The nervous and the immune system coevolved, and the crosstalk between both is critical for the maintenance of tissue homeostasis and mental health. In recent years, several examples have been revealed that the immune system influences behaviour, emotions, pain and even such fundamental needs as hunger. Reciprocally, several examples have become apparent in which neuronal innervation regulates local immune responses, wound healing and immune-mediated tissue homeostasis. Such findings demonstrate how well these two networks are interconnected with each other to sustain the body’s well-being. Nonetheless, the underlying mechanisms that orchestrate this interconnection remain poorly understood. The Epidermal Growth Factor Receptor (EGFR) signalling pathway exemplifies this connection, being involved in both neuronal development and maintenance as well as in immune regulation and immune mediated wound healing. Particularly two antagonistic and leukocyte-derived EGF-like growth factors, Amphiregulin and HB-EGF, have gained appreciation for their role in the regulation of local immune responses and the maintenance of tissue homeostasis. In this review, we highlight the role of these two leukocyte-derived growth factors in the regulation of the nervous system and their importance in the bi-directional crosstalk between the immune and nervous systems during tissue homeostasis and inflammation. Ultimately, we propose that under inflammatory conditions, these two leukocyte-derived growth factors may substitute typical neurotrophic factors in their function.
Researchers and practitioners often emphasize the importance of fine motor skills (FiMS) and gross motor skills (GMS) for academic and cognitive development. However, no systematic review of empirical evidence has compared the associations between FiMS and GMS for key academic-cognitive domains (i.e., reading, writing, mathematics, language, general academic, and cognitive skills). A literature search in five databases identified 59 eligible correlational studies measuring both FiMS and GMS (k = 856, N = 40,806) from an initial selection of 34,811 articles. Mixed effects meta-regressions, controlling for methodological and sample factors, revealed moderate to strong correlations between FiMS and writing, reading, mathematics, and general academic skills, as well as moderate links with cognition and language. GMS displayed small to moderate associations with reading, writing, mathematics, language, and cognitive skills, but no statistically significant links to general academic skills. Overall, FiMS showed more substantial correlations with academic-cognitive skills (r = .302) than GMS did (r = .170, p < .001), although the associations were more similar for language and executive functions compared to those for intelligence. Practical implications are discussed with respect to the role motor skills play in child and adolescent education.
The transferability of single or joint species distribution models ((j)SDMs) depends on their ability to predict beyond the observed environmental range and to remain consistent despite shifts in biotic interactions. Transfer accuracy may be improved by recent advances in the application of deep learning that provide greater flexibility and potentially superior predictive accuracy than traditional approaches. We implemented jSDMs with deep and machine learning algorithms and measured the transfer accuracy from continental to regional areas in communities with different species composition. We ran jSDMs with deep neural networks (DNN), elastic net (EN), and stacked SDMs (sSDM) with random forests (RF). We used 134 689 occurrence records representing 1776 species of six taxonomic groups (beetles, birds, bryophytes, fungi, lichens and plants) from 2387 forest plots in Europe. We employed an agnostic modelling approach that covered most of the environmental conditions by including more than 100 satellite-derived variables and 98 climatic variables. The predictive power of the models within the training continental area was evaluated using AUC, whereas the transfer accuracy in the regional area was evaluated with the Boyce index calculated with independent presence records. We found that the DNN-jSDMs outperformed other models at continental scale, but model transfer from continental to regional extent was less accurate. We found that the accuracy of regional predictions was higher for taxonomic groups with better representation in the continental data, such as birds, bryophytes and plants. Depending on the algorithm and the taxonomic group, we achieved acceptable (Boyce > 0) to accurate (Boyce > 0.5) transferability for 32-78% of the species. Our findings underscored the need of considering trade-offs among hyperparameter tuning, spatial scales and model complexity. Our findings also suggest that the varying biotic interaction structures and, particularly, the different species compositions of the transfer areas, may affect model transferability more than previously considered.
Model-based engineering (MBE) is a powerful paradigm that leverages models as essential pillars of the development process, enabling teams to clarify requirements, streamline design, specify behavior, and perform rigorous verification and validation tasks across the entire system life cycle. Digital twins (DTs) represent revolutionary software systems that mirror cyber-physical, socio-economic, or biological entities, systems, or processes. Built from robust models and data, DTs are deployed for high-impact applications such as planning, monitoring, control, and optimization of the twinned entity. The model-centric nature of DTs has naturally ignited recent exploration into harnessing MBE for the engineering and operation of DTs. However, this organic evolution has created a fragmented landscape of (partial) solutions. To confront this challenge, this article presents a rigorous and systematic literature survey on the field of model-based DT engineering (MBDTE), accompanied by a novel taxonomy for categorizing MBDTE approaches. We also introduce crisp definitions of both the field of MBDTE and the models themselves. We conclude by highlighting research gaps and outlining avenues for further exploration.
Gender stereotypes in educational materials can influence children’s and adolescents’ career aspirations, contributing to persistent occupational gender segregation. However, large-scale, longitudinal studies examining gender representation in stereotypical occupational domains in textbooks across different school subjects remain scarce. This study addresses this gap by analyzing the gender representation in two key occupational domains: science, technology, engineering, and mathematics (STEM) and health care, early education, and domestic work (HEED) across 820 German language arts and mathematics textbooks from 1960 to 2017. Using a binomial generalized linear mixed model with a discontinuity design we tested how representation was related to time, the 1986 governmental resolution on gender equity, subject area, and occupational domain. Results revealed substantially lower female representation in STEM than HEED occupations throughout the study period. Female STEM representation increased from 0.0
