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College students undergo a critical transition from adolescence to adulthood, during which lifestyle behaviors such as physical activity, sedentary behavior, diet, and sleep are key determinants of long-term health. Digital health interventions (DHIs) are increasingly recognized as a promising strategy for improving these behaviors among college students. This systematic review aims to evaluate the effectiveness and applicability of DHIs targeting lifestyle behaviors among college students by analyzing intervention objectives, modalities, functionalities, outcomes, and other key characteristics. In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, multiple scientific databases, including Scopus, Web of Science, PubMed, MEDLINE, PsycINFO, SPORTDiscus, ProQuest Central, APA PsycArticles, ERIC, and Academic Search Premier, were searched for studies published between January 2010 and December 2025 (initial search: August 5, 2025; updated search: December 27, 2025). The inclusion criteria were original empirical studies on DHIs targeting lifestyle behaviors (physical activity, sedentary behavior, diet, and sleep) among college students, published in English. Studies focusing on nondigital interventions, lacking sufficient methodological details, or not reporting lifestyle behavior-related outcomes were excluded. Quality assessment was conducted in 2 stages: all studies were first evaluated using the Mixed Methods Appraisal Tool (2018 version), followed by Risk of Bias 2 for randomized controlled trials and Joanna Briggs Institute critical appraisal tools for nonrandomized studies. A narrative synthesis was used to present and synthesize the findings. A total of 2998 records were retrieved, of which 46 publications met the inclusion criteria. These included 30 (65%) studies related to physical activity, 26 (57%) studies to diet, 10 (22%) studies related to sedentary behavior, and 6 (13%) studies related to sleep. This review enabled an examination of the effects of DHIs on college students' lifestyle behaviors. DHIs primarily used mobile apps, web-based platforms, and mobile communication technologies, with core functionalities such as education, guidance, monitoring, and prompting. DHIs were more effective in improving physical activity and diet; however, evidence for reducing sedentary behavior and improving sleep remained limited. Of the 46 studies, 31 (67%) reported positive effects, with larger sample sizes and intervention durations of 8-16 weeks being associated with more favorable outcomes. This review focuses on college students, addressing a gap in the literature that often centers on general adult populations. Unlike previous reviews that focus on a single behavior, this study integrates multiple lifestyle behaviors and evaluates DHIs across diverse modalities and functionalities. These contributions help refine future DHIs for college students and inform health promotion strategies in higher education. Although DHIs show potential for improving lifestyle behaviors, evidence of their long-term effectiveness remains limited. Future interventions should prioritize multibehavior integration, interactivity, and population-differentiated design to enhance precision, sustainability, and equity. This study has several limitations, including issues related to sample representativeness, intervention refinement, and methodological rigor. PROSPERO CRD420251119078; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251119078.

Xanthoceras sorbifolium, a tree species endemic to northern China, has high oil content in its seeds and is recognized as an important biodiesel crop. The plant is characterized by late-acting self-incompatibility (LSI). LSI was found to occur in many angiosperm species and plays an important role in reducing inbreeding and its harmful effects, as do gametophytic self-incompatibility (GSI) and sporophytic self-incompatibility (SSI). Molecular mechanisms of conventional GSI and SSI have been well characterized in several families, but no effort has been made to identify the genes involved in the LSI process. The present studies indicated that there were no significant differences in structural and histological features between the self- and cross-pollinated ovules during the early stages of ovule development until 5 days after pollination (DAP). This suggests that 5 DAP is likely to be a turning point for the development of the selfed ovules. Comparative de novo transcriptome analysis of the selfed and crossed ovules at 5 DAP identified 274 genes expressed specifically or preferentially in the selfed ovules. These genes contained a significant proportion of genes predicted to function in the biosynthesis of secondary metabolites, consistent with our histological observations in the fertilized ovules. The genes encoding signal transduction-related components, such as protein kinases and protein phosphatases, are overrepresented in the selfed ovules. X. sorbifolium selfed ovules also specifically or preferentially express many unique transcription factor (TF) genes that could potentially be involved in the novel mechanisms of LSI. We also identified 42 genes significantly up-regulated in the crossed ovules compared to the selfed ovules. The expression of all 16 genes selected from the RNA-seq data was validated using PCR in the selfed and crossed ovules. This study represents the first genome-wide identification of genes expressed in the fertilized ovules of an LSI species. The availability of a pool of specifically or preferentially expressed genes from selfed ovules for X. sorbifolium will be a valuable resource for future genetic analyses of candidate genes involved in the LSI response.

