Case Study: Thomas Pak
Despite his high-school maths teacher advising him to study maths, Thomas decided to study Bioscience Engineering at KU Leuven in Belgium for his undergraduate degree. He always had a fascination for science, so he chose a degree that would allow him to explore many different disciplines. He continued this trend during his master's dual degree, where he studied Nanoscience, Nanotechnology and Nanoengineering at KU Leuven and Electrical Engineering and Computer Science at National Chiao Tung University (NCTU) in Taiwan.
During this time, Thomas also participated in iGEM, an international synthetic biology competition, as part of the KU Leuven 2015 team. His main role was to develop a hybrid agent-based/continuum model for pattern-forming bacteria. He enjoyed modelling biological systems so much that he decided to make it his research focus. However, he realised he needed a more rigorous mathematical training before he could truly commit to this path. Thus, after admitting that his high-school teacher was right all along, he started a third master's degree in Mathematical Modelling and Scientific Computing at the Mathematical Institute (University of Oxford).
When he came to Oxford, Thomas was pleased to discover that mathematical biology is a major research area at the Mathematical Institute. He spent the next year learning the mathematical techniques and computing skills necessary to become an effective applied mathematician. For his Master's thesis, he modelled the collective migration of neural crest cells with partial differential equations (PDEs) under the supervision of Prof Philip Maini. During this time, he also became aware of the Interdisciplinary Bioscience DTP. He was immediately attracted to the interdisciplinary nature of the program, as well as its emphasis on cultivating professional skills.
During his first year in the DTP, Thomas had the opportunity to further his programming skills, learn new concepts in bioinformatics, and experience carrying out experiments in a bioscience lab. For his first rotation project, Thomas worked with Prof Radek Erban at the Mathematical Institute to develop a hybrid method for simulating stochastic intracellular processes. In his second rotation project, Thomas was supervised by Prof Ruth Baker and Dr Joe Pitt-Francis to develop a framework for automatically parallelising simulations for approximate Bayesian computation methods.
Thomas then chose to do his doctoral research with Prof Baker and Dr Pitt-Francis. The research objective is to model the processes of cell competition in multicellular organisms, which play an important role in development and cancer. A large portion of his time is spent coding up cell-based models in Chaste and running simulations to study their properties. At a later stage, Thomas will collaborate with experimentalists to validate his mathematical models and hopefully gain new insights into cell competition.
![Vertex based model](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABGIAAAKFCAIAAAAIwNjIAAAgAElEQVR42u3dybLlWHseZlSVLqOuwIOcuU33vR2OsB12yAqKFBtRokRZspXnOipDHjk898hyJ1ESKVINbSlNWZ7ta6jbqAgPUInCQbOwOgALwPNExh9/VZ2NvTeAffJ797eab7ruX+gAAAD46lunAAAAQEwCAABY9ae67htnAQAAYKCbBAAAICYBAACISQAAAGISAACAmAQAACAmAQAAiEkAAAA7sm8SAADAO7pJAAAAYhIAAICYBAAAICYBAACISQAAAGISAACAmAQAALAj+yYBAAC8o5sEAAAgJgEAAIhJAAAAYhIAAICYBAAAICYBAADUZUFwAACAd3STAAAAxCQAAAAxCQAAQEwCAAAQkwAAAMQkAAAAMQkAAGBH9k0CAAB4RzcJAABATAIAABCTAAAAxCQAAAAxCQAAQEwCAACoy4LgAAAA7+gmAQAAiEkAAABiEgAAgJgEAAAgJgEAAIhJAAAAYhIAAMCO7JsEAADwjm4SAACAmAQAACAmAQAAiEkAAABiEgAAgJgEAAAgJgEAAOzIvkkAAADv6CYBAACISQAAAGISAACAmAQAACAmAQAAiEkAAAB1WRAcAADgHd0kAAAAMQkAAEBMAgAAEJMAAADEJAAAADEJAABATAIAANiRfZMAAADe0U0CAAAQkwAAAMQkAAAAMQkAAEBMAgAAEJMAAADEJAAAgB3ZNwkAAOAd3SQAAAAxCQAAYN2fcgoAOND/9/4f/0VnBAAxCYBHR6MPHz93Xff68mn4P8EHClEAiEkA3C4XDdFoUeA/bYUoCQoAMQmAW+SiJIHjaEMBsKtvuu5fchYASPTPC3PRMO6urv6wWyHKX3wAbNBNAuCIXHSkgrF8EhQAYhIAcbmo/WhUJUStJyjxCUBMAkAoep8ltoax5WSVncbd1U1Q66P4BCcAMQmAJ4UixhlpLT5JTQBiEgCPDkVtNn9OtNVtk5oAxCQAGs5FzWabO0WviDGKghOAmATAqelI26dcSYRbSk3/XF4CuFZM+sZZALiy/3dSmldfbmE4vnF3JWdv6er8y84MQLMxCYALB6R5brlQnmn2pVZ8VZNDjf+/yAQgJgFQMx11RtZdKnetvdqlyCQvAYhJAOyQjnZKCzsdtm7r5roBb+ktiEwAYhIAZenocoaA1GeDkvc4PkJh7tpvxF1SXhKZAMQkAJYDUsliazsNP6ty2EkuKgxLd11bYikyCUsAYhLAU9NR12T7aNL8yX6Ra6kmoxe0mKzuuihf/76EJYADfNN1/4qzANCAf7ZHOtp7/FhGXop5STFtpc2fyXvv5464izza19Pub3CAvegmATSRkRpvgIT7P5F5KX5MXXgMXuRx7tRTWlxVXFgCEJMAbhuQut3G1/W1dZXVEWIySTjDZC9mMHlg0nHuvSWusASwH4PuAFoJSPuN0eoOXx1h0lwqX3punsf2eAuXGHEXPEX+TgeoQzcJ4PyAdIC8peSya/3N5lLq0UpSx9V7SvHdPGEJQEwCuFtAqljNby4lFxNdqryY0eJsD3JWKhtdX0kJoMi3TgHAUQHpn334+PmA6nmzRu9fRji97B3Yjs8Pm2ntNrOYPnz83N9vPnUA2XSTAA4ISMcNsYuv9QOrO9x12YM9ktLevbK8a2EMHkB5TPrGWQDYx590x85BSi2pF8fg3XhpuB+++37zZ94SY8/aMd9qTMoq9D4s/as+kABJMQmAywekrni5hYyNYh8Spd5++jEmXy0e562BzCksAYhJANcOSCeuyTYJS00pPyevL58yok6V0NVCW0lYAhCTAC4ZkE4PEuyXlLrctlL1yyosAUSy0h1AlYD0J8esYrd3MV13/e5GwltJKylvxN1iWGqnU/f1ovxJn+0BmNNNAijNSOcmgT0aDnpTe2hkAN58xQ5tJQAxCaBmQDr9FeyUZ+6UlM6albSWlLqIxfSGoXHVL8H8mKP+obAE8C4mWRAcIMP/s98K2q/zyug2c8793lQ4uQ05qu4lXjva+wlL/5rPNkCnmwRQmJGOr6G79L19MpQ3lKrEm/41ZB+qsJVUa2JS6jHHP1BrSfHNqyksAYx941chQHlGetWrZZPq8refftwpsA35JO/4wwNfBbNxxs/+yl0prqmYlHfAt59+7MpmNKWePWEJQDcJICEgdXvOv8+o6ftVAfabwTJ0cuKfYvLzeWHpVWOpg6ZmJZUo3Kk2I2HqLAHoJgHEZqRwrXniFqgVe0pr+STy3YV/LDL8rB0koyVSGJPqdpPKj5ZxoavclkoFQEwCICcjlZek5cPDypPSZsgpj0Dh42weJOkMNxWTah0qaQBexbGgXdcpGAAxCYDkjHRuTCpPSjEvvrDXFD5OlYZVsxmpi1iZo+613m0BRmUD8AjfOgUAVTJS924LmqMzUl+Fl6wIF/M2P3z8PHmPry+f8qa+9MfpD5V3kKvYY7m8zWu934Za/Qw9gNv7pus+OgsAS750WasI5LVW6jYu9l4sYajR9x7pl/eQKrGzylSi8ZXdY928YJ6p7P3qhYoH4OasdAewnJEOaG4MASlQ76bqj1a4WMKm8l2VSoSfvX/ve/RwsgPSTvprPX+WirdT4Px3XScsAWISgIxULUIMrZg9yuhx3fwWDEstjHOr+xomfbmSrJKdsgJPupZqqlzr6RPtMDFpfEBtJUBMApCRataaizV09QJ6HMPeCtZLaNYkiy6e2J/f/oFtpc3nqnihz22Xja+CpASISQAyUn4R3+0/CismLFXZubWdpLR5YocxjTEnfxiulvSo7qhRdkkX/e2QJCwpAXdlCQeAnwNS9fCQmo6qrBkQPkLdjWhPWb9h8vD4MxZIMuN0lHFKUwPSARe67uWOWTOj6zoVBXAnukkA3X4D7dppLwyv562Z4XZHZqRuNAavS5m/FG4rnTLyLf5Jj+wpdaYqAbeLSd84C8Cz/dOdVk8+uICOrJ6bSkqnBMUu2D6qla82LkEDM4v2CEuvL5+67l/3OwW4R0wCkJEqB6S8AvpC1fPpK0CUp9DUh2fkqz2udeqjylNx0rUeTRgTloBr+9YpAB4bkHbKSD98933jUeeH774f5k1dMWgd36mbhKWznj0vWR18uT98/Pzh4+eu+6d+xQBiEsAlA9J4qbSKGekSBXQLSenh6m4r3NrllpSAqzPoDnhcRprskllrgewqGemus1bukUWrX6OkQ5U/aUxSqri/lqlKgJgEcI2AtBiHRot0dVccBlZUoz94LYcHirnWdZtOpioBYhLA9QJSSVjabwhTRrPicg2o7B7FFeNoyeUej8o75o3Pr0vhch2jT5akBIhJANcJSIsl3XzoUbiarJ6aFmetVC+Ur9VQul9G2rzu4/d7VhjuO0KFN4kBeMAVY5J9k4Bb+idd7iC6+dIOm8f58PHzW9Uifm0z0+qnydC7043XcrhxDhx9rP4NFx24REwCEJCWS7pTwkOgaRAzNOswJQ20jBPbVCup7kobCfsCX7mh1L0bgCcpAWISwKUC0rlOqYPzGkqH7Vja3Xq43TFX/O2nHxv7XPwTSQlonH2TgPtkpK/7Wp7jw8fPp3R1anl9+RTZIKo3U+XpLrT2Rt1L9uHj5/5LDQAxCWDfjKRoLvTDd9/3m5DG56UDyu5jXsxVHLkj7U5JacjYX5OSsAQ0yqA74A4B6QbdiRMz0viph//z9vV87tegC8946S/oL6+nvcZL4boLD9xHeHK5TVUCxCSAvTLSUHWduOjCuOx7O2MWzR6T+yd5aacTO79qQ9ydvJ2hkdJCtJic7cAq3tUz0okLOez0Kfv6HYekBIhJALUzUjtJqUrZ3dRT9/91v+bScNUm7aPVV3J2E2b+Aib/uJmaHthH6oJT2qwVDrQZk+ybBFzR/91mHDqrobS3+WC86kkp8qSltpXqTuaJSTiB1FSlF5R6kLrL3OV9GbH5kNEAvH/TbzegkZgEcJ+MdMWG0rV6C3usIpC63ve8rRR4SYHNplJPe96VCgzPe4j4j+TXtpKkBIhJAPUy0hWdm5Gy6/63BrLoOLDtfQ5rTYuqFTLvN0NJUgLEJID8gNRFzI05vaHUj7s7JfMckxnaSUrHvNkbTCV67TC1LLC25GRNjoydi78eXFgCxCSArYx0lSZS0hCyiqOw1kagVRx1dvrpqvj6YxoyLY+Ri2woTSYmVclLm5ds/DVB3hOZqgSISQD1M9KFZijtupb3vNav+ETtNJSqm/TlrjuhaL54w/CP2XkpJta+m45VcJMYgAec6Bu/fYCbZaRxPZddnxU+9pi1ATIeXj0yFa6i9ipYFbBKvFxcB2LXJbzrHipwKSMvzStlX6y861V+k3Rdp1wBDmZBcKBx/9ct+xUTh83Ij1kaLvmVn9FTOixsNCsc8OKTyWh4WxfIS5u7Wu1n9Ar/Lb8QgSNjEsBtM9LiLPM2c1d2UnrmXqV7RMcWcmx8rhu/mPL0Gx6M9yrbCqxKlv46AE9SAsQk4NkBqdttlvnaMnSNrHB9TC1e8bmyi+DXHffh3TXZbq5Lvkde6prps0lKgJgEPD0j7ZpYUuMT+yWlg2PGTsfJeMbDNrctySTzvHT6HTJaK1xYAsQkQEZKlNemmC4Qd0aHJLXJ09SIu6TFAC4ZBeuttDHsMxtzwFqb2wIgJgEy0mkO2M+nNeMFAyJXmr76ZSp/uvFq44G9rc4NSBUvU5WG0rAEpdF3gJgEyEiXyUh9DVc+SCl+59AW0tRkUbXynXmatccyG2thSQcpkJE685QAMQmQkRopy+J/+MPHz2/Fae2KPaWJnfJS9e2SDoiRkfuxlsxcukzOzB3L2t13PCfQbEyybxJwrj+uVQBVbyXFV3WTQPWcpBSzP0/Li6ftfVGSfjhpwtJzrH1b8bWh9G87RcAevnUKABkpUN3+8N33ry+fwuPoXrst8jbUzYX1d+rBK2akSV3b/zk92h2TQzKe6PYBqf9AlWekUQL/Y79GATEJuKH+K+HC+Tw7ZaShsAuEpcBX3YUhpFaY2XyzecdPzUhVTs6FRqPZ9rc8KcV8ASEpATsxNwk40R8Pk3m65qcf/Dxp5P2L3Pyq+61SftsjKY3r+IzjX3SiSK2pTeO7ou6ztDPY8rXPTmKRN9vrwJ24AMQkoLmMNCm7M2qjXVtJMWEpnCUKk9La66kbnDK2Oj0r4cyf+shtpiZHWJxKdI8+0vBJfKv3+RqfrsCsv9RvTExSAsQk4LYZaVbxxBZJR2akeVja+9vu/QruK27NNMwW28xsh721mL2P8g577gWaLL1dHvXn12VtfZS8JpKkBIhJwJ0z0lDxdFvfKA/DgSoWlDsVptlV5mZf69Jdi6TTEtPi2wxO1VtJi2Gpu/4yDPOgUnIPB05In5S66FGsAGIS8NyMFA5L43R0emCIXy48o8rc9R1dK19VWS+ufJTg5suYhKXbZKS8WBuZGMeN2cKMpKEE1PVN1/07zgJwoH+cvblkuOoqLP3zHp602turUvOkyssuPFe11nDPyzzH58O9r0j1Q2Vco5jBrpv3cPb4w6o3lcIGqEA3CWg9I0XWyiU9paa6K3u/mHZWUYt5GVdvzlzFq6wvepsBhwAD+yYBF8hI8VHq4Ko6aa/MmM2CbLbT4B2SdFEq3oSH3c9Jo90mt3G/71a/t1gLt+6Hj5+77h+71QExCbh/Rkpayy6jsjwymYSTUt4knKT3e61W0inR9zbBtd8QeTPGZ8wI6m/jcUBq6o1LSkAVBt0BTWekDEmj79opgvWRqlzQFm6P0xchnMzzGZJSrdW3h7fp5gRuzBIOQNMxKXtbpJg6teJK4klvcP6mytdUiClkz3q/5de0euqIPGALs91SDxW4OuNFGlK3cK342dzpvpq/UxUOUEI3CWg3I5U4fbBWwGQefJWSevEIt1n/4JT+zOn9vWHVuPjl48JJo1ZAuoSv64NLSkB+TPrGWQB284/OqsYiu0mnJ6X99jztVrYPOrf0r96C2Cl3FZ6o8is7fvh4R6bwa45cra6rsZFr9p6zB9xj/Vv7mpT+Xb+IgQyWcADazUh7l9S1Ok5J6901FVryXnNffea95dMv1pHXJfsFL76AfrGEfuGE7Ix0e5P49+Hj5677R04LkMGgO6DRjHRMmXvibPuvX3Ufqtb7zetIvFpqPuyau4aTnLHdavgCLR5WRor4oOkpAWl0