diff --git a/tests/montage_from_records.ipynb b/tests/montage_from_records.ipynb
new file mode 100644
index 0000000..f92f4e2
--- /dev/null
+++ b/tests/montage_from_records.ipynb
@@ -0,0 +1,737 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mrcfile\n",
+    "import mdocfile\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import ndimage\n",
+    "import mdocfile\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with mrcfile.open(\"/data/elferich/CryoTEM/Johannes_20210803/ERHoxB8undiff2/record_lamella22.mrc\") as mrc:\n",
+    "    records = mrc.data.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(458, 2046, 2880)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "records.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o = []\n",
+    "for r in records:\n",
+    "    i = np.fft.fft2(r)\n",
+    "    res = ndimage.fourier_gaussian(i,sigma=50)\n",
+    "    res = np.fft.ifft2(res).real\n",
+    "    res = res > 0.5 * np.max(res)\n",
+    "    o.append(res)\n",
+    "masks = np.array(o)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4201e64c10>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = np.fft.ifft2(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(result[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fd13c1b0340>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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3wCDUMe26JlYQIpwIaGFwFEZSRgt2xLoT6BpYqwkU7DQgReqMwxwkxQGAcds+7WwqJ1ahIeO2QE7AmI6DACfpa0UWJ6AlEpPFaFjh2tFV4OgqqpsPYPj0CMMvPAVc/Apw+QqwsZG5mIpAps8p08tuk3436imyGwi8O0m2Ay9lq8SDkWmuIr6udHwJMi2CKIQArKygOnQQ8aYj2Dh+AON9A9SrqS1iCRlerjEatllrmdQ6cICDq4sEUOi64BSwWD3UchISAJFyIoJaZwTUqOWGrSLtPwpkxIoi4dUVtB5v5WnaZa8z5SAmy45vfxWt68oAL/FTtVFRraWouVYKaXLbXFtZxcaRm1BtHMPgygjDz38F4YtfbkjA4zFiFAA1B/G3l152svQupA5R8mAPXq6L7FRwshmZtjfRLNaZOWUqt2UwAAYDVEePYOP8SawfXtEoICa3xhAQhwBGARsHh41CZz4IEW8VXLRKm1028r/+DqTvNRqFTJYbPSemczAA5XEhi4+f1+Q78WM84BFLkbqbonMZSR2AATjcbj0a0easSSDHSxy0lpdxKiOGRAw2lqv2WL1aoV5ZwcbRExjccgwrX7yC6vMXGwLwtQ3NOTIVzEwihPfSy3aUJVA79gaAAXrrSy+zyazWo64NE2fhJExzKc0AakpuIg57DqsrCMeOYnziMK7ctK+xthBXhK0SIUZgFEhp5++JghexNgyAauSsGqX5iMFFAbw0JF7ioBAYMuAomV6MZcVU5cm51B7muDBAQ2j6q4BGgBuDBLKyKIemark+dSq3GiWwp/V7t1d7TRN51UZstbyZ8doA47OHEG4+gOHlEYZfuozw+S8jXr6sVhnhyvQWmV52tPjUJvw5h+wNALNgiNael73oW5+1v5MsKkACFiXX0TSLS8fvmcWFXUSDAcK+NYTDhxAP7m+4LfsHLW8lKWugBS9jx0tB4qWwSwVAS+K1/wefjJSVtXMBaSSRHK4JALTAombLhbRRs/NavgmDHS0zOsDRtslwUxywUS5OBmKAFlmULTPCsyELD0AWI7YgaX9o3BTQtJ8DIIyQtk9YqbBxeAUbR46hOnsEq1+4bIi/cTyez73UZ/ftZbvJErLwAnsFwPTgZTHpJ7wk03K9lL5POj6H62gqcDlwAPHcSayfONDuHQQT/svkV1a8CiKc6cTsPVQRJ0Wa0OZaYfcIBgQ0SMkLETcVDlXyTRI67lt7CrmqhM+i1zJQov/ZusF1ibWFiboMMnQbAnAZdiykbd6V5blAWj4ScFJgFIMZH43iaq/RiCbXn/FahatnDqI6eQArF9cxuPAlxEtfUSAzNRR7HvCyFxcsvdw4odxRi8rSGYUPPfQQvvmbvxmHDx/GqVOn8L3f+734xCc+Yc550YtehBCC+fvRH/1Rc87jjz+Ol770pThw4ABOnTqFn/7pn8ZoNFq8Yb37qJdZZFKeFjkufxz2PCuZd9pxc0qwrqLBQF1FYThEdewo4tc+E+vPuxVXnnEI432VKkYN+ZUcKFooLMgAFEhIRE6oY2NFaQGAuHNCHZOVJLryQ5skTrLv+tctIiWZQ3IDJcIvNXHsypd2B5gZS11g5G4y9XK6FP/+x/RnLD3s5uJhcxYeHauC5UfcUOxOUqsMua5Kri7jctN7GVAPA9ZP7MOV55zB+Dm3oTp9M6pDBxtX4XDYPBuhKicrBOxz229Z0Mt2EOalLqifl26B+Z//83/ivvvuwzd/8zdjNBrhX/2rf4W7774bH//4x3Hw4EE970d+5Efwxje+Ub8fOHBA/x+Px3jpS1+KM2fO4P3vfz+eeOIJ3HvvvVhZWcHP//zPL7vJvfSShEHJpHMmuYQ2QcxNRRJwKVlczp7AlVMHUa9VVhE6ywTzXvSc9lMTvTmLiTm/RgpHDsEQWcVaoTwWASheITvgATiwwKezq0XqZJDg+4jkEtOEerC/K7k3hNxKRBFOHuSou6xuXUwudJrr4dBwbwXyXBwlDhOvR4XHmqxHbO26dtMaNo6cxODqTVj50hXgc18EhCczxuQwbG1IIT9Rb33pZYfJ0gHMu971LvP9bW97G06dOoUPfvCDeOELX6jHDxw4gDNnzhTLePe7342Pf/zjeOSRR3D69Gm84AUvwJve9CY88MADeP3rX4/V1dVlN7uX7STbyZTt29K1AeM8HJfOqpyrCEjk3KpCOHiwcRWdPIAxAxdvYeH/fcI4UtRqbZFzAyn5ktundbfIdboB4zDnrxh3ToRRxHwtk4Llu5KDnXtL+8AulzZs2gCKFpikXDEEiCoCIuJGo36GNgldGKdrDCfIAzxxR0VolJWPcjJJ8koLTbJKZT/5zMHSp2HA6GDA6NARDE4dxMoXLqP6wiXUl77S5JQZY3pivFnAei+9zCqbCZRZkOax5bbEixcvAgCOHz9ujr/97W/HyZMn8fVf//V48MEHcfnyZf3t0UcfxfOe9zycPn1aj91zzz24dOkSPvaxjxXrWV9fx6VLl8yfkZ4Hs3PkRk6o3sxeihhaAlixVYYcvFShAS+DAcL+/Qi3PQPXnnseV88cbHZ6bq0aTVi0KOrm/8SnCPqGmz2GostlAqiFQq9rXUhclreGiMVDAUSUa6DtEYXPQEkAjufLhBHUfRRDQL1C/aE+pKR7sHwcbRj1Wdon3wWAyDmt5UbdUBGoNqKxopjIorZe1NAxMhJTvcyH0ftTyDysXCAZK76eyw9otmuSv7bcehCwfuYQrj7nLHD7M1AdO9oQuodDiFupcx8s71Lq3Uu9LCr8Ds6qbzepl7eUxFvXNX78x38cf+Nv/A18/dd/vR5/+ctfjttuuw3nzp3Dhz/8YTzwwAP4xCc+gd/5nd8BAFy4cMGAFwD6/cKFC8W6HnroIbzhDW/Yop70smvFb5pY+q1rpTpLdt2p1ZOrCDDuorBvDTh9MzbOHMZ4bdASPIlfASjhlaNotGzimBRzqrALhvgkxrUCCw4kQZtYSditkkKQ2+K7SK9kjVD3k+yLNGDeiCuHLSYlF45EV/kxLiTNy1xdNXLCsxkjGJ5OyV3HFiI5x7iMYtQNLGU8TBI9X4cDYln5oX08Vyrt4/qZQ8Cpg1j90lUM/r/Po/7KU61rqZ4cft1HKvWyTJnVEuM5MNspkd19992Hj370o/hf/+t/meOvfvWr9f/nPe95OHv2LL7zO78Tn/70p/GsZz1roboefPBB3H///fr90qVLOH/+fDqhJ/Fuf7lRrqNZOCzTdpCeqzoHWoA8+dyxo9i49SQ2Dq+kKB4HOIp5UMbJzcNuI1GezP3wwISJpuzmUZBE/BRWvlkUk3fZsLVH/mXOiPudE+epO4lcM77fapWJ1A9yW0m5Zt8k/l3AEZXHVpo4sHV4oCh18th7dxHzh8y9FG5LsGOWWajIiqX3J7TZegHEQUhbOgwD1m8+gOrYLVh98mmEC38FXL5iwq/7PDK93BDxkUelfDBzyJYBmNe85jV45zvfife973245ZZbJp575513AgA+9alP4VnPehbOnDmDP/7jPzbnPPnkkwDQyZtZW1vD2tpadyUyUH1Cu+0rNwK8TOKwTCPrLiCdCeiqCmFtDeHYEYxOH8XVY2sppLbE95AooXGjYDXJnESuBFJy41aJS9Za73IB0jmUMVcVfgwmF0xzQVK+mWuptpYHH/Wk57ErpQBKmMirh6lf3Bb+rFdD447izMD82QK5jF/Sjk9j0bLt5u0NIkIKGw/pswEh5KbyYytjJpYnx78x1h6XZyeBm7xfBtCQhWa8WuHqLYcxvGk/Vi5cRHjy84jXrpltCoph170Vppdlide3HrhsArwAW8CBiTHiNa95DX73d38Xf/AHf4Dbb7996jWPPfYYAODs2bMAgLvuugsf+chH8LnPfU7Pefjhh3HkyBHccccdm2tgD156KckWT9rKQxDOgee4nDuNja9/Ji5/7c24dmyt2ctnGFCvtuCFwoljhUSCde4OBSzIgQW7HjgMOLNqtOUY/ongvNqVlXUUOahpOSNZdA7SuXqMTsl4OmjGgfkiGTeFgU9F/BKkegK7o4K9tjnP1emBhrTNj0GEusC0bKqLyyntpK399q6qiDTBF843YyiH2vD2GICNQ0Nc+arjGD33dlSnb0ZYXUUYNGH5nWHXvfSylbJJ4CKydAvMfffdh9/6rd/C7/3e7+Hw4cPKWTl69Cj279+PT3/60/it3/otfNd3fRdOnDiBD3/4w/iJn/gJvPCFL8Tzn/98AMDdd9+NO+64A694xSvwlre8BRcuXMDrXvc63HfffZOtLJOkBy6LyU5ekU1re5elxf++6WYUrC7iKjpxEzaecVyz5iZrS1DXBQDdnRlIClo3KiRQoa6juommwTjYPYiAnCsiAGncWBYAsqCI5adu6jWcjjpFFTXFJNdNrKC7PRu3itaX8sCEGJtMvGJFInBjXCcEXEpuLSm7BDKY+GsS47XlhorAREAiSkcbFaXlyjg6IGPAoAOMWn4N1CtBx4fdU9oH+SMAFGK6534Hb723SKAsbRUBIARsHF3FxtEzWPurIxj85RdQX/pKG9jkrDGTEi/u1LlgN8lOug+T9O4SAmtCjMsNzwkdDX7rW9+KH/qhH8JnP/tZ/OAP/iA++tGP4umnn8b58+fx9/7e38PrXvc6HDlyRM//zGc+gx/7sR/De9/7Xhw8eBCvfOUr8eY3vxnD4WyY69KlSzh69ChehO/BsOrDrm+YzEsOnAV0lMi085Tv5XrsFN1aXSSXS3X4EMa33Iz1E/tUOQHOuqLtg3ETZRwLUYCwypX3LxLgIXsYsQI0JFevEFuXlCpvzvvirRDkIpE66yEBEzqP22SIrAPk7hMXtWNAVe3KkOHm7QMImBgXGo2fD3vWDS6lDCQAp+3gtjE44yipAmk6jW1+nr+PlhBM1iEHbkw7o70/oXZWpQgMrtVYu/AU8OTnEVt+DNqNIzO3Er8fO0Vx9rJ9xfNeCtaYUdzAe/F7uHjxosEFXpYOYLaL7EkAsxcmmHn7OA3wbFHYaCksWiOLzp7C+tkjGO+rdEXenNd8MIDJ+BBikfCuBOZosGtEDhf25/HKWNsQ06dm2iXXjN1mICZAUCpT+lTlwKnpK5TXYwAXbZKo/RL+C7lnPJgTkMagScmzBF68pcSEeRO4YQDnrTxms0mfK0ZOHTsAQyCCQSP3t2TZMdfxuDpOjVrW+P6TFUrraZ+XladGWH3884hfutjwY8Y1ityYHsT0slXC/NRWZgUwe2cvpL3gQtoLE8u8fSydP0800QKRR8UsuoMBwsGDGN9+BteO72ssLXLegBQo7P8eiZgIG5eEjlfjHJJryL+s+KVMZwnhnDEIwZRlybrRKkU4MASo8lTwwj+ViMHqGmrHTbUvbB4Xbrcjo5htCaIFL1q3Gyefn8VbbQyJVyOJCGh28HjiwIEKKtcn1eN28ZgI6Tjraw11fSmhepCTnDXjsmtDGEeMDgww/ppTWLtwEOGJzwFXriJujPKQ6z7MupfNSsnast1IvL1sc9kOiapuRBvm2QOGJ+lNgpcQGr5LddMxbDz7GVg/uS/xK9o/sQZIKKxJbubABsDK11kziJtSbYgriSwcWT6XYBQb81lYqRqriDdm0T5C2d5AgHGFhDoai0mT+0Xag8zSVGqz1MlRPJp4z7XJj5X+MbdILDUMIBRggMqBjnU1ioZ/o7+7tptxDPRbpLF21iPOgqxghssml5IfH98HzbxcAkVAC06bZ279zEGMnnMrqlMnGyvhYNCA7uzazec+6mWPio9G8jlgFjAy7A0LTC9JrucKqsvcfKPCpWcFMQtMzl3h0WF1FXjGaVw9dwTj1coqq9KOzIC6BLTp5HapV1zafiJttgeaDxc9BEDBg/JSWtHU+ez+YaUrRXurT1sf8zaylT5glLKxpNC1YqEpWY0iGsuDun0EANVly4IxyETqT/u9GlE51JfSfVAOSZVASqSB4azDAtD4dw8wSxFfut+UABIZF247uZw8SNSxb+sVF5rmmOHhEJCL2LRdfq8CNg6vYPTVN2Pt0AFUf/EE4tV13ZIgs8awZbK3yvQyi5S4L9s1D8y2k73gRprFP70ISXZRWfYkN0tZN8BHXyTqVhXC4UMY33Ya6yf2mU36VCLUJcAKtbRzMVsNkjJLSl7LE+ImSJEKCNAIJbIYhGT5MIRbDwSkHFagrbuEQY3yQlpglfYVoo0J+ZPzoUifyF2jv0sSPjoPMZhjQAJhZr+jkmWJPqPHqzyeMlZ8jZTD9dZR22RccsjDp831QLKweGuNVCHPA0LuNqN7m7IR27HKgFobIYY2p0yEgCUAwwpXn3EIa2u3oPqLvwK+8lQ5AV4pgq8HNb1MEg9YRAocmFllb9gBdztwEZll0ph2zrInnlJ5XRaOWSwf09w7/nc2eW/BpOpzu4QQgJUVhHOnsf7c81g/uQ9xEFAPraVFFHM9LFgCSiTc1t0SRklJVyNRWq2FYOCudcpW3CeaI0TcK0LUFWDA9ZbyyJgTUllm3yQJxx6nvqq7SfZQIstMPQwKFDjM2VsRpAwT2kwWDuNW84AwpPZxjpkmmokAowCzMfVZTqdNHrN6CDj4cHMeQ+mnPBPGCufqM3VzaLtzHZnni9pgXHw6/gSIjatQKgq4euoANp7zDFSnTqJaWyvnjSnxw/pdrWeXWS29O91dx3NISRcvCF6AvWSBudEgZq+w90v9nPZ92vFZyylFF5U2Y1ySKHhhou7qCvCMM1g/cyi5jERJEv+DE9HVK84NAjQrZLIQBN/1qrnAuzAylwGFJccqKW9O7tZsGwC1OIjlIOURsVYitWiwDm2vEYtLAwYiJcBzrifAunba35gHEoc5iMhCzAOMkmark17jQpNTFFUaVwkvN2CyAOjS2CcwyRYWS8BOfUr3PbUtkFWpi3Tto7HEspYBJLZGOZeV30KB26Z9CmjmyArKJ9o4OMTo2aew9sWjKW/MtWsIGDeWmJ2uWJcpi8zvs56/G/UG816Y0DsnkNk7AOZGy055CD0I2KqXclnSFSbtpWu1uFm+C4OX1mV07aa1pAx4lUuKWBPNsZuDuSLkshA3RqhbBViH9L+UJyBjCGCUlJnu1twqRY1+YutEkDalNnYRh+W3bB8hsrpIuVweu4k0tJhdJ1S2aY/+QOfSeClgYQVfCmd2lqRYhcSFQQNGMqE2SN+8OynC7r1k3EztOChYLPRXhHk5Yl1i91Oz11Kq3wNRk0cm2HplLPi3MIJazUwoun9WQ8DVk/tQHT2L1S/chOqzFxAvXwE2Rk3uGB0rl+huryzYRPZSX2cVcQ2VjAcMWgS49ByYXjKZdyLhc5eVfG6z5/N1Il07RE+6Zp7fOi+x4EUT0x2/CevPOoXxWgWE1mXUWj8414kx2QtJdZAUEK/CAavES5aY5iTotaxg1Y0jIGbY1iNhwwwy/BwjClrPy3O4eKVnCMNULrtH1J3k8pz4qB6/ZUEpu63Ziwm59cmAFrF2BPebB09AEQB5lw67kZiDIr+rpaiWsZFBgrr7ACQLU7R1mOirAYDYJtYTABjSNZr/ha1OAijHiS+jzyNbZqRvhb7y8xSHFdZPH8DKvluw8mcXUF/6Sntuba0xvVWmF5FJXo+MMO8tMbNVsXeett2Zr2+6zAsUSrswTzvPR/hMS8s/L9jh6yZxahjUTKpjwdVSMUR63xrCM2/B1a89bcALK3QOY+bQ5SzkuFVmstpWZcdDMKBjkx5pdg/wuWR50fpcTpjmGgsYfIRO2uiRymuPlTIFm/4K+CBrEwM2duVo6DWVY+oV8iqBq2IfCLwAUDcJAw8TVh65TZG2b2jb5/rmeSky5rFqgIfskSRlGBDEbUW5nJJLTMuM9noRBcNtedVGzEFaATw1J1PdEY2LKUaMDq9g/TnPQHXieOMunbSfUg9mepkmJb08h67eGxaYvRCBtCxZlK/iLTfLynY7yx4spXomuZWWYXkZDBAO7Mf4q87h2k1ricDKJNpWmXLuEVUqSFYIdg2w6V/JlqCVesdj7C02Je6Gt3io8mOl7fKuGBfIIJXf1Y4SeZVDxj0PgzepFPCGiOQK4nr4HPqudRWsSFkZ7XViiZJ7Jn0zyfzGFgAZF5vLbMyRTrw/VYgRYQO271Js69bjbR7ybL/IQGYx3FuOm3vsrhH3EXL3kmkbgSIto2qeWdQR47UKV7/2NNYeXwUu/JXlxZjx790qvUwRnxtmTkNDD5F7WVxKeVVKYZSbiUyYhRS8qHVn5iaEpo7BQMFLdfQINp7TRhlVIeXxGFiLh3IYXNI2dc8UonyyfXzk60CsAcn64Ym7BsgYK4x1i2jkEu33wxYSdVXwOLQgSqNmQjqX2+8BA4MCn+peonpkfDSayVsFpL/cRrbIsDWH+UTtPTFtFaApfCACJ9n+RG2Z7PIyoIWBBSeQo3NLwE2fB3c8WWnIWjJABlpyt1x57LkvhmPF/ajsZwYKjaWsSXxXr1S4evtxhFvOoFpb63e27mU+WZJHpAcwu0FulKlWgAkDmc1sqlj6bZ6suLNaj+aQUqRRdfwmrD/nHDaOrOiEroRIbm4W4pxbRozroFVUCmxEsUKAixRsPxs+Awg0tccJGHi+g0a5yKdPsAZoVmC/fUDidKT2KjBorQBqeapCVq7JzkukXf0LVpkyWNLxLlkQCAw09cIQi9klwjwRc08ULIbU1gnAzPzPFiECUN7yUnKvAUjRYm04vI6luhNTAzy4Ub5KTKC05FYSQJW13Vl5zHgzWDJgOeDqM44A58+i2r8vBzEyL+xkV9JWtn0nj8syRQi8C3hK9oYLqSuBzm6RrTTVeguIByo+XHkZoYHzlFXi3czblmKxgg4sWTecPYWrtx3HeF+VuSeMuwe0SuY5nRQNgBQqDPpOETEcJu1dIkzczKJOYMswZODWIhFGqW0NEEmWCOGlSEZXk6xubJPPZX2lFTy7RtSN0QI72e5A+mGioyZF1YiVA21drAfYAuJCizOLB1leeGzY0gRAE75lXJR2XJqstvm91vMFHLBVzBsR65jC6eVYjGZMvIvPu4v0Ho+DvS8y5uweZAsatZuT5PF4RwRgAGAsbrL2eKhw5ZbDWNu/gurPn0B86mlgXE+OUNpJspVt3onjsZWygH7eGwBmrxJ4FxE/0cxi1ZgWEbSZcOyua+V413kLhkhndXByutMncfWrTqBeaZOPDWFWsylMOVk9JMspu26AgmVAv7Qr6DEpLmmOWjXasNtBrsxKOz77jRSVcyPfa/r0r0pEtrtzUUlL+0DuiPa3aiMPu1YiLLuBRNFHWGXv+g8kRetzvMhv0s6Me0T3ph6EzNWWcXzEWuPcWppQzlutPNeF+lSKvtLrGAA5V1XmfiLSsrS/HjRh4ajTOGluG+mHs7RlWzHwMfqeGonsnjQgKGD95H6srNyC4f/7BPCVp5rTBcT4RUavuHthMaB8Pl29N2xYXbHoveSyrMllHl6KNzV3mZ67uC+eoLsJs3WognEbaaTRredw9ZnHUQ+CtbyEpGg4bDlWTXZUWzhdU8q2S8o0kXrlfKvkWKFq6DSshUYkkYLZFUSfrJhkVU4WC5OlFigCGSaiNuW6jRuZNNu6MTh7r5Sh5ZPyL1qXAox1Qa0Tck1Me/2ohctZMppNGQuAgkHEAIknxOPD36VsdV/Z+riPxq3mxjxtmgnlJ6nbMaQ/Bc1Up+59RM+CsQR60CmgDDCgyt9n5vsYtxK5BiVCaePoKja+9hmojh5BWBkiDPx247300soChN2S7A0As51lp/pBJ62m5u2Tt6TwsUkcGG9l2eRYFsOkV1dR334Lrj7jMOqVqiF+ElE3jJOi5oy3QA4SlLjJ760o+Jq+kxIz3RXrjlh6KKurbBGQKT0kMBHcfGF2fzYVTTnGVhNS9g1Jl04Loey6QLouI6iyq6ci8JUl1rOKl91KyvOoc/Di25e5AQ0Bu3y/eGxZBDhp+wkMdvGAhD9l6gO07bp1AbnlfBlmXAp9Mv1zv5WyE/vr+D4y34ldfA0vCdg4NMT6s8+huvkEwupKeUfrXnoRMUEMEfMaGnao9lxAtqsb6UaZUxdNPDXNujHJXTQtp8y8+VuWCP6KnJeVFcRbz+HqmQON5WUQEhDhKBIuh4/FpHxkT6B0nmgkpDBrksR9gTkPsMAlXQDLIfHhty1YkP8TcIpZOVJfAyaiaWuooZyQMI42Wka4MmQdEStUyeLU8GmoPukn79OD1G8GEmn36uSeMRaZ4CwX7bj5SCsFYDxugf4HgUm2lACZ9Ub3NqJ7x9YYb+kocZZS/hg3HgAMYChcqwBKktTRc5i5o8SqF+zYelBr7m1pDg0woCZEYLR/gCtfcwrh3GmEfYUIpZ26aOtlueI9I30Y9QTpXUhWZs1kW7pukmWEy53F9z1PvpgtyPZpXEbObYTbnoErtx5uVpcDUsStcFQL81/UMsF/JYmFP21Y+8G5Rzh6h1bmBjQFZFYDMfdLgj2juJErYhOVwnU7ArJxW7Vgx4Mi5mBkfBFxk5E7xIT2kgLWY4VxEl6Q/u/yoQh4U44Ihx278/yeTRZAWkDiibvC62FXUGaRYmDB/aP76MGsgF+TPZdBDI9JRE4QJmBTdJd1iAe3WYI9vpasVgBQr1S48qwTJszaFu7cxbtd9kIfNyO6sHCgZorsHRJvD2A2J9PItPwdyEFO6feuMia5jbbQ6gKgMXkfPIjx7Wd0J2mzx0+X2wVpZa6b8QEGzNTDoBFGvL8REA0I0QRibI3QStpzKFpG09QPYtY+Nv/bOpGUYKvgVYGiTTxXpYiUjCdTABFNv+zKn883ifDasZQ0+bwVgLa5BSYawcT1tZVwltnkzrBjLP/Xw6CAii0/HGIuVrYMQIrFircQECXeAsp6GMzYegmRiNfRnUPl6L5LjoCM1voFpGfMhL9TmXpfmbDLrirOS9RyZzj8nu+DSe4Xoc+SaXuhvxHAlduOYX9VofrsE6jraKOTdoNSnzVAoScuTxYF4/Pp6l3wBPVyXWTRHaTlBZ+F1OstMZMmh80kx/PtE6vLcIjq5hO49tzzuHZ8X1rZA8lC4Df1Y73qd1wWUz27bgDjVrKWEm4XrNnfX89tkP+d6yGRWcmaweDD9UldRmKBqZPCMv0hLoRaO8SSQu1s2tUqUM5bQlYqVpYmn4xxOcC6fmL6riRfSgpoNrCs6RoGd2T5iaHdu8pYctL4SJmZe0WsYIHAmbOGpDaRy8uJjom3yIz4XgczWxvwQXV6gjnfQ2Npo+dA+FNNdFbednM/fRi3s2Bp2QBCBGIArtxyGDh7qiHDr7g187Le4xslm2n7bgBwyxK2wMzhRtobFpjdYH1Zdh6FzYY0etfPtE0gS2HNJesNJ8crlbckN5KPNMLKCsKZm3Hl9uNJmfm8LkI4rezEzsrEKz4FCaIYfd4XtohI21g5q5XCtkVX5rwidgqklAG2uhZb4mhSvtoHsb6Ust0GWOuBF3LzKG+GQFiz4WDqH7t5zGaXXCRbGQhEMSjQMasJKLR1+azIZs+jShR2azGqk+Iv3isqw+RWaQbN9E3HHgmIKeBD6rexSon1hC0uzsojWxsYkDpKfTZ1uvuh5F7k5ZcI4+km2K9mqwi6jyVyMO8HdfX8UeyLEXjic02xG2KK3OQ8tJNlL/ZZxFtaFtTRPQTcKbLZh73ER1lWrpRpeWPEz13K3Mtt8tfx736iW0aSOoo0Creew5WvOt7keKmChs6a9P9RVvyuqUzwHKf/eWWbTnZtIXJvshy0UTRjqpfaoeCjoFy0jmiPq6VikKxKnL/GrMYlHJeub9oTszqLroOBs86IRYEIwQxypF6tn7kmzs1iiKStctRNDRWMpX6xq6Qetjygdjw1lJvD4KWM9lrecsEASamjEHkl4JfPzzL+MnCk/gDpPO/uy8ajBOYE7LChxIf9s7uIIuS0DWydYtcgoJauzsSJej9D9v3qrceUE5OFWO8la8Re6muXdAGWOUm8e8MC00u34i9F9szj053EZeFzJ5H1PPG3q41LCJlm3gtHGl259bAlZZL7Jwvl5VWyaSeS4q5ivrItvJtJMaRzUmQNlLvR/B/z1bmpPw9bZiXkw3R112zJPFuJpSACI6uASu4yWekbSwicy6IdDw88/Jh59xoDP1bA2QaDgwRG2AXDLhvdrJHAJTXYtslbhVw7Tf3qKkkdUjcUl8OuGbbywLVJAAln0i2Qpo21jX9j8CJAhLImh9gk79My2uuzTTrJouJdXlkYt/ByZHwQbDtAxwFcfcYR7AMQ/uICcO0aMHabQO6kjL2LtnWn9O9GiIuKmyZ7Bwpu1zDqGy2z7kk0SznT9i3qimAquZa2YJWSkXarCtXJ47h6y2G7aoZVniaCwz1G5VV1NAAjAYP8OrEgeHIr8zi0TFev3esotVetQaxYXei1fhfgwgnoRkhjIWVx9MswrcSbsoIq9+YA9c1FFZm9kQwgorFinlGrYE2EV9tPbwEwId+AhlOzlUTz5TABlyw2+kkA0uToCakvbF1KA5v6Y85zfdM+ewAFkFVFDlDx8txIPwZpLy4+t5T915BvCfiY/bbYsqTPcb5aLiUWNNfB3n+9bhBw5dajCOfPNtFJXYuSnWCluF5AZCeMxTwyTRfPoat32cgUZEkZ/7a1lB7wRaN3pnFZZrmuVG8X12WSRYgtL8t6iZm0u7aGjXM3WRcQETGZf6FAwXE1zB5DmbsnnciRQHwdK2dRluxuMDyNYH+T76kS2xYGFNVGVLKqce+UrBGABUUVUK+Exr0mG07KhoMKCJxlgtor5WoYN0i5Bbi9nxIA4zZpewhAeEK15GHxY18en6TwjYUoJIsEAxm1VAF58j1vtRAg1G7AKQCiGkVTns/1o8niOAyc63V8FX6Gmn9gAF4WQSRFUqQTX1fMdkwcHpOVV0T6wCHWPjlgcAAXwJXzR4DzZxti741IdrfM+WSryhbZbRabSXyXPgrJiQzIjSbybuahnnbtpP2JJp0z6fikurtS+Jd+n4VnMyvpd0EpbQ+AlRXEZ5zG+k2rJlTaR/Zw9IYJt63oj602okgZvAjPQEmk6a/5niJgTB4ZWAAgdeu/TgE2lbnfW4UsIcTe3cEWAxNV49rIVhWpxxBMTT+CvZbaxm0q7Qdldt1uj9tdmm3fpO0CHBXsxXQfmMycrFZpp3BDTG0jf4qglsd2SGCLeSN0D/ymkEIuZheQzTNDVhQuw48fiY9w4/9t3hZrDfLXMMFZQJ1vj8naK0W4iLQEVlw7KWOvzMdXbjmMcP4swupK91yyVbIsUDDL3NvLfNJbYLahTHqoF1Hwpeu63DHz1jepbnYVTcuaW+K0THNZeXC0SZ94567St5zBlfOHm6YOkmKTVXJzjVVKfiVr97hJf2KKL6Z6Z0DS9QmkNhSUEZDayso9tcuVxcdFidex3Qsofdc2KBiAKnAl+jKQo7bo/y0wMHlGSo+gv6WBwEq0ZQqB1lheCDikE1Egj+Z/BrTVaWz1noW8T1mUFw+B1OUT6NFvqX3p/pgtB0D3Ldoxs/yo/H5IuaZOsnSVJHOLBZvvqGSh8TJpywID+to/ziTd9DXg6i1HzLYDtpGFhVLp9xslW5ijas/KnMaGfsS3gyyqoEtWltKeQl3XzQomJp3jy5mU8G7WOrZiywACL9VNx3DltmOoV9toI1Vo0WzAqBYJIE30JkKpMKt3AQd/3ClgyddiOBdIkz6vkEON5G4YprIMbyXSJ/WluhaTa4M4DyabLvUtjFJZoYaCHm8lUsuIuMnIOmK3UygAoBiNG0lEd6zWcu11WQK3DmDhhcOjvZVKlbWxZAV7jLklpiNUpnxvLW8MjNSVNEjj7jk5knCvGI2FdK+Nq6+994b4WwBUZqsBAs+GC8PAWCxmAkC8i4gBVkyWLZ9R2LcjhmbMr3z1zdNBTElutKVjkpX4eriWdqPEiN4Cs51lKx7iEpCYZaPDLqDBHJVJ15Re4BJvpSuny6xbCCwoxvoCAFWFcOggrj3rlMm/oS6FEIruirSCtMeMcvbkyOiuJW5Dtv9P62YI46j1GstIwQpTD4N1H3jlTW1QS4pkgjXuLFJGvEoO9jzTJ6T+Jr6H7Y8BQ7w3EWCtAn6ceINK2jpAyMWen5TaDWtpqdK5yapB4y+PxtiOt7nXTB5mkCNj2bqnPB8mS2gHAZ4JkGWZbOHrJyBdt0DSW2qA7HnR8XAkbL2OpcOdlCWrg/3NkKX52Q+0AzuDIzkvWkAq/9erFa5+1c2ojhxGGFQ5H2bSPHWjpLRAnCUXVi+TZU6qx94AMNuJxLsVD/Es/JZZ6+3a56jLXDpPOLUvzyfD8+csQwaDxvrSbhEw+upzGO0bZEm9lPTIqfBJ2DIDJMXHxEp2uxjhVThyRZJFzxiCbzDn6d5CYg3yL3yHuV/6KNcWz2cw4c/jS0S5h2D6b3gpPpzbX8tbF8i9ILeTnGtcUS5CjNstSp5dPd5N4jlI3MdErE3ZeC0hGzmgkyJay52JYhPQ5i0X5PYpJemLVQKnHPYuri99ziQP3ASOjHEzAUVgld1z9z8D1Mwa5B89ai/3R3hGnRIbEDO67RTC/v3d+ybx/7Nk8F10HpmV4zdveYv+vtekdyFtY9lqMu+ygUDX6qcrC+80v/Ui9S0gStoFGvByYD82nv0MbBxeSathmrzVJcMcCrIoVBvRupNgJ2vDkYC9NvUN2Qq9xG3JwprVGoOkFBhw+Xqo/VliOECtBrwK1tVxTAonS1YmCtJfAwJvbC2S8jjiSYABZcr13AtVes5S4sGhjotsfaBbIBTuDY9RsGOpfeMQZ3Ifcqh6GKW/1OaQgQMBvL5uHrNUdtS+p0KRPW9ePF/IAC9n5cui11zeHL1vzv1ljsnpLvmfBzY+0o55RhoJJ+cH6JYD6zetAadOIAyHuStJK7+OVoxZEnR6mWZ1nlbPXpfehbTNZTMP6ywrjkXDoKfxYUqAZdYoI2nLNL/wsl9kzbS7gvqZZ7FxZAWRgYkoZFYuSKtdwIb+qtuIVqMmW64oUEN4dcqUVs6ePMt7L0k0TJa/hJW9S/DmeSd+755JlhXdBgHSv2DGpKnDX5Qf7+SF+G0ZKhiA4MEEu0EMh4jqzO5dew/8b9I/BlAKnMRdE9O9USI3uZJMl4p7PtH/ngDLfZoQqaPEbH5mCCwb8i6NkbEWMVAM9J1cZ+oGGqdnz7r4Uj+E4K7WsIKV0nKc0jvgo+PqYQO6PbdJ+hAicPW2Y6iO34SwMrSupC6CP/++LNkM4FhCpvA9LTfahfT617++URr095znPEd/v3r1Ku677z6cOHEChw4dwste9jI8+eSTpozHH38cL33pS3HgwAGcOnUKP/3TP43RqMDym1VudAj1ojINVPj/NxN6PImPMgvvZZpPuuu3aYTdOScCDpkW0i5O34z1k/vBaefrVWdZKHFU2t/4k60DsYKG0wo/gpOQCdBRcigIHDGQAhIAEusAKX7Nb6JlkFtJrAMxKV0tsrLnSHmsvFVRS0Zb5o/AlcUAQJSwhgg3x9nV5gFJBlJ4/J27TBW3cytxW7TfaNpRrwSMV6v8ng3KoGMWkfqZmKrp9MktaKwOfB9kjKMbQ8C4AxngeMugyeHiXZEOCBnLknOxaf8DNAEeJ2iUc3jHbnkm5BNI91wj2Qp7U/H4cRsyt6cASAGLVcDVrzmN6uiRxhLj+TClRVPXwmhZIKIrunNadGXvGppftsNWAs997nPxyCOPpEqGqZqf+ImfwH/