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Abstract Background Activation of brown adipose tissue (BAT) has gained attention due to its ability to dissipate energy and counteract cardiometabolic diseases (CMDs). Methods This study investigated the consequences of cold exposure on the BAT and liver proteomes of an established CMD mouse model based on LDL receptor-deficient (LdlrKO) mice fed a high-fat, high-sucrose, high-cholesterol diet for 16 weeks. We analyzed energy metabolism in vivo and performed untargeted proteomics on BAT and liver of LdlrKO mice maintained at 22 °C or 5 °C for 7 days. Results We identified several dysregulated pathways, miRNAs, and transcription factors in BAT and liver of cold-exposed Ldlrko mice that have not been previously described in this context. Networks of regulatory interactions based on shared downstream targets and analysis of ligand-receptor pairs identified fibrinogen alpha chain (FGA) and fibronectin 1 (FN1) as potential crosstalk factors between BAT and liver in response to cold exposure. Importantly, genetic variations in the genes encoding FGA and FN1 have been associated with cardiometabolic-related phenotypes and traits in humans. Discussion This study describes the key factors, pathways, and regulatory networks involved in the crosstalk between BAT and the liver in a cold-exposed CMD mouse model. These findings may provide a basis for future studies aimed at testing whether molecular mediators, as well as regulatory and signaling mechanisms involved in tissue adaption upon cold exposure, could represent a target in cardiometabolic disorders. Graphical abstract

Abstract ATGL is a key enzyme in intracellular lipolysis and plays an important role in metabolic and cardiovascular diseases. ATGL is tightly regulated by a known set of protein-protein interaction partners with activating or inhibiting functions in the control of lipolysis. Here, we use deep mutational protein interaction perturbation scanning and generate comprehensive profiles of single amino acid variants that affect the interactions of ATGL with its regulatory partners: CGI-58, G0S2, PLIN1, PLIN5 and CIDEC. Twenty-three ATGL amino acid variants yield a specific interaction perturbation pattern when validated in co-immunoprecipitation experiments in mammalian cells. We identify and characterize eleven highly selective ATGL switch mutations which affect the interaction of one of the five partners without affecting the others. Switch mutations thus provide distinct interaction determinants for ATGL’s key regulatory proteins at an amino acid resolution. When we test triglyceride hydrolase activity in vitro and lipolysis in cells, the activity patterns of the ATGL switch variants trace to their protein interaction profile. In the context of structural data, the integration of variant binding and activity profiles provides insights into the regulation of lipolysis and the impact of mutations in human disease.

Alternative consumer networks (ACNs) have become increasingly important for promoting sustainable development. This study provides a conceptual clarification of ACNs and systematically reviews their impact on sustainable development. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we identified 218 peer-reviewed references that report on sustainability outcomes of ACNs at different levels, including determinants of motivation, contextual changes, behavioral changes, and social, ecological, and economic impacts. Our findings demonstrate that ACNs have a multifaceted influence on sustainability, highlighting the interconnected nature of various outcomes. This holistic approach captures the broad and significant role ACNs play in advancing sustainable practices across multiple dimensions of sustainability. We synthesized these results into a comprehensive logic model, which serves as a practical tool for systematically assessing the impact of ACNs. This model helps practitioners focusing on relevant outcomes and to develop and implement effective strategies tailored to their specific contexts. Additionally, it provides researchers with a structured framework for further study, facilitating a deeper understanding of the dynamic relationships between different sustainability outcomes.

We present the "AI-Atlas" didactic concept as a coherent set of best practices for teaching Artificial Intelligence (AI) and Machine Learning (ML) to a technical audience in tertiary education, and report on its implementation and evaluation within a design-based research framework and two actual courses: an introduction to AI within the final year of an undergraduate computer science program, as well as an introduction to ML within an interdisciplinary graduate program in engineering. The concept was developed in reaction to the recent AI surge and corresponding demand for foundational teaching on the subject to a broad and diverse audience, with on-site teaching of small classes in mind and designed to build on the specific strengths in motivational public speaking of the lecturers. The research question and focus of our evaluation is to what extent the concept serves this purpose, specifically taking into account the necessary but unforeseen transfer to ongoing hybrid and fully online teaching since March 2020 due to the COVID-19 pandemic. Our contribution is two-fold: besides (i) presenting a general didactic concept for tertiary engineering education in AI and ML, ready for adoption, we (ii) draw conclusions from the comparison of qualitative student evaluations (n = 24-30) and quantitative exam results (n = 62-113) of two full semesters under pandemic conditions with the result of previous years (participants from Zurich, Switzerland). This yields specific recommendations for the adoption of any technical curriculum under flexible teaching conditions--be it on-site, hybrid, or online.

