Search Results (473)

Microplastics (MPs)—synthetic polymer particles less than 5 mm in size—have emerged as ubiquitous contaminants in terrestrial and aquatic environments worldwide, raising concerns about their ecological and human health impacts. While research has predominantly focused on urban and marine settings, evidence shows that rural ecosystems are also affected, challenging assumptions of pristine conditions outside cities and coasts. This review synthesizes current knowledge on the presence, pathways, and impacts of MPs in rural environments, highlighting complex contamination dynamics driven by both local sources (agricultural plastics, domestic waste, rural wastewater, and road runoff) and regional processes (atmospheric deposition, hydrological transport, and sediment transfer). Key findings highlight that rural lakes, streams, soils, and groundwater systems are active sinks and secondary sources of diverse MPs, predominantly polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) in fibrous and fragmented forms. These particles vary in size, density, and color, influencing their transport, persistence, and bioavailability. Ecological effects include bioaccumulation in freshwater species, soil degradation, and potential food chain transfer, while human exposure risks stem from contaminated groundwater, air, and locally produced food. Despite these growing threats, rural systems remain underrepresented in monitoring and policy frameworks. The article calls for context-specific mitigation strategies, enhanced wastewater treatment, rural waste management reforms, and integrated microplastics surveillance across environmental compartments. Full article

Microplastic pollution is a growing global environmental and public health concern, driven by the increasing production and use of plastics. Due to their ubiquitous presence in the environment, humans and animals may be exposed to micro- and nanoplastics via several possible routes. For micro- and nanoplastics, the development of standardized and validated methods remains an important area of progress to support human health risk assessments. In order to monitor micro/nanoplastics’ occurrence in organisms and the environment, it is necessary to develop accurate and reliable methods to quantify and characterize micro/nanoplastics from various biological and environmental matrices. In this study, an analytical, multi-platform approach was established to characterize and quantify polystyrene nanoplastics in biological samples through a combination of sample pre-concentration, asymmetric flow field-flow fractionation, ultraviolet–visible light, dynamic light scattering detectors and pyrolysis–gas chromatography–mass spectroscopy. Several digestion methods on various rodent tissues were tested and modified, and these led to the development of tissue-specific protocols to maximize yield. These digestion protocols were then combined with a new method of concentrating and retaining plastics to prevent the loss of submicron particles. For identification and quantification, known amounts of polystyrene nanoplastics were spiked into rodent tissues (intestine, kidney and liver). This was followed up by a mouse in vivo study consisting of a single dose of PS-NPs, followed by tissue collection, digestion and analysis. Polystyrene particles were detected in the liver and kidney, but not reliably in the intestinal tissues. Full article

Maize, as the global highest-yield grain crop, can impact social stability and security based on its annual yield. Given that maize viruses have caused up to 91% yield reductions, investigating maize virus diseases is of the utmost importance. In July 2020, a suspected maize yellow mosaic virus (MaYMV) was discovered in a maize field, and a MaYMV detection protocol was established. The MaYMV isolate MA70, discovered in a maize plant from Wuri District, Taiwan, in November 2022, was shown to infect both maize 42 days post-inoculation (dpi) and wheat (35 dpi), causing mosaic symptoms, through aphid transmission with corn leaf aphid (Rhopalosiphum maidis). To determine the whole genome sequence of MA70, a 5642 bp sequence was obtained using RT-PCR and Sanger sequencing. Sequencing results indicated a 94.8–96.8% nucleotide sequence similarity with 54 MaYMV isolates from GenBank and with amino acid sequence identities exceeding 90% for all MaYMV proteins. Phylogenetic analysis showed the relationship of MA70 is closest to the Chinese isolate. The nucleotide sequence identity was lower among isolates of more distinct geographical clusters. Between October 2023 and January 2024, survey results indicated that MaYMV prevalence in corn fields across six areas in Taichung reached 17.5% (130/743 plants) and was present in all the sampled fields. MaYMV was present in all sampled fields affirming its ubiquitous presence. This study establishes the first documented case of MaYMV in Taiwan; however, survey findings hint at a potential pre-existing presence in Taiwanese maize fields. Therefore, this research also develops a practical diagnostic tool for field monitoring of MaYMV prevalence, which is crucial for informing future disease management strategies, including the critical need for cross-strait between Taiwan and China collaboration on viral disease surveillance. Full article

