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Modeling Ocean Flow Cytometry Data With Environmental Covariates – Sangwon (Justin) Hyun (USC)
DeepEukFinder: A versatile multiclass classifier enables accurate classification of metagenomic contigs – Shengwei Hou (USC)
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Elemental and macromolecular composition of Chloropicophyceae – Vinitha Ebenezer (Dal)
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A New Mechanistic Growth Model: Monod-Memory – Mohammad Amirian (Dal)
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Macromolecular composition of dinoflagellates under nitrogen starvation – Olga Carnicer (Dal)
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November 2020 CBIOMES e-meeting – Zhengke Li (Finkel Group, Dalhousie)
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November 2020 CBIOMES e-meeting – John Casey (MIT)
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NEW CBIOMES PUBLICATION
Emily J. Zakem, Martin F. Polz and Michael J. Follows (2020), Redox-informed models of global biogeochemical cycles, Nature Communications, doi: 10.1038/s41467-020-19454-w
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NEW CBIOMES PUBLICATIONS
Joseph J. Vallino and Ioannis Tsakalakis (2020), Phytoplankton temporal strategies increase entropy production in a marine food web model, Entropy, doi: 10.3390/e22111249
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Using Metagenomics to Measure In-Situ Microbial Growth Rates
Growth rates are central to understanding microbial interactions and community dynamics. The Fuhrman Lab, which uses ‘omics data to seek a better understanding of microbial growth, interactions, and biogeographies has been evaluating a promising new approach to simultaneously determine the growth rates of many different kinds of microbes from the within-genome distributions of DNA extracted from in-situ (mixed) ocean populations. Continue reading “Using Metagenomics to Measure In-Situ Microbial Growth Rates”