Destination Areas (DAs)

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https://hdl.handle.net/10919/78628
Destination Areas provide faculty and students with new tools to identify and solve complex, 21st-century problems in which Virginia Tech already has significant strengths and can take a global leadership role. The initiative represents the next step in the evolution of the land-grant university to meet economic and societal needs of the world.

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Browsing Destination Areas (DAs) by Subject "05 Environmental Sciences"

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    Fecal Indicator Bacteria and Antibiotic Resistance Genes in Storm Runoff from Dairy Manure and Compost-Amended Vegetable Plots
    Jacobs, Kyle; Wind, Lauren L.; Krometis, Leigh-Anne H.; Hession, W. Cully; Pruden, Amy (American Society for Agronomy, 2019-07-01)
    Given the presence of antibiotics and resistant bacteria in livestock manures, it is important to identify the key pathways by which land-applied manure-derived soil amendments potentially spread resistance. The goal of this field-scale study was to identify the effects of different types of soil amendments (raw manure from cows treated with cephapirin and pirlimycin, compost from antibiotic-treated or antibiotic-free cows, or chemical fertilizer only) and crop type (lettuce [Lactuca sativa L.] or radish [Raphanus sativus L.]) on the transport of two antibiotic resistance genes (ARGs; sul1 and ermB) via storm runoff from six naturally occurring storms. Concurrent quantification of sediment and fecal indicator bacteria (FIB; Escherichia coli and enterococci) in runoff permitted comparison to traditional agricultural water quality targets that may be driving factors of ARG presence. Storm characteristics (total rainfall volume, storm duration, etc.) significantly influenced FIB concentration (two-way ANOVA, p < 0.05), although both effects from individual storm events (Kruskal-Wallis, p < 0.05) and vegetative cover influenced sediment levels. Composted and raw manure-amended plots both yielded significantly higher sul1 and ermB levels in runoff for early storms, at least 8 wk following initial planting, relative to fertilizer-only or unamended barren plots. There was no significant difference between sul1 or ermB levels in runoff from plots treated with compost derived from antibiotic-treated versus antibiotic-free dairy cattle. Our findings indicate that agricultural fields receiving manure-derived amendments release higher quantities of these two “indicator” ARGs in runoff, particularly during the early stages of the growing season, and that composting did not reduce effects of ARG loading in runoff.
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    Prediction of condition-specific regulatory genes using machine learning
    Song, Qi; Lee, Jiyoung; Akter, Shamima; Rogers, Matthew; Grene, Ruth; Li, Song (Oxford University Press, 2020-06-19)
    Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.