Education•Trier, Rheinland-Pfalz, Germany•
About: University of Trier is a education organization based out in Trier, Rheinland-Pfalz, Germany. It is known for research contribution in the topics: Population & Trier social stress test. The organization has 2827 authors who have published 7409 publications receiving 214286 citations.
Papers published on a yearly basis
TL;DR: The results suggest that gender, genetics and nicotine consumption can influence the individual's stress responsiveness to psychological stress while personality traits showed no correlation with cortisol responses to TSST stimulation.
Abstract: This paper describes a protocol for induction of moderate psychological stress in a laboratory setting and evaluates its effects on physiological responses. The ‘Trier Social Stress Test’ (TSST) mainl
TL;DR: GPOWER performs high-precision statistical power analyses for the most common statistical tests in behavioral research, that is,t tests,F tests, andχ2 tests.
Abstract: GPOWER is a completely interactive, menu-driven program for IBM-compatible and Apple Macintosh personal computers. It performs high-precision statistical power analyses for the most common statistical tests in behavioral research, that is,t tests,F tests, andχ2 tests. GPOWER computes (1) power values for given sample sizes, effect sizes andα levels (post hoc power analyses); (2) sample sizes for given effect sizes,α levels, and power values (a priori power analyses); and (3)α andβ values for given sample sizes, effect sizes, andβ/α ratios (compromise power analyses). The program may be used to display graphically the relation between any two of the relevant variables, and it offers the opportunity to compute the effect size measures from basic parameters defining the alternative hypothesis. This article delineates reasons for the development of GPOWER and describes the program’s capabilities and handling.
TL;DR: It is shown that depending on which formula is used, different associations with other variables may emerge, and it is recommended to employ both formulas when analyzing data sets with repeated measures.
Abstract: Study protocols in endocrinological research and the neurosciences often employ repeated measurements over time to record changes in physiological or endocrinological variables. While it is desirable to acquire repeated measurements for finding individual and group differences with regard to response time and duration, the amount of data gathered often represents a problem for the statistical analysis. When trying to detect possible associations between repeated measures and other variables, the area under the curve (AUC) is routinely used to incorporate multiple time points. However, formulas for computation of the AUC are not standardized across laboratories, and existing differences are usually not presented when discussing results, thus causing possible variability, or incompatibility of findings between research groups. In this paper, two formulas for calculation of the area under the curve are presented, which are derived from the trapezoid formula. These formulas are termed ‘Area under the curve with respect to increase’ (AUCI) and ‘Area under the curve with respect to ground’ (AUCG). The different information that can be derived from repeated measurements with these two formulas is exemplified using artificial and real data from recent studies of the authors. It is shown that depending on which formula is used, different associations with
TL;DR: Empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
Abstract: Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
TL;DR: An up-to-date overview of recent methodological developments, novel applications as well as a discussion of possible future applications of salivary cortisol determination are provided.
Abstract: The assessment of cortisol in saliva has proven a valid and reliable reflection of the respective unbound hormone in blood. To date, assessment of cortisol in saliva is a widely accepted and frequently employed method in psychoneuroendocrinology. Due to several advantages over blood cortisol analyses (e.g., stress-free sampling, laboratory independence, lower costs) saliva cortisol assessment can be the method of choice in basic research and clinical environments. The determination of cortisol in saliva can facilitate stress studies including newborns and infants and replace blood sampling for diagnostic endocrine tests like the dexamethasone suppression test. The present paper provides an up-to-date overview of recent methodological developments, novel applications as well as a discussion of possible future applications of salivary cortisol determination.
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|Oliver T. Wolf||83||337||24211|
|Jens C. Pruessner||81||280||28326|
|Dirk H. Hellhammer||80||178||38769|
|Brian S. Schwartz||69||329||15934|
|Clara E. Hill||67||319||18579|
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