7b/8N/+W//BccPXoUr3nNa/D3//7fxx/+4R8CAMbjMV760pfizJkzeP/7348nnngC9957L1ZWVvDzP//zizdqzgQ5N0zmCVOe9v+s9XSVO6nuLv5K17ld9cwyEc0rbcg0qgrhwAFcO3fEEh9BGwtKd2WVWkjFD1AeFVJQPkSVLResxEymV2dJSNel455LwJadZkVs3T0A8hDvFiiZiCWv6JyYegmc6PkxKTGTII3cDWY8nRh+C3E8EiBx2y+ENO4laZS0tZCl36hRIKXbwWFR0BjdPeRPU3dIoJPAVslSlO6bK4MBM1mSDFht6zF8mBDM8+z7XuQuUfn1IG122ZgkodF45hmGPLtp80+55+qG1EiohgekvB6kaDQfuu6Bu3FphYjxWoX1Z5/D2icr1F/4IjAape0GZp3rZpnHZgE506zMswY8zFpfL3PJlgCY4XCIM2fOZMcvXryI3/zN38Rv/dZv4W/9rb8FAHjrW9+Kr/u6r8Mf/dEf4Vu/9Vvx7ne/Gx//+MfxyCOP4PTp03jBC16AN73pTXjggQfw+te/Hqurq/M1RomLOwC8ADmqL+VS8bLIizGt3FlcO/NycjZ73hQxyeoGg8b6cuo4RvsHVrkJgRSkGGR+JL6GTsZtqLKeK48UgR1RmGzpYGXuM/jK9aiTMuIEasb8Hl29DggUiZjGvSEr5tKgpfI5T0o1irkiD+k8o3gABETTbu86y9wtbJGiPupu0i2QCYhZmQbgrASzVUCWbM2BSCknS+mPdH9NaDmVYdov5Tiw27hQkkXBKOz2mIytB4wKnhgwCUiq2+MMjGnfKHZ56W9ULrtsfJvM+VSvnK/jQlY+w49ii9wAiGhAZ0Tag0r63YxfyuQs96LasBay8VqF9a85g9XRCPHLFxHQ7pk0i6vbg45Zghy6LCpdQQqLyGYXl3tBQsAN58B88pOfxLlz5/BVX/VV+IEf+AE8/vjjAIAPfvCD2NjYwItf/GI99znPeQ5uvfVWPProowCARx99FM973vNw+vRpPeeee+7BpUuX8LGPfayzzvX1dVy6dMn87XiZ1Z+6rDwyk36fJdeLl0mrk0XKm1iVRwcB1bGjuHL+COIgZFFEQAIZCiBcunU9h7kq7TFVFM6VkPFEApVFilddQ6TA/arahDh7Cwe5CVjppvY5K1JM56g7pAAkBGjJeBUTwUk3KEyatykw/SNlz2RRI3TM3CcCbNxvAQ7cR+8yKZXtzxWekBmHCpbgHOhcdhPJ2PBYBlbOVJ6zqOmu2NyWAhk7cYASkJLtHLRsjuYSa0gpnLotO+MGVbDPs9sPSfor7h3P45F6+DmX+6PAkl1Y7bPq3y+Txbl1b43XKoxvP4PqwIE8Msnc0Lr7c57oy2nH2JU0C6dv2vFZ82DNKnvQZbX0Ht95551429vehne961349V//dfzZn/0Zvv3bvx1f+cpXcOHCBayuruLYsWPmmtOnT+PChQsAgAsXLhjwIr/Lb13y0EMP4ejRo/p3/vz55gdhNW+nUOrNSldIc+m3RcVbaErE3WkAa1ZeTdc58wpZX8KBA1j/6lPNhO+USWpDexmZ5gGrmOQ6VnbF8GBZ2cICkSzMlUmpHaZ+tiZkbolW2WXEUnJDiAUlK5pJwo4cHMZIkTUTAIAhFtM+Odo/jnCSY6I0nQXJuKvIIqGgsu2vAVvGypP2+imNZ8nS0HVeMZMtA64AB97Sbz702x9rfrDlN2TYBBjZgsHPDpNh+TdTNLkRGVBZl2HUBIyZRclZUjIXEj1XdnsCer7ZnamgqNDYYI8bsq9Yd8gtd+3YGuJtZ8uRSR4ALCM3jLe8eEs0g6Jpc9i0iMx5ANYssgetNkt3Ib3kJS/R/5///OfjzjvvxG233Yb//J//M/bv37/s6lQefPBB3H///fr90qVLCcTsFumKBlo08qirbC6rxHUp+YVLK5LrkP8g2ypgMEBYXcH49jPYODRMXBCfPE6azVYP53ZJWWkJxNQAQnKvlPaH0bL8arpVUma17ed4NkBwlA65JIxrgyw3MbSmew5tpXJlFa0AKKSVsrTRn8/1JDDhNgoUyw7SmHC5JSKoASkAqnE041VtxMY95FxGbPnSphaS7XHkDNep4c1ttJIPf89IuDQuBiQSGGMLiw/91na7EG3fJ8+DapIats+LlOlAkJ4rbSiEaCfCedBztX/STna38VCGoK7BjLuCdIz7wokBOXKpbnfMNlFoBGQFKHHWY3nH1k8dwP4nD6P+0peBWCc+jDakMB/x8VmlNNd5l1TJRaUDVpg/uYxpdS5LdqILijmqpS05OmTLbU7Hjh3D137t1+JTn/oUzpw5g2vXruHLX/6yOefJJ59UzsyZM2eyqCT5XuLViKytreHIkSPmz8hO4cBMkkVMo/OWLTLJarKZc7uObUYy3stJXDu+TyfFjFjqFAFzRIz1gywq2WQtRZG1QZWUn/AD/ckhyuuidbffmQfDq1r5VHM+WwC8G4LrIgVlXE0t+BhcqY31g8cmE3ERtdEkHN3D3BGODjJ8IMBYgkwd9H+9Eoxy52uZz8GuoC5rCYMvsdqUwpQ9V8VbQjwgNeCnMOGyW9FbnQxxl0CQKvO2fgGbnoCt+Wja8ngPrzAmTgnkWU7uHDM+BO61z84CZ54r11/TbibG03sn1kkPgoruVslwPAhABYTYgM7xM04irK42+5h17Vzt58VF5plpLqBpVpV5ZSuAxk4DL0DSz3N6SrYcwDz11FP49Kc/jbNnz+Ibv/EbsbKygve85z36+yc+8Qk8/vjjuOuuuwAAd911Fz7ykY/gc5/7nJ7z8MMP48iRI7jjjjsWa8RuAC/AbC/Nsv2gXSuSLosNf/rr/G+bFJ7IdJ+jEzfh6q3HJLii48L0ydlRDacg2slZftNrgMz6wqZ4EaN8RBn4FT8IrBDQSZwA22ZTd22vYxeRd40ZBV+lCCINy3bWBSmfU9zreRGW46DJ3sitg7y/0vaiMhOlSMpVfmOypyHsBro3Uv4oXZs4QjR243ys5Xj2v7h7eD8jcnH4+8Jh6J48zUBTy/OWuqq5H7pHUgh5fph27E1OILJuAPSM8jPAVhsHyLQPEWl/LQPu22d86MCdAfn5vZZy2SVo6qOyszEV69wo4trxfcD5s6jW1vI5bqvdMJuNduLzlg2A9rgs3YX0Uz/1U/ju7/5u3HbbbfjLv/xL/NzP/RwGgwG+//u/H0ePHsWrXvUq3H///Th+/DiOHDmC1772tbjrrrvwrd/6rQCAu+++G3fccQde8YpX4C1veQsuXLiA173udbjvvvuwtra2WKN0BbqNgMw8IXzz/L6Zl3nWEG4+v3RtqV1LfnHZdRQGlYKXK199M+o2FwhHs+h1XklVDgjAnp9lR62bazILBCyo8MIgwv+ecTDIgmLAgCs/C0WG9DlmICBV1oINRFVE9UpySRmOT6ACuN1VclUwP8NYcOQccitoEwj0GBees3AV74lakYI9XrK0UZkZ74bHJrhjYglAMIA1KeIUhpxZraQOAQOFiDcplwFb5oqKFAUGAtohla1Rcmzl4j6JtaX93Uc/FZ/TAbXbu66kXtfW9Ay2oMu55tiCyb/XwwAMIjQUvgSomuEGAFx9xmHsG40R/uIJYJ1cSV1RR9OiLLvOm0bU7ZJZ6tuChdyukjmjkJYOYP7iL/4C3//9348vfOELuPnmm/Ft3/Zt+KM/+iPcfPPNAIBf+qVfQlVVeNnLXob19XXcc889+Pf//t/r9YPBAO985zvxYz/2Y7jrrrtw8OBBvPKVr8Qb3/jGZTf1xsosD3AXOCn5Z7eiTdN8yl0cmdK1SxTPe0FVIezfh/XbT6JeqxKwKJjolbDaKigTLgwYzkTTx1SGaUMb7stKAoBxqYgrqVEuzm3h3BweDJm6RJnRKl+vQTRKv+mPbXsqCEaBKHCS9gcBSORmEr6LV1qSD6QKBLJiBhR8FA672YoZa+XaKGMZ0nVRAEvhfoV0jk8+J+0wytWJITdHGFCgylxAEgEI6Ys+D7DK37uHMt4NncNjUQ9DsZ3+ecusPCWul9YR8vqRxs+AkxHfH7vwM6DZgz8gt9jB3hu5V5LR17sYUz3tMzVu+xqA9VuOYt/TV1B//gs2P8ws4dWm8I55rGuO42tKC7dZUkr0Ml3mdCGFGHdTeE6SS5cu4ejRo3gRvgfDsIJtZX0R2Qlkq0kv5rwrFH+tlD9nOQpgBoPEe/mqW3DllkMGSJg9V5CUHRMNgW5Faur0lpz2WiaF6grZgZ5SZIoh1HpCsG9fiZvAbXLKJ3OVdIi6kVy/TV9bZSnKxgM8DZcd8fXtWFRJweZcFu5Iam9x9c/WhMq1gd0idbre83eMS4vqNan5Y36d5jsh6xATdUuAyOTmMWXxSTlwMGVUhTGQfjp+kZbtx6qQPI/HgAF4Vi+QxpvKzu4bP+vi2qLx8s9S5kKiNmecM24vfV95aoSVj38G9VNPA+NxTuoFymBkknRZZEruqi4X1jQr9KQ6e0kSI0ZxA+/F7+HixYs5n5Vk7zjitiNOm2RuvFFSejl9+GDXuYvWtUg5od0qIARUx4/h6pmDNpmWEAeZF+CU3yQ3i1dk+hORbxMwIV6GW3k3vydFrtcFAiyOWJspxZDKMSCA3AYly5Ff2eZky4J7iMaDORtcpsnQy6He0qf2/1IeGMkzYvKqeBIpUl/83juGyyKK1YM3AiZK9K2pnGCvNYrVffq09x6EeWArlh5jiSKwI3wdE9mkZVmroefXJJdM0BxCzIux59CzL/etcv3SumIaC6nPgRozNjR+RVeUs1CZNupY5NZLvZdjOxam6IgmuvD8mYbUOyvp1v/vxVtaukKyvZt93hQSpcjNadajXjpl743OdgQyXjaLyjfz0POL1xVd1AVoZi1/E5YbzrabXEc3o16prKL3FhFW0jLxFkiPfL64bTxosJYYawY3ICkkpSVhu3YsqBy3hYF3KbCCYoWQnQeYlbsBBbwSBo0Br7RBZbBMAHWeuBqr0ObfCfZ3wFjFZIx0r6cuoFYYFwYIJvqlbXty25Q3RzT9bS0HJgzZfSogKFgsFDS35/AeR5mlCFRGsPeEk+CV7ivXo+f7Z6rrPnGdIZj+ch6dhiycHkwdOwKnJcueT7qXCOI0fvS8cVm8oam648gN7BMWhtiEVlcnbkIYVDYqybt/uoDEJJmFeyifXakkJuV+meXYLL/1sjVbCWxr2Y6upGXLMgDQVr04mwBXOlHJLtPDIeItp7FxqH2MeYUnHA24ya+djH3USjWqG6VrFCkpND0ZuqItuaCMe4HzvshqU5rKe+nItRp2Gyzg8e4K56LyK1V2dcgeNwaYcWZWSm5mgJkbu0yc5YL3HuLIFc6nY1bfUgy5/GIIGanacH+4LB4PNz7KN4oAp/Xn7uQuG7ICSFtJ6bN7yiQXJGuPfK85qovGKHOhEaAQ7lKRiCvXsGuKnx167j0Xp9ogEvgI6dmvg3kujTWELD7WqheyPC9Fy0+NLLqvNB6Zm4n7DXpHnIsoBiAOAq6dP4GVi19BvHwZU7ca8OBiGpCYZq2Zdt6sZN29CFAm7Uu43cKot4XsBdDSJYsAhlksJH7lwZ++rGVKSLtMhyOHcfX0AQ0p9dlpm/OT4jAmdSFBOjIk0CpAUg5dq1zDiUCrcIcw7g8pW6KWeNdrACnkl0zuWV0d77RuRUBK1uQs8dYENx5MTuXfZYdtXs2bvCUcNh4S4NLyS3lnnJWqKQBmGwZ28fC2BEaJ6/2iNhcUX55R1vahxKGZJOL6MdfSdfoMOlDllbUnNbPFxpCXGXTJPSCekd7vOpXNz7WMm4yjIR/L/a+CsVQmKwpVM6axc9OoWjqpLQo8B9BwbHM9gTnOY2PHOiSw1oIq2V5A647A6MAA8RmnEYbD8nzVFSU5jyziPp/m1pr1+G6VSfp4Tl29d0ZuwUQ5O17meWm7zJ9drqRZOCzLfjnFdbQyxOiWE4hDUlSkJIC0CvRKRSdewLoNnCLUc2t7PEtqVliJyv+slFlCjKq4qlFSWJrzw6Rot1YkVg7ZCperZ4xJij+LHvH1sMsKFnSU8oeYvCh6TUPsZUAl7gJ2QejvaNqUon2ivR8C8iSDLue5EcXr+CryqWNWqNOMBVlcSu5HU6dae9LYsQL3riZ2SUqb9d6xpSKkfhQtNlSeB9eTcsvo/4N0rQAcw49xADcbxwoGaGebT1K7zb3l+qX9bDmj6CQfqZa1YxAaw1oE1s8dQnXyeO5KKgm7fEou8s3OY9NA0jS+zF6SJenhvQFgeLD2sjVmkvgwwkmJlyYR3Lxs8uUMVdC/1JaAcOxos08KgRe9RpruUquLYgzjqAnR/ATrFa8xX7MLpuMxYlIt+/RVORQ2fowhWPO+1IV0jVGuBcWqYxAI3FA5PkSb+27cMAW+ieFpKLfHgR0P0oL7a0XJp4VQ58xlkVmOaByA1l0CVfrsdmOLSRFwsky4p5rErhjpQuVG1xcGy2LFKE3aND6ef5KB4ticI0ntBGykPofUZscjkn6HmvrKFjr6zpFNvk/GukR9yEAiAT0Ocy+RxbkMwAJFdk3pcxfofwDrt9+McPhwOUsvz1WzBCSUQM2s4GVSOfPKrPXuVFmSHt7FI0TSg5bpMs1nWyLDzZvsbjPSEndDCAgrQ2ycu8muJmE/eefljBAqZmmenAFjkgdgVrPerG6a5lfKyFe6PtLDbEAoQGeMJv9GyULivov5vx6GxGPRH21/Si4lrYPHjBS1gCB2sZnoI961mEAbW1wyAORFXAR+52zAKjAChJpxNiT3SD0Ipm/J2pOOGdIvUxgoakwAEUcKCRnZ9p3qQgH8SBta9yC7Fe0zCps12EsofLKbiMup02/ZZqNi3SPLCAMNAex8D7VfUg0916X76q1axtInVtCxK8eVxf3Iwap7jtQi1OxaXd96anJUEpD/Ni03jJcS8PHh2v6cWRZ+pXMnBUlsI2AThis3tP7tMxK9bH/xCZ1YFo1Kmqf6EICqAoZDjA4MLU+DJ3IxM3MmXrISKL+iIBwFUg9DylILp/xU+aaVZa7YbCVqEWIQU6hfPlW5iEWoVXaGNxFjBqiApCw8eCiBrZIlh105Yj3gSCkZ31SwK1dXyfZ4Nk7iFmJA17bJuxnYhcR1Cvjwli9WpOaeE7DU74A+L6ZtpMx9hBADTQOUnUXKuNI6RDPqBhuJVMzz4sGs462YMaP+mfpqdw4dKzfQPq8ZD4gfBccB8hF/5pyC5Ufe0SKnTeqDgOrm/2sn9iOcPll2Jc0yN3UlrzONrsogpLT46zpWasckYFMKA99GLqfq0MF8l/BFJUb0JN5elieTEjr5Y/ISLuHlkgmolHEXgwFw7AjqtaZuT4o07ghv0ie3TImAa1bufmINtjyjkGJScCIpgZudTL2i9orRcEZqcl84foK4ungM2K0kypUtD0CryCONF1kP6qENfVYgIHwNR3w2EVRibWDLjnd7sWJ14CbbX8icn8aMuRLJWhP1upIyV4Aj94BdKASA+Td2cXW1S6twOW1EYRv3CYERbSv/TmNqCLNs/SNrFVuJ9H564AIoYPQk7Azw6eDQeWMYwJltjkp9YWGejHFvogycDN/ItZsXCj58O+UcaoDMxpmjCPuavZIMiJmX+zIpF5aXWSwqk3LReOuNl2lWoRss44uXEMcTkPk8wrtRz+g12RsAZq8Rd5clfiUyLafCZjLzmmJitorSpHVHDuPqV50wREdDMpVJT65jV0LhGOfvaA6QoiEF7IGHid5gImN7btQJW9LwB3JbtSDC7OTctouz8bqX2PA7XHixV8bGjUMWH5n0NYMwjx25kFThsUIrvEcMljgPiOcpmOihkD5t/0JG+tUxZKBJdTNgSMAPydrjFCgDCv3OFoSSS1K6RGMSW7eVsXKQIi7xhiaKGz+O0GEgNdUi4y0h7ZhxpBfvns6uTg8qxE2o4J5BL42zWrja+oVnU4pcYiuc4WGNqU/+/eVFhZTDe0MBCBEYHxgCN59AWClkBylxXnziuq5rvOXF57LqAkVdwMSXVTpnp8jSo0wD5rHE7A0AA/QgZhHZLBhZ8OH2q6cwqDTyaOO2m41bRyY8TR4GNJOoRPm4VX9emQU4JtcGr4ydYmcSK5OFTQQJK9PWBSNETZ+4TuoukZK1qU6hsVtF+sjuG3aHqGuKQriNImeFy5sD+tU5EtApuUSqEVlS+BpKX69gkN0Lkc9J9ZrQYQCyU7PnW7D1x4c7e4BqxkSsZ61Fw/BjYkQGftkC5sFxSJ+ZlYKsY9pntnDB3i+1Ovjy3TRW2gE9c7uN6bly13mekLEiyjkOHHp+EYDk2qN2eBcQ35dJ98aT3E2GYgL5ZrHQkvM3Th9B2L8vJ/TOaymZdE3JlTMvH3CnApatlt6FVJA5TFK9kGzmBS1934yEgHD0CK4dW22+uvwVnLZeLBTZPjyRJsPojkk1bLoGT6Zthlnyzau5X1awBHYUpMjq2yl1E+HEK3n6rv0aOF6EO1eLbS0xXjnECp07Z3sekbSf99nhuhS4kOKXOtnykcbY7ZMk4Gdkz5Ox17648FnvOjAkVOdyKVlQjIUO9DvrOI7KkvvprDCe96Lle9cVkuJnonDib7h28nMY0zhO4jAx98e4pOj5Ns+fWMRCut67RzUbMAFe3h6C28djbMaOvktfjaVLQZU9h11b7OZLuWwKwF/A3rBCHASM9w2Ak8cbi+0kmSVD77RsvCUX+iQXVcnaMsm9tBdlTl29N0ZsN1hflv1wL1LedSCRyapJN2iTvC+rq9i45URSHiFYJScrSG/SR5pMxcKRpzm3Vo9s5SttG1MIKxzRkFai7DoR4eRqhi8xStcbk3+d/kokzMx1IJYYcgfwitqkZ6c28VhxtJLugM31gRQUu2eQ6pT26zl16ptGu7TlFV1PMZ3L/dBmUOSTsThNEsEFsi8SgYpSzhUPbLR/7AqKMFagrE00ZsZd1QFmm/PItSPJ3BRc5FFzpTq8eBKyVku5X5gvZSLLBBwEytqLdG7GPWMQSePhxzIDZmy9bN8dw9khsM+Ail23DUgH4iBg49RhhEMHy2HVWucS59NJbiBOT9F1ji+nVMZekjn09d4ZmZ0OYpYNGhbNSgls6QslwEXyvgj3JRw7gmtHVyDuGwM+2tWyyd6qBbYfpBQYePCKkxWi546wWd+U511J7DJp28V7z5TyiRiLhrM6qGvD5XrJlDJzImDPadpDbZJ2S5u5XMCY9DMXRbTlmvroU8BTaVxZ+Rk3AWy5rBy5zaJ01eJEit0TTy3ATRYO+Sztxm36RmNiXB10H5jwa1xMfkNCD4JonIwVLkxuV2aJ88984Xytv73O8MAEXDOQhC2f92ni37xlq0RKTyCDQrxDGlMP+KSt6rZqwbTJms3PoTyibWTc6MAA8dzNTXTMZuaqWa+dFJ7NYdaL5snq3U2dsncATC+Ly3V6gTRhXfOlyfuyuoqwuorR6aNmIgXsyizbL4WBwAC52Vn+JS6DrsLF/M6p6MfJVG+udZl2jcWkVUTVhtNCBKpSWTCAgbksCE0+kpJVIKWhjwZEmHFiq0ltgZYlA1N7RnnSNGmz5DUxIJBT9wdbVtcxszoP9rtXjJKAMF3bjlMpP4t3I7GVgwjaoiCVlzQpiRwDGbIQcdvZjeLdjVyeBQb2/vg2Fwno7J6hPYcMaGVAqdYVKs8/xybZp71XGYnXtTnjFwHmWVNXbSRSsBMtu5RagF15ckySNYoVTSwy7ee1mw+iOnK4mUOmZeg1Bc/hNp+UDI8XezudqHu9pXchFWRJm0ftWfEMfJGtdCeJef/Aflw7uqqTNLsAeGLNRK0aTTmG6Nv+DtgVLa8kRdGbyV3cGkTC1J9CsIo30nmgtrKyZmAwsEqZP83+QzI+HHXjkptVG5SNNdo+Nhen8fMgIAMajgvD+zcBbV3cvvY+8WrbKyZum/mEvQ+ZIidritlB2lmcUkWwyppdWMOmXLHclZRnFnkl4MI9E0IYNwn+CGhkz6h7dn2OGa6ruZ6AJIEpdml6QOV5KoADqcz9ImuHaaN8EqgxgMu1T7hNyUITUtuRAI9knjaWtgG9d3zf5DzaKoEtO1oPkenrYcD47MnufZL0wg4Q4v8vSZdbSKwtpbQSe80dtMXSj2ZP7p0svHookdS24oWkyCNUAfXNxzRstYs/AMCSaoFM2WXEw8Jq1AMiBQc6ic/2vBh+B1mAlNDY1mXIk1y0uBGcm8PvO8REWu2bbyJbh1T5tWUzMZItAgSstFykPvF4mU9Tn213zguC7sKs93ZC+/XrmO4LLFDiMdd6BFi2lqx6pbWOkNuxXiGiNJGgjYVDnpu2LyVwo5aoFjx7rpAqclHgMj6cRNBZSrx1sATUsqig2gMJC0CU2O2BVVu+8FCMq8i9J0XeDT1D5tkulcNlxMJ95ecCNLZstSvkXkIFIARcO7EP1U3Huq0wk9w6iyTrnARkgB68zCo9B+YGym55SEs5DrbIFBq6JpjBAGH/flw7vr9buYGUrjfPS74KBjXOlM9WDKCg/CqYLKnFPXHa8tTCI8q/Q8Fq+6qgfvtIK9ESd8JbKkyECVKbZSNIE7U0SNeyxUXdTgSMSi4LBXCAAXOmbgFcEDBmFY3WR+OqwMEnz+saYsPVSOVJvRmpOiDdM/mf9qIScGv6S2Ah4x2x9cO7LDmaC27MAQtS2t/ZYqcK3PNs/Fi0zzWnCGjqp2eYx6gAnjIrjyuf76U5R6x9/EwBunt5xiFrRd2f7h00ZXAUl+tX0/aYHQOQSPCyGGgBZwxAvVphdO64bjFQ3CepJF2ZcScdn1SeNrx3IS1bdom2nSLX08qy3R7SeQCVt7B0JXraSpBGWXfDcIjxuWbHaaP8W4XLuVXYquKVcEacdNwCdvcYBSKukELSuCyvCk/atFJvroEqUCGjapltX6pxgYfBw0IgrB6GzO0hnw0gSOeqm0jM80zc7Mg5w+6mLJSa++yUrLq66Lt8GhBE42Taz+M6oL8KhqBtVuJGueYAMlP00hbQ+VGADg94DmZNGY6QKoBK/8a2rGKOGwFWFJYv7TGuRbZeKIigewB6nnkctG5k95j7yARkk8DQXS8gxZCrB0j7d/niNXw82j4Nm78Sd8a8QzwGjkzu3aEeRMuYbBxbUy7MRNnsvD0pS++886eff2fdP2m3SM+BcbLAHgu7RubxwU5LZb3krQIKPzQfEnl08ACu3bQP9SBMUbiikOQA/XngwtluC9wB427hstrf/crUKwtfDiJQkbumy7ogdTK4MvwZp+g4s6oea7kFhrBZWMkzqVhDVn07mKPCrjlSxqa91D9PCmU3VUZYlXNceLcJb+ax9HwbcLuQxmCElIcHqX2hbonJLvrKAAw3DnI8s87JhpfiMnGcJbt9QTD3KBP/SpDVjS05BlAJyCLAaZ7bLhAmQFDa7VyCGmLeAiZfpynK30eyErIFlJ9pjT4qJMxlC495f32947yvCojdueOzx+ff6HFeKW27wi4nv9njJEAzj6V7uy2YlyG9C6kgu5nrMs/LN+tLwS/atJTbC4i4jTIwU4XGdTQYIB49hHqlMvwXTW9OK+mmQLcqg8sKG/m8VtHw5E4WFJOUi5SnTpCMWZyC6YqmyIiJ3G4xezM/QAALWXd8BArXZ0AYWKkVnvuQQBCnim/cWXQtuXTSCt27Ttw4UBvSOUHHIA+/jcbykLl5uG+w94vL9orW86G6oq2y1X9wv4PGgeqRe5NxhLSfyUqkfSb3m3lO2nM4Hb+xRpElLHMD8SvA4++sTplLEraOVAid41/N9n1K41SeU9WiQ+HlxioY0+9cj4Aeb6E0lhixGA3TuMSh7ZN2s2p2Kl8/sQ/VyePljR43K9PAhk9wt2gW370kvQVmF8osiH3ecjZzziZEcr1E7y6pQtpxev8+bJw8oMpWr61SXpXkTkgTmzWL24RY7J5o/mk/Rck4s71Xsj6FuVF2DBxCmnyNNYHN8wVgxStmdk2pInAcEe+mMEpCDrNbJRBPhNpgcqzwipetWKJk3MrXWCgYgAHJ6uJymnC4qxdjuWnD5H0iM9MvaWtbPke/sDtCuEFaP1/bofx8JI/JKEyWJwM6mcRL15fGvQF91r2i40QWFK6TRUGAa7f+35E6oOhO4nLpOcsAMhfTgl/PGZM6mGDOvC12ZXrwYgBla7Gz4e7pHTNpDog/1nCAmnsQ2vLGJ480G8EuO4HdtP2Q/PmLzNXXC+DsQHfUzmvxopJFDMxuptoWsizE7q0qIqWNyro2KNvkC1VcBUldVROBVN9yMzYODRNYcApfFZSbvI1ikdWyWFzcat2H7YYxNDqlaRN0Ek9kX3LdkKncJOiKTc4SUVB2+4Joj6GwfxBg91digEbtMW4DUTZe6UnbAQUTGRdHT6YGeOXV4cIwbW7bYUBbISGbTfgHBQcZ/whWCbO1hiOLtBwCWnpPRXESuNPtDXjMPZCZpuQduOHrSxYTzv5sXCe+LgKMJnEbAwIau8xKR8++jBNnaA41jbUH0UjlStsyQjs9E+y6zcaajvN4eQtSBhRpwcI5Y4obibp3RN14DMIBIASMDq002XnnzQszi5Sij/zxrg0dJwGgafPsTo1umqR7+72QOiQEGC7MbnYpTZMSCPGrCT7O0mUGnav6doLiiaQKTebMwQDh4EGsn9xvyZ+FFaSu0P2kLLfYTIQpgkj98HItuYD0OjJfZ6tMWRkaUznt0UKKTNqbRbbAKuRUuG1LtvIlywhbFYx7hFexpPQMX8YDi4CsLiUKkyuErRjeZZdW2678gpWCeSd6fGiBoLoqWoXPLjQlPkuCQaqraBEA9D54sCPXcmp6Yy1CGgPDDwIpdBlDN60Yax8BHXYZZdav9ndvjcrqk3K9NSvA3ofSMyRluWelaU9qL1sCi+41HR9qtwNRuqhQCyMDMnq/PaAygNQObNE9LNcYKw6AGBtX9Onj0/PCzCPTNnvsWvDxcc+NmUeWHRV63Sw9Bd27IE91bwAYnRT3MGjxMimt9awP8gwTgV/tFHkv+sKHxvpCeV+UCOiVfUTKX+IAC69g1ZXEbijhL0iILQEOJVoaRR0yS4Ax81ek5D35tD1mOCWt+Ogp41pgvktbhm5YKZM6uaqkX6wQMlKwrFAjMneEjh8r7kDHqU8ScSOKUxSxB2km14kOfuq3X+kby5ecruAr1S3lqEVBgCTzcLjKdtzFjVMPg+5obnkpBBh48cx5SejTjGvJksAWM98uqY+AZbURMbhWm2sBWDKyb5+CkZj6WhIBiKVyCECYZ7kSMjKNBRHpBQgzGZjvhX82DbCgOUA5YAMXIWbObwEfY94aak1MJ6a2+Wdv48QBVEcObw0XBsjnz64Aij65XS5t4IYaGmaUvTFyJrvkHgYx3rIyC69mCX7YqZMFcV/C0SNYP3WgrZMmQrRggvzqJqkVWgUlae4rWe0lUJIalP6YTwOgjeIhIERuBwEzPOkL0DDp6AV4IV3bfFrlx6tXnvR5BVziihRJqDG1Q1fPpHDFnSW5aniFz7sAa5s5iia6eti1hFSOWYWP7Dk87mp5IBK1zyScKapSn4EMTOS5RRzZm9tLLjXTRimXlLTZAFRAK4V5e5DCocXeUsXPLAOReiVgvFaZZ1zHxlkDi2PH3ZT7JJYlGjdtV0eYuHkWxKLF95qAugl/p+fe94GfB5OtuE5jG8bRvHv8fnj3qOeoZZYvY8lr8iyNVyuMz51YPhdGpLSVAP/WZeWet/xeVPbGiOxl0MLS5asVmeSDneRWmlhlzMm6pTqrCmF1BaPTx+ymjGQRUF5BwXogpuksaVxM1zIXQjc2JMJsNYpphT6kTRhdwrSmQmlXqoc5HAJsaomg4nPlO/EkOAJDJup6JeQRJHCrZlGyrOSdItN/q0JZzuIk10sCO0m2p2WF/Ho+zn3RttKYyNgrsJD7Q1wHn6ensTzZbRo4aaBaP9p7qdE8MVe4hgdDAI5DvA2Pgu6ltN9bvpqxzftvuCGOm5WBiXYstO1Ix41Ls+2zB4bqbqXnUN6jFCJOSp3BD5crCp9TFniLXTuu9SCYY8b6yMCpLcdYociCpmW248SWv2QJzfts/uQcP9XL93Z1v3FsH8KBA5vnwnTNjfzbJLAyabuBWercrdJzYAqy0wi7N0L8CzktZ8ICL1PXhCHcF9x0FOsn9jXNccrWK3c91pIiJeoAsMoq0iRrSYQhTaq00mXSpXdJcTnJtZUmTu8iioMmB0zRNeJWvuwaEeCg4ClwHcmVlOXvEJfFyCkLaXMFswkeK9EsmkosV0o6TmClpChMyCytxlmR81gmi0YLHscpVwyPbcaDEHA3pAgVGgMgATozNmxFYUUfE0iQej23yLiOQhoHrbMAGDOw4seLxyOmNhsLBJF4E28o1QF0ABAFj8nyl0nJouLAnQHsgUBc22a5Z8lNRpY4N07++dJcMFUqk9+rbCzpWj/eej8dH0nHT96Z1gqDk8d0n7WFZdr82DVHTltE9jKX7A0AA2BPJ7ObJp4NP8t+H3OaM7vBSwXhvoyPH9IVYGnS99YDXhmWUruz5aAYUhrdxCrKpMB/0EuZSzNMbch9/FQvKR6f7dcrWkmy5i0bqb8J1JiwYL8qN2OD7rGM5XNKuz2nFb0MRrpeQVB7jEOzu9w/xs0Vgl3RhzSGOg5sSSGFp249wLo/2vOLIdgECn3blGskx/w5dN/YuqJg2oE4BkE8tgpG2ILQtjPjgoiVpWRlQwKbJj+S3ItBOseQck0B+XvHeVrYXcZjm/qUxsfwW3wUk4/+gh0bGR9vuWQ+kXd1cl0eBJk2tmVunDg4PbHdvNJFwvVu+t51tFTpR6aX9PLNQjpbALhMy7wLAGHfPmwcW6Pf6JNM7pnpXVZwSIopq4ZIqDyJlpSLKh0CH53J4GTl2AKZjG/RrkLDOBEdVVjxs7ulsGoXC4jsMC3XJ+5Mq6CZd+PdXdH9H2TVTONJdWcJ65y1J1v5w46TcdGIpcMpaLaUmP+dOy2t8gnYkYLKSK5shXBkTpOTJ9A5hfEuuSoZwCVXCdU3LvR3AhhSSwS5rTLLB1t82vo9sNa+CRAvADN1qbljAvLMWEW6BzxuRJw1Fkr59P1jUi8csPNtp7KaCLPCeWLdoTBw026ZLwI62zc+MEQ4uAQ3EsskcNIVyTlrYtC9YqXpo5B6mVkmvWxLRPxTOTBV1WTdPX40rUyJd5KtOFXJUB2ifNkiQMrIgJ4WcBh3j1oNaMI0Fhk36YvCFhKkKCIhycrvfrXL4MQMku2PKsg2Jb70w6yA6VyTj0NcAhVxJ1qlJ4nATF9cOSXXi55PyiGRgV3dmATk6BDnJiEirXJbqG4lSZe2PBBwIyCWExcy0ZTuowIkBjPcR7E4MJGWw6GRn88uqewZ9WUgfU/usGj6oM8SPVsMVDlFgCEit2Oq0Tn87NFnKTeSjqvLGyT1ILZtVVdiUDDK/6eC0mdGSnbjYYj0wk9TvhoNGluiaHFj8tq09ZWy+Tb1A/XNxzSkeksikrSyDvBRilTaS5aWLH0EP4Cz3489NGKt7CU30rz7HnWds4QXqwRixH2ElSFGx/YbU78xQ7s9U9g1IkpZVszqgmFTc6tATSI7l/NDd0Vm5effsUHHRA0kkjBZU1gZyzn1SiIUM2mR08h7To3kuFF3xThdr+UXHmvPI1JysoveSqvkmIBdoOtlIUtWCybd8ne/kvaco+ZYsngoAInRjhcpJ8PREYUmZZm8Ova4IV+TdUPGoJgjhUEml8VjTvfTWIMcVyaLPHKAmHPa+PMVxPrsuwTKzHtC1g8GnRn/ylud6Hy1HMlzJ6BTzhOeS4S5b1KGcqYInBoQEqmM4NrkAJY+f+24+M1LzTWQ55fGyI2nsYIBGN20H2Hf2vRNHmeVWfK4LPJ7qBBWV1Ht37+7AI4HKSYKcXYdvfQReeYzn4kQQvZ33333AQBe9KIXZb/96I/+qCnj8ccfx0tf+lIcOHAAp06dwk//9E9jNCrtgLaA9BFJuUxj0fOxuYsO5rNUZ1hdxejQSloV0mrX+7zldzlHAIFRKkAeBgya2APUTdBcEwzIMStP9zIVd4t2K2whoqqSH5MCdwCACZcmNJTIktoO30cf+uwtRdwmcgfobwUFq0pRlJNYc7zFCxZQSZsNf8dUlpSMSX8f7e/aFsDUmfoICzKijK9tl4ZTFwCJWtzYbeNX+RQllVlduC0MHLhe6gdbCDRKSVwhDIhcWL2QrsVNKhYn5VXRWAs3yiS/o+edI6eYO8XuM3XvkGVKovtK7yGfWyKql54ZwztyoIWjz0zepTEBJeEvsXvXg2omL9NznCx+wHilAk7c1AQQbFZmnS8XSUkRa8SNEeqr6wvPwdtadEziQrq5sB/o5uRP/uRPMB6nZcNHP/pR/O2//bfxD//hP9RjP/IjP4I3vvGN+v3AgQP6/3g8xktf+lKcOXMG73//+/HEE0/g3nvvxcrKCn7+539+8YaFMBey2xXS9RLNEk49SyjgBGHAUjTRViG5j246YldYISm7OCAFCbIQwK2w5Emm1aICG7da96uxhqMiOV6a/+tWaWdmdOmThN5G56t3ijKMmraVXDHGDF5ooyj6MI4KuhAA1CEpYG6PkGYLfJCMM0FWgwwosBUKMKvtlBzQXmKVVduOqumcusGEM8QhvGSFycZHCboWEKkFi6OW6BopxyvLdL9IocEBJ1FyKEePwQ8jgSb5LHFe+Dk2Fppgn7FMjEWi/TdGRC04b5OeQ+CiFFGEAVlhiIiuVjVPwPXTBFlM5b1JVdidwLOx4e807sYCGJC/W+4zID330udqnNyt+g4LCJJyAWycOoyVz30ecWMEoER4mlFm3eV6UQCyG4GLiOoZRr4hB8sdsnQAc/PNN5vvb37zm/GsZz0Lf/Nv/k09duDAAZw5c6Z4/bvf/W58/OMfxyOPPILTp0/jBS94Ad70pjfhgQcewOtf/3qsrq4u3rjdbn2ZJbx5VtfRJs2VsY4IVdDPooQArK5g/cxhXU2ZKAci6MrkZszcXJ8oW0RdkevKsgpKSvWEQj1X6hC+hHuBDLAgl0QMoVEE7fVN2TFfsbZtYeCTZQh2ZSmZmHz5BigMaIJvQY1ZActvLfjh7RTSSQT0AJ38FVgg/Sahy0BB+ZiVt1yQ6jBjIbhALF5gSwgjlbbdrVIC3P0T8ELjK8dLOXt82aY9ERnAzXhYyJ8dY02ic0qRV/4Yc77kMwt/ZwuaA9OxCgiIWV98/qJs/OXUsQVW3n2joKQdW+a/lKQ05lKe4SgBxi1suEP8HEU6Tp9Fqyi3ncPw+XgILbBqrYxVwOrhw8CVqwgxTObqzSMyf/K+crwg9LyX3QxQ5pU5dfSWOtWuXbuG//Sf/hN++Id/2MTdv/3tb8fJkyfx9V//9XjwwQdx+fJl/e3RRx/F8573PJw+fVqP3XPPPbh06RI+9rGPdda1vr6OS5cumb8dL/OCiEVeBL+pWFd5y3zJQtW4DwcDhIMHMN5XmYmmqS8pAlWUZNlQUqeaha2yBUhBtJYLUcDsl0+mfFIi/FdS+C7ahtvLImG1xk1CYb1pp+XUBxMaHQXU0Usd7XiYVXJLTs5yroglh7gOGX+mYNXifk3KW8NKJwvjbc8tRnxp+ayUU/9K0V/e1cPRQuqmEWsUPS/+WuWzuHP0944FuXHxAclVKONYEq6D2lLaC8tv/tlU4p6btm6zH5BR/ARcCnVnQIqsFUCqy5Cf2fpBQNFEk5E1Rzle7f3J0hUgnSvHzTPlgIc5F7Bj7cbAE4mlf8Jza8BL83zVxw8vx43EUnLJzxqh1Avm8ZQs3QLD8o53vANf/vKX8UM/9EN67OUvfzluu+02nDt3Dh/+8IfxwAMP4BOf+AR+53d+BwBw4cIFA14A6PcLFy501vXQQw/hDW94Q3djFvSx3VDZiod7kgtp0nbwC1hkitaXUDXuozZ5XX34oFmxmtwTrMhCVOWtWWKJMKuWEaRjCHmaeF1x0sRei48/QF1HfjUpbqam7KiAgFfRqgRCHq0hIIn98V4ZS/9yIqwoeAtQ2GKgShTpk1fv2o/SI6WKzeZM8XsDcdQVi4kgGkSrcOhesluQr+OIKaOkKh5n+5sBajQWOkYuNFqvYWXqxLt5MkDqjhX5SmI5C1D3oekjkAHPesAWpmDKYEsNt9NbIcW6oGUACubkfwPS6HoGY6a8aN8fv0hILplkOdT/6Vkz4Ej+pzJMfpu2nSFG1FUA5Dl070wGJCtbHltw9ZkLFPG10Zw/PriK4XC4eTeSDhzNpZM2e+yyyPQyl2wpgPnN3/xNvOQlL8G5c+f02Ktf/Wr9/3nPex7Onj2L7/zO78SnP/1pPOtZz1q4rgcffBD333+/fr906RLOnz+fTtAwwx0IZJYhs2SGXGII9aTQRNn3CIMKoxP7dXUE5CtfmywrmBTyOhG6qpQsOgJCaKJOqlG03AMSDgkOEWbiFqVQr6QVPSv6akyKUpo2QjOJmxV30vopX0dyd8nGh6J8de8YKUcHL9VjxtRnM/VCQEKARTMmBATJrcVKI1Pm3NcWKHKbfK4WOU+URxZqKxaGFqjo8ayTdG9rapcoN3HP0TOkG02yaxEdICam4z6cHIDlY9AYxGH6zYBLOadgGeCcRYEsLMb9oSfb8TWWmfZZDHyC1BkcEI1p3AxYcaCUgYp3cWYuKzkn2GuT9TS9exw5x7+nikEgMCQ3rozfIF2nz01mGQymfcqjomg5tryNDgyxcvgQsL6+HDcSz7OTojj94nAWvuI0sONJwjsJHC3IT90yF9JnPvMZPPLII/in//SfTjzvzjvvBAB86lOfAgCcOXMGTz75pDlHvnfxZgBgbW0NR44cMX9GdPWzg8DLMsCEf6hneak2+dAL96VTqqaecOQwNg42GFpcQibXRd0kb6s2Em/EuoiQKXe2gCTTeLTnBvozSjZPny4mZ96SwEc58ATL1wJJaZlPst6wQjRhue05PpKGTe7cZ5O3hK0OhXDb5M4JRpF3kknJYuBD3AELnriNIUa12sh9kOOpUqRoGkCtVT6vSfNbYZyR+u1D37XOYK834+f6ySt8Y9UK9nfpp9wfthB516e6WSTM2O0FVQKkDLSMm8Vb5wg8eLcTu+ekXb4/ei5fH8vnZWX5NpvFRvv+jqLpi+lXlwtPQW3eRv8esxXPpF7QRQLUBRbqmCxd0tYqoL7pSONGWlZOGA586LJic/LQzYAXrmOWUO7tKiEspJu3DMC89a1vxalTp/DSl7504nmPPfYYAODs2bMAgLvuugsf+chH8LnPfU7Pefjhh3HkyBHccccdW9Xc7SnLeBinJVLyx9gKs+B+R53gRdxHQLN1wIlE3gWsYpNdoeVYtdFYJVghssVBJud6aFPSm1BpIK3wWBFIG7yCkEk00iRYyIUi/2dhtaTI2bQNWGXrCY6GY0CKIHXKKhMzxC6ZGpvWtV6kcdMxAZSTYwt0X+toQUGbiM0oEjLXS/mev8D3lnkzHkCZRHn0ODKhmNuqQIssKZnVBwl8SI4dcz9a3kYKI07K17td5HzjFmJXB4NCAgUM2rTfbIxon2fDc4GMP/L75MquRnleneyed60xPHDw/fHWOOlzwfJmxlTazRammPrKlsxmfGy/dFxKTaZEf1keHJ5jOPcOmvd7dNN+hNWVjsFYQLZi3p40j88zx+8y2RIAU9c13vrWt+KVr3wlhsPkpfr0pz+NN73pTfjgBz+IP//zP8fv//7v495778ULX/hCPP/5zwcA3H333bjjjjvwile8Av/n//wf/Pf//t/xute9Dvfddx/W1ta6qpxN9loYtcgs3JZpZs4ZRcBL595HgrSHQ4z3DXWykpV9vRISb4ByknjLQon/wKtko+BJ4fvspKw8S8pG+DDG156ZqNNv5vvAAQ8plwBDBjYEOLUEYMMX6CB4cr8kb4cCAvYQBuKa+LktAt6yItdwuw15msqUDMSdnAvqo1FeYpXwYIbBJCxo9aKRaWyhac9TF5IjtvK5zJ9SK5IACl+fU6hFLgaVn90vBrCAujfYGsOcDeF6MWjJxoKeVbZUmOgpsg5JO8yeTDQ2LOyuY9Aqz1k9CJpHictQy5QLxS6BTO82NFwzuZbdb8KBo/dWCcV1NM+5Xhss6Evh9wHj/QOE/fuXl9ROKyGrNwONRbLv7qYkdiyb1MlbMiqPPPIIHn/8cfzwD/+wOb66uopHHnkEd999N57znOfgJ3/yJ/Gyl70M//W//lc9ZzAY4J3vfCcGgwHuuusu/OAP/iDuvfdekzdmIVnQRLXjpARWJpkeZ3EtzVRt0M9pZtgwGCAMhxjvb2Yytgo0/yBNphEw+/KwAmpX9rFCyq5a0+7JbDKXyzlfBStcPmdkj6dU7lJGagubwYsAgCZwjm4y58jKuJ1kO60gAWZFmRLNJRAo1id1UZEiYy6EkqEpwZ5yUAjMcARKo8go70rpNkfku2FLG1kZSr+c+6LThUWuq5R3BPqspHBxUmgCDsS9MOmxjKl96uIhsOotQKzQ7XYVVtEacACknDiSW4cz23LZAmIHzNHiOm0/tU7vJqJ3xINWzvdiXFpAZjEzLisnmTWQ2u75SgrM3DNktjYogEZNYEcLBG6P8rYcsM+sX2LxkWeqfZfikUPt4mqJWwt4V1Lp+6xWklm4LztRvE6eU0+HGHenWeLSpUs4evQoXoTvwTCs7A3wAsz3UvjrNlXtDOM7GDTgZXUFOH0znv6a4+3FzQfneZHvAE287riIEgWFAOldMUSM5HwayUoRs1WmKsYQUG0koCD1JQVkFXLG1WElUki2p+4NKl+uE2IvK7gsqkpX/M1k7H383iJijtMxE7XDpnzihDRjaUmhfD4nQzPjBCpL6qyggFPGwiQejE1dRVDDeNaROnXM5dTCvTakTh/lwoq7ZDXisVDAbH8zlhCyBpm6ULhHhT5KWQ3BN6RxK7Q/i+Bz/TBWMXePfHSWB//cD9PMQm4hfkbZAumfjfSM2TExfCi6/6UIMo7wk36xVcvz5vzc0GQwBlYuXsPgo/8vYpvxdiYy7zxBD6Vzvbt+J5Fut0LoPRjFDbwXv4eLFy/mfFaSHQzd5pQYsSdcSF07Sk9yEW3RXkdFaVc546P77aqT+QMc1UAkSS3CddFsAGhM5O2/lAHWJAMTa0hwUUbkNmEiqlpgOByWuC/Mo8l8/KJgeYVLk63JCROozUQE9VstqCKVMOYxbQjIlh4GL7TizawsBAJ8yHIzfo1CYTePjAPfI7VkFCxN1g3oErf5VbKMP6/OwdcnIGTcTR3kWM8z0b5r+0La6JP7Lm2L1DbfL6ovVmjKGZISHud1yX03uXOkLgGlBJaMG6dk7WOOTp2eJROZRSCvlOXYWJrknRnkfTb9bS1/5j60H0KmV55UoLbT/dXnm94Ts68St53ab0LyY3lcjLUwuOeyPT46vIJwYE430jzzZtd2LT5iaBmu/FkzA283WcBLso17s2TZKy6kLpGXowRaloT6J4IYrm84xPjgilkRsn9fyZ/IJ9DiBK0kyzR5lnKgGPBSIEHqdgKOTFzyn3uyrCooUmLalsp+l/YZ3gdN6kZZivuKJnIFOe0YMNHTK1MGJF5ZGQDijnkuh3EvFMi32i5Qfx0gMdE5dL5aX0DnKUhLoEk/aQyZf2EApo5PzMGNfkl9FktGGAOD9WjcaUADRrwizD6D9NEBKHke6fow5vsZbTvlg108Aoao7cyj4evYBZWsN1x3c5w3Qs2sTyJcpj8e0rNpuFoxEWoBGlvJqjxKCwi+n9Iv/e6sUByxp1sDsJWocmXRWPmMyeyujlWTiwdHD2vC1eu6Q/U0fkzX/DwtOd605KS7QPYOgNmr4s2UnlhW8sVu0UMu+V/CoDK+bp5MusilzBFRsqwk9HLWCeNiIiViLBIyqflJ16/ynFtLzuU2N/wbKoesBryaTn73tq0ECri+ErBg5ebDUhuAF+01pNQZKHA4bwnM+CisLjM/SzGMWhScJBJzIC4Ogg2xJr5IrJC7tSYJgzca4wSKorpJslU9Uv+k/nolKEjlUGnAPneGGEy/870oRUAZC52AMXJ7mOeDfmdgY0KajTsptcOAAcBEVul4Rfc+wf7GwD4Df2SB4XZZ12yyLso5Pt1A5D9eV3FSPx3PNAcArdWxEH2UCQM0Bj26kSswPnYAWFnpBgbLkklBFSyzbguzG7L8iodkTi9JD2D2gpTcSpNyFCzwAneSd7msqtlCAAf2Y7wqWji5gLJwYogCbifoCjk4aausRtHkjNFJVCw7DHLEVeBcMNwWv1pldw6LWfXxqlvKkNW9fzHJisBWJGlHlhXVRVVlyepIiXq3QT0MCWQRcFOlbsY7almqtFrA4UUVFltbCqCPCbzqykJqY2a5IbDpo5SYzyIkTD2fxwC2TVI+YMGSt954sKZh/CMLWj0YNRYEM0juuFhEHN+qSI5lQBMbBc/1+/w5BlgXRCw5en2NZFmSc9g6JsCJxsGAbjdGRWHw5d6RZJlpAESQBYwsMGQhQO+54R91JL3kZ8X0nXhcpcXB+MBKw8/bSuuLiLeEL9Gdv2NlAQ/J3hitne46msenyeCkxH4X8RaXrUTrBsQE1If3g7NsarTMkFaDrdLrJGhGmlwdt0ByYLBloDGXd+yR4wi5Pi+IEV7J+UmQVrvaL+aKsKISnOSJiSFNsDomEiUi/5cUJ7XDECEHCQSULF4e1GgZA9cfB87K0ShocvVwpJK0UUCZs+p4Pk+niEIjUi5bTfR+tVaPYhh0SAqScwdJe9MzGMz/TfuCrv4T6LFh7VmkTzuuhshcUrruHpYI2+Km8dYcrssW4n6LQHUtgfMG0BOYrPLnSd8beT/oOcosMtReTllgIgw7gA9bBM19i1QW3V+xctnkilRHF57i59+dEytgvFYhHDqYTt9qIDOrG3/StgT+2p0KgEKAZsmfQ1/v0N7OKTuVwDtrQrlZAApLaYfUTTLglf/S9QJV7YMZKsRhlVkbbISK18r2f/XdM/DgBHdUrpRdSuBluCc6YSalr5YRF7ppiINtmayk1DLg2y+WERf1JIox5eogKxBN2hmAKFl9kMbFuMrcOGqdPDZe4Ut/PC+oi+skzab6hc8kW0XI9XoP1SWYAJMBj2rFEWtN1DI4bN6Ml1zKXCUpZ5DO8xFCeh0fV35VPoYl15ZysaQvg/wawztxLibOCcOuIiVqjyYDiHqYAyL5Xq+GBKo4nQDs88T9Ne2XZzEEAzrUBefAh5ZL/QYcqHNj6gGe8r06IrUsKZ+uMffcccyABFLFAlo1oL2+6bBm5TXtuF5WmZK1fNoCcwv4jDdM5tTVW7oXUi+blFkexlkzNvrfGMQswXypL3hH/br/URVQrw4TJ4T88EA7gVWRJngYZSMr/KYumPBkE24dqDw5j4mzZJqOcvIwvTjGwhEKSlsm1WgBl4YZ1846RKv2pv50LLMuAVqu7HGjgCcCAZGUSDrOE30zvrTXUp0+U1vTd9l8kkEZ9xGw/5txIF4Sl98on2iUjPaNQt4bINMoPyVwu7FQ9wgdrzZiskhVAOqQxqZu9gfiPitnqnJlw7XNhX8rqJI9iRwQBGB3HWehZ1HrbsdMP92F2h5/T1teT2aFoToqnzMp5vdcf2aOES8EgnsW5PkPqe4Ygm0nf8o748qX39jyw/1ldyQvSrg/YrGS3FAe2BhulyyGCqBT351B+o4IbNy0D6urK+3mjjRuEwIUpm6dMk0mhVAvogP2kOwNC8xulXk5LNPcSgs3Y7bVSQihiUA6MNTJKuVxQAI0yCcuXa3LilisIK3iMCvtQMqaJ1CyRiQrQD7xePBi+uBW9OqyILeTN6WrguAwaXVfpHKUzDq25Zq6NBU7ASGk8fFgwxJ+bWI7OS4rYgUaorC8FaTg7uCyjcWBFJWx3pA1yFjOSFkZErRcR/yZxMtJyjk464ByduiZkjo1rB30qW1OCtr8Dnd+SKCQ+2WiwiLdF+6/3BcHhtKA0r8hZJ8MJFWhx+TqKd2vWJE1bMjHaZzIEpcllKOxzaT0+hdAubHm0LtVcovVw5CNT/GZgABOeie8hdS1w5OZtY4KqFcqYDjfun4pG0BOOr5Tw6LnlZ4Ds8tl2sM6b7jd9ZIqWV/CvjWM16qcHEiKXFf1FAotJFRrfk/KXlfnpGAy/oxIRGvJkXLs6lLbFQHZC4mtOMaE35bNKesT/8QqAVZ2wmvh31gh8sqZuTnJkmO/yzXSLkOOHQSnqNotG9pj9QrtH0UgUECMuVb4MVyfC+9lxeLdXLo9QkhjKWXzuKdsuO0WE258Tep+uDq6+EssMf2pojeWldzakoCp9N22gcGlISY7xc3jY0i0BOTNeMD+z/lhdLxcpByDT0OMjsiAju/fJDFEWRpDAwrdfTD8HXeOjlEbQeU5allIOvjZ6G5/ends1J15vwRMEq+sHgaEtbW0Z9v1cB1NknnzeE0KztgJ0nNgnOhK6gY/iMuQWdxDXSHRXYmUFq0PUmxhXCe8dOOTRyxBlCdAuZxzrqiFIZmZdfVOlhhd8QvvxYW/Gh88m447IhlEqnHia/CEGWLa/8eLccOIRaSGUWoysUsGYcuJsXk1ivwL2PbUw7SHVMmsbomVaTx19V7bCCwGD1K3iVAKFrgYAEr1lpSkCX8v/BVX9HQ9AGuhk/q439564EOEycowMYqGys431CQgWWoD6Ll2ffIZbDVkmNwrzHPJuTLtde14iVWF+Tc+zFsBb8zbo6CxA/ypy1Kiwsia6C1vvr+e22UAb0CynrUu3szSI/2pU4oC5ag5vhlbArMoQzlHkgwO7Hupvx/cr/lgtky6cr9MklksMp4v05XYdDvLHByYHdCbTYowm7ejzLOR1yy5A7oe8BIJbJa6F3nYC20IIQAtMa5eG+qqB8iBiyGcBprsQvqeiLVu/5aSC9mZiRn08KoxjKNR9Ep0pAgHTvglirgEgAzYausWEz7ns2m4I6Ks2sl0RApsmCf1a861WVl1fKhOrQsWbPB2A/IbAOWYMGfAhKEKmKHfOMS5GicFK+Z/JVrTfRQFVMyYKr87cGSIutzu4H7X1X7ZNWiscsXKbb/ZgqQ8nxByMCRtYK6KWNSI6yPnZ6kAYhonAR6ZpYMASQYMo72mNEYKcsgyw331z4++D9Iuros5RfyTi2Syz09qU8nd5dtsoun8ef7WBiiI4XsQfNlsEWzLYZeqXFsf2a/z1ZaJd+P7gIrSQnSe/ZC2OGnpdpHdD2CAuRDdpmWeh36ejbzmIeteZzE+4FJbq3ZM/IqeeANyDLBKhpNgaZSFr78KFlCQqdisEsnUL8qlUfQ2sRivtI07gHK2eNeXJuxiJSNWJsDkUTFZRMn8bvgwTC40fbV9TmVS2DivYJ1FpBpHAyZSwTTBtwohy2wqAMEo6mDupfZd2krKJ4v6KvRPfgt1k9NHyhJeiQdVfA2X7cPCp/JNCgrdcE1KEUt072QzUeWkOEK5eR6p/ZlSle4QoMx4OXSOtNV8j/YZUFcOW+Hcu2hcNNFt8cB1iOVTwJzj5vAxuU7Hpkq7RWeii5D0HmXtDQXgGwQoQ5+B1BYkaw3s/ZRrS+Hjo0OrTSTS9XQflZKNynd/Dv9W2pJg1ujVZUqH3guDgql0almzj/veADDXU67nQ9PFWu/aU8Nn4e0qY04pupGkXFGWISCsrmJ0sPFz+Pwn2WrOuYY0iyqHf4akUBPvBAawFKOckFaxkgCPw2tt5+R85BYiMZnTH5Nwk/ILSfmPoArGVONzy8SkICQHSaeLQC4hTk+WoI7CZNVaQeG0Psw0RKRVbU1jHF37CXRmoInAD19vTP4UksuWK44ea8oPyqPwdZsxUPdLugds3eB+imtQrUWOR+LdWeaeyb2PIMsYDMlcrWqcOA8EYNsxqgcWpMTQcJKCB13yvAvpWq13BBDIYqJWDMoBVLQ+Bfe/vC8M7iIdB9TlqOHsXRYgV766eIh8LPXUjjOmTXIARIEdAzNqo17HXCN6bgw4CzCLi1gFjPcNEA4fSqdsNZDpmscnBVuUkpFyWdd7gVuoLwwGqA4d7AQ3y5C9AWC2qwtp2dJlcpzmblr2w156saqA+tRNDcsf0ImK0/CLy4QnHJ2cCZiIgqs2kiL2q3wTgUEuI3bDGN4MTbYaEeMAjUmWRX3gupmLAiCRYwFDAq5XgmZG9ZFIRd6P9i0BALUshFQ+txWAsWKkH3PAlFljJrwyDW+AlIq0XcjD7FYTKwVgFAhbqwxPSF1rSJlj23tS2rJBV+CtUveRXxkwEwtAW6a2T5Rz4HbmViw53sVtKa3qGcz4cpq+pHq99dGfn56BBIqtSyTY8UH6TcGaAzkGiIP+d30zFrmYxoHH1IBW50Jtysj7oxY3dm1WiSzO1h3uj/RT2xKjuYfevWeilCL1XUCR1DMA4tFDxXwwS5XSRo7LSDY6bYuB68SDieMxxhcvza9feg6Mk+vpQtoKmZaJcdJ1wPVH477OlgOzcdP+xIngCBRAJxIz8QarSHVyJjKs56VkkRak4GQSzyJDYBV4ZgZHWvVJO4xiZCtD3VpZQOGq7QQr3yXnCiuMMgk2tYtdZyZk1OW5aABLGpcGKHGhMNwfXaFHmHGX1O68slVrCN87Up4aGi+uNgWgBAqcYjXK1ANEpLJN3wn4cFi7sXy139kFmfrWAge2xgV6RujZkTGSZ6tE6JZytW43Nhy2a1IAuOgtHffacoqkDdzW5OqbYXEWBBDQM+wAi5KIySVk+uCtfcP22XL8EY7asyRkpM/Ybs3AddD4c71StrW6Froo5cuj0cV1Cu6TjscBgLacem2lcPGSpcslNG1xKefxX0kmWWMWBTFbCujkYZvd4LA3AMx2ssAs+gB0+UInkXu7iLvXA4G3dWgCu+FQw3nVrROSItf8KD4TJ09WPDkRGNHJR6/JiaDsVjKrcwEYnJ+EFSxZTTQCwmV/zYi8yl9JSpRdBMyxMVEf1A5uT0bapbYbtw2v/oFsB2NtA2WXLY4VjzcrOQYmzL9xkRwmnJjM/z6rbNFdR/eSrUSe0OndMk07Eg8IARqyXQynZ6tA1/xAyttwsapUbga4AqA7TRMwVX5WAfiZZy4iH5cuiTSuhWvYcpF/pvZqvfw8tcfULdVRvukL8nsidZTcnQbMRhpXz98BYPaBorZz1JGpkoC3t9z4aDdenKACUANxbdAQeaW8rXAjdc3p05KL8m+LLG63euuYRWUBPb03AMx2ssAs8uBMyu/StU2A/F4CNP4BXjag4bLbDRzD6qpu4Gi2AgDS5DW2E5FOzKSYWQmn+trzTQQCb3TX/BkCcGu21glS3Ei0qtU8Haw0heDIq3OncHWVWXDfiIla2m3cEdKvdkJu/iiihSZc4zITU3sFDQ/lsdGspX5VP3DAxSnVxPEIytMQhcYKiSOetH1OGHAw+DNuQr7HLQBJBVjFxnUVo3qYzM3gJQJhBN0rK+X1kbZZQBpGMMClFNLOuUqYs6Pt5gzF0f3OTSfLSgYAJ0UeBZjnbJJVJovsEQUvVhp2pbr7IdebCCFzHAaseKumHJOFgIm0ggU/DOwbK6Hth+dNlfqdWWqc1ckOjIDS5ofxvuHWbezIlpYSCZe/+/+nzfksy5jXF0m/cR1lbwAYYHuBmEXFP9yzbrdeKmdJGXhnykJZBcQD+5TMqv5pmfRrWim7FZNf/SlpsD3fT3pmVciWFZlY3XzkjxlTNfv2ARPlxO4Hn1RNFVbbt1L5QlgsZXOV80QxVuNkparGUd0ofvL2eVm8tUf62yjulA9Gy+KxcRM9K2AfZcLt9aCoi6wsxzkbcsrxQwq2rZMVdnKH0DiJAjYAGAaQyrE4aN0fQ2THDUfDJwps+5txspgkTNY6AzLF4lVTv+BAtyOXJrdI/twCSNYRPT+acTLjCQIPHe48015W9tQmUz8BfRlDPU6gMYztrtlSt0Yy8fNC5H3ltLg2cFkMhAy4pOcnszIG2MUAbPZkAKhXK4R9+9Alm7LIeAv6tBQXkwi7XVvCLGtRus0Ai5e9A2CAnQ9iZs3l4hF51wM9zYfaIfLyxjpOfpHpt/rwPk0gpbwU4mroSrlgbgbSJBjGrf+cXEdxjszfJeVrJjuvTJkPIRP8wF7D+TVkIuSkegCtMltrSNW6GUxm3yGBMFqFqzKSZrMrhhWFIfsG5aSY/spYC7iS/vOwsFuCFGBmdeA26A/29xwgQpWtRqI5a0LTl/bTR/A4jpQqZQYuxNUxVpuQ6udxknO1jaREzbMphz3YYyHl6QEpS4kLxPls9NngcrUPNOYCKKMbJ99GAieZK5LL1/a4MSiAnszSMqNOz12QsQA+pWwHZLgferLcZ2m7HddUcbtwiKBzAY72Us7ZIKSEdqHKFmpL2T6ga+7tIu+WcsV0WWTmmdOnRTptY9nerVumhIBtxYVZhsy6caP/PinUeqZq28mlC7xwuYPGlywJ7KwSTN8VyOhk7JJ5BajCq1es0vSTIZukjQuBV/RyPrl9mCeglqGxvcZGUaQ6U312ta9ROTEpbeOikXNjVKuMRBo1irnAQSCLhOFPkNXEAh7bZx0nctHJODLh1otRWKzgaHwMqCBiKJN7RTR77IBW5c5awsBQFV/M26iuvAAT2WXcLMwrEjejgLwo40ZlEgD1hNNONw0BsDgUAJYipjQlAO9bVVCGpTwspdwtxqrA3mIXiaftEqCk3DMHSLzlxQEZbU5M98NYgvi5JDexD+k3rlh+hrmv/l6XFjcM7FwbATc3xMZ62VjC6Bn2w9+OQTywlg4JIN+M5aXkwvfWFT9PlxLedRF4S7lgpknJjXWjZQ5Dw94AMDsVuGw2DK4L5c9inZlYbLLAFKWtT9Nxh9CYZB3ZMyu3FsUKq4R5RUpghN0Q+hmgk2qWidZNgCb8WkJq2W/PnIe2fZkLagztl+FR1Lb9CoZEYUs7fdQHXS+mfOPGKnBGWMRaI242E+5LYIW5GukPCHyOI92y1cVEnZgGoHhNZu0SgMYghy1N/tGKVvEpoGEXEltUKvsbWzSM9YjbBmTZlT03R9or1kDvrjBNFlepWAg4ikssJgJCIj2PAWo9MjlMBACRdUWBkbOE2eeYyuAFhFPuTFAvht57d65YYwqAEiHPIl263luBMtKunpv6wm1KfYFaazyY5roQUyZqtq5mc0UE4kpO5J26eJskXeTbLuBRysbbVWZJZgE08yyCr5fMoa/3BoAB5kJ121JmiS7yMonlvglh4JKBmI6XxeeEUJ4Ir/gisv1mVOkScNHVYyQ3RMfkLXWra4cnKGoH753EoCRbkbcvV72SKz9ZbYvwBoRyblNfspyw1UfcSsZML4sjBgI0ZjJeadWbJnCTOyUiJdRTy1Ea96YM2DJ4HgmiJAgACpi4ltqgGXPdc8EkYCMMTNjdRJ8c9q5WuGFORgWaNnorRSn5obTXjKsAYwIdZsylyQpoymOpUWNyiIjicq7cm+Z7CwaHMGApe/7ba8UNwm0p9onqlnujId3BRvpkgJPalfFOyPJixogXAB7IMvGXfjci7zTlhPGLGHOtuy/Zc8vvO+y5vg0czSR1jtcGKYu4v3SzLqQu8blhvJT4MF30gGXM+dvFKtMhewfAADsPxExD37P8Ns26sokHNFRhCgfG1m1yWADO5C2fSeF6U3u2ipf51e/EixzEMLlSJ/m2LZ4oKKtcjlxKICeBJ4m+8bk9WFGpK4wnX1p9G9dS226/4lbzeGEC9m4D5Rc5heZdDaxADY+GwVZrEcmUCik+hATmYggp9w39iele65JqHeBTEQsEE1+RFL9abwpWB4nWkjBdaZfnQmTPhwcrdI5xNwQHEomLZdxxjmBr+sb3bEBWIth2RW7LONI9cmU6sMDjUZQIA+jNudGBKCQAyG63rG8EfA2XiRYbtnFIYxwsiDJghecKGttkkXR98/hY5hVyTfsyzOVslV2pEIZzEOwWFbaUM6+lK0VGVyb1eSzz21l6F5ITfdkmKNvdINO2DyiRtTaB0ourEC6jrpsQ6rU1jPYPrXJ3kTsmCoSBSnCTCgEPvT4QcIg0WVFOENNEAQe8knSp32XyNonLCsBD2i4uCo5EEgCUJe3jvtUwrghWthxinaxDSdF4lxmLWWkzMRkwiqlEUpbyjeXCWDlcZwgYyu+qGKluz1EohVt3ckva843bRtpJlqwE4CYoqdpeCyRFmoHlQPek/Z2BIltufJkSlcXjpspdxqzd7TyzgkRqJ48xv3NF0GPrY0ulv65TZLqs+Z7m15cSIDK4LD0PRoJtr//NRE/JNCYA3AHBEG09bMHMXGK0aPAuY61b7vOyQ6m7CLYlQi7P2ZMCLkpzfOn3ru87WPYGgPGy0ywxs8osYdVd6H1OUu9MBN6qal7+wsqKibgcRuwtLkz+9Jsg8jnMAxAridl9V/tZVpxGsRJHwUfxZFE9SO1TYKDp8EWJ0yXiqhLgQeDGp2VPodYu8macyKdm1dqKWoaEd6CrVTZX0DFeDSs4EyDAiqC9lN1IrXIwypl+47apFcat0tmiZoi87vmQ/01bCsnxJiV2y+5/dH8OZMnYJ3Bh25K55AjwZO0ISG5IAawteGYAqKCMFK/mmuFxRQK02ZhxGwDKK5T++P56F6XnWmVWH6ozWQsDPBAzwJrOYYufCeWX8xzvK4GlVFZzfbpfxfda3ktZyHD/OB8TEZh1QROAsFLOyLswmXceK8m0c300EpDrgB0QSZRJz4GZIrvdEiMyT6bGOR/yYgj1hDIyUl5hJQmgMyFaV/QOgxZv1jararLgmKgcXdk2v8leTAxWOHpIibgOrHCfxD3D1hu2LiUFQqs/SlzXHEiARtpfjWJKrMYTMo0XgxRWlB5M2NV5AiGZdcsrBwc22PWlSojbxbd5LCHPwYyhv58+IsX3lSOMZPwNUPNET6cQMwVZFdojj9LYgqOKLGA+BDtzRdIYyzV+1c9K2lsPqg1x/yFzIRo3loAVZwnhdjDASeAkjWPRCsILBTlU2XZ61xz3qambvvNz49da5jmxYySWSAYxWWSdr1usLOTWCmPYxITtMc4Bo/ewbt/FoZiMKzPnbYoD0xX6zP/7xWYXWOlajJY+F23rNpbt3bpliU5Au9TywuJdQpOS2ck5C8pUF5IcOri/8fVTKvrmB1ICjohrJ+vCsdbCkXEZeIUnE6bbJsCSZBPo4FVvyleSKwieYOthC3rIfZHcLylU2OQzock2WwmKggzUNr+KHaYysnBpN4Fn49kqFCVLx3SsSIpl7gznJXGWisxtFKVvlNxOrBNja1ngFXxWnldSukpuN/OU4zR2BmxxO+m7coN4w8A6ZucJ0DRuKtB4cfMKhFqTDsCB7EQmT+8AWzHqlaDcI3GRSFtZSqT1DHwEO9am3pBHlRkwFG2ZvnyOXNI+t6Ar4z4ZCwc9W3zv3T5lCvoncF+YZ5VZb5T3BpOpOtTRuq0ZbIUGwMS1VY2mXEootTbYzdPevT8JrPjzuspmoLQoELne7qY56R57A8CYVULMj+0mKT3km8j50iXTCbyheQhDhfrAWqbcAehKPLXd/c8rucpO8PVKikLRSb9KGUQ5jLiLg+LzsmSZV+UcJsIGpyCRt1P7CgcKkPqQ9VfKkTl9bNOtq3WIJ36+Duk6z53xu3UrITTCWpb4NeEEfKI8o2t7cJFWThFxH4tgSn5zLpqMP5O1P/1vylPlS9VX9vei60quFeXaFbWCBEr8s2qsZR3CYCkVCAVUBqiXxrJUJkcNjWG2i5jUD7Y+aUbcYMfO1MMgXgAvt9eBWZ9UzwMOBmNsATPup2AXI8nd1bSTwVHJlWrcRd4ySXV4K5iApVAjWWDk2jpuXQRSU0E3KCnlbJnGiSnJdnYrhdDo5hn18zbtxRZICBbV7RU3EtD9sG7iQZ7V+pJ+I8VfUCBqoQHc5JcAiCFlBpr8/KTViih8Do/WeqXsio7Lb6Q81KVE18n/TCxsPpN7KsscOkj9MOZxR8AURSF1Dq7W2eaP8p37n1a/7XmVUzBI5xhpJ/l6GJI7jAGZc4OUyJA68YdmLGpKTGcUfnTRLzRuaTzSdb6dxmVjVvR0GlkCmu/p/hglyBaXkPrBnBoDNKJ7TiaIt5Rof2ICApnCjW5sgXwMkMaNrT0KLnlc+PzC+5FF75hKLIhUy2Rh01IDbD2urlNZeh8YxxYIwqXoMm5Xsu6l8orPdXu+AcluEeJJ0YbvFRvQVR9YNblgFpZJEaUlS8k00u48wRe+/AWyr19X6TkwE2SvAJdS9kb/m8gWP8xxrZkA7NYBbsdZWk35BFhFSwV/jR2TrEzWA/uZSYDd2M/zMExnkG1MWMk2B8ZvD6NMuqwI2n9ubzs2g6s1BtdqDC+PVTFXo5jM6cgn7yzTK41rsoBR3ZJuvzWn1ytW4bGC5Daa4TMTPwO75joBNNI+bTf9aTnOGsSRax78mUSDtAr349mZPJGBtOsP81a0fAK7noOhljVa9RtXGoGZTEHLNRSx5C0a3iLJYCwjahMInMRTSRwYOxZcbmbto/74nDV8jPvmgYFxH7bWFI4mVItOKIxrFWg/MB5/uufyLMghWRAYoER1+HbTezNeGyAse1fqWTmJkywx/H9XVKm32nSFZ283mcM7ss17siTZK6CFZZG9MJYhPgoJMJNDijhKk2SIUUOC5RhPLGzqLfr3QRM3m/HpU5J4JXM/1EqhJFu2gMiqkciOHhA1/XDtoN2NmeCrYIYUhFxvwpXbc2IIiG0UFe9S7Pf3McqxBVZs7TEACgQgPZhTZR8tQKNcLcIHMpf5yCS/wg7pHO/aM/WSYjdKq21DZkng+t1z4wEEK3JPlOUoKu8CMoqPFKrPuqzjJACsJTKnvqZnW8VYzfKxY0BmADpzaOSZH9MfAZZkGcv30uLEjYn30jaD+UpkuWgsmvQu0/NVAl4MqkrPq7yLkcC1t0QxuDKWunFDPjfPrYnaQgZatM2FMeZ+6/8A4kq1HAvMLBFFQD5vTwuLLm0n0JU/ZtI5O1R2Ry+myW7lu0yTLbSsFFcihPYDK7qCBYX93aJQBWAUV5yg30KasASYVBtR3R8la4hxWVRJETGg0WrIfZTa2/5GAKVrnyWjHCjySNtDQCq1MSdC1sOAjSMDjA4NtFxtY+tKkj+fy4b7asavInAj7SVXigE9tb1ek8OFVIcJC6bzzP0CzL1UgCVArQo2RJ5X7THaMXf8kSyvEBNDxd3mAS8rWw+aC8912d1TALcR9EyRInW5YDxBVpW6PGdOiXM7DOclRnOPusKIEwCNZlz1XnrgIM0jUGrcPTxE/J6GwnUchu6vlfs3AVTIvWvAIN3LQMdA49i6VCvieaXfpfxUZmmO0XYMA8arFTAcmlwwS7HCLCpdex+JTCL3zpoY9UbLVrqQ3ve+9+G7v/u7ce7cOYQQ8I53vMP8HmPEz/7sz+Ls2bPYv38/XvziF+OTn/ykOeeLX/wifuAHfgBHjhzBsWPH8KpXvQpPPfWUOefDH/4wvv3bvx379u3D+fPn8Za3vGXepu58mcYwn4a0SybDTbqPpr68vnyzqkFS+Fp/OmayrA6C8ZnzCpRJqgCyFa4qRXYpkVJWRcWKLKbEcN5KwtfL+RoizRl4RQkVLCDSLj/ZAxbwyHftlwAXAhzKgSFQJUqiifiBghW1MBFwybYnkLbpCpzH1v6poiDACVBqfx6mAY1HIcKIeRxZzp5o26Rg1bu0pM1cR0SKdGKrQntf2fWhvIi2PpPTBqS4XSSY3p/2O4MsA4QEbEf7jCqviQB3DBSmTXXLmCnAo35r5mPY4+qiZYDRBeIIXGn/YvrjepXL4p7jEngyFq1A32lBwBagpg3pN8tlilSX44rF/Hp2W6oVjtpu6q9sG0z/Q0BYtblgtpTEO8+eRbNwGLczUFmCzA1gnn76aXzDN3wDfu3Xfq34+1ve8hb8yq/8Cv7Df/gP+MAHPoCDBw/innvuwdWrV/WcH/iBH8DHPvYxPPzww3jnO9+J973vfXj1q1+tv1+6dAl33303brvtNnzwgx/EL/zCL+D1r389fuM3fmOBLu5gmUTgKhGxuhD5pP015gQ0E/dBomujrjIDxvuGVpFL9JH80QSaRQzwCs2vziDK2ZIvm39Ieft0/6KohtQGqWKcCJc+jJmTqHlLgLbH8zAAzT3BIdv6pwovTcycjp9dFCwGxHjFT/sOmT2naEyZDKyg0YVMZ+0EiOCYojUUKCnxk5QeWSUa0JL6DDigSIrWrK7p94yTEUAWLeJ91Bb4+efDPCdt3w2nhM4xLk6+7Wy5E0vBIHfZsCXKjJ0QYGncuJ8+4stYLKsEaqQ8wAKtDDS68nT81PoEe1/UYkGuo4CGs9J+6lhEW67nnZicR8F+Vu7ZFiK3tlnPD9rGLm5WFoFGv5l2gsCSvGf6LLgyVoaNRbmdKxeywMwLJCaFTU8CLZPywpTKmiQ7wM0UYoxx+mkdF4eA3/3d38X3fu/3AmiU1rlz5/CTP/mT+Kmf+ikAwMWLF3H69Gm87W1vw/d93/fh//7f/4s77rgDf/Inf4Jv+qZvAgC8613vwnd913fhL/7iL3Du3Dn8+q//Ov71v/7XuHDhAlZXVwEAP/MzP4N3vOMd+NM//dOZ2nbp0iUcPXoUL8L3YBhWpMGLdvXGSNeGXrNey1JiuXedO1PxE8YyVAiDClhZQXXwAC5/w3mM91WWTwA3qbE5VyfsdjIj5WNWoUgTj5RXr4pLQsCCVVRajqzoCOgka0VZebLbR88XJU5t5ZUsrzr9ZMsWl4xAXOojKxluHq/49Zom6R2Lyfo7tn3IXHbc5vZTx8XX17HKrgchja1bJRuFx5YRZyXhUGoTgi6/BdsXuTd2MFyfkI+Xuq6MlSBkz55xZbTlKGeKXSXm/jUWRc7xo+PprQg8Ho47wr+za4jdqlk7UCiP6jZlu7FKIALJchkaK1FGRAYSl8U9N4bj5erQZ8M8Z+ldTGPovhMQ8aK8nUA7T4+gCSYTIAKqUSqX2y5W3lBHHPz451A/8STiqHmhttQCM6tMikS6zkEaWyGj+hrei9/DxYsXceTIkc7zlgqx/uzP/gwXLlzAi1/8Yj129OhR3HnnnXj00UcBAI8++iiOHTum4AUAXvziF6OqKnzgAx/Qc174whcqeAGAe+65B5/4xCfwpS99qVj3+vo6Ll26ZP5UQth54MXLJD9n6dM9tGEwyI8vGEY9cRsBKi+EAKysNGRUEZnDmXNAE4q6O0I+STGZVFZN9bDJCRMHaMCLrGoH7XGuWkiP4vsX0qtYP6rUtuKEC5rsRKHqyppOj8mKk/EnZILkxHaw1xpLhkzcboXsrVIa8SLuI1nlTyP0tm1TKxNZwEqcCzav+/DXZnzINaQE3/be6mo873NzvBk32W2Zd5bmlbecm9re/FsPA8ZrIeun5hRx4EdJnw68sKXC8FPcuBirDfOI5L5k/CMipLt+GzBgnjeyQrh7b8KC/XMh1zNgc/fLj4nn35RcnpJQLz3fVGad+mTqcRFCMh7GwkigkKOSUtkpUZ32V/uW6koE/JRduyHEp7K5Hm57sc0RSXdsxiKxbBAxLUJp2jy/na0rNyoK6cKFCwCA06dPm+OnT5/W3y5cuIBTp06Z34fDIY4fP27OKZXBdXh56KGHcPToUf07f/785js0j1yPB6KLqc5uJf59UubGTVh3FlqB6ARocy8ACTz4rJ1RFJYofN1jCC1IIbAAmlgB5c9ULlrBrP5EuSMpEUMuNo2EmXBNojeQQnRkUnFnNOdAJ88mv0zHULVAgTeCZNKxlF8inDLnx4DDAdSVpS4hGUNyC1lyZwJitpK8vQoOndtNxo7Dq3k8DUiAgLE0XinXBz0DMqZGiTWAtR5QWUMkLlWm2FMoe27xcuHDAtoAex6D1JCulU8Jr5dz2cUlbdbnpH1GTU6hmEBW82xZ0MX5baqxteToe8PtJuVtgE/756OQ+N6x28cT5CUxIr97mWtTjruxbJ7r9LzZMSAQKO+Af46o/TJuCjbl2XGkab1U73+hDCo/DpYwt18P/TAxD1cH3WCS3CiQs5Uk3u0qDz74IC5evKh/n/3sZ69vA7bCTDcpXK507oScAHFcyMK1IIjpjEBiqWRZ7F+cpIgAqzwMkY4nX62j4386LylhmuhkdQ1SGgxkJEzUbZ5oJnJRtmTR0LKccvDt4Y0rdXhGyZqkFhICJ76MZiIVpW436uM8Kn5Vb6wJQxirkcmPQxO9Cf+m8eH2KZE6pvJ0TBwJk8OZjULxYMB9CggQMJcGJLVdxrAaRQzWawxkewEZzxJwaoEWK9mM8EttaY45xe0iawQ4ievCk0t1fAg4ZW7AEm7m5ysbs3RtPQhIFky7ZUACKOmZ0HsigJzPRWpLAl5prBkkqavFuGepT956hNQOILl46gGPXdve1gJp958S0EOAiMcK6dlPP9BvsQWWNc0JNBcgCkE/muu2hcyzNcwk8DEpqMPXdyNkDgvMcJn1njlzBgDw5JNP4uzZs3r8ySefxAte8AI953Of+5y5bjQa4Ytf/KJef+bMGTz55JPmHPku53hZW1vD2tpauWFEKN1RMgvAKFle5LiXSWF3MzdpxjFk4CKgBU4hCPcDtPpr/48VABMtY0NGZfI1Sd08x6VVviYagxUoYABKVxp1KdODECMEcMwquGl5dxZT03EYpWiUuirX1J4Qo+GGZJYDUkjsKjPtLXFBqgRkdC8gGfvY3EcODeZ+yjg2GxHS8BQACvfDcEw4EkeALmx5CXgkhdZYZex4GhFFyrefASSf6vguATkoMe4KAhMwLgw39nQ/EFzEU4k/olYJKrO0NYaeD7WqGIKxPFsQ8GafZ28NDNSGDFBXCegLsAYsSEAd7FhTmzKSs5Qrr4MDdlI2j6eMV9nSYy0oxfvOVlnT7+jqiAjj2japCjeGBzNrAjyge66fNyPvZniY10GWaoG5/fbbcebMGbznPe/RY5cuXcIHPvAB3HXXXQCAu+66C1/+8pfxwQ9+UM/5gz/4A9R1jTvvvFPPed/73oeNjQ095+GHH8azn/1s3HTTTYs1bieBl1I2xZJ0PYBbsHVAXvWUF5gsME2ullZhVWnC400QxSTuowiMj573KBFFQMm7NBrFrZyV5EjKIOO4RKRdmcm0b8z8bmVngBBN+HKu5zroirewaiy52ES5MXjS8HB2MxXAi2kzjSWvfgWEiHuCFaaMN4+jX9UaEij1uyGtpvuj4zVqx4f+jPsmJsXieTIKUlymZd4JWsEjl0duMBNWL6cU8tgwB4K5QVKnB1/stjO5ejwooLHJrFTeKsXAQdpep/6anChOkiso9ccDOs9VqcbNdbzTs1qS3HUZ+VcWKHXM7ye5hTjSR8Y2e77ZGkLPgb7jjmcjWXnNsxtcOTKejizN/DXvAuYQ7uj2Q9pxMm3/pFn2XtqmnJm5W/XUU0/hsccew2OPPQagIe4+9thjePzxxxFCwI//+I/j3/7bf4vf//3fx0c+8hHce++9OHfunEYqfd3XfR3+zt/5O/iRH/kR/PEf/zH+8A//EK95zWvwfd/3fTh37hwA4OUvfzlWV1fxqle9Ch/72Mfw27/92/jlX/5l3H///fP3cCcSeGfZhMv/z5/TMjUuQYwlZgpKVz+7W1kl4EG5XWhSNiGPnPuhzUFilBZgInma78HwO7ju1LbceiP+dgNIGIQYDkByN6VrmzIlyR631RJ/8wlfSchFNwat1KOtP4yheVIyCelcUYZClOX6ldhJPAIGeybjK7mttA66xlRPvCEmYDNIqTYot0+kezkol5ERoDtecQ6xbsbQ/u4jfeRPAV1NriIqR/oeRvZZEU6HkLQFdEiyQanHPFsEvI0bkNyYvFeY6bOC7wRcBCiLJZNJtwziGUhGfkakXMByVvh/AkTiNmSydL6ocOPurVl1AlAMXhioGcBUu2edAUtM13qOmo6/tiM9EybtAN0XrFhHBS/evEX6hia56yLqduUH88f81gQ3yvIyh76e24X0v//3/8Z3fMd36HcBFa985Svxtre9Df/yX/5LPP3003j1q1+NL3/5y/i2b/s2vOtd78K+ffv0mre//e14zWteg+/8zu9EVVV42ctehl/5lV/R348ePYp3v/vduO+++/CN3/iNOHnyJH72Z3/W5IqZWWhFuaOlZAqc9KAxJ2YrQIuvq0tWmygk77rwFgsOmxUriw9JzlacOukCbK3Rid+BDZ001eKTJkS/kvWhnUA7oYVC8qxsMi4oSw/g9OKk5NStILsWVwDqFLrMJns1wwdoiKiucGE3JpQxQkTDgwnBJEzrbBv/LuPU1p8ifGDuTYnfomMnwEQUqyg6siz4sVEQ04I65a6AFHOg74iw9ya1r9RmD+C4Xq/M+bnJrBqF/9m1Ug+RgQht4yD1jcEru34y4nugZ5ksDmZfIyemzd7SISCGQYL8bgpJ7w1HXmVpEKQfgxbgU6MMByu2BGRwhuJg+8Dgir5zXboAGlgwymVkCSDJqlgPKWsz7O/16hChqoBxM8/JPFjamXqZrqWiq2rSNgCzWksmBYFsB0vLHByYsJk8MNtZTB6YanX6BdtRSmHPwOQH1UckLWEfDH5hp+V/AQBUAWEwQBgOEW46iqefe0ZXN/Z8JB4MrfJ8Hhae0KdFxWSAR8toDxeAhpTNkRnMJRisJ8WmhFynsHx7jWuB6s/ytRTcUCaaRACd8F14LyRRPo4UWuIFafSWGzejHLlfXhG2QMu2E4kL48abQ96F7yQurwRSbb0mj0ugNsstFQBCgCZWNnutAsKK+qcuJFt+1m5fj+ufdwvGEDKlmPpv26nHHR+ruTaY3wxvC/Q+UJu9FcFEG/lXlNofRlAQy0Dftj1xnNSl2tZhxkAA9NhG1XXmaJF3i8bGhjVbS2npHTJRgCXQ6MnLkHpo3pDpRC1iqW9hhPQ8tu7M/f/fU8AnP4O4MTLz8bbIB9MlHpSU5n2vW7YL1yVGjOLG9c8D08uSZZYHahZ3k5znwc3MzZjxRfXlViGtptykKmQ+5sGom2icJjJjJYEoXC4oHedoHVuX+M7bcmSFyHUEey7QnJ92f24mfo6oMREQTNB0E6+2k1byJU4J/58ieKArQw7dVtdORDKDt3UY3oBYPuh3HjdtO7VB3R+0VUAiTMs1Uhdt72AiiKifaHL0CHhpVvxkhSDQkbnueGfqgeNNwUaoSL84WRlbNbLyHXiRMZW8QsYKQ89LGqOYARe1iNAO2vqTcxfK2BsXloBoit5RoCcgrH3+0ni3PwuodNfoOMA+Q+wKzJ4HGpemLqR3pz2mAICI+Oze5PFga6l8evCi7SJQz9FnnW5DGlMGiCkEP4HprH1yqQM/Jkv1xjiPptxKWQRIzLoJJP+Vrl8iT/J6yM5paS+5MClrFjb5rCmki1UtttqIK0NrUWhX4pLDhQGIaa5f6QHpXOJSJM4IsknSrLRYKdZpXx2d4Pyk5UmSZCVI0ScCeIjwp+G0fiBAOUzKIcjad7KaNO23v/vz/PXsYuFtCTLQhtR+GTfD16FoF17x+s0gTR/VimB/0y0a2HpQeKQ8UTdlOrZ7JxkpWBCacUhjy1ydjINSp2fEhM/qM2uVqo9UkXK9ZCG4agmhy6hdiaha5mtkxGbi7zRlJRDhn6uiC5f7Q6Dau7ik7YnrZdtgOELMOREwQX9M6k0N6HgmpM6BPeY/Swkh03gkAAoUnl1+v9GMgQJYeYbd81ZyHS1V5pmfu+gD8jnvXM8L3R0gO6OVe13YdTSrVabrvHlAj1ZfmJ1nkToCqys6cQNoMmLyyqtrBYg0GSauBBEDAXV/sCnYtFtWyCGBFt2E0VtTq2QNkjbJ/jesRGRFqErGT6CRy2w/B/lvfmXK/W7Kt+OkCoAjraRuAhnFjLW0cpa6k2vADxosuJI6kYBE1l4BZ9Jmp6T1fCmP77lTZAp+aLz8qtwQr3kMOoCMXiM6XMeG6o+pz4Y7JPspkXKWtpoQaRcBxkBRnl3un4LFAumZ+54d9hYkars2jYCQtNUr/owwX8tfNMBZ8rNwX3SnZ6rLjI9YkNy7YHlqXf2zEYDSZr2e8//QYqhLmIgu96kUUabgvK3T5B0a5w/zDSXrsnRtJ8DHlmHR2aay+wHMTotAKsks1pXSeV1hc3LNjCjbb+A41+pjNC6voLgpNAHqhOiUTeNaaq4XRc2RMCYyJQISAsomYw659YpTV4ZipKJ9hFjhZebodgLNIxts9I6tPxjzdgwh2z1XJ+sAC1JI+Xkgljg8FgCqhcEMOq2SwWVAOSvVBk3uMZ2vBOk6dyNw+41FzIM1sZxx1FeEpnw3isnVXwwh9ityXlkLaJLuiFIj7hVHcGmoLrVLh82RlFNUlAU3DCI4EaB5Tt153Bc5R62CXgSEtjwcb7nJwK8TzkLt+6X5fiIBDXn+HOBSUErvqf6moehUb0ifujN0Ow6elJ1zYIJ55vXe8NA5q5QCzbZcGW9d/AiPid4vdQNK30byAt8A/sukDLqT+JE7BIBkMicld/cDmN3JUZ4uXRyYTRcbJu+F5A89fSUlNWMlRhNitrqFXX2FcVQuihxLFbgKdWWYJkutx53nw4UBUn6FlZ1G33QoBymPOSh2x2nqk+iLyl7DlgKAthOgFXc2DqKEN9wYOk4QEzdNDg7uX8ktEmDAkTHLt/fUkITpmMkDQ1E9rFC4z969Y+4v66U6/zT1hXSvGDCoRUSAWEhjzKTcrH6P/wpWH05+BqS2cGI+gO7HgJ69SM9WsOdkViqkZ0ncWezykt85mo3zIQHuWSVrShZNJP+KUmeuFS82BGQyB4U4U035aN1bhbGka73LTqTaiKZN5nqtI21AyfXWg2CsSToGhTK8ewsbI9ywWJdpEUfTdqzeRNLS6y4LjPE279GSZCeAmGVvFDZvkru5qywt6wr1jUYpiZ2YcgcMGJzCoNV2UdgiQRMtJwRLFh06R+r3oEQABLmoxJ2UTsoVRNF0zStHnYyRuV5seHM+KauiEUUPMmm7ybWYE0X6ReUZN4yrj8mNRrlyTgyX94UTfsn5JkpJ7qFYQFSZu36y0oP9X8ryfSytwkv5XZjMK2UNrtUW8MWk9LLwdwK52l45LlYKAlC5WzL9L65LkXqY+p5cpW5s+P8gQESAJ/0/oDwslVfSMq6geqS84HZntjttm/eLFgaZ5SuWn0F1J8b8uiLht62TOTwlrhHzpDTKTayh6ipKwK1mLpd/R6mtJhSd+78VsqhbpwRSfHmz/L8dZQFvyd4AMDtBturh8nskLaEeDafuSpxUR2Xty8pFzfJwoIVXlBF2ApYJsEYKSfUEWT+ZAgYclYCNNpd5LBxxQ+d6oGXcUEaBxUL9Kby0mBMmWEXlRZPgCWHYWTS0HSXxYItJqQ4wqNtsjOJ46ldS4AYctMop8Y243VGVr+Sr4bJKUWhqPZG2M/eB6tF2EXjKNujkYahpLCUrMOw9SLyW0CZCzDeqNBYdiVDzipbG3itqTTpYp+92oKH1GpeIgMFowZBmsPaWEBB4cP3UtkTLhZL/+d0oWmTYCusWCaZNvDCR8ZDjzqJjQAs9owxQ9Flryd2S1oBTB/A45rtl07sg4+SBCxdRhSaaEsjnu81IV0jzJJfRpDBo/327g5UuCWEuILN3AMxOsMJ0ybQXx6Pyrsy8S3oBZ9rMEQReZGVPE1qmXGKbe6K1OnDUSdo3qC3XTZ4m6ogUjdbHE2mgP6CoIEw7WVlw/TLpiltJrCWiYMYy8TakR+ZYZCvdtmxVhDR5mxUyuQI4JFhW84m7kKen5/Bwb5HR/DKyUpX5mt0LNSlVHv+ChUTumbpnBNeuBHMfTF4Pb9onpRbF9E/t4nN07IYFJQaYcZYNLY0FD/TMyD0lV0jmWuE2SNtFGUs9BAr0HkoVY3vvldxaeq0ckPbHtc1ed3uLoZwqodOtcs/eJXGvDUIR8DAgYW6PjgPkGQY9G6khZgNGZ5lhAjG7J8Wqon0d2vHyz6S6qloXGltUsuR73CfAvCOZJXgrQcE8WXP9NbtN5tDVu3QEdpnM+uJMiu2fp5xZZY7yUmr1pBhKK6a0yu0IJc3Mvsl8zuUapcPKMOQTtnEtBdjrkBQT76DNCqLTCiIKtyUEm2t0chUOQ3sucXC0vzIurHQDMiVjODYj2DT6omAjlclWJwFhPoRZdrAepHEJMSY+Aisj74ZiS0mrPPieaw4XUu6mbuYDOYWv40l971K4cWi/m/sqSpvcaFK2AZxyz6n+ZOVwilfHIz3LOi4xB2vmu4lEi/YYWVY4koaBgI0Ecm0JFEUXZVdmm89Id9UmgB75HlKfU0PpU0EFHafzNP+Ldph+Nq7BkPXTWzEzl58vbxybPZ7kuXSghaMI+T0z5PaAxqIMFAHDlkcjdYU0z5LXaxNpM6676GJrPkPDDuhZL0bmJWV1uXnmqnICcVek6yUajTD8yoZODmI6l5woTKRV8CDKFbki8RObmoBJoQt4UfM4T/auG9KOplBomQwAjJWnPU9Wi2FkFZ9ZRcrpw1SvclmMgms3pXOWJFWUAkB4VShtoImWx6JesUDN9KMdXyUXC9kyJmuOHSQowVWSDDIo4HKMdcQLK0DK86M/E7HXjB+DmJJiRDpmEg0SWZnbl6xj7TX+fqkbxIbVl4ilHNHEIIbJwOoC5bYGGIBngIECrGB+4x3A9TgBRxPJA7kX9J6VdIOAH8i5qU26AaPUVxgHKVuet1KEn0bY6TNYjjQ048NN1PfAghUdH+l/bd3EQuTnTWIjj61/r9vjsQpm/kk/5nPclkcjzeM26pKd4E7SOXo+QLh3AMwCBKEdI12bN5bQ+QIP89SXdBIoqiMGX1lXhSVJongVy3le1DTdKn0fnmr+D2mCMWZq4tPIpMmTqCq1dlWs3BJSlCbrbKugOCJEVm1FpUaAgN1SzflIfeUVIai86CbW1jLC4Z7G1C1tpP+V28JjRqCoCARkvHnOJtCkYd3UZyVmE5k2I+J6heTfxZiukUgqdrNVY+sOy8Ab2vY5V0wJDDCfxoRIF8bGgGNnuRMgqrmFxIJFXBztHilgtoQZy5Mq2AS2PVBonsuOecwDoXHU5015RZHCup0Fp7k2KXyI0ieAI+3rBHZizfBWFmqf6SPddx5/facd4duMqbSTypd+eV5LTiDmBZEUaJtrAOeNzPnSlWF3loXoTgAuXnoLzC4XD1Zm2ZXag5qFqp3wYE3J3BjqOk3IXIybGIGkxNXaEpJCTgV2XOvbSBMkh29y1lEAJkW9tx4w+TiGYBO2cdSNq1cVqbMa+ORqJTcZ1w3AhihH950Ul3FzyPg5ICEKxlgMpH20mjfjB5gx59W8cHv0XomCpTJV6floLOHq8JjwXk9IANePibRP/+g3E41DQCEjCNPY8nhkrhtYRavVO+DG5fjnyPA42n5qWQ7IZs+2tJ1+K1ls2HKZueXkt5DGncXkNyKQaFx4DjgymGn66ICFXuOOcZ3mexoHBcxwYIjBpAMxKdQ7mGdDwZzrG7ugvPWTLaq4tqFupKVbXOalB/jPnRQmvQWyd3q8k0m8k6RrX4vS9ujXox2Roo/qGqgC6rWV5piPXOCJt2PVboiSWZ1Qt5Re5siXLBzyqjvPxgQkMisOrZZ9pAqAtOGdEH0FALR1s3vMTvRpktbtBQzBFylZH4EVjZBxSePMCpjJua1yY2WqeWVA5ZByM9YCJ+IeEksHR4exy8RbK/STFCCDIBtRk8ZM28YRXjxfO3I1j4lwHgQgsXvBhN2yK4ytcaTEMn6NV4A+UaPUy2CC65CIGte+kouHXW7elcrAXS0rnCPIjTkAw8dSi0XdsXu5b4u0m7vLwFrBlauXwLsB2jIH0EJCzuP++75rW9o6lUNG76vwe8w7oGNlnx0Bl34Okvc6bmzkg7EsmSdAoxSpxIvHnWhx2aTsDQCzW91HsyYrWnIU0sRViK+jjsCwOeYtJIbHwqt+ViC0kpffTXWtX5zDYuU8Uz6gE5xxDTiiL3MzfARME53hFCevnv0qM8Bm2IU7twsgEaGY9zKyOUeCUfLmerdaz1bjNEYyxgKWWESZMzk3rWSjATtyveEh8Co2u28J8KSDSWGpu4zbHuynuvRKfQuNEjOuEvl0FgLuK4M4aaexyPDt5OdTDgmIiK4cde3Y4wCUR2XI52358gwyEJXoNu+WKblDsnqDPbexGkm5Ug8syIzp2efyyxZPJNCp4J3GxllW1UVJBPksXUCpP/IcSnSdew5kIaD/Rwtgm/antnLUGC98BuvjxgIj9d9Id1LTAGSL0t0EXHoOTIeEgB1vhZlkTSmRvZbAf/Eytwk11ghXmgnARAAwiRcwCpojJYC0upLz9DOkFW228nRKShQRW17Y8sPAgjkqvDpWYGMSY9k+TdqXRfvCQivx7NwsVDRZULJICVAfTCFWmeTgIv2vTSKAxmGnQLpvOs7+GiQgwG4pXQlzSLQHAw6kMom6KGQhAZCUlCizmMaVgSRbKng17jk1rMQzzg3tkSTjE5msynU5N4ucw8+TAmuk9yIOWsVLQEwUdon7xARqz3Mx1jt6N0zklfLS0hj4Z1MASANogkaS8bibeyBN8GMcYa6Lw9SneiWk7/xMMOAOKTJMQI/wkXQMDWcm3X91GzKRu3aWMFqYcBbeLSftTtsKoGs3aaBbN0z7bbtIjHPr6OH0U3aR7HRLzKSHmk2Mnry7RBPjxBVIx0sSxuO06gu0uopAgF0hNhNKmw13EJLfmpSFrj4jjOJkMrBEzRiuCCltTowHtMCCARaighIBVGplGDUT7iybyWnd0u5IkzKSwvHuH1acTREt2ZkBBY0f0EzgJoNwSH30xGEfgsouMB/pwUqOMwL7FW0moqvINVDi+pihcqDF3l+Y+252yBZXSGVdIYnAmtpUihTTc52CVDcC97FVuPo/gQtJgMf3Tn5L/8PkmsnGnJRzKVeJL1P7SJYuzx1qQGNM11WpDh0Xs/s5gWd2eTpXjiGO19GOoUQUtuNkrKxOUZnow0HTDyatA+h0MaEtXy2BMQUGZIBK2oJ87kjlpHcjjKIm5QSa+e+67ETdFX3Ec7mf5yfN7zvBSqPP4uzjuwNg2RJkp1teuqQLsExzGW1yX6TOFziz+LTnjcZF07CEtJqVFW2Oxyv3dI2tW8NmyaJjIiTGaaLiSBS9vi0/1RnTuSF34cQQUK8Gq7gJCHE9zYSf+sdcmxKnwpRVmGQzkz2DMnFvaX/yOrK6SNS9oK6FoOPnx0v/GASIBUJW+4XssgakOWWSuYuoXSYTrxynPDGedJvAH91TAq/e4iDuMQWjMT1L8pshBrd1GKuIKMtxYzFh64XlgVAf3H3WdhP4SGHAjtsE/y7lY2q4UXzP5ZkEnR/4ujQ+mQXSP4ICaCq0C45oAGUzjlQ1u48cj0ffN24qT2ER2XOViPX5vOAjC3W+ccLPiVhpZd4YXL4GjNNFMvct1ZU0bS7uijLtilDaqbKAnt7hPZ5R2H20k8HMtI29SsdLEUhbSeyVumRCqSPCxkgnUTMhSnMoiZqZlAt+eCApIWMyH6RrvJm663q2jDCAkEnXhCETUODJMttLpt3F2axsBVSQ0ldXFqyiMeBHIjFkbAokyqIbpi2HV6ImvNiX0yovA9bU5WPP0wgbpN8MGVQUvdsY0kRl0T0QUqnJuROjAhfTR/fJrizdkZmtWMSR6bS6VNCQ/dy9Yq0YcjxZdQg0ROqvjKHfmiEkMMauDz4n5elprRCcH8gBDx+xpW5Vtp441wm3U4G+vB+86WSAqbfaSEDMR2v5Z4nJ6s3zR+MSkd4tJ55Ib6y1NDbcL31Wgk1GKdYYw4Wp7GcG+hjE1U25Yb3w0ADLtcIsks+rJFu49911EXV5zg4Od1DvNiE7Hbx4km4XeXcSqXdJu1HLPkidL3CRROyAA5qJRyNXZLKtYBUXgQ4gKXFvztey6TxTrtt/p+TyyVbG7pgqHyIiljgnAnp0o0C1ENlNIsVdZkAVYCKqZAwE7HW5qizXwQGqaNuhdbtwW6901MJAk7q0w5j/eXUdgipBiewwuTkYUEp7h7ZvwvNJOVPyVT8DtVAjEbhLj6QobA/GuO2qwMu/GyuB/CQWLgErskZwmYxNU9jiwrwK4cDUlMvG1cfPnE/mKPeKrUJy3POkgHQPJamkbnMh95oAvwHOPMYBpi3i6mXCe6rQXmcAGYHaZFGj8YxpwVMPUwLFrmfQCIFhc7gtn0GPyaTc/lWjiHB13XBgriuJdxkLz53gOvIS48wgZm8AGJadyIPpygHg86907UoNLPbwdzZnAohxQCnG2LiQxjQ5k8Jka4xOXH7lFZM1wpxHE6NE84SRW2mFVKaW59K+l3gZpV13S5u96fUFK4HPs8IrXDOJA4aMrOHVNB6qzH07C0rXhA+P07UcbeI3v8usVtw3Lp5W78Z1RkqrGjVbDQhA80qUQajZpdp3L7p2RZhx4ciVLiUWapv9OKu/pmNuOwnLgymDFx0HdnURUPMh2IYz4l1wrPzbe14PbOJHtuKUrHEcacTHAX4vEojmzS+1jcIjCcH0S68bpPdW+mlcaOJmMfwUGkMFKY27zSxgBFQI6KYxY9eZWAINaKPx9lywUsg985uy9AFidXrqcvu9bj/aNmwWyMzjNpLvvH2AdmKXqvAZjQ27tPcFCWFngheWaRYXT/DakiaE4v/upOZTyG9jp2VDPjkBCYSweyfjSmR1yQQUCADQKr4tV1w7ehmb0SliwYTKUhskGgQArV4T14HBkM/oqv3UFaE7x63+fWSQ4fW4sTEbD7bHqrFYXVz7a5dqnkikeo7nWRRIkjz2RsF3WEAsYBVlhuQ+qtO9N+Z+tigV7r8oOo1aq9NYGetbSONv+DxEHC+RaEtcHQOWIgzYKLVRrRuRrkcaeyYmS5nMDzG8DQGnDOAhzxSQJYsj0MtAVvtYtDq2m49upPtuAFFABhqKYyNlyv0XINeCHA/KrasnqHvMWAG1LAIcDiCzhdCQoKVNzHsqWPfYbRzGEfHylfQbW8Q260KaFljhF5xd3yftVr1TZQ5dvUt6PEV2OnAR8QSuUkhd6SFf4i7UYn2ZcJL9XteIoxGGl4kI11oDmL+i0S4hKRaAlCv577PIokAROLTKbypDIvkFZOBAmy3li7KJQOaTB5KLRMnG9Bu5g4wrjH5LB+hfPp8AnIZ6emXK40iEVamnRJb2gIhzvBjLWAuu6gG5n6i80jixJSy4+diEz4ryMeTYdJ7eWzM2FkTwNg3easNjZaJk2JLDVooCeNE65VpvzVCAkeouKUqpS6/xOV4Au8mkWiGctajtj4LSSECFAHGg8TdRN9ReDYHvsMiw1CvUdwIpRWsjjwEBeQZ3JSuWXuvqV+4RLwC8hUTOc5Ys3TpB24VkkVHQxJWlMgVExRCAClj58lXE0Uj5fFviPpo2N3e5/hcJpd4pEiN6C4yXncp/YSk9nAxQSgz1Saz1OcW7jqa5kNR3PBph5fNPmQm9aTtsroZB+vPEOz3HD4EDPHKO8AnE2mNM/VIPhVqyS0MtLI4n0ig7mqSFr0GuIVm5Zm4uUvIALOmQrSWkpD0Y4JWoiYCZwP9hrkjnOAZS8BHKjUgn06lERE7cEkpORgDD7yelVhx/jwfpvrByZF6OaUdolD+PE1+rPCMmerK7gS0e0n/HyzDkVwJEyeqU+sXWOX+PMlePHO/gq0jkTomMnn6Xa+zzqPeD+q3Awb1rpl4CbKVII61DrTH2MzsXFrALiMhcl7RI8H323BV7DQHqWpJL2qi7FAUGtZYaF6LfoVzrSf9Xl66YEOqlyayhz8BsgGQaz7G0oe92Bjq9BWaXyiSOy6RNv0rpqBduQix+ZvWy1BHh8lU6py2LLCmGrMtNlHMlGmeCIvbRGHrOIJgdoY3JW0zJ3CYCKM0/qV4TrVTTRCnjQYrVR9GoxYRXlsFxHKjfFsi5CZw4Lmy98ZYfVTqt8jKETcCAKL0P0d6PEkdBQWNE4j04UMBKD6BrAo9nGm//yVFGxqLi+yZ9dwrYPlOkIOme+8gU47YC3TP63yj50CbJcyBFrSNKvrX9MtFSTHJmS4dYmXhseHzo+fO/SRnJdWbDlqU/WldI7VLA4u8nV1PobyZUhsmaHAQ4OeDo2m65Sn6eKbRLFhpkgVISPVtZRva9yVI31LE556nLzSLMzZmbtsTMwmGZtOgs5YQpld8FVJYU0LElMqehYW8AmDlYzdtaOpB0qEL+Wwnlb8FDW3yZDbE4tvsisckYafM8MXP7qB6ZdFiBReQrWiSlqN/ZX2983nYik3NSu2Gv4zIzNwMpY0q3b8oixc2gySccy6wO8l36K2NEvIMwSuUa8rO/xR4ctO1hAOLP9wRp7r/ZR8krEe6HrLRbJczuNROpw3N5DaNw9fzarswzZd6OdbI4SNk2CocVOluS/Fh5MMaWEO8KSW4dm/k2u19yfwi0pH7nbVGCqQtHt/XmYCNzVUpdDCRMRdRejhgr/M59CfScGAsU9629drCegEGzvQPSMyKh4O34mHZTZJoH4pmFUqoUdw/lhqlXKdooRoz3VUWXlFzfPKMR8dq1bCwBLCeE2oOILqDiLez+d6B77p+UtdfLdrHIhIDeheRFBmWnS8cDHOuYHnYgPfRbkOho6uqDX5g6JhPsaNTs3iwKl/KhlBQmu214VZVWwzyZ5aZv/m783+yychN2iZjIJnaAABCBF0nvL66UTuKgHkiK2GSebVeMHC6ruTRqq5x1jGQsKmSuNK90MkBiAFoCDNmnKC1aMXP+EpMskPggpr2R/rww2BMOD0enSZi0KinYfDTOHcP9N5YHtdikZ0iuz5qk451y6bCVRC1gDox615OUZdrU8Uwx34sBpr+nBkSRJShZWugaA+yl/nyo2IrYjA29CzG1xfdL351CRJu8g+O19nnQqLFUlr8X/D547pGcn9417ltbjt97LFCGYHrGmvZDPw2nadAS4TdGduG17Nwvkywx07aNmXVLmWnfRbaTRWYOY8PeADA71QIza/KirrTSS3AZ5dW1L38VJoMZV2/c2MDwyjjntAjBkH3+gFF0ujIHTbKyoh4kZakrfplYA3Q/GZmIExk4GiCRuSYAQ3rMFAMsYEjh4bb9liAYE5iQ8p3SyNwNXvnxalSAS2nx5latTFjNXEw0Dr7tTHj19XFYrQAovd4oKVgLQyTQRxwOycKq/WfQGVKZasFr703JHaNKamz7VVLeWUI2BnzRluGthECq37uCsoivto7M6kZARd1C0d1XBau0l5T8FBurhrjyTMhwhFHYYj0pWVZSWdaCIpwf4Ybp+fIOSvh3R9izDlfVtp8WB2kMgs4FWbvJwqkWFnKdeQBaIlnrWI0daIo2GaXe81E0+V8Wknnm3pJFpitQw4dTz8qp6dIH28X6wjKjvt5beyHtNJmF4DXJR1o6vuRcMDNJXQPjGoPLG6gODtxqGFZxjGE22gOac6pRvjlbiBGRZnIfxqnKhw+JIh+kyTrAcSMggAhpYidExURU3w5PqGXTtV5b0W91Yz7nvBXJNUTRNOQO8RFafpfg5IpKq2MlNbe/K8AgRSbnGUtVCcxwun0eL8kPEtw4qOUtgcxqI6JeIXdFzMcoU+LcvxIIqwEM7GqbuShNOxNAYBK3UWD0XPI9RGx3uJb+815YsXlGDGipbX+MZcqHcFP9MYScZEoAw7cVaNsl7yPd35JLSa1KAjiQLFlShy4GuC8xjXvJpZe5PwmsJGtdbHQT3UtvXYtV+77JPRUQzgDRdMiOh++zaWNF4CfaeQBov48jlmKxnyd77qTF5mYWol3gZskJTpcmYmzoXUgkO9H6MovMgry7SFxLqT5O/A6gJcFFINaoLm84EiHsZCt+8ILFwyQIk+spcgOA4dR407rhigCqKFJh7emO4+CjU0S5GVN3ldqhEQ9Iba8H5FYSkEb9sjyQpDCKrg3dcC+4vY9g+RKyOnaEU+0ureI1OVxsQtHrob2O3XXeLeO3YYhVSARJ7huXNYSCHROCHNI5Glbto9SGTuErWLPPSHKX5GOYIqKSVYTbwG0yBGHKT5KuQ3o2ot2AUF1dHVZHPy7SR2MZcffKiChrOtdHKpnTnauUSb0cDdQJfuh5SYDEld+CnATW7DPJ15TC+9mimaKW0rvuSfBSrx9PKY+vMePoQKvypzSH0hIATJcsksRuksyyC7V3V2034LKg7A0Asxv4L0D+EC56/fU2GdY14rhG9dTlfHVEycyANJFnkydNOEaBeEDUHjcWgBHMOexe0fKcEvOKxbQDqYxQu6RffKonovrcLICatJVwOLTp/xkQGQDX9l3dLo7bA7hVMhJQ0WOtomKg6CNkfL88Z8H0Wawdjl+j7ZV2EflYywikDIPvj6tLgU6wyoZW4FnUmPRNwSXlIkLa+sG3t+SySb8nkCLPWEaMDnbMDSFZALv73ytjk8G2BQ8Z74frbMdEo6AIsGRSsFhoW8cJ5Jj3zIXAp+cogXT9bMEB7/0l9Uq/i64y926yJHdTzjljwJK579BGjDF4QQJSDMwGT18DNjby8VqGzLM1zCJ7IJWys88Tun0jZU6+6t4AMLvFAjPvQ3gdIpHY6pK5lGJtSHC4uq6bwqk7YgjrkuEyeJVN4IJXS2bvIF5dyXUhoF5pJzxZ3ZaUEXFUgDSxduVD4clalRbXyyGq7lpWfrr6G7cp+GWiJ2uPBRCpLepOEMBFFgFvyZHVLbuqEncBpq1qfQn2frDLRSw23g2lXXTJ9LTNYlVpz61GEdW1mIEw7atTzJlCLdTNClz4UzIOHPLMFoeSSyorP9C4S1WSit+DZrWU2XaZ/g1aLlDFzyjl1GELSWk8pS4Ga86yANBzwv1BAxKiO65163vjAIgHWXDlVUgk3WA3Lc2sWhWNQXDPVm0Bp+Hm+PsFKEfHWxY9+KrEpVywIDX3tn0unr5aDKFeSOa1uHRl2C1lXF+kvu0scxob9gaA6ZLdYJmZJZOjnFcigl0vqWvEjQ0MLo+sUo9WqQKF1SWSkueVEifG8iRcVoYcKWFWbKLoWQl3WF84VJnLtI1ESoFeslBoB5F4Ea0yN8qb2qArfOnDIEUlZVwAskBwWaqAeFXr+2BWpFHP43P8/0zaZeuOAinAnGNcCDJWQshtgUYGHuCAgUt85sGMWZkH72aLRoF515UqcAYp0dbNY2jcTs4VaNyfXtmKRdBbj+QYKWIFYVUaKzMWms8kGHDS1GujndIPVIaAjUjAFQRO274aoquMWczLMpmCnTAhvrTYaPrUgiDiD2lfyFVkiMjUL44Mk+CANB707NftffAATkDXly6m6+q4uQikablYuhKU+vPmWYTu1P2SQsCWRiG9733vw3d/93fj3LlzCCHgHe94h/62sbGBBx54AM973vNw8OBBnDt3Dvfeey