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Psychosocial determinants of oral health-related quality of life in periodontitis patients

Parent-set bedtime in adolescence is associated with future cardiovascular disease risk: Evidence from the Add Health study

Parent-set bedtimes have been linked to a lower prevalence of key cardiovascular disease (CVD) risk factors in adolescents. However, little is known about how parent-set bedtimes during adolescence affect CVD risk later in life. This study examined the association between parent-set bedtimes and future CVD risk, as well as the potential mediating role of sleep health. Data were taken from Waves I and IV of the National Longitudinal Study of Adolescent to Adult Health, including 4,151 participants. Parent-set bedtimes (10:00 PM, by 11:00 PM, and by midnight) were collected at Wave I. The outcome measure was the 30-year Framingham CVD score, categorized as low or high risk. Analyses were performed using SURVEYLOGISTIC and CAUSALMED procedures in SAS. About 28% of adolescents had a parent-set bedtime by or after 11 PM, while 18% had no parent-set bedtime. Adolescents with parent-set bedtimes by or after midnight (aOR: 2.32, 95% CI: 1.65–3.26) and those without a parent-set bedtime (aOR: 1.35, 95% CI: 1.01–1.79) had significantly higher CVD risk in adulthood compared to those with earlier bedtimes (by 10 PM). Sleep health partially mediated the relationship between parent-set bedtime and future CVD risk. Our study findings indicate that parental-set bedtimes during adolescence are associated with future CVD risk. Further prospective or experimental studies are needed to confirm these relationships.

Research on engine power-loss fault diagnosis method based on time-series data mining

Abstract Traditional diagnostic approaches for engine power-loss faults in commercial vehicles are limited by their heavy reliance on on-site road testing and high consumption of human and material resources. To address these limitations, this study proposed a new intelligent diagnosis method based on time-series data mining. Analyzing real-world operational data collected from onboard telematics terminals, identified key features strongly correlated with engine power loss, including vehicle speed, acceleration, and the rate of change of throttle opening. Building upon these features, a dual-framework diagnostic strategy was developed: the data were first categorized into two groups, “with driver acceleration intent” and “without driver acceleration intent”, based on the rate of change of throttle opening. For samples with acceleration intent, multiple machine learning algorithms were employed to model and diagnose vehicle power performance; for those without driver acceleration intent, a deep learning model integrated with classification techniques was introduced to detect latent power-loss faults. Experimental results demonstrated that the proposed method achieved high accuracy and specificity in fault identification, confirming its effectiveness and practical potential. This study provides a viable pathway toward remote, online diagnosis of engine power loss in commercial vehicles and lays a foundational framework for the intelligent advancement of this field.

A novel prediction approach of three-dimensional thermal fatigue cracks in thermal compression bonding electrodes based on digital twin

Thermal compression bonding (TCB) electrodes that initiate thermal fatigue cracks compromise reliability and takt time in electronic manufacturing, and accurate prediction of three-dimensional (3D) electrode cracks is a prerequisite for crack mitigation. This study developed a digital twin (DT) framework that combined physics-based simulation and artificial intelligence (AI). The framework used the extended finite element method (XFEM) to build a high-fidelity electrode DT and reproduced fatigue behavior under coupled electrical, thermal, and mechanical loading through adaptive updating. To alleviate the scarcity of crack data, a conditional variational autoencoder (CVAE) with a position attention (PA) mechanism was constructed, with an error of 0.7% to 1.3% relative to experimental results. Using the augmented data, the PA-RePointNet model was developed to predict 3D crack morphology. Results showed that PA-RePointNet surpassed PointNet++ and PointCNN in prediction accuracy and stability and achieved a mean absolute error (MAE) of 2.8, a root mean square error (RMSE) of 5.1, and a coefficient of determination (R²) of 0.9378, while the maximum relative error between the reconstructed 3D cracks and experimental measurements was 1.87%. This framework provides a high-precision solution for electrode crack prediction and opens a new pathway for intelligent maintenance of TCB electrodes in microelectronic manufacturing.

