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Memory and thinking problems that aging Latinos in New York City would bring to a doctor’s attention

The number of individuals with Alzheimer’s disease will grow dramatically in the coming decades. Early diagnosis benefits patients, caregivers and society, but depends heavily on afflicted individuals or their family members recognizing early symptoms as possible indications of a medical problem and seeking medical care. To examine the kinds of memory or thinking problems, Latinos ages 40–64 would seek medical care for, we analyzed data from 161 participants in a community-based study in northern Manhattan of the impact of receiving information about one’s risk of developing late-onset Alzheimer’s disease. Participants were asked whether experiencing each of 5 different memory or thinking problems multiple times over 2–3 months would make them seek medical care. Participants often offer a benign or normalizing attributions for symptoms. Disorientation was the most frequently endorsed problem. Considerations found to be associated with an inclination or disinclination to want to see a doctor about a symptom were identified. A better understanding of what Latinos would consider in deciding whether or not to bring different memory problems to a doctor’s attention can help guide the development of educational interventions to encourage help-seeking and facilitate earlier diagnosis.

Bioactive metabolites of UV-resistant Streptomyces alkaliterrae CH-8 and their antioxidant and antibiofilm potential

A High-Performance Rh-TMP-COF Photocatalyst for CO ₂ -to-CO Conversion with H ₂ O Vapor: From Descriptor Prediction to Experimental Validation

Trust the crowd: Crowdsourced fact-checking is as effective at reducing confidence in misinformation as expert fact-checking

The rapid spread of misinformation on social media has created significant challenges for expert fact-checking initiatives to counter in a timely and effective manner. Misinformation undermines behaviour and decision-making in many spheres including health and political domains. X (formerly known as ‘Twitter’) utilises crowdsourced fact-checking (termed ‘Community Notes’) to manage the high volume of and engagement with online misinformation. Community Notes have also been introduced to mitigate perceived partisanship and bias of expert fact-checkers. The present study recruited 102 participants to investigate whether expert or crowdsourced fact-checks on X are more effective at reducing belief in misinformation and engagement with misinformation. Participants were randomly allocated into either an expert or crowdsourced fact-checking condition. Confidence in the veracity of misinformation and willingness to retweet were measured, before and after exposure to fact-checks. It was found that both crowdsourced and expert fact-checks reduced confidence in misinformation and willingness to retweet the information. The results demonstrate the efficacy of crowdsourced fact-checking, a fact-checking variant that is rapidly gaining popularity. Given this, the adoption of crowdsourced fact-checking by other social media platforms warrants consideration.

Dual Role of Microglial TREM2 in Neuronal Degeneration and Regeneration after Axotomy

Spinal cord ventral horn microglia proliferate after nerve injuries and migrate toward cell bodies of injured motoneurons (MNs) enwrapping them. The significance of this microglia reaction has remained unclear. Moreover, some injured MNs degenerate while others regenerate. In mice spinal cords, we found that each MN fate associates with microglia of different activation profiles. Microglia surrounding degenerating MNs form cell clusters that fully envelop the cell body and express high triggering receptor expressed on myeloid cells 2 (TREM2) and large CD68 granules, with female microglia expressing higher levels. Microglia surrounding MNs undergoing regeneration remain individualized and also upregulate TREM2 and CD68 but to a lesser extent. Removal of TREM2, either globally throughout development or specifically in microglia prior to nerve injuries, reduces phosphorylated spleen tyrosine kinase signaling and CD68 expression in all activated microglia but more so inside microglia forming tight cell clusters around degenerating MNs. This effect is also larger in females. TREM2 absence did not prevent microglia clustering around degenerating MNs but prevented the loss of some small MNs. In addition, TREM2 depletion interfered with the retrograde cell body chromatolytic reaction characteristic of regenerating MNs and delayed muscle reinnervation. We conclude that within the same motor pools, TREM2 facilitates microglia removal of some degenerating MNs, while it facilitates regeneration of others. The signals that direct the development of these different microglia phenotypes over degenerating and regenerating MNs, as well as the mechanisms that induce degeneration in some MNs while most others regenerate, remain to be investigated.

Research on the intelligent detection and analysis of floating debris in polluted water

Toward transparent intelligence: Explainable stacked ensembles learning for LiDAR point cloud segmentation

Segmentation of LiDAR point cloud data has various applications, ranging from urban planning to environmental monitoring. Although machine learning approaches have achieved impressive segmentation performance, their black-box nature often limits their interpretability. While stacked ensemble learning improves segmentation accuracy by combining multiple classifiers, it further increases model complexity and obscures decision transparency. To address this gap, this study proposes an explainable stacked ensemble framework for LiDAR point cloud segmentation that integrates multiple base learners with Logistic Regression as a meta-model and incorporates model-agnostic Explainable Artificial Intelligence (XAI) techniques. Experimental results on two benchmark datasets demonstrate segmentation accuracies of 91.13% and 95.71%. The study also identifies the most effective base models within the ensemble to facilitate optimal model selection. Furthermore, XAI-driven feature analysis enables effective feature reduction, achieving a minimum 7% reduction in training time while maintaining consistent accuracy. In addition, variants of the SHAP algorithm are employed to investigate the relevance of features and the impact of neighborhood selection strategies on the segmentation performance of each base model. The experimental results demonstrate that the proposed approach achieves competitive segmentation performance while improving interpretability and computational efficiency.

AI-driven insights into the impact of tourism on local cultures: a machine learning approach

Dynamic right ventricular and atrial volume responses to exercise in endurance-trained and untrained healthy individuals

Aims Left ventricular (LV) function is enhanced during exercise in endurance-trained (ET) compared to untrained (UT) healthy individuals. However, the volume responses of the right ventricle (RV), left atrium (LA), and right atrium (RA) during exercise have not been studied in the same subject simultaneously and while standardizing for the respiratory influence on cardiac volumes. The aim was therefore to investigate how the four-chambered heart responds to exercise in ET and UT healthy individuals using exercise real-time cardiac magnetic resonance imaging (CMR) while controlling for effects of respiration. Materials and methods Twenty ET (9 women) and 13 UT healthy individuals matched for age and sex underwent CMR at rest and during moderate and vigorous exercise. LV and RV end-diastolic and end-systolic volumes (EDV and ESV), stroke volumes (SV) and maximal and minimal LA and RA volumes (LAV max , RAV max and LAV min , RAV min ) were measured at end expiration. Results LVEDV and LVESV decreased during exercise in both groups. RVEDV and RVESV decreased during exercise in ET but was unchanged in UT. LAV max and LAV min were unchanged from rest to vigorous exercise in both groups. RAV max decreased during exercise in ET whilst RAV min was unchanged, and RAV max and RAV min were unchanged in UT. Thus, the RV and RA volume responses during exercise differed between ET and UT. Conclusion This study shows differences between ET and UT healthy individuals in adaptations to exercise when examining the four chambers simultaneously and even when standardizing for respiratory influences. This contributes to increased understanding of the RV volume adaptations in ET.

A positive psychology perspective on self-efficacy and work engagement among psychiatric hospital staff in Japan: A cross-sectional study

Abstract Sustaining work engagement among psychiatric hospital staff is important for staff well-being and the quality and continuity of mental health care, yet modifiable personal resources in this setting remain understudied. Guided by the Job Demands–Resources model and positive psychology, we examined whether general self-efficacy was associated with work engagement in psychiatric hospital staff in Japan and whether this association differed across occupation, managerial position, and age. In a cross-sectional survey of 199 employees, work engagement was assessed using the Japanese Utrecht Work Engagement Scale (UWES-J-17) and self-efficacy using the Japanese General Self-Efficacy Scale (J-GSES). Linear regression models estimated the association between self-efficacy and engagement, adjusting for gender, age group, managerial position, employment status, and occupation; subgroup consistency was evaluated using stratified analyses and interaction terms. Higher self-efficacy was associated with higher work engagement (B = 0.93, 95% CI 0.42–1.44, p  < 0.001). The association appeared similar across occupational categories, managerial levels, and age groups (all interaction p  ≥ 0.43). In this single-site sample, general self-efficacy was positively associated with work engagement. Causal inference is not possible, and longitudinal and intervention studies are needed.

