HIV-1 virological synapse formation enhances infection spread by dysregulating Aurora Kinase B.

HIV-1 spreads efficiently through direct cell-to-cell transmission at virological synapses (VSs) formed by interactions between HIV-1 envelope proteins (Env) on the surface of infected cells and CD4 receptors on uninfected target cells. Env-CD4 interactions bring the infected and uninfected cellular membranes into close proximity and induce transport of viral and cellular factors to the VS for efficient virion assembly and HIV-1 transmission. Using novel, cell-specific stable isotope labeling and quantitative mass spectrometric proteomics, we identified extensive changes in the levels and phosphorylation states of proteins in HIV-1 infected producer cells upon mixing with CD4+ target cells under conditions inducing VS formation.

Graph algorithms for predicting subcellular localization at the pathway level.

Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data.

An approachable, flexible and practical machine learning workshop for biologists.

Summary: The increasing prevalence and importance of machine learning in biological research have created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible or inappropriate for biologists because they require significant computational and mathematical knowledge, demand an unrealistic time-investment or teach skills primarily for computational researchers. We created the Machine Learning for Biologists (ML4Bio) workshop, a short, intensive workshop that empowers biological researchers to comprehend machine learning applications and pursue machine learning collaborations in their own research.

Automating parameter selection to avoid implausible biological pathway models.

A common way to integrate and analyze large amounts of biological "omic" data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable.

Lower Arterial Cross-Sectional Area of Carotid and Vertebral Arteries and Higher Frequency of Secondary Neck Vessels Are Associated with Multiple Sclerosis.

Background And Purpose: Arterial and neck vessel system characteristics of patients with multiple sclerosis have not been previously investigated. Therefore, the aim of this study was to examine the frequency of neck vessels and their cross-sectional areas (in square millimeters) between patients with MS and healthy controls.

Materials And Methods: In this study, 193 patients with MS and 193 age- and sex-matched healthy controls underwent 2D TOF venography at 3T.