Activity

Long-distance migrations are among the most physically demanding feats animals perform. Understanding the potential costs and benefits of such behaviour is a fundamental question in ecology and evolution. A hypothetical cost of migration should be outweighed by higher productivity and/or higher annual survival, but few studies on migratory species have been able to directly quantify patterns of survival throughout the full annual cycle and across the majority of a species’ range. Here, we use telemetry data from 220 migratory Egyptian vultures Neophron percnopterus, tracked for 3,186 bird months and across approximately 70% of the species’ global distribution, to test for differences in survival throughout the annual cycle. We estimated monthly survival probability relative to migration and latitude using a multi-event capture-recapture model in a Bayesian framework that accounted for age, origin, subpopulation and the uncertainty of classifying fates from tracking data. We found lower survival during migratimore human-caused mortality farther north, and suggest that increasing anthropogenic mortality could disrupt the delicate migration trade-off balance. Research to investigate further potential benefits of migration (e.g. differential productivity across latitudes) could clarify how migration evolved and how migrants may persist in a rapidly changing world.Patients with cirrhosis on the liver transplant (LT) waiting list may die or be removed due to complications of portal hypertension (PH) or infections. Von Willebrand factor antigen (vWF-Ag) and C-reactive protein (CRP) are simple, broadly available markers of these processes. We determined whether addition of vWF-Ag and CRP to the MELD-Na score improves risk stratification of patients awaiting LT. find more CRP and vWF-Ag at LT listing were assessed in 2 independent cohorts (Medical University of Vienna [exploration cohort] and Mayo Clinic Rochester [validation cohort]). Clinical characteristics, MELD-Na and mortality on the waiting list were recorded. Prediction of 3-month waiting list mortality was assessed by receiver operating characteristics curve (ROC-AUC). In order to explore potential mechanisms underlying the prognostic utility of vWF-Ag and CRP in this setting, we evaluated their association with PH, bacterial translocation, systemic inflammation, and circulatory dysfunction. In the exploration cohort (N=269) vWF-Ag and CRP both improved the predictive value of MELD-Na for 3-month waitlist mortality and showed the highest predictive value when combined (AUC MELD-Na 0.764, MELD-Na+CRP 0.790, MELD-Na+vWF 0.803, MELD-Na+CRP+vWF-Ag 0.824). Results were confirmed in an independent validation cohort (N=129, AUC MELD-Na 0.677, MELD-Na+CRP+vWF-Ag 0.882). vWF-Ag was independently associated with PH and inflammatory biomarkers, while CRP closely, and MELD-independently, correlated with biomarkers of bacterial translocation/inflammation. CONCLUSION The addition of vWF-Ag and CRP – reflecting central pathophysiological mechanisms of PH, bacterial translocation and inflammation, that are all drivers of mortality on the waiting list for LT – to the MELD-Na score improves prediction of waitlist mortality. Using the vWFAg-CRP-MELD-Na model for prioritizing organ allocation may improve prediction of waitlist mortality and decrease waitlist mortality.The effectiveness of antidepressants in the treatment of major depressive disorder varies considerably between patients. With these interindividual differences and a number of antidepressants to choose from, the first choice of treatment often fails to produce improvement in the patient’s condition. A substantial part of the variation in response to antidepressants can be explained by genetic factors. Accordingly, variants related to drug metabolism in two pharmacogenes, CYP2D6 and CYP2C19, have already been translated into guidelines for antidepressant prescriptions. The role of variants in other genes that influence antidepressant responses is not yet understood. Furthermore, rare and individual variants account for a substantial part of genetic differences in antidepressant efficacy. Recent years have brought a tremendous increase in the accessibility of genome sequencing in terms of data availability and its clinical use. In this review, we summarize recent developments and current issues in the personalization of major depressive disorder treatment through pharmacogenomics.

Intuitive eating is an adaptive style of eating that has generated significant research attention. Theoretically, intuitive eating is a core construct that features prominently in the Acceptance Model of Intuitive Eating, a framework that explains how positive environmental influences can foster intuitive eating practices via body appreciation. Empirically, intuitive eating has been connected to a broad range of adaptive mental health indices. At present, a quantitative synthesis of intuitive eating and its correlates has yet to be conducted. This was the objective of the current meta-analysis.

Ninety-seven studies (89% cross-sectional) were included. Random effects meta-analyses were conducted on 23 psychological correlates, divided into three clusters eating behavior and body image disturbances, positive body image and other adaptive factors, and general psychopathology. Meta-analytic path analyses were also computed to test the validity of the Acceptance Model.

Intuitive eating was inversely associat be identified.This work examines methods for predicting the partition coefficient (log P) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular dynamics simulations. These atomic attributes are transformed into index-invariant molecular features using a recently developed method called geometric scattering for graphs (GSG). We call this approach “ClassicalGSG” and examine its performance under a broad range of conditions and hyperparameters. We train ClassicalGSG log P predictors with neural networks using 10,722 molecules from the OpenChem dataset and apply them to predict the log P values from four independent test sets. The ClassicalGSG method’s performance is compared to a baseline model that employs graph convolutional networks. Our results show that the best prediction accuracies are obtained using atomic attributes generated with the CHARMM generalized force field and 2D molecular structures.