Search Results (7,382)

High-density microelectrode arrays (HDMEAs) have become increasingly important tools in neuroscience and biomedical engineering because of their high spatial and temporal resolution for recording and modulating electrical activity across diverse biological systems. Initially developed for in vitro studies of cultured cells, HDMEAs are now being applied to increasingly complex models, including organoids, animal systems, and even human neural systems. These advancements enable a deeper investigation of cellular interactions, network dynamics, and disease mechanisms, as well as providing novel therapeutic and diagnostic tools for neurological disorders. This review explores the evolution of HDMEAs, emphasizing recent innovations in their design, fabrication, and functionalization. We discuss their applications across cellular models, organoid systems, animal studies, and human electrophysiology, and highlight current challenges such as biocompatibility, long-term stability, scalability, and translational deployment. Finally, we outline future directions for advancing HDMEA technologies in both research and clinical settings. Full article

Industry 4.0 (I4.0) is transforming industrial systems through interconnected, data-driven technologies, raising questions about how these developments affect Occupational Health and Safety (OHS). This study investigates research trends, thematic structures, and knowledge gaps at the intersection of I4.0 and OHS using a multilevel bibliometric framework applied to Scopus records published from 2011 to 2025. The analysis moves from a broad overview of the I4.0 landscape to more focused examinations of specific I4.0–OHS publications, prevention-oriented studies, and emerging-risk research. The results show that OHS has limited visibility in the general I4.0 literature and is more prominent mainly in targeted subsets, where digital sensing, artificial intelligence, machine learning, and immersive technologies drive prevention-focused research. Conversely, emerging risks such as cognitive load, psychosocial stressors, and human–autonomy interaction appear in smaller, more dispersed clusters. Overall, the findings suggest that the relationship between I4.0 and OHS is unevenly developed, with established prevention mechanisms and early-stage conceptualization of new risks. Strengthening this field will require integrating human factors with digital indicators, better characterizing emerging risks, and ensuring that digital transformation supports SDG 8 by fostering safe and healthy working environments. Full article

Objectives: An infectious trigger can initiate a systemic inflammatory response, which in turn activates immune cells and causes the release of various mediators. Local mediators, such as resolvin D1 (RvD1), actively interact with immune cells to promote the resolution of inflammation. This study aimed to determine the impact of RvD1 on the inflammatory response mediated by monocytes in response to LPS. Methods: To investigate the mechanism by which RvD1 affects the monocyte-mediated inflammatory response to LPS, human THP-1 monocytic cells were treated with LPS, RvD1, or vehicle for 24 h. Inflammatory cytokines, interleukin-1β (IL-1β) and tumor necrosis factor (TNF-α), were measured using enzyme-linked immunosorbent assay (ELISA). RNA sequencing (RNA-seq) was used to identify differentially expressed genes (DEGs). The NF-κB and MAPK p38 signaling pathways were validated using real-time quantitative PCR (RT-qPCR) and Western blotting (WB). Results: RvD1 diminished the levels of IL-1β and TNF-α in LPS-induced inflammation. RvD1 significantly enhanced the mRNA expression of CREB, NRF2, and BCL-2. In addition, RvD1 significantly decreased the mRNA expression of CASP3. RvD1 regulated the inflammatory process in human monocytic THP-1 cells via the NF-κB p65 (MyD88, p65) and p38 MAPK signaling pathways (p38, BCL-2) and further suppressed the expression of apoptotic factors (PI3K, caspase-3). Conclusions: RvD1 has been shown to exert pro-resolving effects by regulating the anti-apoptotic gene BCL-2 and activating the NF-κB p65 and MAPK p38 signaling pathways. Full article

