Search Results (2,989)

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. Full article

Diacylglycerol-O-acyltransferase 1 (DGAT1, EC 2.3.1.20) is a pivotal enzyme in plant triacylglycerol (TAG) biosynthesis. Previous work identified conserved di-arginine (R) motifs (R-R, R-X-R, and R-X-X-R) in its N-terminal cytoplasmic acyl-CoA binding domain. To elucidate their functional significance, we engineered R-rich sequences in the N-termini of Tropaeolum majus and Zea mays DGAT1s. Comparative analysis with their respective non-mutant constructs showed that deleting or substituting R with glycine in the N-terminal region of DGAT1 markedly reduced lipid accumulation in both Camelina sativa seeds and Saccharomyces cerevisiae cells. Immunofluorescence imaging revealed co-localization of non-mutant and R-substituted DGAT1 with lipid droplets (LDs). However, disruption of an N-terminal di-R motif destabilizes DGAT1, alters LD organization, and impairs recombinant oleosin retention on LDs. Further evidence suggests that the di-R motif mediates DGAT1 retrieval from LDs to the endoplasmic reticulum (ER), implicating its role in dynamic LD–ER protein trafficking. These findings establish the conserved di-R motifs as important regulators of DGAT1 function and LD dynamics, offering insights for the engineering of oil content in diverse biological systems. Full article

Background: Colorectal cancer (CRC) is strongly influenced by miRNAs as well as the circadian system. Methods: High-throughput sequencing of miRNAs expressed in the rat colon during 24 h light (L)/dark (D) cycle was performed to identify rhythmically expressed miRNAs. The role of miR-150-5p in CRC progression was analyzed in DLD1 cell line and human CRC tissues. Results: Nearly 10% of mature miRNAs showed a daily rhythm in expression. A peak of miRNAs’ levels was in most cases observed during the first half of the D phase of the LD cycle. The highest amplitude was detected in expression of miR-150-5p and miR-142-3p. In the L phase of the LD cycle, the maximum in miR-30d-5p expression was detected. Gene ontology enrichment analysis revealed that genes interfering with miRNAs with peak expression during the D phase influence apoptosis, angiogenesis, the immune system, and EGF and TGF-beta signaling. Rhythm in miR-150-5p, miR-142-3p, and miR-30d-5p expression was confirmed by real-time PCR. Oncogenes bcl2 and myb and clock gene cry1 were identified as miR-150-5p targets. miR-150-5p administration promoted camptothecin-induced apoptosis. Expression of myb showed a rhythmic profile in DLD1 cells with inverted acrophase with respect to miR-150-5p. miR-150-5p was decreased in cancer compared to adjacent tissue in CRC patients. Decrease in miR-150-5p was age dependent. Older patients with lower expression of miR-150-5p and higher expression of cry1 showed worse survival in comparison with younger patients. Conclusions: miRNA signaling differs between the L and D phases of the LD cycle. miR-150-5p, targeting myb, bcl2, and cry1, can influence CRC progression in a phase-dependent manner. Full article

The age of entrepreneurship calls for the evolving of learning and development (L&D) models to meet the dynamic demands of innovation, sustainability, and technology innovation. This study examines the trends and issues of L&D models for entrepreneurs, more so focusing on how these models influence business success in a rapidly changing global landscape. The research employs bibliometric analysis, VOSviewer cluster analysis, and co-citation analysis to explore the literature from 1994 to 2024. Data collected from the Web of Science Core Collection database reflect significant trends in entrepreneurial L&D, with particular emphasis on the use of digital tools, sustainability processes, and governance systems. Findings emphasize the imperative role of L&D in fostering entrepreneurship, more so in areas such as digital transformation and the adoption of new technologies. The study also identifies central regions propelling this field, such as UK and USA. Future studies will be centered on the role of digital technologies, innovation, and green business models within entrepreneurial L&D frameworks. This study provides useful insight into the future of L&D within the entrepreneurial domain, guiding academia and companies alike in the planning of effective learning strategies to foster innovation and sustainable business growth. Full article

