{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "qL_G7B4fovBH" }, "source": [ "# Single-cell genomics" ] }, { "cell_type": "markdown", "metadata": { "id": "y18hMXUWJxv6" }, "source": [ "[Single-cell RNA sequencing](https://en.wikipedia.org/wiki/Single_cell_sequencing) (scRNA-seq) is a technology that provides the [gene expression profile](https://en.wikipedia.org/wiki/Gene_expression_profiling) of a single-cell. scRNA-seq can help describe a cell as a vector of features, where each feature represents the expression level of a gene in that cell upon measurement. Given a large population of cells observed through time, one can sample a few of these cells at different points in time, and produce at each time point as many feature vectors through scRNA-seq, and obtain a point-cloud. Each of these point-clouds provide a clear picture of the diversity of cells at each point time. Taken together, they describe the collective evolution of these cells along a developmental time-course. However, as measuring destroys the cells, one cannot follow gene expression of the exact same cell through time. \n", "\n", "Optimal transport can help us infer individual trajectories from the dynamics of the population overall, and answer questions such as what are the descendants or ancestors of each cell.\n", "\n", "In this notebook, we infer the ancestors of [induced Pluripotent Stem Cells](https://en.wikipedia.org/wiki/Induced_pluripotent_stem_cell) (iPSCs) using temporal snapshots sampled twice or four times a day for a period of 18 days. iPSCs are a type of [pluripotent stem cells](https://en.wikipedia.org/wiki/Cell_potency#Pluripotency) that can be generated from specialised cells. Identifying the ancestors of iPSCs would enable us to know which specialised cells are able to revert to iPSCs. \n", "\n", "**Reference**\n", "\n", "The optimal transport pipeline of this notebook has been adapted from {cite}`schiebinger:19`. The data was downloaded from the corresponding [WOT tutorial](https://broadinstitute.github.io/wot/tutorial/). Downloaded data is capitalised." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "if \"google.colab\" in sys.modules:\n", " !pip install -q git+https://github.com/ott-jax/ott@main" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "\n", "from ott.geometry import pointcloud\n", "from ott.problems.linear import linear_problem\n", "from ott.solvers.linear import sinkhorn" ] }, { "cell_type": "markdown", "metadata": { "id": "96rC8WsdHQEf" }, "source": [ "## Calculates optimal transport on gene expression point clouds\n", "\n", "The optimal transport between pairs of gene expression matrices, measured at consecutive time points, is calculated. As a preprocessing step, the cell profiles are projected to a 30-dimensional space with PCA, as suggested in {cite}`schiebinger:19`.\n", "\n", "- ``DAYS`` is an array of measurement days from 0 to 18.\n", "- ``PCA30_SERUM_DAY`` is a dictionary, which contains as keys the measurement days and as values the low-dimensional representation of gene expression matrices.\n", "- ``CELL_GROWTH_RATE`` is a dictionary, which contains as keys the measurement days and as values the cell growth rate vectors. " ] }, { "cell_type": "markdown", "metadata": { "id": "IZVkjGuZ_7CZ" }, "source": [ "The optimal transport is calculated between each consecutive pair of datasets." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "executionInfo": { "elapsed": 247851, "status": "ok", "timestamp": 1613650194760, "user": { "displayName": "Laetitia Papaxanthos", "photoUrl": "", "userId": "13824884068334195048" }, "user_tz": -60 }, "id": "XQNQFhe2pKR7", "outputId": "6df64c9b-88bc-45cb-e62c-3eebdf5e6283" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Executing Sinkhorn on pair of datasets for the days 17.5 and 18.0 / 18" ] } ], "source": [ "# Defines the optimal transport regularisation parameter\n", "solver = sinkhorn.Sinkhorn()\n", "epsilon = 5\n", "\n", "dict_results = {}\n", "for i, day in enumerate(DAYS):\n", " if day == 18.0:\n", " continue\n", " print(\n", " \"\\r\"\n", " + f\"Executing Sinkhorn between the days {day} and {DAYS[i+1]} / 18\",\n", " end=\"\",\n", " )\n", " # Computes the marginals\n", " delta_days = DAYS[i + 1] - day\n", " n = PCA30_SERUM_DAY[DAYS[i + 1]].shape[0]\n", " # The original data has been transformed with PCA as described in `Schiebinger et al. (2019)`\n", " a = (\n", " np.power(CELL_GROWTH_RATE[day], delta_days)\n", " / np.mean(np.power(CELL_GROWTH_RATE[day], delta_days))\n", " / n\n", " )\n", " b = np.ones(n) / n\n", "\n", " # Applies optimal transport\n", " geom = pointcloud.PointCloud(\n", " PCA30_SERUM_DAY[day], PCA30_SERUM_DAY[DAYS[i + 1]], epsilon=epsilon\n", " )\n", " prob = linear_problem.LinearProblem(\n", " geom, a, b, tau_a=1.0 / (1.0 + epsilon), tau_b=1.0\n", " )\n", " dict_results[day] = solver(prob)" ] }, { "cell_type": "markdown", "metadata": { "id": "mr9ogxkxHYsL" }, "source": [ "## Infers cells' ancestors\n", "\n", "Cells that are identified as induced Pluripotent Stem Cells (iPSCs) at day 18 are selected, and its ancestors at each previous time points, from day 18 to day 0, are inferred. To do so, the method {meth}`~ott.geometry.pointcloud.PointCloud.apply_transport_from_potentials` is used, which enables to perform the pull back operation without instantiating directly the transport matrix. \n", "\n", "``CELL_DISTRIBUTION_IPSC_DAY18`` is a normalised indicator array of cells identified as iPSCs at day 18." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "executionInfo": { "elapsed": 2069, "status": "ok", "timestamp": 1613650660849, "user": { "displayName": "Laetitia Papaxanthos", "photoUrl": "", "userId": "13824884068334195048" }, "user_tz": -60 }, "id": "IfARvic82UnF", "outputId": "de02d1e7-599e-4e53-a7f9-095e8c6aa3e5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating ancestor cells at day 0.0" ] } ], "source": [ "cell_distribution_ipsc = {}\n", "reverse_days = DAYS[::-1]\n", "for i, day in enumerate(reverse_days):\n", " print(\"\\r\" + f\"Infering ancestor cells at day {day}\", end=\"\")\n", " if day == 0.0:\n", " continue\n", " if day == 18.0:\n", " cell_distribution_ipsc[day] = CELL_DISTRIBUTION_IPSC_DAY18\n", "\n", " # Calculates cells' ancestors\n", " cell_distribution = dict_results[reverse_days[i + 1]].apply(\n", " cell_distribution_ipsc[day], axis=1\n", " )\n", " cell_distribution_ipsc[reverse_days[i + 1]] = cell_distribution / np.sum(\n", " cell_distribution\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "7lnZ_-KIHiTo" }, "source": [ "## Visualizes\n", "\n", "Once the ancestors of iPSCs are identified, they are represented on a low-dimensional space thanks to an FLE visualisation. The iPSCs and its ancestors are coloured while all the other cells remain in grey:\n", "\n", "- ``COORD_DF`` is a dataframe that contains the coordinates of all cells on the FLE visualisation.\n", "- ``IDS_CELLS`` is a dictionary that contains as keys the measurement days and as values the IDs of the cells. " ] }, { "cell_type": "markdown", "metadata": { "id": "fIFt6WeDADNp" }, "source": [ "All the cells can be represented in a 2D space thanks to an FLE visualisation {cite}`schiebinger:19`. The cells' coordinates are binarised. