Browse Articles

Spatiotemporal variability of physicochemical parameters, heavy metals and associated ecological and human health risks assessment in Rapti River Basin, India

A role for nucleosome remodellers during resection of deprotected telomeres in yeast

DNA double-strand break (DSB) repair pathway choice is strongly influenced by DNA end resection, a process in which the 5′ DNA strand is degraded to generate 3′ single-stranded DNA required for homologous recombination. Although the enzymatic mechanisms of resection have been well defined, its regulation by the dynamic chromatin environment surrounding the DNA break remains less clear. Here, we used the budding yeast cdc13-1 system to analyse DNA end resection after telomere deprotection. Inactivation of Cdc13, a component of the CST (Cdc13–Stn1–Ten1) telomere-capping complex, exposes telomeric DNA ends and triggers a DNA damage response. Using this system, we examined the contribution of long-range resection nucleases and chromatin regulators. Analysis of long-range resection nucleases revealed that the Dna2 nuclease contributes to telomeric processing, particularly in the absence of the exonuclease Exo1. Genetic removal of chromatin regulatory factors showed that H2A.Z, a histone H2A variant incorporated by the SWR1 complex, did not significantly affect resection, whereas depletion of the major nucleosome eviction complexes RSC and SWI/SNF impaired resection after telomere deprotection. Together, these results indicate that nucleosome eviction allows for efficient resection at deprotected telomeres and the analogies to long-range resection at DSBs illustrate the utility of the cdc13-1 system for studying long-range resection in broader context.

Neurocognitive effects of guided moving meditation on divergent thinking and neural coherence in agri-entrepreneurs: An EEG study

Bioinformatics analysis reveals the association of bile acid metabolism-related genes with sepsis

Background Sepsis, is a life-threatening syndrome triggered by infection. Bile acid metabolism may be involved in the pathogenesis of sepsis, the underlying association has not yet been elucidated. Thus, we aimed to screen for bile acid metabolism-related biomarkers of sepsis and discover the potential association. Methods Sepsis-related datasets were downloaded from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) and bile acid metabolism-related differentially expressed genes (BAMRDEGs), then identified hub genes using protein–protein interaction (PPI) network analysis and CytoHubba algorithm. After GO/KEGG enrichment analysis, a sepsis risk prediction model based on key genes was subsequently constructed using support vector machine recursive feature elimination (SVM-RFE) machine learning and least absolute shrinkage and selection operator (LASSO) regression. CIBERSORTx analysis was performed to assess immune cell infiltration and its association with key genes. Finally, the transcriptional levels of key genes in sepsis samples were detected by Quantitative Real-time PCR (qRT-PCR). Results 9785 DEGs were identified, including 5138 upregulated and 4647 downregulated genes. Additionally, 25 hub genes were identified. Gene enrichment analysis indicated that the hub genes participate in multiple biological pathways. Key genes ( ABCC2 , PECR , EPHX2 , PEX2 , and AGXT ) exert central roles in the development of sepsis, indicating the involvement of bile acid metabolism. Significant correlations existed between the expression of key BAMRDEGs and the levels of different immune cell types. qRT-PCR suggested significant up-regulation on ABCC2 and AGXT in sepsis samples versus controls. Conclusion This study revealed novel insights into the correlation between sepsis and bile acid metabolism, and identified 5 key genes involved in the development of sepsis, providing molecular targets and novel strategies for the diagnosis and treatment of sepsis.

The baseline vitreous proteome in rhegmatogenous retinal detachment: a case–control study of proliferative vitreoretinopathy

Benefits of hypocrisy: Do managers gain more from greenwashing

This study finds that corporate greenwashing raises executive compensation. Mechanism analysis indicates that greenwashing enhances executive compensation by improving short-term performance and enhancing executive reputation. In addition, this effect is more pronounced in non-state-owned enterprises, companies with equity incentive plans, and firms that grant restricted stock.