The widespread use of anticancer drugs (ACDs) in human therapies determines the occurrence of these potent cytotoxic chemicals into aquatic ecosystems. Nowadays, ACDs are ubiquitous contaminants in wastewater effluents and freshwater compartments, raising urgent questions about their environmental impact. Designed to disrupt cellular proliferation, these compounds are inherently bioactive and can exert toxic effects on non-target organisms even at trace concentrations. Conventional fate and toxicity tests provide important initial data but are limited in ecological realism, often focusing on single-specie and single-endpoint under controlled conditions and overlooking complex interactions, trophic dynamics, and long-term chronic exposures. Knowledge of all these aspects is needed for proper monitoring, assessment, and regulation of ACDs. Simulated ecosystem experiments, such as mesocosms, provide intermediate-scale, semi-controlled platforms for investigating real-world exposure scenarios, assessing ACD fate, and identifying both direct and indirect ecological effects. They offer distinct advantages for evaluating the chronic toxicity of persistent pollutants by enabling realistic long-term contamination simulations and supporting the simultaneous collection of comprehensive hazard and exposure endpoints. This perspective underscores the growing concern surrounding the contamination of ACDs, examines the limitations of traditional assessment approaches, and advocates for mesocosm-based studies as a critical bridge between laboratory research and ecosystem-level understanding. By integrating mesocosm experiments into environmental fate and risk evaluation, we can better predict the behavior and ecological consequences of anticancer pharmaceuticals, guiding strategies to mitigate their impact on aquatic life. Full article

Microplastics (MPs) are ubiquitous coastal contaminants that can induce oxidative stress, detoxification responses and inflammation in marine species. We evaluated MP occurrence and associated physiological responses in the digestive tract of the peacock wrasse Symphodus tinca (N = 28) from the northeastern coast of Ibiza (Balearic Islands, Spain). MPs occurred in 60.7% of the fish (58 items in total; mean 2.1 ± 0.5 items·fish⁻¹), dominated by fibres (75.9%). Polyester (38.1%) and polypropylene (23.8%) were the most frequent polymers in the subset of MPs analysed. Fish were grouped by median MP count (<2 vs. ≥2), and statistical differences and correlations were assessed. Individuals with ≥2 MPs showed significantly elevated activities of antioxidant enzymes (catalase, CAT; superoxide dismutase, SOD), the phase-II detoxification enzyme glutathione S-transferase (GST), and the pro-inflammatory enzyme myeloperoxidase (MPO). Production of reactive oxygen species (ROS) and oxidative-damage biomarkers, malondialdehyde (MDA) and protein carbonyls tended to be higher in the high-MP group, but differences were not statistically significant. MP exposure correlated positively with all biomarkers except protein carbonyls. In conclusion, higher MP loads in S. tinca are associated with activation of antioxidant, detoxification and inflammatory pathways, without clear evidence of widespread oxidative damage under the sampled conditions. These physiological responses suggest potential impacts on individual fitness that may signal early ecological effects in coastal fish populations, highlighting their value as early-warning indicators in coastal monitoring and environmental management. Full article

