{ "cells": [ { "cell_type": "markdown", "id": "c0801168", "metadata": {}, "source": [ "# Zivich et al. (2022): Life-Science Examples\n", "\n", "The following replicates the life-science case studies provided in Zivich et al. (2022). \n", "\n", "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "id": "744be0a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Versions\n", "NumPy: 2.3.5\n", "SciPy: 1.16.3\n", "Pandas: 2.3.3\n", "Matplotlib: 3.10.8\n", "Delicatessen: 4.1\n" ] } ], "source": [ "# Loading packages for examples\n", "import numpy as np\n", "import scipy as sp\n", "import pandas as pd\n", "import matplotlib\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "mpl.rcParams['figure.dpi']= 300\n", "\n", "import delicatessen\n", "from delicatessen import MEstimator\n", "from delicatessen.data import load_robust_regress, load_inderjit\n", "from delicatessen.estimating_equations import (ee_regression, \n", " ee_robust_regression,\n", " ee_loglogistic, \n", " ee_loglogistic_ed)\n", "from delicatessen.utilities import inverse_logit\n", "\n", "decimal_places = 3\n", "np.random.seed(51520837)\n", "\n", "print(\"Versions\")\n", "print(\"NumPy: \", np.__version__)\n", "print(\"SciPy: \", sp.__version__)\n", "print(\"Pandas: \", pd.__version__)\n", "print(\"Matplotlib: \", matplotlib.__version__)\n", "print(\"Delicatessen:\", delicatessen.__version__)" ] }, { "cell_type": "markdown", "id": "8b291ac4", "metadata": {}, "source": [ "## Case Study 1: Linear Regression\n", "\n", "The first example considers application of linear regression with an outlier. We will use some simulated data to compare simple linear regression to robust linear regression.\n", "\n", "### Benchmark\n", "\n", "Since the outlier was simulated, we will first load the data and use simple linear regression as a benchmark. The following block of code implements this" ] }, { "cell_type": "code", "execution_count": 2, "id": "11de71f2", "metadata": {}, "outputs": [], "source": [ "# Loading the data without the outlier to generate reference\n", "d = load_robust_regress(outlier=False) # Loads data without outlier\n", "x = d[:, 0] # Extract X-values (height)\n", "y = d[:, 1] # Extract Y-values (weight)\n", "X = np.vstack((np.ones(x.shape[0]), x)).T # Convert to array\n", "\n", "\n", "def psi_simple_linear(theta):\n", " return ee_regression(theta=theta, \n", " X=X, \n", " y=y, \n", " model='linear')\n", "\n", "\n", "# Linear regression without the outlier for reference\n", "lme = MEstimator(psi_simple_linear, \n", " init=[0., 0.])\n", "lme.estimate(solver='hybr')" ] }, { "cell_type": "markdown", "id": "e545c84c", "metadata": {}, "source": [ "Now we can load the data with the outlier and reuse the defined estimating equation above to estimate the simple linear regression model" ] }, { "cell_type": "code", "execution_count": 3, "id": "4a9c9c2e", "metadata": {}, "outputs": [], "source": [ "# Loading the data with the outlier\n", "d = load_robust_regress(outlier=True) # Loads data with outlier\n", "y = d[:, 1] # Extract Y with outlier\n", "\n", "# Linear regression with the outlier\n", "ulme = MEstimator(psi_simple_linear, \n", " init=[0., 0.])\n", "ulme.estimate(solver='hybr')" ] }, { "cell_type": "markdown", "id": "7be6529c", "metadata": {}, "source": [ "Finally, robust linear regression is used with the Huber method" ] }, { "cell_type": "code", "execution_count": 4, "id": "bdd0a577", "metadata": {}, "outputs": [], "source": [ "def psi_case1_robust(theta):\n", " return ee_robust_regression(theta=theta, \n", " X=X, \n", " y=y, \n", " k=1.345, \n", " model='linear')\n", "\n", "\n", "# Notice: the theta from the previous regression is used since\n", "# robust regression can fail when initial values are too far.\n", "rlme = MEstimator(psi_case1_robust, \n", " init=ulme.theta)\n", "rlme.estimate(solver='hybr')" ] }, { "cell_type": "markdown", "id": "83e9020f", "metadata": {}, "source": [ "To distinguish between our results, we will generate a figure of the three estimated regression lines" ] }, { "cell_type": "code", "execution_count": 5, "id": "65549ed1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\pzivich\\AppData\\Local\\Temp\\ipykernel_22584\\1702613546.py:21: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " plt.arrow(159.386, float(y[x == 159.386]) - 2.75, 0, 2.1,\n" ] }, { "data": { "image/png": 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H6N57pc6dpYceorUlAABIUjTZBgAAAIAgNmrUKK1YsSLCc4UKFVLLli1dhXTebHtbZ+sjt8EcPXq0X84XALydPi0NHCgVLy699Za7kC6VQvW0ZmirSmu6OrkP6dKmlZo2lX7/XfrhB6l+fUI6AACQ5AjqAAAAACBI7d+/X32tR1ykSrrGjRsrU6ZMcdqnrbP1th9vffr0cY4HAP5w8aKneq5ECfvvi3TypLt1dfWV1qiSPtRzKqqd7hbdcINn4J311ZwxQ7r11nidOwAAgD8R1AEAAABAkJo0aZLOnTsX4TlrYRnXkC6Mrbf9eLPjTJ48OV77BYDQUOnDD6UyZaS2baUDB9y9JrdruX5SLX2t+qqgte4WlSwpjR8v/fuvNHiwVLAgPwAAABBwCOoAAAAAIAhdunRJISEhEZ6rVKmSz+0uo2P7sf15s+PZcQEgLhYtkm67TXr2WWnrVndrbtImzdVj+lV3qpZ+dreoVi3p88+lTZukl1+WrruOHxgAAAhYBHUAAAAAEIRWrVqlPXv2RHiuWrVqfj1G5P3t3r1bq1ev9usxACR/9p+NOnU8HzYezo0C2qNJaqF1Kq/HNE+p3Myfa9TI/uMo/fST9MgjUpo0fjh7AACAhEVQBwAAAABBGtR5y5UrlwoUKODXY9j+bL8xHRcAorNli6d6rmpVTzWdG9l0TEPUXZtVSi00RWl1OZYF2aSuXaVt26RZs6QqVfiBAACAoJI2qU8AAAAAAOC7yJVtBRNo9pKFdYcPH472uAAQ2f790oAB0rvvWpted69PBp1Te72t7hqqHDoa+4LixaWOHaXnn5eyZOGHAAAAghZBHQAAAAAEIWtD6S137twJcpw8efLEeFwACHPihDR8uDRqlHTmjLvXJbUuq7mmq5/6qbBc/PelRg2pc2daWwIAgGSDoA4AAAAAgtCFCxciPE6fPn2CHCddunQRHp8/fz5BjgMgeNl/FiZOlAYNkrwKcGPVQJ9pkHqprDbGvKHNmnvqKalTJxueGe/zBQAACCQEdQAAAAAQhCIHc5GDO3+5ePFihMcZMmRIkOMACD6XL0uzZ0t9+kg7d7pfV1M/a6i66y79GvOGWbNKrVpJ7dpJRYrE+3wBAAACEUEdAAAAAAShQoUKRXh86NChBDnOwYMHYzwugJTnyhVpwQKpe3dp7Vr3627RXxqiHnpIXytVTBsWLSp16CC9+KJ0/fV+OGMAAIDAlTqpTwAAAAAA4LsqVapEeLxnz54EeRn37t0b43EBpCzLl0v33CPVq+c+pCuinXpPzfSHblW9mEK6O++UPv5Y2rxZ6tiRkA4AAKQIVNQFqL///lt//vmnM6j9zJkzypQpk/LmzavSpUurYsWKfmk3c/r0aa1YsUL//POPjh496jyXLVs2FStWTOXLl1fhwoX98J0AAAAASAhVq1aN8Pjw4cNOqFagQAG/HcP2Z/uN6bgAUoaNG6WePaV589yvyanD6qVBaqOJyqho5lumTi098YRn/pwFdQAAACkMQV0AOXnypMaOHavJkydr+/btMc6iqFatmp588kl1sFYQPvrll180atQoff311zHOsShYsKAeeOABvfrqqypbtqzPxwEAAACQcCwws/fs3pV0K1euVIMGDfx2DNtf5LaXVNQBKcvu3VK/ftK0aVJoqLs1mXVanTVKr2mEsulE1BtZS8sWLaT27T2tLgEAAFIoWl8GiPnz56tUqVLq1atXjCGdsXBtyZIlGjJkiE/HOH78uBo3bqy7775b8+bNi3XYvF3wT506VT///LNPxwEAAACQ8NKmTavWrVtHeG7NmjXatWuXX/a/c+dOZ3/e7Hh2XADJnzXe6dZNKlVKmjLFXUiXVhfVRhO0RSU1QH2jDumse8+IEdK//0qjRhHSAQCAFI+gLgCMHj1ajzzyiA4cOBDh+YwZM6p48eJO9dwtt9yiXLlyxfkYFrrdddddmj179jVfs5aaFSpU0G233aaSJUty4Q0AAAAEiZYtWzrXDd7spryzZ8/Ga7+2/vPPP4/wnB2nhVW/AEjW7D8fb74pFS/u+XzunLt1z+gDbVQZTVBb5df+a75+5bbbpA8+kLZtk1591WZv+P/kAQAAghBBXRKbMmWKOnfurCtXroQ/V7duXS1YsEDHjh3T1q1bnTlyf/31lw4dOuQEbu+//76eeOIJpwWmGydOnNCDDz6oDRs2hD93ww03aNiwYdq2bZv279/vzMOztjabN292ZtctX75cffv2VYkSJRLk+wYAAAAQf/ny5VP//v0jPHfkyBHNmjUrzmGdrbP1th9vAwYMcI4HIHm6dEmaPNlTQWeVdMeOuVtXWwu1SlX0gRqqpLZG+JoV4V18+GFpyRKlWrFCeuYZKwdOmG8AAAAgSKW64p0QIVFt2bLFqZQ7d/X2tHTp0um9995Tw4YNXa0/evSoE7jFxu56tUAwTK1atfTpp58qZ86csa61vx7WMjN79uxKKuvXr1f58uXDH69bt07lypVLsvMBAAAAAsnly5dVvXp15wY/bzly5HDm1RUpUsT1vqxtplXkRQ7p7rjjDqf9fpo0afx23gACg/1WaN48qWdPadMm9+sqa7WGqrvqaNE1XzuTOrVOPPmk8g0eLHEDMAAACHDrkziD4DamJNSqVavwkM7YXatPPfWU6/VuQrqffvrJmTMXxtpbfv3118qcObOrY6RKlSpJQzoAAAAAMbPwbM6cOU5Yt2/fvvDnLWyza4FKlSo57fQLFCgQ7T727t3rdNiIPJPO5M+f32mhT0gHJD+LF0vdu0vLl7tfU0JbNEi99JQ+VmpFvPd7T6pU2vrQQ7pr2jTly53b/ycMAACQDBHUJRGb9/Djjz+GP7aAzpeQzi3vtpo29H3y5MmuQzoAAAAAwaFYsWJatGiRateuHSGsMxa+2YfNvLawLk+ePE43j4sXL+rgwYNOSHf48OEo92shne3X9g8g+fjrL6lHD+nrr92vyav96qv+aqHJSq+LEb62WtKPt96qpz76SHeXLOn/EwYAAEjGCOqSyLvvvhvh8euvv+73Y/z222/6448/wh9b25sKFSr4/TgAAAAAkl7ZsmW1dOlSNWrUyJk5HZmFcdEFclGxdpdWSUdIByQfO3ZIfftKM2d6Wl66cb1OqIuGq5NGK4tOR5g/94WkGTlzqklIiF59/HGnKw8AAAB8k9rH7eEHe/bs0bfffhv+2FrRJES/U++5dKZJkyZ+PwYAAACAwGGhms2Se/PNN5UxY8Y47cPWDR8+3NkPIR2QPBw6JHXsKN10k/T+++5CuvQ6rw56S1tVQn00MDykOyNpvKSbJf3Qrp2mb9umx594gpAOAAAgjqioSwLffPONM/A9zL333psgx/nqq68iPL7nnnsS5DgAAAAAAofNkuvSpYuaNm3qtL4PCQnR7t27Y11XqFAhtW7dWi1atFC+fPkS5VwBJKxTp6TRo6Xhw6WTJ92tSaVQNdFM9VdfFdXO//9C/vxSu3b6KkcOTQkJ0ayQEN12220Jdu4AAAApBUFdErCWlN4qVqwY/mdrVTlt2jQtXrxYu3bt0vnz550ZEqVKldIDDzyghg0bqmDBgrEeY//+/REuxosWLaps2bI5fz516pQ+/vhjZ+D8P//848ywyJo1q3MxbgPoH374YT300EPcDQcAAAAEMXt/37t3b3Xv3l2rV6/WqlWrnM92nWDXGRkyZHDCuSpVqqhq1arOZ5trDSD4XbwoTZok9e8vHTjgft1D+kpD1EMVtPb/n6xUSercWXrmGSl9ej155Yoee/FF/nsBAADgJ1yFBUBQV7x4cSc869Chg6ZOnXrN9jt37nQ+bIh737591bFjR73xxhvOAHhfjmFsH88995wzMD6qeRXr1q1z7ri1i/QJEyaoWrVq8fxuAQAAACQlC99uv/125wNA8hYaKn30kdS7t7R1q/t1d+hXDVM33a1f/v/J+vWlTp2sDZDkNXvO5tAR6gMAAPgPM+qSwJYtWyL+EFKn1t133x1lSBfZ2bNnNWTIEKfi7WQMfSsiH8Mq5mbOnKn777//mpAuKnanba1atfTZZ5/Fui0AAAAAAEhaCxdK1omyYUP3Id3N2qi5ekzLdJcT0l2x2ZYvvSRt2iR9+aV0330RQjoAAAD4HxV1iSw0NPSagK19+/ZOy8uwO9Pq16/vBHHWhub06dPO195///0IAZtVxjVv3lyffvpplMc5duxYhMebN292Zk1cuTox2vbdpEkTVapUSdddd53T/mb+/PkR5tqdO3dOzz77rJYtW+ZU2PnDwYMHdcimWPsgcugIAAAAAAA8Vq2Sune/ou+/dx+oFdRu9VM/Ndd0pdVl7Zc0TtKtb72lJ1q35qUFAABIRKmuhCU3SBRHjx5Vjhw5ovxazpw5nQq2mjVrXvM1a41pg91nz54d4fn33ntPzZo1u2Z7a6P59ttvR3kcC/jGjx+vzJkzX/O1H3/8UY8//niEoM/m423YsMEvrS369evntO2MD2vPWa5cuXifCwAAAAAAwWrzZql3r1B99LH7ZknZdVQ9NETtNFaZdE5/SRolaY6kC5Ly5MmjjRs3Rvt7CwAAgORo/fr1Kl++fJJlELS+TGQWuEUlTZo0TjVbVCGdyZIli1NVZ60rvQ0ePDi8Ss7NcR5++GGnxWZUIZ2599579fnnnzvtOL2r8T6yJvcAAAAAACBJ7dsntXnxvMrefNl1SJdRZ9VVw7RNxdVVw/Wjzqm2pIp2A/DVkM5YVx8bhQEAAIDEQ1CXyDJav/coWFvK2Ia7W3g2ceLECCHa33//rcWLF7s6jlXEWSWdtdeMic3Li1yl9+6778a4BgAAAAAAJJzjx6Xe7Y6rZJHzemdqBl0KTRPrmtS6rBc1WZtVSm+ouz7WMZWVVE/S91Hc2GvddOrUqZNg3wMAAACuxYy6RGaVcVFp2bKlq/XFixdX7dq19d1334U/Z0HdPffcE+txHnzwQRUuXNjVcVq1aqXp06eHP16xYoUuXLig9OnTKz5efvllPfXUUz7PqGvQoEG8jgsAAAAAQDA6f16a0G2nBk28Qf9dyOZ6XQN9psHqqZzapAmS8xHVxPiCBQtq7NixznV3bDf2AgAAwP8I6hJZpkyZnDaXly9fDn/u+uuv16233up6H7Vq1YoQ1K2yydGRRBXU2Tq3brvtNudcz5496zw+d+6c1q5dqypVqig+rN+9fQAAAAAAgOhdvnBZs179XX0nFdLO8ze6fqnu1mINVXdl1XKNkDTLwr4otrNQrl27dhowYICyZs3KjwIAACCJENQlAQuq9llT+atKliwZoZ1lbG666aYIjw8ePHjNNnnz5r3mudKlS7s+hrXJLFGihDM0MabjAAAAAAAA/7ly4qS+eu1H9Zh+k9ZdvM31ulv0lxPQpdYC9ZP0/7f3XstuFg4JCXFu0gUAAEDSIqhLAmXKlIkQ1Pl651rk7Y8ePRrlMWJb54/jAAAAAAAAP9i1S8u6f6FuH1XWksuPuF52o3aor/rosmarm0L1/7fbXuu6665zKuisks5u0AUAAEDS411ZEihbtqx++OGH8MfnreG8D6wNpbfMmTNHeYzIEuI4AAAAAAAgHn77TRte/1A9F9TU53rF9bKcOqzOqYfobOg49dAFxdYD55FHHnFm0RUpUoQfFwAAQAAhqEsClStXjvD4wIEDPq2P3IIyZ86c12xjz9mb7127diXocQAAAAAAgI9sbv0XX+jfITPV77eHNF3DFKo0rpZm1ml1yD5V+4711oDQE4p4i+21ChYs6AR0jz32GD8mAACAAOR+MBr8pl69ehFm0m3fvl1HjhxxvX716tUxzqzzvlsupnUxsVBv9+7dcZ5xBwAAAAAAIjl1Sho7VkdK3Kauj29Wqd9maapedBXSpdVFtSn4hba+/6sG/9dWp568P8aQzn7v0KFDB23cuJGQDgAAIIAR1CWBPHnyqHr16hGemzt3rqu1ly5d0meffRbhuXvuuSfKbSPfLTdv3jyFhoa6Os4nn3xyzcy7vHnzuloLAAAAAAC82I2w3bvrTKHSGtZ+t0rs/F7D1VXnldHVy/RM0RXa+MUWTdj9iPI1qW0pnMaMGaPrr78+2k4+K1as0FtvvRXtNgAAAAgMQdP68sKFC1q/fr127Nihf//9V8ePH9fp06fDhyFny5bNafVYtGhRZz5b+vTpFchat26tX375Jfzx8OHD1bRpU2XIkCHGdZMmTdL+/fvDH2fNmlUPPPBAlNvWqlXLqbb7+++/ncfWBvO9997T888/H+Mxzpw5o5EjR0Z47umnn3b1fQEAAAAAgKt+/10aNUqXPvhE0y43VT/9pr0q6PrlqVNsi4ZMzKYqD9x+zdcKFCigIUOG6JVX/n+uXZYsWTRw4EC1bdtWadMGza98AAAAUrRUV65cuaIAdO7cOf3000/65ptv9PPPPzshnVWTuWFvRsuVK6e7777bCbHuvfdeZczo7i61xGKVbZUqVdLatWvDn3vuuec0derUCG0xvdndcLVr19Ypa5VxVffu3Z035jFVxj311FPhjy3Q/OGHH66Zkxfm8uXLatKkiT744IPw5ywI3blzZ5LNqLOfffny5cMfr1u3zvn5AgAAAAAQcKyTzfz5TkB3ZfFifabH1EuDtEllXO+iSpGDGjr+etWunynG7ewa/q677tLKlSv16KOPOrPoChcu7IdvAgAAIOVYn8QZRMC1vvzuu+/UrFkzpz2kzXKzN5lr1qzRxYsXZZli2Edk3l+zbW2Nra1fv75y587thE8W+gUKC+NGjx6tVKlShT9n1W4WLEaeJWfVg6NGjbompLOZcT179ozxOE8++aRTWee9LwsurUXGiRMnImy7atUq5xjeIZ2xYydVSAcAAAAAQFCwrj8TJ0o33yw9+qgWL76iO/WrntBc1yFdyfynNG3qaX21MlWsIZ1JkyaN03nHRmTYuAtCOgAAgOATEBV11sJyypQpGjdunLZu3eo8F3Za3kGW21ONak3Yc8WKFVP79u31wgsvOC0hktqwYcOcqrjI8uXLp0KFCjmvjb0m1vrTmwVnP/74o2655ZZYj7Fv3z5nJt727dsjPG/tQUuUKOFUzO3evTtCS80wLVu21LvvvquUnGYDAAAAABCtvXul8eOld96RjhzRn6qgHhqiBXrI9YuWN+dF9e2fVrlzf65OnV7RnXfeqY8//pgXHQAAIAVkEEka1J09e9aperP5bEeOHIkQqnmflrWytOqxm2++2enBnj9/fidky5w5s7Od7ccqzfbu3et8bNq0SZs3b47QKtN7n/bnHDlyqEuXLk4vd9tPUrLX4NVXX3UqAd2wuXNffvmlSpUq5foYFsQ99thjTtWc24q/vn376vXXX1dK/0cCAAAAAMA1/vzTaW+pOXOkixe1XUXVV/01S411xWUDo+uzhKprt9R64ol/1b37K/riiy/Cv2bX/dYlCAAAAMk7g0iyoG769OlO28YDBw44AZp3kJYhQwb973//c1o0WtvGihUrKl26dD7t3yrQ/vzzTy1evNiZdWdz2WzunQk7ln22Fps246158+ZKShYu9uvXT3Pnzo02sLNqwI4dO+qll15yquF8ZcFlSEiI03IzrHIxMnvtH3roIfXv3z/CX8yU/I8EAAAAAIDw+XM2VsMCuu+/d546pFwaqN6aqDa6KHfX6unTX1HbtqnUteslzZkzVn369HE66ngrUqSINmzY4HTBAQAAQPLNIJIkqLv99tvDK7vCDm991evWrevMp7PP/n4jam94FyxYoPfff19ff/21M3A5jAV2VatW1YoVK5TUbG7csmXLnIpAmydnlYN58+ZV5cqVnUo6f7G/aH/88YdTgRgaGqpcuXLpxhtvVI0aNZK8wjDQ/pEAAAAAAFK4s2el99+XRo+2O22dp07pOo1SZ43QazqprK52kyrVFTVtmkr9+0uHDq1Sq1atnGvz6Lz22mtOFyIAAAAk3wwiSYI6a6sYVtVmAZG1n7RZaNbSMjHYLDabuzZ+/HgdOnTIec7Oxzu8Q+BI6n8kAAAAAIAUyma5T5ggTZwoHT7sPHVB6TRJLdVffXVQeV3vql49acgQ6cYbTzgVdOPGjXNunI2J3dS8evVqp9MQAAAAkmcG4a5pegLIly+fM5tt165dziy0xArpwo5tx9y5c6dzDol5bAAAAAAAEODWrZNeeMFSNWnAACekC1UqfaBnVEYb