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to design and predict the efficiency and specificity of guide RNAs prior to performing costly in vivo validation experiments. While this tool will be designed for use across kingdoms, the initial focus will
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project seeks to develop cutting-edge decision support tools for accurately predicting cash and cover crop performance across key U.S. crops, including maize, soybean, wheat, cotton, and cereal rye. By
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sequencing in cattle. Developing better statistical methods to improve association studies with QTL and enhancing genetic prediction tools for automation of data processing required for SNP use in livestock
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predicting the ecological success of resistant populations and developing more sustainable, integrated weed management strategies. This project will also involve close collaboration with USDA-ARS scientists
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artificial intelligence (AI)-based imaging solution for applications in poultry meat. The project will focus on detecting and predicting key safety and quality attributes of poultry meat, such as foreign
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to better understand and characterize variability of water at the land surface - i.e. in soils, snow and groundwater - to help in predictions of future water availability, global water cycle dynamics and sea
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drugs and proteins, biosimilar proteins, generic peptides, and nanoparticle therapeutics. Project will apply predictive analytical tools to identify patient populations at risk for adverse immune
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include the development of predictive models for disease resistance, genome-wide association studies to uncover resistance loci, automated phenotyping approaches using image data, and integrative multi
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science to 1) mitigate risks to people, property, and forests, 2) manage for forest resilience and ecosystem services, and 3) monitor and predict land stewardship and disturbance impacts. Wenatchee Forestry
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national scale programmatic decisions and policy. Specific activities include: Operationalize, and where needed, improve Bayesian spatio-temporal feral swine abundance model allowing predictions to be