{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example of phenotypic clustering\n", "This notebook exemplifies some steps involved in working with annotations\n", "from IDR.\n", "It will build parts of Figure 1 of the paper from Rohn et al.\n", "'Comparative RNAi screening identifies a conserved core metazoan\n", "actinome by phenotype,'\n", "see http://dx.doi.org/10.1083/jcb.201103168.\n", "Link to screen on IDR [idr0008-rohn-actinome/screenB](https://idr.openmicroscopy.org/webclient/?show=screen-206)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Figure 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Figure1](./includes/figure1_rohn_phenotype_clustering.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dependencies\n", " * [Matplotlib](https://matplotlib.org/)\n", " * [NumPy](https://www.numpy.org/)\n", " * [Pandas](https://pandas.pydata.org/)\n", " * [SciPy](https://www.scipy.org/)\n", " * [Seaborn](https://seaborn.pydata.org/)\n", " \n", "The cell below will install dependencies if you choose to run the notebook in [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb#recent=true)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pip install idr-py" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from IPython import get_ipython\n", "import random\n", "import re\n", "import omero\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.cluster.hierarchy as hchy\n", "\n", "from seaborn import clustermap\n", "from pandas import DataFrame\n", "from idr import connection\n", "import warnings\n", "\n", "get_ipython().run_line_magic('matplotlib', 'inline')\n", "plt.rcParams['image.cmap'] = 'gray'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Common variables" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "screenId = 206" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method definitions" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def buildComposite(st, n, m, smpl=None):\n", " \"\"\"\n", " nxm shots from st in a grid, as an image\n", " \"\"\"\n", " nr = st.shape[0]\n", " nc = st.shape[1]\n", " if smpl is None:\n", " smpl = st.shape[2] // (n * m)\n", " res = np.zeros((nr * n, nc * m))\n", " for i in range(n):\n", " for j in range(m):\n", " try:\n", " res[i * nr: i * nr + nr,\n", " j * nc: j * nc + nc] = st[:, :, (i * m + j)*smpl]\n", " except Exception:\n", " break\n", " return res" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def getRohnTile(imid, x, y, w, h, conn, chan=0):\n", " \"\"\"\n", " fetches one tile from one image\n", " \"\"\"\n", " im = conn.getObject(\"Image\", imid)\n", " pix = im.getPrimaryPixels()\n", "\n", " tile = (x, y, w, h)\n", " plane = pix.getTile(theZ=0, theT=0, theC=chan, tile=tile)\n", "\n", " return plane" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def getBulkAnnotationAsDf(screenID, conn):\n", " ofId = None\n", " sc = conn.getObject('Screen', screenID)\n", " for ann in sc.listAnnotations():\n", " if isinstance(ann, omero.gateway.FileAnnotationWrapper):\n", " if (ann.getFile().getName() == 'bulk_annotations'):\n", " if (ann.getFile().getSize() > 1476250900):\n", " print(\"that's a big file...\")\n", " return None\n", " ofId = ann.getFile().getId()\n", " break\n", "\n", " if ofId is None:\n", " return None\n", "\n", " original_file = omero.model.OriginalFileI(ofId, False)\n", "\n", " table = conn.c.sf.sharedResources().openTable(original_file)\n", " count = 0\n", " try:\n", " rowCount = table.getNumberOfRows()\n", "\n", " column_names = []\n", " pattern = re.compile(r\"Phenotype \\d+$\")\n", " for col in table.getHeaders():\n", " column_names.append(col.name)\n", " if pattern.match(col.name) is not None:\n", " count = count + 1\n", "\n", " black_list = []\n", " column_indices = []\n", " for column_name in column_names:\n", " if column_name in black_list:\n", " continue\n", " column_indices.append(column_names.index(column_name))\n", "\n", " table_data = table.slice(column_indices, None)\n", " finally:\n", " table.close()\n", "\n", " data = []\n", " for index in range(rowCount):\n", " row_values = [column.values[index] for column in table_data.columns]\n", " data.append(row_values)\n", "\n", " dfAnn = DataFrame(data)\n", " dfAnn.columns = column_names\n", " return dfAnn, count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Connect to IDR" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Connected to IDR ...\n" ] } ], "source": [ "conn = connection('idr.openmicroscopy.org')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download the annotation file for the whole screen" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "dfRhonAnn, phenotype_count = getBulkAnnotationAsDf(screenId, conn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Phenotype clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### A. Transforming phenotypes into one-hot booleans" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "BoolCols = []\n", "PhenLab = []\n", "total = phenotype_count + 1\n", "for iphen in range(1, total):\n", " col = 'Phenotype ' + str(iphen)\n", " dfRhonAnn['Bool' + col] = ~ (dfRhonAnn[col] == '')\n", " BoolCols.append('Bool' + col)\n", " PhenLab.append(dfRhonAnn[col].unique()[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### B. Performing the hierarchical linkage on the gene with a phenotype" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# count a gene as having a phenotype\n", "# if at least one well annotated with\n", "# it has a phenotype\n", "value = dfRhonAnn['Has Phenotype'] == 'yes'\n", "phenMap = dfRhonAnn[value].groupby('Gene Symbol')[BoolCols].sum() > 0\n", "phenMap = phenMap[phenMap.sum(axis=1) > 0]\n", "phenMap.columns = PhenLab\n", "phenMap.replace([False, True], [0, 1], inplace=True)\n", "Z = hchy.linkage(phenMap, 'ward')\n", "Zt = hchy.linkage(phenMap.transpose(), 'ward')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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FqbD2Ri2aKy2loRHYjhJ0D43VxvDw8L5j7TNenaZY9EtqQl3Py6xMvn3ePv9MqE5dlwqv4/+P8bxWcx+/Y+ydFrgAWHeMfa4AFpf0log4p/hEjrX2A94PbN1Jx3XXzznIGwI3RsTNAJKOA94NPBkgR8SfC/tfBKzaxfNrGXR7ZNjMzMyqJmkxYAvgrLypuAT1LRFRDHi/kb8WCpCBHYFTcy3jKk+36zrMQe6pFIvnAbcVHt9O69Hhho8Dv6n0jCaoyuoVZmZm1vskzQBmFDbNystPNxQD4QuAw/L3Iy5BHREXSELSk0PaklYhrbC3SUmnXis9PYI8xoeg1Z8yLS8gL5n4cWDjcs9wXB4dZRR5aHh4eHqVnQ9qFQuzXtSNFKc63nYej25ch9My2lPXz1QdFyMZjxwHzRpllxED4THsQ8pFnpsfvxJ4MXBjHj1eStKNEfHicbRdOz09gjzGh+B24PmFx6sCdzbvJGld4FBgi4j4T+kn2aHR8oudfmFmZmaTISJ+K2lvYJX8+Axgpcbzkh7sl+AYenwEeQyXAGtIWg24A5hKSiJ/kqQXACcBH4qIG7p0Xo0R4qEJHGtmZmbWbfsAp0z2SXRDT48gjyYi5kraETibVObt8Ii4RtIn8/OHAF8Fng38KN8emBsRU6o8r8YI8XgC3SqrVzR0mmJRx1tSdb3t1Q8VP+r62vaLqj8j/ZJmUNdb9NbbBqVSxki1ilttz7WOzy08PpXWKawjttur+roOckScCZzZtO2QwvefAD7R7fMyMzMzs/rySno140oVZmZmZpOrw4VCemsEuUeVtijJeFRdxaIbt1/reku/6tvhg/zaWnuc/lCtOv6f7Rd1TCfqhi4sFGIjWKSTnefF/Fp9mZmZmVl7JH1f0i6Fx2dLOrTw+LuSdpW0mKR7JH2r6fgtJV0u6QpJ10raIW+fKekOSXPy1755+2F53yslnSipZ/KUOxpBnu+gtEwjVaQYanzjdAszMzMr0Z9Ji3scIGkRYAVg2cLzrwV2Ad4K/BV4n6QvR0RIWpxUWnfDiLhd0tNYuCLX9yNi/6b+PhsR9wNI+h5p5b3KCw6UoW+rWNTdSBUpmoLmrqdbPHHPzZVWsegX3ajmMKivrVWnX24715X/z7anrq9THdM4KujjT8D38/cvA64GVpb0TOBhYC3gctLqegcC/we8GpgNLEOKG/8DEBGPkYLoERWCYwFLMsKCbXXU11UselRxZHloEs/DzMzM+khE3Clpbl4H4rWkwPd5wGuA/wFXkkrjbgbsACwPTANmR8S9kk4F/i7p98DpwLERT46eflbSB/P3X4yIswEkHQG8HbgW+FwXLrMUHkGumeLIcnE0uVvpFrPPOr7qLszMzKwikmYAMwqbZuWVhxv+RAqOXwt8jxQgv5YUIP8Z2BL4Y0Q8LOlXwFckfTYi5kXEJyS9HHgzsBvwFmB6brdVigUR8VFJiwIHA9sBR5R3tdVxgNw7upJuUXUVi35R11uEZmWr40I9/ZIqMqgL7wzqdXdLDoZnjbLLn0kB8ctJKRa3kUZ27wcOBz4KvE7SrXn/ZwObAr/L7V8FXCXpZ8AtLAiQRzuneZJ+CXweB8jV6+Kyy0Nd6qeZ0y3MzMysTH8iBcQ3R8Q84F5Jy5NykncGNgaen3OMkfRRYJqki4ApeXU9gPWAv4/USc47Xj0ibszfvxO4vpIrqkBPB8jDw5fP7EY/XQzEFzLZ6RZmZmbWd64iVa/4RdO2pYE3AX9oBMfZKcB3gF2BL0j6CfAI8BCjjx4LOErSsvn7K0iT/npChwHyvKrOw8a20Gjy8PDw9Co6GdQUi24U+PciAma9wwvvVKeu190vqTtjyaPGyzZtm154eGTTc/cCz8kP3z5CmzNbbJsPvG78Zzq5enoEeZA0jSbvPlmj2mZmZmb9rrMAeb4D5DoYqYZyGQZ1BNmjRe2p6wSsfnhtofrrWHKV1w/MKFmzfvmMVK0bd8fqalAni1prHQXI0Tv1nc3MrIl/oZuZtccpFjXmyXhmZmZWJkkrAQcAGwCPAbcCu0TEDZI+C3wLWDEi/pf334Q0Ue9mUkxyXER8XdI+wDsKTS8FrA4sFxEP5mNPAZ4bEa8p9P+G3P+6wNSIOLGqa50Ip1jUW9eXmh7UFIs6jqwN6i3hQb1uqH4iZ7+kEg3yZ8SqU8ffA2XL5dZOBo6KiKl523rAisANpFXzLgG2ZuHJehdExJaSngHMkXR6ROwB7FFo+xjg+EJwvDywPvCgpNUi4pa86z9I1S92q+gyS+ERZDMzM7PBsCnwREQc0tgQEXMAJK1OKvX2eeDLNFWzyPs+JOlS0kjxpY3teYnpFwMfKey+DXAa8G9gKmlkmoi4NR9T66Cyr0eQJW0OHEhaV/zQiNi36Xnl598OPAxMj4jLyuq/hBSJoYrbNzMzsz4yxlLT61AIbJtMA44FLgBeKum5EXFXU9vPBl4N7F3YNgTsC2wSEXOb2vs6KUA+kRwg94q+HUHO637/kLRO+O3AJZJOjYhrC7ttAayRvzYCfpz/LcuEUiTaKOVWegrGoKZYdGpQZ3r71na16vj61rF+9yBXOjEbSxtLTY9kKrB1RMyXdBLwXlIcBfB6SZcD84F9I+IaeDLW+jnwlYi4sdGQpBVJI8oXRkRImitpnYi4evxX1l0dBcjzeyhABjYEboyImwEkHQe8GygGyO8Gjo6IAC6StLyklSPin90/3ZYeHSNIHurSeZiZmVnvuwbYtnmjpHVJg4XnpJvrLEGalNcIkC+IiC1btLcn8M+IOKJp+3bAM4FbcnvLkgLwPUu4hq7oLEDurRSL5wG3FR7fzlNHh1vt8zygFgHyWPWOvWCImZmZdeAPwDclbR8RPwWQtAFpKemZEfFkGoSkWyS9cKSGJL2aNNlu/RZPTwM2j4jZed/VgHPo1wD5icfvEKRb/92urtDKGHk2anFIcyHndvaprSoWDBnUFIt+uP3aL7ed++U6+kUdX1sv9V6dui4GZOXI6Q5bAwdI2h14lFTmbRPg/5p2P5k06nvxCM19nVTa7Y95lLhhG+AFwEWFfm+RdL+kjUhpGieTRpjfKenrEfGyCV5a6ToKkOtmjDyb24HnFx6vCtw5jn3MzMzM+kJE3Am8r439di08PLfF828b5fDntdi/ONK86