9YrGuw7p7rxT+vlnmzl3RVu2fKayZcvq7bffjjWks5uc27dvr+LFi/vpmwIAAEAgSpsUB7WZcPZmM1OmTEpKGTNmVNu2bfXiiy9qzJgxSXouAAAAAAAgCVnDoe++88yfs89hT0taqDrqrqH6Q5Vd765MGWnwYOnRR6V//92lBg3a6YsvvnC1tkqVKk4nIBuDAQAAgOQtSYK6bt26KZBYYBdo5wQAAAAAABLBuXPSrFme+XPr10f40m+q6gR0P+h/rndXsKD0xhvSc8/Zo0saPfptp6vP6dOnY11rc+oHDRrk3FRsbS8BAACQ/CVJUAcAAAAAAJCkbGa9zZ4bP146eDDClzarpHppkD7W0653lz271LOn9MorkjUQ+u2339SqVSutWbPG1frHHnvMaYlZqFAhn78VAAAABC+COgAAAAAAkHJs2CC99ZY0Y4Z0/nyEL+1TPvVXX01SS112+SuTjBmlDh2se5B0ww3SiRMn1K1bb40bN05XrJ1mLCyYs20ftR6ZAAAASHEI6gAAAAAAQPJmgdn333vmzy1YcM2XjyurhquLRquTzug6V7tMnVp64QXp9dctbLNDXNHcuZ+pXbt22rt3r4v1qdWhQwe98cYbuv766+P0bQEAACD4EdQBAAAAAIDkySrm5szxBHRr117z5XPKoAl6WYPVU/8pl+vdPvaYNGiQVKaM5/Hu3bv18ssv68svv3S1vkqVKnr33XdVuXJl998LAAAAkiWCOgAAAAAAkLwcPiyFhEjjxkn791/z5ctKrZlqor7qr1260fVu775bGjZMuuOOiM+fO3dO3333Xazrs2TJokGDBqlt27ZKkyaN6+MCAAAg+QrooG7Xrl1+3V/atGmVLVs2XXeduzYWAAAAAAAgiPz9t2f+3HvvSWfPXvNlmxj3leqph4ZonW5xvdsKFaQhQ6S6daVUqa79esmSJdWnTx/17t072n089thjevvtt52ZdAAAAEBQBHVFixZVqqjeAceT9YEvUqSIqlatqurVq6tx48bKmTOn348DAAAAAAASYf7cTz952lvOnx/tZst0p7ppmJaoputd33ijNHCg1KiRZyZdTLp06aJZs2Zp48aNEZ4vXLiwxo0bp0ceecT1cQEAAJByxPI2MzDYQGZ/fly+fFnbt2/XJ598ok6dOjl3s7Vq1UonTpxI6m8VAAAAAAC4ceGC9P77ks15u+++aEO6DSqjBvpM1bXMdUiXK5enMM8K9Jo0iT2kM+nTp1eItdv0ukm4c+fO2rBhAyEdAAAAgjuoC2PVdZE/3GwT3XbGgrvz589rypQpqlChgtatW5co3wsAAAAAAIiDI0c8fSiLFpWaNZPWrIlys39VSC9qsm7RWn2uBq52bZMy+vSRtm6VOnSQMmTw7dRq1qypFi1aOB18fvvtN40cOdKZSwcAAAAEZevLZs2ahQdqP//8s1MFZ48tXDMFCxZUmTJllD17dmXIkMGpiNu7d6/Wr1/vDHI2YUHdo48+6synO3XqlI4cOaK1a9fqsA2XvrqN7dNm4tWrV0/Lly9X/vz5k/A7BwAAAAAAEWze7Clzmz5dOnMm2hfniG7QEPXQWLXTeWV09SKmTSu1bu0J6fLm/f/nL126pDFjxuj2229XjRo1XO3rrbfeUsaMGZUmTRp+gAAAAAjuoG769Om6ePGiXnnlFSekMxbKvfbaa2rSpInT5z0q1try22+/dXrAf/PNN85zK1ascFpd3nnnneHb/fXXX84bbjtOWFi3e/duvfrqq5o9e3YifZcAAAAAACBKdqPuL7945s998YXncTTOKJPeVnsNVXcdV3bXL+izz0oDBkglS0Z8fuXKlWrdurXWrFnj3CRsn629ZWyus7I8AAAAILm0vmzatKkmT57sBGm1atXSP//8ox49ekQb0hm7a+2hhx7S119/7QRu9kZ6//79uv/++53WE2Gs1aW1vPzqq6+cbcLCuo8//tg5DgAAAAAASAIXL0p2A+1tt0m1akmffx5tSHdJaTRJLVRKm9VDQ12HdHXqSKtWSXPmRAzprFtPu3btdMcddzjhnNm4caOGDx/un+8NAAAACJaKuqlTp+qjjz5y/ly+fHmnOs5aXPri2WefVWhoqFOBd/r0aTVs2NAZ5Ox9F9yDDz7otKZo06aNE9bZ9p999pm6devm9+8JAAAAAABE4+hRadIk6e23pT17YnyZLLb7TI+ppwbrb93s+iWtUkUaOlSqXTvS/q5c0dy5c9W+fXtnrEZkAwYM0DPPPKOSkUvvUihrC7pq1SrnY/Xq1U6HogsXLji/bylUqJCqVKnizOqzj7TWWxQAAABRSnUlbOBbACpevLh27NjhhGcLFixwKuLiqnr16vr111+dfVmF3vPPPx/h6xbOlSpVyjmesWPZMZH0bOagBbVh1q1bp3LlyiXpOQEAAAAA/GjrVmnMGLtjVzp9OtbNF+tuddMwrdAdrg9h+dqgQdKTT0qpI/UX2rlzpzN2Y/78+THuo3bt2vruu++c3y2kVNaxaNKkSQoJCdGeWMJUU7BgQaeFaMuWLZUvX75EOUcAAIBgyiACtvWl3Y0VFpply5ZNdawnRTw88cQT4X+2O+QiS506tR5++GHnDjr72GxDqgEAAAAAQMKw+4aXLrULdqlUKWns2FhDuj9VQQ/pK92jxa5Durx5pQkTpA0bpKefjhjSWVXYyJEjVbZs2VhDOrNixQpt2bJFKdHly5ed9p/FihVT3759XYV0xraz7W2drbf9AAAAIAiCurA+8HaX2o033hjvu9VKlChxzb4jq1y5cvifjxw5Eq/jAQAAAACAKFy6JH34oXTHHVKNGnY3bbTz58JsV1E11Qzdqj+0QA+5elmvv14aONBTrNemjZQuXcSvr1y5Urfddptee+01nTlzxtUNwDarzrrxpDTbt293OhV17dpV586di9M+bJ2tr1GjhrM/AAAAeARsk/BDhw6F/9kfLSXC9mHVct779pYzZ87wP588eTLexwQAAAAAAFcdPy5NnuyZP7drl6uX5ZByaaB6a6La6KL+f9Z8TGwkfdu2Us+eUq5c1379+PHj6t27t8aPH+/8jiA2RYoU0bhx45wuPCnRhg0bnJaf+/bti/LruXLlctpb5s6d25lPZ3Pq7PcuVkl3+PDha7Zfvny5E/otWrTIqWQEAABI6QI2qMuUKZPz2d40W694+xyfwG6r3UJ3VcaMGWPd3s02AAAAAAAgFlY9ZeGchXSnTrl6uU7pOo1SZw1XF53S9a7W2K8MmjaV+veXbrzx2q/b7xU+/fRTdejQQXv37o11f2nSpFHHjh3Vr18/ZcmSRSmRVb5FF9JVqlRJ1apVU4ECBaJdb6+zVS5G7mxk+7P9Ll261GmJCQAAkJIFbFDn/Ubv2LFj+v777503cXFlb8bD2J1eUfG+0+uGG26I87EAAAAAAEjxfv1VGjXK09oyNNTVy3FB6fSuWmlAqtd18Epu1y9hvXrSkCHSLbdE/XW7Abht27b66quvXO3PWmKGhITo1ltvTbE/Rpsl17Bhw2tCuhw5cqhBgwZOpaGb3+3YtjZqZN68eRHGjNh+GzVqpCVLljihKAAAQEoVsDPqrA2CVdDZh931Zj3jrX1CXHz44YdatmxZ+P6sH3pU1q5d63y2bYoWLRqv8wcAAAAAIEXOn/v4Y+nOO6W77pI++cRVSBeqVJqjZ1Um3Va10zjXIZ0d5uefpfnzow7pLl26pJEjRzotFt2EdNdff73Gjh2rX3/9NUWHdGbUqFFasWJFhOcKFSqkli1bugrpvNn2ts7WR26DOXr0aL+cLwAAQLAK2KDO7roKC9QsOLMQrX79+k51nS8+++wzNW/ePDzwM3bHVlR+tnf3V5UvXz5e5w8AAAAAQIpx4oT01ltSqVLS009bAuNqmV2lf6v7VfX6v9VIc7TtYmFX68qUkebNk5YulWrWjHobC5mqVq3q3Ph75syZWPf5xBNPaOPGjXrllVdSfIXX/v371bdv32sq6Ro3bhw+qsRXts7W23689enTxzkeAABAShWwQZ0ZMWJE+Fw6C9ms/WXp0qWd520ocXRCQ0O1cOFCp73Ck08+qfPnz4fPuLOwr1atWtes2bZtm1atWhV+PKvoAwAAAAAAMdi1S3rtNalwYalTJ2nHDtcv129p71Tt/Bv0oL7VHydLuVpjBVlTpkh//SU9+qhnLl1UrM3inXfeqT///NNVtdcXX3yhTz75JNpRGSnNpEmTdO7cuQjP2e9Y4hrShbH1th9vdpzJNr8QAAAghUp1JazMLEANHTpUPXv2jBDYhf25cOHCKlOmjLJly6b06dPr1KlTzqDidevW6ezZsxG2t8/FixfXL7/8ovz5819zHBsmbe0tTNq0aZ1e6Tlz5kzU7xVRW79+fYQKR/v5litXjpcLAAAAAJLKypWe+XPW2vLyZZ+W/nNDNfXOP0Ufb3DfycbGyPfoIb3yioU9sW9/+vRpp93lLgsSo2Fz0Tp27Kh+/fopS5Ysrs8lubN2oTYOxPsG6UqVKl0TsMWHBalr1qwJf2wtMbdv3+78PgYAACClZRAB/w6oe/fuzptnC+usUs67haW94f73338jbO+dO3rPuLM36N98802UIZ2xu+Zef/1158/58uUjpAMAAAAAwJsFcp9/7gnorOekj/aVrKk3CoRo8tKbdfloNKVwkVgo16GD1K2blD27+2Ndd911Gj9+vB5++OEov16tWjWFhIQ4ARQism5DkbsY2evlT7Y/76Bu9+7dWr16tW6//XZ+HAAAIMUJ+KDOdOnSRffcc49eeukl/fHHH85zYVV1kXlX3tmHtVV49dVX1bt3b6fqLjpdu3ZNoLMHAAAAACCInTolTZvmmUG3bZvPy4/f84jezDtSb31ZQme2uAvo0qSRXnhBsvtp49qN0kZf2DgMa2kZ5vrrr9eQIUOc3y/YTcGIOqjzlitXLhUoUMCvL5Xtz/Z7+PDhCMclqAMAAClRUAR15rbbbnPurlq2bJlmzJihJUuWaNOmTU6VXWQ33HCDMzD6kUceUZMmTZzWmAAAAAAAwAe7d0s2IiIkRDp+3LeXLl06nXvmOU3I20+DphXUkSPulz7+uDRokHTzzfH/aY0ZM0bffvutTp486YR2b731FnPoYmG/e/GWUHP7LKzzDuoiHxcAACClCJqgLsxdd93lfIT1nN+/f7+OHTum8+fPO4Fcjhw5om1vCQAAAAAAYmGBibW3/OgjG1jm28uVI4cut35ZM/O+qr6jsiuGEXHXqFVLGjZMiq374YkTJ5zWlm4q4iwMmjhxovP7AquwQ+ysDaW33LlzJ8jLlidPnhiPCwAAkFIEXVDnzd6YlyhRIqlPAwAAAACA4GbdaubP9wR0ixf7vr50aV3p2Elf5WmuHv0yat0690srVJCGDpUefNDGWUS/nY23sDaWHTp0UK9evdS2bVtX+2/cuLH7k4EuXLgQ4VWIaYxIfKRLly7CY7sBGwAAICVKrRTozJkzSX0KAAAAAAAkvdOnpQkTpJtukh591PeQ7t57pS+/1LIpG3X37Jf08JPuQ7qiRaX335dsFH3dujGHdDt27HAq4p5++mnt27dPPXr00N69e307V7gSOZiLHNz5y8WLFyM8zpAhQ4IcBwAAINAFdFC3YMECv+/z3Llzevjhh/2+XwAAAAAAgoaFXD17SoULS1aZtmWL+7Vp00pNm0q//64N435Qg8n1Vb1mai1Z4m55rlw2O07atElq0kRKnTrmMGf48OEqW7asvv766/DnbeacVdbB/woVKhTh8aFDhxLkZT548GCMxwUAAEgpAjqoe+aZZ/T777/7bX/2Bv/RRx/VTz/95Ld9AgAAAAAQNNaskZo185SzDRkiHT3qfm327FL37lbepn8HzdALY2/VLbdIn3/ubvl110l9+0pbt0rt21sFVczbL1++XFWrVlXXrl119uzZa75ubTDnW7tO+FWVKlUiPN6zZ0+CvMKRKyIjHxcAACClCOig7tSpU6pXr562b9/ul5Du8ccf18KFC/1ybgAAAAAABNX8ufvuk2691dNvMlLbwRiVLCmNGyf9+6+OdBmiLm8VVKlS0rRpnl27KcB75RVPQPfGG1LWrDFvf/z4cWf+3F133aW//vorxm1tu9PWvhN+Y+Got8OHD/u9zajtz/Yb03EBAABSioAO6sJaITzwwAPXvIHzxeXLl/Xss8/qq6++8uu5AQAAAAAQsGw+e0iIVLasZCMgfvzRt/V33y3Nm+f0qDzzfFsNHZdFxYtLI0ZI58+720XDhp4Wl2PHSnnzxrztlStX9PHHH+vmm2/WhAkTnMcxSZMmjTOzLlVMw+3gMwvMChYsGOG5lStX+vWVjLw/a3tJRR0AAEipAj6oM1u2bHGGRkfV6iI2oaGhaty4sT777LPwN+95Y7s6AAAAAAAgWO3fL/XpIxUpIr30kvT33+7XpknjSdcsSFm8WJfqPapJU9M4FXQ9eli1m7vd3H+/tHq1NHu2VKJE7NtbJx3rqGPB2347/1jcfvvtWr16tTO/LnPmzO5OCq6kTZtWrVu3jvDcmjVrtGvXLr+8gjt37nT2582OZ8cFAABIiQI6qOvevbtzB50FbL/99pueeuopJ3hzy9Y2b95cH330kbMPe5w7d24tWrQoQc8bAAAAAIBEZ20in39euvFGaeBA6b//3K/Nlk3q0sUSMyddu1L1Nn36qVS+vNSqlbUqdLcb615ol9zffitVruxuTMWbb76pcuXKacGCBbFunzVrVo0fP15Lly5VxYoV3Z0UfNayZUtlzJgxwnPz5s2L0w3U3mz955GGGtpxWrRowU8JAACkWAEd1A0ePFhNmjQJb3dhb9pb2RWCD28sZ86cGR7S5ciRQ999953KWtsPAAAAAACCnV0vf/ONVKeOZMHV9OnShQvu11svy7ffdubP6c03pcKF9dNP0h13SE8+6b4Yz8bYffSRpxDvf/9zt2b58uVOm8Vu3bq5CoDs5t2NGzfq5ZdfdtpeIuHky5dP/fv3j/DckSNHNGvWrDiHdbbO1tt+vA0YMMA5HgAAQEoV0EGdmTp1qmrXru0EbfYxbdo0vf7667Gus4HStjYspMuWLZu+/fZb7rgDAAAAAAQ/C0smT/aUvNWt6ylj80X16nJK5v75R2rXTrr+ev35p2dX997rCdzcsHxl4kRpwwYL0iQ34+KOHTvmhG133XWX/rIqwFjceOONmj9/vtMtp0CBAu5ODPHWuXNnp8Wot927d2vSpEk+t8G07W2drfd2xx13qFOnTvy0AABAihbwDcCtR/ncuXN19913688//3RCt4EDBzqDhq1iLir2Jm/ixInhId3111/vVOMxmBgAAAAAENQOHpQmTPB8HDrk21qrQrMyOQtGvAIY63ZpI+1sntzVhjaxyppV6tpV6thRuu46d2vs+vzjjz9Whw4dXM2hs6o5C4vsZt3r3B4EfmOv/5w5c1S9enXt27cv/HmriLMboytVqqRq1arFGJ7u3btXK1euvGYmncmfP79mz55NdSQAAEjxAj6oM1myZNHXX3+tO++807kLy97cW8WcvamrX7/+NXPtxowZEx7S2Zt5u/PO7tICAAAAACAorV8vjR4tzZwpnT/v21pL1exGV6ucs/l1V1nOZ6PsrCLu4kV3u0qfXnrlFalHDylXLvensH37duc63s0cOmOVXCEhIXTFSWLFihXTokWLnE5H3mGdsfDNPnLlyuWEdXny5FG6dOmcuYMHDx50QrrDhw9HuV/7fY7t1/YPAACQ0gVFUGesX7m9oa9Ro4aOHj2qS5cu6dlnn9X3338f3orB7rKzIdRhIV2mTJmcIcU1a9ZM6tMHAAAAAMA3Vt62cKE0apT07be+v3oWynXoIL34oiesu+rUKc8uhw/3/NkNa2nZrJn0xhsRsj7Xli1b5iqky5o1q4YOHerMp2cOXWAoW7asli5dqkaNGjlzBSOzMC66QC4qdiO1VdIR0gEAAATJjDpvN998s7744gtlzJjRCePOnDmjRx55RP/8848GDRrkDCAOC+kyZMigTz/9VPfdd19SnzYAAAAAAO5Zxdy0aVKFCtIDD/ge0llHmY8/lrZs8bS5vBrSXbggjRsnlShhN7q6D+mskY3Nr5s+PW4hnbGQx6qyYvLUU09p48aNatOmDSFdgLFQbcmSJc7N0fY7mbiwdcOHD3f2Q0gHAADw/1JdsVQryMybN09PPvmkE8jZxw033OAMow77VtKnT+8MmbYQD8Fv/fr1Km8D0q9at26dypUrl6TnBAAAAAB+Z70o33lHGj9eOnDAt7WpU0uPPy517izdeWeEL4WGSh9+KPXuLW3b5n6XtpthwyR/NanZsmWLc213PlLrzhtvvFETJkzQQw895J8DIUHZfMHJkyc7rUl3794d6/aFChVS69at1aJFC6dbEgAAQKBZn8QZRFAGdWbixIlOf/uwCrowadOmdVooWJCH5CGp/5EAAAAAQILatMkzf27GDOncOd/WZskitWghtW9vZU9Rds7s3l364w/3uyxbVho8WLJ7X63lpT9ZN5zelhhKTtXcq6++qr59+zrz5RFcbCTJ6tWrtWrVKuezhXYWwlqHIwvnqlSpoqpVqzqf7Xc1AAAAgWp9EmcQQftOyVph/Pvvv07v+rCwLnXq1Jo+fTohHQAAAAAgsFmK9uOPnmFxX33l+/rChT3hXMuWUrZs13z5t988Ad0PP7jfZaFCUv/+nll0adIoQXTp0kWzZs1yZtFZRVbFihUT5kBIcBa+3X777c4HAAAAUmBQZwYPHqy9e/dqxowZTkg3ZcoUp+89AAAAAAAByQbFffCBJ6CzwW++qlpVevVV6YknpHTprvny5s1Sr16eEXVu3XCD1LOn1LatlCmTb6dz8eJFjR07Vs2bN1eOHDli3d5GVSxcuNBpgWgVdQAAAEBKlyRBXfHixf3aaiHsTq433njD+YiNVeBt3brVb+cAAAAAAECM/vtPCgmRxo2T9u3z7cWy/pOPPuoJ6KpXj7Ifpe3SquEmTZIuX3a3WwvlOnSQunWTsmf3/ef366+/OrPH1q5dq02bNundd991ta5gwYK+HwwAAABIppIkqNuxY8c1s+XiyvZjH3YXn+3X7RoAAAAAABLcP/9IY8ZI06ZJZ8/6ttbmtr3wgidNK1Eiyk2OH5fefFN66y3pzBl3u7VCthdflF5/XSpQQD47duyYevTo4bSuDLuunzRpkpo1a6YaNWr4vkMAAAAgBUvS1pf+Dszc7M8f4SAAAAAAADFceEo//+xpb/nll57HvrD0zObPtWrl6UsZhXPnpAkTpEGDpCNH3O/aOmbamptu8v3nZ9fTH330kTp06KADBw5c83Wrrvvjjz+c9pYAAAAAAjioK1KkCFVtAAAAAIDk5eJF6aOPPAHd77/7vv7WWz3tLZ96yoa5RbmJtbV8/32pb1/p33/d7/qee6ShQ6Xbb1ecbNu2TS+//LK+/fbbaLfZsGGDRowYoZ428A4AAABAYLe+BAAAAAAgWTh61DMc7u23pT17fFtrnWEefljq3Fm6++4o588ZK8qbP1/q0UNav9797itW9AR0DzwQ7a5jZGMmRo4c6cyDP2dlfLF4++231alTJ2WyAXgAAAAAArv1JQAAAAAAQWvrVs/8ualTpdOnfVtrQdbzz3vmz5UuHeOmS5dK3bp5PrtVtKg0cKDUsKGUOrXiZNmyZU47y3Xr1rna/plnntHo0aMJ6QAAAAAfENQBAAAAAOCWlbZZYmbtLefN833+XL58Urt2NtBNypkzxk2tcs66SH7xhfvd58ol9enj2X2GDIqTo0ePqkePHgoJCXG1fdGiRTVhwgTVrVs3bgcEAAAAUjCCOgAAAAAAYnPpkvTJJ56A7rfffH+9rAdlp07Ss8/GmqDt2iW9/ro0Y4YUGupu99dd5xlvZx9ZsypOrly5og8++MBpXXngwIFYt0+bNq1effVV9e3bV5kzZ47bQQEAAIAUjqAOAAAAAIDoHD/+//Pn/v3X99epXj3P/Ll77411SNx//0lDhkjjxknnz7vbfdq00ksvSb17S3nzKs62bduml19+Wd9++62r7e+8806n4u6WW26J+0EBAAAAENQBAAAAQFK7dOmSVq1a5XysXr1au3fv1oULF5Q+fXoVKlRIVapUUdWqVZ0Pq2JCIti+3RPOTZ4snTrl29qMGaXnnpM6dpRuvjnWzc+c8Yy6GzbMkwu6ZfPnBgyQSpRQnF28eFEjR47UG2+8oXPnzsW6fbZs2TRs2DC1bNlSqeM6/A4AAABAOK7wAAAAACCJ7N+/X5MmTXIqk/bs2RPtdtOnT3c+FyxYUK1bt3ZCknw26wz+9+uvnvaWc+e67zsZxkraXnnFU+Jmw+JcdNOcOlXq10/at8/9Ye6/31N5V7my4mXp0qXO36f1NgzPhWeffVajR4/m7x4AAADgR0ly+9sdd9yhxYsXK1D8+OOPzjkBAAAAQGK4fPmyhg8frmLFijnzvWIK6bzZdra9rbP1th/4gSVmH39s/Rylu+7yzKLzJaQrX96TuO3Y4elBGUtId+WK9OmnUrlyUuvW7kO6qlWlRYsk604Z35DOquhq1KjhKqSzv28LFizQnDlzCOkAAACA5BDUrVy5Uvfdd5/uv/9+ff/990oqixYtUp06dVS7dm39Fpdh4AAAAADgo+3bt6t69erq2rWrq1aDUbF1tt6CFtsf4ujECWn0aKlkSenpp6Xly31b/+CD0nffSX/9JT3/vKflZSx+/NFuXpWefFL65x93h7HT+/BDu5aW/vc/+cX//vc/pUmTJsZtrM1q9+7dtW7dOj1o3ysAAACA5NX60kI6+6hUqZLatGmjhg0b6rrrrkvQY54+fVqzZ8/WxIkT9eeffzrPXblyRaliGeoNAAAAAPG1YcMG50bBfdGUUOXKlctpb5k7d25nPp3NqTt06JBTSXf48OFrtl++fLkT+tlNiGXLluUH5NbOnZ75c5MmSSdP+va6ZcggNWkiderkKYlzac0aqUcP6Ztv3B/Kupu+/rr04otSunTyK7sO79Spk0aMGBHl1++8806nJestt9zi3wMDAAAAiCDVFUupEtmgQYM0ZMgQnbGJ2XYSV0MyC+keffRRPf74487depkyZfLL8c6ePeu06Zg7d66++OILJ6wzYd965syZ1bNnT+cDgcdasZS3VjJX2d2c5Xy4IAYAAAACqZIuqpDOQpNq1aqpQIEC0a7fu3ev051kjSU+keTPn9+ZN2YtChGDFSs88+d8bW1pcueWXn7Z85Enj+tlVvDYp480a5b7Q2XNKnXtKnXsaNfJSjB2bWwB765du8Kfy5Ytm4YNG+bMQUydOkma8AAAAAApKoNIkqDO2B2h1qrlww8/VGhoqBPWeVe22d2jdqFaq1YtVa1a1XmRihcv7mrfW7du1dq1a7V69Wr9/PPPzsWs3Ylqwo4R9tmq+OwixO5aRWBK6n8kAAAAQHzZLDkL6VZYUOQlR44catCggYoUKeJ6XxaqzJs3T0eOHInwvM3dXrJkSaztDFMcm+M3b54noFu2zPf1ZcpInTtLjRtLPtxMevCgNHCg9M470sWL7takTy+98opk95DmzKlEMX/+fD388MPOn+36eNSoUcyhAwAAQIqyPqUGdd4vgA1DtwtN7xDNOblI7SjTpUvn3ClqH1myZHEq7mxbm89w8uRJ585U+7hkg8C9eO8v7BhWtdevXz8CnyCQ1P9IAAAAgPgaPny4c6Oit0KFCqlx48Zx6iRiXUNmzZql3bt3X3Oc1157Ld7nmyxYS8tp06S33vKUtfmqTh1PQPfAA3Yx6dNhLRO0jpKnTrlbY7tv1kx64w3pxhsVb3ajqt386lbnzp31wAMPOB8AAABASrM+pQd1YbZs2aIxY8ZoxowZTujmHdRFPsXo5slFt13Y8xbuPffcc+rQoYNK2jRuBIWk/kcCAAAAxMf+/fudlpR2g6F3JZ21FoxPu38L6yZNmhShsi5jxoxOi818Ntwspfr3X2nsWOndd6Xjx31ba+GWVc7Z/DkfZ7NZE5eQEGnAAOnQIffrrJht8GDJ65Inzuzad86cOerSpYu+/vprVaxYMf47BQAAAJK59UmcQQRMw3kLzsaOHetcxM6ePVv169d3LjK9wzcL3qIL6aL6uq21fdSrV8+52/TAgQPOMQjpAAAAACQWC9O8Qzpj7S7jO5Pb1tt+vNlxJk+erBRp1SqpUSPJ5vQNH+5bSGd9Jnv3lnbulKZO9Smks1F3s2d7OmS2b+8+pLvrLumXX6QvvvBPSGcjIKwizqo0bZ5h69atnZarAAAAAAJbwFTUReX8+fPOjLlffvnFmTf3119/ORcc0Z2yhXQ2fL1ChQqqUqWKatasqbvvvlsZMmRI9HNH8kmzAQAAgLiytvxFixZ1ZnSHqVSp0jUBW3zYGIE1a9ZEaKlpVXVp06ZVsmdB1JdfenpNWurlq5tu8lTPNW0qZc7s01K7LP32W6lHD8nr5Y9V2bLSkCGeSjofOmrG2OZyxIgRGjBgwDWB8Pjx4/Xyyy/H/yAAAABAMrY+iTOIgL5ys4CtTp06zkeYixcvOnMYjh8/rjNnzjjPZc6cWdmzZ1fBggWdOXYAAAAAEAhWrVoVIaQz1apV8+sxbH/eQZ1dL9mNjrfffruSrdOnpenTPfPntmzxff1993kCuoceklL73mhm5Uqpe3fpxx/drylUSOrf3zOLLk0a+cWSJUucyrkNGzZE+fUePXo4obDd0AoAAAAgMAV0UBcVC+JsvgMAAAAABENQ5y1Xrlx+D01sf7bfw4cPRzhusgzqLPQcN84zDO7oUd/W2k2dDRt6ArpKleJ0+L//lnr1kj791P2aG26QevaU2ra1dqXyC5tL2L17d6etakxOnDihjh076qOPPvLPgQEAAAD4XdAFdQAAAAAQLKyyzZt1AUkIFtZ5B3WRjxv0/vhDGj1amjPH+on6ttaSsjZtPElZHEPSvXulN96QpkzxdNt0w0K5jh2lrl2l7NnlFzYGYs6cOerUqZMOHjwY6/bW/tRmtIeGhip1HCoHAQAAACQ8gjoAAAAASCDWhtJb7ty5E+Q4efLkifG4QSk0VPr6a8/8OV96TIYpVcpTPWe9Jq+7Lk6ncOyY9Oabng6bZ8+6W2NtLV98UXr99TjnglHaunWr2rRpo4ULF7ra/q677lJISEiEWRsAAAAAAg9BHQAAAAAkkAsXLkR4nD59+gQ5TuRZ3efPn1fQslnkM2Z4Kuj++cf39bVqSZ07S/Xrx2n+nDl3ztNhc/Bg3zpsPvGENGiQdNNN8uvfoREjRmjAgAE6ZycWC5vfPmzYMLVo0YIqOgAAACAIENQBSFL2S6T169frwIEDzi8h7JdXefPmVbly5ZQhQwZ+OgAAIKhFDuYiB3f+cvHixQiPg/J91L590vjx0sSJNoTNt7Vp00rPPOOpoKtSJc6nYG0tLSO0arh//3W/7p57pGHDpGrV5FdLlixR69attWHDBlfbN2zYUKNGjVK+fPn8eyIAAAAAEgxBHYBEt27dOk2ePFk///yz8+fIv1gKuyvc2vTcfffdzt3AtOwBAADBqFChQhEeHzp0KEGOE3leWeTjBrS//vJUz82ebUmmb2tt+FurVlK7dvZNx/kUrlyRvvxS6tlTWr/e/bqKFaWhQ6UHHpBSpZLfHDlyRN27d9ekSZNcbV+8eHFNnDhR999/v/9OAgAAAECiYJo0gETz1VdfqVatWrrllls0ZswY/fHHH1GGdMaet6/bdra9rbP1AAAAwaRKpOquPXv2JMhx9u7dG+NxA3L+3IIFUp06nrRr+nTfQrrixaWxYz1lb1bKFo+QbskSqWZN6dFH3Yd0RYtKM2dKv/8uPfig/0K6K1euaPbs2SpTpoyrkC5t2rTq2bOnc/MbIR0AAAAQnKioA5DgDh8+rHbt2umDDz6I8z6s+s4+nn32WY0dO1a5cuXy6zkCAAAkhKpVq17zvshCtQIFCvjtGLY/229Mxw0YZ896Ei6roNu40ff1NWp45s898oiUJk28TmXdOk8FnVXSuZU7t9Snj9S6tbU1lV9t2bJFbdq00aJFi1xtX716dYWEhDgt4wEAAAAELyrqACSoH3/80fnlQWwhnf2qqsTVzzGx/dj+bL8AAACBzgKzggULRnhu5cqVfj1G5P1Z28uAq6g7cMAz+K1IEU+rSl9COgvknn1WWrFC+uUX6bHH4hXS7dolPf+8VKGC+5AuSxbP6W/d6umy6e+QzowcOdJVSJc9e3a9++67zk1shHQAAABA8KOiLkD9/fff+vPPP7V7926dOXNGmTJlUt68eVW6dGlVrFgxOIfDI8WZP3++nnjiCV2Ioo1RHkkvSrIpGrdKyub1teOS/pD0naQpNnMlihksdevW1SeffKL69esn+PcBAAAQV9aasHXr1urbt2/4c2vWrFHlypVVxEKreNq5c6ezP292PDtuQLCyNauesyo6X+fPZc0qtWwptW/vCfji6b//pCFDpHHjpPPn3a1Jl0566SWpd28pj72BTUCDBg3Sp59+GuMcw0aNGmnUqFHOtSEAAACA5CHVFWuCj4Bw8uRJp6Xf5MmTtX379mi3S58+vapVq6Ynn3xSHTp0iHGfqeI5LMHOo6gNYEhC69evV/ny5cMf2/wF7hwNfFbx9uCDD14T0t0gaYSkJvZ32cV+bPVMSa9JOhrpaxZYL1iwQPfee69fzx0AAMCf9u/fr2LFiuncuXPhz+XIkUMtW7Z0bsiLq7NnzzpzzI4cORL+XMaMGZ338Pny5VOSsUvMhQulUaOkb7/1ff2NN0odO0ovvOAJ6+Lp9GlpzBjPKLsTJ9yva9RIGjDAMw4vscyaNUtNmtg75YiKFy+uiRMnMocOAAAASIYZBK0vA6jyqFSpUurVq1eMIZ2x4GPJkiUaYreDAgHIZqTYLLnIId1DkjZIesFlSKer29n266+u93b+/HnnOJFnsgAAAAQSC8369+8f4TkL1yyUsbAtLmydrfcO6cyAAQOSLqSzIHLqVOmWW6QHHvA9pLv9dumjj2xYmyeoi2dId/GiFBIilSwp9erlPqSzU//9dwvNEjekC6uYq127dvhjq4zs2bOn84uC+++3XhQAAAAAkhuCugAwevRoPfLIIzpgcxu82N2wduekVc/dcsstypUrV5KdI+CLdu3aOe0pvTWX9IX9oiqOL2X+q+ufi/S8HceOBwAAEMg6d+6s2y2I8mJt7q0ibpcNTfOBbW/rbL23O+64Q506dVKis1aNFkRaJdyLL9rtqO7Xpk4tPfmktGyZtHy59NRTlk7Fu6Dv448luwHW2lbu3+9u3W23Sd9/L33zjXSr9WZPAtYRxSrnrHNE9erVnbam1hIzPpWXAAAAAAJbgAwuSLmmTJniXLR7s9lb7du3d9r5RZ5Ft3fvXv3www+aN2+ez0PoK1So4Awo90WStsxBUPrqq6/0wQcfRHjOKuEmS0oTz32nuTqzzqZ2fO31vB3PWgTVq1cvnkcAAABIGGnSpNGcOXOc8GXfvn3hz1tF3NSpU1WpUiXnBr0CBQpEuw+7FrBrgMgz6Uz+/Pk1e/Zs5ziJZuNGz/y599/3VNP5IksWqUULz/y5YsX8dko//CB17y799pv7NaVKSYMHS088YUGZ/M66THz44YfO+1U3owlKliyp5cuXO9dvqS3IBAAAAJCsMaMuCW3ZssWplAubVZEuXTq99957atiwoav1R48e1Q032MSv6HlfCNaqVUs//fSTgk1S94eFb+zv2c8//xz++Iar7S79Gfnar7bKRZpZF6x/vwEAQMqyYcMGp7Whd1jnzbpoWFiXJ08e5/rg4sWLTgcBC+mia/dtId2iRYtUtmzZBD77q+VqlobZDYALFvi+vnBhyeZsW0iXLZvfTuuPP6QePXzrtpk/v/T6655ReOnSKUH88ssvat26tTZu3KiZM2eqcePGCXMgAAAAAEGbQVBRl4RatWoVYaC8zZh4ylq9uBRbSAckNvsPmHdIZ0b4OaQLa4M5XFILr+cWL17s/AeVEBcAAAQyC9OWLl3qzCKzqqnILIzzZf6utbu0SrpifqxKi9L589bGQBo1SvrrL9/XW1/JV1+VHn/cr6nYtm1S797SnDnu11g+2K2bJy/MnFkJwiolu3bt6nRQCWNtSa17So4cORLmoAAAAACCEn00ksjnn3+uH3/8MfyxBXS+hHRAIJo82Rpc/r88kpok0LGaXN1/TMcHAAAIRBaqLVmyRG+++aYzlzoubN3w4cOd/SRoSPfff9KgQVLRolLz5r6FdNbd47HHrKxMWrFCeuYZv4V0Ng7ZumbefLP7kM6mClhWuHWrp/ouIUK6K1euOJVzN998c4SQzhw6dEjdLCEEAAAAgGAJ6ooXL+58lChRQr/++mu89rVs2bII+0tq7777boTHr1vPFSDIRa6me1FS+gQ6Voar+4/p+AAAAIHKZsl16dJF27dv14ABA1SoUCFX62w7297Wvfbaawk3k+7vv6U2bTytKq1kbf9+92uvu05q107avFmaO1eqUcNvw99OnpT69ZPskm7sWOnixdjX2Jg3yxj/+UcaMULKmVMJYvPmzapTp46aNm3qhHJRsRvLrB0mAAAAAARF68sdO3aEz1k7e/ZsvPZl6733l5T27Nmjb72GJ9jgeNr1IdidP3/eaX3p7f4EPmYdSUO8Hq9du9Y5jwx2uzQAAEAQyJcvn3r37q3u3btr9erVWrVqlfN59+7d4e9rLJyrUqWKqlat6nxOmzZtws2fs5m/1t5y/nzf1xcs6AnoWrWyPv1+PbULF6SQEGnAAKtMc7/ukUekwYOlhBwvceHCBac6cuDAgc7PLDY9evRwKiEBAAAAIOCDukAI1RLCN998o8uXL4c/vvfee5P0fAB/sPlwFyPd0nxrAr+0lSM9tuPbeVSuHPkrAAAAgc3Ct9tvv935SHSWgn30kSeg++MP39fbey/rKWmt/P04f86EhnpG41lR3/bt7tdVry4NG+b5nJCsOq5169bauHGjq+0bN26skSNHJuxJAQAAAAgqAR/UJUe//fZbhMcVK1YM//Mff/yhadOmafHixdq1a5dzR2aePHlUqlQpPfDAA2rYsKEK2p2q8bBv3z7t3btXp0+f1g033KBcuXIpf/788doncODAgQgvQgFJ2RL4Zcl29Th7vZ47aANLAAAAELsjR6wnv6eH5F7vd1Qu2A2VDz/sCehq1vRba0vv4j5rQmKz5Nascb/OKueGDJHq1/f7KUVw5MgRde3a9Zo5dNGx8QsTJ050WmMCAAAAQIoM6rwrfdL5+S7P+AZ1Njfv1KlT6tChg6ZOnXrN9jt37nQ+Fi1apL59+6pjx4564403fP4+rC2gHcvmWUTVcqdWrVpq3ry5HnzwwTh8V0jprOWPt0yJdNyMkR67aTcEAACQotnsuDFjpGnTpDNnfFubKZP0/PNShw5S6dIJcnorV0rdu0s//uh+jY3S699fatrU5v8pwVy5ckWzZs1S586do51D582u2SzQ69WrlzLZawcAAAAAKTWosyqyMNdff32SnsuWLVsiPE6dOrXuvvtup5rOzay9IUOGOGHf3Llzffpe7K5P+4jK/v379eGHHzoft956q9577z3dcsstrvcNpE+fPuLf1UR6Sc5Fesx8OgAAgGhK1H75xdPe8osvPI99YR04wubP5cyZIC/xP/9IvXpJn3zifk2OHFLPnlLbtlLGyHdw+dnmzZvVpk0bff/99662r1Gjht555x3mkQMAAACIUYoJ6uZfHYZuM++KFCmSZOcRGhqqkydPRniuffv24SGdnV/9+vX10EMPOUPjrT2lfe3999932lWGseo6q3779NNP/X6OdjybjWFh3VM2Z8KPrC2hmztPYwo2EZjy5s0b4bH9bT2ewO0vbf+RmzRZq1gAAABcZZ1FLPmygG7VKt9fFmvTb+0tn3lGinRjlr/YZc4bb0jWRdJrlHeMrDitUyepSxcpe3YlKOvYMHz4cA0cONBV9wYbL2DbP//8885NmQAAAAAQ0EGdzWFzO//K7bZhLUnOnDnjtHn85JNP9Nlnn4V/rbINO08ix48fd87N2++//+58zpkzp3OeNW3Gg5dnnnlGvXv3doaUz549O/x5q6ibMWOGmjVrFuMxbQadhX+1a9dWhQoVnADQKvGs3aa9pjYAfdKkSfrzzz8jVO41adLECV+s2s9fJkyY4LTtRPJTrlw5p7WPd5tZi5/vScBjev7l/D87vp0HAABAinfsmDRpkvT229Lu3b6/HA895Ano7r03wYa92Sm++ab01lt2/eFujbW1bNFC6ttXKmDDihPYzz//rJdeekkbN250tb1dQ40cOZKbxwAAAAAET1BXtGhRp4osOmGhll3w+MvTTz+tpGLhWFTSpEmjr776yqlki0qWLFmcqrrDhw/ru+++C39+8ODBatq0abSv4cyZM52quMhtCU327NmdDwvv2rZtq5CQEGdOXthdojZzrFGjRk5FW8aE7iODoGctJ8uXLx+hhet3CRzULYz02Nq10voSAACkaNu2eebPWXna6dO+rbX3/M89J3XsKN18c0Kdoc6dk8aNk4YMsfb87tc9+aQ0cKB0001KFJ06ddJbliK6ULJkSU2cONG5ORIAAAAAfBEwfTgskIv8EdvX3X6YsCCrTp06zkdSiS7watGiRbQhXRhrm2IXf97tU/7++28tXrw42jWNGzeOMqSLSljFnvf+9+zZo/Hjx7taD0SuvpxigW8CvSznr+7fmz+rPwEAAIKGXfMsXSo98YRUqpSnis6XkM5amA8YIP37r/TOOwkW0llby2nTpNKlPS0r3YZ0VtS3YoX08ceJF9KFhW+xsY4O1v3kr7/+IqQDAAAAECeprkROxBKZhUIWoiX0adgFlFWe2R2RVp2WVKylZObMma95ftWqVapSpYqrfTzwwAMRqur69eun119/3W/naLPvbD5dmIoVK2rNmjVJOqOuQYMG4Y/XrVtHe8MAZT8bq2rzZmHaCwlwLNtviyiOT+tLAACQYly6ZP3wPfPnLMnylb1v69xZatjQ2iMoodil3pdfSj17SuvX+zYeb9gw6f77E6z7ZowuX76su+66SytXrozy6zVq1HC6kpQtWzbRzw0AAACA/6xfv97pFpdUv2dO8taXz1lrlWhYWORdCZc/f36fAsDrrrtOOXLkcF7Qe+65R7lz51ZSy5Qpk9Pm0i76wti8uFtvvdX1PmrVqhUhqLOQz59effXVCEGd3R1qMwJtXl185cmTh3kNyZj9x8yq2myWR5jXbMSJpHx+PM4+SV2i+HdBSAcAAFKE48c9rS2txaUPc7zD1a3rCej+978ET8CWLJG6d378bUMAAQAASURBVPcU/LlVrJinxeWzz9p1nZKMXbdZEFe1atUI12833HCD3nzzTb3wwgsRupEAAAAAQFAGddOs90k0vMOirl276r777lNyYGHVvn0WNfx/SxVfLvBuitTvxarU/Mkqouwcw/Zr1Y7//POPX4I6JH/2b9U7qDsq6UVJX9gvO/yw/8tXK+lsv966WP8kAACA5GzHDk9by8mTpZMnfVtrFXNNm3rmzyXCnaHr1nkq6KySzi27r7JPH2vJL7ns3p/gKlWq5MyqGzFiRPjs9JEjR3LzIQAAAAC/Cfjb/5K4M2eCKFOmTITHWbNm9Wl95O2PHo0cWcRfoUKFIjz2tV0lUq569erpWbv92cvXV8O6/78POW4uX92P7c+bHc+OCwAAkCwtXy49/bRUooQ0erRvIZ2lX/36eSrvJk1K8JDODtO8uVShgvuQziYTWCf/rVuldu0SJ6Tz5TrTRg3Url1bCxcu1Pvvv09IBwAAACB5VdS5rbZLTi3tbIbBDz/8EP74/PnzPq0/d+5chMdRzbzzx0w/bxcvXvT7MZB8jR071vk77l3tafWxFvdOluS+ie3/23e1ki5ySGfVn3Y8AACAZMVaLX72mWf+3K+/+r7e5qZ16mQlYFLGjEpo//0nDR4sjR9v1zfu1tglx0svSb1723s6JQoL6GbOnKm3335bP/30kzMuITa2jYV0AAAAAJDigrqY5tcFs8qVK0d4bPPffBG51WXOnDnlb/v374/wOBDm+yF45MqVSx988IHq1q0bIYi2kM0id2sc1ESSm5ulbfXMqzPpIteOZsiQwTmOHQ8AACBZsGq5sPlz1urSV/ff75k/Z58TeP6cOX3ac6rDhkknTrhf16iRNGCAVLy4Eo2182/Tpk34TZNWKTd8+PDEOwEAAAAACMbWl8mRtejznkm3fft2HTlyxPX61atXxzizLr52796tnTt3RniucOHCfj0Gkr97771Xn3zyiROmeQubWWd/o3pK+lHS8Uhrj1993r5eJJqZdLZf278dBwAAIOhZz0ibuWst6K0SzpeQznpFvvCC9Ndf0rffSg88kOAhnTXceOcdm7ct9erlPqR78EHpjz+kWbMSL6SzG8f69+/vzOL27mwyevRorVmzJnFOAgAAAACiQVCXBKxVX/Xq1SM8N3fuXFdrL126pM+sBY6Xe+65x6/nN8Xu4I0U0pUqVcqvx0DKUL9+fS1YsCDKOR5WFzpE0n2SsksqKKnE1c/Zrz5vX49YP+ph+7P92v4BAACC2m+/SQ0belKrESN8K0uzzhp9+kh2k529h7/lFiU0G+328ceeUXdt2lgnDnfrbrtNsoxswQKpUiUlmsWLF6tixYp6/fXXdeHChQhfu3z5slq3bu18BgAAAICkQlCXROyC0Ju1XHEzq27SpEkR2lJmzZpVD9gds36yceNGjRw5MsJzDRo08Nv+kfJYxdv69ev17LPPxrjdXknbrn6Oie3H9kclHQAACPr5czVrStWqSR984HnOLeuoERIi/fuv1L+/lC+fEoMFbXa6Tz8tbd7sbk3p0tInn0grVtj7QiWa//77Ty+88IJzU+Pff/8d7XYrV67UO1YaCAAAAABJJGiCutDQUH355Zd6+eWXdccdd6hgwYLKkiWL0qRJ4/NH2rRJP5qvYcOGTusV73kJFt7Z9xmdFStWqGvXrhGes9cjW7Zs12xrLVyslcuZM2dcn5OtefDBB3XS5mJclSlTJnXv3t31PoCo2Ay5OXPmaP78+apVq1acXiRbZ+ttP8ykAwAAQenUKWnsWE969fjj0pIlvq3/3/+kr76SNmyQWrWyN+tKDNaq0u4NtMOvWuVuTf78nixx/XrpiScSZVye48qVK5oxY4ZuvvlmTZs2Ldbt06VLp9M2aA8AAAAAkkhQBHXz5s1T0aJFncqukJAQ567Hffv2OSGUXYjF5SOp2Yw6C9JSeV2xvvfee051XOQZdMePH9eoUaNUu3ZtnbKL+6tKly6tnj1tite1jh07ps6dO6tIkSJq1aqV0ybw8OHD12xnr8XatWvVvn17JwDdZbMxvAwZMkQFChTww3cMeOYz/vTTT1q3bp06duyoypUrO78ciYo9b1+37Wx7W2frAQAAgs7u3ZLd/GZzn9u3l7ZZHwGX7L1S06aetGzRIumhh+xiQolh61apUSOpcmXpu+/crbF7CAcPlrZs8WSJiXmPpN38+L///U/PPfdclNc+kd199936888/r7kZEgAAAAASU6orgZBaxWDgwIHOPIGw0/QOtnw9dVtra+xzoMwhGDZsWJQVa/ny5VOhQoWcuzu3bt16zTyFnDlz6scff4xQlefNQo2oWgPmzZvXqUa6/vrrndBvz549Onr0aJT7ePXVVzXC5mQkMWtzWL58+fDHFtqUs6EYSBas5av9jA8ePOj8OUOGDM4MOvsZ258BAACC1u+/S6NGSR9+aMOmfVt7ww2eIXBt20qJfOPcgQPSgAGeiji3p21v29q18+SRNjovMdl7yKFDh2rw4MHXXDdFJUeOHM51TvPmzSNcXyZ3Nu981apVzofdHLp7927n9UqfPr1z7VmlShVVrVrV+QiELjQAAABASskgAvrdt7W569u3r/PnsAuosHDOKsXsl/nXXXedglm3bt2UOXNmJxS7ePFi+PM2h857Fp23m266yWkDWqpUKZ+Pd+DAAecjJjb3bsKECWrcuLHP+wd8ZWGcVc4BAAAkC9bKfv58T0C3eLHv6+09fqdOUrNmUiJf65w4Idm4avtw2w3Sivuee07q18+u0ZTo7AbFl156KcY5dN6aNWvmhHS5c+dWSmHXlTbr3LrT2I2a0Zk+fbrz2cZM2FiGli1bOjeQAgAAAEjBFXWWWG7cuDG8Es5aMPbp00dPP/20brA7TJORTZs2qV+/fpo7d26EwM5bsWLFnDaAdiFqdz3GNjx9ypQpTtWdtQo9cuRIrOdgcxxs4HqLFi0C6vVN6jQbAAAAiJUlWzNmSKNHS5s3+/6C3XOP1Lmz9QpPtNaWYc6f91TPDRwoHTrkft0jj3jaXCbFW3O73unSpYurOXTGbnJ85513dN999ymlsC4yNkLBbn49d+6cz+szZsyo/v37OyMVbNY7AAAAkFytT+IMImCDOguuypYtGx7SlSlTRosXL3baNiZnJ06c0LJly7R582ZnNl2WLFmcdpVWcWSVdHG1c+dOZ582g85aXZ49e9a58LJALn/+/Lr99tuddpqBKKn/kQAAAADR2rtXGj9eeucdycXNcRFYe8Fnn/VU0CVBhwEr/ps9W+rTR9qxw/266tWthb/nc2Kza8MZM2Y4HUksrIuNzT3u0aOH82HXPynF9u3b1bBhQ61YsSLe+7JZ5rNnz3ZuHAUAAACSo/W0voyaVYGZsJlyEydOTPYhXVjbyQcffND58Kcbb7zR+QAAAADgB2vWeKrn5syRoumIEa3s2aXWrT1D3QoWTPQfh92q+c03Uo8e0p9/ul11ScWKrVKdOqt04cJq9euX+PPNrL2ldRexdpdu3H333U67R+sckpJs2LBBtWvX1r59+6L8ul1XW3tLa/9pPz/7OR46dMhpi3n48OFrtl++fLmqV6+uRYsWOTfTAgAAAEghM+oOHjwY/me7gLCLLAAAAABIMlaCtmCBZ/7cDz/4vr5ECaljR6l5cylLFiUFK7Dq3t1mu7ldsV9Zs05SmjQh2r59j959N2nmm9kNnI0aNdLvv/8e67Y5cuTQyJEj9dxzz4XPOk9JlXTRhXSVKlVStWrVnJES0dm7d69z0+waC6K92P5sv0uXLqWyDgAAAPCzxB1+4GM/fWMXVlSCAQAAAEgyZ8/KSais/Xr9+r6HdDVrSnPnWkmY9MorSRLS2aGfeMLaGLoN6S4rU6bhSpu2mE6c6KujR/e4Oo5VZdlMNGuTOHz48PDruviy68K33nor1u0snLMxCs2bN09xIZ291tbuMnJIZ8GlzSJv0KBBjCGdsa/bdra9rfNm+7Ww1F8/UwAAAAABHtR5X0DYPDUAAAAASFT790t9+0pFinhaVW7a5H5tmjRSw4bW01/6+Wfpscc8zyWyPXukVq08GaNlhW5kyLBdBQpU19mzXXXp0rk4HffcuXPq2rWratSo4VR5+UPNmjXVokWLKL9WunRp/fDDD05ln3VkSYlGjRp1zUw6a0tq1Y1F7O+wD2x7W2frI7fBHG0tXwEAAAD4Taor1kMkAP3zzz/hswRsbtuRI0eUOnXA5opIxoMcAQAAkMKsXeuZPzdrlnThgm9rs2XzJGM2f65wYSWVY8ekYcOkMWM8BYFuWI74xBMbtHhxbR044J/5ZiZ//vx+m29m14V2nWjHNHYO3bt3V48ePZQxY0alVPv373eqGC0gDWMVcRa2ZcqUKc77tZtmJ02a5LzuYex1tvDVn61NAQAAgJScQQTsjDq7I9IGkq9evVonT550Bobfd999SX1aAAAAAJIju3/xu++kkSOlhQt9X1+0qGf+3AsvSNdfr6RiOc24cdLgwdLRo+7XPfWUFQ1uV9OmUYd0gTLfzMInq+hq0qSJatWqpXfeeSf8Bs+UzMI075DOWAvL+IR0xtbbfqZOnRr+nB1n8uTJ6t27d7z2DQAAAMAjoEvUBg0aFP7nPn36OAPEAQAAAMBvLNyYMkWyuycffND3kO6uu6RPPpG2bJE6dEiykM7Ghk2bJpUqJXXp4j6ku/deT3fOOXMuq1evpJlvZsHPz9Ye1CXbz/z58/Xjjz8S0km6dOmSQkJCrglWfW13GR3bj+3Pmx3PjgsAAAAgmQd1999/v1577TUnoLNe+Na2g8HVAAAAAOLt4EHpjTc88+ds7tmGDe7XWkv+p5+Wfv1VWrrU+kUmyfw5Y/cyfv65VKGCp5hv92536yx3+eYb6fvvpdtuS7r5Zha2VaxY0bn222JhpwupUqVSvXr1nM+QVq1a5bQe9WbVj/4UeX+7d+92ut8AAAAASOZBnXnzzTfVrVs3J6ybNm2aqlevru+sJQ0AAAAA+MoCuZYtPQFdv37S1Vlnrli1XOfO0tat0ocfSnfckaSv/5IlUo0a1uLQfc5onSdnz5YsY3ngAQu9PPPN+vbtG2E7q4hr3LhxnFsn2jpbH7myzjql2PFsnl3z5s2d8QY2n/z8+fNq06YNXVTiGNRFniMYW/Wjr2x/tt+YjgsAAAAgiGbUWSsUX9ksAxtY/dtvv6lu3brKli2bKleurDx58vg8NNzuvJxi7W0AAAAAJH9WdmalY6NGSQsW+L7+xhs9bS1ffFHKmlVJbd06qWdP6csv3a/JnVuyLK5VKyl9+qSdb/byyy87rS7/+++/CNsvWrRIs2fPdgI+uBe5sq1gwYIJ8vJZWGcBa3THBQAAABBEQd306dPj1KbE1oTNqTt27JjTJsVXtp6gDgAAAEgBzp+34WuegG7tWt/X33679Oqr0mOPSWmT5NIpgl27PGHbjBme7NGNLFmk117zFAJGNT4vseabrVmzJvy5zz77LNrtO3Xq5NyYGbkSD9GzNpTeclsqmwDsJtmYjgsAAAAgmba+jMxCtrAPAAAAALiGVf0MHOiphHv+ed9COps/ZzPnbPbc8uXSU08leUhnhWeWF5YuLb33nruQLl06qV07T5fO11+POqRLqvlmMTl06JDesNmBcO3ChQsRHqePXDLpJ+nsL5UXa1cKAAAAIP6S7IozrDIOAML+mzBz5kw1bdqUFwQAAMTNpk3SW2950qxIrRxdlZ5Za8v27aXixQPiJ3D6tOfbefNN6cQJd2vsfsZGjaT+/d19G4k538y7bWJ0bG6dzbGDe5GDucjBnb9cvHgxwuMMGTIkyHEAAACAlCZJgjqbNQcA3pYsWaKePXuqUaNGSpMmDS8OAABwx24AtJb41t7yq698f9UKFfLMn2vRQsqePSBedctDbKS2FZbt3+9+3YMPSkOGWOvKwJ9vFlnp0qX1zjvv6N57702Q4ydnhezvcKSqxIRw8ODBGI8LAAAAIIiCuhutBQ0AeJk6daoz52LhwoV60H7LBAAAEBOrGvrgA09A9+efvr9WVat6+klam8tILf2SMnP85BOpVy9p82b366yz5LBh0j33BM98M+9qMLtZq3v37lRoxVGVKlWcOfBhIrcy9Ze9e/dec9zI8w6tQtM+LAC2v1tW3Wc/Ywv1bPuqVas6H2kDYOYjAAAAECh4dwwgyZ04cUIfffSR8+cpU6YQ1AEAgOgdOSKFhEhjx0r79vn2SllfyEcflTp3lmrU8DwOED/8IHXrZq0o3a+xmXVWQffYY3H/VpJqvpm55557nCq6m266KUGOmVJY8OXNKhctVPNnC1PbX+SKyLDj7t+/X5MmTVJISEiMIWFYmGhVm61bt1bLli2VL18+v50jAAAAEKxSJ/UJAICFdGfOnHFeiM8//9zV/BIAAJDCWIlZ27ZS4cJSz56+hXSZM0uvvCL984/02WdSzZoBE9L98Yf0wAPS//7nPqTLn196911p/Xrp8cfj960k1XyzcuXK6YcffiCk8wMLzCK3LF25cqU/dh3t/qxCrlKlSho+fLiKFSumvn37uq7ks+1se1tn6y9fvuzXcwUAAACCDUEdgCRnVXTev8SZOXNmkp4PAAAIENYLcvFiTxWcVV1NmCBdvbnHFasospKzf//1VOCVLKlAsXWr1LChVLmy9N137tZky+b5drZskVq2lPzRPTCp5ptVq1ZNqQIkLA121kbSKtS8rVmzRrt27fLL/nfu3Onsz9vTTz+tWrVqqWvXrjp37lyc9mvrbH2NGjWYYw8AAIAUjaAOQJLasGGDli9ffs28uiv2izkAAJAyWfXV7NnSbbd5Bq998YUntHOrUiXp/fel7dul7t2lHDkUKA4c8BT33XyzZ8SeGxkySF26SNu2eb4dKxD0l8hzxpJqvhnix9pIZsyYMcJz8+bN09mzZ+O1X1tvHS+8ZciQwbmxbsWKFVGuyZUrlypWrKjatWvroYcecj7bY3s+KnYtUL16dee6AAAAAEiJmFEHIElZKBfZ2rVrnSH0t9kv5wAAQMpx9Kg0aZL09tuWGPm+/uGHPfPnatUKmNaWYU6ckEaO9HycPu1uTerUUvPmUr9+no6fyXG+GfzDZr3179/fqVALc+TIEc2aNUuNGzdWpkyZ4hTS2XrbT+R2qZErJI21wrRKyZj+7tjfBWujGblCb9++fU6gt3TpUqclJgAAAJCSpLoSwGUrM2bM8HtLkGzZsjkfN954owon1NUu/Gr9+vUqX758+ON169Y5My0Q/KzNpc3TiKrF0ksvvaSJEycmyXkBAIAk6AM5ZozdweM+xQpjAcRzz0kdO3raYwaY8+eld96RBg60EMz9Ouv2OXiwVLZsQp6ddOnSJRUtWjRCJZ0FLg0aNPDbMayyyzuYsXab27dvd67P4D82680q0yJXuuXIkcP5eRYpUsT1vqxtpv3cIod0WbJk0alTpxJs/3fccYeWLFmiNGnSuN4XAAAAEOwZREBfGTVv3jxB5xbkzp1bd911l1588UWnJQczEoDENX/+/GjnoMyePVsjR45UZn/2dgIAAIHD7hdctsxTYjZvnm+tLU2+fFLbtnZ3j/XaU6AJDfV07+zTR9qxw/26GjWkYcOku+5Sos4369u3b/hzFqpVrlzZp+DFl/lmdjxCOv+zcGvOnDlOWGcVamEsDLMuFvGpeDPXX3+9Tp48GeE5C13jUrFnf7esXadV7O3evTtCG8zRo0frtdde82l/AAAAQDAL6Iq61KlTO+FZQp5iWDh30003ORV8tGAJPEmdZiPhPPzww05YF533339fTZo04UcAAEBycumS9Omn0qhR0sqVvq+vUMHT3vLZZz3D2wKMXbp8841nltxff7lfZ293hwyR6tVL/K6d+/fvd9oNnjt3LkKVlAUpcWmZ6N06cdKkSRGqpmyOmlXTWatGJAyb9WZtJL3DOm82K87Cujx58ihdunROlwtrZRlVi9IwefPm1dGjR3XhwoXw5/g7AgAAgORifRJnEKkV4LxDOgvVvD9i4mbbsOftGJs2bXKq6z788EM/fwcAomK/CPj6669jfHGmTJnCiwcAQHJx/Lineq5ECU/I5mtIV7eutGiRlXt5Wl0GYEhnHQfvvVd66CH3IZ0VrU2f7vm26tdPmtF6YfPNvIXNN7OwLS6im282YMAAQroEVrZsWWfWm7WRjIqFcX/99ZcWLVqkBQsWOJ/tcXQhne2nUaNGEUI6Y+0u4xPkGlsfuc2qBcaTJ0+O134BAACAYBLQFXXvvfde+EXe8OHDtWPHjvDgzu74tGCtTJkyyp49uzJkyKATJ044v/y3Nh3W1/78+fPhQZ3dDWoXGNZP3y4W7ULkl19+ce4c9A7sbD+LFy92WoIgMCR1mo2EMXToUPXo0SPW7bZs2aIS9gs9AAAQnLZvl95+W7JfvEeabRUrC+OaNfPMn0voYW3xsGmT1KuXNHeu+zU5cnjWvPyyVZkpRcw3Y/5Y4v9MR40a5bQ19a6WdMuqHy1YbdeunfN+nDmGAAAASK7WJ3EGEdBBnbHgzebHrV271gnSatas6fyC/84774xx3bFjx/Tuu+9q0KBBTh9969c/YsQIdejQIcLgdOvh/+qrr+q///5znrNj2L7tDkQEhqT+RwL/s39n1m528+bNsW7bq1cvDRw4kB8DAADBZvlyTwWdpVc2sM0XefJ45s+1aWODpRWo9uyR3nhDmjrVQhF3a6wAyTp3dukiZcumgLJw4ULVr1//msopE9/5Zvnz53euseyGSyR+a1OrUAsJCYkwDy46NnfO5gi2aNHCqX60uXGRr79btWoV498FX9nfHbt+92bHvf322/12DAAAACBQM4iADuqsks7emNuLYlVv3bp10+DBg33ahwUBderUce7qtH1YK73mzZtH2MYuVqw6z+4QtJfDtvvxxx919913+/k7QjD+I4H/WTWr239fBQsW1M6dO52wHQAABMH8uXnzPPPnfv3V9/X2Hs9SrEaNAqPMLBpHj0rDhkljxlibPndr7K1MixbS669baKWAYtVWdoPjsGHDnHllMYnLfDML6ay9orVkRNKxG1VXr16tVatWOZ/tOti60FhXGQvnqlSp4sxst89p06YNXzdu3Dinqs7778Arr7zi9/Oz43j/HbLHbS2wBwAAAJJ5BvH/774DkLXZCAvpHnnkEZ9DOlOqVCnNnTvXCfys9Uf79u1Vr1495fa6M9cuSmbMmKH77rsvvA2m9eonqAMShi+z5yxA/+6771TX5tIAAIDAdPKk/R+8J7nascP39Q88IHXqJN1/f9IMaXPJxrWNGycNGeIJ69x66inJGgSULq2AYwFamzZtnHbjbliQEl0gFxVrdzl79mwq6QKAhW92XexrlZqFepFvpEsIFgB7/92KfFwAAAAguUqtAGXtViZMmBD+OPJwc19UrlxZjz76qPPn06dPRzmY+p577nFauYQVGFrFDwD/s1mSH3/8sU9rplo/KQAAEHh27fL0cCxUyBO0+RLSpU8vvfiitHat9M03nrAuQEM6a2tpb0csaOva1X1Id9990sqV0kcfBV5IZ1VwTZs2dbqPuA3pfJ1vZnPGbXY47S6DW+R2md43vfqTVWnGdFwAAAAguQrYoM7mF9gv9K3Czfri33LLLfHa3wN24X+VVcvFtI2FdVwUAAnjww8/1JkzZ3xa8/nnn+vQoUP8SAAACBS//SY1bCgVLy6NGGF34rhfmyuXp/+jhXx2A51Xe5FAY/fwff65ZJcilim6zQ1uvVX69lurVpNuu00BJTQ01OlucPPNN2vmzJmxbm/tLR9//HGnC4kbtp11Rtm+fbtee+012pcnA5FnFqa3kD0B2N81b9aWEwAAAEgJArb15YYNG/zaWiNsHxbCbdy4McptypQpE/7n//77L97HBOCf6jibfTJr1ix17NiRlxQAgKQsK/viC8/8uSVLfF9/882e+XNNmkiZMinQWYON7t2lZcvcr7HcctAg6emnpdSpA/Ma66WXXnLdPcRGA0ycOFGlS5eO83wzBL/IwVzk4M5fIs9HtL9bAAAAQEoQsFdQx48fD/+ztauML+8KHu99e7vhhhsS/OIDSMnsl0PLly+P01q787tDhw7hcyQBAEAiOXVKmjZNeustads239fXru0J6Kx7RSCmV5FYJ86ePaX5892vsY59fftKLVt6OnoGmrNnzzrzvocNG3ZNGBKVXLlyaeTIkU5rzLD3XnGdb4bgF7maMqE6XVg71piOCwAAACRXAXulHBaaWQXcjh07fG6VF9lau+KOtO/Izp07F/7nTEFwly8QbOIza27dunXOHdwAACCRWJ/Hbt088+fat/ctpLMWds2bS3/+KS1cKNWtG/Ah3c6d0nPPSRUrug/psmSR3nhD2rpVats2MEO6RYsWqUKFCho4cKCrkO6FF17Qpk2b1KxZM26QgsOqJL3t2bMnQV6ZvXv3xnhcAAAAILkK2KvlIkWKRAjQPvjggzjvK6xtnt0Nah+FCxeOcrv9+/c7n22bhBqQDaRUVqU6Y8aMeO3DquoAAEACW71aatxYKlZMevNNa0fhfm3OnFLv3p7Uy6rwKlRQoDt82FPwV7q0ZG9VbC6dmxzSsksL6KySzgK7QGPVSVYRV6dOHW3ZsiXW7W1m3U8//eS838ppP0fgKmtp6u3w4cPXhGrxZfuz/cZ0XAAAACC5CtigrlatWsqYMaMTmllVXffu3bXLBs7HQe/evbVt2zZnP+bBBx+Mcrvff/89/M/FbcAEAL/56quv4t0mZ86cOfGurgUAANHMn/v8c3sTbr8dl2bPli5dcv9SWco1YYJk79cHDJDy5w/4l9m669s8uRIlpNGj7aai2NdYF0jLMP/+WxozxtPyMtCEhoY6YZsFbzNnzox1e5sDNmDAAK1Zs8a5BgMis8As8tz4lStX+vWFiry/sLmHAAAAQEoQsEFdlixZ9PjjjzvhmoV1dnddjRo1tMSHwfU2i8FmWo0YMSLCbAVr4xKVhdaW56pbb73VD98FAH9Ww504cUKffvopLyoAAP5Mq8aPt3IqqUED6eeffVt/773Sl19KGzdKbdpImTMH/M/Guj++845UsqSn+O/ECXfrrHvnH39Iln1ZsWGgzgO+55571KJFCx09ejTW7f/3v/85IwLsxkYL7ICo2DV069atIzxnwW5cb6SNbOfOnc7+vNnx7LgAAABAShCwQZ2xgC1btmzOny1o2717t3OXZ7169TR79mxndl1kVm3z66+/qlevXipVqpTGjRvnhH1hgV/Hjh1V2u74jWTZsmXO/sICvbvvvjsRvkMgZbBWNgsWLEjyOXcAAOAqa1vXs6dkLeFfeUVy0RoxnP3yvGlTa0ch/fCDVL9+wM+fM9Zc46OPpHLlPJni1a73sapWTfrxR+nrrz3z6wKV3dB055136pdffol121y5cun99993blS0ayYgNi1btnQ63nibN2+ec3NsfNj6z62a14sdx8JmAAAAIKUI6CvqfPny6aOPPgq/IAhrg/nNN9848xZKlCihzJkzO9vZTLscOXIoa9asTuXd0KFDnXAgLKAzdevWdYaoR+VNm7/hXMBfcar5ateunYjfKZC8vffee04bJn+w2SlbbSAMAADwnVWtWHeJokWlIUMkF1VX4W64QerRQ7Kb5WyYWxB1oPj+e0/g9swz0ubN7tbcdJNkhfzLl0v33KOAZ9dBXbt2jXW7F198UZs2bVKTJk3Cr5OA2Ng1d//+/SM8d+TIEWcWfFzDOltn620/3qwVqx0PAAAASClSXQkb3BbAFi9e7ARzVlEXdjEZ22lH3s4uSCdMmKB0Nvk9Cj///HP4tlbFV6lSJT9/F4ir9evXq3z58uGP161bp3J2KzSCgv27sirWLb7cqR8Lq5iNLnQHAACR2M0yVg42apSnNMxX1iOyY0epeXPpuuuC6uW1or/u3a3Fvfs1BQpI/fpJzz/vKR4MJhcuXHCuYzZaK9JIypQpo5CQENWsWTNJzg3B7/Lly6pevbpWrFgR4Xm7YbZBgwbOzbNuWdtMq8iLHNLdcccdzriLNGnS+O28AQAAgEDPIIIiqDMnT55Uv379nLZ3x48fD38+qrtAvb8le6Nvd+TZ/AUEp6T+R4L4sRDcWtb6kw2zt1kWXMADABCDM2ek99+XRo+W/v7b95fKWsF37uxpbRlkvzS34nubP/fBB+7XZM/uCfXatQuKUXvRstaX3m38bfZcnz591KVLF6VPnz5Jzw3Bb/v27U5Yt2/fvmu+ZiFxtWrVVMDS7mhY15uVK1deM5PO5M+fX0uXLlWxQB0CCQAAgGRrPUFd3HrY2112q1at0p49e3Ts2DGdP3/eqYSzu/nKli3rXCA8/PDDEQIeBKek/keC+GnevLnT+tLfvv76a6edLQAAiMSGr40fL02cKP33n28vjwVyTz/tCeiqVg26l/bAAWubJ4WESJcuuVuTIYPUvr0npMuRQ8lmntjkyZOdmxUnTpzIHDr41YYNG5xREVGFdWEzEC2sy5Mnj9PR5uLFizp48KAT0h0+fDjKNRbSLVq0yLmWBwAAAFJaBhE0FXVIuZL6Hwni7sSJE85F9xm7o9/PnnjiCX3yySd+3y8AAEFr7VpP9dysWdYD0be12bJJrVtLr7wiFS6sYHPihDRihKe75+nT7takTu3p5mltLgP9W7abFS3ksBndblg7QZvr3bBhQ+bQIcEq6xo1aqTlNsQxnqwLzuzZs6mkAwAAQIrNIFIn2pEApDgffvhhgoR05osvvtChQ4cSZN8AAAQNu+fum2+k+++XKlSQpk3zLaQrXlx6+23p33+lYcMCP7GK5Px5acwYyfIrq6RzG9I1aODJNadMCfxveeHChbrlllv0yCOPODPo3LAuIxaiRDUmAPAHa09pXW7efPNNZcyYMU77sHXDhw939kO7SwAAAKRkBHUAEswU++1XArEWOjNnzkyw/QMAENDOnZMmT5bsjj9rBb1woW/rq1eXPv1U+ucfz1C2669XMLl82TN+7+abpY4dpWi66V2jZk1p6VLps8+kQO+wZ60CmzRpovvvv19bt2512g2OsLJBIEDYvGibfWjVdTYXvlChQq7W2Xa2va177bXXmDsNAACAFI/Wlwh4SV12Cv/83BKC/T1Yu3Ytd4sDAFKOgwelCRM8H75Wltv8uSeflDp1km6/XcFaQLhggdSjh/TXX+7X2VuSoUOlhx6SAr3ILDQ0VFOnTlXXrl119OjRayqQ7L1PyZIlFUguXbrkzA+3j9WrV2v37t1O9V/69OmdUKZKlSqqWrWq85E2bdqkPl0k4N8D+/l7/z2wWfIZMmSI8PfAPvP3AAAAAIFkfRJnEFwlAUgQ9gumxPgP6G+//aZq1aol+LEAAEhS69d75s9ZNbn1e/RF1qxSy5aeyrkbb1SwslFY3btLixe7X1OkiKclZuPGnpwy0Nl7m9atW2uplf1F4dy5c3r55Zf17bffBsSNSvv379ekSZMUEhKiPXv2RLvd9OnTnc8FCxZ0vr+WLVsqX758iXimSAwWvt1+++3OBwAAAAD3aH0JwO/sDur3rR9VMgkEAQBIsvKx777ztLa0O/uspbQvIZ2Fchbu2fw5a5kYpCHdpk3S449Ld97pPqTLmVMaNUr6+2+pWbPAD+nOnj2rXr16qVKlStGGdN4z62wOcFK6fPmyM1vM5or17ds3xpDOm21n29s6W2/7AQAAAICULkkq6orb0HovdjeozV2IbTt/ie54APxj/vz5OuRrO644mjNnjkaNGqXMmTMnyvEAAEhwFsbNnu1Jmtat8339HXdInTtLjz1mJS4KVpb99OtnN+VYO0h3a+ztgH3rr70mZcumoGDB20svvaRt27a52r5FixbO3LqkYnPFGjZsqBUrVsR5H1YZaK09586dq9mzZzvBHQAAAACkVEly5b5jxw4nLLtidwlfDc7cbOcvgdAmBkjOErPK7cSJE/r000/VtGnTRDsmAAAJwm5yeecdafx46cAB39amTu0pO7OUykrPgpiNZbN5cm+/bYGOuzWWR1p3zz59pPz5FRQOHDigzp07O0GVG2XKlHFaTNasWVNJZcOGDapdu7b27dsX5ddz5crltLfMnTu3M5/OuizYzVtWSXf48OFrtl++fLmqV6+uRYsWqWzZsonwHQAAAABA4EnSW2zdhnD+DNb8HfoBiMh+EbNgwYJEfVmmTJlCUAcACF4bN0pvvSXNmOE+mQqTJYuVWEnt20tBXpV09qw0dqw0ZIh07Jj7dU89JQ0aJJUqpaAQGhrqvHexirJjLr7RDBkyqHfv3s72Fn4lZSVddCGdtey0mcEFChSIdv3evXu1cuVKrVmzJsLztj/br7X8pLIOAAAAQEqUJEFdkSJFXIVvbrcDEDhmzJjh/AIqMS1evFhbtmxRyZIlE/W4AADEmd089sMPnvaWX3/t+/rChaUOHTwhXbD0eIzGpUvS9OmeNpcuR505/vc/T+Vd1aoKGuvXr1fr1q1jnUMXxgKsiRMnJvl7HJslZ+0uI4d0OXLkUIMGDZzrtthYiGfbVq5cWfPmzdORI0fCv2b7bdSokZYsWaI0gT5QEAAAAACSS+tLf24HIDBYxWpitr30Nm3aNA2y2+kBAAhkFy5IH3zgCej+/NP39bfdJr36qqfNZbp0Cvasct48qWdPadMm9+sqV/YEdHXqKGicPXtWAwYM0PDhw3XJkslYWOvI0aNHO+FVINy4aPOAI8+kK1SokBo3bqxMmTL5tC8L9Vq2bKlZs2Zp9+7dEdpg2vf8mg0YBAAAAIAUJHVSnwCA5OOXX35xKtuSwvTp0527vQEACEj//ScNHiwVLSo995xvIZ0FNY89Zv9HK1lY8swzQR/S/fyzdNddnrzRbUhXooQ0Z47022/BFdJ99913Kl++vIYMGeIqpLMQa9OmTU4IFggh3f79+9W3b99rKuniEtKFsXW23vbjrU+fPs7xAAAAACAlSdIZdQCSF2tb9MILL/i8LqoqvLjsZ+fOnSpevLjP6wAASDD//OOZP2e9HW0Imy+uu056/nmpY0dPSpUM/PWX1KOHb90+8+SRLCdq2VJKwhFtPrPWju3atdPs2bNdbV+2bFmFhISoRo0aCiSTJk3SuUizE62FZVxDujC23vbj/T7QjjN58mRnJh8AAAAApBSprlivOiDAZ3nYXchh1q1bp3LlyiXpOcG/It8tbr+osp87AABByd5eW8mYtbf88kvPY18ULCi1b+9Jpm64QcmBdbS3sG3mTPcvx/XXS126SJ06SVmyKOicPn3aeU+za9euGLfLmDGjU0lmLR/TB1gSaRWARYsW1R6v4YGVKlVyAjZ/sXl1a9asidBSc/v27UqblntKAQAAAKSMDILWlwAAAIA/XLwozZolVa0q3XOP9MUXvoV0NnzNkqzt26WuXZNFSHf4sCdou+km6f333b0c1tWzQwdp61ZrhRicIZ257rrrNH78+Bi3qVOnjtauXauePXsGXEhnVq1aFSGkM9WqVfPrMSLvz+bWrV692q/HAAAAAIBARlAHAAAAxMfRo9KwYVKxYlKTJtLvv7tfa1Xljzwi/fSTpSJS48ZBP3/OnD4tDRwoWUdq6/x54YK7l6Jp0//vFpo7t4Je/fr19eSTT17zfJ48eTRr1ix9++23KlmypAKVBXXecuXKpQIFCvj1GLY/229MxwUAAACA5Czo+ols2bJFn332mX755Rdt3LjRmf1w/Pjx8EHt9913X5Rzsy7aHc5XZyHkTg5X/QAAAEhaW7ZIY8ZI06Z5kilf2Hwvmz9npWOlSyu5sLfckydLb7whHTjgfl3dutLQoVKFCkp2xowZ4wRyJ0+edB63bNlSQ4cOVY4cORToIle2FbS2rAnAwrrDVn4ZzXEBAAAAIDkLmqBu27ZtevXVV/Xll18qbKye93i9yDOuvPXr188ZSm4spLP2LWnSpEmEswYAAECyYu8/ly6VRo6UPv/c9/lz+fJJ7dpJrVtLOXMquQgNlT75ROrVy5NfunX77Z5ixFq1lGxZCDVkyBBNmDBBISEhqlGjhoKFtaH0llA3PFqFYUzHBQAAAIDkLChaX3766aeqXLmyvvjiC4XabwG8xBTQhbGALyzYO3TokObPn59g5woAAIBkyErFPvjAkyzVrCnNm+dbSGelYu+9J+3YIfXsmaxCuu+/97wszzzjPqS7+WZp7lzp11+DL6Sz65F3331XU6dOdb3mpZde0h9//BFUIZ25EKlnaULN0UsXqd3r+fPnE+Q4AAAAABCIAj6o+/rrr/Xss8/qxIkT4c9Z4JY3b17ddtttEarqolO6dGlVr149/PFc+60AAAAAEBtrsT5ihFSihNSwofTbb769ZvXqeZKsNWukZs2kDBmSzWtuo/juv1+qXdszXs8N65w4aZK0dq302GOeuXTBZN26dapZs6Zat26tjh07au/eva7WWTePhAq5ElLkc44c3PlL2JiCMBmS0b8TAAAAAAjqoM6q3xo2bKjLly87lXMWyj311FP6888/nYviFStWuK6qe+KJJ5zPto9FixYl+LkDAAAgiG3fLnXsKBUqJHXpIv37r/u1GTNKrVpJGzdK1snBZigHWyIVA6uae/ZZqUoVaeFCd2uyZ/e0uNy8WWrRQkobNA34Pc6cOaMePXro1ltv1bJly5znbOachXXJWSH7+x/p+iwhHDx4MMbjAgAAAEByFtBB3YABA8KHrps333xTH374oW655Raf93XvvfeG/3n//v3atWuX384TAAAAyYT1YnzqKalkSWnMGOnUKfdrbc5W//6Svc8MCfH0d0xG9u+X2raVypSRPvzQfWbZtavNm/Z8zpRJQeebb75R+fLlNXToUF26dCnC1z7++GN99dVXSq6qWBrrxWZ9J4TIlYmRjwsAAAAAyVnqQJ79MHPmTKdazj6efPJJvfbaa3HeX9myZSO0btlodzgDAAAAFr58/LF0553SXXdJn3xib0bdvy7ly0s2r2znTqlPHyl37mT1mloHevu2LLucMEHOyxWb1KmlF1/0VNBZJd0NNyjo2M191t2jbt262m4VltFo27atTp8+reSoatWqER4fPnzYdbtPt2x/tt+YjgsAAAAAyVnABnXLly/XsWPHwmfQ9e7dO177S5s2rQraUIyrqKgDAABI4SyBGj3ak0A9/bS9AfVt/YMPSt99J/31l/T8857ysWTk/Hnprbc84/kGDpTcZlENGtgsN2nyZE/n0GBjNwyGhITo5ptv1gcffBDr9gcOHNDKlSuVHFlg5n0NZfz9vUben7W9pKIOAAAAQEoSsEHdZrv99qo8efLEqd1lZNltOMZVx48fj/f+AAAAEISs8u3VVz0pUufOnsduZcjgGbJmSdSCBVKdOslq/py5fFl6/33pppukTp2sisrdupo1JRvf9tlnnvaYwWjt2rWqUaOGXnrpJVfXC/fff7/Wr18foc1+cmI3O7Zu3TrCc2vWrPHbTY87d+509ufNjmfHBQAAAICUImCDurBB5db20l/DxL0v+CLPlwAA4P/Yuw/oqMquDcMPvYNSpCpSpUsTRJCiqCgWVFBABQsQelV670W6lACK6Ac2BAv2LqCCICC9iSJFuvRO/rXPMfmTUHIGMsnM5L7WmkVmct6ZlzMhJPPM3lsAQptV7jRs6JaIjRkjRZuFHCdrZ9m/vzt/bvp0qWRJhRprZGHj1sqVk5o08Z5f2vvpbN0PP7jdQ4PRiRMn1KNHD5UvX14/25zCONgbCd966y1nfl3BggUVypo3b660sapFP/jgA508efKa7tfWf/jhhzFus8dpZkE4AAAAACQhARvUJbfBFtHaz8SHgwcPRn18fTAOygAAAIDv5WHz5knVqkmVK0vvvOPe5lWJEm4PRwvo+vWzhCYknwHr+lmzpvTgg1ZV5m1N/vzSG29IK1ZIDzwQvIWFFraVKlVKw4cP9/RmvhYtWmjDhg1q2LCh86bCUJcrVy4NHDjwot+rZs+efdVhna2z9dF/PzODBg1yHg8AAAAAkpKADepy2LuWnXf2RjiD3OPjXbLWWiXyl+nI+wcAAEAIsmq5CROkIkWkxx+XFi/2bb21tLTWltbi8oUXQm7+XKT166VHH3Ur4X780duabNnc0X4bN0rPPCOlSKGgtHv3bidsu//++7Vt27Y4jy9ZsqQWLVrkzK9Lam/669y5sypb0B3Njh07NH36dJ/bYNrxts7WR3f77berk/VaBQAAAIAkJmCb/998881RH1tQZyFbfnvb7lX67rvvot4ha2Fd2bJl42WfAAAACCB//y1NnChNm2ZDiX1bmzq19NRT7mC2eJiPHMgsI7FOnjNnWvcKb2vSp3dH+r34opQli4KWdeuYNm2aunfv7mkOnbVj7NevnxNWpbavkSQoRYoUTqvPqlWrOgFnJKuIe+2115zfrSpVqqQ8