The Multiscale Systems Immunology project: software for cell-based immunological simulation-0

Bution. (B) The trajectories of 4 cell modules, each starting from one of the corners of this 2-D plan. (C) The concentration profile of chemokine of the middle section through the 3-D tissue volume. (D) The trajectories of 8 cell modules starting from the corners of the 3-D tissue. This simple simulation of cell chemotaxis involves the interaction between the Motility (as part of Cell), Soluble factor and Diffusion (as part of Environment) classes in the system. (B) and (D) were generated by simply changing the "dim" template argument, as an example of the generic programming abilities afforded by the C++ language and built into the system.<p><b>Copyright information:</b></p><p>Taken from "The Multiscale Systems Immunology project: software for cell-based immunological simulation"</p><p>http://www.scfbm.org/content/3/1/6</p><p>Source Code for Biology and Medicine 2008;3():6-6.</p><p>Published online 28 Apr 2008</p><p>PMCID:PMC2426691.</p><p></p

The Multiscale Systems Immunology project: software for cell-based immunological simulation-1

Lors indicate degree of activation of pro- and anti-inflammatory genes.<p><b>Copyright information:</b></p><p>Taken from "The Multiscale Systems Immunology project: software for cell-based immunological simulation"</p><p>http://www.scfbm.org/content/3/1/6</p><p>Source Code for Biology and Medicine 2008;3():6-6.</p><p>Published online 28 Apr 2008</p><p>PMCID:PMC2426691.</p><p></p

The Multiscale Systems Immunology project: software for cell-based immunological simulation-3

Lors indicate degree of activation of pro- and anti-inflammatory genes.<p><b>Copyright information:</b></p><p>Taken from "The Multiscale Systems Immunology project: software for cell-based immunological simulation"</p><p>http://www.scfbm.org/content/3/1/6</p><p>Source Code for Biology and Medicine 2008;3():6-6.</p><p>Published online 28 Apr 2008</p><p>PMCID:PMC2426691.</p><p></p

Additional file 4 of fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

An R script for comparing the empirical rejection rates between the JT method and the linear regression method. (R 2 kb

Additional file 5 of fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

An R script for producing the figures presented in this paper. (R 6 kb

Additional file 3 of fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

An R script performing the benchmarking of the fastJT algorithm reported in this paper. (R 3 kb

Additional file 1 of fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

A figure of the schematic for two-layer cross-validation machine learning model. (PDF 203 kb

Additional file 6 of fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

An R script demonstrating using the fastJT package for feature selection for machine learning based on data from CALGB 80303. (R 8 kb

Additional file 2 of fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

An R script verifying the accuracy of the fastJT package results compared to the examples in the literature. (R 1 kb

Gnidimacrin, a Potent Anti-HIV Diterpene, Can Eliminate Latent HIV‑1 Ex Vivo by Activation of Protein Kinase C β

HIV-1-latency-reversing agents, such as histone deacetylase inhibitors (HDACIs), were ineffective in reducing latent HIV-1 reservoirs ex vivo using CD4 cells from patients as a model. This deficiency poses a challenge to current pharmacological approaches for HIV-1 eradication. The results of this study indicated that gnidimacrin (GM) was able to markedly reduce the latent HIV-1 DNA level and the frequency of latently infected cells in an ex vivo model using patients peripheral blood mononuclear cells. GM induced approximately 10-fold more HIV-1 production than the HDACI SAHA or romidepsin, which may be responsible for the effectiveness of GM in reducing latent HIV-1 levels. GM achieved these effects at low picomolar concentrations by selective activation of protein kinase C βI and βII. Notably, GM was able to reduce the frequency of HIV-1 latently infected cells at concentrations without global T cell activation or stimulating inflammatory cytokine production. GM merits further development as a clinical trial candidate for latent HIV-1 eradication