b tagging in ATLAS and CMS

Many physics signals presently studied at the high energy collision experiments lead to final states with jets originating from heavy flavor quarks. This report reviews the algorithms for heavy flavor jets identification developed by the ATLAS and CMS Collaborations in view of the Run2 data taking period at the Large Hadron Collider. The improvements of the algorithms used in 2015 and 2016 data analyses with respect to previous data taking periods are discussed, as well as the ongoing developments in view of the next years of data taking. The measurements of the performance of the algorithms on data as well as the dedicated techniques for the identification of heavy flavor jets in events with boosted topologies are also presented. Finally, the effectiveness of heavy flavor jet identification in the complex environment expected during the high luminosity LHC phase is discussed.Comment: 6 pages, Proceeding for the Fifth Annual Large Hadron Collider Physics (LHCP2017) conferenc

DataSheet_1_Body composition and testosterone in men: a Mendelian randomization study.docx

BackgroundTestosterone is an essential sex hormone that plays a vital role in the overall health and development of males. It is well known that obesity decreases testosterone levels, but it is difficult to determine the causal relationship between body composition and testosterone.MethodsTo investigate potential causal associations between body composition and testosterone levels by a first time application of Mendelian randomization methods. Exposure variables in men included body composition (fat mass, fat-free mass, and body mass index). In addition to whole body fat and fat-free mass, we examined fat and fat-free mass for each body part (e.g., trunk, left arm, right arm, left leg and right leg) as exposures. Instrumental variables were defined using genome-wide association study data from the UK Biobank. Outcome variables in men included testosterone levels (total testosterone [TT], bioavailable testosterone [BT], and sex hormone-binding globulin [SHBG]). A one-sample Mendelian randomization analysis of inverse-variance weighted and weighted median was performed.ResultsThe number of genetic instruments for the 13 exposure traits related to body composition ranged from 156 to 540. Genetically predicted whole body fat mass was negatively associated with TT (β=-0.24, P=5.2×10-33), BT (β=-0.18, P=5.8×10-20) and SHBG (β=-0.06, P=8.0×10-9). Genetically predicted whole body fat-free mass was negatively associated with BT (β=-0.04, P=2.1×10-4), but not with TT and SHBG, after multiple testing corrections. When comparing the causal effect on testosterone levels, there was a consistent trend that the effect of fat mass was more potent than that of fat-free mass. There were no differences between body parts.ConclusionThese results show that reducing fat mass may increase testosterone levels.</p

Table_1_Body composition and testosterone in men: a Mendelian randomization study.xlsx

BackgroundTestosterone is an essential sex hormone that plays a vital role in the overall health and development of males. It is well known that obesity decreases testosterone levels, but it is difficult to determine the causal relationship between body composition and testosterone.MethodsTo investigate potential causal associations between body composition and testosterone levels by a first time application of Mendelian randomization methods. Exposure variables in men included body composition (fat mass, fat-free mass, and body mass index). In addition to whole body fat and fat-free mass, we examined fat and fat-free mass for each body part (e.g., trunk, left arm, right arm, left leg and right leg) as exposures. Instrumental variables were defined using genome-wide association study data from the UK Biobank. Outcome variables in men included testosterone levels (total testosterone [TT], bioavailable testosterone [BT], and sex hormone-binding globulin [SHBG]). A one-sample Mendelian randomization analysis of inverse-variance weighted and weighted median was performed.ResultsThe number of genetic instruments for the 13 exposure traits related to body composition ranged from 156 to 540. Genetically predicted whole body fat mass was negatively associated with TT (β=-0.24, P=5.2×10-33), BT (β=-0.18, P=5.8×10-20) and SHBG (β=-0.06, P=8.0×10-9). Genetically predicted whole body fat-free mass was negatively associated with BT (β=-0.04, P=2.1×10-4), but not with TT and SHBG, after multiple testing corrections. When comparing the causal effect on testosterone levels, there was a consistent trend that the effect of fat mass was more potent than that of fat-free mass. There were no differences between body parts.ConclusionThese results show that reducing fat mass may increase testosterone levels.</p

DEclust: A statistical approach for obtaining differential expression profiles of multiple conditions

<div><p>High-throughput RNA sequencing technology is widely used to comprehensively detect and quantify cellular gene expression. Thus, numerous analytical methods have been proposed for identifying differentially expressed genes (DEGs) between paired samples such as tumor and control specimens, but few studies have reported methods for analyzing differential expression under multiple conditions. We propose a novel method, DEclust, for differential expression analysis among more than two matched samples from distinct tissues or conditions. As compared to conventional clustering methods, DEclust more accurately extracts statistically significant gene clusters from multi-conditional transcriptome data, particularly when replicates of quantitative experiments are available. DEclust can be used for any multi-conditional transcriptome data, as well as for extending any DEG detection tool for paired samples to multiple samples. Accordingly, DEclust can be used for a wide range of applications for transcriptome data analysis. DEclust is freely available at <a href="http://www.dna.bio.keio.ac.jp/software/DEclust" target="_blank">http://www.dna.bio.keio.ac.jp/software/DEclust</a>.</p></div

Results of clustering evaluation.

<p><i>DEclust</i> is our method and <i>existing</i> methods are conventional hierarchical clustering methods. The vertical axis shows the mean AUC values, and the AUCs for each method for each number of replicates are plotted. The error bars are drawn in accordance with the corrected sample standard deviation of three simulations for each parameter set. For existing methods, the group average method was used for the inter-cluster distance measure, and the Euclidean distance, Pearson’s correlation, and cosine distance were used for the inter-gene distance measure. For our method, the evaluation results of DEclust based on the statistical test results of DESeq2 were shown. Also, the group average method with cosine distance was used as the secondary distance measure of DEclust (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.s001" target="_blank">S1 Text</a>). The complete results are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.s006" target="_blank">S2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.s009" target="_blank">S5</a> Figs and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.s014" target="_blank">S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.s017" target="_blank">S4</a> Tables.</p

Pairwise DET profile and definition of gain and loss.

<p>(a) An example of a pairwise DET profile for genes. All pairwise combinations of four conditions (A, B, C, and D) constitute the six-dimensional pairwise DET profile. (b) An example of the calculation of gains and losses. The pairwise DET profile of Cluster 1 is (0 1 0 0 0 0), and the pairwise DET profile of Cluster 2 is (0 1 0 1 0 0); thus, the pairwise DET profile <i>v</i>(<i>C</i>₁∪<i>C</i>₂) is (0 1 0 0 0 0 0). Accordingly, the number of elements that are 1 or -1 in <i>v</i>(<i>C</i>₁∪<i>C</i>₂) is 1 (<i>s</i> = 1), and that in |<i>C</i>₁∪<i>C</i>₂| is 4; therefore, gain_1,2 = 4. The total number of elements that are 1 or −1 in <i>v</i>{∀<i>g</i>∈<i>C</i>_<i>n</i>∪<i>C</i>_<i>m</i>} is 7 (<i>t</i> = 7). Hence, loss_1,2 = 3 and <i>D</i>(<i>C</i>₁, <i>C</i>₂) = 0.857.</p

Cancer transcriptome analysis using DEclust.

<p>(a) Hierarchical tree of clustering result from DEclust for the transcriptome data. The vertical axis indicates inter-cluster distance (Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.e004" target="_blank">3</a>)). The DEGs are divided into 16 clusters (which are color-coded). The cluster numbers correspond to the numbers in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.s023" target="_blank">S10 Table</a>, and each cluster is assigned a pairwise DET profile of six dimensions. (b) Line plots of the expression patterns for each gene in each cluster (the details are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.s022" target="_blank">S9 Table</a>).</p

Results of DEG detection evaluation.

<p>The F-measures for each method for each number of replicates are plotted. The vertical axis shows the mean F-measures and the error bars are drawn in accordance with the corrected sample standard deviation of three simulations for each parameter set. <i>DEclust</i>, our method, uses the statistical test results obtained from edgeR, DESeq, DESeq2, or cuffdiff2, and the evaluation results using each of these tools are separately shown as “DEclust_[DEGs detection tool]”. For the secondary distance of DEclust, the group average method with cosine distance was used. The results for edgeR, DESeq2, multiDE, and DEclust are shown in (a), and the results for only DESeq2 and DEclust with DESeq2 are shown in (b). The complete results are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188285#pone.0188285.s019" target="_blank">S6 Table</a>.</p

Reduction of Systematic Bias in Transcriptome Data from Human Peripheral Blood Mononuclear Cells for Transportation and Biobanking

<div><p>Transportation of samples is essential for large-scale biobank projects. However, RNA degradation during pre-analytical operations prior to transportation can cause systematic bias in transcriptome data, which may prevent subsequent biomarker identification. Therefore, to collect high-quality biobank samples for expression analysis, specimens must be transported under stable conditions. In this study, we examined the effectiveness of RNA-stabilizing reagents to prevent RNA degradation during pre-analytical operations with an emphasis on RNA from peripheral blood mononuclear cells (PBMCs) to establish a protocol for reducing systematic bias. To this end, we obtained PBMCs from 11 healthy volunteers and analyzed the purity, yield, and integrity of extracted RNA after performing pre-analytical operations for freezing PBMCs at −80°C. We randomly chose 7 samples from 11 samples individually, and systematic bias in expression levels was examined by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR), RNA sequencing (RNA-Seq) experiments and data analysis. Our data demonstrated that omission of stabilizing reagents significantly lowered RNA integrity, suggesting substantial degradation of RNA molecules due to pre-analytical freezing. qRT-PCR experiments for 19 selected transcripts revealed systematic bias in the expression levels of five transcripts. RNA-Seq for 25,223 transcripts also suggested that about 40% of transcripts were systematically biased. These results indicated that appropriate reduction in systematic bias is essential in protocols for collection of RNA from PBMCs for large-scale biobank projects. Among the seven commercially available stabilizing reagents examined in this study, qRT-PCR and RNA-Seq experiments consistently suggested that RNALock, RNA/DNA Stabilization Reagent for Blood and Bone Marrow, and 1-Thioglycerol/Homogenization solution could reduce systematic bias. On the basis of the results of this study, we established a protocol to reduce systematic bias in the expression levels of RNA transcripts isolated from PBMCs. We believe that these data provide a novel methodology for collection of high-quality RNA from PBMCs for biobank researchers.</p></div

Workflow of the study design.

<p>Workflow of the study design.</p