Search Results (102)

Background/Objectives: This study examines how seasonality, pollution, and sample type (water and sediment) influence the presence and distribution of antibiotic resistance genes (ARGs), with a focus on antibiotic resistance genes (ARGs) located on plasmids (the complete set of plasmid-derived sequences, including ARGs) in a tropical urban river. Methods: Samples were collected from three sites along a pollution gradient in the Virilla River, Costa Rica, during three seasonal campaigns (wet 2021, dry 2022, and wet 2022). ARGs in water and sediment were quantified by qPCR, and metagenomic sequencing was applied to analyze chromosomal and plasmid-associated resistance profiles in sediments. Tobit and linear regression models, along with multivariate ordination, were used to assess spatial and seasonal trends. Results: During the wet season of 2021, the abundance of antibiotic resistance genes (ARGs) such as sul-1, intI-1, and tetA in water samples decreased significantly, likely due to dilution, while intI-1 and tetQ increased in sediments, suggesting particle-bound accumulation. In the wet season 2022, intI-1 remained low in water, qnrS increased, and sediments showed significant increases in tetQ, tetA, and qnrS, along with decreases in sul-1 and sul-2. Metagenomic analysis revealed spatial differences in plasmid-associated ARGs, with the highest abundance at the most polluted site (Site 3). Bacterial taxa also showed spatial differences, with greater plasmidome diversity and a higher representation of potential pathogens in the most contaminated site. Conclusions: Seasonality and pollution gradients jointly shape ARG dynamics in this tropical river. Plasmid-mediated resistance responds rapidly to environmental change and is enriched at polluted sites, while sediments serve as long-term reservoirs. These findings support the use of plasmid-based monitoring for antimicrobial resistance surveillance in aquatic systems. Full article

Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This study comprehensively analyzes the recent landscape of obstacle and aircraft detection techniques tailored for UAVs acting in difficult scenarios such as fog, rain, smoke, low light, motion blur, and disorderly environments. It starts with a detailed discussion of key detection challenges and continues with an evaluation of different sensor types, from RGB and infrared cameras to LiDAR, radar, sonar, and event-based vision sensors. Both classical computer vision methods and deep learning-based detection techniques are examined in particular, highlighting their performance strengths and limitations under degraded sensing conditions. The paper additionally offers an overview of suitable UAV-specific datasets and the evaluation metrics generally used to evaluate detection systems. Finally, the paper examines open problems and coming research directions, emphasising the demand for lightweight, adaptive, and weather-resilient detection systems appropriate for real-time onboard processing. This study aims to guide students and engineers towards developing stronger and intelligent detection systems for next-generation UAV operations. Full article

Coastal ecosystems are increasingly threatened by climate change and anthropogenic pressures, which can disrupt microbial communities and favor the emergence of pathogenic organisms. In this study, we applied metagenomic analysis to characterize fungal communities in sediment samples from an urban mangrove subjected to environmental stress. The results revealed a fungal community with reduced richness—28% lower than expected for similar ecosystems—likely linked to physicochemical changes such as heavy metal accumulation, acidic pH, and eutrophication, all typical of urbanized coastal areas. Notably, we detected an increase in potentially pathogenic genera, including Candida, Aspergillus, and Pseudoascochyta, alongside a decrease in key saprotrophic genera such as Fusarium and Thelebolus, indicating a shift in ecological function. The fungal assemblage was dominated by the phyla Ascomycota and Basidiomycota, and despite adverse conditions, symbiotic mycorrhizal fungi remained present, suggesting partial resilience. A considerable fraction of unclassified fungal taxa also points to underexplored microbial diversity with potential ecological or health significance. Importantly, this study does not aim to compare pristine and contaminated environments, but rather to provide a sanitary alert by identifying the presence and potential proliferation of pathogenic fungi in a degraded mangrove system. These findings highlight the sensitivity of mangrove fungal communities to environmental disturbance and reinforce the value of metagenomic approaches for monitoring ecosystem health. Incorporating fungal metagenomic surveillance into environmental management strategies is essential to better understand biodiversity loss, ecological resilience, and potential public health risks in degraded coastal environments. Full article