Alzheimer’s disease (AD) is characterized by progressive neurodegeneration and prominent neuroimmune remodeling, but the contribution of macrophage and myeloid states across disease severity remains incompletely defined. We integrated bulk transcriptomic, single-cell RNA sequencing (RNA-seq), and spatial transcriptomic datasets to characterize AD-associated myeloid immune changes across Braak stage and disease status. Across datasets, M2-like macrophage and myeloid signatures showed progressive enrichment with increasing neuropathological severity and were accompanied by pathway changes related to macrophage proliferation, TGF-β signaling, and myeloid homeostasis. Immune-feature-based classifiers identified macrophage-related variables among the informative features distinguishing AD from controls. CellChat analyses further inferred that M2-like myeloid populations occupied communication-enriched positions in single-cell and spatial interaction networks, including apolipoprotein E (ApoE), CX3C chemokine signaling, and fibronectin 1 (FN1)-associated signaling contexts. Collectively, these findings indicate that M2-like myeloid programs are consistently associated with AD severity and neuroimmune network remodeling. Rather than establishing a causal disease driver, this study highlights M2-like myeloid signatures as candidate neuroimmune components that warrant experimental validation in human-relevant systems. Full article

Alexander disease (AxD) is a rare and fatal neurodegenerative disorder caused by dominant mutations in the gfap gene, which encodes glial fibrillary acidic protein (GFAP), a major intermediate filament in astrocytes. As a primary astrogliopathy, AxD is marked by white matter abnormalities, the formation of GFAP-containing Rosenthal fibers, astrocyte dysfunction, and progressive neurodegeneration. While GFAP mutations are known to cause toxic gain-of-function effects, the precise mechanisms by which mutant GFAP drives astrocyte dysfunction and central nervous system pathology remain unclear. To address this, we developed a novel rat model of AxD harboring the R237H mutation in the endogenous gfap locus, which mirrors the R239H mutation commonly associated with early-onset AxD in humans. This model recapitulates key AxD pathologies, including GFAP aggregation, widespread astrogliosis, white matter abnormalities, and motor deficits. Using homozygous mutant rats, we dissected the distinct contributions of mutant GFAP and elevated GFAP expression to astrocyte dysfunction and neurodegeneration. Our findings reveal that AxD pathogenesis results from a synergistic interaction between the toxic gain-of-function properties of mutant GFAP and its elevated expression, which together drive GFAP aggregation, proteostatic stress, and astrocyte dysfunction. These insights provide a deeper understanding of AxD mechanisms and a foundation for developing targeted therapies for this devastating disease. Full article

This study investigates the determinants of economic growth in the Next-11 economies over the period 1996–2024, with particular emphasis on the roles of structural, institutional, and human capital factors. Using a comprehensive panel dataset for eleven emerging economies, the analysis employs three robust estimation techniques—Driscoll–Kraay Standard Errors (DKSEs), Feasible Generalized Least Squares (FGLSs), and Panel-Corrected Standard Errors (PCSEs)- to address common econometric issues such as heteroskedasticity, serial correlation, and cross-sectional dependence. The empirical results reveal that industrial output, energy consumption, human capital, institutional quality, and foreign direct investment significantly contribute to economic growth. Among these factors, industrial output and energy consumption exhibit particularly strong and consistent positive effects across all estimation methods, highlighting the importance of structural transformation and energy availability in supporting economic expansion. In contrast, trade openness shows a negative and statistically significant relationship with economic growth in most model specifications, suggesting that structural constraints, import dependence, and limited domestic productive capacity may restrict the growth benefits of external integration in these economies. The study also explores the conditional effects of foreign direct investment through interaction terms with human capital and institutional quality. The findings indicate that the growth-enhancing impact of foreign investment depends significantly on domestic absorptive capacity, particularly the availability of skilled labor and effective governance structures. These results emphasize the importance of complementary policies aimed at strengthening education systems, improving institutional quality, and enhancing regulatory effectiveness. From a policy perspective, the findings suggest that the Next-11 economies should prioritize industrial development, energy infrastructure expansion, human capital investment, and institutional reforms to maximize the benefits of globalization and foreign investment. Overall, the study contributes to the literature by providing robust empirical evidence on the interconnected roles of structural, institutional, and human capital factors in shaping economic growth in emerging economies. Full article

