A Land Cover Map of Africa. Carte de l'Occupation du Sol de l'Afrique.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization

Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N=293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease. On the electrocardiogram, the PR interval reflects conduction from the atria to ventricles and also serves as risk indicator of cardiovascular morbidity and mortality. Here, the authors perform genome-wide meta-analyses for PR interval in multiple ancestries and identify 141 previously unreported genetic loci.Peer reviewe

A Coding Algorithm for the Covariance Matrix Representation of Polarimetric Radar Data

Abstract not availableNA-NOT AVAILABL

Generation and Use of Topographic Features for Improving the Classification of the Regional Scale GBFM Siberia SAR Mosaic

Information on terrain features like slope, orientation and convexity may be very useful for thematic interpretation of single band satellite radar data. Accuracy of the co-registration is the key issue. The map-projected GBFM radar mosaic of Siberia has been co-registered with a digital elevation model, using a cross-correlation technique in the Fourier domain. The mosaic was produced in the framework of the Global Boreal Forest Mapping Project, an initiative of the Japan Agency for Space Exploration (JAXA), and is based on JERS-1 synthetic aperture radar (SAR) data acquired in years 1997-8. The SRTM digital elevation data (90 m horizontal resolution) have been used for areas up to 60 degrees of latitude and the USGS GTOPO30 elevation data (500 m horizontal resolution) for the rest of the area. Since SAR and DEM data-sets capture completely different features of the landscape and SAR imagery is affected by geometric and radiometric (shadow and layover) distortions due to elevation and local terrain slope, automatic matching by homologous features of the radar image to the DEM image is not possible. Due to the unavailability of SRTM data at the time of the mosaic processing and, in any event, due to computational constraints (the mosaic is composed of some 400 SAR strip-images covering 135 000 km2 each) the classical geo-coding procedure using slant range data had to be ruled out. The a-posteriori solution entailed the simulation of the radar reflectivity dependency on the local incidence angle based on available DEM and radar viewing geometry. The radar mosaic was then matched with the simulated image. The cross-correlation moving window was composed of mutually overlapping squares (60 by 60 pixels) in a regular grid with 20 pixel spacing between the centers. The co-registration gives good level of correlation not only for mountainous areas but also for hilly ones. High correlation occurs also in flat areas with pronounced hydrological features like river courses and lake shores that are reflected in SRTM as fine-detail features. The density of control points was on average 1400 points per 100 square kilometers. The geometric effect of topography (like shortening of the slopes oriented towards the radar) has been then corrected by inversion of the same model which had been used for generating the simulated image. Radar backscattering coefficient dependency on local incidence angle was modeled and corrected by a simple inverse sine function model. The corrected radar image was then fed to a classification algorithm together with layers extracted from the DEM, such as slope and convexity. Preliminary thematic classification results confirm that geometric and radiometric corrections afforded by this technique greatly improve the classification accuracy.JRC.H.3-Global environement monitorin