Search Results (31,517)

Background/Objectives: Recent investigations point to red foxes (Vulpes vulpes) as a very potent sentinel species for monitoring the dissemination of antimicrobial bacteria in wildlife habitats. Methods: This study investigated antimicrobial resistance in red foxes from 16 hunting grounds (peri-urban and peri-rural) in western Romania, between 2022 and 2024, in order to evaluate the species as “One Health” sentinels at the wildlife–human–animal interface. During this period, 137 bacterial strains previously identified from 216 samples were phenotypically tested using both the Kirby–Bauer disk diffusion method and the Vitek 2 Compact system. Results: Among the Gram-negative isolates, particularly Escherichia coli and Salmonella enterica, notable antimicrobial resistance and multidrug-resistant (MDR) phenotypes were observed, including resistance to third-generation cephalosporins (ceftazidime) and reduced susceptibility to carbapenems. Resistance patterns observed in Proteus spp. largely reflected intrinsic resistance traits. Methicillin-resistant and MDR staphylococci (Staphylococcus aureus, S. pseudintermedius and S. sciuri) were detected in both peri-urban and peri-rural hunting grounds, with higher frequencies observed in peri-rural areas. Although MDR prevalence was slightly higher in peri-urban compared to peri-rural sites, no statistically significant association was identified between area of isolation and antimicrobial resistance or MDR status. Antimicrobial susceptibility results obtained by disk diffusion and the Vitek 2 Compact system showed a high level of concordance for antibiotics tested in common. Conclusions: Overall, these findings support the use of red foxes as effective One Health sentinels for monitoring environmental antimicrobial resistance occurrence across wildlife, domestic animals, and human-impacted habitats. Full article

Regarding the issue of abnormal data mining of electricity meters in the PLC application area, an intelligent measurement network architecture integrating protocol message interaction and an anomaly detection module has been designed. Based on an improved convolutional neural network (ICNN), abnormal messages during the transmission and reception process are monitored to enhance the reliability of power information collection data. Firstly, common anomalies during the meter reading operation are analyzed using protocol analysis tools, including abnormal power data, excessive delay, message out of order, etc. Subsequently, a dataset containing these anomalies with a preset proportion is constructed, and through data splicing and matrix processing, it is transformed into a two-dimensional image set to optimize the recognition effect of the convolutional neural network. Ultimately, an anomaly detection algorithm based on the ICNN is developed. Gray wolf optimization (GWO) is adopted to improve the algorithm’s performance, and the algorithm is integrated into the anomaly detection module. The experimental results demonstrate that, compared with the CNN-LSTM and CNN-SVM algorithms, the proposed algorithm offers an advantage in terms of complexity while achieving an accuracy rate of 98.8%, providing a reliable anomaly detection solution for metering network measurement systems. Full article

Ex situ conservation in breading centers is one of the key strategies for saving giant pandas (Ailuropoda melanoleuca). Abnormal behaviors (e.g., inappetence) are key symptoms of potential health issues (e.g., Klebsiella pneumoniae) for the captives. Therefore, monitoring their normal activity patterns could set a baseline to detect these abnormalities for implementing timely interventions. However, traditional monitoring methods are labor-intensive, which often rely on manual observations. Here, we proposed a deep learning framework, termed as DeepPanda, for automatically recognizing four essential behaviors (i.e., eating, walking, resting and drinking) of giant pandas based on videos from common surveillance cameras. Experimental results demonstrated that the DeepPanda model achieved high performance on the self-established APanda dataset, with the testing mean average precision at an IoU threshold of 0.5 (mAP@0.5) of 98.8%. This methodology provides a powerful tool for monitoring the captive giant panda’s behaviors. Full article