Mathematical creativity is perceived as an increasingly important aspect of everyday life and, consequently, research has increased over the past decade. However, mathematical creativity has mainly been investigated in children and adolescents so far. Therefore, the first goal of the current study was to develop a mathematical creativity measure for adults (MathCrea) and to evaluate its reliability and construct validity in a sample of 100 adults. The second goal was to investigate how mathematical creativity is related to intelligence, mathematical competence, and general creativity. The MathCrea showed good reliability, and confirmatory factor analysis confirmed that the data fitted the assumed theoretical model, in which fluency, flexibility, and originality constitute first order factors and mathematical creativity a second order factor. Even though intelligence, mathematical competence, and general creativity were positively related to mathematical creativity, only numerical intelligence and general creativity predicted unique variance of mathematical creativity. Additional analyses separating quantitative and qualitative aspects of mathematical creativity revealed differential relationships to intelligence components and general creativity. This exploratory study provides first evidence that intelligence and general creativity are important predictors for mathematical creativity in adults, whereas mathematical competence seems to be not as important for mathematical creativity in adults as in children.

BackgroundMorphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life.MethodsInterregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed.ResultsAcross the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth.ConclusionsOur findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy.

Abstract Key message The application of material selection principles uncovered eight possible alternative tree species (two deciduous and six coniferous species) to substitute Norway spruce (Picea abies (L.) H. Karst.) and potentially prevent economic loss in European forest. Context Climate change is a major challenge for the Central European forest and timber industry. Increasing biotic (e.g. beetle damage) and abiotic (e.g. drought) calamities have led to major losses in forest value, especially on Norway spruce (Picea abies (L.) H. Karst.) stands. Therefore, a transition to climate change adapted forest management is necessary. Concurrently, neophytes (e.g. tree of heaven (Ailanthus altissima (Mill.) Swingle), Douglas fir (Pseudotsuga menziesii (Mirbel) Franco), grand fir (Abies grandis (Douglas ex D. Don) Lindl.) and Paulownia (Paulownia tomentosa (Thunb.) Steud.)) are increasing their dominance in forest communities and causing additional adaption of the forest ecosystem. Both factors will lead to significant changes in wood species distributions in Central European forests, mainly at the expanse of Norway spruce, over the next decades. Aims Choosing the “right” tree species for afforestation will become ever more complex and will require a holistic approach that combines forestry and technological aspects alike. Therefore, this review presents a selection approach based on available wood material data from literature and the material selection principles proposed by M. Ashby with the aim to identify suitable alternatives for Norway spruce (Picea abies (L.) H. Karst.) and further concisely assess their silvicultural relevance. Methods For this wood species comparison and selection process, dry and raw density, bending strength and modulus of elasticity were chosen as key properties. Beam- and plate-like components subjected to a bending load were chosen as representative use cases. Results European birch (Betula spp.), grand fir (Abies grandis (Douglas ex D. Don) Lindl.) and silver fir (Abies alba Mill.) were identified as suitable alternatives for Norway spruce (Picea abies (L.) H. Karst.) from a technological as well as silvicultural point of view. In addition, Paulownia (Paulownia tomentosa (Thunb.) Steud.), Sitka spruce (Picea sitchensis (Bong.) Carrière), yellow pine (Pinus strobus L.), western red cedar (Thuja plicata Donn ex D. Don in Lambert) and loblolly pine (Pinus taeda L.) offer a technological advantage but currently lack relevance in the forest sector. Conclusion The proposed selection process offers an evaluation of technical performance, and in combination with an assessment of the silvicultural relevance, it will be possible to optimize the wood-supply chain and prevent future economic loss of Central European forests.

Abstract Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

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Meaningful gamification is the use of game elements and play to build engagement and help learners find personal connections in real-world settings. This study examined the impact of meaningful gamification design on preservice teachers' perceptions, performance, and motivation levels within a gamified lesson planning unit. Participants were enrolled in a language arts teacher preparation course at a regional campus over five semesters. Results show meaningful gamification incorporating mastery learning and leveled curriculum created a learning condition where preservice teachers surpassed expectations and felt intrinsically motivated. Challenges to gamification included waiting on feedback and learning within a new format.

This paper presents the outcome of the research project WOODWORKING REVISITED. Within the framework of the two-year project, the two university teams collaborated with the Austrian Open-Air Museum Stübing and the Waldorf Vienna West School to document selected woodworking techniques on video. Formats and dissemination strategies were developed in a collaborative process including pupils and experts at all project stages. The formats were based on established content such as the "Nifty" style found on Buzzfeed (ref.) and further developed for the new immersive possibilities of 360° video. In order to compare the formats and test their efficiency, content produced in both formats was evaluated together with pupils of a technical school for woodworking. The team conducted a study that showed the suitability of both formats for the purpose.