Rat hepatitis E virus (rat HEV) is an emerging zoonotic virus detected in rodents worldwide, with increasing evidence of presence in environmental sources such as surface water, wastewater and bivalves. This systematic review compiles and analyzes all the published research on rat HEV contamination in these matrices, as well as its implications for human health. A comprehensive literature search was conducted using databases such as PubMed, Scopus, Web of Science, and Mendeley, including studies published up until 27 May 2025. Studies were included if they evaluated rat HEV in water- or food-related matrices using molecular detection. The risk of bias was not assessed. The certainty of evidence was not formally evaluated. Limitations include reliance on PCR methods without infectivity confirmation. Following PRISMA inclusion and exclusion criteria, eight eligible studies were analyzed. The results show high detection rates of rat HEV RNA in influent wastewater samples from several high-income European countries, namely Sweden, France, Italy, Spain and Portugal. Lower detection rates were found in effluent wastewater and surface waters in Sweden. In bivalve mollusks sampled in Brazil, rat HEV RNA was detected in 2.2% of samples. These findings show the widespread environmental presence of rat HEV, particularly in urban wastewater systems. While human infections by rat HEV have been documented, the true extent of rat HEV zoonotic potential remains unclear. Given the risks associated with this environmental rat HEV contamination, enhanced surveillance, standardized detection methods, and targeted monitoring programs in food production and water management systems are essential to mitigate potential public health threats. Establishing such programs will be crucial for understanding the impact of rat HEV on human health. Full article

Gastrointestinal (GI) parasitic infections in non-human primates are of growing concern due to their implications for both veterinary and public health. Long-tailed macaques (Macaca fascicularis), commonly found in peri-urban and temple environments in Southeast Asia, may act as reservoirs for zoonotic parasites, posing risks to humans and domestic animals. This study investigated the prevalence and species diversity of GI parasites in free-ranging macaques from four provinces in Northeast Thailand (Loei, Khon Kaen, Bueng Kan, and Sisaket). A cross-sectional study was conducted between April and May 2025. A total of 445 fecal samples were examined using two parasitological techniques: agar plate culture (APC) and the formalin–ethyl acetate concentration technique (FECT). The overall prevalence of parasitic infection was 86.5%, with Strongyloides sp. (65.2%) as the most prevalent helminth and Balantioides coli-like (29.5%) and Entamoeba histolytica-like (28.8%) as the predominant protozoa. Other parasites identified included helminths (Trichuris sp., Ascaris sp.) and protozoa (Blastocystis sp., Iodamoeba bütschlii, Entamoeba coli, and Chilomastix mesnili). Mixed infections were frequently observed, with both helminths and protozoa co-occurring in 37.3% of cases. The high infection rates and parasite diversity reflect substantial environmental contamination and sustained transmission cycles. These findings underscore the importance of integrated surveillance in wildlife populations and the need for One Health-based approaches to minimize zoonotic transmission risks at the human–animal–environment interface. Full article

Background: Antimicrobial resistance (AMR) is a growing global health concern, driven in part by the environmental release of antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs). Aquatic systems, particularly those exposed to urban, agricultural, and industrial activity, are recognized as hotspots for AMR evolution and transmission. In Chile, the Aconcagua River—subject to multiple anthropogenic pressures—offers a representative model for studying the environmental dimensions of AMR. Methods: Thirteen surface water samples were collected along the Aconcagua River basin in a single-day campaign to avoid temporal bias. Samples were filtered through 0.22 μm membranes and cultured on MacConkey agar, either unsupplemented or supplemented with ceftazidime (CAZ) or ciprofloxacin (CIP). Isolates were purified and identified using MALDI-TOF mass spectrometry. Antibiotic susceptibility was evaluated using the Kirby–Bauer disk diffusion method in accordance with CLSI guidelines. Carbapenemase activity was assessed using the Blue-Carba test, and PCR was employed for the detection of the bla_VIM, bla_KPC, bla_NDM, and bla_IMP genes. Results: A total of 104 bacterial morphotypes were isolated; 80 were identified at the species level, 5 were identified at the genus level, and 19 could not be taxonomically assigned using MALDI-TOF. Pseudomonas (40 isolates) and Aeromonas (25) were the predominant genera. No growth was observed on CIP plates, while 24 isolates were recovered from CAZ-supplemented media, 87.5% of which were resistant to aztreonam. Five isolates exhibited resistance to carbapenems; two tested positive for carbapenemase activity and carried the bla_VIM gene. Conclusions: Our results confirm the presence of clinically significant resistance mechanisms, including bla_VIM, in environmental Pseudomonas spp. from the Aconcagua River. These findings highlight the need for environmental AMR surveillance and reinforce the importance of adopting a One Health approach to antimicrobial stewardship and wastewater regulation. Full article

