diff --git a/analysis/2023-04-plotting-ndvi-shannon.html b/analysis/2023-04-plotting-ndvi-shannon.html
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+++ b/analysis/2023-04-plotting-ndvi-shannon.html
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) 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cDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg+FB1ubjlnRNvl3o6IEU2w7fdNPhm/hh+FLysUu6++DLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK/m4szFFR7ch0toUgBTdWHr7EpaWru6+6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d/jLST9v5YflasP6v/CO7+GNAPC2BMZWmsOjp2NNbfHwMCJD+LPVL+D/OYlWEEI/9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2+bBDWBud1Va4pcCn8CPqxlh/fgtG8IPaPH8C5wk6/nZDv69jurV5QhtwE0x2iqOsj9Mx8B9/0EaUdiPfOYYDCi/q9jhWRuupMDEU0+CtX0sDFxv07T/K5niBPqN9+tQjgEc31NGCXFeMcCEuQBIc/BK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF/R0/u5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y+RBgogrXPZ8E1yIHoHIFUM+AbJhE7lbMtt8ApL+xmZW7PwbjAO0fAVoXQOuiSP/ksIVdFZ0aulsamKUzwPZ/NYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7+pg+jUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd/J5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7+RHJHP+2hqfugo/MvI2H/mqr4b9tFnKSRY1Y5Ek80Nm/WIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw+nSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv+rWa/Wih0vlbX6Zb75T5C0qNKVFvH1QL/vazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f+XdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS/xTo+P5wGFak62ap1PVFFN4v/y+xuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f/6dhK6gQokFKhWX3urrjk/rnI0pgfpGMeuQIUaEM7+GF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x/GdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY+9yGFQtEo0GQPNv6vS2drj4+1jHbv3aJSMUWP+QTZrmbNTjU8wyG/iXNNpskybLcJ3CiTF5Ir+JYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d+/XeNRTZ9/1OPfG7+2hwzd5W3D+hmyjsRcUg/+Cavb++Vh2ls3L7zT/etOnHNxeerv313vzLVqPai4nJv+K1FC6040/4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk/U+D4gTXW+j0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7/R561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l+WKHmLTJwRv9E8GWJ6dYvf/FmEyEGr+gyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b+tMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1+Q2BIRvf6J6Kgatnrbiem+CFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ+CuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih/xELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD+uOe+kWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby/+SSbPY8/NH6vpl/Esq3Ae4ZU1HC44KFiI9o7CEgab/RqHbj7s5KAg06s39ZP/zxI/mVuF/TbTSy+3Fb8If9/cv7+wt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi/C8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0+Xxx/5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB/qM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J/UKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc/THK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd++U/3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg+e4As38MfGquNt7oUws6Ysarj1/efE+yst86YUVNvDdts3Pv5c8m/aP0C+f8/Qb+IMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT/v27wm09jRYVc/e++iyx2tyzJb/n3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy/t27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj/S5ki1jL/a0GC6+2L6Um+aoddlNFuj+bJ8mH/iaLh8I0/U51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz+1UmhTJyJGxZzw+wsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ/9pPA4RKTASWahmh+8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ/tHnODtENw/2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8+/Elg5j4Gxur3J8o2PJ4rg+2d08T/fwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r/aMMj8onCP/DuDZOuN+GPPr/+p7bx+7JlbYdppcNhzKU/1Px5aiaGDn/s1iGMaBcleKUo/v9rcxkZj7DBEKOfrayytXNLYiUdBY+pleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF/Qa9Pdf/QqOSqCiE/EE1/XIVKTc2tzWbHnimrEd+Vyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu/kzIdx+BxC0ay9zRSvoS0F2lIxI+X7klU63I40gLQ3w5ep5na+SFnba3z5D64zv+QtM4n4ffG3tq4aNHGRfxgrXPMim+5487abL7xhdseIRn1KDl+7aINixdv0OD+JSPwKf5+xoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz+9a/v2PH1Hfz9236w+ZrPXvWfAxlj4NLLHpq3c/PQ3uvmvbrjG7fe+o2y/cLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF/tkkOGPW2ibWaBTkeZ/dvPWazXfOnnvL6jkRXpi85sFzZt+55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG/yVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6/OU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR/rg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x+MJiOA3YwhDRQrWU0u/0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0+a0uoiFH/xcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL+j3D/uwhBRdyAyozeZwvQzs79soi+BKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo/ybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM+3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho/VDkzQfLE+IrYoJXkD19pdP7OwG/voIUtagiWiZ4PAFTHHlTVhRZ7dYmPar+NJ+8JhmR6DFK5DV1foHoLNO/pHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj+i5++WZfKeViJfW5HnUakVL4UCNVkA4+ETfIqx4B5xSaP2L1yn0zn2ltPn4+OqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL/NNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE+cPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP+Kps8J9/3mGQIteY/LGPC+L7872SPR2br5fy8MtKBMHedGuM28/MZmPJMrGgi3Gb1S+Si1/L/zrZwO9XH1ce/z7ZQ1WSoY/+pMb5FT4ua0Wm+Jf/298nFmChEQ+Ti71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa/1IFO1/jdWr/8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc+KJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3/tM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB/yox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt/qMDnvLpPNTXfNa60z5/yjXQOMq+lNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd+RiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb/pBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap/30lnVESgNnvjbUoT6w9N+Xoio0qcYOIM+heg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X+PE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa+dAdAbMYX6HVF8A+7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf/Nf+Ny65BhYxxxV+77XJ2wfQ389/IQPgajXbwMsuAz/0IaQcXJavKbRqR2IqyZruXjVC2+hdee/5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab/wXl2CNe7s/qCtTvWgG5kpBmCBlSzDS/r8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8/NCfiOUVirYcBbIeA2+iF68rQIo3B/S628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935+k1efkV51mzzrM0LL3/20avnwMeKuWyOUZg2TasSqZ+KcZQiOn1Iu2Vh497ALUVZiCKt/gh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1/uzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb+okJ5JRDyf54Axur1D+WS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt+t/qW2+93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz+tJo+wijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF+kuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj+Z4i+42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA/hRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz+x+Fkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3/RTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY+uSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN+fiK86LeXj+Sc5lPKy+k/vCUI/DaLFYCWHr6nbXuILTIsb5imNKY/rCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T/yOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH+L0eCPxmsa5ZpgRJSDZ11yDv+jmbd86vxZfc1WcZJ3UkMq1BOOOVtvu/+pB+en186d3GTwWAw2jheaJs09/+LNfZft37DALyrNj1wABMuUKbODyTVnT/KYbJ3Tpq8IrNh92dkxOj5P/YpZx4/ycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB+cV99/eTF6294dA8+TK6v74MHVpYNRt/I30e8QGTOOdfGWzzxcy+87a7bLjw37rHw1nPzp0KyyRSeZO+QQhInt3dYgvycjrPOv+T8s1rptaP84VeywdWX2T4ysr0/7TLIs6+x9zib56ye1dM9e/XsZmePY3NDs9zlnNVt4+WgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS+XPplV2ts4bvCwZu+KzgITtxepaPRzWdpv74muvv6RO0SorX6cu/dqKn/XWnrtp/Zragz13DUCl5myiFW2Ycvb0PtsXnU+tx8pvLFbUspLX68mdegwmOif/NPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3/jdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx+6arPqbkcNAHoFPzKUUQ+qL0k97pjbZv1I/egC9zTFbrrlFpNdmea+gIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV/ScS+zjlASyUTVv/AJ46gkJI4bHX4lTnloctxPZE1ckS3+jG2fKIjkQFyzuo8jvYQG1OrGvJPSTu/nSp9PHNTl4z5hK/8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt/wkSx/JCmP5/cBKQfl/5gd+rOS/+p91/+YCg5CXK2W4M9fu+/6xxX+vnelVuldIDCG0VQTpU9Dw4pRfei+6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15+5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl/5bqzYqT9hMmptEXDgTqP3Wiye+sD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC/v8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4+Ti7W9D/OZrLChdwxmPNiBRqVjnpK/aGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3+OfU5/ieLS/NuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD+N2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad/3t2KTtPobnX6D8C8pd0MDP+Kx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE+JGTS3JoEhTrz8/CB+5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk+htwN5A0V3RCPoD/yXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps/t+0iCT3AHVtZC7JDCXfR7OSb/Xja5H3zQbZL1B+ULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset+nQWdS/ZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5+LhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW/3Q5Nn8BZEuzqEI7HK3n0+zFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj/mQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2/BWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY/yCZu/Amd5Or9uS3DYaeqVOhH7gZN/8I/wi1fEuLXvyNivibjuKvN+1Nc01HF/3h+ef/sOhox8MPd5SFucPjorQwXT+ytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP/79nbvCXFOY+PUjrT7/otsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9+pbs6E/Vu7Nk642/PYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu/19/5QW9Rv1U1ridT22i+53pzumbs+XFFXYC++CGsTj5JUT/GCgRt3n78i2n71FHG4/u6X++9+raya7os3ZbDmgWfXun44e+u2NZKuGZ0HiF8M4TlMPR+EU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l+uXrbjPUyep+8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq+NzK/OKSbsfl79o9G20R+brBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery/7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1+SdazRrfddkBU98t1htvWrbjqSqjaCguxrffM/5zDCpBALUycmajhd+R6ww4SWafuZ5eU+tPid4lgd3gt+b/Y9rQoZNmiXYPXyRHbRs8zX/f4WIFjWZJtUdSD55AP3xtXH+ZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy/8F0GoqYPv75Yh9j3x4DuJ+uEzHRpAq2lMqb+qfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT+LUgx0Pg/p87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv/u9ZUWUx+PYFueUKwaNvbtu+Xps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L/BehXuHqfaBwBEU7hfVLcXvS4VQv+T/vaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq+k4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S/tkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP+EaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP/dsZ8+fP75D/9Uu5Gs3FY/2SxPld0DHOciXI9gqjcEidXjE+3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8+LTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7++wW4gRDRbaxw2nrOGm+xOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot/FpHvIUjJovFEoYvHYV9C5Y/xN9OfcalvII37UEhTbTg/AQIaPb4Vz6j5u8/aViycMod/fkDcpu8QZbZoeBi/vbzP3XPsZvOubMtaPHkD9jt6+U2O7vqU/9C9SMvgrXpQNG/E0oJxun+CiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp/VAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y+pGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr+P2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr+P30f3OkPQ72aO0xYo3/EsmO3QO5qEF8S0qQH0UsKXv0brnl9+8M7jF174+DsfvPOl1au/RL5/9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x+4ofw+3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ+pYl+Sf8YcurfmXP5F/kj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0/OM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw+1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g+Rn533qkocdvLs2HmhU75br/MmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3/3pYNi3bS+Rd+GjOfVpAPNd6y64Gsz1GaZleWIPoYL/v9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk/Iojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB/36kP+K38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL+x03nhh+oNo5e77psxg9Q5LzebIKD+fsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O/kdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P/F7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF/BRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm+RdmIJQST/ZfVdudUvh9S/qqNvqT98g9SQ3lHibZY0mRVHooyDN/FHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi+d0s4+ztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr+1VZFcEZ/PdJkn1hOs8SXS/NFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq+rbyg1Z+O8VsNC1UmcvORPRfxtPrfRwL2p/oA1eZp6Z/aGffoewaXcA/xBlKlQLfhQL/oPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp/bXh5e5YXW2Ngfvza0ZF6UgFL/E0fTq4LBlvTE2qb/KuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8/VWi33c3bBZnGY05+dm+3qc7fNmj4YGKLj2nfqFP+g7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j+SYEWHlZ0K/Hgsh/Ztsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D+YVyKd0jw3iumwi/BC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc/J5uGm6CViW5mulYMk+HqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br+fcLyoAyu8D9B7bgmzUqfFobF5nKnK4+t8MPJkI/xHUNWk117jugWF+xazTAALQn6+UE9lhoI5ApGA/iuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL/3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n+jspIsHFjJrTOdzSMuOa9DbDcj/nH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U/8JbJZPJJLBLolH1La/RbF9AbC8JJjv/mMnssKjLRBJyqj9QXxNko0Ux/X79epfiXkm6fmKwF/en1HLc6LxloXWKvGa5rVCVL83VuiPcDEX/K5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS/kTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv/36k2sbqCeCFNa+URpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR+D79H56NOz0EVWCTy2/fffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ+T/El3uZqL+FyzSZ8XxpTiI/G0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ+euOeG2SORmKZr/qKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya+vlKqv6aXK1qIj3imL+L6hL+yvUlFfE0VKZ7E8gBY3M/8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry/LgZIlCeP11Z4zs/AwvVwtGFEut5S1JY4lfyT0N/evOLo+rUEgjcqc9IkGpQbv3iW7Co5b+KgjvpzYdH85PLcc4X21ouwEGl/S4qnUAvoSlXUUhR1eKr2VWFTB+GMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH/qPepXQ0Db77CJOAImohB+RPWr31ev5g/kE+zTa4lbvZo8xdWPffQu9yJTPCNB66s+zXoJt/0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0+3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80+UxfUHPlBZIRVNQ+v0xRm8REKPoLmNr0+Uo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV/+mfr0C0r/NkeXbbpPlOTBBwT0mVz1zx9S/wJecBF9Wgv3p032iP2v4VSgfgW2G+HUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt/QRasn2kri+H6L80MJRsmVYJrAKyDItpJUy3/15WYIJqcJ9Q5N/LFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb+IP8gHed+6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha/fJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG/XbOFz+Tcq2MLrcmV28Nmi/OHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw+luPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs+31Wm5qpH+iJWSzqdPr2O7zc2TmtnrzCig6bBd/vgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I/MAp9q9S3Q/clADNm1wEeO+xbwg5OIYHZLY3ehG5lJk2xhco+6JWybpEVz2wrR6hZyD0QXZbeDVB+onmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P+1fAll+SyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y+cf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY/f+ucuMtCuP/e4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn/+7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7/6B23+f+sp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz/Q0hI0m3NKDRfkgsrnamo+p0CAq1FyvC3a3Nak/s5VX282x9Ufy3E39VAx6o7LpCvO2wK+ch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5+jP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00/Wavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR+gufwA8GegxUEo4A+gd0BASHiH6jYMLIsUdQJTs/C641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW+AMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA/xUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA/E+DXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX/k/7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh/TK18StOPhwG9Xo5mqXAxvw/79YQwwDR+nAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw+oTFqxtz8dxwtogBnF9MjIl/in+K8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O+GC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6/OC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx+v1UKPsfvzQU0Kc83i2VdILmd2/yX55zT9luN2+Cu4nKfwPcK/CvDVU+pHh8+LaldIf1fA5h3ndT6Fln9/W/9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW/uib/ud+VK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M+VG0KsUdgdvpgP/6NqqP0/gHZFUfGEijZLHpiIgvV5/Bltrj8Qd7XQd5p4P+7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z/vvacG/rjpsvnpzs0B1Id6fmX8119l88XnOxe/uGrzzHcdu7UtY3+2vmXN5zUyj3ZcPl8p1sZSs6/nGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q+lwg1RdRSpFDNzosb44YJXnAfoEOpZW//6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21/BWW5DnPQbn2X1WK43/aM2n18DfSoybbNHijFpamzXI31eRibGUOxSu/lT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO/VjC/EinToFyoNDz2p9XD6g78WgQdPG7Z3R0T/Z5dTM9lsL8Ktek7szl2L+gQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T/4VeamYPlLtffdQsk+2sUTY/zDXl/05W53/Bz9UK3p7LjapZ2ZxOm+UlZXrL3HHGqO8+wVroDaCTTnTxitMxmiAAYQzVJQH+nj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj/9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz+3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62/qN/tjQxnSnhnKPw+LNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh/LvQZnt0evgIhTFV0kE/PYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8/Cj1Y9NY6cnGVW0CjTLaz7E3epvaT/LtTV72Rs+0WVVmd0dz/MGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf/z+fdka+eKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ+bewnf18ap4Ib+mSMrtDU5zqlD8QANa5MBGh/OwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq+7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2+YuMbmE9dwGYB4KFTsvnDE6Ah47r/fE3AYI+oXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU/A/Jsw+vWDa/7emewLaoeibaF1B9qUNnuqWK3+UfXYVL1v/omD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk+gk8pL0mcCDOn08MDAQlf+/SlTZ1z12fnqntOhbOTL9/ZdevbAPN+yby1f/uUtC/ixm8ZBo59LTXEW060hGrTDplNprWd58fwB/b/E27BdS/s7U+rGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt+Kh6HN4ti6lxIhT/wahnZtWwzlY9QHQ2c79C+dxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah/PFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn+2MnYv36dbMDBTonl/T/ma5IKAyEGz+4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK/yp/gLEBVat5WnPDdKA574ubuFJ/IUfZ/Y2Nt6mN+ZNNTSTaeI56gKwkXerTe9DDHUw8/H35FY3nNN7GGuBKWhrV9ep+0k1WjNWVaHkW1yA+QHWNu8rtBw2a5YXuE40rs7/GA+j09V3hA98yRnFPOGr8ltGlsFdD/7tRce3LH6Trcneuiy7K7J3khKu+3qUaXPWaX7T6/Kfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM+0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz+XmcOs1H31U6cC6RTVLfCg7RNBF1UF2/wBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH+WC0E1qOGtP66s1MYv+VLu8/XFXvV/ZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86/1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp/lYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm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+<style>h1 {font-size: 34px;}
+h1.title {font-size: 38px;}
+h2 {font-size: 30px;}
+h3 {font-size: 24px;}
+h4 {font-size: 18px;}
+h5 {font-size: 16px;}
+h6 {font-size: 12px;}
+code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
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+
+<h1 class="title toc-ignore">Plotting_NDVI_Shannon</h1>
+<h4 class="author">Marte Persdatter Tangvik</h4>
+<h4 class="date">2023-04-20</h4>
+
+</div>
+
+
+<pre class="r"><code># Labels
+
+labels = c(bacteria = &quot;Bacteria&quot;,
+           fits1 =  &quot;Fungi (ITS1)&quot;,
+           fits2 = &quot;Fungi (ITS2)&quot;,
+           oomycetes = &quot;Oomycetes&quot;,
+           nematodes = &quot;Nematodes&quot;)
+
+signif.labs &lt;- c(&quot;pval &gt; 0.05&quot;, &quot;pval &lt;= 0.05&quot;)
+names(signif.labs) &lt;- c(&quot;p value &gt; 0.05&quot;, &quot;p value &lt;= 0.05&quot;)
+
+# Pivot table
+
+
+all_taxa_data_long &lt;- pivot_longer(all_taxa_data, 
+                                        cols = c(ndvi, 
+                                                 Shannon),
+                                        names_to = &quot;type&quot;, 
+                                        values_to = &quot;value&quot;)
+# Add significance
+
+
+all_taxa_data_temp &lt;- all_taxa_data %&gt;%
+  add_column(&quot;pval&quot;) %&gt;%
+  mutate(pval = ifelse(Taxa == &quot;nematodes&quot; , 0.00496, NA)) %&gt;%
+  mutate(pval = ifelse(Taxa == &quot;fits1&quot;,0.6, pval)) %&gt;%
+  mutate(pval = ifelse(Taxa == &quot;fits2&quot;,0.6, pval)) %&gt;%
+  mutate(pval = ifelse(Taxa == &quot;bacteria&quot;,0.6, pval)) %&gt;%
+  mutate(pval = ifelse(Taxa == &quot;oomycetes&quot;,0.6, pval)) %&gt;%
+  mutate(signif = ifelse(pval &lt;= 0.05, &quot;pval &lt;= 0.05&quot;, &quot;pval &gt; 0.05&quot;))
+
+
+all_taxa_data &lt;- all_taxa_data_temp
+
+
+# Scatter plot by group
+
+theme_set(theme_bw())
+
+ndvi_shannon_all &lt;- ggplot(all_taxa_data,
+                           aes(x = ndvi,
+                               y = Shannon,
+                               color = Taxa,
+                               shape = Taxa)) +
+  geom_point(alpha = 0.8, size = 3) +
+  geom_smooth(method = &quot;glm&quot;, formula = y~x, se = F,
+                      method.args = list(family = gaussian(link = &#39;log&#39;)),
+              aes(color = Taxa, linetype = signif)) +
+  scale_shape_discrete(name = &quot;Taxa&quot;, labels = labels,
+                       breaks = c(&quot;bacteria&quot;, &quot;fits1&quot;, &quot;fits2&quot;, &quot;nematodes&quot;, &quot;oomycetes&quot;)) +
+  scale_color_viridis(name = &quot;Taxa&quot;,
+                      labels = labels,
+                      begin = 0,
+                      end = 0.8,
+                       breaks = c(&quot;bacteria&quot;, &quot;fits1&quot;, &quot;fits2&quot;, &quot;nematodes&quot;, &quot;oomycetes&quot;),
+                      discrete = TRUE) +
+   scale_linetype_discrete(name= &#39;Significance&#39;,
+                           guide = &#39;none&#39;) +
+                     #labels = names(signif.labs)) +
+  #scale_linetype_manual(name = &quot;Taxa&quot;,
+                        #breaks = c(&quot;bacteria&quot;, &quot;fits1&quot;, &quot;fits2&quot;, &quot;nematodes&quot;, &quot;oomycetes&quot;)) +
+  theme(plot.title = element_text(hjust = 0.5 #,size = 20 
+                                  ), 
+        panel.grid.major = element_blank(), 
+        panel.grid.minor = element_blank(),
+        panel.background = element_blank(),
+        strip.background = element_blank()) +
+  
+        #axis.text = element_text(size = 20),
+        #text = element_text(size = 24)
+    xlab(&quot;NDVI&quot;) + 
+    ylab(&quot;Alpha diversity&quot;)
+  
+
+ndvi_shannon_all</code></pre>
+<p><img role="img" 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" width="672" /></p>
+<pre class="r"><code>save_plot(ndvi_shannon_all, plot_name = paste0(&quot;ndvi_shannon_all&quot;),
+          plot_width=13, plot_height=18)</code></pre>
+
+
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/analysis/FITS1/.Rhistory b/analysis/FITS1/.Rhistory
index 7a1d1af5a5e3c8e6947ba8030dee0e71df8b503b..7e4ef78c49c742993b94f04fbefa566eef088399 100644
--- a/analysis/FITS1/.Rhistory
+++ b/analysis/FITS1/.Rhistory
@@ -1,7 +1,238 @@
+parameters$biom_export = "FALSE"
+# CHANGE ME to the directory that contains 'seqtab_nochim.rds'
+path = "FITS1_DADA2_results_260821/"
+# CHANGE ME to TRUE to list all samples and generate an empty metadata file
+optional_sample_check = TRUE
+# CHANGE ME to TRUE to update cuphyr
+update_cuphyr = TRUE
+# Initiate by loading packages and setting knit options
+################# NO CHANGES NECESSARY BELOW #################
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(root.dir = paste0(path))
+knitr::opts_chunk$set(message = FALSE)
+knitr::opts_chunk$set(warning = FALSE)
+if (update_cuphyr) {
+devtools::install_github("simeross/cuphyr")
+}
+# Sequence and microbiome specific libraries
+library(dada2)
+library(Biostrings)
+library(DECIPHER)
+library(cuphyr)
+# The export of phyloseq objects to a BIOM format and the generation of fancier
+# ordination plots require the phyloseq-extended package. The first command
+# installs the package that is currently on the dev brach of the author's
+# repository, the second command sources some extra functions, including the
+# better ordination plot implementation.
+remotes::install_github("mahendra-mariadassou/phyloseq-extended", ref = "dev")
+source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R" )
+library(phyloseq)
+library(SIAMCAT)
+# CHANGE ME to the directory that contains 'seqtab_nochim.rds'
+path = "FITS1_DADA2_results_260821/"
+# CHANGE ME to TRUE to list all samples and generate an empty metadata file
+optional_sample_check = TRUE
+# CHANGE ME to TRUE to update cuphyr
+update_cuphyr = TRUE
+# Initiate by loading packages and setting knit options
+################# NO CHANGES NECESSARY BELOW #################
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(root.dir = paste0(path))
+knitr::opts_chunk$set(message = FALSE)
+knitr::opts_chunk$set(warning = FALSE)
+if (update_cuphyr) {
+devtools::install_github("simeross/cuphyr")
+}
+# Sequence and microbiome specific libraries
+library(dada2)
+library(Biostrings)
+library(DECIPHER)
+library(cuphyr)
+# The export of phyloseq objects to a BIOM format and the generation of fancier
+# ordination plots require the phyloseq-extended package. The first command
+# installs the package that is currently on the dev brach of the author's
+# repository, the second command sources some extra functions, including the
+# better ordination plot implementation.
+remotes::install_github("mahendra-mariadassou/phyloseq-extended", ref = "dev")
+source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R" )
+library(phyloseq)
+#library(SIAMCAT)
+# Phylogeny libraries
+library(phangorn)
+library(ape)
+# Plotting and figure export
+library(gridExtra)
+library(viridis)
+library(ggpubr)
+# Tidyverse
+library(tidyverse)
+library(stringr)
+# Various packages for specific analysis
+library(readxl)
+library(openxlsx)
+library(ggpmisc)
+library(betareg)
+library(BBmisc)
+library(aod)
+library(betareg)
+#install.packages('MicrobiomeStat')
+library(MicrobiomeStat)
+# Checks whether output path exists and creates it if not. Throws warning if
+# directory exists.
+outp <- paste0(path,"/analysis_output")
+dir.create(file.path(outp))
+if (optional_sample_check) {
+seqtabcheck <- readRDS(paste0(path,"/seqtab_nochim.rds"))
+samps <- rownames(seqtabcheck)
+lensamps <- length(samps)
+blankcol <- vector(mode = "character", length = lensamps)
+blanktable <- data.frame(SampleIDs = samps, ExampleProperty1 = blankcol,
+ExampleProperty2 = blankcol,
+ExampleProperty3 = blankcol)
+write.table(blanktable, file = paste0(path, "/descriptors_blank.txt"),
+sep = "\t", row.names = F)
+cat("'seqtab_nochim.rds' contains samples in the following order:\n",
+samps, "\nThe number of samples in the file is:", lensamps, sep = "\n")
+rm(optional_sample_check, seqtabcheck, samps,
+lensamps, blankcol, blanktable, update_cuphyr)
+}else{rm(optional_sample_check, update_cuphyr)}
+# Dedicated environment containing all global analysis settings for better
+# overview and collected export of settings
+parameters <- new.env()
+# CHANGE ME to "TRUE" to remove control samples from the analysis or "FALSE" to
+# analyse all samples.
+parameters$prune_controls = "TRUE"
+# CHANGE ME to a list of unique identifiers that only occur in the names of
+# samples you do NOT want to analyse. Common examples are provided.
+parameters$controls = c("NegativK-4-Nem", "Vann", "Neg", "Positivkontroll-Nem", "Contr", "POSK")
+# CHANGE ME to "TRUE" to remove certain taxonomic groups from the analysis by
+# name. This is useful to exclude non-target organisms or noise from organelles
+# such as Chloroplasts and Mitochondria. It is recommended to first look at all
+# data before using this setting.
+parameters$prune_noise_taxgroups = "FALSE"
+# CHANGE ME to define the taxonomic groups to be removed as noise.
+parameters$noise_taxgroups = c("Chloroplast", "Mitochondria")
+# CHANGE ME to a number of ASV counts [~reads] that analyzed samples should
+# minimally have. Samples with lower ASV counts than 'minread' will be pruned.
+# Set to 0 to not prune any samples.
+parameters$minASVcount = 3000
+# CHANGE ME to "TRUE", if you want to provide a custom taxonomy table instead of
+# using the default dada2 output ('taxa.rds').
 parameters$customTax = "TRUE"
 # CHANGE ME to the location of the custom taxonomy file. This only matters if
 # parameters$customTax="TRUE", otherwise it will be ignored.
-parameters$taxfile = "FITS1_DADA2_results_260821/custom_BLAST_taxonomy_nt.txt"
+parameters$taxfile = "Nems_DADA2_results_260821/custom_BLAST_taxonomy_nt.txt"
+# CHANGE ME to "TRUE" to generate a phylogenetic tree. This process takes a
+# long time depending on the number of sequences (up to days for thousands).
+# If a tree is provided as 'phylotree.rds' in 'path', then it will be used
+# regardless of the value of 'parameters$maketree'
+parameters$maketree = "FALSE"
+# CHANGE ME to "TRUE" to root the used phylogenetic tree (if one exists) on the
+# leaf with the longest branch (outgroup). This makes analyses that rely on the
+# phylogenetic tree reproducible instead of picking a random leaf as root when
+# calculating UNIFRAC distances. Implementation based on
+# http://john-quensen.com/r/unifrac-and-tree-roots/ and answers
+# in https://github.com/joey711/phyloseq/issues/597
+parameters$roottree = "TRUE"
+## CHANGE ME to "TRUE" to export all generated phyloseq objects as .biom objects
+parameters$biom_export = "FALSE"
+# Prat
+set$prat_data$ndvi_temp <- (set$prat_data$ndvi+1)/2
+# CHANGE ME to the directory that contains 'seqtab_nochim.rds'
+path = "Nems_DADA2_results_260821"
+# CHANGE ME to TRUE to list all samples and generate an empty metadata file
+optional_sample_check = TRUE
+# CHANGE ME to TRUE to update cuphyr
+update_cuphyr = TRUE
+# Initiate by loading packages and setting knit options
+################# NO CHANGES NECESSARY BELOW #################
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(root.dir = paste0(path))
+knitr::opts_chunk$set(message = FALSE)
+knitr::opts_chunk$set(warning = FALSE)
+if (update_cuphyr) {
+devtools::install_github("simeross/cuphyr")
+}
+# Sequence and microbiome specific libraries
+library(dada2)
+library(Biostrings)
+library(DECIPHER)
+library(cuphyr)
+# The export of phyloseq objects to a BIOM format and the generation of fancier
+# ordination plots require the phyloseq-extended package. The first command
+# installs the package that is currently on the dev brach of the author's
+# repository, the second command sources some extra functions, including the
+# better ordination plot implementation.
+remotes::install_github("mahendra-mariadassou/phyloseq-extended", ref = "dev")
+source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R" )
+library(phyloseq)
+#library(SIAMCAT)
+# Phylogeny libraries
+library(phangorn)
+library(ape)
+# Plotting and figure export
+library(gridExtra)
+library(viridis)
+library(ggpubr)
+library(cowplot)
+# Tidyverse
+library(tidyverse)
+library(stringr)
+# Various packages for specific analysis
+library(readxl)
+library(openxlsx)
+library(ggpmisc)
+library(betareg)
+library(BBmisc)
+library(aod)
+library(betareg)
+#install.packages('MicrobiomeStat')
+library(MicrobiomeStat)
+# Checks whether output path exists and creates it if not. Throws warning if
+# directory exists.
+outp <- paste0(path,"/analysis_output")
+dir.create(file.path(outp))
+if (optional_sample_check) {
+seqtabcheck <- readRDS(paste0(path,"/seqtab_nochim.rds"))
+samps <- rownames(seqtabcheck)
+lensamps <- length(samps)
+blankcol <- vector(mode = "character", length = lensamps)
+blanktable <- data.frame(SampleIDs = samps, ExampleProperty1 = blankcol,
+ExampleProperty2 = blankcol,
+ExampleProperty3 = blankcol)
+write.table(blanktable, file = paste0(path, "/descriptors_blank.txt"),
+sep = "\t", row.names = F)
+cat("'seqtab_nochim.rds' contains samples in the following order:\n",
+samps, "\nThe number of samples in the file is:", lensamps, sep = "\n")
+rm(optional_sample_check, seqtabcheck, samps,
+lensamps, blankcol, blanktable, update_cuphyr)
+}else{rm(optional_sample_check, update_cuphyr)}
+# Dedicated environment containing all global analysis settings for better
+# overview and collected export of settings
+parameters <- new.env()
+# CHANGE ME to "TRUE" to remove control samples from the analysis or "FALSE" to
+# analyse all samples.
+parameters$prune_controls = "TRUE"
+# CHANGE ME to a list of unique identifiers that only occur in the names of
+# samples you do NOT want to analyse. Common examples are provided.
+parameters$controls = c("NegativK-4-Nem", "Vann", "Neg", "Positivkontroll-Nem", "Contr", "POSK")
+# CHANGE ME to "TRUE" to remove certain taxonomic groups from the analysis by
+# name. This is useful to exclude non-target organisms or noise from organelles
+# such as Chloroplasts and Mitochondria. It is recommended to first look at all
+# data before using this setting.
+parameters$prune_noise_taxgroups = "FALSE"
+# CHANGE ME to define the taxonomic groups to be removed as noise.
+parameters$noise_taxgroups = c("Chloroplast", "Mitochondria")
+# CHANGE ME to a number of ASV counts [~reads] that analyzed samples should
+# minimally have. Samples with lower ASV counts than 'minread' will be pruned.
+# Set to 0 to not prune any samples.
+parameters$minASVcount = 3000
+# CHANGE ME to "TRUE", if you want to provide a custom taxonomy table instead of
+# using the default dada2 output ('taxa.rds').
+parameters$customTax = "TRUE"
+# CHANGE ME to the location of the custom taxonomy file. This only matters if
+# parameters$customTax="TRUE", otherwise it will be ignored.
+parameters$taxfile = "Nems_DADA2_results_260821/custom_BLAST_taxonomy_nt.txt"
 # CHANGE ME to "TRUE" to generate a phylogenetic tree. This process takes a
 # long time depending on the number of sequences (up to days for thousands).
 # If a tree is provided as 'phylotree.rds' in 'path', then it will be used
@@ -142,14 +373,12 @@ cat(tmp$samp_pruned, "\n", sep = "\n")
 }
 # Remove 0 count ASVs (e.g. control ASVs that remain) from the base object
 ps <- prune_taxa(taxa_sums(ps) > 0, ps)
-# Get a tbl of the base object for easier access in some phyloseq-independent
-# analyses. Takes some seconds, potentially up to minutes.
-ps_tbl <- as_tibble(psmelt(ps))
+sample_data(ps)[["ndvi"]] <- as.numeric(sample_data(ps)[["ndvi"]])
 # Transformed per sample (per-sample relative abundance)
 ps.trans <- transform_sample_counts(ps, function(ASV) ASV/sum(ASV))
-# Read NDVI values as numeric
-sample_data(ps)[["ndvi"]] <- as.numeric(sample_data(ps)[["ndvi"]])
+# Read descriptor values as numeric
 sample_data(ps.trans)[["ndvi"]] <- as.numeric(sample_data(ps.trans)[["ndvi"]])
+sample_data(ps.trans)[["ndvi_july"]] <- as.numeric(sample_data(ps.trans)[["ndvi_july"]])
 # Get a tbl of the base object for easier access in some phyloseq-independent
 # analyses. Takes some seconds, potentially up to minutes.
 ps_tbl <- as_tibble(psmelt(ps))
@@ -158,7 +387,7 @@ ps_trans_tbl <- as_tibble(psmelt(ps.trans))
 genus_abundance_tbl_per_sample <- ps_trans_tbl %>%
 group_by(Genus, Sample) %>%
 mutate(Genus_Sample_Abundance = sum(Abundance)) %>%
-select(Genus, Sample, ndvi, Genus_Sample_Abundance, Alias) %>%
+select(Genus, Sample, ndvi, ndvi_july, Genus_Sample_Abundance, Alias) %>%
 unique()
 if (parameters$roottree == "TRUE" && parameters$maketree == "TRUE") {
 phyloseq::phy_tree(ps) <- cuphyr::root_tree_in_outgroup(physeq = ps)}
@@ -229,7 +458,7 @@ width = wp, height = hp, unit = "cm", dpi = res)
 ################################################
 # CHANGE ME to the sample group for color coding. Accepted values are the column
 # headers in the descriptor file.
-set$color_by = "prat_mol_asv"
+set$color_by = "Symptoms"
 ##### Optional settings (sensible defaults) #####
 # Can be changed to change the width (in cm) of the saved plot.
 set$wp = 17
@@ -281,232 +510,3 @@ xlim(0,set$xmax) + ylab("ASV counts ('reads')")
 plots$combo_overview <- ggarrange(
 plots$overview_all, plots$overview_ps, nrow = 2, align = "v",
 common.legend = TRUE, legend = "right")
-if (parameters$minASVcount > 0) {
-plots$combo_overview <- ggarrange(
-plots$overview_all, plots$overview_noise, plots$overview_ps,
-nrow = 3, align = "v",
-common.legend = TRUE, legend = "right")
-}
-#Save plots
-save_plot(plots$combo_overview, plot_name = "Overview_all_and_pruned",
-filetype = tmp$out)
-#Clean up plot parameters
-rm(list = ls(set), envir = set)
-#Print plots
-plots$combo_overview
-# CHANGE ME to the desired sample categories on the x-axis. In this case it
-# should be the Sample names.
-set$x_axis_value = "SampleIDs"
-# CHANGE ME to the taxonomic level for color coding. Use "OTU" for ASVs,
-# "Genus", "Species" or "OTU" recommended to compare pos. controls.
-set$color_by_taxlvl = "Species"
-# CHANGE ME to the taxonomic level for labeling the tree tips (if phylogenetic
-# tree is available). Use "OTU" for ASVs.
-set$label_by_taxlvl = "OTU"
-# CHANGE ME to a sample category to shape the tree tip labels by (if
-# phylogenetic tree is available).
-set$label_shape_by = "Symptom"
-##### Optional settings (sensible defaults) #####
-# Can be changed to generate a tree for just the control sequences IF no
-# phylogenetic tree for all seuquences is provided. This may slow down this
-# chunk when running it for the first time
-set$control_tree = TRUE
-# Can be changed to change the width (in cm) of the saved plot.
-set$wp = 17
-# Can be changed to change the height (in cm) of the saved plot.
-set$hp = 20
-# Can be changed to change the resolution (in dpi) of the saved plot.
-set$res = 300
-############### NO NEED FOR CHANGES BELOW ###############
-if (set$control_tree & class(try(phy_tree(ps.transcontr),
-silent = TRUE)) == "try-error") {
-# generate phylogenetic tree of ASVs only if there is no file called
-# 'phylotree.rds' in the working directory and 'parameters$maketree' is "TRUE"
-if (!file.exists(paste0(path, "/controls_phylotree.rds"))) {
-set$ASVs <- phyloseq::refseq(ps.transcontr)
-set$ASV_align <- AlignSeqs(set$ASVs, anchor = NA)
-set$ASV_phang <- phyDat(as(set$ASV_align, "matrix"), type = "DNA")
-set$dm <- dist.ml(set$ASV_phang)
-set$treeNJ <- NJ(set$dm)
-set$fit <- pml(set$treeNJ, data = set$ASV_phang)
-set$fitGTR <- update(set$fit, k = 4, inv = 0.2)
-set$fitGTR <- optim.pml(set$fitGTR, model = "GTR",
-optInv = TRUE, optGamma = TRUE,
-rearrangement = "stochastic",
-control = pml.control(trace = 0))
-saveRDS(set$fitGTR, file = paste0(path, "/controls_phylotree.rds"))}
-set$fitGTR <- readRDS(paste0(path, "/controls_phylotree.rds"))
-phyloseq::phy_tree(ps.transcontr) <- set$fitGTR$tree
-}
-plots$topnpplot <- plot_bar(ps.contr, x = set$x_axis_value,
-fill = set$color_by_taxlvl) + my_scale_fill +
-theme(axis.title.x = element_blank(), legend.position = "none",
-legend.key.size = unit(3, "mm")) +
-ylab("ASV counts") + guides(col = guide_legend(ncol = 3))
-plots$topntplot <- plot_bar(ps.transcontr, x = set$x_axis_value,
-fill = set$color_by_taxlvl) + my_scale_fill +
-theme(axis.title.x = element_blank(), legend.position = "none",
-legend.key.size = unit(3, "mm")) +
-ylab("Relative abundance") + guides(col = guide_legend(ncol = 3))
-plots$combo_contr <- ggarrange(plots$topnpplot, plots$topntplot, ncol = 2,
-labels = c("A", "B"), align = "hv",
-common.legend = TRUE, legend = "right")
-if (class(try(phy_tree(ps.transcontr), silent = TRUE)) != "try-error") {
-plots$tre <- plot_tree(
-ps.transcontr, ladderize = "left", label.tips = set$label_by_taxlvl,
-color = "abundance", text.size = 2.5, shape = set$label_shape_by) +
-scale_color_viridis_c(aesthetics = c("color","fill")) +
-theme(legend.position = "left", panel.border = element_blank())
-plots$combo_contr <- ggarrange(plots$tre, ggarrange(plots$topnpplot,
-plots$topntplot, ncol = 2,
-labels = c("B", "C"), align = "hv",
-common.legend = TRUE, legend = "right"),
-nrow = 2, legend = "right", labels = c("A"))
-}
-# save
-save_plot(plots$combo_contr, plot_name = "Controls", filetype = tmp$out)
-plots$combo_contr
-#The character vector can later be accessed by calling 'tmp$tops'
-tmp$tops <- cuphyr::abundant_tax_physeq(physeq = ps,
-lvl = "Genus",
-top = 10,
-output_format = "tops",
-ignore_na = TRUE,
-silent = FALSE)
-fits1_list <- cuphyr::list_subset_ASVs(subv = c("Fungi"), taxlvlsub = "Kingdom")
-cuphyr::summarise_physeq(physeq = ps,
-ASV_sublist = fits1_list,
-sublist_id = "Fungi", samp_names = FALSE)
-fits1_list <- cuphyr::list_subset_ASVs(subv = c("Fungi"), taxlvlsub = "Kingdom")
-cuphyr::summarise_physeq(physeq = ps,
-ASV_sublist = fits1_list,
-sublist_id = "Fungi", samp_names = FALSE)
-ps_trans_tbl %>% select(OTU, Phylum) %>% unique() %>% group_by(Phylum) %>% summarise(length(OTU))
-# Total ASV counts
-ps_tbl %>% select(OTU, Abundance) %>% summarise(total_sum = sum(Abundance))
-# Total ASV counts per phylum
-ps_tbl %>% select(OTU, Kingdom, Abundance) %>% group_by(Kingdom) %>% summarise(Abundance_sum = sum(Abundance))
-# Percentage of ASV counts belonging to Fungi
-its1_percentage = 1219571/1246983 # ASV counts Fungi / Total ASV counts
-its1_percentage
-# CHANGE ME to the desired sample categories on the x-axis.
-# Accepted values are the column headers in the descriptor file.
-set$x_axis_value = "ndvi"
-# CHANGE ME to the taxonomic level of interest (color coding). Accepted values
-# are the column headers in your descriptor file.
-set$taxlvl = "Genus"
-# CHANGE ME to change the number of Top n taxa to be plotted at
-# taxlvl.
-set$top_n = 10
-# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
-set$ignore_na = FALSE
-# CHANGE ME to an entry at the chosen taxonomic level you want to highlight.
-# Comment out to not highlight anything.
-#set$highlight = "Meloidogyne"
-##### Optional settings (sensible defaults) #####
-# Can be changed to change the width (in cm) of the saved plot.
-set$wp = 20
-# Can be changed to change the height (in cm) of the saved plot.
-set$hp = 13
-# Can be changed to change the resolution (in dpi) of the saved plot.
-set$res = 300
-# Can be changed to change the y-axis label
-set$y_axis_label = "Relative abundance"
-# Can be changed to change the x-axis label
-set$x_axis_label = "Sample"
-############### NO NEED FOR CHANGES BELOW ###############
-# Estimate Alpha-diversity (Shannon)
-set$alpha_div_ps_trans <- estimate_richness(ps.trans, measures = "Shannon") %>%
-as_tibble(rownames = "Sample")
-#rename("Shannon" = Simpson)
-# Make physeq objects of top n taxa and top n ASVs
-set$ps.topnTax <- cuphyr::abundant_tax_physeq(ps.trans, lvl = set$taxlvl,
-top = set$top_n,
-ignore_na = set$ignore_na)
-# Plot
-set$my_scale_fill <- my_scale_fill
-set$topntax_tbl <- psmelt(set$ps.topnTax) %>%
-as_tibble() %>%
-left_join(set$alpha_div_ps_trans, by = "Sample") %>%
-select(Genus, Alias, ndvi, Abundance, Shannon) %>%
-filter(Abundance > 0) %>%
-group_by(Genus, Alias, ndvi, Shannon) %>%
-summarise(Abundance = sum(Abundance)) %>%
-arrange(ndvi) %>%
-mutate(ndvi_rank = c(1:length(ndvi)))
-plots$topn_tax_custom <- ggplot(set$topntax_tbl, aes(
-x = fct_reorder(Alias, ndvi),
-y = Abundance,
-fill = Genus)) +
-#title = paste0("Top ", set$top_n, " ", set$taxlvl))) +
-geom_col(color = "black") +
-set$my_scale_fill +
-ylab(set$y_axis_label) +
-xlab(set$x_axis_label) +
-theme(strip.background = element_blank(),
-legend.position = "bottom")
-plots$ndvi_dot_plot <- ggplot(set$topntax_tbl, aes(fct_reorder(Alias, ndvi),
-y = ndvi)) +
-geom_point() +
-theme(strip.background = element_blank(),
-axis.title.x=element_blank()) +
-ylab("NDVI")
-plots$shannon_dot_plot <- ggplot(set$topntax_tbl,
-aes(fct_reorder(Alias, ndvi),
-y = Shannon)) +
-geom_point() +
-theme(strip.background = element_blank(),
-axis.title.x=element_blank()) +
-ylab("Shannon")
-plots$combo_topn_custom <- ggarrange(plots$ndvi_dot_plot,
-plots$shannon_dot_plot,
-plots$topn_tax_custom,
-nrow = 3,
-heights = c(1, 1, 3),
-align = "v")
-save_plot(plots$combo_topn_custom, plot_name = paste0("Customized_NDVI_Shannon_plot"),
-filetype = tmp$out)
-plots$combo_topn_custom
-topntax_data <- set$topntax_tbl %>%
-mutate(Taxa = 'fits1') %>%
-ungroup() %>%
-select(Alias, ndvi, Shannon, Taxa) %>%
-distinct()
-write.csv(topntax_data, file = "../topntax_all_taxa/topntax_data_fits1.csv")
-# Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
-# containing the sample abundances for that genus.
-ldf <- data.frame(genus_abundance_tbl_per_sample %>% pivot_wider(id_cols = c('Sample', 'ndvi'), names_from = 'Genus', values_from = 'Genus_Sample_Abundance'))
-ldf_genus_data <- data.frame(ldf) %>% select(!c('Sample', 'ndvi'))
-colnames(ldf_genus_data) <- gsub(' ', '.', colnames(ldf_genus_data))
-ldf <- cbind.data.frame(
-Sample = ldf$Sample,
-ndvi_01 = (ldf$ndvi + 1) / 2.0
-)
-ldf <- tibble(cbind(ldf, ldf_genus_data))
-n_samples_by_genus <- data.frame(ldf_genus_data > 0) %>% mutate_if(is.logical, as.numeric) %>% colSums() %>% sort(decreasing = TRUE)
-keep_n <- 100 # Maximum number of genuses to include in the analysis
-top_n_occurence_genuses <- names(n_samples_by_genus[1:keep_n])
-top_n_occurence_genuses <- top_n_occurence_genuses[!is.na(top_n_occurence_genuses)]
-l_genus_ldf <- ldf %>% select(all_of(top_n_occurence_genuses))
-l_genus_ldf_transposed <- data.frame(t(l_genus_ldf))
-l_meta_ldf <- ldf %>% select('ndvi_01')
-l_model <- linda(
-l_genus_ldf_transposed,
-l_meta_ldf,
-formula = '~ ndvi_01',
-feature.dat.type = 'proportion',
-is.winsor = FALSE,
-alpha = 0.05
-)
-# Print model info
-l_model
-# Show effect size and significance plots
-linda.plot(
-l_model,
-variables.plot = c('ndvi_01'),
-alpha = 0.05,
-lfc.cut = 1,
-legend = TRUE
-)
-l_model_df <- as.data.frame(l_model$output)
-write.xlsx(l_model_df, file = "supplementary_table_ndvi_regression_fits1.xlsx", rowNames = TRUE, colnames = TRUE)
diff --git a/analysis/FITS1/FITS1_Analysis_pipeline_v4-3.Rmd b/analysis/FITS1/FITS1_Analysis_pipeline_v4-3.Rmd
index 29e2310779dcc9c33dcbffa6f38cae177204c859..9f71e2932db49fcc4c1b92b2222e8b4cf625e663 100644
--- a/analysis/FITS1/FITS1_Analysis_pipeline_v4-3.Rmd
+++ b/analysis/FITS1/FITS1_Analysis_pipeline_v4-3.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "16S Data Analysis"
+title: "FITS1 Data Analysis"
 author: "Simeon Lim Rossmann, Marte Persdatter Tangvik"
 date: "23.12.2024"
 output:
@@ -86,7 +86,7 @@ remotes::install_github("mahendra-mariadassou/phyloseq-extended", ref = "dev")
 source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R" )
 
 library(phyloseq)
-library(SIAMCAT)
+#library(SIAMCAT)
 
 # Phylogeny libraries
 library(phangorn)
@@ -676,7 +676,7 @@ its1_percentage
 
 This chunk plots abundance of the Top n ASVs or taxa at a given level as a bar plot, giving an insight into the presence of the n ASV and most common taxa for the primary and secondary parameters. The default for n is set at 20, a larger n may lead to delay/skipping of the plot in standard out, but it should be saved as a PDF regardless for ASVs. For taxa, a large n  may lead to unreadable plots. **The chunk does not require any input**, but it is possible to adjust the **default 'n'**, and to change **width**, **height** and **resolution** of the PDF-output if necessary.
 
-```{r custom-Bar-plot, tidy=TRUE, message=FALSE}
+```{r custom Bar plot, message=FALSE}
 # CHANGE ME to the desired sample categories on the x-axis. 
 # Accepted values are the column headers in the descriptor file.
 set$x_axis_value = "ndvi"
@@ -785,7 +785,7 @@ plots$combo_topn_custom
 
 ### NDVI+Shannon regression
 
-```{r ndvi-shannon}
+```{r ndvi shannon}
 topntax_data <- set$topntax_tbl %>%
   mutate(Taxa = 'fits1') %>%
   ungroup() %>%
@@ -799,7 +799,7 @@ write.csv(topntax_data, file = "../topntax_all_taxa/topntax_data_fits1.csv")
 ### NDVI regression 
 