Hamstring-strain injuries (HSIs) are the most prevalent time-loss injuries in professional football. While player monitoring of muscular strength is ubiquitous in professional football, the utility of in-season testing for predicting HSI in non-league football (NLF) settings is unclear. This study aimed to investigate if short-term, in-season changes in eccentric hamstring strength are associated with HSI risk and compare the predictive performance to a baseline model. This was a single-season prospective cohort study (36 weeks) in 20 male professional NLF players (nine HSI events). Eccentric hamstring strength was measured twice weekly during Nordic hamstring exercise (NHE) using a NordBord device. Cox proportional hazard models (Andersen–Gill) evaluated the association of HSI with bilateral peak force and inter-limb asymmetry as time-varying and baseline predictors. Nine HSIs occurred (29% of all time-loss injuries; n = 31). The predictive analysis revealed that the baseline model with hazard ratio (HR) of 0.20 (95% CI: 0.09–0.46; C-index = 0.824) outperformed the time-varying model (HR 0.29; 95% CI: 0.15–0.56; C-index = 0.776), with higher bilateral peak force protective across both models. Conversely, inter-limb asymmetry showed no association with HSI risk (HR 1.10; 95% CI: 0.95–1.27; C-index = 0.527). A key related finding was that while single test inter-limb asymmetry measurements were unreliable, stability across the season was good (ICC(1,k) = 0.895). In this cohort, a greater bilateral peak force was protective against HSI, with baseline testing more effective than twice-weekly in-season testing. Inter-limb asymmetry did not predict HSI, and the utility of its isolated use remains unclear despite the stability of players’ season-long profiles. These exploratory findings require confirmation in larger cohorts. Full article

Torque teno sus virus 1 (TTSuV1) is a ubiquitous, non-pathogenic virus in both wild and domestic pigs and has potential value as a molecular marker for monitoring cross-population viral transmission and biosecurity breaches. In this study, we integrated genetic data with Bayesian transmission inference to examine the dynamics of TTSuV1 transmission within and between wild and farmed pigs in Arkansas. Transmission steps, defined as the number of transmission events linking two hosts, were used to identify barriers to transmission, such as biosecurity measures or geographic separation. TTSuV1 was highly prevalent in farmed pigs (96.59%) and invasive wild pigs (47.76%), with sequences from both groups distributed across four major subtypes. Transmission step analyses revealed that wild–farmed pig pairs were consistently separated by numerous transmission steps (>10 steps), indicating strong isolation and little evidence of recent spillover. In contrast, few transmission steps (≤5 steps) were common within populations, reflecting localized circulation. Our findings support the use of TTSuV1 as a molecular marker to characterize cross-population viral movement and spillover, as well as to delineate population structure in swine systems. Practically, this approach offers a novel framework for using viral genomics to detect cross-population spillover events and monitor biosecurity breaches in swine production systems. Full article

This paper presents a ubiquitous, blockchain-based system designed to improve transparency, traceability and trust in supply chains involving autonomous vehicles (AVs). The framework integrates Internet of Things (IoT) sensors, radio-frequency identification (RFID) and QR identifiers, global positioning system (GPS) tracking, and mobile communications with smart contracts implemented on the Ethereum 2.0 blockchain. The main contributions are as follows: (1) an architecture enabling real-time monitoring and automated verification of logistics transactions; (2) a proof of concept integrating blockchain, the IoT and Android-based OBUs; and (3) a quantitative analysis of gas and smart contract execution costs. Experimental tests show gas consumption ranging from 21,000 to 5,000,000 units and transaction costs ranging from 0.0001 to 0.0033 ETH, confirming the system’s technical feasibility and cost-efficiency. As well as cost and efficiency, the process improved transparency, real-time traceability and decentralized verification, confirming the system’s efficacy for supply chains involving autonomous vehicles. Full article

Three-dimensional (3D) reconstruction is increasingly being adopted in construction site management. While most existing studies rely on auxiliary equipment such as LiDAR and depth cameras, monocular depth estimation offers broader applicability under typical site conditions, yet it has received limited attention due to the inherent scale ambiguity in monocular vision. To address this limitation, this study proposes a safety helmet-based scale recovery framework that enables low-cost, monocular 3D reconstruction for construction site monitoring. The method utilizes safety helmets as exemplary scale carriers due to their standardized dimensions and frequent appearance in construction scenes. A Standard Template Library (STL) comprising multi-angle safety helmet masks and dimensional information is established and linked to site imagery through template matching. Following three-dimensional scale recovery, multi-frame fusion is applied to optimize the scale factors. Experimental results on multiple real construction videos demonstrate that the proposed method achieves high reconstruction accuracy, with a mean relative error below 10% and outliers not exceeding 5%, across diverse construction environments without site-specific calibration. This work aims to contribute to the practical application of monocular vision in engineering management by leveraging ubiquitous site objects as metrological references. Full article

