Search Results (15,310)

This study evaluates the predictive performance of advanced machine learning models, including DeepBoost, XGBoost, CatBoost, RF, and MLP, in estimating the Ω₂, Ω₄, and Ω₆ parameters based on a comprehensive set of input variables. Among the models, DeepBoost consistently demonstrated the best performance across the training and testing phases. For the Ω₂ prediction, DeepBoost achieved an R² of 0.974 and accuracy of 99.895% in the training phase, with corresponding values of 0.971 and 99.902% in the testing phase. In comparison, XGBoost ranked second with an R² of 0.929 and accuracy of 99.870% during testing. For Ω₄, DeepBoost achieved a training phase R² of 0.955 and accuracy of 99.846%, while the testing phase results included an R² of 0.945 and accuracy of 99.951%. Similar trends were observed for Ω₆, where DeepBoost obtained near-perfect training phase results (R² = 0.997, accuracy = 99.968%) and testing phase performance (R² = 0.994, accuracy = 99.946%). These findings are further supported by violin plots and correlation analyses, underscoring DeepBoost’s superior predictive reliability and generalization capabilities. This work highlights the importance of model selection in predictive tasks and demonstrates the potential of machine learning for capturing complex relationships in data. Full article

Lipid metabolism disorders represent a significant risk factor for the pathogenesis of Alzheimer’s disease (AD). Apolipoprotein E (APOE) has been regarded as a pivotal regulator of lipid homeostasis in the central nervous system (CNS), with polymorphic alleles identified as genetic risk factors for late-onset AD. Despite advances in APOE research and the development of numerous pharmaceutical approaches targeting distinct APOE isoforms, there remain limited treatment approaches for AD that focus on lipid metabolic homeostasis. Consequently, it is necessary to reevaluate the lipid metabolic process in the CNS. Apolipoprotein A1 (APOA-I), a major component of high-density lipoprotein (HDL), plays a crucial role in reverse cholesterol transport from tissues to the liver to maintain lipid homeostasis. Over the past few decades, numerous studies have suggested a connection between reduced APOA-I levels and a higher risk of AD. APOA-I is synthesized exclusively in the liver and intestines, and there is a lack of conclusive evidence supporting its functional significance within the central nervous system, in contrast to APOE, which is produced locally by glial cells and neurons within the CNS. Moreover, APOA-I’s ability to penetrate the blood-brain barrier (BBB) is still poorly understood, which causes its significance in central lipid metabolism and AD pathophysiology to be mainly disregarded. Recent advancements in tracing methodologies have underscored the essential role of APOA-I in regulating lipid metabolism in the CNS. This review aims to elucidate the physiological functions and metabolic pathways of APOA-I, integrating its associations with AD-related pathologies, risk factors, and potential therapeutic targets. Through this discourse, we aim to provide novel insights into the intricate relationship between AD and APOA-I, paving the way for future research in this field. Full article

Background: Borderline Personality Disorder (BPD) is a growing health concern, characterized by emotional dysregulation, impulsivity, and unstable interpersonal relationships. One of the core features of BPD is self-harm, which has significant implications for clinical management, risk assessment, and treatment planning. Accurate assessment tools are essential in evaluating symptom severity and identifying individuals at high risk of self-injurious behaviors, thereby guiding clinical interventions effectively. This study aimed to assess the psychometric properties, factor structure, and diagnostic utility of the Greek version of the Borderline Personality Disorder Severity Index-IV (BPDSI-IV), providing preliminary evidence for its reliability and validity. Methods: A total of 128 individuals with BPD and 32 healthy controls were assessed using the BPDSI-IV together with the Brief Symptom Inventory-53 (BSI-53), the BPD Checklist, the Rosenberg Self-Esteem Scale, the WHOQOL-BREF, and the Defense Style Questionnaire-40 (DSQ-40). BPD diagnoses were confirmed using the Structured Clinical Interview for DSM-5 Personality Disorders (SCID-5-PD). Internal consistency, confirmatory factor analysis (CFA) of previously suggested models, exploratory and confirmatory bifactor modeling, and validity assessments were conducted. Results: The BPDSI-IV showed strong internal consistency (α = 0.92, ωt = 0.96), with most subscales demonstrating adequate reliability. Exploratory bifactor analysis using the Schmid–Leiman transformation supported a model with a dominant severity factor (ωh = 0.69), reinforcing the dimensional nature of BPD. CFA supported this bifactorial approach. BPDSI-IV scores significantly discriminated BPD patients from controls (p < 0.001). Strong correlations with measures of psychopathology and self-esteem, and correlations with quality of life further supported its validity. Conclusions: The Greek BPDSI-IV demonstrated strong reliability and validity indicators. Structured assessment tools, such as the BPDSI-IV, can enhance early intervention and research on the course of borderline personality disorder symptoms. Full article