Background/Objectives: While artificial intelligence (AI) is rapidly transforming education, its specific effectiveness for students with learning disabilities (LD) requires rigorous evaluation. This systematic review aims to assess the efficacy of AI-based educational interventions for students with LD, with a specific focus on the methodological quality and risk of bias of the available evidence. Methods: A systematic search was conducted across seven major databases (Google Scholar, ScienceDirect, APA PsycInfo, ERIC, Scopus, PubMed) for experimental studies published between 2022 and 2025. This review followed PRISMA guidelines, using the PICOS framework for inclusion criteria. A formal risk of bias assessment was performed using the ROBINS-I and JBI critical appraisal tools. Results: Eleven studies (representing 10 independent experiments), encompassing 3033 participants, met the inclusion criteria. The most studied disabilities were dyslexia (six studies) and other specific learning disorders (three studies). Personalized/adaptive learning systems and game-based learning were the most common AI interventions. All 11 studies reported positive outcomes. However, the risk of bias assessment revealed significant methodological limitations: no studies were rated as having a low risk of bias, with most presenting a moderate (70%) to high/serious (30%) risk. Despite these limitations, quantitative results from the stronger studies showed large effect sizes, such as in arithmetic fluency (d = 1.63) and reading comprehension (d = −1.66). Conclusions: AI-based interventions demonstrate significant potential for supporting students with learning disabilities, with unanimously positive reported outcomes. However, this conclusion must be tempered by the considerable risk of bias and methodological weaknesses prevalent in the current literature. The limited and potentially biased evidence base warrants cautious interpretation. Future research must prioritize high-quality randomized controlled trials (RCTs) and longitudinal assessments to establish a definitive evidence base and investigate long-term effects, including the risk of cognitive offloading. Full article

Corn smut, caused by Ustilago maydis, significantly threatens maize production. This study evaluated 199 maize inbred lines at the seedling stage under greenhouse conditions for resistance to U. maydis, identifying 39 highly resistant lines. A genome-wide association study (GWAS) using the mrMLM model detected 19 significant single-nucleotide polymorphism (SNP) loci. Based on a linkage disequilibrium (LD) decay distance of 260 kb, 226 candidate genes were identified. Utilizing the significant loci chr1_244281660 and chr5_220156746, two kompetitive allele-specific PCR (KASP) markers were successfully developed. A PCR-based sequence-specific oligonucleotide probe hybridization technique applied to the 199 experimental lines and 60 validation lines confirmed polymorphism for both markers, with selection efficiencies of 48.12% and 43.33%, respectively. The tested materials were derived from foundational inbred lines of domestic and foreign origin. Analysis of 39 highly resistant lines showed that the advantageous alleles carrying thymine/cytosine (T/C) predominated at frequencies of 94.87% and 53.84%, respectively. The genotype TTCC conferred high resistance, while CCTT was highly susceptible. The resistance exhibited high heritability and significant gene-by-environment interaction. This work systematically dissects the genetic basis of common smut resistance in maize, identifies favorable alleles, and provides a novel KASP marker-based strategy for developing disease-resistant germplasm. Full article

