Studies about the influence of turbulence on the sound propagation in the atmosphere and its simulation

Noise is still an unsolved problem of our time and influences the public health and well-being. So sound-exposure gains more and more in importance. This study examines the influence of turbulent vertical profiles of wind and temperature on the sound propagation, using the model SMART (sound propagation model of the atmosphere using ray-tracing). For several states of atmospheric stability, ten-minutetime series of vertical wind and temperature profiles were constructed and used as input data for the model. Simulations of the sound attenuation showed that turbulence affects the sound propagation in the atmosphere. This influence is reflected in a reduction of the sound attenuation level in the downwind area, whereas the sound shadow remains almost unaffected. The influence increases with the distance to the source and depends on the atmospheric stability. Beside the average influence due to turbulence, a “worst-case” scenario with the highest noise immission during the simulated time range was analyzed. Based on the results of this study, a new SMART-module, taking turbulence into account by parameterizations, was developed. The developed turbulence module is an upgrade of the sound model SMART and helps to improve the sound immission forecasts, including meteorological effects

Correlation between weather and incidence of selected ophthalmological diagnoses: a database analysis

Purpose: Our aim was to correlate the overall patient volume and the incidence of several ophthalmological diseases in our emergency department with weather data. Patients and methods: For data analysis, we used our clinical data warehouse and weather data. We investigated the weekly overall patient volume and the average weekly incidence of all encoded diagnoses of "conjunctivitis", "foreign body", "acute iridocyclitis", and "corneal abrasion". A Spearman's correlation was performed to link these data with the weekly average sunshine duration, temperature, and wind speed. Results: We noticed increased patient volume in correlation with increasing sunshine duration and higher temperature. Moreover, we found a positive correlation between the weekly incidences of conjunctivitis and of foreign body and weather data. Conclusion: The results of this data analysis reveal the possible influence of external conditions on the health of a population and can be used for weather-dependent resource allocation

Spironolactone alleviates schizophrenia-related reversal learning in Tcf4 transgenic mice subjected to social defeat

Cognitive deficits are a hallmark of schizophrenia, for which no convincing pharmacological treatment option is currently available. Here, we tested spironolactone as a repurposed compound in Tcf4 transgenic mice subjected to psychosocial stress. In this ‘2-hit’ gene by environment mouse (GxE) model, the animals showed schizophrenia-related cognitive deficits. We had previously shown that spironolactone ameliorates working memory deficits and hyperactivity in a mouse model of cortical excitatory/inhibitory (E/I) dysbalance caused by an overactive NRG1-ERBB4 signaling pathway. In an add-on clinical study design, we used spironolactone as adjuvant medication to the standard antipsychotic drug aripiprazole. We characterized the compound effects using our previously established Platform for Systematic Semi-Automated Behavioral and Cognitive Profiling (PsyCoP). PsyCoP is a widely applicable analysis pipeline based on the Research Domain Criteria (RDoC) framework aiming at facilitating translation into the clinic. In addition, we use dimensional reduction to analyze and visualize overall treatment effect profiles. We found that spironolactone and aripiprazole improve deficits of several cognitive domains in Tcf4tg x SD mice but partially interfere with each other’s effect in the combination therapy. A similar interaction was detected for the modulation of novelty-induced activity. In addition to its strong activity-dampening effects, we found an increase in negative valence measures as a side effect of aripiprazole treatment in mice. We suggest that repurposed drug candidates should first be tested in an adequate preclinical setting before initiating clinical trials. In addition, a more specific and effective NRG1-ERBB4 pathway inhibitor or more potent E/I balancing drug might enhance the ameliorating effect on cognition even further

Combined thalamic and subthalamic deep brain stimulation for tremor-dominant Parkinson's disease.

Deep brain stimulation (DBS) in the thalamic ventral intermediate (Vim) or the subthalamic nucleus (STN) reportedly improves medication-refractory Parkinson's disease (PD) tremor. However, little is known about the potential synergic effects of combined Vim and STN DBS. We describe a 79-year-old man with medication-refractory tremor-dominant PD. Bilateral Vim DBS electrode implantation produced insufficient improvement. Therefore, the patient underwent additional unilateral left-sided STN DBS. Whereas Vim or STN stimulation alone led to partial improvement, persisting tremor resolution occurred after simultaneous stimulation. The combination of both targets may have a synergic effect and is an alternative option in suitable cases

Classical blood biomarkers identify patients with higher risk for relapse 6 months after alcohol withdrawal treatment

This naturalistic study among patients with alcohol dependence examined whether routine blood biomarkers could help to identify patients with high risk for relapse after withdrawal treatment. In a longitudinal study with 6-month follow-up among 133 patients with alcohol dependence who received inpatient alcohol withdrawal treatment, we investigated the usefulness of routine blood biomarkers and clinical and sociodemographic factors for potential outcome prediction and risk stratification. Baseline routine blood biomarkers (gamma-glutamyl transferase GGT, alanine aminotransferase ALT/GPT, aspartate aminotransferase AST/GOT, mean cell volume of erythrocytes MCV), and clinical and sociodemographic characteristics were recorded at admission. Standardized 6~months' follow-up assessed outcome variables continuous abstinence, days of continuous abstinence, daily alcohol consumption and current abstinence. The combined threshold criterion of an AST:ALT ratio > 1.00 and MCV > 90.0 fl helped to identify high-risk patients. They had lower abstinence rates (P = 0.001), higher rates of daily alcohol consumption (P < 0.001) and shorter periods of continuous abstinence (P = 0.027) compared with low-risk patients who did not meet the threshold criterion. Regression analysis confirmed our hypothesis that the combination criterion is an individual baseline variable that significantly predicted parts of the respective outcome variances. Routinely assessed indirect alcohol biomarkers help to identify patients with high risk for relapse after alcohol withdrawal treatment. Clinical decision algorithms to identify patients with high risk for relapse after alcohol withdrawal treatment could include classical blood biomarkers in addition to clinical and sociodemographic items

Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data

Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB-specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations

Leserschaft, Nutzung und Bewertung von BILDblog: Befunde der ersten Online-Befragung 2007

Der Bericht fasst die Ergebnisse einer Online-Befragung unter Nutzern des BILDblog zusammen, an der Personen teilnahmen. Neben Befunden zur soziodemographischen Zusammensetzung und den Lektüremotiven der Nutzerschaft wurden auch Informationen zur Einschätzung der publizistischen Leistungen des populärsten deutschsprachigen "Watchblogs" erhoben