The compiler serves as a bridge connecting hardware architecture and application software, converting source code into executable files and optimizing code. Fuzz testing is an automated testing technology that evaluates software reliability by providing a large amount of random or mutated input data to the target system to trigger abnormal program behavior. When existing fuzz testing methods are applied to compiler testing, although they can detect common errors like lexical and syntax errors, there are issues such as insufficient pertinence in constructing the input corpus, limited support for structured Intermediate Representation (IR) node manipulation, and limited perfection of the mutation strategy. This study proposes a deep fuzz testing framework named BoostPolyGlot for GCC compiler frontend IR generation, which effectively covers the code-execution paths and improves the code-coverage rate through constructing an input corpus, employing translation by a master–slave IR translator, conducting operations on structured program characteristic IR nodes, and implementing an IR mutation strategy with dynamic weight adjustment. This study evaluates the fuzz testing capabilities of BoostPolyGlot based on dependency relationships, loop structures, and their synergistic effect. The experimental outcomes confirm that, when measured against five crucial performance indicators including total paths, count coverage, favored paths rate, new edges on rate, and level, BoostPolyGlot demonstrated statistically significant improvements compared with American Fuzzy Lop (AFL) and PolyGlot. These findings validate the effectiveness and practicality of the proposed framework. Full article

Background: The plant AT-rich sequence and zinc binding (PLATZ) transcription factors constitute a zinc-dependent protein family implicated in various developmental processes and responses to abiotic stress. Nevertheless, comprehensive investigations on PLATZ gene functions in pepper (Capsicum annuum) have not been extensively performed. Methods: In the present study, bioinformatics methods coupled with quantitative real-time PCR (qRT-PCR) were employed to characterize the phylogenetic relationships, chromosome distribution, structural composition, cis-regulatory elements, evolutionary dynamics, and expression responses of CaPLATZ genes under drought stress conditions. Results: Phylogenetic analyses categorized the CaPLATZ genes into four distinct subgroups, each exhibiting similar gene structures and conserved motif patterns within its subgroup. A total of 11 CaPLATZ genes were nonuniformly located across eight pepper chromosomes, and synteny analyses identified a duplication event involving a single gene pair. The assessment of cis-acting regulatory elements indicated potential involvement of CaPLATZ genes in responses to abiotic stresses and various phytohormones. Furthermore, qRT-PCR results revealed differential expression of most CaPLATZ genes under drought-induced stress. Conclusions: Collectively, these findings support the functional roles of CaPLATZ transcription factors in mediating developmental processes and enhancing drought tolerance in pepper. Full article

Background/Objectives: This study aimed to identify the mediating effect of parental support on the relationship between immigrant mothers’ mental health and adolescents’ self-esteem. Methods: This study utilized data from 1077 Korean multicultural adolescents and their immigrant mothers from the 9th Multicultural Adolescents Panel data obtained in 2019. The data were analyzed using descriptive statistics, Pearson’s correlation, Baron and Kenny’s regression analysis, and bootstrapping using the process macro. Results: Immigrant mothers’ mental health was significantly and positively associated with their adolescents’ self-esteem (r = 0.14, p < 0.001), and parental support was also significantly and positively associated with adolescents’ self-esteem (r = 0.50, p < 0.001). Parental support had a mediating effect on immigrant mothers’ mental health and adolescents’ self-esteem. Conclusions: The self-esteem of adolescents from multicultural families was found to be influenced by the mental health and support of their immigrant mothers. These findings highlight the mediating role of parental support in the relationship between immigrant mothers’ mental health and adolescents’ self-esteem, contributing to a deeper theoretical understanding of family dynamics in multicultural contexts. Therefore, these factors should be considered when developing parent education programs for immigrant mothers. Full article

