PLOS One
https://journals.plos.org/plosone/
Correction: Gut microbiome is associated with stereotypic behavior of the giant panda (Ailuropoda melanoleuca)
by Anqi Hu, Jinyuan Liu, Peng Chen, Kuixing Yang, Fan Xu, Gai Luo, Jiali Zhang, Mengnan He
Occupational health risks from climate-related and infectious-disease stressors: A scoping review of worker safety and resilience
by Muhammed Abdul Odud, Omar I. Abdul-Aziz
ObjectiveThis scoping review examines how climate-related stressors and infectious-disease conditions, including pandemic-related workplace disruptions, interact to influence worker health, vulnerability, and occupational resilience. The review focuses on shared exposure pathways, vulnerable worker populations, methodological patterns, policy gaps, and strategies for strengthening worker safety under compound hazards. Rather than treating pandemics as isolated events, the review uses pandemic conditions as a lens to understand broader, recurring occupational risks and to inform strategies for building safer and more resilient workplaces under compound hazards.
MethodsFollowing PRISMA-ScR guidelines, a structured literature search was conducted in PubMed, Scopus, Web of Science, and Google Scholar for English-language peer-reviewed and formally structured evidence sources published primarily between January 2020 and April 2025, with one pre-2020 policy study retained for foundational context. Out of 612 records initially identified, 10 studies or sources met the inclusion criteria across healthcare, agriculture, construction, emergency response, and other high-exposure sectors after systematic screening and eligibility assessment. Data were charted and synthesized by occupational sector, geographic region, exposure type, worker vulnerability, and methodological approach.
ResultsThe evidence indicates that climate-related and infectious-disease stressors frequently converge, amplifying physical, environmental, and psychosocial pressures on workers. Disproportionate risks were observed among migrant workers, informal laborers, low-income populations, older adults, women, and seasonal or cross-border agricultural workers, particularly where access to healthcare, legal protections, or adaptive infrastructure was limited. Most studies relied on descriptive, qualitative, cross-sectional, conceptual, or review-based designs, with limited use of predictive modeling, geospatial analysis, longitudinal evidence, or integrated datasets. Policy responses were often fragmented, with climate adaptation and infection-control measures addressed separately despite overlapping benefits.
ConclusionsClimate-related and infectious-disease stressors interact to reinforce occupational health inequities and expose persistent gaps in worker protection. The findings highlight the need for integrated, multi-hazard occupational health strategies that align climate adaptation, infection prevention, and psychosocial support. Embedding data-driven early-warning systems, equitable protections for vulnerable workers, and cross-sector coordination into occupational policies may help strengthen workforce resilience during routine operations as well as future climate extremes and public health crises. However, the findings should be interpreted with caution because of the limited number of available studies combining climate-related and infectious-disease occupational risks, heterogeneous study designs, and the emerging nature of the evidence base. Furthermore, since this scoping review maps evidence rather than formally grading evidence strength or testing causal relationships, recommendations should be interpreted as evidence-informed directions for future occupational-health planning and research.
Nurses’ perceptions of an audiovisual material to support patient education on oral health management in diabetes care
by Rie Kudoh, Taiga Shibayama
Educational support for nurses is essential to promote oral health management education for patients with diabetes. However, few studies have focused on supporting nurses in this context. This study explored the effects of an audiovisual material designed to support nurses’ patient education on oral health management in diabetes care. Semi-structured interviews were conducted with eight certified diabetes nurse educators. Data were analyzed using content analysis. Four main themes emerged: (1) enhancement of nurses’ individual learning (8/8 participants), including improved knowledge of the connection between oral health and diabetes; (2) promotion of professional development and reflection (5/8); (3) provision of new insights into patient care (7/8), particularly regarding patient motivation strategies; and (4) identification of implementation considerations in diabetes outpatient settings. The findings suggest that the audiovisual material, designed to complement an existing evidence-based educational guide, was perceived as useful and may support nurses’ learning and educational practices related to oral health education for patients with diabetes.Cognitive effects of untreated unilateral and bilateral hearing loss in midlife: Preliminary evidence
by Lakshmi Satheesan, Usha Shastri, Gagan Bajaj, Mohan Kumar Kalaiah
Hearing loss has been identified as a modifiable risk factor for cognitive decline and dementia, although most of the evidence is derived from studies in older adults. Identifying such cognitive impairment in midlife is crucial, as it serves as a baseline for understanding changes in cognitive abilities that occur later in life. The present study aimed to compare the cognitive performance of age- and socioeconomic status-matched middle-aged adults with normal hearing and those with untreated hearing loss. This study included 48 participants aged 40–65 years, comprising 24 adults with normal hearing and 24 with untreated hearing loss. Among the latter participants, 9 had unilateral loss and 15 had bilateral loss. The cognitive evaluation involved assessing memory (forward and backward digit span and contextual memory test), attention via the Go/No-Go task, and executive function via the letter and category fluency tasks. Results showed that adults with untreated hearing loss performed significantly poorer on all the cognitive tasks compared to their normal-hearing counterparts. Large effect sizes were observed for memory tasks, the category fluency task and Go/No-Go accuracy, and a moderate effect size for the letter fluency tasks. Furthermore, multivariate linear regression analysis demonstrated that better-ear pure-tone thresholds independently predicted cognitive performance, but not age or education. These findings provide preliminary evidence on the association between untreated hearing loss and cognitive performance in middle age. However, considering the modest sample size and cross-sectional study design, longitudinal studies are needed to determine the temporal relationship between hearing loss and cognitive changes.Comparative study of quadratic regression, Lasso, random forest, and multilayer perceptron for modeling optimal solid density in offset printing
