Corrigendum: Semaphorin 4C: A Novel Component of B-Cell Polarization in Th2-Driven Immune Responses

Background: Semaphorins are important molecules in embryonic development and multiple semaphorins have been identified as having key roles in immune regulation. To date, there is little known about Semaphorin 4C (Sema4C) in immune biology. We report for the first time that Sema4C is inducible in human and murine B-cells and may be important for normal B-cell development. Methods: Human Tonsillar B-cells were studied following activation via anti-CD40 antibodies in the presence or absence of representative Th1, Th2, and regulatory cytokines. Murine B-cells from WT and Sema4C-/- mice were similarly stimulated. B-cell phenotyping in WT and Sema4C mutant mice was performed by flow cytometry and lymphoid architecture was studied by immunohistochemistry. Sema4C expression and synapse formation was analyzed by confocal microscopy. Results: Gene Array studies performed on human tonsillar B-cells stimulated to produce IgE revealed that Sema4C was among the top genes expressed at 24 hours, and the only semaphorin to be increased under Th2 conditions. Validation studies demonstrated that human and murine B-cells expressed Sema4C under similar conditions. Sema4C-/- mice had impaired maturation of B-cell follicles in spleens and associated decreases in follicular and marginal zone B-cells as well as impaired IgG and IgA production. In keeping with a potential role in maturation of B-cells, Sema4C was expressed predominantly on CD27+ Human B-cells. Within 72 hours of B-cell activation, Sema4C was localized to one pole in a synapse-like structure, in association with F-Actin, BCR, and Plexin-B2. Cell polarization was impaired in Sema4C-/- mice. Conclusion: We have identified a novel immune semaphorin induced in human and murine B-cells under Th2 conditions. Sema4C appears to be a marker for human memory B-cells. It may be important for B-cell polarization and for the formation of normal splenic follicles

SAKE: Estimating Katz Centrality Based on Sampling for Large-Scale Social Networks

Katz centrality is a fundamental concept to measure the influence of a vertex in a social network. However, existing approaches to calculating Katz centrality in a large-scale network are unpractical and computationally expensive. In this article, we propose a novel method to estimate Katz centrality based on graph sampling techniques, which object to achieve comparable estimation accuracy of the state-of-the-arts with much lower computational complexity. Specifically, we develop a Horvitz–Thompson estimate for Katz centrality by using a multi-round sampling approach and deriving an unbiased mean value estimator. We further propose SAKE, a Sampling-based Algorithm for fast Katz centrality Estimation. We prove that the estimator calculated by SAKE is probabilistically guaranteed to be within an additive error from the exact value. Extensive evaluation experiments based on four real-world networks show that the proposed algorithm can estimate Katz centralities for partial vertices with low sampling rate, low computation time, and it works well in identifying high influence vertices in social networks

Local Community Detection in Dynamic Graphs Using Personalized Centrality

Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addresses the topic of local community detection, or seed set expansion, using personalized centrality measures, specifically PageRank and Katz centrality. We present a method to efficiently update local communities in dynamic graphs. By updating the personalized ranking vectors, we can incrementally update the corresponding local community. Applying our methods to real-world graphs, we are able to obtain speedups of up to 60× compared to static recomputation while maintaining an average recall of 0.94 of the highly ranked vertices returned. Next, we investigate how approximations of a centrality vector affect the resulting local community. Specifically, our method guarantees that the vertices returned in the community are the highly ranked vertices from a personalized centrality metric

Numerical and streaming analyses of centrality measures on graphs

Graph data represent information about entities (vertices) and the relationships or connections between them (edges). In real-world networks today, new data are constantly being produced, leading to the notion of dynamic graphs. When analyzing large graphs, a common problem of interest is to identify the most important vertices in a graph, which can be done using centrality metrics. This dissertation presents novel advances in the field of graph analysis by providing numerical and streaming techniques that help us better understand how to compute centrality measures. Several centrality measures are calculated by solving a linear system but since these linear systems are large, iterative solvers are often used as an alternate method to approximate the solution. We relate the two research areas of numerical accuracy and data mining by understanding how the error in a solver affects the relative ranking of vertices in a graph. To calculate the centrality values of vertices in a dynamic graph, the most naive method is to recompute the scores from scratch every time the graph is changed, but as the graph size grows larger this recomputation is computationally infeasible. We present four dynamic algorithms for updating different centrality metrics in evolving networks. All dynamic algorithms are faster than their static counterparts while maintaining good quality of the centrality scores. This dissertation concludes by applying methods discussed for the computation of centrality metrics to community detection, and we present a new algorithm for identifying local communities in a dynamic graph using personalized centrality.Ph.D

Local Community Detection in Dynamic Graphs Using Personalized Centrality

Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addresses the topic of local community detection, or seed set expansion, using personalized centrality measures, specifically PageRank and Katz centrality. We present a method to efficiently update local communities in dynamic graphs. By updating the personalized ranking vectors, we can incrementally update the corresponding local community. Applying our methods to real-world graphs, we are able to obtain speedups of up to 60× compared to static recomputation while maintaining an average recall of 0.94 of the highly ranked vertices returned. Next, we investigate how approximations of a centrality vector affect the resulting local community. Specifically, our method guarantees that the vertices returned in the community are the highly ranked vertices from a personalized centrality metric

The Application of an Exogenous Linear and Radial Electrical Field to an In Vitro Chronic Diabetic Ulcer Model for Evaluation as a Potential Treatment

Chronic diabetic ulcers affect approximately 15% of patients with diabetes worldwide. Currently, applied electric fields are being investigated as a reliable and cost-effective treatment. This in vitro study aimed to determine the effects of a constant and spatially variable electric field on three factors: endothelial cell migration, proliferation, and angiogenic gene expression. Results for a constant electric field of 0.01 V demonstrated that migration at short time points increased 20-fold and proliferation at long time points increased by a factor of 1.40. Results for a spatially variable electric field did not increase directional migration, but increased proliferation by a factor of 1.39 and by a factor of 1.55 after application of 1.00 V and 0.01 V, respectively. Both constant and spatially variable applied fields increased angiogenic gene expression. Future research that explores a narrower range of intensity levels may more clearly identify the optimal design specifications of a spatially variable electric field