efJc9j527dqlpUuXXjSTzuTOnVtz5sxxHgcAAAAAkpqADersHZUZM2bU8ePHneuvv/668wvy1Rprb/v9z0033RTysyQAAACSlGXL3Llz777rW2vLyBKx1q3dS4i33Tt0SBo+3C02PHXK2xob89y8udS3b/CfntWrVzutK3+xXp8e3HfffZo8eTK/O0gqUKCAvv76a9WuXTtGWGcsfLNL9uzZnbDOZvilSpVKZ8+e1d69e52Qbv/+/Zc8xxbS2f3a/QMAAABAUhSwQZ39YlenTh3NnTvXuT5mzBg1bdo0RqWdVzNmzNC3334b1fbyiSeeiPf9AgAAIIFZILdggRvQee3bGN0tt7jVc9a/0crFQpiNErNCw2HDpH//9b7uySelwYOlwoUV9CxIuu222zzNocuZM6fGjRunJ598MknMofOqRIkSWrx4sRo3bnzJsNPCuMsFcpd7c6ZV0hHSAQAAAEjKAnZGnbEKuuTJkzu/HB89etR5R+uff/7p033YDIm2bds692Hz7tKlS6cXrV8PAAAAgpN1XJg0SSpWTKpXz/eQrlYtN+Bbt04KCwvpkM4yqRkz3FF93bp5D+lq13aLFN9+OzRCOnPrrbeqZs2acR7XsmVLbdiwwZlfR0h3MQvVbFbfyJEjnbagV8PWjRo1yrkfQjoAAAAASV1AB3U2sL1Vq1ZOwGa/JG/evFmlS5dWnz59tGnTpouOj/xF2mba2Tsz77jjDrVu3VpnzpyJuo/+/fsrR44cifC3AQAAwDXZuVPq0UO68UapbVtpyxbva1Olkpo0kVaskL79VqpbV0oe0D8KX5OICGn+fHfUnrWttFPnRfny0pdfSl99JVWooJBivwtMmTJFadKkueTnS5Uq5VSL2THXXXddgu8vmNgsuZdeeknbtm3ToEGDlC9fPk/r7Dg73tbZmyeZSQcAAAAAUrIIS7AC2Pnz51W3bl19+eWXUVVxkYFchgwZdOzYMedjuy1r1qw6efKkc4kUebz9aa1rbAg6gsvatWudF04irVmzxglxETpiv1vd2irZ8w4AgMPCNWtvaeVdHtoWxnD99VKrVlKbNlKePEnihFqBoVXPeRzD5ihUSBoyRGrQIKTzS8eQIUPUu3fvqOvWccM6eXTu3Nlpvw/fWTvR5cuXa9myZc6fO3bs0OnTp51Q1MK5ChUqqGLFis6fKW3oIQAAAAAEkLWJnEEE/G9J9i7L+fPnOy1o3nzzzagX9C14s5AuMoSzy4EDB2KsjX5sWFiYJtpgDgAAAAS+CxekTz+VRo+Wvv/e9/XW69Hmz1kVXYYMSgpWr3YLDj/5xPuanDmlvn2lZs2k1KmVJFgl2OzZs7V+/Xqntf7kyZNVsGDBxN5WULPwrXLlys4FAAAAABBiQV3ku1xnzZqlBx54QAMGDHBmRkQP4i41OyIyvCtcuLDTXsWq6QAAABDgTpyQ3nhDGjtWukSr8zjVqCF17iw9+GDol4b9x0Y4W9j2v/+5LS+9yJTJAis3y8yYUUHvxIkTOnv2rLJkyRLnsalTp9aMGTO0fft253cE5tABAAAAABJTUAR1kewXabt88cUX+vTTT7Vw4ULnnbDWViX6uznz58+vWrVqqU6dOqpXr56SJ5EXaQAAAILW7t3SpEnSlCnSwYO+rbVWevamLEudQm2w2hXs3++2q5w8WTpzxtsa6+zYurXUq5cUKmObP/vsM2cude3atTV9+nRPa2yWtV0AAAAAAEhsQRXURbIWNXaJ/g7af//9V+nTp2fwOwAAQDD5/Xe3em7OHO9pUySrngoLk9q1k/LlU1Jx/Lh7ykaOlI4e9bbGGlA8/bQ0cKB0880KCbt371bHjh317rvvOtetSq5p06aqVq1aYm8NAAAAAIDgCOqeeOIJ1ahRw7lEH9TnKwvo7AIAAIAgmT/3xRfSmDHS11/7vt7miXXsKD33XGj0bfTo7FnJCsYsbNuzx/u6Bx6Qhg2TypRRSLhw4YLCw8PVo0cPHT58OMbnbC71ihUrnPaWAAAAAAAEg0QN6ubOnav333/f+Thr1qy68847o4K7smXLKinbuHGjVq1apR07djgVgzanL2fOnCpatKhuvfVWpUmTJrG3CAAA4JuTJ91BalYOtn6972fPKqVs/tzDD0spUiSpXPO999x2lVu3el9XubI0YoQ7ti9U/P77704Y98svv1zy8+vWrdPLL7+snj17JvjeAAAAAAAI2taXEREROnDggD788EPnYmwQvLWtiQzuypcvH/Kz5o4ePaqJEyc6bXu2bdt22ePsHcKVKlVS/fr11aFDh2t+3NWrV6tChQo6a2/TjmbmzJl69tlnr/n+AQBAEmflXzZIzS42WM0XFsg1aODOn6tUSUmNFRx26yb99pv3NcWKSUOHSvXquS0vQ8Hx48c1cOBAjR49WufPn7/isYMGDXI6dxQuXDjB9gcAAAAAQFAHdcmivYJgoZ2xmXOffPKJczEZM2ZU1apVo4K72267TSlC6J3UCxYsULNmzbTHQx+jM2fOaNGiRdq8efM1B3X2QscLL7xwUUgHAABwzdascavnrIrO1/lzmTNLLVq48+duuinJPRnLl0vdu/vWGTRvXql/f8neZ5UyIH7Kjx+ffvqp2rRpoz///NPT7xXPPfecsmfPniB7AwAAAADgWiXqr/D9+vXTjz/+6LSuOWmtkK4Q3Fm12RdffOFcjM2kq1KlSlRwV7lyZaVKlUrBaOzYserSpUvU3zVS2rRplSdPHueFBjs/u3fv1n5f34Xu4bF//fXXeL1PAACQhNnPM19+6c6fsz99dfPN7vy555+XMmVSUrN5s9Snj/TOO97XXHed1KOHm2mmS6eQsWvXLnXs2FHvWd9PD0qXLq1p06bp9ttv9/veAAAAAAAImaDOnDt3zgmLLLSzy+LFi3XkyJErBnfW/uabb75xLpGhloV1kcGdhXjBMMft1VdfVWebtRLN/fffr/bt26tWrVoX/R3sBYtvv/1WH3zwgZYuXXpNj71161b17ds36nqGDBmc8woAAOCzU6ek2bPdCrq1a31fX6WK1KWL9MgjoVUO5tE//0gDB0rTp9vPxt7WpE0rtW/vVt5df71ChnV8CA8PV48ePWL8TnA5Nsu5f//+6tSpU9C+cQ8AAAAAkHQFxKsgKVOmdII1u3Tr1s0J41auXKkffvjBCe6szWP0SrJLBXdWcWbH2yVyjpu1x4wM7u644w6nCi+QbNmyRW3bto26bi8szJo1S40aNbrsGquwe/rpp53LoUOHrvqx7bw1b948qpLxoYcecl4IiTx/AAAAnuzdK02Z4s6fs499YfOHH3/cnT9nQV0SZDnUqFFuAeKJE95P23PPuW0u8+VTSFm1apXCwsK0ZMkST8fbG9wmTZqkAgUK+H1vAAAAAACEbFAXmwVx5cqVcy7W7sasW7cuquJu4cKF2rlzZ4zjYwd3p0+fdirz7DJ06FAnDKxQoYIT2lWvXt35pT6xtWjRQqfs3ef/mT17tho0aOB5/fXX8Nbp6dOn67vvvoua/2cvcDzzzDNXfX8AACCJWbdOGjdOeuMN+8HLt7XW0rJZM7cczFpdJkF2yizfHDxYOnDA+7p69aShQ6XixRVSrKvDgAEDNGbMGKeiLi65cuXShAkTVL9+/Ri/CwAAAAAAEGwCMqi7lBIlSjiXli1bOtf/+OOPqIo7u2zbtu2Kwd3Zs2edd+baZdSoUU67zcT04YcfRgVlxgI6X0K6a2EhZ9euXaOuDxkyRDfeeGOCPDYAAAhi9nOVtR238q/PPvN9/U03SR06SC+8IGXJoqTIMijrEGrdx//6y/u66tWl4cNDs/Dw008/VevWrfWXhxNiP+e3atXKeSNeliT6NQQAAAAACC1BE9TFVrBgQefynPX9+S98igzt7LJ+/forBneJzQbdX2peX0KwF0IOHz7sfFypUqUY7TcBAAAuWf711ltuQLd6te8n6Lbb3Plz1uYyCc6fM/Yj6KefuvPk1qzxvq50aWnYMOmBB+xnWoWU3bt3q0OHDnrvvfc8HV+mTBlndt3tt9/u970BAAAAAJBQQuaVkrx58zqz3SLnu9lMOwvs3n33Xc2dOzdgArrIUPGLL76Iul62bFmVLFkyQR777bff1kcffeR8bO1ArQVmcht0AgAAEJvNCJ46VXrlFWnPHt/Oj6VKjz4qde4s3XFH6KVMPvj5Z6lbN2nhQu9r8ueXBg2SGjeWUqRQSNqzZ4/mzZsX53Hp0qVz2mJaS3yb6QwAAAAAQCgJmaAu0o4dO2K0xNy0aZMCzeeffx5j9katWrUS5HEPHDig9jYL5j9dunRx3pkMAAAQw8aN7vy5WbOkkyd9OzkZMritLe1njkKFkvSJtQYPPXtKH3zgfU22bFLv3lKrVlKaNApp9ma1Tp066eWXX77sMTZXevLkybo5ic4yBAAAAACEvqAP6rZu3RojmLvcbItAqqj79ddfY1y/9dZboz5esWKFZs6c6fydtm/frtOnT+uGG25QkSJFdN999zkVg1Y9eDWstdC+ffucjwsVKpSg7TYBAECAs5+Vvv/ebW+5YIHv6+3nEwvnmjeXrr9eSdnff0v9+0uvvy5duOBtTfr0bvHhiy8mrfF9/fv3dzpg2M+90eXKlUsTJkxQ/fr1Y7SxBwAAAAAg1ARdULdu3TonkLMga+HChc5si9hhnP0yHz2Ys9aOpUuX1p133hl1CaSgzmbtHTt2zAnSXnvttYuOt/DRLl9//bX69u3rtP2x9j++tP759NNPNXv27KjrU6dOddoIAQCAJO7MGemdd9yAbuVK39eXL+/On2vQQEribQkPHpSGD5cmTHDH+nlhI/ss2+zb18Ipf+8w8GTIkEGTJk3SQw89FPVzfKtWrTR06FBlSUqJJQAAAAAgyQrooM7CtpUrV0YFc4sWLXLaN0b//KVYgFWxYsWoUK5q1aoB9Yv+li1bYly3ILF69epONV1cTp48qWHDhjlhn830yJQpU5xrjh49qpYtW0Zdf+aZZ1S7du2r3D0AAAiZVCk8XJo4UYr2xidPrMLp4YfdEjB7A1QSr3g6ccI9jRbS/fuv93VPPikNHiwVLqwk7cEHH3Qq56xlfXh4uG6//fbE3hIAAAAAAEkzqLO5bRZARbaxXLx4sY4cORJnMGdhVZUqVaKCucqVKytNgA71uHDhghOcRWdz4yJDOnsXsb1Y8cADDyhfvnw6fvy487k333xTu3btilpj1XXPPvus3n///Tgfs1u3bvrbejBJyp49u8bYO+YTyd69e6Pab15tsAkAAK7B5s3S+PHSzJluwuQL68/43HPWT1sqUiTJPw3nzrmn0dpcRvsxLU72fikL9SpUCM1TaD/TW/eGatWqxWjxfiXTp093qut86RgBAAAAAEAoSNSgzuav/fLLL1HB3M8//+xUjMUVzNnMNvvFPzKYs0H0VpUWDA4fPnzR3+u3335z/syWLZvmz59/UWvOJ598Ur1791ZYWJjmzJkTdbtV1L3xxhtq0qTJZR/P2oPaCyWRRo8e7YR1iWXy5MlO204AAJCA7GePhQvd9pYffeRe90WePFK7dlKLFlLWrErq7PTNny/17Clt3Ohbl1AL6O65RyHLumHYz6xLly513jxnb7xLkSJFnOuuu+66BNkfAAAAAACBJlGDOvuF/IzNRYkjmCtQoECM+XJFixZVsLJZdJdiL2B88sknzgsal5IxY0anqm7//v368ssvo263+R3WytIq8WI7deqUXnjhhajzau0urxTqAQCAEHP2rDR3rhvQLVvm+/qyZd32ltajMXVqf+ww6Pzwg3UrkJYs8b6mUCFpyBB3jF+QvLfMZ9YFon///ho7dqxTUWeWLFnitLJs3bp1Ym8PAAAAAICAlegVdRYwRQ/orDKuVKlSMYK53LlzK1SkTZv2krc3a9bssiFd9HMzZcoUFSlSxGmhaTZu3OjM76tZs+ZFx/fr10+brb2VpHTp0sWorAMAACHMBqVNny5NmCDt2OH7+gcfdAM6+/kiic+fi7RqldSjh/TZZ97X5Mwp9e0rNW9uM5QVshYsWKA2bdpo+/btF32uR48eqlevnvJYVSYAAAAAAAjcGXUW2JUvX17du3d35rNZsBSKrDLuUprbKzgeFCxY0KmMi15Vd6mgztppWpvLSH379lUhezt3IrN3VDewt5P7OKPOXuABAABx+OMPd/7cq69aiZNvp8veTNS0qdSpk3TLLZzq//z5p9SnjzR7tveOoZkySV27Sh072s9+oXsqd+7cqQ4dOlxxZrLNm+7YsaPefffdBN0bAAAAAADBIiCCusi2jRYuPfHEE0qZMqXKlSun6tWrR1XVhcrcCgsgrc1lZEsgkylTJufv61WNGjViBHXLYrWyOnfunJ5//vmoxyhTpoxefPFFBQKbL2gXAAAQTyw9+vlnt72lDU77r+res1y5pLZtpbAwKRHn2AaaffvcdpVTpkjROrVfkXUHtS6PNrsuRw6FLPsZ07o89OzZU0ePHo3zeGvv/tdffyl//vwJsj8AAAAAAIJJogZ1VuG1devWi24/e/asfv31V+diVWEW5JUsWdIJ7CLDu2Buh2lB1e7du6OuFy5c2Glr6dUtsd7lvnfv3hjX//e//2mV9Wf6r13mtGnTnPATAACEkHPnpHnz3IDOl4FpkUqXlrp0kRo2lNKk8ccOg5KNEx47Vho1SvKQQTnsPWdPPy0NHCjdfLNC2sqVK9WiRQvn53QvrFPGpEmTCOkAAAAAALiMRE1vbH6aBVY//vhj1GXdunXOzLrIuXUW0tk8tjVr1jgXe/duZAvI6MFdILR19Kp48eIxgrrMmTP7tD728YcOHYpx/V+bS/MfO3e33377Ve3zueeecy6RHnnkEX3wwQdXdV8AACCeHD7stra0FpeXmAkWp/vvd+fP3X038+eisao5G+s3aJC0Z4/301m3rjR0qHUwUEg7duyY+vfvr3HjxsXoDHE59qa6CRMm6PHHH4/qngEAAAAAAC6W6GVW9kv8k08+6VzMwYMHtXDhQie0s9lrVhlmLwZED+7sY6vE++OPPzRr1izn9ly5ckUFd3YpVaqUAlWJEiX07bffRl0/ffq0T+tPnToV43r69OnjbW8AACBA/fWXG87NmOG91CuSVcw1aeIOTStRwl87DErWKdTGp/XuLV2i0cNl2fugRoyQqldXyFuwYIHatGmj7R6CYftZ3WYSDxkyRFmyZEmQ/QEAAAAAEMwSPaiLLWvWrE7lll2Mzb1YvHhxVMWdzWM789+gEAvsIoM7q1B77733nIuxmXbVqlWLCu8qVKjgzIYLBOXLl49xfY8vb9u+RKvLbNmyxcu+AABAALK2ltbecu5c3+fP2VzYNm2kli3djxHDV19J3bpJK1Z4PzHFiknDhlmngdAvSNy5c6c6dOig999/39PxNhfZWq5XrlzZ73sDAAAAACBUBFxQF1umTJlUp04d5xJZTfbLL79EVdzZxydPnnQ+F1l1F9kO0t79a5fIqjNrARkZ3NWsWTOR/kbWIqmuMzvO2lKabdu2OZWEFlJ6sXz58ivOrLMWQ1dTUdilSxf9/vvvUddfeukl3XvvvTFm6wEAgARgrQWt3bQFdD/95Pt6q5qz9pZPPSWlTeuPHQa1Zcuk7t2lb77xviZvXmnAAKlpUynUR/9aNwtrN9+zZ0/nTXNxsZ+zBwwY4IR6qVKlSpA9AgAAAAAQKoLuZYa0adM6IVtk0Hbu3DlnmH1kxZ1V3x05cuSi4O748eNOu0m7WBWerUssFnhVrVrVafEZad68eWrWrFmca23f8+fPj3Fb7NDxxhtvdC6+uv766y9q0Vm7dm2f7wcAAFwlC0Vee81tcbltm+/r7Q02FtDZn6Fe7nUVNm92W1xaq0uvrrtO6tFDatdOSpdOIW/lypVq0aKF8/O11zegTZo0Sfnz5/f73gAAAAAACEVBF9TFljJlSlWpUsW5dOvWzQnn7AWGyIq7RYsW6cCBA1HHRw/vElNYWFiMoG7UqFF65plnlMZmyFzB9OnT9c8//0Rdz5w5s+677z6/7hUAAPiZzf6aOFGaNk367w1HnqVOLT39tDt/rnRpf+0wqNmPTgMH2s9R9qYnb2usELF9e7fyLtZ7mULW3Llz1bBhQ6eizsuc6YkTJ+qxxx5z3gQHAAAAAACuTnKFGHuhoGTJks4cOLvYrIxAfPGgUaNGKh3txbRNmzY54V1kO8xLWbJkibp27RrjttatWytLlix+3SsAAPATq1pq1EgqWFB6+WXfQrrs2aW+fd2Q79VXCeku4fBht4KuUCFpyhRvIV3y5JI1ObDquxEjkk5IZ+666644W7Hbz9Vt27bV+vXrnXbrgfhzNgAAAAAAwSToK+rMiRMn9NNPP0W1v1y6dKlOnz6tQGYz6saOHat77rknqspv1qxZ2rlzp4YPH64KFSpEHXv48GG9+uqr6tevn44dOxZ1e9GiRZ3ZIQAAIIhYtdLHH0ujR0uLFvm+vlgxt72lVdElhV6MV+HUKTeYGzJEitZYIU6PPuquKV5cSZKFdPbz6dP2tXUJt956q6ZNm6ZKlSol+N4AAAAAAAhVQRnU2Qw6axsZGcz99ttvMWbORW9vGf1dvoHS9jLS3XffrWHDhqm79VT6z9dff62KFSsqV65cypcvnzNbb+vWrTpz5kyMtdmyZXPaE2XKlCkRdg4AAHxmb7Z5/XVp3Dhp61bf1999txvQ1anjln3hkhno//73/4WGXlWv7lbP3X47J7Vx48Z6/fXXnZ9JI6VPn14DBw5Uhw4dnLbzAAAAAAAg/gTFb9r79++PCuXssnr16hgtImMHc5HhnN0e+TkbcF+jRo2oS6CwuXr24keXLl109uzZqNttDl30WXTR3XLLLfr4449VpEiRBNwpAAC4Kjt2SK+8IoWHS//+69vaVKmkxo2lTp2snIkn4DLsx71PPpF69JDWrPF+mqwL+fDh0v3328+QnF5jP0dPmTJFpUqVcjpU1K1bV5MmTXJ+lgYAAAAAAEkkqNu1a5cTyP3www/Onxs2bLhsVdzlgjkLsapXrx4VzN14440KVO3atXNaYPbv31/z5s2LEdhFV6BAAXXs2FEtW7ZU6tSpE3yfAADAB7/9Jo0ZI73zjrfhaNHZnLBWraQ2baTcuTntV/DTT/bGJ9+6iFrmNGiQm4GmSJE0Tq+1ik+XLp2nmXKFCxfWmDFjlDNnTj322GPMoQMAAAAAINSDum3btsUI5ux6XNVykZ+L/HyJEiViBHPWOjKYFCtWTG+//bbT1tPm7W3evNmZTZcxY0bnRZLy5cs7lXT+9P333/v1/gEACHlW8b9ggRvQ/fCD7+uLFnWr55o0sX6D/thhyFi3TrJRvR9+6H1NtmxSnz5Sy5ZSmjRKMqwTQ5s2bZw5yNba0ovWrVv7fV8AAAAAACCRg7qnnnrKmTW3c+dOn9pYJk+eXKVLl3YCOQvn7JI9e3aFgsyZM6tOnTrOBQAABInjx6VZs9z5c5s3+76+Zk13/lzdusyfi8Pff0v9+7vj/qJ1Qr+iDBnc0/vii/azlpIM+xm7ffv2TscG06lTJ+dnzKxWsQkAAAAAAAJCogZ1b731lhPERQZwlwvmUqRIoXLlykUFc3feeaeuu+66xNw6AACA9euWJk2Spk6VDh707YykTCk1bOhW0JUvz9mMg53eYcOkiROl06e9n+KwMLeKLmfOpHOKz58/r8mTJ6tXr146evRo1O179+5V9+7dNW3atETdHwAAAAAACLDWl5FhXWQwlypVKlWsWDGqjWXVqlWdFpAAAAABYeVKaexYe9eRdJnZspdlbzay9KhdOylvXn/tMGScOCFNmCANHy4dPux9nWWgNoeucGElKStWrFCLFi20bNmyS35++vTpatKkiapVq5bgewMAAAAAAAEY1Fk4lzZtWlWqVCkqmKtSpYoz7B4AACBgWJ/Fzz5z5899+63v6wsVkjp2lJ59VuINSHE6d06aOdNtc2mFi17dc49beVehgpKUY8eOqW/fvho/frwuxNETNCwszAn0UqdOnWD7AwAAAAAAARjUDRw40AnmKleuzAsFAAAgMJ08Kb35pltBt2GD7+vvvNMdkPbQQ9bP2x87DCnWYGH+fKlnT2njRu/rLJizqrvatZXkfPTRR2rbtq3+tgF+HjpZ3H333Tp79iw/fwMAAAAAkNSDut69eyfmwwMAAFzeP/9Ikye7lwMHfDtTFsg98YQ7f+622zjLHn3/vdS9u7RkifdTZq0thwyR6teXkidPWqd6x44dat++veZbsumBzXwODw/XbXxNAgAAAAAQMBK99SUAAEBAWb3arZ6bPVs6c8a3tVmySC1aSG3bSjfd5K8dhpxVq6QePdzOol7lzCn17Ss1b27zjZWknD9/XpMmTVKvXr2clpdxSZ8+vQYNGuSEeilT8uM/AAAAAACBhN/UAQAArN/iF1+48+e++sr383Hzze78ueeflzJl4nx6tG2b1KePNGeO2/LSCzu9Xbu6pzspjvr77bff1KJFCy1fvtzT8Q899JAmTpyo/Pnz+31vAAAAAADAdwR1AAAg6Tp1Svrf/9wKunXrfF9/xx3u/Ll69Zg/54N9+6TBg6UpU6SzZ72tSZ1aat1a6tVLyp5dSY5VzvXt21fjx4/XhQsX4jw+b968mjBhgh599FFnLh0AAAAAAAhMBHUAACDp2bvXTYkmTXJTI1/YIDQbiGbz526/3V87DEnWpdGKFkeNcj/2wjKmZ56RBgxwCxeTog8//FDt2rXT33//HeexFsrZsdbqMnPmzAmyPwAAAAAAcPUI6gAAQNJhVXNWPffmm9Lp076ttZ6LzZpJ7dsn3cToKtmov2nTpEGD3IzUq7p1paFDpTJllCRZMGdz5T744ANPx5crV07h4eG67bbb/L43AAAAAAAQPwjqAABAaLPhZ19/7ZZyff657+tvuknq0EF64QUpSxZ/7DBkWYfGd96ReveW/vjD+zorVBwxQqpeXUnaxx9/7Cmky5Ahg1NBZ5V0KVPy4z0AAAAAAMEkeWJvAAAAwC+sYu7116Vbb5Xuvdf3kK5SJTdl2rrVnUNHSOdTNvrll1LFilLjxt5DuuLFpfnzpZ9+IqQzYWFhqmRfh1fw0EMPad26derUqRMhHQAAAAAAQYi33AIAgNCyf780dar0yivSnj2+z5979FE3mKtSxR2QBp/8+qvUvbv07bfe1+TN686ga9pUoiDs/6VIkcJpZVmxYkWdP38+1jnLq4kTJ6pevXrOXDoAAAAAABCcqKgDAAChYcMGqWVL6cYbpT59fAvpMmRwZ89t3izNnSvdcQchnY/s1D3xhFuI6DWku+46t8WlrbXOooR0FytbtqxTLRcpefLk6tChg9avX69HH32UkA4AAAAAgCBHRR0AAAjuHovffefOn/vkE9/X58vnBnTNm7upEXy2e7c0cKA0fboUq+jrstKmdcf+desmXX89Jz0u/fv317vvvqvs2bNHVdgBAAAAAIDQQFAHAACCz5kz7vw4C+hWrvR9fYUKUpcuUv36UqpU/thhyDt8WBo1Sho7Vjpxwntn0eefl/r1czPSpMraWM6YMUNPP/20Mlg1ZxzsmG+//Vb58+dnDh0AAAAAACGGoA4AAASPgwel8HBp4kS3lMsXNsfr4YfdgK5aNVpbXqVTp6TJk6WhQ6UDB7yve+wxacgQqVgxJWnLly9XWFiY8+fWrVs1cuRIT+sKFSrk970BAAAAAICEx4w6AAAQ+GyIWdu27vy5nj19C+nSp5fatJE2bZI++EC6805CuqtgbS1nzZJuucXNOr2GdDVqSD//LL3/ftIO6Y4ePaqOHTuqUqVKTkhnxowZo1WrViX21gAAAAAAQCKiog4AAATu/LmFC932lh995F73RZ48Urt2UosWUtas/tplyLPTbuP/evSQ1qzxvq5MGWn4cKlOHXLRDz74QO3atdOOHTsuaoFp1XWLFy9WihQp4v/JAwAAAAAAAY+KOgAAEFjOnpXmzJFuu80tx/rwQ99CurJlpTfflLZtk7p3J6S7Bj/9JFWvLj30kPeQLn9+9/SvWCHdf3/SDun+/vtv1atXT48++uhFIV2kJUuWKNzauQIAAAAAgCSJoA4AAASGQ4ckm9dVoID01FM2zMu39ZYmffed9Ntv0tNPS6lT+2unIW/dOqlePalqVWnRIm9rsmeXxo2TNm50T3/yJPxT5rlz5zR27FgVL15cH1rQHIehQ4fqzJkzCbI3AAAAAAAQWGh9CQAAEtfWrdL48dJrr0nHj/u2Nl066dlnpQ4d3OFpuCZ//y316+fOortwwduaDBmkzp2lF1+UMmfmCVi2bJnTzvI3C4w9eOSRRzRx4kSlJlgGAAAAACBJIqgDAAAJz1pZWl/F0aOlDz7wff5crlxS27ZSy5ZStmz+2mWScfCgNGyYNHGidPq0tzUpU0phYVKfPlLOnP7eYeA7cuSI+vTpo1deeUUXPKSc+fLlcwI6a40JAAAAAACSLoI6AACQcM6dk95/XxozRlq61Pf1Zcq45VsNG0pp0vhjh0nKiRPShAnS8OHS4cPe19npHzRIKlzYn7sLHvPnz1e7du20c+fOOI9Nnjy52rdvr4EDBypTpkwJsj8AAAAAABC4COoAAID/WQo0Y4abCm3f7vv6Bx5wA7q77pKSJfPHDpNcXmqdRgcMkHbt8r7u3nvdyrvy5f25u+Cxfft2J6D76KOPPB1fvnx5TZs2TRUqVPD73gAAAAAAQHAgqAMAAP6zbZsbzllId+yYb2vTppWaNJE6dpSKF/fXDpMU6zA6b57Uq5e0caP3dZYrjRgh3X23P3cXPM6dO+e0rbRWl8c9zFXMmDGjBg0apLZt2yql9QwFAAAAAAD4D68UAACA+Pfzz257S0uFPMzriuGGG6Q2baRWraQcOXh24sn330vduvnWcdRaWw4ZItWvby0beSrMsmXL1KJFC61YscLTCXn00Uc1YcIEZyYdAAAAAABAbAR1AAAg/vopfvCBNHq09Msvvq8vWdJtb9m4sVtNh3ixapXUvbv0+efe1+TMKfXvL73wgpQqFU9EpNGjR6tr16664CF8tmDulVde0SOPPMIJBAAAAAAAl0VQBwAArs2RI+7As/HjpT//9H39ffe5Ad099zB/Lp67jvbpI82Z47a89CJTJqlrV6lTJylDhvjcTWioVKlSnCFd8uTJ1b59ew0cOFCZ7IQCAAAAAABcAUEdAAC4Otu3u/Pnpk93wzpfpE4tPf20mwiVKsUzEI/27ZMGD5amTJHOnvX+dFi30Z49pezZeTou584771SzZs00w2YuXkKFChU0bdo0lS9fnpMIAAAAAAA8IagDAAC+sSFnNn9u7lzp/Hnf1loK1Lq1e7H+iog3x465T8uoUe7HXiRLJj3zjDRwoJQ/P0+GFyNGjNCHH36ofZaI/idjxowaMmSI2rRpoxQpUnAiAQAAAACAZwR1AAAgbhbIffihmwQtXuz7GStWzG1vaVV06dJxxuPRmTPStGnSoEHS3r3e1z34oDR0qFS6NE+HL7JmzaqxY8fqaftalvToo49qwoQJzkw6AAAAAAAAXxHUAQCAy7PSrJkzpXHjpD/+8P1M1a7tBnQ2hy55cs50PLJRae+8I/Xu7dtTU6WKVYVZG0eejujOnz/vuRqucePG+vrrr1WvXj098sgjnEgAAAAAAHDVCOoAAMDFduyQJk6UwsOlw4d9O0OpUklPPSV17CjdeitnN55FREhffSV17y6tWOF9XfHi0rBh0sMPuy0vEXk+IzR//nx17dpVn3/+uQoXLhznqUmWLJlmWoANAAAAAABwjQjqAADA/1u+3G1v+e670rlzvp2ZrFmlVq2kNm2k3Lk5q37w669uQPftt97XWEfGAQOkJk2klPzkF8P27dvVtm1bffzxx871Vq1a6csvv3SCOAAAAAAAgITAyzUAACR11kPRggoL6H780ff1RYtKnTq5SVD69P7YYZK3aZPb4vK997yfiuuvl3r0kNq2ZSxgbOfOnXPmyvXt21fHjx+Put3aWb711ltOa0sA/vv3t2zZMueyfPly7dixQ2fOnFHq1KmdWY8VKlRQxYoVnUtK3l0AAAAAIAkgqAMAIKmygGLWLGnsWGnLFt/X16rlzp974AHmz/nJ7t1uNdyMGTZDzduatGmlDh2kbt3csA4x/frrr2rRooVWrlx5yVPTqVMn1alTR1mtQhRAvPnnn380ffp0hYeHa+fOnZc97vXXX3f+zJs3r8LCwtS8eXPlypWLZwIAAABAyEqe2BsAAAAJbNcuqWdP6cYb3TaVvoR0Vt3wzDPSb7+5/RcffJCQzg9sLGCvXpKNS7MxgV5CuhQppObN3adz+HBCutiOHDmi9u3bq3LlypcN6czevXvV3fqLAogX58+f16hRo1SgQAGnivVKIV10dpwdb+tsvd0PAAAAAIQiKuoAAEgqLJyw9pZvvy2dPevbWivNatnSDfby5vXXDpO8U6ekyZOlIUOkgwe9n47HHnPXFCuW5E/hRSIiIjR//ny1a9dOuyykjkPy5MmVOXNmZx2z6oBrs23bNjVq1EhLliy56vs4deqUunbtqnnz5mnOnDlOcAcAAAAAoYSgDgCAUJ8/9+mnbkD33Xe+r7eSro4dpWeflTJk8McO4VScSG++KfXrJ23f7v2U1KghjRghVa7MabyUv/76S23bttWCBQs8nSCbiTVt2jSVK1eOEwpco3Xr1ql27drabT18LyF79uxOe8scOXI48+lsTt2+ffucSrr9+/dfdPwvv/yiqlWrOrMkS5QowfMDAAAAIGQQ1AEAEIpOnHCTH5s/t3Gj7+urV3fnz1lrS+upCL+IiJAsQ+rRQ1q71vu6W2+Vhg2T6tSRkiXjyYnt3LlzGj9+vNM274T9W4hDpkyZNGTIELVu3Vop+HoH4qWS7nIhXdmyZVWpUiXlyZPnsuut+nXp0qUXtam1+7P7Xbx4MZV1AAAAAEIGQR0AAKHkn3+kSZOkKVOkAwd8W2sBxZNPSp06WWmRv3aI/yxeLHXr5v7p1c03S4MHS40aMRrwcn799Ve1aNHiinPoonvsscecUC9fvnx8bQLxwGbJWbvL2CFd1qxZVa9ePd10001x3oeFeHZs+fLl9cEHH+hgtF7Adr+NGzfWokWLCNYBAAAAhITkib0BAAAQD37/XXruOSl/fjfJ8SWky5JFeuklK4GQZs8mpPMzq5x75BGpWjXvIV327NL48dKGDdJTTxHSXcqRI0ecOXSVK1f2FNJZWPDRRx/p/fffJ6QD4tGYMWMumklnQXjz5s09hXTR2fG2LnaQbm0wx1rFOAAAAACEAII6AACCuW/i559L99zj9kJ8/XXpzBnv6wsUcNOfv/+WRo6UbrzRn7tN8uw0P/+8VKaM9NFH3k6HjQW0uXV//CG1by+lSZPkT+NFIiIinLCtePHieuWVV5zrV5I8eXJ16dJFa9eu1UMPPcQJBeLRP//847ScjV1J99RTTyldunRXdZ+2ztbb/UTXp08f5/EAAAAAINgR1AEAEGxOnZJmzJBKlZLuv1/6+mvf1letKr3/vrR5s5v+ZMrkr51CknVss4LFIkWkmTOlCxfiPi2pUklt20pbt0r9+/MUXc5ff/2lhx9+WPXr13dmWsXltttu07Jly/Tyyy8rY8aMfH0C8Wz69Ok6Zf9HRWMtLK82pItk6+1+orPHmWH/FwIAAABAkCOoAwAgWOzd66Y21jqseXNp3Trf58/98ou0aJEN5nJvg9+cOCENHy4VLCi9/LJ0+rS3dTZ/bv16aeJEKWdOnqAr6d27txYsWBDnScqUKZMmTpyon3/+WeXKleOkAn5w7tw5hYeHx7itbNmyPre7vBy7H7u/6Ozx7HEBAAAAIJgR1AEAEAxDzSyYsxc7BwyQ9u3zvjZzZqlLF7c06+23pcqV/blTOC9WS9OmuRV0PXpIhw97Oy333Sf99ps0Z45UqBCn0osRI0Y4IdyVPP7441q/fr3atm2rFITTgN9YterOnTtj3FapUqV4fYzY97djxw4tX748Xh8DAAAAABIaQR0AAIHI5mx99ZXb2tJaXFp7L68lWSZ/fmnsWHf+nJVz2XX4/SmzjqIlS0phYZKHToyOihWlb75xxw1S7OWbPHnyaNiwYZetvvnoo480d+5c5c2b18d7BnA1QV102bNnd/6Nxie7P7vfKz0uAAAAAAQbgjoAAAKJhXE2yKxMGenee930xhe33y699560ZYvUsaNbUQe/++4799TXry9t2uRtjVXcvfuutHSpdNdd/t5h6GrZsqUzey6SVc116dJFa9eu1UMPPZSoewOSktiVbf4KyGOHf1TUAQAAAAh2KRN7AwAAQNL+/dLUqdIrr0h79vh2SpInd2fOde4sVanC6UxAK1e67S19yVNz5XJHDT7/vJQqlT93lzRYMDdt2jRVrFhR5cuXd2ZWMYcOSHjWhjK6HDly+OVxbrjhhis+LgAAAAAEG4I6AAAS04YN0rhx0qxZ0qlTvq3NmFFq1kxq314qUMBfO8QlbNsm9ekjzZ7t/fRYcWO3blKHDlKGDJzWKzl37pw++eQTPfLII55OVNmyZfX999+rSpUqzKEDEsmZM2diXE+dOrVfHidVrHc4nPalLTQAAAAABCCCOgAAEmOYmfVKHDNG+uQT39ffeKMbzjVvLmXJ4o8d4jL27pUGD3aLH8+e9Xaa7LXqNm2kXr2kbNk4tXFZsmSJwsLCtGrVKi1YsEB169b1dNKqVavGyQUSUexgLnZwF1/OxvrmmyZNGr88DgAAAAAkFII6AAASir1o+fbbbkC3apXv6ytWlLp0kR5/nJ6JCezoUfdpe/ll6dgxb2uSJZOaNJEGDJDy5/f3DoPf4cOH1atXL02ePFkRFmbLAs42qlmzpjJQgggEvHz58sW4vm/fPr88zl57x8QVHhcAAAAAgk3yxN4AAAAh78ABaehQ6eabpaZNfQvpLO159FFp4UJp6VKpYUNCugTOVidOlAoVcufKeQ3pHnzQfZpff52QLi4Wys2dO1fFixfXpEmTokI689dff2mAJZ0AAl6FChViXN+5c6dfHmfXrl1XfFwAAAAACDYEdQAA+MumTVLr1m6rSut7uHu397Xp00tt20qbN0vz5llfPze0Q4K4cEGaM0cqXtztMuq1MOSOO9xM9eOPpdKl/b3L4Pfnn3/qoYceUoMGDbT7Mv8+xowZ47TBBBDYKlrVdzT79++/KFS7VnZ/dr9XelwAAAAACDYEdQAAxCerBvr+e+nhh6VbbpGmTJFOnvS+Pk8eafhwaceO/y/lQoI+fV98YRUa0lNPSX/84W1diRLSBx9Iixa5mSrinjE1atQolSxZUp/EMafx/PnzeumllzilQICzwCxv3rwxbltqleDxKPb9WdtLKuoAAAAABDuCOgAA4qtH4v/+5yY8tWq5JVW+KF/eXb9tm9Stm3T99TwvCezXX6W775bq1JFWrvS2xkYjvfaa9Pvv0iOPUPToxS+//OK8oN+1a1edOHEizuPr16+v162HKICAljJlSoWFhcW4beXKldq+fXu83L+1wrX7i84ezx4XAAAAAIIZQR0AANfi0CG3Aq5AAemZZ6QVK7yvtVaWVnlnFXjLlrklXKlT83wkQofSBg2kSpWk777ztsZy1FGj3LXPPSelSOHvXQa/w4cPq02bNrrjjjv0uyWbccifP78WLFig9957T3ms0hRAwGvevLnSpk0b47YPPvhAJ32pLL8EW//hhx/GuM0ep1mzZtd0vwAAAAAQCAjqAAC4Glu2SO3auSVVPXrY4Bzva9Olc2fXbdgg2QuPNWpQipUI7Cmz4g9rWzl3rvenrnt3tyXmiy+613FlERERTthWvHhxTZ482bl+JSlSpNCLL76otWvXqm7dupxeIIjkypVLAwcOjHHbwYMHNXv27KsO62ydrbf7iW7QoEHO4wEAAABAsKNPCAAAXlnAYEPIxoxxA7Y4AoeL5M4ttW3rpkPZsnHeE8m//0ojR0rjxnkfH2gVc88/L/XrJ8UawYQr+PPPP50quk8//dTTeapUqZLCw8NVtmxZzisQpDp37qz3339fS5Ysibptx44dmj59uurVq6ebbrrJ831Z20yryIsd0t1+++3q1KlTvO4bAAAAABILQR0AAHE5e9YtubKAzlpU+urWW+2VS6lhQ1pbJqJTp6RJk6ShQ63Cw/u6xx+XBg+WihXz5+5Cy9mzZzVu3Dj169fPUxVN5syZNWzYMGfelFXUAQhe9m/4rbfeUtWqVbV79+6o2y1se+2115wg3kL5K7W03bVrl5YuXXrRTDqTO3duzZkzh+8VAAAAAEIGQR0AAFcqvZo+XZowwcoBfD9P1rbPArpatWhtmYjOn5fefFPq21f6+2/v62rWdMcPVq7sz92Fnl9++cUJ3LzMoTMNGjRwQj3m0AGho0CBAvr6669Vu3btGGGdsfDNLtmzZ3f+3d9www1KlSqVE/Dv3bvXCen2799/yfu1kM7u1+4fAAAAAEIFQR0AALFt2yaNHy+9+qp07Jhv5ydtWqlpU6ljR0qwEpl1Jv34Y6lnT2ntWt8KIC2gu+8+8lVf2Wy5MWPGxDmHzuTPn1+TJk1iDh0QokqUKKHFixercePGToAfm4VxlwvkLsXaXVolHSEdAAAAgFCTPLE3AABAQLBg4aefpPr1pcKF3aDOl5AuZ05p0CC3ZGvqVEK6RLZ4sXTnndIjj3gP6W6+Wfrf/6TffpPq1CGkuxrXX399nCGdtcV76aWXtHbtWkI6IMRZqLZo0SKNHDlSae2NLFfB1o0aNcq5H0I6AAAAAKGIoA4AkLSdOye9+65UpYpUtar0/vvShQve15cqJb32mvTnn1Lv3lL27P7cLeJgoZyFc9WquWGdF/aUWS67YYP01FNScn46umoWwBUvXvyyn69cubKWL1/uvGifIUOGq38gAEEjMpzftm2bBg0apHz58nlaZ8fZ8bbOqnWZXwkAAAAgVNH6EgCQNB054ra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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Displaying results\n", "plt.figure(figsize=[6, 4])\n", "\n", "xlin = np.linspace(159, 170, 100)\n", "\n", "# Plotting linear model results\n", "plt.plot(xlin, lme.theta[0] + xlin*lme.theta[1],\n", " '--', color='k', label='Before outlier')\n", "plt.plot(xlin, ulme.theta[0] + xlin*ulme.theta[1],\n", " '-', color='red', label='After outlier')\n", "plt.plot(xlin, rlme.theta[0] + xlin*rlme.theta[1],\n", " '-', color='blue', label='Robust')\n", "\n", "# Plotting the data points (including the outlier)\n", "plt.scatter(x, y, s=40, c='gray', edgecolors='k', \n", " label=None, zorder=4)\n", "plt.scatter(159.386, y[x == 159.386] - 3, s=50, c='gray', \n", " edgecolors='k', zorder=4)\n", "plt.scatter(159.386, y[x == 159.386], s=50, c='red', \n", " edgecolors='k', zorder=5)\n", "plt.arrow(159.386, float(y[x == 159.386]) - 2.75, 0, 2.1, \n", " head_width=0.2, facecolor='k', zorder=5)\n", "\n", "# Making nice labels for graph\n", "plt.xlabel(\"Height (cm)\")\n", "plt.ylabel(\"Weight (kg)\")\n", "plt.legend()\n", "\n", "# Outputting result\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "44d668fc", "metadata": {}, "source": [ "As seen, the simple linear regression model is sensitive to the outlier. Robust linear regression is less sensitive (and is closer to the benchmark, the dashed line).\n", "\n", "\n", "## Case Study 2: Dose-Response Curve\n", "\n", "Next, we consider estimation of the dose-response curve using some publicly available data that come pre-packaged with `delicatessen`." ] }, { "cell_type": "code", "execution_count": 6, "id": "67b28522", "metadata": {}, "outputs": [], "source": [ "d = load_inderjit()" ] }, { "cell_type": "markdown", "id": "92b9b53f", "metadata": {}, "source": [ "The following code implements a 3-parameter log-logistic model (the lower limit is set to zero, i.e., it is not estimated). Additionally, we append an estimating equation to compute the 20% effective dose." ] }, { "cell_type": "code", "execution_count": 7, "id": "ad30a72b", "metadata": {}, "outputs": [], "source": [ "def psi(theta):\n", " # Asserting that the lower limit is zero\n", " lower_limit = 0\n", "\n", " # Estimating equations for the 3PL model\n", " pl3 = ee_loglogistic(theta=[lower_limit, ] + list(theta[:3]), \n", " dose=d[:, 1], response=d[:, 0])\n", "\n", " # Estimating equations for the effective concentrations\n", " ed20 = ee_loglogistic_ed(theta[3], dose=d[:, 1], delta=0.20,\n", " lower=lower_limit, upper=theta[0],\n", " steepness=theta[2], ed50=theta[1])\n", "\n", " # Returning stacked estimating equations\n", " return np.vstack((pl3[1:, :],\n", " ed20))" ] }, { "cell_type": "code", "execution_count": 8, "id": "f6f056cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==============================================================\n", " Estimation Method: M-estimator\n", "--------------------------------------------------------------\n", "No. Observations: 24 | No. Parameters: 4\n", "Solving algorithm: lm | Max Iterations: 5000\n", "Solving tolerance: 1e-09 | Allow P-Inverse: 1\n", "Derivative Method: approx | Deriv Approx: 1e-09\n", "Small N Correction: None | Distribution: Z-stat\n", "==============================================================\n", " Theta StdErr Z-score LCL UCL P-value S-value \n", "--------------------------------------------------------------\n", " 7.86 0.15 51.02 7.55 8.16 0.00 inf \n", " 3.26 0.27 12.28 2.74 3.78 0.00 112.75 \n", " 2.47 0.29 8.45 1.90 3.04 0.00 54.92 \n", " 1.86 0.14 13.00 1.58 2.14 0.00 125.89 \n", "==============================================================\n" ] } ], "source": [ "# Optimization procedure\n", "mestr = MEstimator(psi, init=[8., 3, 2., 2.])\n", "mestr.estimate(solver='lm')\n", "\n", "# Printing the results to the console\n", "mestr.print_results()" ] }, { "cell_type": "markdown", "id": "152669f9", "metadata": {}, "source": [ "Here, the first parameter corresponds to the $ED_{50}$, the second to the steepness of the function, the third is the upper limit of the response, and the last is the $ED_{20}$.\n", "\n", "These results can also be shown through a visualization of the estimated dose-response function. " ] }, { "cell_type": "code", "execution_count": 9, "id": "da6eed80", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Displaying results\n", "plt.figure(figsize=[6, 4])\n", "\n", "x = np.linspace(0, 100, 5000)\n", "theta = mestr.theta\n", "y = 0 + (theta[0] - 0) / (1 + (x/theta[1])**theta[2])\n", "\n", "# Plotting points and drawing dose-response line\n", "plt.plot(x, y, '-', color='blue', linewidth=2)\n", "plt.scatter(d[:, 1], d[:, 0], s=40, c='gray', edgecolors='k', zorder=5)\n", "\n", "plt.ylim([0, 9])\n", "plt.ylabel(\"Root length (cm)\")\n", "plt.xscale('symlog', linthresh=0.3)\n", "plt.xlim([-0.02, 100])\n", "plt.xticks([0, 1, 10, 30, 100],\n", " [\"0\", \"1\", \"10\", \"30\", \"100\"])\n", "plt.xlabel(\"Ferulic acid (mM)\")\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "24925a93", "metadata": {}, "source": [ "As we can see here, the higher the dose, the shorter the root length.