{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Examine Assay Matrix Distributions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When employing TReNA, we are applying a solver to an assay matrix to infer relationships between transcription factors and target genes. Both the solver type and the assay matrix are mutable entities; there are currently 6 different solvers built into TReNA. The assay matrix can also be transformed in various ways. In this notebook, we will examine these two variables in an attempt to answer the following questions:\n",
"\n",
"1. How do the different solvers compare to one another on the same data?\n",
"2. What is the effect of transforming the assay matrix in different ways?\n",
"\n",
"To this end, we will be using the ampAD data of 154 transcription factors and 278 samples, with MEF2C as the target gene. Our three solvers will be LASSO, Random Forest, and Bayes Spike. The 4 different data transformations we will apply here are: as-is (unchanged), log2-transformed, asinh-transformed, and voom-transformed. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Details on the Data\n",
"\n",
"For this exercise, we will be using RNAseq data gathered for Alzheimer's disease (AD) and related diseases [Original Paper Here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5058336/). These expression data are from samples taken during brain autopsies from 4 groups of subjects:\n",
"1. Normal/control group, with no discernable cognitive impairment and no diagnosis of any neurodegnerative disease\n",
"2. AD group, with cognitive impairment and a diagnosis of AD\n",
"3. Progressive supranuclear palsy (PSP) group, with cognitive impairment and a diagnosis of PSP\n",
"4. Pathelogical aging, with the presence of amyloid plaques associated with a diseased state, but with no discernible cognitive impairment. \n",
"\n",
"MEF2C is a gene identified through genome-wide association studies to be associated with protection from AD. To better understand which transcription factors are associated with MEF2C, and thus may be associated with AD, we will use TReNA to identify meaningful relationships. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Transformation Characteristics\n",
"\n",
"Let us first get a handle on what our data look like under the 4 different transformations. \n",
"\n",
"1. The \"as-is\" data are fairly straightforward; we have RNAseq data in units of RPKM with expression levels concentrated near 0, but the range of the data is quite large. This skewed dataset may not be ideal (though we don't exactly have a standard to compare to), so one way to adjust that skewed nature is to do a log2 transformation.\n",
"\n",
"2. For each value in the matrix, the log2 transformation subtracts the minimum expression value in the matrix, adds a very small number (0.001) to prevent any attempt at taking log2(0), and then takes log2() of the value. As a result, the log2-transformed data have a much smaller range and appear much more Gaussian than the as-is data. They also cross over 0 into the negative range, a characteristic that differs from the original data. \n",
"\n",
"3. The third data transformation is using asinh, the hyperbolic arcsine function. The actual math here is to simply take asinh() of every value in the matrix; the resulting distribution is scaled down, much like the log2-transformed data, but all values are positive. \n",
"\n",
"4. The fourth and final transformation is VOOM transformation, a technique that estimates the mean-variance relationship in the data and uses it to weigh each observation. The transformation is performed using the *voom()* function in the *limma* package from Biocondutor, which is documented [here](http://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf). The manuscript for the VOOM transformation can be found [here](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). \n",
"\n",
"For a better understanding of what these distributions look like, we can look at histograms of all 3:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA\nOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9\noU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvu\nuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd\n/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs\n4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTv\nYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7n\nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8\neUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m\n6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiY\nMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpk\nhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thK\nbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpX\nzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJ\nmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477h\nLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549\nHQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQ\nUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgY\nhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjz\nhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VVBg\n/m8AAEAASURBVHgB7N0HmCR1ubZxYIElZ5QgsCxRMgIGFAFFkiIYCKIgCqggx3AUUDEHUPTg\n4dMDKioiQUGMBI8ogoIgUXLOack5x+9+dqs8RdPT3TPTPTXTfb/X9Wzl9OvemfpPVVfPNJOl\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAITWGDmCbzv7roCCiigQP0CG7ALryx24/d07+rSLm3BetYkl5KTRrHOd7Dsog3LP8zw\nteSChvEOKqCAAgoooIACCiiggAKjEriapV8ost+o1vTihQ8r1vmzF48e9tB5xXrKfax2f8e0\nBYe5xtcz/4nkh8NcztkVUEABBRRQQAEFFFCgzwXW4/iqDY7Lu3i827Ou/yY7jnKdZQPpfNbz\nfXIIOZk8QbLv15DZSKf1bmYsl+t0GedTQAEFFJhAArNOoH11VxVQQAEFxpfAe4vdya1q65BV\nyFrkIlKtqQzsQpYiuWXu7+RC8jwZqu5hwg0k3Wrl1rtNyVzkTPI3cgtpV//LDJ+vzLQ+/Vl+\nBbItOYak5iA5rhzLk+Rs8g/yAFmdvIGk5iVpvJ1C7iWtlmOypYACCiiggAIKKKCAAv0sMImD\nu5PkasrOpLxS8236q/VGBtLQqF5pSv/R1Zma9De7xe47TdbzHOPe2WT5clS5X18vR1S6Z9Gf\nfTm+GJdG12XFuOr+TmPcFPKVJtNey7h2yzGLpYACCiiggAIKKKCAAv0ssBkHl0bE02QB8rli\n+Fa6s5CycoUn86XhlIcl5OpM2fjIlaWhqrGB9HJmLJfbiP5cjfpJMS63zw1VrRpIR7BQ1nlu\nsXCuJGU4V67eStK4u51k3CfJkuSzxXCuWr2GzEPaLccslgIKKKDARBHwFruJ8kq5nwoooMD4\nEkhDJ/UX8iD5LfkGeQVJw+J0ksqVptTm5Ebyd7IsKa9ALUd/4xWgNI4aK7+vnidpfO1BfkW+\nSvYnGT+SSsMnlXWmLidbkZtIGnp5it7MJJXGURpL12aAylWxc6b3tV+umM2OAgoooIACCiig\ngAIK9KNAbil7hKSB8WPy9iL3FeOqDZztGJfb4DJvmVzV2Yak0iApx5fdqYzLOjL8M1LWsfSU\n86T7DDmSrESGqlZXkHLlKOv5XbFwGm1fIZeQcjvlvuf2vlSzhzR0styMpf1XAQUUUEABBRRQ\nQAEF+k7gPRxR2YBo1r2f6bNXjvqV9Odqzz/Js6RcJg2btcjPGpJb8Zo1kHI1Z8ti2g10y/Vc\nTP9QNVQDKVe6HidZR26bSx1AMnwX2YfkFrpfk4xr1UDqZDlWYSmggAIKKKCAAgoooEA/CpzI\nQaXRkEbKLyspGxOZlitE85PfF1mebiqNn+tJ5tmXDFWNDaQ0jLKuH1UW2Iz+rCfJVadm1dhA\nSiNrPVI+oCGfoVq8WPACullXGkepXCnL0+syrrGBdB3jyupkuXJeuwoooIAC41zAzyCN8xfI\n3VNAAQXGmcAi7E8aJqlcOUlDplrXMLAC2ZHk1rWViqxMN59TSuXqTepfMzod/TuNud5G8nmh\nKeR0si5J3UvymaFWlatEafhk+dwSl8rtc+8lWXcqjaZXke3JY2RrsgBJlcs8NGNwpmXoprH2\nDdLJcsVidhRQQAEFFFBAAQUUUKCfBD7KweSKyjMkjaXGOpARmf4EmY+sTtIQyrgyaXx8nrSq\nxitImXc3ktv3yvWkm6tRG5KhqryCVF3mTmY+nry1YaE04vI9TZk3D344iXyvGL6Ibq4+TSYX\nFuMyXx7z3clyzGYpoIACCkwEgfywtxRQQAEFFOilQH7X5Ba4XDnKwx1ye9rDZCQ1Jwvldr00\nznLlJ+t6lnSzVmJl95FcmRqq5mfC3ORuUm6/k+WGWp/jFVBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECBUQjM3GbZjZl+Bnm2zXxO7kwg3m8hs3c2\ne8/mupo1X9uztbtiBRRQQAEFFFBAAQUmqEC7BtJ1HNf85FhyFPknsUYu8CoWvYA8OvJVjHrJ\nNM7S6N1k1GtyBQoooIACCiiggAIKDJjAZI53K/Jz8iC5hnyBLE2s4QusxyIvkLmGv2jXlvgy\nazqta2tzRQoooIACCiiggAIKDKhArjyksXQZeY6cSN5MrM4FbCB1buWcCiiggAIKKKCAAgqM\nucAsHWwx82xAvk0OJYuQ75Fchfgx2Y1YCiiggAIKKKCAAgoooEDfCxzIEU4j+cxMPoO0OZlE\nynojPfk8i9WZgFeQOnNyLgUUUEABBRRQQAEFahGYtc1WF2P63uS35LEm817KuI82GT+eRq3E\nzqQhN1S9ggmnEz+XM5SQ4xVQQAEFFFBAAQUUGBCBdg2knXFYkMxH0kDK7XX3kTxoIPVAkekD\n4/SffE7qEy32LY3A9YkNpBZITlJAAQUUUEABBRRQQIGZZtoRhKfJrgXGnnQvI5sWw73q5Ol5\n7yNHkj+TP5Cvk+3IAqSb9RtWdnA3V9hiXd5i1wLHSQoooIACCiiggAIK1C3Q7iENn2YHP0R+\nUuzoIXS/QQ4qhnvRySOwTybfIrl6ldv4LiS5ivUZku8RWoVYCiiggAIKKKCAAgoooEBXBVrd\nYjc3W1qd/KJhixlOAynfhXRLw7RuDOZR4nOS5ciTTVaYL639ItmhyTRHKaCAAgoooIACCiig\ngAIjFmh1BSmfObqd5Da7am3GQD6LdG91ZBf7V2RdZ5NmjaNsJlexXpUeSwEFFFBAAQUUUEAB\nBRTopkCrBlK2k9vr8p1HV5Jfk9zqdhLZhzxOelGnstLtyRpNVj4v4/Yi5zWZ5igFFFBAAQUU\nUEABBRRQYFQCrW6xy4pPISuQ95BlSR7QsDu5gPSqcvUoV4nSCLqB5Da+Z8gUMpVcTfJ9TJYC\nCiiggAIKKKCAAgoo0FWBdg2k2dnaWsUWpxXd3GKX7F8Md7uTR4hn3UeTdcmKZFFyJjmXnE6e\nJ5YCCiiggAIKKKCAAgoo0FWBdg2kPGY7V2vOJ726pa7ZAU1m5AYkDbHFyBMk289nn/I0uwdJ\np5UG3qYtZs4VsnzeylJAAQUUUEABBRRQQIEBF2jVQJqEzdZF/jSGTnnM9wlkZfIvksd8P0wW\nIp8hB5A86e4K0kmtzUzvajHjEky7rcV0JymggAIKKKCAAgoooMCACLRqID2HwQMkV4/GstL4\n6eZjvg9nfclQ9Rsm3DrURMcroIACCiiggAIKKKDA4Ai0e4rd56A4jLyCpDGV+cvQ25PKZ47O\nJj7muye8rlQBBRRQQAEFFFBAAQWGEmjXQNqPBd9BcoUlT5LLVaUy9PakTmWtPua7J7SuVAEF\nFFBAAQUUUEABBVoJtLrFLsvl4Qbt5mm1/pFMy9UjH/M9EjmXUUABBRRQQAEFFFBAgVEJtGv8\n5HuI8qCG7cgc5GPF8A/p9qp8zHevZF2vAgoooIACCiiggAIKtBRod4vdR1j6R+Ru8kbyNNmT\nfJ/0siaz8g3INuRNZHkyDykf802vpYACCiiggAIKKKCAAgp0V6BdA2kfNrcF+WSx2Xvovo18\nkOTx2L2oPOb7ZPItsiDJY74vJPn+ozzm+wKyCrEUUEABBRRQQAEFFFBAga4KtLrFLt+DtCS5\nsWGLNzE8jaSBdAfpdnX7Md/rs4NZ51CVxtZTQ010vAIKKKCAAgoooIACCgyOQKsGUp5Wl+9A\n2pd8sUKSL11dlFxVGdfN3k4e851Hj3daSzFjvix2qFqACbl9z1JAAQUUUEABBRRQQAEFWgq8\nkql5xPddJJ8/uonkasvOpFeVKz63kTWabGBexv2KHN1k2khH/YYFDx7pwsNcbj3mz0Mochth\nXfVlNnxaXRt3uwoooIACCiiggAIKjGeBVleQst9XkpVIHpawPMlnkP5MriO9qrNZsY/57pWu\n61VAAQUUUEABBRRQQIEhBdo1kLLg4+SYIdfQ/Qk+5rv7pq5RAQUUUEABBRRQQAEFOhBo10Da\ngXXMOcR6Dh9ifLdG38yKEksBBRRQQAEFFFBAAQUUGBOBdg2k97EXCxd7MhvdqSSP3v476WUD\naTLr35ZsRhYjT5BLipxC90FiKaCAAgoooIACCiiggAJdFWjXQMp3HjXWToxI46VXlQcYnEBW\nJv8i+R6kh8lCJN+DdADJY7uvIJYCCiiggAIKKKCAAgoo0DWBdg2kZhs6kpHfJmmw3N9shlGO\n6/b3II1yd1xcAQUUUEABBRRQQAEFBkWgXQNplgaIfHlsvgfp5eT5hmndGuz29yBtwo69u8XO\nrcm0Xh1Li806SQEFFFBAAQUUUEABBcabQLsG0u3scD4DVK18gexBpFefAzqVdR9HjiD53FG1\n8j1Ie5HzqiPb9OchE/OTmYeYb3bGt3MYYlFHK6CAAgoooIACCiigQD8JtGsYbMzBVufJI7hv\nJHn0d6+q29+DlM8zJUNVvig2X4ZrKaCAAgoooIACCiigwIALVBs/zSg2ZeQ8zSZUxuVR3EdX\nhkfbm0bY/sU616WbW+4WJWeSc8npxFviQLAUUEABBRRQQAEFFFCguwLtGkhrsbldSJ4kdz1Z\nimTcxeQ2kspT57pdaRTtSl5BfkT+Rsp6Jz2rk6+UI+wqoIACCiiggAIKKKCAAt0QaNdAymd3\ndic/qWxsffp/St5BcrWn25XG0UXkAnID+SP5NDmEpJYkq07v8x8FFFBAAQUUUEABBRRQoIsC\njU+pq646T6zbnBxfHUn/WWRmskLD+G4N5olz55ANyPvJJiTffZQvqbUUUEABBRRQQAEFFFBA\ngZ4JtGogPcdW7yIfadh6rt4sQ+5tGN+twflYUfUpdWmQHUb+q1sbcD0KKKCAAgoooIACCiig\nQDOBVg2kzP8h8kVyB8mjt08leVDC/qQXXxLLamc6n+S7lpbOQFFfpbsO+RRpt8/FInYUUEAB\nBRRQQAEFFFBAgeEJtPsM0l9Y3bJkRzKVXEm+TM4gvao8dvvt5CZyFNmZPEy2JSeS7POfSaeV\ndWX/h6r1mGCjaygdxyuggAIKKKCAAgooMEAC7RpIoXgdSSNiDvItsjXJbW+5Ba8X9TwrTaMo\nD2ZYqrKBfC4pn3vakEyujG/X+xgzPNBipqeZ9kyL6U5SQAEFFFBAAQUUUEABBaYL5PNH+RzS\nd8k9ZFFyGTmU9EvlitXBY3QwaWjmyX+9eDR6p4fwZWY8rdOZnU8BBRRQQAEFFFBAgUESaHdr\n2T5gbEE+WaCkkfQ28kGyRDHOjgIKKKCAAgoooIACCijQFwKtGkh5zHe+c+jGhiO9ieFpxAZS\nA4yDCiiggAIKKKCAAgooMLEFWjWQ8hmjPFFuXzJ75TDzhLncandVZZy9CiiggAIKKKCAAgoo\noMCEF2j3kIbdOMJTyAfI/OQmsjjZnTxKrIkpkNd9wRp3PZ/DerDG7btpBRRQQAEFFFBAAQWa\nCrRrIOWx3iuRbcjyJJ9ByiO2ryPWxBR4Dbv9BnJ/zbv/IbZ/WM374OYVUEABBRRQQAEFFHiR\nQKsGUm6/ez25kBzzoqUcmMgCeVx7vldqgxoPIu+nXJG0FFBAAQUUUEABBRQYVwKtGkjZ0V+Q\nj5E8Cnui1vbseJ66N1StyYTzhprYp+Pz+bJLajy2J2rctptWQAEFFFBAAQUUUGBIgVYNpHxh\n66fI3uRu8i9SrceqA+O4/xb27YIW+7cM0/w8VQsgJymggAIKKKCAAgooMCgCrRpIMfgOyaO+\nz8hAQ83cMDxeB89mx5KhamUmpAFoKaCAAgoooIACCiigwIALtGsg5QP9+T4kSwEFFFBAAQUU\nUEABBRToe4FmDaR8z1GuqnyD3EFmJ5nvcWIpoIACCiiggAIKKKCAAn0r0KyBtDxH++rKEX+E\n/jeTrSvj7FVgtAK5Mjl5tCsZxfJ5UMWzo1jeRRVQQAEFFFBAAQX6UKBZA6kPD9NDGmcCK7A/\n3yxS1649w4aXJbfXtQNuVwEFFFBAAQUUUGD8CYzXBlKuLGxLNiOLkTwWOo+lTk4hDxJr4grk\nffcPkick1lELstGTyHzEBlIdr4DbVEABBRRQQAEFxqnAeGwgzYXVCSSfg8qjxS8l+WLThchn\nyAFkK3IFsSauwAPs+tk17f6UYrv5/qsXatqHbPYakvd0XZX/a/eRuv/gcH1dAMV2czXR2y1r\nfhHcvAIKKKCAAuNFYKgG0hR2cI9iJ99AtzpcjJ7p0LKny900fuYky5Enm6z7WMZ9kezQZFqz\nUR9gZHkszaZnO+c2m9CDceXJ+N9Zd75nqo5aiY3mxHisjrnZMc7ByLyv6tqHbD8194xObf++\nii3niqhVr0AaR7fWuAvzFNu+u8Z9WJxt548WdX2/3SxsO99JdxOp62djfh7kqvJtpK56ORvO\nHRN5Leqq/E7M9wfmDwd1VH4+5/14Ux0bL7a5MN28J+v8P5n/D/eQuh6QNYlt5zb060hdlf+P\n+fmYB4bVVUuw4XxXZv5QX0fNzEaXJzeSfHa6rrqIDe9e18br2G6zBtJD7EjelPs07FDjcK8a\nSCuy3VxZaNY4yi4dQg5LT4eVq1C/ajHv0kw7tcX0bk66jJV9guTJgHVVfvAvQOr8q33+o+UH\n/4OkrlqHDee9UdfJWH7o5713Bamrsv2nyZ117QDbXZNcRZ6qaR/yf3EVkvdkXfUyNpw/Ct1c\n1w6w3ZVJTkLqPAnI/8l8qXf5hyR6x7TmZ2tpoOTKbl2VE9KcjOXnY121FhvO76r84aCOSgNp\nBXJpHRsvtpkGWhoIdTaWV2X7OSl+nNRRaSCuTfJ/sq7KnUNJnY20NE5yp8UDpK6q+3wlx13n\na1CLe1qm463WZ4eOI1uSSxp2bl6Gf0pyUvfehmkTYTAnQQeSyyfCzrqPPRWYm7W/glzd0624\n8okgsAg7mZ8NdV7FmghOg7CPuWqQBlJOyKzBFsgfLfJHk1xRtAZbII3lT5O6/pA4kPrNriDV\nDZGrR7lKdB65gZSX+qfQP5XkhHJzMhHrjez0XiTHUNdfSSeiWz/uc/5avTDJe9wabIFFOfxc\nyfKBIYP9PsjR548muXvi3gxYAy2QWx3vJo8MtIIHnyt5K5JcODhDDgUikL+kvYt8lhxEPkPe\nRPJmmai1HjuehtFcE/UA3O+uCXyANdk46hrnhF7Rf7H3f5jQR+DOd0vgT6xo/26tzPVMaIH8\nwWTHCX0E7nw3BPLU3Zw3rtGNlbmOzgXG4xWkcu9zaTmxFFBAAQUUUEABBRRQQIExEZjIV2PG\nBMiNKKCAAgoooIACCiigwOAI2EAanNfaI1VAAQUUUEABBRRQQIE2AjaQ2gA5WQEFFFBAAQUU\nUEABBQZHwAbS4LzWHqkCCiiggAIKKKCAAgq0EbCB1AbIyQoooIACCiiggAIKKDA4AvmmaGvs\nBJ5jU3l8+S9JHttoDa5AniA5mfzv4BJ45IXAfHTzxaD/VGTgBRZHIF+Qnu/KswZbIN+DlN8P\ndw02w8Affc4bVyHHEL80eODfDgIooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKBAa4E5mbwimbn1bE7tA4HJHMM6ZHkyS5PjWYJxL2syPqN8\nnwwBM8FHT2X/l2tyDL4XmqD06ahZOa71yFJNji+/F1YmczSZllGt3idDLOLocSyQ13mlIfbP\n98IQMH00emmOZYEmxzOa195zhyagjhr/Al9jFx8it5FpZE1i9afAdhzWE+Qm8iw5i8xHUvOQ\nM8h95EHyJ5IfamX5Pikl+qu7CIdzB/l25bB8L1QwBqB3J44xP/svJy+Q/UlZG9KT3w23ksfI\np0hZ7d4n5Xx2J47AYexqfgfcRa4ibyVl+V4oJfq3m//Td5KcK1RrNK+95w5VSfsnjMCb2dN7\nSf6CnMovv1tIsysLmW5NXIH8VfApsk1xCIvRvZl8pBg+lO7JZBKZjZxB/pukfJ/McOjHf0/g\noHJCVG0g+V7ox1e6+TEtw+j7Sa4epV5HHicLkpws3UR2J6mVyaPk9RmgWr1PZszhvxNJYBN2\n9nayKMk5wGdJGkm5cuB7AYQ+rskc20HkUvICqTaQRvPae+4ApjUxBX7Mbv+gsusvpz//Od5Q\nGWdvfwhswGGkMVytnOD8vhiRq4hvqkzci/78VTnl+2SGQ7/9m9f4d+RIcmDl4HwvVDD6vPfz\nHN85Dce4CsM5KdqU3E2qfzD7G8P/j6RavU9mzOG/E0lgX3Y2dxWUld8HaRDn5Nn3QqnSn938\nAXWfIvl/XW0gjea199yhB++X6g/kHqzeVRYCK9K9pKKRy+r5zzG1Ms7e/hA4m8Oofr4gt89t\nSX5P0jDOrXb561FZ19CTq0yZz/dJqdI/3dU5lP8ku5H8UaQs3wulxGB0V+AwLyY/J1eS/yGz\nk5wYr0QuJ8+TsvJzIb8f2r1PyvntThyBY9jVZclPyWfID8khJHce+F4AoY/rSY4tfyRL8n+/\nWqN57T13qEp2qd8GUpcg26wmPwwfbpgn95vnpNjqL4FnOZwnikN6Ld0LyfXkFyTvg1Qax2Xd\nWvTkveD7pFTpj25e05wM7UEaryr6XuiP17jTo1ieGXcmafjsSRYkZ5IpRRp/P+TnQvkzgd4h\nf2ZkmjWxBJ5jd28gm5MtSG6/TOMoNYX4XojE4NUUDnmkr73nDj14v9hA6gFqk1XmTZ+rBNWa\nl4H8JdHqP4G5OaR8CPckkr8U5/7gNJoeIanqeyFXlO4i9xPfJyD0UX2RY8nrmhOiTcjiZArZ\ngPheAGHA6p8c79fJaeR95HGyKcl7ofozgcHpV5qvKKZluDq9+jMj06yJJfAddjcNoleQDckb\nyH5kCvG9AMKA1mhee88devCmmbUH63SVLxW4jlHLVUbnF1x+4V1SGWdvfwhM4jD+SHLy80py\nNynrFnqeJnkvpD+VS+OXTu+baSbfJwVEn3TSKE7j6HPF8eTD98+SmckHie8FEAak8v8974Wy\nnqcnT7HMz4vG//eZJ7fb/IG0+5mRea2JJbAOu/sTkvdA6lySP5KlseR7AYQBrdG89o3Leo45\noG+iiXjYO7DTD5ClST6Um/uNzydW/wnkyVN5rVchaQSXWYj+1K/JCSR/nEgD6iKyL0n5Ppnh\n0K//5qTowMrB+V6oYPR579s4vvyVd63iOLehm6sI+WNJTmbyeYQ0mlPvJvlrchrUqVbvkxlz\n+O9EEjiWnc3v//xuSG1J0nhelfheAGFAKk8y3K5yrKN57T13qEDaO/EEvscu5xfi/eRmkl+M\nVv8JpLHzQpPktprUoiRXDu8juYKQk5/8Fbks3yelRP91GxtIvhf67zVudUTfZGJOhO8gucK8\nMylrM3rSSMqVhMfIrqSsdu+Tcj67E0Mgt9r+lqQRnCuEOS/4OCnL90Ip0d/dxgZSjnY0r73n\nDv39fun7o1uYI8xVA0uB3Fr3siEYfJ8MAdOno30v9OkL2+Sw8nCGNclcTabNXkyb3GRaRrV6\nnwyxiKPHsUA+q7oeyXuisXwvNIoMzvBoXnvPHQbnfeKRKqCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKDAIAt8moN/xSADeOwKKKCAAgoooIACCigwfgXWY9fOHMPdm8a2XjuG23NTCiiggALjUGCW\ncbhP7pICCiigwGAINP4OmtRw2HMzvHLDuOEMTh7OzA3zztEw7KACCiiggAIKKKCAAgoo0HWB\nj7PGn5MjyKPkYvJm8ifyDLmWrE5WIfeR50mu7GTcrWRLklqSXEe2yEBDvZ7h00jWfw/5IEml\nsZV1VRtmJzK8A0ll2v7kDvIs+QdZllgKKKCAAgoooIACCiigQE8E9mOtafR8mKSRcwp5gexF\nFiUnkCNJGjGbkzSSciUp9VWSRtJ8JA2b40mzOpuR7yWTyKbkMZJlViPZVrWBlHk/QFJpIF1J\nXkdWIn8laWhZCiiggAIKKKCAAgoooEBPBNJAOqey5k/TfzuZuRj3EbrnFv0b0b236E9nNnIh\nyVWnXOVZmDRW1vMvchRZrpi4EN1ZSScNpL2LZdLZhKRBNW8GLAUUUECBwRCo/hVtMI7Yo1RA\nAQUUqFsgjZuynqHnapKGSCpXl9KYaVaZ95tkDfJ9kqtLjZX15IrQgiRXg24ge5DcMtes0uiq\n1t8qA2lopeaf0fFfBRRQQIFBELCBNAivsseogAIKjC+BNIJGUnOy0FfIWeQ/yWKkseZgxOPk\nrSSNpFwR+ix5NSkbYbn1rqypZU/RXaIynIbYteS2yjh7FVBAAQX6XMAGUp+/wB6eAgooMIEF\nnmbfJ5PyKs8B9GfcRuQichhJ5bNLHyb5nFFusfsnWYvks0f5rFJu05ud3EieI+8kqZ1IGlHV\n2ouBbC+35X2UnEwsBRRQQIEBErCBNEAvtoeqgAIKTDCBNILuKvIWummw7EZyq126G5FdyTLk\nB+Rl5AlyILmAZPncZncqOZPkytLB5JfkTrIt+Tup1i0MTCNpTK1E/otYCiiggAIKKKCAAgoo\noMC4EMgVoVzNGW4tzgKvIbnlrrEWYESrdc7D9OVJtm0poIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCgxLYOZhze3MCiiggAIjFViTBV9DniQ/\nH+lKRrjcXCy3Ormb3EyeJ0PVKkx4w1ATi/H30z2+zTxjOXkWNrYNWYlcRX5LJlptyw7PS/5O\nrmuy8/MwbseG8S8wPI1cQm5pmOagAgoooIACCiiggALjWmBv9i4ntPeO4V6m4fBj8izJtpNL\nybpkqNqDCeW8Q3VzQj6e6kfsTLmvR4+nHRvGvtxQHMPOQywzpZheHme1+zTTPjnEcq1G78vE\nE8lOrWZymgIKKDBoArMO2gF7vAoooMAACXyOY921ON6/0c0VltXIH8iq5AHSWLkCc3hl5C70\n526Ds0mmpW6b0Rk3/25V7EkaSj8cN3vVux05llWnoT0HyZXJNHgPIouTfUin9SpmfCu5qNMF\nnE8BBRQYBAEbSIPwKnuMCigw0QSWZoffQ9KYeYhcQI4kuRJUVhot25PXkjR0coL8drIwyVWB\nXJHYkqT2JIeS3MKV2+xyIv0WchxprNMYkZSVKxqTSLafdVTrPxjIfqRB9TryevIlklqO5La3\nqeR6kgbWeaQ8ht3pn41kH3Kinv25nKTxllv4ypqTnl1JGgJpFOQWtDPJI2R+khP8hUjqHpJj\nLKud4/LMuCXJbWqnkHeSJ8j5ZFMSqzQsy+P4E/2nk1cW4xag+0vyL1JWPN5N1icvkHPJ8aQ8\nbnqnV26pyzx3kKOmj+n8nwOY9eLK7HntcwVpT5JpeT+kYprXJO+JHNM/yHUkFbf4pFYjOcbf\nZYBq99rNmMt/FVBAAQUUUEABBRQYhUCnt9htzDbSQMjJdTVnMJwGQVnH0VOdnsbHzcW4nOzm\nRP335H/JUiSVW+5uJVnuQ6STyol95s+td431PCMy7X+Kbk72UxuSR0mmVZPGT1nl9JMZUZ0n\nDZScsKfmIWk0VaenP8eZRt6qTaadwLhUJ47vYr6sL42Hfxb9B9PdvuhPYyKNu+r292P4ocq4\nJ+lPIySVPzrGuzp/+v9IcixlpVFVnSfbuLcYlwZps5rCyHKZNRtmWLIyLfue+iYp5y+7eb3K\n9V/RMD3vi9SGpHxtyuXSrb52mc9SQAEFFFBAAQUUUGBUAp00kGZnCzeQnJCeQ3JFIyfkZYMp\nJ72pt5Py5PW/6d+LXF0ZlwZSsyqXe46JaWB0Up00kLIvacB9t1hh2Xg7nuHXkDSuyv19RTFP\neRJ+O8NpqOxLHiaZ79sklePK8EUkjbxXk/LEPvPPQdYhj5DM92GSq0KdOpYNpCybbf+O7EjS\nyMi4JONypSyNv3LcCcW4e4px5RWgjxfDd9J9P9mB3FKM+xLdVPkaZF0nkuxzjq9cd9mAYdSL\nagpD5TyNDaTM+HgxPe+zucljxXD2KUbla3I2/ak1yF9I1nk4WZukyvlavXYz5vRfBRRQQAEF\nFFBAAQVGIdBJA2lz1l+eBK9Q2VZum8r4nJCnjiYZ/mMGilqLbrnsNuXISncz+h8o5snJb2o5\nkv2qZoFMqFQnDaT/V5k/vZuQt5FFSBpEZUMn+5dGTqpsIKWBUNYv6Mk8xxYjPlYMZ96vko1I\n1pf9XpSU9SA9WS5XjVKdOlYbSG+Ysej0f8sG0mMM5Upc6vMk27gtA0V9l27G/aUYPqsY3p/u\nfEW+UIy7hm7qGJJl0qAsK8f0LMn4kTSQso+lZxqOC5L4b0pmJ6uSX5Os/2ZSVpwz7uvlCLqd\nvHaV2e1VQAEF+k8gt1tYCiiggALjQ2ClYjdyEn5tZZdOK/rT4FiILFsMX1B007mY3F8ZLntz\n8vwVksZUGj85QX8PSa1CDmxI1j/cKhsI5XLX0ZNGxuXkVnIwGarKhkOmpwGXKn83ZV/TKMwV\nkTQ04nAhyZWZXDEZqjp1LJd/gp6zyoFKt2xQZlRpe1dlerkPsxbj0nBLfZY8VCQNu1T5mk2d\nMTj9s01F7/RG183lwAi6S7NMjFJ572S/Y/hpkitjl5FmjWZGv6SG89q9ZGFHKKCAAv0gUP5Q\n74dj8RgUUECBiS6Qk9lUGin5y//TGaDK2+Gepz9XNXK1IFVtzOSqQePVn5wk52pTGhRPkb3J\n90hZabwcUQ4U3UcahjsZTCOmWn9mYHlyJtmn6KbBNzNprFw5GaruZcKKJI2tXBF5I1mUfJ1k\nXek2q04dy2XT+IntSCtXYVJlgymm/5g+5sX/ZJ9ze2Nq4Rmd6f/Oyr85rpHWFpUFz6F/PfIb\nMokcSk4ii5Efk3Y1nNeu3bqcroACCkxIgfzytBRQQAEFxlZgHjbXmMmMK68IzUV/edKbk9yt\nSSpXAtLQuSQD1DvJkiTz7EEaf6Z/jHFpHOWKwutJtXHE4PTPvuxCt5rGxg6Th1VTmXv5Yomd\n6aaxsCpJ42C49Q0WyPJPkq3IIuQHJNXqikinjjPWNPp/ywbSVcWqlqZ7WJGMy34/QzLfFSS1\nCZlvet+MJ8rNW/QPp5OrRnkPfKdY6HS6uQL0JpL3xPlkT5IG0sZkqMq8qW6+djPW6L8KKKCA\nAgoooIACCgwhsDfjc4I8VMrb0H5bzJMrGn8kOeEtl8nJcCqNosdIxucq010k86fxlHFl4+Hi\nYjjzlLd8ld3PMa6TyhWerDMNsMbKNjPtdZUJuRpyezE+jYTPkhuK4cy7PknlKliGN8hAUYfQ\nzbhfFcNfK4azzz8j2ecrSebJust6kJ6MqzYCOnF8V7HcbeWKim6uWGV91fFpaGRc2fiid6Y0\n4DLu1AxQG5LS5A/0H0nK16n0W4VxpWmuXGXZrKMctzP9zWoKIzNfksZWXtNcjSrHxXgpknoj\nyfhs+xPk+6R8b1SPKYaZL8t+lXT62jGrpYACCiiggAIKKKDA6AQ6bSDlqkI+e1Oe+KabxsSu\npFprM5DPzTxBLiK7kTw9LfPnassCpHoCXV1f+nNC3EmVJ+7lCX51mecZyLqqDaRM353cSzIt\nt53tR3LlJMNfIKlOGki5qvZz0ngcJzNuQVJWswZSJ47dbiBlf95L0mDNsSZpzHyRlFdp6J1p\nO1Iefwy/S/JaZv6dSbOawshynWU3y15GclVtGVKtNDKz7cx7E/lQ0R/LNNJSG5A00jLPrSTV\nyWs3Y07/VUABBRRQQAEFFFBgDAXSwHk9WY3M1rDdeRhehGSe8ta1NAjKE+fGBguTxrxyNWIt\nMnsXtrwo63gNiceSw1xfK8dhrqrj2fN65XVbl8w1xFJzMH4dsvAQ07sxOuteuc2K0nB7GYlx\nWd187cp12lVAAQUUUEABBRRQoGcC/8ma0xh6knyUvI9cSjIunzeak1gKKKCAAgoooIACCiig\nwEAI5ArSH0njrWfTGLfhQAh4kAoooIACCvRIoLw1o0erd7UKKKCAAj0UKG8fW4xt5Glpl5BH\nerg9V62AAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooEDdAjO32YGNmX4GebbNfE5WQAEFFFBAAQVGKzA7K9iEzDLaFY1y+YtZ/tZRrsPF\nFVBgggq0ayBdx3HNT44lR5F/EksBBRRQQAEFFOiFwNtY6Qnk0V6svMN1Tma+X5KdO5zf2RRQ\noM8EZm1zPKsyfVOyLflfcjc5khxBbiGWAgoooIACCijQLYFJrOghskC3VjiC9fyIZeYewXIu\nooACfSLQroH0FMeZv+Qkuey9GTmAfJn8kXyXnEosBRRQQAEFFFCgHwRyd82cZIkaD+Z5tn1n\njdt30woo0EYg9wFvQA4mt5H8h/1v8ilyI9mNWAoooIACCiigwGgFtmYFD452JaNc/hKWf2Ec\nZKtRHoeLK6DACAXaXUE6kPXuROYlvyNpDP2ZPEdS55FvkB9nYJzW+9mvD7fYt8WYdiL5WIt5\nnKSAAgoooIACgyGQc6PbyJtqPNwz2XbOvSwFFKhBID8EWlUaD3uT35LHmsx4KeM+2mT8eBqV\nfcwtgkPVe5mw0FATHa+AAgoooIACAyeQp/deW+NR+/TgGvHdtALtGkh5gsuCZD6SBtIi5D6S\nS8+pB4pMHxin/1zIfiVD1XpMyDFZCiiggAIKKKCAAgooMOAC7b5nYEd87iJbFk7b0c0VmTzZ\nzlJAAQUUUEABBRRQQAEF+kqg3RWkT3O0HyI/K476ELq5anQQWa0YZ0cBBRRQQAEFFFBAAQUU\n6AuBVg2kfAfA6uQXDUea4TSQlia9+i6kyax7W7IZyeegniB5qkxyCqn7CTfsgqWAAgoooIAC\nCiiggAL9JtDqFrt85uh2ktvsqpVGSz6LdG91ZBf752JdJ5NvkXz+Kbf05TNE85HPkAvIKsRS\nQAEFFFBAAQUUUEABBboq0OoKUjaU2+t+Q/YhV5BlyRpkb/I46UVtxUrzBW3LkSebbOBYxn2R\n7NBkmqMUUEABBRRQQIF+EJidg8gfjeuqZ9hwYikwcALtGki5nW0F8h6SxtFlZHeSqzi9qhVZ\n8dmkWeMo2zyEHJYeSwEFFFBAAQUU6EOBRTmmw4vUdXi5kyh3DA11PlbXfrldBXou0K6BlL9e\nrFXsxbSim1vskv2L4W53TmWFx5EjSD5zVK15GdiL5AtqO61NmXG7FjOvzbTyseUtZnOSAgoo\noIACCigwJgL5CMSvyHfGZGsv3Uj+WH0kmUxsIL3UxzF9LtCugZT/HJuT80mvbqlrJD6bEblK\nlEbQDSQPgsgl3ilkKrmaZJ86rdmYMf/Bh6r8EJp5qImOV0ABBRRQQAEFahC4m22eW8N2s8mn\na9qum1VgXAi0aiBNYg+3LvKnMdzbXM3J1amjybokf8XIpeYzSX5QnE6eJ53WScyYDFX5jNWt\nQ010vAIKKKCAAgoooIACCgyOQKsG0nMw5DuPcvWojrqZjSaWAgoooIACCiiggAIKKDAmAq0a\nSNmBz5E8EOFj5E5SvXJT7WdSVyu3xG1LNiOLEb8HCQRLAQUUUEABBRRQQAEFeiuQz9+0qv2Y\n+A6SW9DyOaBcVSpDb09qLtZ6MvF7kHrC60oVUEABBRRQQAEFFFBgKIF2V5A2ZcF28wy17pGO\n93uQRirncgoooIACCiiggAIKKDAqgXaNnzxFLg9qyGOy5yC51S7DPyS9qjyUIU+yG+qxknnC\nXW77sxRQQAEFFFCguwJ5WuwC3V3lsNaW7ftk2WGRObMCCnRboF0D6SNs8CvkGPI+ksc+7klW\nJ3uQXlS3vwepF/voOhVQQAEFFOi2wMGscIdur3SY68tTY+tuoPTyM87D5HB2BRQYRIF2DaR9\nQNmCXEjSQLqHvI3ku4i+Ru4g3a5cPcpVom59D9IarGuTFju5HNMeazHdSQoooIACCoyFwMps\n5Czyi7HYWJNt5HsDjyKfJD9pMn0sRn2JjfznWGzIbSiggAJDCbRqIE1ioSXJjQ0L38TwNLIE\n6UUDqdvfg7Qe+/leMlQtzYQcj6WAAgoooEDdAleyA8fVtBOT2W4aSLnF/ZGa9uGpmrbrZhVQ\nQIF/C7RqIOVpdfkOpH3JF/+9xEwzvYv+XIK/qjKu2735Ib0B2YwsRvKY78fJImQ+8iDptPJX\nsFZ/CfsN0/OUPksBBRRQQAEFFFBAAQUGXKDdY753wydXX9KAmJ/cRPJ5pHz+6FHSi/Ix371Q\ndZ0KKKCAAgoooIACCijQVqDVFaQsnEv9K5FtyPIkn0H6M7mO9Kp8zHevZF2vAgoooIACCiig\ngAIKtBRo10DKwrm1LVeNxqpWZENnk9wD3azyAIfDmk1wnAIKKKCAAiMUyOdR30PqfILbFLbf\n+LlfRlkKKKCAAmMp0K6BtAM7M+cQO3T4EONHO/pUVpAPqB5BLmlY2bwM70XyhDtLAQUUUECB\nbgnkO/7yedvG3zvdWn8n61mWme7tZEbnUUABBRTonUC7BlIe7b1wsfk8/jNf4LYg+TvpVQMp\nV4+6+ZhvVmcpoIACCijQUiBXjnL7+OtaztXbibmN3VJAAQUUqFmgXQMp33nUWDsxYtvGkV0c\n7vZjvru4a65KAQUUUEABBRRQQAEF+lmgXQOp2bEfychvk4XI/c1m6NK4m1lPUtaB9CxFni9H\ndNh9OfOt3WLelzHNWxpaADlJAQUUUEABBRRQQIFBEWjXQGp8DHi+PDbfg5RGx3AbKp2avoMZ\n128y86aMS0NnNXIxOYp0Ursy0xdazJhbB3t1LC026yQFFFBAAQUUUEABBRQYbwLtGki3s8P5\notZqPcfAQWQ4X9ZaXb5d/wLM8EmSD8peVpk5D4uYm+TKVR7W0Gntz4zJUOUXxQ4l43gFFFBA\nAQUUUEABBQZMoF0DaWM8qvPk80E3kjz6u1eVhz9cS35K0kj6L5Lt/pBcRA4llgIKKKCAAgoo\noIACCijQdYFq46fZynNb2zzNJlTG5XNCR1eGu9F7Jit5NUmj6C/k/cRSQAEFFFBAAQUUUEAB\nBXoq0K6BtBZb34VcSq4neUhCxuUzQLeR1FwzOl3/90HWuD35IDmLPEByBclSQAEF+kkgn+1c\ntOYD2pLt5+d7nfUoG/8u8TOhdb4KblsBBRRQ4EW3zzXjmJ+Ru5OfVCbmAQq5/e0dJLe+9bqy\nrVxR+gK5vtcbc/0KKKDAGAvkZ9uXxnibzTZ3JyOvbjZhDMblD23rkXwHUS9v4W51KCszsfze\nv1bzOU0BBRRQoM8FWl1Byl81Nye5glOtXM2ZmaxArqlO6GF/trNTD9fvqhVQQIG6BPLQmdPI\nHnXtANvN5z1PJfly8Doqn3f9K5mNPFPHDrDNXMXLQ4AsBRRQQIEBF2jVQHoOm7vIR8i3Kk6r\n0r8M8buDKij2KqCAAqMQyO1ldV29GcVud33Rr7HG87u+1s5WeDyz5VZDSwEFFFBgwAVaNZBC\n8yHye/JxktvccvvBa8n+pJdfEsvqu1azs6bGR5VXV57Hhzd+31N1uv0KKKCAAgoooIACCigw\nIALtGkh5gtyyZEcylVxJvkzOIBOlvsiO7tdmZ89uM93JCiiggAIKKKCAAgooMAAC7RpIIciH\nZvPh2TlIbrXbmuRzSLkFbyJUbtn4SYsdzfcq5XuXLAUUUEABBRRQQAEFFBhwgXYNpHz+6Cvk\nGJIP7z5N9iSrkz3IRKin2MkbW+xonpj0fIvpTlJAAQUUUEABBRRQQIEBEWjXQNoHhy3IhSQN\npHvI20g+TJwrM3cQSwEFFJioAquw43mCXLufhb08vnwO0p+lvRR23QooMFKB2Vlw8kgX7sJy\n+cP8C11Yj6tQYFgCrU4KJrGmJUnj1ZebGDeNLEH8pQ6CpYACE1YgD3DJ453fXeMRHMi2565x\n+25aAQUUaBTIOV7q7hmd2v79A1vORzssBcZUoFUDKZ8xyuNW9yV50EFZ76InJxRXlSPsKqCA\nAhNYIH+d/E2N+/85tm0DqcYXwE0roMBLBOYpxmxD966XTB2bEbuwmdXGZlNuRYEXC7RqIGXO\n3cgp5ANkfnITWZzsTvK9HZYCCiiggAIKKKBAfwrkIxa31nRob2a7NpBqwh/0zbZrIOWx3iuR\n/AVheZLPIP2ZXEcsBRRQQAEFFFBAAQUUUKCvBFo1kPLlqa8n+evBMX111B6MAgoooIACCiig\ngAIKKNBEoFUDKbP/gnyM1Hl/fvZjNPUZFv58ixVMZtpZLaY7SQEFeiewDKteqHerb7vmXBm3\nFFBAAQUUUECBfwu0aiDlu4E+RfYmeYrJv0i1HqsOjOP+I9i3S1vsXz4gnceWWwooMPYCF7PJ\nfL6xzvIRsnXqu20FFFBAAQXGmUCrBlJ29Tskj/o+IwMNNXPD8HgdnMaOndRi53Zl2hMtpjtJ\nAQV6J5AruHnEdj7bWEf9Bxv9Wh0bdpsKKKCAAgooMD4F2jWQXsNuTxqfuz4h9ypfuLYLSbfO\nytXAf9S5A25bgYpArkY/XBkey17/ODKW2m5LAQUUUECBCSDQrIGU7zlamXyD3EFyMp/5HifW\n6ATWYPEfkitIbmGso17GRi8nb6pj425TAQUUUEABBRRQQIHxLNCsgbQ8O/zqyk5/hP43k60r\n4+wdmUB5W+J6LF5Xg/PLbHvDke2+S/WZQP4Y8taajyk/gxaseR/cvAIKKKDA+BN4Obs0hRxU\n467lM6qHk8tq3Ac3XYNAswZSDbvhJhUYOIG1OOIDSNlorgNgTTb6HDm1jo0X28wtvFNr3L6b\nVkABBRQYnwK5myl3vSxX4+5twLbvJTaQanwR6tj0eG0g5YPb25LNyGIknxO4pMgpdB8klgIT\nWWBtdv515NAaD2J9tn0jeX+N+7BTjdt20woooIAC41sg53913sF01vjmce96JTAeG0hzcbAn\nkPzlIA8TyCO68wHufFdKvtMof3XfilxBLAUmssD97Py+fgHcAABAAElEQVRnazyA99W4bTet\ngAIKKKCAAgqMS4Fmt/fsy57uSH5Q7PHGdFeqDBeje/aX7+3ZwMdJHiLwZLmxSvdY+nNP6A6V\nca16P8DEPVrMkEu355ItWszTrUnrsqLzyAWkroc05Ham2Uid3/2UR8en0fsIqaPyvl+R3Ebq\neh0WYNuLkwtJXZVb7J4leWhHXZX/E7eTaTXtwMvZ7tIk/y/rqlXYcB6Gc1FdO8B21yFpsOeK\nYh01LxvNH8XyXqzr85n5XZD/l/n5XFflynJ+711Z0w7kZ2P+T95E7iF1VH4/5Gfj+XVsvNjm\nanRnIblzpa7K65DvoLylph3IH6XzfyI/l56paR9y7jk3qfP3ZP5P5v9FzjvrrFwUqOt1yHFf\nTHZNz6BUsytID3Hw85F9GhAahw9tmN6twZy4nk2aNY6yjUPIYenpsHIV6lct5s3J0Vh9BuMy\ntvUJkpOhumphNpyTgOvr2gG2uwLJL98Ha9yH/PLJiVBdP/TmYdt57+WHXl11Dht+mtxZ1w6w\n3WvIVeSpmvYh/xfTQMlJQF31MjY8J7m5rh1gu7lSfwfJHy7qqJyApJFW5//J+dl+Gsx5T9ZV\naRQ8SupqnOS4ryV5P+SPJ3XUHGw0vyOyD3XVmWx4Eskf0eqq/G7IHywer2kHcvz5I1qdjZM0\n0pLrSF2Vc8j7yAN17QDbzc/G/I56rsZ9qPM1qOWw80tpvNX67NBxZEtyScPOzcvwT0lO6t7b\nMG0iDOYk6EBS51/sJ4KT+zg2Aq9gM/m/dPfYbM6tKDCkQH4XrUVyMmIpULfAYuxAriDljwaW\nAnULrMoOfJrU9YfEuo+/lu03u4JUy45UNpqrR4eQ88gNJJeXc1lxCplKriabk4lYb2Sn9yI5\nhrquXExEN/e5NwJLsNr8ldgGUm98XWvnArntN7fz5K+UeU9aCtQpkAZSGu3T6twJt60AAmmo\nr0iOI2cQa4wExuMVpPLQl6Ent0HljbEoyUncueR08jyZiLUeO51jmJvUddl8Irq5z70ROJ7V\n5gTgP3qzeteqQMcCZeNoKZao87amjnfYGfta4EccXX5PT8Q7Vfr6hRnAg1uQY87nQ3O7Y+Nd\nVQPIMXaHPB6vIJVHfzM9iaWAAgoooIACCiiggAIKjIlALt1ZCiiggAIKKKCAAgoooIACCNhA\n8m2ggAIKKKCAAgoooIACChQCNpB8KyiggAIKKKCAAgoooIAChYANJN8KCiiggAIKKKCAAgoo\noEAhYAPJt4ICCiiggAIKKKCAAgooUAiM56fY9eOLdAsH9SvyZD8enMc04QROY4/z+FBLgboF\n8jUOvyG+H+t+Jdx+BM4kc0ihwDgQeJR9+DW5fRzsi7uggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACwxaYNOwlXGCkAvOx4OLkwSYrWIJx\nc5LHmkxzlAK9EpiVFS9I5qpkFvqfIZYCYymQn39Tyf1juVG3pUCDgD8TG0AcrEXA88Va2N1o\nXQI/ZcPHN2x8HobPIPeRNJz+RHKiYCkwFgJbsJEXGnLoWGzYbShQEfga/Q+R28g0siaxFKhD\nwJ+Jdai7zUYBzxcbRRzuS4FdOKqTyXOksYGUk9FMy5W82UgaS/9NLAXGQuA/2MiPydyVTB6L\nDbsNBQqBN9O9l+TqUepT5BaSK5mWAmMt4M/EsRZ3e1WBXRjwfLEqUmO/v4R6j/8EmzidnN1k\nUzsy7jskjafc1nQs2Z5YCoyFwPJs5HKSWzvLPDUWG3YbChQC76GbPxzdUAwfRXcpsn4xbEeB\nsRTwZ+JYarutRgHPFxtFahy2gdR7/DR6DiTnN2zq5QznPtNLK+OvoX8xMmdlnL0K9EogJwNv\nI9eT28ihZH5iKTBWAiuyoUsqG7uL/txuN7Uyzl4FxkrAn4ljJe12mgl4vthMpaZxNpC6C/9q\nVveWIsu0WfWyxfScDJR1a9FjA6kUsdstgbwfy/dm3qepPKAhjaM0kvYiG5PDiaXAWAnk5+DD\nDRtLY92fgQ0oDo6JgD8Tx4TZjQxTwPPFYYJ1Y/ZZu7ES1/Fvga/St04x9EW6+Yv8UPVIMSFX\njG4p+nNFKX9B9UlOBYidrgm8lTV9pVjbBXQ3J9XbmK5kOFeP8uHQPNXucWIp0GuBNI7yM7Ba\n8zKQ96OlwFgL+DNxrMXdXicCni92otTlebyC1F3QnHQuWqRV4yhbTaPoabJcBorK7SbVW+7K\n8XYVGK3AIaygfG/mfZqT0B1J9aEMeYJYnmr3PLEUGAuB69hI9Wdg/kiUBlP1trux2A+3oYA/\nE30PjFcBzxfH6yvjfnVFIE+nO75hTb9m+ASSK3mvJBeRfYmlQK8FZmYDeb99q9hQTkpPLlKM\nsqNAzwV2YAsPkKXJPCQN+fOJpcBYC/gzcazF3d5QAp4vDiXj+L4UaPaGz1/085fS+0iuJqXB\nNIlYCoyFwJvZyBUkn0PK0+vOITlRtRQYS4HvsbG8/3Jr8c2kekWJQUuBMRPwZ+KYUbuhFgKe\nL7bAcdJgCeTWupcN1iF7tONIIE9uWmIc7Y+7MngCC3PIuYpuKTAeBPyZOB5eBfehmYDni81U\nHKeAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgpMJIH12Nltu7jDy7OutcmcXVynq1JAAQUUUEAB\nBRRQQAEFhiWQhs6Zw1pixsyfonPSCJZrXGQZRlxD7iX3k3vIG4mlgAIKKDAAArMMwDF6iAoo\noIAC40ug8XfPpIbdm5vhlRvGjXRwZhacd5gLH8T855JFyeLkD+RLxFJAAQUUUEABBRRQQAEF\nuiLwcdbyc3IEeZRcTN5M/kSeIdeS1ckq5D7yPJlGMu5WsiVJLUmuI1tkoKGqV5DSCEuj5kHy\nOLmcvIGUlW1fRR4ivyDfJ+8nqWz3TdP7ZvyzMZ1nyVyVcfYqoIACCiiggAIKKKCAAiMW2I8l\n0+j5MEkj5xTyAtmL5ErNCeRIkobN5iSNpFxJSn2VpJE0HzmRHE+aVbWB9EFmuIlMIZPIR0hu\nmVuELEHSSNuNvJx8gWRfPklS2W6uPJV1CD1/LAfsKqCAAgoooIACCiiggAKjFUgD6ZzKSj5N\n/+2kbIikAZPb2lIbkTRmypqNngtJrjrdQRYmzaraQDqbGQ5omOluhrcn/0kyvazswzSS8dVK\nw+1okmmvrk6wXwEFFFCgfwXylzpLAQUUUECBsRBI46asZ+i5muTKTSpXl2ad3vfSfzLvN8ka\n5PskV5fa1VRm+GfDTDcynKtHy5BrK9OyD43z7sK47F+2vRopG2/0WgoooIAC/SxgA6mfX12P\nTQEFFBhfAmkEjaTymO2vkLNIrvIsRtpVGlErVGbKbXarkttIpq1EqjWlMvAJ+g8gbye7kE4a\nZMxmKaCAAgr0g4ANpH54FT0GBRRQoL8EnuZwJpPcWpdKYyXjNiIXkcNIWTvSs3Y5UOmeTP97\nyJRi3DZ0czXoFPIX8iqyCUntQNac3jfjn73pfInkYRBpjJUpbwdklKWAAgoooIACCiiggAIK\njFxgPxb9dWXxPNXur5XhD9Gfzxml8rS4NE7uJ28hadisR1JTyCNkV5LKbXOfnd4300zVzyDN\nz7g/kyx7KclnmrYkZe1EzwUk20ij6XSyJ8mtebnlrlnygAdLAQUUUEABBRRQQAEFFBhzgVyt\nWagLW12WdaxBZq+sa176lyqGy/FnM7xhZR57FVBAAQUUUEABBRRQQIGBEMijvfO5oneSJcge\n5A4yH7EUUEABBRRQQAEFFFBAgYETyGeS/kCuJbn1Lw9wsBRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nYAQCM49gGRdRQAEFFBi+wJIs8tZiscvonjX8Vcw0hWV2IM+Q/yKd1hLM+CbyJDm+04WYbw6y\ncwfzH8c8D3Yw31jMMgsb2YasRK4ivyUTrbZlh+clfyfXNdn5eRi3Y8P4FxieRi4htzRMc1AB\nBRRQQAEFFFBAgXEn8HX2KCexyUUj3LtNi+UfH+byWxbL3TnM5RYuliv3e6juysNcby9n/1Fl\nn4/u5YZ6uO4bimMYqnE6pZje7PV4mmmfJMOtfVngRLLTcBd0fgUUUKDfBGbttwPyeBRQQIFx\nKJCr9e+t7Nea9K9KLq+M66T3ZmY6mOQkeCzqKTZyeGVDG9I/laSh9cfK+PFy9Si7tFWxX2ko\n/bDo7+fOsRzcvSRX+/K+WpccRBYn+5BO61XMmCucI228d7od51NAAQXGvYANpHH/ErmDCijQ\nBwLrcwxTSBo2V5PVyY5kP1KtORnYleRENye9ucXqTPIIST1BcnUht9iVtTs9s5Hc5paT3LeQ\nNLz+QO4njbUAIz5EliUXk5+Q6voY/Hc9St8H/z0000zH0J8G0pUN4zPL5mRFkn3Ofm5Hfkmu\nJcuRbUiWvZ6cTc4jz5LUm8jKJOOyzcw7C/kNybaqtQUDuZI2F4nN38gtZH6SE/yFSOoektvU\nylqanveQ1chD5AJyJCn3YXn6c6Utt6mdQt5Jchznk2zvbpJtlcfxJ/pPJ68sxsU1x/svUq21\nGXg7WYLEO6/LbaRauaVufXIHOao6oYP+A5gn6y0rjaNPkj1Jpj1AUq1eg7jFJxWfHOPvMkC1\nWm7GHP6rgAIKKKCAAgoooMAwBQ5h/twOdQLJX/XTfyOZmZQ1Dz1p2GRaNTcznKsBqZyoZ9rj\nGSgqDYqMO7nolsvm5D4nu6mc+Gf8QyQn0+U86f6VdFrHMONQy+RKRqYdTrJ/6X8D2ZCU+5hx\nZdJQKOtwejL+VPJU0V/O91mGy/oOPeX4svsc49KYWbXJtHinNiZpLJbLlN0zGJeGVepdJOPP\nJ/8s+g+mu33Rn88CpXFXLptuGrgxLcflM16vJ2V9lJ40Psvp6aYBtjYpK42q6vRs495i3M7l\nTA3dKcX0LLdmw7QlK9Oy76kNSavX4AqmV/fh1ixEtVtuxlz+q4ACCiiggAIKKKDAMARydac8\n4X0//SuQ8mS0ejK9VzH+IrpLkVeT8sR1X/pTrRpItzM9J/mZ92GSbXybpMoGUsaVV0GOoL/c\nj3UzUwfVSQMp68xJfq6ErEiOIxl3PHkN2aMYzrhXkNThJMPPk8+RXFG5jGTcTST1cpLhZCMS\no5+QDJ9P5iDrkEdIxn2Y5KrQ7OQGknHnkDSm9iNlg+mb9KfKBlLmi1+uoOxIticZl2Tcf5A7\niuGMO6EYd08xrrwCNJXhNJgyz7fIJuSkYjjHlsqVpXLdJ9Kffc7rX44bSQOJxf/dQN07A1S7\n12AN5vkLyXYPJ2UDrt1yzGopoIACCiiggAIKKDA8ga2YPSeeT5MFi0XLk///KYbT+RjJfI+S\nr5KNSBoQucVpUZLalGSeXKEpK/NnXE6uy/oFPRmXqzqpagPplTNGTW9QlFc3tmBctpMT6moW\nKOYtO8fQk/X+tRxR6WZbmZYrLXNXxqdh8DayCMnxlA3BzJtGYOpwkuFTM1BUGjIZl0ZTKldG\nniMZl229myxDst/LkrIepCfz5KpRanOS4SSN07IOoCfj0rBJVRtIb5gxavq/ZQPpMYZmLsZ/\nnm6Wva0YTue7JOP+kgHqEyTDaeTmKtV8JA2RHE/Gr0VKzzPoLytGz5LMM5IGUvaxfE/sS3+q\nk9egfP2+PmORjperzG6vAgoo0B8Cs/THYXgUCiigwLgVeG+xZ9fT3YC8ndxQjNuO7qxFf06W\nc7KexsUXyGnkQrIDqTaIGGxa11TGPlD0N/6Mz/gri2m5upET6dRksgo5sCELMTzcOpsF0pgo\n6zp60si4nNxKDiZD1dWVCeUxlI2SXCHLVahU3H5Fsu4vk9nJULVSMeE2utdWZopvKg236nE+\nwfBZmdBQ2Z80WlL3z+jMdFfRTad8jcrXc/li2ivpptH2ELmYlMczhf6pJPW3GZ3p/2Y/b64M\nD7d3aRbIeyiVdaWG8xrMWGLGvyNdrroO+xVQQIEJJ1D+IJ9wO+4OK6CAAhNAIA8JSIMotTL5\n/fS+//snJ+ebkpPJvWRFksZErri8kSxKvk5yUp1uq3q21cRiWq5iDVVpvBzRMDG3qw230sir\n1p8ZWJ6cSfYpummo5Jgaq90xpLGYfXwHeTNZlryPrEHWJM0qt8ul0ghKQ6o0KD/X9Tzjqg26\nNH4ybqRVNqLKBlMahgc3WdkljMsVsdTCMzrT/52Vf/O6j7S2qCx4TtE/nNegsvhMI12uug77\nFVBAgQknkB/ElgIKKKBAbwTeyWrnJDkRLq9+lFvaiJ6Xk/eSNJC+QVYjvyG5LW828v/IR8g2\n5Oukl3URK9+lyxvIFZLli3XuTPdGkgZjs8ZRMduQndwm+GFyF9m9mGszuv9L0kDKtm4gjXVB\nMWIuumk8pJE6iWxNUpeRPBiiW1U2kK4qVrgC3ZPIHSQNn91IKg3iK8j6ZBMyH3mYvJWkYT3c\nylWjeHynWPB0urkCNNzXIDap4S43Yyn/VUABBfpAYNY+OAYPQQEFFBivAmn8pE4jufpRrTSI\nPkdyop6T21y1SONhI7Ixuabop/OSR0dn3ESoW9jJNAyWIDnWNGDKxg29/769MP3tahozvI3M\nQqaQ08m6JJXGxq3T+176T67U/I6kkflb8ieSRstyJPWVGZ2u/Vs2kI5ljfuRNDROIX8naeQt\nQ/J+OIB8l3yApBF5E/kXeRNJg7psqNDbss5naraZ+WOTupGkQZq6hXTyGqRxlsoVzGfIV0kn\nyzGbpYACCiiggAIKKKBAe4HcwpVbxnLyWl41qC61XjEt03ckk8nPSU6OM65Mri4tSFKbkowv\nb9/KuEeLcRtkoKhD6Ga+XxXDOTHP8J3FcNl5oBifxkMndQwzZT1/bTJzGgSZdlDDtDSI0oDJ\ntOx3Gg1XFMNfoJs6nGR6rpiVlUZixiVlxfF+Uo5P93qyISnrQXoyPsuXlasz5b6Xy8Zt13IG\nuuVDGm6rjEtvGgxZpjp+z2JceXWKwelXADPfqRkoahW6/yDlNtNNAy3vjbK2o6d8DdNITqPp\nLJJ5y0YOvS+qKQxV15n+LHsZ+QFZhlSrk9cg75/S9tZi4U6Wq27HfgUUUEABBRRQQAEFui6Q\n27BeQ15Pluz62utZ4axsdi0yexc2PyfrWJ2kAZTPdWXdndYCzBjX1chsnS7UhfmWZh3Z7suG\nWNccjF+HVD+LNMSsIx7dyWuQq1DZx7wHy+pkuXJeuwoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCihQm8DMbba8MdPPIM+2mc/JnQnE+y1k9s5mr32u\nu9mDc2vfC3dAAQUUUEABBRRQQIExEmjXQLqO/ZifHEuOIv8k1sgFXsWiF5BHR76KMVtyElt6\ngcw9Zlt0QwoooIACCiiggAIKjHOByezfVuTn5EFyDfkCWZpYwxdYj0XS6Jhr+IuO+RKbscUn\nx3yrblABBRRQQAEFFFBAgQkikNvC0li6jDxHTiRvJlbnAjaQOrdyTgUUUEABBRRQQAEFxlxg\nlg62mHk2IN8mh5JFyPfIaeTHZDdiKaCAAgoooIACCiiggAITXmDWNkdwINN3IvOS35E0hv5M\ncgUpdR75BklDabzWkuzYui127mVMy2erLm0xj5MUUEABBRRQQAEFFFBAgemfPXofDkN9UH9B\npq0xzp32Yf/y+amh8jTT/jpGx+AtdmME7WYUUEABBRRQQAEFFOiVQBpBixcrz+117Z5816v9\n6NV6f8OKD+7VyhvWawOpAcRBBRRQQAEFFFBAAQXGk0C7zyDtyM7eRbYsdno7urkVbdNi2I4C\nCiiggAIKKKCAAgoo0DcC7T6D9GmO9EPkZ8URH0L3AXIQWa0YZ0cBBRRQQAEFFFBAAQUU6AuB\nVleQ8rmj1ckvGo40wwsTvwupAcZBBRRQQAEFFFBAAQUUmNgCra4gPcah3U5ym93hlcPMF4jm\ns0j3VsZ1u3cyK9yWZFuLkSfIJUVOoZsHLlgKKKCAAgoooIACCiigQFcFWl1ByoZye12+8+hK\n8mtyITmJ5Mlwj5Ne1Fys9GTyLZIHROQzT9nufOQz5AKyCrEUUEABBRRQQAEFFFBAga4KtLqC\nlA3las0K5D1kWXIZ2Z2kkdKr2ooVz0mWI0822cixjPsi2aHJNEcpoIACCiiggAIKKKCAAiMW\naNdAmp01r1WsfVrRzW1vyf7FcLc7K7LCs0mzxlG2lQdFHJaeDmsB5ntli3kXYtpdLaY7SQEF\nFFBAAQUUUEABBQZEoF0D6UgcNifnk17dUtdIfSojjiNHkHzuqFrzMrAXOa86sk3/J5j+pTbz\ntHNos7iTFVBAAQUUUEABBRRQoB8EWjUMJnGAWxf50xgebK4e5SpRGkE3kFvIM2QKmUquJmm0\ndVpfZsZWV7tyy162YSmggAIKvFggXww+24tHjduhF9iz/K6wFFBAAQUUGJVAqwbSc6w533mU\nq0djWfkllwbN0WRdklvuFiVnknPJ6eR5Mpx6usXM2Z6lgAIKKPBSgVzJ3+mlo8ftmN3Ys5+M\n2737vx37Fb2v/b/Bcd2X37fvImN9LjCuUdw5BRTob4FWDaQc+edIPu/zMXInqTZMqv1M6mpN\nZm0bkHzWaTGSx3znFr88XjxPs/Mx3yBYCiigQI8F8jM3f6z6nx5vpxur/zEryf5OhMpne3Nn\nxukTYGd/wD4uTWwgTYAXy11UQIHuCLRrIO3HZvI0uXc02VxuvehF5THfJ5CVyb9IHvP9MMnD\nFD5DDiB50t0VxFJAAQUU6K3A7aw+tz6P93pkvO9gw/79k+GjGsaNx8GDx+NOuU8KKKBALwXa\nNZA2ZePt5un2/vmY726Luj4FFFCg/wWW4BB3I2+ZAIeafV1mAuynu6iAAgoMpEC7xs8NqORB\nDduR/9/efYBLUtZpG2fIQRiSSGbIKCIoYEbEAKIouAQjKK5IMKyiAuoqsioIuqwRAwJiQMEs\nRoKCoEhSQFAyQ5acgwwD3/3MdPkVTYdzmO6aqu77f13PVOyqt34955x+u6qrFya51C7TXyPD\nqnzmKO9WPthlB7mBQy77sxRQQAEFFCgE8sXiuRS8CWe7Xkw7VyGWAgoooEANBfp1kPagzQeQ\nY8ibSW52sBfZgOxJhlEns9FB3uZ7GG10mwoooIAC9RO4jCZ9tH7NelyL9nncHGcooIACCtRG\noF8HKb/EtyZ/Iekg3UK2IbnV9ifIDWTQlXf/cpZoULf5XohtrdyjkYuxbN4ey12kgAIKKKCA\nAgoooIACYyLQq4OU70FaiVzVZjGd6RtJrqEeRgdp0Lf5zruJudlEr8oX0FoKKKCAAgoooIAC\nCigw5gK9OkgzscltPfclHys55fsQ8r1EF5fmDXo0Z302I1uR5cmc3OY7lwh+lXSrr7Pg8m4L\nna+AAgoooIACCiiggALjI9CrgxSFt5MTyK5kKplOViC7kXvJMGrQt/meQSOv69HQ3AzCL4vt\nAeQiBRRQQAEFFFBAAQXGRaBfB+kfQKxLtiNrkXwG6UQyzDMu3uYbYEsBBRRQQAEFFFBAAQWq\nF+jXQUqL7ie5i11VtQ47OoPkzE6nyg0cDu+0wHkKKKCAAgooMFCBfB55E5KrMepeaWPexM1H\nBCwFFFDgCQv06yC9ni0v0mXrR3WZP6ezT2YD3uZ7ThV9vAIKKKCAAnMukJsY7U3yNR91r7Q1\nn18+ve4NtX0KKFBvgX4dpNzae5nWISzAcA2SL+P7AxlWBylnjwZ5m282ZymggAIKKKDAExT4\nPI/LDZvqXvk8cc54WQoooMAcCfTrIOU7j9prZ2bs2D5zgNP5BXcg+S7Jaf1ccpe75uUdobPI\nKeQRYimggAKFQL7LbNliogHD22ijlwE14ImyiQoooIAC4yfQr4PUSeTbzPwMWZrc3mmFAc27\nmu0kc1pT2EC+DLZb+W5TNxnnj7tAfnaWbAjCh2nnBxrS1jTzj+Q3DWjvmrTxxga00yYqoIAC\nCigwMIF+HaS8K1uudCbyPUhPIVWfxTmEfV5EjiaTqQNY+aN9HvCnPstdrMA4CryHg/5cgw78\nBtr6sga0N1+d8CySs+V1r9Vp4B11b6TtU0ABBRRQYJAC/TpI17Oz5dt2mMtCDiV3ts0f1ORr\n2dDzO2xsS+Y9kzydnE++QyZSn2Wln/VYMR2vS3osH9dFC3Hg6SC/qyEAV9HOXzakrU1pZs4e\nnUve1oAG58Yu+YB2vpqg7pU7bV1DNqt7Q2nfzQ1oo01UQAEFFFBgoAL9OkhbsLfyOnnHMy9E\nc+vvYVVelL2PXEAuLO0kd9PLpXK5tC8vhCZad7NiXuR1q7tY0ITbl3Zr/7Dmr8+G89zvPqwd\nDHC7+T8zhaw8wG0Oc1NLtNo7zH0MYtvpJN9H8rNY93qABk7m90Ldj8f2KaCAAgoooMBcEih3\nfjo1IWdtntRpQWne1YznhgqDqqPY0GXkSJIXZv9L0jH7GjmPfIVYwxdIhyO1wexBrf99I63L\nmcImVO4Mmc/xNaVua0pDbacCCiiggAIKKDAIgX4dpI3YyVvJ38gVZBWSebnE7TqSWnT2YKD/\nns7Wnk3SKTqJvIVYCoyCQM52XU52bMDBfJ02rt2AdtpEBRRQQAEFFFBgYAL9OkhT2dNu5IjS\nHvP5oJzdeS3JmZ1h1Z1s+HUkn3/ITRTyQeHziKVA0wUe5ACa8H/53qZD234FFFBAAQUUUGCy\nAvkQfrfKHeteQX7YtkI6K7n8qqp3ltMZexnJ5XY5i2UpoIACCiiggAIKKKCAAkMR6NVBmske\nbyJ7tO05H95fjdzaNn+Yk5ey8Z3JCcPcidtWQAEFFFBAAQUUUECB8Rbod4ndO+D5Gfkvks8F\nLUOeSw4kw/ySWDZvKTBhgRVYM5/taT/bOeENVLjimuxr2Qr3564UUEABBRRQQAEFJiHQr4OU\nGySsTt5I1iD5jpGPk9NIU+pDNPSjPRq7IMv+2GO5i+ovkP+jC5Mb69/UeTakjenMWQoooIAC\nCiiggAI1FOjXQUqTn0c2JXkBejDZluRzSLkErwl1FI3s9YH4/2b5JU04ENvYU+ARlr675xr1\nWJgvNF25Hk2xFQoooIACCiiggALtAv06SPn80QHkGPJm8hDZi+S7cfYkTah/0shf92ho7tL3\nQI/lLlJAAQUUUEABBRRQQIExEeh1k4YQ7EO2Ju/LBHUL2Ya8jaxILAUUUEABBRRQQAEFFFBg\nZAR6dZBym++VyFVtRzud6XzWww5SG4yTCiiggAIKKKCAAgoo0GyBXpfY5TNG55B9ycdKh7k9\n408mF5fmOaqAAgoooIACCsxtgZ/SgIfndiMmsP98YfizSRNuLjSBw3EVBUZLoFcHKUf6dnIC\n2ZVMJdNJbqmcz+3cSywFFFBAAQUUUKAuAj+hIb+qS2O6tGNx5h9J8pUPdpC6IDlbgbkp0K+D\nlNt6r0u2I2uRfAbpRHI5sRRQQAEFFFBAgToJ5K61df9OvKXrBGZbFFDg8QK9Okj5fNILyF9I\n7mJnKaCAAgoooIACCiiggAIjLdCrg5QD/x55D/lxJhpa+W6c9/doez5P9ecey12kgAIKKKCA\nAgoooIACYyLQq4OUL95Mx+KD5GbyV1Ku+8oTNR7Ptch392hfOlBX91juIgUUUEABBRRQQAEF\nFBgTgV4dpBB8luRW36dloq2mtE3XdfIKGpZ0q21ZcE+3hc5XQAEFFFBAAQUUUECB8RHo10F6\nDhT5PiRLAQUUUEABBRRQYHACC7KphQe3uaFtKbdNb8Kt04cG4IbHT6BTBynfc7Qe+RS5geQH\nOOvdTywFFFBAAQUUUECBJy6waOuh+a7JJlS+sylf8XJnExprGxUYhECnDtJabDhfXlbUHoy8\nlORSNEsBBRRQQAEFFFDgiQss1HrofgxPeeKbqeSRq7CXH5B06uwgVULuTuog0KmDVId22QYF\nFFBAAQUUUGAUBR5tHdRlDM+s+QHeXvP22TwFhiJQ1w5S3l3ZkWxFlicPkAtaOYGh72KAYCmg\ngAIKKKCAAgoooMBgBerYQcpp3ONJPgeVW4v/jeQ23fnm6ZyOPoi8mvydWAoooIACCiiggALD\nEViqtdnLh7P5gW/1NrY4jcwc+Jbd4FgJdOsgTUNhz5bECxmWp1uz5/lKMTLgYTo/i5A1ST4Y\n2F7HMuNj5PXtC7pM78r84lg6rZL9nNVpwRDmFafV/8C28z1Tda58IDNVlc3svT2xf1flYfM2\npK35/5YbnzTBdV3amTcsmtDWtWnnAg1p64q0Mz//TXBdknau35C25u/G8xrS1vztfVVD2pqv\n9NiZbEGaUHvTyDfXvKG5Sib1aZI3futcS7Qal5+vJtTKNHJ6Axqa3wGLkaZ0PM+nrXk9PTbV\nqYN0F0efH4h92hTap4fVQVqH/Z5BOnWO0qTDyOEZmWDlLFQ+YNit8uL65G4LBzz/Qrb3XpIX\nyHWv/ALPWbz8UNS98iJ+dXJR3RtK+6aS5cklDWjrMrQxL5CvaEBbn0Ib83/2mga0Nb/f0kG6\noQFtzV227iW3NKCt59HGW8kdDWhr/hZcS2Jb97qYBuZFXC51r3vlMz35OzCj7g2lfRuTvD7J\n74I6VzrIzyLn1rmRrbblNe0GJK51r/y9WpdcUPeGttrXlI7cwDjzH79u9XwadBx5JWn/j7M4\n844kD5E3kaZV3oE5hDThhXzTbG3v4AVyaUU6SFcNftNuUYGBC6zBFm8jeZPPUqDuAhvRwLwB\nWVxZUvf22r7xFcib0LlC4+BxIqhjBylt+hDZn1xJ8o5w3g2aRvIHMO+8v4LcRJpWW9Hg35Ac\ng78Um/bsjV97cwYpvxjzTrelQN0FVqOB9xDvulX3Z8r2zQtBrpbJa5y84WspUGeBXFWWq1+e\nVOdGjlPb8sdue5LO0qFkP/ISkl8sTa1NaXg6RnnRaSlQd4H9aeCpdW+k7VOgJfBnhvuqoUAD\nBJamjXktkMvBLAXqLvBmGjh2b5R2+gxSXZ6oq2lIYimggAIKKKCAAgoooIAClQg0+WxMJUDu\nRAEFFFBAAQUUUEABBcZHwA7S+DzXHqkCCiiggAIKKKCAAgr0EbCD1AfIxQoooIACCiiggAIK\nKDA+AnaQxue59kgVUEABBRRQQAEFFFCgj4AdpD5ALlZAAQUUUEABBRRQQIHxEZhvfA61Fkc6\nk1bk9uXfJ7nFp6VAnQVyO/r7yGl1bqRtU6AlsDLD3Or7KkUUqLnAw7TvaeQY8kDN22rzFEhf\nYRHyaykUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBgsgLzTfYBrj9HAovw6DXI7XO0FR+sQDUCa7Gb1chd5OFqduleFJgjgfx+XZrcMUdb\n8cEKDFdgfja/MclrsLuHuyu3rsAcC6zEFhYm98/xltyAAh0EPsG8vNC8jtxINiSWAnUUSKfo\nUnIrSWf+FvIiYilQZ4FladwN5DN1bqRtG3uBnRHIa4CLyKPkQGIpUEeBdWjU70leA9xDTiLL\nEUuBgQm8lC3lxWbe3Uy9n1xD5s2EpUDNBH5Ee75DppCFyBHkZGIpUGeB42ncbcQOUp2fpfFu\nW958yptOm7YYnscw78ov1Zp2oECdBL5GY75JFiCLk9PIJ8lYlC/Qq3ma38BufkiubO0uLz5X\nIc9vTTtQoE4C+X95JMm7m/8i+f+6OVmUWArUUeBdNGom+RXJ/1tLgToK7EyjLiNntxp3BsNN\nyIzWtAMF6iSQy0DPJ/n/mTNIF5LijX5GR7vmH+3Dq83R5TTl90utuYnxXG6X/2inl+Y7qkAd\nBNaiEeVrjXdk+sS2eXVop21QIAIbkL3Js8mhxFKgrgJr07C84PwWyVmk35HDyb3EUqBuAl+g\nQZ8mU0nOcu7QCoPRL88gVfMcr85u2j+IeR3zctMGS4G6CdxHg/Iu/JPJd8lryf7EUqBuAvkd\negzZk+QyZkuBOgvkzaddSD7juRfJi868STqNWArUTeCfNCg3Z3gV2YrkdUFuLGIpMDCBi9jS\nB9q2djXTL2qb56QCdRF4Kw3JtfLfJMsQS4E6ChxEo04lL2slZzqPI5sRS4G6CfyRBp1SalTe\npL6ZvKM0z1EF6iCQzx/nzOY7S43J5zvPKk2P9Oj8I3109Tm4y2nKmqXmLMH48uSC0jxHFaiL\nwHtpyL7kNSTvbloK1FXgARqWzx59uNXA9RjmlvRTSD5QbClQJ4FraEz+vxb1CCN3Et+VL0Qc\n1kUgv0sXI78sNejnjOfN/kVJ+TL80iqOKjA5gdezer6XY1XyJHIYOYdYCtRR4HoalXc004kv\nJy86LQXqLHAEjTukzg20bWMtsA1Hn8vtN2opbMcwN8Ipv4HaWuRAgbkqkM5RbszwaZKzSQuQ\nr5O/EUuBgQp8ka3lF2EuW8rldf5CBMGqncAatCjXGXfKsrVrrQ1S4LECdpAe6+FU/QTygjNn\nkW4geRc+n0myFKijwCtpVC6pyxv8yVVkY2IpMHCBZdjiUwe+VTeogAIKKKCAAk0RyM0ZNiS5\nVMlSoO4Cq9DA3C10wbo31PYpoIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKjLjAfCN+fB6eAgoooIACnQR2ZeYM\nckunhaV5mzH+XHJRaZ6jCiiggAIjLDDvCB+bh6aAAgoo0ByB/6apH62wuduyrzUnsL8Xsc7r\nJrCeqyiggAIKjIjA/CNyHB6GAgoooEC9BfKG3COlJuYKhpml6RUYn1KanuxotpftP9rhgflb\nl/nl/W3XYb3Mam9XebVs5+HyDMcVUEABBRRQQAEFFFBAgYkKnMSK+5GzyAPkh2RLciV5iPyU\nLEz2Jfe1chTDvcmFZCpJvYWcT5bMRFstzvTPyK3kZvIjkm2mliDfJv8k2f+3yCIk9Uvy2llj\n88yTdr6TnEPSAfo7WY+kPkJ+Q44laWP28zZiKaCAAgoooIACCiiggAKTEjiPtS8nLyDPIneR\n68nzyJrkHvJqsgD5OjmcLEQWJOkQfYOsSO4g3S5z+wDLjiHLkHSI/kjeS1LvI8eRbG8tkg7Q\nm0kqbdt51tjs8asZ34RMI6eTdNRS6SDlzNReJPvIZYA5jjk528XDLQUUUEABBRRQQAEFFBg3\ngXRC0oEp6kxGPl9MMDyZFMu/zPhhpWXPYPxBcgH5Tml+++g6zFiKpJOVTtdvSblzcxnTm5Fc\nHrdYKwwe10FKZ6qoPRj5c2siHaScASvqyYw8SlYpZjhUQAEFFBgtAW/SMFrPp0ejgAIK1E0g\nZ4yKmsHIxcUEw5yZScelU6VjdDzZgBzQaYXWvKcwzOVvubzueyQdmKIOZeQE8hNyGzmCLEo6\nVS7DK+p+RnImq6jyMeRSvVR5+ew5/quAAgooMBICdpBG4mn0IBRQQIHaCuRsyxOpXIb3KpLL\n4g7usoFc5nYk+QNZljyb/IkUtTIjOUO1HNmS5KzPPqRT9WpnOnKWAgoooMCYCNhBGpMn2sNU\nQAEFai6Qs0uLt9qYszzfIoeQHcjLyS4klTNEu5N83mgRsjxJB2kmWZ3k9t3F54P2Z3xfksrl\nfecSz/xEw1JAAQUU6CrQ7dKGrg9wgQIKKKCAAkMQKO4U9022fR/JXe4ObA1z1ucL5Pckl9R9\nlZxMcgOIT5OfketIOlmfI/nc0Hat8Wx3N3I7yU0h3kQsBRRQQAEFFFBAAQUUUKD2Ajkj1O0z\nQr0avyQLVyutkMvtijNFuTHDxiTfs2QpoIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCgxdYMrQ9+AOFFBAgfEUWJ7Dfk3r0H/D8Jo2hp2YXpJc\nQk5tW1aXyYVpyC4TaMxxrHPnBNarYpV52cl2ZF1yMfkJaVrtSIMXJ38gl3do/JOY98a2+Y8y\nfSO5gLT/X2tb1UkFFFBAAQUUUEABBaoXWJRd3kvywnX/tt2nYzSjtWzXtmV1mlym1cYcQ6+s\nV6NGf73U1u/WqF2TacqVrWPo1jmd1lre6Tl5iGXvI5OtfXnAL8jOk32g6yuggAKjJjD/qB2Q\nx6OAAgrUROB+2vFLkjNFW5MDSFEvZyS/f/Nits5nOP5F+44iRW3OyBrkn+TXxUyGdTl7lCa9\nutWudJS+1hof5cGxHNytJGf7NiSbkEPJCmQfMtF6Fiu+ipw30Qe4ngIKKDCqAnaQRvWZ9bgU\nUKAOAnnxmg7SpmRZkheyqVfOHsxzAsNy52ILprckq5MryG9JLrNqr37rbcUD1iRnkEfItiQd\nth+Rq0heCKezczv5CrmLdKqcAXtbacExjKeD9I+2+VnlFWQdkvY+QHLc3yeXkbRlO5LH5rjS\nrrPJwyT1ErIeybzsM+vOS35Msq9ypbMZo5yhO52cSq4hU0mOa2mSuoXkMrWiVmXkDeTpJMd7\nLvk2KdqwFuN5XnKZWp6X/yA5jnNI9nczyb6K48hzcwp5amvekgxzvH8l5XomE7nUckVyPvk5\nuY6UK5fUPZ/cQL5TXjCB8YNYJ9stKp2j95G9SJbdQVK9noO4xScVnxzjTzNB9Xrc7DX8VwEF\nFFBAAQUUUECBCQoswnr3kEfJm1qPmcIwL8LL87LoU615mV8knZv9Sbkmsl46QtnGSWRmazzT\n15JPl6YzL5/TWZBMpI5hpTzmdx1WTmcwy44i6Yxl/IVkc5JOT6bLSUehqKMYybKTyb9a48W6\nH2K6qM8yUswvhjm+dGbW77DseOal0qFMZ7B4TDE8jXnpWKW2J5mfDtGfW+OfZ/i61vjlDNO5\nKx6b4UfIXaV5DzL+AlLUOxmZQcqPyXP/zGIFhulUlZdnH7e25u3CsFNNY2bxmA3bVliptCxt\nT21Oej0Hf2d5sb0M8/8k1e9xs9fyXwUUUEABBRRQQAEFJiGQz8HkRWdxZmDj1nTOThRnOPKi\nOuukQ/QNshX5FinmbcJ4aqLrFR2kdM7SocpZhYdJtpf9HkC+2JrOvG3JRGoiHaRsLy/yc7zr\nkONI5v2QPIfs2ZrOvJVJ6iiS6Rz/h0nOqFxIMm86ST2FZDp5MVmFHEEynU7NwiS2OebM252s\nRdL5u5Jk3pkknamPkKLDlA5januSdZK7Sc6gvJG8jhTzM+/d5IbSvONb83LGKusVz/MajKfD\nlHkHk5eRXHKZ6Rxb6jWk2PYvGE+bzyvNeyIdJB7+7w7qBzNB9XsOnsE6J5G05ShSdOD6PY5V\nLQUUUEABBRRQQAEFJieQzkdeeOYF9Lzko63pdGKKOpKRrJMXx+VKRyPzD2vNnOh6RQfp6NLG\nzm9tK52loq5jJNvPC/NcSpUX1OUsyXS5JtJBupwHLFZ6UDoG25BlSTpE7yLZZ/JskjqKZPrk\nTLQqHZnMe6Q1nTMjM1vzcrZqB7IaSbtXJ0XdyUgel7NGqVeQTCdrk6IOYiTz8rykyh2kF86e\nNevfooN0H1NTWvP/m2EeG7+i/o+RzDupNeO9remcnZlKliDpiOR4st5GpPDMmayiYvQwyTq7\nFDPbhtOYzvJkQ1KutPFekmX7thZM5DmIaR7zydZjMpjI40qrO6qAAgqMhsD8o3EYHoUCCihQ\nW4Hf0LKckUgHYROyNUnl0qqi1mmN/K6Y0Rr+nmHORKzbmp7oeq3V57mtGGF4e2v85tK8+1vj\n8zF8GjmktCyj6WilwzGZOoOV05ko6nJGPkGOIMuRosPD6OPqktKcO1rjRafkeqZzFmqnUtKR\niGP5RT2Tj6nC7jrmXlZaEtv9SJ6XpUvzH2D8T6XpYjTtSQciVVjeNHty1r+FZfF3da3Wsqcy\n7GQ4jfl5blOnzh7M+jftvJoUy0qLJjS6KmsVHdRsKzWZ52D2I2b/+0QfV96G4woooEDjBIpf\n5I1ruA1WQAEFGiKQz9T8jOzcynMYpgPxS1JUOlCpFWYP/v3v8q2xYnkx7LfevzcwiZFrWffo\ntvVzudpkqzgjUzzuREbSWTid7NMapqNSdHwY/Xelw9OrXs/CtPG15KVkdfJmkjMz7WdSmDWr\nCrOlmVqQPDR79r+t02Erd+jS+enViWs9vOug6EQVHaaLWPPzHda+gHk5I5ZaZvZg1r/5u/zk\n0vRkR4sOeB53ZuvBk3kOyvt7oo8rb8NxBRRQoHECdpAa95TZYAUUaKBAPsuRDtIeJJfZ5XMr\nxQtoRmfdUS0vbPOifyq5i+RF8+Ykde7swYTXa60+4UFe1J9H3jrhR0xsxZwFSecotQu5iryG\ndOocMbtnvZKlu5ObyG6tNbdimDN06SBlX1eS9irsFmVBjNNZnY9sS1IXknRiB1VFB+ni1gZz\nWV86wzeQdHzeTlK3kr+T3L3uZWQJks7cq8jiZLKVs0bx+GzrgacwzBmgyT4HsUlN9nGzH+W/\nCiigwAgI2EEagSfR5vgzwAAAImNJREFUQ1BAgdoLnEALc5nVkq2Wli+vy6zPkXeTvIC+lvyW\n5MV8XvTeRr5EUhNdb/baE/+3eFE/8UdMbM1rWC0dgxXJh0k6MEXnhtFZ3wWV4UTqRlbahsxL\nppFTSC5ZTKWzEbdOlTM1PyXbkZ+Q2KbTsiZJHTB7MLB/C8tj2eJHSDoaef7/QNLJW438nhxE\n/o/sStKJnE7+Sl5Ccmap6Kgw2rPOYWn2mfVjk7qKpEOauoZM5DlI5yyVz1zNIP9DJvI4VrMU\nUEABBRRQQAEFFJi8wFE8JC9k7yQLdXj4M5l3Cck6RS5ifANSroms9yMekG0cWnrg71vz9ivN\nu7Q1b/fSvF6jx7TW/12HldIhaN9nVkuHKB2YLMtZs3QacuYk0x8lqaNIpr+QiVZtwTDzkqJy\n9uV2UszP8AqyOSkqvpmfxxeVszNF24vH3su8/yxWYLg9ybLrSvMymg5D+/y9WvPOzQqt+hTD\nrHdyMYPh08gfSbHPDNNBW4EUtRMjaUuWPULSafpTa7ro5DD5mJrGVHmbxWMvZP5XyWqkXBN5\nDjbjAYXtta0HT+Rx5f04roACCiiggAIKKKDAwAXy4nZzsmqfLU90vT6bqWRxrlbYiCw4gL0t\nwjbSaUwHaD2SbU+0lmTFF5CnkwUm+qABrJfnMvtdrsu2Fmb+xiSXVQ6rJvIc5CxU2pgzmUVN\n5HHFug4VUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nYK4JTOmz5y1Yfhp5uM96LlZAAQUUUKCpAhvQ8NXmQuPzt/VEMnMu7NtdKqCAAgp0EejXQbqc\nx00lx5LvkD+TptX8NPjJPRq9AMtuJDN6rOMiBRRQQIHRFfgHh7Y6qfrvwJPY54vJqWTUa2EO\ncCeSv8lV12XsMG/2WgoooMCEBPr9olqfrWxJdiS/ITeTb5OjyTWkCfVxGvmRPg09heVb9FnH\nxQoooIACoykwH4f1bnJ4xYeXM0fZ9zjU8znIvHa4suKDXYL93UqeWvF+3Z0CCoyJwIIc56vJ\nhSS/1H9BXkrqXnnXap0eOYFlX6r7Qdg+BRRQQIGhCVzKlncb2ta7bzh/S1/SffFILcnrhblx\nuf7u7PeSkZL0YBRQYOgC/c4gpQHzkheQHcj2JI/5IrmWfIN8qjVkUMt6kFblj1+3upcFXv/d\nTcf5CiiggAIKKKCAAgqMkUC/DtIhWOxMFic/JW8nJ5KiQ3E243XvINFESwEFFFBAAQUUUEAB\nBRToL9Cvg7Q8m/gg+Qm5r8Pm/sa8d3aY7ywFFFBAAQUUUEABBRRQoHEC/TpIu3BES5F8yDEd\npGXJbeRRkrqjlVkT/qOAAgoooIACCiiggAIKNFmgXwfpjRzcN8me5AiSW3TuRfYmubmBpYAC\nCiiggAJPTGAKDzuA5A56VVbe8MxNKR6ocqfuSwEFFGiKQL8O0gc4kHeQb7YO6DCGOWt0KHl6\na54DBboJ/AcLPt1t4ZDnf5Xt5/+ppYACCtRZIFdkXF1hA3NFyK4kX39R5X4rPER3pYACCsyZ\nQK8O0mJsegPyvbZdZDovPFclTfgupFwmmNt8dqt1WXBWt4XOnyOBfO9E7oJ48BxtZfIPfisP\n2XDyD/MRCiigQOUCuQFSlW/mTGN/6SDlb3iv1wAsHmitMNCtuTEFFFBgiAK9fjnmFPz1JJfZ\nHVVqw1aM57NI+eK1JtRFNPKXPRo6lWW391juojkT+CcPr/rLF5/HPsflyxfn7Nnx0QooMG4C\n+fud+sPsQaX/Fp9frnSn7kwBBRSYrECvDlK2lcvrfkz2IX8nq5NnkA+S+0kT6lwamXSrTVhw\nW7eFzldAAQUUUGCEBBZqHctrGZ5T4XHtwb4+XOH+3JUCCijwhAX6dZByI4a1yRtIOkcXknyw\ns1eHg8WWAgoooIACkxZYk0esP+lHzfkDckn5gnO+mUZt4WZae12FLb6zwn25KwUUUGCOBPp1\nkPIHY6PWHm5sDXOJXXJga3oYg7zDtSPJfvJdTLnTzgWtpNPmL1oQLAUUUGDEBD7P8bycPFjx\ncS3O/jateJ/uTgEFFFCgpgL9Okjfpt2vIDkNX9UldYuyr+PJeuSvJF9GezdZmuxHDiKvJrnk\nz1JAAQUUGB2BfHbw/0h+11dZ/2JnU6rcoftSQAEFFKivQK8OUv5QbdvKbys8hHR+FiG51KLT\nu4jHMv9j5PXEUkABBRRQQAEFFFBAAQUGJpBbMHermSy4g1T5Ic60ZR1yBunUOcryw8izMmIp\noIACCiiggAIKKKCAAoMU6HUGKfvJHWdyi+b3kNyu+RFSVHm8mDeI4cls5DhyNMnnjsqV68Tf\nRc4uz+wzvgHLX9pjnZypurfHchc1TyCfYcslmZvNhabnslD/P80FeHepgAIKKKCAAgoMQqBf\nBynftJ0ORG4H2l7Dul47Z49yliidoCtJvox2BplG1iCXkHwuaqL1HFZ8S4+VV2VZOn/W6Ahs\nzKGsSybz/2RQR583FQ4a1MbcjgIKKKCAAgoooEC1Av06SFvSnH7rDLrF+SK53CHvuyTfUZRL\n7p5MTidnkVPIZM5efYP1k26V73m6ttvCEZk/jeP4EOl1SeUwDjV3QJw6jA332WaO8wqS/ztV\n1insbIEqd+i+FFBAAQV6CizH0uQrPdcazsJcEfPD4WzarSqgwDAF+nV+cgYnN2rYiSxMcqld\npr9Ghlm5RCqXR21Flie5zff9ZFmyBPE23yBMop7LuruQH03iMYNYdSU2ku8XmVs1mY70INro\nt8QPQtFtKKCAAoMTyNUEed2w1OA2OaEtbchaqxI7SBPiciUF6iXQr4O0B809gBxD3kweInuR\nfK5nTzKM8jbfw1Cd3anMc1hl5TurcomjpcAgBfIGynyD3OAEtrUM6+xOqj4LmzOw2fdFpOo6\nhR2eVvVO3Z8CQxB4mG1WfefbQ9jn+kM4FjepgAIVCPTrIO1DG7YmfyF5cX0L2Ybkc0CfIDeQ\nQder2aC3+R60qttTYDQEVuEwcma73++uYRxtzhCeOIwN99hmLlNNB2nFHusMY9GabPQZxA7S\nMHTdpgIKKKBArQV6vciYj5bnEqmr2o5gOtM3kvzBHkYHKZ8bOYP0us334Sy3FFBg/AQW55Dz\ne2tLUuWltjmbvivZilRZP2FnOdYXVblT9vUl8pSK9+nuFFBAAQUUqIVArw7STFp4DtmXfKzU\n2u0Zz00TLi7NG+RoPtR4HDmaDOI234Nsm9tSQIF6CJxHM3JGu6rKZy8tBRRQQAEFFBgDgV4d\npBz+20k+R5J3TnMt/HSyAtmNDOu7XnL26DByNsmlNNeQGWQaWYPk8r7J3L45dxXr9U5obj4x\nhVgKKKCAArMFcoVAPmv6xYpBnsr+bq54n+5OAQUUUECBxwj06yD9g7XXJduRtUjesc01+JeT\nYdWgb/O9Pw39SJ/GplNmKaDA5ATexur5fEyVtVyVOxvjfeX3/Uokb4hVWcuzs1xmbSmggAIK\nKDDXBPp1kNKw3F77mIpbuBD7G9RtvnMziaN6tD9nqy7tsdxFCijweIH8jB5B/k7ue/zioc1Z\nsrXlpRlWeYnd0A6oxhu+jbbtUHH7PHtUMbi7U0ABBRR4vEC/DlJui5k7ynWqXp2OTutPdN6i\nrHg8WY/8lfyN3E3ygmg/chDJne7ywmwi9S9WuqLHinlx90iP5S5SQIHuAv/Joj93XzzwJfnZ\n/znxstiB07pBBRRQQAEFFIhAvw5Sbu1dXEKTz/LkM0D5srU/kGF1kPICyNt8g2ApoIACCiig\ngAIKKKBAtQL9Okj5zqP22pkZO7bPHOB0rj/PZ4Ie7LLNXBJ3eJdlzlZgbgrk52k5skHFjcgZ\n0Hxe0DOhFcO7OwUUUEABBRQYPYF+HaROR/xtZn6G5JK32zutMIfzTubx3uZ7DhF9+FwRyB24\nnk/eORf2ns+K/Ggu7NddKqCAAgoooIACIyXQr4M0b9vR5stjtye5bfaw3q3O2aOcJTqbXEmu\nITPINJJL/CZ7m28eYilQiUA+F3MayV0fq6x8Hm9t8owKd5pLbi0FFFBAAQUUUGDkBPp1kK7n\niHPb1XLNZOJQcmd55gDHH2VbB5Lvkk1ILrnLF9OeTs4ip5Bhdc7YtKXAHAmkMz+MM6u9GpXP\nCebmJUnVlY5ZlTdpqPr43J8CCiiggAIKjJlAvw7SFniU10nn5SpyfwVOV7OPZE7rrWxg9x4b\nSQcsHS9LgaYK5MzVEWSfCg9gcfY1nSxY4T7dlQIKKKCAAgooMHSBcuen0862ZOaTOi0ozUsn\nJmd7BlnptOT2wSuTr5NTSVH/wUg+BH9AMaPP8HyW/6zHOrlT3209lrtIgSYI5Hb2VZ65ypky\nSwEFFFBAAQUUGDmBfh2kjTjit5J8F9EVZBWSeel0XEdS+d6iQVY6R+eRc0k+g/Rr8gGSzyWl\nViLrzxqb2D/5LqWkWz2bBXaQuuk4XwEFFFBAAQUUUECBMRLo10GaisVuJJfvFJW7dB1JXkty\nyd2gK3fjOpNs0drw1ximk/Qbkg6TpYACCiiggAIK1Fkgn51ei+w/Fxp5PPv8y1zYr7tUYGQE\nenWQcse6V5C3tR3tn5ieQvLh7Evblg1icgk2kjvYFZX9HU7+l6RTZimggAIKKKCAAnUWyNc+\nTCPFm71VtTVX2OTGPXaQqhJ3PyMp0KuDNJMjvonsQQ4uHX1++FYjt5bmDXL0HDaW/X2J5Bbf\nqf8hF5L3k4eJpYACCiiggAIK1FUgbyTnbr8vrriBP6p4f+5OgZEU6NVBygG/g+QGB/9FTid5\nV+K55EAyrA+E/5htv4ZMJ98hu5C7yY7kFyRtPpFYCiiggAIKKKCAAgoooMBABfp1kE5ib6uT\nN5I1yD/Ix0m+DHNY9QgbTqfoAyQ3hSjqTEZyWd/mZKFipkMFFFBAAQUUUEABBRRQYFAC/TpI\n2c/zyKZkYZJL37Yl+VxQLsEbZt3MxpNy5XR1zmhNpp7Byi/v8YC1WHZfj+UuUkABBRRQQAEF\nFFBAgTER6NdByuePDiDHkHxf0ENkL7IB2ZM0odK5e0OPhq7Msht6LHeRAgoooIACCiiggAIK\njIlAvw7SPjhsTf5C0kG6hWxDLiGfIE3oWOQW5Um3ymeeru220PkKKKCAAgoooIACCigwPgLz\n9jjU3OY7X8p6Vds605m+kazYNt9JBRRQQAEFFFBAAQUUUKDRAr06SPmMUW65vS9ZsHSU2zP+\nZHJxaZ6jCiiggAIKKKCAAgoooEDjBfpdYvd2jvAEsiuZSqaTFchu5F5iKaCAAgoooIACCiig\ngAIjI9Cvg5Tbeq9LtiNrkXwGKd9BdDmxFFBAAQUUUEABBRRQQIGREujVQcrldy8guUFD7mJn\nKaCAAgoooIACCtRXYDGalje0d6q4iY+yv5PIHRXv190pMBSBXh2k7PB75D0kd3prauXzU8v3\naPwiLOv1WaweD3WRAgoooIACCihQG4H1aUle82xScYuWYn97ky9WvF93p8BQBHp1kB5hj+8n\nHyT5wta/knI15ctVP0ajP1JueIfxMzrMc5YCCiiggAIKKNAkgSk09iKyUcWNPo/9+WZzxeju\nbngCvTpI2etnSW71fVom2io/hE2ofF9Tr+9B+grLL2vCgdhGBRRQQAEFFFCghgIL0aacvXp1\nxW3LHZdPJDMq3q+7G3GBfh2k53D8+T6kJte/aHz7dzmVj+d+JnK2zFJAAQUUUEABBRSYvMCq\nPCR3PH795B86R49YnEe/kvx6jrbigxVoE+jUQcr3HK1HPkVuIPkMT9ZLR8JSQAEFFFBAAQUU\nUKAskKuKfk7yGrLKuoedNf2N/Cq93NcEBTpdL7oWj3126fF7MJ6bNVgKKKCAAgoooIACCiig\nwEgLdOogjfQBe3AKKKCAAgoooIACCiigQDeBTpfYdVu3yvn5sN+OZCuS21U+QC5o5QSGdxJL\nAQUUUEABBRRQYHwF8lUtPyC5WUOV9SA7ezr5Z5U7dV/VCdSxg7Qoh388yeegcmvxv5G7ydJk\nP3IQyV1S/k6aVkvS4INJPtdVZa3OzvLlcZYCCiiggAIKKDAqArkS6ifkuxUeUF7LfYfkjfzr\nK9xvdnUbObXifY7l7vKhuvbalxlvJF9tLdiC4bql6dbseb5SjAx4+Dq291/kJSQ99PY6lhn5\nxubXty/oMr0r8/fssiyz1yRnka0zMeTKF7edPeR9dNt8zM7ptnBI89dhu08ifxnS9rttdoPW\ngnSuq6xnsrPczOSSKnfKvvL/6hZydYX7zR+ljUnuEHlrhfvNH6a1Sc4o5w6VVVW+7mAFUvXP\nUD4TOpWcS6qs9dlZ3kA7v8qdsq+NSJ7Xf1S83/xfvp30uuPpMJq0KRu9htw0jI132WZ+Jz+V\n5E3G+7qsM4zZy7PRVUjVfwNXZ595g7Xqn6EY52qY80iVtSE7e5hcVOVO2Vd+hu4kV1S83/z9\nyw3Fkqoqz+szqtpZ237yWu7CtnlVTOb/8S5V7Kgu++h0BukuGrcE2aetke3Tw+og5UX1GaRT\n5yhNOowcnpEJ1l9Z7wc91s2tKU/usXyQi/Kf+r1kwUFudALbylm5/JGo+hdm/igll5Mq60+t\nnVX9zk46gneQvNCqsvJCZzqp8sVOju9Skk5o/hhXVemYbURiXWUtzM7SMcvxVllLsrPlSKyr\nrNPYWX5P5cV7lZUO6L0kHf4qK7+bryP3VLlT9nUZyc/vQxXudwr7ehbJz1BebFVV+f+UTkPV\nne4l2OeK5GJSZeXnNn97p1e5U/Z1JsmbDDdVvN88r9lnXkNWWZews/x+7PaacVhtSYcwry+r\n/JqYvGbPG8DZb9VV9eu4qo/vcfvLL8q61fNp0HHkleSCtsYtzvSRJH9M3tS2rAmTi9DIQ0jV\nHZUm2IxCG9MJvZOkk2SNlkDeMVyL+LM7Ws9rcTTrMJI3VKp+k6HYv8PhCeSMWTpIeRFtjZ7A\nUzmkA0nVHcLRk/SIHiPQ6QzSY1aYCxM5e3QYyWn4K0newZxBppE1SN4teAVpYr2IRr+L5Biq\nfOeuiVZNbPOqNDovsG5rYuNtc0+BxViaF1l5x98aPYH8bcnPbdXvfo+eZP2OaCpNypUMVV8+\nWT+J0WxRzuznktyvjubheVQKPF5gNWZtTz5EDiX7kZeQeUlTa1Mano5RTrtboydwKof0sdE7\nLI8IgdeQu5UYWYGcXdhtZI9uvA9sdw4/b0paoymQy2K3Gc1D86jmpkAdzyAVHlczklgKKKCA\nAgoooIACCiigQCUCTT4bUwmQO1FAAQUUUEABBRRQQIHxEbCDND7PtUeqgAIKKKCAAgoooIAC\nfQTsIPUBcrECCiiggAIKKKCAAgqMj4AdpPF5rj1SBRRQQAEFFFBAAQUU6CNgB6kPkIsVUEAB\nBRRQQAEFFFBgfATmG59DrcWRzqQVuX3590lu922NlsBKHM65ZOy+cXq0nsaOR5Of16XIzzsu\ndWbTBaZxAL8j+bJYa7QEFuBwcsfeE0brsDyalkC+B+mn5HZFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFxl1gfgA2JauMO8SIHv/T\nOK4FR/TYxvGwVuWgl+xw4FOYtx5ZuMMyZzVHYMMuTV2G+c8hy3VZ7uz6C3T72S1avgYjaxYT\nDhsn0O1n19dYjXsqbbAC88yzMwg3kovIo+RAYo2GwJs5jMvJ9eR2cgixmi3wJJr/T7JT22Fs\nzvR15FpyH3k/sZonsDVNzu/hdHaLmo+Ro8mD5BryCDmAWM0S6PazWxzFsozcQD5TzHDYKIFO\nP7s5AF9jNepptLEKzBZYjUFeOOfsUep55H6yVCasRgvkjFFeSG/XOoqnM/wX2ag17aBZAgvR\n3EPJ30heQJc7SHnhNZ3sRlI5i3QveUEmrEYI5OfyW+QW0t5Beh3z0vFdnqS2JTPIUzJh1V6g\n189uufHHM3EbsYNUVqn/eK+fXV9j1f/5a1wL521ci5vZ4LyzcRk5u9X8MxhuQvLH12q2wMo0\nP5finNs6jIsZ3kFWb007aJZAziikw/ttcndb05/P9KLkiNb8PNd53vPC2mqGQDpFF5IfdGhu\nOrq/JHn+Uz8jd5GXZ8KqvUCvn92i8e9iZCb5Fcn/Bas5Ar1+dn2N1Zzn0ZYq8BiBo5n6Osk7\nl/8gXyaeYQBhROonHMefyd4kf3jzAiwvpK1mC+SSyfIZpHcz/fu2Qzqc6V+0zXOy/gLpDOUF\nV15UF5UzEAsUEwy3ILncbtnSPEebIdD+s5tWb0CuJHk+87fYS6FBaGB1+tn1NVYDn8i6N9kz\nSNU8Q2uxm13IpWQvkkvrTifTiNVsgXxuIWcS8qHRV5LnkpxBWoRYoyUwjcNpP6uUS7J8rkfj\nec6lsTNIfqZzpiFnkD5GbiVWswXyM3oM2ZP4fDb7uezUel9jdVJx3hwJ2EGaI75JPThnGD5J\n8g50PtR/P9mSWM0WeA3Nzwf11yYvI/m8wvJkV2KNlsA9HE6e23ItwcTfyzMcb7TA+rT+HPKf\nJL+fPcsAwghUOrr5HHAur8vv6RXINLIZsUZDwNdYo/E81uYo5q9NS0a7IddwePnFXNQjjNxJ\n8k6l1WyBjWn++eS61mHkHehfkxeTzxJrdARyp8I12w5nXaZ/3jbPyWYK5LksXmTlA/z5PW2N\nhsADHEb+Bn+4dTjrMXyYTCGnteY5aK6Ar7Ga+9zZ8jEX2Ibjz6U5xeeOtmM8l3O0v9hiltUw\ngR1ob/7wvrDV7lzffjXJO5ZWswXaP8eQs0X3kre1DivPfc4q5cWW1SyBTp9j+ByHcDzJWcJy\nFm3WodlaBNp/dttRjmCGZwfbVZox3eln19dYzXjubKUCHQU+zdy8kL6B5PK6XYg1GgKf5DBy\nBulaknclf0H8XAoIDa9OL7K24pjSSbqJ5HuQ/pNYzRPo9CIrdyTMjRvas1fzDm/sW9zpZ7eM\nYgeprNGs8U4/uzkCX2M163m0tQo8RiA3Z8iH+X1H8jEsIzGRSzVyhnDaSByNB9FLYEEW5uc4\ndz2zFFBAAQXqIeBrrHo8D7ZCAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUGDkBf7AET575I/SA1RAAQUUqL3AvLVvoQ1UQAEFFBgFgfLfm4xPaTuo9ZherG3eICaz\nn8V7bCjL2ttSXr3c7syfr7zQcQUUUEABBRRQQAEFFFBgIgJrsNIN5B3kWnITeTc5gNzZSqZT\np5OZ5DayAzmJfIUU9QNGvlxMlIY/Zfx/StMLMH4JeQlJx2Z/kn3dTy4iLyRFbcHI5STLss4+\npKjsfz9yFnmA/JBsSa4kD5Hsd2FiKaCAAgoooIACCiiggAITEliHtR4l3yOrkQ+2pn/McFWy\nB7mPpCOzCLmFbE3mJ88i6YhsRd5CbiZPIe21JzOml2a+kvF0xLKNt5HpZBrJWZ/s71ayLFme\n3Et2JDl7tAHJ43YiqfNIOk8vIGnLXeR68jyyJrmHvJpYCiiggAIKKKCAAgoooMCEBIoOUjpD\nqXSS0mFKpyO1NMl05qfSCcpZnaI+ysg15HaybTGzbZht/Its0pr/LYb/2xo/g+FBrfFikH28\njuxNrihmtoZfZpgzRal0kD4wa2z2P2cy+Hxp+mTGy8tLixxVQAEFFGi6QN65sxRQQAEFFBiW\nwA2tDeeMUOri2YNZnaOM5mxPpzqUmcuQq8nPOq3AvHSejic7kFzyth05iqTWIH+eNfb//7mK\n0RVJlqUDVa5cPpdlReWMUVEzGCnanXmPkG7tznJLAQUUUKDBAnaQGvzk2XQFFFCgAQLpTDyR\n+jgPylmeXNL2BtKtvsWCdJByed2l5EKSyueZ1p41NvufXGa3PrmOtC/LGhu2lmU8lbNblgIK\nKKDAGArYQRrDJ91DVkABBWookLM0i7fatRnD95BdSG6ekMvfirM7z2F8e1LUrxlZghxAirNH\nWfYrko7VNJLK2aXs4wSSZRuT3HghtRx5OTk2E5YCCiiggAIKKKCAAgooMGiB4jNIxRtxK7CD\nnJXJZXOppUimc4Yo9QWSmza8keRyt+LzQ1MY/x35DUkdTP4ya+z//5PPBz1I8pmkoqYyciJJ\np+hvJDdoyFmmovZj5GFyCbmLHEqyr1Q+g/T6WWOz/zmdwZ6l6Ww3j7cUUEABBRRQQAEFFFBA\ngaEJLMmWcyncIGt1NvYMsmCHjaaTtgnJ0FJAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFKiPwP8D+nFq+vFcakcAAAAASUVORK5CYII=",
"text/plain": [
"Plot with title “Voom-Transformed Data”"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Load the assay matrix (mtx.sub) and transform it 3 ways\n",
"load(\"../extdata/ampAD.154genes.mef2cTFs.278samples.RData\")\n",
"\n",
"mtx.tmp <- mtx.sub - min(mtx.sub) + 0.001\n",
"mtx.log2 <- log2(mtx.tmp)\n",
"\n",
"mtx.asinh <- asinh(mtx.sub)\n",
"\n",
"suppressMessages(library(limma))\n",
"mtx.voom <- voom(mtx.sub)$E\n",
"\n",
"# Plot them all\n",
"par(mfrow = c(4,1))\n",
"par(family = \"sans\")\n",
"hist(mtx.sub, main = \"As-is Data\")\n",
"hist(mtx.log2, main = \"Log2-Transformed Data\")\n",
"hist(mtx.asinh, main = \"Asinh-Transformed Data\")\n",
"hist(mtx.voom, main = \"Voom-Transformed Data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data distributions are quite dissimilar, so it follows that we might expect different results when using them, even if we're using the same solver. To explicitly quantify these distributions, we can also take Tukey's five number summary (min,1st quartile, median, 3rd quartile, max) of each one:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"As-is : 0 1.753137 12.34697 43.24747 1027.766 \n",
" Log2 : -9.965784 0.8107618 3.626201 5.434577 10.0053 \n",
"Asinh : 0 1.327453 3.208193 4.460219 7.62829 \n",
" Voom : 6.379003 8.825687 11.33609 13.10218 17.47137 \n"
]
}
],
"source": [
"cat(\"As-is :\",fivenum(mtx.sub),\"\\n\")\n",
"cat(\" Log2 :\",fivenum(mtx.log2),\"\\n\")\n",
"cat(\"Asinh :\",fivenum(mtx.asinh),\"\\n\")\n",
"cat(\" Voom :\",fivenum(mtx.voom),\"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we expected, the as-is data have a much wider distribution but are concentrated around small values. The log2 and asinh data both scale down the distribution in different ways, with the log2 transformation incoporating negative values and the asinh transformation finding its minimum at 0. \n",
"\n",
"Now that we've examined the distributions fairly thoroughly, let us move to using the solvers themselves to infer relationships between transcription factors and target genes. This will give us a better idea of how our distributions affect the solutions we infer and what we might want to be careful of in the future"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing Distributions and Solvers in TReNA\n",
"\n",
"First things first, we'll go ahead and load our function; this function will do several things:\n",
"\n",
"1. Load the \"as-is\" data and create the 3 different matrices we've examined above\n",
"2. For each of those 3 matrices, create and solve a TReNA object for each of 4 solvers (LASSO, Bayes Spike, Random Forest, Square Root LASSO) with MEF2C as the target gene. This means we'll have 12 total solutions. \n",
"3. For each of the 12 solutions, pull out the top 10 genes, then take the union of those 9 sets of 10. This will give us a list of genes representing the best hits for each combination of distribution and solver\n",
"3. Compile the scores of those genes from the 12 solutions into a unified table (tbl.all), along with the gene names and their Pearson correlations to MEF2C.\n",
"\n",
"Let's do that and produce our table."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] --- Testing LASSO\n",
"[1] --- Testing Bayes Spike\n",
"[1] --- Testing Random Forest\n",
"[1] --- Testing Square Root LASSO\n"
]
}
],
"source": [
"# Source the desired function and generate the table\n",
"suppressMessages(source(\"../utils/evaluateAllSolvers.R\"))\n",
"tbl.all <- assess_ampAD154AllSolversAndDistributions()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Just to get an idea of what we're looking at, here's the table. We can browse through this to see what the data look like. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom | bs.as.is | bs.log2 | bs.asinh | bs.voom | rf.as.is | rf.log2 | rf.asinh | rf.voom | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 0.77458827 | 0.134199069 | 0.209422903 | 0.24105628 | 5.930056e-01 | 3.228768e-01 | 8.583764e-05 | 3.468772e-01 | 1266161.9489 | 57.37132247 | 25.91603826 | 53.05247084 | 0.69519133 | 0.140776460 | 0.142896092 | 0.15897283 | 0.92323940 |
|---|
\n",
"\t| 5 | STAT4 | 2.60534321 | 0.157190023 | 0.076155038 | 0.09551869 | 1.708536e+00 | 1.832979e-01 | 8.946250e-05 | 1.708533e-01 | 649234.9844 | 46.37406567 | 22.47867569 | 48.94942356 | 3.47937884 | 0.190961358 | 0.158960329 | 0.17336532 | 0.90475987 |