High-altitude cognitive impairment (HACI) results from acute or chronic exposure to hypoxic conditions. Brain lipid homeostasis is crucial for cognitive function, and lipid droplet (LD) accumulation in glia cells is linked to cognitive decline in aging and stroke. However, whether high-altitude exposure affects brain lipid homeostasis is unclear. Microglia, key regulators of brain homeostasis and inflammation, play a significant role in pathological cognitive impairment and are implicated in LD formation. This study investigates whether lipid dysregulation contributes to HACI and explores microglia-driven mechanisms and potential interventions. Mice were exposed to a simulated 7000 m altitude for 48 h, followed by a week of recovery. Cognitive function and LD accumulation in brain cells were assessed. Microglia were depleted using PLX5622, and mice were exposed to hypoxia or lipopolysaccharide (LPS) to validate microglia’s role in driving astrocytic LD accumulation and cognitive decline. Minocycline was used to inhibit inflammation. In vitro, co-culture systems of microglia and astrocytes were employed to confirm microglia-derived pro-inflammatory factors’ role in astrocytic LD accumulation. Hypobaric hypoxia exposure induced persistent cognitive impairment and LD accumulation in hippocampal astrocytes and microglia. Microglia depletion alleviated cognitive deficits and reduced astrocytic LD accumulation. Hypoxia or LPS did not directly cause LD accumulation in astrocytes but activated microglia to release IL-1β, inducing astrocytic LD accumulation. Microglia depletion also mitigated LPS-induced cognitive impairment and astrocytic LD accumulation. Minocycline reduced hypoxia-induced LD accumulation in co-cultured astrocytes and improved cognitive function. Hypoxia triggers pro-inflammatory microglial activation, leading to LD accumulation and the release of IL-1β, which drives astrocytic LD accumulation and neuroinflammation, exacerbating HACI. Minocycline effectively restores brain lipid homeostasis and mitigates cognitive impairment. This study provides novel insights into HACI mechanisms and suggests potential therapeutic strategies. Full article

A Digital Elevation Model (DEM) provides accurate topographic data for planetary exploration (e.g., Moon and Mars), essential for tasks like lander navigation and path planning. This study proposes the first latent diffusion-based algorithm for DEM generation, leveraging a conditional decoder to enhance reconstruction accuracy from RGB satellite images. The algorithm performs the diffusion process in the latent space and uses a conditional decoder module to enhance the decoding accuracy of the DEM latent vectors. Experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of reconstruction accuracy, providing a new technical approach to efficiently reconstruct DEMs for extraterrestrial planets. Full article

Genomic prediction has emerged as a powerful tool in aquaculture breeding, but its effectiveness depends on the careful selection of informative single nucleotide polymorphisms (SNPs) and the application of appropriate prediction models. This study aimed to enhance genomic prediction accuracy in Russian sturgeon (Acipenser gueldenstaedtii) by optimizing SNP selection strategies and exploring the performance of linear and machine learning models. Three economically important traits—caviar yield, caviar color, and body weight—were selected due to their direct relevance to breeding goals and market value. Whole-genome sequencing (WGS) data were obtained from 971 individuals with an average sequencing depth of 13.52×. To reduce marker density and eliminate redundancy, three SNP selection strategies were applied: (1) genome-wide association study (GWAS)-based prioritization to select trait-associated SNPs; (2) linkage disequilibrium (LD) pruning to retain independent markers; and (3) random sampling as a control. Genomic prediction was conducted using both linear (e.g., GBLUP) and machine learning models (e.g., random forest) across varying SNP densities (1 K to 50 K). Results showed that GWAS-based SNP selection consistently outperformed other strategies, especially at moderate densities (≥10 K), improving prediction accuracy by up to 3.4% compared to the full WGS dataset. LD-based selection at higher densities (30 K and 50 K) achieved comparable performance to full WGS. Notably, machine learning models, particularly random forest, exceeded the performance of linear models, yielding an additional 2.0% increase in accuracy when combined with GWAS-selected SNPs. In conclusion, integrating WGS data with GWAS-informed SNP selection and advanced machine learning models offers a promising framework for improving genomic prediction in sturgeon and holds promise for broader applications in aquaculture breeding programs. Full article