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "AvCJXwxsF8wb" }, "outputs": [], "source": [ "nbins = 500\n", "xrange = COORD_DF[\"x\"].min(), COORD_DF[\"x\"].max()\n", "yrange = COORD_DF[\"y\"].min(), COORD_DF[\"y\"].max()\n", "COORD_DF[\"x\"] = np.floor(\n", " np.interp(COORD_DF[\"x\"], [xrange[0], xrange[1]], [0, nbins - 1])\n", ").astype(int)\n", "COORD_DF[\"y\"] = np.floor(\n", " np.interp(COORD_DF[\"y\"], [yrange[0], yrange[1]], [0, nbins - 1])\n", ").astype(int)" ] }, { "cell_type": "markdown", "metadata": { "id": "tSz-NCpiAF7E" }, "source": [ "The coordinates of cells identified as iPSCs at day 18 and its ancestors are filtered." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "cwxHtlUiGvgK" }, "outputs": [], "source": [ "coord_ancestors_ipsc = dict()\n", "for day in DAYS:\n", " cell_ids = np.array(IDS_CELLS[day])\n", " coord_ancestors_ipsc[day] = COORD_DF[COORD_DF.index.isin(cell_ids)][\n", " [\"x\", \"y\"]\n", " ].values" ] }, { "cell_type": "markdown", "metadata": { "id": "UH8lBYfPAHWK" }, "source": [ "To be able to discriminate between likely and unlikely ancestors, ancestors are binarised according to their inferred distribution at each time point." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "Vr2lcp7dAX0u" }, "outputs": [], "source": [ "alpha_bins = [1, 0.5, 0.0]\n", "binned_cell_distribution_ipsc = {}\n", "for day in DAYS:\n", " tmp = cell_distribution_ipsc[day].copy()\n", " tmp[tmp >= 1e-2] = alpha_bins[0]\n", " tmp[np.logical_and(1e-2 > tmp, tmp >= 5e-4)] = alpha_bins[1]\n", " tmp[5e-4 > tmp] = alpha_bins[2]\n", " binned_cell_distribution_ipsc[day] = tmp" ] }, { "cell_type": "markdown", "metadata": { "id": "hOqccdlVAPLF" }, "source": [ "iPSCs and its ancestors are coloured according to the day the cells are measured on the FLE visualisation (see colorbar). All other cells are represented in grey. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "height": 646 }, "executionInfo": { "elapsed": 1536, "status": "ok", "timestamp": 1613651475608, "user": { "displayName": "Laetitia Papaxanthos", "photoUrl": "", "userId": "13824884068334195048" }, "user_tz": -60 }, "id": "n7yB9dag77eg", "outputId": "787fd479-8a9c-4e3f-9809-c97d67d7c37a" }, "outputs": [ { "data": { "image/png": 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p6levJvRHf4SnqYlyuUwymSTa1YX7mmsoBQKEK4Fx9cyTbevXU7dxIwWPxw6Q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\nbiGEENWQgFusW26a2Kj9KnV62L6ttAezUgpN7aFWrwUWH2B5PB4KJ09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p06fn7GkN80/a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QqFafb3e9aedDpNrVbTAj2bzWrffaFQ4NSpU6TTafL5fN98w372uGtSr67rsrGxwdWrV0kkEkxP\nT+vFtmqRploroLqR1mo1HSkJ6Hb0r7zyio6XfN/73icLLQVBeFsj/4IJwn3AUcRIhod4l/2vyVx6\nCONW/6hLly5x+fJl/uIv/oIHH3yQU6dOkc1mmZ2dpV6v68WQP/MzP8MzzzxDq9ViZWUFy7K4du0a\nCws9n/Dy8jIf/vCH2djY0Iv4on7uSawc41xrNBJQWUrUeVQu+DhjRcc7Lo7rfJZl6ZQYFQtYKBSo\n1+tUq1XtxzdNk2q1qmMBp6amdFrJwsICpmmyuLjIiy++yOHhIWtra7z73e/uq26Pmlv476ia7CST\nSR5++GGuXbumGyHNz89j2zZ7e3vkcjlWVnoNgdV5tra2SCQSetxCocBzzz3HJz7xCRHbgvAO46S7\nQJ4E998VCcI7iDsRhQYGWR6+bfsDDzxAKpXC932d82xZFplMpq+N+gc/+EHeeOMNPv3pT/PUU09h\n2zbPPfccq6urpFIpKpUKjz76KPV6nb29PWZnZ/WcVXxg2Kpwp6JqWOII0Je+Mo7YP8p8Bnmyx2GS\n/T3PY2lpiXq9zuLiIuvr66ytrWkBrh4sVISg+r3VanHx4kU6nQ7dbpdGo0Gj0dCLY5UQDy9qHHUt\n4W8TstkslUoF6NleVCTkmTNnSKVSXLlyhYceeoh8Pk+tVgPgxRdfpFQq4TgOuVxOe8BfeuklPvrR\nj4rYFgThvkBSSgThbcxxWDPiWF5e5pVXXqFer2vbghLJgF78t7a2xo/+6I/ypS99iUqlwurqqk4u\nsW1bV7VLpRLXr1/Xi+jUOEqcOo5DrVY7cnrFoOuJLpoM21CGVcoHjTeK47LMjHOeubk5AJ0S0263\nCQjArJLL976JKJfL5HI5vYB1fn5et4Gfnp7Gtm3y+Ty5XI50Os3DDz98W3V7GNF7Va1WtZWkVqvh\neR4LCwtks1ndRj6fz7O7u6vnXSqVSKVSOtP9m9/8Jvv7+zz99NO3fSMhCML9z/2aUiKCWxDuQ8YV\nisNE55NPPsn29jblchnf92NTP5LJJMVikY9//OPUajWq1Srnzp3jzJkznD9/Xgs3z/PI5XLU63Wd\nOqHGsSyrr1ulEoRHISp41QLN8HWNO0b0/b3I9vY27XabVqvV80RPVTELn6fVuUEqleLcuXPcvHlT\nV53hzYY4169fx7Ztkskk7XabRCLR57NXjGPhgV7s3/r6Ot1uF8uyyOfzvQ6WQC6Xuy2hRT0EKLtR\nu93mxRdfZHFxkSeeeOLY7pEgCMK9gAhuQbgPOQ57hmVZXLp0iW984xt8+9vfplKp6Iq1soWon06n\nwwc/+EFKpRIbGxt89atf1V7ehYUFms0m6XSabDbLM88807fQTlXPd3Z29AK9Wq2mz3VUwtnb48QD\nTlrNvlOO43xLS0ucO3cOuPWtwWGBoP5hrr3RoFKp4Loup0+f7vO2T01N9T2QXLt2jVarxezsrLbd\nhP+u46D+Vtvb20xPT7O8vEyr1QLANE06nY5+kLp58ya+7zM/Pw9As9mkXq/zV3/1Vzz55JMjk0sE\nQbi/kQq3IAj3PJOIuHH2zefzfPSjH6XRaGhhDG82c+l0OjoO0LIs5ufnefjhh3n00Ud58cUX2d3d\npVarkcvldFX7/PnzfPOb3wR6LeE9z9Nxcio5I5lM9om/Sa8NRleqj0tgxzXbGYewCFZMYqnJZrO0\n222uX79OOp3G933SqTQW8ywsLPYaFt1qRBP2yR8eHur7W6vVmJ2dpdPpsLi4eFuiS9wco9eubEHr\n6+vk83mWl5dxXZdsNksqlaJer3Pt2jWq1SrdbheASqWi33/rW9/itdde4+mnn77nv1EQBEE4KiK4\nBeE+YhLBMu6+xWKRn/iJn+DixYsUi0XgTQ93NpulWCzqBZBhi8jZs2epVqs6anBxcZFsNovrujqh\nolKp4Ps+mUxGt32PeoijMX+DuFMBfVSxN6gRjmKYWI2ed5Jscs/zODw8xHEcnX3tOA75fF5vc123\nL10mlUpp0e26rvbU27ZNLpeL9bxH5xidv4oDVH+7Gzdu0Gq1aLVa+L6P67rs7u7SarV47bXXaLfb\n7O7u8rWvfQ3oLdL9m3/zbx5LLrsgCMK9ipQTBOE+4W5nSocFdVwzmahvenl5WdsGnnnmGba3tzl9\n+jQ7Ozu8//3vB3oNdL797W/znd/5nX15z9GujEp0R6P/7sRvfacRfePe70FidZiIHffcMzMznDp1\nCsdxWFlZYX19Hd/3tcBuNBpYVq99O0C329UZ2LZtY5omW1tbFItFSqUSMLrJjUI1u7Esi3q9rn3k\n6iEgnU7TarV0RT2fz/ONb3yDK1eucHh4qH3aZ86cGXkuQRDeWdwrjW8Mw/gd4MeA7SAILt3aVgA+\nDZwFrgKfCIJgb9RYUuEWhPuEsBCddKHgOEQXvUXPG1epVUL8B37gB3jqqac4PDzkh37oh3QiRS6X\n090RO53ObaI6ep47tYlELRuD9j+JlJFJzxueq7oX5XKZra0tCoUCAIuLi2xtbWkrx+rqKqdOndJN\nZqanp7l58ybQuxfT09M0m02Wlpb0uOGFrsPmEPZ6b2xs4Hke7XabpaUlbNvm8PBQz8myLF5//XVS\nqRRzc3M89dRTPPDAAyOvWRAE4S3mk8CHI9t+AfhCEAQPAl+49ftIRHALwn1GWDSp399qLMtiYWGB\n973vfeTzeT2/5eVlSqUS9Xqd/f193a58mJd5mKgeda3Ryu04leRBvx9F8A8bL5oRHiXufO9973t1\nVjr0FijOzs7i+z6pVIrLly+Ty+X0gsVms6m91evr6+zv7zM1NaU91yopRs1H/R3CPvXwtmQyycbG\nBrZt47outm3r9JNisUi5XObq1avUajX29/dJJpN83/d9H8vLy0PvkyAI71x6iyatE/kZOZcg+BJQ\njWz+GPCpW+8/BXx8nOt66/9LLAjCsRIQ0OANZjiP7/l3RXAf1b6ivN7hfOVCoaA7I0ab4oSzvxXj\nWEHivOCTEtfEZtQ1j2p6MyxyL3xsWHQPGmtrawvf95ma6tVNHMfBtm1KpRLlcpnFxUXdREY1HWq1\nWti2TaVSIQgCpqamODg46LvH4Q6dcdcTXcT60ksvaXvJ4uIiN27cwHEc3njjDRKJhP5bzM/P8+ST\nT0q2tiAI9xJFwzC+Hvr9N4Mg+M0RxywEQXATIAiCm4ZhzI9zIhHcgnAf4XkeTesaXw1+mfcZv0SG\n4/PHxgnPSYR3VAyrxZWe52HbNjMzM9o/HPVqh8857LzDFiBO+pBwlCY2d+qjj4ruYeMqMasWpaZS\nKd3pUVl2lFdbRfRZlkUqlaLT6ZBIJNjZ2cF1XW3vCEcoDrqWsMe7XC5TqVQ4deoUy8vLdDodTNNk\nb29PL9pcXl4mnU6zvLwsYlsQhJGoWMATYicIgveexInEUiII9xGWZZH21nif8UvMcP5Yq9ujBO44\n8XjRirUSdYVCQYtMVdlWnQqj8XTjiNo4W0ZY7N8txvVgT2qLGVRhXl9f18kxhUIB0zR1a3fHcdjd\n3dVVbugtUn3llVd0RGO9XscwDF0BD9tJ1N9C3bdOp3PbgsrnnnuORCKBZVlsbGxw8+ZNEokEh4eH\n1Go1ZmZmyOVyLC8vc/bs2aH3ThAE4W3ClmEYSwC3XrfHOUgq3IJwn3HKOkWOB078vOOI4Lj3YQGd\nz+fZ3NwE0BF34X0msYmMI1xPwt8eV7G+kwQUtV2lkaysrPCNb3wD0zQxTZNSqUS73aZarZLP57lx\n44Y+RtlPkskkN27cYHZ2lkwmo88XrXCHX9VnN2/eZHV1lUajgW3bTE1N0e12aTQaNBoNcrkc8/Pz\nuK7LpUuX+hrvCIIgjMNJN6WZkM8BPw386q3XPxznIKlwC8J9yLBK7lEqvKMSK+5kLLVQT73OzMyQ\nTCZ1U5zwMVHhNu4cBi1CjI5/Nxi0CHLUMaPIZDI651p1i1SLF6GXra1E8f7+PgAbGxv62wTP89jb\n22NhYSG24U0ymex7uFGNj0yz9x/CV199VQv4/f19vWAzmUxi2zbvec97yOVyupnRUbmbfxtBEIRh\nGIbx74AvAw8bhnHDMIy/S09oP2UYxmvAU7d+H4mUHQThPmSYYDtKtfEox8QtthtU6VWV7PBiybW1\nNcrlcp9gi3rHx53XqGpxdJ7RhZ13yjgLPSdFZZ1Dr6HN0tISGxsbPP/883z3d3+3HndqaopMJqP3\nK5fL2LZNIpHAcRweeeQRnVCiLCO1Wu22bxKUvaRcLrO8vIxlWezs7HBwcKATSBYWFnTHSmVnuVOk\nOi4I7ywCjHsmhzsIgv98wEc/OOlYUuEWhPuUSaq/R6kiDvNEx4nIUcIpnMPdaDR0zJxqHa/8xcPO\nPWhb3Pu4OauxlQfa8zxtcblbRBeHjou6H5ubm5w+fZrr169TKBR4/PHHKZVKWJbF+vp63zGqOY2K\nBjx9+nTftaq0EbWYVT3UKHsKoCvcm5ubbG9v02q1dGObVqulm+2cPn2ahYWFO709giAI9wUiuAXh\nPiYgoMsLBAQD95mkUjzusXHWj1GCUh2TTCZZXl6mUChQrVaxbbvv+LAwjhP1qlIbHTfsAx92frWv\nqvouLy/rbPCw6D9uJvkb1Go1XYGfn58nnU7rtBfTNHnllVdwXZdisdh3L9LpNDMzMzQaDZrNJhcv\nXuxbKKkIL05V4+7u7vLcc89x/fp1oLcAM5PJ4DgO586dA3qdLF3X5fDwEN/3JZVEEISJuZdyuI+T\nIwtuwzDyhmHMTbD/BwzD+OBRzycIwmRYloXLi2we/pe4vHii547zaUeTRqJE7R3JZJJsNsvLL7/M\n9va2rrrGjR8lbIcIC/Rxq8hR8ZvNZrWnWaV1vJXe4m63i+/7bG1t6c6R0Kt6q28GdnZ2KJfLWJal\nPdyNRgPoXV+32wV6dp5isaitNOrzbDZLrVaj0+ngui71ep2dnR19vm63i2EYrK6u4jiObuGeTqf5\ny7/8y2OxkwiCINwvTCzvDcP4+8B/A5y79fsW8L8DvxoEwf6QQ/8voHSUcwqCcDRsHmV56rewefRE\nz3scHnLP81hcXMT3fcrlMvPz89paEpdUMk429536gcMPDipaz/M8Hc13Uly5coVcLke9XqdQKFAs\nFtnZ2SGVSmHbNr7vAz2bTrVa5eDggICAqVSHdtMnlUqTTqc5d+6c9myHH3jUIlbLsmg2m1SrVRzH\nodls6v2mp6d11naz2dQWlWvXrjE3N8fq6uqJ3hNBEO4f7vGUkiMxUYXbMIx/BfwGcB4wbv0s0usj\n/y3DMD4waoijTFIQhKNhYGB6D2Pcxf/rHXelV1XAla84l8thmqa2dCSTyYE525POOa5iPmxbmHw+\nr6vDr776Kp1OR4vwQecbZ06j2NrawvM8fN/X2duKK1eu0G63tUhWiyALhQJd9sg+eh17xqfdbrO8\nvKyj/sLNhsLiu9Pp0G638X2fq1evksvlyOVyAJw+fZoLFy5QLBZZXV0lmUziui6+75NOp8e6FkEQ\nhHcKYwtuwzA+BPwcPdH8DPAzwI8D/wPQBM4Cf2YYxl8/9lkKgnAkBlV9x1k8OC7HkSIRFbdKKCaT\nSYrFIisrK7z66qtjLYActpBz0NzjquBR7/ew8R566KG+ynfU6z3OPRon8rDT6fDaa6/p6rJpmjiO\ng2mazMzMMDU1heu6JJNJLly4oEXw3t4eCWZxX3+Ag/1e5VrFAaqHG+VZz2az+j30kk329vZIJpNs\nb2/rBjazs7Nsbm7ieR5TU1Pk83leffVV8vm8jgsUBEGYFNVp8iR+TpJJ/kv5D269fiYIgqdD2/+D\nYRi/Bvxb4IPApw3D+C+CIPjd45qkIAhHI07EjVokebdi2EY1c4k2WAkL3mw2S6lU6hPTyk8dtZcM\nEspHtZYMEttxCzZVkodCdctUAnachjdqrLh9lUcb4MaNG8zPz/d5pbPZLOVymVKppBd6GlMGdu7W\n8buH2LZNNptlbW2tb5Fp9JuFZrNJuVwml8vRarV0GolqG18qlajX6xweHuq5FYtFisUiTzzxxNDr\nFARBeKcxSRnie4EA+GfRD4Ig2KCXSfi/0hPxv2MYxn91LDMUBOFYOKnOioMYdO6whSO6sDLcAVG1\nH+CjpYsAACAASURBVA+PNemDwyjryLiMK5zDCxLj0kDixh12n65cucL8/DypVEqL7UKhgOu6rK+v\nc3h4iGVZ7O7usFN/g2QyQX4xjXvuOm3qBEHAqVOnWFxc1C3Zw9cTfjjIZDIsLi5SLpe1CH/Xu96l\n/eGO47CyssLCwgILCwvYtq0fgk7a0y4IgnCvM8l/fReAZhAEV+I+DILAB/6BYRh79Dzd/5NhGJkg\nCP7lMcxTEIQ75K2oXE9CuLI9KJtaicFBlfpBHSkVw6wjkzDqmuOq32pxIrwZ6zeJMO10OnS7XUql\nEtDrJHn16lVmZmbwfZ9Op0Or1WJmZoZa6wbFx16n89w5OrU0qdo5Uu/JMT09TRAE5HI5fa8qlYoe\nM3y/1YLLnZ0dbNumWCzqtvEA9XqdZrNJp9NhenqaGzdu8K53vYt3v/vdY1+TIAhCFGUpud+YpMJ9\nwBgCPQiCXwT+W3pe7181DOOXjjg3QRCOmXAm8zAmrfqGK9KTjjXM6hEW1qNi/UaJ7eNk0nOouauu\nmYuLi+TzeTY3N7l69epY+d7KsmKaJr7vU6/XmZmZIZVKcf36dWzb5vTp0z0/tr3I5jdXSc4USCaT\nHDomBgZBEJBKpTBNU1ejZ2dntTVHXdfe3h7tdputrS0ODw+5efMmp0+fpl6va0uJih5cWlrC930u\nXLggYlsQBGEAkwjuN4CEYRgPjdoxCIJfAf4xPdH9zw3DGKvPvCAId5e4OL04JvU4D6sWjzNWXOJI\nOOtatRVvNpt6n7v18HAcxF1PuIKvxO38/Dxnz57Fsiy2traAwde1sbGB7/tUq1VSqRSO4+iOnErI\nr6+vUywWmZoyOZxKs714nWawr+czPT2tK+I7Ozt6rtlsVt9vy7KYnp4GeqK60WiwtLSkIwfVw8HN\nmzd57LHH2NraIpfL8YEPjAqpEgRBGA8P80R+TpJJBPdXb73+2Dg7B0HwrwHl4/4n9CwpgiC8DQkI\ncHhlZMfKqNAcVxTHVbZVSkm4yq2SNcICdtA4kzS8OUpFf5LjB1Xfw/aY8LVtbW1p37TCNE1t/VDJ\nI41GQ0cmZjIZvWjy8PAQfz/AemmarSsVDMPQ52u1WhQKBXzfp9Vq9eVuK+ENPY/2zs4OBwcHPPHE\nE1rkb29vA+jK9+rqKg8++OBE908QBOGdxiSC+4/pVaz/nqH+9R5BEAT/C/B3gMMjzE0QhLvAUSq+\nTV7l5eCf0OTVoWNGBWVcRX0cER6ucIftJOr3cEfE8DFqH1URH2UzibYwH5c44Tzo92F+7/C5VY53\np9Mhl8vRbDa5ceMGAM888wybm5u88cYbJJNJLl/eIggC5ubmuHr1KisrK2xtbelOkuVyGe/Aw2ia\nzEzPsLDQq3csLCxQLBbxfZ8zZ87g+75+uKnVaty8eVPfb9d18TyPc+fOsbGxgeM4pFIpfe93dnZI\nJBI88MADb+liXEEQ7i/u19buk5ztPwBfuHXMB4Avj3NQEASfMgzDAWTxpCDc44QFYFhEZXiIR4x/\nSYZ4R1mcwBwkNMe1taj9otF/UT93tHFLdD7hOQ2b+91YPKnGDe+nHgYGzaHZbFKpVMhms9TrdQDm\n5+d5/fXXSSaTvPjiOn/yJ9t85CNLtNttDg8P+drXvqZFu+d5JBIJAKamppidndVCvNlskslkdLRg\nmHw+r33dGxsbNJtNXNflgQceoN1us76+Tjab1ff90qVLPPLIIxPfM0EQhHciY/8XJgiCLvDUUU4S\nBMFngc8e5VhBEI6XYbnSg8SjgUGWh+/4POOgKqhKZNdqtb7uklGRq/aLE/3jiOFBjCumRxF9AFHz\njXtAyOfzWjCrBjQAhUKBtbU1XnjhBc6dW+Rv/I0ss7OnyGQylMtlLMvi8PCQ5eVlAB0XqOwla2tr\n+jzZbJZCocCNGzdIpVIkk0nW19fJ5XLaH767u4tt25w9e1Z3j1xZWaFSqWiRfenSpZHXLgiCMCmS\nUiIIwn1D1NZxnJaAOx0rLErV73E2lKigDltConne0ffDOO60k0EV9EE2FiWKo/5t3/dZWlpib2+P\nfN5id3eX9fV1Xdm2bbtPULuuSzqd1l5sgHQ6jeu6tNttbNsmkUjQ6XRYW1vDdV1s26ZarVKr1Xjp\npZe4dOkSKysr1Ot1HMfhwoUL+iFAEARBGB8x3gnCO5C4KmuYt9KTGxXR+Xy+bwHhOBnb6thxvdTD\nxrkTJs3rjla91XvXdSkUCuzv77O8vEwikWBxcVG3ZK/VauTzecrlMisrK9pqoh5eVOUbwPd9HMdh\naWlJ31/P83Qlu1arUalUePzxx7WtJJVKkcvltKAXBEG4m7yjK9yGYfy+YRj/24DPftgwjI+OOP6r\nhmFcnnSCgiDcPYYt5jtJ4jpAQn+lOhwTGN0vjqjPO+yhnmQ+d8JR8rrDr0rcrq6u6u6SYbF97tw5\nUqkUp0+f1lnY9Xqd7e1tGo0G+Xye1dVVWq0W8GbDHdWuvVgssru7S71e1x0kNzc3sW2bxcVFoBcl\n2Gw2dXa3LJAUBEGYnEn+5fw4UB7w2aeA0ojxVoH5Cc4nCMJdZJyFhHc6Tvjz6OLHQeeLLjJUVdit\nrS3m5uaGjqMqvYOuQ1WER3FUUXnUqvqgOUTTVmzbxnVdyuUyU1NT2nNdr9cxTRPbtrUwnp2d5ebN\nm3rhJPQWX7bbbQqFAo7j8Oqrr2KaJpVKhcPDQ6rVKltbW7z//e/Hsiza7Tb1ep3l5WVmZ2fHXvAq\nCIJwVAKME8/IPgmO08M9VlSgIAj3BpNG4UF85XfchYWTiLVopJ/neeRyOT3OoHkre8SgfO44sT1o\nrKNUuUfFBY5DeP6qGu95HrZt68WLuVyO8+fP47ourutSKpW0iF5bW2NmZob9/X1mZmb6rjmbzWLb\nNuvr67iui+M4WrS3Wi2uX79OKpXizJkzwJve8bNnz4rYFgRBuANk0aQgvIOxLCu2qU3UunESbdOH\nLeQMWy0GJZCE5xiujkc/jzLuNR2XzSRunKiNRv2uxLK6N0tLvShA9fBRKpV0q3bHcWi327qKn0gk\nOHv2LKdOndL7q/FKpRLVapU33niDra0t9vb22NnZwTAMPvGJTwDQarUol8vaIy5WEkEQToL7NYdb\nBLcgvMOJa2oTFbujLCNRBvmkh4nWcOzfoM+HMennYX/3OIwaf9xK+ahFquoeRB908vk8lmXh+z6m\naerqM/QWVZqmyblz57SoVjF/Fy9epFAoAGixnkqlKBQKPPLII8zMzOC6ro78U01vfN+nVCqxtLTU\nZ9MRBEEQJkcEtyC8wxnV1AbeXLQYxyTNbSZNCZlkkeS454tmeY/jYx+ndXtcVT1u/DihHxXX0fsX\nrnovLCzg+z6ZTAbo2T6gt7CyUqnQbrd1Yxvf99na2qJareqxVPSf7/vkcjnW1tZYXV0lmUzyxBNP\n0O12OXPmDKlUilQqdeILaAVBEHzME/k5SeQ7QkF4h6Oa2oxaRHkUS4Fa9GdZ1sAFi8Oa1AxaTDnu\nYsSoD3zQeZTgneRBISra4841aKxBonzQGGreKmVE7WvbNrOzs+zs7LCzs4Nt2wCkUimg1zDHtm3a\n7TaAtpwoMd1ut6lUKly5coXv/d7vxXEcHMfBdV2WlpYGXrsgCIIwGfIvqSAIYwnYSRI34roqqmYu\nkywsDAtQx3G0tWGYAA6/hvcdlYd9J8IynJwSFdPqs+i1xwnz6Hv1wBJuB7+9vU25XCaVSpHNZrl8\n+XJfRVv5tff29uh0OuTzeVqtFmfOnuFy+VWShxkWFxapVCpUKhWg5+1uNBpcu3YN27bJZrPs7u6S\nyWQkd1sQhBPlfu00KYJbEIQjNYQJCKiyToE1jEhI0SBRPc55wuI1vH+0++Koc4yqGg9j0H5xVpSw\nwFdV8mj+9yAby6C5RB9Yksmk/oYgmUxqW8j09DS2beM4DtCzmzQaDd3aXW1fWVmhyT6nvtvjYf9B\naE7pZjYAa2trVCoV6vU62WxWhLYgCMIxM6mHe8EwDD/6w6187bjPQvssHPvsBUE4Nib16lZZ5/PB\nr1NlfeKxhzWfGRY/pwTkIL/0ICYR/GGiizijx4ctM9GKfvgY9dkk842OoR44VPOZ+fl5fT6VVNLp\ndPQiSN/3deXbNE0yTHPOeZRpM4fv+7Tbbba3t8lms5TLZer1OouLi1y7do1Go3HbfRAEQRCOzqSC\n27iDH0EQ7nHG6cIIPQFWYI0fNn6WAmu3fRYlKiAnzXQOV4zjukcO4yhWkah3ehDR6xgWbTjuXMLV\ncDVedNxwx8dMJqNtIABbW1sEAWy3ppibK7E/lcHz/N63EE0T33sz5SSfz2uBrnzf09PTfPrTn554\n3oIgCMfFO33R5N++a7MQBOEtZ1wfc0BA07rGDOeZ40zsOHHcSddFdWw+n9fic5TlY5zfJz3/qN/D\nFpCjEh43bK1R2x3H0ds7nQ6tVot0Oq3btwNsNnw+ezXLjz0Cf1LJ8PRKi0V6lpNOp0O5XKbT6XD2\n7Fk8zyOVSlEqlYBeisnFixfHug+CIAjCeIz9L2cQBJ+6mxMRBOGtZxxB1eANvhr8Mu/xf5GC9fDY\nY4dF6KjzRBNNkskknU5HL74MWzmGEVdhPopoHFWpHnfhp/r9KNYWNffw4lMltrvdLul0mt3dXRYW\nFkjWG/zkYwes5hOcLZ4i4/UqOTs7OziOQ6FQ4Nlnn9Ut36enp3VlXXWxHOc+CIIgHDf3a2v3E/vX\n0zCMNYAgCEYbPgVBeEuIy4yObpvhPO/xf5Fpzo+1uDCc0qHOMUq4xS3YcxxHi85h8X1vdRV2UPLI\nsA6Z4W3hCrY6PnrdShwXCgU6nQ6O4+D7PrZt3xLNM+Ru7Ze3OjTdrh4vlUqxvr7O448/DvT83WoO\nSsw/9NDgTHZBEARhck7kv0qGYcwBV4HDkzqnIAh3RkBA3droSyFRwkxVtqNNXOIWJyovclyFe5g4\njn4WJ8Kj444z/t