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Analysis of river water quality in Rourkela Odisha using multiple indices to inform sustainable water management

Genetic diversity of alpha and betacoronaviruses in cave and temple-roosting bats in Vientiane Province, Lao PDR

The emergence of MERS-CoV, SARS-CoV-1, and SARS-CoV-2 highlights the significant public health and economic threats posed by coronaviruses. In Lao PDR, SARS-CoV-2-related bat coronaviruses capable of binding to human ACE2 receptors have been found in northern regions, but little is known about coronavirus diversity in anthropized environments like temples. This study investigated coronavirus circulation, diversity, and prevalence in bats from caves and temples in Vientiane Province, Lao PDR. A total of 648 guano samples (505 Chaerephon plicatus , 100 Hipposideros spp., 43 Taphozous spp.) were collected between December 2022 and June 2023 and screened using pan-coronavirus RT-PCR approach. The overall positivity rate was 17.28%, significantly higher in caves (18.8%) than temples (4.41%) (p = 0.003). C. plicatus showed the highest positivity rate (21.38%), followed by Hipposideros spp. 4%, while Taphozous spp. were negative. Phylogenetic analysis revealed diverse coronavirus lineages within Alphacoronavirus (80.4%) and Betacoronavirus (19.6%) genera. Although none were closely related to known human pathogens, coronaviruses of Decacovirus genus related to Chinese bat viruses and Pedacovirus genus similar to porcine epidemic diarrhea virus (PEDV) were detected. Unclassified betacoronaviruses identified were also related to viruses from C. plicatus in Thailand. This study provides valuable insights into coronavirus circulation in both natural and anthropized environments. The detection of PEDV-like viruses underlines the need for continued surveillance at the human-bat interface, where activities like guano harvesting and temple visits increase contacts. Further genomic and functional studies would enhance our understanding of their evolutionary relationships and potential for further cross-species transmission.

Improved black-winged kite optimization algorithm with multi-strategy hybrid and its application

Placing anurans in water can improve photo-based individual identification

Identification of individuals across time and space is required for investigating numerous evolutionary-ecology and conservation-related questions, and photo-based identification is commonly used for a broad taxonomic range. Success of available photo-matching software may greatly depend on image quality as well as the focal body parts. We tested the hypotheses that individual identification by colour patterns can be facilitated by taking into account the natural medium surrounding the animals and the natural body posture they tend to take. We optimised photography methods enabling individual identification by whole-body assessment of agile frogs (Rana dalmatina) , and compared the reliability of those photography methods for computer-assisted identification in the HotSpotter software as well as for observers operating it. We found that photographing either hand-restrained frogs with towel-dried skin, or frogs moving freely in clean water enabled comparison of the dorsal surface of the whole body including the hind legs, and HotSpotter identified matching images at rates similar to those anuran studies that reported high success before. Specifically, the true match ranked in the top 10 for >92% of photographs taken with the above methods. By contrast, submerging hand-restrained frogs in water significantly improved identification: images of the same individual were always ranked as the most likely match. We attribute this outstanding performance to the combination of advantageous effects of in-water light refractions that improve the visibility of pigment patterns, and uniform body postures facilitating comparison across individuals. Observers in general successfully identified matching images and ruled out non-matching ones, but some mistakes were recorded when images featured freely moving frogs. The photography methods described in this study should be easily adapted to most frog and toad species for reliable individual identification. Our study highlights that taking into account features of the natural environment of the studied species can improve individual identification by photographs.

Chemometric and metabolomic profiles of global tea brands sold in Saudi Arabia

Numerical analysis of low-frequency electromagnetic field effects from three-phase transformer on coronary stents and cardiac tissues

