Health promotion in school environment in Brazil

ABSTRACT OBJECTIVE Evaluate the school environments to which ninth-year students are exposed in Brazil and in the five regions of the country according to health promotion guidelines. METHODS Cross-sectional study from 2012, with a representative sample of Brazil and its macroregions. We interviewed ninth-year schoolchildren and managers of public and private schools. We proposed a score of health promotion in the school environment (EPSAE) and estimated the distribution of school members according to this score. Crude and adjusted odds ratios (OR) were used, by ordinal regression, to determine the schoolchildren and schools with higher scores, according to the independent variables. RESULTS A student is more likely to attend a school with a higher EPSAE in the South (OR = 2.80; 95%CI 2.67–2.93) if the school is private (OR = 4.52; 95%CI 4.25–4.81) and located in a state capital, as well as if the student is 15 years of age or older, has a paid job, or has parents with higher education. CONCLUSIONS The inequalities among the country’s regions and schools are significant, demonstrating the need for resources and actions that promote greater equity

Generalizability of clinical prediction models in mental health

Concerns about the generalizability of machine learning models in mental health arise, partly due to sampling effects and data disparities between research cohorts and real-world populations. We aimed to investigate whether a machine learning model trained solely on easily accessible and low-cost clinical data can predict depressive symptom severity in unseen, independent datasets from various research and real-world clinical contexts. This observational multi-cohort study included 3021 participants (62.03% females, MAge = 36.27 years, range 15–81) from ten European research and clinical settings, all diagnosed with an affective disorder. We firstly compared research and real-world inpatients from the same treatment center using 76 clinical and sociodemographic variables. An elastic net algorithm with ten-fold cross-validation was then applied to develop a sparse machine learning model for predicting depression severity based on the top five features (global functioning, extraversion, neuroticism, emotional abuse in childhood, and somatization). Model generalizability was tested across nine external samples. The model reliably predicted depression severity across all samples (r = 0.60, SD = 0.089, p < 0.0001) and in each individual external sample, ranging in performance from r = 0.48 in a real-world general population sample to r = 0.73 in real-world inpatients. These results suggest that machine learning models trained on sparse clinical data have the potential to predict illness severity across diverse settings, offering insights that could inform the development of more generalizable tools for use in routine psychiatric data analysis

Half-wave phase retarder working in transmission around 630nm realized by atomic layer deposition of sub-wavelength gratings

The realization of half wave phase retarders based on subwavelength periodic gratings typically requires small periods with large aspect ratio features. The required aspect ratio of the grating features can be considerably decreased when high refractive index materials are employed. Because the nano-structuring and processing of such dielectrics is quite difficult, we have designed and developed a half-wave retarder relying on (low index) fused silica (SiO2) gratings that are over-coated by titanium dioxide (TiO2) using atomic layer deposition. The period and depth of the fabricated structures are 400nm and 1700nm, respectively. Half-wave retardation is achieved at 628nm and the total transmission lies above 90%

Testanlage fuer Orbitale Servising Technologie (Unbemanntes Servicing Element) Abschlussbericht

Available from TIB Hannover: F97B116+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Forschung und Technologie (BMFT), Bonn (Germany)DEGerman