ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use-1

the comparisons. 4 cluster data 20 cluster data. White bars = Cluster; black bars = PKM.<p><b>Copyright information:</b></p><p>Taken from "ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use"</p><p>http://www.biomedcentral.com/1471-2105/9/200</p><p>BMC Bioinformatics 2008;9():200-200.</p><p>Published online 16 Apr 2008</p><p>PMCID:PMC2375128.</p><p></p

ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use-0

He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrate the number of compute nodes where one finds statistically significant increases in speed. The p values presented are for tests of differences between the number of compute nodes for a given number of genes or clusters. The effect of the interaction between the number of genes and number of compute nodes on the speed of execution. The effect of the interaction between the number of clusters and number of compute nodes on the speed of execution.<p><b>Copyright information:</b></p><p>Taken from "ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use"</p><p>http://www.biomedcentral.com/1471-2105/9/200</p><p>BMC Bioinformatics 2008;9():200-200.</p><p>Published online 16 Apr 2008</p><p>PMCID:PMC2375128.</p><p></p

ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use-3

He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrate the number of compute nodes where one finds statistically significant increases in speed. The p values presented are for tests of differences between the number of compute nodes for a given number of genes or clusters. The effect of the interaction between the number of genes and number of compute nodes on the speed of execution. The effect of the interaction between the number of clusters and number of compute nodes on the speed of execution.<p><b>Copyright information:</b></p><p>Taken from "ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use"</p><p>http://www.biomedcentral.com/1471-2105/9/200</p><p>BMC Bioinformatics 2008;9():200-200.</p><p>Published online 16 Apr 2008</p><p>PMCID:PMC2375128.</p><p></p

Heat map for genes differentially expressed among the four groups of T cells

Only those genes with a FDR (q) ≤0.01 and fold change ≥5 are included in this map. Data for each gene are standardized separately before being plotted, as is standard in drawing heat maps, so that all genes have a similar scale and the relative differences for all genes can be visualized on a single plot.<p><b>Copyright information:</b></p><p>Taken from "The IL-10 and IFN-γ pathways are essential to the potent immunosuppressive activity of cultured CD8NKT-like cells"</p><p>http://genomebiology.com/2008/9/7/R119</p><p>Genome Biology 2008;9(7):R119-R119.</p><p>Published online 29 Jul 2008</p><p>PMCID:PMC2530876.</p><p></p

Molecular network for the highly upregulated immunity and defense genes

The network was created by extracting the direct interactions between these genes from the literature. Three types of relationship are shown in the pathway, binding, expression and regulation. Binding refers to physical interactions between molecules. Expression indicates that the regulator changes the protein level of the target by means of regulating its gene expression or protein stability. Regulation indicates that the regulator changes the activity of the target; the mechanism of the regulation is either unknown or has not been specified in the sentence describing the relationship. This network highlights the importance of two key nodes, IFN-γ and IL-10, which regulate many genes in this network. These genes are also critical for the immunosuppressive function of the CD8 NKT-like cells.<p><b>Copyright information:</b></p><p>Taken from "The IL-10 and IFN-γ pathways are essential to the potent immunosuppressive activity of cultured CD8NKT-like cells"</p><p>http://genomebiology.com/2008/9/7/R119</p><p>Genome Biology 2008;9(7):R119-R119.</p><p>Published online 29 Jul 2008</p><p>PMCID:PMC2530876.</p><p></p

The role of IL-10 and IFN-γ in the generation and function of the CD8NKT-like cells

Cytokine levels in the cell culture media. Cultured CD8NKT-like cells and freshly isolated CD4CD25Treg cells were stimulated with anti-CD3 (1.5 μg/ml) and splenic APCs. At 72 h of culturing, the culture supernatant was saved and used for measuring IL-10, IL-4 and IFN-γ using ELISA. Results are representative of two independent experiments. Suppression activity of CD8NKT-like cells cultured from IFN-γand IL-10mice. CD8NKT-like cells (Tr) cultured from knockout mice and wild-type B6 (WT) mice were co-cultured with naïve CD4CD25responder T cells (Tn) at different Tr/Tn ratios in the presence of splenic APCs and anti-CD3. The cultures were pulsed with 1 μCi/well of [H]thymidine at 72 h and proliferation (cpm) was measured by [H]thymidine incorporation in the last 16 h. Results are expressed as the mean of triplicate cultures. ANOVA -values are <p><b>Copyright information:</b></p><p>Taken from "The IL-10 and IFN-γ pathways are essential to the potent immunosuppressive activity of cultured CD8NKT-like cells"</p><p>http://genomebiology.com/2008/9/7/R119</p><p>Genome Biology 2008;9(7):R119-R119.</p><p>Published online 29 Jul 2008</p><p>PMCID:PMC2530876.</p><p></p