|---|
\n",
"\t| 35 | SATB1 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -5.683068e-01 | -1.444623e-06 | 1.154825e-05 | -3.633166e-05 | 577058.4385 | 21.19406994 | 11.98698783 | 27.98823933 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.86282704 |
|---|
\n",
"\t| 15 | SATB2 | 0.13675630 | 0.216133895 | 0.228754703 | 0.26140384 | 1.035786e+00 | 6.942847e-05 | 3.566778e-01 | 2.853092e-01 | 424329.6976 | 26.35567531 | 14.22518618 | 30.46469273 | 0.02139235 | 0.181298914 | 0.226526723 | 0.23814460 | 0.84232989 |
|---|
\n",
"\t| 33 | ATF2 | 0.00000000 | 0.000000000 | 0.009811994 | 0.00000000 | 8.928837e-01 | 5.648769e-05 | 7.409788e-05 | -5.996884e-06 | 137474.9788 | 5.41129360 | 2.34014879 | 7.35197201 | 0.08809962 | 0.008485763 | 0.000000000 | 0.00000000 | 0.82786857 |
|---|
\n",
"\t| 4 | FOXP2 | 2.61508513 | 0.106667287 | 0.120449138 | 0.13231032 | 3.972476e+00 | 8.526235e-05 | 2.309917e-01 | 4.508482e-05 | 112640.2790 | 3.91445249 | 2.97052657 | 3.80445761 | 2.44496515 | 0.101105760 | 0.106358127 | 0.10469561 | 0.81961721 |
|---|
\n",
"\t| 11 | TSHZ2 | 0.69243601 | 0.104562826 | 0.115311122 | 0.09676352 | 1.086813e+00 | 2.277099e-01 | 1.056974e-01 | 1.235520e-01 | 84319.1960 | 5.12252917 | 2.25117903 | 4.60443070 | 1.00207756 | 0.100506784 | 0.094864545 | 0.08923052 | 0.77043615 |
|---|
\n",
"\t| 40 | DRGX | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -4.072208e-03 | 1.835087e-07 | 7.377633e-06 | 1.398560e-05 | 88685.5631 | 10.12862043 | 3.22875065 | 8.62751278 | 1.18904754 | 0.000000000 | 0.000000000 | 0.00000000 | 0.73094500 |
|---|
\n",
"\t| 1 | HDX | 11.26492428 | 0.000000000 | 0.000000000 | 0.00000000 | 3.241756e-01 | -2.498712e-05 | 6.947728e-05 | 5.735278e-05 | 53018.0228 | 1.43641155 | 0.71578023 | 0.86613053 | 13.37342127 | 0.000000000 | 0.000000000 | 0.00000000 | 0.72419056 |
|---|
\n",
"\t| 8 | LHX6 | 1.27950800 | 0.066352215 | 0.090529696 | 0.09315715 | 2.312552e+00 | 6.697943e-05 | 1.139972e-01 | 1.365092e-01 | 33707.1603 | 2.90733938 | 1.70273480 | 2.28153347 | 2.69414714 | 0.058348346 | 0.086557473 | 0.09288872 | 0.71461154 |
|---|
\n",
"\t| 12 | ESRRG | 0.66926539 | 0.019431010 | 0.046640155 | 0.01141497 | 3.927342e-01 | 3.936057e-05 | 2.318925e-04 | 7.762502e-06 | 66430.6662 | 4.29280477 | 1.61084504 | 3.77395550 | 0.45898430 | 0.004540240 | 0.010904356 | 0.00000000 | 0.70903181 |
|---|
\n",
"\t| 19 | TSHZ3 | 0.00000000 | 0.101236743 | 0.064662784 | 0.04451921 | -1.089643e-02 | 8.721712e-05 | 1.673391e-01 | 2.551630e-05 | 23971.7861 | 6.25117359 | 3.70888147 | 9.28067022 | 0.00000000 | 0.103275043 | 0.119569865 | 0.09920060 | 0.70562897 |
|---|
\n",
"\t| 38 | POU6F2 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -3.743652e-02 | 1.548796e-01 | 1.126459e-05 | -6.461413e-06 | 7735.8553 | 1.54216776 | 0.61268809 | 1.62421935 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.68969765 |
|---|
\n",
"\t| 14 | NFE2L2 | -0.32653069 | -0.015204118 | -0.057125918 | -0.04777479 | -7.803000e-01 | -1.304394e-04 | -8.658551e-05 | -1.444340e-01 | 16250.1818 | 0.62406681 | 0.26788921 | 1.05073252 | -0.01666623 | -0.010311681 | -0.001796439 | -0.01195379 | -0.63838196 |
|---|
\n",
"\t| 17 | CUX2 | 0.03676364 | 0.074610279 | 0.134597803 | 0.11559182 | 1.240175e+00 | 1.096828e-01 | 2.066298e-01 | 6.543902e-05 | 13627.7613 | 3.79983083 | 1.69602385 | 3.33980250 | 0.00000000 | 0.059819887 | 0.070841315 | 0.05577775 | 0.62765043 |
|---|
\n",
"\t| 18 | SOX12 | -0.02284905 | -0.065376926 | -0.009410002 | 0.00000000 | 2.385374e-02 | -4.011697e-05 | -2.834563e-01 | -2.815619e-05 | 75863.7094 | 1.74741861 | 0.71539459 | 1.06994890 | -0.05066787 | -0.062815144 | -0.063477372 | -0.09422693 | -0.62404571 |
|---|
\n",
"\t| 10 | HOMEZ | -0.75952764 | 0.000000000 | 0.000000000 | 0.00000000 | -2.571221e-01 | -2.773147e-05 | -1.190609e-04 | -2.070808e-04 | 2075.6167 | 0.23303020 | 0.11305491 | 0.13354243 | -0.05711910 | 0.000000000 | 0.000000000 | 0.00000000 | -0.58326686 |
|---|
\n",
"\t| 34 | STAT5A | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -5.317519e+00 | 1.619035e-05 | 5.634046e-05 | 6.416900e-05 | 2473.5968 | 0.70519199 | 0.07501966 | 0.34651510 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.57464748 |
|---|
\n",
"\t| 21 | FOXO4 | 0.00000000 | -0.059932873 | -0.038120414 | -0.09406223 | -7.525646e-02 | -1.089069e-01 | -7.967480e-05 | -1.649238e-01 | 100624.4622 | 2.53966391 | 1.35220598 | 4.10752152 | 0.00000000 | -0.060840837 | -0.054144051 | -0.09392238 | -0.55645498 |
|---|
\n",
"\t| 16 | STAT1 | 0.10044635 | 0.058977896 | 0.039676356 | 0.01249171 | 1.452155e-02 | 1.550252e-04 | 5.356998e-04 | -7.697100e-06 | 49426.5314 | 1.67592402 | 0.76834548 | 1.64999002 | 0.14023646 | 0.061726178 | 0.056424616 | 0.04170972 | 0.44791101 |
|---|
\n",
"\t| 28 | ZHX3 | 0.00000000 | 0.000000000 | -0.085656799 | -0.04603107 | 8.553841e-04 | -3.468578e-01 | -8.150376e-05 | -1.474584e-04 | 2775.8698 | 0.12460750 | 0.05507819 | 0.06762929 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.43410051 |
|---|
\n",
"\t| 23 | FOXD1 | 0.00000000 | -0.020514333 | -0.043780174 | -0.04197126 | -1.447640e-01 | -8.077647e-05 | -1.312632e-01 | -1.355494e-04 | 3940.9648 | 0.17796819 | 0.07402906 | 0.17825892 | 0.00000000 | -0.018612142 | -0.030152933 | -0.02430439 | -0.41483052 |
|---|
\n",
"\t| 20 | STAT5B | 0.00000000 | -0.072874603 | -0.124676757 | -0.24554706 | -8.615242e-03 | -1.028715e-04 | -6.899009e-05 | -5.172639e-03 | 1962.9467 | 0.14380142 | 0.09304024 | 0.14962969 | -0.03142925 | -0.044682367 | -0.088535616 | -0.20937298 | -0.37452622 |
|---|
\n",
"\t| 6 | NFE2L3 | -2.44526737 | -0.042077199 | -0.012117383 | 0.00000000 | -3.535628e+00 | -6.050182e-04 | -1.180878e-03 | 8.473173e-06 | 1780.2860 | 0.90328191 | 0.43550208 | 0.85687215 | -0.26084529 | -0.038226731 | -0.036547702 | -0.02745371 | -0.33931986 |
|---|
\n",
"\t| 29 | FOXL1 | 0.00000000 | 0.000000000 | -0.055243544 | -0.02501204 | -2.179699e+01 | -1.023130e-05 | -1.109603e-05 | -3.033104e-04 | 745.0412 | 0.04530632 | 0.01952745 | 0.06490894 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.28785265 |
|---|
\n",
"\t| 30 | RAX2 | 0.00000000 | 0.000000000 | -0.054872150 | -0.07062620 | 2.104629e+01 | -2.984847e-05 | -3.858662e-03 | -1.693215e-03 | 10191.6573 | 0.15838869 | 0.08666600 | 0.13254073 | -3.68593268 | 0.000000000 | -0.079070690 | -0.05256948 | -0.28106544 |
|---|
\n",
"\t| 32 | FOXQ1 | 0.00000000 | 0.000000000 | 0.012481789 | 0.01484751 | 1.497996e-01 | 1.024722e-04 | 9.058601e-02 | 1.950611e-05 | 1912.3582 | 0.07622154 | 0.02708449 | 0.04248428 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.26418329 |
|---|
\n",
"\t| 7 | SOX15 | -2.00952281 | -0.047167519 | -0.081356107 | -0.04556199 | -5.808046e+00 | -1.649264e-01 | -2.072168e-04 | -1.738663e-04 | 3073.3194 | 0.09674146 | 0.04575365 | 0.17657099 | -0.20871913 | -0.034963116 | -0.033217226 | 0.00000000 | -0.26062515 |
|---|
\n",
"\t| 13 | ZFHX2 | -0.44426232 | -0.064360137 | -0.076382960 | -0.03506666 | -9.485212e-01 | -1.449449e-01 | -1.972800e-01 | -5.449061e-05 | 4242.0609 | 0.10108451 | 0.05349746 | 0.11198257 | -0.05434429 | -0.044781408 | -0.056811985 | -0.02709282 | -0.24922722 |
|---|
\n",
"\t| 36 | FOXD4L6 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 2.001216e+01 | 5.598696e-05 | -3.493371e-05 | 3.952855e-05 | 2138.4440 | 0.38560704 | 0.08815874 | 0.26016141 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.24249449 |
|---|
\n",
"\t| 39 | SOX6 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 1.117300e-02 | 1.410110e-01 | 2.635437e-04 | 6.368482e-05 | 1286.4206 | 0.02964059 | 0.02545302 | 0.07550225 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.18744712 |
|---|
\n",
"\t| 31 | FOXE3 | 0.00000000 | 0.000000000 | -0.023970663 | 0.00000000 | -3.409200e+01 | -7.539808e-07 | -2.472980e-04 | 1.070491e-05 | 1436.7861 | 0.09033598 | 0.02945399 | 0.08047401 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.15567610 |
|---|
\n",
"\t| 3 | FOXD4 | -6.12834538 | 0.000000000 | -0.018794219 | -0.01121346 | -9.572871e-01 | -2.277717e-04 | -2.120234e-04 | -1.566068e-04 | 7525.4338 | 0.13565668 | 0.05591150 | 0.14653822 | -1.39091255 | 0.000000000 | -0.005870526 | 0.00000000 | -0.11598769 |
|---|
\n",
"\t| 26 | OTX2 | 0.00000000 | 0.000000000 | -0.104669744 | -0.06159170 | -8.116979e-02 | -6.058279e-06 | 1.893926e-05 | -2.229342e-01 | 1023.9351 | 0.11237214 | 0.05414230 | 0.15764033 | 0.00000000 | 0.000000000 | -0.035312214 | -0.01896358 | -0.11114757 |
|---|
\n",
"\t| 25 | FOXP3 | 0.00000000 | 0.004366561 | 0.061610938 | 0.02631893 | 8.865602e+00 | 7.248455e-04 | 1.555273e-02 | 1.446537e-03 | 1611.4674 | 0.06884909 | 0.02423162 | 0.08531106 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.10724617 |
|---|
\n",
"\t| 37 | ELF3 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 7.590501e+00 | -1.672171e-04 | 3.211889e-04 | 4.752853e-05 | 3326.0816 | 0.14770484 | 0.06169517 | 0.18053208 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.10576117 |
|---|
\n",
"\t| 2 | FOXD4L1 | -10.64033243 | 0.000000000 | -0.051396874 | -0.05015705 | -7.279581e+00 | -5.366559e-05 | -2.090862e-03 | -1.683918e-03 | 4436.9676 | 0.19584650 | 0.07824253 | 0.21936956 | -2.38450203 | 0.000000000 | -0.014643134 | -0.01698541 | -0.10509003 |
|---|
\n",
"\t| 22 | ZBTB16 | 0.00000000 | -0.020936732 | -0.035129792 | -0.07343806 | -1.758344e-01 | -2.965382e-04 | -1.843154e-04 | -2.008173e-01 | 1771.1880 | 0.07772086 | 0.03064144 | 0.06150837 | -0.02690810 | -0.011124576 | -0.014971105 | -0.04979472 | 0.06296197 |
|---|
\n",
"\t| 24 | ESR2 | 0.00000000 | -0.012150312 | -0.093702601 | -0.05702639 | 5.680673e-02 | -1.076067e-01 | -1.918502e-03 | -1.278182e-01 | 1254.3418 | 0.06494721 | 0.02666922 | 0.10061373 | 0.00000000 | -0.002660900 | -0.008303913 | 0.00000000 | -0.03964059 |
|---|
\n",
"\t| 27 | ESRRB | 0.00000000 | 0.000000000 | -0.091401413 | -0.05857560 | -3.068364e+01 | -1.117221e-03 | -7.120074e-04 | -1.932052e-04 | 1860.4361 | 0.08006934 | 0.04726370 | 0.07232247 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.03485913 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllllllllllllllll}\n",
" & gene & lasso.as.is & lasso.log2 & lasso.asinh & lasso.voom & bs.as.is & bs.log2 & bs.asinh & bs.voom & rf.as.is & rf.log2 & rf.asinh & rf.voom & sqrt.lasso.as.is & sqrt.lasso.log2 & sqrt.lasso.asinh & sqrt.lasso.voom & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 0.77458827 & 0.134199069 & 0.209422903 & 0.24105628 & 5.930056e-01 & 3.228768e-01 & 8.583764e-05 & 3.468772e-01 & 1266161.9489 & 57.37132247 & 25.91603826 & 53.05247084 & 0.69519133 & 0.140776460 & 0.142896092 & 0.15897283 & 0.92323940 \\\\\n",
"\t5 & STAT4 & 2.60534321 & 0.157190023 & 0.076155038 & 0.09551869 & 1.708536e+00 & 1.832979e-01 & 8.946250e-05 & 1.708533e-01 & 649234.9844 & 46.37406567 & 22.47867569 & 48.94942356 & 3.47937884 & 0.190961358 & 0.158960329 & 0.17336532 & 0.90475987 \\\\\n",
"\t35 & SATB1 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -5.683068e-01 & -1.444623e-06 & 1.154825e-05 & -3.633166e-05 & 577058.4385 & 21.19406994 & 11.98698783 & 27.98823933 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & 0.86282704 \\\\\n",
"\t15 & SATB2 & 0.13675630 & 0.216133895 & 0.228754703 & 0.26140384 & 1.035786e+00 & 6.942847e-05 & 3.566778e-01 & 2.853092e-01 & 424329.6976 & 26.35567531 & 14.22518618 & 30.46469273 & 0.02139235 & 0.181298914 & 0.226526723 & 0.23814460 & 0.84232989 \\\\\n",
"\t33 & ATF2 & 0.00000000 & 0.000000000 & 0.009811994 & 0.00000000 & 8.928837e-01 & 5.648769e-05 & 7.409788e-05 & -5.996884e-06 & 137474.9788 & 5.41129360 & 2.34014879 & 7.35197201 & 0.08809962 & 0.008485763 & 0.000000000 & 0.00000000 & 0.82786857 \\\\\n",
"\t4 & FOXP2 & 2.61508513 & 0.106667287 & 0.120449138 & 0.13231032 & 3.972476e+00 & 8.526235e-05 & 2.309917e-01 & 4.508482e-05 & 112640.2790 & 3.91445249 & 2.97052657 & 3.80445761 & 2.44496515 & 0.101105760 & 0.106358127 & 0.10469561 & 0.81961721 \\\\\n",
"\t11 & TSHZ2 & 0.69243601 & 0.104562826 & 0.115311122 & 0.09676352 & 1.086813e+00 & 2.277099e-01 & 1.056974e-01 & 1.235520e-01 & 84319.1960 & 5.12252917 & 2.25117903 & 4.60443070 & 1.00207756 & 0.100506784 & 0.094864545 & 0.08923052 & 0.77043615 \\\\\n",
"\t40 & DRGX & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -4.072208e-03 & 1.835087e-07 & 7.377633e-06 & 1.398560e-05 & 88685.5631 & 10.12862043 & 3.22875065 & 8.62751278 & 1.18904754 & 0.000000000 & 0.000000000 & 0.00000000 & 0.73094500 \\\\\n",
"\t1 & HDX & 11.26492428 & 0.000000000 & 0.000000000 & 0.00000000 & 3.241756e-01 & -2.498712e-05 & 6.947728e-05 & 5.735278e-05 & 53018.0228 & 1.43641155 & 0.71578023 & 0.86613053 & 13.37342127 & 0.000000000 & 0.000000000 & 0.00000000 & 0.72419056 \\\\\n",
"\t8 & LHX6 & 1.27950800 & 0.066352215 & 0.090529696 & 0.09315715 & 2.312552e+00 & 6.697943e-05 & 1.139972e-01 & 1.365092e-01 & 33707.1603 & 2.90733938 & 1.70273480 & 2.28153347 & 2.69414714 & 0.058348346 & 0.086557473 & 0.09288872 & 0.71461154 \\\\\n",
"\t12 & ESRRG & 0.66926539 & 0.019431010 & 0.046640155 & 0.01141497 & 3.927342e-01 & 3.936057e-05 & 2.318925e-04 & 7.762502e-06 & 66430.6662 & 4.29280477 & 1.61084504 & 3.77395550 & 0.45898430 & 0.004540240 & 0.010904356 & 0.00000000 & 0.70903181 \\\\\n",
"\t19 & TSHZ3 & 0.00000000 & 0.101236743 & 0.064662784 & 0.04451921 & -1.089643e-02 & 8.721712e-05 & 1.673391e-01 & 2.551630e-05 & 23971.7861 & 6.25117359 & 3.70888147 & 9.28067022 & 0.00000000 & 0.103275043 & 0.119569865 & 0.09920060 & 0.70562897 \\\\\n",
"\t38 & POU6F2 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -3.743652e-02 & 1.548796e-01 & 1.126459e-05 & -6.461413e-06 & 7735.8553 & 1.54216776 & 0.61268809 & 1.62421935 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & 0.68969765 \\\\\n",
"\t14 & NFE2L2 & -0.32653069 & -0.015204118 & -0.057125918 & -0.04777479 & -7.803000e-01 & -1.304394e-04 & -8.658551e-05 & -1.444340e-01 & 16250.1818 & 0.62406681 & 0.26788921 & 1.05073252 & -0.01666623 & -0.010311681 & -0.001796439 & -0.01195379 & -0.63838196 \\\\\n",
"\t17 & CUX2 & 0.03676364 & 0.074610279 & 0.134597803 & 0.11559182 & 1.240175e+00 & 1.096828e-01 & 2.066298e-01 & 6.543902e-05 & 13627.7613 & 3.79983083 & 1.69602385 & 3.33980250 & 0.00000000 & 0.059819887 & 0.070841315 & 0.05577775 & 0.62765043 \\\\\n",
"\t18 & SOX12 & -0.02284905 & -0.065376926 & -0.009410002 & 0.00000000 & 2.385374e-02 & -4.011697e-05 & -2.834563e-01 & -2.815619e-05 & 75863.7094 & 1.74741861 & 0.71539459 & 1.06994890 & -0.05066787 & -0.062815144 & -0.063477372 & -0.09422693 & -0.62404571 \\\\\n",
"\t10 & HOMEZ & -0.75952764 & 0.000000000 & 0.000000000 & 0.00000000 & -2.571221e-01 & -2.773147e-05 & -1.190609e-04 & -2.070808e-04 & 2075.6167 & 0.23303020 & 0.11305491 & 0.13354243 & -0.05711910 & 0.000000000 & 0.000000000 & 0.00000000 & -0.58326686 \\\\\n",
"\t34 & STAT5A & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -5.317519e+00 & 1.619035e-05 & 5.634046e-05 & 6.416900e-05 & 2473.5968 & 0.70519199 & 0.07501966 & 0.34651510 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.57464748 \\\\\n",
"\t21 & FOXO4 & 0.00000000 & -0.059932873 & -0.038120414 & -0.09406223 & -7.525646e-02 & -1.089069e-01 & -7.967480e-05 & -1.649238e-01 & 100624.4622 & 2.53966391 & 1.35220598 & 4.10752152 & 0.00000000 & -0.060840837 & -0.054144051 & -0.09392238 & -0.55645498 \\\\\n",
"\t16 & STAT1 & 0.10044635 & 0.058977896 & 0.039676356 & 0.01249171 & 1.452155e-02 & 1.550252e-04 & 5.356998e-04 & -7.697100e-06 & 49426.5314 & 1.67592402 & 0.76834548 & 1.64999002 & 0.14023646 & 0.061726178 & 0.056424616 & 0.04170972 & 0.44791101 \\\\\n",
"\t28 & ZHX3 & 0.00000000 & 0.000000000 & -0.085656799 & -0.04603107 & 8.553841e-04 & -3.468578e-01 & -8.150376e-05 & -1.474584e-04 & 2775.8698 & 0.12460750 & 0.05507819 & 0.06762929 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.43410051 \\\\\n",
"\t23 & FOXD1 & 0.00000000 & -0.020514333 & -0.043780174 & -0.04197126 & -1.447640e-01 & -8.077647e-05 & -1.312632e-01 & -1.355494e-04 & 3940.9648 & 0.17796819 & 0.07402906 & 0.17825892 & 0.00000000 & -0.018612142 & -0.030152933 & -0.02430439 & -0.41483052 \\\\\n",
"\t20 & STAT5B & 0.00000000 & -0.072874603 & -0.124676757 & -0.24554706 & -8.615242e-03 & -1.028715e-04 & -6.899009e-05 & -5.172639e-03 & 1962.9467 & 0.14380142 & 0.09304024 & 0.14962969 & -0.03142925 & -0.044682367 & -0.088535616 & -0.20937298 & -0.37452622 \\\\\n",
"\t6 & NFE2L3 & -2.44526737 & -0.042077199 & -0.012117383 & 0.00000000 & -3.535628e+00 & -6.050182e-04 & -1.180878e-03 & 8.473173e-06 & 1780.2860 & 0.90328191 & 0.43550208 & 0.85687215 & -0.26084529 & -0.038226731 & -0.036547702 & -0.02745371 & -0.33931986 \\\\\n",
"\t29 & FOXL1 & 0.00000000 & 0.000000000 & -0.055243544 & -0.02501204 & -2.179699e+01 & -1.023130e-05 & -1.109603e-05 & -3.033104e-04 & 745.0412 & 0.04530632 & 0.01952745 & 0.06490894 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.28785265 \\\\\n",
"\t30 & RAX2 & 0.00000000 & 0.000000000 & -0.054872150 & -0.07062620 & 2.104629e+01 & -2.984847e-05 & -3.858662e-03 & -1.693215e-03 & 10191.6573 & 0.15838869 & 0.08666600 & 0.13254073 & -3.68593268 & 0.000000000 & -0.079070690 & -0.05256948 & -0.28106544 \\\\\n",
"\t32 & FOXQ1 & 0.00000000 & 0.000000000 & 0.012481789 & 0.01484751 & 1.497996e-01 & 1.024722e-04 & 9.058601e-02 & 1.950611e-05 & 1912.3582 & 0.07622154 & 0.02708449 & 0.04248428 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.26418329 \\\\\n",
"\t7 & SOX15 & -2.00952281 & -0.047167519 & -0.081356107 & -0.04556199 & -5.808046e+00 & -1.649264e-01 & -2.072168e-04 & -1.738663e-04 & 3073.3194 & 0.09674146 & 0.04575365 & 0.17657099 & -0.20871913 & -0.034963116 & -0.033217226 & 0.00000000 & -0.26062515 \\\\\n",
"\t13 & ZFHX2 & -0.44426232 & -0.064360137 & -0.076382960 & -0.03506666 & -9.485212e-01 & -1.449449e-01 & -1.972800e-01 & -5.449061e-05 & 4242.0609 & 0.10108451 & 0.05349746 & 0.11198257 & -0.05434429 & -0.044781408 & -0.056811985 & -0.02709282 & -0.24922722 \\\\\n",
"\t36 & FOXD4L6 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & 2.001216e+01 & 5.598696e-05 & -3.493371e-05 & 3.952855e-05 & 2138.4440 & 0.38560704 & 0.08815874 & 0.26016141 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & 0.24249449 \\\\\n",
"\t39 & SOX6 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & 1.117300e-02 & 1.410110e-01 & 2.635437e-04 & 6.368482e-05 & 1286.4206 & 0.02964059 & 0.02545302 & 0.07550225 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.18744712 \\\\\n",
"\t31 & FOXE3 & 0.00000000 & 0.000000000 & -0.023970663 & 0.00000000 & -3.409200e+01 & -7.539808e-07 & -2.472980e-04 & 1.070491e-05 & 1436.7861 & 0.09033598 & 0.02945399 & 0.08047401 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.15567610 \\\\\n",
"\t3 & FOXD4 & -6.12834538 & 0.000000000 & -0.018794219 & -0.01121346 & -9.572871e-01 & -2.277717e-04 & -2.120234e-04 & -1.566068e-04 & 7525.4338 & 0.13565668 & 0.05591150 & 0.14653822 & -1.39091255 & 0.000000000 & -0.005870526 & 0.00000000 & -0.11598769 \\\\\n",
"\t26 & OTX2 & 0.00000000 & 0.000000000 & -0.104669744 & -0.06159170 & -8.116979e-02 & -6.058279e-06 & 1.893926e-05 & -2.229342e-01 & 1023.9351 & 0.11237214 & 0.05414230 & 0.15764033 & 0.00000000 & 0.000000000 & -0.035312214 & -0.01896358 & -0.11114757 \\\\\n",
"\t25 & FOXP3 & 0.00000000 & 0.004366561 & 0.061610938 & 0.02631893 & 8.865602e+00 & 7.248455e-04 & 1.555273e-02 & 1.446537e-03 & 1611.4674 & 0.06884909 & 0.02423162 & 0.08531106 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.10724617 \\\\\n",
"\t37 & ELF3 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & 7.590501e+00 & -1.672171e-04 & 3.211889e-04 & 4.752853e-05 & 3326.0816 & 0.14770484 & 0.06169517 & 0.18053208 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.10576117 \\\\\n",
"\t2 & FOXD4L1 & -10.64033243 & 0.000000000 & -0.051396874 & -0.05015705 & -7.279581e+00 & -5.366559e-05 & -2.090862e-03 & -1.683918e-03 & 4436.9676 & 0.19584650 & 0.07824253 & 0.21936956 & -2.38450203 & 0.000000000 & -0.014643134 & -0.01698541 & -0.10509003 \\\\\n",
"\t22 & ZBTB16 & 0.00000000 & -0.020936732 & -0.035129792 & -0.07343806 & -1.758344e-01 & -2.965382e-04 & -1.843154e-04 & -2.008173e-01 & 1771.1880 & 0.07772086 & 0.03064144 & 0.06150837 & -0.02690810 & -0.011124576 & -0.014971105 & -0.04979472 & 0.06296197 \\\\\n",
"\t24 & ESR2 & 0.00000000 & -0.012150312 & -0.093702601 & -0.05702639 & 5.680673e-02 & -1.076067e-01 & -1.918502e-03 & -1.278182e-01 & 1254.3418 & 0.06494721 & 0.02666922 & 0.10061373 & 0.00000000 & -0.002660900 & -0.008303913 & 0.00000000 & -0.03964059 \\\\\n",
"\t27 & ESRRB & 0.00000000 & 0.000000000 & -0.091401413 & -0.05857560 & -3.068364e+01 & -1.117221e-03 & -7.120074e-04 & -1.932052e-04 & 1860.4361 & 0.08006934 & 0.04726370 & 0.07232247 & 0.00000000 & 0.000000000 & 0.000000000 & 0.00000000 & -0.03485913 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom | bs.as.is | bs.log2 | bs.asinh | bs.voom | rf.as.is | rf.log2 | rf.asinh | rf.voom | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 0.77458827 | 0.134199069 | 0.209422903 | 0.24105628 | 5.930056e-01 | 3.228768e-01 | 8.583764e-05 | 3.468772e-01 | 1266161.9489 | 57.37132247 | 25.91603826 | 53.05247084 | 0.69519133 | 0.140776460 | 0.142896092 | 0.15897283 | 0.92323940 | \n",
"| 5 | STAT4 | 2.60534321 | 0.157190023 | 0.076155038 | 0.09551869 | 1.708536e+00 | 1.832979e-01 | 8.946250e-05 | 1.708533e-01 | 649234.9844 | 46.37406567 | 22.47867569 | 48.94942356 | 3.47937884 | 0.190961358 | 0.158960329 | 0.17336532 | 0.90475987 | \n",
"| 35 | SATB1 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -5.683068e-01 | -1.444623e-06 | 1.154825e-05 | -3.633166e-05 | 577058.4385 | 21.19406994 | 11.98698783 | 27.98823933 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.86282704 | \n",
"| 15 | SATB2 | 0.13675630 | 0.216133895 | 0.228754703 | 0.26140384 | 1.035786e+00 | 6.942847e-05 | 3.566778e-01 | 2.853092e-01 | 424329.6976 | 26.35567531 | 14.22518618 | 30.46469273 | 0.02139235 | 0.181298914 | 0.226526723 | 0.23814460 | 0.84232989 | \n",
"| 33 | ATF2 | 0.00000000 | 0.000000000 | 0.009811994 | 0.00000000 | 8.928837e-01 | 5.648769e-05 | 7.409788e-05 | -5.996884e-06 | 137474.9788 | 5.41129360 | 2.34014879 | 7.35197201 | 0.08809962 | 0.008485763 | 0.000000000 | 0.00000000 | 0.82786857 | \n",
"| 4 | FOXP2 | 2.61508513 | 0.106667287 | 0.120449138 | 0.13231032 | 3.972476e+00 | 8.526235e-05 | 2.309917e-01 | 4.508482e-05 | 112640.2790 | 3.91445249 | 2.97052657 | 3.80445761 | 2.44496515 | 0.101105760 | 0.106358127 | 0.10469561 | 0.81961721 | \n",
"| 11 | TSHZ2 | 0.69243601 | 0.104562826 | 0.115311122 | 0.09676352 | 1.086813e+00 | 2.277099e-01 | 1.056974e-01 | 1.235520e-01 | 84319.1960 | 5.12252917 | 2.25117903 | 4.60443070 | 1.00207756 | 0.100506784 | 0.094864545 | 0.08923052 | 0.77043615 | \n",
"| 40 | DRGX | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -4.072208e-03 | 1.835087e-07 | 7.377633e-06 | 1.398560e-05 | 88685.5631 | 10.12862043 | 3.22875065 | 8.62751278 | 1.18904754 | 0.000000000 | 0.000000000 | 0.00000000 | 0.73094500 | \n",
"| 1 | HDX | 11.26492428 | 0.000000000 | 0.000000000 | 0.00000000 | 3.241756e-01 | -2.498712e-05 | 6.947728e-05 | 5.735278e-05 | 53018.0228 | 1.43641155 | 0.71578023 | 0.86613053 | 13.37342127 | 0.000000000 | 0.000000000 | 0.00000000 | 0.72419056 | \n",
"| 8 | LHX6 | 1.27950800 | 0.066352215 | 0.090529696 | 0.09315715 | 2.312552e+00 | 6.697943e-05 | 1.139972e-01 | 1.365092e-01 | 33707.1603 | 2.90733938 | 1.70273480 | 2.28153347 | 2.69414714 | 0.058348346 | 0.086557473 | 0.09288872 | 0.71461154 | \n",
"| 12 | ESRRG | 0.66926539 | 0.019431010 | 0.046640155 | 0.01141497 | 3.927342e-01 | 3.936057e-05 | 2.318925e-04 | 7.762502e-06 | 66430.6662 | 4.29280477 | 1.61084504 | 3.77395550 | 0.45898430 | 0.004540240 | 0.010904356 | 0.00000000 | 0.70903181 | \n",
"| 19 | TSHZ3 | 0.00000000 | 0.101236743 | 0.064662784 | 0.04451921 | -1.089643e-02 | 8.721712e-05 | 1.673391e-01 | 2.551630e-05 | 23971.7861 | 6.25117359 | 3.70888147 | 9.28067022 | 0.00000000 | 0.103275043 | 0.119569865 | 0.09920060 | 0.70562897 | \n",
"| 38 | POU6F2 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -3.743652e-02 | 1.548796e-01 | 1.126459e-05 | -6.461413e-06 | 7735.8553 | 1.54216776 | 0.61268809 | 1.62421935 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.68969765 | \n",
"| 14 | NFE2L2 | -0.32653069 | -0.015204118 | -0.057125918 | -0.04777479 | -7.803000e-01 | -1.304394e-04 | -8.658551e-05 | -1.444340e-01 | 16250.1818 | 0.62406681 | 0.26788921 | 1.05073252 | -0.01666623 | -0.010311681 | -0.001796439 | -0.01195379 | -0.63838196 | \n",
"| 17 | CUX2 | 0.03676364 | 0.074610279 | 0.134597803 | 0.11559182 | 1.240175e+00 | 1.096828e-01 | 2.066298e-01 | 6.543902e-05 | 13627.7613 | 3.79983083 | 1.69602385 | 3.33980250 | 0.00000000 | 0.059819887 | 0.070841315 | 0.05577775 | 0.62765043 | \n",
"| 18 | SOX12 | -0.02284905 | -0.065376926 | -0.009410002 | 0.00000000 | 2.385374e-02 | -4.011697e-05 | -2.834563e-01 | -2.815619e-05 | 75863.7094 | 1.74741861 | 0.71539459 | 1.06994890 | -0.05066787 | -0.062815144 | -0.063477372 | -0.09422693 | -0.62404571 | \n",
"| 10 | HOMEZ | -0.75952764 | 0.000000000 | 0.000000000 | 0.00000000 | -2.571221e-01 | -2.773147e-05 | -1.190609e-04 | -2.070808e-04 | 2075.6167 | 0.23303020 | 0.11305491 | 0.13354243 | -0.05711910 | 0.000000000 | 0.000000000 | 0.00000000 | -0.58326686 | \n",
"| 34 | STAT5A | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -5.317519e+00 | 1.619035e-05 | 5.634046e-05 | 6.416900e-05 | 2473.5968 | 0.70519199 | 0.07501966 | 0.34651510 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.57464748 | \n",
"| 21 | FOXO4 | 0.00000000 | -0.059932873 | -0.038120414 | -0.09406223 | -7.525646e-02 | -1.089069e-01 | -7.967480e-05 | -1.649238e-01 | 100624.4622 | 2.53966391 | 1.35220598 | 4.10752152 | 0.00000000 | -0.060840837 | -0.054144051 | -0.09392238 | -0.55645498 | \n",
"| 16 | STAT1 | 0.10044635 | 0.058977896 | 0.039676356 | 0.01249171 | 1.452155e-02 | 1.550252e-04 | 5.356998e-04 | -7.697100e-06 | 49426.5314 | 1.67592402 | 0.76834548 | 1.64999002 | 0.14023646 | 0.061726178 | 0.056424616 | 0.04170972 | 0.44791101 | \n",
"| 28 | ZHX3 | 0.00000000 | 0.000000000 | -0.085656799 | -0.04603107 | 8.553841e-04 | -3.468578e-01 | -8.150376e-05 | -1.474584e-04 | 2775.8698 | 0.12460750 | 0.05507819 | 0.06762929 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.43410051 | \n",
"| 23 | FOXD1 | 0.00000000 | -0.020514333 | -0.043780174 | -0.04197126 | -1.447640e-01 | -8.077647e-05 | -1.312632e-01 | -1.355494e-04 | 3940.9648 | 0.17796819 | 0.07402906 | 0.17825892 | 0.00000000 | -0.018612142 | -0.030152933 | -0.02430439 | -0.41483052 | \n",
"| 20 | STAT5B | 0.00000000 | -0.072874603 | -0.124676757 | -0.24554706 | -8.615242e-03 | -1.028715e-04 | -6.899009e-05 | -5.172639e-03 | 1962.9467 | 0.14380142 | 0.09304024 | 0.14962969 | -0.03142925 | -0.044682367 | -0.088535616 | -0.20937298 | -0.37452622 | \n",
"| 6 | NFE2L3 | -2.44526737 | -0.042077199 | -0.012117383 | 0.00000000 | -3.535628e+00 | -6.050182e-04 | -1.180878e-03 | 8.473173e-06 | 1780.2860 | 0.90328191 | 0.43550208 | 0.85687215 | -0.26084529 | -0.038226731 | -0.036547702 | -0.02745371 | -0.33931986 | \n",
"| 29 | FOXL1 | 0.00000000 | 0.000000000 | -0.055243544 | -0.02501204 | -2.179699e+01 | -1.023130e-05 | -1.109603e-05 | -3.033104e-04 | 745.0412 | 0.04530632 | 0.01952745 | 0.06490894 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.28785265 | \n",
"| 30 | RAX2 | 0.00000000 | 0.000000000 | -0.054872150 | -0.07062620 | 2.104629e+01 | -2.984847e-05 | -3.858662e-03 | -1.693215e-03 | 10191.6573 | 0.15838869 | 0.08666600 | 0.13254073 | -3.68593268 | 0.000000000 | -0.079070690 | -0.05256948 | -0.28106544 | \n",
"| 32 | FOXQ1 | 0.00000000 | 0.000000000 | 0.012481789 | 0.01484751 | 1.497996e-01 | 1.024722e-04 | 9.058601e-02 | 1.950611e-05 | 1912.3582 | 0.07622154 | 0.02708449 | 0.04248428 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.26418329 | \n",
"| 7 | SOX15 | -2.00952281 | -0.047167519 | -0.081356107 | -0.04556199 | -5.808046e+00 | -1.649264e-01 | -2.072168e-04 | -1.738663e-04 | 3073.3194 | 0.09674146 | 0.04575365 | 0.17657099 | -0.20871913 | -0.034963116 | -0.033217226 | 0.00000000 | -0.26062515 | \n",
"| 13 | ZFHX2 | -0.44426232 | -0.064360137 | -0.076382960 | -0.03506666 | -9.485212e-01 | -1.449449e-01 | -1.972800e-01 | -5.449061e-05 | 4242.0609 | 0.10108451 | 0.05349746 | 0.11198257 | -0.05434429 | -0.044781408 | -0.056811985 | -0.02709282 | -0.24922722 | \n",
"| 36 | FOXD4L6 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 2.001216e+01 | 5.598696e-05 | -3.493371e-05 | 3.952855e-05 | 2138.4440 | 0.38560704 | 0.08815874 | 0.26016141 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.24249449 | \n",
"| 39 | SOX6 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 1.117300e-02 | 1.410110e-01 | 2.635437e-04 | 6.368482e-05 | 1286.4206 | 0.02964059 | 0.02545302 | 0.07550225 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.18744712 | \n",
"| 31 | FOXE3 | 0.00000000 | 0.000000000 | -0.023970663 | 0.00000000 | -3.409200e+01 | -7.539808e-07 | -2.472980e-04 | 1.070491e-05 | 1436.7861 | 0.09033598 | 0.02945399 | 0.08047401 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.15567610 | \n",
"| 3 | FOXD4 | -6.12834538 | 0.000000000 | -0.018794219 | -0.01121346 | -9.572871e-01 | -2.277717e-04 | -2.120234e-04 | -1.566068e-04 | 7525.4338 | 0.13565668 | 0.05591150 | 0.14653822 | -1.39091255 | 0.000000000 | -0.005870526 | 0.00000000 | -0.11598769 | \n",
"| 26 | OTX2 | 0.00000000 | 0.000000000 | -0.104669744 | -0.06159170 | -8.116979e-02 | -6.058279e-06 | 1.893926e-05 | -2.229342e-01 | 1023.9351 | 0.11237214 | 0.05414230 | 0.15764033 | 0.00000000 | 0.000000000 | -0.035312214 | -0.01896358 | -0.11114757 | \n",
"| 25 | FOXP3 | 0.00000000 | 0.004366561 | 0.061610938 | 0.02631893 | 8.865602e+00 | 7.248455e-04 | 1.555273e-02 | 1.446537e-03 | 1611.4674 | 0.06884909 | 0.02423162 | 0.08531106 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.10724617 | \n",
"| 37 | ELF3 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 7.590501e+00 | -1.672171e-04 | 3.211889e-04 | 4.752853e-05 | 3326.0816 | 0.14770484 | 0.06169517 | 0.18053208 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.10576117 | \n",
"| 2 | FOXD4L1 | -10.64033243 | 0.000000000 | -0.051396874 | -0.05015705 | -7.279581e+00 | -5.366559e-05 | -2.090862e-03 | -1.683918e-03 | 4436.9676 | 0.19584650 | 0.07824253 | 0.21936956 | -2.38450203 | 0.000000000 | -0.014643134 | -0.01698541 | -0.10509003 | \n",
"| 22 | ZBTB16 | 0.00000000 | -0.020936732 | -0.035129792 | -0.07343806 | -1.758344e-01 | -2.965382e-04 | -1.843154e-04 | -2.008173e-01 | 1771.1880 | 0.07772086 | 0.03064144 | 0.06150837 | -0.02690810 | -0.011124576 | -0.014971105 | -0.04979472 | 0.06296197 | \n",
"| 24 | ESR2 | 0.00000000 | -0.012150312 | -0.093702601 | -0.05702639 | 5.680673e-02 | -1.076067e-01 | -1.918502e-03 | -1.278182e-01 | 1254.3418 | 0.06494721 | 0.02666922 | 0.10061373 | 0.00000000 | -0.002660900 | -0.008303913 | 0.00000000 | -0.03964059 | \n",
"| 27 | ESRRB | 0.00000000 | 0.000000000 | -0.091401413 | -0.05857560 | -3.068364e+01 | -1.117221e-03 | -7.120074e-04 | -1.932052e-04 | 1860.4361 | 0.08006934 | 0.04726370 | 0.07232247 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | -0.03485913 | \n",
"\n",
"\n"
],
"text/plain": [
" gene lasso.as.is lasso.log2 lasso.asinh lasso.voom bs.as.is \n",
"9 HLF 0.77458827 0.134199069 0.209422903 0.24105628 5.930056e-01\n",
"5 STAT4 2.60534321 0.157190023 0.076155038 0.09551869 1.708536e+00\n",
"35 SATB1 0.00000000 0.000000000 0.000000000 0.00000000 -5.683068e-01\n",
"15 SATB2 0.13675630 0.216133895 0.228754703 0.26140384 1.035786e+00\n",
"33 ATF2 0.00000000 0.000000000 0.009811994 0.00000000 8.928837e-01\n",
"4 FOXP2 2.61508513 0.106667287 0.120449138 0.13231032 3.972476e+00\n",
"11 TSHZ2 0.69243601 0.104562826 0.115311122 0.09676352 1.086813e+00\n",
"40 DRGX 0.00000000 0.000000000 0.000000000 0.00000000 -4.072208e-03\n",
"1 HDX 11.26492428 0.000000000 0.000000000 0.00000000 3.241756e-01\n",
"8 LHX6 1.27950800 0.066352215 0.090529696 0.09315715 2.312552e+00\n",
"12 ESRRG 0.66926539 0.019431010 0.046640155 0.01141497 3.927342e-01\n",
"19 TSHZ3 0.00000000 0.101236743 0.064662784 0.04451921 -1.089643e-02\n",
"38 POU6F2 0.00000000 0.000000000 0.000000000 0.00000000 -3.743652e-02\n",
"14 NFE2L2 -0.32653069 -0.015204118 -0.057125918 -0.04777479 -7.803000e-01\n",
"17 CUX2 0.03676364 0.074610279 0.134597803 0.11559182 1.240175e+00\n",
"18 SOX12 -0.02284905 -0.065376926 -0.009410002 0.00000000 2.385374e-02\n",
"10 HOMEZ -0.75952764 0.000000000 0.000000000 0.00000000 -2.571221e-01\n",
"34 STAT5A 0.00000000 0.000000000 0.000000000 0.00000000 -5.317519e+00\n",
"21 FOXO4 0.00000000 -0.059932873 -0.038120414 -0.09406223 -7.525646e-02\n",
"16 STAT1 0.10044635 0.058977896 0.039676356 0.01249171 1.452155e-02\n",
"28 ZHX3 0.00000000 0.000000000 -0.085656799 -0.04603107 8.553841e-04\n",
"23 FOXD1 0.00000000 -0.020514333 -0.043780174 -0.04197126 -1.447640e-01\n",
"20 STAT5B 0.00000000 -0.072874603 -0.124676757 -0.24554706 -8.615242e-03\n",
"6 NFE2L3 -2.44526737 -0.042077199 -0.012117383 0.00000000 -3.535628e+00\n",
"29 FOXL1 0.00000000 0.000000000 -0.055243544 -0.02501204 -2.179699e+01\n",
"30 RAX2 0.00000000 0.000000000 -0.054872150 -0.07062620 2.104629e+01\n",
"32 FOXQ1 0.00000000 0.000000000 0.012481789 0.01484751 1.497996e-01\n",
"7 SOX15 -2.00952281 -0.047167519 -0.081356107 -0.04556199 -5.808046e+00\n",
"13 ZFHX2 -0.44426232 -0.064360137 -0.076382960 -0.03506666 -9.485212e-01\n",
"36 FOXD4L6 0.00000000 0.000000000 0.000000000 0.00000000 2.001216e+01\n",
"39 SOX6 0.00000000 0.000000000 0.000000000 0.00000000 1.117300e-02\n",
"31 FOXE3 0.00000000 0.000000000 -0.023970663 0.00000000 -3.409200e+01\n",
"3 FOXD4 -6.12834538 0.000000000 -0.018794219 -0.01121346 -9.572871e-01\n",
"26 OTX2 0.00000000 0.000000000 -0.104669744 -0.06159170 -8.116979e-02\n",
"25 FOXP3 0.00000000 0.004366561 0.061610938 0.02631893 8.865602e+00\n",
"37 ELF3 0.00000000 0.000000000 0.000000000 0.00000000 7.590501e+00\n",
"2 FOXD4L1 -10.64033243 0.000000000 -0.051396874 -0.05015705 -7.279581e+00\n",
"22 ZBTB16 0.00000000 -0.020936732 -0.035129792 -0.07343806 -1.758344e-01\n",
"24 ESR2 0.00000000 -0.012150312 -0.093702601 -0.05702639 5.680673e-02\n",
"27 ESRRB 0.00000000 0.000000000 -0.091401413 -0.05857560 -3.068364e+01\n",
" bs.log2 bs.asinh bs.voom rf.as.is rf.log2 \n",
"9 3.228768e-01 8.583764e-05 3.468772e-01 1266161.9489 57.37132247\n",
"5 1.832979e-01 8.946250e-05 1.708533e-01 649234.9844 46.37406567\n",
"35 -1.444623e-06 1.154825e-05 -3.633166e-05 577058.4385 21.19406994\n",
"15 6.942847e-05 3.566778e-01 2.853092e-01 424329.6976 26.35567531\n",
"33 5.648769e-05 7.409788e-05 -5.996884e-06 137474.9788 5.41129360\n",
"4 8.526235e-05 2.309917e-01 4.508482e-05 112640.2790 3.91445249\n",
"11 2.277099e-01 1.056974e-01 1.235520e-01 84319.1960 5.12252917\n",
"40 1.835087e-07 7.377633e-06 1.398560e-05 88685.5631 10.12862043\n",
"1 -2.498712e-05 6.947728e-05 5.735278e-05 53018.0228 1.43641155\n",
"8 6.697943e-05 1.139972e-01 1.365092e-01 33707.1603 2.90733938\n",
"12 3.936057e-05 2.318925e-04 7.762502e-06 66430.6662 4.29280477\n",
"19 8.721712e-05 1.673391e-01 2.551630e-05 23971.7861 6.25117359\n",
"38 1.548796e-01 1.126459e-05 -6.461413e-06 7735.8553 1.54216776\n",
"14 -1.304394e-04 -8.658551e-05 -1.444340e-01 16250.1818 0.62406681\n",
"17 1.096828e-01 2.066298e-01 6.543902e-05 13627.7613 3.79983083\n",
"18 -4.011697e-05 -2.834563e-01 -2.815619e-05 75863.7094 1.74741861\n",
"10 -2.773147e-05 -1.190609e-04 -2.070808e-04 2075.6167 0.23303020\n",
"34 1.619035e-05 5.634046e-05 6.416900e-05 2473.5968 0.70519199\n",
"21 -1.089069e-01 -7.967480e-05 -1.649238e-01 100624.4622 2.53966391\n",
"16 1.550252e-04 5.356998e-04 -7.697100e-06 49426.5314 1.67592402\n",
"28 -3.468578e-01 -8.150376e-05 -1.474584e-04 2775.8698 0.12460750\n",
"23 -8.077647e-05 -1.312632e-01 -1.355494e-04 3940.9648 0.17796819\n",
"20 -1.028715e-04 -6.899009e-05 -5.172639e-03 1962.9467 0.14380142\n",
"6 -6.050182e-04 -1.180878e-03 8.473173e-06 1780.2860 0.90328191\n",
"29 -1.023130e-05 -1.109603e-05 -3.033104e-04 745.0412 0.04530632\n",
"30 -2.984847e-05 -3.858662e-03 -1.693215e-03 10191.6573 0.15838869\n",
"32 1.024722e-04 9.058601e-02 1.950611e-05 1912.3582 0.07622154\n",
"7 -1.649264e-01 -2.072168e-04 -1.738663e-04 3073.3194 0.09674146\n",
"13 -1.449449e-01 -1.972800e-01 -5.449061e-05 4242.0609 0.10108451\n",
"36 5.598696e-05 -3.493371e-05 3.952855e-05 2138.4440 0.38560704\n",
"39 1.410110e-01 2.635437e-04 6.368482e-05 1286.4206 0.02964059\n",
"31 -7.539808e-07 -2.472980e-04 1.070491e-05 1436.7861 0.09033598\n",
"3 -2.277717e-04 -2.120234e-04 -1.566068e-04 7525.4338 0.13565668\n",
"26 -6.058279e-06 1.893926e-05 -2.229342e-01 1023.9351 0.11237214\n",
"25 7.248455e-04 1.555273e-02 1.446537e-03 1611.4674 0.06884909\n",
"37 -1.672171e-04 3.211889e-04 4.752853e-05 3326.0816 0.14770484\n",
"2 -5.366559e-05 -2.090862e-03 -1.683918e-03 4436.9676 0.19584650\n",
"22 -2.965382e-04 -1.843154e-04 -2.008173e-01 1771.1880 0.07772086\n",
"24 -1.076067e-01 -1.918502e-03 -1.278182e-01 1254.3418 0.06494721\n",
"27 -1.117221e-03 -7.120074e-04 -1.932052e-04 1860.4361 0.08006934\n",
" rf.asinh rf.voom sqrt.lasso.as.is sqrt.lasso.log2 sqrt.lasso.asinh\n",
"9 25.91603826 53.05247084 0.69519133 0.140776460 0.142896092 \n",
"5 22.47867569 48.94942356 3.47937884 0.190961358 0.158960329 \n",
"35 11.98698783 27.98823933 0.00000000 0.000000000 0.000000000 \n",
"15 14.22518618 30.46469273 0.02139235 0.181298914 0.226526723 \n",
"33 2.34014879 7.35197201 0.08809962 0.008485763 0.000000000 \n",
"4 2.97052657 3.80445761 2.44496515 0.101105760 0.106358127 \n",
"11 2.25117903 4.60443070 1.00207756 0.100506784 0.094864545 \n",
"40 3.22875065 8.62751278 1.18904754 0.000000000 0.000000000 \n",
"1 0.71578023 0.86613053 13.37342127 0.000000000 0.000000000 \n",
"8 1.70273480 2.28153347 2.69414714 0.058348346 0.086557473 \n",
"12 1.61084504 3.77395550 0.45898430 0.004540240 0.010904356 \n",
"19 3.70888147 9.28067022 0.00000000 0.103275043 0.119569865 \n",
"38 0.61268809 1.62421935 0.00000000 0.000000000 0.000000000 \n",
"14 0.26788921 1.05073252 -0.01666623 -0.010311681 -0.001796439 \n",
"17 1.69602385 3.33980250 0.00000000 0.059819887 0.070841315 \n",
"18 0.71539459 1.06994890 -0.05066787 -0.062815144 -0.063477372 \n",
"10 0.11305491 0.13354243 -0.05711910 0.000000000 0.000000000 \n",
"34 0.07501966 0.34651510 0.00000000 0.000000000 0.000000000 \n",
"21 1.35220598 4.10752152 0.00000000 -0.060840837 -0.054144051 \n",
"16 0.76834548 1.64999002 0.14023646 0.061726178 0.056424616 \n",
"28 0.05507819 0.06762929 0.00000000 0.000000000 0.000000000 \n",
"23 0.07402906 0.17825892 0.00000000 -0.018612142 -0.030152933 \n",
"20 0.09304024 0.14962969 -0.03142925 -0.044682367 -0.088535616 \n",
"6 0.43550208 0.85687215 -0.26084529 -0.038226731 -0.036547702 \n",
"29 0.01952745 0.06490894 0.00000000 0.000000000 0.000000000 \n",
"30 0.08666600 0.13254073 -3.68593268 0.000000000 -0.079070690 \n",
"32 0.02708449 0.04248428 0.00000000 0.000000000 0.000000000 \n",
"7 0.04575365 0.17657099 -0.20871913 -0.034963116 -0.033217226 \n",
"13 0.05349746 0.11198257 -0.05434429 -0.044781408 -0.056811985 \n",
"36 0.08815874 0.26016141 0.00000000 0.000000000 0.000000000 \n",
"39 0.02545302 0.07550225 0.00000000 0.000000000 0.000000000 \n",
"31 0.02945399 0.08047401 0.00000000 0.000000000 0.000000000 \n",
"3 0.05591150 0.14653822 -1.39091255 0.000000000 -0.005870526 \n",
"26 0.05414230 0.15764033 0.00000000 0.000000000 -0.035312214 \n",
"25 0.02423162 0.08531106 0.00000000 0.000000000 0.000000000 \n",
"37 0.06169517 0.18053208 0.00000000 0.000000000 0.000000000 \n",
"2 0.07824253 0.21936956 -2.38450203 0.000000000 -0.014643134 \n",
"22 0.03064144 0.06150837 -0.02690810 -0.011124576 -0.014971105 \n",
"24 0.02666922 0.10061373 0.00000000 -0.002660900 -0.008303913 \n",
"27 0.04726370 0.07232247 0.00000000 0.000000000 0.000000000 \n",
" sqrt.lasso.voom gene.cor \n",
"9 0.15897283 0.92323940\n",
"5 0.17336532 0.90475987\n",
"35 0.00000000 0.86282704\n",
"15 0.23814460 0.84232989\n",
"33 0.00000000 0.82786857\n",
"4 0.10469561 0.81961721\n",
"11 0.08923052 0.77043615\n",
"40 0.00000000 0.73094500\n",
"1 0.00000000 0.72419056\n",
"8 0.09288872 0.71461154\n",
"12 0.00000000 0.70903181\n",
"19 0.09920060 0.70562897\n",
"38 0.00000000 0.68969765\n",
"14 -0.01195379 -0.63838196\n",
"17 0.05577775 0.62765043\n",
"18 -0.09422693 -0.62404571\n",
"10 0.00000000 -0.58326686\n",
"34 0.00000000 -0.57464748\n",
"21 -0.09392238 -0.55645498\n",
"16 0.04170972 0.44791101\n",
"28 0.00000000 -0.43410051\n",
"23 -0.02430439 -0.41483052\n",
"20 -0.20937298 -0.37452622\n",
"6 -0.02745371 -0.33931986\n",
"29 0.00000000 -0.28785265\n",
"30 -0.05256948 -0.28106544\n",
"32 0.00000000 -0.26418329\n",
"7 0.00000000 -0.26062515\n",
"13 -0.02709282 -0.24922722\n",
"36 0.00000000 0.24249449\n",
"39 0.00000000 -0.18744712\n",
"31 0.00000000 -0.15567610\n",
"3 0.00000000 -0.11598769\n",
"26 -0.01896358 -0.11114757\n",
"25 0.00000000 -0.10724617\n",
"37 0.00000000 -0.10576117\n",
"2 -0.01698541 -0.10509003\n",
"22 -0.04979472 0.06296197\n",
"24 0.00000000 -0.03964059\n",
"27 0.00000000 -0.03485913"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tbl.all"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\t- 40
\n",
"\t- 18
\n",
"
\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 40\n",
"\\item 18\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 40\n",
"2. 18\n",
"\n",
"\n"
],
"text/plain": [
"[1] 40 18"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dim(tbl.all)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see here, the union of top 10 genes from all solutions results in 40 genes. We can see that both LASSO methods are behaving much as we'd expect them, with some coefficients being shrunken to 0. We can see a little of what's going on with the scores in general--Random Forest looks to line up with itself regardless of distribution, whereas the others do not--but rather than draw conclusions based on a cursory look, let's examine the correlations more closely. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A Broad view\n",
"\n",
"We'll take a look at the correlations between all different columns to give us a broad look at our table. We'll output these correlations as a plot of pairwise correlations. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA\nOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9\noU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvu\nuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd\n/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs\n4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTv\nYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7n\nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8\neUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m\n6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiY\nMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpk\nhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thK\nbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpX\nzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJ\nmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477h\nLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549\nHQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQ\nUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgY\nhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjz\nhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VVBg\n/m8AAEAASURBVHgB7J0FmBxV2kaBEMEJLgkZ3N0tQCD4Lu4s7m4LLLoQYPlZFl/cFnd3ggR3\n9yQkBHd32f+c0He36O2e6a7u6elhvvd5DnXr1r1VX711rWqGzBhjhMKBcCAcCAfCgXAgHAgH\nwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAc\nCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFw\nIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfC\ngXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwI\nB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAg\nHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KB\ncCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgH\nwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAc\nCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFw\nIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfC\ngXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwI\nB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAg\nHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KB\ncCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgH\nwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAc\nCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFw\nIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfC\ngXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHKjYgW4Vl4yC\nzeLANAQyDnxdQUDlyk5M3fFg3AK92H4HXV1jYsBs8BX81IYZPTk+H+jjZ/BvCP3WAdvpDPDJ\nb7NL7vUhd1LQy9D/OlCNl/o4D/wIlYwT/3u1rpNTTZ/vOq7kv9N6+encNCGkOcqt7fkXSCo3\nv6XjnWlbjW/lyo7NDfeGrGdjsa9vXVnl/CrlSczrpVyJvHCgyR0Yn/juh4/BReTt4KKplNoq\n+yWVXNAnPip1ki6Wtwz3+xa8CS4q94ZyWp8D38JI8EXqIXAyD/3XgUEkPwc9fRfmhVKagMwh\n4MLnA3gFVobQfx2o1MtuVPkX+LFjFOjpYRAq7UA1fb70GSI360A9/TyXE6f5KW0HFC7W1vyW\njakzpKvxrbWyq5Tw7LTOYEA7xtiaX8WXjXm92JHYDwc6iQMOdLeAi6DucD+cAKXUWtkpqeAX\nfX/ykfCLU1eWE+5I2LZgQvop0pKF/ezGn7Z9D2sWMqdi+wbsUNiPzRhjLI8JvnT70yPly6YL\n9rHcKdIB7D8Bc8OYMAgehtCvDlTj5QZUeRNsk2oN8OuxfT70Wweq6fO/rRl7pRyot5/3cZHV\nIM1RbrsVLtza/FYo0mk21fjWVtldueuzIetZz07jRP0Dbcuv7BVjXs+6EelwoJM54Nf4AZmY\ndyHtl/lSaq3sUlTwJx6h/zqwIkl/epFdwA9h/6T/FvlPamlSH/1n79eEE/b1RXldeddJ+vSM\nAS7Q/Qps2yvWP8jYNJO5OukvwF8XCf264KnUS9trtqz+2Vaz/poXGmOMavp8+NW2A/X28x0u\n2afMZVub38pUadrsanxrq+yJ3OWeTXunjQ+sLb+yEcW8nnUj0qMdyC4Iw5LmdcAFpr/C9Xwm\nxNdI+6W4+Nfs2io7E3UmgsfBxdNdMD90Zc3Kzb8I2d9v198ZSpjiTzf6ZvL1f1W4PpPX1ZOz\nYMBzGRPeJ/05lPLTny5dBNODv+JwDJwNP0FojDGq8fLPGOZX5KTlSIwPt6WM2P7HgWr6/H8q\nRaKsA/X005+ATAEng2OHY3P66X5b8xtFO5Wq8a2tss7tfmAaDm/BaeBc31XVll9ZXx5mJ+b1\nrCORjq+0TdoG+hGXCyPlwjK9yJpO8ldplAv0b0enfv2PC01VrqwTj78ithd8A/vCnTAnOBl1\nBS3CTaaJwxehFvCnFlnpb6kFvQv3tHhfjPR54IR0KXRF+asJS2du/H7StsFiP52wbavltBQH\nDoIZ4WIYE/ypU1dSrV7ar1U32BGOgkPADyGh3zrQwm5xGy3X539bM/ZKOdBCZr38dKH6MdwE\n+8Fq4E9G/cdzRoAqN79l58JfSzbXf2uZe1q4ldY87s3xF2AX8OXgaHB+Whu6gmrxNub1rtBC\nqrzHsassH8Ub44ATwmGFSz3J1q/syp8YjRqd+vUnSr7Q+P8TZfVlYadcWb/QS5ILqXdgRbgw\nZf7Ot4dzfwsW7tEFpJ7pV1b+xO6lbEYm7RdO//8vJ55D4Z/Q1Rbz3PJoTcp/Lymk3cwHTuLF\nfk5A3stQTrY9WQbuhTvgcehKqoeXc2LYRQXT7NOPdCUDq7jXavt8FafukkXr6ecrODhlxkU/\nYi0PG8FfCvnl5rfC4abd1DL3tOXxEpm7dqz1I6D/2MW48A383lWLt3oT8/rvvYVUeX9jVVk+\nijfGgVO5zOQFVmY7Cn6AGSFpFhLPp53Mtq2yq1J25kx5F7N+mfs5k/d7T+pp8vc00sMg6633\n7xe450wUqRv7t4JfOWeHU6Crvhxx62O8DclLt+4X++nLpguaUn7608tlIek+En49dqHf1VSr\nl7ZZX4gugwULaTahEg4Ut1GLlOvzJapHVpED9fTTvu8LUVbvseMc1db8lq3TjOla5p7WPPYD\n1MbQM3PT75J2bsr+6njm8O8uWYu3Ma//7ppD3FBXcuBqbvZG8Kd+Lsyfgf1AuTDdHlyIqtbK\nHshxfzLSC3rA3vAlTAldVfrmS+JWBQPWZasnsxX2F2W7TiG9JNtPYQ5w0Z+YhHToVwc2ZKNH\n04H/D4wv/E9AkhP5/IWdE9neCJYdC7aEH8HFamiMMarx0p9q6mVqk2nrF+TQbx1oq8//tnTs\nteVAPf10fvODoOOuWgT8aOLYoFqb334t0Xn+25Zv2bmntbL+SrJrgv8r3Lp9/5YChawut2nN\nL83IehvzepdrHnHDvycHfAnyC7wThZOHk4RfPdRC4JeimdxBrZV10LgT/Lr0PnwCa0BX10oY\n4EuSnnwNW0OSk85ThZ392Op1MfcUjsfmVwdOZuP/E2P7egNmhKQRJNKvyvjTzNfAl6IP4QvY\nBkL/daBSL5+kSnG7dH+n/54qUhkHWuvzmWKRrNCBevp5GNd8B4aDPwHxQ4ofUFRr89uvJTrX\nf1vzLTv3eFetlV2e43781DPH3kfBD09dWa35lfU25vWu3Eri3n83DvirdVNUeDetlfUnHv6E\nxJ9IhX51wJ+ozQs9w5C6ODApZ5m9gjPZBm2L/v9L/h546H8dqNTL/60ZOa05EH2+NXeqP1ZP\nPx0X5oYJyoTR2vxWpkrTZlfjW1tlZ+Iup2naO218YG351fiI4orhQDgQDoQD4UA4EA6EA+FA\nOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD\n4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAO\nhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4\nEA6EA+FAONBpHJiISLN/Obs7+8V/qHOeoruZg/3sH4ttYT/7h/kmY39q6IrSP/+AaVb+wc5p\nsxmkp4fxi/LmYn/MTJ5/BX7OzH5XTM7KTftH+1Q3sO0l+YcN9TbJPxCZpLd6nOQfQhynsKPH\net3V1Icb9o89JxV7VMoX/db3pL4kJk47bO33LZn9rpL0nrNjnr5m+3ipcaB4HM22bX2zfrY9\nm9dVNDM32itzs84v2b7uIecp56ussn3efP8g+pTZAqSLyxQd/t3u2t97F92dc3t27vZwsT/j\nkud4mdVU7EyezSA9C3SVP5BevE5KVhT3afOLx1nzSrVn84vHAPNC4UA40CQOHEwcd2Ri+SPp\n9zP7/Uj/Gxwgkz4jsVLaYTsE9s3sn0T64sx+V0quyc2+U3TDf2f/qqK8h9jfM5PnIvRnWCST\ntxTpHzP7XTH5Bje9fuHGB7L9ImPCZaSPK+y7WLWdphfKPUg/XDjm5nnYurC/KFu9zi78C4d+\n15trubujM3eoR7bDpAVI/ALZRc9X7C+fCrC9GQ7P7Nvv783sd5Vk8Zj3f9z4NZmbX5v025l9\nX+Ztn75gJo0isW7aYXslHJvZ70rJV7jZzTI3vArpTzL7Jm+HQzJ5Lvb1NPvSdAb752bKuNi3\nTc+Tyesqyeu40aOKbvZj9lfN5CUPJ8zkbUv6ucy+yQvgn0V5r7O/UVHe73W3eJ3kfU4Btr/p\n3cnIcSA7znrI9dJnJoo0kv0NivJitxM74Fft0O/HAb9sZL8oldr3btsq09rx349bbd9Jd4pI\nVsWeeqw4z34l4aPu/FdZn7JpS2S9Ti87yb/isu6n52Jar8eErqTWPNEHj+tJdozP+pbKmJek\np9n9lP9733rP2fuuZF9PiuukNpmOZY+b11XkfbflRSmP9Sf1fdPFZTxmm+6Kvupn1lN2/8ef\n5EvaliqT8to6l+V+rypuV95n8ixt0727X6lXpc6bzhPbTuhAdvLshOFHyOFAOBAOhAPhQDgQ\nDoQD4UA4EA7Uz4F4Qaqfl3GmcCAcCAfCgXAgHAgHwoFwIBzo5A7EC1Inf4ARfjgQDoQD4UA4\nEA6EA+FAOBAO1M+BeEGqn5dxpnAgHAgHwoFwIBwIB8KBcCAc6OQOxAtSJ3+AEX44EA6EA+FA\nOBAOhAPhQDgQDtTPgeJ/saN+Z+78ZxqPW1gNGvES+RHXGZzTMv9Jz5ULcfpP+84EJ4KaEfyn\nUdN++idUDyPPf/JX9YDtYEV3UAt43+nvTyxJ2n9GeUN4H+6BPPJvOHgN/xWi9tbbXOD+nBfx\n7z4tD8bpvdsOkn8kx1ga9DybNx37fwT/ZoJKbcZ/ejn9s5/+XRSlj0lvkMj+89Upv5Kt/1T7\nspUUrEMZ/wnYx3KeR0/8J86Vf6vnT+A/zd0P/NeBko/zkLa9um+bVP6z0/YN23VfSGX9J1nX\nA/9Z2/Q3utYn7T8v/BTkkfEsnqdijjovUaf4n96t9DR6tDBMD/5z08mTYo9S//07Zey/qhvs\nAKu4g2aF3mAbV55XP1MbNUZjzSPPPX+eijnqPE2dV3PUs8ocoI/ZMc/2OjEkb2cmbdtN+6bV\nIfDF6NSvY8JmpBcr7Pv3aBxvk5dmPw7DTeSQ/cNYGyHHJMemPLId6p33bczKttoTkn/mJU/T\nnJT+ZtRRHPveAshxwrE01XO8UCvBLHA/vA15tAiVZshTsco6/tPRQ+C9Kuul4o5Jjk16aJ9O\nXpAc7ek2bJd3B5Xy0HZYXM9n5J+cyJ7LZ5bqk6xaS1MjzXFVV66ign76p0w+raJOtuhy7Hj/\n2XWSx9MYeCDpz80oaE623lfWqxb2naOyeeyO7u/64Hj7C9wGaXwgWZUGUrqW51HpxYzzZvi6\n0gpdqdyYXelmq7xXF1yXwgdV1qu2uB1tEnDw/6naypTfEs6CD8G/HzMO+Fxt+MoJxnR61tlj\n2eOmVSrnQKTcNy470LjQC/JoFyodDy54i2XcXuPbzAHjdvJ0gHHAqVTGZ+x6mkf7UukI+Bg8\nlzEkL0n+xx/TymvpUfIr+Z22lkmyXGpPPqcvoW86WOX2MMrvD59UUc825kTwWVGdNDmUGiR9\n5u/CbEV1Kt09loI+eye0yYsqZT3SG0leZ4+Z1t9ij8kaLY/7wj8UFhydU/1/TqPKFlDsTfZM\n9Win43PCp6F/9sRVpC+grH9vx5edsSF5QnK0f+U8LPY3lXebzpHq2ke9Vxeg6WWKZFW6ltIr\nQnaBYF+yj/8A9dKEnOhW0JM8chHjYkQlH4rbm8fMS22zkn29dKGfFlve+1WwGeSRz2I+sJ0r\n5w37Zmvt1XLVamIqnA87VluxUP5JtsaZvNQHldrWr3u/7ptO5UwXe5zqFpdxbLZ9ngL7QB69\nQqWp4Zs8lVup47hu30zPyXnoaDgU8uhNKnmvntf+nqQnWb9SOm1TObf6mPWwnK/XU24tK+SQ\n85Dn/S5T17akD9l1Ta3tdjLOtwf8E/IoxedcmPXEcyXvsv4Up1OdVDYbg3mOd45xznXbwnlQ\nrXzOP4Ke1mOsdIz0POneSY6+V8ckfdgIroBQOFCxAxtS0oVhe2sJLmCns6Hm0dZUGpapOIC0\nLxQ9M3kmd4LhRXnV7A6kcC2ddTfqP1fmgq+TXzwh+3LigmS5MnXKZa/Bgc/KHawgf3/KPFxB\nuTso848S5UaR58DYljamwNttFWrl+CCODW7leKlD25D5VokDfyev3Lm8l9dK1Kk06zgKOvG2\nJRcVttuVigr6Ymb/6FeUX7zrpPl0cWYV+2dQ9pI2ytvPfNnLygn/J1ghm9lK+kCOPdDK8bYO\nXUQBP4iU0ixk6tUMRQf/wH61ffco6txedJ5qdn3mJxRV8PnYv+opr3FtDSe0H3uvSY6feuhz\nrad8Zj67vHqQiradpP4kjNOFcz11CSezL+SVz9i+mHQ8idvSTh23ti/HlrxyTKtknK72/D7j\nCzOVHFcdq/PKOWLjTOU3SG+X2Tfpi7JtwTaRV855tfTNz6i/RubifvgzpjkzeSZXAOf1sd3J\nIdcQriXyynFwYBuVfX6l+upj5B/cRt102Lli67RT5bY75fVuiSrrlSv+FgeKY/FDndf4ADaE\nUAkHxiqRF1md24FnCP9HyA6qPme/EjwCzSjjMr5sezT+78H7aUYZ85qQfvpijMuAE0Mz+zwt\n8S0LScbvV8OOjtmXo8dhE8jqT+y4SHgzm9lBaRcRttNumeu77wuSC8OO1usE4IS3aVEg7jf6\n+frltVcmjvlIz9UBcWRCKJnMjjkW0KunwIVUMynb5ozLOF0s+rW6mZT100WYY2Sj215H+uEY\n4Uee4p+W1ysmvXRuzPrsmGk7eBaaRS7KpdRY9CT5PzVLoCXiKPUMHbscwzzW2VSqzfhc/Omp\nv8Z3MbhmLIXtqvgll6yuobG7xm12qbv8hLs9BM6EJWAYuACeGbaAZpTxPgoPwbUwE2wJftH6\nFJpRxxOUE9UTcBE40GwPZ8Dz0Ix6gaBOh5vB9vEh+ALiYraWr7FUr4v24SyDYQq4CxaCdcAv\nXH517GgdSgB+RbSdXgMzgu30IPgYOlo/EcBecAHMBr60rQhLwrLQaPny6MutC2X7xnVwLzST\nfPFwfHScWQ6WBz1rNjlX6+cP4IeYgbAKNJt6ENAa4IeXzcBF1wnQVXQuN7o1OC+cA/2gnovq\ngzmfC94H4HpI87q/Gv45NIv+TSB7wOXgOOnHrwFgH7PtNrOKn6E/pd0BnDednzqbnJ9sg/eD\nbWZW2Bz2Bn8L5gY4FUrJOeWVUge6Ql72K0RXuN+uco/HcKMuKh2cnVRdGLvYHA7NqGEEZXwv\ngvFOBxvAsdCsckG1GNwKLuIXBBenO0IzayeC2xPmh3XhNvA+fLHuaDnpG8tnsCn0AhesV0Ez\n6HWCsJ0+D7bTlsL2/9g2iy4mEBf4E4Ae+hK8KPhi10j9zMVeBRfLvqAdBhtCs8kFvPOgXn0F\ni8MQaDZ9T0BjgnH6VVdP74Zmk372hbXhJtBP+3NXkc9mGfCj2R9gQqinXuNkjkGvwEag1+tB\nM3zgIozf6Gr2fCnqCbbbL8D24EK9mfUdwS0LF4LPsD8cAfrcGWVbWRhsOxvDtOCa5URQb8Lg\nMtxLvmN5l9TYXfKuu8ZNOzhJZ9EIAt26swRbiPMjtr4UdSb9m2DPLNCMcT9DUM24kE5ejSSx\nTdpp0u1dxCUdrYcIwK/IzSz7g+PkAc0cJLEZ5yVwZJPH6WLqFDihyeNsz/C+5OQHFnDhWW/5\noWarep+0nc53H+eVziaf4UEFOlvspeIdRuaWpQ5EXnkHxip/KI6EA+FAOBAOhAPhQDgQDoQD\n4UA40LUciJ8gda3nXXy3/ph1c+gN/o7qbbAFTA+vgr/i5o9nO0rjcuHdwd9Z/gH8qjMZ+Gsc\n18IZ8As0k5YgmF1gAfB3l98Dff0H+CsGzaBJCOJg8PnrsV8k/TWxZviJ42zE8Wdw+wb4awCP\ngvInSxuDv7biV0k9/Rwaqam4mL/v76+5fABnwe2Q1WLs2G6ng5fh72B/apRsg7tBX3gJjoGh\noPqA/s4P78LpcA+0h/yVsJ1hTXCuuRVOgu+gWP5a4HYwBTwBemZ8jdDkXORfMBMMB9vVs1Ct\nfO67QvLde/B89dQsnMznNweMAv107K5WvamwNywF9iF/negqqIem4SQXwIzgl2t//SuPn1Qb\nLdvRVuB41RPuhLGgPeWz3AsWge7gfdhGzgN/mtcZtBJB2qemgifBceAtSBqPxJ4wAKZPmQ3e\nzsD19oO5wNiugGVhXrD/nwb3QrOouP85N9mHHLceB/u8c36zyvXetuBY9z3YtxaAfaGUXGs5\nx31V6uDvPa+9B5nfu3+d8f6cGFeGc+Ay+BiGwEYwGPrAvTAruFBZFDpCToR3gwu9l8AFqROk\nL0ivgIP9JdBMWp9g7oP+4AA0LswDDkgPwPjQ0ZqUAHyuO8NE4OJoQbgcDoWO1MJc3Nj0bCRM\nC/r2R/AF7gL4EB6BP8FD4D00Si58XWisCPfDT3ALuMhYHlaADcBjPmsXcrOB96THjZB9xDZo\n2/P6c4Axzwczw3OwOhhTN7DPbwXtoTU4qf3UhY7X2wdug7EhK9viraCfejcQjFm/21suELaA\nOWE4OP656BkA1WhtCttWbY+Op/r9FMwL9dLUnOhp8AX4cRgP9GstqEbOAfYhx/yH4RO4FAZB\nrRqLE9gf5oKXIfm5LOm8OpeKJ8FI0NPdYRFoL63BiVM79OXZ9ro4/BNcLDaruhGYL3arwr7g\n2PQD3AXOSfap6UGNA7bTHcG++SM0WvNzwWfB2PTbdnklrA6OYfpu7C7qm0HGq1e2haFgGz8O\npgTjXwn02DbfjLqIoGy/tpFvYCJw/JsJnDdKsS759oFQOPAbBzZk793f5LTPjpOdX6S65zz9\n1tQbVqaujd9GfyacAseDHcNB8xf4DK6Fa+BrMN8F1NxgXV+gHga1Gng8r3ajoueuRE6yTorf\ngoP492C8CQd+B1EH9RVgO7gJzHcA8L7yan8qpnuu5BzTU+gIOB++hE/A5/kz/AQj4E54C06H\ni8BB31jfhrwaRMXBOSq7YNXX5KVb24Tx6mdfyGpbdl7LZlSZdgK5vsI6D1BuFOif3rl1/x0w\nTttgT3BSvxw+h9vBSfUVeB7y6gwqXtJG5XM5/gzcAT67F+EeSF7qoVwFSfajq8F2bLs+BLzP\nvLL92MZLqRuZenUyHA7nwZ/BvvEY+NyTt/Ypj+0D+jguuGhy3HNMGA552hfVRstn7rX0w+17\nMAC81uagxoZJwLFnZ0jqQcJ4z0sZRVs93Qy8xki4DvLqHio6rhmnz9F2Z1t6AUqpO5nGnVXy\n/W+ZTJ/1DWAbuRFGgc8urx6gYnp+KdbbyDsabIter5SMTT+zOoqdYTBBIdMyp4P3b/uxL+TV\nq1Q0vtR/HVsegkrG/l6UK9YSZHi+xUDvd4W7wLHKsSWvHNO2Lao8KfuO1Sl+/bCf2H6/gJfA\nY4tCpbIPDaq0cIlyPtuNS+QvTd5J4FiwIcwKtjVjNWZjvw9OhIthdxgKb4Ltxr7zPkwNtueH\nYX/IK+fcNcpUnoP8/4NzYUdwnFkK0jykp8Zsm9Rn0/8E59b9wHPbNnqC7ci1RF7Z1wdWUNkx\nZh04E06BAaC/tmvj1V/Rw+/A5zAv+ALluOW9bA151J1KPscl8lQuquN96Pe94DmN2e1HYL90\n3zYSCgeqcsBB592qauQrbCewwdop8shOmAZEO64LESel1IHT1oHB9I8ljplvDC7uPwc7vAOC\nA4nH7Ewet25eOag5+JW7foqzte3b1HeAsoz3alxOWl/Ck5AWEN5/XjlJ6MPr4KA3FSRtRkJ/\ny8WoRym2T0mngdTnc2fh2FC2+mtZy+SVk67P6mvw/h+BI2AIjILB4DNMmovENZBiMs4Ub/Z+\n7iJ/FngUjNPn5fny6jgq6qf37YTeG5Kc8I4BJ8D0XFMs2dhS2thFP33p0D/LG+vdYFvIqzOo\n+DE8D3+HDeEAuBXegKfA9pb1L8Xq1jYnKVa9s02uBeuB9dJxF/955SLb+/e52KfT9gPS54DX\nL44x663X1i/rmu99Wsf6KXbbkmXugbx6gopZf1L6afIvBV/avH6KzTb2MtiGx4FdYBiU0llk\n2tfPBs93A+SVC8YUW3arFxfAIqDnb4H9TW/1/HroA8r+YvkZ3MnIvug5bVu+cHmevEptPRuj\n6dSmZiyceEK29rnh4Hirv8Z8N8wJ6n44cnTq1w9h15K2rJ46xhpvXnmO4hjd1x+va3xZDWDn\nGbA9Wsax7CgYG9R+8Cx0g9tAH04GxwzvM69GUNH+bB/15fw5SHEbR0rb1xaAxwt5Psd9oFIN\npuCgSguXKGeMPkMxFseo4v5trM4D9iHHneS3268KWN99n/OJ4Lks67n03rXO/pBXXsd+4flS\nfGnrdROWMda0b5nXIcWd2oE++4yXBJ+HbcCt19kN8srrvw++HGxQOMn8bF+BNCaWijvF69ZY\nLWvassbleGm+6wP3vcetIY+6U8lztBVHNib73aswFEaC7dq+7DmyfdLzWi9tPW57CJVwYKwS\neZHV+RywQ9nQ/VowBfSCJPOUE46dwonmTcjKgchjE8EI+BecCruDOhT2AsvUonGp7PVbU4o3\nlXFAS5qKhPfp4GSck8CscDP0gz3ASbPWOJ2E/wELgwNpb/gLnAv6W6zimN13IN8KjNXnsyx4\n3glgJzgSHKhqkc/5ZXDSmwsOAK99GDjh3QGrgwujh2FmyMZayqflKPMSLAh3w4uQFiskc8n2\ndgqsDPeCcfvc3oB9YHxorV2kOG2nxq+fK4CLLRcF84CxOunXIuO5B/aEi2EQGPMIuBVsb15f\npW1K+7yNK8Vqu+0LV4ITkPlbgm0olSGZS9Z37Pa56JuxTAabgkqxpa1llZPndbAouMi0bf4V\nlG3F854D+ulC1eN5NWmZivOS/0eYGNYHF4DGab/Q863gRpgSPodiLUaGZQbCNjAEHBPyyjhK\nSS9WAfu/bXU8+AS+Bdua8d0DjmkpTvOSliexJNjntwf7Xy2yz5RSytdv25+xrQnmOy7o7zCw\nb9wLU4Pxplj/QNr7XA5sJ69BLUrtsfgc+qkfr4BxKscm293coJc3gX7uAieD8h5s2xvAUuCY\nvCt8DbXINncL3AbHwlygzDfWJGO9Ai4sZOifMTVK+ulzEWPpDcao0tb0ONAHnoGLIKsT2LG+\n+gUOA/uM5/snrFNIs8kt40zPPsWVtp40pS1jrHosjpMt4FjmvsfVNfAAnOQOegeWhw/cqVH6\n8wS4PQIeA8dqY9GnFCvJkupBrrFaLpX9M2ljnwhehc+gVmXP39a5elJgZpgB3gPH2QnB+/RY\nknGrtE3x/5ob/w0HKnRgQ8plO+Na7DtorwppsHFA+gccA7NCKdlgD4HjYYVMgXS+vcmzsTpY\n5dHWVPoQXATbwR0APZ/bx+HlQjrlWfYtcIB8H8x3snkRrPMkLACp/J2k1UCoZcG0G/W9pudw\nsvZaiXSttJ/dWifdl/nWd5J9BKzncVkd1Brw5ehUvv/sT7W0mBmX9AhwEP0WsnGZzsZtOuGx\nn2A/cOGcynp8OVAbw7ujU/n+M4hqIwpVHZS9ngPzRzA2KCdGn/9VcB+kOFJ8blOesenbm4W8\nv7JV28LQ0al8/zmOatcXqk7K1va3PdjefK7fgGXWgRSPW9vLp4U8Y/scbK/exyeQylrPuM+D\npyGvzqDipfAG3Aipnfm87B/GnK7pc/MlMu0bn/7br6xnvv3mYzAm941PHQgPjE7l+89FVPsO\n9C1tvfa94LWNxes5MT4K2Ti9tjEtCpODZe07xr0LDAO1O3j+293JqfupZxwuJlN/93pivOPD\nXvA22J+9lz/DTGB5Pd8TimWfejaTaRu/NrNfbXI4FYzTBY3bRIrTuPRhFLiw6w8eWwQ+Asdf\n5fN+DKZxB50O1nV+UGeBzy6vbPspNv0zhrRv2peLzcD72AYcd6aAqcC8LeEVOBw2BNvKH8D4\n7oKLwUXo5XAG5JX9NMXlNhun/dS8nUA5Ntn2znGnoMPYOkbYLqcE4/defOY3gPL+bCOOG3n1\nGhUd25TXSrHqledO95DiN84f4Vswrko1mIKO1XnlWKcfp0GK09iMceOiPONTq4Bl3He7Pwwv\n7Nt29gUX0R57GJRt13J5lea5dF7PnXDMMW2bS356L3dn8sxPc49t3fNtBdaz3YwJ6jlwbsgr\nYxhYqOyYpx96uSN4re8KW+Nx7DIvi+VfBY/7XDzmWHk+vALmHwDDII0NJKtSd0pn/cj6lo3F\ntPHopeXdd13k/uZgPOZ5LB133/NZL+WdSDpUwoGxSuRF1n8dGIfkCtAfbHDng4OPk4xyIeWA\n8BTcDtNDsU4hYyhcASdBH8iebzH2a5Wd+hkYG9JA4jntuA4C5tkZ3Np5xgMnAu/P/HFhNlDz\nwx2Qym9nZp3k9Y3RASApG5tpUen6ttFsO+3G/iwwNyg7useHuFNn6d9tsBT0ghQbyd+oON99\nYzoa9DjJwen+tMO2uF7mUEVJJx7lNfTlbZgU+oG6Gnyui8NCkJTiK77+5akAW31N8lnUQ8Z7\nH6wBc4AxjwT9vQhSPG4dtCeGJGNw33vpDans3aRtT9l42c2lyajVB/4E9qn34R44EzaGpKlI\nzJ52Cluf97TggsPYlgf71XyF/YPY1ks9OZHt0dieAH0dDvatpAVILAIpTmNycXErPAIvgDLu\nPcFFiP56jnthHKhFPluvOQGk/p7akcf0ZkG4C9YEn9/f4H7oAa+AbaBYxjkJGHc95CLSOCfK\nnMx9ZZwvguPNY+Di2Pbri9HM8Ax4TG0OejcK3oHtwXPX67k7bhuXGJdepjj1bn7Qz4dgCngZ\nPoD3wLx54UEw3svAl4vrwDiXhQGwFnidWuQYl+L0PClO814zA3kt29dshe29bJMcs+yHY8GM\nYPzrw6ywKtjWTwXvr15KPno+20Fqr+n8HrfP6/tO8D40SvpgX/U5mU7eGuMQ0IeUZ/ubMLOf\nvD+QvBnAMcJ7mAfuA/uYY4TteQ6oRcagfP7ZNmB+bzAW21aKyfyhkNW0hR3br2sUx13LX1rY\nsqmrbGv6MQyWBuPuAcrrOnYlpftzax/y+KSFg73YbgS2Uc9Ratwiu2qNTw09+7RQ02tn4zBt\n/LYL4/HaxuL+HfAqmJdkecvZTlIdj9m2Pi+DbSPNISS7ljQpVN6Brzh0PzgY2fj7wcHwN5gL\nHLz/DpfBkZBdRLE7Wrvy3yfAzmTjtF72fGexX6s8tx3F82dlp98MzE8dy8HnC3DQGhd+BI8f\nD+p88Outk6qd08G0njKO7wsnTB02nT8bf4rXvGwnN/0YPAou+PTSMouCWg1S3dEZNf7HwfDt\nMudI8aZtKua+ixYHNtMj4Tuwvy0HanrwmdUiFxnqM9AXn6fPzEFNGbsDn5Om11fGY1nLqeSt\n+fuD7UH/3NZbnncmSAPuD6Q/BCfp8yCre9hJMZpvPzQ+49Vb5T3ZTm2vTnK1jme2Jdvml4Vz\nOdkoY0yTpffwCPwLsvHpn3WXAuNbEXwOLqI95qRUL3k+mb7A+GyNNfvMDmJ/FzgWlOUvh3fB\nCdw6qa30IX0zeP/HwRLgvdTipx54zbtBP5NS7D6792BmGAn2lXPgQPgITofUNkn+Ry7qfUFy\nDE79x2eSVylOn3G6XorR/WngA5gfeoCLd8dO+9XcMALUO7Aw+NwPgDXB8vtCLT5SfbRSnO4Y\nqzJO5fn17H2YEUbCLGCcHjPPezC+FK99aVY4AjzPyeC4WqtsU55P71J8aWv78lm9CT5/PfS5\nLwZJxu24YJ2RoG6BlcC6N8AM8AbUU57ba+rjloU0m//cg/dlmzzPzAZrGa6XxngvbZzqFLDv\nJpl/OMwFpn32bg8G/Z4ebEevQX+YDTaGPcBnUqv00DnI6yZ5/RRL6l8e0+dt4Ft3UCpnO912\ndM6vbcMXQONsD9nWvK592vs3bu8h6emUKGzTsVHsp3i9J2N8HpyHrils2dQsx02vOVHhTOma\nKQ73lVvzjP+bwv4CbJ33i8tmx2IOj657J9vNWmGoBUPhQNaBDdmxsS1UyFyR7dnwOmwPm4AD\nVNJGJP6ZdgrbbmyHwEWwF9wFK4NK53ubtA3cySOPtqaSg/d98A7YYT2f21L8SL6DUzrmvhOS\n+w6e4kQ5BZwKTroOog5alsur3ahonF5HX9P1W9t6Hym2VPcr8pxU9W0wfALeg8edcC1fPAiQ\nVbH2p+Tj4OByMOiHbeB28Drl4i3lubEYp5PAgWBc1ncSsHxa6JCsWoOooRfrwtHg/RvfHdAD\nVgCf3YmQYvNeiuNPx1wQ25ZSmZGk5wM9cELIq+OoeAu4WDse9EIfnEyeAeP5BywIKRbznNCz\nsT7LvvFl83323rfntE04UeXVGVS8HvThTvA6+rkjeN7zIcVjnJbL7qe027PgdPAcPuN3wfjt\n86fAI5BXjiXeq8/Je3fr4ulzMKbkoflDwTIpNtvjF+B92QeNZx+wHe0E9nGfi+WN/R7Iq6ep\nmK5bvPUat8ImYCzDwLbqQugScHyaBMrpjxzwPrxvz3Uj5NU7VCyOL+2/XjjmM/M5DgbHhjfg\nLngTHCfKaQMOGJ/PS799dnnlvaa4stv0fPVyOvBZ2pb19FG4Dry2bds20g+KtTkZ38LHYLzW\nz6s0xmVjNJ3ybZd9Cyc/hm1qy38lbRz67D1cAMXagQzbeCrj2JJXw6lom58VimNN+/8uHDPG\n7SAtNklWLNuMY3Ve2c6N43hIcZXaplhtkz771F7s80+C/cx69mvnH9MvwKKwFJi3P+SVbcxz\npvZYHGOKL+WfT1n7Udp3a9swTp+x+7eB84Jt80xYAIbCbpBX+rEqLASvwDPgtW6B5FE2ptbS\n6V5tj8bu/lkwH/gctoY86k4l/UqxGUOxf+Xi8rk/DbZZ43HuSXFm62TPdyJlQuFAVQ5sSOlP\n4G+wBQwANTs8DFOADXhscOD8F6wGahroCYvDg6AmBTvSyrAFpPNtRNrGaqfIIzth6kDZRm86\ni2Web6XsSI79BVxYjwWqBzgwO/h5LgeQvHJQs342pmrSDkB3Q5owrPsi+BW3H9wJDn7mO8Hm\nlZNEul8HGZ+Pmgzuh0piNtZUzsH9IFC94XZIPtge8moQFdN50vN/k7x0XfPOAa/pAGm+/qSy\nqVzx1ns+BbL34CIyr1zEfA9ex0XwKuBkfC4Yl4uybEzZtHWK980zthtgJ5gB1oFLwf6YV2dQ\n0fN6PbkX7oE0uaRY0nH3U17aJp/TMfc9r+PBtZA8fYx0XrnITucv3ha3hxRXinl16h4EjhkT\ng3Lsss07sVre/nUF3AS21bwaSsXi+Ny/FeYDx8V0PPVb90fAItCWHE83AmPU27xK951iSVs9\nsy+cC44n7qdjbofAzNCWnCc2gXuhlhek76ifvX5KG9ffIWl5EvbX7HHTT0Nrvk7N8T+Bz+UM\nyKvss0wxpK3H1s2cuAfpcyDrrX3kLOgFpTQtmZuBfcixJa/eoGK6rttEijVtn+XYZHkvQr3B\n4FidV9n5rlyMxmobdT5McVs2fQhNeVeTtxlsAY7DepiOfUDacSCv0pyZzldqm+L3JTx73Ngd\nKz1u/gvgOilpORIvg8es61oir36kYrrOzaQdB88v5KX8bGzl0pa1T9pWtwX7nZ4OBevox9aQ\nR92p5DmqiSdb3nr2tTTfpG22jGnR93q/IC3JOU+Cm+AuOBn0aDYI/U4c2JD7sOPODj7Yp+BK\nuAc8pg4GJx6PmZ8G9VdILwFOAE+AE/id4AR5PWTP59cdG6qdIo/shKkzOEDuDEeBDd/zimkb\nrJoEFoBFYUboD75kuFAqJ1/2NgCvk1cOag4eDiqPwqvwFjiQ2KGL43V/e9gOvMfxQY0N84PP\npThm/d8CXPTk1f5UfAaWgnFLnORM8jy/cRtjGsTcvlfYX5ptX1gYJoRijUPGTvB28YEq9gdR\n9hH4IwwEXxRUP1gW+kCSE4HP7li4EbIxO3iuDLvACtAN1ASwOZwPr0FeHUfFe2FxsB2pU2EY\nrAcXg+3hY/gehkM2vlHsfwSrwJ6wMYwHxdqDDPtiXrkotI/2Bz29AgaDi8/VYS4wth8hG9+3\nhf3V2Pqs7VfGugRMDlmNz45984FsZpXpiyjvM9wWdoHL4Fb4G9i3fZ72sWyMPvsPoTXZr6aB\n7oVCxnl7IZ1ncwuV0vhjLHJI0YmMV3rBkmB/Se2PZEU6gVKOr3n1MBVTnD+TNk6fz1KQ+r/P\ntT/MA76Y9YZqdRYVfHZ59RIVjdP4Upz2/1Kx9CB/sQLGOxlUqksoaF/IK188Upy2RWN9CjaD\n1LZI/kZ92HMRvBDYFirR9RQ6rpKCZcq8Rr59e01w3EltIMVunrE7VteiwVR2rM6rd6noM/8U\nngHb0AgwL8U6lLTPWU0Pa8PU7iC9XRb6QSlZb2KwHzj35dVnVPR5fALGZju4C74A4xT9PBbG\ngllgWZgd1BSwLMwM5TQlB56H3coVqCDfsXBvmKmorH7tBa75HgLvIXmcYrc9rwxzwDLg2FVK\nxjkcti51sII8+4nX9HpuRX/fhGcheWy+nhqvc473MA/0hwnBOdz77AGzwl/gZNC/A8E+5zVO\nhHpJb/VwCDjmHQI+8xvhS8jrCVVDzeTAhgTj4JRVCzvdshmknUT7FuUV73p8zEJmdkJrIW8p\nsKGXmzw41KpscHYcO4qd//VC2s79ACwJNlKPu4jLq4FU9Px5Zad8DtaHb2EUvADGKS4A7FxO\nfO6/CHm0BpUcTPLKScLJopycMAeDfhrnj+AkoNfuPwqVaGMKvV1JwTJlBpFvHJWoD4XeAeMT\nn+Mn4D048Z4O5bQtB14rd7CC/OMo4zPNakJ2HgTbwVPg83JCvRqM7x5wML+5sH8727a0BwWe\nbqtQK8fP4NglrRz3kG3D+PTNZ/ddYX8k20rlxGRbyauLqOjEU05PcsAYv4e34KfC/mlsq9FR\nFK7E93LnvJUD34DtzHiMYxqot07ghNfWcFJfMozTNpie7Tw1nK9cVZ+Zzy6vHA9deHwOKc5Z\n8p6slXr2AftCXo2koguhFKfPvS/UW44px9VwUse0PeEDcMxObfTrQtp921atGswJHKvzynHG\nfuw84/N/D4zN/r0qrA0j4UqoRc55jm95Zf9x7u0PaW702TvXGK/jvYvk92FSyCvXELvlrUw9\n4xnYSv1dOaa3+m3czu1uvSc/Mrneq0TDKOTaLI+6U8k14dIwJfhsvfZHkOYcY3oZNgHXUhdD\nHvmMnG93LsP25Fd6z37csP/MBqW0Lpke71nqYDPmjdWMQTVxTCOJzY6SlZOrLyityeM2eOWC\nNGkkCRt6rbJDLwF2mGnA53oe+IXAgenwwv4+bDtaVxDAzHA0XAr6aed8BQ6GhcAJy0GiGfUt\nQe0Ieuxi4nnwGfaDF8DjzSYn2BZwseoA5aA4EtaDzWAb6A2NkpOOg7/Xtz34k5A5YFG4ECaE\nA6AvuFizrINvR2tlArgZfJl3kndRcBr47GeEjpa+zQMuxp1QJ4PhYD8bAI2UC5GxoRt4fV+A\nt4Rmk/1B9QTHSsfzuaDZ9BUBOeY4Lj4A9uFZodlknD5z4xwCznvlFkwc6lAtztUdr/vDKjAU\njN3x3JfuPaAZdBhBXAQ/g336bWiBW+AacJG8LrRAR+s+AnCseQdsr653jH9JMEbXKs45zaqT\nCew2sH/ZNhzjHU/nBduG99Ao+bx9ofT5ngnG45iqp77IOU5dDJvDxjAtVKsxqTA3uKYpxQ7k\nTw2VqIVCn4BruVK6isyvoFnHg/+J2QYc+n048Ci3YSdeHRzgt4cfIelxEi1pp4O3b3H9U8HB\n3Q5/E/glbGKwkx8JLdCsaiEwPXZgWgCMuy94T9NBM8oF6zjwJ/CLkHHrv+3Cgd/4Gyn987kf\nDU7+Tpw++/NhQZgA5oETwLinhI5WCwHcCE4ovWAq2ANcBLZAR6sPAdifBoFpY3QRfSm0QCPl\n87U/TASbwFPQAs0mF0KpjS1N2sm9BZpNjuWOi/bd/jASWqDZZJwHgHEuB29DCzSj/MjxLDj2\n3A6zg33mDhgFzSJf4rcE+9K78NfCls1oPVHYthS2Hb0ZQgDOLb7AOW4fBkqfn4MWaGZ9SXBX\nwHjgfL49vACvQgs0Ws7dB4Jz9MfgnPNP+BmUc7jq9+umqv86d10MvmyVYn7yh0Ml0p8xYVso\n9W6xFfl6+jJ0CpW6iU4ReARZ1oHXOGIjXRYcUGcAF00DoNybPYc6RG9w1e9gPTDmpGaMNcXm\nNg0EG5HumTnQzHHr7whYNROvSWP+HjzWkXKh+joYj57OCA6m7n8BfpHsaNl/jKcHGJ8vccuC\nst91tFJ/MsYJwRi7g/sd0ffH57rTgIvlRTsoBi7bpnyOUxWYg639uxk1MUFNAS0wPXTEM+Wy\nbcqfRk8OM4GLumb1831iWxj8EONYo+w3C0GzxpzGIOf0GcA5fllwodsMYxBhjJZx6qP93ziN\n136m383qLaGNlvEtCS0wLSj7nS8QHR17ev5jEksLTAbLgS9Lw6AjZRvcBHyZew/ugevhUfgI\nToJ14AfoFLLRhn5fDozidi4Cv3S7kBsLvi2kl2XbLJqaQM4Dv9j9A+xUx4KLgN1gA2hGueDc\nE/xCfiGcBZfAj7AWLAvNppUJ6DRogTlhIOwHTl6HwfHgV7OO1l8JwDaxL9h2fWly0P0r6G9H\n60gCcNBfE3yJc1JysP8XvAkdra8J4EQ4G86FbmDfN9b1oZHy+W1dwLZlbOdAs8mfTu5S4Bu2\nb4CTerPJBfw+BYzzWRgMzSbjPKSAbe8RuA+aUb64TwF66fhyJ7jgtL06tjejjiCou2FtcO50\nHnIMOh/egWbR1QRyNLwFY4IfuVwkOw5cAM0s2+vBMKIQ5Ei2euz+NdCRGsTFbwOfvy/zyrar\npx+408EawvXngiVgFpgengZf3m4A20Gn0didJtIItBoHelPYycmO41ebz8CJwPxmkAu3W8AF\ncH/4I+wIR4ELlI3BAbYZdQxBbQKbwdLwJ9gK3oZV4UFoJs1LMNfD6XAaOLAeBJfD++Ak9n/Q\nDHJxYptwEJ28sHWhbX4zaEaCcEHiRD8tuFBVLgCaRS0E8lUhGPu7aT1s9FivTz5LX3D1yes3\nk0+EM1rGZ6wugD4HFx0uPJOHJJtCKc7vicYFvB+SfK7uN5NSnN8RlHFOCt3BuaiZ5By0CrgQ\nXg6WBfdHwDLgQr4ZNQlB6fGn4EdG2+nP0CxzO6GM1j/5rzG+AAuCL6PTww7QbH2LkP4j2+uV\n4EvSOLAI9AXHsvmho9vxx8Tg83e8Uq7tbKsLgG3attDR8vneUaCjY6np+mPVVDsqN6MDMxPU\n6rASONk7OU0D58Le0AwaQBB+ZfDF6H74M4wPg+FeuAKaUQ6YO8JucEkhbdwHgIPWndBs2pWA\nfGnbHfzx/FHgROVAvyX8DVwgNoNsn4fAFGC7dbLaDvRc7ztaxvcP6APGZ/9aH/4Ek0FHy7g2\ngLXAhZQx6uUp0Oi+74LCRZIvGy3ghL4pNJtcyPvRwwXcLGC8PtNm0zcEdCj4E5o5wMWx43yz\nyTgdz43TMb4frArNJl/YbwXHw4HgT1nXAxfDvig3q+zHZ4Jzuv17IlgN/Km243ozyGe+DmwM\nS4Bjt/6eCltAM8ux3DFBT5cExwPHhnehGfqba497YXLw+ftivBDMCctDtfIdYGt4pwyjyJ8V\nuqQa/VWxs5ncg4Dt4O2puetwcjtxitPO4oI3m+cl/JozC6Ry5lUjJ+Va5UDj9X1B8kuIA3p2\nUHcQclLNGyNVa/4XUsbkHBOUiWEq8h3o9Tcb49fsuzhdqnCMTZvyWdQqJ8dsHKXONw+ZQ0uU\n+5C85aCtxcCMlKlVLtbbitOBelrwJ5/Zsqktr0L+e1BO2XZUrkxb+b7kZK9dXN5rfFlUxrjF\nCVWf29J0bRWo4PiUlCkVp31HOa5nj/uMfY7ZPHZblc+iVk3DCRYrnOQttovDk4X9em28Ri2y\nv3sOY1MuFJaEV9ypo3xmX9R4PhfvKc73SS8Djpn1lAsv23gtcoGc2lqK0/GmnnJMGV7DCX3u\nP0CK01M51rjotC+PgnrIMbpWZeftmTnZfZCN25iVL6JPj05V/x/nvFo1GyfwOc9bOJFxZeP0\npx0zFeUVila8cQ1Ri3zurmWcs0vJ8cqxasGig+YtAtn7KSrym13XX7Vq7hInMO9lKI7jI/IG\nwFdQjfygcQHcX6aSH7pGlDlWnN1CxrHFmSX21y2RF1mdzIGBxPvvBvEp1xkrpz9rNChGvXDx\nkFcbU7FRflayUC13H9s0MM5nywVRQf7uDYzzoQriKVfkgAbGeWe5ICrIP7KBcV5XQTzlipzQ\nwDgvLhdEBflnNTDOMyuIp1wR77FR45LPLq9sM42K076QV/bBRsXp2JJXj1GxUXE6VueVc0Sj\n4nTuy6thVGxUnK4l8so1TKPidG2WR64FP4VGxblCniBL1PE3Ks6Fn+EBcLwrBdmdQ75Nh8o7\nMDaHGuGRDcqvV3nVWeLsnvcGq6xXq5+NitOvMw6CeRVx/ta58PO3ftS692ONJ2hU++wKcToP\nOc43QrX4GXH+7xPqCn66qO/2v7feLjm1+NmoOJ3XnY/yqrPEWer+biHzXjim1MHOlNeoAbcz\neZKNtZYGnj1Pe6c7S5y1DGzt7WH2/BFn1o3a0+Fn7R5mzxB+Zt2oPd0Z/HTBFXHW/qzTGcLP\n5ER9tn7greUjb32iaPssEWfbHtVa4gpO4K/Kh8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAc\nCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcKCsA/7Lf7eVPdrkB/wf\nwULhQDgQDoQD4UA4EA6EA+FAOBAO1MuBiThR8T+ZXq9zt/t54gWp3S2OC4QD4UA4EA6EA+FA\nOBAOhAPhQGdxoFH/LGNn8SPiDAfCgXAgHAgHwoFwIBwIB8KB2hzwT668DY/XdpqoHQ6EA+FA\nOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD\n4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBBYMhvUAABAAElEQVQOhAPhQDgQDoQD\n4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAONLsDYzZ7gB0Y34Rcex1oxN+K+ojr\nXJ/zXntTb80Gxfke17k5Z5yTU+8P0Ig29xbXuT1nnFNTb5UGxTmC69ydM86+1BvYoDiHcp37\ncsY5PfUG5KxbbbWXqfBQtZUK5Wdm2z9n3WqrPU+Fx6qtVCg/O9slctatttrTVHiq2kqF8nOz\n9a+oN0J6qad5tACV5s9TMUcd26ZtNI/0Uk8bIfu6fT6PbJu20UbIsdMxNI/s6/b59ta/ucCd\n8GbOCzl2Ooa2t4zzVng354VWol6fnHWrqWacN8KH1VTKlF2N9FSZ/fZK/sKJr4NPc15gDepN\nlrNuNdWM82r4oppKXaVsIxarndXLDQj8YhjZxg2Mw3H/WrAvD1n54qLa6iC9KDMtdIefoFpt\nSYUz4Y1qK1ZZflzKTwo9q6yXiu9C4h+Qd6JI5yne+re8poQPQP/GA2OcBPJoPyodBr5k1VM+\nX18S3wf/NsAE8AP4opNHh1NpH3inysrTUP5j+D5TzzZoe303k5eSfij4BGZLGVVufeY7QKlz\nF5/KicuB+pvMgR6knSis7+RYThNzYBS44M2j06m0Cfh86qlib/X5JVg650UuoJ4fRGzv9ZAf\nBByjvsuczP5jn3IBunImv5rk1RReHvz4k5XtXk+KFzj2DWP4EqqRbeMuWKeaSpmyt5FeFOwT\n5WQfsB0W38sU5NlWv4K2ZFkXTJu1VbDM8fvJnwPamk+s7litz8Vt2fHbMfJzKCef+0WwY7kC\nbeT7Qj0dfNZGuXS4lIdjc9D8NKanstmt7dY+u3c2s4r0q5S1Lzre1LtNZsNwvD0WDslmVpF2\nvrTtVdsvWrtEqfbsy82h8H+tVWzlmHOEc8rXrZTJc8hnpFK7d770mZ9iZg4Zo33dfltPud74\nBVK770d6OzgPqpXt/0d4G7LjcrXnKS5vnyke61vI2xiugFA48BsHHCjsgKXYmvxKFnU7Ue51\nKJYd+KbizBL7S5Dnws8FdB4Z57A8FausM5DyP1RZJ1t8N3aey2bUKe3iRv/SQLoG6TRI5bnE\n/lR6OE/FNuosx/GfwcWhclByAMyrQVQcXGVlF0h6tXBRPRfr5vtyWaxtyXitOLOK/eMoe30F\n5ceijP6sUlR2dvaNrV9RfvHuHmQ8XZxZxf4ZlL2kivKVFt2Kgu9kCh9I+oHMfrVJF69nVVup\nTHkX0nrbv+j4goX824vyq9m9lsInlKhgeyh1Xl9USpUvcYrfZFnHa+WVsRzVRuXTOF6qDQ8h\n/29t1E2HfWY+u7yyzdh2KpFj2DMlCvpy3VYb97h9Ia/sg3tUUdm+YR/JamJ2bJeLZTOL0j4P\n21JeOaY5tinb0K2jU7/9zx3s1nINz+YY7VidV84RzhX11Jmc7JqiEzrn2W7y6jMqrpG3civ1\n7J/HZ467htgts19t8gcqDKy2UgXl7Z9HZMq5JnNtlkfdqWT7XyJP5TJ1Jiqcc/Gi436E2LAo\nL3YLDrgo6aqamxv/HD4pw9nk+zWtLT1IgRZwAZzkwL4J+JXCL2Gh/A7MSlW/ZjqJTlXiNA6Y\nX0DewajEKWvK6knttcFBfEDmTC4cvoctM3mNTvrl7BXQywXAn+rZTneAZ6DeX/84ZcXy69tD\nUOyPsY4q4AveZrAzzAMdJSeZXWEDGL+NILwn223en8S0cfpch30htn2uBj53Pc7KvmRbaQ+5\nkPD6s2ROPnMhzzagr/VcGGQukztpzI7vLZkzzEl6UfBYUg8Sa4HeLg9jQkfImOaC7IcQ2+Cq\nkI13PvYdAzaF3tARMh77dLfMxW2PjunPZfKM33zngmzbyRTJnXQO7w8zZc7gNZaC18FrOi4Z\nw+9Bem779KOTH6D8yNtM6xSfhX1oXXgE1gEX+M0ox/8NwbXe5lDqIyPZHS7Xui+AfWhs+APo\n8TgQKuOARnVVPc+NzwG+rZfSvmTa8NvSsxQ4DW6B82EecAH1IzipDgcXoTdAqDoHDqH4ofAm\n2JH9qdxmcBUkfUtiL/Cr2CLwC2QnW3Ybphm50m0wDRiz+0Pgj+AXtv3Ae3Di9X7KtT0OtZtc\ngBrj9vApTACOAw6WHS2f4z1wH9wNC8FK4AS5AlwJ/pRJL08usDvbRkmfLgHjGQZTw1fgi4Yv\nwKX0CpnHw/XwL5gZfInuCHldPXSh7LhkO/Wl2EVSX7gfHLuWhathUqi3ruOE9gkXPucVTr41\n27FgG3gXZoJrYCP4CTpalxGAMT4O54PtYEu4HRz3VQu4r4+jwL7/ALgQsY00Ul73UrAv6bHP\n2HHTZ34OqFNhB3gdJoGTYG1otA7kgg/DY3ATzAbrwvbwDaj1wL7zJTjenwIvg/dTD9nWt4FH\n4XxQPt+34ETweY4HXlcfLd+Z5Ri2FbwI44K+en8drZ4EoLcrg+NrH/gYvodnwLgnh2bR/ARy\nM/iS9D5MC++BfWliaDY5x98Krmt7gb66DZVxwEmpK8tB9rky2DEr1c4U3AGWg8XBzrwQTAmn\ngR17CghV7sBAih4KduYWmAqOhQvBgTMrJ7ZzwYXeIvBv6AhdzEXfBAfK2cAX8JnhKFC2Axcm\nC8Ci8As0Wi1c8Adw4f41uJhy0XoQOFl2pFyA2m+GworwBSwFQ+ByuAjsUy4+PW6fs+/5krQS\ntPd4tg/XWAH2gsvgEHgSroRuUE57c8AFie2iBX6GjtCBXNR2Nx/MCr4gPQG2WReDevot/BOM\ndUyot2zzfjAYBMbhIuNHuA3s48a1IAyAP0M5zcIBj9vf21s+r1XgGLB9zg0++3UgjTUXkn4f\nHJvs+9IPzoT9wHttpDbnYrY7/XRO8pkuBy6KtoAtQY9nAvvU3XATzAmN1FAu5nj4CNi3uoF9\n+WxQ+nkB/B1sH2vDFeB91Uu2yT/AkdAf1oBXYUZYH6YHr+2HDp+zfaMzKvWZvxD8LTAOvAiP\nw+vQ0TqCABzv7TN/hb7wEthmLwKfzdjQDDKOq+EtOAWOgJXB/I1hLGg23UNAj4F++ryHwdcQ\nCgeqdsAvRz9VWMuFxLngRPoxOOjbCDcEO8oHsBWU0hJkOsl2L3WwgrytKWNDb28N5AI/1HAR\nv148V0X9syh7Y1F5fR4FO2XyXST9As/DM4X0F2zzan8qPpyjsoshn+O8RXW3Z/9dcCH3EbwD\nD4Ltw3aRV4OoODhHZSfD78CB/SFwQXwX+GxXhWJtS8ZrxZlV7B9H2eurKF+q6HpkfgnFfeQO\n8n6EETASXoa8OoOKl7RR+WmODwc90zs9/Bx87otCJTqQQg9UUrBMmYvIt2/k0atUsr9k5eLP\n+F30bwq2A5+37fQeyKtrqXhCBZXtF17fBWhWh7Bjfy4l26Rjs8/7PSgeJ8iqWLdT8qiKS5cu\nOA3Z3oMvT1ldzI5j0wvwIfjs8so2Y9uph27lJKdlTuSYZ5zOX7Zn+0Je2Uf2yFu5RL2dyXPM\nd+w/HIzTaziGObbklW3cdpSV7cDzPwWfFNK7sU0yhrdgh5RRwdYx2rE6r96m4sZ5K2fqbUPa\nsfIV8KOI9zkUkpzzbAd59RkV18hbmXqzg/E5ztvWncPt/wuDfcvjyjVE9pmMzqziPz9QdmAV\n5csV9YXauLxv47W9ODccD0/BMHBtlkfdqeS5l8hTuZU6E3LMPu7c9T34zL+BDSFUwoGxSuR1\nlaxZudHXwcG3FFuSX6k/m1B2I7gfrgU789lwHkwBdvZxIVS5A+NR9NOi4j4PO7aLOier+eH/\nYHNwgTcfHAOVPjeK1k3GqxwwVU+YGSYCn/1lcAe0wJLgQtV7aKR6cLF54CVoAb94bQB654SZ\n7oFkh6o3V3exad9RxuVA7gSaNCmJpcEFxIxwAtg22lN9OflkoF9OXi1wHTiZGU9WU7LjPUyc\nzWxgutT19dEFSFapvfYj0zHrUJgFzoes3+zmkm18XtCzbiXOYN+w7RV/1DCuUu3RZ30q7AWO\ns/arn6BW+fwWgdTmqjlfGtuTl9adE1x4GNtccB3US8bqwtFnnEf6mmL1PL4YOIf5cvQ81EP2\nk0XBbS0yVuPqDwfB2uC4/y7UU8tzsv3gj7AADAZfJI6HxUDZz22npdqlx5tRfQhKz04D55zZ\nwHHpfpgBvN+Olu35KnDxboxLgXNnd9gbVDN5PjnxDALbg2OQ8U4P78Aq0EyxEs5/1IuUayPH\nI9er9in7VqiMAx2xkCwTSsOzR3LFA+DAMvh27cRdiRyAhoMT7NbgQm0nsEE+Bw5E90Kocgfu\noeg6cGeBu9nq60ywD7wHO8CTcCEk+dwcuBqpHlxsJfgOjMdJ1PRr4Auc7cAB1MH/B1AfwU+j\nU437j+3TSWcW+AYcHK8HB3zv4XXoSDkenQUfwuPgM74RHgUn0fVhSXgM9K8nvA/jgGrEc9e3\nd0Zf7b/Pz5eAm+Fr0MO34V3wHj4A20CjxloX67dCur4+uThyITR+If0xWyf4sWE3+ARcSPlB\n4mhIqtXPTTmRbfwZcBz0uQ6ArOwv+rZLJtN7cBy1zxdrdTJGwUnFB2rYX426xmY7s83Zf1eG\nSuXYb0x6mbQWCduK41e9ZD8dCcZqH/AZvwzTQTXS101gElgTvG/bwgTguWvV5pzAdv9IYfsl\n28GwM3idanQPheeAXcG4fdFsgSmgnlqbkzle+0LkPLMe+CJhv30AjoEVYTYwpmbX1AT4ZoGr\n2XaDxSHpChL2TdtBR8mxwDbsGOUz7gknwErwPhwFts+P4TnoaC1LAMbyAfjRR90O44Jz6ZEw\nC9wHzaYlCGgIOKZPALYJfXc9ECrjQLWDVZnTdMpsB8HLWoncwXH5Vo6nQy6O+oOTzUhoAfMc\nfNxOBk4Qr0Mza06CWwAubJIgFySOXrA0OCg54Kvb4AnYD7aAp6Aj5cRzC8wPI8AJVPmC5AKp\nNzwLC4PHXDx3lNJg6ICu9HVicBHg17uzwHb/C3SETuKiW8MwuAnsV6uB49RhcCk4wLsg/Qq8\nj6ngcnAB1t7yRcJ+/gLcDHq1KBjT8+Dk2Bf072tw4amnZ0LxywdZ7aIbOety8Dg8CH+E7WE7\nOBfWAOPdGzaHaWBTsJ06JnqsHrKt6ZWTsP1gcnBivhXsByNAfQm+WJwDxu2zXxVsk4dCsTyv\ncdZL9t+5wPv2ZcNn7GLCPu1C7U5oS9bVYxfv88HTsAH0BH2ul3bkRMarbFuzFniR7czwHlSi\nf1BoLXge3oc+cD78BYy/FtlXnUuMz/TcYD+1nywMepraIMk2ZTs+Fbx378++tC58DvWSntpP\nnGN+AtvAFPAD+Aw/gD1gL3CMegqaXT5b+98ocOyxja8HX8NWcDYcAj6L02E6aKRsD3eAfce5\nshfYr3vArXA7TFPY99n7LDpSC3LxweC49DI4hhm7vg6Fa2BtcM13DDiWNYvWJ5BLwdic523v\nxu547H6ojAM+7M4iB9sBkCYI494IboBrYUvoiPtxonFx4cDqoD0KzgdjMdajwYbpANzMaiG4\ngU0SoIORC7oVYW+YFBw8v4BXwU59I3SHRcB2keSiQb8bJdugg/0S0AeuAtuCA70T+s0wGRj/\nCZA0DolsW0757bmduHDyz9jaTl0IvFTIc1E/H3gfHaVtuPDz4DPcC3wBuQVWAgdzn/1Hhe2x\nbPX0cvAlSu/bWz5L+/gV0AJ9YSS46DDm5cC2Z7t0TFoKbAeHwy7Q3nIcMobrwZehb+Gvha3P\nd1tw8XomvAB6tgpcBoML+5uyrZfsp17L5/oE2NYcF7eErM5nZ2l4B6aHC2FBsP8U6w4yZoO1\nig/k3PdZqUfhOvCFNj0rPaxUt1FwYXgRZgRfrJyzWqAe8lxpvHABeSLMBfaB8WAQVKovKWg/\n9xy2kUlhX3BhV6t6cYLPwWc9L9iP9wNj/DMMBBfl1WhXCh8LfgyZGQ4C21O9tAEncoy2bT4E\nk8PfIfk9Pmn7i/e1BzSrehPYjnAJ+EwfgX5gn/befNabgZoJvC/HMn1ttPTX8cE56BlwbL8H\nxgQ1AKaAIXAldLSOIwA9tF2vD/eBY4bxTg22S9cntktfmJpFjm+nFYJxDrgG3od3wfYQasUB\nB93OookI9C6wU38N28Dp4MT0ARwLLvB2h0rUh0InQzkP5uRY6qytna8/B3+AnuCE4Jv5FqB+\ngidhFEwD7SUXj57fBYZaCxxgbgX9sWN4v3uCg9I54IBUTtWULXeOWvKXpvJwcCK/GXxOPnM7\n+27wOEwMPp+34HZI97kq6UZ2fJ+/g+Us4HNwcrVN2Q6mgxnARYP3sTLYHvR+FfgRGilj1TO9\nE9tnav89SLsAmBY6Qi5GjEEvr4O5wPY8GxjzCjATfAV7wOvwFpwJ+rgOfAftqcM5+fKwJTwD\nLqr07zWwz5wP3ofPfh4w7lNgV3ASdb89tTAn9xpLwZKgP7uA7c/87WBT8Nk/ApbXTzUUDoAL\nYHPwft6DvNKXsWBv+Au4sHCMMn8GKNZDZEhbeo4Cg+AquBNmhFcgr/TFOPXCZ+bzcwFhvh6M\nD8kjkq3qeY7umCnheW4Cx6c54EHIK2MUtQwMhGFgP/Dc9u1q9DWFjylwKtujwfPOB84beWWM\nE4Jt6N9wPDwL+tkbHgDH9+ugGu1PYfvQhuA4tQB4//WQ3vms+oEeeA/7gvoBxoUh4NzjfXhf\nzSafmzG6DkntZCrSev4CHAaHgvHfCMvDbbAF/AIPQ6N0NhdaqnCxydnKo+CYZSzG3wOMdUvo\naB1LALZZ1Qccg3zBsJ2neGch7RrV9tksmpZA/gyTFAL6me168CP8BMljkqFSDqSOVOpYs+ft\nSYA+/NVhG1gFXIi4SK1E31Do1Vb4tJKTUGYccFCy0dmh9dSGqNJiwM5jp2ovOYjfC967g/3m\ncD7oyeGgHBRd8DwFTtjTQzmVK+v9tbdckGwEM4GD5uvghGRn1ms79kg4Acx30bU9vA3en5O+\ng1Yj5MTpQK/PN4DxLAPqM/gEjMWYF4dz4U7weVneNtNIfcTFkjc+y+7wHRi3OMC3Zzvl9GVl\nnzGGncE+PQMsCZOC+SuDnhn/maDOgbXB+7A/t7efjgkXgD6tCPZ74+4LvpTb941Vb52I9NLF\n3CB4HjzWnnq5cPJebCeH+cE+k2SfeQIuggFgPNnFv31nGXgFvgfbbV7pgRiLvvjByf7i8zOG\nWnQolQfCcLD9+gzySn/0wbHa+LxnxxTzTOtDXu1DxdXgDTDOWpSeo56ati/PCs4ttnvHmrza\niYprwTtQ68clYzNGXzTFZ+/iXT9fAtvll5BHm1NpQ/gAao0ze33783RgjMabldey3+8CH4Nl\nmlF3EtT44FgoqgVONYEOgy/A+L2PLWFdSPMByYbIeLYC24gYz1BYFBwr9T+NOyuRHgkdqc25\n+F6Q4vWl6EmYGvTOeN1eCHPBs9DRch14JbwFuxeC0Wdj/Vdh67gcasMBDeusmozAb84E/xjp\nEdCSyWst6YSyPziJleIB8m1UbSmVcVB6Gj6ENKk6cbk4GgJ3QXvJBYKLIReJxuHE2Q8Ohr+B\nHfc9+DtcBkfCxlBK5cq2lCrcDnmncc6pwHt5GJycfKFIg/4tpB3YTwbv6Q1wYtgBtgfbQaN0\nChfqDbaBw8CBckz4Gsx/EXqB7WAiOARsc1vA7dBoOYkan/oO9OpzME8eh5eho5T6jc99G0h9\ny+1HYPvVxwGwOKwMR8H9cBbUslCmeptahxJHw54wKZwHtsv0vGck/RUY77WwAIyEvnAYNErj\ncSGvvyU8BT5bdRmcA6+D7dJnb5ms9HJXsJ/ZnvNKD9Jzu7xwkp5sHXfPLuzXsrmbyjvDXeB1\n8ip54xhzJXQDX5aUHtp3a5H9fCdwPqlFxuTz8F4/BRdBpo3X/IugFt1E5R3AxV8tSm1mBCcx\nPv01dhe9zi1zQmoPJKuWz2Q7eKnqmm1XsD84zryaKerL8gvgfUwG9vFmU38CMjbHz/VhYvgG\nlPt/hqHg4v5p2AJ8BulZkWyIBnIV29hw+Bm8vr7OBMar1+a9CY/As9CRcmw/HhyzzgPbs1oQ\n/ADqeGaesR4K70IzyLnQuWcQ2KZtF0rP1wDbguoG6WV0dEb857cOdMYXpGW5hT7wECwNSX6B\n8OtVoxd4U3JNO4kdfV6YHMYFZZ6N0EaZOhfJussB8UHoBXfB7rAquDjaFIxrOCR9RcLBqJTK\nlZ2tVOE65/XmfBvD9uBi1IHIhUDqxHqol/rqYORk6wLGCbjRcrL5EzhZu1A7AFJ/Go+0Ma4E\nxqc+hPdGpzruP4sVLq2PPWERSO3XQ9k24n4jpXe2XxcoS8CZoId+9fTY7PAo/AXMt//fBC/B\nmtAI+ZzPhtPASdNJ8jXQQydT49LXn2EO8CXUvmn+YGhv2X+UsenJOeAztr8rx4QX4GLQO+9j\nUWgPpT7ri+QGhQvow+GQ4ilkd+jmB67+BDhOrw/OIUk7pEQTbF043gBvg546xtgv9PRfcCo0\ng2zzd8BUYHyi9Pdg2BpehmZSetn02Y8Ps4C+Krf2afuO/X1haDY5Z6tj4WpwUex87Thk/MfA\njPAmOCd1lHbiwqPAMf15cFxwrLKNpDnTeG1DG0FHawUCmAgeKQTyDNv0Uue4brv4Eu6CZpHr\nT8ex7WEYuP4wzk/BPmjczqX6LD6LUBkH0uBV5nBTZX9LNOfBfvAY/AGOArU83A8uXtLETLJV\n2VD+CkeUYTHybUBtyQnLci46XSw5OL1R2HcweB++gfaUi8pXYSnYAvRgG1gNtoA7wWNjg7Gu\nDLdAN+gHWZUr+1a2UDulp+S8tknvZ0Nw8eIg/1Jh68DpROWg6mQ7EJzMbodGy4FIP2cFvXag\n1FvbqeoLDkzngoOqC9KO1jQE8DUYz7vwGdwM74Ftxnbb0bqPAJ4G/bTf2J+UXneH/uCENS34\nQrAe2B4aIf3TMxeq74CTuBOok9Bm8Ci8DHrrYsX4HAMeBO+nvfV94QKvs3Us8pp6mdrkbKT7\ngGPfDqCH9rX2kOOhY5Jty+djPzbPPtJMMqYpwHHPOH12xui43qh2xaXalH12CfgYnFP0No03\n25P2PppFjuO2uw9gKBjnMzA9XADNphEEZIzOKcbrWJj665Ok9f1xmBD0v6PViwCOBPv4m+Cc\n6PM3TudP5Tjl4tf7suzy0ALt1d85dUl5/V3gWTDOHjAXOD9eD86jaU7yOThPzgsjoSPlmLA/\n6KtxLwK2i/HBtqAc//W50Z567XKaigN6/iLcDbZZ54VHwHvx45T3cRv8AI7LoTIOuOjoLHKx\ntFUmWL842MmUk7CdysVJpbLhLArlPLChqYHgYG8ncPBZAFz4PAUOljfCDuCxWcFGaCeykRrf\n0VBOs3HAjlerjMdFjx1iRrgEhsNkcCI4UV0Ndmzj+xzuAgcnJwQHraRyZe1M1q1VM3CC1WA6\nsBPPD3rns3PAcSHwMBj/PbA6/D975wFmRZH97WdFMIs5oCKYMOc1YMDEmhczijlgWrNrWF1z\n1jXnnLNrWl1FUcGcc0BEMoIJsyKG//e+Q9d+RXsvc2/P3DuD1u953unq6q6u06dOnaoeFFaA\nceAmRn//ArvAmnAs+Jx6yjE7Ckw0YXz1zU8wNVh+EFxkXbTGgPc3t2bggduCc2MgDIBFoDM4\nroMhqD2FqWAa0N/Gt75eD6zX5iXgL/AIGFM+ew1oqnzWWvAiaGspaYtxuQWMh7agf+cDbTsJ\nVgH9uwF8BPXWEDr8O7jI6Hvt0pdBXusD08Li8C6YZ9aB5tTcPMxx8YNMXwUNpeBiuCLoT3PQ\n0tAG9OUNsDvYZifYHraEWshxWhT0kWiLGjThMNFPc7lzSrvMUfE7cVpzdaQH4+xTMCdq70Bw\n7NxkzgLmIDdDr4C+rbe0aQ7QPnP0wmDdnZCXvndtU8bIuIZSfX64nroWa9twWCgr/5ejdpfS\nAlT6PkNAv9dLztGNYFjW4RkctVu5hqvvQL9fA0OhP1QiY9oxME80t+7ngd1AXxuLYXzXpvwu\nvAnrgnlX+48H53+t5RxaH94B89BycDisBc5/18dpYQpwTrlGjQV99SSsDq453tdSct3Rt9eB\n/jN36mNjRb0FC8KrYA72PUrNQaqbLHPOWuCYfg+OpWufc2UB0A7XzDPhavgFXOd/AP14LhwM\nF8GGYAxMA+ZXn/MadICk5IGqPXA+LZwYBpWT/V/g5DCBWmdSOgxcDLzP+lJHAzkvg/hBCM/y\n6MQsot1pNLhEw07UObljmZxMYpUof28PGjmxiuoAGn4Mvms5gv/Cdc8/A48Bz+8Bx8cElZd2\nupEpqiNp+FyZxi6a70Owr9QxvINJ3oTVB1wA8upFxah8ZRXnJ3GvfQS/aMto8Gh8erwJpoK9\nwLELtsV2h7rwHI8uVtoe7mvKx8g5PMf54/McO8cnr9mpWAfGQGyP/Xvu+4R66zw/BGIdxMlr\ncUWV5cu5/5ZG2jiWwY5SR+v8eLoBroajYA6IdTQnT8cVVZYdU/sRx6U7rA5dwTjT17Ft4V5j\n7YPsmrnL+46AcjqVC75vUdlXiJ/4aH08H3py/gWEeBtBeTWoVOdx4z2V3lzivs+pi+2Lffc1\n124GNxxhTr1DeVGoVlfSwLErKscstjOU3TgtET3Ujc8nEMbdsnWVyjlweaU3l7jPPBNsC8fg\n00FcmzdqY27SJ94X/Hs35Tg+OC2p+6g1txSVH0XBrvxRe/J1A6lbvMLO4pg2dszVReW87RU1\n3pyytgX7go/1e6hzLok+3Qcq0XPc5NpXVN/QMIxj6DvYk/fnV9zrPA+5yvvNm7tAY3qTG9xL\nFJV+MmeWUlcqh0OwN9jvMaCtlvWt+5n1oJQGUbl7qQsV1LXlnjCusQ2hLtgXnxtng7N2wca+\nnO8AcWzkn2de2RaSSnhgihJ1rb3KxfMCeAAegwuhNxRZtGg2SRlMfnG7wXRD5nlH8OPBxHMa\nvAzKYPW3JEGeG6ilkqoL0MLgbwi6QVOlXXkNpcL+Y7mYmpgqUf5eJ2D8fpU8I3/PTFToF+Ux\nj/XfRfWetwM31S70ysXeReJAeArqqX/TmeMW3sG+S72DdcaosbI++E61kMltUzgJjIHZwKSo\nz7qBHx03wiWg3IgGez23rDw6vvrZZLo6jIQu4KL5EzRF2tQerobbYREIOoXCR+A9c2aVvoux\nFuK6DeXXoAMsC6PhX+A71lL22xPciB0N3UEFv004m3D+HCe+n/lCf+8Op0KIW4rNpod50tng\nOPsR8yQ8A46ZdcoxCxsQz+eGxWBN2ArmgzOgVnITFPzkURzPzuBHjVoG3CCfDzPArPAY/AeM\n5Xoo5Bv7CvZaZ/y9BdvBueCmXZ+NAO1rC/WU8zPYF/z5DXVTg/HgsRP4gXELmGvlVrCuE9RD\nxl3ezg+yjhfg+FBkxGmUzVHdwLhdGZaDC6HWMhb1qfnuZ4ht5rRB1hkH74Dr+LvQmPIx/UJj\nDaq87hqktEv7AlNSHgN+QHwLb8OOcCnUS3647AT6VvtUsC/Ya51zfS4YDtfCX2FeuA5aSrPS\nsfN6GGhzkOXwLta5LzK3uw/pBK5btdagrIPgy2BfOPfyVNARVgDXoOvAueX4h1z1DWVljCjb\nu8YllfHAFGXqW2v1oRjmhsAkNBr6g5sAJ9hLYOBWKoPGjfbWZVgoe5AJf2hWHsvRBdKkei3Y\nfxxgJlztCQpJK5x7dNGyz/3hVQjBTrFVK56MRQ3VHybvj7IH/BI9KCQhF3oTrP1Z55/aPA/W\nj4K1oCU0LZ2uN4mOtTW8g7fNDdpbSw3j4Q/C6VknfqybFGeEJ+Eo2Ay+BDehM0NQ8K/n2m1S\ndYHQZs9vgoFgzMcxzWnVcgPiuB8B74GLqNoLDobtwf7DXLD/ryGW7Zzzb0AP8J4DoVbSHjfr\nV4O/odfGIONzJHhU2rIy9AYXzGryELdXrfVpsSvMkrV8huPN4Lgr/egcewIc+yBz1dPwANTi\nwy3049GNhDJ/Bukn1xw3bs7nXeBFOB6831jTh8bbllAPBf/EdjrX1fxgzM0O2uSY94LOsDbU\nU19lnYWY89TcqF1uNruDMToMnFPOHzkIrIvjl9OayXmujWEu29FCoC3PwZKwNPwJ9gBzlLnK\nNsbCIaCtzr9ayjj8ENx8TgnaExT72Pdw/s8XLjZy3IXrcUyb+5pLfaIHjY/KFrV/Dlge2oN7\npFuhXtJnw8ExfRX0rzaV86uxsDCYK13Dwjyk2CLagl61tWuud8c/HkNzw7Vgnv8BaqkPePhI\nWBBiP4byL1nn2midPje3zwy7gWuCz/C6ecwP0/+CsTEMbDMOksp4wMQwucgF9URYAQaUMHor\n6q6Hm8BFozEZdBdDOR/4G0MDTpl4fKaBFytO4gbh8XApvA0mVOtegVizcOJzB8eVf5CyE/Jh\n2DR73zYcrdNPynLwuefWW3cjfAcmqvzCQFVd5NgH27Qr2Gbn2hgrfx5fa85ySG4uiNpmwvY4\nK7jgGGNtQT+HeNM2Y9kFzfj1GXEc+5tc3+1zqIW0ww2nOgCMh+HQCbQt+HUQ5S9gHVDxwhXe\nZa4Jl2ry8zCeuii4ORoG+vAnUM5v+9Z31k8F+l1p29wNpdr9eJxHrwdD4VdYFXaCrcHxU2Og\nM7hA+vGmwoI64ay2P41Fx9Ox/RSCDdbprxnBOPgQYunjERBiJL5Wi3LYmH3Lw50H2qaNI8E1\nQPtiW8Zybpu4jtOa6+usB+eE5U6gndo3P2iPDAHnUJDloVAve7+iL/OP/eqnkDevp6x/jVVt\ncT13w+Z8ieW5MWxOM4ZqKeeO8bkYBJ/pU+2273geuwcYAo3Jd8vHdGNtGruuTZeDH8HK8+fB\nze5pEGQuMmZDrIT6ehyD/zrQ2UJRh67Zo6AjWHZstf8BaE2aB2OMx2Cj72Pemg3cc8RrJKd1\nkR9j+tO1WDv0m3bJd+AcUtYHmb/OhV7gXFoNlPtO4/19+CuEtTNuS3VS7IGwqMd1rbXcCcNc\nnAaUMfAu6l3kFi1zPV/tcww+E18prqLegFKvgYH3kSeZ9ue4SjjhaKCdDiYnk0EIvJA4qGrQ\nMH468baZcPqH+qkvTPIhSfry1ukrj3lfBR/Oz7VbwCTWUok1JBRMmMjmYGO+/mYraqyw6fmR\nfn6B+cDYMsaUMea5C+cZEGw1lkPCN8nGfjcpq3ETDg0/Q7uoqlDRebYWOJ9ugsVhA3gO7oFY\ny3OyblThfAzanoJz86lQUYPjpjzzMgi+1L9BS1OYE/SVsazcVPkb/Q3hJailXMR9/9HgnPgC\nNoK7QTlefpCYC+McZbt6yZgzrhaE2AZjVZ9+Ai+D+SD+xVMXzpfJrnGouRxDNRM4F/Sddjve\nH4Bj/SoErUPBDwBtr6e0Tdl3Z9BOY8Cycac9shoYE0GWu0KtYzL0p53uK7TPTVmw8zjKm4M2\nvwF+gLwD+XXQ8xHwMdRSjvF0sHquE+uNR/cGQd9T0NZK5BjkY7qSdpO6R9/1Bn0pSrtPh1Cn\n3V+B+4+WUNhLunEPZW0yD3QE7XTdUdaf2VBqPT+0WVtF+5Trq/XmiOB3inWTa7B8lvWoXcGO\n6SlPCc6jYC/Fhg/nHhzbwmZgPCrfy3c5BFwnfE78PE6T8h7QwZOL/PJ1UE0UV4OJNtZunJjw\n3osrm1i2Pz+EtgMDdS1wozkWTgVVKshCwI6ccMtEP114D4NrYAHwWX8ktedl9U/w0aTe3XtM\nDsbpQbAXDIZay+SyOBhzYWPsxiQoJBfP8+/heX940Is11lw8/y3ww8NEuSA8C3tCN9gCtodr\nwQ+ivK2+h/Loe/oLBheD8XAhbAU+23nVFBnnxvy+MBRcMP8CH2Rl7VoUgvJ2Wn8CuClwYfDo\nR8GZ0Jxal4fpP39j5/gvDEvAP8FYiKWNwX/W94UX4Ce4GGqpeXn4FLAqaIexuS3Em6NSPuSW\nusmNuQp26CvLzq1DQV0Je4B+uxyM0QPgcegD9VDnXCfaKMaZY6mf3Wh8CXOD68Gl8D7UU455\nkPbpT3HTeR28Ce/C3+A5CDHoufW3Qz00Z9RJsNOqD2EmOA3M6erv8B+YHfrDKrA9GMu2raX0\nXWdwjEsp9O/xGDA3VqJ8TMfjVkn7/D3mY+NOe4NN3hOfW1Y7Tji0yE/nySmwGATbwlGDgu3W\nuda0pDrT+WwQ4nA/ykdmBvkhoYK94ajdaqcJh7r8dN1Uro3BjvioTeZMFeo9bwfPgGv3huD8\n6gY/Q/hQ8h7XefNHUhkPGNSTiwwAk+fRMAaegPvgBTDQL4AtwUGvVAbgImUwmSsXxEHgRsmE\nvhY4oQzOO+AVCAqTKJxfHQq54/Wc/xU6gYvBH0W/8qL6KO+nMLnzfuhLxbnghsQkcQ3UQ26O\n34FhsFHW4eEcXUx9h1IK72T83VzqhhrUfc8z5weT3avgQm7dITADrA33Q1vQvtj2YK9H/e8H\n1oewKSwEz4IbFueHibUpcoHfBe4CbdoRXoZOoO3DYQjECvaFuoEUbLsCPAbOx8+hOfUxD9NX\nG8OPoF86wPOwIMTK29eVi/3Ao5vpWqoLD3csf4GvQTtXBhfDIO3L2xiu1ePohjK2QRuVufqB\nhtKE/0xkTcr/hd1hK7gINoNwP8WaynEOdoY+PXcMH4E/w+vgh8a64EbKcr3lWMfjGWw9n3p9\np5yn3cH8s0OGZeuaOod5REUyLoM/baCdnk8D+4Prd9DDFNYGc5U5ay7YAO6EWks7HWPzXqzY\nx5bNNefENzRS/o7rcUzP1sj9lVzWjuDHUvd7zV86+LHZUtLGJSDYqh0hRi1br6xzn9ZS0o7L\nwVx/I5hrLgD/9M31XcV2T6iZUOccmilU1OHonFfBnuDDCbX/v95YHgfmqRWz8tscXRM+Auf/\nVRCeR7Fh/fiW4zeeJJX2gL+Zn5zUH2OXBDchbtw6w2swCNwIulmoVEtz4xuN3Gxg2k/QJRTE\nRfNFGAFuKGOFYLbOjXY5PcgF8V2eKXfT76z+E95nTnATP332bk762GdO9DtgRxgG/sax3nqf\nDk2cB8E94MesmyMXeJNpULBd+y27mK4H7aEeeoVO7C/WKfEJZRfotmByd/GeEWIF3z9F5UbR\nhTWycm+Oh0X1RYrGd4+o4SyUtdvN8l0wL5jYg/RlLG1cBWqdzIfQh/N5V7gYnN9Lgb57CfIK\nvrPevHBq/oYanZu3tacTzA76S1vGg78RtOzC+B6sCtNCvWWsaUc8ltYZj479g6DGgvOspWQM\nzl2i87OpuzSr363E9XpX6aeQM0PfbowCoc5Nzz8yQl09j85RYzCMu0fXZfPO/ZDX01TEeSd/\nvVbnbob105WgD8spny/L3RfXxzHdN75QoBz8uC1t/bg0HynrQ/45lPK5Vraggi3moCnAHJWX\n9xwA/mK7paQNPeFLuAI2hh+gM/SCy8B7fAePsZ/f5Ny1q17Sh37UaIt2GKfa5ByTWcFfPLg+\nDQLXAt/LtXsYBHl9z4xQF46jQyEdf+sBHT+5yQXgEbgITAzHw02wIJjwKpXBPgd0KMM+1H8C\npfQKlSbBvcDNh0HsZPOorgcDuqsnSf/zgB9GbUDfOqH14QBQ+utYuBv0m5PahbYlNI5OTTAH\ngxvRPUDdCG46tc04NFn9CKfDeFgcjAHLrUVuAPX1EJgRtPkE8DdmJtzXQd+vBPWSPvSjzbnj\nx9MWcAVom6jh8BR8CtrXAWqtmejAGL0BtOth2BveBcc52HcW5dvgYhgFyoWpXtIfH8At4G/c\nO4M+dG4pbbkWnFvnQPApxbrJTYe8BS9kvX7BUVvmzM5bw+FrjHCO+MuyOzKD9G+XrNxaDtrp\nXHgV7syMcrzrMS+y7io6+BFsbtHO+8HxDvuMOSi3Zl2Kce9kjOWo7c6tlpZ27AqrZIa4vlg3\nBrSvpT+OMKEhR2tTPzAunUP3ZUcODfaaq8yZLS3z0CPgR4M531zqev4AaLdrgO9iXrgawhq0\nNOVnoV7aio7Mn+5HtMf1+gI4FeYD13XtvRxehPNAG4dBUjN4wC/U34sW40UM+NOqeCEDv5zc\nZLSD7mVuOJn6s8DANSH4sWQA3w7fgPWqXPsJVyf9p0zhnsaObjQb66exZzR2ffnGbqjgun7S\nRyHpzEzZj6Dgq8MozwBuRkeD16p9r2Vp0xSZcNpD6NeF0mSkTcfC42Cs+R4/gTb+A3yH2cCP\nJ4+hPcWSCr8JLHmxwspZuK+xfnzUtXCiBeT7HQVtYTD0AX3m+5R61uLUN1Wz84D42W6cVgDz\nz2mwF3QDbdMOfTkdrA5uAtQa0LGhVPpHl9LVVdXap2wLPu8j+BSczyNhJpgKDgcX0alBm7XX\nTX/8jpyW1EIla6urNO6+g/9mzRbmuB98DtrhvHKMfwDfR/tUJfZNuLPpG0PHdhpYFCxrg/4y\nB7SFamzh9rKaVEyUbRRd0Bbn0fTg5iLI8W0uG33mPGAuKSrtnBXMj8tlD9GnX0Fz2jkXzzPe\ni8qPoRlhCYjtNBY7gL5uDplTPmzig8xt+s75pH/3hXhec9pwrSn+bY73HY4d64PjYr5xX6KO\ngUUyPG+KXPOaQ2/zkNXA+bQRhNzzAmXVFF/a3uc2Rfpv+ewB5nPPzfUbZ3Wu3yFvOt92g6/B\n9/AdjO9K3mFa7muqOvGA+eE+6AnmTvO8R+U+RLv6Q5izf6ZcjVzPkpIHqvbA2rTwy318I5hc\nnVQS7s2fh/pyRzdiTrwicmK7eS/37OasH1zEwKzN1pmd+iv2WShrZ7gWykVtfyvrs8hhZxrF\n/WpTOA/lYLPjHNdZDuehzaSOYdGgWdVyMZ/Us0td07ZfILaxklh9vGrr/n+Dv1MsZ0voO9ij\nbdbF93stXxdfj8sPcG9RHUdDn2V/gfDsYF+oL3UM91ZyvL2okbQ7A2I78+XYp8FO68I7VGJf\nuOeaJth5MW1jW+JyeH5zHS9qgp03Z3YGX5WKweay07ErqntpGMZRW2tp57FFjaTdI5mdwdZw\nbC4fxs8xtxSVOS1+VsiLIQ6027r4nqLlfYoaSTvXsjB3tSkQ6oraVKqda19RvUvDeKy1rzl9\nGOx1r+NeoqgG0zA8y2OwM/iz3HncppKydoaPrmptdS/4cWab9gSbgn9DXThWYk+5e9zjutdN\nSh5IHkgeSB5IHkgeSB5IHkgeSB5IHkgeSB4o7wH/eHFy02oY7B83LgDTgL+5eBOeggGQlDyQ\nPJA8kDyQPJA8kDyQPJA8kDyQPFDIA+G/ZSzUuAUaHUqfV8OvMAz8MPK/b/ej6VT4BF6DpOSB\n5IHkgeSB5IHkgeSB5IHkgeSB5IHftQf8H3y/A/+n31Laikqvp//prJR3Ul3yQPJA8kDyQPJA\n8kDyQPJA8kDywO/KA34YjWjkjfwf25Zp5J50OXkgeSB5IHkgeSB5IHkgeSB5IHkgeWCy94D/\nv9RI6A2l/sa33aj/FsJfgUkxKXkgeSB5IHkgeSB5IHkgeSB5IHkgeeD364FuvNpQ8P81egL8\n++FfgM/Aj6P1ISl5IHkgeSB5IHkgeSB5IHkgeSB5IHmgkAcmx7/Fzn8orCv4D6R1Bv+BrEFw\nP/gPeiUlDyQPJA8kDyQPJA8kDyQPJA8kDyQP/CE9sBJv/fAf8s3TSycPJA8kDyQPJA8kDyQP\nJA8kDyQPNLsHSv2/PM3eSQ0f2J5nr1DD56dHJw8kDyQPJA8kDyQPJA8kDyQPJA/8gTwwuX8g\n/YGGKr1q8kDyQPJA8kDyQPJA8kDyQPJA8kCtPTC5/UOxeX/8QsUoeCl/IZ0nDyQPJA8kDyQP\nJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQP\nJA8kDyQPJA8kDzTNA5PjX/PdtDeuvPXM3NoL6vGfIfrvON1SuWkT3Tk7Zz2hHv8/2Wj6uXOi\n3is/mZtbt4J6xNxw+rm3ctMmurMjZz2gHnYOpp8HJuq98pMFuHVjqIed79NPn8pNm+jOLpz9\nBeph59v08/hEvVd+sgS3rgP1sPN1+nmyctMmunNZztacqKZ2J/6ny88VfPyKtPOfY6iHnqWT\nlwt2tCrt/lywbbXNHHPHvogcc8e+1vo/OnAOvVOwI+fQkgXbVtNMO81JA6tpFN3rv5lobqq1\ntPNBGFywo01ot0DBttU0007/Xcnh1TSK7t2McsfovFZF7bwLRhfsYGvazV2wbTXNfuXm2+HT\nahpF97r3nC06r1XR/03FvecXtepgcn5uPTYDk6t//Oi4Cd5r5AVm4Prs2T3fcvwK/KiaB5zM\n/htNk9K0XFwQ2sFPk7qxzLXdqL8U3i9zvbFq/ybAWeFz+AGmBiem7+FHoglzPEwPvtNUUET7\n0+hM+KBI4yrazMi9vpO2F9ERNDoWPizSeBJtTMraNBJ+BH38M8wJRXQijQ6GIVU0dr67KXA8\n/ceWjTfHdRZQpcbGa99B0c3E2bTdA4ZBY1qAG1xQ/LfNgozHecENhotOrA6cTAmfgeM+BpaG\nIrqMRj1hxCQaT8M1+/wSnOttwbnjeNp3KenfOUD7lWX9vIYnBXQjbTYC/9/L5pC5R9sd4/my\nB/puc0E/6A5FdDeN1oSPco2Np+kg72fH+HsYC42pIzcYC+Ys51F/cINWRP4zEctDufHzmY7x\n1JD3uXYYq6U2F841rzvHfSfH/X7YEYroKRotDM7bxmT+mwnyc84cpD3xpm0KzrXTnKCdjrsb\npr2hiF6lkc9wTlYi+/4anFNBzulOENYe67VzfhgH+tt3uQIOhSJ6n0bGoe9sTDpP20B4vuPn\nfPgVPgTX8iLqTKNz4dgijWnjeqENsX+KPEo/+xznzIwwA/wCzkNjYkE4Ec6AInJMvgLHsoic\nY9pl3JhPpwXrXKM8/xiUc+BwuNCTAjLO9ak5rilyfhk3+tN9k+uCeyPnp75wvdwHroFq1ZYG\n2mncmRObS46x9upXx+obWAB2gNshKXmgYg9sy52jK7jbSWCwPQsm1aBrKZiANggVZY5dqTf5\nOimKaHcaDSrSMGvjBsaEE2tPTkwg2jVfdqE7RydtUR1AwzeLNq6iXQ/ubcpiciTtn6uiv0pu\nnYebXJyMBxO/6gUh6TdUVPnjJO7vW2WbFbnfMd0t1+6RrD7YFl/uzcnAuKLK8jncf18FbdwA\nuVBvlLt3cc612QU+1sqcWL9IVnkQx9eycpHD5TS6pZGG+umG3D1dOdcOF59S2pXKOI8czfnT\npW6ssO4m7ruywnsbu20abtD2brApuDmcC/4M1veBorqHhueVaHw2dY+WqLevc0vU56v8iHUT\n5iKv7MO+isp+T22k8SVcv7/EPU9Ooq1zzM2emz7lmDl2RWXMGDuV6AhueqPEjTdSd3Oufn/O\nR0GY+84B50JROQedi5XKvl3DYs3MifHnHA8yLw+GdlmFOcXcUlTmNHObMoY+yGhrRabHObpB\ndSyLyhxtri4q/dOraOOs3TYc3QyHOeP4Omdcj08H5Zqnj4vqSxr2KNjYD1Vzz3a59vrNfYjj\nE6TN7iWKajwNuxdtnLVzn+fafUj0nKcoPwRfQBtwT7Y7FJExaPy7tjSXzEM+sx849kGfUNg2\nnKTjxB6YYuLTdFbAAy6SM4CbIANQzQEbwlhYEFqr3CDNDc/mDPTcpOXk2Td3LZ1W7wF/i2hS\ndbHdK2ruR1M95QJpjK4RdapdM4ELlPa1lPSFi4z+ifPSPpwPgREQawFOPgc3OvWSc9mNRKwX\nOPHDt9w8953MB5tBa9MPGPQi+KcF+tMN6JjsPP5TBqqaTf15kvG3ZPTEJSivCV5rTPrZMXfs\n6yXtWhcWiTpcjvIqUM5m7XwHvoZ6S5v0bzzP5+N8E8jbq51+TLXU3Nee3aEdBLnmuOHWriDt\nfAXc4Da3+vHATmBc/QRqKegKH4JzY3KW9sdzph/nfiS8B+Hd3Au0lOah46mg1D5Eu7aG2aC1\naHoMMafH9vbjfGlwLQ0fohRbjcxDr4M2Brv/RLk9+GF6bBmOot6Pqz+kpvxDvnXzvrSL4Gvg\nBugG8DcI24AbjcXB30y1VrlB8jdUbk78DWWQC+tX4EJ1GyyfnTuhkqr3gIusHyZXwb+gGzj3\n4k0BpzWXv9VyDE2IC4Gbfcfazd5L0NI6CAP6gx8d/cA/8XKT8lfQf7F8Fxci59i78YUalp3L\n+uvSqA/tawPl5rl2ngp3wh3QCeo97nRZVgdw5TFYFhYGfe/m8N/gJqC59R8e+F94Fm7JHt6L\nYx+4Lzuf1EE/d4HZoVYfcfn+Hbud4UXQZueuNt8N2l1K2rkPzARflrqhhnXP82xzzaOgvd+B\nc/5NuBZiaWdPcNPnb+vrraPo0DzkGvoQLAobgv72lzZB2ul6NDXE9eF6U47G3UDYAK6AX2F7\n0H+rgXN4cpb2O2ecz5+AeWgX2BJehyvB9aClNJKOHVP3ITdGRniu7cbvG+A8nBNaWt9gwBjQ\nvuczY1zXdwdjx5ieGVqb9sOgfnAkmD+Nbf+0agUwT5XSz1TeDC3xi55S9tS1boq69vb77Wwv\nXs2J0QtM4t/DtPAq9IXWpM0xxsVIG98FN0fHwhGwChwEZ8EZ4CapK3wEC0J+k0pVUs4DfoAc\nAh+CPn4WFoVrwPjQt1PDSlBvadNt8AMsA4fCsmAeMGm2tFwElwMXHe3S3qPhTNCXb8MOoF6C\nR+A/sA10BpN9LXU6D7evc8F5sSPoTzehQ0D1BDei2vsWOObHwNbQDlyYXHRqKfPR+6ANL8PG\nUE5+EOnzfuBGxI3SYaD8ECgqY2oP+AYcIzdoyhyiL/4OHWAesL8toZL8ch/3DYOHwfeyfVPX\nMT8I3wX9ZW7cHGKZ2/1IPwo6gpu0A8GxLafbufA5PAQbwNzQVK3MAwaCdr4I60M5GQP6f2Yw\nd58Mf4GfINYNnIyHB8HrvltT1Z0HDIXvoB+4rpTTUC4Yf/a/NIyFNeAmiHU1J1OBsbQuzAbN\nJePOD8hfwE3ubjAcHLPPwLGcHOQG+AMwPpzXjqdyzgwG58wmoH9/zPiC4xwwCuqlBejobnDT\n/TGY3y+GC2FfMF6OhUPhJNBery0Krq/11nx0aAx8CZ/CleAacAIcDtprzLSHR2ExaC2aGkP+\nBR+B4+/apJ3G+ydgbnOf160MzjXzbVLywEQe2Jaz0RPVlD5pR/UbYAJy8phsDTr5CpxMBmQ5\ndeWCbYpu7kzog8o9PFe/Neduzs6DreA90E4Xhp9AO3yHwyCfiFz0XEiL6gAaOjlrrR50YCIr\nqiNp+FzRxrS7BFx89KNHfSu7gonKRUE/j4FK4ovbSsqFo2/JK5OudNy1xziIY7Uf5ybTvHpT\n4aasqM6hoQt0pdqGG98FbTSBa+fZ4LieDsagm6cb4FLw2T+A72K7orqchrdU0NgNxgCwv2/B\nhf3voE0PgfPoMXDhD3nAe4KOpvB0OClwtB9zSjkdzgV95BzQh27S9aFzP9h5FuVFIa/ZqHAj\nYHvf73Eoqvtp+Dy4uGqHvvB580NTNRcPcIOlr32ufRXVozTUvm/AZ43Lzo3Dpmo+HvAf0P8+\n27ErKnOS+eRZMO58nlwCTZUb1odBP+hT50JRvU1D7TTPaZ9ln7k8NFVdeMBj4HMdJ3NLUZnT\nzG1qBvgQtP0DCL7VH/IOOH+KqC+NzNVFNYqGvSpofBz3fAfHwGZwJWj79+D7jADzjmPh3La8\nDAQZX0eGkwJH8405ujHNwQ2ue0/CDtAfwvwwZnwH7dNe18y83qTCvURRmdvcy1Sq9tw4FF6A\nnaAfBHt9Z23WXtcqP+iCBlHYPZxUeWzL/T6za5XtSt3+AJXG0D6gfe5JQ3zbh+XLYJYy+P5J\nyQO/8cC21DiRG9OO3OBEeR3cjLhZGwsG3jNgInZRmwJKyUlgoDopishJ6GSsRNpyRnajZW10\n8owHJ73Jcwkope5Uel9RmdRMbrWWSdrxKCoXCReLItJH+tQYMHF+DMbCGNB384Fx4ILcC/R9\nUZ1Ew74FGr9Pm3HgXGG3hAAAQABJREFU4jkUhkM4/zflvHpTMTBfWcX5Odxb6QdST+41Ds+E\n9UHf6bfDQU0HYcN9M+UnwPfYG7znNSgqN4WVfCCF5zuGs8O7YJ64EYIfPR4FvsNTYExsBepo\ncHNSVG6y3fyU0tRU2rccCfav//WRfhwJN8CL4D0bQym1pdIx6FPqYoV193Cf/ToX3AS5UXZs\n9dVM0Bxqx0MuBPsqKm3TTm17BhxPx+t7aC5NxYOuAceuqF6ioeuLtg0AN2yWtd2Yag4ZP7eB\nc6GozBXaZHz1g0GgnaOguaSd94OxXVTa2TtrfCDHj8Bffjjull0Ltdu15DIwPop8JPWlnbm6\nqPRbr0YaT89159nO0X2PUtZ+6x8H38vzjWBayOs5Ko7MV1Zxrp96VHD/idzzPpg7B4M2GSPG\njGhv2KNQ/I3cQxzwm9rKK8yD3Su/veGXxa6Rc8GHoL3GjvEQ4tzx/RPE8p12jyuqKLflXveE\nXatoU+rWVajU3iVhXdDmcfANGA8hJuxrUizF9T+kpvhDvnXzvrQfFF/ALPAaOFGmBINvJdgS\nVgA3Ky0pNxMLw6rwbVZ+jKObJCeki4PxcDEkFfPAJTRz/N0QqfAbST+UrD8ITFgmqJaQNhgD\n9j8G3Gi4WLrxcuy3gKWhpXQCHd8Da8GdMDO40LsBNH4PAW3W1oNh7ex4Lkev11P68Dhw7vSF\nDcBxd6FxIdKmPuCY6/eTodZahA604Rg4Hexfn40Ac9It0A28bxRcB9qfl5tD36Ep8rm+t8+Z\nCcw1Y8Ex3QOaQ2523KQ0RcFON3iLwhvwCEwDm0Jz6Ece0lQ72/AMffcVdMiedxtHfez8mBqa\nKjdP5qemKMxDx6Yz3A6vw9ywMDSHtNN51lxyDX8GXPtuhBngVDBeLe8AL8IJ0Bq1IEY5/ovD\nEDA3rQsvgfNvHegI7kkuyo4cWkRu1p+Ao0Cb/CAyrr+GJ8FxPRD0e2uQsfEU+FE2L3wAs4Dz\neTSogyGs+Q0VreSHvh4J78Jd4LppPBgHxslboL9vBtf9UixGvff9IaXDkprmAQPQBet9WAuG\nwQBw49kGroRBYPJqSc1K5y5+LlQGvBPc4L8VXHD6wxhYDUxaLmpbQFJlHtCvC2W36lsT5rKw\nHJioXGz1d0vKmPwTzASzgQuT52Ex+pJyS9loLuoCm4OxdxwYl3+BGaEz7JWVTerXg3PKBd+k\n7yahHupKJ0+Ci8yeMCcsD0eA88Y5r5+9Ty0D3uvmUF/XUm2zh3/O0UV8KZgeHGu1M9wPzvP5\nwfrLoRZ2Tclz9cex8BisD6NhKnAhjuXYu2EaCvpK/wb/Uayp9Jl50f6PBDdEoe8NKVejvbnZ\ndcD3fhk2huZSGNv/8sDdYAj0BGN/GnCzWY2MjYfhWxgFp4Nj01T5DN/fuXEO7AEdwBjbFIrI\n+X8pfAJfw79hamguuYY7h/Vhf3DOuOn1+Dk4n1eGRWEGaG36CIPMicaf+fA+0N+uP5/BUmCc\nPALzQJA5817Qp8Z9PaSvnf+9wXl/AxwB+lV724Ex5Fh/CIdALfITj61I2qv/NgDnoDafAuOz\nc+2dDr4AY2dVaC3S9rngnzATfAPHgP50nTfmLX8O7glLMYD6P6wc7KSmeeAWmuvHdcHN0SKw\nAvwIJi2DsAsMg3pqLTp7FlysXEy102Q5BzhRtNUNskl/angN5gavbQv94A7YBiY31TOu2+Mc\nF6QRkZP07Y3gZmZKMAmZQIdDS8oPNzeD2tcuQ/vcCBirJtHtsiOHukq7foKxsDu4uXKhdCy1\n7RIwdj3/D+jTp2EBcOFyA1BrrUgHj4Nj/h0E37mQv5HVcWiw7U6OR8GZ8BKMAN+jlvqAh+tH\nN5PO9Tfha3CDp7+0x03U87AvqB3gjIZS8/7QN27cHccDskd35qgP8vPgAuoOAzd324O+egL+\nDLWW8STacBm4GXI+6EfHuFIdyY3nwq1g/nwRzAubQHPIGFdbwNnQC7RbP2vraKhUC3Kjc8c5\nszOcmh1dI5pDxtuFsAZo35ygHONqx9R3fBi6gz7uDT5vFWguXcuDXPvUzRMODTGgz50/X0F/\ncA551KaW1Jp0/gy4tg+FI0Dbvoe3wfmttL8jvAmfwmrgvkTpQ59hzt8Dqokfbq9Y5vIBMA5e\nh0FgHjVGvoQ2EOzXFn1rjtgfroITwPist+anw7vgQFgSlgDtMp7NaeYI7VXOv91gFDwBK0At\ntTQPfwQc74/A/D0V5NWXioFg3lczgLbPBrYLsbAB5VvLcAP1c0E1Ms7Mpw/AY2Au6A2LwmSl\nlp7ok5WzyhjrZkk/mqCCLBuwJjATgNerWWy5vUkyQB+FEfACzA7dQDtMnuuAcgOjXdfBwqCO\nBxf2g8CF+GSY3GQiq4ccWxekTcExt9/xoHaDHRpKE+o7Ub4oO2+pg0k9KPjIpKm0/1tYHPqA\n71ZPGZsuPrPBTxkmcO0SYzbYZExuDIPhTnChcOGttY6hg7Ggj5z3v4B+3AQeh/7gom/dzKCd\n74GbQudSreVm7hNoW6IjF/Fd4S7YG7rD56APDwb93pz6mYc5bsqxtWysWX8pBM1DYR/YHv4F\n92TlhzgeC7WWGzfVDrQzzAvtnRUq0dTcdDS4mToezJ/7wmVwEjSHQl5xXekI2jk6e7C2dsvK\nlRyO4KZ3YVP4N1wMzqctIGz6KBaSc1EZT1uC80BbHXfXInPLnFCpnFvLgfP/Grgd1oMwThSb\nrOl4gn7NP1O/Wv8GrAy+25KwIbSUVqXjvqBfXbtnB+POnGn9f8ENaXiXTymvktV773BQ+4O5\n6i9wB5jXmlvaZWyNBJ/vBvk8+BB+Bd+hNywA1pvvle+1LpwGe8Ch4L310ix09CysCfpRe8yp\n5odOYI713UKeHUL5VugFj8CxUCstxIOfBn21A5wMO8EtEMuY7gkPgTGsjGffxzh2DnqP8llf\nlcE1xXWuUjlWfWAZGA39wef/FV6C3WGy0ZSTjaWt11AXdRUSkkcDUQzAH2AUdAGDtR46nk7u\nh5XgM3AD4kLdEdYGJ7ybFTUt7GIBafsrDaUJP7T3cHDz0Fz6Mw/aFt4Dk4q2uME4GOaH28BJ\nFfxJsUEmUROCifJBcDFQ+edNqK3PTzcXbvC09VOYE0yawfYQB1Q1JIi3LbSg3ITG0k5t9Pgr\n9ANj5X0wod0D9ZL9Bxlv2hXyk/a5KC0GbjqfhddhYXCOuYly8a21jLW5QHvcaHSANqCmh9XA\n9wgxrf22OQHctNRaM9GBMVhK+rMrDIZh4Pkb8CWYm8Rc0Vz6mQfpJ2VfynPjy3wY5JgqNxax\nzD1HxBU1Kgcb84//ggo/GPaAODbz93neCRz/hyGW77BnXNGEcogzfanNHo1FNx+3wwHwAFSi\nJbhJf8fv/irnzrH2MAKKKjwz2Bjs/Q8P7AkDYUf4F1QibX0HnG9B4yh8Hk6a4XgOzwj2BvvD\nY+el4Lxy/roWDoIl4UFoCR1Lp8amee8YcAN7MnQCc43xMBRCPpyD8vPgezmuncH86js8DuOh\nFjL3HQcvwopwOgyBK8Bc45yyb9dLdQhoozG4Mzh3DsqO3mMb36MeOpBO9Jv5sR/sB9oWYtmP\n//OzOj8e4g8I7fYjoVYyBt1HuD5rk3oOnL/LwWvg2PcB57LzJtznvJkGjB1lvePwBPjOTdXU\nPOBEWAEGlHjYVtRdDzeBcdrqNUWrt7D1G+gC78Qx+Aw2f5PTHww+MbHODU1ZdGhelVxUnARO\ncjcqy4AfRNopY8FF1Yn9PTjZDVjLLghBtjNhNVcStW8T5G2gHp5waPhPUxakfDeY7HfO6uPD\nRZx8AHfABaCdvlOp59Ujrqen703BMR8NLpxhzPWr8fAueP0jeARaWmthgL7RPjfGP2RHDg1x\ncR3HkeCGxBiqp+Ixe5aO34RnQFuNm1nBhdJN1l/gPnBO3QMhjijWVG6U1E5wNrioBPsoNiym\nfbM6F6lH4Xo4Deoh56u+Mt7uBRfNm8FYtN7jt3AWOG/WgsdAOe7NqXY8zDzzBLwFzg214oTD\n/346ho79Uv+rmVBYlkNz25TrouF0Sn7qm7DJCHN4Rurag/O8MTn/jQP9H8vz5noHbbQP84mb\nTo/KvPM6mA8rlTblbe1A3WzgGtAUTUXjb2AwfAb6U1tXAtfG96BaW10b8uNg7m8u+Xxj0Fh8\nEoZDkLab59cGc9Ic4H0tpeXpeBa4CnrAjuB8Vr7HVrAJGC/K2DwXNoS/wrQwMxgDS0OtZDzZ\nz2rwMujD/cAcrm3jwdzzc8ZLHI2VB+AV8J55IMRpc80jHtmotNX90J1wIIyB60H7Pgdj4lnQ\nRu9rA0HaWytbO/HsDcA5pi+nA/UauE9b0hN0EzjP5oNdQTu1/TnQ74PAXBLk/tRxKsXK1Nu+\nEnXiJnP+gDI330X9t+DamDSZe2Bb7De5NCaD0wkTExYF694AA3JqiLUiJzeA7b3fxFFEu9PI\n5xvkLupqKMT2hLL9OKE9iu/3Aig/mKzbH0xu28NXcBSo7uDkKqoDaPg2jII9weSnvS5M2j8l\nqIXh0YbSxD9M/l7bBNzAmyjcNOef14M6F+iiOpKGJpLGtA436K/g2/gY/KuvZR/IqxcV2l5U\nJ9GwbxWN5+Jex7uUnaHuAq4vAia5e+BxeBUcn6I6h4b3VdDYOAh2eIx9a9nEavx9BrfCv+Bn\nMIGrg8C5VFSX09AEbkyVk/3HduXt9Zqx50LTHsbA3yDW0Zw8HVdUWXbxu7JMm/WpL2eftrpo\nel0c41PhA3Cs8/Jan3xlFef9uDc/jp67AZ8TgsyLLqiOr/Pu7+BYOrZbQGM6jxtK2d9Yu3Dd\nmAl2Bt8FHzmWHcC59gTYTzmbruLaSNgYbOPG5Ds4AJRj5tgV1RAaamewNZS/ps5x8hdHakrY\nDx7OsGxdrNU50b8nQEfw48V1wLl+CzgXiupTGgYbgz/DufPrM9gjeviilPVNf7gWloFYbgKH\ng++zFCwAl8IPYG4pqoE07J01DjEQfBoffQd5CbRxKPiRMSkZ04fBo/ARGD9F5RrxNwh9DqPs\n2MU2Bv8aC8Fe73kqO5+dozoSPgZz7RIwDs6HzqAPvF5UX9KwBxhr80J7+Anydroxd14Nhdch\n2Psp5bPgR7gWtM0PlQ/BuAl6k0KYU6GumqM5vHuZBnNTPyO4TynlY9/FuAs2+x7OcePCdz4Y\nbLc5DILdoYja0sg+HHv9OgCOBnOkY+zRfvTvM7AX6Nd/grnAtpfBzLBkdu4HVIiT/JiE9yl1\n9Lk+oxL9iZtGQm8wxvLajQptb5e/kM4nPw9si8mjc2abaEzuh8Pq4IRwQoeACwEWAtHzN2BB\niLUJJ07Uh+E68L62UEROwpAY7XccBHvyRye3E+UCMFAHgAvFgWC9ZSed9vgcN0kh0LtT1uai\nMqmZ3EzMZ8JbcC3MA29DkEnqnXCSHdtwNAndBIfAY7ABqPzzTNImrqI6kobPVdDYjYR+CmOd\n97XXTJ5HQSn1onJUqQsV1p3Eff1gzgrun4977MvNabBT+wLWOe7vgfHsOA8Fx+kZGA5F5Sbm\nvgoam1yDbR6Db0PZRO24Bpu1Vx8GHUThtXBS4Hg5bVx0fL7jvzjkZZ/Bxrx9Y7k2DJxjV4C2\nDITpINbRnDwdV1RZdg5cWabNstTn7Qr2elTHg3M/3OcvRtwU5OXc75OvrOL8Re4NfYe+PNc/\n92bPacfxKfgUPgCve68xWOqXClT/RudRc89vaiuvGMKtod/YXu1w/mqv95wBN4AxcDzkNQ0V\nV0OIIefasdFNjpljV1Sf0bCUndZp09LgHHoAvgRzvFj+D3gtVk9OPgbfU8yp84B5zblQVMZW\nKTv1i/X+Bt4PCLUK6GNj4BTQBted9SCWH1EvQ7B1KOWnwdxSVM7N3llj+9W2vN1xnX2/BIvA\npBRi+hNu0r4hYK4uKueGfes/fRv8GGyLj+b4+cBY2Cm717a7g/E7HvaEINdQc7v3GK+ufUVl\nnF0FYyHYq22WYxst+w5PgNoBrHMMNoeHsvPQ7lbOZ4Ag9xAHhJMCR33QPdduXc7fB/v8BVxn\n8jaHc/OBcfAemHfCeNhWH+wNahDo9yJqSyOfF3ygzfZv38EO65z34VybteVesJ1rke80L/hO\n4b64HJ5vnpiyDG2or0bduHkoGP9PgGv/C2D+Mn7Xh6TfgQe25R1GR++xDmUnwBhwkhpocYAa\ngAZcCDrPDdi8XKgM3rOzC1052sZJUUROQoPva3gLtDG2IS5rk+/UHtYEk6KbEZPkSaANM8GS\nMD3EMqk4KYvKpDYADsoeoB9GgP34EdkBlO9zVkNpQt1UlFeFZ7K6WTm6aJjcl4P883pSZ7Io\nKheJ5ybReGaubQ/5RKOfg689bgTTQjn14sKochcrqHe8TJj2pf9WgnK6lguDwfEPNlqOsV7G\ngXExI6jeYLIvqnNoeF8jjd1ULAx5e8K5du2VPcOEfzHo/39mdR6MAz9KiupyGj4MK8N/wXni\nWMcK9ngM/grlK6i7BhwT59SNMBfkdTQVbu6K6iYaytYZy3B0jqhuoF3BzrhsXdDUFJaA2UNF\nieOp1PUpUV9plXMo9lGwSf+YF6cDY2ssOKZqDugJXl8DKtF53ORGpagG0rCUnW7k3wNzvDkh\naCsK2jd/qMgdjRnzp+8X60pOHLeiij9mgi89GmsenT+bg3N3UQiybJ3X8pqSisUh5F6v3wLO\nhaL6goal/KkN+lR/tgf1IthfrEs5eT+uiMqdKHcB1477wNxSVI678XcIxP7M226e+QoWgUqU\nj+m+NDJXF9UYGn4Ejv9nkLc1nGu3toa400ePg/PNj1D3BttDXlNQYYy8DHGc5+9r7FwfGYvn\ngmP8DmhTbF8oe3QeqX3AdvavnY6LY2J+mg3ycu91QL6yivPx3Ns9un8pyuPgMtgQnKexnfl4\nsL3zX5ufgJXBvKW9IQ9TbJYPpB15zs7gfkY7HF/jQfuCXR6DvadTdjzdX14Og+BqCNc9+ozB\nUZ3n50Nzanoe9hfYD9znHg87wIwwWckEmVTeAwbbamCg3Q63wv7ghPCjwoBrA3mZnAxc2+fV\niYqOcEX+QhPOp6GtLAn2G6QdToBgo9eGgEnMDbzJaE3w4yroSwpSC5lcNgETkeU7wN8qHAcP\ngIvrguDkUo/DbvAymHzugemhD+wDW8JZED/PZFcLufDcAJtDGN9w1K+hTLFBz/PTTUEt5WKh\nHw6DR8Fkb1zmtQYVs+QqtVdpu/LYGR6C+SGOCeO8VvLD5nhoD8GWYFvs07W5btIfCcfCvuC4\nnwzNJTfrL8AW4JzXt9rlIvg5BAX7wrl2bwsuAO3A+bYj1Er2tTW0BW1xE3QC9IJK5BxxA1NL\nOb+Dgr/009RZpUdzj/HmmKpP4HY4HLz2FNRa+k67tDHYaZ99YV1wjjn+QXdRcG50hWGhMjqa\nw6S5ZS6J7QzlZ6n/M6wOS4M+GwBBlq0zB9wTKrPjzxzfzdU19VQ7nTNKfwY7F6Z8CuwEy8Nz\noN1HQCw3qHvDHGA8xBoanzRDeX6ecVQjz/EdnNfhnRq5/Tcx3dj9jV1vww1zZje9wXHWrKxf\nVfCxZfcbr8KToI+7gGup419Ov3LBGPmp3A0V1mvn/bAdWF4c8optNX/tAdrnnupiaAkdQKfv\nwwawV2ZA8G12OtFhSs6eh3XB3BGUj9VQ35TjmTSeK3qA4zs7uKdzndefxrD12uxccsz3BfOU\na1b4EHWcvU/mgTdhGbCdY2a+LSVzRE8YXOpimTr3dI9klLll8qh2sJPKe8Ck+DSMAzcjh4AB\nsySox8CJFZSfWPlz7/sarHcBcGI2hxzHG8DkuXHugSar2A6T7CxwOlwBLmj11Hp0ZsL/DpxI\n6l64DxaAoWACUItOODT8XJGf88FI8H1mBsci/7we1IX2FJtNV/MkffsxOHYmmlhjOXFs5weT\nkee1lr9ZckHcAV6Hi2AR6AwD4SS4A9pBfoF3QQwbbIoNPh3GUdv16xmwGxgrI6AW2oWH2s/f\noS+8C46tmPxjjY5OXCTUFxMOzf7T+e6mwQXHfu+BjhCUt09bx8OqsBXsArWUi7F9/hvsT3+c\nDEMhKO+/UF+vo3knSH/Feo8TF++voEt8gbJ2zwZeq4e0M/gq2Ol5d3Dsjf+3IMhflIhzvZ6a\nks7ydtp/V3DO6i9tMjflZd1z+coanZeyU79eBGuB76CdzpcfIG+v5+bv76GWMicelXUQ+zXE\ngJes17fBZusak+9mDm4u2fcg2AUeg9g+TieSectfMCwGT8A28CHUQ9q5GTifHNdpQFkflLd9\nKBfWgGfCDS1wXIo+l4DLwFw6JwQF27XbskdZG36EWmtaOnCN95fCQdqhjzuDZRXsO5+y7zAU\nlgPXfnOVMoe5bzX36nM/oIKsc40rJfcCH5e68EeoM5kllfeAi85C8C/oDeuDm/mPwCB1AY0V\nAjXU/RIK0dGNQR84F/4a1Tel6GRdARYHAzqMq/bktU++ogXOS004bW0smY+IbI03x6WeF93a\n5KK/cemZPcUF3MSU9+3M1Iky4dRTxtm3sBH44fs8rAU3g8lf+/P2GiOhzvcxhpaH+WAGMN7d\nQJhUe0AtdAQP9QNvAVgm14GL/VRR3Z8pu6nRtjtB213UaqEuPHQN0CdHw91gf9tBkDnAWDAP\nKDdST8H1cDvUUnPycH3gGF0Ou4L+6gxB2uNHsD5rCTl2Ib7i/q27Iqu4hePT4HtcCfryVPCD\n7x6oh9yEhE1w6E8bx4LjuSd8AGoa0N/m8CegnirlT+30Q8JxfhQGgHPWeXUmqMPBOdzLkzpo\navrQn8q8orTTHPIlmBtfA++5DU4Gc4A+7gjafR+Yz2qpKXi4NnwE8+Y60l5t9+g92vc+VCJj\n2rgJMV1Jm0nd49jOAvtDu9yN2qadwc8XUj42d0+9TrXBNcW5ob3BJn2ownko70DBd2tpTY8B\n34Exty8EeymWLLvX+NGLdZC/9Fwt60f/xbZZ7flocD0wd7rWnwLOqUfBdewAMC5cX5+DVcHn\nzg+297menwdJOQ8Y0EnlPWAC+gaOgd3A4LsX3oZnYCdQ8eSfUDPpnz7rARgOLsRNlQu3k0O1\nnXCYaDKVmlzZbelQgQc6RvfoS+PCBbacXw+M7q9l0f63gaVgJXgc/gnK+HLTfDA8DKuAC5cq\nlWiNm1fgIdgQ2sPx4EffGGiK1qTxh+CzjwVj/iowgfsOK4M+DXZZNxXEcqH4AbymzgffsTk1\nNw87Dpzvjq+bp5ugH/SAIG3okJ0Em104e8ITWX0tD9q2JbjJeAQ2AzfH60FQ7MNgY7hWj6P9\nh7GK+/+K+q8zA57leBBcDKdB2ABuS3kU1EP2qT9VbOdcnO8ObnZPAjeozokvQX/7YVJP+eER\n/Gm/2irWbwquU++CNl8GR4JyHu0G73lSB9lf7E9t1k6PbkSNW+e6cuzvBT8+PgLn34uwF9Ra\n2mT8rQKWtTnYGWymqsFu53Wlcl/ge4WYdnyeq7Rxifu0xRxsnlfBNo95fZCvqOP5L/Sl/8Ia\nE7oO9notyPH/KZy08PFz+l8YLszs0LYQv1bFfvYdHreyTloj6if2X/CpR+eM12QFWBNcy10b\nz4bLYQj8F7qC980PyjHzXRcH80Yp/Uzl7TCu1MVcXSfO/cOExrRVYze0luvpA6mykfiE2/4D\nLox9wMXb38q1gTCBDLxY1htcpTSayj/D2rAOHAVNkZNaW5Jq44GBPNaxdL44riGBhjEPMcCl\nhv+0rV5JdEX6WxXGgzYtDYuA9qqH4WiYE0yGxom2SrCdYoP8CPIjYF5YG3xWV1gJukNTNJzG\nV8F+0A2ug13hG/DDYmGI7dK2vI0uYL7jD3AF1GIzsDrP1b4h8Bk8A5eBG6lDIW8TVf/T+5Se\n+N9ZbQuOo75wYbsGpoEOEEtblb5sCY2POg1+0xY/MuLN+gWc3w1rgW36gh/Q9ZLxVErXUuli\nPwycU13hS3gU3OjXW9/T4QxZp/GYnk7d05ExN1B2jVonqzMXOcfqpR/pyI+kIG01XmUFcC0N\n8kNZO513+ngw9If4/TitiYzJ18Hx3R7K9fkt17SrGsUxbd5oitrRWFu1z2Ne8bWl8hfreO76\notxIm48mpSm42BZ+mtRNdbr2Cv3MBdNl/WlbUPCt58H3I8LFOhxD//o23quHWA3HYNtL3Od7\nzA6bwkBQj8Cs8A9YBYbAC2Be+De4j+gMpfQzla5/lay35m3n9M7gLwVehslasdMn6xepsfEG\noJvMh8AJ4m90hsGH4EdTObmglpMLxmPgAn1UuZsqrPfj6A5wM9sJtDfIcphIoS4dJ+0BF3g/\nYN+A8fA5XAQHQpDJ3SSv9G/w+SbZufW1VkjqbuRXAzcf18OqoJYF42s52AOuhFLxYJ2bBRcL\nk7G/9fTdfJYLdHdoiobS+EK4BQaA8e5CdD9sD/eCyfQkKCX9ew4MLXWxGeue4llrwJKwE9jn\nA3A5bAZhHoXxDudcmuivqve81tJ/x4B55D3oAn7kzg3a9SM4jm7wpod6yw197B/7N876gItn\nrJGc3BRX1LHs/MjbqU8PBjcHamBGw0kL/SjlT+12XuXlxufWfGWdzv14nCHqyzE3Vs+C+OMo\nuqXhA+/puKIOZcfYvLgm9AJt1J9xLGh7iAGKVSnE9C5VtSp/811c2iq6rJ3aF45ectPbUtJ/\n5pxgk3aUK2uz62dLaTE6dh1yL3ce7ALDYQ5wvVPaaIz4Dspztc6EQ11+uu+YGb4EP3D8ADEP\nzAtB2qed+t91ehx0A3NWrK84OTKuyMq2vQHivU2J2yqq8uNoN5gL+sGZMFlLpyaV94AbjN2h\nLywOBtHe0BPGw5sQy2ANE8r6qeKLNSzbpzZ1grh/Tif6T0HCJLc+qbwHFuXSizAKtsxuO5Tj\nN1nZgx8QJpdYX3DyQVxR4/LbPN8YNTFeDCbOVWB58MPjZLgCnOdu+OLYsByfL8W5eh38cHdB\nXg9cNKaE5pAJ/0mYMXuYdvrR1BlOgGBP/silho9Uj7WU/TqmfiTqt+tAXxwAK0JewU7r589f\nrOG5YxQ2bo6ti7ofcy6iSruMCxVvVifU1OendmlH7CN9G+ZTfaxovJdgY2ynG73g38afUJ87\nSvnTj183Ta1J5sXgU+1yzfEj9B+etCJp16rg2q69YW2Mbaf6f/PIcksoxOXWdB7KwQ5tdkMc\nbG8fLrTA0Rxkzgl5J2+CNob10vdorjUl309j5/Z9PgyHu8GPjzXgMzB2lbbGvg7+9Vpnf9RJ\n02b9mNe1x+N8WV18iH3bhwv+SVJL6g46H9aSBjRX3ybdpPIecNKfCG58TaaDIOgtCm4g3UyV\nkkFr+3rIj7U94SqIJ7N9h0mWr/daUmkPvEf17OAm+TZYDkzu/nY0yIQVNi2h7spQqNNRm8JG\n7ijKD2T9vsLxargEDoFPII7TOKH+yDXPQ5xQbPgrpN/laLI9GdpAc0ifLQz+htvfhJns+8Oy\ncDhoRz5OPbfdPFBrfUoH+sPftNnv32BN+Aluh1IK9jaXj0r1ka97lYqb4RvQN53BD+D4Tw2C\nXeHI5boqjrfQsRtlx701ybHNyxhobQrzPLartflS20rZ6XxvqTiM/RWXzd3O2bWjythGyxI2\n9dFtdS3av3aYh5cG53uQZT9InFfKDXRLyXkU+y/YpD3aKfo8vE98L9V1k/32gNXBXwpeBe+A\ne7lFwPcIPg7ru8dgrx+k9ZK/YA8+028hFoN9oc7YsGzODfdQrEg+f19wvErhGrM4VKPruLnc\nelnNc1r8Xp2TVN4DX3NpHtgKBuRuO41zE1ZYEJxYll1c/WBRfljVQ47jJdA915kb/G1gGDip\nWuOCilmtTo6fv1E6Gp6B3qD0Y5AJczT4pyLK5PTPhlL9fnSgq/AbO/vXpoGwBLhYHgUmTI9L\nQohH64wHPwhCDjDWg7zPmJ8NbNvUDaO/KNCe82Ah0K8m/+ngAnCB+hco7TBRnwTrgtdUU22Y\n8JRJ/3T++ouGY+BF+DfcD+9DiAGKDX9l6h0c7wP9qJ6ccKjbzz705NgNhpugI/SDsJC7sL0H\n40HVe+7ry2/BzXHQh6HQio7GmjHnXAhyvFubHL+vwLwU9GYotKKj/jR/jI1sci61NplPzG3m\nJf0aNJSCsRDm9ahwoYWOzmdzu7beAmF+U2zInRtxDDm8n5UtJG28E7TP9WXaqOwaaf29oK0j\nwPtbSsao6/pe4Do3B6hB8DBov/aaO81hEuQaVi+Z17VDm84AYzL417K+PBzOAs/94L8VqtGX\n3HwubD4JmisfLkcf/4DJRlNONpa2jKFuPntMoms3xMeDm722YJDqU4NYDYVJtfeeRf3RRLkZ\nciPiJsnJHRKmCUC0Rx6HxuzhlpJatmRtdZUzcnvR/ivtaeVKbyxzn2M4MwQ7TabLZ+dPcoyf\n34FzpW/d9LtYVaoVK71xEvfNwLWh8B50BhP9CbBwBocGuakyse894bQhkfqes4PxotwUhHdu\nqMh+zBufFCyvRzttMBmfmR1P5Lg7zAddYBh0gvZgPK8Ou8FcoPxY0sZyWqrchSrqfVfH+xDo\nBvr3RugPvoPXpgH78pcjKswt25byX8NN0Y/FonLRYicajgM3oZ1hMFwIxp8bD+e/c020z7qP\noBL7uK1Bi4RCwaN5cPoMbVAjoBobGho18mNBrv/cyD2TumyO108q2DmGcnPb2YlnmqOLyg1n\n+6xxsNONfXPbaRybT4rK+WH+VMFO501z2zk3z/zQTpog33VNMPcsDX+CTqCC7c6tpthujm2K\ntOMuWAjMHeZr57c6IMN7vgP7KmprGDMeUViOR1/oBm2yp2irH6FqMzAXnQFF7QxzlUcUkmPc\nFZxPvrPn28IwUO/Chg2lCXu6dllZH38BvmMltk+XtWvKQVvM8+vDxtmD9F8Yf6sOhBOzuiEc\n/UV9JfZxW4N8/1fh4QmnNf1p/O4Jp9W0l2Z8uM5JKu2BNai+D+JgLHWnPnQzoII/nUzKhTuU\nGyrK/HDj5ybR5FetnDy3QrDTxKQdnnu0f/HZTq6myA1O0Y2oyfFaCD5qih2NtfU3His3dlOZ\n672ovxiCnY6tvgtjo1+Dj73H+krHmVsn0mucrT1RTeUne3DrWRAWIlsGGy2XkvaGWPW65yE+\nJ/UO/rYtJGjbVSMX8RMh7ivfPsSqx1LSRu1rTI9yw9aN3VTm+pHUy6Skr7UxtlOfV2pfeLZ5\nZedwUuXxRO4/OGuT96l2aJu+8hiuh2Nj8UGTiXQLZ/tOVFP5ydlZW20SNakYm3BHsZ9X0ezv\nxZo2/Ge0O9K2HnZeQD/HFrTTsdi8TnaeTj9SRPfTqDvE/nTTVgvpS31aRA/SaLWsoXMl5FHL\nse1NjVmfdRgYo0X0FI2WgXju+stY7fXZynVdwrl11cq2fwPjrIhepNEiWUPXGO3Td/oz+DTY\nSVVhaeeucG/BJ7xNu3mztubFYGf8uGCz1y0r30X7K5XjtR30qbRBdJ99DoTZQPuUzwvjG/xr\nnTZKtfbRpEE+ww+qpyacpp/JA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJAyU84JdnUvJA8kDy\nQPJA8kDyQPJA8kDyQPJA8kBTPOCfzPaEBWAa8D8VfBP8U6oBMNko/PHdZGNwMjR5IHkgeSB5\nIHkgeSB5IHkgeSB5oFV54FCsuRr8T/eGgR9G/ieBfjSdCp+A/2tBUvJA8kDyQPJA8kDyQPJA\n8kDyQPJA8sDv2gP+hTffwaJl3nKr7PpUZa6n6uSB5IHkgeSB5IHkgeSB5IHkgeSB5IHfjQf8\nMBrRyNt8zHX/0pGk5IHkgeSB5IHkgeSB5IHkgeSB5IHkgd+1B/w7DUZCbwh/+1/8wrtx8i20\niytTOXkgeSB5IHkgeSB5IHkgeSB5IHkgeeD36oFuvNhQ8P81egL8Jy1eAP8dRD+O/GdpJhul\nv8VushmqZGjyQPJA8kDyQPJA8kDyQPJA8kCr9YD/QLj/GK//LlZn8B9+HwT+G2lfQ1LyQPJA\n8kDyQPJA8kDyQPJA8kDyQPLAH9IDK/HWD0+ub17qvxOcXN8l2Z08kDyQPJA8kDyQPJA8kDyQ\nPJA80PIeaI8JK7S8GcUsSB9IxfyWWiUPJA8kDyQPJA8kDyQPJA8kDyQP/A49kP6h2N/hoKZX\nSh5IHkgeSB5IHkgeSB5IHkgeaEEP/ELfo+ClFrQhdZ08kDyQPJA8kDyQPJA8kDyQPJA8kDyQ\nPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA80EweSH/Nd3lHzsYl/2Grevx/Wv4d8VeVN2WS\nV+bm6k5Qj7EcTT/XT9Ka8hc7cmk7qIedw+nnlvKmTPLKAlzdZpJ3NN/FD3nUnQUf14V2mxds\nW22zATS4t9pG2f1LctykYNtqm71FgwerbZTdvxzHev0bDa/S1yMF7fRvBVqnYNtqmz1Pg37V\nNsruX43jGgXbVtvsKRo8U22j7P61Oa5csG21zR6nwYvVNsru/wvH5Qu2rbZZHxq8Vm2j7P6N\nOS5VsG21zR6gwdvVNsru34zjogXbVtvsHhq8X22j7P6tOS5YsG21ze6gweBqG2X39+LoGl9r\n/R8d3Aqu8UW0M43cM9Va2nkDuGcqoj1o5B601vqVDq4B96BJOQ/UY7Oa63KyOe2JpTfCK41Y\nPDPXTQxOCDe834FaDKaF18H/DrOcZuDCEtAOfip30yTqd+PaJVB0QZuftlOBtmun/19aZ1DT\ngYl9HPi3kSwE2llE+9PodHizSOMybaam3kVuCnChHA/zwkygX4voSBodDUUX3kr7NPlNA9pb\nRCfRSJ++V0Vj5/uK8AU43sasY98FpgQ37nnNQcXP4D1FdDaNdoVKNghLcd8I+BKCnEMLg+MR\nzyPH3Dnmvf43zi56vpcfOkV0GY22gg8m0XhOrjlug+BH0J/zgfOk3DgYi46x9qt5YDis7kkB\nmZPcLA8u0LZUk6WpHArfg/78BMxhjnc/sK8iuptGq8LQXOO5OHdu5v3sGPvvZYyBSaktF7Vz\nNHwKneA52AKKqA+NzL/GXTkZW46x4x5rEU6+go/jyqw8FccuYGx+DguAH8U7QhE9TSPXGZ/X\nmIzR2SEfk52p+wlGQtA0FHwP338sOA53wd5QRK5DromOTyUyf+sfxzLIMV4c3gfXHhXywDDK\nznl9ey0cCkXks815xrsx6Tx1zIaCY6qMC+vegR+hiHyPC+CYIo1p41j9AE3ZwOrPeM504HxG\naAdDwHm3JJwCp0MRmXudBx6LaCEa/QpDs6NjsyBou+MR5qf56ki4EIrIPYLzOIxxkWfYphNo\nm3nYdUlbzRH6Up8uB/vCNVCtfK52Gnc+rzn0Jx7i+uocN9b1gXNrBTAn3Q5JyQMVe2Bb7hxd\nwd0uJG4ors/deynnJtWdcvX5065UuFF1UhTR7jTKL9yVPqcNN2rjZrkGa3FustKu+UF1Bydt\nUR1Aw+b8ONKO48BNgHa6KVA9oClJxeT7nA+qsXrx/FFN6OMk2vatsv0G3K+v8pveq7N6NyF5\n9aZiYL6yivNzuPe+Cu73g8fN2ya5e124tXm+XP2mnJvg3cCog8DNWVFdTsNbGmn8Btf/mbvH\nuNO+VXP14XQXCm4cgo6m8HQ4KXC8iTZXFmhXqsk0VGr72mAeGQMupP4plfV9oKjuoeF5JRr/\ni7pScfso9cZKYzKPDIlusg/7Kirf8dRGGl/M9QdK3OM4nlKi3qp/wNvRNcfMsSsq+zJ2KtHh\n3FQq195Mfd4G88gL0UOdA5dH59UWnYPOxUo1khv3yN08K+fGn3EYZGw8EU44mlMqiZeoyURF\nc5q5TfmcYWAMxnqMk8/ANaGojHV9XFSjaOha0RTtR+Oh0QMuo6z/nDdhA++a15T3/JL2PaCI\nOtDI8XazHsuN+3jwAzPIuDYHFJXP6160cdauHcefYJPoOU9Sdu78AtPDIDCnFlFbGumPrkUa\nl2kzQ/ZM17D4I9hfTGxbps0fvtpNSVLTPGDSdqMxR/SYeShvBd9D2MBFl1tNsQ2WOBn9wIvl\nuRul0VDNYhc/ox5lfetvH13EDq5Hh5N5Hy4OJl4Td5j7HlcEY1VaSn6QO5f+BlNmRhiDB8IH\nMCKrCwfH3kVK6iX7zM8Vf7ur7VOXMaI/9W74titzvSWrtf1Z2B+033Olz+OPuobKZvrhpnMN\niDdDy3O+JnitMZUag8baNPW6dq0LS0UPWoXyylDO5pawM5j3OIUlId4ILsj5ppC3Vztbct5r\nz97gGhrkmmNedzMXVEt/6i/X7JB37NOcuAZoh31PzsqPsT43Nnzf8G6lfjlWr3cONuRzazjv\niSFz18uYCvppwz0S7LOJPjVHuJ66p2pt+gaDXoJ5Icx3bQ2+p5iU90CcEPLXfu/nblr+CeV8\n4ALTvgInvM89fWB9+C98BJuDk6cDPAmtVT9j2KtwCvjbztHgpDkUXoej4R74M4wFN6wtKX8z\n48bETfHz8CgcDsfB8eBvXPztjskr6bceeJEq47IHmCwd47WhI9wBLS03Rs6Xd2AgzA8LwUaQ\n11NUuBC5ubokf7FG58bbXnANfJX14Vxx8dG3sZbgRPsHwLFwE7jQzwP1kPnLcX0Phkyiw/24\n9gQsBtrrbz7ngH9Dc2xK/PByzjonX4CH4C5w/OxDbQmWH/SkEfXl+hmwMVRyfyOP+9/lRSgZ\nax+COT2WOdDc/hxo55SgzTfC41BKxsoxsA6Uu6dUu0rqOnOT4zUczNt5vUyFv3XX5rvBOb8F\naL82x9JOf7m0Kni9udSFBy0IxpNzuZyO5sIz4Hs8AovC6rAd/AhB2nkbLAevhcpmOj7Ac3z3\nbnA/OLe3Av24MpiPJke5XuuvH0C/bgK+qzG8JziH9P3NYOy3lH6lY/dNp4B+/z+YCvyTImNi\nVngT7oW5oKWlDe/CsWAe+xnOhQPhJzA/zQKtTX/DIO39Jzjf54G2kFTGAyb6P6qcgHNDOR/M\nyzXvqUQ9uMkJ3B1M6m4KZgblBN8a8hsor7WEnBBbQFdwkXeSmJBGgQuCtrtR8l20eQXYG0y0\n3tdS6knHl8P04EfcN6Bd18Bx0A86wQLwPST91gPfUuXC6CapCzimyoV0ebDufWgpDaJjN5PO\nFxdsx/lpcEzXhMFwF4yDj+FguBg2h/Zg7NZSx/Pw9cDF0XntBnBV2BG+AzUHuJlbGlw4zS/X\ngZsTY9j3MnZrpZl5sBugteEX0IfXgeOuPXm9RsUysB84vxeFfuC8/wyaorVo/ClMCz7b994D\ndoBesCkobbu5odT4D+09E+4DN3vGrHHTFJkP/wH6qw3cA9oY8oi2G5OO80bgfbvA7VBOz3Dh\nEugD94M+fh6aIuP7KVgdfgZjy1gzrr6AWP6yoT9sCTPA4WCutF0s298A/UA7VwRjuylyjM+F\n4E/jcScI/qT4P42kZO4x/paDAXAovAqxHG/H5Vmw7L0fQnOpGw/yF0aO79fwEzi3fYc74Xro\nDda3Vs2IYX5gzAujwHm1EmizMazfnDPfwirwJowG8/9wqKe0az3wIy34eQvKX8H9sDJMD8Z6\neBfH6FdoCRlvG0IPCD5dgrLzzg+3ZcFcexF0gpayk65/I+NB3y4C+ncOWB+MifGQVMYDJtg/\nqj7ixbedxMufz7W/TeJ6fGlvThaG72G67IKJ34VnGTAped3gbEk5MVxgO4KTWb4GF3EnkUnT\nReIWWBLegrfBxas7PAj1lklxH3CD8iVos5tj/XwzHAhuEP4KjqnJqjcklfaAmx/9NzeYINVY\n+BRcmJaAn6EldCKdOt6HgIv6TPBPcLF0w7Q4nJeVf+T4JuwIq4H31Hrz8nnWz4XZ8TOOm4F2\nPQH23xXcyGpbJ3DO+yFgbO4CR4MLba10Ow9eHezXzebSoI++hOGwCcwMH4Abduf6MDgsw03L\nNmCuaEocuLbYtwuwGzDH70m4FZYF564EGY/bgf2+DPeAm9NSOpLK/uDmfxEodx+XGlUb7lgY\ntPMHMK84XpfCzhDkhuf6jFCXP+rXXmB+fQf+Do+CMdLUDZM+dIzawXcwFQyC+eFK2Ary0odS\nTn4MGCsD4SBwDjXFlzRvsG8xjuPAOTo9GHPnwl5QSs6rE0pdiOrMS/rU954JmhKbNP+NjqFm\nSRgNc4LrjPPnMTCO9a/vU+4duNSi0nZjbVrQn8aF8XwFGHvG954wK3wD+8FN0A3WAce+Xjqf\njuzfNcf5rvrBbXAaGJfuOx4GbTWWLsgwr9ZbJ9PhUaC9s8H/wXtwNRwHm2TnR3B0Lupv52Zr\n0OYYcQt8Ah3AfDcGjHPPjYek5IGqPeAkriQJL8V9LiqDwYkR48T2Gd/CjvD/2DsLeDmKrG9/\nSEgguEuMhEBwt2DB3V2DBXcN7izL4rrYEtwluCQEd3dCiAe34ATZ73lu+vA2szP33umZO3cC\n/f/9nlvV1V1Vp09Vnaqe8O5bTD0pdMG1KXazGWW78ExzF+PbPKt9HpDDTgOQAfUXGAfa7KZg\n2WCYA9Tq4P2s2o+K5Qa3f1NH34atpvrzQ/gKtN3rLhDakMzXcZEh7UudZzLUK7fKNlQYU26l\n1PMnkR+Qum5u9k4eDH+mffsl5V6vXNBQH66dB1l1FhX7N7Py5zxnX85FU8c21tBO5MclZdop\nMW+3I+8B7xXIqkuo6EbSmDx8PAYeUAfBcAgfehiJ/L3k1XTgXHKzd82po+DJhly2Px5qLitR\n1c3OsX0epkyemY3UvsNnYaPP+R6uo55QqFMpeLCwsIxrfWAfjln0OZb8G/APSKsXF9/AMNCv\nxks/pvR3UzqHB+5o6qFG7j/FvVgPjmHktblNI/UKb3mI1s/GpoGgXx33mUA5Zo5dVjmmYZv+\nibwHNW2dHpqrSXnQOODacj6/m+Q3JXUNuBayajgVtU2bHHvx2jVtv1m0N5Vs71VwvIwL74Ox\nJasGU9HYplYAbTwSnKPmHT/TsN0+ze8B5WgADxurs2oMFbdpRmX94Xhq/0jwTKG9Hur7w9cw\nGl4BNRH8BxyXJ8FY2heyyvY3bEbljXnGOWF80T7x2lRbRoG2a5Pv4b60DIQ8Q+wXFxlS+1q9\njHqr8qxzbzPQPu38OUnDZtMnwHjqejJuDYFdIIvaUEkf9MxSOVVnRvKekVwn+jJtr3nt1R9b\nQa4iHpi4SFleVJ4HNuJxN8Y5wAnnYloWDEaTgRubC6Y7tKYMTD3gNIiN5R7yzgE31/dgUtBu\ng0I3MNieD62hTeg0AowHZ4OShysD+7RgINL2b2B9yNU8D4SvXuBxf71zbrq5TQceEONAR7am\ninnYnl7nhdXATcID5yRwITwKBnznwPLg+nIO/xt8j5bWUXQwF8wHK8PjoC3fgb7UXq/XhiXA\nQ9ax4PvMAN5rSdmPfZwAbuazw6fwCVjuGh8JnUC/TQ7+aHId6ONqyvF0zc4NHcCDTluYBdJz\nzDG0/xtB364M80NXOBpaWh5G1NowNezrBdL+dRtyzftzNY89C9q9KhjvPYScCdWQY6WOBD9+\nFwH3m3lAW43hzdWePNgLFoeVwH3hX3AlhD/IZtKk1PoVFgLt7AfORctdN+VKP54Le4PvvBys\nAh2hWnKvGQQ7w+PgWol44ry9CQaCOh+6NeTq549+de08BK6v3UB5FnF9Od6Oh+80J6gtYRtY\nEYylg6EW0tcPwqFJZ7uTaqN+NgbNDkqb9fN9cB1MDK0h7dWva4H2XQ6eP4aA/rXM+bILxDgc\nSb4etDpGaOOsMBsYL16Cj8F91HXp/VwlPNBak66EOTUt7kpvz8PLJfCr2onflFzc04CpG4Ib\nwTkwHNwgFwDVbnxS078eet1QDDS+jwe4J8AgqpYEg6byUOIC+tEL9DWcDB4SWmOeaO84cAxm\nAv37BQwDy0eCtmuv93I17QH9pT+dB0vB5/AoeABR3jeAtoZcK/IDPA8jwM3HMuWYu4buBt/h\nXbgGYuyd4y2tDejAA5K2eXBbH9xsXPebgvYq7XNjXxCcn64ff7nV7y2pT5PGjyP142wMWDYv\nqMnhLBgNu4F2toEusBBUW34QuV59d9erYzUjvAYhPyRnh74Qm7X+PQ82hJaWPvgJHFvjywXg\nOMmK0Bx14qGF4WiwLeXaOg2q9Q7OLdfACnA1vAjOO+UcG26mmdKmfvBW6vnjyTtPZ06VZcnq\nt7Fg6qHdNRBr9HbyMRfJNkvuP8Pg36mnHyf/Seq60qz2TQ3GkPVSjbkHPglrgevId3Jc14HW\nlod093Z9vExiTF9S5197CFunIm8ceDMpi3jqHLgNnoGWVgc6WB5mAX3tfq4djusa0AemAdei\neHD3nukR0BUWgFpLW+cAU+ehP/isAs7v7hDx3vU3FIxb7g/VWvM0VVKO8bIwX8knxtttzHBv\nmhSsszRMB1+Bc8PyXCU88Hd2jgH2Gijlg424ZwBqSgYYDx5pOQmVQcBNzAXkgq+lLqWznSEW\nsR8/X4ALWHuUhxWDkEyR8BJp6FcyE4NBqxYykO4Di8BqUNh3L8oMTr6T76b8ZeThhlz+pykP\nOBdDzk1ZEPxYUo7zA7AVpOcBly0ux1p1G580/NWersm1G772urZeBAO889P1a13vt7Scd24y\no8C5qtzY1b/Bj7uPwHfwuafAtabdB0FLazAduM796Ig162ao9I9+0mfKd1Ftxyd/XCeXFSeO\nS9jggciDt9facS2EtMOysCvK/ViKORFlLZFqk/F7L7DP4dAZ1OLjkyb/hi8L38Hrar2DhzTH\nysO5/noe5gfnmeV+OA2C5ig9D+J523SexphFebmp4+6+8hbY5ndJqi9cC4/AfOD6bY70baFf\nrWfb1dJzNLRnQWNtuPYjxJgpW4D222+MN9lW0cn0ejjoazUaIsa8TN6PDu2cBfTdmbAxzA2f\ngnIOeHhuSU1B4zfC+kkn2vQuzAXOsy9hA/BfabzWVn2rXWfBbhBjX2ufb0jf14HrS2m78aEr\nGGf1ZWgombAz3iHuVTNdiMZ2h/gwct0r92rnp3aEZiUzD0wP4TvnSUdwbps6B5w3uXIPlO2B\nc6kRkz4qe9jYAfaHZeBYGAtOsmK4oD4C2+kGae3Bxfvgx4mLz0mbRbtQyc3mJ/AXDtvzoFbM\nHsu0Rcz7rKkL5xvQjgNAGdweg3u9QKtDJQF1P+q/bkMFmoFr+yhlb5QXvtP3SZ2TCtozsH1d\nUFbOZV8efqacChmf3YZ6YzLWtZrv/S3ohztgTmhMK3DzEwh/FktHct8x/wIGgXPK+T0UssqN\nrn8zK8d8LGZbusz5uiZ8CQ+CNjpur0BWXUJF39d1cB3MCoW6gIK0Hel8+Mo1b7m2mfpOjnXo\nKDJPxkWG9FrqXFainge56D9tW+Qf5/47MBN4cNG2F8B52AbSOpULfZtVthv9ptO3Cxp0E9d3\nxhZ9djnMBR+Ac6cpncMDzv+sGkrFtH3pvDZtAANAGz+Gf0E7KJR+dd5MktxwLJ6Hm5Nrx8yx\ny6rPqahtxui0jebvA2Ooct5eD85jMW9ZWody8Rl0SRUeQt4Yexu4FrLKD6JC++L6Su6Nhv1T\njW9J/k3Q1+/BzpDW/Fw4p7dKFS5K/kdozvxIVftTdjBXfZIS3zdsLEztexgcB64Xr3tAYzIW\nOyeNzcbowj2KombrU560X/3qOzsPC21MX+tHfRxlxvx3wRisLbYxKfQG21wAlHte34Zctj/u\nucaM10B77UdfhR3F0vR99xyfsWz7JPVscDGMglhXniH2g6zSP6sXVHaOPQDarL/1RTF7/0t5\n2Ky9cY56kbyaGYbCGTAEdoEsMhbbV9igP4cl19F/3NOGD8Extl9j1M/ge+q3dyCeLZb6zltB\nriIe8AsyV/M8sCKPfQBng782PQ1HwuRQShNxYxZwQ7JuyGvbuQYqCUrRXqQjyHwFxTZwn9Ee\nx9wF9BMYdAzi1msPLsrD4VZws+oK+0JLaCkadfM2mK/djA7S72RQ8F32gGOaUSgywIgAAEAA\nSURBVDfLIxtT6XxYB2KddCB/JpwOHuwKdSAFHVOFR5N3fpSq5ztUoqFU7g0zwROwGtwAz4Fz\nayFQS8Mg8LlSMnjeD8vBtAkGzrvAedIS8v2dB+tDZ3DjLiaf0z7lZjE7aGtbWAl2AudzpXqB\nBnaH+eBZ2ARmg5D3So2Zm5rzxPtfgva5hpzjUgvNSScxV4v1tzyFc8HHsAXoU991R9Cv1dRk\nJRqbm/I9k3segAZCxEYPcNr1Dni4PwFaWs6hxsb0Tu7rG9fC8bAtFBvPnShfF94G4+dgmBEO\ngmrI+aX+Oz7546/XXWBOmAIegx6wGzhfzVvmvdB5ZN6A18H3c66fBnuBe0MlKhUrPLDtANqr\nrWo7uBbuA+PtzXAx7A2ht8gY4/X5ILgHnoFPoBpyf+vTSEMTcc+4eRz4bu7570IpOaefAMe+\nNwyFSqS/3NN/SBqZrERj2qlGwPcQ19OQ/xBWA33ZDrTxGngQjHl3QzeoRPZ3GPi+5kclKUlR\nDac0bPQB7TQejQY/fI1j7mPOmR3hN2gJdabRJ0A/bwODYGkoJp/RZtPJQRvNLwC3g3HLD8UT\noRoaSSP3gv10hh8h7TP95b1ZYW7YEY6Hz8D14T7umKTrcPknlZpPf3ro73qhc//OcnI40Ysx\nCeVOrAtgZzB4uwicjKuD8v6kDbn/m4SFk3Ef7p+dPGNinwZ8N84TwWBfqdz8bHcuMPiFtOVX\n+CkKSA22bvhjYUByPYjUd/T5W8AD3jmwIBjwqikXujY8B1uBATtkv4X+83pIPED6BRwMHeBS\naAm1p9He0A/WBsdJuZF8DC+Dm8uckJbv5gFKLQ7rgkGtWL3pKI+5QzaTDITa4VyyrQdgavAQ\nMRvo4xXA+WYwDYWP0+nE3PRApa//C7eDh6fHwcNNJVqKyo9BX4jx7kVe+7XxDnCe2a8qTC27\nCPSp8/JVUB7w5gVtrYZG0chLMDk4ljfBGHC9mBoTtC3sI/uH9OVIeB581z4wFYStZKsmx8rx\nXg6uhCfgEjgUQumxtczrs8Dx/QS060xwc38Yqi3nfTFp+/Fgugc4t5aA+eFq6A8ehk4DY1RL\n61s6KBxPfSW/gu+hr+6Ef4NremNYFNJ6lgvf4Rr4Ev4JC8NoqIZ+SBqJcfXSvLb3ANfS9jA9\nrAI3wg1J3rLtIPQzmTVgL/gYHgXXVj+oVGFntBO+dL5+CHOA80+dBKfBYeBB0Dh1FBwPrrXQ\nqWRWgJdhBLhvvAaVqisNnNtEI9rv/mpMdb05ro1pD246p/WvH8qOSyUybjt+M8FkoD0hxz69\nzlw3z4Jr/Btw/NvCO+Dc8Nq993Ow3c3AeTEKCseNorLken4G1gHzc0HaVi7/2IMs175t4HtQ\nL8LcMC+4H2if/nNNDYSW0kE0PBzWA8d4TVBhe6RRps+13dhwIDwEI8Fxdn9bFrxfDRk7XDf6\nUzsmT1KShmvLtEOZvwX0lWtsFXCNHAzql/FJw9x8k7z+tU6UJ7fzJO0BnT8haWqMdVGtDf3B\nBR8BYnryt8HK0BwtyENOICdJKbkYOsNO0A4M5E4oF7ETTEI+W6wtg1VaHbnw4OTCqpa0w/6f\nguUhbUcbrmOcfeYjGAAG8llgLBhATwEDxH7QUpqOhodB2r7oyzLHUDmmBgOlzdp1gBfoIPAQ\n0pLSlnmgMxwDjvkC8DH8C5Rj6FzUb6GryVwKp8GW0A9K1ZuLe+n5w2XZ6kQNA3Nb0E8G5k3h\nJ/Dw0Q/OgfmhDfiM7xYp2f8ZC58zINvuP2C55Joks7TrWXDs1gIP8s5BN8enYRnQrrRt5tNa\nmQvn5itwCjhv1bDxSdX+9qelkfA1eGjU7iXhYdgBVNhqPnxp6nv+CtPCfWDZlVBt7USDO4J2\nODc9OLnu7S9k3vtRZnpIAkmLK72Zhw12qk0zw0wwHzwBrvf3wbWmuoOHpVrItaNNKm2n1/rW\nw4R2hpx/rg3XlPm0xnBxcrqginnHOOy02cgbQ1yv+lE7nwPnbsj886C9aTlPr01Il1eab0cD\nYVvan9oY+5A+dX50gQchLa//Bc6Rj1I3niIvoZ0ikzGdhHquh/BfNJO2PfKmp4C+bUrpOd3U\ns82534aHtFEb/LBN+9eyKSD8rH/3AWPjHnAuKGNXdzgBjoXfQFnPM5M8A5VIW5YGU9vXv8pr\n+zF1DoSczzeCe9Xt8CF0gLVgM9B2PzhaWq6LAaDNHWEa0N7wKdkG203jPV4iPyXMA6tBL0jP\nTS6rIsdRf0S/4Usbt0x/pn3qfuUzytRx38ULFONxM3l97blEGQdylfBA2rklHqmbYm19AI4A\nB/g4uBdiQrjgekFzZZBeAjycFeMmyg1M68OBoPYdnzQcoJxwbqBpOWklrcLA8wk3x8Ei6Ycq\nzGtLetLbXNjiO6RlAL8KesO84EK5BO6HraAlNXvSuAfjQj/FtenkyXMxtgck16+Tuqm3tL6j\ng/1hHXgZtoOF4QMI+Uy8T5T5bFuYEzYAN4BS9TpzL96ZbCbZvwHQMdRXbpDnQagfmcXAteEG\noKLPYqnz3E3COWM7Bv/2YLuV6F0qHw6ut0XhOnDOnplcn0U6CMImsv+jO1IltqGGjk+q9ndG\nWnLT2xn0qePdEy4DN28Vc9KNJW3vtw13x39QrkTeQ79p+rCaPFLVxAPUpdAFxkEobIs0ymuV\nakuxvi1z/X8Jo2AhCJ+SbTh4eJjzXi0U4xi2aot5y8fAXJC2xXngPBkJtZR7jXaFnZG6L7pe\nHwft9MDnnAiZtyz9DnGvJdIY97AvbDb1MK9/3wPXx+cQa5lsg7z+Ab4Yf9lif9vRsjYZx9MK\ney1L50enH2okr58L53Qjjzd5y/E1/uozxznmZ9hmGor7Xl8J74DjYew8EDw7nQEtoUloVLTB\nNJS2L13mD0jK8f4J3Ccfg5PAvedQqIVcx9rQE+6EQnu9du+MctNVYGlYAdaDp6Al1C3VqP2G\nDVEcZVHu/uqZYzCcCL7XKPC+80a2h83BeZGrCQ+4+CYUdcXQpcCJuSf0gNlgb8giJ83L8FwJ\nPkk1ehd5F37vpMwD31iIgJ8U/ymJQBYHp7hpQL4cLoCNwF/KKpUBaQ6YFuw3rVgYUfYZmdPg\nP/A4TA1TgT714NKSClvCxjic2GeUpfuPhW/Z07AspMssbwlNR6PatiusCzvCw7A8uFlp61pg\nkNf3nSF0NRkP/s4t50ipeq9xr9L15xx9AQ4D/XIl7ASOp5odDO7aW8y/FP8h77uBapO4LraE\nh6BaPh9JW4/AnKA2hS/BNTw9hI2mhX32oczNaCfoD67Hf0E1NQ2NuVmfA37oXg9qBMzYkPs/\nuxz3tDyIGps8gEwOa8NQaAm9SaP6SB+IvjFGGZdC4UPT1pB2qcJxdF0Z+zzwXwrd4XKYGxaB\nW8E4ZFoLOW5phb3aPxyMBa75OaEn3AkvwZNQSxXaad/aqp2O+0C4Goznzlt/CBPzU4L3aqFi\ndtrvi6Adj8FgUK6zk2E76Age3IydF8I4aEk5D/VZu4JO0uvFvD6+GcYUPFfqsnBOty/1YDPL\nY79xfXjeiPlZrLo+07fuS+/D7LAkWM/8udBSivUe45/2Y7E+/deON6Av9IZuYNycAY4H961a\nyLm2CrieF4Jidk9cUH43187lBeFBaCk55uGHmIuFfRXaqx9HwwYJ2u6c8TnbMt8ZJgPni/tc\nrhIecPFNKDLwO/AfJwb/SLoT3AsGsJaUfRpM1wAXh8HSwOqEK1w8FDUaxLx/EDhhb4FqjUHh\nQqHpBhWWn0+ph1EPJbdBBDayNZELVJ+pCKbjr/4cnHxuLLixngqDoFb6gY7OgA/Ag7Eby6eg\nv16AsM05MRO4GRlw1HVwGqzlBWqs3jbjH8n8dw5qvgfa+SjYp/NpFugEp4BztQNcAntA4Xyg\nqEGWt4MrYGcw0NquGt7wtzp/3Kj1r33NA99AjyT/EGlIe/RzSD+79tT3sAW86kUVtTht+eGz\nBPiB2B30p315HRuodhXa9x1lfiC9AS0tx9P+H4U2sCjMDLGuyDao0MYor1Vq/2npN/EQrIbC\nuhBzzjLX1xqgP2ulQjvt1/Xs+J8Cu8LuoJyjvaHWcbPYPqM9rtE14Tcw1ui7q+AtUG+C971X\nC6XtjHViv0vCE7CRF4n+QToFXAbGg3FwMRwJtdAoOvGD1/WdVno+mN8lfbOJfOGc9geXAU3U\naex2zLNCv1onbafX+tc9al54GK6GL6AW+pVOwp5IS/Xr/ddA224E90+lr2qteejQtaP9fqAp\n7UvPXcu8Vt+CcziuLWspaYfxPfpyDhRT3Df1nLwKuN49X34FzvGDwT1UTTI+aYhxnqNzlfBA\ntQ7nJZqvarEB34Ofk9MBV8/C7WAgOBDKkZNtBSjlAw8hoW5kFoMrwMmlHQaizSEmJ9mGfAQH\ny110xfQzhXuBv56sAzdAJbKfz2G2pJGwwUvtiGvzJ1vYSgpb2ib9ex2Ke6a/gL/m66fWkP0u\nC11gFBhA1UlwJswAlquPwcNU6EMyzpG0itXzMFBqfqTrNpa371XBTXla8BCvXBfa+ADcA2eD\nB1H9asB103X+27/zX58b+D1kuyZ2ATcO59P6sDtUKvs7BBYH19E20B701W6wP6TnQ9gYZdql\nrT/AKxAHB7JVk74aA76/fWhjL3C+ngp9IOyx/1hXZBs2ozfM1EDT0cdXoE++g+/hZVgL0tJW\nbQyb0/daOu84FfbrnJdV4CZQj0F36AreGwW1lB/ohXY6tv+Eo5N7x5N2g7HwCbSGvqbTaVId\nO64eKB8F52zoJTILQJekYHiS1ioxjrQr6OxLrv1Rzjjie4T081Hg2uoIvof1ayH7dr0sCu7j\n+lM5FySutcc1Vo4e4+GY01eWU7HIsx5gPfTqN2N8SPti3oat11PWLx5ohVQ73FOM6WmFfZG6\nvx6QfqAV85vQt35zPvSGYWAsUpaFzQ0F/LkZwu9R1lKpfTk39ddkUGiL18bZ/rA1qBtgU5gd\nfoOQ8WxO6AXvgLHkLfAHqVwlPFDq46DE461a7CRxAruxGrQMqAYPF9r98DSUo3l5+EEo5QMn\nnwyAZeEZ2Ae0IzSEzDFxQTpxko8F5EG1Mbk5j2zsgWbesx2Dp/ZG3+l8NDMwMq2UumCL+Vtb\nQ46pPkn7Oe7VOh1epEMDkpSrrPUa62dmbvrR8TAsDp3gXHgeBsOL4JxcDTZI8s6P2MDakI/5\n4tzQ564jD9+ngGvLuZUeHy7L1hrUiHZ2Jm8Q9+NoW5gOPCS5pp0fYZt2h22Wu/5aWo7R3uCm\nsSN8CDOCB75bQHv0RaE/LL8TainH+VjQf8rDnnbpK31XaKMfy7XUFHQWNsQ4Hk+Z83UySMv7\nH6QLaph3HhbaOYoy52PYrU9dT62pKek8bae2ub8U+jJsHB6ZGqeF466dW4D7aClb/cB/F2op\nD/JtwXEdl+RJGnysn2Ps+1uYQdZ3Ttt2JfKQfhFsD2FXzINo17587tooaIXUmKMNV8KuSf9h\nZ/gy7I/7yWOtmjgnPXP0gx3hHegCvk96D+KyYX/c00yNtBT9GHucp+FLu9af4Uvt3yop+4J0\nFnCtWa9QwyiQXM30gBNgQpJf1H75bgy/JIYbXFeG9aBvUtacxK9nDz6TluACyp2Ir8AOsAYU\nHtqPo8zAVUzWPanYjRYoMzBNBdtAHDTtX8VCGkF+o4aS1vvzMV27YWhTWrGYv6NwcvDwl6tp\nDxjM/TC6GhzbvcCPmuvBjyPl3HC9+C9BfkipmBvjr8ZfX5xc+JGwHewMQ+BQMAhXolep3Bs6\ng7a6du3jHhgNT4K/Mg8GVWhfqTU2/unq/+1Hk72gI7iueoAbz42