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How microclimate influences the spring phenological responses to decreased snow cover in four tree species seedlings in a boreal forest
Analysis of people newly diagnosed with HIV and intervention measures in Chongqing
Association between serum Klotho levels and thrombocytosis in aging adults based on evidence from the National Health and Nutrition Examination Survey
The interpretable machine learning model for depression associated with heavy metals via EMR mining method
A vision attention driven Language framework for medical report generation
Topology-preserving contourwise shape fusion
Multicenter, international, retrospective study of prognostic factors for Ra223
Author Correction: Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery
Impact of host switching at different larval instars on the performance of the polyphagous pest Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae)
Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones
Exploring the effects of olfactory VR on visuospatial memory and cognitive processing in older adults
Abstract This study examined the effects of Olfactory Virtual Reality (VR) Gaming on cognitive performance in older adults. A VR game environment (“Interactive Smellscape”) was created to enable this, and 30 participants aged 63–90 years completed both VR gaming sessions and cognitive assessments, conducted with a 6-day interval between the two sessions. Significant improvements were observed in spatial tasks of Japanese characters and words, with notable enhancements specifically in visuospatial rotation performance and word-location recall accuracy. However, no significant changes were detected in olfactory identification or other general cognitive tasks. These findings suggest potential cognitive benefits of incorporating VR and olfactory stimuli into interventions for older populations, particularly for tasks requiring attention and spatial processing. The results further underscore the importance of task-specific designs to maximize the utility of multisensory VR systems for cognitive rehabilitation.
Effects of climate-related disasters on loneliness, social support, social functioning, and social contacts: longitudinal analyses of impact and recovery
Publisher Correction: A hybrid fused-KNN based intelligent model to access melanoma disease risk using indoor positioning system
Unveiling sources, contamination, and eco-human health implications of potentially toxic metals from urban road dust
Duchenne muscular dystrophy gene product expression is associated with survival in head and neck squamous cell carcinoma
Abstract Mutation of the Duchenne muscular dystrophy (DMD) gene causes neuromuscular disorders, but increasing evidence has implicated DMD in the development and progression of several major cancer types. This study investigates the prognostic and biological significance of DMD expression in head and neck squamous cell carcinoma (HNSCC). Analysis of The Cancer Genome Atlas (TCGA) data revealed that high DMD expression correlates with improved overall (median survival difference: 22 months, p = 0.0083) and progression-free (p = 0.0237) survival. The Dp71ab transcript is most strongly associated with better outcomes (median overall survival: 42 months, p = 0.0007). Notably, DMD expression levels stratify HPV-positive patients, identifying a DMD low/HPV-positive subgroup with poor outcomes. Immunohistochemical analysis of 50 HNSCC tissue cases confirmed dystrophin localisation in the nucleus and cytoplasm, with high nuclear expression linked to longer overall survival (mean difference: 31 months, p = 0.0497). Functional assays in HNSCC cells showed that Dp71ab overexpression disrupts nuclear morphology and reduces proliferation. Differential gene expression analysis additionally identified 388 upregulated and 30 downregulated genes, with pathways linked to muscle processes, ribosome biogenesis and non-coding RNA regulation. These findings highlight DMD as a potential biomarker and/or therapeutic target in HNSCC, warranting further mechanistic studies of Dp71 isoforms.
A novel broadband reflectarray antenna employing equivalent magnetic dipole elements
Evolution of strain field and crack prediction in cemented paste backfill specimens based on digital image correlation and computer vision recognition model
Predicting the academic achievement of students using black hole optimization and Gaussian process regression
Abstract Academic achievement is vital for campus life and education since it indicates the caliber of the teachers, administration, and students’ learning abilities. Issues such as poor study conditions and family disruptions can impede a student’s capacity to achieve. Teachers are looking for practical solutions to these concerns because solving problems one at a time might be tough. This study uses a combination of black hole optimization (BHO) and Gaussian process regression (GPR) algorithms to predict students’ academic success in higher education. The method is divided into three stages: data pre-processing, identification of effective indicators using BHO algorithms, and forecasting of academic performance. The presented approach makes use of the GPR algorithm to choose the relevant features and the weighted combination of GPR models to forecast that the GPR model would be used for the weighting operation that is, to determine the ideal weights. The experimental findings demonstrate that our method has a lower error rate of 0.95 and 0.81 in terms of RMSE and MAE than the competing methods. The proposed method can assist teachers in analyzing student behavioral patterns, understanding academic performance impact mechanisms, and developing effective learning supervision plans.