The widespread clinical adoption of novel magnesium alloy coronary stents, combined with increasing densification of urban power transmission infrastructure, highlighted a significant research gap regarding the effects of power-frequency electromagnetic fields (EMFs) on these implants. This study employed field-circuit coupling numerical methods to simulate electromagnetic field exposure in simulated patients with implanted coronary stent positioned at various locations near a 200kVA three-phase transformer. The analysis focused on the distribution patterns of induced electromagnetic fields within both cardiac tissues and the stent, as well as the resultant Ampere forces acting on the stent. The results showed that the simulated patient directly beneath the three-phase transformer was exposed to the maximum electromagnetic radiation, but the magnetic flux density ( B max ) and the induced electric field intensity ( E max ) of the cardiac tissue were lower than the public exposure limits of the International Commission on Non-Ionizing Radiation Protection (ICNIRP). The B max and E max of the stent at the same position were 1.245 μ T and 5.086 × 10 −4 mV/m, respectively. The maximum Ampere force density of the stent in the Y- axis (perpendicular to the coronal plane) was 3.714 × 10 −6 N/m 3 . The above findings indicate that, under the conditions of this simulation, a 200 kVA power transformer exerts minimal interference on the magnesium alloy stent and cardiac tissues. The magnetic flux density and induced electric field in the heart tissues, as well as the Ampere force acting on the magnesium alloy stent, all remain within established safety limits.

Response to multigenerational graphene oxide exposure in acheta domesticus strains selected for longevity

Abstract The development of new nanotechnologies and their use in everyday life always carries the risk of environmental hazards and consequences for human health. Among them, graphene oxide (GO) is a promising material. Due to its excellent physicochemical properties, GO is attractive not only for industrial applications but also in medicine. There is still a lack of sufficient reports on the long-term effects of GO on organisms, including studies of a multigenerational nature. We investigated the health status of two strains of Acheta domesticus : the wild type and the long-lived. The strains were exposed to GO for five generations and a sixth recovery generation. We investigated parameters that may indirectly explain the mechanisms involved in transmitting the informational pattern of the stress response to subsequent generations: DNA stability, mitochondrial potential, apoptosis, and autophagy. GO intoxication induced multilevel cellular responses in five subsequent generations. GO cessation in recovery F5 acted as a new stressor. Across five generations, variation in the response to GO was observed. GO is most likely responsible for changes that persist over generations. We believe that epigenetic inheritance is a likely mechanism underlying the multigenerational adaptation observed in GO-exposed insects, and future research should aim to elucidate this phenomenon in more detail.

Disparate estimates of intrinsic productivity for Antarctic krill across small spatial scales under a rapidly changing ocean

Understanding the spatio-temporal dynamics of Antarctic krill ( Euphausia superba ) productivity along the Western Antarctic Peninsula (WAP) requires the application of robust analytical approaches. Both design-based and model-based methodologies have been employed to address this challenge. The Spawning Potential Ratio (SPR) is a indicator that provides valuable insights about population productivity. In this study, we analyzed the spatial and temporal variability of the SPR for krill by modeling Length-Based Spawning Potential Ratio (LBSPR) with 20 years of fishery-dependent length composition data. Results showed spatial and temporal heterogeneity among five fishing strata in the WAP, where SPR in Gerlache Strait stratum was consistently lower than the 20% SPR reference point, compared with Elephant, Bransfield Strait, Southwest and Joinville Island strata. Moreover, we demonstrate the sensitivity of LBSPR to changes in growth parameters, such as k and L inf , which are influenced by environmental variables like chlorophyll. Our findings underscore the value of incorporating environmental variability into stock assessment models, such as those based on SPR, to accurately assess krill stock conditions. Given the apparent spatial heterogeneity in intrinsic productivity identified through our SPR estimates, we propose using this approach to establish a management procedure based on a control rule for each stratum. This method adjusts the allocation of catch limits in line with the new management strategy of the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). By integrating knowledge about spatial krill dynamics and its intrinsic productivity, advice can be recommended to promote the sustainable management of krill populations in Subarea 48.1.

Mitigating shoulder spoofing vulnerabilities in mobile payment systems: a security framework

Pathogenic bacterial species and the microbiome of cat fleas (Ctenocephalides felis) inhabiting flea-infested homes

