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The 3D microstructural analysis of gustatory papillae and taste buds in the dog (Canis lupus familiaris, Canidae, Carnivora)
Improving ricotta cheese shelf life using saffron petal extract double nanoemulsions stabilized by plant proteins and Lepidium sativum L. gum
Assessment of water quality and microbial contamination in institutional water resources: a necessity to understand health risks
LSTM-based safety-oriented prediction of big toe skin temperature in extreme cold conditions for mountaineering footwear evaluation
Immense data processing within brain networks in professional gamers
Gender differences in the association between physical frailty and cognitive function among older adults: A cross-sectional study in rural Guizhou, China
Abstract To examine gender differences in the association between physical frailty and cognitive function among older adults in rural China. A total of 1,654 older adults (41.9% male) aged 60 years and above from rural areas of Guizhou Province, China, participated in the study. Cognitive function was assessed using the Chinese-adapted Mini-Mental State Examination (C-MMSE), and frailty status was determined based on Fried’s frailty criteria. Multivariate linear regression analyses were conducted to examine the association between frailty and cognitive function, with sex-stratified analyses performed to explore gender-specific differences in these associations. Females exhibited a significantly higher prevalence of frailty (12.9% vs. 9.2%) and lower mean C-MMSE scores (19 vs. 24) compared to males (All P < 0.001). After adjustment, frailty was inversely associated with C-MMSE in both sexes.Among the five frailty phenotypes, only low physical activity (β =-1.156; 95% CI: -2.019 to -0.293; P = 0.002) and self-reported exhaustion or fatigue (β = -0.963; 95% CI:-1.658 to -0.269; P = 0.005) were significantly associated with lower C-MMSE scores in females. In women, the presence of any one or more frailty phenotypes was linked to cognitive decline, whereas in men, cognitive impairment was observed only when three or more frailty phenotypes were present. According to results from the study of older adults in rural Guizhou, there is a gender difference in the relationship between frailty and cognitive function, with women having a stronger association. These findings suggest that women’s cognitive function is more susceptible to the effects of frailty, while more prospective research is needed to confirm this conclusion.
Prevalence and predictors of immediate treatment need for anterior crossbite in primary dentition: a Baby ROMA study
Major Ebola outbreak is escalating: what happens next
Modulation of redox state, antioxidant systems and photosynthetic capacity induction of osmotic stress tolerance in Crocus sativus L. through exogenous melatonin
Daily briefing: Ebola outbreak — alarming trajectory and possible origins
Facies classification and channel detection for reservoir characterization using self-organizing maps in geologically complex Groningen gas field, the Netherlands
Hit a lab project glitch? Thinking about your thesis title like a storyteller can help you focus
Attention-enhanced multi-task learning for binary segmentation and fine-grained aquatic plant classification in UAV imagery
Abstract Accurate monitoring of aquatic vegetation from unmanned aerial vehicle (UAV) imagery remains challenging due to complex water backgrounds, severe inter-class similarity, and the lack of balanced, dual-annotated datasets. Existing studies primarily address segmentation or classification independently, limiting their effectiveness for integrated species-level analysis. To address these gaps, this study proposes a clearly defined attention-enhanced multi-task learning framework that simultaneously performs binary segmentation and 14-class species classification, enabling unified structural and semantic understanding. The model employs a shared encoder with attention-guided skip connections and a joint optimization strategy to enhance feature discrimination while reducing redundancy. Comprehensive ablation analysis demonstrates that attention improves both segmentation and classification performance, while joint learning with Gaussian blur achieves the best overall balance, confirming the complementary role of spatial and semantic features. On a newly collected UAV dataset from diverse wetlands in Bangladesh, the proposed model achieves a Dice coefficient of 0.7344, mIoU of 0.6904, and pixel accuracy of 0.8757 for segmentation, along with 98.77% classification accuracy and an F1-score of 0.9874, indicating strong performance across both tasks. In addition, computational complexity analysis shows that the proposed framework reduces parameters by $$\sim$$ 50% (31.10M vs. 62.09M), lowers FLOPs (54.66 vs. 96.31 GFLOPs), and improves inference speed by $$\sim$$ 48.6% compared to deploying separate single-task models for segmentation and classification, demonstrating its suitability for real-time UAV deployment. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-CAM++ are employed to provide visual explanations of model predictions, improving interpretability and reliability. The results demonstrate robust performance in complex aquatic environments and highlight the framework’s suitability for large-scale biodiversity monitoring, invasive species detection, and data-driven freshwater ecosystem management.
Neuroflix
Quercetin accelerates full-thickness burn wound repair by modulation of oxidative stress and inflammation
Data harmonization processes of cancer data into the observational medical outcomes partnership common data model
Abstract Cancer data is inherently complex and heterogeneous, originating from diverse sources with differing formats, terminologies, and structures, leading to significant interoperability challenges. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), provided by Observational Health Data Sciences and Informatics (OHDSI) initiative, has been adopted as a standardized framework to mitigate data fragmentation and enhance evidence generation. However, harmonizing cancer data into OMOP CDM remains challenging due to granular data, unstructured formats, and lack of cancer-specific harmonization approaches. This study develops a generic harmonization process for integrating cancer data into the OMOP CDM by examining existing methodologies and identifying patterns and challenges. Following the Design Science Research Methodology (DSRM), the process was informed by literature and refined through expert feedback. The proposed process consists of five steps: Initiation, Requirement Analysis, Design Planning, Technical Implementation, and Maintenance. Each step incorporates cancer-specific considerations. It addresses challenges including source data quality and complexity, mapping issues, and maintenance, supporting oncology research and evolving technologies.