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 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 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 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

Table_1_Nasal Immunization With Small Molecule Mast Cell Activators Enhance Immunity to Co-Administered Subunit Immunogens.docx

Mast cell activators are a novel class of mucosal vaccine adjuvants. The polymeric compound, Compound 48/80 (C48/80), and cationic peptide, Mastoparan 7 (M7) are mast cell activators that provide adjuvant activity when administered by the nasal route. However, small molecule mast cell activators may be a more cost-efficient adjuvant alternative that is easily synthesized with high purity compared to M7 or C48/80. To identify novel mast cell activating compounds that could be evaluated for mucosal vaccine adjuvant activity, we employed high-throughput screening to assess over 55,000 small molecules for mast cell degranulation activity. Fifteen mast cell activating compounds were down-selected to five compounds based on in vitro immune activation activities including cytokine production and cellular cytotoxicity, synthesis feasibility, and selection for functional diversity. These small molecule mast cell activators were evaluated for in vivo adjuvant activity and induction of protective immunity against West Nile Virus infection in BALB/c mice when combined with West Nile Virus envelope domain III (EDIII) protein in a nasal vaccine. We found that three of the five mast cell activators, ST101036, ST048871, and R529877, evoked high levels of EDIII-specific antibody and conferred comparable levels of protection against WNV challenge. The level of protection provided by these small molecule mast cell activators was comparable to the protection evoked by M7 (67%) but markedly higher than the levels seen with mice immunized with EDIII alone (no adjuvant 33%). Thus, novel small molecule mast cell activators identified by high throughput screening are as efficacious as previously described mast cell activators when used as nasal vaccine adjuvants and represent next-generation mast cell activators for evaluation in mucosal vaccine studies.</p