by Bingzhong Qiu, Liangfei Li, Xiaoli Liu, Xiaolei Sheng, Yujun Jiang, Xiaomei Liu, Ying-Leh Ling
This study compares four modeling methods, namely quadratic regression (QR), Lasso, random forest (RF), and multilayer perceptron (MLP), to determine the optimal solid density for four-color offset printing. Based on 110 valid samples for each ink color, random 5-fold cross-validation (CV) and paired t-tests were employed to evaluate model predictive accuracy (R2, RMSE) and the stability of the estimated optimal solid density (CV%). Under random 5-fold CV, non-parametric approaches (RF and MLP) outperformed parametric counterparts (QR and Lasso) for black, cyan, and magenta inks, and MLP yielded the optimal performance for yellow ink (R2 = 0.9770). Pairwise statistical tests showed no significant differences between RF and MLP for black, cyan, and magenta inks (p > 0.05), while MLP was statistically superior for yellow ink under random 5-fold CV (p = 0.0296). However, this ranking cannot be generalized to blocked cross‑validation (blocked‑CV). Two blocked‑CV strategies, temporal-block and spatial-block CV, were further implemented to mitigate data leakage arising from high lag-1 autocorrelation (0.96–0.98) in sequentially collected process data. Under blocked-CV, mean R2 values were negative across most models and ink colors, limiting the interpretability of inter-model rankings. While surfaces fitted under random 5-fold CV revealed nonlinear features including broad high-quality plateaus for cyan and magenta inks and multi-peak patterns for yellow ink, such features require cautious physical interpretation given validation-strategy dependence. Despite substantial drops in predictive performance metrics under blocked-CV, the estimated optimal solid densities were insensitive to the fold structure, with deviations ranging from 0.0034 to 0.0152 across all four ink colors. The recommended optimal solid densities are 1.67 (RF) for black ink, 1.44 (RF) for cyan ink, 1.42 (RF) for magenta ink, and 0.99 (MLP) for yellow ink. This study shows that although predictive performance metrics are highly sensitive to the validation strategies, the estimated optimal solid densities are insensitive to the fold structure examined here. This consistency is dataset‑specific and does not by itself establish out‑of‑sample validity under different printing conditions. For offset printing process optimization, the stability of the estimated optimal solid density offers greater practical engineering value than raw predictive performance or model ranking consistency.Predictors of preeclampsia with severe features: A multi–center analysis of maternal and health system factors in Thai tertiary hospitals
by Tippawan Boonyaporn, Piyanun Limruangrong, Ameporn Ratinthorn, Thitipong Tankumpuan
BackgroundPreeclampsia (PE) with severe features threatens maternal and neonatal health. This study examined maternal and health–service factors associated with severe features and compared maternal and neonatal outcomes in Thailand.
MethodsThis case–control study enrolled 156 postpartum women with PE (52 with and 104 without severe features) and 111 nurse–midwives in antenatal care, labor, and postpartum units across four Thai tertiary hospitals. Data were collected using postpartum and nurse–midwife questionnaires, and a maternal–neonatal health record form. Group differences were assessed using independent t–test, chi–square or Fisher’s exact tests. Multivariable binary logistic regression examined factors associated with severe features, adjusting for antenatal care visits and gravidity.
ResultsWomen with severe features more often had a history of PE (32.7% vs. 13.5%, p = 0.006) and differed in BMI distribution (p = 0.007). Three maternal factors were independently associated with severe features: history of PE (aOR = 5.99, 95% CI 2.13–16.83), BMI (aOR = 0.92 per kg/m2, 95% CI 0.86–0.98), and moderate versus low PE knowledge (aOR = 0.13, 95% CI 0.04–0.50). No health–service variable, including nurse training, was independently associated with severe features after adjustment (all p > 0.05). The model explained 29.4% (Nagelkerke R2) of the variance in PE severity and correctly classified 75.8% of cases. Infant birth weight was lower among women with severe features (p < 0.05); delivery mode, hospital stay, birth asphyxia, and NICU admission did not differ significantly.
ConclusionA history of PE was associated with higher odds, and moderate (versus low) PE knowledge and higher BMI with lower odds, of severe features. These findings support prioritizing screening for prior PE history and structured antenatal knowledge counseling, together with adequately powered, multilevel studies to clarify the role of health–service factors in PE severity.
Dual burden of malnutrition among mother-child dyads in Mozambique: Findings from the Demographic and Health Survey 2022–2023
by Nahin Shakurun, Fernanda André, Nazeem Muhajarine
IntroductionNutritional challenges are a global public health concern, especially among children under five in sub-Saharan African countries. The coexistence of an overweight/obese mother and an underweight child in the same household is recognized as a dual burden of malnutrition (DBM). This study estimated the prevalence, identified associated factors, and assessed socioeconomic inequalities in DBM among mother-child dyads in Mozambique.
MethodsWe used nationally representative, cross-sectional data from the Mozambique Demographic and Health Survey 2022–2023 (n = 3,605 mother-child dyads). Child undernutrition and maternal BMI status were calculated using the World Health Organisation (WHO) standard guidelines. The outcome variable, DBM, was defined as the presence of child undernutrition condition (stunting, wasting or underweight) with corresponding maternal overweight/obesity. Multivariable binary logistic regression, Erreygers’s concentration index, and concentration curve analyses were performed, accounting for the complex survey design, to determine associated factors and social inequalities.
ResultsThe prevalence of DBM was 5.5% [95% CI: 4.6, 6.6]. Mothers aged ≥ 34 years [aOR (95% CI): 4.01(1.44, 11.14); p < 0.05] and mothers with four or more children [aOR (95% CI): 2.68(1.29, 5.57); p < 0.05] had higher odds of DBM. Rural residence and using unimproved toilet facilities (latter an indicator) were also significantly associated with DBM. Additionally, maternal age modified the association between women’s empowerment and mother-child DBM. Women aged 15–19 years at the lowest and highest empowerment levels were more likely to experience DBM than women 20 years or older. A positive and statistically significant concentration index indicates that wealth-related inequalities exist, with DBM more concentrated among wealthier mother-child dyads.
