Utrecht University

About: Utrecht University is a education organization based out in Utrecht, Utrecht, Netherlands. It is known for research contribution in the topics: Population & Poison control. The organization has 58176 authors who have published 139351 publications receiving 6214282 citations. The organization is also known as: UU & Universiteit Utrecht.

Sugar-induced signal transduction in plants

Abstract: Sugars have important signaling functions throughout all stages of the plant's life cycle This review presents our current understanding of the different mechanisms of sugar sensing and sugar-induced signal transduction, including the experimental approaches used In plants separate sensing systems are present for hexose and sucrose Hexokinase-dependent and -independent hexose sensing systems can further be distinguished There has been progress in understanding the signal transduction cascade by analyzing the function of the SNF1 kinase complex and the regulatory PRL1 protein The role of sugar signaling in seed development and in seed germination is discussed, especially with respect to the various mechanisms by which sugar signaling controls gene expression Finally, recent literature on interacting signal transduction cascades is discussed, with particular emphasis on the ethylene and ABA signal transduction pathways

PROBAST: A tool to assess the risk of bias and applicability of prediction model studies

Abstract: Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance. PROBAST enables a focused and transparent approach to assessing the ROB and applicability of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be used more generally in critical appraisal of prediction model studies. Potential users include organizations supporting decision making, researchers and clinicians who are interested in evidence-based medicine or involved in guideline development, journal editors, and manuscript reviewers.

The genetic architecture of type 2 diabetes

Abstract: The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

‘Governance’ as a bridge between disciplines: Cross-disciplinary inspiration regarding shifts in governance and problems of governability, accountability and legitimacy

Abstract: Modern societies have in recent decades seen a destabilization of the traditional governing mechanisms and the advancement of new arrangements of governance. Con- spicuously, this has occurred in the private, semi-private and public spheres, and has involved local, regional, national, transnational and global levels within these spheres. We have wit- nessed changes in the forms and mechanisms of governance by which institutional and orga- nizational societal sectors and spheres are governed, as well as in the location of governance from where command, administration, management and control of societal institutions and spheres are conducted. We have also seen changes in governing capabilities (i.e., the extent to which societal institutions and spheres can, in fact, be steered), as well as in styles of gov- ernance (i.e., the processes of decision making and implementation, including the manner in which the organizations involved relate to each other). These shifts tend to have signifi- cant consequences for the governability, accountability, responsiveness and legitimacy of governance institutions. These developments have been generating a new and important research object for political science (including international relations). One of the crucial features of these developments is that they concern a diversity of sectors. In order to get a thorough understanding of 'shifts in governance', political science needs, and is also likely to adopt, a much stronger multidisciplinary orientation embracing politics, law, public admin- istration, economics and business administration, as well as sociology, geography and history.

Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

Abstract: Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.