HIV self-testing in Argentina: acceptability and use of an oral-based test among men who have sex with men (MSM)

Exploring the predictability of distributed lag nonlinear models using SARS-CoV-2 wastewater-based surveillance in multiple communities in Alberta, Canada

Background Wastewater–based surveillance can be an important part of pandemic management, especially when testing capacity of individuals is limited. Statistical modeling can be used to examine the relationship between wastewater pathogen levels and clinical cases. The objective of this study was to examine the utility of distributed lag nonlinear modeling to derive the relationship between wastewater SARS-CoV-2 RNA levels and COVID-19 clinical cases across communities in Alberta, Canada when clinical testing was comprehensive. Methods This retrospective cohort study used data from 24-hour composite wastewater collected and tested two to three times per week from 11 wastewater treatment plants (WWTPs) in Alberta, Canada during May 10, 2020, to March 15, 2022. The number of daily new cases of COVID-19 downloaded from Alberta Health’s centralized dataset of clinical surveillance of COVID-19 were mapped to each sewershed. Distributed lag nonlinear models were fit to describe the exposure-response relationship between the 7-day rolling average of SARS-CoV-2 RNA and daily new cases for each WWTP separately. Results The 11 WWTPs served a population of 3,422,062 (77% of Alberta’s population) and 386,528 cases were documented during the study period. From 2021 onward, peaks in both wastewater viral RNA levels and cases tracked reasonably well. For almost all WWTPs, the best fitting model was a Poisson additive model with a P-spline for time. Models for the larger communities had better fits than smaller communities as represented by adjusted pseudo-R 2 ranging from 80.7% to 94.4%. Models followed the same general trends as the actual COVID-19 cases over time. Conclusions With relationships between wastewater viral RNA levels for SARS-CoV-2 and COVID-19 cases expected to vary over time and to be non-linear, distributed lag nonlinear models are promising. While the form of the models was similar across WWTPs, the resulting estimates were different among sites suggesting site-specific analyses are essential.

Personalized learning path recommendation for middle school students based on joint optimization of deep reinforcement learning and knowledge tracing

Abstract The rapid advancement of personalized education has created an urgent need for intelligent learning path recommendation systems that can dynamically adapt to students’ evolving knowledge states. However, existing learning path recommendation systems predominantly rely on static knowledge graphs or simple heuristic rules, failing to capture the temporal dynamics of student knowledge acquisition and the complex multi-objective nature of learning optimization. This paper proposes Path-Mentor, a novel framework that integrates a Temporal-Aware Graph Knowledge Tracing Network (TA-GKTN) with a Multi-Objective Curriculum Planner and Executor (MOCPE) through a Counterfactual Causal Inference-based Co-Training (CCI-CT) mechanism. The TA-GKTN module models students’ dynamic knowledge states as graph-structured representations by incorporating temporal convolution and gating mechanisms into heterogeneous graph neural networks. The MOCPE module employs a hierarchical multi-agent reinforcement learning framework to decouple high-level knowledge point sequencing from low-level learning activity execution, enabling systematic balancing of conflicting objectives including knowledge consolidation, novelty exploration, and cognitive load management. The CCI-CT method establishes an end-to-end joint optimization pipeline by generating counterfactual training data and enabling bidirectional knowledge transfer between the knowledge tracing and path recommendation modules. Comprehensive experiments conducted on four benchmark datasets (ASSISTments 2012-2013, Junyi Academy, EdNet, and Eedi) demonstrate that Path-Mentor significantly outperforms baseline models across six evaluation metrics, achieving a 12.4% improvement in AUC-ROC compare with DKT model for knowledge state prediction, 23.7% reduction in learning path efficiency, and 18.6% enhancement in long-term mastery improvement. These results validate the effectiveness of the proposed joint optimization framework in advancing personalized learning path recommendation for middle school students.

