Wie kleine Betriebe die Weiterbildungen für ihre Mitarbeitenden organisieren und finanzieren, entscheiden die Geschäftsführer:innen. Von welchen Einflüssen sie sich leiten lassen, rekonstruiert ...Christian Müller in seiner Dissertation. Mit der Grounded Theory analysiert er anhand qualitativer Interviews die Weiterbildungsentscheidungen in Klein- und Kleinstunternehmen verschiedener Branchen. In Anlehnung an das Garbage Can Model erweisen sich diese Entscheidungen vielfach als nicht linear, nicht rational und nicht planvoll. Die Analysen belegen, wie sehr situative Passung und Formalisierung, persönliche Intuition, Bildungserfahrungen und Werte aber auch Spezifika der Branche die Entscheidungen beeinflussen. In der Zusammenführung differenziert der Autor fünf Entscheidungstypen: planvoll-routiniert, inkrementell-pragmatisch, strategisch-achtsam, progressiv-partizipativ und puristisch-funktional. Als Pars pro Toto lassen sich aus den Ergebnissen Rückschlüsse über Weiterbildungsentscheidungen und -teilnahmen in Deutschland ziehen.
16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ...ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.
The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we ...searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect.
Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo.
The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% highest density interval (HDI) 0.98, 1.02, REMA: 95% HDI 1.00, 1.02). The between-study standard deviation τ under the REMA with respect to lnVR was found to be low (95% HDI 0.00, 0.02). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed.
The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.
Conducting polymers offer new opportunities to design soft, conformable and light-weight thermoelectric textile generators that can be unobtrusively integrated into garments or upholstery. Using the ...widely available conducting polymer:polyelectrolyte complex poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) as the p-type material, we have prepared an electrically conducting sewing thread, which we then embroidered into thick wool fabrics to form out-of-plane thermoelectric textile generators. The influence of device design is discussed in detail, and we show that the performance of e-textile devices can be accurately predicted and optimized using modeling developed for conventional thermoelectric systems, provided that the electrical and thermal contact resistances are included in the model. Finally, we demonstrate a thermoelectric textile device that can generate a, for polymer-based devices, unprecedented power of 1.2 μW at a temperature gradient ΔT of 65 K, and over 0.2 μW at a more modest ΔT of 30 K.
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•E-textiles offer wearable sensing and energy harvesting functionality.•Thermoelectric energy harvesters can convert body heat to electricity.•Our thermoelectric textile delivers a record 1.2 μW at ΔT = 65 K.•Adapted thermoelectric models accurately predict the textile device performance.
Abstract
Motivation
Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of ...microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data.
Results
Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi’s wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children’s rooms between samples from two study centers (Ulm and Munich).
Availability
R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.
Contact
Tel:+49 89 3187 43258; stefanie.peschel@mail.de
Supplementary information
Supplementary data are available at Briefings in Bioinformatics online.
Molecular doping of organic semiconductors is critical for optimizing a range of optoelectronic devices such as field‐effect transistors, solar cells, and thermoelectric generators. However, many ...dopant:polymer pairs suffer from poor solubility in common organic solvents, which leads to a suboptimal solid‐state nanostructure and hence low electrical conductivity. A further drawback is the poor thermal stability through sublimation of the dopant. The use of oligo ethylene glycol side chains is demonstrated to significantly improve the processability of the conjugated polymer p(g42T‐T)—a polythiophene—in polar aprotic solvents, which facilitates coprocessing of dopant:polymer pairs from the same solution at room temperature. The use of common molecular dopants such as 2,3,5,6‐tetrafluoro‐7,7,8,8‐tetracyanoquinodimethane (F4TCNQ) and 2,3‐dichloro‐5,6‐dicyano‐1,4‐benzoquinone (DDQ) is explored. Doping of p(g42T‐T) with F4TCNQ results in an electrical conductivity of up to 100 S cm−1. Moreover, the increased compatibility of the polar dopant F4TCNQ with the oligo ethylene glycol functionalized polythiophene results in a high degree of thermal stability at up to 150 °C.
Molecular doped polythiophenes with polar side chains display strongly enhanced processability even at high dopant fractions. Oligo ethylene side chains prevent coagulation of the polymer dopant pairs. The resulting films have a high electrical conductivity of up to 100 S cm−1. An enhanced thermal stability compared with doped poly(3‐hexylthiophene) is demonstrated.
•We review the role of the serotonergic system in the establishment of psychoactive drug use and transition to addiction.•There is a distinct involvement of the serotonergic system in both ...processes.•A new functional model suggests specific serotonergic adaptations during controlled drug use.•Induced serotonergic adaptations render the nervous system susceptible to the transition to compulsive drug use.•Serotonergic adaptations often overlap with genetic risk factors for addiction.
The use of psychoactive drugs is a wide spread behaviour in human societies. The systematic use of a drug requires the establishment of different drug use-associated behaviours which need to be learned and controlled. However, controlled drug use may develop into compulsive drug use and addiction, a major psychiatric disorder with severe consequences for the individual and society. Here we review the role of the serotonergic (5-HT) system in the establishment of drug use-associated behaviours on the one hand and the transition and maintenance of addiction on the other hand for the drugs: cocaine, amphetamine, methamphetamine, MDMA (ecstasy), morphine/heroin, cannabis, alcohol, and nicotine. Results show a crucial, but distinct involvement of the 5-HT system in both processes with considerable overlap between psychostimulant and opioidergic drugs and alcohol. A new functional model suggests specific adaptations in the 5-HT system, which coincide with the establishment of controlled drug use-associated behaviours. These serotonergic adaptations render the nervous system susceptible to the transition to compulsive drug use behaviours and often overlap with genetic risk factors for addiction. Altogether we suggest a new trajectory by which serotonergic neuroadaptations induced by first drug exposure pave the way for the establishment of addiction.
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