{
"cells": [
{
"cell_type": "markdown",
"id": "ef3b72d2-9d5e-4732-9397-0713dc3df8d3",
"metadata": {},
"source": [
"# TS-DAR Tutorial for HP35\n",
"**Reference**: Liu, B., Boysen, J.G., Unarta, I.C. et al. Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space. Nat Commun 16, 349 (2025). https://doi.org/10.1038/s41467-024-55228-4\n",
"\n",
"\n",
"## TS-DAR Framework: An Overview\n",
"\n",
"In this notebook, we introduce **Transition State identification via Dispersion and vAriational principle Regularized neural networks (TS-DAR)**, a computational framework that utilizes out-of-distribution (OOD) detection to accurately and simultaneously identify transition states involved in specific biomolecular conformational changes. TS-DAR leverages a deep learning model to map protein conformations from molecular dynamics (MD) simulations into a hyperspherical latent space. This low-dimensional representation preserves the essential kinetic information while still capturing the protein's dynamic behavior. To distinguish metastable states from transition states, TS-DAR employs a VAMP-2 loss and dispersion loss function:\n",
"\n",
"1. **VAMP-2 Loss:** Encourages the latent representation to capture the slow dynamics of the system by maximizing correlations between time-lagged data.\n",
"2. **Dispersion Loss:** Ensures that the latent space does not collapse by promoting a well-dispersed representation of the macrostates.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "3394d9b7-811f-40e5-af00-07bdd6cddb9b",
"metadata": {},
"source": [
"## Before starting, make sure you have done the following:\n",
"\n",
"#### 1. Download and install Anaconda:\n",
"wget https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh \n",
"./Anaconda3-2024.06-1-Linux-x86_64.sh\n",
"#### 2. Create a new conda environment and install the ts-dar source code locally:\n",
"conda create -n ts-dar python=3.9 \n",
"conda activate ts-dar \n",
"#### 3. Install dependencies\n",
"!pip install matplotlib numpy==1.26.1 scipy==1.11.4 torch==1.13.1 tqdm==4.66.1\n",
"#### 4. Clone the repository\n",
"!git clone https://github.com/xuhuihuang/ts-dar.git\n",
"#### 5. Install the package\n",
"!python -m pip install ./ts-dar"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "885c8576-ddda-4d1a-b0d7-55c9c718f190",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from torch.utils.data.dataloader import DataLoader\n",
"from torch.utils.data import random_split\n",
"\n",
"from tsdar.utils import set_random_seed\n",
"from tsdar.loss import Prototypes\n",
"from tsdar.model import TSDAR, TSDARLayer, TSDARModel, TSDAREstimator\n",
"from tsdar.dataprocessing import Preprocessing"
]
},
{
"cell_type": "markdown",
"id": "9bcb65c7-d0a9-4ce8-a3fe-829e6a40522c",
"metadata": {},
"source": [
"## Load the HP35 Data:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "20a53a16",
"metadata": {},
"outputs": [],
"source": [
"data = []\n",
"for i in range(153):\n",
" filename = \"./HP35_data/ftraj_%03d.npy\" % i\n",
" if os.path.exists(filename): # Check if file exists\n",
" data.append(np.load(filename))\n",
" else:\n",
" print(f\"Warning: {filename} not found.\")"
]
},
{
"cell_type": "markdown",
"id": "a5b6f4f1-daa6-49df-ad5c-e041c6e1b377",
"metadata": {},
"source": [
"## Visualize the VAMP-2 Loss:\n",
"\n",
"The VAMP-2 loss is derived from the Variational Approach for Markov Processes, which aims to extract the slow collective variables from the data. Given an MD trajectory of length $T$, a batch of transition pairs $\\{(x_t, x_{t+\\tau})\\}_{t=1}^b$ with lag time, $\\tau$, is sampled. This produces two batches of data: $\\mathbf{B} = [x_1, x_2, \\dots, x_b]^T$ and $\\mathbf{\\hat{B}} = [x_{1+\\tau}, x_{2+\\tau}, \\dots, x_{b+\\tau}]^T$, which are fed into two parallel network lobes with shared parameters. Each lobe outputs SoftMax-transformed basis functions ($\\chi$) applied to the input features, yielding: $\\mathbf{X} = [\\chi(x_1), \\dots, \\chi(x_b)]^T$ and $\\mathbf{Y} = [\\chi(x_{1+\\tau}), \\dots, \\chi(x_{b+\\tau})]^T$. The remove-mean time-instantaneous ($\\bar{\\mathbf{C}}_{00}$ and $\\bar{\\mathbf{C}}_{11}$) and time-lagged ($\\bar{\\mathbf{C}}_{01}$) correlation matrices can then be calculated as follows:\n",
"\n",
"$$\n",
"\\bar{\\mathbf{C}}_{00} = \\frac{1}{T - \\tau} \\mathbf{X}^T \\mathbf{X} - \\boldsymbol{\\pi}_0 \\boldsymbol{\\pi}_0^T\n",
"$$\n",
"\n",
"$$\n",
"\\bar{\\mathbf{C}}_{11} = \\frac{1}{T - \\tau} \\mathbf{Y}^T \\mathbf{Y} - \\boldsymbol{\\pi}_1 \\boldsymbol{\\pi}_1^T\n",
"$$\n",
"\n",
"$$\n",
"\\bar{\\mathbf{C}}_{01} = \\frac{1}{T - \\tau} \\mathbf{X}^T \\mathbf{Y} - \\boldsymbol{\\pi}_0 \\boldsymbol{\\pi}_1^T\n",
"$$\n",
"\n",
"where $\\boldsymbol{\\pi}_0$ and $\\boldsymbol{\\pi}_1$ are mean vectors of $\\mathbf{X}$ and $\\mathbf{Y}$, respectively, given by: $\\boldsymbol{\\pi}_0$ = $\\frac{1}{T - \\tau} \\mathbf{X}^T \\mathbf{1}$ and $\\boldsymbol{\\pi}_1$ = $\\frac{1}{T - \\tau} \\mathbf{Y}^T \\mathbf{1}$. These correlation matrices are then used to calculate the VAMP-2 loss, which is defined as:\n",
"\n",
"$$\n",
"\\mathcal{L}_{\\text{vamp2}} = - \\left\\| \\bar{\\mathbf{C}}_{00}^{-\\frac{1}{2}} \\bar{\\mathbf{C}}_{01} \\bar{\\mathbf{C}}_{11}^{-\\frac{1}{2}} \\right\\|_{\\mathcal{F}}^2 - 1\n",
"$$\n",
"\n",
"Minimizing this loss drives the model to learn latent features that capture the slow dynamical modes in the molecular system."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "895b18c5-fc47-4792-927e-b76158300e2b",
"metadata": {},
"outputs": [
{
"data": {
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qbJXbmxq8h8KQgygwBaAAAXIm8Mlphujj11W57XetZyHHbzfiTgFx6SbEJQGt0k248LgJYcVfWb1xSzvruHkGsn134Fi9xsgOa4LCoCbw8WsCf0QrM1ZVOxWyAlKQEXAE2f5HkeN/FHmmozgneWKlkZmQglO47NcpCPmvy7PINwD5gZHqkVq3LfY3u6TK645N3oCQ3FT4Fhf+9yhAbnBdHEnoX+W2XTa/h7qndlVYb4IPTse0reLcT6LJgb/xX4aW2bbH8FefS6vc9uzNuytsaxCXshFDbmmFVT8dKtPNGBkbjMG3tELhrRtxKvN8i9vWxH2ARouQaqSgsBC/r16N1NR0xMREoV+vXghwdWL31h+w6KexeKVBIo4UnwmTJ/gG48GfxmKQvLCWsFtYiCWL85TJspywuwm//ZoG08JV5bYMkSO4/Bd3rl8m8v3zYPIpRrFPkYouybNfcQDq5sZXeSNx5NhO2d4HCArxV8nOQaEB6j/+lEOZVZ53cHiAyh0qtpBzZOt5m1CMYv90mAJOwuR/SkXTAjO7V3nszCavoDhsJ2Qa3mIfqOciky8i9r6qzLd5OzkTFdqH7JBT+F/7H5DnZ0KOjw/yzHJifIu24q4/T1XeHVR4Csvzt8BUVPa8fIqOoH/hkCoNy5T3dyLIaoJ11TduoWnSTgDyKOFIg/Owo80NVWredvtnaHj8rwrrk2K/xmb/SiIzO74uNVmCX3GBMk3yiDy9HycSz6vSsNQ5+hHyA/zhUwz4FPvAxwTEpBYiLa5LldsWFu3G/jgxeCWP4v+eQ/JMaLHja2wOqOzcv0GBnwmB5T8v/LdtoJVtN5dsa5gsaV+5AUHICQhGdmCo+o40Tj+OhJBU3PTsORV+eOZu3IAD8n9U+0vhLtBoEVJNUaW5vy7CvoVZCMqOUNGaZORg4zfz0WxoGK4YrOyOVezOGSouwqKlj+P++Fj0b3AWHo0chZiiOKT6JeHb0x/j/uJcvLZgAgb98pj8vBNnJX1fyC8MwNGclth6+hzk551ttQvO/Be7Naq6cZ+KWYtjYceQ4p+FPL9c5PvlIjInBucduLrKbTfUm4mjdfYgL1D+MwbyAwCTFB80+WD0mqfU6Kth4yve+GV5se8p/B0zGdFZgaiTHYiorCBE5gYiPL8F0hpcU+Wx66T/ifi4OggMj4JfeCh8w8LgGxqGzIJAbN9YtVkadmcnlcNlfm5ibGc88EOlhsUn6CQCGz+GFFMujvqGIwV1cdpUB9EZrdH7UNURjuXNZuNA9GbkBGQqg2rgWwTc9WfzKnNfpscfQ7GftOGyDPb/BiH/jlZdwuUT6aWNHA/7Frn5Pog9LQ8TYk8XIzbdhNgMIDHJhAC/ryvvDtr+NfqsPHO+uobFuskqoaobtyVCc06UaluZ5mKM7I3MpIdFIatBYxRFxwL14hAQH4/ghIY4EXIS9d/7FvsCLGsuAyeaHPkOJ95+DY3CuiA9p6D0cfrPP9F3YdVG6eM+E3G0WVuYfDPUo9jvtHrOKNqJ1z79Cz2xCjuOXFD23GOC1fKCoi146pFr0LXe2Yj1jUA9hCKsqBDZf/+JjrPfxwUXhmP12swK133+oDD4LN2I9AefRM/rLoVfWBh8zEx5YUEh1vU5C0nvv4fG77xbZiSojGhMnv4+MqJC0eOyC+Eu0Gh5Cf42RkWIY5rZG1USk3XkOx+07tS03Ai4/dj/XQrmYlEZsyU5RpKvJCOycrMLsHhWicmyKbG7qAA4ugE4sBJFW+fhlRDgYtyAdkv7Y3PqaUnrVdv3irkB4a0b4umYr7D5VAZy81qhKLsDAjLbISS7GXxxJn+rql/sJ8IOICX8EHICMpAdkIHcgAzk+Wdj2M5RlZodk+8pHMv5BNHJJrTMMCE6E6ibYUK9Uz7Y2mNQlb+4H/1qV4XuhcLAQBQGBCI9/FtsKbRyE9qcis5bv8WgpELZAkD2mf3jMP6KvbDKY3dbP8ti10YEfHCgz3OVb1+UjgZ1coD0w8Cp/UDaXvUoPvgXDvvFoKASw7Iv/Dv8EiVJ1UZidbp6HAw/gPP2n1dlVCji5EqccxyIyAYickyIzAbCc4C4UyYE+VRhdrZ9jffXFCIrBMgLKHnkBvgg39+ELoc2Ie/sP7HjqKUb75+4YOEm3F5ULrxhR1TJXsOSGRWJgNYdEZCQgJDGjRDZvDHCmzSGT3x9rLloKEIruXGnRUWh99KlQGYGik6eRFFaGgrT0hCxZi22HzxVZVup3yYawQ2vgI/kAqlHAI7vPYLApb9UafD8nngWA6+oaBqKiovwwIYZuGLpB9jrO1J1wxuE+uag45bPMPf8w3i12wXwK5eLWdivGf7+8R30wB/YeWRghc9LlhcV7Mf0lz+Hf4CFkZiFhbjrwHm4d94sDOh/FAWXjkJBZDwCTp9AwI8fI3P5UrwxvA5mj5iEwHL/zxZe2APrFnyG+N8+xI1vvYPje06XXnf9FpE4PHECTkSFouct11o8tizLGfMwsl59CocmjEfsmLEIatUaebt2ImX6+8hathw5DzxlcduauneyvIMXlHcgejhUPfq/EV0yisp8RNf+f9Mw5I4OSGwbrbp0VFJ3TqFK7M7JzMMvX2xAizYJVodS79+ajIBwX6AAKMpTQSXt4cymuseRH7wX2TiMjKAUnAw6icMh6fDPboMhO0ermjdlbtw/78P+zWlIDj2MOrn1EFAcZPXaqzr2D+3fwtGo3WqZb7EJMaeBuJPFuHVlV+xuKceOtfiLu+PWmYhLWm/xmEmxXbC50xirZQ46/jtd3WAr5X+fYPXawjLGWG6+Pbr5wefxUVY3U8fuOAZNO1s49qYUdNxc+bFt3t7HhOLQYmSHA8mRwN5IP5y93Q9Fva7HjpgLyp53TDBapy4B1n6FD88vRki+j0oODssDIrP9EJ9mQnx2B4fOWwj46FdlWMprdvbFjVB46+BKt2361ZcI6tzFsmm4YYT1DSU/xmSqcvvIy4cjMLFRmU1zDx1C5rx5VW7b8MMPEXXuORYPv3zGbNR79WmED+iP2DFjzG7c05G5bDmSH5iM/rdfV2E7U1ERFl86Fjsa32C1jbc++BUG/fgefPzKmZ2CQvzT53wk9uiIRu+8UyZqIwbv0IQJOLR2M876Y6lV07DowCJ89f69GPN7CAoLGyAvMBJB+afh738M0/vl4Pqxb2BQk0GVXnNY//4ovPTWUqPk/+NHyFpu/ZoNXl7xDbYveBrjfg9GeMqZrvjM2DC81y8XbS+ajIf6XlPJsZ/6T++yRqlE76cqPbaxj4Dpb6Hu6TMG82RkLArG3FXlttV9v6bR8gKjJR9hTk4OQkJCPCqxuyawJyIl0aX0pBzMe+VvxDW3Xqvn4Ja0SovsOVo3pqo6P9bw8YWq/3OxxnmfDD6B5Oh9SKl7CL13DEbLts0sRqV+fncjDv67B3UyX0PsqUJEpOUiIj0fvsX2m50iyOikEEQVZFe57b/xzdAwMBWJRcnwKZSeUh+YCn1QmOuLojw/tFm7BggJrXDzNWVnY2fPnghJzEdkfDb8g4vhH1Kknv2CirHtt4bI7H4DdsYMrGB2WqUuRsj6r/DTpcBOv2Lk5/uiTiZQJ0seJrQ8YkL7wzVkWBwwSvKR+dpw7ILwSARJvk5ODlBQdjCC6C1dqBU+08wspXdQu7YI6dIFAQ0aIqBhAwQ0KHn4xcRg69BBiGjXUUWVypuOgxPGI2P7FnRYtKyCYRGzs6F/X0R36mx129TN/6Lbst8rbFvVjTstMhaFVdy4T//6K9Y/+xH2droJ2cWSm3gmqtT838/R7clbETl4sFMNXnmz9crfLyFq+xHUzQROhgOn2yXigd4PWTVZjl6zudn6aufbaHkkpfTYuxPq4frWE6yaLGcaJTGr639aioyjxxHRsD66XXK+zZGsqu6dNFoeiisLliYlJdWqmdvtiUpVFZHqd31rRNULwamkHKQnZZc+yw1LjueoWarKKOX75iI3IBP5fnkoUI9c9exfGIQm6e1dWhzQ1ycX9UJOol5AGuJ9UhBelA1TTg6KsrOxb28etra9za6olFCV2fmtWRv8Ve9spIREITU4CieDwlWE45PFU9CkVyckvPV2he6FIxPvwv7V/+KHB6eiZYMoRBWno+PRb9D6wFcIyk9D1olAHFwaq0xDSNeuFc4pe/16ZRoan5+CsPj8koUBoUDLQUC7S7Fp/j/w/3Q+wvoPsPBrfxnyR12ByNsew4rtR/D3jnnYl7MMJ8KOoMCvGO0PFOOpL4qrNCzbE0qGqseeBiLLDXysyrBURpVRoauvRmiXzvCvWxd+Zo/Vx3NRePPlaNyzm1XDcnDtevh//iPOaV3yo8RUUIDi3Fxk/rEKR++9t2q9P/kEYWf1tmpYDt9zr1XTkfjmG1YNi7Ft2ID+qGe2bfL06aorqbJtnXHjluMfn/IyUrJCSqNKseE5qP/wQ1Uet8RwvI26p1PsMjtGN+Ka42uw58QetIhvgZ71e1boLnSVWZFuxC82LsPB08fROLI+RnQZUKG70FXHduW9k0bLQ6HRqrmolFG5OrqJRHa62BWRssUshQbsR3pIEg4HFuB0QL5K7A4pCEfHE32rNDupTV9CdPBWFEqalQyi9wEKfXxwHP7ovOklNOzawmI0TXJ5jq/fg6Zp0xAc0hL5RRHIKwpGDsKQHtQQWeEJVZ53+60fon5SJWbNxqiUT1Qd5MfGIy08Gicy89F5z9oqb74P9xmLf+u1QqCfL5rXC0PLuHC0qBeOHd/8gLtXfIiIAQMQM+aO0ptn6vQPkLFsGd47/3a8/vZ98DM32AU5wIYvULzsZez92gdBPQcg0UK3zOEJE5C3ZhmaD8+Ab8fLgLaXAC3OBwLORCQ2vvQYgr/5AcUZZ/pxfSP9kHv1Zejy8P/K6lhYjL/2HcPsrQuw/vjneOnjFDTp2g+J77xt4dh34cCGFRh/B+BnaoRo/+ZoEdQC3X3ro/u+44j4aHqVmkVeey3COneCb3gEfCPC4RcRAZ+QUOy+dTQiOrZH43crXvPB8ROQsW0HOiz5zWJkp6jYhHsnvI47l82wanYsav5fVGnP4CEIbN3KajdY/q7daPHLwkqjSmJYTkx5CYVHjpQuC0hMRJwNhsXStv6JCYh/+GGbTJajiAbZa9aiMDkZ/vXqIbRnj0qv1dmGo7bdBzzJaDGDmngUVc2HNvDmdmpZenIOTqfklD6nHs1Cbob1ytXWRs/5++Sgjt8xBPpk4WhBpypHF33V6sfSXKXSfZh80Da1U+Uj4PxP4dFe/eBfdyQKjh1D/uFjyD+ajPxjacjefQSH8qsekRWXIv8ZlP0P4WSdVljf9d4qz1t+gVdGmyEd0OZyK1Gpx4GVA6/Hl/V7YW/WGaPqayrGJ0m7SpOMy998Jck4JTISZw0fiKnnNkejuiHw9zvznoUNbsTzuYW4Z8PPyDTrLsuoG4c3e92MkeNvqHDDV0ap12j4BkUgbvtEHFm2XBmb8kZNTEPCuWnwvXQq0MVy1EDMVOE9k7Hj8/eQf/QAAhs2QaubxsE/qGIuW6C/L/q1SkC/VrfjzVXh+Oj8Z/Hg3GVWjr0MH13hg5tbP4IHz5NZf8t1g/34TemIKkua5dSLRtvJkyzexBtPelxFdg6Nn4DYsWZG6f3pKu+m8ZtvWL35i5YXT7gBz2flK82zbNVcvkN+foh75GEcuedei9csUaWESo5tIIYoYuBAZK1ejbQ9exDdogXCevWyybAY29prdhxFjmMtWlcVYqp6WUh4J94BjZYXIH3LnljhXLf7T94vkazKJiNd8tn2SqNSVY2eSwz6BwnBaxAacAzBAcfg55uOQj9f5AbVwd5DU9QoQUu5TrJc6gQdi9yjlvn7+uPchudicJPB6F+/D9Z/cTe2VjYCbse3OLA3HYWHvlLdMeWJ0xmR5eenIhy+ERGo51ugaghVNSqqxS0XI7xrZ/gEB8M3NBS+wcHwCQ5B9ubNOCrRn127VISl/KS62btKagr9kFunjMlSn5ePL6a1uwJPLPtURTTKR0iylv2Oab1uxsQODdEstuLnMrRjA+DhW/DAjz1RZ/cWROdlIC0oAumtOuCxSzqUrLdGRANENsoFnrwLSTO/K5PXJBGShCfvQuTOR4HIhtb3IZ9jUBA6jL4HOpzVtDlmtPFFwbN3I2/a7ArHLnjmXvyT/RbuaN7W4s26xaSnlFk6OGGChW6w39GyEsMiZkO6ySSyU/64tnSfOaK52vebbyDJwrHFZNkaVSoxLGchv3VrhNWtq/X/miNmx9Px1PtAbdCMRssLkEYSHR0Nb+v+kznYJE9KJmEVU3J4R5p6v0SytGs6+eQAppAqIzvvtvjbLCIlN/8zBqCZzxwEy7D7dzeixzCzHC81p1kaVrSeg871OuO6ttehX2I/hGUXI3/ffmTM+xT1D65H/XERWL02q2LdmAvD4bNsvQw4rJSqhoE3fP11RPTrC5/Q0NL/OCRCknTF3djwb5TVWjtdU5YgbsxUizfvrekdURgeg/D3p1vsBpNoRXJ4DLbENkdooB86NoxC58QodG5UBx0aROKWj0LwQ2Ikrvq7rNmRLp0frrgLh+q0Re9m1tuu3NgvbF8f/+zrhqSMXMRFBKv3W4qqlEGmF6rTGJH4HRELFyB73fozUY7u3eDzzUigTpOS9zmZXvV7ICaoPqbX2Yg3f1mA3LVnjh3coxvuWXYfYooaqPfpmCXRzBaz5Ghkx27NnRhV8sT/02oaaua+mjFHy0tGHWZmZiI8PNwjfs2Yd/9VvPGnokGrKOTnFOHk8SyLla+ryjfKDtuK41HbcSQ0TRX1Ox2cqhLLR254Cu1bWc9z2rJ7Dz7v8hRMUjrZAr13FOPmFZ2tji7aHbMBnet3Q2xqIfIPHEDx6dNaSeESiQpq3gyBTZsisEmTkuemTVVE4MCNNyGodSvr+UaV5L/YOyrqZFY+Hvx2I3IWL8aTqz9FuIVcKekGe7bXzeg/+hrc3rd5hZvxws3HcOesdRjUOhbjYzLRsDgLR33D8G5qOBbtTMG0G7tXHplyhK0/AF/fDLQeCvS9H4hrByRtA1a8BuxcCFz7aZXTD9mLjAS7f9n96JvQD3d0vh2t6rbCrpO78MGmGVhx5He8NuC1KkeEOZLz4+l42v9p7gA1c65mTIb3ULwtGd6ekX+yzWePr0JsYrjLyiSY13Qyp1lqZwzZNdqywducil9azURoq0LEhMYiwDdAdf8Zz6cykjFi0kok9uiLhhZGwR29awIyV6yUWhBWz7uqBOdGH32I8HMs1/kRsyT5L9bMTlVdMyWJwi8j2WxUVL3wXMQ//GCF7aRy9MwVe/HhH/uRmSfFPIHvO+cjfOY7KCiXpJx563hc/m8gvrzjbJzTIsbiscVsPTd/Gw6fPDO0rlF0CB4f1s51JsvcbP36OHDKbD5FiWQNfs5lJsvcbL28+mUczTozt2BCeAIe7PlglSartsPEbn2omT5MhideMfJPalClHs7EsT3pOLE3HYd2nERuRgGG3NGxiu4/E0LD0lEYeBApgXuxN/wYtoYn4/Idd1eab5T1X56UL3xRP6w+EiMS1c2tYXhDfLnlUxScXogTG/uUqaIcUpyBgoZ/IL/hYcy9eIkquJm3Zy9yt2wpfeRs2QIUFCF27Fj4+ftVyFWKGTtWJViXXIiPqg0U2LQkKhXQqBFSZ36oEpKtdb+JaQnr3dtl+S9Gl07DSiIkGbkF+OiP/fhgxV5k5JYYLMHf1wdv5Sdi+sKFyF237kw3WPfuGPvFejSKzrCx+y9NuyvKYcRMtb0YOLAKyDwBhMeXdBfaOPTdEcRMnd/ofLuH3RNCvAMaLWJ/11+n2LIj/xYeUMu7XdhYFXoWc5V8IAOFBcXaSenLWnyO7XFrKqxf0XgOQq3kSUmV85Wt5+CFtrdgcO+7VTTKnI6bMxE9eybC+meg6NLbUBAZh4DTSfD78RtkfbUM/Xq2w6G5NyJ3xw6Ycs8YSHOCWrWysry1eo576EHUvekm+JYbmSbTflgbkWVEpGwdkWVvd5Ikp2+KbYGkoIQSs+NTMslOdn4hPll1AO//vgenss9kigX4+eDano3QoWEkHv9+szJVdw5ohTZdu2P78QxM+2I9Fm9PUt1/VZkmWW8t4uVyxNg061sjhxZT1at+LzTxbcJIAyG1FBotL0D6lqurKrwx8k9M1rBxncqO/BvXSXX9bVh0yGrXn9zziouqnoT1dNCpMsv94YOWgTGIS9mEjluml8zttTmtwtxe++tuwdCeH8HPNwDF2dkoTE1FUWoqCpKTEffOdwgZUL6uUhOYLuqBw+PHw2fFSuRY6frzq1cPRcnJpaPvyiOmSQju2KmCyXL+iCz9UVWWuu8S6oTgnObRWLYzGSmZ+WVM0VXdEzDxglZoFB2qlkWHBartr5q2qkz3n0tzrLyE6vx+egvUTB9q5r6a0Wh5AdJIoqIqmhZXIDlZOiP/ZFSdGKroJiE44b8e8/e+hqYbH6iy+88UeRCXxZ2FDonnomP9nmgT3QaB8MeWd85FZKe6OG/q0IrVwu/6Ebf97IO9f1+kJn6Vyublka4/8667kvP2LdP1F9C4MYI7tEdIhw4Ilkf79vAND1cFGavq/pMIk7vV+TES0ge2jcPUG7qhTXwEdpzIwNtLduPbdWdyruSjuLxrAu4e2ApNy5VbqNHuPw+nOr+f3gI104eaua9mNFpegIxnOH36NCIjI13qzMUISTkAW7r+ugxshAZ9AvFP+krM2fk9Vh/egXyY1GRqh6T7b/Noy8U3N6diZas5uLfdjRh21v1l9p3511/wSzmNelbypMRESWHEwqNnko91u/7qP/sM6l5jeY6uygoy2tr9V911fqTat0SixGRNH9mzdLBC98Z1MePmnrj90zVYvjMZQzvE474L26iq7NYQU3V28+hqaWveRHV9P70JaqYPNXNfzWi0vABjYswImYbDBY2lqKAYO1cfx/pfD+Lk8Wy1rKquvw9Pv4bVv620uL99MZvQtns4jvyWWbamVEww2l4XjvcObEK9RhNLry1v+3ak//QTTn07xyaz5BMWhoAG9eEfEwv/mGj4xcSqbsT0OXOq7PoLbNzEqg7O6v6rTiQCJd2FEskqPyJUXk84vyWWbE/CTWc3rdRkVVdb80aomT7UTB9q5r6a0WjVcior0ZCXU4gtK45g0+JDyEo/k8Mj3X1VVUhf4/dHmePEFxaiX6Ef+ja+AC+eWo9Na17GJX+dKltuICwHP9Wtg4TEaHQsqI+U995D+o8/IX9PSbV1g6rMUqN3360QNVI1if76y6GuP3eY5kMX6eYTpLvQEm3qR5R5HyGEEOdCo1WLsVaiQfKsTiVlY8vvR5CfWzY5PKQRsLL4F5g2D7bY9XdgcypWtJoDE4rROTcf/bNz0D+mM1qfOx4+bYaqbPiAWa8geuZMBA0YgG5jx6oIlZin5Pffx5AZyzAwoR72P3VRxRP284NPUBBS3n/fYuHOysxSVXOx2dr152nTfEgulSA5WdJdWJ4dxzPKvI8QQohzYcHSWloZ3ijRIPMGirEyzJJEpCpOY2NCfuM0bGy4BGt9SroDZ7dfgDXfHyxj0iTxvcfwRrhu6zA8eSob17a9Huh5GxDT4syeiopUUnlg61ZoZKnK+fjxFQp/hvTsgahLLkHEkCHIXr3a4cKd0vVXvvBm3MMPuWXXnzNytM6bsgTtGkSqnCzz7kOJZo75bI0yYcsePN+mxHZWn9aHmulDzfShZu5bGZ4RLS9AGoj0MduK3GCXzBaTFV2m+09yruS1UZ29qLgA2+P+wcaGS5EeklRmHwVNU3HjU72xb/4/yDxxGuHxkWh2cW/8e3IzsBVo1uchoOe4CsfOWLJEmZyEV1+pdPSfFPmse921iBw2DAENGzq9cKendP05ipinmPBAlYclie+SkyXdhRLJmrZst811sOxta4Sa2QM104eaua9mNFpe4spPnjyJujbOdH94ZxryTwE9xzSrtETDb60/UYnrBr4+vmgXHI9D2cex+NPnEfZzmjJNUjVKylzueysBS4dFI6Eu0D0wGqaCAuTu3ImcDRuQs2Gjei44dMimhPZ699yDqEsudolZkveF9u6lNIu0UTNPZdHWE9h8pGTOxZW7UpThcqQOlm5bI9TMHqiZPtTMfTWj0fKSxpKfn6+ebWksWw/tsKlEg78pAJ3rdUbP+J7q0S0gGuHfjsbKLScRvfRfBA4YoCJT5XOs+jfKx+GfP0fOnhct1rKyJaFdzJMr86R0NfNEcguK8PRPW0pfv3x1Z8RFBjtUB6s26OZsqJk+1Ewfaua+mtFo1UKyAiTCEVhliYZru12J6y+4TFojsP5zYOH1MOVmov7qhspkmedYiWmS10aF9eyDuyrsVxLZg9q3V6MI7UloJ3q8v3wvDqWVGN1zW8Tgsq4N+R8wIYRUMzRatZAo/0ic8M21WqLBqM7eq20ikJUC/HgPsP0ntT47ORAFGUCCLRXWGzZUBkw9unVFcJs28AkMVAnpzhj9R6xzKC0b7y7bXTop9NOXdaDJIoSQGoBGywuQG6itlW2TD2Vg77cFMBVD5WFJ4rv5qEMxWVL9fWvnpXgg42Jg1o1A1pm8nryocwHsrDLHKn7Sk4gecSZZ3d0Kf+po5ok8+9NW5BWWTOY96tymaGWljpYu3q6bK6Bm+lAzfaiZ+2pGo+UFSCMJDS2Z/LcyTqfk4Ke3NqLgv9pYSWEHULQ3BwdeOlPOoSg8B4tbf4mJ8Sb4fXld6fLioBikFV2O5AVLbMqxCmrR0q1H/9mqmSeybEcSft16Qv1dLyII9wyybIrtwZt1cxXUTB9qpg81c1/NaLS8gOLiYqSlpSE6Ohq+5brzDHIy8vHD1A3IPl1S4f14+D7Mb/8O6viaEHC6KUILIpEdcBo+EfvwYEYuBv17TL1P0rMyTWfjxOJCFBxeULIzPz+kvPc+Et91LMeqJgt/2qKZJ5JXWISnf9xa+vqxYW0RERzgtP17q26uhJrpQ830oWbuqxmNlpdQWFhodV1BXhF+emcT0pNKEqNPhhzHgrbTMfp0CsbVOwfrBlyC5OAw1MvNQvdNP8Av7Vf1vrzscJzY1xlZ/+4/szNfX4SeczYyly/3+ByryjTzVGas2Id9KSWDGXo3jcblXROcfgxv1M3VUDN9qJk+1Mw9NaPR8nKKiorxywebkbS/pJZSVsApzG/3Htr4ZGNsvXPge+3naL9uPQoPSvddInyv+wJFn16PlJ/WIW1nGFB0xmSF9uqF+CceV0ntRoV1T5lcuTZw5FQO3l5SkgAv4xueHs4EeEIIqWlotLwYqQ2y7LPtav5BIc8vW5ms4tBsvLj/KLKb34mkoReVmY7GL7ouTLk5KM6Wue9Kcrn8GzZA/MMPqylwjBt3TedYkYr8b/425BSUfGY3n9NUTbtDCCGkZqHR8gLE/FiqbPvXvL3Y/tdx9XeRTyEWtpmBtLBjeC5xOKKWfoAjs95WcwaaFx1Nee+90vIMPgH+iBk7DjGjb4NvSIhHT65sq2aeilR9n/9fXl1MWCDuu7Bk9Kez8TbdqgNqpg8104eaua9mzJjzAqSRBAUFlWksm5YewrqFB9TfJpiwuNWnOBa1B0OaDsGlCYOQtClamazEd95Wowd9w8LUc+K77yJ8QH/4Bvmi+fSnUe+uCRZNljdq5qnkFxZj8g+bS18/clFbRIU4LwHeW3WrLqiZPtRMH2rmvprRaHnJyIkTJ06oZ2H32iSs+PpMZfaVzb7F3piNiA+Nx5NnP4mc1CBVdDR27BirRUeL84pR4GP7HHierpkn8/GqfdiTXJIA361xHVzdPdFlx/Im3aoLaqYPNdOHmrmvZuw69HCKi004svMkjh86hfxG/vAx+eC3j7ZIGEuxPvE3bKm/Ej7wwQt9X0BUUBTSU9LUuqqKjhb+9z5vzmHzdE6czsWbi0pMtfwoe3Z4R/hqzl9YG3WrbqiZPtRMH2rmnprRaHkwe9Yn4Y9vdyMjNfe/JUfg6+eD4qKShnM4YTP+TiyZOue2jrehV/1eZSZsdnRiZ1Lz/O/nbcjKL0mAH9G7MTomVJy7khBCSM1Bo+XBJmvh9M1o2ikWg0d3KJ1CR+YvlKl1CmIyML/RTMAHaB/THhO6TijdVkYH+oUFqsR3ycnixM6eyV97UzFvw1H1d93QADw0pE1NnxIhhJByMEfLQ7sLJZIlJmvYuE6o3zwKgcH+6lkmiW7SKQYFWdLnbEKIfwhe7PsiAvzOJEcXn9gHU16uGl14ePx4ZK9fj6LMLPUsRUil6Gjcww95dakGSX6MiYnx2MTRgqJiTJ63pfT1Q0Paok5ooMuP6+m61QTUTB9qpg81c1/NGNHyQI7tOqW6CyWS5VMuH0deyyTREtVqcLoF7hh6I5pFNSvznpSXnkRxYYnHzv77z9JyDrWp6Kh8sfz8/DzuP6WiYhP+2ZeGb9Ycwo4TGWpZ58QoXNerUbUc31N1q0momT7UTB9q5r6a0Wh5IFmn89SzdBdawljeK/IcXNXqqjLr8g8cQNqvG9TfPn4mNPv4HRTkBta6oqMyyiQpKQlxcXEeMy/Yws3H8Nz8bTh8smQqJcHP1wcXdWygnqsDT9StpqFm+lAzfaiZ+2pGo+WBhEUGqWfJyZLuwvLIcuGmHtdXcOpJzz0J/DeSNbpXJAK7nAfXdziR8hGppIxcxEUEo3ezaJtMkpisO2etw8C2cZh6Qze0iY9QEa13luzGS79sR7PYUAzt6L3lOAghxFOh0fJAGrSqg4iYYJX4LjlZ5t2HpmITVi/Yh6C6PmjdoWx3Utbf/yBjxWr1t19wEWJuv73az702YykilVg3BE9c3M6qSZKhx0kZeZj0wxZc0DYO00f2LC3f0L1xXXxwc0+M+WwNnv95Gy5sX7/aIluEEEJsg0bLA5EbbUT/bOz/Lgfzp21SOVnlRx0mXGkqU0/JVFSEEy++UPo6rmsu/HpeX0NXUPuiUtYiUu8u3a2W/++KTmgWG4YDqVnYn5qtng+o52xk5pXMLj/hppYVamTJ6zsHtMRV01apczqnRYxLr50QQogeNFoeSFFxEd7PeAXdzumPyJ39MeeltaXrJNJ15Jy/8VPG77is+Hz4+ZbkW6V/Pw9527arv4PqFCDqkiFAcO2ddFj64+3pl7cnKiXGTLaxFJGS17d/ugZPfL9Zva8yxJxZXF6/ZLkYP3fVrTZDzfShZvpQM/fVjEbLA1mXtA5HMo9gyrBh6DSysxqFKAnykrsl3YqbUkLx04Iv1fukSKmUbkh64/XS7eO7pcOn+02ozUiXXFFRkcphs3XESVVRqanXd0W7BlFm0agsHEjLxvZjGTh+OldtYykiNeH8lliyPanC8SRKJiZO5i3cdDhdHUvMWXl2HC8ZfSjRNXfUrbZDzfShZvpQM/fVjEbLA0nOTlbPreq0UjfqhDZlb76t6rYq877UGR+gKDlF/R2RmIOw1vWBpn1RW5HI0d97U7D7cDJaJtbDWc1jq+z6syUqde/sjZVGpaqKSPVvHYsL2sajSUwomsaEIaFuCAL8fNU++7+8VBk682MbNdWmLduNRtEhqguzOv5jSk1NVb8C+Z+5bVAzfaiZPtTMfTWj0fJA6oWWTI2z69QudKnXpcL6XSd3lb6v4OhRpH30cckKXxPiupwGuo6RUApqIxW7/vZZ7frLLyzGrqQMbD16Gou3n1Db6EalhEA/X+QXFVcZkRrXv6XFHCsxgXJ+EjWTxHfJyRJzJtuJyVq8PQnTbuzORHhCCHFDPNZorVq1CvPnz8fq1auxd+9eVQsjLy8P9erVQ8+ePXHrrbdi+PDhdu//hx9+wMyZM9X+U1JSEBgYiCZNmmDgwIG499570bx5c9QU3eO6IyE8ATM2zcCbF7wJX58zpqnYVIyZ/85U6+V9xx96BKa8/+putcpCYEQR0OUG1Eaq6vq7b1BrRAT7Y8vR08pcickq+G/eSFujUt0b10GflrFoEhOmIlPyiA4NxIBXljkUkRITKGZKTKIkvhvIdrKcpR0IIcQ98TF56HTf119/PWbPnl36OiIiAgUFBcjNPZMQfNVVV+HLL79EQMCZ6WdsKWAmJu3TTz8ts2/Zr+xfCAkJwddff41LLrlE65wTExNx5MgRJCQk4PDhw3CERQcW4f5l96N/Yn+M7jRadRdKJEtM1vLDy/HagNdwbloMDtwwQr3fL6gILS5Ogl+rPsCt81HbMLrf2taPsGh2pOtv+c7kKhPSvxt/rsWo1NoDJ5UB+vKOsy1GpcxNnrWIlC1myd46XM5Cvh/JycnqBw2Tbm2DmulDzfShZs7VzJn3a4/9NPr374+pU6di/fr1yMzMxOnTp5GTk4MDBw7g/vvvV++ZM2cOXnzxRa39ShTLMFl33XUXjh49Wrrv33//HR07dlR/33TTTTh16hRqikFNBikzJd2HIxeMxNlfnK2e5bUsH9joApwwu/Z6HTPgF2gCupYYr9qGmBPp+ht/fkurXX/mJkve0iouHMO7NsRjw9ri01t7o2GdYBWVEmNmji1RKSMitf14hjJkHSf/op4loqYTkRJTJUZueNcE9Vzd3YXyn1F8fDz/I9eAmulDzfShZu6rmcdGtKpCjNCsWbNUF9+ePXts3m7AgAFYvny5el66dGmF9dJN2aJFC/X3N998g6uvvtrmfTvTIZuXelh7Yi2OZRxDg4gG6BHfQ5V0SP/xJxx96CH1nqA6RWg2+AR8gsKAB3cCQeGoLaTnFODnf49hxoq92JOchS1PD0FYUMUec6lVJebnup6NcMNZjVXkKzjAz+lRqZqOSDmK/HeRn5+vutKZcGsb1EwfaqYPNXOuZs68X3tsjlZV9O7dWxktiUjpcPz4cfXco0cPi+vFuEVHRyMtLQ1ZWSVT3dQkYqp6xvdEks+Z+ZqKc3KQ9Oqrpe+J63IKKo2rwxVeY7IqMyySxC7dgHPXH8aibUnqtUFVCemXd0tA10Z1XJYnZUSkPPk/ppMnT3JkkwbUTB9qpg81c1/NvNZoSbK8oJu03qxZM+zYsQNr154pAlo+oiUmS+jWrRvckdSPPkLhf4YxrEUYwhv8Zza9pNvQWtHQEb0bq3pVP248ipPZJfl05gT5+6q5AWXaGntLJIiZkqluPDkqRQghpPrwKqOVkZGhjNC0adNKE+XvvvturX2MHTsWCxcuxLJly9S2jz32GOrXr6+S5sS83Xnnnep9kjDfuXNnuBsFJ5KQ+sGMkhd+vohvvb/k77pNgSbnwltHDr6tJlfeUeH9seGBuLRLQ1zZLRGHT2Zj/BeOl0jw9KgUIYSQ6sPjjdbu3bvRqlVJgU5zZGTgpEmTlHHS4fLLL1cJ9I8//jjeeust9TAfdSgRr1deeaU04b4ypNyEPAzM0+HEuAlGRVpZZ76+quXG9sa+/PxK8omSXn8dppySSE+dvm0QFFXSt1zcZYScQMnjvyTA8vvWXW7vudtyTZaWV1Y0dMbNJUVDpcvQzwcY3KE+rujWEOe1jEVQgL86XoeGEXjnhm7434LtZbv+6oao5YPbx6v3Vec1VbXc3T4nQdqa+Xl6+jVVx+dUXjNvuCZXf07mmnnLNbnyczLuA8Z6b7gmk4s/J3PNrL3fGXi80fL391ejBgTp0hMzJOUcnnjiCYwbN86ufT788MOqZtaYMWNUlEweBjLiUEYbynEkga4yXnjhBTz99NMVlsuHKnW/DEMYFRVVOrLRICwsTBk86T+WZD2DyMhIhIaGqmstLCyZbFioW7cu8rZuw+nvv1evfcLDEd14J5APmOCDlIQLUfzfMQXpk5apB6QqroE0MtFSjifHNdc4NjZWnZ+cp4Fcv+SryahP83w1Z15TUFCQGn4rDX/5nlM2FQ196bIW6NO8JM/qVFpqmWvqHueL2SPb4d/jOcjzDUJUoFTY91VRKvlMqvuaDGJiYtQX3mgX7vw5yfGltpw3XZMrPyfZr6wzNPOGa3L155Senl5GM2+4pur6nEQzb7smwZXXJJqVv6by5s8RvGrUoXwwu3btUhGpTz75REWfpKhpu3btbN6HfCAjRoxQBUuHDh2KyZMno3379upDke7ERx55RCXYy6jE3377TX3Itka05DxkWxnFcPDgQaf8YjAVFSF7zRpkHzmKzE8/Rf7OnWp5vTHXIvb0GyUbNeuP4pElBswTf9ltOXIKH686gO/WH1FRrapGDr5xXRdc1qVhpecuf8tnExxcdn7A2vrLTieiJd8R+c/KeO3p1+Tqz0nem52dXUYzT78mV39O5TXzhmuqjoiWGAdDM2+4JlM1RLQMzWQf5u9v3LgxRx1aQpxx27Zt8fHHHysH/cYbb2DkyJGqurvxH1xVPPDAA8pknX/++fj5559LtxOnLiUjpOq8JMGL6ZKaW5V1TYqDl4eB+TmUr9thfPjlsbZctj/9669ImvISCo4cMRcB/tHRiGmcBGz+b1m3myzWCdE9prOXVzZyUNb9uuUEPvpjH/7eVzL4wNaRg/GRJV+ays5FvmBinsVouVIba/VZdJbX9OdkjugmvxDlV2v5c/XUa9I9d2vLre1bsKSZJ1+Tq5db08yTr6k6PqfymnnDNbl6uaGZ8Z7K2qS9eG1ls3vuuUc9y+jBdevW2bSNdBHOmFGSSC45WJbEFiM3bNgw9ffcuXNRU4jJOnLPvQhq3RpNv/oSbdauUc/hfc9DYUoKMn77r/p7UCTQVq+CfXUltUul9hs++Av3fLVBPcvrOWsPY/rve9DvpaUY9/naMiYrPMgPkcH+eMfOoqGEEEJIdeNVES1zJNxnIAVLrdXFMmfnzp2q+7GqshDGuv37/xvRV81Id6FEssIHDEDiO2/D579fAyFduyLx3XdxeMIEJK1ZhoiL0uEjtbMCQ+EJIwel9MID32ys8P7m9cJw67lNcWX3RKzYlczJlQkhhHgMXmu09u3bV/p3eLhtRTrNw5eSQyW5WZYw8qukO7EmyF6zVnUXJrz6SqnJMpDXMWPG4MDSZchODkRYt5vgThgjBwdaGDko9a3M5xw8v009jOrTDH1bxpa+z1mTK0u0khWU9aFu+lAzfaiZPtTMfTXzSKMloxkqS0IXXn75ZfUs7zvnnHNs2q90C0rOjpRyeO+991QyvCWTtWDBAvX3WWedhZqgMDlZPQdZKGtRsrx1yfv8E4DEXnAn/tmXatPIwdeu7aIiWK4qGipfLBmRQvSgbvpQM32omT7UzH0188gcrc2bN+Pcc8/FZ599VmaKHUnUlUmmb7zxxtJcKyk6Konx5hjJbk899VSZ5TLyYNSoUervefPmqaKkRvegDPdcvHixMl+SyyUGbvz48agJ/OvVU895u3ZZXJ+3q2TkoX+XQXKxcAd2ncjAq7/uwMQv16vX0l1oCekKFKoyTY5OriyjSuRz9KJBt9UCddOHmulDzfShZu6rmUdGtIQ///xTPQyDJDUwZBSZeU2P0aNHY8qUKVr7lWKk27ZtUxNLy+hFeci+Jcpl5G9JqFFGHOqUjXAmoT17ICAhASnvTy+ToyWYiouR+v77CIgAQq8qGRDgKqqaIFkqsf+48RjmbTiC7f+NCLR15KDsz5XIF0vKFMhny1C77VA3faiZPtRMH2rmvpp5pNFq06aNmjB6yZIlqnSDTAQtBdLEcLVu3Vp1FUo0ytYuQ3NEcNnv559/ji+//FJFyGTfUqahUaNGuOCCCzBx4sQaM1mCj58f4h55WI06PDzhLsSMuUN1F0okS0xW5vLlSLgqET51G1X7fIN3D2yF3IIi/LDhKNYcOFnx3MUYB/o5POcgIYQQ4gl4VcFSdycxMdFpBdAES3W0JJIV1zkNkfe9C3S8Cq4eNTj+/JZl5huU/CpLdG1UB8O7NsTFnRtg3YGTpdtbGzloa1K7vRjV+aUCsbXaLqQi1E0faqYPNdOHmjlXM2fer2m0PNhoGaUestasQcaeHYhY9RjCojPgExoFPLATCAh2SXeh1LtqWz+izKhBIyJlPmqwdXy4yqG6tHNDNI4JrTIiJpGsx4e1c7nJEqTZS1ezjBxlmN12qJs+1EwfaqYPNXOuZs68X3tk1yE5g7SN8Lh8hJ9YA8T+lwfV6RqXmCxBcrJsGTU45apOuK5XY6v7ccbIQUeQL5XMd0X0oG76UDN9qJk+1Mx9NaPR8mS2/gD8+jhwqqSul8LXD4iyXBbBGe5/weZjNo0aDA7wq3J/xsjBmoC//uyDuulDzfShZvpQM/fVjB25nmyyvr4ZiOsAjF4EPHqk5LnFhcCip0vWO5F/D6fjmvf+xKd/HlCvJSfLEtU1atAZXzCZTJQ953pQN32omT7UTB9q5r6a0Wh5IsVFJZGs1kOB678AGvUCgsJLnm/4smT5r0+UvM9BUjLz8Mi3m3DZOytLRxFKJEoS3znfICGEEFI5NFqeyIFVJd2FfR+QxKiy6+R13/uBUwdK3mcn+YXF+OD3vTj/5WWYveYQDMPfPDYM4/o3x9IdSWq+wbUHTiIzr1A9y2sZNSgJ7ZxvkBBCCGGOlmeSeaLkOc5KLS9jufE+zaKjv+9MxrM/bcXelKzS90UE+eOeQa1w8zlNEejvi04JUQ7PN1iTSH88C/vpQ930oWb6UDN9qJn7akaj5YmEx5c8J20r6S4sjyw3f58VLJVYCA30Q3b+mS5HaX/X92qEBwa3QWx4kNuMGnQU+WJFRFhO6CfWoW76UDN9qJk+1Mx9NaPR8kSanAvUaQyseLUkR8u8+7C4GFjxGlCnScn7bCg6KqUaLBUd7dW0LiZf2gEdE6LcbtSgo0jy48mTJ9U8mPwFaDvUTR9qpg8104eaua9mzNHyRKSEw+DngZ0Lga9GAIf+AfIySp7ltSwf/FzJ+6x0F0okS0yWFB2VOQfDgvzV84ybe+KCtnGIDQ/El3ecbdVkecMXTObF5AgdPaibPtRMH2qmDzVzX81otDyV9pcB134KJG0BZl4IvJBY8py0tWS5rK+i6KhMn2Ot6GhKZj5W7684VyEhhBBCbIddh56MmKm2F6N4/x84fWQnIhNaw7dpH6uRLAPJqbKl6KjxPkIIIYTYB42Wp+PrB59mfRFYvyd8QkJKsterwCgmKjlZ0l3oqUVHHUH641lBWR/qpg8104ea6UPN3Fczdh16AdJIQkNDbW4sUli0thcd1dWMlEDd9KFm+lAzfaiZ+2pGo+UFFBcXIyUlRT1XRWpmHu77eoNKiJfRhXd8WjuLjupoRs5A3fShZvpQM32omftqxq5DL6GwsLDK90i06oFvNiIpI0+9bls/XHUfemrR0erQjFSEuulDzfShZvpQM/fUjEarFjFj5V4s25Gs/pbyDZ+OPgsxYUEeW3SUEEIIcXdotGoJ6w+exEsLd5S+fu3arqXJ7p5adJQQQghxd5ij5QVIIl9llW3Tcwow8cv1KPwv8f3OAS3Qr3U91Gaq0oxYhrrpQ830oWb6UDP31YwRLS9AGklQ0Jl5CM2RirePfrepdD7D7o3r4P4LW6O2U5lmxDrUTR9qpg8104eaua9mjGh5ATJi4sSJExZHTsz6+yB+/ve4+jsy2F/Naxjgx4+9Ms2IdaibPtRMH2qmDzVzX814x/USLM3VtO3YaTzz09bS1y9f0wWJdUOr+czcF84JZh/UTR9qpg8104eauadmNFpeSnZ+Ie76Yh3yC0uc+i3nNMGQDvVr+rQIIYSQWgWNlpcyad4W7EnOUn+3bxCJR4e1q+lTIoQQQmodNFpektAXExNTOnJi7vrD+HbtYfV3aKAf3h7RDcEBlU80Xds1I7ZB3fShZvpQM32omftqRqPlBUgj8fPzU897kzPx+NzNpev+d0UnNK8XXqPn5+6aEduhbvpQM32omT7UzH01o9HycGTOwlW7k/H579uxfPsJTJi1Dtn5RWrdNT0ScXm3hJo+RbdERpkkJSVxhI4m1E0faqYPNdOHmrmvZqyj5cEs3HwMz83fVlojC9hXOn1Oy7hwPD28Q42eHyGEEFLbodHyYJN156x1GNg2TtXGahMfoSaIfnvJbizZnoTrezdCaCA/XkIIIaQmcXnXYV5eHhYvXozZs2fjn3/+cfXhak13oUSyxGRNH9kT3RvXRViQv3qecXNPtfyTVfvV+wghhBDioUbrwIEDePjhh9Xj1KlTFdb/9ddfaNGiBQYPHowRI0bgnHPOQc+ePdV2xH7+2ZemugvHn98Svv91FRrIa1l+KC1HvY9YxtfXF3FxceqZ2A5104ea6UPN9KFm7quZQ3v/7rvv8Morr2DRokWoU6dOmXWnT5/G5ZdfjmPHjqnKq8Zj3bp1uPjii1FQUODouddakjJy1bN0F1qiTf2IMu8jFZG2WFRUxErKmlA3faiZPtRMH2rmvpo5ZLR+++03NSxy+PDhFdZNnz5dZfMLd999N+bNm4fx48er19u2bcMnn3ziyKFrNXERwepZcrIsseN4Rpn3kYrIFys1NZX/KWlC3fShZvpQM32omftq5pDR2rt3r3qW7sDyfP3118qEXXnllXjjjTdw6aWX4u2338Y111yjLmrOnDmOHLpW07tZNBLrhuDdpbtRXC4PS15PW7YbjaJD1PsIIYQQUnM4ZLSSk5PVc4MGDcosT09PV12EwqhRo8qsu/7669Xzxo0bHTl0rUZKODxxcTss3p6EMZ+twdoDJ5GZV6ie5bUsf3xYu9JSD4QQQgipGRwa/5+RUdJFJX2c5qxatUoVAPP398eAAQPKrGvUqJF6TktjorYjDO3YANNu7K5GH141bVXpcolkyXJZTyqHFZTtg7rpQ830oWb6UDP31MwhoyUJ8NK/efTo0TLLly1bpp67du2KsLAwi9sGBzN/yFHETF3Yvr4aXSiJ75KTJd2FjGRVjYwyiY+Pr+nT8Diomz7UTB9qpg81c1/NHOo67NChpPL43LlzS5dJdMvIzyofzRKOHDmintkgnIOYqrObR2Nou1j1TJNlG5InKDXemDiqB3XTh5rpQ830oWbuq5lDRuuKK65QJ/jZZ5/hkUcewU8//aTqZRl1sq699toK26xZs6ZMFyJxHPkMTp48yS+YBtTMPqibPtRMH2qmDzVzX80cMlpjx45Fu3bt1ElKPS0p8/Dtt9+qdTLK0NJoRIl+SbTr7LPPduTQhBBCCCFuj0NGKygoSE2vIyUcJPFdDFdAQABGjhypolzl+f3337F161b195AhQxw5NCGEEEKI2+PwrMP169dXUSzp55SRhDExMQgMDLT4XukuXLp0qfr7vPPOc/TQxAwxukQPamYf1E0faqYPNdOHmrmnZj4mduhWG4mJiWowQEJCAg4fPlzTp0MIIYQQF9+vOfukFyBeOTs7m0mQGlAz+6Bu+lAzfaiZPtTMfTVz2GjJScrDGm+99Rb69u2rkuaHDRuGH3/80dFDknJII5FJvPkFsx1qZh/UTR9qpg8104eaua9mDhktMU0RERFqCh6jSrw5t912G+69915VKX7Hjh345ZdfcPnll+OFF15w5LCEEEIIIR6BQ0ZLjJM4wcsuu0wZLnNWrlyJjz/+WP0dGhqKbt26qWrw8v5JkyZh8+bNjp05IYQQQog3G62//vpL1cQ6//zzK6ybPn26em7YsCG2bduGtWvXYvv27WrkocyD+P777ztyaGKGfAYy0pPzXNkONbMP6qYPNdOHmulDzdxXM4eMVlJSknpu06ZNhXULFy5UJz9x4kSVvS+IyZLXEtVavny5I4cmZojO0dHR/IJpQM3sg7rpQ830oWb6UDP31cwho5WcnKyey3cbbtmyBSkpKepvqRZvjlEt3pimhziOGFfJkWMSpO1QM/ugbvpQM32omT7UzH01c8ho+fn5qWcpVFo+P0uoV69ehWhX3bp11XNubq4jhyZmSCPJysriF0wDamYf1E0faqYPNdOHmrmvZg4ZLSnkJWzYsKHM8vnz56tQnJR1KE96erp6jo2NdeTQhBBCCCFuj0NGS4yUOMG33367tKtw9erVKj/L2nyGkhhvTN1DCCGEEOLNOGS0xo8fD19fX+zbtw/NmzdX+Vf9+/dHYWGh6iK87rrrKmyzZMkSFe1q3769I4cmZoieISEhTILUgJrZB3XTh5rpQ830oWbuq5lDRqt79+54+eWX1UlmZmZi3bp1KvcqICAAH3zwQYUkeek2lG5FYcCAAY6dOSlF9I+KiuIXTANqZh/UTR9qpg8104eaua9mDk9bfd9992HQoEH49ttvcfz4cVUl/oYbbrBY8mHZsmXo1auX+vuSSy5x9NCk3DQCkZGR/JLZCDWzD+qmDzXTh5rpQ83cVzOHjZbQqVMn9agKKfVQvtwDcU5jycnJURFEfsFsg5rZB3XTh5rpQ830oWbuq5nDk0oTQgghhBAXRrTKO8S9e/eW1taSqquSKE+HTQghhJDahtOMlkwwLWUeJA8rOzu7zDqZVFrmQ7zrrrswePBgZx2S/IeY2LCwMJpZDaiZfVA3faiZPtRMH2rmvpr5mBwsiZqfn49Ro0Zh9uzZ6rW13RkXIiUfPv74YzWRY21D5nw8cuSIKvR6+PDhmj4dQgghhLj4fu1wRGvEiBGYO3euMlj+/v648MILcdZZZ5UWJJWRiP/88w9+++03FBQUKEMmdba+/vprRw9N/kO0P3nypKpdxl8ztkHN7IO66UPN9KFm+lAz99XMIaMlNbG+++47dYLSNfjhhx+iSZMmFt978OBB3Hbbbapg6Zw5c/Dzzz9j2LBhjhyemDUWiSzKM79gtkHN7IO66UPN9KFm+lAz99XMoVGH0gUodOnSRU27Y81kCY0bN8aCBQvQtWtX9fqjjz5y5NCEEEIIIW6PQ0brr7/+Ui7wgQceUNXgq0Le8+CDDyr3KNs6wqpVq/D444+r5PqWLVuqgmNBQUGqX/Xyyy/HvHnz4CgZGRmYMmUKzjnnHDUJdnBwsDKTEol77bXXHN4/IYQQQrwbh5LhxXhI3tWaNWvQrVs3m7aRaXpkTkQxRVIozF6uv/760gR8QQqOybnIFEAGV111Fb788kubTGB5/v77b1x55ZU4evSoei3J+zInkkwjJPj5+alcM3dIhjeKrnGeK9uhZvZB3fShZvpQM32omXM1c+b92qGIlgyLFFJTU23eRhLPjJIPjiCTV0+dOhXr169X8yxKGX0R7MCBA7j//vvVeyQX7MUXX9Te9+bNm1WkTEzWRRddpEyXGLhTp06pKJeUsJBSFe6CNBDRk18u26Fm9kHd9KFm+lAzfaiZ+2rmkNEy5jM0jyxVhfFeS3Mh6nDnnXdi4sSJKufLMHxGLtirr76KG2+8sUwema0UFxfjlltuUcZNSlFIwn/v3r1LP4jw8HBl8t544w24C3LOKSkp6pnYBjWzD+qmDzXTh5rpQ83cVzOHjNZll12mQm+S2G6Lofnss8/UyEQxLZJH5UrEHAlG15+tyGhI6d6UbtF33nnHY34d6HZjEmpmL9RNH2qmDzXTh5q5p2YOGS2JKDVo0ECZrdGjR+OSSy5R5R6kX1PypeQC5G9ZJuuksKk4x4YNG7q8602S5QWZ/keHzz//XD0PGTIEMTExLjk3QgghhNQOHKqjJV12P/30EwYNGqRyr6R8gzysIYZMCoPJNo7maFlC8qdknsVp06aVdlHefffddhm07t2749ixY3j66adV92FSUpIyXn369FE5YDISkRBCCCHEZREtQUYb/vvvv2qEn6+vrzJTlh6y7uqrr8amTZtU3S1nsXv3btW9Jw8p8SA5W++//74aRfDCCy9g7NixNu8rLy8Phw4dUn9L4nvnzp3VvsRkiTEU4/Xtt98qs/Xmm2/CXZBrZzVgPaiZfVA3faiZPtRMH2rmvpo5ZVJp6Qr85ptvlBGREXkyai8tLU2ti46ORseOHTFgwADVzehsZNqf+Ph49bccU7ospZzDE088gXHjxmntyxgRKYiREnMlXYnXXnut2ueePXswYcIENYG2RLUk6tW3b99KjZs8DMwraRjJd4ZJNAypQVXLyyfvSfkJS8utvd/cFNu73N5zt/WadJfrXpOUGJFl5vvx9Guqjs9Jvgvm67zhmlz5OQnlNfP0a3L151ReM2+4pur4nMw185ZrMscV12RoZqyztP8an1RaF4kUyXQ8gkSMnElRURF27dqlSjp88sknaNasmer2a9eunU3bS+K81MwwkGjWmDFjyrxHSki0atVK5Z5J4VLZvzWeeuop1fVYHjGcknAvSOQtKipK1ecyrysm3bJSG0zMo0wRYCBROzGAMlLCSOKTRmrkviUnJ5dpJNLdKTW/JCpnTlxcnNLLvDSHNDIxrWIOzU2nmFkp2Jqdna1GY5qbOzHS0mWblZVVutwZ1yTILw0xQydOnHD6NRlfTDknKQ/iDddUHZ+TDBKRKLKMvjVMhKdfk6s/J6PsjBxLNPOGa3L15yTHlP/LDM284Zpc/TmJXnKesl95nzdcU6qLPydZbmgm12N+TUb6kDPqaFW70RIDdOutt6ovjyuz/e+77z5VgqFHjx5YvXq1TaFB+UBEcEGeRXBpBOX53//+p6rSS4OSD8nSeyxFtMTwGWbOMJvO+MUgf8uXzIjsudsvBnuuyZ7lOuduDOutV69embbhyddkbbkzr0ley3+WopthtDz9mlz9OckNQ24k5pp5+jW5+nMqr5k3XJOrPyfDbBmaecM1mVz8OZlrJvdx8/dLqShnFSx1StehPbja391zzz3KaK1du1ZFj8RwVYXhaMVwybQ+1gxU27Zt1bM4aHHc4r4tIQ5eHgbmN3TjP1zzdZbMoLXllra3tNza++05pquX65y7teX2nIsrtampa3LVcvneGpqVP1dPvSbdc7e2vKpjltfMG67J1cvLa+YN1+TIuVtbbmhVXjNPvyYfF5+7uWbGe6y9v0aT4d0V8y5Aya2ylQ4dOmgdx9kfCCGEEEK8B681Wvv27Sv9W/JJbOXCCy9Uz5KHYq1rc/v27epZol/uUGtLzJ6cB02f7VAz+6Bu+lAzfaiZPtTMfTXzSKNlS27Xyy+/XJoop1PzSqbukS5Dyb2aOXNmhfWSEPjuu++qv4cOHWo1FFqdSCMx+peJbVAz+6Bu+lAzfaiZPtTMfTWreZdgB1I+4txzz1VT+phPsSMJdTLJtJilGTNmlBYslREP5hh9sDIqsDwyB6Mx0vDBBx/EF198oUpGGF2QV155pUqQk9wrKSHhDsh1S+Io57iyHWpmH9RNH2qmDzXTh5q5r2Y1lgzvKH/++ad6GMM1ZbipRKHMh5rKtEBTpkzR3vfrr7+uKsxLvSwxbbfddpsaYWgMKZUh7p9++qnTy1MQQgghxLvwSKMlUadZs2ZhyZIlqnTD8ePHVSkGMVytW7dWXYVSQsLeaXIkWiVTCckE2FKOQiJoUm9D6nINHDhQRbrkHAghhBBCvM5oiaEaMWKEeriqtIR0LUpETB6EEEIIIS41WtJV5gz++OMPp+yHnEES8qWWlzsk5nsK1Mw+qJs+1EwfaqYPNXNfzWw2WqNGjeJoBjfFqHDrikJr3go1sw/qpg8104ea6UPN3FczLRtnlKZ39EGci2gqFeqpre1QM/ugbvpQM32omT7UzH01szmi9dFHH7n0RAghhBBCvA2bjdYtt9zi2jMhhBBCCPEymDXnJbBPXh9qZh/UTR9qpg8104eauadmHlnegZRFRkzEx8fX9Gl4FNTMPqibPtRMH2qmDzVzX80Y0fICJJEvLy+PSZAaUDP7oG76UDN9qJk+1Mx9NaPR8gKkkcj0QPyC2Q41sw/qpg8104ea6UPN3FczGi1CCCGEEBdBo0UIIYQQ4iJotLwEf3+Oa9CFmtkHddOHmulDzfShZu6pGT8VLxk5ERsbW9On4VFQM/ugbvpQM32omT7UzH01Y0TLC5BEvuzsbCZBakDN7IO66UPN9KFm+lAz99WMRssLkEZy+vRpfsE0oGb2Qd30oWb6UDN9qJn7akajRQghhBDiImi0CCGEEELcMRk+NTUVfn5+qFOnjs3bSH/omjVr1N/9+vVz5PDEbK6mwMBAznOlATWzD+qmDzXTh5rpQ83cVzPtiNbRo0dx++23Izo6GnFxcYiJiUHDhg1x77334vjx41Vuv2/fPgwYMAAXXHCBvedMyiGNRD4PfsFsh5rZB3XTh5rpQ830oWbuq5mW0fr333/RvXt3fPTRRzh16pRKIJOHGKy33noLbdq0wWeffWbTvpiw5zxEy4yMDGqqATWzD+qmDzXTh5rpQ83cVzObjVZubi6uvvpqJCUlqZNq0KCBen3ppZeqOhTGCY8aNQoTJ0506UmTsoj2WVlZ/IJpQM3sg7rpQ830oWb6UDP31cxmoyWRql27dqkQ2/3334/9+/fj66+/xrx581R3okS0oqKi1Am/++67GDFiBIqKilx68oQQQggh7ozNRuu7775Tz4MHD8Yrr7yCgICAMiXsJ0yYgLVr16Jz587KbM2ePRuXX3458vLyXHPmhBBCCCHeYrQ2btyooll33HGH1fc0b94cf/75Jy666CJltn7++Wf1t4TmiOuQzyUkJIRJkBpQM/ugbvpQM32omT7UzH0189Up5SC0aNGi0vfJSf/www+q61DM1vLlyzFo0CCVPE9cgzQS6bblF8x2qJl9UDd9qJk+1Ewfaua+mtlstKTWhCAJ71UhtbU+//xzjBs3Tpmtf/75B+effz5SUlIcO1tiEdE4PT2dSZAaUDP7oG76UDN9qJk+1Mx9NbPZaDVu3Fg9b9u2zeadS1L8Qw89pC5i06ZNqkDpkSNH7DtTYhXRNycnh18wDaiZfVA3faiZPtRMH2rmvprZbLS6du2qTubXX3/VOsCUKVPwzDPPqG137NiB6667zp7zJIQQQgjxOGw2WkYl959++qk0X8tWnnjiCbz++uvqb5kpmxBCCCGkNmCz0ZLCpJJ7lZ+fj1dffVX7QPfccw9mzJgBX1/OY+1sJJEvLCyMSZAaUDP7oG76UDN9qJk+1Mx9NbN5UmmZ13DatGk4ceKEOjF7uPXWW9W8Qt9//71d2xPLSCOJiIio6dPwKKiZfVA3faiZPtRMH2rmvpr5mJg5V20kJiaqwQAJCQk4fPiw0/YrH+HJkydRt25d/pqxEWpmH9RNH2qmDzXTh5o5VzNn3q/Zj+cljUW6dOmZbYea2Qd104ea6UPN9KFm7qsZjRYhhBBCiCcZrS1btqikd5kDkRBCCCGktuLSiBZDmNWD9C1HRkayX14DamYf1E0faqYPNdOHmrmvZgw5eQHSSEJDQ2v6NDwKamYf1E0faqYPNdOHmrmvZszR8gKKi4vVPJLyTGyDmtkHddOHmulDzfShZu6rGY2Wl1BYWFjTp+BxUDP7oG76UDN9qJk+1Mw9NaPRIoQQQghxETRahBBCCCGelAzfrFkzLF261BW7JlYS+lgNWA9qZh/UTR9qpg8104eaua9mLjFaksXfv39/V+yaWEAaSVBQUE2fhkdBzeyDuulDzfShZvpQM/fVTLvr8NixY3jooYfQqVMnVX9CJphu1aoVxowZg61bt7rmLEmlyIgJmeybo01sh5rZB3XTh5rpQ830oWbuq5lWROuPP/7AZZddhlOnTpUpSLp37171+OSTTzBz5kzcdNNNrjlbYhUWh9WHmtkHddOHmulDzfShZu6pmc0RLTFX11xzjZrpWk5MHjExMahfv75aL68LCgpw++23Y/Pmza48Z0IIIYQQj8BmoyWRquPHj6s+TTFce/bsQXJyMo4ePapCbw888IB6n5itV155xZXnTAghhBDiEdjcdbhgwQL13LdvX8yePbvMutjYWLz88svIyMjA9OnTS99LqgcxvxJd5GgT26Fm9kHd9KFm+lAzfaiZ+2pmc0RLugPlZMaPH2/1PRMnTlTPUtJeHqR6kM/Fz8+PXzANqJl9UDd9qJk+1Ewfaua+mtlstCQ3S2jdurXV95ivM95PXI+MmEhKSuJoEw2omX1QN32omT7UTB9q5r6a2Wy0JPdKCAwMtPqegICACu8nhBBCCKmtcAoeQgghhBAXQaNFCCGEEOIuU/Dceuutqhq8o++T5LPFixfrHp5YwNfXF3FxceqZ2AY1sw/qpg8104ea6UPN3FczbaO1Zs2aStcb2fuVvU+Km3JkhPMQPYuKipSm1NU2qJl9UDd9qJk+1Ewfaua+mmnZOKMivKMP4lxE09TUVGqrATWzD+qmDzXTh5rpQ83cVzObI1ocMkoIIYQQogc7cwkhhBBCXASNlpfAPnl9qJl9UDd9qJk+1EwfauaemmknwxP3Q0ZMxMfH1/RpeBTUzD6omz7UTB9qpg81c1/NGNHyAiSRLy8vj0mQGlAz+6Bu+lAzfaiZPtTMfTWzOaL1zDPPOP3gkyZNcvo+ayPSSGRuSakHwtCxbVAz+6Bu+lAzfaiZPtTMfTWz2Wg99dRTTj8RGi1CCCGEeDPaOVrOCrHRcRNCCCHE29E2WiEhIRg+fDhuvvlmtGvXzjVnRbTx9+e4Bl2omX1QN32omT7UTB9q5p6a+ZhsDFFdeOGFWLp0qSpcakSjevTogZEjR+L6669HvXr1UJ2sWrUK8+fPx+rVq7F3714kJSWppDY5j549e6q5FsUQOgvZ1w8//KD+vuWWW/Dxxx9r7yMxMRFHjhxBQkICDh8+7LRzI4QQQojzcOb92uZRh7/99hsOHjyIKVOmoGPHjqoLUeYzvPfee9UJXXbZZfj666+V2akOpk6div/973/qvPbs2VM6VFOEmTdvHi6//HJcffXVKCgocPhY3377banJckfks8jOzuZoEw2omX1QN32omT7UTB9q5r6aaZV3aNiwIR566CFs3LgRGzZswH333adqUIiZ+emnn3DDDTegfv36GDNmDH7//XfXnTWA/v37K7O1fv16ZGZm4vTp08jJycGBAwdw//33q/fMmTMHL774okPHSU9Px913342oqCi0bdsW7og0Erl+fsFsh5rZB3XTh5rpQ830oWbuq5nddbQ6d+6MV199VYXUFi5ciBEjRqj8LTEmM2fOxPnnn49mzZqpkYU7d+507lkDuPPOOzFx4kR07doVYWFhpcsbN26szuvGG29Ur+3p4jPn4YcfxrFjx/D888+zGBwhhBBCqrdgqXTXDR48GJ9//jlOnDiBTz75RJksyeOS6JIYFEma79u3L6qT3r17q+ejR4/avY+VK1figw8+QK9evZSxI4QQQgipscrwElmS5PhFixapfC4pchoUFKTCcmvXrkV1J8sLzZs3t2v7/Px83HHHHcpIvvfee+rZXRFTGxgYyJIZGlAz+6Bu+lAzfaiZPtTMfTVzybjGP//8E5999lm1JscLGRkZagTitGnTMHv2bLVM8qvsQRLtt2/frrbv3r073BlpJNHR0TV9Gh4FNbMP6qYPNdOHmulDzdxXM6cZLRn5J92H8hCzI0gkKzg4WI1IlLpbrmD37t1o1apVheWSLyb5YWPHjtXe57Zt2/DCCy+o5P/nnnvO7nMTk2luNM0T7qRMhvFBy0PWma+varmxvbHfrKwsREREVEjqs/R+QSJ05fetu9zec7flmuxZrnPuxmgT8/w+T78ma8udeU2CJI+Gh4eXvvb0a3L15yTvlR+B5pp5+jW5+nMqr5k3XJOrPydZLgPDDM284ZpMLv6czDWTfVh6f40bLZkj6KuvvlLRq7///lstkxOUk5WcLOlGvOaaaxAZGQlXFhszktTT0tLUCMiAgAA88cQTGDdunPb+5Pxl1KR0Hb7xxhvKvNiLmLWnn366wnL5cKXul2EIZUSjMWrSQAyAHFs0lnMxEC1DQ0PVtRYWFpbur6ioSDWW5OTkMo0kJiYGfn5+pcczkLmdZJvU1NTSZfK5iZZyPDmuucaxsbHq/OQ8DSTkKr8GpKGK0TNwxjUJdevWVV3Prrgm4wYo6+X8veGaquNzkh9Okosp+zG60z39mlz9OcmPLckVlfMSzbzhmqrjc5LzMTTzlmty5eck75fBaHIO8j5vuKZUF39OstzQTK7H/JrKm79qKVhqIEbmxx9/VOZqwYIF6rWxi9atWytzJQ8Z/VfdyAeza9cuVdJBkvJl1KMUNdWpYP/+++8rg3bRRRfh559/LrNuwIABWL58uc0FS8tHtOQ85D9cKYAmOWzO+sUgf0sDtjQqsqZ/MbjrryDZNiUlRRW4NaIMnn5N1pY785rktRgt0c0wWp5+Ta7+nOT/JbmRmGvm6dfk6s+pvGbecE2u/pwMs2Vo5g3XZHLx52SumRg+8/eLh3FWwVJ/nRF40i34zTff4NSpU6UnLo5UKsNL16CMzqtJRCipdSUmSBy0RKTE9En1ePObqTWkjMMjjzyiHO/bb7/t8PmIg5eHgfk5lE+uNz788lhbbml7S8utvd+eY7p6uc65W1tuz7m4UpuauiZXLTci1sZ/5N5wTbrnbm15Vccsr5k3XJOrl5fXzBuuyZFzt7bc0Kq8Zp5+TT4uPndzzYz3WHt/tRitfv36lbo9MQ+SdyUmZujQoW45v9I999yjjJaMdly3bp2aLqgqHn30URVGfOyxx1To0rxLSRD3K0hI1FgnIVRnfyi6yPHFHNb0eXgS1Mw+qJs+1EwfaqYPNXNfzWzuOjQcn+RoDBkyBHXq1HHswD4+qrCpq5AuTemjFWQE4rXXXlvlNkbXoA779u1D06ZNbXov5zokhBBC3B9n3q+1Q1G5ublqLkFn4EqjJQbIQJLEvRnxypLYJ4mI/DVjG9TMPqibPtRMH2qmDzVzX820qnAaSWLOetiL+WgGa7z88svqWbo1zznnHJv2u2zZskrPV+ZXFCQZ3lhmazTLlch5yIgLRzStbVAz+6Bu+lAzfaiZPtTMfTWz2WjJqABnP+xl8+bNOPfcc9XIR/MpdmSfMsm0zHM4Y8YMtUwKjkpivDlGsttTTz1l9zkQQgghhFSF+2Wxa1Sfl4cgyWySlC4hQPOaHqNHj8aUKVNq8CwJIYTUJDKISXJ2vR0JNMh1SnqPO08ZV1NI75ZRwqHajw0PpE2bNpg1axaWLFmiSjccP35cFUgTwyW1vKSr8NZbb7W5y9DTkYbjDqMfPQlqZh/UTR9qVjOaSXeQ3BukHFFtwah0TiwjRksqCkiBUqNnqzq+m9oFSx1FitCJSfr0009VN19tgqMOCSGkepC6iGKy5MYq1cppdGsvJpNJ5XZLr5c8pGpCgwYN3HfUoT1IdfTvv/9emavffvuttB4VcV4jkmkDJBeN/5nYBjWzD+qmDzWrfs3kHmOYLCmqXRswKp3XVPeYJxAREaHqgMqsINI2pIu1Or6bLjVaK1asUObq22+/LZ2DyAigyS8M4hxEU8lNM6p2k6qhZvZB3fShZtWvmZGTVdvuMxxxWDXSVSjT7hi1Nqvju+l0o7Vnzx5lrmS6nv3795f58KWe1bBhw3D11VerZ0IIIcRV0NiS8nhsMryEaL/66itlsP7+++8y5kouavDgwbjzzjvVdD3mc/8RQgghhHgzdhst6QueP3++MlfybITfBOkjFnO1YMEC9fq2227D8OHDnXfWpAxiZlkNWA9qZh/UTR9qpg81sw+WdXDPdqZttNasWaPMlUSwUlNT1TLDYPXs2RM33XQTbrjhBtSrV48fejUhjaS25SI4CjWzD+qmDzXTh5rZp5kEOWr6HGQGFZllxRPwqaZ2ZrPRevHFF1Ul9u3bt5cxV82aNcOIESOUwZL6VqRmaqdIHbHo6GiaWxuhZvZB3fShZvpQM/tLGEiCt+52nsKPP/6IX3/9FevWrcOGDRuQnZ2NyZMn2z3LS3W1M5uN1mOPPabcn3woMhTymmuuwciRI9GnTx+XnRxx7vyPpCzUzD6omz7UTB9qZh+TJk2q0BX2xhtvID09XZkSV7Jt2zaXRoheffVVLF++XHX3NWzYELt37/aIduZvTx2KJ554AjfffHOtqU9CCCGEeAIS3SlvtD7++GNltFw9v2/btm1duv9nn30W9evXR8uWLTF79myVpuQJ+OrUnpBoVkZGBh588EHlJi+77DJ1sTK3EiGEEFKbKCo24c89qZi34Yh6lteegpRfEkM2atQoFYm64oorVPBElhmlmebOnavMjBgbiVTJ1DV9+/bFnDlzLO5Tth0wYECZZaNGjVLL9+3bh6lTpyozJtUHmjRpgqefflp139mKHLtVq1YeN0jC5ojWiRMnVOFRydNaunSpKvYlow3lIVGuK6+8EjfeeCMGDhzo2jMmFZBGx6rTelAz+6Bu+lAz79Rs4eZjeG7+Nhw+mVO6LLFuCJ64uB2Gdqx8ehdXYU8yvHS/nX322ejUqZMyRTLIzcjzevTRR9Xf5513npqyRgp9/vDDD6oWppimiRMn2nychx56SHX7XXLJJRgyZIiaLUYibFKx4Pnnn4dXtzOTHRw+fNj0wgsvmDp06GDy8fFRD19fX/VISEgwPfTQQ6YNGzaULp89e7Y9h/E6RBuRXJ4JIYS4hpycHNPWrVvVsytY8O9RU9P/+8k0+uN/TGsPpJkycwvUs7yW5bLenWjSpIm695izb98+tUwekyZNsrjdnj17KizLyMgwderUyRQVFWXKysoqs0721b9//zLLbrnlFrW8WbNmpqNHz+iSnJxsqlOnjikiIsKUl5enfU1ffvml2u/kyZNd0jaceb+2K81eJln8v//7P2zevBmrV6/GXXfdhdjYWNW1ePToUZWw1r1799L3S98wcR0SepWIo04ItrZDzeyDuulDzbxLM+kelEjWwLZxmD6yJ7o3rouwIH/1LK9l+fM/b6v2bkS5/0pPk+4oQsl5evzxxy2ua968eYVlMsOLRL7kvi73f1t58skny0zkLJ5B6mtKOtKOHTvgze3M4crwPXr0UI/XXntNFSiVGlsyBFPCgQbjxo3D22+/rcKNV111Fdq3b+/oYYkHD9F1F6iZfVA3faiZ+2h26VsrkZyRZ/f2eYVFOJldgKk3dIOvb9kuJ3l954CWuGraKvR87jcE+dtX16peRBB+nHgeqoMuXbpYLQmRlJSkSjvJvf3AgQPIyTnTTSpIYMVWevToUWFZYmJi6ewy3vzddNpch/7+/rj00kvVw3xKnr/++kutl+iXPKRPVuptiel65plnnHV4QgghpErEZB0/7fgArjbxEZaX1y9ZLmYMKJnc2p2Jj4+3uFzqS/Xq1QsHDx5UZZwGDRqEOnXqqDwwqWE1b9485OXZblgjIyMt+gZjphlvxumTSgvyYUgUSx6SaPfJJ59g1qxZpSMZpOipJL/RaBFCCKlOJFrkCEZEa8eJDNVdWJ4dxzPUc93QAIciWtWFtUTwmTNnKpMlJRWkpJM5EuUSo0WcbLTuu+8+3HLLLejatSt0kGGh8kHJ4/fff1dRLhm9KP2yxHlfFGNYLrENamYf1E0fauZemjnaJSe5V/1fXop3l+5WOVnm3YfFxSZMW7YbjaJDsOzB8+FXrmvR1ThzCp49e/aoZ0vzFK9YsQLegE81fTdtToZ/8803VR9r586d8corr+DYsWPaB+vXrx9mzJiB48ePqwgXce4cV/yP3HaomX1QN32omXdpJuZJSjgs3p6EMZ+twdoDJ5GZV6ie5bUsf3xYu2o3WaKV8XAGUudKWLlyZZnlX3zxBX7++Wd4Az7V1M78dZPGtmzZgkceeUTV17jgggtUlEsKnYWEhNi8n+DgYFx//fX2nC+xgIyYkKTFuLg4zgtmI9TMPqibPtTM+zSTOlnTbuyuRh9K4ruBRLJkeU3U0TLmOpS8J2cYB5lib8qUKapWltTOFOO1ceNGLF68WNXN/O6771DdfP/99+ohSAFUY5mRliTFUKUigru1M5uN1i+//KKKlUql2KysLJW8tmjRIvWQ4Z4ymlA+mPPPP99lJ0sIIYS4A2KmLmxfH//sS0NSRi7iIoLRu1l0tUeyXIWMCJQCow8//LC6z4uJk7JNMqnzoUOHasRobdiwQeV8myPmTx5C//79tYxWdeEjxbR0NpDZskVgMV3ibI36E4aDlg/npptuUqbL1fMeeRqizZEjR1QdssOHD9eaX3/uCDWzD+qmDzWrfs1kWjiJeDRr1kz1oNQGnB3R8lZyzdqGlLWw1s6ceb/WbsEy35EYKYlwiat96aWXVN6WfMjykGUyIqFDhw7o3bs33nnnHVXSnxBCCCGktuHQzyup8ioTTEs4T0J3DzzwgJps2jBda9euxd13360c4eWXX64iYVK5ljgXceL8tawHNbMP6qYPNdOHmukjUSxGs9yznTlt7zIh5csvv6wiWtKHK12HEv0SwyVV4qVa/DXXXKPM2YQJE0oLmRLHEY0lZ47Vp22HmtkHddOHmulDzfQxAhzUzP3amdNtnLhpqSArCWsyh5DUzbrwwguVY5SLkWqz06ZNU7OBE+cgukr3LL9gtkPN7IO66UPN9KFm9uHtFdY9tZ25NF5mns8l3YuSt2WENfkFIoQQQoi345IpeAwkH0u6DD///HNV4Iz5WYQQQgipTbjEaP3xxx+q/MM333xTOiu3EcGKiIhQE0pLoVPiPJgAqQ81sw/qpg8104eaEW9pZ04zWrt27VLmynzyaMNcSYl7ydu6+eabVRX52lLXpLqQ/DdrM7ATy1Az+6Bu+lAzfaiZfYYhICCgpk/Do/CtpnbmkNFKSUnBV199pQzWmjVrKuReyUhEMVc33ngj6tev7/jZEosYIzul+Bp/BdoGNbMP6qYPNdOHmuljjDh05nyH3o6pmtqZttHKy8vDvHnzVN6VJLlLJVpzgyXucMSIEcpgdenSxflnTCog2p88eVLVA+EXzDaomX1QN32omT7UzP5Rh1JLi7hXO7P5E1m2bJkyV3PmzMHp06dLT1KQrsDLLrtMmashQ4aorkJCCCGEkNqOzUbrggsuUI7PMFfyt9TCEnN17bXXIjIy0pXnSQghhBDicWjFGMVktWjRQlV9l4dMykjcA4aL9aFm9kHd9KFm+lAz4i3tzOYjjBkzRkWvzj33XNeeEbFr5ERsbGxNn4ZHQc3sg7rpQ830oWaeOepQzqF///4q1cgT8K2mdmZzZfj33nuPJstNkUhjdnY2q+1rQM3sg7rpQ830oWb2z9tnjDq09eEppKamYvr06SofvHnz5ggKClIm6aKLLlID89y5nTE26wVII5EBCjIowZO+ODUJNbMP6qYPNdOHmtlHcXExJk2aVEGzN954A+np6Zg8ebJLj79t2zY19Z4r+Oabb3DnnXeiYcOGGDhwIBISEnD48GE1QG/hwoV46aWX8NBDD7llO6PRIoQQQuyhuAg4sArIPAGExwNNzgV8a3bU/VNPPVXBNHz88cfKaMk6V9K2bVuX7bt169b44YcfcPHFF6suP4MnnngCZ511Fh5//HFVs1OMmLvh0kmlCSGEEK9k6w/A1K7AJ5cAc0aXPMtrWe4ByAwuYshGjRqlIlEya0tMTIxaZszuMnfuXNxwww1o2bKlilRFRUWhb9++KopkCdl2wIABZZaNGjVKLd+3bx+mTp2qzJh0+zVp0gRPP/20isLZWvng0ksvLWOyhDZt2uC6665TcymvWrUK7ggjWl6ANGJWUNaDmtkHddOHmnmhZmKmvr4ZaD0UuOpDIK4dkLQNWPFqyfJrPwXaX1btp2WPXrt378bZZ5+tZnIRUyS5UKK98Oijj6q/pZRTgwYNkJycrKJKMl+xmKaJEyfafJyHHnoIy5cvxyWXXKLqbX7//fcqwiaV2Z9//nk4gjEIQHcEYbW1MxOpNhISEiTjTj0TQghxDTk5OaatW7eqZ6dTVGgyvd7RZJp1nclUVFRuXVHJ8tc7lbzPTWjSpIm695izb98+tUwekyZNsrjdnj17KizLyMgwderUyRQVFWXKysoqs0721b9//zLLbrnlFrW8WbNmpqNHj5YuT05ONtWpU8cUERFhysvLs/va0tPTTfHx8abg4GBTSkqK09qGM+/XjGh5AdK+MzMzER4e7r6/AN0MamYf1E0fauZmmr3fH8hMsn/7wjwgJ7UkklWuG0u97ns/MPNC4OVWgH+QfccIjwPGLtfWTLrhpGtNRzOZh1jymywho/sqnFp4uIp8PfDAA1i9erUq52ALTz75pIqKGciIweHDh+OTTz7Bjh07VETNHsaNG4cTJ07gmWeeUV2f7vjdpNHyAqSxZGVlISwsjP+R2wg1sw/qpg81czPNxGRlHHV8P9JdWNlyMWPVjGG0dJA5iY2uwvIkJSXhxRdfxIIFC3DgwAHk5OSUWX/0qO069ujRo8KyxMRE9Xzq1CnYg3Rtfvnllxg6dCgee+wxt/1u0mgRQgipPUi0yBGMiJbkZDXqVXG9LBdCYhyLaFUT8fHxFpenpaWhV69eOHjwIPr06YNBgwahTp06ai7jDRs2YN68ecjLy7P5OJEWpukzcqqk/pcuEiETEyhJ8t99951bz7FMo0UIIaT2oNklZ7Gkg4wulMT3678o230oI+hWvAbUaQLcvb7GSz3YgrVIzsyZM5XJevbZZ1UJBXPE4IjRqimefPJJPPfcc2qE448//oiQkBC4Myzv4AXIF0UaGrslbIea2Qd104eaeZlmYp4GPw/sXAh8NQI49A+Ql1HyLK9l+eDnasRkOVOvPXv2qGfJoyrPihUrUNMmq3///pg/f75DBVKrq50xouUFSCOR+ibEdqiZfVA3faiZF2ompRukhMOvj5ckvhtIJKsGSzs4c4JkqXMlrFy5skyi+hdffIGff/4ZNcGkSZOUyZJaXo6arOpsZzRaXoAxjYD0gbvlL0A3hJrZB3XTh5p5qWZiptpe7DaV4Y25DiVXyRmajRw5ElOmTFG1spYuXaqM18aNG7F48WJceeWVKi+qOvn4449VN6aYyd69e+Pll1+u8B7pSixfMNUd2hmNlhcgjUVGg0RERLjvf0puBjWzD+qmDzXzYs3EVDXrC3fBmZMjy4hAKTD68MMPY9GiRSgsLET37t3x66+/4tChQ9VutPb/V61ezuPVV1+1+j5do1Ud7cxHimm5bO+kQsM9cuRI6WSYzhzSK8Nw4+LitIf21laomX1QN32oWfVrlpubq6Z8adasmZowuDYgt3IxIRLxcWtzWsOYtw0pa2GtnTnzfs1vPSGEEEKIi6DR8gLk1wuLIepBzeyDuulDzfShZvbBiKl7tjPmaHkB0kikj5nYDjWzD+qmDzXTh5rZp5k7F+2sze2M9tdL+ualii/T7WyHmtkHddOHmulDzezP0aJm7tfOaLS8AGkk+fn5/IJpQM3sg7rpQ830oWb2Qb3cs53RaBFCCCGEuAgaLUIIIYQQF0Gj5SUJfW5dQdkNoWb2Qd30oWb6UDP74KhD92xnHHXoBUgjcXTOp9oGNbMP6qYPNdOHmunDUYf6VFc7o/31kirKKSkp6pnYBjWzD+qmDzXTh5rpIwndBQUFTIh3w3ZGo+UlyLBeogc1sw/qpg8104eaEW9pZzRahBBCCCEugkaLEEIIIcRF0Gh5SUJf3bp1OUJHA2pmH9RNH2qmDzWzj5pOhpfPa8CAAfAUfKqpndFoeQHSSIKCgvifkgbUzD6omz7UTB9qpo9oJeUd5CF/2/rwJCZNmoRBgwahUaNGCAkJQWxsLHr27InXX38d2dnZbtvOPLa8w6pVqzB//nysXr0ae/fuRVJSEvLy8lCvXj0l/K233orhw4dr7zc5ORlz587F4sWLsW7dOhw+fFh9CAkJCejfvz8mTpyILl26wJ2QERNy3nLtrKNiG9TMPqibPtRMH2pm/1yHYkbKG4c33ngD6enpmDx5skvPYdu2bS4tl/D222+jVatWGDx4MOLi4pCRkYFly5bh/vvvxyeffKJ8gc7xq62dmTyU6667Tsawlj4iIiJMwcHBZZZdddVVpvz8fK39+vv7l9lHaGhomf36+fmZXn/9dbvOOSEhQe1Dnp1JUVGR6dixY+qZ2AY1sw/qpg81q37NcnJyTFu3blXPrqSwqND0z7F/TPP3zFfP8rqmKC4uVvc7eS5PkyZN1L3H08mx8nnedNNN6vrefvttrbZRWTtz5v3aY38qSHRp6tSpWL9+PTIzM3H69Gnk5OTgwIEDyt0Kc+bMwYsvvqi1X/lF0KdPH8ycOVNFs7KystRDolv9+vVDUVER7rvvPixcuNBFV0YIIcTdWXRgES6eezFu++U2PLLiEfUsr2W5J7B//34V+Ro1apSKRF1xxRWIiYlRy2SdIL07N9xwA1q2bKkiRVFRUejbt6+6t9qaozVq1Ci1fN++feqe3bZtW9Vd16RJEzz99NNaNayCg4MtLr/mmmvU8+7du+GOeGzX4Z133mlxeePGjfHqq6/ixIkTmDVrFj7++GM8+eSTNu9XwpBi4syRkGK3bt2wYMEC9OjRA9u3b8dLL72EoUOHOnwdhBBCPAsxU/cvux/9E/tjSr8paFWnFXad2oUZm2ao5a8NeA2DmgyCJyDm5Oyzz0anTp2UKUpNTUVgYKBa9+ijj6q/zzvvPDRo0EB1s/3www+4+uqrlWmSVBpbeeihh7B8+XJccsklGDJkCL7//ns89dRTyM/Px/PPP+/QNUgakdCxY0e4JSYv5c0331RhP+n2cyaTJ09W+42MjHSbrsPKQsbEMtTMPqibPtSs+jVzZdehdA8O+XaI6a5Fd5mKist2OclrWS7rq7sbUbSSLjBbuw737dtXmhIzadIki/vcs2dPhWUZGRmmTp06maKiokxZWVll1sm++vfvX2bZLbfcopY3a9bMdPTo0dLlycnJpjp16qi0n7y8PK1rnTJliroX33PPPaaePXuq/Q8ePNimVCHztlFZO3Pm/dpjI1pVIUlxQvPmzZ26Xxnl4G5Vi405rjxtBElNQs3sg7rpQ83cS7PrfroOKTkpdm+fX5SPU3mnVCTL16ds9o28Ht1pNEYuGIkBXw9AoF9JZEiX2JBYzL5kttY2hla6mtWvXx+PP/64xXWW7p/h4eEq8vXAAw+owWjle4Cs8eSTT6qomPm9VAasSRL7jh07VETNVqRHSSJvBjfddBOmTZuGgIAAuON306uMloxAkBGIIvjs2SWN9O6773bqMST06W4hSunjllGXMgqDI3Rsg5rZB3XTh5q5l2ZispKykxzej3QXWlxet2S5mLGaGHXo7++vZRxkFL3RVVge+Qwkz1nSZiT/WfKgzTl69KjNx+nRo0eFZYmJier51Ck9rWR+QuH48eNYsmQJHnnkEZx11ln45ZdfSvfpTt9Njzda0r8swz3LIzU2ZJjr2LFjnXastWvXquRAQcpHVIWUm5CHgflkn0YCoFHLRNaZr69quXkCofxtvKd8YqGl9wvSqMrvW3e5veduyzXZs1zn3I1tZZn5fjz5mqwtd+Y1VaaZp15TdX1O5tt4yzVVtdzRayr//6St527s09L+JVrkCEZES3KyutSrWOpn18ld6rlOUB2HIlqWJoc2tLG2zPzZ0nvNlxvr4uPjLb4/LS0NvXr1wsGDB9UAsYEDB6JOnToqArRx40bMmzcPubm5Fs/T0rLIyMgKy40Cq2IQy39Otly/nLsk64sH6N27t4qyffXVV5Xux1wj494pz9bapDPweKMl7l3ENhqGzF4u4cMnnngC48aNc9pxpAbJiBEj1KhDqdN1++23V7nNCy+8oEZVWHPRhiGUkRzGqEmDsLAwRERE4OTJkypZ0LyxyugPuVaj+1L2J+clSLKieSORUSTSmI3jGYiDl23Mw6/SyERLOZ4c11xjCfPK+cl5GsivoOjoaDXqU0ZmGjjjmgSp2CujU1xxTcavFzk/OX9vuKbq+Jxk1I9EjuXcDQ09/Zqq43OSX+yGZt5yTa78nEQvc810r0n2Y0R4zN8v5/nlsC9L/780kHuG+f+j5stlWZkftqZiDP9xOD7Y9AGmXjC1TPehrJvx7wwkhCXg+0u/h5+vnzp/0be8mTCWyz3LHFkm6ywtt5S2ItdkXKtx/vI5ybnLcvNrkvcY12rsx3iP7EeWG9f6wQcfKJP17LPP4v/+7//KnPvLL7+sjJb5fswxX1Zspp21lBs5fvnPqfy5G5+HpeViCKUtyGA2Yz+iQflrMo4lSJsUjeXeLvuUdmf+fdIZDVklJi+isLDQtG3btjLJd5L05iiSqHfhhReqfcbExJh2795t03a5ubmm9PT00kfDhg1Lk+skadE8cdFIZLR1ufmygoIClWRovqyy9xs1Q8rvW3e5veduyzXZs1zn3EUzqZ8ibcZbrqk6Pid5lrYm+nnLNbn6c5I2Vl4zT78mV39O5TXTPffs7Gz1f788y3uMh3HM8g/d5b/t/83U6eNOpgmLJpjWn1hvyszPVM+SCC/Lf933q0uOa2m5+TK5V1labiTDmy/fu3evWib3S0v7HjNmjFq/adOmCsccNmyYWvfhhx+WWW4kw5svu+W/+7Ek35ffjyThy7olS5Y4pIsk6Pv6+qp7a1XvN28bxr1Tnsu3SSbDW0Ecv9TokJIO4m6lGu7IkSNVwp69yW7ijq+77jr89ttvyu3+/PPPaNGihU3byi8teRiYn0P5/mBr0yFYW26+vfwtvzIr62O2tE73mK5ebu38dZbbekzZtrJ+eU+8pupYbkQ0LJ2np16T7rlbW25t3/L/kiXNPPmaXL3cmma2nruxT2tt2BI6y6V0g5RweGXNKyrx3SAhPMFqaQdnHNfacmOZRHzM11u79vK6mC83aNq0qXpeuXJlmUT1L774Qt0HLe1H99x9ND4nyb+WrkuJkJojUal7771XRaCGDRtW5X7Mj2n0iJm3H2vX5AheZbTMueeee5TRkrwqKTZqKRGvKiTEKKMZpN6HhK1/+ukn1Q/sbhihVFc0EG+FmtkHddOHmnmnZmKmzm90PtYlrUNydjLqhdZD97juqruwJjDPLXKGZhKkmDJliqqVtXTpUlVgVHKzZHq6K6+8Et999x2qk99//12lA0lNLxkNKV3ekgy/aNEiVVy8Xbt22vW4qqudea3RkrkJDfbs2aNttMQdyxBWGb0oUSlJgpfK8O6INBbJo5AIjbv+p+RuUDP7oG76UDPv1UxMVa/6veAuGLlWzkBG78ko+4cffliZGend6d69O3799VccOnSo2o1Wnz59MGbMGKxYsQIbNmxQOXzSyyQGS6oLTJgwQXuexepqZz7SfwgvZOfOnWjTpk1p1VgJKdqKSCLJ7h9++KEKxcp0A5deeqlTGu6RI0eUCRQH7iw4fFwfamYf1E0falb9msloOJnypVmzZlanbfE27C3vUNvINWsbMljDWjtz5v3aI7/1thQLlVERgjS6c845R2v/4ozFZEmegPRHO8NkEUIIIaT24ZFGa/PmzTj33HPx2WeflSmYJr+CZJLpG2+8ETNmzFDLJKQoifHmGP2xMs9SeaQOhxQ8FXf76aefqjmdPAH+gtGHmtkHddOHmulDzYi3tDOPzdH6888/1cOoySJ1U6Qmi3k9mdGjR6tkPluRmiGvvfaa+luM1v33368e1pDRjI0aNUJNY4w6JLZDzeyDuulDzfShZvYZBt0paGo7vtXUzjzSaEnu1axZs1TpfTE7MvJACtmJ4WrdurXqKpTK7bpdhuULq504caLS95cvmlaTffNiMKW/mb8CbYOa2Qd104ea6UPN7B916M4jNWtrO/NIoyWGSqq0y8MerOX/S90QTxwbIOcs1WzdfYSOO0HN7IO66UPN9KFmNT/qsDZgqqZ25pE5WoQQQgghngCNFiGEEEKIi6DR8hIYLtaHmtkHddOHmulDzYi3tDO2ZC8ZOREbG1vTp+FRUDP7oG76UDN9qJk+HHXovu2MES0vSejLzs72yET+moKa2Qd104ea6UPN9DHm7aNm7tfOaLS8AGkkUkOMXzDboWb2Qd30oWb6UDP7MC9RRNynndFoEUIIIYS4CBotQgghhBAXQaPlJUmQrKCsBzWzD+qmDzXTh5rZR03rJccfMGAAPAWfampnNFpegDSS6OjoGv+SeRLUzD6omz7UTB9qpo9oJaUKZCSdMQ2PLQ9PZvbs2aXX8dVXX7ltO2N5By9AEvkyMzMRHh7u8V+c6oKa2Qd104ea6UPN7NNMkuEnTZpUQbM33ngD6enpmDx5skvPYdu2bQgNDUV1cPz4cUyYMAFhYWHIyspy63ZGo+UFSGORhiYNjv8p2QY1sw/qpg81817NTEVFyF6zFoXJyfCvVw+hPXvAx8+vxs5HjNZTTz1VQbOPP/5YGS1Z50ratm2L6mLMmDGIiIjAqFGj8Oqrr7p1O2PXISGEEKLJ6V9/xZ7BQ3Dwlltw9MEH1bO8luWewP79+5W5EKMikagrrrgCMTExapmsE+bOnYsbbrgBLVu2VJGqqKgo9O3bF3PmzLE5R2vUqFFq+b59+zB16lRlxoKCgtCkSRM8/fTTdpWkEOP4448/YsaMGSoa5e7QaBFCCCEaiJk6cs+9CGrdGk2/+hJt1q5Rz/JalnuK2RJ2796Ns88+G8nJycoU3XLLLSpBXHj00UexZcsWnHfeebjnnntwzTXXYMeOHbj66qvx1ltvaR3noYcewrPPPotzzjkH48aNU8skwvbkk09q7efQoUO49957VURr4MCB8ATYdegFyK+FkJAQtw6xuxvUzD6omz7UzLs0k+7CpCkvIXzAACS+8zZ8fEviFSFdu6rXhyfchaSXXkbEwIHV3o1oj15//PGHyuuS6FJ5fv75ZzRv3rzMMslpOvfcc5VBGj16tM05WevWrcOmTZvQoEED9Vq2b9WqlTJskjtmmLuquvrkmJGRkXjllVfgKe2MRssLkEYiIV1iO9TMPqibPtTMvTTbd9XVKExJsXv74vx8FJ88iYRXXyk1WQbyOmbMHThwwwjsPK8vfG0wD5bwj41Fsznf2jXqUJf69evj8ccft7iuvMkSpKtOIl8PPPAAVq9ejf79+9t0nCeffLLUZAkyx+Dw4cPxySefqChZp06dqtzHe++9h99++w0LFy5U+Vme8t2k0fKiaQTE5bvjL0B3hJrZB3XTh5q5l2ZisgpPnHB4P0GtWllZ3lo9ixkrroG5Dv38/LQ069Kli9VoUlJSEl588UUsWLAABw4cQE5OTpn1R48etfk4PXr0qLAsMTFRPZ86darK7ffu3au6H2+77TYMGTIEnvTdpNHyAqSxyBdAHD7/I7cNamYf1E0fauZemkm0yBGMiFberl2qu7A8ebt2qmffunUdimjZgz1z9sXHx1tcnpaWhl69euHgwYPo06cPBg0ahDp16igjt2HDBsybNw95eXk2HycyMrLCMiMCJwaxKqTLUI7/2muvwdO+mzRahBBCag26XXKWcrRkdGHK+9PL5GipdcXFSJ3+AQISE9Hil4U1WurBVqwZjJkzZyqTJQnsTzzxRJl1EuUSo1WdrF+/XpWoELNlCRkdKY/XX39dJcu7EzRahBBCiI2IeYp75GE1ulAS3yUnS7oLJZIlJitz2TIkvPmGR5isytizZ496ljyq8qxYsaLaz+fmm29Gdna2xSR7MWHnn3++yinr2LEj3A0aLS9AfpG4e2E/d4Oa2Qd104eaeZ9mkYMHA2++oUYfSuK7gUSyxGSp9TWATL/jLKTOlbBy5coyiepffPGFGo1Y3UydOtXicikRIUZLyj1cf/31btnOaLS8AGkkzhiBUZugZvZB3fShZt6pmZgpKeHgLpXhRTPJn3IWI0eOxJQpUzBx4kQsXbpUGa+NGzdi8eLFuPLKK/Hdd9/B0/GppnbGgqVegCT0SeKiPYmQtRVqZh/UTR9q5r2aiakKO6s3oi65WD3XZHehaFVYWOg0zWRE4PLly1VR0EWLFuH9999Hfn4+fv31V1x66aXwBkzV1M58TO7ekr0IabhHjhxBQkICDh8+7LT9yhQGMgw3Li7OqaFjb4aa2Qd104eaVb9mubm5asqXZs2aITg4GLUBw2jJSD537XJ1B8zbhpS1sNbOnHm/5reeEEIIIcRF0GgRQgghhLgIGi0vQMLErDqtBzWzD+qmDzXTh5rZB7um3bOdcdShFyCNxNaJPUkJ1Mw+qJs+1Ewfalbzow5rAz7V1M5of70kcTQlJUU9E9ugZvZB3fShZvpQM/uS4QsKCtx+pGZtbGc0Wl6CjDYhelAz+6Bu+lAzfagZ8ZZ2RqNFCCGEEOIiaLQIIYQQQlwEjZaXJPTVrVuXI3Q0oGb2Qd30oWb6UDP7YDK8e7Yzjjr0AqSRBAUF1fRpeBTUzD6omz7UTB9qZp9mNKbu2c4Y0fICZMTEiRMnOEJHA2pmH9RNH2qmDzXTh6MO3bed0Wh5Cfxy6UPN7IO66UPN9KFmxFvaGY0WIYQQQpzSFTdgwICaPg23g0aLEEII8aJpeIx8LVsensSAAQOsXkfTpk3hrjAZ3guQRhYTE+NxX5qahJrZB3XTh5rpQ83sH3U4adKkCrq98cYbSE9Px+TJk116/G3btlXLlDaTLVxHnTp13Lad0Wh50RxX/E/JdqiZfVA3faiZ92pWXGzCsV2nkHU6D2GRQWjQqg58fWvmnA2tnn766QrrPv74Y2W0nnrqKZeeQ9u2bVEdPOWk66iudsauQy9ARkwkJSVxhI4G1Mw+qJs+1Mw7NduzPgmfP/knvn99PX6buVU9y2tZXlNJ3TKdjK3J3fv371cGY9SoUSoSdcUVV5RGd2SdMHfuXNxwww1o2bKlilRFRUWhb9++mDNnjs05WqNGjVLL9+3bh6lTpyozJiUVmjRpokxhTX7G1dXOGNEihBBCNBAztXD6ZjTtFIvBozsgumEY0o5mYe3CA2r50DEd0aJbHDyB3bt34+yzz0anTp2UKUpNTUVgYKBa9+ijj6q/zzvvPDRo0ADJycn44YcfcPXVVyvTNHHiRJuP89BDD2H58uW45JJLMGTIEHz//fcqMpWfn4/nn39e65y/+OILZQbF/HXt2hX9+vVTuWnuCo0WIYQQotFd+Me3u5XJGjauE3z+6yqs3zxKvf75vX+xas5uNOtSr8a6EXX4448/VF6XpS7Hn3/+Gc2bNy+zLDMzE+eeey6efPJJjB492uacrHXr1mHTpk3KsAmyfatWrfDWW2+pnCvD3NnCjTfeWOZ169atMWvWLPTs2RPuCI0WIYSQWsPX/1uN7NP5dm9fVFCM3KwCFckyTJaBvO4xtAnmvLQWHz20En4B9kVZQiMDce1jvVAd1K9fH48//rjFdeVNlhAeHq4iXw888ABWr16N/v3723ScJ598stRkCbGxsRg+fDg++eQT7NixQ0XUqkLeL5Gxbt26qalzJKr1/vvv4+2338aFF16IjRs3onHjxnA3aLS8AAmZxsXFuXXo1N2gZvZB3fShZu6lmZisrFN5Du9HugsrWy5mrDqRPCh/f3/txO4uXbpYjSZJ/tKLL76IBQsW4MCBA8jJySmz/ujRozYfp0ePHhWWJSYmqudTp07ZtI/77ruvzOt27dqpEZWRkZF49tln8corr6guTXf7btJoeQGS/FhUVOSRdVFqCmpmH9RNH2rmXppJtMgRjIiW5GRJd2F5ZLkQHBbgUETLHs2MRHgdzeLj4y0uT0tLQ69evXDw4EH06dMHgwYNUiUUZJTehg0bMG/ePOTl2W5YIyMjKywTYyjIZ+0IY8eOVUZLukHd8btJo+UFSGORBEZx5vyP3DaomX1QN32omXtp5miXnORoyehCSXw3z9ESTMUmtTwyNhg3PnNOtedoiWkwzIutWNN35syZymSJgXniiSfKrJMolxgtdyHmv9GSWVklJtfdvpuMZRNCCCE2Iuapz9Utsf/fFJX4fnxvOvJzC9WzvJbl517V0iMS4Stjz549pXlR5VmxYgXciX/++UeZJnetDs+IFiGEEKKBlG6QEg4y+lAS3w0kkuVJpR0qQ+pcCStXriyTqC6lFWQ0YnWzb98+VccrOjq6zPIjR45g/Pjx6u8RI0bAHaHR8hLYJaEPNbMP6qYPNfM+zcRMSQkHd6kM72xGjhyJKVOmqFpZS5cuVcZLRvUtXrwYV155Jb777rtqPZ/ly5fjzjvvVAVTmzVrpkYdivmaP3++6jKUkg9yzu7Yzmi0vAAZMWEtoZFYhprZB3XTh5p5r2ZiqhLa1IU7IIYhICDAafuTEYFibh5++GEsWrRIVZ3v3r07fv31Vxw6dKjajVb37t1xzTXXYO3ataqshNTzkuR8SdS/7bbbcN1117ltO/Mx2Vqvnzil4UqYMyEhAYcPH3bafuUjlOq6MkTX3X8FugvUzD6omz7UrPo1y83NVdEOiXwEBwejNmCMOuToVtvbhkwFZK2dOfN+zWR4L0C+XCdPnrR5jitCzeyFuulDzfShZvbhaJmE2oapmtoZjRYhhBBCiIug0SKEEEIIcRE0Wl6CbpE6Qs3shbrpQ830oWbEW9oZW7IXICMnZIJOYjvUzD6omz7UTB9qVvOjDmsDvtXUzhjR8gIkkS87O5uJoxpQM/ugbvpQM32omT7GvH3UzP3aGY2WFyCN5PTp0/yCaUDN7IO66UPN9KFm9lFcXFzTp+BRmKqpndFoEUII8Upo1Ig7tAkaLUIIIV6Z4CzVzAkxp6CgQD37+fmhuqDR8pIkSFad1oOa2Qd104eaVb9mchOVh3QL1SbYxqqOZqWnp6uK8DJwoLq+mxx16AVIIyk/ozmpHGpmH9RNH2pW/ZrJ9nFxcTh27Ji6qYaFhdUaE8IonmWDJZEsMVkyR6JMq1Od300aLS9pRNJ4wsPDa81/Jo5CzeyDuulDzWpGs6ioKOTk5CAlJQXJycmoLcnwUrKAWEZMt5isyMjIav1ueqzRWrVqFebPn69m8d67dy+SkpKQl5eHevXqoWfPnrj11lsxfPhwu/cv4r/yyiv49ttv1QSUEl7s0KEDRo8ejVGjRrnVf5jSWLKysmrVrzZHoWb2Qd30oWY1o5ls16BBAxXZMvJyvN1kpaamIiYmhmbLAtKVXL7OWHV9Nz3WaE2dOhWzZ88ufR0REaEal8y2LY958+bhqquuwpdffqldxE3Czf369cPu3bvVa/kQ5MP4448/1OPHH3/EN998U63JdIQQQuzP16oNRkvudcHBwTRabobHfhr9+/dXZmv9+vUq+iRJjxImPnDgAO6//371njlz5uDFF1/U3vc111yjTFZiYiKWLVum9i9Ga/r06Sr0OHfuXDzzzDMuuCpCCCGEeBM+Ji8tNHLTTTdh1qxZaN68Ofbs2WPzdhKtuuyyy1QY8e+//0avXr3KrJfuxIceegihoaHK1OmU7xfjJtE26SM+fPgwnF10Tfqd2TVhG9TMPqibPtRMH2qmDzVzrmbOvF97bESrKnr37q2ejx49qrXd559/rp4vuOCCCiZLGD9+vOqmlLL93333HdwBaSCS+Mkvl+1QM/ugbvpQM32omT7UzH0181qjJcnygkS0dFiyZIl6Hjp0qMX1Esnq27ev+nvx4sVwp9ogXhqcdAnUzD6omz7UTB9qpg81c1/NvMpoZWRkYOPGjRg3blxpovzdd99t8/YyBFiGAgsdO3a0+j5j3datW+EOSCOR/DR+wWyHmtkHddOHmulDzfShZu6rmceOOjSQpPVWrVpVWB4SEoJJkyZh7NixWqMNDRo2bGj1fcY68/cTQgghhHid0ZI5reLj49XfaWlpql6KDHF94oknVGRLBxldaN5FaA1jnUTQKkPqesnDQGp9GQZNEu2cCQvV6UPN7IO66UPN9KFm+lAz52lmBFKM+3atNlpNmzbF8ePH1d9FRUXYtWuXKunw+OOPY8aMGaqoabt27Wrk3F544QU8/fTTFj9YGc1ACCGEEPdFfAVqu9EyR4rStW3bFh9//DHq1q2LN954AyNHjlTV420ZVSBl+A1kVKE1jHUy+rAyHn300dKaXkL9+vVVhEvOU6oVOwvpX5bRldKlyREntkHN7IO66UPN9KFm+lAz52omkSwxWVIA1lG8to7W/v370axZM/X3mjVr0KNHjyq3kUR4mcJHWLBggdWRh4888gheeukllRT/77//oqaROiAyRFVGTxhzOJHKoWb2Qd30oWb6UDN9qJn7aua1nbnG7NyCrQVLpfioYbS2bNli9X3Guvbt2zt8noQQQgjxXrzWaMlE0Ja6BKtCCpUKCxcutLhehoKuWLFC/T1w4ECHz5MQQggh3otHGq3CwsIq3/Pyyy+Xjko855xzbN73jTfeWFqMdO3atRXWT5s2TYUbZeThlVdeCXdA5l+cPHmyeia2Qc3sg7rpQ830oWb6UDM31szkgaxfv950zjnnmD799FPTkSNHSpcXFRWZ1q1bZxoxYoTknanH/fffX2F7Y93kyZMt7r9Pnz5qfePGjU3Lly9XywoKCkwzZswwBQUFqXWTJk1y4RUSQgghxBvw2FGHf/75p3oYxUnDwsJUpCk/P7/0PaNHj8aUKVO09/3NN9+gX79+qhhq//791b6lPpex7yuuuEIVQyWEEEIIqQyPNFpt2rTBrFmz1LyEUrpB6mhJsVIxXK1bt1ZdhbfeeqtWl6E5DRo0wPr161X345w5c1S+l+xbJpm+7bbb1L45fJYQQgghtba8AyGEEEJITeORyfDkzCTYDzzwgJrrUSJuUp5i8ODB+P7772v61NwSKWQrkcjKHjojVL0BmUbqxx9/VAmhF198sZrOytBi2bJlVW4vv9M+/PBDnHfeeapIsHSzS325p556CllZWfBGHNGsqvYnj2+//RbexqFDh/Dmm29i+PDhajYPST6W71qHDh1wzz33YO/evZVuXxvbmSOa1dZ2tmrVKjUrjNwHW7ZsqWpjiW4y5d3ll1+OefPm1Uw7q+kkMWIfmzdvNsXFxZUm9kdERJj8/PxKX9999901fYpux0cffaS08fX1NcXHx1t8NG/e3FSbmDt3bmmbKf9YunRppdvKAJHLLrus9P0BAQGmsLCw0tetW7c2HT9+3ORtOKKZ8b66detabYM//vijyZs4ePCgycfHp4xOkZGRqr0Yr0NCQkyzZ8+2uH1tbGeOalYb25lw3XXXldFM7ovBwcFlll111VWm/Px8U3W2MxotDyQ3N1cZAvnwO3bsaNqwYYNanpWVZXruuedKv6AffvhhTZ+qWxqtJk2a1PSpuJVpqFevnmno0KGmJ554wvTFF1/YbBoee+wx9T4ZiTt9+vTS/7yWLVtmSkxMVOv69etn8jYc0czW93kT+/btU/8niV5ffvmlKTk5ufTGJqO6O3fuXHpj27RpU4Xta2M7c1Sz2tjOhHfffdc0depUVZkgMzPTZHDgwAFVgcDQ5ZlnnjFVZzuj0fJApCHJhx4aGqoaUHkmTJig1jds2NCic6+t0GhVpLCwsMzrjIwMm/6TPnHihPpFLe975ZVXKqz/559/Sg3//PnzTd6EvZrV1hvgqVOn1I3PGhIlEOMqutx6661l1tXWduaIZrW1ndnCjTfeqHQp33Ph6nbGHC0P5PPPP1fPN9xwAxo3blxh/cMPP6z64GWyzKVLl9bAGRJPQSY4twcZjSuzJEgOxJ133llhvYzQNWZZMNprbdestiJzyXXt2tXqeslxGzZsmPq7fJHo2trOHNGMWKd3797qWe6N1dnOaLQ8jMzMTFXSQrA26bWYr3bt2pVWuCfE2UhpFaFv375qlgRLDBkyRD2zDZKqkIE8lmb9YDvT14xUniwvNG/evFrbGY2Wh7Ft2zY1MkKQ0RDWMNZt3bq12s7Nk0Zrdu/eXY0okVE8Ykrvuusu7Nq1q6ZPzWMw2pUtbTApKQmpqanVdm6ewH333acmsJcRUQ0bNlQjy+bOnVv63a5tLF++3GJ7YjvT18wctjOoUcIbN27EuHHjMHv2bLXs7rvvrtZ2RqPlYRw7dqz0b/niWMNYZ/5+UkJ2djY2bNiA4OBg5OXlYfv27XjnnXfQqVMnVQKCVI3Rrmxpg+bvJyVI+5O2FxAQoLT54Ycf1NypMutEbm4uahNSjmbNmjXqbykGbQ7bmb5m5tTWdrZ79+7SMhbSHSjdsO+//74qg/TCCy9g7Nix1drOaLQ8sOvQwFqI03yduHly5ovy9NNPY/Pmzeo/GflVInrOnz8f7du3V/8hybRNzGuzvR3a0gYFtsMSbrnlFixcuBAnT55UU4aJjhKlNm6WUudn/PjxqC0cPHgQY8aMUX9LtKV8OgTbmb5mQm1vZ/7+/iqPTR5iMgV5fuKJJ1Rkqzyubmc0WqTWIEXsZI5KKfgXGBiolklIXZJKpe9eCtwVFxfj0UcfrelTJV6KREwl16NOnTqly9q2bauKJD700EOl75Gbordz6tQpXHrppaorX3JmRAPiHM1qeztr2rSpmppPHpLkLtc5YsQIVcxU0kaq+7pptDwM88rl0gVmDWNdREREtZyXN4zyeeyxx9Tf//zzj/qPjFTdDm1pgwLbYdVIpXnp2pD8GYmyejMSQbjooouwadMmFWn+7bffEB0dXeF9bGf6mlVFbWpnxihhMZliLO+99141d/HIkSPL5Km5up3RaHkY5v3E5YeommOskwmyiW2cddZZ6lm+gPv376/p0/GIdmhLGxTYDqvGmO5DkJuBtyI3LJm66K+//lKJ2osWLaowCsyA7Uxfs6qoLe3MEjJ1kVESY926ddXWzmi0PAxx5pLgJ2zZssXq+4x1kntEiLMx2pUtbTAuLg4xMTHVdm7EfZHcSOn6+v3339VcchKVMUrRWILtTF8zYp2EhITSv/fs2VNt7YxGy8OQEKdRdE2SHS1x+PDh0uGqAwcOrNbz82T+/vvvMn38xDpG8T75z19yICzxyy+/qGe2QduQSWtloIbQrFkzeBsy2EQm9pWaRTISTP7/6tKlS6Xb1PZ2Zo9mtb2dVYZ5BM88Dcfl7Uy7ljxxmyl4ZMJLmXy0PBMnTuQUPOUoLi6udH16erqaNFR06927d7Wdl7thzxQ8r732WoX1a9as8cqpURzRrKo2+Mgjj6h9iG5btmwxeRPy/5AxYa/8v7Vy5UqbtqvN7cxezWprOysoKKjyPbfffru6dn9/f1NaWlq1tTMaLQ+fVFomF924caNanp2dbXrhhRc4qbSVSVrPOuss04wZM8rMD5mXl2dasGCBmpxbNPP19TUtXrzYVJuQCWuNx/79+0tNw/fff19mXVFRkdVJWEVX4z86mfS2UaNGXjnZryOaXX311Uqz1atXq3ZnsH379tIbgDxGjRpl8ra5Ia+55hp1bXIzW7Jkidb2tbGdOaJZbW1n69evN51zzjmmTz/91HTkyJHS5fIdXLdunWnEiBGl1y4TTFdnO6PR8lA2b95siouLK204kZGRJj8/v9LXEtUiZY2WoY08goODTTExMaaAgIDSZTJJt3xJaxvmulT2EA3Nkf+IjF/c8ggMDFS/vI3XEiE8duyYyRuxR7P+/fuXLpfvanR0dBm95CE3Sfkh5U3Ijcq4PrmJxcfHV/ooT21sZ45oVlvb2fr168tcoxjU2NhY1V7Ml48ePdpi9MuV7YxGy4ORcOd9991natmypfoyyhdq0KBBprlz59b0qbkdEu2TLtdrr73W1KZNG6WVhI+joqJMPXv2VOF0iUzURuw1WkY3xcyZM019+vRRWopZ7dChg2ny5MmmzMxMk7dij2a//PKL+gHUq1cvU4MGDdR3Vm4GzZo1M91www1qvTci3am26iUPS9S2duaIZrW1nWVnZ5tmzZqljJT09EggQv6Pj4iIUD0Wd9xxh2nVqlWV7sNV7cxH/tHP7CKEEEIIIVXBUYeEEEIIIS6CRosQQgghxEXQaBFCCCGEuAgaLUIIIYQQF0GjRQghhBDiImi0CCGEEEJcBI0WIYQQQoiLoNEihBBCCHERNFqEEEIIIS6CRosQQgghxEXQaBFC7Gb//v3w8fFRj48//thlxxkwYIA6hjwT72LUqFHqs23atGlNnwohLoFGixA3Ni+OPIj7YhhHncepU6dq+rQJIXZAo0UIIYQQ4iL8XbVjQoh9JCQk4N9//7W6vlOnTuq5Z8+e+Oijj1CTSHePyWRy+XGWLVsGb6Wyz9qcyMhIl58LIcT50GgR4mYEBASgY8eOVb4vLCzMpvcR94afISHeDbsOCSGEEEJcBI0WIV5E+dF5u3btwl133YVWrVohNDRUrZNke4Njx47h3XffxdVXX63eI1GyoKAg1X05fPhwzJ49G8XFxXaPOnzqqafKJOfn5ubi5ZdfRvfu3REREaEevXv3xttvv43CwkKbr6uqc/jtt99w6aWXon79+up6mjVrhjvvvBOHDx+uUsPU1FQ8/PDDaNOmDUJCQhAfH48LL7wQc+fOVevlGMbxzLWsbsqfR15eHl555RWlbVRUlOpqPOuss9TnW1RUVOX+kpOT8cQTT6Bbt26oU6cOgoODVdfwyJEjsXLlSpvOSc5h+vTpuPjii1UbEu2lTXXo0AG33347fvnllyq7miXpf9KkSWob2VbOpV+/fpg1a1aVx5fP6PLLL0diYqI6trSv5s2bo2/fvnjyySfxzz//2HQdhDgVEyHEo5CvrTz69+9fYZ0sM9Z9//33prCwsNL3G499+/ap9xYWFpp8fX0rrC//uPDCC00ZGRkWz0X2Zbzvo48+qrB+8uTJpeuPHz9u6tq1q9XjXHrppaaioiKLxzG/rqrO4f/+7/+sHqNevXqmrVu3WtV206ZNpvj4eKvbjxkzRh2jvJa6GNfjyH/B5uexbt06U48ePayed79+/ax+hsIvv/xiioyMrLQdTJgwwernI6xfv97UrFmzKttTec1uueUWtbxJkyam7du3m5o2bVrpOVhC2vI111xT5bFFI0KqG0a0CPFCDh48iJtuuklFsV588UX88ccf+Ouvv/DWW28hPDxcvceILFxwwQUqyrRw4UKsXbtWJZ5/+OGHOOecc0qjQxMmTHD4nK688kps3boVd999t9qnHOuLL75Au3bt1Poff/wRH3zwgUPHkO3levv376/2vWbNGixatAg333xzadTmtttusxpJGTp0KE6cOKFeSyRnwYIFah9fffWV0kOiNe+99x7cjbFjxyo9r7vuOvz888/qnOX6e/Xqpdb//vvv6nossWHDBhX9O336tMoPvO+++7B06VIV/Xn//fdVNFB455138Oijj1rcx7Zt21TUaN++fer1FVdcoaKhq1evVu3u008/Ve1RIlTWyM7OVuchEUWJrEk7lOuQz1QiVMY5SFSsPNOmTcM333yj/j7vvPNUtG/FihVYt26damuvvvqqikr6+flpa0uIw1S7tSOEuDyiJY+GDRuaDhw4YHU/xcXFpl27dlV6rEmTJql9+fj4mHbu3OlQRCsgIMC0dOnSCu9JTU0tjSJ17tzZoYiWPO644w51beW5/fbby0SAynPvvfeWrn/jjTcsRk2GDx9eaXTGVsw/p3///bfKx+HDhyuNaMnjf//7X4X3FBQUmIYMGVL6nvnz51d4T69evdQ6Pz8/FdkqT1pamql9+/bqPRIB3bx5c4X3dO/evXT9l19+afW6U1JSTNnZ2RYjWvKIioqyuH9pp8HBweo9l112WYX1ffv2VevOOussdc3WkLZGSHVDo0WIlxqtTz/91OFjibmIjY1V+3vllVccMlr333+/1eMY3X1i6E6dOmW30WrQoIEpNzfX4jGkW8p435tvvllmnWxTp04dtU6MhzWk+9O44TvLaNnyEDNSmdESg2rJXAqHDh1SJlfed/HFF5dZ9/fff5fuY9y4cVbPd+XKlaXvGz9+fJl1Ys6MdWJWdTE3WlOnTrX6vuuvv169Jzo6usK6Vq1aqXX33Xef9vEJcTXsOiTECwkMDMQ111yjtY0kvR89ehQ7duzA5s2b1UO6hIxum40bNzp0TjfeeKPVdT169FDP4iON7id7kKR+SYK2hCS3G92me/fuLbNOuqiMyuvSxWUNSYwfMmQI3I1bbrnF6mwA8vkNHjxY/S3dceaJ8dKtajB69Gir++/Tp09pF6/5NsJPP/1U+ve9995r9zXI+Y8YMaLKNpKWllahSn6DBg1Ku59TUlLsPgdCXAGNFiFeiIwglFFjVSHG5vPPP8f555+vTIiMFGvbtq0qimo8JIdHcPQGJvu1RnR0dOnfGRkZLjmGULduXYvHEFNZ/oZuDSkU60z+61mo9FHVPJJGLpY1ZGSnkJWVVcZkGtctxrxr166V7kNGMBojWfPz80uXr1+/Xj03btwYTZo0gb3ExsYiJibGrjYiRlPYvXs3WrZsqfLwvvzyS5tGmRLiami0CPFCDENRGVJqQYbhS5K0RDpycnIqfX9V66tCEvOt4et75r8iW0oR2HMM8+OUP8bJkydL/65Xr16l+6hqfU0QFxdX6XqJxBlIRKj832Ji/P0rr18tpTIEMX7mehkG3Igqufqzs/T5ibF67LHH1DWkp6erGRMkOtaoUSNlvB544IEKUUxCqgsaLUK8EFtGVz3//PNqVJ0go/S+/vprFRHIzMxUNzIjmiKjyYTqmGqH2Iejk4h7wyTk0p6l/cqzjKQ1jNuePXvw2muvqWinO44YJd4PjRYhtRAxTTNmzFB/i5FasmSJyulq0aKFGoJvHj0wj4DUhgiglICojKrW1wRGSQpb1pt3wRl/S0mFygrGCsePHy81ZeZ6SZefUfy2ppGuS4lsLV68WOVxSVmTe+65R3WjFxQUYPz48aVdnYRUFzRahNRCxDwZN04xWObGyhyJbklyvLcjVcgNpB5VZUjivLsh9apsWS9RHqmUXn6eRcm5MnLxrGFUVZf8P8npMpBK9EbttgMHDsBdkJpg5557Lt544w1VU8z4gfHtt9/W9KmRWgaNFiG1EPPohSRIW0OiXlVFOrwBSXCXaWsEGRxQWWTIUsHMmuazzz6z2rV75MgR/Prrr+pvmcLIvFt50KBBpX9LkVpr/Pnnn6rYbPltBCkyavD666/DHRk4cGDp3xyVSKobGi1CaiGS0C1zyAkyOkvmqLMUBZH54WoD0rVkVI+X637zzTctlr+QCuwyiMDdkGiUVPcvj5jkO+64o3SUoMz3WH40ojGKUiqwS5dbeSS5XK5bkMhn+X2I8TJGasrMA1JF3xrSRenooApLiDmu7AeBYTQFo9I9IdVF5cNMCCFeidwwpa6VTGmyadMmNW3J/fffr7qF5MYq07jIZMRS8qFhw4bYuXMnvB2ZAFumcZEuVakHJV2IopGYUkmyFvO1atUqZU6MbjRnJJGbl5aoKv9IJkm2hJilRx55RBkuMYwyClHKMEgSuHGuEnm65JJLKmwrBktKN4gZGzZsGCZOnKjeK7l6ks8kUxoZI/YefPDB0u7G8hE10UW6mm+44Qal4/XXX6+6KWVghegnZke67eR6ZbJqZyIjZ+XcZJon6S6UXEMxzxKBlCl4ZIoeQdpzZfXcCHEJLi+JSgip9srwltaVRyqwVzbJs1TgXr58udaEzpVVhq8MmZrHeJ+laXocOQdzZOJia5XWhQ0bNqiJp61pMmrUKNPMmTNLX0ul+OqoDC+PuXPnVjqpdLdu3axu26dPH9Pp06ddOqn0mjVrTI0aNXJoUunKqGwyb1v0k+l9FixYUMUnQ4jzYdchIbUUyUmSUVnPPvusKkwqEQD5xS8VwCU6IJXg+/Xrh9pEly5dVC6S1F2S6J5UmZdRdVLQVRKqpT6TTL5sYOR11TQyClCibS+88IIqPCqRL/kspZCpdOctX77cajRMkMrxEnWSEXuyfWRkpLp2KUIqESCZoPntt9+2OmhCkO5DGTgxdepUVV5BompS10rOQ9rXmDFjVNeks6NZgkTJpkyZoiJx7du3V4VPJRdNusfPPvtsTJ48WZ2bTBpOSHXjI26r2o9KCCEeyu23346ZM2eqqW0OHTpUY+ch1eJvvfVW9bdMW+QKA0MIcRxGtAghxEYkkXvevHnqb4mUEEJIVdBoEULIf0gVcWtBfknqlhF3RnkAY349QgipDI46JISQ/5B8NRmlJyPmZCSe5BlJFEtGZsrovHXr1pWWNJB5IgkhpCpotAghxIxt27ap5Glr9OnTR9WK8ob5AQkhrodGixBC/uPRRx9F69atsWjRIuzfv1/Nayhz5MkoNqlVdd1116loV2Wj7wghxByOOiSEEEIIcRH8WUYIIYQQ4iJotAghhBBCXASNFiGEEEKIi6DRIoQQQghxETRahBBCCCEugkaLEEIIIcRF0GgRQgghhLgIGi1CCCGEEBdBo0UIIYQQAtfw/+VuiO70vYQJAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val_vamp = []\n",
"for i in range(5):\n",
" j=i+1\n",
" file_path=rf'./trained_model_examples/{j}/validation_vamp.npy'\n",
" dip = np.load(file_path)\n",
" val_vamp.append(dip)\n",
"\n",
"# Define custom colors for the line and markers\n",
"line_color = 'royalblue'\n",
"marker_face = 'white'\n",
"\n",
"plt.rcParams['font.size'] = 35\n",
"\n",
"# Plot with custom colors, line width, and markers\n",
"fig = plt.figure(figsize=(8, 6))\n",
"for idx, dip in enumerate(val_vamp):\n",
" plt.plot(dip, linewidth=2, marker='o', markersize=6, markerfacecolor=marker_face, linestyle='-', label=f'Train {idx + 1}')\n",
"\n",
"# Set the axis labels\n",
"plt.xlabel('Training Epochs', fontsize=20)\n",
"plt.ylabel('VAMP-2 Loss', fontsize=20)\n",
"\n",
"plt.yticks(np.arange(3, 4.1, 0.2))\n",
"plt.xticks(np.arange(0, 35, 5))\n",
"\n",
"# Customize tick parameters for readability\n",
"plt.xticks(fontsize=17.5)\n",
"plt.yticks(fontsize=17.5)\n",
"plt.gca().set_box_aspect(0.85)\n",
"\n",
"# Add a dashed grid to improve the readability of the plot\n",
"plt.grid(alpha=0.3, linestyle='--')\n",
"plt.legend(loc='lower right', fontsize=14, markerscale=1, ncol=1)\n",
"\n",
"# Adjust the layout to ensure everything fits well\n",
"plt.tight_layout()\n",
"\n",
"# Display the plot\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "83c8f0ee-6cc9-4d8b-afbe-fa2c4609a0bc",
"metadata": {},
"source": [
"## Visualize the Dispersion Loss:\n",
"\n",
"While the VAMP-2 loss focuses on capturing the slow dynamics, it can sometimes result in a latent space where the features collapse to similar values. To counteract this, we incorporate a **dispersion loss** that encourages diversity in the latent space. One way to formulate the dispersion loss is by ensuring that the pairwise distances between latent representations are close to a target mean distance. The dispersion loss is defined as:\n",
"\n",
"$$\\mathcal{L}_{dis} = \\frac{1}{C} \\sum_{i=1}^{C} \\log \\frac{1}{C-1} \\sum_{j=1}^{C} \\mathbf{1}_{\\{j \\neq i\\}} e^{\\frac{\\mu_i^T \\mu_j}{\\sigma}}$$\n",
"where:\n",
"- $C$ corresponds to the number of states.\n",
"- $\\mu_i$ is a unit vector representing the mean direction of all conformations (i.e., the state center) in state $i$.\n",
"- $\\sigma$ is a scaling hyperparameter, which is specifically defined as 0.1.\n",
"- $\\mathbf{1}_{\\{j \\neq i\\}}$ is an indicator function that equals 1 when $j \\neq i$ (i.e., excluding self-similarity) and 0 when $j = i$.\n",
"\n",
"This loss penalizes deviations from the target dispersion, ensuring that the latent features remain well-separated and informative."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ed901e4c",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define custom colors for the line and markers\n",
"line_color = 'royalblue'\n",
"marker_face = 'white'\n",
"#marker_edge = 'royalblue'\n",
"\n",
"val_dip = []\n",
"for i in range(5):\n",
" j=i+1\n",
" file_path=f'./trained_model_examples/{j}/validation_dis.npy'\n",
" dip = np.load(file_path)\n",
" val_dip.append(dip)\n",
"\n",
"plt.rcParams['font.size'] = 35\n",
"plt.figure(figsize=(8, 6))\n",
"for idx, dip in enumerate(val_dip):\n",
" plt.plot(dip, linewidth=2, marker='o', markersize=6, markerfacecolor=marker_face, linestyle='-', label=f'Train {idx + 1}')\n",
"\n",
"# Set the axis labels\n",
"plt.xlabel('Training Epochs', fontsize=20)\n",
"plt.ylabel('Dispersion Loss', fontsize=20)\n",
"#plt.ylim(0, 2.25)\n",
"#plt.yticks(np.arange(0, 2.1, 0.5))\n",
"plt.xticks(np.arange(0, 35, 5))\n",
"\n",
"# Customize tick parameters for readability\n",
"plt.xticks(fontsize=17.5)\n",
"plt.yticks(fontsize=17.5)\n",
"plt.gca().set_box_aspect(0.85)\n",
"plt.legend(loc='upper right', fontsize=14, markerscale=1, ncol=1)\n",
"\n",
"# Add a dashed grid to improve the readability of the plot\n",
"plt.grid(alpha=0.3, linestyle='--')\n",
"\n",
"# Adjust the layout to ensure everything fits well\n",
"plt.tight_layout()\n",