ConclusionOur study highlights the occurrence of household-level DBM in Mozambique. These findings emphasize the need for targeted interventions addressing social and economic inequalities, including poor sanitation, a marker of broader household deprivation. Prioritizing integrated maternal-child nutrition interventions within national strategies is essential to improving equity in nutrition and health outcomes.
Parsing-based approach for entity extraction from natural language software requirements
by Sabin Amjad, Arooj Saleem, Ahmad Salman Khan, Muhammad Waqas Anwar
Software requirements are written in natural language, so they are inherently ambiguous. This ambiguity results in the misinterpretation of the requirements and leads to an incorrect implementation of the system that does not meet the user’s needs. So, it is important for all the stakeholders to comprehend the requirements. The requirement statements revolve around the requirement entities. Identifying requirement entities is essential to understand requirements, and they are used in identifying coreferent entities, creating glossaries, producing summaries of documents, feature extraction, requirements classification, and domain understanding. Manual entity extraction is a labour-intensive task. Recently, transformer-based models have played an important role in improving entity extraction results, but they require extensive pre-training of models for domain-specific tasks. To overcome these challenges, the proposed automated approach is used to extract entities from software requirements without the need for manual efforts and a large dataset for pre-training. In this study, two widely used parsing techniques, constituency parsing and dependency parsing, are applied to extract noun phrase-based entities. In addition, a hybrid approach comprising the combined extractions of both parsers is developed. The dataset comprises the software requirements from the healthcare domain and is manually annotated to establish a gold standard for evaluation. Experimental results indicate that the hybrid approach achieves the best overall extraction performance among the evaluated methods. While the constituency parser achieves the highest precision, it exhibits comparatively lower recall. In contrast, the hybrid approach achieves the highest recall and F1-score while maintaining precision comparable to that of the constituency parser. These results demonstrate that the hybrid method provides a more effective solution for automatically extracting entities from software requirements by leveraging syntactic structures of sentences rather than relying on domain-specific model training.24-hour movement behaviour composition associated with desired social-emotional functioning in the early years
by Samah Zahran, Dylan P. Cliff, Devan Antczak, Eivind Aadland, Emad A. Anam, Katrine N. Aadland, Jade Burley, Catherine E. Draper, Dorothea Dumuid, Diego Augusto Santos Silva, Esther M. F. van Sluijs, Hayley Christian, Mark S. Tremblay, Timothy S. Olds, Rute Santos, Zhiguang Zhang, Ian Janssen
IntroductionMovement behaviours (sleep, sedentary behaviour, and physical activity) influence numerous health outcomes in children, including their social-emotional development. The optimal 24-hour movement behaviour composition for positive social-emotional development is unknown. The purpose of this study was to determine the movement behaviour composition across the 24-hour day associated with desired social-emotional functioning in the early years.
MethodsThis cross-sectional analysis included 560 children aged 1.2–2.9 years and 1,500 children aged 3.0–4.9 years from the Sleep and Activity Database (SADEY) for the Early Years study. Sedentary behaviour (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) were measured using ActiGraph accelerometers. Sleep duration was assessed using parent reports. Children social-emotional functioning was measured using the five domains of the Strengths and Difficulties Questionnaire (SDQ), a widely used screening instrument of children’s behaviours, emotions, and relationships across five domains (emotional problems, conduct problems, hyperactivity, peer problems, and prosocial behaviour). Compositional data analyses were used to estimate the 24-hour movement behaviour compositions associated with optimal scores for each of the SDQ domain z-scores and all five domains combined (the Goldilocks day).
ResultsThe movement behaviour composition was significantly associated with all five SDQ domains (all p < .01). For 1–2-year-olds, the Goldilocks day consisted of 12 h 12 min of sleep, 4 h 36 min of SED, 5 h 48 min of LPA, and 1h 24 min of MVPA. For 3–4-year-olds, it consisted of 12 h 08 min of sleep, 4 h 27 min of SED, 5 h 49 min of LPA, and 1 h 36 min of MVPA.
ConclusionThe duration of total physical activity and MVPA associated with more favourable social-emotional functioning exceeded the minimum levels specified in the current 24-hour Movement Guidelines. These findings should not be interpreted as behavioural recommendations but rather as movement behaviour patterns associated with more favourable outcomes within this study population.
Implant selection at elective aseptic revision of a primary total knee replacement: A scoping review and map of the evidence
by Ben Tyas, John Norman, Shivam Nitin Kolhe, Melissa Harden, Catherine Hewitt, David Deehan, Paul Baker
PurposeThe recent James Lind Alliance (JLA) Priority Setting Partnership (PSP) on ‘the problematic knee replacement’ highlighted implant selection at revision knee arthroplasty (rTKA) as a ‘Top 10’ research priority. The aims of this scoping review are to map the evidence for the implants used at first-time elective aseptic rTKA, to identify gaps in the evidence-base, and to provide recommendations for future research.
MethodsA scoping review was conducted in accordance with the Arksey and O’Malley framework and the Joanna Briggs Institute Manual for Evidence Synthesis, and reported according to the PRISMA extension for Scoping Reviews. A comprehensive search of MEDLINE, Embase, CINAHL, CDSR, CENTRAL, DARE, HTA, SCI, PROSPERO and KSR Evidence was conducted on 17th August 2026, covering studies published between 2000 and 2026. Annual reports of 17 national joint replacement registries (NJRRs) were also sought. The review was registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/SD49V).
ResultsFollowing deduplication, 7,096 research articles and 155 NJRR reports were identified. After applying eligibility criteria, 121 research articles and six NJRR reports were included. Twenty-two studies (18%) had cohorts clearly defined as exclusively comprising first-time elective aseptic rTKA procedures. The evidence consisted mostly of retrospective case series (45/121, 37%) and retrospective comparative studies (41/121, 34%). Most evidence was Level IV (48/121, 40%) or Level III (46/121, 38%) with no Level I evidence identified. Approximately half the studies were from the United States (58/102, 48%). Five studies (4%) reported mean or median follow up ≥10 years. A map of the evidence, tabulated by five key themes, is presented.
