Search Results (1,485)

Astragalus membranaceus is an important perennial medicinal plant whose roots constitute its primary medicinal organ; however, its cultivation is severely constrained by root rot caused by Fusarium oxysporum. This study aimed to characterize differences in the rhizosphere microbiome between healthy and diseased plants, identify antagonistic microorganisms from healthy rhizosphere soils, and investigate their suppressive effects on F. oxysporum and the associated host metabolic responses. High-throughput sequencing was used to compare bacterial and fungal communities in the rhizospheres of healthy and diseased plants. Microorganisms were isolated from healthy rhizosphere soils and screened for antagonistic activity against F. oxysporum, followed by validation in pot experiments. Metabolomic analysis was further conducted to assess host metabolic responses to microbial treatment. Root rot disease significantly altered the dominant composition of rhizosphere microbial communities and was associated with reduced fungal diversity and lower bacterial richness in diseased soils. Co-occurrence network analysis revealed increased complexity in bacterial networks and strengthened positive correlations among fungal taxa under diseased conditions. A total of 81 microbial strains were isolated from healthy rhizosphere soils, among which Penicillium halotolerans exhibited the strongest inhibitory activity against the mycelial growth of F. oxysporum. Pot experiments further supported its suppressive effect on Astragalus root rot. Metabolomic analysis indicated that P. halotolerans treatment was associated with changes in host metabolic profiles related to energy metabolism, defense-associated protein synthesis, and nutrient uptake. Overall, this study identified P. halotolerans as a fungal strain with antagonistic activity against F. oxysporum and provided initial evidence for its association with the suppression of Astragalus root rot. These findings offer candidate microbial resources and mechanistic insights for understanding rhizosphere-associated disease suppression in Astragalus membranaceus. Full article

Background: YES1 is an Src family non-receptor tyrosine-protein kinase that regulates cell growth, migration, survival, and oncogenic signaling. Although YES1 activation mechanisms and substrates have been extensively studied, its phosphosite-specific regulation across diverse biological contexts remains poorly understood. Methods: We performed a large-scale integrative analysis of 3825 publicly available human mass spectrometry-based phosphoproteomic datasets to map YES1 phosphorylation events. Co-modulation, co-occurrence, evolutionary conservation, and disease-association analyses were conducted to characterize the functional and clinical relevance of site-specific YES1 phosphorylation. Results: Y426 emerged as the predominant YES1 phosphosite across diverse biological conditions, localized within the activation loop of the kinase domain and conserved across Src family kinases. Co-modulation analysis identified 421 positively and 102 negatively associated phosphosites enriched in biological processes related to cell cycle regulation, transcription, cytoskeletal remodeling, apoptosis, and carcinogenesis. Among these high-confidence protein phosphosites, we identified 24 binary interactors, 5 upstream regulators, and 8 candidate downstream substrates. Comparison with DisGeNet cancer biomarkers showed overlap between YES1-associated phosphoproteomic signatures and site-specific oncogenic markers across multiple cancers, such as breast cancer, colorectal cancer, leukemia, and lung adenocarcinoma. Conclusions: This study provides a systems-level, phosphosite-focused view of YES1 signaling and supports a central regulatory role for Y426 within global phosphoregulatory and cancer-associated networks. Full article

Inland waters are critical nodes in the global nitrogen cycle, where microbial processes govern transformations that impact water quality and ecosystem functioning. Inland waters are critical nodes in the global nitrogen cycle, where microbial processes govern transformations that impact water quality and ecosystem functioning. To systematically map the knowledge structure and to identify evolving trends in this field, a bibliometric analysis was conducted using CiteSpace on 2459 publications from the Web of Science Core Collection (1990–2024). The results reveal a significant increase in publications after 2010, peaking at 228 in 2024, with China (1541 articles) and the Chinese Academy of Sciences (776 articles) being the leading country and institution, respectively. Keyword co-occurrence and cluster analyses identify a core conceptual framework centered on microbial communities, nitrogen transformation processes (e.g., denitrification, anammox), and aquatic habitats (e.g., lakes, rivers). Based on keyword emergence and temporal trends, the analysis suggests an evolution in research focus across four dimensions: research subjects (from microbial biomass to keystone taxa), core questions (from process rates to predictive manipulation), methodological tools (from culturing to multi-omics), and mechanistic understanding (from linear pathways to complex networks). These observed patterns indicate a progressive refinement of the field. The findings provide a structured overview of the literature and may inform future research directions, but should be interpreted as bibliometric trends rather than definitive conclusions about the state of the science. Full article