gtC89h3/k+ilv1FDH0dfX\nSX+OmevK8VodtMdx107T92BLqKWMyyrGUhvcwP01fBDUiwrtfBvDOkOslXqx0zFV4U/XzNLw\nkIV1pEI73bM3hDHgnlsvMqY59u49ngFK6dJSN2pUbpx5GqaDfyV9arPzIGKQa3wN+BVaS2GL\n8ecn0EYV89W8Zb3hWi/qRK4fY+OnYHxfF4r58R3KO0O8F9kW1yP0sAXMD8+CvhwJHyV5fT4J\nqM9hLPisZ4BcVfBAY4GhCs23SBMuvicKWnbSekiYuKC80ksn5KFNNHId91eBHSA22/bkr4QB\nUAt5iNMnQ8GPOH30ErSDEXA33AkG0taUB7quMBO4oC+H7cF/XdDmGeBeuAxyNe0B/6VntaYf\na3jiPv7KFeBc/Ric37NBP0gfCB/keh5w41g1yZNklpvPbUVq70HZU9AJHgWDu/PgVZgRhsEh\n8Aa0ltwsPdyr/WAJ6Ah+5Om/KaA3GH9qpTfpqAfY/0DoBh741gR91gW2AsdWX+r736CWck3r\nIzdxx+8LuAQOgNFQL/LD3Ljo4cLDxwLwLzB+1pM+wZgOoJ2DwXlorPQDpJ7kWvcw73x7BuYC\nP5A2glrPQbosKeemfnT93ASnw+ZgXFSzw8XwpBetqDb0vQ9sB65v7fwe1gJ/TPSAfzY4P1pT\nxslHwLPQEHgB1gb1GDgX/g3fQj3pUoxxbhon3R9fh4VAf/aHY8E53Rragk4d4xlhaTAuuf7b\nwQBwX/LMtCN0Bj/iLoLWspeu/1qa9C/0OvPyLrvBP1rhnXahzwdgB/DQcjXcCrWSm/yH4CHp\nFLgcDKL1prYYtDx4+HCD6g3XwabgPYP9jeCBJVfLeCA9V+3hYLilSFdjKDsL3NAOLXK/GkX2\nsQjsDm5KbrDbwI8wDwwHN9t6kYf8xeBAWAlcc2fAG1BLPUtnHjr7QCcYBH58aJ8yfQqmgZeh\nNQ6mxsG7YDB0BDf24+BpqCf9jDHapH2O5+HwINSbtFO7/AFBWz0U3wz6uZ5kXL8SXMMe7Fzj\n28J7UE9yj7kTLkuM2o7UA7Lxx3fwo8T529r6FQPka+gLN4A/MJwE9abHMehCWBFmBm3Uv/V2\nFnHP8ccQ5+Q48GNze1gZjAX7ge/S2joPA4yd78MFoB8/grch5IfcYXGRp9X1wF/pA+l6XCPN\n1dQ8aBAs5YOluOevn83Rmjx0PhgU1JLgJvawFzXQ9PSxCbiJPgH1FpAwqUEu9oHwAxwIl8ND\nCSS5auABN4FzYTbwcLU0OF/ug9aQG/8/k47bkF4MO4O2TQweYnYAP9TqQYdixJEwCWhfd9gM\nPFzXUkPp7IgiHfag7DaYDzwEeqDW5ougljKu+hFnDB0FW4GHj3rTZBi0HGjnCLgC6lFtMaon\naOcw8MDk+Nab2mHQLuDaeBdOBQ+i9a5NMdA9fFrQx4uCv8T7Y0RryjFuD1vDlmCsvBQ8u/jh\nVC9yvR8PjvujoK31+C8Zju1pcDp49jAufQV+1Es96V6MeQa093bQdv37GGwB9ehfzPrrSGf/\nXTUdL75qI8zMvd+a4ZwuPHMH3Ax+qMwATmbLOkEt5K8IBtGDwcPv2lCPcmOfCo6FS2AlyFU7\nD8xOV3fB3eA8dQ1cCx6ou0Fr6wQM2ADWAA+Ey8BCcCHUg3bCiL6wA0wOPWASuBXqQR7274FR\nYOyZEg6D80Gf1lK/05kfuzPBINCuGaHeZIw/E4z3T4KHEuN4vcnD8EkwK7wA2jkN1Jt+wSDX\nSEf4ALSzHdSzjDE3gB+d+tS58DjUw1zww8O4vSQYE93bN4djoJ7kOtKuBcF95XqoR/0Xo9aF\nRcE52g/qWXtjnGwBriP960d8vfoX0/46mhA/kPy17zxwwx0Ibv59wMNKORrBw6uCh/RiHEX5\nF9CUtuMB29of/DX8K9gXxsC2UAsZnH4CDyQ3wW5Qj9LOH+Bs8JDuuOWqnQf8FfIz2Au+hLFw\nMLwPHvpbW87bY2EAeCB8DlxX2j0VtLa070K4EcbBe6DflgU3rtaW8awTbAV+JP0I2usHXK3X\n2u/0+Qt8DruC8ckDVL3JmKSdroudwetNoN6kXc65T6A3eHDeCOpN4c/RGLYNzAJr1puRBfb4\nw8fzYOz5FpwLu4DzYjNoTXlG6wcvgjHxQTge6m2P98PDsX8TXEfGoq5Qj9LOV2F38Ae5WaFe\n5TifBbeD83FC8C9m/jVkkJ2Q5GHuBHgJBoOHgKnBSe4kOgCugGppchoyyDem5bnp5u8hLi3L\nVgA/nhrT3I3dbOY9D49hp78yzJe6bmYTTT5WjQPgtCm72pBfKHXdpAHNfGCJZj5X6rGJuDEj\nhD9LPVdp+XKVNkB9A3s5dvbieT9Qt4S0fuaiJxRra+n0gxnzHUq0nW5Ov/vLvQf8tB2zcW2c\n2gk8bJfS4qVulFHehWfTfRdWdcN/u+CZSbj2Y2AraM4aWZjnKtVcNFDMTuPN97BOQQf+8jwP\nFKtT8Ogfl/P/kcue8Uer6FO7PDT5UV5N2YcfgpXId03buRrXrpNqyjEbU2GDzp2w8zvya4CH\npmqqC429UWGDrsWw03FfD9pX2GZhdWPKB4WFZV4b27RvSfgNwmayDfKec0FfZ5UxulIVxk+v\nZ4ZCeyvpxz2vUvlDs+PsuUltB0MactX74xmiErnPrAIzQbTlDw6eJaspz2SVyvXtflh4Hqmm\nf6OtSm39S9Z3skwo8uD/BRh83y1i9GaUXQUesjzwVaplaeBmmLiJhrw/KYwreG4yrn8FD09N\nyYPfIuCvMOXKQ0c/CDv98LBPA361NZoGsx6Y3ST/DTHnWtLOwfSzMmTR5lTyX7jCzixtNLfO\nqzy4bnMfLnhuB65PhXLszDpXn6Ef11cW7UGlY5pZ0TnhGnDdhPwAkcL1FffT6UAu9EsWHUSl\ng5uo6DrX3+lDaSmfNtbU3dzUL1l0FJX2KlFR2/Sh9qVjSda1Zvw7ELLoFCrtmKpYTjxMVWtW\nth9P6Zcscq1vkVQs5b8s7RarcxGF+iWLjJ3rJxVb2s4z6Ee/ZNHVVHJPUi1t50n0oV+y6FYq\nuccr44ukY0y1bHcdHgn6JYseopI/JKb382JxKEvb6Tra6Vq/JV1YRn4Qz86dPJ8lJja3K+00\ndt7T3AoFzz3HdYekrCXt9AzWGx5J+ionce55NvDDyLFW6T2xmnZrp/HPPT7XBOyBHtg+qgn7\nP+G+v7Llyj2QeyD3QO6B3AO5B3IP5B7IPZB7IPfAX9oDflX7Lxh9wC/oQu1MwXfgL5W5cg/k\nHsg9kHsg90DugdwDuQdyD+QeyD3wl/fASrzhcPgU/Cfd/uA/mfqfqPlxtCbkyj2QeyD3QO6B\n3AO5B3IP5B7IPZB7IPdAJg/4rzITmqbE4J7gf+86J3wLQ+Au+AZy5R7IPZB7IPdA7oHcA7kH\ncg/kHsg9kHvgb+mBpXjrB/6Wb56/dO6B3AO5B3IP5B7IPZB7IPdA7oHcA1X3QLH/W56qd9KC\nDU5D2/6v2uXKPZB7IPdA7oHcA7kHcg/kHsg9kHsg90DFHpjQP5AqdkDeQO6B3AO5B3IP5B7I\nPZB7IPdA7oHcA7kHwgP+b/9PyPL/N8AYeGFCfonc9twDuQdyD+QeyD2QeyD3QO6B3AO5B3IP\n5B7IPZB7IPdA7oHcA7kHcg/kHsg9kHsg90DugdwDuQdyD+QeyD2QeyD3QO6B3AO5B3IP5B7I\nPZB7IPdA7oHcA7kHcg/kHsg9kHsg90DugdwDuQdyD+QeyD2QeyD3QO6B3AO5B3IP5B7IPZB7\nIPdA7oHcA7kHcg/kHsg9kHsg90DugdwDuQdyD+QeyD2QeyD3QO6B3AO5B3IP5B7IPZB7IPdA\n7oEJzQMTTWgG19DeWelrT6jF/6+oz+nn3Izv1pF6faAWY/kh/Vyc0c6u1NsRamHnCPq5HLJo\nHiptl6VihjrvU+fqDPWssiBskbFuudXepsIN5VZKnl+MdOOMdcut9ioVbiu3UvL80qTrZaxb\nbjX/3xLcVW6l5PkVSNfIWLfcak9R4YFyKyXPr0raK2PdcqsNosIj5VZKnl+LdLmMdcut9hAV\nnii3UvL8BqRLZqxbbrV7qPBcuZWS5zclXSRj3XKr3UGFl8utlDy/Nel8GeuWW+0mKrxZbqXk\n+R1Iu2esW261a6nwXrmVkud3Je2csW451f7Lw/1gaDmVUs/uRX621HVLZbXzMhiVsYP9qTdj\nxrrlVPudhz3TfVxOpb/Ls7U4rE6ovtwKw6+Cpja0BXhmengSnGzKj6rl4Qt4CxrTNNxcAiaD\nXxp7sMS9nSm/AJ5O7k9Huih4WPwyKTPxoDoFaGcW+Y6+q3Zm0b5UOhXceLtCO/DgreYH3709\nfApjQL/Yn4fJcdBczcSDnUA/ZFFfKsmLBZUn4XoZeAO+Ad9hShgNPUD/t4XFwc3QZxrTrNyc\nFjo09lAj907i3t6Q9YDQSNN/ujUHV7773H8qbf7FWTy6PbzWjCq9eMZA/W7qWTddfe28bWx9\nOObfg3M/iy6h0obg2JXSStz4Cl5PPeA4etB6Fn5IlZfKduGG72h8yKJrqLQKvFOksutnKdB/\nH6Xu+zHteng8VdZUthsPDIY1m3qwxP3bKTeu2cak0BP0neunmnJeulY3ydjog9SzjQ9gYvAD\n1BhUzL8UZ9a81HwEXAtZ5Px3rg2HNuD8GQnaXU0Zc/vD7hkbfYV6zkNtmwyWg2EwHKqphWnM\ntXBQxkadl7+Be40xuDto+9egjHnOX213PmTVYlS8EI7J2ID2uW6MGWoF+A60NTQDGf1hmc9m\n0ZJU+geclqUydezXMf8sqT8LqXPedfRJUmbSEWaGl7zIoKWpcyScn6GuVcaBMT59NrJ8dvDD\nSbu00XOE/uwB1vFHV+fDEPgcmlJPHtgH/tPUg0Xuu77t07g2Flz3zlGv09K23+AjWBg8C/wI\noQXI/AzvR0GR1Pmkna75YvqVwkJfFXsuL/ubeWAr3teJ15SclBEU0s9+zkXhhE7fj7wL6b/g\nosiiXajkog0dSOZ38PCd1k1cuOiyanUqVlJ/P+rHwfIy8renDBlOfjcwOB0NSn/4Hqt6UYY2\n5NnY5Mqo9sejfck988fV/2UMmI6TQVINgNNhTTBIxfi5oe4OTWkbHhjT1EON3D+Je9rQ0upD\nB4Mr6OQs6pYKvulmPZDo36PSheTXTcqXKSgvvDyAAje0rPID6fpGKnt4dj5eUPDMolxr95YF\n5aUufT8Pu1l1LRVdP8W0KYXa4maZ1nlcaHs5OpWH/XjIqjuoeE6qsofM11LX1crah31lle/o\nu4bGkim2/uN+1tQxc+yyyjmTXhs/cd2cdVVuf64B10JWuQZdi6FfyDS2ruK5clPf3diSVcY0\nY5u6Aozha3uR0kDyWT8YohljtLE6q9wj3CtC+vO6uEjSGUld93sXlJdz6Zx378uqr6m4Yary\n/uS1aZ5UmdmVwfI2XmSQZwjPElk1joqrF6l8GGXx443r1B/H1Y1wdUNufFw4Nsk3lQzhgV2a\neqjEfX2jj3om9w8ifTfJp5NruHBtbQvFPuK1talY5r5gX42xEPf/lnLTz1WZB16gur/grASd\noCMYBKaHZ6HWeogOJwI3Dxdad9CuNSH9Sw6XrSZtdDNaOLFgKOkmsCh4T/lLuO/hhjAbtLb8\nCH4VDgUP88PAQ/uBMAj8gPK6A/g+ucrzgAcUf/3aGaaGuZP0CNLf4TVoTWnDl+A8bQuub238\nJ6jm/Bgy/smW+/sITbvReSBrD9rnvNwCvoFayrU7FcwOS4KHuKY2ax6pubRzGpgVPOBpc2vE\nbbptVNrpnPPX9x1hMnC861Huh847D8nGynq1E9MaNEeSupd3BmO462cBMM7XkzwIu3c6F9zb\np4OzQT08PmmVv+mz5BRY4KH9V3BPnBKMRZMn16NJ3dfrSfpuflgPHHN/CF0QNoDnwBjRA1pj\nPmib/tsIQvOS2RiMqdpkfO0Gaen7pux1X7sG5iuB/b4Of0tN+rd86+q+tAe4HWAQuImp/8JP\ncIwXNdZb9Oei2RF6g4FLe9S+45NW/3srFmwO2nkPGFB7wVA4CNSq8B085gXyWd/nfS9aSbvR\nr8HKw7q/EC0HbgLfwxhQjvu4hlz+p1wPnEaF48FfI11LMW9vIJ/+Twe4bBX5K+Pl4LycBMLG\nd8h/AK2trzDgblgfvoWwj+z/O8Q/NVQ7+vJjVxxHx+9gqDf5Mb5XQth5cr0ZiT3TgrFRtNOP\n9Yuh3jQ9Bh2doJ2fwVVQb2qLQc7N7aEnOD9HgHuR+gG+geu9qCO5jq+DzyG9vt0fB0NraWI6\nPhM2gV6gLgX/NfIKMF66VzonnMP1plcwyP2nP/hB78en+7w+vgD+CV+AZ5da6w06NCbdBg+A\nMcs5q8/PgyfBj7i7wPnxETi3VwOfa0ruG+5huQo8oINzVeaBSanuIiqUZQaF1tBTSadpuwxO\nm5YwptbzwCC5FbiI/bhwcX4Cc8KW4MfTDPAs+EvJEuCG9RDEBka25vIXxgXhTtCeD6ENTAe+\nk7wO90I3UOkxGF+S/y3lAX/NKqbJihW2Qtkt9OlBKv1x5JjPA/7yWKharyv79+OyUNo4c2Fh\nC1/bp4r0N/ITwloYh53faXidKXwX/vwF+/RpvSrs1J/1aKf7oT59GdYB4/jkEDI/Fbi/15NK\n2dNYrGnsXrXeTX+692nfxbAIeJA3dsfc1Q7vF/svQmphI103qiO561z4FNJnN+ey5w5/pIjy\nWtt7LH2vCa57f5h1r3wUPDt5rU/fgrvBj721YQN4EYopxqTYvbws8UCtB/mv6PgTeSmDgBPS\nQ70H/yHQFkaDv564sGolf7ndH16F9uDCEX/J2xDmg1VgBTgb/FXETcyPqqVBRRAYf9Uyf13g\nN8JO8BV48AxbzyWvvQvBMBgMPtsBxoIfVdfCTFBrjaLDo+FJmB6+ha7gZnAALAKfwEPwDejb\nAbAA5GrcA3257Xx0zN2U/CVsBGwK+jfUWnHrLAxwo1wd5gDXl2WO8QmwIsT6G0n+V3AD2wJq\nIQ9524F9jwF96Nr5Epyb+s0fG1YDY1VL6ica99fWJ0D/TAlu1l2gMdUi9qT7d40+BPpLTQNX\nNeSy/2mJdzBGngpHwNcwCxhnHNdKVO21pJ13g7Yp18kFDbnq/KmWvX60uTfrv23AtbMqzAj+\n4PUfcK1HPNqSfD3oNIxwzjr+s4M2us6WBn+oWQxc3/4gYkz6GH6Bl8DyYqqWT10324I2uv71\nq/1OBdo6LQyHg0Dp88NhKBgrR8NhMBG0xBqi2Sb1IE94dlM9wTOG86E3+B7Oi+GgvSPhQFC1\nsHcA/XQE+/Yct3KSnkzaCW4A49es0B0egLQW5OJecP6MA9vT1+uDa7YYd1DeAf6WmvRv+dbV\nfeklac4DyBkwPzjhPJgoP5w2BSfmMvA7hFxQq4OTvFIZaFwkH8IH4Lj6ITEaDKCWPQ8bwcug\nXWGnG9l7YAB7Cvz11EU2BE6At2BPWBgqlYFnA5gTpocn4BnYGp4G32Ez8JD5ObgwPwIDq9Jm\nfah/N4Fe0A16wB6wFEwOlQZ821gBXoIfIK0uXCwKh8D3oK/1kfJdvO7qBdJOP/S053FYBXZI\n0qlJaxFU6abV5fgtB/rTjbNQBvs1wbmq72cA5Zz8DRz3V2CqBOev89sD9w0wHVhfP1ciD0fL\nwwvwc9KQc3FecD6uBs6//qCt5n2ntuCceBTceJwDn4DzewRcD4tBpyT9irQSaefG8Cm49l2v\nY8G+9cFsSap9nUF/We7z+jbi03fkz4JrYV+YG3zPj8F49SNklXPbeGNfjp9pF3gajJPavx6o\n+6EL7Aba9yp4UBoApeSY2L4HQ+3NKv2yCoSNtrM52OZR4BpX+nknWBtiDlxDPnxp/YMSnLfv\nwrHwDhjznB+WZZX9HA76Nfp0PZwMUyYpyR/Sni3A/cf5+TBcCs4RtSScDcvCt/AAvAfzwXOQ\nVfopxjV8ujtltrsW/ASFmpmCA2Fh+Ai0M22Dc0JbfRfn8nBwDNzXqqElaMS1YR8LgHMirS5c\n3ACOr+P/JTSlzjzgvJqpqQebcX91npkGnD/66hG4AxxD45R59Th43/khv4BjamzQb66zK8C5\nbRvatycYT5wX/aES6Tv76pSgH+3fcZoO7PdR6A0PwsrgfNFW4/zncBIcC1OAY3IRGKP2gJVg\nFqhUC9PAcHg/1ZDjtDl8AYuDfZ8Gy4K2GZPUZuOThnjhvDwdfM73eAOct7OD71upVqSB5eB5\n0Le2OQ+MBn2nba6xp0AfrgPOhe9B+WxHcO85EGYFn4t1uWpy/RWp41RMv1L4Q7Ebednf2wNb\n8fofFbigD9dPwltwGTj5HoPfwYkXeC0uLBec6boQmoPMa2BQ+hCs5wLLol2oZPvpviOfTsMm\ny8LewjKvfwED2YXg4vB6INye5EkyaT9q+b7pvtO2pG0tzIedpr6rdonXw5LU/NgkHwGCy7LV\nlxoGBNszcGwDanp4CAptK3Zt3W8h2vmZvHbG+zsn9GtzNloeKyo3kgFF71S30Dk/uIIm3dx8\nf33ippNeB1ty/XVyL/zoc5E3jetIo8w5qg8t159D4R3IqkuoGHNKm9zsXJvaHvb8SD5tR9hi\nWRDPOv7PgmP+ZnL/JtJnwA+wrPJjJvqINPrW7rAp7sW1zzgfXT/DktQy14rvqE0PgGU+41gN\ngqx6nYppG8IO234PvoPL4WawzH61YyBcBY7tclBMe1DoWOlXY/TdkFW+Z9pO7Qhb9ecx4AHE\ncrFP7dOXjkXoeDKO+SGwGpwO8V7GevtJP89lWbLttJ2RD7+1S1rrQGqsjrk8gvw1YKx5BNxn\neoD+1/drwa1gO77vZ3AJZJXzPWwrTPWHaz4tD26Otc8av91btd39V00KL4HlHv600+d8xtiS\nVcY0Y1t70Ee2K2Fz5E1PBMdP+1+ESaAx+SHlO30MjpuxOqvcg9I2hV1RZhplpkGUF1477h7i\no3wk+YfB2Obel1W2a5tpu4rlo994Vp/GXI0yU9sbA85J/SgXwiewH2SV4xL93Ed+OrgsVVbM\n5iizXtSNvKnvEHPS61vAPWkXyCLXqH1GX9F/pKXKfbeTwXcylkcbPl9YJ8pMz4VcRTzgF36u\n5nnAYOxEMrhcAYvCK7AcKCejC8UJF5qIjGVqw/FJw98r+fsTdITNGkoq+2Of2uSmrWIhpdPx\nd8bbY2BI3zNAhZ2jyXvQnA9sz0C1KlwM1qlE+kNFO+k0bU8676JPyw3YYP4waHfn5PoE0mng\nNIh2yWaSm7FtnQlXw5JwDfSEtG2FeW7/0bdjMid8Awa8OWAScBOaAQ6E8DnZv7QG8nZ+YOpD\nD2Pd4VC4HqaGQfAEhPTr92Ca1g1ceODQbzMnN0aQ2sZV4AG7Et1O5fPAQ1MPmAU8gH8O60Nb\nUNrlHEzb53oW5di/DQvBPeBa8tmtkutK7RxLO65L27Qv866FKSHkPddv2saXufaQ7rw8Flxb\n1nd+9oFe4Px0nKxXuPYoarac62GfadgxEXnHfzk4AnqBZY7pOeC4Lgx3wlFQqLkoOB/2hwXA\nj07fPavsO+wLG00tt90TQL8ad7THueb6XRG2gNVhCjgM9oUzYADoQ9swnvo+/aFSpe2MvG1O\nBnuDdj4G+te+jwHnr3FreVgMeoNrz7mg/V/CJmCZMe9FqETOp7AtnUabrvmOycXOpPrLQ+/a\ncDp0hYfhImgHrjvXou+yAWwIM8E3UKkmpoGHoEOqIW0ORV7f7gX6dEFYD0ppBW74Tq6nWcHz\nQiUKH45INRJlxpuwMcrWpGzT1LO/kDf+OJd9pg2cDb+BOgCcw+95UYGif9uNfLE0utAe54pj\nr1+V1+L1a+Dc/Bxmhl7gOPh8JdKmdWEJmBMGwS5wP7wC7i/F7LZMGfdjjjcU8Md5dBPo689g\ncvgKKpU/0BWzxXaLlbsP7AdPQCdwHoRvyf6pjte5mvCAA5urcQ9Mw+2usD9sBgfDWXAJTA/h\nQyei+fSE5LLhGVMDk5oRDEi2U+lip4kGuZB6gQvT/gttoKhBltunG6b525N0UlJRbkrHQi+Y\nCqYFN+BqyD49HA4DF3jaTvMB2QZ5HXZZ4KZoADMQvA361EDrAeUCUM+BbVci27Sfk+FRcOzX\ngfaQVqH93osybfRA/IyFSFu9F3Ya/O2nXqVPj6+ScW4cX4Nz3o24D5wIjpMbyQfgB2OMm36y\n//Al2Yb6XUhvAueE91xvI+AHmBOiPtlMcp57yN0MnKevwFwwHDYAFTa5TkKWOb6uv9jgR5Mf\nCsYI74+BSu2jiQZNzV/9Mwpcz/rjZiiU9qTluwxMCi4m9X2nBO1zrjonT4cLwUNJJfY6Jir8\nFXmvZSTsAc4N+3kUtGc16Aba4sG4UB5OR4A2VkMeeErJsXPe6t+H4R/g4UrUc7AyeBjR149A\nyIP9cPCQXA1FrNB3hdJ/O8L24HjeAm/CKbAGWLYS3APaq1/D1g3JPwtngnPJuVWJPCCnlbb3\nHW64Zg9KHjiO9Ds4Hh4A7e0LS8N0sDDMC9ZbEbT5bvBj1bhRqZakgQUgbIxUf4Yscx4sBb/D\nh1BsXlLcINfRAOjXcFX5H/t3XOZImvI67GyblJlYpm+fBu1VI6ANOBddb0OS61VIP4OhsAGo\nxtbB+Cca/xv96x/ldRDXpqFJyGiD7xDPhd/1s2eUf0Pn5L7nkGrJtfQS7A7Osc/hZFgU2kMp\naWc7uBPMh72m88Nt4Mf7fFANzZg04ny3P2Vf4a+GguRP2HY/19qyB7iGvoF4PtI3KNMHXquu\nsHEJ1qPc+Pe3lAsqV2kPuCjdIB8HA8h9oNxEzgIPUTHJYrFQ9KfJ52JXUdfNSrkoq6WpaMhJ\nri1pO2w/FoV573kgcdxNVwaljV6rYeCCDBnE0tdRniW1fwPMnEllr9P2uWjTivtx2PIQ4qI3\nCK2dPGh9ZXlL6H0a7ZBqOPpLFf2R1V7lM47JYrAMhCyvZqCPdlsidU582gIN+zG0ODgPnIej\noTs8A2nfmg9/km1Yc87DOSDmqs/4keC8cC1WKsfmJ7gLfP/vwTl5K3hAUmFT2lbLvbauhxnz\n1vOw4Lq0ztlQLUXfr9PgEHAT1Df6M614zjLzX8ASXqBZxyd//JI8luvw6/TkI24lj5WdFKsf\nvjPVPz3gWdBux9XDsv57C5wjI6BQHqyqFY9s27bSfrIsrk2HwmQQ8cXDhTFR2y3TZsdZ34Vv\nyTYcKjz8FXsH75cr29ee8KH1w0597Zhp0wvwJTiXve/csCxtrzYtCcrDjz7oDDODc74S+YEU\ndtmO9sa14+x1R5gCOoHzLnxLtuEjKK717XAwPvh8zE/bmQYqVU8acGzDPtvzwyvtY8vU/mC/\nM8JwKCXjfjX3dvt0XJz3obAv7I7UcteVMVF1AO91Ae2aC7x2jJwzzpFqHX5t1/71T9hHtkFx\nL8q9Fn8sc6zT42r5AFDG9ag7oqGkun/eT5pzPeurb0F/F1PY4T3PfyqeNe67ztqDz42Easlx\nco4q207bEdemynNS+HIIeeOA6yak/8XYb7uh1cn8pxFcf39LxQD/LV++GS/tZrEsuME40dYA\n5UboYnCSxaIn+0c+go4T1wnrobA/qBEJezZcVeePwe9JiMWRbnUwF2kbO3HtwtFGN1XlPIhn\nXiR/EgwHA+lYiA2LbEXSH/ZjYIpFHf2augnENdk/gkEEiDaUabfPGdB8D/PD4XwwcHkQiLbJ\nViTHeC3Qtx7A0+Md70Jxg9J2m7fuDhAfRAYvN98LwXviu1RDG9OI778OFFvTXSk/Fs6G1SDU\nVD39XU25ea4EcySNfk06E6wMF0D4sDDlVsMYz0W6JoyEeGZx8h5I3BAqlQdFN+WFoR1or5oT\nPmvIjf/zLskRqWuz2qMNbj6umw3Bjcd39dpNJvxZbIy43Wy5zm2jFxiLXMezgXMsZJ/KZ5X2\ndYO94V54Fn4F57Vr/AnQ1n3hNKjUnzE+9uFakbR24mIE6Jc3oAd8DIvBQrAonAmFepACn92s\n8EbGa99fWyVsjGvvzQsDwcP0btAFnBfLwjxwG3g4ugwugi3AdzImzwYPQDUUdkZb2ho+Nga+\nAvpzQbgPZoYToB3oz+lgbbgJzgH3sjPgbVgeBsDzUOnh3vEOu7Qx7LRsblCPwA/wKfivQ4eA\ndittsY03QdvuAG1aBlaHbeFKcD+uRPrM8XPuh72mthvxRduV5eY/Audofyilx7ixDnQq9UDG\n8kHU0w4JaUus8yhzbF0fv4Pv6PN7gXPW/FfQD2YH44ZrfgHoAJUo7HKvjjG3Pct/Ssosj3uO\n/wrgGasNKO8pf4yWXSHm0yLkq61NaVD/dYNh4JpVYeeoJG9ZvJ/5VSHew+uX4HHYAHzudKiG\n3qMR+3EslW3HeJsPosw1ri+93hwegpjfPhuamUxbiLJLyFu3GD7rGs2Ve+BPHtiKKwOictJ9\nCwYkNz03SyeXE3EcOIHFsnTe6wHQEdJyo3LDGwjXgs9FkCBblnbhafv8Agw6hXaEPZEarMJO\nUwNQ3DNY+U6Wm3cDHQy2Oxp+gazaj4rpvqLPptKwJ2xOP+/GeUvSrvZG++azqi8VP4B94DUY\nCgaOY8H3T/efzhfaF9em/WBd0PeOu+1oq5tVVp1ExWdhRbgT/FA4H06GQt1HwdawLLwNHaCp\ner7zMOgDQyGrzqLi63AgDIFXwbX0bnKtD11XT0P4zLI0UV6YxtqzfdutJJBfQn03Om1xc/aA\n9h1Y5nhdB8Vssky7nKdxP65NH4b1wbGWL+FFyCrjhesx3Z/+jOtCH8V12BZz2HLztvc86MtP\nwec88DtWgyCr3qJi9JlOXQOHg+vgE9BucQxdE9pluiuU0rHcsE19q513Q1Y53rYVforUsjHJ\nPdfVKaBd+knbfY/eEPJgeB5YbhtfwyPguz0Aw0FfZ5VjUsxO29ee7jADOIZ3wWHgPNYe7fa5\nIyHknBwB8b7e158eCl0LWWV/2lkKfdomaVwbnbuvgP3HWOi7eSHUlcxjoK2iL3xPY0tWDafi\nIGjM3vBN9OvzXaAx+W6PgnuT82Y4GKuzSv/Yvz/QlPJp4bzQl2G7Y2/cies3yN8JETtNvWed\nvpBVxspC+6LPtH2Rj/7Tz2iDe3c8Y3oDnJ2UDSPVH54lssqY57xxbMyfAvpI+10P9tkYYa+2\nxnNpf1s2FGzPs1kWOYfsx3eNPryOvqOsMPXD3ne5OEkvIj0VCp+L62jvXJ7JVcQDkxYpy4v+\n1wMuJCfdvvAhRFAx8FwGBvqJQU0ETjwXyBlwAhTqfgqWBttbqPBmxmsPtMq+tUFCsRC8NkDO\nCn78uZi6g5vEu+Dm6CK7F14D3/Mo8INuVdgdKpEb+eRJA+GnQjuj3MfMG3zGwt1wG8wF08Bz\n8BgYUNeEjaALqJ7jk8x/9eWBMBCOBcf5RNAf/wD9qbSvmLTtTXCuXAeDQS0IfWBxmAI6QyWa\ngcoG03nAto4B52qhnGfOT5/T9gXA55qqxyNV0Ry0sgfcA8eDc+1K2AS6wcwJJA1K+1V71Sfg\n+xrc3wMPm8NgDNjO8hBrkGwmzUktDza2Mz/oo0XAeet6LZR2hn3OUQ8x1p0MHP9/wuOgfM91\nYHOYESqRYx7ShilBv9incyyU9qNly4Dvoy/fAefnd6DNq0N3aAsdYSUwPmSV7Ub/4SPXj+/u\nvethDdAvm0LY/Tz57eB9KKUTufEYOO6zgDEiq4x9YadthK0e1o2DT8JJ0An08VtwJdwGzr2Q\n7+bBrS84n73n/FkNNgTrVyLbKrRTW9+ErSD8ZX//AeeeGg5XwzUwBEJ3k7kHHOtvwfm9fnJN\nklmORdgZvjTVfsdssyRP8v/+Bc632D+Nux7etwfjemgomZVgJnANOY99z0rknH8dPCz3TzVU\naLs+2gucJ8aGpuR7rgl7gvN7UqhExh7X94ygH7UvUrIN8lo5T13HrqnhcB+4RjrAC3BLkm9H\nug28BPpxatgWKpE2fAfaqsLOyDcUJn/0pevLuaINw2BVmB0sc7yfgosg1tjl5FeEQ6FS+a6u\nBdObwTlnPOoCoZgHcR0+9h2/hsngRXAvcl76Ps4l88vDUVCpwpfRd6S2G/41/T3pSN8Za+6F\nAbA/dID7YV7oAqGoH9d5mnugLA+46XyUquEiclNcFgzSBncn5vfgxDTvwpFrk3Q20qbUkwec\n+G2aerDE/V0o93D2KWjDcLA9824A+8DhoI1eZ5WB10NAVnl4eAsMMDeA9uwGbuTaKr+AG8IP\nMAwGQrkyQDgGWdWXis+UqOzGog/0t5tmjLt2h/2jyRs8m9I2PBDBv6lni913M9RHag24HIbC\n7pDWJFw8Bs7Jg0CfrgWqsXoeVmy/DwyGrCo8gNjOOTAC5obFYAt4GvTjr0n6Oam+tkzaQmM6\ngJuvNPZAE/cu4f71yTMenjwwrg2zJGUmX4Lj7Lh/AW70MQf8eGuOjuKhJ5vzYIlnHMfLUve6\nktdWN9NZIezTd9qrPy37BsrRqTz8YDkVCp51k461HD46peCZ9OUcXMyULmhm3rl0RzOfLfbY\nExQ6v4wZ+knOgLQ8THSCadOFZeYdM8cuq16lomP6FYSdOzfSmHNBypVrwLWQVe9TUTuNkWHn\njuQbk2u7Cxhfm6v+PGhsyarhVHSOqn+DtsY81X5x7fhhUokGUNlYnVXuEZ+BtrmetUtbf4JH\nwXg/FNzzO0JWPUPFvlkrU8/14967AcS4++Gt3T+Ca+y5JD2WNKs8w+yXtTL19N/qjdQ/mnsR\nM7Xb/AcQfl+AfHM0hId2ac6DRZ5pQ5njeTH0gINBn+pLfWr+YfgI/MDrDNq5CjRHxjPninu8\nbfmezq1ieK704+pvqUn/lm/d/Jd2oi4N28G6sAI8C+p+mB52AjeDqWEaUD1hHXAC10JObBeU\nh6TJk7wTf0E4HwxO6sDxSav9dYFvDW7C5i8AD/D9YEmYH2JOvkdev9eT3JT8MD4U9Kt+nwHC\n5jfJbw4G01rIw9KOMBJ2BQPZf8ADzuzgId4PEO3Tl9p6BKgdoVg957Rjk1a8X7qskvxRVHas\n34ah0AFGwbFwIkwM2uqcdu6eCT9DreTacTMv1J4U3ABuMPpJaeMdoK9bQ/pPlD8+3AabgGPm\nBqh9yjhVSzmGkyUdaoMx8rTkulgyplhhDcr0kxi7tdOD3tGQluWuldaUvnQ/8iNNe0bD1VBK\nH5e60cLlYae2aqfr+pom+nRtD2/imWrfNsatAUdCP1gVuoFrW9uNO9vC59Dact3sDzODa9w5\noI0rJbgv3QT6urV1FwYcByfAFGAsbQv61T3+TjgF6lXnYdjuoO3GT+3uCs7l48E9vlbalY72\nAPu+G5aAmZLrVUhfBeP9ffA8DILmyPZirjg+A+A/JSr+SvmQEvdKFS/HjS1Bv00O7vOvwxPw\nLkwwcmPIVdoD7bn1LLwH6yd5kgY5yXaBC8APogWhOzihrgfr1UoGe3/Z8KPMg/E74MLaCBxj\ng+p+MBBaWy50F44L6GDoBDvBCFgL/Oj4FEZCPeoYjHJD9yNpRnAjfQuOh/5QS31NZ84z59sH\noD3ngnoEdoYXwQ3KQ7wf0A+CB/3DoVi9UynX9xdDyCBaTX1PY6tDL3DejgaD/DhwQzoK3KC8\nvhSOhnqQhxA3zTPA2OC6uwW2h3qRhzo3u61gEvgBXGdupLWUcdAYqYxHxs9vvagzuZaVtr4B\n64EHznpT2KSdL8AGoI/rTWk7jU0bguuk3qRNxr99wAP7WHgaZoAfwTh6M9SDPsKIuWFdmC1h\nL9JpwTngR8mOUC86CUNuBz82lgfPIM6LR8EPvXqcD5jVoG/4uzJckqSuN/er06DWH3bGIs9D\n7sefwY7gnj4TTAOLwTVgbN8bIt6SLUtDedrzQTXkXnMCvASDYRRMDcars+AAuAImCDlxc5X2\ngAfQecG0lF7hxhFg8DKgesDzcOlhf2uolQxAtyed7UC6CQwAF9eqcGJy/TFpa+srDNgQDPD9\nwAXktYeofaGe5cfCVeCvTPryAZgfboUtIcaAbItLW94FA2UXMBjF5tODfGgJMh3BDxGDqId8\nx6BYvT0oD81Jpg/YT0voURqV0AJkDoEP4FkJBSgwAABAAElEQVTwele4AwZBa2sWDDgMPoVH\noANsAc4B50Q9aDWM2Bw87Dk3/DXvJNDe96GWepvO3oKNYf8EkrqS6+F10DfGIA8afaHe5Lp+\nEVzDG4Dr8mSoN2mna9c1sh70hjOgHvUkRu0I08MV4D55C0wK/4aVYUeoB/2EER6EZwfX9pdw\nK3SBTeEuuA7qRa5748598AI8ByvCa7AS+GNEvWoYhv0C38Cd0AaOhTnAD9Na6Vs60nfKH+dc\nT87PIWCM96yxDfwM9aB2GOE5c3Fw7ynUZhS4T14L9WJzoY1/ujYQ5GrcA419HFnTQ70b1tLg\n4lfnwTOwDhggaqkp6ex8OBL+mXQ8Ban2nAAe7FtbO2DAUrAoDE2MWYX0YbgaIigkt+ou0a8j\nYGWIhX4qeTfVu8HgWmsNb6LDUan7fhyFhkemDtJzsMF56nryoKUuhUtgbi9aWa6f72BZ+D6x\n5TBS15ub1bdJWWslE9OxvvIHmgMSI9zcH4QzQb/WSr/SkT/QaIcHT9f2NfAi1JPGYcy9YLxc\nF1y/buBvQj3pZ4y5H06BLcHD8PUQ8ZNsXUg7Pcy5lv04ugxugDFQj/ovRrn3rAFLwNugLoQn\nwf3oEagX+dFh/O4JPyZGHUWqvXfAD0lZPSTuh87RXRNjJiG9EzwfuXfWq7bFMP27GPgxoi4F\n54ExzD2qlvLDaCNYGl5NOj6X1B8iVgPjV1ZNRMXl4F8lGjCOnw7pM0OJR/9fF258CcU+jqxz\nKzhPe0CclcnWr9xQc1XmAYPr45Ae8Je5Nrh6r9YyyPuRdEGqY4Omv5C1hj0pM/7Iasc9kN7c\nDT5vQr3YiCklZXD3IOphIGTAmgkWiII8LcsDE/N0LzCAxscR2YbNtDtpRy9aWc5N19H3KTtc\nZ+1hiVRZa2X1UwdwLoZ+IXMxtOa6Gkj/b7WyDXTfpO7lCWNSPR/efAk/QD6HFb2oY12Fbe49\nK9SxjZrm2vBjPj6OLHsuoTXXjXYUSnsug/g48r4fHNOAB/p60ewYMg9oW+g3MsZ3560fS/Uq\nfXwfxMeRdj4GnvFaIzZoz+MQH0dkG/4HiZ4g9V6lmp4G5m0E97fm6D0emgj6wMRFKuxMmW29\nU+ReXRZNWpdWTVhGuQFMC0fAeonp/gppmfdqLft0cm4OW8DM8BL463Zr2EO3f5Kbpb/OuCiv\ng9PgDXBhGeTrwUbMKKk23HHdHAu7w/3gr/OOt6p3+8dbWX9/f8ckN/1FYAfwsD8UHgZVD37V\nhs7gAWVR+ARuBNdbvdiHKQ3/913+63VH8MPkffgeail9sgV4aPNf16aGevARZvxJHtT8lw5j\n0q0wFdTaV3TZpIw5e8CqcAt40KhHf2rngbAuuDbaQT3aiVl/yPF2PzoG1gYP8v2htfZwui4p\nbTUG+S8ari1jkPNW1ZOfjeX+69zycBx0AQ/Qr8JPYLyvV+nHOeAEWBPGgTGstc4n2mPfaRmn\n/KjpAf44p33+WOfcLUeOkefV/cupVOJZ29oWroJT4C34BmaFbmAs2BT05wQhN7FclXngNqo7\nQY+EETASjgIPUN6rtV6iQ39d/A84Yd+GXuCm9RC0ptzUB8FocIEvAM/DSnAlzAYDoV41EYbd\nCZPDTGCw3xtehmvgNXATyJXNA09Q7WToAm+C88H/TEM/fwGtLX9ldiNx03ddGT/dDD6DF6G1\nNQoD/BjSZ64vfbggHA+uu1pK34yFD+Ef4IGjP9SbJsGgr+BTOBM8KN8L9SY/PIzrX8K54A81\nrR3PMeF/pF368ju4BDywPQL1LA+XPeFgGA5j4ASYD26DepIx6BBYAYxB7klXgD43TtaLXFPu\nhxeAe6WxqDsYCx6F/0K96g4MWxX2gQ/gI9DuLuD+X2vdQoeLw55Jx9ORDgH3xwfheTgRboDW\n1mMY4LluO3DtaKc/Ih8As4P2TjAy6OaqzANzU/1X8PC/Vaopy7xnkKilpqYzcWNaB/ylxiD6\nEzhBW1MGytPhSDgGjoefIQ5v2vwM7A7++lhvWheDDJxLwz9hN9C3M4P6EPxV72UvcpXtgU7U\ncJ7q3yXBQ/Yv4KG1HtQZI7RvHnBta59zdiqYAr6F1pTxfEbQZ6uAa18bW2Pt++Ghn0Rpi/lR\nXtSRJsOW+RLi0NaV60/qyEZNaQvzw8KgnY51F6inQzHmNPxPu3uYE8fcH5NmBQ9K9SoP7q4Z\n982tUka6tl3n76XKWjs7Gwa4Z7qWIgZ51pgGjENjoR7kvjgL6NcVYDmIWOR8qGfNhXHjwA+R\nrVOGOp+7wfBUWS2yniH3hfPgBJgB9K/2bAZ94Dp4AVaGOE+RbZZ8T/8lqpicW+WuXe0zrl4O\nzsuDQT92gLNA304QMsjmqswDa1HdhX8JvJU0tSDpLrAm3JKU1SpZlI7awLPgojHvZD0JVofW\nlPPt6sQA7XEhD0yuDfaj4HC4BvTlG1BP6okxz4MBy4WuPx3fOeF98BBzN3SHHyBX8z3Qnkfn\nhy/AD2jlAeUg6AJ+XH8GralV6XwScI4a5LVvO/CDzoPrk9CacmN3s3Mung/GJTkOloVaSv88\nBq4P+/bguRoMgHrSjxijTf4yvCJ44HCcn4F60vcYc1+C+8o6oL319oGknXfAI7AurAEejodA\nvUpfujddCIPBD1D30d6g/cb0epFryDXdF34GY9D2sBQYgx6HetAcGDEbDIezQZvV0aBv9fev\nUI9yPnhuco8fAX6ILANbg3v+QKi1LqLDl8CY6j7o+W5LOAKuBX36OiwLnquaK8fF+SOl5Lyy\n7eboNB5yz/4GvoKh0APuB8sXgm1ggpCTNFdlHuhE9V8g/vnT1iYCg2tnL2osf0G2fxeOiym0\nERknZ2vqv3TeAd5NjFic9FtwkcYGeiJ5A5QL9jCoJ32JMbOD/t0JbgcPfs/BaDgJPoW1wHu5\nmu+Bn3jU+aHfzklVm578cRAbbOpWzbN+AL8A/0r17IHqQfADrrXlvFTngoe9kJvmJnFRo9Sx\nfA3c2C+HraEz1Jt+wyAPHtrZDzaHOaHe5GHSjzbtvBH8z+3q0Z/a+QQ4//zoHAXukfWsjhjn\nh/K+KSPj4NglVVYP2XYYYQw6PWVMxCBjZb3IjwpjgGv/vJRRC5J373Se1Kv8uPNcckjKwMvI\nG8O6pMpqne1Jhx/BvTAf/AzHg+elHWBW8IxSjjyvHAeu1WJynL4odqNI2SSU9YGl4T24BLaA\nacH1dQa8DwdCvf0LPSb9r/IPpP/1SbpkCi4c8Mbkocl/ofFXJieuWh/agMGsqfrdeKZSTU0D\n0U8P8gYmJ+el4EJ3MS0JTtJ4jmxZso1K5WHkKnCTd2FuCtPAo5C2y4W2bEEZl83SIs16qvRD\nE3FrZkjbE09rl5vpA0nqR5EH+sXhftgGPOhvCDNAYzKIVCoDeTE7K203XX+F9EXGvAe5puzU\n78qA+iGMAQO+hxbv7QiNBX/nS6WaiwYas9NN33E7E94F5+7u4HpbDvxxoikt0dQDzbjvGi9m\np/NWHQlTwRcwJ6wL+rBYHYqLqhrryP8W3R+ONgP711/l2MDjTWpBnvimyadKP+Ca9l33hi2T\nx1y71bbTMRuRtJ8l0U7nzj6wLTjnZoNq2+kaeAWySjtdi66V3qCdXaHadnamzQ+gEkVsa0Mj\n7vV3gLHdj6MNwXdpD5XYboyuVKvQgHYo/WkMOgveAdfUbqCMQTM15Mr/E7Gj/Jr/V2MNstHO\ndORd8/snt93vO8HmybVx0zlSrir9CNSmdaBLIx17LvZMdRMMBOdBuTHM+lmljWoDmL8hN/7P\naiS/wFhYCW6ER0D7tgN94zwuZ75OzvO2V40Plvlo5zd4DRzbG6A7/AhqCHwErt1q9EczLasY\niJbtZcJsfSnMvhoMlo3J+22hcLFb/nORcor+R/4aaHAz+JWrFangh1DaTu3x2vbEcRZ/DXCB\nZdVoKq6SsfKa1POXpLRt2ig/QPrdfUZbXWxZ9D6V1s1SkTobwT+h1NrQXjdUg6j5caBPHX/r\naLvjnn4fLovqdUoNvFm0NZVOyFIxQ51nqbNDhnpW2Rn6NrPuZDynX/Vlet56rU+b0iAe2L2p\nh0rc35vy2MxLPNIw7o59Vvui3fvIHBAXZaaH8HwciIpVdf7F3E370LXkXC1Ht/HwEeVUSD17\nHHnHXoU99q/vqq1raPCkjI2eRj3XUtho+hPou2rLOO2vqFl0PpU8MNXCznPp58IsRlLnclgd\namGnY/cfyCLnjB8aygOmsSc9N7VfjDvpci7LlmvhhrJrja9wK8lCqbrGn2rEoFSTDVnn++Fw\nZ+GNZl7fy3PdC541FrlH2raET7PEIqo3yHb2gwfHX5b99xFqdGiiVqn5YLXmxjDnjHH6cSuV\nKf30FMxYUE9fOvbaYN69Ujl3PS9lmavauT28AJVqShrwY2sruAXagz82jQS1EjwKc8CHkCv3\nQO6B3AO5B3IP5B7IPZB7IPdA7oHcA39pDxzN2/kD01UFb/kPrj+Cm/4/e+cBb0Vx/u+/SBPF\n3sACqNh7iYqCWNDYsCtiQxE1iSIxRrEX1BhjEGyREBUNYkXEjiBiQY29Il26HbtSLP/nuezk\ntzk5995z9p577yHO9/N57s7Ozuy++84778weE82pj6fRA9ED0QPRA9ED0QPRA9ED0QPRA9ED\n/9Me8J8k7p3zhv4vH3YC/+lYVPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0\nQPRA9ED0QPRA9ED0wC/ZA/5/+R9fXB3g/9ErKnogeiB6IHogeiB6IHogeiB6IHogeqBUHvDf\nsrhNqW5W1/eJH0h17fH4vOiB6IHogeiB6IHogeiB6IHogeiBsvWA/zrDqOiB6IHogeiB6IHo\ngeiB6IHogeiB6IFSecB/pftsKMW/RrxUNsX7RA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9E\nD0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9E\nD0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9E\nD0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QP1J4Hlqi9Wy/2\nd16TN+gFdfEf0/2U51yR0WNt6Hcq1MVYzuE5V2e0cwP6nVRHdk7jOddCFm1KpxOydMzQZyJ9\nbsrQzy5bw9EZ+xbb7R063FJsp6T9DhwPz9i32G6v0WFwsZ2S9h04Hpixb7HdXqTDPcV2Strv\nwXGfjH2L7fYMHR4otlPSXhu1tS40ioc8mvFBjrljXxfSRm3NIueQc6ku5Jg79llkTjI31YWc\nQ86lLDLHm+vrQuYkc1MWnUKn9bN0LLLPz7S/Fcz1WdSTTq2zdCyyj3YOANfOLDqTTi2zdCyy\nj3ZeD+8X2S80P5fCyuGkFo8/ce9rwP9WUVSOB+piU53zyMXmtAuWDoIRBVi8FW0WgsmlMfwA\nBt5uMAGqCr4Vub4z2M97FKvudPBjIOvCuxd934QPIWgFCtuD9/Rd1CqwLWhnFp1Gp8tgTJbO\nqT7BTyagtmASeSF1vQXljaB5qq6YYm8anwXPFtMpp63zqhEsSOqX5dgOnoL5Sd2aHE3U2ptF\nfejk4vl8EZ1zx7UJfbVnHdCOsZCrVlQ0g/VzLxR43pd2R8JLBbbP2mxdOjp/nItZNIBO+8Kr\nVXTeiWsfwNRUm4aU/RBwA/QpOO+rkn78DHauqlEV19xstYc3qmhT1aX0/Em3Mx6dT8aocg69\nD+aHLLqfTluAOTHEWZb75PYxHiVs4N3kmr8OhiwaQac28B44lirkvEVn2f7mzqktuY055ehs\nt/t/z9FvJZgI5hbjzP8IY01lPLoGhQ+NbSg/AidDFr1OJ/3oHCmlncbjMhD+g5O/onwnnAFZ\npB+/g+lJ5405mudegXT+zjfnky4FHcz7N8EFBbX+70azqXJtnvXflwqqaUCr1rAWPA1pGZPG\n0NvQHq6CKyGLvqDTBNDWYuW4mg/Hwtepzi0obwKjUnUdKZ8P16XqiikuoLFj/EkxnXLams86\ngmvFpxDkPDIuR8Ku0BNuhmLlvNFO5/zcYjtX094fWRyr8Um7vTh2g7uS83iIHqjwQGv+uhlw\n0cqHCd4gLUTDafQYTIGfwX6D4QM4EapSOy7ax0mRRd3pNDlDRxdw39+F1ue7gTVhKj+OrFvJ\nk0SdOBbqj9AnfTRZvJWuKLJ8Ku1Natr1DfwZrgCTaloHcJJOsulrhZT9QHqhkIZ52ixJXR/w\n+drpRtgFfLvk3I/MoK4UjI+s8jnphaOQ+3SkkQviRWCS1MYv4XF4F/KpB5UT810osK4v7Zwf\nta1ePMDNWVb5gTSkms7juH5mTptunIc5NI/yQHCjVZnO44ILX1aZV3xGsTqWDnPAMf8eboCm\nEHQKhRnhhKNzy4+HrBpGR+dmOs58dzefNdEf6OzHTFA/Cj4rq3zHm+AZ0DfyPLgxq4kupLMb\nsSDHzLHLKmPmRngNtNGY0/a1oCYyj6Y3zs4B50JWOQf7w9ugneabh6AF1ETX0fnR1A3MKeaW\nrDKnmduC/kbhYfCe34G2fwTT4ALIKnO0uTqrZtPRtaJYNafD7bAAfBePB0Jad3Lyz6TCNc+1\nL6uc5wcU2fkg2k8D7RPnsXYHHUbh23CSHN1D9MypK+ZUP3QqpkOqbWfKU0FbnX8jYVkI2omC\n15YD92TdIYsa0cn7tMvSuZI+3tPYdj56b2P7JPgAukBUHg80yFP3S6may4u6wFSGv4QsWaAz\nJtBuL3Ch2QrOBZPFapBezDktCy2NFU+CmxUXcX8JcaJY1xMGgRP8K6hvbYcBQ+EacJOxOfwO\nTD7rwA6g74M2plDTTVi4V7HHS+ngh9y94Ab0AbgMtFH/umkKtjkGhcYXTUsiP4K/B+PzVrge\nHHMXjM8gqmoPuEH7LRwBF4AbwVtAn24PR8GeMAjKSW5EtPNvsCVcC0eDY78CKBf7lnCCJ4lC\nrIbzYo723RbOAefs2Qme59NKVJ4M+nVvqOzZj3NtAzgCSqWu3Gge7Axuctzs6RttKkbL0Lgb\nOM+939awP5RKx3OjiWCs7Q7NYRQ0gSzqSCff0fculT8dNz+2XwVz917QAhy3hpBVr9PRuXUT\nGFel1mPc8NdwHPSALcC6taE9/B5cbxYX3Y2h7eAwOAT0/VDoAMo84B7FeDoXVoa6VEce5jqp\nnVvBTNgD7oTO0AeuBOO7HOQcuR/0ofa+Dfp3OJwMF0NfeB6+hHJTfwxyfXJP8jG8CP7gshRE\nRQ8U7QED6ocCew2jnUE3Hz6FnxL8Ul8AXaEyOcls16iyBtXUd+f65DxtlqeuG5wFu0NaboK0\n0wV2PZid8CNH0eZvwV9rVgXVCazPqp509H5VSTu1txssB38Ffelm4zNYCG4SlAu6/nactPkJ\ncEFzzL6CrOpNxxeK6Owm4Ezwo+07mAv61gSkD2fALHBx97o+uA9MosZMVvWh46giO7t50Tf6\nS79qXxjzrykvCblyszAxt7KIcxeN4UW0z9q0Fx1fz9qZfgNgSDX9V+G645b2nWVjNsgNtvO5\nss3UeVx7LjTOcBxMn4HV9HPR6wpnw/5gLF4PzWAMfA/vgbYbq9uA+j1Y9ww45k9BVj1IR+/h\nuGyc3MQ54vxokJyHw44UnN8fwEvgfB8BTSGf9KF2at9UeAiyaiQdnZfnw6FgHm4C0+EPUKg2\npOFM8D309zcwB5xfzlPzgGOXVd7zczA/7QPO5RXB+dwFipH+N9YXwivgPfTnq2Cuci5k1Tt0\n/BiMveDP1Sg7pvtBFp1AJ239ArRTtNncklXGprktaAUKjpXrx7swFsKzjI/JsACOg2Lk2Jur\ns2o2Hbvm6bwHdeYd7XGtTGszTn6GLVKV51D2fT4B54vjoQ98Z/OmvjW2ssr+B+Tp7Fw6AoyH\nAyGsMQ9TvguCtqfgmGq3YzA/OWrv1hD0FoWe4STD0THsVGC/TWl3BpwK5qP7IGgDCp+D9upL\n0b/Pge9svHSHLGpEJ+/bLkvnPH2MbX36N7gStNVxN649doGo6IH/8sA61Bjo+biNeoOqELkQ\nOjkMtpBUb6DshL8Dvoc1IJ+cBE4GJ0UWOQmdjGn9ihMXaO3XJhkLjUFdBk9XlBb9WZGD93DC\nmCxXTXiDY0hinSibXLLKpGZyyycTyvMQbNVufeaiuCd4vje4YdGGNtAK9Jtjtzv0h+vgEjBZ\nZ1VvOr5QYOc7aRfGXRu1521oBmpNcMMRxtd4uxT+DjfDbMiqPnQcVWTn5WmvLSFGw1GbrW8F\nuepBxcTcyiLO3cQML6J91qa96GjsZpWbwiHVdHbsXLiD3zw67h9CI1ANwXpjMp/Oo9JFNKsG\n03FgFZ3bcu1T0K4wn5xHjuNVMBOcP8uAY24MTYIGoNyoXA1ulkdCVj1Ox+Afj87LbcBnrgRB\n+m0G3A7Bh76DPrVPZdqJC8aWOeqByhoVUD+WNtpnXhE3xy3AmDWHF6qXaeg7L510aMlxPDwC\n/cD7OnZZ5RwNdjqez4Jj+AqcC8XoFBp/DY6HcuzvhXkwAZwLWfU+HYOdzpXgz/coZ9nYGquO\nixtV5cehY2N8O/5ZNZGOPVKd/0HZ2HTOaL9YvhhcS9UfwXdq5UmBcn6Zq7NqNh27pjq7Vr4I\nYW7rB8dyWwjyQ8W1PFfXUuEHxw3wZ9Cvu4FyzetdUcr25wu6+dy09JPzWBuDvZMprwTj4HRI\n6yZO9PsY6AWrwH1gTC4Byj1Eljiq6Mwf37lTOKnieA3XtEXbxfJQCGpEwbHxmnlS35k7P4KL\nwPfsDlnkvY3Fdnk6H0Td6+D+yDl1AlQn5473mwPGsu8yGkaA/ugCUdED/+GBzTkzaKrCQKpO\nLtS2M9BMVPIlWOe9fc4HcCLkk5PAdk6KLHISOhmDGlLw+Sbys+FAeAi0xwS0HbiYO7GdICbI\nFUEbnPDnQNDBFOaBC2gn8B2zyqRmcsunv1OpfQ+C9mqDCdXnLQkmUxP6CmAingW+s9ebQ1om\nadtklYnOxaI6+T7a/BIcCheBPtSvx8AoeBdmgnW56kqF/s6qPnT0GcVoGxprs7Gh3dr2LVgn\nnSFXPaiYmFtZxHlf2g4von3Wpr3o6MKRVQPoWN0HkpsL/fQIHAQTwDh13NuDOhU812fG886Q\n1nmcPJeuKLI8mPYDq+gznWvaNAVmwPugzY71ZNBPal+w3ZagvVtAWldwMiJdUWT5UdobWz43\nLMrXU9aH5pOgHSn4/JVDRXI8h+PbOXX5TvtROSzfhQLrHIv58A6YT+RZcG6eDoVoLRr5Dvoy\nrWM4+TypcMwcu6wyd+pHc+HChHuTukM5FqMnaHxdTodGnDteT8OAnGvFnBp3X8EE0E7Xj2dA\nH/8aipV5dmqeTsa2uSWrnJ/mNrUHOEfEDa7PMw48vwNegyDn1+/CSQFHc3SfAtpV1sQ47Jq6\neDNl7XoADoRzQVuNs87wBEwC4/FISMs8fH9S4XvdlbpYGx9IxsCPcDVom3NA258HbfwC/Nj7\nAzQEN/DavQEEtaZg3aZJhfOgZ1LOctBXnarpqF+1czx0gd+AsWydz9fnHUC7ZBMIOo/Cm2Cu\n7R4qizw2or33bZfTz3n+A/QH/XkZOL+q8sfqXHfuabu52OMFoB8eBnOK7xiVxwPphSrP5f/p\nKgN9VWhRCf+g3mCqTk4I5QQ3eE1oHpW/nvgcg7gx1IX24iHLQjfwo8JEuj/4a8PRMBa06zNw\nwzwSzgeljQMrSov++CuF71XbceIEdeF30mvvn8CEbaLwfVxgzoDx0AxMrGuCfr4N6kMmpbmw\nI/gr192g9NUgcLPhYtoSrNsM6lshVvWrH5b3w8TEKBOy9VGVe2AFLrnB3xfclJ8M+k05t/rC\nteC43wguTGNgP6gLrcND1k4eNJaj9jjvtXENWCU59uA4CAbALFClzk/mzqbgR465Rp0Cl0M6\nr/pc7dNXaZl7Sm1T+v6hbG5uBK4FQ8F8txNYNwgKUbBTm9Mq5Tt8x43NfcuA+WZJOATmgBvf\nYqS93i8tc+lCqGmudxy1UfucI01gZzB3PwHFSltz/eo90jFU7D1z259PhTH4NZgXxyXnHCo2\nj64/QaUc03DPYo6H0/gdcBPvWnkFnATLg/52Pt8IfugNBjfDHcC5vjdcCaoyvy66WvO/5pr1\n4Xo4Ex6EHvAo7ADGhXHiPLsIxsKq8ApMgKAw9tpbV+rNg8wLW4M+1u6Qw1zP/wh+qBozvte7\nEFSb8XEZDzGnnw4+17jV1ouhIeTTXVTqY/26EiwB9p8CxoPvGVWJB2qaDCu57WJT7Wbnw0r4\nrsC32CBpdwxHE6znTZM6F4sjYG14Mqmr7YPPd+KaiNIyqS4Ft8P+4OR/FlQv0PZ/weegTEhn\nwGhw8axN6S/tS8vk7nu0hzvhHnCCO9lNUpfCjuC77Ap1LRekafBj8mA/NGYkZf3VGTaDaWCM\nXQ71rRYY4MbCjZCb/Z6wOvgOJs6xEFW1B5xD+k6NgfvBOD0J9OfrsD70g8PgWrgG6kJtk4fc\nzPFY0IZ9YBwo7XSzchUMBO11gf0Y3oRSyhh7GrYE858yx2hTWi9z8gWclapcnvIpMCJVV1tF\nN/KvwkvgeCnnwqfwpScFaAptJoPvYF9lnPSCLB8F9s+Vdjo/p4JrinI8zS36uhjpV+PDPBr0\nGwrmYdfEmkg7XXs+Av2pjfpE//wExWokHTaCzqmOG1NeNXVe02KYNz7HueG64vqn9MewitKi\nf2rs3Nam+pJj9FbOw4M9D1B/Aphv2sD7cCE8Db7TPmCcK2PgYAjvbl0p1Sq52eicmxoLsg3s\nDY2gOWwHM0G/++EUZH4yxt8OFXVwXJlnGL/fwfGwFriWzwftMz+tC67z6X2L68IpUKo5z63+\nLT+A3Ns9/u+aRYXHOPjcNXLqPdXmXUD/Of7Gtja7j2oD5r2FEFWJBxpUUv9LqDaorgCDJh87\nUe9Erk4u+spFch0wWbkoKAPRzb1JagLUhUxI2v2n1MNMQHuA4609ajYcBFuB7bvCzvAm3Abv\nwuZwGtS23Ix0Au0M2pKCdrmJuxt2Be2/ElYEx05btfNXUNeaygM3g5CYfqI8FrTZhHQH/Agm\nNuNre6hv+SuSPvwAtEu/zgI3Ndq9NNSnNuHhx2Q0wPeS2pSLZjN4D/4Jz4ObDH1n/OrHU2EB\nBDnf1gNjtrYVNrcb8CBtUk1glYrSogXWPPUtrAnar73dIW0zpyXRW9ylJbhIq68WHf7jr5uQ\nk+Bs0B79qn+dT5dAbct58AbsB85l56xzYiNoBIVKHx4BfiDfBuOgNfweSiHtNL90hHXBNUY/\nbQ3Fqh8d3gc3d+apJ+F60NZ5UBM5B58C10/tVObDTStKxf8xx18BfqQ8DOasl+EzKJWMQefL\nanAxrAzGg7LsMx+B+6APvAP1Jd97L1gmZUD/pHxtqu57yueAR3OWa/koCBpEwXh6De6CdaCU\ncl12Dp8B+lY5n9qDc8xc5V5Ju7TP+eK8WRqM69vBcf4NOLcWQl1JW9YCc8CvwPmxPZhLnTva\n+yIMh7Mg5C37aeclUGr5YeM82iLnxp774fZxTr2n2v4RrAYHwsXgh5LtG4PjsyxERQ/8lwfc\nIBjgJr58uIAYQFPBRcRk/zt4CSaDE9jJPx1slw+T081gMOaqMxVPwxxwsTN5ZJHJw+e4wXFx\n84Mtny3WOcm8btsvwWdb9pqJ0kXtMvB9L4RV4XB4DpycNUlSPen/FWir9/FooqzM1tx629rv\nUzD5aqcfVCYGfXA0vAAmhPDRSrFo+YuVi9BEuAlWg7Ru4EQ/5toXzh3Lb8Gx+A5mwSswAZS2\n/gu0W1uzqg8dvwB9qr2j4BwYDdPgCegIag0YCtoT7Mx3/Ibrt8D6oC99B30+A7KqLx2dZ4Vo\nXxo5r7LIjem4LB2TPgM4zoX34CpwA7IeDIEp4Ly/FfL5TR+56HhNHzqvHHdzg/PKa47NG+BY\nOf5ZNZiOjos4h2eC9rnJ2R7y2WedcfJ70AbnkH3+DhvBrjASpsFo2AuugBGQVW5ucm3RD4dA\nc/gLjAfnmT7SV5/AW3AmLA2FqB+N3Dxnlf7LtdNcIwNgWzAmXQ+cr2KcXQm5Nq5D3Z/A/Hku\nrAhBAyk4dllVWV439hzDoLRv9adMhhthFQhqTOEk8N2ugx1ADQHfO6vM67n+DOdPck370tqF\nk+fBnGlffXsM5KoTFTfBIDgKzCnmlqxyPD8Hc6g+DDbmHo0D49a41IZCtBKNrgdj2/lurs4q\n4007ne/aWZmtP3PtTgjvYz/nl/bbx/XmZAhagUJ/+AC8v7mvN2SVeW8BuDbqr8rs1L+Otf6f\nA8bnd2C9/fWZ80T+AhvCGnALTAFt7QlZpV0+9xk4NLmJ978ZnCf6z3fIjQPP9aW5M1z3PbXR\nONTeS8D5MxW+hO6QRY3o5HgGG7RZ34TzfEftehccc5/v+00C24Zjvn76vgtERQ8U5QGThxPi\nt/AsmLyd2C6Ap8IMMOBMRCHwDOp0YNvfCT0c0jqeEyeZC5ELrX2cFFnkJDTIxYkUbAnHtD2h\nzvfRNs/tMxu0x3usDUG+p9dN9tckZQ6ZZFILk9znBFtyj7n2BjtDu3n0NRGY2E10JvaLwXtr\n4w3gZiKrXCSmgva+DVNgeVD3QrAjfdTmXLv1p32117J2mkyNI4//AGMnq/rQ0QStLz4EY1Ob\nxsBJcAf4XJOfvsr1Yz6btdF72E9GgBsD/ZxVfen4eNJ5GY7aZt2WSV36sC8nbthUUzgHboKO\nsASoZnA6nAG7g/dxQ+IG4HXIKufiK3AmuFn3XvrDhcYc4DzVJ2Hcc8f7c66lfewCqd/dONjH\nmLwIHoSXIKtciB1rNxJhrN6g/BR8kdTls1Hbv4JLwfd5AYyb48Br/4QecAv4HveD459Vxr4+\nkmBPOE6mbho4vtowH/T5ucn5ExzDeFOsUv24OqzKFlVf1CeV2TmLa/rmZZgJbnadvy8m589x\nXBIK0UAaOXZZ5XMrs9Mc3RYagbE1DcaD72YMT4NxMAFyP1Co+g8N4WzAf9QUd+JY5rNTGx1/\nc4l2Kue7/vWauXYs2Mbz3lCVXFPNI1llvBnnuWtRru22MUa1y7lbncxx7yWcztFx6ANZ9Qkd\nfbbrm8c02po+tzwJjLN5yTVjwLltLHvd/NYU3gTb9oI/gvmqOp/TpFIZn/rKPJhrk+e5tn5M\nXa7vzWvew5jQLrUqOPdehd8l5Z4cs8rYMmfcAD7nbAj3d37n2p4bD+G6fcXzC2ENME7Mq+ZX\n1/bukEXOD5+rP/KNu8/M9ad1wXejKXtuf8ejsnvY3nZdICp6oCgP9Ke1E0C1BQPpMU/QKmDQ\nzQbrq+IdrnufXUA1BJNeSEbtKBvsTooschJ6vzfAJKUt6cnjwpO2T1vC9XDcnbpOSTsXSGUS\nNbme5gnyuu+cVSY1k/YlYEIKz07bVllZH4ZrYbKHyX0B13zvE0AdAF9UlLL9cVxMcmppmAbn\nw/IQbNCHoRyO4X3Sx2CrG5MpYL8jQHUF4yer+tBxRtJ5OY7e2wXPJL0kqGvB2HAcw2KUtk/b\nw3l4jw+os+z9VQ/wvlnlJsbFtzFcmNCO42jYA9Lal5Pbk4q/cfwH7AnPQTdQxvnxcAr4bieD\nm5Ir4XXIKjeFIfZbUnajNxEaQFDwkc8+BoxBsV7/b5mU9ekC8Dg+OY7jqM4D3yerBtPxLlgI\nvwFj32d3BO31mdrkMWxWtc/zDhDUkIK+nAvXhMrkeBlHx2xETn0xp75j8It2WA5oXwv4PcyB\ntvAFHJuUzRN7QyHqR6NhhTSspI0+0C79kz5a1g5tfxA+BfP+LmDbXcGP38OgEA2kkWOXVZPp\nGOxz7IO9oe5h6o4B/XgkaPcGoJ8dS+fxLDgTqpJzYEBVDaq5Zk4LsR9s01br9KF1xq1yTrwP\n+jfoUgpuVI1r81plGs4Fc0tWTaOj80V/aVMuYV5bb+58HHwPfVqVQkwH20fROOTSqvpVdk2f\nOd6XQ9omy+Z3536w3TptNG6ts5+2h/XAGHLTHHKnPywFvUKhdzjJcHQu+K5+GAR7wnF0Uqc9\noc5jeB9tPg6MT+eyY+/9msGfYDw0AfUWuJfIKuOwU9LZ++irCdAdtCn4U5vGgEf7pO32Pe6G\nNuA7eP16MJc0BKWvvWcWNaKTzz0PfJZlCbalbfGHN5/vdW15Amx3OrwO90C6veUzQB9btm0X\niMrjgfTin+fy/3TVRrydv2K4QcjHSdQH/2xF2QXHxUZtCV7zV42gJSjMABPSzeC5Wh+cgNt4\ngtaGleE+T0okJ5QLRlMw6J0sc8CJ8wAEWzyabFwUnHj9wffaEEYm5V9xVG2hOZTSTu0aAquB\nZZW2LX1ecTG5rr3K5D4FpsPf4SXQn753Ke3kdhXyeY+BY7cLaKuYiJRl/ehRefS9PGqzyf5S\ncEzeAGPGulLJDwS1FLgIzgR92xqUPjHWtMk2QS4Kvlvabsu+i7GkjJ2gMFbhvNjj53ToCN7b\n+PK4PzwL+aSfOoELuQn/eDgKNgPj91a4CYxZ7XYx/RBKJeeO8+IDcOyU/g36iIJ+9b2c29qg\nzS5Wls0nI2A0eK9PwY2V10qh5ZKbOAdegc9gW2gN6kvYEg6F9SCMn22DXBgfhRXAOElrKCfL\npisylPWf79sbWib9PRd95RxyXo2CSTA2ObfsJijkS4q1Kv2gTbeBGzR9Fex0U/YOGHfPgPPt\naXA8fSfzT13ZuZBnKf12CAQ7rVOOvzwPbUC7jU1jWN9uAs6X2rZXfzp+2jkDQuw55x8HtTuY\njzYC48x4CzIWVwRz+sahshaOxqBr9XOpe7tpDGNvngllbXUd8F22hqqkf41p52AppA3TYV2w\nrIJd+ui91Lnv1B4uT+r0q7a7Rqo7oBnsDGPgMwgK8RXOsxyNvZVgHgQbPZ6TnIdYsG4BmCNs\n61pk+Qnwmu+1NJgz9efD4PVSy1hzjr8AxqS5PuR5bZ0GwU7tUta7b1oV3gdzhrG9IwwH479U\n2jC5Ubhn2jbtEcfXvYVyDPWl7e4F7dkcbGese7wH+sKdybnzNKoSDzihFje1wuD9wEnkZvAq\neAR6Q2MoVJNp2L0KRnEtbI4+puxEckOkXCgNwpU9SeTEMRGZgI5O6gzIueBi+lFSZ1IyKN24\nlEoNuVEv0AafKT5Tf5wE1gc5sbXJSW0b+/p++tJ3tKzCse2i05L81a4LIUx0z9O25T4kXA/j\n4JhrlxvEf4H+ng6qlHYuuuOiv46Tz5yaVGqvvtM2Zdm69HtY9h3DgmF/Y8U+60Cp5HipL0Af\n6R+Pjq/SdpOmCVaUttmumSco2O3Rd3FB0E4XrlLJxW0VuAxM3KeDG7jNIJ9aUOnzg83fUDZW\nfwVuToOmhAJHYyH4I1WdqWhedPzS9wsLib65A/4KbuTWB6X/9LtH553x6Vz7EIxXc0bwNcUa\n6Xt6O28nwE6gby8Hx175vDfhIdBH2uyzXUzTas2J8dE2XUnZuAm+z7lU8Kn+85lXwuykl+eB\nryg7L3yW9q2bnOs7x9JrdaFGPESbjoU1kwd6rvSnsei4ap92ugFcAYzDNlCXdvK4iri8xwIK\ndnrUHtcY84s2rQXGr7Ec6hzn2rZXf/oR4XMdR6V9EsZ6FmXntzHghs76IG00JlVt2qo9jqe+\nUZbNi8FW57XlIPOP0sdVSZvT71NV20KuOY+Ms32TxtoZ7OpOeaOk3oM56gUY6glyHGzrnkNt\nAcb0TCiljdyuQqvz1+fpx7TGJifGhLKN5Q/A8TcvBb+l8451YkzUhpzTqjX4IaZN2qaP1Y5g\nPC/tCQr1bSgbBw3Bdd0+c6DUdvp8x1+Uz1HBjuBHbVfWWyfa4hg71sq8qkK8eG8V7rXoLP5d\nrD3gYusGzsVgEoyA6XATOJGGQKnUnxs5WV1kTgQTy1vQCqybCtZpj8d8mLDcCJoImkOQdk4G\nN3ztwYB2ImZRdzoFG+ZTzmdHuu472pycamfy3ArGJ3UHcgx6gMI4MNH+GnxOVvWkowufPnXM\n0jZVV7ZPaPM0ZZPTneDGxQQ1EtwQbgaHwJeQVb3p+DKYFM8Fn+04XQbBhkKO+uo88Oh4GwvG\nz0tgkjoWjIus6kPHr2FP0Dbv77NGQ0PYDT6Eu8BrkvZjvnewfWgzlfKm4DvMgKzqS8dPwHG6\nBlYC9Rv4U0Vp0cdPE8r7wu1JnePZMikb43+B9qAPfT/Rl6fACmAcvw5ZNYCO94L3uhuMVX12\nGywL60LaZ2M493qoc169mpxb/yBYdyLo04mwFlwML0BWDabjTPAZ4fnO+1AO9vhM50q63nnS\nGpaC34LXhoJxuCuYg3YGx/s1ML9m1bt0DLbkHo3TG2AnsPwyuFHaCnw/x3JFKET9aDSskIaV\ntPmIeu1L+ynYOzW5Zmz4UToQnoJJ8A/4ClpAIbKv75ZVPivYle94NNddm/Sjz3KNNJ5vBuv0\n00Jwk1yVzFW+b1b5LO3L9ec3qfq1kptfzfFL0LeHQweYA9o+CqrScC6aW7JqGh21dT78APl8\nat08eBuM00+gIVSlzbhoW/NzcxiTlDlk0lx6aUc4pu00X6fP9fnxsAzoV6+9B0vDhcm5OWF9\n8L30/3JgbjanuPZllbnO9zbG0jZVVjae3wV9r933gH21Q9sfBrULeP0M8D3s0xOyShv3gi1h\nHLiGaONU8DlVxYLtbCNd4DWw7mX4NVhvXjW/vg/dIYvMwz/DbMj1p8/wmYFce42TCWBOt21n\n8J2D7R6dY6G/R98lajH3wJLY78K5FTSDf4LJzWBU64FBtZonBcgg3B8OqoSHqA9BZTK5Et4E\nn2HgmdQNNM9t5zFNqJtMvTantSwnwyHcy6P2ZJGT0GeF54Vj2pZQdrJZDm3C0TrfqQ+ktQIn\nj0PoY/+s6knH9POCTVUd0+1DORync792iTGrcBwNwU4X46zqTUeTvf74Eo4G/WCs3QTh+VXZ\nHewIR+9zHLSEsRDqXXCzyrHKHU8XzHBvj4OgMZwIIdFaXxUm2BvAxBvaGedZ1ZeOE5POR3B8\nE+6AZ2EjUOPBsdwXwgfSgZRfgydhGqwP6mJwUdOP3qM7OI8dHxe8rBpAx/DOjvFb4AeX5TDm\nwYfhXP+ky8Ff4Rj63kk7F/bQ3gU1qwbTMfeZxqq2O07BxmBDeOYJXPOdwvm3lH8HTeC2VL3X\n74KrYARk1RQ6pm1Il3txzQU81Gl/eKdplHeAQuXGvyYfSM7NYEf6qD1e0zf6KtgXjrOo2wUK\n1UAaOnZZNY+OaftCWXuuS920E+W0b21nG+f1YVCdhtDAuZBVC+gYbMs9GpvmgKDGFAZBsDG0\ndxO/KlSl4Vw0t2SV60cYy9xjsCNtlx9tGxT4sENop7+9r/sEc3VW+dy0fZbDedpO57/rSdrm\nMBah/Ttcd++k9oOPIVwz1ntDVrlmBnvCPcN57jE3R9k+3Wc05yumDDHHf5208Tk9U9eKLfps\n8402md+uBeey9w/1ufaG81w7rZ8Gy4Iyn4ZcoZ3anUWN6BSeWegxbZvl9Frme4V385r3DEfb\ndYGoxdwDm2G/E7pB8h77cHwxKYeDG4RfhZNqjm7OPgITWT5MbAbRbhAm6xKUt4bdYSv4HAww\nJ91zEAJzalI3nuOSUJnW48JpYMA6KbLISaj9JsNnwImgHeI7aN9McEFoCNvD6dARVgD7y/JQ\nmdbnwh/BZ2RVTzqaNEwg3udNcLz8mNFWx8IE7kbAZH0k/AH8les3sBfsDB3Ad/BdcuWYng9f\n5F4o4txF4g3oCMuA2gEco/PgfdDer8AFyXfxXJ6F9mBsGDe7wi4Q7kOxQpvw9zKYveg0098+\n9DL+9ZML8hag1oHdYW1PEh3D0TiYD8bHBNBe43YSeM2x2AMagFoWToDbYSJkVV86Dk91XpJy\n69R5VUXn27pgH6VNxkA4v5tyO1C94PWKUrY/A+j2JBij56Ru0ZayH54DQd/ps/S8N36NjSGw\nJ3SAnWBj2A3WAKVfzR3e5znIqsF09HmO/bNgrO0OR4KxOB+007LHsGDbVn/aviMsB2kZL9rb\nJqm8guOIpJzl8AidjCvRZ8bbvTALToDG4NiJ49oeKpvXXKpU/bgyrNKr1V94mia5dn5G3c2w\ndNJdX+0Kri2O7Y5QbL523B27rHqdjsGXwZ9zqLsrzw2bUKedIRb1bbM87fJVDaFyQL4LBdZN\npl2w0xh03I13Y9Y8mk/GnjnsKDCHFyJzirklqybS0bVlD3C90M4ZkBsLxmsncO4UI/2t/52n\nfYrpmNP2Y84/AOd18KNrpPZqq/O7e3Kujdp6BmwJytzgubknV02p2BkclxegN2TVl3TUpsGg\nzeZRfaeN5iGP48GY/S10BG1yTh0IXeFY2ADyqTmVrqUToGe+BgXWGZNnwYZJ+8c4XgPevyN4\n/09Bm/W5dotlc/2V4J7NvKPfcmWu6AjTwHHJInOL88V8eQt0gKnwE3wE5ieP2vg9HAf7wnS4\nHDqCdrQF89bqoHrBWBgJw+AYMLa6QNRi7gE3mQbEYcl7uHitnZQ9OHkMqpaelED9uYcTozIN\n5MJL4DMXQhNon5x3S46PcqxO7WjgPZwUWeQkNBkdCxuD9/oC/EVkFKibIZQrKjL86UQfk0tW\nmdTmgfYdCt3ARGRCdZI7+fXBy+DClVUH0NH3z6redHwhp7Nxpt3ngzGhverP8DpMBmNzBBSq\nrjScXWjjPO36UFfImLpoulBdBPpfO10MTPRToC34bmMhn3pQOTHfhQLr3MS4mSmFGnATx+Za\n8N2fBeedMvk7Flk1gI5PgT5qlHOTCzk3Lp3nn+dc+xvn+m+/nPrKTs/jwnOVXSyg3vniGN4C\nUyHYatw6ztryOKT1JCfGbTG6gsbFxHPuvR1z40utDz+B46MP94JSqR83GlaDm42mr++qtgHt\ndDNkXi2lBnIzxy6rjL+Lks67cNTOufCnpK5UhyHcyLmQVePo+ETSeXeO2vkYGJctoVQyvswt\nWWVOM7cp57x2hrlkXTew7i2oicxTfWpwg0/ouwCOgdVBmx4C5/NIUOeAm+OayLxqDsmqr+jo\nGLtveh+MI/c/+tl3cE51Be02LrLK8XAvkVX6slOq8z8oBz+uQtl3eBX085GgzFfmXOvMN4Vo\nMo26F9IwTxvjUDvaJdcac/TZ+m5/0ObRoI9ttx5ouz7eF4rRBzTuUkyHX1JbNxyLi/x6vwj+\nCbeBv5zMAOUicRfcA3OgLrQFDzEBOHGWhAmwNGinScLAPR/qQk4o39sPjvdgWXBzsjWcA93g\nGqhvmZz0y5pgEtJuk+YksF4bNwU3E+Uk48wF+Vjwg0O7Lwc35V9CS3AuNYVy08oYtAY8CMam\nGg/GyjrwFCg/mMpdLhLt4U44F3YDF4VSyfs7lx3ftJbixNj1h4jl4G7YFs6E7mDseq2uNJkH\ndYKV4D44Cy6A4Ivc+fMm14zPulZrHngTaI8+ag0T4UkoFzXEEDcdbiz82FXGwb0VpfL5o51H\nw+EQcnlzyjdDuakjBpkf/5oY1o7jUKirtTl5bMEHY1O5du4BxsIVYK5/EepTIR+ZsweBOdwc\n+AO4vp8I50NNPhbpXhKZI4+Dt8E92p6wHkyC6eAPin7gjIFykfmpIzjeK4Nqs+hQ8Z8a2Ijy\nJsm576f/60o9eFBL0I/G4mcwAq6HnWB1UO6ZXN/1s9ejSuQBk+7ipMsw1s2Jky4tA+dQeD5d\nWU15Ta7fAJX5wH8aY1BWJr+824K/jtnW+1lWLrBuSl7zpI40MnnOvOS4OUftPwPcxD0C9S3H\naWkwmWvb5zAHNgN1ABwIEzwpM5n4bwXHXJ276FDxf7w3js6EB5K6cjr4Afc9mOjd9OlfN9Z/\nAdUCZoG/Si4OcmPwQi0Z6kejMXk1nAY/govPyeACasyuAoeBG1X1IbhQmQ/qSmN5UCM4Cjon\ncKj4SHOzdzY8Dt+C42u7ulzYeVzFB5kfZfouSBv2BcewXGSudowfTgzSf9fCV8l5uRyMRdc9\n1z+lnX4oTfakjOQcMTZDftTO8XAClKtmY1gbaAVhHQ0xens9G60/1UvwBrhGDoXG0AT6gbmp\nP9Snwo9Lxun+CfM5aueOoPxwcv7bplz0CoYcAX+DcxKjmnGcBtvDOFBzwVieBnUlx7pb8jCf\nrd9WBuOgC4RcMIzyaHBNCnFLMaqmHnABW9w0CYMfyzHaDY2bBoOoULl5eLcKPqvmRjdy3SB9\nFQxck8F3SdlFy41LXcn39vlu1vzguxnmwYXQAgZBOcgNkjZ+D5angBNe+0+C1jACylF+aBwM\nZ8LCBD8s/FXpDzATXKzKTdo3EP4KT4A2628TqTHyEewNUYvm8ZE44miYDm5KXoNnwLG9AVrB\n6dAVDoWP4Sl4D+pK5ptjYHXYBjaBtWEzeAS2Ajd92j8VVoLwQUyxTuTG7gt4H4w3c+OWMA3K\nSa6B5nrz0k+gzX2g3KSdn4DzVTuNuwuh3LQkBunL4E9t7gDl9sGJSf+WHxjGqD52TdK3vocb\n+uegPuVa6XrjnHdtfwhci1zvLwDXz8uhHPQ3jFgW9KUfFH4c3QyjwLjdCT6ActP9GLQGbAEd\nwfFvDc3BfaIx8Tw4BvdBXckPJG1aF+6GFcG9pXn9NjBG9a/rwO4wE6JK6AEn3P+K3BT8Gv5U\n4At9Trtzq2jbn2su6JXJjfwp4MeZSUF9CkPgAnCBqCv5rG9go+SBx3D8M/jR6CbuNZgI9S0T\nZycwIW0A24IL53FgAlgc5IfGUuAYr5kYbPLcH3y/ctRZGLUMDAU3rm6wpsCTcCVMh6hFHtAn\nbcF544J0HowEdQe0hKugKSgXfz+W6kMu5JKWH3i3w0GwHfwAfeEyqEsZY8snuHAfAp9BuUk7\n/YBUzgn9Zi4tN2nnKolR4zgeDP7AUW7STjdsSjv1p5v5ctYtGNcCLoB1E0P9MHKDWt9yTTkD\nukPYj7hvGwDG647wLDjP61vmQvdN58Dh4FpzIkwDc5B51b1IOUr/vZUYtiHHS+FkWBqMCcfh\n1+APKHWlH3mQNvn8x8F51RHM6167DowN51xULXigQS3cs75u6cfBSUU+3AXHxJiPZgXca2DS\n93yO34J9jgI/Rg6DutICHrQxtIIdwKRpgnJjdyOMT44mrPqWi44T3k2ISUm/LU4bdBen9aEx\nfAkmTpOolKvmY1h3MM57gz5fE44BNzEeo/7PAx9RvAH6QPg48qrzpw00ga/AhUk/+iFQLvJD\nWJuMS+PTeNVm47Uupa8WgrHXHFaDcpR+0k5zqOPoJqRcpS+10w+6VcrUSP05D8ztjrvxuDho\nJka66fSf2Gj/5tABykFzMWIraAU7wp3QE8xRo8BN9DpQDpqMEa41K8Lu4AdRazgLXoWHoJC9\nFc3qTd/xZPdw5k7LYc7VRw67hudPgn7QHqZBF1gVekH8OMIJtaX/pQ+kITjJjUChMgF+DHMq\nwV8+CvHPWrS7CK6CZRMM6sHgLyZ1qRk87CUYBLPBxd5EtQe4CT4N6lMmxsvgONBXbkiehOGw\nuCyk52LrPrATaL+bUZOoi1a5S/9fAtfDcgl+BNwKm0JU1R5w/jiPOoH+c345z4aCHwTloNsw\nYkloDcbndrAzXAp1qR952I3QHP4B98BaUG76AYOuBvPRXeBYuvkoN/kR51xdAR6FB8AYLDe5\nmTwH/Ij7F5jbwz9tpViWci9wCzhHjFdjwdg1p7eGcpHrezs4GHYF1/YW4A8698ESUC4yXk+F\nJrAurAxbJFzFsZz1K4zzfy54NhgPcjvoY/1dV+rMg04G9xvOe/PSe3AJfAlRteyBBrV8/9q4\nvRvTa+FhcHPtP2bsAf5j0WL0Fo2duBtUwu+p938/XZ2OpME4MLm6KXDB9YNpIvilX9fakgea\n8LtDsH805kwQUQAAQABJREFUZX12LNSnXHjcKPkx+zN8C46d//TFf3y9OEgf/hmeT4z1A/s3\nYFyuk9SV6+FwDJsO54ELmPF6JbwGR0FU1R5w7J1H5h3l/HKebQZbQX3LXzj3hNNgRmLMKxz7\nQF3Pfee3Ms7Ogg/hUCg3/ZQYNJ/j6fANHJTUldPBuaq+g1OgIewL5abgz68w7AQwJncrNyNz\n7OnK+atgXtfPxuy5MBPMmeUk53E/GJMY9THHE8H8s2lSVw4H1/oDwH3U1MQg91wXwDHJebke\njsawZ8BcbzwvgDNgLhwCdSX3RDfB48kDff7x4J51+6QuHmrRAybZxUl/wFi/nk1mfoCYwJyI\nnaEv9IKboVCFiZuv/dZULgXesyrtysXGkNuuEXUuDF9DVWpT1cUCry1Pu/D8tpSd1IeBH2tB\nTqpWENqF+kKP9q+pmnEDf3XPtcEFyc3TmlBTuVmtiZags78S5doY7qn9m+dc9wNP/RZmVZSq\n/7NN9U2qbbE2LSqzM1/nTlQ2ADeCaTXlpD3ku9eO6YYZy+tUcu+Mt8vbbZe8tcVVGuP5fBDu\n4vzJbWMOdb75odQBqlO76hoUcN2NUD47V0n67sXxV6n72H4lyNcn1ew/iv6TJ9+rJnLTFp7p\nvNKu8OFUk/um+/qMuemKIsva5bsGO31nPzzM/aWUYzClhjc0doKd5syDYdUa3jO3u/H9Sm5l\nkefpuejmsgusX+Q9qmu+Lg1q6s89uIe529yXb603V5ozfYesWpuOL2TtnPTbm2MYZ++3IYQ4\nsIl7DdUDplaUiv/jmldTuQ8L+5nlKOu/3WETCDIOmoMfHM61YrVysR1y2jvfnTdpm3KaVPzf\nFLQ97WPbmLsci0L2zcvbIaO0UbkfMs7TdmiX+7pukM7xnGaS8R9ViQfCQFRyuayq3cR9Bm4s\nx+exzA32bbAi+GtgTbU1NxgIBmRV8rofSD4zLP76tQmYWAtJAp/Sbk8I/SkWLBfN6yE9lvrK\nBfRHCNJG7299Vrn53z9j593odw2YzNPjk89/GR/x726TKfmBmEX70qkPpP2Zvo9+VOlFc0nO\nfa95XihCb9P22CLap5sewsn56YoCypX5Ol+8pG/3EicnpyuKKB9N2z8U0b4mTZ+mc3oxKeZe\nbi78wK1KjrFxUdOxH8E9elf1oCqunca1E6q4bt5x3ruIBuWL2XCtquMDXLykqgZVXDuLa0cm\n1/VZMfmwitvmvXQntVflvVJ95UU0OTBpVtt23sJzrqvepLwtrqR2r+RKZfM4b8cMlTfQ5x8Z\n+tmlP3RI+ta2nX/lOYOTZxV7GECHsMHMl79LGQuX8ayhxRqYtL+d42apvqXKQalbVhTdG1wA\nj+ReKPD8Xtqtl9M2Xy7KZ39OtypPtfNMGF1lq8ovPsylNSq/XHGlFPGgnafC89U8K99lY+8J\naAGWa7recItK9RNXXPteq7RFvLBYeGBDrJxZjaUfcX2LatrEy9ED0QPRA9ED0QPRA9ED0QPR\nA9ED0QOLvQf8kvafYPi16y9TuTqBim8g/Fqaez2eRw9ED0QPRA9ED0QPRA9ED0QPRA9ED/xP\neWAX3mYafAxPwXD4F/g/UfPjKPxPEChGRQ9ED0QPRA9ED0QPRA9ED0QPRA9EDxTnAf+pzOKm\nZTC4HawPbeBrmAwPwlcQFT0QPRA9ED0QPRA9ED0QPRA9ED0QPfCL9ID/J8vHf5FvHl86eiB6\nIHogeiB6IHogeiB6IHogeqDkHsj3/+Up+UNq8Yb+qyS3qcX7x1tHD0QPRA9ED0QPRA9ED0QP\nRA9ED/yCPLC4fyD9goYqvmr0QPRA9ED0QPRA9ED0QPRA9ED0QG17wH/f++Is/3sfs+Hlxfkl\nou3RA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED\n0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED\n0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED\n0QPRA9ED0QPRA9ED0QPRA4ubB5ZY3AyuQ3tb8azeUBf/rahPec55Gd+tLf3OqCM7/VeqX5rR\nzk3odxrURcy9z3OuzGjnVvQ7uY7sHM9zrslo5/b0OyFj32K7vUWHG4rtlLTvwPGojH2L7ea/\n7v8fxXZK2u/B8bCMfYvtNpYOtxfbKWm/L8fOGfsW2200He4utlPS/iCOv87Yt9huj9NhWLGd\nkvZdOO6asW+x3YbT4dFiOyXtj+W4U8a+xXa7lw6jiu2UtO/BcduMfYvtdgcdnim2U9L+dxw3\nz9i32G630OFfxXZK2ru2b5CxbzHdfqbxAHi9mE6ptu6V2qTOa6uondfBuxkfcBH9WmbsW0y3\nn2jcFyYV0ynV9nLKK6fOa6uone6VptfWAxbn+9bFZnVx9Y8L561Q3cJrUtgUHoK0XCQaw/Pp\nyjzlVahzc2bbhXmuV1fVnQb9IPf51fWr7voyNNgHXNC/gdVhZ9DOLOpJp0vgsSydq+izPNf2\nhAdhHqwJLnzWZ5GJ/kx4IkvnKvqY7HYD48lxbg2tYA3Ioj50cjMyuojODWl7cNLHj/Kg1Sh0\ngKFgwkxrPU705frpyiLKLhKHQyEbmUNp9wLMhqDc8Q31uUc3EQ3AD9wscnOwFzyfpXMRfTah\n7dfgXMqiwXTaEbJuuNLP1F+HgGPzUeqCsbo7uEnWJ1lknG8Mr+Z0dm6uBE/l1HfkfC74MV6M\ntqHxOPCDLItG0Mmc8WYVnY2p5pAbw+btD6CQzZo/aLwAR0MWPUcnbSjkWc7VdSE31xo3/rcD\nX4LK1I4L+uTkyhpUU+8G2xwyoZp24bIf/ONhSqjg2BQ6gzn4C8gn89U94AdEFk2kk/eeDFvA\nCjAG0tqVk8+g2JhM32M3TgbCBenKIsrmwukwrYg+1TU1nl3fn0013JPy1ZD1h0V9qZ9mQSm1\nMzf7Dl5LbuqeRF9el5wXe3Dt9b0/LLZjNe07cv1zCHlkf8q94GYoVo3osABGQnqdLvY+6fZL\ncmKuN+9+krpwMOVucFeqLhajB6r1QBdauPhVpxNpkK+dE+P+6jpz3QXpZ3BSZFF3OpnkS622\n3FC7PKpO4KTNqp50rMlCU9lzt+SCdrZMGhzA0WSdVX4gvZC1cxX9duKaGwcXYtUVZleUsv3p\nQ7dRRXZdlvba4OYiLRfHH6FJujIp9+A4MU99oVV9aTi8gMZL0OZ7MGGntTUnjm+LdGWesouR\nm7OsGkDHIVk7F9HvPNq62c2qwXQcmLVzTr/GnP8Ae+XUt+dcn7tRzqphdOyXp7MbsGfz1D9N\n3Z/z1FdX5TN8Vlb5jldU07k/15/I08YPjUvy1Oercswcu6wyZoydQnQGjSbkaXgvdbfmqU9X\nOQecC1nlHHQuFqppNPxdTuMWnBt/W+XUp0/NKeaWrDKnmdvUVWD85co4/VNuZZHn5mhzdVa5\nRrhWlFJ+XOR+PLvmufZl1Rd0PCBr5yr6Pci1m1LX36LsXiKrFtCxU9bOVfR7kmt/TV13T9Y9\ndV5MsRGNjf92xXSqpm0zrrv2+8Gelh9L7nWj8nigQZ66WFWcB1xgV4TTk26Wb4PjYDNwsVgS\nylHHY9QYMOmYhNaAoEkUXGQvBSdsfas5BriReQ1ehLNBu96G6XAZlIufnVenwVh4A66B98Ff\n6bWzvubdVzz7GbgA1gXH/B24G/wVfj7Ul1wQHgHjbRg4ro+Ci/mr8AFEFeaBPWmm7/ShY7sF\n5JObhZHgxttflNVSYHzM8KSGMs7PhX+BY2jsu2HcCfaDoH0p+FH2cKio4+MaPM+Ycy48ALkb\nk4eo2w06QdDBFLaFurZ5S555Dzi2zpe0TZxWyLFfD7pVnC36syOHzlAX9rbmOfox+NNnVyZ9\n2wtWSxqYw40T4891qS6kT3aGfVIPc04Yp4eD/q7qY43LZalDscr5ZqwMAmNC+b7Gze6eJFou\nFOr4uCnPuxO00Y+29BhwWmHrURw386QMZC41p2qv8yzMP33qfmp9KEd9h1Gj4W/wPLwB18Gy\ncCSY8/NxTtKGQ1T0wP95wK/qQjdlp9LWr/N58GNSfoejv5Z+CSaAyuRi7OawUWUNqqnvznV/\nrShUe9DwafgGtNWJfRb44TEbWkLQdhTmgpt7NzkLIat60jHrgteUvto3E9xo6dMfwPd2Q9ce\nrPPc675bVvWm4wtZOyf9xnLUvq9hAkyDiXAgaJt1b4K/3mRVHzqOytDZBP4pGK+Ov3aaPI3d\nv0CuelCh7VnVl47DC+ysf8I8Gkf52+R8KscPwcTeGfLJTdbr+S4UWDeAdkMKbFuTZm68nqvB\nDQbTd2AV/Y/hmuNqbPlB7Fg7tjtAPrWhchro30fBHCA3wQjIKsd8ARhfzvurwU3vS/BncJyf\nTbBsrsyifnTy4yarnqCj/voM9Jd+0+bcjdo11NnO3On81uaLoFA5Zo5dVhkzT4I2OJ7mEMfH\n86MgV2dQoY3m7adgIdwM1ck54FzIKvOaNn0BX8Ic8Nl7Qz4tT6W5/XNwgzwZHIcOUJWML3NL\nVpnTzG1B/Snor+9hflI2B+lH54U+3wmK1Sg6mKuzyrnYtcDOx9LuFXAuGx/63bE8E4wffbwB\nKN/XcTKezavfQW/IKsf7gAI7t6TdINBObXB/cTYMAs+7Q9CSFIaC/teXvkNPyKoFdOxUZOdV\naP93CPZOpayvnM/aezQ0AuNEPz4Bxn76PTgtWN7rZ2hXcI/KG3qvC2A8aJsx7h7Ec+PD81nw\nTCWMpr4V/CLV4Bf51qV96YbczgnyEcwFJ4zlJeAS2A0Oh1IEO7epkfaj9+PwMSwFJk0XrnVg\nT3AzdQ4EvUxhI7genERO2rqUielYuAXagL41Zv8Ibri0eyz4HtppEtNO29W1tK8HPAU7ghuN\nU+EdWB2Wgc1BO2+F2fAD1LVa88AVYAG8B/8Ck+jN4GbA96gvGXt3wY3wEbhwG3OrgQvom3A/\nHAVR/+2BhlTdAPpM3z0Ar4PjOxDy6X0qN4NLYSJcCZuAuawmcp66uXkULHcFPzrWg8nQHpy3\nYrk31Ica89AlwLgzf2ib9lpO6/ec7Ab+ePI0tINLoK6kL3eFCXAamKt3h4egL3g9Leu2hSfA\nPK7vu0NtaykeYPxdC6eDGzF1O6xVUfrPP26sd4BeYFv9viE8A3WlZjxoD5gDU8B4+Bb8UPM9\n9N294JpTrjoTw5zjY+BiWBfURJgHfwJzQR9Qjs3u8CI8BZOgLrQiDzE3mXPMMe9CEzgYXoF/\ngn62Tv0Ih8Jh8AY4LnWpZXiYOcr57g+eb0FzOBH8JzAXgXPNmN8XXJveBn1eDhqMEY71k9AU\nPgT1LLimavdQ6FAJ5rzpEBU98B8e6MLZB/9Rk//Edt/DguToBJ4PX8FvQI0DF9h8cuIZpG5i\nsqg7nVzUC5HJqD/4waaNP8FnYBL6BEywL0E+daLSd8yqnnQ0uRSqzjQ0IX2aHLXVxXRpCDLh\nL4S9QgXHA8B2WdWbji8U2dnFXTtmg3bKMRB0HwWTzCOhgmNXsH1W9aHjqAydp9FH+/Stz3dM\nTZAuVl+CsZFWD04mpiuKLLt4DC+gTwPaGIfGiL70mdqpXc6P1qAuhlngJiYtx8B4yKoBdByS\ntXMR/c6j7XNFtM9t6oI3MLcyOd+Eo75yLpuT9OEP4BzSt8XoChqPKKZDTtuHOfeZk5Kj8fYX\nuBf+AaVSP240rAY3cyzehFfBnKjPtFs/rgOlkmPm2GXVy3TUrtPBdcn1RZwj2toWSiHngHMh\nq6bS8YGk88kcF4B+1UaP5pNSyJxibskqxznY8lvKxudCcAMZbHUOHQFqb3Au5eYdr1Ulc7S5\nOqtm07FrNZ39wPsG9LfaBXwH84Ax47vqe9egmZBPrnmufVn1BR1de6vTBTQwRrRZn/tuT4Nx\nrG3fgbbvA/nk+tAz34UC6xbQrlOBbW3mujIHlgP7mkv1tX7VZuNQe9tCWpM56Z6uKKLciLbe\ns10RffI13Sa5j+PiD44hFrT7ffDHDOv6Q1QeD7gpiaqZB5xsTeAMuAPcEDwEy4Bf341hNfBD\npD7lpNsYXgEnrudHwq/BOHgKTFafQ31rdQxwob4RVoW7YR4sC0eBti8JK8MHsBXUl7bnwS7U\np8Ah4ALqQnorHAjKJFoOMbAhdqwNs+BBuBLugoNgBTBmXQDqQyZtFyDH/li4AfSlm0AXixCX\nbr7WgI3ARTbq/zzg+CnHcE1YHxxz54u+dP70gN/CJlCbcsy+h2vgbPD5B0MrqO9ciAn/lna1\ngJXgEjgU+oG6atGhLP5qp/7sAx+CubsbOGf09WFQLnIu60vz98Xgh6F1xsFNsCmUk/bAmKXB\neWFe0dZ7wLXbusPBuWQO0tflprYYpP1PgPHbEZTrkDGzM7QG36c51Kdcq0fCd6A/z4MtYDr0\nhwNAXbHoUO9/t8SCMfAtOAedexfDVLgD3OMZE3Oh3KSvZ8K54Meyeyjn5Ghwnb0MVGd4tBLc\nJ7iW/CLV8Bf51qV9aYPHRWpV6AZLwuagTLy3ghPIQKtLrcbDjgCPr8BwMCkNAu1VQ8BJr/y1\nwV8UbFtfasqDDwft9peNy+Ec6AZ+xKkBYCJ9D5YDFwE/kupLblQmwroQxt15pV/vB/3phtDF\nyVioT7l46i+l3V3BzYAyRqfBs1BfcoyXh8EQPoxaUtaXIVcZG9r8Lvgu94GbmKhF/wRQP+jD\nU8GYcx55VHPB/ORYqxvBdrUhn9EMrgfHz/O1E07gWC7StpVB+8w3os8WQmfQdyFfUqw3aaf5\n0Tnips0f4dzwWD8J9ocroL6lfQeCG13Ll4H+dIPWD44BP+begXKRm0XXRm16BIyFo5PjDhy3\nBX0/BMpBzmF9vDV8BE+CNr8M2ul647lxbVvbfAgrgnnUjyk3/HUhP8i6QBsYDx+DH6Har9/X\nBGN4WegJX8BzsDOsC1OgvtSOB2uDtrsv0Xcbw19A/64D38CXoM9rW/6Ioy9dE9+CoeAaWJnc\nF7n/Wwts7/6uL+hv19DfJGVtfxvyyfvXVazke3691jngUTXzwPt03x3OB4NpLpiYlBv4zrAX\nOPHrSh14kAuoz5wOvWAqmDyd0CZLk5MbGBPoj9AalO9TH2rBQ0eDdn0K2ubCry8HQnfQVhNT\nE9gCTADWDYP6kInqOHDBMZm2B+37HFwYnF/bgroLnqoo1d8f7TRG10yO+s5kKdp9DrgxrA+5\nmQr5yLL+065QZxz4cXQ2mMyPB/3vYnU/+APAL12OrXJRvBic12HOUKxYJE/iuB5cD6fAv+Cf\nUGr5YaFciB1PpS3Og3c8KROF+A9zwKP26kvzTCNYAPUtbQl+1Bb96obHMb4bDoJykGOstNWc\noj+t04fOZXPjMlBOcm3cHsaC4/41LAfa7vgby7bZA1yX/JiqL5nDHwc/jl6F1nAJzAft3BfM\nj2569bvvMA/MCY6H42Lc1MWmd32e8yQYu+PgN+D4rwWul9pi/BoXynr9vjO4ofdd60v9ePBp\nYK5ybzIY9N9H4B5Fab/r1DSobW3DAxx359Fk6Al/gE7gB1o+jaTSvd7a0Bq+An1tDBsXTcE8\nYty7rkbleMABjqqZB0yc+tGAM5muAiZYZZ0bzuc9qSNpwxC4F9aFDrBJUh7P0YneFpYFJ4sL\nwg3wR1Beqw9dy0NdeLT5MDC5iwl1N3BD9y2Y7PWrPm8JB4MTvz50HQ+dCyatm0GbpoB+dRy0\n06RqEuoK9a0JGGBSNCa1VdI2ukmoL+kj5UK/IxwCLpSOtz40do2B2aCdr8HD0Bk6QisoJ/XG\nmBXr2CBzkbHoPFHOHzcgQTtReAX8SDkSHP9uUFsytrTBMQ36NQWfWy7SXyH2wlz4gDptnAHB\nlxTrVeZp5602Ku17HTw/AZ6CcpA5PIx38Ke2rw6ngHO6XGzFlAqZs4NfzdvLg3Fh3nkDJsBm\nsBJ0hPrUhTy8NWwMHWAdeAn8CHkOxoAfR8p9iHnyCGgNM8C10h8g60K38BDzdhvoCOuC67l2\nmBcagGvl9/AH0NfLgh8mH4MfVfWhfXjoqbAXbAH7gTnCGDGOtf8sOBqsq+39h89wT+e80Ze7\nwAbg+nIFVCZjeG8wjvXrcuBcvAqmgfLeUZV4wACNqtoDJsy0/PVl1VTFypTDJsSJ7gJhsjIR\nuKA1g8rUmAv2r6kM8lawNrhhXAPuBDeQJsuLwYTj5D4H/gnzYA5oc3v4C7wLLaAlmLhKLe30\n/mvCDqC9G4FJSLvvACevk/96WAFclNaDk8AEqu+nwQDwfnNBmSxsWwo5L1ap5EaOrXZr//5w\nLcyHW0GZSE1GI0G/DgIXgxBHxo/+LUVi0s7w3j5D+Rx9nG/89LvP9dos+AC+SbD/gXAwBK1G\nwRitqXyeduZK+31G8Km2yQPgJn4Y2Ne67vAeDIXlIWgSBWPcmC8nuQkJeaHUdumvNqBPPTqu\njp8+cLy8rk9cIL+AIOe60u/vg37NN2eM36ryFperlYuyi/HX4EeG88LnOaclV+ZBY8HYtV0h\ncsF3PtVE+sJxkg/B/O199edLEKRthfyire+d3/YP8n2tr4m0U59+DvpS34a50ZRyH0jL2Mg3\n59JtLLsmLJNU2j7kqaSq6IN2qhkwsaK06M9CDlfDg+C6lJbvoc+qe7bjYl42Zu1TKjk+X4Jx\n5xzRVu+vryeA80vbjI3mUKi0NR0HhfbLbef4ODZt4RD4O5wJzvHn4UXQ9ktgPbgSXN+NuQ1g\nV7gL9LHrZe7a5poaxo1iZpmXfaZzcmPYCYbCMPC5/eFv4Prpmnk97ANfwYlgX2P8BDgPSjnG\n3O7f0pf51jX9oq/18SjYET6G68C4cM45996CteBS0N6PknPHoJQyF3YC9xrrw43QCraCnuDz\nusF+4Dw21sxR5irVDIzlYNc7lI2Xg8A5ZL2cDMZ6Pj6hfiOIih74Dw904cxJ4QR4GZzId4AB\nZ91keAGcMJ6HY27ZxGBiSsuJbxL7FmwvJuAs6k6nBRDuk/v83Ho3AtZ5fAa0exx8A+m+TpY/\nQpAT1edklRPapJ22J5TTvquszjbTwXGxfAE4PlvAsxD6uaClN4WcFqXetA7jYiJsl/RuyvEm\nMEmGZ+UetSv9LiYXk7+Lr4vGAAg+0L8fQFb1oeN8CDZ8TXkM+DzrvP9ZoLYGE33attAvbbPj\na3wPA223jddnQVb1pWOw80XKmyc3Mim7sIRnhGPannQ53CPYPYO+JvrHk3tYbxxnlWPzGPwV\nXKyXhly5OToN/gbHg4uL2g5y+51C3XKwMbiAeW6/m+E5yKrBdAw+0D/p8pvJedpvoeyxCxwH\nxp39rHsDjE3VEh4B670+GrJqPB2DbeHofZ1bLuRB21LQhmCPR+07EirT2lwYCcHOBytrWEC9\n8yTYF47hvnO4dgZMSto49++BFSFX5u+r4TvwPp/CJTAGwn0du6wy14X7hKN2msdPTd3UeBsL\noY3lfBucX1PvGmY730t7Q9m5kFW58zT40qPPWybnxuaoMAZfUdZnDXLarM15ery101xlbsmq\naXTUVvNd8FXuUZvDdcv62rlenWzzOng/x81cnVXmbe/j84N96XL62pO0ce0L7fTnq/AY3AaO\ncehrrjgJ3gbbG7e9Iauc18GW8IxwHuwJ5+G6x/vBmDU3Gofptvr+RmgKQW9R6BlOMhzDePq+\nxo/ztgO8C7n2pW0JNuvTJ+BRGJf0Ce1c582xyljvXlEq/o82hXsGm9LnueVg2zz6GaNed06F\nueg72ya0C8dw74e4dmAl7Ed9Q4iKHvgPDxjon8HuMAgMvGmwP2ydlA00J3UIvnxHJ7RJyKAP\nupqC9z4OjgIDNX2d04LlJHSx8NlO3nw2pOu09y5wMlk/CUziboh9xzvA+5gATCIhGXWi7HOy\nyvsEX4Vj2q7KytqUe80PO5PRX2A2mLDawfbwEvixl1W96eh4uXH7J3wN68IgMOHk2lLZuQuQ\ndnjdjZHMhENhM7gH9HlW9aGj43YDjIIQA47bpvA7cNG6DMK1fL4M9jsmvnsYG+Pjr/AwzIKs\nchFyvHaAB8F3/i1ok3b7/AngeAZbPKaTuuf63o2z9eK7iPd5Ga4Cxy2rBtDRDcR2cCI8C7k6\nj4orYGsYDodBc3AzlNvP8W8FB8Fc8J7Hg3PqOciqwXR0fqb9o+/CuAUfBt+E83C0fhJ4NLZn\nwT+hIeg/fbkb3AxPQla9R8fwzNzjRclN1+Lohs554ceI/n0VPgPtcxOfKzdLE0Ef7gJDwLHI\nKp+lfT4v105jwDo3l1vC3vAuWL8EpNWfE2P7aNgEnEv21Q87wwPg2GXVt3TMtS+c6zM/KlYA\n58hjsGOCZeu8FrQNBefNtdAe9IEx9SY8Ds6FrDJvaFeuP41X67Qn6PcUfK/fgTnrRDAeLoGg\nMN7mda+NAW38HswtWTWdjtoT7A2+zHcM8TiN9uOhCVSmtbignXeDa4h294GsMidpk/M7rCfB\nxtw5b72xcCvoH89Hwu7ghtn3PQUc/wfB6/fB1uDc7w1Z5TgaZ+G5wcbKjtrpNf3vvDG/h7b9\nKf8ZjFHtHgRBb1FwL5FV3rMHHAUfwR1gTr4FnAP6KNiR76jPbTMjaef5aXAx+C5e2xMmQ3fI\nokZ0+hlC3OWzo7I63+8VMFacc4dB2rf5+unvqOiBojzQhdZOeLUdGLBuPtU64LkTKh1wTg7r\nQ53lM8GkcQCoZuBE6uoJage2c1JkkZPQieCkMDF7r2CDx3dBe0K9trgQPgxOQK9ptxO9Nyjv\n+TWcA7NBdQInX1aZ1MKz3DAGmzwGgt3pc308OtXXNtadBy6sjpE+DTqEgskhq/TBC0lnN0H6\n1Q2DNgX79FUoB1tzj17/EkbC+KT9rhyDHH8TdFb1oePEpHMTjtr0Gji+S4Ey9ly4tM2xS79D\nuqytsgN4T8t/B9UDwnMqKor805f2w5M+jTlOgQ/hSnAc3wF1MKR96KLp4hjs9P3WhA4wCYLN\n3m8Z6AWvQ1Y5xsb8SbAGLAu5akWF1/aAe8F3WAmcI7n9nGO2PwiGQtBMCmPDSYajm+yQZz6l\nLPNAexx7/aLP3oO7YVZybp3tvG4sPA0rgh9D1nUD+68K6goYUVHK9ucpuvnMYKvlgPOzIVwG\nE8Dn7whqeTCPjYEnIVdHUuG8WiG50I/jsKSc5fAqnbRrYXIMNmrTZDAGfUaQud82O4UKjs1B\nn7oZCTqRgnMvzJ2BlAeHixmOU+mjLyXY6NF5YZ3j2AuMr6YQZNk6rwVpx2PJyekcZ8GG4P2e\ngAGQVebj4Et9KMFefeH5uqA+BHNUWvrNvNAoqXS8Pb8EHA/rG4Bxb27Jqml0dI7MAO1L2xnO\n03VH0cb5Yo44HCrTZVwwpy2ZNBjFsU9SznIIa7R5xGcHX3o0vhw77RRj4U4YAdrh/Anv4jXv\nEXQNBfv/OalwzeudlLMcnJPaYI5xLgSbPIbzdOwaZ1eCY2sb7XSu3QBBjrnv57XVk8q3OPZM\nylkO2tIp6XggR216Ec4A7dBPwZ4Qx/bRBrF9uO6xAwSdTGE+OOaToTtkkTHuvcfCFxCeHY7h\n+Z4H20Ldc0n7zhzdrz6TnJvbQxuP4T19n9vB+Z+P9aj/xcpEE1W9B9ahiQG2RtLUoDHInLxB\nb1Nwglv/GiyRlF0M3oc2oFpAE3BSllImxeXA50uYACagYAvFigX7fo77QV/w2pFgQh8O6l+w\nDEyAltAYSiUnvPdW2unzA+Hca8pzP360U5mE9fmf4HJoA/r9OwgK7x3Oa3L0+frCxKGNyqOJ\nKdgajuEdbKNuBcfDhN8KVO6Ye5+ayLFVK4Ljp2+bQohTn6f/tM2kG3QPBRcyFd7L92gDS4Hl\nd6HUMpk7N1YCF+Sl4WlQXtMWn+1xPFyTnHOoiGfH3oS/tRXI5D4D3HCXQtri3H4c+ue54W+p\nux12gI+T625g9oSq+s1N2nowPmuqBtzA2HEBdZwcd2PBvKIc27tAvzh39KmyXQ+w3S6gXSEm\nt6T8PoT3olgjOTaO41DYO3Un6xx3Y6ANTAPf5WVQ2ujYO9fXgVzZZxKYa0sh484xeR6mgdJG\ntQpMBJ8ZNJXCJ5Cuc745v4IvKVZct226nfVZ5ZjqU/3k2CntdGy/BZ+jv94E2wZZti5th2Xz\nmrL8Fuhzx96xqYn059fgmP4ZtG+JhHHJuc9sBqtB2mecVpz7wbmyJ8i2jkFLeB0Wgvc2Pmoi\n59B8eCO5iXZ6X22dAx+BCrZPoex8mQzaVJm89go4VqWSNjwJ+izY471dl++1kMg14BTYC86H\nk8H25lFjYBgEaec08Fgq+c7Og/TY6NOQny0rbXJf1BuWBee8dV53HgYZG6uD11pDqeX9jQPj\nazvQfn1oLCjzqzaFGLZO9QNtsr1tgpxTjonvVgoZ803A5/g8UcEezxtCut69lefmCd+vTXI+\nm2Po15/yI6Bsewz4Y0E+zLWbwy9SBkdU9R5wAi0FnyZNp3A0sNZKzj20BRcG6zeAEIx+ZDhh\nvIcyUF3QOkIptRc3c9Pg8yVMHBNOsMVjG9Am1QH8uNgQ3GB1BNURTFpbwHRYAKXS8tzI91fa\nqU2BcO415bnvtD/YxsnvxirY73ErWA6CTFD2K4VMljvD2xASpXZ4/2BrOIZ34FKF7kyOHTm6\nqNrfcloN/j975wEuRZH97U9JRlTMCTFhznERBQPmLCqiLgqGNSy6RtRVUMxrznnNOWHOIkGM\nmFHJ2Yw5Yvje99K1/97ZmXtn+s6dO1fq9zwvXV1d1X361KlT1cPKpk8ylF1clD4yibYBP+SN\nMdUJ9LXP/hWCtqRg4lXarXwP/ekGybILapDn5ZBzaH1wIxZsMwaVY6st4VlnUb4uObe+BfgR\nojYD6xyfz6BccsN2HLggbAHapE+NL591EOwIp8NKoIy/LpDbz2sNJcfa2NkA1oD5En7mqGaD\nm0Af3g/Bp45vB9B3QZtSMD5c3M1T6ZzGaWb5TO87Bs5P3cVnm2vMpebE5cF32RjUgrAyuEHW\n3lzZx3zlWJVDjqv4IRfeXRvlI9AWnxnk+UKQrpvIuR8inSFI241X5345ZPwrbR1QU/q/cXTO\nao/PXAeM2yDL1qXttdwZlH3WBtvo02+hPnIsQ675B2XjIPhzzeTcTdcPMBk6Q1qdOXHtCfNa\n+xzvKWC8m0P0hTFfH2mTvvS9lXaGmHUNMbcGu0McL0rdCqBNhaRvO4D3KJe0Y2swlwebPE6F\n8PFBsebj0bkVtBkFr78E+tCPjSDHYBnwWC7pz+ngGq+00XgI6/1vViLr368pzchftnf+mBM6\nQ1BnCr6j/cZAudWJG3rvVWAEaL/nxoHSdjFWtTloQwo/gu03DpUcO8PPEN6NYr00kd7ez+f4\n/GBDek4519L12mW8bgadwHj0+pIQ4tu1rTMor10LxkY+zMNvQylaisbbw2ywMJwLj0IfKOe8\n4HZRjeWBbjzYJO3kORsMOifrXrAJmNytM4A9isEm4dzk5MRz8zErBPWl4EJ0JBwC9gkLIMWS\n1IvWv4DP9J7h2Wk7Qp3HSbAN3AJOplPBd3gKTAQPg/VPgvftCaoLeJ5VvenoZNYGk1DapkK2\n2ib0sTwB3oTmoOaE0TAMnJC+17ug37PKSfwebA4Pgh8fbeEKMPnn2p3P9jG02wTcoGr/rnAV\nGE8HQSd4LDnnkEn96fUTXAdDk7K2PQMd4Z/guDq+v4LX0n7X7rTttgnxY7tpcBgYJ5Mgqy6g\n44uwFTwHU2B/8HmDQbtcpB0zy4G0bdYZe1+AfrNsf8fDebg3XAaOfVZdTUdtM+6dA2FT77id\nCMry82Abn2ecLA7PQG6/t6hbCnaBayFoHAXHK6tupaO+8v31i2P1VXIMvgvHtA9ttyXYzzHp\nBMaiMXklmJ/cSI2ArnA7PAtZ9SEdfX7ahmDv0clNXTw/BcfQeLsURoIbJe3cBHLVggp9K/r2\nfhgAWaXv8tlpjDlO2nwvbAp7wVh4AnJ1OhVfQ2/Qbue+fUfDjvA4OHZZ9QMd89npHBiU3HRu\njuNhCGybYHk8eC1oFQreT3ts5xiY81+H5+BqyCpzUj473bgbg3enbmz8/Qz/hI5wLHyfHDnU\nKIy3c/sLMP+/Atp/AWTVODrqO8fMWHOsJJ/t5gNzuBvF4dAcCinEtDHifLO9uTqrnJ/ape/0\nbSEbrfc9zoDN4DgwT5wE2qvf3oJdQLuc297zSegCI6APZJXjNgqMI22pzU6vHQn7wUR4CE4D\n76FNd8JN4PsYN5dBkGPQO5xkODqvffah4Np+DfiM+0H/5sZCeJdwdD59C6ckbe3rXL8KjCf7\nd4TR0AuyyJg3Dn2O/vDZ+eIy2JQ+asNQcF75rgdAGBPPc9t6fjGUS+6VfY453HgwviaA/jHP\nuK5E/Qk80I13MLAMTCfxXnA1OImtM+EYiAaw5wZabhDb/1ZoA2nNwokJ7BMIfZwUWeQkdDKE\n++TaEs61cyS4IGjry7ARqAPBd7StE9yjQd0TgrpQMPCzyqQW/On96yL406OYvEycC0JaS3Iy\nAEJyeo3yV+kGJZb70N4x9pmDwV/olYvMuRD8k89++0wFFzWvmyT3BOX4ngkmZa9NgY8gq/rT\n0XH3md7PcX0cvKfnk+AgUFuAPrE+F/uHe9jXeNoW0u21NasuoKNjZ/w9CcuD2hvGgfaE5wdb\ncs+foE2wJ1xzodwErgI3At7nPcgq57bJe2GYq5abLMK1lsn1eTk6l1Vd/Wa0mrFhGRJOMhzN\nJ7l+CmPq+z+Wuh58a/u/gdoR3gevTQNj0thW5qlb4Eewz3OQVW5og13haP4w76W1Eic+x+eJ\nbbXPHzsKaSEu3AUh/h8q1LCIehftYJ/HYIfHQdADXgWvmReugXzxMSv1J0C43xjKh8H94Hsb\n/45dVv1Ax7Sdls1F+qE1BLWj8Ch4TR6BdpCrDlS8AuG9xlM2h4pzIasck7SdYUy9740Q5g7F\nGh3En+Yq+5h/joRc5Y639/Q5F+Q2LOHc9S3XzmBrqPd8Mng0h90BC0JdMqafBcfccesPWWU8\n+fy0bemytnqubebUUWCdOfsfEKTdrp++h3Y9D7vAC2B/Y7sPZJX9037LV7bOZ48An+ma5Y8i\nc4B51OeHPG9b8/rpEPITxZqP1N4WMso49N5+bPeFZrAuvAjW5/rWulDvtcGwDqhD4RsIbfT5\nVqBc+3vVlEr/owVdwj3Ds9PnueVgs2Pg+HrduPUjxfLIpBzahaPXLD8F+xdgH+png2KkL/Xr\nWuCY3gLO09lBLQc+07Uyqol7oBv2m7CXACdvkAthGHDrjgeD0Y2GC6kL0tIwEY6CutSZBgaN\nkyKLnIROzJvBhHMPGNCLwqbwIJgU5oGgdMIJdR7DRAjH9LUunLjQZ5VJ7W1oBauDNo+BG2Bn\nmB92AH2xCijt0N/6xslXm3wn2QlMslllkh4G2plP2jsITgATy+1wHeibrSAonw/T17pzog+y\nqj8dnwXtnBf0U1A6PkOdx7NhHJwB9r0XVoWP4O+Qq3ZU9IORkFVuYtzAtsxzA8f5YrgNtGkq\nHAdtwHheDPYCk77v5Lvq19x7OT+Phjcgq66mo2PZ0DqJBwypx0Nupa/xNicY7y5E5ijnj3Pc\nuX4QXAWjoB847pbTqi0+jSX7PJnuUGLZvGN8vwg9YSocBoXUkgvOcce4WNn+Enig2A552j1N\n3WQYBl3BBX5/0J60tCs9x9LXcsu5vtVOx8yxyyrt058DYU9wM6S9hWRsSF3yvZw/yvZ3gHMh\nq8zxk+Ep2B1+Bu0Mz6CYV4VyVrqxfmyRMICjuSWrzGn9YEk4F5wfvWAoPAq7wEdwCPhMn12q\n7PcM9C+1Y6r9FMrmtpdgGpwFzvVlwLXINX9vSKu2cQ8+TLc31oeBa19WuebqvytgOtwNR8IC\n0An075UQVJuNzp/cORT6GV+9w0mG4y/02b5Av1bU+/F2I1wPj8Bj0Bd+A98ln7TVvmmN5kR/\nZJFx4z7oIvgYzPHOj3agf7+DwbAjrA4LguuBMkelfZf2s2O/aNJmbo6uF34gOXZjC+A8aQ/F\naDUafQohT25L+aWcjlM5Xz+nrmpPw4tUrYFVYNhkbDBYgwyoH8MJxzXAADWhvgfLwmswAdpC\nXXLC1lfzcgMn6FywKzihP4LnwWTiJFkEgn4NhZyjG1EVjjPOyvunE3oQmGw+gY5wDSwEj4P+\ndRIr7fDchGuCqk2+U6H3qq1foWsu7Pm0FJU+5wwIcdGVsvYtA0G1+bC2a6F/MUefr50mOP0U\nlI7PUOdxSdDPLqjG9dLwChgrS0CuxlPhAl1faWdunO9E3XDYAPRnD3Dcx8A0+CuYTF8HY3th\n8F31Xe69vH9d8UGTP4183+9Bv/0AjqUb+8XAuX4h6NehoB8PgLaQVm0xaCxJfeRirI3O8avA\n8cm1gar/yDG1TaF595+GqYLt62un/nKTqf/uAv2pnbkxpl3FPivXt9rpmNVH2vkZeO874FtY\nEgrJ2JC65HsF22wfynX1K3TdzZ2bOj+I9Oc34PpY130L5Sy6/kf60bVAyiFz2yRoA24w3ZB6\nru4FbVoKill/aPY/Koed+vF0WAE+hz3BjwT92RNGgTamVdu4Bx+m2+fGevpaKeWtaXwwGKPa\np93XwWB4HNLxWpuN9s+dQ1SVTYVyjPXtwPV8A9Dfy8Hh4F7Z/JpP2lronvnaF1vnh/C7YE5a\nGIbANqBdK4J5wNg1L5hrlTkq7bu0nx37j5I25g/znu1vAvcu+fDjaCQUo3E00ubdksYvcNwj\nKXvoBIvCZE+aghz0qPp5YH26mww6wH7gV/QwcIKNgErICbInvAcPwfGwFqgu4ASb6EkV6Gps\neBKeATfAq4Ib5ctgc3BB+ACqVS6gnWF/2Aq6gx/HrcCkVc1aGeOaw3rQA9aFO8GP/PehUtJX\nLpxu5DcEbdE2F5l/QlrG7zfgZiaqdg9M4LIL3muwNuwHwa8/Uq6kXJgfA+fGrrA4FPuBQdOK\n6ReeNAC2h16gnd9Btcm5cQ+4CT0SFgJ/GKk2uTm7FZy3J4MbJtenatZcGLcwdIRusB2Y39vB\n19CYMl+/DH68zQJrwhgwdy4Gzu9K5m4eV1BujPuB+b0PrA9bwL7QGarFTkwpKPdyH4Fr4n7g\n/sRzc9dYqKTM5+bxNnA+jAfX7hZwBtwO14NxUQ0yb/aFW+Am8KMt7DvPouxe425w39ck5OSL\nyu4BE+uy4ObtORgMTrAVwCB+ECohn7k/PAFHwedwGIwGN5wGZ6U3SDzyfzQrNWvBwWDsvQBX\nwiDoD07+gVDNMsnrbxcD/6bDBeoY8Bfoav9AaoeNLvhuXIeAY7AROC5vQKVkDCwA56Qe+A3l\n28A49vgpzAt7gkl3OkTV7oFFuexYrgvXgT61bmH4FSqpZjxseTD3+COCG/xZoNrkHNgQroEt\nQTurUS0xqhMsDVuBH3atoNqknf7K7Q+F+tO4mw2qWdr8A9wAL8JvsDno48aOB+fzJ+BH0dzg\nnkIb/wHPwnvwEDS2tHMSaNM+4PpyIrwOZ4IxcDFUq5bDsG1hDnCOXQCuk34oec3cVelYMDf5\nkfE8tIfL4WFwLTRWzesHwLIwGrLI99oMzH/55LP6QbE/cpxO27tgOUjL/VFXMHabjJo3GUur\n01CTv8nUD5P9wMmkfk/4tuas4f9wopiETKAmejeW+8NkOAmqKTH5cTE7DAIX+nNgP3Ci+lG5\nGrgYuABcAdWmKRj0HcwJD4ALw4+g7Z0TBnKsRpngn4E9YZ3EQGNV+U6VkhsPZRx8WVOa8YdJ\n3mt7pepcdG9JncdiYQ8EvzqWzv+gCRTmCScVOjrPt0lwfrhB2h5Ohmr62HUN3Bj8+FDm82rc\n0JtftgD9aq507fFYbWqJQV1AO82NjnWIS4pVKTehrpVrwlqJhfrXHxiM3caU8bkl7ARuUvXp\nUWD9S3A0GLPVoIUxYgi4Ri4C14MxOhWcY/q4GnU6Rp0A+te4/QnMVYeD73I3uCaFtZJiRdSL\np/jBNj/4Y8jf4VFw/TY224Gq7/wyt8xVc6f//cP52+x/q2utGcVVSeu89ElTKTvZorJ7wIlk\nQugJJ4H+XABMCGrxGYcG/9MPsUWhDbwLzWFtWAqq6ePIBPMcnAF+xD0Ph4B6H/zoWBCOhMtg\nPag2PYVBJgz/9shkuiu4gLlIDYX7oNKbUR5ZlPw46g7GhDFiUtTvvofjUSm9xYPGgr/ShV/B\nl6XsHPoRVgR9vAR8DjdAVN0emEQTF86WoD/14aZgHnKhr6TMhdrj4u7CbowtBidCNUm7rgV9\nZV7y6C/J1SY37G4ynLeXgnZ2gmrTLxjUB1rAnaC9lVoHeVQmjaTXCnAuGLfOHz8+XE89Nqam\n8/CJsDS8Dc6hMXAj7A+uQ9Ui/aYPlwTHXDtngYNhBFSjdsGo42E38KNuIJhD5wPHf13ww3kA\nuMZXUn4Umz/dU7guTgBzqfHgXskfl19PzjlkkvnvSehegB7Ufwzl0FrcxA/RJiOTQVT9PPAe\n3f1QOhFMpuPAv8UxOewOldDcPOQt0BY/Kn4GE3416gCM8mNuPOivoeAisBn4AaWuhCGwlydV\npk+xx6SxCzjup8AguBW2BheJraAa5UZAmw+FYWCidRF4A7pBpeRC4/M2B214BYzdVnA0aKeJ\neyocBfp1Poiq3QOLcLk1OP9fA+fXYzAFvFZJuYE3L70I14LjeDa4EFeT3MDtDObrPtAfOkCl\n/cUja9WsXN0H/GHhYLgY3EDp52qSdh4O42FHeAj0bzWrHca5bh8Hb8KHsCF8DW6QG1OujX5s\n+MOn6+byYG40T1aTHPdnQbtGw5Ogrc6vV6FaZT66Cx5MDOzJ0f3bvKDd5gXnmB8mjaGfeKjz\n3R8aHHfXxpdhIqwK5oSmopUw9KCmYqx26vSowh6Yi0snFb5cc