The ubiquitous presence of microplastics across environmental compartments presents a formidable ecotoxicological and risk assessment challenge, fundamentally complicated by the link between microplastic morphology and differential toxicological outcomes. Current analytical methods face a significant measurement bottleneck, hindering the precise, high-throughput characterization needed for robust mechanistic and exposure studies. To address this, we introduce MNv4-Conv-M-fpn, a novel deep learning model specifically engineered for multi-class microplastic segmentation and morphological characterization from microscopic images. This model is designed to provide the toxicologically-relevant granularity required for rigorous risk assessment, segmenting images into six classes: five distinct microplastic categories (fiber, fragment, sphere, foam, and film) and the background. By incorporating advanced architectural features—including transfer learning, a Feature Pyramid Network, and a Feature Fusion Module—our approach achieves high accuracy, computational efficiency, and near real-time inference speed. Comprehensive validation using a diverse dataset demonstrates that MNv4-Conv-M-fpn outperforms existing segmentation methods while maintaining low computational load. This makes the model well-suited for high-throughput deployment in environmental laboratories and resource-constrained monitoring efforts. This approach offers a valuable tool for environmental monitoring, enabling more precise and scalable analysis of microplastic pollution in various ecosystems. Full article

The use of floating wetlands has been receiving increased attention as a minimally invasive method for oil spill remediation, but the species of vegetation incorporated in floating wetlands may influence the success of oil degradation. Therefore, a freshwater microcosm experiment was conducted at the IISD Experimental Lakes Area, Canada to assess the potential of common wetland plants Typha sp., Carex utriculata, and C. lasiocarpa, to remove phenanthrene, a polycyclic aromatic hydrocarbon ubiquitously found at oil spill sites. Triplicate microcosms containing 3L of lake water were established with either Typha sp., Carex utriculata, or C. lasiocarpa and then treated with nominal concentration of 1 mg/L phenanthrene and monitored over 21 days. Two types of reference microcosms were also included: one set with the same plant allocations but not treated with phenanthrene and another with water only and no plants or phenanthrene. Phenanthrene declined by over 89.30% in all microcosms that received the compound, but the decline was more rapid in microcosms that included Typha sp. and C. lasiocarpa, than those with C. utriculate or no plants. Declining phenanthrene concentrations in microcosms without plants may have resulted from biofilm stimulation. Specific conductivity and pH were influenced by plant type but not phenanthrene, while dissolved oxygen was influenced by both. There was no influence of phenanthrene on plant growth rates or root biofilm bioactivity, measured by adenosine triphosphate or oxygen consumption. Results indicate there may be plant-specific factors influencing remediation success which should be explored in future research. Full article

This study presents the development and validation of an unobtrusive automatic sleep quality assessment index (ASQI) designed for elderly individuals. The proposed method utilizes features such as sleep duration, sleep latency, and sleep efficiency, calculated from physiological data—heart rate, respiratory rate, body movements, and bed-exit behavior—captured by a non-contact bed sensor system installed in home environments. Based on these parameters, a six-component sleep quality index was constructed to objectively evaluate nightly sleep. To assess the reliability and validity of ASQI, sleep data were collected from eleven elderly participants over a one-year period. Results showed strong test–retest reliability ($r=0.91$, $p<0.001$) and moderate correlation with the widely used Pittsburgh Sleep Quality Index (PSQI) ($r=0.52$, $p<0.05$). Furthermore, ASQI successfully differentiated between self-reported good and poor sleepers, achieving a classification accuracy of 85.7%, with a sensitivity of 66.7% and specificity of 93.3%. These findings demonstrate that the ASQI system is a practical and scalable tool for continuous, home-based sleep monitoring in older populations. Its non-intrusive design and objective scoring make it well-suited for personalized sleep management and early detection of sleep-related issues. This work contributes to the growing field of unobtrusive health monitoring and highlights the potential of sensor-based systems in elderly care. Full article