 
-```{r NDVI-new}
+```{r NDVI new}
 
 # Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
 # containing the sample abundances for that genus.
diff --git a/analysis/FITS1/FITS1_Analysis_pipeline_v4-3.html b/analysis/FITS1/FITS1_Analysis_pipeline_v4-3.html
new file mode 100644
index 0000000000000000000000000000000000000000..2926761f112968bc45845b6047ddebc59e6e49c6
--- /dev/null
+++ b/analysis/FITS1/FITS1_Analysis_pipeline_v4-3.html
@@ -0,0 +1,2284 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Simeon Lim Rossmann, Marte Persdatter Tangvik" />
+
+
+<title>FITS1 Data Analysis</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
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yr6EIj+LzNj/mfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h/C/PkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn/pdpgHKNGrGIdkRK+KPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho+EIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA+q/j9m3LM/O7WJka4tSidVCjsvo2lQ/2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3/5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho/bcwQdeboqfQartuU3CsCf+cXkgYAqp/0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid/NIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A+TRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC+JE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW/WDHA60cYFaI/PjpzquUqdaYGcIq+mLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC+1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A+P/oFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E/vwOiKxRtCWsDM+eTHUrmwrCK5BIfMzGkD+0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3/kFutpQGNc3pCR7gvC4sgwbupDu3DyEN+W6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc+h1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0+bC5zgpGz7Io+mjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO/ENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn+3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif+pZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx/k3QgnAFSq27/2i4GEBA+UvTJKK/9eISNvG46Em5RZfjTYLdeD8kdXHyrwId/DQZUaMCY4gGbke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS/2ToPjqkaq62/7WFG8advGlRRqxB9diP07JrXowKR9tpRa+jGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq+nsp3YMuXt/GkXxLx/P6+ZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar+gMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud+YlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl/zh575R5rsrmRnKAzq4POFdgbYBuEviM4+LVC15ssLNFghbTtHWerS1hDt5s4qkLUha/qpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI+yjEldJfSo4y0QhG4i4IwkRFGcjWY8+EzgYYJUK7BXQksLxAww/YYWBMhJILB9e8ePEJ4OP7z+4/wOQDl64iOYDp26DaONPxpKtBxq/aTzRGarm3VkPYTLJKx6Z/Mw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ+lPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k/UuGkNpP1DBI5ch/EehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv+J41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI+HYexTUevLUeta4/DqKrbMVS+Yqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ++KkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9/Wx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k/90B8+yRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB/mQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go/n6j0cS+a2gEU8gIHJ+BwfgZX4GL+Bd/gW34FZ+BS/gUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh/F062yJ7AAAAEDAWAAABWhJ+KPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg) 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+<style>h1 {font-size: 34px;}
+h1.title {font-size: 38px;}
+h2 {font-size: 30px;}
+h3 {font-size: 24px;}
+h4 {font-size: 18px;}
+h5 {font-size: 16px;}
+h6 {font-size: 12px;}
+code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
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+
+
+
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">FITS1 Data Analysis</h1>
+<h4 class="author">Simeon Lim Rossmann, Marte Persdatter Tangvik</h4>
+<h4 class="date">23.12.2024</h4>
+
+</div>
+
+
+<div id="introduction-and-initialization" class="section level2">
+<h2>Introduction and initialization</h2>
+<p>This is an R Markdown file containing code to parse the results of a
+dada2 analysis into phyloseq for further analysis. It is separated into
+chunks that may be run independently by pressing the <em>play</em>
+button. You will need <strong>3 files</strong> in the same location in
+order to run this pipeline successfully:</p>
+<ul>
+<li>A sequence table called <strong>‘seqtab_nochim.rds’</strong>
+(automatically generated by the dada2 pipeline)</li>
+<li>A taxonomy table called <strong>‘taxa.rds’</strong> (automatically
+generated by the dada2 pipeline)</li>
+<li>A table describing the sample properties called
+<strong>‘descriptors.txt’</strong> (Has to be provided)</li>
+<li>Optional (some plots require this): A phylogenic tree file called
+<strong>‘phylotree.rds’</strong> (can be calculated if it is not
+provided, takes a long time)</li>
+<li>Optional: Instead of <strong>‘taxa.rds’</strong>, a <strong>custom
+taxonomy file</strong> may be provided (tab-delimited text)</li>
+</ul>
+<p><strong>Recommended use:</strong> Set the individual chunks until you
+are content with the ouput, then knit the whole document into a
+PDF/html, so you have a full record of a successful run.</p>
+<div id="optional-custom-taxonomy-file" class="section level4">
+<h4>Optional custom Taxonomy file</h4>
+<p>A custom taxonomy file may be provided instead of using the taxonomy
+output from dada2. This may be used to supply taxonomy derived e.g. from
+BLAST searches of the ASVs. Custom taxonomy files must be tab-delimited
+text with as many rows as the original, colum headers (for all columns
+except for the first column). For example:</p>
+<blockquote>
+<p>Kingdom Phylum Class Order Family Genus Species<br />
+ESV1 Kingdomx Phylumx Classx Orderx Familyx Genusx Speciesx<br />
+ESV2 Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy<br />
+ESV3 Kingdomz Phylumz Classz Orderz Familyz Genusz Speciesz<br />
+… ESVn Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy</p>
+</blockquote>
+<p><em>Friendly warning: Parsing the results of a BLAST search into this
+format may require some effort.</em></p>
+</div>
+<div id="descriptor-table" class="section level4">
+<h4>Descriptor table</h4>
+<p>‘descriptors.txt’ should be a tab-delimited .txt table describing the
+samples. It must have the same length and order as the samples in
+seqtab_nochim.rds. To check the order and length of samples in
+seqtab_nochim.rds and generate a template to fill out, you may run the
+chunk below with “optional_sample_check” set to “TRUE”.</p>
+<p>Any number of descriptors is possible. The sample names may be
+retained as one descriptor, but this is not necessary, as they will be
+added during parsing. For example, if there are 4 samples (order: s1,
+s2, s3, s4), the txt file could look as follows:</p>
+<blockquote>
+<p>Subject Species Time<br />
+Kar1 A.thaliana 24hpi<br />
+Kar1 A.thaliana 72hpi<br />
+Mec2 S.tuberosum 24hpi<br />
+Mec3 S.tuberosum 24hpi</p>
+</blockquote>
+<p>Finally, the file should end with an empty line, since it may throw
+an error otherwise. However, this is usually not a serious problem.</p>
+<p><strong>If you choose to use the blank file, you MUST retain the
+original order of the samples!</strong></p>
+</div>
+</div>
+<div id="setup" class="section level2">
+<h2>Setup</h2>
+<p>This chunk also loads required packages and defines the location of
+the input files. It requires the <strong>correct path</strong> as input,
+and allows <strong>setting the pruning of control samples</strong> and
+<strong>choosing generation of a phylogenetic tree</strong>.
+<strong>Beware:</strong> The generation of a phylogenetic tree may take
+several days for &gt;1000 sequences, it is therefore recommended to only
+use this feature for the final analysis or small sample sets. This
+scricpt assumes the packages <em>Biostrings</em>, <em>dada2</em>,
+<em>DECIPHER</em>, <em>ggplot2</em>, <em>ggsci</em>, <em>phangorn</em>,
+<em>phyloseq</em> and <em>stringr</em> to be installed.</p>
+<pre class="r"><code># CHANGE ME to the directory that contains &#39;seqtab_nochim.rds&#39;
+path = &quot;FITS1_DADA2_results_260821/&quot;
+
+# CHANGE ME to TRUE to list all samples and generate an empty metadata file 
+optional_sample_check = TRUE
+
+# CHANGE ME to TRUE to update cuphyr
+update_cuphyr = TRUE
+
+# Initiate by loading packages and setting knit options
+################# NO CHANGES NECESSARY BELOW #################
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(root.dir = paste0(path))
+knitr::opts_chunk$set(message = FALSE)
+knitr::opts_chunk$set(warning = FALSE)
+
+if (update_cuphyr) {
+  devtools::install_github(&quot;simeross/cuphyr&quot;)
+}
+
+# Sequence and microbiome specific libraries
+library(dada2)
+library(Biostrings)
+library(DECIPHER)
+library(cuphyr)
+# The export of phyloseq objects to a BIOM format and the generation of fancier 
+# ordination plots require the phyloseq-extended package. The first command 
+# installs the package that is currently on the dev brach of the author&#39;s 
+# repository, the second command sources some extra functions, including the 
+# better ordination plot implementation.
+remotes::install_github(&quot;mahendra-mariadassou/phyloseq-extended&quot;, ref = &quot;dev&quot;)
+source(&quot;https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R&quot; )
+
+library(phyloseq)
+#library(SIAMCAT)
+
+# Phylogeny libraries
+library(phangorn)
+library(ape)
+
+# Plotting and figure export
+library(gridExtra)
+library(viridis)
+library(ggpubr)
+
+# Tidyverse
+library(tidyverse)
+library(stringr)
+
+# Various packages for specific analysis
+library(readxl)
+library(openxlsx)
+library(ggpmisc)
+library(betareg)
+library(BBmisc)
+library(aod)
+library(betareg)
+#install.packages(&#39;MicrobiomeStat&#39;)
+library(MicrobiomeStat)
+
+
+# Checks whether output path exists and creates it if not. Throws warning if 
+# directory exists.
+outp &lt;- paste0(path,&quot;/analysis_output&quot;)
+dir.create(file.path(outp))
+
+if (optional_sample_check) {
+  seqtabcheck &lt;- readRDS(paste0(path,&quot;/seqtab_nochim.rds&quot;)) 
+  samps &lt;- rownames(seqtabcheck)
+  lensamps &lt;- length(samps)
+  blankcol &lt;- vector(mode = &quot;character&quot;, length = lensamps)
+  blanktable &lt;- data.frame(SampleIDs = samps, ExampleProperty1 = blankcol, 
+                           ExampleProperty2 = blankcol, 
+                           ExampleProperty3 = blankcol)
+  write.table(blanktable, file = paste0(path, &quot;/descriptors_blank.txt&quot;), 
+              sep = &quot;\t&quot;, row.names = F)
+  cat(&quot;&#39;seqtab_nochim.rds&#39; contains samples in the following order:\n&quot;, 
+      samps, &quot;\nThe number of samples in the file is:&quot;, lensamps, sep = &quot;\n&quot;)
+  rm(optional_sample_check, seqtabcheck, samps, 
+     lensamps, blankcol, blanktable, update_cuphyr)
+  }else{rm(optional_sample_check, update_cuphyr)}</code></pre>
+<pre><code>## &#39;seqtab_nochim.rds&#39; contains samples in the following order:
+## 
+## 1-its1
+## 10-its1
+## 11-its1
+## 12-its1
+## 13-its1
+## 14-its1
+## 15-its1
+## 16-its1
+## 17-its1
+## 18-its1
+## 2-its1
+## 3-its1
+## 4-its1
+## 5-its1
+## 6-its1
+## 7-its1
+## 8-its1
+## 9-its1
+## NegativK-4-its1
+## PositivK-1-its1
+## PositivK-2-its1
+## PositivK-3-its1
+## PositivK-4-its1
+## Vann1-its1
+## Vann2-its1
+## 
+## The number of samples in the file is:
+## 25</code></pre>
+<div id="parameters" class="section level4">
+<h4>Parameters</h4>
+<p>This chunk allows the adjustment of several parameters, such as
+<strong>setting the pruning of control samples</strong> based on
+keywords, <strong>requiring that a phylogenetic tree be provided or
+generated</strong>, <strong>defining a minimum ASV count</strong> and
+<strong>providing an alternative taxonomy</strong>.</p>
+<pre class="r"><code># Dedicated environment containing all global analysis settings for better
+# overview and collected export of settings
+parameters &lt;- new.env()
+
+# CHANGE ME to &#39;TRUE&#39; to remove control samples from the analysis or &#39;FALSE&#39; to
+# analyse all samples.
+parameters$prune_controls = &quot;TRUE&quot;
+# CHANGE ME to a list of unique identifiers that only occur in the names of
+# samples you do NOT want to analyse. Common examples are provided.
+parameters$controls = c(&quot;Pos&quot;, &quot;H2O&quot;, &quot;Neg&quot;, &quot;Kontr&quot;, &quot;Contr&quot;, &quot;POSK&quot;)
+
+# CHANGE ME to &#39;TRUE&#39; to remove certain taxonomic groups from the analysis by
+# name. This is useful to exclude non-target organisms or noise from organelles
+# such as Chloroplasts and Mitochondria. It is recommended to first look at all
+# data before using this setting.
+parameters$prune_noise_taxgroups = &quot;FALSE&quot;
+# CHANGE ME to define the taxonomic groups to be removed as noise.
+parameters$noise_taxgroups = c(&quot;Chloroplast&quot;, &quot;Mitochondria&quot;)
+
+# CHANGE ME to a number of ASV counts [~reads] that analyzed samples should
+# minimally have. Samples with lower ASV counts than &#39;minread&#39; will be pruned.
+# Set to 0 to not prune any samples.
+parameters$minASVcount = 3000
+
+# CHANGE ME to &#39;TRUE&#39;, if you want to provide a custom taxonomy table instead
+# of using the default dada2 output (&#39;taxa.rds&#39;).
+parameters$customTax = &quot;TRUE&quot;
+# CHANGE ME to the location of the custom taxonomy file. This only matters if
+# parameters$customTax=&#39;TRUE&#39;, otherwise it will be ignored.
+parameters$taxfile = &quot;FITS1_DADA2_results_260821/custom_BLAST_taxonomy_nt.txt&quot;
+
+# CHANGE ME to &#39;TRUE&#39; to generate a phylogenetic tree. This process takes a
+# long time depending on the number of sequences (up to days for thousands).
+# If a tree is provided as &#39;phylotree.rds&#39; in &#39;path&#39;, then it will be used
+# regardless of the value of &#39;parameters$maketree&#39;
+parameters$maketree = &quot;FALSE&quot;
+
+# CHANGE ME to &#39;TRUE&#39; to root the used phylogenetic tree (if one exists) on the
+# leaf with the longest branch (outgroup). This makes analyses that rely on the
+# phylogenetic tree reproducible instead of picking a random leaf as root when
+# calculating UNIFRAC distances. Implementation based on
+# http://john-quensen.com/r/unifrac-and-tree-roots/ and answers in
+# https://github.com/joey711/phyloseq/issues/597
+parameters$roottree = &quot;TRUE&quot;
+
+## CHANGE ME to &#39;TRUE&#39; to export all generated phyloseq objects as .biom
+## objects
+parameters$biom_export = &quot;FALSE&quot;</code></pre>
+</div>
+<div id="parsing-input-data" class="section level4">
+<h4>Parsing input data</h4>
+<p>This chunk loads the input data into a usable format.<strong>This
+chunk does not require any user inputs</strong>. If no phylogenetic tree
+with the name ‘phylotree.rds’ was provided and
+‘parameters$maketree=“TRUE”’, it will be calculated here. The
+phylogenetic tree is necessary for certain plots that incorporate ‘true’
+taxonomic relationships beyond the annotations, such as PCoA.</p>
+<pre class="r"><code>############### NO NEED FOR CHANGES BELOW ############### Make dedicated
+############### environments to contain temporary values and manage other
+############### objects
+tmp &lt;- new.env()
+plots &lt;- new.env()
+set &lt;- new.env()
+
+# Read in variables
+tmp$seqtabp &lt;- readRDS(paste0(path, &quot;/seqtab_nochim.rds&quot;))
+if (parameters$customTax == &quot;TRUE&quot;) {
+    tmp$taxap &lt;- read.delim(parameters$taxfile, header = TRUE, sep = &quot;\t&quot;)
+    rownames(tmp$taxap) &lt;- colnames(tmp$seqtabp)
+    tmp$taxap &lt;- as.matrix(tmp$taxap)
+} else {
+    tmp$taxap &lt;- readRDS(paste0(path, &quot;/taxa.rds&quot;))
+}
+tmp$samp_table &lt;- read.delim(paste0(path, &quot;/descriptors.txt&quot;), header = TRUE, sep = &quot;\t&quot;)
+tmp$samp_list &lt;- rownames(tmp$seqtabp)
+
+# Check if descriptors has the same samples as seqtabp
+if (length(tmp$samp_table[, 1]) != length(tmp$samp_list)) {
+    stop(&quot;There are &quot;, length(tmp$samp_table[, 1]), &quot; samples in &#39;descriptors.txt&#39;, but &quot;,
+        length(tmp$samp_list), &quot; samples in &#39;seqtab_nochim.rds&#39;. Please make sure that the correct samples 
+    are contained in descriptors.txt.
+       
+    You may use &#39;optional_sample_check &lt;- TRUE&#39; in the first chunk to generate an 
+    empty template for &#39;descriptors.txt&#39;&quot;)
+} else if (!identical(tmp$samp_table[, 1], tmp$samp_list)) {
+    warning(&quot;Warning: The samples in &#39;descriptors.txt&#39; do not have the same names 
+          or order as the samples in &#39;seqtab_nochim.rds&#39;. This may be fine if 
+          abbreviated names were used or the sample names are not contained in 
+          the first column of &#39;descriptors.txt&#39;. Double-checking never hurts!&quot;)
+}
+
+
+# generate phylogenetic tree of ASVs only if there is no file called
+# &#39;phylotree.rds&#39; in the working directory and &#39;parameters$maketree&#39; is &#39;TRUE&#39;
+if (!file.exists(paste0(path, &quot;/phylotree.rds&quot;))) {
+    if (parameters$maketree == &quot;TRUE&quot;) {
+        tmp$ASVs &lt;- getSequences(tmp$seqtabp)
+        names(tmp$ASVs) &lt;- tmp$ASVs
+        tmp$ASV_align &lt;- AlignSeqs(DNAStringSet(tmp$ASVs), anchor = NA)
+        tmp$ASV_phang &lt;- phyDat(as(tmp$ASV_align, &quot;matrix&quot;), type = &quot;DNA&quot;)
+        tmp$dm &lt;- dist.ml(tmp$ASV_phang)
+        tmp$treeNJ &lt;- NJ(tmp$dm)
+        tmp$fit &lt;- pml(tmp$treeNJ, data = tmp$ASV_phang)
+        tmp$fitGTR &lt;- update(tmp$fit, k = 4, inv = 0.2)
+        tmp$fitGTR &lt;- optim.pml(tmp$fitGTR, model = &quot;GTR&quot;, optInv = TRUE, optGamma = TRUE,
+            rearrangement = &quot;stochastic&quot;, control = pml.control(trace = 0))
+        saveRDS(tmp$fitGTR, file = paste0(path, &quot;/phylotree.rds&quot;))
+    }
+}
+
+## parse into phyloseq object
+row.names(tmp$samp_table) &lt;- tmp$samp_list
+if (file.exists(paste0(path, &quot;/phylotree.rds&quot;))) {
+    tmp$treep &lt;- readRDS(paste0(path, &quot;/phylotree.rds&quot;))
+    p &lt;- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
+        tax_table(tmp$taxap), phy_tree(tmp$treep$tree))
+} else {
+    p &lt;- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
+        tax_table(tmp$taxap))
+}
+
+## Adding nucleotide info and giving sequences ASV## identifiers
+tmp$ASV_sequences &lt;- Biostrings::DNAStringSet(taxa_names(p))
+taxa_names(p) &lt;- paste0(&quot;ASV&quot;, seq(ntaxa(p)))
+names(tmp$ASV_sequences) &lt;- taxa_names(p)
+p &lt;- merge_phyloseq(p, tmp$ASV_sequences)
+
+## optional pruning
+if (parameters$prune_controls == &quot;TRUE&quot;) {
+    if (!is.null(parameters$controls)) {
+        tmp$samp_clean &lt;- tmp$samp_list[!tmp$samp_list %in% grep(paste0(parameters$controls,
+            collapse = &quot;|&quot;), tmp$samp_list, value = T)]
+        tmp$contr_pruned &lt;- setdiff(tmp$samp_list, tmp$samp_clean)
+        ps &lt;- prune_samples(tmp$samp_clean, p)
+        # Physeq object for Just controls
+        ps.contr &lt;- prune_samples(tmp$contr_pruned, p)
+        ps.contr &lt;- prune_taxa(taxa_sums(ps.contr) &gt; 0, ps.contr)
+        ps.transcontr &lt;- transform_sample_counts(ps.contr, function(ASV) ASV/sum(ASV))
+
+        message(cat(&quot;\n&quot;, &quot;Number of control samples that were pruned and will not be analysed:\n&quot;,
+            length(tmp$samp_list) - length(tmp$samp_clean), &quot;\n&quot;, &quot;The following controls were pruned:\n&quot;,
+            tmp$contr_pruned, &quot;The controls are contained in a separate phyloseq object: ps.contr&quot;,
+            &quot;\n&quot;, sep = &quot;\n&quot;))
+    } else {
+        warning(cat(&quot;\n\nparameters$prune_controls is TRUE but &#39;parameters$controls&#39; is empty. 
+    No samples were pruned.\n\n&quot;))
+    }
+} else {
+    ps &lt;- p
+}</code></pre>
+<pre><code>## 
+## 
+## Number of control samples that were pruned and will not be analysed:
+## 
+## 5
+## 
+## 
+## The following controls were pruned:
+## 
+## NegativK-4-its1
+## PositivK-1-its1
+## PositivK-2-its1
+## PositivK-3-its1
+## PositivK-4-its1
+## The controls are contained in a separate phyloseq object: ps.contr</code></pre>
+<pre class="r"><code># Prune ASVs defined as noise
+if (parameters$prune_noise_taxgroups == &quot;TRUE&quot;) {
+    tmp$ps_taxlvls &lt;- colnames(tax_table(ps))
+    tmp$noise_ASVs &lt;- character(0)
+    for (lvl in tmp$ps_taxlvls) {
+        tmp$noise_ASVs &lt;- c(tmp$noise_ASVs, cuphyr::list_subset_ASVs(physeq = ps,
+            subv = parameters$noise_taxgroups, taxlvlsub = lvl))
+    }
+    tmp$noise_ASVs &lt;- unique(tmp$noise_ASVs)
+    tmp$no_noise_ASVs &lt;- colnames(otu_table(ps))
+    tmp$no_noise_ASVs &lt;- setdiff(tmp$no_noise_ASVs, tmp$noise_ASVs)
+    if (length(tmp$noise_ASVs) &gt; 0) {
+        ps &lt;- prune_taxa(tmp$no_noise_ASVs, ps)
+        tmp$no_noise_ps &lt;- ps
+        cat(length(tmp$noise_ASVs), &quot;ASVs were pruned because they belonged to the following 
+        taxonomic groups:\n&quot;)
+        cat(parameters$noise_taxgroups, &quot;\n&quot;, sep = &quot;\n&quot;)
+    } else {
+        cat(&quot;No ASVs were recognized as belonging to the following taxonomic groups 
+        defined as noise:\n&quot;)
+        cat(parameters$noise_taxgroups, &quot;\n&quot;, sep = &quot;\n&quot;)
+    }
+}
+
+# Prune samples with fewer than reads than minASVcount
+if (parameters$minASVcount &gt; 0) {
+    tmp$samp_pruned &lt;- names(which(sample_sums(ps) &lt; parameters$minASVcount))
+    ps &lt;- prune_samples(sample_sums(ps) &gt;= parameters$minASVcount, ps)
+    if (length(tmp$samp_pruned) &gt; 0) {
+        cat(&quot;The following samples were pruned because ASV counts were lower than&quot;,
+            parameters$minASVcount, &quot;:\n&quot;)
+        cat(tmp$samp_pruned, &quot;\n&quot;, sep = &quot;\n&quot;)
+    }
+}</code></pre>
+<pre><code>## The following samples were pruned because ASV counts were lower than 3000 :
+## Vann1-its1
+## Vann2-its1</code></pre>
+<pre class="r"><code># Remove 0 count ASVs (e.g. control ASVs that remain) from the base object
+ps &lt;- prune_taxa(taxa_sums(ps) &gt; 0, ps)
+
+# Get a tbl of the base object for easier access in some phyloseq-independent
+# analyses. Takes some seconds, potentially up to minutes.
+ps_tbl &lt;- as_tibble(psmelt(ps))
+
+# Transformed per sample (per-sample relative abundance)
+ps.trans &lt;- transform_sample_counts(ps, function(ASV) ASV/sum(ASV))
+
+# Read NDVI values as numeric
+sample_data(ps)[[&quot;ndvi&quot;]] &lt;- as.numeric(sample_data(ps)[[&quot;ndvi&quot;]])
+sample_data(ps.trans)[[&quot;ndvi&quot;]] &lt;- as.numeric(sample_data(ps.trans)[[&quot;ndvi&quot;]])
+
+
+# Get a tbl of the base object for easier access in some phyloseq-independent
+# analyses. Takes some seconds, potentially up to minutes.
+ps_tbl &lt;- as_tibble(psmelt(ps))
+ps_trans_tbl &lt;- as_tibble(psmelt(ps.trans))
+
+# Condensing to Abundance per Genus and Sample
+genus_abundance_tbl_per_sample &lt;- ps_trans_tbl %&gt;%
+    group_by(Genus, Sample) %&gt;%
+    mutate(Genus_Sample_Abundance = sum(Abundance)) %&gt;%
+    select(Genus, Sample, ndvi, Genus_Sample_Abundance, Alias) %&gt;%
+    unique()
+
+
+if (parameters$roottree == &quot;TRUE&quot; &amp;&amp; parameters$maketree == &quot;TRUE&quot;) {
+    phyloseq::phy_tree(ps) &lt;- cuphyr::root_tree_in_outgroup(physeq = ps)
+}
+
+if (parameters$biom_export == &quot;TRUE&quot;) {
+    suppressWarnings(phyloseq.extended::write_phyloseq(p, biom_file = paste0(path,
+        &quot;all_samples.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps, biom_file = file.path(path,
+        &quot;samples_without_controls.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps.trans, biom_file = file.path(path,
+        &quot;samples_without_controls_rel_abundance.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps.contr, biom_file = file.path(path,
+        &quot;just_controls.biom&quot;), biom_format = &quot;standard&quot;))
+}
+
+ps</code></pre>
+<pre><code>## phyloseq-class experiment-level object
+## otu_table()   OTU Table:         [ 1013 taxa and 18 samples ]
+## sample_data() Sample Data:       [ 18 samples by 19 sample variables ]
+## tax_table()   Taxonomy Table:    [ 1013 taxa by 7 taxonomic ranks ]
+## refseq()      DNAStringSet:      [ 1013 reference sequences ]</code></pre>
+</div>
+</div>
+<div id="output" class="section level2">
+<h2>Output</h2>
+<p>The chunks below will produce various plots and other output. Each
+chunk is headed by a description of the output and may contain some
+parameters to adjust the output.</p>
+<div id="plot-looks" class="section level4">
+<h4>Plot looks</h4>
+<p>This chunk sets the background structure and color palette. Viridis
+was chosen because it is optimized for grey-scale printing and various
+types of color blindness and More info on the Viridis palette can be
+found on <a href="https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html">the
+Viridis info page</a>. It also establishes save_plot as a shorter
+variant of ggsave with customized date-time structure to save plots with
+the same name mulitple times instead of overwriting them (overwriting
+can be triggered with overwrite=TRUE).</p>
+<pre class="r"><code>##### Optional settings (sensible defaults) #####
+# Can be changed to adjust the output format for all plots. Default &quot;pdf&quot;, 
+# possible &quot;eps&quot;/&quot;ps&quot;, &quot;tex&quot; (pictex), &quot;jpeg&quot;, &quot;tiff&quot;, &quot;png&quot;, &quot;bmp&quot; and &quot;svg&quot;
+parameters$output_format = &quot;pdf&quot;
+
+# Can be changed to preferred ggplot2 theme. Recommended: &quot;theme_bw()&quot;.
+theme_set(theme_bw())
+
+############### NO NEED FOR CHANGES BELOW ###############
+
+my_scale_col &lt;- scale_color_viridis(discrete = TRUE)
+my_scale_fill &lt;- scale_fill_viridis(discrete = TRUE)
+
+# Custom, more narrow color ranges based on viridis
+# Base order to have adjacent colors be distinct from each other
+tmp$sort_colors &lt;- c(rbind(c(1:5), c(6:10), c(11:15), c(16:20)))
+
+# Customized vectors
+tmp$n_col &lt;- 20
+tmp$viridis_greens &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 0.85, 
+                              end = 0.7)[tmp$sort_colors]
+tmp$viridis_reds &lt;- viridis(tmp$n_col,  option = &quot;B&quot;, begin = 0.7, 
+                            end = 0.5)[tmp$sort_colors]
+tmp$viridis_blues &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 0.2, 
+                             end = 0.4)[tmp$sort_colors]
+tmp$viridis_yellows &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 1, 
+                               end = 0.9)[tmp$sort_colors]
+tmp$viridis_dark &lt;- viridis(tmp$n_col,  option = &quot;A&quot;, begin = 0, 
+                            end = 0.1)[tmp$sort_colors]
+tmp$viridis_light &lt;- viridis(tmp$n_col,  option = &quot;A&quot;, begin = 1, 
+                             end = 0.9)[tmp$sort_colors]
+# Collected list that is available in the global environment
+sub_viridis &lt;- list(tmp$viridis_greens, tmp$viridis_blues, tmp$viridis_yellows, 
+                    tmp$viridis_light, tmp$viridis_reds, tmp$viridis_dark)
+names(sub_viridis) &lt;- c(&quot;greens&quot;, &quot;blues&quot;, &quot;yellows&quot;, &quot;lights&quot;, &quot;reds&quot;, &quot;darks&quot;)
+
+tmp$out &lt;- paste0(&quot;.&quot;, parameters$output_format)
+
+#################### Function ############################
+
+# Generic save function for plots that checks whether file exists and if so, 
+# creates a new one with d/m/y+time info to avoid overwriting. Overwriting can 
+# be triggered with overwrite = TRUE. Width, height and resolution are taken 
+# from parameters in the &#39;set&#39; environment or set to 20x20 cm with 300dpi.
+save_plot &lt;- function(
+  pl, filetype = &quot;.pdf&quot;, plot_name = &quot;my_plot&quot;, overwrite=FALSE){
+  wp &lt;- if (!is.null(set$wp)) set$wp else 20
+  hp &lt;- if (!is.null(set$hp)) set$hp else 20
+  res &lt;- if (!is.null(set$res)) set$res else 300
+  name &lt;- paste0(&quot;/&quot;, plot_name,filetype)
+  if (file.exists(paste0(outp, name)) &amp; !overwrite) {
+  name &lt;- paste0(&quot;/&quot;, plot_name, &quot;_&quot;, 
+                 format(Sys.time(), &quot;%d-%m-%y_%H%M%S&quot;),filetype)}
+  ggsave(file.path(outp, name), pl, 
+         width = wp, height = hp, unit = &quot;cm&quot;, dpi = res)
+}
+
+################################################</code></pre>
+</div>
+<div id="total-asv-counts-ranked" class="section level4">
+<h4>Total ASV counts ranked</h4>
+<p>This chunk plots the absolute abundance of all samples (including
+controls) and all samples without controls and other trimmed
+samples.</p>
+<pre class="r"><code># CHANGE ME to the sample group for color coding. Accepted values are the column 
+# headers in the descriptor file.
+set$color_by = &quot;prat_mol_asv&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 17
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20  
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+############### NO NEED FOR CHANGES BELOW ###############
+# Rank samples
+set$ranked &lt;- cuphyr::make_ranked_sums(p, myset = tmp$subset_id)
+set$ranked_ps &lt;- cuphyr::make_ranked_sums(ps, myset = tmp$subset_id)
+set$ymax &lt;- max(set$ranked$Abundance)
+set$ymax &lt;- set$ymax + round(set$ymax/10)
+set$xmax &lt;- nrow(set$ranked) + 1
+set$title2 &lt;- &quot;Samples (without controls)&quot;
+
+# Stabilize colors
+set$color_vars &lt;- set$ranked[,set$color_by]  %&gt;% 
+  unlist() %&gt;% as.character() %&gt;% unique()
+set$color_vars &lt;- sort(set$color_vars)
+set$color_varsPalette &lt;- viridis(length(set$color_vars))
+names(set$color_varsPalette) &lt;- set$color_vars
+set$my_scale_fill &lt;- scale_fill_manual(values = set$color_varsPalette)
+
+# plot
+plots$overview_all &lt;- ggplot(data = set$ranked, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + ggtitle(&quot;All samples&quot;) + ylim(0, set$ymax) + 
+  xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+
+if (length(tmp$noise_ASVs) &gt; 0) {
+  set$ranked_nonoise &lt;- cuphyr::make_ranked_sums(
+    tmp$no_noise_ps, myset = tmp$subset_id)
+  plots$overview_noise &lt;- ggplot(
+    data = set$ranked_nonoise, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + 
+    ggtitle(&quot;Samples (without controls), noise ASVs removed&quot;) + 
+    ylim(0, set$ymax) + 
+    xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+}
+
+if (parameters$minASVcount &gt; 0) {
+plots$overview_all &lt;- plots$overview_all + 
+  geom_hline(yintercept = parameters$minASVcount, linetype = &quot;dashed&quot;) + 
+    ggtitle(&quot;All samples (ASV count cutoff indicated)&quot;)
+set$title2 &lt;- &quot;Samples (without controls and low count samps)&quot;
+}
+
+plots$overview_ps &lt;- ggplot(data = set$ranked_ps, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + ggtitle(set$title2) + ylim(0, set$ymax) + 
+  xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+plots$combo_overview &lt;- ggarrange(
+  plots$overview_all, plots$overview_ps, nrow = 2, align = &quot;v&quot;, 
+  common.legend = TRUE, legend = &quot;right&quot;)
+
+if (parameters$minASVcount &gt; 0) {
+plots$combo_overview &lt;- ggarrange(
+  plots$overview_all, plots$overview_noise, plots$overview_ps,
+  nrow = 3, align = &quot;v&quot;, 
+  common.legend = TRUE, legend = &quot;right&quot;)
+}
+
+#Save plots
+save_plot(plots$combo_overview, plot_name = &quot;Overview_all_and_pruned&quot;, 
+          filetype = tmp$out)
+
+#Clean up plot parameters
+rm(list = ls(set), envir = set)
+
+#Print plots
+plots$combo_overview</code></pre>
+<p><img role="img" 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" width="672" /></p>
+</div>
+<div id="controls" class="section level4">
+<h4>Controls</h4>
+<p>This chunk generates an overview over the controls (positive AND
+negative)</p>
+<pre class="r"><code># CHANGE ME to the desired sample categories on the x-axis. In this case it 
+# should be the Sample names.
+set$x_axis_value = &quot;SampleIDs&quot;
+
+# CHANGE ME to the taxonomic level for color coding. Use &quot;OTU&quot; for ASVs, 
+# &quot;Genus&quot;, &quot;Species&quot; or &quot;OTU&quot; recommended to compare pos. controls.
+set$color_by_taxlvl = &quot;Species&quot;
+
+# CHANGE ME to the taxonomic level for labeling the tree tips (if phylogenetic 
+# tree is available). Use &quot;OTU&quot; for ASVs.
+set$label_by_taxlvl = &quot;OTU&quot;
+
+# CHANGE ME to a sample category to shape the tree tip labels by (if 
+# phylogenetic tree is available).
+set$label_shape_by = &quot;Symptom&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to generate a tree for just the control sequences IF no 
+# phylogenetic tree for all seuquences is provided. This may slow down this 
+# chunk when running it for the first time
+set$control_tree = TRUE
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 17
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20  
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+############### NO NEED FOR CHANGES BELOW ###############
+if (set$control_tree &amp; class(try(phy_tree(ps.transcontr), 
+                                 silent = TRUE)) == &quot;try-error&quot;) {
+  # generate phylogenetic tree of ASVs only if there is no file called 
+  # &#39;phylotree.rds&#39; in the working directory and &#39;parameters$maketree&#39; is &quot;TRUE&quot;
+  if (!file.exists(paste0(path, &quot;/controls_phylotree.rds&quot;))) {
+    set$ASVs &lt;- phyloseq::refseq(ps.transcontr)
+    set$ASV_align &lt;- AlignSeqs(set$ASVs, anchor = NA)
+    set$ASV_phang &lt;- phyDat(as(set$ASV_align, &quot;matrix&quot;), type = &quot;DNA&quot;)
+    set$dm &lt;- dist.ml(set$ASV_phang)
+    set$treeNJ &lt;- NJ(set$dm)
+    set$fit &lt;- pml(set$treeNJ, data = set$ASV_phang)
+    set$fitGTR &lt;- update(set$fit, k = 4, inv = 0.2)
+    set$fitGTR &lt;- optim.pml(set$fitGTR, model = &quot;GTR&quot;, 
+                            optInv = TRUE, optGamma = TRUE,
+                            rearrangement = &quot;stochastic&quot;, 
+                            control = pml.control(trace = 0))
+    saveRDS(set$fitGTR, file = paste0(path, &quot;/controls_phylotree.rds&quot;))}
+  set$fitGTR &lt;- readRDS(paste0(path, &quot;/controls_phylotree.rds&quot;))
+  phyloseq::phy_tree(ps.transcontr) &lt;- set$fitGTR$tree
+}
+
+plots$topnpplot &lt;- plot_bar(ps.contr, x = set$x_axis_value, 
+                            fill = set$color_by_taxlvl) + my_scale_fill + 
+  theme(axis.title.x = element_blank(), legend.position = &quot;none&quot;, 
+        legend.key.size = unit(3, &quot;mm&quot;)) + 
+  ylab(&quot;ASV counts&quot;) + guides(col = guide_legend(ncol = 3))
+
+plots$topntplot &lt;- plot_bar(ps.transcontr, x = set$x_axis_value, 
+                            fill = set$color_by_taxlvl) + my_scale_fill + 
+  theme(axis.title.x = element_blank(), legend.position = &quot;none&quot;, 
+        legend.key.size = unit(3, &quot;mm&quot;)) + 
+  ylab(&quot;Relative abundance&quot;) + guides(col = guide_legend(ncol = 3))
+
+plots$combo_contr &lt;- ggarrange(plots$topnpplot, plots$topntplot, ncol = 2, 
+                               labels = c(&quot;A&quot;, &quot;B&quot;), align = &quot;hv&quot;, 
+                               common.legend = TRUE, legend = &quot;right&quot;)
+
+if (class(try(phy_tree(ps.transcontr), silent = TRUE)) != &quot;try-error&quot;) {
+plots$tre &lt;- plot_tree(
+          ps.transcontr, ladderize = &quot;left&quot;, label.tips = set$label_by_taxlvl, 
+          color = &quot;abundance&quot;, text.size = 2.5, shape = set$label_shape_by) + 
+          scale_color_viridis_c(aesthetics = c(&quot;color&quot;,&quot;fill&quot;)) + 
+          theme(legend.position = &quot;left&quot;, panel.border = element_blank())
+plots$combo_contr &lt;- ggarrange(plots$tre, ggarrange(plots$topnpplot, 
+                                                    plots$topntplot, ncol = 2, 
+                               labels = c(&quot;B&quot;, &quot;C&quot;), align = &quot;hv&quot;, 
+                               common.legend = TRUE, legend = &quot;right&quot;), 
+                               nrow = 2, legend = &quot;right&quot;, labels = c(&quot;A&quot;)) 
+}
+
+# save
+save_plot(plots$combo_contr, plot_name = &quot;Controls&quot;, filetype = tmp$out)
+
+plots$combo_contr</code></pre>
+<p><img role="img" 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" width="672" /></p>
+</div>
+<div id="get-a-list-of-top-n-taxa-at-a-given-level" class="section level4">
+<h4>Get a list of Top N taxa at a given level</h4>
+<p>This chunk lists the top n most abundant taxonomic terms at a given
+level. Change the function parameters to the desired values. For more
+info, check help page of the function with
+<code>?cuphyr::abundant_tax_physeq()</code>. Change ‘ignore_na’ to
+include/exclude NA values at the given level.</p>
+<pre class="r"><code>#The character vector can later be accessed by calling &#39;tmp$tops&#39;
+tmp$tops &lt;- cuphyr::abundant_tax_physeq(physeq = ps, 
+                            lvl = &quot;Genus&quot;,
+                            top = 10,
+                            output_format = &quot;tops&quot;,
+                            ignore_na = TRUE,
+                            silent = FALSE)</code></pre>
+<pre><code>## 
+## The top 10 most abundant annotated groups at the taxonomic level &#39;Genus&#39; are:
+## Mrakia
+## Mortierella
+## Sistotrema
+## Candida
+## Solicoccozyma
+## Juxtiphoma
+## Saitozyma
+## Sporidiobolus
+## Tausonia
+## Naganishia</code></pre>
+</div>
+<div id="asvs-belonging-to-phylum" class="section level4">
+<h4>ASVs belonging to phylum</h4>
+<p>This chunk gives an overview of the number of ASVs belonging to which
+Phylum</p>
+<pre class="r"><code>fits1_list &lt;- cuphyr::list_subset_ASVs(subv = c(&quot;Fungi&quot;), taxlvlsub = &quot;Kingdom&quot;)
+cuphyr::summarise_physeq(physeq = ps, 
+                          ASV_sublist = fits1_list, 
+                          sublist_id = &quot;Fungi&quot;, samp_names = FALSE)</code></pre>
+<pre><code>## There are 1013 ASVs in the phyloseq object &#39;ps&#39;.
+## Of this, 982 belong to the provided subset (Fungi), representing 98.17 percent of abundance per sample on average.</code></pre>
+<pre class="r"><code>fits1_list &lt;- cuphyr::list_subset_ASVs(subv = c(&quot;Fungi&quot;), taxlvlsub = &quot;Kingdom&quot;)
+cuphyr::summarise_physeq(physeq = ps, 
+                          ASV_sublist = fits1_list, 
+                          sublist_id = &quot;Fungi&quot;, samp_names = FALSE)</code></pre>
+<pre><code>## There are 1013 ASVs in the phyloseq object &#39;ps&#39;.
+## Of this, 982 belong to the provided subset (Fungi), representing 98.17 percent of abundance per sample on average.</code></pre>
+<pre class="r"><code>ps_trans_tbl %&gt;% select(OTU, Phylum) %&gt;% unique() %&gt;% group_by(Phylum) %&gt;% summarise(length(OTU)) </code></pre>
+<pre><code>## # A tibble: 17 x 2
+##    Phylum                 `length(OTU)`
+##    &lt;chr&gt;                          &lt;int&gt;
+##  1 &#39;uncultured eukaryote&#39;             2
+##  2 &#39;uncultured fungus&#39;              223
+##  3 Apicomplexa                        1
+##  4 Arthropoda                        13
+##  5 Ascomycota                       337
+##  6 Basidiomycota                    244
+##  7 Chlorophyta                        2
+##  8 Ciliophora                         1
+##  9 Mollusca                           2
+## 10 Mucoromycota                     114
+## 11 Nematoda                           1
+## 12 Olpidiomycota                      9
+## 13 Platyhelminthes                    1
+## 14 Rhodophyta                         1
+## 15 Streptophyta                       4
+## 16 Zoopagomycota                      2
+## 17 &lt;NA&gt;                              56</code></pre>
+<pre class="r"><code># Total ASV counts
+
+ps_tbl %&gt;% select(OTU, Abundance) %&gt;% summarise(total_sum = sum(Abundance))</code></pre>
+<pre><code>## # A tibble: 1 x 1
+##   total_sum
+##       &lt;int&gt;
+## 1   1246983</code></pre>
+<pre class="r"><code># Total ASV counts per phylum
+
+ps_tbl %&gt;% select(OTU, Kingdom, Abundance) %&gt;% group_by(Kingdom) %&gt;% summarise(Abundance_sum = sum(Abundance))</code></pre>
+<pre><code>## # A tibble: 4 x 2
+##   Kingdom       Abundance_sum
+##   &lt;chr&gt;                 &lt;int&gt;
+## 1 Fungi               1219571
+## 2 Metazoa               27068
+## 3 Viridiplantae           211
+## 4 &lt;NA&gt;                    133</code></pre>
+<pre class="r"><code># Percentage of ASV counts belonging to Fungi
+
+its1_percentage = 1219571/1246983 # ASV counts Fungi / Total ASV counts
+its1_percentage</code></pre>
+<pre><code>## [1] 0.9780173</code></pre>
+</div>
+<div id="customized-top-n-genus-bar-plot" class="section level4">
+<h4>Customized Top N Genus Bar plot</h4>
+<p>This chunk plots abundance of the Top n ASVs or taxa at a given level
+as a bar plot, giving an insight into the presence of the n ASV and most
+common taxa for the primary and secondary parameters. The default for n
+is set at 20, a larger n may lead to delay/skipping of the plot in
+standard out, but it should be saved as a PDF regardless for ASVs. For
+taxa, a large n may lead to unreadable plots. <strong>The chunk does not
+require any input</strong>, but it is possible to adjust the
+<strong>default ‘n’</strong>, and to change <strong>width</strong>,
+<strong>height</strong> and <strong>resolution</strong> of the
+PDF-output if necessary.</p>
+<pre class="r"><code># CHANGE ME to the desired sample categories on the x-axis. 
+# Accepted values are the column headers in the descriptor file.
+set$x_axis_value = &quot;ndvi&quot;
+
+# CHANGE ME to the taxonomic level of interest (color coding). Accepted values 
+# are the column headers in your descriptor file.
+set$taxlvl = &quot;Genus&quot;
+
+# CHANGE ME to change the number of Top n taxa to be plotted at 
+# taxlvl.
+set$top_n = 10
+
+# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
+set$ignore_na = FALSE
+
+# CHANGE ME to an entry at the chosen taxonomic level you want to highlight. 
+# Comment out to not highlight anything.
+#set$highlight = &quot;Meloidogyne&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 20
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 13
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+# Can be changed to change the y-axis label
+set$y_axis_label = &quot;Relative abundance&quot;
+
+# Can be changed to change the x-axis label
+set$x_axis_label = &quot;Sample&quot;
+
+############### NO NEED FOR CHANGES BELOW ###############
+# Estimate Alpha-diversity (Shannon)
+set$alpha_div_ps_trans &lt;- estimate_richness(ps.trans, measures = &quot;Shannon&quot;) %&gt;%
+  as_tibble(rownames = &quot;Sample&quot;) 
+  #rename(&quot;Shannon&quot; = Simpson)
+
+# Make physeq objects of top n taxa and top n ASVs
+set$ps.topnTax &lt;- cuphyr::abundant_tax_physeq(ps.trans, lvl = set$taxlvl, 
+                                              top = set$top_n, 
+                                              ignore_na = set$ignore_na)
+
+# Plot
+set$my_scale_fill &lt;- my_scale_fill
+
+
+set$topntax_tbl &lt;- psmelt(set$ps.topnTax) %&gt;% 
+                    as_tibble() %&gt;%
+                    left_join(set$alpha_div_ps_trans, by = &quot;Sample&quot;) %&gt;% 
+                    select(Genus, Alias, ndvi, Abundance, Shannon) %&gt;%
+                    filter(Abundance &gt; 0) %&gt;%
+                    group_by(Genus, Alias, ndvi, Shannon) %&gt;%
+                    summarise(Abundance = sum(Abundance)) %&gt;%
+                    arrange(ndvi) %&gt;%
+                    mutate(ndvi_rank = c(1:length(ndvi))) 
+                    
+
+plots$topn_tax_custom &lt;- ggplot(set$topntax_tbl, aes(
+  x = fct_reorder(Alias, ndvi),
+                           y = Abundance,
+                           fill = Genus)) + 
+                           #title = paste0(&quot;Top &quot;, set$top_n, &quot; &quot;, set$taxlvl))) +
+  geom_col(color = &quot;black&quot;) +
+  set$my_scale_fill + 
+  ylab(set$y_axis_label) +
+  xlab(set$x_axis_label) +
+  theme(strip.background = element_blank(),
+        legend.position = &quot;bottom&quot;)
+
+plots$ndvi_dot_plot &lt;- ggplot(set$topntax_tbl, aes(fct_reorder(Alias, ndvi),
+                           y = ndvi)) +
+                             geom_point() +
+    theme(strip.background = element_blank(),
+        axis.title.x=element_blank()) +
+  ylab(&quot;NDVI&quot;)
+
+plots$shannon_dot_plot &lt;- ggplot(set$topntax_tbl, 
+                                 aes(fct_reorder(Alias, ndvi),
+                           y = Shannon)) +
+                             geom_point() +
+    theme(strip.background = element_blank(),
+        axis.title.x=element_blank()) +
+  ylab(&quot;Shannon&quot;)
+
+
+
+plots$combo_topn_custom &lt;- ggarrange(plots$ndvi_dot_plot,
+                                     plots$shannon_dot_plot,
+                                     plots$topn_tax_custom,
+                                     nrow = 3,
+                                     heights = c(1, 1, 3),
+                                     align = &quot;v&quot;)
+
+save_plot(plots$combo_topn_custom, plot_name = paste0(&quot;Customized_NDVI_Shannon_plot&quot;), 
+          filetype = tmp$out)
+
+plots$combo_topn_custom</code></pre>
+<p><img role="img" 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ICFTAs+gM7jtfIfNAF0uz7/XBpAcREJFrCAxQAW/ABtuAp/R8dP2lV4DvphGRMsYAELdotB1oG+W/pX6WDTP/eV5rOrmtfq4qAfFtLDAhaw4LZYyp1I0d2dj3WfXdW9WBcD/RiSOq2WhQUsYDG4xQAAPbjln6vcC7/dwM9sNRMEQdAUxQ7QYK+wG9PuLJyHJtszKVVuThq78hAEQTbiB2iwdztE5bVozhkBdO43AZSqapijWIgIAhawgIVciyXvSE+ViKSKCAIWsICFXAsAVHgQsIAFLORaAKDCg4AFLGAh1wIAFR4ELGABC7kWAKjwIGABC1jItQBAhQcBC1jAQq6FUIBCEATJFwAKQRBkKAAUgiDIUAAoBEGQoQBQCIIgQwGgEARBhgJAIQiCDDU+QA/+46e+77/9y/o7+9dfS3/w8w3zzCwi1R+83Mfh0Xu+v/7zBoNGi4PbM9+/djf+Ze9nJhbVepQr1d+iFJKZxSNVkd4WtVz0jyK3OLiVbfnV0C8IURj2rGoGDVqkZHFwJ/zlx+/3tCgVbJvOgKVRGcaIUvNApXVwy3R2d61cowN0d5aEWn8EyGa6GV76mYbaUCyUDm417fNMcdhu+kSrxX5hr9TgOyOLWj02mzcJpFiUQjKzuBN/QvfUqxaLUsHJZ8wtOnt5jyh69qxaBvu3SMki2VDXf6eXRalg63SyNCrDGFFqHqikXFinc0oAVblSXz3hV1elDx5sZruJzlu3FSVZKIXNq28XisPubD38SturfaLDYlO9nOzWH7Zc+MvBHYso6pXqb1EMycxi7ke5uNXQvSi5iD/TOwpN7NEOtKYWZj2rEoVJi1RycS7sF//ewC5KizCkk6FRGcZIpMaBSs2FbToTNXWtosYG6DxNVemRSkF8MJAP+Xdrf9jTIoi/efTtQnFIYkgeAEW1KD0vKn1QVPoYU6N6VCrV30L3CKt+FmEX/CD+RK+KlAo2i0LzV2Ew+s5BsjDqWZUoTFqkZDHP5m9al+EatfhXHI3KMEbiFxoBSskFQzpjNXatosYGaF7Lcl8Ov4Te+S77Smn9IqBYRC7+3zWcWiE6KO1f13eOBov0ZfXE0rSLNk17SFFoQuppUQzJzCLLQQN+CLlIc5DlxMAifalhrFEszHpWOQqjFtH2i34WpYLt08nRqIksxkhSiaaBSskFRzrTVxrPxOYaG6DzhiOG7XO/zECzf/3878Jvh7cbTm9TLILo/EbTuWmig1JPi80CM7PR2uBBikITUk+LzXaM98hF01gj5KJ7ytRtEalpwkSzMOtZ5SiMWqRo0cSbDotSwVzpDKwaNZHFGIl/bxyolFxwpDOIjdpJHGtsgIZRrv/8f+vfy3tHckZXXx+KRTxzb2oXmoPSd7NeQZSY2QVQchTNw5VgwRZF83Al5CI/aaevCsUiUsNxGtHCqGdpoujbIiUL9d93P/X9c5rLws0W5YK50mnXqLFsxkjQPlApueBIZ6TmrlXU2ABVp5xDvf2vmiql1ZmHk+vHwZ9uN10gJFjE/aKxXSgOgfpu9NcbmkVvUe4cm9lXrD4MYhRtVz66LboASo6ipYcRcqGupUVXShsOkigWQfNwJ1oY9axeACVYhP/djjHecMaN0iI86QwsGzWwHiMdA5WQC5Z0Bu1dq6DRARocRCdv06d6FpVWZzsDT0NKui3iv21ZB9rtEH7oX/525q//fY96FDpHeNwQznfCDq66etPCRUoU7ZeOOy3KIVlEoS7bNvWw7lwEu/EnzjUtOyRYBIUrAmYWRj1Lk8G+LVKymMfgC/tFUz4JLcKTTttGtR8jXQO1Oxcs6Qw6ulau8QGq9Ae1tLU2nqvdsrVK7RbbyTKLtpyQgnjUdHyitah8uybL5BpPkROjaF9702HROQMlRzFbbz1F1J6L7Muk7Wu+I53dFwwIFknFeuSi3wy022KezpVaVpN2tQhPOm0bNZHNGKEM1PZccKQzoHStWDIAGkSrX99NHyifRF7tlo1zpk6LZEFXR7uQguj6YipZVPvXo+iKRVcYXVF0DddWCxpAu6PYbh2rGovq6QxCL2+3CLp7BMEioPh0WfRtkcohvH2L8KTTtlFTmY8R6kDtHuu2HZzUtYLRAVo40aCy3gVQXUYoFsnUT0mTlgGD0F7n1A83ehSNw5Vk0X4VnhpFy1yHkosOjFPT2QKuXi1i26i9W6RokQ1V/ZiltAhTOi0bNZN5FO0DlZQLhnTWKtSikQFamCjXv7aqi9v0daJYtLdLryD0naPJIl3bVlp0qT/jRolC80tvC21IPaM42Gy8XY+Wi44RT01nyzp4WhRGPauewd4tos2FfuJG650M6WRoVOsx0gHQXrkwT6dS+y0WucY+hN/OElU/+igsDFP/NK8S7raI1TSp7xFE00FSg0Vpfd52eqOG/nueXI/mL0eKRceSQVIUTesIWi10N63YpLNjHTwtCpOeVc9g7xbRRdG8hJ3SO+3TydGo1mMkfalxIX2fkWqczs5bLHKNDVC1Kuv9MFl7mqUk5TPke+81NDDFIkiMzB06TtM3WMT35ZbuNn40a/huI9ejfdVhl0UpJDOLjls0aLmwTmfHmmmKhVnPqmewd4tUo7h2t3cuSgUzpJOhURnGSKzWdaCEkWqbzu7l+JnGBmiyKitU/fJfVp1kYv9qwxoNikWkjnWg7Q7zpk+0Wuxp9rtpX+PWXY/OVYftFnvtuzF1W6R7wPlNt4BTcpEerLUu1mu36LhM0COKnj2rlsH+LVKymM9MulapYOt0sjQqwxiJ1LUOtD0XDOkkXMZKNDpAgwO1DaH/Y83NAXl1on00mxYJ0yyU2taBEoNo2S5Rb7GnVvWmK87UHRLN+xRS69G+6pBQkWJIBhbzbKg1nvoj5OIP6iNNN1HSLDqu9lIszHpWNYMGLVKOQm2I2TsXpYJt08nTqAxjRKl1HSilg1unk7gKVAJAIQiCJioAFIIgyFAAKARBkKEAUAiCIEMBoBAEQYYCQCEIggwFgEIQBBkKAIUgCDIUAApBEGQoABSCIMhQACgEQZChAFAIgiBDAaAQBEGGAkAhCIIMBYBCEAQZCgCFIAgyFAAKQRBkKAAUgiDIUAAoBEGQoQBQCIIgQwGgEARBhgJAIQiCDAWAQhAEGQoAhSAIMhQACkEQZCgAFIIgyFAAKARBkKEAUAiCIEMBoBAEQYYCQCEIggwFgEIQBBkKAIUgCDIUAApBEGSoJQN0FYIgaMIaF6DEz33/vXVR9hYigoAFLAay8EKNH8XELADQZTrAAhZiLTyPgaASKgKA1iUiqSKCgAUsBrHwPA6CCqjIci0A0GU6wAIWQi08j4Wg41dkyRYA6DIdYAELoRYAqJkFALpMB1jAQqgFAGpmAYAu0wEWsBBqAYCaWfQB6LepHnSYPnrP99d//rj40sGdme//+P1a8bQoZSRVRBCwgMUgFriIZGRBBejTm/lXlPfCl62e236kc1/lL+1dj197p1o8LUoZSRURBCxgMYwFljGZWBAB+vyCRwbo7mw9nGnuvee/lr10cMs/dzc4+Hd//VeV4mlRykiqiCBgAYuBLLCQ3sCCCNAtzzv65uepfvOwzXLTf1f9szt7NZuCzv345+0CVOPiaVHKSKqIIGABC1jIsaABdHHDO943gv3rGUDDCegHDcUTzUQkVUQQsIAFLORY0AD6/MLKx30j2J2dTy8jFVhaLZ5oJiKpIoKABSxgIceCCtCO60Z1fTfLZ52Kpd/91PfP/bJQbqzvIQiCpirqIXzPGeim76/ntAwBeju+Cv8uAApBkDMiX0Q624efB//ytzN//e/TX+dqAdPj4OAOrsLDAhawcMiCCNBwCnqlZwSP8mP4eTr13MRVeFjAAhbuWBAP4T+85Hkrpy8neqN1GVOquZ9eRdqdJTPPwoWlpHhalDKSKiIIWMACFnIsqBeRPPpC+lQ5LbMlocW1oXHxtChlJFVEELCABSzkWPADNFv0mQP04FYyA80npWnxtChlJFVEELCABSzkWAywG1N6orNwwjN/6d3yZyulN0pEUkUEAQtYwEKOxQAA3Z3VLrmHL127i6vwsIAFLNyyGGI/0Hm85nNdHckn14/ms/ylUvFESxFJFREELGABCzkWdIA+++yU562ceqtrM9BQez8LWRlOOYP8Avze7RChb9+tFU+LUkZSRQQBC1jAQo4FGaBb2SWkXkvqu4onfk5EUkUEAQtYwEKOBRWgip9HTl++dJKXoKudn4glIqkigoAFLGAhx4II0Cdr3rEvop+e3vD678zUXDzxcyKSKiIIWMACFnIsiADd8I6ldx+Z7A3aXDzxcyKSKiIIWMACFnIsDHZjerJ2jHQrJ6l44udEJFVEELCABSzkWBjsB2qwOWhz8cTPiUiqiCBkWODxObCAhRIAukwHV7iDBzjCAhaRyM9Eupr9suPhEN5MrnAHjxCHBSxijX0RaewNpZeplDtjx2GrfFeZsSOBoJFFX8Z09LfRT99cxDImM+XcGTMKBgtnKgILWNha9FlI7506dYr5VqTVzk/EEpFUcCeWMxWBBSxsLci3ct5bSwZN72d7tBZP/JyIpII7sZypCCxgYWtB30xkcf9SOAM9/RHbBaSoeOLnRCQV3EmEi0iwgEWsIbaz66HVzk/EEpFUcCeVK8sJYAELSwsAdIkO7nDHlQWtsICFnUUfgH6birAlKFGrnZ+IJSKp4A4sYAGLkqgAfXozP4VHfConqXji50QkVUQQsIAFLORYEAFafiwnAAoLzMZhAQsyQLc87+ibn6f6DW7lhIWlgTvng2FxmC3I98L3uX3z0Xu+v/7z0hPgH+WPSSoVT3QUkVQRQThi4c6KBFgcagvqbkx9bt/cjp/Kee6r/KU7yYM6K081BkAPqYU7a2JhcbgtDLaz69LubP39INh7z38te2nuRy/d8l/9qvzZSumNEpFUEUG4YeESQHEy9zBbGOxI36VN/131z+4so+XBLT96IPz+db/yYPhK6Y0SkVQRQbhh4RBAcTL3UFuQLyL13kFk/3oG0OzHhK2F4olmIpIqIgg3LNwBKE7mHm4LIkDDKWjfPUR2Z+cfV18DQGERyxXuuPNNAAsjC+Ih/IeXPG/l9OVEbxCWMX03qx6uq5lodhVpNdHY+6FCIymmzthRWCsH6NiRQKOIehGpuI6esJB+0/fXf1l7dTu/rgSAHna5AR0A9JBrGIAe/Mvfzvz1v6+8OscyJlg4ZoFDeIEWy1wXMdxuTI+qx/Dz2XrtoH61+kKDRLSLiCBgIcvClZO5DlksdV3EgNvZzf3SVaTt+vwTAIXF5C2wjEmYxXK/0gYEaPky/B0dPwFQWIxrwQA/LKQXZbHkkyr8+4Gmq+ZLAD3YLN3ZmRdPi1JAu0gJAsOV0wLTR3EWtg0iFKA99gPdTK61bxbu5dz064tCo+JpUcpoWglBYMRzWuAEpjgL6waRCdA++4Huzvx3HgcHxWP27QZ+AqA9hRHPaYFL6OIs7BtEJkB77Qc6T7ZeUkfyu7MQo/vX/VSvlT+6qvt7jQQ0rYQgMOJZLZBOaRYcLSLxIlLP/UD38s0/I4DOfQCUxQIjntUC6ZRmwdIiSz3NNcR+oD202vmJWOM3rYggMOJZLZBOaRY8LcLAT3aA8j0GqVw88XPjN62IIDDieS0cOqW8TGgMZzG9Dj7AfqB9tNr5iVjjN62MIBwa8SIslnq0N6SFKxWZXAcfbj9QWvHEzwloWhlBuDJQpFi4MXGbHncaNbUOPtx+oLTiiZ+T0LQygnBkxMOC02J6R77NmlgHH2w/UGLxxM+JaFoRQcACFjW5BNCJWQy0HyhVq52fiCUiqSKCgAUsagJAR7MAQJfpAAtYDGEBgI5mMeBuTKTiads+q64x6L7SEDRlpfwcO47Dp7EBSvqUkCtz0/pqPAQWE7veMKSFkCFy+CymAFApazSm1bLuWwAaBeHLZByLHgBdpNuB3v+rpZ4DFXOCZ1ot67yFlO9V0O8wWwywH2gfrXZ+Qg5A0ctFWUjpFphKH2oLo/1Ajx1GgKKXy7IQ0i0YLByaSh8+C/p+oCunL62p/zzOe5JWOz8hZaSglwuzkNEtGCycqcihtCDvB3rsofr/VcXSY2w3Ik3nIhJ6uTQLZ1rEmYocSote+4HGW4psKIwyabXzE1FQ4x89o5eLs5Dwvcphga41ZYte+4HuRPvS7/Tanb6jeNrHGPgJgDpnIeB7lcMCXWvKFj0Bqo7en1/gO4Zf7fxErNGTil4u0GL871UWC1em0ofSoteGyk/WFEe7t6d/lD8TKZV6TJJffikqnhalgKSil8NiKAtHptKH0oJ4FX4jOvsZnwqNMdqiO8lTOX+Vv7Q7q70UF0+LUkJS3enljkzcHLJAi0zWggjQJ2vemS/UZfizCqbth/Bzf/39cMp5y3/1q/Slg1v++bvqperj4SulN0pCUl3p5e58E8ACFmNbUO9E2ojuP9rxvJU1r/3xHiEs1QPh1cPgP0hf251FMN2/Xp2CVkpvlIikiggCZ9xgAYvBLcgjhHwv/NfqwH2xoYzbJ6D715OZ56b/bvraPH4efMrWQvGkIIUkVUQQuOYLC1gMbUEfIT02E/nPkJuLe6dOvUW8Bl8AKD9RMegAACAASURBVGagYiwAUFjAokM9hshw29kVaZmdA30tLzfW2PuhHjrlAB07EgiSqT5jZDiAbue0DAkaX5l/J7uGBICOJAAUgto1FEDT/UC/fUDg57y8jOm9CKDnqgtBK6U3SsC0XkgQuIgEC1gMa9HnNBcVoIue+4HOZ+uF60W7MzX5DKehOAc6vgWWMcECFm0aAKDl/UC7AbpdRmV6PWmzeFgfFd8dYaTxkyolCCzbhgUsBrbgv4i05XlH3vw81W+6rsNXppoHt5Jfd2eVlfSV0hslIKlCgoAFLGAxtAX3MqbFjT47MB1s+ue+Kr0AgMICFrCYjgXzQvpkP1CiNmt3bOIQHhawgIWDFr22s6Npu8bPYO7jIhIsYAEL5yx6bWdH0v51P9Vr6qA9YuZ28sq7lQ+vagx0EpFUEUHAAhawkGNBvojUuoFIUXNfA9DgD2o/0LcnvB+olCBgAQtYyLEgL2PifBZnoXji50QkVUQQsIAFLORYdAF08eHlSK+r5xpfTvTG4Xukh5ggYAELWMix6AJoeQU9/U4kolY7PxFLRFJFBAELWMBCjgUAukwHWMACFk5ZDLcbE6144udEJFVEELCABSzkWACgy3SABSxg4ZQFALpMB1jAAhZOWdAA+m38z9Obp0698YV5UJriiZ8TkVQRQcACFrCQY0EA6OJmctVoK76ERF5STyl+8N2lIQhalg7fkw66Afr0QnLZfSdMzpGTvARd7fxELBHfSiKCgAUsxFocwr26OwG6uOF5Rz+Kf3ghPHy/x7mKCQCFBSycsTiMT4vpBOhO+hz48Ier6t8NzinoaucnYolIqoggYAELoRZ9noQxXBRLtugE6EbCzWArmXlmROXQaucnYolIqoggYAELoRYAaKC5F/6GF+9kF/4Qc/PJGu5EggUsYFEVABpob+WMcRn+cLb8CodWOz8RS0RSRQQBC1gItQBAgxaAhhPPq+VXOLTa+YlYIpIqIghYwEKqBS4itQB0KzmUxyE8LGABC62wjKn5HOhGeukIF5FgAQtYaMXATxkVYb0Kr859hjPR46UX2vToZ76/fq32/I7aQ40BUFjAAhZTtugEaHjErqagxSP4rgfM3YmfiFR9AufBrfrjOiulN0rEd+O0WhYWsIDF4Bbdt3KGM07v1JqXTEDvZz81au6vvx8Ee7f8V78qvb7tjwjQw3d2GxawgMXgFt0AVfdyhjqqLhxF+9Mfbb+EFE40P1D/7l+P/021OxsRoIdwfQUsYAGLwS0o29ndf/3Uqbei60YKoGc6riDtX09mnpulp8CHXP270c6B8qxQm1bLwgIWsBjcot+GyosP33pADqEM0E3/tfEuIgGgsIAFLAawGG5H+v3rxatI8/DwvQjQ1UTL2bQvB+hyyoMg6FBoOIBu+69VaAqAQhDklAYD6Ly0jCk6nB9xHSguIsECFrDgtxgKoPPZeuEa/HZ0/X3MhfRYxgQLWMCC3WIggG6X5p+7s+i3Ue9EwkJ6WMACFtwWwwD0Tvk2pG0/U2Vx/SoEQdCExQ/Qg03/XImTACgEQW6KH6Cb9TuOImkO4amqhjmKhYggYAELWMi1YADodgM/AVBYwAIWblvYA3T/ena8/lp2/SgSAAoLWMDCaQt7gM59ABQWsIDFobTguxOJVSKSKiIIWMACFnItAFDhQcACFrCQawGACg8CFrCAhVwLoQCFIAiSLwAUgiDIUAAoBEGQoQBQCIIgQwGgEARBhgJAIQiCDAWAQhAEGWp8gB78x09933/7l/V39q+/lv6g3Ruvj0Wk5ntLKQ6P3vP99Z833pzaYHFwe+b71+7Gv+z9zMSiWo9ypfpblEIys3ikKtLbopaL/lHkFge3mvZM7BGFYc+qZtCgRUoWB3fCX378fk+LUsG26QxYGpVhjCi13ARO6uCW6ezuWrlGB+juLAn1XC3UzfQxdelnGmpDsVAK89K4b1S3w3bTJ1otkhEaR/6dkUWtHqVf+luUQjKzuBN/orSLdrdFqeDkM+YWnb28RxQ9e1Ytg/1bpGSxl2D8nV4WpYKt08nSqAxjRKl5oJJyYZ3OKQFU5Up99YRfXZU+eLDppy/Nm/sn1UIpbF59u1Acdmfr4VfaXu0THRab6uW9W8kzotQvB3csoqhXqr9FMSQzi7kf5eJWQ/ei5CL+TO8oNLEXt6/pbWHWsypRmLRIJRfnwn7x7w3sorQIQzoZGpVhjERqHKjUXNimM1FT1ypqbIDO01SVHysfHwzkQ/5dW4sg/ubRtwvFIYlhd6bvXw0WycfjF9ONpzf9D3pYVOpRqVR/i1JIRhZhF/wg/kSvipQKNotC81dhMPrOQbIw6lmVKExapGQxz+ZvWpfhGrX4VxyNyjBG4hcaAUrJBUM6YzV2raLGBmhey3JfDr+E3vku+0pp/SKgWEQu/t81nFohOijtX9d3jgaL9OXt8MW0izZNe0hRaELqaVEMycwiy0EDfgi5SHOQ5cTAIn2pYaxRLMx6VjkKoxbR9ot+FqWC7dPJ0aiJLMZIUommgUrJBUc601cI23GODdB5wxHD9rlfZqDZv37+d+G3w9sNp7cpFkF0fqPp3DTRQamnxWaBmdlobfAgRaEJqafFZjvGe+SiaawRctE9Zeq2iNQ0YaJZmPWschRGLVK0aOJNh0WpYK50BlaNmshijMS/Nw5USi440hnERu0kjjU2QMMo13/+v/Xv5b0jOaOrrw/FIp65N7ULzUHpu1mvIErM7AIoOYrm4UqwYIuiebgScpGftNNXhWIRqeE4jWhh1LM0UfRtkZKF+u+7n/r+Oc1l4WaLcsFc6bRr1Fg2YyRoH6iUXHCkM1Jz1ypqbICqU86h3v5XTZXS6szDyfXj4E+3my4QEiziftHYLhSHQH03+usNzaK3KHeOzewrVh8GMYq2Kx/dFl0AJUfR0sMIuVDX0qIrpQ0HSRSLoHm4Ey2MelYvgBIswv9uxxhvOONGaRGedAaWjRpYj5GOgUrIBUs6g/auVdDoAA0OopO3vn+tlrK0OtsZeBpS0m0R/23LOtBuh/BD//K3M3/973vUo9A5wuOGcL4TdnDV1ZsWLlKiaL903GlRDskiCnXZtqmHdeci2I0/ca5p2SHBIihcETCzMOpZmgz2bZGSxTwGX9gvmvJJaBGedNo2qv0Y6Rqo3blgSWfQ0bVyjQ9QpT+opa218Vztlq1VarfYTpZZtOWEFMSjpuMTrUXl2zVZJtd4ipwYRfvamw6LzhkoOYrZeuspovZcZF8mbV/zHensvmBAsEgq1iMX/Wag3RbzdK7Uspq0q0V40mnbqIlsxghloLbngiOdAaVrxZIB0CBa/fpu+oC6JPJqt2ycM3VaJAu6Op9yRwii64upZFHtX4+iKxZdYXRF0TVcWy1oAO2OYrt1rGosqqczCL283SLo7hEEi4Di02XRt0Uqh/D2LcKTTttGTWU+RqgDtXus23ZwUtcKRgdo4USDynoXQHUZoVgkUz8lTVoGDEJ7nVM/3OhRNA5XkkX7VXhqFC1zHUouOjBOTWcLuHq1iG2j9m6RokU2VPVjltIiTOm0bNRM5lG0D1RSLhjSWatQi0YGaGGiXP/aqi5u09eJYtHeLr2C0HeOJot0bVtp0aX+jBslCs0vvS20IfWM4mCz8XY9Wi46Rjw1nS3r4GlRGPWsegZ7t4g2F/qJG613MqSToVGtx0gHQHvlwjydSu23WOQa+xB+O0tU/eijsDBM/dO8SrjbIlbTpL5HEE0HSQ0WpfV52+mNGvrveXI9mr8cKRYdSwZJUTStI2i10N20YpPOjnXwtChMelY9g71bRBdF8xJ2Su+0TydHo1qPkfSlxoX0fUaqcTo7b7HINTZA1aqs98Nk7WmWkpTPkO+919DAFIsgMTJ36DhN32AR35dbutv40azhu41cj/ZVh10WpZDMLDpu0aDlwjqdHWumKRZmPauewd4tUo3i2t3euSgVzJBOhkZlGCOxWteBEkaqbTq7l+NnGhugyaqsUPXLf1l1kon9qw1rNCgWkTrWgbY7zJs+0Wqxp9nvpn2NW3c9Olcdtlvste/G1G2R7gHnN90CTslFerDWuliv3aLjMkGPKHr2rFoG+7dIyWI+M+lapYKt08nSqAxjJFLXOtD2XDCkk3AZK9HoAA0O1DaE/o81Nwfk1Yn20WxaJEyzUGpbB0oMomW7RL3FnlrVm644U3dINO9TSK1H+6pDQkWKIRlYzLOh1njqj5CLP6iPNN1ESbPouNpLsTDrWdUMGrRIOQq1IWbvXJQKtk0nT6MyjBGl1nWglA5unU7iKlAJAIUgCJqoAFAIgiBDAaAQBEGGAkAhCIIMBYBCEAQZCgCFIAgyFAAKQRBkKAAUgiDIUAAoBEGQoQBQCIIgQwGgEARBhgJAIQiCDAWAQhAEGQoAhSAIMhQACkEQZCgAFIIgyFAAKARBkKEAUAiCIEMBoBAEQYYCQCEIggwFgEIQBBkKAIUgCDIUAApBEGQoABSCIMhQACgEQZChAFAIgiBDAaAQBEGGAkAhCIIMBYBCEAQZCgCFIAgyFAAKQRBkKAAUgiDIUAAoBEGQoZYM0FUIgqAJa1yAEj/3/ffWRdlbiAgCFrCAhRwLAHSZDrCABSycsgBAl+kAC1jAwikLAHSZDrCABSycsgBAl+kAC1jAwikLAHSZDrCABSycsgBAl+kAC1jAwikLAHSZDrCABSycsgBAl+kAC1jAwikLAHSZDrCABSycsgBAl+kAC1jAwikLAHSZDrCABSycsgBAl+kAC1hM3sKzEVsUUiwA0GU6wAIWU7ew4icAyqzVzk/EEpFUEUHAAhajWnjeK8YCQLm1+j0EQROSHUDHjp5dYwOU+DkRBxfT+mqEBSyGsMAMtCSHAGrHTwAUFrAgfAgALckpgJq3LKlpp9WysIDFEBYAaEkAKAAKC1jQLQDQkgBQABQWsKBbAKAlAaAAKCxgQbcAQEsCQAFQWMCCbgGAlgSAAqCwgAXdAgAtCQAFQGGxDIuB19gBoONYAKAAKCyWYDH0ImUAdBwLABQAhcUSLJzhjjMV4bHoBdDFA8Nomosnfg4AhcW0LZzhjjMV4bGgA/T+6573wpfP//Kth+ZR1Ysnfo4G0IGPkqbVsrAQZcHBHRFnAQDQkqgAXXyiGiEE6AXv6Jc2gVWKJ34OAIXFtC0YuDN0/wZADSyoAN3wvKN/vfbCl4t/8LxjfHPQ1c5PxKIB9EfmAkBhMagFC0AloEtGFGIsiADd8bwrwfMLL4STz3tr3lWr0ErFEz+3LIBiEguLYSwYepYMdMmIQowFEaAb3tkgAWiw5R23iaxcfF6wdQdjAKhdEJIAOnA9JtbLBVgw9CwZ6JIRhRgLGkAXN1Y+zgD6ZO0FtrOgWel2/YuLfs5cyLdPBUcUsMhl8/UOgPaUOIDG6EwAmvzDoqx0BnQBoLnQy6VZsABUwrciulZJTgH0ZXMBoL3qMbFeLsACAHWza1EP4dWFo4ScO4yX4bPSAVCq0MsnacECUGsLloqgaxVEvIgUXTiKARrCdICLSAAoVejlk7RgmD66A1ARU2meKIgAfbLmvfQwAujTi566oMSkrHQAtCD7lrVyAEAHsGBoEWcAKqN38kRBXUi/Ff7NqbWV0yfDf88SoiMqK10KQAdu2uWNtWHrgcVUfS1seqd7ALW2kFGRoMe98F+vpR2bkZ8AqE4sXVTGWANAUwGgrxxugAbPPjsV9uojZ74gxEZWVroUgJp3Ub67QR0CqHVFGKKQMQ8GQF855AAdRFnpAGguAPQV3oECgMpqVCn9giMKABQAlTzWHIoCAAVAg+BbQmQ9lJUOgOYCQF+RN1DsowBADzVAF5+9GN2M5LGeBM1KB0BzAaCv4BBeC1D7ithbCAGoiIoEdIDurHnx3Zyet8K3mx0AqhMA+goAOgxAWSwYeieDhYRcBH0W0sc3cP7+5tpAC+mtqwOAvgKAaqOwz4X9WGMBKIMFQ9dyxsK6UYMe+4EeTY/c9bdyHtye+f61u/nvt/xUr34VBPvX85+Lykq3qw3WgdY6BwCaR2GdC4ZuAYDKs+AYI9TdmAqzTt1+oAkgcz5WALo76wSoeW1+BIAO0zkEVIQlCutcsLQIA/0YLBgq4owFxxjps52d7pdEm/75u8HeLf/848obu7P1X4X/zP3XtMZZ6VIAam3RLgC0Z0VYorDOBQD6ijj6MVhwjBGeGejuLJpa7l+PaFlQOBF9V/27Gf9TU1Y6AJqL5YDRvnNwRGFtwZJO61wAoK+Iox+Dhf0Y6XEO9Lj250TbyQRzu8rJ7XhOenCrSta0eI5eDoBq0GXdOViisLZgSSdDLhiGq3XPAkCZLaz7RdDnqZxnHkQ/PfvEqz+Vc9P/IPq3eqS+fz1+Y//6+d+95/tv363+YVY6AJpLxt67YrqoiHQy5MK6ZwGgRQuGr3eO3tnjufDeyqlTp9SeTLUJaDbB3J2VT4KmM9P0GlLC2ajcWN8nsgRobGHev15ms2CQXcvyWdj0cq4opKSTIRfWPYvFgqEiMixsxNg7qQBdfJoWvvI3tTebAJqdE537/juPgz/d9rMjeQB04BHPYMHQy62jkJJOhlxY9ywA9BXOrrVcgIYIvX8pnIGe/oXmeUgFgJbWKc3Tq/LpTLR2LSkrHYfwuaScwLTp5VwVkZJOhlxY9ywcwr/C2bWWewjfqoYZ6MGt/JA91ry6zikrHQDNBYD+CADV9CwA9JXDBdDKfFT3Sla63YgHQGudw7oeMnq5lHQy5IKhRWQ0qgwLhka1tgj6AfTbVA+q7+ivwtdXz1cvMgGgOgGgP+IFqG3PAkBLFZFhwTBGrPtFQAfo05uFP64tpE/Xf5bXgWZnPLNj+RpSs9Jl0A8AfZl3rHF0Uft0WkcBgL4CgOp7JxGgahu7XLQ7kQqr5zdjcNZPimaly6AfAPqyiwCVkQuGKGRURIaFhDESkAG65XlH3/w81W+qV+JDMp6r3Qu/fz074bk7U8uY9t6r3SuflS6DfgDoy8KgwZJOR3IhpSIyLCSMkYAKUP0WdgXtFXZjSvYPKZ3w3E42Y6reipSVLoN+AOjLhbFmPX0EQF8RB1CGRmXIBYOFhDESGG0motXe7ZCP1yJgpgAtrVna+5nvr79T3asJANVJRucAQF8GQAHQ1nQGRtvZcSorXQb9ANCXC2NNQhQs6WTIBcOIZ4iCwYKhURlywWAhpHdSD+EZH+NRVFa6DPoBoC8DoNpcMIx4higYLBgalSEXDBZCeif5ItJZQlftr6x0GfQDQF8GQLW5YBjxDFEwWDA0KkMuGCyE9E4iQMMp6BVCX+2trHQZ9OMAqP1pJpYTVQz1ENJFWwSA9rZgaFSGXDBYCOmdxEP4Dy953srpy4ne0GwoYqasdBn0k/FYJQD0ZQBUGwWDBUOjMuSCwUJI76ReRCqNUb4rSlnpUujHYGHeORgecC9srFlHYfdtwpcLhhHPEAWDBUOjMuSCwUJE7wRAB7Aw7xwAqCYKGQCVEQWDBUOjMtCPwUJC7wyYdmMyV1a6FPoxWJh3DgBUEwXDWGOIAgAFQOvpDADQASysB5tDY40hCoax5kwuZFSEoUUYLCR8pQUA6AAWzgBUQhcFQF8GQLUWEnpn0Augi3Q70Pt/hXOgLRbmXZRha333ACojCgbuMEQhoyIyACohnQHXfqDGykqXQj8GC/MuKgygMiwAUAB0+gAtX4Y/BoC2WJh3UQBUY8Ew4p2JQkZFAFCTWzm9ldOX1tR/Huc9SVnpUujHYGHeRQFQjQXDiGeIQsY8WEZFAND+dyLd8I49VP+/qlh6jO1GJAAUAO2yAEAB0MkDNNkPNN5SZENhlElZ6VLox2ABgEob8TIq4owFAGoA0Oi60U60L/1Ox+70fZSVLoV+DBYAKADqtAUAag5QdfT+/ALfMXxWuhT6MVgAoLBw2gIANTgHGh3CP1lTHOXcnj4rXQr9GCwAUFg4bQGA9r8KvxGd/YxPhcYY5VFWuhT6MVgAoLBw2gIA7Q/QJ2vemS/UZfizCqY4hG+zAEBh4bQFANofoCE11f1HO563suYxPt4jK10K/RgsAFBYOG0BgBoANPhaHbgvNtS1TawDbbUAQGHhtAUAagLQIPjPkJuLe6dOvcXHTwAUAIXF1CwAUDOADqGsdCn0Y7AAQGHhtAUACoAOaQGAwsJpCwC0D0CffVvXgx6IbFdWuhT6MVgAoLBw2gIA7QHQyuPkYmEdaJsFAAoLpy0AUAB0SAsAFBZOWwCgPQC6+P3nkT71vJU3/unzz//xpLfy1m+ELqS33njCIYDapkJIF4WFPAsAtP9FpHAimq6ev8c4AQ1Wv09kia7YwkpsUVgB1DaK2MEyF9YtAguXLWzox2chIRehet0LH2tL7HZ2J8zl3AzUNhVCvuNhIc/Chn58FhJyEfTcjSnWkzWp98IDoAAoLAa3AEB7A7S0g53c7ewAUAAUFoNbAKAGAC3NQAHQtiisZBsFAAqLoS0A0P4XkTYK5z03cA60NQoAVNqIl2Fh2y3EVMQZgFq3SEAG6I7nvRSf+Fx84ol9qJwMgI5qAYCKtQBADzFA1X6g3ouXL18+Gf77EgWNNGWlA6A8FgCoWAtnWsQZgFq3SEAHqJp4JuLbThkAZbdwb7g6Y+FMiwCgBgANgmefqdnnkbf4dhIJAFB2C/eGqzMWzrQIAGoE0EGUlQ6A8li4N1ydsXCmRQBQALQhIwDoCXHD1RkLZ1oEADUEqPj9QM0TAoDWOoeU4eqMhTMtAoAaAPTpzcIlfCykb4tiVAv3hqszFs60CADaH6DlbUEB0LYoRrVwb7g6Y+FMiwCg/QG65XlH3/w8ldT9QM0TAoDWOoeU4eqMhTMtAoD2BujiBuPtm0VlpQOgPBbuDVdnLJxpEQC0N0BLm4lwKisdAOWxcG+4OmPhTIsAoAYAZdyFvqisdACUx8K94eqMhTMtAoAaHMJjBkqOYlQL94arMxbOtAgAanIRifMO+FxZ6XZbowCgZQeXhqszFs60CADaH6DhFPQKiYg9lZUOgPJYuDdcnbFwpkUA0P6H8B9e8ryV05cTvTHEMibz2pxgBCgDxs1bFgB12cKZFgFADS4ilUAxyEJ689oAoEN0DinD1RkLZ1oEAAVAh7Qwb1kA1GULZ1pEBkBtZzrLPQc6lLLSpdCPwcK8iwKgGguGgSKjIs60CAAKgA5pYd5FM4BK6BxShitDLmRUBC3iiaIfAFqpjhQL8y4KgGosUBFpFWHonTI6uLVFQAbos2+LGmQ/UPPauAdQ2yBcGq6oiLSK/MhYnBYM6bS2CHARaQAL8y4KgGosUBFpFQFAAdAhLcy7KACqsWCoCMMBI4OFMy0CgPZfSP/7dCvQf7zorfzTIPuBmtcGAK0G4dJwZagIAAqA6tJpbRGYXER6snaMjZ8AqK6PyugcUoarjFzIsJDRIgCo3VV47cYiB7dnvn/tbuGV/et+pFe/0r8fF5+VK4R+DBbmXRQA1VjIyIUMCxktwkA/GRN6a4vACKC6KWhCyxiWsXZnBYBq3o+Lz8oVQj8GC/MuCoBqLGTkQoaFjBZhoN+hBqhud+VN//zdYO+Wf/5x9tLcf631/bj4rFwh9GOwMO+iAKjGQkYuZFjIaBEG+jFEIaFFAsMZaA2gu7Nknrn+q+y1Tf/d1vfj4rNyhdCPwcK8cwgDKAaKNAsZLSLDQkKLBCYAXWx4tUP47WS2uZ1D8+BWAZaa95Pis3KF0I/BwrxzAKBCB4oUCxktIsNCQosE9P1A061AL19a8+oXkTb9D6J/C4ft+9fP/+4933/7bsP7SfFZuULox2Bh3jmEAZTBwpGB4pAFQ4vIsJCQzsBsIX1tAprNNndn2UnO9BqSQqfm/dVE3yeyRJckC/PO8bJ9FLGDGAsbiaqIMxa2nVOMhYR0huoP0CNv1a7B6wA69/13Hgd/uu2HbwGgvXqHjM4BgDppwYAuGRYS0kkGaIcKgMwWKqWnPdW1JN37CUezcoUcfzNYmHcO9w7hYSHNwrZzirGQkM6AaTs73Qw01dw//7j5/ax0KfRjsDDvHAAoLIa2YECXDAsJ6Qx6AnTxrf71NoCqSScA2qt3yOgcsHDSggFdMiwkpDPoA9D7r0dnps58oXmv8Sp7wkxche/TO2R0Dlg4acGALhkWEtIZ0AG6uJGd3H+pvpdIur4zX+d5cKvIzPr7afFZuULox2Bh3jkAUFgMbcGALhkWEtIZkAGq+Lly+hef//pSSNDjtbc1dxptxpPNGKS4E6lP75DROWDhpAUDumRYSEhnQAboTrZ6fvGp512tvh1i8lzlXvfdmVrGtPde9JLm/aT4rFwh9GOwMO8cACgshrZgQJcMCwnpDMh3It0o3H20oZmC7hV2W9qdRfPM7WQzprvV94vKSpdCPwYL884BgMJiaAsGdMmwkJDOgH4n0srH2S+azURCQt4O+Xgtml8mAA32fub76+88rr1fVFa6FPoxWJh3DgAUFkNbMKBLhoWEdAZ0gBaYqdvOzlRZ6VLox2Bh3jkAUFgMbcGALhkWEtIZ0A/hSzNQvmd6ZKVLoR+DhXnnAEBhMbQFA7pkWEhIZ0C+iLRVOO+pfaSHobLSpdCPwcK8cwCgsBjaggFdMiwkpDMgA/T5Be+HybRzi/GpxgCornfI6BywcNKCAV0yLCSkM+gGaLoT6KV4Heh/u7zmeaffwCF8i4V55wBAYTG0BQO6ZFhISGfQDdDyTqCJcBGpzcK8cwCgsBjaggFdMiwkpDMAQAewMO8cACgshrZgQJcMCwnpDJi2szNXVroU+jFYWMk2ClH9CxbyLGTQj8FCQjoDAHQACwAUFnItZNCPwUJCOgMA1DULUf0LFvIsbL/dxVhISGfQC6CLbxPd/yucA5VqIap/wUKehQz6MVhISGdAB+jTm4Uq4CKSWAtR/QsWsHDaIuizkL6gYwCoVAtR/QsWsHDa3HSwWQAAIABJREFUIuhxK6e3cvrSmvrPW7lCYiNJWenTR5cMC1H9CxawcNoi6LEf6LGH6v9XFUv59hIBQLktRPUvWMDCaYug536g8TYiG/Ud6Y2VlT59dMmwENW/YAELpy2CnvuB7kR7Mu1odqQ3VVb69NElw0JU/4IFLJy2CHoDVB29P7+A/UDFWojqX7CAhdMWQc8NleOHeWBHesEWovoXLGDhtEVAvgq/EZ39jE+Fap+JZKis9OmjS4aFqP4FC1g4bRGQAfpkzTvzRfJwzg3Gy/BZ6dNHlwwLUf0LFrBw2iKg34m0Ed1/tON5K2seHukh10JU/4IFLJy2CHrcC/+1OnBfbEQ3ImEdqFgLUf0LFrBw2iLotZnIf4bcXNw7deotPn4CoNwWovoXLGDhtEWA7excsxDVv2ABC6ctAgDUNQtR/QsWsHDaIgBAXbMQ1b9gUbRwZA9MWJwAQN21ENW/YHECAHXZIgBAXbMQ1b9gUbT4c2MJqwgsAFBnLUT1L1icAEBdtgjGB+j3iSy5A4uSAywEWthIVEVgkVqEGhugWbmTn/vJsBD1BQ2LogXOgTpnEYw/A83KnTy6ZFiI6l+wKFr8mbGEVQQWpgBdPOjgYW9lpU8fXTIsRPUvWJwAQF22CPoA9P7raj+R53/JeScnAMptIap/weIEAOqyRUAH6OITdSomBOgF7yjbbqAAKLuFqP4FixMAqMsWQa/t7I7+9doLXy7+AbsxSbYQ1b9gcQIAddkiIAN0x/OuJM/yuLeGp3LKtRDVv2BxAgB12SLo90iP5GFIW3gqp1wLUf0LFicAUJctgn4PlUsAimciCbYQ1b9gcQIAddki6PdY4wSgeCqnYAtR/QsWRQsspHfOIgBAXbMQ1b9gcQIAddkioB/CqwtHCTl38FROuRai+hcsihY/MJawisCi/0Wk6MJRDNAQpriIJNZCVP+CxQkA1GWLoM9z4V96GAH06UVPXVBiUlb69NElw0JU/4LFCQDUZYuAvpB+y/O8U2srp096nI+FB0C5LUT1L5z3OwGAumwR9HoufNqxGfkJgHJbiOpfAOgJcQBFi4wE0ODZZ6fCHB4580U3FunKSp8+umRYiOpf2IX9BACqjcIRiwD7gbpmIap/AaAnxAEULQKANlQHFieE9S/Md04AoNooHLEI6AvpeY/cM2WlTx9dMixE9S8A9AQAqo3CEYuADlDPW2HdSTktPit38uiSYSGqf7Hc/+0MgwFQab3T2iIg34n0SXQN/sWPaFikKyt9+uiSYSGqfwGgJwBQbRSOWAQ9zoGqJ3qE01DmQ/ms9OmjS4aFqP7FAlBnRrwMgDrzfSTBIuh1EWlxT62i945cYTyUz0qfPrpkWIjqXwDoCQBUG4UjFkHfq/DPPosO5f9vbCYi1UJU/wJAT4gDKEOLMEThiEVgsIzp6U31bLmuT1GVlT59dMmwENW/ANATAKg2Ckcsgt4AXXymDuMBULEWovoXAHoCANVG4YhF0A+gi/sXuU+CZqVPH10yLET1LwD0BACqjcIRi6APQAe5DJ+VPn10ybAQ1b8A0BMAqDYKRywCMkDVmc9QR7kXgmalTx9dMixE9S8A9AQAqo3CEYugz51Ig9yKlJU+fXTJsBDVvwDQEwCoNgpHLIIeAD3zWxoS+ykrffrokmEhqn8BoCcAUG0UjlgE5Fs5f9M1+Ty4PfP9a3eLLz36me+vJy/tX/cjvfpV+c+y0qePLhkWnP2LYc01AAqA6qJwxCJg284uAWSRj3diZK7/Sv2yOwNAl2LhHkAZopCRTgBUFv2WA9DFh5ffeKj+X9Qbtfnopn/+brB3yz//OH1l7q+/H6iXImbO/deqfwKADmHB2b8Y5n4M9ANAAVCpFgSAPr+gls1HF5Fy1RbS784iTO5fj+eboQ5u+R8E0UvRv5v+uwDoMizcA6gzIx4AlUU/SQDdTiaY2xkn968nR+sROg9uZWQtKyt9+uiSYSEMoAzQcGbEA6Cy6CfpHOhmPN3UHalHAN2/fv537/n+23er72alTx9dMiwAULEjHgBlPb3OEIW1RcAE0GyCuTvLT4LGio/q02tICWejcmN9n8iSO7AoOfBY2AA0sbCBRmJhM+L5csFgwZALhihkpNNGgho1FHEZ04eF60ZPLlW3s2sBaHxwP/f9dx4Hf7rtZ0fyAOgwFgAoANoahQx0MXQthiisLcgAfX6hcNqz9EsVoJV1SvMYmek50tq1pKz06R88y7DAIXwRoHy5YLBgyAVDFDYS1bUYorC2COh3IhWY+WStBaDlGeh8tv5B6Xe/MkPNSp8+umRYCOvlDNCQMeIZopABUBlRHCKAVi7ARzpGPITf9isX32sz1Kz06aNLhoWwXs4wXBnQxZALhihkoEtGFIcIoMFOvUdcrX5GfxX+TpWf9XOkWenTR5cMC2G9nGG4OjPiZVRERhQM6WSIwtoioAB08f9dvnxpbeV0dh/Sm/VtRdL1n9uFc5wHm/65ZLqZrqqvL3PKSp8+umRYuDfrcmbEy6iIjCgOE0CV6teNyqrfiRTd3fk4/zkCZwbSTFnp00eXDAsAVOyIl1ERGVEcNoCWljFpFJLxXOVe+O3i9aLdmVrGtPde9RoSAMptIWzNNcNwdWbEy6iIjCgOG0A7tVfYjWl3Fs5D0/3rQqnJ53ayGVP1VqSs9OmjS4YFACp2xMuoiIwoDiVAF98muv9X9eP5vdshH69F88sIoHO/BNBgT20O+s7j6p9lpU8fXTIsAFCxI15GRWREcfgAmjwUKRYeayzWAgAVO+JlVERGFAyn1xmisLYIyAAtrwY9BoBKtQBAhxnxACgAWrcIyADd8ryV02ox06U1b+UKiY0kZaVPH10yLABQjQXDcAVApe0JJYHBAfkq/A1191H4/6uKpdUbkSyUlT59dMmwENbLGYYrgwUACoCOC9DnF1Y+DhQ7z4b/36jfiWSsrPTpo0uGhbBezjBcGSwY6AeASgMoQ4tYVyTouZB+xzue/Z9HWenTRxenhYRvVwD0zwFQbRQMvfPQAlQdvT+/wHcMn5UuBV0yLABQHMLrKsLQqDLSedgAurgRHcLHG9l13dfZR1npUtAlw0JC52AZKAzDVcaIZ2gRhoowNCpDFAy5OGwADTais5/xqdD6fqDmykqXgi4ZFgydgwEaDBYMw5XBQsZwZaiIbc8CQEtRWKczIAP0yZp35gt1Gf6sgikO4Qe2AP0AUF1FbHsWAFqKwjqdAf1OpI3o/qMdz1tZ86LZKI+y0qWgi8HCml0s9GPoXwxjTYaFjOHKUBHbzikGoAwdnMHCOp1Bj3vhv1YH7osNVTzWgbZaWDetQyNehoUz6bTtnGJOKcuwsE5n0Gszkf8Mubm4d+rUW3z8dBKg5mPtzwHQQSwY0skwXBkqYts5xQCUIQqGRrVOZ8C2nZ2pstKl0I/BAgAFQJ0GKEM6ZURhnc5AEkCt+6gUgDJUhKFz2AYhhn4MFgzpZLBgqIht5xSTThlRWKcz6Abo4sPLdbVvT99HWeks3DFPCABaDUIM/RgsGNLJYMFQEdvOKSadMqKwTmfQDVDdU42H2Q+U4cjXPCGcADVv2T9jBCiDBUMvl2HB8GUiI50MFWGIQkYuGKKwTmcAgAKgzRYMvVyGhfVAkZJOhoowRCEjFwxRWKczkHQOFAAFQOVayEin7RBxKRcMXybW6QwA0Cr9rNsFAP2BuOkjQxQy0mk94h3KBUO/sE5n0BOgiwcdPOytrHSHAMpgwdBFZfRyGQOFIQoZ6bQe8Q61CIOFdTqDPgC9/7o6+fn8LznX0cu7Cm+e0z8XwuAknQwjniEKhl7+E2MBoLURD4D+YCSALj5R1Q8BesE7ynYJyU2AmrfsDxgBKsOCoZfLAKiMXFiPeCnokmFhnc6g12YiR/967YUvF/8w1L3w5t/xf+YcQBk6B8OIl4EuGVEAoNLox9Ai1ukMyADd8bwryU7K99aGeSYSAMoKUAYLGeiSEYUMC+sRL6UiDBbTAmi0oXKyFf3WMM9EYgCodVJZLMw7BwAqNgoZFradU0xFGCwY+sXyABo/0iMB6EA70gOgAKjcKGRY2HZOMRVhsGDoF9bpDPo9VC4B6EDPRGIAqPk3CsM+SH8GgA6CLhlRyLCw7ZxiKsJgwdAvANAKugBQaV3UmShkWNh2TjEVYbBg6BfW6Qzoh/DqwlHp4cY8ykqXcgjPEIV55wBANVFYTxOkpJPBwrZziqkIg8WkABpfOIoBGsKU8SLS94ks0RVbWInNwrxz/MDeInbgsbDponxR2DaImHQyWNgOETEVYbBg6J3W6QxFfyrnSw8jgD696EXPiGcCaFaunWIL84RwTmLNOwdmoJoo/s1YwqZMDBa2/VtMRRgsGHqndToD+kL6rZAPp9ZWTp/0OB/KKQ+g5i3LQD8AVBMFAAqA6iwmBtDg67UUVoz8LALUPKc/AEArDmK6KEMUACgAqrNg6J22U7aIYBnCEpA1su7ZZ6fCvzty5otuLNKVlQ6A8lgI66IMUQCgAKjOYnIAHURZ6QAoj4WwLsoQBQAKgOosJPTOwBCg/znEMibz2mTcsf5KmX4UrnVRALRoYduzxFSEwUJC7wyMALr4ZJCF9Oa1kYEuGVG41kUB0B8AoFoLCb0zoAH0/uunTr34Ufrbk4vDPFTOvDYyDp4zC/OW/QkAWo8CAIWFzkLCl0lAAejTi3Gh8e1H0cbKAGibBQDqIEAlDFdY/IC3RawtAgJA8+caK4JGND06yEJ685wCoNUgxACUoZcDoNKikGHB0C+s0xkQALrlqb2Uo3+uqhuSPO8lxociZaVLoR+DhTMAZRiuDBYyAMrwZSKjRRiikGHB0C+sGzXoBujiRrJyfsPzjit+Mk4/AwBU2y5SxhqDhXUXdQigMtLJkAsGC4ZcTAeg8a3vITyPXuSdfgYAqLZd0EV/AoBqW4QhCoZcMFg40juDboA+v5BcMorOha5cobORpKx0KfRjsDBvlgygMsaahC7qEEBlWDB0LRnplNA7g54AZdyGKS0+K9e+aQHQYi+37hwyuigAymzB0LVkpFNC7wx6ApRzG5Gk+Kxc+6Z1CKDWLetMFwVA5VnISKeE3hn0Ayjf8s9MWekjnzoEQDUWErooACrPQkY6GabS1lEEAGi5aQHQnwCg9TYV0yIyLGSkEwCNis/KdQeg1k0rZaA4A1AJY80hC9shwhPFH40FgOqrI4V+DBbWLSsFXTKisG0QMLhkwQBQhnQCoFHxWbnuANR8xP8bACo1CoYRzxCFDAtnAGodRUACaF2DbCZi3rIsq3/4TmCaj3gAFFG0RiHDggGgDOkEQKPis3IB0H8DQJ2OwrZziukXzgDUOoqAcCvnh5fremOIHenNW5bh4jWnxagYFwYNRAGAHm6ADqysdCn0Y7CQAVCG4crQy2VEwZBOhihkVITBQkajAqBR8Vm5QujHYGHesn8EQAeJgiGdDFHIqAiDhYxGBUCj4rNyhdCPwQIAZYUGgwVDOmVUhKFRGSxkpBMAjYrPyhVCPwYLGQBl6F8MY41hoDBYMKRTRkUYWoTBQkY6JXTwQBJArasDgP4EANVaMKRTRkVkWMhIp4QOHgCg5aaVAlDrlmXpXwwWDAOFwcK2TcVURIaFjHRK6OABAFpuWgD0jwBovU3FzMZlRMGQToYoJHTwQBJAzWvDcOTLuQaJoSK2DmIsGAYKgwUDdxgsZFTEmSgkdPCAD6AHt2e+f+1u40ua96Pis3LHnrgBoENYMAwUBgvbNhWTCxlRMKRTRi6sLQI2gO5f95Ve/arhJc37cfFZuQDoHwHQgYYrg4WMXMiIQoYFQy6sLQI2gG765+8Ge7f884/1L2nej4vPyh2XO85YiOpfUgAqIxcyKsIQBRqVHaC7s2huuX99/VfalzTvJ8Vn5U4eXTIsRPUvZwaKQ7lgiEKGBUMurC0CLoBu+68l/76rfUnzflJ8Vu7k0SXDQlT/cmagwOKPaFSNRcAF0E3/g+jfeQLK6kua95Pis3Injy4ZFqL6Fyxg4bRFwATQg1vJofnuLD3JWXpJ8/5qou8T2X2vebAoOcACFrAY3iIUAOqahaD+BQtYOG0xAEDThUqllzTvJ6qU3qjvvzeJi9lCRBCwgAUs5FiMNQNNiyeWICKpIoKABSxgIccCAF2mAyxgAQunLKRche+QiKSKCAIWsICFHAuudaDvlv6tvqR5PymeWICIpIoIAhawgIUcCyl3InVIRFJFBAELWMBCjgUPQA9u+ecq97qXXtK8nxRPLEBEUkUEAQtYwEKOBdNmInuF3ZZ2Z9E8s/hS+Zdi8UR/EUkVEQQsYAELORZc+4Hu3Q75eC2aXyYALb5U+aVQPNFeRFJFBAELWMBCjoWUHek7JCKpIoKABSxgIccCAF2mAyxgAQunLADQZTrAAhawcMpibIBCEARNWAAoBEGQoUYFKFXVMEexEBEELGABC7kWAKjwIGABC1jItQBAhQcBC1jAQq4FACo8CFjAAhZyLQBQ4UHAAhawkGsBgAoPAhawgIVcCwBUeBCwgAUs5FoIBSgEQZB8AaAQBEGGAkAhCIIMBYBCEAQZCgCFIAgyFAAKQRBkKAAUgiDIUOMD9OA/fur7/tu/rL+zf/219Adf/4w6ukWk3Vn1KaF9HB695/vrP28waLQ4uD3z/Wt341/2fmZiUa1HuVL9LUohmVk8UhXpbVHLRf8ocouDW36qhn5BiMKwZ1UzaNAiJYuDO+EvP36/p0WpYNt0BiyNyjBGlJoHKq2DW6azu2vlGh2gu7Mk1HO1UDf918qfaagNxULpoP6Y5R4O202faLXYLz6e9Dsji1o9Sr/0t9hveGJqD4s78SfihwmSLUoFJ58xt+js5T2i6Nmzahns3yL7mmfY+u/0sigVbJ1OlkZlGCNKzQOVlAvrdE4JoCpX6qsn/Oqq9MGDTT99ad7cP6kWSmHz6tuF4rA7Ww+/0vZqn+iw2FQv78UdImy58JeDOxZR1CvV36IYkpnF3I9ycauhe1FyEX+mdxSa2NOHwhpZmPWsShQmLVLJxbmwX/x7A7soLcKQToZGZRgjkRoHKjUXtulM1NS1ihoboPM0VfvXy8Gqg4F8yL9raxHE3zz6dqE4JDHszvT9q8Ei+Xj84mbymU3/A/N6VCrV36IUkpFF2AU/iD/RqyKlgs2i0PxVGIy+c5AsjHpWJQqTFilZzLP5m9ZluEYt/hVHozKMkfiFRoBScsGQzliNXauosQGa17Lcl8MvoXe+y75SWr8IKBaRi/93DadWiA5K+9f1naPBIn15O3wx7aJN0x5SFJqQeloUQzKzyHLQgB9CLtIcZDkxsEhfahhrFAuznlWOwqhFtP2in0WpYPt0cjRqIosxklSiaaBScsGRzvSVxjOxucYG6LzhiGH73C8z0OxfP/+78Nvh7YbT2xSLIDq/0XRumuig1NNis8DMbLQ2eJCi0ITU02KzHeM9ctE01gi56J4ydVtEapow0SzMelY5CqMWKVo08abDolQwVzoDq0ZNZDFG4t8bByolFxzpDGKjdhLHGhugYZTrP//f+vfy3pGc0dXXh2IRz9yb2oXmoPTdrFcQJWZ2AZQcRfNwJViwRdE8XAm5yE/a6atCsYjUcJxGtDDqWZoo+rZIyUL9991Pff+c5rJws0W5YK502jVqLJsxErQPVEouONIZqblrFTU2QNUp51Bv/6umSml15uHk+nHwp9tNFwgJFnG/aGwXikOgvhv99YZm0VuUO8dm9hWrD4MYRduVj26LLoCSo2jpYYRcqGtp0ZXShoMkikXQPNyJFkY9qxdACRbhf7djjDeccaO0CE86A8tGDazHSMdAJeSCJZ1Be9cqaHSABgfRyVvfv1ZLWVqd7Qw8DSnptoj/tmUdaLdD+KF/+duZv/73PepR6BzhcUM43wk7uOrqTQsXKVG0XzrutCiHZBGFumzb1MO6cxHsxp8417TskGARFK4ImFkY9SxNBvu2SMliHoMv7BdN+SS0CE86bRvVfox0DdTuXLCkM+joWrnGB6jSH9TS1tp4rnbL1iq1W2wnyyzackIK4lHT8YnWovLtmiyTazxFToyife1Nh0XnDJQcxWy99RRRey6yL5O2r/mOdHZfMCBYJBXrkYt+M9Bui3k6V2pZTdrVIjzptG3URDZjhDJQ23PBkc6A0rViyQBoEK1+fTf69sjPSFW7ZeOcqdMiWdDV0S6kILq+mEoW1f71KLpi0RVGVxRdw7XVggbQ7ii2W8eqxqJ6OoPQy9stgu4eQbAIKD5dFn1bpHIIb98iPOm0bdRU5mOEOlC7x7ptByd1rWB0gBZONKisdwFUlxGKRTL1U9KkZcAgtNc59cONHkXjcCVZtF+Fp0bRMteh5KID49R0toCrV4vYNmrvFilaZENVP2YpLcKUTstGzWQeRftAJeWCIZ21CrVoZIAWJsr1r63q4jZ9nSgW7e3SKwh952iySNe2lRZd6s+4UaLQ/NLbQhtSzygONhtv16PlomPEU9PZsg6eFoVRz6pnsHeLaHOhn7jReidDOhka1XqMdAC0Vy7M06nUfotFrrEP4bezRNWPPgoLw9Q/zauEuy1iNU3qewTRdJDUYFFan7ed3qih/54n16P5y5Fi0bFkkBRF0zqCVgvdTSs26exYB0+LwqRn1TPYu0V0UTQvYaf0Tvt0cjSq9RhJX2pcSN9npBqns/MWi1xjA1Styno/TNaeZilJ+Qz53nsNDUyxCBIjc4eO0/QNFvF9uaW7jR/NGr7byPVoX3XYZVEKycyi4xYNWi6s09mxZppiYdaz6hns3SLVKK7d7Z2LUsEM6WRoVIYxEqt1HShhpNqms3s5fqaxAZqsygpVv/yXVSeZ2L/asEaDYhGpYx1ou8O86ROtFnua/W7a17h116Nz1WG7xV77bkzdFukecH7TLeCUXKQHa62L9dotOi4T9IiiZ8+qZbB/i5Qs5jOTrlUq2DqdLI3KMEYida0Dbc8FQzoJl7ESjQ7Q4EBtQ+j/WHNzQF6daB/NpkXCNAultnWgxCBatkvUW+ypVb3pijN1h0TzPoXUerSvOiRUpBiSgcU8G2qNp/4IufiD+kjTTZQ0i46rvRQLs55VzaBBi5SjUBti9s5FqWDbdPI0KsMYUWpdB0rp4NbpJK4ClQBQCIKgiQoAhSAIMhQACkEQZCgAFIIgyFAAKARBkKEAUAiCIEMBoBAEQYYCQCEIggwFgEIQBBkKAIUgCDIUAApBEGQoABSCIMhQACgEQZChAFBIur65edLzvCNvfmFrtOEde8gREASlAkAh2Vrc8FK9ZIk/ABTiFgAKiVaBn54t/wBQiFsAKCRaW563cuVB+MOzz9Y876qVFwAKcQsAhSQrnIC+kJ77fLJmCUAAFOIWAApJ1vML3vHslw3vhS9tzABQiFsAKCRZ4Qz0eP3Vb26Gh/PeqbceZh+5fzE81FcXme6/Hr5zJpq0hvA9u1AH/kc/iv8sA+hT9fcrZ6wv60OHXgAoJFpbnvfDyrRx8Ul6UemompCGAP2/NuLfX/gyeWvl4yAC6A9vFK8+pQDdyi7rL7s2kGsCQCHRCinoeaf/6UHhpa14QdPTcNKpZqfRdfrwBcXVI96ZB8Hi05iY0Z+q3z+JP5gCdCueoj77BASFbAWAQrKVrmM69VYC0ey0aPiDAqL6wNnk9+Sd+GRp8fdoShoD9MlaelZgK34ZgowFgELSFZ/xVEfs0UnLnRR76gr9l9k/QcbJlIwhQJM3EubGAN3KLkXpT7BCEF0AKDQBLX7/4cn01GaujRSgybWh7Cr9TgrQ49kn1Uei/xepicvykKUAUGgiUqc2s2VMz775XCE1AWhCxDpAzwbFdyJeRmdGM9mti4IOvQBQaDLaSm5FuneyyL82gF7N/hIAhYYQAApJVmntfDyljK8qnbr8iwcbhjNQnPiEuASAQpK1Vbz//cmaIuJWti1TH4AWz4HixCfEJQAUkqyQmdm98CEAQ5rmAEyusrcBNP/k2SDHaHotCiyFbAWAQqKl7jE689vwh4W6W7M0g9zqPgeaTEGr60ATgx3b7Z2gQy8AFBKt0jWfCIPJIfw3F5MXWgGq7gN9ejMBaX4nkro7Pr1hCYLMBYBCslXYUflodOj9/GLy65lPoxlkG0BPJ0vw04lo5V548BOyFAAKSdfTD0+pyeaLyZ5KweKzk2ovpd8mF9RbLyKp2aeXbrtU2I1JLYTKDCHIVAAo5KoKV+EhaBgBoJCrAkChwQWAQq4KAIUGFwAKuSoAFBpcACjkqgBQaHABoJCrAkChwQWAQhAEGQoAhSAIMhQACkEQZCgAFIIgyFAAKARBkKEAUAiCIEMBoBAEQYYCQCEIggwFgEIQBBkKAIUgCDIUAApBEGQoABSCIMhQACgEQZChAFAIgiBDAaAQBEGGAkAhCIIMBYBCEAQZCgCFIAgyFAAKQRBkKAAUgiDIUAAoBEGQoQBQCIIgQwGgEARBhgJAIQiCDAWAQhAEGQoAhSAIMhQACkEQZCgAFIIgyFAAKARBkKEAUAiCIEMBoBAEQYYCQCEIggwFgEIQBBkKAIUgCDIUAApBEGQoABSCIMhQACgEQZChAFAIgiBDAaAQBEGGAkAhCIIMBYBCEAQZCgCFIAgyFAAKQRBkKAAUgiDIUABoKK+Hxo61WVOuhAst4EAVqDpEVe2S8xUkqM/olZuvKVfChRZwoApUHaKqdsr5ChLkea9QJbhDeN7LRMmrhOf9OVXygk/keT8gSmwVqPK8fyNq8lXtlPMVJAgAHVsA6KQEgOZyvoIEAaBjCwCdlADQXM5XkCAAdGwBoJMSAJrL+QoSBICOLQB0UgJAczlfQYIA0LEFgE5KAGgu5ytIEAA6tgDQSQkAzeV8BQkCQMcWADopAaC5nK8gQQDo2AJAJyUANJfzFSQIAB1bAOikBIDmcr6CBAGgYwsAnZQA0FzOV5C9CaBSAAAW4klEQVQgAHRsAaCTEgCay/kKEgSAji0AdFICQHM5X0GCANCxBYBOSgBoLucrSJA1QL+5ueZ5K2e+IJW24x0Pgi3vbPkFjlpYAHTnyOlfPEx+Xvz60n/5sqGQxWcvBeXom/X8wgtfqv9IsVsA9PmFLBxicTXVKrRReIFWW0uALj476Xneix/pS4zT3vXS0mQJ0FJdO1UbKvX22PCuav/UtDv0EQBqDdDFjXTvQ9JAkwnQMPqr+c+N/S6OVRxAVz7OiyQUV9PoAH2ylnSho1/qStR0EaZeYyQ7gJbr2ikAVLzsABqO3yNXwtnbs09oBK31fBkAPbKWRrFxZK0DoDQtEaBpVGwjZoMGzaJsABp+Bx9Txy/3L+rz6xJAu+rapoZKA6Djyg6gG96x5OB3q3nmVpBQgB69mAT//MJfNPc7mQBdOZmMoKkCdCftQ2FdPtZ4uwTQrrq2CQAVKSuAPsmna+F3azTs7r8eHp8ceUt1k7Bp7530vOT06L01b+VK8TAkf6H0V4a1sALosU+TXriz8s9Jv3t600tD3/HOhrEeiY68jqfRP1Wnfl8svH/04+KLBYB21c0SoC/896QR0hFTLG8RHhkc/WKrtWnid5+qN09dCaI3zz65GL75MHuTUAdzgG5lYIjIXQknPkd0vNgi6Uu6vKuahcGv/E0QfB22WXyq0bZ3VapqAdBKXZNgk8hqXe7ox7WhkvW+7KOhx/2TGo+oOyTfhXGxhSZmEgBqCdDiXOVZ1IKfJKd4jkfvnop+jsb1RvTj5bwTFF4o/ZVhLewA+j+TY/iNF/5HjKGd+FzVytXo/dPhb0cvFgH6pPZ+OLUovJgDtLNutgD9MhmVCUCL5cWoWbkUhdzUNFGF0nNz8Zuno9/UZGmr9qcNdbCZgZamSpVwMoDmLZIDtJ73MPg3o9/ORv0rmuZZ965KVa1moKW6psFGkdW73LGHtaESv7BTqvBlT+dRA2ixiZkEgNoBNOzJlcOHHW9Ffenfi3pu2O7HH6pvz6vRG1eCxYaXdYLiC6W/MqyFHUD/142oYz+/cDzGUNjZzjxQ8zcV0U7MkuJFpOcXvJceBotPo/GQvl98MQNod92sAZo0Qxx5qbwt9cvTi16ScH3TqAqFFj8Mq3B/LXnz2BeFN0l1MAeoug721oPs11o4cdorLXI80OddBf/Sw/Bn1b2e3uDpXZWqWgC0UtdiprVdrjZU0q+X/KNNHlWAlnLKJADUDqD1EzlJg8VDOjlBmhysnI1fTztB8YXSXxnWwg6g4Ve9Kjr8f4yh9FBrIxmBH8efywCavp928I/jX/IXM4B2180aoMmZlDjyYnlJoeGwOtvSNHG4hZasvkmqgzlAI8KHR+C/eFDIXiGcNO2lFokBWs97Enxc5fiUo33vqlTVAqCVum7EE9ItFaa2y9WGSqn3bcSDR+9RBajBWdfuXHAbTlC8AFV69vt/vOjFozRqzmLrbaWdoPRC6a8Ma2EJ0GhILm4k1MsGWzQC0zP/OUAXN5KKP1nL3y+9WLqI1F43e4DGmc6vGqTlRdEFhTMtuqZJp3xHf5t6Vt8k1cECoMHifoSV/KxrKZykacotcjz9sZL3pK5J98qu2dj1rkpVbQBarmvSMIXDiEqXqw2VJDuFjzZ5aGageU6ZBIDaHsLXABp/w3rJKC18/cWDeycHaOGF0l8Z1sISoMlMLok0+2ZIjsLrAPVS5e+XXswB2lk3BoBG8SYpLZS3U5yaNTVNOqkJdfQXRd4WAEqogw1Alb5RC8yzU7LFcHbib4dyi5QAWmwMDUCte1elqlYALdU1uYKuxpG+y9WGSml+njBS71E7B1rMKZMAUO5zoOpE9cqLb/72RmWUppfrs05QeqH0V4a1sARo1A/VcXwC0GxZUzZciwBVqy+LAE0u4ugA2l03BoDm3wGl8koAbWqahJHqorynzh9q3qTUwRagoZ5eyK/CF8LZKU2vSwCt5z3hSRGg9r2rUlVrgGZ1LcFP1+VqQ6VIVA1AC2/Ur8IXcsokANTyKny+KkO170sP1XmaZFLQYwZa/ivDWtgCNAxEHcHrZ6B1gBan3tlALryYdmFC3TgAqnKdnnzIyyvOYxqbJjtKX/xaLXM5W3+TVAdjgBbSthOfx6uE0zIDree9DlCG3lWpqjlAq3VtmYGWT6cHgf0MtJhTJgGgfOtAkxPXcQuGk4IqQFvOgZb/yrAWtgCNFlSmUx3dGbfKIfzV4t9H75deTLswoW4sAA3L+eci+qPyiudAG5umeHfg4tPCWbVaW7XWwRighbSVAJqF03IOtJ73OkAZelelquYArdY1ybQaRvouRz8HWvPQATRIc8okAJTvTqR78dKJ9Hp1dSbTdhW+/FeGtbAFaPgNfslLl8/prvlWrsKnp0VLvT1/sQrQlrqxADRQ96MWABqVl61vKgzDetMUDxML12HqAG2tg/khfH4P24Y2nJar8PW8NwPUondVqmpxCF+p60a6WOl4Q5cjXYXXe0Q9I34h+tNSTpkEgHLdC69ugDgbJAd7ahVedZRG60rUfTGF82rpC+W/MqyFLUDVOfZ8MVC8oO7ZzcKqw/xz8TpQdU9zusb1eJqM7MXyIXxr3XgAqtYDZofwaXlbKv54HWhz0ySTGhX60+KqssohfEcdzAGq1kYmfSheu1oJZyddmFRskWMP9XlvOoS36l2VqloAtFLX4nJcbZerDpXiOtBnJQ+v6pGcCvCi5bDxKMtzyiQA1Ho3puwKZ9w7d+Kfj31aHYjxNcDi3RSFF0p/ZVgLa4CGnU/9k16VKd0WEof1pHBzSPK+91Lh/eKLhYX0XXVjAmgYXbKqPy+veCdSY9OUbv05+qXmTUodzAGa96GVeC1uJZw47aUWiV/S5V1zEcm+d1WqagHQSl3Tu4jOFmpR6nK1odJ8J9JLFY/0XJS6tvZf00u1aU6ZBIAy7Ad6//XifqBqlduRK6WzM8lxxb2T1XvhsxdKf2VYC2uAJjfzp0Qq3ZicjLyv1zKAJrdff1R6v/Bivoyps25MAE0PD0vlxffCxw3R1DTJSiUVenqjfPVNQh0sABoskpvVH8TFVsKJ015qkfglXd41ALXvXZWq2gC0XFe1JcGaii2pRa3L1YaK9l746Fb5qkfcMxY3VQdIFgsWcsojABQ70o+v4Xekb9qwh0/YkZ4K0JKGb5hhNfW25BAAOraGA2hy3WCIm/gqAkAB0EMqAHRsDQfQ5xei6ycbPMeubQJAAdBDKgB0bA14CH8vuWAx9AQUAAVAD6sA0LE15DlQdcWC+sA/GwGgAOghFQA6tvBY40kJjzXO5XwFCQJAxxYAOikBoLmcryBBAOjYAkAnJQA0l/MVJAgAHVsA6KQEgOZyvoIEAaBjCwCdlADQXM5XkCCvj8YOtlFTroQLLeBAFag6RFXt1DQq+Owzdbe5d6rwND9GuTB8p92rXWgBB6pA1SGqaqemUMFod4BEZ4a+oQSCIIiqCQA03YMqFuNOVBAEQVaSD9CIny9+pGae36jNqPj2QoUgCLKSeICq/VBfyG7Ee3phCXc1QxAEkSQeoDue90LhqD2cj2IKCkGQDIkH6IZX3m3gkzMfxT9Em0unu0Soh4YtPjuZ7TYdPUQsUvoMq+jjyZbdEARBHJIO0JCEL2gvG22lV5VeSj72FxcLV5lqAL2Hi1AQBHFLOkCbDtm34seePPskJmgITG/lbx5GT9+LH+tTBqh6XNoX0ROhcQYAgiAuSQdow6Osc65Gj62NABod6asnlz6sAzR7GPVGw4QWgiCot6YD0PjxpJ6XPDk75eAiesizei52vMR+I3m0eRWguHgPQRC3pgnQmJqxosfdqEffxL9uaQG6o643/XbZwUMQ5LakAzQ/Vi8CVB2x5woJWQVm7SLSJ9EnjwxzMz0EQYdT0gGaH5sn2jIDaPRonOgqPBYyQRDEJOkAra0DTQFavpjeDdBQ33x4UiF02g+xgiBIjsQDtHInUgzQ5Fp7rmaAli+7q3VMgz8hHIKgQyLxAFWnPgtX0J/eiH7byF6LWaoB6PH07fD3wkUnrGOCIIhL4gEan+88+pG6+vPtry8my5jUwvh4IrkTHZNXAZpNUZMJbLaMqTZ3VXJjg9gpV8KFFnCgClQdoqp2aQIVfHKx1CJHo7vft+LLQekhee2cZ/j+sS+CxWdrXjojXbnyMJrA1s+B9ugOgjvElCvhQgs4UAWqDlFVOzWFCi4+ydsjwqBSdi98NKGsATRd9HTs0/j3fFfm+o1NnvcjqgR3CM87QZS8Snjen1ElL/hEnvcTosRWgSrP+yNRk69qp6ZRwcX96JlIR4pr4Z/eVNfUk+2V6lfd472ZrhR+P6UcXtIsBAVAxxYAOikBoLmcryBBAOjYAkAnJQA0l/MVJAgAHVsA6KQEgOZyvoIEAaBjCwCdlADQXM5XkCAAdGwBoJMSAJrL+QoSBICOLQB0UgJAczlfQYIA0LEFgE5KAGgu5ytIEAA6tgDQSQkAzeV8BQkCQMcWADopAaC5nK8gQQDo2AJAJyUANJfzFSQIAB1bAOikBIDmcr6CBAGgYwsAnZQA0FzOV5AgBoA+v9Cyy+jiM/Xo+q3qNiYb2gc2G8sKoDtHTv8i3eVv8etL/6WpMoVK1OpjFbsVQKN9D9JtEerxxvkfWnYAbaxC1LM4c80gG4BueQXZ1kpAXgDQ4QG6E+3mLBugXr7NX/UZAEWJBGi209bRWthRkDuV578MIyuANlcBAG2RgLwAoMsCaE2yAHpkLduz/8gaBaC8sVsAVO2RrfaIvX+xAZTyAdpShdaeNZKsD+ElVspQACgAGoV49GJSg+cX/qK5MhIBupM+Y+D5hcKzX0ofkA7QlipIZA0AmgsAZQRogsStcMAmQ+LJ2gv/f7S18/GYPRve1ScXvZW31Hvhp9Vm+2fiofP0ZvipM9Fu+8mH/iYIvl7zjsSnxaKnMh95q+V5eJYAPfZpcgy/s/LPUWV2vLP31ryjH5fKjgH6ZC0c5glLuwMjyAqgW4XnXVVDCoNcJPkvpjh7LPbKxym7lItZ4rM6mAO0VoViqPkhfDhBVVvcBuVPVN7Qf4i1xqwALRRXGD9h7Or1U/XahiHfO5n8wtkHDQWADgPQ+Odw8F5dlAH65lo6nje802vZlvo78UmwlatB/qGzG3GPD19K9+RvmUrZAvR/JsfwGy/8jwSgKrwwuGLZUSVCfl5NOy8hMIIsZ6Dl6Uwl3gyghRQXcJKMZdVShonP6mAzAy1XoRRqBtCtwonDYn8pvdHwIdYacwK0WFxx/KRnhY9Xa7vhnYp+iR91xtcHDQWADgTQZJam6Fi4iLQRPaopnNldLf8cdpczD9SzS+JHjnovPVRPe1q5oh7ipMa+t6JmB/c8/TFqUgs7gP6vG1Gnfn7h+PMEoAnai2WrSoRj8WqQXZ/pDozUAhYAVc+7eit/0EAt3iT/pRRHChESHxQESQOaJT6rgzlAK1UohpoDNHz1hw/DUFTjlCpTeUP/Ic4aMwK0VFxh/ISRhvUI7q8lFSyNjuMPk2HD2QcNBYAOBFD1/+SMVgmgySPvQjZtxICK/ig9htuIJ6/qjbDXnA2S02OJcTRpaK6FHUAfbiVcvJoCNO6QpbLjGV1Sy7MBKTCC7K7CP42eOvjiLx4Eunjz/BdSXPhpJ/+/WeKzOpgDtFKFYqg5QJNXN+J6FSqjeaP+Ic4aMwK0VFxh/JROBmtGRzps+PqgoQDQoQAaNujJ5EiqCNCz2ceTfp183yYnIPM+nNE3ub7w7Pf/eFHzSNFCLSwBGoW5COehKUDz5z9nZW+pg734OlJ2PakrMIJs14Hejx/ceiYNuBhvkv9yigs/pE2XDeC+ic/qYAHQUhVKoWYALQKi9AntG9UPsdaY+SJSVlx5Bno0fQBadXRkw4a1DxoKAB0KoPmz60sAjRp5cSPssbrv24SstV79NH2084AAjcp+spZMevLhVCx7Kzu/lnReQmAEMdyJ9I1aiR4Ny3K8KUDLKQ6ScyxKqk2K7dc38VkdbABarEIp1AygxTlZ5ROaN6ofYq0xJ0CLxRXHT9TTjv7iYa0i5Q/x9UFDAaCDAfT5heTIgwLQbA1R1EXic1R5r1Zn1FdefPO3gx7CR+VuZWMrXfxTKjvs1qeTYZiel+sMjCCeWzmfXqiFVARoMcVBeiY3iN/eSS5imCQ+q4MtQNMqlEItADSftdU/UX2j+iHWGjMCtFRcafyoS+2eOkVbGx0VgDL1QUMBoIMBdCv9Tuw/A6306vAY5vjDYOhzoCpOdQRfBmi57K1oOUFSvbMBKTCCbABamHTVc9U4A03P5EYvHHsYp9ws8VkdjAFaqcIwM1DGGvMBtFxcefwEi1+/Hh3vtM9AufqgoQBQO4A+eT3qCk/WagANX/pva8l17Oo50Ojj5TM+pbM8lV6d9qB8DqGthS1Aw/7539eyydpOOo8ull25Ck8KjCAbgBYGTj1XjedA8wsr4c8r/2+8AsEs8VkdjAFaqcIw50AZa8wH0HJxFYCqOnxazUcNoFx90FAAqCVA1wqnj7KrR+mA3UiubhYAeixe3328esandJ2xoVfvtN09bA/QcF58ybvaANC47ARIcWXPBqTACLJcSJ9f0j2uj7d2Fb608FKteM2vXfdOfFYH80P4chXar8LX+4vmjfqHOGvMD9C0tbLx82QtPW8b9rXK6NAD1LYPGgoAtQOouov5oTqNHZPSi5bTxR02mtOpxs2BU1rEVlk2fOZB8OxmstJNe1yllusNClB10kF1W80hfFr2VnJ7wNni4VNHYKQWsACoWkR55WF0t0qYMU28O+lanSzF2eWU1CAO3yzxWR3MAVquQinU6jrQ+4V1w89uFtaBZm9oP8RaY+5D+LS4wvhJdgeI16ZWRofuEN6+DxoKALU8B7rjZfdFBPFNLy/813witJWel1fLLCOAXs5uFCkdsJTvtaie2U8KOfZpy90WDABN1g2ULyKVyk5v5UwHNSUwUgtYADS/Chtlrx5vnP9iije8VMmUJ13yapL4rA7mAK1Uof1OpNq9OaU3Gj7EWmPWhfSF4grjJ7sTKdqeqjw6KheRmPqgoQBQ24tIT14PW/aleMXP4mbY4l8o9JRW9369lgP0ajj9jG9VLp/xKd/tG1TWliQ3OBfXZtZrYQ3Q5DpDGaClsrfSiUtyayolMIKs14HGN0M/aIj36/iLIU9xGSfx11xgmvisDhYArVSB/1541hpzLmMqFZePHxX7Wn6De2l0VJcx8fRBQwGgS92RfmO4E93Ykd5YLHtMTWpHersaY0f6XM5XkCAAdGyNC9BoUZm1pgRQyxoDoLmcryBBAOjYGheg91hOnk0JoJY1BkBzOV9BggDQsTUmQDc8nm18pgNQ6xoDoLmcryBBAOjYGhOgn6qt3Bg0HYBa1xgAzeV8BQnCY43HFh5rPCkBoLmcryBBAOjYAkAnJQA0l/MVJAgAHVsA6KQEgOZyvoIEAaBjCwCdlADQXM5XkCAAdGwBoJMSAJrL+QoS5PXR2ME2asqVcKEFHKgCVYeoqp1yvoIEuTB8p92rXWgBB6pA1SGqaqecryAEQdBQAkAhCIIMBYBCEAQZCgCFIAgyFAAKQRBkKAAUgiDIUAAoBEGQoQBQCIIgQwGgEARBhgJAIQiCDAWAQhAEGQoAhSAIMhQACkEQZCgAFIIgyFAAKARBkKEAUAiCIEMBoBAEQYYCQCEIggwFgEIQBBkKAIUgCDIUAApBEGQoABSCIMhQACgEQZChAFAIgiBDAaAQBEGGAkAhCIIMBYBCEAQZCgCFIAgyFAAKQRBkKAAUgiDIUAAoBEGQof4PXHwGQHh0oOQAAAAASUVORK5CYII=" width="672" /></p>
+</div>
+<div id="ndvishannon-regression" class="section level3">
+<h3>NDVI+Shannon regression</h3>
+<pre class="r"><code>topntax_data &lt;- set$topntax_tbl %&gt;%
+  mutate(Taxa = &#39;fits1&#39;) %&gt;%
+  ungroup() %&gt;%
+  select(Alias, ndvi, Shannon, Taxa) %&gt;%
+  distinct()
+
+write.csv(topntax_data, file = &quot;../topntax_all_taxa/topntax_data_fits1.csv&quot;)</code></pre>
+</div>
+<div id="ndvi-regression" class="section level3">
+<h3>NDVI regression</h3>
+<pre class="r"><code># Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
+# containing the sample abundances for that genus.
+ldf &lt;- data.frame(genus_abundance_tbl_per_sample %&gt;% pivot_wider(id_cols = c(&#39;Sample&#39;, &#39;ndvi&#39;), names_from = &#39;Genus&#39;, values_from = &#39;Genus_Sample_Abundance&#39;))
+ldf_genus_data &lt;- data.frame(ldf) %&gt;% select(!c(&#39;Sample&#39;, &#39;ndvi&#39;))
+colnames(ldf_genus_data) &lt;- gsub(&#39; &#39;, &#39;.&#39;, colnames(ldf_genus_data))
+ldf &lt;- cbind.data.frame(
+Sample = ldf$Sample,
+ndvi_01 = (ldf$ndvi + 1) / 2.0
+)
+ldf &lt;- tibble(cbind(ldf, ldf_genus_data))
+
+n_samples_by_genus &lt;- data.frame(ldf_genus_data &gt; 0) %&gt;% mutate_if(is.logical, as.numeric) %&gt;% colSums() %&gt;% sort(decreasing = TRUE)
+keep_n &lt;- 100 # Maximum number of genuses to include in the analysis
+top_n_occurence_genuses &lt;- names(n_samples_by_genus[1:keep_n])
+top_n_occurence_genuses &lt;- top_n_occurence_genuses[!is.na(top_n_occurence_genuses)]
+
+l_genus_ldf &lt;- ldf %&gt;% select(all_of(top_n_occurence_genuses))
+l_genus_ldf_transposed &lt;- data.frame(t(l_genus_ldf))
+l_meta_ldf &lt;- ldf %&gt;% select(&#39;ndvi_01&#39;)
+
+l_model &lt;- linda(
+l_genus_ldf_transposed,
+l_meta_ldf,
+formula = &#39;~ ndvi_01&#39;,
+feature.dat.type = &#39;proportion&#39;,
+is.winsor = FALSE,
+alpha = 0.05
+)</code></pre>
+<pre><code>## 0  features are filtered!
+## The filtered data has  18  samples and  100  features will be tested!
+## Fit linear models ...
+## Completed.</code></pre>
+<pre class="r"><code># Print model info
+l_model</code></pre>
+<pre><code>## $variables
+## [1] &quot;ndvi_01&quot;
+## 
+## $bias
+## [1] -0.1560564
+## 
+## $output
+## $output$ndvi_01
+##                          baseMean log2FoldChange      lfcSE         stat
+## Mrakia               340184.59007   -0.204087169 0.15117277 -1.350026034
+## Candida               44204.60411    0.313554734 0.33157232  0.945660163
+## Sistotrema            74105.32511    0.110212031 0.29324305  0.375838517
+## Bactrocera             7806.33499    0.679090318 0.43293090  1.568588222
+## Mortierella          192461.65172   -0.043290897 0.10349192 -0.418302196
+## Sporidiobolus         14874.06202    1.578834180 0.32349836  4.880501396
+## Juxtiphoma            39774.07212    0.014101235 0.13977953  0.100881977
+## Tausonia              26322.26036   -0.093883636 0.20364465 -0.461016964
+## Naganishia            25647.20607    0.369171189 0.18162449  2.032606935
+## Saitozyma             34589.98512    0.236064479 0.07448041  3.169484105
+## Botrytis              14364.12697    0.695648156 0.31302174  2.222363697
+## Solicoccozyma         46636.49367    0.013954771 0.06928624  0.201407523
+## Holtermanniella       12365.63902    0.157407640 0.17455343  0.901773426
+## Tetracladium          14309.09600   -0.769677521 0.19939424 -3.860079020
+## Ascobolus              4672.21703   -0.242018800 0.22181684 -1.091074945
+## Trichosporiella       10518.07870   -0.441756988 0.18542568 -2.382393812
+## Dendryphion            3285.61699   -0.518529490 0.20130949 -2.575782666
+## Papiliotrema          10698.66436    0.140491327 0.15715957  0.893940617
+## Incrupila              6995.67328   -0.411028144 0.13784184 -2.981882344
+## Paraphoma              1637.31059   -0.561378782 0.46430853 -1.209064120
+## Paraphaeosphaeria      6106.05411   -0.155745024 0.16111694 -0.966658302
+## Didymella              1307.00291   -0.260531888 0.35851067 -0.726706098
+## Haplosporangium        4078.80668    0.150041293 0.15062673  0.996113350
+## X.uncultured.fungus.  16818.86991    0.103333263 0.14308774  0.722167139
+## Burgoa                 1522.54815    0.637244740 0.33030186  1.929279894
+## Pseudoanguillospora    1166.01391   -0.132895032 0.31691179 -0.419343919
+## Apiotrichum            1184.39462    0.735278334 0.33262155  2.210555332
+## Exophiala              3276.55056   -0.113893840 0.11244333 -1.012899900
+## NA.                    5499.72627   -0.266086115 0.17017266 -1.563624351
+## Aaosphaeria            2209.49427    0.001404389 0.23712799  0.005922492
+## Pleotrichocladium      1228.03054   -0.246732117 0.24353919 -1.013110509
+## Polyphilus             1015.79623    0.122375715 0.19369307  0.631802245
+## Vishniacozyma           766.26376    0.859195545 0.25898552  3.317542830
+## Anungitopsis            700.15888   -0.141167271 0.13627747 -1.035881236
+## Cystobasidium           423.89482    0.014674994 0.35558944  0.041269489
+## Herpotrichia           1784.66557    0.343009347 0.56174244  0.610616760
+## Dioszegia               776.03696    1.412501022 0.50024785  2.823602412
+## Boreoplaca             1100.10727    1.625276241 0.31549585  5.151497958
+## Epicoccum              1171.99679    1.037309278 0.40750798  2.545494361
+## Minimedusa             3376.06624    0.266690354 0.25753461  1.035551498
+## Pseudeurotium          1512.71376    0.391183616 0.32992436  1.185676676
+## Gorgomyces              426.09565   -0.988371202 0.35509022 -2.783436882
+## Neoascochyta            377.90003    0.546276025 0.37093243  1.472710321
+## Rhizophagus            1106.70653    0.298554887 0.30439906  0.980800960
+## Umbelopsis             1187.02092    0.043999878 0.26503403  0.166015955
+## Verrucula               374.76993    0.421711104 0.11501219  3.666664436
+## Cystofilobasidium      3038.89858    1.990167444 0.55363080  3.594755632
+## Olpidium               1036.85803   -1.214407794 0.55815189 -2.175765808
+## Barnettozyma            591.32654    0.375901602 0.50843364  0.739332678
+## Lambertella             988.62434   -0.390124959 0.20065390 -1.944267992
+## Yunzhangia              345.19851    0.356066407 0.35140288  1.013271159
+## Pseudogymnoascus        248.04469   -0.119729939 0.33608735 -0.356246488
+## Crocicreas              186.96982   -0.209384360 0.46692977 -0.448427947
+## Bullera                 534.75171    1.417392235 0.45393514  3.122455446
+## Waitea                  144.14805    0.005147444 0.45269064  0.011370777
+## Cladophialophora        286.52298   -0.036082125 0.40761705 -0.088519667
+## Cadophora               523.10058   -0.882878199 0.20416232 -4.324393531
+## Hamamotoa               200.47423    0.033836839 0.45217192  0.074831801
+## Stromatinia             162.45048    0.482428813 0.35097822  1.374526362
+## Galactomyces            142.75030    0.424632322 0.38808822  1.094164425
+## Itersonilia             355.85415    1.626555802 0.35562550  4.573788403
+## Knufia                  289.11314    0.012580795 0.39208622  0.032086809
+## Cutaneotrichosporon     146.60817    0.036707892 0.52795779  0.069528081
+## Cladosporium            204.06860    0.836685957 0.33513887  2.496535102
+## Chromosera               79.95274   -0.529805523 0.37203130 -1.424088590
+## Rhodotorula             265.30898    1.169297348 0.41330993  2.829105369
+## Atractiella             127.43030   -0.041339967 0.45658407 -0.090541852
+## Funneliformis           173.97744    1.004949930 0.41280887  2.434419397
+## Alternaria              114.82214   -0.327229518 0.31510939 -1.038463236
+## Trichosporon            597.64206    0.557732733 0.38101737  1.463798714
+## Rhizoctonia             214.33709    0.747246164 0.61251324  1.219967362
+## Setophoma                77.48646   -1.196881984 0.36665321 -3.264343371
+## Monodictys               83.97420   -0.630170569 0.37448172 -1.682780565
+## Cantharellula           197.81389   -0.699373413 0.30905178 -2.262965160
+## Paraglomus              117.01600    1.410850242 0.65735957  2.146238221
+## Coprinellus              92.70154   -0.996851740 0.45364323 -2.197435488
+## Pseudosigmoidea         100.80873   -0.105332522 0.32489559 -0.324204222
+## Ganoderma                40.23399   -0.108813056 0.35932951 -0.302822485
+## Cryptotrichosporon       86.47389   -1.239531075 0.41707480 -2.971963476
+## Sporobolomyces          433.97502    0.868493893 0.32540557  2.668958322
+## Filobasidium            162.05987    0.345731693 0.44425968  0.778219825
+## Hyaloscypha              59.69257   -1.698611980 0.36960975 -4.595690393
+## Laetinaevia             215.56526    0.048539437 0.30093676  0.161294477
+## Paraboeremia            181.82217    0.012563635 0.30707594  0.040913772
+## Lachnellula              72.61733    0.220247703 0.53297331  0.413243401
+## Crassostrea             111.26717    0.909067503 0.32733500  2.777177817
+## Hannaella                88.81468   -0.230277245 0.20822486 -1.105906599
+## Pichia                  159.95283    0.547036400 0.32341908  1.691416609
+## Fusarium                120.37675    0.363078794 0.34250016  1.060083583
+## Slooffia                 46.28587    0.942862502 0.39337700  2.396841949
+## Degelia                  42.33919   -0.913593597 0.28366338 -3.220696249
+## Varicosporium            47.19529    0.103580146 0.30483430  0.339791638
+## Aotearoamyces            25.47022    0.608030309 0.28996993  2.096873653
+## Boeremia                 94.21359    1.045401610 0.55802848  1.873383965
+## Sampaiozyma              80.61828    0.397577050 0.47806504  0.831637989
+## Coniochaeta              57.76716   -0.177008246 0.40718654 -0.434710453
+## Claroideoglomus          93.40685    0.588468302 0.25426341  2.314404195
+## Entoloma                 66.92444   -0.160796858 0.26299201 -0.611413463
+## Archaeospora             46.88894    0.312411209 0.38832908  0.804501203
+## Pulvinula                46.31055   -0.014919876 0.28655940 -0.052065562
+##                            pvalue       padj reject df
+## Mrakia               1.957979e-01 0.41659120  FALSE 16
+## Candida              3.583854e-01 0.55997716  FALSE 16
+## Sistotrema           7.119734e-01 0.86826029  FALSE 16
+## Bactrocera           1.363066e-01 0.32730173  FALSE 16
+## Mortierella          6.812867e-01 0.84557241  FALSE 16
+## Sporidiobolus        1.666677e-04 0.00780430   TRUE 16
+## Juxtiphoma           9.208975e-01 0.99469356  FALSE 16
+## Tausonia             6.509912e-01 0.84557241  FALSE 16
+## Naganishia           5.903332e-02 0.16866664  FALSE 16
+## Saitozyma            5.946949e-03 0.04955791   TRUE 16
+## Botrytis             4.102506e-02 0.13890315  FALSE 16
+## Solicoccozyma        8.429182e-01 0.96887148  FALSE 16
+## Holtermanniella      3.805520e-01 0.58273252  FALSE 16
+## Tetracladium         1.385568e-03 0.02309280   TRUE 16
+## Ascobolus            2.913986e-01 0.54363551  FALSE 16
+## Trichosporiella      2.994949e-02 0.11519036  FALSE 16
+## Dendryphion          2.031486e-02 0.09673741  FALSE 16
+## Papiliotrema         3.846035e-01 0.58273252  FALSE 16
+## Incrupila            8.805964e-03 0.05993242  FALSE 16
+## Paraphoma            2.442023e-01 0.49837201  FALSE 16
+## Paraphaeosphaeria    3.481002e-01 0.55253995  FALSE 16
+## Didymella            4.779042e-01 0.66751979  FALSE 16
+## Haplosporangium      3.340222e-01 0.54757732  FALSE 16
+## X.uncultured.fungus. 4.806143e-01 0.66751979  FALSE 16
+## Burgoa               7.162601e-02 0.19358380  FALSE 16
+## Pseudoanguillospora  6.805409e-01 0.84557241  FALSE 16
+## Apiotrichum          4.197762e-02 0.13890315  FALSE 16
+## Exophiala            3.261813e-01 0.54363551  FALSE 16
+## NA.                  1.374667e-01 0.32730173  FALSE 16
+## Aaosphaeria          9.953478e-01 0.99534778  FALSE 16
+## Pleotrichocladium    3.260838e-01 0.54363551  FALSE 16
+## Polyphilus           5.364397e-01 0.73337271  FALSE 16
+## Vishniacozyma        4.355308e-03 0.04839231   TRUE 16
+## Anungitopsis         3.156609e-01 0.54363551  FALSE 16
+## Cystobasidium        9.675917e-01 0.99469356  FALSE 16
+## Herpotrichia         5.500295e-01 0.73337271  FALSE 16
+## Dioszegia            1.223066e-02 0.07084152  FALSE 16
+## Boreoplaca           9.653983e-05 0.00780430   TRUE 16
+## Epicoccum            2.159908e-02 0.09817763  FALSE 16
+## Minimedusa           3.158101e-01 0.54363551  FALSE 16
+## Pseudeurotium        2.530607e-01 0.50612141  FALSE 16
+## Gorgomyces           1.328745e-02 0.07084152  FALSE 16
+## Neoascochyta         1.602279e-01 0.36958745  FALSE 16
+## Rhizophagus          3.412898e-01 0.55046743  FALSE 16
+## Umbelopsis           8.702241e-01 0.98188829  FALSE 16
+## Verrucula            2.084051e-03 0.02977215   TRUE 16
+## Cystofilobasidium    2.425943e-03 0.03032428   TRUE 16
+## Olpidium             4.490436e-02 0.14032611  FALSE 16
+## Barnettozyma         4.704137e-01 0.66751979  FALSE 16
+## Lambertella          6.966079e-02 0.19350221  FALSE 16
+## Yunzhangia           3.260094e-01 0.54363551  FALSE 16
+## Pseudogymnoascus     7.263101e-01 0.87507238  FALSE 16
+## Crocicreas           6.598576e-01 0.84557241  FALSE 16
+## Bullera              6.563640e-03 0.05048954  FALSE 16
+## Waitea               9.910682e-01 0.99534778  FALSE 16
+## Cladophialophora     9.305622e-01 0.99469356  FALSE 16
+## Cadophora            5.233783e-04 0.01046757   TRUE 16
+## Hamamotoa            9.412762e-01 0.99469356  FALSE 16
+## Stromatinia          1.882254e-01 0.40918566  FALSE 16
+## Galactomyces         2.900821e-01 0.54363551  FALSE 16
+## Itersonilia          3.121720e-04 0.00780430   TRUE 16
+## Knufia               9.747997e-01 0.99469356  FALSE 16
+## Cutaneotrichosporon  9.454308e-01 0.99469356  FALSE 16
+## Cladosporium         2.383980e-02 0.10365132  FALSE 16
+## Chromosera           1.736316e-01 0.38584796  FALSE 16
+## Rhodotorula          1.209238e-02 0.07084152  FALSE 16
+## Atractiella          9.289804e-01 0.99469356  FALSE 16
+## Funneliformis        2.700101e-02 0.11250421  FALSE 16
+## Alternaria           3.144943e-01 0.54363551  FALSE 16
+## Trichosporon         1.626185e-01 0.36958745  FALSE 16
+## Rhizoctonia          2.401549e-01 0.49837201  FALSE 16
+## Setophoma            4.871706e-03 0.04854770   TRUE 16
+## Monodictys           1.118259e-01 0.27956467  FALSE 16
+## Cantharellula        3.790077e-02 0.13535990  FALSE 16
+## Paraglomus           4.753503e-02 0.14404555  FALSE 16
+## Coprinellus          4.305998e-02 0.13890315  FALSE 16
+## Pseudosigmoidea      7.499831e-01 0.88233311  FALSE 16
+## Ganoderma            7.659267e-01 0.89061246  FALSE 16
+## Cryptotrichosporon   8.989864e-03 0.05993242  FALSE 16
+## Sporobolomyces       1.680599e-02 0.08402996  FALSE 16
+## Filobasidium         4.477957e-01 0.64897927  FALSE 16
+## Hyaloscypha          2.983997e-04 0.00780430   TRUE 16
+## Laetinaevia          8.738806e-01 0.98188829  FALSE 16
+## Paraboeremia         9.678708e-01 0.99469356  FALSE 16
+## Lachnellula          6.849136e-01 0.84557241  FALSE 16
+## Crassostrea          1.345989e-02 0.07084152  FALSE 16
+## Hannaella            2.851188e-01 0.54363551  FALSE 16
+## Pichia               1.101390e-01 0.27956467  FALSE 16
+## Fusarium             3.048479e-01 0.54363551  FALSE 16
+## Slooffia             2.910159e-02 0.11519036  FALSE 16
+## Degelia              5.340247e-03 0.04854770   TRUE 16
+## Varicosporium        7.384331e-01 0.87908697  FALSE 16
+## Aotearoamyces        5.225156e-02 0.15368105  FALSE 16
+## Boeremia             7.940044e-02 0.20894852  FALSE 16
+## Sampaiozyma          4.178527e-01 0.62366068  FALSE 16
+## Coniochaeta          6.695787e-01 0.84557241  FALSE 16
+## Claroideoglomus      3.426050e-02 0.12689075  FALSE 16
+## Entoloma             5.495151e-01 0.73337271  FALSE 16
+## Archaeospora         4.328997e-01 0.63661713  FALSE 16
+## Pulvinula            9.591210e-01 0.99469356  FALSE 16
+## 
+## 
+## $covariance
+## NULL
+## 
+## $feature.dat.use
+##                                 1            2            3            4
+## Mrakia               4.863046e-01 4.417461e-01 4.333489e-01 3.948711e-01
+## Candida              3.535210e-02 2.892206e-02 1.839322e-02 2.370730e-02
+## Sistotrema           5.102202e-02 1.738643e-02 6.272360e-02 6.080056e-02
+## Bactrocera           1.674407e-03 9.803210e-03 1.736921e-03 3.136881e-03
+## Mortierella          1.907876e-01 1.282301e-01 1.200938e-01 2.170519e-01
+## Sporidiobolus        1.579629e-03 2.184715e-02 4.225644e-03 1.257643e-03
+## Juxtiphoma           1.747070e-02 3.965891e-02 5.552963e-02 2.769707e-02
+## Tausonia             1.766025e-02 3.539529e-02 4.097319e-02 2.068607e-02
+## Naganishia           1.307933e-02 2.994906e-02 1.884689e-02 1.877792e-02
+## Saitozyma            1.980855e-02 2.314387e-02 2.386322e-02 1.711552e-02
+## Botrytis             2.748555e-03 1.705447e-02 3.694198e-03 4.784827e-03
+## Solicoccozyma        3.383566e-02 3.713809e-02 3.962514e-02 3.798951e-02
+## Holtermanniella      9.920071e-03 1.375561e-02 9.501218e-03 7.314570e-03
+## Tetracladium         2.293621e-02 6.857060e-03 2.897029e-02 3.389855e-02
+## Ascobolus            3.759517e-03 2.904655e-03 9.138280e-03 6.100293e-03
+## Trichosporiella      1.295296e-02 6.857060e-03 1.893763e-02 1.520737e-02
+## Dendryphion          2.590592e-03 1.794662e-03 6.053300e-03 3.845209e-03
+## Papiliotrema         8.214071e-03 1.143189e-02 8.166122e-03 9.381731e-03
+## Incrupila            7.361072e-03 2.759422e-03 5.807020e-03 1.464360e-02
+## Paraphoma            2.274666e-03 1.141114e-04 1.970239e-03 5.652168e-03
+## Paraphaeosphaeria    2.527407e-03 4.886044e-03 8.904962e-03 7.329026e-03
+## Didymella            6.634442e-04 1.192983e-03 1.944315e-03 2.240629e-03
+## Haplosporangium      1.421666e-03 2.738674e-03 4.523772e-03 4.669182e-03
+## X.uncultured.fungus. 1.105740e-02 1.253151e-02 1.198994e-02 1.512063e-02
+## Burgoa               7.898146e-04 5.083146e-04 9.591953e-04 4.914928e-04
+## Pseudoanguillospora  6.318516e-04 3.008392e-04 2.359102e-03 1.301010e-03
+## Apiotrichum          4.107036e-04 2.686806e-03 1.425831e-04 3.903031e-04
+## Exophiala            1.895555e-03 1.670176e-03 4.964484e-03 4.336702e-03
+## NA.                  9.540960e-03 2.136996e-03 7.466169e-03 2.124984e-03
+## Aaosphaeria          2.274666e-03 4.564457e-04 1.024006e-03 2.009338e-03
+## Pleotrichocladium    4.107036e-04 9.855078e-04 2.786851e-03 1.272099e-03
+## Polyphilus           4.107036e-04 6.120522e-04 1.348058e-03 3.613918e-04
+## Vishniacozyma        9.477775e-05 1.628681e-03 3.759009e-04 2.457464e-04
+## Anungitopsis         3.475184e-04 2.282229e-04 9.980816e-04 8.962516e-04
+## Cystobasidium        9.477775e-04 7.365374e-04 2.722041e-04 1.734681e-04
+## Herpotrichia         3.791110e-04 2.450284e-02 3.759009e-04 1.893693e-03
+## Dioszegia            2.168351e-05 6.535473e-04 2.009125e-03 4.336702e-05
+## Boreoplaca           5.782269e-05 6.016785e-04 7.518017e-04 1.156454e-04
+## Epicoccum            7.459569e-05 8.796954e-03 5.314461e-04 2.023794e-04
+## Minimedusa           4.644110e-03 2.686806e-03 2.203557e-03 1.069720e-03
+## Pseudeurotium        1.579629e-03 5.913047e-04 6.351428e-04 1.156454e-03
+## Gorgomyces           1.611222e-03 2.697179e-04 5.055219e-04 2.255085e-03
+## Neoascochyta         6.002591e-04 2.800917e-04 3.759009e-04 5.782269e-05
+## Rhizophagus          9.883376e-05 1.078872e-03 1.114740e-03 2.023794e-03
+## Umbelopsis           1.705999e-03 6.742948e-04 5.703323e-04 8.817960e-04
+## Verrucula            8.394614e-05 2.489704e-04 1.814694e-04 3.613918e-04
+## Cystofilobasidium    1.579629e-04 3.459651e-02 1.685073e-04 7.898146e-05
+## Olpidium             3.190851e-03 4.564457e-04 1.186032e-02 2.934501e-03
+## Barnettozyma         3.791110e-04 6.639211e-04 4.605748e-05 4.605748e-05
+## Lambertella          1.042555e-03 5.809309e-04 1.568414e-03 1.445567e-03
+## Yunzhangia           8.529997e-04 3.215868e-04 1.425831e-04 4.858352e-05
+## Pseudogymnoascus     4.738887e-04 2.074753e-04 2.592420e-04 3.469361e-04
+## Crocicreas           2.843332e-04 9.336390e-05 4.277493e-04 3.758475e-04
+## Bullera              3.971721e-05 8.506489e-04 1.296210e-04 1.416656e-03
+## Waitea               2.211481e-04 4.149507e-05 1.944315e-04 7.227836e-05
+## Cladophialophora     3.791110e-04 1.244852e-04 2.203557e-04 7.806063e-04
+## Cadophora            1.137333e-03 1.214168e-04 1.101778e-03 1.792503e-03
+## Hamamotoa            1.895555e-04 4.356982e-04 1.036968e-03 8.673403e-05
+## Stromatinia          2.527407e-04 1.348590e-04 3.306550e-05 3.306550e-05
+## Galactomyces         2.593442e-05 5.186883e-05 9.073469e-05 8.673403e-05
+## Itersonilia          4.536735e-05 4.149507e-04 9.073469e-05 4.536735e-05
+## Knufia               4.422961e-04 1.556065e-04 5.962565e-04 5.203575e-05
+## Cutaneotrichosporon  2.527407e-04 6.327998e-04 1.685073e-04 1.445567e-04
+## Cladosporium         4.354967e-05 3.527081e-04 1.166589e-04 4.354967e-05
+## Chromosera           1.579629e-04 3.112130e-05 9.073469e-05 2.891134e-04
+## Rhodotorula          5.398169e-05 2.074753e-04 5.398169e-05 5.398169e-05
+## Atractiella          3.159258e-04 7.261637e-05 2.699084e-05 2.699084e-05
+## Funneliformis        3.112130e-05 6.224260e-05 3.112130e-05 3.112130e-05
+## Alternaria           3.260681e-05 3.260681e-05 1.296210e-04 1.734681e-04
+## Trichosporon         3.159258e-04 9.855078e-04 1.579629e-04 1.579629e-04
+## Rhizoctonia          3.349590e-05 1.452327e-04 9.462332e-04 3.469361e-04
+## Setophoma            1.263703e-04 4.149507e-05 1.036968e-04 2.081617e-03
+## Monodictys           2.312700e-05 1.659803e-04 3.110904e-04 2.168351e-04
+## Cantharellula        1.200518e-03 6.029262e-05 4.018251e-04 1.011897e-03
+## Paraglomus           1.156350e-05 1.156350e-05 1.156350e-05 1.156454e-04
+## Coprinellus          2.211481e-04 1.491914e-05 1.491914e-05 1.734681e-04
+## Pseudosigmoidea      3.294459e-05 1.659803e-04 3.294459e-05 3.469361e-04
+## Ganoderma            1.037377e-05 2.074753e-05 3.888630e-05 1.037377e-05
+## Cryptotrichosporon   4.107036e-04 2.282229e-04 1.166589e-04 3.469361e-04
+## Sporobolomyces       1.381724e-04 6.950424e-04 1.381724e-04 1.381724e-04
+## Filobasidium         9.477775e-05 4.149507e-04 4.475741e-05 4.475741e-05
+## Hyaloscypha          1.895555e-04 1.156350e-05 4.018251e-04 1.301010e-04
+## Laetinaevia          8.038954e-05 4.253244e-04 4.277493e-04 3.180248e-04
+## Paraboeremia         2.843332e-04 6.588917e-05 6.588917e-05 4.336702e-04
+## Lachnellula          4.107036e-04 1.445567e-05 9.073469e-05 2.891134e-05
+## Crassostrea          3.349590e-05 3.349590e-05 3.349590e-05 3.349590e-05
+## Hannaella            3.732829e-05 3.732829e-05 9.073469e-05 1.734681e-04
+## Pichia               5.796765e-05 9.232652e-04 1.814694e-04 5.796765e-05
+## Fusarium             4.477345e-05 2.904655e-04 1.425831e-04 8.384290e-04
+## Slooffia             1.296210e-05 1.296210e-05 2.592420e-05 1.296210e-05
+## Degelia              1.263703e-04 8.299013e-05 5.184840e-05 1.734681e-04
+## Varicosporium        1.811489e-05 1.037377e-04 1.555452e-04 1.734681e-04
+## Aotearoamyces        9.949655e-06 3.112130e-05 7.777259e-05 9.949655e-06
+## Boeremia             2.159268e-05 3.215868e-04 2.159268e-05 2.159268e-05
+## Sampaiozyma          2.487414e-05 8.299013e-05 2.487414e-05 7.661506e-04
+## Coniochaeta          1.989931e-05 1.141114e-04 6.481049e-05 3.758475e-04
+## Claroideoglomus      4.149507e-05 8.299013e-05 4.149507e-05 4.149507e-05
+## Entoloma             1.579629e-04 2.978791e-05 2.978791e-05 2.978791e-05
+## Archaeospora         1.678923e-05 1.678923e-05 7.777259e-05 1.678923e-05
+## Pulvinula            2.009754e-05 2.009754e-05 7.777259e-05 2.009754e-05
+##                                 5            6            7            8
+## Mrakia               3.878983e-01 3.420844e-01 3.388772e-01 3.387759e-01
+## Candida              4.793325e-02 2.142744e-02 4.334255e-02 2.659605e-02
+## Sistotrema           4.343373e-02 1.455636e-02 7.296035e-02 1.490535e-02
+## Bactrocera           3.508949e-03 6.116451e-03 3.264392e-03 9.909920e-03
+## Mortierella          1.758168e-01 2.692628e-01 1.668176e-01 1.421501e-01
+## Sporidiobolus        2.367281e-03 1.153785e-02 1.501144e-03 2.911690e-02
+## Juxtiphoma           3.384708e-02 3.171419e-02 5.089592e-02 6.247760e-02
+## Tausonia             2.778617e-02 2.615379e-02 2.725886e-02 6.761179e-02
+## Naganishia           2.300124e-02 2.889427e-02 1.348647e-02 2.238694e-02
+## Saitozyma            3.042208e-02 3.417666e-02 3.628955e-02 2.543970e-02
+## Botrytis             3.139586e-03 3.713559e-03 1.048418e-02 2.607569e-02
+## Solicoccozyma        4.479366e-02 5.179125e-02 4.870377e-02 4.841638e-02
+## Holtermanniella      1.061079e-02 1.342442e-02 1.103222e-02 2.843465e-02
+## Tetracladium         2.810517e-02 1.263007e-02 2.073008e-02 1.696366e-02
+## Ascobolus            2.719855e-03 2.859639e-03 6.195196e-03 1.084656e-02
+## Trichosporiella      1.259192e-02 9.810152e-03 1.748952e-02 1.408434e-02
+## Dendryphion          4.566670e-03 3.097943e-03 5.551849e-03 2.867748e-03
+## Papiliotrema         1.235687e-02 1.062435e-02 7.434236e-03 6.498688e-03
+## Incrupila            1.170209e-02 5.361824e-03 8.625620e-03 2.358954e-03
+## Paraphoma            3.223532e-03 9.929303e-05 1.667937e-03 6.417743e-03
+## Paraphaeosphaeria    8.931869e-03 7.685281e-03 1.122284e-02 6.718394e-03
+## Didymella            9.234075e-04 3.455398e-03 9.769348e-04 4.417257e-03
+## Haplosporangium      2.904536e-03 9.849869e-03 5.623332e-03 4.995432e-03
+## X.uncultured.fungus. 1.764548e-02 1.622448e-02 1.596454e-02 7.516276e-03
+## Burgoa               6.547799e-04 9.730717e-04 5.718643e-04 3.700320e-04
+## Pseudoanguillospora  4.700984e-04 8.142029e-04 5.742470e-03 1.537946e-03
+## Apiotrichum          1.208824e-03 5.480975e-03 4.527259e-04 6.568069e-03
+## Exophiala            2.417649e-03 3.296529e-03 3.312047e-03 2.081430e-03
+## NA.                  5.439710e-03 4.150449e-03 8.434998e-03 3.191526e-03
+## Aaosphaeria          1.947550e-03 2.164588e-03 2.883149e-03 4.394130e-04
+## Pleotrichocladium    1.410295e-03 2.164588e-03 3.121426e-03 1.237295e-03
+## Polyphilus           1.242403e-03 3.971721e-04 9.769348e-04 4.162860e-04
+## Vishniacozyma        3.022061e-04 6.354754e-04 9.054518e-04 1.491692e-03
+## Anungitopsis         5.372553e-04 5.361824e-04 1.096073e-03 4.047225e-04
+## Cystobasidium        5.708338e-04 7.149098e-04 4.527259e-04 6.128656e-04
+## Herpotrichia         2.182600e-03 9.015807e-03 9.245139e-03 1.965795e-04
+## Dioszegia            5.708338e-04 5.957582e-05 1.191384e-04 1.680177e-02
+## Boreoplaca           1.175246e-04 1.310668e-03 2.382768e-04 3.815955e-04
+## Epicoccum            4.029415e-04 5.361824e-04 9.769348e-04 2.012049e-03
+## Minimedusa           1.309560e-03 2.601477e-03 2.430423e-03 1.167914e-03
+## Pseudeurotium        1.393506e-03 1.072365e-03 4.765536e-04 3.700320e-04
+## Gorgomyces           3.928679e-03 2.978791e-04 9.531071e-05 2.890875e-04
+## Neoascochyta         4.868876e-04 4.964652e-04 2.891134e-05 3.815955e-04
+## Rhizophagus          9.234075e-04 1.548971e-03 2.096836e-03 6.475561e-04
+## Umbelopsis           1.208824e-03 2.065295e-03 1.286695e-03 1.381724e-04
+## Verrucula            1.678923e-04 3.971721e-04 4.527259e-04 3.122145e-04
+## Cystofilobasidium    1.024143e-02 8.856939e-03 7.898146e-05 1.055748e-02
+## Olpidium             5.204661e-04 8.737787e-04 1.119901e-03 3.353415e-04
+## Barnettozyma         4.751352e-03 5.758996e-04 1.382005e-03 1.271985e-04
+## Lambertella          1.091300e-03 1.886568e-03 1.596454e-03 1.260422e-03
+## Yunzhangia           1.007354e-04 4.567480e-04 4.050705e-04 2.197065e-04
+## Pseudogymnoascus     3.861522e-04 5.560410e-04 2.859321e-04 2.312700e-04
+## Crocicreas           3.861522e-04 3.177377e-04 3.335875e-04 1.156350e-04
+## Bullera              3.971721e-05 7.943443e-05 3.971721e-05 1.364493e-03
+## Waitea               8.394614e-05 2.074753e-05 1.191384e-04 1.156350e-04
+## Cladophialophora     6.883583e-04 4.567480e-04 3.469050e-05 6.938101e-05
+## Cadophora            7.051476e-04 3.177377e-04 6.195196e-04 2.428335e-04
+## Hamamotoa            3.294459e-05 3.375963e-04 2.096836e-03 6.591196e-04
+## Stromatinia          3.306550e-05 8.737787e-04 7.148303e-05 1.503255e-04
+## Galactomyces         2.182600e-04 5.361824e-04 5.003812e-04 3.931590e-04
+## Itersonilia          4.536735e-05 4.536735e-05 4.536735e-05 9.597706e-04
+## Knufia               3.693630e-04 5.203575e-05 7.624857e-04 1.040715e-04
+## Cutaneotrichosporon  1.678923e-04 4.567480e-04 1.667937e-04 3.469050e-05
+## Cladosporium         1.007354e-04 2.581619e-04 4.354967e-05 6.591196e-04
+## Chromosera           2.854169e-04 1.191516e-04 1.556065e-05 1.556065e-05
+## Rhodotorula          5.398169e-05 2.978791e-04 5.398169e-05 1.387620e-04
+## Atractiella          2.699084e-05 1.588689e-04 3.478841e-03 2.197065e-04
+## Funneliformis        3.112130e-05 9.929303e-05 4.050705e-04 1.040715e-04
+## Alternaria           2.014707e-04 3.260681e-05 1.429661e-04 4.162860e-04
+## Trichosporon         1.579629e-04 8.142029e-04 8.673275e-03 1.942668e-03
+## Rhizoctonia          3.349590e-05 3.349590e-05 2.621045e-04 9.250801e-05
+## Setophoma            7.219368e-04 2.074753e-05 4.765536e-05 2.074753e-05
+## Monodictys           1.175246e-04 2.312700e-05 1.191384e-04 4.625400e-05
+## Cantharellula        1.343138e-04 3.177377e-04 3.812429e-04 1.271985e-04
+## Paraglomus           1.156350e-05 1.156350e-05 1.191384e-04 2.312700e-05
+## Coprinellus          3.357846e-04 3.574549e-04 1.906214e-04 8.094451e-05
+## Pseudosigmoidea      3.022061e-04 3.294459e-05 3.294459e-05 1.850160e-04
+## Ganoderma            1.511031e-04 9.929303e-05 1.191384e-04 9.250801e-05
+## Cryptotrichosporon   2.048286e-03 3.971721e-05 1.429661e-04 3.700320e-04
+## Sporobolomyces       1.381724e-04 1.381724e-04 1.381724e-04 4.741035e-04
+## Filobasidium         4.475741e-05 2.184447e-04 8.101411e-04 1.376057e-03
+## Hyaloscypha          7.387260e-04 7.943443e-05 1.191384e-04 2.312700e-05
+## Laetinaevia          1.678923e-04 4.964652e-04 8.038954e-05 7.631911e-04
+## Paraboeremia         6.588917e-05 6.588917e-05 6.588917e-05 6.588917e-05
+## Lachnellula          1.445567e-05 1.191516e-04 1.445567e-05 1.445567e-05
+## Crassostrea          3.349590e-05 1.588689e-04 7.148303e-05 3.349590e-05
+## Hannaella            1.175246e-04 7.943443e-05 2.144491e-04 2.081430e-04
+## Pichia               5.796765e-05 5.796765e-05 5.796765e-05 5.796765e-05
+## Fusarium             4.477345e-05 2.184447e-04 4.477345e-05 6.591196e-04
+## Slooffia             1.296210e-05 2.780205e-04 1.296210e-05 1.296210e-05
+## Degelia              1.678923e-04 1.492448e-05 1.906214e-04 3.469050e-05
+## Varicosporium        1.811489e-05 1.811489e-05 1.811489e-05 9.250801e-05
+## Aotearoamyces        9.949655e-06 9.949655e-06 9.949655e-06 3.469050e-05
+## Boeremia             2.159268e-05 2.159268e-05 2.159268e-05 3.353415e-04
+## Sampaiozyma          2.487414e-05 2.487414e-05 1.906214e-04 2.543970e-04
+## Coniochaeta          2.518384e-04 3.177377e-04 1.989931e-05 1.271985e-04
+## Claroideoglomus      4.149507e-05 4.149507e-05 4.149507e-05 1.387620e-04
+## Entoloma             2.978791e-05 5.957582e-05 2.144491e-04 2.978791e-05
+## Archaeospora         3.357846e-05 1.678923e-05 9.531071e-05 1.678923e-05
+## Pulvinula            2.009754e-05 1.985861e-04 1.667937e-04 2.009754e-05
+##                                 9           10           11           12
+## Mrakia               2.348949e-01 3.194431e-01 2.877716e-01 2.602708e-01
+## Candida              3.379126e-01 1.420964e-02 7.338417e-02 1.522200e-02
+## Sistotrema           1.086512e-01 1.037229e-01 5.190868e-02 4.971905e-02
+## Bactrocera           1.251894e-03 1.928628e-03 4.012587e-02 2.371960e-02
+## Mortierella          7.853990e-02 2.051563e-01 1.469291e-01 1.632507e-01
+## Sporidiobolus        1.103644e-02 4.852678e-03 3.312049e-02 1.508382e-02
+## Juxtiphoma           2.658628e-02 4.308929e-02 2.161330e-02 8.235077e-02
+## Tausonia             1.083877e-02 1.873880e-02 2.680417e-02 4.789978e-02
+## Naganishia           2.141398e-02 1.067589e-02 6.304837e-02 1.027082e-02
+## Saitozyma            2.642156e-02 3.215210e-02 2.995085e-02 2.001197e-02
+## Botrytis             7.840812e-03 5.366564e-02 7.556617e-03 7.039886e-02
+## Solicoccozyma        2.302827e-02 3.870944e-02 3.539437e-02 4.069178e-02
+## Holtermanniella      8.400870e-03 4.790464e-03 1.796132e-02 8.935151e-03
+## Tetracladium         3.525071e-03 2.627912e-02 6.017732e-03 3.369105e-02
+## Ascobolus            3.525071e-03 4.093669e-03 4.639625e-03 1.667281e-02
+## Trichosporiella      6.358305e-03 9.817340e-03 5.282742e-03 1.358696e-02
+## Dendryphion          7.577255e-04 2.724965e-03 1.837475e-03 1.360999e-02
+## Papiliotrema         1.311195e-02 6.320924e-03 2.133768e-02 4.053058e-03
+## Incrupila            3.162680e-03 9.244973e-03 2.664339e-03 6.655306e-03
+## Paraphoma            1.647229e-04 3.658172e-03 5.971795e-04 2.003500e-03
+## Paraphaeosphaeria    2.734401e-03 5.313060e-03 3.238550e-03 5.296610e-03
+## Didymella            3.294459e-04 1.567788e-03 7.809270e-04 1.215917e-02
+## Haplosporangium      3.096791e-03 4.193211e-03 2.825118e-03 3.799742e-03
+## X.uncultured.fungus. 1.202477e-02 1.011597e-02 1.146125e-02 1.066231e-02
+## Burgoa               8.071424e-03 2.550766e-03 1.584822e-03 7.599484e-04
+## Pseudoanguillospora  3.953350e-03 8.958790e-04 2.526529e-04 1.612012e-03
+## Apiotrichum          9.224484e-04 4.354967e-04 2.159033e-03 1.427782e-03
+## Exophiala            2.009620e-03 3.160462e-03 1.309201e-03 5.273581e-03
+## NA.                  5.831192e-03 2.376568e-03 2.204970e-03 8.566691e-03
+## Aaosphaeria          2.372010e-03 2.961378e-03 1.791538e-03 9.211496e-04
+## Pleotrichocladium    3.953350e-04 1.605116e-03 3.904635e-04 2.740420e-03
+## Polyphilus           9.553930e-04 1.368704e-03 5.282742e-04 9.902358e-04
+## Vishniacozyma        4.282796e-04 2.115270e-04 4.111351e-03 7.138909e-04
+## Anungitopsis         3.623905e-04 8.834362e-04 5.053057e-04 1.082351e-03
+## Cystobasidium        3.953350e-04 7.465658e-05 1.171391e-03 1.151437e-04
+## Herpotrichia         3.689794e-03 4.106112e-04 1.527401e-02 9.441783e-04
+## Dioszegia            6.588917e-04 5.848099e-04 2.848087e-03 5.526898e-04
+## Boreoplaca           1.680174e-03 2.264583e-03 4.685562e-03 5.987472e-04
+## Epicoccum            2.306121e-04 4.417181e-03 3.606045e-03 1.842299e-03
+## Minimedusa           4.216907e-03 4.504280e-03 5.348599e-04 3.270081e-03
+## Pseudeurotium        1.548396e-03 2.077941e-03 1.745602e-03 1.850160e-04
+## Gorgomyces           3.349590e-05 8.087796e-04 4.364004e-04 3.914886e-04
+## Neoascochyta         6.588917e-04 2.364125e-04 7.349901e-04 1.381724e-04
+## Rhizophagus          1.976675e-04 3.857257e-03 5.282742e-04 7.369197e-04
+## Umbelopsis           1.054227e-03 1.717101e-03 1.148422e-03 2.763449e-04
+## Verrucula            2.306121e-04 4.603822e-04 3.215582e-04 4.375461e-04
+## Cystofilobasidium    1.910786e-03 8.709934e-04 3.256925e-02 4.836035e-04
+## Olpidium             4.593688e-05 8.585507e-04 9.187377e-05 1.476142e-02
+## Barnettozyma         6.259472e-04 2.861836e-04 2.733245e-03 9.211496e-05
+## Lambertella          3.953350e-04 1.070078e-03 4.823373e-04 1.473839e-03
+## Yunzhangia           1.087171e-03 3.732829e-04 2.296844e-04 4.375461e-04
+## Pseudogymnoascus     3.306550e-05 4.354967e-04 2.756213e-04 3.306550e-05
+## Crocicreas           6.259472e-04 2.488553e-05 1.244276e-05 1.244276e-05
+## Bullera              4.612242e-04 5.350388e-04 5.121962e-03 5.987472e-04
+## Waitea               1.976675e-04 2.074753e-05 6.890532e-05 2.302874e-04
+## Cladophialophora     1.647229e-04 8.834362e-04 8.498323e-04 3.469050e-05
+## Cadophora            1.214168e-04 7.714513e-04 1.214168e-04 1.519897e-03
+## Hamamotoa            6.588917e-05 3.294459e-05 2.526529e-04 1.612012e-04
+## Stromatinia          1.647229e-04 2.861836e-04 1.837475e-04 5.296610e-04
+## Galactomyces         8.565593e-04 8.709934e-05 9.187377e-05 2.593442e-05
+## Itersonilia          9.224484e-04 7.465658e-04 7.349901e-04 1.842299e-04
+## Knufia               5.203575e-05 1.032749e-03 4.134319e-04 5.203575e-05
+## Cutaneotrichosporon  4.282796e-04 1.734525e-05 3.904635e-04 1.734525e-05
+## Cladosporium         4.282796e-04 8.709934e-05 2.756213e-04 2.763449e-04
+## Chromosera           6.588917e-05 8.709934e-05 1.378106e-04 4.605748e-05
+## Rhodotorula          4.612242e-04 3.608401e-04 6.201479e-04 2.993736e-04
+## Atractiella          2.699084e-05 1.368704e-04 9.187377e-05 3.454311e-04
+## Funneliformis        3.112130e-05 4.106112e-04 9.417061e-04 1.612012e-04
+## Alternaria           3.260681e-05 9.954211e-05 3.260681e-05 6.217760e-04
+## Trichosporon         6.918363e-04 5.101533e-04 1.194359e-03 1.579629e-04
+## Rhizoctonia          3.349590e-05 1.356261e-03 3.349590e-05 3.349590e-05
+## Setophoma            2.074753e-05 1.244276e-04 2.074753e-05 4.605748e-05
+## Monodictys           6.588917e-05 6.221382e-05 2.312700e-05 1.635041e-03
+## Cantharellula        6.029262e-05 2.364125e-04 6.029262e-05 6.029262e-05
+## Paraglomus           4.282796e-04 1.318933e-03 3.904635e-04 1.156350e-05
+## Coprinellus          1.491914e-05 5.599243e-04 1.491914e-05 7.829772e-04
+## Pseudosigmoidea      6.588917e-05 1.866414e-04 1.148422e-04 2.533161e-04
+## Ganoderma            1.037377e-05 3.732829e-05 1.037377e-05 6.908622e-05
+## Cryptotrichosporon   1.985861e-05 1.617559e-04 1.985861e-05 4.605748e-05
+## Sporobolomyces       1.381724e-04 1.381724e-04 1.584822e-03 2.763449e-04
+## Filobasidium         1.647229e-04 4.475741e-05 2.756213e-04 7.829772e-04
+## Hyaloscypha          1.156350e-05 1.156350e-05 1.156350e-05 2.993736e-04
+## Laetinaevia          8.038954e-05 2.239697e-04 1.607791e-04 6.908622e-04
+## Paraboeremia         1.317783e-04 5.599243e-04 2.067160e-04 2.533161e-04
+## Lachnellula          1.647229e-04 1.445567e-05 3.215582e-04 1.445567e-05
+## Crassostrea          2.965013e-04 1.617559e-04 3.349590e-05 3.349590e-05
+## Hannaella            3.732829e-05 7.465658e-05 3.732829e-05 1.151437e-04
+## Pichia               5.796765e-05 4.852678e-04 4.593688e-04 3.914886e-04
+## Fusarium             4.477345e-05 1.368704e-04 4.477345e-05 4.477345e-05
+## Slooffia             6.588917e-05 4.977105e-05 1.296210e-05 1.296210e-05
+## Degelia              1.492448e-05 1.492448e-05 1.492448e-05 1.492448e-05
+## Varicosporium        1.811489e-05 7.465658e-05 1.811489e-05 6.908622e-05
+## Aotearoamyces        6.588917e-05 3.732829e-05 6.890532e-05 9.949655e-06
+## Boeremia             2.159268e-05 5.300617e-03 1.561854e-03 2.302874e-04
+## Sampaiozyma          2.487414e-05 9.954211e-05 7.579586e-04 2.487414e-05
+## Coniochaeta          1.989931e-05 1.989931e-05 1.989931e-05 1.989931e-05
+## Claroideoglomus      4.149507e-05 1.741987e-04 4.149507e-05 1.151437e-04
+## Entoloma             2.978791e-05 7.465658e-05 2.978791e-05 6.908622e-05
+## Archaeospora         9.883376e-05 1.678923e-05 1.148422e-04 1.678923e-05
+## Pulvinula            2.009754e-05 8.709934e-05 2.009754e-05 9.211496e-05
+##                                13           14           15           16
+## Mrakia               2.197703e-01 1.847039e-01 2.393617e-01 2.380455e-01
+## Candida              1.929306e-02 2.627716e-01 3.700627e-02 1.496428e-02
+## Sistotrema           2.575358e-01 9.886585e-02 4.268718e-02 8.602471e-02
+## Bactrocera           1.390568e-02 6.745363e-03 6.739375e-03 1.727260e-01
+## Mortierella          1.984151e-01 8.134114e-02 1.768048e-01 1.229180e-01
+## Sporidiobolus        3.749568e-02 4.708528e-02 5.094056e-02 1.085607e-01
+## Juxtiphoma           1.825661e-02 2.218695e-02 3.506351e-02 1.724275e-02
+## Tausonia             1.141173e-02 1.213504e-02 3.632295e-02 1.597915e-02
+## Naganishia           1.482337e-02 3.746321e-02 3.774318e-02 3.936083e-02
+## Saitozyma            2.937684e-02 3.085011e-02 3.731443e-02 2.474479e-02
+## Botrytis             2.154949e-02 1.196971e-02 3.872126e-02 1.643683e-02
+## Solicoccozyma        3.461306e-02 2.529511e-02 6.423174e-02 3.105287e-02
+## Holtermanniella      3.843496e-03 8.464769e-03 2.486736e-02 9.302927e-03
+## Tetracladium         8.809812e-03 2.579109e-03 1.548850e-02 5.442461e-03
+## Ascobolus            1.425117e-03 1.950865e-03 4.716223e-03 1.452650e-03
+## Trichosporiella      6.693729e-03 2.182323e-03 2.053969e-02 3.989812e-03
+## Dendryphion          2.418380e-03 1.190358e-03 6.511603e-03 7.661234e-04
+## Papiliotrema         4.912334e-03 8.167179e-03 1.677475e-02 7.661234e-03
+## Incrupila            7.762567e-03 5.521939e-03 7.248513e-03 4.736036e-03
+## Paraphoma            2.925808e-03 1.183745e-02 3.751541e-04 1.273556e-03
+## Paraphaeosphaeria    4.156590e-03 1.388751e-03 7.114529e-03 3.362983e-03
+## Didymella            8.852997e-04 3.306550e-04 5.091377e-04 5.273317e-04
+## Haplosporangium      1.522284e-03 3.174288e-03 3.724744e-03 2.477464e-03
+## X.uncultured.fungus. 1.510408e-02 3.356149e-02 1.006217e-02 9.402424e-03
+## Burgoa               3.228105e-03 3.835598e-03 3.215606e-04 1.771039e-03
+## Pseudoanguillospora  8.529107e-04 9.588996e-04 1.152259e-03 1.989931e-04
+## Apiotrichum          3.454828e-04 1.818603e-03 1.768584e-03 2.885400e-04
+## Exophiala            2.170064e-03 2.512978e-03 2.947639e-03 2.069528e-03
+## NA.                  3.422439e-03 6.315511e-03 5.613913e-03 5.213619e-03
+## Aaosphaeria          3.238901e-03 2.116192e-03 1.728388e-03 1.601894e-03
+## Pleotrichocladium    4.534462e-04 7.935721e-04 2.438502e-03 3.183890e-04
+## Polyphilus           2.483158e-03 7.935721e-04 1.594405e-03 6.765765e-04
+## Vishniacozyma        8.313180e-04 1.289555e-03 7.503082e-04 1.741190e-03
+## Anungitopsis         9.284851e-04 4.298515e-04 5.225360e-04 5.472310e-04
+## Cystobasidium        3.238901e-05 2.645240e-04 1.339836e-03 1.492448e-04
+## Herpotrichia         9.828976e-05 3.504943e-03 1.085267e-03 3.880365e-04
+## Dioszegia            2.699084e-04 2.215389e-03 2.036551e-03 3.681372e-03
+## Boreoplaca           2.774659e-03 1.917799e-03 7.235114e-04 3.024695e-03
+## Epicoccum            8.421144e-04 9.588996e-04 3.496972e-03 1.492448e-03
+## Minimedusa           6.726119e-03 4.926760e-03 3.148615e-03 4.855432e-03
+## Pseudeurotium        4.016238e-03 1.124227e-03 9.646819e-04 1.601894e-03
+## Gorgomyces           4.858352e-04 1.322620e-04 6.699180e-05 1.492448e-04
+## Neoascochyta         2.807048e-04 3.240419e-03 3.215606e-04 1.492448e-04
+## Rhizophagus          2.018915e-03 2.645240e-04 1.219251e-03 7.860227e-04
+## Umbelopsis           6.585766e-04 1.025031e-03 1.045072e-03 1.164110e-03
+## Verrucula            4.318535e-04 3.306550e-04 3.081623e-04 3.581876e-04
+## Cystofilobasidium    1.271809e-02 1.223424e-02 2.827054e-03 5.810598e-03
+## Olpidium             3.562792e-04 1.653275e-04 1.574307e-02 4.593688e-05
+## Barnettozyma         3.994645e-04 5.852594e-03 3.081623e-04 1.691441e-04
+## Lambertella          5.182242e-04 1.976675e-04 1.393429e-03 1.976675e-04
+## Yunzhangia           9.716704e-05 9.588996e-04 2.277721e-04 4.858352e-05
+## Pseudogymnoascus     2.159268e-04 6.613101e-05 1.071869e-04 1.790938e-04
+## Crocicreas           1.187597e-04 6.282446e-04 2.143738e-04 1.293455e-04
+## Bullera              1.079634e-03 1.190358e-03 1.822177e-03 2.397867e-03
+## Waitea               1.079634e-04 3.604140e-03 2.074753e-05 1.591945e-04
+## Cladophialophora     3.454828e-04 3.469050e-05 2.679672e-04 5.969793e-04
+## Cadophora            5.506132e-04 1.214168e-04 3.617557e-04 3.681372e-04
+## Hamamotoa            1.079634e-04 3.294459e-05 5.225360e-04 8.954689e-05
+## Stromatinia          4.102608e-04 6.613101e-05 1.741787e-04 3.306550e-05
+## Galactomyces         2.593442e-05 2.645240e-04 1.875770e-04 2.593442e-05
+## Itersonilia          1.295561e-04 1.256489e-03 7.637065e-04 3.124192e-03
+## Knufia               3.346865e-04 5.951790e-04 5.203575e-05 3.880365e-04
+## Cutaneotrichosporon  1.734525e-05 7.605066e-04 3.751541e-04 1.734525e-05
+## Cladosporium         4.354967e-05 4.354967e-05 4.019508e-04 1.144210e-03
+## Chromosera           1.556065e-05 1.556065e-05 1.556065e-05 1.094462e-04
+## Rhodotorula          1.079634e-04 2.645240e-03 6.203441e-03 3.283386e-04
+## Atractiella          5.398169e-05 3.306550e-04 2.699084e-05 2.699084e-05
+## Funneliformis        4.210572e-04 1.388751e-03 3.112130e-05 1.989931e-04
+## Alternaria           1.511487e-04 3.260681e-05 1.607803e-04 2.188924e-04
+## Trichosporon         1.579629e-04 1.579629e-04 8.173000e-04 1.579629e-04
+## Rhizoctonia          4.318535e-04 1.613597e-02 6.699180e-05 3.349590e-05
+## Setophoma            2.074753e-05 2.074753e-05 5.359344e-05 2.188924e-04
+## Monodictys           2.312700e-05 2.312700e-05 2.312700e-05 2.312700e-05
+## Cantharellula        6.029262e-05 6.029262e-05 1.205852e-04 6.029262e-05
+## Paraglomus           6.693729e-04 1.554079e-03 1.156350e-05 2.387917e-04
+## Coprinellus          1.491914e-05 1.491914e-05 2.813656e-04 1.491914e-05
+## Pseudosigmoidea      1.727414e-04 3.294459e-05 3.294459e-05 6.964758e-05
+## Ganoderma            6.477803e-05 1.037377e-05 1.071869e-04 1.037377e-05
+## Cryptotrichosporon   1.985861e-05 1.985861e-05 1.985861e-05 1.985861e-05
+## Sporobolomyces       1.813785e-03 4.298515e-04 1.902567e-03 2.019780e-03
+## Filobasidium         4.475741e-05 4.475741e-05 4.475741e-05 4.875331e-04
+## Hyaloscypha          3.238901e-05 1.156350e-05 7.503082e-04 1.156350e-05
+## Laetinaevia          8.038954e-05 8.038954e-05 2.545688e-04 8.038954e-05
+## Paraboeremia         5.290206e-04 6.588917e-05 6.588917e-05 1.591945e-04
+## Lachnellula          2.807048e-04 1.445567e-05 4.823410e-04 1.445567e-05
+## Crassostrea          3.454828e-04 4.629170e-04 6.699180e-05 2.686407e-04
+## Hannaella            3.732829e-05 3.732829e-05 1.607803e-04 3.732829e-05
+## Pichia               2.699084e-04 3.306550e-04 5.796765e-05 5.796765e-05
+## Fusarium             4.477345e-05 4.477345e-05 1.607803e-04 8.954689e-05
+## Slooffia             1.296210e-05 3.306550e-04 1.741787e-04 1.492448e-04
+## Degelia              1.492448e-05 1.492448e-05 6.699180e-05 2.984896e-05
+## Varicosporium        1.811489e-05 1.811489e-05 1.811489e-05 5.969793e-05
+## Aotearoamyces        9.949655e-06 9.949655e-06 9.949655e-06 1.989931e-05
+## Boeremia             4.318535e-05 2.159268e-05 2.143738e-04 2.159268e-05
+## Sampaiozyma          2.487414e-05 7.935721e-04 2.487414e-05 4.974827e-05
+## Coniochaeta          1.989931e-05 1.989931e-05 1.989931e-05 3.979862e-05
+## Claroideoglomus      1.403524e-04 1.983930e-04 4.149507e-05 3.382883e-04
+## Entoloma             2.978791e-05 2.978791e-05 2.679672e-04 2.978791e-05
+## Archaeospora         2.483158e-04 1.678923e-05 1.678923e-05 1.691441e-04
+## Pulvinula            2.009754e-05 2.009754e-05 4.019508e-05 4.974827e-05
+##                                17           18
+## Mrakia               1.761057e-01 1.168765e-01
+## Candida              3.755579e-02 5.599650e-02
+## Sistotrema           1.718886e-01 1.681387e-01
+## Bactrocera           2.325952e-03 1.287024e-02
+## Mortierella          2.290809e-01 2.376519e-01
+## Sporidiobolus        3.520086e-02 2.538243e-02
+## Juxtiphoma           2.935337e-02 4.684609e-02
+## Tausonia             8.615443e-03 1.155736e-02
+## Naganishia           1.268767e-02 2.152334e-02
+## Saitozyma            4.953336e-02 5.331105e-02
+## Botrytis             3.433134e-02 8.185634e-03
+## Solicoccozyma        4.683062e-02 4.034135e-02
+## Holtermanniella      1.078923e-02 8.275149e-03
+## Tetracladium         5.970668e-03 6.146685e-03
+## Ascobolus            1.630340e-03 4.107736e-03
+## Trichosporiella      5.130137e-03 4.664717e-03
+## Dendryphion          1.318764e-03 3.361779e-03
+## Papiliotrema         7.231465e-03 1.112968e-02
+## Incrupila            8.383572e-03 4.595095e-03
+## Paraphoma            2.847661e-03 8.553639e-04
+## Paraphaeosphaeria    6.753232e-03 5.778680e-03
+## Didymella            1.521651e-03 1.790297e-04
+## Haplosporangium      2.195525e-03 4.356388e-03
+## X.uncultured.fungus. 1.793374e-02 4.230073e-02
+## Burgoa               3.746160e-03 4.654771e-03
+## Pseudoanguillospora  1.796997e-03 6.663882e-04
+## Apiotrichum          7.173497e-04 2.456685e-03
+## Exophiala            3.941800e-03 3.540809e-03
+## NA.                  4.463509e-03 5.868194e-03
+## Aaosphaeria          5.731552e-03 4.406119e-03
+## Pleotrichocladium    9.419744e-04 5.370890e-04
+## Polyphilus           1.072402e-03 1.392453e-03
+## Vishniacozyma        4.275114e-04 5.669273e-04
+## Anungitopsis         6.086604e-04 5.967655e-04
+## Cystobasidium        6.086604e-04 5.072507e-04
+## Herpotrichia         5.217089e-04 3.471186e-03
+## Dioszegia            9.709582e-04 1.889758e-03
+## Boreoplaca           4.528723e-03 9.399057e-03
+## Epicoccum            5.492435e-03 1.491914e-04
+## Minimedusa           4.543215e-03 6.007440e-03
+## Pseudeurotium        6.492377e-03 8.135903e-03
+## Gorgomyces           1.014434e-03 1.790297e-04
+## Neoascochyta         1.811489e-04 8.155796e-04
+## Rhizophagus          1.202829e-03 2.924151e-03
+## Umbelopsis           2.195525e-03 3.312049e-03
+## Verrucula            5.144629e-04 5.470351e-04
+## Cystofilobasidium    6.992348e-03 7.628653e-03
+## Olpidium             6.347458e-03 6.166577e-04
+## Barnettozyma         1.659324e-03 1.342722e-03
+## Lambertella          6.376442e-04 1.064232e-03
+## Yunzhangia           3.767898e-04 1.581429e-03
+## Pseudogymnoascus     7.970552e-05 1.412345e-03
+## Crocicreas           5.796765e-05 5.370890e-04
+## Bullera              8.695148e-05 1.312884e-03
+## Waitea               2.970842e-04 3.779515e-04
+## Cladophialophora     2.608544e-04 5.171968e-04
+## Cadophora            7.028578e-04 3.481132e-04
+## Hamamotoa            3.294459e-05 1.989218e-04
+## Stromatinia          2.753464e-04 1.094070e-04
+## Galactomyces         1.304272e-04 1.292992e-04
+## Itersonilia          7.970552e-04 1.680890e-03
+## Knufia               7.390876e-04 7.161186e-04
+## Cutaneotrichosporon  3.043302e-04 1.734525e-05
+## Cladosporium         2.246247e-04 4.674663e-04
+## Chromosera           9.419744e-05 4.774124e-04
+## Rhodotorula          5.398169e-05 4.475741e-04
+## Atractiella          1.811489e-04 2.088679e-04
+## Funneliformis        7.390876e-04 6.763343e-04
+## Alternaria           6.521361e-05 6.962265e-05
+## Trichosporon         1.376732e-03 7.658491e-04
+## Rhizoctonia          1.086894e-03 9.747170e-04
+## Setophoma            9.419744e-05 2.074753e-05
+## Monodictys           1.014434e-04 9.946092e-05
+## Cantharellula        1.739030e-04 5.967655e-04
+## Paraglomus           3.637470e-03 3.878976e-04
+## Coprinellus          2.391166e-04 2.983828e-05
+## Pseudosigmoidea      7.970552e-05 3.294459e-05
+## Ganoderma            1.037377e-05 9.946092e-05
+## Cryptotrichosporon   1.985861e-05 1.985861e-05
+## Sporobolomyces       4.564953e-04 4.575202e-04
+## Filobasidium         4.475741e-05 8.951483e-05
+## Hyaloscypha          1.156350e-05 1.156350e-05
+## Laetinaevia          8.038954e-05 8.038954e-05
+## Paraboeremia         4.057736e-04 3.282210e-04
+## Lachnellula          4.275114e-04 1.690836e-04
+## Crassostrea          3.478059e-04 3.182749e-04
+## Hannaella            3.732829e-05 1.392453e-04
+## Pichia               1.159353e-04 1.690836e-04
+## Fusarium             4.477345e-05 1.591375e-04
+## Slooffia             1.666570e-04 1.094070e-04
+## Degelia              1.492448e-05 1.492448e-05
+## Varicosporium        3.622978e-05 8.951483e-05
+## Aotearoamyces        2.173787e-05 1.392453e-04
+## Boeremia             2.159268e-05 3.282210e-04
+## Sampaiozyma          2.487414e-05 2.487414e-05
+## Coniochaeta          1.883949e-04 1.989931e-05
+## Claroideoglomus      4.149507e-05 3.580593e-04
+## Entoloma             1.594110e-04 1.193531e-04
+## Archaeospora         1.678923e-05 2.287601e-04
+## Pulvinula            8.695148e-05 2.009754e-05
+## 
+## $meta.dat.use
+##       ndvi_01
+## 1  -1.9625407
+## 2   0.9980715
+## 3  -0.7571646
+## 4  -1.1410253
+## 5  -1.9896865
+## 6   0.6228720
+## 7  -0.8421145
+## 8   0.9838132
+## 9   0.6887884
+## 10  0.5747102
+## 11  0.5945529
+## 12 -1.0529250
+## 13  0.6121273
+## 14  0.8302155
+## 15  0.3130208
+## 16  0.5095020
+## 17  0.3558477
+## 18  0.6619353
+## 
+## $wald
+## $wald$beta
+##                 Mrakia   Candida  Sistotrema Bactrocera Mortierella
+## (Intercept)  8.8942074 5.9501581  6.69553872  3.4486788   8.0724606
+## ndvi_01     -0.3601436 0.1574983 -0.04584439  0.5230339  -0.1993473
+##             Sporidiobolus Juxtiphoma   Tausonia Naganishia  Saitozyma  Botrytis
+## (Intercept)      4.378760  5.7977898  5.2022449  5.1647632 5.59631589 4.3284318
+## ndvi_01          1.422778 -0.1419552 -0.2499401  0.2131148 0.08000806 0.5395917
+##             Solicoccozyma Holtermanniella Tetracladium  Ascobolus
+## (Intercept)     6.0274208     4.112298297    4.3228940  2.7081407
+## ndvi_01        -0.1421016     0.001351223   -0.9257339 -0.3980752
+##             Trichosporiella Dendryphion Papiliotrema  Incrupila  Paraphoma
+## (Intercept)       3.8788327   2.2001977   3.90339220  3.2904963  1.1953614
+## ndvi_01          -0.5978134  -0.6745859  -0.01556509 -0.5670846 -0.7174352
+##             Paraphaeosphaeria  Didymella Haplosporangium X.uncultured.fungus.
+## (Intercept)         3.0942738  0.8702958     2.512180538           4.55604227
+## ndvi_01            -0.3118014 -0.4165883    -0.006015125          -0.05272315
+##                Burgoa Pseudoanguillospora Apiotrichum  Exophiala        NA.
+## (Intercept) 1.0905213           0.7056184   0.7281833  2.1962112  2.9433932
+## ndvi_01     0.4811883          -0.2889514   0.5792219 -0.2699503 -0.4221425
+##             Aaosphaeria Pleotrichocladium Polyphilus Vishniacozyma Anungitopsis
+## (Intercept)    1.627750         0.7803799  0.5066444    0.09994638  -0.03021235
+## ndvi_01       -0.154652        -0.4027885 -0.0336807    0.70313913  -0.29722369
+##             Cystobasidium Herpotrichia Dioszegia Boreoplaca Epicoccum
+## (Intercept)    -0.7541884    1.3196872 0.1182307  0.6216776 0.7130020
+## ndvi_01        -0.1413814    0.1869529 1.2564446  1.4692198 0.8812529
+##             Minimedusa Pseudeurotium Gorgomyces Neoascochyta Rhizophagus
+## (Intercept)  2.2393766     1.0811724 -0.7467174   -0.9198901   0.6303061
+## ndvi_01      0.1106339     0.2351272 -1.1444276    0.3902196   0.1424985
+##             Umbelopsis  Verrucula Cystofilobasidium   Olpidium Barnettozyma
+## (Intercept)  0.7313788 -0.9318895          2.087582  0.5362518   -0.2739397
+## ndvi_01     -0.1120565  0.2656547          1.834111 -1.3704642    0.2198452
+##             Lambertella Yunzhangia Pseudogymnoascus Crocicreas    Bullera
+## (Intercept)   0.4675277  -1.050468       -1.5272946 -1.9350892 -0.4190255
+## ndvi_01      -0.5461814   0.200010       -0.2757864 -0.3654408  1.2613358
+##                Waitea Cladophialophora  Cadophora  Hamamotoa Stromatinia
+## (Intercept) -2.310343       -1.3192438 -0.4508063 -1.8344779  -2.1378947
+## ndvi_01     -0.150909       -0.1921385 -1.0389346 -0.1222196   0.3263724
+##             Galactomyces Itersonilia     Knufia Cutaneotrichosporon
+## (Intercept)   -2.3244010   -1.006609 -1.3062605          -2.2859292
+## ndvi_01        0.2685759    1.470499 -0.1434756          -0.1193485
+##             Cladosporium Chromosera Rhodotorula Atractiella Funneliformis
+## (Intercept)   -1.8088405 -3.1606752   -1.430221  -2.4881863    -2.0389944
+## ndvi_01        0.6806295 -0.6858619    1.013241  -0.1973964     0.8488935
+##             Alternaria Trichosporon Rhizoctonia Setophoma Monodictys
+## (Intercept) -2.6384939   -0.2586130  -1.7380132 -3.205879  -3.089877
+## ndvi_01     -0.4832859    0.4016763   0.5911897 -1.352938  -0.786227
+##             Cantharellula Paraglomus Coprinellus Pseudosigmoidea  Ganoderma
+## (Intercept)    -1.8537510  -2.611189   -2.947229      -2.8262741 -4.1514081
+## ndvi_01        -0.8554298   1.254794   -1.152908      -0.2613889 -0.2648695
+##             Cryptotrichosporon Sporobolomyces Filobasidium Hyaloscypha
+## (Intercept)          -3.047558     -0.7202827   -2.1413678   -3.582271
+## ndvi_01              -1.395587      0.7124375    0.1896753   -1.854668
+##             Laetinaevia Paraboeremia Lachnellula Crassostrea  Hannaella
+## (Intercept)   -1.729770   -1.9753666 -3.29950881  -2.6838667 -3.0090247
+## ndvi_01       -0.107517   -0.1434928  0.06419128   0.7530111 -0.3863337
+##                Pichia   Fusarium   Slooffia   Degelia Varicosporium
+## (Intercept) -2.160248 -2.5703379 -3.9492510 -4.077829   -3.92117996
+## ndvi_01      0.390980  0.2070224  0.7868061 -1.069650   -0.05247627
+##             Aotearoamyces   Boeremia Sampaiozyma Coniochaeta Claroideoglomus
+## (Intercept)    -4.8110111 -2.9238876  -3.1487158  -3.6295732      -2.9362944
+## ndvi_01         0.4519739  0.8893452   0.2415206  -0.3330647       0.4324119
+##               Entoloma Archaeospora  Pulvinula
+## (Intercept) -3.4172896   -3.9305752 -3.9484818
+## ndvi_01     -0.3168533    0.1563548 -0.1709763
+## 
+## $wald$sig
+##               Mrakia              Candida           Sistotrema 
+##            0.6233013            1.3671077            1.2090721 
+##           Bactrocera          Mortierella        Sporidiobolus 
+##            1.7850198            0.4267081            1.3338179 
+##           Juxtiphoma             Tausonia           Naganishia 
+##            0.5763258            0.8396484            0.7488569 
+##            Saitozyma             Botrytis        Solicoccozyma 
+##            0.3070906            1.2906217            0.2856745 
+##      Holtermanniella         Tetracladium            Ascobolus 
+##            0.7197022            0.8221235            0.9145743 
+##      Trichosporiella          Dendryphion         Papiliotrema 
+##            0.7645297            0.8300203            0.6479855 
+##            Incrupila            Paraphoma    Paraphaeosphaeria 
+##            0.5683365            1.9143931            0.6643021 
+##            Didymella      Haplosporangium X.uncultured.fungus. 
+##            1.4781773            0.6210499            0.5899659 
+##               Burgoa  Pseudoanguillospora          Apiotrichum 
+##            1.3618695            1.3066608            1.3714338 
+##            Exophiala                  NA.          Aaosphaeria 
+##            0.4636157            0.7016399            0.9777038 
+##    Pleotrichocladium           Polyphilus        Vishniacozyma 
+##            1.0041378            0.7986170            1.0678246 
+##         Anungitopsis        Cystobasidium         Herpotrichia 
+##            0.5618864            1.4661328            2.3161234 
+##            Dioszegia           Boreoplaca            Epicoccum 
+##            2.0625747            1.3008227            1.6801985 
+##           Minimedusa        Pseudeurotium           Gorgomyces 
+##            1.0618424            1.3603130            1.4640745 
+##         Neoascochyta          Rhizophagus           Umbelopsis 
+##            1.5293936            1.2550695            1.0927633 
+##            Verrucula    Cystofilobasidium             Olpidium 
+##            0.4742074            2.2826783            2.3013192 
+##         Barnettozyma          Lambertella           Yunzhangia 
+##            2.0963256            0.8273172            1.4488712 
+##     Pseudogymnoascus           Crocicreas              Bullera 
+##            1.3857237            1.9252008            1.8716225 
+##               Waitea     Cladophialophora            Cadophora 
+##            1.8664913            1.6806481            0.8417828 
+##            Hamamotoa          Stromatinia         Galactomyces 
+##            1.8643526            1.4471203            1.6001287 
+##          Itersonilia               Knufia  Cutaneotrichosporon 
+##            1.4662815            1.6166129            2.1768257 
+##         Cladosporium           Chromosera          Rhodotorula 
+##            1.3818130            1.5339243            1.7041205 
+##          Atractiella        Funneliformis           Alternaria 
+##            1.8825444            1.7020546            1.2992293 
+##         Trichosporon          Rhizoctonia            Setophoma 
+##            1.5709749            2.5254568            1.5117499 
+##           Monodictys        Cantharellula           Paraglomus 
+##            1.5440277            1.2742531            2.7103630 
+##          Coprinellus      Pseudosigmoidea            Ganoderma 
+##            1.8704190            1.3395788            1.4815535 
+##   Cryptotrichosporon       Sporobolomyces         Filobasidium 
+##            1.7196435            1.3416815            1.8317296 
+##          Hyaloscypha          Laetinaevia         Paraboeremia 
+##            1.5239400            1.2407940            1.2661065 
+##          Lachnellula          Crassostrea            Hannaella 
+##            2.1975052            1.3496368            0.8585331 
+##               Pichia             Fusarium             Slooffia 
+##            1.3334910            1.4121643            1.6219349 
+##              Degelia        Varicosporium        Aotearoamyces 
+##            1.1695741            1.2568640            1.1955767 
+##             Boeremia          Sampaiozyma          Coniochaeta 
+##            2.3008104            1.9711127            1.6788731 
+##      Claroideoglomus             Entoloma         Archaeospora 
+##            1.0483549            1.0843439            1.6011218 
+##            Pulvinula 
+##            1.1815147 
+## 
+## $wald$X
+##    (Intercept)    ndvi_01
+## 1            1 -1.9625407
+## 2            1  0.9980715
+## 3            1 -0.7571646
+## 4            1 -1.1410253
+## 5            1 -1.9896865
+## 6            1  0.6228720
+## 7            1 -0.8421145
+## 8            1  0.9838132
+## 9            1  0.6887884
+## 10           1  0.5747102
+## 11           1  0.5945529
+## 12           1 -1.0529250
+## 13           1  0.6121273
+## 14           1  0.8302155
+## 15           1  0.3130208
+## 16           1  0.5095020
+## 17           1  0.3558477
+## 18           1  0.6619353
+## attr(,&quot;assign&quot;)
+## [1] 0 1
+## 
+## $wald$bias
+## [1] -2.2308186 -0.1560564</code></pre>
+<pre class="r"><code># Show effect size and significance plots
+linda.plot(
+l_model,
+variables.plot = c(&#39;ndvi_01&#39;),
+alpha = 0.05,
+lfc.cut = 1,
+legend = TRUE
+)</code></pre>
+<pre><code>## $plot.lfc
+## $plot.lfc[[1]]</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+<pre><code>## 
+## 
+## $plot.volcano
+## $plot.volcano[[1]]</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+<pre class="r"><code>l_model_df &lt;- as.data.frame(l_model$output)
+
+write.xlsx(l_model_df, file = &quot;supplementary_table_ndvi_regression_fits1.xlsx&quot;, rowNames = TRUE, colnames = TRUE)</code></pre>
+<div id="credit" class="section level5">
+<h5>Credit</h5>
+<p>This script is based on ideas and code from the <a href="https://benjjneb.github.io/dada2/tutorial.html">dada2 Tutorial</a>
+by Benjamin Callahan, the publication “Bioconductor Workflow for
+Microbiome Data Analysis: from raw reads to community analyses” by <a href="https://f1000research.com/articles/5-1492">Callahan et
+al. (2016)</a> and various pages of the official <a href="https://joey711.github.io/phyloseq">phyloseq website</a> by Paul
+J. McMurdie.</p>
+</div>
+</div>
+</div>
+
+
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Controls.pdf b/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Controls.pdf
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diff --git a/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Controls_23-12-24_142001.pdf b/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Controls_23-12-24_142001.pdf
deleted file mode 100644
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diff --git a/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf b/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf
index cd3b5224e26835874c72aad2411f736d8c597fca..52a7bcdff24666a8a16a1f6a92618285337da82d 100644
Binary files a/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf and b/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf differ
diff --git a/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Overview_all_and_pruned.pdf b/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Overview_all_and_pruned.pdf
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Binary files a/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Overview_all_and_pruned.pdf and b/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Overview_all_and_pruned.pdf differ
diff --git a/analysis/FITS1/supplementary_table_ndvi_regression_fits1.xlsx b/analysis/FITS1/supplementary_table_ndvi_regression_fits1.xlsx
index edc8bc9e2b79d161f34e9899501f7b37a9783bd1..69907b3ab6d103e7735c148b1120b950cf481255 100644
Binary files a/analysis/FITS1/supplementary_table_ndvi_regression_fits1.xlsx and b/analysis/FITS1/supplementary_table_ndvi_regression_fits1.xlsx differ
diff --git a/analysis/FITS2/FITS2_Analysis_pipeline_v4-3.Rmd b/analysis/FITS2/FITS2_Analysis_pipeline_v4-3.Rmd
index 5a147ebeb8c54583d389924a2d24b634da45dde6..1519ff25d06d3702281dfb5fee9f3f9614910061 100644
--- a/analysis/FITS2/FITS2_Analysis_pipeline_v4-3.Rmd
+++ b/analysis/FITS2/FITS2_Analysis_pipeline_v4-3.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "16S Data Analysis"
+title: "FITS2 Data Analysis"
 author: "Simeon Lim Rossmann, Marte Persdatter Tangvik"
 date: "23.12.2024"
 output:
@@ -86,7 +86,7 @@ remotes::install_github("mahendra-mariadassou/phyloseq-extended", ref = "dev")
 source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R" )
 