With the rapid development of digital technology, the use of digital learning resources has become increasingly widespread and is now an integral part of higher education. However, there is a lack of research on engineering students’ behavioral intention to use digital resources and their digital literacy. This study, which is based on two waves of longitudinal survey data from 422 Chinese engineering students, employs the unified theory of acceptance and use of technology 2 (UTAUT2) model to systematically analyze the factors influencing engineering students’ behavioral intention to use digital learning resources and explore the longitudinal relationship between this intention and digital literacy. The results show that engineering students’ behavioral intention to use digital learning resources positively predicts their digital literacy. Effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit are positively related to engineering students’ behavioral intention, whereas performance expectancy and price value do not have significant effects. The findings extend the application of the UTAUT2 model in the context of digital education and reveal a longitudinal link between the use of digital learning resources and digital literacy. This provides theoretical support and practical guidance for optimizing digital learning environments, enhancing digital literacy in engineering students, and improving the design of teaching resources in higher education, contributing to the development of engineering education in China in the digital age. Full article

Research practice partnerships (RPP) between schools and researchers present a promising approach to co-creating scalable professional development for trauma-informed schools. This study used an RPP to develop an implementation model for a trauma-informed professional development program across 15 schools in a major urban school district. The primary study goal was to describe the RPP’s co-design processes used to develop and mount a large-scale professional development program with accompanying implementation supports. A secondary goal was to provide representative case examples of feedback loops for real-time improvements to the implementation strategies. A rapid mixed methods approach drawing on the principles of developmental evaluation was used to collect implementation process data including RPP team meeting notes and documents, informal discussions, training and survey completion reports, attendance, and implementation workshop exit tickets. These data were triangulated to conduct preliminary analyses which were then presented to RPP team members for collaborative review. Results highlighted seven co-designed elements of the TISE implementation support system—engaging and supporting school leadership, implementation teams, live and asynchronous training, ongoing consultation, delivering practical resources, relationship building, and continuous improvement. Exemplar feedback loops highlighted immediate improvements to implementation resources via exit tickets and enhanced strategies for building long-term school-level team effectiveness and engagement via attendance tracking. Full article

To address global climate change and the energy crisis, there is an urgent need to meet human demands through utilizing renewable energy sources. The deoxygenation of lipids to produce liquid biofuels has emerged as a promising alternative, particularly for carbon emission reduction in the aviation industry. This review critically examines recent progress in catalyst development and reaction control strategies for lipid deoxygenation. Emphasis is focused on the design of different kinds of catalysts to meet the requirements, including noble metal catalysts, non-noble metal catalysts, and non-noble metal compound catalysts, with strategies such as morphology control, utilization of metal support interactions, and constructing synergistic effects between metal acid centers and metal oxygen vacancies. The reaction networks, mechanisms, and selectivity control strategies for lipid deoxygenation, cracking, isomerization, and aromatization are comprehensively discussed. Finally, we propose that it requires focusing on the precise regulation of multiple active sites to optimizing deoxygenation performance and reusability. It is essential to integrate in situ characterization to deepen the study of structure–active relationships and explore the reaction mechanisms within complex reaction systems. Full article