ConclusionsThere is a lack of high-level comparative evidence to support implant selection at first-time elective aseptic rTKA. Future research should focus on more clinically homogeneous cohorts and compare outcomes associated with cemented versus hybrid/cementless stems, constraint levels, and metaphyseal fixation devices.
Monitoring seasonal and long-term forest canopy dynamics using Sentinel-2 imagery and Random Forest on the Google Earth Engine platform
by Pavan Kumar, Rahul Kumar, Soumyajeet Sahu, Manmohan Dobriyal, Pankaj Lavania, Manish Srivastav, Prem Chandra Pandey, Benson Turyasingura
Understanding forest canopy dynamics is critical for assessing the condition, resilience, and conservation priorities of protected landscapes. This study evaluated the spatiotemporal and seasonal variation in mapped forest canopy density within the Orchha Wildlife Sanctuary, Madhya Pradesh, India, using multi-seasonal Sentinel-2 imagery and a Google Earth Engine (GEE)-based Random Forest (RF) classification framework. Sentinel-2 data for pre-monsoon (March–May) and post-monsoon (October–December) periods in 2016 and 2024 were analyzed independently. Canopy-density information was derived from NDVI, NDMI, elevation, slope, and GLCM texture features and classified into four classes: non-forest, open, moderate, and dense canopy. The same RF procedure was applied to all four season-year datasets using NDVI-informed canopy-density strata and balanced reference sampling. Overall accuracy ranged from 97.80% to 98.95%, with Kappa coefficients of 0.970–0.986. The results indicated clear seasonal and interannual redistribution of mapped canopy-density classes. During the pre-monsoon period, non-forest and open classes decreased by 39.62% and 7.15%, respectively, while moderate and dense classes increased by 231.64% and 240.43%. During the post-monsoon period, moderate canopy increased by 26.01%, whereas dense canopy decreased by 9.46%. Within the common comparison area of 4,533.54 ha, transition analysis revealed substantial class persistence and redistribution, particularly among adjacent canopy-density classes such as open-to-moderate and dense-to-moderate transitions. Spatial changes were distributed across riparian, interior, and peripheral zones. Because 2016 used Level-1C top-of-atmosphere data and 2024 used Level-2A surface-reflectance products, interannual differences should be interpreted as changes in mapped canopy-density classes rather than precise biophysical changes in canopy structure. The framework provides a replicable approach for seasonal forest monitoring and spatially targeted conservation planning.Predicting visual impairment in type 2 diabetes using key performance indicators identified by the Black Widow and Whale Optimization Algorithms: A case study in Saudi Arabia
by Faten Al-hussein, Laleh Tafakori, Mali Abdollahian, Khalid Al-Shali
Visual impairment (VI) is a severe and prevalent complication of type 2 diabetes (T2D), particularly in high-risk populations, underscoring the critical need for early predictive strategies. A retrospective cross-sectional study was conducted to develop and validate machine learning (ML) predictive models for VI among patients with T2D in Saudi Arabia. To the best of our knowledge, this is the first study to employ WOA and BWO as feature selection techniques to identify the most informative predictors of VI among Saudi patients with T2D. Predictive models were subsequently developed and compared using both clinically recommended features and algorithmically selected features derived from WOA and BWO to determine the optimal feature set for predictive performance. Medical records of 1,000 T2D patients (555 VI [55.5%]; 445 non-VI [44.5%]) from King Abdulaziz University Hospital were analyzed. Models were developed using an 80/20 stratified train-test split, with 5-fold cross-validation performed on the training set for internal validation. Patients were divided into two groups: those with HbA1c ≥ 6.5% (full dataset) and a clinically relevant high-risk subgroup with HbA1c > 8% (reduced dataset), which incorporated 19 recommended demographic, lifestyle, and lipid profile features. Models utilizing the reduced dataset consistently achieved superior performance across all algorithms. Among them, the WOA-CatBoost model demonstrated the highest performance (Accuracy = 0.95, AUC = 0.96, 95% CI: 0.94–0.98) using only 9 features, outperforming both the BWO-Random Forest (BWO-RF) model (Accuracy = 0.92, AUC = 0.90, 95% CI: 0.87–0.93) with 10 features and the baseline Multi-Layer Perceptron (MLP) model (Accuracy = 0.91, AUC = 0.94, 95% CI: 0.91–0.97) with 13 features. Bonferroni-corrected paired t-tests based on five-fold cross-validation accuracies (α = 0.017) confirmed a significant performance advantage of WOA-CatBoost over BWO-RF (t = 10.17, p < 0.001), supporting WOA-CatBoost as the overall best-performing model. Key predictors identified across all three best-performing models included Hemoglobin A1c (HbA1c), body mass index (BMI), age, occupation, triglycerides (TG), type of food, and smoking. These findings demonstrate that WOA-CatBoost provides a clinically actionable, parsimonious predictive tool for VI risk stratification in T2D patients within the Saudi Arabian healthcare context.Predicting continuous load-settlement trajectories of bored piles: A GA-optimized weighted ensemble framework with explainable AI
by Loi Van Giap, Tuong Lai Nguyen, Tuan Anh Pham
Accurate prediction of bored pile settlement is a core element in reliable foundation design. While machine learning (ML) models demonstrate superior predictive capabilities compared to traditional analytical methods, their black-box nature and tendency to generate non-physical trajectories significantly hinder their practical application in geotechnical engineering. This study proposes a new computational framework that ensures physical consistency by combining Weighted Ensemble learning with SHAP (SHapley Additive exPlanations) interpretation capabilities. The integrated structure incorporates linear regression (LR), Random Forest (RF), XGBoost, and multilayer neural networks (MLP) to capture the complex interaction between piles and soil through a database of 563 field static load test records. A two-stage hybrid optimization strategy, using Random Search combined with a Genetic Algorithm (GA), was implemented to optimize the global weight set. The results showed that the GA-Ensemble model achieved excellent accuracy on the independent test set (R2 = 0.958, NRMSE = 5.27%, RMSE = 0.380, MAE = 0.2322), successfully neutralizing the ladder errors of the tree model to recreate a smooth, continuous, and mechanically compliant load-settlement (P-S) trajectory. Notably, by converting the SHAP value to a real physical scale (millimeters), the study clarified the algorithm’s inference logic. SHAP dependency analysis demonstrated that the model successfully captures the coupling characteristics of elastic compression and geometric nonlinearity by identifying the interaction between external force (P) and free length (Lf). Local diagnostic decompositions precisely extract failure mechanisms, successfully quantifying the “systemic penalty” caused by elastic shortening of structures and foundation puncture even in very stiff soil layers. In short, the proposed method has gone beyond the limits of a purely interpolation tool, establishing a transparent and geomechanically reliable expert system for predicting settlement.Examining the performance of the International Trauma Questionnaire with clinical checks in a multinational European sample
