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, scientific, and regulatory steps required to translate in vitro molecular hits into small animal preclinical evaluation models. Processing and integrating complex transcriptomic, proteomic, and image-based
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, implementation of management treatments, and the use of coding and process-based models to analyze natural resource dynamics. Learning Objectives: Under the guidance of a mentor, you will build experience in plant
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instrumentation, quantitative analysis, and modern modeling approaches that help connect fiber properties to fabric performance. Learning Objectives: Under the guidance of a mentor, you will: Develop
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pathogens affecting U.S. agriculture. The selected participant will join a multidisciplinary team investigating the bovine respiratory disease complex, with emphasis on bovine viral diarrhea virus (BVDV
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sensors, RGB/IR cameras, video systems, insect traps, and other devices to build predictive, AI- and machine-learning based models for monitoring grain quality and detecting deterioration due to mold
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. coli, Salmonella, Listeria monocytogenes, Campylobacter). Analysis is typically complicated by the complex nature of food matrices and the frequent need to detect very low numbers of targeted pathogens
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to model fungal population shifts and identify environmental or biological drivers of mycotoxin risk. Collaborating with plant pathologists, microbiologists, chemists, and data scientists to develop
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is typically complicated by the complex nature of food matrices and the frequent need to detect very low numbers of targeted pathogens amongst relatively high numbers of generally benign background
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extrinsic stresses. Foodborne pathogenic bacteria use complex molecular mechanisms to adapt and survive in the variety of harsh environmental conditions (meat animal host, production and processing chain, and
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affecting the overall quality of the food products is the desired goal. Learning Objectives: Learn to investigate and resolve food safety problems related to microbial inactivation in complex food products