2Ud/jRY3wI3m0+Am739YSsw\ncdSmtrVdLPKaG6KPwV8ZXZAOgG5ggiqXlivDjRbiHnvDLbAKuOk1mbqR0ua09OXGUJf/030s\nOwnrIxO6C1Jdz3XMTVifw3Xg+bHgR14xvl+DdvVVO25Ql53pZ2zKiR94Q2BheAFceLtCJ8h3\nr3JsENoXuPdV1JvknWd3w96wCbgRCDJmHJMTwA1LIbmpra+MyXw+qO990/07p08ylteiXz47\n50/upy8Xg3kgLPx7UM7Xh+q80p/mlaz6jY7fwXgwJ84Ja4NjW4odNK9V63P1k1pb1H5xOpe1\n8314AIxFdRx8UVMqzx+O2Qf1uJVj8X1yj3s4mh9ng3+Ceb9ccg74YZ1Vv9DR+ToSRoBj7tpR\nzjHndjX/TYab1/poWzovBMbQl/AkLAuTwVjYF3YB22RVOzoOy9qZfq4x5mmP84Hri/c7FMqp\nxctws+bc4xJYESy/A3vD8aB/y6H6jIXPNza7g2Ou1oDPIB2f7k+0eTo8AiPgAChFjlVWaaPq\nAZvWlGb87dVblF8DffA8uM/cDeoj96f6Izwn917mlt2hvnPN+96eYLlJKAxEkzC2wkauzvMu\ng7o+cLzeAlwYTGJKv7YC60xodelzGpiIQ/+62qevO9HPhzCW2tMSXFCz3I9uBTWFK3sWvFr7\nBTe+Z0CwM7QuZK/+c3K60SpVTuYepXZK2m/J8ZQi+vqrkouAfg4q9C7heu7xXSr+lltZ5Lm/\nzh5XZNvQrJB9+trFoFCsmpSPDDcp8bgH7XsX2ce4NWa1JUgf6+u0n8O13OMQKvrkVhZ5/lfa\nHVRk2/o2e5obnJrxJtqorYXkWDpn0ptm85PzznxUih6m8TmldEi1PYJyN0jP33zjm+qSuegH\nw8UZexsv26f6lhJvqW5FFW+m1TVFtfzfRv2o2iJVnXVMU7coWNRGbc2is+nUMdXRMTevpOMx\ndbleRTfj/hiQRRfRad2kY75c7nxxLpVjHT2X+zyUPKvUw1V0WDXplM/OUu9XW/vTuPhUbQ1q\nuXYL19pD1txdy63/65Lrw0kw6L9qiz+5i6aLp5rn8+msXDdu6zP22nk0vAKlytjzA32BpGOw\n56dSb1RE+yVocz+MK9DW8bwZfihwPV/1RlS6T1wGZocR8DYMhg+gyciBiIoeiB6IHogeiB6I\nHogeiB6IHogeiB7I6gE/Ck+F12EkTILWsAJ0Bn9ovR6iogeiB6IHogeiB6IHogeiB6IHogei\nB/7UHpiNt/N/Buz/xDKfulLpdf9mNip6IHogeiB6IHogeiB6IHogeiB6IHrgT+0BP4z8G6Pa\n5H8v6n/3FRU9ED0QPRA9ED0QPRA9ED0QPRA9ED3wp/aA/8nOZDgQ/O+mctWTiu/A/74rKnog\neiB6IHogeiB6IHogeiB6IHogeuBP74FOvOF4+BSehwHwMvgPkflxtBU0GcV/pKHJDFU0NHog\neiB6IHogeiB6IHogeiB6oGo9MBeWdQD/VcOl4VsYDf5Ljt9AVPRA9ED0QPRA9ED0QPRA9ED0\nQPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0wMzsgfV5+SeaqgPy/YdUTfVdot3RA9ED\n0QPRA9ED0QPRA9ED0QPRA43vgXkwYZ3GNyObBfEDKZvfYq/ogeiB6IHogeiB6IHogeiB6IHo\ngT+hB5r9Cd8pvlL0QPRA9ED0QPRA9ED0QPRA9ED0QON54DcePQVebTwT4pOjB6IHogeiB6IH\nogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IH\nogeiB6IHogeiB6IHyuyB+H8UW9ihy3KpL1TiH7Lw/2X4yMKm1HplJa6eAJWw0/8t6fG1WlP4\n4ppcOhoqEXNjec4phU2p9Yr/LOXfoRJ2vs9zzqjVmsIXN+bSQVAJO9/kOecVNqXWK1twdb9a\nW5Tvov+P3ZdmvN129NsrY99Suw2iwzWldkra78pRKqGnechNGR/UjX7bZ+xbardH6HBnqZ2S\n9j04dsnYt9Ru99HhgVI7Je2d65tk7Ftqt9vp8FipnZL25s4NMvYttduNdHim1E5J+2M4uiY1\ntP7gAc71wRkfdBL9XOMbWtpp7nwl44NOo98yGfuW0k07zwfXpCw6h06LZ+lYYp/faX8WuMZn\n0UV0WiBLxxL7aOepMKbEfjNF8+YzxVtme8n16NYVbiui+5a0mRvmBH36MQyDrWE4fAiFtDAX\n9oZjYXqhRrXU+/9YvDPcVUubQpfcXPcE/516P36CFqSwE9wCK8AqMAf4H9wdD1nk4r4t3J+l\nc4E+61K/KDwCLnYuJLPDT3AKZNFmdHI8/X99LpeCP2/lhvpyRZgNpkHWDyRt1NZSNjL6yjEw\nNlcDY9b/Z+vPoA3cB7nSVj8as34g+Tw/5p6CYuQi6z8L2hq+h3fgPchVOyo6QdjAr0pZ32b9\nQNqRvn+B56CQduPCSNCmoBYU/gqPwsehMudoTtCHy4Ht24Obpizy48i4z7rhKvaZ69FwQQj+\nLbZfaLcnheXBWMuVuURfL5lcmMTRdj8k57UdjFv9F2LV+8wJd0IWdaeTOfjVOjrPxXWftQS4\nSZsI2vwj5NNaVLaFAclF54DK+oFkjJkz3vAmRagVbTaEdtAMpoD2Ot/TcpwXAuNXmVO+h8c8\nyaCe9DH/vltCX32qHeYg/fk+uAHWz0EdKJivnkwqzH+fQtYPpIPo+wl8AMpc4sZZv/ncEJM+\nZwIU63ea/pfMf+Mh63w9lL7mnNGQVeZG8/i9qRsYzzuAc9F1/Rd4D16BLOpNJ+fQ+AydtaUb\naN9Xqf7tKHeGh8BYXgS01ecYH1l0FJ2eBudDVjmf9oPHYSoEtaewCfye8AJHY7lUuUYcAQ+D\nMVpO7cnNWoJxbi7w6FwfA1HRA0V7wAn7URGtZ6HNZ/A17APbgRt2N6oGsicAAEAASURBVL+f\nw35Qm0z8JmQnRRb1olPW5OnGzcRookzLxVybzgMn0dFwPNg2q0ygb2ftXKDfqdSbLC+CL8Gk\n0hdc4LOqDx2HZe1coN/a1OvP28BYcdE7Cz6FrOpPx1I3B46ryfsnOAW6wGnwK0yCfDqQypH5\nLhRZdwHtwiaxri7OOX8kOBu07QRwLB2TXO1ExY/QIrlwJMc3knKWw9V0ur2Ojn4YaVNa83Gi\nTzdKV+aUXZDHQQ+4CV6GrPIj+9qsnUvodyZtw2a0hG7/afoAJedlruakwnzlRmzXBP3hAu1G\nqS45x9P5zmf4rKzyHX3X2jQPFyfAUNgZusJwcPPjR0s+nUjlW6kLjpljl1VD6HhSkZ3N66+D\nG17nlPn9WfgY/BhK6wxOXkxVOAecC1n1Bh2di8XKDzLzz+WwJbhOTIOLIS3H+ZlUhTnF3JJV\n5rQDk87G3VfwJYSYND5HgXPescwqbTZXZ9UUOnbP2jnpp0+dX0HzUpgIrkXGxe7wBeTLs1QX\nJf23U1Et/7fRElS5Pq6Zc8n3/ha8t+O9LZhHfZ+s+oWOXbJ2Tvq15GjMbpO6jx+gP4BrgTE0\nFdybZVELOumPDlk619Lnb1z7DQbBFtAPXEPNEVF5PDBrnrqZpWpxXvQeeKAAJ1LfBurS5jSY\nD5w0JtRHwURh4M0D9dlk0L1B5STX3pPBd1Bzw2kwGA6HQ+B8GA7VJpPmOvB32B8uBn8RMblU\nk97CmEmwF+wLV4AL3yxQSb3Kw0zgb8AZ8DTcCCb21tDYOhUDjDUXam3zI/JocC46v9IayMnP\n4HsEP4YjVQ2iB7mrsbZMcnc3ov+Cj0Df5lNHKs0RW8NNMBKmw8wq499Y6wL3J1h2k/pXqEvm\nq6XgsLoalvF6T+7VDLYEY+BecIOxMDin8+lhKleD/fNdbOC6Hbm/GzZtvBO0xc3cN3AopGUO\n3RD2SFdWsHwKz7oNHM+n4BLQZ54vCEH6fVPYIVSU8WjcmReNy5/AuDQmF4KV4SFoynLOtAVz\nl+oFs4H7k0vhHhgDjaXJPPh1OANmT4xw7E8Ar42GXeAx+BYaW79gwOPQF+ZNjHGNsv5ZMH5c\nU6tJs2CM9j4Jq4B+7QfGe1QBD8zMH0gGxoRa+I1rzQv4LV3tQmRyuRuGwDB4D1w8Pbp5qmb5\nEWSi9B1egLGwNJwNrcAJX60ajmFXgnF8Kphk+4CLXTXJWPpXYtB1HAfBeVDpD7m5eaYbvVXB\ncR4M/k9hPgQ3B15vLDmG7eGZHAM8167Fc+q/5nxfMH5dQHuD8dqQOpObfwD6TN/pw92hO7g4\n5pP5YRLo46gZ/zPYV3GE4xfkxv0V0Fd1ybF2zC8Gx2FPKCZP0yyztOsl+D51h2mUzT+FbH6H\na0fD9fAW7ASVkjaNgPTaY3was7n26veT4U7wffwwqKS0J9+cb0b98ilDBlI+GwbAa7ARlEsr\ncaOhcB48AsanOdq845punDVljcH4w+BCcE9yPCwA14Afnuq3GYdG+7MHT14LxoP7EOe56+Mn\n8DxU25p+KDa1AdcA7e0G2nsQVKP8kFsEToOX4E0wtmeDqAIeaOiFpcBjq6L6C6w4phZLXIBX\nq+V6uDSBwlLQEW6EzuBCujcYhNWuKRi4BrjRcIN6F7hYfAYmzXXgUWhMtePhP0N6wQ/2uGk1\nWbmoORa3wSnQmGrNwxeGiaDd6gmYBe4A616HPaCS+pKH+QtcP3Bsl4erYT5YBr6DxpILoB8S\n60J6w+S5/nKhVAuBG5dx8DCsALvDVuAC0JD6kZtvATvD5vAI3AjBNor/I2NyMVgU8sXv/3T4\nk1eM5/12gVbguKqWsDq8DMvBePgVCsmYfQEcBz88amvL5XrLMdwMXC/Ds2anvArcD2kZm8ah\nc98N6dOwI+wKlZL2Orfnga+Th87KcW14Kjn3YN3S4Eb5MdgOukIlpa3O8VtSD/Vc6cO0LuPE\nGFkTnPPlkjbsALvBfbA1uNk11zwI+ugnaMrz1zEeBM6ZLWFZOBJmgXbQAhpT7/HwlaAbtIMB\ncDP4UewepNrkWrUGuG9aDZxrH4PrUjXKPPAVrAnbgzl4MzDvRhXwwKwF6mN18R54gqaj4SH4\nBSbCSWDg9YAXoB1Us0z+t8Mc4KL+JoyCt+BacHFfDCqtTjxQO0w6U2EouIFKawond0AX0N4f\noLHi2l9jXIi+gJHgR+YxoHwPY8TE9CKYYJtBJeXm7iI4DfrBYXAznA9XwR/QmPIX3JPhYHCc\nXSwvhStgXngKPgHn23jYAfTjBfA4VOJXxv14jmPsR/mZcAm4OBbSc1x4Bx6GTWBBcKM9s8p4\nc57cDWsl3M/RHxROBeeJG41eUJs+4OLZ4Ia5oXUDD5gfzDNuitaB+8D5Yt5UreBKCHP/c8p9\n4F0wTt6ASkl/ToMHYQNYFf4NzinnuTIPOXecS5+CNl4HI6CSMvc4l46F5cE5bYzcCZNBafeL\nMBUGwL7wJZRLN3KjOcGYnA6PwLrgmP4dxoLPHgLLQlNVmDP78AKt4WmYCL6fcd3Y+hoDWsDf\nwLgwD/iRuhFcCivDbFAtaokh28KRoP+2BNehFcFr1aTfMca1/2x4DMxlrv9zQFQBDzQvUD8z\nVJsQ94dCPlizSCeYULcGFyATqLLODZEbu5PgSVgNfoHGlAn/QNgUvoO7wIVbHxwBJk4Xn1fA\nNm7+XODvBBPXr1ApLceDnMg++1bYGFaBF8FE6QYk6AAKl8F94Lt8D42hy3noDvAC6Osv4XQw\n8V8L+tdNlB9KzSD9DpxWRM/wFGNS+5SJ8wfYDGaBxvxIMt78m5aLQfuMN8f+BHAj7PzZCPTb\nMeB4d4DXoBJybB1H/Xc3uFky7hxvN5va56b0NtCv6jfYDm6AgaCPK2Uvj2pUdeTpxrrjdT68\nB1/AFmC+HA7K+fExHASjYXfwb4nsNwAaWu15gHE2DwyCC8HYC9K2LuAYvplUmiN9j6+Sc2N2\nR9gT3oCtwPt8A64D5dBC3MR4Wx5GwNngDwa5+pEK7dXHLyUX3RxvA+PgL3AP/At8p0XgInDN\nGgX1VSdusAkY+3eC87SQnEfzwplwLoQ5fxhlNQc8DWNhPfgenPt7QbnmkXGWG5P6YS5wfO+H\np6BbclyVoz6uNrlG9oD5YRgYdy1gPpgAjoeaCgeC6/+soL6bcajon7vwtK6gffrX3HABHAcD\nYGW4HHwXc+jh0Jh+d83eD3aFH8D8PydsDWOgL+wL70Nj2snj/0dtqTGejQFzk+uQNoaYoBgV\nPfB/HliG4qvgYpaPT6kvJXhOp/3v8EdydGH0HuuCSd1JlU9u8OxjIsuiXnRyU1GXWtFgCHwO\n18GTEOz1+b7rKZDWUZx8DCaBfcENYFb1puPbJXQ+j7avw3PwJbwCPj/YehnllpDW3JyY+MOm\nJX2t2HIfGpqQS1UbOuhDfeox2OnYj4S0WnNyBExJV5ZY7k/7Z0rsY/N7QX+GsZ9OOfi1I+Vc\n6c9c+3Pb1HbugudiV4xcEI3P9+AWcMxdiI4G7XQj5zyaDPrXd5gIC8KR4DzOKjfkt9fR+Xmu\nX5vT5i3OtSWMt3bq43wyRv4FzsOsupWOuTZkvVdt/dywmiOyys22sa8/wlg5pmFDRrHmY3ij\n5Lp5Mi3HY2C6okD5IuofKHCtmGrnkPPVzVkYx28o59pDVY0W40/jMC3ns++5c7qS8skwOqlz\nzBy7rHqRjr+C8yHY+TPlTaE2LcTFJXIa3MH5Qzl1S3KuH54GfZ9Vzl3H/WsIdo6i3BZqU3Mu\nLg3m8LT25sR8ro+D3Nx9BuaWrDKnmdvSmo0TYzTkQ/OLfjeWLbs2GBvdoFgZX+bqrHKN6F5E\n50Npo42PguM3FRwDx9RxcB3fC4IuoeA6tzgYz5Z9v6xyjHYqofOFtDWe3aRrn7br98shrc6c\neH0xaAvvQm/IKp/RJUNn89ZrEPypvdqVO4+upW4ojIFekEUt6OS9O2TpnKfPWtQ5Pq6twW6P\nl8JHUEo803zmUXqxmnneesabjuWwHhg8+XARMUiLUWcanQgGoQl2C3DjZqCfAhOgLTSmDuHh\nK8AacBSsAm+CE2U/MBaOgfRiOpzzhWE6mGArKf3lZsCxOR9WBRPjeHCBdFKbZNP6lpNP0xUV\nLOtbfajN3WFZ0F4X3WUgLRdZF/jG0CY8dB44A7SxJ4RFqivlxtRFPPxVMEb3hQ3gNjgWPgLt\nvRtuAf19B8wHA6ASMiadE0H9KKwGzqH9YTvQzl1ge8jVNCrMETODzJ3NoBc4bkPBDVpfCNJX\ns4P+MxelpZ/1d0NLG3+GIWCu2QGcs8+DHxe5cuOZmwsXo84Nfjo27FfOd3AtUXeCuXsj+B7c\nvOvDQjIfTs65mBvHXp4EbqDm8KQeMge2BOftytAfHH8/FMI7UPwfuVkeB+bwtJbiZDSYM4P+\noJA+D/X1Pbo53xQeAHOiuWjt5PxAjqfCBNCmatKCGOMaeTiYgw6GUWAcmy/bw5VwK2wByhh4\nA6ZAbjxT1aBah7sfAbPAWaB9PcA5tCOkFeaU9k6E39MXK1h27I0F/ai9fkwah+b5jhCkvYuA\n16pFl2HIl+A+Tp9vA6/DoeBcjSrgAZNZVP09cBK3MPDuASfRIHCTNC9sCcvBCGhMbcbDtc+E\nuCc4MZzYk2FOcAK5uT8AgrpQ8JeQX0JFBY/6aw14CPYHE+kjsDBMBTfFLlpzQzXITZJJ8Va4\nC8aCC65J3dioBjnf28BPcDJo4y1wKWjj4tBY0rZNwWTuZknpz4vBMV8C/gnGwAkwGpaHB2BD\nWBoaWsakcyKoJ4UvQN89C09AV/BddoWZWfpkKNwMxtkR0AyOTI4cavQBf9p28xmn//nTvPn+\nf84aruCz3TjsAX6kGV/3g7G3HxSjCTQyd6Zjw37lfIfm3M8PmF5gHL4I/wA/aEqNNf26Bfju\nQatTWAi+CRUZj95zIrj58jmnwFewFGwLpcp3XRnSuakV5+axcmp+brYfHATmmtfA9ftCOBpu\nAD+k24M2VZM6YIzxenVi1JocN4FrYDkYBaeCH9fGjPIdOkMLqLQ244Hfwe1wGmjfLTAN9L2x\nGOSc+hVs05j6Kw+fAn6Eaou+VMb7PjWlGX9obzXFhx/JxkdbOBBcr1aCw2BW8HpUAQ+YdGdm\nOREL+cBfZYrVoknD+znuBo/CFfALtICh8DQ0pn7k4fMkBrhYuTH5Cfw4Mln1h3/BjvASbApH\nQQ9oDOm/48BfO1wMW8Mz8A6YREeDvl0MPoRqUEiW4zDmbdB2E5OJSFunQ2NKHzYDNxj3wo2g\n/w4G5QassfQ7DzYeQ4wGO7RZPQ/G5ONgjB4AK0A3cOPlZqahdToPGAw3wt2wALSE62ASqDfg\nD9DHM7OcC5+mHOA46hePjpULtZoM14CbpVPBeb077ACbQENLO417N79B2qh9S4WKOo7m1rPg\nElgI3oQtwU1IVyiHtFOb9GGQsfcrFGtn6HcuhdfhPvg3LAJ94SH4Huoj7XRMg7TRTdhnUKqd\n3uNheBf8AcL5p329wTxWTi3BzczT+uUHGA/ml+1gGdgf5oKR4PpeTTL+9PPs4Fqun60zpj0G\n+W49k5NLOR4Ij8HlMB9UStpkTGhPWq7jf4GzQftWBn/I80eyL6AxNQcP/zJlwETK5v0DYC3Y\nFvaA7WFjuA2qQeYH9x0t4FU4DZz/d8EfYH1UAQ/MWqB+ZqhejZd0IXOS5mMv6ov1j5vhr6AP\nuCl2s3cPmLTGgpPGYGxM3c3Dd4et4H1YAy6CVvA4XA1fQ1twAdgB9oXboTH0KQ91IXQT6qL7\ndxgKL4DJ/Bf4HkxU1SAXGsd9GhwKLuz62gTlBqyxP44woWZ8P7GA1oQH4AzQPuPzFmhMOWf6\ngjGo2oDJ/HkwHp1LbljuhTlgE5gH5ofwXhQbTC9z5y6wIgwA84O8BUHaqxrSlz7/AnAMc/UP\nKpZMVf6T8uywBJwP2ufGLyhf/cpcbA/F5r9wr/TRObtcUuEcdtMzChwn50hah3Pihsj86Sbd\n5+vnl6AS0gfzJg/qynFraA3myWLl5v0EOBic+5uD93oQyiFzi/7Ul2oZcPOon0dAKXKT3wnM\no/fBmeDc88eG+kp7HL/mCRdy/BkWglL8SfMa/caf5tEXwY2869F34Fwsp8wt2rkF3A3dwTXH\nOGgJV8JP4Lg6FtWkwRjzGTiHtFU/O+cPAd8lyHcLY/AR5Y7gunQnLA6VUsid2me8qF1gQ/C8\nLTiHjoRz4DhobLnHc/4ZF0HuQbTXet9pRdDHL0O16FcMMacas+7/LoETYQ+Iih6o1QNzcHXu\nAlxBvcn5LfDjwU3HP+BNmAgmlYvAxd7gy4fJxwneCnLl4jkMTGxuTltAFvWik4k9/Xyfmz63\n7ESZkNT7XuG69bZ30TFpzgf55IbFhJBVven4MbwG3ic8v5Rj6Bfsd2GYDdLaiZOv0hUllt2k\nTYa74CDIHZctqfsSarNbf+rX0GY45SUgre6cTElXlFjuT/uv4Qf4FkzKp8NAMD6fgU1BLQjP\nQdqmYFv66AbAj4/V4UX4EfS5cZNVF9BRf40GF/AQXy6G74CxK8ZFR8i10bF+F9Ix7Zz5KxwA\njtUD4GL6BmSVc9y40c6B4ILofJgEU8F7u2HSR2mfhbJ2Bxu12XPfy35uRoNOojAknGQ43kof\nfdUBnoMtIC1zkjGs1gFzjNKOY6EbjIelQeWr34V6bX/aBhllzAffpI/X5dxvIc71/Tj4EM6C\n2aFY+b4PFNs4TzvjJ21fKDt+t8Bf4A4wDozX46AZlKpr6eDYZZWxGWxLHx2nNVI3nYeyPhmd\nYNm6YnU7DR2PrDJnpO1LlwdzLdcW4/d5MBd6fQcoRgNoZG7JqrF0dD36FL4Bxztta7rsvLZN\nZyhGxvQ1MB6mQX/Iqk/o6Noc1jztSpfDeT/qv8+55js5HrY3b5nL14J8GkZlyBv5rtdV9y0N\ngh9z7Uv7UpueBeM22J72vffYFwrpbS70LnSxiHp9YJxdDCsn7dtyvAVcN40J3yVtc23l8bTN\np9FU9sp3oYg69xzuCYMfPYZyPluMz3PgRDBHTQLX7zfBeTUW8vWzznd1TYjK44FZ89TNTFVh\nc2mQ5GLQqdtgXfgATgMT8+mwMThR/cgqJDd2PeD+nAYHcX4nuNGpz2IUbts8KTipVLMZh//6\n07FeMqlx0oS21oubkvkgdwGjqmzyGWtDsLeYG5vYg0I/k77168FDMAuUU/rPZ5wLT4AJS+0G\nntflI/vrU+PLj4s1wLH2Q6Wc8uPwPfgaVgUTpM8+DT6Cp2FPeB86gzYVUkiWu9JgOGwALpoj\nIbw/xUyaSC8XpK3gBdgchsBK8CwMgrWSY66Njq0LmfE6DtT8cCM4Pv7yvReUQ76rc8O5vTC0\nhjnB8R4KbjZbggrzZ8bZDN/qe6XNbgB+gdXBd+wC5ZJx1ALcTA7OuenNnHdN6hz7G8HYcOH/\nF5h3zoDuUKieSzUbR2Miq/RdPvWkcr/kgj9OGQcbgbZdA3+FR0AfVkLakE/GYWfQv+3gNDDW\nToRrodJqVeCBxsHrsAIYm8+BcXFJgmXrQtxSbFDpt3xyPXWcP4Rgy46UzacT4J8wAh6AfaCh\n5VxdAJzbc0Ehu53nU8G1S1/XJePJmP4LnAPOu/pIO91jpOdDuuy9PT8FtNHnvQw/gu+Uttn1\nc2toCHnv4Mdc+9LP06ZN4Qv4AGxrXcjvvusNcAw0hHye6+X6MBy6wUuwPHwOri3m/Lqkvd9B\nWzi1rsYZr2un0mYpJGPkWNCOu8H4c/1uD59AO8gn30F/R/1JPOCi+zcYAD3BhBDUhsLz4aQM\nx4u5h79sqJXAYHIzrhaB38BN0O8JTpbJYDs3RqF+U8ouDh6VicTkEBJAB8r2SScyTotWL1pq\nxzh4HdLP9r4XgBMt2KMtg8Brbq5fBZPq5mDdXZBPXaj03lnVm47BX1Mo+ywTknZZDvblHrXX\nX0JCfbCvHXUuAtrkxjtoJwpfhZMMxz70GZb0W5rjl2DMzQKfQrAj95j7Dr5rV1Au+sbHGZ4k\n6s5RP2RVfzqOSzo7L4zVD0GfGmPKDb02G6v6UZu1U5+l7Q1lj9pkOzes6kAYVVPK9ofx53xV\nLj6fwVjQ3r0h6O8Ugk9bJJUjU3Vh03Qqdc4f3+k8CDqSwhvhJMPxavpopza4aDp++tPFeyj8\nG4J9B1HuDMGn1vs+E0G7noQFwLF4FL4F56Y6CVzAsupWOg6E+2EcrAu5eocKY/cDmAf082UQ\ntBeFy6FQ/S5c05e+R1Y9T0f9cjU45sF3HqeB8+loMN6M36BlKfwE24WKOo7GuPMrq96mozaZ\n2x3DtJ3aIf0gqCMF58nKoaLI47W0c+yyagwdg23apA3h3PJj0AO+hIUhyLJ1XitGt9PIMcsq\n15IwP/VnsNN5EeLgsOTmH3C8MCmHQ18KxoTxUZucq+aWrBpPR32qb4Iff6asbz23HOpdZ26A\n4VCXjqHBZAgx/Qxlc3VWBfse5gZhvQx2jaJOO/WtddNBmXs8dx001zcHZT71R7t8vnXN6wNZ\n9R0dtWMqBPvC8Y2kzmeHunspDwbfyRjR9s3hENDf38OckCvna+/cyhLO9UeXpP0lHL+BEeAe\nwmvBPo8vgrYF/1pnTBu3tt0ZPgTHoCWkNZqTXumKEsotaOtz9ZvPtixp29Ll0EbbdgTf6XB4\nH0K/8A7OLd839NHv3SAqjwdc/JuKtPUJOAGchH3BzUeY7AZoZyhWLnAmHzcv+XBTHPyzGuWf\nYElQnvvc8cmRQ83/HjxM9pdT9U5yJ9FaoJaCNvCgJ2WSdt4B2hVsNhk5OcZBSNbabPJeBXwf\nF+w5wEVUG01M60BDqRk31obZQNuU55Y9OmnDkWKNTO4mX2UCWLWmNMP3JhB9HnybXCrbQd/d\nDybuRWBB0L4gy/ox1HkMmKTWBuU9rC+3nW5ElGOob50X88NSoHyuNhsTYZGkWBMDxkew1TrL\nzoNWniDtDwpjFc6zHrV3EDiPtPcBCBpDQRuUSdt5s7wnyPrLakoz+jh/fKeNkrpyHeblRvrF\nj3AXPTd7LkRuxtJj91ly7objFNA+7fG9PHpuXA4B59RcsCaE96NYL02k965wLuwG+jKMOcWa\nfznufI7D4Wt4GjqCMaANW8NjUKieS/WWfvNZN4MxGGTdfGCd8+NZSMeacfB2co1Dg8ucok2j\nwPFUnotz4V1Ij71j2pA5h9vnlRsypV+d785JbQzSl/IifBIqk/Iwjul3SF0ue/E37jgOtC9s\n0LTTefEEqM1gdlgBzFFpmRMWA9ekhpQ2me+Gph5iLDjmXnskOVp2vXoL1kjqOBSUfn4O0jFd\nsHERF/SbNjj+2qQ8V4uD8TkQrHN+t4Z1k3Pnnptt85K6A/T7Qp6UWT7fuHNemzs9D1gf7KNY\nU9aX+krffgeuXZ67L7LOGG8PDakLuPnc4PxYB8w92hlkTpgOjmWo96iNrhPaexu0hHZQbrXj\nhs4j41IFG0I55FhjROl39yq+k/PKfOZ8tN80UMbEw6DPVbj3jLP45395wAnVVLQMhq4PS8DH\n4EQfDofBZVCqDJ5uYALJpwOp3C654AQ3cZpQlc836NqAAaxWAyeKcjNtvW2+hGAzxZpf0Qza\nFWC0FWWQz3Jx1E4XFjdMJhnlMUwkz+eHT2E+cNPmBt8koB9s6z0aStopPk+F8xlnM+y2HCa8\n/rON9ivjVd8rfb00mAxCHcWyy/vrI5Ok45a2jdOauNBGbQ3y3HgJdjnW9g3nFMsi54D6CvSR\n5z7HDZxaHlxYHVuva3uwbTbKKh0bbuQdf9/F9y23vK82fQ/zgAvgm6D0sbYF+WGhDfpfuTFV\nK4ILgf6dDOWUNvhh5nxNzyHHL8wL30E/O5a2+ycobfe97G98h3d1k+gYfAHp9+M0s7al523Q\nFg4CNyWjIOQfr50NW4Nyvt8Hr4I2fA3Pgu+br34b6sshn7Uy6K8g64xRbdCHG0FajutS4LVK\nKMwJx23x5IHa6Pg5Nxzj1yFIX5s7QzyE+oY+BjuNy6uTh2mn8vgZ6DPH3LbariwvB4M9qYD0\nm/5RS884/CfunUdenwDGnjFgXdo2z53f5rRKSN8o7TIug0/NTSrEgu/kPArXvZZP+WI6X7tS\n68whq4F2Bhvc5JoDVgGl/eagcZ6gjcG2YT1Yi7Jx0VC+1UfmbeeS8tnaa8yqX6E5WP8ReM11\nwD2J0neOf+jX0HPM9U7pw1dA+3120AQK2jsHBJs8aqO5Nsw365x/5Zb7zdagn1SwLZx7TNfp\n5/FJnTbOC6Ht3El5XY7GyGygzA9RfwIP+AEwPuc9NuT8C/AXkUUgBAvFeuti7uCENoD2BBPL\nCHCDZwKYCtbZxmM+/JXhQZgCYTJSrPmKH8exI3QG7W4BWdSLTi4oPv+D5Ji2RRtMnKHO8gWp\nc39ZuB/cYNlmB8inLlSapLOqNx0dK5+RtifYFY65/pxE++lJP9vcCSuBfnWRNYm2hqCdKNRn\nAehD/2HJzbbh6LN3TM7v5RjsrOtov41gf9BvvvOGENSdgnGRVf3p6AJpbJ4H+s1nPgkm7y1A\n39wNwdba/G5f7Qn+H0nZzcNJMAGy6gI6Pg5twDnlAnoO+LyxYMI+Cr6GtJ19OQ+2WP8G9AAX\nIhO8dR0g6EgKtskqN52O7zvgwqedPv/m5HgYx2Cfx+GQ9qc26W+vfQ4PgPf4MjmezFHpzyE1\npWx/3Eq366Bdhu4u9Evm6Zev/kzaGUtZ9R4d9YXjnPabZd9BrQLmLuPBzcli4Px2jD0vRhfR\nSF9nVRizdKwFe8dwU8v/grlgeXgOXAeaQym6lsbhvUvpF9qm50ewL33ci4aOrZsqY3nBBMvW\nLQHF6HYa2SerzEnalevPYL/1wRbH/VPYCtywdQbz/VVQlwbQwNySVa6/+sU5+hukfZkue/01\nmAanQF1amQY/w7lgzhsE5uqs8rna4zruMW2rvs71s7nUuRP6jaesHc5n+z8L+eSa59qXVSEn\nT+YGPqcuHL8LIbyP9hoH4+Eb8Ho+vU1l73wXiqz7hXbuZfxgeBI+AG04Fdw3/AS12R7s1cbz\nk7ZDOOZqNBW9ciuLPG9BO/eEA6E2W7wW7LHsu7mePgjmNa99BOEe6bahr3XdIKqJe6AV9pus\nds55DxeeJ2AlMKjKpYu5kUFkYnKR99xE6TPEhGByD+eFjk7A1SEtF9t7IN3HSZFFTkKDPH2v\nQmX9V+iaifZkKCSTihMwq0xqYfFM26CP0+eFytqnz9PX3bivBWntxImJLqv60PFreB+0zc1R\n0DwUxkDahnxlF8h0vX7fG9LqzsmUdEWJZRdd4zL9nEnJuXbLDdASjoFiY8SPAxcu/R3ubZLN\nqgvoGPzhvbeGWeEq0MbwDMs/5dR5zfp0O+uMw0MgrSM5eSNdUWL5atqHdw72hucHG53/ubak\nz/P52OuXQTNQJ0G+BbXmYhF/uMm+toh29W3ihurJetyk0DwZyD3dDAc5X/0gCj6233rhYhHH\ni2jzQBHtCjVxrodn5x6ncU1fu5EO14ZTXhZKlfdx7LLKuRFsSB+NLzdqQZtRmAyhjeVNw8Ui\njrfTxrmQVc7N8Ozco9d2Sd3YNU+/pOfQbZzPnmpTqDiAC+aWrJpIxzDf08/Ptdlz5/WlEOYw\nxVq1I1fD3sDc37/W1rVfTM+NfLZZp/2ueeMg3WZ6zvmbnKfnHqf/0TBKrn1Z5T4p2JK2Ibes\nrbl25frfvDNfAUPq+4Hks0eD8348rAA9wPNga649oT7f8R36+bGVK5/RK7eyyHPnRb5n1Van\nzZNS/dI+ru19XOu6QVQeDzTPU1etVQ5kD7gLXLCWBJOPG6PH4UUoRUvQ+HIo5AN/CTIgDR43\nRlNBrQpO3rfAXxGOhXNA+1qCmmXGoabvvZQN0LT8UNgd2sJ2cAXURyZHn2kCnwt83kPgvX0/\nr10FvcGEsBz8BC4S64CyvX5tSLkxfh2ugX+B53OCC5C26xdtdSN9FDjGbeB9eBR8r/VhWTAx\nmfBzfUtVvfUJd9DG58BxDupFoR34TBd7fWgymwNMSGfBI+BGvR10hI9gMBir5ZZxeTpoiwl5\nHLSDpWEUTAZ1HtwEJ4ExoK3GhZplxqHmH5Bwozk8OT+F496wCayb1GU9ODdPAO+trepvoE/0\nqXbOC8Zxe9Am596vYIz0hGngtSlgLLggl1vPcMOTwfHzWcbeeNCfX8DRsCYYr/rPOGgGboZ2\nhrdhJVgIXDR9V9/deJrZ5JxdKnlpx1NuAcfbcQ0aQOEJWAv0l/NN/1ZKb/KgjZKHBTsP4dyx\nNA6c4/8Ax/0reBcaQ+/x0DWSBzsnlDn0TPjak0TPcTRetVf5fs73SskctGzysGCnNl0L90F6\n7LXrQOgLy4F9J0El5LheBUNgeTgVtK0ZqD9A+3vAU1DKHH6I9q5djsFFUB+ZYxxf58b34H0t\nm48WBW207LqzP6wHrpFDwbHvANrxAhhDDSXH0vw5LziW5j/Xdv0Y9kYhb3ZMri3McQRMhQ3A\nddQ1bSw0lLTHeHweBoDriDwMWyRl/Wv+/wbcT9nHtUr79gLtdS4OgobMB8ZcG2gOH8BEWBD0\n8VLgeJqnLoed4TDYGLT3LbDtEmBffe1cWxX075fwKNwJUQU8oOObku7GWJPPehCS/veUN4UN\nwYlXrOz3HhTygQHVFnxeWrkT4gAu/g5uhAfCLLATGMRrgzYXkgFvINdX/ip0IZjo9wAnuInm\nEvAZ78O24AQyIUmQk6eS2pKHuVn+FKbAOmAC1a+dYAJY9w3k0xAqpSHlgqM/0zqJk75g8hkN\nxs8qYMzdDovCqeB1NSah5qSB/tCGR3PuPZ5zydVnVGwC2meCH5iUd+TYBhaB8HFEseZj+SqO\nLmrrWlEPOZYv5em/C3Wzg/Hoe/QH58+scAoMAueS9cvAY9CQ8iPs1eQB6TkyOalzcdS+V8B5\n4ybAd1gIjGHHw3kXNWOzaY52Pl8PZ4H+aw57Q1r6Ll98pNs0VNnc7bi5UfgcTgDn8aIQ5vJ3\nlBs65/CIWqWd5vU7wLz5T3DD49zIlX4PcZx7raHPfbZyjujTi6AzPAW/Qj6Zj6TScu3VxqeT\nB+vXgUl5K47mxE3hlqSulIMx/TIYW/WR+eY+MN8Yl83gUDA2tbc9XAC7wf7guKfH/kXOpaGl\nPcana7dj7rqyMjjmxuhD4JroXDoXOkFaj6RPGrh8D/cPY74g5WEwFfrCUtAH/CBaH0ZCK7CN\na9CSYP+h0NByH+I6uA90gHdhazAeBsN+MCtsAsaHOaId3ApKX4+rKc34W/ojknI8FOkBF6um\npp8w2OBIy03cD2CwFKsvaXhiLY0v5tqatVwPl9pRMIm5oHZLyhxq6jayUAH5Li6Yd8MfMBuc\nDE7sifAxzAONLW1zMneFSeCkdwGZH1YC/bgI/A1MotUifac/H4cdYDnQbv37MLgQLg9LgO9V\nrXIxVYvBnqC/Tapq3RmHiv65ME97E4yHZnAdTIa2cDz0g/egHVSDb51X5pmOsDrMCX5Uuch2\nguEQNcMDri3Oj3bg3PkW9F13OA/egGpQS4zwI70H/JjgeG4Hj0C1yNjTp/vC1+CHyBxwIFwI\n1SJ9qcwnS4MfCt9BP7giKXOoKq2KNa5Ni8MeYC7Sv2qDGYdG+1M7noXb4QjQxn+Dm+f5wI+k\nFuDex3zuezSWdubB+u9+CHshbTsFzoTrwTnmWl8t+geGuJZvDCtDf1DG8QfwNJgbbgHfwZxW\nKR3CgwbBC6AtrpPrg7nAdfsk6Ant4BNoDceB/v8BourpASfVn0VOuoMa4WV+5ZkmJhOBSUss\nq0olq59mPO4//5OWVznXBifJIuCGUwW7ZpxV/k+ffy+4AXGTGTZRV1FeDvSldXtANcnYMjGu\nkBjleDvOJtaFwc2UCctNYDXrN4zTdjcAIVZ9L+t+gUrL/GMszAXa5vi7AVCfgTbpc+dRNfhW\nO9yUOtYfg+PvJkW5EYz6Pw84to7fVzA7+CPDJJgCa0E1yXH8GuYGbbVcbTZiUs0c0LZ5wVw6\nGarNTsfddce/NV4AzC/jQL+G/EmxquS8zs2L2qvMSdUi87a26ltjYCwsBvr8XfBaY0n/qUfA\nj4mlwTrzorF6MHQH86e5vVrk/HksMeZBju9AeBfzQheYALuBsfwMVEoh9txjOLbmz7XBc+05\nEZaCaXAFuIbOD2dDVBk84MT6s8hfWJyUxcpN2VFwXAEMxGL0ZdLIAB4Kr4F+9dzNfiVk4lT+\nAuLmbd3k6C8JThplm2NrSo33hxsmk8/ExITgJ3+ZuQ4+SQib5KRZox/cDKuwwPsO48AFSunb\nZ8HNYDXLhVVp/8vwUlI2VsNHNsWKyThwUzoCrgcXUmNCbQQbQohfbWxsObeUH5eDYTSEufc5\n5aj/84Ax5nx/EcKYfkR5IQjziWKjSzungHMh2OlGqJpsxJwa25wvr0CIOTfx1Wjnt9jlj3TB\nzkmUlfm9GuXfaijnt2v4MDAuVFjfZ5w17p/GpTJnjgTnlnNM+TEa4remosJ/mJ8d776wM+g/\n/WpOnw4XwWygbphxqIo/nT8rwMbgvuNyCGuN+7dRYFx0AP1tXFdSS/Awffc0mDvd2y0P74Lz\n/znwI/Q06Acnwz4QVQYPVGoDXwZTy36LBbnjruAEzqdFqQwTJd/1UOfGaDHQlzuCScxfHuaE\nllAJteEha4DPHAJOdpPlXmCikmtgDzgTGksukHvCftAaTEj+ojQG3HQ+BU7usKBSrAqNx4qX\nYQMwSZroNwPjQ/SvCaza5a/P+tzY2A6MTz+MTLRh8aVYMbkY9YfHYd7kqb9ydE6+Di4IY2E5\ncKHQ/sZUGG9zx96g/34Bfecvd1H/5wHH0R+htk+q3GRsBBPgmaSuGg7O3ZXAGPsN3OTJvVBN\n0n+rgz+CTQfniPPj31BNcj4sCW7YgzpTeAQmh4oqOzruyrzonsCjdc2hsWUsuka2BeeTOUj7\nPHdjb040f+vzNWE4NJbe58G9YTBsBWdDPzBPOs/Ej+cLoVrkvmgI+CH8I7hHmgatwfFfGpxr\nXrsdKin3Q47tO6AtH8GqcAU47z8E59uzsC/od2PED2h9baxE1cMDOr2pyUV2T1gGDJ4R8DYY\nHB9AsRpHw461NO7GtevgzFraeMnkpX6AUK6p4A8Dta7+i4fG9Ti6QXsz6a9/nBiPgYvqUuAk\n9wPKJFqXPTTJK+9RX83HDVyAvoF7wGTqAtAS/CDdHbxm0s9qZ3v61kcmlnx+epX69UH/Kn07\nBuZJcEHw/YrVKsU2rKXdslwrxU9+2BkbJvsQq54r3yPfvVx06ys3oPnu7X1N/n7Iu+grN9Zu\nTp07X0F4/t8ou7gW0nqFLpRQ7xwpZKe3ccx/AxdMfamc9y2hMxg3dalDXQ2KuO671mZnEbeo\ns0lnWtTm77pu4Dh+CvNDGFsX7/uhH5RLzsfJ9bjZd/R1XM2hyvngvD7WkzLKMfNX36wyL+pT\nY01p50jY15MyyjkwpB7305+O+4JgLlX6dxSUM2bNKY5TfbQLnV3X9Kn+9MeikBd9D/dHbaA+\ndpujh0FWuT5em3R2M6wvX4QvwdyzGKwL7oWOhKxzoZjcxe1r1Viu+r7bJK1O4TgIPobdwRie\nAKdDVi2StWPSz5jsCZum7vMI5V3BnO7e0ji4GVwTXdffB/cnu8ESUIzMe1mljcqcZO50Tn4G\n5k5j1nE3XvWnz3kXjItDwPVyKpwBxch1NqqAB8JAFLhcddVHY9Gp8Dq4OEwCv/RXgM5ggrge\nyqGVucn5YIDWJn1ocvotwbYmNXEjaiDXpc9psHddjQpcX5t6E7h2aKtJXVxMnTTWTQfrtMX6\nrJpCx54ZO/+FfqeBNgW1pOBm08Svbb6DeF4fuXAemvEGm9KvT4G++tAk+jME32qv0sfFjHVN\n4+SPERz/ka4oobx1hr7amiVWh9PvhBJsSzfdmRMTd13SttzYtc4YMWaLiYkXaXcqZFE3Ou1f\nR0fntItWeqyNX8fdRbVYPUfDc4ptnNNuP873yqlrqNPHuPHFGW9+MP3cFCl9ZB4qNh/apxS5\ncbi6lA6ptkdQ3gFC/Hl0LEudy3SpU7fT4qY6W+VvYE7aAvSjcdiQdt7A/e+CLOpHp46gfY67\nctwbQldy0wcz3vgs+rl2Kn1qXg9rpnWlruH2KaQLufBEoYt11Ns3/WOadirzoT7W9rB2FpMj\naV5Q+mRgwau1X3AslgH9pk2uj5Y9aqd53LxZXxt911NhGGSRsb14gY6uPyFmtdP3UMaF9em8\nb31t0s4TwbUzi26j0wKg71TIR8G/2uK1YK/12mldKXY6Pu6r3YtENWEPmBi+hxULvEPX5Hqr\nAtdjdfRA9ED0QPRA9ED0QPRA9ED0QPRA9MCfxgN+GE2q420+4bp/HRkVPRA9ED0QPRA9ED0Q\nPRA9ED0QPRA98Kf2gH91OBkOhPBXn+kX7snJd+Bf5UZFD0QPRA9ED0QPRA9ED0QPRA9ED0QP\n/Ok90Ik3HA/+R6DPwwB4GfxvePw42gqiogeiB6IHogeiB6IHogeiB6IHogeiBzJ5IPwHYJk6\nN1In/yWkDtAeloZvYTQ8BP6rHlHRA9ED0QPRA9ED0QPRA9ED0QPRA9EDM6UH1uets/7rMDOl\nw+JLRw9ED0QPRA9ED0QPRA9ED0QPRA8U9kC+/5ancOvquzIPJq1TfWZFi6IHogeiB6IHogei\nB6IHogeiB6IHmqIHmvoHUlP0ebQ5eiB6IHogeiB6IHogeiB6IHogeqBKPeD/uVRTlv/nrFPg\n1ab8EtH26IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB\n6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB\n6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiBJuOBWZqM\npZU3dAUeeSYU80+h60f/RcBfIShfXbiWe/yMioNyK4s8X512faEYO/PdUrv/gN9TF72X9vuv\nBKblvxh4eLqihPK6tD0Ryh1zzbln2u+aNBaOtpBBG9HHvuW2M5+fR/CckzLYaJfNwLEo1U79\n5bg65kH5bAvXPA6H/umKEsrb0vYAKNXOYh6R+y7D6HRuMR3ztNmFur/mqU9X5ZsXvpf+y43B\ndL/c8kAqLs6tLPJ8L9rtUWTbUpvl+tP/E+6rS71J0n4/jjtl7FtqtwF0uLHUTkn7gzlunbFv\nXd30Zzou7uL8zro6Fbh+BPWdC1yrb3WunTdzwwcy3vQ4+v0lY9/auuXOM/PXdfBYbZ1quXYK\n19ZKXc+9f7iU65tQX+xROy+D54rtkNPuDM5XzqnLcpo7t71Huk47z4ehXsig8+izbIZ+oUu+\nNShfnXa6L3stdCzx6FgsXmKffM3z2ZZeH36nUz94J1/nIuquoc2CRbQrtUnaRvu6F3AP8qEn\nUf/tgYbYtPz3E5ruWTdMvx6uLeIVdqDNd/B8TtuenL8C7+bUp08X42R3aAnT0xeKLPeinYnt\nxiLbp5s5WdxkPwgTUxcWpuz7uzn6Kalvy3F70M4s6k0nF6Vbs3Qu0KcD9frv3tT1ZSl3hrlT\ndaUU+9DYzYibmXIp+PMqbvhzctMVOa4BiybnpR7606EH3F9CxyVouytcAemN29qcrwK3QK5W\no2JJaJ97ocjzC2i3EzxcZPtimy1Hwy5wZdLBzU5rSG96kktFHYz1zvB4La3/yjUXvDdSbdxk\nHAqOw+RUfaHi+lxw8exYqEEd9c6fNeGZOtqVenl1OqwD/046bsRxGmyVnJd6eIAO7eCFUjuW\n2L4T7cfDLiX2C82fpNAGsm4Mw31yjxtQ0Q5CHtmC8puwD2TREDqZr11PyinjcCEwftU2MBD8\ncMwi58Y3kJ4jWe6T26czFa3hoeSCa+4AOCo5L/Uwkg6ueWFt3ozyHPAIpLUnJ+Ph5XRlCeVd\naXsTnFxCn3TTKZyYcz5IV5ZYXpv2+fJ7mNvOAd/zYjgbsugrOg2GMRk6O677w83wZaq/a7k/\nXlyeqnP+nAaXpOpKKbrHci1y7LOqGR0PA+dMOucvwrl+dJ3fC44G95ClqgUdfoF7YGqpneto\n343rYyHkkQMp94KsP9zQNWpm9ICB9FGRL+4G3SSY1qycfA5uqmqTm/w/wEmRRQb36Cwd6eNE\n/xl2zunfmXM3cfOk6rtQdtJmVW86vp21c4F+fakfnnNtJ86/zakr5bQPjYeV0qGItm5oHWM/\nNIK6U/g4nGQ49qfPMyX2c9OrHfPn9HPxLrShMYGOzGlfyukFNHYjU265QfoJWiU3PpJjoXdI\nmtR6uJqrt9fa4v/9v7e4/s+cNgtwrk//klNf6PQkLrjZzapb6Xht1s619DOPGI+zJG3O5Phk\nUs5yeIBOF2XpWGIfn+Gzssp39F3LLfNI2Hx7b8fMscsqY8bYKbfMI+nNv3PAuZBVzkHnYrll\nHnk+dVNzinVZZU4ztwW54X46nKSO71F2LLPKHK2Ps2oKHV0r6qPD6Tw+zw2cNzck9a559XnP\nr+i/U3KvUg+L0cEcuk5Ox305975puYfona4osfwL7d3L1Ect6Twdts+5yRac/wZzgXsyc2oW\ntaCT/uiQpXMdfcwjZ6fauFfyR5yjC3AE9b7PTCk38VH198B93KIbdEpu5SbDXzlmgyeSumo8\nOJldaPrBwqDmgzPAxP41VLNM8GvCgSkjN6EcNnmp6kYtmtRHgQu6MaH0d7OaUuX+8FejSXAR\nmOTVSuCCYww3JQ3E2O/hPAh+bOhxv5dn6St9pvShvvTXyNegKcv/qVJr6AvBj+HYlN+rsWw3\nr64I/tJczTKHrgf7V7OR2GZ+cn31F/qGkPffHLqmbn445RXgwVRdUyw+jNGLwvEp47elvANU\nQ96fih0vwTkwN6jF4SSoBvu0Jy0/svSpe7wFkwttOJ4O/uDyXVJXjQf9+TdYIzHOvzV1Xu1V\nC/4N80yp5jPlW5f/pe/glhvDc+AvTvOCE2dv+BSqWS4CT8MY+ADaw8fghKl2+eHhLxxXwrHg\n33otD99DNUm7/IB+HCbDBFgdvoRKajoPc4NhctcOWRWegnOhKelbjNWnJvzdQB9b15ByAd8Q\n3gL/dmAJ8Eem7UHfNmV9hPH7wc3gZnk2GAFR2TzwPt3MrZfCP8ANlB+h1abhGHQ0XAd9wM3Q\n3VBtGopBJ4NrbT9YBFyzyqUXuFE/8N0/AH/8WAr8wPW8Kcv15gBwjN0cuz6uDP+CR6Ea9FeM\ncB/ij02Oq/aZY4+BatShGKW9Y+FDcN/kh15XqGaZj/ybqddA/6q7wH1UVI4Hmuecx9PsHnDC\n3AidwQTkL4iTodr1KQauCzuBk9wJ/wD8DE1BTvgnYTuYBbT7DKg2uRHRv7uCi7vJdV+otIbx\nQO3YBdwMHQf+bWFTlD70g3hn8COlLTSk/OXQOOsCzhnnzv1Q6Q9dHtkgupu7Gh/mAuPjN4jK\n7oGr6OrcMmb2yn6bBu95IU/w421b2LvBn5b9AeZ1/zZna2iI3Hka93Xt2xJ+BT8eRsOfQbfw\nEoNhB/DHD+PyDagWjcKQlcH10Tx+OjwM1ZqDPsa2tcG1xzVoDBib1b5vMq53h86wIawGUQU8\nED+QCjgmY/Ur9JOmpukYfG9TMzpl70jKotzcVau+xrB/J8Z1b0Qjp/Hs6xvx+eV89Cfc7GqY\nHXqU88a13MsPM/kzahIvdRksBuv8GV+wwu/kBvtiWBWM0WrVhxgm61WrgYld73GUTRrIzne4\nr/wZNZ6XurSKX+wHbLu1iu3LNc190z25lU3kfCB2ij86+IPfTZBPflCdCK6zM53iB9JMN+Tx\nhaMHogeiB6IHogeiB6IHogeiB2r+QYhCf1Nn/R8zq4/iB9LMOvLxvaMHogeiB6IHogeiB6IH\nogdmVg/48eP/3PKImdUBtb33rLVdjNeiB6IHogeiB6IHogeiB6IHogeiB6IHZiYPxA+k8o/2\nwtzS/zh3FLwJ/u83W0BT0DYY+RyMA//FtY2hMbUAD78cRsLbcAq0hGpWV4wbBGPhIfA/5q82\ntcOgm8H/PsJ/zaY3+A9cVLOaYdzR4D92od03wBJQaa3IA++CMfAS9ISmJP+jXP/lP+NzKOwD\nDSn/uxvHzX89sRzajZtsUI4bNcA9/A+2XwB9+whUq52YVvMvtN3I0bn0OhwJ1bofaIVtfcE1\nwP9O6jKoxJq6IM+5AsL6czLlal9/MLFoLUzLK8H3a190r4ZtuAa39x++Cfmpe8M+rt5378Ud\nXAdcD1wXXB+qXVtj4LPgPs853w1cV/Phf1O/DMyUiv8Tu/IN+yrcysXbv6r8HC6FecDNwTrg\ntUpqfh7mMxcCF8AnwL9OLaS9uXAz3AhO9E3hOdgenoSGlht0J66++hS01/8Q/jfwI2ku0Ldu\nOvxXoapFG2FIJ/gWWsOpcA3cDtvAENCX/utg1aAlMeIN0F5t+gjOABP7odDYcuPjRlN7JsC9\n4H+8ewPsBP4H75/BfvASGC+fQCWkTS+Di/e7MDu4gVoaToZqkXOlKzjWI2AA/AprwIswGM4F\n30e/+qF5NjSEjH3Hc/cy3fwb7mM8VFKdeZjz/Ctw8+acydWBVLjZvA6c+1vBINgC9Hcl5Vq0\nbfLAxzi+l/PwxTg3jp1fF8KicBqsDJVWSx64C7QHN2x+vP8IaT3AifP8EvgODoZF4AMop1bn\nZluDa85AuAOmQ3r9WZ/zHaAxNQsP3wb0ycegz6ZBKZqXxi+Cc8kPziOhITQHN90N2oEft46l\nPs2ntal0vRwI5qeV4EZYHP4FlZb2bAm/wEPgjwlp9efkWNB/Y8Cc67xyj1Kt6oZht8Eb8BK0\nBefRg5BPrhuf5LsQ62ZuDxhI+RbCfF45nsrfwUD6GUywx4BaAzzv6EkedaDODxc3hlnUi065\nE/cv1H0BJs9X4Cd4EmaDfJqVyqnQN+fiFZy/ldR14WiiyKredHy7QGftegq0U3u1+3vQn60h\naCUK2uCmo5B24oIbmazqQ8dhRXR2kboeTCDDYTwYA9dBWi6yL6QrknJ3jlPy1BdbZXJ+ptjG\nSbtmHEeBdr4Gk+A78J2taw+5OpCKkbmVJZxfQFs36MVoARq9Cd/CSzAN3DT5QewcMa6DWlEY\nAf+fnfOAk6JI+/CniBkVFROKK+aICQMmDIgZc8CAijmdZ+TEnLNguDPnnDDnLOaIWUCiiJlg\nBNP3PEuX144zuzO9M7PDUf/f79muqq7qevutqreqB+/cSJUbvEE/qy6j4c2NNL6D+1/DBHCe\njgZtdcOfC4pRbyp5CMiqG2l4RQONl+DeCHD960PX0WvQGu6HByCtPch4IJ01XUj6dDBmZFU/\nGj4Ft8Mb0BlytSMFxph/whzJzXZc9dHxMF9Slr5sRKZDUtCRq88utLkn1Rq8+I6+az4ZF50T\njq/rRb+6XjaBtIzdztUj0oWkr4MXkzLHzLHLKueMfmlMR1HB/eb9BNOWpdWXzADQ7qB1SLjG\nnCOuhaxyDboWi9E8VHoPxoNzdSy4ly0IQV1IGPOd10GzkBgHxpasMqYZ24Kcb7/Du/AR6Lcx\n0AqCliLhXFgvFBRxNUYbq7NqFA27pxrPQNpn/gzGoM/BmLQKlKJjqTwMZkoavcS1V5LOcnHs\nuuU0bE9+COhHx/c7cN61gXx6iMJ7c27sRf5HCOPgGeKQnDqlZJ1LzqnGdAYVnA/a61xx3NPz\nZa6kbHuuQZ4JnoJbwXncE7KoJY1ci52yNG6gjfa5Hzi3jZvDwXfsC1F5POAGENU0D6xCcxfT\nLvAKXAm7w9mwEoQFZroamoZOXKAPQTvQvuWgA/wL8mk+CueFO3NumrfttDnl5c4ewwOXTdBe\n7f4KZoQfIehDEm78K4eCZrzuSt/8PKAYAABAAElEQVTdYW1YETYHA9BOkN4APFRXa+zpqkHt\nz92F4FLQhx5EroKjwA3O92hO9aHzFrAIrAZ1MBQuAA8KbuJBE0jcB9WcCx6MXAvO1TBP7yGt\nzc3tO0yo1w38/QAcW324KLSCc0Ff5a5x5+f0sDSUU+4t38J7MAyGQFprkNkZroFZ4WRQV8Pb\n8Do8A9qe1sZklgfLL4eBUCnty4M3B/2o7+rAPm8CbQ5qT6I16Mu09HU152dH+jsTeoDjKaYt\nS9thPLoXPPQFPUviK5g9FFThejF9TISFIaz30aTdQ4O0+z34KBRwHQ9fp/JNTa7FA06E7WFZ\nWALeAsfYdRTkupK0L8O9al2PpSM/1JaBVcC98jG4FYxDxco58DB4YK6UXMsjQB86vo6zunDS\n5W9/9atrJi3XlB+FvnO1tCEdHQWu/Q6wGBwO/wbjqbJ8KuhnJtEfXO+C5pwfwZZ810Mp9Ey1\nGzj+daDNUQU84CYW1TQPbEVzD24G8flhUxgHr4L3nJDzwZdQDbk4DZqHgZuPGgh9YWszeTSG\nMjfLEMBCFfNjwYV1QCiswFW7tG9Q8mztfgJmBhdy0HQk9HE+X1ruB6C/OBm4Ki1t9qD0YtJR\nsMlfZwywQR72wz3L3Cx6gx9Y1V5/2jwcWoNj2geGwYwwK6TtJFt1uV7Ohm3gQtgbPNy5Kc0J\n2phWrm/T9yqRdv69A64n5Zq5CJxv6Y9isn+Th9WTYROo1LgvwLNd/73ge1Cfweng2Du++iyt\nkO9KoT53Xjg/mqrfecCn4MF2NIyAtPThvqBP3aTDOje/DoyEFeBHyKdpKZwL9HulNnl95iHv\nDVDaZV8zwX4Q5GHd8uDLUG7+i5CpwtX18zLcmPTlXFgK9P/hSZmXfPNgdsod95+tUAVNQx9b\nwIkwHvYA14c/gq0PYa1rq7HdeZGWY1AuOc5Pg32dneB89eNhSwjShrZgveaStl4AgxMD3CsP\ng4XA9VKsfIeOcA4YY2eEcmoOHuZ6OQYcX2Wfp4A+NWbmyvueOdJyDakO4HvPbqbC2obn+/H4\nYKqfC0kPBees0tYWUAdBxvWNQF+2CoU1dPVc4vlkXfB9DgbH4SD4tQDGA/euKVIOaFTTPOBG\n7aJ9C6YHP078FaE9uOFcDwba+6Ea0h4369yN7ifKvJdP2ncz9IXVwM3LTcrA6TvtC/4LU76g\nRnGTpV3al9Y1ZHyPfcAPJQ9Dlqm7J13+/LsOqQ9gD5gHDFyVVq7NHoTuA3/tMsjrw03BDeJy\nUBuCdu4C2mmdakqbP4Yd4HjQzl5guQH/OWguGYtawtmgbQvBIXALKO27GTws6WOD+1ZwBVRL\n4+loVdgJ9NlicDE4T0dAIe3OjQHgfGgDvmclpE3qx0mXP/+a957z8J/gAUAb3PhuhJ9hb9Dn\nh8P7YEyrpFyzrpfNYFyqI237Ci6CF8D4k0/fULghzAaVWu/6LMSlC0k/DiuCcfAMOBmUthjz\n/w0rget6IzgOwtonWXGl7T2C3l6FdUEfbgfXgra7ZowB+4P33LNcZ0PBdVYNud71k2P3CpwH\nxqOuoI1rgzLWm78aXDvuBSeC414u6bcF4WlYBVzjHuJngvWT61xcr4PfoR80l9JjHGxw/Srv\nFavWVHQubwVrwaJQTgVbwvoJzzYWtQTHP1euFeetNllnGbgKnJN9YSmYESotbdfOXFkW3msA\naeftDbAYOFfehE3haygUt7jVbGpFz87fHrACHA1/wD3guivEh9yLih74iwd2JDf6LyX5Mx52\nnXTnJrd34/pDUubkGwxOxkLqxA3rGRCyqCeN7CPIAPItnBIKuLqZfAQeOgrJjcfNSFv8lcF3\nGgu3gBtZF5gIWXUIDd8p0NhDpotw1tT9U0l/B2NAe7RrKKwCabUgMxyuBNPdQLuzqhcNXyqi\n8aHUMRC2S9U9kHSw1at4sHIzmA4+B8fADb87jIKscnyfKLHx8dR3Mx0EBvtgq2Nd6J335t5A\nyKrzaXhvkY1HU28czJnU12cjQFvdHD+AMD+/J70fBDkeb4VMhutltLm5kXaOpWPuOgi+G0na\nOVpo456Hex4S3PhVb3ihPpXtz40084CbT84rx/YqMK3cqPtDPzPoTPgFgv0e7l8EN3jlx+ej\noN+9ZpX99YHtwfWt7GPu+tSkZxtT1AnwMmjr5RB0G4mukG7Xl3wPMKY65vYR3o1kyfIdTy/Q\nyrFyTu4Av8J64Bp3De0OrptVQM0GD0KYn977NxiTlGPm2GWVY6g9DWlDbjq2O4Hjuxt0AssO\nBu3eFpT5HyHMg3dJLw6ugcsgq96ioeNSjJ6i0jAwvvjxo69uh6/A+Rd8tyrpYaBv9esY8HBq\nbMkq+zS2qWPA5x5kJlEvrpa5lwcfDSXdEUqRMfqUUhrk1B1Fvnuq7D+k34dZU2VnkDYuuX6K\n0UZUck6cCsZb38+54Ttn1Vgadstp7H7vfJo6KZ+Wq/4oFFeMWWeDay34/CPSngOWBuUzPUtk\n1UQahrhT6BmuH9fGsqkKm5B2PqyUKmtL+iVIr3njkxoMPetTpf9pSROf2an0pg22GMTdCfAw\n5LO5wcZT4s1ppsSXLvM7G5R+AjechcBF7gQfCUNgA3CxV0su7H3AwLQ+uFC1wQB2IhSSB86t\nYRGoAwPoM3A8GLAqqRN4uLZ+CE+ANqwMBqpHkrQL+3XItWU5ytqBdlbTz25UW4IB+yGYE/Tz\nEUl+fq4fwGeg3FjnguPA4NQcupxOT4I28ADo5w5wIbjpOJfdKJtDrpvZ4Qd4GfrDimCZh3YP\nRcuAZa3gDRgP1ZS+c4ydg++AftQmDzCuu3yyvvfOS92s1Pj73L3gQXAjHwBrwbSwKygPQRfA\nsuChSj/6XvpdGctOgefB9VhObc7D9oQN4WY4B4aCh0DXxupgnPLwa+ycDZ6CbSC0I1mvd/lr\n+0XA96yE9NNmcC18Cb1hXTgQroX9YVN4FbTb9OKwAOg736uaeozOroPrwfE0PmmTZReBc8H3\nuTPJ38DV9aTtb8PvUE0dRGeuo2/gHOgIHjp3AOO+selNcD44zu4J08Hr4Pwpl2bjQY7vubAO\ntAT9NhpuhfvAuOhayd1/KKqq3Odcmx/Ak7AouNb1WbGxezPqPgHHwpmgX/tAubUPD3wUXJ/6\nbg1oBfo4n4xfR8H5sAx8BmfBM/A+VEu30dG24D70IEwPfiA5R32PINe3MWtZcM35sfQPqEXN\nhFELw3uwGjin54cVoDUsCfnkfB+U78aUUDbNlPCSVXjHcfRxIBh41PawOTjxfoNq6046/Bj2\nAA8eLuwr4HtoTIOpIIs1VrGM97/hWW6Oe4PB3sC0L3gIUs9OuuT9a1BtDk2g0y6wGxjwDSKn\ngJuX+mjS5c+/zWXnnwaQ+DHJXMXVefEC7AceEPxAam4bPaAdBgvAEvAA9Ac3KeX91+tTzfMn\nPU9XxgQ3mx7gIa8hTdXQzTLfc60sBx5O9OOVcBloe9AXJGTaUFDB6+08W9StCaavg3tMIOPn\njGCsfBrczFvB56DS7dIHkA2450d/GytVQK6XzvAQeID4AHrDy6DyjatxV5pLe9Hx1LA1jIEd\noR+oXHvHUvZU/Z3m+aM/PwPXT0vQzkvBeZArD2rB77n3ypE3Xl8CG4P9bwVHw0RwTdWKvsKQ\nsFeuSPpFcN80FhWrdJz/nkbPwE/FNi6hnuO1LLiX18EN4Ph+CQ3pc26KSts6qaTyf91ntgM/\nOruC68Sz3SOQT55TvoVK+DBff00pO4LGC8La4AfdCrBrApe86kBpY3tc3oaTe2H8QGr6CD7K\nIwysC8BhyePquLq4Dk7yzXFx0f4LZgWDaqmBZiBt3OhPht2g0jJQXwAe2jywa3Mx8j1HwKng\nRlFN/UJnV8G94GFKCsmDvRuDdvox0hwaT6du9h6gtwYPpT/Ag/A4TIDmkvNTOw6ALjA9OCdu\ngTdgNNSC9FdfaAPjoDGfPUGdGeBIOBtU7kF1Umn5/g7hUcfB7OA6csPPJw9/j4GH/hdAf2vr\n8TASKi39F5ReO/pYilG6XTH1G6ozEzedd9+kKjm+Z4CHI9f5y6BcPyvDgWaqLGO6SvtvUsmk\nv86zHuCPNf0mFdX/0r0F6e5JvlqXmenImO4BMp/uo7ArrAFfQgu4HJx/A6AaeoBODoez4B/Q\nEjqBNvWCWtN3GHQ+eH6bA76GUuT7PgjrwVNJwzCnkmzZLu7NJ0Cw87cSn6yt58LF8F6JbZtS\n3Zjp3uO69+Ncnzck7TTmXgYDG6rYTPeMp0+D83kbMJZ9D/uDP6IdC/nkuxdau/nq/0+VxQ+k\npg+ngfw06AN9k8e5uEZBBzDIeviotDajgzbwOnwC2rMbTAcGqSPgDkhL+9xIPUh5cL4RfoEg\n2xsgXPBuXpU42Nn3BjAjrA07gYeU0XAMXAvKvruBm7yL/W5wwRtwdwEDvu39tadS87olz9bW\neeFtaAX/hqVAOww6+4EBZX3YCGYG7bwNdoV7oAv4a5MHh2rLgPgq6Kep4Q/wEGjQbC51pONl\noR+4hvSftv0OP4NrKFetKfBwrO3V1LZ0diE4B1wrN8DB4Jx1E7WsLewF7eBDOAwugp3A+aDv\nm6oWPGA9WAA+gJdBOff1of07v9wEj4cLQLnexoMbnzoAnoFB8CYsD+p+WKg+9b//Z0Fe8VOY\nD4wzH4M+M166Pp6Ai+FR6A/6vhOcDK9BteRYa9tiSYcjuRr7nkvy4fIRiSPhajAeuU7WAufq\nXVBpOcevgS1gNnAtvwv7QJinJOt1LH/1pe/1EiwKc8Nm8BtUQ/rvMnAPUc4Bx/0d2BNWhyuh\nHOuWx5SsNWlhXHkctEH79Jtj7B5k2ZlwNmh3kPNkDzAe6XfHxH3nUXDfegycz+5rdVAOed5w\nf2sDxpNt4FDQzm/gVOgDQZYfDI77CHDODoegq0hsBK/D8+AaqITa81DXiPFSPy8CzomVQZ9a\n5vzV19q7MAwD7fsUjA8bwtugnb5/U+W49YBgk3G7KTqcxq4xx8E59Bso58SX9an4J3qgSA/s\nSL3RRdT1oPYd/A4uJDHt5HshSR/NNVczULAzXAq2MUhlUU8aeYi0z1/AZw2DHyDY9GuS9pC0\nLRwBBinvhzq2fQtmhLTcrKx/N0xM3ygxfQj13XDS2pzMOAgHtmCL1zGgD9+HncAAGe5rq+nT\nIKgtCf18H/jMrOpFQ4NIrgyIH4M+MCDqZ/0qpkP+C9IeSMxPAAOmAe5BmAbawb/gYShmflEt\nr06h9Im8dxouDHMy2Kwf9bPslqfp3pQNzFNebNH5VLy3QOVpKb8LtEWfhnmqTZZ5lesgaAMS\nfhAE/15PelY4FJy/WXUZDZ2Hbs4Xw2yQq64UBNvS9rnWzDvuHqrHwkdwK7jpi3afCP3hRciq\nG2l4Eziv9NdI0CbnlzHltiQf7PQqN4Dvpp3aewHof9UKDoS+4Dr13U+HRyGr+tGwT9bGJbSz\nD/vKqidpGHwUrvpIPoUNIagzibPgDFgdStEVVHbsssoDbq592mjZ9gUeuiLlp8I5kH6PAtXr\ni2/mr2shq/wQ0qbgw5D2auzsALmaloLdwbE0hhvLG5MxxdiSVcY0Y5uaDjwgamMg+PZbypwH\nruE6KFa+p/vm+2Cszir7Nza71l23fizcA2m/jiLvHnMkGKOOAsfdNrb1Xdwzh8LsELQuCT+q\nTocB4N6XVWNp6Pj9BNo2AexXG8S0tsi5sA9cBM6JUM939Sy1NqQ1FZkt4DxwLA6BrLK/LjmN\nzycffOzVOK6vwzton+3cH9L2jiE/DkIsmJr01uDzPAf0hCxqSSP71I+Om3Z8DetAsfIZm8F+\nsCa4rsKcCc/2XS17CHYowDaUuz6jogf+4oEdybkADCBOeif/snAmXAY9YB54B5xkacIEdLHv\nDE7ExSBoYRJDwKDyIVjfCZ1FLkIX7SfwObigQv/BprAQQj59XxsN/oPAer5fPhlU7CerDGoe\n0NzYRsPPoD25tgUbc6/B5qG0+QB8T+usCGl1I6Nfs8pNYjAcCyHw+aw34Ckw2OjrxuzWXv31\nK3ioMpDuC0HdSYwKmQxXN139cBNcCnvA7LAjHAPbQnpObUl+IKT9qo3Br9qpT1cFpa0vwzDw\nfbPKzeJdOA5WTj1E2wzMzgPXwN2gDcG+YFfIf8y9r0C/Ow6rwVbgOz0Bh8JbkFWu6VfgABgK\nbsRu5K4H8fDhphfsSV+DH4PN2jgSroYO0B/uAdUbzGfVjTR0zX4LrqUXQFt/grsgbVdu2vmi\nj7XTsX4ECslD06OFbhZR3o86fYqo19Qq9mFfWfU+DYOfwviZdwzfA+fnUtBUXcEDHLusGkXD\nfHZaNhFaZH1wTrubybsWsuobGmpT2pfBbufc0KwPzmnn3De2ZNUnNHwVjB2mg4251/Aexp4H\noRidRiWf49waA8bqrLLf62Aw5NqWzrv+nbOWBZvND4Kx8Dl47w7Ip5co7JXvRpFl46nn852L\nxmrjU9o+08E+037QhfvaG+yfQPpL8KMonzxvHZLvRpFl2nc97AWzwK6gHR/BnnALpO0MNqav\n2ivGUsfH/StXjlfP3MIi8+6NPv9HMNZrs3HI/lxDYjrkjf9XgXv2oeC68KylL4dAsD3YHfLh\n+gN1nB/58NlLQFT0wF88sCM5J86TydVDnhPSQ9Td4MQtZiGdSb1P4CAI6k/iKXCBdgInrosi\ni1yE2hUmu1efl87nprU9XXYl+W/BRTgM8qkLhS7UrDqEhrZP95tO57M5tywEB219PnmW45FW\nNzJuCFnVi4Zuan4QOb6XgAFCW/4FaZsbSrthaJt1HB8PHvdAUHcSo0Imw9VNV3+GsXeDMe/Y\nvg5uQANgbngYgq25Pg3l6euQpP44rj53NGTV+TT8GvSnfZwKfsgNTPLpNRRsC9dgU8in67qG\npoHFwfv28xZk1WU0dIw2Azcm/Ro2cQ99HjSCPblX+3duWm5anEPGD3He6EfVG7Q9q26kYbo/\n+3SuOfah/1z7Qt4Df1foAP2S+ubzaUr5QHJ8gn/SV33s+nwBLoam6goe4NhllbE5bV86ra1H\nZX1wTrumfiCFeZi2z7Q2uga8Lg1NVVM/kDxAOvbpmBJs1saQ9r5554LpQgd3btVrQ/5ab4tJ\n2foPMGN1Vhl7gy3hmrYvXWb5h/AchDrG3u5wEVjXeZRPL1Ho3pdVPjf0mbYppNPXdD1jl/fC\nXuM9MZ7n0zsUHpLvRpFlxun3wY+wz+AT8LzQEm6DtJ0NpYdS91sI9taRTmswGc9mWaQtPtf+\nnUteQ74hm7znR1E4m3recO6l26TXZ3hmX+qUU2vwsAvhAXgSnHt7w2T3oTU1RkcV9oCLd31Y\nCwzqDvaqsAM4cdPBMkw2J2OQZS6SacEDl5obnEAGI59fDrnofX7u87Qlt8xg4CJRV0+61P+z\nfTfS08GMSVklLi2Sh2pvrvSVSvvUvOXhnu0NYvrzJrBuxwQuZdNHPGklcOxd2PpGnTTp8uff\nXFvTeQ/vBvmnQLtXgzAHSJZFBsK2sDk4dgZHN8iVoT3oN4OUm3bwIcm80nbntM+og+thVnAz\n+gmaohdorD+3BD8WPCjWgf25Mbq2Hoe0rD8yVeDa88Pvbfge9Kdj8zH4ATMnNFWOk+/9b/gE\n9IlzQDuvhSD9oX/Tsu7wpEBf+wHih9Y7sC/4jHIqxBn7cuxzDw1HUzY1OB+CnCePwgDYFbT5\nMJiSNTp5+ee4hjWiX1QbcL4tYKaZNSZP/8FOb+2Y535zFDnnXR/Bl2kb/CCxvEe6sJnSrh/j\nSNpO/emhNy3XkLoVTBt3GtI23HwY7muoUgn3XNtqyKRL/d9gs3MzLd9pKQh9u8/PAa75g8FY\n6d6ZnjdkyyLjm3b5Lypp2VewN11u2vng+vN+KxgBIU7uT3pmKLfs61BYEN6AdvAJrALbQdpW\n/Zkr9/CHwPZ+eIb65d7beXT9PApjFfqxvCHpT/2mbe5dx4DPCLgH+HFY7POoWpIOp7bzrQNo\ny7MwAbaA16AnTDYKi3+yMbiZDHURuQBmSfp3MXl4HJbkvehLJ4BXJ0SYkK1Jzw0GTTXdpEt9\ncEiSTb605AnXQ7DPB7oAtOUR0BblNQQg73swNYh6yNYuyz6FSin4JNhjP+l0vn59h7HJDQPW\nsmBA2x2013RnqISe4aF3wfLwHejnQvaGcq/ifDFI/Az6vA7ugXJqJA/7Ap5PHvom144wLxgE\nT4AVIdhEst5n+k1ZrsJVO22n3pt0qf9bruDvxv00rAv60nXiL5wD4ThIyzXmugtahMRgsE0r\naAHbw5LgISB3Y6aoZLWhhZvL8WDfL4L2Xg5uLEHPkDgA9GPwpfO0Dtz09afv1x++gQXBTbVc\nck75/JfgQ/gRtCf88KBNpjtBul/nS9BaJKw3eyiYQq8hVi/O+xtf0tLH68MH6cJmShung3qE\nRHJ1HN1nakHaOT0YM0LccD1o44ygasFW1+vn8KwGJdJGY4lxUNu1W1neFZwrC0FDch81BpRL\nxjntMLYr09qjjH3G62Cn89d7z4MKdhjTFoQ6sO5cUCn5Q5V2BJvsZ1ySD3Z71cfG7IVBv34G\nxizfdwg4hxaFSuknHnwUTAOeg/wgU86LIOeHtqbfR/uGg+eBbcD3dK5U4ty0Ks8NY2o/aZ+G\nfPpKlfr9xqt7gwSfh+fcRplzKaxNkvUf/adyzceJlM8GxcgxOxlWhnVg7yR/BFf3zz3gQnCN\nTBZyckQV9oAfHH1gIXBh1IF5PyiceOnDhXkntAoTwDIX3FPwTwjy18Db4XGYLxQ24WofLtZ8\n2iGn0M1pFHjIDPZq56OgXOi+Y67a5RY0IZ8OQrmP0RYXvdegGZKE7dwUwrwdSXoe2AgcE7Xw\npEvmv/ZdB8EHy5H2oPQRdISgtI2mc+WmdDjYVhn8DRyilph0adJf5452Gpi0oT0oA50BfAGw\nPNgXfBry3PqLhpGbNynZmuv8sGySb8plGRoHfzo+wSfaUwdujm70aV1KJthruf5UjoHlvoPP\n9SNmOAS/ksysxWnpHHsNpgXXinavBgtC0MYkNoG0fd5zE3IsLN8etNHnmR8PPmsVsF5TNJbG\nzvsVwY1uRnBdhOfa7/ngNdho+i14D4xba8KPYNswNiT/1Bqkvv0zly2xDs3yPTvb0/K3so9h\n+W8VXaqP5k5qp31muWtoDmjqe6zOM96GrNIu5fW6+tR/x9ZxdJ411UYfuxI8YyKjnIPaGNZ4\n+jHuoX5AGfuaaqtr/xNoilxDjm2u9KUo54Dv5P7ivr4ZuIYLyXm0PjhG7vOLwUuQVWGv2yj1\nAP2rtCfM2/oC/gS/arPxS/uNZ65538kPk2OTK5c/VfdnKlsiHXvST7D/2ZKCYLdZ0wsm5cZM\n9x33M5/juLaH/cG5nVZTz0v263M3Bc93yli/Y31q0h/raLe+NS0qlNve+3smVz9Eg99J1mvO\nkMhwDf05ZqXKOa1td4Hj75ibD8/0nZ1L4V285/isCvnkucU2Y/PdzCmrI++e8VFOecjeSeIS\ncP0PCIW1fA1Oq2Ubm8u2xen4NDCoK4ORhxEDjHID8J4+XBT8sAh5780FHkZs4yTMlYHPcvFw\nuDdk0XI08kBskPFZM0EbGA9OaheIfbnYDTZfgJuUQUHbxXeyrdfwfiT/plGUHPS30uIKPMCe\nDPpmAviBFuzQdn3nYrTMtPW06XuwXLusF/xpPeX7WZbWJ2SOSBeUkF6DuodDWBv6zr61xTlg\nMDGI26e+9jpDcrXcOt+A7xg2jdA+5LlVrw/42ztJl3pZnwYHg3Ypx9O0fdif0jdh/EO+HYlx\nZpDv5n3fYzgY6PW7z/oJgr1vkj4FssgPib0g7U/tdIx9vumQ1zYP7KPB8dU+A77vMxBCPctt\nH+ZFsPMlys6GLNqKRntCsMs+w9oIZfNT5qY1DLyvHa41bdY+pT8de2017VX7go0k/+8Z6Gsi\ng3aizfYQfOAjfLa+sGzJJK0Pgw0LkHbtj0zKLA/KXTuh3OsjcFm6oIT07tTtVkL9plS9l8bX\nZnzAvrTrDo61/quDwaC/9GlD/uF2SbqN2reW1OK/lf9B0jmqPLC4LpxzrlnH3/mYHleymXU9\nLftlbH0U7bombf34cN/xwGQc1z79XA6f+qwr4SHIohNo5Meg9rgX1cFQUK736aAtvAX613r2\nmV7HZPPKdS/Wtc2F8BRk0Wk06gTGFG2YE4zX7uPOT211TzIGab9l9utcdr+cmOSDLYXewfLz\n4AXIonNptATYr7Zoq7HSfcRziLZom/PWMuOTCr6yvve113dqyM7Tuf86ZNHFNGqbNLRv+w3+\ncsyD31qTHgaWuR/KrDAcXGsq+FS7tTct750I76YLS0hfRd15wf610atzwOfODN+Be7V9K88g\nloW8bcL7+U7Wdd1ZblofOy4/Q2/4GJoqnz0STgLt19a09iTjWnBeOM5R0QPRA9ED0QPRA9ED\n0QPRA9ED0QPRA//THliHtxsGX8LT4I9Xr8DX4I8kXWGykV98UdED0QPRA9ED0QPRA9ED0QPR\nA9ED0QNN8YD/wuW/ei4GC4H/sjUY7oPxEBU9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0Q\nPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0Q\nPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0Q\nPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0Q\nPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0Q\nPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0Q\nPRA9ED0QPRA9ED0wRXtgqin67Rt++aW4fR5M3XC1+rst+CsTU3X1bcucstTtvyS/JrfzX0qK\nz6xI1dMh61hq4x/wKwRNQ8Ln/RIKkusornvmlBWbXZ2KJ0BWO/P1E3ysnb6DsmwI7Gcmg9al\nTa8M7RprMi0VfksIdT8kcWjIlHjdiPr/LLGN1fPZ4Rz4HbQvn96k8F/5bhRRtiV19i+iXpYq\nvovzVtvVi3BSfar0PzvSZI9GmrnGjQfpdZFv7TfymP97igpnNVapwP0elHcvcK8pxa4b50F6\nLT1Evm/Gh+5Lu60ztm2oWT4776bBZQ01auDeP7i3SQP3y3nrZh52XcYHHk279TK2bahZPn9e\nTYPbGmrUwD1jfKcG7me95bpzX0rPz/+QvyfjA8+gnXtnUHh+vj083WeoX8r1Aio/UkqDVF3b\nehZpivKNsc/LjZ/65BlvZJBj0T5Du9Ak3x7keKv02cR9/iR4yRsZ5Nxum6FdbhPt1Za0belz\nk/eOAffOLLqJRnNmadhIm9w9zL3zcPigkXZT5O0wAafIl2/kpZfj/lpwYSP1vL0djIPHzKR0\nIOkX4O1UWW7Sxbob7A4G4lK1Ag1WBQNUqXJTOApugeGpxvOQ3gM8HP2YlC/EdRfYM8mXeulI\nAzekK0tt2ED9ztzTfwaToMVI7AxZP5D05dJwPZRL8/GgHnA+TEgeah87QNYPpDVo67s6dsVq\nQSr6IeCHfzqwOzbOo8shV8tTsA1k/UBam7bOnTuhnFqCh60D+lT5DlvCSWYyaF3azA/3NtB2\nH+654b2RqmMMPQJy11Cqyl+SnchtDlk/kLrQdm7w46Wccm12gEuTh3bm6oeDMSCL/ICfHR7P\n0riBNq5PD4xXJXX0h31l/UDyHWeGZ6GSsh9tvS5jJ84Z1+yLGdsXarYeN5xPIY50I21Z1g8k\n16A/+L0G5VRXHtYKQhzZlrSx5R7IImPaYHg7abwx1xng7iQfLu7NQ6B/KCjxuhP1jdVZP5C2\np+3r8D5klWvG/ebKnAdsRn5quA98z9XgGcgi3/MJGJih8Wy02R9cw9+m2i9K2vl0TqpsL9LG\n+pdSZaUkd6Wyc2hoKY1y6hrzj4QbYWTqXtjn/ajdA9xT3S9KVUsadAfPIKNKbdxIfe36GMIe\ndgjp5eADiIoeKNoDHiRHF1n7duq5WNKalYyLfZd0YZ60B6Y/wEWRRT1pZKDPIn9N+BncLIIM\nmFuA/5rghhTUhcTEkMlwdSG+k6GdTWaG6fO0PY6yATnlbvDf5ZSVku1F5azBN/SjXw36QQYg\nx7guFHA1AH6eypeaPIUGbkilaHUqa8dcSaNZuDrvToIQMJNbf172JjXwz1zpifNp0tBHR6lP\nnJYGzstNYQJ4qFGHwlv1qWx/3JxvbqSpzz8hqeP6dqP0cKlPPYQUo95U6l9MxQJ1jDNXFLhX\nSrHz03ka5Mb5Jbj+1enwaH0q259+NOtTYlP9qV8b0kHcHA5TJZXsw76yynf0XSstx8yxyyrn\njHOn3DqDB6bXvmvAtZBVb9HQtVhuncsDnwf3AmVMMbZklTHN2BbkPHoqZFLXj0gfC9OlykpJ\nGqON1Vk1iobuFU3RATQeAWHNhGf5YXQl6NOXwb0vq8bSsFvGxvPQzhi6Sqr9bKSNSd+mykx6\nhjgkp6yU7EQqdymlQZ66LSmbAOF9Q9zqStmvMCMMhp6QRT5ff3TK0riRNs9x37VknNfHo2FH\niMrjgbAZ5rkVi0rwwG3U9ZeejcFfN14FA4YT0PJwICVZU/Ij6E44GeaHY+BrcPP5BfxVqTm1\nAp2/BH7wfA/3wLwQdBcJfxk7NBRwNfjlbgSp2xVNGihPA4P6GBgJPeBd+BAuAjcjpb9b1Keq\n9+c1uhoKd4C/SI4D/Xo0OOa1rNYYdxM4F8aDh9ofQJ9OC6rS8cx17r8WDQfXt/57AfRp+pBJ\ntma1HZZpr/PTd/CQqf8eBDf2MyH4sVrryB8/HEf9qU2DYEvIJ+epH6UnQLAvXPPVj2X5PdCZ\nYg+bHopXgFdgVqhFGfM7whrg+n8R/JGknLqdh3WG9J7Xh/xi4AeOcbIfeJif3OSHUBs4EcJa\n2Za0PzIFny5LurnkD4XPwXngR8UwMD5dCSOgJdSSPBs5F9zrr4EQt4xNr8OPUKtynh8E4YxS\nq2u+JvzngW5KlYHubCjkAwNj6yKdcxf1+oKHjD/ABeQi8flbwyNggPeDpNbkrzEPg4cmg+fv\n4K8ft4IB61doyi+KNM8kPyD8Re8ZWAdmAjeqx2FF8JegD2A/uASOBP07HzRXgDqHvt1gDwcP\nH34wG+R/hu3hIfAXm1GwKPgxWk05lsfCjeAcdfPRz/prLahVOS/vhzlgB/gK9oHFYRtwjflu\n30Al9RoPdx6KvpsT6sA5af+1rk0w8BYwLrmZ6z/Ts8BesBN4f3cwLg6AauhqOlkbHNPBsB3c\nCRuCMSCtkWRcY9eCa38G8HAVVbwHlqOqe5JxQB/uCvvCF2BcdQ7UiqbFkCfgJ7gCPEB3gOnh\nbSiXXuRBR4Fz0X1mOpgL3oDDoBVYHvYf4+fkok8x1DVzHTjexvt24Fi/B667/0BzqgedPw+O\nsXHcePoJuJ9fDM7PWpIfGR+Ddn8GxstxsDKsC7Wq2TBsajDmu4d5Do4q4AEHdUqVh+nvoJAP\nDJAtS3COB/S5oTOcAA+AB7nLYSh4WLas1vQtBq0N/mJzH9wMD4EBagL0hub4QDqAfv2Q2BYc\nK/U66MtucAeoK+Ex2Ahc+NY9B6otf4k5ELqDhzv1KhiIjgE39SVgMzAoedjfC6ot+38SPAi0\ngddgLHwAHZM8l5rSOlizOiwCjr96ARYC8x6gtoSFoZI6iod7eLwE3Ai/BDdz5197GAK1LOeh\n4+5VvQIeih+G4+B+8D02hR3A9VRpOYY7gTHIA5LSLmNpL3gKcnU7BdbdBHYGY1VU8R44nKr9\nIcQfPw5uhadhENSSXNftwHnyNZwNXeEsKLfO5YH3QBfYAhaE1SB3//HeXTA56Q6MDWtmetLG\nrxXAdf47eBZqTg2jc/f798F45H5kXN8YjEvGJ+Ntrch9fXY4En6BIaDd14FxqxbVEqO09xB4\nA5zbJ0JUAQ9MU6B8Sij+ipc8sIEX7cu9JRu4n+9WawrvhWtSN0eT/gQWSZXVWnIuDJoBToIP\nU8b1J30KTJsqq1ZSf70MYXOyX8fsY8j15QjKLgflx1NzyM3UAKTP0nJT2i8p+JGrhzvVfdKl\n6n/1nR/CHvTT+paM915LF9ZIWrvcPIfm2KOv/XC6AeYAD/eVlHZ4gPJAKaoFOEe95yZZy9LG\nsE6Cnc7PqUDfGas8hBi/FoWVoNJamA48oL2U05Fj29BBQ1uvgmXBtRdVvAecB4/nVH+WvGM/\nPqe8ubPaasz340i5l/4bDjdTAQ3mmbI+OAfT+4/+GQjaNDnqc4y+OjE8xDHXXq3IWHAo3JQy\nyPjkDzXGJ/1fK3IOODcuSK7BLufMEfBHKKihaxts8cNOG98D9/pjIKqAB5x4UeXzgIHVA5sH\njqB5SLjwDey1qi8w7HtYI8dA85/CxJzyamT15arQItXZnKQXA+/VmoZjkL8k5fNhLY29vsu1\ncQnK/DWsFv2KWfV2teVaZyYl36Oavs3nO3+Fc45W046UC0pK5rN/TZ7gZt5cH3f6zX1IP6bl\n2NbqfEzbOTmm9XluDPDA5w9l7gO1JOfA4mDsD3K9zRYyFbrqo7C2QxceMP3h4H9hXvp+nlVq\n6QyoX3Pn5ZqU+RE3FGpJ+s95ODnFLX9g9geQXB9TFJXPA9PkK5xCymbiPXtAIR/4n0SVqoto\n0BNuBtMG8dPAf5Xxn19rVX4AnZfQkuursC4cC4dBc+g/dLo/3A7nw8xwKowA/5Wu1jQOg/xl\n8zKYFd6BjcFfxHaFWpHj7L9+6N9rYT44G56AWvzXI8yq/79g9l8TH4ReYKDfFzrCAVAtnUVH\nj4H/euG8XBjOSdJumLWuMzDQtaP/7gE/jLX/WvCdmkMefG6FW+Ao8JC0HXSHrhBVfg8YA16B\ny8F/UZgHzoRn4RuoJTlPT4SH4Dj4AdyTpodK6hIevi/cBv4rQStw/xkG98Hkrot5gb3BddcX\nfL/m1hkYcDc4B41TS4F7k3P0C6glGe/dA4xdR4L57WFH2BBcW7WmXzDI/4zUmO+592WYDqIK\neEAnTalyU/BjppAP5uPeVCU6x19hu4DB9QX4Fe6H/ZM0l5rVyVimvaeDH3ZfggcWD9LNoZF0\nuj7Yf3/4Ddwk94OJUIs6AqN+gj7ghvMZ7AMG0VrRGxiyKVwIwZfadzDUqvwXjs3BdXUXtAQ/\nQDeC96FaeoqOtoXzIIz1taQr9Z/78Oiy6kGetjOcBb3hB7gSjobm1J507sZ9FcwAxtHt4Emo\npuz7APgUbkt17FhfA9+kyopNtqPiatAKjK+1oAEYsQkYAzwkG0/vgIPg31BL0rYNwB+enL8t\n4JUELhWTP8S5/+iPsP/YvzHTg+bkLn+YCO/nWeXHGnghPzx3hTPhWDA+XQ69oBa1O0a5F1wN\nIW65P7hP1KpOxTDPUqdAa/C8ElXAA9MUKJ8Sij/hJVdq4EX9VeXABu4XumXwXhn8V4QJ8DNM\nDvodI108/oqj7WPAg2lz6k06XxX872bdlGp9MXsA+hccB9pcCz7EjL/pCUqWAj+E3Rhr9YMT\n0/7Ut6R2An/xcjMaC82he+hU3Fy+h8ntsHQbNsvsMB5q4dDuujbWHgr+S7Hrpjnkv5pvCX6c\npbUDmbuh1A+kVWjzKPjxsRY8DbUiD3HLwGyg/92ralX+0LQ5uO79ccR5ey9UWm/Qwaowuew/\npfrjNRp0TN7vsVIbV6j+LTxXaik+FXpV140/qPwDmjNuFbIvX7lnOn8E9yPUtf8+RBXwwJT8\ngVTAJZmLV6elG+lM8AzcCn6pT27SZg+jQXUkeoKBtLnkhtiYFqHCHuCGNnVjlSt830Nn2ofp\n7pYgszv4q3Jz2TkPfe8FC8MguAK+glqQG6O/auun4XAlfApBHuRq4TBX6iHew/JOsCbUwkdJ\nen62wSZ9vigMAedDpeW/YGwG/iv9A/Ag+LFZql9p0iS5BhcEDzqdYUZw7n0O+bQGhbvBx3A9\nfA3KH8W2Bd9labgMdgE/+q6Dq2FaqKY609k24I8Kj8OdkPuj11jKmlvume4xy4N+vwYGQa48\nkEqltCkPdk7qI8fxIVDF7D+Tak5ef7th7sbgnu8Y1JLS8akFhhk7O8OcUGsKccuPd9e8MX6W\nWjMysWcursZ6z0uDwfgbVcADbg5TqpwY/guSB+p8zF2CYw6j7guwHHjAc3M0wE4Dk7PWwfj3\nYEtwc6/VxdQV294BD13aWavz2g1pAGyQ2Nkc88M56pj2gOnBg4n5JaG51R4DHMf9Qdt2AG3z\n42Jy1oEY/zKsCP4K3hzjTrd5tQSl74LzwIP0rqDP3UgrpUt4sP8C0A4WgHvgP9Acciz88edD\n8L2HgR+J+dSWwuvhCdBXz4Ax0Xl6EjwLR8DxoC6Fu+tT1T+Anki/T8Hi4Aewdt8OtRYbtc1/\nqfkX+HHqgd0YsBFUU46V89D56Lx0fjpP/1d1LS/mfJgXjLtSi/Kj42EwPswGrrtalPvVk3AB\n+HFU7R9D6LJRLUMNY9weoB+9amtUAQ/U0kZdwMSKFTtZXoOpGujBX5Iak4HlbDgafgDbXAz9\nYC8w8DaHWtHpFuBB53V4HkqRfrkGboV9YH3wl5FKam0e7kfrF3A/fAeNyTmsnVfCP8B3vg6q\npeXpaB3QVm3+CvLJg/FVYADtBd3hHKi2/Hj/ADwMfAP/hGvh37AuNKf60vkQOA8Whrtge9Bv\ny0KtaC0MWRm+hPugoXk6P/fPhwPAWNAbPARWUm5+m4MHvffB/3zGuJRPjvsAcN1MAA8kd4IH\nVNuWW+vxwH3BePJs8vBduLpmfwJ99SlUUx6Gn4Y2MBeMgHzaisLL4Y7kpntIJ3COHg4fwSfw\nHKgPJl3q/4fbXUk/kOQrfXF+HQ9XwFngmloOXoYd4WaoFZ2OIR4uLwL99SCcC8Z056+/zlda\nG9CBe/Vu0Bp+gxvgJnAtODdqTetgkD+4fAHGoO+hWBkbdoK14ZWk0XvJtdwXPxQ2gzpwfTwC\nv0Ox2o+Kxlr3WdeWH8/V0hJ05NyYCA9BQ3HJs8fi4D41EgZDrcmPTM+8J8Hq8BR4Hokq4IFp\nCpRPCcXv8pIG5qkLvKxB2sXZmFxAHpAM9J8llefj+jJsBJcmZdW8GDjdaGYAbfIgfj+4kRe7\n4bjYFwLfq5SARvWS5aHMX7MM3ANB/50Hm8Bb0JDc+OeF0+CPhipW4F5fnnkwDAI31j6wAzwM\nuTLIzw76s7k0Kx2vBo7nXDAnGCBPBee788VDanNoKjrdEIaCc0GfOv++hDpoC6OgOeU8vQ38\nmAjzVL9tCm9CPvlBMB6qFQfa09ej4JoYBouChyBtNE6lZfzrDPp9Aijjw5nwIlTiA8mY+Dw8\nC+o4OBHsf284AHrCTVAt+c7FqAOV9G3Q9ySMVavA10nhMK4esFULuAbcZ43H1dCBdGJc+hX0\n9Z5wKFwC94FlN0MtaA6M2A1c+93BuTsEdoJDQH+/DpWWPrHf68HDreM1N3jI3RiehlrRtBhy\nJ7g3fgzGRWOQdg6AYmTdx8G4EJQbG0J5U64L0Nj1siAY1xeBt8H+x0AxcmxuAT+Oqqlj6ewk\nGAbTw4XQUFzyna4D508tamaMWhP6wcswCDyPuKdFFfDA1AXKp5TiibzozwUIm1xjvnCDnBXc\nmAwEYnBfA2aEastN+Q7oD/PCUrAirAVHQbH6JaloQK60jqYDF+8KoL3a/QL4Hr5PQ/o1uVkN\nO9N27EhmP+gCfkxq81VwKxh4cqWdHgSaMyB5GPkD/gFLgDa72R4Pv0Oxc56qZZd2GY9awaKw\nDLSD8aDCOE/KNc/fI+l2bXA9hXn6PGnnqYeqfNJux7xasfYG+hoF84M+dJzbwtmQqzDmuWtn\nOio6HpWQ/vD5aj3wEOJa8gPzYjgZroY6aE65ho3raT1CZv2kYCauneBxGAihfCPSLUD5bo5F\nd/gRKi1/Zb8I7oIR4F7kDzgXQgdwnGthHWFGvbRVnQLOVe3VT/7QpKpl61z05eG9JywEC4B7\n5WIwC9SS/oUxq4FjvTR4/ngZjEHFxhj39tw1T1HZ5ToeB46r42tcd02F8SXZqKpla9qQdckY\nh3aChcFY6lr2fZwf+dQcduazo1CZe7vxflNYD8KZZSLpqAIeKHZBFWgei/FAa/Aw4a/xQW+R\nsOyHUFDF60r01R7cGMO/BrxDui9sD8XqEyq+B2fC9MU2ylhPu7Tv3aS9dmu/wcnDaEPSRm31\nAFiNoB9s0eab4cmkwADkAVp1nXT5y9/XyX0K50Chw/RfGlQg041njgRtd+M3qJ8ArUD7aiFY\natOPoH5OMK1/m1v6zcOm60mFeep6c93l0+MUOt76OWiqkCjz1cNSJzgcxibPHsr1VND2XDne\nD4G2GceU8+JkGG6mArqHZ3rA2wG2Az86lHbfC6eD62RLaE5px4E5BjxMfjZ4HgbBg6Cf/wl7\nwnPQHZzDyl+Vd4Nh4PtWWtvSgeu4NywIB8Gl8AYcAZtDP6gFtcCIreFR6AkLwFdwLKwLztuw\nzkhWVCEeh7hjZ6OTHj1U1pJcxxeA+57SZvdKPz6Wh2J0D5XWB+dDUFj/Id/Uq8+zj6Pg6+Rh\nI7meCM7TYmOgtu4MHaFa0sfOy9uTDv/gegZo/5ZJWe5FO40BHXJv1EjevcpxGANvJTbNxHXq\nJB0veTwQAkOeW//zRUvzhm9DQz5wYTQmA+j7cCL0ACfgyvA5fALV1ox0qE3f5XQ8jrz3StEu\nVDZQDIMvodigRtWSpF3al5b26//GbPZd/aXHw8tQGAsNjSm3yyLtMmCm5S+eP8AM6cIk7aHJ\nw9P90AWsV80POrqrt+terpuBvnLeLgPqmkmXZvubDtSumwGwBIQPo0p/pBfz4o5rvnnqHCw0\nTz307QE3gOM/HXwJlVCwYXzOw7U535y02oHwJAyBd8G4aP2HYBEot17lgcfBLWCsNKZsBMfD\ny6C0v5C99RXK+Gdi6lm3kxa136RL/d+OqbSxZi5wrX+blDs3PcQZezyo9gW10qRL/d8r+Fvp\ndwrzczB9HQSXwP4wP7gnXQTGn1qQMdr4dxkcAx/Bm7A4OCf0v+uqGnLcPobbQFvsfzkYAWGM\nSdaEwhinjfkuyYT1n76XL/00hWfBveBhuSW0g3JKO/VjvlhkDDTeh9hOsqCu48568CL4oV9u\nO3nk36TtuXZbqaG45A8R68Jr8DrMDbWmgRjkfu/e7we2accoqoAHqnGQLNB1sxd/iAVO6EI+\ncIPZsggrPVwcAB6A1gE3z7tgK/BeteXi/B4uhtagPQOgM5Rqj+2WgB1grSTNpezSrv1hKegA\nX4KblgHJ92lMBiU3Vu10DDwQVFrafAT8ASuDm9QnYGB8FvLpeQoXg+3BoL86VFNP0dlucBXs\nCG42Hqb0+X3QnPqdzp8BN88RsBAMA+eCh85Pobml/zy4OdeWB23zkO96cw4W0h3c8P42sAm4\nAVdCzr/hcAb8Bo6vcc41rO35pF8df+ekc/MauA2OhaZqNh5wJSwNjumF8AKcBg+CfWwGxtkw\n/1wXHk71c63KcU+rLRnj7XPQA/aCSsmYfiSsDcbHm+FGUI6x+9aK4IeHcci4egAYq86Hakq/\n9IKVQJ9dDg+BmgDOhb2hM2wBrqmW8BU8BtWSftsdtoPVQN+6ltqD67uWpK0HgmtEvgDnwVh4\nE4qVH4J3w8ZgrPDdy6nPeNhHcDL8CHXgAX1BcJ3YZzFyf90NjEvrwHxQaenjS+B8cO5OhPfA\nOOlayiffZ1vYENYA969aUhuM0ZczwiBwjR0G50FU9EDJHuhLi1+LaDU1dUbD72BA8Jco06PA\ne42pExWcuG4MWdSTRoNzGl5PXhsMUq+Bm9Ev4GJPS/sWhjnShQXSXSg3UGTVITR8p0DjlSnX\n1z/DqxD8eR1pPzjqoFh1o6KbRVb1ouFLRTTWLgO/drsxDQF9rv3FqDuVnCNZdQoNnyix8SzU\nHwcG8/dhKGjzQGgB+bQ3hd7PKjeZe4tsvAP1tOd7eBu+TfLHcG1Mh1LhrcYqNXDfA6WHzYbk\nwdN15HpyXbm+tPcGCGpLYoGQyXPtTVn/POXFFnkYvqKByidzT5u+AX34Q5J3XZSi06n8aCkN\ncur2I6+f/IHjJLgHXCvbQZBz7mFwTl4Nt8BEuAiKVR8q2ldW+Y6+a1M1Dw/YEOYr8CDHLHzI\nFKjSYLFzxjXvHjMYzgX9pY/ThxzLjEvuAWL6ZihW1r2s2Mp56rkGXYsLwufgHNS+u8C4cwgE\nLU9iDAyAS8A15drvBI3JmGJsyaqBNDS2qangbnCtuAe5dn6BIaDNe0JWGaMdt6xyj+iearwq\nadeRdqb3StdPPs1A4SLgwbghuef1aqhCI/fGcr9bTp2jyRuLHONX4Lskn34fiuo1LX+1c5ZJ\n2YJ/PUOk51DBigVuTKS8S4F7oXh6Et+CY+/c/Bh8D+eMag0LwzRmCsg12rPAvcaKW1LBM2Gn\nxioWeb8N9dzrtd81aUzw3T4E59GOEJXHA1PnKYtFpXlgG6rPCi7+xWExMIA5KZsSWGmeSbPR\nyl8yDPgGA4OOi2ME/BOUC3sf8FcEF7LXN2EuaA4Z8Oz/VpgfnJcu5l3ABa39ci08CNeDh9Xm\n1L507kb6PCwK7cCNtiPoz8fgcugEvk8taB2MMPiPh6WgDpyrC0AtBMmDscNxdlPvAM7lYaCv\nnRduot5zvjhXLoU1oFqy3w8SluDqeN8HO8CT8AV8Cq61j8By7a2WWtDR3jAM3MT1oeM9DA6D\nY+B26AvLQiXlJi8rwbGwHnwC/wbtVG7Sm8MR0Ao8hOwEzoPJTZ9jsGv+swoavhbPXgCMNY6n\na9bDjnF9cVA7g7HdtSKmLaumjHf3g3NwadDWreEzOAtc1+ptcI7qN9d3f1geXoRq6g86Oxvc\nF6eD2cGyp+ESuACquY7prqCMQf7ocAPUget7IDgXXEPm24LvciWMh0EwDvpAWHskKypj437g\nfu1+uAR8A45tWN8Lke4Nb4HnJ+20jnY3p7970H8Y/wVJt4EHQHuNYdo4GHwv69a69LFrrj3M\nDc4RY60x4weIKuABF9GUKif79VDIBy4MF3lj6kIFg6qTLshn2vYK2AN2hSGQK9uV46PE/uxr\nJhgOPncreAPmhUVBe3wnN6xlwDZ/gPKeG5OLflYI5SQrrlXoYXMYDfpJmw3qHqiUC/kn8FCw\nG2ir2gU+gE3Bd66G5qcTbdCnG4D+Xg1CwCFZ7zs3WO9r694wFrrDw9Cc0qdukGGMtc9DrPSF\nx+FLqKbcCB33ZWF10CZRXuuS60iuBnOx/EFYCJ6Hg6Ecctw6ghv2r3ke2Jkyx15/OS9nhi1A\ndQbbWO76WRxuhYlwGpwM5ZQbXQ9oB+/D9TAfzAP2r4/U1FCXsARX/efB9UBwDWljJWR8CdKG\nVqC/tGt3uAqUPjN2SVTDHlib22Fcrek4G3vU3uCHpvPvxgQuzaJz6NUxD9JOD8DOAfemznAP\nqBFwZH2q+f64bp6D9Jx1je8Bwd93kXav8V2KlWs0/cxi26Xr6Ufjm2PvGlkDXoWeoH4D/doC\nXoClkrTryrKg8BzrHx4KK3A1fu4Ls0AdzA9+xJt3vzReqaFQB8Gf+tn0x9AVLoUeUA3Z93aw\nDXj2MMZPC+uC60nfbQbWaw/BZvfRa2AM3Afllvvi/jAcbod3oBS1pbJnvf3A99F+bffqvDTt\nmEQV8EBTF2+Bx04WxV9jpUHaQJhPLggD3CXwMtwCC4G/1HjA8JeQOWE3cMLlKiwiN7DHwIn6\nc1JJv58BB0I5filZgOfsBSosAK8rQ1gUP5G2Lz+EVLDPA9yV4GLfGS4HN9tKyAOofjOIzwgG\nQQI/sQAAQABJREFUHlH2r60G2PAOJOul3cHHBqzHYQXwOU+Avp0A5ZKHzPPgSXgoeeg6XP0V\n6StwXuSOm+8RfKqt2vk9+J6+273geJQa5GhSUO24o00ePvqDfWwMC8FAuA0cd23zl8cDkjSX\nP/0ZbHZsPoQu4IdnL3DDcK431beL8owz4WF4FtR0cAT8K0l/yzWMce74mx8OdWBA933vhAdh\nf7gAToOmagMe4Ob8OdwP46El6L9vYM4kz+Uvhw/t9uAxBBYH80eCPvRg0Bt8Vh8oh7SxB7h2\nxT7OB/vLVRhfy+eAp8D32xNuANfP11BuOU76IShtx3EUXhVuxGtRHliEWsGfaV+6jsyvV9RT\nKl+pPV24XlTaTg/xYY2vRTp8IFmvOaSN3aEbrAjTQFrBdte1HxobgTFhM2hMq1DhClgOfobn\nIavcZ1ZL2Cd5iHFfOR+0zRjg1X0wyLzy/HIuuLefCoeAceIXKKe05QaYGdLzVDsce6XPfwV9\nXQdB2m+Ze+uS8AzsCu4PlpVb7hmeg66Hr+EVcA6k7SZbr3zzwg8+5drz/f4D90G5tV/ywN+5\nHgNeg43JrfrLGP4+DYNgLnDPbg+5Zz2K6tuHuW0+9/0si5qMPbAGtl8IHgyfhItgb1gCyqm+\nPMzg6MQfBx4aPSi+C4+Ci9oJ2xBOxIHwHWwHQR6+XZi7wE5gPQ9iWdSTRtrZkB3pe9oyHuwz\ntHPBu9jNfwH51IVC3zmrDMwN+Ux70nbmS3vYfz6ptyNX27gBmQ5ywxsbMhmubh6OjQdHx/sm\nMAiOgKcgbVeuzem8dp4NQ5I2zqHLIKg7iVEhk+F6Cm20z41OmxxDNx8P8k+DhxHnXjvwozTY\nnbYxlHm1PNz7MqnvM5wroyGrzqehc+o5cH75MdMGhkC6/4bS2mVb567jbV3nkhuAG4Y2XgNv\nQVY5NnfCzqBP7c8xsy836XeSdD47te+H5L7trNMb7oZnwbEZDsry/vWpbH9upJnPd6yDLc73\n0G8Yw3AvfU2vEzdQ614F+XQ6hY/mu1Fk2WfUy2eLZeIcKIf68JB+TXiQ7+i7VlpX0IFjl1Vh\n3qfHM53+OOuDc9rdTN61kFUNxfgxPNT7jllTdS8PMLZkletxAoR1oy8LzVfjwS1gnfmhIbXj\npnHD+iuCh29jdVaFWJwe63x2Bvt3pqNDwTq+m7EnaAAJy+cOBanrS6Td+7LKvTltY7CnkK3e\n10/a6N5q/DS+G9css53+y5Vx+JDcwhLyjqVjMgS+hetAWzyThdhqvhDOYe3Tdsc11JuFdFqD\nyfRMF5SQ9izo+7tW7Cv0UcxVHzq39aN+9eqzwjjsQNrzTXiW99P7Atmo4IGpQ2IyuR6OnW5o\nHcBD27NgkNsCXoOsE5KmeeWk8tmdYHEwiCwLu0Gu76aiLFe2r4Nh0A7UjHAgHAwuSCdzU+Uk\nd7G7eFWwxas2eA18SjosGg//+m8GWB98pxZQKYVnu/DzKdgd7pl3IRsk1HTwHnwMHUF5OKwz\nUUYN4lkbgOO+FRwNC8Ba0JjCO6xAxXfheXAMpgGfUU4ZDN3s1gV94yYzPMm35+p8uA9WBW1Q\n4Top99e/zgXxX0qcG7ODay69yZItWS/TYm3oCs57nz0/hHm7MOmHIUgfanvwpeUXgBvb3WC5\nbV1H0yaYb6qcZxeBBzjnlXLM9dmtZhLZf7gfyvS/5a4hr8vDaqDNri/f1/JyyOdoa9BMJByn\n3LF1I0z3mX4H15XSzkpoCA/VHvsPdoW0fX8PUcV7wLFUwYeTcpP+WjY0XdCMacdVjEVK25RX\n9z7H/kNobmnHN3mMMP4Fm8NtD8PXJpk1QmGB676Uj4Bd4E0wRjdFLZPGr3NNrydtTMcA877T\n7RDsd6+fHlqBcg/yGV+ZKbPCGk+va8tCud0Fu0xbrm1Dwdg4GgaD8VP8kPkEyi37PRYWg0dh\nJ3C8jI3dIS39mStj7QPgPNGfyveatz5V3j+PJM9O+62xHr6gwgKwA8wM6bOWz/kPzAbKfHp8\n6gvjn/96wIk4ucjFdDKsDOvA3kn+CK6bwx5wIXhQKUZzUOlc6FOAtSkPE9MDnIvFAK+WA303\nDNIKgcfFHdoaMFyM7ycVXUja6MGpXNKW66B16oFOfHkmKQsLYVHyN4L2bQQeMF1EllnnNaiU\n7FPS8868CvaG/KTSSR9sY0OGqwHA93TM3SBc7AOhEnqDh94FzgVlQMy1r/5Gqtz3sI62PQcb\ng/61/AMop0byMD+MB4A+fRtWgPlAn50Cy4L2pH1Ott4eywPa57wMvn6PdJDvUg49wUOeBP3p\nxm/fj8NQODPJc6mXvvspSXvZHhyLrWE8zABLwvngGhsETVUbHuCmchy0ANfsC3AZbAFBl5Do\nB7+CflP613VvnJDV4WfQn35geigMdUk2Wc7F58Gx9/2NSenn30L+GngIgjokCX3nB6e2fZGU\nlfuiD5Tz85X61H/tG0E+PbbJ7XhpwAPOLeWYjalPTVo/Jh33E5Ky5r64JlxDyo8DbXOdK9e8\ne+QNZppZrlfnvmsoyDnrOUObTWu36dtgE1CNffDUUcd9o1wxUzu1YzikpV328TkE/2qz8+Ru\n8P4C4L2twIPxkvAlhLVJsmyyP+UHRPCdfYs2eQ39mnae+G6fgaqDhcB38lme5cZBpWT/x4Nz\n0li0LyhtC9I2bZFQ7r7QCmaH3UG5vw+vT5X3j3uIPtNW+w82kPwzH8rDPfdG0y/CgxDmc3iP\nHyizTlBoF/LxmvKAm+zkojoMdbP9qIDBd1LuwWUJ8NDQmJw4C0IhH7gQnFRXQDtwMVvfvIvD\nidUGgqwb8i66ID+qtHubBJ9rwLgGPNjPDU2VzzQI5tO6SWFYCL7HGmCgnzO5p72zgYvRw5bv\nmKu2uQVNyGtDYwr2Wm+OVOXjSHtP2/W59rp5bQyqrv5v9j8+uz0EH6xKWv8aWAz+adl/IRlE\nP4Hwrr+Q9uM4PHcR0k2VG6DP88NGhWf64a+9bcH3EcfWa1CwK+S9joJZkgLnq89zPTVVy/OA\n8N5LkQ5rTv9tAdeD6yStbmS0N/jYQ9ce4FwN62sV0h3hCSg0/7lVtHxX/XI1OH5hvFYkPT8E\nHUBCf+b60HkSfOx68gB2CqjRoA9WAD+cmiI3ZH/YWAz05QzgB6Q2aYPaadLlLz58kzIP12Ee\n60fbhrEh+af0q/ZnVRi31jzAjd58GE/tyNcnxSXLZw8qudV/G2iT8aPNf4sqklqLp77ehCcH\nf86aekYoc0/cKyF1O1PSeOd6yirnoHKOBfvC9RvKnoO+0FQZU4yvTZHxc67UA8LatSi9ttcl\nb2xw3W0LDcWaxblv3LwK9MWS8BJkVbDJuBHS+tO0cX8eCHLt29fXYPwPY3At6aBXSeRbe+1D\nhYzXMMbBxvAYy41VSp+aF+OW/lwTbOO9sPd8Rtp5ns/OdBymSsmyr3+C8TL055gZB4Oso432\nFdLpe52TTDi7fUj+olAhuabnVc6tRrP2qdyDTIe5qE2NaT4qWO92aA2/QNgvSdafCbyqYp43\nqeYU+jcMxOTw+to6Ek6CEHzSdu9J5kLw48WF11QtzANOgDA5m/q8htob0A5tqEID9wzA/4JS\n7dSfpS6QUbQ5GrLIDe1wqMacG0I/x2cxkjarwMFQDjsdk3BgIPk3GVhP+1tpcQVrUW0faIqd\nxc6Bt+nn3OLM+lutDSjZ/W+lpRcUY+srPDZ3oyq2p02pGD4qim3TUL2G7H2Ohpc31LiBe1tz\nT4pRQzYU0/5xKl1XTMU8dXakbLM85ZUoeoCH3prxwT1o1yVj21Kb3UWDfqU2Suq71tdO0k0d\n18ZMuJkKDzVWqcB9Y+eqyb1K23kt/WT9mDuCtu5JlZZ7rGv9+Ywd9aade3wp0u+qlP3dusZO\nP6Cy6GQatc/SsMQ22nkeuCdl0Vk0apulYYlt3PfPAPf4LOpDozmzNCyxjXaeBJ+U2C5Wr0EP\nrINNw+BLeBruBQ9FfmB8D10hKnogeiB6IHogeiB6IHogeiB6IHogeiCTB8IvDZkaN1Ojmem3\nEywGC8F3MBjug/EQFT0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9\nED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9\nED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9\nED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9\nED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0wWXhg\nGqxcD1qkrN2J9H3QD/aAqSEqeiB6IHogeiB6IHogeiB6IHogeiB64H/eA3Pwhn/ATMmb7sX1\nV3gAroRvoC9ERQ9ED0QPRA9ED0QPRA9ED0QPRA9ED/zPeyD3A+l93vifqbdehfTv0CpVFpPR\nA9ED0QPRA9ED0QPRA9ED0QPRA9ED/5MeyP1A+oK3XCznTT8hv2xOWc1m/W8Go/J7YDmKL4Zi\n/ptJ/5tLfTkBgmw3LVjmPzs2pK+5uRU0Vi/fM/wqPw+myneziDJttN9fUnV9F+2fmCozOQp2\nyCkrNrs2FU+DrHbm6yefj33+UNglX4MiyjakzvFF1Cu1ynQ08J+bf0s1/ID0Pql8KcktqHxU\nKQ2SuvnscA74y4725dPrFB6a70YRZdtT55Ai6mWpMj2NnKParvpDr/pU6X92o0ljY9GSOs6v\n9LrIt/Yb6/1xKpzUWKUC97VRW8st38u5kfbn/eTPytjRP2i3Xca2DTULdqbj6h00yPqfbhxN\n280b6jDjvXyx6XqedXnG551Auy4Z25baTBu1NYvOpNGaWRo20kZ/uv7Se+yF5G9vpF2h2324\nsXLqZr51HOZaek2kmhSdPJua/u8wsuhSGi2TpWGqTXiP9Jrxdu5ecDJlj6XalZK8jsoLl9Ig\np662uDem96B88dazSm94DrLoNhq1zdIwp417Zu65KW2v9w6HV3PaFZN1vPzf7MxZTOUS6+Se\n7dw7D4J3SnxOQ9U7c3MAvAhrwUBQq8IM8KGZyUE6Kyq/B5aieAUw4DemXakwBh5OKrbnKsuD\nk+RpKKR23PDQ41ikP1IK1c8t92tcW8/PvVFE3k3hJLgahoBByg/DBWBFOAfGgVoEdq5PZfuj\nLxaFi0psXkd9+zZw+kHxJQRtSGJu0P6gJUl0C5kMV9/bMbksQ1ubGAD04azwBbwH88G+cAqE\nDb4D6aYczDrSfh64BoqVG1gPcMz1qe/5MxjYtdlDQ648RGwCh+beKDK/GvX8ZenGIus3VG12\nbnpYcJ6qpeHU+tT//V8nrl2hV5Iv9bIGDVrB7Q001Acvw3fgJvsDOCf/CdeCa6gxdaZCF3AM\nsmhtGvlh2C9LY9pMDfptXvA93Bh9DzcvfXgBKG1cD84yk0GdaWNfDxbZdmbquSZmglGgXz1k\n5MoN19h6cXJjU66doW+SL/XiOxpbHi+1YZ76jovvMCu0Bt/pclBbgWMX8paVIsfjJ3imlEYZ\n6vqDhmvh+gxtbeIa/AxeNFNG7cCzjPXOi8GwJhhbGlqv3C4oY9r78HpSY0uuxu57kny4HELi\nR7BP638FpWgPKneE+0pplKrrHvECDEiVlZrMXTOh/TYknKcjwfjv3vcYZFE3Gj0AH2ZobFw/\nDC6AMWCMnweM88vDiRB0MAnLngsFJV63pr57keOZVS1peAJcCd+AvjNufQ/O/9Ngf1gWXoVS\nNQ0N9KexYkSpjRupfwD3PwJjv2Pv3FwK3oGmyvjkeeRoWATmgk5wFawPD4Pn6V8hajL3wI7Y\nP7rId7iVejIVXAu/gEHNifAb7AmF5ATyIOCiy6KeNMq62KemrcF/B1gcPoXP4S34HQbCfKC6\nwMT6VLY/bjSlLsJLaaMP9aUbhL50gQf1JvFeyCRXA4uLP6t60fCljI1XoN2XoB+fhLGg3R44\nHGM/UIK6k9DXWXUKDZ8osfGq1NeOx+BneBYGgvN1EOTT3hRaJ6vOp+G9WRun2rkeJ8KH4OZo\n2vkwC6hDwXmbVX4Q39xI4ze5Pwx+gKdhKDjX9KkbTTFyzvYvpmKBOjdSfkWBe40Vt6LCy6DN\nT8FwGAOdoAd8DdOAOh0erU9l+9OPZn2KbNqZeuNBfz4NHjQc4xkhV65/15exS9mHfWWV7+i7\nNlXL8QB/EPkMXPvGVddYG1COmWOXVf1p6NyptFwDroWscg26FsupA3mY+5Hzwr3APWEQGFuy\nyphmbAvyWcbDtC4nY7/Oy7D/7JeuUETaGG2szqpRNOyetXHSTpt9TlgzFntWGQHGUOeWB333\nvqwaS8NuGRt7kP4DNoBXwVhgfNIm7VsNgjxDHBIyGa4TadMlQ7t0k2nIuLZPAGPpEDBu/QTO\nl9lhMHg2y6KWNNIfnbI0bqTNK9zXv8Yq45T2+i6W5cMxWBKyyI/GJZKGS3PN+pwsfZelTXrB\nlOWBU+hDbuK9twWDrB8bneEdcPEcB/+BdlBrcnHcBqeC7/ABrAnOi4dgHFwMzaGt6HRPWBfW\ngA5wEFwIi4C6AxaDY8CArzaHkK4vqOKfG+nreVgY1gdtM9j5Hs4H50FrUAtBi/pU9f74a6kf\ncOuAPvW6O7gJtYVZoRblBnoVnAIG2bXBeaoehxnqU389ACRFZb24WSwAu4DzcgWw7Fd4D2pd\nJ2Pg3OCmtR4sDPeDa9/17ly9ILly+cuBynwlNC0Ptf9bwXWtX91M28OxkKt7KHANnQlh/TTX\nek/bdj2Zl0G71wfnp/50fk4NUdk8sCjN+sIZ4Dq/BdYD12E55XPXgv2Th27HdU/4BezP/ceD\n+UXgupmcdC/GzgZnQVgz7uvzw8HgXB0IzSX3pCfhBjDWG58OBH+IGgTGh1paQ79iz51wLNwH\nztEeMBz82DgMalXGzhlhd1gfPAM+BjsXYBfKHYMscvw+Shq+z/XDLA+JbWrTAzti1ugSTDuR\nuk62H8EPIw9OBlb1KexTn/r7H38l8NcCN9Ms6kmjwVkaJm08FPcHbfB9XfyvQBvoBhPA4NQF\nJkJWubn4kVCsrqWiH0C58l3/kSp0Yevzb0Gfa+M4yKpeNHwpQ2MPd/pw8Zy2u5P/CvxYMtDo\nz5Fg3S8gq06h4RMZGvsLnb/2GciDHdeS/gkc71ztTcHA3MIS8v5o4AbdFHWn8RjI3STdpHwP\nx993akoA9lfzm6EhvctNPzIdO31n326Krvs1oRj1ppLrLatupOEVGRsPoZ0HorTmI+P7rAAb\ngn42fn0PT0NW9aNhnyIad6KO/c+eU/do8m6q+bQ5hePBde4mfD9k1aM0PD1r46RdO66+w7I5\nz+lL3rmhT11fjl1WOWecO5WWa8C1kFVv0fDQrI3ztPNZxk21Lxg/v4ZfwNiSVcY0Y1taB5Dx\n+cZrr/axHaSVbw2l7+emjdHG6qwaRUPjX1O1GQ9wvYj7zm/wBgS557n3ZZXxt1vWxrRrC/r8\nVxgBrpuHYVFwbS0HyjPEIfWpbH8m0qxLtqZ/abUROW3Uj+4B2q0PTwb3icHQE7KoJY18Z2Nj\nOdWah/ncu5Prp1x9B+NUuTQLD9oP7oU9YQYIMsY/HTKTw3WaycHIycTGE7FzFZgZLoLHwGCk\nXEQt6lO190cbtwaD5pXwDDiJXTgueg+lzfELrf6y/1xZlvblTeT/n52zgLeqWP/3TykVu1sQ\nBbsTG8XuRCwUsfV6xe7uzmujYmB3YmB3i4pKKKViYAvG/3kOa/wv9937nL332bHOdb6fz3Nm\n1qyZNe96Z+adWRvvHQjrgLZ6r5iDGdUqKvtWuTYHe4dwb1EwOM8Bs4Ebcq3lJnQFeODyI9gD\nv//64UEg7VcuMyPtcj5KWqO5eAYug61hEaimtOMW6A3LwRfgpmjqvaxLG/PNT+32njGrI6wL\nO0FbqLbs10071y4Pp2FN5drgB1EH0M5dIbctRTVVGPtcO57Dit1gX+hTU4v+dzpLz9nLea37\noRucU4VXvJRn3ps8fw/SsXAbpOUYh/FOl2c9r9/C2m5H3nj5dYaMHoUtfpjeDu/CYHge3CtV\n1nzuDx7GrY1gbhgKnpv6QtZsxaQGhXh6BlfHwErgmqqUfP7D4Mfug3Ac7Ahrg75yP1kTWoyC\nw1qMwRk31MW9NLi4w8eRG7gTxsNHVuUBz1+TFoOnwYPoVHAoaLcfeLWWC8xfpJZKddyD/ILg\nIkzLj7sb4Qbw19p66CM6/RgMPCFA+mvKQeC7qAnwAFwFw6Ae0pZd4E24Gt6CQ0ANmpRk7u/j\nWNQe9k9ZtgB5g6+HZX8RexkMwtWUvtsHxoC+s+/DwIOG/Wdd2n8A+EuemgyOhdHgPFD+Enwr\nvONFDfQKfXwF2hHkh/u+8FAoyJPqcz9W389zr9ZFruUP4GgIa39q8geD73ATGBuiSveAsb4z\nGPuVB2kP0NN4UQWN5JnXw2WwISwJQT3JdILc/Sfcz3oa1sx1GHoHbAXu+VmR8ak7aNvzEOKT\nY/I2ZEnGe88a3cD9/Akwbu0DvkcWZZzV7iPB84p7mJoX1i2A7xdiGtlGNT93V4AVYW9YCPzA\nNZa3SLVukVZn12gDzybgpu+hbjpYFfrCJ5Bl7Y5xA8EDh4dnJ7laY1JS878D6HEzeBG0qz1o\ny1EwGLImD+e94CHQd+/BKvADhA8QsnXXeViwPngAfhLmhGVgVzCAZlGjMcqD/WWwPXwJa8Nz\nYFmtdBIduWG8D4PADcFNYBv4GbKuozFQuz3MPwMePH2HzWEi1EO/0Klzzx+X1oGh4Dr/FE6A\nliLfwYOzscm11RV8ty0hqnwPGEePgZtgT/gRnCdjoZq6mYe7/7wE7j9+8K4OR4Drp6Xrel4g\nnFV8v04ZeCEP7k/Dh2B86gIdYVP4HbIk4/1ucCv4cRHi1gjyJ4IxNYvqg1GeTd3D3oDJwXmw\nIeTTbxQuD8Wcuaal3kgIa1MfGRcfAP3U4qRzoirnARexG+LO8Bn4tb4yeCjNuvwoWhSuBn9p\nOh8WBxd+PfQHnXoY7gEGnddhVTgNsioP7PrQzVUfngr+i+IYyIo8tK0N+4GBzGCpjTdAlnUF\nxi0HbpyjwUBvUK/lwX48/bmeDwc/0u4D14hpS9BXGOlmdxyMgzthMXgM6qn76Tz4Ub/q35VA\nf7cUvYihrn3XkWv/DFgSPDBENc8DxtHVwAOde0EPeAuqKfef7cA9yD5fA3/wclz/F+T7bQ07\nwmfwE9RbxiRj/PFg/nZwTblHZVH3YtQSYPwPccv94TvIqt7GMH16FRinnAeXQLsCtKe8mI8j\nqjX8KDwbafrj0LjoPnM9zAAtSq1blLXZNdYPzX3BwO2E8pf5Y8FF3pL0OcbmfoB4CN0fusBk\nUEv9SWd3JzTVr4tyH+gM9fzwd7M5EQppW27sAdpZr/Xn4dPNsRN8BM7ZrGgZDDkCFobhcC48\nAcoDktRTv9L5lQnF2uG62Qt6wgLggate8le9y3I6X5Hrw8A5+QmcDdXUlDz8UNg46cQPpDPh\nmOQ6i4lr5HDYADxU3AP6yfkQNIrMyeEipkV7wHnn2C8FY8D5eRek5Y9PErRryFQodU66BjZK\nnueh9yzQjlxbkiotOpka68N89l8Jfs/I2/ihdmmOLbNwfTT4L8tz5dyr9+UQDAhxaw7y7ler\nwsyQRXXFKGOv+9DHFTbQWLgLDIDvYR74GQ6Eh+B5aFGq50GyRTmqCWOv4f4p8CzcAm6iTobp\noSWrN8a7UXwOj2b4RfyAux0+BQ/Ttf6Qo8ui5IZ0IxiYnoF62LkJ/T4FE8F5q7Rl3YZcff+s\nRveum6lA2/wl7jHYDlqyLsN4D1svgb98Zynudscex1+b9PkE8AeeBaEaasVDHwY/GP0wkj3h\nEWgNWVQbjBoIHsr9MHKz/xdoey3HcjH6C4cxsiVrCVrs0Egr15191FKL0tkrMC/0gxFwGxwA\ntZLzzv1tD3BM/U+C9gbH2fn6v6a2vNDj4GH2bnDt+RGSRc2IUf4rhHHKs9UvkEXNhlEvgx9H\nN4JxNGvaEIMGwe9grP8DKh2/buWZ08EW4BlD/QhrwcbgGajFKKsbUotxIIYuBzuDByCDvfIX\nkIPAIH8i1ENOyN3Bhfsq+MvGF1CsDKJngxPadzNA7QbV0qw8uC8sC36QXQlPQVOamgqngb7W\n75