The demand for advanced detection methods and accurate staging remains a global challenge in cancer diagnosis. Even though traditional deep learning models in medical imaging achieve high precision, they suffer from limited explainability and multimodal reasoning due to their black-box nature, thereby limiting their clinical applicability. To address this gap, recent research has increasingly explored multimodal approaches that integrate visual and textual clinical data to enhance diagnostic accuracy and interpretability. This study presents a bibliometric analysis of 408 publications from 2021 to 2025, collected from Web of Science and Scopus, using VOSviewer and R-Bibliometrix to map citation networks, co-authorship, and keyword co-occurrences. The results reveal a rapid growth from 1 publication in 2021 to 269 in 2025, with significant contributions from leading countries and institutions. Thematic analysis indicates a shift from conventional convolutional approaches toward transformer-based and self-supervised methods, alongside increasing attention to multimodal learning in cancer imaging tasks such as breast, lung, and brain cancer analysis. Overall, this study provides a structured overview of the evolving research landscape, highlighting key trends, emerging themes, and research gaps to inform future developments in multimodal artificial intelligence for cancer diagnosis. Full article

Background/Objectives: Down syndrome (DS), the most common chromosomal disorder, is associated with obesity and related metabolic complications. Although physical activity (PA) improves health outcomes in individuals with DS, global research trends in this field have not been systematically synthesized, and evidence regarding the effects of aerobic training (AT) on obesity-related parameters in individuals with DS remains inconsistent. This study incorporated a dual bibliometric and meta-analytical approach. Methods: First, the bibliometric analysis included 321 original research articles published between 2001 and 2024, retrieved from Scopus, Web of Science, and PubMed. Second, a meta-analysis of 15 randomized controlled trials (n = 477) was conducted to examine the effects of AT on obesity-related parameters, including body weight (BW), body mass index (BMI), fat mass (FM), waist circumference (WC), and waist-to-hip ratio (WHR) in individuals with DS. Results: Keyword co-occurrence and collaboration network analyses revealed a notable increase in research output since 2018, with “adolescent,” “obesity,” and “intellectual disability” the most co-occurring keywords associated with DS and PA. “Obesity” emerged as the most prominently growing keyword associated with DS and PA. A meta-analysis concluded that AT reduced FM (standardized mean differences [SMD] = −0.44; p < 0.001) and WC (SMD = −0.39; p < 0.01), while subtle changes in BW, BMI, and WHR were found. These findings suggest that AT improves body composition, particularly reducing central adiposity, even without changes in traditional weight-based metrics. Conclusions: Our findings demonstrate that AT can be an effective non-pharmacological strategy for improving body composition in individuals with DS and obesity and highlight the urgent need to shift clinical and research paradigms toward multidimensional, individualized health strategies that support PA and healthy body composition throughout the lifespan. Full article

Biopolymer-based materials have gained significant attention as sustainable alternatives for wastewater treatment due to their biodegradability, low toxicity, and high adsorption potential. Despite extensive research on individual materials such as chitosan, cellulose, and alginate, a comprehensive synthesis of global research trends integrating multiple biopolymers remains limited. This study addresses this gap through a large-scale bibliometric analysis of 13,720 publications indexed in the Scopus database from 1995 to 2025. The dataset was systematically analysed using VOSviewer to evaluate publication trends, leading journals, countries, institutions, collaboration networks, and keyword co-occurrence patterns. The results reveal a rapid growth phase after 2016, driven by increasing global demand for sustainable water treatment technologies. China, India, and the United States emerged as the most productive and influential contributors. Keyword analysis highlights adsorption, wastewater treatment, cellulose, and chitosan as dominant research themes, with growing emphasis on composite materials and functional modifications. Beyond descriptive metrics, this study identifies key research gaps, including limited focus on scalability, regeneration efficiency, and real-world application of biopolymer-based systems. The findings provide a comprehensive understanding of the evolution and current direction of the field, offering strategic insights for future research and development of high-performance, sustainable wastewater treatment materials. Full article