Willingness toward kidney donation among patients’ relatives at Muhimbili National Hospital, Dar es Salaam, Tanzania: A cross-sectional study

Background Kidney transplantation provides superior long-term survival and quality of life over dialysis for patients with end-stage kidney disease; however, its use is limited by the availability of donors. In Tanzania, only living-related kidney transplantation has been performed since 2017 at Muhimbili National Hospital (MNH). Success relies on donor readiness, yet little is known about public willingness to donate. Aim To assess willingness of patients’ relatives to donate kidneys at MNH. Methods Cross-sectional study design among 424 in-patient relatives at MNH from May to June 2023. Systematic random sampling was used to recruit participants. Data were collected using a questionnaire comprised of inquiries on socio-demographics, knowledge, attitudes, and willingness to donate and analyzed using the Stata 18 software. Frequency distribution tables summarized descriptive statistics. Modified Poisson regression with robust variance identified factors associated with willingness. Results Of the 424 participants, Mean age 36 ± 11 years; 240(56.6%) female, 362(85.4%) urban, 400(94.3%) educated. While 361(85%) heard of organ donation, only 32(7.5%) had adequate knowledge, 289(68.2%) positive attitude, 200(47.2%) willing to donate. Age 35–44 years (aRR = 0.69 [95% CI: 0.49–0.99], p = 0.046), female gender (aRR = 0.82 [95% CI: 0.67–0.99], p = 0.042), informal traders/farmers (aRR = 0.77 [95% CI: 0.60–0.99], p = 0.041) had lower willingness versus counterparts. Positive attitude showed 74% higher likelihood (aRR = 1.74 [95% CI: 1.31–2.30], p < 0.001). Conclusion Low kidney donation willingness was influenced by attitude, age, gender, and occupation. Majority of the participants lacked adequate knowledge. Educational programs needed to improve knowledge, attitude, and willingness.

Low resource word sense disambiguation in Oromo with fine tuned small transformers

Abstract A key task in natural language processing is word sense disambiguation (WSD), which attempts to determine the accurate meaning of ambiguous words based on their context. While transformer-based designs have achieved significant results in high-resource languages, WSD for low-resource languages such as Oromo remains hard due to inadequate annotated corpora and lexical resources. Contextual representation learning has been greatly enhanced by recent advancements in transformer-based language models, allowing for more reliable disambiguation in situations with limited input. This study uses a manually created dataset from the Oromo–English Dictionary to examine the efficacy of transformer-based models for lexical-sample WSD in Oromo. The dataset contains sentences annotated by two native speakers, attaining an inter-annotator agreement of 0.82, indicating good annotation reliability. The dataset was filtered for experimental usage following preprocessing, normalization, and elimination of noisy cases. 472 training sentences, 71 validation sentences (15%), and 140 test sentences made up the final dataset. The dataset has a highly unbalanced long-tail distribution and encompasses 43 sense classes. BERT-base-cased gets the best performance with an accuracy of 0.862 and a macro-F1 score of 0.2897, according to an experimental evaluation of transformer-based models, including BERT, RoBERTa, DistilBERT, Davlan/afro-xlmr-base, and multilingual variations. Significant differences between models with χ 2  = 34.03 and p  = 4.0 × 10 −1 are confirmed by statistical analysis using the Friedman test. BERT-base-cased performs much better than most transformer variations and classical baselines, according to post-hoc Wilcoxon signed-rank tests. These results show that contextual transformer representations are quite successful for low-resource WSD, although there is still a significant class imbalance that limits performance.