lj9T7ZFJvsEKnQJsIak1SQtQfor6NSmfU4FPqzk1cD/apR/bGZmZmaToKdHkEcTEXMl7QicTSrzdnhEXCPpk/n5Q4AzSSXebiSVefvoZJ2vmZmZmdVD3wbIABFxJikILm4rFscO4NPdPq92uMaxmZmZlU1SAD+PiA/lx4uRihNcnFfLmw5MiYgdJc0EvgAMNWoiS3owIpZu8f3bSWtLbBYR/+j2dZWtrwPkEo1Vbm0kQxPos+vLTMPgTtIzMzMbEA8B60haMiIeIa0Xccco+98DfA744kg7SNoMOBh4az8Ex+AAuS3jrRbhEmz9qxuzsAd11n2/XIfZoHBVip70G+AdpBXuGivojfTD93BguqRvR8S9zU9Kej3wU+DtEXFTRefbdQ6Qu6jDtImhCk/FzMzMBtdxwFclnQ6sSwqCRwqQH8zP7ww032V+GnAKaZnp6ys610nhALm72k6b8OizmZmZdWqMNSIAiIgrJQ2RRo8Xmqs1goOAOZK+27T9CeDPwMdJAXTfcIBcX+PNezYzM7MBNcYaEUWnAvuTFgl59hht3ifpF8Cnmp6aT6qp/DtJX46Ib3Z+xvXkALmmqlglrx2epGdmZjYQDiet/3CVpE3a2P97pDUmFoodI+JhSVsCF0j6d0QcVvqZToJ+XijEzMzMzFqIiNsj4sAO9r+HtET001o8dy+wObCnpHeXd5aTxyPIZuPgSgtWB4Na6aSuVRMG9f3o1Hiuu47vea9eR6NucdO2c8nLSUfEkcCR+fuZTfvtCuxaeLx04fvbgNVKP+FJ4hFkMzMzM7MCjyBXq3mi3dAknYeZmZmZtckBcoWaJ9oVg2UvJW1mZmZWTw6QJ8+kLCU9FlexMDMz62+StgZOAtZqLPAhaUNS2bcVgQAuBC4Hts+HrQ38FZgHnBURu0vaHNgLWBZ4ND//+Yj4h6T3AjOBtYANI2K4S5dXCgfIZmZmZoNlGikAngrMlLQicAIwNSJmSxKwDXBBRPwQQNKtwKa5mgWS1gEOBt4VEdflbe8ipZP+A7gaeA/wky5eV2kcIJvh2eftqsMM7FYG9f0Y1OuuK78f7anrz5FO9ep1SFoaeB2wKWmxkJnAp4GjImI2QEQEcOIYTX0R+GYjOM7HnVr4vhE0l3n6XeMqFmZmZmaDYytSisQNwL2S1gfWAS7tsJ2XAZeVfG614RHkmvNkPjMzM2uXpBnAjMKmWXn56YZpwAH5++Py44n2+Wzg98BSub/9J9rmZHOA3F3Fsm9DbR7T1cl8gzpJz7dG2+PXycYyntvO/lzZWLrxGelGykSn1zGec8rB8KxWz+VA9k3AOpICWJQ0Ie8o4FXAKR10dQ2wPnBFRPwHWE/SbsBTFiLpRQ6Qu6hY9q2pPvJoI8VD1Z6VmZmZDYhtgaMjYofGBknnAb8DjpZ0RkRcnLd/EPhdRPxrhLa+A5ws6aJCHvJSFZ57VzlAro+WI8XNgXTe5rQLMzMz69Q0YN+mbb8iVbOYCuwv6bnAfOB8Uim4liLiKkk7kwLrZYD/kKpXfA2eLCV3MPAc4AxJcyLibSVfT2UcINdf82p8AEPDw8PTq+hsUFMs6ljFoo7nZDaW8XwOe7UagPWXbqQ/TPZnPSI2abHtoMLDEV+EiBhqse0M4IwR9j8ZOLnjk6wJB8g117waH7QeVTYzMzOzcjhAnjzNI8NDEzjWzMzMzEriAHmSNI8MdxLwthpVLsugplj0Q3qCqwcMHqfhWNnqmiJTxwoT1t+8UIiZmZnZgJAUkn5WeLyYpLslnS7pZZJukLRk4fkzJE2VtJykoyXdlL+OlrRcYb+zJN0n6fSm/t4k6TJJV0s6SlJPDM72xEkOql6oVuFRy8HjUcvqDOpr243JUda+QX19B+i6HyLVQV4yIh4B3gLcARAR10g6CdgD2FPSVsDiEXGcpBOBqyPiwwCSvg4cCrw3t7sfqcxbsYTcIqQay5tFxA2S9gI+AhzWheucEAfI9dbVRUKg+hSLfvmFPqgG6BeI2UDy//GB8RvgHcCJpNJvx7KggsVewOU5IN4XeKekF5MWEtmu0MZewI2SVo+ImyLi95I2aern2cBjeVlrgHOAL+EA2TrQspzbJJyHmZmZ9ag2lpqGtMT0V3M6xLrA4eQAOSIezivinQ98LyL+JuldwJyImNdoICLmSZoDvAy4aYTTuQdYXNKUiBgmLVTy/AlfZBc4QK4Jl3MzMzOziRptqenCPldKGiKNHp/Z4vnTJN0H/ChvEmlJ6mYjbW+0E5KmAt+X9DTgt8DcNi5j0jlArjeXczMzM7MqnArsD2xCSoVoNj9/AVwDvFLSIhExH57ML34FcF2LY58UEbPJo9OS3gq8pIyTr5oD5BqrspzbSAa1zJuZmdmAORz4X14yepPRdoyIGyVdDuxJyj0mf39ZRNw42rGSnhsRd+UR5C8C+0z4zLvAAbJ1VTeqXtR1kkmn11HHeqSeZFmtOr6+df3/VLW6/qyq48/Dfrnubvz/60almrmP39HWfhFxO3BgB01/HDhY0o2k1IrZeRsAki4A1gSWlnQ78PGIOBv4vKQtSaWFfxwRf+igz0njANmsTwxqIGNmZu2LiKVbbDsXOLdp21DT4/8CHxyl3ZbRfER8Hvh852c6uRwg10Av1Ds2MzMzGxQOkOuh6/WOR/LEPTfXrg5yv4yMVn1rrY63B6GeaQN1VfVnpF/+L9VVHV/ffvmM1PG8+mWJbWvNS02bmZmZDYjRlppu2u8USbObts2UdIekOXnp6Hc1Pb9tbn9Kfjwk6erC89vnZaefKWlvSVfmtn4raZVqrnh8BmIEuQdSGIaKDybzfGefdfxkdGtmZmbdMeJS0w2SlgfWBx6UtFpE3FJ4+vsRsb+ktYALcpWK+ZKWAXYCLm7VqaQPAZ8B3hQR/5W0X0R8JT+3E/BV4JPlXur4DUSATI1SGFppUet40s636jJvvl3U2+qaLtGNmeHdUNfzsmrU8f3uxjnVNR1swH4/jbbUNMA2wGnAv4GpwLeaG4iI6yTNBVYA7gL2Br4D7Na8r6T3AbsDm0XEPfn4+wu7PINRFhyZDE6xMDMzMxssxwFTJT2dtNR086hvI2g+Nn//FJI2Ii0kcrekVwLPj4jTW+z6QuAHwFsj4l9Nbewj6TbgA6QR5NoYlBHkumteMW8IeiI1xMzMzGpE0gxgRmHTrLz89JNGW2pa0orAi4EL81LRcyWtExGNXOLPSvog8ACwHakm8veB6SOc0t3AvcD78n7F89gD2EPSl4AdgdosVuYAuQaaV8wrBMtdT7XoNMViwG5JWU3V8Va1K34Mnn5YzKKO59Qt/VLxIwfDs8bcceSlprcDngncIglgWVKaxZ75+e9HxP6NnSUtB6wDnJv3Xwk4NU/guwd4GNgCuFDSXRFxTItz+QVwBjUKkJ1iUU+NEeWhST4PMzMz60+HA3tFxFVN26cBm0fEUF4s5FWkALmliPhfRKxQ2P8i4F0RMVzY525gc+Cbkt4GIGmNQjPvAq4v4ZpK4xHkGmqMKLeYvGdmZmY2Ya2Wms5pFy8gBbmN/W6RdH/OOZ5If7fkUeUzJb0H+IKkl5LymP9OjSpYgANkMzMzs4HRxlLTz2vx/Pr525Yl3Jr23aTw/a2k9IvG4ysK7W/T3hlPDgfI9dY8ec/MzMzMKuYAucaaJ+91Q9V1kM3MzMzqzgGyTUi/zF7uhk5nPHdjhrTfv+rUcTGE8ZxTXWfq11E3Xqs6vh/9slBPHV9bmzyuYmFmZmY2ICStJOk4STdJulbSmZJeIukRSZdLuk7SXyR9pOm4LSQN5+evl7R/0/PbSgpJU/Lj9STNlnSNpCslbdfN65wojyDXlBcJMTMzszIpFSo+GTgqIqbmbesBKwI3RcQr87YXASdJWiQijpC0Dmk1vHdExPWSFqOwGImkZYCdWHgS38PAhyPib5JWAS6VdHZE3Ff5hZbAAXJ9dX2REHAOcpXqWODfelsdb1XXNY2jjq9VNxblqOPPhX5ZjKSun/UxbAo8ERGHNDZExJxc3o3Ctpsl7Qp8FzgC+AKwT0Rcn5+fC/yocMjewHeA3Qpt3FD4/k5JdwHPAe4r+Zoq4QC5BkYYLR6ahFMxMzOz/rUOcGmb+14GrFk47rutdpL0SuD5EXG6pN1G2GdDYAngps5Od/I4QK6Hp4wWtyrv5rQLMzMzG42kGRTSH4BZefnpjptqo69FgO8D00fZZ2XgZ8BHImL+OM5jUjhArq9WNZCHhoeHp1fZ6aCmWNRxFnYNbsWZdeWz3o20gTr+Hx9U/fLa1vVndA6GRwqIrwG2bbOpVwLXFY57FXBF0z7LkEaXz03pzawEnCrpXRExLGlZ4Axgz4i4iB7iALmmWtVA9qIhZmZmNgF/AL4pafuI+CmApA2ApYo75Zzk/YGD86b9SJP2LoyIG/LI8S4R8T1ghcJx5wK75eB4CdKEwKMj4oSKr6t0DpB7i1fWMzMzs3GJiJC0NXCApN2BR4FbgV2A1SVdTkrlfAA4OCKOyMddKWkX4FhJSwFBGhkezfuANwDPljQ9b5seEXPKvKaqOEDuId1YWW9QUyzqeMuvjudkNpZuVCjo0eoBPaFfXttuXEevvlYRcScpeG225BjHnQ6cPsY+mxS+/znw83GcYi04QK7AOCbTDVV0KmZmZmbWIQfI1eiohvFYaROuXmFmZmbWPQ6Qa2SUQLjy6hUNT9xzc0cpFk4DMOsddU1/6FQ3blPX4Vb4ZBjU6wb/PrOFOUCul5Yjz56YZ2ZmZmXJE/VOAtZqrI6XF/PYn7TsdAAXApcD2+fD1gb+CswDzgKuJ1W3uANYnFQS7sMR8XD3rqQ6DpB7Q9eqV8w+6/hudGNmZmaTZxopAJ4KzJS0InACMDUiZisVNd4GuCAifggg6VZg04i4Jz+eDvwyInbMj38BbEdamrrnOUDuAd2oXtEwqFUsrD3duEVv1anrzP466tUKBZb4vRiZpKWB1wGbAqcCM4FPA0dFxGxI5eCAEztoczHgGcB/yz7fybLIZJ+AmZmZmXXNVsBZEXEDcK+k9Umr4V06jra2kzSHlGbxLOC0sk5ysnkEuR4aKRRD4znYVS7MzMwMQNIMYEZh06y8/HTDNOCA/P1x+fF4/TIidswpGT8EPg907a53lRwg10AjhWKkPOM2AuDSqlw4xcJG0y+3z8djUFMNzMpW12oq/ZKWkYPhWa2ek/Rs4E3AOpICWJQ0Ie8o4FXAKePsMySdBnwGB8jWRaPWVZ4yZcrurnRhZmZmY9gWODoidmhskHQe8DvgaElnRMTFefsHgd9FxL/abHtj4KayT3iy1DJAriBlYKjEtmqnzEl8HkG2yVbXiYAeEa6PQR4d7MZ19MNnvV/e7/G8F3Mfv2O0p6fx1BHeX5GqWUwF9pf0XGA+cD6pFNxotpO0MWlO2+3A9I5PuKZqGSDT4Up0Y+mh0dWRyrkNdfk8zMxsAPVDcGwji4hNWmw7qPBwxA9ARAw1PT4SOLKcM6ufugbIA2mkkeAJ5CabmZmZWYccIPe2UkfawSkWNvk8gmVj8VLT9eIJrNXx53DyOEDuDU69MDMzswmTtBKpzNsGwGPAv4GNgBuAFwD/y1/3AJ8AbgF2ioiD8/E/AIYj4khJRwJvAV4UEY9JWiE/N1To77PAt4AVI+J/eduGLKi0IWBmRJxc4WV3zAFyVnK6wlBJ7QCjpl64eoWZmZm1JdcrPpm0at7UvG09YJmIuCAHvKdHxIn5uSHgLmBnST+JiMdbNDsP+Bjw4xG6nQZcAmzNgpzlq4EpETFX0srAFZJOi4i5E7/KcjhAXqC0dIVuBa1VLEFddYpFXSsU9AO/tjYWpyb0vk7/z9bx/ahrHeRO1bWiyBhVLDYFnoiIQxobImLOGE3eDfwJ+Ajw0xbPHwB8VtJTnpO0OrA0aQGRL5MD5Ih4uLDb00m1mGvFS02bmZmZDYbxLim9L/A5SYu2eO4fwIXAh1o8Nw04FrgAeGkuIQeApI0kXQNcBXyyTqPH4BHknuGKFWZmZjaWNpaa7lhE3CLpL8D7R9jlm8CpwBlN26cCW0fEfEknAe8lLUlNXpDkZZLWAo6S9JuIeHQi51kmB8i9o/SKFa1UnWLhW/rV8WtrVp66LkZSx5SJOhrk12m0paaBa0ir6Y3HN4ETSQuINPd5o6Q5wPsa2yStC6wBnJNSn1kCuJkcIBeOvU7SQ6TR7eFxnlvpnGJhZmZmNhj+ADxN0vaNDZI2kPTGsQ6MiOuBa4EtR9hlH2C3wuNppOoUQ/lrFeB5kl4oaTVJi+X+Xwi8FLh1XFdUEQfIZmZmZgMgIoJUTeItkm7KOcAzgTvbbGIfYNUR2r4GuKywaSqpYkbRyXn7xqTKFXPytk9FxD1tnkNXOMWiyyaQSzxU8qmYmZnZgImIOymkQjQ9N73p8a2k1IfG4ysoDK622P89he9Xa9H+roWHP+voxLvMAXL3jSuXuFE6zpP1zMzMzKrlALn3VDpZz0tNm5mZ2aBzgGzWJZ3OqnZVCrPJU9cqCP2wUEg3frbVtQqJ9Q5P0usdj+Y0i6FJPg8zMzPrUZJWknRcnqR3raQzJYWklQr7/EjS7pI2kfQ/SXPy1+/y8zMl7Za/P1LSLfn5yyS9Jm9/r6RrJM2XNGVyrnb8PILcIxrLSheXsXY+spmZmbVLqSDxycBRETE1b1sPmA3sD3xQ0vqkKhOvAl4HXBARI5V2a/h8RJwo6a3AT4B1gauB9+THPccBcm8rPR/ZOcjVccqEWe+o6y36qvtwmkG9VPB+bAo8ERGHNDZExBxJVwJ/krQpqZTbjhHxRF7goxPnAy/O7V4HMI42asEBcjUeLY70Nhkqse2JtmVmZmaDYx3g0uaNeSno/yMtJHJqRBRXy3t9rlcMcEJE7DNK++8ErirrZCeTA+QKNNIhWhklcO647SlTpuw+0fbMzMysf0iaAcwobJqVl58eVR5Jvhr4UdNT7aRY7CdpT+Bu4OMdnXBNOUDuYaMF4uP1xD03d5Ri4bSB9rmKhfWifqiaMB7juY5Bfa061Y3rHuSfnzkYHikgvgbYdpTD5+evTn0+Ik4cx3G15SoWZmZmZoPhD8DTJG3f2CBpA0lvnMRzqiWPINfYZFSpmH3W8d3szszMzLokIkLS1sABknYHHgVuBXYpu6/cz8HAc4AzJM2JiLeV3U9VHCDXW6Wr5rXSaYqFtW+Qb/n1g0FNkanjddf1Fv2gpkxYdcbzOZz7+B2jPh8RdwLvG+G5TZoenwuc22K/mYXvp4/Q1smkknI9yQFy941W4aLZUIXnYWZmZmYtOEDusk4m1jUH0t1IuXCKhZmZmQ06B8i9pfKUCy8UYta/6pr+0C/pKHVU1/e8U/6MWLc5QK635nSMoUk6DzMzM+sDkuaRFvMQMI+0at6f83MbkpacXhEI4EJgJ1LO8n7AHaQ72T+JiO93/+y7xwFyjTWnY0xGyoWZmZn1lUciYj0ASW8DvgW8UdKKwAnA1IiYrbRG9DbAMvm4X0bEjpKeDfxV0okRcdsknH9XDEqA3M7EuKEunMdEPWVEeXh4eHqZHXihELPW/Fm3QVDXShx1rKbSDV14P5YF/pu//zRwVETMhlQSDjgRIMXKSUT8R9KNwMqAA+Re1s7EuF5YsnmsEWUzMzOzMSwpaQ7pDvTKwJvy9nWAo8Y6WNIL8rFXVnWCdTAQAXIf66RkXFtcxcLMzKx3SZoBzChsmpWXn24opli8Bjha0jptNL2dpE2BlwLbR8SjZZ1zHTlA7mGdlIxrVx2rWNTxll+/3L4zK1u//N/oRjWObvxsq2OFCVc6ad94rjsHw7PG3DHtO1vSCqTV7q4BXgWcMsLujRzk15BWxvtNRPyr4xPsEYtM9gmYmZmZWfdJWhNYFPgP8APgI5I2Kjz/QUkrFY/JOco/A3bu5rl2m0eQzczMzAZHIwcZUqm3j0TEPODfkqYC+0t6LjAfOB84qUUb3wYuk/TNiHigGyfdbQ6Qu8hl2czMzGwyRcSiozw3G2iV13Fk/mrsdyewUov9+oYD5O6qfCW8iapjDrKZmZlZNzkH2czMzMyswCPINiHdKN5ex5nh4zGohe5tsAxyhYI6VozoBv9sa1+//D4bBA6Qa8r5ymZmZlY2SfOAq0gx4HWkSXoPS3owIpZu2vcg4O6I2Ds/3gNYJSI+LelI4C3AiyLisVwubjgihiQN5bb/SpoI+BDw0Yj4a16q+kRgA+DIiNixgmvcdbTnI+J7Y7XhALkEHQSzQx00Oyn5yp3mIHdjJKBfRmVcK9SsNY+Sta+Oo7X98nOnjqO743lt5z5+x1i7FBcKOQb4JDBSwLgnMCfvF8AngFcWnp8HfAz4cYtjbyr0swPwZeAjwKPAV0gr97WzQMl4LDPRBhwgl6OtYLa46l0bQfXQhM/KBkq//JIyM7OuuQBYd6QnI+L+PGr8g7zpqxFxX2GXA4DPSvrpGP0sC/w3t/kQcKGkF4/3pMcSEV+faBsOkCfPqEH1lClTdi97GWkzMzPrb20sNd3YbzFgC+Cs0dqLiGMl7QTMi4ifNT39D+BC4EPAaU3PrZ7rLS8DLAVsRJdJWhU4GHgdaQT8QmDniLh9rGMdINdUFctIt6MfyrzVNe2j6vOq4zl1Sx1vOw+q8by2TrGoTr/836hjOkNdP+uLr/CisZaaLi4UcgFw2Gjt5SBzJSAkLR0RDzbt8k3gVOCMpu3FFIvt8jlt3s41lOgI4BfAe/PjD+ZtbxnrQAfI3fVoYVR4aBLPw8zMzAbTkznIbToQmAmsBXwN+HzxyYi4MQfc7xuljVNJgWm3PSciiv0eKWmXdg50gNxFxVHh5vQJV60wMzOzOpG0BfBc4GhSmsQVko6IiGubdt2Hp44gF20M3FTNWY7qHkkfBI7Nj6cB/2nnQAfI9VGLVfaqTrHoxu2lOt6Kq2sf/WJQX6s63j53ukS91PH/RjfSwcbTRx3/P3XZUpKKubk/IlWd2DYiAnhI0hdIE/beVDwwIq6RdBmwfmFzIwdZwOOkChgASLqVNHFvCUlbAW9tEXSX4WP5fL+fH/8pbxuTA2QzMzOzAdFc67iwvdXqyt9s2uck4KT8/fSm595T+P5WYMlRzmGo3fOdiIj4B/Cu8RzrAHnyPNqUZjE00o5OvzAzMzPrjKQXkXKoX02qYjEb+GxE3DzWsQ6QJ0lzlYoxSrp1Lf2i6hSLus769SIeZq31y/+/QdUvaQN1fM/reE72FL8AfghsnR9PJeUjj1lyzgFyfTSPKBcNdfE8zMzMrE9JWom0wMcGwGPArcAuEXGDpM8C3wJWjIj/5f03AU4BbibdzT4uIr4uaR/gHYWmlwJWB5ZrlIKTdArw3Ih4TaH/TwKfJq3C9yAwo6L8YwA11W7+uaS2lrZ2gFwTo9U99qIhZmZmNlGSBJwMHBURU/O29YAVgRtIVR4uIY24Hlk49IKI2FLSM0hLT58eEXsAexTaPgY4vhAcL0+atPegpNUi4pa86y8i4pC8z7tIy1yXWh9Z0rPyt3+UtDtwHCnFYjtGr7bxJAfIPaCbi4bUcaGQOt7yq+M52eDpl8+h0zjqoxvpY91ItatrOl+nxnMdcx+/Y7SnNwWeaASoABExB0DS6sDSpDrHX2bhALmx70OSLiWNFF/a2J5Lqb2YVPWiYRvS6nr/JqU2fCu3cX9hn2eQAteyXZrbVX68Q+G5APYeqwEHyGZmZmaDYR0KgW2TaaT83AuAl0p6bkTcVdxB0rNJE972LmwbAvYFNomIuU3tfZ0UIJ9IDpDzMZ8GdgWWoKlkXBkiYrWJtuEAeYHRcoDHMlTieSzEFSzMzMysXZJmADMKm2ZFxGhLTzdMBbaOiPmSTiItz/zD/NzrJV0OzAf2jYhrcl+LAj8HvhIRNxbOYUXSiPKFERGS5kpaJyKuBoiIHwI/lPR+YE8WHnkuTT6/d5DitCdj3oj43ljHOkDOJpLGUHF+cFcXEKljioXZWPplpr5Vp463z+uorv836lhpqK4LXy2+wotmASMFxNcA2zZvlLQusAZwTkpTZgnSpLxGgHxBRGzZor09gX82LecMKdf3mcAtub1lSQH4nk37HQf8eIxLmojTgEeBq0jBfdscIJuZmZkNhj8A35S0fUT8FEDSBsB3gJkRUUyDuEXSC0dqSNKrgeksvHpewzRg84iYnfddDTgH2FPSGhHxt7zfO4C/tTi+LKtGxLrjOdABspmZmdkAyOkOWwMH5OoOj5LKvG0C/F/T7ieTRn0vHqG5r5NKu/0xjxI3bAO8ALio0O8tku6XtBHwfklvBp4A/ktF6RXZbyS9NSJ+2+mBDpArVFL+8FCJbZmZmdkAi4g7gfe1sd+uhYfntnj+baMc/rwW+zdGmkcKuKtwEXCypEVIAbnSqcSyYx3oALlaE84fLuQ3dyUX2TnIZmZm1ie+C7wGuCoiOiont0g152NmZmZmNqn+BlzdaXAMHkE2G2j9Uv2hrudlNtn8f6M6/bIYSZ/7J3CupN+QltUG2ivz5hHk+mvUZx6a5PMwMzOzHidpJUnHSbpJ0rWSzpT0EkkvkPRbSdfl7UN5/yUkHZD3/5ukUyStWmhvnqQ5kq6WdFpeYhpJQ5Ieyc81vpaQtKak2ZIek7Rb07ktL+lESdfn83hN3r6epItyG8OSNmzzcm8Bfk8qW7dM4WtMHkGuuUZ95uZay560Z2ZmZp1QKjdxMnBUREzN29YDViStjrdPRJwjaWkW1A3+JimofElEzJP0UeAkSRvl1IVHImK93NZRwKeBffKxNzWeK5zDvcBOwFYtTvFA4KyI2FbSEqQqGZDK0H09In4j6e358SZtXPKvGouTdMoBcu+qZNKeJ+kNFt9+Netv/ZJG1alupD/06Gu1KfBERBzS2BARcyStDSwWEefkbQ8CSFoK+CiwWkTMy88dIeljpCWif9/U/mxg1LrDefnquyS9o7hd0rLAG0i1lYmIx4HHG4eRFhsBWA64s83rPSQH2kcCv4iI+9o8zgFyD2leCntoks7DzMzMetM6wKUttr8EuC8vMb0a8Dtgd9Jy0f+IiPub9h8GXkYhQM7LOm8GHFbYb3VJc/L3f4qIT49ybi8C7gaOkPSKfJ47R8RDwC7A2ZL2J6UHv3bsS4WI2FjSS0hB/rCkvwBHtlMX2QFyj2heCrsYLDvdwszMzAAkzQBmFDbNioiRlp5uWAx4PfBK4B/AL0kjucOk0dundFPYvmQOgodIQe05hf2ekmIxxjmsD3wmIi6WdCApSP8KaRGTz0bEryS9jxSEv7mdRiPiBkl75ms5CHhlTjX5ckScNNrJWG8qjigPDQ8PTy+j0U5TLAbklpRZX/AMehvLeD4jdfy5Psif9RwMjxQQXwNs22L77cDlEXEzgKRfA68GjgNeKGmZiHigsP/6wGn5+0ciYj1JywGnk3KQDxrHqd8O3B4RjYVETiQFyJBW29s5f38CcGg7DUpalzR6/A5S4P7OiLhM0iqkdJARA2RXsehRw8PD+w4PD8/Meci3TvLpmJmZWf39AXiapO0bGyRtADwNeKak5+TNbwKuzekNRwHfyykUSPowafLcH4oNR8T/SJPvdpO0eKcnFhH/Am6T9NK8aTPg2vz9ncAbC+f2tzab/QFwGfCKiPh0RFyW+7oT2HO0Az2CXI7m/OCGoUnu38zMzAxIayxL2ho4QNLuwKOkQbZdgN2A3+f0g0uBn+bDvgTsD9wgaT5wPbB1q8U3IuJySVcAU4GWw/iSViKlOywLzJe0C7B2znP+DHBMnlh3M2n0F2B74EBJi+VznvGUhltf7xtyn4tLeiVwR54kSET8bLRjHSCXoDk/uKFbQetI/Y9HpykW3bi15jQOs9YG+TZy1bwIRHX8OrVvPJ/DuY/fMerzefT0fS2e+hstKlBExGOkwPUzI7S3dNPjdxYertNi/38BqzZvz8/NAaa02H4h8KpWx7Qi6RDg4Ii4Jqd+zAbmAc+StFtEHDtWG06xMDMzM7N+8vqIuCZ//1Hghoh4OSnI/kI7DThANjMzM7N+8njh+7cAv4YnR6/b4hSLSeLSbGZmZmaVuE/SlsAdwOuAjwPkHOYl22nAAfLkqWQlvInySnpmZmb9SdK5wLci4uzCtl1IC4V8jVQtYseI+ImkTfK+rynsuxgp6FwP2BiYCawFbBgRw924hjbtQCo1txKwS2HkeDPgjHYacIqFmZmZ2WA4llRhomhq3v5e4CJgWt5+PrCqpKHCvm8Gro6IfwJXA+/J+9VKRNwQEZtHxHoRcWRh+9kR8bl22hjvCHLVZcWGKmzbzKwvdDrDva6LQNSxqkEdz8mq1S//n8ZwIvANSU+LiMdy8LsKcCHwTeBzwC8kPS8i7pB0ArAd8O18fCOYJiKuA0hV4frPuALkMsuKteKavmZmZmblioj/SPoLsDlwCing/SWp7NpKEfEXSceTguLvkYLhWcC3JT0NeDvw2Uk5+S5zDnLNdXsyXzdGTar+C/qROy+ow1/pZrWz5Cqv7/j/uEdSB0/VPz/H8znsF924bkkzWHghjVl5+emGRppFI0D+WP73+Pz8ccBhwPci4hJJS+fV7dYCLoqI/1Z9DXXgALlao6WiDLXZRlcn8z1xz82VTtLrRuDq4NistUENSqx9g5pS009yMDxrlF1+TVo6en1gyYi4TNKhwIqSPpD3WUXSGhHxN1LAPJUUII+5wEadSFqRlDqySkRsIWlt4DURcdhYxzpArtBoqSgjBc4tRoyHxni+VLPPOn7snczMzKwnRcSDuZrF4cCxeXT4GRHxvMY+kr5OCor3JgXFpwDLkcul9ZAjgSOAPfLjG0gpJQ6Qe9BCI8YtAulKR5Rd5s2sf3n5ZOtVg3pnsIqlprNjgZNIQfA04OSm539FGjneOyKulfQwcGlEPNTYQdLWwMHAc4AzJM2JiLd1fMLVWiEijpf0JYCImCtpXjsHOkA2MzMzGyARcTLQKD8xs8XzVwJrFx6/YoQ2mgPrunlI0rOBAJD0auB/7RzoALn+mvOYh5p38Kp8ZmZmZk+xK3AqsLqkP5FGu7dt50AHyDXXnMc8Qu5yaWkXnU7SG9TbXmbW35yO0tu6UaPYn5H6yxMQ3wi8lDRi/teIeKKdYx0gT56RKlwMjeO4sY4xMzMzI+fgXkWKAW8BPhQR9xWevwK4NiKmFbYtTpqwtw3wGPAw8LWI+I2kfYAPA8+MiKW7diHt25AUJy0GrC+JiDh6rIMcIE+SkSpcjLVISqvjpkyZsntZi6u4ioWZmVlfeyQi1gOQdBTwaWCf/HgtYBHgDZKeUZiUtzewMrBOXoFvReCN+bnTgB8Af+veJbRH0s+A1YE5QGNyXgAOkAdBmSsbdppi0entIqdkVKdHlz01qyXfCq9OXevhd+P3WTeWs+7QbGDdwuP3Az8j1Tx+F6kM3FLA9sBqEfEYQET8m7ywSERcBLVdcnoKsHZERKcHLlLByZiZmZlZjUlaFNiMNImtYTtSneBjSeXfAF4M/CMi7u/uGZbiamCl8RzoEeSa63aFCqdYmJmZ9a42lppeUtIcUl7upcA5+bgNgLsj4u+SbgcOl/TM7px1ZVYArpX0F1LuNAAR8a6xDnSAXH+1Xmp6UG/R1zGdYVDfC7N21PH/x6CmcdTx52e3+ujUeM6pjaWmH4mI9SQtB5xOykE+iDRivKakW/N+y5Im5f0CeIGkZSLigY5PaHLNHO+BDpDrZ8y6x2ZmZmYTERH/k7QTcIqknwDvBdaNiDsAJG0K7BkRh0o6DDhI0g4R8biklYHNIuLnk3cFY4uI8yS9EFgjIn6X86kXbedYB8g106LucWkVKtrhFAszM7PBEBGX57Ju7wPuaATH2fnA2jkY3hP4Bild4VHgIeCrAJK+Q5rct1ROzTg0ImZ28TJGJGl7UrrJs0jVLJ4HHELKvR792HFM7KvclClTZnYzrcAWWGyJ59XvA9EFrsbRnrreGu0XVX8OB/WW/njUtQpCp7pxTt34XHXjs94vP6vmPn5HLctJTIaca70hcHFEvDJvuyoiXj7Wsa5iYWZmZmb96LGIeLzxQNJipDrIY3KKRY11u4KFmZmZWR85T9KXSZU73gJ8irSwyZgcINdbVytYACy2xPM6qmLRL/rl1lrV/DpVqx8qnXTj1vYgp4rU8dr9c6F9dUzD6XO7Ax8nLa29A3AmcGg7BzrFwszMzGxASJonaU7ha0jSJpJOb7HvuZKGC4+nSDq3aZ8DJd0hqXYxZUTMj4ifRsR7I2Lb/L1TLOqqg9SJoQkca2ZmZtbskYhYr7hB0tAo+z9X0hYR8ZvmJ3JQvDVwG/AG4NzyTnP8JL0bWDUifpgfXww8Jz/9xYg4Yaw2HCBPjrZSJ0Yo71Zp2sWgpliY1UEdq1h02odvCbevjukS4+G0gfb16LXvRyrz9pQAGdiUtJzzL0kLjZzbvdMa1ReAqYXHTwM2AJ4BHAE4QO5xzYuGgBcOMTMzs/FrLDUNcEtEbD3G/rOBrfPCIc0r6U0DjgVOAb4pafGIeKLUsx2fJSLitsLjCyPiP8B/JD2jnQYcINdY86Ih0HpU2WkXZmZmBiBpBmlxjIZZefnphqekWLThG6RR5C8W+lkCeDvw2Yh4IKcxvBU4Y1wnXq5nFh9ExI6Fh8+hDQ6Qe0/LUeXh4eHpZTTuFAszK+qXNACzOuhGOkoOhmeNuWNnbf5B0t7AqwubNweWA66SBLAU8DD1CJAvlrR9RPy0uFHSDsBf2mnAAXKPaXdU2czMzKxE+5CWab45P54GfCIijgXIqQu3SFoqIh6epHNs+Czwa0nvBy7L215FykXeqp0GHCD3h1ajymZmZmbt2kzS7YXH7y0+GRFnSrobQNJSwNtItYUbzz8k6ULgnaRJe5MmIu4CXivpTcDL8uYzIuIP7bbhALkPtBpVHq+qUyy6MYverFf1w0Ih3VDXtI9BXfCkjgvQ1PWzXofzioilW2w7F1iyxe6bNO33qsLDZ7Vo5z0TPL1S5YC47aC4yAFyiSZS39jMzMzM6sEBcrkmUt94TK5WYWZmZlY9B8g10kYAXFq1ipFUnWJRh9tLrfTL7Tuz0XQjxalf0gbGY5CvvR/494AVOUCul1FHoD0Rz8zMzCZCUgA/j4gP5ceLAf8ELibVOT4ZeEVEPJKfPwP4GWklvYOB1+Wm/gR8JiL+l/c7i1QG7sKI2LLQ32LAXqRJfw/lzSdExD5VXudEOUDuLa5WYWZmZhPxELCOpCVzEPwW4A6AiLhG0knAHsCekrYCFo+I4ySdCFwdER8GkPR14FAWVLvYj1QLeQcW9g1gJeDlEfGopGWAz1V6hSVwgNxDyqxWMZJBXSikH26VuULI4OnGLX2nDbTP6SjV8Wtbut8A7wBOZMFy0Y0XeS/g8hwQ7wu8U9KLSXWEtyu0sRdwo6TVI+KmiPi9pE2KneRycNsDQxHxKEBEPADMrOi6SrPIZJ+AmZmZmXXVccBUSU8H1iWlVwCQF/nYDTgfOC4i/gasDcyJiHmF/eYBc1hQZ7iVFwP/yEFxT/EIco9xJQszMzMbiaQZwIzCpll5+eknRcSVkoZIo8dnNrcREadJug/4UaNZIFp1N8L2kc7to8DOwLOB10bEbe0e220OkCfHSLnEQ20c21YpufEa1BSLftAv6RJOFbFeVcfb+oP6f2NQrxsgB8OzxtwRTgX2Jy0G8uwWz8/PXwDXAK+UtEhEzAeQtAjwCuC6Ufq4EXiBpGUi4oGIOAI4QtLVwKLtXM9kcYA8CUbKJW4OmkcYLR6q5qzMzMxsgBwO/C8irmrOHW4WETdKuhzYk5R7TP7+soi4cZTjHpZ0GPADSTvkSXqLAkuUcgUVcoBcb08ZLR6rioVTMMzMzGwsEXE7cGAHh3wcOFjSjaTUitl5GwCSLgDWBJaWdDvw8Yg4m1QRY2/gakkPAI8ARwF3lnIhFXGA3HvGKvU2ocVEnGJhk22Qb41WbTyvbR3TBqx9XvzCmkXE0i22nQuc27RtqOnxf4EPjtJuyw9PRDwB7J6/eoYD5B4zVqk310k2MzMzmxgHyP3Hi4n0CU9Ws7H4/a6XbtTqrbqPR+68wJ8rMxwg101zcDvUaQMTXUzEKRa9y7/UzGyi/HPELHGAXCPNwW27I8GemGdmZmbtkDQPuIoUA14HfCRXm3gwIpbO9ZGvA/5KqjZxPvCpiJhfOBbSAiDvym2eC+wWEcP5+HOAHYFh0mp9GwBHRsSOhfNYGvgu8GbgUeA/wOcj4slFSyaTA+T+UFptZI8g14dHcgZP1ROqPOHOBoVT1Eb1SESsByDpGOCTwPea9rkpItaTtBjwB2Ar4KTisa1IWhU4G/hcRJwt6RnAV4B18lfRocAtwBo5+H4RsNYEr600PRUg98BI6VAVjbZx3ZX0a2ZmZn3tAtJS0y1FxFxJfyYtGT2WlYCjgT0j4tR8/EPAhZIWOl7S6sBGwAcaC49ExM3AzeO6igr0VIBMxavITVSFk+NGve4pU6bs7ol5ZmZm1s5S03m/xYAtgLNGaWspYDPgq3nT0yUNA3OBfSPi14XdG8HxCW2c5suAORExr419J0WvBcjWwkQn5hU5xcLMiuqYxjFAt8KfwmkyNpY2lppeUtKc/P0FwGEt9lk97xPAKRHxm7z9BRFxZ06H+IOkqyLipvzc74APSToyIh6e8IVMMgfI9daoajE0yedhZmZm/WHUPOLsplb7RMSd+d+b88S8VwKNAPk7pIVETpD07oiYO0r71wCvkLRII8Wibhwg11hjZLg5faIHcrHNzMysj0h6JvBwRDwmaQXgdaSguOizwC+AwyRNj4ho1VZE3JRTNb4u6asREZLWANaOiFOqvI52OUDuTZXlYjvFoj68ROzgqWM6w6De0ncVhN7m96ISawE/kTQfWISUg3xtcYcc6H4EOJ0UPH9e0q3AssASkrYC3pqP+wSpzNuNkh4ml3nr1sWMxQFyb5jwAiJmZmZmEbH0aNsj4laeWpKNiPgz8PIRjt2k8P3jwFsLj4dGOOZ+YPu2T7zLHCD3gBYLiLhqhZmZmVlFHCD3oDKrVjRzikV9+Bbh4PFCIe3pl/SH8ZxTv7yH1h6/35Nnkck+ATMzMzPrDkkPNj2eLukH+fuZku6QNEfS9ZJ+LGmR/NyRkrZtOnZI0iOSLpd0naS/5BzkxvNrSpot6TFJu3Xj+sriEeQe5UoWZmZmVoHvR8T+OTA+H3gj8MdR9r8pIl4JkOsjn5TLtx0B3AvsRFqquqc4QO5dlVSycIqFWe/w7dfq1DElA+p7XtaXliANxP233QNyfeRdSdUpjoiIu4C7JL2jonOsjAPkLqhotHeo5PbMzMys/xVX0gN4FnBq4fFnJX0QeCHwm4go7tuOy4A1J3SGNeAAuTtKH+11FQszMzNrJmkGMKOwaVZefrphoZX0JE0HphSeb6RYLA6cKGlqRBzXySmM47RrxwGymZmZWZ/IwfCsMXccu50nJJ0FvAHoJEB+JXDdRPufbA6Qe1fz4iFmZmZmpZAk4LXAnA6OGQL2Bw6u5qy6xwFyj6qqFrIn6ZmZmQ20Rg7y4sCVwI8Kz/1E0gH5+9uAacDqki4nzbV6ADg4V7BA0krAMGmp6fmSdgHWzqvo1ZoDZDOqX6DBrB1Vf676peqF//9Vp18WYemX6xjPOc19/I5Rn29eajoijgSOzN/PBGaOcNz0EZpccpS+/gWsOuoJ1ZQD5HKNlPYw1OXzMDMzM7NxcoBcopHSHpwrbGZmZtY7HCDXgFfFMzMzM6sPB8j1UMmqeOPhSXpmZmb9TdIewPuBecB8YAfgl8CUiLgn77MJsFtEbJlrJe8H3EFaYe/7wFxg59zk2sBfc3tnkXKajwDWB/aIiP0LfS8PHAqsAwTwsYiYLelI4PSIOLGw74PNOdPd4gDZzMzMbEBIeg2wJbB+RDwmaQVS0DuWX0bEjpKeC1wDrFOoVnErsGkhuH4usBOwVYt2DgTOiohtJS0BLDXRa6qCA2Qz6jl72arTLzPcB1W/vH/9UlWkU4N63TWyMnBPRDwGUAhq2zo4Iu6SdBNpKep/j7QPcJekdxS3S1qWtPDI9Lzf48Dj47qKijlA7o6xFvUY6tJ5mJmZ2WD7LfBVSTcAvyONDJ/X7sGSXgS8CLhxHH2/CLgbOELSK4BLgZ0j4qH8/H6S9hxHu6VzgNwFYy3qMZ4qF57YZ2ZmZs0kzQBmFDbNystPAxARD0p6FfB6YFPgl5J2J+UDNytu207SxsBjwA4Rce84Tm8xUl7yZyLiYkkHArsDX8nPf745B3kcfZTCAXLvqmRinyfpmVnd1TFdwtrn969aORieNcY+84BzgXMlXQV8BPgP8EzgnrzbswrfQ85BnuDp3Q7cHhEX58cnkgLk2nGAXA9jpWC0MlTBeZiZmVkfk/RSYH5E/C1vWg/4O6kKxYdI6ReLAh8Efl1m3xHxL0m3SXppRPwV2Ay4tsw+yuIAuQbGSsFoxYuPmJmZ2TgsDRycy63NJeUSzwCeAH4s6QpApHJtPx9PB5JWAoaBZYH5knYB1o6I+4HPAMfkChY3Ax+d0NVUxAFy7xrPqLOZmZkNsIi4FHjtCE+/f4RjjiTVNh6pzaGmx/8CVh1h3znAlBbbp7fYNik1kKG+AfJIwd9Ql8+jtsYz6twO5yCbmZnZoKtlgDxS8OcRUzMzMzOrWi0DZJs8VRdw75fZy/2yUMGg8nvR2wb5/1/V1zGe17aOC3/U9f2u42tlrS0y2SdgZmZmZt0jaQ9J10i6UtIcSRtJOlfSP1RYUk/Srxu1iCUNSXok79/4+nB+7ta8ZDWSXiXpFkmvlLSmpNmSHpO02+Rc7fh4BNnMzMxsQEh6DbAlsH5EPJYD2yXy0/cBrwMuzFUuVm46/KaIWG+Uttcl1TbeLiIul/RcYCdgqzKvoRscIFekV1e6m33W8ZN9CmZmZladlYF7IuIxgIi4ByAPHB8HTAUuBN4DnAS8rM121wKOAj4UEX/Jbd8F3CXpHWVeQDc4QK5OJSvdVe2Je252FQszM7MeNdZS08BvSYuB3AD8jrRC3nn5ud8DP80LhUzN7XylcOzqkuYUHn8mIhqJ1acAH4yIC8u7msnjANkWUteJDXUzqK9TNyaYDOprC/WcwNPp+1HHa4DOz6sbn8NB/qwPqm6852MtNR0RD0p6FfB6YFPgl5Iayz3PI40ebwcsGRG3FlKSYfQUi98Bn5B0dl7Kuqd5kp6ZmZnZAImIeRFxbkR8DdgR2Kbw9HHAwUCnOZc75n9/VMIpTjqPII+ghBzioZJOxczMzKwUkl4KzI+Iv+VN6wF/B9bJjy8AvgUc22HT84FpwNmS9oqIr5ZwupPGAfLIJpRD7EVNzMzMrIaWBg7OVSrmAjeSco1PBIiIAPYf4djmHOTDI+KgxoNcFePdwHmS/g38ChgGlgXmS9oFWDsi7i/1iirgALkH9GpFDDMzM6uXiLgUeG2LpzYZYf+l87+3AkuOsM9Q4fv/kUalG1Yd14lOMgfIvaFrFTEWW+J5rmJhZmZmA80Bsk1IHWeGW3XG8/7VtarBIPL7Z2bWHgfI1Xm0xDzkoZLaMTMzswEmaR5wFSkGvIW0sMd9koaA0yNincK+M4EHI2L/vAT1HsBHgADuAHaMiGskXQw8DXgWKQ3jjtzE+4H9gNVJJeROi4hGSblac4BckeHh4X3LamukQNu5yWZmZtahRxq1jCUdBXwa2KeN4z5Nyl1+RUQ8LOmtwKmSXhYRG+X2pgNTImLH/HgpYP+I+KOkJYDfS9oiIn5T+lWVzAFybys9N7nqHOTx3K51WkZv8/tXnX5ZxKNfrsOsB80G1m1z3y8Cm0TEwwAR8VtJfwY+ABzW6oC87x/z949LuowembTnALk3jJSuMdTl8zAzM7M+kJeT3oyFg9vmMm4rAftLWhZ4RkTc1NTMMPCyNvtbHngncOB4z7mbHCD3gJHSNaZMmbK76y2bmZlZg6QZpLrGDbPy8tMNS+YgeAi4FDin8NxCS0nnHORRuyPlI491TouRFh45KCJuHmv/OnCA3MPKzHNuqDrFwrfb2+cKIVa2bnxGxpP+0I2UiX5J46j6PXSlk3oZz2u7+AovmgXMGmWXRyJiPUnLAaeTcosPGmV/IuJ+SQ9JelFTgLs+cF4bpzUL+FtEHNDGvrWwyGSfgJmZmZl1V17QYydgN0mLt3HIfsBBkpYEkPRmYGPgF6MdJOkbwHLALhM64S7zCHINuTqFmZmZVS0iLpd0BTAVGGu4+mDgmcBVuVTcv4B3R8QjIx0gaVVSabjrgctSpTh+EBGHlnH+VXKAXE9dWzmvmVfSqw+nTAyeOr7n/ZL+0C/X0SmnavW28bwfcx+/Y9TnG0tHFx6/s/BwnabnZha+D+Dr+Wukto8Ejiw8vp2Up9xzHCB3UQcjw0MVn4qZmZmZjcABcne1NTLsyhRmZmZmk8eT9MzMzMzMCjyCXE8jLQxiZmZmNiGStgZOAtaKiOslDQG3ADtFxMF5nx8AwxFxpKQjgdMj4sRCG6Me08XLqYQD5Bqqor5xuzxJz8zMrO9NAy4kVa+YmbfdBews6ScR8Xib7YznmJ7gANkmxDOee1s3ZrjXsXrAoPJ70b66ftY71S/vh5VH0tLA64BNgVNZECDfDfwJ+Ajw0zabG88xPcE5yGZmZmaDYyvgrIi4AbhX0vqF5/YFPidp0Q7aG88xtddrI8jdzM0d6lI/T/ICIWZmZjYRkmYAMwqbZkVEcenpacAB+fvj8uMfAkTELZL+Ary/3f7Gc0wv6KkAuZu5ucVAvMTAdWiM5ydtgZAG5yAPlm7cfvUt3urUMUWmjmkG/aTq13eQ/7/2SwpSDoZntXpO0rOBNwHrSApgUSCAHxV2+yZwInB+B92O55ha66kAeRKVEri6MoWZmZlNom2BoyNih8YGSecBqzYe56oW1wJbAn9pp9HxHFN3DpD7gFMzzMzMrA3TSDnDRb8Cvty0bR/g8qZtP5F0QP7+ttzWWMf0LAfIPWKMIHhoeHh4ehn9OMXCrHfUMUVmPLepu9GH2VgG4XMYEZu02HYQcFDTtisoFHKIiOkjNLnOSMf0OgfIvWPENA+nbpiZmZmVxwFyf/DKe2ZmZmYlcYDcB8qs7uEUi8HSjSoI1tu6cRu5F29V9yu/F+3zz8P+5gC5u8Ya6R1qfNMi53ioeWczMzOzTknaGjgJWCsirs/bNgT2B1YklX67kDTpbvt82NrAX4F5wFnA9cCUiNixqe0lgB8AmwDzgT0i4leF568Aro2I5kl+teIAuYvGGultCp4XyjkeKbB2BQszMzPr0DRSADwVmClpReAEYGpEzJYkYBvggoj4IYCkW4FNI+Ke/Hj6CG3vAdwVES+RtAjwrMYTktYiTeR7g6RnRMRDlVxdCRwg975SFxdxisVg8S3CweLb5zaWbiwmMx7+WVUeSUsDrwM2BU4FZgKfBo6KiNkAERGkhT/G42PAmrmd+cA9hefeD/wMWAt4F3DsOPuoXN+U4zAzMzOzMW0FnBURNwD3SlqfVK7t0ok2LGn5/O3eki6TdEIenW7YDvglKTB2ioWVYqT85aEun4eZmZnVlKQZwIzCpll5+emGacAB+fvjKDdQXYy0Kt+fImJXSbuS8po/JGkD4O6I+Luk24HDJT0zIv5bYv+lcYDcI0bKX24Omp2TbGZmNrhyMDyr1XOSng28CVhHUgCLkibkHQW8Cjhlgt3/B3gYODk/PgH4eP5+GrBmzmUGWJaU53zoBPushAPk/jOhnGTnIJuZmfWtbYGjI2KHxgZJ5wG/A46WdEZEXJy3fxD4XUT8q93GIyIknUaqYPEHYDPg2jxZ773AuhFxR25/U2BPHCCbmdlkqusErDp65M4LBnJiWF2vuxtLmA+IaUDzHelfkapZTAX2l/RcUnm280ml4EYzXdJWhcevBr4I/EzSAcDdwEeBNwB3NILj7HxgbUkrR8Q/x3c51XGA3Puac5OHJuk8zMz6xqAGV/1y3f1yHWWLiE1abDuo8HDEFy4ihpoeHwkcOcLub2h6/A9S8Fw8fh6w8kj9TTYHyPVSDHaH2jmgOTe5GCw7H9nMzMyscw6Qa6QY7I6x4l67Os5Hdg6yWf8a1HSJ8fBrZTbYHCD3n45Hoc3MzMxsAQfIfaZpFHr3kkaizczMrE9I2po0AW+tiLhe0hBwHfBXYAlgGPh4RDwhaRNgt4jYsnD8kcDpEXGipMWAvUhVKhpLR58QEfuM1F/e1tzn+cCnImK+pLNIOcsXtuj3jcD/8qbpETGnnFdlYQ6Q+9hItZNH88Q9N3eUYuGJEGaTp1/SADr9OdIv123t68bvmk4/Vz3++28acCGpcsXMvO2miFhP0qLAOcD7gGPaaOsbwErAyyPiUUnLAJ9ro79in4uRysJtRQqk9wOWAnbgqT4fEeNdBrttDpBH5lQFMzMz6yuSlgZeB2wKnMrCASsRMU/SX4DntdHWUsD2wFBEPJqPf6DY5lj95WPmSvoz8OL8+Pd55HrSOEAeQQUT5satm9UoZp91fDe6MTMzswq0sdT0VsBZEXGDpHslrQ/cWzj+6cBGwM6FY14vaU7h8QuA00kB7T9yUDySp/QXEZc1nfNSpEVFvtrGJe4j6avA74HdI+KxNo7pmAPk3jCh1fE6MahVLAbs1ppZ2wY1/aFfFlXpl59V3fgZ3S9pHKMtNZ1NAw7I3x+XH/8QWD0HwWsAJ0bElYVjLmiRC/wUkj5KCqyfDbw2Im4bob9GgNzoM4BTIuI3Y1zel4B/kXKWZ5EWJdlrjGPGxQFyfTnFw8zMzEoj6dnAm4B1JAWwKCk4/REL8oFXBs6V9K6IOHWMJm8EXiBpmYh4ICKOAI6QdDWw6Ej9SfpCPv6miFiv3fMvrLj3mKQjgN3aPbZTDpBrytUozMzMrGTbAkdHxJOT3ySdB6zaeBwR/5S0O2m0dtQAOSIelnQY8ANJO+RJeouSRnhH629j4LZOT76xLLUkkVI3ru60jXY5QO4B46lGMV5Vp1iM5xZkN2579cttyE45taS39cst4TqmJtTxnMajX66jG+r4WlVwTtOA5pjiV8CXm7b9GpgpqZ3/0HsAewNXS3oAeAQ4CrhzlP7eD3x7pAYlXQCsCSwt6XZSybmzgWMkPQcQMAf4ZBvnNy4OkM3MzMwGQERs0mLbQcBBTdsCeEVh07lNz08vfP8EsHv+ajZSfw3rjHCeLQPziHhTq+1VWKRbHZmZmZmZ9QIHyGZmZmZmBU6xqIFu1jk2MzOzwSVpHnAVKQa8DvhInmz3YEQsnfd5O3AgqTbxx4AvkBYDuSs//2BELC3p+cDRpJX05pNqLh/Y9YuqgAPkeuhaneOxDGodZDMzswHxSKO0mqRjSBPdvtd4UtJmwMHAWyPiH6lgBPeQlo/+YlNbc4HPRcRleYnpSyWdExHXVn8Z1XKAbBPSL1UQ6jh7ua6vlVWnX/4/mfWiAV0U5wJg3caDXLXip8DbI+Kmwn6HA9MlfTsinlx1L9cl/mf+/gFJ15GWqHaAbKWkSAyVdCpmZmZmY5K0GLAFcFbe9DTgFGCTiLi+afcHSUHyzkDLO82ShoBXAhdXcb7d5gC5HBNKkfAiIGZmZlYGSTOAGYVNs/Ly0w1L5uWdIY0gH5a/fwL4M/BxUiDc7CBgjqTvtuhzaVJ9410i4v6JXUE9OECuh0cdJJuZmdlE5WB41ii7PJmD3GQ+8D7gd5K+HBHfbGr3Pkm/AD5V3C5pcVJwfExEnDShk68RB8g10M2V8sbiSXpmZmaDKVez2BK4QNK/I+Kwpl2+B1xCjh/zks+HAddFxPfoI66DbGZmZmYA5El4mwN7Snp303P3ACeT8pUBXgd8CHiTpDn56+1dPeGKeATZFuJZ9O3pl+vul+swq4Nu/H/qk8oJHavjz6o6nhPA3MfvGPX5Rq3j0bZHxG3AavnhKU377Qrsmr+/ENAETre2PIJsZmZmZlbgEeQe0M2V9mafdXw3ujEzMzOrLQfI7RmrysRQxf13baW9J+65eSAn6dX1VplZmbpxe35QUwDAKWpV6sbnqhsLhfg97x0OkNswVpWJdku0TWAkeGgcx5iZmZk9haQ9gPcD80jl3XYAvg2sDDxKWhjkY8C+pFzkpYHnALfkJj4FfDPv/whp0t73G/WWJd0KXBoR2+TH2wJbRsR0SWsCRwDrA3tExP5VX+94OEDurnGNBDcC8G6kWjjFwszMrH9Jeg2wJbB+RDwmaQVgifz0ByJiOC82sl9EvCsfswmwW0RsWWinuP+zgJskHRkRj+ddpkh6WURc03QK9wI7AVtVc4XlcIDcWypPtRjUOsi+NWq9qBuf20FOmaiaX9ve1o3/TxX9rlkZuCciHoMnS7c1At6G84FdOmhzaeAh0oh0w/7Al4EPFHeMiLuAuyS9o9MT7yZXsegNjRzooUk+DzMzM+ttvwWeL+kGST+S9MYW+7wTuKqNto6RdCXwV2DviCgGyMcD60t68cRPufs8gtwDGjnQzbnO3axuYWZmZvWX0yNmFDbNauQGA0TEg5JeBbwe2BT4paTd89PHSHoEuBX4TBvdNVIsngP8WdJZEfH3/Nw8YD/gS8BvJnRRk8ABcm9prqYxNDw8PL3MDqpOsfCsX7OR+bM+WJzyMni68X88B8OzxthnHnAucK6kq4CP5Kc+EBHD4+jzbkmXARsBfy889TNSgNych1x7DpB7SHM1jXarZ5iZmZkBSHopMD8i/pY3rUcKateZQJtLAa8EvlPcHhFPSPo+sDvwh/G2PxkcIPe2seozm5mZmRUtDRwsaXlgLnAjKSXjxHG01UjJeBpwZERc2mKfw4A9Gw8krQQMA8sC8yXtAqwdEfePo//KKCIm+xx6Xge5wKWnRJTtiXturt0HwredzcrRjRQnpwC0r44/2wb5/evG+9GNKhZzH79DY+9lY/EIcgnGWkikwaO9ZmZmZvXnALkHdLNahRcKMTMzs0HnALk3VL5ASEOnVSwG+Xac2SDw//Hq1PG17ZfKGnVMXxmPOr62g8IBck01jRoPTeKpmJmZWR+Q9Gzg9/nhSqRaxXfnx68AvhcRn8v77gYsHREzJc0EvgAM5ZXwkPRgRCxd/F7SesCPSRPw5gH7RMQvu3JxJXOAXF9PjhqPlbvsBUPMrB1LrvJ6j0jZqPrl81HXmvvd6GPu43eM+FxE/IdU1o0c9D4YEfvnx48C75H0rcby003uAT4HfHGU7h8GPhwRf5O0CnCppLMj4r5xXMqkcoDcG8Yq51ZadYyqFwoxs8nTL8GPmVViLmmBkc8Ce7R4/nBguqRvR8S9rRqIiBsK398p6S7gOcB95Z9utRwg94CxqmRMmTJld1fIMDMzs7GWmh7DD4ErJX2nxXMPkoLknYExB9MkbQgsAdzUZt+14gC5uzpZ2GOo3UbbLTPXjifuudkjyG2o6+27TnWjJqdVxyPC1emXyWqdGtTrHo9uXPd43o92lpoe5dj7JR0N7AQ80mKXg4A5kr47WjuSViYtM/2RiJg/nnOZbA6Qu6iTQNYjwmZmZjYJDgAuA45ofiIi7pP0C+BTIx0saVngDGDPiLioqpOsmgPk+tp4MoJk10E2MzMbXBFxr6TjgY+TUiqafQ+4hBYxpKQlgJOBoyPihEpPtGIOkOtrsW7VPl6oU0/SM+sZXga6OoP6Wg3qdY9HN9JRJvH9+C6wY6snIuIeSSeTJvM1ex/wBuDZkqbnbdMjYk4VJ1klB8hmZmZmAyYiZjY9Xrrw/b+BpUbZd1dg1+ZjI+LnwM8rOeEuc4BcX7eO9ITrHpuZmZlVxwFyfd06ynOVLT3tFIv2uJqD1YFvhw+eqn/2+DNVrcleKMTa5wC5vkYrCTfUxfMwMzMzGygOkGtqtJJwXhjEzMzMrDoOkHtQmQuDNHOKhZmZmQ28iPCXv8b8Ama4D/fRa330wzW4D/fhPvr7GrrVh786+1qkutDb+syMsXdxH+6jdn30wzW4D/fhPiav/X7qwzrgANnMzMzMrMABspmZmZlZgQNka9cs9+E+erCPfrgG9+E+3Mfktd9PfVgHlJPDzczMzMwMjyCbmZmZmS3EAbKZmZmVStIzutjXBt3qywaHFwqxMeUfdFsD0yLiHSW2+54Wm/8HXBURd5XUx84RceBY2ybYx9OBjwMvA57e2B4RHyurj26T9Ezg+RFxZQVtrwOszcKv1dEltv9CYI2I+J2kJYHFIuKBEtt/DrA9acn3J3+Glvl+S3oasE2LPvYqsY+ufG4lvZanXkdp73fuY2PSe35Efn+WjohbSmxfwAeAF0XEXpJeAKwUEX8pq49ukPQd4BvAI8BZwCuAXSLi5yX28VrgUGBp4AWSXgHsEBGfKquP3M/awFRgGun3xpQS234dMCciHpL0QWB94MCI+Hsv9WET4xFka0nSEpK2knQ88E9gM+CQkrv5OOkH6Qfy10+BXYE/SfpQSX18pMW26SW13fAzYCXgbcB5wKpAaQEZgKQ1JJ0o6VpJNze+Su7jXEnLSnoWcAVwhKTvldzH14CD89emwHeAd5XY/vbAicBP8qZVgV+X1X52CrAc8DvgjMJX2X28G5gLPFT4KlM3Prc/A/YHNgY2yF+lBTK5j68BXwS+lDctDpQW8GU/Al5DCsYgvU4/LKNhSQ9Iun+krzL6KHhrRNwPbAncDrwE+HzJfXyf9Jn6D0BEXAG8oYyGJb1Q0u6SriB9fj8FvCUiSv1MAT8GHs7B/ReAvwOl/lHXpT5sAjyCbAuR9BbSL4G3AX8k/RDaMCI+WkF384G1IuLfue8VST80NgLOz32Pi6RpwPuB1SSdWnhqGfIP7hK9OCLeK+ndEXGUpF8AZ5fcxxHA10i/fDYFPgqo5D6Wi4j7JX0COCIiviap7BHkbUmjVpdHxEfze35oie1/GtgQuBggIv4m6bkltg+wVER8seQ2m60aEZtX3Ec3PrdTgLWj2tngWwOvBC4DiIg7JS1Tch8bRcT6ki7PffxX0hJlNBwRywBI2gv4F+nnXmPEuuzrWDz/+3bg2Ii4Nw2Olysibmtqd95E25T0Z9IfpscB2+b/27dExK0TbbuFuRERkt5NGtU9TFKrwZa692ET4ADZmp0NXABs3LhFKam0dIQmQ43gOLsLeEn+of3EBNv+M2nkewXgu4XtDwBlB32Nc70vpw/8i3RLuUxLRsTvJSnfgpsp6QJS0FyWxSStDLwP2KPEdoseiYj5kuZKWpb0nr+oxPYfi4jHG7+cJS0GlB2cnS7p7RFxZsntFv1Z0ssj4qoK++jG5/Zq0ij1P0tut+jxHGgEVJb7+oSkRcmfpZzGMb/kPt4WERsVHv9Y0sWkuyxlOU3S9aQUi0/l63i0xPYBbstpFpH/iNgJuK6Edu8m3eVYEXgO8DfK/7/d8ICkLwEfAl6f3/vFxzimjn3YBDhAtmavIuV1/S7fwj8OWLSivi6QdDpwQn68DXB+/gV330QazkHk30m3Ras2K+fsfgU4lZR799WS+3hU0iLA3yTtCNwBlD0yuhfpD6Q/RcQlkl5E+iVUpmFJy5PSaS4FHgTKzOM8T9KXgSXz3ZBPAaeV2D7AzsCXJT1GCjIFREQsW2IfGwPTJd0CPFboY90S++jG53YF4FpJfyFdBwARUVpaDXC8pJ8Ay+cUm4+RPl9lOgg4GXiupH1Id0L2LLmPeZI+QPqZG6Q7eRMeeS2KiN0lfRu4PyLmSXqYlMpTpk8CBwLPI6Vx/JZ0Z2dCIuLdkpYj/Z74uqQXk97zDSvIBd+OdAfyYxHxr5xzvl8P9mET4DrINqI8iWAa6QfSHODkiCitmHme+LIN8DpSAHAh8Ksyb8cqTQT8NimYFNUEM5VTmqV9HbA8sDfpVuN3IuKiyTyviZA0BCxb5kTA/EfEx4G3kt7rsyOi7GCpckoTDZ+i1ybwSHpjq+0RcV7J/byFhd/zc8psP/exJmkuhoDfR0QZo6LF9odIgeXrSAHyn0gT6G4tsY+lSPM8XhARMyStAbw0Ik4vsY/nR8RtTdtWioh/ldVHbnNFUpA5lTSh+Pklt1+c7LsUsGiZk3271YeNnwNkG1MOOt4CTK0oF7kykm4E3ln2L7OmPlYEvgmsEhFbKM2ufk1EHFZVn1XII8YHAq8m/YKeTfoF3TPVAJQmd/66+EtG0pZlBACS1oyI6yWt3+r5iLishD6WzXngzxqhj3tL6OODEfFzSbuO0EepEzN72UjvQ0MZ70c3Sfol6c7NhyNiHaUqL7MjYr0S+5hLuiv4sYh4JG+7LCJa/r8pqc8XllxhYntgBvCsiFg9/yFxSERs1kt92MQ4xcIWkgOWVq6j3HzXbo3u/rvK4Dg7kjSJrpG3ewPwS2DCAbKkAyJiF0mn0SLfruRb1b8gzczfOj+eSrrdu9GIR3TuR6TczTeRUjoeAH5Fqm5QhoOBz0maVnjf9wLKGCHblfQL7bstngvSNU3UL0gVBi7NbRZnOgXl5Gs3cnTLngD2JEkXRsTGkh5g4c9t6f/HJb2a9L6vBSxBSgl7qKQ+Wr0PDWW9HwBIeglpkvKKOXhdF3hXRHyjrD6A1SNiO6VJzETEIyp/lt5VpHksF0p6X0TcRAkTiiUdwcg5x0G6c1SWbkz27UYfNgEOkK3ZGbT+xfwcUiBbZj7yd6h4dJeU8/pLUqmvYg7kSSX2sUJEHJ8nXBARcyWVlTvYqOSxf0ntjUYRUawc8vOc71ymyqoBZLeQflGeKGlmRJxASdU+ImJG/naLiFhoYpNSTeEy+tgy/7taGe2N0MdP8r9fr7CPjfO/lQXhBT8g/TF3AqlqxoeBF5fRcJXvQws/JZVca7w/VypVFikzQH48jxo3JhuuTuHnYkkiIn6kVIrtNElfZOTAthOt/sh9AbAL5c+T6cZk3270YRPgANkWEhEvLz7OeXFfBN5MSiMoUzdGd5cFHiblJzYEUGaA/JCkZ7Pgl86rSYXrJywiLs3/lpqzOYI/StqdBZOEtgPOaNxmLul2ctXVACIiLsu5r8dK2ojyf3n+mVTUf6xt4ybp9823WlttG2fbB432fETsNNE+Cn39LCI+NNa2iYqIGyUtGhHzSPW7/1xm+/DkHa+NSZ/dCyLi1yV3sVRE/KVpQHduyX18jbRAyPMlHUPKd55ech8CiIg/SdqMdDdtzYk2GhG/erKDlA72ZVJ95X0p4W5dk25M9u1GHzYBDpCtpZwPtQfp9vp3gZ0iYqKl15pVPrrbpZzpXUlVAFaX9CfSaPu2ZTQs6SpGGVUouarBdvnfHZq2f4zybidXXQ3gnwARcY+kt5FSeNYpo2FJK5Fm5i8p6ZUsGJleFliqpD6enttaQanCRLGPVcrog5Q2ACk4WpsUwAC8t/BcWV5WfJBHyV5Vch8P57sQc5RWivsnC9JISiHpR6RR6WPzpk9KektETLg6Q8E9eUS38cfjtpRcHi8izpF0GWmegYCdI+KeMvsg1Vhu9PdPSW8CXltGw5LWIv1eeiWp4sMnI6LsPyIgDQp9gpQusgNwJuXWa+9WHzYBnqRnC1Gqh7oH6Rfbd0jF5EstNVTo64gWmyPKXbK30ry+PBq6EykH8qWkXzp/LeuPiUI1g8Yv4kYKxAeAh6PEpYerlid7vhq4lwqrAVRFqYj/dNJt/EtYELzeDxxVxh92knYm3TJehVTKr9jHTyPiBxPto9DXH0krqz2RHy8O/DYiNi2h7S+RRviWJN3BgXQtjwOzIuJLIx07jr5eSKqnvTjwWVKFlx9FxI0l9nENsE7kX5j5s3xVRLxs9CM76uNFwCxSMPlfUrrQB0qefFbZ8sZVT/6U1Eih2R84nqYSeGVNmMzv7ZURUcof1pPVh02cA2RbSM6dvY2Ui/yUwLjM26/dIOk8cl5fRLwyb7u6zB9Mks6NiE3Kam+EPv4UEa8ba9sE+1gUeAdpsYgn7y6VWdVA0uyIKL02dTcnM0rapni7twqSPhMRB1fcx19J1VbuzY+fCVwUES8tsY9vlRkMTxZJJwGfbQSSOSjfNyKmjX5kR32sFhG3KNWBXyQiHmhsK7GPK0krWa5LWtb4cOA9EdGyHF+Hbe8QET9RWvr7KSaa8y7pVhb8337KPJmIKHPC5DHAlyLiH2W1ORl92MQ4xcKalTZ6OxJJX4iI70g6mNbBTJlBeDfy+v4k6QekW9UPNTZGCWW/Cp4haeOIuBBAaaWqslcMO420qtZVlL9KWMNvJW0DnNQYjStJNyczvirnA98HTwaWn4uIMlNF5ktavqmPaRHxoxL72Be4PI8kA7wRmFli+wB/kbRcRPwPQGmRmE3KyN/tRvpR4Q+u5YDrlBY8CVLqWdl5zr8C1o+IhwrbTqTclJTi8sYHRYnLG1c9+TMihqpodwQrA9fk97v4M73MqkHd6MMmwCPI1nWS3hkRp430gzkijiqxr98AOwIn5OoJ2wIfj4gtSuzjjy02R0SUUfar0cerSKM9y+VN95HqjJYWhEu6suSc5lZ9PEAK7OeSgvHKFm7JQeXzo8SFSHK7lzfuRhS2lVrnVdKcaKpN26rfEvpZiRTsBfCXKH8xh8quQyMsptJQUtrAqCOrZUyeVVqApJHS9vnCU8sCny85jeM80iS9j5ImuN1NSrl4+agHdtbHd0iVNx7Jfb2CVE/95yW0vRiwBQsm/V1LWhim1EGPkd73Mt7vbvZhE+MRZHuKHLjuTMqphVQD+aCIOLqM9nNwvCgpp+/zYx4wMZ8m5fWtKekOcl5fmR2UkbPZRh+XAq+QtCzpD9tSqmQ0+Y2kt0bEbytoG6i+7Jekc4F3kX62zQHulnReRLTMixynRSU9LSIey30uCTytxPYBFpGkQs7roqQav2XbEHh9/j4ofxb9Ii22lfJ7p8zc3FH66Eaw8lJS7evlgXcWtj8AbF9yX43ljT8e1S1v/NaI+IKkrUlLTb8X+CMwoQBZ0iq5nX8Cl5P+uN4S+J6kTSPizomd9gIRcV7+43FD0v+LS8r+47EbfdjEOEC2hUj6MGmS0K7AZaQfQusD+0mixCB5Xh4VrUwOKv4vIt5czOurqK93kEaBnqyHW+YEOknLkUo0vSE/Pg/Yq+RA+SLg5DyB5AkqGt3NI7trsPBrdX5JzS8XaSW6TwBHRMTXct5lmX4O/F4LFi74GFDaXY/sbOB4SYfkPj5JGo0rjaR9SQu0HJM37STptSXnDA9L+h5pAZoAPkNJlTK08CIkjRyqRm5qqZ9bVbgYSUScApwi6TURMXui7Y3hAdKkvHlKE5jXZEFljrIsnv99O2mS970qZy2SbwI/jogDihsl7QR8CyglVSS3+Qngq8AfSJ+ngyXtFRGH91IfNjFOsbCFSLqItKT0rU3bh4DjIuLVJfb1XVKgdAIL52CVVuZN0h/KTHUYoY9DSKW5NiWV6dmWdLu6tJWdJP0KuJoFgdiHgFdExHtK7ONmYCvS7PxKfjDkXwo7A6uSRnhfTVrqtpT3KOelvpX0Ou0REZdUkToiaQsWVOL4bUScXXL7i5BKPz3ZB3BolFhRJv/hsF5EzM+PFwUuL/O1yn+YfoVUR71xHd9oyrOtPUnDPHUxkjUi4ssl9vF00iI3zX9ol1nV51LSHYNnkv4gHiZVwyntrlr+w2srUorFhqSR8dMjYkIrckq6PiJa1lOW9Ncod3LpX4HXRsR/8uNnA3/utT5sYjyCbM2WbQ6OASLi1nx7v0zPAv7Dwkv0lr2Ix+WSTqXCIJz0Q27dHIh9PQf+ZbYPaYnYbQqPvy5pTsl9/A24uqrgONuZNGp5UURsmvMvy5zUsxdp9PXCHBy/iHRdpYqI3wC/KbvdQvvzSeUJf1xVH9nypLJ7sCC/vTQ5EN697HabSdqYFLAeIWkFYJkosfoDdGUxkp8B1wNvI32OP0BKbyuTIuJhSR8HDo40WXpOmR1ExO6Svg3cn0eqHwbeXULTj4zy3MOjPDcet5NG2xseIFV36rU+bAIcIFuz0X4IjfZcx6I7i3h0IwhvvC4P5zy5/wBlL1H7iBauYvE6Sn4/SLl95+aJjcWFW0or8wY8GhGPSiLn8V4vqbQRk0hLS59QeHwzsM3IR3SuytvthT7WIN02XpuFRxNLK2WV229UsRApfafUkmxKKyV+gaeOipY5gfVrpFHdlwJHkN6Tn5MWQilL5YuRAC+OiPdKendEHKW0zHSpdyYASXoNKfhu3OEqe6VJIuK/he8fojA4MQHLKa1m2EykCY0TpgU1nO8ALpZ0Cun3xbuBv/RKH1YOB8jWbK0RcjZFOSupdVWXgvDTlcpX7UfK2w7gpyX38X/AUTkXWaRRv9Jy7rJb8tcSVDMhDOD2/Fr9GjhH0n+B0ibXdMkPeOrt9heX3McRpJzz75NSdz7KwnVfJywijs2TGjfIm75YwSShY0jlD7ck5VF/hFQ5oUxbk1ZWuwwgIu6UVPZk0A+RAskdSYuRPJ+S//Ai5f0D3Ke0YNO/SDXJy7QL6Y+gkyPimnyHpVUVnjo6j4UnMRaVNYeh8bm5KX81nFJS+93qw0rgHGRbiLpQOqmbVPFKei36exrw9IqqTNBIc4mI+6tov5uUyhwtB5wVEY9P9vm0S9JwREwp5jZL+nNElLKcbm7v0oh4laSrIpfgknRBRLx+rGM77Oc9wMakP+oujIiTS26/cR3F1+q8KGFhikIff4mIDZVL7eW859ll551XLefn/4q0iMcRwNLAVyPikAr6ekav5YGbdZtHkG0hvRYAt+Gn5JX0ACLiynzrspIAOVLpr8fG3LFDeQLH18jBjKQLSVUs/lNiH3+k9cItlUxyjN6t99mN2+2P5ol6f5O0I+l27HPL7EDSj0gj340qBjtIenNEfHqUwzrVGBX9p1KllztJEzTLdLyknwDLS9qeVFWklDs4ko6PiPdphEVJygzCI+LQ/O15VHS3LqdXHEYKvl8g6RXADhHxqRLaHrUOeJS7cFIl1IUVObvRh5XDI8g2afJo6zY8dWnjMsujXRIRG6iwOIFaLF5Qd5LOId1GbNQS/QBpRbI3l9hHseze00nvzdyI+EJZfVSlkNfXUpl51Pkuy79JaSifJY2C/ygibiyxjw1IE7SWB/Ym5VjuFxEXldjHNaRa5I1ay4uQKpiUuTDFlsAFpJSEg0nX8fWIOLWsPnI/byFVLxFp4YhzSmp35Yj450h31socUBjhM/w/4NKImFNSHxeTquycWvh5eHVErFNC26OlakTV1YTKIGn9iLhMFS7i0Y0+rBweQbbJdAr5FwAVjLpm90hanfyXutJKev+sqK8qPSsi9i48/oakrcrsINJiJEV/Uqq33AsqXYCkqBAUPUq5FTiKfVySv32QlH9chb8CLwAa1/N8oNSa0RFxev72f6Rc6kpExDk5+FsMQNKzIuLeMQ5rp91/KpW/O6zMP0ZHMCV/NRZreQdwCfBJSSdExHfK6CQibtPCdYlLKR0YXVgwqQv2I5VWfHtEfLGH+7ASOEC2lnIe3yOxoEbqIqTc2jLL6awaEZuX2F4rrVbS+2BVneVgfBqplvSER2UK/ihpKnB8frwtcEaJ7SPpWYWHiwCvAlYqs48Wfb4OeP9Eb+tHRCWBap97NnCdpMbM+Q2A2UplEXvmVq+kHUhl0R4B5pMXCqGkNIVGqTJJy1U1tyB7NrB+RDwIT1bnOJFUXeRS0lLUE3WbpNeS0rSWAHaipFJyI1SYeFKUW9/+tTz1zmMZi1itnEd23yXpOJomxpaUJtKNPqwETrGwlpQWDHlz4Yf10qQFEcqciDSLVIvzqrLaHKWvylbSk7QyC5ZwXZdUPuukMq9LadWwZ5ACAEgBbGOSTZRRYkzSLSxYiWwu6Y+JvRql5coiaT3Sa/W+3MdJEXHwBNs8aLTnI2KnibTfj0a6xdvQK7d6Jf0NeE1E3FNhH8eTFrU5h4XrqZf2uZJ0HWnxn8fz46cBcyJirWKK2AT7WAE4kIUXbtm5jLkMSitLjiSipAVPJP0MWJ200FBj9DvKeC/yHcaPk+Z6XMLCwWspaSLd6MPK4QDZWmqVp1t27q6ka0mThG4hpVg0logtcyWvyvKc84SgaaRJR8fnr1MiouwayD0vVxOZSnq9/kMq/bVbRIxaNaWD9kcteRcRE14KWtLPIuJDknaOiAMn2l4dSFqRBWXe/hIRd03m+YyHpLOA95R8d6u5j5afrzI+V4U+vkIqWdco9/VO4FTgu8CsKHG1u16W/5BYOyoMXiR9pSmlrSf7sIlxgGwtSfoT8JnG7Z48gesHEfGaEvvoxsSXs1iQ5/xkrl1EfLeEth8HZgOfi4jhvO3mKHcxh2J/7yLdbgU4t5DfOdF2K781Kmk+abLWxxuT2Sp+rUovY5X/oNuCFLRswlNvjU4457XQ11Gkkb378uNnAt8taxQut/k+Uj7kuaRreT3w+Yg4sYS2uzlp8pWksmgXs/ACNz1310DSFNICJyKV3Rsuuf1ufK5WBL4JrBIRW0hamzTCf1hJ7Z8A7BQRvTiXxHqIc5BtJLsAJ0hqLOLQSCOYMEnLRqrjW3q6QwtV5jmvArwX+F7+pXA8sHgVHUnalzTSd0zetLPSynplLOM7UvF9KG/VwW1II8h/zH+0PCX3rgxVlrECDgHOIuW2XkrTrVHKLc21biOIgbQyWQ4Ey7QHsEFj1Fhp1bvfkfJeJ6prkyZJJRz/AFzFghSkUqk7KxsCXE4qhdeYbPiCiPhHie1343N1JOkPlj3y4xtId4xKCZCBFYBrc+588Q+insiZt97hEWQbkaTFScu3Crg+Ip4Y45B22z09IrZsynltiDJ/6XQrz1nSqixIIViKtFLVl0ts/0pgvcKkyUWBy8tMR+mGnAu+Fel1ehNwFOm1+m1J7VdWxqrQx48j4v/Kam+EPq4glfH7b378LOC8yIuGlNTHVcX28kTcK8rsoxtU8iItI/RxIQtWNnwneWXDiPhaiX18Jvfxb9LdripSzrrxuaq0tKbLo1m3eATZFiLpTRHxhxa33deQVMrt9ojYMv9bWa6uFhT2Xwz4qKSbqSjPmdTg7cD+wP6SXkoKlsu2PGmJaUi1d0sl6ZvAd5puv34uIvYsq4+c9nAMcEz+5fxe0tK3pQTIuY9KylgV2v+/PDLdWNXu/IgotTwaKe/0z5Iao7nvBfYpuY+zJJ3NgoVCtgPOLLMDSU8nTUh6GQuPvJZ2S590V2IGqTxacUSxtJQXYMmI+L0k5RSwmZIuIAW0ZdkZeGkZE+ZG0Y3P1UNKCxs1Smu+mpTmVoqIOC+n560REb+TtBRpGfDSKFUjuj0iHpO0CWny9dHF0fde6MMmxiPIthBJX4+Ir40wI7m0mci5r99HxGZjbRtn25UvmS3pDaM9HxHnT7SPQl/TgH2BP5KC/DcAX4qI40rs48kRn8K2yyJi1BWy6iT/4v8e8ANS1YGdgCkRUdofLJJ2AmawIPVka9IkqglV4mjRz9qkUXYBv4+Ia8tsP/exDQtyXs+P8peaPgG4nlS1ZC/SAjfXRcTOJfZxS4vNZd+J+hPpD6ITSekcdwD7RsRLS+zjj8BbImJuWW2O0E/jcwXwh7I/V0or6h0MrANcDTwH2LasPyLz5OgZpNrwq+f0l0PK+L1R6GMOqSb1EHA2ad7BSyPi7b3Uh02MA2RrSdJqEXHLWNvG2fbTSWkIf2ThyU7LAr+JiLVK6ONZoz1fxuiS0lKhT2kaeAUp97nsUY2VSXnIAi6OiH+V3P6VpJzUx/LjJYHhKGFlNaUydY0fNo33uzHCv0RElHI3SxWWsSr0cSVp0tFD+fEzgNll3JVo5OeP9PkteVS0co0/uiRdGRHr5rSts6PHSlmpOysbHkZKaTuDhUfCS5vQmPt5BekP7AAuiIgrymw/97EYC9Lz/lpWel5uew6wIelnYCOFY6F0oRL6uCwi1pf0eeDRiDi41QBC3fuwiXGKhY3kV0DzyOGJpMUjJmoH0iTAVVh4stP9wA9LaJ/cbiO/+QXAf/P3ywP/ACac3hERC01uk7QxaWLKP4EdJ9p+brP5Pbg9/7uKpFWi3KLyPwd+n+8eBPAxUo7whEXEQpO2JC0DfIr0WSht1DJSLdyqy2GJhdM2GvmiZfgFsCULf36L/054VLTpj5WFnqKkmtoFjcDoPknrAP8ijZiVKrfdPIGujIUjGuZGqglf5cqG/8hfS+Sv0knaGdie9PNdwM8llXr3Q9KngWMi4pr8+JmSpkXEj0rq4rGIeLyRRpWD8bJH+p7Id+0+woJJzGVPwO5GHzYBHkG2hUhak5Qv+B3g84WnliWVgJrwaGKhr8+UfVu6RR+HkCZsnZkfb0FaAOVzJfaxGfAV0g/pb0bEOSW2/cdRno6yR+IkbU5h9DUizi65/eVJfxx9mBQMfr/k0d1ulLHalfRLrRHYbwUcGREHlNVHv5D0CVIw9nJSdYOlga9ExE9K7ONrpDtRa5NyqLcglUjbtsQ+/kiq5HMCcFwj+KtC/uMxckBedtuV3f0o9NGqhn5pI6OSvgPcR/oZ8hnSH9rXRsQeox3XYR9rA58kvTbHSloN2C4i9u2lPmxiHCDbQiS9m/QL/12knKiGB0i/GP5cYl+NkYb78uNnAmWONCDp0oh4VdO24YiYUkLb7yCNGP8P+EZE/GmibfarnPrwOdJEsMNJlUVKX7Z3hDzq0m9b5pH9jVmQu3t5me3nPtblqQvclFGTuitpHEpVMbaNiOPH3Hli/VxFSmu6PCJeoVRy8dDmOzwl9LMSafXH7UgDBr+MiG+U2P46wM+AxvtyD/DhMoPx/FptEBGP5sdPBy4pOT3hStKKgI1JeosCV5Y1uJI/Vx8H3kr6/3c26f2uJJjJv5eeX1YO9WT1YZ1zgGwtSXpNRMyuuI9KRxpye2eTFqj4OWmE94PAGyLibSW0PZ+U8nAFLW7xRQl1OTVyVZFGH2XUKK6cpIeAu0n1UZ9S/7qsPEt1oYxVN0g6nDSr/RoW1PaNMkbC1d0yi+dHxKiTWUvo4y8RsaGkS4FNSZ+vq8u829XU38uBL5BG+0pLhZD0Z2CPiPhjfrwJ6Y5UaSXsJH0WmE6Fdz8k7Uf6w+4Q0ufrk8BtJd+1WwJYM7f/18jLc5fY/rmkQaLFSEta3036OTLqAjh168MmxjnINpLbJJ1MmuEewIWkW9e3j35YRxaRpKaRhrJz76aRSjE1fiGcn7eVYdOS2hnNG0iz5luNhpW1iEc37MeCPyKqXESiWMYqSCN+ZZex6oZXR8TaFbXduIW7VmMksULnSNqNtFDEkysbljzZcDin7vyUlLv9IPCXEttH0lqkkeNtSUulH0e6I1KmZzSCY4CIODenQJQij7xeDJzHgrsfH63g7scXSVUm/o8FE2UPLavxfOfuEOCm3P5qknaIiN+U1QewXL7L8gngiEiVncoe3e1GHzYBHkG2liSdQ8oR/Vne9EHgAxHxlhL7qHykoVuUViEjIu4uud2dI+JApVXzLiyz7RH6WxJ4QUT8teq+qqIulEerWq5o8N0qzr2RdqQulPBTF0qwNfU3BCxb9q1qSReR6kWfEBF3jrX/OPs4GbiMhX/mTomIrUrsY3ZEvKas9iaDpOuBLWPBkvWrA2dExJol9nEVKYXjKNKo/iXKlVh6qQ+bGAfI1pKkKyLiFU3bSlsNKbe3CKmKwWYURhoiorSFHXLg+gWeulDBhCe3KU2j/hqpYoWARYC5pPzavSbafu5jTkSs16Vg5p2kxU6WiIjVJK0H7FVGqkhuf1PSa9X4RXYd8IOIOLeM9vuJUo3t00hVH0pd4CYHe9cB7yCNhC4kInaaaB/doKdWeFlIlFvhpXI5D/XrFHLbgZmNdKGS+vg6cCVwUlU5u1VrTtvJP4fPKzOVR9J7SROv/xRpYaAXkcr6bdNLfdjEOEC2liT9jjTrvLHK1jTS7bjSirF3g6Tfkm7v7kYaof4IcHdEfLGEtj8LvB2YEbk+dP4h92PgrIj4fgl9HAv/3957h1lWVunb99MtWUnKqD+kAZEwyNCIIKlH1AHTCAKCSDCAIpga0NExjQT1mxHTAApGegjCKKYBUYJIgyCxoQkSRkQwouKQo90+3x/ve7pOVVdVA2ftfeqcXvd11VW9d1W96+2qE9Zee63nYRuK2P4vu79EvA3tPErlda5H9EVDKhr1tugXKGYRV1P2vznwUeDdriojUxlNrOUcLo8m6VbgvcD1jPQgRxncPIOiVPIp4GNjv247RNqvxlqR8v+YYfvtKqYOG9r+QcDarSq8DAP1MbwSRZqw014T+thtGknHA2sD36I8//YAbgEugcGZy0imPpkgJ+MiaQYlodmG8iL0M2C27V8Hxlgf+HcW1y+NHBLq3E5elOhJutD29gFrX0NxvrprzPk1KBJpUbJGz6JMai9WyY1ImLriXG57q+5BycAEeS6lh/3aMec3pVTce/57DBOSftJ0gidp5ti/RwMxvknpC36T7U1qC8+lkXeiBh0Vw6EJ34ij7uAMCxrf5bVD1CDrBpRCxzPr43ZTYGfHqpY0HiPpjRzSSyZirbEvzJK2owjZRzGH0qLwecrA234QZrjQoWNU8Idaxfw98JygtZcZmxxD6UNWcQwLwcUxb+YSv7F3bpC0NzC9XrzMplwYRfCs8ZIx29epyHINFCqmMOvbnlMrsk9zgMtkFzdLOpXSZtHtqhZWHWs6Oa6sZ3tPFUMEbD9cb4kPJJJWctUQDuQzwetNiooizixGnPS+H7RuK4m+7aaMWrr5KsUH4Ms15nX1+RiZvLYRI+mBTJCTiTiWxZ30xjvXCyvYPr8qWdwBHC7pp5SkOYpPSFqFMnF+LEW/9NCgtSeTFgqVHWqJ91B0nR+ltNacQ7HVjWCypKLnhEMTu8MBENz+cBiwBcVKdw5FeeUUiuJLFCtQ/g4v7zo3SKolHR6rVeOOUs16dCX8g4KkbSlKDE8FZqjYNR9o+529rm37wq44jQ7JSjoOeB4jrXMHSdrR9rsClu8k+rsBz6I8J6C0590esD5AR7v5rSw+VxJmBgSsaPuKMddyCwLXbytG0gOZICejkLQNsC2whopjWIeVgenB4R6pg3q/kPRu4HfA30UG6Op1vJd4WbaZku4b57zoeuEeFGw/REmQwxypulhP0hnjnBcB9smuVtaSjqQMtp1c196HeFm5XYEXUHqpsf17FfezMJqsknUpo2zn5s1tDgPOBtaS9A3KRcRbIhbu7F/ScrabTro/D7yCap5k+9o6SBlG95AsRbpsMwKHZCvbA5t0BvRUnCevj1i4k+hL+viYgbkzJV0UEaNyMnAz5e9xJOU5flPg+gB31Yu5zu9pd+APAxgj6YFMkJOxLEupkjyF0YnFfRQN0EgOAVak3Mr/OCWBfXNwjEVEK0HYjr5gWAxJJ9t+YyepaShGG7dGXzvJ1yJvMb/C9lZdx8dLupxinR7FY7YtqfPGFqZV20HFTvcTwMOUBHMmcIjtUyb9wcfHfsDRxN8RWgzb50m6GtiacsFy8HhtSU+SY4AXApfS8P8DwPZvxlT7wtR2KocDLwLm1njzVWTrIrkFmAF0ZhfWoqhaRLKGpOfavg1AxUJ5jcD1n2d7D0mvtX1ibUs4J3B9gHcBXwE2kvQ74FeURHzQYiQ9kAlyMopaBbhQ0n/ZvkPSyuW0F3M/C4h1Zf3nA5Q37aYZxN7HF0paG9hf0kmM+T84xnCh8R7I7tvIDbNQ0j4U+TJTbu9GJzLfkvRlYFVJBwD7U/oJI3m57Q9I2pXi1rgHcAEjt6174SZJt1MSme7kKFwZpbI8cDfl/WZjSdiOqCj+tQ5srSnpmLFfdKxc3W9qm4VVXNxmE1+1XGD73oZbtJ9O+ft3jFS2BC7t3N0Juhg+FJgr6bZ6vA5FzjOKzlzJPSr23HfWGCGoGFa9w/YO9eJ3WvT7Xxsxkt7JBDmZiDUk/YBaRZZ0L7C/7Xn93VZPnNXvDTwJvkSpID6XogYwyhaYmPaEtpLXNtibUh09mvL7uaSeC8P2ZyTtSLmrsiHwMdvnRcYAOkOerwZOs/1/UYmT7b0mU0aJRNKnKA50oyyzKRq/vfIailzdyyjPjSY5iPKYWpPSCnYOpQIYSZNDsh0Wk/WLxvbZdf8dvfObg1tgvqKiGf1vlJaXpxL4/7K9UNIL67+jBzJbi5H0Tsq8JeNSK0vvsv3TejwLOK6B6lLyOJB0vO13NBzjV4zTahEpuzcMqOhfn+5Y2/WxMf4D2IXSYvEiYFXgB2PaRyLiLAtsUA9vsf3Xyb7/Sax/C7Bpkz3CakGurg1UNKM/wshg5jnAJ9y8HXgoGtG+Xtv2AQrUvm4LSZ8F1gdOZ7RFetiQbBsxkt7IBDkZF0mX2N5uSed6WH86RVe5ZzONJcTZjWKI8HeU6mu4qUNb1Mn5f6yHFzneTvfpXYfLU27rr2678apTFGpHv/Qw4PXA/1FaOb5t+49R63fFWQ24r1abVqRYKN8ZuP72wEkUhQFR+lHfHNT+0InxI2AP2w9ErTlOjOdQ+qm3o1zgXUzpdQ67gFExADqa0kttSt/zoZ0+22QENax9LWk54HWUtopFd8Ed5F5aY4yntexIpYw2YiS9kQlyMi6SPk8ZoDuN8oawJ6WP8DsQY+Mqaa7tl/S6zhJi3ArsZDu6X7BtabHZwNsZkfnaFfiK7WOjYkwQ92LbswLWaUUjVdKFVG1Rj5id3GB7k4j1x8TalPK8eB3wW9s7BK+/CYub6JwUuP48YO+OpFi9uDjN9gsD1j6W8vdekzJgeD6j9ZzD+oMlnQecSlE3ANgX2Mf2joExLgO+yIg82huA90RX9IcBSVfZ3kKjDYeutR2i5S7pbIoq0Ty65gtsfzZi/RpjMYWXaNWXNmIkvZE9yMlEbFY/j9Uk3pbyxhfh8nWJpC9QrKC7bzH1nHx38ccmkmNoXVrsbcBWnX612tt5KaVyFoKkbiWAaRSt36j/RysaqbSrLfonyt/9LwTLE9Yq9UsoCfIPgVdRKqNhCTLF6GaR3q7t/1Wcwc1V9fM8qjRag/yd7e5q3H9JOiQ4hmyf3HV8ioo05cChhrWWaV77+jm2Xxm43ni04QPQRoykBzJBTsbFdrRm8HhsWz933xqLSr47XFVv+X2fhhzJaEdaTIxWY1hIvCpHdwVmASVxfX3Ewm5PI7VxbVFJ76BUjtcAvg0cYPvGyBgUScWZwDW291NxG/xacIyrJH2dkcrrPgQNu9k+sfPv2ue8EeVvcovtaBOdP0val5Hq7l6Ui5ZILpD0QUbUUfYEzpK0OvSuJtNiy1kbWsuNaV9XfibpH2yH6Dd3oxZ8ANqIkcSQCXIyLiruc4cBnWTmQsoL6b1RMVpKwlcGHqJZR7I2pMXmAJdL+l493gX4emSAlv4eTWukjqctum/g+gBrUzSJ5wev283Dtv8maYGK1OKfCFAsGcM7KL+v2ZSLrYuA4yIDSHo1xUr3lzXGupIOtP2jwDD7A1+gmHmYovwQ3ce5Z/08Vq5sfwLUZGqf+Wsp/4cmOZyGtZbdkPa1pOspv+unAPtVGblHiZUnbMMHoE2vgaQHsgc5GRdJ3wFuADqVoDcCM23vFhznn1ncMjRs2KIN6hvM0YwMCV1CSaBuD46zOTCLmszYviZ4/adTLopmMTLsdKTtsGqcpFdSEthRGqm2Q4X+1bC2aFV1Wd/2HElrAE+1/avA9Y8DPkzpdX0fRSt8vht02GsCSTcDr7F9az1eDzjL9kaT/+TSh6RPAqvQYMuZpMttbzWmP/i6SHUiSdtRHqsP1sr+5sDRtu9Ywo8uad21J/t6r+uPjdVZT8Xt9am2x3NNndIxkt7IBDkZF0nzx04dj3euxxhfogwCvpRy+3h34Arbbw2M0fiE+7BQh50uYqQ/eB/gJQ0Mny1HQxqpkg6mVNvvp5h3bA580Pa5gTEOo/Rnb2h7A0n/jyL7FqLwMk68dSgKFtGOZ40j6aLulhqV5vALx7TZDAQtDE1eMM5p2w5rOastNecDH6QMl86m9KIfFBjjOkp70KaUnvkTgN1sbx+0/nqUodhHJb2kE8f2PRHr1xinUrSvF1LajlYBPmf704MUI+mNaf3eQDJlebhWyYBFVYGHg2Nsa/tNwN22jwC2oUhNRTKHMiT0/ygT9WfWc2FI2kDS+ZJuqMebSvpoZIyWWN32x23/qn58gqK/G4aKXNn7gXe7aNfOkPSawBD71yrMyymDc/sB/xG4PhQFkZ2pVT7bvydomFHS5mM/gNWBp4wZohwUfi7ph5LeIunNlOfflZJ2U5FgHAjqRdGx9eOllPmCUJMV2y8d5yNyHgPgPZQ7do9SerbvAw4JjrHApfL2WuAY20cTO7T8HUpb2/MobWbrUlRMItm4vo7sQhmSnUG5izpoMZIeyB7kZCLeAZxYe5FF0Xx9c3CMTsL9UK3C/YXyYhfJGi1MuH+VKi0GYPu6Wh0I095tiQskvQH4Vj3enXj3wTmUask29fi3FKH8KBOBzuDiq4E5tq+Vwr17H7NtSZ1BwJUC155MqipsgLUOhf2H7fdHrDcJywN/BDrVwz9TEv6diJ8FaJLGhyZbmvt4iGJG8pGoNcfhfkkfovT+v7g+1qLUUQD+ZntBvcD6T9vHSgptNwOWUVF02QX4gu2/dp7vAxYj6YFMkJNxqQNIM+uAEA31Rv1A0qrAp4GrKW+Y0ZP6d7Uw4d6otFh9gzknutWha/2OnrMoDlidFotplN7XsVJ/vbCe7T0l7QVg++HgBHaepHMpF1ofkvQ0RiyOo/iWpC8Dq0o6gDKo9dWIhVsalFxkdStJbrDPro2eabVgHEE7Q5MnUOY+Osoxb6RcUPZcaVdLOuSVPSn27m+1faekGZTX+Cj+Wl8/3kS50ILYBBxKseN24Frgotr/HP0e2EaMpAeyBzkZlzYGtsbEWw5YPrJaUtedQZlw34aRCfeDgwc6fgS8m9KHurmKtNhbbb8qMMYZwBujfz9tI+lnwD8Bl9Tf1XoUc4oXBa0/jaLhfZvte+rjeM3o/l1JO1LaOES5eDkveP3lgXcy8vz7KfAlB9oOq0GrW0kfsH2URgxDRuFYo5A2jCMaH5pscu5DxTVxQlxlGAcBSRtTencvtX2aihLOnrajW6m6YwqYbrspTfVWYiRPjEyQk3FpY2Cr9qO+jyJaf4Ck9SmDT1G321tBxYb2KxRty7up0mIOVLGQ9C2KbNJ5jE5mIhONcQenHGs9vCPwUcqw07lUjVTbcwNjrEZJ/LqHqSK1lhun/r3vZ7Shymq29wiM0ZjVraSdbJ9Z+47HC3LieOefZKxGnBInibcODQxNSroUeL/ti+vxdsBnbG8z+U9OLSRtTenV/nuKpNl04AHbqzQQazVgrUEcYE2mPpkgJ+MiaZ7HWM6qWogGxvgmperzJtubqLgvXRqslLEGcACL334N97tXg9JiLSUaZ3YdLk/RS50XPShUq7odjdTLHKCR2rX224CDgecA82ucSyP+D5rYWryjwxppLb6YNe9456YybfU5S/oKcKwbMI4YE2dNigZ29+tI5MXjTIrqQyeRvBt4c2TyJ+lXjF/RD2sXkXQVpdJ+OkXt5U0UScQPB60/lzIg+RTKc/zPFGWU907yY0nyhMke5GQi2hjYarofFeB/KLenf0y8eQewuLRYVRsIlRazfaIatoi1vVP3saS1iHUD7NZIPav2hn9YUs8aqV0cDGxJSbxfKmkj4IiIhV2txVviGklb274MQNJWFH3tMCRtABwPPLNeoG4K7FzVS3qm0+ccsdZ4qB3jiE6sT1F6a29k5HXElLtsIbioujQ999Fd4Fge2IMyNBmK7VslTbe9EJhTW6uiWMX2ffVieI7tw1Sk5ZIklEyQk4k4kMUHth5UscaMqpY9VpO+jhrAenTZQQexou1/DV5zLPvbPlrSKxiRFptDaSEIQe1YxI7lt0D0revjKUnATIryxwmUqlmIRirwiO1HJCFpOds3S9owaO022Qp4k6Rf1+MZwE2dpDAo+WtDfeWa2j8f3ucMRMoDLoldKO1f0a9Pi9FQYtxZe+wMyX9Kuhj4WGCYh1TsxedLOopi9R6p9PIUSc+mDDM2psYhaVsWv/MYpnvdVozkyZMJcjIuLVXLDgPOBtaS9A1qP2pwjB9IerXtHwav200b0mKHs7hFbKgk3piBqs6w27WRMagaqSq2usfY/vpE7SNPkt+qKKN8HzhP0t3A7wPXb4tXthCjUfWVyuoU1ZjuFpcQeTePuJCdbHuUfqykk4nVlL2NopTQeILcJBqtpT2NUlGOfq1/Y1373cChFG371wWufyRwDnCx7SvrDMgvAtfvPH7Wo7RwdN8xiDSGaTxG0hvZg5xMSBvDTk32o9b176dULx4F/koz/aJzKCYk61K0UqcDc8f2cPcYow2L2O5EdQFwu+3o2/oXUi6K9qPovf6Z0nLxD5FxaqztKf2cZ9t+LHr9pqlV9n+shz+tt+Aj129cfaUNJF1te/Ou4+nA9bY3Dli7c9G4JuW5fT5dSXLUkKyK+srWtiNbEcaL0+3Wt4AiM/aZ6LatptvBmkbSTRQjj8YSpDZiJL2RFeRkXCYadiLAqECLO4L9oX6eIWmG7at7jdGhpUr4WxmRFnuoJv3R+q83SNobmK6i9jGbIlkXyaourleLkHTw2HM90rRGKioOkOvbnlOHNNekKIsMDLWv/QBGKq2nSPqK7WMDw7yLor6ykaTfUX5H+wSu32ifs4oZxYeBFSR12hIEPEb5f0VwVf08j+LI2QguGsufZcRAp6k4jetsN9UOphalAyl61M9i5L2pCdqIkfRAVpCTcam9jp1hp806w0629wxY+4JJvuxo1YQ2aLrariKJ9xG6tHeBjztWF3dUJa6eW1SxHgRULIG3oPSLbqDi0Hi67e0C1p5IxQKA4LsS1wHb2H6wHq9EUeMIu2PQFatJ9ZULqX3OXXc+QmXZJP277Q9Frfc44jUiLSbpCOA64LtNVRXVgr69pHmUQsrcyLtdalc68AJK0eMKRt8xCJv5aCNG0htZQU4morFhpzaqGG3SZLW9g6tFbJ2md2Qyo6Iisjel2tNdJXsawa6Dal4jdVfgBRRnRmz/XsVNr2c6dyMkHQncCZxMuVjZh/g+TjFadWUhI73uMQHGJEt1WCvaDKixPueuO1Gnj3NXisg7URpHWkxStLTYeyntYAskPUID7WDAf1OUNzo9wfsA3wQiXToX2L43egzD9pn1c1giPAmHD0mMpAcyQU4movFhJ0nvAr5h+556vBqwl+3jIuO0QGPSYh0kbUlRfOgkafdS1DPmBSz/M8ptvmcA3e5j91MqWpF8gXE0UgPXf6wOAXaUUSKn5zu8wvZWXcfHS7qcWEm8OcDlkr5Xj3eh/P0jaSNZuktFnabz99iduFvKncfq8pTH0rWUpHJT4HJK4h9F49JiLbWDrW77413Hn5C0S3CMRtvBJG1BuZs2VpM67O6KW3AWbCNG0huZICfjYnvX+s/D662