A roadmap to improve the quality of atrial fibrillation management:proceedings from the fifth Atrial Fibrillation Network/European Heart Rhythm Association consensus conference

At least 30 million people worldwide carry a diagnosis of atrial fibrillation (AF), and many more suffer from undiagnosed, subclinical, or 'silent' AF. Atrial fibrillation-related cardiovascular mortality and morbidity, including cardiovascular deaths, heart failure, stroke, and hospitalizations, remain unacceptably high, even when evidence-based therapies such as anticoagulation and rate control are used. Furthermore, it is still necessary to define how best to prevent AF, largely due to a lack of clinical measures that would allow identification of treatable causes of AF in any given patient. Hence, there are important unmet clinical and research needs in the evaluation and management of AF patients. The ensuing needs and opportunities for improving the quality of AF care were discussed during the fifth Atrial Fibrillation Network/European Heart Rhythm Association consensus conference in Nice, France, on 22 and 23 January 2015. Here, we report the outcome of this conference, with a focus on (i) learning from our 'neighbours' to improve AF care, (ii) patient-centred approaches to AF management, (iii) structured care of AF patients, (iv) improving the quality of AF treatment, and (v) personalization of AF management. This report ends with a list of priorities for research in AF patients

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.

Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or  ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention

Causal effect of plasminogen activator inhibitor type 1 on coronary heart disease

Background--Plasminogen activator inhibitor type 1 (PAI-1) plays an essential role in the fibrinolysis system and thrombosis. Population studies have reported that blood PAI-1 levels are associated with increased risk of coronary heart disease (CHD). However, it is unclear whether the association reflects a causal influence of PAI-1 on CHD risk. Methods and Results--To evaluate the association between PAI-1 and CHD, we applied a 3-step strategy. First, we investigated the observational association between PAI-1 and CHD incidence using a systematic review based on a literature search for PAI-1 and CHD studies. Second, we explored the causal association between PAI-1 and CHD using a Mendelian randomization approach using summary statistics from large genome-wide association studies. Finally, we explored the causal effect of PAI-1 on cardiovascular risk factors including metabolic and subclinical atherosclerosis measures. In the systematic meta-analysis, the highest quantile of blood PAI-1 level was associated with higher CHD risk comparing with the lowest quantile (odds ratio=2.17; 95% CI: 1.53, 3.07) in an age- and sex-adjusted model. The effect size was reduced in studies using a multivariable-adjusted model (odds ratio=1.46; 95% CI: 1.13, 1.88). The Mendelian randomization analyses suggested a causal effect of increased PAI-1 level on CHD risk (odds ratio=1.22 per unit increase of log-transformed PAI-1; 95% CI: 1.01, 1.47). In addition, we also detected a causal effect of PAI-1 on elevating blood glucose and high-density lipoprotein cholesterol. Conclusions--Our study indicates a causal effect of elevated PAI-1 level on CHD risk, which may be mediated by glucose dysfunction

Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.

Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways

Suche nach einem diffusen kosmischen Neutrinofluss von allen Neutrinoflavours mit ANTARES

In this analysis, a search for cosmic neutrinos with the neutrino telescope ANTARES from all neutrino flavours is introduced. The Cherenkov telescope ANTARES, which is situated off the French coast at 2.5 km depth in the Mediterranean Sea, has been taking data since 2007 with the goal to measure high-energy neutrinos of cosmic origin. To this end, two multivariate classifiers are developed in this work to select the cosmic neutrino signal from the atmospheric muon and neutrino background simultaneously for all neutrino flavours. While former analyses targeted either muons originating from muon neutrinos’ charged-current interactions producing a long particle track in the detector, or electronic and hadronic particle cascades from other neutrino interactions, the classifiers incorporate common features from both signature types. The analysis approach increases the overall sensitivity of ANTARES towards the cosmic neutrino flux beyond that reached in former signature-specific cosmic neutrino searches.In dieser Analyse wird eine Suche nach kosmischen Neutrinos aller Neutrinoflavours mit dem Neutrinoteleskop ANTARES vorgestellt. Das Cherenkovteleskop ANTARES, das sich in 2,5 km Tiefe vor der französischen Mittelmeerküste befindet, nimmt seit 2007 Daten mit dem Ziel, hochenergetische Neutrinos kosmischen Ursprungs nachzuweisen. Daher werden in der vorliegenden Arbeit zwei multivariate Klassifikatoren entwickelt um das kosmische Neutrinosignal für Neutrinoereignisse aller Neutrinoflavours von dem durch atmosphärische Neutrinos und Myonen verursachten Untergrund zu trennen. Während Vorgängeranalysen sich entsprechend der Ereignissignatur im Detektor entweder auf durch Myonneutrinos verursachte Myonenspuren oder auf kaskadenartige Ereignisse durch elektronische oder hadronische Teilchenkaskaden bei anderen Neutrinointeraktionen konzentrierten, vereinen die Klassifikatoren Observablen beider Ereignistypen. Dieser Analyseansatz verbessert die Sensitvität von ANTARES für den kosmischen Neutrinofluss im Vergleich zu den früheren vom Ereignistyp abhängigen Suchen nach einem kosmischen Neutrinofluss

The KM3NeT Open Science System

International audienceThe KM3NeT neutrino detectors are currently under construction at two locations in the Mediterranean Sea, aiming to detect the Cherenkov light generated by high-energy relativistic charged particles in seawater. The KM3NeT collaboration will produce scientific data valuable both for the astrophysics and neutrino physics communities as well as for the Earth and Sea science community. An Open Science Portal and infrastructure are under development to provide public access to open KM3NeT data, software and services. In this contribution, the current architecture, interfaces and usage examples are presented

The KM3NeT Open Science System

International audienceThe KM3NeT neutrino detectors are currently under construction at two locations in the Mediterranean Sea, aiming to detect the Cherenkov light generated by high-energy relativistic charged particles in sea water. The KM3NeT collaboration will produce scientific data valuable both for the astrophysics and neutrino physics communities as well as for the Earth and Sea science community. An Open Science Portal and infrastructure are under development to provide public access to open KM3NeT data, software and services. In this contribution, the current architecture, interfaces and usage examples are presented