Millisecond Laser Oblique Hole Processing of Alumina Ceramics.

Alumina ceramic substrates are ideal materials for next-generation microelectronic systems and devices, widely used in aerospace, 5G communications, and LED lighting. High-quality hole processing is essential for system interconnection and device packaging. Millisecond lasers have emerged as a promising choice for hole processing in alumina ceramic due to their high processing efficiency.

Explainable machine learning identifies key quality-of-life-related predictors of arthritis status: evidence from the China health and retirement longitudinal study.

Background: Arthritis is a prevalent chronic disease substantially impacting patients' quality of life (QoL). While identifying key determinants associated with arthritis is critical for targeted interventions, traditional statistical methods often struggle with complex interactions, and existing machine learning (ML) approaches frequently lack the interpretability needed to guide clinical decisions. This study integrates a comprehensive, explainable machine learning (XAI) workflow to identify and interpret key QoL-related predictors of arthritis status in a large national cohort.

Comprehensive insights into the disease burden linked to air pollution exposure across U.S. states: findings from the 2021 Global Burden of Disease Study.

Background: Ambient air pollution persists as a critical global health threat, ranking fourth among risk factors for premature mortality. Despite decades of air quality improvements in the U.S.

Global trends in smoking-attributable rheumatoid arthritis burden: Insights from GBD 2021.

Background: Smoking is one of the most significant environmental risk factors for Rheumatoid Arthritis (RA). However, there is a lack of research examining the impact of smoking trends on the RA disease burden globally.

Methods: This study utilized the Global Burden of Disease (GBD) 2021 database to analyze the burden of RA attributable to smoking.

Prediction of protein-protein interaction based on interaction-specific learning and hierarchical information.

Background: Prediction of protein-protein interactions (PPIs) is fundamental for identifying drug targets and understanding cellular processes. The rapid growth of PPI studies necessitates the development of efficient and accurate tools for automated prediction of PPIs. In recent years, several robust deep learning models have been developed for PPI prediction and have found widespread application in proteomics research.

Neural Evidence for Tonal Prediction: Multivariate Decoding of Predicted Tone Categories Using Functional Magnetic Resonance Imaging Data.

Predictive processing plays a central role in language comprehension, allowing listeners to generate predictions about upcoming linguistic input. Although considerable evidence supports segmental prediction, less is known about whether listeners can form predictions about suprasegmental features such as lexical tone. This study investigates whether listeners can generate and neurally represent predicted tonal information in the absence of auditory input.

Predicting mortality risk in Alzheimer's disease using machine learning based on lifestyle and physical activity.

Alzheimer's disease (AD), a progressive neurodegenerative disorder, significantly impacts patient survival, prompting the need for accurate prognostic tools. Lifestyle factors and physical activity levels have been identified as critical modifiable risk factors influencing AD outcomes, but their precise impact on mortality prediction remains understudied. This study aimed to employ machine learning (ML) techniques to predict mortality risk in AD patients, leveraging data on lifestyle and physical activity to enhance personalized care strategies and inform public health policies.

How drawing practice enhances distress tolerance in university students: the mediating roles of psychological resilience and self-disclosure.

Introduction: University students often face significant academic and emotional pressures, making distress tolerance a vital skill for maintaining mental wellbeing. Drawing practice, as a creative outlet, has been shown to promote emotional regulation and psychological health. This study examines how drawing practice influences distress tolerance, focusing on the mediating roles of psychological resilience and self-disclosure.

The impact of growth mindset on subjective wellbeing in elementary school students: a moderated mediation model.

Introduction: This study examines the relationship between growth mindset and subjective wellbeing among elementary school students, with a focus on the mediating role of academic self-efficacy and the moderating role of perceived teacher support.

Methods: Utilizing the cluster sampling method, a sample of 1,740 elementary school students completed measures assessing growth mindset, subjective wellbeing, academic self-efficacy, and perceived teacher support.

