3,684 research outputs found

    Experimental data and model for the turbulent boundary layer on a convex, curved surface

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    Experiments were performed to determine how boundary layer turbulence is affected by strong convex curvature. The data gathered on the behavior of the Reynolds stress suggested the formulation of a simple turbulence model. Data were taken on two separate facilities. Both rigs had flow from a flat surface, over a convex surface with 90 deg of turning and then onto a flat recovery surface. The geometry was adjusted so that, for both rigs, the pressure gradient along the test surface was zero. Two experiments were performed at delta/R approximately 0.10, and one at weaker curvature with delta/R approximately 0.05. Results show that after a sudden introduction of curvature the shear stress in the outer part of the boundary layer is sharply diminished and is even slightly negative near the edge. The wall shear also drops off quickly downstream. When the surface suddenly becomes flat again, the wall shear and shear stress profiles recover very slowly towards flat wall conditions. A simple turbulence model, which was based on the theory that the Prandtl mixing length in the outer layer should scale on the velocity gradient layer, was shown to account for the slow recovery

    CoCoCoNet: conserved and comparative co-expression across a diverse set of species

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    Co-expression analysis has provided insight into gene function in organisms from Arabidopsis to zebrafish. Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying conserved gene modules and comparing co-expression networks. CoCoCoNet is a resource for both data and methods, providing gold standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at ftp://milton.cshl.edu/data

    Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

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    The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63-0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited

    Exploiting single-cell expression to characterize co-expression replicability

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    BACKGROUND: Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes. However, co-expression analysis is often treated as a black box with results being hard to trace to their basis in the data. Here, we use both published and novel single-cell RNA sequencing (RNA-seq) data to understand fundamental drivers of gene-gene connectivity and replicability in co-expression networks. RESULTS: We perform the first major analysis of single-cell co-expression, sampling from 31 individual studies. Using neighbor voting in cross-validation, we find that single-cell network connectivity is less likely to overlap with known functions than co-expression derived from bulk data, with functional variation within cell types strongly resembling that also occurring across cell types. To identify features and analysis practices that contribute to this connectivity, we perform our own single-cell RNA-seq experiment of 126 cortical interneurons in an experimental design targeted to co-expression. By assessing network replicability, semantic similarity and overall functional connectivity, we identify technical factors influencing co-expression and suggest how they can be controlled for. Many of the technical effects we identify are expression-level dependent, making expression level itself highly predictive of network topology. We show this occurs generally through re-analysis of the BrainSpan RNA-seq data. CONCLUSIONS: Technical properties of single-cell RNA-seq data create confounds in co-expression networks which can be identified and explicitly controlled for in any supervised analysis. This is useful both in improving co-expression performance and in characterizing single-cell data in generally applicable terms, permitting cross-laboratory comparison within a common framework

    Rotation-induced 3D vorticity in 4He superfluid films adsorbed on a porous glass

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    Detailed study of torsional oscillator experiments under steady rotation up to 6.28 rad/sec is reported for a 4He superfluid monolayer film formed in 1 micrometer-pore diameter porous glass. We found a new dissipation peak with the height being in proportion to the rotation speed, which is located to the lower temperature than the vortex pair unbinding peak observed in the static state. We propose that 3D coreless vortices ("pore vortices") appear under rotation to explain this new peak. That is, the new peak originates from dissipation close to the pore vortex lines, where large superfluid velocity shifts the vortex pair unbinding dissipation to lower temperature. This explanation is confirmed by observation of nonlinear effects at high oscillation amplitudes.Comment: 4pages, 5figure

    Transverse NMR relaxation as a probe of mesoscopic structure

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    Transverse NMR relaxation in a macroscopic sample is shown to be extremely sensitive to the structure of mesoscopic magnetic susceptibility variations. Such a sensitivity is proposed as a novel kind of contrast in the NMR measurements. For suspensions of arbitrary shaped paramagnetic objects, the transverse relaxation is found in the case of a small dephasing effect of an individual object. Strong relaxation rate dependence on the objects' shape agrees with experiments on whole blood. Demonstrated structure sensitivity is a generic effect that arises in NMR relaxation in porous media, biological systems, as well as in kinetics of diffusion limited reactions.Comment: 4 pages, 3 figure
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