K/sx2V10DocC11bvHoq4DmaqBwi/wCqWOuBXidU/wCRtPMAoMutns7S81BzTEGWGyHdReSV5brWDF7gFa2RjHPBDQgBkJfSfxXi6MvPdfeArocV8NyvNcFdeP8ABwbfPkroa1fEXGv5GA+igHQOh3lzsgo/KMhg/wBDmFEZHfZbw/uQWTn0nookwAVnYf8AJRcYnCtO4iquC4gqOEnlLa21B9Ki4wChs7jZoRtDQqlkdlxD2V5jSoG6fPdXaNWZ9lmR2V3aNPLxefQK9QaT2dpearuz91ZMIeWH4b+D9brIzdLDQTwbCzoULVBoMKAqrT6v0WcPSShUMjugVCC+E5X3BqoInUq25Wn+2JZtGGkudoaFUo7TFiV/GLSzA7L4hWYPqF/I2yvis90T4jT5Aq2/DiMsKKFh0W6FWniCiJH8lZbAegwIHd4u07CSPyjLk4hWNpx/vDtOV6g0lssy1x4jLCb5DdFvsotz6jf68kDg6Li/C6eyvsh6K47ipysRmrXXrhf1GUvhbM+pw6vC41R7ZItyxLbEHtRe7IK07rydg5jAIkhy0Oj6YMBm6kthnGfxgxKuDurxjJdcobS6dd9puWJYPC5WW8DeUDm5hQyfpP2kdy4cDgNGgktHsi5xiTPBRKrgQdVn6URutM9sanJxCsu4x+Zu0juXa9iiJ+2+JyCj8oy5Pw3ZHLfabnLGbyWSq1RZULXf5Y4cwwIUNs3uFFpBEjn9pGP1py7VGIgc9993Zfx7P3Xyjssx7IWwIjTf4Lf9t1FfXDuyWSu1xQ7r13xGagd0JoS29nxaa4vCslUSxYeyiM+o3tGzbEwVXQ9FUlUJVhxiFXlg21Qabv43dlBrbJ6lRMkdxd16KJzUAvOc+WI9neWE8ZvFZn1waKs1ZLTVdaovmrz8FDoN0cB/riDzHJ04XZbzyLcCChzxwneuI3ky3r03nFjJFAjrg3mnnhgu864jnaDEhgHQ1RPJ+Ge2FaGR5yGEHjpQ4lczXlGFWRkJBjxGaj83UYEMnDoiCoGSg3Z7qjlYYBBUMKvCM8aBnaN9MtVTPVWm9M+QtNURn1ExURmo9eqERWNN8TNFqrycRgxwYBWW764V0br4WcsTuD30Z+1BogNwcjDLpgwntMMCoG66WG/ZbPQR3XlxieIz5SIni7Bv0Cg1VVMO07hUAN8WqEZfE2w9BLTNVwI4MH3gqO993nJEyXHEKG2p5qksdcaivLJZBcIVKKqz354Ga6lUG6mJDoM1ASxElraXWyjdEUcrLqGavLwNWfpRBiORgMCLebGpznLGeqv7T2V1tdTgwcO6+pmu/PErJ6YXhO4XZSHlo68yB0zwHeYwLUDZ1kgVa2NDooPaQeROG1/XrvOEGt/+IiMUMI8y53bAE/8AXqVYFNJoPEV/EY+RUHAjBjMHYb2d98MGgg3VDY7Lidmdw5fxXdllA+So4GSk3fAJ0EsGgn0UdrQaKAy3eK3vOzHLVA4XbCouE1UMgAnO6dNw5YN6dVSekowLR+aTgCoIS2tl7KBoZWuPIWx1zwoqirNBrSfRR2t3yUGiG6xk3r5744UMT+xqcUieHyjPEvNBV1zgviH2V5zihs2CEM0R16cgWnqrJwYMBKvkNWZKi3fdbTVRdfd5qgkMnkcGJw7R4WzZrNZqg30XDPSjdVZbyDj57vFaKHPAi4wVxsVSA7Li/CrA9lfb7K46O7+wywQ059ZLLREqO2qdFASkzQM9AqldV1VHKonDevWSDbzledJQqG0HdRG+oV1WR+1w/lVgFei5U5Gu6BURwH8Teeii4zRChtajVRaYhW2cU9t3C2UwzjWf1wK76KL8p4slGjayWNkaa4FMtFabJRQdxDlHN0O+y4RCtMiWfqSJUTg/16hRaaK0zilpRvUqy0QAl7zw05S0JI/Vv8Jh9cKvCc5Q5uYQcOvJh3R0trY0OisvEDus4dh3Cd1c9Vqsl/IaaaqDRATCePKkbwNxPXEh1bSUscYA5Kh5KyVZdmJYPEVaY7so4gdIIdFGWHyjNRbebLBgii4mLsQYhJFUwOFYIOFWoDELdRLBUKo+PqobW6deipyGjh1UHiEp9MVwkrm2iLZLLKlQG6rVR5C+IfZVi71UGiCLTkUWHphjEsbi12RTmRjDEbJGXVmiD2Gh5CDhFXHQ9V091w/lXnw9FtICtnPFfIRqJPpHmoNwfFYLwzwoMCLHZjE2h8t7sRslmavAc+VI1RB6YnrI1RF1mq4fyrrQMQ7TYinUYFKN1Vlitto/9qBzwLI37XtvY/UYjnaDfFEz+E806cra6Pw4KEjQM4oNx7TbrlB4kuNKjtjHyUBvtM4v2oGhmpJtO28bIdKnErma74a4AQPXryhb16KBwoyx+kcjDr0Vl7BFcCoxs94d1cNpX2kLOd7tTuLz0CLnZnDrwjOQ4EU9vflfE2YvdRrj2zm/ko5O1UHjCLXZKyZ2t69d0PqOHZarI3k4XblrQuv1V9tNZqzQ+UZqA5OBVIt9F8T8IutRhgQPZQMto8Ld+y776qLazRN1qg2SzggeacdBzFWQ9FTaOVl+BZYFZb3PMf1OWBTOSyxBjd+yPrLUKhIXGVVxKutmjgl2itfVzVl4iFHZ32/lVkoo7S4PyrLBDmbLslWrdZqSZWW6lWWSR+muN64NEGDIcR52+wFUtN7rjcq2nepVxgHOxbdVCCsh7qpAV3ar5T3Xy+6vvA9FGFo+cxaeqLHdJqziIwYw/wDSg37N8VgvDPCDWCJKvPJVG+6svCoLTdRNDZtJUdqbRUAPs87TZD1bgWWCJWrzmZatgdQrm091R7Ve2g9lei71UGiA+0rTbrlVsRqJINEVfuBQYO/2/e2bT2Xw/wAr4YV1oH/gG//EAC0QAQACAQMEAQMEAwEBAQEAAAEAERAhMUEgUWFxgZGhsTBAYMHR8PFQ4YCQ/9oACAEBAAE/If8A9/WSgNM9Rqx+1R90lzYxGl/STNN+q38cBEAN1itrk5wEqVKlSpVNmjKuP+jWK0n1QLAR5P4yiJ3vd7QIEqVKlSokSXOblHafhxP/AKfv4et9G/Qqt/Mw7wM1LvGSexBLS0E1IR/CKOoJsksdmw/hrip+B