This study examines the global research landscape on the relationship between pesticide exposure and cancer using bibliometric and scientometric approaches. A total of 3908 records published between 2005 and 2024 were retrieved from the Web of Science database using an elaborate search strategy incorporating pesticide-related keywords (e.g., atrazine, glyphosate, DDT, chlorpyrifos) and general cancer descriptors (cancer and neoplasm). The analysis explores publication trends, citation patterns, keyword co-occurrence, and co-citation networks to understand the evolution of research in this field. The results reveal a consistent increase in publication output, indicating growing global attention to pesticide-related health risks. Keyword burst analysis and temporal thematic assessment highlight a clear evolution in research focus, shifting from early studies on occupational exposure and epidemiological risk assessment toward recent emphasis on toxicity, oxidative stress, and mechanistic pathways underlying carcinogenesis. The findings provide important insights for future research, public health policy, and regulatory frameworks, emphasizing the need for interdisciplinary approaches. By identifying emerging themes and research gaps, this study offers a broad understanding of the development of pesticide–cancer research and supports efforts to mitigate the health impacts of pesticide exposure. Full article

This study investigates the relevant literature on applications of Artificial Intelligence (AI) for wood as a material using a systematic review and screening process. The Web of Science (WoS) database identified 50 peer-reviewed publications dealing with AI applications for wood as a material. Bibliometrix and VOSviewer software were used to evaluate publication trends, country contributions, keyword co-occurrences, and AI application areas. Based on these analyses, an annual growth rate of 23.28% between 2014 and 2025 (November) in publications published per year was measured and an average of 6.92 citations per publication was observed as of November 2025. Most notably, a considerable increase in AI-focused research after 2023 was identified. Before 2022, work done using AI tools (such as neural networks, deep learning, and others) did not necessarily use the term AI and hence were not found by our search. China, Canada, and Poland were the countries with the highest number of publications. The leading journals with publications on AI applications for wood as a material were Forests and Wood Material Science and Engineering. The most frequently occurring keywords in the publications reviewed were “AI,” “machine learning,” and “deep learning.” In general, according to the publications reviewed, AI applications for wooden materials improved productivity, material evaluation, and quality assurance. The findings highlighted the impact of AI on the sector and show that AI will change the industry. Full article

Precast concrete (PC) component production scheduling is essential to the efficiency and reliability of industrialized construction. Although intelligent algorithms have been widely applied in this field, the relationships among research evolution, collaboration patterns, and industrial applicability remain insufficiently understood. To address this issue, this study presents a bibliometric review of 1272 publications indexed in the Web of Science Core Collection from 1990 to 2025. CiteSpace was employed to analyze publication trends, collaboration networks, co-citation structures, keyword co-occurrence, and burst terms. On this basis, a technology adaptability evaluation framework was developed to assess the alignment between algorithmic advances and industrial implementation in terms of dynamic adaptability, verification completeness, and technological generation gap. The results indicate that the field has evolved through four broad stages, from early static optimization to multi-objective coordination, digital twin-enabled dynamic scheduling, and emerging human-centric intelligent autonomous systems. The analysis also shows an increasing convergence of operations research, computer science, and civil engineering. However, a gap remains between academic output and industrial application. Specifically, 32% of the retrieved studies focused on genetic algorithms, whereas only 6% reported full-process industrial validation. In addition, Gen 4.0-related studies showed a technological generation gap of 82.5%, indicating that many frontier technologies have not yet reached broad industrial implementation. The collaboration network further reveals a “high-output, low-synergy” pattern, in which major publishing countries contribute substantially to the literature but exhibit limited cross-institutional integration. This study provides a structured overview of the development of PC component production scheduling research and highlights future directions for digital twin integration, human–robot collaboration, and cross-sector validation platforms. Full article

