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              <text>This project focuses on developing a graphical model learning method for stochastic Ising networks, specifically using data generated by the Glauber dynamics.</text>
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              <text>Antimicrobials are a crucial component of modern healthcare and are used in managing a range of ailments from infections to surgical procedures. However, the rise of antimicrobial resistance (AMR) threatens these practices, and without effective monitoring and efforts to curb this proliferation we face the risk of a serious public health crisis. This report seeks to illuminate the current state of AMR and stewardship within the Coventry and Warwickshire area, with a particular focus on the three acute Trusts (University Hospital Coventry and Warwickshire (UHCW), South Warwickshire Foundation Trust (SWFT), and George Eliot Hospital (GEH)) associated with Warwick Medical School. Data was extracted from the 'Fingertips' public health database and a subset of indicators were examined to present an overview of the threat of AMR and compare outcomes against goals laid out in the governments 2019-2024 National Plan to tackle AMR. Our analysis suggests increasing rates of E.coli specimens resistant to key antimicrobials in the region compared to the national average for England. An exploration of key antibiotic prescribing practice targets indicated that Coventry and Warwickshire (CaW) Trusts were below national average performance, suggesting that a review of such prescribing practices may help address AMR trends in the region. The variance in these findings reflects the complexity of the challenge in fighting antimicrobial resistance and the persistent hurdles healthcare systems encounter in addressing threats of this magnitude, particularly in settings with limited resources and prioritisation of events like the COVID-19 pandemic. Further efforts, monitoring, and investment will be needed in the coming years to address the alarming rise of antimicrobial resistance in the face of current efforts to curb its proliferation.</text>
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                <text>Warwick Medical School</text>
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              <text>This project studies how to decide between investing in risky stocks and safe bonds using Apple Inc. (AAPL) data from 2020 to 2024. We look at two scenarios: one where we know the chances of stock prices going up and one where we don’t. When we know the chances, the best strategy is to invest about 67% in stocks. When the chances are unknown, we update our estimates with new data. Our results show that investment choices are sensitive to changes in these estimates. This research shows how using Bayesian methods can help make better investment decisions.</text>
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              <text>Tenam Cuauhtemoc, Skyrmion, magnetism, single crystal growth</text>
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              <text>Cyanobacteria exhibit gliding motility, a unique form of self-propulsion with ecological implications, particularly within microbial communities subjected to fluctuating environmental conditions. Aggregates of cyanobacterial filaments engage in collective motility, with their dynamics modulated by environmental cues such as light exposure and oxidative stress. Building on established frameworks, notably those involving Fluctiforma draycotensis under stress conditions [1], this study examines the aggregation and motility dynamics within cyanobacterial aggregates, incorporating methodological adaptations to elucidate underlying physical mechanisms at both individual and aggregate scales.</text>
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