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 & Context (language use). The organization has 58176 authors who have published 139351 publications receiving 6214282 citations. The organization is also known as: UU & Universiteit Utrecht.

Stressing the role of FoxO proteins in lifespan and disease.

Abstract: Members of the class O of forkhead box transcription factors (FoxO) have important roles in metabolism, cellular proliferation, stress tolerance and probably lifespan. The activity of FoxOs is tightly regulated by post-translational modifications, including phosphorylation, acetylation and ubiquitylation. Several of the enzymes that regulate the turnover of these post-translational modifications are shared between FoxO and p53. These regulatory enzymes affect FoxO and p53 function in an opposite manner. This shared yet opposing regulatory network between FoxOs and p53 may underlie a 'trade-off' between disease and lifespan.

The epidemiology of traumatic event exposure worldwide: results from the World Mental Health Survey Consortium.

Abstract: BACKGROUND: Considerable research has documented that exposure to traumatic events has negative effects on physical and mental health. Much less research has examined the predictors of traumatic event exposure. Increased understanding of risk factors for exposure to traumatic events could be of considerable value in targeting preventive interventions and anticipating service needs. METHOD: General population surveys in 24 countries with a combined sample of 68 894 adult respondents across six continents assessed exposure to 29 traumatic event types. Differences in prevalence were examined with cross-tabulations. Exploratory factor analysis was conducted to determine whether traumatic event types clustered into interpretable factors. Survival analysis was carried out to examine associations of sociodemographic characteristics and prior traumatic events with subsequent exposure. RESULTS: Over 70% of respondents reported a traumatic event; 30.5% were exposed to four or more. Five types - witnessing death or serious injury, the unexpected death of a loved one, being mugged, being in a life-threatening automobile accident, and experiencing a life-threatening illness or injury - accounted for over half of all exposures. Exposure varied by country, sociodemographics and history of prior traumatic events. Being married was the most consistent protective factor. Exposure to interpersonal violence had the strongest associations with subsequent traumatic events. CONCLUSIONS: Given the near ubiquity of exposure, limited resources may best be dedicated to those that are more likely to be further exposed such as victims of interpersonal violence. Identifying mechanisms that account for the associations of prior interpersonal violence with subsequent trauma is critical to develop interventions to prevent revictimization. Language: en

Protein instability in poly(lactic-co-glycolic acid) microparticles.

Abstract: In this review the current knowledge of protein degradation during preparation, storage and release from poly(lactic-co-glycolic acid) (PLGA) microparticles is described, as well as stabilization approaches. Although we have focussed on PLGA microparticles, the degradation processes and mechanisms described here are valid for many other polymeric release systems. Optimized process conditions as well as stabilizing excipients need to be used to counteract several stress factors that compromise the integrity of protein structure during preparation, storage, and release. The use of various stabilization approaches has rendered some success in increasing protein stability, but, still, full preservation of the native protein structure remains a major challenge in the formulation of protein-loaded PLGA microparticles.

Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set

Abstract: The application of the convolutional neural network has shown to greatly improve the accuracy of building extraction from remote sensing imagery. In this paper, we created and made open a high-quality multisource data set for building detection, evaluated the accuracy obtained in most recent studies on the data set, demonstrated the use of our data set, and proposed a Siamese fully convolutional network model that obtained better segmentation accuracy. The building data set that we created contains not only aerial images but also satellite images covering 1000 km2 with both raster labels and vector maps. The accuracy of applying the same methodology to our aerial data set outperformed several other open building data sets. On the aerial data set, we gave a thorough evaluation and comparison of most recent deep learning-based methods, and proposed a Siamese U-Net with shared weights in two branches, and original images and their down-sampled counterparts as inputs, which significantly improves the segmentation accuracy, especially for large buildings. For multisource building extraction, the generalization ability is further evaluated and extended by applying a radiometric augmentation strategy to transfer pretrained models on the aerial data set to the satellite data set. The designed experiments indicate our data set is accurate and can serve multiple purposes including building instance segmentation and change detection; our result shows the Siamese U-Net outperforms current building extraction methods and could provide valuable reference.