Perspectives on mental health and well-being

Abstract: Introduction: The aim of this study is to provide an overview of Paralympic athletes’ views on mental health in a competitive sport context. Although research in the field of Paralympic sport has increased exponentially over the last two decades, mental health and its promotion have hardly been the subject of research so far. Previous research shows that the practice of competitive sports influences the mental health of Paralympic athletes both positively and negatively. Mental disorders are no exception, even in elite sports. Well-coordinated health care for the prevention and treatment of mental health challenges and mental disorders in elite sports is crucial for this purpose. Methods: The methodological approach of the present study is based on a qualitative research design. The data was collected through semi-structured interviews and their evaluation with the help of applied thematic analysis. For the interviews, 15 active, adult, Swiss elite Para athletes were recruited. Results: The results suggest that athletic success, the athletic activity itself and an improvement in physical health can lead to an increase in mental well-being. On the other hand, athletic failure, pressure to perform, and physical problems can cause psychological stress and facilitate mental disorders. Conclusion: In particular, the coordinated handling of mental health challenges and mental disorders among Paralympic athletes seems to need improvement, especially regarding the use of sports psychiatry and psychotherapy services

A stereotaxic breed-averaged, symmetric T2w canine brain atlas including detailed morphological and volumetrical data sets

Stereotaxic systems and automatic tissue segmentation routines enable neuronavigation as well as reproducible processing of neuroimage datasets. Such systems have been developed for humans, non-human-primates, sheep, and rodents, but not for dogs. Although dogs share important neurofunctional and -anatomical features with humans, and in spite of their importance in translational neuroscience, little is known about the variability of the canine brain morphology and, possibly related, function. Moreover, we lack templates, tissue probability maps (TPM), and stereotaxic brain labels for implementation in standard software utilities such as Statistical Parametric Mapping (SPM). Hence, objective and reproducible, image-based investigations are currently impeded in dogs. We have created a detailed stereotaxic reference frame for dogs including TPM and tissue labels, enabling inter-individual and cross-study neuroimage analysis. T2w datasets were acquired from 16 neurologically inconspicuous dogs of different breeds by 3T MRI. The datasets were averaged after initial preprocessing using linear and nonlinear registration algorithms as implemented in SPM8. TPM for gray (GM) and white matter (WM) as well as cerebrospinal fluid (CSF) were created. Different cortical, subcortical, medullary, and CSF regions were manually labeled to create a spatial binary atlas being aligned with the template. A proof-of-concept for automatic determination of morphological and volumetrical characteristics was performed using additional canine datasets (n=64) including a subgroup of laboratory beagles (n=24). Overall, 21 brain regions were labeled using the segmented tissue classes of the brain template. The proof-of-concept trial revealed excellent suitability of the created tools for image processing and subsequent analysis. There was high intra-breed variability in frontal lobe and hippocampus volumes, and noticeable inter-breed corpus callosum volume variation. The T2w brain template provides important, breed-averaged canine brain anatomy features in a spatial standard coordinate system. TPM allows automatic tissue segmentation using SPM and enables unbiased automatic image processing or morphological characterization in different canine breeds. The reported volumetric and morphometric results may serve as a starting point for further research aimed at in vivo analysis of canine brain anatomy and function

European larch sapwood: A model for predicting the cambial age and for a more accurate dating

International audienceEuropean larch (Larix decidua Mill.) holds significant importance as a forest tree species throughout the Alps and in certain regions of central Europe. Its extensive use as construction timber has made it a subject of substantial interest in dendroarchaeological studies aimed at understanding the long-term interactions between human societies and forests. Precise dating of felling phases, accurate estimation of the age of harvested wood, and information on the geographical origin of wood play a crucial role when it comes to characterize these interactions. In this study, we compiled a large dataset of L. decidua samples from across the European Alps to provide a robust statistical model that predicts the cambial age of L. decidua trees based on the number of heartwood rings. By extension, this model can be used to estimate the number of sapwood rings so as to approximate the felling date and to more precise date archaeological larch timber. The model requires almost complete heartwood sequences (<5 missing rings) to achieve accurate estimations. Our results also evidence that the ratio between the number of sapwood and heartwood rings varies across the Alps. At the same time, the indicator developed in this work is not suitable for a determination of wood origin, raising doubts about the effectiveness of attempts aimed at dendroprovenancing L. decidua based on sapwood

The sex-specific associations of the aromatase gene with Alzheimer's disease and its interaction with IL10 in the Epistasis Project

Epistasis between interleukin-10 (IL10) and aromatase gene polymorphisms has previously been reported to modify the risk of Alzheimer's disease (AD). However, although the main effects of aromatase variants suggest a sex-specific effect in AD, there has been insufficient power to detect sex-specific epistasis between these genes to date. Here we used the cohort of 1757 AD patients and 6294 controls in the Epistasis Project. We replicated the previously reported main effects of aromatase polymorphisms in AD risk in women, for example, adjusted odds ratio of disease for rs1065778 GG=1.22 (95% confidence interval: 1.01-1.48, P=0.03). We also confirmed a reported epistatic interaction between IL10 rs1800896 and aromatase (CYP19A1) rs1062033, again only in women: adjusted synergy factor=1.94 (1.16-3.25, 0.01). Aromatase, a rate-limiting enzyme in the synthesis of estrogens, is expressed in AD-relevant brain regions,and is downregulated during the disease. IL-10 is an anti-inflammatory cytokine. Given that estrogens have neuroprotective and anti-inflammatory activities and regulate microglial cytokine production, epistasis is biologically plausible. Diminishing serum estrogen in postmenopausal women, coupled with suboptimal brain estrogen synthesis, may contribute to the inflammatory state, that is a pathological hallmark of AD.European Journal of Human Genetics advance online publication, 5 June 2013; doi:10.1038/ejhg.2013.116

Same data, different conclusions : radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed