Enrichment of lymphoma and CLL risk SNPs in DNase-hypersensitive sites of lymphoblastoid cell lines.

<p>(A-I) These histograms represent the distribution of how many random loci overlap a specific annotation. The blue represents the mean of the empirical null distribution while the red line represents the real number of loci from the lymphoma and CLL GWAS that overlap the DNase hypersensitive site in the specified cell line: (A) GM19238 (B) GM19240 (C) GM12864 (D) GM12865 (E) GM06990 (F) GM19239 (G) GM18507 (H) GM12892 (I) GM12891. (J) Th0 (K) CD20+ (L) Summary of distribution of tissue of origin for cell lines in which lymphoma and CLL risk SNPs are either enriched (p<0.0004) in DNase hypersensitive sites or not enriched.</p

UES algorithm visualization.

<p>This represents the generalized workflow to determine the SNP enrichment in an ENCODE track. A full description and details of the algorithm can be found in the Materials and Methods.</p

Overlap of lymphoma risk SNPs with regulatory regions in GM12878.

<p>The histograms represent the distribution of how many random loci overlap a specific annotation. The blue represents the mean of the empirical null distribution while the red line represents the real number of loci from the lymphoma and CLL GWAS that overlap the specific regulatory annotation. A, Overlap of SNPs with DNase hypersensitivity regions in GM12878. B, Overlap of SNPs with active promoters and strong enhancers as annotated by ChromHMM in GM12878. C, Overlap of SNPs with active promoters and strong enhancers as annotated by Segway in GM12878.</p

Framework for distributed monitoring of services

Title: Framework for distributed monitoring of services Author: Lenka Skotáková Department: Department of Software Engineering Supervisor: Mgr. Martin Děcký, Department of Distributed and Dependable Systems Supervisor's e-mail address: [email protected] Abstract: Monitoring of servers and its services enables early detection of problems.Distributed monitoring provides the advantage of load balancing between multiple nodes. Most of the tools providing distributed monitoring still retain the master node as a single point of failure. Distributed system working without a central node is more reliable. Redundancy of monitoring can be also introduced for further increase of reliability. Then it is appropriate to ensure that reports of failures do not repeat. This thesis presents a distributed system for monitoring of services, resistant to failure of nodes including a node that currently acts as a coordinator. Nodes automatically distribute tasks among themselves and found problems are collected and stored so that the notifications are not repeated. Keywords: distributed systems, distributed monitoring, network services, Invitation algorith

Additional file 1: Figure S1. of The influence of a short-term gluten-free diet on the human gut microbiome

Baseline characteristics of the GFD study group. (TIF 1068 kb

Additional file 2: Table S1. of The influence of a short-term gluten-free diet on the human gut microbiome

Results macronutrient intake per participant. (XLSX 13 kb

Additional file 6: Figure S4. of The influence of a short-term gluten-free diet on the human gut microbiome

Measured butyrate levels vs. the predicted activity of butyrate metabolism. (TIF 6 kb

Additional file 10: Table S5. of The influence of a short-term gluten-free diet on the human gut microbiome

Correlation of predicted HUMAnN module activity and levels of fecal biomarkers. (PDF 143 kb

Additional file 3: Figure S2. of The influence of a short-term gluten-free diet on the human gut microbiome

Unweighted unifrac distances when comparing inter-individual vs intra individual distances. In group 1 the intra-individual differences are shown regardless of diet. Group 2 shows the intra-sample differences are shown within the same diet. Group 3 shows the intra-individual differences are shown between the two diet groups. In group 4 the inter-individual differences are shown regardless of diet. Group 5 shows the inter-sample differences are shown within the same diet. Group 6 shows the inter-individual differences are shown between the two diet groups. The main difference is the intra- vs. inter-individual difference. Also the same diet points in the samples are slightly closer to each other. However, we do not see such a phenomenon for group 5 vs. group 6. (TIF 1862 kb

Additional file 9: Table S4. of The influence of a short-term gluten-free diet on the human gut microbiome

Correlation of predicted HUMAnN pathway activity and levels of fecal biomarkers. (PDF 228 kb