The study of hybrid zones offers important insights into speciation. Earlier studies on hybrid populations of the marine mussel species Mytilus edulis and Mytilus galloprovincialis in SW England provided evidence of admixture but were constrained by the limited number of molecular markers available. We use 57 ancestry-informative SNPs, most of which have been mapped genetically, to provide evidence of distinctive differences between admixed populations in SW England and asymmetrical introgression from M.
View Article and Find Full Text PDFProteomic analysis was carried out on the Crab (upper-shore) and Wave (lower-shore) ecotypes of from a hybrid zone at Silleiro Cape, Spain. Proteome profiles of individual snails were obtained. Protein expression in F hybrid snails bred in the laboratory and snails with intermediate shell phenotypes collected from the mid-shore were compared with Crab and Wave ecotypes using analytical approaches used to study dominance.
View Article and Find Full Text PDFMislabeling samples or data with the wrong participant information can affect study integrity and lead investigators to draw inaccurate conclusions. Quality control to prevent these types of errors is commonly embedded into the analysis of genomic datasets, but a similar identification strategy is not standard for cytometric data. Here, we present a method for detecting sample identification errors in cytometric data using expression of human leukocyte antigen (HLA) class I alleles.
View Article and Find Full Text PDFBACKGROUNDDespite a rapidly growing body of literature on coronavirus disease 2019 (COVID-19), our understanding of the immune correlates of disease severity, course, and outcome remains poor.METHODSUsing mass cytometry, we assessed the immune landscape in longitudinal whole-blood specimens from 59 patients presenting with acute COVID-19 and classified based on maximal disease severity. Hospitalized patients negative for SARS-CoV-2 were used as controls.
View Article and Find Full Text PDFEvolution has direct and indirect consequences on species-species interactions and the environment. However, Earth systems models describing planktonic activity invariably fail to explicitly consider organism evolution. Here we simulate the evolution of the single most important physiological characteristic of any organism as described in models-its maximum growth rate (μ).
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