Background/Objectives: Atypical sensory processing is increasingly recognized as a transdiagnostic dimension of neurodevelopmental disorders (NDDs), with critical implications for emotional and behavioral regulation. This study aimed to identify distinct sensory profiles in preschool children with NDDs and to examine their associations with emotional–behavioral and cognitive/developmental functioning. Methods: A total of 263 children (aged 21–71 months) diagnosed with autism spectrum disorder (ASD), language disorder (LD), or other NDDs (ONDD) were recruited. Sensory processing was assessed using the SPM-P, emotional–behavioral functioning was assessed via the CBCL 1½–5, and cognitive/developmental levels were assessed through standardized instruments. Latent profile analysis (LPA) was conducted to identify sensory subtypes. Group comparisons and multinomial logistic regression were used to examine profile characteristics and predictors of profile membership. Results: Three sensory profiles emerged: (1) Multisystemic Sensory Dysfunction (20.1%), characterized by pervasive sensory and emotional difficulties, primarily observed in ASD; (2) Typical Sensory Processing (44.9%), showing normative sensory and emotional functioning, predominantly LD; and (3) Mixed Subclinical Sensory Processing (35%), with subclinical-range scores across multiple sensory and emotional domains, spanning all diagnoses. Higher cognitive functioning and fewer internalizing symptoms significantly predicted membership in the typical profile. A gradient of symptom severity was observed across profiles, with the Multisystemic group showing the most pronounced emotional–behavioral impairments. Conclusions: Distinct sensory–emotional phenotypes were identified across diagnostic categories, supporting a dimensional model of neurodevelopment. Sensory profiles were strongly associated with emotional functioning, independently of diagnostic status. Early sensory assessment may therefore offer clinically meaningful insights into emotional vulnerability and inform targeted interventions in preschool populations with NDDs. Full article

Photoperiod sensitivity significantly affects the reproductive process of plants. The CONSTANS, CONSTANS-LIKE, and TOC1 (CCT) genes play pivotal roles in photoperiod sensitivity and regulating flowering time. However, the function of the CCT gene in regulating flowering varies among different species. Further research is needed to determine whether it promotes or delays flowering under long-day (LD) or short-day (SD) conditions. CCT MOTIF FAMILY (CMF) belongs to one of the three subfamilies of the CCT gene and has been proven to be involved in the regulation of circadian rhythms and flowering time in cereal crops. In this study, 60 CCT genes in Chinese cabbage were genome-wide identified, and chromosomal localization, gene duplication events, gene structure, conserved domains, co-expression networks, and phylogenetic tree were analyzed by bioinformatics methods. The specific expression patterns of the BrCMF gene in different tissues, as well as the transcriptome and RT-qPCR results under different photoperiodic conditions, were further analyzed. The results showed that BrCMF11 was significantly upregulated in ebm5 under LD conditions, suggesting that BrCMF11 promoted flowering under LD conditions in Chinese cabbage. These findings revealed the function of the BrCCT gene family in photoperiod flowering regulation and provided a prominent theoretical foundation for molecular breeding in Chinese cabbage. Full article

Grassland degradation influences arthropod community structure and abundance, which, in turn, modulate element cycling in grassland ecosystems through predation and soil structure modification. In order to explore the influence of degradation on arthropods in Bayinbuluk alpine grassland, we selected four degraded transects (i.e., non-degraded: ND, lightly degraded: LD, moderately degraded: MD, and heavily degraded: HD) to collect soil samples and determine their composition, spatial distribution, and diversity patterns, in addition to the factors driving community change. Following identification and analysis, the following results were obtained: (1) A total of 342 soil arthropods were captured in this study, belonging to 4 classes, 11 orders, and 24 families. (2) With the intensification of degradation, the dominant groups exhibited significant alteration: the initial dominant groups were Pygmephoridae and Microdispidae; however, as the level of degradation became more severe, the dominant groups gradually shifted to Campodeidae and Formicidae, as these groups are more adaptable to environmental changes. (3) Common groups included six families, including Parasitoididae and Onychiuridae, and rare groups included 16 families, such as Macrochelidae. (4) As degradation intensified, both the species diversity and population size of the arthropod community increased. Our Redundancy Analysis (RDA) results demonstrated that the key driving factors affecting the arthropod community were soil organic carbon (SOC), electrical conductivity (EC), soil total nitrogen (TN), and available nitrogen (AN). The above results indicate that grassland degradation, by altering soil properties, increases arthropod diversity, induces alterations in the dominant species, and reduces mite abundance, with these changes being closely related to soil carbon and nitrogen contents. The results of this study provide basic data for understanding the changes in soil arthropod communities during the degradation of alpine grasslands and also offer support for the sustainable development of soil organisms in grassland ecosystems. Full article