k4BGM9JhJfWJs5hidlJqyqQcP76ttNZLyU4yi3/q3l2BFlB2wh8Om52RqqT06jdw4Utay+16SoCYBHDE7kOLRzi+NXRwLFwsxz98/BxZcBfm3nGqKS/Nd20hzg8eGZZSX1V/zHv0kfo3csp4TgAxCTjGlVpJFZNSnqs0lDaXUAtXt3UvaMnFGjZFPSWgroWlU17VKcM+N++u7LAUHgKqoQSIScDlM9JZnpOUUt9pTMsivq2UV0bP+zMZJ21oSe0aw8LHn4elyFfVpvK7N9yljLyjYqbJSUqAmAScmZHKC+WSzkOVdZyzH3v8d/OtrZW3eAPs1BtMmvPT2rkah6WLBqTJuc2bNrbZpaw7DE9SAuLZNwnYpVC+ek/p+OFPB29BE/82U2e/DEmp1j2wuTFU5B5ZGdN+Uq9I3rNc+tM6efjb1zxT8dba3BU36S18vT//Pb+rgc2YBFDFPxzPLjglKdWKN5El8mKBW/LGG2wsZK+iNpyKA6aZha9XxpLcV7l8tdZvyL5p5w8c/vFt1P9ZPC0Z8TscltKTvKQEiEnAgRkpOyk1tbzVpPJeTESrdXnEG5+/2f4hR/aUNtNgYRV+5AoBa+/lHkPa9jN8SDOSUsSSCd1aZMq+tcZhqSv4RkZSAsQk4ISMlFTBjCuekry0R0E8pKPkkVTv3/haKFo8aW8t7cFaqDwppe4U3L3fdLV8olrkEbKf68QNjgu/10jNNuPIVN4OiolqAGIS0HqVPC9l5umoQdnFa5+UNkPR6UkpUKNfcUOe8VQlfaRNk4yRlJSy80k7q5BrKAFiErCrfxiuliZDemLS0W06KtkxI/IM7JcEGslIeW/w+Ix0xUi2mHMil99op4dT2ASTlAAxCTgnI6WWX+3UqacXvpOktLYeWvnrnIeKihnpdfG423JXaqcouzlVqe7KeI0ELUkJWIxJFgQHjqtInITUpLR56hrvvE0CXvxLLcknR8ab27SS5uFhcazs/T7FX5PSv+93DjCJSQB5/sF+BVP8uLs9tp1ppPA9piTtT+D4NNZ90smZzE5NZwlP39r7KU68r/buAEtKgJgEyEj1K/75GnSHbY/TYIWX11Dqz9jkuuw9vT4yNbU81G3yas/dH3bX7D2fWHjjyCQpAWIScHRGqlj9B1boHq9yVlgEXy5rpSalxYA0LhZrbVq6+ZLCqakwj1XMWuEb796r6jXeVjprM2tATAI42fz7+83iODLqzIvyKlmrqzG9PrXyi0lKw/vNm5dygM0lK866/VpLQf0NlppebrCK906GM6mhBIhJQLZ/cGQBnZFYxkOhJgu4rRXlD4mavnHf6fYrbyilbmXbX8rxIvsx+zi7AQIB6X0glJQAMQloOCN1ZRu8domdqIpV7ykCwxRTr1p5Q6mFpcALr2PSY0+8Z5LCUvbVrDUOs50hfGsvQ1ICxCTgEa7YMioZHDUkJR2kEpfbLikcllpoJQ2vIX6n6YMDEsAkJtk3CYj0R88pLK47L3/YbanwYoWno9z7Tsi79C3cM4thqc3hduOXd9gdFflcX2/+/8AvfXh4TAKQkW6VlKoUneHaOjyhv5FbZb4lVBfRXTx+W9vhFW4+NnJpkAOG4VW8nao0lzbfbOrZkJQAMQmQkdpSPinomOXaw6/h3HgZWJ4unJrKg/FmUgovJRJ4bOryie1sdhTzGjKaS0Oy+mWBxKXHGmIHiEkAld1+S5z72Vy/O3J/27qvJ/4p3HKbzaV5Opqc27f3rc7sgKShBGISQNgfHbwFEHTBHV3zAtJmaqq1WdOweVde7lrc+yt7J67Td1/Ne/bF5lL//+P3LC7fvkxSAjEJYDUjPfz9H/ztfiORsvBlxOx1Gx9gNhs+be4AW7iNUtfwantH3s9JAWnhw1vjBUhKICYBcH5SukHKKjQ52+FhchUDUmsXetyV0o819hUQk4B2/FE7s8BhXi7ffhrPfJfkDIXj7vpOznxtw80DtvB7Q0MJKIlJ9k0CFv3hUFucPr2hhWo1tSLPWF/rHiPuCiWd5zYbfXVfUvkEm51uhsVF4RvcrKlqUvoP/cUAj4pJALFVwsN7SpFzYMZ17avSolvxwYyD8/CNhT/yi/+pcPsjADEJaNwfzqucJyelcek8HgG1uJzA+BQtbuFSPThNVgO77jUSUa6SkTazU8X78FVjORANJUBMAtjX2hLSMZVcYBOY7Mg0qUfLJ6JcK2VV7//csqGUelfc7wsRSQkQk4Byf7hWTGSU4DcYCRYomueRKbUOG/98Rm26+JCL9v20kvYWeVcYXispAWISkJCRkkrwyRSFpPUMxo8t2X6nzy2FxXf8w8fLNx9T8gZO6eWSUslluvGEolrrNwx3y+ZkuRtnpCoNJUBMAshJSmsTuCPD0vwH+qTUpe+aMp9NlFFGpxbfhXVYfLaJ+bG81p8isjy2tWZ8WY+ZLHfvpKShBA/xTdf9R84C8NXfz569EN8vmv9kzOpY8dO410LR0OqJP05esiopwiI7dXtMMqm4Rln/pK/ofFslk9QKNsNxDkvX+91O+90thx2qfP2GPc7q18+LCgruTDcJyMlI3WwAT/xjx52l+MfGDMAL17VJzaWzmgnhFlDGXkzxgyRrrVE2PLz/37xm4CkmN8YQlq7bVjq3Pdhyc1LjFNikmwRkxqSK9XTSQxZr1owv/sPNpeziuNYX1XUnjYQfO/+vec8VSHGBnkDd3kvJTLbCVziZmValPzZ8GZF3OQ7+RM8Plf3ix7dNdlsvfDWzP6rvJ3opokBMAmSkBmLSUKNMlpgrXKSh+gHrDpQqrDXDZzv132dfzbUxeC3EpM0HBir1tbxdWNxP7qLUIZHZn80qSanKDTyJ1uX3yeInPemFTd6ImARiEiAmNRSTxiVUxa+Zh0NVqcYqFpq7ThdJ7TJV+cnu/QDIihlp+P+pfcW86WqRoze7rAVIwgMvu62JfCdunBVe3yX+PqmbqMPdwsilO5cWwFBEgZgEyEgtxaRut7ndtcZKVUk1Xe2Vx8bpK+bgkT2ijEZBV3Uh78UAs0eGyQtjSU8Uef+sF+7duZ/K8hsmPESzK1v3Mi+XBv+rOgruyRIOwN8/ZSqzKdQxqp+i1Okim0tK5L3CYXWHPTJSF7dcR15Cy4t28UvnxWfsyVIoV9kpazOlh7/+yFhXI+aH+4XCu6UVOP2aAjEJQM6RvhKSUiM3QLgIXssnp6xftxmWMvqQtdYnjAzGtX4nrIWlyBZx/IDbpO2h++ie9FWChRzg3jHpG2cBHuwPrht1YpYIP0WVLSxbO9WTFcO7Gt+yF17B+PFX43wy/jdn3R7zF5+6lkDdeHPW9yaTsJQ0jDamrZTX98v68Cql4J4xCZCReIqSYXIV958tVzLyrYVoPUluT/4kDmGp7hjI7At9v685gDzfOgXAhYr1p5WP4014Gyln6164Dx8/T7Yb2ikjtemH775v8I1k3HgnfqL7pDS+i/p/POzEfvj4uev+wO8rEJOA29BK2suwK2hTIeceye3gCviAhFZrXcQTr87p33r0abNPSv0FukeKBsQkgPy6MKMXcUBG6l9ba+2gZpt4k25AOKKogJvSzk1VcX15gM7cJHiqP9hpB8mHm3cGUhfgHp/hJ5zq8bt7+/qW6041udYN09Sl6Y6aNPVqci2WeF8/5v+x34EgJgGPzkhVyq8nZKShhOpStnMdP6piwdp+3JrkpdPX775uvV5+q/yyBF/waK3dVBpKQN2YZBVL4NCMNMSG1qr2kgJrsy0QftfhReTmG4levRAPF9nD5rPj8vc5ufr0T+j4bC/uu3qV4F0UtywLDmKSUwAP8/sn7q8yft7s0WjzA4YHax1Q7Ma/i8m7Tko+1bctaiQwbwbLipdVqyEpI40/Vm+3uOv2/vqg6/4TpwLEJODyf6MfXPHMnzFpNFrggIHBWgdkpMItNfMuXzcbuxg+1HW/+G92E+GWw2f2dLjAeR7CUrPdYAAxCcj2+3WbOSUZaV70x7+StVc+HqyVWlgPqwkvVoe1MlJ8sEnNutea8aXI3mPE3eSLg9TPVMxHJjAGr+Rli8GAmAQ0JDufpFa6MT8ZH9siB2vFjMQbh6K1Y76tx4+mCv3y85ad0PbOSHdtKNXNiovd2j2+Cpm0lVpz4ioOxt2BmARc1O+vTZePKdcW663IB8bP2wn/fFLNF142LT7nrK24cLlmyIlz0m5j3HIsKcSHVlKtZTnCi2FsXveMfk7dtlKDc8ZyV3EAxCTgRgIF9OYKbOHoklGXrx0zu8SfL5um9Kki74rkPerchtKQq+dhO+MljYfbVVmWI6a/Gv72Ie/EVlnaYXj28vA5ibKFh2pt32rgeN/oDsMz/H5S2yf1S+66wWbx9ZRnm4q9lCpb05xyhLpn8pUYPguH6lWpoZMOslm7Lyao+JiU9K1E4Sld+5BWbIuVLxpRGG/G5/ZVMEfxlxCYPoXMuDu4DfsmAb+UudlDgOalUnk5XvJ6rnK2j39f5dOKxpdjvgdu4HoVvt+DG0qRzY1ftmGNy0vhsjupuZSaTOYfzIprJ2SMIVxbfDyvrTRfl39oI0ceqlY7q+s6lRXcJiYBt/f3Umup8lKpSgA4YCfTcw/VfkKbL6AXXrEwMjIdb4gxa4OpxkO/Msrl1LxUmDpKxqC+Ku0zNo982e3otQsRn3ACt2V46cvw9TJDCcQkgGqxZFyHcbkLHd6OKfLKLkam8lozr6EUWXDXaiYE8lLqCK611FHel9t7De7NvLT5AiLDUszmzmttparto/kvwP/U7xkQk4DG/UqABtMAACAASURBVD1fhVrhLT7NVj9Ri5vhlkiavhL/w0NdXqtunuel7HNbZaWHs26zeV6KD2nhi5IUO4e20vwCbb4GDSUQkwAZiWclrqT3cvp7H15AzA7CO/UKsvNSlZXTarVqyyd6lXTGchYff381Y5pIgcSet/q536UgJgFwRMUvrRWmu7Ww1E5AYqeo2fjNb9wd3MO3TgHc12mtpKamJ7WWKF5fPvUvySSuwuvYn8O3n34cL8/QD9DKzkhD46KujHWlW76Za72dvLB01lP7tMLTWBAcWEg4hpccU+ufeLYPft7yhcjDK+y9ZW2SA2kROu0eVl/BtekmwV39XVGnq7pyQFe8O+riw/WUqpz2inf7Tg2l2zirlXQtHz5+7rq/6zzApZmbBNy/vD53fbDNZ9fBg0uIaSj51gPEJKBlT28lTZLJiWEpMv9ISoUnsKuxettOGmy/ZJfyWkmLSWlxP2V5CcQkgLbq/rXnLdyaM/WBqaks/oyVn9irRLKzXmc/7u6WM53Gt2WbqfISIS28CzMgJgFtKm0lXbqzsfnKh7lAGW8wPsbkHV9P6YpZ7kInZL6CSFJS0kqSi0BMArheXRgfTjLaSpNpToFSqbCWvXdS2nvcY8VxdzdrKK2d+WZHKgKISUBdl5+VNM4h/TSAmMI6I1pEtpUWpznNX2pJk2rthe2UKE6cMhE/Sew2WbGFDkz4ZEYmJa0k5wEeGJOs6w+30lR9udl7mf/kpFzr/zG8ulT2Ww63lWJWqNvjtI8PVTHSjKPX8Ulp/KY2w1LJyWyqPXL6wuKR0f1RPaXD3uzXD9p/5m8luG5MAm7j7xSuUjD5Cz57qYPFwPMWLM3DVcvaOrxVGjiLjaBG0ubaa4h/47s2pkqi+2QQ4zgZNpLzs8fdDeno3PXos8/kYrq7TQul5DsCrSQQk4BrKx8A1j88dTWn/ufXysr5v0+qQedJqWI9PY6Xeafu4DlF4fo7vonXTg6smCWO741McsXkXZR/eTFc0Mgrm3Ey+5MWOPINhkGOf0HddSVDQEwCisrozWJisYB42/oWdu+yY5yU9ijaWlg+IekFTK5yfOPoyHcav3NU47V4YARdzPnMO+fhoYlrgbNkyOJ+X740EpB++WWSmJS0kkBMAm4YliL/dk9tBw3Vw2Hfy8Yv6uAqN1KepvY0WnsjkxF05Tm8SxktGTk1rjtkdOWJd9pwFVKjWuB32o13xwLEJGDs76yVDjHFWTggNaUvbtqMK4UVpJ2CKl6LefMztXWwR+SISRrZwz67Q5YxLNx5LC8jrU10LPydFp+UtJJATAJuWzV2waks1/pKdW1Fh0KPSikH9G1OPJ+LTx0YNTq+/3cKSJtJ49zFNvJGe+Zta/YW/aVMfy3mPzn8m8W8lPSlj54SICYBC2Hp3CZSSYGyU1K6qCMnGh0TIfZ4isAx3w5f9KJ81ZBGknaXuPPYOCyFP/sxvxzmeSnjF9rQoA6MK/arBp4Zk+ybBDfwe3nz/i/9HerNktIlelmX2B82b6/hrnjxg+w9jq97D5fsPBZYkDAcWgJ5qeRrl26vaZaqLLhwTAIe6gbjTCompSrV6tWr3sjzs7bl0bUiHxXD0iQRJW1r280GPTbV39ZKAjEJ4OlJqZEQcnB1G7nq2tr+sN0Ok2oKdzdW1J6SlPLuhPEAvIwm0jFJCRCTgIv6PXXhFVPKzUrk7uxFCK579m5z+2W/l+N3BI5MSm0uqgkc41unALi6H777/oB1kJMqxSNDWjsL1n34+PlCm/PS1Pn/8PFzeSaptVvA+DgZn+j33yD8nhsDxCQASemXevFCpW2Dr7akNoV2khJwXQbdAfeRN+irbgtiONpVRqBt7ql1lf6MVtLVNTX0bvLhvfHqLEAgJlmqEi7tb/ubu5utjvUWvRHNfl8Sz2fstJZD1havG7/y0wfRdQ+Y6aQEb/NXyuSKFFwmhRZcNSYB3CojdcMuKEv9nEkuCvxkYfBYjEav2tuY1l0RLn5bpCMjXMxJEzNOuYuqH6SRhtLaIuACLYhJABfOSJMINE5B3cpyw/Of3KkSmkemU0qucKlXvtfq3iet2ZfK6cpX9A5vlCQpgZgEXMLfzu4k3GNjkMh6KL5yWkxWkSc5tXhqp3VTPdXst3bf5N+bWH8/5zaUYjaTlZTgIax0B5ypZAHfXXeB/OG77/s/fUm06xSmg9fRarzCS3p5py9BPq+ezz11l7hqUUnpjA2LYjJSI9caEJOAHQuC/s91X/8x3zf/+j/73H1dcDxQGBWWiYdVXYdlpLy3k7056dVr1sK30N+cQ+RWwe+akSQleAiD7uC6ita4+2V02SgpXWUk3mEZaX7G9pu8lDGSp82+UPaS6IWbkzayLt8pzzh+46nztfqD5C0usmuX9cihd6kZqZ27DhCTgJql1bj4eLeOdkpkGv9w+ZzpZqPa5K1NJi/VXUJg+H56p8Lr4JJuvlRd4NmvXm6Ow2H2G3klLmS/udLA5tEWpym+zcLPWuckbxX+A77RSPqNlJeRJCV4QkyynD88NyOFip6t8XiT4/Q/nxRyFh8yf975D7QQpSbNpbrRYo/C68RibpKXdto86sSCdbIDVcbLiF8APT5Khe+l/jjhhR/HEWjzQzf+RNT61uCAhlJJRopOpAotuGpMAtiOTLFF0teQE9OMii3R3genptpNk3ddMVfUrfiPnJIUs9T43pmw5PWv5ZPAzxfuQzr54cAC6BkJZF7EBwJS3a8PClc73Dsp9R/bin3gtTGQry+fuu4/93cKiEnAMf5Wxt/u2euA5yyoHWwuJXWEknpcZ4Wlt9q1/niY01o34KJDfTabS6fEy8kDCwcKxr+SmGBZeMUn6fSALxqGsJT3a2r8xrOTUviXTHkTqfDmAcQk4KECzaVGRs2VV1qHJYrClkIjraRwFDw3Kc0fUj5QMOaVHJl1+9ez9xcl868PusQ1IeY/nJqUhgg0GRM7/hJn11bt4vhJQEwCGnXKlrLz5tKFMlJGRbhTybs2QSVyjn7LX2nvNKEo6ev81IGCSbODwlODbt9tiB+DFz4hfVLqtlphk3F0k6PtsUxl0s0DiEnA3v7W5Gv4zb/1X+ftav+uWrppRtojKSWNxYq/E26flFKHP0U+9bg/kNExmzykZHuoK17icFiKTIxDgygwlHezv3eJ7w4AMQkoLS4XC+WWa+XybtKN+1F73AlH3gxtlp7h5eMyXnNGVpk85LE1+jwsZbTUFgfgVVyM4Qb3PFAxJlmnEm5VKM8rwtNbSReVPRmjvHIqnKwfSE3N3sCvnc/buTPshzeosC5fCm8yAC9jolGVCxG/REfX/Rd+ncJFYxJw59RkWPzxVeCb75iv8AE5+KMhI4UjfcZjT9zQ1qWEJ/jWKQC1CC3o910Z775yrRdfa4/OG1eu5e9xj7UB/X6TkYBFuknwiErizbi7A4UbSmtl7h49wGt1MCruesQud/WNpgVq7gFiEiAp5aiw1EREHLpHDXfRclOhfGlnXTv3DIhJALO6v8Z3yY9a7E45dUx0UblyzM3mTgMxCbhnTaChRGr8OPF5m61Hz21DaYKd+HFw2kFMAlr2f/qr+iGxtkpZlt2fSUrUtRb3m+wv1MVtnewTIbHISEB133bdN/7448+l/hRVzP0+jCfqx8ud/jJiXuSTa9CMDaPKW0+TSvTDx899xrvo6n+B1Kr4eNjl8NeWP/5c8o9uEjyuLDhx6N0p8WOIZJcLP6cMrzprM+K1dxpuLvmOv/0re4kzE7jx3GbwWGIScEJGKlyGIfLhfUAa/9j83zwkG0fWeSWVdMm+ujEvb7wn7MNr1odPT6r43l9xC/fLSCAmAU+psZIaSuMBcnnZZjGc7L1g3eLB+38zvKP289KRBXF5tyEvKSW9wXFYKq9fn5Y3TlxkssFxtuFLr5sEiEnARlkzqYpSGzLVs9BmvbX5Cof/NPzk/JhPm55Ua0RWSU8pKd505y3E184lSw2Kk1t9jzt87eO5+GvkGdfov/S3CYhJwGWsNZRiSqihIRNTY4V/LOnL6Xlsmz82NcKF38vNRugdFiqSktIDt6Ytb2HN1wOMz0vzMajZkWkx6uz9eenfe/nYy9QepoYSiEnA4+SVSsPX0oGHxESgmKS09kTjx+4RabKPXPeb8ipVdeQ8roOT0gNLz6G+f1WaxxWZlxbvgbXItPm5PiYRhT8UJfePzAOISXA//0etv937hlJJrbPZiimsomKqsZi0tvks4cduhqV5wPil39VANRY5mm6PqWLhr/zLS9XLfcc/frV5LZHA+83uLy1+yha/PTl9JOo8ItY9h3e62YAqMekbZwEeq7zomQeVjJFvk/Ir6QjH7MI0Wfsh8hzWmqhzuRJtfEHfLOS9khhT1+6LPGnzvFT4m6GRJU8W335qW+mMG0+VBReOSQB1IkRhR6L9Begy3mDFJQ0yVihOWphhp7UHf749RmGgbql68DSV6sePDEsZL3KclwoXuOvaXnThmO8RNJRATAKa9qpXc1efyZNdZO+9OPjVk9L8os8bBfPjN7Wd6Dws1f0s5IWl4Qg7lb/xryrwMu63VdTbTz9WXzU+ZgCekAOISXBnVVZ5YijXjgkS1ZfJXium58nk+LwaOMIQlqrfvYX7KWU0CoZPYre+akLey+jeN4Ju+Unf45dYOPG+Hjn8FRCT4HFJqbvjd8z3lp2UUmekrKWm+9XZazNVIj8di+sBRM4RWny6zSh7QN674i+xzZOWsXh3Z39YQEwCYSmplnr4jpyXS0rHl3flE8yiVoHf+X1tho21lBU44YGjVW9f3Hj/3PGIu5imaJfVG+x2G1GpoQRiEtCg/z2w0FOgVlsrON5amrsiKTkP1dNjxsyleRF8+8bOWRkpfMnCwSk+z1R/C/0x3RXwkJhkqUq4vMB3z+EltiSllhPCWV9aZzeUkh51ZGKcf0DCn4uMRQVp8xdj9WUV10a3rj+LKgsuHJOAu4WlpHH8N0hKeWX9ievyxSQEA3uqn5mkDYVamO5/s/FdGQvclZ+EiucwZjqcXA138q1TAMJVyzui3NUP333f8syTwzbtPf4kxBTNO43XerK8jHShcP7h4+f+z+vLp+FP1/1XLj2IScDlk5Kw1E5SumIPIa/DlpqUrnVmbhO3rrKt2U4BKbVF3//xKw6uzqA74Oe/2juLOpyRlN5G63E199rWi+OKobrNZS0saFY3ZZ04+LDk4e4BEJOA9v1vB/xtfdZUpeO/q27n2/E+JHSzbV7aKXDXXnbF4BSZlB5Ys94grV13uJ2MBGISoK46Pyk91mIR2UJx/ORxVnLOfgFbRgKuwtwkYCkpJZY4w9Smh0xwqrXCQbNftB+fkTYnKR1ftt5jZlH/FvpP6K4fz+Ep+j91p+gUXovUh8tIQGffJGA1KUX0lIaqa/hJ60DUykgP7EIcMEnplFN6+pLWk02lJ5/Zkht47VkufSfXerVfs9l/7XcdXDcmAaQlpXk6elfpnjRYK/55WxhO1vKEjRPPzzBTK1zrk13xD/84Ps/xH5y8K1IlKR0Qt16zWYKAmAQ07n895W/uPil1szFm4aLq+KQ0PF3/Im8zo+ZaX8NXue6htfUu2AhquYJfbDEFPj5X3/goZtydgASISUBaLZW6qMORSWn8RDFhqf/5wkBVeJBjKs5A0y8vqBx5KZdvqsuOQqy1JvVQ6MccKv4Zw6PyqtyupwfO19YvMQOGATEJHpFtKlYkr0MWvsuLHIuF9RCWFhet7v/lOFAlPe/4dQaeqFZGyruUk0sW3wlsNiPdICllf/rG7zcvL2V8MzLOS+2c8OyPQ8xn/IF3FyAmASeIbChN0lF8Qd/FNB9mB5/8/OR5854xqa10ysilvMjUWkYqr2Xzvjuo8qVDX6mnHifw85t5qcpcoK7qFl6nNJSSvuWRlAAxCdilyEhKSovRYq2gn/9kZPOhi2sZbY7WCwehyJlReRkptbgMX7JAZGp/7Y3rfogmqabikLl5XurMsSn49SUpAWIScIS14js+5ESmpuy0tpl24jtF4TF4Dc6AD4TSPYJxrYx0fCGb3QaZh6JxiyZwwIynG/98syt/VGzNpd7hkhKQEZPsmwSN+5tn/YW9x6yk7GUPAmPn9ggPeesfdCtj8A7LSNmXrNaGuak3w2GF7MEbxYaDUDgsXWLt7FN+HXXp0wgboMqCC8ck4J4mdeFZNdPQYbjWat0lg9DylnZ4WtVbeH7ik9I4jRxwGuOH1S2+pNYudPV7r8qqJAd8hDWUADEJbms+pTvmx1qLHNc17s803ko6/jJVeaJwLbu4tkF20R/5wLzBckOyej2jNE96p6lNpJqfX0kJxCTggalpMz51N9qn9dykVKXS2qwsj1m3vUpAqnhrLdaymwPeStJIePBe3mEP63Q1ZbPdfVZAmtxdhRcXEJOAtuqPpJ15MspBWqgsm02Gk/ld8+y9R9sqfiuhkiUZwkl11yR8vy8R3uXnpbPXQvJfXEtQcAIxCTjd3/Q3MTHRt0qCqjXu7sh5aMNX/kmflPFQt8gAtl/6euDNvLYr9PwuauF+XgtFix86NwCIScDNffj4+a3SOK4HTkw6OCPVvV5VosuR9WtebRo51C21N1uSlG4ftE4fR1f9l+ROX1gAjcQkS1XC40rqp7n0GhLXuqDx57mRixJOJj5NFc9D6vS5i35sJ+sWqrLgur51CoDA3/fN7D2SXIUrT68SX6scp+Qr/GEAXpWTv3a0Yx5e997r//zw3feFZ7i7zhIjAGISqKo5ItSVXI5TruYp+8y28I3AOAb08eCxH6VxOhqyTZ+U8sLSibseFx6hPB8C12VuEoDEuxDw5vXifqmskQXN+nNefuYvOkNpyANrl+PnVQrjFrSQLl5fPnXdn/arDMQkYA//Sxexu8jBFbORM81mpIqrOOQtO1bSMWgqKT2yoO/iJ5X1YSm88tv4aIX9HGvAAGISsFC3BQqRmJ+vXqy/rVRCnJuRKqbixcsac62zq+H7lcKXaChtto82Ltnsd1GgDXXi9RW0ADEJ7udPz0uliosX5xXr43/5FsxsV69LTqmuLt3NeOa8ppbzdswlK7nc3SGrqsg5gJgENFRgxRTr4/3pw32nI/cbbUT/lmNK1fBpPPg1X+sr/5ZT2VkNpfgRdOVn73Lrv2femZbGgafGJCv6w31UGauTcYTF5w0Ep4xqqS/pmtpEdfOlJtW13RkTz5qrR99X0pt1/Lsffnwte7PNW2+RspRYcO2YBHBcihvnpfiiZ7yydstJKTUgjU9LlZXBSk5OI2XoOBpdfcTjYQ2lvIB0cPY4MeoYtgeISUBpZXbMuK/+KWKaS4vBo8GklBeQKl64cN64ELXsJPzsPcXoQjdGdtTpPwuBT4S7DhCTgPMz0iQsreWlcPDok1Ij9U3hNrI7Zba8kY3XrRfbnEOSl36TukMXysPHd3UiPwuWHgHEJHiE7MrsrGbIPC91EZ2ZoSV1YmVf3kSqdQ4nxegV71vDorpbzy86PmUlrTAhRAFiEkhK9TNSxZed+mLCA/DG9U3hanuLpZL1r5oroy/bUDolIB3f3jnmGesurWmxOxCTgAb9N8fsMnnpCmCSlMZ5Zm21vfjlkoejpW71e0y+nb+kwp1wzmrpnN5KGiYCVdx5LPJTpoNU8oXF/LxpSwK1YpLVKuGe4r/Dbm3MWHZSWsszk5/cDEt1h9LRsvlq7P2/yd6wKBDR50d+ZkDKTuOLj5q3eVs6n0osuHZMAmirbC1ZQDnpJ+dhqYWAdGLaPEvFr//jx0etxaHJ+uzhQ73SZ9ONH3LpgHT8YgyLTzf5/O70koy7AzEJuJVJwb04SOzJf/FP1iVPPRs75ZnJMtDXukDtb1DzSuk6ztPUZsso8q57VVp6pPxsn3LJdlqMwf5IgJgE5NSFi9WDr0gz1o045iUllfXPyTlRb2FleFtXvPmvfsLx2rkhXxpKICYBdxKuMOoOJrnE5rZ180zd1zw/2mIf4061WvUieNjGqu4Zq3uVdTx2CuQ7HXZyUwlLICYB1xZZjS1++84p1ytwCRZ7I+P/1ELx3VeoJ379P97qt2Kf8N4Txs66ZDHPm/HCdn07whI8x7dOATw8Iw2FxQ/ffb/HCte31xfQe2ek+ZP2P9z/n9eXTxWvXV9lZmekpCPULWfHGan6Bbqxydr3Tb2wvNsj+x6OvK82P3evL5+67s+4tUBMAnb1Zw4r8p6clM4tpjM6FeOH9EXbh4+fT6x0JxVtTJ06tJ6qvOzFjETkhev/HH//BJ60/Tlye3xJAbTDvklwT9njr6pMVcobnvSoSU07vfjJTrtnZaRxBRzYnGpoPXVP2hK05YlJQ2g58RXWimq1ht5txu/1YXjqK7h8TALIT0rzr1EfOMapqYkrVZJSUokZ+MnF4ywW4oVF7TNbSeUzweaPPT61ThpKw/OW56Uj51w1uGAmICYBC9GlQpW8NEd5MRQ54YUXq/o5PKunFFOqbmaqLr2VEfN9f1P1a5WOzWQmWMbRSq5FrfhxevNqj29M/FoDMQm4s5+/VM7aTDOjKr36t7Dz8ijm7ez3rg9LSnU3Cc1oZVyrjzT+CmPolpQnnIyEWX4tChPO5sOr9IKO71LOF6MHxCSguTqsVsHtrGacq5g6addze0BSiixAU4v4+NL20mPtJnmpPA90OwyWWxs2WfLiD+4g3WDHZEBMAorS0R5FsIwUf/7XtoU98pLVTUrzySHjQyWVnqmrqEXuqJO0hPrpHcu1rzCGhBMZHsoHLuZdu8UklvTiUwNSrYSTdxy/AEFMAu4QkMZ/nbcwL2WxKm18IMrBlXTJ08U/MDUphUeCLU6130kLq64dKTJvVBksl7Fh62Y8C7/4Fi5lOPYDzGOSBSvhwumoW+paVBltVeWb1PjpOoby75rHYu6HmEK24ipkhdX55b7pjxwNG84b8QlnLWSWNGc2ezKLL74wIFVpKK0dIXwbl+xP0HW/4jcP3CAmAe37laF7EEhHbRbo8T+Zt8Wqm6M8KR3wTX/d6jwvI7W23l1qWEo9h2dtSDV+8V0DHZvNJljgQwGIScAFLG1fmFkW3+A8zGNY0vkJnN68tJb3vEcW7ou3xFVGtY2T0hVnjOQtrJI6Zylw6rrDVzKo+HQlh7J4AyAmwf1lrEubnZTKK9GM6j+cGTbbaNnbO07y1YV6DiVJ6XLTfoZy/8RLsxbR937jXXE77pmBQUYCxCTgPnX8JKKkDjJMCjlrDajbJ6WuxvLTp4zmOmso1OZ6JOF5d/co1s9NHdU3htozS5uYBGIScIWa+HIlWuF39pEhJ/wzhyWl47fine+E++QcXnKovNT0HLWy9GSbpsI10xNe/E2/KwHEJCAzKZ0796NKIRszfq/We3yppY5y1p0Zc4kDqcm4r1oZqYveUMs5B8p96xTARfzKhToALSSHxZ7J68un/rXFvLwnd13uLemy5t3M/T1W61Pw2Lp/8Y33SWlt+KWMBNRi3yS4v5iG0i1Xv530lKqvKrHT62TXgPTzegZHLax/4zUnT9T8ohQqK7hJTAIeahyNFldNaCTelB+qpCCWYe6XkbrxZkTBiXAu/bm/oDY3O55vqHVucHp9+dR1f9a1AzEJuIzhK+21aHTvYLDfTCdl9JFFc8mpXltu7l2R/T5R3+ni9vnh+FXgs1dxiHzUWbtCAWIScLekpKYvqbNr5a6SaPrkYJb93iOX5J60mG6WkbrrbKCU9AofuysUICYBgz9bUiIfVl7feMehxchUPYMdk/fqrJh8VG06tJIyVinM2LZomL90gzt5fo3y2kpHXuuMJ9JNAsQk4KBIcG7aqfUCdjrIeFmIKqHlsFFe46e4ytIC4+F247AUk5dez14+YS053LIDc3pSMjEJbsaC4EC1EqH/0zWwCMROuWItzpWcsfGy0XsvQT55Lx8+fg4srNxgRhq/8v7PcMu1nJH681w96pQ/MLyydlO5Likp1U4+wEPpJsFTMsxOa2Gvfa/fwtC78tcQua9o6hMFRo4NSanu2Vs7Zv9vmm0rbS7bsNZc6v9NhVGFd58JdkBbKb7JU+Vl1O0plS+VCVw6JlndH8gsu7snLZdXJSnFRKDAALxxsy7+3G6+tgb39ul7ApHvcT4G0hyV1FUQjl8Eb4+MVDEpDRF9cYTt+p2ppoJbxSTgQn71yOwxr/6TvlgtTEqnB626z56abRZP/uLq1eHDRr6LPik1EjCy1/6eNJdkpKRo0d1otlJhUlob6pn3mxAQk4BGFZb7JQthnxh1yt910sPDeyvllVPDMReHkG1Gpoxg1jUwAK9wf6T61fbVmqIl8eCsttIe8WwyTyn++DF34MoiLr/qrxsQk4AHqbI3a3epr13zItZa863kjffH3KzwAnukZjzjiW2lKhmpwTGE3ddhhKlV+3hb2JjHVlnnfXKcKosihA+yx8WanIrIc5h6B1Zc9BIQk4CLZaTydlDGatdDzXGtCU6T/s8pr3wocwuv11tcPKvYB2iqj/TuPRZ3JuenaO0CTSr78bawmxV/9ek9VY4fc1mrx9rF3aLC579/ywbRAYNv9Ijhav7n1EXVDljtrcqhxl/HTorF1Bdw4j68dQNSyZLW5TXffLDfrotHV1/cr4X8lvoyMsaJzfPMTkkjNSklrcNRd/X2jERX+Hl5ffmkoIKb0U2Cy/nV664gt9ib2lyd7Mj5IVVaZ7cZfjO0lVKTTN5pfM7SiAEZQ93K+4d7yBi9VqundPxCFDIS3DUmWbwSOCEpJa3d3CelzTJ9nE8yau7WyvTC79erxEvRZX5rdSd1SLYvd9UBkIuHinyWvM5Mm5PKoqmm4IYxCbh80RauPNpMSpkLFq8sUL6YuN4s2nvNCF3raFVq7sWKf/EDOG+TXrTiDwehcFJKGmi3x1W7zZrmgJgEZBYT8YGqyiiyujsIFS1YvDKFaS1ZRealm7WSxqdLSswuuNfusgdBdQAAIABJREFUmcDK75E35x5qbauavXTHjRcqBMQk4BJ+Lb6mP32T1p3KwbyfX5tmY2hZxeh46T2FSyr+QJ/zIany9E7gKa2k15dPXfdrfl3A/XzrFMDtNbWowLkjkX747vvhz+vLp+HPjTNS/04fe+dnLGzw9tOPpy8MfdbIsfjnnezmVP2M5V07gIp0k+BBSen0MNDUbI3xkmK3HHHnXh2u7+SKh3/40pm5ZNxd6gPHSWmPkzbsdBz/QTZUDxCTgEOrz1uO3JuUWW+3foNvl1r3r8r9Nt/AanNW25N3F83OGHufsZhV9U48b0bcgZgEtObXMurIc9POq9KyYzt9YRy55vhVTtSTre3wG4hMheuzNZeKb9FXifwgZOw0BRAZk6z0Dw9y775QO2GptZN8uSXvsm/UyEdNItPDPxH3iFXn5UN1FNw2JgFPTEqRFWQt7XdIhgKrcAzeUKZnV/lDc+PEM9ZIzHulRNZXbr6933cHSYGhzYykpwqIScBpSWmtLlwLUa+C5HDJQjO97B6fotTiuz9L76Ja1bB0xT2UhsC5GeObyjmvNjZIjRyEJiOVxfg/5y8UEJOA1vy5nUrD8B6sGclhsnZwgxYL06GtFPOWF8/M0LiLefjqC6gUlvrjXLFtMnnBi6m7tT2UG4n6taJU5FcDTXy7Yb07QEwCzipYIwujSXukvHzZKWuFX1jMGLzACYk5Y+HvzsvD0vixMeewkbXjw6e0zTK95di/U66YDOItvxaG2wFiEnDhsLRZm85rnSpJ6axviwNj8GLK9MAZS1rOKzUszX/+ikPv2CkjVfyFMPlypGJqAhCTgOslpW59oshioVZl78u6Kxwkfe/ezcbgxbcyFs9Y6hfnQ1gKPyq8oerNklKt8XL3GHd3+pCz8AjJvNGnyd9o5J6E9MmEJibBzWOShSzhun793D1A54XFeKBdxSJm8pDItLBHTTkOS13il+XjM7Z5ojYLwcWHRwbIQFIqvKMMgbtczsnOFTG78RampiNP+3i9kOgXpoKCm8ckgKKwNBQWO00qWKvhTlw4u3/qvCKvyomaj8FLPRUt95TOyloVG0qFc8kOfuDB360spqb9foEk5b2ssASISQDrpU98iVNxU5eS5Q1OrCk/fPz8VqMirL5u+BWXbu9qj5d7ZbUKxw8frkXepcnL/yX381lT/gKTmsq/xYj/DTO/0Jsrr7y+fOq6X/fLH8Qk4J7Z5sTxUdXLssma45tHrrOYRDPdmPzBe6O3MN4YtyQnXNfiCNLFmj4mI2Xk2Okex2fvMnz8b6SDnzTcE45f0B8Qk4AG/XojyzfnfWW+uRRB0iiy8QMLI8QBReFbAwsfD0lpsjHuOCccfHe1M7spdV5NYOWSmMwzv9sna7gnPfbhYr43iZ9bZcYdiEkAJ5Qya+Vd4SCiQGSqVVPeY8m4wLu40DyN8u7o5sM3d7kN31TZDaLNIXxV7uc7bcw6P1GL24XF3y3aSiAmATRU6NSq2ObVUiO1YCMNpc3v1FPD0kUnOGVcvvFbTltWftYgirnhF4OWPlLMr47Jv8lYf2UyHtXEJBCTAPZV/h12xsC8dirL8qR02HuJCUtVxiZlp6yShtLBo6omDaIucYnCbqU9wt4fh1lYAu7s2677xh9//Ln4n9+49F/bk1kE3aW+IP/hu+8fVTN9+Pi5TyMqxVr3T/byG+PFHnb6JJ7wrUHuC3j76cdjf2/4e8cff+7/RzcJaCUpHdbqaWoaRiND70q+U7/6NPfrvvK6d3IfUTIaXKc78uP8+vKp637DL214AjEJnmW+n2PXxrzkg/eKrVhf3mMhh+yw1LW0xsO5y9xf1+JHr/3lImUkQEwCakajeaVbvkSYSeSFxf1ZDaWMuexr4eS6n45Lx6r9ZvdVXCku6TdJfDvrUftKAWISkOc3AjtjxuwNktoQmBy/VpV/ZMVTq6HUH+TtjI6KVc4WM8+1ks+5FzHy2ecflreffqzbRRy+bYlc3+L486aVBGIScOEaMT4azZNSF/HN+mQ6yh5J6VqGWu3niVW5BfpjT+Ae4mP/k0foFbZifgkzK78T8jLS8lNkrZ8OICYBmdEosr4sSV+NK2wozR9b0laSlMq/JhhOe0zsv01GyriN99iXLPvO3xy1W7J++uJ7z3uRWkkgJgEX9RtVyr75xiCRx3xalR+e0ZHRVuovX19rHnMaq0xMGgfsE1PH4rM3tUhJO/ftTjfYcOcn/d5ImtnY1BqVwBNi0jfOAlCl6r1iUsorvDYfkrr83XC2+/8dak0VYXZGmsT+eYP0gcviHbPa/s/PtRVQX1nfCJySlF5fPnXdb/qgwQNjEnAbv1mr7HtU+RhTeGXsehk/AG9+tod/fFpe6kvnV1bC3Iz989NbKymd/gVB5D188BIp3XpzyfKYgJgEXDUpHVwvNjiWJrBvTGRqihmAF75ek7w0WWosownWeEYa78UUcxvH3+1riehOPaXJ9Z1M6Tnl87XYXLpWRtJKAjEJ4OSkdKLxbi1r0ajo4MUzZPIG403e1PwFNJsQYqYV1XrxN0hKi5m5yoIHdfPSkVPvZCRATAImfvP0mu9ySWnvr9vX2kqpV2o+GC81FzWbENZGHq69vLov+9JJaXN/2HZatYvfR+QdxJg9QEwC7qPNQV+HlVyTdR1K6vJJcyk+Fx0cdcofuLZIffV3WpKUXiup9erxfr+kdInXrJUEYhJwP600