+i1vtZ8sEOYd6Hfm4sfUCGYyDxAq9pXNuobon9cEtdI4HAt18zl0J7nJ/CNI/tyZ9Lnv9f0VURO5eiKze50AtWkY9R/NGZrHMyy6Q9sqVKlSoUlBskuRA7f5Q1/gYmu+A3m0D6zHHYhfMu6WE/H1koFsURQ6wCVKlSpUSXjd7JQNQdsjH0hKlSpUqVEjITf/h/Au9HCq1VuuO0jYa+5UMXsGHq817f1AgSuipUSc4NnqVKiQ/W60jo2S6X8j/AAY1dg7sZPeouEBFsRk91eQ0rdJPjG4qNpbD9BlR4lUqJKstdDGWTdH7/wDtObfc0MV8N0+6HRu3xdzoQ3yejA6KlZY7fjKlRJUXvgEqJljPEj/AF8Qhhn+jdQ6Dos7dv+/TAySokcM429Ms+Vpgwxwxh9z+ACk9+cu+E6KEp26E7Jr94QyS4xwzQy9OWfWzAy4uWM8SJ/ALdwbwOIjwDPnAHqX/4mp1Tx0WgOJ+cDouXllV2bywHWyVEdbjWL6GM1hsX8/wDpufULFukO7ufhhH5IQB1h3dTRvgR/Qoj3fiaWG4jVrOXWwDiHUHeYae5SkKTRIfoMvrdaesMYzTW2vqWMTR1/6MNQh3lvd99hFSpUqVES3v8AJCbbB6FotjurectBfMUX/wA46aXYoc4Y3o0ovlnxbvmDB6rm4W/aAnQUS5cWLNXN9cuXLlxYscO2LywP8Ves/HDU/wC8T/IDNJvdnR/8JAtQI+2fQfWbDnq2Jbz0Tvf2EeADvoYl9Ejl6KC1m9BKCVKlSpUqJNaa/AeYZe9QcvZ6d8oqlJpO3GmNLIGBgAFCPJEptwsUoUd39S09uXCpUtly5cuXNAv6ybDPbNSpbuPmLFilkctSXLly4sG8PJ9yj49Ajiz7jKVtbXCsgsNQ0F2gN6nuPtU6RUV+k+sELETvLbXHA3Aui+o3z3I3qO16/t94T5Gaqdk7QipUqVhRg9/kif7kqwLWg3gQOtIkCpfCBEsmpHF8SoMoQvCkIYIEqJNLdbQLlQofqgSpUqVKiTUwFJU8Ep/DHiblR2ge0p2g9oPIHZmqDwYPoAZr8e1R6gPZU1Yjxqyqfr9EHwnditvXAgSpUqVKiRJ3p5Y1xrBVH5tOgVFKhTKXef6qBADlh2lXfYi/2OEkdF6Iu5qRsGj1glSbahj5NXY7ECVKlSokBhMGw/LxDrYwpjiLQHAFwB2etNSUSiPJDBAn3HQjDKgQOqoyzaLNHzyTkdXGYHDti+X84HAE/wBrytFsRh2zAh1JEghAQJUqVKiQpKibJBFwHZ/TrU0/MvXgnAZ0r/TuOcKjvpuZUzmdj/44JUSOXCkwSWN05WzLIaPYZXD2jFHFBlxYbw62XPEqWntes08Bgcl7ceRTxLKUPJAbqv3GtTeWqB0yvZ94IfoOIQOmpUSCN0GokI+HQ+f0Votj3er/AHmTGXI7X4lZ2isvN7BRvH++Ydp9JuE+ZWDY1l2r/GEIxy5dRIXTU47zbweO0RmoL9PYHbowMDgdFRILzH40Sma4G77fkggYJBBiu+Xi4xADghDNSpWWOrAh1sSUjeZ74/RTnCNHIEauaP0dVzxgLhtFEr0+ZHdXoDeIO25EAz6CVZpGPvkdoMWOXA6MMSa0/wBLNTtT6veLdu03bczJjcdmSAa79opr9IRUqVGFopNJqXS37vUMPQjp8ro2mnxpCGCGGONuDoqVljFUJuakD82a1w/oexyDSGCNN/Yd2b/ZL18yHBRHuAbECVKlSpUSN6yQISWOyRJtjTnoMZ2QKKwxwWCJdugaFpnmh1491NVt3gSpUqVKiTUERNkhX/e8O+HKofhXQGwKumEMkuLHC24NskCJHLgnFNaneHN/k7PWOzc5Vt6C1iK7sHop7Ss7R14NoErpqJEmrf4zEjNcPkd44Ys8YI4Y4O64QfQQGi9w/X7XJPFoHIxFaCzQ7rsIEbOLouLrQJXUkScYRMLSk/76QQ9cjFFisW50So1GVJsLQh0XLw9KxUMkGLHLh8mXCKLQFwQWNmDtMfqhXSPl1HLKuxh8fFCr5TSinK+V/WEVBWLqVeCbaPmA7EVuH4g/F7k1DR+87IIfoMZ8RPaOA8X84VgpiwNSAQRywpDmGRlSRCFc4atJTKvONs2YcWKPZ3ghkTsLBeUe9m4BhwTWbOHK2ZsbG5uYSSIk3F6FzdXw0R3A9sNnvTLGbLL4j9CEOlam7CVHouXljhbQFQaIl7y9KHdqQ2bjP6jbIq8sqVKiri4gWBwHdiJLWqxSDVnfXJ693QiHZCGaldDhqjbZHKDrv3hfzQDRLjllrGhiEMFBHRt7+YTR3lnGXJrkhEKE8zDDQrFku0ZsUxyoZuwEWk+82lPbKii+xPF0q1oSpu3DB6VeCXGHWxY6uam6VKlSpUs2lCGJNC+7mjIcJKz3whggSo5Yyodwj0Ng1Ad9OhjDJxEMCaoebw8S5oQ0XmOJEoIJoOudDGpHqZujKlQe+TOnuKoj3JcuXODouXL6rixQro2uv7iG8qBDDBKlQQQAJw/zCGCGHLGfZMcrzRbIB8YWLFo6sz5+e09zZHa+Y5K7ZoyHKtLwjvN8JMbjcZsIXiMhyrShQ3ozZN54/SBly5cuLFhycKTaDjnrNB7z7qbD9DHuqCGbly8sZbxKPrFFHhNes7MiJVwDWA4oMuLFNB9lPEkYw3msE0iCCCVKghhlijv0fm8YyIYYdIKXhJod3fcP0bl5A8EElSokSDw41BgJUcKykGVrHHFLlxRyhMFD9Bilb5KKOGLWPdGOjfQypRPDvLDKGBwtfyGSBnOkUtn6Iexju6Ca0NmbZA6w9yGDoaVNtpn0CPeGX6Zm/UTx/pB7IzcM6NvpqJBDWbMIYZwmOQnP46mBwuAaRWfEGX1MWfNGHAE2sbU27RjhxhlXncfrMGBwrfIvOmtDdbSui+SjuHdFgRRxS5cUccrb1GH1PvLO0PglxYo80AL0NmFc0Oya/YtbtCETte0AUGWySUkDsvaIkFJjb6mM2ZtYQZcq0N+gsZhdfWGAsrFZGo0wZcuXLlxZwr5e0K7B94wGzVitrLgbZDG4rEu00xK7wzSA957wO8D1Y/0THxZOYDyBsGLtMOvqEAKt9GeOoE36eglRIBoECK+z39dIjTkY4PDozzZF7hHqOiacW+WoCWQxuaVuDHSbMqVKyxwWrml56mgC6OjD7whjzwdmrHVGnaBRRAOs4OyGC8LvaLX/AGpRaibwiaNBFyx6AYTgflNAAjAJtE4DDNlc80RuxY12mgeQv9xM3A7sPCNoo1oTc6w0CDOpctlSpUvuluZcC/ycA0B7aMIGhuHXVjNUtcqvL0Bu/wAJHwh9u0oChHkj0CNHbRsQwxwxweFC4ttsRULY2p14J3j7gB/VNy+hEc1NrWdybDAHaPgm6RzDQqEMb0TyMVu5z35Jwom4MdlStb6WM3ccjKil5UGAoOMXO6e8TUS5cRLe3y9K1B2IIVUo57ywGiefB8p54GxtiFxvAlSpUqVKiS+8pdiHBZ8yztEGsCBKlSpUqJN5e6n9IfMmyQYmWrxriJcWOGOG1LliOMCNDXvgOjlpNl+zoGt4IMuLlaJaZdyy+tYoTmZUSUxOaDA5BYQ2zbdxE7QdhFLu9hxcUeJ/bl+F4bkuC2bf5w7cX2JvOC4gQOsSonsQziGPUz0gSpXRUSJGv/wHof2GW5eWOF6zixTZ3c8dQJ0JtloOi+gWTZpgy5cuXLixZugqGDW2aoIEqJBDBNK/QRlPxzRpiUU0QBHhl37zt8Tx+JMXDWHWCtEpecBn+8aw/QY2NjSTv4K9o45TLcvUxYus+QN4HeHRFGrOYaXmaRBBBKlQQw5EbwetizVLSEXLly4xV7wxcub36YGHoWj1vQ5DpfQwYnoI4XRMoYKRzx4mr0/klyp2TAhmpWNC+7hIMjQP0wzUroYysX0zOgptZeblxcHssPaflEiQU4Wz9EPejB1gwZcuXLlxYuLqPq/mXT7Uff8ANJugfWeuHezebOqp7qKDhYo4p2G6BHeX7T4RCzwIROhxhVHTj/csxGFxtE8sIZIESegMwQEqVBU5IpApMECVHLGWOu6wF5oi24rly5cWalBXxFUELVtC2iMhTdq9I2JQnmPQiigy5cUccqbGC9WbQZcuXLl4qNWadX4lQBQbZDTl4Z5fo6fgFwGByDTiLuO/jo2LS7wWiHjodYqfl/xiUKHD0JplTkWQhkhGe5YIIECVKlSnPT7mCEuOWMC1wMM0VsS5Y1OgWxfhNXKdm8p3E5TRNpofSNHdAgTbJVOaOoE/ISz1mpU1lMrzKM0okRIIYJRaECVLdTeOjTn0tvCY3Pn3nJ/qABRt+mDUhn+Rwn4qaQeY1eTKVO72hDoufBwwQIECVKlTbRhxw/fAy5eXC5s8TUDO27K2q/c30o4yr9Z0JpD9h9JUoD0QQQ6TX7YEqVP9WqX4zp6GisTgOP09OuX54w5dkt7IdlE8IbwZd2Im5IHcM3FR7HZ0YKaMMbPlnzuSdCg/Ye3qGL6VeGEOii/fGngd2aQnm6Rb7ONS6/RPzChO+eYDsPDnBaMCKpKTpYxlPNevtkLFLl7NiVLHa7EIAANgw5PAhN2GaspjcDwwE3y3GIasddknFfqclfzGrf64h/dOyffrsbfr7S4sUa0vBsR3UrsaHQN7yan0CakCPJGWdkGX3kO0dbL80mtVPcbiP3cRFr52gBQo/YMt7c42QCO4yp9to/hDJq1BQE79e0alX9QJUqVKitCPcjR6zvBpE2SV0PI7z30LFiVGvftjkGkDALakUGXFiiliekOipWqJ0Mc2Q7keKL2R658IAoKMBjYtNoGoU9oiNO/RdnuIYGGb5w8+oEqVKlSok5Ce8AUsY5Go3oTZnC2958/tB5gjji4CbjKI/dxcuazFniiR9WBKldFRJ3woFiK2YFwrkd42qSkly4s4wWJE2wI5NkVLysBgcjpRBklRlLz0brsbyozhgcr9xzi8W73V/EMav1U/ECBK6KiRI2q/2ayo5EaMobLf2fs6pND79DF95Z0cz2YySHDFNYdjVgQ/QZqV2fDhrwrsTiKdybqtcPrG4ECDBwS4sUUrGVLAY3GFFZermGSXFjN0b53HfEYIEqJBBLhniiMuAJ2MFW+B7ha27sP0GCaqgSCHARtqk8M2Yfn9kz+B7MZBW5kTHWSPMSWUa2l2LmEM1K6GMtbetfcUcqJez12MADzg5Xrg5Ic1fuEDvDF5YWcfiUrBFDYMCGbl5s1N5cP0urOTbN+MMAh5lfE7jRhKV9F7RzwGuB0VKyxnYb8USCCCaB2JYtB8Mc1x21ylCQAWrH9heTsodbF+cRnNndbYEMECVHLh623Hg06mhQK/FqdAVpFk3RO698bHX3NGfldrjFr+clMA8TadKZvKuoQ62c0ug44oYWKKLSWKbtSnpoovrA8WFJKho2MIZJUSOGMVV8xjgKLiOpXdlSokDlvM/wCpcA/YFBY8w938NUpdnATYe4Y0hGrlZq1cCGCGHLg6+n+8VBFa52llbPiU74NRryxRTV3Xd/R9cEckGDLxeLlxZxuc8EACl26CDA5AAmM73hRL6OwLyhZyDCGTDHDGeh2xjhpDxqypUqJEmszwu3mIAjY/tBR2FRW6lTCGbl9DgL3uxXoxWvMtQhv3+oLk+1PxyfqXJN/Z9QYMuXLlxZoV3HBxacvebADnj2irYGiZUxueWN3vN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F+o27PA4YqZd+oaZHB9Ka77geVi7ctMNjdw8/O9KaZZSWQQXQH9si9WfvzPv2gz3dtPT4rYZXY427eoBraOzqwezgQzO7Pa3jY9XBs0b+PYXuxPBZvPE/cvKMI4N9c8SkA9MRHXa+/FmerV4Cni2+bzPSJZZBd3qVvUJ59mk4ABrOz5/tK2xv1LbbDYvPa5BCwmTPUuG3Z2yyCPxKcCMwwz3vIjBZYt+3z/AOcTcSLycHvDsmyJpPC0PUmmTk8Ct8yTeBjIffOjJw3haC8wDwPB2vyf65PQEvABxqyLYBp6s2U4ADx6E8Z683Qrq8WPdbQiGkRRf2/6vhAvtX77l/J/xA/FiC/z8WLWQWj6lhspbX34jv2gPMrxwmMR3o/VtY3g2M4+fcR0jepCXgJQ6JbYbEOl3J18f2RP027B6izEzx5zx