Land snails have the highest recorded extinction rate, and these small animals are often overlooked, leading to data gaps. Past data for land snails is often lacking, making the analysis of trends difficult. Here, we compared past presence surveys to new quantitative estimates to infer changes over time. We surveyed 55 sites for land snails and habitat characteristics in 2024 using visual encounter surveys for medium to large snails and litter samples to assess the density of small to medium snails. We assessed habitat on two scales to assess associations and selection. We identified 27 land snail species, including a non-native species (Oxychilus cellarius). Sites with higher snail density and a richer assemblage generally had deeper litter, higher canopy cover and taller understory vegetation. Rare land snails were detected at most sites where they had previously been found, and we detected several species at new sites where they had not previously been documented, due to litter sampling. Vertigo arthuri (V. paradoxa) selected sites with a higher canopy cover. The abundance and density of land snails will enable better estimates of long-term trends and help assess how they respond to management actions. Resolving the taxonomy of Oreohelix and Succineidae is critical for direct management of these species. Full article

Long-term ecological data are rare but are highly desirable for assessing responses to ongoing environmental change. To assess temporal trends in abundance over time and establish a baseline for future comparison, we monitored a plethodontid salamander community for ten years. From 2014 to 2023, we sampled forest plots at Wooster Memorial Park (OH, USA) using a regular and standardized monitoring scheme. Of nine salamander species detected, four were common enough to permit statistical analysis. Three species (Eurycea bislineata, Plethodon cinereus and P. electromorphus) had no statistically significant abundance trends over time. The slimy salamander (P. glutinosus), however, showed a statistically significant decline in abundance. We also report on ecological differences between P. cinereus and P. electromorphus, which occur in sympatry at this site. Specifically, we document significant microhabitat differences between these species, which are suggestive of competition avoidance. Additional data are presented on color morph frequency, body size, sexual dimorphism, frequency of hybridization, mate choices, and phenology of surface activity. As global environmental change accelerates, such baseline information is essential to track organismal responses. We also provide a brief review of other long-term field studies in plethodontid salamanders. Full article

Managing rutting aggression is critical in sika deer (Cervus nippon) farming. To mitigate rutting aggression in male sika deer, this study evaluated the efficacy, safety, and physiological mechanisms of exogenous progesterone. Twelve sika deer were randomly assigned to either a control group or a treatment group, with behavior monitored for 60 days post-administration. Serum hormones, non-targeted serum metabolomics, biochemical indicators (including reflecting liver and kidney function), and subsequent antler performance were assessed. The treatment group exhibited significantly reduced aggressive and mating behavior throughout the study (p < 0.05). HPG axis hormones (GnRH, LH, FSH, and T) and PRL were significantly reduced throughout the study (p < 0.05), while TRH was elevated, T4 declined, and GH showed time-dependent fluctuations. Differential metabolites were significantly enriched in pathways related to nucleotide metabolism, pyruvate metabolism, and arachidonic acid metabolism. Except for a transient decrease in the ALB/GLB ratio (p < 0.05), no significant changes were observed in other biochemical indicators or antler performance (p > 0.05). This study confirms that exogenous progesterone effectively controls rutting behavior primarily via HPG-axis suppression and multi-system endocrine interactions, without inducing detectable organ toxicity or compromising production, supporting its use as a safe management intervention. Full article

Background: Building on the validation of the Your Memory test for mild cognitive impairment in English speakers, this study adapted and validated the Memory Test for Mild Cognitive Impairment (TYM-MCI) for older Spanish-speaking adults, highlighting its potential utility for the early detection of amnestic mild cognitive impairment and cognitive profiles associated with increased risk of dementia. Methods: A total of 151 independently functioning adults aged 60 or older (Barthel Index 9–10) completed the TYM-MCI, the Addenbrooke’s Cognitive Examination-Revised (ACE-R-Ch), the Mini-Mental State Examination, and the original TYM. Analyses included ROC curves, correlation matrices, and principal component analysis (PCA). Results: The TYM-MCI exhibited strong psychometric properties (Cronbach’s α = 0.832; sensitivity = 81.7%; specificity = 47.8%). The optimal cut-off score was ≥24.5/30. Scores between 19 and 24.5 suggested probable mild cognitive impairment (MCI). Conclusions: The episodic memory components of this test are key cognitive features relevant to the modification and monitoring of early cognitive decline and are straightforward to administer. Notably, the TYM-MCI specifically assesses both visual and verbal episodic memory. It can be used alongside other assessments, such as the ACE-R or MMSE, to support the clinical evaluation of cognitive functioning in older adults. Clinically, it provides an early assessment and follow-up in individuals presenting with memory complaints, contributing to timely clinical decision-making in the context of cognitive decline. Full article

