Work-related exposure to violence or threats and risk of mental disorders and symptoms:A systematic review and meta-analysis

OBJECTIVE: This review aimed to examine systematically the epidemiological evidence linking work-related exposure to violence and threats thereof with risk of mental disorders and mental ill-health symptoms. METHODS: We searched PubMed, EMBASE, PsycINFO and Web of Science to identify original studies that provide quantitative risk estimates. The evidence was weighted according to completeness of reporting, potential common method bias, and bias due to differential selection and drop out, selective reporting, and misclassification of exposure and outcome. RESULTS: We identified 14 cross-sectional and 10 cohort studies with eligible risk estimates, of which 4 examined depressive disorder and reported an elevated risk among the exposed [pooled relative risk (RR) 1.42, 95% confidence interval (CI) 1.31–1.54, I(2)=0%]. The occurrence of depressive and anxiety symptoms, burnout and psychological distress was examined in 17 studies (pooled RR 2.33, 95% CI 3.17, I(2)=42%), and 3 studies examined risk of sleep disturbance (pooled RR 1.22, 95% CI 1.09–1.37, I(2)=0%). In most studies, common method bias and confounding could not be ruled out with confidence and strong heterogeneity in most outcome definitions invalidate the strict interpretation of most pooled risk estimates. CONCLUSION: The reviewed studies consistently indicate associations between workplace violence and mental health problems. However, due to methodological limitations the causal associations (if any) may be stronger or weaker than the ones reported in this study. Prospective studies with independent and validated reporting of exposure and outcome and repeated follow-up with relevant intervals are highly warranted

Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy

Time-resolved X-ray tomographic microscopy is an invaluable technique to investigate dynamic processes in 3D for extended time periods. Because of the limited signal-to-noise ratio caused by the short exposure times and sparse angular sampling frequency, obtaining quantitative information through post-processing remains challenging and requires intensive manual labor. This severely limits the accessible experimental parameter space and so, prevents fully exploiting the capabilities of the dedicated time-resolved X-ray tomographic stations. Though automatic approaches, often exploiting iterative reconstruction methods, are currently being developed, the required computational costs typically remain high. Here, we propose a highly efficient reconstruction and classification pipeline (SIRT-FBP-MS-D-DIFF) that combines an algebraic filter approximation and machine learning to significantly reduce the computational time. The dynamic features are reconstructed by standard filtered back-projection with an algebraic filter to approximate iterative reconstruction quality in a computationally efficient manner. The raw reconstructions are post-processed with a trained convolutional neural network to extract the dynamic features from the low signal-to-noise ratio reconstructions in a fully automatic manner. The capabilities of the proposed pipeline are demonstrated on three different dynamic fuel cell datasets, one exploited for training and two for testing without network retraining. The proposed approach enables automatic processing of several hundreds of datasets in a single day on a single GPU node readily available at most institutions, so extending the possibilities in future dynamic X-ray tomographic investigations

Methanotropic microbial communities associated with bubble plumes above gas seeps in the Black Sea

Bubbles evolving from active gas seeps can be traced by hydroacoustic imaging up to 1000 m high in the Black Sea water column. Although methane concentrations are not distinguishable between the water column above the deep seep and reference sites, atmospheric noble gas measurements clearly show the constant input of gases (mainly methane) via seepage into the Black Sea. Archaea (ANME-1, ANME-2) and methanotrophic bacteria detected with specific 16S rRNA-targeted oligonucleotide probes are related to active gas seeps in the oxic and anoxic water column. It is suggested that methane seeps have a much greater influence on the Black Sea methane budget than previously acknowledged and that ANME-1 and ANME-2 are injected via gas bubbles from the sediment into the anoxic water column mediating methane oxidation. Our results show further that only minor amounts of methane evolving from Black Sea gas seeps reach the atmosphere due to the very effective microbial barrier. Hence only major thermodynamically and/or tectonically triggered gas hydrate dissociation has the potential to induce rapid climate changes as suggested by the “clathrate gun hypothesis.

Noninvasive ventilation in COVID-19 patients aged ≥ 70 years—a prospective multicentre cohort study

Funding Information: COVIP study did not have any funding. Publication of this article was funded by the Priority Research Area qLife under the program “Excellence Initiative – Research University” at the Jagiellonian University in Krakow (06/IDUB/2019/94). Publisher Copyright: © 2022, The Author(s).Background: Noninvasive ventilation (NIV) is a promising alternative to invasive mechanical ventilation (IMV) with a particular importance amidst the shortage of intensive care unit (ICU) beds during the COVID-19 pandemic. We aimed to evaluate the use of NIV in Europe and factors associated with outcomes of patients treated with NIV. Methods: This is a substudy of COVIP study—an international prospective observational study enrolling patients aged ≥ 70 years with confirmed COVID-19 treated in ICU. We enrolled patients in 156 ICUs across 15 European countries between March 2020 and April 2021.The primary endpoint was 30-day mortality. Results: Cohort included 3074 patients, most of whom were male (2197/3074, 71.4%) at the mean age of 75.7 years (SD 4.6). NIV frequency was 25.7% and varied from 1.1 to 62.0% between participating countries. Primary NIV failure, defined as need for endotracheal intubation or death within 30 days since ICU admission, occurred in 470/629 (74.7%) of patients. Factors associated with increased NIV failure risk were higher Sequential Organ Failure Assessment (SOFA) score (OR 3.73, 95% CI 2.36–5.90) and Clinical Frailty Scale (CFS) on admission (OR 1.46, 95% CI 1.06–2.00). Patients initially treated with NIV (n = 630) lived for 1.36 fewer days (95% CI − 2.27 to − 0.46 days) compared to primary IMV group (n = 1876). Conclusions: Frequency of NIV use varies across European countries. Higher severity of illness and more severe frailty were associated with a risk of NIV failure among critically ill older adults with COVID-19. Primary IMV was associated with better outcomes than primary NIV. Clinical Trial RegistrationNCT04321265, registered 19 March 2020, https://clinicaltrials.gov.publishersversionpublishe