As soil becomes more acidic, aluminum toxicity has emerged as a key issue impacting seed germination and crop productivity in such environments. Therefore, it is urgent to investigate the mechanism of the influence of aluminum stress on germination. In this study, we focused on one of the major bioenergy crops—rapeseed. Seeds of aluminum-sensitive (S) and aluminum-resistant (R) lines screened from the recombinant inbred lines (RILs) population of rapeseed were treated with 80 µg·ml -1 AlCl 3 (ST, RT). Purified water served as the control (SC, RC). On the 3rd, 5th, and 7th day after treatment, the root tissue was collected for transcriptome sequencing. Utilizing MapMan software, the genes showing differential expression in S and R lines were assigned to the aluminum stress signaling pathway, resulting in the identification of 1036 genes. By weighted gene co-expression network analysis (WGCNA), five co-expressed gene modules associated with aluminum stress were discovered. A total of 332 candidate genes were screened by combining the genes related to aluminum stress signal transduction pathways with the module hub genes. Among them, 26 key genes were located in quantitative trait loci (QTL) with confidence intervals for germination-related traits of rapeseed under aluminum stress, and primarily distributed in 11 QTL regions, such as qRDW-A09-1 , qRDW-A10-1 and qRGV-A01-2 , they were associated with relative root length (RRL), relative root dry weight (RDW), relative germination vigor (RGV) and relative bud length (RBL). The roles included transcription regulation, stress protein production, redox processes, hormone signaling, cell wall alteration, and calcium-based signal transmission. Compared with the R line, the S line exhibited quicker and stronger activation of genes related to aluminum stress signal transduction, suggesting that the S line was more responsive to aluminum stress. This research offers an empirical basis for identifying aluminum-resistant rapeseed varieties and investigating the molecular regulation of aluminum tolerance during germination.

Background Digital health interventions have gained increasing prominence worldwide and demonstrate substantial potential for promoting healthy lifestyle behaviors. However, accumulating evidence suggests that not all population groups benefit equally from these interventions, raising concerns about persistent social inequalities in access, engagement, and outcomes. Methods This study conducted an umbrella review to systematically synthesize evidence from review-level studies that examined social inequality indicators in digital health interventions targeting lifestyle behaviors among adults. Comprehensive searches were performed across seven electronic databases, identifying 41 eligible reviews published between January 2000 and June 2025. Data were extracted on targeted behavioral domains, intervention outcomes, and reported social inequality indicators. Results The included reviews primarily focused on interventions targeting physical activity and diet, followed by sedentary behavior and sleep, with behavioral outcomes serving as the main evaluation metrics. Among social inequality indicators, age, gender, and place of residence were most frequently reported. In contrast, indicators such as income, race/ethnicity, socioeconomic status, education, digital health literacy, and employment were substantially underrepresented. This uneven distribution indicates significant gaps in the current evidence base and suggests that the differential effects of digital health interventions across social groups may be underestimated or overlooked. Conclusions Current review-level evidence on digital health interventions insufficiently captures the full spectrum of social inequalities shaping intervention access, engagement, and benefits. To support more equitable and inclusive health promotion strategies, future research should systematically incorporate a broader range of social inequality indicators and conduct in-depth analyses of the mechanisms underlying unequal intervention effects across diverse demographic and socioeconomic groups.

Yellow-seed trait is a desirable breeding characteristic of rapeseed ( Brassica napus ) that could greatly improve seed oil yield and quality. However, the underlying mechanisms controlling this phenotype in B. napus plants are difficult to discern because of their complexity. Here, we assemble high-quality genomes of yellow-seeded (GH06) and black-seeded (ZY821). Combining in-depth fine mapping of a quantitative trait locus (QTL) for seed color with other omics data reveal BnA09MYB47a , encoding an R2R3-MYB-type transcription factor, as the causal gene of a major QTL controlling the yellow-seed trait. Functional studies show that sequence variation of BnA09MYB47a underlies the functional divergence between the yellow- and black-seeded B. napus . The black-seed allele BnA09MYB47a ZY821 , but not the yellow-seed allele BnA09MYB47a GH06 , promotes flavonoid biosynthesis by directly activating the expression of BnTT18 . Our discovery suggests a possible approach to breeding B. napus for improved commercial value and facilitates flavonoid biosynthesis studies in Brassica crops. Yellow-seed trait is preferred in rapeseed breeding as it can greatly improve seed oil yield and quality. Here, the authors assemble the genome of two rapeseed lines with yellow-seed and black-seed phenotypes, and clone an R2R3-MYB-type transcription factor encoding gene as a key regulator of seed color.