vBllAL2bf9+svWD3AT3AH5ZKA/HhyT66AnrAm11gV0aCCfFlaCq8Ffbc+DMH/J1kXONw8n\n2mMwHwFXgbbdBgb0esuDhPPUde96cp76QVFIi3HDD4A1YRAcBRtANeUa2g/mgcFwDujLfDIu\nOAc8EAY5Dq71V0JBBdNteNYyoF+0qR1MDkeCB6GL4Hr4E7IgN/t+oE8fhjfhPtDGd8B4cxfU\nQpvSycnN6OgX2o5vpL1j4Zx5oZE6lb51Pg/Upt9gITA+vQ7OQQ9xxtVqa1s6WBLOhK7wO5wH\nx8DWMACyJPf0vuA6GgvGIGNLsdqJigvCImB7tdGkpOJ/l+CJB0BH+ADOhU+gWB2YVFye9Edw\n36yFXAvGxPVgAtwJjcWlQ7n/HawA/kvKHpA1OaedK6NhfdBe42w3sDyfXJfHwpf5bhYoM874\no1tarqnnEtLlmc5PnmnrWoZxK2LmT7AXfAqfgZuYE8t79VAfOh0IU8EbsDm8BnNBsepCxenh\nxmIbNKPe3LR9HTYF7dXux8GPiaa0KBXagx8ntdTUdPYs7AODweB9C5wG+bQUhW3AOvVSJzp2\no5oPPNwZtO4B44Cbpe9UL2mDHx1uSG4yztcO0AvmgHmh3nL9OE9dT87TKcB11thmaAwYBaUc\nYKhetlxDL4C26sNV4E1wneTKdeOHSu4ady3NkFu5Qtf64zkYAa4Hf73eF0ZCO/gPXA1ZkPFP\nX64N2uth/S44Gj6Bl6BWMb4VfSkPirnqQcGl8G+YKbnpWnZengvGHuUa8zlTwn6wPtiuD9RD\nrvNu4AH0VegMr8Cn4B6wONRCK9GJhzo/OobCaPBQ6HjXanzpqijNQy1j0MZgDHKcn4DdoFj5\nTv6oMzbVwDGotNblgS/D/OD4Gt+12Q+7YuXY3A3ur7WScelhcJ1/BF/AZXANFJI+vRP8OMqi\njGWuL8dkT3gXnPOTQWswJhQixB6q/LOkY6Ka5wEXv8F8aXgredQlpG+Cgb7WmpYOzwOD/flJ\n5y705+FE6J2UNZV8lVTwMD26qcrNvK9dBmsPcyHAuNn7HgPgeyikYOe8VPi2UKUqlB/AM2eG\nJSHYcBv5e6AffAhpWWdymBs8XNVDe9GpvxgdAjclBtxHej38nJAU1zz5gx5/BzckN0Wv1d2w\nKXzjRZ11Av1/CStDmKfOg3PhFvAXuVw57jNBe6j2Ju/88sB7ATjGys3NMT4H1oe0HHNtco37\nsR/kWnIsqiH94SHPjXlnWAqWgAfhVhgIz8DV8BzUU/pwCrgC1oKe4Pq+EW4A30O7ayHXrQea\n13I6M2buAMbQjZJ0X9KDQN0Ozs9TYRrYFJ6Gs+ASuB+cL667t8B5WitdSEfGdn3p2lL6+qKG\nXO3+N7xz0J9rtAt8nPT9H1J9PUNynZXkZAwZBatC+KjpS9693vXzAzQl1+DSTVWqwH1jkevY\n+ahc885H59tqUIzGUcn4VEvtRGf6x719RNKx72GMDGlS/FeiT42bWZVxfiJMB50h7Kc9yD8K\n/4KoHA+4oUY1zwNuoAaqw2F68KPTxa9vXdy11rJ06C8Bl6c69jB3LayRKmsq60fRI+AvJws2\nVbmZ97VL+7QzSPvdrH2fxuSG5oHqSuiYVDQQV1vafBsYGIPuI6PfHP9cvU2BG24/mAdUrdff\nKvT5FpwEHkzVENBfBv/foV7SF63ATWalxIhFSRcH7TOw11v55qnzzvW2XAHj3HycI9fBjBDe\nk2zF1YknzgUe7oIcU23U9lz9QcH1cBosn9xckvQceC+5rnRyEw/UzjOgGzwM+8AC4D3/xca1\nks9eimsqbdCmq8F5eCLcDd+BcXEO8MO4FnKsJsI7OZ25fv1FuB38CSFetiG/AphuAs9AWh6k\nDwE/8O6EhaGW0t4VQf/2hfXAdX4PdISX4COohfSrvtsd3M9dz9uDZT9BluScvAY8cwS5V04D\nHuqLUX8quc6PB8fBH3jngkrK57nO07FIf14BXaE1FKNrqbQF7AG2MX5WW/r4IRiR6uhF8u6d\n3ssnx2QH2AlaQS3spJui5Rz/EowHri+1DLjmogp4oNhJWqB5LMYDX8NQWAW+AieigWAUDIZa\n63s6dHF6GNOGoJnJeK8U7UzlO8BN+Feo1mLSLu1LS/vtrxibe1DPg4vjoJ2/QbWVz+a2dDot\n5LPZObEVaOen4AbnQauW0i4PvnPAG/AzeBj4Ay6FekobfoQP4TkItr1GXt95oKu38o35DBjl\ness35trrAWsT8BDqDyZ+sLjRVkPBBtfSJ6kOGlv7B1NvNngZgs8fJP8+LA6Vlmt0W+gH4aN3\nPPntQJtd86798C5k66Yw3vqiJ1wFh4P7pvF+a/gM6ikPbNo0EMamDDmZvHHxX7A0bANpfcuF\n60oZM9s05Kr/r5xJNw375C9cPAWu/YfAa+ORdnnQrJXcJ93jPIT3TTp17EdDvcc3MeevJMzJ\nvwrIzJRceK8YuQfsCFfCkWD8Mk5VUsZyx9HYk5bX9lXsHv0YdQ+CCxOcK9WWfuyS00lTceke\n6h8NV8EVENYW2czodSxZAtxTf4aw95ONyucBF0ZU8zwwgOad4SJYG7aC08FDqP/kXWu5CD6A\n/8A0Secrkh4INyXXxSZfUHE1WA5OhWoteu3SvhVATQva73t4kG9Ko6ngO9r+bCg2+FK1bGlz\nD9g0eUJbUueAH8gPJ2W5yQgKloWVwboToJa6mc52hPPBw+/OcB98Do9AvaVPZwd/Td4euoPS\ntq8bcvX9o30eOJ1ryvV1OXwIrrtCcg53hrXAMfgFqiEPyE+B4zsrqIXhGLDffPKwshX4r3X6\nfAnYCKo5N+/l+fPCIeDBw0OaBwz3Iw/2s8FdUG853r1gXbgdFoBB8A10goegHmpFp/MlHfch\nPQIOh+mSMhP3oPthSzgTHOMsyYOue+dJcC7MD/vCEHAufAS10i101AW0ZWNw/l8MzkPHPUty\nTvrB4J6sHPPLwI+et6BYeTaZBzaFDWAwVFLf8jDXh/vxnMmDXT8ngP4uRRdQWVu3hpGlNCyz\nrrFybdgtaW9ccm64NzUWl1xzxrVtYRxkTf0xaA7YB4z1S4Hxd09wvPLhe7iH/CPV+h/51pV9\n6Xd53B5wKfyQPLo96e5Q6aCTPL7RxI3HQPIA+IEjLloXhxtROfIXhxnLaVhkm3Oo58HsJfgU\nZoXPwY3K9ylGfry9AiEYF9OmOXXupPFZYMAcC9PCBND346GQfJ8XwQOBQaqW6kdny8Cj4K+m\nM4C/9G0GP0O95YF5QXBjdR7MDa6v3SALOg8jnKeOX5inri/n6e/QmPxw9nC9Kjj21dLOPPhB\nGAmjwYOFv8J6iG5Mxqpaxitj5bmJQf5YcDy0g9bQE7S/3roBA1wvD4O+nB5+gk2gngegWejf\nD4i2cBMYh4aBa3pWWAuco0+A62de2AMWgizpXxjjDzRD4DNwvb8JHthqKfe3/eB8MHa3gimh\nF3wMWZJjvTi8Asag2cC5aQwq9QfM72jj3FYnTkoq+rc3T3sQRoDr2XloDDwIStWXNLgfTi21\nYRn1n6NNX7gcToO20AZ2AOdpY/Lc4px2r8iaBmDQsnAJOGemA+eM+8NVkE+/UWis+UfKzSiq\n+R64hkcYaNYBJ9xAGAP10nt07Ga4HrhhugG8Dvk0OYV/5LtRwzIPlzuCByYXsIfOR+AXUFmw\ncZIlf/97JJf9YDX4HrTZTSfLOgDj/gNd4RvQZg+rWZA+XBtWh4VhOLiWmvr4oEpNpB07g5vf\ncpA7Tylq+EXOtNTDim0qITfwpaE7zAsekN3w66Wm1q5r/g7oBr/Co1DPj49ce/+NPVfAKuAv\nrK4X52k9dFHS6VhSD23qOri7ITfpcD8Veeepvrwd5oHhoN6Huxpyf//flR6blJn440SlNVny\nwNw14cfIGgmdSYfCE/AH1FqX0eF9YPzRfx4aPexmTR5Ye8I54Me7Nrpmwl5J9r+UO6f/q0KV\nCpyny4M+7QAfgh9IhVQvO/PZY4y/E9aCiaCP/UgLypKtwaZi0sOpdDWsCp5VbgbX3T0QleMB\nBzmq+R5wE7oJnHjXwK2wCxgYpoB6yF8ZPWSKH0u5H8MrUfY8uPjd8K+EKaFe0r60vZ24vha0\nLRycViTvL2XrggeBesuNvwto14nggflHuAWcE91gE3AssqI5MORC8EAwAF6CbSB3flBUF7lu\nPDD7Eec6OgSypPQ8db6GsdWv2uu/MPwMT0IfcO3VUs7JI8Cx1YceiHeAWks7fH8PdH5YPAQd\nIZ9GUOhaN4bW6+PoePr+HH6Hr0F71wf1PlwFfnAYj7ImPzREOf+Ml8p3GW6mDlqCPh3/d2BC\ngv+aMBek9ScXT8EVMBDq8XFEt//XAS4B7XAfH5TkdyOt9Rqmy0bVhrvGnsDMBWr3ovwzcB6Y\n7gW11rR0GOzUj+3zGLAnZcYAY8VoMH5l4Wy6AHZou3bPA/r9NDA+aOtgWAuyrskwUJ8Gu18g\nvyw8BVGNeCArh6JGTMz8ramx8G2YDtyYXESrJBjsPTR7AH0OaqV16OhemAhuTvvAQbAm+K8F\n64O/Oho4XTxTQm/Q7kWg1tKHbo6LgYep6eFUGAt7gWXnggtbn2qzZduB7eqhVnTqQd4PNu1x\noze9CTaF4aD0vzoULmrI1e+P4+xcnQkM8L6DG8AAGArabdCvlw6g4/Mh+HNa8s6DjuDG5HzQ\nz7uCc/Ub6A8vQy3kPH0SFofvoS0cBzdAD/gFToKesCasDm70N0MvCHOBbNV0DU/eBYIP/YDT\nvhlhClge9Jt+9uBfLbXiwaIcx/XgEzgP+kKW1BtjZgVji9LedaE73AI7gfMuqjgP7E41x1mf\nOQ+NNT+BPnWtdoYfISuaHkOeBvdvbW0H2tgFfBffwXnguqrFGqabgpqGO4+Dcdt1bCw/CZzD\nN8Cc0BFWhdNA293nLb8IfLcLoBZalE4GgXHnZ/Dj6Egwdo8E/fsAdAJt1FZjqId5x+QwqJeu\npOPdQB8bw48C/zV+IfCM5zxxDAbC+vAYZFWnY5jnP2WM07d7wR6g5oF1GnL//cf3fAYcn3+c\nWv/j3rjyL3wCj5wueewUqce7OQyF58F/vlwQXGxpzc6Fi6y5sl/7cWM39cPBsrfA4NMWloEh\nYJCyntLGz+AV2BS05RA4C2qhVnSyHxwMs4HXnyasRGpQ7wcTYCpQBlEDlge+h2Fl0P5aaCM6\n2Ru0y83Uw6aBw4CpXfrTTVR5rfT9/eCm9B48AfWSG48bqnLtBxu1ewa4F5wDE6FWcpPUriVg\nyaRT7Qn+tKgP7AFusuNBfz8CflC/AI5JJeQ69uPHcQqH5fRzT+di6aRg5qSOdu4Oph5eTgbt\nD3qJTDc4CSq54a/F8w4ENzc/ak8D402Yf7k+dP55+DAuaKs+1dZjoRpyjTr37UsFn/yb/Ntw\nnYUZkOtglsQObdZOUw8G38KWYJzpD1FNe6AdVRZKqjn2+tO4PiWMg9lhV7gYsqLdMMS4qI3p\n+ZpeQ5tz7xzYH4qR88r45DObI313BhwAzsGOMC+4jucG7XV/7AfGpznAsrDeXiL/EWwCxqdT\nQN9X68C7E8/eB9yvtdM+vwDjpXHbeDUIvO9cCPIdjBeuPeOYceJUGA/VlmcJP4DWh+/hDXBO\n/AHeC75cnHyQ42u5dl8P+r3SOpYHetZw3dwMV0GwhWyT6kyNFeBgcE6I8hmir9Vm4PzIJ88C\nnnPcE6OiB/7yQA9ybpBOyt3BxbsoGGAuBQPBkeAEciFJmHjh2tTJ+RXsAkEGB4Nduo3Boxz1\nppHBLl/f6bJfqBP6C/YZfLTfD6KlkvtDSfOpO4UGg3JlgB8D78CP4AFEu4MtwbZgc0jDfetr\nq4fk4fAg2N7g62YR5GJ33MrV4TT8FK6GnhCCyN7k7e8+MIgGu9J2p8uGUMcPt1D2MnkDaZDP\nHhUuykg9cH8Mj8O94OF7QdDP58M+4Oak3GT3hV8h2JPr31C+P3Wci0fD26CNQ6FcnUvDD+EC\ncAMKak/mInBMf4JhoE3BjrR95kck98x/Dd1A+b7OCe11YytXl9PQOebzP4N+8B9wHlwFHj6c\nt/nss8xN7OHkvvNkNXgVXHeOz5egjoJnG3Ll/TFuOOft83MYDfah7deC9nvPNOSDzY7nmrAQ\nOB8t7wr55OHkkXw3iizzHYMNaTvMGw8rJef6Xc142Me0zWdn8KEHkwHNeH5oeiUZx65c6U/n\nTrV1Ex24FsrVdzQs5M9vuKdfK+HPe3iOsaVcfULDF+BJMJ6ENRJsD9e/cs+ysTABwiGTbEFt\nwB3Xpe2MTcbqcqXPcm0KtoXyYGMoN/We6Tmg/EBxv7d8NsiVvjg8t7CEa8fdOJ7uO9iQLjPe\ne50uM7a7Z/sextjwXsuQz5UxzJhfrhzDR+EU6AjTgXtciP1pm4ONwZ5gt3NnDAyBUKZ/0zKu\n9E4XlJBvQ93Qp6m22Y/7SkjNBxzXh+A0uBCMM/opvEtIbRueG+z2Ge7NUXk8EA5/eW7FIjzQ\nGlxAZ4Jf0G/CejAXXAUng4fPoDD5wrXB9BDwIDNjKCS9GFaH7uDzKqHcvn1muqxt0olByA1B\neUhVHoot117ft1qaiQcvClOCc8/+gswHe0Ma7pla3/J+4HMeAcumhS2gkmrHw/TDFXAPTAPO\ngVthIwh+I9ugYK/vEN7J9s6RJxpqTPo1TbsrqXl52CqgTUfDB0namfRYMEh2gufAjxEDb7Av\n2ExRg0K5wXIknAhzgu/ueDVHPsND+X3wH5gdPoR9wfUzBcwH2hSUa59r7gfwwDE9+IG8KLiW\n3EB8z+ZK+/YG33tH6AG7wmawDqT9kLZP3zne64J56QkzgHZ2S/KWV0Juxr7zLKAvw6FiF/JB\naftC2cZknoIPwLz2OE+qofmTh+azw1joO2RBc2BEsNE0SN94eJgQCmJalAdcy4X8OTR5wudF\nPam6ldzbl4bVwHiiHPP0HLDMeupSMN/Vi0ZknLsL3CuMI/5I0hw5/4I/w3NCHAnlxnVl+RZw\nhBfoD3BvV+PgY7BN2P/JVkza4NirtA/TtnovfV76letfYGrQt6PBvVdp+2cNucr+0R5tMP69\nA/3BGHAG3Atp27n8y/eW2/Y76ADuBe6VynJjfaVl/LFfzznKfpRpGvdXz5GHwe7gfrMIfALh\nGaHtWpR9CUGhPFxXIvVMciHcD4+DZ48+4NpoUQqOb1FG19DY7+lrG3DA3fCfgeVgH3BBpxeT\nE208pOX9maALvJjccDLvBB4OnTw/QHPlYekL8FcYFSa9aToYem2QHAnKAGRbg5JB3Xw1ghKP\nbZCBSRkYi5U+1C6lz+8BN9plQBlU52vIVe6Pz98aHOs1wMBjELcsreDndFnI69ObYQT4DjND\nmANkKyLH20PyajAVOP/GwoawIIyCu2F50IagdD6UhfQHMj6zH2hzX/CXweboFRqvBwbu3qBf\nZoXfwXk7N9wJwZ+mO6SuyTZ8sPmcdL0DuHZOtIEwR8iWrYm0dKM8BdwI9dOyYPl9EKSPFwgX\nqVS7bWPqBuzBy4NVe3gNGvM7t4tW6Cc08ONMX6R1OBdd4OVU4aepvBu88gOrGhrOQ33fs5KU\npMEvlnn48UM3CxqRGPE9ae74/EiZa/6BpE5MmvaA8cP4bszI9ediSfPTkrSeiXPwqzwGGJO0\nO9juGcmyhcAYMx80JuPbO3AgjIFS9jqq/5faJiXPkWpTeu0PyantOz0E70Ow3/i4FOwMS4Lr\nznhWaYX+XDNBlkm+2Gz5KPAd/LjQLteg/vY9+sOXUGnZ7+mgT9yH1gdjgGt8E0jLcVe2Uaae\n354H720LyjEe2pCr7J/Hksc55sVqJBWN6T1gFvADO7Q37QRvgQrlk64q87cvj3kEnGvO/0Gg\nfzYF92/XR4uRiyeqaQ90oYoLwkOyWhScXMOgIygXjwciZT49+W7h+gVvIA+G+n2wFxWSQeUG\ncHIq+1emHppnSvIkDQe740iXgPlAO63nIbUVnATVkn2J9gaF/rVBhetJV5PqWj/c9+C3SMK3\npAaDD6Aa8rl3QPgYC+sl2JLuU7uDzE8Fp0HPpNAg4a8qlZQbjJuKG6I+ehvWAMfSQHkq3A/B\nNu0Wr0MZ2QZZ/jR0BOf5hxAUNopwXW76DA0HQjdoA27Urgs/cvXNVhB0ExltCtqNjIfts8Fx\nd6154DoTPIx9DMtBczQzjaeAk8EPjvfgDbgCNoIg+74bgi8td+24GbX1As0Il8PFYL1/Q6Xk\n85yLg2BacMzWBMcpzFEPAX7sBR863r6DBwHfUT/qt9egGtIX6lDQLvsPtvjR9iNkQeGw6Php\n3+SJUeZdw3dC/6QsJk17QH+6PhxzD7360LFXrvmLYIwXdZbj/CUY59YE5cE8rB+vw5z9ivyW\noP3G2sY0Jzcrvbdrx1gIfgz9L0DGddYuKdB+rz1Y/wDGcdsYw7xn/lKohlwvPr89pOOQfbm2\nvBdsMG99zyXu35abN/5a/izsCdWU/ZwGu4N5UxVsMx9igWXp8lWSa1PVD3znSmt5HqhvtK91\n8nDzSnty5b3vwXvPw33QEywXy69K8iR/qRs596p8+o3C48G10pTcV06EZSHfmWxryq+D/vAr\nZF7B6Zk3tE4GGug9qM0BLpb5kmt/RVCzTkr++uskDAr5oRQ4MX2OMu/GMQCGwCzQXGmbCyGt\nsCC0Odhi6gFubxgNc4JlLkJ/UXZSb5VA8jcZyCoh+9PeoGBbvutwT/tCm9WTim7E+tKD1rYJ\nJA0fB6blymcuCGG8ViKvHV+Dv3SlFeyzLJ33Wnv1s/Leq3CZF4k6hEwz0vloq51u3PqoCxio\nLwLnmGMW7LLcjSoolIdrU4PgMknBdqT+CrRAct2cxIAZ/Lk4eX2jvdq0ITwKEyDYZBryjod5\n19zu0BZ8X8vcQHz28+AcaK4W4QH2dwt4uJgHtHsxmA2C3AgWBuuqXFu9dl66yVv3PdgvYVHS\n76E5+oXGPle7tMFDyWagT4MtppK28T6u7VsfGvutbwzzHXPl2I/MLSzhOthhH2HthuZjyeTr\nM9wvJXX8B5fSIKeuc1B5kFfBbsufBdP+0FytyANeaOZDnOPOn2qqKw9/pBkdGJeVsSbXp47T\nLFCJsXfcP4HmaB4ap9e1ayWMv88Necs9KH4Oh0BjMga7f7g+nTuu0eaMe7BpOZ6jtEksd12F\nWEi2wefDSL8B7yvrarvjMQbmhnz+1+bmyH5c68Eur4PCO6Rt0jfOj/DxZF3bvAuj4BrIp3kp\nTD87X53GyrThCNgV3FN8VgfYEFShZ4fy9Dvo/59hOsj1aXpecbssaZ/92U/oPzwo9zqUz5XU\n9Qe8qcH16BxJ281lg3yG+LFiX/kU5k6+e7llHSjwnPRB7o3k+nbSS2AhCP+KldzKZhKclk3r\n6mvVfHR/OORu7tWwahwPParMBxvYDoJa2GngOrFMO93Y94dazLlh9HN6mXYuTbs9a2SngeS8\nMu30wLVbmW1LbfY2DQxs5Wh1Gu1QTsMy2rxCm6vKaGeTdWCbMtuW2uw5GlxfaqOk/kakm5bZ\nttRmT9BgQKmNkvpbkK5fZttSmz1Mg7tKbZTU70G6VpltS212Dw0eLLVRUn9n0lXKbFtqs9to\nMLDURkn9PqThMF/mI4pudiM1ny669t8r7svlEn8vqtrVNTz5pTKf7t7uR1e15UH5cnijzI48\nK3Uss20pzbTzInivlEapuseSnyt1Xa3sHzz4XPiozA5Ood3MZbYtpZl2elYaUUqjAnU9230G\nJ8DV4LPT8rxyIcwI/iAaFT0QPRA9ED0QPRA9ED0QPRA9ED0QPfA/7YE1eLvh8AU8Cf4o5I8D\n/iPAD7AetBj5xRcVPRA9ED0QPRA9ED0QPRA9ED0QPRA90BwP+J/2+Z/qdoaO4H/W/THcC99B\nVPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0\nQPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0\nQPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0\nQPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0\nQPRA9ED0QPRA9ED0QPRA9MA/xQOTtcAXXQWbt4P5YUoYDG/DM/ABREUPRA9ED0QPRA9ED0QP\nRA9ED0QPRA+U5YFWZbWqX6O+dH01/AEjwA+j1uBH06nwBbwBUdED0QPRA9ED0QPRA9ED0QPR\nA9ED0QP/0x6Ygrf7ERYq8JZbJ/fbFbgfi6MHogeiB6IHogeiB6IHogeiB6IHogca9UBL+k/s\n/DB6DOZp5I0+59668FYjdYq9tQwVr4TJi2jgv2L5r3G/JnXbkFpm+jNMhMY0jpva/WdjlQrc\n60r5xVDuWLalrf1qo+8a7PZ5P4H/Whc0iszG4aLEtBv1z4ZS7dSHYQx+J/8bBFmu/fo97bvh\nXG8B5WgjGp0EpdoZ+nIeiLbpO+019QNfH/sOQf7noTuEixLTrah/dIltrK4d2uT7Bb+aWlZo\nnr7MvT2hHO1II//ltxIK68xn6VP9PCHJk/zfIDjQTBnqQ5t9mmgX5qJzLcwP8/rvlybapm8/\nwsXh6YIS8vtTd7cS6udW1W7fI9jvuDsnvfbHpbQ/7+b6BChHh9Jo+xIaOpaOrwrxKL2mJ92Z\n5OvcNX8zN88MFUpMj6P+5iW2KVQ9vfato0+dF+E9riF/EZSj02m0XjkNC7TRNv0d4n16n7qE\n8qsKtGuq+AIqrN5UpQrdP4fn9C/zWZfTboVUW8fOdZFex/qoPbg+jDe5+w9FRelkat1RVM3/\nrnQ9RYv/d3FJJcan3DXjA9wLlPPT9zsMHoBydBuNFiinYdLG2KN/JcRZx8Tr9Jho68HwBJSj\n+2k0VzkNc9porz4T15DSXq/Dmt+P/PNQqpx3j8LMpTYsor6+1V796rww3u8Kr0NUjgfCwOYU\nZ/LyQ6xy4vSBq8GJmJaHBoPZ++nCZuQ703ZBOLaIZ+xBnS/BSe3iHQ8ujM3ASTgUroR86kjh\nAeBYFDqg5msXyhYmMy+cHApKSF3QfrRcDC7GbeFZ+A78ENLH18B70AV6Q7lajIazQ7EHGYP3\nIfAj6Mu2sBZ8AZeB0kbfvR8EuZn4HuXK9jPC+WU8QPs2hKdhNDg2S8FdsDUcDr+CWhbWaciV\n98fnOt8vLaF5Z+ruCQbwkfAqTAvrgmN9BORqZQp8r3LlDw2twTXbHO1C4w7wJPwA3WBWOAFc\nb2skkJSl5WilD25opPUx3NNfH8BbMBPoG+fmf2AINKXuVFi1qUqN3F+Re47fgEbqFLo1DzeM\nNYPhHdB/+k2fus5Wg1NBbQRdG3Ll/XHeGEec+01pEyqsAk/BOFgSXDsetkdDWmtzsTRcnhRu\nQWpf5cp3tM8Hyn1A0k7fGe+fAdeW83MOCPNiO/KOXbkfSM4ZffEYNFfT84CDYQz4gTwNzAbv\ng/vV8lDuB9LqtB0Og6Ca2p2HG1v6l9mJ6/YNeCFp7/i451yXXE9Jqo+MsR8l2OZzcEyL1T5U\nNFbfUWyDnHrO9yfgtZzyUi6di+43Z6UaTUX+WHD874N1YIj2wcAAAEAASURBVHEodx0Y13xH\n40qpmpkGR8G1sBO8C+/BomAseBg8W6lDYTHQJ+VoPRo5tz8sp3HSpi3pGfA6aKPx8yswDs0P\nrvGeYAx7HkpVaxo4HhfCsFIbN1HfMXf9PwsjYGvoDL5LVAv3wBrYPxw8JDsp74GXwA3uB3Dy\nF6upqfgv6FuAAZR/D8XoRirdBifCEDDQtoPxcBAYhNaEfHKD/hPa5LtZRFlv6nxcRL18VSan\nUL/tAJ/DEaA85E8EF7qHKWUAnNCQK++PB7O3S2h6JHWHQ/tUm07kf4X1kzLtdVNPy0NKseOW\nbhfyh5N5IVyUkE5JXX25V06bflwbfBzjLhDUk8zYcFFGehJtBpbYzoOPdmiPYx/Un4zlC4eC\nVNqH/JDUdanZc2ngOm2OlqWx9nkgCtqAzB9wSVJwIKkHnnJ1OQ1vaqKxm+AnOXV251rb1ssp\nL3TpQcDNqVw5VleW2fgh2t2Z09Z56JraE74GN391KjzSkCvvz100O7+IprNTx/joh05a+Wz1\nvnaOhlZeIPuwr3LlO/quzVE7Ghvr9089xLhv2ctJmWPm2JUr54xzpxJyzbwK+vAYeAdWAufx\nA+BaKFeuQdditWVMMbaUK2OasS3obDLPhQvSo2EYDAXHUi0IrpV1vShSxmhjdbkaRUPXaHO0\nB43HQFgzPut4+BFCLHmBvHtfufqWhpuV2XgW2hnLXSueo4Jc666hX2C6pNAzxAFJvpxkAo26\nl9Mw1aY1+Z/gd0i/86ZJ2Z2kH0NvKEdtaORa7FpO40bauOfrS8c6yHnRI1zE9O8eSB+S/n4n\nm1eDMGsx2BH8tcJJ6EZqQJ4TStnQ/fV0+0ZYg3tTQTG6gUpbwsbwKOhXNxkX41XwESwFWZNB\n6UY4DfSHm84C4GbhIhcPzW2h1lqSDh8Hg3iQh9P3IPhyAPmOcDIYtNwA/EVkMqi19Jsfc/ow\nrXu56AKvwhUwG6iFIL1hNRRW+c9rPN/APg3MmPS1DunmYLk+z6Ic75Hwesq4J8j7IbwNTJuU\nVzueuSHOBfpMzQxuLm5mknXpx3zz0/Xtr4m+w6UwJahq+9M+jOeu1/u9SEk7wzpPFf/f3VxM\nDedDu+RGPdZ70nVD0pG/zkHXepAHPe1bBjzwZEn69QFwPt8CncEfHL6A6eGfKPfBFeFgcN7r\no+9gdgiHdvfx98F7LUl3Y6x704XgnHS9bA6u835Qb32JAQ/D0vBsYozr5ji4DrTZ/TIr+g1D\nBoF+9ANauYbOAvfYJSCL8uwRYtJGiYG+Q1QBD9RiAyzQdcnFrWnRDX4GP0IuBg+dLqStEkp5\nHyf2SrBcATx8F3vocXEfAR4w94RvYX0wCPkRMjeMhizqIIwanBj2IqmbgBvlXrAwfAV+6NVa\nY+jQ/tMyoM8H3lOOYU/YH75L2JrUjb/WGkuHzpdcm7127LeFmZK8Pj0GnBu1lP09D7PC5/A1\nuJZuBX0b/Eo2U9Iug/uMKat+Jf86+COG/jwV2kI19SEPHwL6TN/pw/nBTcYxzrryrSnnp/Ld\ntoBNwPhlXDDmVlv6zbjdJacj7co3H/W58d51Px72hlr/0ECXf5M2uba0OegzMjeBc8PYtAtk\nRel5YLzfERzvWWB5+CfqDV66D5wIjtdmsAhsB46lMtbMC/nmpfezqi8wbEvwXVwzvp8/THwM\nz0EWtCtGTIDzYBy8Bi/AbaCy5vPjsMm1/TZor/FzFDiPsmYrJjXoG/66bz4C94LX00FUAQ+U\n8kFR4BE1K3YgH4cpkh53J70BfAcPSGfDeVAvnUnHfpW7aG4HNxoPzQPAw9T9kEX9iFHrw51g\ngNofPCStBieBvyjXQ1fR6bKgX924O4IHDm28C4K0248mD0w7gZucHyq11pd0aDC/AlaFacAN\n6XDQh8NgSVgH9oOD4TeotVwnU8PlcAg4ZzvDO/AsZFEDMUr/6d8uMCMcAavDprAx3Aq/QDXl\nOC4Eftz2hX3AA8fT8C5kXdp/IOwM08IK0A/8gUf/+h7zw5bwILjWqq3BdDAI+oPr3ThvbPfD\n5xLIp8co7ADbgPl6rCO6/UvfkLsZ9K9z0rXvDzVbwQnQA3zHrOgyDNE2Y9AM4Nz1oDcSXoB/\nqq7lxf0A2gVc33/CKhD2H8f4Z7gbWpqMoR3ANbMrrAcd4RSYGdpCPeWPDAeBMecW0O/u/e5T\nD8CnkCW9jDHGyw/hHFgbnB+7wSWQRbk/XgPGWef4v6HaeyZdRNXCAzPRiQGrfdLZe6QOcJCb\n/R/g5lQJXcBDytl43RjHgbbKO7AIFFJXblivTaEKTZT35v7HTdQp5vaUVLoe9KH2+O4XQStQ\n3aE5B6YDaO8mXIo8/Bo4gy/fJ+9HRmPajJv+Al6uDqdhuYcED513QLBXf50Gk0GuelIwKrew\nhGs/Xt30ylEfGn0HwU7fd74CD7LukAL3iik+l0r3FFOxiToLcP81CDZ7KPWDOMiD/xvhoozU\njdgP8KbkofInCHY8Tn7Wphql7h9F/tnUdanZ/jS4stRGqfpHk3dTDPY/SH7G1P2QPZXMI+Gi\njPQu2pxfZDv951wONunfQ4psax/2Va58R9+1ufJHh1shvMNE8mfB5KAcM8euXDlnnDuVkge5\n8RDs9cDngdk14FooV65B12K1ZUwxtpQrY5qxrTFtxs0vIPhoMPklGmuQ557z2lhdrtwj3Cuq\noS156Jfg+7nm3PvK1bc01F/N1XE8IB2f7ud6htRDPUMckLouNTuBBt1LbVSgvnHL+B/mhz50\nf1Afg2ezctSGRj6zazmNm2gzBff7QTjn6eseEJXHA63zlLWUopkx1F8Wggzww6AD+FHSlLpQ\n4WEo5IPpuBc2t6aelb5/Oxf3wpLwI3iod7JnXf4ytjMcBh3ABW7wrKf040OgL13IfhRn2Zff\nYd9WMA/MBR/CN5A1eVi7ERYH7fOwkHU5H5cF/wVnWnCjdE7UWmfToQfIRcHD01BoSToZY/3h\nY2EYC8Oh3tKP68D84KHDdf49tCT9gLHbwtwJrqmvIau6BsNuBmOAH0rGqqi/e8CPMPcfP4pa\nwv7zd+ubvrqTKn6A+H5XNF29JjVOoJcLwDg/BkZAVmXcWhtaUtxyHveCI6AD3AVRBTxQ6OOg\nQPVMFK+JFW/B87AahMPdiuT9V5D3oRgNp5KTpJAPduSem3ax8lC8GfhL4pPwErREGZQkyPfx\n0L9GKKhxOpH+Xi2iz+moo51rwWRF1K9mlc94uOTTjBRuCc6tetnZjr6dqwb2j2A4TIAsyPWo\nbW6QI8B/kfsZgj4ImTqmHt5fLLF/P5g3h9WhXuMeTPZAnLa/LdfatiD4wefBqdryA23DpJMH\nSY3b9i1Z1WIYtj78AR4sw95D9i+NJCdZUEeM2BRc74/BG5CW6+rldME/PL8I779B4oMwJ42L\nxew/LdF1fhw7n3+DLP3w6L9GpeMTlw3/F9qeQdzns6Z03PJMuipMlTUjE3uM9Z4/OsEnkKVx\nx5xsycNIS5HB/Fo4DBYAf2nsClfD2vAQnA4u9mL0K5VuaaTi8txbp5H76VvbcHE9eHD6EU6B\ny2EfqJfa0PHqoJ9ehw+hVLlhPAp+eI6Dah/sutDHMvAFDIJix3Jp6jr+rcDgalorzU1Hq4Bj\n/xT8BIW0IjcegN/BeeIY1Vrz0OGTMCd8Ch7cj4XuMAbqqZno3IPcQjAaZoMTQdsM5llRZwxZ\nFpynT8NEaEweUo01riFjru9WTblO3aQd6/fAH5QKaXZuDIT54H1wDR4PrvtqqS8PPhM+Ajdo\n8zfBbfAUfAdZkr45A7YF46jxRZv/DRdB1jQjBp0Ae8FI8F+3Toez4DCI+m8PHEKRPgox0PE9\nFM6BLGohjHLf+xyMQcXulVRtkDH/eHDNuw+5D1RDxqKVoQO4dl6DUmT7y6E3vAPTQ63k2ckz\nlPHdPbOxuDQ596+HHqCd00DW5Bg/Bu4Lzps5IKoRDzioLUUePHcDJ6yHO39JWAPUWFgSXPS1\nlgeMG0BftgcX1S/QB1ws9dCCdOoi9ZBzMXwAl0Gp430jbTxczQv7gYeZaki7/gPaqb3arf0L\nQFMygN4Mz4EL/2AodbOgSVk6klbDwAB+N3iIXxnyqTWFA+Bh0M6jwcBbaxno54cJ0An8oNT/\nl0C9dREGOOZTghuh68nDnusrC3KuXQphnj5C3nnqeiukmbjRH3y3DqCff4VqaXYe/BIMggvh\nTbgd2kE+BXucEytAB/gKNoRqaFke6kF9N/CQdz64XneAW2E4rA9Z0UkY4rreFv4A52QP2Ae0\nfXHIkrbDmE9hX9DemeFk2Bj6wgZQba1JB9rRUrQ8hp4BxmPjjjHHNeo8XQayJGP1FeCHjXul\nB963wVherFaj4vHgnF4U/MFnFFRaxr5nwL35AngV7oOpoFgZF3YCz3pLwQiohfagE/u6Fm6B\n4dDY2unD/c3B/T98uJLNlP6DNdPClGBcaANtIaqAB1xsLVU/YrgHFfUeGDBKkZNjM9iqAMUG\nnF60d5K5CTn5/HAzsOrbnaHWmowO74DPYDYwSK0NO8L+UKzmp6IB6VDQ19XUATy8J3QD7dXu\nUeB7+D6NaTFudoG+8EtjFSt8zwPHSbALuKnOAG5Wd8M0kKvlKJgP/g1+nKg/JyU1+9uLnhzX\n40Cb9fVLSboJab2D5dbYoG+cdwZw1+CX4KYzC9Rb+2GAm/U6oO9mB9fZneB6z6fuFDrOR4IH\nVlXNce/H81tBB9CHzruu4FzNlTHQcT8e9LP6Bvx47+hFFbQFz3wZroMV4VI4GJyH58P1cDvM\nAfXWdhhwGDwAg8A1/iLcA9rpwXRzyIqMgzfA0zAcPIieDZZ9DL7HVlBNuQ4WB/3VUrRnYuix\npO7hcgG4TneFLOlAjHFergkhBo0l75qZDIqRc+BJsE3QVyFTwfRKnuV5qBMYv5eEJeBMKFba\nejM8W2yDCtRbgWdcBq597Zfr4DYoFJe081p4BbIoP4o2APcsPzqnh/BDANmofB4wmP1TtQAv\nfnkjdONeMf5ZhXoTwQPI7+CvocfDt9ARaq2l6dANancYl3T+BKkB38NdsWqXVPyx2AbNqOeH\npPYZtJV2a7/B1KDamGppZ9oObb4VbkoK9dMe0B7WS8rSiR8fbrg/pwtrnDcwuokOTPr9jnQ3\ncKNtlUBSF7nWWkN/eCuxYBjpGUneDbbecswvBNeTcp72hsXAdZdPzs9fwdhQbc1GB869A+DT\npLPXSI1N2p4rx1yf567xnygr9rCV+8ymrvWHz1c7wiC4CLTBD7aD4BvYEuot4+X18Blo8/fg\neOvnbmCZ75MV9cCQ98E4qm3OuRPgA9ge9HG17XVOOY4LQZBxvFe4IPWDfaPk2jlwDWhf2rZ8\n5ftTZyU4B+aGSsn90ljowf0P+A38QeMX8F6W5Do+D1w36kvoA0tBsbbq59w1T1FFNS1P2wxc\nz0OTJ79NeiyUeg6ptq2JeX8lzr2n4UJwPkyAvvA1bAX5VAuf5uu32DLjvGvzcRiQNPqBtJ7n\nkcSM7CaTZ9e0qlvmRjI7+CtrPq6k3MXRlL6ighuCAT6oA5lpYHQoqGHqLwPa/XlOn2O4niGn\nrLHLD7n5KRwK1Toshf61WfvS0n4/KJqy2cO0dQ+DWiqfzW6oHu7y2fwq5d/CIVAvTU3HzkmD\nvcFSeZByvnwAWQiWa2CHH5PK+LRxQ676G3rSTaNJvjF37uk/7+WTh1Xnw675bla4bLrkeblx\nx+t89jlfnwEPMcYw5bxwfuSuR+9VQo/xkNVgZdAmbVsJVodHIcSufPZyu6Zy3PSDNq8Dy8L3\nCdq8ImhzVqTPgr2LkA8fIZbND5tCte01ZhsDh0KQ/Z8N7ZKCI0j9CNkO9oSboTtcDqpQ+e7c\nOwaego5QKY3jQe7XjmeQ83NKyF1L4X690jDG6f79FyRV7JpxDqwLS9sokXtDJeUHkvE7139e\n62sP7MVIW7eHeYupXKE6+jHX7qbiknbuDLNXyIZKP8a4NR46QPC9aViTZKNyPeAEjmqeBx6k\n+US4Ecyb+kuJvr0Caq3X6fBX2CXVsQvBX0WeS5U1lTUg9IZe8Ap4aKrWh5J2aV9YuGQb7PcA\n5/s0Jn3fB/YH//MX0/RzuKyKtHlLcCMIWofMXPB8KEilfoh4GDgSnk3ybUlrKW2eAGvDW3AN\nDAP9dSDUU863N8Bfnt8DbXPsNwA/Qj6FestxzTdP9Wmheardh8HV8DBsDdXalD7h2foq/THm\nmu0Fjn0+7Uuhh31jlj53XnQHba2GHuWh18NT4CFefwyCG+ERWAaWhEL2cqtm0oZtwQ+k28F1\nKzPA4XAFPA1Zkfb68fkDnAH3wEDoBr7HE3ATVFOu4/HgvA/yXzlcO91B3y0K+nQbODjJ70bq\nB8oUUKicWw3/pcF9pF95USEN4Dm/gWN5GzjW/rDhu9wIWZJ+3AlapYxyr3d/eTNV1lj2bm7e\nBT7Ld78T5odKahQPGwHpWOTzvX4J9HcxuoxK78Jb0B/mgGrLdbQ+pPtamuulwHv5dB6Fznlt\nvQFmgqzJ/+KlE4RYb9o2a0ZmyZ7Js2RMC7XFYGpg/Q7mBBfRZPA4GGxrrW/p8Ai4FFyox8Er\nYAA8FkqRm6vv42bhQvoTqiHtcuG+DNqr3ZeAhxA326bkhmkAM+BrpxtbtXU+HfwMHupPAv19\nP1wEHvDzyfmwHLwKfpT8DrWUhyY1Dr6G1cFN4ELw4Fpv7YMBjl0r0E9TJXk/LKs193h00XKe\ndgTXk/P0enADd719A4V0LjfWgGFJhWIPB4WeV6jc+XQAHAkeeo4BY9M6cDDk07sULg7OzSnh\nnuR6LGm11JsHbw/2bdz8AcaAh4yn4I4kJamrzqR316lr/GPwcLQyvAMe4p2vWZJjro0vgmvo\nLlgFvgDX0BZQ65hDlw1yrWwN2nALuM6XhE8gyH1zOihUbr1CsdV75eomGuoz43kXWAgmwoMJ\nJJnR0ViijS+DMciPBvecQ8G1VIz+pFJP6AW+8zcwHCop+/DHyn/BvWAsegI2h4OgWP1Kxe7g\n+6lqxc5JT5/0tx/JR/AanArnwSBwfT0J+fQThWuA7zk51MJOuilJh1F7BMwGy4Nj7zhFFfCA\nA/lPVQde3Mn+TAEM5gbspmSg3whOAT9OvoTjYRPwXj10AZ1uBtPCBuCmuSwMh1I1hAaHwGml\nNiyh/jDqat8LoL3avSlcCMVqMBUNvOdALfw+nn66wgDoBp1gT3BDaExvcfNAuARqfVhxbq4A\n94AHP8d2R9CeLOgljDBwPw1uOG/AaqC9WZCbi/PU9eQ8nR5cZ+dDUzLO7A3+oFLNcb+V56+V\n9GFccqNfDvRlIY3hxrHgR8tR8BlUWx42doGOoP8c5yXgCNgBsiB/SFgRPOB5SJsArpclIStz\nElP+knFvYzgdXOezwDGgj6+Has47Hv9fmpGS6ZLS+0gd452gHyj/lXLthtyk//fK78h/3kh5\nUrXiiYfZ9UC/fQvGyaNhS8jaAXIoNhmDjJXGoGnAMXc/KUW+1wDoBb1B31daYcyN5dpoXDG+\nu8+XoolUvhJce45NteVHmXv6peCcDXGpJ/nGZLvLwPg1vrGKdbr3Df3qf335PehLxyaqgAc8\nJP1T9TUvboBuU8ABG1I+e4F7ucVunGcl5N6r1/X9dCwtRSMxdN+WYmxipwHnyBZms0Hx4Azb\nPBjbemXYPufpfhm2T9OeSci4mQ3m/cjfkxKyaO84jDoki4YVsMm96OyEAlVqVuyv75+CqYfH\nR8HD5hBQHiYvgd7gR7ypKlQ+6W51/v7CY/1AkqxLn+6TdSMT+14k7dFCbE2b6YfDyQnp8pae\n99x7eOol/HEsqoAH/skfSP5ickYBv1g8C6zQyP30rSm58F8u/FcjdS/0BYNuvTQbHW8Hs8Jr\n4C+e6X9ZmZPro6AreNC/LoGkLpqcXjcDfx37Am6DbWBb0L+Pgxutv/BlSWtjzDrgBq+vfwJ9\nfS5MgKypHQb5Mb856PMH4N+g3VnQ8hhxEXSAseC/KDwEWZE+2xQcb+epv8J+DvrVNe+94Ff9\nXA+/uo786HA++ovzvvAG1FKT0ZlzbAsYD67nC5M8Sea0MhYdAguD/6LwElwPT0PWdAoGbQ/O\nOe07DD6FLMg95s7EkL1yDNo753ow12vBvPAl/AyqUPmSk27/VSd12exse55wKGwA08EouBtu\nhK8gS5ocY1xby8DnMACMRbkyljo3FgTjgB/Oz0EtNQ2d+YHUAT6AW8GP5bQW5uJIWAL0+8Xw\nINRbxoR1wX3cPd15uR3sDrPAK+BH1AjIutbHwBPBtTYSnOtPQFQjHmjdyL14qzgPtKLahzA3\n/JE02Zt0I+gEvydltUxWp7OHwfH1oCJvwargJjQHvAz+OnoLzAWXwyJgQK21pqJDA6IBaSJM\nCefBr2Cw9GO2F+jTFeEHqLfcpNxAtUnpY/+zhfvgYFgDNoQs/Sca2vwOLAAeAp27fcBDrHNA\n39dTG9O5Py7oM3ETegD+DRdAveW8fAaWgmDjGeQ3gKPAA5zryDW/J3SHNUFf10qu39Mg2KcP\nXwVtfBRqJefWbOCcmxNOgGPgCDgHsqQFMeZZcP63BcdrUegF14JrJAsyTnpYnwHca7R3W3Bs\nnXsjoCXq0wJGFyovUL3s4ja09LDomPvR6dw1RnaFY0H/vgJZUHuMeAhWAMffOXEuaKfrPmht\nMtZ7BG6AlWEQGOvdo2qhznTyPPjBGXQKGW33xy/lvHXtGaP6w2KgfX5cXwn10hV0vDuE2K3d\n+tMPpqthIGwF2u37DIOsyvfwfYwZ42Fp0H4/WJ3rUQU80LpA+T+h2AOtv1gYHPNp1nyFecqO\no2xu+BLGge381WReuAr2hl+gmvJddgEPQ6/DPeDYngSfwT6wHFwKu8KR4EeHgXUnmAIMoh7s\nL4PhUCvNQ0cu3hXhT/Dw9BucANo1BrT7VngN3oXBcCo8C7XU1HS2GcwJnWBjGAGzwZ3gPctu\nh63BzcF3ux+cH/XWgRiwAPwO48DDgJoZ+sN2XtRYzl03Hdfi8aAehx/BtbkhnA3693NoDW6e\nq8C3cD0MglroQjpZBt4ENxrnq3Y8Cr7HbeAHnvc/Bj+W+sIZUGm5NpxvxhnXw0Pgx4gbuXb5\nA4nXbWEtGADnw1LgmnJeame1pA2uYw9wxj/9o86Cr+FaLzIi/ejhQV9pmx8hc4NrxPg4EPRf\nvXUABrSHnyF3/Z5K2Q4QVboHHGPXtfPUOatv3cOdu9+A63p+cI7UWq6bDWBa8MNhf1gUWoH7\nykewBbju3etvh5ngPHgQlAf5QeA+agzwOdXSdDzY9TQrHAgzwEXwJawJa4M2rgr6VN86nyeC\n664XvAVnwQ1Q7bMTXTTI8V8TfgDHeXf4BO6BVuAccRweA+3uBMb33nA87AKVVhceuBJ8D47Z\nWChV7vEXgn7Ur8vCaJgJfP4EiIoe+C8PLE6JC8GNvBDFBMRhtP89z7NsK07GVaCQunLD/gt9\nqBVqF8pdoC5qN/Z3wQDv81zgBs2fwEWgLdr5WpJaJ9hocDJv20KH5O7ca85icnN/G9LakYtf\nIdinDXfC46CtwUY3qXSdUD6E8nkhLYPzt+mCEvOHU/+FPG2cL6NAW3wPbdWmz0F7gg9DuWmw\n03v7Q1o9ufB55eokGg4ssfFz1NeuQLDPa/3txpurPhTo53J1Lg3dZPKpPYVPgnPA4J1rV/Cr\nqTiXnef6/ArwgGCbf4Gb8RtQrtzsBoA2FdJ4bmhr8Jc2mTd1k/8M9GNYg+HezZS5Uamj4NmG\nXHl/+tPsFvgEvgP95ubn2G4K2uJ8Mw2Eual91h8D2rgNFJIH7kcK3Syi/AHqhPc3DWiT4xc+\nmMg2Sx747mrGEx6nbfBTSLXVmOq1c8Lxa66u5AGOXbl6iYbBn9oV8o6jccixrYRu4iGuhXLl\nGnQtVlv30IGxpVwZ04xt6iFw3QafhnmQ9rNrNqxhskXLGG2sLldf0tD1OhKMMaPAA7NrX5sd\ne39M0lbT9NoP72Md8+6L1vPjJVcvUODeV658dl/4AozRg8G+RoC+Nm+c0g7xfSwLNoaY+TFl\n7kPeWwpy5d57QG5hCdf6ontO/Qu41o4PwfNaWFP6WH/q52CfdollpsayjyBXvkfv3MIir9tQ\nz2dr0/vg+BuPNoFiZXztCsdDeFZu6vOdUz0gKo8HWucp+6cUvcOLehhqVeCFz6R8T7gDXoTL\nwIW7I8wIBpQ5YF5Ib/ZOwrRe5uJeWAC+Sd3Ym7wBZb5UWbnZdjT0XaZPHqANVyT5kIwnMx0s\nnRQEOw1a54PB1UC+LXgoqIYMzJ/A3ODc02+iLSHdIufacu1Oyw3cIKdPn4RF4FeolLTvBngc\n+oPBUJ+8CpfAXaAMZLM05CbNo/AOvpttDMaTg+NjEH4PnoBKqRMPch7+BE+D82xD8BeuIXAV\nfAX2fwKsCMFG07S89v22h0fhFPDAPS0YnJujhWl8KTwE9yUPmpn0MXBNubnmKtgX1qfvOCX4\nLyf6djDsAX3gYtDe5morHrANvALvgn21BfUluMYcT2Ua1pCpc0EFu32W62o22AhcW4dCJbQ5\nD/kNxoG+tW/H9lxQwWeTrv7/9ZwUDIMPwc32RngYvodKy3URfGEafGU/s4L3v/Cizuqc03+w\ncyrKwwHCca+3FsSA4M9gi7Y6Dy3/MRTGtCgPuEb2gt1hcUj7NswBiv/am7qSN+4bX5tSRyq4\nFtcF55Axulxp1+wQbJqDfLDVMtd+uHbOKmODe5A6Fk4G99XbQHuqsd6dh2eAtpifHtQ8EOxz\nH5oAxlRjUXgn927f4wNwnl8L6utJScX/HsETHZsrwPPQPjAUjAX6TvtlatB27QzvYN4zk3J/\nMI5pezVkn11Am8zfA0Fpm/Tf6+CeNS14Pl0I5gX3yiDbqJCar5btPrvFy0nQ0rQKBl8I98Pj\ncBH0ASdEqfqZBj8UwF8OlAfMA+FDeB4WBYPMWeBCCwuH7N8mntfem98M2nRS0vDXg9K5cD0c\n3lDSvD+OY3rSp20KTw4fGb7vx0mh72HQ+jfoVxfi8lAtuXA7gosy2Ji2237DdW7qPXUCON77\ng8+YCwz+lZT2uYGGebYEeQ+ib8HD4EE9rWBrusyNYCX4Nik07ZXkK5W4ySwJa8DB4AfOnuCB\nzs3/HXDzfwOcc+n1rs1pu/Wlc+FOGA3/AjcAy02bI+1xo7wDroaO8AFou36eCbQzrbR95rX9\na3Bzt8050B2uAjcIn9lcPckDTgfXwA6wEWwJa8IikOs/ihqkj8S5qMw7DrPDaaC9u0Kl5Dh5\nAHIDNB0GX8H8oK+UaaChgD8HweqwLawAznPtq4bm5qGh/2BT6Mc4E9ZFKKtXakzKZ6dj6EfH\nVuCHfb3l4TefndqlrTeYiSraA64bY84yEOKbfsydq65590nj4vrggbgxGcuegZlhFxgKzZF9\n59oU5oHPNW98CWXGLmN30NFkvPcR/AI+z3NPpRX2zLQP7TdcB/usZ165vr4Ax2IidATlOzwL\nn3pRYWmPe/Na4H7uvqmeA8dNX6YVbLXMvHa2BWPYfaCmB8e70rIv+9Q/uXb5HkHuBytDH9gU\nnNfuPcF/PsP6cgCMh6giPODib0nqi7GPgAerMTAIPBw5KV6B3lBJObH2gNXBCfckdIMjwUlp\nsAlKT9hQZvvO8Dm4ESsXl0HLw8qJ8AI0Vy7WUfBDzoO0yaAY5PVI+AnSi8YNwg8Afap91VJY\n5AaoXKXtCffy+dSNaiislVTyoNUhyVcqGcaDeoKbp4FnG1COe1MKNl9ExXfgCfDd9PFMUEn5\n7gbm5cAD1DfwPfQAx3Mw3A5dQBtUSCddTfobbDYgu2lNC1fCXHAEWNYc+eG2CawBO8GNYB+/\ng3NOv1wLaXkv2GV6DLwJDyTlzqFe0C5B25sr/bk/HAc+TxYCDxRu2EGWO65pufa0M7AWeefQ\nveDYzADeq5R8b+OP4zkfnJzkQx+maR9y2fDBb6rCmPqhVA19wkODLeH5XmvvWHD8siAPEdo0\nDoK9IfWHEMf5Oqi3tE95uNHetLR3QLog5pv0gGvHeZjrSxuG8TevvoNrGnKTYliSzZvsRalz\nuzsYe7+E5qhN0th5qK1p28KHjmXi3DDWhjZfkXc/Nza9Da55Y0I1pD+1z7NFkDZZls/Hlmnb\nYWA941n6Q8B4Xw3Z7zmwPPSDpcExuhpWBaU9KtgdUss8M3uOmRrc05T1je+V1pM80GcHinn+\nZ1SaAzaG9uBcDO9DtmEPmN4MKuW5k1r8w/462C1FU2CoHxQeBteAPsn1waRO1F3hQnChFSM/\nePpB/wKsS3mYWH6Q+RESJpaHTzUMQh2vw4Szbij3MNUFXgQ1D0wDj3pRIblgbwIXrXJBG7CU\nm3ywxevOcGNS5gHeun5k6lttewaqpeCf9LxL26YtKpR5bd5fmZR+3QCcC9uA7zgdDIFq6GMe\negcsBB6e9XOwjexfsiyUB5udp3NDd3AO+M5hDpCtiEbxFDekT8HnvwuLgOPoh/FZsCB4T3KV\na3cbKngYUMMb/k76E+ZSqqis7Au0Ggj6xr6cdwZwbb0Zgg/JNmyWYT54fRDcCluCH4HOgQ5w\nAWizY9VczcwDtOt0+B2cVx/BVbAepLU2F9obbHRNO0/DQeFS8rfDWuD7vQShLtlmyTH3WU/C\naxA+7LRZBRs8cPzWUDLpzxJJ3o3zfPgKPFRVQ855bfwsSdNje0k1OizzmfpH29KHTR+l7SfB\nFhD8SrZu0p+qHbiWtTn41APeMIgq3gP60Pk/KNXEcQ4+dfzNm94IOyb5MA5c5pXx9xlwzVdC\nYaz98Sttm8/O3dvD/u9ZQLu9r4yd24OH+PehWrLPqSDtR20OhH691o+mvcB47vUP4H7guz4P\n1ZS2ngnGSM91+4Fl2mSqgo1hDCwzb1w3xr6cpNo9FCotP+LsJ8RwbQsyn4v3wgeq4/wguE/6\nPu61pu6VKjwrC7FtkkUZ/OvkaCnqgKFfwwcFDHbSuvEuBG8VqJMuduK5KRbygX05oe6BWcCF\nMU9y7QHDe3MkKUnDtanymd4XNzQPCYeDagUGT4Pu5zAjNFfatn/OQ3w/yw04bZN72uPC6AXj\nIByYLLsKXETTgO+cK31QCWlDWsVce3BV+vU00N4QOH4kv0sCScOYmJYrn90Fgg/8ONbGkeDH\nWFrB9pCGe153g2HgeCuD6EoQnju3hc3U/LT3efrFQOfcd7yvBMfSMQvzwPvBFrINyrX7U0rD\nfOxJXnvna6jZvD8r0Dy891LJowz62jQ7jAEDd7DH1E3Szdb147Vz2E3Md3Djt2w58Hmvw87g\nhtYcLZo01lbXeEcw3xmCX8g2rCvnXTigBLudH84ftQNsA7NBmKvhWR7MmiM3QfteHXy2vgkH\nE+eC697xN75NDUGvkhkPtrWddV1b2pWrhSkYlltYwnXwgzFT/wQf2W9XyNcnxSVrcVq8WXKr\n/9/A9aK0U9uC3fp4WbgLKiHn6TPNeFA4JHngSdtpfjjcCZWQ7/xAMx/Um/ZrNfMZTTU3pnzS\nVKUm7nfgvnt4kOsmzFPnQZi3W5I3ljpX9oDdoJCMFdYN83tJ8i8UqlxEebBp/aRusM9L46fj\n7486yrpvgB8c38L0YP1zwTVvfff9YBvZv9Tlr1x5GfvRFuOzpO20PLxH8Kuxx/i0BijrTwva\nOBRug3yaj8L0s/PVaazM/k8A9xJjo/1p22YQbCPboOBfy4PMbwXa4Fqx7TDIXX/pecXtsjQd\nrULfue+cvjYf6nmu0N8PgbHCOev+IEofp9uGdg0345+/e6AlOUdbP4MT4GpwEqS1GxcXggeZ\nsOml75ead6IdCC6AasugdWqZnXSknYu9FmM5mn7OLtNOA/AeNbJzOP04F8rRYjTaFWrhzyH0\n859yjKTNMrBjmW1LbfYuDa4ptVFS34+sbctsW2qz12nQv9RGSf3VSTcvs22pzV6kwa2lNkrq\nr0O6YZltS232NA3uLrVRUl8btbUWGkgnD5bZkWPu2NdC2qit5cg15FqqhRxzx74cGZOMTbWQ\na8i1VI48LxjrayFjkrGpHO1Fo87lNCyxjYfma8FYX44OoFGHchqW2EY7Lwf3znJ0MI3mLKdh\niW2082IYVmK7UP1IMjOHiyqmnqPPg1FV7CM+ukYeWIN+hsMX8CT4S8hLMA5+gPUgKnogeiB6\nIHogeiB6IHogeiB6IHogeqAsD9TiV/KyDGuk0dTc6wr+qtIR/Ofkj+Fe+A6iogeiB6IHogei\nB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogei\nB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogei\nB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogei\nB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogei\nB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogei\nB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogei\nBzLjgckyY0n2DFkBk66HyatgWjue+Tv8ljx7HOkq8GdyXUqyOpWvhPRY+nyfbR9Bbcn4/Imh\noIx0JG26ldHOJuvBhZC203LVBiyf4EWiVqSt4ddQUEL6EXU3KqF+uurmXJwB+exM19M2bUzb\nZxt9b1kxY/k29baGcrQ9jU4osWEh+5wbf0CYj7mPfZGCnXMLi7zejXqHF1k33xzN5+dCj3uS\nG3sWutlE+b7c/1cTdfLNU+OD5el50MRj/u9BKhzYVKUC9w+mfI8C9yzOt/bz2d3II/66dQe5\nI/66Ki1zHNV3SDUJc8817lyrpG7gYSeV+cDTaLdVqq3zzTFNx6LU7WZlr6D12WU+4QLabZBq\nW0077euSVF+lZC+n8lqpBs7H9F6XutXs7Ok84Zoyn+KcWbHMtqU2cy3cXGqjpP7tpEuk2uZb\ny+5DzodSYlDqkQ1Z96vD4O7cG0VeP0C9BXPq5hv7fPbnNGv0UjsPgEcarVX45hPcmrvw7YY7\n+fxp/HJ/8vxUTPyyjnH6aShV9vUczFxqwwL19blKn/3CO/5M+c7wshWi/u4BF1VUfg/MT/Ec\n4IGkknLSnwpHwxfQCQxMjkV6AnNZlAxKM8BRSe2FSD187QfpA++aXHeHUI9sSVqE2h4iy1UX\nGraHE/I8QH8Y8Aal7ukPN+lz4cNUeVPZpaiwQ1OVGrmv/+zbzbcx6eOR4AaWlu2ehGfThXny\nbs7pA0+eKo0WOR4ePPRPsVqVipvASTkNPCDOC+fllHu5GqyUp7zYosWo+BM0deByU/gPeDgb\nDEGzkzkR/Mj6OhTmSdehzB81ytUSNPwWrmzkASdzbyA8larjRuO7aff7qfJC2Q25sVyhm0WU\nO78/h+vz1J2VMm08Ar5K3ffd9oJ9UmVNZbegwjJNVWrkvm0/hQGpOseQfwseTpU1N9uDBzTH\nzmVp/zHclRhinOoLe4Prq1LyEOLYlSvnzHvwYPIA4/4hYEyemJRVIunDQ5wv5co1+Aa4TtS2\n4D7q+qikfG9jS7ky/r4Iz5T7gCLbHUQ9Y3W5WpmGD8FLyQNOI3UOpO1uw7Ux6GwYAuXI+LpQ\nOQ2TNquQ3ghvJtczkJ4BfhyOScpMFoYDwfXlR0Sp8nmu0UdKbZjUdz+7GAY30n437rkfXZ1T\nx7j6LjyQU57v8hQKF4Sn891soqw19x13/fdJE3WLue1z7oXnUpXbkb8IfoD54WWIih4o2gNu\nvOmFXXTDJioaONx4PdCpruCvIm28KEO9aeMGH7QkGZ/XKRQkqQf35iyC7rRvzq+qB9D+7cSW\n3OQVCk7LKVyAa9+j1M16M9p8m/OsUi7dKF4oosHN1Lktp157rn+CLXPK8132pHBUvhtFlp1E\nvXAIKbLJ/21ORe3TzrRu5WJAuiCV70O+3E3Xx5wL95gpQh7q7S+t9bj4DaZLF+bJu+l6MCtX\nl9PwpiYae6A6M6eOm4vzdOmc8kKXR3Hj2UI3iyjvT51CH3Ezc89Dx5qQ1n5cjEkXFJEPP1oU\nUTVvFTfk81N32pAfC7njm6pSVtY+7iqr5aRGj5L4rkG9yIwHD0iVlGPm2JWr52no3Anaicz3\nMHkoqFDqGnAtlCvXoGsx6E4yN4aLCqbGFGNLufp/7J0HvBRF1rc/CYKCgDkSRTDniIqoYFZM\nq4hxRQyrYs6uCUXXgGJGDKiICUEUFRQQMWBAwSxBMhhQgqKCab/nuXbt2zs7c+9M3zRo/3+/\n53Z1dVX36VOnTlWP+/Ka0yo6FrPZYo42VyfVbDq6VgS9R+GacBIdW3M0B22UUV/I6Rgau/Yl\n1QI6dox1XoGyeXvvWJ1F9yvzM+oKOXUP0a2QDhltf+a8Q0Zd5unNVLyaUVmLc8filIz6XKeT\nueC7JlFtOjmebZJ0ztLnfequzKjfkHOf8RW4102VxQMVnVyzPCKtyvCAyWEQ3AoGaUXLBPIu\n9IXG4BjvDyaV+6EYpV3ap51uTLS7L4wF36cY9QBGHQxdweS5EvQFx3coFKNexKh58CBor3af\nAIdAMcSGNlwFYWHwF+Je8BS4aa1uaZ8fGweA82od6AvvwXiobn2DAc+Av5CGX4N3pnwZVPX4\nOo+3hGVhBbgb6sBAKCY5B1aNDNqe4zXg3HbzUEyqjTFrRAZty9GPOufx71FdsRy0cznQrydB\nR6jq2OORf1rpyzNgX3CONQHr3oaPoVjkx/uT4A8Y5nHlfxXxY9H5Vcx6COO09SIwf9UH/wud\nR9eipU3Gx9ngx6ox0wzuAz+Kf4dUOTxgEktV9R7oyiNNHia0JRX8eBd2N7xPwwxYDE7ym8FN\nSjFKu9YFbfYXnrowDg6CYpUfGyad2yL08XTwI8//SlOM0i7tc5PqZlpfq3NgWEmpev9cyuPX\nhtfBuDUOhoMbrWLQPRjREvTfL6B9/jp3IBTLhtpfLQeA/3O/4EMX/CugKvUbD9se3Ci5zvhL\npZvlb6GY5LidAEeB4/kEXAjFJjcyp4G2aqcbtfOg2OQPB9fA5aBvz4QRkKpiPOBG3bXSH0LC\nWukPNOagYtPJGOR8+hBCLnqcsh8exSz3HsfBXXAl1IS5oI+/hqVNt2JwCxgCIWbGUnZ/9Q6k\nyuEBF65UVe+B+TyyPWwOe8C/oCLlRn0r2AZWByf8TChWuZCeC71gC3Az5cQt9l83tPcR2A4W\nwRvgxrmYZSy0gh2gAbwFJv9ikD8WdAYXpdZgHL8PxaTzMeY2cO7qN3+5LaY49QNkV3AeNQY/\nlCZBVcs5fS88C46rc8NNUrHpVwzSTjcPE+AzKEaZV4w7PzYmguNajHKs3cQPBXOLP8SkqjgP\nOK/OgpvBHORaaQ6yvti0EIP2hE2hOTi3nGNLg/phpLmrDfhRYf76CZZGGRtnQE/YDL4E91fW\nLwMrwlqQTebHpfGjMNu7FFyXfiAV7LIK7TCeuy1foXf8v5u5aXOBWprkR1wxf8hl86UbgOey\nXSjiOjdbo4vYPhfRYl5Il4Y49UNYqlNuLF6oTgPyfLYfuoPzbFudzdwMLw12fo6dS1tOrM5x\nTfLsGXSSpUEfYKQsbfIDb2nIX/n61R8cJa7VOLkzIl4fL/tRtTSOX/wdEpXTD6REbks7pR5I\nPZB6IPVA6oHUA6kHUg+kHlhqPeAP6f7XMv9nsdnkf0Gamu3CX6Eu/UD6K4xy+o6pB1IPpB5I\nPZB6IPVA6oHUA6kH/tsD/pey6vifYf+3FUV4VqMIbUpNSj2QeiD1QOqB1AOpB1IPVJYH0h+H\nK8uz6X1TD/xJPJB+IP1JBjJ9jdQDqQdSD6QeSD2QeiAvD9TMq1XaKPVA6oG/rAfSX1H+skOf\nvnjqgdQDqQdSD6Qe+Mt7oBMeaAv+z4weAv8lyCZwNPgh5b9yOAfqQ2dYH2znP7KkdoRjwH9Y\nxvr0X+7DCalSDyztHkj/C1LFj2Ajbnkt+E9vvgynwNLiZxO9//81/P+r0B/8Z0SrUyvw8KvB\nf43vFTgdiv2Xv/bY6L809S48CC6mxabVMagXjIWXwI3A0qAuGOk/c+w/UXoTrAJVLTdOd4Pj\n679wdDAsTWqBsW74tN9/2npfKAYdhBH6U7t6Q1NY2rQrBj8NvsPDsCEUq/zXq24G59JwOBaK\nVeb8buAa8CZ0B+sqQq55R8ID0BCuAnU/+AFkjhwFK8DZsAYMAP/JZHP92vAQ6MM6MAqWgaVB\nvm8PcH1dt0gMbokd+j7kp32KxK5cZpj/Q95yXXB9KHbtgIHGsPs8Y1Vq5WBp2btifsXrL/3y\nFe/O/7c19/SfODWZ14Mf4Qa4D4pBJvDT4CkwqR8Au8Oh0AVcgJwsT8DK4GK0M1SHluOho+Fo\nmAsuTDeB/9zkvlAfik3HYNAw2Ai0fw8wCenjYpEfR++AG9LlYWNwQboBikFuOJwv2vgYtAbl\n/w+Y2+FjeAb2BeNzJagqNedBY8F57hyaA86V/qCtYryaV7eHw2ATqGo5h8PzHd8g4/MTcOy/\ngAWgL/8BlaGduOkQGAynQK6xOodrT4I26dctQT+3gKqQMfY8jIHu4GahUB1BBzfJS8B3MY6N\nYWOlorQaN3IOjICBsDc41oVqVTq8BXuCYzMBesONUBHahZuMhJfhpAq4ofPLcdGfQ8E43g4q\nQhO5iTbWgX/DVqA89z1mwhbwI9SGbaPj/hxfBefSPeCYXwvvQxuoDrXloebOQXA+NADr/gZ+\neMRVn5PXoBM8Dz9BVUvf9QPHVB8eBc771jAA5sGzcDIUg2pixPGgvfrODyPzv+uAecu5rv2u\nE8Wmphh0A7wN2t4SHHfzx+nwSylszLVUqQf+ywMmji/+q6b0kz25HILsV8q/we8wPzpuwzGb\nTKYmZpNvEnWh0+Q8OtaljRPjG+gDL4H2yXfR8Q2OcT3IiYuS6gA/l5SS/elGtw8K6HoGbb8C\nFyETpR9L+lV7PX4Le0GmOlLhxi+pLqTjmASdHb/vwXF/ExZB8K91Z0FcnTmZHa8osNyd9sML\n7GPzm0F/Gqva5ZgGO7P5syvXJ0JS9aTj4Dw7b067xeB8CEfH2g28dbtDUD0Kk+HqqOJMjuOi\ncpKDG0Q3YqXpIS46R2rGGnmu/x4Hr/8IX4P2Goce3TzVAXUJOA+Tqh8dnb+5tBoXtCn+fG07\nH7RzLviu5rb34GwwVutDXD04GRavKLDsxmYhuPEKuVDfHA1xrciJbbrEKvXv6/BwrC5X8RYu\nDMp1MY9655B+0V8h5vRRI8hXtWhorro8o8MAzl+O6hwzxy6pHFPnrLZpq/PC41jwg6cQXU/j\nz2C5WKd9KOuHZ8D4SKqP6aid2mZu8eizVoAk2oVO3m+LWOdVKJtrzS1JNZ2O5rZDQR9eB+YQ\nc7dyHp0Ho2A81APn8LEwEKbC1uC4eo8gffe3cMLR+OoeOy+06BrhWlGWzJGO33NwNzi/nVfW\nOQ893gk1QPlu3tv5p8bAhSWlZH8W0M21N1+5Djmu08AY0T6P3mdNCDqDgvuT5aMK9xDdonKS\ngzHZIUFH/TYEQkyHo/auGt3PvOU8dR1wbeoCSVSbTvqiTZLOWfo4d7RzIoRcrL99B/PIE7Bj\nDraj3o+ov6TCZPlLvnwFvrQB1BcWg5OkPzihR4OT22DcHapSjXnYBXATdAYTaGvYDE6FdeFd\ncCJeBr6DCf8ICHKiW+eErWy5ydBO7dXu3cAkszG4qXIC/wu+BOP2BXADshZUl/TZfnAD3AH1\n4UZoBS6028MccPytbw/VrT0woBFouzYeB21APQbLlpSq589gHms89gAX86vB+WNM6McRoFyQ\ndgYX9R2gquSz9NHhoE33gfPD8X0fesFbsDJ0Bf28DRi710BV6AEesgLou/vB57YDfaov+4Eb\nqE1AP64Cbv48r0jpE3PguzAEPgY3tdq3KQS5eBtz2hX0G4X+EOIy1FfGsQ43XQSO4z5wCzh+\nH8JekI9a0MgN9UMZjR/mvKLewRzsR5i+6Q1PwSxoBY5zIdKmAfBTrNPzlOfBSrG6JEXHciE4\nV9wwjwBtfBUaQ6HyPh/BuFjHbyjPjZ2Xp+g8vQj8OGgY3aguR+ePeb0dTICd4Dowlg+G6+EQ\nGAq7g3Ie6duXoCY0hcqQsXAk9ITzwbl7E5wG+8LJYKyYS5+ETnAYHAXh40K/+i7zoTK1Ijf3\nmdp6EuijreAMeAzqwzlwEJgzfgbnTZBzagXYOFRU4XE3nuWeozvo5z3AHHomnAXOxQbwNCjP\n+4O+LSbdjjGjYXWoAe6dhsOX0fkXHF/PgeuZcZQq9cB/ecCkYuDko940Moic4GJ5EZgYfgAn\njm2yyYRqe5NeEnWhkx8ScbXnxOdPhDdhMfwIJksXsI7wK5hEtVf7PLpgzoUOoI4FE4KyzuSV\nVPrigxydTZLa6bOGwST4BbR5LPQFxyL42ONnMBPOBhcz7/0RuHFYAEl1IR3H5NG5Jm18lr7V\nFp+pXXOio77yfAZYHgivgokqjIttk6o7HU1yhepbOoQYDcfpUZ1xYOzE1ZUT7U2qnnQcnEdn\nE7f2zAPjYEpUDn41Nt1wu1AtgeBf698HY9nxTyrnp4tbaXqHiy4qxqY2OIeCD4M9nmvPzRB0\nIoVvopNLOL4WLiQ49qNPnxz9/OAx5tz0OM76UF96rl2izbZ5A24F55fn60Mt0L/vgvYmiS+6\nlchNg8/z3uFo+RNwcxm0BQXrm4eK6NiDo/7OpmWpvBTGge+ZT3zRLKscC1kIwUchHzqm+tsf\nE0rTalz0HXbOaGTO+x6MB+PaeyWV42SuCbZpq3EYbF6Fcr56hoYPxBrXo3wjeC/jxbmQVOaK\n10Fbg23h6JrUHgqRc8fcXwsawA3gPHf+9YSkMueZ246F8TAIbocpsCsYo24OH4VhUBsOB8fy\nETCXbwDmJNt4PgeuA7UGGD/OIXN1Us2mY+dY5xUoa5dxNQGMf30tNcFY3RGMx6+jo3aI7+Z+\nxDnoDxYvQpBr3oXhJMHRPN0xo99GnDt2+sVnOv6Wrwafbx/zkbZpr+Ufo7I+3wY2ic7X46g+\nAOdVUvmsDnl0vps22jMVXMONYc/j9mqz/vTaSNgNHH/HZzJ0gSSqTSfv3SZJ51gf42FN8F6+\nh3Za9hjK5pNekCr1QEEe6ERrJ3dpWp2Lz0IItvjR8hJwA+dCdhFkk5PAoHVSJJGT0Env8yRe\nDnUew8KqLWGSaFuoNzHZzqRl3fkwE0xUb4MbJvsmlUnNe+uTuF2llYOdwf6R9HUhCEnK5DQN\nZsNTMAu8Zn1SuUjMBTdfbhpWgqAjKUyCbDbHbdWvLjjex7ba8zro15dBvzpO8yCputNRf7ro\nuLl7DS6HETAVHLddIGh7ChMhm+3xuhdp0xIeAJO8G4nPIancxLiIfwaWG4JyY3EbfAWOmb7R\njnD8d+w8lIOdLrZnRtftqx8HwseQVL3p6MePfuwHQ8HNkHZPA+eA/gg2ZNqkj/RvqF9M+WVo\nD/uB76XdjpULaFJpm/HlPDKGgu/cMD0GPj+bD7X7AfD5R8NYML/pu0/hFTAHOMcfhZdgJCSV\n9/eZwR/Bb96/L9wKU2Aa+FzniHZcCfrLuD4VsmkIlc6ty8AfHJ6BpHIuZrNTu48Efe0zpoIb\nTmPZsTaWV4Cg5yiMhxZRxfYcHSPH5RJ4Exy7pHLcstkZ6raLbqxN14Nxq48c3xlwL6wB6mBw\nHDpBLTAe9f98MO85F5JKfwWbPAb0p/PDOVYHgnahMAZ8/mKYBCdA0KoUfHdteg8ch8Gg/Y5B\nUmlL16izOSmel4J99agVIL65AABAAElEQVQPPoualnyENAsnseNqlONrRbg0nIK5Oqm+oaPv\n6pwOBJ9mHvvSxpjT1xLGVJ+F+HFenQMh7o1n85p5/kJIqu/o6DrsM81JmbbFz30P7bPOeDE2\nP47OrfPaCPBdPgRziXNHm13zukFS6csp8ALsHd1kA47vg7aYiyRubyhrl7abByReb0z63uI8\n0h9dIIlq0yn4JzyjrKN5xnf4BD6HD8C6su7j+/SCVKkHCvKAi8cXpfTYlWtuUMoKXCe5Qbgd\nZFMbKg1iJ0USOQnLsiHzuoksXjeNcxOndmqLicKjk8x3vAlcdE0uSWVSKytxxm2ynG1ymyD1\n6fNgG+95A7i4/gv6gAkuqVwkpsFFMAlMOG46zod87dfu4EuP4b0+o+wCcg3cDS5OSdWdjvrB\nDY1x6vj5nFfhJOgP2rsP7AtxO4I9uY7e9204FZ4FF8+kchNjHJ0Nn4P2NgE3SuH52laaffrT\nd8nW5uDo2p0cvXdS9aaji7GbYe3Sp8aRfnXBuw2CvdmOc7nu/PCa9i4ENzfa7WLqPDKmBoK+\nTSo3Ct4r7gvtDD7MNmeCvXfR7h7w3Di0rTGojc+A93gyOnfBHwZJNZOO4bnxo8/Un5PBmNDP\nvo/YLrzXrZSXgUy1p8K2G0YXbuE4KConObiJiNsXLw/lmr51HM2ZHs2Nr8NU8KOnFqjVwHrf\nz7H3aDy0BmVecuySyufGbcss68va4MfONJgI5sp5oK3vgz5vCOqfoH3hvo7X5mDecC4kVRjH\nTPs8D2PbNrr5PhyNPRkH5i7bLQHtC9qNwnfgNcdD3gVzS1Lpn65JOxfQbzhtuxfQPrOpY+h7\nl4XxZpsx8AiEc/OSfgq+d+4tD59C8L3+tuzal1TOo/CMYGuwIZxnHhfTJ8RL6GsfMS49mqdc\nK3wvx8uP/W6QVMa8a++D4DMvAG3Qls8h08Zc5/rMdw7vGPKI9+8C2uwxiZzH3jf4JJcN2ep9\nvnPDdzJ2XgLbBTtDn/h5L65XpHbkZubvITACbgPHbn1YqlRjqbK2eo3VV+fChzALhoFBFuSE\nMfgyZT8XRheuypKTQpwUmQp18c3GqrFG1ntuIrXsInUpqBZwBpwDTrT4+3JasEK8uYGIK25b\nvN5y5jPrUzcUnIT2ExPmiWCyc1KaWMqjL+h8LWwNDeAsuAIyFbc72OJRmz0aDzNAeb4uHAOX\nwGgIY0MxkbRzC/AXsLowCUxC90FnuBNugAfABVAF+/44+7+/1t8C2muCvhLugGegvHZO5R4u\n0ttDY3ATvgq4+H0FdcC6uDLnkjE4Fj6CRVFDF7WB4OapXlRXnoP+2wHOBO3TFytHxxocg6ZT\n+D6ccNR3tgty/D8GNyK2c365QBhTYfGimFjBN24izDvLwotRmUOJ3Aj5bH0TdASFg8DxWC6q\ndAxuhuEwGf4G18CuUB7poyD9EZdzaiewXvzY0Y5/gfnmF3gBMvtRVfI/uzEGPvGkAjQ7xz0c\n0/agj6eBPm4Fe0MbOBY2gkNBfQ2+kzF+AvSGN2ECVIQczyD9on1xOa6doDVcDE1ga9gYVoLb\nwHc4GVR3aAaOu/PK3DQeyivHMa64nX6MK/2qboBZ8CyYx3YGY28RmCMbgRoJ5oc3oDP4bvb7\nKyjMgam8bCiH9/45FGJHY7QthLYrUzYGzJXOK/PkcWBMPAZDwLgx95VXjnXm+HvPYIvrcogH\nbakJtaI68+tMsK3jrIzjujAPdoE+sADKI+//FBwLF0J3UG2gGXg9KNezbONcMr9bdp14DaaA\n7zMYjOHy6sPoBnGbst3T68GvzuHN4Sj4EtpDuBaOVP3Xe3peUTqHGw2DzeALeAWWwAHwDnSB\npUbxRX+pMboKDa3Dsy4Fg+xu+CeYqB+G2mDAhaDzaHI3WP2FK9S/SvnvUJlyHB8CJ22mPqdC\nW8IkciE3YH+Hl6J6J7pJyTafgX1MYCbTp6EiFWzxWSqcx49/XPnDHt9telRhHxeFRjAaTMa/\ngu9d0XZyy5Jfgl1AdgM3lDUhH/ku2qmv9WFIlr7LMxBku/Lom6izi4i2udlbBZqCGgAbwqpg\nvAa56XMDGp4fjn2p85p+dsMSFMYqnCc9zqXjK2AC117jzsXEMewHwQ6P1oVziiXz8C2OzskV\nwGu+96FgPMyE8spNg+N2O+iHr8C58ii4gAbpj+mgjXG5OLqoed1F3b6hzbOUK0r6QL0Mbm7d\neM4B/RHkc92QaIPSX26UzoPb4FowTzUG545jswYY54NAH5dHYfz0Q/CB99M3zgOPW8FwmAzm\nH+eKvn8fvJZN2rk2OOcrQou5ib7RF0GeK2NUP3wHr4LvMgqcd/rtHcicJ8bok+AHnBt571ER\nMi71mTJ3Ww52WnaObw36sRl8BPp1DrwOjv0IiNtrvrB9AzDfV4SCncHWYKe2GoueO1+NM3OT\n/jXegvSd89AY93rQFxTqgbGq//8qcq7oO8fTY4BiyTxaFNV57rWL4cGo/HZ03ICj95kKrgM7\nwGhYB2aB/l8A5ZH3V+Z08462KI/Gq0fbhHhwvjnPjQnbW2/+su1JUdk4MS6fB+OqotWfG+qP\nSdACzPnhPSiW/AMS2hN/H+38EjyaFzzaxjn0Hvg+30NFqCk38d4+Q/8Fn1L8z3ncr7bzfXyH\n0TABtF15TfmO5rxwbt2J4BqSja+oN37ykeN3FZiHdoGu0fm5HPeHv8Ot4NxeKhQPhqXC4Co2\n0gHvCE5QB/tgMACGgQHmBj0E2rKULwID1iQRdAQFg7yyZdArny9B61HQxlC3GuVxYOAbxE5m\nr+8OtvkAboYBYH1rqEh5z7itniuPwcaSiujc+jlRhddN6FNgH7gcTAhqS6gPx0LmfahKrKb0\nNCFmk7Zlk/XyNZjkXYBC8t2DspsmN4Dl3Twtxz2Ui5sx5rlHE51qCd+VlP7PJ9plfO4a1YeD\n9TMhxKrxURlal5uGZ7j4rR89ZFp0DIclFLQp6BUK14EbwdDfRfMxeADmQ3n9qT36cCUwhoJ/\n16bs/ZX1zaAF+EEUl/3diL4LLgjG6gTwPcprG7f4j7yfubsVNAU3jnXhVwhaOSroL2WfB6Eh\nOKfPh3+C8eH5EPgRbLMNeK/y2OzzRF+GOUqxRI6fMev8MEZ9F49fgHm0CXgtmwZSqd/vBe9t\n2TFJKvtqp/6LK9hv7hkN5lHb6tcVwU26sazN2fQElc79u6KjdpZHPjsQxkUb1eegHc5Z4/JL\naAq+k761Tn86zpn29qeuMdwCbvLKaye3+I+doRzsXIsKY0wbXAOMPdfQ9SGoJQXnkYrb+jDn\nG8B1YJ6vCDu5zVIj4y34MRz1gb4IcjPcBy4D86fz2Lb3gDHTHMyZ+nWniAc4VpSMT2XMBfn8\nEK/hunV+CDcD5XVzwiqgPcah6grmz9aeVILMM8o8on+X9wQFO/Wv75Jpv2u59ppLJ4I5Qr/u\nAw+Dvq8ILeIm2qK/VDgG+8K5R+sk7OccX/0Y2prPlfnVvBDqnWvGh77OxWSu5aNmNJoHn+Vo\nPIB63yk+33M0TauL3QOdMDAk6HMoOyE8KieSid1JYX0mBqx1LgZlqQ0NbF+7rIY5rneh3uD3\nWTMh05bMcyf33WAyFd/BCR1s9jgITLwG9ETYHvYD2yZVNzr6PO1xUga7wnPDeeYxJCMnltfc\nWI0Ax+fR6HwxR695/4XgwptUF9LxLXA8gs07Uh4O+jjTvlznjslQOB+0/RLQdt/X99dWNy1J\n1Z2OJsMD4Wrwft73JTCx7wpfwnPgtUw74363bAwcA97Dtm6YboP3YAYkVU86aoOLyY3gWA0E\n58834LNugFcgbpO+DnZbPx4OAn1mHNrPxXM2eN22n0JS9abjYzAJjHn9IDeDNp8LPjNO3F6f\nbzuvnwn6z3NzyAJ4EVrCVTAGkqofHf0ADs8yzox3Y0Ebgk2W9bHHYLPv9xm8DIeCuh7cWO8F\nbcDFPtzDdkk1jo7hufGj9hhrfWAXcCz9oNT+zcANhu+3MuTSDlwwJrXT+z0LSeUcidsXL/sL\nt/e/BPTlg6BPJsND4LiuDrnUjgv6M9zTsUsqnxXuEz/qPzkCmoJ2GsvOk6ehL1h3J9huY8jU\nnlQYA97XmLd/UjmO3iced56bx72387oJKPOB+XoJHAW7gfPF8R8KmepIhddCDJlbkmoiHbsm\n7VxAv+G0NVcnVRj3SdxAP+bCueC1XrA9jI/OzQ3mxddB/9vOnGFbfbwqrAnm0QshqXKNey57\n7+NBZ8JisI22fQThPazrA23B8b4IGsIn0A2SyjnQAdYDfaRvfJa50ZjIjFuvxQm+094poL32\nCXafR7kBTIUukES16eT99Ef82aWVw/NdU8fCItCuMF/sm/lu4bwT1ypCfnTNgq5QI8sNj6dO\nu/xIS7WUe8CgMVmrtmCAve1JJK/HA9YADYRgDG1LO7aJ+jkpkshJ6PPCBAnlYEs4Wu/kltA2\nfjTxHAetIMiENBjCPUwKSWVSMzmFe2U7amMu+53Mj8P8WJuplLeFtcCPmnBPJ2FSXUhHNxSL\no+OJ0Y1W42jCCs8o7RjeU5v1t5tQk4ebKWMo9J1LOalcdB2PuM9mc+69fa5HN3RPg7/W6ZN4\n22BDOAa/u2EZktHWTV5SuYlxA+T9fd/9QF9Ogkz7tSXYkWlXvN5+YZE0EW8Dd8I4SKredNRO\nCePmps7nhmeHcjiP22uf4Pd4/aPUbwIuxuGd3qGcVG6y48+PP+tHrsXPw/Nsbxxmk3nnHgj3\n9Pg8aPcwSKqpdIw/P9h1H/VuhGfGruu38PzplHeAsqTdO8IAGFRW41KuG+/BzvhxBPV14XTQ\nr/Gxtd008PllqQ4NdgbnVHk+kMxHcfss6zNzzcUQtAeF+Edf6OMH04GhUZaj77oLvAS9s1zP\ntyrkvvDccNRWN2sdYjdalrI5yjZh/C1rQ64P5OW51g5Gg7klqSbSsWvSzgX0G05bc3VSLaBj\n8E3mMe5by/E5ZVvH3PoQu64Dzj3pCF9AuIfPce1LqpArw/1yHbUrzLnwPj7b/BTsfJfyZhB0\nNAXbeE/nYsj9FAvWL/QIc+lVyqvBE6AtwZ5stmde01bXipthL1gP3Cv4QRrs7EI5icxt2WwI\ndZm2hPpw1DbnoWNine8crmW+p+06QUVpF240DYy9l8H9o3szP9zcg+wJS41qLTWWVq+hr/N4\nB3z9iNkcVwGDzkTbGrwekroLjJOlPZggK1tzeMAUaAbLwNpg4KsaMBm03cSwIrSCNcEFy/P5\nMB58n7hMZCbSZnAgXA/l0TQ6XwvnwgYwCepDQ9CWxqAN0gtcAPcF36k/mHzqwdbgu5hITaxq\nO2gBf4OLoDzSlxfCGJgX3cjxDbqFguO7LGirPhaTz7/gCjBh6uOP4CtQHrcFrx0JXaE88v1v\nAH1hHOpPfSCWp8NzMAvWgadgF9CPjUB7w9jbznu8BdPgShgNu8HhUB69SedL4R3QVrUJHAV7\nQF3Q1lMhLDzW/QL6dzk4BVzMZsBA8BdL5Tt43x1hByiPHO+bwONm4HyeEB2/5TgWaoDPNm61\nbQnUAxfDwbABrAU/wGcwFdTmsDGcFh05JNYwer4IxuZK4BzWB46li77PrgM1Qfss69ds0sfG\n8uWwLmiv+a0HeO+kMu6bgD4K8TaKsn5SzUEfLwP6eAv4GfRxmNMUc0q7X4e/QdOcrcq+8B5N\ndgbt8Pn66nE4AtRt8DBon371ffTl+9GRQ6nS/6/CMWAcJ9WHdNQGpZ3OD+00nozNIONCf5gj\nfSefb8yOj8ocssp5+QqclPVq/pWTadoa9FOw8zHKd4Bjqz1BXj8WLgPfrTY4Hp9DLrlBHgXG\n+l9BxtxwWBVawhrguPr+DcDr+tTrR8OXoP/NB3PAWFgPpkRw+I+ep2ReMp7v+E9tsoLzsSdM\ngCNgLzAGtM/4c165YV8eNgKfuSGYS98Fc7vn2vwpxOX8GwDa+kD8QoKyNpnbngOfqw6DA+Bp\ncJ7Uh+ZgHloWtNW48x1s9w2sCOYAy0GTKDwC5rX+obIcx9H0dU6Mg1NgBqwNNWEmGAtToSGY\nq/8JL0EjeA/0bQNYCFvBPuCc+wS2hDfhfqhImUM2hjbQCvSj9psXngHXglR/Ag904h2+iN7D\n5OMmczY4wWQR/ANuAhNYpl6m4vrMyiznBpL3cyIkURc6GXxBHSj8DtMh2Ori52SZB0nlfZ1c\nSdWNjh9EnR/laNLR9hVgIARbre8JJtUk6kinBUk6Rn0u5DgmS39jQBv1dT9YE0ZBsPtXyhdB\nvupMQ+MpqbrTMVvcZd7vDCpM4k1hGbgBtDXYPZiyCTZobwqOswuD6goTS0rJ/jiWPqMsOd6O\n/dzoGOxz3ll2YShNZ3LRRJxUvelY2qIWt8/5FOxzQ5KPfcGuSyi8Fk4SHI29Pjn6bU+9tnwe\nHS071i5K5qtC1IPGwwrpkNHWxTD4yPgzJ72Y0aYiTm/hJoPKcaNR9A12Oh+18zaoaDlmjl1S\njaVjsNPNkXZek/RmpfRzDjgXksrNbbDTNUg7z0t6s1L6mVPMLUllTjO3VbbM0ebqpDImO8c6\nu9l1PsdzUFjr28XaFVp0zXPtSyrX3I5R5zocg43GaoiHnymbF8qjD+jcrRw30IYOWfqfT92H\nsDv8Ap5/DcF247iQ506mfRdIotp08rltos5nc/wsVv6B8hIItmm3NjeBQvUFHToV2umv0r7W\nX+VFE77nKvTzq70ROLGcnNNAvxmkB0NTaAAmwrj8lWRt2DJemaUc35xmuVxmlZNkLQjPr0/Z\nyeKG/tuoXI9jTVCh3R9n+f9dKf+mWVtqZ3Pw+S3gO7gdrgbt1Z9uyqfCpvAiJJHv7bPKow3o\nnOkn/eeiJAeCv5BpsywH74DJVfLRGjQqr53GVqadmc/2o+hX+Ajmg8lXfxvXLr7GxlMQZDz6\noTIUXBSM4fLKRF+WnT7DpL88+DHvr40utMad9Y+A75FLLg6ORXm0G51Ls9Oxd6y1R19qn/7K\nxz6alagZf/2oKo/2oXM2O/WdeWpNCHPfOvOVY56tD9VZtS61E7Neya/S2PZXVz/M/BHEYyso\nxAaalynv6cdDUhnjYR6vSNkxdg5XtJ3mlJGQVNrpvNC+VcCY6wjbQUVqY26Wzw8auZ7puAc7\nV6NsPB4Je0JFajNu5g8B5dHZdD68PDfIo685ekwe7XI10Z8Xw/FRA+PAdTLkIPP5qqCfrwPn\nWRJtSKfyjvtV3OP06OHmnyZgjjTfhX1Jtr0Sl/OWe4jyrpv/4h4XZDxxLc7NmxfBLLgC5oHr\nkD52rTwggkOZ8n7ltbMX91gIa0SYk1zPnV/681dQLWES3O9JgXJ9La+dBT5y6Wmus1Nl94BB\neQrUyH65Qmu/4W5OhiRqTKeuUBVjOYfn3JXESPq0gOOgKuycznPuhSRqTaejknRM0Mek9lCC\nfnbZBA5L2LfQbp/Q4dFCO0Xt3SAclLBvod3G0+GpQjtF7bfjuF/CvoV282P6mUI7Re135rhH\nwr6FdnudDkML7RS19yOjXcK+hXZ7mQ4jC+0UtfeHjh0T9i2024t0eLXQTlF7N2fbJOxbaLch\ndHir0E5R+0M4bp6wb6HdBtHhvUI7Re2P4OhHQVXocR7yUcIHHUO/9RL2LbRbPzpMKLRT1P4E\njk0T9i2km5v5vjClkE6xtv+g7IdQZUs7+8DMhA86g37+EFLZ+p0HuKcr7492lW1nev/UA6kH\nUg+kHkg9kHog9UDqgdQDqQdSD6QeSD2QeiD1QOqB1AOpB1IPpB5IPZB6IPVA6oHUA6kHUg+k\nHkg9kHog9UDqgdQDqQdSD6QeSD2QeiD1QOqB1AOpB1IPpB5IPZB6IPVA6oHUA6kHUg+kHkg9\nkHog9UDqgdQDqQdSD6QeSD2QeiD1QOqB1AOpB1IPpB5IPZB6IPVA6oHUA6kHUg+kHkg9kHog\n9UDqgdQDqQdSD6QeSD2QeiD1QOqB1AOpB1IPpB5IPZB6IPVA6oHUA6kHUg+kHkg9kHog9UDq\ngdQDqQdSD6QeSD2QeiD1QOqB1AOpB1IPpB5IPZB6IPVA6oHUA6kHUg+kHkg9kHog9UDqgdQD\nqQdSD6QeSD2QeiD1QOqB1AOpB1IPpB5IPZB6IPVA6oHUA6kHUg+kHkg9kHog9UDqgdQDqQdS\nD6QeSD2QeiD1QOqB1AOpB1IPpB5IPZB6IPVA6oHUA6kHUg+kHkg9kHog9UDqgdQDqQdSD6Qe\nSD3wJ/XAMn/S98rntZrT6BGolaPxytQ3hrk5rservUcN+Hd0/J2jvv05Otbm+At4PZu+oXJz\nyHU9W59QtzuFvuDzy6Nl6fxbRLiPdb7bEtC2WbANJNF+dLob8o25mrQVfRhk32y+DG21Xzsn\nQVtIor/R6WbI1874M/TXr+D4B+k/7+X4Z9r5PnV7QRIdQ6ceUKidmWNqf21U+jpbDI6h/lAb\nJNDJ9Plnnv2MYW3Rf6Jt+sxx9Zg5h4wF23ldjQD9kkRn0+mcMjrGxzI0DTbH4zRcC0f72c7Y\nUM/CiSWlwv9cQpd/FN6tpEcY52CHldbVgcXg2Aef2+YxOAuS6Bo6HZejo+OmwrjVjcrmmSDb\naE/c1nAt89iXCv2SRM71w/LomGmz8aiMSeW88n3CO1mXGS93UqdfksjcuX8BHbXXsQz+017P\njVPLxmOwnWLJNftYdwPolyR6iE67F9BRm3xutjmvL8N17YrnJvtcBvoliQbQaYdYR++nwvg5\nnvroRysj5bIlXM921OaLQb8k0fN02ixJxwL7aKdz/ckC+4XmL1NoFU4SHrPlJ8fA/OD4Ozba\nae4cAkn0Fp3WSdIxSx9jxPll7KoQM9qpvUfDSChUxtl4WKXQjmW015fLgbZpt89RB4FrfKoM\nD4SAzKj+S5z64WMyyOWDA7jWDM6D0rQ+Fy+B86EzfAsPQw8YB24yXHCGwzDIVCsqLgftMHAL\nVTM6ODGTbmR83kpwG/gOs0G5aeoNLhzHwIZQ1gaSJjnVgismkgtztvjvC6dz6obpnv+uLtlg\nvEmdm0xVD+6Eu8D6reF4SKp16fgTOCaFqDGNr4NTYUGsox+UJ4O+08c9wbjYEQ6EpFqPjj7H\nOCtED9L4M7g21smPn45wB+jDuHbjpG28osCy8f0l3JRHv3/RRt/0i7Xdk/Lh8AOcG6u32AmM\nK32wN2wCSdWajtPh9lJu4Ni5YXkp1sbNZl/Q9o8gU8bTlXARzATH3LmUVBvQcSL0SXADx/dx\nGB3r24tyQ7gZJkX15j7joTx22vdDeAjiasfJEeB8WAQbgTnB8R0EbrTU7uCPKud5UopCbiql\nSamXtHMs6Jdc6sAFNxHabG5QDcCY7gtT4EY4A76BoC0onAnHQldw7JJqYzq+Dk/ncQOf2w2c\nL99G7WtzdB16DTaDT2AAxGUOnQfrxysLLDsHR8ILefZzXnwP8Xm3Hef/iDAOPL8E4rqZE3NL\nUm1KR23UVnPcYeD4GofKH0xs8ySMgiDzpuMwJFSUcfTjaL0y2pR22bFyzH1mZcocZa5KKmPu\nfhib9Ab0uwseAWM0yLmzB1wB5qfrwJyfVFvS8Ub4MOkNon7OxwvgOPCDqAs0AW3fG2pBc0gi\n+xp7jom5vqLkHGsI5tuZ0U1dR5rCmOg8PaQeyMsDvWjlV3ZZOo0GIYgNNhNuPbgF3EwtDwvB\nTUE2taHy31A728U86pyYk/NoV1oT7f0ZjgEnp3KCatd8T1AHsE1SdaPjBwV0vo22r2S0r8P5\nXDg+Vr8DZe1cCxpBR1gASWXySJIsVqefdrTNePD5nE+B9mAi1derQWeYDUnVnY5+dBeiVWms\njc+CvtQOZUK3fl9PMtSV84kZdYWc9qTx4Dw61KDN72CcxbUeJ9q2GEzucQ3g5DFYDtxAjYOk\n6k3H/mV0foPrvs8yoO/0YSvQvs0gm06gckbsgna+FjsvtNiPDuaZJBpPp6tjHetT1nZpGavf\nPqobFqsrtDiIDubATFn3fKzSDbXPfxlujdX78VHWPNR+N1U+K6l8xx5ldDY2nsrSZgR1/4KV\nwdjdHeI6ixM3Ig3gQXDskuo1Oho7+ehiGo3N0vB+6pwvAyEz1lei7hcYBb5vUjkHzyyg85e0\nPSajfYjLNtS7vn0NzrWgRhS+AudiUpnTzG3KuDMnBq1I4R74BuIxXJfzb+E4yFfm6O75Ns7S\nbjZ1nbPUV3SVc821L6kW0LFj0s5Rv4846ivXUtcD9SaYH5p7gtxDdCspJfvzM90y15gkd9qM\nTtrVGrT3DbgCbobXwT2ZsZtEtenkvdsk6ZyjjzHtPX3/g6I27gecW52i8/SQ4YEQhBnV6WkB\nHphF23XATeauEd9xPBL8Nerh6BhPwFQVlU7GGhd4F3ET3RXQEpxQ5dkk0T2xXMxNEJeDC6Yb\n0gdAucAH1YoKjsN8uDZcqOKjC/ZguBu2AJPcfnAx3AMmIuebC6xtb4vOOVSZHNvF4IfQ96Ad\nM0H7/Hh7EapLxp+bpU0yDPDcjZvX3NQ515YDF0kTvUne+XY1xDdRnFa49NNp4FjqO5/7KrwD\n70M2GZdrgHZWt7T/LDgEjM+m8CtMABd07XwGXOCVbSpas7nh+hDm7YeU3ZxtD8bmstAJTgXt\nzabGVL4AC8HcVRMqU9q8IcTXS+10c+T4OqefgjthK/Dd9gZz1xJw3mV+BFBVadLeFlAv4wkb\nc669feBwOAXqQjuYBNq9E1SltDXbnNcGr5nrlwFz/y7wJpjnK3I++ZwNYAvw/vPgBFgJNgfX\nHzfBfcE8OQhSVawHjL05cAl8GdGd43rg/JoKxSTz/Qzwo057t4Yu8A/Ilbe4VG1yrXLNN77v\nhpngOtYQUuXwQDzh52jyp652UrrQZSNf37hQG2jPgpOlJ7jpWBkOhc3ADekiKEadgVEmokvB\nX0RdVC+DATAF3Lh0gEZQlRrHw46CM8HJ7YZ0B9CXbjhUA+gPc2EWnA4/Q22oDv2dh5o03wM3\ne4PhIbgezgXrtNV2bgqNu6qUHxra9+/ooX6UrANrwxDwenWqFw+/HI4AP4j3gdvgvqjcnKOJ\n3bnUAyaBth8MN4HvU5mazc1rworgsxw/FxhjM5ecU9o5ENxsrQDmneqQG3j9+RgYix+B7+Tm\n7zAYDhuC8+1jCHFCscLkfNB/HlvCuuAGQ5+cBz+B166DByBTfgS/BOYj85LvUhl2ctv/SDuM\nM+OwBawHj4C2mH/UCTAZxoIfRc4nN/b6d1d4BqpKT/GgH+EJcOPfFG6HTaAPuGaZ92+EH+Bl\nMJY7w0ioSt3Kw7rBSWActoMHwdw5HeaDOX8nGAXbwvfwKVSUjDc/uMzJ3vtv4Lx1jvtc54Mx\n6rO1ZSGkqlgPON+3gqHwG6wMl4K+N8deA+uAcVoMOgcjjBnXI+W6YI7wfDjUhmKSPr0TNgXt\n1pfGt3anSj3wPx7YmBqDxsU1FwZQPupHIydyuI/J1An/C7jJK01tuGi/pBOqC30nl/aAMq59\nwfXzYm3WpdwTXChMTI+AftBG3yepXAQ/SNB5efrsAtuBm6i4TuFEX68BftAFO3+inFQX0tGF\nsjxqTefdYa3oJiZO/dcRnonKns+DpOpORxNxIVqPxj73SXAsLftB+QkMg2zqSuXEbBfyrDOW\n3OzkIzeUPcANprY5P++FuqBqgIuoG06xTStQfkiPKykl+9Obbv3L6PoS191MrQKOrxvOHUA7\nWkIuNeXC62A7eQeSylzTJ2nnqJ8bUe3fCGqCYxTi4VfKd4D5K1dMcKlMDaLFLTlaOZcnQPCH\n8bU96E/tcgHPpU5cMNeuFDXwGT4rqXxHY64s7USDzyHY7JwxFjNlPLYHN1DfwAqgHDPHLqle\no+MlBXT2Q3c8BHtnUN4jo39Dzm+Fr8E8q5wDvUtKyf44B52LhehcGi8CbTWHm8u1La6LOZkN\nxoe2mlOM26Qy5sxtQXdTWALBX8anOcYxdC5kW3+oLlPm6O5ltsrdwHfunPtyhV1xzXPtS6oF\ndOyYsHM9+i0G57YK+ckNveN0MLjOOzY/QjdIqp/p2CFp56if65Rz5qzoPOStRzgPe0rt7BJd\nL/RQmw6+a5tCO5bRvhbXJ0Ow0Vzvx37wO8VUcQ/osL+qPubFt4dcPjDJH5Knc9wwPQ0G25rw\nHDiBjoL1oFhl4lkDDoBX4G1wE3A/OPlNXEeCvrDtbVCV0ncHwXIwAlrAgTAHnoJ14QMweR4K\nJlYn+9VQ1dqGB+4FJp+x4EK/AywGk516EQaDMXIM6NeqlP7TPv0zG9zM6T8TctLFja4VJv10\nBfwAbUHbrgJ9qPTpu9AA3FQtgElQVdJ/A8FNk/GoaoI+1X8uPn501IDPwUXyU3CR3xGawtmQ\nbWNNdYXJzaX+8UPdjc/t8AusCtr3BQT7KZbY5H8FOxG2hvnQA8qrTbmBz/Yjwec9Dd/BW7A+\nGH9K/4Q5UlJRyh/HwPbzSmmT5JIbYHNeHXgWHoWfIOg1Ci2hNRiH2pBN1oub64/he6hImQuP\nhT3BODSfPAeZ/vuEus3BHOk7fQbaHddCTqwzXxmrFSljvAO4CdNmn6Od4yFTN1JxJ5jvzeXG\nYqaaU/EGxOM2s015zpel88ugn4xXx9uNsPaYb4zZVBXvgRW55fFgjJpDPX8MHGfH5EQw5z4D\n5ownoDpljF4J5tImYIx8COpb6AztoC8Uk/Tr4dAI3oen4FF4HVKlHijYA73oYXLPR2No5ALw\nG7hQiWXr5oIb5VxqwwXb187VoIx6f6VwY1aIatH4HdC+YK/lkaD+AQvAdkEdKPwcThIcu9HH\nDW+++jsN3dS5wXRx0r6ANrtpvg38EHUBC/obhUXhJMHxQvo4noWoB421bRzot7hPjSE3Wl7f\nE4JMpNk2AuF6WcfuNBheVqOM624utU1b4kfL+mwDyFRXKiZmVhZw3pO2g/Nsvw7t3Jxrj37z\nqO+2gaDTKPwA4R2MDzeBZ4L+T6redOxfRuehXHdRies+TuK+1I9uiq1zvnjsBy726hJw85VU\n3qtPKZ3bcM254XODD+dRNp85n6wXY2cVCNqHwhJYO6owpodF5SQHNzTh+WGsvP9RSW4W6xPm\n92pR3S0cB8WuF1r0HWdD8ItH7Z0DjSGpjqOjfm8U3cAxc+ySypgx9vRh3FbXmRFQB5LIXO9H\nQP2os3Ogd1ROcnAOzoC4jcbBBNCvl0ASnUen6VA36mxOMbcklTnN3BY0koL2LYQQA/HjI9SH\nOUwxbznPzNVJZWx2Ttq5gH5jaOval1Suex0L7OzzQo4I8WJ8u2faAq4F83tc7iG6xSsKLJuT\nOxTYJzS/i4IxEmz16HuvBUq7/EiqCZOhCyRRbTp57zZJOmfpszl17pNC7vAdxPXAHNUJUmXx\nQI0sdWlVYR7YgebbRV3CIvAm5/6yYEC+AM9CWCgpVrsuxoKtwElzJWjnfGgHbmyuh+vA5FUd\naspD7wYTqJt2P+aUk/pY2BUWw0nwE+jjvWAP+CdUpXbjYRfAATAeTJhukk1wZ4N+HQXqUTge\ndoQjoKrnX0OeqV3ixupqMFY915+Ovcm5uvQiD64H+0At8MNIOx1fZb0bfTdLbuhGQ1MYBX4k\nVbbtLthu0LVhJ3DT+3dw3JVx8DM4ri6WvsM/oD1cBVWh53iINmwOPt+4bAB+WHaGuuA1PzD6\nQ9AwCu+DxwOhCdg/qcx/+mEQbAxfgHH2YHTOIZGeptdUGAr7wzrgc5LKd1wTvoEWYG7xA3cN\n6AtJ9Tgdvad2GrdrQXmkPw8HY3wI+N7PgznRfHIuJNHDdFoC3mtP8L3Lo2XprG1PguPdA7z/\n2nAcdIdtoVDdR4fw7h0oxz/uC71XZvt2VOwCv0BNGAfzQPvlbtgdtD1VxXjAWL4ajN858DCY\n65Xz2TxkTNumGKS9J4ExsgdcBdpubn0DzgHXBwnvQbEo1A8rtEm/ut7/Cj+COaU+pEo98D8e\nMFjawm458D9BGkhl6ToamEwHg/9FwIS6GPwlwbILruVjIJvaUGk7k38SdaHT5AI7zqK9k3v1\nqN/JHGeDdvjOJiYnT1wuSm68kqobHf31Jx+dQqOZsYbfUZ4Cj8IjUf1eHH2Ha8BNoXabBFzc\nFkBSXUjHMQV0vpW2L4Ibe/1zA2ivm81LQd+6UXoGPoKFoJ+/iOCQSC7WwwvseSXt9dPXoK3a\n8QlMj871n/Mhrq6cTIxXFFjuSXvnRj5yPHtnNDyNc+1sCd7HhTTI5O65/cK7hGuFHn1u/zw6\n7UubT8Hn6ct3wUX8HVgR9KHXNoKX4SYwBvS5ugReKykl+9OPbn1ydN2Uep/dJeO6+UkfxeUP\nJLZtHqtcmbJzzA2t10ZCUg2ho4uweVadCnNBn90A5ZF5awAYw76XcyupXqGj99g9dgM/gh1H\nqROrL7S4Dh20zU2Vz3DskupNOuo7bVohuom2meu8t3kvqYyBF8D7a2vmHKQqb02i5Sy4DkKc\nH01ZG48AN5M9IIla0Wk46APXWHNLUpnTzG3qFhgNxvx40FZ9oW+Nj8ngHDZ+C5X2di+0U6z9\nbMoDY+eVVRzDjV37kkpfdSyg80jauk66Hp4I5okbwTEIXEA5Ux9Q0S2zsoBzc0aHAtqHpm9T\nMC7cGwVdQiHYan4/K1zgaMxk5uHY5VKLtbnqfduU2iq/i+tF9/J+P8ByYK6bCdY5lzpBqiwe\nqJWlrlirtLUtmLAcVGXCFc9diB4EgzgfbUCjlyCXD5bJ5yZRf+/xJbi4mNDuBSfh1rAS+OHk\n5qO61QIDjoZVwcmhD5zYd0MfMHksAhNVdaohD3ciuwB+DPVgUnTkUPJ/F9WAo2PkZmFfWDY6\n34ujcVBV0ra1wEXWxGYMzIdGsB08C479dKgJm4Lv5mJyA1Slludh2vA9zIKfwIVxR2gCLlir\nQHUobKTn8vBzYBv4FBx3dQy0hMc9iWSsGs+O/wuwIVS2hvIAY24H8NlPgR9q5gDzk+/h3NJe\n/W39DDAP5JtTaJpIYeyMvztgXRgLzo0gfWRsTokqVuc4NSp/y9F8qs09YDNIKt9VP7wGzmF9\n4Lnx5zPLo6/ofCg438wR60BSaaf4vvPgNpgOYRy1N6lm0fEA0P93QXk+toKd3KYk5m7n6Jrn\nMzaC8tzb8d8btPMhKI+0U3WGuuB/LXIOW+84ucYYX0k0kU7twXd9MskNcvSJ2/MNbbTVj4XD\n4FdYA6aDP4B4zTiuSplX/ixyXh0I5hbzo+tlC3Aunw+nQYjl5ygXg9bHiJaRITtxHA/myuvg\nctB219CwVlGsdq2KBa5P7jeCfqKwH7wBF8OdEHxNMVWmB+KJIfNasZ03xKARUB9+gBPAjb0b\nlq/hRtgczoB85KJdWnD04vqpedzI5/vLQdeorQnUSROSqAm2NbwF1SmT0hPgmGujGgkfwe5g\nYrLe8+rUFjzcRNkItMtx1q4tI/SrC8ZyYPkbUC68VS2TzyHQAPzo+B3OgzCvGlPeDxbA/nA/\n2Mb4rQ4N5qF+fDSHEANtKOvHRVAf3oHqkH5ZCBeBtgX7tE2MAzcq+tfEHsa9HeUVYRq4+a9M\nuaAPA/PMGHCcu4N2a8PToK1Ke62zz5rwNoRrFCtFo7mr+WZAdHfnxJ7gc92IjIWtQLlYWveJ\nJxnyHlIe1aGzc3SHCG34An6DV6EipP3erzxyDLVt2+gm+svxtG4i/AjlleNgfJdH2hnmRQfK\nMgucE977WSivKsrOtTAkzF/XPe3Un8bcTnAVlEdL6Oz9Kkqu4SeD99w1Ol7PUYX3eJjyB1CR\nz/X+hWgFGh8HG4L5pC9oTxM4GmrCvTAHzOWdwc39QzAe1I5wDEwA60MepVjp8gPcj/rdwHhW\ndeECOAVug9pgHH4J2XIT1VWqo3haX6gRPdUfPDpG5a84+h7G9dSorhgOW2PEMPge9GOI2ZUo\nPxGdz+Moa0KqHB4Ig57jclFXn4V1bpb2gxNgbzgdTCJVKRfokERdqJzcISA9PgYGpV/t1SU3\n8CZ4J7Ob9bagnWpjmAX/BN/F5FmdMmm/DNqzCdQDN0L6Uty8LAcm0m/BGFgdqkN9eegr0A/8\ngDNRukhppxtMN4SW9b8L1q1QndK+IGPVc+1T+vl+mOpJNcmPNOeSsfk+OO5qMawFO4ML6qdw\nElwJLrj3wFyobF3EA1rARrAHHAja6/i+DsGXFEs+8H2HZnAwGKeVLWPOOay+AzcY+k45X5rC\npXB7dG6sbgeVoeAL7dEu5WI8A5zjxSLHT2nvD+C88Mc4zw+HYpGbS6V92qZf1wbHcDpcDcWg\nGpERrjOuK9qqneaam6E3jIRi0hKM0W5tVc6dIP08DlYE40J/V5e68WDnUB8w9xwKyrw9HsbC\nKHAPdDb48TwAekJ7cByce8OhDoyCEP8UK13avwPUAnO9vg3xrM0Xg/Y4Dn8Hr1WnVufh+trY\n8OMtSPtktahCu0OOi6qq9fAwT3eMt4AWYH4I0udqJWgMxWS3dhWVHPilVatg+HMx4/1FZSo0\ni9VVRfFMHmKQOWH8Wv8ZFoKT23p/gTwKqlNtebgfFU4Ok9Sr4OQOm0o3T1Ng8+jIoVrUjKf6\nwXYZXAODYA68BvrTRXZ50N43YB3wVxIXi6qWi40J6HI4Bi4APzJCgtcubXWxlfmwAKpT+/Hw\n4Mfw65cbaP2qtLO6pN/06fMwAZqAm5FRYOy2hNfhZDC5uyh1hCvgFKgK7c9D7oQZ0cO25jgb\nzKPaZw74GNxoG7fG6fugz9+FytZWPMCNz13wNTQDffkmKG3ww3JbOAGeBP1ZGTKmJsKP0c3D\nwnwT50uiumI4OGajYBroO8+NRe39AIpF2jQMBoK+DXZ63ADMN8Ug17/+4Bq4GoSxtt75eioU\nm87AINfqg2AkGAe/gL41Dj6HPaAZtIXqUj8efAeY//Sn811p7y4wE1yTnHPmHue5R/OWa77v\ndw8476+F96ENVJW0Yzr43GD/iNjDzZMTwXh2HKpbu0cGjOZoTv8UXgftVM65eeC7FItch9aH\ny2A3cE9ivDjmxrSxEeLa9/gOUuXwgAv70qZ2GOzG+A3YGYK2o+BGyiDORw1odDEYSNkwubgo\nlSWfaRLVl21gBVgRvoBvoD54vTrle4gbzq8iQxZy7ANObq+1BTd31angbyeu8vx5uBesc5K7\nGHwET4OLrz5uCFWtYKvJRk0EF1mlXSYl4+BycENjXFS3tNk4nQJrwbLgR9y74DXnVXUp+FNb\nNgNt0daXQK38x6FkcbXezYEf9D0hxAvFSpU2hvH2QWvCZ1FdF47a5QfHK2B8bgzngJsU80Rl\nK8TYMzyoFRiD+shFXO0FTcBc+SBMAH9lrgzpp6FgnvX9HwP9Z7mYpJ2fQwvQNvO+qhVRclIk\nf5y3fwM3Pe1A6VPzTbFIf46DZmCu9sNCG/0hxlxejDIHmkOcNx3ADfw14Fo5HfT5CFgAlTVf\nuHWZ+gctHoLtwR9Agg6hMBdug9ehLlwNrpd+/LlebgLmVWM9aBEF14GqknHgmuNGfQZ8AHuD\n6+NsMPfr+6lQDNJe0dfm+g9hZ7gL3D+59nwGXisWaa8ynhvCd2AONtf7kWf5ZtDX7gdTleIB\nF4GlRU6iB+AC8Ct5NWgD94Ff+i/AdRA2qRRLlZvX3aB2jlaNc9RnVj9BRXtwkeoFTvp5sDZ8\nCjOhOrQSDz0YVodZYFKqAV3BTWdTOBlMWCanOVDd+hYDtMXN1GUwCTrBNmDi3A92gg1gHOwA\nG8GrUNXSp9p0C7wMblr0728wGYI6UjARjQ4V1Xh8jmfrV/13PjgHfoQdQburw488tkQm9IVw\nEug/x3UaaKdz2oSuTgAXJT+Mq1pDeOApMAU2A/23M7hxMgaMh1PBvDQQlPZ+DPM9qWQZY87z\nXnATrAs+uy24cGrXG6CMSefTGE8qQd7fHLQyHAWHw79BHxaTtNM8Yj48Dsw3xuJM0JfFIsev\nPWwJ5vVD4Wf4AYpJrifGmZv1beFhUJ/8cSjKv87VdnArOLdrgfN2DbgDlPPceB7vSTXIOD0R\n1gHHfCS8DeFjyGs3wOOwE+wFV8ODYM46BIaCMTQAXK8cpwuisu9cGVqOmxqv64EfGlvAmqDd\nLeEIUGtBbRjkSZHoS+zQLweCcbA3bA3HwvIwEVyfekKxaDKGaNcVcA24/5sDrqvPw/bwd3DN\ncg01J6fK4YHKmhQ5Hleu6h/pfXzsDk7wxtG5geyG5dPoPJ/DdBq1L6Whm4zT4El4E+4GJ/fR\nYKJ8A3rDvXAdWNcRnEwGnbSCPSCbTBgmrs2zXSxnXTv6D4ca0X1cXJU2ubAugjrgdetOgKqQ\nSf4+MFn7/N9AG7RPlgVlsjRRWqd9jrNJfTUYBTPA9zgWHoTK2uRx6/+R42aiaQHap3aBYKvH\njWEsNIJm8B1cBBWt1tzwI/gJXoNnYB9oDiZJk/ps0Nengv6yrYuWMRtspliyKTQO/Lh3w2By\nNZ4bwvdQHm1K50fgBegPbkDj2oGTLeEJ6AoPQty2LzjXn63Ad3kUjJWfoSp1Iw87G3yHuH3G\n6KswFw4HNzAtYQT48enCWpHalZs9BE+DftkDtGEYmLfOAXNT3EZteQ58B/OlsdAEDoLK0Erc\n1Fg8CoIdzndtaA8zQZkTjgHfwfE09gZDVWkFHrQBTIVgp8+uBSeCsXY8OI4LoB+MhqrWijzQ\nDbvjHbfzvQxDvGYM7hfVD+H4OBgfVSHtNKYWQ7DTZ+8K5oAjIa61OTE3tQLH4M7oyKFSZdz5\n3JPAfPgrnAYq2G3ZMb8UzoX74VMoS/GYXo/GY8rqkMd1547j6Hg6Tz6B/eEW0Ma3YAo0gpGx\no+tDEzCWp8Oh8CqsC+YQ+3YC25RXJ3CDo2ECLAHnufNGfyqPv5eU/ojHz2J18ymbN++KrlfX\n4QAe7P7EGK4P2ux4ngHa/nZ0/ICja+g4GAyVoXu4qeNpbH4Oa4LjtCxoU/CrY2jszgLz7Uw4\nELYH+znWqjM4/5yPzlOPP0Gq1AMFe6AXPUxK98IXYPA5QYbCfeAmw8nRB6zPhpMom3ah0mTw\nDjwNBmptSKIudJoc67gr5Wy2WPcjmLhMsIthPGwH+agDjeyXVN3o6PNz2Zar/jv6OIm9Pg9G\nwih4Hnz3kCQolqgjf00YSXUhHXMtaDtxzZjIZWuo10++6zfgpqAxZMpkNTuzsoDz7rR1LH1X\nn6effP4k6A0ujN/CVvAm/ArGXLAx87iIa2L9wuiofd7D+E+qnnScDv3A5z8JYcxqU34K9Kl2\nZ9qUee6Hmj613verA0FnUnA+JpU+619G56+4nmlTONc230NfeZ9XoS9sCnFdwslr8YoCy/rR\nZ3wOPs/nOz98vvEQ6oJd4eh8N7Y/BnOZm611IZd6cGFYrot51OcaT+uHRv1rchwC2u5mbQD8\nAjdDvrqFhoPybZylnfEdfBSOzif96HxwLjju94E5xzZnQKHqQwfHLqkcv2Bf5lEfBD1MwXgw\nBsWyvs1X9nEuJJW+y7TPMTWXWH8TBG1CQf87l32m89c8tj2UpcE0MLcklTnJ+aJtmfZmntv2\nfDBey1JmTDvXupfVqZTr+s21ImgNCstGJ404hlxaj7LX4tKWZvGKqLwax5Uy6sdwfmFGXSGn\nzpVPoS/oV9ebTD/Gzz/h+nxwTTAGnF+Z9lP1P/qAmm7/U5t/hfHZIUdzY9N4KM32eVx3DZoM\nN4J+zyavd8l2IY+62rRxT6gdpdkS92cov0gffTkXjD3nleVwPX5cQL337wSp/iQe2JH3uBVc\nWEfAbdAV1oeKlB9IBo9qBQbWME8ircxxNsQDzskXD2iDfGPI1EQq7gWTWxuoqA+kYKf3i9v1\nW+zcCX4MFCqTiu+XVCY17TABPQval+kr6xZH14L9PnMVcKG3bksoTR256MRPKhcJF4ts8r7B\nrsxj/F28dmy2G8TqOlM2fpLKRdeNsotla/D5frRPAlUL9PNk+BEmgG0yYyO8h2OzHJj8rbsb\nlHPr85JSsj896eZmRm0KjuPBnqDLwOS9mSco2OLROAlxq83GT9AdFLx2TqjgeCa4wUoqF5L+\nGZ034HwgOE4uzHH7ZsTOte8zaAYu+idDLl3ChddyXcyjvh9t9I2bkWCPG8zp4EYjjO9blOfA\neAjtKOatHrSM57u8O0YNR3H0uZnzwli0fgU4GtxYGb9B5hmvbxMqyjjewvVBZbQp7bK+83n6\n1KOEeTKJsnF2HgQ5H5bAWqEiz2Mf2jl2STWTjsG+cDQ36k9tbAp7g3Vx31m2bi/IR84B50JS\nfU3HYF846i/n+YdguR6oV8AcZa5SNeARMGbLUnk/kKbyAH2qPZkxGuwOR9+pNuSjzJgeTidz\ndVLNpmPnpJ0L6Oea59qXVN/RsSPcAFPAmDQXSXxuBZ++SH0SmYfja0Gh93AudMjSaSvqgm2Z\nx7D3sN53yUeTadQln4ZZ2hhr+m0i+Oyy4jNu71e0bwarwiy4E7we7uEeYevY+feUO0GqLB4w\nIS1NOgdjh8Fm4ObQBGuCOwDegUICsgntX4CXcnAg9cuA8iPHibGyJ5HcjLwXTjj+AOfBWRAm\nkUHeHOJqysl60BO8XlFyLEeBNntf7XPTZjm8h0cTbqZNVFWJfP7T0C56mjYH26Kqkg2/kzn4\nxmQhk8DJvztUh9bioQ0hbq/lYGfN2DXr94PKlnPAhO9Y+3zHuyU0BX3oR34LcI6YKPW3sr3x\nqp0uZB7FZGy9mvHHoeSvbSpCLm7DoX10s2M5Ov/qg/7VBuXxGrjbk0inhwLHK8B3cY5Wltbj\nxm4aVoB/wkAI2oPCyzAH9KVqBdPAduH9KFaKnuOujutM+Bw2hINAW5UxOQ2eAW0MqhMKVXA0\n/tRQCD7yfDlwfJ3T+mkITIAg48E4qWwfhue5QdBW1w83Esq5pFaBD2F7TyLdy9F5snOoqKJj\nsPNjnhfmaC3K+nIxtAN9Nhp8lyDL1lWVP7VtNuhLc5NaFvTlR+C4bw3W6UNzVIgV+9wMru+2\nr0zV4ObG5StgWfl8/ekm0pypPF8VNvUkD+nnIRCP6Ty6LfVNwtzx/fVn3KfOZ2UMB7UIhSI5\n7o8d5s2FMXuuoOx7GateU+G9/jir3L/Gnh/nPtuyBM2joG2Z12xzC8yFh2AfsM48ZtvHYCyM\ngMx7UpUq7oFa8ZMiL9fFvqtgK/gsi62HUvcg9IOQ3LI0+0/Vd5Teglw+aMC1daLWLkAumiHh\nW+2k2ckCMtDqwbXwBIyD7UBlJkoXVxU2M3+cle+v95ofu6f2rAhOdhegsyBM8OaUM22iqsq0\nJU/SV5kK9lnvmISJ73kjcMPq+zgWla0VecCJMAomgmr9x6Hkr7aZKOM2xi6X1PvLbmUrbOSM\ny2CTzww+8sPDejkdjAvl5sM6NQealJT++BMWurBpiV2qkKKx6vx8FFrAWnAEfALBVu28HIzp\nIDctQWHuVJaP9VsfcJE5F94HbbosOr4YHbU3+Cv4077B/xQrReYe58hnYI76FsxFC8DNnLYe\nBkHBtt9CRRUcQ6y5QGfGkhsmF3hzobGYKX0Y8mTmtco415c7QPBTOLo5WR7ituh7PzQre4x5\nxH/JfKOdG8MvsSvaoy3a6DHMDYr/kXVVZa82rh092Y8hpT+dS/uCcaEtxqLvkWmv57Y3p1WF\n4s8P+XQNHmyOUtoSbC6pKONPrpguo9tSf1kf7Q3O5zD+vpRxu6EF5J4r+Puzkprq+bM/j/WH\nmqEQ4sx1SDm3g66kEHJBqKvKo3ve1eBT2CR6sH7WppWjc+eQPg6aQGEvsJ2+9gcL25tTXava\ngGr+xyHn/je6/Nc+xB1b7J5ohoEuqrkm1gCuLYL1IR+5mbgCLs3Bm9SHyWHbEJguAMrrDUtK\n/9euLufHwLZRvYvAxKgcDi66r8L14Ma/ImRSColHm8WxdRIcC0HWT4OBoaIajmFihkdrUzYF\nfzvhn4RvwQ+XZ6CyZbL8J5iYLo8e5seZ0t5gW0lF7E/8XfrH6iur2Jgbu0HePXqAqqIgvQAA\nQABJREFUH59j4Ctwkb8K/BHAONXv2hegWFL2HspYbQkmZLUXuPnyXcNGh2K5dCS924Lj2AF+\nBJ8xEzYAFXzrRiM+P5z7xrQb1ofB97gfKlrbcMNJsDO4CI2DeyAojPHiqCLk0BGc67ODwVxU\nmfKDw+fuCq3hZ3D8XoagMM7BXuszP1RC28o4Ok5BIWd6rg3HRxf0kz47KDr3cD40gcGeVIH8\noFRxP3k+G4aBc+IDUL7HTWDcjoSqVIPoYdoZ9+c0zt0kvQRPgfHbBYIsW+e1qpAbzyDncpD2\nuz5PhXfBeB0EV4K5SjnfroUX4TuoTP3OzRvDdrGH6NsQB3Wiett9EhFVlXrIFtOldviTXHSN\nOAzWzngf/Rl8Gb/WI6NdVZ0ak4fA4/AhNAc1BrzmehQUYsFzr6lv/zhUyV/3HD7XHKTi8Rls\n83q8/mXOnVvGtXn2AfD6uvA5bA2+g/e0PtWfxAMGwizoCmFTEn81g8EEHA/w+PVCy73oYKA5\niVzUXRD9UPoK/EgzuGbCD2ASDVgfyo9RzqYWVE4GFwE/AOxjgkkiF0Dtc5MUnhuOcVusextW\nhyTqQCefkVTd6OjGItiW65hps2PguC6BI6EsdaSB45RUF9LRZKlMpD5/b6gJ30A+duezGenM\nvWZDUnWno+Ohfdqkf8R4dFOvrz2uA8ZHNrvjvv4lauP97Gd76+z7JSRVTzo6HpPAe18AzuM5\nMBKcT9+DdsftiZeD7b5veJf+lOM6kxPtTqredJwCxprjPxz6wM5g3cmg/cGucNQ2y9PB8o1Q\nmi7h4mulNSjjWj+u66/gh+CbN6kLtoS6uI3WFaIeNB5WSIeMto53sCMc9V/bjHZXRu3MqfrQ\nGD4G8tUtNByUb+Ms7eZTF+wL/tJON8XWm/89/wicB25azIWFylhy7JJqMR2z2Wks7BO76RmU\njY3PIyxbl6+cV86FpHKO5rLTub517MarUh4HIVc5zz6FdaAs+QFtbkmqGXSM+zTY7DHEgUfz\n0iZQiOIxbe4zVyeVa4RrRWXLnOfal1SOXdyH2crBr8ZmUvljhXuJpDI+nb8rwSjwvZtDyJ/Z\n7I7XnUrbfDSZRu7Nkqg2nYKvfHa8HLcls+y7iTnNnNUXloFOENrG7+W8+ym6ziFVpgdqZVYU\n8bkDeyQ8CNfAx+AHxhqwLtSFQ8AAqSj5zIfASeSmZhXoCNtBK3gALoa47BPk/6wgm6ZQuTF4\nr7bgvcqjGnQOz1o9dqNgi0cTgot7dcoPDBc/f8HQHidvprRVXNhvh9ngQjYEXNSqUn7ouBCb\nYDaFhlCW3LC8WFajCro+kfsYm84DP36fh73AhO81fdYIaoLSp6FcUsGfMAZfUPZdbwD93AFM\n8C0hn/emWU5N5UpfcMP9GVwOy8E/4DGYDo55awjyPEgbz4U2YCzcBc7Hitay3HB52Bz0599h\nLDwA+kI7gl3hSFWJXPSdz+P/OK3Uv/Wiu2uDNrn4bQvzYGVQXguYH6paU3nguhkP1dbTYXSs\n/nLKT4Lx5sfRczAdqkrTeNBm0cP0V9C9FEaC47k97AQLwDkSci3FKpMfF42jp8XtNC/p66Be\nFMwDe0cVL3D0Y7WqtJAHxWPQ5zruz8Ip4PUg/bgN7Aeugb6H/v0ZKlvGmja5vmRuuOP+NV99\nCIXochqHmDZv/BXkhjxI/5lz4n40Bjw3Ng+G6pa58kwYB59CTXBfZs6K281pibT/XVjxj9Mq\n+asdwW8enS/WrQL617zvUd9PhjXhe/DHmBfBvYFyjTXn3gybgLnE9XMGdIdUOTxQK0d9sVa/\ngmF+WLhRMqE2BwPc4HgG3Cjmqw1p+Bbk8oH1Bt8NEOQG/z4w2LrC0eDEMmgXwQqgLNcHF9Zc\ncqP3OMwEN4rlkRPkW3CxnAgtwAml3Oi1BydOdctFSbvOgwPhANB3+ll9AivBU6BPfoWeUJ0y\nka4KV8BJ0A2MQd8j2D2Hsm2Mw/XBsa0KfclDjMO4XJjjMmbdIDWFEOs/Uq4HKiTZnyifXlLz\nx5+XOIj3d7zKoxl07hW7wUeUtwEXKOftYXAZXAFBIX71vzExEG4KFyvp6Pj5rHbwGtwLPWF5\nUMaqc+0KaAeOu35tCwNgPFSFfuEho+AN+AG0qT0YlytDfHy9FsaYYpXJ58bH0I2F8/lQ8IPk\nfQgyHqQ6FHKSz3YDsgpotxvjMJ5vUpbqlGMa/OkGZ7XImAM4xueW1c53qQ4t4qH6UH0N5kVz\nS0fIzFVUlcTE0xaqWM5lN5F9oFv0bNfIBlHZuFgW6kfnhR6MZ9m30I5LaXv3Qcp9iD6rCyFe\n9bVcAtdCsWheZIi2rwfaOA3Mqa6PwX7X2dXBvHU+VJXu4kHHQVh/nEvfwGEwAPS1dVeB/v4E\nngRjN1OuVYdnVnJu31Q5PFArR30xV5uATWwS1xac7AX5TsAJtD0IcvlgN66dANnk4vMc7AlO\nKjUL1gft+gVMjCHZUqxUzefuG4KL+hrRk7TrA9guOi+Gw08YsQDuAe1zQTIJmVB9h6OhB3SC\n78DN6v9n7zzArCiyv/03AGbMIiCCoBhQMSuYUDEr5oDuYsIsZhdzwLjmLEYwoGtYI2smmHMO\niEhGzBlFTN/7Drf8envvzNzbN8wg9Xued7q6uqvr9KlTp6qvu9qQakXn28EA2BbuARPNf8B3\nMSn5Hvrc8+bgJrC2mOJSg6gPvT4EIeH7IdAJtNVN4JrQDKol7XkO1gUT/Z2wIwRp50kwFYwH\n5ZyqtFxwZoUV4WkwPtuDP6QMAeNSPx0MxoL3+XGkxk8/VOVvU3o5AMYmejMn/Tt37oLquFrn\n/F8CQp6iWBW56XCu67uNcz0+wNH5tBokP5BylxvkYKyNhvdhs5wF1nUH53ljkeM3Ej6CTXNG\naecauXJjOTh/JsHboA/V57AkuD6+Co1N0zDIOfUjPAwdwc2wqkbemd7TX+PvNbzGieCPMubQ\nDUCNgXNqSo3nzyGY4g85fsiOBfUlzF1Tmv6hYf5ynfJ97oMhUC0NoiP3QFuDeyTlXLoYzAcL\nwGdwJmhfVJk90Ng2cqW83nI03h8KnYROjCfq6NDN7hHwfC33uBAYnIuBZZO/G7r1oBkYsAZ3\nbe25VKN5QyHj0YliP/bdIfEMk77U13+iSZ1F/VGKtLMjfA3z5R5kIvoV9JUbqmegCfgu4kag\nWPtNGvZVilahsRve+cFfY7rBTzA8d5zIsSXYj5sU1QyMA+8/BvaDurQwF0u1c3WeUah/XASc\nI/p3JTD+lc/Q/8ZhvmctSr1jVIqcE+lnj6GuLewAjvUUcGOvLbPDqaBvPdenbv6DzRT/R4tT\n89X/1BZXsQG3T4DLwL6Ng97gOK0A2ugYO+cPAeu1SfqBG4P61IobjK2ssk/H40H4IfGQhSnr\nR6X/HGPxPWzj5i89BlTVqiW48m6tVwu7YM5wDit9pz/VUaBfy6G2PKSY90r36Xj6jHa5C87z\nZrANrJ2rK8dhKR7yWAkPMj92gKVzz9DOprAulPL+ucf9efD5/miRVfrT3CjKmJwPtLc/lOuD\noyPPGgWlqC+N94Hgy8Uo75x4oPNmISjFv8uX2F4bTofDoJLqxMPvL7EDbdWnIU+vT9m6aeDG\nvhQ/0rxG7fnrM7PKfHhrrrEfHdpmzAfbJlNeBlQTcH7Z3yRoDeE+inXKe0ux04dfC84fnyPa\n7j5TOY/08xfwLGSV8V2qnVn7bvTtXEj/KhrEi0i5NIQHnQQm/ErLIM+6cDxM2xPByVNpmTyy\nyuTrBrgado7PaiTt7iyhbbFNPyq2QeJ+k7ybzmpoRAmd3Ejbz0poX0xTf7XOqmtoOCZr4yLb\nvVbk/cnbL+ek1A+X5PPqKr9Q18V6rp3OdXNoNfR0CZ0cR9u1SmhfTNNS/OEGedViOivh3kdL\naHsgbVcsoX0xTR8q5ubUvY572GyW+mGQevR/nfrse/+rpriTI7ndj4JKSztLWfv2p32bShvJ\n8/2nOKWM136098e0Sks73ZtlkXvB3uCPXpWWsflkpTuJz48eiB6IHogeiB6IHogeiB6IHoge\niB6IHpjBPVCNX/PL7aKuPHBXWArmhPfgLfBXxFJ+6aZ5VPRA9ED0QPRA9ED0QPRA9ED0QPTA\nzOyB2Wawlz8ae2+A32Ec+GHk/0zQj6azwf8pz+sQFT0QPRA9ED0QPRA9ED0QPRA9ED0QPfCX\n9sAcvJ3/n4vwvxtOv+xOuevN0hfiefRA9ED0QPRA9ED0QPRA9ED0QPRA9MBfzQN+GE2o56U+\n5frK9dwTL0cPRA9ED0QPRA9ED0QPRA9ED0QPRA/M8B7w/y81Efy3e+T7N8vtQ/0P0BSiogei\nB6IHogeiB6IHogeiB6IHogeiB/7yHtiANxwL/n+NhoL/uscXwX9Nth9Hm0FU9ED0QPRA9ED0\nQPRA9ED0QPRA9ED0QCYPzIj/Fjv/415dYBloB9/DKHgAvoOo6IHogeiB6IHogeiB6IHogeiB\n6IHogZnSA2vy1o/MlG8eXzp6IHogeiB6IHogeiB6IHogeiB6oOweyPf/5Sl7JxV8YHOevVoF\nnx8fHT0QPRA9ED0QPRA9ED0QPRA9ED0wE3lgRv9AmomGKr5q9ED0QPRA9ED0QPRA9ED0QPRA\n9EClPTCj/Ydi0/74jYpJ8HL6QjyPHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHoge\niB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHijNAzPiv+a7tDcuvPV6\n3Op/Z6m+/5+WPvR/qvgrJGXdH/B7srKW8ufUdyzw3vQj/G8/3Q712ZluV8j57Nzk/4zR91AT\nYMWaUvF/tqPJTVCJmEvb+QH9rFW8iTUtevL3SqiGna/TT7eaXov/sx9Nzodi7dRXxmQyLo0d\nSccwVTV6lr9b5crFHvrQ4IwCG+WbM/XZlnz045zsnKwootyXe6US0udJ35pXemXsSF/q03Kp\nrni4mU4OztjRBbQzRtMyXvPlS+1I5pp0u7rOr+fiMXXdUMe1q7m2ex3XvVQumy/jWafU01dt\nlwdyoUdtF1P1ddmbnvuppjWn5/JXsuguGnUvoqGxoBz7oNrsD9fDUV/q0ywaTKOuuYa19VdK\nTAabXDuPBWM0i4bSaJUsDetok+99tfMQGFRHu7ou+d+jdA9TaWnn3nBfxo7eoV3rjG3rauY6\npV9DHDvPzCuPQrHyWSNh4WIb1nN/vlyvP7eFp+tpO1Ne1mFR+T3QimoDvlf+y3/Wbklpa0hv\nJPalriX0g7q0LBddjFwonFTFysn+MxxYbMN67l+S65eCyehr6AwnQla1oeG3cHjWB9TSzv8e\n1j/BZPQT+GGUHguqCpZ2fgr/KLhFYTeuxG2ngRt4k+gGsCtkleMzHk4p4gHzca+b3iNhTKKd\nC9t5sBtMTdRb3BSK2fCkmv9fWyo+gLPTF1LnzrV/w2nwJgQtTuFq2B8+C5V5jib5VfPUF1rV\njhvfggsLbVDgffpuF+idu9/xb58rZzksRaOX4YosjVNtmnH+L/DDcETimr64GEqx07ZPQ3pj\nuC91+fLiSdR/kud+qurUflwtxU79+QTcUkcvB3HNueMcSeoMTkaBc6o+HcoN9pVVvuND4AdI\nfdqJG9aGY1I3HsZ5U6grxo/muuOfVR1oeA88UOADBnCfDIOgOSjcAem4DNc9uha1tZBRS9Pu\nNngMjKEWcCYkZW6dCDcmK4ssGyPm6qxahob2PzzrA/K0c330vyfpuhlkbLcJJxmOriFXwYsZ\n2hbTxD1JqXY6zm8U02kB957DPa4ft+fuvYZj61y52MNsNHC+1xX/xT5zbhoY7+YEc1aQucu9\nblQeD8yepy5W/X8PTKV4//8/zVvSh3vCUPgucYdJyI1efe0/T7TJWpxSQD/FPntRGlwC42EI\n/AilSv/U549i+3DxMdG74Q/JWd+XIj8Iy23nhzzTxdJfht4BE1YpH0g0/z9jpxg73Rz1h89S\n7fbh3LG5C36HpIyD7smKDGU3voXYqV3fpO71w+dXcOH5AWqTm7pVa7tYYL2boULsLPBxNbdp\ne294Gr6CTtAWStFYGpfDzll5zrfwZep5/tjwC5Sqj3hA2s5lqdMfD0H4tdUNwfngxjp9P1V1\nqhtXS9mA+nDnZF39rsL1neAB+ANUE7gMHNe62nK5Rv6INmeunPXwPg0L6WtB7tsC3Pz/BEFu\nuF6Aup5Rak6yr7fr6cN7gsZSSK+za+Qu3snRvJ5P5qxS5SZZXywPe8ODEPKfMemH5L+gLn9x\nuU4dVufVwi6+wm2l2JDuxf80ynaQjGdjo1S5/pbTznz29MtXWUSd8/c5eLyINoXc+jduMp+G\n9zd2SpW5RVvLIfep10J6z+D6FFWLBxzQqNI88B+au7EzkXYAv8YfBTeVbWEXmFHk5s1J5MJ6\nMjwMbqi7wpxQbTWjwyNgMPwbekJa46hwDG6CtUA7Z4GG0up0fCPow4ugNaj3YDjcCquCdlZ7\n/k2jz+tAuwaCi8Qw8PwaCJsDig2iK+n1dHDxnhe6weVwM/hx1APcNBmXx8M8UG0tTIdngXP8\nFtgA6pI+Hgfa3REcdzdfDaXF6Pg80H7njJvCs2Er0Oebgov7m1AJ6bNFwThsmcMc0wqcO9ql\nPYtAY9EADGkHV0ELcE47x+eDQRC0CoUbwLnvP4FrAw2he+jUjc9t0Bb0pR+g5p3rQTnWJ8Ej\n4D+t8QOuIaRPzfFuMPXnmjAAnONjIKgnhX+Da4H3zwLlkjG5OFwLISaNT/3mfLVPfbo7/BU0\ngJdoD1fDaTAEXPujivfAhjQxfpaEneAUWAD8IGlMMh8Y3/+E/cH89Spoq2V/FM6HH1TLwUyp\nxjaIM+Ig/ITRW8C/4MPcC/zBcThMAjfEa8NR0FjVFMP8IOoLo8BFsyv4a+/L8AwoJ1m1pE2P\ng5NT384NTuT14CBI6kxO3Jj466iaMv1Q9b9u7O+CofAWdIe/g778AHrD/WBiUl9MP1T179n0\n1gvckLjJMFbVN9MPDfpX2xaCu2G2nCX+k6PD4Aw4AfzQGAmHwK6wI2wB+rrSH5xuoowxbRsH\ni8EQOBCug3yaRuWWoN0jcje8kjtW+7AkHT4PjvXD4AebvjNeHwD9Zzw456dCOTehPK5GH/N3\nKzAv7l1TM/2fYM1BuQm8A47pnrA2eH8+daByW/Cj6vN8N5SxbizPsq+B4FirMeC4fuYJ2hrc\nwD8Fb8DG0At8j+VhZTBuq6Hv6GRzuAO0U2mnG7h3oTk8C/OBNrcA85L51o+Casan88b+Pd4M\nytjUb0HXUNgLzAXGifn+NxgF5dBEHuL42f++uQe6WZwMh4J+nBcGwLpg7pkR1R6je4A+PBHO\ng6agpk4/xL9FeMB9yBXgvHaeOcdOhdPhJ2hs6otBy4Dzydz+R44nObp/yqdfqfww34VYN3N7\nYDde3wRZqEzwJu1fYByYcHaBTeF3WAnyqQuVBqqbgywyoZeyULSgvZsSbf8EXBh8Xju4B56G\nJaAPuNnLKtv70VCoDuDGr8C+gzagoJ3+yhh0FAXr3oMRoK/dIGRVXxo+n6HxbLQxXs5NtJ2d\n8hPwAKwBbuT08eugL8PmimLR6kcLn12sbqHBr/ApuFkyTt8E7WkFafWmYmS6sojzi7jXzVcx\nWpib14GWuUbtOTrGO+TOPfghpT9/hvHgnHsfsqo/DQfV0/h6rn8L+uxF+BimwI8wH9SnZbnh\nKnimvhvruH4r18w1WXQbjYztponGJ1B2zreBU0B/fgTG8lDIqntpeEkdjZ0vnWFd+B6OhKA5\nKLwG+juf9qHSPGtcOocehKx6lIZnF9jY+bwqrAyzJtpYngAXJup8P+eX+ci89CU4dlllzJxY\nZGM3QSvC6pBcX/pxPgbmB3UsOL+01VhwLmTV6zQ8IkPj5rRZG9qm2q7FubatD6eCNjrPzWHm\nlqwydsxtSZ3Jic//AMzTlpM+3zBXpz8L1RPcqL+zahINe2ZtnGi3N2XnzIfgGPlu5q1NYWUw\nL/SFrPqGhj2yNi6i3Vvc26eI+9O3TqOie7oyw3lH2hiXfgjpO99/FLgPuRecX/tCFjlX/4Au\nWRrX08a543ppLJhjfYdLISqPB5JJPs/lWFWgB/bkvl7gImjwnQLnwgB4Ez6CDaAxaV6MMSG+\nCi3BWNgM2sJkuA5cKLuCi4UTq5rakM703T/hPjgMXgATUPBlZ8oXgIloeVg2d17tuJ6Nfk+A\nFtAJ9gA3Jy7i18KGcDs8CUvCKuCmxHuqKTfGu8BEeBpGwOmg3SZKx7qhtQQGGJcn5nBc1wM/\nhv4NQb9QmAeMzbZwMfwMldR2PNwxWwnWAsdSm+bInf+N411wD+wDxkVS+tsNT6VlDF4FD4Hz\npxUo580NMM2TnLxvfvCdjod+0B5uAn1cijamsTa48Vw09aDfOH8D3AzMBeaaoKkU7F9701qK\nimvgOFgGBoHzrFR14AFuFLT3ErCftOznNTAvucEM0l+tQV8GdaRgPrKNR+OkXFqfB+kfPwxP\nguaQT39Q+Ta8Asmx1K/mo29gNfCfJPTKnftRVy7NyoP2grvBeWEf1uXTt1S+AGNTF7VVm/6A\nU2FnWA4mQDllrBr/2uD4jsmVz+BoDlLDwHVQm2YEtcNI8+KTcB1cAEvDKvAsNMvh+0YV5oGu\n3HYjPAeuBc6fdaAt+KOjMl7Mb41JnTFmIJiLzMWbgD/2OK/Mvy1qYWHqZ1rVlqxmWodkeHEn\nyTm5dt9xnA3cFO8DXjPpzg/fQ2OR9jwPR8HiOaOcKIeAC9WJsBG0gp/ARb7aMsm4EVAmntPA\nX4/ag4vkTrAduGEZAEEmft+lWnIO3Qdu2JR9Xw+Pws3QF5R2Hw0/e4Lc8Fc7ia5Jn03AcXUT\n2hrOgKbgYtnQMerG3jH+G7gJCuPblvI8oJ1BG1LwXV6HsFmt9Li7kLwMI0H9As5957l+7A9T\nQDuuhvHg9ZZQLW1OR/7o0QP04V7gBsiF8QdYCJIK56tS+Q2clbhYij9n5znzgfnQHzfGwjHg\neVLaNCvMn6ykrF1eS2trKiaAG79yaRke5CbcuDMfbgRvwBpQiIKdwZe20f+TwXxaTh3Lw4aB\n49wGDgRjcmEoVNobbN2e8kvgR73xXY5cvyDPuRAcJ9dC56d9+gE5CIqR7RaAHWAoaKdy7pdL\nxl+w0w8g1zzH3nxk/1eCczjEqXWNXathoLl0T1gJnMvOw66gRsMnoF+jCvPAvtz2FKwPxqR5\n/3Hwg8PceTKYZ3+ExqRtMcY53g6MA+1+BE4DY3o/MFflw32K8TNTSudEleYBF2wXVQNuBLho\n6Vc3oQZiH5gXBkNjkLadCyb8hXMGaaeLmBOlC7hAWHci3AGlbJRoXrRWpsWKoA0PwiEwHpaG\nOcCNvQvtNqCtDamd6XwjcEF9DpaAL2ET2ApMmE1B6eOkqu3XdXKda4cbuFXABNgclBvqhtR1\ndO4m7Qt4ET4GN9S9wQ+6y0H/XgbXgLE8Caql7+lIH/qhqeYBbXEc14L3YDRsCe+DC+ce8CYY\nB5WW82UA6JdxYE7Sl+afgeDCfSa8DM4dx93N39tgHPwM5YpJY8zNdnewrG3nwD2gfUGvURgJ\njqf+VM6lw+F2T1JyLhkL5ZJ2bQ+/gfZ2hGVAH10BhciNxXC4GBbJNXBNML/me4fcLUUf2tHi\nPNBOx8lxXQiagZuzQqVNvcCxCf40Dr6FT6EU6c9jwbVEH0yDreEqWBf09VZQqO7nRt9xPQi5\n3jkV/EyxZO3IE9rAU/AvWBp8D+OxCXSCN6E/+CH/ADRWtcCw5cExnht8D9dMj8ocq7zu+HTw\nJKpeD5grzfXmida5u3/hqA9vzJ235aifzXGNRa6f5tZLYVLOqNk5mj+Ozx1v5uj6lA/j4y2Y\nKaWjokrzwEY0Hw0m/bBwhwB08TGpzwkmrs+hIeVC5WRplTPCyf4JaKMLmYuOi4TnTiAn1FFQ\nbW1Ihx/CTeDkvQVmBfUzvA6bQ2fQzg1gOKiwuE0/q/zfbnTxOIyAu+FCCIvRApS1b0+4NcfG\nHJUxYfKqphajMzerLvqinSZ4bfRX0bPhNih1k8QjipbjuyYYj0uBi7zxORbaw25wNewHbhC1\nX7v3B22fDJWWm6de8AKMA/3ZDJS+XA1WhXfA2BwMY6EDXALGbCWln7TJMTTmlH56G9aA78Dc\ntDq48dS/E8APOuPiCtgdbodyqB0P0S9u5L+ChWEz2AnuBKUNnuurz8DYWxK0wbmUlnPtn9AD\nfIdSpX+MPeejfA3WrQ1zgZvMKVCf/s4ND8PEHG05+u7aWy7tlXuQ9ulLfWd/C0I3KFS3cKOx\n+ijocz/kV4Zt4GAoRc1oLHPkHuK4m2McX/t5GrTV80I0iZt6gvHg3NJe3/c9KJc24kGuKevB\nhuB4u6Y7n0JcOD/2Aj+mJkNjk74dCMkco82uST/DYeC4LAfGzjj4BbqAMdQcyjGfeMxfUvrV\nWHDNcf4pc6k+3hLGgHnrCzgH9oLGIMfb+WdsrwmzgArz0/I38IGFqP/2gAtDVGkeMJnOn3tE\nmEBfJh55KeVRYBJuSLlh+ze8Cn4MOdE9mjRNjr6Hst64OAg2hG+h2vqRDueDB8HNsIncSfwS\nmJBMVj+BtrqwPQH3wT3gJt97qiVtdfxNPPatH61z86Id1p8Hj8CG4DvcAudDtaUPg2/0nWXt\ntDwP6Ot1oaGkrxYCP3pawQ7QAtRA0E7f4UhYAYxRY/VY2AeSSZ/Tsus0njgBnN+fgv7SZjcY\nLjAb5847cZwKG8AScCV0A++tpNz4qO9Be4zD58G5pEaCG/8e8B7MBofACPDaSXAbPALe0xSy\nyryinA/O0TnBfONc1k9Jvc1JR9gDnCtr5srGZlpvUuE8M5cNBjcnblSySjuNq8/gZwhzdi7K\njq/2FqLx3NQZjFnntuNt7r8XHoD0O1NVlLTTWFfGljHneTvwgyGfr6iuVUdwZUU4BZ6EueFo\n6AKlyHHWFmNQXzYH58sisD7MD8ZlMbqPm9vAc2D7x2EBKJeMUWNAfzpH54X2oL6F0WB8eP0h\naGzSZn1k3jEunGdBd1M4HJwzznff81p4BZzny8FpYLuo/B5YguqLQT8b23dCTzA/hJhx/hjz\nm4L3NBaF/GVOVcPAsTYOglpT2KgWnLO+40ypmfbFyzjaJiY3Jk4eJ8zvYIJVnu8Nnhe7KNCk\nrDqYp30O20AY96GUF4JH4Wtw0vjh4Walf+6cQ9XlRsUF1STu4jU7fANrwDjwupsOE34L2BF8\nN++5EByDamkCHZlEvgQ36OPBTYLqA/4CagJqCvp5OKj/QEheNRVV+OOCGH790keOteNu7Ir2\nN1SchjFzzvQC/eRm2Y2qOgS0tzl4z/vgQr8OuCHwvkr78yv6cGEcB24s7FObtoYO4BwLGkXB\n61uAc85NbXJR4rTsMvbU/uCG2vmwKDhH1PbwIjh31gXH/CgIOofCRjAa9LFklTnGjYIbzGVB\nXzhf54V8Gwj9eC9cA877unQSFzcH555jnu95VBckc4vyI+MjeBqsc6zGgrFVqLRlMPgOT8GR\nsC18AsU8h9v/R3PmakKOHsF5sNNL+q5YvUuDa6E77AzmsFLt1CbH2dgxvszLxqB2mw9Xgbug\nWPmc9aAnfAyuAeWSHz2uLdrtHFdhrp5BeRFwbvg+XaGxaU0MMg+6D5kCU0Fp73k1pen/8+SQ\n252T5ojtYSRcD+aKUrUuD7gKjHv3FWoesK+LoDME2fflsCWYK1RruBD+CR0hn1yjqqkF6OwN\nWAzC3FuN8jGwEwTdSmF5eD1UNPBxO/ofCq+CdhsLjr/zZj4wRkKMOxaP18FyXJspFQJzpnz5\nMr10CB4D0EXBJOsm1A2fi4XJ1U2dSbghtQmdLw4vweCcIVtwdHOxB7QHJ8wLMBEaSvvR8Y0w\nBPSjvjNOlwST/yrwIbQF7dW/+ro37AvaXy31oqMLQJ9qp/aEeHiUctgoUazZeN7A8Vj4G/wH\nqq236FAblT617EKmXy27ORoODaGQi+ai827gwppcJI0H7X8NzoBwvwu888wEbyxXUh14+DBw\noTkJXGTmhF3gANgBzAHmAuPgcLgE9oI7odJyc+dcsP9WiSPFGk0KBY5uRlTYyEw/m/5+B3My\nGHxWVhlP5kI/iBxTbXKcrL8DSpXjfSB49JlZpY3qMzDeeniS072hUMLRee4G8ekSnmHTpmB8\nfwvG3ObwC2j/d3AWlKL7aWzure/jtL4+tMe4OQ9ehqUgSN8aW2+HiiKPjvPdsC+8W2Tbum5f\nmYvOW9UMwhyyPz889Pmh4OZyCWhsMi9pq/PrPbgeglwv/TFpPBg3E2FvuA1Kmd80/y85vzeA\nm6A5mKPVUdACHDc/kjaB9aEXDIAtINz7IOVPwBzv+tkOkpqHEz8CqyntmB/M+Y6/+Uy7OkPI\nD59S9j2T+ZXTBpN5UX9/AyH3aozlNWA+MKZnBXU5OG/z4Xwo51zjcTOOnFBRpXnACa5MUC+B\nv4RuBiH4TBxuiCdDQ8kJ3Ra05QT4ALTRRddJYb0TXx03/dAgf5vQ63lwPJwPF0JP0Lcm2blB\n212krHsDVCeo9geoftPGk+FsMOkPA31potwAPoMFQLmZvqem1HB/ts51re9cSJcHbQ2baZOq\nC0FDyMXaBcijcaCN+lJ75oCP4SYYBOYtF0o3NdZNgFGwNlRSp/JwF4sNQTvtb3foC/pSex3z\nxcHN6wGwDGjvWVBptacD844fSnOBPvwthz7Unv7QGq6F7+EVqITsdxpoxxfgpqkpuFl7ChqL\nHCe1KEwEfaMPHcubobHIWDfnfA3mQccu5OyNKOvvxiDHXP/1gw9Au4x/54ub9XegsWl9DNLG\nN8F8PRqct81gR9B27XYONUb7f8Au43Uo+CONcbI9LAnWu8G3znxwOlRCPvsq0GeWVwNlLnfc\nh8M2YByvCx1B+04G52An+ARcU5Wx7dqfzJuOw2SollaiIz8oHoMvYTcYCcaGtuhbdc70Q6P4\nq//PhWPBHOE4aOtCYP6dB6bCwuA4SbnVlQfuCkvBnPAevAVPwwiYYWQiiyrNAwaAk94EsD5s\nDEn14uSeZEWVyy3o71D4GZwoV0MXuBaCnCRe19bw0RGuVfPYns4WhDuhFbjAOuH9JUSZkFrn\njq9wdDFoB25sqq0l6XARuBu6geePg9J255ZJSQ0G36WhZbKflqMTR+NWfxoXxkBfaCjpLxO4\n4/08uBD9Ai6wXmsJd8GDoK2j4XPwPbaFamwQV6cfx9sFck8YBG5Yte9t0C4XnrGwN9wIV8D3\nUI0YdQOnToRzYCgcCfpN25z7P8EEWB7UP6cfyv7X/lyIZwPntONqXX9oTDJutNPjEmCOEcfs\nHWgsMj87T9uA8TYPaKcbt1ehsUgbtUnblgPHXzk/GpM/a4zK/THHmAu3hG/BjbHx6juYg64D\n11HnkRu9xqB5MaIH7AZjwLm1DvQB60eB9ssCueMFHG+ASsh+zDtbgz4MOpOCfjscHP8V4cnc\nuf5+DcylK8NHEPQDBXN+Uq04Sdclr5e7vBEPNF92hL7wHZj7jXHnoMcR4IdhY9HSGNIc7gTX\nHOPWHO8+1TgPa6zlsGYZ74fUwgHUzwWF6mhufBQcz8kwHMxd28LLsC/MMJp9hrG08Rhq4jRg\nvs6Z9ARHB3/d3HnSpy9Sd2uuPn0wiHeHLukLZTj319Bh4MQ2cZk8PU6D9nAzuAGYBE6gR6AX\nhHeiWDHNx5PPADfrJpkpMBU+hr1AW0eD8poT2zbKa076vmCy7Q8mrXuhmlqVzi4G7Xkf9K3y\naDIwCbkx8GPzMTgCekO51ZoH/htcTJ6DB2ArWApGwh1gglf62rFWxrC2myQ9eu1HuAjOhrdg\nM1gPQgxRzCxj7jT4D7wE+RQ2Vsbo+qBdyqPX9oHtYBl4FNy0GK/PwC+wMVRan9PBfnAJBPsc\nb8ddtMP6c2AYdIUT4Er4FcotP4i6wyLg4jMW7P9yMP5UT/gNnCfGw86gzE3HwihPKiTnrfZo\ni0f5AtLST5uCY38fvAvVkrGvHx0/7VMendcbwjDYArTRjd+dMA6qLW3U1qBgb77N2WrcZB5Q\ng6GaH1DmlIXsOKdg5yecm388JmV+3w2c1+b928F8Vk2ZN93YO66zg/km2K3Pt4Wb4FQoVCGm\nzdHl1j944FmgbcaqmF+cZyfBPDAWfgHfx5z1FIyHSklbboEzIOmnczk/EwbCQbAjdAJt0a7l\n4EboAeYj7TVfbQ7ONdUS/OjeCaZANXQYnVwAvlcbMDbddzivNgBlrLq26+dK6Goeqj8mg2ty\nM9Bf7cDc6lwzJz0Ml4LzZmdQu4Pr1EjYC7wW8rHvNBV8pmPhR+sikE/G1ROQ/HjNd591c4Dj\nb/4ZAWk5fsbBreC6GTUDe8Ck/QX0BgPI4HJwHVgTkguoi/qBYEK1LuC5jAMDPK3lqZgEn8Cr\nYDuDPYv2pdGoREMnhgkm2OCzQ9n3cRPi5Aj1j1BuCvWpOzfYNqv60FCf6b9gT/oYbErWj+H+\n8D79KY8F75NXoCMkZaL9JllRZLkv9z9fR5tzuZa0z3La7p9y91j/PRwFafWkwhjIqn40dDxM\nztrgB47J7EsYmjuO5Nga5oKXIPheuwLhXYwJ23v+BnjvM2CMGsdZdREN/bCwf599DiS1Mif3\nwwQI7+J92hdsS54HG/3YmgWCXKheDycZjsbWoHraPcv1YEvavhe4pm0uOo6L1z1eDMkccCLn\n+jWrbqWhdhqn+lWfif2FvoPfkjY694vR2dz8aDENUvd+wLn9J23QLu1YCoIuo+D81n9v5sqH\ncixUbgLuLfTmPPc5X4KN4aidjp3jORacC0/Dh+Dc3g6K1XU0cOyyyn6DfeGondq4S+Khp1K2\n3twolk+BQmVsOReyKsS+Nibt1A79uEHiwa0pj4SQsz6jbK7pAPXJnGFuySr77Q09wbL2Sdpu\n59aaUIySMW28m6uzyjVCG9Vs4JgGv4ZjsF3/Opesdz2w79uhELnmmVOy6lsajgbn4hW5cjeO\nu4Lz+jZwDrnBXxZeg7tgKOwG6mQwh3vN+jlAjYAu0Am+gT6QVdNo2L2OxnNy7XwIPtWXko4N\nY6cujeLivnXdUMe1JlwLfQY7wh4onAd7kue+mznKcff+J6E9GAvJ90i2sXwplEOO64R6HvQp\n11eu5554eQbwgJPWRWk8GFwOvMG3PZgwrwQDMhm46aD2vCuk5WbxPzAXOPG9z0mRRU5CJ6Na\nCdLB73nSLsvabDI9FQqVScX3zSqTmv7UnndyRxf3pL3a5i8daZuvpe470O6loSMsCfnUg0qT\naFa5SLhY5NPWVKbtTdsaru/JvStASPLp5/WkYlK6sojzftxrIlwYNgb7dZF6BdT88CLcB89C\nsKu2oxuBlqCfvWcbUL1hdE0p25+LaHZ/rulWHB1DF07VGfxFcDDsB2nbjIdQN5Byc2gCj4DP\n2QuCjqDg4ppVbgrzfSA5R9eAdhBsCXYljw9xvQtMhb2hE8wHaZ1IxTPpyiLO3WT77mHueLwa\nXOSsT9oU7A1HLhesUj+QnqOn0G+wyXPj7MmcFc4nz43foP0p+E7O8UJ0CTfdW8iNtdzzEfXB\nvnA0N1oeDtp8DKhZQL84zxaAYnQdNzt2WeXGItgXjsaa9plXF4G1c+f+Sh+0EwXv8VohGsRN\nzoWs0jfBvnD0RyJtdVz9GGoKytxkjprfEzQPDIFhUJ/u5wZzS1aNpOEI0DdJgs2hzvMx4K/u\nhSgd00/QyFydVa4RrhXqONCutI3B1h9y1/Wz95iT8uUgqv9HpX4gfcMTdwdztDJvNqspTf+w\na5srJw/WzZasoGy7JVJ1ydO3OOmTrCiyPI37u9fSZlXqJ0Lwp0fzU9Ln5thDoD6N4oZ967up\nluuucY7fLfA5GH/aYN9J29Jl2wyDVuB4ep5s41rr++8DybaXcl4OmR/1X2/IN1/s1xgN859i\n41a+l2jcFlfXOid9GzgAWoMLnIvxS+AkmQwGRVKeG5QeDdCQ/CnWaHH+uuE6CfyyL5dO5EGv\n5h5m34EwQbxk3Whw4jmJTodqyoT5NfhLgwrxF2zVXyZIJ2/woddOACeei6ub7A9gHFRbAxId\napfyqN0ek3Wbc/4uuDGolPTJF+C42/cb4D/ebgnGbj9wwV4HngWVtNFysH02yh/DhFzd0hyD\nXHDLocE8ZAj0AMdxGPgBaTykF/JgG5dqbF6Co5svF6yQgPekXEkdzMPdmDrfnTdBP+cK+k7U\nFvAc3AObwjvgx2Yl5IbY8TK2BsAecBaE/oJNYay5VKMmoVCFo+Nk/+J8Vto1O3QDx9s4MCae\nhKBrKYwC47YaCj7TzuC34KdO1JlrnFPK66dAeAfrqqUpuY6CPz02hRCLm1DWnyEGKdbobv5a\n57VqKKxpwZ8e5wZzvxv2BWF10IfmAHOUuUq5eToVNoD0uklVWWX/rcH5nVbwcahvS2GFcFLP\nMV9M19OkoMt3cNe5iTu10bE3LiyLa6e5VL8uB86hEN8UKy7H3hytLIfYdC0fC2mNpcJrSdnO\nNajaMj7vhYmgL5VH48R5L0FXhkKFj86T+2AR0JawX6JYo2BTsNfzVaAvrAM3wyfg9fdhThgA\nN8KzYL1tNgTfKR9+PC0MhchnuRadCPY7FO6HF+ELuAz88caPtBlCaYfPEEY3gJFOejcjGyX6\nPomym7akDJDPIASs19xcJeXGRrmBKJfcbJwB4dk+V1tMPtqdtKcN5wPBgG0IuVgGezyKtiZl\nXEqoX5tyR5gCyXfktGrqRE8LJXrTtkCi+s+iibXSChtPfaXCefCRMeY1Y8CEGVSX3WFsKmW/\nNrUHE6d2Xg8vwNkQ+vb4NiQXdzdWQWHuhPcM9eU8/o2HXQ7Oc8d9VQjSN+YE/RhsDte0rZJ2\n2c/HYN/a4SLqgrMtBFs8ToZh8D0EhevhvJJH+9JG/RT6TR6NS+0PY0nxT1lXqfj7s5NcQZuc\nN+aWIOu0wfyZtsV7ra/0GNPFf0l/2XfI5/pWOUdCLNTlz2rZG3wX7PNo3Vvgh73n2uL7SHr8\nw+bJa5WUNoyHl/N04viG/sN7FBqPtY1Bnm4KrrqMO3fJ3a0vlXY5x9PjOpG6R2EERBXugR25\n1Q/mNhDG3JxgPAafU6yq/KD5ex09apc5QVnW7g9hD1B+pPijg/ULgPKj1Xt9zyD7WaQOwg9G\n4f66jsO56F5pT/DHwlHwMBwBLcHYnGFU6KSfYV6oQoY6yHNA+JL2g+QUcLGyPsjAWxQMyMDn\n4WLuaAJ7B04Eg6gc0i77Tiskea8Fewzaoekbq3SuDU3BXwzDL4TWqXAMtk6vnT6h7+RkGHSD\nhppgfqCpYJ/HIG0P9ltn2V9+Kq1WdOAHZ1swUa4E78IEMC6Pgy+hOQT7PGp78j3CNe9rkbvm\nhiYovQiH+mKPXWiwCYyDm8BFqTcEW4IdVNX84qztzjX19PRDzb0XUvZ9787VleuwBg8aBMvA\narmHnsTxK7gld66t+sM4thw0icLy4Dv1CZUVOr7Jc9uD+dsx1w4XIO10DJXjuDgkfRo2n16v\ntMJCnIw9+zRnvgHfwH/gZlgVXgO1A6wAj3hSBYXNsPMo+Mqj9a9Dd9DGoCMp6PfhoaJKx1/o\nx3zuvA522rW5X1ufBGNQ+7rBUFCW14MzPKmCtLNJrh/tNDY9DoRz4Ad4GYxFbTZHDQfjwnYn\ngD8qGsuVlj8SGntpOb+Djz1+DG+nb6rl3M2g75qM6VpuLajazev8uTuDL0ND643FoHEUfgwn\n8ViwB07mztNA/5o3g8wJv4FrjXNPeV4ttcl15LwI8WhViINwDNc8fxVWBtvuDTfBseB7+R4H\nwIawBHiujNnDa0rl+eMcfyxHeZ7YQE9JTq4GMmGG6HYEVk6BlrAzGGz6zoBMLwhU/Sl/4cmn\nfal0o/8uTMx3Q4a6j2izVJ52TvIgJ0TPcNIARzdNLvAh4TuxwyQP5oTJHs5N+J9ANzgF3oGG\nkAu3/tNelbYz+R6O679q7qrsH/34ObhZ99dZz/WXfa8F1vtxeTCotP3Jd/Hap2Cb8WC7kbAk\n+OxStDqNB0N3uBmMQTf29qX+Cb+CPtavsjQkdQgnu0IzmBdehv5QTjmntXFB+A4uA999ABiD\nKoy7G5ek9N8r8Bj4jpVUFx5ubmkJ00A/doS0gq3p+mqc+/Ger39tDYuxH7jbw3PwOOjTjeAM\n8COwGmpLJ/nsdPzXAz/STwZ9bt5fE3rDZ1BNtaazfHY6hw+Dj3Ncw1FfijKer4ahnlRBi9FH\n0s5QvoB654g/Ck4F5ZweDiPgRfBHCXPYxlBpadcyYDwmFey1LuT0vSibnwrRXdy0HYSYXoHy\n84U0rOWeWanXDuPRvJeUvlLa7D1nehJVlAe25e7TwTzv3kR/hxjw6LkKsXDx9NOq/w3923HS\nPj/mk9qJk6/BD/o34FTw3U4B30WMSZV8v+k18e9/ecANQVTtHpibSxeCCc/No4nuZnAiqZfB\nBTPIIFYhgH+Zfvo/f1+ixiD1Q2mN/7lafIX9zQNfwIKQnjReFzfuLlINpd9yHSf9FHzlpWQS\n8Hw4uHj6YXUAPA3VUFs6eRiehCuhDawKJpyVQSXfIV124558r5oGFfgziWdOBj+KnoIHYCtY\nCq6C60Gf6zsTY1JJm603Lozvc8ANjB80xqgxHDY0FDNpCq3Gwg7wEHQD581keAb81bsJBKVt\n05fXgptU7RkIV0C5Y9lY2xx2gTvAxbMz6IewqadYo7SN/uJ9LNwD5barpsPEn5aUB0F70I/a\nYi5P92t9NeKQbv5H31OT9pH2aU+r3N2W94AesClMgzPAWK6WtNOcqZK++pDzS+E+2B3WhVFw\nMJgHqq2wgbPfYKfHOyH5Q8GhnDvHtgZ1CTxaU6rOH+dnmMvBTsfdNWkzMKcG6eNOsB8sAzfC\nDeBaW2lpzyKQb87Yd7DdzeYTVhQo2yVjevkC29V2m/lbfQzO97BnS88t7/EjM6p+D5zLLT3h\nSjgVzDvLgmvKNpBU0s+ug64R1ZJzfs5cZyEek30H20LdOAqLg/P/fnCN+BVOA3Pq5dAa3AsY\n1++BP6BsDytCPtne+Tk+38VUXVvOL0jV5TvdKV9lY6wLk60x2tZYbDJwboOLwaDaG+aCd2Bt\nMEhD8Ho04RqA1htctcmE1w+6QHpS1tamtnoXpUXhB7DvpD2c1vwfjU0AhQS591dKLpz6RXtD\n7GmrdcGPYyibsFwMtPcgqLZclFy83fDumDty+K/xDD623rK+/wZaQXrRpaoiGs1TN0k9+c3U\nuadu9PqAm4KgYGPw/QQudINm4HPvgf2hFxwHpeh9Gh+SeMC9lI+G+eBBGAhu5NywBIW5o83a\n2A8mhYsVOjqvp8BdcAx0hsHguK4GQfoubFyMacff+4dAtdSejhYD58pO4Pw+C5qCcnH9GfSx\nOaHasm/9JI6hGgUdoLknOem7+3KEumoev6Mzx1I7Q04y3txsGgfq5hw1Jw3053v6dSzTdppL\n03qECmkI/Uinc4A+Df503VwYPKb1FRX/TFdW4XwqfZwIreHgXH8hjxuvbkw9Nz6KVTKmnyi2\ncep+nyXOd+d68GmoNy4s63M3vFF1e8C5/RG0hOfBfP8BOOabg9f1p3KuhdxpvfEbcgLFikt7\nroblEz05r7Q12KhdxoB2Pgbng3uXtIZQsUK6knOf51706TzXrHId/raWa+lq57LzpRfo21dg\nhlaYbDP0S1TQeCfPpqnnuyAZMP5C9yT466NBqgxaAzVMLDdP1VAIzGXoLG2LG5WOYMAuCQ0p\nJ88iYNwFO5MTXr+1BW1+BwqdmNxaVk3gaX3gHHgL1gJtcZN8B+wGyncIicrNtePtBqGh7Kbr\nvPoHtdp3AASbg/9DrAabu3BPC/D9XZBVeMfpZ6X//SePOBJc1K8AY2Ag7A1BLgLaGOJjnnCh\ngkd9MDesm+vjJo5uoHYEN1RBznHtU/pGOz/zpEqyzzXABbsHuDD2BhdGF0F95gbPMS/32PHI\ngqQN+kmCDQvlzp/l2FikbY6lBDu1zXzZmOQ8zWfnM43JSGxJj7tzw7xorL4LjUWO9ZdwLuwO\nC4C2zgvJOJjEeUPKtdINrDnZ+ayNwT593Rz0q/N+FXgVomr3gL67Dh4H1/djoD24pjvW80Mz\nMH+au/S3ci3cGH7wpErShhGwJLguKXNAsMlz30e7XQdGQrGy/YtwarEN89zv/m4fMFaHgev8\nDC0DICqbB96kWRvw12WDzA+nF8BNSvBrtSaTHx1fwLOgLeKHnRs9k78J4E5wQ9qQ+oTOzwIX\ne3/1cNHUVhUmt3WHQWe4BxpSk+ncX7e7wClgYloUxoLvoO1PgfIX+1tqStX9n7Tkuqzz4K9A\nB8GToM3abuJ9H0KsjqWsTMre7/tUSp/x4EvBPmQc+PEZdCsFF7FjQRvVfNMPFf3rfHUMB8Ma\nYExOBf3hXFLaOx78tfYdCPG7HOVqydxjvy9BV3gOukGIReP0XrgDHgflmFdT/lDgBs6jP+Bo\nrx9I2myObCyagiGO7wSwrKbB3TWlxvPHfKk/3QzpU6WdN9WUGs8fN0n6U3udO467Hx2Hgz98\nNUaZC5W26t+xuTKHmrnlsaHkXG4B5pqw5minsbArOP9FNfT6Pt2KGefvVZjqB6hr3gngj3Dv\n5c71tevll6C/94IQJxSrooH0shk4n4Lc410AxoU2joZO4FrVWORec1xjMaYUOwyOqPwecFIs\nDP4ThLpk8Bqos8FaYDvr3HguCvW1n4t7SpH92dc6uYdoi3U3gR8bysm0BLi5q88ebskrk4fP\nzSrbLg3bwuegb0zyLvLSHrSzKVwOH+eOHIqSG+lS7LSzlSD4ycVJn2q7i/7a4ELvZsoFaT0I\nPu9J+VMYAvXJX6pKsdO2xluws77+vL4AaHsT6Ag+wzGwLjzLWPoFRsG3sCCEjSPFomUfG0I+\nO41P/dAavE8/yvZgTGinaONN4LXa5Fz1Azyr7H9HcI44rlPhDNA3E+E20CZtMEbd9GmXPjLm\nzoSToT4Z96V8IGhnG/gIVgPHTbu00cVUm8R5FuS59+Qbg3BP+mjcv5auLOLcGHI+60v9ZVkb\n/CW0GDu4vU615OpTdd5R90VtMne3An2rjMty2ugzjfHBFjJKO/Wh4xL0FYVXw0mZjsbWHSU8\ny/lhDnFd015lTjw2h+fl0JI8xByVVY71qXAYLAHarRaffqg51+e7wRa5uiyHdjR6LkvDXBvt\n0tYVc+fmI8+d06fn6hbk+ANcA17LItdffwzMKvu9FPplfUCB7ZbmvqzvGLroT0F/mZfM95ab\ng7lpDTB3OfZdwfXH+D0+B4eCZEyVaqdrozYaA7/CZ7ACmPcdf+vcw78OWeVc3Rd2reUB9rEJ\njKjler7qAfkqZ8Q6nRuV3wOPUX0EuHhWWl/QgZMyix6k0eFgoFdak0vo4O5c27BolvCoepuO\nr/eO2m8YxCU/CKph5+jazaj3ygDu8EOzGnZ+UK81td9wLZfGQjXsfKd2M+q9cjl3vFslO9+o\n15rab7iQSy/XfrmsV0rp50wsGVJWa2p/WCkbUDc+boqqoadK6OQo2nYuoX2hTd3UlTJuB9Pe\nX7QrLe18tIRO+tDWH4kqLe0cXEInf6PtUiW0L7Spdt5f6M157tubujZ56stdpZ1hL5Hl2XvQ\naPEsDYts8zv3uzfLIveC2rlwlsZFtlmT+58BfxTKJz+QRue7EOuiB6IHogeiB6IHogeiB6IH\nogeiB6IHogeye2AVmvqDVFT0QPRA9ED0QPRA9ED0QPRA9ED0QPTATO+BnnhgzEzvheiA6IHo\ngeiB6IHogeiB6IHogeiB6IHogRnRA9X4/wXMiH6JNkcPRA9ED0QPRA9ED0QPRA9ED0QPFO6B\nrtzqv/TB/+/cnPAe+C+9eRqK+Zc9cHvDqhr/Ag80ybcAAEAASURBVIKGfcPYe/RA9ED0QPRA\n9ED0QPRA9ED0QPRAJT1wNA+/AfyXVIwDP4z8l8H50XQ2+G/iK+XfukfzqOiB6IHogeiB6IHo\ngeiB6IHogeiB6IHG7wH/kw7+G4CXrcXUnXLXm9VyPVZHD0QPRA9ED0QPRA9ED0QPRA9ED0QP\n/GU84IfRhHrexv+m1Mr13BMvRw9ED0QPRA9ED0QPRA9ED0QPRA9ED8zwHvDfaTAResOsed5m\nH+r8j/L6H7+Nih6IHogeiB6IHogeiB6IHogeiB6IHvjLe2AD3nAs+P81Ggr+x4dfhC/Aj6PN\nYIZR/LfYzTBDFQ2NHogeiB6IHogeiB6IHogeiB5otB6YB8u6wDLQDr6HUfAAfAdR0QPRA9ED\n0QPRA9ED0QPRA9ED0QPRAzOlB9bkrR+ZUd883/9OcEZ9l2h39ED0QPRA9ED0QPRA9ED0QPRA\n9EDDe6A5JqzW8GZksyB+IGXzW2wVPRA9ED0QPRA9ED0QPRA9ED0QPfAX9ED8D8X+BQc1vlL0\nQPRA9ED0QPRA9ED0QPRA9EADeuA3+p4ELzegDbHrCnigG8+cCtMK4Jc89+Srq+1ZH9M+6z/N\n24q2P+fpv7a+iqlPv8No+smqnWlYLTvfzmok7XpBMT4q5t60P/23u2TVwTQspu9wb9qG+uq9\nPiSrkbQ7pgg7s9gW7Pf4UAl2nlqEnck+Cymn3+tfJdh5XpntTNsW3sf6G0uw84o67MzXZ766\nYEt9R/vKKt+xvud7PZ99+erqepZjl1XGTF3PTl/LZ1u+unQ7z0/JaiTtnIP5nllbnTblsytf\nXfoZx5Rgpzkt+bx8/eWrS7YptHxQCXa+mLKz0D7ruy/fu7n2ZdU7NKyvz6zXk7a6h3AvkVWj\naZjVjmLaaad7syxyL+iesJj+Crk36cdwv3tc97pReTwwe566WDXdA4tx+BF2rcch23J9e9g7\ncd9SlN3AzgvHw1dQm5bnwiXgP837vbab6qhvwbUvoZTkFh5vPKwDvruT6RDw/b+GVaEfZNXi\nNPSXhAOKfEBb7l8FnNAuFv7rIpNajpNLoQf8BF3gSMgq7fTfuHJ4xgf47/jXhoVhLLwCync4\nB0ya/qqyEfwdsko7XZT+UcQD/N8D3wUu2E1gBfgB9Km2BR9S/FNbUtLmrGpJQ33gB0hdmoWL\n/p85j4fXEjfafgDoK5P52jA3vAUfQtAOFLyWVfbzDOiH+rQiN/hv6PkWngPzRG3anAt75vCe\nnrC0hYxqRbsn4OKM7W3WGTqA/3aho+EIeA+CzF/XgH1llW0fgqtTDzD2vHZSqv5szo2BV2Ek\nOL6FKDyvkHvz3aMt98CN+S7m6swFzp0zUvecx7m23pCrd051gUVgHBj3f4AyJ5Xqz9t4xiAf\nVo924/q6cGjqPse6Gehr1RE6wRQwjr8D51+pduqPf0Mh+hc3XQfGdNAcFB6AZFyaT9cCffwG\nuN62hKxqTcMr4T/gWt0CToGkzuREn3wI0+BF+AKKkTFSqp0X8owhxXRaz73GojnU92sH5oNt\nwDUlq/TnWWAclSrXyvbgvsn8qf+NEdUfSrFzCdqfBMk1htOyayBPNKayyL2g75iM/yzPSbaZ\nhxPnpDlhNKwN9rE7LAZR0QNFecBFZnIBLbbjHjdIi4KJdgT8Dj+BC4/XdoDa5ILqImriz6J9\naTQqS8NEG5PbFeBi4ELwPmi/77EjqO7gtazqQ8O3imxsAteGD2AiTAUndFImPP33KLh5cDH5\nBrKqLw2fz9h4GdqNAf3ou/4Mw8Hk5H9lWjvvADdkd8IkyCo/Vp8osrEx5gfR66Bf3eC7STZG\nPc4KafWmYmS6sojzi7j3/gLv1x9u4pIy/vSj80wbP4H3QPuvhvXBxdN3Kja+aPKn+lMyfuqS\n/nPT/ysYY8bjl7AG1CYXeOfNIrkbTuT4TK6c5XArjcJmodj2c9DgEdAeffU1/AbGUpD2PgXW\nPx4qMxzvpc0ledodQ51zRF8GrUVBn5ov34ZfwJhpCrVpWy64CfkA7qvtpgLqH+Wes+u572Su\njwA3LkHaZk46LFfRnuNH4Nx/E4wNx3lPuAVGgWOXVT7L2ClEf+emr8BNcFIvc3IBzALXg2Ps\nXPoU9P2T4Ds4F7LKeXhEEY1f4V5tSqoLJ+bKNrnKnhz1p/52TXbu+37mlqwaSUNzm/oH+N7G\nZC+4HRwrc6V9GWP27Zq4KxSjJ7g5Ob+Kaeu9k8D3L4e24SHOGefYx3A++H7GtmtBX8gq82GP\nrI1z7VbiOBaMS2NSmxyDZyHIvNUnnGQ4TqNN9wztim3ifN+32Ea5+41D479Lxvb5ms1GpbnJ\n/Ps+/AyOmbl2N4jK44F8G6I8t/0lq5bnrdwguOnKh4lkUahPD3ODScwk+k9YGkw+zWAAmMRd\nIFtAY5RJyaTjwuAi6eRpDbuACWoAbAoLQDXlRu142B46whJwJtwEYeGk+H+ngwl1HXATegjM\nDg2h2+j0Q9B/+nVZ0NZzoBO4GLlINYGNwA1rNWUyHAMrgxuCB8BEaayaC+aChtRldH4q7AHO\nl63hcnCzMgCuh1bg3O0GLkDDwLqpUOlxP40+jMvP4U54HuaHwVBb309w7UO4F1aF5lDbvVyq\nqE7m6Y79imB86jc3is4z5/uF4LusCV9CJew0F+qDm2FpcI7oI+ew46ptq4Cbg+Mgn7TzHpgb\nbOfiX0kN4OEt4QZoD+ajQeC88ah8r3GwBOhj73HOD4Cm4Nyvlv5NR+byu2AFaAtX5srXctwH\nesJ6oM8fA23sCrNANeWcPwwOhMXBeT0Q7oPxYP405/eDF2A+GJ47ciiL7G9B+AiugjmhO8wF\nQ8CxdFzPAe81v89oOh+DjQvnzAgwvx4NvaEH/AgNqY3o/BUwBsytX4M5/T1YB/S9Y2CcRhXv\ngd9oYg44G9qBH0cvwx8QFT3wPx5w8Xez4yYsH/dT/ysUIpP3NDDY5GPwH60blMuAv4bsC/nU\nhUrbNMl3sYA6nzuqgPtqu8XFxo88F/D1YVa4Fd6EPUEf+B7a+AtkVR8avlVE4xu41zFIaxwV\nh+QqN+aoTR5Nqr6Ddv4EWdWXhs9naNyWNvbtBi+p/Tn5BL6AC8B38j5xEciqfjR8osjG83K/\n/jI+PWqDcfslOM7Og7RcQEemK4s4v4h7841jvkfMQuVZ4MKobdp0HfSE7yC5YXeB9/oboI6A\n12tK2f70p9mgepp+znXnsn4MOpOCtnYLFXmOS1L3NHifuDBllXNTn2SR4+imKKmlONGmEA/6\n9Ao4Fx6FrLqXhpfU0nhN6kdA8IfHLVL3nsS5OSitValwnm+Wu2Af9pVVvuPZBTTuyj2jINj8\nLmVtUa3A+nBu3Xpg3vzWE+SYOXZZ9QwNTyyisR8+zodgr3mze679IxzdLCn9aA7oAubNYeBc\nyCr7dC4WI2Pye9BWx/Yu8ENIHQpjYQfQvpVATQBzS1Y5F8xtQSdQCOucdnwDr+Xq1uWozE/2\ne5AnBeoJ7jNXZ9UkGvbM2jjXbhWO+tX9TtBTFMJ66ft+BX3DxQxH/dUjQzub6NePwLkSYqcp\n5SdhCGifudejH3J9IKum0TDMg6zPKKSduWLfQm7Mc08T6nzXLnmulVK1AI0d8zDu+sJx2w2i\n8nhg9jx1M0vVr7yoC0VtKmYSdeYh/wITkc99Llf+meNx4D8mnhMai1pgiInmQFgPTMImKeXk\nuQTcxDmBrG8JJj83TtXSHHT0C/QGfeeGxI3VgnAwuFguDX7MmEhlUXCynwXV1rK5DrfhqN3d\nwM2HG1ATkwlfu74G/d8LwmJAsSpag15mg/tgEvhBpF3WzQ/6vCHlonATrAYrwFi4FtzsOd7O\nrSDj9jf4LFRU4TgffQwGN3NBF1M4AbqAY7sOLAnGpuP+PlwI2tsG3Az6fpWUtpwNrcD+D4cx\n4DyaBovDZFDfTD/UbJh3pLxirs72pWoZHvAAOC/t52G4Gl4C54s+WgsGghu2pPRxvpy5MfXm\ngkeTN5dYXoj22uD8+A5ugf6QjLdnOdde38m4GwVBwU5tDtLOj6B1qCjTUb+dDJ1hClwDN0Ja\n71HhemT+aQYfgLldaW+wdRPKfnw9B1OhHHuCxXjOrWC8fwwj4RN4BEZDWs6Pq6ADfArJOa2t\nrp+bwBPwFqjk2EyvKe1vW5o/Cc+D47sROH/08SWwKziejW0tx6T/0tycmaf0t+9wCpg/He93\nIEgf+z4j4Egw5htCnej0JDBOXTfXh2vhR3C/MQjUVuBY3OVJVNEecB7tAu7nnGOuB4/B/hBV\niwfKkQxrefRMVW0i3RL+gFlhZTDJ/g69YDYYAg2tDTDABdVFVluDtN/zh6ENzJs735fjcAgT\nimLV5IbqIHCj4eR2kQ82t6TsgmpyD3UUaxbWcRaqrOPo7xxw0T4DzgQTkTFgUg82LkLZTbSL\n1wTwnmpqKp3Z54G5TkP/2mnZDdXduWsNcViPToeBtohx+SIcC34Y7wEvw9lgXDQBfTsbVENu\nnruC8yNsMJ1P2uqYi740Byjn/BrwOqwPb0Jy88dp2dWbJ7q51w5jzY3FFnAULASXwmWgHXuB\n7/MlvAe7g+9YDrm2bA76xjHSni7gR7n1nn8IH4MboSPgLFDzgO/xpCcpGcP6v1zSvv0gjJm2\nrgn6ax1IjpfX/NBI6yMqxoIbzYNB/QLmqXzv4PUs2o1G/SDp0+s53wu6gT5Na3S6gnNtcmN0\nIfwE+nMfmBs+h1LkuP8Dgo3hWfrR2HOcrwyViaN2vJ04D0VtPQ9GQBj3DpQXhXJqOR7mXOgM\n84PvoXyPVeF9OBE6gvO6sUmbr4adQNv9J0ILgDJufY8xYEy/Ak/AtvAajIJqa0k6vA/cK2mb\nMo9vB2PBfBnG25h8A6aBa2xUcR5YhttfAGNEOXdcC5xHc0JULR4Ii0Itl2N1AR5YhXs6Q5jk\nv1N2cdS3Jqrg4zkoN6RcAB6GZ8ENiXJBdWFykzIFnCxulG7Pna/OsQ9UW4vT4d4wAZrDj2CS\nV9pp2WTaCVaCgyBoQwphLEJdJY9u+s6FXjAewnhro/41ybuJMbHfA3OB8h19h2oq+C70+S4F\nN/rabNweB62gofQgHTt2p4NjewjoR/17HtwCbqL0ub7T5g1A/6pKj/u19OHCMhL6gxuNHUE7\ntPN50JeO9UmwERwKw+BiqLTs+3KYCC3BvOTiaD5yc6pdfgDJwjAY/gEHwAMwD5wBwY/hSFXR\nCrE9jJZ3gec+vyvsDkHGpOPsmLsZ1q/GZTM4FdLSTufOCYkLpdhpzrP9NLgRXgXVFtxwFiLH\n3g+6vcGNiB/N+4Bz/U0oh1xLVgD7Mkc/BPr0W9CnR0KhuogbP4F3oBOsBs4hY8H1oBT5zs6H\nUbmHGGuezwGHwWXgprhQuYF37vSA9WE4OEbfQLm0Ng/Sh2oB+Bn0s/L4dk1peg66gPIbufPG\ndLgbY1YB89FWMD+oO+BY8D2MlwdBfQrG1JZwE7SFaslYGAbGgb42hl3jZwHnobnJfYrzqCmY\nn6yPyuYB82rzXNNxuaOxsBCYZ6Nq8YAL6syqBXnxc8GEl491qXfC1qeduOHr3E0GnSTbfcb5\nSDC5N6RcnEzsbtoPhpCMTEBTQbuVyclNw0Dw48NFtNrahg7dOLkwTQYX3R9y5272XMSCn4dS\ndtEdAy7KfcAFuVramY5eh8VgKXgFtFHpW/3tZskFrANMgrfA2KumnXRX84uh9gS54ZoHQp3H\ndcLFKh/NRfPBM3AvdAL9+iK4kJ8I2qdvXTw9dyPg+V5wAlQ62Z9GH4/AIrAnuCFRN4Jjugc4\nr7T3LNDeC+E6WA+SeYHTssux0wfG4Hgw1izrI7UyGKMXgBtP5fy5Bz6Bv8FRMBH2hyaQVeFd\nN+QBbkCHgfoD0rnwNuq8xzxp/ukPq4O5M61xVDifToEJ8HcIuYti0Uq29bkd4GuYFXqAc7gQ\nPcFNxoPxuigMgqPBHx3GgnmiFAU79OvyoL+eguagrcZeoTKXGo/nwO/wQu7oGrEllCLtM+6X\nAsvmce3TTnO0cbcTFCP9uCO8Dto9FeaGcinkcGPTuTtP4sFfUV4cXgbf515obFoVgzYGY+9f\nMBj0+acwB1wE5ivfz3XKeeacPw+OAX3pmFVL5sYlQX+ar8xTm8GvYJxbvyAYp+aKxuhzzGrU\n0odd4XhoDZ6Lc8f5biwo4ySqFg+YDGZWuenuCLX5wE14ITLx+2uNwedioww6y7Jw7vw7jg2p\nFem8M7iwKifI07AJfAQmyn+DC9oiMA0aQiZwJ7V+6w4mTG0dDcvBZPgCXgI3Bb6T7+Z76Hc3\ng+dANaSfdoMWYCypDmBMfQhDYSVYB9rAg/AAuOAaX72gmrJPbdOf4oLkUb+5UTSGv4eGUEjg\nS9H5m/A1aK9zSB0AfUHfvQ2Xg2oHY2AcNIFKythyXN1EBnudJ9ro/N4Lgp6kYCyvCXuDftXX\nlZT2qW3AcdYmc1PoVz+pfnApfAvGbpDz3/i1/U4Q2lEsWvpHmU9coJcA68y7wU6Kf8oPOSlE\nt3HTU7A17ArGcVYZM9oVNohfUm4LvrtzwuuF5sIR3Hs4JOXmzg/5PZKVGcpz0kY7w5g4dl1z\n59aHDyiKBekn7rokhw0c9y3gb56UIDfk2qLPPP4MSruXB2PSdylW5k0xz24Ge0O5tCAPMk+b\nB42DkAstPwMbw3WwNrSD56ExSZv09y6g/6fmjuaf1UHdDkfDajAAHoS3QfWHar3TFfR1sJ0i\nY0LmgWtBG/cE41l1hzdrSvFPMR5wjbwf1oCQ1/Tp92De/DvcDcZLuE4xKu0BE8LMqom8+Pbg\nZiAfJpCwGFH8UyZ3N+9BJiSDz03SW7nKRzjqWxdXNwi28XlpzU6FG5hyyQVpUdCeZrAvDIWW\nsCJY1w8eB2UCUutDt5rS9ERpsq2k3CRtCG6cFgATpEcX6edA3+n7h2FsrrwsR+uvhiVyeI/+\nXxlMvJfBx1ANufjcB27O3ASYcJTjadLxXe4Ex97zdeBmuBXOh3egVOkPF3fH3FhSHj13kU/r\nDSr0mTjGLkjXgPdaZwIdDkHGuTFcqnx+vjjXfm21D99DPxmr+4C/2H4DStsGwofwPhwNyfc1\nnl4A76ukDuDhsiXoy0mgHVuA4+vmeCw4/z6BpcEN8nagryul5jx4PngPgg+0ZRQcAkqblq8p\nTf/jfFIfTD/8+df50x/0f3jWnxeLKNif7c2J3cH86Lk4D9IKeSBdX9v5BC6YCxyHUmQMatNE\n2Dj3IG1X9mH+Vs4F53KxGkuDK+HdYhum7ncOOWe19Q7Q7h9AW3+GxyApY0IKlbFyOXgsRdrz\nCzwE2mtuD7Hg+rQePAlJ+W7JHJa8li4br+b58ekLJZw7rtqgb4fCV6Dt1nWAL+As0Ofp+UJV\nrTKmbVMu6T/9pD/NO8/AEfAZeM0Po9XA9TyoRa6wJsfOYKycDW9DpRXm9FZ09CyYtw8E7TUf\njAB9ro1tYHPQX17/BEqdMzxihpZjbM5uAovAHKCvHMulwLxq2WtBxrJrjX7smat0XdXPzcB9\ny+uwJVgfFT2QyQOX0srNbwhQF5tbwcRpsI2Ek2BK7jy5AbAc8LoTPykT75nwHfgscRJk0b40\nclHT3h/BZ02FpD3WhfMXKe8BTo5gY7ju+3aAfHIy+u5Z1YeGJuWPINhiv6HvfHXp697j4qud\n2j8MXoW+ENSDwjfhJMPRZz1fR7u1uaaf0/YGW8P7uBBpq/eJm5mkTF6TkhVFlvtxv32Efo2z\nYeC7W/cluCApF9Q7INgcjsHWcO7RDe1u4IeI1/X1GMiqi2gY7HyOcqfcg3pzdBG0D8cyaUuw\nJ1n3U+4e62RPWA6eAuPqSDDxZ1V/Gg6qp7H2h3kT7Ejaal1yzB2TcH2J3LNP5PhMrpzlYA66\nCVwsV4cREGx5LVe2z9BvKHt042G+OAiME8d2Bcins6l8NN+FAuve4b5gVzhqw2QwHoNWpfAS\neI/XH4E2UKgu4cZ7C705z30hBoONwQ6PH4F5K/jYsXXz4cavWF1HA8cuq8wfSRuDnfpsLCwE\nallwToR7LXeEQuUccC5kVZjroX/tC7Yab3ekHmyOMld5jzF5Mri5q0/3c4O5Jatcv81Bq8C3\nEOxNHrU9YM43NgtRMqbNW+bqrHKN+DtcCmF9DzYFWz23/DV4bzfQl+G+7yl73fPjIZ+ep7Jv\nvgsF1tmfa69aA1yXQ5/BznBuXhkD2jMxdwy2GiPG0DaQT65Pzsmscg9zBVwNe0PIRdp8IewH\nc4Py435/MM46Q1BXCv3hKFg4VOaOtjkNRoG5Noua0Cj4Kt/RuiT6zjn0MJijvDYWLoZxcCME\n/6afp693g6g8HigkEeVpNtNU6Z93YTI8C06inWB1eA3OgGZg0KlwDJPOyd4a0onVzcfBcCTs\nBaXKSerEdlJOgaaggj3JowvCLWCi0L5gq4vE1uAzKqWleXA7sE9tCnYFG8J58qhdTu6g2SnM\nBg/C3uCGwMWuGpqfToyDOSBpc3iXcPSaY6Cdr8Nd8AaUW/rlCngS9Mv68AKYzI3NfnA4PA67\nQLCZ4p++t6x8lnHhB8xt8AwY71dBsh2nRcuP8nXhKxgCbtCvBH1pvxPBxT+tEAf2/x0cCm+D\n9dr6Hjj/XEzDvRRL1ko84XwYBI/Bc3A/rAVJX4Q+w5HLNWPue9n+CHB8voBJUC714kHOcz8s\n2sM40I9toC4tyMXrwTHV5q3gXaiGgo/MVSfmOmzN0didAOvAJjAvPAHOsWpotjydOMbaq12X\nwCvgXNgenFv3QWNQiEXjy7m1ADi/3JSvn2Mqx6EwP1RDYZxDX8FG49PyPOECR3NTPzBX6dd/\nwDFwElRDjuerMB9od4Din9Lm32Ag7Phnbe2FdEy/U/utBV85kDt7w9eQ9m8492gONd8YA+Ph\nWwg+/5Wyvj4HKqldebj5b+VUJ0nfduea4/wGtARtFGP1NnCuub5XSubB62A72AnMOdfCHaDC\nfs0PoBZwN1wE5qdWcDOYo1x7hoG2B/1Awec2CRUlHJ3H+s34S45z0pfh8c79TUG/qmFwCBiP\nO0NS2usznJP58l/y3pm6POtM/fb/939tef8OtdCcegNoSfgXrADnwQNgUnWiqKQPk0HsNSeJ\nCSCpOTnpA4fBDfAhlCoXnV9geZgbwoQNk4CqPzU7pftga/gSTJxXwqIQEgPFiqgpT9Um+wzy\nPPjNOn0e5HuYvNykhvqRlHvCQPA99J9jUg1dSifBt/aXtDvZv/VuXnvAUHBhPRPKLRdB48ix\nNNG9ARuAPtHWU+Ek2Ag+B+2StHwn6QX61/JQcMF10TK2SpEL+7OwA7iAnAr3gwuJG7vnYRoE\n2f/3EMZcm104jW3n6wCw7l5YC8ZCubQHD3oNuoHjp++WBO3WLjF+vwBtUNZp/xlgnecPwYLQ\nGxz78C4US9ZQnuAvf/Y1CZaD1SHML4o111xgk1qYky6wDLigPgqV0hQerH2+t8egWSkcA+ah\nA+Bj2AX8sB8CxrK5qJDNKLeVLOMvaaflYG8ryt+Bc+cVcEy3gfVAP1ZTP9BZPjuNxVVgQ/g7\n6G/n1dM5jGHrekE1FOIy6UfL+nEUbAVLgeoLp4K56k3oD8eC8dEEKinz5aagXdoXFOwOdR69\n9zQwputTOqa/ra9BPdfNJWuDPxgslrhXuxxX86TyvsXheNgIBsN88A10hQXgVKik9uPht4Nz\n23mujdrlMa2rqDgOLgavjwTX+l7wFlRSD/DwBcGcvRqYN805q8DDYIwqY3DN3NF575zaHq6F\nu+AceBPSueBm6tznlarXecAnoC+T0qdKvxkDyroQA19S/hCGgPE9N/iR5XXjwXXBeuPa9T2q\nFg+kHV/LbX/J6pV4qzFgIOXDiap/xsNT4GTaAZTBb9JycU0q6U+D0QBeKnkD5ZZgsns+VV/K\nqcGvfWGxD8/SnrRNP1K3BTwBi8BZcBj8AtWQEzpMav0TJnbyGOzwuvZvAKPhUzD53gZ3wAjo\nDm4SqiH7UtqqwjGUw7nHzvAAmFh3gkeg3AqLo4ufyc7kZ2wZY8oYWxi0x/hQwcbpZ//7V5/L\nufAuHAsuIOWQNrwKC8EmYCweB7vBrpC0bV7OfSdl/ZFwBPSFw8E6NwTlVBMedg2cAi+CGzrn\nr/Fq/Cl94336NTm3rDsVXERXhaegD+i/S6Gc6sbDzEHa1QbOBhdTxyn4UNu8J8h657gxYb6r\ntH6mA/tM+sg+PW8OxsBS8DKYv4K+pjACvFYNTaWTYGfwnUdZFD6ApC3GxOepOk4rrqQ/k3aa\n+6aANraHN8ANUJBl65LvEK5V4hj8GXzofLFsjnoGPNeWuaAFpNfB56hz7ju/KinnhvnFWEwq\n+Na68A4e2yVvqqPsu6Vjuo7b670U5vQN3Bl8Geyalbr5wHPl8WIYAoeC2hf06Q+eVFD60h83\nkrZY1mbzjuUQl5aNh8fAvP4+dIVkHuC0YnLNWRs+y/XwJcdNoQM8AiFfn0n5Ljgc3oEVYWX4\nCIL0a1hvQ90gCq7DpUp7FoH0uIfnBr8Gn1u/D/wDzoBWYIx43fFR5ooNYThE1eMBnTcjyWRw\nINwP+0ByA7Ag50OhUL3FjSbo1rVgQnIDoj4EE5UBq9YEA85NUQjOqZQnggoT3Wu2TWoSJz/C\n+snKEsuOowHvpiPY4yOdWNoU6jxvBtpwJfjup4H11dLsdBQ268FP9h9I2qHd1pt4l4D5oS+Y\nfFxgdwU3K9WQtpisgk32mfZbOPd4ARifHeE+qITC4v41D9eXzgEXIcdXGWPGpdcc6yCvB9+H\nunA0lnxHn7McuNDWdi+XipLj5tz5BhzLNyHMg+THjv5zfrqwB7lQuRBdBrbxnnehnHJT1gTO\nh3Xh/7V3HmB2VVUbRnpvobeE3ntHIPQiTTqiIEVEFARFERCkihVEioD0Jh3pXSGE3pEOAULo\nvXfw/993cpYerneSzJ17z73JrO953jn79HW+vffa+5wZggPdMDgJ1gGlN8+C/frfEPqCgt75\nXH8B26cD1DHQbOmbdeIA/xL8EKaHqHeKXZOk+1gOdgXpl4NjVXJCb4zmH9tgSM9sr2+AHi4P\neh6yDhaA2rwZ+5u91BO9sZ5dKpfyCtgHyrHMx/p0NdtYbbkcM/TOeG1ryhgdhyYBY3wSlijW\nWXTJfUtC+RmG72nNT+Nzwmlsxhv5Ui8HFutDWPo8tteVoSyPMT+8Xt7YgrKx2TfsQyHbqfHq\nr/GpeI6nh6+O9Kc+17bpkZ40ggPMKcYwN9ifLIt6FYwz1j+nfB7cC9fCQLgYqpC+Wcf6GvG4\ndPvdoCYcvujabzu5AbaFBcF8UIWM5wA4FOzbanFYE/aCRWANmBR+C1fAJvB72BSugdVB2bdW\ngOuLsjlY6UP00a4NDf5wnmT9xrX0M7z1kuZNn0fPQ24zB/g80xUbPUe/lf3qHhjoSmrEDjhZ\nHV1korBxzgxXwQHwHbCx2gB8gVkFeiITTHeKBuV+Jxomfe+9FUwNajLw3srO737XHbRc2rAf\nh7I+YeVw+DPYCe0EvZWdxM6rvK/roZmiUCyfYmlCcAJTtbynPk1c3NjOHP4Vm7pij20unQye\nDCaBZ+AS+Byq1tLc0P4S/kaMEUd53Xo/FqzrVmpWLn4qzAPe0+R+PSwGA+FXcEuxHm3WOG2n\nqhyzdXM+zA22n0fABLsw2Pd6Iye9Djp7g3WuNweAA8m2sBnYF8rxeN/yugnf86eC3cHn/S00\nU1/nYtbxOeDANAMoPypEmzOmAVCrf7DhAhgAR4MvmK2SfcLB3fisGydOp8MsEFqFgvXYLk3L\njY0t2lrEYay/AZfHw4/APm1ONC8cCM9DVRO7/txLzTp88Z+fti8nE/aH2WAlmBEOg+vhTqhS\n3tv6jNwZ99bj22EwPAB7gePjr0H9EhxjznClAhmn/UUZW/RhY5gdrNehoA6FI+HfMAiWg0PA\n7frfSnl9Y4vxV28dm9xmDhDl+glgWxgV1bZp81Vv5LxGrTx88ZWf/VhzrmHs6iLYpqtU/Q/r\n0HqPWCIC/VuhWCnv25Btttl2yDa2KzwKG4D5en1YF2wPjoMfwB3wT3gYZoPvw3PgeGXsc4L9\n6h3YCnaAtUA1I/+X87k+hqIcfsbS7eeCbcA8MTksDzeCH1N9tkXgE7B9u26bT40BDszFM5jU\nZiieZSKWj4ENXbk9Gk7Xhl7++DPne7+NwcTzITgBcuLkfWxct8KXxboJwu1iWZ6EenLg2Bfe\ngDhnvHoHjsK2HTnGyZtEDHH/uHbEZKedCRrRmpzkMzeqH3OiScfOGXFGXN3FG/v1+HKIuqfY\nrTZijwmrUe3NiU44anUZG4zHNlEv3ojVfQNhZNqaA14c2UEj2G+S/xTCS7/KXw2vgbG8BLvA\nrPAmWPdxbCzLMUf5CY6zju4Ft1nnz0KjOoITvZ51eAPMA+OAcUZfKvchY4v4ymX7n/GIvpn4\ny9qDlfvLG3pYPoHjb4F34VV4BYzP/qV/B0DEE/EZS7l8G+t+vRuRnLB6n0Z1Fic+DlH35fbo\ntogp4oqY9bgnOoyDr+3JCTXH/ov1uLcxRVzDao5biPVBxbHmsL/DjDCqcoLtOY2qnIPLcVq2\nPq1/25Xr9qGTYTLoqU7kBOuuUUVfiRhd6q95ccrSReegfA1Y32LZbaOqv3HgCaN6cJ3jog1G\nnNEOjcXnj5enONUc9RJ4vDnhZxATPord6lL2mFsa1RBONKYzIMbOiLUcu+24p2NzuU1bb+bq\nRuVYZlzmo4jPZZQjVpcDoVE55u3d6MmcZ940pnI+qhen274Njcr6cC7RqBzPtoDxiwvYd6K9\nTU950mJ7LByrBsRKaTkd5alL67VF25e5oxHZ3qzPqOPyMsrlenebvjs//QTcZ85aGZQv0i9D\nnBtL2//7sBWk6jgwdp1tnbppcgJ7AUwUysFqezgIbKytkB3nbJgKTD6bg4OjnWhtWBhsZDZI\nFY12+Fr3LxQ20MPAr6xrxMG9WNo5fPlQxmDcqhyXHfZKcDBql5xoOtE1vvAqYjWm2ObybdgX\nrHe/cmwAUfcUK9U03O0bYFx3g22v/AxudyC0Hixbv1XoFm4yMdgmTdbrgkne9ZngOHgeVoQ7\nQBmfchll128DJy9rggl6f5gEdgMnEL3RdZxsnLb1J8E+sxF4H9uDg7P+hcptwm3GaZJ3MDOm\nmcFzmq1hXHBz8B7WuZO9E8H+vh/UKjx0uQSsAPfVHtSC9Xm55rhwFwwqru/E4VkwlpDl8FKf\nq5QTu7i3ccizMAvoU+hhCgPB9iEbw8tQlXwZVhGjS7UDGOfJsDjYJmwHTnqcVFStt4obluM0\nz9iv9Tr0DIV1IPy07LaqVB6HjNU28DgYq7nEPlWWOWomMGeZu/4IUQcUWybjORYWhNp5UHjs\nzR+Dnua/cpvubZ4yL34MznHKcbHaJbc57hij922XfJEIGY8q93/jexOcT0k75dzisyIA+060\nN3NBtN9id9dYNTRWSsvXKEefLG1uavFWrvYPqG1/5XZg2Xq372wJ18IgMGfdDErfZwRfCueD\nCWFamABqn5dNqXCgNjHE9k5cPkJQNoJvloK7g/LFcAb4EtMTzcbBNiYbYD0cqJUDzWpwjyvo\nS3CycSM4UXkDQnaYVyAasG/zI5LHmfx6Kzvrn+AKsDOZ/JXbTVb3gh3kQGin3uXmdkx9s2Ma\npx58CiasSPTWx/UwN3hcbYJgU6UyDgeA02BZ0FefxdjVnXAILADDiiWLlsv728b0KGJx6XpZ\nj7GyKtwPtl99tt0+BbY/rzEF6LPxHw3nwz7gJNHE2hsZk3VclhMH730u3AjbQgyoFLsGn3gO\nt88Ael5+kWK16bqOK84DTxTsxHIH2ABeB2XcQ8H+bmw+ny9wVepMbmZO/DocD7+EOcF4rMfn\nwAH/JbBvOShWKevbthb15UvFb8A+Px/UyjZo3FXLNmas1qkyf+vh4q6U5HP4PO3Se9y4HKf1\nak5ygl9PHitVS5+sR+tTGae++UJv++xO5RzW3THN3D4+FzPnXV1c1BiN+W0wN8Z4NB3lRuX1\nzA29kef/GA4Bx/GQfftxcL99yr7lRLhdsi2qX8ETYFxiX3K+9DTYx/aG1Kg58AsO86PijBAv\nnda77eBFiPmd234Od8AqsDvUk9ewbmzbtvG4JsVUPQfGrrexQ7eZ7L8L54GVOxGoPWBiuM2V\nHsjJ1q0j4OVRuJbHPAA2tEHgJGRyuBlMDkOhKh3MjTaElcGkpF8mLZPmC7ACPAztlsn8SdgL\n9M2B38HKiYkT86fABLAoPAedoIjjLILZF2xvk4LPMgSWh9+Ck8CZIY6n2FF6hmhsG/uBHlsP\nPwMnL8Mg9BMK+8D6BbblVuhZLupLxv5wEZj0B4H384u9k60zwbbsIFCV7DOTgbnmZDgbroFH\n4QZwYJoBrP+/gnl0KFQl72v/8KVsR/ghLAW2O/vUCeDLnOX74BQo1y+rLZcTTtuaWKdzwzKg\nV8bZKTJOY3J5OTiBs/11Qq4kjP/ImIzTOr0QjijKT7PsJPlyZJyfgX13MJgXjX8YdIqMx/y2\nHgwFfd0f7gInmk5Ox4Eh0G75YnEAmJPtT/eAsc0K5vAfgfvbKXOSsf0YnocV4CDQZ9vEZbAk\nmONTPXNgUg63vsPLaSg7Hi0Cy4J+Lw22XT/sPAipJjjgxGh00vkEa0ezMdjplF8nVoXlYEUY\nVfmCdPAIDp6CfXbokck393+BE4AdwYbs4KWcfFah6bjJTvAB/L644aYsryrKnbg4naD2AhPm\nvOBL3HwwAOaHGeFk6AT5FfRvcBo4AKwMuxTlp1gav0nLen8MboBOlH5vDD8t+JKlMdtmbcch\nE/HRBbar8r44phnLP3IR69iX4yvhetgW/Njhcik4Bo4DJwmtln3eFw8H9gnhDCjLeM0/TkYu\nhDnhKLgUqpxI2cZmgt/C/bAbOLH3Y9FisDnsCu5bGw6HPaFK+VL7NbAvWH969j14Fm6ETpET\nO18mbeeLwF/hfTgLOklfEMxb8ENYEKx7txlvJ+mzIhjr3I8M34E3wb70GnSKnD/YZ06EFeAW\n8EXjZ2Dc5kXbxi+hnfLjoTlpevg1fAArgf2rk2RbdDwZGy6CZUEv1bfh6q5S/uiJA+bMc2Bf\n+BCehiVBn0N+ONwhVnLZXAfGbe7lKrnaJ9xlcM2dbDAfgZ2zaj3CDZ3MnQrnFjc3eW0BQ4v1\nVi+sxxgovfeP4apW37SB68/IOdbdTeBkcw3Qt2kLWHRpLn46sXth+GrlP+fgjoPgn3A4OCg5\ngXIy7Ev6ePAGHAMbwuOgrodNwMGiE/UsQdkufdk7uwjwc5bbge24ap3JDSeHg0GfnVw9DMuA\ng4H9+gLYC6qQbe4b4G+F14Q3oawrWfElxMnpIeAE6ibYBqrUndzMNnk6TAB+7HEid0Kxbvu1\nnZoPzQe/ASesVer/uJn33gDsI65bt/qqb50i+6p5ybpVL4F56SNXOkj20+ngoiImXzb09fli\nvZMWtjtfPpT+/h12d6VDdRtx7QZHQvj7MWVzgT63U+Ny88h/5serYAA8AZ0kP4a8Bf3gpCIw\n+9D2kC9HhSE9XKzF8WLufBGs//PAXP4PSLXYARPZmKL5eZDvt+Fh9NAXpPjqeAZlE8P3wKRR\nhZwg+YI0DBww/wadKAfLwfAduA18yRgIviDNXDAbS7+QOglsl5zYGZ91eDNMDFGnU1MeAE6q\nfgyzwxwwHawFJrJOle1xO/Ajw1lgW30PtoNxoB06lptODwPAyfNccAf4MnoDbAbrQBW6nJvM\nAovDg3Vu2J9tvogMAeO7EFaB7aBK+YLhhNO2aPuz/xwB6iBYFaxbB9IXYGuYCqrU2NzMF6IT\nwL5zKSwCr0InycmHuca6NM4b4THoNBnnreAHg4/BF7p25khuX1e2zUHgx0LzqG1wZzDndKr8\nyOCY5Hhkvzkb/DizHbRb+qan5kI/dMwA94C/SegkfY1g/gm+DBvvfjAZnA6pxhzYnNPWh7fB\nvHk16O11sCOkWuzAuC2+fpWX96VARlXjceAG0J0Hc4/ihTbluBVhURhSnDMPS79CbwwXF9ta\nuXiHizsITQLedy/YGzpNrxPQvvA7eAic5B0CDkydpBcJZh84HJzk/QBiAvoBZQk5cXk2Vjp8\nuR7x+RK3BDxaxDo7y3/BltCT/lOc3pTFF1zlueL+V7A0lpB1cDS4Xa9bqY+5uHXfnQ5ih3Ha\n3/2ir6JtnEnZflilnNAPLd3Ql8ufw0agX8pJijlhT9gPqpITzFthD/DjzV2wGjiJ6iR9RjC3\ng3lp6aLsS91g6CT5snEN/BqWh1tAX++ATpJx+jJ8JKwNTuqOg8egU7U9gTlmLwQvF0EuxVJv\n9XhQsa0dC+cnvgxvWNzcHHQhmBdXgU6R/f0csO5/BvuDY6Y5NdWYAy9x2k/A8XkN0GNlLtdb\n/XYMSLXIAb/y9VXNyYM78eqOldj3tVEwx8nSzTCkdOyTlB3Avl7aVkXxQ25yXhvu29Nne5cT\nLgK962T54nYZdHqco+qhz3E7xMuR5/lydyO0+xnHIYbl4BQo62RW+sMs5Y1tKuuRL0LxcmQY\np8EE4Etnu7UCAbwJ8XJkPO/D+dDO+r2f+0s7Y+D2I9XdHPEQdHqc9mFfOKoeX7hlj3QtRzvJ\n6/Q4re+r4WUI3UPhQeiE2M3Pof+jcCp0QlwRU+3SnD05LFK7I9d77IBt83SIlyMv4Bipvwu7\nkmqdA6PyAtC6uzd2ZRPDljAHTARO9nzD9ovf49AseY+zYehILjgZ+ycEfztS1rSsfAJOUEYk\nz50ZxoMvRnRgN/u2Z7tfuZ4r9k/J0hfft4r1Zi38M7N+4GSwEe3KSYfD88XJUxXLt4tlsxaT\ncCFjnLrBC/6C8w6CF4rzvY6/1n6nWG/WwnbzGcza4AUP5jy/1DkBGVVNyoHW42s1J4yorZqI\nbUvz1ZwzqqvW+Q+gPPno7twZ2aHPH5cOGJ/yNOD5Tg66k+1+GDT6onI8534b/FOG7lTPJ1/s\npgf7/+fdnVjabrs3Z61U2taT4hkc/E2orUOvYS7x+rVe601Pc4J9/U5YBxrRRZy0OviRQenR\nB+BHnGbKtvEP2LTBi17DecvCm8X5xmnObvaX2em45iWwLTQix7cFIPLlDJTfg2bH6fOfBbtA\nI7qPk2aDd4qT7dPG7FjYTHld++yeDV70Cc6zr+jhFDAuRBug2KVmtNmZuNIf4VfDL9njn69w\nhrGFn17AHG5edl+zNAsXOgB+1+AFHSM+Bfu38drezaXliT2rvZbjpXV+TINXMkbrudn9pjac\n/mz4Pvgy21Ppn2PJi2A9G6u5M9RMfwdw0a3h/Lh4Lv/rwOj2gmTHcPJ6LzwJTrZtQPPCKrAH\nnAzNkNfdFJxYtFpOIi5t8CYm+W9CFXGakK9sME4nlxtAFW3uBe5zbYNxzsh561YU57Pc558N\nxulAsWZFcT7FfW5uMM7ZOW+1Bs/t6WmPccJtPT2pOH5ulis3eG5PT3uIE+7q6UnF8fOzXKHB\nc3t62v2ccF9PTyqOX5jlMg2e29PT9FJPG5Ev1Is3cmID59g2baONSC/1tArZ1+3zjci2aRut\nQuZOc2gjsq/b51ut/+MG14NzlUZk7jSHtlrGeTXUflwZ1fuuzYG+ZLVaxnk51H6QHtX7rseB\nflxotf7NDS6B+KDR0/ttxAl+/Gm1jPMi8ENBajR2YEJi9+vEfN08w2bF/gm62Z+b04F0IB1I\nB9KBdCAdSAfSgXQgHRhjHPDFaGRfYfyV7qJjzBPng6QD6UA6kA6kA+lAOpAOpAPpQDrQjQP+\naZZ/OrUTjF3nmB3Y5t9p+t8spNKBdCAdSAfSgXQgHUgH0oF0IB0Y4x0YyBMOhdfgRvC/27kT\n/G94fDny72BT6UA6kA6kA+lAOpAOpAPpQDqQDjTkQBX/wXxDgY3gpEnZ538IOg/4Hy++D0Pg\nMsj/0AwTUulAOpAOpAPpQDqQDqQD6UA60Dcd8F/2uaZvPno+dTqQDqQD6UA6kA6kA+lAOpAO\nNNuBev8tT7Pv0crrTcHFl2zlDfLa6UA6kA6kA+lAOpAOpAPpQDrQdxwY3V+Q+k5N5ZOmA+lA\nOpAOpAPpQDqQDqQD6UDLHRin5Xdo7Q38vzS/CHe39jZ59XQgHUgH0oF0IB1IB9KBdCAdSAfS\ngXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1I\nB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHejrDoyO/6PYKutsXG5WhUf+YxP/\n7sWDjS5xjteLZ+zJqb31s6o4v+Ch/q8nD1ZzbMb5VUPSz6/60du1z3t5garaZ1+I03HIPF+F\neuNnxvm/NdQX/PRfRK7qH/3qjZ9Vxem47njUqEaXOBt9vjxvNHdgDeK3kVfB29yn0X9yfaOK\nYtSHV6BRbc2JVXjpPZ5rNEjO+16FcT7aizh3rzDO23oR574Vxnl9L+L8dYVxXt6LOI+sMM6z\nexHniRXG+ddexOkzVpWXrLtGdQknVhWnfaFR2QeritPc0qjMaVXFaa5uVA9yYlVxOvY1qiGc\nWFWcziUa1UucWFWczs0akXPBt6GqOJ3rpuo4MG6dbblpuAPTsHgLNhiJIfuxfx3wN0C1Lzl+\n6XgIFocT4CwI2Si/BTPDlODXl0Z+i2ScL8IWoLze+WAHmxy8rl8yjO0d+DssBQuB2/zi9wZ4\nHX/z8lt4DIz1A5gEBsIS8AdoVF7/adi2zgX+zLaFwVhtk8bxGUwExvAJ9AOfQz4Cn834H4Y5\nwInOmbASWCeNyjgfge+P5AJHs38xMDbjVHppUlOfgrGq8Qvc9he4ENaCnaFRGec9sHsPLnAU\nx1qPtYq432fHRqD3oQ0pbBIrDSyN8xb4xSicO5hjIhbrNgYIt9mebSffA/vLUFgQ3GebtZ2u\nDI3KOJ3cHTiCC/gc9lEZr1gao+32YzCWCeBDOBKuhqXhT3A7/By+C8bdqIzzcvCZa2U79Bns\nP8ZkPMaqLBursbnPNnkgbAvzwLmwPDj5sK5WBu/VqDzX+6mIw7jGBbdvA6vDejAtfA4HwgNg\n/xgEi8CWUJbxDoQdi417sPT8RjUTJ0acsTRepVdDwXy6D3wXbJd6dTC4Ta9eAI8xB5uL1fdB\nD73+arA39MbP6Tk/4qP4FT3J2g6wGXwHDgL7+i7wFBjbxbAVHA/mnoeKsvljVvgZXA3PQW/i\nnIXzy3H+m3Xbm9ssG9s/QF0C74L5fFewfWwMu4HtwbF3c1gbHgZzfuR220hv4vTc38Fl8BtY\nCYzFe1j/Es/xGmX7y42wOFwBtoHuNDU7fgDLgH2yt3Hq2XWgHHfmhokhfH2V8gzwNLwEPsNc\n4DHGbgyTgs9zKthGfwnuexz2h9Ogt3HuzTUGg7Kf/xM+Bu9vrNa/Y6JL69b6N8YPYDIwPo8f\nBjvCcvAHGBt+DPfCGdCbOM0V5oy7oTv9ih32kU9gwuIgc8EkRfkzlhOA8er3y/AIrAvTQeSA\nRuPUqynBNmQ/baUcSxqNs5VxdcS1bcSp7h2wI9zW/e6uPQsV++3EtRqPDReBSdWOFtf6IWWT\nksnuPTgAeiM7clzbRKLsYMpObD27nAocxIdAxGvCMkH1AweF9cFB1UHLpGrCugUiUVBsWF4z\n4ixfZE5WTDjKWExE3s+YLUdieonybODycTCBeq5xmvDfghnA5+iN3ufkenGWrzmAFe9j8q+n\nYWw06ZuQvywOGJ+lcerpAOhtnA7oI4uTQ/4jB07vqa9lue52fbYeboLQglHoxdJ6GVmctlHb\nZMSiR7YB4xLbrpMjBw+PuRMWhifAAe/X0Fu9wQVGFqcxGsPnYMzGpmzbxui6g/7esDScD/bx\nBcBrrwq99fTV4losvqJNWPP+xqeciER8rlu239wAu8CB4DOo28GXY/08BO6G3shJYijq1HsZ\nkx5eAeuB9zHneP/p4Rp4GKz7N6G2PmZn21bgMfpqPusPjco+ESp75Tb7wpwwN1wPF4A6Cp6C\nO8D+fS6ooRC+LUR5YzCf+QzW2UTQqMp+Gqeehual8Aro711g3nkI/grqTvgELoNZwGMjFy9L\neXnQz/HBvtobxbgT14g+7brlPcH2ZR/RO5/lNzAYlHX6M9B7c8CjsAHcA9uCz+eYZTvqrXyh\nsG6sXzXF8EVXTGV/bSN68w2wTRqT59WTx94PjiH7wk+gt3qSC8T9bP+Tgr7pwzhgv1FzwPNg\nXU8CeqSHQ8A69pxN4EJ4CWwn94HX9rl6q8e5QMS5KGVjMw4VsTonMo7jwTYwFPqBfsuEMB/0\nB8/9FOw3N8BjYLvtjbzHiOrPa89T3MBY9NB44znc9Q5MAz7fTDAzOMbfDWvAa2A76K3sw+Fn\nb6/V3fmfdbcjt/93kpxeNO6ASSZk56tVJMhnih0miEPhF/BTuA6aKZOOehtMJvPD1yFkcl2s\nWImkNR3rDpomgjnhCDCJKhNTvefq2tmkH5FMvsn1HBwdXJTxeO8YDGdxI3IgWBhMUPqpVoZF\nYE9XKlDU+z3c63P4Ts09HXSN3xjHh+eL9c1ZzgXrgfuqVPgc93yOgv4apzJZxnN1bajwh3Uc\n7cw4bce2y7KcAL4OP4DtwXNi0rkEZfdXoUu5yQLgQBmamsIuxYp+fghrgX3dyZ79Lnym2BKV\nr+/Ewglg9B1veAH0h3NgcdAv9w8DJyzPwWWwEri9N+0z6sLJQlle0zidLBvfXODLhjnJiZ0+\nLgjLwrlQq4vZ8BZcAsuAx3utRhX5w7YX7c9rGaOYI50wOZlX9mvbpbE7STJXvQyD4QjoB+ou\nsA1/AEvCVNAbhZ9eo9zuImbvPQSsVyfFxqEvxuA24/UZjFdfvwurwhkwIfwLPgLroDfqri70\nchhMDt8C76Vvxh/eUhxrRdAzZayXgvt9Hpdnw8/BOmiWop0bY7m/xPX15xiwHxuHfaQ77cwO\n873PcRL4jM2U/hnnsWDbNZ43IGS/maNY8VmuhjVhKDhOTQme53Us2y5aoQ9LF/0NZe/pXMSY\nrPOIcRbKk8BN4HOJOg72AWN2LvMYVCVfetTvwX73C1dKmoLyRVBuK46b+uyz1ctbbE6Nbg50\nl8xGt+doZ7zhoR3bxF7u5MY1TbHtKlfQbOBgaeJvhWYuLmqHfQhMhFsW21w4cXq/WN+dpZNi\n4zZZGrvJSjmIvQcOStJK6aGJ5QDYDF6FL0GV/fS4J+FxuByU+z+Gv8ODYHL7AlotY/HeDtRO\nhhaBskzs7nu92GidG+dp8BRsCPGMFCuRbUKfQwMo+AxukwngdmiHHECNRR0MS8L1rhQ6maX1\nals8EvT/GbCNO4HZBGKiQ7Glsu6sQ/tM6C0Kq4P17nNcC4PAyZLP9lpRZtGrCb3nd6dFSzv2\npWwfPra0bRvK1u8D8M9iu/XuQO9EZe1i2xEs9VcalW1dPQsbd5X+W7/e8/vgS9n0oF9OOvaA\nF8EcdDX8EWrlddcEfb0TfKZoNxR7LNuU5/uC8xh4z7ie7ckXjFlhWTgRroAbYQ3w5e4CUNuC\nbfMFeALuhnvB/n8PbAq9kZ6FzHFngHGK9bcEuM3jvJfjztngZN5ttsu14G9wOtif/gHGab+f\nGaYGn6s38l6Rf1+mfHPpYjMU5W8Wy9+wtA/tD9+GH4GTUvO/bfRR0M/vgO1Fea7HRB25rbeK\neL3OO8XF4vouZZdiu/3ppaJcb7EYG+1bjqmtkH1Sj+2ry8G6YH5RtgPrUa88xnXzon3mG+Bz\neP72YH+z//g8Q8G4m6n+pYu9Rtl8bizhp/E9BS6vAfvXMxAyvnHhE5gcIodQbLliHDEm6932\nZpzqczgMbIcXQmg+CtbDXWD+TI0BDtgAU71zwCQUqp2Axva/ULivWHHANCEvAE8X25q5eL64\nmJ3cDm6H/RLxNPDWAAA1IElEQVTs4CYnyyZJdQy47sBuXE40L4abwEFeroIX4W3w/FbI2D6E\nRcHY9DTu5X31aSmIJDWA8rzwJpj8T4TvwiwwKVTRrj/jPhPBrNAfvg4qPDZZmvBnBOM2Jo/R\nXycKnh+JmGIl+pS7GLMyzvAz1h0wret2yNginj0o/6S0bjwXwBA4CHyG88BB1Da6IhwPThpa\nrWib0T7jfvYjB3EndYvA0mD9epxt2phtr57/JLRC75Yuejjl2jqeg20O7KuA+6eH22B8eApe\ngJnhXngAbNuNyucUc9D5RVkf1KFgPe4NemRcxmocj8E+cDl0J/OBdW78h4ET+0Zlm5OViwsY\nS8jttjlfNox9R7Dfel+f67vgfjUUrPd1Qd9sBzeC15gR9Ls30svQWxSMw2srvfsI3oFV4QSY\nALYE2+V7cBT8DG4GtSucCOYk282VMAk4JvRG3s9cF889HWXjc/1+WA7eBHU0eOxv4CyINuNy\nCwhdROEW0Fuf6xHYD5olc7Uyxn7F0vWQ230u2+oNsbGb5ctsj7bUzSG93mw8s8Pt8AUYm3oO\ndoGFXUE+l233d+DLSdTLQpSN8c9g3U8LzdYrxQWte9u+3kWcxq9mA2MyXut8VlCnw3dhT7CN\n/hJ8GXkNJoNW63NuYLx6tAoYr8+hjHcj+BDWBGXZ9rgUbA3DwLEpNZo7YGWneufAw5zuIBOd\nPhJpXPUaCg5GIQers8CB6AOwIzZTDo6/hejU91GOJOR9JgYHmAXBTu8+lybJT2F7eB9Ci1FY\nA0wGu8XGJi+dTBiPg48D1OKgfIapwISujHN2MPGb2FcCJ18fw/xgnA6i20CrdSU32BSMyTid\ntExdrJvsnVD1B+vXNuJEwbrwS5NebgDrQ5U6mpvtX9zQmMX4Q4dGoU1LB3vbo3FZtp4jTopd\nX/fXYrkaOOA7MA2EzWEeaLUcoK1H+7gToclhUjBG+49eWr/Wv8cuA4NgFVgUnFR/C2aAVugy\nLvrH4sL6Z5x6aFtUD8G90B/MA76QnAzngZOBueEZuBF+DbNCo7Ieva9eWI52djflX8FfYPVi\nu/3+IzBml6OqVznwfbDfNSo9KGtCVqxP9SLogfnI+pwSVoIvwZjdVpYvLZeWNxRl24ptdaI6\n+0Z1UznOCThJQvp2ZrHyFEv7h+3S3OnY5LHW6TAo60FWJOQLlrm0N4o4o77Nf/qpZ3MU5VNZ\nhv5E4c+wCrhfr64HvSzLuj6ttMGxqlnSl2mKixmrRPyWn4YnwD58E4xIJ7NzVzgCHIfL9cRq\nrzWEK8wFLm+BhWApULPCTuB2/fHeY8POYHt4F+6HRwsuZ7kaWDd7QDP1CBczBtvDm+DYZz9d\nGpS+Kn2eGYxT2Rb6gzE6Xqn94XRYCfaDVss8ugXol7LfR5sw3oXBnBb6K4Uji5VDWNrnDi7W\nczEaO1Cu5NH4Mdoauh3XDqHsPHoanckBdhOo1Y/YcBL8s3ZHE9bt1CYUO6rxLAGRjCh2aSZ+\nOnExKX1RLD9naaxOOMpyvy95JtHdyjuaWF6Paz0Oa4Axq4jZOJ20hFx3UD0QHBh8sboRHFCv\ngvFgG2i1duAGG4MeGnM/MGaxDRiPA9SJYEKNuv6EsgPTZOBzV6nruJltQxmzhM/vUda/dup2\nbr4S6KlEbB9Q1rOzYDqwTb4Fh8M5YD+bB1qty7jB98BJ38zFzSJG+4fbnRAMg2XgIJgLHoDn\nC+ZluS60QkO4qHHonf1AIj77tb7ZZ5wE+4L0ezgX1KCCrpUm/bB96YmKOL49fLXrv3k8uyi3\nc2F/VOW+EOtO8KyrJ92A3oGhFtog84mKOGPptsHwBwsl2Wck6re0q6VF25ixqahz120HfkQ4\nCO6Gsszp5sfIkeV9VZRP4yaOkyHjFeO3P80Gvtx+E6K9UKyrx9i6KZwEPwGPvwmapTW5kO1x\nboicZ5x6aMzmpXVgAnC725wTmBNsv+av0IsUzixWWjG2b8m1/w7WuzFFe7CNGMefwXYxLqiX\n4VtgzGtDWUNYET1ttbbhBmvBlGAsIcecqWG82MDyQdintP4EZdm3tK2Ti85DzMnzdROkdXU0\n2Ib6nOw0qd45YAOLyYeDmA3KZPU63Az1EupHbN8aTGbfh2br11xwY3gIjGMoGNf6YKw29ivh\nDTA5HQdzwdXQDg3jpnPAdcXNTTC7w2FwCVwPn8OjoHdO7o6Fa+AvcA9Uram4ocn9UjBxvg23\nw62gnoQzYCXoD+Ukympb5KBjm/gUbA966ouR7XUQxABGsetL309Zft2VCuRAtBxsDnqon0PA\n+raP3QK26fnhNHBy8Ed4EarS8tzoULBf69vzsD3o32/haNDTseFscHJtzD+CKjQ7N7FNWreR\ni1zq4XgwKzjJ2wX0r5Vt0rzn5DJ4nPKrsDJ0kt4lGNu9nhnzx2Bb3AbmBuu6E2R/ULYvc/iH\nYJx7wBpgPXeCzIX6GbnFlzR1FCwGB7nSYbLOzYnGHP3GdjsMHoZNwbYwquOMY+tssBTcD83U\nc1xsAFwLtt0XwZcb47U9PALGae58B9x2MfwYFoJnoSpdyo3MmfeCsdi/roA74Qi4CBwzlePS\nyXAAzAsPQLtkO5gJHHPM7bYJ28eFcBnYvn2Wb8ES4L7RVRMRuP1y4AiYcnR9uN7GPW5vL5Dn\n/yehOjEx0SobnYnLjjYivcROE1qzZWL+K4wHxjE/mCgHw6KwB5iEXgAHARNsOzU+Nzfe1Ysg\nZmT5S1gPTK7bwtJgMtoZ1gQTlNvPg3bIpKk2gsdhKtDbY8E47waf4xI4El6Fdsv2aMK37m0D\ntovnYDF4GpTt5ATYEZ6AmeEdaLUcdJyk+qIxCzwJc4CTgElAnQNHgXG1Q/q3OcwF1rne/AHG\nhhtgEPiStDt4jBMV26jPUoWMTzlJtT2ajyYC84zrvig5QalC1qeTNjEP2Qd+AxEjxY6QsX0I\nxjsZ3A/23+ugk+RYbT80V9of7oAV4FQw9k5RxDkBAU0Kt8BKsB98AJ2oTwnKvKi38YJs/C/A\nG9BIn7GdO3bZ55op4zoR1oDHwFypt1/AQTAXuO0SuAmuBHNqO8af2bjv6WA8ji8LwtKwOGwJ\nPsPrYOz7w2XQKfoFgawItgu9mxGM/Vm4EZx/nAeju8wdvkA7ZqVqHHBgT/XOgaGc7uTDrwsO\nCPJncLLsYFu1xuWG58NNMBMsDMuBctB/HvaAvWB2OBvarb0JwBgXAwcVB/9bweeYGfaBv8Nn\n4AR0Q9gC2pmgVuP+Js/7YBkwzjNgT7gcfMH4Bhh7OwYnbvs/ctC0Xa4JP4V1wTZh/PqrtoPv\nwEBYAH4OTnKr0CvcZFZYHoxzfpgY3oXV4UfwBLRLb3LjeWBjsF8Z6/ugP3eDGgK7gd7uCk9C\nVbJvOwk1BwyAyUEvbZtVt0EHXtuU9bcvHAcTwbXQSTLOU2AKsH0tDg/Ca9BJMs6/wGTwB7Be\nB4Ptr5NkTvwjOA66dJJ5M3TqyxGhdX1QsG06QdffSeDPsALcDp0k+5IT9UUK7NuOlV+AeXs/\nMPccDOb426Dqvs8tu2S/ehNmA/P5gTAtnAj6uxFEzrqJcqfIsf0A2B4+hrNAXxcuyvY9c1ur\n5VziZ2AOVeuD86SD4EiYC9TYsDOYY7eGerJ97w8nwAb1Dsht/+uAA2mqdw58ndOfgU3hXTBR\nTQkOsP2gaq3NDX3xeRR+DQ6q98AZsA08Cy/D4uCXsaOhnTLZ7w7GZAL4ATiRt0NPDU/BY2Ci\n6AQtRhDfg2+CcU0Pz8ETYAJV/xy+6LifDxGRg+Zf4XD4N9hWHUxvBrU52FZucQU5MbNNVyG9\nfB3uhPfBgcGJlYPqNPAGtFPGZ/9xcHwPJgD9MY9a98ZdheblJufCJ3ABXAlqJpgUPgLr+klw\nIvUSGHuVsm1tAWtAtB9j7bQXD0LqepH9FktzzqdQVT1yq1HW1zjSD0k7gf1Cf6+BTtPYBGQ+\n+TEYp/3jWuhk+fHF8XI9sF/bBsyLb4N9u5O0JcH8Cw6FF8Bc7vhp2Xw0BOz75ghz5xrQDk3N\nTfXVXGmMzkGOBT2Wu8Cc1A8cc/S9E+R87mjwY52x2d9OLdb9EHYRnA2nQKt1KzeYDy6GdWBN\nsD5/DqvAScVyP5ZzwzmwF8wGv4Wy/sTKW/A3+CPYThwbzCupbhwwmaV658CEnO5A8AWYpF6D\nz8GBwX1VagFuZuf13q/DWmAyXQUeACfyvwcnVCapTcC4q5aT3RVgB7gd9MmXy13B+E1Sdnqf\nw46+LDhYtVubEcDd4GTYvmMiMmkeAk4CdgR9r9JT2978MAWMTCbDlcGJ9bPwIhhrefJsXbwP\nVcq4pgMH+MlBD18G+5ITfuXktd2ahACMz7b4Khijcf27WLKoRPYH69BYLoUjinJ4tA7rB8C9\nsDM4oFqvVco61Z83wf7+Fvixo9MUudIJmvVrzHrbabKNvQL2Tfv8O/AxdJrMh76QfwT2XeO0\n3MnST9voZzAUbKu2C9tB9CmKbVd/IpgTFgV9XRXs48uB+l7BdSz3hAWgXX1uK+6tZobPYXe4\nG44E27JzECfq88DV0AnahSAGg2Opfcu8eRA47hu3ef9k2BZsH62W9zsBlgDbpzoeroIDYH5Q\nW8Ap8CocBd+GspyrrA6/hEGwNVgHm0MVz8FtRk+NO3qGXWnUU3E3G2p3cmI/A5hcTUjKL1DT\nw4jO87hmy85xD6wEdmI7hvoHOLheBsdAu+Sk6VGYDyzbOR8BE+fSsAzcBCYmO/t7cDR0gpxg\nmpzOhQ1hclBOqkw0ftkxkU4LN0EVmpWbvAETgS86Ju/dwfZXT7aJdeF+WA6iPnaibNJ1oLKt\n/AgcvKwDNc7wRUt+four/h5mAduDzzIBOLDapz4E2/Hz0G45aPYHfbOejfcjcIJlu65Kf+dG\n28H84ATjJwWDWOqTHxncbr1tD9bjDVClrLOZiht+wrIf+PLRaRqPgMzfyrocAE9Bp8k47RPK\nOPXzCVc6TOMTj3lJ2XeN8yFXOlT2ZZkOzDcLgTKHzgh+9OwU/YFA/OBgvrFfG7c56Dzwhclx\n1BzVbjkmHgbOfxaFZUAZ6wlwExwEnSTz+RGwGzinOwn019xwBdiWHZvcXpVW40bOlU4FvVO+\nvCvbpzlhUhgA60DonCgUS/Ow58c1Ir9ZN82U7xMrwyD4srjwt1iK684/TwdfzkYLxQR6tAi2\nDUFOyT1tkE/AN2AveBheggthXugPyoEhGqFlNaDrZzU/nAytAnZi9TWIDmE9zwgm0HZqTm5u\nh7eDvFEEsiDL7cHlizAP/BAOBH12QHBCcAH40tkuLcaNHey3gkkgvNXnFcAEejo4oDqAVaHZ\nuMmxoKe+nG0Ef4JarciGB+A08OVjWTB+k9YcYPsweTlB8BpOqJ3UOBjsCibiVmg9LnomnApL\ngTGZZKP/eF/7oDHbftutWQhAr6xz5TLq2jqoSptwIwdxvx6/AsPgaPgCHOiNxTiV8c0OHlul\nHLitT7H+lHF0mmxfyjgjd0addu3okB9TF3GU44wXkQ4JsSuMqUpxho/tzNsj8mYldr4MOxcH\nmXeizUYOGlDs64TF6gRhbrZvleXY70eITng5Mq5twQ+I9q3wkWKX3P5gUe6khWOk85LjIeKO\ncc82EW35acpV6ThudCgMgLehnj5g4x1wGvwCLoSI1ZwxBTgncWx3/FJHwbpwjStNlPf6B0xY\nXPN7LB3fHYucx/0R/gSjjWIQHW0CrjhQJ73LwrVwOewP58F+YOO7HdaE7rRadztasN3O/Tk4\nWY+k5ATOzh06JgptWtpxBsNGYLzGZ2Ivxxhl95lM14LNYS64HiJpUaxUxqkcjIzx8WLJomvd\neJUvUTdBFRN6Y9kD7gKT4o/ApFQeQJ0s234dlIw55GRaYvL6BOXzwRcVn+E3YMJ7D0yurZAJ\n/RT4FdwH4aHLaB8Uu/R7ft4Dl4HJvR2yPYainbpuvGvEjgqWN3MP89G44Jdv28GjsA1EfRpT\nOUa9rlLle8d9vxGFDloaZ22s7WpfI7PFOKOPeOyIxp6RXatV++23tXFWOQ6O6nPZZ7YG4zUP\ndqdOarOfEuTCpUDLPs/A9jlL+9pVXJQbOwkut1NjKcfqONVp+piAnDc5XvoCETLukM/0h1ip\nYHkE93Dcvgpsp93p1+w4Fa6GM+HvoA6D8Nr8fxH4AuNvoq+B48B3gE3BMaUeHt8fGtFPOOnn\nsD44LzGv7gaTwWghzUl174AvHHfBsaBXTuIOgVPACdFTMBOEvqTwCbhUNsQq5MTNyW/tpOgl\ntjm5jXh8QTGRtksmGzvKBjAtuG5Mxm2cJim3hfRPjy+FtWEO2AjaoX9xU2NUp4KJtKyIexI2\n+huvXco7W1R+mOta936ZOQMcQH2B1N9ol75APQD7w+wQ8jgHhIhb77cC27bnvgonwGMQx1Bs\nmqbhSr6MbQlvwhVgOwgNoXBDrLBcGC4GX9g8dmdolXbgwoPBlw796A8hY7R+X4OXYyNLffZF\n3jzRajmhexf+AXODfUlPloTw0GOst+j78eLEpkpkHlQO7NF+Fuza0lk/PizCKce5dGeF2BXN\n+/ws16cbV+va01k/7BvlOG2P5qNOkx8XXoDyx6Rop8Ya5TlcabN86bgVpobo3+9QNv/YzyLW\nmAyzqS1aiLteBuYeFbG+XZQjTnOR408n6WaCeQuuKQUV8ca4764NS/tbXfwtN1gMTi9utDvL\ns4uy+cr2oG6E5eDH4Dh5L6gfgC9J6krwmE3AFyKfzfHN5TC4vhscg62/RjQNJ3nfkHPpZ2FA\nbOj0pUkiNXIHTFBOIKcsHepEeFaIJOAu/RQbnarKX2OZCpw09YNQTJIjHmOdC16JA9qwvJx7\nzgPGZDzhn5N15XYTbPi4MeUjwQmpk3XPbYdMSG/CjLATRPwU/yMnMQ64Y0MVcU7BfWyXvrT7\ndeYkUH8D4zsZZoch8C8Ij8Pz8tL2o0yI34Wjwev70uVzN1PGdD9MCF7/A1gHytI/8TmUifaw\nrtLw3yT9gbJfy5yQNVO/42IONMeDA8dWcDcsBcp47G+iIr6JKJ8O34ZNIV5MKDZda3FF+4MD\nkHV4KbwAe0Eoco9t0Rg9zpg/hCqkH95zHAiPLHeaJieg2jirfpkcFU8ce6xLFX6WJ/fD97T/\nZ+S/cpzmkU6TddwPym3SdiBljV9eaUPZOv85OP68DRGP+TpydrSH1dnWLunjIcXNaz2MOMux\nmZ8+L29oc9lx5Pfwh1Ic0R5sK+FxlX3OHP9eKZ6RFf2YPCL5DO/WHOC2uyHqrmZ3Q6urcNaD\ncBusBE+CWhYcF5zHjRaKhDtaBNvGIH2hcDL3fCmGwylPX1q3WP4KGes1h7Rk1a8xNnS/KPhy\nEbJzXRMrxfLZmvUqV/VnHjBGk4+TSOOWkF9rTLZu8xgnq8pnmxeecaXFmo/rD4NTYebSvZzI\nR7zGFuVYRvKcgX1VxOnL2p9hfZgbbI9O7E1C68KGoG+bwM0Qsh5UxB1Lt60O+u/zTwt7QPkL\nGqu9li+8k8FRxfIClpeBcSiX0Ta6NvDjn1FgeTp4/kKlbc0oTsxFfga+FP0E/gR66MTEsop6\nr43vLfb5EqV/20Ir9Xcubr2/B/Yl62pt+AaErONyjHpa1cuRMTj5MQbpZDkx0ptynOVyp8Re\nL85yru+UOP3gUVvvzc4fzXhWY3q6IPKO17VcXrcPtVO+SBjD2DBVKRBjHAr3lLb5kaRdMj5l\n/dd6GP2p7GVs6zqpwh/m73PgLvhO6b6OnQdCOUZ3+yyfWCjUiX0uYmv38mMCOBV+Afq7ARwG\nynFxMJwEo42H0aiJOVXHAV88lgcnTMrJ8vxgotoO7GwSqi07SahKJpwHwQ4dmpyCE6eQ+16M\nlTYsvb8emfRdjgOh8O4lNliO9dkpO8G/Al4GJ4et1pvc4FBYBPwKMjX48jMHhOJZIs6I2aXP\ndX4c2MLl41x7IFwEfg011l3AhH4t/BJ8cTL2ZcGYVbnfR9wTs/002BFUvBhY7u1gtgbXeB/0\nZBZw3RiMbzPwXt+Espfuj3WKX/mSZj9Uvrg0U36l1ztf1pTxDYO5wDaowsNy23X7YHgUrAtf\nTlspB/Er4QEwnt2K9XK9Wi6vs1qpbE/RtuLG4V2sd8JyiiLOsledGKd9WD8jzlpvO8FLY/C3\nlO1ue6PihS9I9utZoZxnyue6vd2TOfPMuHAH/BVCxjYAlowNLJ8plasu2mdeB1+QVLkP2R5c\nj5xpuZ2+nsL9zdcufwLK/P4B6HVZ+jwBuFTxfMPX8mfZAT/i7AArw0xgbh0Iyl8yLAq/cmV0\nUSTb0SXequM02TvpXAW2BZOqk6C3wJenW6GcCOxE0ZHc/m+oQt7Tyef1UFunEU8VcYzsHiYg\nfTGmWt881+1zWEB693lXafi/zOIEe02oIkGZ6B2MVgQTuRNQJygmeCfQquxruRzPNWz4YS39\nafv8G7wExvBTKOsJVoxbL/1tULlteHw57ulYt40/B7Zzr9ksPcKFdoT+cDNEHQ6gfAnMC+tB\nKNpIrLv8RrHiy5F1czs8VWxr1sLnngj8mrgVHADGfTLY12tV9u+dYqd5ofYrZO15vV3fnAv4\nMcQBaA2wHYi5qqxyfOXtVZRtc96/nTGMynPWqyv7eacp8krE5XrtZC72tXNpvddq/NoNHbBu\nnGeB40q00/IyQmznRN4Yoi0eTvn7ERTLaA/l/hVjU+mwyor2I+vZeKPvl29ejtNyeSwqH9fq\nsr45T9oTdoeDwZzvxzHndWVFzC7D79pjysdn+asOfMjq48Um5wCPfXV356+1q5F2vjPDI3TS\n4yRkATCZ+hV+fnDCbBJYDGoTaHQkdlUm6/Hb8HPw/hFDuczm/4nVbVUqJsZORJVfFfzqUI7X\n7cptw+BOMHmtXayzqEwfc6frwHp+G96FCSESpvvD41h+zrYnoYpEOjX32QYuhpchXiIodsmv\nYiaof4Ixh+8U/6N4ltfYos/Lg+3JF4RmydjOh9VhAvAjg4O5fWoveAmuhGgHJlYHXP2N/nUg\n5XfgeXBA2xKarTe54GNgXD+CU8A4toNjQOlXKOJ1Xf9WgE3hEmilzuPixjMPDIbnYHuwP5Vl\nfOV4y/taXY6PIeUYLPdr9Y17eP33OL5cj55unXeaIs7w03qNvtFJsXZX706eO01+9PDDkJ4G\nxlhuD/VypsdUpZijXcANzX/OO0IRcywvjB1tWNoex4WJwZhdL/vIatc247d/OU62W+ZpPyBu\nDjvCAlBW+Ooy8ujd5QOy3CMHFufofXp0RpsPtkGnRuyAA1NZTjhfhMPgaVgUVCSEWLrNwaIq\nmZTsyOPU3NBtsc8Jfjvl1/WrYB3QpxmKZSQiVv8zUPkcM8E3oLYO2FSZ5uVOT8BxEIOTExPj\nc6JeTp6WfcZdoAr5W4Q1ihvdx/J0mBHugIHwHdgYnPivDsYWirhdKpf6LAfCaeA5TmibNbmx\nP9wGLn1RWgIWA+8dcVDs+m2I7cOX0ZDtYDLwZetWiLqg2DRtxpW876zgvZaDHeAQOBfOhlA5\nXrf5wubL3mngC0wr5UA+M5wJDjqzwTFwHZTls7RLk9e5sfFcDl+HWv/qHF7Jpmnq3GUcttn2\nfInvFMWLpR6Gd/ZLc+RLnRIkcUS918Z5NPt27qA49W598INQWeGt2yz7saadijynn1ND2deI\ny23ieNQuxUtRxGIcEWvZ0zhuPPa3+yXJsd3YToD3QUWsEXssY5/zvzFNPqM5r570I9pgvf09\n2TY/B38fftOTk9p5rI011XMH7Ey7wiKlU6NjlRtTVV+fjOcViBhs8MpYooM7qZ/YjW2U8RjD\nXyBiLX8FdSKvjNljnaRE4qJYmUzeU8HBsBJcDNvBVmA8JhPji2eIJZu65PlV62xu+E1w8nwI\nzAbrwOXwQ3gYIk5j1/dYp/iVl/nDWPdZp4eFwOObIX2zzzwDy8Kz8AAcDg5WZZVjs3wtnA+D\noVnxcKmvyGtvCP8CJ0Z+AJkN9LNWxlSO8X7WfVn9Xu2BLVi3jq+HncBYj4JXYU2ImPTIcqu8\n4tIjVPnlImJyvLHeB47wzGp3flzcruyTcW5abRgjvVv4WY7T8cUJRycpJr3lOB8iQPtFvZfR\ndsVufOY2c9ynpSBsq9Fe3Vwulw6rrGgdG1/4GfHEuoF85A/Uzpc545q4iMFy7fjCpq4XIvcZ\ne7QTt1etCbjhcvBXeB4cr28E83fMQYzzSwifXXdeMheMSTLX7QbWVz2sJ/tJM/Q3LjJ7My5U\n1TU0J9WYA2dw2u9Kp0YHKns6Tml/K4vjc/EZoNyZLTupjGQ0LmWPa6cm4+arghN2k7qxxUvk\nu5T7gctb4AVQWwxfVPrTCehb4J9ZbQ0fgMnxdnC7cj0Sius+y/YW0E+GLyr/eQV39IVuFlgN\nbgC1PBwP4bVt1EHBZwj5MlrWRaysB78Fn7M3mpST/Xpkn3FCcgro17RwGPwcnoGQcQVus7yC\nhRbrFa5/FawLts+Z4UJwAPkDhF/2dT0UvXH9PbgJqpB5ZWPYHnYG89APwO0Ro7FFmWJb5P1F\nf5SeOuDO6UqHyBckY9SvkOvzxUqHLH1Bqo3TfN5JXmqV9Vsb59xs098B0Cmy35rbzHGOjSHj\nLLeFqWNHm5bmbD8iGZO+iroO/LNv86YvJmrp4Yu2/DT3KHP9a+DLRcRqeRjEuOj2ds1FvLcf\nDR3LHwPXjXcr+AdsAyHb8uPgWGWeULMMX4wxP53PHAQrdYPj7iPQE32dg/1o53xET4+GnaDT\nciohjVjlRDDiI3NvPQfsYMoJQEycXfeLjx0vJgZua6VMlNEon6XsvcWJXiQuXzxMtu3Uc9zc\nyfCXMAkYW3hkvE6gPoSb4Alw2xtQtby3LxUzwwXwNJgkB0J/OBOMzZcMB1cTiH3pDlDlAXf4\nlvb+tG2a2I1Lv8ueOrFXrcwFK3L9R2EJWAdeBPUUmJiV99fTYaDXL8EtcBWoGYYvKvn5Pne5\nBy6EZ8EXJJ9BOVENz/TyITBut1cl+49957zSDe3/7xTrT7LUQ+Uy/O7aUNGPz0v3eZWyceiT\nEyPrvVNUjlOfvgDjDC87Jc5y7n6uCMo2MLRTAiziiImkq1HPE1K2zdqXOlmOoyEnzcq20G45\n2ewP9iNzjl6uCVPD7BB6MwptWEa+2ZJ7O9aYO5X+2U6N/49gflflfjd8SzU/9c8XNX3bqJtb\neowyVy0Axh/eltsIm0d7WQ++BDrW1uNOtocfFEeqPTniWlgUXoZB8ClsCHfDjjDayMabqu/A\nFmw+BxwwY2JZPtIJnUnBZSgaUvgaSSOOjeNceoyN0xcCE9144L16qu054SRwQLcjTwRxrejM\nk7FtAvAFxN+GNCLPnxgc7BrRrpz0J3gDpqu5QPgVm8u+6mn4WvYxzol9rjuoGZ/b9LQR7cVJ\nh0IkxLiGz66veqyPsW79OTmeFJRJVd9HNgBYT07EZ4VGdBAn7Q2+/PQD4wovKHa1y2h/se5+\nfYql26Mcy/I5HuvE7DmYDxqRg6J17wt6+dpeSz99UdY//fI5vKee2k59JuvRbdbtiOR1noIl\nR3TQCPZdzr71IGK0remJKGMQVfaq3P/jXI+pbas+n+3CdnI/rAyN6AxO2gq8/ntgztBH271l\nfXDpvZTHTQXG/AaMqswZg2HdUT2h5rhLWHdQLPsXh4RPZY/cV7uu37Xnl9fth55zNWwGjehG\nTloB3gc9tF97j5G1Nw7pkabg6Ath2x6d9d+D76Zo29a7clv0iPDTcrmNuq7KPg7fUt9rr2Pb\nOQ12gUb0ICctAPZhxwzxuvbn8ssTq72SbfoY+FmDV3mc82YE29CUYJzmOr1yvA8fR+Yth3bJ\n4z22to+Zv34LB0Aj8kOROcPYJofI8+ZT68r79oN692bzKMtr7Ae/H+UzvnqgL20h+4/9MmRs\n0bdduj/88pjw2u2q3rrtSaaBPeBYaERO1r2OS2W+dCy2bbpNf/XZOG0bHmtcthHzrPOC8rOy\nWlfTsnUnOLXu3hFvtP05Xji2W+/TQXhD8X8U3v7PjmKD59a2yzjWa38Lzo8NvViaP/XHPGX/\nqtVmbDgd7BPhf+0xHbUeDbGjguqQYOw468E+oE+DIGRn2RZOAjtRaE0KNsYbYgNLk9sOYMMw\nqYVWpWAn+APYeMvnsDrKsjOvA3aSVutVbnBjgzdxQFsL9NIB40W4B+aBFeEUUN8HvXgGloMZ\n4e9gEjPhnAlO/tYFk9pNMDNsCnZ05bWd3DUiE9tqYJyt1nPc4PYGbzID5w0E4/SF7nW4C9R8\nsDyc6kqhXVleCs8X66uydFJwTbFuotwK/gJfFNu2Znkd/Bni2sWuUV7MwpHWbxV6kpvc1+CN\nvs15tq2ji/OXYjkALizWN2Bpu7ulWLct/hOeBie/34WTwQG11n+91tOYfDxK+V/QiObkpKUb\nObGBc4zRWBvRvJx0GbwDdxQX+B7LQfAU1PZn28k34RhQi4E+nusKWgO+Bte7grYEr2VuuB+e\ngEa0ACct0siJDZxzN+fYXhqRMdpXbeMPFheoNw7ZNvpDtFsPNS8+Bg+4gmrbq9ts7+bRvcGc\n9Bw0oiU4yZxehczx5vpGtAwnzVHnxL3YNg7cVGffymybFK6qs0/fZ4B9a/b9H+u201dqto/q\nqnnc+my1jPMf8EaDN/L5HoGHwX66G1wAL8PU8B34K3wCc8NAOAmUdWH/v9gVtA543E2gtobr\n4HIwTstvQyNalZOmb+TEHp7zb46/Bt7r4Xlx+JoU+hUrJ7K0rZs3a1Xrbe3+RdlgLv5J7Y5i\n3TivhA+72d+TzfNxsPl51hGc5BxyLYgcNoJDc9fo4ICdPAbtiNcBwI46Y2wolqezPKVmmx3f\nY53YlHUcK+eWN/Sh8m086z7F85r8yoPcW6xvXOw7mKVJW00H+ji/K8h6ObarNDzZuq+v6iYe\n/IDSw29DeVhp3eLn4CQzdDyFc2KF5dKghxOXtv2LsgNdX5ADp4NFaF8Kt8YKSwfnP5bWnUhs\nWqw74OvdTMX6tiyfK8ou7Ovnldb7StHB8tDSwzqJ2rJYr+3Pq7P9i9KxDuj3ldbNq+bX0D0U\n9oyVPrJ0UvHj0rPWG4d+yf5bSsdY9AVyj9K2uSjbXmcubTMfm5f7uuynf+nGhKPYflE3+/Zj\nu5PYvqonePDvFw8/Dkvb14rF+kLFekz4t2DdiXLoAAo3xQrL2jroS+NQyYb/FPVKz+ppQTbq\n9TT1drLN8Vv/qtDXuMkLsBOMXeeGO7DtAxi/zr6O3DRuR0aVQaUD6UA6kA6kA+lAOpAOpAPp\nwOjggC9q3wY/ZP0aHoH3YAbwFwQTgh8VP4PRQvmCNFpUUwaZDqQD6UA6kA6kA+lAOpAOdKwD\ng4jM3xiuAPPA7OBvsIfAZeAL02ijer8GG22Cz0DTgXQgHUgH0oF0IB1IB9KBdKAjHPDP6K4D\n/9MU/1xyOTgLRquXI+Kt+3eCbk+lA+lAOpAOpAPpQDqQDqQD6UA60IgDU3DSko2c2Ann5G+Q\nOqEWMoZ0IB1IB9KBdCAdSAfSgXQgHegIB/K/QRp5NfgvK0lZsR7L2Of6l7FSLOOYWMZu12u3\nxb4xfVl+9nLZ5y6vl8vh6+eFOS6j7HGxv9jdpxZln3zw2nW36ZXbQ7XHuO5/ZFn2sfaYOHdM\nXNY+a0/WPVaVl1F2e7mtut5XNKI2F/6Ul1HWH8u167bPUO3+2D4mL2ufOfyJpc9ee0xsi1wZ\n6+VllMvXcVtfVD3/wodG98X5Y/Ky7E2MI9Ge6i1jm56Uz+1uvdx+x2Qf6z1brT/lY8LHWJb3\nWR7RubXHtmL9SS56QCsunNfsDAdmIox6/4TiInXC89/X95+vrVW9Y71m7T8TXnvemLo+gAeb\nrHg4/8lH/53+0PwU4sXdX8/OFjtYln0s14u/CfWfu+yr6s+DT156+Ako+x9IlqU//vOroWkp\nlNuf/0TnwrGzWPovz5T/2e+a3WPUam0bsn0OKD3hLJSnLq37/3wYr7Rebpu1/vfVvm7f9f/V\nE9Kz8j/xWvas1v9JOHaOOJGledX8GnLfpLHSR5b1+mPZQ22obbdu0yv9LGtUzisf31fK5sR6\n473P7z9T7bhTT5OzcUC9HX1km/+rg4lKz+p/qO+YEiqPLeZNc0HIcd4xLFRbB/XafRzbF5a1\nY03tM5e9rd3n+K1/qXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfS\ngXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1I\nB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQg\nHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KB\ndCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAc61IHZiGvKOrF9jW3zwYR19uWmscbqqT+TY9qA\nboybie3TdbOvr2+eBQNm78aEntZBN5cZozePatsaBxcWBNtpatQcyPY3aj6N6lHN9rMfN14W\nanPrxGxzX5lxWe8LarbHY5JnPfUmx/TGa38iTp0H9HxEcm5a7qdTjejg3JcONNOBSbnYK7BF\nzUUHsv4CPA8fwp6Q+q8DjfhzCqdf+N9LdJX0fzC8Ce/AtWDiSI011mSYMAj+Da/B47AOhBqp\ngzi3Lyx70racRNoG34N34Uao99GEzanCgWx/zW0KzfTTl/3T4RMYBuaQgyBkLv6/GlaLnWPw\nspkej2k2NeJNjumNtYJDOM1xxjnmy7AodKf32VHuq290d2BuTwea5cAEXOgIeAhsfOUXJCdW\nQ2EnUP4W6QP4uiupsXrqz3Z4dhV8CRdCWcex4j4H9PHAl6UjITXWWPtiwj2wMPiVyaR6O6ie\n1sHws/rWz560rfux5hjw69wkcB/8FFL1Hcj2V9+XRrc2288tCeR5mKEIaCOWn8P0xfrNLNcD\n23pgDh6T1WyPxySveurNdjx8jumNtYDVOe0NmKM43Y/vfsQYu1gvL+yvb0H0UZf+9jeVDrTU\nAf9sbq8C3+S3KN1tLcp+sS83WL/kH1U6pi8Xe+qPg7Ve3wIX1hin96uVtu1K2S8qqbHGOhwT\nvlMyYn3K/obDP4XpaR2ULtNniqPatvxYchfEZFKDzgC/wKfqO5Dtr74vjW5ttp+OVcfXBOOk\nLPLJS5T9092+pGZ7PCZ511NvckxvvPZP4tRy3/QlyI/0K9a5pNtuq7M9NzXRASdUqa868Amr\nvy827f7VXWPNy/oj4J8lhJ6kMEes9PFlT/05r/DL/xakPCibGPwbZn+LF9JnJ6r+md3HsbGP\nLv2ypGaHpeFAMLl+AT2tA07pU+pJ2/oUZ5Yp3Fme5VKwCWxQbMvF/zqQ7e9/PenNlmb7+XOC\nKY9fq7I+KVwDfoWeDo6GFcAXJ39rfyKMyWq2x2OSVz31Jsf0xmt/Hk49t3T6q5T9mOf80o/I\nZc3FyhRwNzgPeBB+Bv7FQ6pJDvT1FyR/W7RSycvBlH1B6k4D2OGX+rL8c4W++oLk5NFOqnyB\nGQDN8McOr0wOIX1Wfe0FaURt1K9I+8GccDZ8DQZAM+qAy4wRqm2jMxZP1dO25Z/Vbgj+93B+\n1UvVd2AAm7P91femka3N9tOXfjUO7AKHwa/Al6H5wP/e7gr4BawHx8MHcA6MqRrAg2WbHV67\ntfmyWd7kmD7c3/hZb1zXo9p2+ALbnPPUyg8Z9uWfwkewF1wPC4IvVqkmONDXX5D64eHfSj4u\nRvnF0npt8X02+FuMsvxNx6PlDX2ofDDPumTxvA6yzfLH6yi9HtZVGv4bJTu+f3fbl9SPh+2u\njZ7JPhkIN8F10Kw64FJjhGrb6M3FU/W0be3Aeb6AHg2nQgz4FFMlB7L9lcxoQrEVfjqJOquI\nbS2WdxTlx1n6G9aQH71Wh2/BmPyC1AqPw8PRbVmbL5vljddRPc27w88a837245Fqx3VfjvSn\nrMlYeay8oSj7V07xl05u8mPHS2B/dk6QaoIDYzfhGqPzJXwZmrbEiF6OfM4hMKeFkvwV9L9K\n632puA4PG/4dR7lZ/gzjWp9B2et5WH8I+prqtVG/FK1SMuJmyn75deLTrDooXX60Lta20Z60\nrYV48ntg7MIBf3N0MQyASSD1vw5k+/tfT3qzpdl+Ol75QnQuLFmUWXTJ/OELUVmvsOI/ojMm\nq9kej85e1ebLZnnTk7w7Ovs3qrHXG9drvfbjuy9M9eaX32D73KWb+XLlb3rH9L5aeuQsttsB\nG/EWpSBssDbCHYptm7H0y8h8xXpfX4zMn2UxaNM6Jvl37hfWbL+I9cvB33LODw/ALyA11lh/\nxgS9mQ2cvG8P/ktUTn5GVgcc0uc1orblC//OoI/+xsg/cdgTJgS3XQD3Qaq+A9n+6vvS6NZm\n+2muNXc48SozMevmWT9MmafVMuCHF/PLmKxmezwmeTUyb3JMb15tb8Wl3gbH9UnhL+AHutDW\nFBYvVn7J0r9cclwaHxyj3ofpIZUOVOJA7QuSN10bfEnyz70+hB0h9V8HRuTP7zjsvv8e+p9S\nvRckJ6p+OXGAdtB2UjsOpIZ/OXoSI3wpeh38evQ9CI2oDuKYvrwcUdtaCmP8TdFchUHbsvwY\nbIefwCOwEKS6dyDbX/feNLKnmX7eSwC271p+WAR2EEv/VOdp+Df4MSZ+g0pxjFUzPR7TTBqR\nNzmmN7e2j+Zy/rdFb8FzUP4rmmdZ3weUL67+JcnL4FzU4zeCVDrQdgd8Y18UJmh7JJ0ZQDP9\nmYdHnK4zH7OtUfmbNX9zuRjU+3OvZtZBWx+0hTcf1bY1GTEsCb405Uv6qFVItr9R82lUj6rS\nT3PLwmC770uq0uPRzddmejMPD59jevctoB+75u9+91f2TM2a8wD7bCodSAfSgXQgHUgH0oF0\nIB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfS\ngXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1I\nB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQg\nHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KB\ndCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH\n0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAd\nSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0\nIB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfS\ngXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1I\nB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQg\nHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KB\ndCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH\n0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAd\nSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0\nIB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfS\ngXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1I\nB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQgHUgH0oF0IB1IB9KBdCAdSAfSgXQg\nHUgHqnTg/wHt1e9TSL3O9gAAAABJRU5ErkJggg==",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot all pairs\n",