Smart surveillance cameras are increasingly employed for automated tasks such as event and anomaly detection within smart city infrastructures. However, the heterogeneity of deployment environments, ranging from densely populated urban intersections to quiet residential neighborhoods, renders the use of a single, universal model suboptimal. To address this, we propose the construction of a model zoo comprising models trained for diverse environmental contexts. We introduce an automated transferability assessment framework that identifies the most suitable model for a new deployment site. This task is particularly challenging in smart surveillance settings, where both source data and labeled target data are typically unavailable. Existing approaches often depend on pretrained embeddings or assumptions about model uncertainty, which may not hold reliably in real-world scenarios. In contrast, our method leverages embeddings generated by randomly initialized neural networks (RINNs) to construct task-agnostic reference embeddings without relying on pretraining. By comparing feature representations of the target data extracted using both pretrained models and RINNs, this method eliminates the need for labeled data. Structural similarity between embeddings is quantified using minibatch-Centered Kernel Alignment (CKA), enabling efficient and scalable model ranking. We evaluate our method on realistic surveillance datasets across multiple downstream tasks, including object tagging, anomaly detection, and event classification. Our embedding-level score achieves high correlations with ground-truth model rankings (relative to fine-tuned baselines), attaining Kendall’s $\tau$ values of 0.95, 0.94, and 0.89 on these tasks, respectively. These results demonstrate that our framework consistently selects the most transferable model, even when the specific downstream task or objective is unknown. This confirms the practicality of our approach as a robust, low-cost precursor to model adaptation or retraining. Full article

Vector-borne parasitic diseases represent a critical public health challenge in Africa, disproportionately impacting vulnerable populations and linking human, animal, and environmental health through the One Health framework. In this review, we explore the existing burden of these diseases, particularly those that are underreported. Climate change, urbanization, the introduction of alien species, and deforestation exacerbate the emergence and reemergence of arthropod-borne zoonotic parasitic diseases like malaria, leishmaniasis, and trypanosomiasis, complicating control and disease elimination efforts. Despite progress in managing certain diseases, gaps in surveillance and funding hinder effective responses, allowing many arthropod zoonotic parasitic infections to persist unnoticed. The increased interactions between humans and wildlife, driven by environmental changes, heighten the risk of spillover events. Leveraging comprehensive data on disease existence and distribution coupled with a One Health approach is essential for developing adaptive surveillance systems and sustainable control strategies. This review emphasizes the urgent need for interdisciplinary collaboration among medical professionals, veterinarians, ecologists, and policymakers to effectively address the challenges posed by vector-borne zoonotic parasitic diseases in Africa, ensuring improved health outcomes for both humans and animals. Full article

Chagas disease, caused by Trypanosoma cruzi, is a significant health concern in Latin America, with triatomine insects serving as its primary vectors. Among them, Triatoma barberi is an important yet understudied species in Querétaro, Mexico. This study employs ecological niche modeling (ENM) to predict the potential distribution of T. barberi in the region, using occurrence records and environmental variables. The MaxEnt algorithm was used to generate the model, which was validated through AUC and TSS metrics. Results indicate that temperature seasonality and altitude are key drivers of T. barberi distribution, with high-suitability areas found in semi-urban and peri-urban zones. Additionally, six new occurrence records were documented, suggesting a growing urban presence of this species. These findings highlight the need for enhanced vector surveillance and targeted control measures to reduce the risk of Chagas disease transmission. Full article

