Determinants of Behavioral Intention to Learn Arts Education of Postgraduate Students in Chengdu, China

Purpose: This research delves into the factors that impact the behavioral intention of university students to engage in arts education. The conceptual framework encompasses social sphere, academic sphere, educational satisfaction, attitude, social influence, self-efficacy, effort expectancy, and behavioral intention. Research design, data, and methodology: The target population and sample size are 500 postgraduate students who have experienced arts education at top three universities in Chengdu, China. A quantitative research approach was adopted, using a questionnaire. The sampling techniques employed in this study include judgmental, quota, convenience, and snowball sampling. Both the item-objective congruence (IOC) index and Cronbach's alpha were used for validity and reliability testing, respectively. The collected data were analyzed through confirmatory factor analysis (CFA) and structural equation modeling (SEM), which served as the main statistical techniques for this research. Results: Social sphere and academic sphere significantly impact education satisfaction. Furthermore, education satisfaction, self-efficacy and effort expectancy significantly impact behavioral intention. Nevertheless, the relationship between attitude, social influence and behavioral intention is not supported. Conclusions: Understanding these determinants can inform the development of strategies and interventions to promote arts education and enhance students' engagement and intention to pursue arts-related fields

Formation, Structural Characteristics and Emulsification Properties of Mung Bean Globulin Amyloid Fibrils

In this study, the structural evolution of mung bean globular amyloid fibrils (MBGFs) during the formation process was investigated with different acid-heat treatment times, and the emulsification properties of MBGFs were also explored at each stage of the formation process. The results showed that the subunits of mung bean globulin (MBG) were gradually degraded and hydrolyzed into fibrous structure units mainly composed of small peptides during 0–12 h of heating. And a large number of β-sheet structures were produced, and its relative content increased from an initial level of (18.28 ± 0.75)% to (53.61 ± 1.15)% over the 12 heating period. Enhanced fluorescence intensity was recorded after combination with thioflavin-T. Morphologically, MBGFs gradually became elongated and pliable, and oriented fibril aggregation occurred in the heating process. Between 16 and 24 h, the structure of mature MBGFs gradually was dissociated and the structural characteristics of the fibers were destroyed. The emulsification activity, emulsion stability, protein adsorption rate and interfacial protein content of MBGF were significantly improved compared with those of MBG. MBGFs formed by acid-heat treatment for 4 h had the best emulsification properties. Oil droplets in the MBGF emulsion were small in size, uniformly and orderly distributed, and the emulsion had the highest apparent viscosity and showed an elastic gel structure. In conclusion, different acid-heat treatment times had significant effects on the structure and emulsification properties of MBGFs, and MBGFs had better emulsification properties than MBG. This study will provide theoretical support for clarifying the formation of MBGFs and ideas for the development of highly efficient food-grade emulsifiers

High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component

All‐In‐One OsciDrop Digital PCR System for Automated and Highly Multiplexed Molecular Diagnostics

Digital PCR (dPCR) holds immense potential for precisely detecting nucleic acid markers essential for personalized medicine. However, its broader application is hindered by high consumable costs, complex procedures, and restricted multiplexing capabilities. To address these challenges, an all‐in‐one dPCR system is introduced that eliminates the need for microfabricated chips, offering fully automated operations and enhanced multiplexing capabilities. Using this innovative oscillation‐induced droplet generation technique, OsciDrop, this system supports a comprehensive dPCR workflow, including precise liquid handling, pipette‐based droplet printing, in situ thermocycling, multicolor fluorescence imaging, and machine learning‐driven analysis. The system's reliability is demonstrated by quantifying reference materials and evaluating HER2 copy number variation in breast cancer. Its multiplexing capability is showcased with a quadruplex dPCR assay that detects key EGFR mutations, including 19Del, L858R, and T790M in lung cancer. Moreover, the digital stepwise melting analysis (dSMA) technique is introduced, enabling high‐multiplex profiling of seven major EGFR variants spanning 35 subtypes. This innovative dPCR system presents a cost‐effective and versatile alternative, overcoming existing limitations and paving the way for transformative advances in precision diagnostics