Search Results (257)

Hydrogels are endowed with exceptional hydrophilicity and biocompatibility by their network structure, while also exhibiting soft physical properties similar to living tissues, which renders them ideal biomaterials. Responsive hydrogels—particularly those constructed from multicomponent systems including proteins, polysaccharides, peptides, and polyphenols—have emerged as a frontier research focus owing to their tunable responsiveness and controllable functional properties. In this review, hydrogel response mechanisms were categorized according to pH, ionic strength, temperature, light, enzymes, and multi-stimuli interactions. Key preparation strategies, encompassing chemical, physical, and enzymatic crosslinking, were systematically introduced. The preparation of hydrogels from various food-grade matrices, such as polysaccharide-based, protein-based, peptide-based, and polyphenol-based systems, was also summarized, with emphasis placed on how their tailored structures govern functional performance. Furthermore, innovative applications of responsive hydrogels were highlighted, including targeted delivery of nutrients and bioactive substances (e.g., probiotics, anthocyanins, vitamins) in functional foods, smart packaging and sensing for real-time freshness monitoring of meat and fruits, food quality detection through colorimetric and photothermal sensors, and 4D food printing for personalized nutrition and dysphagia-friendly foods. Full article

Efficient RFID spatial exploration in dynamic warehouse environments is challenging due to occlusions, sensing geometry constraints, and the weak coupling between information acquisition and navigation decisions. Many existing inventory robots treat RFID sensing as a passive data source during exploration, without explicitly optimizing sensing pose or prioritizing inventory-driven frontiers, which can result in incomplete coverage and redundant traversal. This paper presents ArmTenna, an articulated mobile robotic platform that formulates RFID inventory exploration as an active perception problem. The system integrates dual 4-DOF robotic arms carrying directional UHF RFID antennas and a 2-DOF neck-mounted RGB-D camera, enabling adaptive interrogation of candidate regions. We propose a multi-modal frontier exploration framework that combines newly detected EPC tags, average RSSI values, and vision-based product detections into a composite utility function for goal selection. By embedding articulated antenna control directly into the frontier evaluation loop, the robot tightly couples sensing geometry with exploration decisions. Experimental validation with 150 tagged items across three separated warehouse zones shows that ArmTenna achieves up to 97% map coverage, compared to 72% for a baseline platform, while reducing missed-tag regions. These results demonstrate that integrating active sensing pose control with multi-modal frontier evaluation provides an effective and scalable solution for RFID-driven warehouse inventory automation. Full article

The detection of underwater organisms represents a particularly challenging frontier within the field of computer vision. This paper proposes an underwater organism detection algorithm named the Enhanced Feature Extraction-based Underwater Organism Detection Network (EFE_UODNet). Firstly, this paper designs an Enhanced Global Context Guided Feature (EGCGF) module to extract organism feature information in a serial interaction manner, thereby enhancing the feature extraction capability for blurred organisms in low-quality underwater images. Secondly, this paper proposes an Advanced Multi-Scale Fusion Pyramid Network (AM-FPN) to achieve multi-level feature fusion. EFE_UODNet uses high-level features as weights and combines channel attention with spatial attention to fuse low-level information with high-level features. Finally, the Dynamic Head (DyHead) is introduced to better utilize the features output by AM-FPN, thereby improving detection performance in underwater scenes involving dense and highly similar targets. Experimental results on the DUO dataset demonstrate that the proposed EFE_UODNet model, using YOLOv8m as the baseline, achieves significant improvements across multiple metrics. These improvements include a 2.3% increase in Precision, 3.5% in Recall, 2.1% in F1-score, 3.5% in mAP[50–95], and 1.3% in mAP50. These accuracy gains are achieved while ensuring that inference time and parameter count still meet the real-time and resource requirements of this work. Additionally, its generalization ability is validated on the URPC2019 dataset. Compared to other underwater organism detection algorithms, the proposed model delivers outstanding overall performance on this task. Full article