/+8i//0pTxzGc+EyEE8/fmN7/ZnPPhD38Y3/7t3459+/bh/PnzeMtb3jJvU6fLbrDMdLHKvQnSvyxbkI2x6yVXNv94jOGXm4R2RuHpibDui9gobHZ1sN+8ZCLWcjjlfSspuoNX0GViIEffyPcutwnXK+1hImpzPWXepXLlN67TlycTrygksTr50GUFAaSUNARZqo40wROvg0OXpWwzdgWug/TFRzeZMODgFK+0q3W7GLcWgzJSjmbs2cLAQ8XWCnbViGLuOMdbblhpG9AVLScpy14L267s2WbLB1tsWmDFz3JyPcbMcmjdUalsBunSZymrtBiIvq2unwaEt3XUg0C/JcAk7jhroWzbz7ws87sDRPI8MkCjjVllvCva/FEBiyRSFAsZuSJr2sA00P1ULhY/s1LHqEa8trG1WwgsIvOGVXel05i13BslQuSdQeZu+dNPP41v+IZvwK/92q9lv12+fBkf+tCH8G/+zb/Bhz70IfzO7/wOPvGJT+Dv/t2/m537xje+EU888YT+vfa1r9XfLl26hLvvvhu33XYbPvjBD+IXfuEX8PrXvx6/8Ru/MW9zd5/M+rD58LstfEhnWqHE2PBgLj6V9hMiHknTRjq/nRw5NX9RGYIBAJEvOdGaO0+rIEVsV2r5y6MWGF4pj+1kCSTFFyKt9mklrf02ZVMfqa1eQSnwcEkAZdXZbGBp74NuZujAlCo9BjctmOBQWemPcZPFVKYhqbL1itroc+cYiwdvI1AQ48ojBZ6AFgFeVzZbpAxwcW4EbQtZLDgPDI+ZAhZn6eLjagF0FgImYJdcZhzxY1wnDoQw/8S7LzUKjACRtyYlQAd1+2Qh38aSU7h33Gdn+RDirvzeuMnstX7sPfjLCNzOvSn1Ne5BW6YCZ8ef8eH5maW1Lbe6MtqaLQRYJs3HXZaWSYnrpkWezlLfDpS5w6hf8pKX4CUveUnxt6NHj+Lhhx82x371V38V3/It34LHH38ct956qx4/fPgwzpw5Uyzn7W9/O65du4b/+B//I1ZXV/Hc5z4Xjz32GH7xF38Rr371q+dtciNzoLptLfNwX65zqFxxpRJroG5NJ4MBMBq1Zusm9NhzBQznIgSgahWIMZ3nIKA5v5k8Tcixjzhx1+tkHalus8JL7fPuELVc6E6/MJwcDaEex6zNugKNyHk8EQiwe8EYDoYQRge2bgMEue3ajvYaxPQ/EYYTWMktDXx9Vj6fj6i/5VYL7r+9tuTOKkbwDKgdzkJiXBoESPh54PEx/aoIZIZ8la/1kyJVN5Z3OyFda0B0CSwTGPJhwsaqIQoZ9FxzWQSgfTs8IGc3jALDmJ6Jppz2nBqohzlwtBY4twBRgAFyqQZNhSAbr8YQ9HlRS5SAeLE4yTvXzgN8vrFoSf3ybnp3JOgc45Kk/oRmzMNGDaYX+D3gliaebOu3fPGffE3pt1K5XP5OEXJlTpMttx1dvHgRIQQcO3bMHH/zm9+MEydO4K/9tb+GX/iFX8BolEJFHn30UbzwhS/E6uqqHrvnnnvwiU98Al/60peK9ayvr+PSpUvmT4Vf6N3Ae5lVPBGMw+euMx+m2U6gRhyNUW3UyeXREkt5IgVgSJhNAbBuDDZ7kwUji3KiFahR6CH9CXnSWDoABQbquiqtIP3ky3WPLDjRDSJV2UDN7JLYTeo1n1IVcz0opb91TREApAiR5KZpfxOyb8GVl1lEXHtM5l3Kc2PCXAHjNsvAiCgo4kVEItimlbt1j6lrg8EBmu9mxd0CCsMDiXYMs+ehFYlIUnDtuCGZZQZ0nowPby/hFa2MTWFBpXU6lwy7/aQdWj9bL5zFRnOxCMBqLSRNmemYuG88iV5IuQx2zL1zVh2TRJIsOyFGww9LRHwBTwTyGfRB3sEckHiw5nlJ6Tj1l8GWjMu4tcq1lp/q6auZBWYh8NJlQZk1TLorQemksuRYiTvTJdvJhRQj5tlKYEsT2V29ehUPPPAAvv/7vx9HjhzR4//iX/wL/PW//tdx/PhxvP/978eDDz6IJ554Ar/4i78IALhw4QJuv/12U9bp06f1t5tuuimr66GHHsIb3vCGckMMwXMHW2EWSWjkMzZ2+UqXAGBmSm43GmH49AZG+wc2yVurMDgZGoBkZUCa3AwXhlbgsQqNVUdWkzIZelOxEx/hYCY3ZznJclfQdgYcYcMJuFLdllcg4Ka0avbHdfKNBBqYZ0OWm9SemEAaAxKxkJCLq+TWyFw/aJQ7k6cza0arqIy7JKRVM9+TUAP1ih136bOs0hN5GRYgxXQNj0UxhLY934T5tmNixl7aF9N5DeAki4xvq3fZhQAMEmBKA5P6zICJo8QQUhi9WsSk7QXidfG5JUuPnifPIlJelODO0ySDgP4e+XoFKu1wCBclpLZJ+ZllDLAWP/nOvCl6dzg8XX6zGavtvdDnyPWnHgQFazpnAI2VhSMY9d60GYefvoI6RiDWyyXvdqWxmAYgJllb+PdF2rfD88FsGYDZ2NjAP/pH/wgxRvz6r/+6+e3+++/X/5///OdjdXUV/+yf/TM89NBDWFtbW6i+Bx980JR76dIlnD9/frHGb1eZtj3ApMy8bIGZxHrfdBOngJhYo3rqGnDzPjPhKtmvtLKmycdOUnYCBLkvMp+7bxKvjAuKiJUc3CSoREeJnmG+h0Q2RLo2IKufrUulLLmhRpNVtl2Jx2FKkuYjmPy4MBeD88r4cdDIDkdC9dYVJYQOmpOUu+RIskDTf95agC0FGReDktnpOIgyHyRFx22xpGxqY+ua6CZCh1zhS3kOXHCZGRkXCQixm9K4qvw9b8FTRVmhw7jEbXFRVXS8+QepPk9gJ6uXWASN5YjGOZL1yQB9qU/cu+251pol5fHz0YKaGM1xKV/Gktud89jS/zrOQyiRt+TCqphLR2MgLtYK0YBWw29io0YICKE5d3B1jPj00zb9A7B5F5Kfd6dlRe9yA01yD80LZLYrcLnRLiQBL5/5zGfw8MMPG+tLSe68806MRiP8+Z//OQDgzJkzePLJJ8058r2LN7O2toYjR46YPyNzmKW2tUx66Jb127IfbMkHEyNQxyah3TgaU3sKn4SCGnZxNC6WYCceXlUVJkDNA0G5Q7KIFP9IBPcZ7WRvVsNeCSBdw+4WNdGL4h/ba3gCLuUP0RV55Y47949Rtq3S5NDRDMQByXVA+UyycF1H9kVolERKQhjtb9wuuY8aAYak3GUsC+AOoBwiMY0ZW15MZFKk8TQWVxglVhwrMyD296LVjsCb9o2eF2P1KxB+1QJG1yX3jh1L8wk6V8rJ8phEvfd8reXuuPJcfplkiXFtI4IwRwdZ8jmIaNseE6uk40RxfbFKfLhkCZM60vNTXIjwp7RBOGsxASzleMkz78FiAAZPbyBujLAlUgqqYDfPtLwu3mLStQv1Tpc59PTSAYyAl09+8pN45JFHcOLEianXPPbYY6iqCqdOnQIA3HXXXXjf+96HDcqE+PDDD+PZz3520X00VYTAu5PdR8uQWSw3S9gvqXOlUteIMSJeuYrB1dpNuLBKCaTsABs9wr/rlzQRS14ZJupqaG2dFIWsvsXlZCbDSN9h/2eeDitYaYevW47xZoUmOkgupTZollhS2MatJlYYWj1nYd6xmbitv9/VS7+ZfnjlyGNQuL0KFjUhIawFIyRAlQBH7v4oZR32vKQiEKOkfJ50a8i21Bd9vgQQdURCaXsiUli6VMHjx+PRKnV2E2ZgiAAK8zd85JDhdnD7C7wiSYjIYMiUJ32hZ8nwmCgCSvtBbQSgYINTFTDI1T4zwGQwxO2g3/02ChmnzFnfUgh1+bnRtrl5Qp9FCWensR586WlgPM7myk0TeEuZdbuy5JZIuZvJvL6LZW4X0lNPPYVPfepT+v3P/uzP8Nhjj+H48eM4e/Ys/sE/+Af40Ic+hHe+850Yj8e4cOECAOD48eNYXV3Fo48+ig984AP4ju/4Dhw+fBiPPvoofuInfgI/+IM/qODk5S9/Od7whjfgVa96FR544AF89KMfxS//8i/jl37plxbr5V4HLiJdJsxS1sdNCu9anclohOHlDYzXqmZCqdxk5Vb7fMxG8shERqAAsCvbOjdra3lkJTGTLexvGXiJUK6JaSM3u/XLxxrGYpOFcrfliOspDpFZhkrRRNpuH4FB7VReSwWA3UCuj/565iP4eyFjrpsN+vui34ONrpLrB8ndYtw43HbaSymM8kSHAn4UeMpBAmbi1pGIK3GlqRLmmY9AjCrq4mZCAspi8Z6bfsf0XBrAQEBd73NBuRetg+576Zn27j11m4wBVHmOGQ8ieBwE/EYEA3IBN35I4FVcueqOa58XSYRn3DfRghZvsbFWlQQA9bwQAHFDDsopBPQ5o+eS+yucJwXJl57a/C7UXVKKJJLjXWTdSblf5s3wuwslxDnv1nvf+158x3d8R3b8la98JV7/+tdn5FuR//E//gde9KIX4UMf+hD++T//5/jTP/1TrK+v4/bbb8crXvEK3H///Yb/8uEPfxj33Xcf/uRP/gQnT57Ea1/7WjzwwAMzt/PSpUs4evQoXoTvwbBanX7BdpbNkrRK30vmxyWBF/EXG/ASKqAKCMMhwsoQ8dazuPKMw6rU9DRKEGYLbSf9Kud0sKXAr0hjSAqFLRe+bA2lNWHJUiidqiTIqIq0NAlrW3jRL+G2pbw3bJEwFpR8xRrGuQLR4zRGHIHjuRamnT7fhvSla2ogpSvXmHBil1RMsyVXAKeZZ7eS4QtFe5+yKCnmT4T8f8Np8spRAURHlloeB8BaQhyPhjkwIjkh3e9v5ZS6tLGtQ5+vdgy5L5nbi5WwFDVKoMAAXOpDamzhHnC51M/MesV95yJrej/kXP/O0Ttr2k7HSnw48zwU7j0T6JUQ7Pc+YvAeYfYaW/3SNQz+z6eaJHabJfBOk3nn22n8x10mo/oa3ovfw8WLFydSUOYGMDtFMgAjbqSdLvM+xF0M9utgXiwCmMEAYTgETp/E5a8+oas9rwR4hV5yLfmJWX8jxeGJgoabIHkpROGLpYZBBwMSVq7RrWQFxLjzTXizAz1GMVLIrAFIrn8MXBQYkJLxQM7nbSmudJHaJgBIwtoz0m3b52ojWgsMAQcDfpwiNKAQCcip4mpJm0YpS9+ZNEp5VwwxNNB97ABE/jcGCzp2DjRkxGAnpTZUo6j7G3FfTZl6ACliJiJT/hqZRAq+GvEuy64vdK846geAtUrw8+yAiBJz62TB4vZ6gCsuskp3c7aAg0GJRJ550GsWD+SS9e+Y9ocWGv550XrpHTFtr53rCcD+z3wF+NSfI7YpPbYUwLCUFpUlrswyAcx2BUOtnp4VwOx+JxkAJQXtdKy2zIfuOoXP6SQgRN66RhyPEZ66bKIyzDUum24GXjxvpP3kHCu+LKPEQZNvSP97UmhplQggZRblFW37v+Rz8TldNAcMLNhouC7pXLaeZK40Wql6km8nx4JBwIjAS+vCYGAlUklq9mjHVDabzIChO88DUu0zgSKj/EoRKVSWHmvvi2wIaoBSLCs6XyaDF0kfb4jjbuxMyHMHJ6gzBF5ynLT3M2WMtvXEEFTxe9K1uqxiGgN1RTrrC4+X4aWU/iB9d22P9v/kGsoz+nrLpE8xwECZXUDmnWZrzpD6w++lALOYfuNzeZ8mft8lWZ55dyhPjbnnNVA9dRlx3DT6uoEXAMUtX+Q4yzLn6+0IXgDMa2TY0jww20Z2g+Vls7IdrDAAYozN3HHlClYuXcP6icZtKJOVTkYyV1KIcpqESCHJyo6tJmydaC0c/3973xpsyVWd9/U55z5mYB56oBmNkYSEbGGeAdkWExvFFCo9IBgHUsVDcUEiQyDCCY9gSkkM2H9EIEXFRXASVwXkVGGEqTJQAZtYkRgJ2wMYFYMsAWMkRoxe8565dx73dU7v/Ohea39r9e7zuHPvzH30qpq5957evXvv3X16f/tb31rbC4ABGBBiXmpyvAOdMAyLU6mI+stMDrWHX9oMQAqdRrlnixitkNU9k2VAK8QwbVlJglwaBCL0d+lf7valkQmCGRmnY+HcKhJ5wpqj4pzEpCyTEk+mvaKtPqzbrrTT41tZzQvbxYBCz6tmPfYMkQEE0t7yHhe7o7s6/HPhorqYyZPPfdI6Zu5MqDXVafRdzEjSxVPsIrMnFf0UA8WOjFkM4dbMyShCp3U8qN/8DCbdigLkBVyUGiLDyIlLrCO/O4bIfWeAyPpkeeyjAdz8N5v/3iDec8PWELBpz+UIJ6aqfVuMDXIPpbLr9tPHeBuUF2Yt2JCh1OuDgQGw6tkXYHEPa90XwNOVy/BFkFWMYWEAyL5InUPTVZGsa0ZdQrosh4mE4LBLnx2WE7rZCTu++CuWIe4azH3yE2Og68lLuhUjMSrghV6gnOxNsp0WH8AKTnW1LQnk4nkCfFh3YsYhixOntINdR2YbAE4c1qNxzt350v48agj0X48y8npWQ9rJAImilkx5Ygs8sySbQDI7oqGzngXg8GvEnyZ8vw3zjKVcidp3imqK7EosaxLQ6XVCnNhRbUtRsLj/yn6Bnl/jGszggZeJ0GOgz9qV8nnm6DjzTGYxgWDRVts2vx0DAwp5xjjXjJwXv7M1X7QAs+FoJaWC9juOr69P0yS02PXnxs252/y5YyfnEWbnis/6BSAMY8MkppOfgzbX9YBmHUQWjWLrYzTWiv5lKayOqlyGL0byBVC6kZDnCNMn0Z7r1SJtE1bsJhj5TCdpo60of5FzOd9DuTrmvBj6uZvQTRZdt2LzQkJ2ZQF2ewRmiXjyYZaoYBSim8UIRaVJWeyrUvu8unXgqrVQFZxW2svuMwFVZb+SieG473QttjoND987M0nJvF6CGQWnOf3uct5wOyzbEcdI2saTtuqQkpEuESTq89COeYSYvTHnMssgYyqgx7nHNHQ/Jcg1kziBBQ/uGBj4KLSyjXHHcrtrNV9Pn6MWPQfsftNxpnEjt2MlpxIIYGSZgtn4jGXUR3oeMxh2UOtgFzL12bSNXIlBvh8c4k1pEwzrFeLxLA9oHz1ZRB/Re3HZ3UiDtgGQfz7NRd0WAcMApxUMgFqLSGK7cnuzlKYrn4A1wcQsxvw+SCshDK/bxdjhM/pyMitU92LTCc/pNbQ8vdx5ZQ6g+hJFfCnb3BKhump17pW02wJEz9MLlJvoNDFMcxtwQSt5ADaiBnHCqbjWPKAIRV9bC0EZC3W70CTCrItvg1L4wiK4SVkBpAATYQTKeiuTNN0bo0kBrAvC3T/9XLC3B5aJcqassFeJyDZzP9yqnu9FbUQWYJ89AjsKnGUCpeecQZKyhFIdHdMx9fob0Jj7Z82xIDIelgWkZ48BkQIRC6J4vBi8MNvD99yI1L3OKtj7wWwLb2mg/WnZhUXl++8BNOuWpF7Kb2TOD6X7aGq6kn132a2faLdOF9PPhnmfr2CXUz4f876tiL2QVpytdhZmsRFI/rPU73Xnn4UlqdiQI/SArNVC6OVoTZ1Clm8qox5oh2p/q8oXNYMcM0HKx/KdF/8+V+GFsbRiNddxXx7VgngNiAMOCip4Ys5itJNpByIY89S3Wa2DJ7Jissk7WUxYhvjC5ggeH/kBxLalQsmN3gOojFMy/FyO9aStmbm2RndRZFidJYWw5fVa8wGBkxMSG5aKLCrGsfiZyiEjx809DIDoiYrJFTEKh91+qXEgIFLUHZkYD4Q8C+KBNutGAJQMkD1WpPmXa7n2lxXyPlLcTh5X1UZx20nromCZgZRjmSQFQpGTxbI++gyI9sdri3y72gBCvJYCaGJPOJoKudwTaVO8Dz5HjhdvMyszdmIWYWYW58z65Xjx7MqoIGY1m/EKuHd1ja0PBmat2FJs5OhR+DJ/MWr9yOJGmpkp3Ei0kuubEVUrRqTWaXVu6iGwY1wpNHFUfPbOWPPBn1VW/ol2sfhVV5HlC1fT5HMdMhGyC6x80TKYEZ2Eulx6EbxodRy2SwxPBbjUjbWsttnF0/LulHg8pXHQ1T1PnKmXUkj/U3fchItgEQZD2kTAkTVHPprJC0a9ONuCjdg2nyGWART31YQiM2jhfgHmfhhXDAt3mRmp+QqZ7R5yuT/u+l4HUpY1QmqKIDJ6K10MuDHS69NiQrQ6DHYdWGPWht1S5l8J/lNgmTVpLMqPW4X4AYrtrSyKMhS7TwcU7qMy++45iT6qc+WkAixWOGuybDYkA7M+AMx6dhvJz2EyPS4TsAl5sC+GkMdsl/MLGDtern7oJWfcAE7bwaJLjhQyUQy0wpKJwiQEK8/h8GYBD6q/6NnjQHltjhiiFby2lUCUlJHrm2RdycmlbCO1QUSOuortIrJJ5QQbOlBAI+HcldW+jB9rcsidwS4OwyyR1ojby+HiRkQLajtNaqYtWfU6FfDgyZN21Q3EGh7dm4nF1wIA3LNl6sjl3lMqfZlkmeHw7gjEY9IWk9I+c/2WU7gP+tzYSDgzNgSGuX7OWKyhzgRO9B6xC0aeF5cQMjJMDHzjPwZklgWN7fHC2+KEODZm0SHjIMC9A/ts+vGihUqRxqCG2SKwbZ7XUm9m3NQtFJs3nphafvdR3X5z/aKMUp83Zmx9uZDWm6W2Cxi0I3W/DcUW8QXqq+gPASEEtI5OI9u2sZigJKInkYTNh7XqC7BDq7HM7vqrFDjiy5ZX3yaLLkdmyEre63LK3/O2uIYyBShajidonhQym7NCQk29poB1Jbyy54iU1kLQhGDG7SPXo8841NlQ8AGF24GSifmMueySYzcW0/EmrFnujQvZZpeSuLuyLAJMw87IcOUBsqwuWJZg74ncb2KzjFtIGA5k1LdyLHvxXrOIVYyFpPHehEobi7LUPxJds1vFgLg2uW2IhWHQYjQk4qZB3B5B2s2aLn0Oy/uqbUCs0wAZF5LM95nvWywPiBteAJ/Ps8RRWPJc+ecrbjFh3YtJAETtLo4H02bug0TxqW4sxGcGiKJ0Bl3jh09r5t1lNf/u9MJcX2YQc94YgPXCwKx27csw1o+WHEWtXsfInGXOgQo1K9FIvR7CmTPonOmB3RLGtUEToEyaRvSXoOBZsKjVEKthJiCpj1bNPtOv19sIg8FiW8NauPwT/Fng/kiV5Us4b0cXjZk8CRjpOQwmtC8hlmX2gSZrBnXyki/CoRFDaanPRovgWJh8jFbHwsiksCpPdIALB+f2x8mbI4KYMeKymgqeVuAaUqsC5RBZM16hg56jhEiWmTzv8uMVfhSay6QJHS+N2sljWR1L91zoWBBAV9CnAI2AbotclAl2Sl07KQY6dY9S5xNbqQJtTgbn+iIAvbL1AEcd+aa4DNLGNSbiYFlc8PMeYEC9lG91g0nayKkW7PsgIDs2VbiPkHhHnY2NEiCRYsj7WcPEqDUjsVas38PPO6FyJFI/SwGipdylmt1IC12MHTkVX3S6Apey8je9rIQxyaJQVCn2chLRaJ3yJV55MRqxYTymIdByKXaVMBPBTAG9tH0ad2Z4shBKDYusaO1k2iKWIbQypdYr0VCkW/CRWXGyie1nTYQRY1LElDA0eq6yAdA2qZtGJrOS7dIJw08UAgKcUJrH1rjtmCUox1LYGb7H/BykImL0vpEoNt7zRPQNhVVzcj1uZ+oasZ1xXLwZN5eAZzdXalQcAWkgCrVZc+MjqvReMJNHbbUsRTDPLugZNNFWXA2fR9dXvYwbfxMlZgYJ+ryYZzKE6jPjxk36yakO+Duhrj+6vl6WItFkPBCA9nyhwVsW99EwGpdBO1MPU89atBEkH+sDwIQw0qCsWuvnN2XQMihF9TIifKOHKVmY0OshOz6N9lxOL9myKTyhKnPhVrIZTUD0oi8uGCcNnpgizV0WYzZEBLJO12HCnrN4Ld4JN3BiOX7kBJRkdvIJrq3aZppoWJDqJ3mdmPgF7tiWihbATbaqI/KTqwAnqt/kCUlM8HyftN/KojhwSsDMmIAywEyopk7WUjAwpfwfzObEqJoqo+U1NyzCNpFrA4hcD4Y5nLqiSSJAKefy1gM6FJxHxj1PJmKIXUmwZb2bSN2RwQLBZP+IJaq4S7mYdxGHuOWDfJ/NsxEQo+YqYCyODbOjWpcDNjx+3pWpx4nFkfPHjs0Uyevc+681OYGsPUwkwYg27NYAdYEW64l5GdJrsj5GJMuKf2sdxIyC+kcJp14G0yy9JbgMs7MYO1aIeWWFlFzJEUCp5HagCUg3PcxpQklFNzDg4ZdpcOcT4+PLyGRXCH4HPGP+e5nFyZZ3mObrGEYkuMlCJmFmDTICXs4d4VevAL3wE+0r2Ki48vbgMAVCfCSQifaRUwUkOECaa7h0pnvZxAzEbqJzUUVG3C3n1ESZsatSQ60d86fMHa/cpR4CNSZyDLDuHALVLKrlcRB2kAW3wjx5wMuA1WhOuD0epJX3hDPeynEZI+PuCsRSleUMqGLtjBQhF6+2M6u2n91RlWeO6uKf6pZiVzG9Awxb2LPn8r1itjHrBbQOHVcWmNnhsNCtBxdnY4O2BRjk2l+ONq3E5HYjSD5WWMuXyYSBWetamEFIvY6mPF+UJGe97OVoHz5hJyZmTFxGUz8ZmWP0eyVMmN+lIjKUF3KqjE9Ahzh56H4yOtEVx802AkDiBR7dV1HQWEwwdjVqXVxedyN9VyGt1O9z0RBbU+fKkb5yav/QpvYxK5JbYJIUX4bYXnYTaZvz+LueIhNXJ07sPPbKOJTuwbhXjtVBMNjUj7IyM2zCXcBJ+pgxMt0hNoWBq5/cvUU2R/6OO0szwLTjVoITV23lOgxW4Nqm189i3wA7TgwKEgxKBaRpu+19KVgZlNouBwYdQ8rPDI8RuzuNvojXWZ6VQzyHmSHJaO31MizQH5+aRz59EvARkgBCr7c84dQpNtwHR6Tc/VJuPdmQZMP6ADDCwKxVqwMsdSzLStoETMS8eY5w6hTGT8zZScGtIuUzznorL0BZZbJLwGo2oC8yr4cB3ApPLKAyORfRUmk2wrs6mII37hCOlOhCdTQ+Q7CZTIWB8aJPGg/9TIZXXFo0oasGxrVXEgUyCxAHCQoeKoDBA0w3ZsomcMp/n03XMzm8kqdJyIg5qV3Sfulf8TNUgSkB20p4vhMIy7i2uuJuSdTnJlqd4Mt7KRFxHIocGSUkTSdwOgeAYUhkPIY1BqdyjVyeYQLFBjRmcfyjC466z+Jp335Ux9PnrbHnlNczz3Fm9FraZ3rG1JWo7FmIP/WzWLf0r33kJFDmfjkvxkBFbCnByrB7J63y6Kb1AWDWug1iVHj7ALFRaMNlesBDHqBi3l4PYaGLzlPHzKofiOBEfye3kKGdaULhFR2X59U/l1NmhMCDEc7yaptW1GaiKcGIujDkpUzMhxfc6lhomGgVRMhqXQS96hZiN1EWf2fg0aJJl8dVwZrfFgB2bNnlIZE4NoIJMU+NZ7tostF2MdhxALCSMM5NSn6s+adxcZAYWduprheqX0wmt9zVkdv6Usn6uA7+aVmyYCZZAMYN4rUxnI9GXZI1l2bAYaLEMvcZ3YNKqn7PAiE+g9x+I+L1bkzGdb7N/GzQvfBsqjKN+v2L90xz+/D3m4FyRmPqwKi31nwOHD0RgwjOh6X2PJLP+ecwNkxG3zVq6wPArHXti9gwTAxQRd2e2qw7vsSmuWF44jh1GmMn5qhQfLFpGdE50IpSPjeaCr7txDR49wf75T27wRE9UVRIm+UJXS0vUwEy9MzpebInEaA5ZCTSKE6Spfgxj6v+Clij0GK/iq9doQfqPwMFYo5kcmh1QwxPpr54kOjbAhDICnwsVMYk5QqMAxbHrLVAfZF50ekeGMRWXFIB6kapgJfMleXr8PNV/q4sVlatq6K9qLmOiYLyxiCBdR78LHH0WG7PLa5vmUEW30pd5pkJBchl1oSZFq+dkj6Ku0g3UKQIKmUnS7DLuWEqrrk2qsyZF90DRmjuRdfexaiMHLm8BAyGLMP4kdMIc3PFomk5XEXD2lK9V88W7KxiW1u9qTNdMa5xIDOMWj21xwYzNOdw23Z9eQgLEwJCt4vOoSkbFg33sgbsBJh6MdIEYMJpqa7U/jxxsqaJgFeL9GLNesG4sEx0j0y2nBVWQJRbpau4kCl0YXx6VLecT8yGj3ypiDo9SCnbl3JNmXa645VU7IBhbERwC5S/S1SSbHFAE5qCAB/1xPimBKFR50L3rUXtK9sqSf28AFTHmSa7ivsiS7vLjEuxbJtmZhbQ68LQY/vd8wNY5iHATtTuflW2mmCAwmMg9XI7Qxxrjk6Tekx2ZMfYSX3J6DABKwr2AU4ep2V0TKEMkEk2mGD/9BIJZsUck/IenLftc8dmBfGleLeXSvyzhDZMmopR2ZFhF6j9hLlrjJFZHwBGadY+FPBqtmFFu6lsunUZec+R+ZBq5DnC1EnVwugk7UNl3U+fiIxXy6w34ZemdUtVWQt5CfukevJCbi0EkwK9qCtE4OH8/ura6EVAkhJE8nWkTUKjF4AlU0ZA2m/awK6Esi6ehEMb1YgU0CTkfpe2y7kc0l1ZBWu4Kk1SBDZ5sk1Fl6VCarNc0sfHcuaeZ7a8JOXzYygsVCWnTzL3SbC/C2Ml1XUicFTXBydYVFcjAWnKE8RJ+pKRcWzURh+ez31IhZ5HptECDFO3S0xnNDhAXEg4UKR1E5DkMWvPBwtqya2qEXcB8KwRsrK8bI9BoLTCOPpnCLFuk/YABdiaPDxbK95dMltqkDBqojtpw2plW0YgGlZpD0e0tQpcxIZhXurSVPstBurqHsFqN3DsZ46FaR85qZN1cdy+rNSFIwwHU87EcKhLJcHSWBdCZlkHKsOrUVnl5Z0M+XhsG7ugTJIuYk98VE56hV2WZV0JMzfkZuBJ2fSJWBev9zErW8dIWf2PZb4UQDFDRGOnyfw0HwwVYGBCk22cyD21E9umY0J94ySFEmrMbVKwIxMwgcnIlti0/z5PTCU6h+9PoLHgdnYSYFfqkPuSEWCgeivuIGK9lJVwAlt+tivhymU9ej8ovN8D3CiCLlyHgZ43BWKepXEMSaCfJo0/n+PcqlKPfC73zQBMHpMMBuwoiHLfYwAG4GvZbo72k4d148ZzboO2c+m3mFwMW7Na2ZYRgm7WB4BZL1aXZ8Ar3euASmrDx37sTs0XZNSVTYWF6fWAE9NozxZvOXlRsZuGGQVNsOZW26y/YHeAunxIWKj5R1JfHGIQ1OQl2qV2yTVlkmzTpBmoHppYtP7EZcVdYbQdQuHT5Mmr7IoGgl1LTqjL9eokWQInBVhE5wtgYCDGboGQxagnZUnKtkpflDnxrEJZB7t7THI8vg9l/R5wCCvC5+g4cZ1uck3mWREwF+g6WQS6fG8ViGX0M5X/xIGkfs9V6CAyCQa82vGrRCY5oKSfKcBwfS3Hn0PvZR+sSvsE0JQARxcRDiQDxfepN9myiwgPMGEBqIJaZq2yTKP+8rZl7cx3mlxECnacC3Bsuj50eigbJdX/oHdpnfA2FZ00artWeWQRADRh1GySB2a9WL+opEGIfljEv8QJkExEEgDML2D80CmjG6jLCZEEHfzCJxeR8e/TCzqVY8LkB4FM3uU+K8xGIE7qem3EyYbFoEU9mZ183YRo9A4OjLD+g2l26aOmavfX4CR2vTguPryWWRs+zuMac5vEiceMg7RXAA5PhrC/+/HWvCUBBvj5CCE/EVfaEOxY+rZF90dM4mbEptQ/z94xmMnHiHHzQIQBtUyoBKq1qGunv36KrUuxZlp3Zusxz6nL5WLGTVMOVN+VlW08cnnuIoixUV8oBejuu8lgqGRYDYANbkfwYDU8/jvF7JyE+QMENMl11jlyEqF7Fknqhn3fpXSG8nNQoETq2KBtYoa11eJSalxI68gWm71x2NA7/+VbbitZmBCKjdZac/GaZnLM4iTCkSJGi9GDAT3FienLMjMAxJWmnONDq9mtw+4MI5oMsUxFbBviBop6DZ9bRfUVWbUf1B/vttL2I072rD1RtokBhlQnbXSrbxYf67nlJO2jSJLNpOR4miWZ2RgFVcGAKL2Gq5t1GxVGg0BnJbya2COTVI4ArM9MbCKDtDJEgOAARkU/Q7+nwAG7CiugOsR26zNUjqVs/GnYHmLEpB7PWvooJHZzGmMWg/akkrFMuaykrNHP8HeHmaM24oIi9b2UZ7sEl60yEaERwvfiOAEoWbh4fQG/nTM9hGPHl2ffo7OxfvqWYbL0jqqLWWO2fgDMWtXBDNr/yJfr5xbiCKRzGI3kTfPCnJnBxOEzRTM4f4pzX9iT7YveaDhc+K0BOCGW8/oOXd1RvVKWz1cNDL/Aywk6lSOlEh5eTu5eKMtJ56LrhNonw0KAwIT70upfxtKsXGmValbSNC6srVGx7AK5uBwLoqcTQ8R1spstFWHmo4kqkSrcPvnHbiI5TFE7ev+YWfETOl2LXUiSxE7/pb52LuIn6HND+1S5iKdKRBixdObvBIsn+hHjimPgRoCCAX8/S4GhOC7VkGzPHMXxiS5D7rPZnNKZd4NJAkH/7BnXKQNNBqEuZH/86Snd9yjpPlrKyb1O2+LdQz7bbt05w+6FtFoYlkE2wly9RnrcWK0Nsz8Sf17nfuoHipbIxI1kNnk8cBSdMz1duQG0N0oHFeaBV5zGVUQrR/nJk6enpHnlB5STvtNk6MrYhTpLO8w5pMXwOwwnXVoMfDILTkyUjZxaEQ8HndSZEeLkYDpkZrKL45GPWbeTYaQoOVsF9FGoNNfLolTWHyk7I+NHjJWZrNiE/QhukkOs17NH7FLwKfbN7zT+Oj4yBqXp/klApe+Vdrq2cfg8Wy14EKYxi25BzwqZUOryfKOV4uaSm06zJLcsM5fx2MKer3VI8wi01EWvqQaLk+NxGQYppMcx38HE68akFQAso1eOb2emBxw+htBbYgZi0PuvH6OdOt8DHC/6HfZ668jWB4BZa+zL2WbRZWrSC8f8l2XYyKYlMqOFCQHhzAzGnzphXm7JJGDcrNT3mOh+v9GjuhaYbZCf5Xl5JzNAJRW+CdgXqrA2LChl1kez2IImG1enCW0uX/SVKBsX0aJMCoW/cjmjZaAcNMU1RKRKNDwiAJHx03I0lgoofEi6MEfksmL2ymg0fEi8Y6o0EeCCfQbMxCnXgr1PGnnEbZP2ybVKkFBoW0h8Su03glEZG/dMGpcHgTl2p1RYuczWY0So1E4Ru/prctoAdm2aCV4iwGgrDA5vZkBdASPiqhXdkIAeqt+MKQEnzv1SAWmOfdJjAip5/MtjMXKqPFXuBUX4ybM4/vQUwswMatkXYHHv1FEDHFKBEnXbCcixtSDIXUZbHwBmrdkwX6C64yl6MyUs6ycq89dZDuOIpONT6Mz2zMsw+c+5C5IvVcSXnrICoFUgivN0Z+lQJkkrJ4Rid2ZEYJK4hq6GOYok2Ov4yJ6Um0ndRzqJU8RJRtfSl3WcsD3AYubFC3N1UqEJVtkbpyHRSZzG2DBezPqIy8JNcNFNRhM7s1jufphIKaASQZSMwkFso4hFhXEAUGEshNEy/XcTLId8q/BXtEyaHI7YrMy6XCosTOyKQgAAZvVJREFUEfWTo+p8+wwQZYBCAmRmKmIliIC/vLYXdqvbh1jASvI7yGfUZi1L96Y8l7U2LBiOi4j4twEigeuBPkfqmk1obkT4LnmZNPNu+b0Ym14ADhxeWvZlGFdOirFOuYOGYWdSmz2uZWtEvOvERgnrS53XT+A7KBHScq8KhIXJc4S5eYwfOKkveJ+KnfeOYa1HUY+rtgQLrMFgir6oz57TWhAQYScJ444iGlxcXHLMu3sqoIcPmUgKRLDjXAA84enk7IWgxARpH8klxX1gQbFhfeg6JlrGgUGuq6+7hImSUjDrE7qZST2zYEn7TxoQbScBhWrekjiJc2gvr9xTYMiwBSHWY0KD/bzKIMmBGgZqlcg1eg5NO5wZlx/3w4C3yHjEE+PzH/h5YrdTiNoVHlt9xsqiukkosVByDQZ18RkOldw52naOHHILCXWNcr+lPoqoMiAuB5AXQHf8qePI5/poX+rMg4l+rp9hI4cGgZW6MOt+bVnn1gCYtW4p8JL6cgy7K2odGFpC47wwIujF4WPonOnFF56wJJQjhicLE9pKYAKIx2RlrtEozDaQdkUjVniyI12FGO+ErawGX5tWrMWHSOoUOFTaMBzO/WEmSgElCvJsnXpJpvJJoMzRRwwIORqLNUCthQADsBDHUsdDJsKc+iNtCHzdrHq/mA0i00mVk9jJ5J/ZfXvExeGT66n7hUBAvID9m9kCcZswE6JhvcwYUd0qZiV2yrBxofovyyMA4MnZsih8U6kfiXvOZTJ+PqjP3G7DzhG4KVxl8V/xnNoFg08EyC5BdV8x2ydf9Y4AsdheBaiBtnBA9XtmQCD1a+LIDPJDR0aPPErlbOmXw6VfwMPZ5nPpZ2udiRnCmhFYq1aH4OXvOiCS+jLWfTFTmpklMhX0ApGFeXrKruQ9Y+676ZPF+WswSJCXqgxDqU3JxylTrGcdGJD4icO9oFWY2LLtNL57mbAoLJhf6BpZ5MNxZfJL9b1shwmlJrbJu6R8P1OsSwR0EWwZkOUAo2kXheVKnRXNTN18wywSrcR14qdxSWlJFJwxc8J1uGvLvTDuHWJcfJizGSMXscNlrNianjlmhFp2TH3ET8a3n4Sx6v5hUKTjYNuo5Xuh8qz6exddOXG8Mld/AaKpHnoWgDJ79VhWvd9A9XmW7wo/gx5ctTJUmL0SSHZme2j97ADC/AJGYl8GMc9cru6cUVJX1NU9jAZxrTIxTRRSwkbwq60ZGxSiV3cO//S/p6wu0mkpvmDKxuTAoSOYODprXA+VLLdABDf8TnSJ2vwEX5l0ZbJ0K12vxzDi0uDOT0RkpCJ2gAiYWIchE4lMhMI4MfPC2W6lXfp3IKDA1/LhtmzMEHDm3cRxHhdTV7Bt8kwHRw6Z8O6cxozvYep9JowLaVLkGhUQk9nzfLt9wkBmCoyr0kUNAVDBsxGnSh15sGCA2Q+ZpIkdqnODsUupwo7ptWhM+J5TCDWzlOzaY90JC84N4JNmlaxl8ewl8tpQ+/0Gm6aYyx3kgbKI5s39lz4ouLGADwHKwIw/NYVw6vTw4IXfV4OYkGFBziC2OqVnWQ8alyW09TNSay0SqZ8NyuFSlwWyro