The way robots represent the environment, make decisions, and express themselves can positively influence human–robot interaction if they clearly communicate their intentions and needs. To improve human–robot communication, biologically inspired models that mimic human communication skills, including task and scenario-specific contextual information, can facilitate mutual understanding and successful task execution. This paper presents a Context-Awareness and Biologically Inspired Behaviour system to generate a more natural human–robot interaction. The architecture combines sensory information processed by a Joint Attention System that prioritises stimuli based on internal processes with task-related motivations to generate context- and goal-adapted verbal and non-verbal interaction. We evaluate the system through a video-based user study that compares two robots with similar appearances but different behaviours, one using the proposed approach and the other not using the internal state and joint attention mechanisms, to make verbal and non-verbal responses. The results show that participants rated the robot endowed with the proposed system as significantly more sociable, agentic, and animated than the robot without it. Additionally, the robot not showing the responses developed in this work was perceived as more disturbing than the robot integrating the proposed system. Full article

Employment recommendation services are increasingly used to support graduate job search. However, limited research has examined how graduating computer science students perceive a proposed employment recommendation approach that combines static profile-based matching with dynamic interactive functions. Drawing primarily on the Technology Acceptance Model (TAM), with selected dimensions of the Information System (IS) Success Model used as supplement, this study conducted an exploratory questionnaire-based survey of 386 graduating students. The respondents evaluated existing employment recommendation systems and provided open-ended comments, and the findings show that only 38.3% of respondents reported willingness to use existing employment recommendation systems for job hunting. The main reported problems were delayed matching to individual qualifications (71.0%), information lag (55.4%), and jobs not matching students’ majors (54.1%). In contrast, respondents expressed relatively favorable attitudes toward the proposed static-dynamic approach: 67.6% indicated willingness to use it and 59.6% indicated willingness to recommend it to others. Exploratory subgroup analyses further suggested that positive evaluations of the proposed approach were higher among students from emerging computing fields and those with more active job-seeking engagement (p < 0.05). Overall, the findings provide exploratory evidence that graduating computer science students may respond more positively to employment recommendation concepts that integrate profile-based matching with dynamic interaction. However, it is a proposed design concept, not an implemented system, evaluated by the respondents. Therefore, the results should be interpreted as perceptions and stated intentions, instead of evidence of actual adoption or real-world system effectiveness. Full article

Hand gesture recognition (HGR) is a critical area in computer vision that supports intuitive human–computer interaction and sign language communication, yet existing systems remain sensitive to lighting variations, background clutter, and diverse hand postures. This study introduces two contributions to address these limitations: a Gradient-Based Augmentation Validation (GBAV) framework that establishes structurally safe augmentation ranges before training, and a multi-backbone Convolutional Neural Network (CNN) architecture combining ResNet50 and InceptionV3 with optional attention-based pooling. GBAV uses magnitude-weighted gradient orientation histograms with Pearson correlation and Kullback–Leibler divergence thresholds to verify label invariance under spatial transformations, providing a classifier-agnostic pre-training calibration mechanism. The proposed framework is evaluated on three static gesture datasets, Indonesian Sign Language (BISINDO), American Sign Language (ASL), and Hand Gesture 14 (HG14), yielding validation accuracies of 96.87%, 99.92%, and 95.25%, respectively, with 5-fold cross-validation on HG14 confirming result stability (93.51% ± 2.31%). Quantitative attention localization, cross-dataset transfer evaluation, and computational efficiency analysis (26.8 ms per image, ~37 FPS) further support the framework’s robustness and practical deployability. These findings establish GBAV-calibrated augmentation as the principal performance driver, which complements the multi-backbone architecture for robust hand gesture recognition across diverse visual contexts. Full article

Alpha male replacement occurs in many group-living non-human primates, but its characteristics remain under-explored. Species of the genus Macaca live in multi-male, multi-female groups and are structured into four levels of social styles, which may impact alpha male replacement. Rhesus monkeys (Macaca mulatta) are classified as Grade 1, yet little is known about alpha male replacements in this species. This study investigated the occurrence and characteristics of alpha male replacements in rhesus monkeys inhabiting the southern end of Taihang Mountains, China. The types of alpha male replacements included succession (8/11), Rank Reversal (1/11), and Group Fission (2/11). The average age at which adult males took the alpha rank was 10.2 (±4.1, n = 11) years of age. Their average social rank prior to attaining alpha rank was 3.91 (±3.05, n = 11). The average tenure of alpha males after 2017 was 2.6 (±1.4, n = 5) years, which appeared shorter than that before 2017 (>7.5 ± 2.9 years, n = 4). The occurrence of alpha male replacements did not significantly correlate with group sizes, natality, the ratio of adult males to adult females, or the proportion of immatures. Compared with tolerant Macaca species, alpha male replacement in rhesus monkeys tend to be biased toward Succession, a pattern that may be linked to their extremely despotic social style. This study suggests that social style could interact with changes in social structure, deepening our understanding of the evolution of primate social systems. Full article