\n", "\n", "## Case Study 3: Standardization to external population\n", "\n", "For the final example, we use inverse odds weights to standardize scientific results to an external population by drug use. This approach helps to generalize study results beyond a particular convenience sample" ] }, { "cell_type": "code", "execution_count": 10, "id": "e9a460eb", "metadata": {}, "outputs": [], "source": [ "# Loading Kamat et al. 2012\n", "d1 = pd.read_csv(\"data/kamat.et.al.2012_biomarkers.csv\")\n", "d1['drug_use'] = np.where(d1['Cocaine'] + d1['Opiate'] > 0, 1, 0)\n", "d1['S'] = 1\n", "biomarkers = ['IFN_alpha', 'CXCL9', 'CXCL10', 'sIL-2R', 'IL12']\n", "d1 = d1[['drug_use', 'S', ] + biomarkers].copy()\n", "for bm in biomarkers:\n", " d1[bm] = np.log(d1[bm])\n", "\n", "\n", "# MACS/ WIHS in 2018-2019 of cocaine or heroin use in previous 6 months\n", "d0 = pd.DataFrame()\n", "d0['drug_use'] = [1]*300 + [0]*(4016-300)\n", "d0['S'] = 0\n", "\n", "\n", "# Stacking data together and adding constant for model\n", "d = pd.concat([d0, d1]) # Stacking data sets together\n", "d['constant'] = 1 # Creating intercept for the model\n", "d[biomarkers] = d[biomarkers].fillna(9999)" ] }, { "cell_type": "markdown", "id": "bb3f6e38", "metadata": {}, "source": [ "The following code calculates the means for the study population. Note that these may not be generalizable" ] }, { "cell_type": "code", "execution_count": 11, "id": "6c93b4e9", "metadata": {}, "outputs": [], "source": [ "def psi_standard_mean(theta):\n", " # Returning stacked estimating equations\n", " return (np.asarray(d1['IFN_alpha']) - theta[0],\n", " np.asarray(d1['CXCL9']) - theta[1],\n", " np.asarray(d1['CXCL10']) - theta[2],\n", " np.asarray(d1['sIL-2R']) - theta[3],\n", " np.asarray(d1['IL12']) - theta[4], )" ] }, { "cell_type": "code", "execution_count": 12, "id": "670fb35e-2d7b-4a63-a429-5bf61f9ae2a5", "metadata": {}, "outputs": [], "source": [ "nm = MEstimator(psi_standard_mean, init=[1., ]*5)\n", "nm.estimate()" ] }, { "cell_type": "markdown", "id": "6966c472", "metadata": {}, "source": [ "The following code generalizes the results from Kamat et al. using data from the Women's Interagency HIV Study (WIHS). The WIHS study is considered to be more generalizable, but we don't have access to these biomarkers in the WIHS data. " ] }, { "cell_type": "code", "execution_count": 13, "id": "47fddcd6", "metadata": {}, "outputs": [], "source": [ "# Weighted means to standardize to population\n", "x = np.asarray(d[['constant', 'drug_use']])\n", "s = np.asarray(d['S'])" ] }, { "cell_type": "code", "execution_count": 14, "id": "e98453e3-e24b-4c56-8a7c-5d62b524722a", "metadata": {}, "outputs": [], "source": [ "def psi_weighted_mean(theta):\n", " global x, y, s\n", "\n", " # Estimating weights\n", " nuisance = ee_regression(theta=theta[:2],\n", " X=x, y=s, model='logistic')\n", " pi = inverse_logit(np.dot(x, theta[:2]))\n", " weight = np.where(s == 1, (1-pi)/pi, 0)\n", "\n", " # Returning stacked estimating equations\n", " return np.vstack((nuisance,\n", " s*weight*(np.asarray(d['IFN_alpha']) - theta[2]),\n", " s*weight*(np.asarray(d['CXCL9']) - theta[3]),\n", " s*weight*(np.asarray(d['CXCL10']) - theta[4]),\n", " s*weight*(np.asarray(d['sIL-2R']) - theta[5]),\n", " s*weight*(np.asarray(d['IL12']) - theta[6]), ))" ] }, { "cell_type": "code", "execution_count": 15, "id": "426a2397-4a5c-434c-89e2-eae379859e1d", "metadata": {}, "outputs": [], "source": [ "wm = MEstimator(psi_weighted_mean, init=[0, 0] + [1., ]*5)\n", "wm.estimate()" ] }, { "cell_type": "markdown", "id": "cdde9e4a", "metadata": {}, "source": [ "Finally we can show the difference between the raw results and the results generalized to the WIHS population. " ] }, { "cell_type": "code", "execution_count": 16, "id": "a02ad1c7", "metadata": {}, "outputs": [ { "data": { "image/png": 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txzBP1aBOpy8c592xxx57ZMNj5n322Wct00xAWy688MKi5RhOtfCapRbU5fSFQLqr1l133XT44YcXrbvsssuy+rxRCOqgTNHL4fjjj+/wi7eeYjLMQjEZM33TD37wg5Z/R6/QGK+5USy00EJFyzE3QKUiJO/oPekbGjmQ7g6NADTLtYH6nL4QSHeH+pxy6s44hmebrfxmmeWXX75oOcLpalCn0xeO8+6I8scN0oW0udCe559/Pt1111093suoGnX5K6+80uF7Qr0D6e445JBDim4miZ7RjXAzSJ6gDioQXWVjkvvCL7AXXnihIfZhYW+/MH369LqVhfoqHJI1JuyOY7azx7hx44reI47r0uf8+9//7nbZSv/oeumllyp+j9LXrLDCCt0uF82nkQPp7tAIQLNcG6jPaRSNerOa+py2rLjiikXL8803X0U7qvT5b775ZlV2tDqdvnCcd5c2F8oVPf5jGoO8GAFr5ZVXrvkOLK3LX3zxxYpeH6F44fQkc845ZzYcLTRSIN0dCy64YKsR6Rrlb4cgqIMKRG+NwYMHF6179dVXG2IfzjHHHEXLH3/8cd3KAu0ZPnx40ZjQ0UW+krA7nls498ygQYNa/cFE39DIgXR3aQSgGa4N1Oc0ikauMxu5bNTH6NGji5ZnzpxZ0etL53ceOHBgVcqlTqcvHOfdpc2FckRAF0Fdob333rtHdl7pTczPPfdc+uijj8p+/VNPPVW0vOyyyzZ07yj6ZiDdm6/PBXVQ5Yu1eikNDAvHT4dGEUHIKqusUrTu3nvvLfv199xzT9FyvFdhL1foDTQC0AzXBupzGkUj15mNXDbqo/Qu7v/9738Vvb50CMAhQ4ZUpVzqdPrCcd5d2lwoxx133JEmT55c1Cttt91265GdF/O2x6MwJH/ooYe63N6y2mqrVbV89B71DKR78/W5WBwqMGPGjGz82kIxUWYjuPvuu4uW9TLqu/7yl79U/EXz6KOPpsMOO6zouL7kkktazS1QDdttt1267777WpZvu+22tOuuu5b12nhuoe23374qZYJGohGAZrk2UJ/TCBq5zmzkslEfMVx3DIv62WefZcvRmBt/X5aO2tKe0gbX0mHOukOdTl84zrtDmwvluOiii1q1WZR77Ffr/Dv//POL2lDWW2+9sl6rvYVmCKR78/W5oA4qEMOrFXbrjZN50UUXrfs+nDRpUlZJdjRfB33HJptsUvFrSoczmGuuudLmm2+eamGHHXZIxxxzTMvyVVddlc4666w0zzzzdBqUx3MLfeELX6hJGWl8jR5Id4dGAKo1POyf//znml4bqM9pBI1cZzZy2aiPhRZaKG2wwQZFc7pcc8016Rvf+Eanr/3kk0/StddeW7Ru0003rVrZ1On0heO8q6K9JYYRLKTNhVIxtcfVV19d1zm7oi4vDOouuOCCrP2ls5GI4vgubFeMXkfbbLNNTctK86p3IN1V0cv0gQceaNzr8xxQlvfffz83atSoSOlaHnvvvXfd994nn3yS23LLLYvKNWTIkNzMmTPrXTSayPjx44uOoeHDh9d0e2uttVbR9o455phOX3P00UcXvWbdddetaRnpfXr6OO+KCRMmFJUxHs8991y9i0UT2nfffYuOoznnnDP3yiuvVH076nPq6c0338wtsMACRcf6+eef3xAfivqc9lxyySVFx2z8jfnhhx92usPOPvvsotfNN998ubfeequqO1qdTl84ziv17rvv5lZZZZWicq288sp1LRON6aKLLio6ThZZZJHcxx9/3KNliPNsiSWWqPjaaPfddy96zS677NIj5aX5RJ04zzzzFB0v119/fa4ZXHjhhUXlHjBgQO69997LNQpBHX3OD37wg9z9999f0Wtef/313Oabb150Ms8+++y5xx57rMPXRSNw4WsuuOCCDp//ox/9KPf000+XXa6oTHbddddWjbq//OUvy34P6G6AUXr8xXt15uabby56zRxzzJG74447OmzsiucUvub222/34dGrgjqNALTl5z//ee7BBx8se+dEY8D3v//9VnXzd77znU5fqz6n2fRUIF0p9Tkd+fTTT7NG/sJjd88998zWt+df//pXq0axI444osPtqNOpp0Y9zuN66OWXXy7795g+fXpus802a7Wda665puz3oO8YN25c0XFy6KGHduv9Jk+e3OrYi3WdOeecc4pes+CCC+YmTpzY7vMvvfTSVu2dlbRN0rc0QiDdFVOnTs0tvvjiRWXffvvtc41EUEefs+qqq2Yn49prr5077bTTco888kjuo48+avW8zz77LPfUU0/lTjjhhNzQoUNbfTkedthhnW6r0qAunj/bbLPltthii9y5556bfTG2dSE7bdq03G9/+9vcsssu26pc66yzTll3qkE9g7pQ2hN0rrnmyp1xxhlFd7NEQ9fpp5+e/V/hc7fZZhsfIA19nGsEoFo22WST7Jhbf/31szry8ccfb/MPobjb/LLLLsutttpqrY7XuF547bXXOt2W+px6aeRAWn1OLcQNZ/369Ss6FuPG0NLzIOr2+Ju1NLyI3knvvPNOh9tQp1NvjXic53tQ7Ljjjlmvv/ZCjxdffDF38sknZw3QpduI10KpKVOmtDre47q9HkFdtHGOGTOm6HWDBw/OApbCvyOiU0KMXBTtkIXPPeCAA3zANGwg/corr+R+/OMf5954442KtpHPA/KPOF8feuihXCMR1NHnlJ6Y+btvl1566dzqq6+eBV2jR4/OzTvvvK2eV+6dYN0J6kq3NWjQoNxyyy2XW3PNNXNrrLFGm8/JP1ZYYYXsji9ohqDu1Vdfzc670tfPPffc2UVlnIelAV2+wTnCamjk41wjANUO6gof0cAUdeHYsWOzYcqWWWaZVn9g5x/RwPTMM8+UtS31OfXSyIG0+pxaOemkk9qtt+NvvxVXXDH7O7X0/2Oag85Gdskfu67RqbdGO87bKksMr7n88stnN3NHm9Biiy3WbpvLRhttlE2LAqXiJv/CYyXa77qrq0FdePLJJ7NwrvT1EYhHu2gE4aWjFsUjzgPHOI0cSE+e9fw4lnfbbbfcn//853Z7Sj/77LO5o446Kjf//PO32sZ3v/vdhvug+9d7jjxoBB999FGaPHlyp8+bb7750kknnZS+9a1vdToRazUno3322Wc7fd6ee+6Zfv3rX6d55pmnR8oF3bXwwgun8ePHpy984Qvp0UcfbVn/wQcfpIkTJ7b5mtVWWy399a9/TcOGDfMB0BQTFV933XXZI/8dsuiii6b5558/ffzxx+l///tfeuWVV9p87UYbbZQuu+yyHi4xzXRsxYTvnYkJ4GMC+YUWWqim5VGfUy333ntv9ggDBgxISyyxRFZnzj777On1119PU6ZMSZ999lmr1y2yyCLp5ptvTkOGDKnJh6E+pxZ++MMfpoEDB6ZDDz00uy7Ie/XVV7NHW5Zffvl0/fXXp+WWW65mH4o6nb5wnBd65513skdHZptttnTYYYelE088Mc0xxxw9Ui6ay0UXXVS0vNdee6V6WnHFFdM//vGPrL3lhRdeaFn/7rvvFrW/FNp8883TVVddleaee+4eLCnN5I9//GN0+mpZXmONNdJKK61Ul7K8++67WZtJvt0k/g6Iv3uj3SXaFadOnZqmT5/e5mt32mmndNppp6VGM1u9CwA97fLLL0+/+MUvsi+gOHk7E4HcKquskk455ZQ0adKk9O1vf7tmId2pp56afZmPHDmyrG0suOCCaZ999kmPPPJIuvDCC4V0NJ3hw4en+++/PzsnF1tssXafF/938sknp/vuuy8tueSSPVpGqJZoAHj66aezYz7q7bZCumgEOPzww9Pf//53fyCROeqoo7IbhMaMGZOFFZ2JG3biD4877rgj3XjjjTUP6fLU59QqkH744YfTAw88kJ5//vk2Q7oIpKPBqacadIP6nGo5+OCD02OPPZa++tWvdtj4v/TSS6czzzwze25PHOvqdHrrcX7uueemXXbZpey/KeNGkEMOOSS7ho+/WYV0tOXuu+8uuoluzjnnTLvttlvdd9aqq66aHn/88XTkkUdm7YftifPtvPPOS7feemtaYIEFerSMNJdGC6QLxU19Tz31VNZuGN8jbYV0cSNgBHR/+tOfsraXRtMvutXVuxBQL/HHfvRWiwDuxRdfzP7ojru85p133uzu3REjRqSxY8eWFehVW5TlySefzO58iTvNomddhHfxpTl48OAsPFxhhRV6rGcf9MT5+NBDD2WNbdOmTcvWRQNz9KKL87ARv0RpLhMmTEjjxo0raoSK3hnlKK1rozfopptu2u7z4w+duIPxnnvuSS+99FJZjQDReHHQQQdlN2tAW95///3s2iCO27hDMO4ijLozrg3ij+/Ro0enlVdeuaxAr5bU51TqtttuS9dcc02666670n/+85/06aefdhpIb7311lmdufHGG1e0LfU5jSr+/osepfH36dtvv50d59G7La6Do4dRvajT6a3Heb5RN9pcokE32lziGiquqYYOHZpWX331tMwyy/RomaBWoq0zAownnngiO/bjWI/RXuLci78foJxAOkb+KQyk42/SaKPujvjbNm7UKBSj3kWbfFuit1y0t0SbzD//+c9spKLORNvP17/+9azzTUedBOpNUAcAUEMaAQB6RyCtPgcAgMYxderUrNdzdMB57bXXsr8lIkSMvxvi5v+11lqrocO5QoI6AAAAAAAAqAPjiAEAAAAAAEAdCOoAAAAAAACgDgR1AAAAAAAAUAeCOgAAAAAAAKgDQR0AAAAAAADUgaAOAAAAAAAA6kBQBwAAAAAAAHUgqAMAAAAAAIA6ENQBAAAAAABAHQjqAAAAAAAAoA4EdQAAAAAAAFAHgjoAAAAAAACoA0EdAAAAAAAA1IGgDgAAAAAAAOpAUAcAAAAAAAB1IKgDAAAAAACAOhDUAQAAAAAAQB0I6gAAAAAAAKAOBHUAAAAAAABQB4I6AAAAAAAAqANBHQAAAAAAANSBoA4AAAAAAADqQFAHAAAAAAAAdSCoAwCAOvv000/TpZdemr785S+n4cOHp0GDBqV+/fq1PPbaa69ub6Pw/TbddNOqlBsa2aOPPpoOPPDAtMoqq6TBgwen2Wefveg8oG+qdV143HHHFW1jwoQJqVbU673XlClTqn4dAAA0rv71LgAAAPRlL730Utp+++2zUAHovs8++ywdfvjh6bTTTrM7AQCAhqdHHQBAO0aMGFF0N3P+8bvf/a7L++zCCy9s8z1jW/Q9M2fOTNtuu62QDqropJNOEtIBAABNQ486AIAK/fGPf0z7779/l/bbRRddZH/T4vzzz0+PP/54y/JCCy2UDjrooLTqqqumgQMHtqxfbLHF7LU6iaHx7rjjjpblXC7ns2hgb7zxRvrJT37Sshw3Qnz9619Pn//859PQoUMNeQkAADQcQR0AQIXuvffe9Nxzz6Vll122ote98MILRQ3+EKFv3hxzzJHuvPPOtPzyy9sx0EVXXnll+vDDD1uWjznmmHT88cfbnwAAQMMy9CUAQLkXTrPN1mbAUq54Tb43TuF70Td99NFH6eGHHy7quSWkg+755z//WbTc1d7P0AzimiL/mDBhQr2LAwBAF2khAgAo07hx41r+ffHFF1c8BF5huLfZZpvZ733c5MmT08cff9yyPGbMmLqWB3qDZ555puXfCyywgGFjAQCAhieoAwAo0x577FEUstx1111l77t77rknTZo0qc33om966623ipbnm2++upUFeuN55ZwCAACagaAOAKBMW2+9dRo2bFiXhr+86KKLWv690EILZe9F31Y4j1bo169f3coCvfG8ck4BAADNoH+9CwAA0CzmmGOOtNtuu6UzzzwzW7766qvTr3/96zTXXHN12nB85ZVXtizHe/TvX73LsKeffjo9+uijafr06entt99OgwcPzoZ723DDDbN/d0eU/cknn0xPPfVU9v7vvfdemnfeedOQIUPSyiuvnFZaaaWazLcXv9N9992XXnnllTT77LNn4ebaa6+dVlxxxVQvL774Yrr//vvT//73v5b9vMgii6QNNtigKMAtV6VDpzaC+FweeeSRNG3atOxYGDp0aMuxNv/883f7/T/55JOW3qexn+PcWnbZZdNGG23U7WO5UX366afpX//6V5oyZUqaOnVqthzDoG633Xbtvuazzz5Lzz77bJo4cWJ2jrzzzjtpwIAB2T4aOXJkdq7EcrXF5x49ifPDtsbnP3r06LTuuutm52lXvfrqq9l8jbEP4neJ32/gwIHZeb/MMstk9cw888zT4+dVzCMZc97F7xu/e74uWm655bJ9XO26b+bMmdnx/9///jc7FmJ7a621Vtpkk03Kfo8HHnggOzZefvnlrHxx/sSwzZ2dn1HX33333VldP2PGjLTgggumFVZYITv3qvF99f7772e/W5SrcF/G8TN27NiqhKoPPvhgdk7EvosyDx8+PK2//vpp8cUXT40k9m+cR/E5RT0a3x9xnG+88cbZdUZvq9cb/Rh99913s+PmP//5T3r99dfTBx98kJUl9kMcm6NGjUq18MQTT2S/SxyvUYaFF144G22hHsdAXnz/xHkU38FxPESdFMfn0ksvnV3rVPt7JfZ3fP/F91hcY0Y9//nPf77DfR7DG8c1b+y3OA7isx80aFB2nsexFHVKNa+xAaDH5AAAaNPw4cOjxbfl8eabb+YeeuihonWXX355p3svnlP4mocffjh7r8J1sa1KzJgxI3fcccflll566aL3KXzMPvvsuU033TR35513VvTeL730Uu6UU07JXjtgwIB23z8eCy64YO773/9+7uWXXy77/SdPnlz0HnvuuWfL/91yyy25Nddcs93trbjiirm//vWvuZ7y6aef5s4///zcSiut1G6ZZptttty6665bVrlKj6lyHoX7pzsK33OTTTYp+3Uffvhhdjwsu+yy7Zaxf//+uc997nO5u+66q0tli238+Mc/zg0dOrTdY/mrX/1q7oUXXuj0GKqGY489tuLPqb39Wvpe48ePz9a//vrrucMOOyy3yCKLtHqfVVddtdX7vPPOO7mLLroo98UvfjE77zoqR5y3X/nKV7L6qhKFx2dhnfT0009n241jva3tDRkyJHfaaaflPvroo4q2d+WVV+bWW2+9TvdrfP6rr7567vjjj8/2W0f7t9xHR55//vnc7rvvnpt33nnbfX0cqwcffHBu+vTpZf++cZwWvkccx/k695vf/GZugQUWaLWdL3zhCy2vj2On8P/idw+ffPJJ7swzz8wtt9xybZZ14MCBuR/84Ae5Dz74oM3j6vDDD8/NN998bb522LBhud///ve5rrr77rtzW2+9dYffJQsttFDumGOOycrSFX/4wx/arZ/69euX23LLLXOPPvpot+vCcrV3zk+bNi233377tbsvBg8enDvyyCPb/JyapV5vlmM06rS4horv7vg9O6oroo6OuueNN97o9n74+OOPc2eddVZuzJgxbW4rrg3zuvo9F/triy22KHptfM7PPvtsu6+Jbe2zzz7ZMdjefojPKOrFqB+7+51y77335rbffvvcHHPM0Wo7p59+epvH60knndTh8Zp/zD333LnNNtssd84555RdTgBoBII6AIAKgrpQGNp8/vOf73T/xXPyz1955ZWzdd0J6q6//vqsYbOzxorCRzQCRwNRZ6IxMxo2K3nveMw///y5m266qazyt9f4FI105W7vhBNOyNXaf//739zYsWMr2g/RIN1RY3OzBXVPPPFEbplllqmovHvvvXdFgU2EbyussELZx9mECROaPqh75JFHcosttli779NWUBdhWKXliXP5pz/9abcaVa+66qqs4bOc7W277bZlhQzR6BrBX1f28W233VaVz6o90Ug855xzlv0+ER78+c9/7nJQF79PHNftvX9nQd27777bqlG+vcdGG22Ue//991veb9KkSe0GJ6WP7373u7lKRLl22mmnij6TCETuv//+srcRx9p2221X1nvHZ3rZZZdlr+vsnO2uts75xx57LLfooouWVdZRo0a1hLidabR6vRmO0biG6kqdsfjii5d9fLa1HyLo23DDDTvcRneDuqlTp2Y3NRS+bq211spC4vb85Cc/qajOi+eWG4y29Z3yi1/8osPrzNKgrpJrhNJHOde9ANAozFEHAFChGJoo77bbbsuGbWtPDM0Tz2nrtV1x7rnnph133DEbkqhQDBMXw0LGcGwx9F3pkGzxuq985SudDgsXQ72VPmfOOefMhhNaffXVs/ePYd9KhxWKoSBjqL7x48d36fc64ogj0imnnNKyHMNrxvB/a665ZpvDSv74xz9O11xzTaqVGOouhkyL4fgKxX6NIcqiXDGsWqmbb745G8LrzTffTM0uhr+KIcWef/75ovUxLFccA7EPYni0UhdccEHaYYcdsmOpM3HuxP6KIccKxTB4sZ9j2L/4mR8WL46z7bffvtXzm8lLL72Uttpqq2yor7wYsmuNNdbIhvtqbyjdtuY0XHLJJdMqq6ySDT0Z50vUA4XiXD7qqKPSCSec0KWy3njjjWmXXXbJhoLLf/ZRxqgHRowY0ebzDz/88E7f9xvf+Ea69tprW62PoRCjnsn/Pl0ZUrY7jjnmmPS9732v1bG7wAILpFVXXbXNIThjqM6dd945O+4rFcMNfuELX8iO67yoV+Lcijq3syHw4vONz6fwOybOyXh9DP9WOhxpDLd4yCGHZP+O75DNNtssG4KwrXOu1BlnnJEuvfTSsn6veO8YrvOqq65q9X9LLLFEdqyvttpq2dCFpfXBpptumg1vWM4wuV/+8pfTDTfc0OY28vsw/10Yn2l8/06YMCH1tBjGN4bzi+uB0jLGvi79vo6h/eKzKawjmqleb/RjtLQuDXPPPXdWt8WxGWWLoR5LP5cYqjOOzxiuslJxvMb+Kzy24/iP+jse1RhiNL4X11tvvaxeyYv5kOO6rK26NIa53GuvvbJ6r/RzjeHNo1yxL0qHjo3nRh1++umnV1zG3/3ud+mHP/xhy3VmXF/Gfo/PNLZTOgRufPdsvvnmrb7z47OJujI+r/g+iiFQyx0eGQAaVr2TQgCAZutR98orr2RDseXXn3rqqe2+x8knn9zyvHhNvLarPepuv/32VkPPxdBB0cOo9K7hGB4u7louHbothg7qyAMPPNByZ37c1Rx33rd1R3L0ZLjuuutya6+9dtH7Ry+hGJazI6V3iRcO37nOOuvkbr311qJtfvbZZ9nd6aV3VMfd7bW4WzreM8pRuK0YGuuII45oNcRn3O3/jW98o9Vd3DFMY3vDwEUPmnjEcVP4mq9//est/1f4mDhxYlV+r0p6XkSvwBEjRrQa9iqO59KhB6MX5o477thqH/zwhz/stEw77LBDWfs5ejfG0Gf5IcpKh3ytdo+65557rmX/r7LKKkXbauszyj8efPDBTnvXLLzwwi31wbe//e1Ww5FFb5LSXmNh0KBB2TkQQwTGsGHvvfdem0O1/vOf/8ztsssuRduMbZXTE6SwzothGPNDbC6xxBLZ8IKlvUWfeeaZrBdd4baijop6oz1RjtLPPIb7mzJlSpvPf/XVV7NefXvssUfWs6903xR+VvHI79/8vm7vsyoVQ9eWHsPRe/pvf/tbNmxf3syZM7MhO5dccslWvUwKh1csp0ddvqxzzTVX7uijj86O80LxPXHHHXe020un8Bzdddddc08++WTR61977bXcAQccUPSa6MkSvbu22mqrlm