 library(phyloseq)
-library(SIAMCAT)
+#library(SIAMCAT)
 
 # Phylogeny libraries
 library(phangorn)
@@ -668,7 +668,7 @@ its2_percentage
 
 This chunk plots abundance of the Top n ASVs or taxa at a given level as a bar plot, giving an insight into the presence of the n ASV and most common taxa for the primary and secondary parameters. The default for n is set at 20, a larger n may lead to delay/skipping of the plot in standard out, but it should be saved as a PDF regardless for ASVs. For taxa, a large n  may lead to unreadable plots. **The chunk does not require any input**, but it is possible to adjust the **default 'n'**, and to change **width**, **height** and **resolution** of the PDF-output if necessary.
 
-```{r custom-Bar-plot, tidy=TRUE, message=FALSE}
+```{r custom-Bar-plot, message=FALSE}
 
 # CHANGE ME to the desired sample categories on the x-axis. 
 # Accepted values are the column headers in the descriptor file.
@@ -786,7 +786,7 @@ topntax_data <- set$topntax_tbl %>%
   select(Alias, ndvi, Shannon, Taxa) %>%
   distinct()
 
-write.csv(topntax_data, file = "../../topntax_all_taxa/topntax_data_fits2.csv")
+write.csv(topntax_data, file = "../topntax_all_taxa/topntax_data_fits2.csv")
 