5hvlSD1PWLMBOR1OshLHTRPnTCvOw4P0aFTs7s8eIE+kksSdYjFqI81uoGs4tuMrpJqvOiU8da1LpGalgP1YYAUaip+h0HuNwkz2BNLyOaAY2CsWGvqk/giUwneBoX6q9s0FdhnzI6p7wH6g5ItAkQMEL1E5CJQM6Or07owh7Q2BrtDoED/dy3Wa4lwCUVEdayIK34LFOg6Jkc1vdEVyT1QYr6aCS5bBD3aGRVKjlwiBXjtpqxpucuPkvFQd7tuyr8JgCcxfslmjCum8dE7z9i/yvfvyz2RV2miOfpNgUJfU9sbxb7Rs/X+In50YS7KZcR26gZePnvfou4fmkr6v5eCjvHkaWLsiHn65Fb+8ADD+D1r389duzYgSzL8JWvfMUcf8c73oEsy8y/m2++2ZQ5duwYbr31VmzevBlbt27FbbfdhlOnTpkyDz30EF71qldhcnISl112GT7xiU+M2tT1bWfLgAzLwAyTIG+YevqZaGFCQJg+hbGp+agt4LDbEFerAIzrQCydgIxe1IFAT/m3vLiNyJToduO3J0pbmSLSl5iVNU06Jv9KJ6Pde6M40etL4h+xXcpKySHnGjBjRm3kicDUy6COPveJ1gA79jIeqUnX5JYB7P0qr+fdDGayS4iFfVvMuDDLE+xkaMS+1DYGKylwq1oOuPZKuwS4dR0YzeU6tu/cVp9ZWAFlAqRX8sGwS00iclwZHYeyfCUijcdYziOQkmTlgMj6ldogbocHjQbM8fPqgJ0J1c5i/xj0mO93L2DsiaP9hbt1C7i6rLjDvv98ff3sfCalG6Y/58vK9/yy7YV0+vRpvOxlL8NnPvOZ2jI333wznnnmGf33hS98wRy/9dZb8cgjj+Cee+7B1772NTzwwAN417vepcenp6dx44034oorrsCDDz6IT37yk/jYxz6GP/7jPx61uYWtR/fRYiy1ChnmgT7bh77P+RUtzPw8xp45HgW8IRhRLQDLcGhF5Y8WvYD9ytqdZl7UPCmVqz1ZJZr6CIywGZeTm9hVEMzAyOtSAFqRW6DCm+MBBMD4Olks52n/lCAUIIBRhg/7FPER0NHEWNatWVSpTb6M+dut0DlUvcKiAeAVvLrf+PzgXDKOJahoW4C42qc+MbhiDRK7w/S4A0NGy1G61MzEK+d7to6uywnzKsBcTuvFNnFkT+FigdE51e1NxGH6Hgh65sxs38FjmEFFvZXvAINJ+Yhcafp32RZJVyCfK0PjIwUDinw7JVM6Nr2AfNCWAcNkKu8HSFIC32E0MGz96heAtVLYkHNpI3pKOqPWf8stt+CWW27pW2ZiYgLbt29PHvvRj36Eb3zjG/i7v/s7/NIv/RIA4NOf/jRe+9rX4r/8l/+CHTt24POf/zzm5+fx2c9+FuPj43jRi16EPXv24FOf+pQBOkPbKIMSwsiDeM6sbs8Mb/1yDaTq6ad7GZTKepQvmf+Sj7JdQZnJMwsBYeokxk5swfwFE1FgC1jGJSMgQxS+HDe/J5KCAYi0Ok2CHHGT1GcEeuHSatWLOH3kStQ4AMgCTVYoIz4oCqpl65YZhCen1kKo0PICFHRSpFWxtJddR6Jh0H6FIgGaz4yqbjjEXDRWh1H0VyJNjGsKZRsUuAQdI24PRzJFRgp6L407gfQrSRAjbaZxN8f5nng9Tfmj1Qs2f46AMNOX2HfWu1Su4ZCzfxYVXLQA5LGfOg7eBRVAgKVoW97Jija770jeLiOpEF013KaK/iWRfE+BDKJb0oAO+S5IckRpf8veX67TJAn0hEinPEdD0iOoGTtwAr1yw8aB2pd+LvbFgIdhFoApcFT3Tj/fbMj5skweqsFFlwXi7dq1C5dccgmuueYavOc978HRo0f12O7du7F161YFLwBwww03oNVq4Tvf+Y6Wuf766zE+Pq5lbrrpJuzduxfHjx9PXnNubg7T09Pm36JspYKXpbTUl2qYsnXU6mLcVSO8IFIsTOepo+qLN9k4HdVfYRQC/c7X4BDcjCYMt+oXBicZYVMCLOPeolWkVsMTQYL1AaAiyRbtwcPaEqNTEbdLiGU5v43XQgig8yvoVHp4mQBb3Tj5ySaCRn9S9oVBU0WfBLoOg4oQryfjU8mXQtcwjI30y4+lTqp0Dn2ecnX5vmt5t3eR9MewMDKmVI+wdflYprmBLPNHIIsYGyA+jzpecm3qpwdVRlNEuXLkXM/cKHuRh6Q7j/uof7P7rxxXdoNVsh0zOyOLCv/Mt2M542pzP/XZp53gQ1kvWsDE0VnkR44Vix0PXobJwSLAZVRX+LB5YEZxT61H9oVtuVxIg+zmm2/G//7f/xv33nsv/vN//s+4//77ccstt6DXK564AwcO4JJLLjHndDodXHjhhThw4ICW2bZtmykjf0sZb3feeSe2bNmi/y677LJ4cK24kAaF1i3W91rnD+afyxGJNCroYS3MyVOYeGqquDRFYQA0sYgbwudH0eu7F25Zl9clSNnUi5eBjKwGjVC07vyM/qGcfBy7U4lC6cX6zXVZA+TcABV2gX6PGon4u7RNBbrU18hwxD74HCoVrUNGWZJldZ8QMXMYO7tZTMJB3wcCF8ZdRa4ufx8q7hQ+xm4jPl/GSlizEOsxIcvUJ8O2EduTdyhRHU3+mhVXxboJVoZAqrTBuxmlLpNjBlVQxkC/kgrAASw9vxvbVeTXsdmvfaJHE2Fk2pZVxl76ntpKwnx3Bez2JPy7eB7bMzlajx9AmJ+vBw/8+zBs8ijggvUzde/MYaKQBl27MWMju5AG2Vve8hb9/SUveQle+tKX4vnPfz527dqF17zmNUt9ObU77rgDH/jAB/Tv6enpCGLWGqsyCuU4zJdokDtpkOtqsar2EVmYrJUDebEkD70esoOHMXHBRsxdMAEIVe/BqptQ9eO2m0wN9U+fIX5uyvDE6uozWxXQS9cAG5qsjQ6hnSmosGxMrEfakhJTisvJ9B9FubxTugZC9XzWzIQOAYoEaNB2BET3RwbTb+POaWUuciozfZFIG9bupEKW2TWn/W4HA0oq7ETZ7gpwAiKAYCAmIEFceSy4LttlBd6xvxrRIwwYMyqtYiwlF0urF++DtktAuD4fxDSF+C+0oovHgwZ/T3Tc5FmS+0/fC3Vz0vPpMw6HLNOx5mtx3/T6pSbHb3gp2jFmxrwb1bsVTSi+jFXO1wtAlmHi4CmEkyfT7AtggUXqb/5sEBPT79y6d+Wo4uD1aiNKOJadp7rqqqtw8cUX49FHHwUAbN++HYcOHTJlut0ujh07prqZ7du34+DBg6aM/F2nrZmYmMDmzZvNPwDFYCRFnauQlRlEb9axJoNcRIOOL5bmXI6opEAbPS500fnZIaXBeWLzK3l5WVbcQrCTl4mEcS4BfQk7dxCDFICuo4UQV5wevADG3ZSVOgUvWgwt6N5K0ha+lrzoW7RPUEyel6l7S8eIfrJ4WPUF5I4y7hVOlEZjYNwYHiPLZ0GuGyffOj+3ESWn2Cb6PK7c67/T2nfRx1AfKvl6ZPxDor/ybLBWpzynPZ/Hz+U5Ks9r9YJO9AoWsupzUpws14jnV7ax8FFH7nx+vliTZES0nI6gBXO/jb4JNfeVWJ3A7JscV0Yxlrf5bKDfOXm+/XeLxzhe1/arPdcDDh4pwqYHvatSEUh+Aeffn6O43FPX9TrC1RDGfL5sRLJh2UfrySefxNGjR3HppZcCAHbu3IkTJ07gwQcf1DL33Xcf8jzHddddp2UeeOABLCwsaJl77rkH11xzDS644ILRGlCH6FYjKzOsS2YU9iRVns2HGPryg+pbQjNbDPR6QK+H/NRpTD45HV0gbmJlelxfiO7lL9lImVkA3ORS1lEcsC/7Oj2LMAuxA+UPF1Xj9SJA3GtG21sCKvH/q2usNFntMtNg8si0rO5DwRCt0tmMTsS5fjjiKpncjQCVjHsyMssDPgU4cey8JgKIE6+d4OVaoQJkfLbWCsMG+jygCgqy2GdmgFgPkoWA3ngr1smsDspzfGI2ZpL6rKe8e6/vc5knxiez5RQ8sfiZj8GBBnpu2Squ2hDBt49OMqxSQOl+gnnGFEzXXTPEMoHaPP7E8cFJ6+pyrvTLXTUqmBjEjNdlLR81gqkxtZEBzKlTp7Bnzx7s2bMHALBv3z7s2bMH+/fvx6lTp/ChD30I3/72t/H444/j3nvvxRve8AZcffXVuOmmmwAAv/iLv4ibb74Z73znO/Hd734Xf/M3f4P3vve9eMtb3oIdO3YAAN72trdhfHwct912Gx555BF88YtfxB/+4R8aF1FjQ1g/OnPY8ovJczCsKHgRqw0V9IoeptcDnjmMzqkFMP2vYtt2/Gk2WfQAVuhpeeETxV5xQSmrESewigkQEWAhbFA5kbUWgg3/5dU34sRmtAt6bVoxE5gwq35iF5Sal3JGUGs/M+HADrCZc8rPIwsSz/fRKsYtgDjh92NfQhtl2nkaF/nH94PARdalYzLZZVEPktqGwmtDFPS6++FBKueJ0fvKoczC3Phr+Sy6FNHFAEf3cRL2jMZXhN06Xqwx4q8UgRdmnpKar8S9iHqrLP7Nzx7pYySlgGwK6nMp8fhWxOIJ3YyEarNexrCs5bhOHJ1FEPbFNH5I1mQQ2zKsMfjpF105SIPY2Eg28gzyve99Dy9/+cvx8pe/HADwgQ98AC9/+cvxkY98BO12Gw899BB+4zd+A7/wC7+A2267Dddeey2+9a1vYWJiQuv4/Oc/jxe84AV4zWteg9e+9rX4tV/7NZPjZcuWLfirv/or7Nu3D9deey0++MEP4iMf+cjiQqjZltNttJKpv2HzFAwboj1qoqd+K59Byv1+9QIalTT+syMxQyjfZpkQhKZuZdWXpkukpYnd4OqCmyAAnTRSEUcGHDggJC9onqDNFgGUyM24kej6kteFJyDJ/SEveMnHIm42XTWXglRxZ/j8H0Yo6k3HM81iGpcUC00ZRDhmoJLRl1bbycR8IV7Hh+uapopImVkgAqkV95EAX2q3Rqip28f1Xdov7aPPK+4dOd+FdyswIFdbsh7Q/Sp/rwDcEjyEjgWmwsTJ30V/I1g1TA+DRS6nY5SZcTHuKn6uqe8+ozKnQNDvadleAfiaDZtM3FXt2Rztnx1U4e5Iu033C3OuKztsXf3er8O6khobyrIQVqMYZLBNT09jy5Yt+HW8AZ3W+OAT1qINAi3D5pXpV++wVieiS4UxDtWErCjbKjM+j48Dl12KmeduKujptgUs0Q3j3AtGK2DzlHjRKE+mlZc0u5K0chgAVQn/ha1bcnVofTwpcj3GzRVZF4448hOq5GHx9QER3HGeEJMnxblcdCwp/DYyB7HfqtuQCaxLxzinS4KN8WObMs2nk9oqgseZxk6eAX4ukhFMBAArbgypqxynFAhq9VyfuK9w/U60VcoAMAkTFSyFYvzN803tYYAu973VjeWBCCi89sbmHMrisbIfwoRIsjnPBkk75N7k7QxZsDuYc3/5e6bX6KFITkfgVsE5gKybY8NPDqF34FCxzUg/1xGQBiz93jmLzQWTOr9fGxpLWjcsYFf4CqampqKeNWHrAwKuTYyWNg8S5N+oX9KU9iVVf8o8q8JgpV8+hsXkhildSaHbBQ4cxvjUvL7cs57bU6WcuPQFyS4FXmnq76gyKxn97iYoDhv14aCVlS3iTwFQvNKUicasPtlNIf0gtibqF6gPWdzXx0fh8DksLJXPeSzUnZEQj7KrjtkCHXun/TCuBSpvXDc0Lt59Jz9l4lPNRx1zxuBFXTYuqVuQOhIZhOm4Z6l8GHnhWqHrU3s8UDOaJNYcAco+SC4eGR9N19+K4EKFscSwKHtU5oJRNxs9K+qiIQbPu5NiuLJ9DrU9VJ+G5Qt4keda2D9qM5thvKh+aL/jJqYF6xYw+cQU8sNHB4OXOvEuH0/lcVmOqKEGuAxvQ87Z6wPAAOsHxAxiWobNdcDHFyscrsuJcDaq/rrr5znC7Bw6Tx5Fez43E4aGBLP2gkAKALM6N5EuPNl6Wt1HR5QJ3yLIoegbAj6ad4UnXKmDw6Ud0OHfeSWvriJhRAI0XJc1Mx4wSD1x8uOGxD4UE7Nrh7SVyjJQ0MlG2szjrceFSaC6QhwDLc/AIfE1lugVATMm/4jc95zccuXkXtEXgSZYntDlc98GAcoh3nMORTaJ6DzrEOJ5FTcds1kyrqTlMtonOc7g0mlcKnX5/C6eYWvZfsgzbDY27UW9izJVrNWR+0yuLu1eCY6zbhwbm36gek5clARMHJlBePogwgKLyKQcvY+GyfRdB1o8QzyoHq6vnyupATFLausDwGQZVmXU0VLZKDqTFO3Zj6WpW6kMShzlo5oWQa+aqKRS0JtPTWPi6ekYVURiytCxK8DgsiCJe8loVkL8myeDigsJ8cVcVAa7mg30chfg48AEgMqk7yN6/MudI3nkp8+lUXFRaH/j3yK+VZFpufJWZoDp/cRiILJUfAHqrwNhLGKNoIaKuER2ZtIv//ah1qncPMys+QnRRMC4vqnglv5mrRCDFqmPzbApnOxOLtmzzwsDmghYssr4JxMVEpCqJIMTZkmevcQYmAgvEjQLU1n0oRxjAcl1gJL6WdHZ8BgrOCK2qwRAlcg9Av/t+Ryt/YdG070Mk07CWz/wk2Js+tUx6rUbG9rWx6iGAGVg1gsTs1gbJUTai9JS5fq5serCC0cFMaUrCUARlXTgCMaPzynbwStH1pyYyTtEsSezLUUSNpi06ub6BBwMayEv3syWTQGNiuCRwBNQBVoVEOFW+5Kd1Ea70HkZjQEcGAqoTGhSh7A8MqEyyFBBLQMFWsFr7hkHSKTtvHlfUgTqmYrMlolRK/QZhQTztgcyARtQAljmjRkDs5WAm1y5LIE8c48Sa6dKuDCxhBWGLI/jFNsHC8LKugpWg/7OYz1G4yXMV6I/nFfJPKfsipR265YVCZehM+NyasWowFR5jfJyIH/8yRNFwrp+riOtZID2pE6wOwic+MXXMKLdYdz1jY1s62vkQsCaZWKW+kvQT+vi2Rn/ZU6FFKbqSV1zsaHVQMHClFFJ7Z8dROd0twSvsay+ZFkX4FgMr13R1b0wIjlN2qn26N41NGHycc/UtON5HiRVXVA2Qklzw/jU825i8loODqvmchW9iVyDo7YqTAG1NTVZM7AgtiMyAFWNhLqD+N74SbwVx8xMqi6iigGGAp2MzkPsC0/SfD8qoeX0u3HT9GFlAFRT5aN679g8k2PCrokd0vIE/pg1MZFqnIQvo3Pc+Pp6iuimMry7TeeFeC6zQH68bEQUjVGI19G+5dXPxo/NIhw4jNAbcqPGYVxA/J5Ksc79IigHRXcO665fSpf6OrP1MVLiQlqr4AUYDA7YhlkJ1H2pUiAj9ZLwbMogOvVs6VYS9CIEhDMzGH/0GXTO9AoWZj7mXdFQ2ZI1yLqoTDaaLC6351QSqukxO7EaQSXVB8AILr0bgyNEon4lM6vmCCqg4bK66s8jS5KcfN0kNcg1w/S9Zw2YpYoAILoyvGBTw3ozWDcEs07yN02KFdbKgQXv0vICXQZcLOI15cpxSiUE9KDMj613FZpx4ftGY8rPgrQHIFDB4edAleVBHHfD/Mg4UfQcgx0WbUt9IeM9mmL/+DmoCqwzvRa7mgpxcBZZQ+l/ef1cE9hRmx3IsTmJivNb8zna+/rsdcQ2zLswpdPj8+uY5Tp3eGpRt9hIpkYnM7StDwCzEt1H5wplj5I4KQVa6ijQFHWa+mIPatuoouIaM1FJvR6wsIB8+iTGf3qwmBzoMhzZk+URAFQii9zkY8BMO56jWWPZLcV76HB93H0WUJZ/s9tDV6Yptw8BMAueqiDd5E6RiZlAQizoTiS2psI2MSvEYbyOMYrRKYgTfckIaW4SnTBjiv8sh670AQsAzSSPeB0RBisTIGLXYMvUunrKvjDjpPdIx4GYM/N7rNMzI5yMTnO0OPedjF3cVoK2MMhiu7z2JQjIS4huWa9lwviz+Jlvs7KM5N7T9hJTYsBdgPlOGOBD4Fjdm3mMsDLsS7mYEEZR+t/qBUw+frS/6ygFOureXWf77l1M4MPZ5H1pGJlaa0bmfNlSoOxRVfajWj8fcj9xW6ptqZDGVFj1WeRKSGXpDSdPYfKpk5WtBpQy9xlWGXD41SGzMMGex3Vyvf6zCitTuoAiWArV+lKrbm6jc534fCOpiUonQ57cnetFzpFQXO4vT5I8tiyc9aG3PiGZD/uVyRiARtvo6V7fQuJXXanLRO+BoBtDab/snySC2op7TforLJWMQQLosUuE74MZx5psxzy+DBQAaHJGCXv2kTo+DwvnxeFn2YwluzhNH4L2RbaskPYaUTBovHg8aOwqIer+d7mmABeNdrP7MWUBmHxyGvmBQ1XXUZ2+rk6X4nV5dQuous8XIwb211yMrSdGhvWqQ9j6ATAriX1ZKluE6HVkG/Rl9aCmTgTHlGrKd7zUodVCyfd6wNOHMHngtEbWAA64hPiy1oldVpKBQAvsT+PGcBEwlSa1UM08KkZMjj9Hjgs4MKJcOl+FyD2bD0RzyUgd6vqAmUCkbHEsM/VqeQfmKv3gv+lakZWi/sivrOcJMAArFSXkz2MBtvztQZ6Kdink2QuqOXFbaDvXnpuQdeNCeTZo4jfGDI8DvBzFlIWgW10I46HtBrU9j9fjCV8AgAeZPGYFqxHbXWEaAQtgHQCSvoggXoXFIujmTMp0H81wlM+id81xH4yFgImDZxCeeKYIme73jkgtrlLHBy28+O+6BdugqMu66/T7vLHCRpR6rI/R1JfR8AOzKm25vjR1It5UuToWJXVOP/fW2biSyvOL3WmLJHfZEweKJHet4qXbmvdLcsC7VDRDKKoTuBznVah3s/i/K5EwdF1hGCrRN3wtwAID2N9lklJGhOj96F5I0PY9mG0YUhmDzcqfAJeOkbguiO1g8JQEXmV/vA4EiBEqDCREo1IBNrAgIetZHRD3n5kLrYM+r+s3gx4DtMQ1lrlxU4BD9brnKwUi1F3oAHEU5Nr6uc3e1ZeKmpNrSLJDjl6LYmwrXJfrCQuk45HZfDJ+3NQNR8LlCnAmxgoBQB6PTxydRWvfkwizc8X32bMvejG3KJJ30DDvkDqN3jBRRny+v+awbvrGzsrWB4BZi+xLykb90gybmKkf0PAvjZSvOcW2DEPjDtOHZJMZxPQKV9LsHDqPHyxEvZSuP5UDwyQNk0kZiOGetPr2pi9rVzcnSNPopzLfiq5a3QraCkwRdSIh6Gqd285gS9wjKZeTEQhzzhsk2lyCj3wsPRnqROfGQ4CUlFFxLG/ymMhnItfVyZQ1Mhk0PLy4hg3rjin3PQqJ/fP3zo+xZyeYaaqEPpPbSiZizcei0TjBnCPtqQMXxvXl+lJx45GrrOLuZBGyXFMZJzu+LHKu9F3AmgO/vr0M2HybjQarfKYKfRM3WMoWv4+fmENr39PIZ2ar4AVIvx/qFkx1UUX9FlvDvs/qyvSzhoVZMls/I7nW2ZfFWD9QUEeZerV9P7AxyG9cp61JuaFG1MaYJHe9HhAC8pOnMPEPB9CeK+owET3doKs/vWTNxoOV43xd1snw3/ISJ2peIzZatqxckyODKroLaYuwLD50mFbTer5MIiIQVvYhAhmv/wg1XxsFeWblDmWBONKL3Qsm74rTyJiu0QpdJ9ASvHgwyMBT3RSlENRszyCiYce2qBsqwZBYhgXqJkpFBRlAEar12A7GYwxMWf+R0u94BkSrS2yLYRgObk5Wimj9VhcGQJIrLvWd4Pp8nhx+xtvx/jOY8QJ2btv49ALaP3kS+anTw4l2h7F+C7E6QNFPFOzdTMMELMjPhoXpb40GJmFrgYUZFrkvJ8L3AINBjF+59Fud+JeCp2z5OmfTNxH1lkxMfmIKkz89rG4GoxEptRCyDw0AZWGMaBGIL2llT2IiPKMrkc9IECn1FpmB3eTGTAhNPrzPTIzegQEivDmfAQ0+pT2VVxAgmhPpT3m9Fmc0ZiF0uZrmvCQKfgKNgydDKjoHVHKjKIMhehUae2U5enRNBxZMjhLYeg1jUfarteCAFIEAozsi5kP6LL97gbIBvPR7bL+bwF0/2VUmu477shqunlndDN9nifiRHC7iAgNATF05FgS0bIZcx/JkhctRdor2QFRZMQI/JiFeCXR5nOS6E8fn0Pnx/nrwkgIMde+IupQOKatbYHmQUqftG1S/dzUtla1zNmf99H4tMzApUezZWj9RXD+Ffz93kX8RDNLFDNLQDOxCQCoyKT96HBuemIb43AOzA7xBniTkIveEZwr0Wpw3I9BP/p3cDUl3SR7LGfcE6OXfS0/E6uKgyY2ZDgEfGm6cUc4TZY2C1mmAlYwPRxjlMJoPBXpuZ+isS33IrNvAMwMM9JSZyDwzQe4MwIAUH5JsIpccU8JmcpbIT2Jjcmkz3bdK7px2/JxZlUq9psME1iRsuASczFAIM8TJCIVJ0hw8ho1JsBsCQvmZBKwbMVD7vRCagJIyby5Bodxb3UQ18LXp+SZArM97BnROd9F+9KnB4GUUXUrKBrmfpMwoGr46wa9fyC01A7PO2Zy1D2DWEnAZJJ5dSkup6vsBl7r2DFLne6ZlGb6QFRCzsAA8cxgTR2ZN+KvuA8QRLilBa40gNUWvAwRGfMp0mZBdOf3bsC/VNrSIRdIJTbQPBJC0btj6/b42uvoWjQznCgn0z3SOJiZAU9krmJKMut1Ynvum1+nE81gUy8nmTAg494n6UyuSJSaq0g9ly2yEjAKo3B0r25C6n5XJX/rLwM7VEXO/xDbGqDgLeNntJcBbx4aAVOV+6Xmxb0WfY7mKZqb8J9dTUJzZuvW568VIKbYoRC7YrpjrR3auDmjP5Rj/ydPIT57qD15GtRSY6AdshnFV92M96t7Fy/h+W3PWRCGRrQXX0fmwlKjtbOrp5zLiv1PXH6Z9w7ZFwMncHNqPPYWJo7O6KlXffzszEwWzG/xZ8QcqIkhmMow4UtkVmaChP4Xq96v/kGUxgy0iIDBAwLECCgBUSIrYHj95k8tHw8ZpEpTzRTOh7fQmdRMYMtqejnxuRa6VthDrxCHtRT2ZAp58jHLT0FhKu6UN7MbgRG6Vf3UsibTf62bITaXJ8RyYMNdDBGb604MNIEZQ6dgR0POMVYgAo1KWwujZTWeYEcTnmtkvuTetXojupg61sUuATICmYwq1PTReeSfTZy3LQ4w46gVM7juC/MTUcHscDWsp5rgfk8Lua/5cO5RXf9YxL3zOoDKNVW1IENOM5lqy8/nl8DQpf+ZXQamIgUEgpO4lNKRP2UQmhVDsmTQzi9ajT2LiyExxCXoJa0pzBQLcFqqXXsxmRdqmOqisaZMDDBI6LHVIm3IXrSHuHd0ryE3cXFbqZ21DdFnYtqQ2M+TIq7hnUcwsy0Jok7xPxinRtphbJrqzTMI9ajcCqhoV+tNENxHbwJmKPSujUVMuoZy0SX6PGo/Y7ur2A/G58dfivgnQ9UxOJekcAVoPDir5U/LYNj3OYIgYHWmrqdPPEQziHHvDTJ3s6s7uSOMmonHhMc5yxBknK7LytuZzbHj8BPKDhxG63Xrwsth32zDvFn98sXqVVPmziKZc1zYk8bD2AQwjuRCGHphVYcv55Rg1HHCQT3oYv3QKBA269gjaHxOZJCBmbg6tx57C5OEZnWRYD5N3olaEJ1WTLZRX/6DfaSIIHZrsTURS1KPoZFBZyfu+u37RBJLsN+dCKdtlI4UyWk0XRQqqP45HPiYRU3Eysqvt4nwFc9zcPK66dfWNCIi8nsS6uWK7NSy8LCcCW58Xxidkq4yHgC/S5ui1ygk+glHKkUPMhjcz9gJWEpE2ktWW/2nOG845JGNBPxkw+IgnH5FUycsiwmdK2Gh2Bu85tq4cO+PCCnZsDHNV/s5AWJm7Uhif9YR1iW0bPzqL8GSRqC6Z58Xr50axUc9ZigWgZ1uGjVJazjatYVv7o6MvxoA1pYdZbhv2y18n2h2mnhT4qAM6dazNKKGMQFUPwyDm0Iyu+Hlyje1F5QVvXuzkjlBQQ2CGdRAqwJTJmVfZAnp4InarYcMwgACJ6HYI0FRcWojn8ETOGxlWmCC+Jp1vAAcBQEnRr5FRBJqsW8deJ3nMbEwY9BizNvyT3Wh17j7V9/CkHKj+EGxfQzDPBbNtzKLxFg0aEQRbTxTCxs/0+r04rgoEcionYyRdcs8YJ19kPVc8IdbPrrHQzkz/+X5ojqHyWiIyljax/qbCCoL2PVJgXn6+kKP1xAGEbiLLLoCzjthZTMRiv8/6pYIYtr5RrWFs+traBzBiukpbQyBmqUPyhr1m6vd+Zeto1WHEdL5cnatqBOsLYg7PVicS7z6SiVHqa7tQ1yyuRCu7WmeZuoSYiZCfRofAWgqp17EFUlZW8UaESjk4JEJEJ3Z2k8nkWdbjJ28jLmb3DOhz+VOBTlYR8+qEXQp6U6yFgic6x7gl+FFwk2gKzBT3J7OABvb+aT8IkDK75lkFA1RL9yInB2T2RtvJYCpQXaBnjc93jFgEM441onunXTGuQQLB0k/Euk2enHZmniMjIjfh1g7AOQutzOzvpUCxV7Q3tDK053qY/IeD9aJdrewsJvBh3dLe5T3MOXXXGKSfaWxJbX0AmLUEWrydiy/GoCR0dblefPl+L4hBrqB+EQSLEPTa3avzCGJ++hTGj89HBoYjTeSF3KKJP3MveGkusQdGFBuia0bZEjlG2hFfF7ufFCzJRJei9isdhnE5pNxSlUmdGX3eGNBpHYweRvpODE50JcBoZRREMXtCDIAPiwasu8WAET//EZjj6CQDGqWMtjUxbojHOJS94t5xGhz+acLZ+VElJiZ5bQJCoQ0FBSZM2oEXbQtgGD7eY0kFwgIqZTzdLt++Xu6TsmFMUPZsnT6fTzEWRZbd8R8+ifzwkXrw0g9ILPWizbur+oGQYRdtZ/NebtxGQ9v6Gam1pH05FzYIUDC70g9ApEKkU6DHvzxSL5JBEQSDyla6ELQcC3s7P3kS41MLCgw4eyhgV8zFB+6nAB1iN7S55b5DlYgSCg022gTSgPiJ1mgacluWs7tG0Wyshydc1t7w6rpOR2IzB0dAkTKJqOHJVOs0bIADLgHmulJHxaVHEWAmKaG6oUg3I313+X48WLLXjH00/c/iPw79FheL6a9c02l1DDtHwMAzZL793jVoQtLl3ij7RKeyS4rvMTFOJlGgJDcM1CaN1EPl+fKskbG8AC9jJxfQfvQphKlpK9pNuYfrJvKlWrTV1XO+o4bWK1uzCI3q+gAwrINpbDgbVbsyqJ5BGpdBSfJSL7Q60OLL9lk5eSYm9HoIZ2bQ+YenMH5ivijEOg2eeE377e8MEJR6F5DSIWAk7eBoJQEoolVw4EAnuY4FE9oOYgcMi+FAAbugOKzZsCvsYsrjpOsT7TEAs1sTZGZCNhocynVi2CceF0n0x5+rhiJYgBgKhkYTwlWywTITQm6QjPrjWaSMQFBW7YO02TAofD3PxrTiGBornw8zTu5eMrhkXYwmKlQ3ls2QLNdNRsQRiyjuQ+uyC1q+cMWV16WszCzO1ozRXQfSy7rGHz+McOp0NeKoLjrxfNtKaMN6MWWIh5+n1weAAaDbdK8mELNSqcTFqPvrslOm2Je6Ovz161xXKVamD0BiJgZ5ycScPo3OPzyJ8eMFiDEb8+mJ8QUvriX5XZviNRuOkQHs5O+Fq5zh1mfWZa2EnlOaRn8wUAqJf0CFHWHwoWwFuxlAYMYldFM9SAmehJ1gIGPca6TJ0XGkZHxGP+LGXupiHQe7jRikaDSNnpspOMmCrVPuBWBBELuqOPQ7tWeQaWpWXD9uNgnNZVPRGvm6JYQ+A3rjLTNWFSFzy7WRwKQHTSzAzrqI2YjlvhCgCx1+9kBj6vsZBeQK/MrzOmd62Pjjg8iPHK2Cl8VGGQ1rZxOCPazbu7GlsRHlHuvvDqwmPcz50rcsZXmg2o9Bq6w6MDJqlMAIUQNJd9KZGXR+8iQmj8zqitxnRjWJ5DhhHGiSQCyrq+U8ls/HMvjVf3JykLwaGeVhSegxUi4dTlfvN1fUz2omYA+A/GoegGWDOH9OiCyBTGwcas0p8Tmc2kRTUViwBzI+Gsu4aqT9HFlUAhIFPXSvvCCVx9yEetOYFMfT4yV9FreWgFEjhkUEC3ETRW6EBa+cBI9z0BT3JDNuNK+d0iR5vAWAtJm2qDDPXojnBwV8QcectU9FOzIDkLM8oD2bY+IfDhS5XnqJnaW1Ecs0HZ3te/RsQ6EbG92GnKfXzx1ZTczLubRRv9x1TMmgL/egsGguM4oQzlPPwwCdQe3k6KQzM0Wyu4Nn4sTjUs77CctH7bBGo9L8knq37ED5K+8QjTihAsVEIWHPMvnKijxWDste1Li+VFtC7gEDMLwmhRgfBhCcGyZGsMQtDvx1pb+8BxCzWszacHu8/iiWs/smydgYt5WEjI9lZsxT9Zn2UTkTgVYmZNNJPFA7evHcIg8K3dMyp44CJwI0ZtPIktlJJp4rTXU/gLkmC6zzTlYwPk6vwmxNMV4W2BlgTUDdsjmUzyePfRDwMvnYYeTHT6iLtmIj6NbOuY2y2Grs7G1Eucf6GP3VxLqcaxv2CzisqHcx1xmGOUkd75cDxgOZEbP1VkKs9z2JyQOno79fWAHHsACw+U+AuCLVtpQfy+TvJlgTvm0m7mA2RwRg3DqqYUhk1OVrGzbDuQRaXRLdUpvMBF9hQTIFL6zNUGEwT4oECHJKjFcRO0t/XS4Uw3w5QCV9MGNdMjh6jOowgICvnQdbPyxI4vLCRmg+FjmcZcqsVZgdYUBC1OzUhZSHLAIHBjmmPgF4JVuTdzKjh+FnjLeiYCCi7ihmgAhMs+bFRMDJWHD7y89bCwGTPzmI/NCRIlGdBy8+j9P5tlEjn1ZKu9eSidRjSFsfAKZhX+ptFGZiKa9Tp2lZTP6EfvoarmPIyKRknpj5BWQ/ewaTB8+UL3IRTpYrTk4axhMqhfBqFlUXfupFt1k3oD1bvOwrm/1RJmCOADH5PDwAoXBwszUAuZW8O8sAD1jQYiJPyvorCeoY4IU0KOAkerp650kRMtnbTMIaDiz6kxDb55MFsjutEl6ceC1oBJH83ZYoJRgmSkPhWwQyHADjv4tMxvEaJjkdsUze3SZMhoyzsTrXFYnOY/3erUYAGzDAklko78pk96YCUXlmBMgEoHNqAZM/egr54SMI8/P9mZeVYv3eD6MEHTS2OFvEPL3CnqBltgbIrBzrF200bGRT3Wf9EuT1O26qS4CYuTlkP30SG35yGJ0zvQLE0ORvJv4SLNhJj5gPwKy6ZcLP8oDQydCbbEcw4PNt8CQiK/VKVEkwK/a6aCWf04UBh58gjd7HiDVpBd92dcm1IEAnMhitBQes2NiFU07CzMKwe0VPcSHgrNUw4KpOg8F1l79r1I3/XMTJJchQBibEvZtMMjgnKK5keQbdVyonwCkvNxhlLUwy/03JwqgIXJPIOYBL7snQAd1HchnSuNlotGBAFW8QmQVg8sgsxn78BHrHjg8OldbOr7KpqHEnLb1JkE0ThZSwEampVWfDZJEctp7lsLr2LeZ6dXXVhVWP6qLS6iyICb0ewvw88kNHMPbjuAlkURhALpOO0xmUxzlXiK66aWJmCt+kbm9nZjdgI6IVsEETd4xKiT9NvhGe+APXAwMwvDvGJoSjrrfiNgTcZqPBEXBHWwxovaotop/Sfmmrgg8LZCpuJJlweV8p/kyYBWI6NNU9IghiNkOBZer+kRXsmv2cmSUv6q6ANsBsvGmvH+szieYUNLtxoOsbBpD0PZxtmiObDJOjgJgrrfa7qC9g8sBptB59Mp1ht1/EYcNkNAaMPE+vDwCzVoBLv4l4qXzJZ6NlGcb6bRMw7DUH9bWfqHfEcWIQo2xMr4dw+jRaP30aE0dmku4jk2vET7Kwf/vEaJWyAiZ8GZrsAWgbzEaM5eesa2BdCbtXZHNE0y69jmVk2B2iYAUwO2crS5DZ36VNDBYq2o4Q6zAgsBXbZRgXD1rKcefrqsiaI22EhdAcFPE8bSe5TAyTkoMEt0X9xp2SYKBMlmKXYweA1U7RWPiILA5RNmNGwJO1ThKOXWGRCDBxhmeO3AJcm+jeiJuqM9vDhseOAo89gfzU6Sp4WYk5XpbaBqWCaGw4axgYZyMqm1es9QtHPpc2ypc0pUPpp3lJXaOffsYfr6Ol/XUXA2JS4t7HnsTkE1PlKh4FCyNunZadFE2dCRdLnKDpPLhj7DYQN1DP/uOQZn9Nz3JIhIruF0STs17DgSm1DDGEuJwwW5wrJwHIxM2kO1uLi4QiqvT03E7UyjoJo1LR1KRBELs4tN0yUZfAUPLmeNPxYsGrd/2QFkr6yCyHCYF2bYuMFWIfM0SmiIExsT6SrC61hYXpB/3a6oY069Oqnqe5h3iTTXcv5f50zvQw8ZODCE8fRJidA0IiVHq9TOxrEZitYBv5qXrggQfw+te/Hjt27ECWZfjKV75ijmdZlvz3yU9+Uss873nPqxz/+Mc/bup56KGH8KpXvQqTk5O47LLL8IlPfGJxPbSNO/s61rsNYjBS4KKfS6fOlzyKoK7u+v3AzIgv1NoIpdk5hCcPYPLRQ2jP9oqVOEdsiCuI5xSi642rppy4WMQJILIjvcTkxI80z4+JTfbk2qxTkb2F5HxxMxSrcZp42RUEXn3bvrHuoihHIEdYEGkH6XHYlWHaBD4nM/98vznMnEGG1kdMBo+HSQpY9kHFr+SqMwBSyofEPdHyVW0SAxA+RzLhqtA7VBkWBnwAgT0GnSGWixqnavuUCSIwa/IDud26DVAi8DtxeLbY1+jIUY00qtW8rEVbpHu6sYSdCxHv6dOn8bKXvQyf+cxnksefeeYZ8++zn/0ssizDm970JlPuD/7gD0y53/md39Fj09PTuPHGG3HFFVfgwQcfxCc/+Ul87GMfwx//8R+P2lxrIwqEVpythi/LMMnmmI3pF3k0iKFJbbpW96L0oGUR+WJSEUohBGBhAfnho5j48VMYm55XQacKHFPNN3sYBTO5ea2LSbPPuwKXeouK2yfYeuJFqS8tmInOFHMC00qem8wlWzOuJTvpaqZcmfRl4s9i+6sbRQZTjtsFIApmEevUrLM+iR1pXawrqPy9BIUaXgwHDEoQqkJXHhcXwRWFrMG0O46NvZecyVfcd9K3FPPBLI9Pgpe0LB63IuL4nJhtAzg83+mHWCcjjNWGn51A+8c/Q35iqhDrSqTRkC7b1sQEWhs39v+erzZbq0DtXJiIeEewzuAi1m655Rbccssttce3b99u/v7qV7+KV7/61bjqqqvM55s2baqUFfv85z+P+fl5fPazn8X4+Dhe9KIXYc+ePfjUpz6Fd73rXaM2Gcq8rHYGZqW4kEY1z6jI38Mmv6uLHqoLj+7H4KSOcVuGzBeTtXIgL8oWFHyOfPokOj+aR/uybZj5uU0anZIhFKtWmmBDhybPRKSSJD2rTKrEZkgOEU4lL8c4fFaSyQFQQWh1fyUCDTwRl/Vp+LfUndMqn7QWfrVv0uGngBVdL7nPEAGZ9kyO3mQrhkHTueq6ChFEpVwiAgJ5rIyLKvCeQCGKcpmhoUld/0Z53eDKBhTLRHddzn4suXsKEBPPU2DLot1Q9DWlk9Ffaad0w06BQu/Lz3XvKBprZYAgDAyKhvUytLoBndNdjO87VCSn63bTO0oP8W7K5xeQtbrDvQtWovG7ZFRdX2P1loquq7FlfWoOHjyIr3/967jtttsqxz7+8Y/joosuwstf/nJ88pOfRLfb1WO7d+/G9ddfj/Hxcf3spptuwt69e3H8+PHktebm5jA9PW3+rTtbiS+BfmBilIy7KQZl0HVT7fDsziJBYT9dTLb/GWx46mR5nfIE0rYIIyEsSBYiA8BiXbOyl/KyQzFHGtFKm3dzlp2vOZKGmR5lJlL5XsStk1HZTNoRAUkMJ86qdSTcLEbwS1sKBJ7kM64//t7d2DYMT2WvJ8AyOIAFd8J6sM4IcTw4QqeS1p+AgImqYmDJYI5ysJj2eBcYMWdy3zR0O7fghSOwUskO1eXlgaJcuxOjxXw/4mDEPujeTRmAkkkbm5rH+E+eRn7sOML8QuE26heW3s9CnmZtVpMN2uqksdFsRBZmZAZmFPuTP/kTbNq0CW984xvN5//23/5bvOIVr8CFF16Iv/3bv8Udd9yBZ555Bp/61KcAAAcOHMCVV15pztm2bZseu+CCCyrXuvPOO/H7v//7y9STxpbM6tiPYTQvw5Ttdx2gns1ZhBVMTFaCmJYyMWF+AdnjT2MSOzC7Y1MEDD2axBEnI05rn4WAbAEqdvX7K5lJnVfR/ktPQEXOM/leYEGQOV8m+jE5jxgcOLFrTm0p22ZcHbS6z1Cs6k1UUIIUVeajjYrLppikS2CRF6yC9KH4Jei1ZDL2/SuEy8R2UPsr7WYGIw/KflSYmeD6k3gHq0A3K64dWnZPJm1jTowQItCU50GvLy4pGkP9nLMxt+LYReBj2R3vktP6ekWfWws5xp+aAg4fQz4zk96QkW1EofyKs2HaP6ILurEhLISRGJhlBTCf/exnceutt2JyctJ8/oEPfEB/f+lLX4rx8XH863/9r3HnnXdiYmJiUde64447TL3T09O47LLLij+UCl/lLqRBdi6+PEvxYhomQ+5irpNiWQYBGflskX2qgJiCeEeYn0f2+NPYsLANM5dvMQLJZNMFzHQyK+4VYEA7N+tk6VgGoJgQhdng9P5GNGsuTAxBZj9PAgABKeROMpFRVE9lEu0BaMfNEZPjqexIMNdit1dkIaKLp7UQgQXXZYCBuEhapcuJ518nADZuG0QGhFkPL6I1Yyrn9WI7JPFdcb14HruWlJHjKLaa62Sp+bMVx51/jwUQ3UNSp7Qjj+dIG7IAjB+fQ/uJQ8hPnkKYX0Alyii1OGgm8sYWYyuFgfnWt76FvXv34otf/OLAstdddx263S4ef/xxXHPNNdi+fTsOHjxoysjfdbqZiYmJ/uBnLYKXc7HK8dcY9XqLXaUMI8YdZYXUD7D00+QM4Z+Xl3nWKuoMPQCtDJifB548gI0nz2Dh8ouxsHm8XH2XfgJmVUrqP7qXIlhhXUJWeloF0PiNJYW1MDlemDEgk4lM9BFxgi9ZFHIfqV6DGQ+ehNkNRYAn64Vil2QCYxWmgvrHLhzulzdtk5vVQxvFGLn6pR6j1yldd7XanPIzBoCc3E3bwSyGA3jF2MRIKNVFkYjY91G3RxAAQs+BsG16/0mgnEqCJ6yLhES3yuder9sOBogp+MkKvcvkM6eQ7T+AfG6ufj+j1O8r3c4m0nEpr9PYWdmyiSb+1//6X7j22mvxspe9bGDZPXv2oNVq4ZJLLgEA7Ny5Ew888AAWFha0zD333INrrrkm6T5aFbYS9SnD2ChfvlE2hjyb8UgBEPl80Dl1gGoJRHjJUOuFBeTHT6Cz9wlMPn0SrYW8nCgCsm4UigKImgcFOVJxBAi6ESBAmhbSbrhcI37n4OJEROBRRumYhHTBlpFrtbrBAg2qQ9klYnRCq3SP+WtLO8vJntkPBWqszxGWgMESsQYpobNhYwKVS4CUVM4bBoYM/Diqh+8JQO3gBHLluSbdfo7KOHnj7Lvx4gQUy9+zbqzPFHXRXVI+79gdqaVdUTxe9Gv85AI2/vgg8NMnkZ85k97PaKkn5uXMAu7tXIGKc8WKrzRbbJtGjEIa+SqnTp3Cnj17sGfPHgDAvn37sGfPHuzfv1/LTE9P40tf+hJ++7d/u3L+7t278V//63/FD37wA/z0pz/F5z//ebz//e/Hv/gX/0LBydve9jaMj4/jtttuwyOPPIIvfvGL+MM//EPjIhrZznf49HI8yCm/83LYsPWOkp9lKcdjKepaIn92EsT0eggzs8C+p7Dh0cPozPQqE5Gpw28oKDypcyupqXuidHvwhB5cWTpeCEYz49oACDxl9vyU1sbkVWGvAm1YKPvtVHKgZDJRB3OdSobZzE3G1E7Vd+iFYZgny0Dw2JBbSfpMbZdjKY2Jdz3F82A23tTPacfwyDhZZkTuhWqXyvFoLQQwAGOQa6KMWvEna5wqGXSJOTL7ZZXPYXsux4b9JzH28M+KXaRn52yU0SjC+1FtuSb7tc6ArMT+nU2bRvCWjOxC+t73vodXv/rV+reAire//e246667AAB33303Qgh461vfWjl/YmICd999Nz72sY9hbm4OV155Jd7//vcbcLJlyxb81V/9FW6//XZce+21uPjii/GRj3xkcSHUYhrZEEYaoFVlK/kFMAjcrIQvYR0dPmKYZ0oXU2z8mCM/fBTjJ08BF1+I+Us3F5E1WYbWfDCgRUWbPGHS5KYb75W7IUfdiRWZSrm8bQGFcav0QkxIx+4cZxxhw64gdcdQ5E2rG0N91X1VAqJWN1SyvEooMStrWYsBWBAjbpkMIYYvowoc9Kf0zeloGNgYzYy6g2j8ZeyAQkAsOqAe3ZusOr7FvbSak4yAlhczF22h6DTR3IRgtmrgNmkSuzbde9L+mHwvPrFdANpzOcYPnUZ28CjCmTPI68KjG7O2Ut5d/Ww1tHGRloVwvqmJ5bHp6Wls2bIFv443oNMaH3xCY/1tJX8JRmnbYvuxCHYrM9liW0CryDqNVgvIMmQbNyK/YhvmL5zULKzGLQQYjYYHAqJ7YOEnu57spAW7GvdAhRkXcvOYPZAY4ORxkpf6WDxa/HRAiOpWnQ8Q3Wh8bdIEqbAZMOBANUCJyZv7ae4JC1xJtFowRJkyEWZ8Um4nzuEj7XXjweZFwT63S2WsZPwkUZ6wYLTXUqVt5XU1UsmzdSVYZWYtC8DEwTNoPXkI4cyZArT0CobUsC4r+fu/UqwZo7O38jvQDQvYha9iamoKmzdvri2+rFFIjS2BrZQvxVK2Yan7lBLkDlN2kNXVNSQjE8W9xMa0APR6yLIM4eRJtP5hDhsuvhCzz7sIvcmWTuJRRJtZPQdNSLJDtYTiZiDWoxcnVc3cK0wBr/wR6/MgxiRyA4zWxohtZSJvh+jmaEWGSIcTwbA1pi8u9NcIij0okfYxc5FZ9sWEOcvfpB8SQWw5EOWYlWCJ3Epe4KzXDxHgyOaQ+VhWYaX0+hWkQXXRvRDxbAHGQhybLvVZ3E0g8CXn0f0v+l1orfKxljIxEsbdObWA8SeOIj92AmFhIbo7vbtopbyDVro1Y3TObX0AGJPjYpW5j9bSl6LuRTiMCHcxkUfDXHtQXf2u61emNaDGRClx9t4sK/JpHDyMiZOnES69CPPPeRZ6460YrSIRS4DVgTgBqrqLhJUhRkDEmz7st5KdVyZ8XrXT30UGXkSNDrMNrJdhMKHgoGxjnNHt+Qkg4yOTTFh4O5ZBO+jkXNH7cJ+9KwXEoKh4GjEKC7a97GbyOiTVKTkWS9tNYfCyOzjvR8TiZe6bluskAFCI9YoeKF6UAJBslFn2qbWQY+LAaWTPHEI+M1uIc8vEjEmX0Vp6BzW2sm2lhFGvKJNBWW3gZa1ZnQDwbMKqB5nPDeM/H+bc1O91n/VhZ3QLAqDKxpw+DTx2BhMHn4V8x3OwcOEkQjuzwllJcOaqN3sokYvDsiRVkGOFqxYY6MRasjxaJ13PuENCwRJIeWV7OFxZypZtrYQ0O8YEWdAtF/S6/BUOEWCoJkZdMq6tJVhIbSEgfRExs0lwR2OqjA0AtEIU1EodZuuCCAor9ysr7odheJwZgMjtkJ8EolQ/pKyVtkIF0lnxH8aPzqLz1NFi/6KUu6ixxs6njahoWR8AhsFL3e+NrU3rl09mlHM9kKnbO2mUnDG9oryKfLMM4eQpZI/OYPLCrZi/7CKEZ3UAnViDshshy3TVryv8UgPCK/uKBkQmqrxMqd+OOV94ouVcKqJH8WG54rZAu+xTCAiBmB5YkKR/h8j+mDIqeKXLlP1UwazsOOJcOuJyCa0ICLSs9I0ZH9AxxHw6mbhnpC2c04VYDYD2TPLJ4uiarE/yrJOAn1witpSFiuBFgI7vs4Y9k2uMhd3MiIVWhvZ8jsknp4GnDyJf6KYT0rE1rEtj58MaBmaAMWBpwMv5sdXsU/cs0iJD102kEmC2IkAZrTQ2fQrjF2zBwrYt6G4ag+7NEwBwAjZnZhID4gSvTAat7lMegywrXBYENDi02oMQca0oQ6EuqgRr4yNkpDy7kVhbwiJiIDIyGZWlTqQARTIDcclKBKqcE85J9tuYMM+1j66p/WQGJNjzeUNNBiEFwEEESL3iQgwsfWI+3vQytOh4KNot0VsoAc2Gp04hO3AU4eSpte0uWs3vlcYKaxiYAdawLuff1tJLZtjcN8lTaeIt9TEBocji2+sBMzMIc3PoHDmGsYsuwNwVF6E32Va2wQp7o75C65e9c8QNJC6HnkyqKNw0wjKAyknVeTGZhrbbaVqO1UX8ZJERyDsZRUxREdaUlOeY7LJGuyadoh2U/deYGRxiMLTtAsAoSid0CKRkNAa+as5G7CO+ssiAKFAs25G3s2LbAmZO5PcuKkCRd8KOgCb2XdvPEVdUdxaA1nyOkKHYNfrxw8Wu0YPcRWvhO7mSAw0aG2zKxGbJ72DK1h+AWW92Lr+Iy3mtlfRC6SdGXiQz4/UxAFQjgzxHOHgY4yemkT37WQibNmLhOc9Cd0Px9Q0ZkGnoDJSpybrQCBbWoCTZD3I5GVdM6YaQcwFUssQaEOLYHhEPc/6VOkbEb5/gj1fcOAG6uWIqDFnapmJWcedwvhYu60Kd5bOCUXEvVWaM3AaW3H9x95ntHkpmyexvRX3kz1oLuQnV5sR6rH0qwq2La4xNz6N99BRw+Cjy2bnRgctK+q6dL1vutAyNVW0RxML6ADDr2W10Lr9cSxkBNGzd58OGeemPmPyuOKXqVgIIyMzMIMzMAMfaGDs0ifaVOzB30aSd7EuGRQFGL7p5AFSTpqluAkbvwZN4RcMR0Bc0AFB9jjAb6g6S41kGtIL+LiJTyXsSvIuM2BkT2l1Jl+8+FwCWwYAHH77Mrh4ZFyMARmRPKnUIeAm2DUUOm9h+M45Sb2ZDzVsLhXCZN1vUnbU1Agwmb46M69j0AsaePIowNV1Et5XuosoWAEB8PvtteNrYYFsPY3WuQVqjgXHmB2S9gZjzbav9S576AtdtcjlM2HUfq+SOASKQKd1LWa+HcOo0sh8/jo2bN6G346ICyEgdLUQNRxbM5F8cz6JOhr00qskoJ0wBEnS8+AkTxeTdKfKZGS5lQSLw4Xp4R2sTCQVymcj1KLOu1i/5V8pjkmiOGSC/fYCOl8/ZIhob5xITa8/nRc4XCtHOQlD9TGjFPYaK3CwRdOTjsToZC21TFnPJcE4Xn/RO8v4UpxVC6omDp+Omi/10Lg1YaWxUW6HgBVgvAMZscDfaADW2Cmy5VwjD7JOUCtcelEem7yUTQCZrFStqATILC8hPTKF18hQ2PmsjetsvwsIFk8jHW3F7AdVNBOsqAmICuiwyDjI5VjYDRJzoOZy4olMhYBFDk0n0ShoStKpMiAEtwjSYRsS2qoaGri2MUtGHzJSVdjOLk/UAtO04MNNkXGMl8MrHMmWhYl6ZzIwRAxN2sbVEzMvXoP4rsGzZ0GgNh6aIpPZcjvEjp9E6dhLh1CnkM7ODgQt/Vxrw0tgqt/UBYNga9mV12CigZCW8iAeFa59FtFJxemavIXlkZM+abhfZqdOYmJwELtiMhW2b0dvQLibDHiDidWUiRGsBGBcL4JgBQBOsmb9LQKBuJ5rgNcJH8sOktitwOV1YpyNtYJBhBL/M1JBuREBSZYNFcgHxtgNyXNqbdeMx1prIeQa48e0mEW9KDG22QZBjQJUZkrJZdPNlAQUoKc/L8oCxkwvoHD4JHJ8C5heQh4AwP1+4GYHBrEtjja1ka0S8zprIo9Vna+ml63PHpNgb+XxANt94Sl7JI4NeD1hYAE6fxtihIxjfshm952zB/IWle6mXA2hFFrIF3Y8+ILMrfr/NALmGRIAK+MmX2hiiwNYLchn0SN2ahh8RrHC2Ws/GGPdWea2ibjiWCfHzsmwRYlzdqZnP9dFAutEjC6IZZOWwL10RQ8vLmFiuDMSuJJgerkcBVg5MHJ1F+5ljCNMnC8DSahUC726R8GYoke5a+l41tjat0cDUWANkVpatFwX/IMqeBZVDVynMTK6uJQBR8DubI8wfRXbsBDZs2Yx86yb0Nk2gt6FTuEBQAo4yzLjViztEm52ZWTCbRe1MlgcFDUCc1DWEmN4sMumb3bYBZTWU4RH3EAl4GaTw7961ZEKn69xPZTkRyHL4OAuHza7Y0mQBJCwe9m6kdqxHWR+/eSMDHQKKvHeRXDQLoXATHTyN1pHjyE+eQt7rFXsW5QHodhu2pbF1besHwDTg5fzbWgIsS9EXz8wAfVmYlJnwa6ASuZTlOfJjx4HjJ9BptzG2cQPCszci37IR3U0TyMdb5f5GgEQwaY4YoNwGwIYXq2C2hQoTwkyHhit3YM7X4247g4poNqCySSGLkT27oqn7vXbH554hMOVDvBVcECsSwUeAZ5NSu1+rm4pz2nTjOMpYBEeVZ3kh3s26OTqn5tE5eALh1GmEmVn0RJgLoEn931hjha0PANMAl/NjdZE6dX+vJluKtteNjd9dO3UefV5JiAdE95IwMih+hvl5YPoksmcyjE9OIHv2s5BfsAkLF0yiN94qvyshAghJ3kYTvQ9XNhsk9qoAArDAoxDwlu3NXYh1wg0TK4HNtJtF8GAEvc4Fkwq7NtcTHQ3v+iznkCCZo7lCViaoC7GshqT7fDKl5ifrhaLfMn7SlwzonOlh7NgMWkemEM7MIMzPIxdNSy+Kw5vU/401Fm19ABggMjCeiWmYmeUzPxGf7YaMa9Xq9DEjbhZZHBbXUin67cVzOctvsYHkGYSZWeDYCYyPjyHbsAH51k3oXrQB3cm25kXR/YHYdUO6DnUZea0HAQcT+QOfwp82PuwQaHIJ87z7hT9X1wu5noxQtzzHAB2qh7c98KZgR1w+orkp2yihzABMjpjQoQR3XvbUzdFZyNGZmkPr+EmE6VMI3S7yhYXoIipuaANaGmusxtYHgOknCmrAy7mxs3nZrtYXdV3+GGA0NopdTUO6l/pFLwElmAEKFiDPERYWCjBz/ATGnuxg/NnPRn7RZixcuAHdDW2d/DVcmfdD8sAgwLAkmg/F70TN6wins9FcLronEqLbiUCTMCZad2av65P8mWF17h/V/dRoaIxGRj5zmY1VnCyMCxDlNN0crW7A2JEzaJ0oQp/D/ALyPI+C3EFsizwHq/U70VhjS2jrA8AAGHWXyxVhzYtq7dkw99MzLywCHpKJiZezz7zRywBx/yUgfj96PYTZOeD4CYyPj2Niyyb0Lt6MhS0T6E20IjgAYt4SRIBj9g2iDSSzPAC96IIxrqYShGiSthDBi81nQ6CmG9kRzcPigYecRztTCwDS7LskHJZyZmdnEgfzpowZ7XFU1EfXJZDTnu1h7MRsZFrm54vQZ3Ht5aGIEBvWTdS8Expbi7aI+Xn9ABhg9bEtzYtqddti7t+woPUs88rEauharJsBioy/5fYF2eGjmJicQLZ5E3oXbcLClgnkY7KDdihcJC0UP417JzIv+lMy05aAR3+nxHZmzyBkcX8hZoAQgYvJHlyCDmF9dPfmEBmS0M6APGbKNVstAAagyd/KNslOz6V+JmRl37uFO3rs5ALaJ2eRzXWRnZ4pmJaFbtS0iIuI7nNfUW7zHmhsPZgytMMDmfUFYBprbKVbP9Gz/8yDmDp3Vd/LsQDY6maUmWllRcK80z2E02eQHTpSgJmNGxE2bUT+rAl0nzVW7JQtItVuZCdMPhjWyWQxxX7UwWRGj6Kh1/KjZ0GOCn0NSxMsGwKncQnxHKNZaWXRJcRDqaxNKF1DJWhBUU9nrofO1ByyUzPIZucLbdH8fKEP9kzLsKClKND/eGONrXNbHwAmJd5trLHVbik3E/+9iJDs4vQ+upkSzIRTXWSnzwBHW8iyDONjHWSTk8DGDehd8Czkk2PIx1vojbdKUOKS1wFF5txeZt0w3VB8XSkVf5aH6AIqToRED6kLSbcCINFs+XerWybK87tswzIuUYiLUrhc1NHqBrTmi/HIujnacz20Ts8hOzNb6Ibmih2fQ6+HwHoWx7QMHfrcAJfGGhvK1geAaayx9WSL2Anbnt7HzZRT+DaFaIduF9nsXBGifTBDu9VCp9MBxseQTU4gTE4gTI4hjLXR2zCG0MmQtzOETiu6YLTiCERUCCvRPCUYCgR6TOZgbXQ8FrLICHEul0oOFwCt+RythRzt2W4BUmbmEM7MAHNzMVlgyaoEAMiJUelJCHuOWqbFg04v6j6L+9ZYY0lbw1rKBsA01thqtrqX06BcMr7sKKHZYhSiDZQMTS8vWBqg2NZgZgZhCgUrASBrtzGWZUC7DXQ6yDpthGdtACbGETotIASEiTEAQN5pFZ9RhFI+niHr5kA7U0Y1gMAIuZGQZUAeDPgBgPZcD9l8jlY3RzbfLf6dmQW6PYS5uYJhWugWQAWwIIWBEoU6+7Hqaz5xoR3swec31tgotpqeqWY36hpr3EeNrUUb5eXEIbhnEZrNVolqAqqh2gBMhJOcm5XbL06fNGWy8rvabhXuKbSyoo2trAA9rVYBiFotoN1CGOsAnSKkKZtbKFihbq+4FudUES1PCU6Q5wVgAgrXDwqAkmVZPE/AWIpFqRkTLVOnUVpNE0pjjZ0ra6KQGmussYqlhME+n0iKFVjUJpNZeoLOq+crwOm58rRnUkAENPxZLNtKH8vTICHUvSSpH8XmmOIOonNHSd9fx4ytYTq/scYWbfK9HDHdSQNgGmtsvRqDGA9S6tiD1P5NpliKpSlAjXFF9TN2TYUcoY4hamXAQtcAHmVN+KfvxwAQMRJQSYE9Nv95A14aW6l2PsH1Ij0k6wvANJFIjTUWrd9eVYN2zq5jF5KnhL5/2yok+qgAPLWMDhDZEb8FQM3n8bo1ewb0M+92qzue+ryxxlaDna9nleflRgOTMBmgBrw01li0pXxhpRibRUTUjAJ2zqn5jMgpa8BKY42NbmcxLzcxe4011tjS2UoLB/ago04PVAdOGtdPY42tWFshb5lzYCEsSuXcWGPr1upAyKCwbQEEHiCkwMEwbqt+bairu65snSbFi5gHtWulALTGGlvHtj6+hbpTbeNCaqyxoW1U1mEQsBn2c//Pl+8HPnw9Z9OeYUBRY401dt5sfWhgGmussfNvwyTdk3KpzweBj2FBxUrSsGQtZGMdhPn5c3/txhpb5TYSA3PnnXfil3/5l7Fp0yZccskl+M3f/E3s3bvXlJmdncXtt9+Oiy66CM9+9rPxpje9CQcPHjRl9u/fj9e97nXYuHEjLrnkEnzoQx9Ct9s1ZXbt2oVXvOIVmJiYwNVXX4277rprcT0US7EvjUupscbOnQ0CCAxcFutaWm0W8ga8NNbYIm0kAHP//ffj9ttvx7e//W3cc889WFhYwI033ojTp09rmfe///34P//n/+BLX/oS7r//fjz99NN44xvfqMd7vR5e97rXYX5+Hn/7t3+LP/mTP8Fdd92Fj3zkI1pm3759eN3rXodXv/rV2LNnD973vvfht3/7t/F//+//XXxPU2ClcSk11tjKsbUMVBprrLHBNuKcnIXa1JSD7fDhw7jkkktw//334/rrr8fU1BSe85zn4E//9E/xz//5PwcA/PjHP8Yv/uIvYvfu3XjlK1+Jv/zLv8Q//af/FE8//TS2bdsGAPgf/+N/4MMf/jAOHz6M8fFxfPjDH8bXv/51PPzww3qtt7zlLThx4gS+8Y1vDNW26elpbNmyBb+ON6CTjVGPG9DSWGONNdZYYyvGHAzphgXswlcxNTWFzZs31552ViLeqakpAMCFF14IAHjwwQexsLCAG264Qcu84AUvwOWXX47du3cDAHbv3o2XvOQlCl4A4KabbsL09DQeeeQRLcN1SBmpI2Vzc3OYnp42//pa4z5qrLHG1qs177/GVpItMk/bogFMnud43/veh1/91V/Fi1/8YgDAgQMHMD4+jq1bt5qy27Ztw4EDB7QMgxc5Lsf6lZmensbMzEyyPXfeeSe2bNmi/y677LJqIR4g/r35MjfWWGONNdbY+bNFzMOLBjC33347Hn74Ydx9992LrWJJ7Y477sDU1JT+e+KJJ+JBRneSD4b/9dtAiss11lhjja0FW4wrvXkPNractohnclFh1O9973vxta99DQ888ACe+9zn6ufbt2/H/Pw8Tpw4YViYgwcPYvv27Vrmu9/9rqlPopS4jI9cOnjwIDZv3owNGzYk2zQxMYGJiYl0g/lLN2jPBb9f0oi7YzbWWGONrUlr9IONLZctcp/CkRiYEALe+9734stf/jLuu+8+XHnlleb4tddei7GxMdx777362d69e7F//37s3LkTALBz5078/d//PQ4dOqRl7rnnHmzevBkvfOELtQzXIWWkjrMy3rabwUwq2R2vOFJAZlhgs1oB0Gptd2ONrXcbli3x5eT3Ub776/09sd77vxS2SKJgJAbm9ttvx5/+6Z/iq1/9KjZt2qSalS1btmDDhg3YsmULbrvtNnzgAx/AhRdeiM2bN+N3fud3sHPnTrzyla8EANx444144QtfiN/6rd/CJz7xCRw4cAD/6T/9J9x+++3KoLz73e/Gf/tv/w2/+7u/i3/1r/4V7rvvPvzZn/0Zvv71r4/cwaR5t5H8nkKAcmwxD+lybCLJbe/Xbi7bWGONrU5b7HfYTwj93m/9zq/Ln7VS3itL1Ra/I7L/ve44cPbXX0njOYwtV3sXAWJGCqPOahr9uc99Du94xzsAFInsPvjBD+ILX/gC5ubmcNNNN+GP/uiP1D0EAD/72c/wnve8B7t27cKznvUsvP3tb8fHP/5xdDoRT+3atQvvf//78cMf/hDPfe5z8Xu/93t6jWGsNoy62qmlQdD9vuj9Hv7UOXX1+TJcLgXKhqnT17XaANEw48Vll/pl11hjbIt9NkY5L8UKp777qb9TLnT/Dhz1XeVtlPeH/6xfm1Pt7mepBWrd+cO+R0Z53yz1+Wd77eW0xYDdPvPusGHUZ5UHZiXb0ADmXNpSgKV+ddSBmX7nDyrnv+Sp8ilGaNgXcuqlOswLsN/vdf3kelKfD2sNgGlskPV79oZ9Vof5nqfO71f+bN5Bg9o17Dn9Flr9QFndeypV/6jtHLbt3JZ+7626BeWge1AHtga1bxjgdbasWt04+N/ryoxgwwKYNbsXkuCyLhaAlQLRFtMO/yXpW0c2uJz/XP6uUM4Dyic/E3eb+3sg6JF6huijaa87J+vX/5qXZj4CKJM28rX7AT3uUz9KutLHAavqfi+us3lBNdYfRA8CvnUvank2pQwXy7LiGZTfQ4B5Vuvq6/d3xWqu3feURBsW8/7q+75IHOfvdx7qyw1b/9mYr0fGZJh3Y7/2D/N+G6YPw9YZG5V+BuTZzEccuGGuP0yViWeti4Xi9AHgZ80CmKNHjwIA/hp/cZ5bcpY2yjN1Nl/cpfjS132h+71kFnvdUV9cw7w0U5+l2pn6fVCdw5xT14Zhf+/32TDXayzaKPfmbOpbzHMxqi0V8DiXdr6vn7Jz9S5eLlup74U+1z958iS2bNlSe3zNAhjJDrx///6+A9DYaDY9PY3LLrsMTzzxRF9qr7HRrBnX5bFmXJfHmnFdHmvGtbAQAk6ePIkdO3b0LbdmAUyrVUSIb9myZV0/CMtlmzdvbsZ1GawZ1+WxZlyXx5pxXR5rxhVDEQ9ntRdSY4011lhjjTXW2PmwBsA01lhjjTXWWGOrztYsgJmYmMBHP/rR+u0FGluUNeO6PNaM6/JYM67LY824Lo814zqardk8MI011lhjjTXW2Nq1NcvANNZYY4011lhja9caANNYY4011lhjja06awBMY4011lhjjTW26qwBMI011lhjjTXW2KqzBsA01lhjjTXWWGOrztYkgPnMZz6D5z3veZicnMR1112H7373u+e7SSvaPvaxjyHLMvPvBS94gR6fnZ3F7bffjosuugjPfvaz8aY3vQkHDx40dezfvx+ve93rsHHjRlxyySX40Ic+hG63e667cl7tgQcewOtf/3rs2LEDWZbhK1/5ijkeQsBHPvIRXHrppdiwYQNuuOEG/OQnPzFljh07hltvvRWbN2/G1q1bcdttt+HUqVOmzEMPPYRXvepVmJycxGWXXYZPfOITy92182qDxvUd73hH5fm9+eabTZlmXKt255134pd/+ZexadMmXHLJJfjN3/xN7N2715RZqu/+rl278IpXvAITExO4+uqrcddddy13986bDTOuv/7rv155Zt/97nebMs24DmFhjdndd98dxsfHw2c/+9nwyCOPhHe+851h69at4eDBg+e7aSvWPvrRj4YXvehF4ZlnntF/hw8f1uPvfve7w2WXXRbuvffe8L3vfS+88pWvDP/4H/9jPd7tdsOLX/zicMMNN4Tvf//74S/+4i/CxRdfHO64447z0Z3zZn/xF38R/uN//I/hz//8zwOA8OUvf9kc//jHPx62bNkSvvKVr4Qf/OAH4Td+4zfClVdeGWZmZrTMzTffHF72speFb3/72+Fb3/pWuPrqq8Nb3/pWPT41NRW2bdsWbr311vDwww+HL3zhC2HDhg3hf/7P/3muunnObdC4vv3tbw8333yzeX6PHTtmyjTjWrWbbropfO5znwsPP/xw2LNnT3jta18bLr/88nDq1CktsxTf/Z/+9Kdh48aN4QMf+ED44Q9/GD796U+HdrsdvvGNb5zT/p4rG2Zc/8k/+Sfhne98p3lmp6am9HgzrsPZmgMwv/IrvxJuv/12/bvX64UdO3aEO++88zy2amXbRz/60fCyl70seezEiRNhbGwsfOlLX9LPfvSjHwUAYffu3SGEYoJptVrhwIEDWua///f/HjZv3hzm5uaWte0r1fxEm+d52L59e/jkJz+pn504cSJMTEyEL3zhCyGEEH74wx8GAOHv/u7vtMxf/uVfhizLwlNPPRVCCOGP/uiPwgUXXGDG9cMf/nC45pprlrlHK8PqAMwb3vCG2nOacR3ODh06FACE+++/P4SwdN/93/3d3w0vetGLzLXe/OY3h5tuumm5u7QizI9rCAWA+Xf/7t/VntOM63C2plxI8/PzePDBB3HDDTfoZ61WCzfccAN27959Hlu28u0nP/kJduzYgauuugq33nor9u/fDwB48MEHsbCwYMb0BS94AS6//HId0927d+MlL3kJtm3bpmVuuukmTE9P45FHHjm3HVmhtm/fPhw4cMCM45YtW3DdddeZcdy6dSt+6Zd+ScvccMMNaLVa+M53vqNlrr/+eoyPj2uZm266CXv37sXx48fPUW9Wnu3atQuXXHIJrrnmGrznPe/B0aNH9VgzrsPZ1NQUAODCCy8EsHTf/d27d5s6pMx6eSf7cRX7/Oc/j4svvhgvfvGLcccdd+DMmTN6rBnX4WxN7UZ95MgR9Ho9c9MBYNu2bfjxj398nlq18u26667DXXfdhWuuuQbPPPMMfv/3fx+vetWr8PDDD+PAgQMYHx/H1q1bzTnbtm3DgQMHAAAHDhxIjrkcayyOQ2qceBwvueQSc7zT6eDCCy80Za688spKHXLsggsuWJb2r2S7+eab8cY3vhFXXnklHnvsMfyH//AfcMstt2D37t1ot9vNuA5heZ7jfe97H371V38VL37xiwFgyb77dWWmp6cxMzODDRs2LEeXVoSlxhUA3va2t+GKK67Ajh078NBDD+HDH/4w9u7diz//8z8H0IzrsLamAExji7NbbrlFf3/pS1+K6667DldccQX+7M/+bF18CRpb3faWt7xFf3/JS16Cl770pXj+85+PXbt24TWvec15bNnqsdtvvx0PP/ww/vqv//p8N2VNWd24vutd79LfX/KSl+DSSy/Fa17zGjz22GN4/vOff66buWptTbmQLr74YrTb7YpK/uDBg9i+fft5atXqs61bt+IXfuEX8Oijj2L79u2Yn5/HiRMnTBke0+3btyfHXI41Fseh37O5fft2HDp0yBzvdrs4duxYM9Yj2FVXXYWLL74Yjz76KIBmXAfZe9/7Xnzta1/DN7/5TTz3uc/Vz5fqu19XZvPmzWt6gVQ3rim77rrrAMA8s824DrY1BWDGx8dx7bXX4t5779XP8jzHvffei507d57Hlq0uO3XqFB577DFceumluPbaazE2NmbGdO/evdi/f7+O6c6dO/H3f//3ZpK45557sHnzZrzwhS885+1fiXbllVdi+/btZhynp6fxne98x4zjiRMn8OCDD2qZ++67D3me6wtu586deOCBB7CwsKBl7rnnHlxzzTVr3s0xrD355JM4evQoLr30UgDNuNZZCAHvfe978eUvfxn33XdfxYW2VN/9nTt3mjqkzFp9Jw8a15Tt2bMHAMwz24zrEHa+VcRLbXfffXeYmJgId911V/jhD38Y3vWud4WtW7caNXdj1j74wQ+GXbt2hX379oW/+Zu/CTfccEO4+OKLw6FDh0IIRSjl5ZdfHu67777wve99L+zcuTPs3LlTz5eQvxtvvDHs2bMnfOMb3wjPec5z1l0Y9cmTJ8P3v//98P3vfz8ACJ/61KfC97///fCzn/0shFCEUW/dujV89atfDQ899FB4wxvekAyjfvnLXx6+853vhL/+678OP//zP2/CfU+cOBG2bdsWfuu3fis8/PDD4e677w4bN25c0+G+/cb15MmT4d//+38fdu/eHfbt2xf+3//7f+EVr3hF+Pmf//kwOzurdTTjWrX3vOc9YcuWLWHXrl0mnPfMmTNaZim++xLu+6EPfSj86Ec/Cp/5zGfWdLjvoHF99NFHwx/8wR+E733ve2Hfvn3hq1/9arjqqqvC9ddfr3U04zqcrTkAE0IIn/70p8Pll18exsfHw6/8yq+Eb3/72+e7SSva3vzmN4dLL700jI+Ph5/7uZ8Lb37zm8Ojjz6qx2dmZsK/+Tf/JlxwwQVh48aN4Z/9s38WnnnmGVPH448/Hm655ZawYcOGcPHFF4cPfvCDYWFh4Vx35bzaN7/5zQCg8u/tb397CKEIpf693/u9sG3btjAxMRFe85rXhL1795o6jh49Gt761reGZz/72WHz5s3hX/7LfxlOnjxpyvzgBz8Iv/ZrvxYmJibCz/3cz4WPf/zj56qL58X6jeuZM2fCjTfeGJ7znOeEsbGxcMUVV4R3vvOdlQVLM65VS40pgPC5z31OyyzVd/+b3/xm+Ef/6B+F8fHxcNVVV5lrrDUbNK779+8P119/fbjwwgvDxMREuPrqq8OHPvQhkwcmhGZch7EshBDOHd/TWGONNdZYY401dva2pjQwjTXWWGONNdbY+rAGwDTWWGONNdZYY6vOGgDTWGONNdZYY42tOmsATGONNdZYY401tuqsATCNNdZYY4011tiqswbANNZYY4011lhjq84aANNYY4011lhjja06awBMY4011lhjjTW26qwBMI011lhjjTXW2KqzBsA01lhjjTXWWGOrzhoA01hjjTXWWGONrTr7/81lVbuFp/XsAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(records[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mdoc_data = mdocfile.read(\"/data/elferich/CryoTEM/Johannes_20210803/ERHoxB8undiff2/record_lamella22.mrc.mdoc\")\n",
+    "IS_to_camera = np.array([13374,6053.4,6394.1,-13172]).reshape(2,2)\n",
+    "\n",
+    "\n",
+    "result_tiles = pd.DataFrame(\n",
+    "    {\n",
+    "        \"tile_filename\": pd.Series(dtype=\"object\"),\n",
+    "        \"tile_movie_filename\": pd.Series(dtype=\"object\"),\n",
+    "        \"tile_pixel_size\": pd.Series(dtype=\"float\"),\n",
+    "        \"tile_x_size\": pd.Series(dtype=\"int\"),\n",