7WrLbDDFgP++N1pEPuBzxNWvPB4Ig/qezAfmSSyCfy9QK4W9zaCU8lk36lJXC6QSkHth1weCLzH3ltPE9aaZOTMiJ59IL0WT5Ja4cDyWl/r/fDbT4thsmHTfaGkJLIIWgPqGR79hBkvjgB6BPgvh8/cV12/cTLJTWwISWWQQ4D9v/yFkHBKfXt7eJTgRB4sgsu/fVY+GzC6t/UuF3GYzw4ECzv/AKTq8jYRPBw8fcyS+JfngX7/AOAoGs44sLF175w7m17PN0U6FORofaSyCEMM9/EAfwUPmIDOdCEkPlaeITxNOoE8Ctl2/iNvEo4Ix8kPEAfi8P8Al3//xAAtEAEAAgEEAQMEAQUBAQEBAAABABEhEDFBUWFxgZGhscHRIDBAYOHw8VCAkP/aAAgBAQABPxD/APTdyyWdy5f+PXMQJi+0fLsS/L/lLf1L6fysQFE0U32nrPiXsn1lByU/mMfSFFl8Hs79oP8AjYYmtFAdse0HRY9DomXOWEmkyyoEQbEaSIqtsoPp+0NJ3xHzB2Wwlj/jJ67sX/QJj0CDUOgxw7Moet4m9gW716kwIBvbHkf4e2N97t6vH9DCl8459jMKqlVbV50AgQg2Sl3EeqE5RTcSWqtidJBjDcOnkgJVrwEYQCY4O/SeH/DGdTCf0nn7QDKKu68/0HSb5/x0ECyJ/wCNHNVPXEM0J5XDG0DqB6ieoDxDMwNXqZV2l3rbEBM3NnD6/fMXuqwMkTyXoGDoq/7U/uWm4WuPUIf4NZKwk3Vn0SZTpve2eeHtKAvL2wQ2/ktFsIiMIyPXn2hul3Qoh4aGMMFyNQp5QYH7hpedPRHxngnji4cJ63X5hJr2WlItFJEUhxtj4/aIFmTh/wADAF6ctfqIJxSvuRmgYR54RQ58rdeIqVk+jMwkxfxQIAFq8RqSADn9T7ymqhJqHQgWuH4e/WBwBCjk0OhzDGA+kINQwxjjnUVI7eX4h/gKiwHl4HfrFLEvIWCcSy+8Ha/1CO0QuvMxEH8XKyXJPHH7QtAgJUqMOgRzaD5L9Rl0v/W6IECVKiRNAIIiNicMz/fAOfff/ADyo3W5sRWzLHLKQxMII2+hGBtfzgSoSiRQ3Phi/gH1i07eD5l35k7WCBAgSpUSJBLoaBXvtGHQbU3B+kCBoSJEghmKymHhk+l/4Agq452v9feGDExER9VN7qghCVGitFI/5T+DI043YX90gxBAgQNDCRgjlg1h9yMOgXizr2agfwIkdVS3cPpefp/99glN52rrExE2JVDSi+g/2aCEIP4PRYub3tqNmghDDRCOgqCrCemyVEgmBmyfen8MN9Jp3R1fpf3/APvsSiXdv23HgixEE3B6KV9mEIR3TKtk/wAAJqyPUy+5NughHARxR0c4GD0bnEdtF1BVhg6BjoLHQ4gt5Curz/8AUvWz+jiShH7P2mAm3EA7B7rCBZE7cE2Y8BKzDuBUUPd8fwdobGDgfs+sWIoMIMJYWLFF4pXj2/MI6N5dL0ZiGUvaDoWLFijmCnc/LB+fj/6Vx/St8g9eCKDjyF8FfeOCUuBz8y4u104+IUOdIXw395XE9TPTuX/JahugZGd1ufaGjMwGZVVSoPVmyfg4IBiC6lOoPX8DxNm7xuPrBcFqNxOIoMGDLlxYsUoDsPQ/3cuKKOWQY6+vMGXLixdB20bwnmNLudHt+f8A6O8u5W/g8xHhvD87x6aBr2VjZYmzFwsVbPv8kBAVh/AEQAyrxFAK4Rx7HM9TAMHtxKpFTAoKwMBoO4HMADExbTDtPTPH/B12sZf7jFwsgQHYvuHoDBly4sY3vHL05ZWEAPBGHUVoVvWuTmGodRqwFsHuehCXpXtZe28XRU5AH50Scry9H2RG3730GH/wVJwWq0Ephki6cDy+Zx9IWWkbEfiUxhG865zAer9TEKCTznp3qQ15HBGytKV28vl0CDUMOho6Xh9o8/eEZIAcI6LRcfNHpHL9QgMQ+pY2EUjzAqQds47p6hAxo4/4CKqViWJCfFaKAg8dlHL6OPeId9S2w0RG0C3zAvOowpcS/oBs/F6zfz6EY8/d8fO0wD/Qb1IkRBHCPMY3vfHWlllagFrsEFuqdj8A8SsKaBv0BFyDtXzLx7RTci1W1hosMAqBwItejvF71In7sRTbeHHpUrg3tr/JEhbFSE3nIGxlG8wT4JXsPusVcN3ZilMuBPg5ln9lj+FElro3eibd3d4evega9lys38TgRgn6/JMMZ5/GfuBxeyj3WsxaAQJUqJE1LvCl37e0IoIliN3EBxhU5OT+PeGINRwyivYD3cv4mE1jDqwlSxSp72TBgdxs9veiQah0C+SUYf2JBcTByss83zN7vTvrCZ3TBDo3/wDUyO+qTv6Og2wJnoYYTVqm7nrXFT6xo5XP/FDaR8tvHBEsUMHv1E9BjOHo0SDUOhQbMMAC9PpLOPvHsgoYW6M+EBqYtmE2TiGAz3ZceHcgem7vPt3Bv+oKNrUoCJirn/6Mswbr91zD+n9R+0HQfio0MG//ALErGDn/AHQ/b2Ep0RRCm5jiDQINQjQYgMpb5ghtB6geo6XW2HL9QQIEqJEiQQxt/K/WIIZdUD7/AFmDVJtR+Y9qLJY/wVJ7T7E2Gts02ws6wECVKiRggW4JYoY7ZeKsoRHqy1yplXmABieOeiY9oHUIYKTkh7fHZggcykCNt9x9pUqAqABavESJsfE799IQIEqVE/gHYzHomodCkC1opGCwJSYB2ef6dPIYA3XRMhAcjB5e2CG0PqbNoFNkdcM5nw9SnWmpTuQIECVHCzEAME8MxbQ8IeMBEQR3EiJcrc/5tKzBDSiMdAxBH3ofW/1o5WW3IHwhZXL+vMENphKj3XZ+0wmtBUppDCCBAgSpUSUBVoInUNiEDhAp6OYCEyOzMerw6V4ywvBNmAWMHhmPsjG5Zg+qPsxACL36fDFZm1WQr4Yx7zBBAgQJUqJEggyGuBAlSow6BVK8gMwWMc47ej/RBEAC1eI8K2VwP7MEDE8eh6IxQDZn0P3hFSoWhNyEQ94ECN4JUGIAtAeZc064LfaA/e/UQBXqt/ECllI86BQAjhGKjFbn/dtOybNLHUF+5joKVOJWw6