 
 ```
diff --git a/analysis/FITS2/FITS2_Analysis_pipeline_v4-3.html b/analysis/FITS2/FITS2_Analysis_pipeline_v4-3.html
new file mode 100644
index 0000000000000000000000000000000000000000..3bcd94ffed25e97108344d05d5cac5998da88050
--- /dev/null
+++ b/analysis/FITS2/FITS2_Analysis_pipeline_v4-3.html
@@ -0,0 +1,2494 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Simeon Lim Rossmann, Marte Persdatter Tangvik" />
+
+
+<title>FITS2 Data Analysis</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
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) 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+<style>h1 {font-size: 34px;}
+h1.title {font-size: 38px;}
+h2 {font-size: 30px;}
+h3 {font-size: 24px;}
+h4 {font-size: 18px;}
+h5 {font-size: 16px;}
+h6 {font-size: 12px;}
+code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
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+
+
+
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">FITS2 Data Analysis</h1>
+<h4 class="author">Simeon Lim Rossmann, Marte Persdatter Tangvik</h4>
+<h4 class="date">23.12.2024</h4>
+
+</div>
+
+
+<div id="introduction-and-initialization" class="section level2">
+<h2>Introduction and initialization</h2>
+<p>This is an R Markdown file containing code to parse the results of a
+dada2 analysis into phyloseq for further analysis. It is separated into
+chunks that may be run independently by pressing the <em>play</em>
+button. You will need <strong>3 files</strong> in the same location in
+order to run this pipeline successfully:</p>
+<ul>
+<li>A sequence table called <strong>‘seqtab_nochim.rds’</strong>
+(automatically generated by the dada2 pipeline)</li>
+<li>A taxonomy table called <strong>‘taxa.rds’</strong> (automatically
+generated by the dada2 pipeline)</li>
+<li>A table describing the sample properties called
+<strong>‘descriptors.txt’</strong> (Has to be provided)</li>
+<li>Optional (some plots require this): A phylogenic tree file called
+<strong>‘phylotree.rds’</strong> (can be calculated if it is not
+provided, takes a long time)</li>
+<li>Optional: Instead of <strong>‘taxa.rds’</strong>, a <strong>custom
+taxonomy file</strong> may be provided (tab-delimited text)</li>
+</ul>
+<p><strong>Recommended use:</strong> Set the individual chunks until you
+are content with the ouput, then knit the whole document into a
+PDF/html, so you have a full record of a successful run.</p>
+<div id="optional-custom-taxonomy-file" class="section level4">
+<h4>Optional custom Taxonomy file</h4>
+<p>A custom taxonomy file may be provided instead of using the taxonomy
+output from dada2. This may be used to supply taxonomy derived e.g. from
+BLAST searches of the ASVs. Custom taxonomy files must be tab-delimited
+text with as many rows as the original, colum headers (for all columns
+except for the first column). For example:</p>
+<blockquote>
+<p>Kingdom Phylum Class Order Family Genus Species<br />
+ESV1 Kingdomx Phylumx Classx Orderx Familyx Genusx Speciesx<br />
+ESV2 Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy<br />
+ESV3 Kingdomz Phylumz Classz Orderz Familyz Genusz Speciesz<br />
+… ESVn Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy</p>
+</blockquote>
+<p><em>Friendly warning: Parsing the results of a BLAST search into this
+format may require some effort.</em></p>
+</div>
+<div id="descriptor-table" class="section level4">
+<h4>Descriptor table</h4>
+<p>‘descriptors.txt’ should be a tab-delimited .txt table describing the
+samples. It must have the same length and order as the samples in
+seqtab_nochim.rds. To check the order and length of samples in
+seqtab_nochim.rds and generate a template to fill out, you may run the
+chunk below with “optional_sample_check” set to “TRUE”.</p>
+<p>Any number of descriptors is possible. The sample names may be
+retained as one descriptor, but this is not necessary, as they will be
+added during parsing. For example, if there are 4 samples (order: s1,
+s2, s3, s4), the txt file could look as follows:</p>
+<blockquote>
+<p>Subject Species Time<br />
+Kar1 A.thaliana 24hpi<br />
+Kar1 A.thaliana 72hpi<br />
+Mec2 S.tuberosum 24hpi<br />
+Mec3 S.tuberosum 24hpi</p>
+</blockquote>
+<p>Finally, the file should end with an empty line, since it may throw
+an error otherwise. However, this is usually not a serious problem.</p>
+<p><strong>If you choose to use the blank file, you MUST retain the
+original order of the samples!</strong></p>
+</div>
+</div>
+<div id="setup" class="section level2">
+<h2>Setup</h2>
+<p>This chunk also loads required packages and defines the location of
+the input files. It requires the <strong>correct path</strong> as input,
+and allows <strong>setting the pruning of control samples</strong> and
+<strong>choosing generation of a phylogenetic tree</strong>.
+<strong>Beware:</strong> The generation of a phylogenetic tree may take
+several days for &gt;1000 sequences, it is therefore recommended to only
+use this feature for the final analysis or small sample sets. This
+scricpt assumes the packages <em>Biostrings</em>, <em>dada2</em>,
+<em>DECIPHER</em>, <em>ggplot2</em>, <em>ggsci</em>, <em>phangorn</em>,
+<em>phyloseq</em> and <em>stringr</em> to be installed.</p>
+<pre class="r"><code># CHANGE ME to the directory that contains &#39;seqtab_nochim.rds&#39;
+path = &quot;FITS2_DADA2_results_260821&quot;
+
+# CHANGE ME to TRUE to list all samples and generate an empty metadata file 
+optional_sample_check = TRUE
+
+# CHANGE ME to TRUE to update cuphyr
+update_cuphyr = TRUE
+
+# Initiate by loading packages and setting knit options
+################# NO CHANGES NECESSARY BELOW #################
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(root.dir = paste0(path))
+knitr::opts_chunk$set(message = FALSE)
+knitr::opts_chunk$set(warning = FALSE)
+
+if (update_cuphyr) {
+  devtools::install_github(&quot;simeross/cuphyr&quot;)
+}
+
+# Sequence and microbiome specific libraries
+library(dada2)
+library(Biostrings)
+library(DECIPHER)
+library(cuphyr)
+# The export of phyloseq objects to a BIOM format and the generation of fancier 
+# ordination plots require the phyloseq-extended package. The first command 
+# installs the package that is currently on the dev brach of the author&#39;s 
+# repository, the second command sources some extra functions, including the 
+# better ordination plot implementation.
+remotes::install_github(&quot;mahendra-mariadassou/phyloseq-extended&quot;, ref = &quot;dev&quot;)
+source(&quot;https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R&quot; )
+
+library(phyloseq)
+#library(SIAMCAT)
+
+# Phylogeny libraries
+library(phangorn)
+library(ape)
+
+# Plotting and figure export
+library(gridExtra)
+library(viridis)
+library(ggpubr)
+
+# Tidyverse
+library(tidyverse)
+library(stringr)
+
+# Various packages for specific analysis
+library(readxl)
+library(openxlsx)
+library(ggpmisc)
+library(betareg)
+library(BBmisc)
+library(aod)
+library(betareg)
+#install.packages(&#39;MicrobiomeStat&#39;)
+library(MicrobiomeStat)
+library(effectsize)
+
+
+# Checks whether output path exists and creates it if not. Throws warning if 
+# directory exists.
+outp &lt;- paste0(path,&quot;/analysis_output&quot;)
+dir.create(file.path(outp))
+
+if (optional_sample_check) {
+  seqtabcheck &lt;- readRDS(paste0(path,&quot;/seqtab_nochim.rds&quot;)) 
+  samps &lt;- rownames(seqtabcheck)
+  lensamps &lt;- length(samps)
+  blankcol &lt;- vector(mode = &quot;character&quot;, length = lensamps)
+  blanktable &lt;- data.frame(SampleIDs = samps, ExampleProperty1 = blankcol, 
+                           ExampleProperty2 = blankcol, 
+                           ExampleProperty3 = blankcol)
+  write.table(blanktable, file = paste0(path, &quot;/descriptors_blank.txt&quot;), 
+              sep = &quot;\t&quot;, row.names = F)
+  cat(&quot;&#39;seqtab_nochim.rds&#39; contains samples in the following order:\n&quot;, 
+      samps, &quot;\nThe number of samples in the file is:&quot;, lensamps, sep = &quot;\n&quot;)
+  rm(optional_sample_check, seqtabcheck, samps, 
+     lensamps, blankcol, blanktable, update_cuphyr)
+  }else{rm(optional_sample_check, update_cuphyr)}</code></pre>
+<pre><code>## &#39;seqtab_nochim.rds&#39; contains samples in the following order:
+## 
+## 1-its2
+## 10-its2
+## 11-its2
+## 12-its2
+## 13-its2
+## 14-its2
+## 15-its2
+## 16-its2
+## 17-its2
+## 18-its2
+## 2-its2
+## 3-its2
+## 4-its2
+## 5-its2
+## 6-its2
+## 7-its2
+## 8-its2
+## 9-its2
+## NegativK-1-its2
+## NegativK-2-its2
+## PositivK-1-its2
+## PositivK-2-its2
+## PositivK-3-its2
+## PositivK-4-its2
+## Vann1-its2
+## Vann2-its2
+## 
+## The number of samples in the file is:
+## 26</code></pre>
+<div id="parameters" class="section level4">
+<h4>Parameters</h4>
+<p>This chunk allows the adjustment of several parameters, such as
+<strong>setting the pruning of control samples</strong> based on
+keywords, <strong>requiring that a phylogenetic tree be provided or
+generated</strong>, <strong>defining a minimum ASV count</strong> and
+<strong>providing an alternative taxonomy</strong>.</p>
+<pre class="r"><code># Dedicated environment containing all global analysis settings for better
+# overview and collected export of settings
+parameters &lt;- new.env()
+
+# CHANGE ME to &#39;TRUE&#39; to remove control samples from the analysis or &#39;FALSE&#39; to
+# analyse all samples.
+parameters$prune_controls = &quot;TRUE&quot;
+# CHANGE ME to a list of unique identifiers that only occur in the names of
+# samples you do NOT want to analyse. Common examples are provided.
+parameters$controls = c(&quot;Pos&quot;, &quot;H2O&quot;, &quot;Neg&quot;, &quot;Kontr&quot;, &quot;Contr&quot;, &quot;POSK&quot;)
+
+# CHANGE ME to &#39;TRUE&#39; to remove certain taxonomic groups from the analysis by
+# name. This is useful to exclude non-target organisms or noise from organelles
+# such as Chloroplasts and Mitochondria. It is recommended to first look at all
+# data before using this setting.
+parameters$prune_noise_taxgroups = &quot;FALSE&quot;
+# CHANGE ME to define the taxonomic groups to be removed as noise.
+parameters$noise_taxgroups = c(&quot;Chloroplast&quot;, &quot;Mitochondria&quot;)
+
+# CHANGE ME to a number of ASV counts [~reads] that analyzed samples should
+# minimally have. Samples with lower ASV counts than &#39;minread&#39; will be pruned.
+# Set to 0 to not prune any samples.
+parameters$minASVcount = 3000
+
+# CHANGE ME to &#39;TRUE&#39;, if you want to provide a custom taxonomy table instead
+# of using the default dada2 output (&#39;taxa.rds&#39;).
+parameters$customTax = &quot;TRUE&quot;
+# CHANGE ME to the location of the custom taxonomy file. This only matters if
+# parameters$customTax=&#39;TRUE&#39;, otherwise it will be ignored.
+parameters$taxfile = &quot;FITS2_DADA2_results_260821/custom_BLAST_taxonomy_nt.txt&quot;
+
+# CHANGE ME to &#39;TRUE&#39; to generate a phylogenetic tree. This process takes a
+# long time depending on the number of sequences (up to days for thousands).
+# If a tree is provided as &#39;phylotree.rds&#39; in &#39;path&#39;, then it will be used
+# regardless of the value of &#39;parameters$maketree&#39;
+parameters$maketree = &quot;FALSE&quot;
+
+# CHANGE ME to &#39;TRUE&#39; to root the used phylogenetic tree (if one exists) on the
+# leaf with the longest branch (outgroup). This makes analyses that rely on the
+# phylogenetic tree reproducible instead of picking a random leaf as root when
+# calculating UNIFRAC distances. Implementation based on
+# http://john-quensen.com/r/unifrac-and-tree-roots/ and answers in
+# https://github.com/joey711/phyloseq/issues/597
+parameters$roottree = &quot;TRUE&quot;
+
+## CHANGE ME to &#39;TRUE&#39; to export all generated phyloseq objects as .biom
+## objects
+parameters$biom_export = &quot;FALSE&quot;</code></pre>
+</div>
+<div id="parsing-input-data" class="section level4">
+<h4>Parsing input data</h4>
+<p>This chunk loads the input data into a usable format.<strong>This
+chunk does not require any user inputs</strong>. If no phylogenetic tree
+with the name ‘phylotree.rds’ was provided and
+‘parameters$maketree=“TRUE”’, it will be calculated here. The
+phylogenetic tree is necessary for certain plots that incorporate ‘true’
+taxonomic relationships beyond the annotations, such as PCoA.</p>
+<pre class="r"><code>############### NO NEED FOR CHANGES BELOW ############### Make dedicated
+############### environments to contain temporary values and manage other
+############### objects
+tmp &lt;- new.env()
+plots &lt;- new.env()
+set &lt;- new.env()
+
+# Read in variables
+tmp$seqtabp &lt;- readRDS(paste0(path, &quot;/seqtab_nochim.rds&quot;))
+if (parameters$customTax == &quot;TRUE&quot;) {
+    tmp$taxap &lt;- read.delim(parameters$taxfile, header = TRUE, sep = &quot;\t&quot;)
+    rownames(tmp$taxap) &lt;- colnames(tmp$seqtabp)
+    tmp$taxap &lt;- as.matrix(tmp$taxap)
+} else {
+    tmp$taxap &lt;- readRDS(paste0(path, &quot;/taxa.rds&quot;))
+}
+tmp$samp_table &lt;- read.delim(paste0(path, &quot;/descriptors.txt&quot;), header = TRUE, sep = &quot;\t&quot;)
+tmp$samp_list &lt;- rownames(tmp$seqtabp)
+
+# Check if descriptors has the same samples as seqtabp
+if (length(tmp$samp_table[, 1]) != length(tmp$samp_list)) {
+    stop(&quot;There are &quot;, length(tmp$samp_table[, 1]), &quot; samples in &#39;descriptors.txt&#39;, but &quot;,
+        length(tmp$samp_list), &quot; samples in &#39;seqtab_nochim.rds&#39;. Please make sure that the correct samples 
+    are contained in descriptors.txt.
+       
+    You may use &#39;optional_sample_check &lt;- TRUE&#39; in the first chunk to generate an 
+    empty template for &#39;descriptors.txt&#39;&quot;)
+} else if (!identical(tmp$samp_table[, 1], tmp$samp_list)) {
+    warning(&quot;Warning: The samples in &#39;descriptors.txt&#39; do not have the same names 
+          or order as the samples in &#39;seqtab_nochim.rds&#39;. This may be fine if 
+          abbreviated names were used or the sample names are not contained in 
+          the first column of &#39;descriptors.txt&#39;. Double-checking never hurts!&quot;)
+}
+
+
+# generate phylogenetic tree of ASVs only if there is no file called
+# &#39;phylotree.rds&#39; in the working directory and &#39;parameters$maketree&#39; is &#39;TRUE&#39;
+if (!file.exists(paste0(path, &quot;/phylotree.rds&quot;))) {
+    if (parameters$maketree == &quot;TRUE&quot;) {
+        tmp$ASVs &lt;- getSequences(tmp$seqtabp)
+        names(tmp$ASVs) &lt;- tmp$ASVs
+        tmp$ASV_align &lt;- AlignSeqs(DNAStringSet(tmp$ASVs), anchor = NA)
+        tmp$ASV_phang &lt;- phyDat(as(tmp$ASV_align, &quot;matrix&quot;), type = &quot;DNA&quot;)
+        tmp$dm &lt;- dist.ml(tmp$ASV_phang)
+        tmp$treeNJ &lt;- NJ(tmp$dm)
+        tmp$fit &lt;- pml(tmp$treeNJ, data = tmp$ASV_phang)
+        tmp$fitGTR &lt;- update(tmp$fit, k = 4, inv = 0.2)
+        tmp$fitGTR &lt;- optim.pml(tmp$fitGTR, model = &quot;GTR&quot;, optInv = TRUE, optGamma = TRUE,
+            rearrangement = &quot;stochastic&quot;, control = pml.control(trace = 0))
+        saveRDS(tmp$fitGTR, file = paste0(path, &quot;/phylotree.rds&quot;))
+    }
+}
+
+## parse into phyloseq object
+row.names(tmp$samp_table) &lt;- tmp$samp_list
+if (file.exists(paste0(path, &quot;/phylotree.rds&quot;))) {
+    tmp$treep &lt;- readRDS(paste0(path, &quot;/phylotree.rds&quot;))
+    p &lt;- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
+        tax_table(tmp$taxap), phy_tree(tmp$treep$tree))
+} else {
+    p &lt;- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
+        tax_table(tmp$taxap))
+}
+
+## Adding nucleotide info and giving sequences ASV## identifiers
+tmp$ASV_sequences &lt;- Biostrings::DNAStringSet(taxa_names(p))
+taxa_names(p) &lt;- paste0(&quot;ASV&quot;, seq(ntaxa(p)))
+names(tmp$ASV_sequences) &lt;- taxa_names(p)
+p &lt;- merge_phyloseq(p, tmp$ASV_sequences)
+
+## optional pruning
+if (parameters$prune_controls == &quot;TRUE&quot;) {
+    if (!is.null(parameters$controls)) {
+        tmp$samp_clean &lt;- tmp$samp_list[!tmp$samp_list %in% grep(paste0(parameters$controls,
+            collapse = &quot;|&quot;), tmp$samp_list, value = T)]
+        tmp$contr_pruned &lt;- setdiff(tmp$samp_list, tmp$samp_clean)
+        ps &lt;- prune_samples(tmp$samp_clean, p)
+        # Physeq object for Just controls
+        ps.contr &lt;- prune_samples(tmp$contr_pruned, p)
+        ps.contr &lt;- prune_taxa(taxa_sums(ps.contr) &gt; 0, ps.contr)
+        ps.transcontr &lt;- transform_sample_counts(ps.contr, function(ASV) ASV/sum(ASV))
+
+        message(cat(&quot;\n&quot;, &quot;Number of control samples that were pruned and will not be analysed:\n&quot;,
+            length(tmp$samp_list) - length(tmp$samp_clean), &quot;\n&quot;, &quot;The following controls were pruned:\n&quot;,
+            tmp$contr_pruned, &quot;The controls are contained in a separate phyloseq object: ps.contr&quot;,
+            &quot;\n&quot;, sep = &quot;\n&quot;))
+    } else {
+        warning(cat(&quot;\n\nparameters$prune_controls is TRUE but &#39;parameters$controls&#39; is empty. 
+    No samples were pruned.\n\n&quot;))
+    }
+} else {
+    ps &lt;- p
+}</code></pre>
+<pre><code>## 
+## 
+## Number of control samples that were pruned and will not be analysed:
+## 
+## 6
+## 
+## 
+## The following controls were pruned:
+## 
+## NegativK-1-its2
+## NegativK-2-its2
+## PositivK-1-its2
+## PositivK-2-its2
+## PositivK-3-its2
+## PositivK-4-its2
+## The controls are contained in a separate phyloseq object: ps.contr</code></pre>
+<pre class="r"><code># Prune ASVs defined as noise
+if (parameters$prune_noise_taxgroups == &quot;TRUE&quot;) {
+    tmp$ps_taxlvls &lt;- colnames(tax_table(ps))
+    tmp$noise_ASVs &lt;- character(0)
+    for (lvl in tmp$ps_taxlvls) {
+        tmp$noise_ASVs &lt;- c(tmp$noise_ASVs, cuphyr::list_subset_ASVs(physeq = ps,
+            subv = parameters$noise_taxgroups, taxlvlsub = lvl))
+    }
+    tmp$noise_ASVs &lt;- unique(tmp$noise_ASVs)
+    tmp$no_noise_ASVs &lt;- colnames(otu_table(ps))
+    tmp$no_noise_ASVs &lt;- setdiff(tmp$no_noise_ASVs, tmp$noise_ASVs)
+    if (length(tmp$noise_ASVs) &gt; 0) {
+        ps &lt;- prune_taxa(tmp$no_noise_ASVs, ps)
+        tmp$no_noise_ps &lt;- ps
+        cat(length(tmp$noise_ASVs), &quot;ASVs were pruned because they belonged to the following 
+        taxonomic groups:\n&quot;)
+        cat(parameters$noise_taxgroups, &quot;\n&quot;, sep = &quot;\n&quot;)
+    } else {
+        cat(&quot;No ASVs were recognized as belonging to the following taxonomic groups 
+        defined as noise:\n&quot;)
+        cat(parameters$noise_taxgroups, &quot;\n&quot;, sep = &quot;\n&quot;)
+    }
+}
+
+# Prune samples with fewer than reads than minASVcount
+if (parameters$minASVcount &gt; 0) {
+    tmp$samp_pruned &lt;- names(which(sample_sums(ps) &lt; parameters$minASVcount))
+    ps &lt;- prune_samples(sample_sums(ps) &gt;= parameters$minASVcount, ps)
+    if (length(tmp$samp_pruned) &gt; 0) {
+        cat(&quot;The following samples were pruned because ASV counts were lower than&quot;,
+            parameters$minASVcount, &quot;:\n&quot;)
+        cat(tmp$samp_pruned, &quot;\n&quot;, sep = &quot;\n&quot;)
+    }
+}</code></pre>
+<pre><code>## The following samples were pruned because ASV counts were lower than 3000 :
+## Vann1-its2
+## Vann2-its2</code></pre>
+<pre class="r"><code># Remove 0 count ASVs (e.g. control ASVs that remain) from the base object
+ps &lt;- prune_taxa(taxa_sums(ps) &gt; 0, ps)
+
+# Get a tbl of the base object for easier access in some phyloseq-independent
+# analyses. Takes some seconds, potentially up to minutes.
+ps_tbl &lt;- as_tibble(psmelt(ps))
+
+# Transformed per sample (per-sample relative abundance)
+ps.trans &lt;- transform_sample_counts(ps, function(ASV) ASV/sum(ASV))
+
+# Read NDVI values as numeric
+sample_data(ps)[[&quot;ndvi&quot;]] &lt;- as.numeric(sample_data(ps)[[&quot;ndvi&quot;]])
+sample_data(ps.trans)[[&quot;ndvi&quot;]] &lt;- as.numeric(sample_data(ps.trans)[[&quot;ndvi&quot;]])
+
+
+# Get a tbl of the base object for easier access in some phyloseq-independent
+# analyses. Takes some seconds, potentially up to minutes.
+ps_tbl &lt;- as_tibble(psmelt(ps))
+ps_trans_tbl &lt;- as_tibble(psmelt(ps.trans))
+
+# Condensing to Abundance per Genus and Sample
+genus_abundance_tbl_per_sample &lt;- ps_trans_tbl %&gt;%
+    group_by(Genus, Sample) %&gt;%
+    mutate(Genus_Sample_Abundance = sum(Abundance)) %&gt;%
+    select(Genus, Sample, ndvi, Genus_Sample_Abundance, Alias) %&gt;%
+    unique()
+
+if (parameters$roottree == &quot;TRUE&quot; &amp;&amp; parameters$maketree == &quot;TRUE&quot;) {
+    phyloseq::phy_tree(ps) &lt;- cuphyr::root_tree_in_outgroup(physeq = ps)
+}
+
+if (parameters$biom_export == &quot;TRUE&quot;) {
+    suppressWarnings(phyloseq.extended::write_phyloseq(p, biom_file = paste0(path,
+        &quot;all_samples.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps, biom_file = file.path(path,
+        &quot;samples_without_controls.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps.trans, biom_file = file.path(path,
+        &quot;samples_without_controls_rel_abundance.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps.contr, biom_file = file.path(path,
+        &quot;just_controls.biom&quot;), biom_format = &quot;standard&quot;))
+}
+
+ps</code></pre>
+<pre><code>## phyloseq-class experiment-level object
+## otu_table()   OTU Table:         [ 1671 taxa and 18 samples ]
+## sample_data() Sample Data:       [ 18 samples by 19 sample variables ]
+## tax_table()   Taxonomy Table:    [ 1671 taxa by 7 taxonomic ranks ]
+## refseq()      DNAStringSet:      [ 1671 reference sequences ]</code></pre>
+</div>
+</div>
+<div id="output" class="section level2">
+<h2>Output</h2>
+<p>The chunks below will produce various plots and other output. Each
+chunk is headed by a description of the output and may contain some
+parameters to adjust the output.</p>
+<div id="plot-looks" class="section level4">
+<h4>Plot looks</h4>
+<p>This chunk sets the background structure and color palette. Viridis
+was chosen because it is optimized for grey-scale printing and various
+types of color blindness and More info on the Viridis palette can be
+found on <a href="https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html">the
+Viridis info page</a>. It also establishes save_plot as a shorter
+variant of ggsave with customized date-time structure to save plots with
+the same name mulitple times instead of overwriting them (overwriting
+can be triggered with overwrite=TRUE).</p>
+<pre class="r"><code>##### Optional settings (sensible defaults) #####
+# Can be changed to adjust the output format for all plots. Default &quot;pdf&quot;, 
+# possible &quot;eps&quot;/&quot;ps&quot;, &quot;tex&quot; (pictex), &quot;jpeg&quot;, &quot;tiff&quot;, &quot;png&quot;, &quot;bmp&quot; and &quot;svg&quot;
+parameters$output_format = &quot;pdf&quot;
+
+# Can be changed to preferred ggplot2 theme. Recommended: &quot;theme_bw()&quot;.
+theme_set(theme_bw())
+
+############### NO NEED FOR CHANGES BELOW ###############
+
+my_scale_col &lt;- scale_color_viridis(discrete = TRUE)
+my_scale_fill &lt;- scale_fill_viridis(discrete = TRUE)
+
+# Custom, more narrow color ranges based on viridis
+# Base order to have adjacent colors be distinct from each other
+tmp$sort_colors &lt;- c(rbind(c(1:5), c(6:10), c(11:15), c(16:20)))
+
+# Customized vectors
+tmp$n_col &lt;- 20
+tmp$viridis_greens &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 0.85, 
+                              end = 0.7)[tmp$sort_colors]
+tmp$viridis_reds &lt;- viridis(tmp$n_col,  option = &quot;B&quot;, begin = 0.7, 
+                            end = 0.5)[tmp$sort_colors]
+tmp$viridis_blues &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 0.2, 
+                             end = 0.4)[tmp$sort_colors]
+tmp$viridis_yellows &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 1, 
+                               end = 0.9)[tmp$sort_colors]
+tmp$viridis_dark &lt;- viridis(tmp$n_col,  option = &quot;A&quot;, begin = 0, 
+                            end = 0.1)[tmp$sort_colors]
+tmp$viridis_light &lt;- viridis(tmp$n_col,  option = &quot;A&quot;, begin = 1, 
+                             end = 0.9)[tmp$sort_colors]
+# Collected list that is available in the global environment
+sub_viridis &lt;- list(tmp$viridis_greens, tmp$viridis_blues, tmp$viridis_yellows, 
+                    tmp$viridis_light, tmp$viridis_reds, tmp$viridis_dark)
+names(sub_viridis) &lt;- c(&quot;greens&quot;, &quot;blues&quot;, &quot;yellows&quot;, &quot;lights&quot;, &quot;reds&quot;, &quot;darks&quot;)
+
+tmp$out &lt;- paste0(&quot;.&quot;, parameters$output_format)
+
+#################### Function ############################
+
+# Generic save function for plots that checks whether file exists and if so, 
+# creates a new one with d/m/y+time info to avoid overwriting. Overwriting can 
+# be triggered with overwrite = TRUE. Width, height and resolution are taken 
+# from parameters in the &#39;set&#39; environment or set to 20x20 cm with 300dpi.
+save_plot &lt;- function(
+  pl, filetype = &quot;.pdf&quot;, plot_name = &quot;my_plot&quot;, overwrite=FALSE){
+  wp &lt;- if (!is.null(set$wp)) set$wp else 20
+  hp &lt;- if (!is.null(set$hp)) set$hp else 20
+  res &lt;- if (!is.null(set$res)) set$res else 300
+  name &lt;- paste0(&quot;/&quot;, plot_name,filetype)
+  if (file.exists(paste0(outp, name)) &amp; !overwrite) {
+  name &lt;- paste0(&quot;/&quot;, plot_name, &quot;_&quot;, 
+                 format(Sys.time(), &quot;%d-%m-%y_%H%M%S&quot;),filetype)}
+  ggsave(file.path(outp, name), pl, 
+         width = wp, height = hp, unit = &quot;cm&quot;, dpi = res)
+}
+
+################################################</code></pre>
+</div>
+<div id="total-asv-counts-ranked" class="section level4">
+<h4>Total ASV counts ranked</h4>
+<p>This chunk plots the absolute abundance of all samples (including
+controls) and all samples without controls and other trimmed
+samples.</p>
+<pre class="r"><code># CHANGE ME to the sample group for color coding. Accepted values are the column 
+# headers in the descriptor file.
+set$color_by = &quot;mel_mol_asv&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 17
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20  
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+############### NO NEED FOR CHANGES BELOW ###############
+# Rank samples
+set$ranked &lt;- cuphyr::make_ranked_sums(p, myset = tmp$subset_id)
+set$ranked_ps &lt;- cuphyr::make_ranked_sums(ps, myset = tmp$subset_id)
+set$ymax &lt;- max(set$ranked$Abundance)
+set$ymax &lt;- set$ymax + round(set$ymax/10)
+set$xmax &lt;- nrow(set$ranked) + 1
+set$title2 &lt;- &quot;Samples (without controls)&quot;
+
+# Stabilize colors
+set$color_vars &lt;- set$ranked[,set$color_by]  %&gt;% 
+  unlist() %&gt;% as.character() %&gt;% unique()
+set$color_vars &lt;- sort(set$color_vars)
+set$color_varsPalette &lt;- viridis(length(set$color_vars))
+names(set$color_varsPalette) &lt;- set$color_vars
+set$my_scale_fill &lt;- scale_fill_manual(values = set$color_varsPalette)
+
+# plot
+plots$overview_all &lt;- ggplot(data = set$ranked, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + ggtitle(&quot;All samples&quot;) + ylim(0, set$ymax) + 
+  xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+
+if (length(tmp$noise_ASVs) &gt; 0) {
+  set$ranked_nonoise &lt;- cuphyr::make_ranked_sums(
+    tmp$no_noise_ps, myset = tmp$subset_id)
+  plots$overview_noise &lt;- ggplot(
+    data = set$ranked_nonoise, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + 
+    ggtitle(&quot;Samples (without controls), noise ASVs removed&quot;) + 
+    ylim(0, set$ymax) + 
+    xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+}
+
+if (parameters$minASVcount &gt; 0) {
+plots$overview_all &lt;- plots$overview_all + 
+  geom_hline(yintercept = parameters$minASVcount, linetype = &quot;dashed&quot;) + 
+    ggtitle(&quot;All samples (ASV count cutoff indicated)&quot;)
+set$title2 &lt;- &quot;Samples (without controls and low count samps)&quot;
+}
+
+plots$overview_ps &lt;- ggplot(data = set$ranked_ps, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + ggtitle(set$title2) + ylim(0, set$ymax) + 
+  xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+plots$combo_overview &lt;- ggarrange(
+  plots$overview_all, plots$overview_ps, nrow = 2, align = &quot;v&quot;, 
+  common.legend = TRUE, legend = &quot;right&quot;)
+
+if (parameters$minASVcount &gt; 0) {
+plots$combo_overview &lt;- ggarrange(
+  plots$overview_all, plots$overview_noise, plots$overview_ps,
+  nrow = 3, align = &quot;v&quot;, 
+  common.legend = TRUE, legend = &quot;right&quot;)
+}
+
+#Save plots
+save_plot(plots$combo_overview, plot_name = &quot;Overview_all_and_pruned&quot;, 
+          filetype = tmp$out)
+
+#Clean up plot parameters
+rm(list = ls(set), envir = set)
+
+#Print plots
+plots$combo_overview</code></pre>
+<p><img role="img" 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" width="672" /></p>
+</div>
+<div id="controls" class="section level4">
+<h4>Controls</h4>
+<p>This chunk generates an overview over the controls (positive AND
+negative)</p>
+<pre class="r"><code># CHANGE ME to the desired sample categories on the x-axis. In this case it 
+# should be the Sample names.
+set$x_axis_value = &quot;SampleIDs&quot;
+
+# CHANGE ME to the taxonomic level for color coding. Use &quot;OTU&quot; for ASVs, 
+# &quot;Genus&quot;, &quot;Species&quot; or &quot;OTU&quot; recommended to compare pos. controls.
+set$color_by_taxlvl = &quot;Species&quot;
+
+# CHANGE ME to the taxonomic level for labeling the tree tips (if phylogenetic 
+# tree is available). Use &quot;OTU&quot; for ASVs.
+set$label_by_taxlvl = &quot;OTU&quot;
+
+# CHANGE ME to a sample category to shape the tree tip labels by (if 
+# phylogenetic tree is available).
+set$label_shape_by = &quot;Symptom&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to generate a tree for just the control sequences IF no 
+# phylogenetic tree for all seuquences is provided. This may slow down this 
+# chunk when running it for the first time
+set$control_tree = TRUE
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 17
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20  
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+############### NO NEED FOR CHANGES BELOW ###############
+if (set$control_tree &amp; class(try(phy_tree(ps.transcontr), 
+                                 silent = TRUE)) == &quot;try-error&quot;) {
+  # generate phylogenetic tree of ASVs only if there is no file called 
+  # &#39;phylotree.rds&#39; in the working directory and &#39;parameters$maketree&#39; is &quot;TRUE&quot;
+  if (!file.exists(paste0(path, &quot;/controls_phylotree.rds&quot;))) {
+    set$ASVs &lt;- phyloseq::refseq(ps.transcontr)
+    set$ASV_align &lt;- AlignSeqs(set$ASVs, anchor = NA)
+    set$ASV_phang &lt;- phyDat(as(set$ASV_align, &quot;matrix&quot;), type = &quot;DNA&quot;)
+    set$dm &lt;- dist.ml(set$ASV_phang)
+    set$treeNJ &lt;- NJ(set$dm)
+    set$fit &lt;- pml(set$treeNJ, data = set$ASV_phang)
+    set$fitGTR &lt;- update(set$fit, k = 4, inv = 0.2)
+    set$fitGTR &lt;- optim.pml(set$fitGTR, model = &quot;GTR&quot;, 
+                            optInv = TRUE, optGamma = TRUE,
+                            rearrangement = &quot;stochastic&quot;, 
+                            control = pml.control(trace = 0))
+    saveRDS(set$fitGTR, file = paste0(path, &quot;/controls_phylotree.rds&quot;))}
+  set$fitGTR &lt;- readRDS(paste0(path, &quot;/controls_phylotree.rds&quot;))
+  phyloseq::phy_tree(ps.transcontr) &lt;- set$fitGTR$tree
+}
+
+plots$topnpplot &lt;- plot_bar(ps.contr, x = set$x_axis_value, 
+                            fill = set$color_by_taxlvl) + my_scale_fill + 
+  theme(axis.title.x = element_blank(), legend.position = &quot;none&quot;, 
+        legend.key.size = unit(3, &quot;mm&quot;)) + 
+  ylab(&quot;ASV counts&quot;) + guides(col = guide_legend(ncol = 3))
+
+plots$topntplot &lt;- plot_bar(ps.transcontr, x = set$x_axis_value, 
+                            fill = set$color_by_taxlvl) + my_scale_fill + 
+  theme(axis.title.x = element_blank(), legend.position = &quot;none&quot;, 
+        legend.key.size = unit(3, &quot;mm&quot;)) + 
+  ylab(&quot;Relative abundance&quot;) + guides(col = guide_legend(ncol = 3))
+
+plots$combo_contr &lt;- ggarrange(plots$topnpplot, plots$topntplot, ncol = 2, 
+                               labels = c(&quot;A&quot;, &quot;B&quot;), align = &quot;hv&quot;, 
+                               common.legend = TRUE, legend = &quot;right&quot;)
+
+if (class(try(phy_tree(ps.transcontr), silent = TRUE)) != &quot;try-error&quot;) {
+plots$tre &lt;- plot_tree(
+          ps.transcontr, ladderize = &quot;left&quot;, label.tips = set$label_by_taxlvl, 
+          color = &quot;abundance&quot;, text.size = 2.5, shape = set$label_shape_by) + 
+          scale_color_viridis_c(aesthetics = c(&quot;color&quot;,&quot;fill&quot;)) + 
+          theme(legend.position = &quot;left&quot;, panel.border = element_blank())
+plots$combo_contr &lt;- ggarrange(plots$tre, ggarrange(plots$topnpplot, 
+                                                    plots$topntplot, ncol = 2, 
+                               labels = c(&quot;B&quot;, &quot;C&quot;), align = &quot;hv&quot;, 
+                               common.legend = TRUE, legend = &quot;right&quot;), 
+                               nrow = 2, legend = &quot;right&quot;, labels = c(&quot;A&quot;)) 
+}
+
+# save
+save_plot(plots$combo_contr, plot_name = &quot;Controls&quot;, filetype = tmp$out)
+
+plots$combo_contr</code></pre>
+<p><img role="img" 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" width="672" /></p>
+</div>
+<div id="get-a-list-of-top-n-taxa-at-a-given-level" class="section level4">
+<h4>Get a list of Top N taxa at a given level</h4>
+<p>This chunk lists the top n most abundant taxonomic terms at a given
+level. Change the function parameters to the desired values. For more
+info, check help page of the function with
+<code>?cuphyr::abundant_tax_physeq()</code>. Change ‘ignore_na’ to
+include/exclude NA values at the given level.</p>
+<pre class="r"><code>#The character vector can later be accessed by calling &#39;tmp$tops&#39;
+tmp$tops &lt;- cuphyr::abundant_tax_physeq(physeq = ps, 
+                            lvl = &quot;Genus&quot;,
+                            top = 100,
+                            output_format = &quot;tops&quot;,
+                            ignore_na = TRUE,
+                            silent = FALSE)</code></pre>
+<pre><code>## 
+## The top 100 most abundant annotated groups at the taxonomic level &#39;Genus&#39; are:
+## Mrakia
+## Candida
+## Mortierella
+## Juxtiphoma
+## Sistotrema
+## Cladosporium
+## Gibellulopsis
+## Alternaria
+## Chloridium
+## Fusarium
+## Solicoccozyma
+## Sporobolomyces
+## Umbelopsis
+## Trichosporiella
+## Tetracladium
+## Aspergillus
+## Cadophora
+## Vishniacozyma
+## Colletotrichum
+## Botrytis
+## Paraphaeosphaeria
+## &#39;uncultured fungus&#39;
+## Urceolella
+## Tausonia
+## Laetinaevia
+## Podospora
+## Minimedusa
+## Exophiala
+## Rhodotorula
+## Acremonium
+## Alpinaria
+## Naganishia
+## Cystofilobasidium
+## Plectosphaerella
+## Papiliotrema
+## Dioszegia
+## Sporidesmium
+## Holtermanniella
+## Trichocladium
+## Talaromyces
+## Chrysosporium
+## Barnettozyma
+## Ophiosphaerella
+## Sphaerobolus
+## Coniochaeta
+## Apiotrichum
+## Paraphoma
+## Trematosphaeria
+## Delitschia
+## Chordomyces
+## &#39;uncultured Orbiliales&#39;
+## Protoventuria
+## Neoascochyta
+## Leucosporidium
+## Leptosphaeria
+## Pleotrichocladium
+## Devriesia
+## Glonium
+## Verticillium
+## Pseudeurotium
+## Halenospora
+## &#39;uncultured Spizellomycetales&#39;
+## Truncatella
+## Cephalosporium
+## Hyaloscypha
+## Cladophialophora
+## Coprinus
+## Cyberlindnera
+## Didymella
+## Dendryphion
+## Arthopyrenia
+## Gorgomyces
+## Waitea
+## &#39;uncultured Ichthyophonida&#39;
+## &#39;uncultured Agaricales&#39;
+## &#39;uncultured Hypocreales&#39;
+## Sakaguchia
+## Apodus
+## Pseudoanguillospora
+## Rhizoctonia
+## Rhizophagus
+## Trichoderma
+## Nullicamyces
+## Stemphylium
+## &#39;uncultured Glomerales&#39;
+## &#39;uncultured Basidiomycota&#39;
+## Spizellomyces
+## Neopyrenochaeta
+## Zasmidium
+## Penicillium
+## &#39;uncultured Ascomycota&#39;
+## Ochroconis
+## Fimicolochytrium
+## Pseudogymnoascus
+## Lachnellula
+## Polymyxa
+## Sordaria
+## Phoma
+## Neohendersonia
+## Neohelicomyces</code></pre>
+</div>
+<div id="asvs-belonging-to-phylum" class="section level4">
+<h4>ASVs belonging to phylum</h4>
+<p>This chunk gives an overview of the number of ASVs belonging to which
+Phylum</p>
+<pre class="r"><code>fits1_list &lt;- cuphyr::list_subset_ASVs(subv = c(&quot;Fungi&quot;), taxlvlsub = &quot;Kingdom&quot;)
+cuphyr::summarise_physeq(physeq = ps, 
+                          ASV_sublist = fits1_list, 
+                          sublist_id = &quot;Fungi&quot;, samp_names = FALSE)</code></pre>
+<pre><code>## There are 1671 ASVs in the phyloseq object &#39;ps&#39;.
+## Of this, 1502 belong to the provided subset (Fungi), representing 98.81 percent of abundance per sample on average.</code></pre>
+<pre class="r"><code># Total ASV counts
+
+ps_tbl %&gt;% select(OTU, Abundance) %&gt;% summarise(total_sum = sum(Abundance))</code></pre>
+<pre><code>## # A tibble: 1 x 1
+##   total_sum
+##       &lt;int&gt;
+## 1    888567</code></pre>
+<pre class="r"><code># Total ASV counts per phylum
+
+ps_tbl %&gt;% select(OTU, Kingdom, Abundance) %&gt;% group_by(Kingdom) %&gt;% summarise(Abundance_sum = sum(Abundance))</code></pre>
+<pre><code>## # A tibble: 4 x 2
+##   Kingdom       Abundance_sum
+##   &lt;chr&gt;                 &lt;int&gt;
+## 1 Fungi                878383
+## 2 Metazoa                4195
+## 3 Viridiplantae          2265
+## 4 &lt;NA&gt;                   3724</code></pre>
+<pre class="r"><code># Percentage of ASV counts belonging to Fungi
+
+its2_percentage = 878383/888567 # ASV counts Fungi / Total ASV counts
+its2_percentage</code></pre>
+<pre><code>## [1] 0.9885388</code></pre>
+</div>
+<div id="customized-top-n-genus-bar-plot" class="section level4">
+<h4>Customized Top N Genus Bar plot</h4>
+<p>This chunk plots abundance of the Top n ASVs or taxa at a given level
+as a bar plot, giving an insight into the presence of the n ASV and most
+common taxa for the primary and secondary parameters. The default for n
+is set at 20, a larger n may lead to delay/skipping of the plot in
+standard out, but it should be saved as a PDF regardless for ASVs. For
+taxa, a large n may lead to unreadable plots. <strong>The chunk does not
+require any input</strong>, but it is possible to adjust the
+<strong>default ‘n’</strong>, and to change <strong>width</strong>,
+<strong>height</strong> and <strong>resolution</strong> of the
+PDF-output if necessary.</p>
+<pre class="r"><code># CHANGE ME to the desired sample categories on the x-axis. 
+# Accepted values are the column headers in the descriptor file.
+set$x_axis_value = &quot;ndvi&quot;
+
+# CHANGE ME to the taxonomic level of interest (color coding). Accepted values 
+# are the column headers in your descriptor file.
+set$taxlvl = &quot;Genus&quot;
+
+# CHANGE ME to change the number of Top n taxa to be plotted at 
+# taxlvl.
+set$top_n = 10
+
+# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
+set$ignore_na = FALSE
+
+# CHANGE ME to an entry at the chosen taxonomic level you want to highlight. 
+# Comment out to not highlight anything.
+#set$highlight = &quot;Meloidogyne&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 20
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 13
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+# Can be changed to change the y-axis label
+set$y_axis_label = &quot;Relative abundance&quot;
+
+# Can be changed to change the x-axis label
+set$x_axis_label = &quot;Sample&quot;
+
+############### NO NEED FOR CHANGES BELOW ###############
+# Estimate Alpha-diversity (Shannon)
+set$alpha_div_ps_trans &lt;- estimate_richness(ps.trans, measures = &quot;Shannon&quot;) %&gt;%
+  as_tibble(rownames = &quot;Sample&quot;) #%&gt;% 
+ # rename(&quot;Shannon&quot; = Simpson)
+
+# Make physeq objects of top n taxa and top n ASVs
+set$ps.topnTax &lt;- cuphyr::abundant_tax_physeq(ps.trans, lvl = set$taxlvl, 
+                                              top = set$top_n, 
+                                              ignore_na = set$ignore_na)
+
+# Plot
+set$my_scale_fill &lt;- my_scale_fill
+
+
+set$topntax_tbl &lt;- psmelt(set$ps.topnTax) %&gt;% 
+                    as_tibble() %&gt;%
+                    left_join(set$alpha_div_ps_trans, by = &quot;Sample&quot;) %&gt;% 
+                    select(Genus, Alias, ndvi, Abundance, Shannon) %&gt;%
+                    filter(Abundance &gt; 0) %&gt;%
+                    group_by(Genus, Alias, ndvi, Shannon) %&gt;%
+                    summarise(Abundance = sum(Abundance)) %&gt;%
+                    arrange(ndvi) %&gt;%
+                    mutate(ndvi_rank = c(1:length(ndvi))) 
+                    
+
+plots$topn_tax_custom &lt;- ggplot(set$topntax_tbl, aes(x = fct_reorder(Alias, ndvi),
+                           y = Abundance,
+                           fill = Genus)) + 
+                           #title = paste0(&quot;Top &quot;, set$top_n, &quot; &quot;, set$taxlvl))) +
+  geom_col(color = &quot;black&quot;) +
+  set$my_scale_fill + 
+  ylab(set$y_axis_label) +
+  xlab(set$x_axis_label) +
+  theme(strip.background = element_blank(),
+        legend.position = &quot;bottom&quot;)
+
+plots$ndvi_dot_plot &lt;- ggplot(set$topntax_tbl, aes(fct_reorder(Alias, ndvi),
+                           y = ndvi)) +
+                             geom_point() +
+    theme(strip.background = element_blank(),
+        axis.title.x=element_blank()) +
+  ylab(&quot;NDVI&quot;)
+
+plots$shannon_dot_plot &lt;- ggplot(set$topntax_tbl, 
+                                 aes(fct_reorder(Alias, ndvi),
+                           y = Shannon)) +
+                             geom_point() +
+    theme(strip.background = element_blank(),
+        axis.title.x=element_blank()) +
+  ylab(&quot;Shannon&quot;)
+
+
+
+
+plots$combo_topn_custom &lt;- ggarrange(plots$ndvi_dot_plot,
+                                     plots$shannon_dot_plot,
+                                     plots$topn_tax_custom,
+                                     nrow = 3,
+                                     heights = c(1, 1, 3),
+                                     align = &quot;v&quot;)
+
+save_plot(plots$combo_topn_custom, plot_name = paste0(&quot;Customized_NDVI_Shannon_plot&quot;), 
+          filetype = tmp$out)
+plots$combo_topn_custom</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+</div>
+<div id="ndvishannon-regression" class="section level3">
+<h3>NDVI+Shannon regression</h3>
+<pre class="r"><code>set$my_formula &lt;- y ~ x
+set$plot_title &lt;- &quot;Fungi (FITS2)&quot;
+
+
+topntax_data &lt;- set$topntax_tbl %&gt;%
+  mutate(Taxa = &#39;fits2&#39;) %&gt;%
+  ungroup() %&gt;%
+  select(Alias, ndvi, Shannon, Taxa) %&gt;%
+  distinct()
+
+write.csv(topntax_data, file = &quot;../topntax_all_taxa/topntax_data_fits2.csv&quot;)</code></pre>
+</div>
+<div id="ndvi-regression" class="section level3">
+<h3>NDVI regression</h3>
+<pre class="r"><code># Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
+# containing the sample abundances for that genus.
+ldf &lt;- data.frame(genus_abundance_tbl_per_sample %&gt;% pivot_wider(id_cols = c(&#39;Sample&#39;, &#39;ndvi&#39;), names_from = &#39;Genus&#39;, values_from = &#39;Genus_Sample_Abundance&#39;))
+ldf_genus_data &lt;- data.frame(ldf) %&gt;% select(!c(&#39;Sample&#39;, &#39;ndvi&#39;))
+colnames(ldf_genus_data) &lt;- gsub(&#39; &#39;, &#39;.&#39;, colnames(ldf_genus_data))
+ldf &lt;- cbind.data.frame(
+Sample = ldf$Sample,
+ndvi_01 = (ldf$ndvi + 1) / 2.0
+)
+ldf &lt;- tibble(cbind(ldf, ldf_genus_data))
+
+n_samples_by_genus &lt;- data.frame(ldf_genus_data &gt; 0) %&gt;% mutate_if(is.logical, as.numeric) %&gt;% colSums() %&gt;% sort(decreasing = TRUE)
+keep_n &lt;- 100 # Maximum number of genuses to include in the analysis
+top_n_occurence_genuses &lt;- names(n_samples_by_genus[1:keep_n])
+top_n_occurence_genuses &lt;- top_n_occurence_genuses[!is.na(top_n_occurence_genuses)]
+
+l_genus_ldf &lt;- ldf %&gt;% select(all_of(top_n_occurence_genuses))
+l_genus_ldf_transposed &lt;- data.frame(t(l_genus_ldf))
+l_meta_ldf &lt;- ldf %&gt;% select(&#39;ndvi_01&#39;)
+
+l_model &lt;- linda(
+l_genus_ldf_transposed,
+l_meta_ldf,
+formula = &#39;~ ndvi_01&#39;,
+feature.dat.type = &#39;proportion&#39;,
+is.winsor = FALSE,
+alpha = 0.05
+)</code></pre>
+<pre><code>## 0  features are filtered!
+## The filtered data has  18  samples and  100  features will be tested!
+## Fit linear models ...
+## Completed.</code></pre>
+<pre class="r"><code># Print model info
+l_model</code></pre>
+<pre><code>## $variables
+## [1] &quot;ndvi_01&quot;
+## 
+## $bias
+## [1] -0.06003912
+## 
+## $output
+## $output$ndvi_01
+##                                    baseMean log2FoldChange      lfcSE
+## Candida                          64855.7339    0.223427960 0.29625914
+## Mrakia                          137712.5246   -0.126149897 0.13824709
+## Colletotrichum                    9975.0863   -0.555137076 0.34043978
+## Cladosporium                     40663.1338    0.958216259 0.24164878
+## Sporobolomyces                   15851.7473    1.615421093 0.31411693
+## Juxtiphoma                       60592.5372   -0.003133777 0.13831708
+## Sistotrema                       49356.2769    0.024813154 0.25181444
+## Gibellulopsis                    31844.0120    0.024176825 0.15775986
+## Botrytis                         17417.2529    0.664397243 0.30134099
+## Podospora                         9177.1238   -0.291139885 0.23814170
+## Alternaria                       30206.5050   -0.119394651 0.18607697
+## Fusarium                         25205.8716   -0.366581496 0.21721201
+## Mortierella                      97189.2687    0.074171290 0.10057144
+## Cadophora                        21893.8184   -0.324458149 0.09727133
+## Aspergillus                      23934.0302    0.212046916 0.06210173
+## Laetinaevia                      11010.7263   -0.229434231 0.22306853
+## Paraphaeosphaeria                17523.4236   -0.119369481 0.14381724
+## Tetracladium                     19683.1695   -0.473325140 0.20726442
+## Solicoccozyma                    28483.9636    0.011181980 0.10380672
+## Chloridium                       29641.4667   -0.315352933 0.12511179
+## Trichosporiella                  19578.7390   -0.722571471 0.18211055
+## Vishniacozyma                    16229.8737    0.883288344 0.28199229
+## Umbelopsis                       24225.0522    0.274862969 0.17646619
+## Plectosphaerella                  4286.3430    0.642633262 0.28259447
+## Tausonia                         12318.6719   -0.105809988 0.19900516
+## Chordomyces                       2045.6662    1.037165724 0.28996476
+## Minimedusa                       10394.5710    0.367241472 0.17080732
+## Urceolella                       14279.9809   -0.283974754 0.10316279
+## Naganishia                        6543.3855    0.400619651 0.17028459
+## Acremonium                        7127.7850    0.306642537 0.19276356
+## X.uncultured.fungus.             15910.3745    0.131545738 0.10951640
+## Talaromyces                       3907.3448    0.207260292 0.32210852
+## Holtermanniella                   4875.6600    0.189052140 0.18132252
+## Sporidesmium                      5287.4513   -0.173566541 0.14323542
+## Protoventuria                     2869.6767   -0.042575329 0.16328045
+## Trichocladium                     5011.6517   -0.163317004 0.12665952
+## Gorgomyces                         992.4232   -0.982940191 0.29618703
+## Papiliotrema                      5207.4970    0.033355458 0.16379285
+## Ophiosphaerella                   3430.3770   -0.069319372 0.11596455
+## Pseudeurotium                     2052.5011    0.348650782 0.31921908
+## Coniochaeta                       3102.3335   -0.327716837 0.14880418
+## Exophiala                         7794.7155   -0.189252171 0.14667745
+## NA.                               7299.8862    0.170515771 0.12505966
+## Sakaguchia                        1172.6853   -0.329665815 0.21313105
+## Pseudoanguillospora                970.3718   -0.056484147 0.30338656
+## Halenospora                       2187.0233   -0.341543400 0.16392615
+## Pleotrichocladium                 2059.7164   -0.423911605 0.28149263
+## Cephalosporium                    2114.8551   -0.158147249 0.17447955
+## X.uncultured.Spizellomycetales.   1851.2812   -0.119947362 0.32879296
+## Verticillium                      2382.0107    0.391487585 0.19095141
+## Pseudogymnoascus                  1044.1890    0.139213124 0.19405191
+## Devriesia                         2646.5150    0.072390136 0.12140692
+## Polymyxa                           914.2938   -0.489528074 0.17254518
+## Apodus                            1188.6065    0.422169395 0.25272443
+## Arthopyrenia                      1706.0135    0.065833583 0.14541611
+## Zasmidium                         1124.8244    0.350946743 0.15187707
+## X.uncultured.Agaricales.          1006.1689    0.151604556 0.40180240
+## X.uncultured.Glomerales.          1124.9771    0.078365323 0.20223420
+## Ochroconis                        1024.0389   -0.097038195 0.18421722
+## X.uncultured.Basidiomycota.       1063.0512   -0.086452242 0.23249945
+## Microdochium                       553.3013    0.042877532 0.29280961
+## X.uncultured.Hypocreales.         1438.7679   -0.123848877 0.20716917
+## X.uncultured.Mortierellales.       604.1388    0.342821980 0.31073789
+## X.uncultured.Ascomycota.          1038.8782    0.213464170 0.25042139
+## Allovahlkampfia                    584.6582   -0.092153191 0.25210743
+## Rhodotorula                       4085.0962    1.029864285 0.41127426
+## Dioszegia                         2071.6538    1.061786375 0.49955429
+## Alpinaria                         1953.8021    0.170573036 0.60055393
+## Leucosporidium                    1400.4022    0.824491727 0.36171309
+## Trematosphaeria                   1522.2950   -0.933633545 0.30043503
+## Paraphoma                         2093.4314   -0.538998661 0.49466838
+## Neoascochyta                      1708.7532    0.072967586 0.41829934
+## Barnettozyma                      2458.8383    0.544038170 0.42964477
+## Apiotrichum                       1900.7476    0.050058510 0.44301095
+## Waitea                            1090.9631    0.279643651 0.40086194
+## Dendryphion                       1400.4955   -0.556690470 0.25411140
+## Hyaloscypha                       1545.4426   -0.526521960 0.28568895
+## Neopyrenochaeta                    976.3556   -0.544164691 0.26491958
+## Cladophialophora                  1811.1913   -0.401131745 0.18253724
+## Nullicamyces                      1138.5811    0.411352123 0.21819542
+## Rhizophagus                        738.4522    0.775040286 0.41413520
+## Spizellomyces                     1006.4791   -0.248987420 0.25291903
+## Neohendersonia                     946.5213    0.225157790 0.18357875
+## Oidiodendron                       707.4774   -0.041568378 0.32905841
+## Fusidium                           439.4076    0.311219171 0.19138955
+## Leptosphaeria                     1215.8903   -0.911638026 0.44620438
+## X.uncultured.eukaryote.            356.1635   -1.227915622 0.36919530
+## Stemphylium                        731.5821    0.838836467 0.41712173
+## Trichoderma                       1041.3259    0.006247107 0.30202328
+## Fimicolochytrium                  1001.5063   -0.111933140 0.19780177
+## Volutella                          318.0905    0.895861134 0.32804608
+## X.uncultured.Monoblepharidales.    766.6738    0.631824602 0.19618789
+## Chlamydomonas                      251.2449    0.199145173 0.35607893
+## Peziza                             185.3868    0.892439184 0.17438466
+## Delitschia                         650.9164    1.070370873 0.76757682
+## Cyberlindnera                      415.7661    0.511003399 0.57754124
+## Chrysosporium                     2661.7691    0.402014214 0.45456357
+## Neohelicomyces                     494.1081   -0.296970544 0.51451954
+## X.uncultured.Ichthyophonida.       857.5788   -0.655428134 0.47810120
+## Coprinus                          1265.6112   -0.973061109 0.29838891
+##                                        stat       pvalue        padj reject df
+## Candida                          0.75416394 0.4617067039 0.678980447  FALSE 16
+## Mrakia                          -0.91249584 0.3750525626 0.599352818  FALSE 16
+## Colletotrichum                  -1.63064692 0.1224895909 0.301119445  FALSE 16
+## Cladosporium                     3.96532633 0.0011100979 0.027752449   TRUE 16
+## Sporobolomyces                   5.14273811 0.0000982459 0.005165325   TRUE 16
+## Juxtiphoma                      -0.02265647 0.9822044499 0.983753336  FALSE 16
+## Sistotrema                       0.09853745 0.9227294758 0.941560690  FALSE 16
+## Gibellulopsis                    0.15325080 0.8801166331 0.941560690  FALSE 16
+## Botrytis                         2.20480208 0.0424491117 0.161565743  FALSE 16
+## Podospora                       -1.22254895 0.2392041635 0.449404417  FALSE 16
+## Alternaria                      -0.64164121 0.5301917728 0.726290100  FALSE 16
+## Fusarium                        -1.68766676 0.1108687504 0.291759869  FALSE 16
+## Mortierella                      0.73749856 0.4714973566 0.683329502  FALSE 16
+## Cadophora                       -3.33559899 0.0041926729 0.048279840   TRUE 16
+## Aspergillus                      3.41450895 0.0035497969 0.048279840   TRUE 16
+## Laetinaevia                     -1.02853699 0.3189960996 0.531660166  FALSE 16
+## Paraphaeosphaeria               -0.83000815 0.4187467500 0.624995149  FALSE 16
+## Tetracladium                    -2.28367771 0.0363936161 0.161248995  FALSE 16
+## Solicoccozyma                    0.10771923 0.9155576367 0.941560690  FALSE 16
+## Chloridium                      -2.52056924 0.0227135477 0.130447675  FALSE 16
+## Trichosporiella                 -3.96776276 0.0011044220 0.027752449   TRUE 16
+## Vishniacozyma                    3.13231386 0.0064293598 0.052085622  FALSE 16
+## Umbelopsis                       1.55759562 0.1388868856 0.321515588  FALSE 16
+## Plectosphaerella                 2.27404759 0.0370872689 0.161248995  FALSE 16
+## Tausonia                        -0.53169471 0.6022422554 0.747638543  FALSE 16
+## Chordomyces                      3.57686823 0.0025193717 0.048279840   TRUE 16
+## Minimedusa                       2.15003363 0.0471891164 0.168532559  FALSE 16
+## Urceolella                      -2.75268581 0.0141556861 0.092522931  FALSE 16
+## Naganishia                       2.35264776 0.0317688567 0.161248995  FALSE 16
+## Acremonium                       1.59077023 0.1312232470 0.312436302  FALSE 16
+## X.uncultured.fungus.             1.20115109 0.2471724292 0.449404417  FALSE 16
+## Talaromyces                      0.64344866 0.5290484274 0.726290100  FALSE 16
+## Holtermanniella                  1.04262910 0.3126186901 0.529862187  FALSE 16
+## Sporidesmium                    -1.21175709 0.2431977675 0.449404417  FALSE 16
+## Protoventuria                   -0.26074970 0.7976117872 0.896193019  FALSE 16
+## Trichocladium                   -1.28941752 0.2155818993 0.431163799  FALSE 16
+## Gorgomyces                      -3.31864698 0.0043451856 0.048279840   TRUE 16
+## Papiliotrema                     0.20364417 0.8411990793 0.934665644  FALSE 16
+## Ophiosphaerella                 -0.59776349 0.5583643135 0.735978158  FALSE 16
+## Pseudeurotium                    1.09219907 0.2909190809 0.503485701  FALSE 16
+## Coniochaeta                     -2.20233628 0.0426526860 0.161565743  FALSE 16
+## Exophiala                       -1.29026085 0.2152961499 0.431163799  FALSE 16
+## NA.                              1.36347537 0.1916112153 0.399190032  FALSE 16
+## Sakaguchia                      -1.54677514 0.1414668588 0.321515588  FALSE 16
+## Pseudoanguillospora             -0.18617880 0.8546445466 0.938813988  FALSE 16
+## Halenospora                     -2.08351995 0.0535991518 0.175377167  FALSE 16
+## Pleotrichocladium               -1.50594208 0.1515685764 0.336819059  FALSE 16
+## Cephalosporium                  -0.90639416 0.3781754133 0.599352818  FALSE 16
+## X.uncultured.Spizellomycetales. -0.36481122 0.7200294433 0.827620050  FALSE 16
+## Verticillium                     2.05019474 0.0571018221 0.175377167  FALSE 16
+## Pseudogymnoascus                 0.71740147 0.4834694971 0.690670710  FALSE 16
+## Devriesia                        0.59626037 0.5593433999 0.735978158  FALSE 16
+## Polymyxa                        -2.83710081 0.0118941512 0.084958223  FALSE 16
+## Apodus                           1.67047323 0.1142683480 0.292995764  FALSE 16
+## Arthopyrenia                     0.45272550 0.6568248570 0.801005923  FALSE 16
+## Zasmidium                        2.31072897 0.0345093512 0.161248995  FALSE 16
+## X.uncultured.Agaricales.         0.37731123 0.7109001847 0.827620050  FALSE 16
+## X.uncultured.Glomerales.         0.38749788 0.7034938563 0.827620050  FALSE 16
+## Ochroconis                      -0.52675963 0.6055872198 0.747638543  FALSE 16
+## X.uncultured.Basidiomycota.     -0.37183848 0.7148916578 0.827620050  FALSE 16
+## Microdochium                     0.14643485 0.8854072592 0.941560690  FALSE 16
+## X.uncultured.Hypocreales.       -0.59781518 0.5583306590 0.735978158  FALSE 16
+## X.uncultured.Mortierellales.     1.10325130 0.2862356512 0.503485701  FALSE 16
+## X.uncultured.Ascomycota.         0.85241989 0.4065604066 0.616000616  FALSE 16
+## Allovahlkampfia                 -0.36553144 0.7195022327 0.827620050  FALSE 16
+## Rhodotorula                      2.50408153 0.0234805815 0.130447675  FALSE 16
+## Dioszegia                        2.12546744 0.0494697626 0.170585388  FALSE 16
+## Alpinaria                        0.28402618 0.7800330469 0.886401190  FALSE 16
+## Leucosporidium                   2.27940802 0.0366996537 0.161248995  FALSE 16
+## Trematosphaeria                 -3.10760544 0.0067711308 0.052085622  FALSE 16
+## Paraphoma                       -1.08961615 0.2920217065 0.503485701  FALSE 16
+## Neoascochyta                     0.17443868 0.8637088687 0.938813988  FALSE 16
+## Barnettozyma                     1.26625110 0.2235490086 0.438331389  FALSE 16
+## Apiotrichum                      0.11299610 0.9114392608 0.941560690  FALSE 16
+## Waitea                           0.69760589 0.4954370238 0.697798625  FALSE 16
+## Dendryphion                     -2.19073391 0.0436227506 0.161565743  FALSE 16
+## Hyaloscypha                     -1.84299031 0.0839360597 0.226854215  FALSE 16
+## Neopyrenochaeta                 -2.05407499 0.0566835155 0.175377167  FALSE 16
+## Cladophialophora                -2.19753371 0.0430517779 0.161565743  FALSE 16
+## Nullicamyces                     1.88524633 0.0776902616 0.221333203  FALSE 16
+## Rhizophagus                      1.87146684 0.0796799530 0.221333203  FALSE 16
+## Spizellomyces                   -0.98445507 0.3395454644 0.556631909  FALSE 16
+## Neohendersonia                   1.22649160 0.2377578593 0.449404417  FALSE 16
+## Oidiodendron                    -0.12632523 0.9010483201 0.941560690  FALSE 16
+## Fusidium                         1.62610328 0.1234589723 0.301119445  FALSE 16
+## Leptosphaeria                   -2.04309522 0.0578744652 0.175377167  FALSE 16
+## X.uncultured.eukaryote.         -3.32592430 0.0042790512 0.048279840   TRUE 16
+## Stemphylium                      2.01101121 0.0614860981 0.180841465  FALSE 16
+## Trichoderma                      0.02068419 0.9837533355 0.983753336  FALSE 16
+## Fimicolochytrium                -0.56588543 0.5793223568 0.738886328  FALSE 16
+## Volutella                        2.73090033 0.0148036690 0.092522931  FALSE 16
+## X.uncultured.Monoblepharidales.  3.22050773 0.0053423642 0.048566947   TRUE 16
+## Chlamydomonas                    0.55927255 0.5837201991 0.738886328  FALSE 16
+## Peziza                           5.11764731 0.0001033065 0.005165325   TRUE 16
+## Delitschia                       1.39448046 0.1822344813 0.396161916  FALSE 16
+## Cyberlindnera                    0.88479119 0.3893724699 0.599352818  FALSE 16
+## Chrysosporium                    0.88439602 0.3895793318 0.599352818  FALSE 16
+## Neohelicomyces                  -0.57718031 0.5718504168 0.738886328  FALSE 16
+## X.uncultured.Ichthyophonida.    -1.37089832 0.1893315960 0.399190032  FALSE 16
+## Coprinus                        -3.26104985 0.0049055962 0.048566947   TRUE 16
+## 
+## 
+## $covariance
+## NULL
+## 
+## $feature.dat.use
+##                                            1            2            3
+## Candida                         3.232412e-01 2.575758e-01 7.743115e-02
+## Mrakia                          9.112224e-02 6.456154e-02 2.026302e-01
+## Colletotrichum                  7.123106e-03 1.790024e-02 1.126378e-02
+## Cladosporium                    7.182996e-02 2.651515e-02 9.435631e-03
+## Sporobolomyces                  1.236031e-02 4.176723e-02 2.034558e-03
+## Juxtiphoma                      3.808759e-02 2.862698e-02 3.824379e-02
+## Sistotrema                      5.974999e-02 5.390185e-02 4.393466e-02
+## Gibellulopsis                   1.729169e-02 1.505095e-02 3.190423e-02
+## Botrytis                        8.257196e-03 1.146420e-02 5.278056e-03
+## Podospora                       5.186233e-03 4.592384e-03 6.988264e-03
+## Alternaria                      9.480485e-03 8.950121e-03 1.627646e-02
+## Fusarium                        1.197803e-02 1.759855e-02 2.002123e-02
+## Mortierella                     3.706819e-02 3.955484e-02 9.211535e-02
+## Cadophora                       1.279356e-02 1.065969e-02 3.096067e-02
+## Aspergillus                     1.634874e-02 1.541968e-02 1.559828e-02
+## Laetinaevia                     6.027244e-03 4.022526e-03 8.845904e-03
+## Paraphaeosphaeria               7.645552e-03 5.933226e-03 8.108746e-03
+## Tetracladium                    7.084878e-03 4.223652e-03 2.046353e-02
+## Solicoccozyma                   1.590275e-02 8.782515e-03 2.633131e-02
+## Chloridium                      9.926475e-03 2.336417e-02 3.249396e-02
+## Trichosporiella                 6.944710e-03 3.620274e-03 4.968450e-02
+## Vishniacozyma                   5.810620e-03 2.353178e-02 2.712744e-03
+## Umbelopsis                      1.224563e-02 1.008984e-02 2.146606e-02
+## Plectosphaerella                5.632223e-03 5.095200e-03 1.710208e-03
+## Tausonia                        5.504798e-03 4.424779e-03 9.818954e-03
+## Chordomyces                     5.976273e-03 1.672700e-02 5.012679e-04
+## Minimedusa                      1.178689e-02 7.978010e-03 1.379961e-02
+## Urceolella                      6.065471e-03 7.978010e-03 1.851743e-02
+## Naganishia                      4.574589e-03 6.167873e-03 4.275520e-03
+## Acremonium                      9.149177e-03 1.421293e-02 5.396002e-03
+## X.uncultured.fungus.            1.078023e-02 2.269375e-02 1.783924e-02
+## Talaromyces                     3.415013e-03 8.380263e-04 4.069116e-03
+## Holtermanniella                 3.415013e-03 2.614642e-03 4.098602e-03
+## Sporidesmium                    5.020579e-03 2.849289e-03 6.781860e-03
+## Protoventuria                   9.939218e-04 2.815768e-03 1.916613e-03
+## Trichocladium                   3.313073e-03 3.050416e-03 7.312614e-03
+## Gorgomyces                      1.656536e-04 4.357737e-04 4.629357e-03
+## Papiliotrema                    5.453827e-03 3.184500e-03 4.481925e-03
+## Ophiosphaerella                 1.822190e-03 1.743095e-03 2.388394e-03
+## Pseudeurotium                   2.803369e-03 1.106195e-03 3.066580e-03
+## Coniochaeta                     2.089784e-03 9.050684e-04 5.219084e-03
+## Exophiala                       6.001758e-03 2.111826e-03 7.577991e-03
+## NA.                             5.058807e-03 5.564495e-03 3.626821e-03
+## Sakaguchia                      8.282682e-04 1.005632e-03 1.533290e-03
+## Pseudoanguillospora             2.293666e-03 8.715473e-04 6.192133e-04
+## Halenospora                     6.753571e-04 1.106195e-03 3.096067e-03
+## Pleotrichocladium               5.734164e-04 1.005632e-03 1.769181e-03
+## Cephalosporium                  7.518126e-04 1.139716e-03 2.358908e-03
+## X.uncultured.Spizellomycetales. 2.803369e-04 1.541968e-03 2.388394e-03
+## Verticillium                    4.077628e-03 4.123089e-03 9.435631e-04
+## Pseudogymnoascus                6.880997e-04 5.698579e-04 7.371587e-04
+## Devriesia                       8.664959e-04 1.173237e-03 1.946099e-03
+## Polymyxa                        4.842183e-04 4.022526e-04 1.592263e-03
+## Apodus                          1.567338e-03 1.910700e-03 3.243498e-04
+## Arthopyrenia                    9.811792e-04 1.139716e-03 2.123017e-03
+## Zasmidium                       8.664959e-04 8.380263e-04 4.422952e-04
+## X.uncultured.Agaricales.        8.282682e-04 8.380263e-04 8.845904e-05
+## X.uncultured.Glomerales.        1.924131e-03 7.374631e-04 1.356372e-03
+## Ochroconis                      4.969609e-04 2.681684e-04 8.551041e-04
+## X.uncultured.Basidiomycota.     1.019407e-03 9.050684e-04 1.326886e-03
+## Microdochium                    3.185647e-04 7.709842e-04 2.948635e-04
+## X.uncultured.Hypocreales.       9.811792e-04 7.039421e-04 1.710208e-03
+## X.uncultured.Mortierellales.    1.146833e-03 7.374631e-04 3.833225e-04
+## X.uncultured.Ascomycota.        2.930795e-04 1.877179e-03 3.538362e-04
+## Allovahlkampfia                 2.930795e-04 1.676053e-04 4.717816e-04
+## Rhodotorula                     5.479312e-04 5.665058e-03 2.739656e-04
+## Dioszegia                       1.057635e-03 2.044784e-03 2.064044e-04
+## Alpinaria                       3.389528e-03 3.184500e-03 7.371587e-04
+## Leucosporidium                  8.282682e-04 4.123089e-03 7.666450e-04
+## Trematosphaeria                 4.587331e-04 1.411273e-04 2.034558e-03
+## Paraphoma                       6.626145e-04 1.518504e-02 4.039630e-03
+## Neoascochyta                    5.097035e-03 1.863770e-02 3.243498e-03
+## Barnettozyma                    1.745734e-03 1.803433e-02 1.032022e-03
+## Apiotrichum                     1.376199e-03 7.374631e-04 2.299935e-03
+## Waitea                          6.243868e-04 9.888710e-03 1.179454e-03
+## Dendryphion                     1.540642e-04 1.005632e-03 1.651235e-03
+## Hyaloscypha                     6.753571e-04 5.698579e-04 7.666450e-04
+## Neopyrenochaeta                 1.656536e-04 6.369000e-04 1.798667e-03
+## Cladophialophora                1.121348e-03 2.388858e-04 3.155039e-03
+## Nullicamyces                    1.656536e-03 1.642532e-03 2.121168e-04
+## Rhizophagus                     4.205054e-04 3.352105e-04 1.769181e-04
+## Spizellomyces                   4.842183e-04 1.461134e-04 1.385858e-03
+## Neohendersonia                  7.390700e-04 1.005632e-03 7.666450e-04
+## Oidiodendron                    1.401685e-03 2.346474e-04 1.179454e-03
+## Fusidium                        2.421092e-04 2.681684e-04 8.361004e-05
+## Leptosphaeria                   1.656536e-04 6.369000e-04 1.680722e-03
+## X.uncultured.eukaryote.         2.293666e-04 2.011263e-04 3.862712e-03
+## Stemphylium                     1.911388e-04 7.136740e-05 1.474317e-04
+## Trichoderma                     1.363457e-03 1.550084e-04 6.486997e-04
+## Fimicolochytrium                6.371294e-04 2.616805e-04 8.845904e-04
+## Volutella                       6.116442e-04 3.251542e-03 3.917697e-05
+## X.uncultured.Monoblepharidales. 5.097035e-04 1.039153e-03 7.076723e-04
+## Chlamydomonas                   6.371294e-05 5.698579e-04 2.358908e-04
+## Peziza                          2.293666e-04 1.340842e-04 3.361571e-05
+## Delitschia                      1.340554e-05 3.687316e-04 1.340554e-05
+## Cyberlindnera                   1.302292e-02 3.486189e-03 2.653771e-04
+## Chrysosporium                   8.231711e-03 6.804773e-03 1.828154e-03
+## Neohelicomyces                  3.567924e-04 6.369000e-04 2.358908e-04
+## X.uncultured.Ichthyophonida.    2.930795e-04 6.391138e-05 3.774253e-03
+## Coprinus                        5.097035e-04 1.704303e-04 2.683258e-03
+##                                            4            5            6
+## Candida                         2.805780e-02 5.264330e-02 1.998296e-02
+## Mrakia                          1.349000e-01 1.626853e-01 9.759267e-02
+## Colletotrichum                  1.288943e-01 1.956231e-03 5.112910e-03
+## Cladosporium                    2.543032e-02 4.009479e-02 1.565829e-01
+## Sporobolomyces                  3.780364e-03 2.772123e-02 9.614401e-02
+## Juxtiphoma                      7.752426e-02 6.091354e-02 2.381764e-02
+## Sistotrema                      3.524318e-02 1.359819e-02 5.372816e-02
+## Gibellulopsis                   2.516221e-02 5.342261e-02 1.246272e-02
+## Botrytis                        4.678535e-03 2.407914e-02 1.785258e-02
+## Podospora                       6.153145e-03 3.308098e-03 6.753302e-03
+## Alternaria                      2.839294e-02 3.093390e-02 2.699190e-02
+## Fusarium                        2.048367e-02 2.260004e-02 4.793353e-03
+## Mortierella                     5.001609e-02 7.137859e-02 6.766084e-02
+## Cadophora                       2.962625e-02 1.245308e-02 1.369834e-02
+## Aspergillus                     1.556384e-02 2.072333e-02 1.670217e-02
+## Laetinaevia                     1.979999e-02 7.156944e-03 3.067746e-03
+## Paraphaeosphaeria               2.258834e-02 1.529996e-02 7.413720e-03
+## Tetracladium                    3.154325e-02 1.269165e-02 8.947593e-03
+## Solicoccozyma                   2.092606e-02 2.390419e-02 2.030251e-02
+## Chloridium                      2.943858e-02 1.596794e-02 1.938645e-02
+## Trichosporiella                 3.545767e-02 1.205547e-02 6.497657e-03
+## Vishniacozyma                   6.970883e-03 3.058401e-02 3.003835e-02
+## Umbelopsis                      7.198777e-03 2.255233e-02 1.991905e-02
+## Plectosphaerella                4.102097e-03 5.773268e-03 2.236898e-03
+## Tausonia                        1.422328e-02 1.458426e-02 6.902429e-03
+## Chordomyces                     3.887608e-04 3.514855e-03 9.586706e-04
+## Minimedusa                      4.986863e-03 9.701635e-03 1.301662e-02
+## Urceolella                      1.132769e-02 6.170876e-03 8.990200e-03
+## Naganishia                      3.941230e-03 7.999873e-03 1.205795e-02
+## Acremonium                      2.225320e-03 3.260386e-03 6.305922e-03
+## X.uncultured.fungus.            8.874470e-03 1.059228e-02 9.544099e-03
+## Talaromyces                     1.863371e-03 3.737515e-03 4.686834e-03
+## Holtermanniella                 3.472036e-03 5.471086e-03 2.876012e-03
+## Sporidesmium                    4.477452e-03 1.669954e-03 5.048999e-03
+## Protoventuria                   3.740147e-03 1.431389e-03 1.597784e-03
+## Trichocladium                   3.324575e-03 4.151027e-03 1.896037e-03
+## Gorgomyces                      4.825996e-04 7.793117e-04 1.917341e-04
+## Papiliotrema                    3.203925e-03 5.121191e-03 4.154239e-03
+## Ophiosphaerella                 2.895598e-03 1.797188e-03 2.258202e-03
+## Pseudeurotium                   1.246716e-03 7.793117e-04 3.451214e-03
+## Coniochaeta                     6.528500e-03 2.242509e-03 2.023860e-03
+## Exophiala                       9.745831e-03 2.608308e-03 6.668087e-03
+## NA.                             3.994852e-03 4.325975e-03 1.035364e-02
+## Sakaguchia                      6.166550e-04 6.997901e-04 1.193012e-03
+## Pseudoanguillospora             1.327149e-03 7.156944e-04 1.917341e-04
+## Halenospora                     3.163709e-03 1.654049e-03 5.965062e-04
+## Pleotrichocladium               3.981447e-03 2.306126e-03 1.320835e-03
+## Cephalosporium                  1.635476e-03 2.560595e-03 7.882403e-04
+## X.uncultured.Spizellomycetales. 1.702504e-03 4.453209e-03 2.045164e-03
+## Verticillium                    1.152877e-03 2.417457e-03 8.521517e-04
+## Pseudogymnoascus                4.289774e-04 7.793117e-04 9.586706e-04
+## Devriesia                       2.037643e-03 2.083466e-03 1.896037e-03
+## Polymyxa                        4.021663e-04 3.180864e-04 5.112910e-04
+## Apodus                          2.386187e-03 4.612253e-04 1.022582e-03
+## Arthopyrenia                    1.246716e-03 8.588333e-04 1.427354e-03
+## Zasmidium                       1.260121e-03 4.612253e-04 7.030251e-04
+## X.uncultured.Agaricales.        4.772374e-03 6.202685e-04 1.193012e-03
+## X.uncultured.Glomerales.        1.273527e-03 7.634073e-04 2.556455e-04
+## Ochroconis                      9.517937e-04 1.256441e-03 4.686834e-04
+## X.uncultured.Basidiomycota.     5.228162e-04 4.930339e-04 4.686834e-04
+## Microdochium                    6.702772e-04 3.657994e-04 4.686834e-04
+## X.uncultured.Hypocreales.       8.579549e-04 2.385648e-04 9.373669e-04
+## X.uncultured.Mortierellales.    3.083275e-04 2.226605e-04 1.150405e-03
+## X.uncultured.Ascomycota.        1.420988e-03 1.161015e-03 7.669365e-04
+## Allovahlkampfia                 2.681109e-04 1.161015e-03 2.130379e-04
+## Rhodotorula                     1.112660e-03 3.912463e-03 4.546229e-02
+## Dioszegia                       4.758968e-03 2.290222e-03 6.327226e-03
+## Alpinaria                       6.702772e-04 3.319232e-02 3.621645e-04
+## Leucosporidium                  1.251849e-04 1.431389e-03 1.043886e-03
+## Trematosphaeria                 2.667703e-03 1.001972e-03 7.669365e-04
+## Paraphoma                       1.206499e-03 1.272346e-04 1.299531e-03
+## Neoascochyta                    1.193093e-03 1.288250e-03 7.243289e-04
+## Barnettozyma                    1.479458e-04 2.799160e-03 1.171709e-03
+## Apiotrichum                     7.238994e-04 4.787200e-03 2.130379e-04
+## Waitea                          3.190520e-03 5.248425e-04 1.363443e-03
+## Dendryphion                     1.984021e-03 6.361728e-04 6.391138e-04
+## Hyaloscypha                     1.809749e-03 6.520771e-04 2.982531e-04
+## Neopyrenochaeta                 8.043327e-04 4.135123e-04 8.947593e-04
+## Cladophialophora                1.300338e-03 8.270246e-04 8.308479e-04
+## Nullicamyces                    1.313743e-03 6.043641e-04 1.278228e-03
+## Rhizophagus                     1.916993e-03 1.590432e-04 2.428632e-03
+## Spizellomyces                   8.445493e-04 8.588333e-04 1.278228e-03
+## Neohendersonia                  9.517937e-04 7.634073e-04 3.621645e-04
+## Oidiodendron                    9.383881e-05 5.566512e-04 4.260758e-04
+## Fusidium                        4.021663e-04 1.908518e-04 3.195569e-04
+## Leptosphaeria                   2.748137e-03 3.498950e-04 8.521517e-04
+## X.uncultured.eukaryote.         1.206499e-03 2.862778e-04 4.260758e-05
+## Stemphylium                     1.152877e-03 2.465170e-03 2.982531e-04
+## Trichoderma                     9.249826e-04 6.043641e-04 1.746911e-03
+## Fimicolochytrium                7.104939e-04 9.224505e-04 7.030251e-04
+## Volutella                       1.608665e-04 3.180864e-04 1.917341e-04
+## X.uncultured.Monoblepharidales. 5.630329e-04 6.838857e-04 1.427354e-03
+## Chlamydomonas                   6.702772e-05 3.657994e-04 3.185647e-05
+## Peziza                          1.206499e-04 4.135123e-04 8.521517e-05
+## Delitschia                      2.681109e-05 7.156944e-04 1.695782e-02
+## Cyberlindnera                   5.362218e-05 8.111203e-04 2.681109e-05
+## Chrysosporium                   5.496273e-04 2.748137e-04 4.942480e-03
+## Neohelicomyces                  4.960051e-04 6.361728e-05 3.180864e-05
+## X.uncultured.Ichthyophonida.    2.547053e-04 1.272346e-03 1.278228e-04
+## Coprinus                        3.016248e-03 4.135123e-04 3.408607e-04
+##                                            7            8            9
+## Candida                         6.361909e-02 4.021124e-02 1.701996e-02
+## Mrakia                          1.363809e-01 1.351605e-01 1.407039e-01
+## Colletotrichum                  9.966216e-03 6.707097e-03 1.103616e-02
+## Cladosporium                    1.334459e-02 5.359410e-03 3.401169e-02
+## Sporobolomyces                  1.900338e-03 1.457383e-03 5.955573e-03
+## Juxtiphoma                      4.691723e-02 4.049332e-02 4.626154e-02
+## Sistotrema                      3.086993e-02 4.466175e-02 6.102346e-02
+## Gibellulopsis                   3.593750e-02 1.869525e-02 2.133845e-02
+## Botrytis                        2.871622e-03 6.158620e-03 5.173728e-02
+## Podospora                       2.286740e-02 1.750427e-02 3.415281e-03
+## Alternaria                      3.203125e-02 3.184304e-02 2.610856e-02
+## Fusarium                        6.528716e-02 6.526883e-02 1.608852e-02
+## Mortierella                     7.060811e-02 7.940388e-02 1.099664e-01
+## Cadophora                       1.910895e-02 2.783132e-02 1.572158e-02
+## Aspergillus                     1.982686e-02 1.317913e-02 1.803607e-02
+## Laetinaevia                     6.841216e-03 1.283438e-02 6.181377e-03
+## Paraphaeosphaeria               2.588682e-02 1.968251e-02 1.069745e-02
+## Tetracladium                    2.685811e-02 1.957281e-02 3.014480e-02
+## Solicoccozyma                   2.398649e-02 2.040337e-02 2.958029e-02
+## Chloridium                      3.274916e-02 7.094166e-02 1.673770e-02
+## Trichosporiella                 3.317145e-02 4.693401e-02 1.227808e-02
+## Vishniacozyma                   2.787162e-03 6.299657e-03 4.120918e-03
+## Umbelopsis                      2.217061e-02 1.914970e-02 2.613678e-02
+## Plectosphaerella                1.815878e-03 1.551408e-03 2.229812e-03
+## Tausonia                        1.203547e-02 1.018601e-02 8.382963e-03
+## Chordomyces                     9.290541e-04 3.447573e-04 1.044342e-03
+## Minimedusa                      3.462838e-03 3.400561e-03 1.171357e-02
+## Urceolella                      1.651182e-02 1.847586e-02 1.591916e-02
+## Naganishia                      4.032939e-03 3.494586e-03 2.963674e-03
+## Acremonium                      6.989020e-03 4.904957e-03 5.475740e-03
+## X.uncultured.fungus.            1.184544e-02 9.355460e-03 9.907138e-03
+## Talaromyces                     8.889358e-03 2.805071e-03 4.628976e-03
+## Holtermanniella                 3.272804e-03 2.664034e-03 1.637077e-03
+## Sporidesmium                    3.483953e-03 5.547459e-03 5.927348e-03
+## Protoventuria                   2.385980e-03 4.528858e-03 3.133027e-03
+## Trichocladium                   6.228885e-03 2.209581e-03 5.024133e-03
+## Gorgomyces                      7.369088e-03 4.152759e-03 1.072568e-03
+## Papiliotrema                    4.624155e-03 6.205632e-03 1.891106e-03
+## Ophiosphaerella                 2.934966e-03 3.760989e-03 2.229812e-03
+## Pseudeurotium                   1.393581e-03 1.112626e-03 2.173361e-03
+## Coniochaeta                     5.067568e-03 2.225252e-03 2.173361e-03
+## Exophiala                       4.983108e-03 9.010703e-03 5.757995e-03
+## NA.                             6.355574e-03 5.422093e-03 4.798329e-03
+## Sakaguchia                      1.625845e-03 4.622882e-03 3.669310e-04
+## Pseudoanguillospora             4.645270e-04 7.992102e-04 1.693528e-04
+## Halenospora                     2.554899e-03 1.990190e-03 8.185385e-04
+## Pleotrichocladium               2.449324e-03 2.319277e-03 1.608852e-03
+## Cephalosporium                  2.766047e-03 2.099886e-03 2.455615e-03
+## X.uncultured.Spizellomycetales. 1.942568e-03 3.290865e-03 2.794321e-03
+## Verticillium                    1.435811e-03 1.190980e-03 1.580626e-03
+## Pseudogymnoascus                6.967905e-04 9.715889e-04 1.749979e-03
+## Devriesia                       2.217061e-03 2.679705e-03 2.963674e-03
+## Polymyxa                        2.956081e-03 1.285005e-03 1.298371e-03
+## Apodus                          4.222973e-04 6.111607e-04 5.645093e-04
+## Arthopyrenia                    1.097973e-03 1.128297e-03 1.608852e-03
+## Zasmidium                       5.912162e-04 7.365270e-04 5.927348e-04
+## X.uncultured.Agaricales.        1.140203e-03 5.641484e-04 2.822547e-03
+## X.uncultured.Glomerales.        6.123311e-04 4.387821e-04 7.338621e-04
+## Ochroconis                      1.731419e-03 8.618933e-04 9.596658e-04
+## X.uncultured.Basidiomycota.     6.334459e-04 1.629762e-03 7.338621e-04
+## Microdochium                    1.478041e-04 1.520066e-03 6.491857e-04
+## X.uncultured.Hypocreales.       9.079392e-04 1.911836e-03 2.766096e-03
+## X.uncultured.Mortierellales.    3.378378e-04 9.402473e-05 1.552401e-03
+## X.uncultured.Ascomycota.        6.123311e-04 6.111607e-04 4.798329e-04
+## Allovahlkampfia                 1.309122e-03 1.426042e-03 7.620876e-04
+## Rhodotorula                     9.079392e-04 1.582750e-03 1.806430e-03
+## Dioszegia                       1.858108e-03 1.032022e-04 4.233820e-04
+## Alpinaria                       2.343750e-03 2.397631e-03 5.927348e-04
+## Leucosporidium                  1.520270e-03 4.387821e-04 1.213695e-03
+## Trematosphaeria                 2.850507e-03 1.968251e-02 2.822547e-04
+## Paraphoma                       4.961993e-03 3.243853e-03 3.245928e-03
+## Neoascochyta                    3.652872e-03 9.559181e-04 9.032149e-04
+## Barnettozyma                    1.135980e-02 3.604281e-04 1.411273e-03
+## Apiotrichum                     3.103885e-03 3.290865e-04 6.209602e-04
+## Waitea                          4.011824e-04 4.701236e-04 2.540292e-04
+## Dendryphion                     2.048142e-03 1.190980e-03 1.241920e-03
+## Hyaloscypha                     4.687500e-03 2.131227e-03 3.330605e-03
+## Neopyrenochaeta                 1.245777e-03 2.805071e-03 3.387056e-04
+## Cladophialophora                1.710304e-03 2.899096e-03 1.383048e-03
+## Nullicamyces                    6.967905e-04 4.387821e-04 5.927348e-04
+## Rhizophagus                     6.828269e-05 2.037202e-04 4.657202e-03
+## Spizellomyces                   5.067568e-04 7.521978e-04 2.907223e-03
+## Neohendersonia                  7.812500e-04 1.081284e-03 1.129019e-03
+## Oidiodendron                    7.390203e-04 8.148810e-04 1.552401e-03
+## Fusidium                        2.111486e-04 2.193910e-04 4.798329e-04
+## Leptosphaeria                   2.681588e-03 5.735508e-03 1.665302e-03
+## X.uncultured.eukaryote.         1.393581e-03 2.820742e-04 8.185385e-04
+## Stemphylium                     7.136740e-05 2.507326e-04 1.129019e-03
+## Trichoderma                     8.445946e-04 1.206651e-03 1.185470e-03
+## Fimicolochytrium                1.140203e-03 1.833482e-03 1.270146e-03
+## Volutella                       1.689189e-04 7.835394e-05 3.387056e-04
+## X.uncultured.Monoblepharidales. 2.744932e-04 1.372466e-04 7.056366e-04
+## Chlamydomonas                   1.055743e-04 3.134158e-04 4.798329e-04
+## Peziza                          3.361571e-05 1.410371e-04 2.540292e-04
+## Delitschia                      9.712838e-04 4.544529e-03 7.959581e-03
+## Cyberlindnera                   5.067568e-04 4.544529e-04 3.951565e-04
+## Chrysosporium                   2.491554e-03 1.206651e-03 3.189478e-03
+## Neohelicomyces                  7.390203e-04 8.305518e-04 3.180864e-05
+## X.uncultured.Ichthyophonida.    2.618243e-03 3.212512e-03 2.681419e-03
+## Coprinus                        5.236486e-03 2.225252e-03 1.100793e-03
+##                                           10           11           12
+## Candida                         1.779902e-02 4.037171e-02 3.993721e-02
+## Mrakia                          8.027768e-02 1.128019e-01 1.216132e-01
+## Colletotrichum                  3.068169e-02 2.460524e-03 1.444803e-03
+## Cladosporium                    4.536247e-02 6.684026e-02 3.132191e-02
+## Sporobolomyces                  1.356549e-02 2.704188e-02 1.257514e-02
+## Juxtiphoma                      8.665073e-02 8.623779e-02 4.955140e-02
+## Sistotrema                      2.936156e-02 9.746542e-03 1.023848e-02
+## Gibellulopsis                   1.843633e-02 4.082559e-02 4.955140e-02
+## Botrytis                        5.870035e-02 3.244070e-02 5.012218e-03
+## Podospora                       6.304768e-03 2.221638e-03 6.135954e-03
+## Alternaria                      8.214408e-02 3.571343e-02 1.669550e-02
+## Fusarium                        2.626607e-02 4.304723e-02 3.865294e-02
+## Mortierella                     7.017184e-02 6.282697e-02 1.284448e-01
+## Cadophora                       2.993058e-02 1.414204e-02 2.192177e-02
+## Aspergillus                     1.131217e-02 1.595757e-02 2.286713e-02
+## Laetinaevia                     2.897462e-02 2.195361e-02 7.491572e-03
+## Paraphaeosphaeria               1.067486e-02 1.748644e-02 2.042345e-02
+## Tetracladium                    4.197109e-02 2.195361e-02 1.967429e-02
+## Solicoccozyma                   2.014339e-02 2.226416e-02 2.894957e-02
+## Chloridium                      2.023444e-02 1.889587e-02 2.272444e-02
+## Trichosporiella                 1.666098e-02 2.195361e-02 1.496531e-02
+## Vishniacozyma                   2.053033e-02 2.601467e-02 1.448371e-02
+## Umbelopsis                      5.121202e-03 1.151430e-02 3.610224e-02
+## Plectosphaerella                2.754069e-03 8.074341e-03 2.675561e-03
+## Tausonia                        1.684306e-02 2.350637e-02 1.275351e-02
+## Chordomyces                     1.206328e-03 2.102195e-03 2.586376e-03
+## Minimedusa                      6.190964e-03 3.153293e-03 9.614184e-03
+## Urceolella                      9.514055e-03 3.846062e-03 9.774718e-03
+## Naganishia                      2.458177e-03 5.470486e-03 6.849437e-03
+## Acremonium                      2.503699e-03 2.818853e-03 4.209550e-03
+## X.uncultured.fungus.            8.740184e-03 6.593249e-03 1.298539e-02
+## Talaromyces                     3.414135e-04 1.146652e-03 3.317696e-03
+## Holtermanniella                 3.527939e-03 9.459879e-03 5.654353e-03
+## Sporidesmium                    4.028679e-03 1.791644e-03 2.051264e-03
+## Protoventuria                   1.934676e-03 2.102195e-03 2.176123e-03
+## Trichocladium                   4.210766e-03 2.149973e-03 6.528370e-03
+## Gorgomyces                      9.331968e-04 9.555433e-04 6.956460e-04
+## Papiliotrema                    2.822351e-03 2.078307e-03 5.297612e-03
+## Ophiosphaerella                 4.415614e-03 2.149973e-03 6.207302e-03
+## Pseudeurotium                   2.048481e-04 3.344402e-04 2.140449e-03
+## Coniochaeta                     1.866394e-03 1.146652e-03 2.354494e-03
+## Exophiala                       8.603619e-03 4.514942e-03 6.688904e-03
+## NA.                             3.550700e-03 2.556078e-03 5.957583e-03
+## Sakaguchia                      7.283487e-04 5.972146e-04 7.491572e-04
+## Pseudoanguillospora             1.001479e-03 1.003321e-03 5.707864e-04
+## Halenospora                     2.503699e-03 2.412747e-03 4.138202e-03
+## Pleotrichocladium               3.391374e-03 2.078307e-03 4.066853e-03
+## Cephalosporium                  2.549220e-03 3.869951e-03 1.819382e-03
+## X.uncultured.Spizellomycetales. 1.320132e-03 1.242206e-03 4.494943e-03
+## Verticillium                    2.731308e-03 3.392179e-03 2.211797e-03
+## Pseudogymnoascus                1.024240e-03 2.866630e-04 1.212921e-03
+## Devriesia                       9.559577e-04 1.982752e-03 3.282022e-03
+## Polymyxa                        1.092523e-03 7.166575e-04 9.275280e-04
+## Apodus                          1.342893e-03 2.388858e-04 7.491572e-04
+## Arthopyrenia                    4.779788e-04 6.211032e-04 1.962078e-03
+## Zasmidium                       5.462615e-04 5.733260e-04 1.516151e-03
+## X.uncultured.Agaricales.        1.866394e-03 4.538831e-04 7.491572e-04
+## X.uncultured.Glomerales.        9.559577e-04 4.299945e-04 1.105899e-03
+## Ochroconis                      2.503699e-04 6.927689e-04 9.988763e-04
+## X.uncultured.Basidiomycota.     1.001479e-03 6.449918e-04 1.284269e-03
+## Microdochium                    1.160806e-03 5.016603e-04 6.599718e-04
+## X.uncultured.Hypocreales.       8.876750e-04 4.538831e-04 7.491572e-04
+## X.uncultured.Mortierellales.    5.462615e-04 7.166575e-05 1.962078e-04
+## X.uncultured.Ascomycota.        1.229088e-03 3.822173e-04 1.302107e-03
+## Allovahlkampfia                 2.731308e-04 2.149973e-04 1.088062e-03
+## Rhodotorula                     1.060658e-02 5.398820e-03 4.494943e-03
+## Dioszegia                       1.411176e-03 3.944005e-02 4.280898e-04
+## Alpinaria                       7.966314e-04 1.672201e-04 1.275351e-02
+## Leucosporidium                  2.503699e-04 1.552758e-03 1.266432e-03
+## Trematosphaeria                 1.889154e-03 1.600535e-03 2.033427e-03
+## Paraphoma                       2.731308e-03 1.194429e-02 6.361728e-05
+## Neoascochyta                    5.462615e-04 3.822173e-04 3.014466e-03
+## Barnettozyma                    2.958917e-04 1.409426e-03 1.551826e-03
+## Apiotrichum                     1.024240e-03 8.074341e-03 7.331038e-03
+## Waitea                          6.373051e-04 4.538831e-04 1.270146e-04
+## Dendryphion                     5.030158e-03 1.242206e-03 1.266432e-03
+## Hyaloscypha                     3.801070e-03 6.211032e-04 8.383426e-04
+## Neopyrenochaeta                 8.649141e-04 6.449918e-04 8.282682e-05
+## Cladophialophora                1.638785e-03 4.777717e-04 2.532865e-03
+## Nullicamyces                    9.104359e-04 5.494374e-04 4.994381e-04
+## Rhizophagus                     1.365654e-04 2.388858e-04 8.026684e-04
+## Spizellomyces                   1.934676e-03 3.822173e-04 1.373455e-03
+## Neohendersonia                  5.007397e-04 5.255488e-04 2.211797e-03
+## Oidiodendron                    2.731308e-04 4.691941e-05 5.172752e-04
+## Fusidium                        8.876750e-04 1.672201e-04 4.816011e-04
+## Leptosphaeria                   3.778309e-03 5.016603e-04 5.886235e-04
+## X.uncultured.eukaryote.         4.324570e-04 2.130379e-05 6.064606e-04
+## Stemphylium                     3.573461e-03 3.153293e-03 6.599718e-04
+## Trichoderma                     7.283487e-04 4.061059e-04 5.707864e-04
+## Fimicolochytrium                7.283487e-04 6.688803e-04 8.205055e-04
+## Volutella                       1.820872e-04 5.255488e-04 3.032303e-04
+## X.uncultured.Monoblepharidales. 1.372466e-04 5.733260e-04 1.462640e-03
+## Chlamydomonas                   4.552179e-04 5.733260e-04 5.172752e-04
+## Peziza                          6.828269e-05 2.388858e-04 2.853932e-04
+## Delitschia                      1.340554e-05 4.538831e-04 1.048820e-02
+## Cyberlindnera                   1.593263e-04 2.149973e-04 5.172752e-04
+## Chrysosporium                   6.828269e-04 2.748137e-04 2.748137e-04
+## Neohelicomyces                  4.324570e-04 3.105516e-04 2.069101e-03
+## X.uncultured.Ichthyophonida.    1.820872e-04 6.391138e-05 3.103651e-03
+## Coprinus                        1.934676e-03 8.361004e-04 3.817134e-03
+##                                           13           14           15
+## Candida                         0.0251802065 0.0582199385 5.860949e-02
+## Mrakia                          0.1039109682 0.1205157484 1.066295e-01
+## Colletotrichum                  0.0062828755 0.0053129064 6.477720e-03
+## Cladosporium                    0.0294174946 0.0087385416 4.741625e-02
+## Sporobolomyces                  0.0367962205 0.0013480509 4.474032e-02
+## Juxtiphoma                      0.0298801870 0.0676245758 5.085907e-02
+## Sistotrema                      0.1560734463 0.0487042852 2.586193e-02
+## Gibellulopsis                   0.0253993766 0.0365242491 3.284546e-02
+## Botrytis                        0.0222092344 0.0132426174 3.441187e-02
+## Podospora                       0.0068673290 0.0054715006 7.424087e-03
+## Alternaria                      0.0270066238 0.0198718559 2.925580e-02
+## Fusarium                        0.0064289889 0.0221714721 1.605561e-02
+## Mortierella                     0.1130187025 0.0787578901 8.499355e-02
+## Cadophora                       0.0148792129 0.0201731849 1.641457e-02
+## Aspergillus                     0.0206993961 0.0233450693 2.352864e-02
+## Laetinaevia                     0.0043103448 0.0146541060 9.218921e-03
+## Paraphaeosphaeria               0.0144895772 0.0288324293 1.664301e-02
+## Tetracladium                    0.0113968440 0.0322897834 2.377339e-02
+## Solicoccozyma                   0.0228910968 0.0272623466 3.654935e-02
+## Chloridium                      0.0161698812 0.0370000317 2.398551e-02
+## Trichosporiella                 0.0100331190 0.0283725061 2.507873e-02
+## Vishniacozyma                   0.0217708942 0.0172074730 2.082008e-02
+## Umbelopsis                      0.0283947010 0.0173026295 3.220911e-02
+## Plectosphaerella                0.0008279758 0.0019665683 1.811151e-03
+## Tausonia                        0.0075735437 0.0131474609 1.413024e-02
+## Chordomyces                     0.0008279758 0.0012846132 9.463671e-04
+## Minimedusa                      0.0183128775 0.0075966632 6.738786e-03
+## Urceolella                      0.0162185856 0.0168268468 1.168274e-02
+## Naganishia                      0.0037015391 0.0041393092 8.142020e-03
+## Acremonium                      0.0057714787 0.0035049323 4.046535e-03
+## X.uncultured.fungus.            0.0150009741 0.0128619913 1.173169e-02
+## Talaromyces                     0.0072082603 0.0012053161 2.137484e-03
+## Holtermanniella                 0.0026056887 0.0037428236 8.207287e-03
+## Sporidesmium                    0.0052844341 0.0076601009 3.638618e-03
+## Protoventuria                   0.0026543931 0.0025533670 1.240067e-03
+## Trichocladium                   0.0036771868 0.0072636153 3.899685e-03
+## Gorgomyces                      0.0012906682 0.0006660957 1.631667e-04
+## Papiliotrema                    0.0030927333 0.0039014178 8.223604e-03
+## Ophiosphaerella                 0.0038476524 0.0027119612 2.480134e-03
+## Pseudeurotium                   0.0055279564 0.0007453928 9.953171e-04
+## Coniochaeta                     0.0027030976 0.0023947727 1.158484e-03
+## Exophiala                       0.0069160335 0.0092619025 4.128119e-03
+## NA.                             0.0079875317 0.0083737749 6.853003e-03
+## Sakaguchia                      0.0009740892 0.0006978146 1.337967e-03
+## Pseudoanguillospora             0.0002435223 0.0042027469 1.044267e-03
+## Halenospora                     0.0010714981 0.0031560250 1.713251e-03
+## Pleotrichocladium               0.0006331580 0.0041075903 3.410185e-03
+## Cephalosporium                  0.0018020651 0.0018079741 1.517451e-03
+## X.uncultured.Spizellomycetales. 0.0001461134 0.0006185175 2.839101e-03
+## Verticillium                    0.0008279758 0.0027436800 1.191117e-03
+## Pseudogymnoascus                0.0010471459 0.0008564088 2.937001e-04
+## Devriesia                       0.0026787454 0.0032670409 2.773835e-03
+## Polymyxa                        0.0008036236 0.0005392204 8.647837e-04
+## Apodus                          0.0027518021 0.0004916421 8.974171e-04
+## Arthopyrenia                    0.0013637249 0.0027753989 9.790004e-04
+## Zasmidium                       0.0018020651 0.0007771117 5.874003e-04
+## X.uncultured.Agaricales.        0.0020699396 0.0008722682 1.256384e-03
+## X.uncultured.Glomerales.        0.0006331580 0.0008881276 1.794834e-03
+## Ochroconis                      0.0007792714 0.0017286770 1.289017e-03
+## X.uncultured.Basidiomycota.     0.0007305669 0.0019824278 1.958001e-04
+## Microdochium                    0.0006331580 0.0004440638 1.142167e-04
+## X.uncultured.Hypocreales.       0.0014124294 0.0019031306 7.342503e-04
+## X.uncultured.Mortierellales.    0.0007305669 0.0007136740 2.284334e-04
+## X.uncultured.Ascomycota.        0.0016315995 0.0009832842 8.158337e-04
+## Allovahlkampfia                 0.0004870446 0.0002378913 1.142167e-04
+## Rhodotorula                     0.0056740697 0.0018872712 7.424087e-03
+## Dioszegia                       0.0025326320 0.0004123450 6.738786e-03
+## Alpinaria                       0.0001704656 0.0107368287 9.790004e-04
+## Leucosporidium                  0.0024352231 0.0005709392 1.240067e-03
+## Trematosphaeria                 0.0009984415 0.0030132902 7.342503e-04
+## Paraphoma                       0.0026787454 0.0026009452 7.668837e-04
+## Neoascochyta                    0.0010471459 0.0003806261 1.533767e-03
+## Barnettozyma                    0.0015585428 0.0054239224 1.223751e-03
+## Apiotrichum                     0.0001065190 0.0083896343 2.268018e-03
+## Waitea                          0.0014367816 0.0002854696 8.321504e-04
+## Dendryphion                     0.0010471459 0.0025850858 2.170118e-03
+## Hyaloscypha                     0.0001491265 0.0019189901 2.300651e-03
+## Neopyrenochaeta                 0.0017046561 0.0013163320 9.463671e-04
+## Cladophialophora                0.0017533606 0.0025057887 1.256384e-03
+## Nullicamyces                    0.0010227937 0.0017921147 4.242335e-04
+## Rhizophagus                     0.0015585428 0.0023947727 1.468501e-04
+## Spizellomyces                   0.0002922268 0.0020141466 6.526670e-04
+## Neohendersonia                  0.0007062147 0.0001810822 3.916002e-04
+## Oidiodendron                    0.0015098383 0.0009674247 5.221336e-04
+## Fusidium                        0.0003652835 0.0004599232 5.058169e-04
+## Leptosphaeria                   0.0171683226 0.0012846132 7.016170e-04
+## X.uncultured.eukaryote.         0.0004626924 0.0006819551 9.790004e-05
+## Stemphylium                     0.0014367816 0.0001427348 1.142167e-03
+## Trichoderma                     0.0001550084 0.0008564088 3.100168e-04
+## Fimicolochytrium                0.0005357491 0.0005233609 7.668837e-04
+## Volutella                       0.0003165790 0.0003330479 1.305334e-04
+## X.uncultured.Monoblepharidales. 0.0004139879 0.0007136740 6.689836e-04
+## Chlamydomonas                   0.0003652835 0.0001744536 1.305334e-04
+## Peziza                          0.0002435223 0.0001585942 1.468501e-04
+## Delitschia                      0.0004139879 0.0013004726 2.773835e-04
+## Cyberlindnera                   0.0005357491 0.0003489073 2.681109e-05
+## Chrysosporium                   0.0043834015 0.0011577378 2.300651e-03
+## Neohelicomyces                  0.0005601013 0.0011735972 3.100168e-04
+## X.uncultured.Ichthyophonida.    0.0018507695 0.0011577378 6.391138e-05
+## Coprinus                        0.0001704303 0.0012528943 2.268018e-03
+##                                           16           17           18
+## Candida                         8.335901e-02 5.146564e-02 4.669838e-02
+## Mrakia                          1.089542e-01 4.171709e-02 7.119479e-02
+## Colletotrichum                  4.128921e-03 9.748555e-03 7.321929e-03
+## Cladosporium                    5.850332e-02 7.032406e-02 2.760063e-02
+## Sporobolomyces                  3.580452e-02 1.737932e-02 3.566488e-02
+## Juxtiphoma                      3.412009e-02 5.328089e-02 4.221075e-02
+## Sistotrema                      3.204536e-02 5.580207e-02 9.501636e-02
+## Gibellulopsis                   1.345494e-02 2.033750e-02 1.815298e-02
+## Botrytis                        8.648138e-03 8.370311e-03 3.360664e-02
+## Podospora                       5.443602e-03 6.171843e-02 8.401660e-03
+## Alternaria                      1.670056e-02 2.477477e-02 1.568985e-02
+## Fusarium                        1.314681e-02 1.593384e-02 1.245065e-02
+## Mortierella                     7.230747e-02 8.340056e-02 1.143840e-01
+## Cadophora                       9.777942e-03 1.085787e-02 1.531869e-02
+## Aspergillus                     1.774820e-02 2.659002e-02 2.982758e-02
+## Laetinaevia                     1.170888e-02 8.807315e-03 3.880285e-03
+## Paraphaeosphaeria               9.551981e-03 1.079064e-02 1.717448e-02
+## Tetracladium                    7.148580e-03 5.781901e-03 8.570368e-03
+## Solicoccozyma                   2.087057e-02 1.855587e-02 2.439518e-02
+## Chloridium                      1.053799e-02 3.781767e-02 2.334919e-02
+## Trichosporiella                 8.833015e-03 6.756757e-03 1.147215e-02
+## Vishniacozyma                   7.805920e-02 1.321097e-02 5.668590e-03
+## Umbelopsis                      2.801915e-02 2.131236e-02 4.281810e-02
+## Plectosphaerella                6.717200e-03 4.097754e-02 3.003003e-03
+## Tausonia                        1.123642e-02 3.664112e-03 4.116476e-03
+## Chordomyces                     3.574290e-03 6.386984e-03 1.180956e-03
+## Minimedusa                      7.538876e-03 1.290843e-02 1.032493e-02
+## Urceolella                      1.372199e-02 1.015194e-02 1.393528e-02
+## Naganishia                      1.454366e-02 3.832190e-03 3.914026e-03
+## Acremonium                      7.600501e-03 1.179911e-02 1.703951e-02
+## X.uncultured.fungus.            1.337277e-02 2.524539e-02 1.724196e-02
+## Talaromyces                     2.711530e-03 9.244319e-03 1.687080e-02
+## Holtermanniella                 5.053306e-03 1.949711e-03 5.668590e-03
+## Sporidesmium                    3.676999e-03 6.084443e-03 5.061241e-03
+## Protoventuria                   1.047637e-03 7.429071e-03 1.923272e-03
+## Trichocladium                   2.916949e-03 2.487562e-03 6.613355e-03
+## Gorgomyces                      1.848771e-04 4.033885e-04 1.687080e-03
+## Papiliotrema                    9.572523e-03 3.193492e-03 2.901778e-03
+## Ophiosphaerella                 1.314681e-03 2.756488e-03 3.542869e-03
+## Pseudeurotium                   1.643352e-03 5.849133e-03 6.680838e-03
+## Coniochaeta                     2.629363e-03 4.370042e-03 2.260688e-03
+## Exophiala                       3.409955e-03 9.378782e-03 1.211324e-02
+## NA.                             4.888971e-03 7.529918e-03 1.248439e-02
+## Sakaguchia                      3.081284e-04 5.042356e-04 2.091980e-03
+## Pseudoanguillospora             3.697541e-04 2.050558e-03 8.772818e-04
+## Halenospora                     1.437933e-03 1.579938e-03 1.315923e-03
+## Pleotrichocladium               5.135474e-04 2.016942e-04 1.484631e-03
+## Cephalosporium                  1.088720e-03 1.042087e-03 7.423153e-04
+## X.uncultured.Spizellomycetales. 5.751731e-04 9.076240e-04 2.294429e-03
+## Verticillium                    2.547195e-03 3.563265e-03 1.180956e-03
+## Pseudogymnoascus                5.751731e-04 3.865806e-03 9.447650e-04
+## Devriesia                       1.396849e-03 2.554794e-03 2.665587e-03
+## Polymyxa                        2.465027e-04 4.370042e-04 6.748321e-04
+## Apodus                          1.479016e-03 2.823719e-03 1.147215e-03
+## Arthopyrenia                    1.766603e-03 2.050558e-03 2.631845e-03
+## Zasmidium                       1.684435e-03 1.613554e-03 2.294429e-03
+## X.uncultured.Agaricales.        4.108379e-05 7.059298e-04 6.748321e-04
+## X.uncultured.Glomerales.        5.546312e-04 2.084174e-03 1.653339e-03
+## Ochroconis                      5.135474e-04 6.386984e-04 1.687080e-03
+## X.uncultured.Basidiomycota.     1.540642e-03 2.722872e-03 2.699329e-04
+## Microdochium                    1.437933e-04 1.008471e-04 1.079731e-03
+## X.uncultured.Hypocreales.       2.403402e-03 2.487562e-03 1.788305e-03
+## X.uncultured.Mortierellales.    7.805920e-04 1.042087e-03 1.214698e-03
+## X.uncultured.Ascomycota.        1.437933e-04 2.050558e-03 1.889530e-03
+## Allovahlkampfia                 7.395082e-04 5.714670e-04 8.097986e-04
+## Rhodotorula                     1.994618e-02 5.916364e-03 9.447650e-04
+## Dioszegia                       1.066124e-02 1.479091e-03 7.423153e-04
+## Alpinaria                       1.700869e-02 1.848864e-03 8.361004e-05
+## Leucosporidium                  2.384914e-02 1.210165e-03 4.386409e-04
+## Trematosphaeria                 3.286703e-04 8.740083e-04 1.147215e-03
+## Paraphoma                       8.627596e-04 4.706199e-04 3.104228e-03
+## Neoascochyta                    3.594832e-03 5.378513e-04 1.903131e-04
+## Barnettozyma                    7.805920e-03 2.991798e-03 7.524378e-03
+## Apiotrichum                     2.403402e-03 1.210165e-03 2.665587e-03
+## Waitea                          4.313798e-04 3.596880e-03 3.070486e-03
+## Dendryphion                     3.081284e-04 1.411860e-03 3.374161e-04
+## Hyaloscypha                     2.937491e-03 1.815248e-03 1.045990e-03
+## Neopyrenochaeta                 8.216758e-04 1.109318e-03 1.079731e-03
+## Cladophialophora                2.670446e-03 1.613554e-03 1.788305e-03
+## Nullicamyces                    8.833015e-04 2.353099e-03 2.395654e-03
+## Rhizophagus                     5.751731e-04 2.353099e-03 1.822047e-03
+## Spizellomyces                   7.189663e-04 4.033885e-04 9.785066e-04
+## Neohendersonia                  9.038434e-04 1.075703e-03 9.110234e-04
+## Oidiodendron                    7.395082e-04 1.479091e-03 4.386409e-04
+## Fusidium                        8.216758e-04 4.370042e-04 5.736073e-04
+## Leptosphaeria                   7.600501e-04 8.282682e-05 8.282682e-05
+## X.uncultured.eukaryote.         2.130379e-05 1.344628e-04 2.024496e-04
+## Stemphylium                     1.643352e-03 6.723141e-04 5.398657e-04
+## Trichoderma                     2.690988e-03 3.361571e-03 2.024496e-03
+## Fimicolochytrium                2.403402e-03 2.616805e-04 1.045990e-03
+## Volutella                       6.778825e-04 3.917697e-05 3.036745e-04
+## X.uncultured.Monoblepharidales. 7.805920e-04 9.748555e-04 8.772818e-04
+## Chlamydomonas                   8.216758e-05 3.185647e-05 5.061241e-04
+## Peziza                          3.492122e-04 6.723141e-05 2.024496e-04
+## Delitschia                      1.848771e-04 8.740083e-04 3.745318e-03
+## Cyberlindnera                   6.367988e-04 7.059298e-04 2.681109e-05
+## Chrysosporium                   7.292373e-03 1.469006e-02 9.447650e-03
+## Neohelicomyces                  3.180864e-05 4.941509e-03 3.272936e-03
+## X.uncultured.Ichthyophonida.    1.417391e-03 5.714670e-04 2.361912e-03
+## Coprinus                        1.704303e-04 4.706199e-04 2.260688e-03
+## 
+## $meta.dat.use
+##       ndvi_01
+## 1   0.6887884
+## 2   0.8302155
+## 3  -1.9625407
+## 4  -0.7571646
+## 5   0.9980715
+## 6   0.5095020
+## 7  -1.9896865
+## 8  -1.1410253
+## 9   0.5747102
+## 10 -1.0529250
+## 11  0.9838132
+## 12  0.6228720
+## 13  0.6121273
+## 14 -0.8421145
+## 15  0.3130208
+## 16  0.5945529
+## 17  0.6619353
+## 18  0.3558477
+## 
+## $wald
+## $wald$beta
+##               Candida    Mrakia Colletotrichum Cladosporium Sporobolomyces
+## (Intercept) 4.3943512  5.480705      1.6935183    3.7208385       2.361759
+## ndvi_01     0.1633888 -0.186189     -0.6151762    0.8981771       1.555382
+##             Juxtiphoma  Sistotrema Gibellulopsis  Botrytis Podospora Alternaria
+## (Intercept)  4.2962572  4.00035068    3.36813923 2.4976342  1.573231  3.2919764
+## ndvi_01     -0.0631729 -0.03522596   -0.03586229 0.6043581 -0.351179 -0.1794338
+##               Fusarium Mortierella  Cadophora Aspergillus Laetinaevia
+## (Intercept)  3.0308770  4.97791414  2.8276407   2.9561805   1.8360267
+## ndvi_01     -0.4266206  0.01413217 -0.3844973   0.1520078  -0.2894733
+##             Paraphaeosphaeria Tetracladium Solicoccozyma Chloridium
+## (Intercept)         2.5064018    2.6740797    3.20726703  3.2647339
+## ndvi_01            -0.1794086   -0.5333643   -0.04885714 -0.3753921
+##             Trichosporiella Vishniacozyma Umbelopsis Plectosphaerella
+## (Intercept)       2.6664050     2.3957689  2.9736169        0.4749363
+## ndvi_01          -0.7826106     0.8232492  0.2148238        0.5825941
+##               Tausonia Chordomyces Minimedusa Urceolella Naganishia Acremonium
+## (Intercept)  1.9979638  -0.5922402  1.7529473  2.2111112  1.0852263  1.2086428
+## ndvi_01     -0.1658491   0.9771266  0.3072024 -0.3440139  0.3405805  0.2466034
+##             X.uncultured.fungus. Talaromyces Holtermanniella Sporidesmium
+## (Intercept)           2.36708491   0.3413776       0.6607865    0.7777615
+## ndvi_01               0.07150662   0.1472212       0.1290130   -0.2336057
+##             Protoventuria Trichocladium Gorgomyces Papiliotrema Ophiosphaerella
+## (Intercept)    -0.1039228     0.7004752  -1.635784   0.75577912       0.1535562
+## ndvi_01        -0.1026144    -0.2233561  -1.042979  -0.02668366      -0.1293585
+##             Pseudeurotium  Coniochaeta  Exophiala       NA. Sakaguchia
+## (Intercept)    -0.5874280  0.008542792  1.3376854 1.2430630 -1.3949950
+## ndvi_01         0.2886117 -0.387755956 -0.2492913 0.1104767 -0.3897049
+##             Pseudoanguillospora Halenospora Pleotrichocladium Cephalosporium
+## (Intercept)          -1.6682014  -0.4958424        -0.5823653     -0.5442522
+## ndvi_01              -0.1165233  -0.4015825        -0.4839507     -0.2181864
+##             X.uncultured.Spizellomycetales. Verticillium Pseudogymnoascus
+## (Intercept)                      -0.7362870   -0.3726311        -1.562428
+## ndvi_01                          -0.1799865    0.3314485         0.079174
+##               Devriesia   Polymyxa     Apodus Arthopyrenia  Zasmidium
+## (Intercept) -0.22071715 -1.7540812 -1.3755398 -0.854181939 -1.4551112
+## ndvi_01      0.01235102 -0.5495672  0.3621303  0.005794465  0.2909076
+##             X.uncultured.Agaricales. X.uncultured.Glomerales. Ochroconis
+## (Intercept)              -1.61593844               -1.4549153 -1.5905404
+## ndvi_01                   0.09156544                0.0183262 -0.1570773
+##             X.uncultured.Basidiomycota. Microdochium X.uncultured.Hypocreales.
+## (Intercept)                  -1.5365999  -2.47867380                 -1.099977
+## ndvi_01                      -0.1464914  -0.01716159                 -0.183888
+##             X.uncultured.Mortierellales. X.uncultured.Ascomycota.
+## (Intercept)                   -2.3518590               -1.5697845
+## ndvi_01                        0.2827829                0.1534251
+##             Allovahlkampfia Rhodotorula  Dioszegia  Alpinaria Leucosporidium
+## (Intercept)      -2.3991455   0.4055591 -0.5740281 -0.6585266     -1.1389698
+## ndvi_01          -0.1521923   0.9698252  1.0017473  0.1105339      0.7644526
+##             Trematosphaeria  Paraphoma Neoascochyta Barnettozyma  Apiotrichum
+## (Intercept)      -1.0185630 -0.5589414  -0.85186696   -0.3268341 -0.698244019
+## ndvi_01          -0.9936727 -0.5990378   0.01292847    0.4839991 -0.009980609
+##                 Waitea Dendryphion Hyaloscypha Neopyrenochaeta Cladophialophora
+## (Intercept) -1.4992086  -1.1388736  -0.9967909      -1.6593325       -0.7678721
+## ndvi_01      0.2196045  -0.6167296  -0.5865611      -0.6042038       -0.4611709
+##             Nullicamyces Rhizophagus Spizellomyces Neohendersonia Oidiodendron
+## (Intercept)    -1.437574  -2.0622345    -1.6154938     -1.7041040   -2.1240550
+## ndvi_01         0.351313   0.7150012    -0.3090265      0.1651187   -0.1016075
+##               Fusidium Leptosphaeria X.uncultured.eukaryote. Stemphylium
+## (Intercept) -2.8111793    -1.3427979               -3.114200  -2.0757193
+## ndvi_01      0.2511801    -0.9716771               -1.287955   0.7787973
+##             Trichoderma Fimicolochytrium Volutella
+## (Intercept) -1.56638930       -1.6226395 -3.277302
+## ndvi_01     -0.05379201       -0.1719723  0.835822
+##             X.uncultured.Monoblepharidales. Chlamydomonas     Peziza Delitschia
+## (Intercept)                      -2.0081261    -3.6176448 -4.0562009  -2.244267
+## ndvi_01                           0.5717855     0.1391061  0.8324001   1.010332
+##             Cyberlindnera Chrysosporium Neohelicomyces
+## (Intercept)    -2.8909671    -0.2124255     -2.6419124
+## ndvi_01         0.4509643     0.3419751     -0.3570097
+##             X.uncultured.Ichthyophonida.  Coprinus
+## (Intercept)                   -1.8464698 -1.284977
+## ndvi_01                       -0.7154673 -1.033100
+## 
+## $wald$sig
+##                         Candida                          Mrakia 
+##                       1.2215077                       0.5700074 
+##                  Colletotrichum                    Cladosporium 
+##                       1.4036692                       0.9963434 
+##                  Sporobolomyces                      Juxtiphoma 
+##                       1.2951373                       0.5702959 
+##                      Sistotrema                   Gibellulopsis 
+##                       1.0382575                       0.6504606 
+##                        Botrytis                       Podospora 
+##                       1.2424607                       0.9818834 
+##                      Alternaria                        Fusarium 
+##                       0.7672150                       0.8955881 
+##                     Mortierella                       Cadophora 
+##                       0.4146667                       0.4010600 
+##                     Aspergillus                     Laetinaevia 
+##                       0.2560520                       0.9197351 
+##               Paraphaeosphaeria                    Tetracladium 
+##                       0.5929737                       0.8545731 
+##                   Solicoccozyma                      Chloridium 
+##                       0.4280061                       0.5158491 
+##                 Trichosporiella                   Vishniacozyma 
+##                       0.7508610                       1.1626840 
+##                      Umbelopsis                Plectosphaerella 
+##                       0.7275887                       1.1651668 
+##                        Tausonia                     Chordomyces 
+##                       0.8205193                       1.1955553 
+##                      Minimedusa                      Urceolella 
+##                       0.7042566                       0.4253511 
+##                      Naganishia                      Acremonium 
+##                       0.7021013                       0.7947845 
+##            X.uncultured.fungus.                     Talaromyces 
+##                       0.4515477                       1.3280874 
+##                 Holtermanniella                    Sporidesmium 
+##                       0.7476119                       0.5905748 
+##                   Protoventuria                   Trichocladium 
+##                       0.6732226                       0.5222306 
+##                      Gorgomyces                    Papiliotrema 
+##                       1.2212104                       0.6753352 
+##                 Ophiosphaerella                   Pseudeurotium 
+##                       0.4781341                       1.3161740 
+##                     Coniochaeta                       Exophiala 
+##                       0.6135353                       0.6047666 
+##                             NA.                      Sakaguchia 
+##                       0.5156342                       0.8787618 
+##             Pseudoanguillospora                     Halenospora 
+##                       1.2508948                       0.6758848 
+##               Pleotrichocladium                  Cephalosporium 
+##                       1.1606239                       0.7193976 
+## X.uncultured.Spizellomycetales.                    Verticillium 
+##                       1.3556481                       0.7873128 
+##                Pseudogymnoascus                       Devriesia 
+##                       0.8000965                       0.5005736 
+##                        Polymyxa                          Apodus 
+##                       0.7114220                       1.0420095 
+##                    Arthopyrenia                       Zasmidium 
+##                       0.5995660                       0.6262052 
+##        X.uncultured.Agaricales.        X.uncultured.Glomerales. 
+##                       1.6566737                       0.8338330 
+##                      Ochroconis     X.uncultured.Basidiomycota. 
+##                       0.7595471                       0.9586198 
+##                    Microdochium       X.uncultured.Hypocreales. 
+##                       1.2072850                       0.8541804 
+##    X.uncultured.Mortierellales.        X.uncultured.Ascomycota. 
+##                       1.2812051                       1.0325138 
+##                 Allovahlkampfia                     Rhodotorula 
+##                       1.0394656                       1.6957272 
+##                       Dioszegia                       Alpinaria 
+##                       2.0597151                       2.4761473 
+##                  Leucosporidium                 Trematosphaeria 
+##                       1.4913813                       1.2387254 
+##                       Paraphoma                    Neoascochyta 
+##                       2.0395700                       1.7246924 
+##                    Barnettozyma                     Apiotrichum 
+##                       1.7714708                       1.8265809 
+##                          Waitea                     Dendryphion 
+##                       1.6527961                       1.0477282 
+##                     Hyaloscypha                 Neopyrenochaeta 
+##                       1.1779257                       1.0922914 
+##                Cladophialophora                    Nullicamyces 
+##                       0.7526203                       0.8996428 
+##                     Rhizophagus                   Spizellomyces 
+##                       1.7075232                       1.0428119 
+##                  Neohendersonia                    Oidiodendron 
+##                       0.7569146                       1.3567426 
+##                        Fusidium                   Leptosphaeria 
+##                       0.7891193                       1.8397478 
+##         X.uncultured.eukaryote.                     Stemphylium 
+##                       1.5222312                       1.7198369 
+##                     Trichoderma                Fimicolochytrium 
+##                       1.2452739                       0.8155576 
+##                       Volutella X.uncultured.Monoblepharidales. 
+##                       1.3525686                       0.8089034 
+##                   Chlamydomonas                          Peziza 
+##                       1.4681510                       0.7190064 
+##                      Delitschia                   Cyberlindnera 
+##                       3.1648003                       2.3812635 
+##                   Chrysosporium                  Neohelicomyces 
+##                       1.8742136                       2.1214184 
+##    X.uncultured.Ichthyophonida.                        Coprinus 
+##                       1.9712618                       1.2302890 
+## 
+## $wald$X
+##    (Intercept)    ndvi_01
+## 1            1  0.6887884
+## 2            1  0.8302155
+## 3            1 -1.9625407
+## 4            1 -0.7571646
+## 5            1  0.9980715
+## 6            1  0.5095020
+## 7            1 -1.9896865
+## 8            1 -1.1410253
+## 9            1  0.5747102
+## 10           1 -1.0529250
+## 11           1  0.9838132
+## 12           1  0.6228720
+## 13           1  0.6121273
+## 14           1 -0.8421145
+## 15           1  0.3130208
+## 16           1  0.5945529
+## 17           1  0.6619353
+## 18           1  0.3558477
+## attr(,&quot;assign&quot;)
+## [1] 0 1
+## 
+## $wald$bias
+## [1] -1.26042852 -0.06003912</code></pre>
+<pre class="r"><code># Show effect size and significance plots
+linda.plot(
+l_model,
+variables.plot = c(&#39;ndvi_01&#39;),
+alpha = 0.05,
+lfc.cut = 1,
+legend = TRUE
+)</code></pre>
+<pre><code>## $plot.lfc
+## $plot.lfc[[1]]</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+<pre><code>## 
+## 
+## $plot.volcano
+## $plot.volcano[[1]]</code></pre>
+<p><img role="img" src="data:image/png;base64,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CAPjMADS2iChd8S1717CMva919j4lX3T+x/CA0FALAQAjywnMZa+C1l5lzuaDrs6kZc9QQfAQBgYgI8sLSGZfjJ0vsEB8/USdz0pY371wk+AgDANAR4YJn1Z/hp0vvEH8ncBHPdxyot8ZgJPgIAwJQsYgcsueVbU23ExPXlu1gAAO4J8JALGxsbi64CYUicuC7Dz4hOCgAsnCn0AMEwcR0A4CET4AHGJiQDADB/AjzwoJlwHqVrhJ5jJvgIAABTEuCBh07OjJIaYeBfJ/gIAADTEOABpM0oGt4IIxpngo8AADAxAR4gigLJnLOeuN7/2cTStre3M6wAAAAj2EYO4NZ98kxco26BGXVvb29E9aav2AQlbG9vP3r0aMrzAgCQSICHXLi8vIxsNJ0bsw7JUd93BGOVObp6zIhOCgAsnCn0AAPM9O3u/vjd85uTuGlKBgBgaQjwAINN8EJ4GsMy9v3v08T7xPIn/goAAIDcMoUeYKjM33VPk72H/emmMmnS+MCvAKwtBwAQOiPwAHMy5Uj4Yj8OAMDCCfAAwZDhAQAeMlPoAYaaZq34fDKXHgAgXEbgAQZLXEwOAADmyQg85ILNpaeX7Wj5iLXi+7dhN6b9EOikAMDCGYEHlkG2o+XjrhWf8lxyPgAA0xDggeAl7qyeSWmZfEqGBwBgYgI8ELaJd1afc03SHzNTvkEAAAiXAA8ELPPMPNN92qR3AACmIcAD5MuUMXvYx6V3AIDQWYUeIHd6wva4Q/fWyQcAWEoCPMDyuA/qEjsAwPIxhR5y4fLy8vLyctG1IGxC+0zppADAwgnwQMDSRNaxYu2M3j9PX/jAYzK/TAAAQiTAA2GbPjNP/5H0H5y4tplfJgAAwRHggeBlvu767GL/iOXoEkuwvDwAwAMnwAPLoD/EznQy/GQHj7tL/EncwBNJ7wAAD4dV6IElsZAom/6kabaCOzk5uS9wYJ7f29uT2AEAHiwj8AADpNx6fdwd2qc8+4xOBwBAEAR4gF5j5eRsQ/X9bPn5nA4AgIAI8JALGxsbGxsbi64FUTRRQp5zqJbhF0InBQAWToAHAACAAAjwAPNg8TkAAKYkwAPMiQwPAMA0BHgAAAAIgAAPEDPZOPnJnVkUPvB0mZQDAEBABHiAXtPE7Ckz/N7eXsqzy/AAAA+NAA8wwDQZ/u3bt5MVfv97GR4AgH4CPOTC5eXl5eXlomtBzJwzvCXuck4nBQAW7qNFVwAgv4aF6jRD32/fvt3e3h54/E2xEjsAAGMR4Ol3fXF68ubXs6ur23+vra198sXXe082V8MoH/Ji2Dh8f/4X5gEASCTAE3N98fJ/L86u4r+8urq6unpx9npt6+C759Ol7FmXD4E6OTmR4QEAGM078HS5ePmsL113uTp78ezlRY7Lh5ANnGY/mswPAPCgCPDcuT6tvji7+8fa1sGrVz/feHXwdGvt7g9nL6qn17ksH+Zldmu/j5XhpXcAgIdGgOfWxcnr+3fSn76qPd9cvZvLvrr55Hnt1dO7jH31+mSSUfJZlw/zMeud21JmeOkdAOABEuCJoiiKrk/f3I2Obx3UnvS/h776pHawdfvz2ZuxB8lnXT4sLxvOAQBwwyJ2RFEUXf/2693w+FZxc8hBm8Wt6OwsiqLo6tffrp8MSOELK38JbGxsLLoK5JfEngc6KQCwcEbgiaIo+uddcr6+Sdg3rt79k6vyAQAAlp4ATxRF1+/+vv1p7fHHI477+PHdi+p/vxtnkvusywcAAFh+AjxR9wD5J49HTVxfffzJ7U/jDZHPunyYn1nPZjdbHgCAYbwDT/oB8tsh8qsouhki30z5lvpsyv/888/TnT56//59yiPzIKzaTizoy9ze3n779u2MSg60ZQKt9mQeyMU+kMsEgLAYgQcY2/b2dhBlAgCwTAR4ume4h1k+LEK2eVt6BwAgkSn0ABPqTt3pJ9XL6gAATEaAJ1S///574jE378k/evRo9tWZyvv37y8vL6Mo2tjYyH9tp3TzYu3yXebe3t7JyUmaw+ZQmXla1hvaQycFAPLAFHq6d28Ls3zIh8RwvnzpHQCAeRLgATIzIqJL7wAATEmAZ5zt19Nu6D7f8iFPBgZ16R0AgOl5B54o/fbr6Td0n2/5kC/dy9R5kRgAgKwYgSdKP0Q+6QD5rMsHAABYfgI8UdS9zNxZ82LoURfNs9ufxh0gn3X5AAAAS0+AJ4qiaPXfXyQm7OvTN/f5+ot/jzdAPuvyAQAAlp4ATxRFUbT65Out2x/PXlRPr/sOuD793+u7+e1bXz8ZN1/PuvzwbWxsbGxsLLoWwFA6KQCwcAI8tzb3nt4Nkl+9flZ9eXpxl7KvL05fVp/dx+u1p3ubOSwfAABguVmFnjurT747ePfsxc009quz1y/OXg84auugNnh4/OLll7efjbYOfn7en8GnLB8AAOBhMwLPB6ubz18dbK0N/fva1sGrAck8N+UDAAAsMSPwxKxuPq/9vHdxevLm17Oru0nt0dra1hdf7z0ZvoF7bsoHAABYViudTmfRdYBZ+fzzz6Mo+v333xddkQTv37+///nRo0cLrMkc3Fzscl+mG7p83NNZu/nPdRTCf7EBYIFMoQcAAIAACPAAAAAQAAEecuHy8vLy8nLRtQCG0kkBgIUT4AEAACAAAjwAAAAEQIAHAACAAAjwAAAAEAABHgAAAALw0aIrADP3+eefL7oKAAAA0zICDwAAAAEQ4AEAACAAK51OZ9F1gIeue5L/77//vsCakAk3dPm4pwBAHhiBBwAAgAAI8AAAABAAAR4AAAACIMADAABAAAR4AAAACIAADwAAAAEQ4AEAACAAAjwAAAAEQIAHAACAAAjwAAAAEICVTqez6DoAAAAACYzAAwAAQAAEeAAAAAiAAA8AAAABEOABAAAgAAI8AAAABECABwAAgAAI8AAAABAAAR4AAAACIMADAABAAAR4AAAACIAADwAAAAH4aNEVAEa6ePnli7Mo2jr4+fnmoutCkuuL05M3v55dXd3+e21t7ZMvvt57srm6yFqRHf0RAFgoAR7y7OLli7NF14FUri9e/u/F2VX8l1dXV1dXL85er20dfPdcig+e/ggALJgp9JBbFy+r4kIgLl4+60vvXa7OXjx7eTHH+pA9/REAWDwj8JBPFy+roxIhOXJ92pXs1rYOvtvbXF2NotsZ9a9vb+PZi+rjV7UnhuGDpD8CALlgBB7y5/q0+qW0EIyLk9f377w/fVV7fpveoyha3XzyvPbq6drtP69enxiFD5D+CADkhgAPuXJ9cVr98tlrYSEY16dv7kbftw4GDbCvPqkdbN3+fPbm9HpuNWN6+iMAkC8CPOTF9cXL6pfPXnwYzd06eLo18hMs3vVvv97dsK3isHXJN4t3N/Lq198k+DDojwBADgnwkBPXv735MEt3bevgVe355uNFVog0/nmXnN9jCf7dPzOvExnQHwGAPLKIHeTM2tZTG4cH4/rd37c/rT3+eMRxHz9ei6KrKIqiv99dR+5uOPRHACBPBHjIjU9EheB8GID/5PGoG7f6+JPbAH/17p8oco8DoD8CAPkjwENOrD55/nzRdWBMaQfgDcEHR38EAPLIO/AAAAAQAAEeYFIfZtADAMDMCfAAAAAQAAEeAAAAAiDAA0zq48dri64CAAAPh1XoYUauT6vPXo94Q3rt6avaE4uRAwAAKRmBB5jU6uNPbn+6evfPyCPTbhgPAABDCfAAE/swh/7vd9cjjku/YTwAAAxjCj3MyOqT2s9PFl0JZmz18SdRdBVFt0PwQ8fWDcADADA9I/AAk/swBH/WvBh61EXz7PYnA/AAAExMgAeY3Oq/v0hM8Nenb+7z+xf/NgAPAMCEBHiAKaw++Xrr9sezF9XT/hfhr0//d78bwdbXNh4AAGBiAjzAVDb3nt4Nwl+9flZ9eXpxl+KvL05fdm0muPZ0b3MB9QMAYFlYxA5gOqtPvjt49+zFzTT5q7PXL85eDzhq66Bm+B0AgGkYgQeY1urm81cHW2tD/762dfDqudF3AACmYwQeIAOrm89rP+9dnJ68+fXs6m7SfLS2tvXF13tPNg29AwAwvZVOp7PoOgAAAAAJTKEHAACAAAjwAAAAEAABHgAAAAIgwAMAAEAABHgAAAAIgAAPAAAAARDgAQAAIAACPAAAAARAgAcAAIAACPAAAAAQAAEeAAAAAiDAAwAAQAAEeAAAAAiAAA8AAAABEOABAAAgAAI8AAAABECABwAAgAAI8AAAABAAAR4AAAACIMADEEVRFLUbjVqlUiqVVrqVSqVSpVZrtBddPQCAB0+Ah9xp17oDVKn2AINTu1GrVUo9SbJUKlUqtUY7VXvEG3FSlcZsr7NRyfRGT1pcu1GrlFZWVtZ3d/ePj5vNZuyvzWazeby/v7u+srJSqqTL8d01mXkrAgA8GAI8kCftWqW0srK+u79/3OxJks1m8/h4f3d9faVUMR6ckXajUlpZ390/biYfG0VR83h3PXWKBwAgYwI8kBftWmllPU2UbB7v764/yKkJmWo3KqX13ZTRvUvzWOsDACyEAA/kQrtWWt8fJ0s296XIKbQblQHhvVg+PKy3Wq3OB61Wq35YLhbjB2p9AIAFEOCBHGjXvulJ78ViOZYkW61WvTdGNvfX071fXa53JnK0k90l5kqjsr57HPtNsVxvdTrnR9XqTqFQ6PpDoVDYqR6dn3da9XKs+Zv734jwAABzJcADC9cb34vleuv8/CiWJAuFws7R+XmrfhhLkcffC5Fja1Ti6b1YrrfOj3YKw46/Udg5Om/FWl+EBwCYLwEeWLT2Lyfd8b1cHxEmCzvV83q56xfNk19kyLH0xveR7d2j0Nv6+z9YYh4AYG4EeGDB4vm9XE+ct75z1B0iJfixtGvfd8f34mFrzPcE4q1vCgQAwBwJ8PAAtduNWmXALutpN1nvK6+3uNvC0n269Wd3fv8qTZzc+ao7wf/ZGqu2Wcq6Jcc82TjNfKPxQ+xlhfJ/qynH3rvsfNs9kX6sL1BuL2Gl9xLGbqx2u1GrVEo9TX/T+FO1fnY1nKpTjKpbacD1TlEqABCUyVZ2AmYn/p5x8bCV/JH0ZfctBNeneLOcWfryRpV1W1T3JfUuKBe73LRXm/iheCNOuojdqBpk0ZLdY9kjL310O9+dJ7m42OD55M/Wh9YtFsuHAy4ydp6b1k/5qCSeun44spy0JXbV8K4ZMqphFp1igqsep9sCAMEyAg8Pxt223wl7tTWbx7vrpUqKQb3kbcTT7BheqJ63WvX6YblcLEaffTr+gPD8Zd6Sqc424kRpN2Zv/BSbPr/3nwlbu1D9sV5vtTqdzvn5UTXFC/TtWhaPys0Tt59y5/rxtqtPWcPEXQ+y6RT9RSZedTYPGwCQcwI8PAzJ+SQmOWS0a6V05TX310s//DnykEJhZ6d6dHR+nnrftti0+/mm/sxbcrSB27UPOs/+euWnpLIyyu9RFBV2dgppP/3H96X1/ZSPyoiAnPqJS13ivVYtbQ2Pd0eVl2WnuNWorKS/6ubxbsqNFQGAQAnw8BA0Kr355Gab9Q+zcQbusj58k7C+Aovlw67yWvEpv83jcYNXglgSLf5rPdPCR58565ZMOl3fdu3D2/n4+LivgG7tv/7o/ufcvvdo3k9V6GmrVv9k86Fr4vVuNXg7eT82a/ymOXpL/Ckp0Tb3d++L7il0nBrOoFO0a6WR97+v0CiKjnen+b4IAMi7Kafqe+crAAALJUlEQVTgA5nL/B34eIGj3pbtiyuDz95b4JDyBr8LPP376GkaaDbvwGfdkgkvrcffWB9+tt5qpSpuBusCDK35qNq3eq8yxcv7iQsGxA4eeKWZ1zD7TtH3tA2/6J5Ds104AwDIEQEecifrAD/mwmXJ6WecINifk6YNjj1ZZUhxMwnwM23J/uLGaee+DN9fXLqGy0bfbR95tuQLHfe7h9jxid+dzLuG6TrFmE/bzJdtBADywBR6WHLxfb+Lhz8m7Ru2cxSLAv0zhmPz15O2be/ZNXxqPTOpi4ffpnlr/nh3ZSwDJyFn35IjxU+X1M6F6o8DhuFHmOuLBwm3KbYrYPTHX33NFH95P8VWg2PvM5hpDTPpFD0lnic9bfEnIPnFAQAgSAI8LLn4Luuptv0uVP/bHX969vmOB4sUWerb8ZLlKD1vGacI0RnKvCXHOV1yO8fPlSuJy+Wt/2v0I7Jz1Gm16oeH5WIxKqZoi+QSM65h9p1i3BKjKCr8Z0+CB4BlJ8DDcpsgB0QjBzDHLzCWK6bQqKzEVvSab3zPviUzP92YoXVukpe7L3z62Yd/DG6lQmGnWj06P++cp92pYAzT1jD7ThFbcDDt0xYrdMA0AQAgfAI8LLVYDhhj1nQsC3ZHgUmCRSz8TKbdm97TTCrOVOYtmf3psvqmJGtz3Oav3W43arVKKeWucHemrOEMOkX3/Iv0T1t3oeN8WwQABOOjRVcAmJsxYkrh08+i6C5CNP9sRdHtR2MTu9Na/1fxQ2nja/ft0l2up94xfqLjk2TRkjM4Xexcy6/dbrdarb/++unPP//444+u3ermLftOEftKoLm/vrI//gn++Ksd7czzOy4AYA4EeFhqE0WLtFKPDE4TLAel99YMZlEnmWlLzuV08bsw4XcJi9du1H74fj95D/UFyaZTzPlpAwCCYQo9kFvtRmUlnt6L5XrnyLDiZOLvyAf4jnS7VimtrO/mN73nijn0ALCMBHggnxqV0nr8tffiYWsWK5g9GPHXrkPLd43KynpydC8Wy+XDeqvVym7rAwCA/BDgYallsjD5sFnBqSNg7I3eVB+olVZ2j3uG3lvzXbWux0xbckanG13odEPwjcrKykqpUqk1GnMYyW/XSvEvc6IoiqJisVgulw/r9Xq91Wp1Op1O5/z86Ki6U1jcg5JNp4jdqXK9MxFfdgHAEhLg4eEYI7ENTRcTLao+3hu9/W+93wy952jifBYtOYPTJZ0rvkz9eNvS97jZOK15fLy/u7u+UqrNNsQ3fuh9kaLV6nQ65+fnR0dH1Z2dnZ1CT2af60vkM+4UAb7sAADMigAPSy3FBtuDtH856UoX3YugT1LgOBm2USn1vfW+2KH3W5m35AxOl5gJM0vwsY3PU2yjPp3Y2W6/zVn8E/FB9p1iwqcNAFh6Ajwst52vyh/+cfxTI9WH4qkzPu87NtqYqsB4aaM0KqWeifM5GnrPvCUzP108VQ/Uk+D3f0h3GT3ate8Xlt/L/03zbU6KpshS9p1ioqctatdKK6VSpVZrtA3aA8ByEuBhycWzxfdp5jr3pM54PItHwBTRIm1+b9fi6T0vQ+/3sm7JBPEIl+J0qUJr/PalvIzeE/VMaJ9xfo9J9R1IzxcMszeDTjHB03ZzY5rN4/393fX1lcpEX84AAPkmwMOSK1T/2xUEm/vrSf9f36jE5rD3xbN4gce7o8tr177ZT5Pfe8+ao6H3O5m3ZIJYgm/ufzM6wzUqA1Z5GyB+FWkuI+FE6YbEs5Ji2n/aRy5L2XeK3qct4fZHfTem/JU17ABgCQnwsPR2vo1tqXW8W6oMWzi83aiUkuNZvMAR5fUvRzdEPHsUD/M19H4v85Yc53TN/fVha8W1G5WVdPE9iqJo56jeHeFHXkb/iUq9W/t9O/OcGBuMbu7/MKKy7b41FKJoPqvAZd8p+m//8NvUd2PmcV8AgAUQ4GH59Qy6Rs3j3fVSqRJ7T7bdblRKpfX4K+hRuT5wK6reUdzj3fVSpfu923a7UauUVtIFlZ74Xq7nM71H0QxaMul0P8a+MWjur6/0NfTNycYqtzfC39zAhBDfblT6rqp4+OMc7lXvtP/epy2K2jcPXGmlt4JzlHGniAbc/uPd3vs/5HGbz30BABZhsu1lgdlpxf6vfRLFw1YGhQ4oJYta9u5qPZPL7Sl10p20M7rwoS3ZnaOHHRTP2qNPc1ju+seouzfsMorlw8P7PdVvjmu16ofl4qCDR7RqrNIpWj/p+PSNMFBSiRnUsNPpZNgppiox6dYDAAEzAg8PQ6F63qqXU4eB5BXkCtXzlOUVy4cjUkj6NepzIvOWTLBzlO505fp59dP0xRaq5wPC4e0CaOsr99bX13f3j5u996hYrncmmFMwqZ2jsZJssVyvdx8/r53Us+oUsRLHedpy/P4JAJAJAR4ejMLOUZowUCwf1tOtIJeivGK53jo/+s/wA4LL71E0g5ac9nTFcr01fpweOxx2ne18juk9iqLbLxySK1ssH9ZbnfOjnZ3YTuqT73g/riw6RVzKpy2KiuXDVkd6B4DlJsDDg1LYOTrvdFr1w8NyMT4tulgslg/rrVbn/KiaPnIWdo7OO7eTrLuL+1BYzpaSz0zWLZnmdP0NXbwPrJOd6KbY+uAp8gMUy/XWwvYHKFSPzoe2ePmwXm91uto8tgbcHBP8TDrF7dN2W2b/43Z4e+1L2tkAgHsrnU5n0XUAllz3Iumm+OZVu9345Zef/jz544+o2T1lvlgsRp99tvfVt//ZKbhxmdEpAIAJfLToCgAPy2efCir5VCjsVKs7UXXR9XiAdAoAICVT6IHxtGulUqlUqcV3sxql8dOHXc6K/1qfUb1gYXQKAGA+BHhgbM3m7WrlK6VaYl5p177v2qXcWCPLSacAAOZAgAfGU4gt773/zci00q6V1ve73qcufzXnxcthDnQKAGA+BHhgTLHlvaPm/nqpVKk12rG5w+12u1GrlFZiQSUq1+e9+RjMhU4BAMyFVeiB8XWvoJ1aud4RVVhaOgUAMHtG4IHx7Ry1DlPuHH6jKKiw5HQKAGD2BHhgEoXqeadVL6cILMXyYb11Lqiw9HQKAGDWTKEHptNu1H756eTkj6jZ7Hq1t1gsfvbZ3lffVncssM2Do1MAALMhwAMAAEAATKEHAACAAAjwAAAAEAABHgAAAAIgwAMAAEAABHgAAAAIgAAPAAAAARDgAQAAIAACPAAAAARAgAcAAIAACPAAAAAQAAEeAAAAAiDAAwAAQAAEeAAAAAiAAA8AAAABEOABAAAgAAI8AAAABECABwAAgAAI8AAAABAAAR4AAAACIMADAABAAAR4AAAACIAADwAAAAEQ4AEAACAAAjwAAAAEQIAHAACAAAjwAAAAEAABHgAAAAIgwAMAAEAABHgAAAAIgAAPAAAAARDgAQAAIAACPAAAAARAgAcAAIAACPAAAAAQAAEeAAAAAiDAAwAAQAAEeAAAAAiAAA8AAAABEOABAAAgAAI8AAAABECABwAAgAAI8AAAABAAAR4AAAACIMADAABAAAR4AAAACIAADwAAAAEQ4AEAACAAAjwAAAAEQIAHAACAAAjwAAAAEAABHgAAAAIgwAMAAEAABHgAAAAIgAAPAAAAARDgAQAAIAACPAAAAARAgAcAAIAACPAAAAAQAAEeAAAAAiDAAwAAQAAEeAAAAAiAAA8AAAABEOABAAAgAAI8AAAABECABwAAgAAI8AAAABAAAR4AAAACIMADAABAAAR4AAAACIAADwAAAAEQ4AEAACAAAjwAAAAEQIAHAACAAAjwAAAAEID/A3BPI3ErnoYBAAAAAElFTkSuQmCC" width="672" /></p>
+<pre class="r"><code>l_model_df &lt;- as.data.frame(l_model$output)
+
+write.xlsx(l_model_df, file = &quot;supplementary_table_ndvi_regression_fits2.xlsx&quot;, rowNames = TRUE, colnames = TRUE)</code></pre>
+<div id="credit" class="section level5">
+<h5>Credit</h5>
+<p>This script is based on ideas and code from the <a href="https://benjjneb.github.io/dada2/tutorial.html">dada2 Tutorial</a>
+by Benjamin Callahan, the publication “Bioconductor Workflow for
+Microbiome Data Analysis: from raw reads to community analyses” by <a href="https://f1000research.com/articles/5-1492">Callahan et
+al. (2016)</a> and various pages of the official <a href="https://joey711.github.io/phyloseq">phyloseq website</a> by Paul
+J. McMurdie.</p>
+</div>
+</div>
+</div>
+
+
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/analysis/FITS2/FITS2_DADA2_results_260821/analysis_output/Controls.pdf b/analysis/FITS2/FITS2_DADA2_results_260821/analysis_output/Controls.pdf
new file mode 100644
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diff --git a/analysis/FITS2/FITS2_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf b/analysis/FITS2/FITS2_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..a6d4eb21ed817eb4826a7310a95f09e54c201cec
Binary files /dev/null and b/analysis/FITS2/FITS2_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf differ
diff --git a/analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Overview_all_and_pruned_23-12-24_141959.pdf b/analysis/FITS2/FITS2_DADA2_results_260821/analysis_output/Overview_all_and_pruned.pdf
similarity index 64%
rename from analysis/FITS1/FITS1_DADA2_results_260821/analysis_output/Overview_all_and_pruned_23-12-24_141959.pdf
rename to analysis/FITS2/FITS2_DADA2_results_260821/analysis_output/Overview_all_and_pruned.pdf
index 553ead2a4b20aea868ee4c5d00db6d98d9d768b8..9e715b19d419c4b1b50e6fde971616b21c585ec4 100644
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diff --git a/analysis/FITS2/supplementary_table_ndvi_regression_fits2.xlsx b/analysis/FITS2/supplementary_table_ndvi_regression_fits2.xlsx
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Binary files a/analysis/FITS2/supplementary_table_ndvi_regression_fits2.xlsx and b/analysis/FITS2/supplementary_table_ndvi_regression_fits2.xlsx differ
diff --git a/analysis/Nems/.Rhistory b/analysis/Nems/.Rhistory
new file mode 100644
index 0000000000000000000000000000000000000000..0a8a1aab7b156f9a07920a077e4f59df4c49a04d
--- /dev/null
+++ b/analysis/Nems/.Rhistory
@@ -0,0 +1,512 @@
+top = set$top_n,
+ignore_na = set$ignore_na)
+set$topnASVs <- names(sort(taxa_sums(ps), decreasing = TRUE))[1:set$topASVs]
+set$ps.topnASVs <- prune_taxa(set$topnASVs, ps.trans)
+if (set$unify_colors | exists("highlight", envir = set) | set$fuse_ASVs) {
+set$toptax <- union(phyloseq::tax_table(set$ps.topnTax)[,set$taxlvl],
+phyloseq::tax_table(set$ps.topnASVs)[,set$taxlvl])
+set$toptax <- sort(set$toptax)
+set$taxlvlPalette <- viridis(length(set$toptax))
+names(set$taxlvlPalette) <- set$toptax
+if (exists("highlight", envir = set)) {
+# It is possible to change the highlight color here by substituting
+# 'sub_viridis$reds[4]' with a hexcode-string, e.g. '#ff7f7f"'
+set$taxlvlPalette[set$highlight] <- sub_viridis$reds[4]
+}
+set$taxlvlPalette <- set$taxlvlPalette[sort(names(set$taxlvlPalette))]
+set$my_scale_fill <- scale_fill_manual(values = set$taxlvlPalette,
+na.value = "grey")
+}else{
+set$my_scale_fill <- my_scale_fill
+}
+# Plot
+if (set$unify_colors | exists("highlight", envir = set) | set$fuse_ASVs) {
+set$my_scale_fill <- scale_fill_manual(
+values = set$taxlvlPalette[
+sort(unique(phyloseq::tax_table(set$ps.topnTax)[,set$taxlvl]))],
+na.value = "grey")
+}
+plots$topn_tax <- plot_bar(set$ps.topnTax,
+x = set$x_axis_value,
+fill = set$taxlvl,
+title = paste0("Top ", set$top_n, " ", set$taxlvl)) +
+facet_grid(paste0("~", set$panel_by), scales = "free", space = "free") +
+set$my_scale_fill +
+ylab(set$y_axis_label) +
+xlab("Sample") +
+theme(strip.background = element_blank(), strip.text = element_text(size = 16),
+axis.title=element_text(size=16), legend.text = element_text(size=14))
+if (set$fuse_ASVs) {
+plots$topn_tax <- plots$topn_tax + geom_bar(
+aes_string(color = set$taxlvl, fill = set$taxlvl),
+stat = "identity", position = "stack") +
+scale_color_manual(values = set$taxlvlPalette, na.value = NA)
+}
+if (set$unify_colors | exists("highlight", envir = set) | set$fuse_ASVs) {
+set$my_scale_fill <- scale_fill_manual(
+values = set$taxlvlPalette[
+sort(unique(phyloseq::tax_table(set$ps.topnASVs)[,set$taxlvl]))],
+na.value = "grey")
+}
+plots$topn_ASVs <- plot_bar(set$ps.topnASVs,
+x = set$x_axis_value,
+fill = set$taxlvl,
+title = paste0("Top", set$topASVs, "_ASVs")) +
+facet_grid(paste0("~", set$panel_by), scales = "free_x", space = "free") +
+set$my_scale_fill +
+ylab(set$y_axis_label) +
+xlab("Sample") +
+theme(strip.background = element_blank())
+# save
+save_plot(plots$topn_tax, plot_name = paste0("Top", set$top_n, "_", set$taxlvl),
+filetype = tmp$out)
+save_plot(plots$topn_ASVs, plot_name = paste0("Top", set$topASVs, "_ASVs"),
+filetype = tmp$out)
+# Clean up plot parameters
+rm(list = ls(set), envir = set)
+# Print to standard out
+plots$topn_tax
+plots$topn_ASVs
+# CHANGE ME to the desired sample categories on the x-axis.
+# Accepted values are the column headers in the descriptor file.
+set$x_axis_value = "ndvi"
+# CHANGE ME to the taxonomic level of interest (color coding). Accepted values
+# are the column headers in your descriptor file.
+set$taxlvl = "Genus"
+# CHANGE ME to change the number of Top n taxa to be plotted at
+# taxlvl.
+set$top_n = 10
+# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
+set$ignore_na = FALSE
+# CHANGE ME to an entry at the chosen taxonomic level you want to highlight.
+# Comment out to not highlight anything.
+# set$highlight =
+##### Optional settings (sensible defaults) #####
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 20
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 13
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+# Can be changed to change the y-axis label
+set$y_axis_label = "Relative abundance"
+# Can be changed to change the x-axis label
+set$x_axis_label = "Sample"
+############### NO NEED FOR CHANGES BELOW ###############
+# Estimate Alpha-diversity (Shannon)
+set$alpha_div_ps_trans <- estimate_richness(ps.trans, measures = "Shannon") %>%
+as_tibble(rownames = "Sample")
+# Make physeq objects of top n taxa and top n ASVs
+set$ps.topnTax <- cuphyr::abundant_tax_physeq(ps.trans, lvl = set$taxlvl,
+top = set$top_n,
+ignore_na = set$ignore_na)
+# Plot
+set$my_scale_fill <- my_scale_fill
+set$topntax_tbl <- psmelt(set$ps.topnTax) %>%
+as_tibble() %>%
+left_join(set$alpha_div_ps_trans, by = "Sample") %>%
+select(Genus, Alias, ndvi, ndvi_july, Abundance, Shannon) %>%
+filter(Abundance > 0) %>%
+group_by(Genus, Alias, ndvi, ndvi_july, Shannon) %>%
+summarise(Abundance = sum(Abundance)) %>%
+arrange(ndvi) %>%
+mutate(ndvi_rank = c(1:length(ndvi)))
+plots$topn_tax_custom <- ggplot(set$topntax_tbl, aes(x = fct_reorder(Alias, ndvi),
+y = Abundance,
+fill = Genus)) +
+#title = paste0("Top ", set$top_n, " ", set$taxlvl))) +
+geom_col(color = "black") +
+set$my_scale_fill +
+ylab(set$y_axis_label) +
+xlab(set$x_axis_label) +
+theme(strip.background = element_blank(),
+#strip.text = element_text(size = 12),
+#axis.title=element_text(size=12),
+#legend.text = element_text(size=12),
+legend.position = "bottom")
+plots$ndvi_dot_plot <- ggplot(set$topntax_tbl, aes(fct_reorder(Alias, ndvi),
+y = ndvi)) +
+geom_point() +
+theme(strip.background = element_blank(),
+# strip.text = element_text(size = 12),
+# axis.title=element_text(size=12),
+# legend.text = element_text(size=12),
+axis.title.x=element_blank()) +
+ylab("NDVI")
+plots$shannon_dot_plot <- ggplot(set$topntax_tbl,
+aes(fct_reorder(Alias, ndvi),
+y = Shannon)) +
+geom_point() +
+theme(strip.background = element_blank(),
+# strip.text = element_text(size = 12),
+# axis.title=element_text(size=12),
+# legend.text = element_text(size=12),
+axis.title.x=element_blank()) +
+ylab("Shannon")
+plots$combo_topn_custom <- ggarrange(plots$ndvi_dot_plot,
+plots$shannon_dot_plot,
+plots$topn_tax_custom,
+nrow = 3,
+heights = c(1, 1, 3),
+align = "v")
+plots$combo_topn_custom
+save_plot(plots$combo_topn_custom, plot_name = paste0("Customized_NDVI_Shannon_plot"),
+filetype = tmp$out)
+set$my_formula <- y ~ x
+set$plot_title <- "Nematodes"
+topntax_data <- set$topntax_tbl %>%
+mutate(Taxa = 'nematodes') %>%
+ungroup() %>%
+select(Alias, ndvi, Shannon, Taxa) %>%
+distinct()
+write.csv(topntax_data, file = "../topntax_all_taxa/topntax_data_nematodes.csv")
+# Excel file with field data
+morphological_id <- file.path("2020-patch-larvik-a-counts.csv")
+# Reduce abundance per sample and genus data to genera of interest
+genera_of_interest_mol_morph <- c("Globodera", "Meloidogyne", "Pratylenchus")
+genus_abundance_tbl_per_sample_mm <- genus_abundance_tbl_per_sample %>%
+filter(Genus %in% genera_of_interest_mol_morph)
+# read in and parse morphological count data
+morph_data <- read_delim(morphological_id) %>%
+mutate(genus = str_replace(genus, "cyst", "Globodera"),
+genus = str_replace(genus, "mel", "Meloidogyne"),
+genus = str_replace(genus, "prat", "Pratylenchus")) %>%
+dplyr::rename(Sample = rute, Genus = genus)
+# Combine morphological and metabarcoding data
+set$data_mol_morph <- genus_abundance_tbl_per_sample_mm %>%
+left_join(morph_data)
+### Plotting
+# New facet label names for Genus variable
+genus_names <- list(
+'Globodera'=expression(paste(italic("Globodera "))), #"spp.")),
+'Meloidogyne'=expression(paste(italic("Meloidogyne "))), #"spp.")),
+'Pratylenchus'=expression(paste(italic("Pratylenchus ")))#, "spp."))
+)
+genus_labeller <- function(variable,value){
+return(genus_names[value])
+}
+signif.labs <- c("pval > 0.05", "pval <= 0.05")
+names(signif.labs) <- c("p value > 0.05", "p value <= 0.05")
+# Subset data for modeling
+set$glob_data <- subset(set$data_mol_morph, Genus == "Globodera")
+set$mel_data <- subset(set$data_mol_morph, Genus == "Meloidogyne")
+set$prat_data <- subset(set$data_mol_morph, Genus == "Pratylenchus")
+# Quasi-binomial model for Globodera
+model_glob = glm(
+Genus_Sample_Abundance ~ total_count,
+data = set$glob_data,
+family = quasibinomial
+)
+# Quasi-binomial model for Meloidogyne
+model_mel = glm(
+Genus_Sample_Abundance ~ total_count,
+data = set$mel_data,
+family = quasibinomial
+)
+# Quasi-binomial model for Pratylenchus
+model_prat = glm(
+Genus_Sample_Abundance ~ total_count,
+data = set$prat_data,
+family = quasibinomial
+)
+pval_glm <- function(glm){
+coef(summary(glm))[,4][2]
+}
+set$data_mol_morph <- set$data_mol_morph %>%
+mutate(pval = ifelse(Genus == "Globodera", pval_glm(model_glob), NA)) %>%
+mutate(pval = ifelse(Genus == "Pratylenchus", pval_glm(model_prat), pval)) %>%
+mutate(pval = ifelse(Genus == "Meloidogyne", pval_glm(model_mel), pval)) %>%
+mutate(signif = ifelse(pval <= 0.05, "pval <= 0.05", "pval > 0.05"))
+# Other plotting variables
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 20
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 13
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+set$my_formula <- y ~ x
+set$plot_title <- paste("Molecular analysis vs",
+"morpological analysis", sep = "\n")
+# Plotting quasibinomial model
+plots$mol_morph_quasi <- ggplot(set$data_mol_morph,
+aes(x=total_count,
+y = Genus_Sample_Abundance)) +
+geom_point(alpha = 0.7) +
+#ggtitle(set$plot_title) +
+theme(plot.title = element_text(hjust = 0.5, size = 20),
+panel.grid.major = element_blank(),
+panel.grid.minor = element_blank(),
+panel.background = element_blank(),
+#axis.line = element_line(colour = "dark grey"),
+strip.background = element_blank(),
+strip.text = element_text(face = "italic", size = 16),
+axis.text = element_text(),
+text = element_text()) +
+xlab("Nematodes / 250 ml soil") +
+ylab("Relative abundance") +
+geom_smooth(method = "glm", formula = set$my_formula, se = F,
+method.args = list(family = quasibinomial),
+aes(color = signif, linetype = signif)) +
+scale_color_manual(values=c('black', 'lightgrey'),
+name= 'Significance',
+labels = names(signif.labs)) +
+scale_linetype_discrete(name= 'Significance',
+labels = names(signif.labs)) +
+facet_wrap(~Genus, scales = "free",
+#labeller = labeller(Genus = genus_labs)) +
+labeller = genus_labeller)
+#Print and save plot
+plots$mol_morph_quasi
+save_plot(plots$mol_morph_quasi, plot_name = paste0("Molecular_and_Morphological_Plot"),
+filetype = ".pdf")
+# Generate table overview of morphological counts for manuscript
+morph_table_manuscript <- pivot_wider(set$data_mol_morph %>%
+select(-Genus_Sample_Abundance,
+-ndvi),
+names_from = Genus,
+values_from = total_count) %>%
+arrange(Alias)
+write.xlsx(morph_table_manuscript, file = "t1_morph_table_manuscript.xlsx")
+# Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
+# containing the sample abundances for that genus.
+ldf <- data.frame(genus_abundance_tbl_per_sample %>% pivot_wider(id_cols = c('Sample', 'ndvi'), names_from = 'Genus', values_from = 'Genus_Sample_Abundance'))
+ldf_genus_data <- data.frame(ldf) %>% select(!c('Sample', 'ndvi'))
+colnames(ldf_genus_data) <- gsub(' ', '.', colnames(ldf_genus_data))
+ldf <- cbind.data.frame(
+Sample = ldf$Sample,
+ndvi_01 = (ldf$ndvi + 1) / 2.0
+)
+ldf <- tibble(cbind(ldf, ldf_genus_data))
+n_samples_by_genus <- data.frame(ldf_genus_data > 0) %>% mutate_if(is.logical, as.numeric) %>% colSums() %>% sort(decreasing = TRUE)
+keep_n <- 100 # Maximum number of genera to include in the analysis
+top_n_occurence_genuses <- names(n_samples_by_genus[1:keep_n])
+top_n_occurence_genuses <- top_n_occurence_genuses[!is.na(top_n_occurence_genuses)]
+l_genus_ldf <- ldf %>% select(all_of(top_n_occurence_genuses))
+l_genus_ldf_transposed <- data.frame(t(l_genus_ldf))
+l_meta_ldf <- ldf %>% select('ndvi_01')
+l_model <- linda(
+l_genus_ldf_transposed,
+l_meta_ldf,
+formula = '~ ndvi_01',
+feature.dat.type = 'proportion',
+is.winsor = FALSE,
+alpha = 0.05
+)
+# Print model info
+l_model
+# Show effect size and significance plots
+linda.plot(
+l_model,
+variables.plot = c('ndvi_01'),
+alpha = 0.05,
+lfc.cut = 1,
+legend = TRUE
+)
+# Write supplementary table for manuscript
+l_model_df <- as.data.frame(l_model$output)
+write.xlsx(l_model_df, file = "supplementary_table_ndvi_regression_nematodes.xlsx", rowNames = TRUE, colnames = TRUE)
+# Prat
+set$prat_data$ndvi_temp <- (set$prat_data$ndvi+1)/2
+model_prat_count_ndvi = glm(
+ndvi_temp ~ total_count,
+data = set$prat_data,
+family = quasibinomial
+)
+summary(model_prat_count_ndvi)
+# Glob
+set$glob_data$ndvi_temp <- (set$glob_data$ndvi+1)/2
+model_glob_count_ndvi = glm(
+ndvi_temp ~ total_count,
+data = set$glob_data,
+family = quasibinomial
+)
+summary(model_glob_count_ndvi)
+# Mel
+set$mel_data$ndvi_temp <- (set$mel_data$ndvi+1)/2
+model_mel_count_ndvi = glm(
+ndvi_temp ~ total_count,
+data = set$mel_data,
+family = quasibinomial
+)
+summary(model_mel_count_ndvi)
+data_mol_morph_long_temp <- pivot_longer(set$data_mol_morph,
+cols = c(Genus_Sample_Abundance,
+total_count),
+names_to = "type",
+values_to = "value")
+data_mol_morph_long_temp$ndvi <- (data_mol_morph_long_temp$ndvi+1)/2
+pval_glm <- function(glm){
+coef(summary(glm))[,4][2]
+}
+set$data_mol_morph_long <- data_mol_morph_long_temp %>%
+mutate(pval = ifelse(Genus == "Globodera", pval_glm(model_glob), NA)) %>%
+mutate(pval = ifelse(Genus == "Pratylenchus", pval_glm(model_prat), pval)) %>%
+mutate(pval = ifelse(Genus == "Meloidogyne", pval_glm(model_mel), pval)) %>%
+mutate(signif = ifelse(pval <= 0.05, "pval <= 0.05", "pval > 0.05"))
+# Count data vs NDVI
+plots$morph_count_ndvi <- ggplot(set$data_mol_morph_long,
+aes(x = value,
+y = ndvi)) +
+geom_point(alpha = 0.7) +
+#ggtitle(set$plot_title) +
+theme(plot.title = element_text(hjust = 0.5, size = 20),
+panel.grid.major = element_blank(),
+panel.grid.minor = element_blank(),
+panel.background = element_blank(),
+axis.line = element_line(colour = "dark grey"),
+strip.background = element_blank(),
+strip.text.x = element_text(face = "italic")#,
+#axis.text = element_text(size = 12),
+#text = element_text(size = 14)
+) +
+#xlab("Nematodes / 250 ml soil") +
+#ylab("NDVI") +
+geom_smooth(method = "glm", formula = set$my_formula, se = F, color = "dark grey",
+method.args = list(family = quasibinomial)) +
+facet_wrap(~Genus, scales = "free_x")
+plots$morph_count_ndvi
+# Print and save plot
+plots$morph_count_ndvi
+save_plot(plots$morph_count_ndvi, plot_name = paste0("morph_count_ndvi"),
+filetype = tmp$out)
+set$data_mol_morph_long <- data_mol_morph_long_temp %>%
+mutate(pval = ifelse(Genus == "Globodera", pval_glm(model_glob_count_ndvi), NA)) %>%
+mutate(pval = ifelse(Genus == "Pratylenchus", pval_glm(model_prat_count_ndvi), pval)) %>%
+mutate(pval = ifelse(Genus == "Meloidogyne", pval_glm(model_mel_count_ndvi), pval)) %>%
+mutate(pval = ifelse(Genus == "Globodera" & type == "Genus_Sample_Abundance", 0.5574120890, pval)) %>%
+mutate(pval = ifelse(Genus == "Pratylenchus" & type == "Genus_Sample_Abundance", 0.5177548997, pval)) %>%
+mutate(pval = ifelse(Genus == "Meloidogyne" & type == "Genus_Sample_Abundance", 0.0001920515, pval)) %>%
+mutate(signif = ifelse(pval <= 0.05, "pval <= 0.05", "pval > 0.05"))
+# Morph data
+set$data_morph <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'total_count',]
+set$data_morph <- set$data_morph %>%
+rename("value" = "Number_of_nematodes")
+set$data_mol_morph_long
+set$data_mol_morph_long <- data_mol_morph_long_temp %>%
+mutate(pval = ifelse(Genus == "Globodera", pval_glm(model_glob_count_ndvi), NA)) %>%
+mutate(pval = ifelse(Genus == "Pratylenchus", pval_glm(model_prat_count_ndvi), pval)) %>%
+mutate(pval = ifelse(Genus == "Meloidogyne", pval_glm(model_mel_count_ndvi), pval)) %>%
+mutate(pval = ifelse(Genus == "Globodera" & type == "Genus_Sample_Abundance", 0.5574120890, pval)) %>%
+mutate(pval = ifelse(Genus == "Pratylenchus" & type == "Genus_Sample_Abundance", 0.5177548997, pval)) %>%
+mutate(pval = ifelse(Genus == "Meloidogyne" & type == "Genus_Sample_Abundance", 0.0001920515, pval)) %>%
+mutate(signif = ifelse(pval <= 0.05, "pval <= 0.05", "pval > 0.05"))
+# Morph data
+set$data_morph <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'total_count',]
+set$data_morph <- set$data_morph %>%
+dplyr::rename("Number_of_nematodes" = value)
+# Relative abundance data
+set$data_relabu <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'Genus_Sample_Abundance',]
+set$data_relabu <- set$data_relabu %>%
+rename("value" = "Relative_abundance")
+set$data_mol_morph_long <- data_mol_morph_long_temp %>%
+mutate(pval = ifelse(Genus == "Globodera", pval_glm(model_glob_count_ndvi), NA)) %>%
+mutate(pval = ifelse(Genus == "Pratylenchus", pval_glm(model_prat_count_ndvi), pval)) %>%
+mutate(pval = ifelse(Genus == "Meloidogyne", pval_glm(model_mel_count_ndvi), pval)) %>%
+mutate(pval = ifelse(Genus == "Globodera" & type == "Genus_Sample_Abundance", 0.5574120890, pval)) %>%
+mutate(pval = ifelse(Genus == "Pratylenchus" & type == "Genus_Sample_Abundance", 0.5177548997, pval)) %>%
+mutate(pval = ifelse(Genus == "Meloidogyne" & type == "Genus_Sample_Abundance", 0.0001920515, pval)) %>%
+mutate(signif = ifelse(pval <= 0.05, "pval <= 0.05", "pval > 0.05"))
+# Morph data
+set$data_morph <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'total_count',]
+set$data_morph <- set$data_morph %>%
+dplyr::rename("Number_of_nematodes" = value)
+# Relative abundance data
+set$data_relabu <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'Genus_Sample_Abundance',]
+set$data_relabu <- set$data_relabu %>%
+dplyr::rename("Relative_abundance" = value)
+signif.labs <- c("pval > 0.05", "pval <= 0.05")
+names(signif.labs) <- c("p value > 0.05", "p value <= 0.05")
+breaks_morph <- function(x) { if (max(x) < 200) seq (0, 100, 100) else seq(0, 600, 50)}
+breaks_mb <- function(x) { if (max(x) < 0.1) seq(0, 0.002, 3) else seq(0, 3, 1) }
+breaks_fun_morph <- function(x) {
+if (max(x) > 500) {
+seq(0, 800, 200)
+} else if (max(x) > 150) {
+seq(0, 200, length.out = 5)
+} else {
+seq(0, 100, length.out = 5)
+}}
+# MORPHOLOGIAL AND NDVI PLOT
+# Relative abundance and NDVI plotting
+# Axis ticks MB
+breaks_fun_mb <- function(x) {
+if (max(x) > 0.8) {
+seq(0, 0.9, length.out = 4)
+} else if (max(x) > 0.2) {
+seq(0, 0.6, 0.2)
+} else {
+seq(0, 0.003, length.out = 4)
+}}
+p1 <- ggplot(set$data_relabu,
+aes(x = Relative_abundance,
+y = ndvi)) +
+geom_point(alpha = 0.7, color = "black") +
+ggtitle("a)") +
+theme(plot.title = element_text(hjust = 0, size = 12),
+panel.grid.major = element_blank(),
+panel.grid.minor = element_blank(),
+panel.background = element_blank(),
+strip.background = element_blank(),
+axis.title = element_text(size = 9),
+strip.text.x = element_text(face = "italic"),
+plot.caption = element_text(size = 7, hjust = 0, face = "italic",
+margin = margin(t = 0.2, unit = "cm")),
+plot.caption.position = "plot",
+plot.subtitle = element_text(size = 10, hjust = 0, vjust = 0.5,
+margin = margin(b = 0.8, unit = "cm"))) +
+xlab("Relative abundance") +
+ylab("NDVI") +
+geom_smooth(method = "glm", formula = set$my_formula, se = FALSE,
+method.args = list(family = quasibinomial),
+aes(color = signif, linetype = signif)) +
+scale_color_manual(values=c('black', 'lightgrey'),
+name= 'Significance',
+labels = names(signif.labs)) +
+scale_linetype_discrete(name= 'Significance',
+labels = names(signif.labs)) +
+facet_wrap(~Genus, scales = "free_x") +
+scale_x_continuous(breaks = breaks_fun_mb, limits = c(0, NA))
+p1
+# Plotting NDVI vs metabarcoding
+p2 <- ggplot(set$data_morph,
+aes(x = Number_of_nematodes,
+y = ndvi)) +
+geom_point(alpha = 0.7, color = "black") +
+ggtitle("b)") +
+theme(plot.title = element_text(hjust = 0, size = 12),
+panel.grid.major = element_blank(),
+panel.grid.minor = element_blank(),
+panel.background = element_blank(),
+strip.background = element_blank(),
+axis.title = element_text(size = 9),
+panel.spacing=unit(2, "lines"),
+strip.text.x = element_text(face = "italic"),
+plot.caption = element_text(size = 7, hjust = 0, face = "italic",
+margin = margin(t = 0.2, unit = "cm")),
+plot.caption.position = "plot",
+plot.subtitle = element_text(size = 10, hjust = 0, vjust = 0.5,
+margin = margin(b = 0.8, unit = "cm"))) +
+xlab("Nematodes / 250 ml soil") +
+ylab("NDVI") +
+geom_smooth(method = "glm", formula = set$my_formula, se = FALSE,
+method.args = list(family = quasibinomial),
+aes(color = signif, linetype = signif)) +
+scale_color_manual(values=c('black', 'lightgrey'),
+name= 'Significance',
+labels = names(signif.labs)) +
+scale_linetype_discrete(name= 'Significance',
+labels = names(signif.labs)) +
+facet_wrap(~Genus, scales = "free_x") +
+scale_x_continuous(breaks = breaks_fun_morph, limits = c(0, NA))
+p2
+# Combining the two plots
+plots$combo_morph_metab_ndvi <- plot_grid(p1, p2,
+ncol = 1,
+align = "hv")
+plots$combo_morph_metab_ndvi
+# Print and save plot
+save_plot(plots$combo_morph_metab_ndvi, plot_name = paste0("morph_relabu_ndvi"),
+filetype = tmp$out)
+found <- c(40, 24, 16, 37, 10, 27, 74, 100, 100, 78, 32, 28)
+not_found <- c(24,12, 7, 7, 24, 52)
+wilcox.test(found, not_found)
diff --git a/analysis/Nems/Nems_Analysis_pipeline_v4-3.Rmd b/analysis/Nems/Nems_Analysis_pipeline_v4-3.Rmd
index 4f117bcfe05afc090d0cb44686ed4e60ec4f1d67..b498d450866a6e13dc0eebe3dc4376b5c2a16acf 100644
--- a/analysis/Nems/Nems_Analysis_pipeline_v4-3.Rmd
+++ b/analysis/Nems/Nems_Analysis_pipeline_v4-3.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "16S Data Analysis"
+title: "Nems Data Analysis"
 author: "Simeon Lim Rossmann, Marte Persdatter Tangvik"
 date: "23.12.2024"
 output:
@@ -88,7 +88,7 @@ remotes::install_github("mahendra-mariadassou/phyloseq-extended", ref = "dev")
 source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R" )
 
 library(phyloseq)
-library(SIAMCAT)
+#library(SIAMCAT)
 
 # Phylogeny libraries
 library(phangorn)
@@ -811,7 +811,7 @@ plots$topn_ASVs
 
 This chunk plots abundance of the Top n ASVs or taxa at a given level as a bar plot, giving an insight into the presence of the n ASV and most common taxa for the primary and secondary parameters. The default for n is set at 20, a larger n may lead to delay/skipping of the plot in standard out, but it should be saved as a PDF regardless for ASVs. For taxa, a large n  may lead to unreadable plots. **The chunk does not require any input**, but it is possible to adjust the **default 'n'**, and to change **width**, **height** and **resolution** of the PDF-output if necessary.
 
-```{r custom-Bar-plot, tidy=TRUE, message=FALSE}
+```{r custom-Bar-plot, message=FALSE}
 # CHANGE ME to the desired sample categories on the x-axis. 
 # Accepted values are the column headers in the descriptor file.
 set$x_axis_value = "ndvi"
@@ -939,7 +939,7 @@ topntax_data <- set$topntax_tbl %>%
   select(Alias, ndvi, Shannon, Taxa) %>%
   distinct()
 
-write.csv(topntax_data, file = "../../topntax_all_taxa/topntax_data_nematodes.csv")
+write.csv(topntax_data, file = "../topntax_all_taxa/topntax_data_nematodes.csv")
 
 
 ```
@@ -1261,13 +1261,13 @@ set$data_mol_morph_long <- data_mol_morph_long_temp %>%
 
 set$data_morph <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'total_count',]
 set$data_morph <- set$data_morph %>%
-  rename("value" = "Number_of_nematodes")
+  dplyr::rename("Number_of_nematodes" = value)
 