Results: The results show that: (1) growth mindset positively predicts elementary school students' subjective wellbeing; (2) academic self-efficacy partially mediates the association between growth mindset and elementary school students' subjective wellbeing; and (3) perceived teacher support moderates the first stage of the mediation pathway, such that the positive effect of growth mindset on elementary school students' academic self-efficacy is stronger under high levels of perceived teacher support.

Marine anemone inspired cerium oxide doped nickel catalysts for enhanced seawater electrolysis efficiency.

Seawater electrolysis offers a promising strategy for sustainable hydrogen production, yet inherent chloride ions (Cl) in seawater induce electrode corrosion, posing a major challenge to this process. Herein, we developed a novel biomimetic catalyst by doping Cerium Oxide (CeO₂) into a nickel-based system and depositing it on carbon cloth (CeO₂/Ni/CC) inspired by the tentacle architecture of marine anemones. This design endows the catalyst with abundant active sites and high specific surface area, thereby significantly enhancing its seawater electrolysis performance.

A Novel Drug-Disease Association Prediction Method Based on Deep Non-Negative Matrix Factorization with Local Graph Feature.

Computational drug repurposing utilizes data analysis and predictive models to identify new uses for existing drugs and new drugs, significantly improving research efficiency and reducing costs compared to traditional screening methods. Due to the limitations of current computational models in extracting deep key features, we develop a novel drug repurposing model based on the deep non-negative matrix factorization (DNMF-DDA) to enhance the accuracy of drug-disease association predictions. The model leverages similarity and known association data to extract low-rank features from complex data spaces, allowing for the prediction of potential drug-disease associations.

Cognitive flexibility is associated with the age of onset and duration among patients with type 1 diabetes.

Cognitive impairment is a recognized risk in patients with type 1 diabetes, yet its contributing factors and overall impact remain insufficiently understood. This study aimed to explore the relationships between specific cognitive functions-namely memory, attention, and cognitive flexibility-and clinical characteristics, including age of onset and diabetes duration, as well as glycemic factors, such as glycemic control and extreme glycemic events, in a cohort of 331 patients with type 1 diabetes. Cognitive performance was assessed using the Wechsler Memory Scale, Wisconsin Card Sorting Test, and the Sustained Attention to Response Task.

Advances in DNA-based proteolysis-targeting chimeras technology: Novel strategies for targeting previously undruggable proteins in cancer therapy.

Targeted protein degradation (TPD) technology has emerged as a transformative therapeutic strategy for selectively eliminating aberrant proteins across diverse pathological conditions. This comprehensive review systematically examines recent advances in DNA-based proteolysis-targeting chimeras (DNA-PROTACs), which harness the exceptional specificity and binding affinity of DNA to substantially expand the targetable protein repertoire beyond conventional small molecule PROTACs. Through extensive literature analysis encompassing mechanistic studies, preclinical evaluations, and clinical investigations, we demonstrate that DNA-PROTACs effectively target previously undruggable proteins, including transcription factors, cell membrane proteins, and DNA damage response mediators.

Childhood psychological abuse influences adolescent aggression through mentalizing and alexithymia sequential mediation.

This study explores the impact of childhood psychological abuse on adolescent aggression and delves into the sequential mediating roles played by mentalizing and alexithymia within this dynamic. Engaging a cohort of 911 middle and high school students in China, the research uncovers that childhood psychological abuse is a significant predictor of aggression in adolescents. It further identifies alexithymia as a mediator in the correlation between childhood psychological abuse and adolescent aggression.

Different effects of verbal and visual working memory loads on Language prediction.

Mounting studies suggest that working memory (WM) plays a crucial role in language prediction, but how varying types of WM loads influence language prediction remains unclear. This study investigated whether verbal and visual WM loads differentially impact language predictions during speech comprehension. Using a dual-task paradigm combined with eye-tracking in a visual world setting, we asked 48 participants to complete a sentence comprehension task under concurrent WM load conditions.

The impact of multidomain interventions on cognitive and physical function in older adults with subjective cognitive decline: a meta‑analysis and systematic review.

