Monitoring of the operating parameters of the KATRIN Windowless Gaseous Tritium Source

The Karlsruhe Tritium Neutrino (KATRIN) experiment will measure the absolute mass scale of neutrinos with a sensitivity of \m_{\nu} = 200 meV/c$^2$ by high-precision spectroscopy close to the tritium beta-decay endpoint at 18.6 keV. Its Windowless Gaseous Tritium Source (WGTS) is a beta-decay source of high intensity ($10^{11}$/s) and stability, where high-purity molecular tritium at 30 K is circulated in a closed loop with a yearly throughput of 10 kg. To limit systematic effects the column density of the source has to be stabilised at the 0.1% level. This requires extensive sensor instrumentation and dedicated control and monitoring systems for parameters such as the beam tube temperature, injection pressure, gas composition and others. Here we give an overview of these systems including a dedicated Laser-Raman system as well as several beta-decay activity monitors. We also report on results of the WGTS demonstrator and other large-scale test experiments giving proof-of-principle that all parameters relevant to the systematics can be controlled and monitored on the 0.1% level or better. As a result of these works, the WGTS systematics can be controlled within stringent margins, enabling the KATRIN experiment to explore the neutrino mass scale with the design sensitivity.Comment: 32 pages, 13 figures. modification to title, typos correcte

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu).Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1x10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5x10-8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD.This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O'Neil and Betty C Lynch

Regulation of MMP-9 in human ovarian cancer

Available from British Library Document Supply Centre-DSC:DXN028636 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Proteomic Approaches for Studying the Phases of Wound Healing

© 2009, Springer Berlin Heidelberg. Proteome level information is necessary to understand the function of specific cell types and their roles in health and disease. Proteomics is a rapidly developing field with a wide range of applications in wound healing. The ability to use proteomics to assess the wound healing process would have many benefits, including earlier evidence of healing and better understanding of how different treatments affect the wound at the protein level. The basis of what is known about the chronic wound proteome is based on results from a broad collection of studies utilizing a number of different proteomic techniques on fluids and tissues from wounds with different etiologies. The identification of biomarkers associated with healing or delayed healing in chronic wounds could have great significance in the use of current treatments, as well as in the development of new therapeutic interventions