<p class="Default">The gap in educational attainment separating underrepresented minorities from Whites and Asians remains wide. Such a gap has significant impact on workforce diversity and inclusion among cross-cutting Biomedical Data Science (BDS) research, which presents great opportunities as well as major challenges for addressing health disparities. This article provides a brief description of the newly established National Institutes of Health Big Data to Knowledge (BD2K) diversity initiatives at four universities: California State University, Monterey Bay; Fisk University; University of Puerto Rico, Río Piedras Campus; and California State University, Fullerton. We emphasize three main barriers to BDS careers (ie, preparation, exposure, and access to resources) experienced among those pioneer programs and recommendations for possible solutions (ie, early and proactive mentoring, enriched research experience, and data science curriculum development). The diversity disparities in BDS demonstrate the need for educators, researchers, and funding agencies to support evidence-based practices that will lead to the diversification of the BDS workforce. <em></em></p><p class="Default"><em>Ethn Dis. </em>2017;27(2):107-116; doi:10.18865/ed.27.2.107.</p>
With tunable bandwidth of negative permittivity, graphene-silicon carbide (GR-SiC) ceramics were sintered by spark plasma sintering process. Compositions and structure were investigated by field emission scanning electron microscopy and Raman spectroscopy. With its content increasing, GR interconnected into "GR clusters" with larger lateral dimensions, which tended to be connected into conductive pathways instead of agglomeration under the action of ball milling. Permittivity, alternating current conductivity (σac), dielectric loss and imaginary reactance were analyzed in detail. When the GR content exceeded 6.30 wt%, a significant electrical percolation phenomenon occurred, and the conductive mechanism changed from electronic jump conduction to a metal-like conduction. Fano-like resonance, appertained to permittivity, exhilaratingly transformed into the Lorentz model. Owing to the mutual dielectric screening between SiC and GR, the bandwidth of negative permittivity became wider and can be tuned via the GR content.
BackgroundThe fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.ResultsIn this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.ConclusionsIncorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.
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