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https://www.selleckchem.com/products/ide397-gsk-4362676.html Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms that learn how to accurately predict outcome variables using large sets of predictor variables that are typically not hand selected and are minimally curated. Models are parameterized using a training dataset and then applied to a test dataset on which predictive performance is evaluated. The application of ML algorithms to the problem of AMR has garnered increasing interest in the past 5 years due to the exponential growth of experimental and clinical data, heavy investment in computational capacity, improvements in algorithm performance and increasing urgency for innovative approaches to reducing the burden of disease. Here, we review the current state of research at the intersection of ML and AMR with an emphasis on three domains of work. The first is the prediction of AMR using genomic data. The second is the use of ML to gain insight into the cellular functions disrupted by antibiotics, which forms the basis for understanding mechanisms of action and developing novel anti-infectives. The third focuses on the application of ML for antimicrobial stewardship using data extracted from the electronic health record. Though the use of ML for understanding, diagnosing, treating and preventing AMR is still in its infancy, the continued growth of data and interest ensures it will become an important tool for future translational research programs.Temperature is an important environmental factor governing the ability of organisms to grow, survive and reproduce. Thermal performance curves (TPCs), with some caveats, are useful for charting the relationship between body temperature and some measure of performance in ectotherms, and provide a standardized set of characteristics for interspecific comparisons. Endot

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