High precision CA-ID-TIMS U-Pb zircon age for the “Dueling Dinosaurs” locality, with implications for regional correlation, basal age and duration of the Hell Creek Formation, Montana

Discovery of the “Dueling Dinosaurs” and other significant dinosaur localities from remote and isolated exposures of the Hell Creek Formation in central Montana highlight the complexity of establishing stratigraphic context and correlating Hell Creek Formation fossil localities located within and outside of the type area. Stratigraphic correlation is particularly problematic for the lower two-thirds of the Hell Creek Formation, which generally lacks reliable biostratigraphic or magnetostratigraphic zonation and has no dated ash beds. To address these enduring issues for one of the most significant Upper Cretaceous terrestrial fossil-bearing units in North America, detailed stratigraphic sections were established on the Murray Ranch and on McGinnis Butte in central Montana and correlated with other published Hell Creek Formation localities via magnetostratigraphy, biostratigraphy, and radioisotopic dating of ash beds. Results indicate that the K-Pg boundary is not exposed in the study area; however, high-precision U-Pb CA-TIMS zircon ages for two newly discovered ash beds (66.929 ± 0.020 Ma and 66.850 ± 0.026 Ma, 2σ internal uncertainties) that bracket the “Dueling Dinosaurs” quarry provide the first absolute ages for the lower portion of the Hell Creek Formation anywhere. Bayesian age-stratigraphic modelling places the “Dueling Dinosaurs” depositional age at 66.897 + 0.023/-0.028 Ma and suggests that the age of the base of the formation is ~ 67.102 + 0.710/-0.173 Ma (or older) in the study area. Comparison of stratigraphic architecture within the study area with published sections in the type area suggests that named sandstone marker horizons used for lithostratigraphic and sequence stratigraphic correlation in the type area have limited utility for regional correlation and need to be used with caution.

High altitude limits the biogeography of common rats but not house mice

Learning-based multi-objective hyper-heuristic algorithm for reconfigurable assembly line scheduling problems

Reconfigurable assembly lines have emerged as a vital manufacturing paradigm to meet the growing demand for customized and multi-variety products. This study considers the reconfigurable assembly line scheduling problem, involving product sequencing optimization, to minimize reconfiguration cost, production workload equalization, and logistics leveling simultaneously. This study formulates a novel and linearized multi-objective mathematical model, which rectifies deficiencies in prior formulations. A novel Q-learning-based multi-objective hyper-heuristic algorithm is proposed. The algorithm integrates multiple metaheuristic operators, including particle swarm optimization, teaching–learning-based optimization, whale optimization algorithm, and grey wolf optimizer, within a unified search framework. Q-learning is employed to adaptively select the most promising operator at each search stage based on real-time performance feedback. Moreover, the proposed algorithm incorporates a new density-aware leader selection strategy with a survival-time decay factor to select the global best solution for population evolution, favoring superior solutions in sparse regions and increasing selection pressure on high-quality individuals. A numerical case study demonstrates that the models with the ε-constraint method could achieve a set of Pareto solutions. A computational study on 120 generated benchmark instances demonstrates that the proposed methodology outperforms nine other high-performing multi-objective algorithms.

Enhancing composition-based materials property prediction by cross-modal knowledge transfer

Abstract Crystal graph neural networks are widely applicable in modeling experimentally synthesized compounds and hypothetical materials with unknown synthesizability. In contrast, structure-agnostic predictive algorithms allow exploring previously inaccessible domains of chemical space. Here we present a universal approach for enhancing composition-based materials property prediction by means of cross-modal knowledge transfer. Two formulations are proposed: implicit transfer involves pretraining chemical language models on multimodal embeddings, whereas explicit transfer suggests generating crystal structures and implementing structure-aware predictors. The proposed approaches were benchmarked on LLM4Mat-Bench and MatBench tasks, achieving state-of-the-art performance in 25 out of 32 cases. In addition, we demonstrated how another modeling aspect of chemical language models—interpretability—benefits from applying a game-theoretic approach, which is able to incorporate high-order feature interactions.