Clarifying the responses of microbial communities in distinct microhabitats like roots, the soil, and litter layers to secondary succession is critical for predicting the effects of global climate change on ecosystem functions. We investigated the microbial activities, compositions, and networks in these microhabitats of Fagus lucida forests ranging from 40 to 200 years. The results showed that soil physicochemical properties decreased with forest succession, except for NH₄⁺-N and available phosphorus, which decreased at the early stage. All vector angles of extracellular enzyme stoichiometry that were greater than 45° indicated that phosphorus was the key limiting element for microorganisms. The microbial community shifted from r- to K-strategists with forest succession, displaying the replacement of most bacterial phyla by Proteobacteria and Acidobacteriota, and an increase in the Acidobacteriota: Proteobacteria ratio, especially in the soil and litter layers. Soil properties, particularly NH₄⁺-N and pH, significantly affected the bacterial diversity and structure. Moreover, the bacterial network complexity increased with succession, particularly in the litter layer, and the topological properties of bacterial networks showed a stronger influence on microbial activities compared with those of fungal networks. The richness of keystone taxa in the litter layer was higher than in the soil layer and roots. However, the fungal community dominated by symbiotrophs showed lower sensitivity to soil nutrient changes and greater resilience to forest succession, displaying stable diversity and decreased network complexity, particularly in the roots. Ectomycorrhizal fungi (e.g., Russula) dominated the fungal guilds, and their abundance increased with forest succession, accompanied by a decrease in pathogenic fungi. Plant roots with significantly higher phosphatase activities played a stronger role than soils in structuring the litter microbial community, as reflected by similar carbon- and nitrogen-acquiring enzyme activities, microbial compositions, a greater share of taxa, and closer community distance. Our results revealed the increasingly important role of plant roots with forest succession in structuring the microbial community and nutrient cycling in the soil and litter layers. Full article

This study rounds into both the historical context and future projections of sustainable supply chain research practices. It emphasizes the necessity for the advanced analyses of research articles by combining traditional analysis with modern topic modeling and forecasting techniques. This study is organized around four primary research questions. A dataset of n = 8955 indexed article keywords and abstracts for the period of 2000–2025 was analyzed in the Python (version 3.12.) environment using n-grams, top keywords by year, k-means clustering combined with dimensionality reduction, and co-occurrence networks. Time-series forecasting models were also used to project the short-term development of clusters. The dataset retrieval was performed with search string and subject-area filters to focus the analysis on managerial and economic perspectives of sustainable supply chains. The analysis identified four keyword clusters: (1) CSR and Stakeholder Engagement, (2) Circular Economy and Sustainable Production, (3) Decision-making, Resilience and Emerging Technologies, and (4) Green Supply Chain Management. These clusters were then examined to assess current research practices from a managerial and economics perspective and their near-term evolution, with results validated through the additional clustering of abstract-level topics. This study confirms a paradigm change toward the integration of circularity, digitalization, and resilience, with technology-enabled growth. Social sustainability remains underrepresented, revealing a critical gap in current research. This study contributes methodologically by updating and extending current research practices and theoretically by revealing sustainability problems trends in supply chains. Full article

Fraud remains a critical and evolving challenge in telecommunications, costing the industry billions annually. In Mobile Virtual Network Operator (MVNO) environments, conventional supervised approaches are limited because fraud labels are scarce or delayed, and outgoing-call behavior is shaped by heterogeneous tariffs. Using a real-world MVNO dataset (9603 subscribers, 1.78 million outgoing CDRs), we derive payment-based segments and confirm statistically significant baseline differences via Kruskal–Wallis tests with Dunn post hoc pairwise comparisons and Benjamini–Hochberg correction. We propose a plan-aware calibration strategy setting interpretable thresholds using segment-wise empirical quantiles. Evaluation employs both operational metrics (activation rates and workload) and two label-free alert quality proxy metrics: multi-rule co-occurrence and activation stability (coefficient of variation). Compared to global calibration, segment-aware calibration reduces the dominant S4 rule activation (5.44% to 4.59% of user-hours) while increasing sensitivity to rare overnight patterns (F6: 0.0017% to 0.0137% of user-days). Experiments confirm improved alert quality, and the robustness of these findings is confirmed by sensitivity analysis across quantile levels and alternative segmentation schemes. Overall, segment-specific calibration yields a more balanced, interpretable, and operationally fair rule-based screening layer suitable for MVNO constraints. Full article