Following the COVID-19 pandemic, researchers have increasingly focused on monitoring the spread of the virus and improving methods to detect changes in the SARS-CoV-2 genome. Although clinical surveillance provides direct and reliable results, it has limited applicability. Wastewater-based epidemiology (WBE) has therefore emerged as a valuable, non-invasive complementary tool for disease surveillance. It provides a comprehensive picture of virus circulation in a population, including asymptomatic individuals and those who do not seek healthcare. In addition, it facilitates early detection of outbreaks and the collection of epidemiologic data at the community level. However, WBE also presents technical challenges, including variations in sampling and testing protocols, the presence of inhibitors that affect viral RNA extraction, and the need for standardised procedures between studies. These challenges should be addressed for possible future infectious disease outbreaks. One of the challenges facing researchers was to develop efficient methods that could overcome the extraction and detection problems related to inhibitors present in wastewater. To this aim, this systematic review highlights the potential use of WBE, the variety of techniques, and the most effective methods for the detection and quantification of SARS-CoV-2 in wastewater samples. A reproducible electronic search of the literature was conducted in the Web of Science (WoS) and PubMed databases for articles published between 2020 and 2024. Our search revealed that the majority of observed WBE applications emphasised a correlation between SARS-CoV-2 RNA concentration trends in wastewater and epidemiological data. Another relevant issue that the articles often discussed and compared was the techniques used in different steps of sample processing, such as sample collection, concentration and detection, hence the lack of standardised procedures. This paper provides a framework regarding previous research on WBE to gain a better understanding that will lead to functional solutions. Full article

Objectives: This study aimed to evaluate the validity, reliability, and diagnostic accuracy of the Low Sit–High Step (LS–HS) Test as an original, cost-effective, and clinically practical tool for assessing lower-extremity muscle strength and function, with a specific focus on its sensitivity in detecting early-stage sarcopenia. Methods: This cross-sectional study included 205 participants divided into four groups: probable sarcopenia, sarcopenia, and two control groups (young and middle-to-older adults). The LS–HS Test was compared across groups and against standard assessments to evaluate its efficacy in measuring lower-extremity function. Reliability was verified through Cronbach’s alpha and ICC. Multinomial logistic regression was used to determine the test’s predictive power, while ROC analysis assessed its diagnostic accuracy for sarcopenia screening. Results: The LS–HS scores were significantly higher in participants with probable sarcopenia and sarcopenia (p< 0.05). Multinomial logistic regression revealed that the LS–HS performance was a significant predictor of both probable sarcopenia and sarcopenia (p < 0.001). The test demonstrated excellent internal consistency (Cronbach’s α = 0.938) and very high inter-rater and test–retest reliability (ICC = 0.998). ROC analysis confirmed high diagnostic accuracy in distinguishing both probable sarcopenia (AUC = 0.768) and sarcopenia (AUC = 0.704) (all p< 0.01). Conclusions: The LS–HS Test is a valid, reliable, and sensitive tool for assessing lower-extremity functional capacity. Its ability to identify early functional decline, often manifesting before significant muscle mass loss, positions it as an effective alternative to traditional assessments in routine clinical practice, particularly for the early detection and monitoring of the sarcopenia spectrum. Full article