"\n",
"# Display the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cc3ccd1f-2c96-47dc-89fd-410ddc3baa35",
"metadata": {},
"outputs": [],
"source": [
"# Initialize with specified hidden layer sizes and number of output states\n",
"lobe = TSDARLayer([528,200,100,50,25,10,3],n_states=4)\n",
"\n",
"# Load a pre-trained model - the model was trained for 30 epochs\n",
"lobe.load_state_dict(torch.load('./trained_model_examples/3/model_30epochs.pytorch', map_location=torch.device('cpu')))\n",
"tsdar_model = TSDARModel(lobe=lobe)\n",
"\n",
"# Use the model to transform the input data into low-dimensional representations on the surface of a hypersphere\n",
"features = tsdar_model.transform(data,return_type='hypersphere_embs')\n",
"\n",
"# Use the same model to assign each frame in the input trajectory to one of the learned states\n",
"lumped_trajs = tsdar_model.transform(data,return_type='states') # State Assignment"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "261b294c-eac9-4f4d-a8e0-a0ba1385fa00",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-0.27191687 -0.83422726 -0.47971457]\n",
" [-0.5415171 0.7496386 -0.38052765]\n",
" [-0.16964732 -0.0948611 0.9809287 ]\n",
" [ 0.9731338 0.179747 -0.14388044]]\n"
]
}
],
"source": [
"# Initialize the TS-DAR Estimator to compute metastable state centers\n",
"tsdar_estimator = TSDAREstimator(tsdar_model).fit(data)\n",
"\n",
"# These scores indicate how far each point is from its assigned state's center, which helps detect rare conformations\n",
"ood_scores = tsdar_estimator.ood_scores\n",
"\n",
"# Extract the learned centers (means) of each state in the latent hypersphere embedding space\n",
"state_centers = tsdar_estimator.state_centers\n",
"\n",
"print(state_centers)"
]
},
{
"cell_type": "markdown",
"id": "1b7d4e54-8970-4e7e-a82a-3b88eb761f2b",
"metadata": {},
"source": [
"## StateAnalyzer:\n",
"\n",
"The `StateAnalyzer` identifies key frames from MD data by identifying those closest to state centers and high OOD-scoring frames near state transitions in a learned embedding space."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "21d6afc4",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import warnings\n",
"\n",
"class StateAnalyzer:\n",
" def __init__(self, state_centers, features, ood_scores):\n",
" \"\"\"\n",
" Initialize the StateAnalyzer with state centers, features, and OOD scores.\n",
"\n",
" Parameters:\n",
" - state_centers: List or array of state centers.\n",
" - features: List of trajectories, where each trajectory is a list of feature vectors.\n",
" - ood_scores: List of OOD scores corresponding to the features.\n",
" \"\"\"\n",
" self.state_centers = state_centers\n",
" self.features = features\n",
" self.ood_scores = ood_scores\n",
" self.flatten_features = np.concatenate(features)\n",
" self.flatten_ood_scores = np.concatenate(ood_scores)\n",
" self.indices = self._flatten_features()\n",
"\n",
" def _flatten_features(self):\n",
" \"\"\"\n",
" Keep track of (traj_idx, frame_idx, flatten_idx).\n",
" In case that different trajectories have different lengths, have careful record of the indices.\n",
"\n",
" Returns:\n",
" - flatten_features: Flattened list of feature vectors.\n",
" - indices: List of tuples containing (traj_idx, frame_idx, flatten_idx).\n",
" \"\"\"\n",
" indices = []\n",
" flatten_idx = 0\n",
" for traj_idx, traj in enumerate(self.features):\n",
" for frame_idx, f in enumerate(traj):\n",
" indices.append((traj_idx, frame_idx, flatten_idx))\n",
" flatten_idx += 1\n",
" return indices\n",
"\n",
" def find_center_frames(self, center_idx=0, top_n=10):\n",
" \"\"\"\n",
" Find the indices of the closest frames to each state center.\n",
"\n",
" Parameters:\n",
" - num_centers: Number of state centers (default: 4).\n",
" - top_n: Number of closest frames to return for each center (default: 10).\n",
"\n",
" Returns:\n",
" - center_frames: DataFrame containing the closest frames for each state center.\n",
" \"\"\"\n",
" results = []\n",
" diff = np.linalg.norm(self.flatten_features - self.state_centers[center_idx], axis=1)\n",
"\n",
" # Find the indices of the top_n closest frames\n",
" k = np.argsort(diff)[:top_n]\n",
"\n",
" # Extract trajectory, frame, and flattened indices\n",
" closest_entries = [(center_idx, *self.indices[i]) for i in k]\n",
" results.extend(closest_entries)\n",
"\n",
" # Create a DataFrame to store the results\n",
" center_frames = pd.DataFrame(results, columns=[\"Center_Index\", \"Trajectory\", \"Frame\", \"Flatten_Index\"])\n",
" return center_frames\n",
"\n",
" def find_transition_states(self, state_idx_1, state_idx_2, threshold=0.25, top_n=10):\n",
" \"\"\"\n",
" Find the indices of the transition states with the highest OOD scores between two states.\n",
"\n",
" Parameters:\n",
" - state_idx_1: Index of the first state.\n",
" - state_idx_2: Index of the second state.\n",
" - threshold: Distance threshold for considering a feature as close to the midpoint (default: 0.25).\n",
" Note: the threshold can't be larger than sqrt(2), not recommended to be larger than 1\n",
" - top_n: Number of top transition states to return (default: 10).\n",
"\n",
" Returns:\n",
" - selected_indices: Indices of the top transition states.\n",
" - selected_ood_scores: OOD scores of the top transition states.\n",
" \"\"\"\n",
" # Compute the midpoint between the two state centers and normalize it\n",
" state_centers_mid = (self.state_centers[state_idx_1] + self.state_centers[state_idx_2]) / 2\n",
" state_centers_mid /= np.linalg.norm(state_centers_mid) # Normalize\n",
"\n",
" # Find indices where the feature is within the threshold distance from state_centers_mid\n",
" close_indices = [i for i in range(len(self.flatten_features)) \n",
" if np.linalg.norm(self.flatten_features[i] - state_centers_mid) < threshold]\n",
" \n",
" # Check if there are enough close frames\n",
" if len(close_indices) < top_n:\n",
" warnings.warn(\n",
" f\"Only {len(close_indices)} close frames found (requested {top_n}). \"\n",
" \"Returning all available frames. Try increasing the threshold.\",\n",
" UserWarning,\n",
" )\n",
" top_n = len(close_indices) # Adjust top_n to the number of available frames\n",
"\n",
" # Get the OOD scores corresponding to these indices\n",
" ood_scores_filtered = self.flatten_ood_scores[close_indices]\n",
"\n",
" # Get the indices of the top N highest OOD scores from the selected frames\n",
" top_n_indices = np.argsort(ood_scores_filtered)[-top_n:]\n",
"\n",
" # Retrieve the corresponding original indices\n",
" selected_indices = [close_indices[i] for i in top_n_indices]\n",
"\n",
" # Get the corresponding OOD scores\n",
" selected_ood_scores = self.flatten_ood_scores[selected_indices]\n",
"\n",
" # Create a DataFrame to store the results\n",
" transition_data = {\n",
" \"State_1_Index\": state_idx_1,\n",
" \"State_2_Index\": state_idx_2,\n",
" \"OOD_Score\": selected_ood_scores,\n",
" \"Flatten_Index\": selected_indices,\n",
" }\n",
"\n",
" # Add trajectory and frame indices if available\n",
" if hasattr(self, 'indices'):\n",
" transition_data[\"Trajectory\"] = [self.indices[i][0] for i in selected_indices]\n",
" transition_data[\"Frame\"] = [self.indices[i][1] for i in selected_indices]\n",
"\n",
" transition_df = pd.DataFrame(transition_data)\n",
"\n",
" return transition_df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1c26e570",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Closest frames to state center 2:\n",
" Center_Index Trajectory Frame Flatten_Index\n",
" 2 21 1215 211215\n",
" 2 150 2809 1502809\n",
" 2 21 2684 212684\n",
" 2 150 3540 1503540\n",
" 2 151 7972 1517972\n",
" 2 21 3911 213911\n",
" 2 128 7181 1287181\n",
" 2 45 9219 459219\n",
" 2 0 4326 4326\n",
" 2 150 9405 1509405\n"
]
}
],
"source": [
"# Initialize the StateAnalyzer\n",
"analyzer = StateAnalyzer(state_centers, features, ood_scores)\n",
"\n",
"# Find the closest frames to each state center\n",
"center_idx = 2\n",
"center_frames = analyzer.find_center_frames(center_idx=center_idx)\n",
"print(f\"Closest frames to state center {center_idx}:\")\n",
"print(center_frames.to_string(index=False))"
]
},
{
"cell_type": "markdown",
"id": "639f0e03-192c-445d-8b4c-ed785f1a1834",
"metadata": {},
"source": [
"## Find the Transition State Conformations Between State 1 and State 2:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "de6469d7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transition state between state 0 and state 1:\n",
" State_1_Index State_2_Index OOD_Score Flatten_Index Trajectory Frame\n",
" 0 1 0.424043 1127138 112 7138\n",
" 0 1 0.424043 1127162 112 7162\n",
" 0 1 0.424273 196158 19 6158\n",
" 0 1 0.424390 455435 45 5435\n",
" 0 1 0.424422 455541 45 5541\n",
" 0 1 0.424532 196217 19 6217\n",
" 0 1 0.424601 604530 60 4530\n",
" 0 1 0.424729 195996 19 5996\n",
" 0 1 0.425174 196649 19 6649\n",
" 0 1 0.425725 1126842 112 6842\n"
]
}
],
"source": [
"state_idx_1 = 0\n",
"state_idx_2 = 1\n",
"transition_states = analyzer.find_transition_states(state_idx_1, state_idx_2, threshold=0.5)\n",
"print(f\"Transition state between state {state_idx_1} and state {state_idx_2}:\")\n",
"print(transition_states.to_string(index=False))"
]
},
{
"cell_type": "markdown",
"id": "8e64e0d1-0809-4f71-b9a1-682439d034e4",
"metadata": {},
"source": [
"## Find the Transition State Conformations Between State 2 and State 3:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "461274c7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transition state between state 1 and state 2:\n",
" State_1_Index State_2_Index OOD_Score Flatten_Index Trajectory Frame\n",
" 1 2 0.446738 180716 18 716\n",
" 1 2 0.446771 1501421 150 1421\n",
" 1 2 0.446950 460201 46 201\n",
" 1 2 0.447108 1123185 112 3185\n",
" 1 2 0.447181 224263 22 4263\n",
" 1 2 0.447375 224250 22 4250\n",
" 1 2 0.447526 103890 10 3890\n",
" 1 2 0.447535 1287784 128 7784\n",
" 1 2 0.448049 289471 28 9471\n",
" 1 2 0.448070 103844 10 3844\n"
]
}
],
"source": [
"state_idx_1 = 1\n",
"state_idx_2 = 2\n",
"transition_states = analyzer.find_transition_states(state_idx_1, state_idx_2)\n",
"print(f\"Transition state between state {state_idx_1} and state {state_idx_2}:\")\n",
"print(transition_states.to_string(index=False))"
]
},
{
"cell_type": "markdown",
"id": "5cb10d62-b201-4bf6-9da6-b18b24ede2df",
"metadata": {},
"source": [
"## Find the Transition State Conformations Between State 2 and State 4:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "55f7c58b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transition state between state 1 and state 3:\n",
" State_1_Index State_2_Index OOD_Score Flatten_Index Trajectory Frame\n",
" 1 3 0.471023 1484781 148 4781\n",
" 1 3 0.471358 1484847 148 4847\n",
" 1 3 0.472856 41614 4 1614\n",
" 1 3 0.472881 1484808 148 4808\n",
" 1 3 0.478483 41701 4 1701\n",
" 1 3 0.479567 41628 4 1628\n",
" 1 3 0.482969 41615 4 1615\n",
" 1 3 0.483645 41700 4 1700\n",
" 1 3 0.485224 41702 4 1702\n",