Background Ctenocephalides felis is a common ectoparasite of dogs and cats and can transmit a variety of pathogens including Bartonella and Rickettsia species. These bacteria, along with the known endosymbiont Wolbachia , are well-documented members of the C. felis microbiome, but species-level information is limited. Additionally, little is known about the variation in the C. felis microbiome in fleas from different sources and when different sequencing methods are applied to the same samples. Objective This study aimed to characterize the flea microbiome using both short-read (V3/V4) and long-read (full-length) 16S rRNA gene sequencing, determine whether long-read sequencing improves species-level identification especially in known pathogenic genera, and evaluate differences in microbial composition between fleas collected from cats, dogs, and environmental traps. Methods Fleas were collected from cats, dogs, and traps in flea-infested homes in Florida, pooled by source, and sequenced using short- (V3/V4) and long-read (full-length) 16S rRNA gene sequencing. Microbial prevalence and abundance were compared across sequencing approaches. Community composition was evaluated for differences between sources and houses. Candidate members of the flea microbiome were identified based on a combination of prevalence, abundance, and statistical signatures of potential contaminant origin. For Rickettsia and Bartonella , species-level taxonomic assignments were refined using a phylogenetic approach. Results Wolbachia , Rickettsia , and Bartonella were the most prevalent and abundant taxa. Spiroplasma was identified as a fourth core member of the flea microbiome. Long-read sequencing enabled better, but not perfect, species-level classification of Bartonella and Rickettsia compared to short-read sequencing. Important relationships between specific ASVs and flea sources were identified, for example fleas from cats harbored higher abundances of B. clarridgeiae and B. henselae than fleas from traps.

Investigation of the relationship between carotid intima-media thickness and angiopoietin-like factor 3 levels in metabolic dysfunction-associated steatotic liver disease

Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction

Drug-drug interactions (DDI) represent a significant clinical challenge in modern healthcare, contributing to over 125,000 deaths annually in the United States alone. Current computational approaches face substantial limitations in capturing long-range molecular dependencies and generalizing to novel drug combinations. Traditional Graph Neural Networks (GNNs) suffer from over-smoothing and locality bias, while sequence-based methods fail to adequately represent three-dimensional molecular structures. To address these limitations, we propose Graph Former-CL, a novel deep learning framework that synergistically combines Graph Transformer architecture with contrastive learning for DDI prediction. Our approach features four key innovations: (1) a hierarchical Graph Transformer with position-aware multi- head self-attention to capture both local and global molecular patterns, (2) a domain-specific contrastive learning module with molecular augmentation strategies, (3) a cross-modal fusion mechanism integrating SMILES sequences with graph representations, and (4) an adaptive pooling strategy for multi-scale molecular representation. Comprehensive evaluation on four benchmark datasets demonstrates superior performance, with Graph Former-CL achieving 98.2% accuracy on DrugBank and 89.4% on TWOSIDES, both representing statistically significant improvements (p < 0.001) over state-of-the-art methods. Notably, the framework achieves 85.6% accuracy for novel drugs in inductive settings, demonstrating robust generalization capabilities essential for real-world clinical applications.

Complex network analysis of mobility dynamics in Seoul during the COVID-19 pandemic 2020–2022

In vitro assessment of antibacterial and biocompatibility properties of a poly-ε-lysine and hyaluronic acid contact-killing coating to prevent prosthetic joint infection

Prosthetic joint infection (PJI) is a major adverse outcome following total hip and knee arthroplasties. With the rise of antimicrobial resistance, there is a risk of therapeutic insufficiency in treating PJI. This study determined the potential of a poly-ε-lysine and hyaluronic acid (PEL-10/HA144-1) contact-killing coating as a promising prevention of bacterial attachment. A broader development perspective was integrated through a Safe-by-Design approach by adding relevant in vitro tests for implant-host interactions. The PEL-10/HA144-1 coating was deposited on titanium alloy Ti6Al4V (Ti) and ultra-high-molecular-weight-polyethylene (UHMWPE). A combination of the ISO 22196, ASTM E2180-18, and JIS Z 2801 testing standards using Staphylococcus aureus and Escherichia coli demonstrated a bactericidal effect of the coating, up to a 5-log reduction compared to uncoated samples. A bacterial adhesion test showed a decrease in adherent bacteria up to 4-log after 4 h, and up to 5-log after 24 h between coated and uncoated samples. Saos-2 human osteoblast-like cells and L929 mouse fibroblasts exposed to 72 h extracts of the coating for 24 h showed in vitro cell viability of >70%, indicating no cytotoxicity according to ISO 10993−5. Furthermore, while initial osteoblast attachment to the coating appeared challenging, increased proliferation and metabolic activity over time were observed. After 14 and 21 days, no reduction in osteogenic marker expression was found on the coated samples compared to the uncoated samples. Overall, the PEL-10/HA144-1 coating reduced bacterial adhesion, was not cytotoxic to mammalian cells , and supported osteoblast function in vitro , making it a promising technique for future implantable orthopedic applications.

