Search Results (26,229)

Microplastics (MPs) are pervasive environmental contaminants detected in terrestrial, aquatic, and human systems. Emerging evidence indicates that MPs interact with microbiota through biofilm formation, induction of oxidative stress, enrichment of antibiotic resistance genes (ARGs), and disruption of short-chain fatty acid metabolism, leading to dysbiosis and altered host immune responses. These interactions contribute to dysbiosis, altered immune responses, and increased dissemination of ARGs, which pose health risks. This review synthesizes current knowledge on mechanisms of microplastic–microbiota interactions, highlighting evidence from in vitro, in vivo, and environmental studies. We discuss methodological challenges, including variability in particle types, concentrations, aging, and analytical approaches. Recent advances in multi-omics techniques provide deeper mechanistic understanding and reveal functional consequences of MP exposure. We outline key knowledge gaps and propose future research directions to assess the impact of microplastic exposure on ecosystems and human health. Full article

In the context of Industry 5.0, human–robot collaboration increasingly demands intuitive, safe, and sensorless interaction for tasks such as hand-guided teaching and concurrent manipulation. However, conventional admittance control systems are prone to instability due to abrupt changes in human arm stiffness and their reliance on accurate dynamic models. To address these challenges, this paper proposes a sensorless external force estimation and variable admittance control method that models robot dynamic uncertainties and interaction forces as normally distributed stochastic quantities. An improved particle swarm optimization algorithm is introduced to calibrate the variance parameters, enhancing estimation accuracy and robustness. Furthermore, an energy-based variable admittance control strategy is developed, which preserves system passivity by adaptively adjusting inertia and damping gains based on real-time energy variations. The proposed method was validated on a redundant robot platform. Experimental results show that the external force and torque estimation errors remain below 3 N and 3 N.m, respectively, with lower detection delays and errors than those of a first-order generalized momentum observer in collision detection. Variable admittance experiments demonstrate that the system maintains passivity and stable interaction even under sudden arm stiffness changes. The approach is well-suited for industrial applications requiring safe, sensorless, and compliant human–robot collaboration. Full article

This review examines microplastics (MPs) in aquatic environments, their interactions with organic pollutants (OPs), effects on organisms, and implications for human and ecological health. MPs are ubiquitous, persistent contaminants. Their small size and large surface area enhance adsorption of diverse OPs; however, the extent to which MPs influence pollutant transport, fate, and bioavailability remains highly context-dependent and is still under scientific debate. Sorption processes are influenced by polymer type, pollutant properties, environmental factors, and aging processes that increase surface reactivity, further contributing to the variability of MP–OP interactions. Detection of MPs in human tissues raises concerns about long-term health effects, including inflammatory, immune, gastrointestinal, respiratory, and endocrine responses. Despite advances in analytical techniques, challenges remain in identifying and quantifying small particles in complex matrices. This review emphasizes the need for integrated, multi-technique, and environmentally realistic studies to understand MP–OP interactions and support risk assessment. Future research should focus on standardizing methodologies, improving nano-sized particle detection, and elucidating long-term effects, including trophic transfer and potential tissue accumulation. Full article

Ensuring the visibility of road markings is critical for traffic safety, yet current inspection methods remain either prohibitively expensive (retroreflectivity) or subjective (manual assessment). This article introduces the Random Generated Reference (RGR) method, a novel automated solution for quantifying marking degradation using a standard on-board camera. The proposed pipeline is a complete protocol from video acquisition to road marking inspection and validation of the inspection that combines deep learning with computer vision: YOLOv8 is employed for robust detection, while a unique algorithm generates a “perfect virtual reference” that dynamically replicates the real scene’s geometry and illumination conditions, including shadows. By computing pixel-level deviations between the observed marking and this ideal reference, the system assigns a continuous degradation score aligned with the UK CS126 standard. Experimental validation was conducted on a real-world circuit yielding over 20,000 detections. Verification via Cochran sampling demonstrates that 68% of the automated assessments fall within one class of human inspection. This proof-of-concept confirms the viability of an approach based on generating the ground truth and scene conditions—such as illumination, shadows, rain, traffic, etc.—for road marking inspection. Full article

