Calibration and Validation of the Sentinels Geophysical Observation Models

We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided into calibration (Cal) and validation (Val) data sets. Each Cal/Val pair is used to derive the coefficients (from the Cal set) and the accuracy (from the Val set) of the observation model. Combining the results from all Cal/Val pairs provides probability distributions of the model coefficients and model errors. The method is generic and demonstrated using comprehensive matchup sets from two very different disciplines: soil moisture and water quality. The results demonstrate that the method provides robust model coefficients and quantitative measure of the model uncertainty. This approach can be adopted for the calibration/validation of satellite products of land and water surfaces, and the resulting uncertainty can be used as input to data assimilation schemes

Impact of droughts on the carbon cycle in European vegetation : a probabilistic risk analysis using six vegetation models

Peer reviewedPublisher PD

Storage selection functions: A coherent framework for quantifying how catchments store and release water and solutes

We discuss a recent theoretical approach combining catchment-scale flow and transport processes into a unified framework. The approach is designed to characterize the hydrochemistry of hydrologic systems and to meet the challenges posed by empirical evidence. StorAge Selection functions (SAS) are defined to represent the way catchment storage supplies the outflows with water of different ages, thus regulating the chemical composition of out-fluxes. Biogeochemical processes are also reflected in the evolving residence time distribution and thus in age-selection. Here we make the case for the routine use of SAS functions and look forward to areas where further research is needed

Validation of New Gene Variant Classification Methods:a Field-Test in Diagnostic Cardiogenetics

Background: In the molecular genetic diagnostics of Mendelian disorders, solutions are needed for the major challenge of dealing with the large number of variants of uncertain significance (VUSs) identified using next-generation sequencing (NGS). Recently, promising approaches using constraint metrics to calculate case excess scores (CE), etiological fractions (EF), and gnomAD-derived constraint scores have been reported that estimate the likelihood of rare variants in specific genes or regions that are pathogenic. Our objective is to study the usability of these constraint data into variant interpretation in a diagnostic setting, using our cardiomyopathy cohort. Methods and Results: Patients (N = 2002) referred for clinical genetic diagnostics underwent NGS testing of 55–61 genes associated with cardiomyopathies. Previously classified likely pathogenic (LP) and pathogenic (P) variants were used to validate the use of data from CE, EF, and gnomAD constraint analyses for (re)classification of associated variant types in specific cardiomyopathy subtype-related genes. The classifications corroborated in 94% (354/378) of cases. Next, we reclassified 23 unique VUSs to LP, increasing the diagnostic yield by 1.2%. In addition, 106 unique VUSs (5.3% of patients) were prioritized for co-segregation or functional analyses. Conclusions: Our analysis confirms that the use of constraint metrics data can improve variant interpretation, and we, therefore, recommend using constraint scores on other cohorts and disorders and its inclusion in variant interpretation protocols

On a new iterative method for solving linear systems and comparison results

AbstractIn Ujević [A new iterative method for solving linear systems, Appl. Math. Comput. 179 (2006) 725–730], the author obtained a new iterative method for solving linear systems, which can be considered as a modification of the Gauss–Seidel method. In this paper, we show that this is a special case from a point of view of projection techniques. And a different approach is established, which is both theoretically and numerically proven to be better than (at least the same as) Ujević's. As the presented numerical examples show, in most cases, the convergence rate is more than one and a half that of Ujević

Wonen op historische grond. Archeologisch onderzoek naar nederzettingsresten uit de IJzertijd, Romeinse tijd en Middeleeuwen aan de Lostraat te Aalter.

Dit rapport werd ingediend bij het agentschap samen met een aantal afzonderlijke digitale bijlagen. Een aantal van deze bijlagen zijn niet inbegrepen in dit pdf document en zijn niet online beschikbaar. Sommige bijlagen (grondplannen, fotos, spoorbeschrijvingen, enz.) kunnen van belang zijn voor een betere lezing en interpretatie van dit rapport. Indien u deze bijlagen wenst te raadplegen kan u daarvoor contact opnemen met: [email protected]

Feasibility of predicting allele specific expression from DNA sequencing using machine learning

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo

xQTL workbench: a scalable web environment for multi-level QTL analysis

Summary: xQTL workbench is a scalable web platform for the mapping of quantitative trait loci (QTLs) at multiple levels: for example gene expression (eQTL), protein abundance (pQTL), metabolite abundance (mQTL) and phenotype (phQTL) data. Popular QTL mapping methods for model organism and human populations are accessible via the web user interface. Large calculations scale easily on to multi-core computers, clusters and Cloud. All data involved can be uploaded and queried online: markers, genotypes, microarrays, NGS, LC-MS, GC-MS, NMR, etc. When new data types come available, xQTL workbench is quickly customized using the Molgenis software generator

The role of plasma concentrations and drug characteristics of beta-blockers in fall risk of older persons

Beta-blocker usage is inconsistently associated with increased fall risk in the literature. However, due to age-related changes and interindividual heterogeneity in pharmacokinetics and dynamics, it is difficult to predict which older adults are more at risk for falls. Therefore, we wanted to explore whether elevated plasma concentrations of selective and nonselective beta-blockers are associated with an increased risk of falls in older beta-blocker users. To answer our research question, we analyzed samples of selective (metoprolol, n = 316) and nonselective beta-blockers (sotalol, timolol, propranolol, and carvedilol, n = 179) users from the B-PROOF cohort. The associations between the beta-blocker concentration and time to first fall were assessed using Cox proportional hazard models. Change of concentration over time in relation to fall risk was assessed with logistic regression models. Models were adjusted for potential confounders. Our results showed that above the median concentration of metoprolol was associated with an increased fall risk (HR 1.55 [1.11–2.16], p =.01). No association was found for nonselective beta-blocker concentrations. Also, changes in concentration over time were not associated with increased fall risk. To conclude, metoprolol plasma concentrations were associated with an increased risk of falls in metoprolol users while no associations were found for nonselective beta-blockers users. This might be caused by a decreased β1-selectivity in high plasma concentrations. In the future, beta-blocker concentrations could potentially help clinicians estimate fall risk in older beta-blockers users and personalize treatment.</p