Event types classify Chinese verbs based on the internal temporal structure of events. The categorization of verb event types is the most fundamental classification of concept types represented by verbs in the human brain. Meanwhile, event types exhibit strong predictive capabilities for exploring collocational patterns between words, making them crucial for Chinese teaching. This work focuses on constructing a statistically validated gold-standard dataset, forming the foundation for achieving high accuracy in recognizing verb event types. Utilizing a manually annotated dataset of verbs and aspectual markers’ co-occurrence features, the research conducts hierarchical clustering of Chinese verbs. The resulting dendrogram indicates that verbs can be categorized into three event types—state, activity and transition—based on semantic distance. Two approaches are employed to construct vector matrices: a supervised method that derives word vectors based on linguistic features, and an unsupervised method that uses four models to extract embedding vectors, including Word2Vec, FastText, BERT and ChatGPT. The classification of verb event types is performed using three classifiers: multinomial logistic regression, support vector machines and artificial neural networks. Experimental results demonstrate the superior performance of embedding vectors. Employing the pre-trained FastText model in conjunction with an artificial neural network classifier, the model achieves an accuracy of 98.37% in predicting 3133 verbs, thereby enabling the automatic identification of event types at the level of Chinese verbs and validating the high accuracy and practical value of embedding vectors in addressing complex semantic relationships and classification tasks. This work constructs datasets of considerable semantic complexity, comprising a substantial volume of verbs along with their feature vectors and situation type labels, which can be used for evaluating large language models in the future. Full article

Background/Objectives: Exposure to older adults has been shown to influence the formation of attitudes toward this demographic. This raises the question of whether such exposure affects the relationship between the knowledge of ageing and attitudes toward ageing. The current study aimed to assess the level of knowledge and attitude toward ageing, as well as to examine the moderating effect of exposure towards older adults. Methods: A cross-sectional study using convenience sampling was conducted in Kuala Lumpur and involved 392 participants with a mean age of 28.69 years (S.D. = 7.61) and an age range of 18 to 59 years. Data were collected using a questionnaire that included sociodemographic questions, Kogan’s Attitude toward Old People (KAOP), and Palmore’s Facts on Ageing Quiz (FAQ). Hierarchical regression and moderation analyses were conducted using SPSS version 23 and the PROCESS macro. Results: The findings show that the public had a moderate level of knowledge about ageing and a slightly positive attitude toward it. Knowing someone with dementia significantly moderated the relationship between ageing knowledge and positive attitudes toward ageing. Furthermore, the positive impact of ageing knowledge decreased as experience in caring for individuals with dementia increased. Conclusions: Understanding the moderating effect of caregiving for those with dementia can inform public health strategies and caregiver support programs, encouraging a more nuanced approach to ageing education that considers the practical experiences of those who interact with older adults with cognitive impairments. Full article

Background/Objectives: Autism spectrum disorder (ASD) has a wide-ranging impact on individuals’ quality of life and development, and there is a critical need for greater awareness, early intervention, and comprehensive support strategies to effectively address the unique needs of those affected by ASD. Recent studies highlight the gut microbiome’s potential role in modulating ASD symptoms via the gut–brain axis, but specific microbial biomarkers remain unclear. This study aims to investigate differences in gut microbiota composition between ASD patients and neurotypical controls in a novel approach, specifically assessing ratios of Firmicutes/Bacteroidetes (F/B), Actinobacteria/Proteobacteria (A/P), and Prevotella/Bacteroides (P/B) as potential biomarkers. Methods: We analyzed gut microbiome samples from 302 Bulgarian children and adolescents diagnosed with ASD (aged 2–19 years). Microbial ratios (F/B, A/P, and P/B) were calculated and compared against previously reported reference meta-analytic means from European neurotypical populations. The statistical significance of deviations was assessed using parametric (t-tests), non-parametric (Wilcoxon signed-rank tests), and proportion-based (binomial tests) methods. Effect sizes were quantified using Cohen’s d. Significant differences between ASD cases and neurotypical reference values were observed across several age groups. Results: Notably, children with ASD demonstrated significantly lower F/B and A/P ratios, with the youngest cohort (0–4 years) exhibiting the greatest differences. Deviations in the P/B ratio varied across age groups, with a significant elevation in the oldest group (≥10 years). Collectively, ASD cases consistently exhibited microbiota profiles indicative of dysbiosis. Conclusions: Our findings support gut microbiome dysbiosis as a potential biomarker for ASD, highlighting significantly altered bacterial ratios compared to neurotypical controls. These microbiome shifts could reflect early-life disruptions influencing neurodevelopment. Future studies should adopt longitudinal and mechanistic approaches to elucidate causal relationships and evaluate therapeutic microbiome modulation strategies. Full article