Stingless bee colonies are vulnerable to predators of widely varying sizes, and repeated intrusions can cause stress, reduce productivity, and trigger colony absconding. Existing automated surveillance systems detect only a limited range of predators and often struggle with multi-scale object detection in high-resolution images. This study proposes a real-time predator monitoring system that integrates a Multi-Scale Attention module into the YOLOv11-nano architecture (MSYOLO11) to enhance detection performance across both small and large predators. The proposed model combines convolutional features with an attention mechanism to improve global–local feature fusion. Experimental evaluation shows that MSYOLO11 increases overall Recall from 0.830 to 0.853 compared to YOLOv11-nano, with substantial improvements for small-object classes such as ants (+0.096), humans (+0.083), and H. itama (+0.026), while maintaining comparable Precision (0.868 vs 0.842) and mAP50 (0.898 vs 0.896) at a nearly identical computational cost (6.3 GFLOPs). The system operates at 5 FPS on a Jetson Orin Nano, with an end-to-end latency of 181 ms. A Firebase-integrated mobile application delivers instant push notifications, displays detected predators with bounding boxes, and provides real-time data synchronization. The results demonstrate that MSYOLO11 offers a practical and efficient solution for multi-scale predator detection, supporting continuous hive surveillance and timely beekeeper intervention. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder in which amyloid β accumulation, tau pathology, chronic neuroinflammation, and metabolic stress converge to drive synaptic dysfunction and neuronal loss. Rather than resulting from a single mechanism, increasing evidence indicates that neurodegeneration in AD is mediated by the coordinated activation of multiple regulated cell death pathways. These pathways include apoptosis, necroptosis, pyroptosis, ferroptosis, and autophagy-dependent cell death, each characterized by distinct molecular mediators and execution programs. Evidence from human brain tissues, animal models, and in vitro systems demonstrates that core pathological drivers such as amyloid β and tau pathology, oxidative stress, and sustained neuroinflammation engage these death pathways in a spatially, temporally, and cell-type-dependent manner across neurons and glial populations. In this review, we synthesize the current knowledge on regulated cell death mechanisms in AD, emphasizing their molecular signatures, cellular specificity, and stage-dependent involvement, together with recent advances in immunohistochemical, imaging, and biofluid-based approaches for detecting neuronal death. By integrating evidence across molecular, cellular, and system levels, this review positions regulated cell death as a unifying framework for understanding neurodegeneration and developing pathway-specific biomarkers and combinatorial neuroprotective strategies. Full article

With the increasing demand for enhanced security and surveillance, the integration of multimodal AI has shown significant promise. We developed and fine-tuned a transformer-based model, the Large Language and Vision Assistant–OneVision, tailored for human event and action recognition. By utilizing a multimodal approach, we identified specific human actions, including eating, running, fighting, sitting, and sleeping, within diverse real-world settings. Through knowledge distillation and Low-Rank Adaptation, the model’s performance was optimized in demonstrating substantial improvements in context-aware recognition and response generation. Evaluation results showed recall-oriented understudy for obtaining evaluation (ROUGE)-1 score of 0.6844, ROUGE-2 score of 0.5751, ROUGE-L score of 0.6520, and the bilingual evaluation understudy score of 68.20, demonstrating significant gains in accuracy and interpretability. The model’s success highlights its potential for real-time applications in surveillance, healthcare, and interactive AI systems, providing reliable, efficient, and context-sensitive human action detection. Full article

Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual operation or static detection techniques, are no longer sufficient to ensure scalable, autonomous, and secure monitoring. The convergence of two emergent technologies—Large Language Models (LLMs) and the Model Context Protocol (MCP)—facilitates a fundamental shift in radio monitoring. We define this as the AICEBERG paradigm: a novel, stratified architecture where a high-level, intelligent agentic interface (the peak) abstracts the underlying complexity of SCPI-driven hardware integration and radio governance protocols (the foundational base). This autonomous framework provides the necessary objective rigor to audit the stochastic ‘ocean of electromagnetic waves’ characteristic of modern smart cities, ensuring a stable platform for regulatory enforcement amidst high-density signal interference. The proposed system implements a three-layer processing flow, enabling high-level natural language commands to be translated into validated and secure hardware actions on RF spectrum analyzers. A dual-server design separates operational execution from safety validation, ensuring controlled SCPI command handling, parameter verification, and instrument health monitoring. Experimental validation demonstrates the feasibility of autonomous measurement execution. The results show that the proposed architecture reduces human dependency, enhances reproducibility and lowers the expertise barrier required for RF spectrum surveillance. To the best of our knowledge, AICEBERG represents one of the first integrated frameworks to bridge LLMs with SCPI-compliant hardware through the MCP for autonomous radio governance. Full article