Y9uPK3XIypyre3weYsxstwTYgoZgzFMGqCkghAgaKInQ28SsChx3PQmHEs5xGZ6M1/6qYddhmHRtGEkbUfswNbQeoLxMCWd9WGCECGiwkYJkOgIECVKiRINJtmoHjk+ce8P6DhqoPTn6QQlTCahRE5w57vV4lK25ZUqVKm87t4Z2lyiMxFurUa1bw/Yl48EuPiB6lInqL+rP2GKhGAOf6lAUI5E5hIiWOEi3Ff+j0mDRWYx0KUeTMSDSXAcOXHC++0UyKgRvu9HUUQ1bEMRcSS03h1vMGqqKwwhDOUFsOZmmjptoGm6Rk94iBUVLVx2e0GgxDDvBhjIHY+kPCPhB6jRcgvg1jQZUOhjqncqDECBAhDCRIIYhaNYcJFUZpXBk8f0Mx4Sl8r/qY0xGhVs836RGoJsQJUqVKiS/sNoyX8kNvROxokGgw6UGShhc+R+oUM9osSYJnOeBx5IsWLFHLLTdwQTGwVEghhraUNhxB7UPrBjwFhpB1vK9wA3iR7kIQrjK2JSnVbugQaDDoBcb2ikZQEHwrwPzosR4j3mBjBWRT9IeEfDQ4aD1Mb6xBjlNJRmb44lTahCCYJggjHV2qG3AjsxQg2X56n+aF1BQe8s2k2kBSwjYCVkLl8HddsCBKg2yfQihpE9ZUSCqxpOZ8Rn50iAlSokdRPWbs19r0dIw8C3Dt6PMsNJTFi0rlnT5YI6g3h3ITLAIL2i+xeBSe8ZV/ND7W8xS0d7kqYn1g8u+ISu17nvBsYemDQwZRE0GWKj9jrSICBKlRNQUIm4I6WQx7xsByvDMMwsGmKOaR6Zl/EBKN0PeEdkYwLJTyNn0fpHiKDBg6GFlVG7A0CrhCOYJgizGOs5Zm3D1YwI7wMd14hgSbI2abMfYG17ECgvH2olhfKJ9Vn/kpWkaBKXyK/8ywTCS0MDAH4/aELtWwFsNG29f5hYPexb9YcwVeIPXrwYuxq6Zn2vcqh+5cbCPIzBBAgSpUSJEghRsUTIkDo3sN/3DiDEKSA+PVHpBuOgP5d3qbJRpY6F3QoJDaHRjWAJhaW3+/3mUg2kTIx2hTxLRpi2Bux4jn3ZcekrB5TBBAgR+nOguD2e6VAFvtZjdepESCDQWfiTfD5IoXaKy/rzKyKSwdpHAUzfLmIWR00/cpFkKLWpkE7vKZlVICG1HmXPVw8MUUGXLlwBbFbHmGECoMGDCGFixRx4lS6OCHdSwuS7rzO5RZH2jVIU6/9PrGw2tS1hr3M4cwRRiYwLXsRHzVG6ss7bExMs9yVhiB1DwiK2mDaeOESCio3eYYIECGgkSJBBEFsgLzs/jTs0cVHYbkuaq6bTs7u9xlRjotQ2wQwTEY/gjbVbM2dPPccQsB9YY6zuQdysHaL0Qhgl1S89EFTZ9bEqxjwQNbQeooRwlu+lSoGOGGDCJH27o4HTEKtu5fpPKsAoIIFQFbQPUR1DraMrozdi/c0Bly5ct0bEIoQosQYMuXFixaWx6lSowMyEGoYclxeGLch0dXzoMpdPghgY1Ih1QzFv53WINJwhqMdVRNkPi9OyKLDLUTCZPpLvaLFijg0e25iIdtAYlQERBHe5lkWeRzoXKeOlBEu+kCWOXqFaX5mLaYNoYbR8J4ZYbRCQd+1Nj1mJ5hB7KTDAxDraeKHhHwmDaHW0thD9Iu9t7hJoWYe8IMGEMFGDBly4sdQsGWl7lRIkwkECVKiRIUB3yhsHbUM7YAmA1AhEwTNCWoGHu1jQ4pUcY6pVAWrPiOOOYmM24Cb5I1ljpmIcvMXUgZvBHtFDbR4hihU9MbfO0bFAiYR4lLQCNlwTFdBCjLlg0akQ6ovHErYMxmY5XAm2Oh7TJcATBpEOqLxjK+QJsXEEA6F/IgI3wfMIMJcuXFnHDUOh4yL5OoxyEBw4YkMB6YECBKiRJWpgUxGTpFLZ/MWHVAD3I4oQYaLCxijjtmyPlfodS34aK+kslbsVZe0Yk2Vs6Z6RgWC0QSDgcTAZm3/AAXj0zqU+pb/AFg2Jhj1mTbuwRVI8swCW1pYICI0MEwMEfZNz4gQJUNVwTI89kGJglRL0ME3JQ03Mwicib7uV1LibH+0UGDBly5cuZgudLDGyFO2ZXd7dDqfJycRIrXZAlkZESHvCZuVI8kEZ3CYZs6BLjqqhrbeg7P3+ZgigwZcuLFixSiJRUtHXX0mHRc0gjpbGxDMSGMYMTZUW8rFAgjMBmbOZ5dOPRGLwt9H8TKAplOWOhvYV6JvHceE2mnZ/BsmxlZVaO4EqVEWtsNxg4OBJ2e/RixqosRYYz2IidAllo3OyADYfHmVxw54B3FBly1A8Eyz+vD/AGyFr9LiOK7rHzokSElUqVEjoOSKnnmO/Bo2xNNefPL1KjDV6ygUzs8kFDMwmiS6LyyjVuGJ/dsPZ3oEDLlxYsWkhoyCd9GsJaXRyy+YcNAw4g2HxHQWekSJLxUTc8wkMw+55J6p5ofcK9zb6K/MYx0caWHjyzOhGBl/UaKmEdXvBVcxmokugwTCw77YYQlQJUpubw7DplJ1K9yv15hrXDvMTCVeaxGEtsOGIyMEth7PDATqhyZK/aCCKgpw7QKEDxBOIDqBW0cAR3EihH6R/USoikYkN+vKlSokSCGfVjxdMUojO88t3qBKlTnCNp3A4y5OmChmeSHnHHeYN4IbyroEqrR27EtQ0miQaDDoRI1yzAg/VuLuu2eeFovCcTIQ/aMdO2bPpGOZQRIugJu1g94Bdh9ZSMHYl/MIIPvpiKghsm8l4CbPAwOiMGCs2bA/B5gkBoGCYGYB4TvYnxCkM0G2WKhIZ0cUIdAMEHMtCbtvpCEIIRgj5eQbPh7IqOxu4/64jpccIHMrWsMsEFrxvYxZQpkdlX6ZhAiibYuUwnSx9YIQThMzxQwmCeGBW0BQrYe/ECkREwjBMghhIkEMxzMB2DBgzCG7bxMrblgSpUqX0p58yjXwMcED3BreCEFIvES/wmx2hAFBsEqxybJxM76o0DRYuqCvRD13Xy/UKjPg59YTYhG31TllkWOhR4mwHUZTl3jGwUXB/wB2gIAbAQq2jAYZVQ4OYyEHcap3dZZvHuR8Q1ZeBtvP6QgoghwzaO9XtKDZv/xEROEm5BeZg3h5x855YIOY6V9ooOViTy+IL4lnddNjZfmUcp1PiBWxyQ0VGBWj78+8LWvmt9d4IOYrEmxcSlLXLMIckt96yWsNBHUG+E3fwcRy+wb/ANTiAiNYliTBoYJaOIJHW56wXAwIcaHU2yoeSDBvex3EbcYfamwEDE/J+YGUV3lCAEeiX1q4DV9a7I4tLxYm8Vecw7IoswCbyV4xMtOWEAaDEjFjfZiN2DIphnGCCz65Q+kI+/hn5mwPoS6BR2y+JeZcuLFijjhIs9UjFLHcfHXvBRioBgJXqDW0CtoSOImH3VzAWRlSgCrsEHDfWKP040RFHhj6xH5f9TEQngpTHgy8fhPJ2dnmAkA7gQIMHIHQRy3odS3A20yDUMOhGNfKG4H0IwpE+iO/i2MLwHcVy6JqGHQ0Pckb+X6QDQ2ixNATAwG84JsRww0lmOq8RVGgWu0e/g2g5Zd3qGyxbrdg1tMe0PGPjPBCQSyZNU4fuS5cuWeDaEUojpGLLaLYzDeN7wtBgy5cWLqA55Z6p9tFEAQBG8ux7TwT0aGKeGAmJfKXs6B6xoecW35Z6gr/APh7So61wOZde7wx1ljwTjGV70i9jMgExG3jowRk+zCsJ7zhg6Jdt3LCm7K9AIEqVEiQQNsFyLfSAUAgnEp1L8PQGkQRUqJHUV4Kmb8fo6iwxGfa03HogwhhYsUcfEKB8y/F9YSrLlZUGJhmKEOgx7S0cTGtfSZcuGkgwYOhYsWFUwXmNEg1DqMvwQPJjlwj7WnwalSYdLHMbGbT4t6f9v8ABnyzzQgcxEYJqsj6ywNWQwFSFiQho9q2Xq4jkj6UvQbW8wQIECVKiQUFrgh5srdng0zwhG3XYPiCBAlSpUSJDKJyLE3GXZWAHAw6GNh5GUrRBly4sWLSugOsuXPIXiCEBxtUdzxPLohlRMZMegQ6TDMLABWQsizM6AwZcuLFi0ns54git/TQNe6YXUdSgYgw0L63In1fxp2abNPObGJGNWb8fwCt4dbwzmUZLfU8Hn7Q1QwTYTaKYVFI7jMRPPMOmmOct/Q8RF3+2gPfZiHlOlBBBAgQ0ElIHo8ELrXTQw9tf3z8wQIENBIkSCGMmKKP6P4h5hg5lA2DLMU7YRcuMOgqhBYXH+jthxaLftN1fO3zKt4ZRv7hw07Zs/g2TYwD6EwU8yrXh/AHUYVl3g2+3BOOfLxHhbctZ9NpZJHgqWFsOSBt6e8tzf4Jsi+XiEGDLlyoQyp1Kz+AWNjE8N9Vx9n+FLZCxI4Rgnkl+Xwv6hQy0A2jLgT2zZ/5mACmWczFvPXPLA7lYUq1+n+9YhBqFYJzMwmP4Emn4lrw8RECER4YQaWyGMdXjgFXe0MHMRUitiIntcrHQ85hqHQgJa2BcQTALplahvQBbQAG7CLDaNqYP3GblNpZXEC6Fi0gRGhgmBg9cMU2xjiUrGTRJNNllV2gDyy3DGDjp77QIUVAMBPBPDDVidUbkCBELL1gZcuXBToKGOczF/IEmLMMqviW379/4Egngi36zx+AVK1AESx3GBWHOyf86i7cAKJ7QE3la3gdwg029EZZKk6NqPvNughFFFKazkf47hosPwjU47e8NDmyOMdXxIUuxeLiJrizLQczhTHiGgjuFFjhWUvk4L/BDQj1u+rGMqq0bjKjBgG9Gb8XxriLj5rjzBus1n1mHeYNIh0GOUDmEUuX9Imb/tKiSksbMrwh4JfmKPMZnYLh1VXbuPB7TBqWNlTAlkZLi/dA0FJLlwxLn4yf6mPeeSeqYN5h3gl5gVlqw2ry7YCCgUBx/T2AChTJ7xhWXiwHzn6y9fg/uCb8bQ/v6wM5jmVNhfB95sAD2H/VLmERMIxQYMGEUF7ZMxfxoDUExZUzc2WGtnDB0PXGFixRSyq4Qv1np4D/AMIXnmFD6RkqJlNy4g7hAl5zt+Xn2hZgmUYen7Qok9AGlgZepVRkvoEZFMaq83yggRhpvMe8IR3PLAreFgAjfFweIFSpUSEuXLixYh23FezxfmcTZNumloIDfe16O25/eF2B3UNyeowOvGQyTaX+JdYvYX9obuMSw0gjyQ+5XuH3ABgxRFzGPbtjQ+02r2v9djkNlZ8DRMZFinYD3vRiigwZczsy79oYsP8AkqNqo5afuFFkcWX83APiMH2j2R+h9qlML8pZ8S2m5tQ9SJLYaBt34Y1cikSkZcuLFiijvBli0QHvuPbbSxOIREwkvs9AP+qIUix9SeftA5tQKDXzmxgObIwtF3W2CBAiUDDDjLICQJBTeB7g1vADeN0SUQl6x7sEEn0LgTKeg/EUqB7p+pmLvB/UtFjoyR0uXLmVALXYgGK+Xce22jBMEXd7F8rHj1GM9oRSJ6jl+PHHxCoIHh+5+oAZbCWM8E8c+pgQBXkCybcfYhb0vecvFcFRz1x+iHHPGHxPzBJAwAUH9hamt6lluF33XmbIAu9AsSJldhW7fuKDBgSG61AA2MQ2oc238viIGvg49BomoYEDeyUkNLgow9TmGwC0WMLELa49+sbFBScMuLF1Igyp9WHtvDDBhlih1SADL1ejhjr7CwRXKpBAgQIQFr2epdwVxxCqT2nJR7StkvtDXAdsFsLbt/qHzhsBC6mHaJraeKAg2HhnqEeL6dRkCDCPEuXLlVr/ANefOpMNyVeNu+8PDzokmgw6W/Oxm2fTphA/lD06HhlhtBDKrBpGA9RynXp+f7NjrcLPV4mAmxCVHQMJHiFld/V8eYQR7C3NgcbdvUsTfSOjQIIqVEjoAwK7L9zphjHsJQsmbMf7wpcikSkjDoaSWibY8HbDNlQRxxYYsoT73WqvnPNpgDmXesFvq6CEMIELFs2ZkacJLlzCC8/lCACp4tUdP4ZYOIeSofIjjRcMBV16MH5nlle46oDnfR+dYICVKjqAWULQ66hCWUjkTnWvHETs2efE2bjJ25P7N60oL4f6gIZm1nQCiURKR5lmNyj4uvtN+AwfbuHT0jNPqjMUECBKlRIkSGLuF3f9ZigDgeNz37l8/PfpEVHhVKkS2ZpwvEEL9AUR1/jYSlg3uat9WE8zDvHzh9wa3lAAC4m7leghFDDQUqcD6y6cy8QAUzlMR/BV1/hYIksZ14sekVRxhmqEx6QXmM3Hk9pZKqWrywQIECVKiRINCqW1t6cf94/hrjZl8jNHIe59pSZbJQf7LAsufiGNOdQfv6QEMzyzyx8p0C6mT0dyAKi07x0MvvxXDBAgQ0EiRIJcyNJLbF6V6b62aNh5Zwayjlie4GTp6jp93MrHMr3OdGvQ94kDxyy8nPqRWN0Kt4nuGEWx3pg9XiPO5YAyed/pFiKKDBhDCxYP/QxyqAeggwQQQ20MMGGHKGvMRTeohF0ViVUxt6Lzx+Y8r7Bl8XXDFwUii92DEECBA0MJEghjXplZef8A0/wi3YHqD6xUluUIU9mX6zLP8Mr8kKlBYjY/2FowHreHxH08128h5goZg1vE1vCreUnkB9IcaxBoIgjHUVXeMe5/rRqJfTzHFrS3k4/7xEvuEe9v9o+U8sAN4Cb0tjyvBAj2NvdaMLf/APuEuYroH6RCfhfuAEQ4w/BU8GMFQ2rX6SQ/6bbs4Y4oMGXLixYpgByQUnVTETY0EVL1rEzIhVeMSsb3m7b0xbwKhx3EQ3BQoZpLzrEIbmzSEdVe6I+lMMMM4nmDtl8qS3Q6VSsuZdHfRlVDYdk8J/YLuoxtBvw7fO8XgH2U/EeKvofxcLPNAW/WKW/uUx9PabdYijxFxHGOpU0WSKxFUS56HkyfmZmoxfmNEGtDa7BvKVXuKlPBHKNx1p5/oO0cUJ0TPf6msEDouMOgY3EWrHqMdxWK3JaGf4QOi80xOY6GgJWW1bTeN3eXSU8cVf1qicO9K1iEcvEeh1dxV+B/B2qf8a1HUQ4Y1faPSCWBYjYn9pmDS/RKg104vI1HFBgwhhYxYopc+F32MQQcxPFpUjxgvBLFfgIiXTKzr+s3yuzL5lf0kxLkBZ9/LseJlp0TUOhAXi4mD07ZU1G5u+2GKz10Ov2i01NjcZs5nknkjhvPNBreCDmNvx4cEIbnfqKElSxfZ6f93qpmRb5X6Y4oMHQ2xhYsUcdIwg+V+h1EBoFWMzl3KlRIkGg3l4CfR6Q2/s2UFAu4ps+z76SgwZcuLFijiBG0ol7CqvWOb41aFAA7ZmQrl28v9ZIwQbcPlPzAD6C44hqKUHllD3cQQJwvQ9XmFAWoCgjzaDoYfJsdL8xtigKkgPMCt4juD3DBzGs9vfUBZ3XKu7FoBxRJ7K0fo/OpbFearAfn2lhFBgwZcWLFijmdZ1dXt9I45fLLn0IGhIkSCGPTaHaO9PpT+0OoD3OnHvFRnqNxONAYMuXFi6kgmbYh1a3Fijl62QtbOx9/7FLc9E39TO06FCPrAC9+8B+ZtwQc36wBQBwR85YodcMTg13Fe5EDw0X/AETfUqE4fedFK29mFaOiLFpC1tiXWNi+g/a03BYzuuJbERPWKo4dpihAwZcuLqDirfe+j3i41q+F/GEEMZVBHVXuAEitOAPOz+P7SoQ4xjbezz95ahQaRKR0DUMOhtTYd3uFAMA2CLFi0GfwiX12+6/2RgNah+ycy3Xq59BgpA1Axbtf4UUmCU4jXkezzO0ArwNDDGCBSMBuylBOAw3ll0WtEadhl+0ETciUn0e9Eg1DoLeZubDtYUu2ebmdSpZpHuMN9I4jm6P8GCn/ABt/apiLegzHpfmPTOYzvvx7zyQ0GLqgr4iq3jpCgAwHEWLqVPUoFx09WF4GA2A/s1C3uFjKQ+yPhiHs15feVk2Jq1zBQ3PLMWh6tDFFwVk5VLxDpI6TPfkMcIUSwO4ztwH51MX5LTyYfd1E1UID2GbJcabE4YaDDwI+km55TwQRzxLau1/guqTDb6sGUQ0ixYo5b+wX6czpmv3X+n+3UCTcSxi4r8j6TEUkGsAfViNbByOR7Gc1WcQENBh0I5qxfs9rDs5EKe7+3BAESkeY/NhfEf6QUMzHvPJPVPLDreGDmYd0wvEH+smRMu3MwzOMXKYe2Ah2G75XVAasB5RfjWJaTbkhvr81n5g9t+Gn1gqbHOMJYboqPvBD5Z6tDHmitUC2borMGDLixYsUeJUjaD5f6jVFvvw/7z/dKHrbtdjxL+tymx6c+0tSLcFMNFinctgWszquVZR6ce8OborurteYf3Hq8xSPYxahtwfnpiCD7le4HcIYBu926iq1tY6Q4VyBV+jmF645zl7X+CFbH7dn7xRQYMvA46hOH2lvM8kPKHlHynknmlmXKr2hAy5cuLF0KCSigOZ0AQ88erDbAKA4P7up6uRk+YhY8F5903mz5Wv1qKX4Ck+yevkZPmV/duhJuJYx8W97+D+I772VS9Vjx1+/pNp7xagW+rDGvtH55wH4gyudVP8ArKFK5EX4gYBzWHpsgAAAGwfxE23l4SoYmZbs4feOKDBiFqmOY+UZtA9wPcT3A7hHMyZ9mgdk0SDUOg4TeWdHM8H78Qh5ibvt/wACr+mo3sK+f1JaaBAy9AjJJy1yrc+sf4qg4PL4l0e0CftKlf8A9hZiVGWUvYw54yFaHhxDhDTZYs4OQV6jtNmAN9j7sOs1AUH+HJe8t7Ky58v0nDCTUOhT29AceXoiVhToDo8Q1cxxOvJf9PuS2vQF/wBoFnBFsj0oFZGX6wRWuKPgQTtcEB/iNRUg24fKfmInjf1jk95g04ep6o+UEJ/KWGj+Y/bj3laCex6rA/x2ifNgi/mIQC9N8Ebxqb/crK0LajJR/wDwF//Z)
Um nanoporo e software de reconhecimento de imagem podem sequenciar um glicosaminoglicano sulfatado em tempo real. Crédito: Rensselaer Polytechnic Institute
Usando um nanoporo, os pesquisadores demonstraram o potencial de reduzir o tempo necessa¡rio para o sequenciamento de um glicosaminoglicano - uma classe de moléculas de açúcar ligadas em cadeia longa tão importante para nossa biologia quanto o DNA - de anos para minutos.
