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"DNA Viral Diversity, Abundance, and Functional Potential Vary across Grassland Soils with a Range of Historical Moisture Regimes" Soil viruses are abundant, but the influence of the environment and climate on soil viruses remains poorly understood. Here, we addressed this gap by comparing the...
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The rhizosphere represents a dynamic and complex interface between plant hosts and the microbial community found in the surrounding soil. While it is recognized that manipulating the rhizosphere has the potential to improve plant fitness and health, engineering the rhizosphere microbiome through...
Agriculture is the largest source of greenhouse gases (GHG) production. Conversion of nitrogen fertilizers into more reduced forms by microbes through a process known as biological nitrification drives GHG production, enhances proliferation of toxic algal blooms, and increases cost of crop...
The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...
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The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...
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Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...
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Short Biography Caroline (Carrie) Harwood received her Ph.D. in microbiology from the University of Massachusetts and completed postdoctoral work at Yale University. She held academic appointments at Cornell University and the University of Iowa before moving to the University of Washington in 2005...
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Please cite as : Anderson L.N., W.C. Nelson, J.E. McDermott, R. Wu, S.J. Fansler, Y. Farris, and J.K. Jansson, et al. 2020. WA-TmG.1.0 (Metagenome, WA). [Data Set] PNNL DataHub. https://doi.org/10.25584/WATmG1/1635002 To enable a comprehensive survey of the metabolic potential of complex soil...
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Please cite as : Anderson L.N., R. Wu, W.C. Nelson, J.E. McDermott, K.S. Hofmockel, and J.K. Jansson. 2021. Iso-VIG14.1.0 (Metagenome Derived Viral Genomes, WA/IA/KS). [Data Set] PNNL DataHub. https://doi.org/10.25584/IsoVIG14/1770369 Soil samples were collected in triplicate in the Fall of 2017...
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Please cite as : Anderson L.N., R. Wu, W.C. Nelson, J.E. McDermott, K.S. Hofmockel, and J.K. Jansson. 2021. IA-TmG.2.0 (Metagenome, IA). [Data Set] PNNL DataHub. https://doi.org/10.25584/IATmG2/1770333 Soil samples were collected in triplicate in the fall of 2017 across the three grassland locations...
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Please cite as : Anderson L.N., R. Wu, W.C. Nelson, J.E. McDermott, K.S. Hofmockel, and J.K. Jansson. 2021. WA-TmG.2.0 (Metagenome, WA). [Data Set] PNNL DataHub. https://doi.org/10.25584/WATmG2/1770324 Soil samples were collected in triplicate in the fall of 2017 across the three grassland...
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Please cite as : Anderson L.N., R. Wu, W.C. Nelson, J.E. McDermott, K.S. Hofmockel, and J.K. Jansson. 2021. KS-TmG.2.0 (Metagenome, KS). [Data Set] PNNL DataHub. https://doi.org/10.25584/KSTmG2/1770332 Soil samples were collected in triplicate in the fall of 2017 across the three grassland locations...