Rodents represent the most diverse order of mammals, comprising over 2200 species and nearly 42% of global mammalian biodiversity. They are major reservoirs of zoonotic pathogens, including viruses, bacteria, protozoa, and fungi, and are particularly effective at transmitting diseases, especially synanthropic species that live in close proximity to humans. As of April 2025, approximately 15,205 rodent-associated viruses have been identified across 32 viral families. Among these, key zoonotic agents belong to the Arenaviridae, Hantaviridae, Picornaviridae, Coronaviridae, and Poxviridae families. Due to their adaptability to both urban and rural environments, rodents serve as efficient vectors across diverse ecological landscapes. Environmental and anthropogenic factors, such as climate change, urbanization, deforestation, and emerging pathogens, are increasingly linked to rising outbreaks of rodent-borne diseases. This review synthesizes current knowledge on rodent-borne viral zoonoses, focusing on their taxonomy, biology, host associations, transmission dynamics, clinical impact, and public health significance. It underscores the critical need for early detection, effective surveillance, and integrated control strategies. A multidisciplinary approach, including enhanced vector control, improved environmental sanitation, and targeted public education, is essential for mitigating the growing threat of rodent-borne zoonoses to global health. Full article

Amidst global shifts in land use patterns due to urbanization, this study focuses on the rapid land use and land cover (LULC) changes in Midnapore City during the periods 2003–2014 and 2014–2024. The study employs Landsat 5 and 8 imagery with 30 m spatial resolution which were processed through Maximum Likelihood Classifier (MLC) algorithms. The results were attained through ArcGIS 10.2.2 and ERDAS IMAGINE 2014 software, with ground-truth validation using data from 117, 111, and 116 points for 2024, 2014, and 2003, respectively. For the validation, the kappa coefficient was calculated and achieved 87.3%, 88.1%, and 81.7% for 2024, 2014, and 2003, indicating substantial accuracy. Using statistical measures such as change matrix union, binary logistic regression, and correlation matrix analysis applied to classified LULC outputs and spatial drivers, the research highlights significant transformations in the region. The study reveals significant transformations, notably the conversion of 77% of forest areas and 5% of fallow land to built-up land. The increased rate of agricultural land conversion to built-up areas is evident after 2014, indicating rapid urban growth. These factors led to the reduction of LULC classes possessing substantial ecological value like forests and scrub lands which are becoming more accessible due to the increasing population. The results point out the drastic alteration of these developments and recommend a planning approach responsive to environmental needs for safeguarded ecological impacts. The research highlights the importance of reforestation, preservation of water bodies, and socio-economic surveillance in fostering urban management and sustainable development in Midnapore City. Full article

Conventional waste monitoring relies heavily on manual inspection, while most detection models are trained on close-range, simplified datasets, limiting their applicability for real-world surveillance. Even with surveillance imagery, challenges such as cluttered backgrounds, scale variation, and small object sizes often lead to missed detections and reduced robustness. To address these challenges, this study introduces EcoDetect-YOLOv2, a lightweight and high-efficiency object detection model developed using the Intricate Environment Waste Exposure Detection (IEWED) dataset. Building upon the YOLOv8s architecture, EcoDetect-YOLOv2 incorporates a small object detection P2 detection layer to enhance sensitivity to small objects. The integration of an efficient multi-scale attention (EMA) mechanism prior to the P2 head further improves the model’s capacity to detect small-scale targets, while bolstering robustness against cluttered backgrounds and environmental noise, as well as generalizability across scale variations. In the feature fusion stage, a Dynamic Upsampling Module (Dysample) replaces traditional nearest-neighbor upsampling to yield higher-quality feature maps, thereby facilitating improved discrimination of overlapping and degraded waste particles. To reduce computational overhead and inference latency without sacrificing detection accuracy, Ghost Convolution (GhostConv) replaces conventional convolution layers within the neck. Based on this, a GhostResBottleneck structure is proposed, along with a novel ResGhostCSP module—designed via a one-shot aggregation strategy—to replace the original C2f module. Experiments conducted on the IEWED dataset, which features multi-object, multi-class, and highly complex real-world scenes, demonstrate that EcoDetect-YOLOv2 outperforms the baseline YOLOv8s by 1.0%, 4.6%, 4.8%, and 3.1% in precision, recall, mAP₅₀, and mAP_50:95, respectively, while reducing the parameter count by 19.3%. These results highlight the model’s effectiveness in real-time, multi-object waste detection, providing a scalable and efficient tool for automated urban and digital governance. Full article