Characteristics, management, and outcomes of segmental and subsegmental pulmonary embolism in ICU patients: A retrospective cohort study

Objective To describe the incidence, management, and outcomes of segmental and subsegmental pulmonary embolism (PE) in intensive care unit (ICU) patients and to explore associations between therapeutic-dose anticoagulation and clinical outcomes. Design Single-center retrospective cohort study. Setting Tertiary academic hospital ICU between January 2019 and June 2025. Patients Critically ill adults (≥18 years) who underwent computed tomography pulmonary angiography (CTPA) during ICU admission and had radiologically confirmed segmental or subsegmental PE. Interventions None. Measurements and main results Radiology reports of all CTPA examinations performed in ICU-admitted patients were screened to identify the most proximal level of thrombus. Clinical records were reviewed for demographics, illness severity, radiologic characteristics, anticoagulation practice, bleeding, venous thromboembolism (VTE) recurrence, and mortality. Among 896 CTPA examinations performed in 804 patients, 164 examinations (18.3%) identified PE. Of these, 115 scans (12.8% of all CTPAs) demonstrated distal PE only, corresponding to 104 patients (12.9%) (61 segmental, 43 subsegmental). Overall, 96% of patients with distal PE received anticoagulation and 86% of anticoagulated patients received therapeutic-dose regimens. Bleeding occurred in 15% (major bleeding 12%), 90-day VTE recurrence in 7.8%, and 90-day mortality in 24%. No statistically significant association was found between the use of therapeutic-dose anticoagulation and 90-day mortality (adjusted odds ratio [OR], 0.70; 95% CI, 0.21–2.45), bleeding episodes (adjusted OR, 2.34; 95% CI, 0.47–19.2), or VTE recurrence (adjusted OR, 0.69; 95% CI, 0.11–6.22). Conclusions In critically ill adults, segmental and subsegmental PE are commonly detected on CTPA and are usually treated with therapeutic-dose anticoagulation. Although VTE recurrence was less frequent than bleeding episodes and mortality, our study did not find a significant association between therapeutic-dose anticoagulation and bleeding episodes, recurrent VTE, or mortality. Larger prospective studies are needed to define optimal anticoagulation strategies for ICU patients with distal PE.

Serum NO₃⁻ and NO₂⁻ levels among pregnant women from agricultural communities: associations with self-reported dietary and environmental exposures

Abstract Nitrate (NO₃⁻) and nitrite (NO₂⁻) exposure can lead to adverse health impacts, yet data on direct biomarkers are limited. To assess serum NO₃⁻ and NO₂⁻ levels among pregnant women in two agricultural regions of the Jordan Valley in relation to dietary and environmental factors. This cross-sectional study recruited 346 pregnant women aged 18 years or older from four hospitals in the Ghor region between 2023 and 2024. Serum NO₃⁻ and NO₂⁻ levels were measured using a Griess assay. Socio-demographic data, dietary habits, and water source information were gathered through questionnaires. The median serum NO₂⁻ level was 0.63 µM and the median serum NO₃⁻ level was 19.13 µM in the overall sample. Serum NO₂⁻ was significantly higher among women from North Ghor compared to South Ghor (0.83 µM vs. 0.45 µM, p  < 0.001), while serum NO₃⁻ levels did not significantly differ between regions. In multivariable models adjusting for maternal age, BMI, hypertension, anemia, and smoking, we found that variables of region of residency and eating green beans once a week or more were significantly associated with higher NO₂⁻ levels in the whole sample ( p  < 0.001 and p  = 0.031, respectively) and in North Ghor ( p  = 0.032), while hypertension was associated with lower NO₂⁻ levels ( p  = 0.032 whole sample; p  = 0.013 North Ghor). For NO₃⁻, smoking during pregnancy was associated with lower levels in the whole sample ( p  = 0.003) and in South Ghor ( p  = 0.004), while employment in crop harvesting/collection was associated with higher NO₃⁻ levels in South Ghor ( p  = 0.024). Regional differences in serum NO₂⁻ levels exist among pregnant women in rural Jordan, influenced primarily by dietary habits, while occupational exposure to crop harvesting and smoking were associated with serum NO₃⁻ levels. Targeted recommendations and public health strategies are needed to mitigate NO₃⁻ and NO₂⁻ exposure.