lOYVUgtx5cQnHQbgdZX2uSrPDOWNlPGSZW/7BJjukPGBeaeif8jxSSn+1tV+GZ80q8MD8THJguBwS7/VwiCixiuzU2rHn+NEpcZarYXa8uLB/CqnroQe/xaGF7nrjZ10/HGb6+CBpgffug9MWa8vn7rut/xFAg+PSQC7FBkH7IhSEjZ8v14YD9bOXtKSFUmZ55gZRDHPsri8RGpSGobMpY6dy7h1NZQAxCRgkNlQanku++KSxxlVZmfn1pT7YV5zbxbl3T4D8E4Pja/iRQvnd2DSbXyDtFOe2fZOfVpJgJgEktJeRUZeBTOvfta2glkMSzFPeqfv1PdY7W3t9K5dkY2rWbBnVGtJKSYgbTaUAlkoJiyV3L0aSgBiEnBy7V7+PXE4F4UfGC5Sy8vEe9eaMZ2itMUDVtJF+wtwj5NS0j62i0kpslm0Fpa0QAs/s0m/wbSSADEJnuC3rrghTHYzKhBjmoo3GUsdNJ5vNy7KrDOT994P3uAoZpfbyBo96dxOwtLNknn/dg54U0M62ns1eUBMAjin1E5OIO8LU9/En37hTo+F5btUJf1831AqvPGSRpPGfzTyjlbrZQzH2SkpjRtHixcunLS1kgAxCR7lSg2lKpXTZNHtyUC+2wSba30pXnfPqMZVXB/89G7S8OyFqW/+8JK3NnnsYuMocHUWPz4yEjCJSfZNgif48wdU1S20khbzUmf+ehsXru6eUc9R8e5NPdT4h0vC0k6fvqR0FJOUFEXA4FunAJhUzHf9pv+WPaVTMpIz+RCL2eaH777vb4D4eyCQkQrvpQ8fP/d/8h47Xtrx9eVT1/15Fx0Qk+CB/vzics8nVtunVHjt1Pc/fPf9rlfkiletQWvrpMc8sOLNVvHWjTlUn4IC98Y4LAWOtnmc7Lf29tOP5V/oZF9c4AnMTQKe5R6j76zW5dY9+CuGtZc0PKRb2mIrYeH46B/un6vW/T9KSlpJgJgEz7UxQ6kvF8Jfry4+/BKtpMJyc74gROFc9tMDj1bStVRcR3tzH+eMA07CUsVEN3ltqTtZpSQlADEJJKWlgBRTdtyjnogvN8fpaF4UJk2Fnzz8l/+UfvLL9/ORkQ5Qcb27ndbRXry9syNTyRoP4/e1mIt2/b5AKwkQk4DlKiG+EFnrJrXzdiKLyHC5uVg+Lh4h/DORx+lmG7CupaPJhXhlLbFdnpGsHHjiLV2rn7nfFSxZ47srW9QboGJMsvYlPM1vD+VFYUfirIRTsZhbG3qUNDdjcTfb1Hpx2FZouBwxEWjyw+FL2UiaLd9q9kL1cXlDaW0xkrzPS0y+OisDV1mVIeNe6rrf9rcCsBiTgIcmpa7qNOi3NgZxpZZ3k1W2sr8Fz4hYq8fJahBNmksxmeqKmk34p9zPGSsf3KkBWN5QkpEAMQkIFc2csnFn+DjZW8F0910Hb/y+kt7p7XcDC9919wtIAAewbxI81m/XbSnU3SUmj9kyR16sgzehWow6fT+h8eZY9tlOmmW3+BTDtkU+F4t3lFYSEKCbBFQOKk4C0UVqbEsz0A7abCuNl7m/UE8pKfMvTlU6a37RkU9qIQdATAL28NvVK4zyCim7zNJKSq0vz5pONh5SGLnyxOZdOl8hfWgxvUsOo77TkbV16tnOu5mHAXhXHGV38PoNWkmAmAQcnZTKg5bAU1j/HXBBh0I89WJNquHN9Rji385kEYvFV7UYmVpbHrDk/n/gx0dDCRCTgKfIK74lq8OK+HHeSF1sbbGcXQtLGeVvXzRHTumZ5KXAj2WvYD5e1b1/os3lFlq4k+8dt7SSgMiYZN8keLi/4LvYZ8oYCdaVbWkavs3ma5ofc1vG7EE039UqMiDNW2eBsCTtZ9/J6b/EFD/AdkwCqOBVdaJLao9CcbmrcECq23lodk3z8a5WMeP0wmtOzAPqk2/jIycmvb586rq/4EMNiElAjDoNpbp13ny96faLyMsNVdpsKEV2kDbf+MET9A8IS/O380rZF3gyBu8eGckXFoCYBEhK0+pw7TvyKlXpuGQP/wy16t3UcxtISrfJSO/e7Oi2z1ttfHMMnqCSdDJjfoNpJQFiEnCEXfeimRd8a/VfO6XhFbeNmjSUSiYgLSal+2Wk8d34VvwRGJYy55DfVzISEOtbpwD46i8klWv9d7eTAvHDx8/P3GH27acf+4QwHyt4xbdQEh6edgOUh8DHfmrqBmmBExCTgJO9vnwKjG85vuY7vTSfpIv+9Vyr8C0PSIuX466tpCe4WeLVSgLEJKDERkNpCEibKzs/JCkNHZj56yl5ST989/2R34vvN2xSRuJIGkpARfZNAib+4lqnKGmNh9Q9eeYVduMz0WPm8Nx7j87NNy4jJdX3tZY/KdzequR5U5/0sK82Xl8+dd1fdJsBqTEJIKbIyFnIq/rCdy1kkqQyNONVPXmayomh9x7rcQ8f0rdRU6W1tzbc4cOr3XUxGAAxCajll4ZSYflylaS061a2w+i7tcWy5z8/VLoXLRy1kgrzQ2q86R84Pufj/z9EpryPRsW4NX+d41db8ttmPNZu0vfWSgLEJKCyKnvOZiSlkq/2k5LSOL1s9ogKV8ruVtpEgQP2m/PIG88xudZv76fZBBbE35wrOD/g5q2++XriP7lrB1wLS2s/Fph3NHmI9hQgJgE7+YuvegX6wT2lyHQ0qRQDYaniZI+cTtQhSanu3BjlaZWvJGJSU9LZXmwxzWN8YBHL7au/lGSS7ofAMgyp7/TrcbSSADEJkJSCDaXNwDMPS6fPWrlWT6mFjNRaLM/LSJsh4a14QGytQy0etuS3R5+UKt1IMhKQyYLgwEZSqlm8HrU4wXwx7qS9U4flvBuZ2d+vDx5zLV4G6Z1xac5Kgw0eCuA2LAgOhP3OwT2luslkcXxddtY6txx/++nHxpNSrVZS/xYeEvkk253OyevLp677HScTKIlJAMc5bEBUa3sWVXkxkbXjKZV3rUg5vPhhjsqNU4SMtN+JlZGAQgbdAZt+p+6womNG31WJJU01lJLOcMYly7su/dDEvo/UP2/23dI/drKqdcZ7OXIUXH/Gzhp3J/7JSMB+dJOA2KRUq+7pD9XPGr/Bnp6NJ6WdqtW1VdE213TOKKwjB+ANWaX/ycNWccje80crCUBMAiSlaWk41LLjsFRrmFxTw+1OTEoZtXhkQ2lzA5yk2LD5IsNJafG5Doji47lY71egtihChV8R2Q/XSgKq+MZvEyDF/1RYwSw+/BW36+VZGanKAQsPUrI6QmTdGRNsUkvYzZ8vSVORmeS1z6oh4Suy+dpqtZJaO0750Qofq6oBatFNApLk95TCo6rmeamRjNRO0Mq22dvZb3he+KlT76XJPKXIx+6xashmap3c0pMfvvdwu7PenYwEiEnA9ZJS/Aaa19oY9CrWBuAdsJRc4KkznjdvofC6A/CSOnsG410iYgEsxiT7JgENlT4lSWmPpk1/zH7GztXz2zhjHLzW9ritdMoy34tz4fbOSJNnP/60P+qXTNf9JecBqBuTAFL9pfjkk/3df2otu8eUpKGk7v/3BitDnLgZUfYieNVfxtt563fnrdWOjASIScCtklJJTXzuhkWLiShvy9oGw9V1uxlVUtb8CENw2lzsQSNIRgLEJICipFRS0eZNQakyryk8vi4vKdF4xBr+/dv6oou1MlLevb1/0jg/x5qYBIhJwP2TUvnmJ3kzQAqTUkwEik9KQ0PsxNXAVaKpt9A4da/9p5ulx5/HlF7w3tBKAsQk4EpJ6cRaPDspJS3SEFjUYTxWcPxfb9CDunTEypsj974cb/FklvSmXrMFLWLeZjv3gIwEiEnAlZJSI3P0kzJJRoaZLOowpKPwaL3u1OXyrt5KukcrrO5ig/3RXumNr/muu5Ft0p0WwMjYuVhGAvaOSRYEB+5WyMbvk1MYXZLCz22Wy3NbVnklhVsqjVPW4nZYa8HpVbYqekyOOupsK2CAfWMSQLm/3NqGMMM+OYGqrlZiSTpIXlvpZo0UaW0eYJI+QeEzOf9Pr/WlKeY35zExvmQc4+vLp677y37tAmIScI2k1HX/Y2vF9NoAvHPHvyW1lYb1G05ZFeOu4aTBlxHfXCqZZ3XArk0/fPf9YkNpc2Rg5PuSkQAxCbheUmqwFp8npYrfl5ccKqatNF7j7sSdYaWdI2/XcahYHE2363mo0lDqk9LaWwu89803KCMBYhIgKdVPSqcvojCvJgNZa74OePbUf62ky72MeXOp/L28amwslhS3st+1ZesBMQmQlA5NSg2er8Xv7wMlZsV10mg8qrWzgGSqks2+1jKhVhJwpG+dAmCnpFRSUx4wg6LBpDTejjZmhFJ/op52ro7JJ3d6DXl347mvYf5LQEYCxCTguUlpqCkfGAB++O77ISzFF9ZDWDqmUj/rirSwGVfJ/UzhOZeRgOPZNwnYz++WDzdqeWjZTksnZ7zZA5LSMK3l7SKD/U6PKJfOSEeuDB53Y/+u36fA8TEJoN26tm5YuvHWrptT/EvO4Xjq/zgsdSn7/BwZHvqnOzGo6CPtkJQAxCTgPn43fi+Uze0yS0rP4bF9fd9mWCqZ9R5Tbna5q+TNT9fwbwJ5aW1L0/FS0eX3RvX3KyMNl6yFrxVeXz5pJQFiEvDopBRT+JZXvUNYKqz/Tp/jfkxY2lxCepKXxt/9rz1wMTJN1r8uCR7jH64Vlob2VPxJlrtkJEBMAihKSvFVYF7VOz9+9vrgQzoatjzqWu1NxZzG8DlM2mZnGIyXdDYmKSu0027cRd9v6Gb8+3oz4k5GAq7PSnfAcUmpMCONq974GQtrx//w8XN8R6hfgK4fg9T/GUrnFlZPzg5LgeUEX4dvM7XZtvrhu+/D6x9uDt3MW0HxlZ797jGd5sR7W0YCTqebBByalCp+y14+Bi+mpxTTLxqqyZJcsevEpMAZ6GZtllfD07eGntXiVKiYE1h92OHqLdFYT+nV6vbKAM3GJAuCA4f5K5NatjA4bZa8m8dfS0qTwXWx5fs1V9KbzAtq/y30IWQS7ZJupMiwVBItGkxKme/i8Lv69eVT1/0Vvy6B02MSwDlJqeLSDotHi28vDEkpNR0t1pSpD29kzF54EYUGy/fua1sp+0YKh6Xy9ss8ziEjAWISwHZSqlvlZ4/B65NSV6ORkrS0Q/9j2S0RhhxSKx+Or0WtFsrpbaWDo29J7JeRADEJkJR2SUrjojB1FFbFanJzDN4kIFEUQmq04/YYeTjcAMcnpdfS5lRv2fOs4sbdje/qjE+TjASISQB7JaWupcFji2PwAgGpZPyYTlStm6dWH2kSLbKTUvyVfW3t25u9Dn5MOpo8aerNLCMBYhLAu6TU3Xo7znFbSQdpxzh6hZSY8TqHbW3XHrUZjaokpcWG0mI6AhCTAOokpe4BnZC+yoysYq97Ni660N+RpyJ+UYfFVdrnndLsmXiFi9fHPHX8zayVBIhJAMthqWJbqWTds7d99pZJ2hPp0kmJmMzcrWz9tBiQxnGo1sck41YfzwGre3PKSECzMcm+SUAL/tvOBBsKskc74+5iumqLLzgmBdV6j0lJab/xoq8vn/rPPkCDMQmgobBU+H15g0ErqZU0lLAS4ynK12+IH3k4JKW8tRlbvoEj72QZCWjZt04B0FpSGsLS8SXyh4+f3376sZH9Xrm9H777vr/r+j8HP3t/t4cDUklG2vy4yUiAmASQHJZeXz4dGZaG77z7grVWUsquMuvu4/ScyHHua7jcIhZr3wsMAanKPCh3MnBFBt0B7SalLnEQXcVNh/qZG12lbUZ3ynW13j5nZaT9ejXJYen97bT3q9JKAsQkgNKwtNPeSpvpov+XJWvfFRbBw7yOxS/jF48sKZF3888Xk6h4I01mKMlIgJgEUCcpxdRtOyWE09tKSe9ryFTZXbXs1zneQvfEFlz5bZD3Fq64Z9QpoVpGAsQkgMphaY+2UkylmNdWqjWeKmOVvKQKeL5XacY7HXLFz5sCnReWSoLieJm7yXSd03fTunpGGjVFZSTgMjHJvknAVfzVtfIur+ZLbdQ0PlspPi3Mo1FJrT8/IUlhafixwnA1pI68nVgnS4FPXkY4NV2ulbR55++2JP1f9VsMuFBMArhYWKrSVsqoAstnKx2clMZvMz4aRW48uplqxmFp7SfHAWP4+YzTO+/MJIWlze2SklLT1TPSTk8qIwFiEsDuSamw2it57HxZsHlJ3cgKZl3uILRwUkpq+0yGsa394+Ts1coeMWEpY0vZ8c9fa5et+Du/VkPpaz6XkQAxCeCosDR0SJKKuSrFX+AIb+3tEpPxfteSUvYAs5jm0vwnY55rM5QGwtLrpN7gKUH6+D6SJhJwXbaXBa6dlIbOUjuVYr8p5/221Oy3HC0MFeOVHjZ/cugsVckbw3UZLk2VjBTzIlsgIwEk0U0C7hGWomahHFkp7jYJ/jjjhtJZqxSEx+Bl9GTGnaXLLeFdEFcyO4rZi6MMH0wAMQngGmGpvOg8eHbH6UmpYqLIiFt75LRz231HjrjTRALIjkkWBAfu5K9167NQjk8sJUmpnYjVQtdl3lYqDBuRC/pd2sG30Nfk+df8GgLuEZMA7hyWurO7OhnPPsS8FpJSO3FisghEO226PsIdc4ri+6W1liqJPM7ryycBCRCTAC4TlroG2jKJteYvRfANhu3tlEmue06yX/z4TpiMGKy14XJJcpORADEJ4Hp56YA5S+VJSSI6/qJcZdzd5N6YDyjNyOT1MpKABIhJAJdNSt0hCzzkJaXAC2uhoVQrTpy1XN6lxdy04/9afWmKtTtQEwkQkwCEpb0qztPbXFQROT0pdcRdRkI+ZhE/TSTg9mwvCzwzLP21s9aDHqrYfp/Tfs/TFgrfq6g7MenDx8/Nbg6b3UXc9Yb5uj+vjASISQA3DUtfC74jKtd5FRsTkNpJSi3HiVtqaqLaONv33zK4QMDt2TcJeLL/rjtj2JulGh4oKWSW3yF7TGl7ffnUf2QAHhKTAISlC8wROncth/78lKzBUGX9hpaXAp9MT5rkosla3mvvouJ9WOWGGfUwBSRATAJ4cFjS51mslYfT8vblU/d1p9fbGCJNlSA3yUXz9LJ2p7Vz+0lHAGISwLuw1HJbKbU/MBS7JfX34o49V9luKObkjN/d29d/mfTuNqPR2qXs3neZqt912TeMdAQgJgFMk1LX8Bi8zc2XJj88+a+p72jtuU7ZmLUfcTfZSjU7IK29r0leCkemPiBlr0e3ltbOSozSEcDgG78TAdb9D5PitcpX/oUHGR4+yUUxx0wKS5uv8xUxAG8yRadkatP6Dqe7vP35s4xffElAqns/ZB9fOgIQkwDyk1JXe2RUrYn1JePoNh8e/yJfS22ltXFor6ypTTErN4TbMlXag+WNrFOS0uKOxtIRwGZM+u+dBYAtf6NWcdxI0Aonh9Tjh8f7rf18ZFhKXd1ucYOpiiFkj0hzQENp9E/+3gfYZm4SQIy+svwb7a8bHm88cm/SbUh9g6lLBQzrQHQRY/YyXsxh8aN9o4AkHQGISQA7hqWcSUFXCUtH5opxWFrMS23ukrTHWap+TOkIQEwCOC0vzVPTRSNTYDOfY566e99cqrVAwrl78h5POgIQkwBaTE3XbTSdHifGzaUHjpQrSXTSEYCYBHCZyNTdotF0SlpoJH60n2ylIwAxCeDaqWkoahus2i11IB0BICYBtBKZhBMG4caXdARwcEz6xlkAONxfbycv3b6VVH3c3cED+b7eJH/dxwbgyJgEwMXy0tMWcHumUftIQAIQkwDkpei8dEuX2JhIOgIQkwC4bV6qEh60tgqy0PLVB0BMAuD8vHR7pzSUxCEAMQmAi+UlXSBxCAAxCeBWean/XzmnUp4pPJQ4BCAmAdBGXprX/fGp6VqtpHrxRp4BICom2TcJ4NI+BeJE40EoEH5m/+mTKw3AkTEJgMelpgNaSXH9H+EHgBZ9468ogCc5srnk7xcAxCQAAIBb+NYpAAAAEJMAAADEJAAAgDgWBAcAAHhHNwkAAEBMAgAAEJMAAADEJAAAADEJAABATAIAABCTAAAAdmTfJAAAgHd0kwAAAMQkAAAAMQkAAEBMAgAAEJMAAADEJAAAADEJAABgR/ZNAgAAeEc3CQAAQEwCAAAQkwAAAMQkAAAAMQkAAEBMAgAAqMuC4AAAAO/oJgEAAIhJAAAAYhIAAICYBAAAICYBAACISQAAAGISAADAjuybBAAA8I5uEgAAgJgEAAAVdK95AAABRUlEQVQgJgEAAIhJAAAAYhIAAICYBAAAUJcFwQEAAN7RTQIAABCTAAAAxCQAAAAxCQAAQEwCAAAQkwAAAMQkAACAHdk3CQAA4B3dJAAAADEJAABATAIAABCTAAAAxCQAAAAxCQAAoC4LggMAALyjmwQAACAmAQAAiEkAAABiEgAAgJgEAAAgJgEAAIhJAAAAO7JvEgAAwDu6SQAAAGISAACAmAQAACAmAQAAiEkAAABiEgAAgJgEAACwI/smAQAAvKObBAAAICYBAACISQAAAGISAACAmAQAACAmAQAA1GVBcAAAgHd0kwAAAMQkAAAAMQkAAEBMAgAAEJMAAADEJAAAADEJAABgR/ZNAgAAeEc3CQAAQEwCAAAQkwAAAMQkAAAAMQkAAEBMAgAAqMuC4AAAAO/oJgEAAIhJAAAAYhIAAECk/x8w0zCeUfu/JAAAAABJRU5ErkJggg==)
Besides doing research, Thomas also teaches and demonstrates for modules at the DTC. In addition, he sits on the expert board of the DTC Coding Dojo, where he has given masterclasses on the Linux terminal, LaTeX and Vim. He is also involved with the Rhodes Artificial Intelligence Lab, which is a student-led initiative that aims to use machine learning for positive social impact. In his free time, he enjoys working out at the Linacre gym, collecting medicinal plants, and partaking in the rich social life at Oxford.