Rainbowfish of the genus Melanotaenia are highly endemic freshwater fishes found only in Australia and New Guinea. Although widespread, most species have narrow geographic ranges, making them particularly vulnerable to environmental change. Currently, 43 described (and many undescribed) Melanotaenia species occur in the Bird’s Head and Bird’s Neck region of Western New Guinea, 29 of which are currently classified as critically endangered, endangered, or vulnerable by the IUCN Red List, including two that may be extinct in the wild. We generated a high-spatial-resolution baseline land cover classification of rainbowfish habitats using low-cloud Planet Labs quarterly basemap mosaics and compared it with a moderate-resolution Landsat 8 OLI-derived classification to assess how spatial resolution influences land cover classification. Using the full 40-year Landsat archive, we quantified decadal land cover change around species type localities and identified localized disturbance events that may affect rainbowfish habitats. For species described from large rivers and lakes, changes in water-body extent over time were quantified. Deforestation varied widely, ranging from little or no detectable change in remote, difficult-to-access locations (e.g., M. misoolensis, M. sneideri), to landscapes heavily modified by logging, urbanization, mining, and agriculture (e.g., M. boesemani, M. arfakensis). Around the type localities, from the high-resolution imagery, we detected ~2939 ha of cleared land, whereas from the Landsat classification we identified only 31 ha of clearing, indicating that most of the fine-scale deforestation was not resolved at the Landsat scale. Time-sequence analyses indicate that over one-third of type localities experienced one or more localized disturbance events over the last 40 years. Land cover change in this region is highly dynamic and differs from commonly studied frontier deforestation patterns elsewhere. It also underscores a critical conservation challenge where rainbowfish species are being discovered in landscapes that are simultaneously undergoing rapid, spatially heterogeneous change. The same infrastructure that enables biological exploration also accelerates habitat modification. These changes threaten the persistence of highly endemic rainbowfish and underscore the value of multi-scale spatial and temporal remote sensing approaches for assessing habitat change in remote, biodiverse regions. The framework presented here is also broadly applicable to other narrowly distributed endemic taxa. Full article

Air pollution has become a pressing global challenge that threatens ecological security, public health, and sustainable socioeconomic development, prompting extensive academic and policy attention on air pollution control and environmental governance. To systematically clarify the knowledge structure, evolutionary trends, and interdisciplinary characteristics of this field, this study employs bibliometric methods combined with CiteSpace, VOSviewer, and Tableau tools for in-depth analysis of the global literature published in the last 25 years. Key dimensions including keyword clustering, co-occurrence networks, national cooperation patterns, journal co-citation relationships, and policy evaluation methodology evolution are explored. The results reveal that research output in this field has maintained sustained rapid growth, with distinct interdisciplinary integration across environmental science, economics, energy engineering, and public health. Notably, the evolutionary path of research themes presents a clear transformation: shifting from early emphasis on “emission standards” and “end-of-pipe treatment” to market-oriented policy instruments such as “carbon tax” and “carbon emission trading”, and further expanding toward systematic solutions including “green finance” and “collaborative environmental governance”. In terms of policy evaluation methodologies, there is a developmental trend from single-indicator monitoring to integrated assessment frameworks combining quasi-experimental approaches (e.g., difference-in-differences, regression discontinuity design) and multi-model coupling. Furthermore, national collaboration analysis identifies China as a core hub in the global research network, while European and American countries maintain advantages in research impact. While this observation is based on absolute metrics, a data normalization approach (e.g., by population) reveals more distinct relative differences and a complementary global dynamic: China’s scale-driven output aligns with large-scale, engineering-intensive governance challenges, whereas the markedly higher per capita research impact of Western nations reflects a deeper focus on policy innovation and systemic mechanisms. Burst term detection highlights emerging frontiers such as the “Porter hypothesis”, reflecting growing focus on the synergistic relationship between environmental regulation, green innovation, and economic development. This study also identifies critical research gaps, including insufficient attention on cross-regional pollution transport policy coordination and emergency policy evaluation under extreme weather conditions. The findings provide a comprehensive academic map of global air pollution control and environmental governance research, offering valuable insights for optimizing environmental policy design, promoting interdisciplinary collaboration, and guiding future research directions in this field. Full article