 # Relative abundance data
 
 set$data_relabu <- set$data_mol_morph_long[set$data_mol_morph_long$type == 'Genus_Sample_Abundance',]
 set$data_relabu <- set$data_relabu %>%
-  rename("value" = "Relative_abundance")
+  dplyr::rename("Relative_abundance" = value)
 ```
 
 
diff --git a/analysis/Nems/Nems_Analysis_pipeline_v4-3.html b/analysis/Nems/Nems_Analysis_pipeline_v4-3.html
new file mode 100644
index 0000000000000000000000000000000000000000..4ea4596f79646f2267d02dd4dbcb8c170f893c97
--- /dev/null
+++ b/analysis/Nems/Nems_Analysis_pipeline_v4-3.html
@@ -0,0 +1,2453 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Simeon Lim Rossmann, Marte Persdatter Tangvik" />
+
+
+<title>Nems Data Analysis</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+<script>/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
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) 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cDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg+FB1ubjlnRNvl3o6IEU2w7fdNPhm/hh+FLysUu6++DLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK/m4szFFR7ch0toUgBTdWHr7EpaWru6+6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d/jLST9v5YflasP6v/CO7+GNAPC2BMZWmsOjp2NNbfHwMCJD+LPVL+D/OYlWEEI/9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2+bBDWBud1Va4pcCn8CPqxlh/fgtG8IPaPH8C5wk6/nZDv69jurV5QhtwE0x2iqOsj9Mx8B9/0EaUdiPfOYYDCi/q9jhWRuupMDEU0+CtX0sDFxv07T/K5niBPqN9+tQjgEc31NGCXFeMcCEuQBIc/BK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF/R0/u5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y+RBgogrXPZ8E1yIHoHIFUM+AbJhE7lbMtt8ApL+xmZW7PwbjAO0fAVoXQOuiSP/ksIVdFZ0aulsamKUzwPZ/NYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7+pg+jUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd/J5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7+RHJHP+2hqfugo/MvI2H/mqr4b9tFnKSRY1Y5Ek80Nm/WIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw+nSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv+rWa/Wih0vlbX6Zb75T5C0qNKVFvH1QL/vazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f+XdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS/xTo+P5wGFak62ap1PVFFN4v/y+xuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f/6dhK6gQokFKhWX3urrjk/rnI0pgfpGMeuQIUaEM7+GF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x/GdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY+9yGFQtEo0GQPNv6vS2drj4+1jHbv3aJSMUWP+QTZrmbNTjU8wyG/iXNNpskybLcJ3CiTF5Ir+JYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d+/XeNRTZ9/1OPfG7+2hwzd5W3D+hmyjsRcUg/+Cavb++Vh2ls3L7zT/etOnHNxeerv313vzLVqPai4nJv+K1FC6040/4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk/U+D4gTXW+j0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7/R561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l+WKHmLTJwRv9E8GWJ6dYvf/FmEyEGr+gyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b+tMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1+Q2BIRvf6J6Kgatnrbiem+CFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ+CuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih/xELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD+uOe+kWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby/+SSbPY8/NH6vpl/Esq3Ae4ZU1HC44KFiI9o7CEgab/RqHbj7s5KAg06s39ZP/zxI/mVuF/TbTSy+3Fb8If9/cv7+wt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi/C8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0+Xxx/5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB/qM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J/UKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc/THK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd++U/3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg+e4As38MfGquNt7oUws6Ysarj1/efE+yst86YUVNvDdts3Pv5c8m/aP0C+f8/Qb+IMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT/v27wm09jRYVc/e++iyx2tyzJb/n3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy/t27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj/S5ki1jL/a0GC6+2L6Um+aoddlNFuj+bJ8mH/iaLh8I0/U51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz+1UmhTJyJGxZzw+wsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ/9pPA4RKTASWahmh+8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ/tHnODtENw/2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8+/Elg5j4Gxur3J8o2PJ4rg+2d08T/fwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r/aMMj8onCP/DuDZOuN+GPPr/+p7bx+7JlbYdppcNhzKU/1Px5aiaGDn/s1iGMaBcleKUo/v9rcxkZj7DBEKOfrayytXNLYiUdBY+pleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF/Qa9Pdf/QqOSqCiE/EE1/XIVKTc2tzWbHnimrEd+Vyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu/kzIdx+BxC0ay9zRSvoS0F2lIxI+X7klU63I40gLQ3w5ep5na+SFnba3z5D64zv+QtM4n4ffG3tq4aNHGRfxgrXPMim+5487abL7xhdseIRn1KDl+7aINixdv0OD+JSPwKf5+xoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz+9a/v2PH1Hfz9236w+ZrPXvWfAxlj4NLLHpq3c/PQ3uvmvbrjG7fe+o2y/cLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF/tkkOGPW2ibWaBTkeZ/dvPWazXfOnnvL6jkRXpi85sFzZt+55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG/yVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6/OU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR/rg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x+MJiOA3YwhDRQrWU0u/0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0+a0uoiFH/xcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL+j3D/uwhBRdyAyozeZwvQzs79soi+BKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo/ybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM+3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho/VDkzQfLE+IrYoJXkD19pdP7OwG/voIUtagiWiZ4PAFTHHlTVhRZ7dYmPar+NJ+8JhmR6DFK5DV1foHoLNO/pHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj+i5++WZfKeViJfW5HnUakVL4UCNVkA4+ETfIqx4B5xSaP2L1yn0zn2ltPn4+OqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL/NNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE+cPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP+Kps8J9/3mGQIteY/LGPC+L7872SPR2br5fy8MtKBMHedGuM28/MZmPJMrGgi3Gb1S+Si1/L/zrZwO9XH1ce/z7ZQ1WSoY/+pMb5FT4ua0Wm+Jf/298nFmChEQ+Ti71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa/1IFO1/jdWr/8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc+KJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3/tM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB/yox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt/qMDnvLpPNTXfNa60z5/yjXQOMq+lNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd+RiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb/pBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap/30lnVESgNnvjbUoT6w9N+Xoio0qcYOIM+heg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X+PE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa+dAdAbMYX6HVF8A+7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf/Nf+Ny65BhYxxxV+77XJ2wfQ389/IQPgajXbwMsuAz/0IaQcXJavKbRqR2IqyZruXjVC2+hdee/5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab/wXl2CNe7s/qCtTvWgG5kpBmCBlSzDS/r8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8/NCfiOUVirYcBbIeA2+iF68rQIo3B/S628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935+k1efkV51mzzrM0LL3/20avnwMeKuWyOUZg2TasSqZ+KcZQiOn1Iu2Vh497ALUVZiCKt/gh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1/uzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb+okJ5JRDyf54Axur1D+WS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt+t/qW2+93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz+tJo+wijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF+kuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj+Z4i+42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA/hRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz+x+Fkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3/RTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY+uSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN+fiK86LeXj+Sc5lPKy+k/vCUI/DaLFYCWHr6nbXuILTIsb5imNKY/rCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T/yOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH+L0eCPxmsa5ZpgRJSDZ11yDv+jmbd86vxZfc1WcZJ3UkMq1BOOOVtvu/+pB+en186d3GTwWAw2jheaJs09/+LNfZft37DALyrNj1wABMuUKbODyTVnT/KYbJ3Tpq8IrNh92dkxOj5P/YpZx4/ycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB+cV99/eTF6294dA8+TK6v74MHVpYNRt/I30e8QGTOOdfGWzzxcy+87a7bLjw37rHw1nPzp0KyyRSeZO+QQhInt3dYgvycjrPOv+T8s1rptaP84VeywdWX2T4ysr0/7TLIs6+x9zib56ye1dM9e/XsZmePY3NDs9zlnNVt4+WgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS+XPplV2ts4bvCwZu+KzgITtxepaPRzWdpv74muvv6RO0SorX6cu/dqKn/XWnrtp/Zragz13DUCl5myiFW2Ycvb0PtsXnU+tx8pvLFbUspLX68mdegwmOif/NPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3/jdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx+6arPqbkcNAHoFPzKUUQ+qL0k97pjbZv1I/egC9zTFbrrlFpNdmea+gIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV/ScS+zjlASyUTVv/AJ46gkJI4bHX4lTnloctxPZE1ckS3+jG2fKIjkQFyzuo8jvYQG1OrGvJPSTu/nSp9PHNTl4z5hK/8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt/wkSx/JCmP5/cBKQfl/5gd+rOS/+p91/+YCg5CXK2W4M9fu+/6xxX+vnelVuldIDCG0VQTpU9Dw4pRfei+6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15+5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl/5bqzYqT9hMmptEXDgTqP3Wiye+sD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC/v8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4+Ti7W9D/OZrLChdwxmPNiBRqVjnpK/aGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3+OfU5/ieLS/NuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD+N2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad/3t2KTtPobnX6D8C8pd0MDP+Kx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE+JGTS3JoEhTrz8/CB+5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk+htwN5A0V3RCPoD/yXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps/t+0iCT3AHVtZC7JDCXfR7OSb/Xja5H3zQbZL1B+ULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset+nQWdS/ZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5+LhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW/3Q5Nn8BZEuzqEI7HK3n0+zFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj/mQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2/BWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY/yCZu/Amd5Or9uS3DYaeqVOhH7gZN/8I/wi1fEuLXvyNivibjuKvN+1Nc01HF/3h+ef/sOhox8MPd5SFucPjorQwXT+ytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP/79nbvCXFOY+PUjrT7/otsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9+pbs6E/Vu7Nk642/PYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu/19/5QW9Rv1U1ridT22i+53pzumbs+XFFXYC++CGsTj5JUT/GCgRt3n78i2n71FHG4/u6X++9+raya7os3ZbDmgWfXun44e+u2NZKuGZ0HiF8M4TlMPR+EU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l+uXrbjPUyep+8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq+NzK/OKSbsfl79o9G20R+brBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery/7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1+SdazRrfddkBU98t1htvWrbjqSqjaCguxrffM/5zDCpBALUycmajhd+R6ww4SWafuZ5eU+tPid4lgd3gt+b/Y9rQoZNmiXYPXyRHbRs8zX/f4WIFjWZJtUdSD55AP3xtXH+ZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy/8F0GoqYPv75Yh9j3x4DuJ+uEzHRpAq2lMqb+qfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT+LUgx0Pg/p87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv/u9ZUWUx+PYFueUKwaNvbtu+Xps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L/BehXuHqfaBwBEU7hfVLcXvS4VQv+T/vaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq+k4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S/tkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP+EaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP/dsZ8+fP75D/9Uu5Gs3FY/2SxPld0DHOciXI9gqjcEidXjE+3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8+LTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7++wW4gRDRbaxw2nrOGm+xOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot/FpHvIUjJovFEoYvHYV9C5Y/xN9OfcalvII37UEhTbTg/AQIaPb4Vz6j5u8/aViycMod/fkDcpu8QZbZoeBi/vbzP3XPsZvOubMtaPHkD9jt6+U2O7vqU/9C9SMvgrXpQNG/E0oJxun+CiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp/VAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y+pGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr+P2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr+P30f3OkPQ72aO0xYo3/EsmO3QO5qEF8S0qQH0UsKXv0brnl9+8M7jF174+DsfvPOl1au/RL5/9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x+4ofw+3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ+pYl+Sf8YcurfmXP5F/kj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0/OM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw+1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g+Rn533qkocdvLs2HmhU75br/MmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3/3pYNi3bS+Rd+GjOfVpAPNd6y64Gsz1GaZleWIPoYL/v9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk/Iojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB/36kP+K38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL+x03nhh+oNo5e77psxg9Q5LzebIKD+fsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O/kdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P/F7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF/BRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm+RdmIJQST/ZfVdudUvh9S/qqNvqT98g9SQ3lHibZY0mRVHooyDN/FHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi+d0s4+ztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr+1VZFcEZ/PdJkn1hOs8SXS/NFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq+rbyg1Z+O8VsNC1UmcvORPRfxtPrfRwL2p/oA1eZp6Z/aGffoewaXcA/xBlKlQLfhQL/oPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp/bXh5e5YXW2Ngfvza0ZF6UgFL/E0fTq4LBlvTE2qb/KuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8/VWi33c3bBZnGY05+dm+3qc7fNmj4YGKLj2nfqFP+g7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j+SYEWHlZ0K/Hgsh/Ztsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D+YVyKd0jw3iumwi/BC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc/J5uGm6CViW5mulYMk+HqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br+fcLyoAyu8D9B7bgmzUqfFobF5nKnK4+t8MPJkI/xHUNWk117jugWF+xazTAALQn6+UE9lhoI5ApGA/iuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL/3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n+jspIsHFjJrTOdzSMuOa9DbDcj/nH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U/8JbJZPJJLBLolH1La/RbF9AbC8JJjv/mMnssKjLRBJyqj9QXxNko0Ux/X79epfiXkm6fmKwF/en1HLc6LxloXWKvGa5rVCVL83VuiPcDEX/K5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS/kTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv/36k2sbqCeCFNa+URpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR+D79H56NOz0EVWCTy2/fffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ+T/El3uZqL+FyzSZ8XxpTiI/G0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ+euOeG2SORmKZr/qKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya+vlKqv6aXK1qIj3imL+L6hL+yvUlFfE0VKZ7E8gBY3M/8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry/LgZIlCeP11Z4zs/AwvVwtGFEut5S1JY4lfyT0N/evOLo+rUEgjcqc9IkGpQbv3iW7Co5b+KgjvpzYdH85PLcc4X21ouwEGl/S4qnUAvoSlXUUhR1eKr2VWFTB+GMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH/qPepXQ0Db77CJOAImohB+RPWr31ev5g/kE+zTa4lbvZo8xdWPffQu9yJTPCNB66s+zXoJt/0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0+3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80+UxfUHPlBZIRVNQ+v0xRm8REKPoLmNr0+Uo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV/+mfr0C0r/NkeXbbpPlOTBBwT0mVz1zx9S/wJecBF9Wgv3p032iP2v4VSgfgW2G+HUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt/QRasn2kri+H6L80MJRsmVYJrAKyDItpJUy3/15WYIJqcJ9Q5N/LFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb+IP8gHed+6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha/fJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG/XbOFz+Tcq2MLrcmV28Nmi/OHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw+luPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs+31Wm5qpH+iJWSzqdPr2O7zc2TmtnrzCig6bBd/vgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I/MAp9q9S3Q/clADNm1wEeO+xbwg5OIYHZLY3ehG5lJk2xhco+6JWybpEVz2wrR6hZyD0QXZbeDVB+onmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P+1fAll+SyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y+cf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY/f+ucuMtCuP/e4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn/+7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7/6B23+f+sp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz/Q0hI0m3NKDRfkgsrnamo+p0CAq1FyvC3a3Nak/s5VX282x9Ufy3E39VAx6o7LpCvO2wK+ch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5+jP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00/Wavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR+gufwA8GegxUEo4A+gd0BASHiH6jYMLIsUdQJTs/C641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW+AMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA/xUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA/E+DXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX/k/7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh/TK18StOPhwG9Xo5mqXAxvw/79YQwwDR+nAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw+oTFqxtz8dxwtogBnF9MjIl/in+K8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O+GC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6/OC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx+v1UKPsfvzQU0Kc83i2VdILmd2/yX55zT9luN2+Cu4nKfwPcK/CvDVU+pHh8+LaldIf1fA5h3ndT6Fln9/W/9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW/uib/ud+VK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M+VG0KsUdgdvpgP/6NqqP0/gHZFUfGEijZLHpiIgvV5/Bltrj8Qd7XQd5p4P+7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z/vvacG/rjpsvnpzs0B1Id6fmX8119l88XnOxe/uGrzzHcdu7UtY3+2vmXN5zUyj3ZcPl8p1sZSs6/nGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q+lwg1RdRSpFDNzosb44YJXnAfoEOpZW//6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21/BWW5DnPQbn2X1WK43/aM2n18DfSoybbNHijFpamzXI31eRibGUOxSu/lT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO/VjC/EinToFyoNDz2p9XD6g78WgQdPG7Z3R0T/Z5dTM9lsL8Ktek7szl2L+gQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T/4VeamYPlLtffdQsk+2sUTY/zDXl/05W53/Bz9UK3p7LjapZ2ZxOm+UlZXrL3HHGqO8+wVroDaCTTnTxitMxmiAAYQzVJQH+nj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj/9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz+3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62/qN/tjQxnSnhnKPw+LNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh/LvQZnt0evgIhTFV0kE/PYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8/Cj1Y9NY6cnGVW0CjTLaz7E3epvaT/LtTV72Rs+0WVVmd0dz/MGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf/z+fdka+eKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ+bewnf18ap4Ib+mSMrtDU5zqlD8QANa5MBGh/OwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq+7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2+YuMbmE9dwGYB4KFTsvnDE6Ah47r/fE3AYI+oXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU/A/Jsw+vWDa/7emewLaoeibaF1B9qUNnuqWK3+UfXYVL1v/omD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk+gk8pL0mcCDOn08MDAQlf+/SlTZ1z12fnqntOhbOTL9/ZdevbAPN+yby1f/uUtC/ixm8ZBo59LTXEW060hGrTDplNprWd58fwB/b/E27BdS/s7U+rGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt+Kh6HN4ti6lxIhT/wahnZtWwzlY9QHQ2c79C+dxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah/PFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn+2MnYv36dbMDBTonl/T/ma5IKAyEGz+4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK/yp/gLEBVat5WnPDdKA574ubuFJ/IUfZ/Y2Nt6mN+ZNNTSTaeI56gKwkXerTe9DDHUw8/H35FY3nNN7GGuBKWhrV9ep+0k1WjNWVaHkW1yA+QHWNu8rtBw2a5YXuE40rs7/GA+j09V3hA98yRnFPOGr8ltGlsFdD/7tRce3LH6Trcneuiy7K7J3khKu+3qUaXPWaX7T6/Kfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM+0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz+XmcOs1H31U6cC6RTVLfCg7RNBF1UF2/wBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH+WC0E1qOGtP66s1MYv+VLu8/XFXvV/ZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86/1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp/lYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm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+<style>h1 {font-size: 34px;}
+h1.title {font-size: 38px;}
+h2 {font-size: 30px;}
+h3 {font-size: 24px;}
+h4 {font-size: 18px;}
+h5 {font-size: 16px;}
+h6 {font-size: 12px;}
+code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
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+
+
+
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">Nems Data Analysis</h1>
+<h4 class="author">Simeon Lim Rossmann, Marte Persdatter Tangvik</h4>
+<h4 class="date">23.12.2024</h4>
+
+</div>
+
+
+<div id="introduction-and-initialization" class="section level2">
+<h2>Introduction and initialization</h2>
+<p>This is an R Markdown file containing code to parse the results of a
+dada2 analysis into phyloseq for further analysis. It is separated into
+chunks that may be run independently by pressing the <em>play</em>
+button. You will need <strong>3 files</strong> in the same location in
+order to run this pipeline successfully:</p>
+<ul>
+<li>A sequence table called <strong>‘seqtab_nochim.rds’</strong>
+(automatically generated by the dada2 pipeline)</li>
+<li>A taxonomy table called <strong>‘taxa.rds’</strong> (automatically
+generated by the dada2 pipeline)</li>
+<li>A table describing the sample properties called
+<strong>‘descriptors.txt’</strong> (Has to be provided)</li>
+<li>Optional (some plots require this): A phylogenic tree file called
+<strong>‘phylotree.rds’</strong> (can be calculated if it is not
+provided, takes a long time)</li>
+<li>Optional: Instead of <strong>‘taxa.rds’</strong>, a <strong>custom
+taxonomy file</strong> may be provided (tab-delimited text)</li>
+</ul>
+<p><strong>Recommended use:</strong> Set the individual chunks until you
+are content with the ouput, then knit the whole document into a
+PDF/html, so you have a full record of a successful run.</p>
+<div id="optional-custom-taxonomy-file" class="section level4">
+<h4>Optional custom Taxonomy file</h4>
+<p>A custom taxonomy file may be provided instead of using the taxonomy
+output from dada2. This may be used to supply taxonomy derived e.g. from
+BLAST searches of the ASVs. Custom taxonomy files must be tab-delimited
+text with as many rows as the original, colum headers (for all columns
+except for the first column). For example:</p>
+<blockquote>
+<p>Kingdom Phylum Class Order Family Genus Species<br />
+ESV1 Kingdomx Phylumx Classx Orderx Familyx Genusx Speciesx<br />
+ESV2 Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy<br />
+ESV3 Kingdomz Phylumz Classz Orderz Familyz Genusz Speciesz<br />
+… ESVn Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy</p>
+</blockquote>
+<p><em>Friendly warning: Parsing the results of a BLAST search into this
+format may require some effort.</em></p>
+</div>
+<div id="descriptor-table" class="section level4">
+<h4>Descriptor table</h4>
+<p>‘descriptors.txt’ should be a tab-delimited .txt table describing the
+samples. It must have the same length and order as the samples in
+seqtab_nochim.rds. To check the order and length of samples in
+seqtab_nochim.rds and generate a template to fill out, you may run the
+chunk below with “optional_sample_check” set to “TRUE”.</p>
+<p>Any number of descriptors is possible. The sample names may be
+retained as one descriptor, but this is not necessary, as they will be
+added during parsing. For example, if there are 4 samples (order: s1,
+s2, s3, s4), the txt file could look as follows:</p>
+<blockquote>
+<p>Subject Species Time<br />
+Kar1 A.thaliana 24hpi<br />
+Kar1 A.thaliana 72hpi<br />
+Mec2 S.tuberosum 24hpi<br />
+Mec3 S.tuberosum 24hpi</p>
+</blockquote>
+<p>Finally, the file should end with an empty line, since it may throw
+an error otherwise. However, this is usually not a serious problem.</p>
+<p><strong>If you choose to use the blank file, you MUST retain the
+original order of the samples!</strong></p>
+</div>
+</div>
+<div id="setup" class="section level2">
+<h2>Setup</h2>
+<p>This chunk also loads required packages and defines the location of
+the input files. It requires the <strong>correct path</strong> as input,
+and allows <strong>setting the pruning of control samples</strong> and
+<strong>choosing generation of a phylogenetic tree</strong>.
+<strong>Beware:</strong> The generation of a phylogenetic tree may take
+several days for &gt;1000 sequences, it is therefore recommended to only
+use this feature for the final analysis or small sample sets. This
+scricpt assumes the packages <em>Biostrings</em>, <em>dada2</em>,
+<em>DECIPHER</em>, <em>ggplot2</em>, <em>ggsci</em>, <em>phangorn</em>,
+<em>phyloseq</em> and <em>stringr</em> to be installed.</p>
+<pre class="r"><code># CHANGE ME to the directory that contains &#39;seqtab_nochim.rds&#39;
+path = &quot;Nems_DADA2_results_260821&quot;
+
+# CHANGE ME to TRUE to list all samples and generate an empty metadata file 
+optional_sample_check = TRUE
+
+# CHANGE ME to TRUE to update cuphyr
+update_cuphyr = TRUE
+
+# Initiate by loading packages and setting knit options
+################# NO CHANGES NECESSARY BELOW #################
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(root.dir = paste0(path))
+knitr::opts_chunk$set(message = FALSE)
+knitr::opts_chunk$set(warning = FALSE)
+
+if (update_cuphyr) {
+  devtools::install_github(&quot;simeross/cuphyr&quot;)
+}
+
+# Sequence and microbiome specific libraries
+library(dada2)
+library(Biostrings)
+library(DECIPHER)
+library(cuphyr)
+# The export of phyloseq objects to a BIOM format and the generation of fancier 
+# ordination plots require the phyloseq-extended package. The first command 
+# installs the package that is currently on the dev brach of the author&#39;s 
+# repository, the second command sources some extra functions, including the 
+# better ordination plot implementation.
+remotes::install_github(&quot;mahendra-mariadassou/phyloseq-extended&quot;, ref = &quot;dev&quot;)
+source(&quot;https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R&quot; )
+
+library(phyloseq)
+#library(SIAMCAT)
+
+# Phylogeny libraries
+library(phangorn)
+library(ape)
+
+# Plotting and figure export
+library(gridExtra)
+library(viridis)
+library(ggpubr)
+library(cowplot)
+
+# Tidyverse
+library(tidyverse)
+library(stringr)
+
+# Various packages for specific analysis
+library(readxl)
+library(openxlsx)
+library(ggpmisc)
+library(betareg)
+library(BBmisc)
+library(aod)
+library(betareg)
+#install.packages(&#39;MicrobiomeStat&#39;)
+library(MicrobiomeStat)
+
+
+# Checks whether output path exists and creates it if not. Throws warning if 
+# directory exists.
+outp &lt;- paste0(path,&quot;/analysis_output&quot;)
+dir.create(file.path(outp))
+
+if (optional_sample_check) {
+  seqtabcheck &lt;- readRDS(paste0(path,&quot;/seqtab_nochim.rds&quot;)) 
+  samps &lt;- rownames(seqtabcheck)
+  lensamps &lt;- length(samps)
+  blankcol &lt;- vector(mode = &quot;character&quot;, length = lensamps)
+  blanktable &lt;- data.frame(SampleIDs = samps, ExampleProperty1 = blankcol, 
+                           ExampleProperty2 = blankcol, 
+                           ExampleProperty3 = blankcol)
+  write.table(blanktable, file = paste0(path, &quot;/descriptors_blank.txt&quot;), 
+              sep = &quot;\t&quot;, row.names = F)
+  cat(&quot;&#39;seqtab_nochim.rds&#39; contains samples in the following order:\n&quot;, 
+      samps, &quot;\nThe number of samples in the file is:&quot;, lensamps, sep = &quot;\n&quot;)
+  rm(optional_sample_check, seqtabcheck, samps, 
+     lensamps, blankcol, blanktable, update_cuphyr)
+  }else{rm(optional_sample_check, update_cuphyr)}</code></pre>
+<pre><code>## &#39;seqtab_nochim.rds&#39; contains samples in the following order:
+## 
+## 1-Nem
+## 10-Nem
+## 11-Nem
+## 12-Nem
+## 13-Nem
+## 14-Nem
+## 15-Nem
+## 16-Nem
+## 17-Nem
+## 18-Nem
+## 2-Nem
+## 3-Nem
+## 4-Nem
+## 5-Nem
+## 6-Nem
+## 7-Nem
+## 8-Nem
+## 9-Nem
+## NegativK-4-Nem
+## Positivkontroll-Nem
+## 
+## The number of samples in the file is:
+## 20</code></pre>
+<div id="parameters" class="section level4">
+<h4>Parameters</h4>
+<p>This chunk allows the adjustment of several parameters, such as
+<strong>setting the pruning of control samples</strong> based on
+keywords, <strong>requiring that a phylogenetic tree be provided or
+generated</strong>, <strong>defining a minimum ASV count</strong> and
+<strong>providing an alternative taxonomy</strong>.</p>
+<pre class="r"><code># Dedicated environment containing all global analysis settings for better
+# overview and collected export of settings
+parameters &lt;- new.env()
+
+# CHANGE ME to &#39;TRUE&#39; to remove control samples from the analysis or &#39;FALSE&#39; to
+# analyse all samples.
+parameters$prune_controls = &quot;TRUE&quot;
+# CHANGE ME to a list of unique identifiers that only occur in the names of
+# samples you do NOT want to analyse. Common examples are provided.
+parameters$controls = c(&quot;NegativK-4-Nem&quot;, &quot;Vann&quot;, &quot;Neg&quot;, &quot;Positivkontroll-Nem&quot;, &quot;Contr&quot;,
+    &quot;POSK&quot;)
+
+# CHANGE ME to &#39;TRUE&#39; to remove certain taxonomic groups from the analysis by
+# name. This is useful to exclude non-target organisms or noise from organelles
+# such as Chloroplasts and Mitochondria. It is recommended to first look at all
+# data before using this setting.
+parameters$prune_noise_taxgroups = &quot;FALSE&quot;
+# CHANGE ME to define the taxonomic groups to be removed as noise.
+parameters$noise_taxgroups = c(&quot;Chloroplast&quot;, &quot;Mitochondria&quot;)
+
+# CHANGE ME to a number of ASV counts [~reads] that analyzed samples should
+# minimally have. Samples with lower ASV counts than &#39;minread&#39; will be pruned.
+# Set to 0 to not prune any samples.
+parameters$minASVcount = 3000
+
+# CHANGE ME to &#39;TRUE&#39;, if you want to provide a custom taxonomy table instead
+# of using the default dada2 output (&#39;taxa.rds&#39;).
+parameters$customTax = &quot;TRUE&quot;
+# CHANGE ME to the location of the custom taxonomy file. This only matters if
+# parameters$customTax=&#39;TRUE&#39;, otherwise it will be ignored.
+parameters$taxfile = &quot;Nems_DADA2_results_260821/custom_BLAST_taxonomy_nt.txt&quot;
+
+# CHANGE ME to &#39;TRUE&#39; to generate a phylogenetic tree. This process takes a
+# long time depending on the number of sequences (up to days for thousands).
+# If a tree is provided as &#39;phylotree.rds&#39; in &#39;path&#39;, then it will be used
+# regardless of the value of &#39;parameters$maketree&#39;
+parameters$maketree = &quot;FALSE&quot;
+
+# CHANGE ME to &#39;TRUE&#39; to root the used phylogenetic tree (if one exists) on the
+# leaf with the longest branch (outgroup). This makes analyses that rely on the
+# phylogenetic tree reproducible instead of picking a random leaf as root when
+# calculating UNIFRAC distances. Implementation based on
+# http://john-quensen.com/r/unifrac-and-tree-roots/ and answers in
+# https://github.com/joey711/phyloseq/issues/597
+parameters$roottree = &quot;TRUE&quot;
+
+## CHANGE ME to &#39;TRUE&#39; to export all generated phyloseq objects as .biom
+## objects
+parameters$biom_export = &quot;FALSE&quot;</code></pre>
+</div>
+<div id="parsing-input-data" class="section level4">
+<h4>Parsing input data</h4>
+<p>This chunk loads the input data into a usable format.<strong>This
+chunk does not require any user inputs</strong>. If no phylogenetic tree
+with the name ‘phylotree.rds’ was provided and
+‘parameters$maketree=“TRUE”’, it will be calculated here. The
+phylogenetic tree is necessary for certain plots that incorporate ‘true’
+taxonomic relationships beyond the annotations, such as PCoA.</p>
+<pre class="r"><code>############### NO NEED FOR CHANGES BELOW ############### Make dedicated
+############### environments to contain temporary values and manage other
+############### objects
+tmp &lt;- new.env()
+plots &lt;- new.env()
+set &lt;- new.env()
+
+# Read in variables
+tmp$seqtabp &lt;- readRDS(paste0(path, &quot;/seqtab_nochim.rds&quot;))
+if (parameters$customTax == &quot;TRUE&quot;) {
+    tmp$taxap &lt;- read.delim(parameters$taxfile, header = TRUE, sep = &quot;\t&quot;)
+    rownames(tmp$taxap) &lt;- colnames(tmp$seqtabp)
+    tmp$taxap &lt;- as.matrix(tmp$taxap)
+} else {
+    tmp$taxap &lt;- readRDS(paste0(path, &quot;/taxa.rds&quot;))
+}
+tmp$samp_table &lt;- read.delim(paste0(path, &quot;/descriptors.txt&quot;), header = TRUE, sep = &quot;\t&quot;)
+tmp$samp_list &lt;- rownames(tmp$seqtabp)
+
+# Check if descriptors has the same samples as seqtabp
+if (length(tmp$samp_table[, 1]) != length(tmp$samp_list)) {
+    stop(&quot;There are &quot;, length(tmp$samp_table[, 1]), &quot; samples in &#39;descriptors.txt&#39;, but &quot;,
+        length(tmp$samp_list), &quot; samples in &#39;seqtab_nochim.rds&#39;. Please make sure that the correct samples 
+    are contained in descriptors.txt.
+       
+    You may use &#39;optional_sample_check &lt;- TRUE&#39; in the first chunk to generate an 
+    empty template for &#39;descriptors.txt&#39;&quot;)
+} else if (!identical(tmp$samp_table[, 1], tmp$samp_list)) {
+    warning(&quot;Warning: The samples in &#39;descriptors.txt&#39; do not have the same names 
+          or order as the samples in &#39;seqtab_nochim.rds&#39;. This may be fine if 
+          abbreviated names were used or the sample names are not contained in 
+          the first column of &#39;descriptors.txt&#39;. Double-checking never hurts!&quot;)
+}
+
+
+# generate phylogenetic tree of ASVs only if there is no file called
+# &#39;phylotree.rds&#39; in the working directory and &#39;parameters$maketree&#39; is &#39;TRUE&#39;
+if (!file.exists(paste0(path, &quot;/phylotree.rds&quot;))) {
+    if (parameters$maketree == &quot;TRUE&quot;) {
+        tmp$ASVs &lt;- getSequences(tmp$seqtabp)
+        names(tmp$ASVs) &lt;- tmp$ASVs
+        tmp$ASV_align &lt;- AlignSeqs(DNAStringSet(tmp$ASVs), anchor = NA)
+        tmp$ASV_phang &lt;- phyDat(as(tmp$ASV_align, &quot;matrix&quot;), type = &quot;DNA&quot;)
+        tmp$dm &lt;- dist.ml(tmp$ASV_phang)
+        tmp$treeNJ &lt;- NJ(tmp$dm)
+        tmp$fit &lt;- pml(tmp$treeNJ, data = tmp$ASV_phang)
+        tmp$fitGTR &lt;- update(tmp$fit, k = 4, inv = 0.2)
+        tmp$fitGTR &lt;- optim.pml(tmp$fitGTR, model = &quot;GTR&quot;, optInv = TRUE, optGamma = TRUE,
+            rearrangement = &quot;stochastic&quot;, control = pml.control(trace = 0))
+        saveRDS(tmp$fitGTR, file = paste0(path, &quot;/phylotree.rds&quot;))
+    }
+}
+
+## parse into phyloseq object
+row.names(tmp$samp_table) &lt;- tmp$samp_list
+if (file.exists(paste0(path, &quot;/phylotree.rds&quot;))) {
+    tmp$treep &lt;- readRDS(paste0(path, &quot;/phylotree.rds&quot;))
+    p &lt;- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
+        tax_table(tmp$taxap), phy_tree(tmp$treep$tree))
+} else {
+    p &lt;- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
+        tax_table(tmp$taxap))
+}
+
+## Adding nucleotide info and giving sequences ASV## identifiers
+tmp$ASV_sequences &lt;- Biostrings::DNAStringSet(taxa_names(p))
+taxa_names(p) &lt;- paste0(&quot;ASV&quot;, seq(ntaxa(p)))
+names(tmp$ASV_sequences) &lt;- taxa_names(p)
+p &lt;- merge_phyloseq(p, tmp$ASV_sequences)
+
+## optional pruning
+if (parameters$prune_controls == &quot;TRUE&quot;) {
+    if (!is.null(parameters$controls)) {
+        tmp$samp_clean &lt;- tmp$samp_list[!tmp$samp_list %in% grep(paste0(parameters$controls,
+            collapse = &quot;|&quot;), tmp$samp_list, value = T)]
+        tmp$contr_pruned &lt;- setdiff(tmp$samp_list, tmp$samp_clean)
+        ps &lt;- prune_samples(tmp$samp_clean, p)
+        # Physeq object for Just controls
+        ps.contr &lt;- prune_samples(tmp$contr_pruned, p)
+        ps.contr &lt;- prune_taxa(taxa_sums(ps.contr) &gt; 0, ps.contr)
+        ps.transcontr &lt;- transform_sample_counts(ps.contr, function(ASV) ASV/sum(ASV))
+
+        message(cat(&quot;\n&quot;, &quot;Number of control samples that were pruned and will not be analysed:\n&quot;,
+            length(tmp$samp_list) - length(tmp$samp_clean), &quot;\n&quot;, &quot;The following controls were pruned:\n&quot;,
+            tmp$contr_pruned, &quot;The controls are contained in a separate phyloseq object: ps.contr&quot;,
+            &quot;\n&quot;, sep = &quot;\n&quot;))
+    } else {
+        warning(cat(&quot;\n\nparameters$prune_controls is TRUE but &#39;parameters$controls&#39; is empty. 
+    No samples were pruned.\n\n&quot;))
+    }
+} else {
+    ps &lt;- p
+}</code></pre>
+<pre><code>## 
+## 
+## Number of control samples that were pruned and will not be analysed:
+## 
+## 2
+## 
+## 
+## The following controls were pruned:
+## 
+## NegativK-4-Nem
+## Positivkontroll-Nem
+## The controls are contained in a separate phyloseq object: ps.contr</code></pre>
+<pre class="r"><code># Prune ASVs defined as noise
+if (parameters$prune_noise_taxgroups == &quot;TRUE&quot;) {
+    tmp$ps_taxlvls &lt;- colnames(tax_table(ps))
+    tmp$noise_ASVs &lt;- character(0)
+    for (lvl in tmp$ps_taxlvls) {
+        tmp$noise_ASVs &lt;- c(tmp$noise_ASVs, cuphyr::list_subset_ASVs(physeq = ps,
+            subv = parameters$noise_taxgroups, taxlvlsub = lvl))
+    }
+    tmp$noise_ASVs &lt;- unique(tmp$noise_ASVs)
+    tmp$no_noise_ASVs &lt;- colnames(otu_table(ps))
+    tmp$no_noise_ASVs &lt;- setdiff(tmp$no_noise_ASVs, tmp$noise_ASVs)
+    if (length(tmp$noise_ASVs) &gt; 0) {
+        ps &lt;- prune_taxa(tmp$no_noise_ASVs, ps)
+        tmp$no_noise_ps &lt;- ps
+        cat(length(tmp$noise_ASVs), &quot;ASVs were pruned because they belonged to the following 
+        taxonomic groups:\n&quot;)
+        cat(parameters$noise_taxgroups, &quot;\n&quot;, sep = &quot;\n&quot;)
+    } else {
+        cat(&quot;No ASVs were recognized as belonging to the following taxonomic groups 
+        defined as noise:\n&quot;)
+        cat(parameters$noise_taxgroups, &quot;\n&quot;, sep = &quot;\n&quot;)
+    }
+}
+
+# Prune samples with fewer than reads than minASVcount
+if (parameters$minASVcount &gt; 0) {
+    tmp$samp_pruned &lt;- names(which(sample_sums(ps) &lt; parameters$minASVcount))
+    ps &lt;- prune_samples(sample_sums(ps) &gt;= parameters$minASVcount, ps)
+    if (length(tmp$samp_pruned) &gt; 0) {
+        cat(&quot;The following samples were pruned because ASV counts were lower than&quot;,
+            parameters$minASVcount, &quot;:\n&quot;)
+        cat(tmp$samp_pruned, &quot;\n&quot;, sep = &quot;\n&quot;)
+    }
+}
+
+# Remove 0 count ASVs (e.g. control ASVs that remain) from the base object
+ps &lt;- prune_taxa(taxa_sums(ps) &gt; 0, ps)
+sample_data(ps)[[&quot;ndvi&quot;]] &lt;- as.numeric(sample_data(ps)[[&quot;ndvi&quot;]])
+
+
+# Transformed per sample (per-sample relative abundance)
+ps.trans &lt;- transform_sample_counts(ps, function(ASV) ASV/sum(ASV))
+
+# Read descriptor values as numeric
+sample_data(ps.trans)[[&quot;ndvi&quot;]] &lt;- as.numeric(sample_data(ps.trans)[[&quot;ndvi&quot;]])
+sample_data(ps.trans)[[&quot;ndvi_july&quot;]] &lt;- as.numeric(sample_data(ps.trans)[[&quot;ndvi_july&quot;]])
+
+# Get a tbl of the base object for easier access in some phyloseq-independent
+# analyses. Takes some seconds, potentially up to minutes.
+ps_tbl &lt;- as_tibble(psmelt(ps))
+ps_trans_tbl &lt;- as_tibble(psmelt(ps.trans))
+
+# Condensing to Abundance per Genus and Sample
+genus_abundance_tbl_per_sample &lt;- ps_trans_tbl %&gt;%
+    group_by(Genus, Sample) %&gt;%
+    mutate(Genus_Sample_Abundance = sum(Abundance)) %&gt;%
+    select(Genus, Sample, ndvi, ndvi_july, Genus_Sample_Abundance, Alias) %&gt;%
+    unique()
+
+if (parameters$roottree == &quot;TRUE&quot; &amp;&amp; parameters$maketree == &quot;TRUE&quot;) {
+    phyloseq::phy_tree(ps) &lt;- cuphyr::root_tree_in_outgroup(physeq = ps)
+}
+
+if (parameters$biom_export == &quot;TRUE&quot;) {
+    suppressWarnings(phyloseq.extended::write_phyloseq(p, biom_file = paste0(path,
+        &quot;all_samples.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps, biom_file = file.path(path,
+        &quot;samples_without_controls.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps.trans, biom_file = file.path(path,
+        &quot;samples_without_controls_rel_abundance.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps.contr, biom_file = file.path(path,
+        &quot;just_controls.biom&quot;), biom_format = &quot;standard&quot;))
+}
+
+ps</code></pre>
+<pre><code>## phyloseq-class experiment-level object
+## otu_table()   OTU Table:         [ 151 taxa and 18 samples ]
+## sample_data() Sample Data:       [ 18 samples by 21 sample variables ]
+## tax_table()   Taxonomy Table:    [ 151 taxa by 7 taxonomic ranks ]
+## refseq()      DNAStringSet:      [ 151 reference sequences ]</code></pre>
+</div>
+</div>
+<div id="output" class="section level2">
+<h2>Output</h2>
+<p>The chunks below will produce various plots and other output. Each
+chunk is headed by a description of the output and may contain some
+parameters to adjust the output.</p>
+<div id="plot-looks" class="section level4">
+<h4>Plot looks</h4>
+<p>This chunk sets the background structure and color palette. Viridis
+was chosen because it is optimized for grey-scale printing and various
+types of color blindness and More info on the Viridis palette can be
+found on <a href="https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html">the
+Viridis info page</a>. It also establishes save_plot as a shorter
+variant of ggsave with customized date-time structure to save plots with
+the same name mulitple times instead of overwriting them (overwriting
+can be triggered with overwrite=TRUE).</p>
+<pre class="r"><code>##### Optional settings (sensible defaults) #####
+# Can be changed to adjust the output format for all plots. Default &quot;pdf&quot;, 
+# possible &quot;eps&quot;/&quot;ps&quot;, &quot;tex&quot; (pictex), &quot;jpeg&quot;, &quot;tiff&quot;, &quot;png&quot;, &quot;bmp&quot; and &quot;svg&quot;
+parameters$output_format = &quot;pdf&quot;
+
+# Can be changed to preferred ggplot2 theme. Recommended: &quot;theme_bw()&quot;.
+theme_set(theme_bw())
+
+############### NO NEED FOR CHANGES BELOW ###############
+
+my_scale_col &lt;- scale_color_viridis(discrete = TRUE)
+my_scale_fill &lt;- scale_fill_viridis(discrete = TRUE)
+
+# Custom, more narrow color ranges based on viridis
+# Base order to have adjacent colors be distinct from each other
+tmp$sort_colors &lt;- c(rbind(c(1:5), c(6:10), c(11:15), c(16:20)))
+
+# Customized vectors
+tmp$n_col &lt;- 20
+tmp$viridis_greens &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 0.85, 
+                              end = 0.7)[tmp$sort_colors]
+tmp$viridis_reds &lt;- viridis(tmp$n_col,  option = &quot;B&quot;, begin = 0.7, 
+                            end = 0.5)[tmp$sort_colors]
+tmp$viridis_blues &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 0.2, 
+                             end = 0.4)[tmp$sort_colors]
+tmp$viridis_yellows &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 1, 
+                               end = 0.9)[tmp$sort_colors]
+tmp$viridis_dark &lt;- viridis(tmp$n_col,  option = &quot;A&quot;, begin = 0, 
+                            end = 0.1)[tmp$sort_colors]
+tmp$viridis_light &lt;- viridis(tmp$n_col,  option = &quot;A&quot;, begin = 1, 
+                             end = 0.9)[tmp$sort_colors]
+# Collected list that is available in the global environment
+sub_viridis &lt;- list(tmp$viridis_greens, tmp$viridis_blues, tmp$viridis_yellows, 
+                    tmp$viridis_light, tmp$viridis_reds, tmp$viridis_dark)
+names(sub_viridis) &lt;- c(&quot;greens&quot;, &quot;blues&quot;, &quot;yellows&quot;, &quot;lights&quot;, &quot;reds&quot;, &quot;darks&quot;)
+
+tmp$out &lt;- paste0(&quot;.&quot;, parameters$output_format)
+
+#################### Function ############################
+
+# Generic save function for plots that checks whether file exists and if so, 
+# creates a new one with d/m/y+time info to avoid overwriting. Overwriting can 
+# be triggered with overwrite = TRUE. Width, height and resolution are taken 
+# from parameters in the &#39;set&#39; environment or set to 20x20 cm with 300dpi.
+save_plot &lt;- function(
+  pl, filetype = &quot;.pdf&quot;, plot_name = &quot;my_plot&quot;, overwrite=FALSE){
+  wp &lt;- if (!is.null(set$wp)) set$wp else 20
+  hp &lt;- if (!is.null(set$hp)) set$hp else 20
+  res &lt;- if (!is.null(set$res)) set$res else 300
+  name &lt;- paste0(&quot;/&quot;, plot_name,filetype)
+  if (file.exists(paste0(outp, name)) &amp; !overwrite) {
+  name &lt;- paste0(&quot;/&quot;, plot_name, &quot;_&quot;, 
+                 format(Sys.time(), &quot;%d-%m-%y_%H%M%S&quot;),filetype)}
+  ggsave(file.path(outp, name), pl, 
+         width = wp, height = hp, unit = &quot;cm&quot;, dpi = res)
+}
+
+################################################</code></pre>
+</div>
+<div id="total-asv-counts-ranked" class="section level4">
+<h4>Total ASV counts ranked</h4>
+<p>This chunk plots the absolute abundance of all samples (including
+controls) and all samples without controls and other trimmed
+samples.</p>
+<pre class="r"><code># CHANGE ME to the sample group for color coding. Accepted values are the column 
+# headers in the descriptor file.
+set$color_by = &quot;Symptoms&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 17
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20  
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+############### NO NEED FOR CHANGES BELOW ###############
+# Rank samples
+set$ranked &lt;- cuphyr::make_ranked_sums(p, myset = tmp$subset_id)
+set$ranked_ps &lt;- cuphyr::make_ranked_sums(ps, myset = tmp$subset_id)
+set$ymax &lt;- max(set$ranked$Abundance)
+set$ymax &lt;- set$ymax + round(set$ymax/10)
+set$xmax &lt;- nrow(set$ranked) + 1
+set$title2 &lt;- &quot;Samples (without controls)&quot;
+
+# Stabilize colors
+set$color_vars &lt;- set$ranked[,set$color_by]  %&gt;% 
+  unlist() %&gt;% as.character() %&gt;% unique()
+set$color_vars &lt;- sort(set$color_vars)
+set$color_varsPalette &lt;- viridis(length(set$color_vars))
+names(set$color_varsPalette) &lt;- set$color_vars
+set$my_scale_fill &lt;- scale_fill_manual(values = set$color_varsPalette)
+
+# plot
+plots$overview_all &lt;- ggplot(data = set$ranked, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + ggtitle(&quot;All samples&quot;) + ylim(0, set$ymax) + 
+  xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+
+if (length(tmp$noise_ASVs) &gt; 0) {
+  set$ranked_nonoise &lt;- cuphyr::make_ranked_sums(
+    tmp$no_noise_ps, myset = tmp$subset_id)
+  plots$overview_noise &lt;- ggplot(
+    data = set$ranked_nonoise, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + 
+    ggtitle(&quot;Samples (without controls), noise ASVs removed&quot;) + 
+    ylim(0, set$ymax) + 
+    xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+}
+
+if (parameters$minASVcount &gt; 0) {
+plots$overview_all &lt;- plots$overview_all + 
+  geom_hline(yintercept = parameters$minASVcount, linetype = &quot;dashed&quot;) + 
+    ggtitle(&quot;All samples (ASV count cutoff indicated)&quot;)
+set$title2 &lt;- &quot;Samples (without controls and low count samps)&quot;
+}
+
+plots$overview_ps &lt;- ggplot(data = set$ranked_ps, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + ggtitle(set$title2) + ylim(0, set$ymax) + 
+  xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+plots$combo_overview &lt;- ggarrange(
+  plots$overview_all, plots$overview_ps, nrow = 2, align = &quot;v&quot;, 
+  common.legend = TRUE, legend = &quot;right&quot;)
+
+if (length(tmp$noise_ASVs) &gt; 0) {
+plots$combo_overview &lt;- ggarrange(
+  plots$overview_all, plots$overview_noise, plots$overview_ps,
+  nrow = 3, align = &quot;v&quot;, 
+  common.legend = TRUE, legend = &quot;right&quot;)
+}
+
+#Save plots
+save_plot(plots$combo_overview, plot_name = &quot;Overview_all_and_pruned&quot;, 
+          filetype = tmp$out)
+
+#Clean up plot parameters
+rm(list = ls(set), envir = set)
+
+#Print plots
+plots$combo_overview</code></pre>
+<p><img role="img" 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" width="672" /></p>
+</div>
+<div id="controls" class="section level4">
+<h4>Controls</h4>
+<p>This chunk generates an overview over the controls (positive AND
+negative)</p>
+<pre class="r"><code># CHANGE ME to the desired sample categories on the x-axis. In this case it 
+# should be the Sample names.
+set$x_axis_value = &quot;Sample&quot;
+
+# CHANGE ME to the taxonomic level for color coding. Use &quot;OTU&quot; for ASVs, 
+# &quot;Genus&quot;, &quot;Species&quot; or &quot;OTU&quot; recommended to compare pos. controls.
+set$color_by_taxlvl = &quot;Species&quot;
+
+# CHANGE ME to the taxonomic level for labeling the tree tips (if phylogenetic 
+# tree is available). Use &quot;OTU&quot; for ASVs.
+set$label_by_taxlvl = &quot;OTU&quot;
+
+# CHANGE ME to a sample category to shape the tree tip labels by (if 
+# phylogenetic tree is available).
+set$label_shape_by = &quot;Symptoms&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to generate a tree for just the control sequences IF no 
+# phylogenetic tree for all seuquences is provided. This may slow down this 
+# chunk when running it for the first time
+set$control_tree = TRUE
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 17
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20  
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+############### NO NEED FOR CHANGES BELOW ###############
+if (set$control_tree &amp; class(try(phy_tree(ps.transcontr), 
+                                 silent = TRUE)) == &quot;try-error&quot;) {
+  # generate phylogenetic tree of ASVs only if there is no file called 
+  # &#39;phylotree.rds&#39; in the working directory and &#39;parameters$maketree&#39; is &quot;TRUE&quot;
+  if (!file.exists(paste0(path, &quot;/controls_phylotree.rds&quot;))) {
+    set$ASVs &lt;- phyloseq::refseq(ps.transcontr)
+    set$ASV_align &lt;- AlignSeqs(set$ASVs, anchor = NA)
+    set$ASV_phang &lt;- phyDat(as(set$ASV_align, &quot;matrix&quot;), type = &quot;DNA&quot;)
+    set$dm &lt;- dist.ml(set$ASV_phang)
+    set$treeNJ &lt;- NJ(set$dm)
+    set$fit &lt;- pml(set$treeNJ, data = set$ASV_phang)
+    set$fitGTR &lt;- update(set$fit, k = 4, inv = 0.2)
+    set$fitGTR &lt;- optim.pml(set$fitGTR, model = &quot;GTR&quot;, 
+                            optInv = TRUE, optGamma = TRUE,
+                            rearrangement = &quot;stochastic&quot;, 
+                            control = pml.control(trace = 0))
+    saveRDS(set$fitGTR, file = paste0(path, &quot;/controls_phylotree.rds&quot;))}
+  set$fitGTR &lt;- readRDS(paste0(path, &quot;/controls_phylotree.rds&quot;))
+  phyloseq::phy_tree(ps.transcontr) &lt;- set$fitGTR$tree
+}
+
+plots$topnpplot &lt;- plot_bar(ps.contr, x = set$x_axis_value, 
+                            fill = set$color_by_taxlvl) + my_scale_fill + 
+  theme(axis.title.x = element_blank(), legend.position = &quot;none&quot;, 
+        legend.key.size = unit(3, &quot;mm&quot;)) + 
+  ylab(&quot;ASV counts&quot;) + guides(col = guide_legend(ncol = 3))
+
+plots$topntplot &lt;- plot_bar(ps.transcontr, x = set$x_axis_value, 
+                            fill = set$color_by_taxlvl) + my_scale_fill + 
+  theme(axis.title.x = element_blank(), legend.position = &quot;none&quot;, 
+        legend.key.size = unit(3, &quot;mm&quot;)) + 
+  ylab(&quot;Relative abundance&quot;) + guides(col = guide_legend(ncol = 3))
+
+plots$combo_contr &lt;- ggarrange(plots$topnpplot, plots$topntplot, ncol = 2, 
+                               labels = c(&quot;A&quot;, &quot;B&quot;), align = &quot;hv&quot;, 
+                               common.legend = TRUE, legend = &quot;right&quot;)
+
+if (class(try(phy_tree(ps.transcontr), silent = TRUE)) != &quot;try-error&quot;) {
+plots$tre &lt;- plot_tree(
+          ps.transcontr, ladderize = &quot;left&quot;, label.tips = set$label_by_taxlvl, 
+          color = &quot;abundance&quot;, text.size = 2.5, shape = set$label_shape_by) + 
+          scale_color_viridis_c(aesthetics = c(&quot;color&quot;,&quot;fill&quot;)) + 
+          theme(legend.position = &quot;left&quot;, panel.border = element_blank())
+plots$combo_contr &lt;- ggarrange(plots$tre, ggarrange(plots$topnpplot, 
+                                                    plots$topntplot, ncol = 2, 
+                               labels = c(&quot;B&quot;, &quot;C&quot;), align = &quot;hv&quot;, 
+                               common.legend = TRUE, legend = &quot;right&quot;), 
+                               nrow = 2, legend = &quot;right&quot;, labels = c(&quot;A&quot;)) 
+}
+
+# save
+save_plot(plots$combo_contr, plot_name = &quot;Controls&quot;, filetype = tmp$out)
+
+plots$combo_contr</code></pre>
+<p><img role="img" 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width="672" /></p>
+</div>
+<div id="get-a-list-of-top-n-taxa-at-a-given-level" class="section level4">
+<h4>Get a list of Top N taxa at a given level</h4>
+<p>This chunk lists the top n most abundant taxonomic terms at a given
+level. Change the function parameters to the desired values. For more
+info, check help page of the function with
+<code>?cuphyr::abundant_tax_physeq()</code>. Change ‘ignore_na’ to
+include/exclude NA values at the given level.</p>
+<pre class="r"><code>#The character vector can later be accessed by calling &#39;tmp$tops&#39;
+tmp$tops &lt;- cuphyr::abundant_tax_physeq(physeq = ps, 
+                            lvl = &quot;Genus&quot;,
+                            top = 15,
+                            output_format = &quot;tops&quot;,
+                            ignore_na = TRUE,
+                            silent = FALSE)</code></pre>
+<pre><code>## 
+## The top 15 most abundant annotated groups at the taxonomic level &#39;Genus&#39; are:
+## Globodera
+## Meloidogyne
+## Pristionchus
+## Rhabditis
+## Pellioditis
+## Cephaloboides
+## Aporcelaimellus
+## Acrobeloides
+## Nygolaimus
+## Cruznema
+## Sectonema
+## Acrobeles
+## Eucephalobus
+## Mesorhabditis
+## Aphelenchoides</code></pre>
+</div>
+<div id="sequences-belonging-to-phylum" class="section level4">
+<h4>Sequences belonging to phylum</h4>
+<p>This chunk gives an overview of the number of ASVs belonging to which
+Phylum</p>
+<pre class="r"><code># Number of ASVs belonging to Nematoda
+
+ps_tbl %&gt;% select(OTU, Phylum) %&gt;% unique() %&gt;% group_by(Phylum) %&gt;% summarise(length(OTU))</code></pre>
+<pre><code>## # A tibble: 5 x 2
+##   Phylum                     `length(OTU)`
+##   &lt;chr&gt;                              &lt;int&gt;
+## 1 Arthropoda                            10
+## 2 Candidatus Bipolaricaulota             1
+## 3 Nematoda                             133
+## 4 Proteobacteria                         1
+## 5 Tardigrada                             6</code></pre>
+<pre class="r"><code># Total ASV counts
+
+ps_tbl %&gt;% select(OTU, Abundance) %&gt;% summarise(total_sum = sum(Abundance))</code></pre>
+<pre><code>## # A tibble: 1 x 1
+##   total_sum
+##       &lt;int&gt;
+## 1    405276</code></pre>
+<pre class="r"><code># Total ASV counts per phylum
+
+ps_tbl %&gt;% select(OTU, Phylum, Abundance) %&gt;% group_by(Phylum) %&gt;% summarise(Abundance_sum = sum(Abundance))</code></pre>
+<pre><code>## # A tibble: 5 x 2
+##   Phylum                     Abundance_sum
+##   &lt;chr&gt;                              &lt;int&gt;
+## 1 Arthropoda                           426
+## 2 Candidatus Bipolaricaulota             4
+## 3 Nematoda                          404660
+## 4 Proteobacteria                        11
+## 5 Tardigrada                           175</code></pre>
+<pre class="r"><code># Percentage of ASV counts belonging to Nematoda
+
+nem_percentage = 404660/405276 # ASV counts Nematoda / Total ASV counts
+nem_percentage</code></pre>
+<pre><code>## [1] 0.99848</code></pre>
+</div>
+<div id="top-n-asvstaxa-bar-plot" class="section level4">
+<h4>Top N ASVs/taxa Bar plot</h4>
+<p>This chunk plots abundance of the Top n ASVs or taxa at a given level
+as a bar plot, giving an insight into the presence of the n ASV and most
+common taxa for the primary and secondary parameters. The default for n
+is set at 20, a larger n may lead to delay/skipping of the plot in
+standard out, but it should be saved as a PDF regardless for ASVs. For
+taxa, a large n may lead to unreadable plots. <strong>The chunk does not
+require any input</strong>, but it is possible to adjust the
+<strong>default ‘n’</strong>, and to change <strong>width</strong>,
+<strong>height</strong> and <strong>resolution</strong> of the
+PDF-output if necessary.</p>
+<pre class="r"><code># CHANGE ME to the sample category that will be shown in separate panels.
+# Accepted values are the column headers in your descriptor file.
+set$panel_by = &quot;Symptoms&quot;
+
+# CHANGE ME to the desired sample categories on the x-axis.  Accepted values
+# are the column headers in the descriptor file.
+
+set$x_axis_value = &quot;Alias&quot;
+
+# CHANGE ME to the count of top ASVs you want to plot (e.g. &#39;set$topASVs = 20&#39;
+# plots the 20 most abundant ASVs)
+set$topASVs = 100
+
+# CHANGE ME to the taxonomic level of interest (color coding). Accepted values
+# are the column headers in your descriptor file.
+set$taxlvl = &quot;Genus&quot;
+
+# CHANGE ME to change the number of Top n taxa to be plotted at taxlvl.
+set$top_n = 15
+
+# CHANGE ME to an entry at the chosen taxonomic level you want to highlight.
+# Comment out to not highlight anything.
+set$highlight = &quot;Ditylenchus&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to turn unified coloring on or off (same taxonomy term = same
+# color in both plots). Highlighting will unify colors even if unify_colors is
+# FALSE.
+set$unify_colors = TRUE
+
+# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
+set$ignore_na = FALSE
+
+# Can be changed to remove ASV segmentation in the top n taxlvl plot. This
+# improves visual clarity when a bar segment appears black due to the border of
+# many small ASVs overlapping.
+set$fuse_ASVs = FALSE
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 30
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+# Can be changed to change the y-axis label
+set$y_axis_label = &quot;Relative abundance&quot;
+
+
+
+
+############### NO NEED FOR CHANGES BELOW ###############
+
+# Make physeq objects of top n taxa and top n ASVs
+set$ps.topnTax &lt;- cuphyr::abundant_tax_physeq(ps.trans, lvl = set$taxlvl, top = set$top_n,
+    ignore_na = set$ignore_na)
+set$topnASVs &lt;- names(sort(taxa_sums(ps), decreasing = TRUE))[1:set$topASVs]
+set$ps.topnASVs &lt;- prune_taxa(set$topnASVs, ps.trans)
+
+if (set$unify_colors | exists(&quot;highlight&quot;, envir = set) | set$fuse_ASVs) {
+    set$toptax &lt;- union(phyloseq::tax_table(set$ps.topnTax)[, set$taxlvl], phyloseq::tax_table(set$ps.topnASVs)[,
+        set$taxlvl])
+    set$toptax &lt;- sort(set$toptax)
+    set$taxlvlPalette &lt;- viridis(length(set$toptax))
+    names(set$taxlvlPalette) &lt;- set$toptax
+    if (exists(&quot;highlight&quot;, envir = set)) {
+        # It is possible to change the highlight color here by substituting
+        # &#39;sub_viridis$reds[4]&#39; with a hexcode-string, e.g. &#39;#ff7f7f&#39;&#39;
+        set$taxlvlPalette[set$highlight] &lt;- sub_viridis$reds[4]
+    }
+    set$taxlvlPalette &lt;- set$taxlvlPalette[sort(names(set$taxlvlPalette))]
+    set$my_scale_fill &lt;- scale_fill_manual(values = set$taxlvlPalette, na.value = &quot;grey&quot;)
+} else {
+    set$my_scale_fill &lt;- my_scale_fill
+}
+
+# Plot
+if (set$unify_colors | exists(&quot;highlight&quot;, envir = set) | set$fuse_ASVs) {
+    set$my_scale_fill &lt;- scale_fill_manual(values = set$taxlvlPalette[sort(unique(phyloseq::tax_table(set$ps.topnTax)[,
+        set$taxlvl]))], na.value = &quot;grey&quot;)
+}
+plots$topn_tax &lt;- plot_bar(set$ps.topnTax, x = set$x_axis_value, fill = set$taxlvl,
+    title = paste0(&quot;Top &quot;, set$top_n, &quot; &quot;, set$taxlvl)) + facet_grid(paste0(&quot;~&quot;,
+    set$panel_by), scales = &quot;free&quot;, space = &quot;free&quot;) + set$my_scale_fill + ylab(set$y_axis_label) +
+    xlab(&quot;Sample&quot;) + theme(strip.background = element_blank(), strip.text = element_text(size = 16),
+    axis.title = element_text(size = 16), legend.text = element_text(size = 14))
+
+if (set$fuse_ASVs) {
+    plots$topn_tax &lt;- plots$topn_tax + geom_bar(aes_string(color = set$taxlvl, fill = set$taxlvl),
+        stat = &quot;identity&quot;, position = &quot;stack&quot;) + scale_color_manual(values = set$taxlvlPalette,
+        na.value = NA)
+}
+
+if (set$unify_colors | exists(&quot;highlight&quot;, envir = set) | set$fuse_ASVs) {
+    set$my_scale_fill &lt;- scale_fill_manual(values = set$taxlvlPalette[sort(unique(phyloseq::tax_table(set$ps.topnASVs)[,
+        set$taxlvl]))], na.value = &quot;grey&quot;)
+}
+plots$topn_ASVs &lt;- plot_bar(set$ps.topnASVs, x = set$x_axis_value, fill = set$taxlvl,
+    title = paste0(&quot;Top&quot;, set$topASVs, &quot;_ASVs&quot;)) + facet_grid(paste0(&quot;~&quot;, set$panel_by),
+    scales = &quot;free_x&quot;, space = &quot;free&quot;) + set$my_scale_fill + ylab(set$y_axis_label) +
+    xlab(&quot;Sample&quot;) + theme(strip.background = element_blank())
+
+
+
+# save
+save_plot(plots$topn_tax, plot_name = paste0(&quot;Top&quot;, set$top_n, &quot;_&quot;, set$taxlvl),
+    filetype = tmp$out)
+save_plot(plots$topn_ASVs, plot_name = paste0(&quot;Top&quot;, set$topASVs, &quot;_ASVs&quot;), filetype = tmp$out)
+
+# Clean up plot parameters
+rm(list = ls(set), envir = set)
+
+# Print to standard out
+plots$topn_tax</code></pre>