Computational framework and machine learning approach to fractional order soil helminth infections disease model for control mechanism

A novel approach for longitudinal analysis of serum biomarkers of joint metabolism and knee injury in military officers

Purpose To investigate the longitudinal relationships between serum biomarkers of joint metabolism, knee injury, and Knee Injury and Osteoarthritis Outcome Score (KOOS) using novel methodologies. Methods Data were collected from military officers who enrolled as cadets between 2004–2009, with follow-up conducted between 2015–2017. Analyses included 234 officers who had no history of knee ligament/meniscal injury at the time of military academy matriculation, had serum biomarker measurements at matriculation and graduation, demographic data, and KOOS assessment at follow-up. Biomarkers included Collagen Type II (C2C) and Type I and II (C1,2C) collagenase-generated cleavage epitopes, C-terminal propeptide of Type II collagen (CPII), and C- and N-terminal telopeptides of type I collagen (CTX and NTX). Angle-based Joint and Individual Variation Explained (AJIVE) was used to determine demographic determinants of biomarker levels and individual modes of variation specific to biomarker levels at matriculation and graduation, stratified by sex. Results We confirmed known associations of joint metabolism biomarkers with age in both sexes and with smoking in males. Matriculation biomarker data in males suggested a protective biomarker profile characterized by high cartilage synthesis and low cleavage of type I and II collagen in association with healthy KOOS scores at follow-up. CPII measured at matriculation was negatively associated with incident injuries after adjustment for smoking status (p = 0.03, logistic regression), confirming results from AJIVE. Conclusion These exploratory analyses suggest that CPII alone, or in combination with other joint metabolism biomarkers, may help identify individual risk of knee injury.

Balancing noise reduction and neural signature preservation in EEG biometrics

Abstract EEG-based subject identification is an emerging biometric approach with strong potential for secure authentication, but reliable performance requires optimisation of the entire processing pipeline. The key difficulty lies in improving signal quality while preserving the subtle neural signatures that uniquely distinguish individuals . In this study, we propose a complete framework that integrates lenient preprocessing, spectral feature extraction, and ensemble classification. Using the Brain Encoding Dataset(BED), we evaluated three data variants: raw EEG recordings, signals processed with a modified Pre-processing (PREP) pipeline using relaxed thresholds, and expert-curated pre-extracted features. All datasets were analyzed with mel-frequency cepstral coefficients(MFCC), and classification was performed within an ensemble architecture that combined decision trees, random forests, support vector machines, and XGBoost. The experiments covered 21 subjects, 33 sessions, and twelve stimulus conditions including resting state, cognitive tasks, and visual evoked potentials. XGBoost achieved peak accuracy of 98.00% using Visual Evoked Potential Complex stimulation at 10 Hz on cleaned data, representing a 5.3% improvement over raw signals and an 8.4% improvement over pre-extracted features. Statistical validation confirmed that these improvements are robust across all experimental conditions at ( $$p < 0.01$$ ). Cross-session evaluation further demonstrated the expected temporal variability in EEG-based biometrics but showed that the proposed pipeline improves robustness compared with both raw and conventionally processed data, with Rest Closed Eyes emerging as the most stable paradigm. These findings establish a principled framework for EEG-based subject identification and provide practical guidelines for optimizing preprocessing, feature extraction, classification, and stimulus paradigms for real-world deployment with consumer-grade hardware and system approach.

