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While transitioning to clean energy technologies offers countries economic advantages, the risk of "low carbon leakage" arises if production shifts to more favorable locations. We conducted a scenario analysis to examine potential shifts in production and trade patterns of clean energy technologies. Our novel empirical approach combines the global macroeconometric model GINFORS-E with a newly developed gravity trade model and integrates a socio-political analysis based on a Multi-Level Perspective (MLP) framework, innovatively augmented by quantifiable indicators. The MLP-analysis defines the expansion paths of clean energy technologies in an alternative scenario, allowing us to incorporate political and social dynamics into our analysis. The baseline scenario is based on announced pledges. For established technologies like photovoltaics, we observe minimal shifts in global trade shares as production capacities approach their limits, signaling market saturation. Conversely, emerging technologies like hydrogen electrolyzers display greater shifts, highlighting potential for market disruption and increased risk of low carbon leakage. While adopting clean technologies in individual countries lead to sector-specific changes, broader macroeconomic impacts appear constrained by existing market structures and path dependencies. Sensitivity analyses suggest that component-specific price changes influence trade and production. Expanding our approach to additional technologies and countries remains challenging due to data limitations. [ABSTRACT FROM AUTHOR]

Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.

Introduction: Clinical reasoning in veterinary medicine is often based on clinicians' personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures. Materials and methods: Dogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features. Results: A total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year. Conclusion: Machine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process. [ABSTRACT FROM AUTHOR]

Background: It is not known how much information clients retrieve from discharge instructions. Objective: To investigate client's understanding of discharge instructions and influencing factors. Animals: Dogs and cats being hospitalized for neurological diseases. Methods: Clients were presented questionnaires regarding their pet's disease, diagnostics, treatments, prognosis and discharge instructions at time of discharge and 2 weeks later. The same questions were answered by discharging veterinarians at time of discharge. Clients answered additional questions regarding the subjective feelings during discharge conversation. Data collected included: data describing discharging veterinarian (age, gender, years of clinical experience, specialist status), data describing the client (age, gender, educational status). Raw percentage of agreement (RPA) between answers of clinicians and clients as well as factors potentially influencing the RPA were evaluated. Results: Of 230 clients being approached 151 (65.7%) and 70 (30.4%) clients responded to the first and second questionnaire, respectively (130 dog and 30 cat owners). The general RPA between clinician's and client's responses over all questions together was 68.9% and 66.8% at the 2 time points. Questions regarding adverse effects of medication (29.0%), residual clinical signs (35.8%), and confinement instructions (36.8%) had the lowest RPAs at the first time point. The age of clients (P =.008) negatively influenced RPAs, with clients older than 50 years having lower RPA. Conclusions and Clinical Importance: Clients can only partially reproduce information provided at discharge. Only clients' increasing age influenced recall of information. Instructions deemed to be important should be specifically stressed during discharge. [ABSTRACT FROM AUTHOR]

The current study further establishes the reliability and validity of the Bullying Participant Behaviors Questionnaire (BPBQ), a self-report survey that allows for an examination of participation in various bullying participant role behaviors including bully, assistant to the bully, victim, defender of the victim, and outsider. The study included 801 sixth- through eighth-grade students. The results of the study confirmed a five-factor structure (Bully, Assistant, Victim, Defender, and Outsider). Internal consistency of the subscales was high and item-subscale correlations were all significant and moderate to high. Correlations among the BPBQ subscales and with additional measures, including the Behavior Assessment System for Children, Second Edition, Self-Report of Personality (Reynolds & Kamphaus, 2004), the Social Skills Rating System (Gresham & Elliott, 1990), and an unpublished victim measure (Demaray & Malecki, 2003), provided evidence of concordant, convergent, and divergent validity. Gender and grade level differences were also investigated among the bullying participant behaviors.

Aims Severe acute respiratory syndrome coronavirus-2 infection causes COVID-19, which in severe cases evokes life-threatening acute respiratory distress syndrome (ARDS). Transcriptome signatures and the functional relevance of non-vascular cell types (e.g. immune and epithelial cells) in COVID-19 are becoming increasingly evident. However, despite its known contribution to vascular inflammation, recruitment/invasion of immune cells, vascular leakage, and perturbed haemostasis in the lungs of severe COVID-19 patients, an in-depth interrogation of the endothelial cell (EC) compartment in lethal COVID-19 is lacking. Moreover, progressive fibrotic lung disease represents one of the complications of COVID-19 pneumonia and ARDS. Analogous features between idiopathic pulmonary fibrosis (IPF) and COVID-19 suggest partial similarities in their pathophysiology, yet, a head-to-head comparison of pulmonary cell transcriptomes between both conditions has not been implemented to date. Methods and results We performed single-nucleus RNA-sequencing on frozen lungs from 7 deceased COVID-19 patients, 6 IPF explant lungs, and 12 controls. The vascular fraction, comprising 38 794 nuclei, could be subclustered into 14 distinct EC subtypes. Non-vascular cell types, comprising 137 746 nuclei, were subclustered and used for EC-interactome analyses. Pulmonary ECs of deceased COVID-19 patients showed an enrichment of genes involved in cellular stress, as well as signatures suggestive of dampened immunomodulation and impaired vessel wall integrity. In addition, increased abundance of a population of systemic capillary and venous ECs was identified in COVID-19 and IPF. COVID-19 systemic ECs closely resembled their IPF counterparts, and a set of 30 genes was found congruently enriched in systemic ECs across studies. Receptor–ligand interaction analysis of ECs with non-vascular cell types in the pulmonary micro-environment revealed numerous previously unknown interactions specifically enriched/depleted in COVID-19 and/or IPF. Conclusions This study uncovered novel insights into the abundance, expression patterns, and interactomes of EC subtypes in COVID-19 and IPF, relevant for future investigations into the progression and treatment of both lethal conditions. [ABSTRACT FROM AUTHOR]