Laser welding is widely used in complex manufacturing processes and valued for its reliability, flexibility, and high energy density. However, achieving the desired weld quality requires the detection and, ideally, the prevention of defects. Besides other methods, machine learning (ML) has been integrated into laser welding with the primary goal of process optimization and quality improvement, for example, by enabling process adaptation before or during welding to reduce defects. This survey systematically reviews publications from 2015 to 2025 that integrate machine learning and deep learning methods into laser welding optimization or adaptation processes. An extensive analysis identifies which parts of the process and for what purposes ML methods are researched and implemented and how they are evaluated, as well as the sensors, lasers, and materials involved. Furthermore, the findings are analyzed and organized into taxonomies that define overarching meta-categories into which existing approaches can be classified and contextualized. The results reveal that various ML approaches are applied for tasks, such as surrogate modeling, process planning, direct control, and virtual sensing and monitoring. Although many different control parameters and optimization targets are considered, laser power and welding speed dominate as the most frequently adjusted parameters, while penetration depth and weld geometry-related properties are the most common optimization targets. Finally, the survey identifies major challenges, including the lack of benchmarking datasets, standardized evaluation protocols, and interpretable models. Full article

Lateral flow immunoassays (LFAs) are widely used for on-site testing; however, their use for the rapid detection of plant viruses in the field is often limited by inconvenient sample preparation. Here, we present a new sampling method and a simplified dipstick LFA format for the detection and monitoring of cowpea chlorotic mottle virus (CCMV) as a model plant pathogen. The assay employs a monoclonal mouse antibody for capture and a poly-clonal rabbit antibody conjugated to 80 nm gold nanoparticles for detection. Conventional sample and conjugate pads are omitted, allowing the test strips to be dipped directly into wells containing plant extract and antibody–gold conjugate. No plastic casing was required, which could lead to a reduction in waste. It was shown that CCMV concentrations as low as 3.5 µg/L or 350 pg per sample could be reliably detected in 15 min. Specificity tests confirmed that other plant viruses, cowpea mosaic virus (CPMV) and tobacco mosaic virus (TMV), did not produce false-positive results. In addition, we describe a new method for on-site sampling using a manual punch and a syringe equipped with a frit. This step combines grinding the sample, extraction, filtration, and reconstitution and mixing of the antibody-gold conjugate, enabling the analysis of punched leaf disks without laboratory equipment. When applied to CCMV-infected cowpea plants, the assay revealed systemic infection before visual symptoms became apparent. This work demonstrates that simplified LFAs combined with innovative sampling techniques can provide sensitive, specific, and rapid diagnostics for crop monitoring and support early intervention strategies in agriculture. Full article

Maritime nations around the world proactively engage in preparedness, response, and recovery activities related to marine oil spills. In addition to an individual nation’s capabilities, there are a number of response organizations that are actively engaged in the surveillance, monitoring, and remote sensing of spilled oil. A global survey was conducted of these organizations to better understand surveillance/remote sensing capabilities operationally employed today from four aerial platforms: satellites, fixed-wing aircraft, helicopters, and remotely piloted aircraft systems (RPASs). Satellite remote sensing continues to be used for both routine surveillance of coastal environments and in support of response to oil spills. Additionally, there is a strong continued use of fixed-wing aircraft, and in some cases helicopters, particularly to support operational response to oil spills. Many of these fixed-wing aircraft are outfitted with sensor suites optimized for oil spill detection and documentation. Of particular interest is the recent introduction and widespread use of RPASs for the response of marine oil spills and oiled shorelines. Respondents identified operational gaps in remote sensing capabilities to support oil spill response, including the accurate measurement of oil spill thickness and volume, differentiation between petroleum oil and biogenic materials, and the detection of water-in-oil emulsions. Survey respondents also shared remote sensing capabilities used for oiled shorelines, as well as identifying research and operational gaps in the surveillance of oil spills. Full article