Wireless sensor networks (WSNs) have emerged to address unique challenges in urban environments. This survey dives into the challenges faced in urban areas and explores how WSN applications can help overcome these obstacles. The diverse applications of WSNs in urban settings discussed in this paper include gas monitoring, traffic optimization, healthcare, disaster response, and security surveillance. The innovative research is considered in an urban environment, where WSNs such as energy efficiency, throughput, and scalability are deployed. Every application scenario is distinct and examined in details within this paper. In particular, smart cities represent a major domain where WSNs are increasingly integrated to enhance urban living through intelligent infrastructure. This paper emphasizes how WSNs are pivotal in realizing smart cities by enabling real-time data collection, analysis, and communication among interconnected systems. Applications such as smart transportation systems, automated waste management, smart grids, and environmental monitoring are discussed as key components of smart city ecosystems. The synergy between WSNs and smart city technologies highlights the potential to significantly improve the quality of life, resource management, and operational efficiency in modern cities. This survey specifies existing work objectives with results and limitations. The aim is to develop a methodology for evaluating the quality of performance analysis. Various performance metrics are discussed in existing research to determine the influence of real-time applications on energy consumption, network lifetime, end-to-end delay, efficiency, routing overhead, throughput, computation cost, computational overhead, reliability, loss rate, and execution time. The observed outcomes are that the proposed method achieves a higher 16% accuracy, 36% network lifetime, 20% efficiency, and 42% throughput. Additionally, the proposed method obtains 36%, 30%, 46%, 35%, and 32% reduction in energy consumption, computation cost, execution time, error rate, and computational overhead, respectively, compared to conventional methods. Full article

While open-set recognition algorithms have been extensively explored in computer vision, their application to environmental sound analysis remains understudied. To address this gap, this study investigates how to effectively recognize unknown sound categories in real-world environments by proposing a novel Kernel Density Estimation-based Generative Adversarial Network (KDE-GAN) for data augmentation combined with Attractor–Reciprocal Point Learning for open-set classification. Specifically, our approach addresses three key challenges: (1) How to generate boundary-aware synthetic samples for robust open-set training: A closed-set classifier’s pre-logit layer outputs are fed into the KDE-GAN, which synthesizes samples mapped to the logit layer using the classifier’s original weights. Kernel Density Estimation then enforces Density Loss and Offset Loss to ensure these samples align with class boundaries. (2) How to optimize feature space organization: The closed-set classifier is constrained by an Attractor–Reciprocal Point joint loss, maintaining intra-class compactness while pushing unknown samples toward low-density regions. (3) How to evaluate performance in highly open scenarios: We validate the method using UrbanSound8K, AudioEventDataset, and TUT Acoustic Scenes 2017 as closed sets, with ESC-50 categories as open-set samples, achieving AUROC/OSCR scores of 0.9251/0.8743, 0.7921/0.7135, and 0.8209/0.6262, respectively. The findings demonstrate the potential of this framework to enhance environmental sound monitoring systems, particularly in applications requiring adaptability to unseen acoustic events (e.g., urban noise surveillance or wildlife monitoring). Full article

Tennessee’s ecological diversity, spanning forests, farmland, and urban areas, provides an ideal foundation for applying the One Health approach, which integrates human, animal, and environmental health. This review examines Tennessee’s current One Health landscape, highlighting active initiatives, ongoing challenges, and future directions. Key efforts involve workforce development, disease surveillance, outbreak response, environmental conservation, and public education, led by a coalition of state agencies, universities, and the Tennessee One Health Committee. These programs promote cross-sector collaboration to address issues such as zoonotic diseases, climate change, land use shifts, and environmental contaminants. Notably, climate-driven changes, including rising temperatures and altered species distributions, pose increasing threats to health and ecological stability. Tennessee has responded with targeted monitoring programs and climate partnerships. Education is also a priority, with the growing integration of One Health into K–12 and higher education to build a transdisciplinary workforce. However, the state faces barriers, including limited funding for the One Health workforce, undefined workforce roles, and informal inter-agency data sharing. Despite these obstacles, Tennessee’s successful responses to outbreaks like avian influenza and rabies demonstrate the power of coordinated action. To strengthen its One Health strategy, the state must expand funding, formalize roles, improve data systems, and enhance biodiversity and climate resilience efforts positioning itself as a national leader in interdisciplinary collaborative solutions. Full article