Avian influenza (AI), particularly highly pathogenic avian influenza (HPAI), represents a serious and growing threat to global poultry production, international trade, and human health security. Control of AI is complicated by the high evolutionary rate of influenza A viruses, which drives antigenic diversity and ongoing emergence of novel strains. Effective surveillance and disease management therefore depend on timely and accurate diagnostics. While conventional methods—including virus isolation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assays (ELISAs)—remain effective and widely used, they are limited by long turnaround times, the need for specialized equipment, and reliance on highly trained personnel. In addition, strict state and federal regulatory requirements restrict testing to a limited number of authorized laboratories. Although these regulations are essential for maintaining diagnostic accuracy and quality assurance, they place substantial strain on laboratory capacity during outbreaks and delay actionable results. The need for rapid, on-site decision making has driven interest in alternative diagnostic approaches, including biosensor technologies. A major limitation of current diagnostic strategies is the lack of robust DIVA (Differentiating Infected from Vaccinated Animals) capability. In countries such as the United States, where poultry vaccination against AI is not routinely practiced, the absence of DIVA-compatible diagnostics has hindered adoption of vaccination as a disease management tool, as seropositive birds and products face significant trade restrictions. Biosensor platforms capable of enabling DIVA strategies offer a potential pathway to support vaccination while preserving surveillance integrity. This review examines the current landscape of AI and HPAI diagnostics, emphasizing the limitations of traditional approaches and the opportunities presented by biosensor platforms. We evaluate electrochemical, optical, piezoelectric, and nucleic-acid-based biosensors, with particular attention to biorecognition strategies, performance metrics, field deployability, and applications supporting subtype discrimination, DIVA implementation, and One Health surveillance. Full article

Sustainable soil–water management under climate and socio-economic pressures requires predictive capability that is both mechanistic and continuously corrected by observations. Data assimilation (DA) provides the formal machinery to merge models with heterogeneous measurements—from satellite evapotranspiration and soil moisture to cosmic-ray neutron sensing, proximal geophysics, lysimeters, and groundwater hydrographs—while propagating uncertainty. This review (based on 90 references) synthesizes frontiers in DA and modeling for soil–water systems across scales, emphasizing (i) multi-source observation operators and scaling; (ii) coupled crop–vadose–groundwater modeling frameworks and their structural hypotheses; (iii) modern DA methods (ensemble, variational, particle-based, and hybrid physics–ML) for joint estimation of states, parameters, and biases; and (iv) emerging digital twins that enable predict-then-verify management loops for irrigation, recharge enhancement, and drought risk reduction. We highlight how tracer-aided and isotope-informed components can improve evapotranspiration partitioning and recharge threshold detection, and how agent-based or socio-hydrological coupling can represent human decision feedback. Finally, we outline research gaps in uncertainty quantification, benchmarking, reproducibility, and governance needed to operationalize trustworthy soil–water digital twins for resilient food and water systems. Full article

Gynecological diseases—encompassing polycystic ovary syndrome, endometriosis, infertility, and malignancies—represent a significant global health burden affecting women’s quality of life, reproductive capacity, and long-term health outcomes. While traditional diagnostics rely on protein-based biomarkers, clinical phenotyping, and imaging, these approaches often lack the sensitivity and specificity required for early detection and personalized intervention. Glycomics, the comprehensive study of carbohydrate structures on proteins and lipids, represents an emerging molecular frontier in gynecological disease characterization and therapeutics. This review synthesizes current knowledge regarding glycomic dysregulation across gynecological conditions, elucidates how aberrant glycosylation patterns serve as disease-specific biomarkers, and demonstrates key translational applications, such as glycoform-specific CA-125. By integrating glycomics with complementary omics technologies and artificial intelligence-driven analysis, a transformative diagnostic paradigm is emerging that promises earlier detection, improved risk stratification, and precision-guided therapeutics for women with gynecological disorders. Full article