+<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAACslBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZmYAZpAAZrYZGUgZGXEZSEgZSHEZSJcZcZcZcboaGhoenIkflosjio0nfo4nrYExZo4zMzM6AAA6ADo6AGY6OgA6Ojo6OmY6ZmY6ZpA6ZrY6kJA6kLY6kNs7UotDv3FEAVRGCl1GNIBIGRlIGUhIJXZISBlISEhISHFIcXFIcZdIcbpIl5dIl7pIl91NTU1NTW5NTY5Nbm5Nbo5NbqtNjshmAABmADpmOgBmOjpmZgBmZjpmZmZmZpBmkJBmkLZmkNtmtrZmtttmtv9uTU1uTY5ubk1ubm5ubo5ujo5ujqtujshuq8huq+RxGRlxSBlxSEhxcUhxcZdxl5dxl7pxl91xut1xuv960VGOTU2Obk2Obm6Ojm6Ojo6OjquOq6uOq8iOq+SOyOSOyP+QOgCQZgCQZjqQZmaQZpCQkGaQkLaQtpCQtraQttuQ27aQ29uQ2/+XSBmXcUiXcXGXl0iXl3GXl7qXurqXut2Xuv+X3bqX3d2X3f+a2Tyq3DKrbk2rbm6rbo6rjk2rjm6rjo6rjqurq46rq8iryMiryOSr5Mir5P+2ZgC2Zjq2kDq2kGa2kJC2tma2tpC2tra2ttu225C229u22/+2/9u2//+6cRm6cUi6l0i6l3G6l5e6ut263d263f+6///Ijk3Ijm7Iq27Iq47IyKvIyMjIyOTI5KvI5MjI5OTI5P/I///bkDrbkGbbtmbbtpDbtrbb25Db27bb29vb2//b/7bb/9vb///dl0jdunHdupfdurrd3brd3d3d3f/d///kq27kq47kyI7kyKvkyMjk5Mjk5OTk5P/k///r6+v/tmb/unH/yI7/25D/27b/29v/3Zf/3br/3d3/5Kv/5Mj/5OT//7b//7r//8j//9v//93//+T///9OXWxGAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOy973dkx3nn101gJHC1GzP22kYyAAEL62S1sWwnMeQjmgE8HA094Asf6djrxD9kyu/iEWR5ORtFG2+cxHv4Qq9y3MMZiV7ApKNkHe45fkU3ARlDCbQt2zzZN5oWB5BNmssm+//Irbr1896q2/V033ur+vb3+2IGXfe5dQt1n/6gfjxV1ZtAEARBM6kXuwAQBEGLKgAUgiBoRgGgEARBMwoAhSAImlEAKARB0IwCQCEIgmYUAApBEDSjAFAIgqAZBYBCEATNKAAUgiBoRgGgEARBMwoAhSAImlEAKARB0IwCQCEIgmYUAApBEDSj4gF01CvrYObchr0r5+rDQOfY/5LT/OKLG+vZ1ZXHb587r0MQBE1XRwD6cN0A6PjWFICe7hkGHwVCIQiaTd0A6LtP9gyAsk8VAB3fsR+6+tKsvwIEQcut+GOgWePxkTkRdsGIqQFqNUdLytunm7dfz36+vHvVuhOCIIigLgD0hA1nGhjM2rZrfms2QLp6rD4Os487cz0egqBl1eID9OIwQ+BVE6DDKiRmj7ObnJn1vC1gCIKWUwsPUAbE3s7IoCLro/sHUwdFXrIR09ln/yEIWmKlCNCz/QyK/S3Rzc55eLHPBi6PnLdnHXIToBkRPcFLuXmxeTpcufG6+PHi0Hwyg+1BnrZy41wlyduHaqTg4pANpa5sOYoHQVCHlR5AdRDS9rn8fCCn7LdK8z0P/7Obk4kFUDaH9K2TqyYJtao67Hp6Pn8yB6hME0x2ANSY1V8tPxCCoO4qOYCaMUiciQygn1q3ksoyAZr9vCoDPUtjoQP/pLsZPromjTfswrgAaoTtYzQVgpZKyQGU4Yi1HM/2BP9yrq0eGUllmQAdmkGeBXOWmW+Cid23nXXmL+/I+zgaWWEu9mSQahmgbFBgm8dEvYAJfQhaLqUGUD1GORbTPRyga+dmUlkmQAditHTMw5vs+SHWvvUwzphMGonHDBQR1X1lgOqhUPNHCIKWQKkB1OhiC6IxgEoD74z5yL5tR/9od9itDHTfmxmZ9BvkVgavB7q/XgYo1jJB0HIqMYBaIUg5tKxe98DTgBy5hzazvO0JeasFagN0YDx5lFsNyo3LMkD5/Nbjz8mJfAiClkeJAdQKQcqpaDHV10n2ALQUEuoHqDmFJKeR3DNGhSR14+ZvY0koBC2XFgKgVhIFoKUWK6NdMYORE6CiUTodoOadq7dDf2sIgjqgBQMosQVa7vI74kDnBahYTeqJm4IgqLtKGaBDNQY6axfeGtiUTytOQzlGCvTdQQDNdHlvn8eq+tdAQRDUOSUG0BomkapXdZpz+va9jqyrAToownyMQFAIWi4lBlAGKkcYk0wqzapLjdw27g57obH6mgpjMmPxxSy8A6CCmmI4tYh8ABSClkepAdQTSC+SbvkWYhoANYwcO4eU9gPl45fM3jCWDHYAVCN52FNxoOajAVAIWh6lBlDfUk6V5N55rrAWnq/8HN9ddy1cGvPopVUeuHn5Nb5ovv+0fLK6zzfgyTP/n+S8kVzKucrPpmPlw2J4CFoiJQdQ52YieksPTwPPmkSadiintVhet0fNefi8RNUxS59aV41SLTRAIWiJlBxAJ++qIzON7ezEjnF5U9EhexZe7S/n2V6Oby5ajt0cHxbuc025y+L1Dx4KgE5eU3tF9W/OUgMQBC2o0gPoZHLKIoJW7A2V2UnEeuPjkgphTPkOx5v+sPbxvX1m0d+0szxjmyfr+5wxS+OTjXyrUQXQyeVd3kTexCHzELRcig/Qaao+oQOCICiaAFAIgqAZBYBCEATNKAAUgiBoRgGgEARBMwoAhSAImlEAKARB0IxKH6AQBEGJCgCFIAiaUQAoBEHQjAJAIQiCZhQACkEQNKMAUAiCoBkFgEIQBM0oABSCIGhGAaAQBEEzCgCFIAiaUQAoBEHQjAJAIQiCZhQACkEQNKMAUAiCoBkFgEIQBM0oABSCIGhGAaAQBEEzCgCFIAiaUQAoBEHQjAJAIQiCZhQACkEQNKMAUAiCoBkFgEIQBM0oABSCIGhGAaAQBEEzCgCFIAiaUQAoBEHQjAJAIQiCZhQACkEQNKMAUAiCoBkFgEIQBM0oABSCIGhGAaBS3/vYY49933Szv/jfmi+KS+//z4899g/+X/unKsUqZzN647HHHvtHf2OnfTdL+6kYhfnLf/Xx7NGPfeTHf+WvYzweSkkAqFQQQP/2y499to3ClEUDaLxyNiMG0CIt4wD0/a9/7DGtf/i7rRcASkoAqFQIQP88s1kEgEYsZzPiAH3096y0KAD923/+mK3/+m+m3wR1VwCoVAhA2fd4EQAasZzNiAO00ImPAdD/+IuPFVUcWYCWSgCo1MIANEAdBajNyxgAfZkPfv7KX7Gf3//2lz/mGFmAlkoAqBQAmrAEQK1OfASAMh8xn8leSvhbgTooAFQKAE1Ybzj6yxEAyoph+QgnKJqgSywAVKoM0Pe/zsJVPv4r+ktbBNP7X/8l1i768d8tjoN9758/9thP/rFh92OZ3Y//u/zTX/wS7wda9/hy4t1EZusF6N/+zsc/xm+dpZyLIvYL/XKh+VcA6N/xt+X6HcvvMU+l18nLzpksw2uImYb5z8K/vS4LAJUyAMqmCj7Lp7LzjuOv8tTvqnkD8SUyIloe/TUjm0d/Lzd99LM8p5/Ke358ynYiGi2F/qgzp4kx5/vo73kmkf7CnBXOZ4TDyrlY4n8R3rArzQLo+3+kf+tflYnu95jbz1QnL5f+iLFHqGaxI1P26q1pJuPtBfjPzCWFWhIAKlUEqOaQ+J4WwaRIqE3ybB79339RmnGAfu9jhpVxl4KBO6fsifrGR/8PJ0DfeMzS94WXc7HEAcp/EU0jE6B2dNH3CRv3e2SasU7YHJJ/aMSZafEW7WYh/tONt9dlAaBSBYD+shkvzV25ACbbs5Vvsy/Aj+Up2Xed5fTf/KKZ0cvmFz2/xZPT5HtWEX7MAdDvPlbQZ4PLuVjKxyTsORwDoMXoosr3OJm9Tr77WEXYkjvTQh9fj64E+U833l6XBYBKFQD6GFtm8jeyj8z8tgCmN5TJ+9xGfDlz6v3DP846lT9l5JRZ/V/sp48/9thHfu1v8lB3iUFPTvl3hw2cvi866kWA5rn/JBtaff/bvyTJEVbOxZLAjtWJ1wDNa4rX69993Qgtcr/Hyex1wjN89FfcCHVnym4xuv2sqDmCg/ynG2+vywJApUoA/W/z74nZcTQmZ5i5HpPS07PfM8goc/o+baSy4nafrcrpu8Y3/v2XXQDlFp817zSQPKWciyXxC1mdeA1QXg9ySRDvzZt/YMrvcfY6EX+cPvLL/64EUV+mb1jtRmb1U1Xmtv904+11WQCoVBGgqqvGLpTBVJjoVvOzGoylnPgH9d14WX6xfDm9bH5djIhDDVDLwuzaB5RzsSR/C7MTrwFq1wOv5p9SPzne4xx18ud6Rucn/ldrL5Eqh9DdfpUc5j/deHtdFgAqVQSoBcESmHRXTN+tWxa/Z96rcuIQVF902TLx5VTo++mvvwHKv/xX/8z4Pr1MKediSf1CRif+uyYnTa6ohprnPc5VJ9Z01aM6VM2fqTmNpCbtw/ynI2+vywJApQoA1exytuyKrsyMvk9e0D5vf7etGVn5/fflVIhLDVgL7wKot5yLJXvq5R+pcC3FHPN3Uq/P8x7nrJO//bI5LyUR6s/UnEayyjzdfzry9rosAFSqAFANQSdAS/Pf8ntdBqj+Cls9MPld8uVUXGjzciVA3//21/XYX0g5F0v2XwQdVmb/IMQqiNez5z3OXyfv/8WXf0y1QnV72J2p+Uf0ZWv4eqr/dOTtdVkAqNT8AFXNG3s8zjEAJnPwAZTlVFxN9IYHoH/3l7/z33/cKkJQORdLhUFdFVbmGkTW9UwA6Ax18u3fySGav9KKTPU0EiuQMUM41X868va6LABUKnWAftcF0Pf//GPlInQboKoT7wfoG60AlGX5Rx8ThanK1DV7BYB2RACo1CIC9D8Wt/ddAoCqTnwCADXmfKoylWPfVUsgnP7TkbfXZQGgUnSAOudDZwKoK6eALryxTOUjP/Gv/9o1ieQt52LJqgzRiZ+rCz9LnRTiInRhql7jZKIXMBm+EeY/HXl7XRYAKkUHqHM+dCaAunIqfnkcAOWR+R/55f/nr1T24eVcLFmMFJ34uSaRZqkTla8p+b6rMpXTSMZvEeY/HXl7XRYAKkUDaGmXHUc2pZycAPXlVEh3hDHxMMdfc1iElHOxZDcy8058WBiT4z3OXCcvOxqE8uGVmeaRqWZUZ5j/dOTtdVkAqBQNoLwVZPQbv/fPfjLfrpEMUF9OhfaOI5C+AA4jBDugnIulQi+dd+J/R+KsMpDe8R5nrpPvOoYg35jyGvMP/OWZUZ1h/tORt9dlAaBSNIDy1ojROFCrCckA9eZkn4X+8mNTAMqXy5cA6s19sVQAqBr8nb6U0/UeZ60Te2kol/6rVZkpn0Z6o7ioYrr/dOPtdVkAqBQRoN/7mOnbfDTys4VsSjm5AVqVk9ra8g09AWsBVDWI/pYzpQxQX+6LpeI8kdzpb/pmIq73OHOdiL2RdMJf6JwqM2Ul/E+sdZlh/tONt9dlAaBSIQDl06li7R7fH+nRX/lrtduc/h7RAOrLydjK7NvO7ezyzdXYJm6Tb385B4qxLGZKORdLrkglDVBxWKZzOzvne5y1TkTLVxzL+Vf/d/5ajLa+L1O5YakxghrkP914e10WACoVClAGMj7FW4jBNPZzJALUk5O9me4/+F88s/C2rOjPqnIulkoAFXUzfUNl53ucuU5KgbcGFCszfaOUFOQ/3Xh7XRYAKhUCUNlz5N9l27cf/Wwpm1JOHoB6crLTP1sdB5rpH31d5RhQzsVS+ZjR7xktzazfbhHU3D/Q+R5nr5PiDvHmOUVVmebFtcYvQ/ynG2+vywJApUIAavccjYPM2BbipWxKOfkA6s7JTH/0V51r4c0Dln7NfPT0ci6WygDNf0V9yNGXHUjzv8c56sQ6xs88erU6U94Xt3+FAP+Zq6RQCwJApYIAKo4kVuD7+i+xlsXH/4c/dmVTyskLUGdOXH/3Rz8mtp10bybybb4t0Ed+8ncLF6aWc7HkAGjxSHZ1rLFOqniP89TJ3339l/g2Ih/5iX9dHJD0Z+oKgZruP3OWFGpcACgEQdCMAkAhCIJmFAAKQRA0owBQCIKgGQWAQhAEzSgAFIIgaEYBoBAEQTMKAIWg7uvy7v56r9fbuPF67JJ0TAAoBHVdF4c9pa3z2KXplABQCOq4Hq73DK2+FLs8XRIACkHdFufn5m3W8jw7zH5ei12gLgkAhaBOa3yr13vkWH66eLLX/1LM4nRMACgEdVqjjJ9Grz1rj6IJWp8AUAjqtAa93oH5+c7W7fyHC9af72/ljdN3n+ztjO9e7fVWbpzLz7nZUPCXm/c2b7dW8IUQAApBXVZGwkec00ZDOau0Lcx+ZM+YZSoB9ASTUC7FA+gPQ5BQqp6RaLEK9VUtX5c94ydrfF7eyQmaAbPXf/p8cnGrx8lZBGiWzZXMfPwCJqEsRQSoJ/3Bg+AsYNqIadvPLwG0yYcRTIsAbfRhBFMaQEc9SUJLmqtDPqvEAMp7+uNbvSvnZYDKjvxk4GnQLqkAUJjGfj4ASjOdFaBsOp6LAVMBkSWvcWBeyUPsc0KWAYrJe5cAUJjGfj4ASjOtA6A5NXMNGDozYKoWqQugIzbfdER58HIIAIVp7OcDoDTTWcdATYCyHrtWRsgiMEuTSHe45QoW09sCQGEa+/kAKM2UBlDdNxcazgbQyem+mIVHIJMhABSmsZ8PgNJMaQAtxYFKgNqT6dMBmunsC1cZQq3sllwAKExjPx8ApZkSAVpYiZQDVMy1a/kBak+7szimK9jQSQkAhWns5wOgNFMiQNnQpzGDfnGLfxqotJylDoCuycvZZ2PSCXFMpgBQmMZ+PgBKMyUCNB/vXL3NZn/u39sTYUwsMD5vSI54n7wIUNVEFQ1YFcZUarsutwBQmMZ+PgBKM6UCdPJwz5wx6q3y1e/DfDpIdslLY57Z9SvHk/Hd9Z5skfZvnvMGLMZADQGgMI39fACUZkoG6GR8R+OTY5BJrYXnDcoSQGXQ05UX8s96V2bXwqalFQAK09jPB0BppnSAZjg85WcirZix8BeHV/X2SuVZ93xvppvG5w2WwzYCQU0BoDCN/XwAlGY6C0ChhgSAwjT28wFQmikAmpAAUJjGfj4ASjMFQBMSAArT2M8HQGmmAGhCAkBhGvv5ACjNFABNSAAoTGM/HwClmQKgCQkAhWns5wOgNFMANCEBoDCN/XwAlGYKgCYkABSmsZ8PgNJMAdCEBIDCNPbzAVCaKQCakABQmMZ+PgBKMwVAE1ItAH3v2Z+xPn/wlWd2dz/9quODoVS+JjCN/XwAlGYKgCakWgD6yq4F0Pee3WX66T8tfTCVytcEprGfD4DSTAHQhFQDQD94ZdcG6Cu7T7w6eef53Se+U/xgKpWvCUxjPx8ApZkCoAlpfoD+h8/t2gB9+xne3Hzv2U/8fuGDpVS+JjCN/XwAlGYKgCakuQH65u7uZ/4/C6Bvik9v7v5C4YOlVL4mMI39fACUZgqAJqT5AfrJ/3PylgXQV3Z/k//PU60PllL5msA09vMBUJopAJqQaplEsuj4wfOit/72M098x/qQX/9hqQcQxFQCaOwCCRUBGrs8UgBoQgJAG1EvTLGLmYYAUJpoAA10RS7i9x5qGKA//afWB/um3AvmerWx+7peU8JvNZ9vowvPVH91u8q1mF340LqpcDKoQu23QKW4F8z3alv23PCihv9W81UAAMpEqENSbbsBSsiC8rA56qtSvd5/ESoAdAYBoMGm9X9zesH+3WpjtcU/Nrys8wP0vwxRXt2hluVy0bpMVFqHVy0AmpLqByhpFp7gjtF7+6Si/tMAEQFKqau5aqDFugJAZ3hfAGhCagCgMuRTxIH+gpWoRQUowdShdAFKqIBgTsxXAzU0Kwm/Vstd+FYBGuoFc43NVgsAbVYNAJSyEql23/fxI90ufEsVUANASWUNNu0yQIPrgGAKgCalBgD6wfO7n1TL360PlgRAQ/5IU9tqDrU6MwSAEkwBUEKurnJVCwBtVrUC9O1neDvzHXMDpncqd2OKDtBmnDzcNNRy4QAa/F7rAOg/CVE9s/CkSgh+GKW3D4CmpCYAOnnnKxkyPy2anNYHQ/QuPOE7WdJ8ExBNAjT4t0oAoMEFAEAB0OVQvDoDQBcPoM28LAA0dYCOb2XFPCA81alhr3flPMDu3SdreFhbig7Q2n2/wS78DwQIAI0wBgqAelULQB+uZ8VcIzzVKQC0VqURxkRycgJAQ00bqgCCKaWuCLAnFGChViKRHkZwmKQBOmC/UP9LhMe6BIDWKjpAg9sZDi0XQAmmpLoCQJeyBZoR7ZGf7fV2CI91CQCtVQKgPxQiAJTa2w//tSh1RSjAPw4RACoeljZAR1n/PZR+FQJAaxUAmgZAGypAqgANfodegIbkQHq3k8QByqaQdtgw6JxUA0BrVSoADfbc+ACNbpoqQAlMC36HAKhUxs7+l9g46JzTSABorQJAE0FdM7miBdpM2IajvipVA0Bz8I1K00hnh1mrtL/9ujY7GL+QwXbzmH++OLyalXjjxut2PidZan/zdjmjrSPx0QRo4ZLKdkVnG1cAaLDnNgTQEEu6KQDq+RVCq7BcrmUFKO/B51gzp5Eu9kSZ+k/nCQygerr+4lAVevtcGVz51i2RuHpcyki2TzVAS5cm4zulbOMqOkAJvr9IAA3/rQjfR0Ku0QEanGuXu/DBD6NUV9sAlU3PgdUD56GhQjntMj4+xT+uFS7L2zKDVUVE1Zw1LR95iaUogJYv5QFVQnMHptahRQJosGmoLaEATZkmANBmTMMrywPQhuowmGkAqJAEZwZSPTTJ1yax3vvpuoTbkBUwaxZeHOcM7G1mHe/xKUNmjjpu0Ns6Fs3THKvckmV0eXddpEmAOi4xpF5h/fmzvRoCU+tQJwFKcMemTOv9kjXahU8ToIQcmpnXAUBzqfYgY6Zq9DGa5h16xjT+E+OjvD5Ul41loBygOwWDod0WleA8cF8aWv38FJqgiwTQJr4mYYOwizcGSqjWVAEavHC1EwANNm0boEPVfTaAZsJUQo0xUbRQLbqpDyZhZbvWGlkd6rQD96VB+bGRFR2gBHckQCH4awKANmQaXNY6AFpzudAC1TJQqdqa9kR5HuVkkrYwYy8vDM1UYTLSN/FcWZrI3HWJZXGT8Ns3r+gAJfh+7aYTABQA9RYLAGUyAugZSwXSJDRNGU3CYXG+SRJWpwpKWmnsAQeVl9jIQa9/3Yhqiq1FAijBc0O/ewBoU6bBZW15DDS8WO0G0s9ermrNC1Bz7l1PI1mtQyEDeHbQvTGmqVNFcNSgWBk7yt51SSWu3k6g+84UHaAEdySYdhOgBKRENw3+tQBQwm/rKle15gQonwk3lSNw5BiA9AJUtB6ZwY6VOANAjTjQzeNJAgJAYwM0+KtDMiXUVTO5Bpt6AUqYsaq5XNUArbm+0gboqFiCvOc+K0DnboGKmKZcKaz3BECbAWj4l4xgSqiAZgrQRFkBUJpvtQvQEsVyjNXZhS8FI2mAeuKULu/t86KkEAgKgMYGaDMV0EwBmsh14QDaxMNSBWhhDyY1jdTMJNL0S1oskn7uHUprEAAauwtP+K2aMY0MewTS03yrVYAOCy1NGetpbZgkmooG8LxhTDozHcZUIrEOYypeMh9rxfXHEwAKgIaDqgmm1QHQ2k0njnItIUBLkHoo1lSaF4weugRoKZBeRdrvGDnLoHnL8sD6r3DJKs/8++vVoUUCKOFr0gxAg798CwVQginhDQTn6gVo8N4xtZcLABXScUtSA9EsZFd2VJJsYKout3cpZ19kd0cmskHWm9rSWInkuuRY3RlZACgAGte0y2FMwQ9LFKCD0lyRBKfaTOTiVk+valcArdhMhN10phO55ZayNMcHPJf6bCvQ8cm6Yx4rghYJoARTdOEXxhQApflWiwB1bNjBkji3zABRxUc96ePZzu7Dt4qJtuWOfEZpOzvRoLXCqhJogAKgwV9TALRlgBK68DWXCwDNZWwPojSQaXqz423FR2PW/EKj0thQee1dedeWMtUZia2ZXRsqy12bJycKqikEMQGgCwXQ4LJGpyIASpuxShKgcuDRkpxGynS2z4/bkAuCimFHZ+JID5XG40DH/EiPLWsV0em1ddPSnGsvXGKl4kGg/c1E1nJ2FKAEz23CyQFQALQpN2z/TCSoQgBoaKYJALSTpl0GaPDDANBFFQBKcPIGTKPzK3rEEwBK8y0ANCl1FKChqwATAGgwaRKgIsEUAAVAl0EAaLDnAqDowgOgkK1FAigBNQsE0BDLLpsuHEBr/ovXOEAJIn6FIQCU4FoAKAAKgEK2ogO0Znekd+GDXQsABUAXrwsPNSsAtAknB0ABUAB0KRQdoDW7IwC6aKYAKM23qAANbqP00IWfQR0FaNx+eWcB2kR3AQCl+RbGQJMSABrsuQsF0CZQ10yuACjNt8gADdkXQm8OAdHUUYCGfvcA0KZYG2wKgE5If/IB0KQEgBKcvAHTjvbLAVAAdDkEgBKcvAHTBAAa2RQApfkWAJqUANBgzwVAAVAAFLIFgAZ7LgAKgAKgkC0ANNhzAVAAFACFbAGgwZ4LgAKgAChkCwAN9lwAFABdUoDqs93nUfHQJJ/MM5FCrlXZNy4ANNhzAVAAdEkBys8XXvNdDRUAWqsA0CT4Fd0UAKX5VgyAsrOM5z9GGACtVVjKmQS/opsCoDTfigDQjFCP/Gyvt0N4rEt1ALQO+1oFgAZ7LgAKgC4nQEdZ/z2UfhUCQGtVowD9oQABoEmYAqA032ofoGwKaYcNg85JKQC0VgGgSfAruikASvOt9gGasbP/JTYOOuc0EgBaqwDQJPgV3RQApflW+wDNwTcqTSOdHWat0v7269rsYPxCBtvNY/754vBqr9fbuPG6nc9JltrfvF3OaOtIfDSBWMzEuDY+3c8uZTlZAC3kpbJY0eWoVQBosOcCoADoMgKU9+BzdJnTSBd7Yiah/3SewACqp+svDtVUw/a5MrjyrVsicfW4lJFsn2ogljPR1x7K27a/qQFaymsyvlMqR63qKEAxibQwpgAozbdaB6hseg6sHjgPDRXK6ZXx8Sn+ca1wWd6WGawqwKnmrGn5yEssRUHSkYm6NtLXPqShWsorj8ASmjuS1SEANNhzAVAAdBkBKsE56hk9Zb42ifXeT9clq4bsK5W18i6Oc871No9YP3tPkYsb9LaORctSE5FndHl3XaRJSLoysa6x20Qj9cCTF0PqFdafP9urIZLVVb/1ZxmoRgEa7I6xTaPzK7opAErzrbYBqtp8jJmqDcdomnfoGaL4T0OjjTdUl41loBygOwWDod0WlXA88GRiXruZXzvpGQ8o5jW0BgYaaIICoHFNo/MruikASvOttgE6VL1hg08mTCWjhrqFasFKfTAJK9u11sjqUKcdeDIxru0YWSm4FvMalMtZrwDQuKbR+RXdFACl+VbLADVQqdqa9kR5HuVkkrYwYy8vDM1UYTLSN/FcWZrI3JmJ65psbbryYs+8SagusgDQuKbR+RXdFACl+VbLADUC6BlLBaEkNE0ZLbxhcb5JElanChJaaewBB+5LMpOK25yJIz5fdd2IaqpZAGhc0+j8im4KgNJ8q2WAmnPvehrJauwJGfyyg+6NcUudKoKjzEnynhgjFfbOTJzXBnn+rrxU4urt+rvvTABoXNPo/IpuCoDSfKtdgPKJbVM5t0aO8UQvQI0W4o6VSAGo3Tq1rw0rAGrEgW4eT+oXABrXNDq/opsCoDTfahegoyKU8p77rACdvQU6K0BFTFOuBhZ8AqBxTaPzK7opAI8loLoAACAASURBVErzrXYBWoJSTqU6u/Cl2KI5uvCeOKXLe/sG/WsVABrXNDq/opsCoDTfahWghT2Y1DRSM5NIVZeok0glsUj6ubc0LQsAjWsanV/RTQFQmm+1CtBhoaUpYz2t/TtEy8/g18gXxqQzG6kwphKJA8KYLIDLwjjyMstpLQSoTQBoXNPo/IpuCoDSfKtNgJaY81AskTQvGD10CdBSDLyKtN8xcpZB85blgfVfKRNxzSqYSfVCXpbd/BvyOQSAxjWNzq/opgAozbfaBOioNPEyEK28kYbhQDcwVQ/au5SzL7K7IxPZIOtNbWlAcupSTnGNbyviy8uxurNeAaBxTaPzK7opAErzrTYBOijNFUlwqs1ELm719Kp2BdCKzUTYTWc6kVtuKUsTklWbifDns31JLu/o+XVPXn22Fej4ZN0x8TW/ANC4ptH5Fd0UAKX5VosAdey/wZI4hswAUcVHPYfj2c7uw7eKibbljnzG1O3sjOdf+bxrOzvRQB2ZEQTdCmN6kCnYHRswZZbRTWNWQBqmDx6UAJp7RiBAm/KMUrl4seK7Ybm+KjUPQIcO5sidO4y9i7cVH41J8AuNSmND5bV35V1bylRnJLZmNjZULmWir43V1swvDRwbKsttnicnCqpN7GaHFihaoJFN0QKl+VZ7LVA5jmhJTiNlOtvnp2fI9T3FKKIzcRqHSuNxoGN+pMeWtSjo9Nq6aWlOnRczsa+ts6M6zicDg/OFvNivwYNA+5vNrOUEQOOaRudXdFMAlOZb7Z+JBFUIAI1rGp1f0U0BUJpvAaBJCQCNaxqdX9FNAVCabwGgSQkAjWsanV/RTQFQmm8BoEkJAI1rGp1f0U29AA0UAFopALRZAaBxTaPzK7opAErzLTJACSJ+hSEANLZpdH5FN/UC9IdCBIBOEQDarADQuKbR+RXdFACl+RYRoFCzAkDjmkbnV3RTAJTmWwBoUgJA45pG51d0UwCU5ltUgKIL36gA0Lim0fkV3RQApfkWxkCTEgAa1zQ6v6KbAqA03yID9L8KFQA6gwDQuKbR+RXdFACl+RYAmpQA0Lim0fkV3RQApfkWAJqUANC4ptH5Fd0UgfQ03wJAkxIAGtc0Or+imwKgNN8CQJMSABrXNDq/opsCoDTfAkCTEgAa1zQ6v6KbAqA03wJAkxIAGtc0Or+imwKgNN8CQJMSABrXNDq/opt6ARqvXABoQfps90qNAg8ONg82mlWhz3I+rI4CCAGgcU2j8yu6KVqgNN+KAVB+XPCa76oSANqmANAk+BXdFACl+VYMgLKzjANOBQZA2xQAmgS/opuiC0/zrQgAzXjzyM/2ejvTnpUkQJsqgFBHARraeAFAo5sCoDTfigDQDFZrxVPfPXYAaGsCQJPgV3RTAJTmW+0DlE0h7bBh0GnMAUDbVKNfk+8PEACahCnGQGm+1T5AM3b2v8TGQadNIwGgbQoATYJf0U0BUJpvtQ/QvPc+sqaRhhynp3vZC9i8LRNzqJ3tZ43VlRuvGzmcHWZJ/a0j8dHkV+ESyyN72CnPY7uch0ryPuvi8GpWqA2VZjxsfLqfl9cCaLEEMgs7W58A0GDPBUDRhV9GgPIefA4iPY3EAMqjQ5lWj/NEBrU/vCMS+zel7cWe/GMnRlE1v0qXOEC/KVP7T5fykEm+Zx2qv6zbxYc9lHlsf1MDtFyC8Z1iFpX1O9WiKQGgSfAruikASvOt1gEqm54DcxqJAXSgQCOapszyajExDyIVynvdimnlSyyPFSP1oJzHQeCzRGnVw0b62oc0VMslGBhZTI997ShAwxRcAAAUAF1agEpwjnpGv3fIv0Bbx6LNl7NqpBNvWQDrsZ735d11kSaZ5rgk8mCJRh68rcvSTtcl5/zP2jxinfU9ib/Sw0Qj9cBTOIbUK6w/f7YXEvsKgAZ7LgAKgC4hQFULjlFMtcg4QHf0z/xHDjXRmx5I0g3t9uGBkaXjUjGP/PpIPYzZVT5LFkqtPjUeJu1PFEAdJRhaIw1Tm6AdBSi68AtjCoDSfKttgA5V39agDceRgovZRpWJsuNvDZ0KOAmmuS4V8+BMM9HtsVPPUoWSH1wPGxhwLZZgUH5WZf1Oud6cANAk+BXdFACl+VbLADXgpVp/ExumCmBmJ182/EZmuFGWyAzFNdclZx7mpHkeU+V9lh0owLLXD9PXZGvTVYKhOSc1XQBosOcCoADo8gHUCKBnLJW8sRpnLlgaPWdtyHI4UNdclyzQyTwkNE1NfZa8q+phrkQ+NtC/bkQ1VQoADfZcABQAXT6AmnPvRqtvaA4PikAnJ9TMOe2eGDgV11yXnHmMHEHznmcZY5ZG3/2geG3gL5xKXL09rfvO6zfAphlhEikJfkU3BUBpvtUuQPk0tSlBobYBWhqLnA5Qu7lrXxtWANSIA908nl6/Uy2aEgCaBL+imwKgNN9qF6Cj4ndG9KWHZlR9twAqYppyTV3w2VGAogu/MKYAKM232gVoCTGCMZQWaCkUyMk0oba78J44pct7++bfC78A0GDPBUAB0GUDaGEPJj2NNNskkn3NGSXkyqOdSaSSWCT91E1QAdBgzwVAAdBlA+iw0PYbyl6tdUHCzNN6LMHPFVlUzMvMw9r7Q7Qapz/LDmOyIFxROPNZ1tIBjwDQYM8FQAHQJQNoiSAP5YJHY82PtvLBzwpuP7D+K15y5mEWo8qulKERtV9aR+UpnGUXsIUfABrsuQAoALpkAB2VplHk6sqhMcOimqUuqPFRVBGYzuBkMM11yZ3HSON6UNHanbqUU1zj24r4CudaX1pVv1OuNycANAl+RTcFQGm+1SZAB6XZG4myfDMRtg3Hmdq2ww01Hgi1pTb4MJnmuOTOQ20mwjcO2al+lmczEZ4H23zk8o6eX/cUrs+2Ah2frDsmr0r1G/ge6hcAmgS/opsCoDTfahGgjt00WBKDytDadU7MxDihZm8xt2NdK1/y5GGGo1bAunI7OyOPK5/3F86O3EIYEwCauCkASvOtFgE6dBBE7MPB5q/P1AbFYibbDTVjz2KxG7JrQ2Vzo+TKPLarYJ03US1DfVFvAP3SwF+4yeREQXX6bnYAKAAa2RQApflWewBV45KmxDQSDwAa373KDsNQy3U8UJtMTq8xJm3cOC9fK1zy58FP79AP89uJIz3OHRf56R0r2aWBv3DsF+dBoP3NkLWcAGiw5wKgAOhSAbRKITu9LYUA0GDPBUABUABUCAAVAkCDPRcABUABUCEAVAgADfZcABQABUCFAFAhADTYcwFQABQAFQJAharrbHx6bWOdz3ZdBG/RHCoANAl+RTcFQGm+RQYoQeHZAqBCVXUmD6lnAB31eqvTdxelCABNgl/RTQFQmm8BoEmpos5ek/GkDKCDsLBSggDQJPgV3RQApfkWEaBQs/IDdKj+LmUAzYP4ayUoAJoEv6KbAqA03wJAk5IXoCNGzK1jufY1787X2WoHQJPgV3RTAJTmW1SANtOFh4R8dcZX3u/ozQPyFmmdTVAANAl+RTcFQGm+lcYYKCTkq7NRT2/dlwN0rHaSqkcAaBL8im4KgNJ8iwzQfxoqAHQG+epMbLpiAFQxtSYBoEnwK7opAErzLQA0KXnqjDU3eX/dAOjDkP1FwwWAJsGv6KYAKM23ANCk5KkzxU0DoMaPdQgATYJf0U0BUJpvAaBJCQAN9lwAFAAFQCFbFQB1deExBlqzaXR+RTcFQGm+BYAmpYox0OIkEotjmnbIJ0EAaBL8im4KgNJ8CwBNSr46k7TUAJWRoXUJAE2CX9FNAVCabwGgSclXZw/F0ckKoLxNikD6uk2j8yu6KQBK860YAFUd0lZkHXEUfCks09kz8MhbZ3zh0U0F0Pzwuhp78ABoGvyKbgqA0nwrBkB5c6rOL3+lugHQfPuQFXZiXf+5L+QbM9W6gRUAmgS/opsCoDTfigHQQe1bCVWpGwDVpygrrdYXwzQBQBPhV3RTAJTmWxEAmlHnkZ+tdQKkUrVDzsi0RYBOxnfXLX5u19n+BEAT4Vd0UwCU5lsRADrK+u8t7qDcFYDaCN2qdz96ADQRfkU3BUBpvtU+QFlfdIcNg7Y0jdQdgGYa338x09HrdT4zV6Nfk+DduwDQ2KYAKM232gdoxs7+l9g4aEvTSJ0C6HR98JVndnc//ar+/Pyu1E//6WTy3rP6Z0sAaBL8im4KgNJ8q32A5r33kTWNNOQ4PWWxOZu3TeOLw6tZ2saN1w3Lg/ELGYM38z7s2SGbl942m2R50pY8ttKA3PjePusEr8hr8hLLdDI+YY/aPMofm9mt3DiflqnMYGQsSzeel5d+5UZ4g3FugApAaj4WAPr2MzEAii78wpgCoDTfah2gvAefY0ZPIzGAqmlmfdykPIfSmDJhrNOz+Hk4JPv0tLpHJslBVg201/QQYn7NBKi6b0cbKsT7Mq0E6PhOqfTT6zfQzqtXdp94dfLO87tPfKdw4e1nPvH72X9v7f6M+0YANAl+RTcFQGm+1TpAZdNzYE4jMYAOFG4ktx6a087COLN8in9eKxgclO/Rax75xYGRWz5+YAD0557UOenz28RDvZlWAnRQfFxI/VZdPL0un/Fw3W50K739DG9avvcsp6WhrCH6C+z/V/L/ygJAk+BXdFMAlOZbrQNUgnPUM4YPc2SxmWXe6NTtQ96lHvO+/Zq2zBp0F8ciNJKB5HTdWiHOki7vys2KDMj1Vo/ORXZqb6MDmWn/5vlk/EL2w8o6L8mf7ckS+jOtAijfLYl1+c/2wqNeKwB6Ysy7DUVTuaQ3RQPzzSIn38zbpB88XySrFACaBL+imwKgNN9qG6C6g3vLaJeZQBjKH4c6TS/+NPcgGikDBit5j9l+lRsYsf+NJq8cPzABKm4b6JKwq2vVmVYBdGj19wOboH6A3jEbsgNfs/aV3d/k/xd76u89m19479kn/uRzu7s//2rpTgA0CX5FNwVAab7VNkCH5nZsql1mbc020OenqTQTZmpKyEDwUN+jGmZGWnHCfFAGqLjN3GVzoHdAmpKpC6CDUulC6td3YWgOKYzv5GMKpTaoamC+/Yw9CCpbpnIOSXB2wl6/0INMwe5INA0DaHABmjNtqgIWx/TBgxJAm/SM8GIVy8WL1dDD5inXlC/4nAA1oKdajRMbpnKUtDRPzxGlAWwhMY+NskDGrrM0V6jRUKPxwH68SUvBvYpMp7RA+zfDalXLB9B8FNYY92SjFuWBAR9A1ZjoW7u7n/nO5O+/sqt68gCoXdQQyy6bAqBJA9QIoGcsldSxmmiCQHazTRDSTJVJhqx72ANcsZpn9/Z7JYCavfsDO7eKTKsAOuIDq9ePJhT5AFo+Bd65H6gBUCtO6S05Ky9bouW5JHThk+hBRzdFF57mWy134c25d2MaaWgO6IlAJzvU3sG6UflMIGuiXXRyDSRe3ru2Yc6LhwG0ItOgWfjV22Hdd16/7mTXIfDDXnlgwNMC/eB53WXP9VYpzgkATYJf0U0BUJpvtQvQd58ssEggcjpARcuvANAiQCoBqoM52wGoEQe6Gbpy3QNQdSaSIdexxh6AFtqjzhQANAl+RTcFQGm+1S5AR0UWCSoMzfZVQwBVwZwb14/KY6ANAFSEPeUKXPBZAdAiLJ2ncrpn4cvR88VJJgA0EX5FNwVAab7VLkBLLBLcrLELX4rtEXfyXvDmc/fPjecFA9SX6RSATtiowb75l2Ka5gWojP+040DViKfqy5eRCoAmwa/opgAozbdaBWhhDyY9jdTIJJKdHWv8qknxwcyTSHamLoCWN5pikfSB259WjYEWGrHsFyqVy7kSyYiefyUHZ3lQFABNg1/RTQFQmm+1CtBhoc2kgjqtC4JGI18Yk4t1AokjR1PPwWMZTBUG0IpMXQAVc2NmRtaigUp5AOqaMRr0HJlmZPxkaS38e8+qAc+3n2FhTO98rrxWHgBNgl/RTQFQmm+1CdASR1TUuhHJbtHNCqSXlhIkZnYG0Kx7NMnMjviINIlUkakxvqoYO+ipIQNzbcB8AOUDuFYjthzYxPWOsRuT2D/EGvB8U2zGVFqKBIAmwa/opgAozbfaBOio1A8d9FTH3Axp0qs2XUs5rTioHZURbwIOdEed3WMsGjIauepQuzCAVmQqrQ3GnshfxbEANKB+fRf46HFxnyoXlN/5SsbHT3NgSoBaMUvv/Mbu7ic+U2x/AqCJ8Cu6KQBK8602AToozXpIBOYLFdlCmzO9b4hvMxGrJ85vulBhkvyeLXWPsWydQWyV7TV6lu+RRwCoP1NpzUty5VhtwCdN+mwr0PGJI+DIV7++CzIAbOOpTPm8VGieYQJAk+BXdFMAlOZbLQLUsaeGnEke8j2QpIxZol4xsTTdJLXmuEcxVaxN7xXyCwSoP1NlPTIKMiinzRvGlOmiGEJrtEfrEACaBL+imwKgNN9qEaBDB0fEgCFD1ZmMc9e7D1/oo3yNDZWNyRQdG69u0klik2WrlZhr66s5t0MB6s1UW5+sq4IMSmnhZzj7AWrG5ReqqRYBoEnwK7opAErzrfYAKocPLYlpJI6q8d2r7NQMq111Jo70UPcVY4rO9teLN51eWzfv0ZA7ZV3f/mZ2ocC/qQD1ZWpYi9If8T8KMu1e/sTwtZwVAJXZcVnHjdQiADQJfkU3BUBpvtX+mUgutXjKcdqaWmfj+5maqCoANAl+RTcFQGm+BYAmJUKd1SwANAl+RTcFQGm+BYAmJQA02HMBUAAUABUCQIUA0GDPBUABUABUCAAVAkCDPRcABUAXEaAEhWcLgApV1Nn47v56sYZrDKUHQJPgV3RTAJTmWwBoUvLX2UmJngBo/abR+RXdFACl+RYRoFCz8gJ05PwTBYDWbBqdX9FNAVCabwGgSckHULlodWXD0iYAWq9pdH5FNwVAab5FBWgzXXhIyFdnfLeVVdoJnzQBoEnwK7opAErzrTTGQCEhT53luz01OkoMgCbBr+imACjNt8gA/SehAkBnkKfOeA8+cEOnGQWAJsGv6KYAKM23ANCkVAHQWnf/LAsATYJf0U0BUJpvAaBJCQAN9lwAFAAFQCFbFWOgwXuKziYANAl+RTcFQGm+BYAmpapZeIyBWp4LgAKgAChky1dn6lTSxgSAJsGv6KYAKM23ANCk5K2zUa9wrHHdAkCT4Fd0UwCU5lsAaFLy1xlbC99kJD0AmgS/opsCoDTfigHQ8T2+r9DK47cJT5YaBc5Hm4cblRS8eYkrl8qc55SvzsbfePHuenktJ5Zy1mwanV/RTQFQmm+1D1BxdDpX/ybh2bmWE6Dvlg41xmYiAGgTpgAozbdaB2hhWzby4bwAKAAKgAKgywrQfFeM269nP96/w1i6Rng6U7sAJec8pzoK0OC9EwDQ2KYAKM23WgboyOq38z0yiHPLywnQ5gWAJsGv6KYAKM232gUoY4+5omaGJYoAaDNCFz4JfkU3BUBpvtUuQEsLakbkJigA2owA0CT4Fd0UAKX5VqsALa+nYZ14NY90drieNVC3VLDjkI+Qnu5lkN1UEU85QM9YINTKjdeNnERslLzdxNzF4dXs0oY2zwF6kqX2N61YqqKlkcv4dD8viAXQYpllFlbZwgWABnsuAAqALhtAHe3NS8WZiz05FiYZywDKh0n5xNOxyuORP7wjEvVw6mt6cj+/XWPOiJva1jlf+VYxZ5elzuWhLN72NzVAy2Ue3yk9jCIANNhzAVAAdNkAOqzYU+ihEd4k+ugMoAOVKO7MANq/WkycaLOemNhX6DMzlpzLcl7dK2XisFS5jPS1D2molstsFoQaYDAhAfT+/a9dW5RZeAB0YUwBUJpvtQlQ1pr0feV5nM521hy9ZCtucnrxkKfe1rFoGuaJI514y0pcPTpn/ew9AUSJPp7x5pG4JKDmzNllaeXCiicaqQeeMjOkXmH9+bO9mTagqwBoPjRgCwCt2TQ6v6KbAqA032oboL65m6HdEDwQaarLP5Q/coCKnvtAImSgM2ZU25lo9A11Hnw4wJ+zy9LMRTz0pGfkUiyznp1id9KboH6A3inREwCt3zQ6v6KbAqA032oToBVMkdjjkhQamt1gyciRkTgqtDaloQFQ65nqgytnp6WRy45hr+BaLPNA5zHTTL8XoEMXP3tXANB6TaPzK7opAErzrUQAOjJbU5kd56I1ZCphOeppWDoDioYW+kZWT3ooHuPK2WnpykW2Nl1lHs60vl/LB1CxEmmD9+JXNvjYa3+2iX6PANAk+BXdFACl+VbbXXgPQK3mGrM7KCZKWJrYKgP07N5+zwKo3Q7M2CfRXMrZaem65iyeSOQDDP3rM2875wMob4B+9Fyd7cHHf2eYpPILAE2CX9FNAVCabyUyBjoodk5Z33hoMoLdzBJ9AL28d23DnP4W1wYWZ4whzVLOTktnLoM8E2eZZeLqbXL3nckH0EHPGHzYmZjjuTUJAE2CX9FNAVCab7UaxjTwTnvMC1AdkFkJUKPxuFPM2WnpzGVYAVAjDnTzeEKWB6Aal2oI+OH6PKupygJAk+BXdFMAlOZbseNAx/d4d9cH0BLm3ABVAZkb14+GYQCd0gKdFaAipikXvYXoAajeQ0BzczDTA7wCQJPgV3RTAJTmW60ClH37d8pJN4uEEgpugfIG2uZz98+Nu1rpwntGIS/5SOwsgaAVAM1/a/2TGY5Qg1IBaJiCywqAAqD0h6UKUMcgqGiUOmN+gieRGEvU3PcgyiRSSSySnn4M3FSA6sUINffhAdAk+BXdFACl+Vbk3ZhkZNPI1VwbmkOmEpwugJook1P908KYrJynhTFJ8FpPdZTZnNWqiDnwq2IMVK8VzZ86w1aAVUoFoATTYCcHQAHQbgC0uB/oeGCEwltR7IVFRBpHLoCanWnZs/UF0qsQfTNnZyC9TDwowFD+FXCU2bLzd/H9qgCoqDf1NwgADXZyABQA7QZACzvSG9GMA90LVx39oTERo7jhaYHKtIfrNkD9Szn7Iuc7ZmLVUk5xjW8rcuAps2tFKkUegBrPH8mfWP4dBCi68HFNAVCab0U5EynfLJNvpWkuHe9tqZ089Ip1vlvHmd4HxAVQxpLV2zxLfosJ0IrNRAo5V20mwonKNh+5vNNTWHeUmTex2W83PpmJbz6AjhTx1dDnqIcx0EAnB0AB0K4AtLioWyHA2ktuR5quFLf5dIcxDcqZTtvO7sO3iomV29kZp7pd+bxrOzujgahUWxhTDnBOcNk45gXq4Cw8wTTYyQFQALQzANU7E2fqP63TdSy8TGVzQ2dqH2NBOSdAx5qGW1+1pn8mYqDAzoSFMb0rc95Szbiypc5F7+z80sCxobL6TfSxzTPsZuffTGSkqM7/Aj3+1PpshPYKAE2CX9FNAVCab7UP0Aw7X+S7Yaw8fttOP73GkjduSKDxyfXxXXbwxpZa1eMEaHYvi7zsb2b32hGcXGfioA5FSh4HOj6xc3ZZ2rnwY0TOJwODXMUy87NFeFFmWsvprbOcm6zFOS43netQKgANU3BZAVAAlP6wxAEaqrmOfltMVdTZiZyUUk3n1fqmkADQRPgV3RQApfkWAJqUKuvs9LdyYo7vsFZvf5Yzl/xKBaAE02AnB0ABUAB0KRRaZ5f369wLlAkATYJf0U0BUJpvAaBJae46m1kAaBL8im4KgNJ8CwBNSgAoABrXFACl+RYZoAQRv8IlAaAtCgBNgl/RTQFQmm8BoEkJAAVA45oCoDTfIgIUalZFgBoLoFzq4Fp4ADSuKQBK8y0ANCkBoABoXFMAlOZbVIC22YVfQgGgAGhcUwCU5lspj4EuoUoA3d+Q4kfC9x+//uKL957iPz/+1FPX6xsiBkCT4Fd0UwCU5ltkgP5QqADQGeSvM7Zvyao6b/7i0NxXtQ4BoEnwK7opAErzLQA0KXnrjO2ct2a2N+/0unkuPAAa1xQApfkWAJqUfHWmDiNRMk75qEUAaBL8im4KgNJ8CwBNSr46K53Gh2ONAVAAFACFbHnqzNXcLDdK5xIAmgS/opsCoDTfAkCTkqfOXCdw4lTOYCcHQAFQAHQpRABoR0/lBEDjmgKgNN8CQJNSRRfeNQbawS48ABrXFACl+VYMgI7v7TvPRIK8k0iD0owRZ2oHJ5EA0LimACjNt9oH6MWhnguoNxa8A/IBlDU3xcHJQgyptYYxPcgU7I5E0zAqPiCahj6fYNpUBSyO6YMHJYA26RnhxSqWixeroYfNU65qzQ/Qk3VrOrXeg30WXj6A5kdx6srKD2CusQGKFmgaDcDopmiB0nyr7RYoP5139TY70ec+PxutTggsvrzDHg/zvzubN17M9MX8XM4ap5AA0ET4Fd0UAKX5VssAHVn9dt6u2nGYLa38E28ju+Xeq/lUYwA0DX5FNwVAab7VLkBZOI45cFdzLOPiqyJy4WLP5mfNgx8AaBL8im4KgNJ8q12AllYkjtAEtVQZ+nWmp9/62zjWGABtwhQApflWqwB1b4nBm1IZWg/GL6z3+pvHjKqqWcpuOZiIsVMtlsuI/3fKQqJWLJ6cHWZJ/S219xvLezI+YZtobvLEC2awckOXRAZW6XsiqRKgmS6/wcZA/6Buek4A0ET4Fd0UAKX5VqsAdbQ3LwULGOQGvTw0hwDQb8p+bf9pmaPu6kpWs7xVavb818RwohpMeG29V7wnkqYBtDkBoEnwK7opAErzrVYBOvSHLmaXnuqJ0JxwgK4YMytiaOChkSRyye79OX00xsHQymWSh1QqxY0KAEAB0LimXoAGCgCt1FwAZf1135QRp1rWmb84dgPUzoVjeMRfGOu886jInIb8ECGWdnl3XabxvPs3zyfjF3oculvZQ/5sT0KX5bN6lFmOT/fqDU6nq6MADf3uAaDRTQFQmm+1DVBfF5lBTjb+qgE6MMHXu6kSc/IZjVzWFj2QeYtE3tjcUTmviTRZKpYWdU4LAAVA45p6ARryYr4fAJ2iuQCqkOWQOT9fCdChlAXN9QAAIABJREFUIuDIZi43swg4FGTUt3CoSloO8tutJwzSBej47n4pFBS7MQU5OQCKFugSAFTRoAqgBjUlNE0z81aWyBueRqvUBOzQ0R4eRh4E9QP0pERPADTUyQFQALQLAGVd+AqAKppVANRsQY6MAUtjrklDkT2vkGhmVwLo2b39XqoAHTm9FQANcnIAFF34rgC0Ygw0AKDWyiWX2aCImJ1JCEAv713bkHckCdB3RRTByoalTQA0xMkBUAC0CwBleKuYhZ8OUPtooNoAai+STBKg+R4sTYb5A6BJ8Cu6KQBK863YcaDje0fy0nSAqgl4r9ksAFWRoxvXjxIdAx3rOK2mBIAmwa/opgAozbdaBShD1U45iYUihQDUmE33mQ1cAKwGKIfT5nP3z0VaigDlPfgD97WaBIAmwa/opphEovlWqwB1DILKRqkPoCqYs3wQugugrpn1KQBl2aoN9pwAblEVAG1416pUABr6NQVAAdAmPDZpgJZ3Y1KRTT6A6lglcwK+ZGaEMZWXElUD1HxyVZxAKwJAAdC4pujC03wr7n6gY3MFkcJYhkplpIY9y6f4ugBqhZo6mqUOgJqNzmIjt3VVjIE2vMY0FYASTIOdHAAFQLsB0MKO9Bf6ZEkToAYFT3o9TcYCQpxDpQPdIVcDBlNboDKbh9HPGPEAtNx0r10AaBL8im4KgNJ8K8qZSCs32CZ2fNtOibZiR7p35Vgd4HngPRi9DFA+3cK29eQ7g6i18BUAZdRcvT1R+xUnCdDyTqp1CwBNgl/RTQFQmm+1DVDXvnQiWQNipK+vDTjxRr2CPLve2dvZiTn7KWFMhdCnqDuC+gDa/Nb9AGgS/IpuCoDSfKt1gE4eGmHrehtke/pcLfzePqcC1AiLl7lPAWh+ZDDX1lcjn9HkBSivkiYj6QHQJPgV3RQApflW+wDNEPfFDX6AxuO3dVoh/mh896o4lYMM0Mnk9BrLfkMd2TF1KecpWwPf38xuKO+e1658dTb+xot3+R+VFSzlBEAB0MlSAxTyqmoM1CFsJhLk5AAoAAqALoUAUAA0rikASvMtADQpAaAAaFxTrESi+RYAmpTi1RkAmgS/opsCoDTfAkCTEgAKgMY1RRee5lsAaFICQAHQuKYAKM23yAAliPgVhgBQADS2KQBK8y0ANCkBoABoXFMAlOZbRIBCzQoABUDjmgKgNN+iAhQt0EblqzOEMQGg7ZgCoDTfQhc+KQGgAGhcUwCU5ltkgP7jUAGgMwgABUDjmgKgNN8CQJOSF6D75h4ifFuR1edefPEPzmt7MgCaBL+imwKgNN8CQJNSaJ2d7dV9xgcAmgS/optiJRLNtwDQpBReZ4OaCQqAJsGv6KYAKM23ANCkFF5njiOi5xIAmgS/opuiC0/zLQA0KRHqbNSr9ZAPADQJfkU3BUBpvgWAJiVCnbHDn2psgqYC0NCOIgAKgDbhsQDoQotQZyyyqYNhTABoXFMAlOZbrQLUFc3YzAlEo0C4VJ6BNGz/iE4AlGAa7OQAKAAKgJK0BABlY6AAaJCTA6AAKABKUvcByg9j7uAYKAAa1xQApflW6wBt59DgzgP0gh9mj1n4ICcHQAFQAJSkrgHUXsq5sZG33mvswQOgafAruikASvMtAHQxAOrcTKTGBigAmga/opsCoDTfAkAXF6B18hMATYNf0U0BUJpvpQFQk3eW0dnheq/X337dsM2Tto7k5wxza5PJ6V5GlM3bdoZn+5npyg3j7vE9ltRbkbebD7s4vJpd2tDmOUBPstR+MefS3fnN1rNmUThA535UQQBoEvyKbgqA0nwraYBe7Ala9J+WlipJNQ4ZQPmUNNPqsc7wD+/Iu2/Ku19b79m364ddHKpL2zrnK99y5Vwq6vhO6ebZFDyJVLsA0CT4Fd0UAKX5VsoAfahxJ28zk8QdDKADlSh2KMoy7F8tJk60WY83W42HmRlLNmc5r+4VM3EWdVDMd1YBoABoXFMAlOZbCQOUNytZ7/10XV7mPVmWdHlXrQQfcmxtHYtGZJ440om3rMTVo+zH8ancTlM+jGe8eSQuCQj6ci4VlS9LZ4MCc2/TCYACoHFNAVCabyUMUMa7fJ6EAYr/NNR8YmkHIk3Npwzljxygouc+kJkPdL+fPWTHeNhQ58G5XZlzqah6tomlzNMEBUAB0LimACjNt2KvRMp56qISA5lkkSCUxJ6ZxtGmmCUZOTISR4XWpjQ0AGpxT33w5Vwq6qBU0lk1DaDjsxcz3Z9rnNUtADQJfkU3BUBpvpUuQE3eZc1NxkDTjF3nXByavWYJy5EeNnW3e/O5e3ltZPW8h+IxvpxdLVA9UTWXKgF6cagGavs36mYoAJoEv6KbAqA030oXoAKapqzmHWuhHhQT5c2eaX2hs3v7PQugdrtRPjk4Zz5g0L9+NJlbFQA1wgS45pvuLwkATYJf0U0BUJpvpTsGOirHwg+K+GW98KE56siouuPJkOny3rUNc7pcXBtYI5dGwzIwZ1mw1dtzUs0PUCtMIH9YjQs5AdBE+BXdFACl+VbSAC2OJs4LUB1EWglQo227E5azEQe6eRxUkR55ASr4uXL9uRdf/NoX8g+1LpMCQJPgV3RTAJTmW50AaCDmVCNu4/rRMAygwW1bHldljkrMJh9A85UCWwrO+Z+CuUJOCwJAk+BXdFMAlOZbSQPU0YUvMyMYcxxCm8/lU9hVAKV34bku+cjqfIGgPoCO5J8LVZzBnE8qCgBNgl/RTQFQmm+lB1AZ4DltEsmZOG2qR82VD+qYRFKxqEoskn6eTT58AB2Ueuz8z0GNTVAANAl+RTcFQGm+lR5AR71yGJPg3cjV6BqaLVWZkQugJg9lkOm0MCYr51IYkyiqWVIzenUGeQCqg/u1RvWOggKgSfAruikASvOtNABqtjYHvXIgvS/evbCISN/jAqjZTx/Zk0iljFWIvpkzT3QU1YKmc5ghWB6Aug6Qe7heTptDAGgS/IpuCoDSfCsNgBoAO1HzMCPNL7kac6B74YpoQ2PiZmjc62qByjQ+nbQ2cVLYXsrZFznfkYmuorpWmM4mAkBxKmewkwOgAGi3AZqfkHasosXtzUT4fiA7MoPeltrzQ69Y53Zneh8QF0AZ21bZvp5nh8UwpqrNRAo5u4rK7u6z/TnHJ3M2Cyu68MXBC9VQrkcAaBL8im4KgNJ8K/ZKJIGbkRGeOZCUNc1Fo88KJ8+bjBnmVorbfE7fdK6nV9dXbWf34VvFRGdRjbRmwpjM0YSJLgcOlQtycgAUAO04QCcnEmDb5wqgRuC7Wreok+Qmy2xu6GyvYOcE6FjTcOuruYERZK8vGhsqr70rc95SjT1XUU/0KvW5Qot8AC3319kvg0PlwpwcAAVAuw7QyfjuVXFSx8BoxPFDOfpb5uqe02sMVhtqNw0+uS7uVnZOgGb3skjN/mZ2rzF7JB92Jo70UKTkcaDjk6vFEriKOr6XZz3nWk4fQPMtR428WRxonWGgAGga/IpuCoDSfKtVgDaiCEe/NSd/nT3cM7d8Yg3x1bkWjRYFgCbBr+imACjNtwDQpFSsM+M8+HyQYOOpTHw/lH72w3VMIoU4OQAKgAKgPnUboM7z4EvjH4Y++Mozu7ufftVIee/ZXa6f/lP39VwAaBL8im4KgNJ8CwBNSnMDVNAyh2Wut58xAOq4ngsATYJf0U0BUJpvAaBJaW6AvrL7xKuTd57ffeI7Kumt3Z+pvJ4LAE2CX9FNAVCabwGgSWneOnv7GdHO/MTvq7RXdn+h8nouADQJfkU3BUBpvkUGKEGEfOcQAGroTdHafFND84PnDVg6rgsBoEnwK7opAErzrcUHaKc0b529svub/H+j2/7es0/8yed2d3/+Vc91IQA0CX5FNwVAab5FBCjUrOYEqGptvv2MGuSUc0gMnY7rPyz1IFOwOxJNw757D4imoc8nmDZVAYtj+uBBCaAPgl/M9zdSrrxYxXLxYjX0sHnKNUVogTaqBgD61u7uZ74z+fuv7GaXANA6f6tOmgKgjQIUXfhmRaiz+/e/dq04C28AUgUqyWFPNpfkui6ELnwSPejopujC03yLPAYaVF4je4iiijq74Ev1bVUAtBSn9NbuE9+puA6AJsGv6KYAKM23ANCk5K+zO642PgWgrNEJgNb5W3XSFACl+RYAmpS8dTZ0DpJcKQbS+2fZc2ZiFr7G36qTpgAozbcA0KTkqzOxImmD9+JX8o1F+B74Bcn4Th3n+cHzJjPL16UA0CT4Fd0UAKX5FgCalHx1xhugHz1XZ3tceA41dqw0eiVvbOYgxUqkGn+rTpoCoDTfAkCTkq/OBpKX8mwP10HHE47JTxbWur/9DAtjeudzPMlxXQgATYJf0U0BUJpvAaBJyVNnGpcjSdLiFvVC7xi7Lb39DG9nvik2Y3q1eN0SAJoEv6KbAqA03wJAk5Knzvixn/wAD83NgbMJOnnnKxkfP83blwKgk3d+Y3f3E5/5Tum6JQA0CX5FNwVAab4FgCalCoDmIUv6p5F7FHRWAaBJ8Cu6KQBK860oAD37Ap9HXtn8bd0H9Z4ZzzTbjkuVWaapqQDVh3F6+vCzCgBNgl/RTQFQmm+1D9DxXfMIdnXcJQDKVDEGKoLm1Wmc5ZOO5xIAmgS/opsCoDTfah2gD/d6luRJkwAoUwVAxSHGQzn0CYAGeyEACoB2BaAPefNz6w8YDi/vcZjmjANAmXzDHjJ6KR/65D+xmgRAg7wQAAVAOwJQzs9tzcKTdXN2BAD1AZRhs89/FzX0yZIwBhrkhQAoANoNgPJ4xh0zhfGAJwCgTD6A8orjA8YyJJQv7sQsfJAXAqAAaDcA6gi9GYomKADK5AMor7m8EviqzsefWtejH7UIAE2CX9FNAVCab7UKUGMyWSlrgvLpEZN2+daXG3qzjBygJ1lqf/O2eXfJkj3ldD9Ly+wsgJ4dZsjpbx0ZWR6MX8gevsnjAMb39nlklTaIIi9Ac26yPz55Y1TxtC4BoEnwK7opAErzrVYBqvrrhsbfyCmgaXdxqAghR0sZQL8lwbEqI59clsY0//Y3NUAv9orQYQAd8DAAhu/XdGhV1CM+/QDlw8X59Lv8XVbrm0ICQBPhV3RTAJTmW60CdKiCccpSAH1ohokKmmU3rurwJ5mHy3Iy0okfUgA1LUUTOMvyqZ5s1Q2MjOocWCSrAqCTyelv5UUf32G/Tn+7VtIDoEnwK7opAErzrVYBOqgIvJEA5ZMjm0esJ76naJZvJ8zmUHijM4el05Inbr+uWqcHVuLlXbV8ZyjarRfH+QDj6tG5yMnL+BZUCVBDl/fLe4HOJwA0CX5FNwVAab7VJkDZ6J23gywBqiMejT2IOO1EqjLwWorQ/JOekWi2WpWhbGsOdLlYMYqjDC0qFKD1CwBNgl/RTQFQmm+1CVAGJ2//WADUslEfTNpJ3DktLf7J/YqsRDmhr1b0FGbrBwBo0KsFQLtpCoDSfCseQI1xR4m+A96bNrrQMsbJGjwVJk5LK1G2NkfmyEH2HG4x9AwnDKMOggKgAGhc0zoAGigANCR7QyEAtSM+ZYyTlVplaSWyjv1B8W5notTZvf24s0gAKAAa1xQApflWvDFQN0AHFr+MgdE1K5sdj6WdOMjvHhTf3M6kBNDLe9c2zOJEEgAKgMY1rQOgBNPwYgGgnln4QQVAjebijpVYAqiwtBOHoQC9sLaIAkADXi0A2k1TAJTmW/HjQAMBOqUFOhdAVZjoxvUjjIGGvVoAtJumXoAS+uUAqFfzAdS1EqkSoLV14ctMNADKg6A2n7t/Pik+qnUBoABoXNM6AEowDS8WAOpeC18AaOOTSFJG4qinYkc9tG1NACgAGtcUAKX5VvTdmAoA9YYxafBWhTFJ4nLJ9uvINXIwtJbFK8Ay6AKgAa8WAO2mKbrwNN+KvR/o5OJJC6Cl8HgV9b5j5OEMpGeJFv9krHzJstAsNRudLsS3KAAUAI1rCoDSfKvlHen5qnR1kNxkcnmn17MA6l+g2ReLhe6YiU5Lkci3FeGJbBbppraUTDZaoLJ9y6eTANCAVwuAdtPUC9AfDBEAOkXzAlRMeG/eZnthXPKNOwXIAjYTYduBnBX2DSlacpQyQudoNjYT2VKWkrQSoKxMq2yb0bN8AxIANODVAqDdNEULlOZbbQN0cnGrUOPiWM5p29l9uLyPsHM7Ow5LkfR513Z2ooFqDnwW4pwi7ggKgAKgcU0BUJpvtQ7QyeTUjFrv3zDAJzZU1qg0NlReU/sIbym+OSyNHdtXXxo4NlTuP62ytGaOZOZfrfWsS6oAUAA0rilm4Wm+FQGgbFvOfXYSR3/zU3o01NwS6Uwc1KGgyIMzx/xID2MA1WWZJ2YNzpUsTQM0w/a1dcvSjm3igwn9zexq3IOUqgE6Pr22sc75fnG97qNHANAk+BXd1AvQHw0RADpF9QAU8qmqzuQBJmLXqdXjClu6ANAk+BXdtA6AhrxDdOEDs4coqqgzdWwTAygbta1353wANAl+RTcFQGm+BYAmJX+dDVXPJwNoPmhbK0EB0CT4Fd0UAKX5FgCalLx1NmLE3DpmI7R8DJR353GscZgXAqAAKAC6FPLVGQ/O2sn/z4MEhjU3QQHQJPgV3RQApfkWGaAEEb/CkB+go55ev5oD1LEqdi4BoEnwK7opAErzLQA0KfnqzDwfT4SpSqbWJAA0CX5FNwVAab5FBCjUrDwAZc1N3l83AMpWV9UY8g+AJsGv6KYAKM23ANCk5AGo4qYBUOPHOgSAJsGv6KYIpKf5FhWg6MI3KgAUAI1rCoDSfAtjoEmpAqCuLjzGQIOcHABFFz4ZgAZVjvJyiKaKMdDiJBKLY6px4z0ANAl+RTcFQGm+BYAmJV+dSVpqgMrI0LoEgCbBr+imACjNtwDQpOSrs4did30FUN4mRSB9mJMDoAAoALoU8tYZX3h0UwE039+0zq3zAdAk+BXdFACl+RYAmpS8dZZvH7LCNjXtP/eFfGOmWnfOB0CT4Fd0UwCU5lsAaFLy19m4eBRKb7XWjfMB0CT4Fd0UAKX5FgCalCrqbHx33eKnPsSkFgGgSfAruikASvMtADQpVdaZidCtevejB0AT4Vd0UwCU5lutAjQ/HHOnnOadTq46och1Le6JRvNr2h+d8f0XMx29Xv+TAdAk+BXdFACl+VYEgNpzH3yndQBUKF6rHQBNgl/RTQFQmm9FAKhFyymHUwCgbQkATYJf0U2xFp7mW20DtH/V7sNnSSvrswG0DvvU5APou//djQa67aYA0CT4Fd0UAKX5VusA/aLdhx/1ep8CQJW8AM1+r9Xb9c672wJAk+BXdFN04Wm+1TpA/+WTFi4HvUf+LQCqVAXQTJu3G3tytwFab5MoDdQBoE14bPIA/dLA7MNnYFh7CIAqTQFor4H4JSEANAl+RTcFQGm+1T5AR2YfPvtwYAH07JAtVtw6Eh9NIF4cXs2cfEMNBhrXxqf7efvMAmjxBm559yrL/5iVhS0qZ/Na8gY2oXXAy5QV8HQ/u7Kybd5bKFsT8k8i8d+Qq9/IcGi3ARqaa3R+RTf1ApTwRyiktgHQIC+3xVFp8TLrwb9kJOR7ZDAJxmog5iehc20Xrz2Ut21/UwO0fEOmExmKviMAyqAp9+QQSQyg35Q59p+Wt5bK1oSqZuHHp6oEKzdrLwIAmgS/opsCoDTfah+gDFmyD8968BMN0IfGYkW96+VB8ZIgmLo20tc+pKFavkEEneZ6PKclS5IHY4xylmb/rRg3H0w8ZWtCVQCdiPZzrrqHQwHQJPgV3dQL0F8PEQA6RTUAlCFLAo314DVA+UAf6zRf3pXHVUhI8kubR7IZtjYpXmO3iTbnwcRzA4dg/8b5ZJw3RDkHjfbvIL93xHPhGd6yaF0oWxOaAtBMl3cUyWsdDgVAk+BXdFMAlOZbEQBqIcv8PNQR9XJoUkKSNR1Fs9U63kJeu5lfO1EAdd2gDgcWaWpn4twwy4+ncIDelAX0l60JTQfoxBycwKFyQU4OgAKgHQKohaysLScByoCo5udFM1VAkv2ndg+WH4xr6raBAdfSDSb59KnqqkEsevAcoPLekZFfsWxNKAigEzafBYACoE2YAqA034oAUAtZO7oTPTKB8G4eLiogObJWe4phS9c1yUjnDRb4BvJpqgE8kEOqeuBTtXJdZWtCoQCdXHxxHQANdXIAFADtEkDFbLfowSuCWXwTIUUCYHabT9zguiZuc98wMA/BUEiUDWKFRRO+xjBB+SENKAygF4fr6MIjDhQAXVKASgDlPXgF0EHRn3cUwCz0mX330jXRjHTdYE7/GxSXcMwSVLtYgUkC1FW2JhQA0Mu7jcwiAaBJ8Cu6KQBK860YAGXIEvFCO5MZAGq3Tu1rQxdA8xvsVUoaoOKnoaRiygBtLo6p2wANzTU6v6KbAqA034oCUIGsgfoUFaD51exf0XFPFqCNRtIDoEnwK7opAErzrSgAVUDjfWYDoKVzetvowufT76oH7wNonWcIe1UBUGMt5zZ2pAdA2wYoYRgkpLYB0CAvt2VGfK7JHrx7EkmohUkkEQCqevBOgDYYuWTJB9DxHeWhDa3FB0CT4Fd0UwCU5ltxAMqbfwP9QYYxlaKDAsKYrLX1RthRaBjTJB9LGKgbXAB1la0J+QAqd2NabWw/OwA0CX5FN0UXnuZbcQDKePBzogdvBdJbwe8H1n/WJSPI3twOhC9AcgbSi3l2K8BTYTLD4488aa4vLQHUVbYmVAnQlRsNNoIB0CT4Fd0UAKX5VhyAsvbfiuwzm5Hscgklo6IByalLOWXve703ZSnnI8c6zcTkyroaH3UB1FW2JuQHaL+pjUCFANAk+BXdFACl+VYkgI74ZMiX7FTezNpSG4CYkKzaTITDkOHlMh8onLKZCJu/zqezdRj6oGd00Z0AdZStCXnHQO839EClH36QKdgdiaZh/vKAaBr6fEKuTVXA4pg+eFACaO4ZgQANrW1R3WF64CgXL1b9lUD1rXJ9VaougHIeiWacezu7vEEYtJ2d3q69d+XzodvZWQAdGTZugDrK1oQqZuEbFlqgSTQAo5u23AIN08RRruVugfI231opVW9aLDYyNjZUvqWqs7Sh8lheW31poFqH5RsmxobK/X+xbq9u11B0A7RctiYEgBJMg50cAAVAuwZQ1uY7KKVmnetrDHEbcrrEnK85Eyd0SBra19bzSZaB0b0u3sCUL+XZvH1uYtKIop94AVoqWxMCQAmmwU4OgAKgnQBoShqZM0E6ij6yinX27v7GxuZL+f8ObWIzkRAnB0CTBWjw6wZA48uKAx2aUfXDBoc1SSoB9Ekd8+oQdmMKcnIAFAAFQOeXMWxgbytv9eCjCgAlmAY7eXhPMTq/opsCoDTfWiaAqnONJvfv6IONzifjtla6TxcASjANdnIAFAAFQGuQFcUkQupzNCXSAMUkErrwkU0BUJpvkQFKEPEr3IJe08GccmFPHgKVxggoAAqAxjYFQGm+tVwAnYxP9xlDV66rdZFsn6OVxrbooAoAJZgGOzkACoAmAlCoWXkBenn/fnET0PHZ12qM6AdAk+BXdFMAlOZbAGhS8gHU2jwqV817mgCgSfAruikASvMtKkAXvAufuggAdSTNIwA0CX5FN8VKJJpvLdkYaOoiAJRFsgKgQU4OgAKgyQD0Pw0VADqDSnX2jRe5vsY24nvuRUNfY3ubAKBBTg6AAqAA6FKoVGejSseqMfwfAE2CX9FNAVCabwGgSalcZ4Mqx6pxX2cANAl+RTdtGaA/GiAAFApVuc6sjZwLqjP8HwBNgl/RTQFQmm8BoEnJUWcnT2ViO5H2H39K67dffPEPzut8MgCaBL+imwKgNN8CQJMSYRa+ZgGgSfAruikASvMtADQpAaAE02AnB0CTBSgmkQDQOuWrs/E36u6yF9VtgIZ+T6PzK7opAErzrVYBmk+I7JTTat1NbhTaWBtOWwtpHqosJry/cTyZFI5KqlPx/ugAoEnwK7opuvA034oAUBtafI/ORQHoxaE+wR4ADTeNDtDwb3QzFbA4pgAozbciANSiZb4d54IAVJ0fmgJA79//2jWsRApycgAUAO0KQPtX7T58lrSynjpApR7WXNKyKgB6wQ9ptgWABjk5AAqAdgagX7SpldHuUwCokh+gd1yDdgBokJMDoABoZwD6L5+0IDToPfJvAVAlL0CHLn72rgCgIU4OgAKgnQHolwZmHz5D1FrdWOoiQMWpnBu8F7+ykY8l3yjuUT+PANAk+BXdFACl+Vb7AB2Z2Mo+HFhYOjtkixa3jvRN+djfikWLPG1Dp2UsPBi/kGW0eSwAesYOP7LuGt/Lz0NSmecAPcmy6m+apyKNT/czuyzJnkRS23ocGGR1lW4O+QDKG6AfPedzbqyyLtjcW61HMQOgSfAruikASvOt9gFq8TLrwb9kJFzsqc6pYOxYj/1tS+xeHJbSGEAHYj6fAfQP5W39m/JRxomcInMG0G/dEmmr6pi5h7IQ29+cBlBX6eaSD6ADyUtGUtaA59ELdQYCAKBJ8Cu6KQBK8632Acq++rIPz3rwRsfY3HlI9MLN3dzWJiUrg4VPSZsMoH09YS3ZbG0LtyZvWt0rGY509h+aBlBH6eaTB6AalyP5JFYLNR6JBIACoADoDywCQM2hR9aDt6Mre9tZZ/jyrqQDxwTrcp/t9UyrzSPW094zWcgbgRfHcg9idu477+jmz2KJq0fn4q48p6E2PFSGqhCinVsVB+oo3ZzyAJQ9L89fc3NQbxMUAE2CX9FNAVCab0UAqNFlH1ifh5pCjBMHE3OehzHE6sROzIbZ0GgCcoDelA8QTdmBpjbLaUfeJLNSuQ71zSfTAOoo3ZyqAGj+e+ifRj3sSB/o5AAoANohgOo+fEaDDEASSwpsTIJNAw0JkWKhyqKqbI6NbJiqvFVzbWCwcs1IlNnvGGmVAC2Xbl5NBSirvPyviFKSAAAgAElEQVSnmvvwAGgS/IpuCoDSfCsCQDVsRhxWEktW+NG7T0pTPQ00EfcYneWhuGdo3DsyYOpccznU1NVZiXyt7GU7uKIFWijdvKoYA9WDwroMCKQPcnIAFADtEkCz/2S/WnzkSLAacQwZB6I73r9uRDXZTT3XvSaIywA9u7dvzGjrrEq9cl0IL0DLpZtXFQAVXFdtbQA02MkBUAC0SwA1uMRgJbFUOj5tx0hcvS2wNrBG/iTaQgB6ee/ahjVlPjSzEgMLdvaDKZuJlEo3rzwANQZ+R/InnAsf7OQNATRQCVARAO0UQCW58h58NUCNSMtNHqlpE06yeDpAdYipCdAdK6cSQIdTAFoq3bzyAZRhs89/ETX0yZIwBhrk5AAoANopgIo+/EB98gNUxDTlEo2+WQCqgkc3rh8Ng1ug0wBaKt288gE03/WPRVzJyAMeboVZ+CAnB0AB0E4BNMee6MGbAPUA4ZKPW+bDgLN14Tl2Np+7z23CATqtC18q3bzyAVQEt7LfkQdfPf7Uel3MFgJAAVAAdEEAmqNrpAfzypNIJbFYdWY/2yQS44+aLx/UNYlULt288gI05+baRDZGFU/rEgAKgAKgiwJQ3ocf6A8yjKnYiDNBxcCxVhHGVAlQ87rIyI59kvlaK/Wn7EjvKN288gN0crIup9/laO5qncd0AqAAaASAhlZhuVxLDVCGnp8TPXgrkN4KkT+wsZQ3HEtWPJNpADV75qw1WlzTxJ/EcrCeKEOGPAB1lG5eVQB0Mjn9rfzXGt/hB/HVs32JVCoADf5GxQcogROEXCObAqA034oDUIabFUkvlcpmkUQ/W/LMvbrTuZRzWgtUpvHpJAXQvmhC3jFyktnzbUWmBdIXSjenKgFq6PJ+nXuBMgGgAGgEgIZmWi7XcgM0D0H/kp3KJ5a31DYhEl184+DxiQx79G0mUglQxrdVtufnWb5FiAJovnHImc6JQ5lNd1/mIUomQNn/Hz3XCY7SzalQgNYvABQABUAXBqCcggJ57u3sjLhxpYOylbG1ZxVAizFSst//4fKMjNj8nSd9vgxQXjJzbqpQuvkEgAKgcU0BUJpvRQKo3iHYStXR7v2nxT0nCpdqcudCU8/YULkaoMbc9dZX9czTmpqR2VIjisp09aVCGJNos67pBEfp5hIAGjwtGx+gwbCPTkUAtHMAHekmmzXxfXqNIWnjhp4gGfMwy/6muVryTBzpoZIClnKe5tlk9xgDo2tZ75sd6bFlrSPi54qsZJZFgPLzPzLW6gRX6eYQANoMQBtBHQAKgDrqq1LzARSapmKdvbu/UaXN7q2FXyCAEjhByDWyKQBK8y0ANCmVAPpk5Te+g5uJLFAXnsAJQq6RTRHGRPMtADQpAaAAaFxTAJTmWwBoUgJAg79RAGgXABqaablcAChUFiaRANC4pgAozbfIACWI+BWGAFDCNwoABUABUMgWAIoWaFxTAJTmW0SAQs1qkQAazDoANDYVAVAAdDkEgAZ/owBQAHQBARr8vemhCz+DqutsfHptIz8N5aLOo0C5AFAAFADFGOiCq6rOLvKNpDhAR73eai2n2CkBoGQoESqAkGtkUwCU5ltkgP5gqADQGVRRZ6/JfUvy8/jq2r5Eig5Qwtck1BIAjW4KgNJ8CwBNSv46G6ovZAbQfMOoWgkKgKILD4ACoAsub52NGDG3jtnKJD4GyrvzcQ+VI3xNQi0B0OimACjNtwDQpOSrM76kcyf/P1+/Oay5CQqAAqAAKAC64PLV2Ui0NzVAeS++hoOUpQBQABQABUAXXL46Y5NGcmtnsYPIqN4+PAAKgAKgAOiCy1NnrLmpjn4WAH1Y0zl2QghjAkABUAB0weWpM8VNA6DGj3UIAAVAAdDEAVr+zo/mh4Bx7BHhUqJaJIASviahlgBodFMAlOZbrQNUnsWZCwC1VQFQVxceY6CB34fwojZTAYRcI5sCoDTfah+gVuwNAGqrYgy0OIk0LP4xmk8AKAAKgC4CQK1mU7MAXTz5xo0lLTVAZWRoXQJAyVAiDAITco1sCoDSfCsCQM2vPQBqywdQ1l9nv6UCKG+TIpA+9PsQXlRCBQCgAKijvipVC0CN7z0AassbucAXHt1UAL3Y63l68B985Znd3U+/aib9h9/Y3f2ESHrv2V2un/7Twn0AKLrwAOgCAPRT1twHAGrLC9B8+5CVa1nt9Z/7Qr4xk2sKSQDS5OO/z5H5id9nH95+BgCtLGozFUDINbIpAErzrdYBejA0O/ECoKyHKiHIUJH/PL57NcPF1jG7rDF7cZil9jZuvG5mKm69t8/QsrJ1ZF8asv/GJ+y+zaM8j8xu5ca5VYhibvmDVtSD2pAXoIKgplZdf3le2X3i1ck7z+8+8R2Z8tbuJ/7HCUvizHxr92fc+SMOFAAFQBcBoGpVDZNgF0uT/VFFyxO5AeaOAVC5q3Cm7XOdKf9J7ZgpG2cmQPNObw5vaSiK4QTo+E7xQW3ID9Dsz4n+9bylevsZjsn3ns3bm5k+eH73Nyc8if//yu4vuLMHQAFQAHQRAGoFMEp2DTXDRoKlegPM3uPrZvCjh5J8vbjSmnkpy+rnnlSXDoaFHJwAHRQza0UVALURuuXej/5N0cB8U3HyvWdFb52j84PnFVkLQhe+kT8LAGh1xQCgdIBOjE68ZFcGRtkqHeQEY6jsZ53scd4QNWJ3WC98fLpXpGSWVW/16FxcU2HnAqBsCibL7IXsh5V1jp8/YzkcmIUwc+OUZ939s726936vUiVAM43vv5jpyDuq8Ere3HT11DlA33v2iT/53O7uz79auhMABUAB0MUAqNGJHxlROTvSRnbqhQ0f/dObYAr0WrHl7P+BbteypJ2JDVCRGW9YqiflCHYBdKhyU2ZtaBpAp0g1MN9+Rg+C5sp79XIOSXB2wl6/0INMwe7ITAO/JqGmzPJB6DcquKzCNLyogSJWAKFaI5s+eFAC6IPgF/Pr5IoJft2lcvFi1V8JVN8q11elagGo0YkfGWhUKWsTe1pJ7TtksUx+EJlak/GDMkB3dF6Ss4MKgA70k4b1bv1eKSJALwvlqgBo3rl/a3f3M9+Z/P1XdlVPHgClF5VuSqjWyKYA6AIAVBNNsUv14QfFFmCexq1GVm9ajJu6wpiGFlvZR3mfbJxOjGd4WqD9m4SKqUkkgI7v/ueFiXgDoIU4pbdyZMox0vJcErrw6MKjC78YXXijg67YJfvwmQm/MDD7zcZUk9EWFMwtAfTs3n6vBFCzdy+tqwDKRlR7/dpPX58mCkAf7pViaL0t0Lee+cRvWp93iy1UABQABUAXBaCqJz0y98ZgCdkF9p8eExXW3MqiqqPvfnnv2ob08DkBqmbhV2+3F8TkAej4LvutNm5YBeFhVqEAfXO3MPleaqECoISi0k0J1RrZFACl+VYkgMpOvGaXgOTQHr2cmNcKABUR98pURXrWAlAjDnTTHTLUhBwA1cFL5piCGZtgyD0L/++L/HSMkSIOFAAFQBcHoKITr9mV4zD71wpAyhUEUBUiunH9qDwGSgZo1p7VYZetLRUt19nYDEiVjXK5nOBKEaAy/vNNY4zzg1d2PymamzKq3hHmBIACoADo4gBUdOJHhQB60YOfoQvPw5o2n7vPQVkLQCdsTGA/b/u1FQharrOhRaO8aHKN1kdLowvllUh8ded39M8cnAqkWujCdxqgwX/tAFCab0UDaN6JN9jFA0CHkpvkSSQ266O6uIOZAWqGT+VikfR1brxZqVKd5RtYbR3d5ysH8vFhMbiw6hhZyMj4ycJa+DfN+aK3n2FhTO98rjSHBIACoADoDywSQHkn/osGuwYZDweysUcOYzJvkEvrZwDoqFeKKjUX6jetUp3xBuiO/vFA7iriCbJ6x9iN6e1nsnao3L8uE2t8vik2YyotRQJAAVAAdJEAKgctTXb9yJOSdGZL0DpSzQqkZ8YiU7PJyihIAKixjlQewG5BcxARoIOeKjsn544ID/Cshc8I+pWMj5/m7UsO0Ld2LYBO3mGbg36m2P4EQDsO0OCyAqA034oIUDG8Zw4/rqybKzUfOVY/Tl/KaWxHwslMAKjB5ZNerxR7X+7VN6dinanfcpKXqXflpKL5OY8AUAAUAF0sgOadUR2Kw9pWqinINxO5mfEtH/ybupkIs1+9nX0+y2eoCQDl5bhyrCa3JZb7bCtQvplJjccHV8sFUKtKuJpYWQqALh5Ag/vlLXfhCeUCQOcAaE4EDaeRxQZ7+nn6dnYDy97s3U8DaL7sKNfaQG/RpBUtjMk6/F3+8o3MaDUK0NAvVAIADS4rAAqAuspVrXoByiGpAWquU58YGyr3/4WxobLemL2wobKxZfvWV+1l8tMAqh+1fS4BqtNa3M2uGqD5jHxDpQFAOw3Q4LICoDTfigtQPtL5kvHJogM/0qO3eduMFmVd9PxID9lU1ZmesqDN/mZ2pbDKcypA5ekhR2ozkwk/IITn1+ZazgCANtQaBkA73YUPLmsdAP3REAGgdIBOkYyiL2nU4oZycTUdoE3FAwCgACgAutgAHRo9+GFhOLS9UzWiaipAGxtOAEA7DdDwVwCALipArR68CGjnajOOKLKmArSxeAAAdPEASigAANpxgJ7zXTN0O5N3V7fZ2T/376wvTQ8eAAVACabB9YoufNcBWp5gtqKYHmlvQ7m4AkAB0HBTAHQBAUoQId88BskKcHxt6hm+HVRHARr6hQJAYy5lAEB/YHEBeqfXW7ldSDvdZwxdub40+ARAAdAuADSYD8sHUKhZAaAAaCNvgNDmAUAB0IWVC6D9x5/KdW3d+MB1vb65NQC0uXHFZQNozaadAmjwq+jRZuEhLhdAK1RjexSTSJ0GaLApWqA030pkDBTKBYDGBijhtyKYEqq1mTcQbAqA0nyLDNCQ3pjukkE0AaAAaCNvINgUXXiabwGgSalYZ+MvPFWlRRkDDf6aAKAAqPt1A6BQgOLVGQAKgAKgAOiCKzpACcNXhK9v8NcEAAVA3a8bAIUC1FGANjMGSsi1AdIAoHNREQAFQBsQABr8jQJAAVAAFLLVUYAGf00AUADU/boBUChA0QHaxHcSAAVA5zEFQKXsqMaVx6tOG7KPNSqdpjTlNvOGbxxPJoQcoio6QKO3QAlUBEAbKCsASvOtiADtsYPfvU+qC6AXh/kuowBotRYQoMFMAEAB0G4CtOKM85oA+nC9B4CGKJUu/L8JEABKmZsDQDsFUA2x+3d6FYekUcnpfIQB0MUQAEoAaLCTd3UzkSbKCoDSfCsiQPNjO3zHbQKg7WoBu/AE0/CiNlMBNX/LAVAAlIkd5OHbDwMAbVcLCNBgJqALD4ASAUo49S0iQHkT1INHALRdLWAXPpgJCwVQAhWDv4cAaEcBOjIAenbINlzfOrJtHRy9OLya3bZx43Ujo/Hpfpa2ebswiTSQ7nZg5JDfv2Ldn4oAUAAUAHV5BgA6qQToxZ70DHEEvA+gF4fKh7bPZT4P5d3b35wGUHZ8Xen+ZASAAqAAqMszANCJuwufd7Af6jOMxaioB6CmoWTtZKQTPzQNoAPjft8EVjwBoAAoAOryDEwiTSomkXiA6HbWp768uy646AYoN9w8Yn32PUVAdbdonVbFgTIAX2HDBGd7FTFU0QSAAqAAqMszANCJO4xpR/wgYcYIZxCwAFB1R07fPDeWKJY0nUwD6NAaI0iuCQqAEgAa7OQAKABKBCiBcLEAesnbkKoBqpYkCcQ5AWpBT36w7h5MAehAZ6BZmo4A0G4ClEBFANTpGQDoxLWUs895OjLDQTMjhjwnQEdWt3uY32YlFhqwrhZoxQL86AJAu9mFJ1CRkCsAuuwAXc3BZ7UFWdfc2fQ8KDYaBRxdd3sBOuLYvn40SVMA6NIDlGAKgC4zQPsbCmODonfseAA6sIYtnYmDKZuJyGetVu2lF00AaDcBSjAFQJ2eAYBO/MuKZgSoaG3aicMpADXiQDePCb95OwJAlx6gBFMAFADlahOgIlQqV3L72wGgAGi4KQAKgHINHAFFTXXhuS7v7efDCKkFggKg3QQooV9OyBUABUC5XAFFDU0iabFIev9+zpEEgBIAGgolABQA7TZAR462YHgYk7Xh0pQd6c0SMNamFkkPgHYToCFLq38QAK3wDAB04gdoKT7eF8ZUMmRNTwuEcoM8D0AtW9fIQWQBoN3swgOgc5gCoFLezT3ZLJIIb2eI865E8i/lFIl8W5FpgfSFRaMpCQAFQMNzBUABUH2ht6X2CPGvhXduJsJRunU8mVzmIUomQNn/Hz238+uzrUDHJ+v+7fCjCQAFQMNzbWJkFQCdLCBA7V3qdgzboO3sjAD9K58vA5RnKRNGpmul1gCND9AmvpMAKAA6jykAKuUHqLGhcv9p07a8ofIt5Rh6Q+SxTFx9qRDGxPv3rKmqEk4UgpMLYgJAAdC6q/XXAdBZABr6NXDUV6UaA+hkcnqNkW3jxrllWwIoO/vjqmkoE9fZMR3nxTjQDJjMeutcJ4x5EGh/M8W1nAAoABqeaxMvCwAlfA9c5arWfACFpgkABUDDcwVAGwPo7OWqFgDarKIDlOD7hG96sDsCoOjCOy3L5QJAobIAUAA0PNcmXhYAGvzbAqDpKTpA0YVvxBQAncMUAIVCBYACoOG5hlQrAJoYQAkifoWhBABK8P2av+kAKADqtyyXCwCFylokgBIaq8HuCIACoE7LcrkWE6BQswJAAdDwXAkvKzjXOgDahGcAoFCAANAFAiihAgBQAHQiChwu2jcYmiwWQIOnEgHQJQNoyDv8N8sJUAo/AVC6AFAANLwAhJcVnCsAGvzbzgbQoHo0socoWiSAEvgR7C8AaBcA2oRnAKBQgADQbgKUYEooAOFlBecKgAb/tgBoegJAFwighDGM6AANLgC68MG/LQCangBQADS8AE28LAA0+LcFQNPTIgG0ZtNOA5RgSigA4Q0EFwAADf5tAdD0BIACoOEFILwBABQAXQYBoEsP0JpHRgDQmQAaPuIBgKYkABQABUBdngGATqzTMzOtPO46lajy3KRvHE81CdUowUONAVAAFAB1e0bLAA12w4gAzdS/6bTxndx5mJ+jCYA2oB9+kCn4a9KAKbN8EOq53DQMdSTTYIAy00AoNWdKeAPBuT54UAIo2TMCAUryjFK5eLEeBAM01FQ4TJhc5apWzQAVZ8AXbbxnxwOgjQkt0ERaoARTwhsIzhUt0EnaXXhNvvt3eqSz2RVAaxEAagsABUAB0AXowptNx2FG0LXgZwOgTQoABUAB0AUD6PgWhWIAaJMCQAFQAHTBAMqboMGjmQBokwJAEwFo8PAbADpZeoCOBEAzkB6MX8gAuXmsbS4Or2aXV268zj8NpOccWNnkRhvCKM/yyvnkdH89u3X7deNZZ4dZUl8l5QA943Y37MRSYe2SNCkAFAAFQF2eAYBOpgCUE7L/JWkzvqN8Zft84gHoxaFtlGd55Zt7IrH/tHzSRTGJsfIP5TNkOJUToMWSNCkAdOkB2swbAEA7CdChmIbP/n+KE2pN2Shg9vKJJhdAs0691pUcbhkDV4xk8biHpaTMrn9VpYmxASdAiyVpUgAoANrIGwBAuwhQNYk0FM27i2Npw4h35Si7dLYn8VaKA+VhpZuZ0fh0T8FtlDcVs972xS2FVfYgnna6LhGZ220dW3YugDpK0pwAUAC0kTcAgHYRoIybO/KHNctmqJqULIFfKwFU3S4IybPmYBQ98oEk3khZMhzuOOwUVUsAdZSkOQGgAGgjbwAA7RxAL3mzMeeVMRsvbAaaVZJgRYBaPFMfRkZHWw6xMrwWcyvYKdCWAOooSXMCQAHQRt4AANoVgNrq59gcanDpFmhxoXwRoCOrRy2zGGkWW339g0IuLjtfC9SxZL8hAaAAaCNvAADtJEBXBQCNpp2mYwau60fG3UWA2u1BA4wKq8YwZmnw0jlf5Ep0lKQ5AaAAaCNvAADtHED7GxpKZYCque9VteddEaADa0TS4J2LgaWg+WCAOkrSnABQALSRNwCAdgWgzoVHDoAa0ZebfBfQKQBl45wVAC2NXYYDtFyS5gSAAqCNvAEAdFIcPvSJ5obJAnQyubyrgjdl1GcUgJZL0pwAUAC0kTcAgC4fQDNd3tvPu/sMnLG68OWSNCcAFABt5A0AoJnpDwaoWwBlYvHrPHQzeBLJFQtPn0RiEaN2YVVJmhMACoA28gYA0OUCqGkpoziDw5hKYLSeKxquUwEq4pxcJWlOACgA2sgbAECXqwtvoWrgBmgpkF4GyJfAaOZW1dW3mqqDHKCukjQnABQAbeQNAKDLBVC1zchE96WDl3I6W5bSclDRUjWZfNLr6UD6QkmaEwAKgDbyBgDQwEroCkDZf322Aef4RO7/wZI+ej6xe+auzUQckUhyMxG+cchOtd2VY7VPnnxKoSTNCQAFQBt5A8EFAEBpbpgqQMVuSUKqecjxN207O8dEuhnCXwFa86lrA3N7EqskzQkABUAbeQPBBQBAaW6YLEAnJ4qOfb3gs2duGSoalLmMDZWdm8rvFQw9duqp2+cSoI6SNCYAFABt5A0EinsCANoJgE7GPPSyv6lXUJ6wHZC3zs1szsSRHsrGG8rJT+/obx1PsRvfvcrMjvhoqUgsl6QpAaAAaCNvAADtwiQSNE0AKAAa7Q38OgAKgC64ANBuAjT4uwuA+opVA0CDHwaALqoAUAA02hvoPEDRAu28ANBuAjRetQKgAOgSCQAFQOOadhmgwQ9rEqAEEb/CEAAKgIZ/uQDQCQAK2eooQIP9BQAFQD3F6gZAoWYFgBIASsgVAA01BUBpbgiAJiUAFAAFQJ3F6ghAg19wD134GQSAEgBK2B93gQBaM30A0KQASuEnAEoXABrsuQBoFwBKIElHABpUOUb2EEUdBWiwvwCgACgAamUPUQSAEkwB0PBcCaRqFaDBrxsAhQIEgBJMAdDwXFMFKFqg07KHKAJACaZLD9Bg/ACgAOhyCAAlmAKgiw/Q4NcNgEIBAkAJpksPUIIpAAqALoMAUIIpABpuCoACoMugRQJo8HcSAI0P0GBTAJTmhq0C1DwbM9PKJjstONco9MRg4/gkt8xDj8WZRt84nkwqTmRKSAAowbQRgBJ+q+UCaM2vGwANzN5UAaBM2+JSgwC9OMwP0wRAq5RGOyP+WngCJwBQT8XUXC4AVMoBUHFKe4MAfbjeA0CnCwAFQCsBGq9cAKiUjbDxGTssWCTUD1ApBdBFEABKMCV8H8IBSqAigbXxqhUt0M4CNNNJT+IQAOUCQAmmhO9DIy1QABQAddRXpWoG6GTQE3QDQLkAUIIp4fvQCEAJdRWvWtGF7zRAM27mKQAoFwBKMCV8H8IBSqiAZkxrrlYAdJkAera/3uut6OCmyWR8jyX1VraOREIO0JOrWdN187aR0/iUjahmSfYk0kA2GQ6Mp18cXu0VnpOIAFCCKeH7AID+/+2db3MUR37Hd5HI6R1EV5YQsV7DCDAuqVyYHGwszIUXABifgxVd+cqu8iuILge++OLlxFWlIH4sm8PJ+dbIEVx4qgCWjQqkB9K5tLXzPjLd0zPdvTM7f1bb8+vt/X4esNLsb7ub2Z6P+t/0FD6vEOjQCvQ/bwnZ1c9F738zGXeaRLuTCfR/l8Sx8btR4Mt5cej4X/IE2r4Vp3k8sy1LAARaIrTE9QCBFj6vEOgQCbS9pIyB1l+JxRb1uWP9McIFT4FAx+cTgdtStH+TJ9BmIk17cFSgxfBLCrREqtQCLVxWCLRXsSDQFIEGNpSz8AEzQYtyZ0k7OH7nMeufz0eybMnA5TiQLzA9/kgcy14HGvxeO8oGBDbma9aNjkKghatWqVSNCLRwAUqFljitJkIrFmjh7xAC9bsFususGCmMC1T03JvRhFJTThixj86yH7hAZ8ODrZpyUHyYrYzKFKichWIHLGuCOirQwvXFVYEO+FxBoKMr0G6EC5lAI5ltC61qvm0qrpxQDjIZxnYVxzIF2pSfz53RrxwItERo4YsPAsU6UEcFOh5NpMezSX6Pey5bwnsttd8tVLutHmNd9JwWqJyksg0I1Exo8Yuf7gTYEQqBlqtblAKtz8h1SOo60KRAN26zNUqRQGWrMdEr98OJqSyB8sGC+pk7vo1AoGZCIdCioZhEKle3iMZAd9lSInURUS+B7t5+Yyqq35FA5bglk+Ws1iv3+W+Zm4lEs/DjH9nVfWdAoGZCIdCioUMn0AF/t2WHh8gmkdhkjzIHni7QnXm1uJFA4+HOdIG2cgSqrAOdvuvbBQRqJtTFfZMgUH+UBcq70tpoZlKgL6PVnVNn7rQKt0DzBBo0a1fjZaOW7W8HgZoJhUCLhkKgPNV/KQCxQHlXOh66TBMo02Nt+sOHPKa4QPO68JxdPqpq3UJQCNRMKARaNBQC9YdFoNyPytKlhEBZGzWeL28OahJJwlbSK+MBNgCBmgkd7EUGgVZarmyBFvnbWOKP4xAJNOyhi9/TBKp6kWkxEqjctkmsX9I2XMrZkV4tQ5yoNZALtIRqTFwmECh16NAJdMDf7TAJlC+KFzZME6jaM48X2sd3H/mhAJliNRG2apkC1WKbEGiE2wItbkUIFAIdGoEqnfgeLdDoGG+sxgKti0RuCVeqVt2OmrVZC+k/lqnaNYtELtAB1/3hEyjdXxA7QodOoCX+4jknUG6w5I70ivrCW5U2wi1CYoGGG4fwIcxQv9zEbIOR3XCJkipQ9vrqY3mAvdTZVqDtB5NFd3GuDAjUTCgEWjQUAvWHSKByO6bUZUxNvdLywOATP1nSD/naPU5H/zkpUN5AVeemYuxqgEKgEChxKATqD5NAedORdb5TBdqWppz5gwhgy5h+jNbXz2hz75zxz7uWMYk264Q88CBeBWrZIiYIlH4SCQKFQIdIoPFy+h63cvLndNSnzz5WB0Yngt43e6THjHYf0cYyfxzI4+51oPxyw7kAABQ4SURBVD5//kfgWnmgfTtM1r57OSFQM6EjbkUI1A2BgjwgUNpQcn+Rh0KgPgQ6vBz+nHVuNDzv4tc9D6W8z7HjMqEOJfcXeSgE6kOgw8uhz9nBZY9x4s89DqW8H2LHZUIdSu4v8tChE2iJ4RkI1HkOfc7WvNe+9vdXvNd+SD+U8n6IHZcJdSi5v8hDXRZo0UgIdGg57Dnba/C25cHluc9SD6W8L7DjMqEOJfcXeejQCbREZlYItAQl0gUhhz1nm95p8Xop9VDK+wI7LhPqUHJ/kYe6LNDCmUGgw8phz9ma9z5/fSFE2X0o5X2BHZcJdSi5v8hDIVDfpECBWQ4p0M6K6JrvNaJBTu1Qyvt/G/E8oPDfRiOhJQpgKrRopLuhz58nBGpFzUiUixeLvhomzxcgBAKlDS0a6W4oBGpWoIULXEMXvg8GJ9BooZJ2KOX9iF614Pnzwrkj1Eho1fmnduFNZVYiNLULbyqzEqEYA7WI6lugEbZcJgilzh8CLRdaUqBFRlfVIVZQBggUodT5Q6DlQiFQiyCehU+BWh8IpRaCLTUDAgW5HH4d6CXttftQyvsCWy4ThFLnD4GWC4VALYL4TqQUqPWBUGoh2FIzIFCQy2HPWWfFO9V1r7t2KOV9gS2XCUKp84dAy4VCoBZx6HO2r+y2tNfg7Uz1kP6Lii2XCUKp84dAy4VCoBZx+HO2fyPw40XevhQCVQ91/aJgy2WCUOr8IdByoRCoRdCdM1suE4RS5w+BlgutWqC7q1PsoW5jx/p6ItG3d/NjhhgIFKHU+UOg5UKrFejOvLxTqX62rEJ3lq17juZggUARSp0/BFoutFKBbk9qN3uOf54S05uXk/Y9iHiwQKAIpc4fAi0XWqVAXzJ/ztxhDc/dVfaw4aOl2qAQqDlsuUwQSp0/BFoutEqBNmuKAdu3AoPOlsgcAjWILZcJQqnzh0DLhVYo0B9fr9XOy1/bSyWboBCoOWy5TBBKnT8EWi60QoF2C3C7Vk6IEKg5bLlMEEqdPwRaLpRQoFqLdGN5MvDpzB31A+Gx44/4L81o6kl8ZGeZjaJOnX0URW/zBu36QvCZseOPEsnIpFssjfYD9vHpO2FS7DPKooD27QW+1kovjnkgUIRS5w+BlgutVqC9Bj3l8ibZqY+P1f+e/aoLdGc5nss/Lj7BBPoX/TPpSTOBxkeDAn0j1gbEdv9GLhYoN811WCBQhFLnD4GWC61QoGzQs/ZqmpFeKsubjnyePMacqQlUfTOSXCDQsa7PpCcdCPRnr8u4VndCzZrCRInTc2ggUIRS5w+BlgutchZ+mytpJnEPEuvL11ive3d1MtIYty07tj4ZmU8OAfAPsO53e30+llyYOvvIzlJsw7SkuTHr5x777d/XuHRn7vr+/8xH0mXpjLO1VjzxSkddIVCEUucPgZYLrXQhfdTYqx/78LF+OPIUazDGGpuND82KH0RYK34zFG38idq58Gi8YCot6ZbsrjdrcUpMtRPiWNRxZ8dKrbQ6JBAoQqnzh0DLhVZ7K+e6vJWzPhPd165pqhXqi3lxQj8kBRq7TvtlW+lwbwurpiWt6vflpLRlM/y4NrXVhEALJ4FQCNRgKAQasr4gxxdn4sHLI/FNnYG/mCVVjUXijAWqr39qiY9H0hSp8J/TklZbpapgWylTRq1qB0EhUIRS5w+Blgsl2M5uZzWS6BHeCNXMxZqe59PXfMbHdNVFh1WtRgJNS1o9qHo6IdCN2wsVzyJBoAilzh8CLRdKtB9oeC98aCxt1lsMSmpNR0Es0KamtbTWZnQsLekCAt29/cYUxTQ8BIpQ6vwh0HKhdBsq86WYTGg9BJroT/cQaNSwHJhA1T33INDCSSAUAjUYCoEmYPZiyrJMoPHK0akzdzAGOjymcTSUWgi21AwINDmsKaZ/mimWGlwXPinAbIHydVHTHz6MloxCoAVBKARqMBQCjddhxgjlpU1/9zeJlBBoWtI5AmWT+eei0FQBmwMCRSh1/hBoudDqBKou7QyRykrIsns1Jvtg/jKmhEBTN3zKFqjq3GSRzUIoUAAEttYMS4tV3RhoS23b+aGe2JhkYl38eV1dkeV6L6TnxksTaFrSOQJVG53q2vwqgEABPbbWDEuLVe2GyspmIsyR8ZhmZNZ4l2V5Kyd7m5sx/1bOhEBTk85tgWr7mYyGQHtRonog1Egodf7VZuZuuTiH3UyECan+Jttks8036VRuQ6/NxHuDcKPFm4nwnUFmo7BX5RYhKZuJJAWalnS2QJk1xz/y2TaiI7SMqRfk1XHkQ6nzrzYzd8vFGfBTOeO7KLXd6cTBH+WOc0Ji4shs9wfkdnZJgaYlnbOMqWvpU5U7gkKgCLUt/2ozc7dcnMM/F35ZURNv54nj8dr1tJ2Qoy2TW9KmvF2qv5su0JSkcwTalknP/CFlNZVBIFCE2pZ/tZm5Wy7OABbSt28v8Js4p87oj8tYf4M1FafOqu29jYVJddcm3+eP4RBbkGyIR3rEH+gh0GTSubdy8g1P6tPBB7qeg2caCBShtuVfbWbulosz2DuRQDf2nTPy6jjyodT5V5uZu+XiQKBmse+ckVfHkQ+lzr/azNwtFwcCNQvOGQAOA4GaBecMAIeBQM2CcwaAw9TKQF3YIQTnDACHgUDNgnMGAAB9AoECAECfQKAAANAn9ALtPHnrxJ/ZD/v3v0coRSh1/ijXYMoFCCAX6MEVz2MVh716p75GaOWh1PmjXIMpF6CAWqCdFW/u+g/81VtseOHfYIRWGEqdP8o1mHIBEqgF+kLUFf4a1J5LCK04lDp/lGsw5QIkUAt0TVSV8PWF99oPCK02lDp/lGsw5QIkEAu0szL3GXs9uMxfDy737rsg1Egodf7VZuZuuQANxAKNaspeg79mVRyEGgmlzr/azNwtF6DBEoFuhn2WInUMoQMNpc6/2szcLRegwZIuvBgCyhr7QaiRUOr8q83M3XIBGuyYRBJDP51oKB2h1YVS549yDaZcgARqge41vOuswgR/cTtPr2Suf0OokVDq/FGuwZQLkEAtUH/TY7A/vAeXvbAfg9BKQ6nzR7kGUy5AAblA/ScNzzvF127863s5Az8INRJKnT/KNZhyAQLoBer7HYQSh1LnX21m7pYLVI4NAvX/Wnz7GYQaCaXOv9rM3C0XqBobBFpi1RtCjYRS519tZu6WC1QOBIpQ+vyrzczdcoHKgUARSp9/tZm5Wy5QORAoQunzrzYzd8sFKgcCRSh9/tVm5m65QOVAoAilz7/azNwtF6gcGwQKAABDCQQKAAB9YoFA9397ddHzvMXFX+Q+ixChZkIBAH1BLtD9K54k+3GuCDUTGn/kZuGbsM2EVpqZu+UCFUIt0L2GN/eLr7YYz36b/ThXhJoJjemseO8XCDMXWmlm7pYLVAi1QNfUvbeDunMaoVWHSl4U34fSTGilmblbLlAd5M9E0rY+FM/UQmiFoSprxR8lYSa00szcLReoDHKBald2keduIXSgoZ2b1yTvNLKuXTOhKNdAygVosOShcoK9Rv5ztxA60NCDy55OlpVNhKJcAykXoIF6DHTTOyFniPcvZz1MC6EmQrW2D+Pd3lY2EopyDaRcgAZqgXZWPO/Ce/cZ96563qmMP7wINRMKAOgXaoH6nS+UjsvPM//uItRMKACgT8gFGlzqz26+s7i4+Pa7XyGUKBQA0BcWCBQAAIaT4RFoZ8tYyjffNXK3eGfr2Vdbo/mcsEF9WUN/Ck3VLWAJwyPQg8tz/yR/Pnk9O3r/xj+IeZODT/Jq8MFlTyadn2rnk08L6GH/RjQAefJ6iRHIg6sXsuZ7nl71vAvs/7N//085KX33Ccv2u6sN7+TFnDMw+G1Hyn1ZvYpFfQp7lou4bgF7sEegeZsosKVy0WpG9nPmzYmbciXdQeZynyg177UC7ghT5eEn3/63P2UV90VDXPpvBf+c+iwjsrssWSsAN3mac++zOfbgJSuhL/gJ2BQGyjoDfWw7Mtgvqwfkp7B3CrR1C1iEPQLN20QhaNS8Je/QeNrIin7heXGrq3MvM5SnfOK/V7y53GZSkOrPg+5kJ2oYZVynew3v4vd+50tmjqdXsiI7z7SLJfPqDwrwj1tbX3qnfuPNLTa8uQypbHpzgeJYibe2vgsM2fsM9LPtyEC/rN7loj2FvVOgrVvAJuwRaN4mCuy6WJNNmRdZt7utaQ2DzFCRsv9/wfWa82c/TnXNe7/z9EZWcaOCbrKPZO7lwdscF977Pv41M9VLYaLe6R/8zlpGqqHggn8vacVJT7SPbUcG+WX1LBftKcxJQUBQt4BNWCTQnE0UWFWMjZB3J3jYsuj8mo+G5T1rJnw/6MnO/S4/jMHvjCxSAHEPZdZNlwe/Xgw7lWGfuEiqew3+mr2ZCHtPKUbP0D63HRncl5VXLqpTmJMCWd0CVkF9L3zxTRR4pQoaG9eVX7NCWeorJ4pXcp81FLJWnMtkRFEK7OURiSy7AKzJ4YU9wyKpitf80K5i5Pyv8iINfVl55aI6hTkpkNUtYBXkuzF5Onl1fz+6KItspSHaQFndrM6z+/f/2Jj7NLzpsZE1WSE36OANl0zVrGhNvyINne+u8juGiqQaN5+yzgAfm1vTWnHZRfXzIg19WXnlojqFOSmQ1S1gFTa1QLM3URCVai/8Ux5cm5nbCZ9m6TwJunXXt540MqZKy2yDs+lFjQ+Wd85QW3hdhYNsm4WG2tiqnVP/np3q6fCFzesG3cKMVF/wPuNeg4d01jLOQPEdSgx9WT0gP4W9U6CtW8AmbBoDzSaqVMFFyVe2ZFWxoOqe+vT+De/Ef/GlMFl9zauLi0FiJxcjMtYQBpV77mLQlniLT95mVvKgkEEB/niFRbIFM8UmofnCx7xUg0RPf5k/U/tFcIG/e/9LUYysrShNbDtS5svqAf0pTIe8bgGbGD6Bim0yshcs7jXC1X7+flCJ81ZhF66u0SIT3t3KXq+9d0WsN+TTDoUXkj/JXkcULo0MHMdOQc7E7pOGbPpkbiZiYtuRUl9WD+hPYY9yUdctYBHDI9DOTdlj3Poqr+Z37l1dLHoTXYnqyu7Zeft3hS67p+8sXuDTwp1nJS7T/eyydO5du1Yse5bWvWvBJX7h2qe5J2vg246U+7J6QX8Ke6RAXbeANQyPQAEAwDIgUAAA6BMIFAAA+gQCBQCAPoFAAQCgTyBQAADoEwgUAAD6BAIFAIA+gUABAKBPIFAAAOgTCNRFvv3lVC1gbPrsI8M5tZdqtSOfG84EAFuBQJ2jvTpZk4x/ZDYzCBSMMhCoa+zM13SOPzaYGwQKRhoI1DF+fL3WzVGDBoVAwUgDgTpGkw9+nn3Ifm5vLPPe/Ky57CBQMNJAoG7xclIXJhOcScNBoGCkgUDdohX4ckI9wA1qrgkKgYKRBgJ1i6AHX/9YO7KtK3XngynWSK1Pn+0eGW2vvhK8MX0n/G19gQ8FPE6JqU/Lmf0ugbZXF3jiH5mcuALAGiBQt2gmGoRsVimeRlpXp+ij6XkWMRt2/vlhX7RbGZGNWcyEnOEfjyStCVRZQFU/Z/g/CoANQKBuweaQzvd8t6VPz4uGKRfoS7l4dFb6MzYoF6gyw18XmagCVT5lduYKAFuAQN1iO2vZ0nb3AqfQgsyLf6esfqp/3ExYlsX8RBWkEKsiUN2fMCgYBSBQt+CNxHpifFO+V5thg5ztjQVdjrXwnqX279lPU7Xa2LkgiQesVRrqUcTwlDd4Rz7UtCLQVphIcLjNRwq6xmIBcBAI1DFEK3PszTsJiW7LRqcf+k6V44Q8HDdieb/+vIw5clcJ4selQFmsHPlMrAYAwEUgUNd4ICdyjn2o7SXS1KQm1cfleFSZUZLTQs2oKx62bD9OJCVTadW00dfkcgAA3AMCdQ7tZvj6zF35zre//KnitKYm0Mh9fCQz1mxLE6gc1WTNTf7hWKDsB3XwlUVgFBS4DgTqIDvL6n5MqkJVNIHqjcu4JbmtCrSu25f/Ggu0W5jsDfThgetAoG7SXl9+pWvCXHt7Y3VeGwOVi0e1vrcmULWBGXXYY4EmZvhrZncxAcAGIFCH2fjglYRBd7/94I2pyHBSoNJ1vQWqNiijNzIFins8getAoG7TvjWpNAXbDyZTDAeBAtAnEKjr8OVF4nai7r2WywlUHeKEQAHwIVC30EczBfFMunKr0NixXz1qDrwFill3MGpAoC7BdJaYMmJNUO4+vvx97M3/eBgeLy3Q3EkkzLqDUQMCdYpmSjswEihfyik3SdIX0hcRqLpt3VLaMibMuoNRAwJ1iu2UkceoCx+3REPk2GhRgSo3GsW61BbSq3civfzpDDYFBc4DgTqFdldmSCxKXaDtZq20QLUgOa4aGrup59xElx6MABCoW4gtkeSB9XgZU3z7JWNnqVZeoLUJEXUr/qy+mYhMRm43AoDDQKBuIWbaxWM5H94OFy7JDZXqbJs6f2M5nIwvK9Bwt7pwO7vZOD9hZb6JaP3so2g7OwyJAveBQB0jsdZTTiu1ku/EZi0oUIn4gCLQ7pyxGRNwHwjUNbo3hpebdOrvHF2tpS5RyljG9EB+elx5jEc0LqAbtI4OPHAfCNQ9tCfHqZsxqY86OicnlYqvA43vBI2eR9f1ULlbimF77AEFgEtAoC6yu7rAtxEZO/YrfRxyg+/RNDbDZpli95VYSL/Lbq0fOyv3aU4+1pg5duoM9AlGAggUFKH7TiQAgA+BgmJAoACkAIGCIkCgAKQAgYIiQKAApACBgiJAoACkAIGCIkCgAKQAgYIiQKAApACBgiJAoACkAIGCIkCgAKQAgQIAQJ9AoAAA0CcQKAAA9AkECgAAfQKBAgBAn0CgAADQJxAoAAD0CQQKAAB9AoECAECfQKAAANAnECgAAPQJBAoAAH0CgQIAQJ9AoAAA0Cf/D39S1tZB8hRyAAAAAElFTkSuQmCC" width="672" /></p>
+<pre class="r"><code>plots$topn_ASVs</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+</div>
+<div id="customized-top-n-genus-bar-plot" class="section level4">
+<h4>Customized Top N Genus Bar plot</h4>
+<p>This chunk plots abundance of the Top n ASVs or taxa at a given level
+as a bar plot, giving an insight into the presence of the n ASV and most
+common taxa for the primary and secondary parameters. The default for n
+is set at 20, a larger n may lead to delay/skipping of the plot in
+standard out, but it should be saved as a PDF regardless for ASVs. For
+taxa, a large n may lead to unreadable plots. <strong>The chunk does not
+require any input</strong>, but it is possible to adjust the
+<strong>default ‘n’</strong>, and to change <strong>width</strong>,
+<strong>height</strong> and <strong>resolution</strong> of the
+PDF-output if necessary.</p>
+<pre class="r"><code># CHANGE ME to the desired sample categories on the x-axis. 
+# Accepted values are the column headers in the descriptor file.
+set$x_axis_value = &quot;ndvi&quot;
+
+# CHANGE ME to the taxonomic level of interest (color coding). Accepted values 
+# are the column headers in your descriptor file.
+set$taxlvl = &quot;Genus&quot;
+
+# CHANGE ME to change the number of Top n taxa to be plotted at 
+# taxlvl.
+set$top_n = 10
+
+# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
+set$ignore_na = FALSE
+
+# CHANGE ME to an entry at the chosen taxonomic level you want to highlight. 
+# Comment out to not highlight anything.
+# set$highlight = 
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 20
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 13
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+# Can be changed to change the y-axis label
+set$y_axis_label = &quot;Relative abundance&quot;
+
+# Can be changed to change the x-axis label
+set$x_axis_label = &quot;Sample&quot;
+
+############### NO NEED FOR CHANGES BELOW ###############
+# Estimate Alpha-diversity (Shannon)
+set$alpha_div_ps_trans &lt;- estimate_richness(ps.trans, measures = &quot;Shannon&quot;) %&gt;%
+  as_tibble(rownames = &quot;Sample&quot;)
+# Make physeq objects of top n taxa and top n ASVs
+set$ps.topnTax &lt;- cuphyr::abundant_tax_physeq(ps.trans, lvl = set$taxlvl, 
+                                              top = set$top_n, 
+                                              ignore_na = set$ignore_na)
+
+# Plot
+set$my_scale_fill &lt;- my_scale_fill
+
+
+set$topntax_tbl &lt;- psmelt(set$ps.topnTax) %&gt;% 
+                    as_tibble() %&gt;%
+                    left_join(set$alpha_div_ps_trans, by = &quot;Sample&quot;) %&gt;% 
+                    select(Genus, Alias, ndvi, ndvi_july, Abundance, Shannon) %&gt;%
+                    filter(Abundance &gt; 0) %&gt;%
+                    group_by(Genus, Alias, ndvi, ndvi_july, Shannon) %&gt;%
+                    summarise(Abundance = sum(Abundance)) %&gt;%
+                    arrange(ndvi) %&gt;%
+                    mutate(ndvi_rank = c(1:length(ndvi))) 
+                    
+
+plots$topn_tax_custom &lt;- ggplot(set$topntax_tbl, aes(x = fct_reorder(Alias, ndvi),
+                           y = Abundance,
+                           fill = Genus)) + 
+                           #title = paste0(&quot;Top &quot;, set$top_n, &quot; &quot;, set$taxlvl))) +
+  geom_col(color = &quot;black&quot;) +
+  set$my_scale_fill + 
+  ylab(set$y_axis_label) +
+  xlab(set$x_axis_label) +
+  theme(strip.background = element_blank(),
+        #strip.text = element_text(size = 12),
+        #axis.title=element_text(size=12),
+        #legend.text = element_text(size=12), 
+        legend.position = &quot;bottom&quot;)
+
+plots$ndvi_dot_plot &lt;- ggplot(set$topntax_tbl, aes(fct_reorder(Alias, ndvi),
+                           y = ndvi)) +
+                             geom_point() +
+    theme(strip.background = element_blank(),
+         # strip.text = element_text(size = 12),
+         # axis.title=element_text(size=12),
+         # legend.text = element_text(size=12),
+          axis.title.x=element_blank()) +
+  ylab(&quot;NDVI&quot;)
+
+plots$shannon_dot_plot &lt;- ggplot(set$topntax_tbl, 
+                                 aes(fct_reorder(Alias, ndvi),
+                           y = Shannon)) +
+                             geom_point() +
+    theme(strip.background = element_blank(),
+         # strip.text = element_text(size = 12),
+         # axis.title=element_text(size=12),
+         # legend.text = element_text(size=12),
+          axis.title.x=element_blank()) +
+  ylab(&quot;Shannon&quot;)
+
+
+
+
+plots$combo_topn_custom &lt;- ggarrange(plots$ndvi_dot_plot,
+                                     plots$shannon_dot_plot,
+                                     plots$topn_tax_custom,
+                                     nrow = 3,
+                                     heights = c(1, 1, 3),
+                                     align = &quot;v&quot;)
+
+plots$combo_topn_custom</code></pre>
+<p><img role="img" 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" width="672" /></p>
+<pre class="r"><code>save_plot(plots$combo_topn_custom, plot_name = paste0(&quot;Customized_NDVI_Shannon_plot&quot;), 
+          filetype = tmp$out)</code></pre>
+</div>
+<div id="ndvishannon-regression" class="section level3">
+<h3>NDVI+Shannon regression</h3>
+<pre class="r"><code>set$my_formula &lt;- y ~ x
+set$plot_title &lt;- &quot;Nematodes&quot;
+
+topntax_data &lt;- set$topntax_tbl %&gt;%
+  mutate(Taxa = &#39;nematodes&#39;) %&gt;%
+  ungroup() %&gt;%
+  select(Alias, ndvi, Shannon, Taxa) %&gt;%
+  distinct()
+
+write.csv(topntax_data, file = &quot;../topntax_all_taxa/topntax_data_nematodes.csv&quot;)</code></pre>
+</div>
+</div>
+<div id="molecular-and-morphological-data" class="section level2">
+<h2>Molecular and morphological data</h2>
+<div id="molmorph-relative-abundance" class="section level3">
+<h3>Mol+morph, relative abundance</h3>
+<pre class="r"><code># Excel file with field data
+morphological_id &lt;- file.path(&quot;2020-patch-larvik-a-counts.csv&quot;)
+
+# Reduce abundance per sample and genus data to genera of interest
+genera_of_interest_mol_morph &lt;- c(&quot;Globodera&quot;, &quot;Meloidogyne&quot;, &quot;Pratylenchus&quot;)
+genus_abundance_tbl_per_sample_mm &lt;- genus_abundance_tbl_per_sample %&gt;%
+  filter(Genus %in% genera_of_interest_mol_morph)
+
+# read in and parse morphological count data
+ morph_data &lt;- read_delim(morphological_id) %&gt;% 
+  mutate(genus = str_replace(genus, &quot;cyst&quot;, &quot;Globodera&quot;),
+         genus = str_replace(genus, &quot;mel&quot;, &quot;Meloidogyne&quot;),
+         genus = str_replace(genus, &quot;prat&quot;, &quot;Pratylenchus&quot;)) %&gt;%
+   dplyr::rename(Sample = rute, Genus = genus)
+
+# Combine morphological and metabarcoding data
+set$data_mol_morph &lt;- genus_abundance_tbl_per_sample_mm %&gt;%
+  left_join(morph_data)
+
+### Plotting
+# New facet label names for Genus variable
+
+
+genus_names &lt;- list(
+  &#39;Globodera&#39;=expression(paste(italic(&quot;Globodera &quot;))), #&quot;spp.&quot;)),
+  &#39;Meloidogyne&#39;=expression(paste(italic(&quot;Meloidogyne &quot;))), #&quot;spp.&quot;)),
+  &#39;Pratylenchus&#39;=expression(paste(italic(&quot;Pratylenchus &quot;)))#, &quot;spp.&quot;))
+)
+
+genus_labeller &lt;- function(variable,value){
+  return(genus_names[value])
+}
+
+signif.labs &lt;- c(&quot;pval &gt; 0.05&quot;, &quot;pval &lt;= 0.05&quot;)
+names(signif.labs) &lt;- c(&quot;p value &gt; 0.05&quot;, &quot;p value &lt;= 0.05&quot;)
+
+# Subset data for modeling
+
+set$glob_data &lt;- subset(set$data_mol_morph, Genus == &quot;Globodera&quot;)
+set$mel_data &lt;- subset(set$data_mol_morph, Genus == &quot;Meloidogyne&quot;)
+set$prat_data &lt;- subset(set$data_mol_morph, Genus == &quot;Pratylenchus&quot;)
+
+
+
+# Quasi-binomial model for Globodera
+
+model_glob = glm(
+  Genus_Sample_Abundance ~ total_count,
+  data = set$glob_data,
+  family = quasibinomial
+)
+
+# Quasi-binomial model for Meloidogyne
+
+model_mel = glm(
+  Genus_Sample_Abundance ~ total_count,
+  data = set$mel_data,
+  family = quasibinomial
+)
+
+
+# Quasi-binomial model for Pratylenchus
+
+model_prat = glm(
+  Genus_Sample_Abundance ~ total_count,
+  data = set$prat_data,
+  family = quasibinomial
+)
+
+pval_glm &lt;- function(glm){
+  coef(summary(glm))[,4][2]
+}
+
+set$data_mol_morph &lt;- set$data_mol_morph %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Globodera&quot;, pval_glm(model_glob), NA)) %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Pratylenchus&quot;, pval_glm(model_prat), pval)) %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Meloidogyne&quot;, pval_glm(model_mel), pval)) %&gt;% 
+  mutate(signif = ifelse(pval &lt;= 0.05, &quot;pval &lt;= 0.05&quot;, &quot;pval &gt; 0.05&quot;))
+  
+# Other plotting variables
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 20
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 13
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+
+set$my_formula &lt;- y ~ x
+set$plot_title &lt;- paste(&quot;Molecular analysis vs&quot;,
+               &quot;morpological analysis&quot;, sep = &quot;\n&quot;)
+
+# Plotting quasibinomial model
+
+plots$mol_morph_quasi &lt;- ggplot(set$data_mol_morph, 
+                             aes(x=total_count, 
+                                 y = Genus_Sample_Abundance)) +
+  geom_point(alpha = 0.7) +
+  #ggtitle(set$plot_title) +
+  theme(plot.title = element_text(hjust = 0.5, size = 20), 
+        panel.grid.major = element_blank(), 
+        panel.grid.minor = element_blank(),
+        panel.background = element_blank(), 
+        #axis.line = element_line(colour = &quot;dark grey&quot;),
+        strip.background = element_blank(), 
+        strip.text = element_text(face = &quot;italic&quot;, size = 16),
+        axis.text = element_text(),
+        text = element_text()) +
+  xlab(&quot;Nematodes / 250 ml soil&quot;) + 
+  ylab(&quot;Relative abundance&quot;) +
+  geom_smooth(method = &quot;glm&quot;, formula = set$my_formula, se = F,
+              method.args = list(family = quasibinomial), 
+              aes(color = signif, linetype = signif)) +
+   scale_color_manual(values=c(&#39;black&#39;, &#39;lightgrey&#39;),
+                     name= &#39;Significance&#39;,
+                     labels = names(signif.labs)) +
+  scale_linetype_discrete(name= &#39;Significance&#39;,
+                     labels = names(signif.labs)) +
+  facet_wrap(~Genus, scales = &quot;free&quot;, 
+              #labeller = labeller(Genus = genus_labs)) +
+             labeller = genus_labeller)
+  
+#Print and save plot
+
+plots$mol_morph_quasi</code></pre>
+<p><img role="img" 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" width="672" /></p>
+<pre class="r"><code>save_plot(plots$mol_morph_quasi, plot_name = paste0(&quot;Molecular_and_Morphological_Plot&quot;), 
+          filetype = &quot;.pdf&quot;)
+
+
+# Generate table overview of morphological counts for manuscript
+morph_table_manuscript &lt;- pivot_wider(set$data_mol_morph %&gt;% 
+                                        select(-Genus_Sample_Abundance,
+                                               -ndvi), 
+                                      names_from = Genus, 
+                                      values_from = total_count) %&gt;%
+  arrange(Alias)
+write.xlsx(morph_table_manuscript, file = &quot;t1_morph_table_manuscript.xlsx&quot;)</code></pre>
+</div>
+<div id="ndvi-regression" class="section level3">
+<h3>NDVI regression</h3>
+<pre class="r"><code># Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
+# containing the sample abundances for that genus.
+ldf &lt;- data.frame(genus_abundance_tbl_per_sample %&gt;% pivot_wider(id_cols = c(&#39;Sample&#39;, &#39;ndvi&#39;), names_from = &#39;Genus&#39;, values_from = &#39;Genus_Sample_Abundance&#39;))
+ldf_genus_data &lt;- data.frame(ldf) %&gt;% select(!c(&#39;Sample&#39;, &#39;ndvi&#39;))
+colnames(ldf_genus_data) &lt;- gsub(&#39; &#39;, &#39;.&#39;, colnames(ldf_genus_data))
+ldf &lt;- cbind.data.frame(
+Sample = ldf$Sample,
+ndvi_01 = (ldf$ndvi + 1) / 2.0
+)
+ldf &lt;- tibble(cbind(ldf, ldf_genus_data))
+
+n_samples_by_genus &lt;- data.frame(ldf_genus_data &gt; 0) %&gt;% mutate_if(is.logical, as.numeric) %&gt;% colSums() %&gt;% sort(decreasing = TRUE)
+keep_n &lt;- 100 # Maximum number of genera to include in the analysis
+top_n_occurence_genuses &lt;- names(n_samples_by_genus[1:keep_n])
+top_n_occurence_genuses &lt;- top_n_occurence_genuses[!is.na(top_n_occurence_genuses)]
+
+l_genus_ldf &lt;- ldf %&gt;% select(all_of(top_n_occurence_genuses))
+l_genus_ldf_transposed &lt;- data.frame(t(l_genus_ldf))
+l_meta_ldf &lt;- ldf %&gt;% select(&#39;ndvi_01&#39;)
+
+l_model &lt;- linda(
+l_genus_ldf_transposed,
+l_meta_ldf,
+formula = &#39;~ ndvi_01&#39;,
+feature.dat.type = &#39;proportion&#39;,
+is.winsor = FALSE,
+alpha = 0.05
+)</code></pre>
+<pre><code>## 0  features are filtered!
+## The filtered data has  18  samples and  53  features will be tested!
+## Fit linear models ...
+## Completed.</code></pre>
+<pre class="r"><code># Print model info
+l_model</code></pre>
+<pre><code>## $variables
+## [1] &quot;ndvi_01&quot;
+## 
+## $bias
+## [1] -0.01789055
+## 
+## $output
+## $output$ndvi_01
+##                             baseMean log2FoldChange      lfcSE         stat
+## Globodera               754634.19865  -4.559618e-01 0.29935505 -1.523147257
+## Meloidogyne              61866.79371   2.765057e+00 0.40120028  6.891961655
+## Pristionchus             67346.31537   3.169910e-01 0.33017787  0.960061242
+## Rhabditis                56521.12829   4.432402e-01 0.27148299  1.632662775
+## Nygolaimus                3165.10477  -8.427746e-01 0.36100641 -2.334514200
+## Acrobeloides              4388.62890   7.941938e-01 0.24592277  3.229443800
+## Aporcelaimellus           8752.47207  -3.406609e-01 0.16569033 -2.056009379
+## Pellioditis              16832.59394  -2.604002e-01 0.43525867 -0.598265365
+## Cephaloboides             2025.06777   5.911949e-01 0.59646282  0.991168138
+## Eucephalobus               646.22070  -4.151353e-01 0.45558400 -0.911215723
+## Acrobeles                  651.64733   5.235050e-01 0.59599188  0.878375980
+## Mesorhabditis              524.38406   6.847116e-01 0.42269495  1.619871742
+## Pratylenchus               392.39406   6.840229e-01 0.35193863  1.943585597
+## Hypsibius                  242.94694  -2.893259e-01 0.35082408 -0.824703697
+## Aphelenchus                316.56727   2.529986e-02 0.48480642  0.052185488
+## Cruznema                  1363.79265   8.024877e-01 0.49336009  1.626575985
+## Aphelenchoides             822.11300  -2.162205e-01 0.38616715 -0.559914237
+## Anaplectus                 458.84418  -5.849614e-05 0.41156785 -0.000142130
+## Metateratocephalus         536.81600  -7.507380e-01 0.33206049 -2.260847143
+## Plectus                    434.97545  -6.856073e-01 0.42590207 -1.609776919
+## Paramphidelus              498.11205  -3.340229e-01 0.36976266 -0.903343935
+## Filenchus                  259.63928  -3.615399e-01 0.34704143 -1.041777350
+## Ditylenchus                425.99207  -5.460422e-01 0.29454627 -1.853841900
+## Sectonema                  233.31224  -8.387025e-01 0.47130542 -1.779530807
+## Chiloplacus                758.90167   2.180144e-01 0.18503697  1.178220939
+## Bastiania                   98.23107  -3.367691e-01 0.28834536 -1.167936648
+## Clarkus                    119.63544  -5.583309e-01 0.23586714 -2.367141657
+## Trischistoma               275.44460  -8.092726e-02 0.37115882 -0.218039431
+## Tylencholaimus             353.94743  -6.167086e-02 0.25399021 -0.242808008
+## Zeldia                     800.30996   6.173047e-02 0.28823133  0.214169867
+## Zeatylenchus               214.31405   6.287060e-02 0.31691865  0.198380869
+## Prismatolaimus             355.13517   2.221236e-01 0.15762989  1.409146273
+## Diploscapter               691.90325   3.177631e-01 0.25758203  1.233638597
+## Prodesmodora               199.12220   1.613313e-01 0.17248893  0.935314184
+## Panagrolaimus              411.24634   7.120651e-02 0.10241779  0.695255342
+## Diphascon                  142.12094   1.333432e-02 0.11281091  0.118200620
+## Mesaphorura                211.59911   7.592354e-02 0.08824782  0.860344677
+## Tullbergia                  64.19880  -1.033518e-01 0.16089301 -0.642363814
+## Spelobia                  6776.13632   2.047921e-02 0.08477302  0.241577026
+## Bunonema                  1135.86360  -8.197451e-02 0.10298133 -0.796013368
+## Aquatides                  950.11625  -1.370781e-01 0.11024072 -1.243443740
+## Myolaimus                  607.64266   2.047921e-02 0.08477302  0.241577026
+## Altainella                 526.06316   8.944697e-04 0.12485257  0.007164207
+## Dicyrtomina                393.04080  -6.957390e-02 0.11101082 -0.626730773
+## Anguina                    262.10103  -1.370781e-01 0.11024072 -1.243443740
+## Deladenus                  222.46410   2.879846e-02 0.10715623  0.268752044
+## Geobacter                  221.88520  -1.354813e-01 0.12565904 -1.078166078
+## NA.                        221.38501   1.493595e-02 0.12460371  0.119867621
+## Odontocepheus              217.59550   1.596974e-02 0.13517441  0.118141742
+## Theristus                  158.90293   2.879846e-02 0.10715623  0.268752044
+## Thonus                     116.90292   8.944697e-04 0.12485257  0.007164207
+## Candidatus.Acetothermum    116.90292   8.944697e-04 0.12485257  0.007164207
+## Sellnickochthonius          36.82683   2.047921e-02 0.08477302  0.241577026
+##                               pvalue         padj reject df
+## Globodera               1.472409e-01 0.5574120890  FALSE 16
+## Meloidogyne             3.623613e-06 0.0001920515   TRUE 16
+## Pristionchus            3.513092e-01 0.7352513393  FALSE 16
+## Rhabditis               1.220616e-01 0.5177548997  FALSE 16
+## Nygolaimus              3.292834e-02 0.4034160983  FALSE 16
+## Acrobeloides            5.242908e-03 0.1389370590  FALSE 16
+## Aporcelaimellus         5.647602e-02 0.4988715484  FALSE 16
+## Pellioditis             5.580376e-01 0.8450283790  FALSE 16
+## Cephaloboides           3.363572e-01 0.7352513393  FALSE 16
+## Eucephalobus            3.757063e-01 0.7352513393  FALSE 16
+## Acrobeles               3.927397e-01 0.7352513393  FALSE 16
+## Mesorhabditis           1.247991e-01 0.5177548997  FALSE 16
+## Pratylenchus            6.974921e-02 0.5177548997  FALSE 16
+## Hypsibius               4.216652e-01 0.7449418642  FALSE 16
+## Aphelenchus             9.590269e-01 0.9998883537  FALSE 16
+## Cruznema                1.233578e-01 0.5177548997  FALSE 16
+## Aphelenchoides          5.832927e-01 0.8587364852  FALSE 16
+## Anaplectus              9.998884e-01 0.9998883537  FALSE 16
+## Metateratocephalus      3.805812e-02 0.4034160983  FALSE 16
+## Plectus                 1.269965e-01 0.5177548997  FALSE 16
+## Paramphidelus           3.797431e-01 0.7352513393  FALSE 16
+## Filenchus               3.130015e-01 0.7352513393  FALSE 16
+## Ditylenchus             8.229094e-02 0.5177548997  FALSE 16
+## Sectonema               9.415207e-02 0.5177548997  FALSE 16
+## Chiloplacus             2.559357e-01 0.6888466043  FALSE 16
+## Bastiania               2.599421e-01 0.6888466043  FALSE 16
+## Clarkus                 3.086977e-02 0.4034160983  FALSE 16
+## Trischistoma            8.301549e-01 0.9955117434  FALSE 16
+## Tylencholaimus          8.112394e-01 0.9955117434  FALSE 16
+## Zeldia                  8.331202e-01 0.9955117434  FALSE 16
+## Zeatylenchus            8.452458e-01 0.9955117434  FALSE 16
+## Prismatolaimus          1.779308e-01 0.6286888072  FALSE 16
+## Diploscapter            2.351534e-01 0.6888466043  FALSE 16
+## Prodesmodora            3.635293e-01 0.7352513393  FALSE 16
+## Panagrolaimus           4.968695e-01 0.8229401169  FALSE 16
+## Diphascon               9.073799e-01 0.9998883537  FALSE 16
+## Mesaphorura             4.023073e-01 0.7352513393  FALSE 16
+## Tullbergia              5.297345e-01 0.8412595983  FALSE 16
+## Spelobia                8.121767e-01 0.9955117434  FALSE 16
+## Bunonema                4.376758e-01 0.7482844797  FALSE 16
+## Aquatides               2.316162e-01 0.6888466043  FALSE 16
+## Myolaimus               8.121767e-01 0.9955117434  FALSE 16
+## Altainella              9.943724e-01 0.9998883537  FALSE 16
+## Dicyrtomina             5.396760e-01 0.8412595983  FALSE 16
+## Anguina                 2.316162e-01 0.6888466043  FALSE 16
+## Deladenus               7.915551e-01 0.9955117434  FALSE 16
+## Geobacter               2.969467e-01 0.7352513393  FALSE 16
+## NA.                     9.060803e-01 0.9998883537  FALSE 16
+## Odontocepheus           9.074258e-01 0.9998883537  FALSE 16
+## Theristus               7.915551e-01 0.9955117434  FALSE 16
+## Thonus                  9.943724e-01 0.9998883537  FALSE 16
+## Candidatus.Acetothermum 9.943724e-01 0.9998883537  FALSE 16
+## Sellnickochthonius      8.121767e-01 0.9955117434  FALSE 16
+## 
+## 
+## $covariance
+## NULL
+## 
+## $feature.dat.use
+##                                    1            2            3            4
+## Globodera               9.555057e-01 8.863991e-01 7.903606e-01 7.858771e-01
+## Meloidogyne             2.861367e-04 3.180478e-04 1.298566e-01 1.009443e-02
+## Pristionchus            1.449759e-02 7.745909e-02 1.233878e-02 4.214542e-02
+## Rhabditis               7.916448e-03 6.129648e-03 3.989139e-02 3.940085e-02
+## Nygolaimus              1.096857e-03 7.054878e-03 1.920696e-02 3.246965e-02
+## Acrobeloides            2.146025e-03 7.517493e-04 1.677115e-03 7.442899e-04
+## Aporcelaimellus         3.719777e-03 5.002024e-03 2.675398e-03 1.079220e-02
+## Pellioditis             4.816634e-03 8.702943e-03 6.799946e-04 5.312369e-02
+## Cephaloboides           1.047296e-04 3.180478e-03 2.795192e-04 7.442899e-04
+## Eucephalobus            1.573752e-03 1.047296e-04 1.047296e-04 1.442062e-03
+## Acrobeles               4.768945e-04 4.555394e-05 4.555394e-05 1.162953e-03
+## Mesorhabditis           6.199628e-04 8.247649e-05 8.247649e-05 2.791087e-04
+## Pratylenchus            7.100659e-05 7.100659e-05 1.118077e-03 5.582174e-04
+## Hypsibius               3.815156e-04 8.095761e-04 2.795192e-04 6.833090e-05
+## Aphelenchus             7.100659e-05 1.330018e-03 6.389011e-04 3.721450e-04
+## Cruznema                2.816901e-04 2.816901e-04 2.816901e-04 2.816901e-04
+## Aphelenchoides          3.099814e-03 5.493552e-04 2.414224e-04 1.674652e-03
+## Anaplectus              1.099687e-04 1.099687e-04 1.099687e-04 3.349305e-03
+## Metateratocephalus      3.338261e-04 4.337015e-04 1.669131e-04 1.669131e-04
+## Plectus                 1.011970e-04 2.023940e-04 7.187637e-04 4.651812e-03
+## Paramphidelus           1.597253e-04 1.597253e-04 3.194505e-04 5.116993e-04
+## Filenchus               9.237753e-05 2.891343e-04 9.237753e-05 3.628413e-03
+## Ditylenchus             1.559495e-04 5.204418e-04 1.559495e-04 1.721170e-03
+## Sectonema               7.918074e-05 3.469612e-04 7.918074e-05 7.918074e-05
+## Chiloplacus             3.416545e-04 3.416545e-04 3.416545e-04 1.535098e-03
+## Bastiania               4.189184e-05 4.189184e-05 4.189184e-05 3.256268e-04
+## Clarkus                 2.861367e-04 1.156537e-04 5.782687e-05 6.977718e-04
+## Trischistoma            5.722734e-04 1.156337e-04 1.156337e-04 8.838443e-04
+## Tylencholaimus          1.924452e-04 1.924452e-04 6.389011e-04 1.924452e-04
+## Zeldia                  2.670609e-03 4.189184e-04 4.189184e-04 4.189184e-04
+## Zeatylenchus            1.047296e-04 1.047296e-04 1.047296e-04 1.047296e-04
+## Prismatolaimus          2.111486e-04 2.111486e-04 2.111486e-04 2.111486e-04
+## Diploscapter            4.118382e-04 4.118382e-04 4.118382e-04 4.118382e-04
+## Prodesmodora            1.246417e-04 1.246417e-04 1.246417e-04 1.246417e-04
+## Panagrolaimus           2.804437e-04 2.804437e-04 2.804437e-04 2.804437e-04
+## Diphascon               9.303624e-05 9.303624e-05 9.303624e-05 1.860725e-04
+## Mesaphorura             1.451647e-04 1.451647e-04 1.451647e-04 1.451647e-04
+## Tullbergia              4.337015e-05 8.674030e-05 4.337015e-05 4.337015e-05
+## Spelobia                4.856419e-03 4.856419e-03 4.856419e-03 4.856419e-03
+## Bunonema                8.140671e-04 8.140671e-04 8.140671e-04 1.628134e-03
+## Aquatides               6.809430e-04 6.809430e-04 6.809430e-04 6.809430e-04
+## Myolaimus               4.354941e-04 4.354941e-04 4.354941e-04 4.354941e-04
+## Altainella              3.770265e-04 3.770265e-04 3.770265e-04 3.770265e-04
+## Dicyrtomina             2.816901e-04 2.816901e-04 2.816901e-04 2.816901e-04
+## Anguina                 1.878463e-04 1.878463e-04 1.878463e-04 1.878463e-04
+## Deladenus               1.594388e-04 1.594388e-04 1.594388e-04 1.594388e-04
+## Geobacter               1.590239e-04 3.180478e-04 1.590239e-04 1.590239e-04
+## NA.                     1.586654e-04 1.586654e-04 1.586654e-04 1.586654e-04
+## Odontocepheus           1.559495e-04 1.559495e-04 1.559495e-04 1.559495e-04
+## Theristus               1.138848e-04 1.138848e-04 1.138848e-04 1.138848e-04
+## Thonus                  8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
+## Candidatus.Acetothermum 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
+## Sellnickochthonius      2.639358e-05 2.639358e-05 2.639358e-05 2.639358e-05
+##                                    5            6            7            8
+## Globodera               7.743274e-01 7.720015e-01 7.663992e-01 7.303019e-01
+## Meloidogyne             5.805581e-02 2.442002e-03 4.451698e-02 1.048001e-01
+## Pristionchus            3.955396e-02 2.855264e-02 1.336869e-01 2.243361e-02
+## Rhabditis               7.550755e-02 9.711656e-02 2.180516e-02 6.367185e-02
+## Nygolaimus              2.100210e-03 1.131774e-02 5.893286e-04 1.044702e-03
+## Acrobeloides            1.700170e-03 1.737579e-03 1.994651e-03 1.814483e-03
+## Aporcelaimellus         6.650665e-03 1.709402e-02 5.802620e-03 6.983010e-03
+## Pellioditis             1.105111e-02 2.503053e-02 1.359989e-03 4.701160e-02
+## Cephaloboides           1.200120e-03 1.784540e-03 4.487964e-03 2.749216e-04
+## Eucephalobus            2.200220e-03 6.574622e-04 1.047296e-04 1.110683e-02
+## Acrobeles               1.600160e-03 4.555394e-05 3.173308e-03 4.555394e-05
+## Mesorhabditis           8.247649e-05 4.696159e-04 1.178657e-03 1.649530e-04
+## Pratylenchus            3.500350e-04 2.817695e-04 8.159935e-04 7.100659e-05
+## Hypsibius               3.500350e-04 2.348079e-04 1.813319e-04 6.833090e-05
+## Aphelenchus             3.000300e-04 7.100659e-05 7.100659e-05 7.100659e-05
+## Cruznema                2.816901e-04 3.052503e-03 1.128791e-02 1.209655e-03
+## Aphelenchoides          2.000200e-03 2.414224e-04 1.133324e-03 3.848903e-03
+## Anaplectus              3.500350e-03 1.099687e-04 1.269323e-03 2.199373e-04
+## Metateratocephalus      7.150715e-03 3.287311e-03 1.669131e-04 1.669131e-04
+## Plectus                 1.900190e-03 6.574622e-04 1.011970e-04 1.011970e-04
+## Paramphidelus           5.500550e-04 1.690617e-03 1.597253e-04 1.597253e-04
+## Filenchus               9.237753e-05 9.237753e-05 9.237753e-05 9.237753e-05
+## Ditylenchus             3.650365e-03 1.559495e-04 1.559495e-04 3.848903e-04
+## Sectonema               7.918074e-05 3.057199e-02 7.918074e-05 7.918074e-05
+## Chiloplacus             8.500850e-04 3.416545e-04 3.416545e-04 1.264640e-03
+## Bastiania               4.189184e-05 9.392317e-05 4.189184e-05 4.189184e-05
+## Clarkus                 5.782687e-05 1.878463e-04 5.782687e-05 5.782687e-05
+## Trischistoma            1.156337e-04 1.156337e-04 1.156337e-04 3.079122e-03
+## Tylencholaimus          3.050305e-03 1.924452e-04 1.924452e-04 3.848903e-04
+## Zeldia                  4.189184e-04 4.189184e-04 4.189184e-04 4.189184e-04
+## Zeatylenchus            2.400240e-03 1.047296e-04 1.047296e-04 1.047296e-04
+## Prismatolaimus          2.111486e-04 2.111486e-04 2.111486e-04 2.111486e-04
+## Diploscapter            4.118382e-04 4.118382e-04 4.118382e-04 4.118382e-04
+## Prodesmodora            1.246417e-04 1.246417e-04 1.246417e-04 1.246417e-04
+## Panagrolaimus           2.804437e-04 2.804437e-04 2.804437e-04 2.804437e-04
+## Diphascon               9.303624e-05 9.303624e-05 9.303624e-05 9.303624e-05
+## Mesaphorura             1.451647e-04 1.451647e-04 1.451647e-04 1.451647e-04
+## Tullbergia              4.337015e-05 4.337015e-05 4.337015e-05 4.337015e-05
+## Spelobia                4.856419e-03 4.856419e-03 4.856419e-03 4.856419e-03
+## Bunonema                8.140671e-04 8.140671e-04 8.140671e-04 8.140671e-04
+## Aquatides               6.809430e-04 1.361886e-03 6.809430e-04 6.809430e-04
+## Myolaimus               4.354941e-04 4.354941e-04 4.354941e-04 4.354941e-04
+## Altainella              3.770265e-04 3.770265e-04 3.770265e-04 3.770265e-04
+## Dicyrtomina             2.816901e-04 2.816901e-04 2.816901e-04 2.816901e-04
+## Anguina                 1.878463e-04 3.756927e-04 1.878463e-04 1.878463e-04
+## Deladenus               1.594388e-04 1.594388e-04 1.594388e-04 1.594388e-04
+## Geobacter               1.590239e-04 1.590239e-04 1.590239e-04 1.590239e-04
+## NA.                     1.586654e-04 1.586654e-04 3.173308e-04 1.586654e-04
+## Odontocepheus           1.559495e-04 1.559495e-04 1.559495e-04 1.559495e-04
+## Theristus               1.138848e-04 1.138848e-04 1.138848e-04 1.138848e-04
+## Thonus                  8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
+## Candidatus.Acetothermum 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
+## Sellnickochthonius      2.639358e-05 2.639358e-05 2.639358e-05 2.639358e-05
+##                                    9           10           11           12
+## Globodera               7.288732e-01 7.262720e-01 6.823507e-01 6.584559e-01
+## Meloidogyne             3.880282e-02 1.998612e-01 1.409697e-01 1.735998e-01
+## Pristionchus            1.004225e-01 2.835338e-02 8.288670e-02 1.135269e-02
+## Rhabditis               6.035211e-02 1.845513e-02 5.421912e-02 3.682292e-02
+## Nygolaimus              1.901408e-03 1.433858e-03 9.971332e-04 1.675673e-03
+## Acrobeloides            5.070423e-03 2.543941e-03 4.424779e-03 1.390809e-02
+## Aporcelaimellus         2.014085e-02 7.493062e-03 6.855291e-03 5.990532e-03
+## Pellioditis             2.894366e-02 5.134135e-03 5.733516e-03 9.174312e-02
+## Cephaloboides           1.971831e-03 8.325624e-04 1.283809e-02 2.094592e-04
+## Eucephalobus            9.154930e-04 4.162812e-04 3.739250e-04 2.094592e-04
+## Acrobeles               2.535211e-03 6.197965e-03 6.855291e-04 5.864857e-04
+## Mesorhabditis           3.521127e-04 8.247649e-05 9.348124e-04 8.247649e-05
+## Pratylenchus            7.100659e-05 1.850139e-04 9.971332e-04 7.100659e-05
+## Hypsibius               6.833090e-05 1.850139e-04 6.833090e-05 6.833090e-05
+## Aphelenchus             2.816901e-04 4.162812e-04 7.100659e-05 5.864857e-04
+## Cruznema                5.633803e-04 2.816901e-04 2.816901e-04 2.816901e-04
+## Aphelenchoides          2.414224e-04 2.414224e-04 2.414224e-04 2.414224e-04
+## Anaplectus              9.859155e-04 1.099687e-04 6.855291e-04 1.099687e-04
+## Metateratocephalus      6.338028e-04 4.625347e-04 1.669131e-04 3.770265e-04
+## Plectus                 3.028169e-03 1.011970e-04 4.362458e-04 1.011970e-04
+## Paramphidelus           1.126761e-03 1.597253e-04 1.597253e-04 1.885133e-03
+## Filenchus               9.154930e-04 2.775208e-04 3.116041e-04 2.513510e-04
+## Ditylenchus             8.450704e-04 1.559495e-04 1.559495e-04 1.559495e-04
+## Sectonema               7.918074e-05 2.775208e-04 7.918074e-05 7.918074e-05
+## Chiloplacus             3.416545e-04 3.416545e-04 3.416545e-04 3.416545e-04
+## Bastiania               7.746479e-04 1.387604e-04 4.189184e-05 8.378367e-05
+## Clarkus                 5.782687e-05 5.782687e-05 5.782687e-05 5.782687e-05
+## Trischistoma            1.156337e-04 2.312673e-04 1.156337e-04 1.156337e-04
+## Tylencholaimus          1.924452e-04 1.924452e-04 1.924452e-04 1.924452e-04
+## Zeldia                  4.189184e-04 4.189184e-04 2.929079e-03 8.378367e-04
+## Zeatylenchus            1.047296e-04 3.700278e-04 1.047296e-04 2.094592e-04
+## Prismatolaimus          2.111486e-04 4.625347e-04 5.608874e-04 2.111486e-04
+## Diploscapter            4.118382e-04 4.118382e-04 4.118382e-04 4.118382e-04
+## Prodesmodora            1.246417e-04 1.246417e-04 2.492833e-04 1.246417e-04
+## Panagrolaimus           2.804437e-04 2.804437e-04 5.608874e-04 2.804437e-04
+## Diphascon               9.303624e-05 9.303624e-05 9.303624e-05 9.303624e-05
+## Mesaphorura             1.451647e-04 1.451647e-04 1.451647e-04 1.451647e-04
+## Tullbergia              4.337015e-05 4.337015e-05 4.337015e-05 1.256755e-04
+## Spelobia                4.856419e-03 4.856419e-03 4.856419e-03 4.856419e-03
+## Bunonema                8.140671e-04 8.140671e-04 8.140671e-04 8.140671e-04
+## Aquatides               6.809430e-04 6.809430e-04 6.809430e-04 6.809430e-04
+## Myolaimus               4.354941e-04 4.354941e-04 4.354941e-04 4.354941e-04
+## Altainella              3.770265e-04 3.770265e-04 3.770265e-04 7.540530e-04
+## Dicyrtomina             5.633803e-04 2.816901e-04 2.816901e-04 2.816901e-04
+## Anguina                 1.878463e-04 1.878463e-04 1.878463e-04 1.878463e-04
+## Deladenus               1.594388e-04 1.594388e-04 1.594388e-04 1.594388e-04
+## Geobacter               1.590239e-04 1.590239e-04 1.590239e-04 1.590239e-04
+## NA.                     1.586654e-04 1.586654e-04 1.586654e-04 1.586654e-04
+## Odontocepheus           1.559495e-04 1.559495e-04 1.559495e-04 1.559495e-04
+## Theristus               1.138848e-04 1.138848e-04 1.138848e-04 1.138848e-04
+## Thonus                  8.378367e-05 8.378367e-05 8.378367e-05 1.675673e-04
+## Candidatus.Acetothermum 8.378367e-05 8.378367e-05 8.378367e-05 1.675673e-04
+## Sellnickochthonius      2.639358e-05 2.639358e-05 2.639358e-05 2.639358e-05
+##                                   13           14           15           16
+## Globodera               6.070910e-01 5.876176e-01 5.180925e-01 6.273754e-02
+## Meloidogyne             2.026938e-02 3.679888e-01 2.289820e-01 6.050728e-01
+## Pristionchus            2.396012e-01 9.356968e-03 7.322200e-02 1.390942e-01
+## Rhabditis               6.727849e-02 1.398347e-02 1.427800e-01 7.242399e-02
+## Nygolaimus              1.716625e-03 1.299579e-03 3.976369e-04 3.035262e-03
+## Acrobeloides            9.573485e-03 1.871394e-03 5.850943e-03 2.190667e-02
+## Aporcelaimellus         4.489634e-03 5.926080e-03 1.334924e-03 1.470122e-02
+## Pellioditis             4.417008e-02 8.785154e-03 1.448534e-02 9.976774e-03
+## Cephaloboides           3.961442e-04 1.047296e-04 9.259259e-03 1.805321e-02
+## Eucephalobus            2.640961e-04 2.599158e-04 1.047296e-04 6.598395e-04
+## Acrobeles               1.254457e-03 1.507512e-03 4.555394e-05 3.827069e-03
+## Mesorhabditis           5.281923e-04 8.247649e-05 1.050898e-03 5.225929e-03
+## Pratylenchus            9.243365e-04 7.100659e-05 1.420132e-04 2.111486e-03
+## Hypsibius               2.640961e-04 6.833090e-05 3.976369e-04 1.821157e-03
+## Aphelenchus             7.100659e-05 7.100659e-05 1.420132e-04 6.070524e-04
+## Cruznema                1.320481e-03 2.816901e-04 1.107703e-03 9.633657e-03
+## Aphelenchoides          2.414224e-04 2.414224e-04 4.828448e-04 1.055743e-03
+## Anaplectus              1.099687e-04 1.099687e-04 1.099687e-04 4.750845e-04
+## Metateratocephalus      1.669131e-04 1.669131e-04 1.669131e-04 1.583615e-03
+## Plectus                 1.011970e-04 1.011970e-04 1.011970e-04 1.011970e-04
+## Paramphidelus           1.597253e-04 7.797474e-04 1.597253e-04 6.519215e-03
+## Filenchus               9.237753e-05 9.237753e-05 9.237753e-05 1.847551e-04
+## Ditylenchus             5.942163e-04 3.118989e-04 1.559495e-04 6.334459e-04
+## Sectonema               2.640961e-04 7.918074e-05 7.918074e-05 1.583615e-04
+## Chiloplacus             3.416545e-04 3.416545e-04 1.278119e-03 1.847551e-03
+## Bastiania               4.189184e-05 4.189184e-05 4.189184e-05 4.486909e-04
+## Clarkus                 5.782687e-05 5.782687e-05 1.704158e-04 5.782687e-05
+## Trischistoma            1.156337e-04 1.156337e-04 1.156337e-04 1.741976e-03
+## Tylencholaimus          1.924452e-04 1.924452e-04 1.924452e-04 5.278716e-04
+## Zeldia                  4.189184e-04 4.189184e-04 4.189184e-04 4.189184e-04
+## Zeatylenchus            1.047296e-04 1.047296e-04 1.047296e-04 1.047296e-04
+## Prismatolaimus          2.111486e-04 2.111486e-04 2.111486e-04 4.222973e-04
+## Diploscapter            4.118382e-04 4.118382e-04 8.236764e-04 4.118382e-04
+## Prodesmodora            1.246417e-04 1.246417e-04 1.246417e-04 1.425253e-03
+## Panagrolaimus           2.804437e-04 2.804437e-04 2.804437e-04 6.862331e-04
+## Diphascon               9.303624e-05 9.303624e-05 9.303624e-05 4.750845e-04
+## Mesaphorura             1.451647e-04 1.451647e-04 1.451647e-04 2.903294e-04
+## Tullbergia              4.337015e-05 4.337015e-05 4.337015e-05 4.337015e-05
+## Spelobia                4.856419e-03 4.856419e-03 4.856419e-03 9.712838e-03
+## Bunonema                8.140671e-04 8.140671e-04 8.140671e-04 8.140671e-04
+## Aquatides               6.809430e-04 6.809430e-04 6.809430e-04 6.809430e-04
+## Myolaimus               4.354941e-04 4.354941e-04 4.354941e-04 8.709882e-04
+## Altainella              3.770265e-04 3.770265e-04 3.770265e-04 3.770265e-04
+## Dicyrtomina             2.816901e-04 2.816901e-04 2.816901e-04 2.816901e-04
+## Anguina                 1.878463e-04 1.878463e-04 1.878463e-04 1.878463e-04
+## Deladenus               1.594388e-04 1.594388e-04 1.594388e-04 1.594388e-04
+## Geobacter               1.590239e-04 1.590239e-04 1.590239e-04 1.590239e-04
+## NA.                     1.586654e-04 1.586654e-04 1.586654e-04 1.586654e-04
+## Odontocepheus           1.559495e-04 3.118989e-04 1.559495e-04 1.559495e-04
+## Theristus               1.138848e-04 1.138848e-04 1.138848e-04 1.138848e-04
+## Thonus                  8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
+## Candidatus.Acetothermum 8.378367e-05 8.378367e-05 8.378367e-05 8.378367e-05
+## Sellnickochthonius      2.639358e-05 2.639358e-05 2.639358e-05 5.278716e-05
+##                                   17           18
+## Globodera               2.338695e-01 2.761935e-01
+## Meloidogyne             1.566616e-01 3.619716e-01
+## Pristionchus            3.462822e-01 9.648324e-02
+## Rhabditis               5.256449e-02 1.953808e-01
+## Nygolaimus              5.827011e-03 1.002187e-03
+## Acrobeloides            7.465857e-03 6.195335e-03
+## Aporcelaimellus         8.376328e-03 6.741983e-03
+## Pellioditis             4.564492e-02 1.093294e-02
+## Cephaloboides           8.758725e-02 3.325437e-03
+## Eucephalobus            3.095599e-03 1.047296e-04
+## Acrobeles               9.711684e-04 9.110787e-05
+## Mesorhabditis           4.309560e-03 2.186589e-03
+## Pratylenchus            8.497724e-04 8.655248e-04
+## Hypsibius               6.833090e-05 1.366618e-04
+## Aphelenchus             1.396055e-02 7.100659e-05
+## Cruznema                1.566009e-02 2.291363e-02
+## Aphelenchoides          2.414224e-04 4.145408e-03
+## Anaplectus              9.711684e-04 1.685496e-03
+## Metateratocephalus      1.669131e-04 1.138848e-03
+## Plectus                 6.069803e-04 2.049927e-03
+## Paramphidelus           1.597253e-04 1.597253e-04
+## Filenchus               9.237753e-05 8.199708e-04
+## Ditylenchus             1.559495e-04 1.559495e-04
+## Sectonema               4.855842e-04 4.555394e-04
+## Chiloplacus             1.760243e-03 6.833090e-04
+## Bastiania               4.189184e-05 4.189184e-05
+## Clarkus                 1.213961e-04 5.782687e-05
+## Trischistoma            1.156337e-04 1.156337e-04
+## Tylencholaimus          1.924452e-04 1.924452e-04
+## Zeldia                  4.189184e-04 2.687682e-03
+## Zeatylenchus            1.274659e-03 1.047296e-04
+## Prismatolaimus          2.111486e-04 1.047741e-03
+## Diploscapter            1.165402e-02 4.118382e-04
+## Prodesmodora            1.246417e-04 1.246417e-04
+## Panagrolaimus           2.804437e-04 2.804437e-04
+## Diphascon               9.303624e-05 9.303624e-05
+## Mesaphorura             1.451647e-04 3.188776e-04
+## Tullbergia              4.337015e-05 4.337015e-05
+## Spelobia                4.856419e-03 4.856419e-03
+## Bunonema                8.140671e-04 8.140671e-04
+## Aquatides               6.809430e-04 6.809430e-04
+## Myolaimus               4.354941e-04 4.354941e-04
+## Altainella              3.770265e-04 3.770265e-04
+## Dicyrtomina             2.816901e-04 2.816901e-04
+## Anguina                 1.878463e-04 1.878463e-04
+## Deladenus               1.594388e-04 3.188776e-04
+## Geobacter               1.590239e-04 1.590239e-04
+## NA.                     1.586654e-04 1.586654e-04
+## Odontocepheus           1.559495e-04 1.559495e-04
+## Theristus               1.138848e-04 2.277697e-04
+## Thonus                  8.378367e-05 8.378367e-05
+## Candidatus.Acetothermum 8.378367e-05 8.378367e-05
+## Sellnickochthonius      2.639358e-05 2.639358e-05
+## 
+## $meta.dat.use
+##       ndvi_01
+## 1  -1.1410253
+## 2  -1.9625407
+## 3   0.5747102
+## 4  -1.0529250
+## 5  -0.7571646
+## 6  -1.9896865
+## 7   0.5945529
+## 8   0.3130208
+## 9  -0.8421145
+## 10  0.5095020
+## 11  0.6619353
+## 12  0.3558477
+## 13  0.6228720
+## 14  0.6121273
+## 15  0.9980715
+## 16  0.6887884
+## 17  0.9838132
+## 18  0.8302155
+## 
+## $wald
+## $wald$beta
+##              Globodera Meloidogyne Pristionchus Rhabditis Nygolaimus
+## (Intercept) 10.1047233    6.496183    6.6186168 6.3658080  2.2073429
+## ndvi_01     -0.4738524    2.747166    0.2991004 0.4253496 -0.8606651
+##             Acrobeloides Aporcelaimellus Pellioditis Cephaloboides Eucephalobus
+## (Intercept)    2.6788599       3.6747802   4.6182753     1.5630598  -0.08481149
+## ndvi_01        0.7763032      -0.3585514  -0.2782907     0.5733044  -0.43302585
+##               Acrobeles Mesorhabditis Pratylenchus  Hypsibius  Aphelenchus
+## (Intercept) -0.07274706    -0.3862146   -0.8045352 -1.4961972 -1.114326359
+## ndvi_01      0.50561441     0.6668211    0.6661323 -0.3072165  0.007409314
+##              Cruznema Aphelenchoides  Anaplectus Metateratocephalus    Plectus
+## (Intercept) 0.9927140      0.2624983 -0.57883414         -0.3524108 -0.6559045
+## ndvi_01     0.7845971     -0.2341110 -0.01794904         -0.7686285 -0.7034979
+##             Paramphidelus  Filenchus Ditylenchus  Sectonema Chiloplacus
+## (Intercept)    -0.4603681 -1.4003298  -0.6860119 -1.5545764   0.1470745
+## ndvi_01        -0.3519134 -0.3794304  -0.5639328 -0.8565931   0.2001239
+##              Bastiania    Clarkus Trischistoma Tylencholaimus     Zeldia
+## (Intercept) -2.8025871 -2.5181936   -1.3150763     -0.9533033 0.22372041
+## ndvi_01     -0.3546597 -0.5762215   -0.0988178     -0.0795614 0.04383992
+##             Zeatylenchus Prismatolaimus Diploscapter Prodesmodora Panagrolaimus
+## (Intercept)  -1.67711201     -0.9484702   0.01373187   -1.7831843   -0.73683560
+## ndvi_01       0.04498005      0.2042330   0.29987259    0.1434408    0.05331597
+##                Diphascon Mesaphorura Tullbergia    Spelobia    Bunonema
+## (Intercept) -2.269719357 -1.69550491 -3.4162203 3.305552544  0.72887925
+## ndvi_01     -0.004556226  0.05803299 -0.1212424 0.002588669 -0.09986506
+##              Aquatides    Myolaimus  Altainella Dicyrtomina    Anguina
+## (Intercept)  0.4712656 -0.173615293 -0.38160243 -0.80215936 -1.3867154
+## ndvi_01     -0.1549687  0.002588669 -0.01699608 -0.08746444 -0.1549687
+##               Deladenus  Geobacter          NA. Odontocepheus   Theristus
+## (Intercept) -1.62326589 -1.6270250 -1.630280895  -1.655189754 -2.10869272
+## ndvi_01      0.01090791 -0.1533719 -0.002954595  -0.001920806  0.01090791
+##                  Thonus Candidatus.Acetothermum Sellnickochthonius
+## (Intercept) -2.55152743             -2.55152743       -4.218009412
+## ndvi_01     -0.01699608             -0.01699608        0.002588669
+## 
+## $wald$sig
+##               Globodera             Meloidogyne            Pristionchus 
+##               1.2342725               1.6541912               1.3613582 
+##               Rhabditis              Nygolaimus            Acrobeloides 
+##               1.1193531               1.4884675               1.0139656 
+##         Aporcelaimellus             Pellioditis           Cephaloboides 
+##               0.6831587               1.7946175               2.4592792 
+##            Eucephalobus               Acrobeles           Mesorhabditis 
+##               1.8784209               2.4573375               1.7428159 
+##            Pratylenchus               Hypsibius             Aphelenchus 
+##               1.4510802               1.4464847               1.9989081 
+##                Cruznema          Aphelenchoides              Anaplectus 
+##               2.0341758               1.5922080               1.6969377 
+##      Metateratocephalus                 Plectus           Paramphidelus 
+##               1.3691205               1.7560392               1.5245705 
+##               Filenchus             Ditylenchus               Sectonema 
+##               1.4308885               1.2144454               1.9432420 
+##             Chiloplacus               Bastiania                 Clarkus 
+##               0.7629270               1.1888784               0.9725051 
+##            Trischistoma          Tylencholaimus                  Zeldia 
+##               1.5303270               1.0472285               1.1884082 
+##            Zeatylenchus          Prismatolaimus            Diploscapter 
+##               1.3066891               0.6499247               1.0620379 
+##            Prodesmodora           Panagrolaimus               Diphascon 
+##               0.7111901               0.4222793               0.4651313 
+##             Mesaphorura              Tullbergia                Spelobia 
+##               0.3638551               0.6633789               0.3495281 
+##                Bunonema               Aquatides               Myolaimus 
+##               0.4246029               0.4545341               0.3495281 
+##              Altainella             Dicyrtomina                 Anguina 
+##               0.5147803               0.4577093               0.4545341 
+##               Deladenus               Geobacter                     NA. 
+##               0.4418165               0.5181055               0.5137543 
+##           Odontocepheus               Theristus                  Thonus 
+##               0.5573384               0.4418165               0.5147803 
+## Candidatus.Acetothermum      Sellnickochthonius 
+##               0.5147803               0.3495281 
+## 
+## $wald$X
+##    (Intercept)    ndvi_01
+## 1            1 -1.1410253
+## 2            1 -1.9625407
+## 3            1  0.5747102
+## 4            1 -1.0529250
+## 5            1 -0.7571646
+## 6            1 -1.9896865
+## 7            1  0.5945529
+## 8            1  0.3130208
+## 9            1 -0.8421145
+## 10           1  0.5095020
+## 11           1  0.6619353
+## 12           1  0.3558477
+## 13           1  0.6228720
+## 14           1  0.6121273
+## 15           1  0.9980715
+## 16           1  0.6887884
+## 17           1  0.9838132
+## 18           1  0.8302155
+## attr(,&quot;assign&quot;)
+## [1] 0 1
+## 
+## $wald$bias
+## [1] -1.00756447 -0.01789055</code></pre>
+<pre class="r"><code># Show effect size and significance plots
+linda.plot(
+l_model,
+variables.plot = c(&#39;ndvi_01&#39;),
+alpha = 0.05,
+lfc.cut = 1,
+legend = TRUE
+)</code></pre>
+<pre><code>## $plot.lfc
+## $plot.lfc[[1]]</code></pre>
+<p><img role="img" 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" width="672" /></p>
+<pre><code>## 
+## 
+## $plot.volcano
+## $plot.volcano[[1]]</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+<pre class="r"><code># Write supplementary table for manuscript
+
+
+l_model_df &lt;- as.data.frame(l_model$output)
+
+write.xlsx(l_model_df, file = &quot;supplementary_table_ndvi_regression_nematodes.xlsx&quot;, rowNames = TRUE, colnames = TRUE)</code></pre>
+</div>
+<div id="morph-count-and-ndvi" class="section level3">
+<h3>Morph count and NDVI</h3>
+<pre class="r"><code># Prat
+
+set$prat_data$ndvi_temp &lt;- (set$prat_data$ndvi+1)/2
+
+model_prat_count_ndvi = glm(
+  ndvi_temp ~ total_count,
+  data = set$prat_data,
+  family = quasibinomial 
+)
+
+summary(model_prat_count_ndvi)</code></pre>
+<pre><code>## 
+## Call:
+## glm(formula = ndvi_temp ~ total_count, family = quasibinomial, 
+##     data = set$prat_data)
+## 
+## Coefficients:
+##             Estimate Std. Error t value Pr(&gt;|t|)  
+## (Intercept) 0.263912   0.203506   1.297   0.2131  
+## total_count 0.011331   0.004609   2.459   0.0257 *
+## ---
+## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+## 
+## (Dispersion parameter for quasibinomial family taken to be 0.0613813)
+## 
+##     Null deviance: 1.4028  on 17  degrees of freedom
+## Residual deviance: 1.0038  on 16  degrees of freedom
+## AIC: NA
+## 
+## Number of Fisher Scoring iterations: 4</code></pre>
+<pre class="r"><code># Glob
+
+set$glob_data$ndvi_temp &lt;- (set$glob_data$ndvi+1)/2
+
+model_glob_count_ndvi = glm(
+  ndvi_temp ~ total_count,
+  data = set$glob_data,
+  family = quasibinomial 
+)
+
+summary(model_glob_count_ndvi)</code></pre>
+<pre><code>## 
+## Call:
+## glm(formula = ndvi_temp ~ total_count, family = quasibinomial, 
+##     data = set$glob_data)
+## 
+## Coefficients:
+##              Estimate Std. Error t value Pr(&gt;|t|)   
+## (Intercept)  1.149679   0.287632   3.997  0.00104 **
+## total_count -0.004626   0.002444  -1.893  0.07662 . 
+## ---
+## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+## 
+## (Dispersion parameter for quasibinomial family taken to be 0.07214207)
+## 
+##     Null deviance: 1.4028  on 17  degrees of freedom
+## Residual deviance: 1.1402  on 16  degrees of freedom
+## AIC: NA
+## 
+## Number of Fisher Scoring iterations: 4</code></pre>
+<pre class="r"><code># Mel
+
+set$mel_data$ndvi_temp &lt;- (set$mel_data$ndvi+1)/2
+
+model_mel_count_ndvi = glm(
+  ndvi_temp ~ total_count,
+  data = set$mel_data,
+  family = quasibinomial 
+)
+
+summary(model_mel_count_ndvi)</code></pre>
+<pre><code>## 
+## Call:
+## glm(formula = ndvi_temp ~ total_count, family = quasibinomial, 
+##     data = set$mel_data)
+## 
+## Coefficients:
+##              Estimate Std. Error t value Pr(&gt;|t|)    
+## (Intercept) 0.1416789  0.1490480   0.951 0.355967    
+## total_count 0.0024307  0.0005518   4.405 0.000443 ***
+## ---
+## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
+## 
+## (Dispersion parameter for quasibinomial family taken to be 0.0369228)
+## 
+##     Null deviance: 1.40277  on 17  degrees of freedom
+## Residual deviance: 0.59886  on 16  degrees of freedom
+## AIC: NA
+## 
+## Number of Fisher Scoring iterations: 4</code></pre>
+<pre class="r"><code>data_mol_morph_long_temp &lt;- pivot_longer(set$data_mol_morph, 
+                                        cols = c(Genus_Sample_Abundance, 
+                                                 total_count),
+                                        names_to = &quot;type&quot;, 
+                                        values_to = &quot;value&quot;)
+
+data_mol_morph_long_temp$ndvi &lt;- (data_mol_morph_long_temp$ndvi+1)/2
+  
+
+pval_glm &lt;- function(glm){
+  coef(summary(glm))[,4][2]
+}
+
+set$data_mol_morph_long &lt;- data_mol_morph_long_temp %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Globodera&quot;, pval_glm(model_glob), NA)) %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Pratylenchus&quot;, pval_glm(model_prat), pval)) %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Meloidogyne&quot;, pval_glm(model_mel), pval)) %&gt;% 
+  mutate(signif = ifelse(pval &lt;= 0.05, &quot;pval &lt;= 0.05&quot;, &quot;pval &gt; 0.05&quot;))
+
+
+
+# Count data vs NDVI
+
+plots$morph_count_ndvi &lt;- ggplot(set$data_mol_morph_long, 
+                                 aes(x = value, 
+                                     y = ndvi)) +
+  geom_point(alpha = 0.7) +
+  #ggtitle(set$plot_title) +
+  theme(plot.title = element_text(hjust = 0.5, size = 20), 
+        panel.grid.major = element_blank(), 
+        panel.grid.minor = element_blank(),
+        panel.background = element_blank(), 
+        axis.line = element_line(colour = &quot;dark grey&quot;),
+        strip.background = element_blank(), 
+        strip.text.x = element_text(face = &quot;italic&quot;)#,
+        #axis.text = element_text(size = 12),
+        #text = element_text(size = 14)
+        ) +
+  #xlab(&quot;Nematodes / 250 ml soil&quot;) + 
+  #ylab(&quot;NDVI&quot;) +
+  geom_smooth(method = &quot;glm&quot;, formula = set$my_formula, se = F, color = &quot;dark grey&quot;,
+              method.args = list(family = quasibinomial)) +
+  facet_wrap(~Genus, scales = &quot;free_x&quot;) 
+
+
+
+plots$morph_count_ndvi</code></pre>
+<p><img role="img" 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" width="672" /></p>
+<pre class="r"><code># Print and save plot
+plots$morph_count_ndvi</code></pre>
+<p><img role="img" 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" width="672" /></p>
+<pre class="r"><code>save_plot(plots$morph_count_ndvi, plot_name = paste0(&quot;morph_count_ndvi&quot;), 
+          filetype = tmp$out)</code></pre>
+</div>
+<div id="plotting-molmorphndvi" class="section level3">
+<h3>Plotting mol+morph+NDVI</h3>
+<pre class="r"><code>set$data_mol_morph_long &lt;- data_mol_morph_long_temp %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Globodera&quot;, pval_glm(model_glob_count_ndvi), NA)) %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Pratylenchus&quot;, pval_glm(model_prat_count_ndvi), pval)) %&gt;% 
+  mutate(pval = ifelse(Genus == &quot;Meloidogyne&quot;, pval_glm(model_mel_count_ndvi), pval)) %&gt;%
+  mutate(pval = ifelse(Genus == &quot;Globodera&quot; &amp; type == &quot;Genus_Sample_Abundance&quot;, 0.5574120890, pval)) %&gt;%
+  mutate(pval = ifelse(Genus == &quot;Pratylenchus&quot; &amp; type == &quot;Genus_Sample_Abundance&quot;, 0.5177548997, pval)) %&gt;%
+  mutate(pval = ifelse(Genus == &quot;Meloidogyne&quot; &amp; type == &quot;Genus_Sample_Abundance&quot;, 0.0001920515, pval)) %&gt;%
+  mutate(signif = ifelse(pval &lt;= 0.05, &quot;pval &lt;= 0.05&quot;, &quot;pval &gt; 0.05&quot;))
+
+
+
+# Morph data
+
+set$data_morph &lt;- set$data_mol_morph_long[set$data_mol_morph_long$type == &#39;total_count&#39;,]
+set$data_morph &lt;- set$data_morph %&gt;%
+  dplyr::rename(&quot;Number_of_nematodes&quot; = value)
+
+# Relative abundance data
+
+set$data_relabu &lt;- set$data_mol_morph_long[set$data_mol_morph_long$type == &#39;Genus_Sample_Abundance&#39;,]
+set$data_relabu &lt;- set$data_relabu %&gt;%
+  dplyr::rename(&quot;Relative_abundance&quot; = value)</code></pre>
+</div>
+<div id="morph-and-metabarcoding-vs-ndvi-plotting" class="section level3">
+<h3>Morph and metabarcoding vs NDVI plotting</h3>
+<pre class="r"><code>signif.labs &lt;- c(&quot;pval &gt; 0.05&quot;, &quot;pval &lt;= 0.05&quot;)
+names(signif.labs) &lt;- c(&quot;p value &gt; 0.05&quot;, &quot;p value &lt;= 0.05&quot;)
+breaks_morph &lt;- function(x) { if (max(x) &lt; 200) seq (0, 100, 100) else seq(0, 600, 50)}
+breaks_mb &lt;- function(x) { if (max(x) &lt; 0.1) seq(0, 0.002, 3) else seq(0, 3, 1) }
+
+breaks_fun_morph &lt;- function(x) {
+if (max(x) &gt; 500) {
+    seq(0, 800, 200)
+} else if (max(x) &gt; 150) {
+    seq(0, 200, length.out = 5)
+} else {
+    seq(0, 100, length.out = 5)
+}}
+
+
+
+# MORPHOLOGIAL AND NDVI PLOT
+
+
+# Relative abundance and NDVI plotting
+
+# Axis ticks MB
+
+breaks_fun_mb &lt;- function(x) {
+if (max(x) &gt; 0.8) {
+    seq(0, 0.9, length.out = 4)
+} else if (max(x) &gt; 0.2) {
+    seq(0, 0.6, 0.2)
+} else {
+    seq(0, 0.003, length.out = 4)
+}}
+
+
+p1 &lt;- ggplot(set$data_relabu,
+             aes(x = Relative_abundance,
+                 y = ndvi)) +
+  geom_point(alpha = 0.7, color = &quot;black&quot;) +
+  ggtitle(&quot;a)&quot;) +
+  theme(plot.title = element_text(hjust = 0, size = 12), 
+        panel.grid.major = element_blank(), 
+        panel.grid.minor = element_blank(),
+        panel.background = element_blank(), 
+        strip.background = element_blank(), 
+        axis.title = element_text(size = 9),
+        strip.text.x = element_text(face = &quot;italic&quot;),
+        plot.caption = element_text(size = 7, hjust = 0, face = &quot;italic&quot;, 
+                                    margin = margin(t = 0.2, unit = &quot;cm&quot;)),
+        plot.caption.position = &quot;plot&quot;,
+        plot.subtitle = element_text(size = 10, hjust = 0, vjust = 0.5, 
+                                     margin = margin(b = 0.8, unit = &quot;cm&quot;))) +
+  xlab(&quot;Relative abundance&quot;) + 
+  ylab(&quot;NDVI&quot;) +
+  geom_smooth(method = &quot;glm&quot;, formula = set$my_formula, se = FALSE,
+              method.args = list(family = quasibinomial), 
+              aes(color = signif, linetype = signif)) +
+  scale_color_manual(values=c(&#39;black&#39;, &#39;lightgrey&#39;),
+                     name= &#39;Significance&#39;,
+                     labels = names(signif.labs)) +
+  scale_linetype_discrete(name= &#39;Significance&#39;,
+                     labels = names(signif.labs)) +
+  facet_wrap(~Genus, scales = &quot;free_x&quot;) +
+  scale_x_continuous(breaks = breaks_fun_mb, limits = c(0, NA))
+
+
+p1</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+<pre class="r"><code># Plotting NDVI vs metabarcoding
+
+p2 &lt;- ggplot(set$data_morph,
+              aes(x = Number_of_nematodes,
+                  y = ndvi)) +
+   geom_point(alpha = 0.7, color = &quot;black&quot;) +
+   ggtitle(&quot;b)&quot;) +
+   theme(plot.title = element_text(hjust = 0, size = 12), 
+        panel.grid.major = element_blank(), 
+        panel.grid.minor = element_blank(),
+        panel.background = element_blank(), 
+        strip.background = element_blank(), 
+        axis.title = element_text(size = 9),
+        panel.spacing=unit(2, &quot;lines&quot;),
+        strip.text.x = element_text(face = &quot;italic&quot;),
+        plot.caption = element_text(size = 7, hjust = 0, face = &quot;italic&quot;, 
+                                    margin = margin(t = 0.2, unit = &quot;cm&quot;)),
+        plot.caption.position = &quot;plot&quot;,
+        plot.subtitle = element_text(size = 10, hjust = 0, vjust = 0.5, 
+                                     margin = margin(b = 0.8, unit = &quot;cm&quot;))) +
+  xlab(&quot;Nematodes / 250 ml soil&quot;) + 
+  ylab(&quot;NDVI&quot;) +
+  geom_smooth(method = &quot;glm&quot;, formula = set$my_formula, se = FALSE,
+              method.args = list(family = quasibinomial), 
+              aes(color = signif, linetype = signif)) +
+  
+  scale_color_manual(values=c(&#39;black&#39;, &#39;lightgrey&#39;),
+                     name= &#39;Significance&#39;,
+                     labels = names(signif.labs)) +
+  scale_linetype_discrete(name= &#39;Significance&#39;,
+                     labels = names(signif.labs)) +
+  facet_wrap(~Genus, scales = &quot;free_x&quot;) + 
+  scale_x_continuous(breaks = breaks_fun_morph, limits = c(0, NA))
+
+
+p2</code></pre>
+<p><img role="img" 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" width="672" /></p>
+<pre class="r"><code># Combining the two plots
+
+plots$combo_morph_metab_ndvi &lt;- plot_grid(p1, p2,
+                                          ncol = 1,
+                                          align = &quot;hv&quot;)
+
+plots$combo_morph_metab_ndvi</code></pre>
+<p><img role="img" 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58IqyMgUHUgUB8g082YZc8cYXUFBKrOYAJ9x+GBgwOZbsJse+YIqysgUHWGE2iBw2OHBTJdn3n2zBFWV0Cg6gwl0HfeGZNBkem6KOgTYXUFBKrOsAIdi0KR6VrMGfqsQFjdAIGqM7RAx6FQZLoGivbMEVZXQKDqDDoGCoGalWazsNBQ1yfC6goIVJ0BlzGNx5/IdFVUO+8UhNUNEKg6g64DhUDNSrNZWEDo2TNHWF0Bgaoz8EJ6CNSkNJuFBYOuPXOE1RUQqDrhXImUskqR6fMw0CfC6goIVJ2ABJpwfx6ZPhvtzjsFYXUDBKpOMAJNekoJmT4LM3vmCKsrIFB1AhNoogpFpndjas8cYXUFBKpOcAJN0qDI9C6MW58lCKsbIFB1ghFo0tcmIdPb6WPPHGF1BQSqTjgCrRTq5L0MCzK9lX76RFhdAYGqE5JA052JR6a30LP5mSOsroBA1QlLoDkEqlKazcKGorc9c4TVFRCoOoEJNFGQ6TVs6BNhdQUEqk7IAk2nMYpMl+jfeacgrG6AQNUJWqDJdOiR6SKW9ImwugICVSdggSY0J49Mn2Kr+ZkjrBaREg0CVSd4gSZhUGQ6x549c4TVFvU8g0DVCV+gKRgUmU6xqk+E1QotaQaBqhOwQBO6uhOZXmKx805BWPvTdgEgBKqOflXeu7A6mZy6LG7a+dakvqmk/2lIxJ/I9BLb+kRYbQCB9kO7Mt89Myl57O3ppu1VsunYq7WXWjgNFv05WVycWCpKmxgz3XK8rDc/c49hHbLuOKatlweBqqNdm9cnxy/nO+cnxz/iW/bONzZRbJwGawItUqBIAztlaROhQO3Gy4E9c39hHbTuuKaljQKBqqNbobdXSdvz7plpe7NlE8XOabDz7b9IGsmTgbIgUIHOCK3VeLnRp7ewOqg7gzZpZWO2NFAgUHV0q/T1yRPs72m+aYtuKhqiL8qvtXIa7Hz7T3gSDFNtwxTojNDajJeLzjvFT1gd1J0hm7QK0woQqDq6dXqdWZJZs8RpC1T69jfvzrNihmqCBinQWQ0re/Fyp09fYbVfd4brDqktbIFA1dGs1XvnmSW3V6sRz2oMtFLqFxj935707d9jSh4CbTCzYWUrXplLf8Yq0OG6Q9Mp95l5BIGqY0Gg+d5bpD48U22wJ1Cp8vZY1IQufIOZXrAUL5f2zKPtwg/3ba64shoCVcdcoNOFTNvfJNXh8fpCUMsC7bWuHpNIdWansY14OdZntJNIwwt0zssgUHUstEC3V8vGZ9EMdTAG2uzCGxsUy5hk5jSs+sfLuT9jXcY0YHdILXsgUHUsCHSdTcivTwdBKdYnkXpdHo+F9DXmNKx6xsu9PuNdSO+3OySmi1rmQKDq9J+Fbx0WJThYxhTptZ1BCtRlo9yHPkMNqwL+ukNG+QKBqqO/DvS09Ne5QOvf/jH6M9RMd9Uo96PPYMOqgJ/ukGmHDQJVx8KVSE678E0g0MCrt9OVSxKjCqs+amuWWoBA1dGt43vny9l26cL3rYm7SSSJeG/pMJpML06RN33GHVYPlRkC9YB2Ld8R7sa0vUqceZ2tyjhde6nl0xDxLR1iznQdRH2692fMYfVRmY2nXCFQdfSr+c6FwpWnSPuTCTT/X+X9QL/u4H6gIm1Tl7F05yPOdB0WJx7tmcccVj/z8KYTrhCoOiHfkV6ibfFcNFPy8Wa6Dp71GXFY3a0EnXejJSUgUHWiEWjb5RvRLGqKNtO1qPzpa5wl2rC6uhbJUjpAoOrELNBeVyZ5JdpM16DSp79LFKMNqxuBWksGCFSdaATa2YWPwaDRZro6oj99rZWINqwuuvAWkwECVScagbaPu0ei0GgzXRVBnx7vkRFvWO1PIhmvWWoBAlUnHoG2r/yIwp8RZ7oa09FPryvNIg6r9WVMNlsSEKg6EQm0Y+1xBP6MOdMVECbf/V7rEHNYbUfKZkMCAlUnJoF2AIEOi+/FS1OSDqsuFhsSEKg6wwg03osyzUg40z3rU6o5CYdVDf071XXhMK5pM4hAI74o04x0M92zP+Wak25YlbDY5nQZ17QZQqDD/rzGXBw0j0PLdFsf0XfvvVZzQgtrD/TPiM1BT6dxTZsBBDrwD7zNw0XzOLBMt/URffuzXnMCC2sPtM+I1fV7buOaNgMI1OVvavWvUE6ax2FluqWP6H/yqF5zwgprD7TPiM1Vn67jmjZpCVS/StW7Tm6ax64zXasDaPsni3uVokV6AqUnTv+M2L1+BAI1J60uvFatKitvo+vkxu6OM12vA2jlI3bq0+UCC19deG+LRNiJ0z8jdi8fQRfenKQmkbT6NWXlPXCw/k4663KvpHIrUDGeCm/ThkA7/el2gYWfSSTHi0Smp4h/HIMzYvfyEUwiGZPWMiYNg5Z1ZvHg4sFaW7iredzvLTsVqPiWVd5m/x5Ad+/d9QILH8uYHH+G6UeoTsSi/hmxfO0IljGZktpCelWFksp7cLFQaHsTtJY+VQ03e+NOBSq0X1Ryf7J4YLGXIbJuf7pfYOF+Ib3jzyCcoumJq5+31mrm9Io7LKQ3JIFLOWUUm6CkyhYCZU1Q8ZmWRhxPqrLLb9IO9SRQldwnIxcHzJvTM/TpdoFFC07C6vYzSN0F4ZtPqnRtVdDnPXMgUHWSE6ji8BBteB6kAq1PxDcExKr6waZu1fDUhVfIfdb4PGDavprpTwh0LmLpokzFStfsR3i+ayMEqk6CAlXq60xYE5R04eemCtNOc8hUEU+TSPNzv2f/dLY+vV8jEWEXXjpF7SMujTfwjoCL99QAAlUnRYEqQWvpQbXOrKRbg7aJp2VM83O/X/Nqnj99X6Ub4SSSfIpa5/wa58izPiFQHUYrUFZ558wK8ae5QA1zy9dC+rm5P0egM6MxX5+55/vE+F3GZGfmUz5FbWV2C7T/0dXQj+t75x4o6sW9X7vIHq9l+64q7MZftvvGcpbd85LiXkExXoEqLZmsUqn8V9lcDbELLzDPX7PbqDP3VrzyyOedCr0upLf11TC3nOY58v2jC7px3T1bVY7D1ICaAl0rd73nTQhUJnCBzqe+Qt24c+fvmsN5/pr1EWZ+PP8Xbs7H56Wc9jr2c79iGofyfcdwzbgK/swqI+oI9OZy9mtX1fcKitQF2qfuNZsCpq2QgC7a7v4IM1unIfrTZ1i9To+V52jIn1nQjOtGli08/X7xj9tlT3xF/3ibRnuFQeIC7dX5aRkwDHEhvSadH2HW+GiI+vQaVr8LtCbD/tCXXlyLBug9fOyzaEsaNCI3s4WXtHcKhDEI1LQq2kuakATaSffHDbL5macrUL9TRk304nrnRLa/erCW3fOm9vEg0HaGF2i/+Ut73bYoBNr5cUP1Z6JdeO+T7g20W6D769uq0cwrTxb9+8NXN4hXC9Ue2X3jgSy79ylhrmmD1q7CodVet84tZ9lD3+WlvVc+zB6kOzUKabycPFx4mLeK3ZK2QHtWRmsTB1EItOPjKs6+D0Gck0hz8L7qs4n+GOhv1PrtTIV8eumec1yg//Ak3XLfm9XLmgLd4FP6pKzd13gNJDs1Cqm9vHrIH7slcYH2VKitpStxCLT14wbsT79h9bXCdXB9ase1UFqW/YM/eF/YxFS4RtY1EQMygWYLv301v1V49YjwMt6FZw8LA+67SPYiL9qgi6NuFdbcn7cU0nx52fi8/ZofgyYvUNW7i7RjaVVjJAJtftyQ9ek7rJ5WuA6uT/N1oA8+xSVKVXhzmTqtlBoX6ArbYZ+wbkkWaPEq3nxlO9ERAra9Xkjt5cUx2YDChpeR1fQFavvmsybEItA6Yfsz2rDOJsbqSkcpy141HXmkKtzgM0qF5JgL9/HBT/JMq0CrRU10czXDNBWqVEjt5dUxW8dm7TMGgSrfSdFZGyPoTO/81IHrM4Gf9GhlYHvmhnHd/em3H6BDmTlToWCwNbkxyTXXKtCNron89kLkl4vW9LIufxQCVcTdKFfIAu381MH7M9Kf9Ghj6DanjHFcd1/PBDOW8+XsieksvPi4XaBt3rv93vdLPbcVIr+cjMdWGCyp0gUCrXA4zxqwQDs/dfD6jPJuTK0M32mX6RHXDdqhtirQdx8QhQiBBorLlX7hCjS+xZ8CEd4PtIUAZo1q6MVVWjvP9MYFyrvTPQRKZ6ge/Oq/fH9NTaAeBj4FRifQzqrafq2JneGw4QXa9Tm6rrCJQJ/u7ki/eLC88aufJujwqz6b6MV1Q7ySnU28d46BKgmUCZnuvyHc4qldoOLL+fy+N/RTZO/C6mRy6vL08Xmeg5PH3pZeGaJAu6tqq0osDYcNLtDOz9Eu0Bian7kzgR4sf+nloB+BBrBsvoleXAtnVtfClys/V/IZs/AKAq2m1YuCVwQjdhQiv7wogy9e8uNS7Ry5e6bmyvgE2l5X27putobDhhZo9+do7bBG4k9HXfgDi4QDXrrwAepTO67l3Twf/kHxj90rJzNxgWfLOlAFgVbrlEjbcqrBjkLklwu3M/FzjyftJFmfHL+c75yfHP+o9sT26rFX5S0BCnRWdW1axtpw2MACnfU5mp86/Nl3TjIC9XEkDUyuROLUrymqXYmkINDSfMKlRawL/95JVnijkM3GlUj3fZcuCPDRmdfNku1V0sy8e6Zuy6Iherr22gAFOtug9X6utVvwDCzQmZ+j/qnj8WcKXfjgZuBLetyR/j7af+bXwq+xrb+vI1D54vY77ML37OHXSZOyUUjntfBeBkN10+T65An293T9iUabNESBzjRofaZlFAKVP3VE+ox4Ekmof8HZMzeJ661vP1g2EKs7InXfjYk+P1ug8u2VyK2XFh7+AZthbwq0eTemct3T9LFTdBNlffIi+bvFRMq5e4Y9IRCkQHWujR9DF14mKn/GuowpxEanhP1f5YzxxzrU0MyUvfOs6769Kjc4r4tC/QKj97tzgsaoU/qTSDJR6TPShfRBjnrK2IprbUFSklgSqDwmGrZAlS+Nz0ewjEkiMn/GeClniKuWGtiK64awwujI7JdGi7lApTVLW80R0FC78LnWlcepL6QXiUyfMd5MJMhlnw1sxbXw5sLTRfvz3WUfF1UOg50W6N755ghowAL1zvACnU9szc88jrBKxOFPe3HdXOaT835+X2MA7Ai01h5lQKCcCDI9Qn/GENYaEdgztxnX26/RCfFEZ5ByW7Pw9Tl5CgTKCT/TY/RnBGGtE4M+XczCp4v+OtDT0l/KemMRfUkMp8FPfQ490+NavVQRelhbiMGfEKgGVq5Eqjr2MhGcBk8tgsAzPU59Bh9WThTSFIFA1dFNmr3zk8cb18LfPdM2BBqLQD1U76AzPdLmZx54WCui6LVLQKDqaGfNjnA3Jn7/kPqqekb4p8HXlGjImR6vP4MOKyeKWaMaEKg6+mmzc6Hw5ykiTC7Q1lWgMQjU193Ews30iPUZclgrYli11AACVWd0d6Sv4cegwWZ61P4MN6ycOJZ9NoBA1Rm7QP0YNNRMj1qf4YaVE6k/IVANRi9QL2ubA830yP0Zalgr4tQnBKpDPXl2f/p9mT81v4ggltMw1jHQyPUZalinROpPCFSDevrIv6uc9fpp5WhOwygFGnvzMw8zrCJx6hMC1aEh0CcflHnoTeOycRo4AWZ6Av4MMawSkfoTAtUAY6A+CC/TU/BngGHNI7zuqAkEqg4E6oPgMj0Jf4YX1jzeVqcIBKpOYxLp39u781SEp8FR5Q8t05PQZ3hhjfO6oyYQqDotk0gP/cBS2RGeBkfVP6xMT6P5mYcWVm/XtTkHAlWn0QJ9YznL7n3aSjM0vtPgqvoHlenJ+DOssMZyu3kFIFB1WtLovSeL7HrYwj344zsNrup/SJmejj+DCmu81x01gUDVac0j0gy9r/cP08d3GlxlQECZnpA/QwprHu91R00gUHW6Eqlshi4cfr9X2fGdBldtiHAyPSF9hhRWQir+hEA16E4l2gztM6EU42lwo9BQMj2l5mceTlgZadgzh0B1mJlL750bx6WcIk6aoIFkemL+DCWsyQGBqjMzma48OT6BOrmAOYxMT82fAYQ1kT57DQhUne5sulU0P7OFp3osaIr0NKQq0OT8OXxYUxn0rAGBqtORTmQAtO9EPE4DZ/BMzxObPqIMHNZ0pt1rQKDqtCZU2XXvPQcPgU4ZXqDpNT/zocOazrrPOhCoOs2Muv2ahcYnAaeBM7hAk/TnoGFNV59BCvTOCcPJmN1zy/XLgsRN/P7HxlM9jWvhz1ppfBLCOw262MqOoQWapj+HDGvK/kxJoEyQ4s7SppvLtgV6wkrjkxDeadDFVoIMLNBE/RlAC9Tm8cMhIYGuZfsu5rfOZvuutm/azPb3e2MNgf4LK41PQninQRNrOTKsQBPVZwACtXn4gIhUoK8174F0c5nsdufEwkvtm9ayI/3eGG6oPIMkBJpq8zMf+HspWX36FOhatnLzpLhYkgttg7QMyWz2veWzRKDsSfpcfqscynzoolhYY9Ule2nx90jrpt2zU7Oa0ZZXu+R9ZwsP9ezLpyFQG3kyYKYn7M+hR0aSRTuumQptO65lXyODkFU/mnWpd89mK0WTku25v02gbPRyYaUqrFx7WVPoWrYiFtvYdOfEvj85KWtY96M3N727XH3mhV4Kjb5GWpsnGC7TU/an/7Am3OoU8SnQckCyMM4K28C66qSfvUn9825WtBIbAr1zIjt8Nd99XZ7+uVI0Zw9PFVo1MG8u80FQeROfQ6qOr03zY22UjeafFUf62RsP9Cg4T0Cg1gw6mECT9qfvsCY86injU6DEfxvTSR7aQCSOZL4krdGGQOoVYmAAACAASURBVFs654RyFWa1iGiuQDezUsO3z2XGPfnGxyqcXL2jwu99hgjiF6gtgw4l0KT16TmsKU+71/ApUCIbYYqI9KxpD77k9k+/czJrEWiLGjm757IWW/IDyJu4hs3nkhofa0Oa1+81SZWAQC3NIw0j0LSbn7nXsCa97rOOT4GulH+EuRxiSua7WyerDnZToFW58vS8XguUs5nVNaz80WuPa9NS5gXnaQjUzlLQQQSavD8hUEd4nYUv/wjWIf+cThMtPPS1H7R14fkVRDWB6o6BcqYtVF2aC+mlkswLzhMRqJWJgyEEmr4/BxCozeOFy5ACLQc1aQ+++P/+q3nHGKi4spNjMAvPaQ4EqDJHoMaXoJakIVAb+BfozI5TKvgWqM2jhYxPgRIliu20wmV/RR5yRxaNzdYu/EqzsJZ1oEekv7VNVSnmFyRBoD7wLtAx6NNvWEfkT68CJS0/ceKlaI1+hzzkAt3MCtUREdGXlS3T6cS9MMhocCXSmrjs1AgI1Ae+BToOf/oVqM1DBY5PgZbddGEdaE7WUZKHtAu/+3pWCbRQ6dPlVexsHei+i3lt1wZFGffVroWXNt1cLpcx3TppPtUDgfrAs0BH4k/3YR1Rq1PEp0C/SmqquNankBqVziatxfteJ2OexTY69X7PH5Nm4yZbA3945gFuCbdeurlM2qHiJmLr8oHxpUgQqAbG+eRXoCPRp/OwjmnYU8LrJFLRhrz3aXEb7aKXXDmZlc+VvXQqot1zWdF83BSvhZ93rST5ZaKHSfuSCVTcVDwgv99uvtSo5XZ2MhBohXlC+RToWJqfueOwjmnavYb3WfiIgUDVMU8pjwIdkT+dhnVM6z7rQKDq6Cfa3oXVyeTUZWnTW8Wm3/y9+isTE2iPlPIn0DH502FYq3XzEKhTRijQu2cmJY+9Pd20QzdNnqm9NDGB9jCoN4GOyp/uwjpqfUKgOmin2vrk+OV85/zk+Ed8y975yeOX872/mBx7VX5pagI1N6gvgY5Kn+5boDZLjwkIVJ3GtfA//b7Mn8oTVNurpO1598zUllusOXp98oRcVnICNb4gxY9Ax9X8zJ0L1GbhURHgT3oEi+4kErfk9clptqVogL7YXnaKpyHgMdDR+dNlWEesTwhUh4ZAn3xQoCnQdWbLraq5efeMOB4qkuRpCFag4/MnftLDERCoOrPyjSxble95sneedd23V/kgaPmvX/zOZPL4H1Wv+gLD9puNFg+ZPkJ/QqCOQOaqMyPhriyT+91LtAv0Ap2F5516CLSO+0wfoT4hUFcgc9XpTLnd14rmZ+M6KUGgvOO+VS5g+ijfeyv9WXhjnGf6KP0JgToCAlWnK+famp95awt0izc910cwC2+I60wfpz8hUEdAoOq0J13Z/Gz9SePWLnxjEyPx06AxneQ400fqTwjUERCoOq1Zd/Nka/OzpDkLX3Xmp716RtqnQWeloNNMH+P0EQUCdQMEqk5L2nU2P0v4+k9xHShrgW5NRtUC1Vlr7TLTx+tPCNQREKg6zbwrm58Pd94fr+VKJD72uT6dhqckfRq0rup0mOkj9icE6ggIVJ1G4pXNz6fbXkmhF75L18Jvr5Y3ZxrdLLyOQd1l+pj9CYE6AgJVR/t+oDvC3ZjY/NHWKtl0rH5FZ+KnQcOgzjJ9zPqEQF0Bgaqjf0PlnXLZ/CnS/uQT8DvlLUK/frn2wuRrpLpBHbdAbRYeExCoGyBQdVwmX/KnYfgWaD5mf0KgjoBA1YFA+zD8GGg+Yn9CoI6AQNWBQHsxvEDHDMLqBghUHQjUB8h0JyCsboBA1YFAfYBMdwLC6gYIVB0I1AfIdCcgrG6AQNWBQC0xczAUme4EhNUNEKg6EKgyk8XFSfezMyfkPWf67HeaDoMLNNFAQ6DqQKCqFLlS5EvXs7OXhPrN9NnvNCGGFmiqgYZA1YFAFVmkP1vSlS4BCXTOO02IgQWabKAhUHUgUDUmPFs6umyzr+r0menz3mlCDCvQdAMdoEDvnGhcU27A7rnlLHv4Ytcmfh27zqEgUDVYsnS2N2ZfF+8z0+e904QYVqDpBjpVgTJBiiVJm24uQ6CumJstMw0KgToBAnVDqgJdy/ZdzG+dzfZdbd+0me3XLhMCVWN+f22WQdGFdwK68G6IXKC77x5u3X5zmZRx58TCS+2b1rIj2m8MAp3JdJ3K/BmD4Vug9N0mO7fRwFVYFVcnJRtofwJdy1ZunswWnro63UAdtkEag1eeLDrU95bPEoGyJ+lz+a1y9PKhi/Uid99Y7mhIsv2Kv0daN+2enZpVGQh0FuI6lflrVoYeA+XvMNXVNQ0chVU5fqkGWjuu71TM3tZgLfsaGXesjMd60btnsxXy6xgZe7YpUDZgubAiFVjqs/RxUUA23ZsfbEU8RmPTnRP7/uRkm5NnAYHOQG5izG+XDCvQ6btNdH13Azdh1WhYJhponwItxyDfXWYeq7rqpGu9SX/b8t2saBg2BHrnRPnDwbuvSzM+u68tZ/eR38NsEWjVwLy5zAdB5U18Dql6MypAoN3YG+TyIdB0h+Q6cRLWEcaxjk+BEv9tTOd1aJuQOJL5krRGGwJt6Y/ffi1bOPx+57HmCnST/Jj77XOZTk8eAu3G3jSrD4GmOynciZOwjjCOdXwKlOhPmCIinWnagy+5/dPvnMxaBNpqwxm/JpxLtuRHkzdxJ2vNJUGg3UCggQOBusGnQFfKP8L0DTElU9ytk1WfuinQ5q+29W2BcjbFdU7zgEC7QRc+cNCFd4PXWfjyjyBQ8s/pNNHCQ1/7QVsXXvjxS2EQtN8YKGfaQlUAAp2B+TqV2teu50mksTD4JFKiDCnQclCT9uCL/++/mneMgYqLOUX6zMJzJJvOAwKdhek6lXrHxe8yptEw9DKmVPEpUKJEsc1X6OuvyEPuyKKx2dqFX2kvctY60CPS39qmqkitC5Ig0JkYrlOpD/14XUg/HgZeSJ8sPgVKGnsbgrKK1uh3yEMu0M2ssBsRKH1Z2TKdTty3DFiaXom0Jq5BVQUCdUBj9HzoG1cmCsLqBp8CLbvpwjrQvFQjXYlJu/C7r2eVQAuVPl1euM7Wge67mNd2nU1R4H21a+GlTTeXy2VMt07qzCFBoE6ozz4i052AsLrBp0C/SsYpxXVDhcdoh36TjmHue52MeRbb6MDmPX9MWoqbbNl7e2uzlVvCrZduLpN2qLiJqLt8oHMpEgTqhGG68GMDYXWD10mkog1579PiNtpFL7lyMiufK3vpdKXo7rmsaDFuitfCf1fncLeK/dliUSZQcVPx4MksWzis0f6EQF0BgXoAYXWD91n4iIFAfYBMdwLC6gYIVB0I1AfIdCcgrG6AQNWBQHUwXd/iMtNHvOYmVIHGfkogUHUgUA2MV1g7zPQxr/oOVKDRnxIIVB0IVB3za/zcZfqorzsMU6Dxn5IAf9IjWCBQZXrcZcJZpo/7zhdBCjSBUwKBqgOBKtPjPmfOMn3c914LUqAJnBIIVB0IVBkINDQgUDdAoOpAoMqgCx8aQQo0gVMCgaoDgaqjNTsgLWVxd9ugA4uRt3b6EObdmDCJNCYgUA001qfIL3V448oDByJfM9ODQO8HimVMI0JfoHsXVieTU5eFLXfP0O/cx96WX5neaVBumtRaIS5vnV42Qm2WHhGh3pEeC+nHg7ZAmS1FWW6vjkWgqtTHwfDjPU5AWN0AgaqjLdD1yfHL+c75yfGPqk1bkydaXzre01CficXPRzoBYXUDBKqOrkC3V0k78+6ZY69W29Ynp1tfO97TAIF6AWF1AwSqjq5Ar7PW5vWpNPfOCzIVGe9pQBfeCwirGyBQdXQFuj55kfwVuu13zxz/y29OJl+/XH/tiE+Dx0kktJRslcb+jj6sEKgGmgKtWpvbq9UgKJ9DYmrNyxNAsfUmI8TbMqao18v0BGF1w6gzVxMLAt2aTJ75KP/7C5OqJw+BeltIP+KOJsLqinFnrh7mAq0WLfFh0cZcEk4DJ8hrDuMHYXUDBKqOhRYoZ2tS24TTwEGmOwFhdQMEqo5FgQqNUgpOAweZ7gSE1Q0QqDoWZuE5DafiNHCQ6U5AWN0Agaqjvw70tPS3bJR2ORWngYNMdwLC6gYIVB0LVyKtU3FWIq3AaeAg052AsLoBAlVHV6CFJh+vXQu/vVouY9r5Zn0OCTWyApnuBITVDRCoOto3E9kR7sa0vUraodfZzZjqlyLhNHCQ6U5AWN0Agaqjfz/QnQuFLE+RxiYTaL7zrcnk2DP1WXnUyApkuhMQVjdAoOrgjvQ+QKY7AWF1AwSqDgTqA2S6ExBWN0Cg6kCgPkCmOwFhdQMEqg4E6gNkuhMQVjdAoOpAoD5ApjsBYXUDBKoOBOoDZLoTEFY3QKDqQKA+QKY7AWF1AwSqDgTqA2S6ExBWN0Cg6kCgPkCmOwFhdQMEqg4E6gNkuhMQVjdAoOpAoD5ApjsBYXUDBKoOBOoDZLoTEFY3QKDqQKA+QKY7AWF1AwSqDgTqA2S6ExBWN0Cg6kCgPkCmOwFhdQMEqg4E6gNkuhMQVjdAoOpAoD5ApjsBYXUDBKoOBOoDZLoTEFY3QKDqQKA+QKY7AWF1AwSqDgTqA2S6ExBWN0Cg6kCgPkCmOwFhdQMEqg4E6gNkuhMQVjdAoOpAoD5ApjsBYXUDBKoOBOoDZLoTEFY3QKDqQKA+QKY7AWF1AwSqDgTqA2S6ExBWN0Cg6kCgPkCmOwFhdQMEqg4E6gNkuhMQVjdAoOpAoD5ApjsBYXUDBKoOBOoDZLoTEFY3QKDqQKA+QKY7AWF1AwSqDgTqA2S6ExBWN0Cg6kCgPkCmOwFhdQMEqg4E6gNkuhMQVjdAoOpAoD5ApjsBYXUDBKoOBOoDZLoTEFY3QKDqQKA+QKY7AWF1AwSqDgTqA2S6ExBWN0Cg6ugLdO/C6mRy6nJ98/bq8Y9qm3AaOMh0JyCsboBA1dEW6N0zk5LH3pY3752fQKCdINOdgLC6AQJVR1ug65Pjl/Odhi6vTyDQbpDpTkBY3QCBqqMr0O1V0va8e+bYq/JmCHQGyHQnIKxugEDV0RXo9ckT7O9pYWvRgf9djIF2g0x3AsLqBghUHV2Brk9eJH+3mEj51ifGPYk0WVyczHjaVabPOWzqIKxugEDV0RTo3nnWdZd8uVV038UNX2DYeYsRUCRckXTdzzvK9HmHTR2E1Q0jytze2BAoGRBNXqAzmiWLZGHCpDvn3GR6mecHD4441Z2FdfbZTL6JmljmOsVcoNOFTOvleGjqXfgZzZIJT7nOpHKS6cXxSn0ePJByLs/EVVjnnM3km6gQqDoWWqDXyfx74gKd1Sxhz81otDjJ9MKfi4QDNguPCUdhnXc2FZqocQOBqtNfoNurZFPaAp3ZLBlKoItcoGNtgg4jUIUmauRAoOr0n4W/PqmoXZ6U0GmYmVVDdeEPUH+OdxR0mC78/O/L2IFA1dFfB3pa+guB5oNNInGBppvJcxhmEgkCBVMsXYk06i78UMuYJgfKOaSDCfcl5zDMMiZ04cEUXYHunZ883nYtfOICndcsGWYhfanPxYRbQvMYaCE9JpFAhfbNRHaEuzGx+SNC4gLtt3TFVaanvp5mDkNdypl62CFQdfTvB7pzofDnKSLLMQm01+JpZ5me+IruOQx2LXziYYdA1cEd6X2Au144AWF1AwSqDgTqA2S6ExBWN0Cg6kCgPkCmOwFhdQMEqg4E6gNkuhMQVjdAoOpAoD5ApjsBYXUDBKoOBOoDZLoTEFY3QKDqQKA+QKY7AWF1AwSqDgTqA2S6ExBWN0Cg6jgVKKhAXJ2AsLrBZlzTBgL1A+LqBITVDTbjmjYuBSpg85RYPb3BvrFhDjm28oY9zPgOmR4QaJhlDXTIsZU37GHGd8j0gEDDLGugQ46tvGEPM75DpgcEGmZZAx1ybOUNe5jxHTI9INAwyxrokGMrb9jDjO+Q6QGBhlnWQIccW3nDHmZ8h0wPTwIFAID0gEABAMAQCBQAAAyBQAEAwBAIFAAADIFAAQDAEAgUAAAMgUABAMAQhwLdu7A6mZy6PGeTcVm//OZkcuz/+MjSG3ur2PSbv2elrJ1vmb4x40Max7Vj51+WH8FieSXbq8fNYmLzdM0vWdzU/cDPIQvunnmi5xH1DtnrzI8RdwK9e2ZS8tjbMzcZl3WdbJk8rl1WW2E7dNPkGQtl/cL4jRkf0jiuHTu/RT/CsVctlVeyd35iJlCbp2tuydKm7gd+DlmyPukrUK1D9jrzo8SdQNcnxy/nO1LatGwyLWt79VjRANn5pkH9ahZWJPfjl/O9v9CvOG1vrNy095ahLkwOaR7X9p23JiS2581s0fFmii88s4jYPF1z36m0qfuBn0MWn3R90lugOofsd+ZHiTOBbq+Sk3D3zLSWt2wyLmt9clp4pmdhW6zCXNetra1vjNbU9cmLmm/M+JDGcW3fufATeetF08TgI3S8meJ7xcw9Nk/X3JKlTd0P/BySjlL1FajOIfud+XHiTKC8dl+npuvYZFwW4+4ZbYE2C+P1Rp8ZZW31bjqoHtI8ru07VzFdt1NeSRGW3zUbA7V5uuaVLG/qfuDnkGWj/Zlf9K1GOofsd+bHiTOBrjdF0rLJuCyGwcxEszADC3eWtXeefdMbz5loH9I8rnN2Nkqj9vLWJ08YBsTm6ZpXsryp+4GfQ+bXH/+j/t/DeoesXgGBKuJKoC0iMXZL946/WNVujLQUVv7rF78zmRT1tXdZrgVq+5AzdjbqrraXt1V0380CYvN0zS1Z2tT9wM8h6fN9BWpwyN4DFaMiZoGuTybHtJOoPSMv0NlHze/dtjfGp03X3cwieRSo0RBjd0LaFKjZ6ZpbMgRK6Tm4PCo8CJR3uFo2GZdFNv8///vq5Nj/2f+NbZUrYj4qZ841v3jb3tj2KivLzUymzbjO3nnLaJK7tTzSI+wt0N6na27J0qbuB34OSZ+3KFDVQxqe+ZEScwu04JfaffjWb3nWltFddNfx9U0wnDMxOKSbFujW6jGTqZq28q6Txri1Fqjx6ZpbMlqg5JBmZ36kRC5QMrrW941tr1p9Y+Xik69fjnwM9LphK6Q7vBa78FaGmSHQ9kOanvmREvksvEGFbhZW9V60+2cz3pirZUx+ZuHNu8fN8liT3PA6Hpuna17Jgc3C9z+ewSF7DoyMDofrQE9Lf9s3mZZVrQXUF2hbYazOaDdnZ3wiVytBbMa1a+e9dfMrUZvl9ROozdM1r2R5U/cDP4ck9Bao3iH7nPlxEu+VSE9Ify0VppkdLWVd55dIOfoid38lUr8VBJ1vxrDza/N0zS05sCuRcgsC1Tuko7UjCeNMoPSC5ZZrmA2uJ27ZsZrs1q7Q7YWdumxSWEtZ9HLiX666WopsM64dO1/vk0Wdb8ZQoDZP19ySpU3dD/wcktBboFqH7HXmx4m7m4nsCHd5Ye2xHdM72rSUtcXuG6M/YdhW2KphYS1lsRvaOKuJNuPaWh67Pc/E8DrslvdHMJ1+sXm65pYsRbL7gZ9DlvQfStc4ZM8zP0oc3g90p1zufIokDU8kYVP/sozvXNj6xlbLqXMrZZVXyfS/V6XWIY3j2lbe1qRfGrW8P/pvw+8Um6dLoWQhkt0P/BwytzIXqX7Ivmd+jOCO9AAAYAgECgAAhkCgAABgCAQKAACGQKAAAGAIBAoAAIZAoAAAYAgECgAAhkCgAABgCAQKAACGQKAAAGAIBAoAAIZAoGNn9+rQ7wCAaIFAk+Lmcsa493BTjHdOLLxU33bl5Erx/41sv96BNrN9uuJdy1Y09wAgdCDQpJgKNMvuebP+bItAd88SrUGgAJgAgSbFzWWuzSvLTSd2C1QbCBSAHAJNjKlAxX9yIFAA7AKBJoVgTW7LW+eKbv1DF8VNV54sB0mfulp23UtWSBd+LTtCdqRuFHarmO5Xvui/vbacLTx8UTwsPcDN5X3/7Y1i7/u+S3e7XbwwO3yVCVQopP7CKyeLZ56m/247PgChAYEmhSDQTToIuslGRVfySqCvsUHSwpOiQDdZn5+IVNyNI+xXvPi+k+TBwkreFCh7jhqZjcve949JYWIhtRduCP9uOz4AwQGBJkUl0N136RjonRNF2y/ffT0rzUb9tpktFK283deInYRJpDsnyL7kRdJuDGm/zUJuv3E1v3WSaLouUPrcWfJcUdR9F8ui2H5CIfILi0e/cbV4HdupcXwAwgMCTQpxFv6+N/Pp9Dr5S/3GuuqFOo/Is/C0j71JW6bCbgxpv81MfNQQKGt6Ul/T0dJN4sw1eTfxhfRY9A21HR+A8IBAk0JYB/p0qa3ds6wJd3O50Jg0idQUKO3DlxqVd5PhAmVNXeLHukDp3pKvy0blSq0Q6YXidNas4wMQEBBoUlCTlV1fOhdTWEtYFloJdPe973/7gawuUNKHZ/9rX0063Y8PmNIj1gUqPJp6kc/CTwuRXijavev4AAQGBJoUXEkbbPqlVaDvPpBV0zXSQvq14mmm0jaBiftpCJR7kQpULAQCBbEDgSZFNYm0Rqdf5JWffBIpW3jot/7gZ40ufPHMEfq47ZpPeT/TFqhUyAyBYvIIRAEEmhSVQIs23HRKhsON1jGJVDy/76/oGEDL8np5v9ljoOIjPgZKypQL6TCt+fJ+ADwDgSbFdB0onfQu9MXnwPkkElutVK60rAs0X7vn99k/xN0o8n6s+Nps0EbWFChXLdlDLqRu2v15Xi0IaBwfgACBQJNiKtDdanHlvot5/i4RFl/iuf9qvlteAUQF+tt5JdCyg/0SLUfYjSLvV77yt6t1oEUpbK1nU6Dsuf+yTAUqFtJY/fQbV8tr+Fdajw9AgECgSSFdC0/kwy/pOZxzUbELfva9zlYtlS5jAi38xlp84m4Mab/plUhEuJv0ud842xRofovOCO3753SBp1BI7YXsuf0dxwcgPCDQpBDvIMLmkchF5fRqc2EW/t6nWfd491ypLL5gfa1q8Qm7ccT9+LXw/K6j5RXu9z692ybQfFe8Fl4spP7CxrXw8vEBCA4IFAAADIFAAQDAEAgUAAAMgUABAMAQCBQAAAyBQAEAwBAIFAAADIFAAQDAEAgUAAAMgUABAMAQCBQAAAyBQAEAwBAIFAAADIFAAQDAEAgUAAAMgUABAMAQCBQAAAyBQAEAwBAIFAAADIFAAQDAEAgUAAAMgUABAMAQCBQAAAyBQAEAwBAIFAAADIFAAQDAEAgUAAAMgUABAMAQCBQAAAyBQAEAwBAIFAAADIFAAQDAEAgUAAAMgUABAMAQCBQAAAxxKtCby9l+acPu2WzF5QEBAMAjfgVabLnnTZdHBAAAf3gWaL6R7bvq8pAAAOAN3wK9cyI74vKQAADgDd8CLZqg6MQDANLAu0DRBAUApIJ7gd56MsvufWo68IkmKAAgEZwLtPivZOElYSOaoACAFHAt0P/tRJbJBi368JiIBwCkgGuBZtl9F/P83eIf1WjomtAcBQCAeHEuUNraLP5VWXMjw+VIAIAUcC1Qrs21rBr53MwwCAoASAHXAuXDnZvTPnzb4iYAAIgPX+tA2/8JAAAR41GgvDEq/BMAACIGLVAAADAEY6AAAGCIa4HyyzbXpmuXMAsPAEgD5+tAaWNzM8M6UABAari/Eunh9/PdN3AlEgAgPbxdC1/dggnXwgMAEsHX3ZjKK+IpGAIFACSCh/uBnpTvB7qG+4ECANLA++/C4470AIBU8C5Q3JAeAJAKvgWKBigAIBl8CxS/Cw8ASAbPAr25jDWgAIBU8CvQ3bO4CAkAkAzeJ5EAACAVIFAAADAEAgUAAEMgUAAAMAQCBQAAQyBQAAAwBAIFAABDIFAAADAEAgUAAEMgUAAAMAQCBQAAQyBQAAAwBAIFAABDIFAAADAEAgUAAEMgUAAAMAQCBQAAQyBQAAAwBAIFAABDIFAAADAEAgUAAEMgUAAAMAQCBQAAQyBQAAAwBAIFAABDIFAAADAEAgUAAEMgUAAAMAQCBQAAQyBQAAAwBAIFAABD4hPor/7wi0tLS196hT765NnP/ER+/tOXl16YXcLHRw99z817i4lPnl2ahu7aUiNozch+8qwQNwSxnRtLjC/9uPM1n/6HRmTrse7e92XEPSRiE+inP+QV9PMflo9vsL9T5lfG5j5jpMj0KhU/PrrUSMtmlD4+KmxBENu5xuvnUmc1lOJI0AimhmuBByITaNG8PPSVvy3+fnB06f5yw7WlR2svaVbPOpforiPnmiDQS0uPNNKyGVkJBLGdSyyQv3q5M343GqGbE2uR+dUb+CQugRb+/DzrGf2K9icvNbqezerZLES5uibMpaV/xmNXhOxfNYLWjKwIgtjOJ89yv3WbrhnZ2bGWuIYvrqCIS6A3pGG7R6sRoV/94dFq0OnS0qM/f27p0PP0VeIzxYOlpc9975NnSXX9ux8K+xz63sfPlTWTvPxzr/j8TAPxybOH/i1z4Kcvf+Y/PUv+KcSkGVk2uCwFUQ7vP1la+vKHRWk/qYxAOpyFnz/90dFip7x2jAT5+Oj91b8KgbZULDpI+oIYIRZrMTJyzD79YVUti/3KYawvf8h3Lv9Sq37wxaJT8eVUIxsoUQm0OT9Ea9CNo3TU6QXymkP/tHogPcMeFN4oqyt7htTcIuf/49HyRR+zl4+gcfXx0c/8+VH6OW8sPXqNREiMSTOydA6pLYhieD/356U4eJ+U/L229PzL1aCgeIwEmXbGy55QW8W6RkMhRkiKNY2MFDNh76J6v/JyNQXAx06Jjdnoa6qRDZSoBNocaydbivpVfO8W38tlbSuaRkXr89NL5IH0TPHgKx/mPz+6xPb5yt/mn/4ZKfHjo589+o8+JPsWr8g/6B7/T4civ1lrqUjf//wyD1AVk2ZkSZOqHkQpvEUMP1h6pMxzNo5CzXCp0OqP2befdIwEqTrjpIneWrFo316M0I1GjWyPWfHS4lvsi8UDtgaCQSJJ6gAAIABJREFUWZh8tRX/e54MvSYa2UCJSqDNsfZyS1HLSF2kw3JFbauaS9IzfNSu6ELdXz2gnacbrM15jb98BN/iReTYeB3/lxyTZmRvCHHjQWyGly6IYgOARCeFPe5nx6kdIz3I+EX5t/iKKSLQWrGoOsUINSMjxozP1xGbFtX7Uf7vKorsq40UWPQs0v/2D4iYBMp78EJfpax/1cAoqaZir0Z6htesom4+Oh3hv0YrsFTppPWOiVLGkgq0bAORnJZj0ozsNRI3IYjS0zzutG1EX0dL5PtcasQ9PXhnuxzL+ElHxaKNVDFCl4gZxcjIMRMKuUbDTEviz5DTU5wSjH/6Jy6BUrFdqtbZkS1Vu5RogPehyr/SM/wB6VtNV+tRWbDX/c2/fuSRR2Yt4UsGOndBO9gv8KFKISbNyJKvr1oQhacvVc/QgRRetND8asQ9Pfgy+s9+ic34NCsWq8VChFisxcjIMRPm3VmJVJ18wcmlagR7FPOfQRGjQAk36LeuMOPLekL0NaTqic9UE1Dll/enLzdkUe5DzXzokREMI5UNHvK5q3/IMWlGlipXCGJ7eGlLihVN8pu9rMx5+RgJIg8ytVYsNnMuRIhFVv5Kr2ImrXHiJdL2/iVhCLR88kdHacMX+CMugQpV6RodlmPZXz3N1+FRTQrPVA8qY1RMF4Mc+sb/yMex1I58xkulBcn8w7RlxGhGVoqb9EAKL2sVXao28fSmrdi0B0dqn6+1YvGFTtMISbFmO3bEbFrio7UhUMrPnxtB3Q2KmAQqfr+LMx1VUheVi1ema0tSuldr7eiQvFxd2T7cz6NYI84mL1745Fk++inHpBlZKZmlIIrhnY5TP8pUys/IJbHERKldZ9lasW5M1y/dmF5N11ojpZhV/YWqxOlA6f2VcnGlp2eiEqhwAwtaeS4JI5h0TpgN29N13tIzl6r540enz1yig3/S5PuNESyl498/L1z7zE/4F5MUk7bIPtoRRDm8PIi/ziakb4jRlY6RHrWrj1orFu+RTyMkxfqSsApMihmZPqrWLQlDoB/QYSha7BgmQIMiKoEWdenQN8or4YueSll5qkVIz+f5Xz+3xNYcFvXsV7QnIz1zg6wP/TM6xkSf+ZvnlvgkKC29bIj9qOXGGsnBM/D5l6k6ySSEEJNmZGmGtgSxHl4qhXI6mjryEs9s+ioh7ulRW2fXVrGEdvtSbSldo0bymB16pYysYF8q6mtLv/5h/nc/5OtRymWgn/4w0cgGS1wCnd6LiSxMZn0YtvFzPybr8P7NUnWhhvgMnyT5LG3FsmeqCY8Stgbl0Ahu1HaDLZZ/RAyAEJNmZGkytwaRhJetjXikWs3ETDptft1fO0aCyNe0t1asMoJkcxUhOdbiCanGkwnPT5+4xpaSklL/HbHpJ8/RV6Vfd8MiLoEWX9HlNcWHvvQKu5cdHTEvG6Sf/QZdH/f5Dz8ovum/wRJ0+gy176Fv/DXrZZUXDn/2eboPHzb6mLz6fyY+UFdCm0rsooNqoeE0Js3I8tE3KYhSeIsW1mef5xNw7Fr5XBiUfrR2jPSo9Z/bK9bHz4mjTPk01tPI1GP23/k18rxEJuqypn/lQ97f/1F5KXx5qzLgkdgECsLmUs2boAtEKAkgUGCDG/wKGdbo0rhB20hBhJIAAgU2+PjooVdyPnk3jqW0/UCE0gACBVZgMx2fYzNNWI04C0QoGSBQYAU6h8Fm8tjEPOgAEUoGCBQAAAyBQAEAwBAIFAAADIFAAQDAEAgUAAAMgUABAMAQCBQAAAyBQAEAwBCXAv0CqEBcnYCwusFmXNPGqUAdlh0XljPdZmExg7C6AQJVBwL1ATLdCQirGyBQdSBQHyDTnYCwugECVQcC9QEy3QkIqxsgUHUgUB8g052AsLoBAlUHAvUBMt0JCKsbIFB1IFAfINOdgLC6AQJVBwL1ATLdCQirGyBQdSBQHyDTnYCwugECVQcC9QEy3QkIqxsgUHUgUB8g022RiRUWYXUDBKoOBOoDZLodsgwC9QAEqg4E6gNkug2yDAL1AgSqDgTqA2R6fzLOdBPC6gYIVB0I1AfI9L5kGQTqDQhUHQjUB8j0frTqE2F1BQSqDgTqA2R6Hzr0ibC6AgJVBwL1ATLdnKzTnwirIyBQdSBQHyDTTem2Z46wugICVQcC9QEy3YyZ+kRYXQGBqgOB+gCZbsKMzjsFYXUDBKoOBOoDZLo+8+yZI6yugEDVgUB9gEzXRUGfCKsrIFB1IFAfINP1mNt5pyCsboBA1YFAfYBM10HNnjnC6goIVB0I1AfIdA1U9YmwugICVQcC9QEyXRnl5meOsLoCAlUHAvUBMl0RDXvmCKsrIFB1IFAfINOV0NMnwuoKCFQdCNQHyHQFdDrvFITVDRCoOhCoD5Dp89HWJ8LqCghUHQjUB8j0eeg3P3OE1RUQqDoQqA+Q6XMwsGeOsLoCAlUHAvUBMn0mZvpEWF0BgaoDgfoAmT4Dk847BWF1AwSqDgTqA2R6J+b6RFhdAYGqA4H6AJnegdHcUQXC6gYIVB0I1AfI9HZ66RNhdQUEqg4E6gNkehv9mp85wuoKCFQdCNQHyPQWetozR1hdAYGqA4H6AJneoL8+EVZXQKDqQKA+QKbXseFPhNUREKg6+hV478LqZHLqsrhp51uT+qYSnAYOMl3Gij4RVldAoOpoV+G7ZyYlj7093bS9SjYde7X2UpwGDjJdwpI/EVZHQKDqaNfh9cnxy/nO+cnxj/iWvfONTZRBT8NkcXEy5PElkOkCvfQpnVbvYQ2qUrkDAlVHtxZvr5K2590z0/ZmyyaK5dOgVXuL1xavt/sGzIlQoK5c0W/pknxafYe1f6WKw8AQqDq61fj65An29zTftEU3FQ3RF+XX2j0NWrV3kQwqTEIxaHwCdfQF1HPlZ+20eg5r/0oV1te6iHRCIFB1dCvyOrMks2aJnxaoVu2d8FcH8nUfnUDdfAH1XTlfP61+w9q/UgX2tV5RPyMQqDqaNXnvPLPk9mo14lmNgVZK/QLD1pvMdWsve3EwdTU2gbr5Aup74VHjtPoNa+9KFdrXOqN5TiBQdSwINN97i9SLZ6oNDgSqV3sh0H64iF/vCzejF2hotZLQdlYgUHXMBTpdyLT9TVItHq8vBB1OoKF910OgVpYuRd6FD1CgWevXGgSqjoUW6PZq2fgsmqEux0A1a29go02xCdT6F5CllZ9xTyKF9rWetesTAtXBgkDX2YT8+nQQlDLcJFJo852xCdTyF1D/zjsn7mVMIX2td9ozh0B16D8L3zosShhwGVNgK+6iE6jNLyB7+sxjX0gfztf6DH1CoDrorwM9Lf31J9CwlKhHfAK1F22r/pSJL6yB1OFZzc8cAtXBwpVIfrrwURNfptvCoT7HHNZezLZnDoHqoFuv986Xs+3She9bEw+TSHEz2kx36s/xhrUHc+2ZQ6A6aFfsHeFuTNurxJnX2eqM07WXxncaXPWwRprp7alqL8gjDas53V13h2PLaaPfMti5ULjyFGl/MoHm/6u8H+jX478fqLMx/nFmenuqWgzyOMNqzIyRT5erG9IGd6Sf4m6VySgzvSNVLQZ5lGE1JZvlT5fra9MGAq1wuM55jJnenqpWgzzGsBoyc97d7RVeaQOBVji80m6Emd6Rq1aDPMKwmjFn5sjtPQbSBgKtgEDt0ZmtEKh/5s67Q6DmQKAV6MJboztf0YX3jcKyJXThzYFAp2ASyQ4zExaTSF6Z03lnYBLJGAhUAMuYbDCvv4hlTL6YOXMkgWVMpkCgIlhI35v5CYuF9H5QtmcJFtIbEodAA7kHgzFRZ7pW8NVT1gJRh9UxWvqEQI2JQqDh3AXMkJgzXSf4XvUZd1jdot55p6ALb0oMAg3pPrRmRJzpOsH37M+Yw+oSXXtiEqkHEQh0gF9CsD1kEG+mawTfTJ99Qh1vWO0zjaOuPbGMqQ8RCNT/b3FZHzKIN9PVg2/mz16hjjes1qniqK9PLKTvg1+BvmNSjHeB2h8yiDfTlYNv1nvvF+p4w2obHkftzru4NwRqgEeBFt+Q7xRoF+O7C2/neMP+eM98pu9vZidaNRhm/uwZ6nDD6nnZCIujkT1zdOH74E+gRZV6h6FZjudJJCst3mF/PnI+0/c3pxOtFnzDyaOeoQ42rL6XjSz20WeOSaQeeBNoeY7eecdMoY366PQL3oZAh/0B8/lM399cQSrIwHjyPTWBss9zYN6Hsl1/F3vpM8cyJnN8CXSiLtCW2lXb5PYL3kIX3m2fqF9hExI9/v4UPuzcbDdP3MS68OzjLB5YnP2prNdfaeGSkZyxkN4QXwKlVUrFn/Nrl+suff/y3Y7K9yqMhPdA9f76N7d7Lf5MaxKJfZqDiwdnfizb9VeaObIhZwhUHb8C5Q6dsdP82uV+Uql3JQxXoPSdHTxoTaDTho/R6UhqGZOaQC3XX3ni3YqcIVB1vHbhWa0RBVp3qULt8rCsqe8YVbBd+EYns2c2V6lrfDpSWkiv1oW3Wn8z2Z925AyBquN1Eqml0tTbowq1y//Cen1CnUTisWNNUJVJpFmI/hzgdAQTVo7SJJLN+lufOrJTNgSqjtdlTM3OWmNQ9ODBg4tzasAAl3bqE+gypirBDqguY2qDtxpFfQ5xOoIJa0XbMqZ6G9te/W1OvEOgvvG7kL5RZerz8ouFP0nraFbtiuLmImEupBen35UW0rfB9SD5c4jTEUxYpzQX0je/oCzV37Z1S+jC+2bga+FrAl0sR+AXyzH42dPwsd3eLpxMt5C8tcsGBzwd4YS1m7aA2whYx7rPgSaR3jv3QPFG7v3aRfZ4Ldt3VWE3/rLdN5az7J6XFPcKiqFvJiL5k3x/kjbonAoQ2w2WA8r03slbu2yQbhrmdAQU1i7am4S9A9ahz9yOnHXjunu2ejeHqQE1BbpW7nrPmxCojOJpkBqgZV07ePCgw3c1BCFlet/kbfpzMEIKawdOZjy79Zlb+TbTjKvgz6wyoo5Aby5nv3ZVfa+gCECgebWwidS2d4If3tQngkxXZjEYf8YQVvsCrS1ccoFmXDeybOHp94t/3C574iv6x9s02isMwhAoo7reM6r+uQIRZLoyk8VQ/BlDWG0vGXFvz1w3rkUD9B4+9lm0JQ0akZvZwkvaOwVCUAItv68N79gUNhFkujqLkzD0GUdYrS4Z8aJP3bjeOZHtrx6sZfe8qX08CLQdgxpJbxmanEJjyHRlAml+5pGE1d4aBQ+dd4p2C3R/fVs1mnnlyaJ/f/jqBvFqodoju288kGX3PiXMNW3QD1Q4tNrr1rnlLHvou7y098qH2YN0p0YhjZeThwsP81axWwITaG58y7ugiSLTFQnHn5GE1dYaBU/2zE3GQH+j1m9nKuTTS/ec4wL9hyfplvverF7WFOgGn9InZe2+xj822alRSO3l1UP+2C2hCbTl4qQEiCPTVfCVw0qkE9b5eLNnrh3XQmlZ9g/+4H1hE1PhGlnXRAzIBJot/PbV/Fbh1SPCy3gXnj0sDLjvItmLvGiDLo66VVhzf95SSPPlZePz9mt+DBqeQHNxYVMiJJPpQfkznbDOxac+zdeBPvgUlyhV4c1l6rRSalygK2yHfcK6JVmgxat485XtREcI2PZ6IbWXF8dkAwobXkZWAxNo1d2Zdcem+Ag/09X6mUHpM4awmtA8Ff467xSTK5GWWa+ajjxSFW7wGaVCcsyF+/jgJ3mmVaDVoia6uZphmgpVKqT28uqYrWOz9glLoO0D7vG3R4PPdLWZjsD8GX5YTWicCt/6NIzr7k+//QAdysyZCgWDrcmNSa65VoFudE3ktxciv1y0ppd1+UEJtH3JRwJjoqFnutNfjnNH6GE1oXEqvOuzR1x3X88EM5bz5eyJ6Sy8+LhdoG3eu/3e90s9txUiv5yMx1YYLKnSJSSBdiw69jMt7/R67gEzXeVzKa32Dq35mQ8UVrcX/tdPhf/mZ94rrhu0Q21VoO8+IAoRAu2k47I3Lwub3N5RaDiBKn0ulesNA/TnIGF1fOsp+VQMYc9cN67S2nmmNy5Q3p3uIVA6Q/XgV//l+2tqAvUw8Cmgf2b2LqxOJqcuTx+f5+d88tjb0istCdRHE9TxLUYHE6ja51IQaIj+HCKsnn7QkBxhIH3qxnVDvJKdTbx3joEqCZQJme6/IdziqV2g4sv5/L43tM/N3TM1V9oTaHdP0rU/Xd/kfiiBKn6u+S8L0p8DhNX5ryFMDzBI552iF9fCmdW18OXKz5V8xiy8gkCrafWi4BXBiB2FyC8vyuCLl/y4VPvkrE+OX853zk+Of1R7Ynv12KvyFkuTSAQvDVBnLYuhBKr6uea1q4LU5xBhdf97XOwIA+pTO67l3Twf/kHxj90rJzNxgWfLOlAFgVbrlEjbcqrBjkLklwu3M/Fzjyfd07O9SpqZd8/UbVk0RE/XXmtrGRNFEqhlmY5doLNH9sJsfuZpCpSeikH9aXQlEqd+TVHtSiQFgZbmEy4tYl34906ywhuFbDauRLrvu3RBgI/OvO75uT55gv09XX+i0Sbts5B+DpYbpCPvwuczAx+sP1PswpNjDGrPvNcd6e+j/Wd+Lfwa2/r7OgKVL26/wy58zx5+nTQpG4V0XgvvZTBU9xStT14kf7eYSDl3z7AnBNwtrLM+qzTuSaSZhOvPFCeR8gHnjir043rr2w+WDcTqjkjdd2Oiz88WqHx7JXLrpYWHf8Bm2JsCbd6NqVz3NH3sFM2TtHeedd23V+UG53VRqF9g9H53Xdhf2TTqZUyzGDqZZ5HgMqZhFn7WsJ+5Mf5YhxqWBCqPiUYo0FEvpJ/B0Lk8k/QW0oegT3txrS1IShJzgUprlraaI6BOr42L7J53sV5zGEAyzyLWsHYRhj7txXVDWGF0ZPZLo8VOC3TvfHME1HWNhECdE0Q2zyDSsHaQheJPa3EtvLnwdNH+fHfZx0WVw2BHoLX2KMN5jYRAnRJGNs8gzrC2E4w9c4tx3Vzmk/N+fl9jAOzMwtfn5Clea2TQMo0y04PJ506iDGs7AenTZlxvv0YnxBOdQcpN1oGelv5S1huL6Et81siwO/QxZno4+dxJjGFtJaTmZ+5iFj5drFyJVHXsZbwLNFiHRpjpAeVzJxGGtY2w7JlDoDronrO985PHG9fC3z3TNgTqs0a+42Bhk0Xiy/SwMrqD+MLaRmj6hEB10D5rO8LdmPj9Q+qr6hlDtEAhUCuEltLtRBfWFoJrfuYQqA76p23nQuHPU0SYXKCtq0B918iA/RlbpoeX0u1EFtY2AtQnBKpDSHek7wsEaocQc7qVuMLaQojNzxwC1SElgebBLmaKKtODzOlWogprk0D1CYHqkJhARQKSaUyZHmhStxFTWBsEq08IVId0BRpShz6iTA81qduIKKx1Alv5KQOBqlM/fbs//b7Mn5pfRBCAQANxaDyZHmxStxFPWGsErU8IVIf6CZR/Vznr9dPKg56GoFaGRpPpAWd1C9GEVSYL3J8QqAYNgT75oMxDbxqXHUYLFAJVJ+SsbiGWsMoEbs8cAtUh3THQkFaGxpHpoed1gzjCWiN4fUKgOqQs0HAmkqLI9PATu04UYZUJv/mZQ6A6NCaR/r29O08FcRogUEUiSOw6MYRVIgZ75hCoDi2TSA/9wFLZwZ2GwWQaQabHkdoyEYRVJIrWZwkEqk6jBfrGcpbd+7SVZmhop2G4Dn34mR5HZtcIP6wCoU+9C0Cg6rSczPeeLM7xwxbuwR/aaRhuVj74TI8ks2sEH1aBePQJgerQejpJM/S+3j9MH9hpGHBdU+iZHk1qy4Qe1ikRNT9zCFSHrvNZNkMXDr/fq+zQTsNwa+sDz/R4Ulsm8LBOicmeOQSqQ/cppc3QPhNK4Z2GoZqgQWd6XMktEnRYp0SmTwhUh5kn9b1z8V7K2QG68HViy26BkMNaEVXnnQKBqjPzrF55MjmBEoV6P2bAmR5ddgsEHFZOhPqEQHXoPq+3iuZntvBUjwVNEZwGTy4NN9MjTO8p4YaVE6U/IVANOk4sGQDtOxEfwWnw1KEPNtMjzG6BYMPKiVKfEKgOrae27Lr3noOPQaC+ppRCzfQo03tKqGFlxNn8zCFQHZrn9vZrFhqfhPBPg69Z+TAzPdL0nhJmWBmx2jMPUqB3ThhOxuyeW65fFiRu4vc/Np7qaVwLf9ZK45MQ3mmo42thaJCZHm+Cc4IMKyXa1mdJQgJlghR3ljbdXLYt0BNWGp+E8E5DgxELNOIE54QYVkIWtT9TEuhatu9ifutstu9q+6bNbH+/N9YQ6L+w0vgkhHcaWvDShw8w02NOcE6AYSXErc9oBfpa8x5IN5fJbndOLLzUvmktO9LvjaV9Q2UlRinQqBOcE15YSyJvfuY+BbqWrdw8KS6W5ELbIC1DMpt9b/ksESh7kj6X3yqHMh+6KBbWWHXJXlr8PdK6affs1KxmtJ3jXfK+s4WHevblIxGoh/uEBpfpcSc4J7iwlsRuz9wgrpkKbTuuZV8jg5BVP5p1qXfPZitFk5Ltub9NoGz0cmGlKqxce1lT6Fq2Ihbb2HTnxL4/OSlrWPejNze9u1x95oVeCo1GoCJOZBpapkef4pTQwponoU+/Ai0HJAvjrLANrKtO+tmb1D/vZkUrsSHQOyeyw1fz3dfl6Z8rRXP28FShVQPz5jIfBJU38Tmk6vjaND/WRtlo/llxpJ+98UCPgvM4BepmRDSwTE8gxwmBhTX+wU+GT4ES/21MJ3loA5E4kvmStEYbAm3pnBPKVZjVIqK5At3MSg3fPpcZ9+QbH6twcvWOCr/3GSKIVqDWHRpUpieR44SgwpqMPv0KlMhGmCIiPWvagy+5/dPvnMxaBNqiRs7uuazFlvwA8iauYfO5pMbH2pDm9XtNUkUoUEcLQ0PK9ESSvCSksCbkT68CXSn/CHM5xJTMd7dOsl3bBFqVK0/P67VAOZtZXcPKH732uDYtZV5wHqVAHd10OaBMTyXJSwIKayKjnxSvs/DlH8E65J/TaaKFh772g7YuPL+CqCZQ3TFQzrSFqktzIb1UknnBeZwCddOJDyfT08nyPKSwJtT8zIcVaDmoSXvwxf/3X807xkDFlZ0cg1l4TnMgQJU5AjW+BLUkToG6mEgKJtPTSfKSYMKa1veSV4ESJYrttMJlf0UeckcWjc3WLvxKs7CWdaBHpL+1TVUp5hckQaBtpCrQpNI8uLAmE1ifAiUtP3HipWiNfoc85ALdzArVERHRl5Ut0+nEvTDIaHAl0pq47NQICHQuFlwaSKanleahhDWp3jvBp0DLbrqwDjQn6yjJQ9qF3309qwRaqPTp8ip2tg5038W8tmuDooz7atfCS5tuLpfLmG6dNJ/qgUDnYqFDH1Sm23wrwxJCWNPTp1+BfpXETlzrU0iNSmeTxnXf62TMs9hGp97v+WPSbNxka+APzzzALeHWSzeXSTtU3ERsXT4wvhQJAp2HjSmlEDI9PX8GENapPhFXE9aylaINee/T4jbaRS+5cjIrnyt76VREu+eyovm4KV4LP+9aSfLLRA+T9iUTqLipeEB+v918qVHL7exkIFALs/LDZ3qK/hw+rEnqc4BZ+IjRF+jehdXJ5NRladNbxabf/L162WkJtI9CB8/0JP05dFjTbH7mEKgO2qf+7plJyWNvTzft0E2TZ2ovTUOgNm66PLxAkejzS9N8faL2zCFQHbTP/vrk+OV85/zk+Ed8y975yeOX872/mBx7VX5pIgK1MAw6uEDTTPUhw5quPiFQHXTP//YqaXvePTO15RZrjl6fPCG/NhmB9p6JH1qgieb6cGFNtfNOgUDVaVwL/9Pvy/ypPEHFLXl9cpptKRqgL7aXnZBAe94nNIzBOpvvIQgGC2vS+gzyJz2CRXcSaZ3Zcqtqbt49I46HiiR7GrRdGsRgnc23EAYDhTXt5mcOgerQEOiTDwo0BLp3nnXdt1f5IGj5r1/8zmTy+B9Vr/oCw+UbHxLtDv2gAk032YcJa+r6hEB1mFULyLJV+Z4n7QK9QGfheac+dYHqzykFMNth8w2EwhBhTb75mUOgOsyoBleWyf3uJQSB8o77VrmA6aN87610Z+Fr6C9rGlCgKWf7AGEdgT4hUB06K8Lua0Xzs3GdVEsLdIs3PdcTnoWX0F9aP/h0sc3Dh4P3sI5CnxCoDl1Voa35mXd04RubGMmehngEmna++w7rSPwJgWrQXhfK5mf7Txo3Z+Grzvy0V89I+DRo9uGHEmji+e43rGPRJwSqQ2ttuHmytflZwtd/iutAWQt0azKWFmhJDAJNPeF9hnUMk0ccCFSdlurQ3fzMW69E4mOf69NpeEripyF0gaaf8B7DOiJ9QqA6NCtE2fx8uPP+ePTCd+la+O3V8uZMI5qFb2GOS4dcb2PzyIExgEBtHjBYIFB1GjWibH4+3fZKxo5wNyY2f7S1SjYdq1/ROaLTMKdDP+B6G5sHDg3vArV5uICBQNXRvx/oTrls/hRpf/IJ+J3yFqFfv1x74YgEOm9Oyb9Ax+BP72OgNo8WMhCoOrgjvQ3mLWsaasGizaMGiFeB2jxU4ECg6risF+M5DfOW1g+0YNHmQUNk6LsEpgoEqg4EaoWwBDoSf0KgjoBA1YFALTGzDz/Iim+bhwwTCNQNEKg6EKg1QhHoaPwJgToCAlUHArVIEAIdjz8hUEdAoOpAoI6QXOp/xbfN4wULBOoGCFQdCNQRUn/eX6aPyZ8QqCMgUHUgUD0mi4sTlde9M4xANfyp+kkCJkCBJhBVCFQHCFSLIj2KFFF4oTyh5CvTNfyp/EkCJjyBphBVCFQHCFSHRfrjTwoZMohAdfyp/EkCJjiBJhFVCFQHCFSDCU8QhV7aAALV6r+rf5JwCU2gaUQ1SIHeOdHjmvKK3XPLWfbwxa5N/Dp2nUNBoBqw/NBvYnjJdJ35I+NPEhShCTSNqKYrUCZIsSRp081CFX/nAAAWIklEQVRlCNQpQQtUa/49jVSHQN2QqkDXsn0X81tns31X2zdtZvu1y4RANWBdtMUDBzX7aB4yvc2f3VPCaXQ2QxNoEc7FgwcXI49q9ALdffdw6/aby6SMOycWXmrftJYd0X5jEKgORDsHDx7UnWl1n+lt/pw1JZzEdEdoAs1LfZb/9S9pUPwJdC1buXkyW3jq6nQDddgGaQxeebKo0/eWzxKBsifpc/mtcvTyoYv1InffWO5oSLL9ir9HWjftnp2aVRkIVIvCSaU/ddXjPNNb/TnTkSksuIFA3aAd17bbOc67xSNhLfsaGXesjMd60btnsxXy6xgZe7YpUDZgubAiFVjqs/RxUUA23ZsfbEU8RmPTnRP7/uRkm5NnAYHqMTl4YFG/8+s609v777N76Qks+Q5NoKPtwvcQaDkG+e4y81jVVSdd603625bvZkXDsCHQOyfKHw7efV2a8dl9bTm7j/weZotAqwbmzWU+CCpv4nNI1ZtRAQLVxGiiwHGmt84fpTKjMYPQBJpKyH0KlPhvYzqvQ9uExJHMl6Q12hBoS3/89mvZwuH3O481V6Cb5Mfcb5/LdHryEKgmAQq0ff49lWyeAQTqBp8CJfoTpohIZ5r24Etu//Q7J7MWgbbacMavCeeSLfnR5E3cyVpzSRCoJkbz104zvWP9UhoT7TMJTaCphNynQFfKP8L0DTElU9ytk1WfuinQqoteubdvC5SzKa5zmgcEqovJ/LXLTO9c/5nERPtMQhNoKiH3Ogtf/hEESv45nSZaeOhrP2jrwgs/fikMgvYbA+VMW6gKQKDaGMxfu2+Btr0uhYn2mQQn0ERCPqRAy0FN2oMv/r//at4xBiou5hTpMwvPkWw6DwhUH/35a+cCbX9hAhPtMwlPoGmE3KdAiRLFNl+hr78iD7kji8Zmaxd+pb3IWetAj0h/a5uqIrUuSIJAfeB6Eslm6RERoECTwKdASWNvQ1BW0Rr9DnnIBbqZFXYjAqUvK1um04n7lgFL0yuR1sQ1qKpAoD5ApjsBYXWDT4GW3XRhHWheqpGuxKRd+N3Xs0qghUqfLi9cZ+tA913Ma7vOpijwvtq18NKmm8vlMqZbJ3XmkCBQLyDTnYCwusGnQL9KxinFdUOFx2iHfpOOYe57nYx5FtvowOY9f0xaipts2Xt7a7OVW8Ktl24uk3aouImou3ygcykSBOoDZLoTEFY3eJ1EKtqQ9z4tbqNd9JIrJ7PyubKXTleK7p7Lihbjpngt/Hd1Dner2J8tFmUCFTcVD57MsoXDGu1PCNQPyHQnIKxu8D4LHzEQqA+Q6U5AWN0AgaozPoEOsdDEcaYnsXbGgJAFGvM5gUDVGZ1AB1nq7DbT01i9bUDAAo36nECg6oxNoMNcbOc00xO5ftCAcAUa9zmBQNUZmUAHut2Dy0xP5Q4WBgQr0MjPSYA/6REsIxPoQDccc5npqdxDzYBgBRr5OYFA1YFAfQCBOgECdQMEqs7IBIoufEoEK9DIzwkEqs7IBNprdN98ZYpTgR44uBhvY6cX3gSqfeYxiTQWxibQHutLeqxMcZjp5e+EHtD+neU08CVQgzOPZUwjQV+gexdWJ5NTl4Utd8/Q79vH3pZfGeZpMG1H9mlUuMt02vg8eCDOvmJPPAnU6MxjIf040BYos6Uoy+3VmARqSK9hLWeZHvlgW0/8CHR8MYZA1dEW6Prk+OV85/zk+EfVpq3JE60vTeo09JpYdZbpkU/39sSPQMcXYwhUHV2Bbq+SdubdM8derbatT063vjap0wCBhgcE6gYIVB1dgV5nrc3rU2nunRdkKpLUaUAXPjzQhXcDBKqOrkDXJy+Sv0K3/e6Z43/5zcnk65frr03rNAQ8iTSixpFEyJNIMQOBqqMp0Kq1ub1aDYLyOSSm1rw8ARRbbzIMgl3GFO96mZ4EvIwpalLLXJdYEOjWZPLMR/nfX5hUPfk0BRrqQvqI18v0JNyF9HGTXOY6xFyg1aIlPizamEvCaeAEe81h3CCsboBA1bHQAuVsTWqbcBo4yHQnIKxugEDVsShQoVFKwWngINOdgLC6AQJVx8IsPKfhVJwGDjLdCQirGyBQdfTXgZ6W/paN0i6n4jRwkOlOQFjdAIGqY+FKpHUqzkqkFTgNHGS6ExBWN0Cg6ugKtNDk47Vr4bdXy2VMO9+szyGhRlYg052AsLoBAlVH+2YiO8LdmLZXSTv0OrsZU/1SJJwGDjLdCQirGyBQdfTvB7pzoZDlKdLYZALNd741mRx7pj4rjxpZgUx3AsLqBghUndHdkX4QkOlOQFjdAIGqA4H6AJnuBITVDRCoOhCoD5DpTkBY3QCBqgOB+gCZ7gSE1Q0QqDoQqA+Q6U5AWN0AgaoDgfoAme4EhNUNEKg6EKgPkOlOQFjdAIGqA4H6AJnuBITVDRCoOhCoD5DpTkBY3QCBqgOB+gCZ7gSE1Q0QqDoQqA+Q6U5AWN0AgaoDgfoAme4EhNUNEKg6EKgPkOlOQFjdAIGqA4H6AJnuBITVDRCoOhCoD5DpTkBY3QCBqgOB+gCZ7gSE1Q0QqDoQqA+Q6U5AWN0AgaoDgfoAme4EhNUNEKg6EKgPkOlOQFjdAIGqA4H6AJnuBITVDRCoOhCoD5DpTkBY3QCBqgOB+gCZ7gSE1Q0QqDoQqA+Q6U5AWN0AgaoDgfoAme4EhNUNEKg6EKgPkOlOQFjdAIGqA4H6AJnuBITVDRCoOhCoD5DpTkBY3QCBqgOB+gCZ7gSE1Q0QqDoQqA+Q6U5AWN0AgaoDgfoAme4EhNUNEKg6EKgPkOlOQFjdAIGqA4H6AJnuBITVDRCoOhCoD5DpTkBY3QCBqgOB+gCZ7gSE1Q0QqDoQqA+Q6U5AWN0AgaoDgfoAme4EhNUNEKg6EKgPkOlOQFjdAIGqA4H6AJnuBITVDRCoOhCoD5DpTkBY3QCBqqMv0L0Lq5PJqcv1zdurxz+qbcJp4CDTnYCwugECVUdboHfPTEoee1vevHd+AoF2gkx3AsLqBghUHW2Brk+OX853Grq8PoFAu0GmOwFhdQMEqo6uQLdXSdvz7pljr8qbIdAZINOdgLC6AQJVR1eg1ydPsL+nha1FB/53MQbaDTLdCQirGyBQdXQFuj55kfzdYiLlW58Y4yTSZHFxovRCh5mu/B4SxJtARxZkCFQdTYHunWddd8mXW0X3XdzwBYadtxgsRVYVmaXySneZrv4eEsSXQMcW5OQz1yI2BEoGRAMQqOd2wiJZjjBRSSxnmU7fw+KBUTWQKjwJtOtEJ9suhUDVMRfodCHTejkeOnwX3nM7YcLzSiGJXGU6fQ8HDx5cHFMDqcKPQLtOdLrtUghUHQst0Otk/n1wgWo0CG0eT+mIrjKd+rOw50GPHzwc/Ai040T7rm8egUDV6S/Q7VWyaWiB6jQIrRCKQBe5QNPsTs5iSIF6r28egUDV6T8Lf31SUbs8yetp0PGZFULpwhN/LibaFprNkF147/XNIxCoOvrrQE9Lf8cr0FAmkVgDNMlMnsOQk0gQKCixdCXSCLvwoSxjWjx48GCqfck5DLmMCV14UKIr0L3zk8fbroUfXKBDDOqHsZD+wGKqLaF5DLqQHpNIIDe4mciOcDcmNn9EGFygIS8rcZvpAX9wtwx7KWe6YYdA1dG/H+jOhcKfp4gswxJowAubHWd6uB/cLQNfC59s2CFQdXBHeh/grhdOQFjdAIGqA4H6AJnuBITVDRCoOhCoD5DpTkBY3QCBqgOB+gCZ7gSE1Q0QqDoQqA+Q6U5AWN0AgaoDgfoAme4EhNUNEKg6EKgPkOlOQFjdAIGqA4H6AJnuBITVDRCoOk4FCioQVycgrG6wGde0gUD9gLg6AWF1g824po1LgQr0PSU994/88G5LDqWMgHqOvd7JYDsP965HDAQaweHdlhxKGQHlMAQKFIFAIzi825JDKSOgHIZAgSIQaASHd1tyKGUElMMQKFAEAo3g8G5LDqWMgHIYAgWKQKARHN5tyaGUEVAOQ6BAEU8CBQCA9IBAAQDAEAgUAAAMgUABAMAQCBQAAAyBQAEAwBAIFAAADIFAAQDAEB8C3buwOpmcuqy/490zE8Jjb+sXc/fME22HVy6k2t/gTfzyW5PJsdYjGoeiDcPCbL277dXjH/UqY++t4qW/+Xu934hlLIRVrjPzMa/oe+cnnHJnrQP3TRGQexEoO6mqlUlge1WoDprFrE9kAdL91Aup9td/E2/RHY692uPwChgWZuvdFalLBWpaxg5L9md6vhHL2AirVGcUMK/oNYFqHbhvioDci0DXJ8cv5zs83XTY4mdYu5i99QnfV9pPtRBhf+03sTU5VrSqiheROmh0eCXMCrP27q5P2AsNyyhS//HL+d5fUOe4C5MuNsIq1RmlvU0rOmd7lYRR48B9UwQQ3At0e5V9r9IvZx3WJ6fNivnlNye8dkj7qRYi7K/9JgoxvJiTF5V/jQ6vhFlh1t5d2dYhOWZaxhZr5FwvA+0uTLrYCKtUZ1QwrujC8U/XC5pN3xQBFPcCvc5O03XNSlXWCuEk6hRTNI6e+UX1emE/xULE/bXfxN0zrPez3jiieSha36RJYbbeXZGxv0vHQA3L4MrJ2etdhUkXG2GV6owCxhVd2ImcC/UD900RwHAv0HWWKLrdmrJWHv/L4nvy65d1i7n++B9Vr5P2UyxE3N/4TdBcMjm8Gj0L6/nu1idPsEkkwzIq5bDiXIVJFxthleqMAuZ1rCqAN4BVD9w3RQDDuUCrL8Vq0lYZPiJenlLtYlgFkPbTKYRXIOM3QTpBxoefS8/Cer67raLFQ19nWkb5il/8zmRSJHKPs2QdG2GV6owKvSp6yfWWyjqfvikC8rAFulV0Mz7K//7CpJ7rajvbEajxmyCVOliB9nt3xBO9BXqBJvvpkHLYRlilOqNCr4qeCwOWegeGQC3gU6C6KyOuV12M0/rFNGvHY2/rFMIFavomtkglNj78XPoV1vPdkY5qQ6BaZWxNSLLvvVWzhuUw6WIjrFKdUaFXRc9pf6BRkMpu/VIE5GG3QDll/RisBWr4JrZWj73Y5/Bz6VVYz3dHpyz6tUC3JizJy9WI4TSCbIR1+lh7LZTRp5fn49QPjBaoBWIQaO2bcRiB6r2J66wTFaZAe747tuawbxe+a79oBXq93nPWbcUZVfS2w6gdGAK1QMiz8BxyLnWL6TvF2BSoxpt4q0qlEGfh+76769XFL+VSTsNPWOU4+Uc4E8E2wsrRlZBRRW97pdqBMQtvAR/rQE9Lf5WpeibkXOoWMx3DFPbTKKT6etZ/E3vrk8d5A8D08PMxLaz/u5MFavgJq6YO6W66C5MuFsIq1xmFXXtVdGHEU/PAfVME5GFfibQuOky3mOkyJNMLZfjXs/abWBdGoEK7Esniu2PNHNMy1qsAn3YZJl1shHW99uU7f98eFV1cPa934L4pAnIfAqXXPJtcXLu9Ws7T7nxzQsfa9IrhtUPaT6MQYR2o5pu4Lj5revj5GBZm790xgZqWUQT21GU2C+8wTLrYCKtUZxToVdHFSxL0Dtw3RUDu5WYiO8a3d2F9xccuGxRT9WKk/dQLqfbXfRP8jmLsUmPDwytgVJjFd8cH2kzL2KKrvunEtbsw6WIjrFKdUaBPRZcGPLUO3DdFQO7nfqA75YrpUyZfaDvlPRaf+cikmOkwkLSfciHC/npvYms6QviE+eFVMCnM4rurEte0jJ3yxpP8skN3YdLFSljFOqNyTPOKLq9Z0jlw3xQBOe5IDwAAxkCgAABgCAQKAACGQKAAAGAIBAoAAIZAoAAAYAgECgAAhkCgAABgCAQKAACGQKAAAGAIBAoAAIZAoAAAYAgEOlZ2rw79DgCIHgg0Um4uZ0fYP++cWHhJe/8rJ1c6n1vLup8TuXNi39TCV57MsoWHL7InMsI9b5aPbp0r/smemVWYyqfYyPYbfmAAHACBRkohUG4RE5/snp0hSVWB3lw+Uv37NarMhRX25qYCZTad9x4hUBAhEGiklI5i7b/BBLoxPe5mtvB00dY8S525WWpueqR9F8tn9s0eM9D4FBAoCAUINFJII482AIcS6O7ZyolFcUfoWyG7rmXTpunNZd4OnV0oBAoiBAKNlMJLv8+6xdwnt84VUn2IjDUWPd3dolNdtgrfLTYepqIrhymze5+6Wj5fspKzAcqH2ADl7dfIi5lAhfLYy75bewuVJiulEXXunhUEt8FaoxuCVNvenmxF6XjCe0QXHgQFBBophUD/P9YtZj7ZZAOPK3npmV87Sx48TccmyevYMGX5oBIo20kaurzvH5MyxPL4oOYR8S1stGiMCPTOiXv+r5NFOS/RLaTsols/7cO3vT3JitLxxPcIgYKggEAjpewZs5l46pOij1y05HZfJ83SQpD3/N9lx5q08jbINjpMWbb8VqoufFHCvov57XMZK+G+i2UJ5BVSeWvZfvJvOq1O2T0rPqLQt8L1R1ujTKA3l0WBNt+eZEXxeNJ7hEBBUECgkUKGFkX1TLvK+7mT+FonKjE2MEmHK5nX1lizcK3cibcRN4lAxfKYseSB05vL05kizgYpoiigUl4lOzYWyl7WfHuiFaXjSe8RAgVBAYFGCvHRLunEE59U446kpbchde6lSR1RoJURiTvXqkmpYqtUXvG6++TxT7LPSnMT2WlDUF6HQJtvT7SieDz5PUKgICgg0EihPiJtOOITvnadrr5kzcc7J6i0uBp33/v+tx8QetaS3abty3LYUi5vM+PTT1PWGj34zeWFFelxobwOgTbfnmRF4XhyARAoCAoINFKYj8rOsKpA332gPjTJnyf/mM6dNwWaXzlJ/vnwVKF3TtR78Bv1xfKSluUx0DkCFY4nvUcIFIQFBBopTKBlJ/6/nhDHGglthipadQsP/dYf/OysYgu0Jqnd/7dcBDWVZr0Hv/ta42Ij8h7bZ+HnCXR6PLRAQcBAoJHCe8RFJ/4f0jFQUWgthuJr3eePgZKtrSvtN4Rp+FoPvhy2fLP6p1BqxzrQ+ttrtWJ5PIyBgoCBQCOlGlLcYNeZr1Vz6FNriYbi/95cbs7CF4/ILDx7RSZOfpflTW1dWVO6kUhRwprQwFyjR6eqbrsSaZ5ApeNJ7xECBUEBgUZK5TKymvIlvlyyvLLnSKuhCoOVayvfWOZjoL+d19ZYlo3Ii/nuf1kmAhXLo9ezlxe0758eX15UL17qXux6+Gp+6yQRMi1VvhZ+nkCl42EdKAgXCDRSpo1Bfl8mfuXQ4bzdUBvsOqTXyXNrdDJJuhLpFp042vfPpSuRDue12ysR5MuQhCmn/Xl1KPoStu+8IdrWK5HorUlwJRIIFQg0UoTeNJ/9Jteu0/WTnbPw9z7NZnN2z1HX3SqnavjNOneb18Kz9Zjk3wvTdUzCjURKNjNJoKTUBXYFfsv9QOePgUrHE94jBAqCAgIFAABDIFAAADAEAgUAAEMgUAAAMAQCBQAAQyBQAAAwBAIFAABDIFAAADAEAgUAAEMgUAAAMAQCBQAAQyBQAP7/dupYAAAAAGCQv/UcdhdEMAkUYBIowCRQgEmgAJNAASaBAkwCBZgCQ5vqZ7so9foAAAAASUVORK5CYII=" width="672" /></p>
+<pre class="r"><code># Print and save plot
+save_plot(plots$combo_morph_metab_ndvi, plot_name = paste0(&quot;morph_relabu_ndvi&quot;), 
+          filetype = tmp$out)</code></pre>
+</div>
+<div id="pratylenchus-mann-whitney-u-test" class="section level3">
+<h3>Pratylenchus Mann-Whitney U test</h3>
+<pre class="r"><code>found &lt;- c(40, 24, 16, 37, 10, 27, 74, 100, 100, 78, 32, 28)
+not_found &lt;- c(24,12, 7, 7, 24, 52)
+
+wilcox.test(found, not_found)</code></pre>
+<pre><code>## 
+##  Wilcoxon rank sum test with continuity correction
+## 
+## data:  found and not_found
+## W = 58, p-value = 0.04339
+## alternative hypothesis: true location shift is not equal to 0</code></pre>
+<div id="credit" class="section level5">
+<h5>Credit</h5>
+<p>This script is based on ideas and code from the <a href="https://benjjneb.github.io/dada2/tutorial.html">dada2 Tutorial</a>
+by Benjamin Callahan, the publication “Bioconductor Workflow for
+Microbiome Data Analysis: from raw reads to community analyses” by <a href="https://f1000research.com/articles/5-1492">Callahan et
+al. (2016)</a> and various pages of the official <a href="https://joey711.github.io/phyloseq">phyloseq website</a> by Paul
+J. McMurdie.</p>
+</div>
+</div>
+</div>
+
+
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Controls.pdf b/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Controls.pdf
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diff --git a/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf b/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Customized_NDVI_Shannon_plot.pdf
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diff --git a/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Molecular_and_Morphological_Plot.pdf b/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Molecular_and_Morphological_Plot.pdf
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diff --git a/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Overview_all_and_pruned.pdf b/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Overview_all_and_pruned.pdf
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diff --git a/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Top100_ASVs.pdf b/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Top100_ASVs.pdf
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diff --git a/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Top15_Genus.pdf b/analysis/Nems/Nems_DADA2_results_260821/analysis_output/Top15_Genus.pdf
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diff --git a/analysis/Nems/Nems_DADA2_results_260821/analysis_output/morph_count_ndvi.pdf b/analysis/Nems/Nems_DADA2_results_260821/analysis_output/morph_count_ndvi.pdf
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diff --git a/analysis/Nems/Nems_DADA2_results_260821/analysis_output/morph_relabu_ndvi.pdf b/analysis/Nems/Nems_DADA2_results_260821/analysis_output/morph_relabu_ndvi.pdf
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diff --git a/analysis/Nems/supplementary_table_ndvi_regression_nematodes.xlsx b/analysis/Nems/supplementary_table_ndvi_regression_nematodes.xlsx
index 0a839bf429320d51ba511f317b149428d02a501e..17c5dff8dbe99da0c7b7cce1a81a8842eff85cbf 100644
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diff --git a/analysis/Nems/t1_morph_table_manuscript.xlsx b/analysis/Nems/t1_morph_table_manuscript.xlsx
index ed98fdbc57a52a2400420a4eaff6838c01c126ce..6b52e638ca772c1d5b1cfbdf1e8dc66e91460df4 100644
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diff --git a/analysis/OITS/OITS_Analysis_pipeline_v4-3.Rmd b/analysis/OITS/OITS_Analysis_pipeline_v4-3.Rmd
index 1cd9eca62e37b0a8a8ac2485211ba3b5fc41527d..70c01376d72a84901f97d3de605362acf789ab5d 100644
--- a/analysis/OITS/OITS_Analysis_pipeline_v4-3.Rmd
+++ b/analysis/OITS/OITS_Analysis_pipeline_v4-3.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "16S Data Analysis"
+title: "OITS Data Analysis"
 author: "Simeon Lim Rossmann, Marte Persdatter Tangvik"
 date: "23.12.2024"
 output:
@@ -86,7 +86,7 @@ remotes::install_github("mahendra-mariadassou/phyloseq-extended", ref = "dev")
 source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R" )
 
 library(phyloseq)
-library(SIAMCAT)
+#library(SIAMCAT)
 
 # Phylogeny libraries
 library(phangorn)
@@ -668,7 +668,7 @@ oomycetes_percentage
 
 This chunk plots abundance of the Top n ASVs or taxa at a given level as a bar plot, giving an insight into the presence of the n ASV and most common taxa for the primary and secondary parameters. The default for n is set at 20, a larger n may lead to delay/skipping of the plot in standard out, but it should be saved as a PDF regardless for ASVs. For taxa, a large n  may lead to unreadable plots. **The chunk does not require any input**, but it is possible to adjust the **default 'n'**, and to change **width**, **height** and **resolution** of the PDF-output if necessary.
 
-```{r custom-Bar-plot, tidy=TRUE, message=FALSE}
+```{r custom-Bar-plot, message=FALSE}
 
 
 # CHANGE ME to the desired sample categories on the x-axis. 
@@ -784,7 +784,7 @@ topntax_data <- set$topntax_tbl %>%
   select(Alias, ndvi, Shannon, Taxa) %>%
   distinct()
 
-write.csv(topntax_data, file = "../../topntax_all_taxa/topntax_data_oomycetes.csv")
+write.csv(topntax_data, file = "../topntax_all_taxa/topntax_data_oomycetes.csv")
 