Empathetic dialogue systems built upon large language models overwhelmingly adopt a monolithic inference paradigm that processes emotion perception, causal reasoning, memory retrieval, and response planning within a single forward pass without architecturally enforced intermediate representations, forfeiting intermediate-state transparency and long-horizon personalization. Drawing on neuroscientific and cognitive–psychological evidence that human empathy is functionally dissociable, we present MOSAIC (Multi-agent Orchestration with Structured Affective memory for Interpretable empathiC dialogue), a training-free framework that operationalizes empathetic dialogue as a four-stage cognitive pipeline: affective perception, causal appraisal, episodic memory retrieval, and response synthesis. Three innovations distinguish MOSAIC from prior work: (1) a cognitively motivated modular architecture whose functionally dissociable stages enable post hoc failure attribution through logged intermediate states; (2) a hierarchical three-tier emotional memory—perceptual, semantic, and episodic—coupled with adaptive three-dimensional retrieval over emotion, situation, and coping-strategy cues; and (3) a heterogeneous model orchestration strategy coordinating open-source and API-accessible models through role-specific chain-of-thought prompts, requiring no task-specific fine-tuning. We note that the EmpatheticDialogues evaluation pre-populates the memory store with 200 training-split episodes prior to test-set interaction, a data-access asymmetry relative to single-model baselines that must be borne in mind when interpreting comparative results. Experiments on EmpatheticDialogues and ESConv show that MOSAIC achieves a 76.4% weighted F1 and an empathy score of 3.87 (on a 1–5 Likert scale) and that it improves over single-model, training-free baselines on aggregate empathy and—most prominently—on human-rated personalization (3.67 vs. 3.24 against Claude-3.5 five-shot, $d=0.48$). We caution that the comparison against training-free baselines is not data access-controlled (see the cold-start discussion in Methods); the personalization advantage, supported by the ablation without the Event Agent, is the result we treat as the primary practical contribution of this work. Full article

The prevalence of plastics in the environment raises concerns about their complex and poorly understood effects on human health. Research continues to uncover more sources of exposure and wider ranges of plastics within the body. Adverse health effects have been observed in animals, but their relevance to humans remains unclear. To address the growing need for reliable toxicity assessment resources and tools to aid in the synthesis of findings and the identification of data gaps and needs, we have developed a data visualization tool to provide streamlined access to the evaluated data on the chemical impacts of plastics on human health. The Plastics-Related Toxicology Profiles Tool uses Tableau Public to organize the extracted chemical-specific information from ATSDR Toxicological Profiles, the United Nations Environmental Program’s 2023 Chemicals in Plastics Technical Report, and a literature review of relevant research in Google Scholar and PubMed. The tool organizes extracted data from 98 ATSDR Toxicological Profiles representing over 476 substances related to plastics production in 16 tabulated health outcome categories associated with plastics exposure. The chemicals are organized into four categories based on their role in plastics manufacturing. The top four health endpoints impacted by all listed substance profiles are respiratory, neurologic, hepatic, and developmental effects. More than 30% of the substance profiles affected these systems as well as other non-cancer endpoints involving the immunological, renal, and reproductive systems, as well as increased cancer risk in respiratory and hepatic systems. Most monomers negatively impact development and the respiratory system, and most metal additives affect the respiratory system. We explain how this data visualization tool combined with ATSDR’s framework for assessing health impacts from multiple chemicals could be applied to identify the target organs impacted by components of the common plastic polyvinyl chloride. Hazard quotients and index show low toxicity and health risk of components in the cured product. This data provide a valuable resource for prioritizing health risk assessments. Use of this interactive tool can enhance the ability of public health professionals to navigate the expanding literature, synthesize findings, and identify future health risk assessment and research priorities. Full article