Efficient object detection is vital for Remotely Operated Vehicles (ROVs) performing marine debris cleanup, yet existing lightweight designs frequently encounter efficiency bottlenecks when adapted to deeper neural networks. This research identifies a critical “Inverted Bottleneck” anomaly in the Slim-Neck architecture on the YOLO11 backbone, where deep-layer Memory Access Cost (MAC) abnormally spikes. To address this, we propose SvelteNeck-YOLO. By incorporating the proposed EHSCSP module and EHConv operator, the model systematically eliminates computational redundancies. Empirical validation on the TrashCan and URPC2019 datasets demonstrates that the model resolves the memory wall issue, achieving a state-of-the-art trade-off with only 5.8 GFLOPs. Specifically, it delivers a 34% relative reduction in computational load compared to specialized underwater models while maintaining a superior Recall of 0.859. Consequently, SvelteNeck-YOLO establishes a robust, cross-generational solution, optimizing the Pareto frontier between inference speed and detection sensitivity for resource-constrained underwater edge computing. Full article

Although the polarization state, as a key physical dimension of light, plays an irreplaceable role in many frontier fields such as quantum communication and chiral sensing, traditional photodetectors are limited by the inherent optical isotropy of materials and thus are unable to directly distinguish circular polarization information. This paper numerically reports a miniature circular polarization photodetector based on chiral metasurfaces, which achieves an excellent extinction ratio of up to 31 dB through the collaborative regulation of geometric displacement manipulation and tilt angle operation. This device utilizes the symmetry-breaking effect to construct significantly different transmission spectral responses between left circularly polarized light (LCP) and right circularly polarized light (RCP). Our research not only provides a high-performance implementation solution for on-chip polarization detection but also opens up new paths for the future development of quantum optics, integrated sensing, and ultra-compact polarization optical systems. Full article

The continuous detection of emerging pollutants (EPs) in water poses potential threats to aquatic environmental safety and human health, and their efficient removal is a frontier in environmental engineering research. This review systematically summarizes research progress from 2005 to 2025 on the application of activated carbon (AC) and its composites for removing EPs from water and analyzes the development trends in this field using bibliometric methods. The results indicate that research has evolved from the traditional use of AC for adsorption to the design of novel materials through physical and chemical modifications, as well as composites with metal oxides, carbon-based nanomaterials, and other functional components, achieving high adsorption capacity, selective recognition, and catalytic degradation capabilities. Although AC-based materials demonstrate considerable potential, their large-scale application still faces challenges such as cost control, adaptability to complex water matrices, material regeneration, and potential environmental risks. Future research should focus on precise material design, process integration, and comprehensive life-cycle sustainability assessment to advance this technology toward highly efficient, economical, and safe solutions, thereby providing practical strategies for safeguarding water resources. Full article

The concept of individual cellular intelligence reframes cells as dynamic entities endowed with sensory, reactive, adaptive, and memory-like capabilities, enabling them to navigate lifelong metabolic and extrinsic stressors. A likely vital component of this intelligence system is stress-responsive G protein-coupled receptor (GPCR) networks, interconnected by common signaling adaptors. These stress-regulating networks orchestrate the detection, processing, and experience retention of environmental cues, events, and stressors. These networks, along with other sensory mechanisms such as receptor-mediated signaling and DNA damage detection, allow cells to acknowledge and interpret stressors such as oxidative stress or nutrient scarcity. Reactive responses, including autophagy and apoptosis, mitigate immediate damage, while adaptive strategies, such as metabolic rewiring, receptor expression alteration and epigenetic modifications, enhance long-term survival. Cellular experiences that are effectively translated into ‘memories’, both transient and heritable, likely rely on GPCR-induced epigenetic and mitochondrial adaptations, enabling anticipation of future insults. Dysregulation of these processes and networks can drive pathological states, shaping resilience or susceptibility to chronic diseases like cancer, neurodegeneration, and metabolic disorders. Employing molecular evidence, here, we underscore the presence of an effective cellular intelligence, supported by multi-level sensory GPCR networks. The quality of this intelligence acts as a critical determinant of somatic health and a promising frontier for therapeutic innovation. Future research leveraging single-cell omics and systems biology may unravel the molecular underpinnings of these capabilities, offering new strategies to prevent or reverse stress-induced pathologies. Full article