+    "        \"tile_y_size\": pd.Series(dtype=\"int\"),\n",
+    "        \"tile_orig_x_size\": pd.Series(dtype=\"int\"),\n",
+    "        \"tile_orig_y_size\": pd.Series(dtype=\"int\"),\n",
+    "        \"tile_x_crop_center\": pd.Series(dtype=\"int\"),\n",
+    "        \"tile_y_crop_center\": pd.Series(dtype=\"int\"),\n",
+    "        \"tile_mask_filename\": pd.Series(dtype=\"object\"),\n",
+    "        \"tile_plotted_result_filename\": pd.Series(dtype=\"object\"),\n",
+    "        \"tile_image_shift_x\": pd.Series(dtype=\"float\"),\n",
+    "        \"tile_image_shift_y\": pd.Series(dtype=\"float\"),\n",
+    "        \"tile_image_shift_pixel_x\": pd.Series(dtype=\"int\"),\n",
+    "        \"tile_image_shift_pixel_y\": pd.Series(dtype=\"int\"),\n",
+    "        \"tile_microscope_focus\": pd.Series(dtype=\"float\"),\n",
+    "        \"tile_defocus\": pd.Series(dtype=\"float\"),\n",
+    "        \"tile_acquisition_time\": pd.Series(dtype=\"int\"),\n",
+    "    }\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "for i, item in mdoc_data.iterrows():\n",
+    "\n",
+    "    # Initially convert image shift to unbinned pixels\n",
+    "    image_shift = np.array(item[\"ImageShift\"])\n",
+    "    shift_in_camera_pixels = np.dot(IS_to_camera, image_shift)\n",
+    "    shift_in_image_pixel = shift_in_camera_pixels / 2\n",
+    "    crop_x = 0\n",
+    "    crop_y = 0\n",
+    "    crop_center_x = 0\n",
+    "    crop_center_y = 0\n",
+    "    mdoc_data.loc[i, \"tile_image_shift_pixel_x\"] = (\n",
+    "        shift_in_image_pixel[0] + crop_x + crop_center_x\n",
+    "    )\n",
+    "    mdoc_data.loc[i, \"tile_image_shift_pixel_y\"] = (\n",
+    "        -shift_in_image_pixel[1] + crop_y + crop_center_y\n",
+    "    )\n",
+    "mdoc_data[\"tile_pixel_size\"] = mdoc_data[\"PixelSpacing\"]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from decolace.processing.decolace_processing import find_tile_pairs\n",
+    "\n",
+    "tp = find_tile_pairs(mdoc_data,9000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ZValue</th>\n",
+       "      <th>TiltAngle</th>\n",
+       "      <th>StagePosition</th>\n",
+       "      <th>StageZ</th>\n",
+       "      <th>Magnification</th>\n",
+       "      <th>MagIndex</th>\n",
+       "      <th>Intensity</th>\n",
+       "      <th>PixelSpacing</th>\n",
+       "      <th>ExposureDose</th>\n",
+       "      <th>DoseRate</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Voltage</th>\n",
+       "      <th>DataMode</th>\n",
+       "      <th>ImageSize</th>\n",
+       "      <th>ImageFile</th>\n",
+       "      <th>titles</th>\n",
+       "      <th>image_shift_pixel_x</th>\n",
+       "      <th>image_shift_pixel_y</th>\n",
+       "      <th>tile_image_shift_pixel_x</th>\n",
+       "      <th>tile_image_shift_pixel_y</th>\n",
+       "      <th>tile_pixel_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.504, -416.103)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.98390</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>-3048.906807</td>\n",
+       "      <td>-39942.397648</td>\n",
+       "      <td>-3048.906807</td>\n",
+       "      <td>-39942.397648</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.506, -416.103)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.04150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>-240.544809</td>\n",
+       "      <td>-38911.785370</td>\n",
+       "      <td>-240.544809</td>\n",
+       "      <td>-38911.785370</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.505, -416.103)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.11380</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>2567.786922</td>\n",
+       "      <td>-37881.238952</td>\n",
+       "      <td>2567.786922</td>\n",
+       "      <td>-37881.238952</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.505, -416.104)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.06330</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>5376.118653</td>\n",
+       "      <td>-36850.692535</td>\n",
+       "      <td>5376.118653</td>\n",
+       "      <td>-36850.692535</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.505, -416.103)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.16730</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>8184.450384</td>\n",
+       "      <td>-35820.146118</td>\n",
+       "      <td>8184.450384</td>\n",
+       "      <td>-35820.146118</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>453</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.481, -416.112)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.37630</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>-4522.788603</td>\n",
+       "      <td>21185.519211</td>\n",
+       "      <td>-4522.788603</td>\n",
+       "      <td>21185.519211</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>454</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.484, -416.111)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.39670</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>-1714.456872</td>\n",
+       "      <td>22216.065629</td>\n",
+       "      <td>-1714.456872</td>\n",
+       "      <td>22216.065629</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>455</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.482, -416.111)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.41972</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>1093.874859</td>\n",
+       "      <td>23246.612046</td>\n",
+       "      <td>1093.874859</td>\n",
+       "      <td>23246.612046</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>456</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.482, -416.111)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.51189</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>3902.206590</td>\n",
+       "      <td>24277.158463</td>\n",
+       "      <td>3902.206590</td>\n",
+       "      <td>24277.158463</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>457</td>\n",
+       "      <td>0.005999</td>\n",
+       "      <td>(614.482, -416.109)</td>\n",
+       "      <td>-16.4062</td>\n",
+       "      <td>64000.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.111955</td>\n",
+       "      <td>2.66662</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.26200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(2880, 2046)</td>\n",
+       "      <td>record_lamella22.mrc</td>\n",
+       "      <td>[[T = SerialEM: UMass_Krios Camera -&gt; 0:Ceta 1...</td>\n",
+       "      <td>-23737.555550</td>\n",
+       "      <td>16937.558851</td>\n",
+       "      <td>-23737.555550</td>\n",
+       "      <td>16937.558851</td>\n",
+       "      <td>2.66662</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>458 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ZValue  TiltAngle        StagePosition   StageZ  Magnification  MagIndex  \\\n",
+       "0         0   0.005999  (614.504, -416.103) -16.4062        64000.0        29   \n",
+       "1         1   0.005999  (614.506, -416.103) -16.4062        64000.0        29   \n",
+       "2         2   0.005999  (614.505, -416.103) -16.4062        64000.0        29   \n",
+       "3         3   0.005999  (614.505, -416.104) -16.4062        64000.0        29   \n",
+       "4         4   0.005999  (614.505, -416.103) -16.4062        64000.0        29   \n",
+       "..      ...        ...                  ...      ...            ...       ...   \n",
+       "453     453   0.005999  (614.481, -416.112) -16.4062        64000.0        29   \n",
+       "454     454   0.005999  (614.484, -416.111) -16.4062        64000.0        29   \n",
+       "455     455   0.005999  (614.482, -416.111) -16.4062        64000.0        29   \n",
+       "456     456   0.005999  (614.482, -416.111) -16.4062        64000.0        29   \n",
+       "457     457   0.005999  (614.482, -416.109) -16.4062        64000.0        29   \n",
+       "\n",
+       "     Intensity  PixelSpacing  ExposureDose  DoseRate  ...  Voltage  DataMode  \\\n",
+       "0     0.111955       2.66662           0.0  10.98390  ...    300.0         1   \n",
+       "1     0.111955       2.66662           0.0  11.04150  ...    300.0         1   \n",
+       "2     0.111955       2.66662           0.0  11.11380  ...    300.0         1   \n",
+       "3     0.111955       2.66662           0.0  11.06330  ...    300.0         1   \n",
+       "4     0.111955       2.66662           0.0  11.16730  ...    300.0         1   \n",
+       "..         ...           ...           ...       ...  ...      ...       ...   \n",
+       "453   0.111955       2.66662           0.0   8.37630  ...    300.0         1   \n",
+       "454   0.111955       2.66662           0.0   8.39670  ...    300.0         1   \n",
+       "455   0.111955       2.66662           0.0   8.41972  ...    300.0         1   \n",
+       "456   0.111955       2.66662           0.0   8.51189  ...    300.0         1   \n",
+       "457   0.111955       2.66662           0.0   8.26200  ...    300.0         1   \n",
+       "\n",
+       "        ImageSize             ImageFile  \\\n",
+       "0    (2880, 2046)  record_lamella22.mrc   \n",
+       "1    (2880, 2046)  record_lamella22.mrc   \n",
+       "2    (2880, 2046)  record_lamella22.mrc   \n",
+       "3    (2880, 2046)  record_lamella22.mrc   \n",
+       "4    (2880, 2046)  record_lamella22.mrc   \n",
+       "..            ...                   ...   \n",
+       "453  (2880, 2046)  record_lamella22.mrc   \n",
+       "454  (2880, 2046)  record_lamella22.mrc   \n",
+       "455  (2880, 2046)  record_lamella22.mrc   \n",
+       "456  (2880, 2046)  record_lamella22.mrc   \n",
+       "457  (2880, 2046)  record_lamella22.mrc   \n",
+       "\n",
+       "                                                titles  image_shift_pixel_x  \\\n",
+       "0    [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...         -3048.906807   \n",
+       "1    [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...          -240.544809   \n",
+       "2    [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...          2567.786922   \n",
+       "3    [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...          5376.118653   \n",
+       "4    [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...          8184.450384   \n",
+       "..                                                 ...                  ...   \n",
+       "453  [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...         -4522.788603   \n",
+       "454  [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...         -1714.456872   \n",
+       "455  [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...          1093.874859   \n",
+       "456  [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...          3902.206590   \n",
+       "457  [[T = SerialEM: UMass_Krios Camera -> 0:Ceta 1...        -23737.555550   \n",
+       "\n",
+       "     image_shift_pixel_y  tile_image_shift_pixel_x  tile_image_shift_pixel_y  \\\n",
+       "0          -39942.397648              -3048.906807             -39942.397648   \n",
+       "1          -38911.785370               -240.544809             -38911.785370   \n",
+       "2          -37881.238952               2567.786922             -37881.238952   \n",
+       "3          -36850.692535               5376.118653             -36850.692535   \n",
+       "4          -35820.146118               8184.450384             -35820.146118   \n",
+       "..                   ...                       ...                       ...   \n",
+       "453         21185.519211              -4522.788603              21185.519211   \n",
+       "454         22216.065629              -1714.456872              22216.065629   \n",
+       "455         23246.612046               1093.874859              23246.612046   \n",
+       "456         24277.158463               3902.206590              24277.158463   \n",
+       "457         16937.558851             -23737.555550              16937.558851   \n",
+       "\n",
+       "     tile_pixel_size  \n",
+       "0            2.66662  \n",
+       "1            2.66662  \n",
+       "2            2.66662  \n",
+       "3            2.66662  \n",
+       "4            2.66662  \n",
+       "..               ...  \n",
+       "453          2.66662  \n",
+       "454          2.66662  \n",
+       "455          2.66662  \n",
+       "456          2.66662  \n",
+       "457          2.66662  \n",
+       "\n",
+       "[458 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import multiprocessing\n",
+    "from functools import partial\n",
+    "from rich.progress import track\n",
+    "from scipy.ndimage import binary_erosion, mean\n",
+    "def determine_shift_by_cc2(\n",
+    "    im1,\n",
+    "    im2,\n",
+    "    im1_mask,\n",
+    "    im2_mask,\n",
+    "    erode_mask: float = 0,\n",
+    "    filter_cutoff_frequency_ratio: float = 0.02,\n",
+    "    filter_order=4.0,\n",
+    "    mask_size_cutoff: int = 100,\n",
+    "    overlap_ratio: float = 0.1,\n",
+    "    debug: bool = False,\n",
+    "    debug_object = {}\n",
+    "):\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "    if erode_mask > 0:\n",
+    "        reference_mask = reference_mask > 0.5\n",
+    "        moving_mask = moving_mask > 0.5\n",
+    "        reference_mask = binary_erosion(im1_mask, iterations=erode_mask)\n",
+    "        moving_mask = binary_erosion(im2_mask, iterations=erode_mask)\n",
+    "\n",
+    "    # Transform mask2\n",
+    "    diff = (\n",
+    "        int(im2[\"tile_image_shift_pixel_x\"] - im1[\"tile_image_shift_pixel_x\"]),\n",
+    "        int(im2[\"tile_image_shift_pixel_y\"] - im1[\"tile_image_shift_pixel_y\"]),\n",
+    "    ) \n",
+    "    tform = transform.SimilarityTransform(translation=(diff[0], diff[1])).inverse\n",
+    "    moving_mask = transform.warp(moving_mask, tform, output_shape=reference_mask.shape)\n",
+    "    mask = np.minimum(reference_mask, moving_mask) > 0.9\n",
+    "    if np.sum(mask) < mask_size_cutoff:\n",
+    "        return None\n",
+    "    # Get bounding box of the mask\n",
+    "    bbox = np.array([np.min(np.nonzero(mask)[0]), np.max(np.nonzero(mask)[0]), np.min(np.nonzero(mask)[1]), np.max(np.nonzero(mask)[1])])\n",
+    "    # Calculate bounding box for moving\n",
+    "    bbox_moving = np.array([bbox[0] - diff[1], bbox[1] - diff[1], bbox[2] - diff[0], bbox[3] - diff[0]]) \n",
+    "    mask = mask[bbox[0]:bbox[1], bbox[2]:bbox[3]]\n",
+    "    \n",
+    "\n",
+    "    with mrcfile.open(im1[\"tile_filename\"]) as mrc:\n",
+    "        reference = np.copy(mrc.data[0])\n",
+    "        # Cut out the bounding box\n",
+    "        reference = reference[bbox[0]:bbox[1], bbox[2]:bbox[3]]\n",
+    "        reference = filters.butterworth(\n",
+    "            reference,\n",
+    "            cutoff_frequency_ratio=filter_cutoff_frequency_ratio,\n",
+    "            order=filter_order,\n",
+    "            high_pass=False,\n",
+    "        )\n",
+    "        if debug:\n",
+    "            debug_object[\"reference\"] = reference.copy()\n",
+    "    with mrcfile.open(im2[\"tile_filename\"]) as mrc:\n",
+    "        moving = np.copy(mrc.data[0])\n",
+    "        # Cut out the bounding box\n",
+    "        moving = moving[bbox_moving[0]:bbox_moving[1], bbox_moving[2]:bbox_moving[3]]\n",
+    "        moving = filters.butterworth(\n",
+    "            moving,\n",
+    "            cutoff_frequency_ratio=filter_cutoff_frequency_ratio,\n",
+    "            order=filter_order,\n",
+    "            high_pass=False,\n",
+    "        )\n",
+    "        if debug:\n",
+    "            debug_object[\"moving\"] = moving.copy()\n",
+    "    \n",
+    "def calculate_shifts(row_pairs: list, num_proc: int = 1, erode_mask: int = 0, filter_cutoff_frequency_ratio: float = 0.02, filter_order: float = 4.0, mask_size_cutoff: int = 100, overlap_ratio: float = 0.1):\n",
+    "    pool = multiprocessing.Pool(processes=num_proc)\n",
+    "\n",
+    "    # map the worker function to the input data using the pool\n",
+    "    results = pool.imap_unordered(\n",
+    "        partial(determine_shift_by_cc2, erode_mask=erode_mask, filter_cutoff_frequency_ratio=filter_cutoff_frequency_ratio, filter_order=filter_order,mask_size_cutoff=mask_size_cutoff, overlap_ratio=overlap_ratio), row_pairs\n",
+    "    )\n",
+    "    shifts = []\n",
+    "    # use the rich.progress module to track the progress of the results\n",
+    "    for result in track(\n",
+    "        results,\n",
+    "        total=len(row_pairs),\n",
+    "        description=\"Calculating shifts from pairwise cross-correlations\",\n",
+    "    ):\n",
+    "        shifts.append(result)\n",
+    "        # process the result here\n",
+    "\n",
+    "    shifts = pd.DataFrame([a for a in shifts if a is not None])\n",
+    "    return shifts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "decolace",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}