Flow boiling-driven intensive growth of critical minerals during short-lived events

Genotypic variability of Tunisian maize landraces: A valuable genetic resource to mitigate drought and heat stress in the Mediterranean basin

This study aimed to assess the drought and heat stress tolerance of nine Tunisian maize populations and their potential stress tolerance mechanisms. Over two years, nine Tunisian maize populations were evaluated under five environments with varying stress levels and one optimal growth condition in Tunisia. This work formed part of a larger study that includes a total of 223 Mediterranean maize landraces. The nine Tunisian populations were specifically chosen to assess the behavior of landraces adapted to the drought and heat stress conditions prevalent in the southern Mediterranean. In all the locations, the trials followed an augmented design with five blocks and a total of five checks over the two-year study period replicated in each block.The study demonstrated that combined drought and heat stress severely reduced maize yield, with Tunisian landraces experiencing losses of 76% to 95% relative to optimal conditions. Factorial regression analyses were performed to provide a biological interpretation of the contribution of environmental and genotypic variables, as well as their interactions, to grain yield variability. The most representative genotypic covariates were plant height (PH) followed by the number of ears (NE), thousand-grain weight (1000GW), and aerial biomass, respectively, explaining 26%, 12%, 9%, and 8% of the total variability. The significant environmental covariates were cumulative hydric deficit (DHC) and the average anthesis silking interval (ASI_ENV) in each environment, representing 48% of the total environmental variation. The interaction between thousand-grain weight and cumulative hydric deficit had the highest contribution (9%) of interaction for grain yield. The factorial regression indicated that under stress conditions, maize plants appeared to adapt to maintain yield by increasing thousand-grain weight while reducing aerial biomass, number of ears, and grain number. This response likely reflects an enhanced capacity for efficient resource reallocation, supporting the plant’s resilience under combined drought and heat stress conditions. The landraces BK, KAR, and MT2 consistently outperformed in most traits under stress conditions, showing significant tolerance and adaptability for across multiple stress levels with better yields and flowering synchronization. The selected best-performing populations could serve as valuable sources of drought and heat stress tolerance sources for future breeding programs.

Experimental assessment with data-driven machine learning-based prediction of compressive strength of waste natural fiber-reinforced sustainable concrete

Grading by size to reduce the opportunity for domestication selection in hatchery-reared steelhead (Oncorhynchus mykiss)

Fish that are produced in hatcheries often produce fewer surviving adult offspring than do wild fish when both spawn in the wild. This difference in fitness is likely due to inadvertent selection for adaptation to the hatchery environment. Size at release is positively correlated with survival at sea. Therefore, selection should favor traits that promote fast growth in the hatchery even if those traits are maladaptive in the wild. In that case, changing hatchery conditions to reduce the variance in size at release would reduce the opportunity for domestication selection. Here we test whether grading by size and raising each size group separately can substantially reduce the variance in size at release. We graded a mix of 15 full-sibling families of winter run steelhead ( Oncorhynchus mykiss ) juveniles (initial n = 375 fish/tank) into small, medium, and large body size groups. We then promoted growth in the initially-small fish with excess food and low densities, and slowed growth in the initially-large fish with restricted feedings and high densities. We successfully held back the growth of the initially-large fish, but the initially-small fish never caught up with the controls, despite being raised under ideal conditions. This result suggests that inherent physiological or behavioral factors, rather than interactions with larger fish, restrict the growth of small fish. Both the total variance among individual fish and the variance among family means was reduced by the grading treatment, although only the reduction in total variance among individuals was statistically significant when comparing intraclass correlation values (ICC). The reduced variance due to slowing the growth of the initially-large fish indicates that grading could possibly be a tool used by hatcheries to lessen selection on salmonids. However, it would first be necessary to better understand the factors limiting growth in the smaller fish.

Surface enhanced Raman spectroscopy and machine learning as an accurate and rapid diagnostic tool for hydrocephalus

Abstract Hydrocephalus is a severe neurological disorder marked by cerebrospinal fluid (CSF) accumulation in the brain’s ventricles, causing elevated intracranial pressure, neurological deficits, and substantial societal and medical costs. Rapid detection in neonates and children remains challenging, as primary diagnostic indicators such as increased head size often manifest late, delaying intervention. Hydrocephalus stems from diverse causes, including prematurity, intracranial bleeds, CNS infections, and brain tumours. CSF collected for diagnostic purposes provides an opportunity to explore innovative rapid detection methods. In this paper, we show that integrating Surface-Enhanced Raman Spectroscopy (SERS) and machine learning offers a novel molecular diagnostic tool for hydrocephalus. Using silver nanoparticle-layered-cellulose strips as SERS substrates and a portable Raman spectrometer, we analysed CSF samples from 117 patients (70 controls, 47 hydrocephalus cases). Within 5 min of sample placement in strip, the optimized Random Forest algorithm achieved 97% accuracy in blind testing, with 100% specificity and 95% sensitivity. The miniaturized Raman spectrometer and standarised strips enable portability and support clinical use, particularly in resource-limited settings. Our grid search-based ML workflow and scoring system enable the prediction of overfitting, as well as assessment of feature importance within the spectra, improving model interpretability, which could prove helpful in other vibrational spectroscopy applications. Overall, this study provides evidence of an accurate, rapid, portable, and interpretable diagnostic technique for hydrocephalus. Further validation with larger cohorts will refine predictive models and expand clinical utility, advancing diagnostic precision for hydrocephalus and related neurological conditions.

Influence of rice variety and grain form on the development and infestation of Sitophilus oryzae (L.): A comparative analysis

Post harvest losses caused by the rice weevil ( Sitophilus oryzae L .) pose a severe threat to food security and farmer income in Ethiopia. While both rice variety and grain processing form (paddy, brown, polished) influence susceptibility, their interaction in locally adapted Ethiopian varieties remains unquantified, limiting the development of effective resistance-based storage strategies. A controlled laboratory study was conducted using a three-factor factorial experiment in a completely randomized design (CRD) with seven replicates per treatment (n = 7), totaling 126 experimental units. Six Ethiopian rice varieties (Nerica-4, Gumara, Selam, Shaga, Wanzaye, and X-Jigna) were tested across three grain forms. Each replicate consisted of 50 g of grain infested with 20 unsexed adult weevils. Key parameters measured were development time, F₁ progeny emergence (as a measure of fecundity), percentage grain damage, weight loss, and the Dobie Susceptibility Index (DI).Grain form was the dominant factor affecting weevil performance. Polished rice significantly accelerated development (mean = 29.86 days) and increased progeny production (mean = 40.38 adults) and grain damage (21.83%) compared with brown rice (29.81 progeny, 6.79–13.86% damage) and paddy rice (23.29 progeny, 8.29–16.00% damage). This represents up to a 42.3% reduction in progeny emergence and a 69.9% reduction in grain damage when storing brown rather than polished rice. A significant variety × grain form interaction (p < 0.05) revealed that polished Gumara and X-Jigna were the most susceptible combinations, while brown and paddy Nerica-4 showed the strongest resistance. The Dobie Index was strongly positively correlated with progeny count (r = 0.859, p < 0.001) and negatively correlated with development time (r = −0.912, p < 0.001), supporting its validity as a resistance metric. Grain width showed a weak but significant negative correlation with weight loss (r = −0.227, p = 0.011). The high susceptibility of polished rice and the strong resistance of Nerica-4 provide a clear strategy for loss reduction. Promoting the storage of paddy or brown rice of resistant varieties like Nerica-4 can significantly mitigate postharvest losses, reduce pesticide reliance, and enhance food security in Ethiopia.

Prognostic outcome of extranodal marginal zone B-cell lymphoma: a nationwide cohort

The diversity of cellular systems involved in carbonate precipitation by Escherichia coli

Climate change is increasing the need to limit levels of anthropogenic CO 2 released into the atmosphere. One approach being investigated is to generate products based on microbially induced carbonate precipitation (MICP), which can trap CO 2 as CaCO 3 . We recently identified a novel MICP pathway in bacteria that is initiated by Ca 2+ toxicity in cells, causing extracellular CO 2 to be trapped as CO 3 2- by Escherichia coli, although the yield of precipitated CaCO 3 remained low (in the milligram range). In this work, we used the E. coli Keio gene knock-out library to identify 54 genes involved in MICP in E. coli, which could be broadly characterized into four groups: central metabolism, iron metabolism, cell architecture, and transport. The role of central metabolism appears to be crucial in maintaining alkaline conditions surrounding the cell that promote CaCO 3 precipitation. The role of iron metabolism was less clear, although the results suggest that growth rate influences the initiation of MICP. While the impact of repeating polymeric structures on cell surfaces promoting MICP is well established, our results suggest that other structural features may play a role, including fimbriae and flagella. Finally, the results confirmed that Ca 2+ transport is central to MICP under calcium stress. The results further suggest that the ZntB efflux pump may play a previously unidentified role in Ca 2+ transport in E. coli. By overexpressing some of these genes, our work suggests that there are several previously unidentified cellular mechanisms that could serve as a target for enhanced MICP in E. coli. By incorporating these processes into MICP pathways in E. coli, it may be possible to increase the volume of CO 2 fixed using this pathway and yield potentially new products that can replace CO 2 intensive products, such as precipitated calcium carbonates (PCCs) for industry.

