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AIM: To investigate the impact of primary glaucoma on sleep quality and daytime sleepiness of patients. METHODS: Prospective cross-sectional study with consecutive sampling in South-East Asian population was performed. Validated questionnaires: the Pittsburg Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were administered prospectively. Subjects with non-glaucomatous optic neuropathy or concomitant retinal pathology were excluded. Glaucoma severity was based on HVF 24-2 perimetry. Binocular single vision was represented based on the better eye. Frequency of and predictive factors for poor sleep quality and excessive daytime sleepiness were compared. RESULTS: A total of 79 primary open angle glaucoma (POAG), 27 primary angle-closure glaucoma (PACG) patients, and 89 controls were recruited. PACG patients had higher median PSQI scores (P=0.004) and poorer sleep quality (P

Objective: This case demonstrated the possibility of using GATA3-positive circulating tumor cells (CTCs) to detect early-stage breast cancer (BrC). Case report: The 86 years old female patient received a mammographic examination with no evidence of malignancy (Breast Imaging Reporting and Data System, (BI-RADS category 2). However, CTC testing on the same day revealed four GATA3-positive CTCs in 4 ml of peripheral blood. Core needle biopsy was performed in the suspicious area even with no evidence of malignant image on breast ultrasound. Pathologic examination showed invasive carcinoma of no special type of the breast. The patient then received an oncoplastic partial mastectomy of right breast and sentinel lymph node biopsy. The surgical staging was cT1N0M0. Post-operation follow-up examination showed absence of GATA3-positive CTCs and the presence of HER2/ER positive CTCs. Conclusion: The role of GATA3-positive CTCs as a potential biomarker for early-stage BrC should be explored.

Research has addressed the benefits of immersive virtual reality (IVR) for affective learning rather than cognitive performance, indicating the necessity to explore what factors may influence learners' attitudinal learning, defined as the result of instruction for changing learners' attitudes, when engaging in immersive instructional mediums. This study proposes an affection-oriented model to understand the roles of young learners' epistemic curiosity traits and triggered situational interest in their attitudinal learning from the affective, cognitive, and behavioral dimensions in IVR learning contexts. Through a series of PLS-SEM analyses with 90 elementary school students, the results of the measurement model test have verified the reliability and validity of the instruments employed in this study. The structural relationships among epistemic curiosity, situational interest, and attitudinal learning were also confirmed. The triggered situational interests of "novelty" and "exploration intention" during the IVR learning process were further identified as the mediators in the research model. While the students' senses of "novelty" mediated the relationship between curiosity and cognitive attitudinal learning, their senses of interest in "exploration intention" mediated both the relationships between curiosity and affective attitudinal learning and between curiosity and behavioral attitudinal learning. This study contributed to establishing the theoretical framework for affection-oriented IVR learning.

Featured Application: Accuracy verification of large-scale remote sensing fractional vegetation cover products; precision agriculture; ecological research. When recording the vegetation distribution with a camera, shadows can form due to factors like camera angle and direct sunlight. These shadows result in the loss of pixel information and texture details, significantly reducing the accuracy of fractional vegetation coverage (FVC) extraction. To address this issue, this study proposes an efficient double-exposure algorithm. The method reconstructs the pixel information in shadow areas by fusing normal-exposure and overexposed images. This approach overcomes the limitations of the camera's dynamic range in capturing pixel information in shadowed regions. The study evaluates images with five levels of overexposure combined with five vegetation extraction indices. The aim is to determine the best-performing double-exposure combination under shadow conditions and the most suitable vegetation index. Experimental results reveal that the R² value between the best vegetation index and the FVC calculated from the fused double-exposure images and the ground truth FVC increases from 0.750 to 0.969. The root mean square error (RMSE) reduces from 0.146 to 0.046, and the intersection over union (IOU) increases from 0.856 to 0.943. These results demonstrate the excellent vegetation extraction capability of the double-exposure algorithm under shadow conditions, offering a straightforward and effective solution to low accuracy of FVC in shadowed areas. [ABSTRACT FROM AUTHOR]