This paper proposes and experimentally validates a label-free microdroplet concentration detector based on a quad-resonator metamaterial. The device exploits the linear relationship between the dielectric constant of a binary mixed solution and its concentration, mapping concentration information to absorption frequency shifts with a sensitivity of 28.53 GHz/RIU. System modeling was performed through full-wave simulation. Experimental results demonstrate a highly linear relationship between resonance frequency shift and concentration across ethanol, water, and ethanol–water solutions. The relative deviation between simulation and measurement is less than 3%, validating the model’s reliability and the robustness of the detection principle. This detector supports rapid non-contact sample replacement without requiring chemical labeling or specialized packaging. It can be mass-produced on standard PDMS substrates, with each unit reusable for >50 cycles. With a single measurement time of <30 s, it meets high-throughput detection demands. Featuring low power consumption, high precision, and scalability, this device holds broad application prospects in point-of-care diagnostics, online process monitoring, and resource-constrained scenarios. Future work will focus on achieving simultaneous multi-component detection via multi-resonator arrays and integrating chip-level wireless readout modules to further enhance portability and system integration. Full article

Due to the weak electrical response characteristics of groundwater nitrate contamination, traditional monitoring and remediation assessment methods are limited by low spatiotemporal resolution, high cost, and strong subjectivity. To address this issue, this study proposed an integrated technical framework combining field detection, laboratory-controlled experiments, and remediation process monitoring, aiming to explore the application potential of Electrical Resistivity Tomography (ERT) in nitrate pollution monitoring and remediation evaluation. First, ERT survey lines (L1 and L2) were deployed at a chemical-contaminated site in Luzhou, Sichuan Province, and groundwater samples were collected. Coupled with hydrochemical analysis, the feasibility of ERT for identifying nitrate plumes was verified. Second, a quantitative response model between nitrate concentration and resistivity was established through Miller box experiments, and a multi-line layout was optimized via sand tank experiments to mitigate boundary effects and improve monitoring accuracy. Finally, grouped sand tank experiments involving electroactive bacteria (EAB) and magnetite were conducted. Combined with 16S rRNA sequencing, the coupling mechanism between ERT electrical responses and biogeochemical processes was elucidated. The results showed that the low-resistivity anomaly zones identified by field ERT were accurately consistent with the high-nitrate contamination zones, and Piper diagrams confirmed that nitrate-related ions were the primary cause of the low-resistivity anomalies. The power function quantitative model established by the Miller box experiment (y = 1021.97x^−0.74, R² = 0.9589) enabled the indirect inversion of nitrate concentrations, with a small deviation between theoretical and measured values in the deep layer (16–18 m). The optimized layout of one main and three auxiliary survey lines effectively characterized the spatiotemporal migration of the contamination plume. Under high-water level conditions, the ternary system of nitrate–magnetite–EAB exhibited the strongest low-resistivity response. Microbial analysis indicated that electroactive groups (e.g., Pseudomonas and Flavobacterium) enriched in the EAB group were the core drivers of enhanced electrical conductivity. The integrated ERT monitoring technology system constructed in this study realizes the visual identification of nitrate plumes and dynamic tracking of remediation processes, providing technical support for the precise monitoring and in situ remediation of nitrate contamination in agricultural non-point sources and industrial sites. Full article

Respiratory diseases and welfare impairments impose substantial economic and ethical burdens on modern broiler production, driven by high stocking density, rapid pathogen transmission, and limited sensitivity of conventional monitoring methods. Because respiratory pathology and stress directly alter vocal behavior, acoustic monitoring has emerged as a promising non-invasive approach for continuous flock-level surveillance. This review synthesizes recent advances in audio classification and artificial intelligence for monitoring respiratory health and welfare in broiler chickens. We have reviewed the anatomical basis of sound production, characterized key vocal categories relevant to health and welfare, and summarized recording strategies, datasets, acoustic features, machine-learning and deep-learning models, and evaluation metrics used in poultry sound analysis. Evidence from experimental and commercial settings demonstrates that AI-based acoustic systems can detect respiratory sounds, stress, and welfare changes with high accuracy, often enabling earlier intervention than traditional methods. Finally, we discuss current limitations, including background noise, data imbalance, limited multi-farm validation, and challenges in interpretability and deployment, and outline future directions for scalable, robust, and practical sound-based monitoring systems in broiler production. Full article