Computational biology exposed a common pathogenic mechanism in influenza A and Guillain-Barré syndrome

Influenza virus A (H1N1) can lead to acute respiratory infection, while Guillain-Barré Syndrome (GBS) is an autoimmune peripheral neuropathy, which can work as a post-infectious disease. There are clinical observations showing the existence of a possible relationship between the presence of H1N1 infection and GBS disease, and therefore there could be common immunopathological pathways associated with H1N1 infection and GBS. The possible similarity between H1N1 infection and GBS was further investigated using integrated bioinformatics and systems biology approaches. Differentially expressed genes (DEGs) were identified from GEO datasets related to H1N1 infection and GBS. Enrichment analysis was conducted to understand the functional roles of identified DEGs by performing GO and KEGG pathway analysis. For the interacting proteins of the common DEGs, protein-protein interaction (PPI) network analysis helped to find out TLR4, TNF, and ITGAM as key hub genes. These hub genes might have common molecular pathways in H1N1 infection and GBS. To predict possible drug targets for treatment, analysis of interactions between hub genes and miRNAs, TFs, and related diseases was performed.

SPP1 knockdown inhibits invasion, migration and paclitaxel-induced Epithelial-Mesenchymal Transition in Cervical Cancer Cells through the TGF-β/Akt/Snail pathway

Psychometric validation of a novel community norms measure among youth, Eswatini Violence Against Children and Youth Survey, 2022

Social norms define what is acceptable and appropriate for women and men, boys and girls, in a given group or society. Restrictive social norms around women and men’s roles and responsibilities have proven harmful for both women and men, particularly in adolescence and young adulthood, and are associated with increased risk of violence. In global settings, measurement of social norms tends to rely on proxy measures capturing attitudes, beliefs, and perspectives. Measurement of social norms on women’s and men’s roles and responsibilities is particularly limited among adolescents and young adults, a formative age period where sanctions for non-adherence to norms can be heavy. We used data from the nationally representative 2022 Eswatini Violence Against Children and Youth Survey (VACS) to test the psychometric properties of a novel set of social norms survey items among male and female youth aged 13−24 (n = 7,709). Items were largely derived from prior scales and adapted by social norms experts to ensure that they were salient to the lives of adolescents in low-income settings. The items captured norms about women and men’s, boys’ and girls’ education, domestic labor, household decision-making, work, marriage and violence, with the community as the reference group. We conducted exploratory (EFA) and confirmatory factor analysis (CFA) on 16 norms survey items using split-random half samples. We identified a single-factor, four-item scale as the best-fitting solution (EFA: RMSEA = 0.060, CFI = 0.974, TLI = 0.923, SRMR = 0.076; CFA: RMSEA = 0.063, CFI = 0.972; TLI = 0.915; SRMR = 0.048). The scale captured norms in communities on domestic labor for youth and household-decision making by adults. We assessed measurement invariance by age and sex of this final one-factor, four-item solution. We observed latent mean differences by sex in baseline (β = 0.309, p  < 0.001) and final DIF-adjusted (β = 0.299, p  < 0.001) models. In addition, females and males varied in their propensity to endorse two of the scale items (Item 5: β = 0.225 p  < 0.001; Item 6: β = −0.212, p <  0.001). We assessed correlation between the final DIF-adjusted community norms measure and two attitude scales: attitudes toward IPV ( r  = 0.363, p  < 0.001) and attitudes toward women and men’s relations ( r  = 0.087, p  = 0.018). The measure demonstrated adequate reliability and convergent validity. The analysis resulted in a promising single factor, four-item social norms scale, which meets requirements of a brief, theory-based measure and is feasible to incorporate into national and cross-national surveys. Future work is needed to validate the scale in other settings.