by Philip Hyland, Mark Shevlin, Menachem Ben-Ezra, Marylène Cloitre, Matus Adamkovic, Agata Błachnio, Marek Preiss, Ella Salgo, Mihaela Fadgyas Stanculete, Zsolt Szabolcs Unoka, Thanos Karatzias
BackgroundThe International Trauma Questionnaire (ITQ) is a widely used self-report measure of PTSD and Complex PTSD (CPTSD). ‘Clinical checks’ – brief follow up questions verifying respondent comprehension of the symptom item – have recently been developed for the ITQ to reduce false-positive diagnostic cases. This study tested whether the ITQ with clinical checks generates psychometrically sound scores and adds clinically relevant information to standard symptom assessment.
MethodsData (N = 5,070) were collected using quota-based sampling procedures from adults living in five European countries (Czech Republic, Poland, Hungary, Romania, and Slovakia).
ResultsOverall, 82.2% of respondents were trauma-exposed, and 19.2% met diagnostic requirements for PTSD (7.2%) or CPTSD (12.0%) on the standard ITQ. With clinical checks, these fell to 9.8% overall, 3.9% for PTSD, and 5.9% for CPTSD. The ITQ with clinical checks produced scores with excellent factorial validity, internal reliability, and latent classes corresponding to PTSD and CPTSD. Associations with clinically relevant variables including suicidality and anxiety-depression symptoms were stronger with than without the clinical checks. Females, those with more extensive trauma histories, and higher concurrent anxiety-depression symptoms were more likely to retain than lose their diagnostic status when the clinical checks were used.
ConclusionsThe ITQ with clinical checks generated scores with excellent psychometric properties, captured additional clinically relevant information, and yielded more plausible prevalence estimates compared to the standard ITQ scores. The potential value and limitations of using clinical checks in self-report questionnaires are discussed.
Learning environment at home and primary school dropout status in Bangladesh: An analysis of multiple indicator cluster survey
by Md. Emon Prodhan, Ashraful Alam, Sajida Subha, Faizul Islam, Mohd. Al Amin, Mohammad Nayeem Hasan, Mohammad Anamul Haque, Md Jamal Uddin
Primary school dropout is a serious concern worldwide. In Bangladesh, primary education is strongly shaped by the home learning environment. This study aimed to investigate the associations between the dropout of primary school children and the learning environment at home as well as the associated risk factors by analyzing cross-sectional data from the 2019 Multiple Indicator Cluster Survey (MICS), with a total sample size of 14,278 children aged 6–12 years. Bivariate analysis, crude and adjusted log-binomial models were used to assess the relationships between dropout and risk factors. Overall, 1.68% of the children dropped out, with increased rates observed among males (ARR = 2.44, 95% CI: 1.63–3.64) and children aged 9–12 years at the beginning of the school year had a higher risk of dropout (ARR = 1.83, 95% CI: 1.15–2.93). Mymensingh presented the highest dropout rate, 3.82 (ARR = 3.82, 95% CI: 1.59–9.18) times higher than Barishal. Children who did not read books or were not read to at home had 14.47 (ARR = 14.47, 95% CI: 9.30–22.51) times higher risk of primary school dropout. To reduce primary school dropouts in Bangladesh, addressing gender inequalities, regional disparities, and inadequate home reading activity is essential. Strengthening early childhood education initiatives, promoting home-based literacy activities, supporting older children in their grades, and implementing targeted interventions in disadvantaged regions may help reduce primary school dropout in Bangladesh.Retraction: Performance and emission optimization of a CRDI engine in RCCI mode using hydrogen enriched biodiesel through grey relational analysis approach
by The PLOS One Editors
Expression of Concern: Visual edge feature enhancement of product appearance design images based on improved retinex algorithm
by The PLOS One Editors
Expression of Concern: Digital economy, innovation factor allocation and industrial structure transformation—A case study of the Yangtze River Delta city cluster in China
by The PLOS One Editors
Expression of Concern: How to optimize dust pollution control in opencast coal mines: Analysis of a joint social regulation model based on evolutionary game theory
by The PLOS One Editors
An adaptive voxel-wise iterative satellite interference localization method based on a double-layer UCA antenna
by Han Li, Jinjie Yao, Muhua Li, Xu Zhang, Kexin Wang, Hao Li, Congmin Hou
Aiming to balance localization accuracy, computational complexity, and robustness under low-SNR conditions in three-dimensional interference source localization, this paper proposes an efficient method combining frequency-domain covariance matrix reconstruction (FDCMR) and adaptive voxel iteration. First, the proposed method employs FDCMR-based direction-of-arrival (DOA) estimation for multi-station coarse localization and uses the intersection of direction cones to substantially compress the global search region. An adaptive voxel iteration with exponentially decreasing resolution is then performed within the compressed region for position refinement. Complexity analysis shows that the proposed method replaces exhaustive direct position determination (DPD) evaluation over the global search region with a bounded number of evaluations in a local adaptive voxel region, thereby substantially reducing the spatial-search complexity. Under the considered 2000 m × 2000 m × 2000 m search region with a 5 m spatial resolution, the number of DPD objective-function evaluations is reduced by approximately five orders of magnitude compared with conventional global DPD, while a localization accuracy of about 5 m is maintained at an SNR of 20 dB. At an SNR of −15 dB, the localization error is reduced by approximately 70% compared with the fixed-grid DPD method. These results demonstrate that the proposed method provides an effective balance between localization accuracy, computational efficiency, and low-SNR robustness.Cross-national validation of the Depression, Anxiety and Stress Scale for Youth (DASS-Y) in Lebanese and Chinese adolescents
by Changhong Huang, Xiu-Mei Chen, Feten Fekih-Romdhane, Sahar Obeid, I-Hua Chen, Xiao-Ling Liao, Ping Xu, Zeng-Han Lee, Souheil Hallit, Olga Malas
BackgroundThe Depression, Anxiety and Stress Scale for Youth (DASS-Y) has been validated in several countries, but its measurement equivalence between Middle Eastern and East Asian adolescents remains untested, and prior validations primarily relied on single analytic approaches without examining complex factor structures or brief screening forms. This study evaluated the DASS-Y in Lebanese adolescents enduring sociopolitical strain and Chinese adolescents under comparatively stable conditions.