"par(family = \"sans\")\n",
"pairs(tbl.all[2:18],\n",
" labels = names(tbl.all)[2:18])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A few things stand out here; first of all, the 4 different Random Forest distributions and the 4 different Square Root LASSO distributions look to be in very good agreement, producing the linear plots found near the bottom right corner. The other 2 solvers don't show nearly the same level of correlation between different distributions, though LASSO looks at least somewhat similar when comparing the log2 and asinh data. When it comes to the gene correlations themselves, LASSO looks to be the most similar, particularly for the asinh- transformed case.\n",
"\n",
"This broad view can clue us in to some trends, but it's likely that we'll get much better resolution by looking more directly at smaller pieces of the data. Let us now zoom in to look more closely at how the different cases compare. We'll begin by looking at how different distributions affect results within the same solver, then shift to how different solvers affect results within the same distribution. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Within-Solver Trends\n",
"\n",
"We are interested in the question: how does transforming the data affect the correlations between genes? We'll look at the top genes found by the solvers, sorted by their Pearson correlation with the target gene. First, let's look at LASSO:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 0.7745883 | 0.13419907 | 0.209422903 | 0.24105628 | 0.9232394 |
|---|
\n",
"\t| 5 | STAT4 | 2.6053432 | 0.15719002 | 0.076155038 | 0.09551869 | 0.9047599 |
|---|
\n",
"\t| 35 | SATB1 | 0.0000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.8628270 |
|---|
\n",
"\t| 15 | SATB2 | 0.1367563 | 0.21613389 | 0.228754703 | 0.26140384 | 0.8423299 |
|---|
\n",
"\t| 33 | ATF2 | 0.0000000 | 0.00000000 | 0.009811994 | 0.00000000 | 0.8278686 |
|---|
\n",
"\t| 4 | FOXP2 | 2.6150851 | 0.10666729 | 0.120449138 | 0.13231032 | 0.8196172 |
|---|
\n",
"\t| 11 | TSHZ2 | 0.6924360 | 0.10456283 | 0.115311122 | 0.09676352 | 0.7704362 |
|---|
\n",
"\t| 40 | DRGX | 0.0000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.7309450 |
|---|
\n",
"\t| 1 | HDX | 11.2649243 | 0.00000000 | 0.000000000 | 0.00000000 | 0.7241906 |
|---|
\n",
"\t| 8 | LHX6 | 1.2795080 | 0.06635221 | 0.090529696 | 0.09315715 | 0.7146115 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & gene & lasso.as.is & lasso.log2 & lasso.asinh & lasso.voom & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 0.7745883 & 0.13419907 & 0.209422903 & 0.24105628 & 0.9232394 \\\\\n",
"\t5 & STAT4 & 2.6053432 & 0.15719002 & 0.076155038 & 0.09551869 & 0.9047599 \\\\\n",
"\t35 & SATB1 & 0.0000000 & 0.00000000 & 0.000000000 & 0.00000000 & 0.8628270 \\\\\n",
"\t15 & SATB2 & 0.1367563 & 0.21613389 & 0.228754703 & 0.26140384 & 0.8423299 \\\\\n",
"\t33 & ATF2 & 0.0000000 & 0.00000000 & 0.009811994 & 0.00000000 & 0.8278686 \\\\\n",
"\t4 & FOXP2 & 2.6150851 & 0.10666729 & 0.120449138 & 0.13231032 & 0.8196172 \\\\\n",
"\t11 & TSHZ2 & 0.6924360 & 0.10456283 & 0.115311122 & 0.09676352 & 0.7704362 \\\\\n",
"\t40 & DRGX & 0.0000000 & 0.00000000 & 0.000000000 & 0.00000000 & 0.7309450 \\\\\n",
"\t1 & HDX & 11.2649243 & 0.00000000 & 0.000000000 & 0.00000000 & 0.7241906 \\\\\n",
"\t8 & LHX6 & 1.2795080 & 0.06635221 & 0.090529696 & 0.09315715 & 0.7146115 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 0.7745883 | 0.13419907 | 0.209422903 | 0.24105628 | 0.9232394 | \n",
"| 5 | STAT4 | 2.6053432 | 0.15719002 | 0.076155038 | 0.09551869 | 0.9047599 | \n",
"| 35 | SATB1 | 0.0000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.8628270 | \n",
"| 15 | SATB2 | 0.1367563 | 0.21613389 | 0.228754703 | 0.26140384 | 0.8423299 | \n",
"| 33 | ATF2 | 0.0000000 | 0.00000000 | 0.009811994 | 0.00000000 | 0.8278686 | \n",
"| 4 | FOXP2 | 2.6150851 | 0.10666729 | 0.120449138 | 0.13231032 | 0.8196172 | \n",
"| 11 | TSHZ2 | 0.6924360 | 0.10456283 | 0.115311122 | 0.09676352 | 0.7704362 | \n",
"| 40 | DRGX | 0.0000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.7309450 | \n",
"| 1 | HDX | 11.2649243 | 0.00000000 | 0.000000000 | 0.00000000 | 0.7241906 | \n",
"| 8 | LHX6 | 1.2795080 | 0.06635221 | 0.090529696 | 0.09315715 | 0.7146115 | \n",
"\n",
"\n"
],
"text/plain": [
" gene lasso.as.is lasso.log2 lasso.asinh lasso.voom gene.cor \n",
"9 HLF 0.7745883 0.13419907 0.209422903 0.24105628 0.9232394\n",
"5 STAT4 2.6053432 0.15719002 0.076155038 0.09551869 0.9047599\n",
"35 SATB1 0.0000000 0.00000000 0.000000000 0.00000000 0.8628270\n",
"15 SATB2 0.1367563 0.21613389 0.228754703 0.26140384 0.8423299\n",
"33 ATF2 0.0000000 0.00000000 0.009811994 0.00000000 0.8278686\n",
"4 FOXP2 2.6150851 0.10666729 0.120449138 0.13231032 0.8196172\n",
"11 TSHZ2 0.6924360 0.10456283 0.115311122 0.09676352 0.7704362\n",
"40 DRGX 0.0000000 0.00000000 0.000000000 0.00000000 0.7309450\n",
"1 HDX 11.2649243 0.00000000 0.000000000 0.00000000 0.7241906\n",
"8 LHX6 1.2795080 0.06635221 0.090529696 0.09315715 0.7146115"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(tbl.all[,c(1,2,3,4,5,18)],10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we would expect from LASSO, many genes are missing coefficients because they've been shrunken out of the model. What we wouldn't necessarily expect is that the remaining coefficients don't necessarily line up well with one another. To quantify that, let us compare the LASSO coefficients between transformation types."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom |
|---|
\n",
"\n",
"\t| lasso.as.is | 1.0000000 | 0.1891492 | 0.2135705 | 0.1865346 |
|---|
\n",
"\t| lasso.log2 | 0.1891492 | 1.0000000 | 0.8479843 | 0.8549927 |
|---|
\n",
"\t| lasso.asinh | 0.2135705 | 0.8479843 | 1.0000000 | 0.9336374 |
|---|
\n",
"\t| lasso.voom | 0.1865346 | 0.8549927 | 0.9336374 | 1.0000000 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" & lasso.as.is & lasso.log2 & lasso.asinh & lasso.voom\\\\\n",
"\\hline\n",
"\tlasso.as.is & 1.0000000 & 0.1891492 & 0.2135705 & 0.1865346\\\\\n",
"\tlasso.log2 & 0.1891492 & 1.0000000 & 0.8479843 & 0.8549927\\\\\n",
"\tlasso.asinh & 0.2135705 & 0.8479843 & 1.0000000 & 0.9336374\\\\\n",
"\tlasso.voom & 0.1865346 & 0.8549927 & 0.9336374 & 1.0000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom | \n",
"|---|---|---|---|\n",
"| lasso.as.is | 1.0000000 | 0.1891492 | 0.2135705 | 0.1865346 | \n",
"| lasso.log2 | 0.1891492 | 1.0000000 | 0.8479843 | 0.8549927 | \n",
"| lasso.asinh | 0.2135705 | 0.8479843 | 1.0000000 | 0.9336374 | \n",
"| lasso.voom | 0.1865346 | 0.8549927 | 0.9336374 | 1.0000000 | \n",
"\n",
"\n"
],
"text/plain": [
" lasso.as.is lasso.log2 lasso.asinh lasso.voom\n",
"lasso.as.is 1.0000000 0.1891492 0.2135705 0.1865346 \n",
"lasso.log2 0.1891492 1.0000000 0.8479843 0.8549927 \n",
"lasso.asinh 0.2135705 0.8479843 1.0000000 0.9336374 \n",
"lasso.voom 0.1865346 0.8549927 0.9336374 1.0000000 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# For each pairwise comparison, find correlation\n",
"cor(tbl.all[2:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Correlation between the coefficients found for the \"as-is\" data with either transformed data type aren't particularly high, but transformed matrices are in much better agreement. We can similarly look at the results from using Bayes Spike as the solver:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | bs.as.is | bs.log2 | bs.asinh | bs.voom | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 0.593005603 | 3.228768e-01 | 8.583764e-05 | 3.468772e-01 | 0.9232394 |
|---|
\n",
"\t| 5 | STAT4 | 1.708535525 | 1.832979e-01 | 8.946250e-05 | 1.708533e-01 | 0.9047599 |
|---|
\n",
"\t| 35 | SATB1 | -0.568306798 | -1.444623e-06 | 1.154825e-05 | -3.633166e-05 | 0.8628270 |
|---|
\n",
"\t| 15 | SATB2 | 1.035785852 | 6.942847e-05 | 3.566778e-01 | 2.853092e-01 | 0.8423299 |
|---|
\n",
"\t| 33 | ATF2 | 0.892883655 | 5.648769e-05 | 7.409788e-05 | -5.996884e-06 | 0.8278686 |
|---|
\n",
"\t| 4 | FOXP2 | 3.972475657 | 8.526235e-05 | 2.309917e-01 | 4.508482e-05 | 0.8196172 |
|---|
\n",
"\t| 11 | TSHZ2 | 1.086813411 | 2.277099e-01 | 1.056974e-01 | 1.235520e-01 | 0.7704362 |
|---|
\n",
"\t| 40 | DRGX | -0.004072208 | 1.835087e-07 | 7.377633e-06 | 1.398560e-05 | 0.7309450 |
|---|
\n",
"\t| 1 | HDX | 0.324175587 | -2.498712e-05 | 6.947728e-05 | 5.735278e-05 | 0.7241906 |
|---|
\n",
"\t| 8 | LHX6 | 2.312551744 | 6.697943e-05 | 1.139972e-01 | 1.365092e-01 | 0.7146115 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & gene & bs.as.is & bs.log2 & bs.asinh & bs.voom & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 0.593005603 & 3.228768e-01 & 8.583764e-05 & 3.468772e-01 & 0.9232394 \\\\\n",
"\t5 & STAT4 & 1.708535525 & 1.832979e-01 & 8.946250e-05 & 1.708533e-01 & 0.9047599 \\\\\n",
"\t35 & SATB1 & -0.568306798 & -1.444623e-06 & 1.154825e-05 & -3.633166e-05 & 0.8628270 \\\\\n",
"\t15 & SATB2 & 1.035785852 & 6.942847e-05 & 3.566778e-01 & 2.853092e-01 & 0.8423299 \\\\\n",
"\t33 & ATF2 & 0.892883655 & 5.648769e-05 & 7.409788e-05 & -5.996884e-06 & 0.8278686 \\\\\n",
"\t4 & FOXP2 & 3.972475657 & 8.526235e-05 & 2.309917e-01 & 4.508482e-05 & 0.8196172 \\\\\n",
"\t11 & TSHZ2 & 1.086813411 & 2.277099e-01 & 1.056974e-01 & 1.235520e-01 & 0.7704362 \\\\\n",
"\t40 & DRGX & -0.004072208 & 1.835087e-07 & 7.377633e-06 & 1.398560e-05 & 0.7309450 \\\\\n",
"\t1 & HDX & 0.324175587 & -2.498712e-05 & 6.947728e-05 & 5.735278e-05 & 0.7241906 \\\\\n",
"\t8 & LHX6 & 2.312551744 & 6.697943e-05 & 1.139972e-01 & 1.365092e-01 & 0.7146115 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | bs.as.is | bs.log2 | bs.asinh | bs.voom | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 0.593005603 | 3.228768e-01 | 8.583764e-05 | 3.468772e-01 | 0.9232394 | \n",
"| 5 | STAT4 | 1.708535525 | 1.832979e-01 | 8.946250e-05 | 1.708533e-01 | 0.9047599 | \n",
"| 35 | SATB1 | -0.568306798 | -1.444623e-06 | 1.154825e-05 | -3.633166e-05 | 0.8628270 | \n",
"| 15 | SATB2 | 1.035785852 | 6.942847e-05 | 3.566778e-01 | 2.853092e-01 | 0.8423299 | \n",
"| 33 | ATF2 | 0.892883655 | 5.648769e-05 | 7.409788e-05 | -5.996884e-06 | 0.8278686 | \n",
"| 4 | FOXP2 | 3.972475657 | 8.526235e-05 | 2.309917e-01 | 4.508482e-05 | 0.8196172 | \n",
"| 11 | TSHZ2 | 1.086813411 | 2.277099e-01 | 1.056974e-01 | 1.235520e-01 | 0.7704362 | \n",
"| 40 | DRGX | -0.004072208 | 1.835087e-07 | 7.377633e-06 | 1.398560e-05 | 0.7309450 | \n",
"| 1 | HDX | 0.324175587 | -2.498712e-05 | 6.947728e-05 | 5.735278e-05 | 0.7241906 | \n",
"| 8 | LHX6 | 2.312551744 | 6.697943e-05 | 1.139972e-01 | 1.365092e-01 | 0.7146115 | \n",
"\n",
"\n"
],
"text/plain": [
" gene bs.as.is bs.log2 bs.asinh bs.voom gene.cor \n",
"9 HLF 0.593005603 3.228768e-01 8.583764e-05 3.468772e-01 0.9232394\n",
"5 STAT4 1.708535525 1.832979e-01 8.946250e-05 1.708533e-01 0.9047599\n",
"35 SATB1 -0.568306798 -1.444623e-06 1.154825e-05 -3.633166e-05 0.8628270\n",
"15 SATB2 1.035785852 6.942847e-05 3.566778e-01 2.853092e-01 0.8423299\n",
"33 ATF2 0.892883655 5.648769e-05 7.409788e-05 -5.996884e-06 0.8278686\n",
"4 FOXP2 3.972475657 8.526235e-05 2.309917e-01 4.508482e-05 0.8196172\n",
"11 TSHZ2 1.086813411 2.277099e-01 1.056974e-01 1.235520e-01 0.7704362\n",
"40 DRGX -0.004072208 1.835087e-07 7.377633e-06 1.398560e-05 0.7309450\n",
"1 HDX 0.324175587 -2.498712e-05 6.947728e-05 5.735278e-05 0.7241906\n",
"8 LHX6 2.312551744 6.697943e-05 1.139972e-01 1.365092e-01 0.7146115"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(tbl.all[,c(1,6:9,18)],10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From visual inspection, these coefficients are a bit all over the map in terms of magnitude and sign. Additionally, most of the coefficients for the log-transformed, and many from the asinh-transformed, are quite small. Let's look at these correlations:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | bs.as.is | bs.log2 | bs.asinh | bs.voom |
|---|
\n",
"\n",
"\t| bs.as.is | 1.00000000 | 0.05740733 | 0.07552436 | 0.04127314 |
|---|
\n",
"\t| bs.log2 | 0.05740733 | 1.00000000 | 0.16902615 | 0.47330622 |
|---|
\n",
"\t| bs.asinh | 0.07552436 | 0.16902615 | 1.00000000 | 0.30836121 |
|---|
\n",
"\t| bs.voom | 0.04127314 | 0.47330622 | 0.30836121 | 1.00000000 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" & bs.as.is & bs.log2 & bs.asinh & bs.voom\\\\\n",
"\\hline\n",
"\tbs.as.is & 1.00000000 & 0.05740733 & 0.07552436 & 0.04127314\\\\\n",
"\tbs.log2 & 0.05740733 & 1.00000000 & 0.16902615 & 0.47330622\\\\\n",
"\tbs.asinh & 0.07552436 & 0.16902615 & 1.00000000 & 0.30836121\\\\\n",
"\tbs.voom & 0.04127314 & 0.47330622 & 0.30836121 & 1.00000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | bs.as.is | bs.log2 | bs.asinh | bs.voom | \n",
"|---|---|---|---|\n",
"| bs.as.is | 1.00000000 | 0.05740733 | 0.07552436 | 0.04127314 | \n",
"| bs.log2 | 0.05740733 | 1.00000000 | 0.16902615 | 0.47330622 | \n",
"| bs.asinh | 0.07552436 | 0.16902615 | 1.00000000 | 0.30836121 | \n",
"| bs.voom | 0.04127314 | 0.47330622 | 0.30836121 | 1.00000000 | \n",
"\n",
"\n"
],
"text/plain": [
" bs.as.is bs.log2 bs.asinh bs.voom \n",
"bs.as.is 1.00000000 0.05740733 0.07552436 0.04127314\n",
"bs.log2 0.05740733 1.00000000 0.16902615 0.47330622\n",
"bs.asinh 0.07552436 0.16902615 1.00000000 0.30836121\n",
"bs.voom 0.04127314 0.47330622 0.30836121 1.00000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cor(tbl.all[6:9])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There appears to be very little agreement between the as-is Bayes Spike coefficients and the other transformations, but there's some agreement in the asinh and voom transformed datasets. This is somewhat similar to what we saw for the LASSO solver, though all correlations for the Bayes Spike coefficients are weaker. \n",
"\n",
"Let's continue our survey of the solvers by looking at the Random Forest scores:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | rf.as.is | rf.log2 | rf.asinh | rf.voom | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 1266161.95 | 57.371322 | 25.9160383 | 53.0524708 | 0.9232394 |
|---|
\n",
"\t| 5 | STAT4 | 649234.98 | 46.374066 | 22.4786757 | 48.9494236 | 0.9047599 |
|---|
\n",
"\t| 35 | SATB1 | 577058.44 | 21.194070 | 11.9869878 | 27.9882393 | 0.8628270 |
|---|
\n",
"\t| 15 | SATB2 | 424329.70 | 26.355675 | 14.2251862 | 30.4646927 | 0.8423299 |
|---|
\n",
"\t| 33 | ATF2 | 137474.98 | 5.411294 | 2.3401488 | 7.3519720 | 0.8278686 |
|---|
\n",
"\t| 4 | FOXP2 | 112640.28 | 3.914452 | 2.9705266 | 3.8044576 | 0.8196172 |
|---|
\n",
"\t| 11 | TSHZ2 | 84319.20 | 5.122529 | 2.2511790 | 4.6044307 | 0.7704362 |
|---|
\n",
"\t| 40 | DRGX | 88685.56 | 10.128620 | 3.2287507 | 8.6275128 | 0.7309450 |
|---|
\n",
"\t| 1 | HDX | 53018.02 | 1.436412 | 0.7157802 | 0.8661305 | 0.7241906 |
|---|
\n",
"\t| 8 | LHX6 | 33707.16 | 2.907339 | 1.7027348 | 2.2815335 | 0.7146115 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & gene & rf.as.is & rf.log2 & rf.asinh & rf.voom & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 1266161.95 & 57.371322 & 25.9160383 & 53.0524708 & 0.9232394 \\\\\n",
"\t5 & STAT4 & 649234.98 & 46.374066 & 22.4786757 & 48.9494236 & 0.9047599 \\\\\n",
"\t35 & SATB1 & 577058.44 & 21.194070 & 11.9869878 & 27.9882393 & 0.8628270 \\\\\n",
"\t15 & SATB2 & 424329.70 & 26.355675 & 14.2251862 & 30.4646927 & 0.8423299 \\\\\n",
"\t33 & ATF2 & 137474.98 & 5.411294 & 2.3401488 & 7.3519720 & 0.8278686 \\\\\n",
"\t4 & FOXP2 & 112640.28 & 3.914452 & 2.9705266 & 3.8044576 & 0.8196172 \\\\\n",
"\t11 & TSHZ2 & 84319.20 & 5.122529 & 2.2511790 & 4.6044307 & 0.7704362 \\\\\n",
"\t40 & DRGX & 88685.56 & 10.128620 & 3.2287507 & 8.6275128 & 0.7309450 \\\\\n",
"\t1 & HDX & 53018.02 & 1.436412 & 0.7157802 & 0.8661305 & 0.7241906 \\\\\n",
"\t8 & LHX6 & 33707.16 & 2.907339 & 1.7027348 & 2.2815335 & 0.7146115 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | rf.as.is | rf.log2 | rf.asinh | rf.voom | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 1266161.95 | 57.371322 | 25.9160383 | 53.0524708 | 0.9232394 | \n",
"| 5 | STAT4 | 649234.98 | 46.374066 | 22.4786757 | 48.9494236 | 0.9047599 | \n",
"| 35 | SATB1 | 577058.44 | 21.194070 | 11.9869878 | 27.9882393 | 0.8628270 | \n",
"| 15 | SATB2 | 424329.70 | 26.355675 | 14.2251862 | 30.4646927 | 0.8423299 | \n",
"| 33 | ATF2 | 137474.98 | 5.411294 | 2.3401488 | 7.3519720 | 0.8278686 | \n",
"| 4 | FOXP2 | 112640.28 | 3.914452 | 2.9705266 | 3.8044576 | 0.8196172 | \n",
"| 11 | TSHZ2 | 84319.20 | 5.122529 | 2.2511790 | 4.6044307 | 0.7704362 | \n",
"| 40 | DRGX | 88685.56 | 10.128620 | 3.2287507 | 8.6275128 | 0.7309450 | \n",
"| 1 | HDX | 53018.02 | 1.436412 | 0.7157802 | 0.8661305 | 0.7241906 | \n",
"| 8 | LHX6 | 33707.16 | 2.907339 | 1.7027348 | 2.2815335 | 0.7146115 | \n",
"\n",
"\n"
],
"text/plain": [
" gene rf.as.is rf.log2 rf.asinh rf.voom gene.cor \n",
"9 HLF 1266161.95 57.371322 25.9160383 53.0524708 0.9232394\n",
"5 STAT4 649234.98 46.374066 22.4786757 48.9494236 0.9047599\n",
"35 SATB1 577058.44 21.194070 11.9869878 27.9882393 0.8628270\n",
"15 SATB2 424329.70 26.355675 14.2251862 30.4646927 0.8423299\n",
"33 ATF2 137474.98 5.411294 2.3401488 7.3519720 0.8278686\n",
"4 FOXP2 112640.28 3.914452 2.9705266 3.8044576 0.8196172\n",
"11 TSHZ2 84319.20 5.122529 2.2511790 4.6044307 0.7704362\n",
"40 DRGX 88685.56 10.128620 3.2287507 8.6275128 0.7309450\n",
"1 HDX 53018.02 1.436412 0.7157802 0.8661305 0.7241906\n",
"8 LHX6 33707.16 2.907339 1.7027348 2.2815335 0.7146115"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(tbl.all[,c(1,10:13,18)],10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This looks a lot more in line with what we would expect; the Random Forest scores look to decrease in size almost monotonically along with the Pearson correlations. Furthermore, they look to do so regardless of whether or not the data have been transformed, although the coefficient sizes vary quite a bit. Let's follow up with the correlations between different transformations."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | rf.as.is | rf.log2 | rf.asinh | rf.voom |
|---|
\n",
"\n",
"\t| rf.as.is | 1.0000000 | 0.9650516 | 0.9604815 | 0.9529617 |
|---|
\n",
"\t| rf.log2 | 0.9650516 | 1.0000000 | 0.9949891 | 0.9913273 |
|---|
\n",
"\t| rf.asinh | 0.9604815 | 0.9949891 | 1.0000000 | 0.9972757 |
|---|
\n",
"\t| rf.voom | 0.9529617 | 0.9913273 | 0.9972757 | 1.0000000 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" & rf.as.is & rf.log2 & rf.asinh & rf.voom\\\\\n",
"\\hline\n",
"\trf.as.is & 1.0000000 & 0.9650516 & 0.9604815 & 0.9529617\\\\\n",
"\trf.log2 & 0.9650516 & 1.0000000 & 0.9949891 & 0.9913273\\\\\n",
"\trf.asinh & 0.9604815 & 0.9949891 & 1.0000000 & 0.9972757\\\\\n",
"\trf.voom & 0.9529617 & 0.9913273 & 0.9972757 & 1.0000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | rf.as.is | rf.log2 | rf.asinh | rf.voom | \n",
"|---|---|---|---|\n",
"| rf.as.is | 1.0000000 | 0.9650516 | 0.9604815 | 0.9529617 | \n",
"| rf.log2 | 0.9650516 | 1.0000000 | 0.9949891 | 0.9913273 | \n",
"| rf.asinh | 0.9604815 | 0.9949891 | 1.0000000 | 0.9972757 | \n",
"| rf.voom | 0.9529617 | 0.9913273 | 0.9972757 | 1.0000000 | \n",
"\n",
"\n"
],
"text/plain": [
" rf.as.is rf.log2 rf.asinh rf.voom \n",
"rf.as.is 1.0000000 0.9650516 0.9604815 0.9529617\n",
"rf.log2 0.9650516 1.0000000 0.9949891 0.9913273\n",
"rf.asinh 0.9604815 0.9949891 1.0000000 0.9972757\n",
"rf.voom 0.9529617 0.9913273 0.9972757 1.0000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cor(tbl.all[10:13])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The correlation coefficients between matrix types suggests very high agreement in all cases, and nearly identical trends when using log-transformed and asinh-transformed data. This is important information as we think about using Random Forest on other datasets; we can likely expect that regardless of transformation, we'll likely find similar results. \n",
"\n",
"Let's finish our survey of solvers by looking at Square Root LASSO:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 0.69519133 | 0.140776460 | 0.14289609 | 0.15897283 | 0.9232394 |
|---|
\n",
"\t| 5 | STAT4 | 3.47937884 | 0.190961358 | 0.15896033 | 0.17336532 | 0.9047599 |
|---|
\n",
"\t| 35 | SATB1 | 0.00000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.8628270 |
|---|
\n",
"\t| 15 | SATB2 | 0.02139235 | 0.181298914 | 0.22652672 | 0.23814460 | 0.8423299 |
|---|
\n",
"\t| 33 | ATF2 | 0.08809962 | 0.008485763 | 0.00000000 | 0.00000000 | 0.8278686 |
|---|
\n",
"\t| 4 | FOXP2 | 2.44496515 | 0.101105760 | 0.10635813 | 0.10469561 | 0.8196172 |
|---|
\n",
"\t| 11 | TSHZ2 | 1.00207756 | 0.100506784 | 0.09486455 | 0.08923052 | 0.7704362 |
|---|
\n",
"\t| 40 | DRGX | 1.18904754 | 0.000000000 | 0.00000000 | 0.00000000 | 0.7309450 |
|---|
\n",
"\t| 1 | HDX | 13.37342127 | 0.000000000 | 0.00000000 | 0.00000000 | 0.7241906 |
|---|
\n",
"\t| 8 | LHX6 | 2.69414714 | 0.058348346 | 0.08655747 | 0.09288872 | 0.7146115 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & gene & sqrt.lasso.as.is & sqrt.lasso.log2 & sqrt.lasso.asinh & sqrt.lasso.voom & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 0.69519133 & 0.140776460 & 0.14289609 & 0.15897283 & 0.9232394 \\\\\n",
"\t5 & STAT4 & 3.47937884 & 0.190961358 & 0.15896033 & 0.17336532 & 0.9047599 \\\\\n",
"\t35 & SATB1 & 0.00000000 & 0.000000000 & 0.00000000 & 0.00000000 & 0.8628270 \\\\\n",
"\t15 & SATB2 & 0.02139235 & 0.181298914 & 0.22652672 & 0.23814460 & 0.8423299 \\\\\n",
"\t33 & ATF2 & 0.08809962 & 0.008485763 & 0.00000000 & 0.00000000 & 0.8278686 \\\\\n",
"\t4 & FOXP2 & 2.44496515 & 0.101105760 & 0.10635813 & 0.10469561 & 0.8196172 \\\\\n",
"\t11 & TSHZ2 & 1.00207756 & 0.100506784 & 0.09486455 & 0.08923052 & 0.7704362 \\\\\n",
"\t40 & DRGX & 1.18904754 & 0.000000000 & 0.00000000 & 0.00000000 & 0.7309450 \\\\\n",
"\t1 & HDX & 13.37342127 & 0.000000000 & 0.00000000 & 0.00000000 & 0.7241906 \\\\\n",
"\t8 & LHX6 & 2.69414714 & 0.058348346 & 0.08655747 & 0.09288872 & 0.7146115 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 0.69519133 | 0.140776460 | 0.14289609 | 0.15897283 | 0.9232394 | \n",
"| 5 | STAT4 | 3.47937884 | 0.190961358 | 0.15896033 | 0.17336532 | 0.9047599 | \n",
"| 35 | SATB1 | 0.00000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.8628270 | \n",
"| 15 | SATB2 | 0.02139235 | 0.181298914 | 0.22652672 | 0.23814460 | 0.8423299 | \n",
"| 33 | ATF2 | 0.08809962 | 0.008485763 | 0.00000000 | 0.00000000 | 0.8278686 | \n",
"| 4 | FOXP2 | 2.44496515 | 0.101105760 | 0.10635813 | 0.10469561 | 0.8196172 | \n",
"| 11 | TSHZ2 | 1.00207756 | 0.100506784 | 0.09486455 | 0.08923052 | 0.7704362 | \n",
"| 40 | DRGX | 1.18904754 | 0.000000000 | 0.00000000 | 0.00000000 | 0.7309450 | \n",
"| 1 | HDX | 13.37342127 | 0.000000000 | 0.00000000 | 0.00000000 | 0.7241906 | \n",
"| 8 | LHX6 | 2.69414714 | 0.058348346 | 0.08655747 | 0.09288872 | 0.7146115 | \n",
"\n",
"\n"
],
"text/plain": [
" gene sqrt.lasso.as.is sqrt.lasso.log2 sqrt.lasso.asinh sqrt.lasso.voom\n",
"9 HLF 0.69519133 0.140776460 0.14289609 0.15897283 \n",
"5 STAT4 3.47937884 0.190961358 0.15896033 0.17336532 \n",
"35 SATB1 0.00000000 0.000000000 0.00000000 0.00000000 \n",
"15 SATB2 0.02139235 0.181298914 0.22652672 0.23814460 \n",
"33 ATF2 0.08809962 0.008485763 0.00000000 0.00000000 \n",
"4 FOXP2 2.44496515 0.101105760 0.10635813 0.10469561 \n",
"11 TSHZ2 1.00207756 0.100506784 0.09486455 0.08923052 \n",
"40 DRGX 1.18904754 0.000000000 0.00000000 0.00000000 \n",
"1 HDX 13.37342127 0.000000000 0.00000000 0.00000000 \n",
"8 LHX6 2.69414714 0.058348346 0.08655747 0.09288872 \n",
" gene.cor \n",
"9 0.9232394\n",
"5 0.9047599\n",
"35 0.8628270\n",
"15 0.8423299\n",
"33 0.8278686\n",
"4 0.8196172\n",
"11 0.7704362\n",
"40 0.7309450\n",
"1 0.7241906\n",
"8 0.7146115"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(tbl.all[,c(1,14:17,18)],10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Much like regular LASSO, we see many coefficients shrunken to 0 and there seems to be good agreement in which coefficients are 0 in the transformed cases, particularly between the asinh and voom transformations. We'll look at the correlations to get a better look"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom |
|---|
\n",
"\n",
"\t| sqrt.lasso.as.is | 1.0000000 | 0.1854567 | 0.2254451 | 0.1996011 |
|---|
\n",
"\t| sqrt.lasso.log2 | 0.1854567 | 1.0000000 | 0.9597672 | 0.9139551 |
|---|
\n",
"\t| sqrt.lasso.asinh | 0.2254451 | 0.9597672 | 1.0000000 | 0.9490039 |
|---|
\n",
"\t| sqrt.lasso.voom | 0.1996011 | 0.9139551 | 0.9490039 | 1.0000000 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" & sqrt.lasso.as.is & sqrt.lasso.log2 & sqrt.lasso.asinh & sqrt.lasso.voom\\\\\n",
"\\hline\n",
"\tsqrt.lasso.as.is & 1.0000000 & 0.1854567 & 0.2254451 & 0.1996011\\\\\n",
"\tsqrt.lasso.log2 & 0.1854567 & 1.0000000 & 0.9597672 & 0.9139551\\\\\n",
"\tsqrt.lasso.asinh & 0.2254451 & 0.9597672 & 1.0000000 & 0.9490039\\\\\n",
"\tsqrt.lasso.voom & 0.1996011 & 0.9139551 & 0.9490039 & 1.0000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom | \n",
"|---|---|---|---|\n",
"| sqrt.lasso.as.is | 1.0000000 | 0.1854567 | 0.2254451 | 0.1996011 | \n",
"| sqrt.lasso.log2 | 0.1854567 | 1.0000000 | 0.9597672 | 0.9139551 | \n",
"| sqrt.lasso.asinh | 0.2254451 | 0.9597672 | 1.0000000 | 0.9490039 | \n",
"| sqrt.lasso.voom | 0.1996011 | 0.9139551 | 0.9490039 | 1.0000000 | \n",
"\n",
"\n"
],
"text/plain": [
" sqrt.lasso.as.is sqrt.lasso.log2 sqrt.lasso.asinh\n",
"sqrt.lasso.as.is 1.0000000 0.1854567 0.2254451 \n",
"sqrt.lasso.log2 0.1854567 1.0000000 0.9597672 \n",
"sqrt.lasso.asinh 0.2254451 0.9597672 1.0000000 \n",
"sqrt.lasso.voom 0.1996011 0.9139551 0.9490039 \n",
" sqrt.lasso.voom\n",
"sqrt.lasso.as.is 0.1996011 \n",
"sqrt.lasso.log2 0.9139551 \n",
"sqrt.lasso.asinh 0.9490039 \n",
"sqrt.lasso.voom 1.0000000 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cor(tbl.all[14:17])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The as-is coefficients don't really line up with the others, but all 3 transformations are in close agreement here. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Inspect Trends Between Solvers\n",
"\n",
"From looking at the within-solvers comparisons, it seems evident that we can probably expect some major differences between solver types. Let's look first at the as-is data across solvers; we'll look at the columns in a table and compute the correlation coefficients concurrently."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | lasso.as.is | bs.as.is | rf.as.is | sqrt.lasso.as.is | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 0.7745883 | 0.593005603 | 1266161.95 | 0.69519133 | 0.9232394 |
|---|
\n",
"\t| 5 | STAT4 | 2.6053432 | 1.708535525 | 649234.98 | 3.47937884 | 0.9047599 |
|---|
\n",
"\t| 35 | SATB1 | 0.0000000 | -0.568306798 | 577058.44 | 0.00000000 | 0.8628270 |
|---|
\n",
"\t| 15 | SATB2 | 0.1367563 | 1.035785852 | 424329.70 | 0.02139235 | 0.8423299 |
|---|
\n",
"\t| 33 | ATF2 | 0.0000000 | 0.892883655 | 137474.98 | 0.08809962 | 0.8278686 |
|---|
\n",
"\t| 4 | FOXP2 | 2.6150851 | 3.972475657 | 112640.28 | 2.44496515 | 0.8196172 |
|---|
\n",
"\t| 11 | TSHZ2 | 0.6924360 | 1.086813411 | 84319.20 | 1.00207756 | 0.7704362 |
|---|
\n",
"\t| 40 | DRGX | 0.0000000 | -0.004072208 | 88685.56 | 1.18904754 | 0.7309450 |
|---|
\n",
"\t| 1 | HDX | 11.2649243 | 0.324175587 | 53018.02 | 13.37342127 | 0.7241906 |
|---|
\n",
"\t| 8 | LHX6 | 1.2795080 | 2.312551744 | 33707.16 | 2.69414714 | 0.7146115 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & gene & lasso.as.is & bs.as.is & rf.as.is & sqrt.lasso.as.is & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 0.7745883 & 0.593005603 & 1266161.95 & 0.69519133 & 0.9232394 \\\\\n",
"\t5 & STAT4 & 2.6053432 & 1.708535525 & 649234.98 & 3.47937884 & 0.9047599 \\\\\n",
"\t35 & SATB1 & 0.0000000 & -0.568306798 & 577058.44 & 0.00000000 & 0.8628270 \\\\\n",
"\t15 & SATB2 & 0.1367563 & 1.035785852 & 424329.70 & 0.02139235 & 0.8423299 \\\\\n",
"\t33 & ATF2 & 0.0000000 & 0.892883655 & 137474.98 & 0.08809962 & 0.8278686 \\\\\n",
"\t4 & FOXP2 & 2.6150851 & 3.972475657 & 112640.28 & 2.44496515 & 0.8196172 \\\\\n",
"\t11 & TSHZ2 & 0.6924360 & 1.086813411 & 84319.20 & 1.00207756 & 0.7704362 \\\\\n",
"\t40 & DRGX & 0.0000000 & -0.004072208 & 88685.56 & 1.18904754 & 0.7309450 \\\\\n",
"\t1 & HDX & 11.2649243 & 0.324175587 & 53018.02 & 13.37342127 & 0.7241906 \\\\\n",
"\t8 & LHX6 & 1.2795080 & 2.312551744 & 33707.16 & 2.69414714 & 0.7146115 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | lasso.as.is | bs.as.is | rf.as.is | sqrt.lasso.as.is | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 0.7745883 | 0.593005603 | 1266161.95 | 0.69519133 | 0.9232394 | \n",
"| 5 | STAT4 | 2.6053432 | 1.708535525 | 649234.98 | 3.47937884 | 0.9047599 | \n",
"| 35 | SATB1 | 0.0000000 | -0.568306798 | 577058.44 | 0.00000000 | 0.8628270 | \n",
"| 15 | SATB2 | 0.1367563 | 1.035785852 | 424329.70 | 0.02139235 | 0.8423299 | \n",
"| 33 | ATF2 | 0.0000000 | 0.892883655 | 137474.98 | 0.08809962 | 0.8278686 | \n",
"| 4 | FOXP2 | 2.6150851 | 3.972475657 | 112640.28 | 2.44496515 | 0.8196172 | \n",
"| 11 | TSHZ2 | 0.6924360 | 1.086813411 | 84319.20 | 1.00207756 | 0.7704362 | \n",
"| 40 | DRGX | 0.0000000 | -0.004072208 | 88685.56 | 1.18904754 | 0.7309450 | \n",
"| 1 | HDX | 11.2649243 | 0.324175587 | 53018.02 | 13.37342127 | 0.7241906 | \n",
"| 8 | LHX6 | 1.2795080 | 2.312551744 | 33707.16 | 2.69414714 | 0.7146115 | \n",
"\n",
"\n"
],
"text/plain": [
" gene lasso.as.is bs.as.is rf.as.is sqrt.lasso.as.is gene.cor \n",
"9 HLF 0.7745883 0.593005603 1266161.95 0.69519133 0.9232394\n",
"5 STAT4 2.6053432 1.708535525 649234.98 3.47937884 0.9047599\n",
"35 SATB1 0.0000000 -0.568306798 577058.44 0.00000000 0.8628270\n",
"15 SATB2 0.1367563 1.035785852 424329.70 0.02139235 0.8423299\n",
"33 ATF2 0.0000000 0.892883655 137474.98 0.08809962 0.8278686\n",
"4 FOXP2 2.6150851 3.972475657 112640.28 2.44496515 0.8196172\n",
"11 TSHZ2 0.6924360 1.086813411 84319.20 1.00207756 0.7704362\n",
"40 DRGX 0.0000000 -0.004072208 88685.56 1.18904754 0.7309450\n",
"1 HDX 11.2649243 0.324175587 53018.02 13.37342127 0.7241906\n",
"8 LHX6 1.2795080 2.312551744 33707.16 2.69414714 0.7146115"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" | lasso.as.is | bs.as.is | rf.as.is | sqrt.lasso.as.is | gene.cor |
|---|
\n",
"\n",
"\t| lasso.as.is | 1.0000000 | 0.11796108 | 0.14940040 | 0.79793209 | 0.3394498 |
|---|
\n",
"\t| bs.as.is | 0.1179611 | 1.00000000 | 0.07497072 | -0.01188095 | 0.1611727 |
|---|
\n",
"\t| rf.as.is | 0.1494004 | 0.07497072 | 1.00000000 | 0.12236983 | 0.5062618 |
|---|
\n",
"\t| sqrt.lasso.as.is | 0.7979321 | -0.01188095 | 0.12236983 | 1.00000000 | 0.3866010 |
|---|
\n",
"\t| gene.cor | 0.3394498 | 0.16117270 | 0.50626182 | 0.38660105 | 1.0000000 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & lasso.as.is & bs.as.is & rf.as.is & sqrt.lasso.as.is & gene.cor\\\\\n",
"\\hline\n",
"\tlasso.as.is & 1.0000000 & 0.11796108 & 0.14940040 & 0.79793209 & 0.3394498 \\\\\n",
"\tbs.as.is & 0.1179611 & 1.00000000 & 0.07497072 & -0.01188095 & 0.1611727 \\\\\n",
"\trf.as.is & 0.1494004 & 0.07497072 & 1.00000000 & 0.12236983 & 0.5062618 \\\\\n",
"\tsqrt.lasso.as.is & 0.7979321 & -0.01188095 & 0.12236983 & 1.00000000 & 0.3866010 \\\\\n",
"\tgene.cor & 0.3394498 & 0.16117270 & 0.50626182 & 0.38660105 & 1.0000000 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | lasso.as.is | bs.as.is | rf.as.is | sqrt.lasso.as.is | gene.cor | \n",
"|---|---|---|---|---|\n",
"| lasso.as.is | 1.0000000 | 0.11796108 | 0.14940040 | 0.79793209 | 0.3394498 | \n",
"| bs.as.is | 0.1179611 | 1.00000000 | 0.07497072 | -0.01188095 | 0.1611727 | \n",
"| rf.as.is | 0.1494004 | 0.07497072 | 1.00000000 | 0.12236983 | 0.5062618 | \n",
"| sqrt.lasso.as.is | 0.7979321 | -0.01188095 | 0.12236983 | 1.00000000 | 0.3866010 | \n",
"| gene.cor | 0.3394498 | 0.16117270 | 0.50626182 | 0.38660105 | 1.0000000 | \n",
"\n",
"\n"
],
"text/plain": [
" lasso.as.is bs.as.is rf.as.is sqrt.lasso.as.is gene.cor \n",
"lasso.as.is 1.0000000 0.11796108 0.14940040 0.79793209 0.3394498\n",
"bs.as.is 0.1179611 1.00000000 0.07497072 -0.01188095 0.1611727\n",
"rf.as.is 0.1494004 0.07497072 1.00000000 0.12236983 0.5062618\n",
"sqrt.lasso.as.is 0.7979321 -0.01188095 0.12236983 1.00000000 0.3866010\n",
"gene.cor 0.3394498 0.16117270 0.50626182 0.38660105 1.0000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(tbl.all[,c(1,2,6,10,14,18)],10)\n",
"cor(tbl.all[c(2,6,10,14,18)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we might expect, the 2 LASSO variations have the highest correlation, but otherwise there's not very good agreement between solvers. In terms of the gene correlations themselves, the Random Forest solver has the strongest agreement for the \"as-is\" data matrix, both LASSO variations are a little weaker, and the Bayes Spike shows virtually no correlation with the Pearson coefficients. Let us move on to the transformations to see what differences there are; we'll begin with log2-transformed data."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | lasso.log2 | bs.log2 | rf.log2 | sqrt.lasso.log2 | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 0.13419907 | 3.228768e-01 | 57.371322 | 0.140776460 | 0.9232394 |
|---|
\n",
"\t| 5 | STAT4 | 0.15719002 | 1.832979e-01 | 46.374066 | 0.190961358 | 0.9047599 |
|---|
\n",
"\t| 35 | SATB1 | 0.00000000 | -1.444623e-06 | 21.194070 | 0.000000000 | 0.8628270 |
|---|
\n",
"\t| 15 | SATB2 | 0.21613389 | 6.942847e-05 | 26.355675 | 0.181298914 | 0.8423299 |
|---|
\n",
"\t| 33 | ATF2 | 0.00000000 | 5.648769e-05 | 5.411294 | 0.008485763 | 0.8278686 |
|---|
\n",
"\t| 4 | FOXP2 | 0.10666729 | 8.526235e-05 | 3.914452 | 0.101105760 | 0.8196172 |
|---|
\n",
"\t| 11 | TSHZ2 | 0.10456283 | 2.277099e-01 | 5.122529 | 0.100506784 | 0.7704362 |
|---|
\n",
"\t| 40 | DRGX | 0.00000000 | 1.835087e-07 | 10.128620 | 0.000000000 | 0.7309450 |
|---|
\n",
"\t| 1 | HDX | 0.00000000 | -2.498712e-05 | 1.436412 | 0.000000000 | 0.7241906 |
|---|
\n",
"\t| 8 | LHX6 | 0.06635221 | 6.697943e-05 | 2.907339 | 0.058348346 | 0.7146115 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & gene & lasso.log2 & bs.log2 & rf.log2 & sqrt.lasso.log2 & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 0.13419907 & 3.228768e-01 & 57.371322 & 0.140776460 & 0.9232394 \\\\\n",
"\t5 & STAT4 & 0.15719002 & 1.832979e-01 & 46.374066 & 0.190961358 & 0.9047599 \\\\\n",
"\t35 & SATB1 & 0.00000000 & -1.444623e-06 & 21.194070 & 0.000000000 & 0.8628270 \\\\\n",
"\t15 & SATB2 & 0.21613389 & 6.942847e-05 & 26.355675 & 0.181298914 & 0.8423299 \\\\\n",
"\t33 & ATF2 & 0.00000000 & 5.648769e-05 & 5.411294 & 0.008485763 & 0.8278686 \\\\\n",
"\t4 & FOXP2 & 0.10666729 & 8.526235e-05 & 3.914452 & 0.101105760 & 0.8196172 \\\\\n",
"\t11 & TSHZ2 & 0.10456283 & 2.277099e-01 & 5.122529 & 0.100506784 & 0.7704362 \\\\\n",
"\t40 & DRGX & 0.00000000 & 1.835087e-07 & 10.128620 & 0.000000000 & 0.7309450 \\\\\n",
"\t1 & HDX & 0.00000000 & -2.498712e-05 & 1.436412 & 0.000000000 & 0.7241906 \\\\\n",
"\t8 & LHX6 & 0.06635221 & 6.697943e-05 & 2.907339 & 0.058348346 & 0.7146115 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | lasso.log2 | bs.log2 | rf.log2 | sqrt.lasso.log2 | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 0.13419907 | 3.228768e-01 | 57.371322 | 0.140776460 | 0.9232394 | \n",
"| 5 | STAT4 | 0.15719002 | 1.832979e-01 | 46.374066 | 0.190961358 | 0.9047599 | \n",
"| 35 | SATB1 | 0.00000000 | -1.444623e-06 | 21.194070 | 0.000000000 | 0.8628270 | \n",
"| 15 | SATB2 | 0.21613389 | 6.942847e-05 | 26.355675 | 0.181298914 | 0.8423299 | \n",
"| 33 | ATF2 | 0.00000000 | 5.648769e-05 | 5.411294 | 0.008485763 | 0.8278686 | \n",
"| 4 | FOXP2 | 0.10666729 | 8.526235e-05 | 3.914452 | 0.101105760 | 0.8196172 | \n",
"| 11 | TSHZ2 | 0.10456283 | 2.277099e-01 | 5.122529 | 0.100506784 | 0.7704362 | \n",
"| 40 | DRGX | 0.00000000 | 1.835087e-07 | 10.128620 | 0.000000000 | 0.7309450 | \n",
"| 1 | HDX | 0.00000000 | -2.498712e-05 | 1.436412 | 0.000000000 | 0.7241906 | \n",
"| 8 | LHX6 | 0.06635221 | 6.697943e-05 | 2.907339 | 0.058348346 | 0.7146115 | \n",
"\n",
"\n"
],
"text/plain": [
" gene lasso.log2 bs.log2 rf.log2 sqrt.lasso.log2 gene.cor \n",
"9 HLF 0.13419907 3.228768e-01 57.371322 0.140776460 0.9232394\n",
"5 STAT4 0.15719002 1.832979e-01 46.374066 0.190961358 0.9047599\n",
"35 SATB1 0.00000000 -1.444623e-06 21.194070 0.000000000 0.8628270\n",
"15 SATB2 0.21613389 6.942847e-05 26.355675 0.181298914 0.8423299\n",
"33 ATF2 0.00000000 5.648769e-05 5.411294 0.008485763 0.8278686\n",
"4 FOXP2 0.10666729 8.526235e-05 3.914452 0.101105760 0.8196172\n",
"11 TSHZ2 0.10456283 2.277099e-01 5.122529 0.100506784 0.7704362\n",
"40 DRGX 0.00000000 1.835087e-07 10.128620 0.000000000 0.7309450\n",
"1 HDX 0.00000000 -2.498712e-05 1.436412 0.000000000 0.7241906\n",
"8 LHX6 0.06635221 6.697943e-05 2.907339 0.058348346 0.7146115"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" | lasso.log2 | bs.log2 | rf.log2 | sqrt.lasso.log2 | gene.cor |
|---|
\n",
"\n",
"\t| lasso.log2 | 1.0000000 | 0.4912528 | 0.6601102 | 0.9852655 | 0.7078153 |
|---|
\n",
"\t| bs.log2 | 0.4912528 | 1.0000000 | 0.5400662 | 0.5151536 | 0.4787279 |
|---|
\n",
"\t| rf.log2 | 0.6601102 | 0.5400662 | 1.0000000 | 0.7166040 | 0.5547292 |
|---|
\n",
"\t| sqrt.lasso.log2 | 0.9852655 | 0.5151536 | 0.7166040 | 1.0000000 | 0.7057869 |
|---|
\n",
"\t| gene.cor | 0.7078153 | 0.4787279 | 0.5547292 | 0.7057869 | 1.0000000 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & lasso.log2 & bs.log2 & rf.log2 & sqrt.lasso.log2 & gene.cor\\\\\n",
"\\hline\n",
"\tlasso.log2 & 1.0000000 & 0.4912528 & 0.6601102 & 0.9852655 & 0.7078153\\\\\n",
"\tbs.log2 & 0.4912528 & 1.0000000 & 0.5400662 & 0.5151536 & 0.4787279\\\\\n",
"\trf.log2 & 0.6601102 & 0.5400662 & 1.0000000 & 0.7166040 & 0.5547292\\\\\n",
"\tsqrt.lasso.log2 & 0.9852655 & 0.5151536 & 0.7166040 & 1.0000000 & 0.7057869\\\\\n",
"\tgene.cor & 0.7078153 & 0.4787279 & 0.5547292 & 0.7057869 & 1.0000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | lasso.log2 | bs.log2 | rf.log2 | sqrt.lasso.log2 | gene.cor | \n",
"|---|---|---|---|---|\n",
"| lasso.log2 | 1.0000000 | 0.4912528 | 0.6601102 | 0.9852655 | 0.7078153 | \n",
"| bs.log2 | 0.4912528 | 1.0000000 | 0.5400662 | 0.5151536 | 0.4787279 | \n",
"| rf.log2 | 0.6601102 | 0.5400662 | 1.0000000 | 0.7166040 | 0.5547292 | \n",
"| sqrt.lasso.log2 | 0.9852655 | 0.5151536 | 0.7166040 | 1.0000000 | 0.7057869 | \n",
"| gene.cor | 0.7078153 | 0.4787279 | 0.5547292 | 0.7057869 | 1.0000000 | \n",
"\n",
"\n"
],
"text/plain": [
" lasso.log2 bs.log2 rf.log2 sqrt.lasso.log2 gene.cor \n",
"lasso.log2 1.0000000 0.4912528 0.6601102 0.9852655 0.7078153\n",
"bs.log2 0.4912528 1.0000000 0.5400662 0.5151536 0.4787279\n",
"rf.log2 0.6601102 0.5400662 1.0000000 0.7166040 0.5547292\n",
"sqrt.lasso.log2 0.9852655 0.5151536 0.7166040 1.0000000 0.7057869\n",
"gene.cor 0.7078153 0.4787279 0.5547292 0.7057869 1.0000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(tbl.all[,c(1,3,7,11,15,18)],10)\n",
"cor(tbl.all[c(3,7,11,15,18)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once again, the 2 LASSO methods are in close agreement, this time agreeing nearly identically. We now also see much stronger agreement between all methods, with correlation coefficients in the 0.5-0.66 range. In contrast to the \"as-is\", the log2-transformed data demonstrate at least some correlation between each solver and the gene correlation coefficients, though the 2 LASSO methods have the highest coefficients. Now we'll look at the asinh-transformed data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | lasso.asinh | bs.asinh | rf.asinh | sqrt.lasso.asinh | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 0.209422903 | 8.583764e-05 | 25.9160383 | 0.14289609 | 0.9232394 |
|---|
\n",
"\t| 5 | STAT4 | 0.076155038 | 8.946250e-05 | 22.4786757 | 0.15896033 | 0.9047599 |
|---|
\n",
"\t| 35 | SATB1 | 0.000000000 | 1.154825e-05 | 11.9869878 | 0.00000000 | 0.8628270 |
|---|
\n",
"\t| 15 | SATB2 | 0.228754703 | 3.566778e-01 | 14.2251862 | 0.22652672 | 0.8423299 |
|---|
\n",
"\t| 33 | ATF2 | 0.009811994 | 7.409788e-05 | 2.3401488 | 0.00000000 | 0.8278686 |
|---|
\n",
"\t| 4 | FOXP2 | 0.120449138 | 2.309917e-01 | 2.9705266 | 0.10635813 | 0.8196172 |
|---|
\n",
"\t| 11 | TSHZ2 | 0.115311122 | 1.056974e-01 | 2.2511790 | 0.09486455 | 0.7704362 |
|---|
\n",
"\t| 40 | DRGX | 0.000000000 | 7.377633e-06 | 3.2287507 | 0.00000000 | 0.7309450 |
|---|
\n",
"\t| 1 | HDX | 0.000000000 | 6.947728e-05 | 0.7157802 | 0.00000000 | 0.7241906 |
|---|
\n",
"\t| 8 | LHX6 | 0.090529696 | 1.139972e-01 | 1.7027348 | 0.08655747 | 0.7146115 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & gene & lasso.asinh & bs.asinh & rf.asinh & sqrt.lasso.asinh & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 0.209422903 & 8.583764e-05 & 25.9160383 & 0.14289609 & 0.9232394 \\\\\n",
"\t5 & STAT4 & 0.076155038 & 8.946250e-05 & 22.4786757 & 0.15896033 & 0.9047599 \\\\\n",
"\t35 & SATB1 & 0.000000000 & 1.154825e-05 & 11.9869878 & 0.00000000 & 0.8628270 \\\\\n",
"\t15 & SATB2 & 0.228754703 & 3.566778e-01 & 14.2251862 & 0.22652672 & 0.8423299 \\\\\n",
"\t33 & ATF2 & 0.009811994 & 7.409788e-05 & 2.3401488 & 0.00000000 & 0.8278686 \\\\\n",
"\t4 & FOXP2 & 0.120449138 & 2.309917e-01 & 2.9705266 & 0.10635813 & 0.8196172 \\\\\n",
"\t11 & TSHZ2 & 0.115311122 & 1.056974e-01 & 2.2511790 & 0.09486455 & 0.7704362 \\\\\n",
"\t40 & DRGX & 0.000000000 & 7.377633e-06 & 3.2287507 & 0.00000000 & 0.7309450 \\\\\n",
"\t1 & HDX & 0.000000000 & 6.947728e-05 & 0.7157802 & 0.00000000 & 0.7241906 \\\\\n",
"\t8 & LHX6 & 0.090529696 & 1.139972e-01 & 1.7027348 & 0.08655747 & 0.7146115 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | lasso.asinh | bs.asinh | rf.asinh | sqrt.lasso.asinh | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 0.209422903 | 8.583764e-05 | 25.9160383 | 0.14289609 | 0.9232394 | \n",
"| 5 | STAT4 | 0.076155038 | 8.946250e-05 | 22.4786757 | 0.15896033 | 0.9047599 | \n",
"| 35 | SATB1 | 0.000000000 | 1.154825e-05 | 11.9869878 | 0.00000000 | 0.8628270 | \n",
"| 15 | SATB2 | 0.228754703 | 3.566778e-01 | 14.2251862 | 0.22652672 | 0.8423299 | \n",
"| 33 | ATF2 | 0.009811994 | 7.409788e-05 | 2.3401488 | 0.00000000 | 0.8278686 | \n",
"| 4 | FOXP2 | 0.120449138 | 2.309917e-01 | 2.9705266 | 0.10635813 | 0.8196172 | \n",
"| 11 | TSHZ2 | 0.115311122 | 1.056974e-01 | 2.2511790 | 0.09486455 | 0.7704362 | \n",
"| 40 | DRGX | 0.000000000 | 7.377633e-06 | 3.2287507 | 0.00000000 | 0.7309450 | \n",
"| 1 | HDX | 0.000000000 | 6.947728e-05 | 0.7157802 | 0.00000000 | 0.7241906 | \n",
"| 8 | LHX6 | 0.090529696 | 1.139972e-01 | 1.7027348 | 0.08655747 | 0.7146115 | \n",
"\n",
"\n"
],
"text/plain": [
" gene lasso.asinh bs.asinh rf.asinh sqrt.lasso.asinh gene.cor \n",
"9 HLF 0.209422903 8.583764e-05 25.9160383 0.14289609 0.9232394\n",
"5 STAT4 0.076155038 8.946250e-05 22.4786757 0.15896033 0.9047599\n",
"35 SATB1 0.000000000 1.154825e-05 11.9869878 0.00000000 0.8628270\n",
"15 SATB2 0.228754703 3.566778e-01 14.2251862 0.22652672 0.8423299\n",
"33 ATF2 0.009811994 7.409788e-05 2.3401488 0.00000000 0.8278686\n",
"4 FOXP2 0.120449138 2.309917e-01 2.9705266 0.10635813 0.8196172\n",
"11 TSHZ2 0.115311122 1.056974e-01 2.2511790 0.09486455 0.7704362\n",
"40 DRGX 0.000000000 7.377633e-06 3.2287507 0.00000000 0.7309450\n",
"1 HDX 0.000000000 6.947728e-05 0.7157802 0.00000000 0.7241906\n",
"8 LHX6 0.090529696 1.139972e-01 1.7027348 0.08655747 0.7146115"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" | lasso.asinh | bs.asinh | rf.asinh | sqrt.lasso.asinh | gene.cor |
|---|
\n",
"\n",
"\t| lasso.asinh | 1.0000000 | 0.6089727 | 0.6190488 | 0.8586945 | 0.6848150 |
|---|
\n",
"\t| bs.asinh | 0.6089727 | 1.0000000 | 0.2264054 | 0.7004467 | 0.5063666 |
|---|
\n",
"\t| rf.asinh | 0.6190488 | 0.2264054 | 1.0000000 | 0.6842537 | 0.5657811 |
|---|
\n",
"\t| sqrt.lasso.asinh | 0.8586945 | 0.7004467 | 0.6842537 | 1.0000000 | 0.7040126 |
|---|
\n",
"\t| gene.cor | 0.6848150 | 0.5063666 | 0.5657811 | 0.7040126 | 1.0000000 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & lasso.asinh & bs.asinh & rf.asinh & sqrt.lasso.asinh & gene.cor\\\\\n",
"\\hline\n",
"\tlasso.asinh & 1.0000000 & 0.6089727 & 0.6190488 & 0.8586945 & 0.6848150\\\\\n",
"\tbs.asinh & 0.6089727 & 1.0000000 & 0.2264054 & 0.7004467 & 0.5063666\\\\\n",
"\trf.asinh & 0.6190488 & 0.2264054 & 1.0000000 & 0.6842537 & 0.5657811\\\\\n",
"\tsqrt.lasso.asinh & 0.8586945 & 0.7004467 & 0.6842537 & 1.0000000 & 0.7040126\\\\\n",
"\tgene.cor & 0.6848150 & 0.5063666 & 0.5657811 & 0.7040126 & 1.0000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | lasso.asinh | bs.asinh | rf.asinh | sqrt.lasso.asinh | gene.cor | \n",
"|---|---|---|---|---|\n",
"| lasso.asinh | 1.0000000 | 0.6089727 | 0.6190488 | 0.8586945 | 0.6848150 | \n",
"| bs.asinh | 0.6089727 | 1.0000000 | 0.2264054 | 0.7004467 | 0.5063666 | \n",
"| rf.asinh | 0.6190488 | 0.2264054 | 1.0000000 | 0.6842537 | 0.5657811 | \n",
"| sqrt.lasso.asinh | 0.8586945 | 0.7004467 | 0.6842537 | 1.0000000 | 0.7040126 | \n",
"| gene.cor | 0.6848150 | 0.5063666 | 0.5657811 | 0.7040126 | 1.0000000 | \n",
"\n",
"\n"
],
"text/plain": [
" lasso.asinh bs.asinh rf.asinh sqrt.lasso.asinh gene.cor \n",
"lasso.asinh 1.0000000 0.6089727 0.6190488 0.8586945 0.6848150\n",
"bs.asinh 0.6089727 1.0000000 0.2264054 0.7004467 0.5063666\n",
"rf.asinh 0.6190488 0.2264054 1.0000000 0.6842537 0.5657811\n",
"sqrt.lasso.asinh 0.8586945 0.7004467 0.6842537 1.0000000 0.7040126\n",
"gene.cor 0.6848150 0.5063666 0.5657811 0.7040126 1.0000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(tbl.all[,c(1,4,8,12,16,18)],10)\n",
"cor(tbl.all[c(4,8,12,16,18)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Outside of the expected high agreement between LASSO methods, this transformation also shows fairly strong agreement between Random Forest and the LASSO methods. Bayes Spike is less correlated with the others, but still much more so than in the as-is case. Compared to the gene correlation coefficients, the 2 LASSO methods still show the strongest agreement and Bayes Spike has fallen off a bit. Let's finish by looking at the VOOM-transformed data."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" | gene | lasso.voom | bs.voom | rf.voom | sqrt.lasso.voom | gene.cor |
|---|
\n",
"\n",
"\t| 9 | HLF | 0.24105628 | 3.468772e-01 | 53.0524708 | 0.15897283 | 0.9232394 |
|---|
\n",
"\t| 5 | STAT4 | 0.09551869 | 1.708533e-01 | 48.9494236 | 0.17336532 | 0.9047599 |
|---|
\n",
"\t| 35 | SATB1 | 0.00000000 | -3.633166e-05 | 27.9882393 | 0.00000000 | 0.8628270 |
|---|
\n",
"\t| 15 | SATB2 | 0.26140384 | 2.853092e-01 | 30.4646927 | 0.23814460 | 0.8423299 |
|---|
\n",
"\t| 33 | ATF2 | 0.00000000 | -5.996884e-06 | 7.3519720 | 0.00000000 | 0.8278686 |
|---|
\n",
"\t| 4 | FOXP2 | 0.13231032 | 4.508482e-05 | 3.8044576 | 0.10469561 | 0.8196172 |
|---|
\n",
"\t| 11 | TSHZ2 | 0.09676352 | 1.235520e-01 | 4.6044307 | 0.08923052 | 0.7704362 |
|---|
\n",
"\t| 40 | DRGX | 0.00000000 | 1.398560e-05 | 8.6275128 | 0.00000000 | 0.7309450 |
|---|
\n",
"\t| 1 | HDX | 0.00000000 | 5.735278e-05 | 0.8661305 | 0.00000000 | 0.7241906 |
|---|
\n",
"\t| 8 | LHX6 | 0.09315715 | 1.365092e-01 | 2.2815335 | 0.09288872 | 0.7146115 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & gene & lasso.voom & bs.voom & rf.voom & sqrt.lasso.voom & gene.cor\\\\\n",
"\\hline\n",
"\t9 & HLF & 0.24105628 & 3.468772e-01 & 53.0524708 & 0.15897283 & 0.9232394 \\\\\n",
"\t5 & STAT4 & 0.09551869 & 1.708533e-01 & 48.9494236 & 0.17336532 & 0.9047599 \\\\\n",
"\t35 & SATB1 & 0.00000000 & -3.633166e-05 & 27.9882393 & 0.00000000 & 0.8628270 \\\\\n",
"\t15 & SATB2 & 0.26140384 & 2.853092e-01 & 30.4646927 & 0.23814460 & 0.8423299 \\\\\n",
"\t33 & ATF2 & 0.00000000 & -5.996884e-06 & 7.3519720 & 0.00000000 & 0.8278686 \\\\\n",
"\t4 & FOXP2 & 0.13231032 & 4.508482e-05 & 3.8044576 & 0.10469561 & 0.8196172 \\\\\n",
"\t11 & TSHZ2 & 0.09676352 & 1.235520e-01 & 4.6044307 & 0.08923052 & 0.7704362 \\\\\n",
"\t40 & DRGX & 0.00000000 & 1.398560e-05 & 8.6275128 & 0.00000000 & 0.7309450 \\\\\n",
"\t1 & HDX & 0.00000000 & 5.735278e-05 & 0.8661305 & 0.00000000 & 0.7241906 \\\\\n",
"\t8 & LHX6 & 0.09315715 & 1.365092e-01 & 2.2815335 & 0.09288872 & 0.7146115 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | gene | lasso.voom | bs.voom | rf.voom | sqrt.lasso.voom | gene.cor | \n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 9 | HLF | 0.24105628 | 3.468772e-01 | 53.0524708 | 0.15897283 | 0.9232394 | \n",
"| 5 | STAT4 | 0.09551869 | 1.708533e-01 | 48.9494236 | 0.17336532 | 0.9047599 | \n",
"| 35 | SATB1 | 0.00000000 | -3.633166e-05 | 27.9882393 | 0.00000000 | 0.8628270 | \n",
"| 15 | SATB2 | 0.26140384 | 2.853092e-01 | 30.4646927 | 0.23814460 | 0.8423299 | \n",
"| 33 | ATF2 | 0.00000000 | -5.996884e-06 | 7.3519720 | 0.00000000 | 0.8278686 | \n",
"| 4 | FOXP2 | 0.13231032 | 4.508482e-05 | 3.8044576 | 0.10469561 | 0.8196172 | \n",
"| 11 | TSHZ2 | 0.09676352 | 1.235520e-01 | 4.6044307 | 0.08923052 | 0.7704362 | \n",
"| 40 | DRGX | 0.00000000 | 1.398560e-05 | 8.6275128 | 0.00000000 | 0.7309450 | \n",
"| 1 | HDX | 0.00000000 | 5.735278e-05 | 0.8661305 | 0.00000000 | 0.7241906 | \n",
"| 8 | LHX6 | 0.09315715 | 1.365092e-01 | 2.2815335 | 0.09288872 | 0.7146115 | \n",
"\n",
"\n"
],
"text/plain": [
" gene lasso.voom bs.voom rf.voom sqrt.lasso.voom gene.cor \n",
"9 HLF 0.24105628 3.468772e-01 53.0524708 0.15897283 0.9232394\n",
"5 STAT4 0.09551869 1.708533e-01 48.9494236 0.17336532 0.9047599\n",
"35 SATB1 0.00000000 -3.633166e-05 27.9882393 0.00000000 0.8628270\n",
"15 SATB2 0.26140384 2.853092e-01 30.4646927 0.23814460 0.8423299\n",
"33 ATF2 0.00000000 -5.996884e-06 7.3519720 0.00000000 0.8278686\n",
"4 FOXP2 0.13231032 4.508482e-05 3.8044576 0.10469561 0.8196172\n",
"11 TSHZ2 0.09676352 1.235520e-01 4.6044307 0.08923052 0.7704362\n",
"40 DRGX 0.00000000 1.398560e-05 8.6275128 0.00000000 0.7309450\n",
"1 HDX 0.00000000 5.735278e-05 0.8661305 0.00000000 0.7241906\n",
"8 LHX6 0.09315715 1.365092e-01 2.2815335 0.09288872 0.7146115"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" | lasso.voom | bs.voom | rf.voom | sqrt.lasso.voom | gene.cor |
|---|
\n",
"\n",
"\t| lasso.voom | 1.0000000 | 0.7414561 | 0.6195594 | 0.9121495 | 0.6367442 |
|---|
\n",
"\t| bs.voom | 0.7414561 | 1.0000000 | 0.6838455 | 0.6833242 | 0.5055787 |
|---|
\n",
"\t| rf.voom | 0.6195594 | 0.6838455 | 1.0000000 | 0.6444418 | 0.5682585 |
|---|
\n",
"\t| sqrt.lasso.voom | 0.9121495 | 0.6833242 | 0.6444418 | 1.0000000 | 0.6647592 |
|---|
\n",
"\t| gene.cor | 0.6367442 | 0.5055787 | 0.5682585 | 0.6647592 | 1.0000000 |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & lasso.voom & bs.voom & rf.voom & sqrt.lasso.voom & gene.cor\\\\\n",
"\\hline\n",
"\tlasso.voom & 1.0000000 & 0.7414561 & 0.6195594 & 0.9121495 & 0.6367442\\\\\n",
"\tbs.voom & 0.7414561 & 1.0000000 & 0.6838455 & 0.6833242 & 0.5055787\\\\\n",
"\trf.voom & 0.6195594 & 0.6838455 & 1.0000000 & 0.6444418 & 0.5682585\\\\\n",
"\tsqrt.lasso.voom & 0.9121495 & 0.6833242 & 0.6444418 & 1.0000000 & 0.6647592\\\\\n",
"\tgene.cor & 0.6367442 & 0.5055787 & 0.5682585 & 0.6647592 & 1.0000000\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| | lasso.voom | bs.voom | rf.voom | sqrt.lasso.voom | gene.cor | \n",
"|---|---|---|---|---|\n",
"| lasso.voom | 1.0000000 | 0.7414561 | 0.6195594 | 0.9121495 | 0.6367442 | \n",
"| bs.voom | 0.7414561 | 1.0000000 | 0.6838455 | 0.6833242 | 0.5055787 | \n",
"| rf.voom | 0.6195594 | 0.6838455 | 1.0000000 | 0.6444418 | 0.5682585 | \n",
"| sqrt.lasso.voom | 0.9121495 | 0.6833242 | 0.6444418 | 1.0000000 | 0.6647592 | \n",
"| gene.cor | 0.6367442 | 0.5055787 | 0.5682585 | 0.6647592 | 1.0000000 | \n",
"\n",
"\n"
],
"text/plain": [
" lasso.voom bs.voom rf.voom sqrt.lasso.voom gene.cor \n",
"lasso.voom 1.0000000 0.7414561 0.6195594 0.9121495 0.6367442\n",
"bs.voom 0.7414561 1.0000000 0.6838455 0.6833242 0.5055787\n",
"rf.voom 0.6195594 0.6838455 1.0000000 0.6444418 0.5682585\n",
"sqrt.lasso.voom 0.9121495 0.6833242 0.6444418 1.0000000 0.6647592\n",
"gene.cor 0.6367442 0.5055787 0.5682585 0.6647592 1.0000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(tbl.all[,c(1,5,9,13,17,18)],10)\n",
"cor(tbl.all[c(5,9,13,17,18)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, we see the strongest overall agreement between solvers. This is mainly driven by heightened correlation between Bayes Spike and the other methods. The two LASSO methods still have the highest agreement with the Pearson correlations between target gene and transcription factors, but they are not as strong as in the other transformations. \n",
"\n",
"At this point, we've delved into a whole bunch of information and pulled out lots of correlations between the different transformations, different solvers, and the gene correlations coefficients. So let's step back and sum up what we've seen."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Takeaways\n",
"\n",
"Based upon this short data exploration, there are a few key points to take away:\n",
"\n",
"1. Data transformation can profoundly affect results. There is no guarantee of consistency across different transformations, even for the same solvers. Furthermore, different transformations can affect which solvers agree with one another. \n",
"\n",
"2. Random Forest scores are the most consistent across different transformations, so the distribution of the data may be less of a concern if using this solver. LASSO and Square Root LASSO also show strong agreement between the transformed cases, but not compared to the as-is data.\n",
"\n",
"3. Regardless of transformation, LASSO coefficients and Random Forest scores tend to have stronger correlation with gene correlations than do Bayes Spike coefficients.\n",
"\n",
"Though it is tempting to recommend a particular solver and/or transformation to use in every case, this is not a realistic outcome of the exploration done here. Rather, these data should serve as an example of the benefits and consequences involved with these choices, as we have demonstrated that choice of solver and transformation have a sizable impact on the final results.\n",
"\n",
"Perhaps the best summation of this foray into different transformations is found in the [VOOM paper itself](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29):\n",
"\n",
"\"...correct modeling of the mean-variance relationship inherent in a data generating process is the key to designing statistically powerful methods of analysis.\"\n",
"\n",
"Thus, transformation techniques such as VOOM that take into account the mean-variance relationship represent a standard way for preparing robust data for use in tools such as TReNA."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.3.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}