Background: This study explores sourcing risk in supply chains by identifying key risk categories, trends, and management strategies. It responds to increased vulnerabilities exposed by recent global disruptions such as the COVID-19 pandemic and geopolitical conflicts. Methods: The research applies a Systematic Literature Network Analyses (SLNA) combined with textual analysis to examine 687 peer-reviewed publications over the past three decades using the PRISMA protocol. Citation network analysis, keyword co-occurrence mapping, and main path analysis were conducted to map intellectual developments. Additionally, textual analysis using the Semantic Brand Score (SBS) approach revealed thematic relevance, novelty, and impact. Results: A shift exists from foundational supplier optimization models to resilience-building/strengthening, ethical sourcing, and technology-enabled strategies. Responsible sourcing and modern slavery were found to be the most innovative and underexplored areas. Research on sector-specific challenges, particularly for small and medium-sized enterprises, remains limited.; Conclusions: Sourcing risk has become a systemic challenge requiring resilience, ethics, and data-driven coordination across supply networks. Full article

Intensive ornamental planting is increasingly prevalent in urban green spaces, yet its effects on soil microbial community assembly and interaction networks remain poorly understood. Here, we examined shifts in soil properties, microbial diversity, community composition, and interaction networks across successive planting cycles. Bacterial alpha-diversity remained relatively stable, whereas fungal communities showed pronounced sensitivity to early planting stages. Beta-diversity analyses revealed that bacterial community composition was jointly influenced by planting stage and site type, while fungal communities were primarily structured by site characteristics. Co-occurrence network analysis revealed contrasting reassembly trajectories between microbial groups. Bacterial networks exhibited increasing complexity and modularity, indicating enhanced interaction intensity and competitive structuring under intensive management. In contrast, fungal networks displayed reduced connectivity but maintained or recovered modular organization, suggesting structural buffering. Notably, keystone taxa remained taxonomically conserved, indicating that network reorganization was driven by interaction rewiring rather than species turnover. We propose a dual-speed reassembly framework in which bacteria function as fast-responding components with dynamic interaction networks, whereas fungi act as slow-buffering, structurally persistent elements. This decoupling of short-term functional responsiveness and long-term stability provides new insights into how intensive management reshapes soil microbiomes in botanical garden ecosystems. Full article

The increasing demand for high-quality grapes necessitates rapid and objective quality assessment methods to overcome the limitations of traditional subjective and destructive techniques. This study investigated the feasibility of using hyperspectral imaging combined with machine learning for non-destructive quality evaluation of fresh grapes. Hyperspectral data were acquired from four table grape varieties (“Rose”, “Yongyou”, “Xiahei”, and “Jumbo”), and their Soluble Solids Content (SSC) was measured, which varied significantly among varieties. We extracted texture features using the Gray-Level Co-occurrence Matrix (GLCM) from images at key wavelengths, which were a combination of those selected by the Successive Projections Algorithm (SPA) and sensitive wavelengths. Comparative models for variety classification (qualitative) and SSC prediction (quantitative) were built using Extreme Learning Machine (ELM), Convolutional Neural Network (CNN), and Partial Least Squares (PLS) with full-range spectra and texture features as inputs. The results showed that the ELM model using full-range spectra was superior for both tasks, achieving a classification accuracy of 97.56% and, for SSC prediction, an $R_p^2$ of 0.75 and an RMSEP of 0.81. Notably, CNN models also showed considerable robustness. Our findings confirm that combining hyperspectral imaging with machine learning is a viable strategy for fresh grape quality assessment. Full article