13 research outputs found
Parametrization of low-energy symmetric (e, 2e) differential cross section measurements
Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients
Measurements of the Higgs boson production and decay rates and constraints on its couplings from a combined ATLAS and CMS analysis of the LHC pp collision data at root s=7 and 8 TeV
70 pages plus author lists + cover page (104 pages total), 32 figures, 22 tables, submitted to JHEP. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/HIGG-2015-07/ and at http://cms-results.web.cern.ch/cms-results/public-results/publications/HIG-15-002/Combined ATLAS and CMS measurements of the Higgs boson production and decay rates, as well as constraints on its couplings to vector bosons and fermions, are presented. The combination is based on the analysis of five production processes, namely gluon fusion, vector boson fusion, and associated production with a or a boson or a pair of top quarks, and of the six decay modes , , and . All results are reported assuming a value of 125.09 GeV for the Higgs boson mass, the result of the combined measurement by the ATLAS and CMS experiments. The analysis uses the CERN LHC proton--proton collision data recorded by the ATLAS and CMS experiments in 2011 and 2012, corresponding to integrated luminosities per experiment of approximately 5 fb at TeV and 20 fb at TeV. The Higgs boson production and decay rates measured by the two experiments are combined within the context of three generic parameterisations: two based on cross sections and branching fractions, and one on ratios of coupling modifiers. Several interpretations of the measurements with more model-dependent parameterisations are also given. The combined signal yield relative to the Standard Model prediction is measured to be 1.09 0.11. The combined measurements lead to observed significances for the vector boson fusion production process and for the decay of and standard deviations, respectively. The data are consistent with the Standard Model predictions for all parameterisations considered.Peer reviewe
Patterns and Determinants of Care-Seeking for Antepartum and Intrapartum Complications in Rural Bangladesh: Results from a Cohort Study
Prescription medicine use by pedestrians and the risk of injurious road traffic crashes: A case-crossover study
A Novel 8-Predictors Signature to Predict Complicated Disease Course in Pediatric-onset Crohn’s Disease: A Population-based Study
International audienceBackground The identification of patients at high risk of a disabling disease course would be invaluable in guiding initial therapy in Crohn’s disease (CD). Our objective was to evaluate a combination of clinical, serological, and genetic factors to predict complicated disease course in pediatric-onset CD. Methods Data for pediatric-onset CD patients, diagnosed before 17 years of age between 1988 and 2004 and followed more than 5 years, were extracted from the population-based EPIMAD registry. The main outcome was defined by the occurrence of complicated behavior (stricturing or penetrating) and/or intestinal resection within the 5 years following diagnosis. Lasso logistic regression models were used to build a predictive model based on clinical data at diagnosis, serological data (ASCA, pANCA, anti-OmpC, anti-Cbir1, anti-Fla2, anti-Flax), and 369 candidate single nucleotide polymorphisms. Results In total, 156 children with an inflammatory (B1) disease at diagnosis were included. Among them, 35% (n = 54) progressed to a complicated behavior or an intestinal resection within the 5 years following diagnosis. The best predictive model (PREDICT-EPIMAD) included the location at diagnosis, pANCA, and 6 single nucleotide polymorphisms. This model showed good discrimination and good calibration, with an area under the curve of 0.80 after correction for optimism bias (sensitivity, 79%, specificity, 74%, positive predictive value, 61%, negative predictive value, 87%). Decision curve analysis confirmed the clinical utility of the model. Conclusions A combination of clinical, serotypic, and genotypic variables can predict disease progression in this population-based pediatric-onset CD cohort. Independent validation is needed before it can be used in clinical practice