3HsLPRE6fQf/7zn9zGG2/cqsdbZ72q4hgcN25cq6EJY5jF0m3Ec6NOjGHqCp8fx1uUvSM///nPW31ecdw/9dRTRc+Lbf7oRz9qqTtK67We6FFXeF61Vcao76JHeeF1Rbk99hutXm+GYzTqk9jXUXf97ne/y+rgOBbb2l+XXHJJ1sOxcBurrbZadl1SyX4orJc233zz3D333FO0zXi/qJeit3FXetTF+5UOWxm9IDu6Rop9Wvj8GIbykEMOafOaI6519t9//6KecHFOxXY7Unjsx/Vovnd29C6NYcVLv1NiWM3HH3+8zWvofF1y7rnntjkMaezDKOdvf/vbbLjbKKsedQA0E0EdAECFQV3pcJbRkN+ewnlIYljEvEqDunh+4VxW0RgejRydiQaXaNgobEwubewq9L///a/DRvZS0dBUGlSdffbZHb6mtPEp/9hrr72KGsRLxdBNpcMFRlhYbaUBWuyzm2++ucPXRMNR6e9z7bXXdmkOm1qppEH3oIMOKnp+DM0Xcyt25Kijjip6TRyjHc2RVhqMRCPhDTfc0OE24jVtzSdU7aCuUOyrwm1Vqq2hGeN3iMbiSuTnuSpXzGdXuM2dd96509e0NTRrDP/a0bBpcc4W1oedDUFXOsRtBIDlikb9jspS+juUO6RwhJ6FDenxiKH6ChvNS0VDcQxlXNqAX0lQF4955pmn7DnASuuMcm4WCdFgXzrXZ37bHc1hGvsl/9xy67W4GaTw+XHTQ3yvdPY9UlrnfOc732n3+RHqRnhT+PwTTzyxw23E0MxtzYfVE0FduWWMIVRLw7oIhZupXm+GYzTCoPZuDGhL3Ji0zTbbFG2js6G+29sPlQwhW25Qd80117Q6HyL470gEbIU3fsWcm3GjVmeuuOKKotd1dP3b3ndKfF4dXYcWimE786+LuR1LQ+6OxHM7C1QBoJEI6gAAuhDUxVw3hf8Xc06Vit41hc+5/PLLuxzUlfYeiOVy3XjjjUWvjYa3aooeJssuu2zL+0fDSqVB3ZprrlnW/DfnnHNO0eviDu9qitChtKdMZw2Med/61reKXhdz0TRjUBfHZvSyKHz+1VdfXdY2IowufN3uu+/e7nOjV0Hhc4877riythENkM0e1EWvwZ4Q+78wHHzrrbcqqvNi/rVyGrSjQbTwdcsvv3y7z4051/LPi0b4jsL5ruhKUBe9agrLHzcEvP32252+Luqy0vn7oudzJUFd9P4oV1uN/+313i3tsVXaGzsecYNBZ0q/62K+045Ck8JePdFrprSnVnviOFh33XWLjo3C79xCcf4UlinmqStHBGX1CurKLWPp77beeus1Vb3e6MdoV8VxXDiXZMy/2JG29sP6669fUXBUTlD3m9/8pmi/RdAb9Vln8r0V4xGv76xnXEc3W7TVQ7m975QIywt7zHWmcJ+X08MUAJqZOeoAALog5ombb775Wpb/+Mc/tnrORRdd1PLvmH8kXtMVMY/Ir371q5blpZZaKh166KFlv36bbbbJ5n3K+/Of/5yqKeYY2WmnnVqWY36U/JxW5Yo5tDqbjynEXFCFSueQ665bbrklm0MsL+ZAyc+X05mf/vSnRXOExVw0XZnLpt4uu+yy9P7777csb7DBBtlcUOX45S9/WbR85ZVXFs2/VTjXz9///veiOcBi3ppyHHnkkVWZz6de4hiJ36En7L777kVzJD3wwAMVvf5b3/pWm3Mxlor5gWI+o7yYU+rdd99t87mFdUPMM1Q6D1Q9/P73vy9aPv7444vq9/bEPH2l9UPMB1quqMv322+/1FUxn1M58w/m5wQrFJ/rPvvs0+lrY07Iws+ocP6rUvE9+MYbb7QsH3fccWnw4MGpHDFXWeF5EcdP1MelIpu68MILi9b94he/KGsbhx12WFpkkUVSPZRbxphTsvDY++c//1mV75GeqNeb4RjtqjiOY763vHvvvbfi94j9UDoHW3f86Ec/SgceeGD67LPPWubZi7l7v/nNb3b4ujieCs+tr371q9mcvOWK7+rCeYoruaaMbcVcn+Uq/L4o5xoRAJpZ/f8qAgBoQtEgUhgaRSNYBGp5H3/8cbr88stbluO5c801V5e29eijj6ZXXnmlZXmXXXapuMFiyy23bPn3f/7zn/Taa6+lalp66aWLAoEnnnii7NdG6LLVVluV3VgWjdt5haFaNdxxxx1Fy3vssUdRg1RnZSsNY++8887UbEr3QTkNpYWBTWGD30cffZT+9a9/tXpeNHL+X2eQ/xMNxuWeHxF0felLX0rNatttty0rBKr2edmVBuxoVC3Xaqut1vLvaDiOMLa9Bvm8d955J11//fWpniIQKgz84/iKOrZcpefHXXfdVdH+7U5QGeHoqFGjynpuaeP4F7/4xSwc68w888yTBZJ5L774YrvPvemmm1r+HfVmJfsxfO5znyvaH23ty/j+evXVV1uW11hjjTR69Oiy3n/AgAEVl6kaKilj7O/SAK20Tm7Uer0ZjtFq1adRv02fPr3s1y688MJps802q0o54vpyzz33TD//+c9b1g0ZMiS7+WWHHXbo9PU333xz0fLXv/71irYf24pjuit13q677lrRtgq/L+J6qlafLQA0AkEdAEAXRYiT97///a/oDuUbb7yxKAwrfG6lShtBSu86L0dhuBXKuUM/7r6/4oor0v7775/WXXfdrMFk3nnnzRpS467wwkc8p1AlQeDYsWMraqxeaKGFWv5d7l395brvvvuKlittWIuG5kLlNmY2kp7YBw8++GDRciV383fl+Y1k7bXX7tbrIwT7xz/+kfWq3XzzzbNeJ9EjMRq0S8/L5ZdfvsvnZdwMsOqqq3bpvOzo3Nxiiy2Klr/2ta+l0047Lb311lupHuJYLLzJYq211soa/su13HLLpSWXXLJleerUqemFF17okWOhsLG8nMb10nq3K6+NcLUtEbzfc889LcsRzlQaSA8aNKhoW219T91///1Fy5tuumlF26j0+dXQ3TKW/s7N9N3WSMdoW6LeOf/889Pee++dbW/RRRfNjsPSujQehcFYpfVpXLdVozfdjBkz0nbbbVc0ikOElHHurbfeenW5pozwvPDGm2rWeYXfF/GdMm7cuHTVVVdlYSUA9Dbl3R4MAEArG264YVpmmWXS888/3zLUZQwzmf933rLLLps9t6tKGytLh3/sisLhyUpFA0gMdRVDOUajUFdU0uhe2sDfmWhEy6t0iM3OlDawFw7nV47SYKPZ7v6OxrbCXorR0F7YU6Fa+6C0t9WKK65Y0TbKff6TTz5Z1Bu1I2PGjMkaaXu6l1sl/vKXv6Tvfe97afLkyTU/L6OHaDm9Wdo6Lzs6N2OY3Khb4rPJ92iLIQljGLdNNtkkCxCivozG4672Qu7Jcz5/zBeeN3HMlzNkaHeOhTBs2LCyn1s4LG93Xtve5xo3qxR+r8Tn291goq3vqdJjv5Jh9MLKK6+celqlZSx9flfP956u1xv9GC303nvvZUPcnnXWWWnmzJmp1vVpd8/1/DkWdWRhz+gY1jx6slYypGvpNWWl12Cl4kaHCEc7G5I6boAYOnRoRe/9gx/8IF166aUtw7bG9XZcA8fNKTESQ4TaceNOnDONMIwyAHSHoA4AoIuiETKGDIrGnvDXv/41u+M3gq7oUdfVYYVKvf7661X/jNrr7RINXHG3dvTY6Y5KGr56ojG+XG+++WbLv6PRp9z5lfJKG6EK368ZxHGRn++mrR4O1doHpQ2clc45F4105Tj55JOLQvOOXHDBBWmvvfZKtdbVYS8jyCrt0dHI52V7PSyip14Mdxk3NTz99NNFw+nddttt2SM/TGH0EPnCF76QDZcWQ8fVQunxWWlDcnfO++4Ogdqdz6ja9W5PfU+V1h2V1lFdqdO6q7tl7G5v056q1xv9GC3sCRc3BDz++OM9Vp9WY7jjv/3tb616p91+++3ZSAeNcK529j3elX0QPZavvvrqbMjawp6ScU786U9/yh5hwQUXzHrbfeUrX8mGII/h6QGg2QjqAAC6IYa0POGEE7JG6Q8//DBdeeWV2c/8sDwR5nVn2MtQiyHhChvtCh1wwAGtQrq4qz3uWo476mOIt2hsiUaQwt42t956azrllFNSs4vePe3d3d+VXkVd7ZHYCL9/W79PtfZBaQPnnHPOWdE2IsRpVpXOLxkibCwN6eIc3GijjbLG2hiGLBrSY78U7svogbH77runRhM9kWNeuDPOOCOdffbZbc5nF8fIhAkTsscPf/jDbHjd6IlXaaN0oxzz1ToWGlVPfU+Vfl6V1tNd+Xy7q7tlLP2dm+kYb0TRq7c0pItrmwh6Yi7BJZZYIuv9FXVsYS+tGG7y4osv7tI2q3GuR/1e+N0Zv0MMQVo6nHAjXVNWYx9svfXWaeLEieknP/lJNh90W+dDBMfXXHNN9ohr1mOOOSYddNBBVRluFAB6iqAOAKCbDc4xTFt+zo9oyCkcdika0rs75FFpI99JJ51U0bwv7Q3zV+rf//53Ue+jaFSJHkkR3nUWpDz33HOpN4jGuXwvjvxQS5WI4bQKVTtUqLXSublKf59q7YPSO+8rbYiuZA6iZhc9zSKoKrTPPvtk52ZnPWMKe6w1mqjXopfgEUcckc2vFDcIRCgXc2mVDl0X++BXv/pVdkPAnXfe2e2h2upxzPd2pd9TEXiceeaZ3XrPtnrFlAZGldbTXfl8u6u7ZaxkzsS2OMb/vxj5IOqZwnP1nHPOyXrtdjZ04t///vdUTzHkY1yLxZx6IerJ7bffPv35z39O2267bUXnauF36M0335z69+9e02AlQ292RYSnv/vd77Jh2aMXYXyG8V3w6KOPFs0xGqZPn56+853vpDvuuCPrcVfJEM4AUE+COgCAbooec/mg7u677271f91VOuRUBH+bb755qrboDVg4XF0M6fnd736323PeNZMYPikf1MUd4nGXdqyrZEit0vdrJhGgRWNl/u74rgyRVc4+KB1SNOaRW2211creRrnzzl144YXZo5lFg2T0jMvbcsstWxpqe8N5Gcdb3NAQj2OPPTbrjfzggw9mw7xF74lJkyYVBY8xPGnMyVQtpcdnrY753q70eyq+S2rxPVU67G3pvq/HsH+d6W4Zyx3qt971ejO44ooripYj/ImQrhnq0/gMzzvvvCysi3AxRA+7L33pS9nv9cUvfrHsc7UwqBs7dmxVb36opQjqYzjkeIT4PeK6O4abv/zyy4uGZI0A87TTTkuHH354HUsMAOUz2yoAQBXucm7rzv9YF0MsdVdpj7zChutqiiGUChuEvvWtb5X92hiWqDcYPnx40XLcrV2J0ueXvl+ji2GiYgiwvGgEmzJlStX3QfS2KfTII49UtI3o/dlXFJ6XIXq49ubzMnryxtx0caPAM888k37zm98U9XSJ3h9PPfVUw5zzveG8r4boUVP4PfjCCy+0DAFd7V7shZ544omKXv/YY4+lnlZpGUuHZexur/yeqtebrT6NHslx/dZM9Wl8ljFccOFNVNHjOH6PuNmqka4pe0IMxR7zncb3xEsvvZT23nvvov8//fTT250vFQAajaAOAKAKDQX5u3sLxYT28X/dFfOmFCqdQ65aCnvtxBwf5d4xH3fpxxBDvcG6667brX1d+vzS92sGPbEPYl61QjfccEPFw5f1lNLh0Hq60a/wvAzLL7982a+tVV3Rk43SEUzutttuRetLey53x5prrlk0NNoDDzxQ0VCs0cgdDcR5iy66aDZnYF8TAesGG2xQNNxjDGNabWuttVbRcqXfPfX4rupuGUvry67w3da6Ph05cmTZwyJGuPnQQw+lRhEBVOGQyJ988klWT15yySUNc01Zj9525557bhoxYkTLuldffbWpg0gA+hZBHQBAFey5555lreuKaKQrDM2iUeXJJ59M1VYYQMQd2pWEJv/9739Tb7DJJpsULUejVzSAlSOGXLr22muL1m288cap2fdBJUNHxtCEMd9Y3oABA9I666zTZmP7wgsvXNTLodxG0Pvvvz8LU3pKd+fE6q7SYLDcczMapK+55prUGxQGQF0ZSrCz+bsK5/yMz7fcninhD3/4Q4fnT1/y+c9/vmg55hWsthVXXLGo7oh6o9zvwxgmMIbH62mVlDFC4hiyr9rHVE/U682gq9c5cZ5/+OGHqZHEfMU//vGPW5Zjrra47iytkzo7TyPcqkXv13qIufZKj81qfl8AQC0J6gAAqmCrrbZKM2bMKHrEXFLV6qlQOMxRNDTtv//+VW9YiaHLCkOnchoWo1Hx0EMPTb1FfGaFvWEmT56cfv3rX5f12mOOOaYoxIk5t1ZYYYXUbGK+nsJwKuZfvO6668p6bemxEMNxxfxIbR3TpUNUffvb3+60IfSDDz6oaEjWaiidTy+OiZ5UeF5W0pvs4IMPzoKJ3qDW82N94xvfKFqOxu9yetXF8I5nnnlm0br99tsv9VWxHwvnU7v66quzuaOq3csy5iksVNizqCOnnnpqqx6qPaXcMv70pz8tmj8sesJFONkM9XozKKxPYyjLt956q9PXvPzyy9lQvI0oyhXHTOEIB3Ee5uewa0vcmFDYqy56BB999NGpt+gt8ykC0PcI6gAAqtR4GD0zCh+xrloOOeSQol4E0Vj/la98Jb399ttlv8d7772XzjrrrHT++ee3+f/rr79+0fLhhx+eNfq0J0KpL33pS+n5559PvUUMgxX7utARRxyR/v73v3f4uriDPeaNKdSsAWY0tO+zzz5F62K5s7mdjj322KJG+Rgy8nvf+167z4//KwzBopdcDCE7bdq0Np8fDezbb799Np9dNc+tzowZM6ZoOcKHnlR6XkYvis56CESj61VXXZUa0de+9rWKhgKMmwZ+//vfF60r7AFXrTIV1q/RMB9hREc9bqKBP47XwnB+9dVXT5tttlnqqyK8KQyk4vsjAqJKh6qNHmhf/epX2/3/COujV1fh0Lk/+9nPOnzPv/3tb3UNW8opY/TIPuWUU4rWFd6k0wz1eqMrrE/j/D7yyCM7fP706dPTdtttV1agVy8/+tGP0mmnnVZ0M1cMGXzGGWe0+5qf/OQnRcM6n3zyyemEE06oaGjnGEnhBz/4Qc16uMdcpHEDTyVDV0ZZJkyYUHTcl85rCQCNSlAHANAkDaDR8B49kfKi8TNChGigefHFF9t8XdwpHcHC7rvvnhZbbLEshCqcT6lQPKew4SYa5yIYKe1ZF72e4j1XXXXVdNttt2XrqnHHf6OIfVQ4dFL0Stp6663TUUcdlaZOnVr03Agpo3dj3MFe2MAVjcxtzVvYLOIO/cJ5XiIsiQbOONbi34WeeOKJ9OUvfzlr5CsUDXgRXLRnoYUWatUb6dZbb83mYIvGuUsvvTTdfPPN2fCj0TAf6/OBaezznrLFFlu0auCM3oAXX3xx1vh/++23tzxqMYdRDFk3fPjwluU4f2MoyDj3Co+5+Pe9996blTffw6IRz8uoVzbddNM0evToLAT45z//2eZwotF7Muq8OBej51pe1Dul85R118CBA9N5551XtC6OvRh2OPZz4Q0L0bgfQxOuttpq6dFHH21ZP+ecc1Y0nGBvFTd4bLPNNi3L0bs85muNmzpi2Oa2ennGd0oMaRtBVoSwMW9gR8OPRt0UPZgLRf0cc3T95z//aRXwR3Ad32XRC72wXusp+fM3X8YYSrJQfK9EwLnTTjtlwxcW9vDuKLBsxHq90e2xxx5Fy7/97W+z4SIL65j8cRs3Na2yyirp3//+d8PWp3nf//73s97/hTexRKAaAVxb4juksCdeiPo46tYrrrii1fEQ4tiM8CyGyoxRJJZeeumsl2rcBFYLUVfE5xPf/fE9GEPpxnFZeI4UBqq//OUv0+c+97mi/4/PNupmAGgKOQAA2jR8+PBoBW95vPnmm1XbU/Fehe8d2yrH5ZdfnptrrrmKXpt/LLroorlVV101t/baa+eWX3753IILLtjm84499th23/+AAw5o8zVLLrlk9r6jR4/ODRw4sOj/Nt5449x5551XtO6CCy5odxuTJ08ueu6ee+5Z0b7bZJNNil5fC88991xuqaWWarUfZpttttyyyy6bW2uttXIjRoxoc1+NHTs298Ybb3S6jfHjx5f9uVRD4bZiH3bmgQceaPMYmmOOOXKjRo3KrbnmmrnFF1+8zX3w+c9/Pjdz5syyyvWTn/ykzfdo77Hjjjtmn0/hun333TdXS5tttllZZWtrv8bnWvic+NwrdeWVV7a5vfh81lhjjdxqq62WGzx4cNH/Lbzwwrk777yzonOtsM4rt06q9Pecf/75W/0es88+e7a91VdfPbfOOutk9VccZ6XPi7rnoYceqtnvcPTRR7e7n2Mfr7zyyrl55523zXrhD3/4Q6fvH/u/8HVRF1aiO3VGd47DSuvct956K7fpppu2uS8HDBiQ1R/xOcf3VXxG8fm39dyOfPTRR7mtttqq3e+rqKNHjhxZ9N79+/dvtQ/LqQsrVbqvr7jiitxiiy1WtC6+X/JljOOn9HeI/fLSSy81Xb3eDMfoNtts0+bvt8wyy7TUP3POOWfR/+22224Vla8a3+9duVY699xzWx1PJ5xwQrvP/973vtfmvoj3iGuc+H6JR1z3lF77lbMfulMfP/LII21ub+65587Omzh/4hHv269fv1bPW2655XLvvPNORdsEgHrSow4AoInssssu2bCXo0aNavV/cVd+9PCInglxx35bd0TH0I7Rs649p59+ejbMU6noxRPvG73rCnu/xDwnf/nLX1L//v1TbxJDJd1zzz1p7NixReujZ81zzz2XDa80ZcqUVq+Lnncx7FJvmBMlerXceeedrYaNil4pzzzzTHrwwQezIQJLxfxR0duz3LvYo7fLBRdc0Ok+i54CBx54YNbTprQHVq3nS4rec6XHQk+KnjbR+6F0yM84x6MXX/T4eOONN1rWL7nkklkPv/jZDKIHRPRoiWFN77vvvqz+Kp2Dc/HFF89+p1p+DtFbMurA0mM39nPs48cffzzraVNovvnmy3r+lc652JfF+Rg9EaOXT+l3Q/SSifojPuf4vorPva0eMp0du9G7PIaKjDq3re+rqKNjyLz8e8dnGudx9ObsaTGs6i233FL03Ru94PNlLB1ieuTIkVnvwyWWWKJp6/VGFr21o7dsqeghn69/Coe9jeuu+I5qBjFHZpS1cHSEmHOztAdqXvRCu+iii4rmlgxxTMY1Tny/xCOue9rq+TzvvPO2em2tRW/rOG/i/IlH1CGlQ3aut9562TyMUT4AaBaCOgCAJhNDg0Vg9sc//jGtu+66WfjWkZjLJ+ZNiiGKogHzm9/8ZrvPjUa4CN6isXqRRRZp93kxfFYMsxQN5z3dSNNTopE0GoFifqzSecoKRXgSw/PFfrvpppt6VcPQSiutlB1rMW9SR/O8RGN8DDkVDWPRSFg4RGs5ohE4GonjuNt4442zBu14j5jrMYYe+853vpM16scxF+sLQ6meCOqiPP/617+yQCaGrovjIY77ngyoYx6iGDYyhn5sT4RGMTdiBErx2TWiCPxjOLY4XgYNGtTp8yO0iAAtGs+j8bXWYk6wGN4thgKO4689Q4YMSQcffHDWYBzDOlIszo0YUjE+t/jOiaFuOxPfK/HcGAK3rRshSs0999xZnRt1dEf1UwwHG3V5BC71EudjhL0xTHLh/HqF4maFGAIz5o2r5bxaPVWvN6qouyOsjPCqo++OqOcvu+yydPnllzdVQBnDe0YYWfj9dOKJJ2bDlrb3/Djfop5t6yawto7TmCM5rkFfffXVbBjgWojv/rhhKs6JuO4t5/s2hnKNcsXrCucdBYBm0C+61dW7EAAAdN3bb7+dhQivvPJKeu2117I74yMsiobRFVZYIZvfY6655qr4fT/55JOscTMaDV9//fUsEIzwLhplOgoLeqvoARF328ecR++8807WWLXoootmDUPlNEL3BjH/U/R6mjZtWnZ3fYQV0dNpww03rHlYVigCuwhJ8mJusJiLpq+IICmOxfgc4jyNzyHmT4rgvpkalPNzHj377LNZL558b7Wov+K4irom5kGql+hVE/P+TZ48OZsDKXqpxLkejdnRI6ew1wodi2aHiRMnZo/4nnrrrbeywCrqjfiMY87Cjnp7lyO+r2IOqwgPolF/qaWWyuqmOJYaSXx/RPgVx33M7zV06NAsMIvefvUIwxqlXq+HmB8x5smMeih6zkb9GcdhzNVWTmjVG0VdHOdSHA9x7Rf1XNwEEsdEfM8su+yydan74lyJ+iN698V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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Displaying results\n", "plt.figure(figsize=[6, 4])\n", "\n", "# Plotting point estimates\n", "plt.scatter(nm.theta, [i-0.2 for i in range(len(biomarkers))],\n", " s=40, color='red', edgecolors='k', marker='D', \n", " zorder=3, label='Naive')\n", "plt.scatter(wm.theta[2:], [i+0.2 for i in range(len(biomarkers))],\n", " s=40, color='blue', edgecolors='k', marker='D', \n", " zorder=3, label='Standardized')\n", "\n", "# Plotting confidence intervals\n", "plt.hlines([i-0.2 for i in range(len(biomarkers))],\n", " nm.theta - 1.96*np.diag(nm.variance)**0.5,\n", " nm.theta + 1.96 * np.diag(nm.variance)**0.5,\n", " colors='red')\n", "plt.hlines([i+0.2 for i in range(len(biomarkers))],\n", " wm.theta[2:] - 1.96*np.diag(wm.variance)[2:]**0.5,\n", " wm.theta[2:] + 1.96 * np.diag(wm.variance)[2:]**0.5,\n", " colors='blue')\n", "\n", "# Some shaded regions to make it easier to examine\n", "plt.fill_between([3, 7.5], [3.5, 3.5], [2.5, 2.5], color='gray', alpha=0.1)\n", "plt.fill_between([3, 7.5], [1.5, 1.5], [0.5, 0.5], color='gray', alpha=0.1)\n", "\n", "plt.yticks([i for i in range(len(biomarkers))],\n", " ['IFN-α', 'CXCL9', 'CXCL10', 'sIL-2R', 'IL-12'])\n", "plt.ylim([4.5, -0.5])\n", "plt.xlim([3.5, 7.5])\n", "plt.xlabel(\"Mean of log-transformed biomarkers\")\n", "plt.legend()\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "06380eb7", "metadata": {}, "source": [ "As shown here, there are some differences in biomarkers after standardizing the data (particularly IL-12 and sIL-2R).\n", "\n", "This concludes the examples provided in the paper.\n", "\n", "## References\n", "\n", "Ritz, C., Baty, F., Streibig, J. C. & Gerhard, D. Dose-Response Analysis Using R. PLOS ONE 10, e0146021 (2015).\n", "\n", "Inderjit, Streibig, J. C. & Olofsdotter, M. Joint action of phenolic acid mixtures and its significance in allelopathy research. Physiol Plant 114, 422–428 (2002).\n", "\n", "Kamat, A. et al. A Plasma Biomarker Signature of Immune Activation in HIV Patients on Antiretroviral Therapy. PLOS ONE 7, e30881 (2012).\n", "\n", "Zivich PN, Klose M, Cole SR, Edwards JK, & Shook-Sa BE. (2022). Delicatessen: M-Estimation in Python. arXiv preprint arXiv:2203.11300." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }