4 research outputs found

    Performance of the First ANTARES Detector Line

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    In this paper we report on the data recorded with the first Antares detector line. The line was deployed on the 14th of February 2006 and was connected to the readout two weeks later. Environmental data for one and a half years of running are shown. Measurements of atmospheric muons from data taken from selected runs during the first six months of operation are presented. Performance figures in terms of time residuals and angular resolution are given. Finally the angular distribution of atmospheric muons is presented and from this the depth profile of the muon intensity is derived.Comment: 14 pages, 9 figure

    Performance of the front-end electronics of the ANTARES neutrino telescope

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    ANTARES is a high-energy neutrino telescope installed in the Mediterranean Sea at a depth of 2475 m. It consists of a three-dimensional array of optical modules, each containing a large photomultiplier tube. A total of 2700 front-end ASICs named Analogue Ring Samplers (ARS) process the phototube signals, measure their arrival time, amplitude and shape as well as perform monitoring and calibration tasks. The ARS chip processes the analogue signals from the optical modules and converts information into digital data. All the information is transmitted to shore through further multiplexing electronics and an optical link. This paper describes the performance of the ARS chip; results from the functionality and characterization tests in the laboratory are summarized and the long-term performance in the apparatus is illustrated.Comment: 20 pages, 22 figures, published in Nuclear Instruments and Methods

    Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

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    Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Peer reviewe
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