Computational study on B, N, and Si-doped C60 nanocages for acetone detection in heart failure diagnosis and environmental remediation

Abstract Acetone is a volatile organic compound that acts both as an environmental pollutant and as a biomarker for metabolic disorders such as heart failure. Therefore, early and sensitive detection of acetone is of great importance for environmental monitoring and medical diagnosis. Recent advances in carbon-based nanomaterials, especially C60 fullerene, have shown promise in the development of highly sensitive and selective sensors. Building on this background, the present study aimed to design and theoretically evaluate a pristine C60-based sensor and its doping forms with B, N, Si for acetone detection using density functional theory (DFT) and quantum theory of atoms in molecules (QTAIM). Key parameters including adsorption energy (Eads), recovery time (τ), electrical conductivity (σ), HOMO–LUMO gap (HLG), and dipole moment (μ) were computationally studied. The results show that SiC59 acts as a highly sensitive sensor, exhibiting a strong adsorption energy of − 137.17 kJ mol −1 , a reduced HLG of 0.74 eV, a high dipole moment of 19.55 D, and a fast recovery time of 1.50 × 10–10 s. In contrast, BC59 exhibits exceptional adsorption capacity (Eads = − 109.28 kJ mol −1 ), making it ideal for acetone adsorption and environmental remediation. The superior performance of SiC59 and BC59 holds promise for efficient acetone detection and removal, supported by strong quantum mechanical insights.

The effect of retirement on health behaviours: Evidence from Brazil

Objective This study investigates the impact of retirement on health behaviours in Brazil in light of rising life expectancy and recent pension age reforms, focusing on how retirement affects well-being in a middle-income country. Methods Using data from the 2013 and 2019 Brazilian National Health Surveys (PNS), this study analyses health behaviours among 54,741 individuals aged 50–80. Health behaviours (alcohol consumption, smoking, physical activity, sleep medication use, and diet) were measured using binary and continuous variables. Retirement status was defined as receiving a pension and not working, with Brazil’s minimum retirement age used as an instrumental variable to address endogeneity. Probit and IV probit models for binary outcomes and OLS and IV OLS models for continuous outcomes were estimated, with statistical tests supporting instrument strength and endogeneity. Results The findings reveal a positive relationship between retirement and improvements in health behaviours. In the IV probit models, retirement is associated with increased physical exercise (β = 0.393, p < 0.05) and healthier eating habits (β = 0.371, p < 0.05). Men are less likely than women to reduce smoking. Retirement is linked to greater time spent engaging in physical exercise, reductions in alcohol consumption and smoking, together with healthier eating habits. Conclusion These results have significant policy implications, underscoring the need to consider the potential long-term public health effects of increasing the retirement age, as it could result in higher public health burdens.

Machine learning prediction of long-term sickness absence due to mental disorders using Brief Job Stress Questionnaire data

Abstract Long-term sickness absence (LTSA) is a significant issue, causing productivity decline, financial difficulties, and increased mental health issues, with mental disorders being the most common cause. Occupational stressors are also linked to increased risk of LTSA due to mental disorders (LTSA-MD). This study uses occupational stressors data, assessed using the Brief Job Stress Questionnaire from 2011 to 2022, to predict LTSA-MD using machine learning and sampling methods, assessing their performance. This study analyzes data from 231,425 Japanese public servants from 2011 to 2022, focusing on LTSA-MD incidents. We compared five machine learning models and six sampling methods (random sampling, equal size sampling, SMOTE-synthetic minority oversampling technique, bootstrapping, borderline-SMOTE and ADASYN-adaptive synthetic sampling) to predict LTSA-MD incidents, addressing class imbalance. We prioritized average precision (AP) to identify the most promising model–sampling combinations to give the severe class imbalance. The gradient boosted trees model and bootstrap oversampling method demonstrated highest AP among all integrations of machine learning and sampling methods, with a AP of 0.040 and a ROC-AUC of 0.81. However, no significant difference in superiority was observed between the combinations of higher-level AP machine learning and sampling methods. The results demonstrate that machine learning models’ predictive ability for LTSA-MD is generally low, requiring further research.