MethodsThe sample comprised 758 Lebanese (M age = 15.95, SD = 1.78; 63.2% female) and 1,062 Chinese adolescents (M age = 16.83, SD = 0.59; 47.5% female). Psychometric properties were examined using confirmatory factor analysis, exploratory structural equation modeling (ESEM), and bifactor variants of both. Differential item functioning was assessed using Rasch analysis. Reliability was indexed by composite reliability (CR), average variance extracted (AVE), and person separation reliability.
ResultsLebanese adolescents showed a largely unidimensional pattern, whereas an ESEM solution better represented the Chinese data. Reliability was strong in both samples (subscale CR = 0.91 to 0.95, AVE = 0.58 to 0.71, person separation reliability = 0.79 to 0.85; total CR = 0.97 in both samples; total AVE = 0.63 for the Lebanese and 0.61 for the Chinese sample). Several items, mainly in the stress subscale, functioned non-equivalently across samples. The 12-item short form performed comparably (subscale CR = 0.88 to 0.92, AVE = 0.65 to 0.73; total CR = 0.96 and 0.94; total AVE = 0.64 and 0.57, respectively).
ConclusionsLebanese adolescents’ responses reflected a unitary distress construct, whereas Chinese adolescents’ responses showed greater differentiation among depression, anxiety, and stress, though a general distress factor remained dominant in both samples. Reliability was high throughout, so this divergence is structural rather than a matter of measurement precision. The 12-item form offers a practical screening option pending further cross-national confirmation.
The influence of moisture ingression on mechanical and vibrational properties of basalt, glass, and hybrid fiber-epoxy composites
by Dayananda Pai, Jibin Jacob John, Sriharsha Hegde, I. V. Thara Reshma, N. H. Padmaraj
Basalt fiber, a natural and eco-friendly reinforcement, has demonstrated superior properties compared with conventional synthetic fibers. This study investigates the effect of ageing on the mechanical and vibrational properties of Basalt/Epoxy, Glass/Epoxy, and Basalt-Glass/Epoxy hybrid composites. Laminates were fabricated using vacuum bagging, ensuring uniform fiber-matrix volume fractions, and then exposed to ageing in seawater, tap water, and distilled water for a duration of 80 days. The moisture absorption pattern followed Fickian diffusion, with seawater exhibiting the highest diffusion coefficients owing to matrix breakdown and osmotic influences. Tensile tests demonstrated a decrease in failure stress and modulus for all materials after ageing, with Glass/Epoxy composites experiencing the most notable degradation despite absorbing less moisture, indicating a weakening of the fiber-matrix interfacial bond. The hybrid laminates exhibited moderate tensile and vibrational characteristics and maintained mechanical performance better in harsh environments. Free vibration tests showed that the natural frequency diminished as mass increased due to moisture absorption, while damping factors rose after ageing. Overall, the combination of basalt and glass fibers enhanced environmental resilience, indicating their suitability for structural applications under challenging conditions.Susceptibility of human neural stem cells to SARS-CoV-2: Entry mechanisms and glycocalyx influence
by Ann Song, Cori Zuvia, Prue Talbot
Previous work demonstrated that ectodermal cells exhibit greater susceptibility to SARS-CoV-2 than endodermal and mesodermal cells, raising concerns about potential vulnerability of the developing nervous system. We hypothesized that neural stem cells (NSCs) derived from human ectoderm are susceptible to SARS-CoV-2. Using pseudotyped viral particles representing both wild-type and Omicron spike variants, we confirmed efficient transduction in NSCs, with Omicron variants preferentially utilizing endocytosis-mediated entry. Inhibition of endocytosis with filipin and OcTMAB significantly reduced viral entry across all spike variants. Low glycosylation levels on NSCs facilitated viral entry, and enzymatic removal of glycosylation increased their susceptibility. Ectodermal infection by SARS-CoV-2 raises serious concern for potential teratogenic effects on the nervous system, possibly causing latent or subclinical anomalies not immediately evident at birth. Therefore, future clinical studies and long-term surveillance of infants and children exposed in utero are necessary to investigate and identify potential neurodevelopmental deficits.Reliability and criterion validity of abductor hallucis muscle volume measurements using ultrasound and MRI by assessors with limited image analysis experience
by Jacob B. Willes, Emma Robinson, Joshua K. Sponbeck, Derek A. Swanson, Dallin C. Swanson, Austin G. Goodrich, Steven P. Allen, A. Wayne Johnson
PurposeThe abductor hallucis (ABDH) contributes to foot function and mobility, which can influence quality of life, making accurate assessment of muscle volume important. While magnetic resonance imaging (MRI) is often considered to be the reference standard of medical imaging, ultrasound (US) provides a cost-effective, rapid and dynamic way of assessing muscle size and function. This study aimed to evaluate the criterion validity and intrarater reliability of US-derived ABDH muscle volume measurements compared with MRI when image analysis was performed by assessors with limited experience.