Background Leaf color mutants have reduced photosynthetic efficiency, which has severely negative impacts on crop growth and economic product yield. There are different chlorophyll mutants in Arabidopsis and crops that can be used for genetic control and molecular mechanism studies of chlorophyll biosynthesis, chloroplast development and photoefficiency. Chlorophyll mutants in Brassica napus are mostly used for mapping and location research but are rarely used for physiological research. The chlorophyll-deficient mutant in this experiment were both genetically mapped and physiologically analyzed. Results In this study, yellow leaf mutant of Brassica napus L. mutated by ethyl methyl sulfone (EMS) had significantly lower chlorophyll a, b and carotenoid contents than the wild type, and the net photosynthetic efficiency, stomatal conductance and transpiration rate were all significantly reduced. The mutant had sparse chloroplast distribution and weak autofluorescence. The granule stacks were reduced, and the shape was extremely irregular, with more broken stromal lamella. Transcriptome data analysis enriched the differentially expressed genes mainly in phenylpropane and sugar metabolism. The mutant was mapped to a 2.72 Mb region on A01 by using BSA-Seq, and the region was validated by SSR markers. Conclusions The mutant chlorophyll content and photosynthetic efficiency were significantly reduced compared with those of the wild type. Abnormal chloroplasts and thylakoids less connected to the stroma lamella appeared in the mutant. This work on the mutant will facilitate the process of cloning the BnaA01.cd gene and provide more genetic and physiological information concerning chloroplast development in Brassica napus .

Background Tribenuron methyl (TBM) is an herbicide that inhibits sulfonylurea acetolactate synthase (ALS) and is one of the most widely used broad-leaved herbicides for crop production. However, soil residues or drifting of the herbicide spray might affect the germination and growth of rapeseed, Brassica napus , so it is imperative to understand the response mechanism of rape to TBM during germination. The aim of this study was to use transcriptome analysis to reveal the gene responses in herbicide-tolerant rapeseed to TBM stress during seed germination. Results 2414, 2286, and 1068 differentially expressed genes (DEGs) were identified in TBM-treated resistant vs sensitive lines, treated vs. control sensitive lines, treated vs. control resistant lines, respectively. GO analysis showed that most DEGs were annotated to the oxidation-reduction pathways and catalytic activity. KEGG enrichment was mainly involved in plant-pathogen interactions, α-linolenic acid metabolism, glucosinolate biosynthesis, and phenylpropanoid biosynthesis. Based on GO and KEGG enrichment, a total of 137 target genes were identified, including genes involved in biotransferase activity, response to antioxidant stress and lipid metabolism. Biotransferase genes, CYP450, ABC and GST , detoxify herbicide molecules through physical or biochemical processes. Antioxidant genes, RBOH, WRKY, CDPK, MAPK, CAT, and POD regulate plant tolerance by transmitting ROS signals and triggering antioxidant enzyme expression. Lipid-related genes and hormone-related genes were also found, such as LOX3, ADH1, JAZ6, BIN2 and ERF , and they also played an important role in herbicide resistance. Conclusions This study provides insights for selecting TBM-tolerant rapeseed germplasm and exploring the molecular mechanism of TBM tolerance during germination.

The factors and transforms of affective state were analyzed based on affective psychology theory. After that, a multi-layer affective decision model was proposed by establishing mapping relation among character, mood and motion. The model reflected the changes of mood and emotion spaces based on different characters. Experiment showed that human emotion characteristics accorded with theory and law, thus providing reference for modeling of human–computer interaction system.

Point cloud data have been extensively studied due to their compact form and flexibility in representing complex 3D geometries and structures. The ability of point cloud data to accurately capture and represent intricate 3D geometry makes it an ideal choice for a wide range of applications, including 3D computer graphics, autonomous driving, robotics, and augmented reality, all of which require an understanding of the underlying geometry and spatial structures. Given the challenges associated with annotating large-scale point clouds, self-supervised point cloud representation learning has attracted increasing attention in recent years. It aims to learn generic and useful point cloud representations from unlabeled data, circumventing the need for extensive manual annotation. In this paper, we present a comprehensive survey of self-supervised point cloud representation learning using DNNs. We begin by presenting the motivation and general trends in recent research, then briefly introduce commonly used datasets and evaluation metrics. Next, we extensively explore self-supervised point cloud representation learning methods. Finally, we share our thoughts on some of the challenges and potential issues that future research into self-supervised learning for pre-training 3D point clouds may encounter. Our curated bibliography can be found at https://github.com/EtronTech/Awesome₃DSSL.

Cluster, consisting of a group of computers, is to act as a whole system to provide users with computer resources. Each computer is a node of this cluster. Cluster computer refers to a system consisting of a complete set of computers connected to each other. With the rapid development of computer technology, cluster computing technique with high performance–cost ratio has been widely applied in distributed parallel computing. For the large-scale close data in group enterprise, a heterogeneous data integration model was built under cluster environment based on cluster computing, XML technology and ontology theory. Such model could provide users unified and transparent access interfaces. Based on cluster computing, the work has solved the heterogeneous data integration problems by means of Ontology and XML technology. Furthermore, good application effect has been achieved compared with traditional data integration model. Furthermore, it was proved that this model improved the computing capacity of system, with high performance–cost ratio. Thus, it is hoped to provide support for decision-making of enterprise managers.