The Iridaceae family comprises approximately 1800 species, including Iris pallida Lam., which is widely recognized for its ornamental and aromatic properties and particularly adopted in the perfume industry. In this study, we evaluated the effects of planting density and maturity age on biomass production, morphological traits, rhizome biomass, and orris concrete yield in Iris pallida grown in Tuscany (Italy). The experiment consisted of four agricultural parcels, each one containing six plots arranged to test combinations of two planting densities (low density [LD], 8 plants/m² and high density [HD], 15 plants/m²) and harvesting age (2, 3, and 4 years). Results indicated that planting density significantly influenced biomass variables—including rhizome, bud, and stem biomass—with the low planting density (LD) exhibiting higher total biomass (5.48 ± 0.59 kg/m²) compared to that observed under high planting density (HD) (1.82 ± 0.54 kg/m²). Orris concrete yield varied significantly across planting densities and harvesting age, consistently favoring LD (0.055 ± 0.01%) over HD (0.045 ± 0.01%). Also, orris concrete yield showed a positive correlation with floral stem number (r = 0.73, p < 0.001), root biomass (r = 0.66, p < 0.01) and floral stem biomass (r = 0.63, p < 0.01), while no significant correlations were found between orris concrete yield and total biomass or rhizome biomass. A shorter production cycle under low-density planting may improve orris concrete yield without compromising biomass productivity. Full article

IMF (intramuscular fat, IMF), as a key index for evaluating meat quality traits (shear force and cooking loss, etc.), and its deposition process are jointly regulated by nutritional and genetic factors. In this study, we analyzed the molecular regulation mechanism of IMF deposition in the LD (longissimus dorsal muscle, LD) by dietary energy level in Pamir yaks. Meat quality assessment showed that the meat quality of the High-energy diet group (1.53 MJ/Kg, G) and the Medium-energy diet group (1.38 MJ/Kg, Z) were significantly improved compared with that of the Low-energy diet group (0.75 MJ/Kg, C), in which IMF content in the LD of yaks in G group was significantly higher (p < 0.05) compared with Z and C groups. Further analysis by combined transcriptomics and lipid metabolomics revealed that the differences in IMF deposition mainly originated from the metabolism of lipids, such as TG (triglycerides, TG), PS (phosphatidylserine, PS), and LPC (lysophosphatidylcholine, LPC), and were influenced by SFRP4, FABP4, GADD45A, PDGFRA, RBP4, and DGAT2 genes, further confirming the importance of lipid–gene interactions in IMF deposition. This study reveals the energy-dependent epigenetic regulatory mechanism of IMF deposition in plateau ruminants, which provides molecular targets for optimizing yak nutritional strategies and quality meat production, while having important theoretical and practical value for the sustainable development of livestock husbandry on the Tibetan Plateau. Full article

Hard disk failure prediction is an important proactive maintenance method for storage systems. Recent years have seen significant progress in hard disk failure prediction using high-quality SMART datasets. However, in industrial applications, data loss often occurs during SMART data collection, transmission, and storage. Existing machine learning-based hard disk failure prediction models perform poorly on low-quality datasets. Therefore, this paper proposes a hard disk fault prediction technique based on low-quality datasets. Firstly, based on the original Backblaze dataset, we construct a low-quality dataset, Backblaze-, by simulating sector damage in actual scenarios and deleting 10% to 99% of the data. Time series features like the Absolute Sum of First Difference (ASFD) were introduced to amplify the differences between positive and negative samples and reduce the sensitivity of the model to SMART data loss. Considering the impact of different quality datasets on time window selection, we propose a time window selection formula that selects different time windows based on the proportion of data loss. It is found that the poorer the dataset quality, the longer the time window selection should be. The proposed model achieves a True Positive Rate (TPR) of 99.46%, AUC of 0.9971, and F1 score of 0.9871, with a False Positive Rate (FPR) under 0.04%, even with 80% data loss, maintaining performance close to that on the original dataset. Full article