 ```
 
diff --git a/analysis/OITS/OITS_Analysis_pipeline_v4-3.html b/analysis/OITS/OITS_Analysis_pipeline_v4-3.html
new file mode 100644
index 0000000000000000000000000000000000000000..902cf9a1396bd972536faa4a1579455c528dc376
--- /dev/null
+++ b/analysis/OITS/OITS_Analysis_pipeline_v4-3.html
@@ -0,0 +1,2238 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Simeon Lim Rossmann, Marte Persdatter Tangvik" />
+
+
+<title>OITS Data Analysis</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
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) 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Cv3aCgUIAQgIFID+lAgKCggBbAgIpAgIpAgAAAAD//D/8AS6BLoACQANABAAAAAyHwEWFA8BJzcTAScJAQUTA+AmDpkNDWPWXyL9mdYCZv4f/rNuBLoNmQ4mDlzWYP50/ZrWAmb8anABTwAAAAEAAAAABLAEsAAPAAABETMyFh0BITU0NjsBEQEhArz6FR384B0V+v4MBLACiv3aHRUyMhUdAiYCJgAAAAEADgAIBEwEnAAfAAABJTYWFREUBgcGLgE2NzYXEQURFAYHBi4BNjc2FxE0NgFwAoUnMFNGT4gkV09IQv2oWEFPiCRXT0hCHQP5ow8eIvzBN1EXGSltchkYEAIJm/2iKmAVGilucRoYEQJ/JioAAAACAAn/+AS7BKcAHQApAAAAMh4CFQcXFAcBFgYPAQYiJwEGIycHIi4CND4BBCIOARQeATI+ATQmAZDItoNOAQFOARMXARY7GikT/u13jgUCZLaDTk6DAXKwlFZWlLCUVlYEp06DtmQCBY15/u4aJRg6FBQBEk0BAU6Dtsi2g1tWlLCUVlaUsJQAAQBkAFgErwREABkAAAE+Ah4CFRQOAwcuBDU0PgIeAQKJMHt4dVg2Q3mEqD4+p4V4Qzhadnh5A7VESAUtU3ZAOXmAf7JVVbJ/gHk5QHZTLQVIAAAAAf/TAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH+qoIHDxH+qf6qEQ8Hgv6lEQYUAyABYRMT/p8RDPn+bxQLDPb3DAsUAZD7DBEAAv/TAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH+qoIHDxH+qf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP+nxEM+f5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU+AT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR+Cg8PCvuCCg8PVWQCo/3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh/doKDw8KAiYKDw/872QDIGT8GGQDIGQETA8K++YKDw8KBBoKD2RkZA8K/qIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX+cBUdHQJtAZAVHR0V/nAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX+cBUdHQRMHRX+cBUdHRUBkBUdHRX+cBUdHRUBkBUd/agdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA/AE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd/nAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3+cB0VyBUdHRXIFR0dFcgVHR0VyBUd/nAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH/oAICLEHFAirBxYB3QI7CAixBxQI/PAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI/vUBCwgIsggVB/70/vQHFQiyCAgBC/71CAiyCBUHAQwBDAcVBDuzCBUH/vT+9AcVCLIICAEL/vUICLIIFQcBDAEMBxUIsggI/vUBDAcAAwAX/+sExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj+1HeOYrSBTU2BAW+zmFhYmLOZWFj+vJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15/tUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF//rBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI+ATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K/qIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V/nAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P/t6WCg8PCpYKDw/+3pYKDw8KlgoPD/7elgoPDwqWCg8PBLAPCvuCCg8PCgR+Cg/+cA8K/RIKDw8KAu4KD/7UDwr+PgoPDwoBwgoPyA8K+goPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ/AjY/ASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ+frJ+BJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF+frEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD/u0DwoBEzspASwBLDsp/UQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT+1PzgKTs7KQMgZP1EArz9RAK8/UQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg/+1A8K+goPrwoFAmoCYAcH/aAICv3BCg8PCgF3/okKDw8KAj8KAAIAZAAAA+gEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V/OAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd/nAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI+ATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb/oxXJyxejFcnL+fDIKD68KDw8K+goPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr+7Q8KMgoPDwoBXgoPAAAAAAL/nAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp/eYBr9EVohTQ/p4b4BsBkP5wBLD+1AEs/nD+1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN/rkNCxS/HQJUMgoPDwr75goPDwoyCg8DhA8EsB0V/j4XEP5wEBABkBAXAcIVHfzgDwr+ogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K/u0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf+7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY/AT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX/dQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K/XQYGf5XDB4CBggEDRkBqRkYAowKDsgOC/4+Cw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJy/ov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2+DCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ+AjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL/pYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ+AjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L/pcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv+lwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB/AI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA/K4KDw8KA1IKDw/9CDIKDw8KMgoPD9IBwgoPDwr+PgoPD74yCg8PCjIKDw/SAcIKDw8K/j4KDw++MgoPDwoyCg8P0gHCCg8PCv4+Cg8PvjIKDw8KMgoPD9IBwgoPDwr+PgoPDwSwDwr7ggoPDwoEfgoPyA8K/K4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT/tRSdmQpOzspA4QpOzsp/ageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE+ATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH+WwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT++uSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF//tQKDw8KASx03qP9wAwI/jQIDAwIAcwIDAwI/jQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ/AScmND8BNjIXAUEBAgkMDAn+/hUZ+goPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg+EeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY/ATYBQQECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK/RIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd+hGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk/nBkAfQCvMjI/tTIZAJY+7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs/UQBLGRkZAOEZGT+DGRkAfT+1AEsA4RkZGQCWP4MZMgBLAEsyGT+1AEs/tQBLMhkZGT+DP4MAfRk/tRkZGRkyGTI/tQBLMhkZGT+1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI/OD+1AEsyGRkyGRkASzIyMgD6PwYA+j8GAPo/BgD6PwYA+j7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI/jAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAwYCqggI/jAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg/9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj//wRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4+/j4dBLAdFfuCAbz+QwR/FR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY/ATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2/kQLEAFOBw45BhcKAcIKFwY+DgdTARABVpYKFgROBBYK/doKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw/+sf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg/+cA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP/agOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE+BAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT+z76Hh76H/u9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K/7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE/AQEDIQMCqQGBFCgSJQkK/l81LBFS/nk6IgsJKjIe/pM4HAwaBwcBj6wBVKIEr/waMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu/VoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU+ATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD+DCk7AQYLFyEaAdc5dWM+Hy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb/K5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK/gy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL/tQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI+AzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn+cAQOIhoWyBkYJhMZCwgyA+j7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI/OCnpwMgpwACACH/tQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ/nAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V/agVHR0VA+gVHR0V/BgVHR0VAyAVHR0V/OAVHR0VBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V/UQVHR2zBEwVHR0V+7QVHR3dArwVHR0V/UQVHR2zBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V/agVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V/OAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3+6WQVHR0VZBUdHf7pZBUdHRVkFR0d/ulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA/AAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA/AE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd/tQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3+1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG/5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V/gwVHR3++qfIyAHCASwVHR0V/tQVHR0VAlgVHR0V/agVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V/gwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd+7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3+1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH/0SHywsBIT+1AEsA+gsH/12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs/NJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7+4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4+rWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab/G8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ+BQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ/f4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA+hsAgYUFR0OFgoFBmz9BQGQMje7/pApOzspAfQpO8i7o/5wpbm5Azj+lqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp/gwpOzsptsj+tKW5uaUBkKW5/tf+ljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml/nClubkCfwFTCAj+rAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn+DCk7OymZZ6W5uaUBkKW5G/7TBxUH/s4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX+cKW5uQHKAYsHFQdlBwf97QcVB/gHB2UHFQdvCBQETBexOyn+DCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH+AcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE+AQgIFAgBBAcFCqrICggBCAgI/vgICsiqCgUH/vwIFAj++AgFCq/ICgj++AgIAQgICsivCgUDlgEICAj++AgKyK0KBAf+/AcVB/73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ/+MB0VZBUdHQRMHRX+SgHFDggV/BgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ/+MBUP/jAdFWQVHR0ETB0V/koBxQ4IFf5KAcUOCBX8GBUIDgHF/koVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ/+MBUP/hQPDwHsDxUB0A8VBBr8GBUIDgHF/koVCA4B4A4qDgHgDggV/koBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS/NISGRkEMv4OCx4L/g4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2+sgVHR0VyBUdHQGlyBUdHRXIFR0dA+gdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw/+FA8V/jAPFRUPAmQBthUIDv4gDioO/iAOCBUBtv47DggVA+gVCA4AAAH//gACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL+MQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX+OQ4JFAPoFQkP/j4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA+gVCA7+OwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW++4WCQ8B9Q8q/fcD6BUdHRX8GBUdHQQ5/eQPFhYPAhwP/UgdFWQVHR0VZBUdAAEAiP/8A3UESgAFAAAJAgcJAQN1/qABYMX92AIoA4T+n/6fxgIoAiYAAAAAAQE7//wEKARKAAUAAAkBJwkBNwQo/dnGAWH+n8YCI/3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4+rWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX+DBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm/5+HRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI/ARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ+AQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB/IHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm/4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ+AQUiDgMVFDsBFjc+ATMyFhUUBgciDgUHBhY7ATI+AzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ/eMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD/7UDwoyCg/IDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw+5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP/s2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA/AAAAMh4CFA4CIi4CND4BBCIOARQeATI+ATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb/oxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJyg2oHB/7ACBQIyggIagcVB0/FBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf+vwcHywcVB2oICE/FBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ+AQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV/t0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA/kAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ/AgoQEP32EBf92hUdHRUCJhcD8f5VDSYN/lUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3+3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN/lUNJg3+VQ0KFQEjHQSwHRX92hcQ/fYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ/AgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN/lUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I/iH+1BUdFQ5e+QgUCGoHB/leDggEsB0V/tQVCA5e+QcHaggUCPleDhX7UB0VASwVCA5e+QcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ/AScmNgP2agcH+V4OCBX+1BUdFQ5e+QgU/QwBLBUdFQ5e+QgUCGoHB/leDggEYGoIFAj5Xg4VHRUBLBUIDl75B/3xHRX+1BUIDl75BwdqCBQI+V4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3+DA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc+BAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP/nDI/nAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe/okKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf+iQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc+ATc+AScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy/t3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U/M0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc+ATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY+BDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA//cAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl+DiINfWjIWCwQMWv3/Iw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP/cAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ+BTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2/VFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP/ngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U/BQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR/plkGhPNzRMaZP6ZERgWDwFrBLBYPv6t/rsOMRQpFA0M+f75XRRAFRAJgIAJEBVAFF0BB/kMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA/AEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk/HxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd/nD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb/kPD/kOFZ/9qP7dFR0dFdECWPEV/amNetEVHR0VASMDGvkPD/kOFfG1jXqfFQSN5g4qDuYOCBWW/agdFWQVHQJYlhUI/piNeh0VZBUd/k3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7/tQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3+1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6+hUdHRX6+hUd/nD+1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB//8A1ARMA8IABQAAAQcJAScBBEzG/p/+n8UCJwGbxwFh/p/HAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH/p8BYQAAAAAC/1EAZAVfA+gAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW/oP+HOYOCBWWAYHX/RIVHZYVCA7mDioD6B0V/dkVDvkPD/kOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3+1B0qHR8SHQYFyTYUHh4EsBYQoiUY/iUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw+1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz+1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY/ATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO+Q8P+Q4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ/5Dg75DxUCWBUDYOUPKQ/lDwkUl5cUCQ/lDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt/BgpOzspA+gpOzv+q2RkZAGQAtwXLSgV/R1kOylkKTs7KWQpO8hkZGRkAAAAA/+cAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k/V0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj+7BAIHBUBUQ76AgQQDw36/tT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM+ATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU+LwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N/rmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI+ARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW+v4+Mv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3+qBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv/tQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo/rgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl+IZJZkMjIBwvoETCEY/qgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX+DBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP/qoHphwOOmQBJYMGGw/LFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7+rRUoog0CDRElCkj+CVkBUxUoMjIBAgIDBQIZFrdT5B0V/gwVHR0VAfQVHQAAAAP/nABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf+qg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH+Jd0PHAaCASZq/qoCUGQVHR0VZBUdHQRP5gEFEBEXC/3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE+AR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN/fMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V/gwVHR0FDm5UaoMGGw/+qgemHA4OAWsfFm4NAQxUAS5U1ssVExIW/jgCCgIJAwcDBQMBDBGSVv6tZAElgwYb/QsdFWQVHR0VZBUdAAP//QAGA+gFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM+BDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX+DBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf+qg8bBgHP/q1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob+7RAX/u0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm/453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX+7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm+jfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm/5AARMQEP7tEBf+7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg+JFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD+7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ+ASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY+ATc+AzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU/OR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg/KhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh+WYcrIgsW/awQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA+gpOzsp/BgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH+1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E/tTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv+DMj+DDspASw7KcgB9Dsp/BgpOwSwOylkOyn+cGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj+L2oHB8deDggV/tQVHRUOXscIFALLXg4VHRX+1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX+1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX+1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5/d8t2gXl6gAcbgv29vv+C/b2/+LS0gIC0gAUwtICAWDg83ETNIMykfegEJ/octICAtIAIdLSAgLSAEqF+f3fLdoF5eoN3y3Z9Xb7/gv29vv+C/BiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf/YAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr+qw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB/0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH/6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6+RAH/SBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg+PqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB/slVVsn+AeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn/8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ/ARYfAQcXAQKru0KNQjgiHR8uEl/3/nvUaRONQkIBGxJpCgmNQkL+5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl/4/nvUahKNQrpCARv+RmkICY1CukL+5UJCjUK7Qjc3LxFf+AGFAAAAAAMAyAAAA+gEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7/Vj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8/RY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT+6dT+zP7oywEz0gEsAQsjDx0qBKH+5g8qHQ8j/vX+1NL+zcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH/s0NBxMtPGQ+i6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ/soCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc+ATM2FjMyNxcOAyMiLgEHDgEPASc+BTc+AScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig/IhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn//wSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF+YOCBWWyJYVCA7mDioBLBYO+g8P+g4WA4QQ+Q4V/HwDhBUO+Q8AAAQAGf//A+gEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI/tTIyAEsArxkZAH0/HwWDvoPD/oOFgOEZMjI/RL6ZJb6ZAAAAAAEABn//wPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO+g8P+g4WA4SW+mSW+mT7UGRkAfRkyMgAAAAEABn//wRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD/oOFgOE/gwBkGT7UGQBkGTIyAAAAAAEABn//wRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD/oOFgOE/gxkAZBkyMj7tAGQZAAAAAAFABn//wSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk/nABkGT+DAH0ASwWDvoPD/oOFgOEyMj+DMj+DMj+DMgABQAZ//8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD+DAH0ZP5wAZBk/tQBLGTIyAEsFg76Dw/6DhYDhMjI/gzI/gzI/gzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P+cKW5uQJn/gwpOzspAfQpOzsETLuj/nClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml/nCju7wCZP4MKTs7KQH0KTs7/m/9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn+DCk7OykB9Ck7gr4MJAy+DAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX+cKW5uQJn/gwpOzspAfQpOzv+FQGQFQsMvgwkDL4MCwRMvKL+cKW5uaUBkKO7yDsp/gwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf+DCk7OykB9Ck7O/77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn+DCk7OykB9Ck7z/0QGBgQ/REAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX+cBUdHRUBwik7Oyn+PhUdHb8BRBAQ/rwQFvoVHR0V+hYETLml/nCluR0VZBUdOykB9Ck7HRVkFR3p/uQOJg7+5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B/vf+8wQMDgkJCQUCUCcn/tIXCAoQSwENuwUJEASeCQoRC/5TBwEjEv7K/sUFDwgLFQnlbm4TFRRWAS/TBhAAAAACAAAAAAT+BEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4+KTs7KQHCFR0dFf5wpbm5AvEBRBAQ/rwQFvoVHR0V+hYETB0VZBUdOyn+DCk7HRVkFR25pQGQpbnp/uQOJg7+5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj+75wpOzspAfQpO8i7o/5wpbm5pQEsBLAdFf5wFQgObf7IDw+NDykPAThtDhX+1Dsp/gwpOzsplMj+1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx/nugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO/oEOJw3+hQ0JFfod/oUD6BUd+7QdA2dkBLAdFf6iFg/+Vg8PAaoPFgFeFR38fB0V+voVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE+AQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n/fkD6BUd+7QdA2dkBJ7+TQ8g+hQeHRX6IQ8BrxAC/H8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB/7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH/fMICAEjCBQIiwcH/dIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY/ASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A+gVHfu0HQNnZAP/jg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V+voVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ/AQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A+gVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3+WB0V+voVHWQyMgACAAT/7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d/sb+cQSs/TMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V+gH0ZMhkZAPo/K4VHQGQ/nAdFQPoFB7+1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC/tVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf+XAgUCMoICGoHFQdPASkHFQPolf7VXwkJk/5wHRUD6BQe/tQBLMjI/c5qBxUH/lsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72/reWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykD6P7zZ4YPAw7+9v5wHRUD6BQe/tQBLMjI/YxGDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM/qIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA+j+1B0Vlv5wHRUD6BQe/tQBLMjI/agdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75/m6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P/vf+cB0VA+gUHv7UASzIyP2F5Q8V+hQeHhT6FQ/lDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd+1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U/doVHR0VAib+1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX+cAGQFf0vMhUdHRUyyGRk/oL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI/oUdFWQVHcjI/cvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ+AzMyFh8BETMRPAE+AjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX+4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH/uuFR0dFQGNAQgbHzUeAWcfRJEZDA3+Phw/MSkLC/5BFR0dFQG/BA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn+DBlL/nAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr+diUNODgBBQoUDgN4FhkBAjg4DSX+dgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn+2cj+cGQBd/4lASwpOzsp/tQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn+DCk7OykB9Ck7ZMjI/veFo4XGyMhm+BUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE+BRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB/LimdWQ/LAkJHRTKFB2N/sKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V+7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy/tEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI/OAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7/Rv+1DspZCk7BEw7KfwYA+gpO/7UOyn9RAK8KTv84AGQKTs7KQAAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkyMhkZARMsHz+DHywsHwB9HywyP1EArzIZP7UZGQBLGT+1GQB9GT+1AEsAAAABf+cAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE/gxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI/UQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP+cAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj+1AEsAZDIyP7UASwETLB8/gx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP+cAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE/gz+1AEsAZD+1ARMsHz+DHywsHwB9HywyP1EArz+DJaWlpYBLAAAAAX/nAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8/gx8sLB8AfR8sMj9RAK8/agB9GRWQUFUASz+1FRBQVYAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkZMjIZGQETLB8/gx8sLB8AfR8sMj9RAK8yGT+1GRkASz+DAGQZP4MZAAG/5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8/UR8sLAYA4T9RMj+1GQCWGRkyP2oZAEsZGQETLB8/gx8sLB8AfR8sMj9RAK8yP5wAfT+DAGQZMjIyP7UZAAF/5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI/UQCvP5wyDLIlmT+DAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz+1AEs/tRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv/y/50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V/tQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi/bMPCv7tFxD0EBD0EBcBEwoPAAAAAAL/8v+cBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr+hg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ+teGOkHAFoEBD+k3NPRWsOExNkqWP9kuQQF/7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD+8qr+8qr+1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3+s/7U/tRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd/tQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd+x5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc+ATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ/j0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb/7V/tVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s/oQPCgI/ASzIZKLU1KJktP51Cg8DhA8KwwMg/oTIyALzCg/+1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz+ov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9+AFe/doERwoP/tTI/HzIyNT+ZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT+ogHCJCsRBP5w/nAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX+1BUdHRUyHQFzKTs7Kf2oARP2/ro7KVg+ASw+WP201MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7KfzgKTsE9gFGAUQpO5Y+WFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG/2oKTs7KVg+ASw+WAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7Kf4M/tQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP/ogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs+wQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf+TB0U/tIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K+goPDwr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPDwRMDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg/+1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko/PwoKUIcMT4BXmRkyP4+ArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3+1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq/9WrIiCikBSAOvArwVHR0VlgET/EoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb+lR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf+vCcrI/7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW/us5Okxra0w6Of5yHSodZGQdKh3+1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ/68Jysj/s0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2+jGv4KOTpMa2tMOjn+ch0qHWRkHSod/tQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ+AjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz+1CU/P00T/e48PUJtj0r+ogK8FR0dFQ2K/EqJDBUdHbMETBUd+1AdBLChizlmUT9IGVO9VFShdksE/H4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA+gFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U/5wU3V1BRQ7KU4aXDYyMjZcGk4p/kc2LmRkZGRkU3V1UwGQU3UAAAMAZP//BEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr+5eANGiUNWP30Vw0mGg3g/t8KDw8BqmQBRGQETB0V/gwVHR0VAfQVHf1EDwoyCg/gDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn+1AEsKTv+DDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg/4f/fsgIAEnH1n+rAFWAS/+q6IDIBUd/HwdBI39VR9ZH4MCBh9ZHwEoH/5u/qoBMAFV/BsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV/nAVJwi9CBMVAnEdKh19FROo/a0dFTIdFTDILxUdMhUdAocB+hMcHBP+BhMclhUdHRWWHP2MMhUdMhUdASz+1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr+if7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD/7UyP4M/cEKDw8KA7YKD/7UyAAC/5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc+ARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF+goPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb/nP/mBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL/CtqQ0geZgM3FR0cE/0fFyIJKjr+1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g+yD5YrBwVODMQEAtEERzJLUAtLUD+24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz++QFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg/+1MgVHR0LCgQOIhoW/nDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb+YDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj/sIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn+PilUrAFINhWWFTYFFKxn/gwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn+PilUrAFINhWWFTYFFJaz/kIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO/YL+hk5klpZkAQEBBQQvkwKCAVz+ov6iAV4BXgL//uWqPOCWx5SVyJb6BA0GCgYDKEEBG/1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz+yFh8WFh8/shYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP/nAPCpYKDw8KlgoP/nAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o/4Mpbm5Asv9qCk7OykCWCk7O/2xAfQVHR0V/gwVHR1HAZAEsLuj/gylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX+1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA+gVHR0VMjIVHR0VMjIVHR0VMjIVHR0V/BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA+gEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8/V1c/P/75fLB8sAK8sHyw/cB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy/sCwfLB8fLB8sP7UsHywfHywfLD+05AVHR0VjgQAAAH/tQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8I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+<style>h1 {font-size: 34px;}
+h1.title {font-size: 38px;}
+h2 {font-size: 30px;}
+h3 {font-size: 24px;}
+h4 {font-size: 18px;}
+h5 {font-size: 16px;}
+h6 {font-size: 12px;}
+code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
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+
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+
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">OITS Data Analysis</h1>
+<h4 class="author">Simeon Lim Rossmann, Marte Persdatter Tangvik</h4>
+<h4 class="date">23.12.2024</h4>
+
+</div>
+
+
+<div id="introduction-and-initialization" class="section level2">
+<h2>Introduction and initialization</h2>
+<p>This is an R Markdown file containing code to parse the results of a
+dada2 analysis into phyloseq for further analysis. It is separated into
+chunks that may be run independently by pressing the <em>play</em>
+button. You will need <strong>3 files</strong> in the same location in
+order to run this pipeline successfully:</p>
+<ul>
+<li>A sequence table called <strong>‘seqtab_nochim.rds’</strong>
+(automatically generated by the dada2 pipeline)</li>
+<li>A taxonomy table called <strong>‘taxa.rds’</strong> (automatically
+generated by the dada2 pipeline)</li>
+<li>A table describing the sample properties called
+<strong>‘descriptors.txt’</strong> (Has to be provided)</li>
+<li>Optional (some plots require this): A phylogenic tree file called
+<strong>‘phylotree.rds’</strong> (can be calculated if it is not
+provided, takes a long time)</li>
+<li>Optional: Instead of <strong>‘taxa.rds’</strong>, a <strong>custom
+taxonomy file</strong> may be provided (tab-delimited text)</li>
+</ul>
+<p><strong>Recommended use:</strong> Set the individual chunks until you
+are content with the ouput, then knit the whole document into a
+PDF/html, so you have a full record of a successful run.</p>
+<div id="optional-custom-taxonomy-file" class="section level4">
+<h4>Optional custom Taxonomy file</h4>
+<p>A custom taxonomy file may be provided instead of using the taxonomy
+output from dada2. This may be used to supply taxonomy derived e.g. from
+BLAST searches of the ASVs. Custom taxonomy files must be tab-delimited
+text with as many rows as the original, colum headers (for all columns
+except for the first column). For example:</p>
+<blockquote>
+<p>Kingdom Phylum Class Order Family Genus Species<br />
+ESV1 Kingdomx Phylumx Classx Orderx Familyx Genusx Speciesx<br />
+ESV2 Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy<br />
+ESV3 Kingdomz Phylumz Classz Orderz Familyz Genusz Speciesz<br />
+… ESVn Kingdomy Phylumy Classy Ordery Familyy Genusy Speciesy</p>
+</blockquote>
+<p><em>Friendly warning: Parsing the results of a BLAST search into this
+format may require some effort.</em></p>
+</div>
+<div id="descriptor-table" class="section level4">
+<h4>Descriptor table</h4>
+<p>‘descriptors.txt’ should be a tab-delimited .txt table describing the
+samples. It must have the same length and order as the samples in
+seqtab_nochim.rds. To check the order and length of samples in
+seqtab_nochim.rds and generate a template to fill out, you may run the
+chunk below with “optional_sample_check” set to “TRUE”.</p>
+<p>Any number of descriptors is possible. The sample names may be
+retained as one descriptor, but this is not necessary, as they will be
+added during parsing. For example, if there are 4 samples (order: s1,
+s2, s3, s4), the txt file could look as follows:</p>
+<blockquote>
+<p>Subject Species Time<br />
+Kar1 A.thaliana 24hpi<br />
+Kar1 A.thaliana 72hpi<br />
+Mec2 S.tuberosum 24hpi<br />
+Mec3 S.tuberosum 24hpi</p>
+</blockquote>
+<p>Finally, the file should end with an empty line, since it may throw
+an error otherwise. However, this is usually not a serious problem.</p>
+<p><strong>If you choose to use the blank file, you MUST retain the
+original order of the samples!</strong></p>
+</div>
+</div>
+<div id="setup" class="section level2">
+<h2>Setup</h2>
+<p>This chunk also loads required packages and defines the location of
+the input files. It requires the <strong>correct path</strong> as input,
+and allows <strong>setting the pruning of control samples</strong> and
+<strong>choosing generation of a phylogenetic tree</strong>.
+<strong>Beware:</strong> The generation of a phylogenetic tree may take
+several days for &gt;1000 sequences, it is therefore recommended to only
+use this feature for the final analysis or small sample sets. This
+scricpt assumes the packages <em>Biostrings</em>, <em>dada2</em>,
+<em>DECIPHER</em>, <em>ggplot2</em>, <em>ggsci</em>, <em>phangorn</em>,
+<em>phyloseq</em> and <em>stringr</em> to be installed.</p>
+<pre class="r"><code># CHANGE ME to the directory that contains &#39;seqtab_nochim.rds&#39;
+path = &quot;OITS_DADA2_results_260821/&quot;
+
+# CHANGE ME to TRUE to list all samples and generate an empty metadata file 
+optional_sample_check = TRUE
+
+# CHANGE ME to TRUE to update cuphyr
+update_cuphyr = TRUE
+
+# Initiate by loading packages and setting knit options
+################# NO CHANGES NECESSARY BELOW #################
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(root.dir = paste0(path))
+knitr::opts_chunk$set(message = FALSE)
+knitr::opts_chunk$set(warning = FALSE)
+
+if (update_cuphyr) {
+  devtools::install_github(&quot;simeross/cuphyr&quot;)
+}
+
+# Sequence and microbiome specific libraries
+library(dada2)
+library(Biostrings)
+library(DECIPHER)
+library(cuphyr)
+# The export of phyloseq objects to a BIOM format and the generation of fancier 
+# ordination plots require the phyloseq-extended package. The first command 
+# installs the package that is currently on the dev brach of the author&#39;s 
+# repository, the second command sources some extra functions, including the 
+# better ordination plot implementation.
+remotes::install_github(&quot;mahendra-mariadassou/phyloseq-extended&quot;, ref = &quot;dev&quot;)
+source(&quot;https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R&quot; )
+
+library(phyloseq)
+#library(SIAMCAT)
+
+# Phylogeny libraries
+library(phangorn)
+library(ape)
+
+# Plotting and figure export
+library(gridExtra)
+library(viridis)
+library(ggpubr)
+
+# Tidyverse
+library(tidyverse)
+library(stringr)
+
+# Various packages for specific analysis
+library(readxl)
+library(openxlsx)
+library(ggpmisc)
+library(betareg)
+library(BBmisc)
+library(aod)
+library(betareg)
+#install.packages(&#39;MicrobiomeStat&#39;)
+library(MicrobiomeStat)
+
+
+# Checks whether output path exists and creates it if not. Throws warning if 
+# directory exists.
+outp &lt;- paste0(path,&quot;/analysis_output&quot;)
+dir.create(file.path(outp))
+
+if (optional_sample_check) {
+  seqtabcheck &lt;- readRDS(paste0(path,&quot;/seqtab_nochim.rds&quot;)) 
+  samps &lt;- rownames(seqtabcheck)
+  lensamps &lt;- length(samps)
+  blankcol &lt;- vector(mode = &quot;character&quot;, length = lensamps)
+  blanktable &lt;- data.frame(SampleIDs = samps, ExampleProperty1 = blankcol, 
+                           ExampleProperty2 = blankcol, 
+                           ExampleProperty3 = blankcol)
+  write.table(blanktable, file = paste0(path, &quot;/descriptors_blank.txt&quot;), 
+              sep = &quot;\t&quot;, row.names = F)
+  cat(&quot;&#39;seqtab_nochim.rds&#39; contains samples in the following order:\n&quot;, 
+      samps, &quot;\nThe number of samples in the file is:&quot;, lensamps, sep = &quot;\n&quot;)
+  rm(optional_sample_check, seqtabcheck, samps, 
+     lensamps, blankcol, blanktable, update_cuphyr)
+  }else{rm(optional_sample_check, update_cuphyr)}</code></pre>
+<pre><code>## &#39;seqtab_nochim.rds&#39; contains samples in the following order:
+## 
+## 1-oits
+## 10-oits
+## 11-oits
+## 12-oits
+## 13-oits
+## 14-oits
+## 15-oits
+## 16-oits
+## 17-oits
+## 18-oits
+## 2-oits
+## 3-oits
+## 4-oits
+## 5-oits
+## 6-oits
+## 7-oits
+## 8-oits
+## 9-oits
+## NegativK-1-oits
+## PositivK-1-oits
+## PositivK-2-oits
+## PositivK-3-oits
+## PositivK-4-oits
+## 
+## The number of samples in the file is:
+## 23</code></pre>
+<div id="parameters" class="section level4">
+<h4>Parameters</h4>
+<p>This chunk allows the adjustment of several parameters, such as
+<strong>setting the pruning of control samples</strong> based on
+keywords, <strong>requiring that a phylogenetic tree be provided or
+generated</strong>, <strong>defining a minimum ASV count</strong> and
+<strong>providing an alternative taxonomy</strong>.</p>
+<pre class="r"><code># Dedicated environment containing all global analysis settings for better
+# overview and collected export of settings
+parameters &lt;- new.env()
+
+# CHANGE ME to &#39;TRUE&#39; to remove control samples from the analysis or &#39;FALSE&#39; to
+# analyse all samples.
+parameters$prune_controls = &quot;TRUE&quot;
+# CHANGE ME to a list of unique identifiers that only occur in the names of
+# samples you do NOT want to analyse. Common examples are provided.
+parameters$controls = c(&quot;Pos&quot;, &quot;H2O&quot;, &quot;Neg&quot;, &quot;Kontr&quot;, &quot;Contr&quot;, &quot;POSK&quot;)
+
+# CHANGE ME to &#39;TRUE&#39; to remove certain taxonomic groups from the analysis by
+# name. This is useful to exclude non-target organisms or noise from organelles
+# such as Chloroplasts and Mitochondria. It is recommended to first look at all
+# data before using this setting.
+parameters$prune_noise_taxgroups = &quot;TRUE&quot;
+# CHANGE ME to define the taxonomic groups to be removed as noise.
+parameters$noise_taxgroups = c(&quot;Metazoa&quot;, &quot;Viridiplantae&quot;, &quot;Dinophyceae&quot;, &quot;Chrysophyceae&quot;)
+
+# CHANGE ME to a number of ASV counts [~reads] that analyzed samples should
+# minimally have. Samples with lower ASV counts than &#39;minread&#39; will be pruned.
+# Set to 0 to not prune any samples.
+parameters$minASVcount = 3000
+
+# CHANGE ME to &#39;TRUE&#39;, if you want to provide a custom taxonomy table instead
+# of using the default dada2 output (&#39;taxa.rds&#39;).
+parameters$customTax = &quot;TRUE&quot;
+# CHANGE ME to the location of the custom taxonomy file. This only matters if
+# parameters$customTax=&#39;TRUE&#39;, otherwise it will be ignored.
+parameters$taxfile = &quot;OITS_DADA2_results_260821/custom_BLAST_taxonomy_nt.txt&quot;
+
+# CHANGE ME to &#39;TRUE&#39; to generate a phylogenetic tree. This process takes a
+# long time depending on the number of sequences (up to days for thousands).
+# If a tree is provided as &#39;phylotree.rds&#39; in &#39;path&#39;, then it will be used
+# regardless of the value of &#39;parameters$maketree&#39;
+parameters$maketree = &quot;FALSE&quot;
+
+# CHANGE ME to &#39;TRUE&#39; to root the used phylogenetic tree (if one exists) on the
+# leaf with the longest branch (outgroup). This makes analyses that rely on the
+# phylogenetic tree reproducible instead of picking a random leaf as root when
+# calculating UNIFRAC distances. Implementation based on
+# http://john-quensen.com/r/unifrac-and-tree-roots/ and answers in
+# https://github.com/joey711/phyloseq/issues/597
+parameters$roottree = &quot;TRUE&quot;
+
+## CHANGE ME to &#39;TRUE&#39; to export all generated phyloseq objects as .biom
+## objects
+parameters$biom_export = &quot;FALSE&quot;</code></pre>
+</div>
+<div id="parsing-input-data" class="section level4">
+<h4>Parsing input data</h4>
+<p>This chunk loads the input data into a usable format.<strong>This
+chunk does not require any user inputs</strong>. If no phylogenetic tree
+with the name ‘phylotree.rds’ was provided and
+‘parameters$maketree=“TRUE”’, it will be calculated here. The
+phylogenetic tree is necessary for certain plots that incorporate ‘true’
+taxonomic relationships beyond the annotations, such as PCoA.</p>
+<pre class="r"><code>############### NO NEED FOR CHANGES BELOW ############### Make dedicated
+############### environments to contain temporary values and manage other
+############### objects
+tmp &lt;- new.env()
+plots &lt;- new.env()
+set &lt;- new.env()
+
+# Read in variables
+tmp$seqtabp &lt;- readRDS(paste0(path, &quot;/seqtab_nochim.rds&quot;))
+if (parameters$customTax == &quot;TRUE&quot;) {
+    tmp$taxap &lt;- read.delim(parameters$taxfile, header = TRUE, sep = &quot;\t&quot;)
+    rownames(tmp$taxap) &lt;- colnames(tmp$seqtabp)
+    tmp$taxap &lt;- as.matrix(tmp$taxap)
+} else {
+    tmp$taxap &lt;- readRDS(paste0(path, &quot;/taxa.rds&quot;))
+}
+tmp$samp_table &lt;- read.delim(paste0(path, &quot;/descriptors.txt&quot;), header = TRUE, sep = &quot;\t&quot;)
+tmp$samp_list &lt;- rownames(tmp$seqtabp)
+
+# Check if descriptors has the same samples as seqtabp
+if (length(tmp$samp_table[, 1]) != length(tmp$samp_list)) {
+    stop(&quot;There are &quot;, length(tmp$samp_table[, 1]), &quot; samples in &#39;descriptors.txt&#39;, but &quot;,
+        length(tmp$samp_list), &quot; samples in &#39;seqtab_nochim.rds&#39;. Please make sure that the correct samples 
+    are contained in descriptors.txt.
+       
+    You may use &#39;optional_sample_check &lt;- TRUE&#39; in the first chunk to generate an 
+    empty template for &#39;descriptors.txt&#39;&quot;)
+} else if (!identical(tmp$samp_table[, 1], tmp$samp_list)) {
+    warning(&quot;Warning: The samples in &#39;descriptors.txt&#39; do not have the same names 
+          or order as the samples in &#39;seqtab_nochim.rds&#39;. This may be fine if 
+          abbreviated names were used or the sample names are not contained in 
+          the first column of &#39;descriptors.txt&#39;. Double-checking never hurts!&quot;)
+}
+
+
+# generate phylogenetic tree of ASVs only if there is no file called
+# &#39;phylotree.rds&#39; in the working directory and &#39;parameters$maketree&#39; is &#39;TRUE&#39;
+if (!file.exists(paste0(path, &quot;/phylotree.rds&quot;))) {
+    if (parameters$maketree == &quot;TRUE&quot;) {
+        tmp$ASVs &lt;- getSequences(tmp$seqtabp)
+        names(tmp$ASVs) &lt;- tmp$ASVs
+        tmp$ASV_align &lt;- AlignSeqs(DNAStringSet(tmp$ASVs), anchor = NA)
+        tmp$ASV_phang &lt;- phyDat(as(tmp$ASV_align, &quot;matrix&quot;), type = &quot;DNA&quot;)
+        tmp$dm &lt;- dist.ml(tmp$ASV_phang)
+        tmp$treeNJ &lt;- NJ(tmp$dm)
+        tmp$fit &lt;- pml(tmp$treeNJ, data = tmp$ASV_phang)
+        tmp$fitGTR &lt;- update(tmp$fit, k = 4, inv = 0.2)
+        tmp$fitGTR &lt;- optim.pml(tmp$fitGTR, model = &quot;GTR&quot;, optInv = TRUE, optGamma = TRUE,
+            rearrangement = &quot;stochastic&quot;, control = pml.control(trace = 0))
+        saveRDS(tmp$fitGTR, file = paste0(path, &quot;/phylotree.rds&quot;))
+    }
+}
+
+## parse into phyloseq object
+row.names(tmp$samp_table) &lt;- tmp$samp_list
+if (file.exists(paste0(path, &quot;/phylotree.rds&quot;))) {
+    tmp$treep &lt;- readRDS(paste0(path, &quot;/phylotree.rds&quot;))
+    p &lt;- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
+        tax_table(tmp$taxap), phy_tree(tmp$treep$tree))
+} else {
+    p &lt;- phyloseq(otu_table(tmp$seqtabp, taxa_are_rows = FALSE), sample_data(tmp$samp_table),
+        tax_table(tmp$taxap))
+}
+
+## Adding nucleotide info and giving sequences ASV## identifiers
+tmp$ASV_sequences &lt;- Biostrings::DNAStringSet(taxa_names(p))
+taxa_names(p) &lt;- paste0(&quot;ASV&quot;, seq(ntaxa(p)))
+names(tmp$ASV_sequences) &lt;- taxa_names(p)
+p &lt;- merge_phyloseq(p, tmp$ASV_sequences)
+
+## optional pruning
+if (parameters$prune_controls == &quot;TRUE&quot;) {
+    if (!is.null(parameters$controls)) {
+        tmp$samp_clean &lt;- tmp$samp_list[!tmp$samp_list %in% grep(paste0(parameters$controls,
+            collapse = &quot;|&quot;), tmp$samp_list, value = T)]
+        tmp$contr_pruned &lt;- setdiff(tmp$samp_list, tmp$samp_clean)
+        ps &lt;- prune_samples(tmp$samp_clean, p)
+        # Physeq object for Just controls
+        ps.contr &lt;- prune_samples(tmp$contr_pruned, p)
+        ps.contr &lt;- prune_taxa(taxa_sums(ps.contr) &gt; 0, ps.contr)
+        ps.transcontr &lt;- transform_sample_counts(ps.contr, function(ASV) ASV/sum(ASV))
+
+        message(cat(&quot;\n&quot;, &quot;Number of control samples that were pruned and will not be analysed:\n&quot;,
+            length(tmp$samp_list) - length(tmp$samp_clean), &quot;\n&quot;, &quot;The following controls were pruned:\n&quot;,
+            tmp$contr_pruned, &quot;The controls are contained in a separate phyloseq object: ps.contr&quot;,
+            &quot;\n&quot;, sep = &quot;\n&quot;))
+    } else {
+        warning(cat(&quot;\n\nparameters$prune_controls is TRUE but &#39;parameters$controls&#39; is empty. 
+    No samples were pruned.\n\n&quot;))
+    }
+} else {
+    ps &lt;- p
+}</code></pre>
+<pre><code>## 
+## 
+## Number of control samples that were pruned and will not be analysed:
+## 
+## 5
+## 
+## 
+## The following controls were pruned:
+## 
+## NegativK-1-oits
+## PositivK-1-oits
+## PositivK-2-oits
+## PositivK-3-oits
+## PositivK-4-oits
+## The controls are contained in a separate phyloseq object: ps.contr</code></pre>
+<pre class="r"><code># Prune ASVs defined as noise
+if (parameters$prune_noise_taxgroups == &quot;TRUE&quot;) {
+    tmp$ps_taxlvls &lt;- colnames(tax_table(ps))
+    tmp$noise_ASVs &lt;- character(0)
+    for (lvl in tmp$ps_taxlvls) {
+        tmp$noise_ASVs &lt;- c(tmp$noise_ASVs, cuphyr::list_subset_ASVs(physeq = ps,
+            subv = parameters$noise_taxgroups, taxlvlsub = lvl))
+    }
+    tmp$noise_ASVs &lt;- unique(tmp$noise_ASVs)
+    tmp$no_noise_ASVs &lt;- colnames(otu_table(ps))
+    tmp$no_noise_ASVs &lt;- setdiff(tmp$no_noise_ASVs, tmp$noise_ASVs)
+    if (length(tmp$noise_ASVs) &gt; 0) {
+        ps &lt;- prune_taxa(tmp$no_noise_ASVs, ps)
+        tmp$no_noise_ps &lt;- ps
+        cat(length(tmp$noise_ASVs), &quot;ASVs were pruned because they belonged to the following 
+        taxonomic groups:\n&quot;)
+        cat(parameters$noise_taxgroups, &quot;\n&quot;, sep = &quot;\n&quot;)
+    } else {
+        cat(&quot;No ASVs were recognized as belonging to the following taxonomic groups 
+        defined as noise:\n&quot;)
+        cat(parameters$noise_taxgroups, &quot;\n&quot;, sep = &quot;\n&quot;)
+    }
+}</code></pre>
+<pre><code>## 368 ASVs were pruned because they belonged to the following 
+##         taxonomic groups:
+## Metazoa
+## Viridiplantae
+## Dinophyceae
+## Chrysophyceae</code></pre>
+<pre class="r"><code># Prune samples with fewer than reads than minASVcount
+if (parameters$minASVcount &gt; 0) {
+    tmp$samp_pruned &lt;- names(which(sample_sums(ps) &lt; parameters$minASVcount))
+    ps &lt;- prune_samples(sample_sums(ps) &gt;= parameters$minASVcount, ps)
+    if (length(tmp$samp_pruned) &gt; 0) {
+        cat(&quot;The following samples were pruned because ASV counts were lower than&quot;,
+            parameters$minASVcount, &quot;:\n&quot;)
+        cat(tmp$samp_pruned, &quot;\n&quot;, sep = &quot;\n&quot;)
+    }
+}
+
+# Remove 0 count ASVs (e.g. control ASVs that remain) from the base object
+ps &lt;- prune_taxa(taxa_sums(ps) &gt; 0, ps)
+
+# Get a tbl of the base object for easier access in some phyloseq-independent
+# analyses. Takes some seconds, potentially up to minutes.
+ps_tbl &lt;- as_tibble(psmelt(ps))
+
+# Transformed per sample (per-sample relative abundance)
+ps.trans &lt;- transform_sample_counts(ps, function(ASV) ASV/sum(ASV))
+
+# Read NDVI values as numeric
+sample_data(ps)[[&quot;ndvi&quot;]] &lt;- as.numeric(sample_data(ps)[[&quot;ndvi&quot;]])
+sample_data(ps.trans)[[&quot;ndvi&quot;]] &lt;- as.numeric(sample_data(ps.trans)[[&quot;ndvi&quot;]])
+
+
+# Get a tbl of the base object for easier access in some phyloseq-independent
+# analyses. Takes some seconds, potentially up to minutes.
+ps_tbl &lt;- as_tibble(psmelt(ps))
+ps_trans_tbl &lt;- as_tibble(psmelt(ps.trans))
+
+# Condensing to Abundance per Genus and Sample
+genus_abundance_tbl_per_sample &lt;- ps_trans_tbl %&gt;%
+    group_by(Genus, Sample) %&gt;%
+    mutate(Genus_Sample_Abundance = sum(Abundance)) %&gt;%
+    select(Genus, Sample, ndvi, Genus_Sample_Abundance, Alias) %&gt;%
+    unique()
+
+if (parameters$roottree == &quot;TRUE&quot; &amp;&amp; parameters$maketree == &quot;TRUE&quot;) {
+    phyloseq::phy_tree(ps) &lt;- cuphyr::root_tree_in_outgroup(physeq = ps)
+}
+
+if (parameters$biom_export == &quot;TRUE&quot;) {
+    suppressWarnings(phyloseq.extended::write_phyloseq(p, biom_file = paste0(path,
+        &quot;all_samples.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps, biom_file = file.path(path,
+        &quot;samples_without_controls.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps.trans, biom_file = file.path(path,
+        &quot;samples_without_controls_rel_abundance.biom&quot;), biom_format = &quot;standard&quot;))
+    suppressWarnings(phyloseq.extended::write_phyloseq(ps.contr, biom_file = file.path(path,
+        &quot;just_controls.biom&quot;), biom_format = &quot;standard&quot;))
+}
+
+ps</code></pre>
+<pre><code>## phyloseq-class experiment-level object
+## otu_table()   OTU Table:         [ 796 taxa and 18 samples ]
+## sample_data() Sample Data:       [ 18 samples by 19 sample variables ]
+## tax_table()   Taxonomy Table:    [ 796 taxa by 7 taxonomic ranks ]
+## refseq()      DNAStringSet:      [ 796 reference sequences ]</code></pre>
+</div>
+</div>
+<div id="output" class="section level2">
+<h2>Output</h2>
+<p>The chunks below will produce various plots and other output. Each
+chunk is headed by a description of the output and may contain some
+parameters to adjust the output.</p>
+<div id="plot-looks" class="section level4">
+<h4>Plot looks</h4>
+<p>This chunk sets the background structure and color palette. Viridis
+was chosen because it is optimized for grey-scale printing and various
+types of color blindness and More info on the Viridis palette can be
+found on <a href="https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html">the
+Viridis info page</a>. It also establishes save_plot as a shorter
+variant of ggsave with customized date-time structure to save plots with
+the same name mulitple times instead of overwriting them (overwriting
+can be triggered with overwrite=TRUE).</p>
+<pre class="r"><code>##### Optional settings (sensible defaults) #####
+# Can be changed to adjust the output format for all plots. Default &quot;pdf&quot;, 
+# possible &quot;eps&quot;/&quot;ps&quot;, &quot;tex&quot; (pictex), &quot;jpeg&quot;, &quot;tiff&quot;, &quot;png&quot;, &quot;bmp&quot; and &quot;svg&quot;
+parameters$output_format = &quot;pdf&quot;
+
+# Can be changed to preferred ggplot2 theme. Recommended: &quot;theme_bw()&quot;.
+theme_set(theme_bw())
+
+############### NO NEED FOR CHANGES BELOW ###############
+
+my_scale_col &lt;- scale_color_viridis(discrete = TRUE)
+my_scale_fill &lt;- scale_fill_viridis(discrete = TRUE)
+
+# Custom, more narrow color ranges based on viridis
+# Base order to have adjacent colors be distinct from each other
+tmp$sort_colors &lt;- c(rbind(c(1:5), c(6:10), c(11:15), c(16:20)))
+
+# Customized vectors
+tmp$n_col &lt;- 20
+tmp$viridis_greens &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 0.85, 
+                              end = 0.7)[tmp$sort_colors]
+tmp$viridis_reds &lt;- viridis(tmp$n_col,  option = &quot;B&quot;, begin = 0.7, 
+                            end = 0.5)[tmp$sort_colors]
+tmp$viridis_blues &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 0.2, 
+                             end = 0.4)[tmp$sort_colors]
+tmp$viridis_yellows &lt;- viridis(tmp$n_col,  option = &quot;D&quot;, begin = 1, 
+                               end = 0.9)[tmp$sort_colors]
+tmp$viridis_dark &lt;- viridis(tmp$n_col,  option = &quot;A&quot;, begin = 0, 
+                            end = 0.1)[tmp$sort_colors]
+tmp$viridis_light &lt;- viridis(tmp$n_col,  option = &quot;A&quot;, begin = 1, 
+                             end = 0.9)[tmp$sort_colors]
+# Collected list that is available in the global environment
+sub_viridis &lt;- list(tmp$viridis_greens, tmp$viridis_blues, tmp$viridis_yellows, 
+                    tmp$viridis_light, tmp$viridis_reds, tmp$viridis_dark)
+names(sub_viridis) &lt;- c(&quot;greens&quot;, &quot;blues&quot;, &quot;yellows&quot;, &quot;lights&quot;, &quot;reds&quot;, &quot;darks&quot;)
+
+tmp$out &lt;- paste0(&quot;.&quot;, parameters$output_format)
+
+#################### Function ############################
+
+# Generic save function for plots that checks whether file exists and if so, 
+# creates a new one with d/m/y+time info to avoid overwriting. Overwriting can 
+# be triggered with overwrite = TRUE. Width, height and resolution are taken 
+# from parameters in the &#39;set&#39; environment or set to 20x20 cm with 300dpi.
+save_plot &lt;- function(
+  pl, filetype = &quot;.pdf&quot;, plot_name = &quot;my_plot&quot;, overwrite=FALSE){
+  wp &lt;- if (!is.null(set$wp)) set$wp else 20
+  hp &lt;- if (!is.null(set$hp)) set$hp else 20
+  res &lt;- if (!is.null(set$res)) set$res else 300
+  name &lt;- paste0(&quot;/&quot;, plot_name,filetype)
+  if (file.exists(paste0(outp, name)) &amp; !overwrite) {
+  name &lt;- paste0(&quot;/&quot;, plot_name, &quot;_&quot;, 
+                 format(Sys.time(), &quot;%d-%m-%y_%H%M%S&quot;),filetype)}
+  ggsave(file.path(outp, name), pl, 
+         width = wp, height = hp, unit = &quot;cm&quot;, dpi = res)
+}
+
+################################################</code></pre>
+</div>
+<div id="total-asv-counts-ranked" class="section level4">
+<h4>Total ASV counts ranked</h4>
+<p>This chunk plots the absolute abundance of all samples (including
+controls) and all samples without controls and other trimmed
+samples.</p>
+<pre class="r"><code># CHANGE ME to the sample group for color coding. Accepted values are the column 
+# headers in the descriptor file.
+set$color_by = &quot;mel_mol_asv&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 17
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20  
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+############### NO NEED FOR CHANGES BELOW ###############
+# Rank samples
+set$ranked &lt;- cuphyr::make_ranked_sums(p, myset = tmp$subset_id)
+set$ranked_ps &lt;- cuphyr::make_ranked_sums(ps, myset = tmp$subset_id)
+set$ymax &lt;- max(set$ranked$Abundance)
+set$ymax &lt;- set$ymax + round(set$ymax/10)
+set$xmax &lt;- nrow(set$ranked) + 1
+set$title2 &lt;- &quot;Samples (without controls)&quot;
+
+# Stabilize colors
+set$color_vars &lt;- set$ranked[,set$color_by]  %&gt;% 
+  unlist() %&gt;% as.character() %&gt;% unique()
+set$color_vars &lt;- sort(set$color_vars)
+set$color_varsPalette &lt;- viridis(length(set$color_vars))
+names(set$color_varsPalette) &lt;- set$color_vars
+set$my_scale_fill &lt;- scale_fill_manual(values = set$color_varsPalette)
+
+# plot
+plots$overview_all &lt;- ggplot(data = set$ranked, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + ggtitle(&quot;All samples&quot;) + ylim(0, set$ymax) + 
+  xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+
+if (length(tmp$noise_ASVs) &gt; 0) {
+  set$ranked_nonoise &lt;- cuphyr::make_ranked_sums(
+    tmp$no_noise_ps, myset = tmp$subset_id)
+  plots$overview_noise &lt;- ggplot(
+    data = set$ranked_nonoise, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + 
+    ggtitle(&quot;Samples (without controls), noise ASVs removed&quot;) + 
+    ylim(0, set$ymax) + 
+    xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+}
+
+if (parameters$minASVcount &gt; 0) {
+plots$overview_all &lt;- plots$overview_all + 
+  geom_hline(yintercept = parameters$minASVcount, linetype = &quot;dashed&quot;) + 
+    ggtitle(&quot;All samples (ASV count cutoff indicated)&quot;)
+set$title2 &lt;- &quot;Samples (without controls and low count samps)&quot;
+}
+
+plots$overview_ps &lt;- ggplot(data = set$ranked_ps, aes(x = Rank, y = Abundance)) + 
+  aes_string(fill = set$color_by) + 
+  geom_col() + set$my_scale_fill + ggtitle(set$title2) + ylim(0, set$ymax) + 
+  xlim(0,set$xmax) + ylab(&quot;ASV counts (&#39;reads&#39;)&quot;)
+plots$combo_overview &lt;- ggarrange(
+  plots$overview_all, plots$overview_ps, nrow = 2, align = &quot;v&quot;, 
+  common.legend = TRUE, legend = &quot;right&quot;)
+
+if (parameters$minASVcount &gt; 0) {
+plots$combo_overview &lt;- ggarrange(
+  plots$overview_all, plots$overview_noise, plots$overview_ps,
+  nrow = 3, align = &quot;v&quot;, 
+  common.legend = TRUE, legend = &quot;right&quot;)
+}
+
+#Save plots
+save_plot(plots$combo_overview, plot_name = &quot;Overview_all_and_pruned&quot;, 
+          filetype = tmp$out)
+
+#Clean up plot parameters
+rm(list = ls(set), envir = set)
+
+#Print plots
+plots$combo_overview</code></pre>
+<p><img role="img" 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" width="672" /></p>
+</div>
+<div id="controls" class="section level4">
+<h4>Controls</h4>
+<p>This chunk generates an overview over the controls (positive AND
+negative)</p>
+<pre class="r"><code># CHANGE ME to the desired sample categories on the x-axis. In this case it 
+# should be the Sample names.
+set$x_axis_value = &quot;Sample&quot;
+
+# CHANGE ME to the taxonomic level for color coding. Use &quot;OTU&quot; for ASVs, 
+# &quot;Genus&quot;, &quot;Species&quot; or &quot;OTU&quot; recommended to compare pos. controls.
+set$color_by_taxlvl = &quot;Species&quot;
+
+# CHANGE ME to the taxonomic level for labeling the tree tips (if phylogenetic 
+# tree is available). Use &quot;OTU&quot; for ASVs.
+set$label_by_taxlvl = &quot;OTU&quot;
+
+# CHANGE ME to a sample category to shape the tree tip labels by (if 
+# phylogenetic tree is available).
+set$label_shape_by = &quot;Symptom&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to generate a tree for just the control sequences IF no 
+# phylogenetic tree for all seuquences is provided. This may slow down this 
+# chunk when running it for the first time
+set$control_tree = TRUE
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 17
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 20  
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+############### NO NEED FOR CHANGES BELOW ###############
+if (set$control_tree &amp; class(try(phy_tree(ps.transcontr), 
+                                 silent = TRUE)) == &quot;try-error&quot;) {
+  # generate phylogenetic tree of ASVs only if there is no file called 
+  # &#39;phylotree.rds&#39; in the working directory and &#39;parameters$maketree&#39; is &quot;TRUE&quot;
+  if (!file.exists(paste0(path, &quot;/controls_phylotree.rds&quot;))) {
+    set$ASVs &lt;- phyloseq::refseq(ps.transcontr)
+    set$ASV_align &lt;- AlignSeqs(set$ASVs, anchor = NA)
+    set$ASV_phang &lt;- phyDat(as(set$ASV_align, &quot;matrix&quot;), type = &quot;DNA&quot;)
+    set$dm &lt;- dist.ml(set$ASV_phang)
+    set$treeNJ &lt;- NJ(set$dm)
+    set$fit &lt;- pml(set$treeNJ, data = set$ASV_phang)
+    set$fitGTR &lt;- update(set$fit, k = 4, inv = 0.2)
+    set$fitGTR &lt;- optim.pml(set$fitGTR, model = &quot;GTR&quot;, 
+                            optInv = TRUE, optGamma = TRUE,
+                            rearrangement = &quot;stochastic&quot;, 
+                            control = pml.control(trace = 0))
+    saveRDS(set$fitGTR, file = paste0(path, &quot;/controls_phylotree.rds&quot;))}
+  set$fitGTR &lt;- readRDS(paste0(path, &quot;/controls_phylotree.rds&quot;))
+  phyloseq::phy_tree(ps.transcontr) &lt;- set$fitGTR$tree
+}
+
+plots$topnpplot &lt;- plot_bar(ps.contr, x = set$x_axis_value, 
+                            fill = set$color_by_taxlvl) + my_scale_fill + 
+  theme(axis.title.x = element_blank(), legend.position = &quot;none&quot;, 
+        legend.key.size = unit(3, &quot;mm&quot;)) + 
+  ylab(&quot;ASV counts&quot;) + guides(col = guide_legend(ncol = 3))
+
+plots$topntplot &lt;- plot_bar(ps.transcontr, x = set$x_axis_value, 
+                            fill = set$color_by_taxlvl) + my_scale_fill + 
+  theme(axis.title.x = element_blank(), legend.position = &quot;none&quot;, 
+        legend.key.size = unit(3, &quot;mm&quot;)) + 
+  ylab(&quot;Relative abundance&quot;) + guides(col = guide_legend(ncol = 3))
+
+plots$combo_contr &lt;- ggarrange(plots$topnpplot, plots$topntplot, ncol = 2, 
+                               labels = c(&quot;A&quot;, &quot;B&quot;), align = &quot;hv&quot;, 
+                               common.legend = TRUE, legend = &quot;right&quot;)
+
+if (class(try(phy_tree(ps.transcontr), silent = TRUE)) != &quot;try-error&quot;) {
+plots$tre &lt;- plot_tree(
+          ps.transcontr, ladderize = &quot;left&quot;, label.tips = set$label_by_taxlvl, 
+          color = &quot;abundance&quot;, text.size = 2.5, shape = set$label_shape_by) + 
+          scale_color_viridis_c(aesthetics = c(&quot;color&quot;,&quot;fill&quot;)) + 
+          theme(legend.position = &quot;left&quot;, panel.border = element_blank())
+plots$combo_contr &lt;- ggarrange(plots$tre, ggarrange(plots$topnpplot, 
+                                                    plots$topntplot, ncol = 2, 
+                               labels = c(&quot;B&quot;, &quot;C&quot;), align = &quot;hv&quot;, 
+                               common.legend = TRUE, legend = &quot;right&quot;), 
+                               nrow = 2, legend = &quot;right&quot;, labels = c(&quot;A&quot;)) 
+}
+
+# save
+save_plot(plots$combo_contr, plot_name = &quot;Controls&quot;, filetype = tmp$out)
+
+plots$combo_contr</code></pre>
+<p><img role="img" 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" width="672" /></p>
+</div>
+<div id="get-a-list-of-top-n-taxa-at-a-given-level" class="section level4">
+<h4>Get a list of Top N taxa at a given level</h4>
+<p>This chunk lists the top n most abundant taxonomic terms at a given
+level. Change the function parameters to the desired values. For more
+info, check help page of the function with
+<code>?cuphyr::abundant_tax_physeq()</code>. Change ‘ignore_na’ to
+include/exclude NA values at the given level.</p>
+<pre class="r"><code>#The character vector can later be accessed by calling &#39;tmp$tops&#39;
+tmp$tops &lt;- cuphyr::abundant_tax_physeq(physeq = ps, 
+                            lvl = &quot;Genus&quot;,
+                            top = 10,
+                            output_format = &quot;tops&quot;,
+                            ignore_na = TRUE,
+                            silent = FALSE)</code></pre>
+<pre><code>## 
+## The top 10 most abundant annotated groups at the taxonomic level &#39;Genus&#39; are:
+## Pythium
+## Globisporangium
+## &#39;uncultured Haptoglossa&#39;
+## &#39;uncultured Apodachlya&#39;
+## &#39;uncultured fungus&#39;
+## Haptoglossa
+## &#39;uncultured Pythium&#39;
+## Aphanomyces
+## &#39;uncultured Atkinsiella&#39;
+## &#39;uncultured Lagenidium&#39;</code></pre>
+</div>
+<div id="asvs-belonging-to-phylum" class="section level4">
+<h4>ASVs belonging to phylum</h4>
+<p>This chunk gives an overview of the number of ASVs belonging to which
+Phylum</p>
+<pre class="r"><code>ps_trans_tbl %&gt;% select(OTU, Class) %&gt;% unique() %&gt;% group_by(Class) %&gt;% summarise(length(OTU))</code></pre>
+<pre><code>## # A tibble: 28 x 2
+##    Class                  `length(OTU)`
+##    &lt;chr&gt;                          &lt;int&gt;
+##  1 &#39;uncultured eukaryote&#39;            36
+##  2 &#39;uncultured fungus&#39;              272
+##  3 Aconoidasida                       5
+##  4 Actinobacteria                     9
+##  5 Agaricomycetes                     2
+##  6 Alphaproteobacteria               12
+##  7 Bacillariophyceae                  3
+##  8 Betaproteobacteria                 7
+##  9 Chitinophagia                      2
+## 10 Chytridiomycetes                   1
+## # i 18 more rows</code></pre>
+<pre class="r"><code># Total ASV counts
+
+ps_tbl %&gt;% select(OTU, Abundance) %&gt;% summarise(total_sum = sum(Abundance))</code></pre>
+<pre><code>## # A tibble: 1 x 1
+##   total_sum
+##       &lt;int&gt;
+## 1    827206</code></pre>
+<pre class="r"><code># Total ASV counts per class 
+
+ps_tbl %&gt;% select(OTU, Class, Abundance) %&gt;% group_by(Class) %&gt;% summarise(Abundance_sum = sum(Abundance))</code></pre>
+<pre><code>## # A tibble: 28 x 2
+##    Class                  Abundance_sum
+##    &lt;chr&gt;                          &lt;int&gt;
+##  1 &#39;uncultured eukaryote&#39;          3971
+##  2 &#39;uncultured fungus&#39;            26551
+##  3 Aconoidasida                     763
+##  4 Actinobacteria                   109
+##  5 Agaricomycetes                    25
+##  6 Alphaproteobacteria              213
+##  7 Bacillariophyceae               1241
+##  8 Betaproteobacteria               181
+##  9 Chitinophagia                     26
+## 10 Chytridiomycetes                  85
+## # i 18 more rows</code></pre>
+<pre class="r"><code># Percentage of ASV counts belonging to Oomycetes
+
+oomycetes_percentage = (781792/827206)*100 # ASV counts Oomycota / Total ASV counts
+oomycetes_percentage</code></pre>
+<pre><code>## [1] 94.50995</code></pre>
+</div>
+<div id="customized-top-n-genus-bar-plot" class="section level4">
+<h4>Customized Top N Genus Bar plot</h4>
+<p>This chunk plots abundance of the Top n ASVs or taxa at a given level
+as a bar plot, giving an insight into the presence of the n ASV and most
+common taxa for the primary and secondary parameters. The default for n
+is set at 20, a larger n may lead to delay/skipping of the plot in
+standard out, but it should be saved as a PDF regardless for ASVs. For
+taxa, a large n may lead to unreadable plots. <strong>The chunk does not
+require any input</strong>, but it is possible to adjust the
+<strong>default ‘n’</strong>, and to change <strong>width</strong>,
+<strong>height</strong> and <strong>resolution</strong> of the
+PDF-output if necessary.</p>
+<pre class="r"><code># CHANGE ME to the desired sample categories on the x-axis. 
+# Accepted values are the column headers in the descriptor file.
+set$x_axis_value = &quot;ndvi&quot;
+
+# CHANGE ME to the taxonomic level of interest (color coding). Accepted values 
+# are the column headers in your descriptor file.
+set$taxlvl = &quot;Genus&quot;
+
+# CHANGE ME to change the number of Top n taxa to be plotted at 
+# taxlvl.
+set$top_n = 10
+
+# Can be changed to include (FALSE) or exclude (TRUE) NA values in the barplot
+set$ignore_na = FALSE
+
+# CHANGE ME to an entry at the chosen taxonomic level you want to highlight. 
+# Comment out to not highlight anything.
+#set$highlight = &quot;Meloidogyne&quot;
+
+##### Optional settings (sensible defaults) #####
+
+# Can be changed to change the width (in cm) of the saved plot.
+set$wp = 22
+# Can be changed to change the height (in cm) of the saved plot.
+set$hp = 14.3
+# Can be changed to change the resolution (in dpi) of the saved plot.
+set$res = 300
+
+# Can be changed to change the y-axis label
+set$y_axis_label = &quot;Relative abundance&quot;
+
+# Can be changed to change the x-axis label
+set$x_axis_label = &quot;Sample&quot;
+
+############### NO NEED FOR CHANGES BELOW ###############
+# Estimate Alpha-diversity (Shannon)
+set$alpha_div_ps_trans &lt;- estimate_richness(ps.trans, measures = &quot;Shannon&quot;) %&gt;%
+  as_tibble(rownames = &quot;Sample&quot;)
+#  rename(&quot;Shannon&quot; = Simpson)
+
+# Make physeq objects of top n taxa and top n ASVs
+set$ps.topnTax &lt;- cuphyr::abundant_tax_physeq(ps.trans, lvl = set$taxlvl, 
+                                              top = set$top_n, 
+                                              ignore_na = set$ignore_na)
+
+# Plot
+set$my_scale_fill &lt;- my_scale_fill
+
+
+set$topntax_tbl &lt;- psmelt(set$ps.topnTax) %&gt;% 
+                    as_tibble() %&gt;%
+                    left_join(set$alpha_div_ps_trans, by = &quot;Sample&quot;) %&gt;% 
+                    select(Genus, Alias, ndvi, Abundance, Shannon) %&gt;%
+                    filter(Abundance &gt; 0) %&gt;%
+                    group_by(Genus, Alias, ndvi, Shannon) %&gt;%
+                    summarise(Abundance = sum(Abundance)) %&gt;%
+                    arrange(ndvi) %&gt;%
+                    mutate(ndvi_rank = c(1:length(ndvi))) 
+                    
+
+plots$topn_tax_custom &lt;- ggplot(set$topntax_tbl, aes(x = fct_reorder(Alias, ndvi),
+                           y = Abundance,
+                           fill = Genus)) + 
+                           #title = paste0(&quot;Top &quot;, set$top_n, &quot; &quot;, set$taxlvl))) +
+  geom_col(color = &quot;black&quot;) +
+  scale_fill_viridis(discrete = TRUE, na.value = &quot;grey&quot;)  + 
+  ylab(set$y_axis_label) +
+  xlab(set$x_axis_label) +
+  theme(strip.background = element_blank(),
+        legend.position = &quot;bottom&quot;)
+
+plots$ndvi_dot_plot &lt;- ggplot(set$topntax_tbl, aes(fct_reorder(Alias, ndvi),
+                           y = ndvi)) +
+                             geom_point() +
+    theme(strip.background = element_blank(),
+        axis.title.x=element_blank()) +
+  ylab(&quot;NDVI&quot;)
+
+plots$shannon_dot_plot &lt;- ggplot(set$topntax_tbl, 
+                                 aes(fct_reorder(Alias, ndvi),
+                           y = Shannon)) +
+                             geom_point() +
+    theme(strip.background = element_blank(),
+          axis.title.x=element_blank()) +
+  ylab(&quot;Shannon&quot;)
+
+
+
+
+plots$combo_topn_custom &lt;- ggarrange(plots$ndvi_dot_plot,
+                                     plots$shannon_dot_plot,
+                                     plots$topn_tax_custom,
+                                     nrow = 3,
+                                     heights = c(1, 1, 3),
+                                     align = &quot;v&quot;) 
+
+save_plot(plots$combo_topn_custom, plot_name = paste0(&quot;Customized_NDVI_Shannon_plot&quot;), 
+          filetype = tmp$out)
+plots$combo_topn_custom</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+</div>
+<div id="ndvishannon-regression" class="section level3">
+<h3>NDVI+Shannon regression</h3>
+<pre class="r"><code>topntax_data &lt;- set$topntax_tbl %&gt;%
+  mutate(Taxa = &#39;oomycetes&#39;) %&gt;%
+  ungroup() %&gt;%
+  select(Alias, ndvi, Shannon, Taxa) %&gt;%
+  distinct()
+
+write.csv(topntax_data, file = &quot;../topntax_all_taxa/topntax_data_oomycetes.csv&quot;)</code></pre>
+</div>
+<div id="ndvi-regression" class="section level3">
+<h3>NDVI regression</h3>
+<pre class="r"><code># Create properly formatted tibble with columns Sample, ndvi_01 (ndvi translated to (0, 1) interval) and one column for each genus
+# containing the sample abundances for that genus.
+ldf &lt;- data.frame(genus_abundance_tbl_per_sample %&gt;% pivot_wider(id_cols = c(&#39;Sample&#39;, &#39;ndvi&#39;), names_from = &#39;Genus&#39;, values_from = &#39;Genus_Sample_Abundance&#39;))
+ldf_genus_data &lt;- data.frame(ldf) %&gt;% select(!c(&#39;Sample&#39;, &#39;ndvi&#39;))
+colnames(ldf_genus_data) &lt;- gsub(&#39; &#39;, &#39;.&#39;, colnames(ldf_genus_data))
+ldf &lt;- cbind.data.frame(
+Sample = ldf$Sample,
+ndvi_01 = (ldf$ndvi + 1) / 2.0
+)
+ldf &lt;- tibble(cbind(ldf, ldf_genus_data))
+
+n_samples_by_genus &lt;- data.frame(ldf_genus_data &gt; 0) %&gt;% mutate_if(is.logical, as.numeric) %&gt;% colSums() %&gt;% sort(decreasing = TRUE)
+keep_n &lt;- 100 # Maximum number of genuses to include in the analysis
+top_n_occurence_genuses &lt;- names(n_samples_by_genus[1:keep_n])
+top_n_occurence_genuses &lt;- top_n_occurence_genuses[!is.na(top_n_occurence_genuses)]
+
+l_genus_ldf &lt;- ldf %&gt;% select(all_of(top_n_occurence_genuses))
+l_genus_ldf_transposed &lt;- data.frame(t(l_genus_ldf))
+l_meta_ldf &lt;- ldf %&gt;% select(&#39;ndvi_01&#39;)
+
+l_model &lt;- linda(
+l_genus_ldf_transposed,
+l_meta_ldf,
+formula = &#39;~ ndvi_01&#39;,
+feature.dat.type = &#39;proportion&#39;,
+is.winsor = FALSE,
+alpha = 0.05
+)</code></pre>
+<pre><code>## 0  features are filtered!
+## The filtered data has  18  samples and  82  features will be tested!
+## Fit linear models ...
+## Completed.</code></pre>
+<pre class="r"><code># Print model info
+l_model</code></pre>
+<pre><code>## $variables
+## [1] &quot;ndvi_01&quot;
+## 
+## $bias
+## [1] 0.02945993
+## 
+## $output
+## $output$ndvi_01
+##                                  baseMean log2FoldChange      lfcSE
+## Globisporangium              112793.95005   -0.302605527 0.27750423
+## Pythium                      655412.08418    0.130754881 0.11644040
+## X.uncultured.Apodachlya.      27968.52938   -0.128213658 0.46044449
+## NA.                           15176.58132    0.553446370 0.34519770
+## X.uncultured.fungus.          44547.99615    0.601237816 0.27185222
+## Aphanomyces                   13766.97575   -0.058429407 0.33558023
+## X.uncultured.Atkinsiella.      9604.81526    0.522064490 0.43650118
+## Cylindrotheca                  1205.02840    1.081194750 0.49734943
+## X.uncultured.Haptoglossa.     39042.69452   -1.384946394 0.44858375
+## X.uncultured.eukaryote.        2484.16641    1.392704388 0.47441000
+## X.uncultured.Pythium.         15648.99474    0.133331068 0.48523991
+## Haptoglossa                   14282.22089   -0.637514001 0.39236739
+## X.uncultured.Aphanomyces.      2178.03672    0.940636968 0.58933786
+## Myzocytiopsis                  1707.64910   -0.859579949 0.57709022
+## Phytophthora                   1149.07427   -1.002677322 0.51700478
+## X.uncultured.Lagenidium.       7705.70872    0.003827813 0.44751402
+## Hyaloperonospora                215.68551   -0.022379235 0.68265436
+## Plectospira                     756.16359    0.158280710 0.92838816
+## Plasmodium                      640.72788    0.543117366 0.63965969
+## Lagena                         1880.31180   -0.525598920 0.53767413
+## Peronospora                    1351.94432   -0.854799622 0.50549521
+## Pseudomonas                      75.25381   -0.291448322 0.50554485
+## Acytostelium                     42.38269    0.111545592 0.43634958
+## Leptomonas                      521.33949    0.217005057 0.20880005
+## Streptomyces                     22.72933    0.389913036 0.66683207
+## Bordetella                       42.21543   -0.859276722 0.44706378
+## Pseudoxanthomonas                40.82502   -0.269248872 0.23008544
+## Lysobacter                      726.29677   -0.381155849 0.16993904
+## Luteimonas                      331.57091   -0.098030844 0.28463613
+## X.uncultured.Saprolegnia.       468.04565   -0.679764779 0.16469817
+## Saprolegnia                     822.69411    0.320691346 0.44125237
+## Pythiopsis                     1386.31637    0.043104357 0.21175909
+## X.uncultured.Mortierella.       510.14233    0.171445057 0.16090295
+## Klebsiella                      300.46844   -0.125582769 0.13959953
+## Stenotrophomonas                 48.63764    0.056468502 0.24169318
+## Candidatus.Carsonella           835.93841    0.055441273 0.30565007
+## X.uncultured.Paralagenidium.   3948.28326    0.059988730 0.12393506
+## Mesorhizobium                   397.69812    0.166669761 0.21578380
+## Bosea                           272.20321    0.050691416 0.21832841
+## X.uncultured.Plasmopara.        251.58104    0.002406805 0.13650611
+## Xanthobacter                    201.33775   -0.070664907 0.13236021
+## Rhizobium                        43.03216    0.015275322 0.19479509
+## Vischeria                        33.20715    0.012075229 0.20557996
+## Bremerella                       33.57518   -0.224156724 0.15163235
+## Leishmania                       63.32648   -0.108981802 0.11616086
+## X.uncultured.Phytophthora.     4735.80361   -0.086653165 0.07204391
+## Rhodospirillum                 2513.54684    0.011719188 0.08863010
+## Spizellomyces                  1804.91175   -0.007146310 0.07468240
+## Chaetomium                     1113.32856    0.001820354 0.06684901
+## Treponema                      1048.87355   -0.002143319 0.06974949
+## Corynebacterium                 804.15094   -0.003310537 0.09123093
+## Nitzschia                       743.02077    0.003399945 0.07040643
+## Caulobacter                     575.93924   -0.152560587 0.05818710
+## Panacibacter                    550.33500   -0.001109530 0.07421640
+## Mycobacterium                   494.81269    0.001820354 0.06684901
+## Leptolegnia                     441.72331   -0.099053784 0.07427569
+## Atoxoplasma                     569.82829   -0.018704030 0.06002553
+## Syntrophus                      403.45086   -0.007146310 0.07468240
+## Saccharopolyspora               371.10952    0.001820354 0.06684901
+## Niabella                        277.29343   -0.003310537 0.09123093
+## Saccharospirillum               247.40635    0.001820354 0.06684901
+## Phragmosporangium               241.97868    0.020754345 0.07611201
+## Nitrosomonas                    218.53552    0.003399945 0.07040643
+## Archangium                      218.53552    0.003399945 0.07040643
+## Cedecea                         198.01390   -0.099053784 0.07427569
+## Thauera                         191.56646    0.020754345 0.07611201
+## Laetiporus                      179.53494   -0.018704030 0.06002553
+## Ancylobacter                    174.82842    0.003399945 0.07040643
+## Rhizorhabdus                    165.24600    0.021593074 0.07297712
+## Pelosinus                       123.70317    0.001820354 0.06684901
+## Nibricoccus                     110.91737   -0.003310537 0.09123093
+## Eustigmatos                     109.28214   -0.018704030 0.06002553
+## Intestinimonas                   93.75755   -0.152560587 0.05818710
+## Micromonospora                   82.46878    0.001820354 0.06684901
+## Variovorax                       72.56010   -0.154157399 0.07911862
+## Alternaria                       46.83520   -0.018704030 0.06002553
+## Faecalibacterium                 31.22347   -0.018704030 0.06002553
+## Trichoderma                      31.22347   -0.018704030 0.06002553
+## Cucurbitaria                     26.78787   -0.152560587 0.05818710
+## Shewanella                       23.41760   -0.018704030 0.06002553
+## Gramella                         12.80472   -0.154157399 0.07911862
+## Aquiflexum                       12.80472   -0.154157399 0.07911862
+##                                      stat       pvalue       padj reject df
+## Globisporangium              -1.090453763 0.2916638013 0.88576751  FALSE 16
+## Pythium                       1.122933949 0.2780336986 0.87687551  FALSE 16
+## X.uncultured.Apodachlya.     -0.278456277 0.7842287163 0.99328112  FALSE 16
+## NA.                           1.603273609 0.1284294711 0.59235010  FALSE 16
+## X.uncultured.fungus.          2.211634757 0.0418897025 0.41059524  FALSE 16
+## Aphanomyces                  -0.174114566 0.8639593971 0.99328112  FALSE 16
+## X.uncultured.Atkinsiella.     1.196020791 0.2491128434 0.84968866  FALSE 16
+## Cylindrotheca                 2.173913708 0.0450653314 0.41059524  FALSE 16
+## X.uncultured.Haptoglossa.    -3.087375343 0.0070641984 0.25281498  FALSE 16
+## X.uncultured.eukaryote.       2.935655642 0.0096955557 0.25281498  FALSE 16
+## X.uncultured.Pythium.         0.274773502 0.7870066508 0.99328112  FALSE 16
+## Haptoglossa                  -1.624788458 0.1237407096 0.59235010  FALSE 16
+## X.uncultured.Aphanomyces.     1.596091190 0.1300280718 0.59235010  FALSE 16
+## Myzocytiopsis                -1.489507041 0.1558011154 0.65085724  FALSE 16
+## Phytophthora                 -1.939396626 0.0702941395 0.42518355  FALSE 16
+## X.uncultured.Lagenidium.      0.008553505 0.9932811167 0.99328112  FALSE 16
+## Hyaloperonospora             -0.032782674 0.9742533730 0.99328112  FALSE 16
+## Plectospira                   0.170489799 0.8667622173 0.99328112  FALSE 16
+## Plasmodium                    0.849072365 0.4083657563 0.99328112  FALSE 16
+## Lagena                       -0.977541762 0.3428509236 0.93712586  FALSE 16
+## Peronospora                  -1.691014284 0.1102171241 0.59235010  FALSE 16
+## Pseudomonas                  -0.576503397 0.5722967975 0.99328112  FALSE 16
+## Acytostelium                  0.255633547 0.8014910841 0.99328112  FALSE 16
+## Leptomonas                    1.039295999 0.3141187240 0.88819777  FALSE 16
+## Streptomyces                  0.584724478 0.5668878024 0.99328112  FALSE 16
+## Bordetella                   -1.922045054 0.0725923127 0.42518355  FALSE 16
+## Pseudoxanthomonas            -1.170212558 0.2590514198 0.84968866  FALSE 16
+## Lysobacter                   -2.242897540 0.0394162445 0.41059524  FALSE 16
+## Luteimonas                   -0.344407598 0.7350249144 0.99328112  FALSE 16
+## X.uncultured.Saprolegnia.    -4.127336562 0.0007899624 0.06477691  FALSE 16
+## Saprolegnia                   0.726775344 0.4778629430 0.99328112  FALSE 16
+## Pythiopsis                    0.203553748 0.8412685601 0.99328112  FALSE 16
+## X.uncultured.Mortierella.     1.065518429 0.3024571970 0.88576751  FALSE 16
+## Klebsiella                   -0.899593058 0.3816768565 0.97804694  FALSE 16
+## Stenotrophomonas              0.233637130 0.8182297170 0.99328112  FALSE 16
+## Candidatus.Carsonella         0.181388055 0.8583409291 0.99328112  FALSE 16
+## X.uncultured.Paralagenidium.  0.484033570 0.6349205292 0.99328112  FALSE 16
+## Mesorhizobium                 0.772392386 0.4511414416 0.99328112  FALSE 16
+## Bosea                         0.232179659 0.8193421251 0.99328112  FALSE 16
+## X.uncultured.Plasmopara.      0.017631486 0.9861508343 0.99328112  FALSE 16
+## Xanthobacter                 -0.533883319 0.6007617532 0.99328112  FALSE 16
+## Rhizobium                     0.078417388 0.9384683843 0.99328112  FALSE 16
+## Vischeria                     0.058737381 0.9538886394 0.99328112  FALSE 16
+## Bremerella                   -1.478290948 0.1587456677 0.65085724  FALSE 16
+## Leishmania                   -0.938197272 0.3620908147 0.95778861  FALSE 16
+## X.uncultured.Phytophthora.   -1.202782576 0.2465577951 0.84968866  FALSE 16
+## Rhodospirillum                0.132225822 0.8964542256 0.99328112  FALSE 16
+## Spizellomyces                -0.095689338 0.9249555559 0.99328112  FALSE 16
+## Chaetomium                    0.027230822 0.9786123857 0.99328112  FALSE 16
+## Treponema                    -0.030728812 0.9758658636 0.99328112  FALSE 16
+## Corynebacterium              -0.036287442 0.9715020441 0.99328112  FALSE 16
+## Nitzschia                     0.048290267 0.9620825684 0.99328112  FALSE 16
+## Caulobacter                  -2.621897164 0.0184986569 0.25281498  FALSE 16
+## Panacibacter                 -0.014949924 0.9882569639 0.99328112  FALSE 16
+## Mycobacterium                 0.027230822 0.9786123857 0.99328112  FALSE 16
+## Leptolegnia                  -1.333596231 0.2010116133 0.74922510  FALSE 16
+## Atoxoplasma                  -0.311601276 0.7593670651 0.99328112  FALSE 16
+## Syntrophus                   -0.095689338 0.9249555559 0.99328112  FALSE 16
+## Saccharopolyspora             0.027230822 0.9786123857 0.99328112  FALSE 16
+## Niabella                     -0.036287442 0.9715020441 0.99328112  FALSE 16
+## Saccharospirillum             0.027230822 0.9786123857 0.99328112  FALSE 16
+## Phragmosporangium             0.272681588 0.7885859177 0.99328112  FALSE 16
+## Nitrosomonas                  0.048290267 0.9620825684 0.99328112  FALSE 16
+## Archangium                    0.048290267 0.9620825684 0.99328112  FALSE 16
+## Cedecea                      -1.333596231 0.2010116133 0.74922510  FALSE 16
+## Thauera                       0.272681588 0.7885859177 0.99328112  FALSE 16
+## Laetiporus                   -0.311601276 0.7593670651 0.99328112  FALSE 16
+## Ancylobacter                  0.048290267 0.9620825684 0.99328112  FALSE 16
+## Rhizorhabdus                  0.295888263 0.7711211463 0.99328112  FALSE 16
+## Pelosinus                     0.027230822 0.9786123857 0.99328112  FALSE 16
+## Nibricoccus                  -0.036287442 0.9715020441 0.99328112  FALSE 16
+## Eustigmatos                  -0.311601276 0.7593670651 0.99328112  FALSE 16
+## Intestinimonas               -2.621897164 0.0184986569 0.25281498  FALSE 16
+## Micromonospora                0.027230822 0.9786123857 0.99328112  FALSE 16
+## Variovorax                   -1.948433808 0.0691232547 0.42518355  FALSE 16
+## Alternaria                   -0.311601276 0.7593670651 0.99328112  FALSE 16
+## Faecalibacterium             -0.311601276 0.7593670651 0.99328112  FALSE 16
+## Trichoderma                  -0.311601276 0.7593670651 0.99328112  FALSE 16
+## Cucurbitaria                 -2.621897164 0.0184986569 0.25281498  FALSE 16
+## Shewanella                   -0.311601276 0.7593670651 0.99328112  FALSE 16
+## Gramella                     -1.948433808 0.0691232547 0.42518355  FALSE 16
+## Aquiflexum                   -1.948433808 0.0691232547 0.42518355  FALSE 16
+## 
+## 
+## $covariance
+## NULL
+## 
+## $feature.dat.use
+##                                         1            2            3
+## Globisporangium              6.644015e-01 1.547035e-01 4.853431e-02
+## Pythium                      2.015203e-01 5.216436e-01 6.213895e-01
+## X.uncultured.Apodachlya.     6.309169e-03 3.741718e-03 5.379577e-02
+## NA.                          3.517678e-03 2.769101e-03 7.623752e-03
+## X.uncultured.fungus.         8.980739e-03 9.863490e-03 6.356706e-02
+## Aphanomyces                  4.463721e-03 9.405788e-03 2.512617e-02
+## X.uncultured.Atkinsiella.    9.327177e-04 2.826313e-03 1.578439e-02
+## Cylindrotheca                2.998021e-04 1.144256e-04 1.610652e-03
+## X.uncultured.Haptoglossa.    7.584994e-02 2.159670e-01 9.916246e-02
+## X.uncultured.eukaryote.      4.063984e-04 2.031992e-04 2.031992e-04
+## X.uncultured.Pythium.        4.043998e-03 9.852048e-03 2.021999e-03
+## Haptoglossa                  6.882124e-03 5.848294e-02 2.710968e-03
+## X.uncultured.Aphanomyces.    4.663589e-04 6.979964e-04 1.825405e-03
+## Myzocytiopsis                3.577639e-03 3.661620e-04 1.830810e-04
+## Phytophthora                 6.728892e-04 7.552092e-04 7.408998e-03
+## X.uncultured.Lagenidium.     3.804156e-03 5.995904e-03 1.734135e-02
+## Hyaloperonospora             2.265172e-04 2.281508e-05 2.281508e-05
+## Plectospira                  4.577026e-05 9.154051e-05 4.577026e-05
+## Plasmodium                   1.019327e-03 1.056245e-04 2.308601e-03
+## Lagena                       1.452375e-03 9.154051e-04 4.577026e-04
+## Peronospora                  1.037982e-02 3.506853e-04 3.506853e-04
+## Pseudomonas                  2.198549e-04 2.377528e-05 1.073768e-04
+## Acytostelium                 1.693853e-05 1.693853e-05 1.693853e-05
+## Leptomonas                   2.965960e-04 2.965960e-04 2.965960e-04
+## Streptomyces                 1.332454e-05 6.662270e-06 4.831955e-04
+## Bordetella                   1.465699e-04 1.693853e-05 1.693853e-05
+## Pseudoxanthomonas            1.065963e-04 2.281508e-05 2.281508e-05
+## Lysobacter                   4.517302e-04 4.517302e-04 4.517302e-04
+## Luteimonas                   1.863238e-04 1.863238e-04 1.986471e-03
+## X.uncultured.Saprolegnia.    2.803428e-04 5.606856e-04 2.803428e-04
+## Saprolegnia                  4.176536e-04 8.353072e-04 2.883067e-02
+## Pythiopsis                   8.856290e-04 8.856290e-04 8.856290e-04
+## X.uncultured.Mortierella.    3.461805e-04 3.461805e-04 1.771717e-03
+## Klebsiella                   2.045891e-04 2.045891e-04 2.045891e-04
+## Stenotrophomonas             5.996043e-05 2.998021e-05 2.998021e-05
+## Candidatus.Carsonella        5.349437e-04 5.349437e-04 5.349437e-04
+## X.uncultured.Paralagenidium. 2.865330e-03 2.865330e-03 2.865330e-03
+## Mesorhizobium                2.728885e-04 2.728885e-04 2.728885e-04
+## Bosea                        1.842584e-04 1.842584e-04 1.842584e-04
+## X.uncultured.Plasmopara.     1.830810e-04 3.661620e-04 1.830810e-04
+## Xanthobacter                 1.450864e-04 1.450864e-04 1.450864e-04
+## Rhizobium                    2.971910e-05 2.971910e-05 2.684420e-04
+## Vischeria                    2.288513e-05 4.577026e-05 2.147536e-04
+## Bremerella                   2.377528e-05 2.377528e-05 2.377528e-05
+## Leishmania                   9.327177e-05 4.663589e-05 4.663589e-05
+## X.uncultured.Phytophthora.   3.696042e-03 3.696042e-03 3.696042e-03
+## Rhodospirillum               1.961689e-03 1.961689e-03 1.961689e-03
+## Spizellomyces                1.408637e-03 1.408637e-03 1.408637e-03
+## Chaetomium                   8.688936e-04 8.688936e-04 8.688936e-04
+## Treponema                    8.185899e-04 8.185899e-04 8.185899e-04
+## Corynebacterium              6.275970e-04 6.275970e-04 6.275970e-04
+## Nitzschia                    5.798881e-04 5.798881e-04 5.798881e-04
+## Caulobacter                  4.494899e-04 4.494899e-04 4.494899e-04
+## Panacibacter                 4.295071e-04 4.295071e-04 8.590143e-04
+## Mycobacterium                3.861749e-04 3.861749e-04 3.861749e-04
+## Leptolegnia                  3.447415e-04 3.447415e-04 3.447415e-04
+## Atoxoplasma                  4.447206e-04 4.447206e-04 4.447206e-04
+## Syntrophus                   3.148719e-04 3.148719e-04 3.148719e-04
+## Saccharopolyspora            2.896312e-04 2.896312e-04 2.896312e-04
+## Niabella                     2.164127e-04 2.164127e-04 2.164127e-04
+## Saccharospirillum            1.930875e-04 1.930875e-04 1.930875e-04
+## Phragmosporangium            1.888515e-04 1.888515e-04 1.888515e-04
+## Nitrosomonas                 1.705553e-04 1.705553e-04 1.705553e-04
+## Archangium                   1.705553e-04 1.705553e-04 1.705553e-04
+## Cedecea                      1.545393e-04 1.545393e-04 1.545393e-04
+## Thauera                      1.495074e-04 1.495074e-04 1.495074e-04
+## Laetiporus                   1.401175e-04 1.401175e-04 1.401175e-04
+## Ancylobacter                 1.364443e-04 1.364443e-04 1.364443e-04
+## Rhizorhabdus                 1.289657e-04 1.289657e-04 1.289657e-04
+## Pelosinus                    9.654373e-05 9.654373e-05 9.654373e-05
+## Nibricoccus                  8.656510e-05 8.656510e-05 8.656510e-05
+## Eustigmatos                  8.528889e-05 8.528889e-05 8.528889e-05
+## Intestinimonas               7.317277e-05 7.317277e-05 7.317277e-05
+## Micromonospora               6.436249e-05 6.436249e-05 6.436249e-05
+## Variovorax                   1.132586e-04 5.662929e-05 5.662929e-05
+## Alternaria                   3.655238e-05 3.655238e-05 3.655238e-05
+## Faecalibacterium             2.436825e-05 2.436825e-05 2.436825e-05
+## Trichoderma                  2.436825e-05 2.436825e-05 2.436825e-05
+## Cucurbitaria                 2.090651e-05 2.090651e-05 2.090651e-05
+## Shewanella                   1.827619e-05 1.827619e-05 1.827619e-05
+## Gramella                     1.998681e-05 9.993404e-06 9.993404e-06
+## Aquiflexum                   1.998681e-05 9.993404e-06 9.993404e-06
+##                                         4            5            6
+## Globisporangium              5.932849e-02 3.461510e-02 2.320570e-01
+## Pythium                      5.805575e-01 4.974170e-01 4.346976e-01
+## X.uncultured.Apodachlya.     7.036558e-02 5.548651e-02 5.170320e-02
+## NA.                          1.809685e-02 1.337817e-02 3.580671e-02
+## X.uncultured.fungus.         7.046502e-02 4.587324e-02 1.123845e-01
+## Aphanomyces                  1.378807e-02 1.351220e-02 1.386381e-03
+## X.uncultured.Atkinsiella.    2.449372e-02 1.314667e-02 1.991022e-02
+## Cylindrotheca                3.413874e-03 8.650730e-04 6.443095e-03
+## X.uncultured.Haptoglossa.    7.248683e-02 2.123328e-01 2.518020e-03
+## X.uncultured.eukaryote.      4.971662e-04 4.751809e-03 4.224980e-03
+## X.uncultured.Pythium.        2.021999e-03 5.330555e-02 2.021999e-03
+## Haptoglossa                  5.733983e-03 5.421936e-03 3.675733e-02
+## X.uncultured.Aphanomyces.    2.331794e-04 1.056364e-02 2.550832e-02
+## Myzocytiopsis                8.286103e-04 3.387187e-03 1.830810e-04
+## Phytophthora                 1.495074e-04 2.851086e-03 1.495074e-04
+## X.uncultured.Lagenidium.     1.902078e-03 7.980603e-03 1.902078e-03
+## Hyaloperonospora             2.281508e-05 2.631771e-03 1.267494e-03
+## Plectospira                  9.015280e-03 9.747301e-03 2.667019e-02
+## Plasmodium                   4.275629e-03 2.083486e-03 2.112490e-04
+## Lagena                       3.095688e-02 2.887638e-03 4.577026e-04
+## Peronospora                  1.491498e-03 3.506853e-04 5.334037e-03
+## Pseudomonas                  2.377528e-05 1.218413e-04 2.534988e-03
+## Acytostelium                 1.657221e-04 4.873651e-05 1.693853e-05
+## Leptomonas                   7.954658e-04 2.965960e-04 1.214682e-03
+## Streptomyces                 6.662270e-06 6.662270e-06 9.506205e-04
+## Bordetella                   1.693853e-05 1.693853e-05 1.693853e-05
+## Pseudoxanthomonas            2.281508e-05 8.528889e-05 2.281508e-05
+## Lysobacter                   4.517302e-04 4.517302e-04 4.517302e-04
+## Luteimonas                   1.863238e-04 1.863238e-04 1.863238e-04
+## X.uncultured.Saprolegnia.    2.803428e-04 2.803428e-04 2.803428e-04
+## Saprolegnia                  4.176536e-04 4.176536e-04 4.176536e-04
+## Pythiopsis                   8.856290e-04 5.568146e-03 8.856290e-04
+## X.uncultured.Mortierella.    3.461805e-04 3.461805e-04 3.461805e-04
+## Klebsiella                   2.045891e-04 2.045891e-04 6.865593e-04
+## Stenotrophomonas             2.998021e-05 3.898920e-04 2.998021e-05
+## Candidatus.Carsonella        1.905804e-02 5.349437e-04 5.349437e-04
+## X.uncultured.Paralagenidium. 1.060621e-02 2.865330e-03 2.865330e-03
+## Mesorhizobium                2.728885e-04 2.728885e-04 2.728885e-04
+## Bosea                        1.842584e-04 1.842584e-04 1.842584e-04
+## X.uncultured.Plasmopara.     1.830810e-04 1.830810e-04 1.830810e-04
+## Xanthobacter                 1.450864e-04 1.450864e-04 1.450864e-04
+## Rhizobium                    2.971910e-05 2.971910e-05 2.971910e-05
+## Vischeria                    2.288513e-05 2.288513e-05 2.288513e-05
+## Bremerella                   2.377528e-05 2.377528e-05 2.377528e-05
+## Leishmania                   1.325776e-04 4.663589e-05 4.663589e-05
+## X.uncultured.Phytophthora.   3.696042e-03 3.696042e-03 3.696042e-03
+## Rhodospirillum               1.961689e-03 1.961689e-03 1.961689e-03
+## Spizellomyces                2.817275e-03 1.408637e-03 1.408637e-03
+## Chaetomium                   8.688936e-04 8.688936e-04 8.688936e-04
+## Treponema                    8.185899e-04 8.185899e-04 1.637180e-03
+## Corynebacterium              6.275970e-04 6.275970e-04 6.275970e-04
+## Nitzschia                    5.798881e-04 5.798881e-04 5.798881e-04
+## Caulobacter                  4.494899e-04 4.494899e-04 4.494899e-04
+## Panacibacter                 4.295071e-04 4.295071e-04 4.295071e-04
+## Mycobacterium                3.861749e-04 3.861749e-04 3.861749e-04
+## Leptolegnia                  3.447415e-04 3.447415e-04 3.447415e-04
+## Atoxoplasma                  4.447206e-04 8.894412e-04 4.447206e-04
+## Syntrophus                   6.297438e-04 3.148719e-04 3.148719e-04
+## Saccharopolyspora            2.896312e-04 2.896312e-04 2.896312e-04
+## Niabella                     2.164127e-04 2.164127e-04 2.164127e-04
+## Saccharospirillum            1.930875e-04 1.930875e-04 1.930875e-04
+## Phragmosporangium            1.888515e-04 1.888515e-04 1.888515e-04
+## Nitrosomonas                 1.705553e-04 1.705553e-04 1.705553e-04
+## Archangium                   1.705553e-04 1.705553e-04 1.705553e-04
+## Cedecea                      1.545393e-04 1.545393e-04 1.545393e-04
+## Thauera                      1.495074e-04 1.495074e-04 1.495074e-04
+## Laetiporus                   1.401175e-04 2.802349e-04 1.401175e-04
+## Ancylobacter                 1.364443e-04 1.364443e-04 1.364443e-04
+## Rhizorhabdus                 1.289657e-04 1.289657e-04 1.289657e-04
+## Pelosinus                    9.654373e-05 9.654373e-05 9.654373e-05
+## Nibricoccus                  8.656510e-05 8.656510e-05 8.656510e-05
+## Eustigmatos                  8.528889e-05 1.705778e-04 8.528889e-05
+## Intestinimonas               7.317277e-05 7.317277e-05 7.317277e-05
+## Micromonospora               6.436249e-05 6.436249e-05 6.436249e-05
+## Variovorax                   5.662929e-05 5.662929e-05 5.662929e-05
+## Alternaria                   3.655238e-05 7.310476e-05 3.655238e-05
+## Faecalibacterium             2.436825e-05 4.873651e-05 2.436825e-05
+## Trichoderma                  2.436825e-05 4.873651e-05 2.436825e-05
+## Cucurbitaria                 2.090651e-05 2.090651e-05 2.090651e-05
+## Shewanella                   1.827619e-05 3.655238e-05 1.827619e-05
+## Gramella                     9.993404e-06 9.993404e-06 9.993404e-06
+## Aquiflexum                   9.993404e-06 9.993404e-06 9.993404e-06
+##                                         7            8            9
+## Globisporangium              5.209442e-02 7.219529e-02 1.649611e-01
+## Pythium                      6.641925e-01 7.483553e-01 4.450812e-01
+## X.uncultured.Apodachlya.     7.411858e-02 1.064751e-02 7.572657e-03
+## NA.                          1.253308e-02 2.596953e-03 3.261018e-02
+## X.uncultured.fungus.         2.307365e-02 6.107168e-02 1.164552e-01
+## Aphanomyces                  2.114197e-02 3.852147e-03 5.553281e-02
+## X.uncultured.Atkinsiella.    8.913090e-03 4.663589e-04 2.312730e-02
+## Cylindrotheca                7.148724e-04 2.034280e-03 3.138218e-03
+## X.uncultured.Haptoglossa.    4.252730e-02 1.800554e-02 9.278210e-03
+## X.uncultured.eukaryote.      7.300824e-04 2.077562e-03 2.326375e-02
+## X.uncultured.Pythium.        1.662459e-02 2.021999e-03 2.162642e-02
+## Haptoglossa                  2.727162e-02 2.710968e-03 2.710968e-03
+## X.uncultured.Aphanomyces.    2.331794e-04 2.331794e-04 2.331794e-04
+## Myzocytiopsis                3.559152e-03 1.830810e-04 1.830810e-04
+## Phytophthora                 5.384358e-03 9.219183e-03 1.495074e-04
+## X.uncultured.Lagenidium.     1.902078e-03 3.943040e-02 6.815391e-02
+## Hyaloperonospora             4.563015e-05 2.281508e-05 2.281508e-05
+## Plectospira                  6.236121e-03 4.577026e-05 4.577026e-05
+## Plasmodium                   3.042010e-04 1.056245e-04 1.056245e-04
+## Lagena                       4.577026e-04 2.713816e-02 4.577026e-04
+## Peronospora                  3.153044e-02 3.506853e-04 3.506853e-04
+## Pseudomonas                  2.377528e-05 2.377528e-05 2.377528e-05
+## Acytostelium                 6.692422e-04 1.693853e-05 6.822213e-04
+## Leptomonas                   5.931920e-04 2.965960e-04 2.455997e-03
+## Streptomyces                 1.673106e-04 6.662270e-06 1.159776e-03
+## Bordetella                   1.693853e-05 1.693853e-05 1.693853e-05
+## Pseudoxanthomonas            4.563015e-05 2.281508e-05 2.046664e-04
+## Lysobacter                   4.517302e-04 4.517302e-04 4.517302e-04
+## Luteimonas                   1.863238e-04 1.514889e-03 1.863238e-04
+## X.uncultured.Saprolegnia.    2.803428e-04 2.803428e-04 2.803428e-04
+## Saprolegnia                  4.176536e-04 4.176536e-04 1.391731e-02
+## Pythiopsis                   8.856290e-04 8.856290e-04 8.856290e-04
+## X.uncultured.Mortierella.    3.461805e-04 3.461805e-04 3.461805e-04
+## Klebsiella                   2.045891e-04 2.045891e-04 2.045891e-04
+## Stenotrophomonas             2.998021e-05 2.998021e-05 2.998021e-05
+## Candidatus.Carsonella        5.349437e-04 5.349437e-04 5.349437e-04
+## X.uncultured.Paralagenidium. 2.865330e-03 2.865330e-03 5.730659e-03
+## Mesorhizobium                2.728885e-04 2.728885e-04 5.457771e-04
+## Bosea                        1.842584e-04 1.842584e-04 2.387775e-03
+## X.uncultured.Plasmopara.     1.830810e-04 1.830810e-04 1.830810e-04
+## Xanthobacter                 1.450864e-04 1.450864e-04 1.450864e-04
+## Rhizobium                    2.971910e-05 2.971910e-05 2.971910e-05
+## Vischeria                    2.288513e-05 2.288513e-05 2.288513e-05
+## Bremerella                   1.368905e-04 2.377528e-05 2.377528e-05
+## Leishmania                   4.663589e-05 4.663589e-05 4.663589e-05
+## X.uncultured.Phytophthora.   7.392085e-03 3.696042e-03 3.696042e-03
+## Rhodospirillum               1.961689e-03 1.961689e-03 1.961689e-03
+## Spizellomyces                1.408637e-03 1.408637e-03 1.408637e-03
+## Chaetomium                   8.688936e-04 8.688936e-04 8.688936e-04
+## Treponema                    8.185899e-04 8.185899e-04 8.185899e-04
+## Corynebacterium              6.275970e-04 1.255194e-03 6.275970e-04
+## Nitzschia                    5.798881e-04 5.798881e-04 1.159776e-03
+## Caulobacter                  4.494899e-04 4.494899e-04 4.494899e-04
+## Panacibacter                 4.295071e-04 4.295071e-04 4.295071e-04
+## Mycobacterium                3.861749e-04 3.861749e-04 3.861749e-04
+## Leptolegnia                  3.447415e-04 3.447415e-04 3.447415e-04
+## Atoxoplasma                  4.447206e-04 4.447206e-04 4.447206e-04
+## Syntrophus                   3.148719e-04 3.148719e-04 3.148719e-04
+## Saccharopolyspora            2.896312e-04 2.896312e-04 2.896312e-04
+## Niabella                     2.164127e-04 4.328255e-04 2.164127e-04
+## Saccharospirillum            1.930875e-04 1.930875e-04 1.930875e-04
+## Phragmosporangium            1.888515e-04 1.888515e-04 1.888515e-04
+## Nitrosomonas                 1.705553e-04 1.705553e-04 3.411107e-04
+## Archangium                   1.705553e-04 1.705553e-04 3.411107e-04
+## Cedecea                      1.545393e-04 1.545393e-04 1.545393e-04
+## Thauera                      1.495074e-04 1.495074e-04 1.495074e-04
+## Laetiporus                   1.401175e-04 1.401175e-04 1.401175e-04
+## Ancylobacter                 1.364443e-04 1.364443e-04 2.728885e-04
+## Rhizorhabdus                 1.289657e-04 1.289657e-04 1.289657e-04
+## Pelosinus                    9.654373e-05 9.654373e-05 9.654373e-05
+## Nibricoccus                  8.656510e-05 1.731302e-04 8.656510e-05
+## Eustigmatos                  8.528889e-05 8.528889e-05 8.528889e-05
+## Intestinimonas               7.317277e-05 7.317277e-05 7.317277e-05
+## Micromonospora               6.436249e-05 6.436249e-05 6.436249e-05
+## Variovorax                   5.662929e-05 5.662929e-05 5.662929e-05
+## Alternaria                   3.655238e-05 3.655238e-05 3.655238e-05
+## Faecalibacterium             2.436825e-05 2.436825e-05 2.436825e-05
+## Trichoderma                  2.436825e-05 2.436825e-05 2.436825e-05
+## Cucurbitaria                 2.090651e-05 2.090651e-05 2.090651e-05
+## Shewanella                   1.827619e-05 1.827619e-05 1.827619e-05
+## Gramella                     9.993404e-06 9.993404e-06 9.993404e-06
+## Aquiflexum                   9.993404e-06 9.993404e-06 9.993404e-06
+##                                        10           11           12
+## Globisporangium              4.304785e-02 4.534445e-02 6.323563e-02
+## Pythium                      7.708359e-01 6.084998e-01 4.629587e-01
+## X.uncultured.Apodachlya.     1.725200e-02 7.754459e-02 5.308101e-03
+## NA.                          1.930581e-02 7.723970e-03 1.264713e-01
+## X.uncultured.fungus.         1.918258e-02 3.267442e-02 1.652435e-01
+## Aphanomyces                  9.365373e-03 2.152887e-02 4.038772e-02
+## X.uncultured.Atkinsiella.    1.910043e-02 2.286702e-02 4.663589e-04
+## Cylindrotheca                4.066543e-03 1.575283e-03 5.721282e-05
+## X.uncultured.Haptoglossa.    2.518020e-03 8.726731e-02 2.518020e-03
+## X.uncultured.eukaryote.      6.777572e-03 1.609160e-03 7.154396e-03
+## X.uncultured.Pythium.        6.683097e-02 3.529990e-02 8.400646e-02
+## Haptoglossa                  2.710968e-03 1.141657e-02 2.710968e-03
+## X.uncultured.Aphanomyces.    2.331794e-04 5.623592e-03 2.469421e-02
+## Myzocytiopsis                1.667694e-02 1.085760e-02 1.830810e-04
+## Phytophthora                 1.889505e-03 4.082186e-03 1.495074e-04
+## X.uncultured.Lagenidium.     1.902078e-03 1.637956e-02 1.902078e-03
+## Hyaloperonospora             3.696858e-04 1.355082e-04 2.281508e-05
+## Plectospira                  4.577026e-05 1.846300e-03 4.577026e-05
+## Plasmodium                   1.056245e-04 4.573403e-04 1.315486e-02
+## Lagena                       4.577026e-04 2.405271e-03 4.577026e-04
+## Peronospora                  3.506853e-04 3.506853e-04 3.506853e-04
+## Pseudomonas                  2.377528e-05 2.377528e-05 2.377528e-05
+## Acytostelium                 1.693853e-05 3.387706e-05 1.693853e-05
+## Leptomonas                   2.965960e-04 2.965960e-04 2.965960e-04
+## Streptomyces                 6.662270e-06 6.662270e-06 6.662270e-06
+## Bordetella                   1.232286e-04 3.387706e-05 1.693853e-05
+## Pseudoxanthomonas            2.281508e-05 2.281508e-05 2.281508e-05
+## Lysobacter                   4.517302e-04 2.794857e-03 4.517302e-04
+## Luteimonas                   1.863238e-04 3.726477e-04 1.863238e-04
+## X.uncultured.Saprolegnia.    2.803428e-04 1.016312e-03 2.803428e-04
+## Saprolegnia                  4.176536e-04 4.176536e-04 4.176536e-04
+## Pythiopsis                   5.175601e-03 8.856290e-04 8.856290e-04
+## X.uncultured.Mortierella.    3.461805e-04 3.461805e-04 6.923610e-04
+## Klebsiella                   2.045891e-04 2.045891e-04 2.045891e-04
+## Stenotrophomonas             2.998021e-05 2.998021e-05 2.998021e-05
+## Candidatus.Carsonella        5.349437e-04 5.349437e-04 5.349437e-04
+## X.uncultured.Paralagenidium. 2.865330e-03 2.865330e-03 2.865330e-03
+## Mesorhizobium                2.728885e-04 2.728885e-04 2.769444e-03
+## Bosea                        1.842584e-04 1.842584e-04 1.842584e-04
+## X.uncultured.Plasmopara.     1.830810e-04 1.830810e-04 1.830810e-04
+## Xanthobacter                 1.450864e-04 6.097871e-04 1.450864e-04
+## Rhizobium                    2.971910e-05 2.971910e-05 2.971910e-05
+## Vischeria                    2.288513e-05 2.288513e-05 2.288513e-05
+## Bremerella                   2.377528e-05 2.377528e-05 2.377528e-05
+## Leishmania                   4.663589e-05 4.663589e-05 4.663589e-05
+## X.uncultured.Phytophthora.   3.696042e-03 3.696042e-03 3.696042e-03
+## Rhodospirillum               1.961689e-03 1.961689e-03 3.923379e-03
+## Spizellomyces                1.408637e-03 1.408637e-03 1.408637e-03
+## Chaetomium                   8.688936e-04 8.688936e-04 8.688936e-04
+## Treponema                    8.185899e-04 8.185899e-04 8.185899e-04
+## Corynebacterium              6.275970e-04 6.275970e-04 6.275970e-04
+## Nitzschia                    5.798881e-04 5.798881e-04 5.798881e-04
+## Caulobacter                  4.494899e-04 4.494899e-04 4.494899e-04
+## Panacibacter                 4.295071e-04 4.295071e-04 4.295071e-04
+## Mycobacterium                3.861749e-04 3.861749e-04 3.861749e-04
+## Leptolegnia                  3.447415e-04 3.447415e-04 3.447415e-04
+## Atoxoplasma                  4.447206e-04 4.447206e-04 4.447206e-04
+## Syntrophus                   3.148719e-04 3.148719e-04 3.148719e-04
+## Saccharopolyspora            2.896312e-04 2.896312e-04 2.896312e-04
+## Niabella                     2.164127e-04 2.164127e-04 2.164127e-04
+## Saccharospirillum            1.930875e-04 1.930875e-04 1.930875e-04
+## Phragmosporangium            1.888515e-04 1.888515e-04 1.888515e-04
+## Nitrosomonas                 1.705553e-04 1.705553e-04 1.705553e-04
+## Archangium                   1.705553e-04 1.705553e-04 1.705553e-04
+## Cedecea                      1.545393e-04 1.545393e-04 1.545393e-04
+## Thauera                      1.495074e-04 1.495074e-04 1.495074e-04
+## Laetiporus                   1.401175e-04 1.401175e-04 1.401175e-04
+## Ancylobacter                 1.364443e-04 1.364443e-04 1.364443e-04
+## Rhizorhabdus                 1.289657e-04 1.289657e-04 1.289657e-04
+## Pelosinus                    9.654373e-05 9.654373e-05 9.654373e-05
+## Nibricoccus                  8.656510e-05 8.656510e-05 8.656510e-05
+## Eustigmatos                  8.528889e-05 8.528889e-05 8.528889e-05
+## Intestinimonas               7.317277e-05 7.317277e-05 7.317277e-05
+## Micromonospora               6.436249e-05 6.436249e-05 6.436249e-05
+## Variovorax                   5.662929e-05 5.662929e-05 5.662929e-05
+## Alternaria                   3.655238e-05 3.655238e-05 3.655238e-05
+## Faecalibacterium             2.436825e-05 2.436825e-05 2.436825e-05
+## Trichoderma                  2.436825e-05 2.436825e-05 2.436825e-05
+## Cucurbitaria                 2.090651e-05 2.090651e-05 2.090651e-05
+## Shewanella                   1.827619e-05 1.827619e-05 1.827619e-05
+## Gramella                     9.993404e-06 9.993404e-06 9.993404e-06
+## Aquiflexum                   9.993404e-06 9.993404e-06 9.993404e-06
+##                                        13           14           15
+## Globisporangium              6.886768e-02 7.367787e-02 2.226874e-01
+## Pythium                      6.368971e-01 6.680729e-01 5.921753e-01
+## X.uncultured.Apodachlya.     1.087503e-01 3.375019e-02 2.873690e-02
+## NA.                          7.995873e-03 2.109387e-03 2.027006e-02
+## X.uncultured.fungus.         3.927005e-02 7.533524e-03 1.506090e-02
+## Aphanomyces                  2.772762e-03 1.310833e-02 1.126814e-02
+## X.uncultured.Atkinsiella.    3.098401e-02 1.657375e-02 5.948821e-03
+## Cylindrotheca                4.997421e-03 5.721282e-05 1.180322e-03
+## X.uncultured.Haptoglossa.    1.325123e-02 6.569233e-02 5.036039e-03
+## X.uncultured.eukaryote.      1.247743e-02 2.031992e-04 3.117623e-02
+## X.uncultured.Pythium.        2.401986e-02 4.068103e-03 1.893236e-02
+## Haptoglossa                  1.466985e-02 3.585958e-02 1.556451e-02
+## X.uncultured.Aphanomyces.    6.641733e-03 5.725478e-03 4.548173e-03
+## Myzocytiopsis                1.418623e-02 4.821455e-03 2.580970e-03
+## Phytophthora                 1.495074e-04 1.495074e-04 2.990148e-04
+## X.uncultured.Lagenidium.     1.902078e-03 4.309176e-02 1.004060e-02
+## Hyaloperonospora             3.159660e-03 2.281508e-05 7.554059e-04
+## Plectospira                  8.801909e-03 3.616092e-03 1.120519e-02
+## Plasmodium                   1.056245e-04 1.295766e-02 1.056245e-04
+## Lagena                       4.577026e-04 6.629501e-03 4.577026e-04
+## Peronospora                  3.506853e-04 2.712069e-03 3.506853e-04
+## Pseudomonas                  2.377528e-05 2.377528e-05 2.377528e-05
+## Acytostelium                 6.448285e-05 1.693853e-05 1.693853e-05
+## Leptomonas                   2.965960e-04 2.965960e-04 2.965960e-04
+## Streptomyces                 6.662270e-06 6.662270e-06 6.662270e-06
+## Bordetella                   1.693853e-05 1.693853e-05 3.934406e-04
+## Pseudoxanthomonas            2.281508e-05 2.281508e-05 2.281508e-05
+## Lysobacter                   4.517302e-04 4.517302e-04 1.054421e-03
+## Luteimonas                   1.863238e-04 1.863238e-04 1.863238e-04
+## X.uncultured.Saprolegnia.    2.803428e-04 2.803428e-04 2.803428e-04
+## Saprolegnia                  4.176536e-04 4.176536e-04 4.176536e-04
+## Pythiopsis                   8.856290e-04 8.856290e-04 8.856290e-04
+## X.uncultured.Mortierella.    8.382770e-04 3.461805e-04 3.461805e-04
+## Klebsiella                   2.045891e-04 2.045891e-04 4.091782e-04
+## Stenotrophomonas             1.612071e-04 2.998021e-05 2.998021e-05
+## Candidatus.Carsonella        5.349437e-04 5.349437e-04 5.349437e-04
+## X.uncultured.Paralagenidium. 2.865330e-03 2.865330e-03 2.865330e-03
+## Mesorhizobium                2.728885e-04 2.728885e-04 2.728885e-04
+## Bosea                        1.842584e-04 1.842584e-04 1.842584e-04
+## X.uncultured.Plasmopara.     6.448285e-04 1.830810e-04 1.830810e-04
+## Xanthobacter                 2.901728e-04 1.450864e-04 1.450864e-04
+## Rhizobium                    2.971910e-05 2.971910e-05 2.971910e-05
+## Vischeria                    2.288513e-05 2.288513e-05 2.288513e-05
+## Bremerella                   2.377528e-05 2.377528e-05 2.377528e-05
+## Leishmania                   4.663589e-05 4.663589e-05 4.663589e-05
+## X.uncultured.Phytophthora.   3.696042e-03 3.696042e-03 3.696042e-03
+## Rhodospirillum               1.961689e-03 1.961689e-03 1.961689e-03
+## Spizellomyces                1.408637e-03 1.408637e-03 1.408637e-03
+## Chaetomium                   8.688936e-04 8.688936e-04 8.688936e-04
+## Treponema                    8.185899e-04 8.185899e-04 8.185899e-04
+## Corynebacterium              6.275970e-04 6.275970e-04 6.275970e-04
+## Nitzschia                    5.798881e-04 5.798881e-04 5.798881e-04
+## Caulobacter                  4.494899e-04 4.494899e-04 4.494899e-04
+## Panacibacter                 4.295071e-04 4.295071e-04 4.295071e-04
+## Mycobacterium                3.861749e-04 3.861749e-04 3.861749e-04
+## Leptolegnia                  3.447415e-04 3.447415e-04 3.447415e-04
+## Atoxoplasma                  4.447206e-04 4.447206e-04 4.447206e-04
+## Syntrophus                   3.148719e-04 3.148719e-04 3.148719e-04
+## Saccharopolyspora            2.896312e-04 2.896312e-04 2.896312e-04
+## Niabella                     2.164127e-04 2.164127e-04 2.164127e-04
+## Saccharospirillum            1.930875e-04 1.930875e-04 1.930875e-04
+## Phragmosporangium            1.888515e-04 1.888515e-04 3.777029e-04
+## Nitrosomonas                 1.705553e-04 1.705553e-04 1.705553e-04
+## Archangium                   1.705553e-04 1.705553e-04 1.705553e-04
+## Cedecea                      1.545393e-04 1.545393e-04 1.545393e-04
+## Thauera                      1.495074e-04 1.495074e-04 2.990148e-04
+## Laetiporus                   1.401175e-04 1.401175e-04 1.401175e-04
+## Ancylobacter                 1.364443e-04 1.364443e-04 1.364443e-04
+## Rhizorhabdus                 2.579314e-04 1.289657e-04 1.289657e-04
+## Pelosinus                    9.654373e-05 9.654373e-05 9.654373e-05
+## Nibricoccus                  8.656510e-05 8.656510e-05 8.656510e-05
+## Eustigmatos                  8.528889e-05 8.528889e-05 8.528889e-05
+## Intestinimonas               7.317277e-05 7.317277e-05 7.317277e-05
+## Micromonospora               6.436249e-05 6.436249e-05 6.436249e-05
+## Variovorax                   5.662929e-05 5.662929e-05 5.662929e-05
+## Alternaria                   3.655238e-05 3.655238e-05 3.655238e-05
+## Faecalibacterium             2.436825e-05 2.436825e-05 2.436825e-05
+## Trichoderma                  2.436825e-05 2.436825e-05 2.436825e-05
+## Cucurbitaria                 2.090651e-05 2.090651e-05 2.090651e-05
+## Shewanella                   1.827619e-05 1.827619e-05 1.827619e-05
+## Gramella                     9.993404e-06 9.993404e-06 9.993404e-06
+## Aquiflexum                   9.993404e-06 9.993404e-06 9.993404e-06
+##                                        16           17           18
+## Globisporangium              8.285684e-02 7.770948e-02 1.521529e-01
+## Pythium                      4.939195e-01 5.728383e-01 4.092811e-01
+## X.uncultured.Apodachlya.     2.829258e-02 8.651112e-02 9.010749e-04
+## NA.                          1.426517e-02 1.427914e-02 4.692025e-02
+## X.uncultured.fungus.         2.792406e-02 3.575013e-02 7.124928e-02
+## Aphanomyces                  6.276673e-03 2.333166e-02 1.210015e-02
+## X.uncultured.Atkinsiella.    6.989931e-03 4.892122e-03 2.046727e-02
+## Cylindrotheca                3.090786e-04 2.299716e-04 1.570445e-02
+## X.uncultured.Haptoglossa.    2.531829e-01 8.406506e-02 4.441012e-02
+## X.uncultured.eukaryote.      1.652382e-03 4.599431e-04 1.081290e-02
+## X.uncultured.Pythium.        5.480201e-03 2.910186e-02 9.795971e-02
+## Haptoglossa                  4.214168e-02 2.627948e-02 7.420995e-02
+## X.uncultured.Aphanomyces.    3.055123e-03 2.331794e-04 1.486774e-02
+## Myzocytiopsis                3.815932e-03 5.853822e-03 1.830810e-04
+## Phytophthora                 4.493527e-03 4.432179e-03 1.029800e-03
+## X.uncultured.Lagenidium.     4.184449e-03 9.345208e-03 1.902078e-03
+## Hyaloperonospora             1.058000e-03 6.062887e-04 2.767587e-03
+## Plectospira                  4.577026e-05 8.885265e-03 4.577026e-05
+## Plasmodium                   1.056245e-04 1.056245e-04 4.698462e-03
+## Lagena                       1.321905e-02 3.324134e-03 4.577026e-04
+## Peronospora                  7.013706e-04 2.592407e-03 1.454592e-02
+## Pseudomonas                  4.755055e-05 2.508781e-04 1.158525e-03
+## Acytostelium                 1.693853e-05 1.693853e-05 1.693853e-05
+## Leptomonas                   2.965960e-04 2.965960e-04 9.654373e-04
+## Streptomyces                 6.662270e-06 6.662270e-06 6.662270e-06
+## Bordetella                   1.693853e-05 1.839773e-03 1.693853e-05
+## Pseudoxanthomonas            5.943819e-05 2.281508e-05 2.281508e-05
+## Lysobacter                   9.034605e-04 1.860679e-03 4.517302e-04
+## Luteimonas                   1.863238e-04 7.944472e-04 1.863238e-04
+## X.uncultured.Saprolegnia.    8.559099e-04 2.968724e-03 2.803428e-04
+## Saprolegnia                  4.176536e-04 4.176536e-04 4.176536e-04
+## Pythiopsis                   1.771258e-03 8.856290e-04 8.856290e-04
+## X.uncultured.Mortierella.    3.461805e-04 3.461805e-04 3.461805e-04
+## Klebsiella                   2.045891e-04 7.108212e-04 2.045891e-04
+## Stenotrophomonas             2.998021e-05 2.998021e-05 2.998021e-05
+## Candidatus.Carsonella        1.069887e-03 5.349437e-04 5.349437e-04
+## X.uncultured.Paralagenidium. 2.865330e-03 2.865330e-03 2.865330e-03
+## Mesorhizobium                2.728885e-04 2.728885e-04 2.728885e-04
+## Bosea                        3.685168e-04 1.842584e-04 1.842584e-04
+## X.uncultured.Plasmopara.     1.830810e-04 1.830810e-04 1.830810e-04
+## Xanthobacter                 1.450864e-04 1.450864e-04 1.450864e-04
+## Rhizobium                    5.943819e-05 2.971910e-05 2.971910e-05
+## Vischeria                    2.288513e-05 2.288513e-05 2.288513e-05
+## Bremerella                   4.755055e-05 2.377528e-05 2.377528e-05
+## Leishmania                   4.663589e-05 4.663589e-05 4.663589e-05
+## X.uncultured.Phytophthora.   3.696042e-03 3.696042e-03 3.696042e-03
+## Rhodospirillum               1.961689e-03 1.961689e-03 1.961689e-03
+## Spizellomyces                1.408637e-03 1.408637e-03 1.408637e-03
+## Chaetomium                   8.688936e-04 8.688936e-04 1.737787e-03
+## Treponema                    8.185899e-04 8.185899e-04 8.185899e-04
+## Corynebacterium              6.275970e-04 6.275970e-04 6.275970e-04
+## Nitzschia                    5.798881e-04 5.798881e-04 5.798881e-04
+## Caulobacter                  4.494899e-04 8.989798e-04 4.494899e-04
+## Panacibacter                 4.295071e-04 4.295071e-04 4.295071e-04
+## Mycobacterium                3.861749e-04 3.861749e-04 7.723499e-04
+## Leptolegnia                  6.894830e-04 3.447415e-04 3.447415e-04
+## Atoxoplasma                  4.447206e-04 4.447206e-04 4.447206e-04
+## Syntrophus                   3.148719e-04 3.148719e-04 3.148719e-04
+## Saccharopolyspora            2.896312e-04 2.896312e-04 5.792624e-04
+## Niabella                     2.164127e-04 2.164127e-04 2.164127e-04
+## Saccharospirillum            1.930875e-04 1.930875e-04 3.861749e-04
+## Phragmosporangium            1.888515e-04 1.888515e-04 1.888515e-04
+## Nitrosomonas                 1.705553e-04 1.705553e-04 1.705553e-04
+## Archangium                   1.705553e-04 1.705553e-04 1.705553e-04
+## Cedecea                      3.090786e-04 1.545393e-04 1.545393e-04
+## Thauera                      1.495074e-04 1.495074e-04 1.495074e-04
+## Laetiporus                   1.401175e-04 1.401175e-04 1.401175e-04
+## Ancylobacter                 1.364443e-04 1.364443e-04 1.364443e-04
+## Rhizorhabdus                 1.289657e-04 1.289657e-04 1.289657e-04
+## Pelosinus                    9.654373e-05 9.654373e-05 1.930875e-04
+## Nibricoccus                  8.656510e-05 8.656510e-05 8.656510e-05
+## Eustigmatos                  8.528889e-05 8.528889e-05 8.528889e-05
+## Intestinimonas               7.317277e-05 1.463455e-04 7.317277e-05
+## Micromonospora               6.436249e-05 6.436249e-05 1.287250e-04
+## Variovorax                   5.662929e-05 5.662929e-05 5.662929e-05
+## Alternaria                   3.655238e-05 3.655238e-05 3.655238e-05
+## Faecalibacterium             2.436825e-05 2.436825e-05 2.436825e-05
+## Trichoderma                  2.436825e-05 2.436825e-05 2.436825e-05
+## Cucurbitaria                 2.090651e-05 4.181301e-05 2.090651e-05
+## Shewanella                   1.827619e-05 1.827619e-05 1.827619e-05
+## Gramella                     9.993404e-06 9.993404e-06 9.993404e-06
+## Aquiflexum                   9.993404e-06 9.993404e-06 9.993404e-06
+## 
+## $meta.dat.use
+##       ndvi_01
+## 1  -1.9896865
+## 2  -1.1410253
+## 3   0.6121273
+## 4   0.5095020
+## 5   0.3130208
+## 6   0.5945529
+## 7  -0.8421145
+## 8   0.5747102
+## 9   0.6887884
+## 10  0.6228720
+## 11 -0.7571646
+## 12  0.8302155
+## 13  0.9980715
+## 14  0.3558477
+## 15  0.9838132
+## 16 -1.0529250
+## 17 -1.9625407
+## 18  0.6619353
+## 
+## $wald
+## $wald$beta
+##             Globisporangium    Pythium X.uncultured.Apodachlya.       NA.
+## (Intercept)       7.8749497 10.4136622               5.86313627 4.9811787
+## ndvi_01          -0.2731456  0.1602148              -0.09875372 0.5829063
+##             X.uncultured.fungus. Aphanomyces X.uncultured.Atkinsiella.
+## (Intercept)            6.5346924  4.84054355                 4.3211616
+## ndvi_01                0.6306978 -0.02896947                 0.5515244
+##             Cylindrotheca X.uncultured.Haptoglossa. X.uncultured.eukaryote.
+## (Intercept)      1.326471                  6.344384                2.370166
+## ndvi_01          1.110655                 -1.355486                1.422164
+##             X.uncultured.Pythium. Haptoglossa X.uncultured.Aphanomyces.
+## (Intercept)              5.025402   4.8935522                 2.1804321
+## ndvi_01                  0.162791  -0.6080541                 0.9700969
+##             Myzocytiopsis Phytophthora X.uncultured.Lagenidium.
+## (Intercept)      1.829415    1.2578758               4.00333143
+## ndvi_01         -0.830120   -0.9732174               0.03328775
+##             Hyaloperonospora Plectospira Plasmodium    Lagena Peronospora
+## (Intercept)     -1.155595062   0.6541741  0.4151875  1.968376   1.4924395
+## ndvi_01          0.007080699   0.1877406  0.5725773 -0.496139  -0.8253397
+##             Pseudomonas Acytostelium Leptomonas Streptomyces Bordetella
+## (Intercept)  -2.6746877   -3.5029773  0.1176988    -4.401897 -3.5086821
+## ndvi_01      -0.2619884    0.1410055  0.2464650     0.419373 -0.8298168
+##             Pseudoxanthomonas Lysobacter  Luteimonas X.uncultured.Saprolegnia.
+## (Intercept)        -3.5569989  0.5960348 -0.53520688               -0.03787507
+## ndvi_01            -0.2397889 -0.3516959 -0.06857091               -0.65030484
+##             Saprolegnia Pythiopsis X.uncultured.Mortierella.  Klebsiella
+## (Intercept)   0.7758318 1.52866031                0.08637549 -0.67731087
+## ndvi_01       0.3501513 0.07256429                0.20090499 -0.09612283
+##             Stenotrophomonas Candidatus.Carsonella X.uncultured.Paralagenidium.
+## (Intercept)      -3.30437908            0.79887234                   3.03862928
+## ndvi_01           0.08592844            0.08490121                   0.08944866
+##             Mesorhizobium       Bosea X.uncultured.Plasmopara. Xanthobacter
+## (Intercept)    -0.2728506 -0.81984023              -0.93350112  -1.25490658
+## ndvi_01         0.1961297  0.08015135               0.03186674  -0.04120497
+##               Rhizobium   Vischeria Bremerella  Leishmania
+## (Intercept) -3.48103713 -3.85495853 -3.8390575 -2.92364361
+## ndvi_01      0.04473526  0.04153516 -0.1946968 -0.07952187
+##             X.uncultured.Phytophthora. Rhodospirillum Spizellomyces Chaetomium
+## (Intercept)                 3.30101303     2.38712835    1.90933208 1.21228319
+## ndvi_01                    -0.05719323     0.04117912    0.02231363 0.03128029
+##              Treponema Corynebacterium  Nitzschia Caulobacter Panacibacter
+## (Intercept) 1.12624454       0.7429420 0.62887823   0.2613923   0.19578577
+## ndvi_01     0.02731662       0.0261494 0.03285988  -0.1231007   0.02835041
+##             Mycobacterium Leptolegnia Atoxoplasma  Syntrophus Saccharopolyspora
+## (Intercept)    0.04235819 -0.12138135   0.2460029 -0.25213135       -0.37267931
+## ndvi_01        0.03128029 -0.06959385   0.0107559  0.02231363        0.03128029
+##               Niabella Saccharospirillum Phragmosporangium Nitrosomonas
+## (Intercept) -0.7931109       -0.95764181       -0.98964435  -1.13665652
+## ndvi_01      0.0261494        0.03128029        0.05021428   0.03285988
+##              Archangium     Cedecea     Thauera Laetiporus Ancylobacter
+## (Intercept) -1.13665652 -1.27892263 -1.32667933 -1.4202597  -1.45858462
+## ndvi_01      0.03285988 -0.06959385  0.05021428  0.0107559   0.03285988
+##             Rhizorhabdus   Pelosinus Nibricoccus Eustigmatos Intestinimonas
+## (Intercept)  -1.53990899 -1.95764181  -2.1150390  -2.1364667     -2.3575175
+## ndvi_01       0.05105301  0.03128029   0.0261494   0.0107559     -0.1231007
+##             Micromonospora Variovorax Alternaria Faecalibacterium Trichoderma
+## (Intercept)    -2.54260431 -2.7272760 -3.3588591       -3.9438216  -3.9438216
+## ndvi_01         0.03128029 -0.1246975  0.0107559        0.0107559   0.0107559
+##             Cucurbitaria Shewanella   Gramella Aquiflexum
+## (Intercept)   -4.1648725 -4.3588591 -5.2297763 -5.2297763
+## ndvi_01       -0.1231007  0.0107559 -0.1246975 -0.1246975
+## 
+## $wald$sig
+##              Globisporangium                      Pythium 
+##                    1.1441792                    0.4800961 
+##     X.uncultured.Apodachlya.                          NA. 
+##                    1.8984613                    1.4232866 
+##         X.uncultured.fungus.                  Aphanomyces 
+##                    1.1208754                    1.3836327 
+##    X.uncultured.Atkinsiella.                Cylindrotheca 
+##                    1.7997405                    2.0506242 
+##    X.uncultured.Haptoglossa.      X.uncultured.eukaryote. 
+##                    1.8495582                    1.9560425 
+##        X.uncultured.Pythium.                  Haptoglossa 
+##                    2.0006954                    1.6177722 
+##    X.uncultured.Aphanomyces.                Myzocytiopsis 
+##                    2.4299023                    2.3794039 
+##                 Phytophthora     X.uncultured.Lagenidium. 
+##                    2.1316653                    1.8451476 
+##             Hyaloperonospora                  Plectospira 
+##                    2.8146560                    3.8278424 
+##                   Plasmodium                       Lagena 
+##                    2.6373845                    2.2168872 
+##                  Peronospora                  Pseudomonas 
+##                    2.0842101                    2.0844148 
+##                 Acytostelium                   Leptomonas 
+##                    1.7991154                    0.8609047 
+##                 Streptomyces                   Bordetella 
+##                    2.7494191                    1.8432912 
+##            Pseudoxanthomonas                   Lysobacter 
+##                    0.9486666                    0.7006766 
+##                   Luteimonas    X.uncultured.Saprolegnia. 
+##                    1.1735848                    0.6790680 
+##                  Saprolegnia                   Pythiopsis 
+##                    1.8193301                    0.8731051 
+##    X.uncultured.Mortierella.                   Klebsiella 
+##                    0.6634199                    0.5755836 
+##             Stenotrophomonas        Candidatus.Carsonella 
+##                    0.9965265                    1.2602275 
+## X.uncultured.Paralagenidium.                Mesorhizobium 
+##                    0.5109973                    0.8896994 
+##                        Bosea     X.uncultured.Plasmopara. 
+##                    0.9001911                    0.5628291 
+##                 Xanthobacter                    Rhizobium 
+##                    0.5457351                    0.8031607 
+##                    Vischeria                   Bremerella 
+##                    0.8476279                    0.6251962 
+##                   Leishmania   X.uncultured.Phytophthora. 
+##                    0.4789435                    0.2970447 
+##               Rhodospirillum                Spizellomyces 
+##                    0.3654313                    0.3079234 
+##                   Chaetomium                    Treponema 
+##                    0.2756255                    0.2875845 
+##              Corynebacterium                    Nitzschia 
+##                    0.3761548                    0.2902931 
+##                  Caulobacter                 Panacibacter 
+##                    0.2399116                    0.3060021 
+##                Mycobacterium                  Leptolegnia 
+##                    0.2756255                    0.3062465 
+##                  Atoxoplasma                   Syntrophus 
+##                    0.2474916                    0.3079234 
+##            Saccharopolyspora                     Niabella 
+##                    0.2756255                    0.3761548 
+##            Saccharospirillum            Phragmosporangium 
+##                    0.2756255                    0.3138179 
+##                 Nitrosomonas                   Archangium 
+##                    0.2902931                    0.2902931 
+##                      Cedecea                      Thauera 
+##                    0.3062465                    0.3138179 
+##                   Laetiporus                 Ancylobacter 
+##                    0.2474916                    0.2902931 
+##                 Rhizorhabdus                    Pelosinus 
+##                    0.3008924                    0.2756255 
+##                  Nibricoccus                  Eustigmatos 
+##                    0.3761548                    0.2474916 
+##               Intestinimonas               Micromonospora 
+##                    0.2399116                    0.2756255 
+##                   Variovorax                   Alternaria 
+##                    0.3262144                    0.2474916 
+##             Faecalibacterium                  Trichoderma 
+##                    0.2474916                    0.2474916 
+##                 Cucurbitaria                   Shewanella 
+##                    0.2399116                    0.2474916 
+##                     Gramella                   Aquiflexum 
+##                    0.3262144                    0.3262144 
+## 
+## $wald$X
+##    (Intercept)    ndvi_01
+## 1            1 -1.9896865
+## 2            1 -1.1410253
+## 3            1  0.6121273
+## 4            1  0.5095020
+## 5            1  0.3130208
+## 6            1  0.5945529
+## 7            1 -0.8421145
+## 8            1  0.5747102
+## 9            1  0.6887884
+## 10           1  0.6228720
+## 11           1 -0.7571646
+## 12           1  0.8302155
+## 13           1  0.9980715
+## 14           1  0.3558477
+## 15           1  0.9838132
+## 16           1 -1.0529250
+## 17           1 -1.9625407
+## 18           1  0.6619353
+## attr(,&quot;assign&quot;)
+## [1] 0 1
+## 
+## $wald$bias
+## [1] -0.29273099  0.02945993</code></pre>
+<pre class="r"><code># Show effect size and significance plots
+linda.plot(
+l_model,
+variables.plot = c(&#39;ndvi_01&#39;),
+alpha = 0.05,
+lfc.cut = 1,
+legend = TRUE
+)</code></pre>
+<pre><code>## $plot.lfc
+## NULL
+## 
+## $plot.volcano
+## $plot.volcano[[1]]</code></pre>
+<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
+<pre class="r"><code>l_model_df &lt;- as.data.frame(l_model$output)
+
+write.xlsx(l_model_df, file = &quot;supplementary_table_ndvi_regression_oits.xlsx&quot;, rowNames = TRUE, colnames = TRUE)</code></pre>
+<div id="credit" class="section level5">
+<h5>Credit</h5>
+<p>This script is based on ideas and code from the <a href="https://benjjneb.github.io/dada2/tutorial.html">dada2 Tutorial</a>
+by Benjamin Callahan, the publication “Bioconductor Workflow for
+Microbiome Data Analysis: from raw reads to community analyses” by <a href="https://f1000research.com/articles/5-1492">Callahan et
+al. (2016)</a> and various pages of the official <a href="https://joey711.github.io/phyloseq">phyloseq website</a> by Paul
+J. McMurdie.</p>
+</div>
+</div>
+</div>
+
+
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
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