Mycotoxins are ubiquitous and unavoidable contaminants in food and feed, posing significant health risks through toxicity syndromes collectively referred to as mycotoxicoses. With climate change enhancing the conditions favorable for fungal growth and mycotoxin production, concerns over food and feed safety are increasingly pressing. Although regulatory frameworks have been established to monitor and limit the exposure, effective mitigation remains a challenge. This paper provides a comprehensive overview of recent advances in the prevention, detection, and control of mycotoxins, with particular emphasis on innovative strategies such as plant-derived bioactives, nanotechnology-based systems, genetic engineering approaches, antibody-mediated technology, and emerging non-thermal processing methods. Full article

Leveraging ubiquitous wireless signals for environmental sensing provides a highly promising pathway toward constructing low-cost and high-density flood monitoring systems. However, in real-world flood scenarios, the wireless channel is simultaneously affected by rainfall-induced signal attenuation and complex multipath effects caused by surface runoff (water accumulation). These two physical phenomena become intertwined in the received signals, resulting in severe feature ambiguity. This not only greatly limits the accuracy of environmental sensing but also hinders communication systems from performing effective channel compensation. How to disentangle these combined effects from a single wireless link represents a fundamental scientific challenge for achieving high-precision wireless environmental sensing and ensuring communication reliability under harsh conditions. To address this challenge, we propose a novel signal processing framework that aims to effectively decouple the effects of rainfall and surface runoff from Channel State Information (CSI) collected using commercial Wi-Fi devices. The core idea of our method lies in first constructing a two-dimensional CSI spatiotemporal spectrogram from continuously captured multicarrier CSI data. This spectrogram enables high-resolution visualization of the unique “fingerprints” of different physical effects—rainfall manifests as smooth background attenuation, whereas surface runoff appears as sparse high-frequency textures. Building upon this representation, we design and implement a Dual-Decoder Convolutional Autoencoder deep learning model. The model employs a shared encoder to learn the mixed CSI features, while two distinct decoder branches are responsible for reconstructing the global background component attributed to rainfall and the local texture component associated with surface runoff, respectively. Based on the decoupled signal components, we achieve simultaneous and highly accurate estimation of rainfall intensity (mean absolute error below 1.5 mm/h) and surface water accumulation (detection accuracy of 98%). Furthermore, when the decoupled and refined channel estimates are applied to a communication receiver for channel equalization, the Bit Error Rate (BER) is reduced by more than one order of magnitude compared to conventional equalization methods. Full article

Antibiotic use in aquaculture has become widespread to sustain production and control bacterial diseases, but it poses significant ecological and human health risks due to residue accumulation and resistance development. This study investigated the occurrence, dominance, and combined use of sulfonamide and fluoroquinolone antibiotics in freshwater fish aquaculture ponds around Wuxi, China. Here, the term aquaculture refers specifically to the controlled farming of freshwater fish species such as carp and crucian carp in managed pond systems. A total of 80 water samples (collected exclusively from pond waters) were obtained from 40 ponds during the high intensity rearing and harvest stage of fish. Residues of enrofloxacin and sulfonamide antibiotics were analyzed using a validated LC–MS/MS method with detection limits in the low nanogram-per-liter range. Results revealed that antibiotics were ubiquitous in pond waters, with enrofloxacin emerging as the dominant compound in August, reaching concentrations of up to 2.36 µg/L. By October, sulfonamides, particularly sulfamethoxazole and sulfadiazine, became more prevalent, with a maximum sulfadiazine concentration exceeding 4 µg/L. Multivariate analyses demonstrated a clear seasonal shift in antibiotic profiles, while correlation analyses indicated limited combined use in summer but notable co-occurrence of sulfonamides in autumn. These findings underscore that antibiotic application patterns in aquaculture are strongly linked to production stages, with potential consequences for environmental safety, resistance development, and food security. Effective monitoring, stricter regulation, and alternative disease management strategies are urgently required to mitigate risks and promote sustainable aquaculture practices. Full article