Antiretroviral therapy (ART) drugs vary in their distribution into cerebrospinal fluid (CSF), which can be estimated using the central nervous system (CNS) penetration effectiveness (CPE) score. Although higher CPE has been associated with lower CSF HIV RNA levels, its relationship to CSF inflammation is less clear. We investigated associations between CPE and three CSF immune biomarkers (CXCL10, TNF-α, and IL-6) in 275 virally suppressed people with HIV (PWH) on three-drug ART regimens using a training–validation design. Participants were randomized into training (TG, n = 144) and validation (VG, n = 131) groups with similar demographics, ART characteristics, and CPE scores. The CSF levels of the biomarkers were quantified by bead suspension array-based immunoassays. In both groups, higher CPE correlated with lower levels of CXCL10 (TG: r = −0.31, p < 0.001; VG: r = −0.30, p < 0.001) and TNF-α (TG: r = −0.19, p = 0.04; VG: r = −0.18, p = 0.03), with remarkably similar effect size. CPE did not correlate with IL-6 in either group. Multivariable models confirmed the associations between higher CPE and both lower CXCL10 (R² = 0.16, p < 0.001) and TNF-α (R² = 0.07, p = 0.02) in CSF, and supported the relative resistance of IL-6 to ART effects. During suppressive ART, regimens that achieve higher concentrations in the CNS may better reduce some indicators of CSF inflammation (CXCL10 and TNF-α, closely related to the interferon pathway), but they may not fully normalize the neuroimmune environment (IL-6). Distinct ART regimens may produce different neuroimmune signatures, potentially contributing to heterogeneous patterns of brain injury. Full article

Effective water resource management requires an understanding of the interactions between water and environmental parameters, especially in regions with limited data availability. This study used generalized additive models (GAMs) to investigate the relationship between climatic and hydrological factors, namely river flow, rainfall, air temperature, and physicochemical water quality parameters in the Kiso River, Japan. Seasonal and non-seasonal GAMs models were developed for each water quality parameter, resulting in 7 non-seasonal models and 28 seasonal models based on Japan’s meteorological seasons (winter, spring, summer, fall). The findings demonstrated how seasonal models captured seasonal variability, significantly outperforming the non-seasonal models. For example, turbidity in winter (R² = 0.5030) showed significant improvement compared with non-seasonal models (R² = 0.1470), and organic pollution in fall (R² = 0.4099) increased compared with non-seasonal models (R² = 0.2509). Beyond assessing the influence of environmental drivers on water quality, these findings are crucial in regions with limited data, emphasizing the role of model–based seasonal analysis in identifying high-risk contamination periods, and supporting targeted and effective water management and early warning systems. Full article

Objective: To explore the relationship between intergenerational care and life satisfaction of older adults, and to analyze the chain mediating effect of children’s emotional support and depression in this relationship, so as to provide scientific reference for improving the quality of life of older adults. Methods: In total, 2970 older adults ≥60 years old from the China Health and Retirement Longitudinal Study (CHARLS) were selected as the study subjects. The process plug-in of SPSS was used, and the chain mediating effect test was carried out following the Bootstrap method. Results: Intergenerational care was positively correlated with children’s emotional support and life satisfaction (r = 0.123, 0.141, p < 0.001) and negatively correlated with depression (r = −0.096, p < 0.001). The mediating effects of children’s emotional support and depression were significant between intergenerational care and life satisfaction, with a mediating effect of 0.023 (95% CI: 0.015–0.033), 0.028 (95% CI: 0.014–0.043), and the chained mediating effect of children’s emotional support-depression was also significant, with a mediating effect of 0.006 (95% CI: 0.004–0.008). The total indirect effect of children’s emotional support and depression between intergenerational care and life satisfaction was 0.057, accounting for 26.03% of the total effect. Conclusions: Intergenerational care not only directly affects life satisfaction of older adults, but also indirectly affects life satisfaction through the independent mediating effect of children’s emotional support and depression, as well as the chain mediating effect of children’s emotional support-depression. It is essential to create a positive and inclusive social environment for the intergenerational care of older adults. Full article