0W44PGH3d7OI1l0H0M3+dWq8WZM2dwHIdWq4XneXS7Xd2Jstvt6hxu13XZ3t4G\nekI7m81SqVRIpVJ93yC81Q8tgiC8c1Gt3e83TrrTpCyeFIR7mPDCxrgUkmFRfFFPc9wiyXHTLgbt\nF86jDo8bPe4ole5hc5vks+NI9Ah/OxD+Pe6BZmZmhlqtxv7+vv4sk8nozHK1QFK9r9VqXLhwQR8/\nOztLPp8nn8+TTCb7BP1xXY8gCMI7nfvvEUIQhCMTFlrhFJJJxes4CxoH7RfePsqaMuy8g+YwzrGj\n5j3suElF/rj3So0dTjCxLItGo0E6ncZ1XXK5nL5HjuMwNzdHrVbTx29sbDAzMwP0muQo+4qKW4z7\ntkAq3YIgnDT3Y+Obk65wC4LwNsHAYI4zGBgjRVc0ti8uu3qQr3lYBTV8TLfbve3Y8Os4jJvEMmgO\n4zBJNXxcm4njOLH3Svm4AXK5HNlsVqelWJbF7u4ulmVRrVYBODw85MKFC9i2TSaT6Rur2+1Sr9fH\nu0hBEARhIqR0IQjC2MR5o4e1Jx/FJMkdvu9ri0V0UWF0jOP0II/jNw8zaFFkXLU+7lzhMdSr8q+r\nMVRFOtzdslqt6t/V/q7rYpqmboBj2zamaVKv1/X5w57tcXPYBUEQ7hb3a2t3qXALggCMVykeVq2O\nvp9k7OhxUeHX6XTwfb8v63pYtfhOxXb4/HeafBJ3z4alrKjzRQlXuLPZLNlslnw+r/dPp9O3zbdY\nKvLa1hvs7O7oMXzfZ2FhoW9flQBTLBaPdI2CIAjCcERwC4IA9AvCsOAbJJYDApreNocc0qFKQDDw\n2GELKuMIC1LlM3Zdty+pxPO8u1aRHTczfJz7NIxxj1GCOPywoSrdnU5H3xdVpVb+7lc2XuPqfJlK\nZxeAqakp0ul0X0MdQC+YVJVzQRCEtwqVw30SPyeJCG5BEG5jnGpxlz2uG3/KPle56v8xXfb0/oOE\n9qAxB/mxw6kkuVyOnZ2dvu3jdHS802SUcSvb4/jS46ryKo5v2P2JPmgolFDe2dnR9yNc9fbrB7T+\nbJdScg6A+fl5LMuiVqvF3uu46EVBEAThzhn7O1LDMH7nDs6TuINjBUG4B0kwy2rwFCmKWEGWBL2u\nhar6GhBwwC6nmOtbeDksIST6mUrcUL7kuNbpk6aITLrfuMkmgxrbxO3veR7Xrl3jzJkzE+WIh5Na\nVCUbeoI82vJ9f3+frfIWq/kVvZ9qcqPi/8LnCf8uCILwVnI/5nBPckU/A7e+MxYE4R3BoMV9AL7n\nk7Hm8TyPjDV/2+cH7HLT/wNKwY+TthaOPAclALe3t7Ftm2KxeKLCMBzBB8Mj+4YRPu7GjRs6UWRQ\nk5vwq9onaimxrDe7Qyr/tapeVyoVtre3ee9730u5XAZ6C0+VdST8cKCucZxvDARBEITJmeS/WF9C\nBLcgCLcIC8RwNVttP8UcS+bHOcXcHZ0nLARVM5dw9XfSZJKjiHUlUpVIvtPxFxcXR3rZx8nozmaz\n1Go1Lby3trbI5XJUKhVqtRpnz56l0WjolBJV4Y4ujlQVbhHdgiC81dyvKSVj/1cnCIIn7+I8BEG4\nBxk3Bs9lh5v+H7BkfhybnpgzMPT7QURFZZzIVGI3nU7HCu1RjBtbOEzgAmO3PB83Wzvql54kajFc\nce92u1owz83Nce3aNXzfZ2pqitXVVer1Oo1GA4CVlZ69RNlPwhVuVfUWBEEQjh9ZNCkIwkDGFWCT\nVLOHLQ4cJlZV6/JBjCt0j3r8JJXxcWIQhyWcjHooCFtL5ubmKBaLevHlwsICvu+TyWSwbZtWq6X3\ndxyHbrcbO36z2Rzr2gRBEITJmWTR5M8CzSAIfvsuzkcQhLcBUfE5TjVbMUmudbixi2maR7Y8HIfn\ne5LjR1Xto/vE7T/OYlC1GDIs4JvNJhsbGzz66KOYpnnbMcM88FLhFgThreZ+tZRMUuH+NeCX4z4w\nDOO3DcP47PFMSRCEe4lBudoBATVaBLf+59DQWdyjxhnnnMo2YVkWW1tbFAoF7TOedO7HIbaPkrMd\nd+5xmwDF2Uji9lVzU5YXZQ3xfZ9yucxLL73E/Pz80OtQlXJBEATh7jCppcQYsP0jwMfvcC6CINyD\nDBKrddp80X+JqrdPk32+6X+ZJj3bR5yoU2JvXB90MpnUiRuvvfYa9XoduL1L4zABe6dCe5zs8EnH\nO6p4jyaWRO+n+ibgW9/6Fqurq5imSS6Xo1Qq8dBDDwFo20mclUc83IIg3Cv4mCfyc5KIh1sQhLEJ\nC7IcKZ40LzJrZdnB5d3mB8gwfdt+k/iTw/uqRiydTodarUYul4vd9ygicVQ6SHSugx4gjsqoh4Zh\nTXjiFoyqRZCvv/466XSaXC7HzMwMruv2degsFosDBb80vREEQbh7iOAWBOFIGBjkSXPVq/Drh39K\nFQ/j1pdggwTxKE9ydIHi7u4urVaLQqFAtVrV4nvUWMMYxxs97kPCUc6rbB9q26RzUHYbZSMJb2+3\n23zgAx/Q23zfJ51OazvKsNbto2IKBUEQTgJp7S4IghDDOWuefzT1w6xRAHqZ3BtWvc/PPU6rcyUi\n4U3xPTc3R71eZ2pqikKh0CcyxxWIRxHPw+IBx2FYRVzZZcLRfnHfCMSJ7jjBrMZxHAfTNOl2u/r4\nmzdvcunSpb7c7rgKeXgugiAIwvEjglsQhDvCwGDFy+nq9jpVfj34PJtWL/s5mi8dfoU3FwOGc6HV\n551Oh6WlJUqlUl93xEmyuO9ESI5r9Zj0nOFqd/gnemz4vbLYqG0qT1uJ5d3dXQqFAolEgm63S6VS\n4fTp07iu23ff40T7/SS2pUovCG9veikl1on8nCQiuAVBGIthVeqwUJ7vpPlZ44dZ9mYGNnAJi8tk\nMhlbhQ6L0rm5uT6BHRWrd+s6o3M/LmGqHi4GJZKoeUTnEo4BDFetPa/Xyn1xcZFut0sikcB1Xcrl\nsm7nro4b5NU+jujEe4H74RoEQbh3MAzj5w3DeMEwjOcNw/h3hmEcqR3vpP8yFQzDeCZu+61JxX0W\nJgiC4AcnPKcgCPcA4zSWUULwDCmw3hSN0WOVAAx7kaFfZCohXqlUyGazLC0t6f2TyeTIPO6jCshh\n4jfqMx+0/yjCeeJR0R1XvVe+7ahYVtaU69evk8vl6HQ6JBIJWq0W7XabtbU1vd+k1ywIgvBWca/k\ncBuGsQL8LPBoEARtwzA+AzwNfHLSsSb9V9YGnhzy+bDPgCEhvYIgvG0YV8yO2icqIsOiUwnaXC6H\nbdvaOqGSS0Y1wblTK0ncg8Qg3/VRxod+4T3MJhOtTIczygFc12VxcRHotXpfXl7GdV0cx2FxcXHk\n30vNx3EcSSsRBEHoxwJShmEcAGlg86iDjMunjnICQRDuP6LV12G/R7eF36sKbdi/rfZJJpM4jsPM\nzMzI8QdxXFVu9QBw5coVrly5wkc+8pGJxwzbQ5TIVlX8QUI7PP+wh109dGxtbeE4DrlcTr/u7u7q\nBZSmaeK67tAoxmj6iQhuQRDeSk6402TRMIyvh37/zSAIflPPJQg2DMP474F1oA18PgiCzx/lRGP/\nlygIgr99lBMIgnD/ERZq6vXAO6BlrTNjne/bNy4BQ4nscIVaVVeV2FbnUbGAg8RzXKX7uP3ISuie\nO3eOdrt92znHbeYTbXwz7LjoOaKi3LIsEokEV65cYXFxkYWFBTqdDqZpYts2rusC3NbePW5O6n2x\nWBxxJwRBEO4rdoIgeO+gDw3DmAU+BpwDasC/NwzjbwVB8G8mPZEsmhQE4VhoWet8NfhlGrzRtz1a\nvVYLJRWqehwV347jkM/nsW1bjxEeK877HD5H9LNJWsIPwrIsHnroIV5//XWuX79+2/nC1zSI6MNK\n3L7qfoTnrKL/1KvjOOzu7rK4uKi3h/3wxWKRXC43VHBH564yzgVBEN4q7rEc7h8CrgRBUAmC4AD4\nfeD7jnJdIrgFQTgWZjjP+4xfYoZehXtcgasqt+EKcDKZJCBgr7tFIplgZ2fntjHjFmQOayQzauFg\nHIPytC9cuIDrugOvcdQC0/ADwLD5R+0kyoKiHjxs28Y0TU6fPk2n09HJJLZtU9mpUPGbJJKJkdcZ\n/hsIgiAImnXgA4ZhpA3DMIAfBF46ykAiuAVBmJg4YWZgkOMBnccdrWLHEY72UwkkSoh2jAavBn9O\nx2jo/aOxeON4uyfN0h4Uxad+V5+fOXPmtgr1OA8Zat5xDwCD7CbKsx0W7NevX2d6ehrTNHVlOp1O\nk0qlyGazNEyP/3dqg12/PXJOk8xfEAThbnOv5HAHQfBXwP8JPAt8m55u/s2hBw1ABLcgCEdmmECL\nCte4pjdxvmRVxc0n5nlX8imSwQzFYnGgSA3HCMbNLSrCx/Fah4k+OAxKVRk19jhCP7yAMm4eanul\nUqFQ6HX2TKfT5PN5AHZ3d1lYWMDzPBbsaf5G9lFamztDzxs+/1G+BRAEQbifCYLgnwdB8EgQBJeC\nIPjJIAi6RxlHBLcgCEcmrmmNYtjiwGGtz9XnBgYz1hypZKrv+KjIHyRyh83tTonmcavq/O7u7m37\nxonxuKp7nMgOxwcqOp0O09PTJBIJdnd36Xa7WFav06TqKtlsNmm327jeIaurq2Nfk3RpFARBuDuI\n4BYE4cgMWvCniFscOI4VxLIsarUatVpNHxuXbBI97ygxroTxsGsYJjqj1xPOBrcsC9M0ef755wd2\nqwxvG7VPmPA1q+i+brfL3NwciURCp5MsLS0BMDc3h5tN8Rl/l05mtId70LwEQRBOGhULeBI/J4kI\nbkEQjkxYeIa3KQIC1rtvEBAMFHNR8a2sDfl8vi8mUKV2DFuoGOcbj8u/jjv/UTpHRhNYisUiFy5c\n0JVu5a12HOe2uYyTbBK9N+ra1fjq/mSzWarVKvv7+/phJWgnqX19mXrZHdu3Hr0eQRAE4XgQwS0I\nwpEJJ2bEsc0m/8H+t2zfaswVFbdKRKpKcSKZgGSLdqetP496vGGwd3xYNGB0v0GJIKOud9j+ar65\nXE7vE+1OGfcgEB4v7nP1/sqVK+zv7+M4DolEQp+vXC6zsrJCKvWm/WYu6fHTD3us5IdfV7T5jSAI\nwluJVLgFQRAijBK18yzzMf+nmGdZbwsTEOBbDQ45pEWNDlWu+n/MVLKN4zixed3h3+NehxHXsCc6\n3rBjod+WEmdhCVfalcAetGB0VFU7vG13d5dUKsXs7CwLCwtYlkW1WtXfBuRyOf3w43keBwcuq3On\n8P3hTXnCn91N37sgCMI7GfnuUBCEO2KQFUP9vmyduW2bevWtfa77f8r04XexefAyDxkfZJWnSFsl\njLxx23jhCnd0oeE4EYHDiBPgcdX1OIE6bOGmGksdG00jUQ8Ww9JUPM+jUqlw4cIFncnd7Xb7xlbe\n7lqtRrvdZnV1VY89yf04yr0TBEE4TsZsSvO2QircgiDcMZMItHAF9cA5xWrwFIvph5k7PEeKGRIU\ndJb3oGPV+RzHGZj4MWye48x30D7RKnucUI+zi6iHhGi1Wy24HHZ8rVYjlUpRr9dJJpO0Wi3q9TrQ\nE9rdbrdvEafrumxubtJsNvXC03GRaEBBEITjRwS3IAh3RNQ2Me7+AKlkigQFtrpv8Ff2v2Hfuglw\nmzANV4XD78OV4UG523dqkRh2bXG2kEH2mmHjxnnDO52OXmwJYNu2/tw0Tebn5zFNk2QyqV/39vaA\nXlOemZkZTNPsyw0fB7GUCILwVtLzcN8bjW+OExHcgiDcMXEZ0opBPuWAgF3WMS2TVLvIh9y/T4E1\nXaWNWi8GnW+YQBxUgR42t/D28PmHWWdGLYCENxNAoq/h6w0/NCSTSRqNXpdNJZp938fzPDKZDJ7n\nYZqmPr7ZbGKaJvv7+9RqNe3tjotCHIWIbkEQhONFBLcgCMfCICFrWRYH3gFbbBAQ6O1V1nnG/A22\nvStMZ6dZSlzgptcT4Cr9JK4KHLaTKMEK9Fk2wmI9Osc4X3aUOOtJ+L065yhhGk0lGWV/iS5ake6L\n5AAAIABJREFUVFVtz/OYnp5mZmaGRqPRZ/vwfZ9Wq0Umk8G2bUqlUl+EoohnQRDeTkhKyQlhGMaq\nYRh/ZhjGS4ZhvGAYxj+6tb1gGMafGobx2q3X2dAx/8wwjNcNw3jFMIy/9tbNXhDe2QyqAletbX4/\n+B0dD+h5HgXW+GHjZ5m3zmFaJuvet/lD81PsWdt6HyUsO50OiWSCBo7O9FbnUPvEtVyPclyLAaPp\nKaNSQIYlo4STTgZF9DWbTf2+UCjgOA7FYpF8Pt/rKOm6NJtNfN+nWq3q/QMCtoID/aAzKE4xOidZ\nNCkIgnC83HOCG/CAfxwEwUXgA8A/NAzjUeAXgC8EQfAg8IVbv3Prs6eBx4APA/+zYRj33/JWQbgH\niVayBwm1eZb5CePvUPDm9XEGBjlvBQODRqdMw///+GD9h5juFG47RzKZZKdT5c+7X2fPa8TG+0UX\nUI4SjYMqv+OIUnV81G4SN27cQ4japhr6KOGtttdqtb4HDoD9/X0ajQaO49Dtdrl69Sq7u7vYts3i\n4iKO4+D7Pr7v47ounU6HmjXFH2UD9ixj7HsS/WbhOJAquyAIkyAV7hMgCIKbQRA8e+v9PvASsAJ8\nDPjUrd0+BXz81vuPAf9HEATdIAiuAK8D7zvZWQvCO5NRCxTV777nM+ctcMo6BbxZIZ6yprjZeY1s\ncp4HEz/OQ9nvYTo73WeHUII2S5rvT7yXadJ951ACWXVdPGq6iGJUSsegKn543EHCP3xsdJFnOEMb\noF6v60p1JpNhdnYW27ZxHId2u002m8U0TQqFAvV6Hdu28X2fVCpFqVQCYNGy+VunCsx6gT7/MPE7\nSYrLJEjFXBCEdzr3nOAOYxjGWeC7gL8CFoIguAk9UQ7M39ptBbgeOuzGrW3Rsf6eYRhfNwzj65