MethodsUs And MRI were employed to measure the right and left ABDH muscle volume involving 22 participants (females = 12; males = 10). Following exclusions, 37 feet were available for analysis, with 36 complete MRI-US observations. Muscle volume of the ABDH was calculated across the muscle belly every 0.5 cm both manually by utilizing a truncated cone (TC) formula and with a semi-automated tool in the OsiriX (OX) software. Intrarater reliability was assessed using intraclass correlation coefficients (ICC), standard error of measurement (SEm), and minimal detectable change (MDC). Criterion validity was evaluated using Pearson correlations, Bland-Altman analyses, and two one-sided tests (TOST) with a determined ±10% equivalence margin.
ResultsSide-specific intrarater reliability was high across methods (ICC(3,1) = 0.932–0.997), with SEm values of 0.251–0.709 cm³ and relative MDC values of 4.48%−12.95%. Very strong associations were observed among methods (r = 0.906–0.995). Bland-Altman limits-of-agreement half-widths were 6.52%−9.84% for MRI-MRI and 17.86%−27.35% for MRI-US comparisons. All three method comparisons met the ± 10% equivalence criterion.
ConclusionUS-derived ABDH volume measurements demonstrated high intrarater reliability and statistical equivalence to MRI within the ± 10% margin. Although MRI-US comparisons showed greater individual-level variability than MRI-MRI comparisons, US may provide a practical alternative for assessing ABDH muscle volume when MRI is impractical.
Impact of dephased entanglement, mixed input states and varying measurement orientations on the reliability of cryptographic keys generated via the quantum protocol E91
by Adrián F. Hernández-Borda, María P. Rojas-Sepúlveda, Hanz Y. Ramírez-Gómez
Photons and optical circuits are among the most promising platforms for implementation of quantum technologies, including quantum computing and quantum cryptography. For entanglement-based quantum key distribution, on-demand sources of highly entangled photon pairs are essential, and semiconductor quantum dots have emerged as prominent candidates to satisfy the necessary conditions of brightness and fidelity. However, in most cases the photon pair emitted by these sources is an anomalous Bell state whose relative phase and purity are non-ideal, and then, their suitability for distributing secret keys must be assessed. This work studies the performance of the quantum key distribution protocol E91, under the influence of three elements: the phase in the input state, the impurity of such a state, and the relative directions of the polarization analyzers. We derive analytical expressions for the minimum quantum bit error rate and the Bell’s parameter, and validate them by using quantum computational tools. Our results show that the performance of the quantum transmission is highly impacted by the phase and purity of the entangled input states, and that such an impact may be modulated through the orientation of the polarizers. These findings provide important insight for the scalable implementation of quantum key distribution protocols with realistic entanglement sources.Allostatic (over) load measurement: Workflow and repository
by Shawna Beese, Jason Cross, David Rice, Trey L. DeJong
Researchers have long studied allostatic (over)load as an estimated measure of individual cumulative stress over a lifetime. Often called the overall ‘wear and tear’ from social and environmental stressors, allostatic (over)load shows promise as a practical indicator of general health trends in community settings. This data processing workflow aims to document our overall approach and reasoning when calculating allostatic (over)load for data analysis and knowledge sharing. The included repository features an R script for generating datasets using this workflow from the following data sources: All of Us Research Program data repository. Health and Retirement Study (HRS). National Health and Nutrition Examination Survey (NHANES). Our allostatic (over)load measurement process, along with the linked repository, provides a reproducible workflow to process secondary data and offers insights into protocol-driven measurement practices in community environments.Blood biomarkers of long COVID: A cross-sectional evaluation of key cytokines and the coordination of inflammation
by Callum Thomas, Ruth E. Ashton, Etheresia Pretorius, Rebecca Owen, Massimo Nunes, Jan H. Pretorius, Isaac T. Shawa, Corinna Chidley, Mark A. Faghy
IntroductionLong COVID is a complex pathophysiological condition linked to a heterogenous symptom profile. Dysregulation of 1) immunological and inflammatory responses, 2) endothelial/vascular function, and 3) metabolic and clotting processes have been identified; however, there remains a lack of clinically validated diagnostic biomarkers. The study aim was to investigate dysregulation of cytokines, chemokines and growth factors in confirmed Long COVID cases grouped by disease severity.
MethodsOne-hundred-and-twenty-seven participants provided venous blood samples for cytokine profiling utilising Luminex assays. Participants were grouped into those with no knowledge of SARS-CoV-2 infection (n = 21), acute infection within 3-months (n = 22), spontaneous resolvers (>3-months, n = 21) and those with confirmed Long COVID (n = 63). Long COVID severity was grouped according to the post-COVID-19 functional status (PCFS) and defined as those with negligible (4A; n = 9), mild (4B; n = 20), moderate (4C; n = 24), and severe limitations (4D; n = 10). Cytokines were assessed for normality using Shapiro-Wilk tests and differences measured between groups using Kruskal-Wallis tests. Pairwise correlation and principal component analysis were performed.
ResultsInterleukin-12 (p40/p70) was 22% greater in the Long COVID group (Median [IQR]: 58 [49] pg/mL, p = 0.046) and 30% greater in the acutely infected group (64 [36] pg/mL; p = 0.039) against non-infected controls (45 [25] pg/mL). Following PCFS classification, fibroblast growth factor-basic was 25% lower in Long COVID 4B versus spontaneous resolvers (24 [7] vs. 32 [24] pg/mL; p = 0.025). Pairwise correlation differences demonstrated that three Long COVID subgroups had strong, positive associations between cytokines when compared to non-infected controls; however, PCA was unable to determine any coordinated cytokine variation that was group specific.