Development of colorimetric and machine learning based accurate glucose detection platform for point of care applications

Abstract Improving overall health and preventing complications is crucial for timely and effective treatment of diabetes patients. In this direction, accurate measurement and detection of glucose concentration in blood is essential. The aim of this study is to develop an affordable, reliable and accurate Point-of-Care (POC) diagnostic platform for glucose concentration detection using microfluidic and colorimetric principles. A microfluidic chip is fabricated which provides the base for efficient colorimetric reaction. The proposed system requires only ~ 20 µL of sample per microwell, with a colorimetric reaction time of 3–4 min. The chip is positioned inside a compact, USB-powered, 3D printed image capture device affixed with a high-resolution camera so that variables such as camera position, focal distance and lighting conditions can be controlled. The analysis workflow is adaptable for integration with embedded systems or laptops, making it suitable for real-time deployment in Point-of-Care settings without the need for smartphones or calibration tools. The images captured inside the device (1280 in total, corresponding to 16 glucose concentration levels ranging from 50 to 200 mg/dL) were labeled based on known concentration levels and subjected to standardized preprocessing. Key preprocessing steps included Region of Interest (ROI) extraction using a fixed box detection algorithm, normalization of pixel values, image resizing to 128 × 128 pixels, and standardization using ImageNet parameters. These ensured uniformity and robustness prior to feature extraction and machine learning classification. The classifiers used are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and K-Nearest Neighbours (KNN). The RF machine learning classifier achieved the highest cross-validation accuracy of 87.47% and precision of 89.14%, demonstrating its ability to effectively distinguish between different glucose concentration levels. The confusion matrix and ROC curve analysis further validated the model’s robustness, with minimal misclassifications and a high mean AUC value of 0.9509. The results thus highlight the potential of image-based glucose concentration estimation as a cost-effective and scalable solution for real-time monitoring in biomedical applications.

Socioeconomic status and the relationship of physical performance with activities of daily living among US older adults

Background Independence in activities of daily living (ADLs) is an important health outcome for older adults and a predictor of morbidity and mortality. Physical performance is closely associated with ADL independence. It is not clear if socioeconomic status (SES) may alter this relationship so that people with more resources may remain independent at lower performance. Objective To assess whether SES is an effect modifier of the association between physical performance and ADL limitations. Methods Using data from the Health and Retirement Study, we considered two outcomes in logistic regression models: cross-sectional presence of ADL limitations and longitudinal development of new limitations. We separately considered two performance measures, gait speed (gait) and grip strength (grip), and three SES measures, education, income, and wealth, with and without an interaction term, for a total of 12 regressions. Covariates were age, sex, comorbidity count, cognition, and BMI (for grip) or height (for gait). Results Respective sample sizes for gait and grip were 4,825 and 3,401 in cross-sectional analysis and 3,995 and 2,860 longitudinally. They were 57–59% female with median ages 69.0–75.4 years. In regressions without interaction terms, gait and grip were significantly associated with greater odds for the presence or development of ADL limitations (0.95 and 0.97 respectively for grip, 0.03 and 0.20 for gait) while SES measures were not. When interaction terms were included, none reached statistical significance. Conclusions We found that SES did not modify the relationship between performance measures and ADL limitations cross-sectionally or longitudinally. Greater SES may not reduce the effort needed for ADLs, or Americans with low SES may similarly access adaptive equipment. Maintaining physical performance remains key for maintaining independence.

Social and structural determinants of SARS-CoV-2 testing and positivity among immigrant and non-immigrant children in the Lisbon Metropolitan Area, Portugal

Abstract Understanding the social and structural determinants that affect children’s access to SARS-CoV-2 testing and their infection risk informs policies to reduce health inequalities. However, evidence on testing and test positivity among immigrant young children in Europe is limited. We linked national laboratory surveillance data, primary health care records, and cohort baseline data for children born in 2018 and 2020 living in four municipalities in the Lisbon Metropolitan Area to analyse determinants of SARS-CoV-2 testing and positivity rates. Our analysis focused on migratory status and considered perinatal characteristics, socioeconomic conditions, and whether the child had an assigned family doctor. A lower prevalence of being tested at least once was observed among immigrant children compared with non-immigrant children. Among those tested, immigrants showed a lower prevalence of at least one positive test. In adjusted models, being tested at least once was associated with migratory status, daytime location (preschool or home), and having a family doctor (yes/no), whereas test positivity was only associated with migratory status. In this setting, testing appears to be driven by symptoms or contact suspicion, suggesting informational, social, and structural barriers that limit equitable access to testing and primary health care for immigrant children.