Conforme publicado esta semana no Proceedings of the National Academies of Sciences , uma equipe do Rensselaer Polytechnic Institute mostrou que o software de aprendizagem de ma¡quina e reconhecimento de imagem pode ser usado para identificar de forma rápida e precisa as cadeias de açúcar - especificamente, quatro heparan sulfatos sintanãticos - com base no sinais elanãtricos gerados a medida que passavam por um minaºsculo orifacio em uma pastilha de cristal.
"Os glicosaminoglicanos são um repertório complexo de sequaªncias, já que a obra de Shakespeare ou um poema de Yates éuma coleção complexa de cartas. a‰ preciso um especialista para escrevê-las e um especialista para laª-las", disse Robert Linhardt, pesquisador principal e professor de química e biologia química no Rensselaer Polytechnic Institute. “Treinamos uma ma¡quina para ler rapidamente o equivalente a palavras com quatro letras, como 'ababab' ou 'bcbcbc'. Sa£o sequaªncias simples que não tem nenhum significado, mas nos mostram que a ma¡quina pode ser ensinada a ler. Se estendermos e desenvolvermos essa tecnologia, ela tem potencial para sequenciar os glicanos ou mesmo as proteanas em tempo real, eliminando anos de esfora§o. "
Dispositivos comerciais de sequenciamento de nanoporos são usados ​​para sequenciar DNA, que écomposto de quatro unidades de a¡cido nucleico, conhecidas pelas letras A, C, G e T, amarradas juntas em uma variedade infinita de configurações. O dispositivo depende de uma corrente ia´nica que atravessa um orifacio de apenas alguns bilionanãsimos de metro de largura em uma membrana. Fios de DNA são colocados em um lado do buraco e puxados com o fluxo da corrente. Cada a¡cido nucleico bloqueia o orifacio de alguma forma conforme ele passa, interrompendo a corrente e produzindo um sinal especafico associado a esse a¡cido nucleico. Os dispositivos, atualmente usados ​​para trabalho de campo, são apenas uma das várias técnicas relativamente rápidas e automatizadas de sequenciamento de DNA.
Glicosaminoglicanos, ou GAGs, são uma classe estruturalmente complexa de glicanos - os açúcares essenciais presentes em organismos vivos - encontrados nassuperfÍcies celulares e na matriz extracelular de todos os animais e desempenham muitas funções no crescimento celular e sinalização, anticoagulação e reparo de feridas e manutenção adesão. GAGs, atualmente extraados de animais abatidos, são usados ​​como drogas e nutracaªuticos.
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Como o DNA, os GAGs podem ser subdivididos em suas unidades de açúcar dissacaradeos constituintes. Mas enquanto o DNA éfeito de apenas quatro letras em uma cadeia linear, esses glicanos tem dezenas de unidades ba¡sicas, algumas com grupos sulfato, grupos a¡cidos e grupos amida. Por exemplo, mesmo uma molanãcula de sulfato de heparano de ocorraªncia natural relativamente pequena de seis unidades de açúcar poderia ter 32.768 sequaªncias possaveis. Por causa do desafio, o sequenciamento de glicano permanece oneroso, contando com trabalho de laboratório meticuloso e análises sofisticadas, envolvendo técnicas com nomes como espectrometria de massa em tandem de cromatografia laquida e espectroscopia de ressonância magnanãtica nuclear .
Como parte de seu trabalho, Linhardt, um especialista em glicanos que desenvolveu uma variante sintanãtica da heparina comum para diluir o sangue, sequencia GAGs para entender formas de ocorraªncia natural e desenvolver variantes sintanãticas.
"Usando manãtodos analaticos padra£o, levamos dois anos para sequenciar o primeiro GAG simples", disse Linhardt, membro do Centro Rensselaer de Biotecnologia e Estudos Interdisciplinares. "Temos outro em que trabalhamos na maior parte da sequaªncia e levamos mais de cinco anos - e provavelmente levaremos mais cinco anos para conclua-lo",
Raciocinando que o sequenciamento de nanopore poderia ser usado para identificar as unidades de dissacaradeo em um GAG, a equipe de pesquisa construiu seu pra³prio dispositivo nanopore e sintetizou quatro cadeias de heparan sulfato GAG usando o processo quimioenzima¡tico desenvolvido pelo Linhardt Lab. a‰ importante ressaltar que esses quatro sulfatos de heparano eram muito simples - feitos com combinações de apenas quatro tipos diferentes de unidades de açúcar, montados em uma cadeia de cerca de 40 unidades de comprimento e com uma composição e sequaªncia cuidadosamente controladas.
A equipe passou cada sulfato de heparano atravanãs do nanopore e produziu um gra¡fico que descreve a saada de tensão ao longo do tempo do dispositivo. Cada uma das quatro variantes foi executada atravanãs do dispositivo mais de 2.000 vezes, aumentando a probabilidade estatastica de uma leitura precisa, dado o design rudimentar do nanoporo experimental.
"O dispositivo sequenciou o sulfato de heparano mais simples em tempo real e produziu um padrãoque nossos olhos poderiam reconhecer facilmente de imediato para cada uma das quatro amostras", disse Linhardt. "Vocaª pode perceber imediatamente que eles são diferentes."
Para garantir uma análise imparcial, a equipe alimentou os resultados em um software gratuito de aprendizado de ma¡quina e reconhecimento de imagem usando a rede neural profunda do Google, treinando o software para distinguir entre os quatro padraµes diferentes e identificar cada variante do sulfato de heparano. O modelo de aprendizado de ma¡quina de maior sucesso produziu uma análise quase 97% precisa.
"O conteaºdo de informação em uma sequaªncia GAG pode superar muito o de uma quantidade semelhante de DNA ou RNA, o que significa que a capacidade de ler rapidamente as sequaªncias GAG abre uma nova janela de compreensão para a complexa bioquímica da vida", disse Curt Breneman, reitor da a Escola de Ciências Rensselaer. "Este estudo de prova de conceito liga manãtodos inovadores de nano-detecção com ferramentas de aprendizado de ma¡quina de última geração e mostra o poder do pensamento interdisciplinar para expandir as fronteiras do conhecimento."
Reduzir a velocidade com que os GAGs passam pelo nanopore pode aumentar a precisão, e o dispositivo pode ser treinado em unidades de açúcar adicionais e sequaªncias mais complexas, todos objetivos de pesquisas futuras. Linhardt disse que a ma¡quina teria que aprender algo em torno de 10 a 20 unidades de açúcar para sequenciar totalmente um GAG.
"Esta éuma prova de conceito; fizemos com que fossem lidas palavras de duas letras", disse Linhardt. "Assim que ensinarmos o alfabeto completo, ele serácapaz de ler todas as sequaªncias diferentes. Sera¡ capaz de ler todas as palavras."