Background/Objectives: The human performance envelope (HPE) is a multidimensional model that represents the range in which an individual operator’s performance is acceptable or begins to become dangerous. Although several alternative models have been proposed, HPE currently remains primarily a theoretical concept. The goal of the study was therefore to translate this theoretical concept into practical applications, seeking to characterize and measure how HPE manifests itself in real-world contexts. Methods: Multivariate Autoregressive (MVAR) models and conditional transfer entropy (cTE) have been used in the analysis of complex systems in which processes are interdependent and mutually influence their dynamics over time. Professional Air Traffic Controllers were involved in the study and asked to deal with realistic traffic scenarios while their behavioural, subjective and neurophysiological data were collected. MVAR–cTE models were then employed to estimate the interactions among controller human factors and to identify the most appropriate characterization of the HPE. Results: The results showed high and significant correlations among each controller’s performance and the corresponding neurophysiological-based HPE values. Furthermore, high-performance conditions (best) were characterized by significantly higher HPE values and higher inter-human factor connections compared to the low-performance (worst) status. This evidence suggested that a densely interconnected network of Human Factors is a prerequisite for operational resilience. Conclusions: The study provided the first application of a neurophysiological framework to model the directed interactions between human factors, translating the theoretical HPE into a quantifiable model validated against operator performance. Full article

Financial markets are characterized by non-stationarity, regime shifts, and complex cross-asset interactions, which challenge traditional portfolio optimization and motivate reinforcement learning (RL) for adaptive decision-making. However, many RL-based approaches remain predominantly return-centric, with risk, uncertainty, and human oversight only weakly integrated, limiting robustness and practical applicability. This review provides a critical synthesis of risk-aware and uncertainty-sensitive reinforcement learning for portfolio optimization from a human–AI collaboration perspective. We analyze major architectural paradigms—including single-agent, hierarchical, multi-agent, and modular systems—together with risk modeling strategies (e.g., reward shaping, constraint-based optimization, and downside risk measures such as CVaR) and probabilistic approaches to uncertainty estimation (e.g., Bayesian neural networks, Monte Carlo dropout, and ensembles). A structured analysis of 57 fully assessed studies reveals that only 5 (9%) explicitly couple uncertainty estimation with risk constraint mechanisms, while 38 (69%) treat risk and uncertainty as structurally independent components. We identify a central structural limitation: risk objectives are rarely conditioned on epistemic uncertainty, while uncertainty estimates seldom influence constraint mechanisms or capital allocation. This decoupling leads to fragmented frameworks that remain difficult to deploy in real financial environments. By integrating architectural design, risk modeling, uncertainty estimation, and evaluation practices, this review proposes a unified, deployment-oriented perspective for developing governance-aligned portfolio decision-support systems. Full article

Conversational AI systems are increasingly integrated into individuals’ emotional and relational lives, yet whether such interactions can meaningfully support personal growth remains poorly understood. This critical integrative review synthesises theoretical frameworks from humanistic psychology, self-determination theory, attachment theory, and relationship science with empirical research on human-AI interaction to address this question directly. Drawing on 130 studies spanning therapeutic, companion, and educational AI contexts, the review identifies four interdependent domains that together shape growth outcomes in human-AI contexts: user-related characteristics, AI design features, human-AI relational dynamics, and broader contextual factors. The evidence supports a position of bounded optimism: conversational AI can scaffold early emotional stabilisation, structured self-reflection, and therapeutic skill rehearsal, yet it remains structurally limited in replicating the reciprocal vulnerability, rupture-and-repair processes, and calibrated ideal-self affirmation that underpin enduring psychological development. Engagement-optimised design—including flattery, progressive intimacy escalation, and unconditional validation—is consistently identified as a systematic barrier to growth across multiple domains of the framework. An integrative four-domain conceptual framework is proposed to guide both future research and the ethical design of AI systems that support, rather than undermine, the relational mechanisms fundamental to human flourishing. Full article