" 1 3 0.489177 41626 4 1626\n"
]
}
],
"source": [
"state_idx_1 = 1\n",
"state_idx_2 = 3\n",
"transition_states = analyzer.find_transition_states(state_idx_1, state_idx_2, threshold=0.5)\n",
"print(f\"Transition state between state {state_idx_1} and state {state_idx_2}:\")\n",
"print(transition_states.to_string(index=False))"
]
},
{
"cell_type": "markdown",
"id": "24aad710-1cf4-4527-a2d5-3509e7f0f744",
"metadata": {},
"source": [
"## Compute the Angles Between State Center Vectors:\n",
"- `angle_between_vectors` computes and prints the pairwise angles (in radians) between the state center vectors in a high dimensional embedding space to assess how uniformly dispersed the learned states are."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "1e4c7a29",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.8708554530644315\n",
"1.9233548700192482\n",
"1.9310537054099948\n",
"1.9236102600048615\n",
"1.9150272270878264\n",
"1.8999867164522164\n"
]
}
],
"source": [
"def angle_between_vectors(v1, v2):\n",
" \"\"\"\n",
" Calculate the angle between two vectors in radians.\n",
" \n",
" Parameters:\n",
" v1 (array-like): First vector.\n",
" v2 (array-like): Second vector.\n",
" \n",
" Returns:\n",
" float: Angle in radians.\n",
" \"\"\"\n",
" # Convert inputs to numpy arrays\n",
" v1 = np.array(v1)\n",
" v2 = np.array(v2)\n",
" \n",
" # Calculate dot product and magnitudes\n",
" dot_product = np.dot(v1, v2)\n",
" magnitude_v1 = np.linalg.norm(v1)\n",
" magnitude_v2 = np.linalg.norm(v2)\n",
" \n",
" # Calculate cosine of the angle\n",
" cos_theta = dot_product / (magnitude_v1 * magnitude_v2)\n",
" \n",
" # Handle potential numerical precision issues\n",
" cos_theta = np.clip(cos_theta, -1.0, 1.0)\n",
" \n",
" # Calculate angle in radians\n",
" angle_radians = np.arccos(cos_theta)\n",
" return angle_radians\n",
"\n",
"# Well-dispersed data should have the angles between center vectors to be almost identical\n",
"# Useful when the dimension is larger than 3 or the number of state is larger than 4\n",
"n_states = 4\n",
"for i in range(n_states):\n",
" for j in range(i):\n",
" print(angle_between_vectors(state_centers[i], state_centers[j]))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "99f1df4a-660c-4d37-8d7a-1f609f4b3481",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Length of xx: 1526041\n",
"Length of yy: 1526041\n",
"Length of ood_scores: 1526041\n"
]
}
],
"source": [
"xx = [frame[0] for traj in features for frame in traj]\n",
"yy = [frame[1] for traj in features for frame in traj]\n",
"zz = [frame[2] for traj in features for frame in traj]\n",
"scores = [score for traj in ood_scores for score in traj]\n",
"\n",
"print(\"Length of xx:\", len(xx))\n",
"print(\"Length of yy:\", len(yy))\n",
"print(\"Length of ood_scores:\", len(scores))"
]
},
{
"cell_type": "markdown",
"id": "bfff2c4b-e3e8-460b-9fbd-d8e4393dfcc6",
"metadata": {},
"source": [
"## Visualize the Results in the Hyperspherical Latent Space:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8dfad4de",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r = 1\n",
"pi = np.pi\n",
"cos = np.cos\n",
"sin = np.sin\n",
"phi, theta = np.mgrid[0.0:pi:100j, 0.0:2.0*pi:100j]\n",
"\n",
"x = r*sin(phi)*cos(theta)\n",
"y = r*sin(phi)*sin(theta)\n",
"z = r*cos(phi)\n",
"\n",
"plt.rcParams['figure.figsize'] = (5,4)\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"\n",
"ax.plot_surface(\n",
" x, y, z, rstride=2, cstride=2, color='c', alpha=0.1, linewidth=100,antialiased=False)\n",
"\n",
"ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],[0,state_centers[0,2]],linewidth=2,color='black',linestyle='--')\n",
"ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],[0,state_centers[1,2]],linewidth=2,color='black',linestyle='--')\n",
"ax.plot([0,state_centers[2,0]],[0,state_centers[2,1]],[0,state_centers[2,2]],linewidth=2,color='black',linestyle='--')\n",
"ax.plot([0,state_centers[3,0]],[0,state_centers[3,1]],[0,state_centers[3,2]],linewidth=2,color='black',linestyle='--')\n",
"\n",
"c = ax.scatter(xx,yy,zz,c=scores,s=1,alpha=1,cmap='coolwarm')\n",
"\n",
"cb = fig.colorbar(c)\n",
"cb.ax.tick_params(labelsize=10,length=3,width=1.5)\n",
"cb.set_label('OOD score',fontsize=18)\n",
"\n",
"ax.set_xlim([-1,1])\n",
"ax.set_ylim([-1,1])\n",
"ax.set_zlim([-1,1])\n",
"ax.set_xticks([-1,-0.5,0,0.5,1])\n",
"ax.set_yticks([-1,-0.5,0,0.5,1])\n",
"ax.set_zticks([-1,-0.5,0,0.5,1],[-1,-0.5,0,0.5,1])\n",
"ax.set_aspect(\"equal\")\n",
"ax.tick_params(axis=\"both\",labelsize=10,direction='out',length=7.5,width=2.5)\n",
"\n",
"ax.set_xlabel('z1',fontsize=15)\n",
"ax.set_ylabel('z2',fontsize=15)\n",
"ax.set_zlabel('z3',fontsize=15)\n",
"\n",
"ax.view_init(elev=20, azim=70) # Adjust these two values to have better views"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f16eda34",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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],
"source": [
"# This performs bootstrap resampling of transition probability matrices (TPMs) for a Markov State Model (MSM) \n",
"# using the msmbuilder package, to estimate uncertainty in the model across a range of lag times.\n",
"\n",
"from msmbuilder.msm import MarkovStateModel\n",
"\n",
"n_states = 4 # Number of discrete states in the MSM\n",
"num_runs = 50 # Number of bootstrap replicates\n",
"num_samples_per_run = 160 # How many trajectories to sample per bootstrap\n",
"lt = np.arange(100, 1501, 100) # Lag times to evaluate (e.g. 100, 200, ..., 1500)\n",
"\n",
"# TPM generation function\n",
"def generate_TPM(trajs, lagtime, n_states=4,):\n",
" TPM = []\n",
" for i in range(len(lagtime)):\n",
" msm_macro = MarkovStateModel(n_timescales=3, lag_time=lagtime[i],\n",
" ergodic_cutoff='on', reversible_type='mle', verbose=False)\n",
" msm_macro.fit(trajs)\n",
" if len(msm_macro.transmat_) != n_states:\n",
" return None\n",
" TPM.append(msm_macro.transmat_)\n",
" return np.array(TPM)\n",
"\n",
"# Bootstrap loop\n",
"bootstrap_TPM = np.zeros((num_runs, len(lt), n_states, n_states))\n",
"for i in range(num_runs):\n",
" bootstrap_indices = np.random.choice(range(len(lumped_trajs)), size=num_samples_per_run, replace=True)\n",
" bootstrap_trajs = [lumped_trajs[i] for i in bootstrap_indices]\n",
" temp_TPM = generate_TPM(bootstrap_trajs, lagtime=lt)\n",
" if temp_TPM is None:\n",
" continue\n",
" bootstrap_TPM[i] = temp_TPM\n",
" print('Run {} is complete'.format(i))"
]
},
{
"cell_type": "markdown",
"id": "e4fba7fe-0e44-4598-b6ea-2daa5dbca52f",
"metadata": {},
"source": [
"## Create a Markov State Model (MSM):\n",
"\n",
"- **n_timescales**: number of implied timescales to estimate\n",
"- **lag_time**: lag between transitions used to build the model\n",
"- **ergodic_cutoff**: ensures all states are connected (ergodic)\n",
"- **reversible_type**: maximum likelihood estimator for reversibility\n",
"- **verbose**: suppress output "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ec95b68c",
"metadata": {},
"outputs": [],
"source": [
"# Set the lag time for the MSM.\n",
"lagtime = 100 # In simulation steps; corresponds to 20 ns\n",
"\n",
"msm = MarkovStateModel(n_timescales=6, lag_time=lagtime, ergodic_cutoff='on', \n",
" reversible_type='mle', verbose=False)\n",
"\n",
"# Fit the MSM to the lumped trajectories (discrete state assignments per trajectory)\n",
"msm.fit(lumped_trajs)\n",
"\n",
"# Extract the transition probability matrix (TPM) from the fitted MSM\n",
"transmat = msm.transmat_\n",
"\n",
"# Define the number of TPM propagations to evaluate (i.e., powers of the TPM), up to 1500 simulation steps\n",
"order_parameter = np.arange(1, int(1500 / lagtime + 1))\n",
"\n",
"# Convert timepoints from simulation steps to nanoseconds (0.2 ns per step)\n",
"msm_time = order_parameter * lagtime * 0.2\n",
"\n",
"# Compute the TPM raised to successive powers (i.e., TPM ** i), giving transition probabilities over longer times\n",
"# Each TPM ** i gives the probability of transitioning from state j to k in (i * lagtime) steps\n",
"msm_TPM = []\n",
"for i in order_parameter:\n",
" msm_TPM.append(np.linalg.matrix_power(transmat, i))\n",
"\n",
"# Convert the list of TPMs into a NumPy array for further analysis or plotting\n",
"msm_TPM = np.array(msm_TPM)"
]
},
{
"cell_type": "markdown",
"id": "3226342d-1a4c-4b71-bddc-9410b8499f96",
"metadata": {},
"source": [
"## Perform a Chapman-Kolmogorov (CK) Test: \n",
"- This test is done to evaluate the markovian quality of the model by comparing the **MSM-predicted multi-step residence probabilities (from `msm_TPM`)** with the **Empirical bootstrapped MD estimates (from `bootstrap_TPM`)**\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "85e2a8f7",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Font and axis style\n",
"plt.rcParams['font.size'] = 17.5\n",
"plt.rcParams['axes.linewidth'] = 2\n",
"\n",
"# Compute mean and std\n",
"TPM_mean = np.mean(bootstrap_TPM, axis=0)\n",
"TPM_std = np.std(bootstrap_TPM, axis=0)\n",
"\n",
"color_msm = '#D55E00' \n",
"color_md = '#999999' # gray\n",
"\n",
"# Create subplots\n",
"fig, axes = plt.subplots(2, 2, figsize=(9, 7.5), sharex=True, sharey=True)\n",
"axes = axes.flatten()\n",
"\n",
"for i in range(n_states):\n",
" ax = axes[i]\n",
"\n",
" # MD error bars\n",
" ax.errorbar(x=lt * 0.2, y=TPM_mean[:, i, i], yerr=TPM_std[:, i, i], fmt='s', capsize=4, elinewidth=2.5, markersize=6, \n",
" color=color_md, ecolor=color_md, zorder=1, label='MD' if i == 0 else None)\n",
"\n",
" # MSM predictions as dark blue circles\n",
" ax.scatter(msm_time, msm_TPM[:, i, i], c=color_msm, edgecolor='k', s=80, zorder=3, label='TSDAR-MSM' if i == 0 else None)\n",
"\n",
" # State label\n",
" ax.text(0.95, 0.10, f'State {i+1}', ha='right', va='bottom', transform=ax.transAxes, fontsize=20)\n",
"\n",
" ax.set_ylim(0, 1.05)\n",
" ax.set_xlim(min(lt * 0.2), max(lt * 0.2))\n",
" ax.set_xticks([100, 200, 300])\n",
" ax.set_yticks([0.0, 0.5, 1.0])\n",
" ax.tick_params(axis='both', direction='in', width=2, length=6)\n",
"\n",
" # Add clean gridlines\n",
" ax.grid(True, linestyle='--', linewidth=1, alpha=0.3)\n",
"\n",
" # Border thickness\n",
" for spine in ax.spines.values():\n",
" spine.set_linewidth(2)\n",
"\n",
"# Axis labels\n",
"fig.text(0.54, 0.04, 'Lag Time (ns)', ha='center', fontsize=22)\n",
"fig.text(0.04, 0.5, 'Residence Probability', va='center', rotation='vertical', fontsize=22)\n",
"\n",
"# Unified legend\n",
"handles, labels = [], []\n",
"for ax in axes:\n",
" for h, l in zip(*ax.get_legend_handles_labels()):\n",
" if l not in labels:\n",
" handles.append(h)\n",
" labels.append(l)\n",
"\n",
"fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.05),\n",
" ncol=2, fontsize=17.5, frameon=False)\n",
"\n",
"plt.tight_layout(rect=[0.05, 0.05, 1, 0.98])\n",
"plt.subplots_adjust(wspace=0.25, hspace=0.3)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1de1be3e",
"metadata": {},
"outputs": [],
"source": []
}
],
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