VK\n5W5OWxDesUR9zmEBFye69rjB18x/T40NkrfiAAcJ41QyRdpLatGuxGmcBzpq1YjOTY19FMKV6HAU\nX1zVXe0TJ3TDfm01TtiHbpomvu8D0Gg02Nvbw7IsisUipmniui6u61KtVslkMriuC0C73dZz6Xa6\nzHr0PeiMU+U+7gq3IAjCuIiH+4QxDCMLfBb4uSAIGsN2jdkW3LYhCH4zCIL3BkHwXlX9EQTheImm\ncowSbdPeIo+f+luccvJ9ItnzPEzLpGm5HHgHty0+DAvcQSJ3UKV6UHzguKjc7HA04aDxoiI8ah1R\nle5kMkk2myWbzer3lmWRTveq+TMzM6RSKbrdLtevX9fb0um0Ti5RCyynp6dpNptaXG9sbMRevyAI\ngnBy3JOC2zCMU/TE9r8NguD3b23eMgxj6dbnS8D2re03gNXQ4afh1sosQRDeUsILAOPE9xRTzLHG\nKesULepkshkOvAPI29Rp8+fGazRo6yrysDjAsIiNi+oL+6aPIraHRf+N8m2r+cSJ83A3ylqtRqfT\n0S3bo+drt9vkcjlmZ/WacWZnZ9nf3yeVSjE9PY3neSQSCX2Oubm5vnHiHkQGefFrtdrQeyIIgnDc\nBPQ6TZ7Ez0lyzwluwzAM4LeBl4Ig+Fehjz4H/PSt9z8N/GFo+9OGYSQMwzgHPAh89aTmKwjCm0TF\ncFQEh7eF9w+SXV44+AL7XpWWdcAX/ZewLJPvDx5khlSsQJ60I2KcCI/OZ9hngyrYYevMqPMPGl+N\nE01aCZ9/e7tXY1A2k06nQ6vVIplMYts2ruvS7XZJJpN0u92BfuxwM53o3MIPD9JxUhAE4fi45wQ3\n8Djwk8APGIbxzVs/HwF+FXjKMIzXgKdu/U4QBC8AnwFeBP4f4B8GQeC/NVMXBAHGX3ynBJ7vmDxi\nfIhpq0DaO8X3Bw8yZ82Q8WxOWad0o5wtr6Uj7qKV5XBle1gm+LBKddz+0flCvy0lm82O7RcfZHVR\n89rd3QV6dpFwS/Zms4njOKRSKVKpVO+e+T6FQoFaraYXUpqmiWVZLCws9NlzwvMe1Hky/C2CQkS3\nIAjC8XDPLR0PguAviPdlA/zggGN+BfiVuzYpQRCOjWgzmQPvgCDpkrfm6Xa6pJIpUqR6AtuqssYS\nBgbbXps/DLb4mLfAgpW+bSx4U1yGbRrRhZzKWx7XMVK9DkoxCVtDoiI6Lr1EnW/Q2Op3FfWXy+Xo\ndDp0u92+8165coXFxUXdYdLzPHK5HI7jUK1WMU2TXC6n/dzhZkCq22TYbx53fYMeBGq1Gvl8/rbP\nBEEQ7g7GibddPwnuxQq3IAhvc8JiNFo5jlaD2zR4zfhzap3tPtG3yTafmvoD1r2beJ5HgVP8xKkV\n5q0UEN8+XVVo1cLEQZXnuKi+uP3i9okmr6jzDbO9hOcSrcJHq/HZbJZEIoHjOKhEpVQqRT6fx7Is\nWq0WANVqVeduz83NUalUME2zr0lOdJHmoG2DCIt0QRAE4eiI4BYE4dgJC92oyI2KXaNjc/7ge8la\nBb0NYN4r8NOHH2fNWurth0ERG9/zb0ssCRO2lqhs7uhc1LHD8rijY0YJV7QHJZVEU1vi5ug4Tt/x\n4Qq3Sh5Rvu1qtcr8/LzeXy2ibLVaZLNZfN8nl8vp86q0k+jfQ20fB8uyYn3fgiAIdwOJBRQEQRiT\nsNANe4k7nQ6mZbLFhm7Rfso6xbQ1h+/1RKUSoqesUyx4BXzPvy1ZZNBixbAIjqsiD/J8D2LQZ1Gh\nPii7OvqQEW3j3ul0cByn17o+kcDzPJrNJq7r4vu+FtwrKyu0Wi1s22Z3dxfLsjBNk3a7DYDruti2\nTS6X6/Nhq3sUtoQMqv4PIs4XLwiCIEyGCG5BEO4K0ZxqgEQywVVe47PBb7PNZl+WtvJBO46jBaMS\n2gEBHaq9yEAGN9IJ20ziqrjDfNlxDPKID6qqjxKn4XvheR57e3ssLi5qu8je3h7tdptUKkUul8M0\nexUY9V41t3EcB9M0dXMc1WVSVaKVrzyfz1Or1fri/UZFNcZtUz5wQRCEk0Aq3IIgCGMSFZeWZdFk\nn+sHr/MjxtPMs0xAgGs5OnnEtEzIHnLgHfRVyFtehav+H+Oyd1vVOCwQw+3gw+dVtoioaJy0CU6c\n0FfCVYnaOEEerWwrbNvGsix2dnZoNpu6ZXu9XieTyWjBXa/XSSQSFAoFXNel3W7jui6O4+A4DrZt\n62jAcAVb2VWUUA/PLZlMUqvVBlblo0iVWxAE4eiI4BYE4a4QFqGKhJfiu089wVkeBGDTusxz3T+l\nTZ1Op0OTfZ49+Eta7PcJw7RVYungB5jypvvan4er4WrfQRXruDi8cRYEjqrsqvO3Wq2hsX/h92re\nKnXE930ttn3fJ5VK0Ww2teAOz2V2dpZ6va6vaXd3l3q9ju/7mKZ5m9Wm0+kwMzPTNw/1WiwWuXHj\nxsh7AL0qt4otFARBuFsEGNL4RhAEYRKSyWSfYD1lnSLLDL7ns80mfxR8hqXEu0iRAyDDNO823s+M\nNdt3nO/5ZKySzuQO21DCHvEwSniqSq+azyQpHeqY8JhxWJalRXDUHx31kqtzO46jE0WULSSdTmOa\nJrZtk8lk+hYrJpNJWq0WlmVRKpW0Z/v8+fN6sWQmk7ltEWc2m6XRaPTNX1W6O50OxWJxrI6SnueR\nyWTGumeCIAhCPyK4BUG4K4R92dAvPC3LYp5lfsL4uyx7D2Bg6CSSnFXAwIhtba5e49I/ot7k8P7R\n6vYkgluNGT5XHEtLS7ftp+ahtisLSD6fJ5/P69xt6NlLrl27Rrvdxvd99vb2KBaLAJimqTtKVqtV\nHMfRCytN0+xrvtPtdvsWlXqeR6FQ0POK3juVtf2Vr3xl5H2IJp6EcV2Xn//5P9P2FUEQhKMQ3Mrh\nPomfk0QEtyAId40D74BudhfXc2lwGdMytRg0MFhgBYC6VyWTzQz0WCvCNpOomA0L7WiqCcDOzo7e\nb1LiUkbi9hmUrR1+KFCWks3NTer1Ovv7+7TbbarVKisrKziOg+/7TE9P9wl16Hm51XsVFdhut5mZ\nmdHXb5omlUql74FDHaM82+HFnZZlUSwW2dnZ4aWXXhp6fclksi+S8ObNA559dpONjX0+9rE/4dd+\nbZt/+k//csK7KwiCcP8jglsQhLuC53lUuc7nD/9HytbX+Ib531HpvtS38NDzPLpWm28bX6PJ/m1j\nxFWUo9XbuHxvtV90kWA4vWPYQslBixyjFeLogs3o52FLyObmpq6s7+zsUK/XMU2TUqlEKpXSArpQ\nKJBOp7WFBHr+6cuXL+uKttpfLZSMXl+pVKLT6ejzKbtLOElFve7u7rK5uUk6nebixYsD70mUnR34\nhV+4zI/92B/wUz/1Bf7kT7b4a38tzb/4F4+PPYYgCEIc92NKyf3XO1MQhLccJejmOccPeP81p5w8\n78n+IlnzHFtsUGBe75thmu80v5cM0xhJo08MqkhAx3LJYWJgDLSOwPCEDVX1DjeaCY8RXXwZtz18\njvC5Hcchm83q13Cl3fM83RVSiWwlmtViR+gJbcuyqFarLC8v60o3wP7+PqlUikqlQrvdplQq9S20\nDF/j3t4eq6ursQs4t7a2eOCBB/oeCur1OqlUiqWlpXH/vLfuwyELC00++ckP89hjJf78z7f4xCfO\nMzUldRxBEIQo8i+jIAjHjhKiBgZzrDGTnaFgPcyeVeGzh7/NG9aGjgI0MPRCymhjGoA6bZ45eIE6\n7dvOEY0ehP6ovzgBro5RVXL1E118OaipTrS5jhqz0+mwv7/fV9WuVqu88sorvPrqq1y/fp1cLofr\numxsbOC6Lrlcjnq9rv3YN2/eJJVKsbm5STKZ5MqVKwBcu3aNcrlMu93W0YCAzuEOz1dVt9UDRtjD\nbppm34JJJbZnZ2c5d+7coD/nbRweHvL88w1+7uce5qmnzrGyMs3TT18QsS0Iwh0jnSYFQRAmIGq9\n8DyPEkt8/9Rf50t+wC4HffvFNY7xPI9Ex+BDxsPkSA0Uw+FxVFU5Ll86vD28kHKYNQXos7BEx1Sf\nt1otpqen2d7e5nOf+xwAr7/+OoVCgWKxyMLCghbKhUJBR/7Ztq27RpZKJaanpykUCjQajb78bMdx\nODw81BVy1exmenpaz6PZbPZ9O6ASWtQ4tm3rRJJkMkkikdDzGCciUfHM/8/em4c5ct/nnZ9CFQpV\nQOFsoIE+ZqZnhsPhqRFFWaIOS4osKet1bGflQ5b1bGJbdiQna2d3nWOfzWYvObvxKuusN4kjOZGc\nw7ZsWZblY20ltmVblyWapMiROJwhOVf3dDfQuIECqlCoQu0f0O836OZIJDXNQ9Tv8zz9TDdQKBTQ\nfPp58fL9vt9P7fGDP/hFzp1z0TTtGT9OoVAovlVRkRKFQnHoLArmRSE7poc3fYLvTH47SyTRjLlY\nE1EMeKrwSxpJlgz76z7XQbfb9/190Y6DuWVxzDOJoyxe09c6xjAMcrkcvu9Tr9dZX18H5hsiXdel\nUqlQr9dxHId8fl6BGASBFMK2baPruuy6dl2Xfr+/Ly6ytraGaZo4jrOvoeTgQGev19u3GdIwDLkC\nXkRYRKuJ6AB/pot/BG9+8zK/+Zv38eY3Lz/9wQqFQvEsiNGIZs+v+/x8oBxuhUJx6BwcjBRuazLM\ncJv2RpZ8Bw1Nir4bLaVZPM/XaxY5KDgXz7d4DYtVgIv5anH+Gw1Kfr1WksVjxOPPnj1LqVSS0Yp8\nPo9t22xvb8tqviAI0HWdbDZLOp2Wg4+igSQIAj7zmc/guq7sz15bW5MLcer1uhTcURTtc+0zmYxc\nab+4GGgxUtLpdGTbiOM4VCqVZ+xui+dJJBK85S01FSFRKBSKZ4j6a6lQKJ4TFsXt0B0ysObiOWcs\n8ZlPfwaAn/u5n+P8+fNS2C5ukRTnuFGue1EgH8xRL7q1N6oKFLctPv5rCc7FvPeNXtdiPCWKIo4d\nO4Zt21QqFWBe3ee6Lmtra/Jn27ZlS8p4PJatJLZtMxwO+fKXv0wYhrI5BOZRkFqtBsxFs8hei35u\n8RpFpKTX68kPK+12WzrcpVKJUqkkm0tEk8kz5dk64QqFQqGYo/56KhSK54RFceZaSX592uFdyRJp\n1+e1r30tAMeOHeO3fuu3uPvuu7nnFfegr8RUwhVsy953jhs53JZl7YuiHGwscV2XMAxlvEK47EJ4\ni9gJXK/0WzzPjeoAb+Tci2P7/b6MgYhctK7r5PN5KbTFdQVBQC6Xk4tsxODj1atX6XQ6FAoF6YgD\nst1EnEdspRQfHCaTidwC2W63SaVS8gNIJpOh0+kAc3ddvC+pVIrhcMiRI0e+gd+uQqFQPEfEEIYq\nUqJQKBTPmjI6P6TlKIbxvojD93//93PPPffQ7/f51Ff+Mx+ZfZBPBZ9i5I+e4kQvIkSuyGmL2xYd\nbNG3Le6D/avmF6Mkiw73Yub8oGMOT3XdXdfl6tWrtFot8vm8XLku7stmszLKIYS1aZry8cJ9tm2b\nVqtFuVymVqthGIZ0uEulEp1OB9u2CYIAx3GYTCYyPiKuV2TEF9+3brcrny+KItbW1jAMg729vaet\nAvx6UZ5vZIGQQqFQfKuiHG6FQvGcsChkk0aSrDsh+upfHOEOO47D2972Nnbru/zKQ7/HrlbDy3ya\n2d6MtxTfQtJIPm2++EaC+OBSnMXbF6MnBwcqD3KjwUvxNRqNWFpa4uLFi3ieR7FYlEJbEEURvV6P\nKIo4evQovu8TBAGGYUghbJomlUqFT37ykwwGA06fPg3A1tbWvuz1ouMt3G7R2W1ZFu12W4rtQqFA\nGIaMx2MZPQFoNpvk83na7fYzGpb8evereIlCoXguiGONKHzp/X156b0ihULxouCg2F287WB7SaqY\n5PbXZ6i1tyGpM5le4OL2GiW7RhRFcrBvcZvjjQT1otg+6F4fjKAcvDbR7nGj6zv4msJwvkJ9b29P\nimEhiEWGGuaCXTzf4sp2schGxF0uXLjAeDxmY2ODbrdLEASYpimHJkVUpdPpUKvVZLtJOp2m0WiQ\nSqXIZDLS9Revs1Qq0Ww299URmqZJJpORERSFQqFQPPcowa1QKJ5zxMbINgElkvJ2IYgrmSpvSn03\nX/nywwRhQLfSoTkcktGLGPp8+2IQBJTL5X2d2GLor1Ao4LquFMyLzyuYhlNmDgzdocyILwr4gxnu\ngxy8XSywGY1G0mVutVpygBKQMRLTNLFtW+a3+/0+2WxWLskZjUZYloVt20ynUzKZzL6lNo7jyKjK\notsurnlR1O/s7FAqlWRWW1wrzB33xx9/nPvuu+9Z9W4rFArF88Xc4VYZboVCoXhGHBx47GghH/W3\n6Bvxvl7rMAxJGknK1jKvv+fNDJcmPHnyLGdPPUaYmRGnI8IoJF/Is20FpKyUfOxiHnyxHnCx9k9U\n43nGhM9NH2JqzW4YJznYtS0eu/gaer0eYRjKpTT5fJ6TJ09y4sQJwjCkXC7jeZ7MTNu2LXPdYg17\nFEWsr6/LWr5Lly7x5JNPEoYhnufh+74U26KZRHR2m6YpHfTFYVHXdeUHDiH+RUPJ1atX9+XEx+Px\nc/UrVygUCsXXQAluhULxnCIE7NFMgb86yZMPtX2Di7qh0w87BGHAmD53Fu9B655kL+Xj2i7nkg/R\nCzpcjAf8rPYAj4fdfRnsxWo+13X3RU6EqAXIkuHV2hk0d/aU67vRAKAQ4gdFd7PZlN3Zy8vL0rkW\nuW5AOtOLuWuYO9C2bcvIydmzZ6nX69xyyy3UajUZnRE1gOK5dV2XkZJutyufU3zgmEwmMrcdhiG9\nXk+2sIihTYB6vU46nb5pdzuOY+r1CXEc39R5FAqF4inEEIX68/L1fKIEt0KheM4JwxANjdXUfOHN\nohs9YsiXor+gb+xxIf5zVoo5vqN1H6+8ehRrx+TWyctYri1zNLL5O