gVSjDVZEcYPuLXTHvlnQAEJIg18GX6yJv505AG9JiXwfeafunsKjPdg4lGeiJqgZwB7CNpGdSkn2Am9yA7amb1Uj9lqQvA6vWx9L+FDmzSBZK2oeSYJricheqsW37c7VqOYuS9O1n+5rIGLSTLDXW59y5EyXpv4G3uxqFSNoE+JeIGF20JS3W9GB0G/r276H8jh4FTqW0g0VKB36D0rZzPfC3wHWRdLHtWeO0U4VV89uIkcSQCXKyRBq80p0mSZ1+uzp9vmzU4nXw5do6+PfrJf/Ek6YNabH7O8kxgO2L6wtsGJL2oAzXzKW8WB8r6f22vx0YpjGNVJV7ut8ENgLuAzYEPmb7vIj1u9gbOLp+mGLgsXdkAI1Il109zrkoGk+WbN8G7NBknzOwkbtc9GzfUAfDImlDWqzJwehOMiYW17d/gNJq0zP1NfwM2zvQ3IXEn203MjBpe1b93Fg1v40YSQw5pJf0DUmfpoikf4ny4n0Q8Bvb7wuM8RNKRfQKRhsVNDIIoWBpsa7eyjcCKwKnUX5XewJ32w57E5J0LbCj7T/V4zWAHzvQ3ljS2sAfKRdCh1J+V8fZvjVo/cUs0geRsQOTipUu606WVmKkCjeN0g8eOWzYxlDYaZTn9ik1xr7AU23vFRWjDZocjG6TOsfwRtv3NrT+P1Hu2oyV3Iswn+nEGFdbO/ICtY0YSW9kBTnpJ/9Kcex7B+XN+lzga8ExQnuBJ0LNSYt9dsxxd6Un+up2Wic5rvyFkjSFYfsOFY3UZ9tu4m9zmaQtbV/ZwNrA4EuXtVy5aqPPeT/Ka8jB9fgiyt9n0Gi8VUvSi8c7H9zG8QhwvaTzGF2UiJJh249yl2gZRi7uQsxnunh+94GkpwDRF95txEh6ICvIydAzpq/2ijFJYMT6jUmLtUmt6G9KqVJDqVJf50CrbnVppNpeV8GW2ZJuBDag9FQ/yEhfX6ShytBIl9Xkfh1GDztFVuIWq+hLusr2FlExhoU6kLkfcAilreJuYBnbrw6McWbX4fIUd855DpTWVMMybJKudwPGQnXtRReowEOd09QL1IjnZBsxkhgyQU76hqRfMb7g+3MDY7yeYkQxl/Ii9I9AaF+tpPlUaTE353K3KkXxYR1GJzNRTl6i9D5uychg2EW2vzfpDz7xOI1opHatv/Z45+sgYgiSrrS9pUa7Gs63vVlgjO9QBjPPth06iNQV4wTKBdHP6arE2d4/MMZnKGYb3X3Oz7fdc8+rpG/Zfn1tTRjvdSRcM7otolu1JomzFnBUZDtK7Td/pA7hdtqDlnORqoxY/6vA523fGLHeBDEav0Bt6yI4efJki0XST7qrSMsDewCrB8f4CLDl2L5aIHLwrA1psR8Cl9HA5DaUrEjS92u1L/JW5Vga0Ujt4tnAzzvDYCoayBtTKspRNCld1uF4SjXxWEmnA/9l++bgGFtH9DQvgQMpQ2En1+PpwIOS3kvvE/udlorX9LDGpNR9TojtzwXEGO81rzN0+FTg/3qNMQm/BaJVfs6ntNA8UI9XoLTPbRu0/izgzbXA8igN3CUCrpC0SqePuhYoXmL7+wMWI+mBTJCTvjHOoM5/qpgVfCwwTON9tbQjLba8Y00JxqPx/l2at8w+Hug2jXhwnHO9Mp502b6B62P7x8CPJa1CGUg6T9JvKI+rU2z/NSDMpZI2brIS17AaQOei5J+An9oOVZWotNGvPY+RocmxGIi8o9Zt0zyN4uR2bdT6leVtd5JjbD+g4gQaxSsD15qIw7rvntm+p7bSfX/AYiQ9kAly0jc02v1qGqWiHP2GdLakcxjdV/vDqMVblBY7uSbfP2D05HZkdemlwIGSGuvfpXmN1EWygbBI6i/0dc7tSJd1FCD2pSiYXEPRf50FvBl4SUCIEylJ8p00V4lrg3WAfWt7zTzgp5SEeX6vCzc0SDo2xrpNx+jiqq5/LwBOs31JcIwHJW3u6mQo6YXAw1GLR7ZLTcJ4RZTofKmNGEkPZA9y0jdUDEg6LABuBz5j+5bgOLvRcF/t2EGkaFRcBz8J3MNIBcjB/dqN9u/WXsRzXDRSG0HSdyn95h0Vg3cCL7W9S2CMgykmLfdTKrqbAx+0fW5gjO9SLrpOprRX/KHrayFDbpJupbQ/jGrbaSkBCaeqoxxAMQlZ0/b0gDWPmezrgcoMnXg7Ax2libm2fxC8/sG2j17SuR5jbElRL+lowT8b2NMxrp+tUPvz7wG+SHm9fQ+wmu23DFKMpDcyQU6SHpH0RUoS06S02C+BrWzf1WCMrRmnf9f25YExmtZI/TvgGMogoCn9kIc4ULlE0rW2Z0p6BaXd4t8o9sNhbRySXmb7J1HrTRDjJ5HqBf1C0keB7Sj9utdQtJZ/2n1R0cPa4yoydIhSZqix/oMyJPuNemov4KrIQS6N0deu5xYNmwbGWYZyN03AzUEtQa1R7w79G6WXuiNB+gnbD076g1MsRtIbmSAnfUPSchR91HUYrcxwZL/29GRoSVrsDOANUZPgE8S4Bti806KgYtV9VXDi9y2KQ1hTGqmN01HdkHQ0pcr3vegko94x+Ibte+rxasBetkNs2OuaxwGrAmcSbLgwweDZIiJbgyRdTbkDdRZwIcVo45Go9cfEWqmpBEbSdcBmrqol9Y7LNRGvI5L2org9zqK0oHR4GrAw+q6OpG1Z/HX9pMgYSdI02e+S9JP/Ae6l9A0+uoTvncq8qoUYCyn2zBcwOpmJTCwb79+lJDGhdsbdqEETjy7mSToXWBf4UK20RyuLHGD7i50D23fXHvSwBJmiLvAo8PKuc1GGC92DZzMomr6iJOS/pvzuQrC9ef0bzAJ2BL4q6Y+ulr4RSNqGIrv3VGCGpJnAgbbfGRWjsiojqhWrBK77M4rSyjMYbT50P3BdYBwknQysR7HLXlhPGxiYBLmqHX2AYuaxfOd85B2XNmIkvZEJctJPnmO7jYnkpmlDWuz7ND/dfJuk2Yzu370tOMa3GUcjNXD9r1JNPABsXyfpVGIHAd9Kmf6/zfZDdZhuv8D1AaZJWnTBUn9Py0YGsB295+611wWQ9CXgDNs/rMevItZFD0mbUPTNt6cM+v6G0VXSCP4TeAVwBoDtazWBK10P/DtwTb0IFqUXOaS9ovaV3wFso9HGSTfZXhARo4stKK1Zg3x7+huU4evXAAdRBmP/PIAxkh7IFoukb0j6CnCs7euX+M1PfO1xzQM6BLc/NN6a0AYt9e9eBuzQkYGS9FTgXNshGqlqwcSjrrkasD6jKz9hdr0qrobrAF+i/C0OAn5j+32BMeYwvsFGpFFI4056kjqtFRcDVzbR7yrpcttbjXlcXWt7ZnCcZzPa9fPO4PX3oDhZzoXGjJNOB2ZH9ID3i87jVl0mRpIutL39IMVIeiMryEk/mQW8Rc0IvnfMA95VP3eMCvZhxN4zisZbE9SC62BNhN8Qtd4ENK2R2riJh6S3UUwqnkO5jbw1cCnlwiKKfwXeDryDkQGerwWuD0UysMPywK6MKA9EcVcdojuF8jfZl6JFHobtf45cbwJ+U/tqLWlZin73TQ3E2YbyumiKqUqo4g7wUZo3TnoGcKOkKxjdDhZiJ98SnYusP0j6Z8rz4jkDGCPpgawgJ32jaVmxGuMS29st6VyPMdqQFnt61+Ei10HbPZuqSPqA7aM02kRgEZF9zpIuAd7j0RqpX7C9TdD6z6WYeGxL6Xv9FbCv7dsj1q8xrqdU+S6zvZmkjYAjbO8ZFaMf1DsfPw7us1wdOIzSLmDgIuDIyCG9NpD0DOBoRisOHOzFzY56iXEc8DxGa7b/0va7Jv6pJxzjetv/0HU8Dbi2+1xAjHEroLYvjIrRNJJeQ2nTWQs4FliZ8hw/Y5BiJL2RCXIy1EiaD7zb9sX1eFvguMhb7m20JkwQ9+KIQSRJO9k+cyJJK8dKWbWikaoGTTy62jjmU6T3Hm2ijaNtJG0InGX7eQ2s/dTuOwfJ4kj6ObDJmFat620/PzDGp4FNGZ2EX2f7X6NiJMmwkC0WybDzVuAEFcteKMLsYT2W0E5rghp0HbR9Zv0clghPEuvKWnEN1UiVNK4Nt6RO3M/1GqOL30palTI0eZ6ku4lvTWgcSfczojRh4E5Ka0dkjG0prSFNqz80iqQTKRXje+rxasBnI/u1gVsoih+dO2hrEagwofJkOIZy96NjnPQVBxkndS7Yux5Xi75EaZ1bOSJOkrRFVpCTpQJJK1Me7+EGFW1Ii6kF10FJW1BsoNdmtH5pqPWwGtBIlXRY/eeGlASgc5tyJ4p74tt6WX+SuNtT5LjOtv1YEzEGGUmXA7tTlCw6w2032N4kYO0zmXwQN6znVePoXI93rscYF1Ieu1fUU1tSetsfgpj/z3hDk0mSjE9WkJOhRmPMSLoqipFmJI1Li9l+adRak/ANyv9jlPVwJGpII9X2EXX9cymKIh3JvcOB03tZezwkzQLWtz2nDjqtSel37nXd1pK+Gm9NFr8gClPjqOv9pvO8qyyc6HufIJ+pn3cDnkUZBITiQHd7UIwO0yStZvtuWNRbHf3+2fM8wePgMklbukHXz0FG1XZb0na2L+n3fpL+kglyMuy0YUayou0rxiQBodqiYxP9zvngRP/PLQyINK2ROgPoruQ+RvmdhVGr1VtQqtVzgGUoyVnE4GdrSZ+kT1F6UG9k9MVKZILcmPpDZ+hL0sdtd2sSnykpNMmnmGv8TNK3Kb+j1wOfjAzQ0hDbS4EDJTXm+jng7EcZxjwWaESmc6J2sA7B7WBJD2SCnAw7bZiRNC4tRjuJ/mGSvkYZMgy1Hu7iBkri15RG6snAFZK+R/l77ApE91bvCrwAuBrA9u9VzGF6puWkbxdgQ9tNulgeREk41gR+S1F/CFNlqKwh6bm2bwOQtC6wRmQA2ydJuooyiCtgN9s3Rqzdcu9uG66fg8xNkm6nPKa6+78jLyRCXiuS5skEORl2fibpH9yAGUkX76JIi20k6XdUabHgGG0k+vsBG1Eqop0Wiyjr4Q6NaqTa/qSkH1EMEAD2s31NxNpdPGbbkjoXRCsFrw8tJH0Ul8RlaNDm3fZdFO3xJjkUmCup4/q4DnBgdJCaEIckxWPWnVU/t5E4teH6ObDY3kvSs4BzgEZ0mzvtYMnUJ4f0kqFG0o0UbdEmzEjGxmpSWqwx18GuGKM0UhuKMQwaqf9CcdHbkWIPvD9wqu1jA2O8knLRNSrps31OYIzvADNZ/I5BpO71UZRe/IeBs2u8Q2yfMukPPvE4y1Eu7qAoozRZFW+MqlbTMQq5OPriTkPi+tkGtSVog3p4S4Tazpj1l6eoLD2f0Y6coSpLyZMnK8jJsNPYLcWWpcWadB3scJmkjaNuHY/HICXC41Glsr5JScbuo/Qhf8z2eZFxbJ8taX2aTfrOYETtoylebvsDknaltFjsAVzASG91z6g4Mb4XWNv2AZLWl7Sh7R8s6WenEpI+Rvn9dO7Y/Jek0yPVcGjB9XMYqBfyJ1H6/gWsJenNwQOsJwM3A68AjqTcaWnCnTF5kmQFORlqJM0Y77ztXwes3Zq0mNpxHbyJojARnoQPk0ZqG1JZ4yV9lH7hQUv6fm77+ZK+CnynJv7X2p4ZGOOblN78N1WZxRWASz1gxi31+fcC24/U4xWAq23/fWCMxl0/hwFJ84C9OzKaVcrztMjnfUcmUNJ1tjeVtAxwjgOdLJPeyCvHZNg5ixEzhOWBdSmC/D27U7UpLRaZCE9CYz3OLfdZNk0bUllzKElfx4L7t5TH1EAlyJThwpspLRbvrJJ4jwTHWM/2npL2ArD9sMZIygwIt1Neozq/n+WAXwbHOIhiFvJRRlw/3x4cYxhYxl0a87b/tyawkXRaNu6RtAnFqGed4BhJD2SCnAw1Y3tqa49f9ABP49JibdBSEj4MtCGVNRRJn+0PVjm5+2wvlPQg8NrgMI/Vamunr3Y9Ghw8jEbSsZS9Pwr8XNJ59XhH4OLIWG7B9XNIuErS1yltEFDaH+YFx/iKiiPjRyl3H59KO1rYyeMkE+RkqcL21ZK2DF62DWmxZOrQhlTWoCd9L7P9E0m7dZ3r/pZIZZTDKAOAa0n6BkWP+i2B6zfNVfXzPKDb9nluVABJH7B9VFcyPorIwcwh4R0UdaLZlAvgi4DjIgPY/lr950XAcyPXTmLIHuRkqBkzSDeNIv7+dNuvCI6zOSPSYhc1IC2WTBEkbc04Ulm2Lw+MsSOlsrQxRTt4O+AttucGrN24W5+kI2wfJmnO+CFiJ/UlPR3YmpLMXFbl5ZKKpJ1snynpzeN93XZe0LeMpP8POMr2PfV4NeB9tj/a140li8gEORlqugbpoLjb3U4ZForug2yEcYbaRjFIw23DQltSWU0lfV1Se+O69dn+cESctpC0HTDf9oOS9qVcBB+dLUPJVKYzpDfm3NUpuTd1yAQ5WSqoVT7bfqDfe3kySDqSMsRxMiVh2gd4mu2j+rqxpRBJ88cqJHQm0QNjNJ70SbrIo936xj3XY4zGLdJVHM9mAptSpLlOoDjdjau5vTQjaQvgI8DajP57pNV0y9TH7ZYd+cbaUnWV7Z4HyJMYpvV7A0nSJJI2qRW/GygDMPPqxPCg8Qrbx9m+3/Z9to+nJB5J+9wmabakZerHwYwYekRxPPCQpJnA+ylOZycFx1hD0qLeRzXj1vc/lKG8BZSBxs5HJAtqNf+1wDG2j2aA7XzVjDNjh29QFFJeR5Gj7HwkFUnTJX26hVCnAOdLequk/YHzyNmVKUUO6SXDzleA99q+AEDSS+q5bfu4pyfDQkn7AP9NabnYC1jY3y0ttbQhlbXAtiV1kr6vT9Q/2gNtWDS3YZF+v6QPUezdXyxpOsVCe6CQtC3wNYqawYx6cXSg7XcGhvmz7abNYQaaqrbyQkmjTFUaiHOUpOuBf6LcFfy4A50yk97JFotkqBnPlCDaqKANJK0DHE0Z1jJwCcWy9/Y+bitpCEkXUpQZ9gNeDPyZ0nIRagWuhi2a1Y5F+rOAvYErbf+0mgO9xHZ0xb1RJF0O7A6c0elNlXSD7bA7XpL+iXJxPdZePFJVZOCR9FmKnfzpdN3xyN/T0kUmyMlQU6XXrmZEz3JfYIt0jkqeLNVV63jgmdW5bVNgZwdaAreR9KkFtz5JNwLPowF3xmFD0uW2t+oe3oq+mJd0CuWC6OfA3+rpcFWRQadJ9RUNkavosJMJcjLUVOmcI4BZ9dRFwBG27+7frp44bSRlyeOjVnffD3y5qUpfG6gFi2a1Y5G+NXAs8PfAssB04AHbq0TFaANJ3wY+B3yBol4ym3IxH2bsIen66LsQSTKs5JBeMrTUXsTTbc+2vXn9OGTQkuPKV4EPUe1JbV9HOmL1ixVtXzHm3ILIAJK2lnSlpAckPSZpoaR7I2NQ3PqOYuQx9TClihWG7TtqMvwwpVrW+YjkC5S2gV8AKwBvA74YHKMNDqKYU6wJ/A7YrB5HcpmkjYPXHDokbSDpfEk31ONNJYXqE0s6+fGcS/pHJsjJ0GJ7IUUJYKAqSRPQeFKWPG7uqs52HR3k3YE/BMdoI+lr3K1P0s6SfkFpsbiQokP+o8gYALZvBabbXmh7DvCS6BhNY/su2/vYfqbtNWzva/svwWFmAfMl3SLpOknXV7mxZDRtFCRGyblJegrwwuAYSQ+kikUy7DwCXC/pPEYPWwyatWobSVny+HgXRQllI0m/oyR/+0YHsX2rpOn1Qm+OpJ8Fh2jDovnjlHaBH9t+gaSXUhL/SB6StCwl8TuK8rxoUiqtEark3tGU35eBS4FDbUdKCDatKDIsrGj7Co22Rw8pSFTFlQ8DK0i6r3MaeIzyupJMETJBToads+rHoNNKUpYsmZqw7FD1aqe5Wk4H03jSZ/s8SVcz4tZ3cJRbXxd/tf0XSdMkTbN9gaRPBcd4I+Vu6Lsp0nVrMZga4adS7hLsWo/fAJwGbBUVIN0FHzeNFSRs/3t9DnwthyOnNjmklyQDRMNJWTIJkt472ddtfy4w1trAHylDZ4cCqwDH1VaCqBhtuPX9GNgF+HfgGcCfKO5hoTrktVVkhu1bItdtk46KxZhzl9neul97Wlqp1fyOXv7dlILEPsHPjXm2s6ViCpM9yEkyAEg6WNLKwEPA5yVdLenl/d7XUsbT6scWwDsow1RrUoarQgef6huxgGfbPsL2eyOT40obbn2vpQzoHUpp5/glwc5tknYC5tf1kbSZpEE0w7hA0gclrSNpbUkfAM6StLqk1fu9uaUJ27fZ3oHiLLmR7VkNVN8vk7Rl8JpJIFlBTpIBoKOHKukVlHaLfwPm2N68z1tb6pB0LvC6ThVf0tMoailh/Z016fsMsKztdSVtBhxpe+fAGFfb3lzSx4DfVbe+qwftMSVpHvAyYG6X7N51g6a1LOlXk3zZtp87ydeTQCQ9ndKjP4vSZnEx5fkXNjRZNcI3oFyYPkhqhE85sgc5WSqQtJLtB5f8nVOWzrTIqymJ8bUaM0GStMYMykBNh8coNs2RHA68CJgLYHu+iptiJI1bNI9jhgBwL3AV8L6gAbQFtu8d9KeD7XX7vYdkEf9N0czv9LLvA3wT2CEwxqsC10oaIBPkZKiRtC3wNeCpwIx6O/lA2+/s786eMPNq5XJd4EO1avm3JfxM0gwnA1eouDSaMlR1YnCMNpK+PSlufW+1faeKW9+ng2N8Dvg9ZQBNlMGzZwG3ACcQI8d2g6S9gekqboCzgWjFj1aQtAmlXWf5zrlBs8weEla3/fGu409I2iUygO076vvRP9ZTP7V9bWSMpDeyxSIZaiRdDuwOnDHgrmfTKMYBt9m+p94CXLPqcyYtI2lzRt7YLrJ9TfD6XwfOBz5IqWLNBpaxfVBknKaZbPBMQTbKKpbZHwE6PfnnAJ+w/Uiva7eJpMMoFwwbAz+kVBgvtr17P/e1NCLpM5S7HN+qp3YHnm/7sMAYBwMHAN+tp3YFvmL72KgYSW9kgpwMNZ03aEnXdCXIIW/MbaNim70+o6tLF/VvR0lTtJH0qQWLZkmXAp8Hvl1P7Q68tybI83u1ta5tIefUgaqBRtL1wEzgmjpv8EyKFFjoUGMyMV0tQaLIKnbu0k2jPDdWDox1HbBNp/WvKhRdmj3IU4dssUiGnd/UNgtXXdnZwE193tMTRtLbgIOB51Am9remGAm8rI/bShqgJn1n1KTvIw2G+gKl5eF0ijLHmygXYJHsQzG/OI6SeFwG7Ftl2d7d6+K2F0p6SNIqtqOtuNvmYdt/k7SgKtb8CcjBvBax/bQWwwlY2HW8kGCr96Q3MkFOhp2DKG/QawK/o1Ti3tXXHT05Dga2BC6z/VJJGwFH9HlPSQO0mfQ16dZXE/13TFIBvTgo1LC4ZV4laVWKzfE84AFgrL180hKSNqUM3y7Kk2x/d8IfeOLMAS6vswxQ9MK/Hrh+0iOZICdDTXUG26ff+wjgEduPSELScrZvlrRhvzeVNEYbSV+jbn010W/DCGEo3DK7Boe/JOlsYOWcMegPkk4ANgV+zkibhRnpF+4Z25+TNJciJSdgv+hZhqQ3MkFOhprqiHQ0pSXBlLaEQ4Pkpdrkt7W69H3gPEl3U9QBkuGkjaSvDYvma6ppx+mMTvQjK3HfplxALoRFlevlAtdvDUlrAmtT35slvTjnDPrC1rZDzX/GUmcAfm776nr8NElb2b68ybjJ4yeH9JKhRtJlwBeB0+qpNwDvGTtZP0hI2p5iPXy27ceW9P3J4FEHdhZL+mw/FBynUYtmSXPGOW3b+wfGuAzYwfYD9fipwLnRdtZNI+lTFOm9GxnpTXWkOUzy+KgqMp+1fWODMa4BNndNwqpS0VWDZtQzzGQFORl2ZPvkruNTJPU8HNQPJM0C1rc9R9IalL7qydy3ksHlfIopwQP1eAXgXCAs6et26wMaceuzvV/UWpOwfCc5rjEfqCogg8YuwIa2H+33RhJOBC6VdCfwKM243MldFco6oJk52RRiWr83kCQNc4GkD0paR9Lakj4AnCVpdUmr93tzj5eqkfqvwIfqqWWAU/q3o6RhFkv6gOik73CKW989NcZ8gh0BJT1H0vck/UnSHyV9R9JzImMAD1Zd6k7MFwIPB8dog9sIdjJMnjQnUFqQXgnsBLymfo7kNkmzJS1TPw6mPAaSKUJerSTDzp7184Fjzu9P6UkeFBmlXYEXAFcD2P59ddNLhpMHJW3e1Z/YRNLXhlvfHIqL3h71eN96bsfAGIcAp0vq9OQ/m5Hn/ZRH0rGU16KHKAOT51OqlsBAqnEMA7+2fUbDMQ4CjgE+Svn7nw+8veGYyRMgE+RkqLG9br/3EMRjti2p068WpjaQTEkOofmkrw2L5jVsd/ch/5ekQyID2L6yyh5uSLkVfrPtv0bGaJir6ud5QNNJWfL4uFnSqcCZjL5YiVSx+BNlJiaZomSCnAw9kjah2Ld2O9Cd1L8dPSm+JenLwKqSDqBUwL/a5z0lDdFS0vceihHJo5Qq7znAJ4Jj3CVpX0aGZPcC/hIcA4pG+DqU97QXSBqY57jtE8eeq66Za6XMW99YgfK8eHnXuVCZt2TqkyoWyVBTe3dfQkmQfwi8CrjY9u793NcTQeUe+HOAjSgv2KLY657X140ljVIdINdhtFFBSNLXlkWzpBkUx75t6qlLgINt3xEY42RgPYrDZLf6w0C1JlRN3J0pf+/5wJ+BC22/t4/bSpKllkyQk6FG0vXATOAa2zMlPRP42iTuXlMSSfNst2G6kEwB2kj6qj7xGwfdolnSTcDGHvA3M0nX2H5BtZVfy/Zhkq4LVk5IHgdVnnCxx1OEPKGkg20fLWk725f0ul7SHNlikQw7D1f5nAWSVgb+xOAM5nVzmaQtbV/Z740krbAFzSd9jbv1VYe+T1AGDM+mXKweYjtSgeUG4FkUJ8BB5imSng28ntL6kvSPH3T9e3nKkHSUMdN+FPOqY4HUPJ7CZIKcDDtXVQe6r1KGYB4Arujrjp4cLwUOlHQHJZlpQpczmTq0kfS14db3ctsfkLQr8FuKmsUFxEoUPgO4UdIVjB6oGjSDjSMpfeAX1x705wK/6POelkpsf6f7WNJpwI+Dlr9J0u3AGpK6e8zzNX2KkS0WyVKDpHWAlQdx8EXS2uOdj+zlTKYOki4ANqNczDWS9LXh1ifp57afL+mrwHdsny3pWtszA2NsP9552xdGxUiWbiRtCJxl+3lB6z2LcjG02PM5X9OnDllBToYeSWsCa1Mf75JebPui/u7qCfNs4Oe27weoGsgbA/liOpwc3kKMxt36gDMl3UxpsXhndYB8JHD9TISTcCTdT+lBVv18J8WoKQTbdwIzJS0LbFBP3zJg8oRDT1aQk6FG0qco+rE3MnrYaaBuv0q6Bti805MqaRpwle3sYUueFJLm295sSecC4qwG3Gd7YbWAXrkmCL2ue7HtWV3JzKIvUZ7jK/caI0maot75OAm4nfKYXQt48wAWb4aWrCAnw84uwIa2H13SN05x1D2wVQcP8/k7ZLSc9DXm1ifpZbZ/Imm3rnPd39KznqztWfVzOkom4Yy98wgQnLx+jtKjf0uNtwFFLzzViqYI+QabDDu3AcvQ1cc5oNwmaTZwfD1+J+X/lgwRLSd9h9CcW9/2wE+A8eQU03ChC0mT6hzb/lxbe0kKE915BCIT5GU6yTGA7f+VtEzg+kmPZItFMpRIOpbygrYmRVrqfEYPOw2aicDfAccAL6P8v86nyGX9qa8bSwaa+oY8qBbNQ0E1M5oQ20e0tZekIOkWYNMm7zxKOoHyWn5yPbUP8BTb+zUVM3liZIKcDCWS3jzZ18ezd02SpY2m3PqyKpoMMpJ+BOxh+4ElfvOTj7Ec8C5gFuUC9SLguCFoBxwaMkFOlhrqsNBaAyrztgGlveKZtjeRtCmws+1P9HlryYDSpFtfV1V0Q2BL4Ix6vBNwke239RpjWJB0zGRfH7S7XcOApO8wBHcek97IBDkZaiTNpWhNPoWSCPwZuND2pBWuqYakC4H3A1+2/YJ67gbbm/R3Z8mg0oZFs6RzgdeNkSc83fYrm4o5aOTdrqnHRH+T/FssXeSQXjLsrGL7PklvA+bYPmyMe9GgsKLtK8YoASzo12aSoaANt74ZwGNdx49RWjqSytikS9JKth+c6PuT5slEOAGY1u8NJEnDPEXSs4HXAz/o92Z64C5J61GlvyTtTrOJTTL8dCyaz5F0RucjOMbJwBWSDq9tF5cDmXyMg6RtJN0I3FSPZ0o6rs/bShpA0nRJn+73PpLJyQpyMuwcSbH0vNj2lZKeC/yiz3t6MrwL+AqwkaTfAb8C9u3vlpIB5/CmA9j+ZB14+sd6aj/b1zQdd0D5T+AV1H5t29dKenFfd5Q0QjXNeaEkNdnilPRG9iAnyQAhaSVgWqenM0mS4UDS5ba3knRN15zBtbZn9ntvSTySPgusD5wOLGqpsZ0a4VOErCAnyRRmIrmsTi9yymUlT5S0aJ6y/KbK7lnSssBsartF0g6SzmT0c2IUtncODLc68BeKtv2iEKSJzpQhE+Qkmdp0HNXGlcvqy46SgSYtmqcsBwFHU8yNfgucS2mtStrjM/XzbpQB1lPq8V7A7ZGB0hBk6pMtFkkyAKRcVpIkSTtIusj2i5d0rscYqW0/xckKcjKUDKGTV8plJckQI+lE4GDb99Tj1YDP2t6/rxtbOllD0nNt3wYgaV1gjeAYX6Vq2wPYvk7SqUAmyFOETJCTYWXYbh935LK+R+lT25WUy0qSYWLTTnIMYPtuSS/o436WZg4F5kq6rR6vAxwYHCO17ac4mSAnQ4ntI/q9h0hSLitJhp5pklazfTeApNXJ9+i+YPtsSesDG9VTN9t+dLKfeRKktv0UJ598yVAi6ZjJvm57dlt7icL21cDV/d5HkiSN8FngZ5K+TUmaXg98sr9bWjqRtCLwXmBt2wdIWl/ShrYjzabG07bfJ3D9pEdySC8ZSiS9ebKvp5VokiRTDUkbU2S/BJxv+8Y+b2mpRNI3gXnAm+oA3QrApbY3ayBWattPUTJBTpYKJK1k+8Elf2eSJEmyNCPpKttbNGnaIunpwGHALModg4uBI23/JSpG0hvT+r2BJGkSSdtIupEquC9ppqTj+rytJEmSZOryWK0ad/qD1wOie5D/G/gz8Dpg9/rvbwbHSHogK8jJUCPpcsqLzxldlYAbbG/S350lSZIkUxFJOwIfBTamGLZsB7zF9tzAGPNsv3DMuatsbxEVI+mNHNJLhh7bvxkjpbOwX3tJkiRJpja2z5N0NbA1pR/8YNt3BYe5QNIbgG/V492Bs4JjJD2QLRbJsPMbSdsClrSspH+htlskSZIkyVgkbQc8YvssYFXgw5LWDlr7fkn3UXSVT6WYPj1Gabk4NCJGEkO2WCRDjaRnAEcDO1AqAedSqgE5CJEkSZIshqTrgJnApsBJwAnAbra37+vGklbJBDlJkiRJkqQi6Wrbm0v6GPA721/vnAuOsynFpW9Ru6vt70bGSJ482YOcDDWSTqRUjO+px6sBn7W9f183liRJkkxV7pf0IWBf4MWSpgPLRAaQdAKlQv1z4G/1tIFMkKcImSAnw86mneQYwPbdkl7Qx/0kSZIkU5s9gb2Bt9q+U9IM4NPBMba2vXHwmkkgmSAnw840SavZvhtA0urk4z5JkiSZANt3Ap/rOv41pRc5kkslbZxuiVOXTBSSYeezwM8kfZty++r1wCf7u6UkSZJkqiJpa+BY4O+BZYHpwAO2VwkMcyIlSb6TYkIiwLY3DYyR9EAO6SVDj6SNgZdRXoDOzyv2JEmSZCIkXQW8ATgd2AJ4E7C+7Q8HxrgVeC9wPSM9yNi+IypG0htZQU6GnpoQZ1KcJEmSPC5s3yppuu2FwBxJPwsO8WvbZwSvmQSSCXKSJEmSJMkID0laFpgv6SjgD8BKwTFulnQqcCalxQJImbepRLZYJEmSJEmSVKpr3h8p/ceHAqsAx9m+NTDGnHFOOyVIpw6ZICdJkiRJknQhaQVghu1b+r2XpD9ki0WSJEmSJElF0k7AZygV5HUlbQYcaXvnwBhzKMpKo8gK8tQhE+QkSZIkSZIRDgdeBMwFsD1f0jrBMX7Q9e/lgV2B3wfHSHogE+QkSZIkSZIRFti+V1JjAWx/p/tY0mnAjxsLmDxhMkFOkiRJkiQZ4QZJewPTJa0PzAaiZd7Gsj4wo+EYyRNgWr83kCRJkiRJMoV4D/B8ivzaqcC9wCGRASTdL+m+zmeK3Nu/RsZIeiNVLJIkSZIkSQBJ04FzbO/Q770k/SVbLJIkSZIkSQDbCyU9JGkV2/c2GUvSmsDadOViti9qMmby+MkEOUmSJEmSZIRHgOslnQc82Dlpe3ZUAEmfAvYEbgQWdkIAmSBPETJBTpIkSZIkGeGs+tEkuwAb2n50Sd+Y9IdMkJMkSZIkSUb4NvCI7YWwqC95ueAYtwHLUAYBkylIJshJkiRJkiQjnA/sADxQj1cAzgW2DYzxEDBf0vl0JcmRbRxJb2SCnCRJkiRJMsLytjvJMbYfkLRicIwz6kcyRckEOUmSJEmSZIQHJW1u+2oASS8EHo4MYPvEyPWSeDJBTpIkSZIkGeEQ4HRJv6/Hz6YoTiRLEWkUkiRJkiRJ0oWkZYANAQE32/5rn7eUtEwmyEmSJEmSJF1I2hZYh9EmHif1bUNJ62SLRZIkSZIkSUXSycB6wHxGm3j0nCBLOrOuNS62d+41RhJDJshJkiRJkiQjbAFs7GZusX+mft4NeBZwSj3eC7i9gXjJkyRbLJIkSZIkSSqSTgdm2/5DgzEusv3iJZ1L+kdWkJMkSZIkSUZ4BnCjpCsYbeIR2f6whqTn2r4NQNK6wBqB6yc9kglykiRJkiTJCIe3EONQYK6k2+rxOsCBLcRNHifZYpEkSZIkSdIykpYDNqqHN9t+dLLvT9plWr83kCRJkiRJ0m8kXVw/3y/pvq6P+yXdFxxrReD9wLttXwvMkPSayBhJb2QFOUmSJEmSpEUkfROYB7zJ9iaSVgAutb1Zf3eWdMgKcpIkSZIkSbusZ/so4K8Ath+muPYlU4RMkJMkSZIkSdrlsVo1NoCk9ehSzEj6T6pYJEmSJEmStMthwNnAWpK+AWwHvKWvO0pGkT3ISZIkSZIkLSPp6cDWlNaKy2zf1ectJV1ki0WSJEmSJEmLSNoOeMT2WcCqwIclrd3fXSXdZIKcJEmSJEnSLscDD0maSZF7uwM4qb9bSrrJBDlJkiRJkqRdFrj0uL4WOMb20cDT+rynpIsc0kuSJEmSJGmX+yV9CNgXeLGk6cAyfd5T0kVWkJMkSZIkSdplT4qs21tt3wmsCXy6v1tKukkViyRJkiRJkiTpIivISZIkSZIkLSJpa0lXSnpA0mOSFkq6t9/7SkbIBDlJkiRJkqRdvgDsBfwCWAF4G/DFvu4oGUUO6SVJkiRJkrSM7VslTbe9EJgj6Wf93lMyQibISZIkSZIk7fKQpGWB+ZKOAv4ArNTnPSVdZItFkiRJkiRJu7yRkoO9G3gQWAt4XV93lIwiVSySJEmSJElaRtIKwAzbt/R7L8niZAU5SZIkSZKkRSTtBMwHzq7Hm0k6o6+bSkaRCXKSJEmSJEm7HA68CLgHwPZ8YJ2+7SZZjEyQkyRJkiRJ2mWB7dQ9nsKkikWSJEmSJEm73CBpb2C6pPWB2UDKvE0hsoKcJEmSJEnSLu8Bng88CpwK3Asc0s8NJaNJFYskSZIkSZKWkDQdOMf2Dv3eSzIxWUFOkiRJkiRpieqc95CkVfq9l2Risgc5SZIkSZKkXR4Brpd0HsUoBADbs/u3paSbTJCTJEmSJEna5az6kUxRsgc5SZIkSZKkRSStBDxS2y06fcnL2X6ovztLOmQPcpIkSZIkSbucD6zQdbwC8OM+7SUZh0yQkyRJkiRJ2mV52w90Duq/V+zjfpIxZIKcJEmSJEnSLg9K2rxzIOmFwMN93E8yhhzSS5IkSZIkaZdDgNMl/b4ePxvYs3/bScaSQ3pJkiRJkiQtI2kZYENAwM22/9rnLSVdZIKcJEmSJEnSMpK2Bdah626+7ZP6tqFkFNlikSRJkiRJ0iKSTgbWA+YDC+tpA5kgTxGygpwkSZIkSdIikm4CNnYmYVOWVLFIkiRJkiRplxuAZ/V7E8nEZItFkiRJkiRJuzwDuFHSFcCjnZO2d+7flpJuMkFOkiRJkiRpl8P7vYFkcrIHOUmSJEmSJEm6yApykiRJkiRJC0i62PYsSfdTVCsWfQmw7ZX7tLVkDFlBTpIkSZIkSZIuUsUiSZIkSZIkSbrIBDlJkiRJkiRJusgEOUmSJEmSJEm6yAQ5SZIkSZIkSbrIBDlJkiRJkiRJusgEOUmSJEmSJEm6+P8B6C8v+/g1SX8AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "warnings.filterwarnings('ignore')\n", "\n", "cg = clustermap(phenMap, row_linkage=Z, col_linkage=Zt, figsize=(10, 10))\n", "for item in cg.ax_heatmap.get_xticklabels():\n", " item.set_rotation(90)\n", "for item in cg.ax_heatmap.get_yticklabels():\n", " item.set_rotation(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building a gallery of images for visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A. Gallery of tiles for several phenotypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# hard coded, but corresponds to tile size used for CHARM features computation\n", "X = [0, 580, 116, 348, 232, 464]\n", "Y = [0, 348, 87, 174, 261]\n", "ni = 15\n", "w = 116\n", "h = 86\n", "\n", "# some phenotype\n", "phs = range(20, phenotype_count - 10)\n", "\n", "tiles = np.zeros((h, w, len(phs) * ni))\n", "for kk, ph in enumerate(phs):\n", " ws = dfRhonAnn[dfRhonAnn['Phenotype ' + str(ph)] != ''].Well\n", " for ii in range(ni):\n", " we = random.choice(ws.values)\n", " we = conn.getObject('Well', we)\n", " i = we.getImage()\n", " x = random.choice(X)\n", " y = random.choice(Y)\n", " tiles[:, :, ii+kk*ni] = getRohnTile(i.getId(), x, y, w, h, conn,\n", " chan=1)\n", "\n", "plt.figure(figsize=(15, 15))\n", "imc = buildComposite(tiles, len(phs), ni)\n", "plt.grid(False)\n", "plt.imshow(imc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Disconnect when done loading data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "conn.close()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### License (BSD 2-Clause)ΒΆ\n", "\n", "Copyright (C) 2016-2021 University of Dundee. All Rights Reserved.\n", "\n", "Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n", "\n", "Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 2 }