Prevalence, virulence profiles and antibiotic susceptibility patterns of Shiga toxin producing Escherichia coli O157:H7 among children 6–59 months in Longido, Arusha-Tanzania

Shiga toxin producing Escherichia coli (STEC) is a zoonotic pathogen associated with diarhoeal disease and severe complications in children, yet its epidemiology in pastoral settings of Tanzania remains insufficiently characterized. This study determined the prevalence, virulence gene profiles and antibiotic susceptibility patterns of STEC among children aged 6‍-59 months with diarhoea in Longido District, northern Tanzania. A hospital based cross-sectional‍ study was conducted between July and August 2025, enrolling 150 participants from four health facilities. Stool samples were collected and analyzed using culture, serological conformation and multiplex polymerase chain reaction targeting five genes; rfbE , stx1 , stx2 , eae A  and hly A . STEC was operationally defined by detection of stx1 and/or stx2 . Antibiotic susceptibility was assessed using the Kirby-Bauer disk diffusion method. The prevalence of STEC was‍ 13.3% (20/‍150). All stx2 positive isolates co-occurred with stx1 . Virulence genes showed a heterogeneous but significantly clustered distribution, with rfbE (20.0%) and stx1 (13.3%) predominating. Significant co-occurrence was observed‍ between stx1 and eae and between stx1 and hlyA (p < .001). Animal contact, raw milk consumption and use of untreated water were significantly associated with STEC infection. Firth penalized logistic regression confirmed these exposures as independent predictors. Antibiotic susceptibility profiles were uniform, with complete susceptibility to ciprofloxacin,‍ gentamicin, cefotaxime and ceftazidime while ampicillin and trimethoprim showed complete resistance. These findings indicate that STEC transmission in pastoral communities is strongly driven by zoonotic and environmental exposures, characterized by clustered virulence determinants and consistent antibiotic profiles. Limitations include the cross-sectional design and short sampling period, which may not capture seasonal variation. Strengthened surveillance and integrated One Health interventions are needed to reduce disease burden.

Comparative analysis of artificial intelligence models for predicting oil viscosity in the in-situ catalytic oil upgrading process: a study of influential parameters

Intelligent kinematic physics engine construction for hyper-redundant cable-driven flexible manipulator

To solve the problems of difficult kinematics modeling and poor real-time performance for a hyper-redundant cable-driven flexible manipulator, a construction method of an intelligent kinematics physics engine based on Bayesian-optimized Long Short-Term Memory (BO-LSTM) network is proposed in this paper. Firstly, the multilevel kinematics equation of the flexible manipulator is established based on the Denavit-Hartenberg (D-H) parameter method, and an analytical model of the forward solution is established. Secondly, the inverse kinematic model of the Newton iteration method is proposed based on the kinematics equation, and the stability of the numerical solution method is proven. Subsequently, the kinematics database is established based on the Monte Carlo method, the analytical model of forward solution and the high-precision numerical model of inverse solution, and the database is visualized. Finally, an intelligent kinematics physics engine is established based on the kinematics database and the BO-LSTM neural network model. This model combines the global search ability of Bayesian optimization and the advantages of LSTM neural network in processing time series data, which provides a new solution for the kinematic modeling of hyper-redundant cable-driven flexible manipulator. The calculation speed of the forward and inverse BO-LSTM models are 0.018 s and 0.017 s, which are faster than the numerical method. The MSEs of the forward and inverse BO-LSTM models are 0.065 and 0.056 respectively, which are lower than those of the BP, RBF and LSTM neural networks. The SimMechanics module in MATLAB is used to simulate the hyper-redundant cable-driven flexible manipulator. The experimental result shows that the intelligent kinematics physics engine based on the BO-LSTM neural network model has high precision and high efficiency in kinematics solution.