The responsive light intensity dynamic range (DR) of the human eye far exceeds that of existing visual systems, and the development of a biomimetic retinal detecting unit is currently an important challenge in the field of machine vision. Here, a two-terminal Au-contacted VO₂/WSe₂ heterojunction photodetector with the same adaptive DR as retinal cells is developed. It is revealed that the VO₂/WSe₂ heterojunction part-mimics the cone cell for strong light detection with photoresponsivity (R) of 320 mA W⁻¹ and the Au/WSe₂ Schottky contact part-mimics the rod cell for weak light detection with an R of 217 A W⁻¹ and noise equivalent power (NEP) as low as 248.2 fW/$\surd \mathrm{H}\mathrm{z}$. The dual-mode photodetector shows a fast response speed of less than 39.28 μs. Image fusion by the cone mode and rod mode shows enhanced recognition. These results demonstrate that contact engineering enables a photodetector with the functionality of both rod and cone cells, and the resulting visual imaging system can achieve performance comparable to that of the human eye in certain operating conditions. Full article

Asthma is a chronic respiratory disease characterized by airway hyperresponsiveness, obstruction, and persistent inflammation, arising from complex interactions among genetic, epigenetic, immune, and environmental factors. To elucidate the stage-specific molecular mechanisms underlying asthma progression, we constructed candidate genome-wide genetic and epigenetic networks (GWGENs) of human cells through large-scale biological database mining. Using a system order detection scheme, false-positive interactions were pruned to identify real GWGENs corresponding to three clinical stages of asthma: quiet, exacerbation, and follow-up. Core GWGENs were subsequently extracted from each real network using the principal network projection (PNP) method to highlight dominant regulatory structures and pathogenic pathways. Based on the inferred core networks, key stage-specific biomarkers were identified and further explored as potential drug targets. Drug–target relationships were investigated by integrating gene expression perturbation profiles from the Connectivity Map (cMap), comprising microarray data for 14,207 genes across 1327 compounds. This network-guided analysis enabled the qualitative design of multi-molecule drug combinations tailored to each disease stage. Our results suggest that asthma onset is associated with reduced innate immunity, increased disease susceptibility, and impaired endothelial barrier recovery influenced by microenvironmental factors such as cigarette smoke and lipopolysaccharides, together with genetic and epigenetic alterations. During the exacerbation stage, enhanced differentiation of T cells toward the T helper 2 lineage contributes to airway inflammation and tissue injury. In the follow-up stage, T helper 1–mediated responses are linked to mucus hypersecretion, airway obstruction, and sustained inflammation. Collectively, these findings demonstrate that a systems-level, network-based framework can uncover stage-specific pathogenic mechanisms of asthma and provide hypothesis-generating insights for network-informed drug repurposing strategies. Full article

Ixodid ticks are vectors for a plethora of pathogens, including the Crimean–Congo hemorrhagic fever virus (CCHFV), which causes severe disease in humans. Two autochthonous CCHF human cases were reported in 2025 in Greece. The aim of the present study was to gain a better insight into the geographic distribution and prevalence of CCHFV and the related Aigai virus (AIGV) in ticks in Greece. Therefore, 680 ticks (135 Hyalomma and 545 Rhipicephalus ticks) collected during 2024 from livestock (sheep, goats, cattle) and from the environment were tested for CCHFV and AIGV. AIGV was detected in 12 adult Rhipicephalus bursa ticks (12/511, 2.3%), while all Hyalomma ticks and R. bursa nymphs were negative for both viruses. AIGV-positive ticks were collected in May and June from goats and sheep in two distantly located regional units of Greece. AIGV sequences from partial S RNA segment differ from the prototype AIGV strain (AP-92) by 10.3% and 1.4% at the nucleotide and amino acid level, respectively. Integrated surveillance studies are needed in ticks, humans, wild and domestic animals within a One Health framework to gain a better insight into the epidemiology of CCHF in Greece, while clinical research is needed to elucidate the impact of AIGV in public health. Full article

Dragon fruit (Hylocereus undatus) is a high-value crop in the Philippines that has gained commercial importance due to its nutritional benefits and profitability. However, determining the optimal maturity stage remains challenging for farmers relying on manual classification. We developed an automated system that integrates You Only Look Once Version 7 (YOLOv7) for dragon fruit detection. A dataset of dragon fruit images across three maturity levels, unripe, ripe, and over-ripe, was collected and used to train the model. The system classifies maturity stages based on external features such as color and shape, and its performance will be evaluated using a confusion matrix. By providing accurate classification, the proposed system aims to assist farmers in harvesting dragon fruits at their optimal stage, improving yield quality and market competitiveness while reducing human error. Full article

We developed an automated system to address orchard crop damage caused by Formosan macaques, a problem where traditional deterrent methods have proven to be ineffective. The system integrates an Internet Protocol camera with a You Only Look Once version 5 (YOLOv5) object detection model, which was trained on an augmented 6000-image dataset featuring a simulated monkey puppet in an indoor setting to validate its real-time identification capability through simulation. Upon target detection, a high-power laser, controlled via the Message Queuing Telemetry Transport protocol, is actuated to perform dynamic and non-invasive repelling. A web-based Human–Machine Interface (HMI) is provided, allowing users to remotely monitor and adjust strategies. This system offers a low-cost, highly efficient, and scalable solution for smart agriculture, with potential for expansion to other scenarios requiring a high degree of security and defense, such as warehouses and construction sites. Full article

The first influenza A(H5N1) human case associated with the A(H5N1) dairy cattle outbreak in the United States was identified in April 2024. The U.S. CDC response to this outbreak was activated days later and remained active until July 2025. During this time, 70 human cases of influenza A(H5N1) were detected with a range of epidemiological links to sources of exposure. Next-generation sequencing (NGS) of human samples was an effectual mechanism for tracking and analyzing the outbreak evolution throughout the response. Due to the specimens’ importance and their variable physical quality, an assortment of laboratory methods was utilized including influenza segment-specific amplification, enrichment capture, short-read, and long-read sequencing. Combining these methods allowed for high-quality genomic data production with rapid turnaround times—typically 2 days from sample receipt to public database submission. By leveraging replicate sequencing, enrichment capture, and sequencing of diagnostic amplicons, valuable genomic data could be produced directly from human clinical specimens that would have normally been considered too weak for routine virologic surveillance sequencing. The resulting assemblies were characterized and analyzed by CDC and shared with local and state public health authorities to facilitate case investigations and risk assessment. These data were further used for phylogenetic analyses of viruses from human cases to investigate likely animal-to-human transmission events and identify clusters within the outbreak that might indicate trends in the types of exposures. Through the adaptable laboratory workflow and the rapid release of viral genomic data, the public health risk mitigation strategies could be evaluated and adjusted in real time. Full article

Human T-cell leukemia virus type 1 (HTLV-1)-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a chronic inflammatory disease driven by HTLV-1-infected CD4⁺ T cells; however, the phenotypic and functional characteristics of disease-associated T-cell subsets remain incompletely understood. We analyzed samples using flow cytometry (n = 3–5 per group) and RNA-seq (n = 13), focusing on CADM1^highCD4⁺ T cells enriched for HTLV-1-infected cells to evaluate a transferrin receptor (TfR)-expressing subset. TfR⁺CADM1^highCD4⁺ T cells were detected in both asymptomatic carriers and patients with HAM, but their frequency among CD4⁺ T cells was higher in HAM patients. These cells exhibited a Treg-like phenotype with higher Foxp3 and CTLA-4 expression than TfR⁻ cells and showed increased Ki-67 positivity, consistent with proliferation. Despite this phenotype, they produced interferon-γ, indicating inflammatory potential, while Foxp3 expression was lower in HAM patients than in asymptomatic carriers, suggesting a more inflammatory phenotype. Furthermore, TfR transcript levels (RNA-seq TPM) correlated with clinical indicators of disease activity, including neopterin and CXCL10 protein levels, and the Osame motor disability score. Collectively, these findings suggest that TfR identifies a proliferative, Foxp3-low, Treg-like inflammatory CD4⁺ T-cell subset that is associated with disease activity in HAM. Full article