Background/Objectives: Gestational exposure to substances (GES) is associated with adverse developmental outcomes. Early identification is limited by reliance on self-reported data. This study assessed the incidence and predictors of discordance in GES reporting between birth certificates and Medicaid claims among Medicaid-covered births in Nevada from 2022 to 2024. Methods: A statewide, hospital-clustered, cross-sectional analysis was conducted using linked Medicaid billing and birth record data. Discordance was defined as GES identified in one source but not the other. Incidence per 1000 live births was stratified by demographic characteristics. Multilevel logistic regression assessed patient- and hospital-level predictors, with random hospital intercepts. Results: Among 50,394 live births, the discordance rate was 95.09 per 1000 (95% Confidence Interval: 92.5–97.7). Substantial disparities were observed by race/ethnicity, socioeconomic status, and geography, with higher discordance among White non-Hispanic mothers, those residing in rural or frontier counties, and individuals with lower educational attainment or living in lower-income areas. Modest but meaningful variation was also observed across hospitals, including differences by hospital size and teaching or research status. Conclusions: Findings highlight substantial discordance in GES reporting and underscore the limitations of single-source surveillance. Findings also have clear policy relevance, indicating that improved cross-system data integration would strengthen statewide surveillance, enhance early detection, and support more equitable resource allocation and intervention strategies. Full article

Sudden cardiac death (SCD) remains a major challenge in forensic medicine, representing a leading cause of natural mortality and frequently occurring in individuals without antecedent symptoms. Although conventional autopsy and histology remain the cornerstones of investigation, up to 10–15% of cases are classified as “autopsy-negative sudden unexplained death,” underscoring the need for complementary diagnostic tools. In recent years, post-mortem biochemistry and molecular approaches have become essential to narrowing this gap. Classical protein markers of myocardial necrosis (cardiac troponins, CK-MB, H-FABP, GPBB) continue to play a fundamental role, though their interpretation is influenced by post-mortem interval and sampling site. Peptide biomarkers reflecting hemodynamic stress (BNP, NT-proBNP, copeptin, sST2) offer additional insight into cardiac dysfunction and ischemic burden, while inflammatory and immunohistochemical markers (CRP, IL-6, fibronectin, desmin, C5b-9, S100A1) assist in detecting early ischemia and myocarditis when routine histology is inconclusive. Beyond these traditional markers, molecular signatures—including cardiac-specific microRNAs, exosomal RNA, proteomic alterations, and metabolomic fingerprints—provide innovative perspectives on metabolic collapse and arrhythmic mechanisms. Molecular autopsy through next-generation sequencing has further expanded diagnostic capability by identifying pathogenic variants associated with channelopathies and cardiomyopathies, enabling both cause-of-death clarification and cascade screening in families. Emerging multi-omics and artificial intelligence frameworks promise to integrate these heterogeneous data into standardized and robust interpretive models. Pre- and post-analytical considerations, together with medico-legal implications ranging from malpractice evaluation to the management of genetic information, remain essential components of this evolving field. Overall, the incorporation of validated biomarkers into harmonized international protocols, increasingly supported by AI, represents the next frontier in forensic cardiology. Full article

Mastitis remains a prevalent and economically detrimental disease within the dairy industry, profoundly affecting animal welfare, milk quality, and overall production output. Nowadays, Somatic Cell Count (SCC) is widely recognized as the gold-standard indicator for the detection of mastitis; however, its limitations in pathogens discrimination and the lack of early-stage characterization of mastitis highlight the need for complementary diagnostic approaches. This review synthesizes recent research into the development and validation of novel biomarkers for the early and accurate identification of mastitis in dairy cows. The investigation encompasses a range of biological molecules for improving mastitis diagnosis. Biomarkers such as lactoferrin (LTF), β-defensin 4 (DEFB4), vitronectin, paraoxonase 1 (PON1), and N-acetyl-β-D-glucosaminidase (NAGase) show promise in distinguishing between cows not susceptible and cows susceptible to mastitis. Concurrently, nucleic acid-based biomarkers are emerging as a particularly promising frontier. While mitochondrial DNA (mtDNA) has demonstrated insufficient specificity, microRNAs (miRNAs) are gaining attention as highly stable and sensitive indicators of intramammary inflammation, potentially enabling the detection of subclinical infections before they become clinically apparent. Despite these advances, significant challenges related to specificity, reliability, and cost-effectiveness currently hinder the widespread practical application of any single biomarker. Therefore, future research should be directed towards the validation of a synergistic panel of multiple biomarkers to improve mastitis management in dairy cow farms. Full article