A randomized controlled trial in healthy participants to compare the insulinogenic effects of whey protein and pea protein co-ingested with glucose

Increasing protein content of foods is effective in reducing postprandial hyperglycaemia, but animal protein may exacerbate insulin sensitivity. This single-blind, randomised, crossover study compared the effects of co-ingesting glucose with 10 or 20 g whey protein and glucose with 10 or 20 g pea protein, with a reference product (glucose) on glycaemic and insulinaemic responses in 30 healthy individuals. Blood glucose and plasma insulin were measured at baseline, 15, 30, 45, 60, 90, 120, 150 and 180 minutes after product consumption. The trial was registered with Clinical Trials.gov (NCT04871971). Glucose incremental area under the curve (mmol/l*min) at 180 minutes was significantly reduced (p < 0.001) for glucose with 20 g pea protein (89.8 ± 51.6) and glucose with 20g whey protein (98.5 ± 58.0) compared to glucose (143.2 ± 74.0). Insulin incremental area under the curve at 180 minutes (µU/ml*min) for glucose with 20 g pea protein (4304.56 ± 1896.07) was significantly lower (p < 0.001) than glucose with 20g whey protein (6311.81 ± 3489.12). This study has shown a superior effect of pea protein over whey protein in reducing glycaemic response, without any excessive increase in insulinaemic response.

Dynamic graph convolution with comprehensive pruning and GNN classification for precise lymph node metastasis detection

Abstract Early and accurate detection of lymph node metastases is crucial for improving breast cancer patient outcomes. However, current clinical practices, including CT, PET imaging, and microscopic examination, are time-consuming and prone to errors due to low tissue contrast, varying lymph node sizes, and complex workflows. To address the limitations of existing approaches in lymph node segmentation, feature embedding, and classification, this study proposes a novel framework Graph-Pruned Lymph Node Detection Framework (GPLN-DF) that integrates a Dynamic Graph Convolution (DGC) autoencoder with Node Attribute-wise Attention (NodeAttri-Attention) for accurate lymph node segmentation. This segmentation is further refined using Comprehensive Graph Gradual Pruning (CGP) to reduce unnecessary parameters and computational costs. After segmentation, Hessian-based Locally Linear Embedding (HLLE) is applied for effective feature extraction and dimensionality reduction, preserving the geometric structure of lymph node regions. Finally, a Graph Neural Network (GNN) classifier enhanced with CGP is used to classify the segmented lymph nodes as metastatic or non-metastatic based on the extracted features. This comprehensive framework addresses challenges such as small lymph node size, shape variability, low contrast in medical imaging, and high computational burden. The model was evaluated on the CAMELYON17 dataset, achieving a classification accuracy of 98.65%, surpassing existing models in segmentation precision and classification performance.

Effectiveness of dyadic interventions in improving outcomes for adults with multiple long-term conditions and/or frailty and their informal carers: A systematic review protocol

Aim To synthesise current evidence on the effectiveness of dyadic (pair-based) interventions in improving outcomes for adults with multiple long-term conditions (MLTC) and/or frailty (aged ≥55 years) and their informal carers. Methods The review protocol followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines, with the protocol registered with PROSPERO (CRD420251144604). MEDLINE, Embase, PsycINFO, CINAHL Plus, CENTRAL, ClinicalTrials.gov will be searched for experimental and quasi-experimental studies examining the effectiveness of community-based dyadic interventions for adults with MLTC (≥2 long-term conditions within an individual) and/or frailty (aged ≥55 years) and their informal carers (spouses/partners, other family members or relatives) published since 2010 and up to September 2025. Dyadic interventions will be defined as pair-based interventions that directly involve informal carers and care recipient adults with MLTC and/or frailty using various techniques targeted at carers, care recipients, or both to change outcomes for at least one member of the carer/care recipient pair (or dyad). Database searches will be followed by a manual search of the reference lists of included studies and lists of papers citing included studies in order to identify additional studies. Two reviewers will independently screen titles and abstracts against the selection criteria and independently screen full texts using Covidence software. Methodological quality will be assessed using the Cochrane Risk of Bias (RoB) 2.0 tool for experimental studies and the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool for quasi-experimental studies. Synthesis of evidence will be quantitative. If meta-analysis is not possible, we will follow Cochrane recommendations for quantitative Synthesis Without Meta-Analysis (SWiM) guidance. Conclusion The findings will address the evidence gap in dyadic implementation research in later life and help inform clinical decision-making, policy development and program planning for adults with MLTC and/or frailty and their carers, particularly in primary care and other community health settings.