Microbe-host interplay in atopic dermatitis and psoriasis

Despite recent advances in understanding microbial diversity in skin homeostasis, the relevance of microbial dysbiosis in inflammatory disease is poorly understood. Here we perform a comparative analysis of skin microbial communities coupled to global patterns of cutaneous gene expression in patients with atopic dermatitis or psoriasis. The skin microbiota is analysed by 16S amplicon or whole genome sequencing and the skin transcriptome by microarrays, followed by integration of the data layers. We find that atopic dermatitis and psoriasis can be classified by distinct microbes, which differ from healthy volunteers microbiome composition. Atopic dermatitis is dominated by a single microbe (Staphylococcus aureus), and associated with a disease relevant host transcriptomic signature enriched for skin barrier function, tryptophan metabolism and immune activation. In contrast, psoriasis is characterized by co-occurring communities of microbes with weak associations with disease related gene expression. Our work provides a basis for biomarker discovery and targeted therapies in skin dysbiosis.Peer reviewe

Simulation of SVPWM Based Multivariable Control Method for a DFIG Wind Energy System

This paper deals with a variable speed device toproduce electrical energy on a power network based on adoubly-fed induction machine used in generating mode(DFIG) in wind energy system by using SVPWM powertransfer matrix. This paper presents a modeling and controlapproach which uses instantaneous real and reactive powerinstead of dq components of currents in a vector controlscheme. The main features of the proposed model comparedto conventional models in the dq frame of reference arerobustness and simplicity of realization. The sequential loopclosing technique is adopted to design a multivariable controlsystem including six compensators for a DFIG wind energysystem to capture the maximum wind power and to inject therequired reactive power to the generator. In this paperSVPWM method is used for better controlling of converters.It also provides fault ride through method to protect theconverter during a fault. The time-domain simulation of thestudy system is presented by using MATLAB Simulink to testthe system robustness, to validate the proposed model and toshow the enhanced tracking capability

Ein Windows-Programm auf dem CLiC

Auf Grund einer zu verwendenden Windows Programm-Bibliothek(DLL) wurde im Rahmen einer Bakkalaureusarbeit eine parallele Windowsapplikation mit selbstgeschriebener Kommunikation auf dem CLiC betrieben. Der Artikel soll die Vorgehensweise aufzeigen und kann Anregungen zu Arbeiten auf diesem Gebiet geben

Optimal Layer Design

In this bachelor thesis we report on our numerical investigations into the optimal design of protective multi-layer coatings subject to an external force of Hertzian form. In view of mechanical reliablity and durability of the substrate and the coating we aim to find the best composition of given materials with the least computational effort. Numerical studies are carried out using the simulation software ELASTICA being the first non-FEM approach for the computation of stress fields within multi-layer coated, elastic materials. We thereby made use of the massive parallel computer CLiC (Chemnitzer Linux Cluster) where we ran our Windows based application in a Wine Environment. The outcome of the optimization is in general very sensitive towards the input parameters(i.e., material properties) which are not always available in the desired accuracy. However, the scheme outlined in this work is shown to produce very good results and could contribute a great deal to find optimal solutions for real applications.Diese Bachelorarbeit befasst sich mit numerischen Untersuchungen zum optimalen Design von schützenden Mehrschichtbeschichtungen, die einer externen, Hertzschen Last ausgesetzt sind. Hinsichtlich der mechanischen Zuverlässigkeit und Haltbarkeit von Substrat und Beschichtung, versuchen wir die beste Zusammensetzung von gegebenen Materialien mit möglichst geringem Rechenaufwand zu finden. Die numerischen Berechungen wurden mit der Simulationssoftware ELASTICA durchgeführt, welches das erste kommerzielle, nicht-FEM-basierte Programm zur Berechnung von Stressfeldern innerhalb mehrfach beschichteter, elastischer Materialien darstellt. Dafür benutzten wir auf dem massiven Parrallelrechner CLiC (Chemnitzer Linux Cluster) unsere Windows basierte Anwendung unter der Emulationssoftware Wine. Das Ergebnis der Optimierung hängt im allgemeinen sehr stark von der Qualität der Eingangsparameter (z.B. Materialeigenschaften) ab, welche nicht immer in der erwünschten Genauigkeit vorliegen. Es wird gezeigt, dass die in dieser Arbeit vorgestellte Vorgehensweise sehr gute Resultate liefert und für reale Anwendungen einen äusserst ressourcenschonenden Lösungsweg darstellt