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Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images
Author Correction: Structural dynamics of the midnolin-proteasome during ubiquitin-independent substrate turnover
Physician adoption patterns of AI-driven clinical decision support systems in urinary tract infection management
Nationwide spread of multidrug resistant Klebsiella pneumoniae across US communities
How ice forms is a mystery — now scientists are cracking the case
Integrative single-cell and spatial transcriptomics with machine learning identify a Luminal-inflam malignant program and reveal an RPN1–PERK UPR vulnerability in triple-negative breast cancer
Towards robust foundation models for digital pathology
Entropy quantum computing for fixed-backbone protein design
Cryo-EM structure of the LARS1:IARS1 complex reveals a nutrient-responsive switch controlling mTORC1 signaling
Science must be seen as a viable profession for the many, not the few
Big five and higher-order personality traits matter for climate-risk perception among moroccan farmers
Abstract Climate change risk perception is increasingly recognized as an important antecedent of climate-related engagement, yet its psychological foundations remain underexplored in vulnerable, non-WEIRD contexts. This study examines associations between personality traits and affective climate-risk perception (denoting climate-related worry), among cereal farmers in Morocco. Drawing on the Big five and higher-order meta-trait personality framework, we model affective climate-risk perception as a function of both individual traits and their composite configurations. Using cross-sectional data from cereal-farming households, we estimate these associations through different analytical specifications. Results show that personality traits are significantly associated with affective climate-risk perception. At the trait level, Emotional Stability is consistently linked to lower levels of reported climate-related worry, whereas Extraversion and Openness are associated with higher worry. The findings suggest that engagement-oriented and information-seeking dispositions correspond with heightened emotional sensitivity to climate threats. At the meta-trait level, Meta-Plasticity is positively associated with affective climate-risk perception, whereas Meta-Stability is associated with lower worry. Exploratory composite configurations further reveal a non-linear, U-shaped association for a Social Harmony orientation. This indicates that moderate social embeddedness may coincide with lower concern, while very high levels are associated with increased affective climate-risk perception. Overall, the findings do not imply causal effects on behavior but demonstrate that perceived affective climate-risk is patterned along stable psychological dimensions, even under similar exposure conditions. By integrating these personality traits into affect-based climate-risk frameworks, this study highlights the role of psychological heterogeneity in shaping farmers’ responses to climate-risks and underscores the need for more targeted adaptation, climate communication, and resilience-building strategies in vulnerable agricultural communities.
T2Pdecoder enables protein-centric analyses from transcriptomic data
Spatial patterns and risk mapping of opisthorchiasis and soil-transmitted helminth infections in Thailand using Bayesian geostatistical models
Abstract Opisthorchis viverrini and soil-transmitted helminths (STH) remain major public health challenges in Thailand due to their widespread distribution. This study aimed to map and predict the prevalence of O. viverrini and STH infections across Thailand and to identify key environmental, climatic, and socio-economic factors influencing their geographical distribution. Bayesian geostatistical logistic regression models were fitted to national survey data to estimate infection risk and generate high-resolution spatial predictions. Demographic (sex, age), environmental (land surface temperature, vegetation indices, altitude), climatic (minimum and maximum temperature), and socio-economic (nighttime light intensity) variables were included as covariates in the models, and their associations with infection risk were quantified using posterior odds ratios and 95% Bayesian credible intervals (95% CrI). Opisthorchis viverrini infection was significantly associated with sex and age. Males had higher odds of infection than females (OR: 1.57; 95% CrI: 1.24–1.97). Individuals aged 25–59 years (OR: 4.75; 95% CrI: 3.22–7.21) and ≥ 60 years (OR: 4.69; 95% CrI: 3.10–7.29) had similarly elevated risks compared with those < 25 years. Minimum temperature (TMIN) was significantly negatively associated with infection risk. For each 1 °C increase in TMIN, the probability of O. viverrini infection decreased by 33% (OR = 0.67, 95% CrI: 0.49–0.88). Hookworm infection was more common among males (OR: 1.65; 95% CrI: 1.42–1.93) and individuals aged 25–59 years (OR: 1.86; 95% CrI: 1.52–2.28). For Ascaris lumbricoides , infection risk was lower in individuals aged 25–59 years (OR: 0.42; 95% CrI: 0.18–0.98) and negatively associated with land surface temperature (OR: 0.66; 95% CrI: 0.39–0.98) and nighttime light intensity (OR: 0.97; 95% CrI: 0.93–0.99). No statistically significant associations were detected for Trichuris trichiura . Spatial predictions showed that O. viverrini was concentrated in northeastern Thailand, whereas STH infections were most prevalent in the south. Distinct spatial heterogeneity was observed in the distribution of O. viverrini and STH infections in Thailand. Demographic factors were consistently associated with O. viverrini and hookworm infection, whereas selected environmental and socio-economic correlates were associated with A. lumbricoides . These findings support geographically targeted surveillance and control strategies focused on high-burden areas and vulnerable populations.