Identification of Rana dybowskii Ferritin-Heavy chain gene and analysis of its role during bacterial infection

Ferritin is widely present in organisms, which can maintain iron relatively stable and participate in the immune response. In this study, the full-length coding sequence (CDS) of the Rana dybowskii ( R. dybowskii ) Ferritin-Heavy Chain ( Fer-H ) gene was cloned by the polymerase chain reaction (PCR) method and characterized by bioinformatics analysis. In order to further explore its role, the inflammation model was established by using Aeromonas hydrophila ( Ah ). The activity of antioxidant enzymes in some tissues was detected, and the expression level of the R. dybowskii Fer-H ( RdFer-H ) gene was detected by quantitative real-time PCR and Western blot analysis. Bioinformatics analysis revealed that the Fer-H gene was 534 bp long, encoding 177 amino acids, and there was a Pfam Ferritin domain. When compared to other species with the same nucleotide sequence, Rana temporaria has the highest homology (94%) with the Fer-H gene. The activities of antioxidant enzymes indicated that the activities of SOD and CAT increased significantly, while the activity of GSH-Px decreased distinctly. This meant that the bacterial infection had caused serious oxidative damage to R. dybowskii . The qRT-PCR results confirmed the broad expression of the Fer-H gene in all R. dybowskii tissues. Furthermore, the transcription level was significantly up-regulated after bacterial infection, and the protein accumulations were consistent with the transcript levels in liver and muscle tissue according to Western blot after Ah infection. This study hypothesizes that the Fer-H gene contributes to R. dybowskii ’s immune response during bacterial infection. It also broadens the research idea for exploring the anti-infection immune response mechanism of amphibians.

RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI

The dilemma of sweet temptation: How sugar perception confusion in sweetened beverages shapes consumer avoidance behavior

Despite widespread consumption of sugar-sweetened beverages, consumers face contradictory information from health authorities, marketing, and social media, yet limited research examines how this information conflict affects purchasing decisions. This study investigates how sugar perception confusion influences purchasing avoidance through ambivalent attitudes. Based on cognitive dissonance and information processing theories, we developed a cognitive-affective-behavioral model examining relationships among sugar perception confusion, ambivalent attitudes, and purchasing avoidance behaviors. Using PLS-SEM analysis of 531 Chinese consumers, results show sugar perception confusion significantly affects ambivalent attitudes (β = 0.576, p < 0.001), which strongly predict purchasing avoidance (β = 0.593, p < 0.001). Sugar perception confusion also directly influences purchasing avoidance (β = 0.155, p < 0.001), with ambivalent attitudes serving as a significant mediator (indirect effect β = 0.342, p < 0.001). These findings advance consumer information processing theory and provide evidence-based insights for optimizing information environments to support informed decision-making.

Strengthening mechanisms of indigenous bacteria in granite residual soil improvement via microbial induced calcite precipitation

The role of demographic characteristics in US medical students’ professional well-being and medical school experiences: An intersectional approach

Introduction Previous findings have been mixed about the role of demographic characteristics in medical students’ well-being and school experiences when those characteristics were examined in isolation. The aim of this study was to investigate the roles of gender, race and ethnicity, and sexual orientation in medical students’ professional well-being and medical school experiences using an intersectional approach. Method We analyzed data from the 2019–2022 Association of American Medical Colleges Graduation Questionnaire ( N  = 66,795). The independent variable was intersectional groups, composed of 16 intersectional groups that combined various genders, races and ethnicities, and sexual orientations. The outcome variables were professional well-being (i.e., burnout, career regret) and medical school experiences (i.e., general mistreatment, discrimination, emotional climate, faculty-student interaction, faculty professionalism, and satisfaction with medical education). Given the large sample, we focused on effect sizes versus statistical significance. Results The intersectional groups differed from each other on all professional well-being and all medical school experience variables except emotional climate, with at least small effect sizes (ηp 2  ≥ .01). Black female sexual minority students reported the most negative outcomes on all variables. The largest differences were primarily with White male heterosexual (e.g., discrimination: d  = 1.68, 95% CI [1.53, 1.84]) and White female heterosexual (e.g., disengagement: d  = 0.63, 95% CI [0.48, 0.79]) students. However, being a member of a greater number of marginalized groups was not necessarily associated with more negative outcomes, and patterns of group differences varied across domains of professional well-being and medical school experiences. Discussion Examining the combination of medical students’ gender, race and ethnicity, and sexual orientation yielded larger and more consistent effect sizes than examining each factor individually, suggesting that an intersectional approach can identify the unique challenges confronted by medical students from specific demographic groups.