Discussion and conclusionsElevated interleukin-12 (p40/p70) and reduced basic fibroblast growth factor suggest persistent immune activation and possible subgroup-specific Long COVID; however, these findings were marginally significant and exploratory. A coordinated but partial immunological/inflammatory profile may exist in some of those with Long COVID, suggesting a rationale for subgrouping upon a biological, rather than self-report basis but larger longitudinal studies are required to confirm this hypothesis.
An optimization-based waypoint generation method for overcoming local minima in artificial potential field navigation
by Avinesh Prasad, Vishal Chand, Sushita Sharma, Priynka Sharma, Sandeep Ameet Kumar
The Artificial Potential Field (APF) method is widely used in robot path planning due to its simplicity and computational efficiency. However, it is well known to suffer from the local minima problem, where the resultant force acting on the robot becomes zero at non-goal configurations, leading to motion stagnation in cluttered environments. This paper proposes an optimization-based waypoint generation framework to address the local minima issue in APF navigation while ensuring explicit collision avoidance. When the direct path between the robot and the target is infeasible due to obstacle interference, intermediate waypoints are generated by solving a constrained optimization problem that guides the robot along a locally defined reference direction while enforcing obstacle clearance constraints for each path segment. Unlike conventional APF modifications that rely on artificial perturbation forces or random escape strategies, the proposed method constructs collision-free intermediate waypoints through finite candidate evaluation and explicit segment-wise collision checking, enabling the robot to avoid obstacles and overcome local-minima while preserving goal-directed motion and ensuring convergence to the target. The effectiveness of the proposed approach is validated through numerical simulations on two robotic platforms, namely a point-mass robot and a wheelchair-type robotic model. The results demonstrate that the method successfully resolves local minima situations, ensures collision-free motion, and consistently guides the robots to their target locations in complex environments. Furthermore, the framework is general and can be applied to different robotic systems.Nomenclature, classification, and in-silico structural analysis of MSP1a variants of Anaplasma marginale
by Amam Zonaed Siddiki, Nazifa Humaira, Nanziba Sharmin Hossain, Yusra Bint Akbar, Puja Palit, Pann Ei Phwe, Rahima Akter Eva, Hala Alshaar, Marjia Islam Tia, Kethain Marma, Barsha Akter, Samira Sultana, Sabreena Alam
Bovine anaplasmosis, caused by the tick-borne rickettsial pathogen Anaplasma marginale, is a significant threat to livestock health and productivity. The major surface protein 1a (MSP1a) is a widely used molecular marker for characterizing strain diversity, due to its variable N-terminal tandem repeats. In this study, we analyzed and updated the nomenclature classification of 157 A. marginale MSP1a sequences submitted to NCBI between 2013 and 2023, following the research by Cabezas-Cruz in 2013. We identified and classified 189 unique tandem repeats, reported their tick-transmissibility, predicted their secondary structures and B-cell epitopes, and analyzed amino acid variability. Following this, we conducted a comparative analysis with previously published datasets, which revealed new tandem repeat types, new positions of serine within tandem repeats, and high variation among B-cell epitope variants. We have also classified the unique R1 tandem repeats of the new MSP1a sequences into different ecoregion clusters based on the principal component analysis of NDVI values. This updated classification system enhances the molecular understanding of A. marginale diversity and may contribute to future developments in strain typing, epidemiological investigations, and vaccine research.Beyond conventional indicators: Reproductive coercion and its overlap with measured intimate partner violence in Bangladesh
by Md. Nuruzzaman Khan
BackgroundReproductive coercion is an under-recognised form of violence against women and is not routinely captured by standard measures of intimate partner violence, which commonly focus on physical, sexual, and emotional violence. This study examined the association between reproductive coercion and these measured forms of intimate partner violence and estimated the proportion of women who reported reproductive coercion without reporting any of the physical, sexual, or emotional violence items included in the survey.
MethodsWe analysed nationally representative data from 24,951 ever-married women, including 22,161 currently married women, in the Bangladesh Violence Against Women Survey 2024. Outcomes were physical, sexual, emotional, and any conventional IPV, assessed in the past 12 months among currently married women and over the lifetime among ever-married women. Exposures were reproductive coercion pattern and contraceptive coercion typology. Survey-weighted Poisson regression with robust standard errors estimated adjusted prevalence ratios (aPRs) with 95% CIs for associations with conventional IPV. Survey-weighted logistic regression examined factors associated with reproductive coercion without reported conventional IPV and was considered exploratory because of the small number of events. Survey-weighted prevalence estimates were applied to 2022 census population counts to derive population estimates with 95% uncertainty intervals (UIs).
FindingsIn the past 12 months, 27.30% of currently married women reported conventional IPV and 3.59% reported reproductive coercion; corresponding lifetime prevalences among ever-married women were 60.40% and 9.25%. Including reproductive coercion increased the prevalence of any reported abuse to 28.61% and 61.18%, respectively. Reproductive coercion was associated with a higher prevalence of each form of conventional IPV, with the largest aPRs among women reporting multiple forms of reproductive coercion. Reproductive coercion without reported conventional IPV was reported by 1.31% (95% CI 1.08–1.59) of currently married women in the past 12 months and 0.78% (0.60–1.01) of ever-married women over the lifetime, corresponding to approximately 645,483 (95% UI 532,154–783,449) and 437,414 (337,106–567,462) women, respectively.
InterpretationReproductive coercion captures a dimension of violence against women that is only partly reflected in conventional IPV measures. Women reporting reproductive coercion without conventional IPV might have experienced unmeasured or unreported violence; therefore, this group indicates a measurement gap rather than absence of other abuse. Including validated reproductive coercion measures in national violence surveys could improve evidence for health, legal, and social support responses.