Data-driven exploration of electronic nose technology to differentiate bacteria in blood cultures under biofilm-promoting conditions

Abstract Biofilms are a major cause of delayed wound healing, yet current biofilm identification methods are limited by invasiveness, processing times, or specificity. This study investigates the potential of metal-oxide electronic noses for identifying bacterial cultures of Staphylococcus aureus , Pseudomonas aeruginosa , Enterococcus faecium , and Staphylococcus epidermidis grown in blood-based growth medium. We conducted in-vitro experiments to capture volatilome signatures from cultures grown under biofilm-promoting conditions and analyzed data using an interpretable machine learning workflow to disentangle algorithmic limitations from biological variability. This workflow incorporated feature extraction and selection, correlation-based clustering, and Shapley analysis. Six classification models were evaluated using cross-validation. Considering all five classes, classification accuracy reached at most 55.6%, which Shapley-based interpretation attributed mainly to biological factors: E. faecium and S. aureus exhibited high signal similarity to control samples and strong inter-day variability. Accuracy increased to 100.0% for species with distinct volatile signatures, and dimensionality reduction resulted in a model using two constructed features. These findings demonstrate that classification performance in biological sensing cannot be explained solely by algorithmic factors. Interpretable machine learning workflows help for distinguishing biological sources of complexity from algorithmic ones. We provide a proof-of-principle for electronic nose-based identification of bacterial growth under biofilm-promoting conditions.

The relationship between esports and cognitive function: A scoping review

The aim of this scoping review was to map and synthesize empirical evidence on the relationship between esports participation and cognitive function, with particular attention to executive control, attentional processes, visuospatial working memory, decision-making, and lifestyle-related factors. Searches were conducted in PubMed and Web of Science following PRISMA-ScR guidance. Nine empirical studies met the inclusion criteria and were included in the final synthesis. The included evidence consisted mainly of cross-sectional comparisons, together with a small number of intervention, acute experimental, and qualitative studies. The studies were organized into three thematic domains: cognitive differences across expertise levels, lifestyle and intervention effects on cognitive performance, and decision-making or training mechanisms. Five studies primarily examined expertise-level differences, three addressed lifestyle, health load, or intervention-related factors, and one focused on decision-making and training processes. Overall, the available evidence suggests that competitive esports players may show advantages in selected cognitive domains, particularly executive control, attentional flexibility, and visuospatial processing. However, findings were heterogeneous across game genres, participant classifications, cognitive tasks, and reporting practices. Because only nine studies were eligible and most designs were cross-sectional, the current evidence base remains insufficient for strong causal conclusions about whether esports participation improves cognitive function or whether pre-existing cognitive abilities contribute to esports expertise. Future research should use longitudinal, experimental, and cross-cultural designs with standardized cognitive measures and transparent reporting of statistical results.

Fabrication and characterization of ecofriendly Taro starch bioplastic composite films reinforced with coffee husk and enset fiber for packaging applications

Translating injury prevention evidence into safer padel: Protocol of a TRIPP-guided scoping review

Padel is a rapidly growing sport with high injury incidence rates and substantial consequences. However, no comprehensive overview exists of the evidence on padel-related injuries and injury prevention strategies. A scoping review can help identify gaps in the emerging field of padel research. The objective of this scoping review is to systematically map the available evidence on padel‑related injuries and injury prevention strategies across all player populations and settings, from epidemiological description to intervention effectiveness. Studies describing injuries or injury risk factors or mechanisms in padel players of any kind and regarding primary injury prevention strategies or interventions involving stakeholders at any socio-ecological level will be considered. Studies that have been conducted in any geographic location, at any playing level, in any setting and in any implementation context will be considered. The proposed scoping review will be conducted in accordance with the JBI methodology for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) will be followed. A three-step search strategy will be utilized. The search strategy will be adapted for PubMed, SPORTDiscus, Web of Science, Scopus, EMBASE and CINAHL. A structured gray‑literature search will also be conducted. The data extracted from included papers will include specific details about the participants, concept, context, study methods and key findings relevant to the review questions. No critical appraisal of individual sources of evidence will be performed. The extracted data will be presented according to the six stages of the Translating Research into Injury Prevention Practice (TRIPP) framework. The review has been registered through Open Science Framework (osf.io/4t69j).