+0T5NuBbBA5yMEO6kUm/oQc\nGYqF4r64xcHWkYPLccR5+v3+/APCQuXfZDKZ94wPhxiGQRRFsnpPPEb83O/3WVpaIpVK4TgO29vb\n9Ho9crkciUSC8XiM67oUi0W51EY45dvb29JRN02TVCq1z4nP5/P7XrfjOKRSFrvdmGKxJCsJW63W\nobSLNBoBH/zgJo1GcNPnUigUim8FVIZboVA8JxzssxaO82IWOiam2fe4K/VtlCiT0t5I1ihRWmny\neeMBVvaWScc5Lky/xJnka7gtLjMcDgH2udyAFLkHxbg4ZjGjvdg+IuIZIs+9WAUort11XarVqnxe\n2F/tp+u6HLrUdX3fkGIURZRKpX3bH3VdlxGQTqcjr0dESUajEUEQSKEsur1FNKTZbLKxsSE/OCx2\ni4vr325H/Jv/HPEjb4JSRpfvxbFjx276d1utmrznPUepVs2nP1ihUCieBXGsEU5VhluhUCieEYtO\n8TSc4hpXqBstdEOXonVsweecLTQrS4IEafJoaJTtZQrDKi2ry6PJK6x4p0hO58tcKpWKbOFYFNyL\nIntxwc1ixGLxfvGzOM9ihnvR2RbRDTGk6XkenudRqVTI5/Ps7e0RRRGFQoHhcLjPmfY8T65sn0wm\nUmxvb2/TbDZptVqUSiV0XadareK6Lvl8nnK5TLvdlter67r8EsOTBwcm4xgu9kdMp3PH3tYGvOO+\nEa3tR6VQTyQS9Pv9m/7dappGrZZC07SbPpdCoVB8K6AEt0KheE5YjHR4xiZ/Mftf+LXwQ1wJrkmx\na+FzdHaJFB79sMM0nMpoBEmdr2TvRw8ThKZFEARMp1POtVqI5PDu7u6+DDewz7kWPwsHeTE7vuhi\n32irpLhPZKMFi3noTqcjxXUYhrLzWmS4FwU0ILPXa2trzGYzqtUqjuNg2zaO47C2tsbW1hYAKysr\n8jyVSoUgCKTLLRpOFhf5tDWDj81yTLLztfGzWUQlG6HrCem4Axw9evQb/I0qFArF84HGLDKel6/n\nEyW4FQrFc8KioLXDo5zx/yE/bLyb9URN1gQ+OYko9W9nGsY8OP0cE8NjVJhghha3T+9mo3Uvny61\n+Hf5v6SRGnF5POK3zAmXRy6dTodsNiuFtWBRIIt/Dw5Z+r5/wy5vcax47KIY7/V60qHOZrPYtk0U\nRTJ33el0ZFuIEP+VSgVd1+Uw5YULFwiCgKtXr1KtVvdtpMzn83ieR7lcpt/vy/5xmA9h1ut16Z6L\nWI1w3wGW4pC/mY3IeK58TBAEWJYlBbfYZKlQKBSK5xcluBUKxaFz0DWOwojc7AS1sIyGhmVZnGfI\n/2w8zJ9n24wmEa9Ivo4vuJf4N9Fv8iAPcX/pj7ndOsprG/fxjp27OBIXSccD7jG/xGpex0/N0I3r\neenFho7FWIjjOPL7g8OUQlz7vi8z4HC9uWQxliIeJ+IZURSh6zq1Wk0OLi7mrBeP3dzcZHt7m1Kp\nRLfblQJbIOoBK5UKnuextrYmozMwz2yLxpNisUg2m91XfWhZFpoGNQNs25Jr5re3t3Ech2vXrgGw\nvr5+SL9htdpdoVA8R8RAqD8/X88jSnArFIpDJSamZ8yIiQnDkCAMeGJ8lVa3xbVRB93QaTabpAm4\nN9zhtk6GoOnyaODyq7k2r4tex9Zsl2k85c8aO/yD7gabzSTdThd7luHe6T1EmsFnEk+wN+kRpEL5\nXIvZazGgeFD8w3X3fXFIcnGoUrjUwg0vl8sy622aJuPxmHQ6DcyFt8hxizXtQmh7noeu6+TzefL5\nvLyepaUlmQUX693H4zGe51GrzbdrivsB6XQDdLtdJpOJ/BCwOPDpui6+78u172JoU1YzHmKcRK12\nVygUimeOEtwKheJQaTPlE9EuHabExFyYXeWT+YepF3z+1NzlUrdONptlnSLfGb2OO5NHaKU8Ho0a\n/NfDZU54NVa2yiRbKbxCh/fNznOr5nPNadPr99BGOrOuz2vDEySY8VDyHENGjMdjGRVZdLcBKaYP\nZriFgyyiIAIhthdFt+/79Ho90uk0pmliWZas7xPZbNd18TxPZq9hvm3Stm36/T75fJ6jR48ymUxo\nNBr7jltdXZUNJ0KIi+tfzG6L7ZXidS1epzi+2WwSRRG9Xo+rV69yxx13yPMoFArFi5pYUw63QqFQ\nPB1LJPnuuEKiN2LImHPxFaazoyRiixNhDtMPGVk6XSZ8drzFX+q7XClscjKChvvnfHTwBS5wDrO8\nTSI94NbklGEt4A+WP0+jNiRpJhm5I4ZbbcyJwR3DE2TJyOq9xUFC2C+yF+vz4LrDLRxswWKERDjI\ng8EAXddl57bv+3JIUmyBNE3zhqJ2a2uLKIpkL3ehUKBcLssYihiIFDlwmH8IaDQawPVIyd7eHidP\nnpSCW3zAEB8IwjBkMpmg6zqlUolEIsHq6qo8fjHGolAoFIrnDyW4FQrFoaKhkQ8TzKIZYW9CuVek\nE2v8Ppv8VvIsF52Y3/AuM8QHdvl95yEujZr8yWzErn07qXqHnaO7pOqvwNA9MiULfWBje7dxeVZn\nlPAJgmAuhD2fnDZfpnMjRFxERC0WN1CKgcNyubwvdiLEtHiMOE6IarHwpt1u0+l06HQ60oEOgoBO\npyOjIN1uF0DmzIWodxyHfD4vKwN1XZfiXWyx1HWd1dVVebzYNtloNKTDLljchvnoo4/Knm+AarUq\nBbeKgSgUCsULgxLcCoXi0PF9n9JSiYdmDc7mL3HZaFAe9zhhPERmsM1tewFHyXN3uMQZ7UGWxgmS\n4wnVcxEnZqdYfewEq0+c5E3e2yhMspSXbY5qae5L3k2g2+TyOVZWV5hYMQl9/mcsJqYTuQzdeYOH\ncLRF7/TBpTuLcQ5x/+K/YlW8iJ8s1v31+33Zhy3EtuM4mKaJbdtSYB85coQoiigWiziOIzPaQljb\ntk0+n8e2bWzbZm9vT36YCIJAnkdEUmAeC0mlUvIDQqvVwrIshsMh999/P71eD9M06XQ6FItFWTko\n3hOFQqF4URMDofb8fD0DNE0raJr2MU3Tzmua9pimaa/5Rl6WEtwKheJQGQwH/Mlwl4995Yv8vLPN\nTrDH3Y8GfHFqc2T3TaQSFb6S69AjoNQoszr6r9hdtnllmKO6ssKfngy5fFuX/Kky+bEGHFrxAAAg\nAElEQVTDKBqwY57nHmeNcR9+zwoZWkkuBF3+Mr2Jlk8B0I99/mD2OP1MQPzVpm7f90ln0njGhKE7\n3NfPnclkbtjFLQYQRQe3aCmpVCqYpkkul+PEiRNks1miKCKfz8ue7O3t7X21gK7rym2R/X6fSqUi\n6/2E6AZkFWA2m5Wu+OIHApHnjqKIcrnMeDyW11ur1bAsC9M0CcNQDl2K5/U8T36vUCgUimfNLwCf\njOP4NuAM8Ng3chIluBUKxU0TE9MIx3z285/jfZ//JL+YvcT93R2OtXVi/xjt7BKPr5h8POXxO/EO\n7ckm4PNQJuDRMMUrxicppfN8fvkqoVXnDaPXUY1K5PI5OoMhg6bFfwy/iDvt8CP2MpZl8cemxm3R\ncZatAgCul+TS3lEeiJ9gas2kM+0bAZ+dPoirjWm32zKTbRgGjUZj3yp34IabKUVzCcxjIr1ejzAM\n5ZbIer0uox4iHgLIXLdwxEWTieM4dDodYC6IRa2gbdsyRrKYtxbPHQQBrVZLimtxjl6vR7PZxDRN\nBoMB29vbUtCLDZXidRwmi++RQqFQHBrh8/T1NGialgPeAHwIII7jII7j3td/1I1R/39RoVDcNH/0\n4Bf4WHCNu/owPpWh0o4YlSNeVo/4y9NlLpUitChGLzX4C/KEcY1P0OM3a31qoUdgTxhMba7YIUuz\nMW6gs6XrnLQiNo912BtOeMUTJdbMPF6/xVoqx7sMh1rBZOJPsCwLJxjxY8UCmeBWkrMELb81d6jd\nBG90Xk2WNJEe7evirlarMtu96HaLOIm4XTjeB91nUcGn67qMjcD1zLYYhBQRka2tLarVKoZhyDiK\naZpMJhM2NzcplUrSkV50pYVgXlpaYjKZUCgU5Jr5MAyp1+ucP39eut/Hjh2TWXARUTls4jhme3vM\niRNqxbtCofimpaxp2gMLP/9SHMe/tPDzCaAJ/LKmaWeAB4G/G8fx6Nk+kXK4FQrFTXHlyhUufOFB\nTm52uTwbkhs3ecVmQFQuMlwzaKU84sjE1BJMZxX0qUU+DljD4HjkczL3BbrGI9zhB5wY5iiGNT7g\nDHh/+hqXRlOOd6u8Mf0yBnmPxxIjPhWe41p/j2IYE4XXO6YLhTwbOYuikcO2bOlkZ50sOTJoaF9z\nq+Ti7SJCInq3xXFhGEqBnMlkyOfz0lkOgkAuplms7QuCgHw+T6lUYm1tjUwmQxRFTCYTPM/DdV15\n7GK2Wwh+IdyLxSL5fF5umBQOe7FYpNVqsbe3J1tMisUS3XGK8djbVxMoOCyXu9EI+PCHd2g0gqc/\nWKFQKJ4pMc+nw92K4/iVC1+LYhvmxvQrgH8dx/E9wAj4H76Rl6UEt0KhuCmCIGCWjmncFfDAm9ex\n3BydwpRepcGT5Qu8YTPk3ecifvShgNddzFFp6ay3U2yQwQqXuTL8Ni7NjvCIMeWN0zxJ9lgLk3QK\nMbuJHjuFNpNBG8ce8+koz63BLRxfWtu3ol0gHGnx76KbLWIZvV5v34KcG/17cA384vGpVArf99nc\n3CQIgn393YDMegu2t7fxPI8nnnhC5rDFYxZ7tsW/YmX88vLyvmHHfr+/r3JQXGO9Xmd5eZlsNksu\nlyORXuEPztXw47xsNxGLcw5u2nw2HBTq1arJT/3UKapV82s8QqFQKL7puQZci+P4i1/9+WPMBfiz\nRgluhUJxU6yurmJNTe6sr7Dy5Ahvq0FrZcBa3KD4sI79ny5iXbxGUEjwJWtMKZnm5c0cBSzS6GSH\naVL49LQpV5tDRhT43l6GH+hX2fASZLRV4nSOhJnnh0spjsUGQ2PGaDz/P3oHheDB7ZKiaaRQmGe9\nhYgV2W1xbKvVAtgnrhcX5vi+TzqdlmJe1PQJkey6rsxpL9b/lUol8vm8FOEi110ul2XVoGEYZLNZ\nKcSjKCIMQ+l+C3c9iiIuX74sX+PDDz8se7y3t7fJ5XIU7YB3v0WjmA6IoohHHnnk64rsZ+p4HzyH\npmk4zlTFSRQKxeHy/DrcX/9S4rgObGmadvqrN30HcO4beVkqw61QKG4Kx3F4z996D/VGHefLX6JU\nuZVzf7lLz4zZKZTpzK7Q0Ls8cXzKbJzl6JbGQ1aW1zPD7W3hFceUfYtz6RTRis164HDBhT+1LnMp\nGzNw6iwlTuJop7kniOlpEz41fZJvT1WofnXD4iKFQkGK4oMDkUJgCxEOT91CKVxt8f2i2z0ejwmC\nQNb5NZtN6UhfuXJFLrPxPI9yucz29rYckEwkEnIL5UGXW/Rmr6ysMB6PyeVy+wR3t9uVwr5UKuE4\nDsPhkF6vx8rKCt1ul3Q6zcbGBp7nUV3SaTSQHwoODoUuvh83UxW42AWuUCgUL1F+CvhVTdNM4BLw\no9/ISZTgVigUh0KtWuMd1e8E4K3Mm0vaTIlP9vnixfNsbjbRQo/NcMY9V3xO3Zrm9ssJLtprrGwG\n7J2KSCbqbGxXeLJg8TLnHHf2TpIYblAcG9y7fJRo2kLX07zWtaja2X3iMZWyeKg/5h49RNOui2XX\ndeVadeApERHXdff9DNfd3MWKQLFuXTjK2WxWNoBcu3aNcrksxbcQ08Lx1nWdTCYjO7HFYhwhqHO5\nHLlcTn4PSOEtjhOivFQq4bouZ8+elaK+VCpx6623yudbfEy1WqXX60mHfzFyczPEccyDD/Z41auW\nlMutUCgOjxiYvtAXcZ04jh8GXnmz51GCW6FQPCdoaJQxoVzhu8plXk2ARsRes0M/2CJBgtyex0Zz\nQLG8RCGrkzIdsjs9NrQRWrDCw0fOE0YWYdqivGeT6ydwHIe1XFmKxsvakNtJ8Rd7Hu86n+I3Xxbz\n8uw8q+04jhTMYRiiGzoTK0YPp3LjpBiuFCxmuUWUxHVdarWaHHLUdZ3d3V08z6NUKpHNZjFNk0aj\nwcbGhjyXcLu73S7VahVARk76/f6+mkAh0sW1mKbJlStXuOuuu+QwZS6Xo9vtytx2GIYy5iKiKbqu\nYxgGyaTJ5YaHldCl2D74YeNmeOQRl3e+8zwf//gZXv7y7E2fT6FQKF7KqAy3QqF4ztHQqJCiTJo7\nKuu85r75oq53/fAP8/Y3vhXHm/H6R0cUvjjhatzgKxsX+VxhSl3LU58cZ7W/xFY8ZnV9lSAI6Ha7\njMdj/qLxBP+H/iUuMeB2M+DX7wy5tzAXyqI6T9T6WZbF2JjyGe1xAivel982DIPR6Hom3Pd9dnZ2\nGI/HWJZFuVym2WzS6XRkFtvzPI4ePSodZcdxmM1mXLlyZZ/L7HkexWIRXdfltshUKoWu6+TzefL5\nPI1GA8uypNi2LEtupQS4evUqAKPRiHq9DiDXyIdhyMrKCs5X4zXpdJpms0ljCL/yUJLAKMrzHhwy\nvRnOnHH4yEdu48yZw68cVCgU38LEQPQ8fT2PKMGtUCheUFZXV/ne7/ke7n3T9/Mrhbdhjo5y4toK\nRnJMP8zjpgOGWoWPLO/xZ90niaKIra0tXNfldHKJfxi8jBPkCIIJd1pTJhOf0Wi0b8mN7/v0ej3S\nYZJvj28lx/Xtj0KAplLzjZW9Xo/BYIBpmqyurkqRWiwWWV1dxfM8ms2mbBwRDjVAIpGQK9thLrhL\npRIrKyv0+325KGc8HrO0tITrunQ6HfL5vNxQKQYioyiSGelTp07JGIrIZc9mM5aXl6lUKuTz+X0x\nkWKxSMJv8ubaVW5dzz9lbf1hoGka995b2LcRU6FQKBQ3RkVKFArFi4IzWfjo3TOOhnfxuc4mjwyT\nBEUDGkl+pXMbP918nPONOua4RbFQYMsfsQ4s4aClNVzX5fLlyxw/fhzP82SMIgxD2QYy8SfkLZt+\nbx7nyGQyhGG4r3LPsiyiKKLf7+O6rhTCi1V/QTBvABEDlP1+nyAIqFarcsARrme4J5MJa2trMjYi\nGk3EAKQQ9Z1OR7rajuMwGAzk0GW322V1dXXf5khxHiHGLcui2+0SBAG+73H7sRrJ5FOz6YeFrus0\nGgHVqq5y3AqF4vA43KW4LwqUw61QKF4UaBq8tpqmbyZ48tY1VnIlUlqS181s/r6/TU0z+L3XLbG5\n6lDPJvm9zJSeqVEPBsziGIo1Mpn5QKKu60zDKVdHXbpJjYQ+bwhpt9uEYUihUEDXdQaDATB3t4Vo\nbbVa5PPzDutyuUypVKJQKMjO7UajgW3bcpOjiJmIzZHlcnnf4hshkIMgoFwus7y8jG3bsrFEiHDL\nskin0/uE/mLvNswdc9u2uXDhgmwYqdVqchiz2+3KLZW9Xk+eRzzH4iDpYdBoBHzwg5tq+Y1CoVA8\nDcrhVigULxpiYkrFHG8Y9LCs43zab/GFyhUyZpZTE5M4noEBX85NeU1boz/b44HSiHSwwb96bMD3\n2hDqEYUo5po34He1AZGZ5y3dMZV4vgBmZ2dHZqdFBjoMQ7kcRjjK6XSaVqslIyAAleUKEztGd6/H\nKIS4Fi652A4JyA2UnU4H27bpdruy6UQ0kDQaDZaWluh2u2SzWVKplBTFFy5coFqt0mw2ueWWWzBN\nk83NTY4ePYrv+yQSiX3Pn81mGQ6H8nlrtZocDAXkAOlhRUuqVZP3vOeoWn6jUCgOD9HD/RJDCW6F\nQvGioc2UX/Yu03SyTIYTpskk5tCkxFUeXnK4S9ulnDxC8cKQcDYjnS3yij2LUxsm95S6tHe3+aOM\nzi2PtignLL79xBG6g00y6Rwd3+PqoElmOo8+XLhwAcdx5NBiJpPhzJkz7NbreI5FtLlJ2rbRdZ16\nvU4+n+dSe4sH0td4lX+M2a7PeDymWq1imibdbpdisSjjJjCPlHQ6HbniPQgCGW/pdruy8WQ4HOJ5\nHtlslvF4TLPZ5P7775dZ8FarRavVwvd9ut0ux48fx3VdCoUC/X6ffr+P53lUKhXq9Tqu67K3t0ci\nkeDIkSMMBgOZRz+4COhm0DSNQiFWcRKFQqF4GpTgVigULxqWSPK99lE+Fg14Y36ZP5+NudVPcdpx\n+EPP5zVelTuXyvjrJr8d7XCqqXObbnFem/LBW0z+SfE0P+AFNF9xJ8udXQqZPFpnyChtYuctnsj1\nePleipGts7q2yiyasbS0JMUqQKK6xJ9YU36ocjuzegvP8zh9+jT9fp+iXeRI7ijGaEaqmpI5buAp\n2yTFv6KbOwgCKpWKrObrdDoUi0UZA9F1HcuyaLfbXL58mbe+9a1cu3YNmA+W2rYtz7G4NEdUH/q+\nLwcwF7dbFotF6WgLoX1YTSUKhUKheGaoDLdCoXjRoKFxIkzyDtfkFX6C75lleCCT5H0+fCAbcy5a\nIRHr2OOQO3tppnmbaq3KHZi8f5Dm9lmS3dDmvX6Fdnkd27aZ5h3+tGLh+lP+SlCjuFTmz/M+ifI8\np93r9ahUKvT7fWJixoMu3zVKUJjO5GZHsVkybacpahkMfR758DyPpaUlef1ibbvIg6+vr5NKpUin\n01QqFZkf73Q66LrOaDSSgjydTvPkk0/iui6vf/3r0XWddDoN8JTlPYu5bUAOftbrdTzPkw0qa2tr\n+95f0YByWMOTcRzTakXEcXwo51MoFIoX02r3w0QJboVC8aIiaSS5JV8ibaXJRTOYDHlXMOMnJxme\naGt8dpSio5n8jp7hfYU6D2pDAE6HOhoa9xVsPrIccKc+bye5s7rCdw4hm8tSNtKsWlnuC4uU9ZTM\nUwPYtk0nnvCZJY9Z4BFMAtLptFwkI3LXQmgL93o8HsshStH5XSwWAWR8Y3HNvOu65HI5OUxpGAZR\nFMnstYieNJtN2Z+dSCSkiBaCPQgCuTxnOBwSRRGVSoVarSaHK8Vad+FoC4G+uOjnZmg0Aj70oR01\nNKlQKBRPgxLcCoXiRcXiyvV8KsGRRItPBzGfTl+ms9rgp68Z9GKLn8ln+Ufhcb407dBmSiaTIZfL\nkc063JEISKXmgjhlpshkMvxuaoprmXQ0+FRyyqXEBN3Q5Yp20zQJohRb3SMYqaLMYeu6TrvdBuaL\nZwqFAvl8niiKiKJo3/r1yWRCt9uV7SewX9z6/rwj3DAM2ect3Ont7W0ZRRHP3ev15HvhOA6VSoVr\n165JgS/uE80suq7T6XS4ePEi+Xz+ho0kYkj0MFheTnLffWWWl5OHcj6FQqFQDrdCoVA8T+i6wXnN\notPp8l8Gx/iJZJL/cVLl9XaTf3WsxZ3WlELos+LD7eERSnFSikjDMOSGSJgL0mXN4B1ajrWUjTlw\nuXdvwF9Gm2wOW1iWJev7arrGe3MpjqUtSqWSPJ/IQ2cyGdrtNrquS9ErBHWxWCSTyZDNZuVjRQe4\nyG2HYUg+n8f3faIoIp/P02w22d7e5pZbbqFSqchMeKlUkmJZiHpAZrjFNWxtbcnlO6ISUPR/7+7u\nSpdbIFz4w+Chhzx+8idbnD2rHG6FQqH4eijBrVAoXnQ8MJryY+0+v6NfRdM0jmdtjgRJRmHEvUs2\nV8Y+vzd0+TC7vOcxnbNDDcuyZHSjVCphWRapVGpex9fpcszOEoURy5Vlvm1pjVfGNVbsvGzugHkX\neFWPmUx8+v0+k8mETCZDP5UilUoxGAzk8KJ4TBDMoyeGYdDtdgnDUK5fNwxD3tfv9ymXy8C8nk80\nmFy8eBHHcdje3pZOtud5nD17luPHjwOwsbGB4zhywU6lUkHXdZrNJhsbG7KOcGVlBdd1yWQy0vUG\n9rncIsd9GJw5Y/Lxjx/hzJnUoZxPoVAolMOtUCgUzxPHUx5/e+Uqp+NbSHpQjwIausWfcowvRyl+\nIbT5Z6UBGXObX9zociaLzCofzCxnMhlWV1elIA/DkGFS4zPaAM9OMplMWF5eljGOyWSC4zjouk4U\nRVx2R/z7yZQr4zGmaZLL5eSWSJHxHo/HhGFINpuVy2gAxuPxPkdZDD/2+32uXLmC53mUy2Ucx6FW\nq6HrOpubmzz++OMYhsF0OgWQC3J0XZfnAFhbW5Outq7r9Hq9fTWD6XSaTqcj31fRwS3en5sV3slk\nkrvuMlQtoEKhUDwNSnArFIoXHamJxunhSX7i4TIPkubjSZ/mFJZbBSq6zk8tG/ztKM/51IyTKzaa\nNs9HL1bkLTrXAiF+c1N4h32M3BQymYwcWIT5z2LYMZ1OUwG+L5iwkU4TRZGMicBcUOu6TqlUkuvZ\n4bqjXCqVCMNQ/jwajQB49NFH91X7ia2VMK8XnM1mAFKoN5tNXNel0+lIB1xUDaZSKdnnLTq+Raxl\nPB4TBMG+XLz40HFYbSWHvS5eoVAoXooOt/pLqVAoXlQMh0N6qRSb/TGJhEYu0nhTZPPbSZO/Zo/4\nNYYYsY5j6/yd+FWs+Smw5lsqL4/6rKfScqmLiE8s5rt93yfrZOn1eiS/KmiLxeI+t3fx8dmsw2l7\nfpzIXwuBXCqVaDabUsiL5xJtIKJFJJ1OY1kW3W6XJ554QlYJilYScexgMKDT6TAej1ldXZXnEdlu\nkc0WA5Mi2+04Du12W94uRLno5BavrVwuy+sTH1AUCoVC8dyjBLdCoXhRMcpk+Hmvz+dqI/7PRIL7\nHJvNiU+kR+SjKY6W4LtCi1oiRZkE17Qut2DRiKb8f86Md+oaadeXEZLFiIlAuLuLMZSD9y1W+oVh\nuG/w0bIs6T4Xi0V832cymQDzVhNxbD6fx3VdJpMJk8mExx57DJhHQTY3N1lbW5NRkSiK6HQ6+L7P\nXXfdhW3bT3Hooyii3+/LbvDRaCT7vEVmO5/PS1EfBMG+1y4y4octtg+z21uhUHyLEwPTF/oiDh8V\nKVEoFC8qEu0Ob2uN+Ok2vE0LcUcjHk5m+UnT4OWpNO/WCqzNpjTCFBfGO/zL5J9zNe5wLJPlB2MH\nrdWVAlmISlHFt5hdXoxWwPVoxHg8ls0jjuPIY4XAFkIe5q50qzVvOomiSG6tFMcKcS5y15ZlsbGx\nId3nZrOJrutsbW3Ne8A7HXK5nGxFEY61ENKmabK2tiYrAMVQpLhWXdfp9/uk02k8zyOXy+17jeJ9\nuVFdoEKhUCieO5QloVAoXlTszTR+OTawElu8LH2ah4MU7w0m/F+zmO+xk5gzjw/Ej/GRvXv5+eI6\n6VaGVKnEyO1jA05hvnQmDEN6vZ5cZb4ogn3fRzd0WswoGykpSkVFYL/fJ5VK0ev1mEwmpFIpGcUQ\nsRRg35ZJmOfIs9ks9XqdjY0NmSMX7SKJRELmrXVdp1KpsLm5SSaT4fHHHyeOY8rlssxz27Ytzy2c\nbeGGiyrDVGreECJEdBAEUnyLDx1CXAvHXnx/WCiHW6FQHBoxEL3QF3H4KIdboVC8aAjDkN1oyhec\nFGjHcbQUZwKX/zuls51x+VDc49Nk+PDkdn56KcVfseCHI4Oaru1zo8MwxPN83PS8Hk+IwUVh3WLG\nx/QRPUN7SrNJPp+Xy27EbSJOYhgGjuMwHM43XBqGsW8xjtg8CfDkk09Sr9dxXRfTNDlx4gSVSoXt\n7W12dnYAOHr0KM1mk93dXarV6j73WjjcgKwR3Nzc3Hf7ZDLZd/0iXy7Et/hXXCsgXe7DqgdUYluh\nUCi+PuqvpEKheFEQxzGtRIJj3oj/3fe4u7ZCN9b4T06Ot+DT0hLc4884lQj4QDLLm5IzEgmNI3YS\nTYNpOGXsWLTCPktWjlZC5z8EId8XTCDWOBpDFM1d6l6vR3Li865qGdv1CLleayec4rW1tbkTruvs\n7e0BkE6nZba7UqlIoa3rOmEYyqaQxx9/nGq1KltCwjCkVCoRRRGGYbC2tibjJI899hiXLl3CsizW\n19flyniR7Yb5cKVwrsXPtm2TSqXY29vDtm2y2SwwF9xi9TzMIzCiRhCuN6gcltgW75lCoVAcCqKH\n+yWGcrgVCsWLgvos5v3dAX886/FQOs2/9Sb8h/aEt8aw4Yd8d5Th4WySX5oG3KpF7EZzF1tERYZW\nkv8QNfj96By/c2WLRCLBD0RTelqKXxrb1Bf+gBcKBcpLZUohaGj4vi9dYHG+7e1tANkOImr2YO5k\nC+c4lUrhui6XLl0C5tGPYrEon8eyLI4cOSIz2MPhULrXTzzxBDs7O2iaxq233gogYyKiQxxgeXmZ\nfD6P4zisrKzILZWTyWRfRaHo646iSIpz4djD9VpAcd9him6FQqFQfG2ULaFQKF4UhMGMTw/a7Bxv\n8cbhEq9KQDaXpuCPKBaLJN0h78DAnfg0dY0/jKb8WNqmM4k4bsLEi/nBaYEr4xzv3nP4LsvlRyyd\nX+/DRIvnQtO+nuMW7SGpVGrfIORird9wOMS2bTn4GMfwaKPNqqmTTs9jI5PJBNM0yefzbG9vs7u7\ny6lTpwDkWncAz/OA61WBIg/ebDY5ffo0R48elfluIZ49z5PHmaaJ67oUCgWq1Sr1ep1CoSAX8LTb\nbZrNJpVKRVYHZjIZrly5gq7rxDH4UYHY6zEajahWq4f3u1MZboVCcVgoh1uhUCieG2azGX9/a4uz\n2TKpwV38/Mzks8lL+GZAQ4u56g+pRwFLscbISvOJaZLXGBazED7UmfHlMbz/8oRNs8DpjM0PnO7x\nY5mYV+UyvNcJ+AelGMvt4fs+nudx1fNIpVIysrE4TLi4iVGIV7HsppNI8nvJCjtBJJ1t4SgHQcBw\nOOTUqVNya6Vt2xw7dky6251Oh0qlguM4RFHEAw88QCaT4cSJE9i2Lav9RKxExEJarZaMkRiGwWg0\nkveZprmvheTixYuyJlB8eDBNk0Yb/u1vR3SGSel4HxZKbCsUCsXXRwluhULxgnO/6/KAnUKfzchP\nk7wqTHJ8sMKHxvAzfsA/Cdr8z7rLvwzh1xMxQ7vLJ3sTRuMxbygaLBkx7Sz8hDfkK9GUd2hpXunk\nGY9HbDgpjmctslmHaTjlcV3jw77PHvNea9GfLej1egDousHV0YREYi7Kc7kctxSyvMuecGspLxfN\nCOFbKpVIJBL0+305NOl5Hu12WzrVgMxTP/zww7Tbbe666y557MbGhhTv9XpdXpNY3Q7Q7XalE767\nu4tlWdTrdRqNBr1ejyNHjkhXvd1uEwQBmUyG6hK8820+R1dS8n5QsRKFQqF4PlCCW6FQvKDEcczO\no+f4690OdwY+v5JMEIQhuf6EU+6ELxhpzkc2X8jE/Jwe8Vgr4tujIj9uRXzWjfm54YS2FvM/ZSJ+\nJqHzu9GYX+/HPFpvYRgGcQxP9kZ8tjXi6tjlD4MW3+fYpAYDKWI7nY7MbkdRRLfbZW+m8YHGhIFp\n02636Xa7DAZ9jthJut0O1WoVy7IZmGksa+5OZzIZKpUKzWZTnlfkr7e3t6XofvTRR3nsscc4efIk\npmniOA62bTMYDOQ2yVqtJt+jpaUldF0nk8lQLBbp9/uyJrDValEqlSgWi6TTadn3XSgUZNvJaDRC\n0yBrjZhM5rl30VRykG9EgC+utVcoFIqbQkRKXmKr3ZXgVigULxjRLOKfP3GB96cd/jBXoBeHmNkB\nuVnEZT3DPfGU10997F6azsUlvrMDb52M+Xs7Sf7jXoJ/spXnnRmHuwwYWRl+ds/kz9w09/gjMr5L\nk4gHN6/xs80ZPzYqsheleJdR5FQUU1iIVYiWEZg7zdlsltJsyruXdEaeSy6fl671ZDLhxIkTtFot\nOokkv+anuOJ6XLt2jSAIuHr1qjzWsiz29vbodDqUSiU8zyMIAlqtlhyMFPGOTGZeYSiW1kRRJFe3\n93o92QXu+z6VSkW2njiOQ7PZlKvhRd/3pUuX0HWder0uPwDkcjkp+m+0ZVPc/mxJpVJ85SsecRw/\n68cqFArFtwJKcCsUiheMz8ym/IuiQSObpnZlk5ZmMRjm+M+6yd9N5vl3Zx/jSnfEn6WSxJbOWTPk\no77B6WjMv+9kuXd9xJ+5AxoxnEnB33M8PF1nYqXoJXX+X3eXfDHD38sFfGwl4tZSlppukjSS+xbd\nBEEgW0nK5TK9Xo/ZLMJOm3xU12l+9XqXlpaIooh2u42u69QMjb/hROQCj5WVFfm6RF2gyIaLIcZa\nrcZDDz3E1tYWpVKJo0ePShfa933y+TzXrl0jn8/LCkFxHtH7LZx4cd5Op8Pa2i/1ZjAAACAASURB\nVBowF9SJxPzPushuHzt2jPX1dWAeRwnDkH6/j+/7+5zpm3GoH3nE5e1vf4RHHlHbKxUKxSHwEnS4\n1aSLQqF4wfj2RJLv+/xZstkyjeVb+av9AZEbc2dSp5+IeNMrX0ZjpvEboxFHJl3aRob3R1X+2/EO\n7lKR+4/2GDxa4W/acDoz5a1aRC4IeLSpEy3ZTJNJBoMRp4tJRsmQX+wOeHcmzbGvOtD1el0OMFYq\nFWAuSm3bJggCcrrOD0ynHC8W8P0JF9p9lhK6FNC+72EEfcz0PFKyuBkSIJ/PMxqNCIKAy5cvU6/X\nuXz5MoVCgTvuuEPmtU3TpN/vy7YTmLvuruvKIc6VlRWZvRYC3HVd6ZzPZjMSiQSz2Ww+4NnpYNu2\nfI5CoSCvT9d1+WHgMDZPnjnj8JGP3MaZM843fA6FQqF4KaMEt0KheMHQEzr/7HvfThxDI4KqDpq2\n/5gjwCsBqDKbwX3tkBOjCme8gM9dtXhyOmXZTvG+zoDs0OG9pZDausl7ukk+4EzYsWyWElAajfiR\nVIpSGMloRq1Wk+vXoyhidXWVlZUVeT+A0+/TikIaUYJfGqf46ZLBejolnWjRxz2dTsnlcjQaDY4c\nOQLMhx2FqyyiJQBvetObiKJICnsxBCkW3ohrWBTDwlUXDrdY7S6eXzjbQrRvbW1RqVT2DXaKxhXH\nceTad9/3923RhGdf86dpGq9+dfkb+C9AoVAoDqBqARUKheK5QdOgZjxVbB9kNgu5xzD45583+Qdf\nKfBXUmmOndDQkjF5K8l3Z8cMvB5Rcw+tDedaAT/uZnifN+NhNyDrT3hoHDGdzle8tzsdHpoE5PJ5\nLtsJYmJ6vR5hGBLH0NZ1lpeXpWDVNI3RaMzloU87YSIiy7ZtU61W2dnZkS0nMF80E4ahXFqzu7vL\nsWPH6Ha7wDz2sb6+LrdKRlFEtVqV1yDONZlMZI+37/v0ej2iKGJ5eXmfa10ul6VoTyQSNBoN+WFi\n8Xpc12U8HksHHfY73IvfP5OoSRzHfPGLV1WGW6FQKL4GSnArFIpvGgzDoFqAf/RWjY+9MeLVaxly\nSY1KAt6cNBilLT5hpalUC/zOHfDXafKLZpcfGg75/X6Oh4wCPzEqcjHp4DgOl+00f0tP8p+SMT+T\nHXGOQLrAHUPnV6OYrckEXdc5mc/wo8kh3sTjF4Mcv0qJnSCSkY5Go0Eul+Puu++Wglo4zN1ul8cf\nf5xyuYzjOJTLZWq1Gp7nMR6P5Tp2IYzFsptCoQDMhxKF6BfO9urqKsPhUMZbdF1nMBgQRRHT6RTD\nMJjNZgDyvEL4G4ZBqVSSDrdY9/613vOn45FHGvzQD32CX/u1i0p0KxSKm0O1lCgUCsULj6bBkYrB\n3bmQFT3Bf5dzaKLx30Twk90ULzML/FEnRVkPaeVW+I5MzF0rVZjB0cDlI9Upd+rzwcE7gV9Lp/iu\n2OD/8fLcgUkqlcIwDJaBd+kapa+K1TAMCfyATCqDOYz4Hs0j0Z23g4jKPhHvEK7x1atXMU2TXq9H\nt9sljmO5GEc0jIihSSGaDcOQK9iFEBZd4dvb26TTaVn1JyoEt7a28H1fXkOxWJRr6BuNhnTBgyAg\nDEMmk8k+Z1t8/41y5kyVf/pPv4d//I/ranBSoVAoboDKcCsUim9KRJd02TCoGjqfyGZw8Thqp7i9\nnKI+CfgbFzP86zt13pzWeaddJ5kscszr0Qt0GkGCRBRRG7ZJlIrchYWGJttAgiDAcRyCIGCm61zu\neXxiXOSH0wHvMHbJTTyypRJBEMiBS7HgJggCeZ22bXPu3Dmy2SwbGxt4nkc+nyebzQJzIS+2TIoe\nbUDGRgAp6E3T3Je3Fo0jR44ckeLbNE22t7epVCo0Gg0KhYKMo4gMuXDxRVzkZgW3pmn84A9ucPp0\nWQ1OKhSKmyMGpi/0RRw+yuFWKBTftFiWhWEYaJrGbdGUk/k0/5sb8quPhJgznQ+9fMa5rEsjClhf\nX+eDdZPWzMC3C/zCXpp/18ux40WMRiN5TtGVLZbNiK8jjskPlXzsbIps5DErL9HudGQF4NbWFrPZ\nDNu25W31ep37778fy7LY2NgAkFEO4UALh1ysbRebIcvlshTuhmHQ7XZl+4jjOAyHQzY3N/E8b76u\n/upVOp0Ouq4zHo8ZjUbkcjnpiMP8Q0SpVJLr4BeF+82iaRovf3kW7emC+AqFQvEtiBLcCoXim5Lp\nNKTuIgcXLcvi2jjij3smL78NfrcbcUqPeWcix7qVJo5hNNMo5Jd4pBuQN5N8X2nKHStlzk1MPG/e\nSpLL5+kYBqPxeF+uejDooyVn/Itmi8upFB8eT/jsbpcomuG6LoPBgDAMuXTpksxe+75PsVikVqtR\nKBSoVquyhlAs2xFiWLjj2WwWwzBotVoytx2GIUEQsLy8TDqdxvd96ZTbtk0+n+fUqVPUajWCIODE\niRN0Oh1M06TT6cg18SdOnKBer7O0tLQvSvL1ctpqg6RCoXheiYHoefp6HlGCW6FQfFPSnhj82y9F\nNK6b07zSSfLR5ZC/Xoz4iaM66+kktusz8SckkwaZRMzZns/7LusspRIMEmnOjhL86KUcl7R5xKOj\n63xUM9ibaVwIU+TzRWAew/C3tvhef0zK9Xh1e8hXZidpBgm2trY4duyY3CJ55cqV+bk6HQaDAZPJ\nRC6fMU0T27ZJJHQu7PSxLFuK416vx3g8loJaiHHXdWX7SLvdZjKZkM/nZYd3sViUTncURVy7dg2A\n8XhMIpHg+PHjAFLgCxG92MMt7j/IzfRzKxQKhWKOEtwKheKbkmoGfvwenaXUdZGoaXBvRsc0DWop\niKIQ35/w5bGOOxny358wuMMxyKU0fvaKxtsfjujHOr9xV8gdqXlo0PE8fsROcWlm8Y96Wb7YHssF\nMxrATOO3Qod0MOZHTkSspBOUy2Xa7TamaZLL5djZ2QHmmx9vueUWlpaW6HQ6VCoVgiAglUoxSjj8\nUXudkTYfnEyn0ziOI9e5C6cb5q0ly8vLGIYhoy5BEMge762tLfmzcOTF8YDMhbuuSzablT3jBwX2\nzYjrOI55+GFftZQoFIqbR7WUKBQKxYsDTYOaA8nkfpGo6zqtREI2ftTTVb7vMY1fmCZIpnWm0wkn\nbI3/dd3n7673eUN2xh2pKZvuhDgGQ9dhpvGH/ZBjwwnJQU86zadOnaIQTvgvxrss6zMSgz10PcHO\nzg7pdJpr166xt7cnoxqFQoHZbEapVKJWq2HbtnSyrWmf76rtsZyO5XZJ13UZjUYMh0P5BfPstWgs\nESJZuNlCdPf7fc6fPy/Fu3hekQuH+dp6ke8W13hYkZFHHpnw9rdv8eCDY3mbiqMoFArFHPX/ChUK\nxUuKRhzz4fGEv2EarJtJ7rRDPno65FjRZhmNhpkhxuOLSXhAt1jfHXB3xuaPBw7vLUJ7lqSshfxU\nKcF4yeS0tcxoNJQr0ldqNcKtazzZGqL191hfW8NxHM6dO4c/mdBNmBz9qkstnGTLsuj3+3Iwslwu\nMxgMuP1IkcFgQKlUwvd9JpMJS0tLciFNs9kE2JflFivk8/k8nU6HfD6PruvydrGu3nEcPM+jUqlI\nQS/W1pfL5X1r4m+0bfKZsHj8mTMpPv7xI5w+fd3hVnEUhULxrFGbJhUKheLFi3BTq5rGu3RYS87b\nN5JJg5c7UIlnbLoTfntnynvWk/yk4fHyXsj9A4cP7yQ4FXWYzeDn6zabw4CmH/MbmyP+pDMjkZhv\ngex0OvOaPTvHZzO3kqwe4S96Ps3IYAY8Fuicv/21XBnOa/jG4zFLS0sUi0XZdiI6tUXeO4oiuflx\naWmJXq/HcDiU2ydhPhA6mUwwDAPTNOn3+7LL2zRNLl++jOd5uK7L2travufa3t5me3tbPqdlWTQa\nDXzf37c6vtfrPWuBvHj8vKXEkp3fCoVCobiOsh8UCsVLAiH+NE3j2AHRJ+47lrV473FYSoS4msEv\nlEx2xzN+pWVyf6hRHcEfdpOshxr92Yxb0jF//9oSH1jbY30yj3Ds7u5yPJ/hryWhPSzyz5dfxWs6\ne3xbtMfF0jonv/QZ1vNzR3p5eRld13GceTe1aCcRDSSiV7vRaMitj6LhJAgCGQXpdrty2FGI7E6n\nQ6lU4vz58+TzeXk+XdcplUpsbW2RSCSo1WryfYiieQVitVrdVwkYhiGFQuGmHG6FQqFQfG2Uw61Q\nKF4SxDHUXWS9H1wX2rpuUPfmArGWgtHIJZt1OJIxeE0tzd9eGvDjS2O+Ywn+6akZv+xWuDs14bSl\n8/71Abclp2yNpuRyeRIJna9M4A/aCVIJg1d2u7zi2peojBu8Yfwkp40pxa+K5vF4TBAENBoNdnd3\nZfRjsRd7NBrJ+EcYhtJpTqfTUnxns/8/e28eJ0dd5/8/q6u6urq7+u6e7rknmZwkJCQQgXAEEAGX\ngBzLJbqA4L2ri/rVL677c/3pd3+7Xqtfd3VXwFtBEMGIIiLIFe4jCUfuydzTx3RP32dV1++PTlcm\nAUQxkDDU8/GYR7p7eqq7JjXdr37V6/N6e/YbgtOOimQyGTo7O80IiSiK+Hw+c9pkO4LSFu5tQd6m\nLbZfrh7wz8lfW2Lb4mBgGAbxeNFacGvRwhrtbmFhYXH4kijBDc/q5PSXTk1MVOEHQwJpreXoth1n\nSZLQdY0lHT4WRTzYbHCut8Z/LmiwOBzgumGFO+I6G3IOflDvYkZQmBFl7izCkuJuJMmOUS3S5bLT\n39uLUski2+1MT0+bj12tVmk2m3g8HjMKcqDoLhaLyLJsCvRyuWzGPKA1dbLtcPt8Pnw+nymkM5kM\nxWKRaDSKKIpMTEwQi8WIxWK4XC5KpZL5WKlUClVVSSQSZr687XIf2FpiiWmLN4pEosQNNzxFY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GSLq6upiYmDBrBev1OoqioCgK2WzWnH7pcChkCwpud8vpf61um9V0YjEXaMfKYN8x3R4cdeAx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\n4pFHerFardVNnK2trdXnxuO7OOaYMC0trcybN+9tlyk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" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "# Sets a matplotlib colormap\n", "cm = plt.get_cmap(\"jet\")\n", "cNorm = mpl.colors.Normalize(vmin=0, vmax=len(DAYS))\n", "scalarMap = mpl.cm.ScalarMappable(norm=cNorm, cmap=cm)\n", "\n", "fig = plt.figure(figsize=(13, 10))\n", "plt.title(rf\"Cell type: iPSC, medium: serum, $\\epsilon$={epsilon}\", fontsize=24)\n", "plt.plot(\n", " COORD_DF[\"x\"],\n", " COORD_DF[\"y\"],\n", " marker=\".\",\n", " color=\"grey\",\n", " ls=\"\",\n", " markersize=0.3,\n", " alpha=0.07,\n", ")\n", "for i, day in enumerate(DAYS):\n", " colorVal = scalarMap.to_rgba(i)\n", " for b in alpha_bins:\n", " ind_alpha = np.where(binned_cell_distribution_ipsc[day] == b)[0]\n", " colorVal = np.array(colorVal)\n", " colorVal[3] = b\n", " plt.plot(\n", " coord_ancestors_ipsc[day][ind_alpha, 0],\n", " coord_ancestors_ipsc[day][ind_alpha, 1],\n", " marker=\".\",\n", " color=colorVal,\n", " ls=\"\",\n", " markersize=1,\n", " )\n", "plt.xlabel(\"FLE1\", fontsize=24)\n", "plt.ylabel(\"FLE2\", fontsize=24)\n", "ax, _ = mpl.colorbar.make_axes(plt.gca(), shrink=1)\n", "cbar = mpl.colorbar.ColorbarBase(\n", " ax, cmap=cm, norm=mpl.colors.Normalize(vmin=0, vmax=18)\n", ")\n", "plt.show()" ] } ], "metadata": { "colab": { "collapsed_sections": [], "last_runtime": { "build_target": "", "kind": "local" }, "name": "Single-cell application for OTT.ipynb", "provenance": [] }, "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": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 1 }