Background: This study aimed to examine the efficacy of multidomain interventions compared to control and nutritional interventions in older adults with subjective cognitive decline (SCD). Four databases were searched for relevant literature.

Methodology: PubMed, Embase, Cochrane Library, and Web of Science databases were searched for relevant studies.

Examining the effects of competitive state anxiety and goal orientation on sports performance of college track and field athletes.

Existing studies on athletes' sports performance (SP) have identified significant associations between competitive state anxiety (CSA) and goal orientation (GO). However, few studies have explored the intrinsic relationships among CSA, GO, and SP. Moreover, there is a lack of attention paid to college track and field (T&F) athletes as a specific group, particularly regarding the role of GO in this process.

Isolation and Mechanistic Investigation of the Efficient Zearalenone-Removing Strain YJ25.

Zearalenone (ZEN), a non-steroidal estrogenic mycotoxin produced by species, poses a significant threat to both human food safety and animal feed quality. In this study, we isolated a strain, designated as YJ25, from a contaminated moldy corn sample, demonstrating substantial effectiveness in removing ZEN. Our findings revealed that YJ25's ZEN removal occurs primarily through cell wall adsorption, with enzymatic degradation representing a potential mechanism.

Understanding the association of calligraphy activities with flow experience, peace of mind, and self-focused attention among university students.

Introduction: Calligraphy, as a mindful practice, fosters focus, creativity, and relaxation, all of which contribute to a more peaceful mental state. For university students, regular engagement in calligraphy can help develop better coping mechanisms for academic and personal stress. This enhanced ability to manage stress can lead to improved mental well-being, greater self-awareness, and a more positive outlook, supporting their overall academic and personal development.

Mangiferin mitigates neurological deficits and ferroptosis via NRF2/ARE pathway activation in cerebral ischemia-reperfusion rats.

Introduction: Ferroptosis is a newly described form of nonapoptotic, iron-dependent cell death that plays an essential role in the pathogenesis of ischemic stroke. Targeting ferroptosis may be an effective way to treat ischemic stroke. Mangiferin (MGF) is a natural polyphenol that has been shown to protect neurological function via multiple mechanisms.

The relationship between 24-h Movement Behaviors and executive function in children aged 3-6 years: The mediating and moderating roles of fundamental movement skills.

Objective: This study seeks to explore the role of children's fundamental movement skills(FMS) within the framework of 24-h Movement Behaviors and executive function(EF)through the application of structural equation modeling (SEM).

Methods: The study employs a cross-sectional design. Apart from sleep data, which was collected via questionnaires, all other data were objectively measured using ActiGraph wGT3-BT accelerometers.

Exploring emotions and perspectives of families with autism spectrum disorder individuals on sports participation: a case study on Weibo using machine learning method.

Background: Physical activity is known to have numerous benefits for individuals with autism spectrum disorder (ASD). Understanding family sentiments towards ASD sports participation is crucial for developing interventions that promote physical activity. This study aimed to explore the sentiments of families regarding sports participation for individuals with ASD using longitudinal data from Weibo, a popular Chinese social media platform.

Deciphering the Reaction Cycle Mechanisms and Key Reaction Factors of Monochlorosilane by Disproportionation of Dichlorosilane via Experiments and Density Functional Theory.

Monochlorosilane (MCS) is an important silicon-based precursor for the fabrication of semiconductors and integrated circuits. The MCS production partly depends on the catalytic disproportionation of dichlorosilane (DCS). The DCS disproportionation has been critical yet challenging due to the involvement of multiple reversible reactions and other byproducts, often resulting in suboptimal yields of MCS.

The quantitative enamel firing technique based on regression analysis.

Enamel firing, as a key technique of enamel craftsmanship, has had no quantitative standards to follow since ancient times. To solve this problem, linear regression analysis was used to conduct quantitative enamel firing experiments. Through enamel firing experiments on enamel specimens with different masses, the linear regression equation was solved using the obtained experimental data and the regression equation was subjected to the analysis of variance.