Search Results (13,332)

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

Due to their low permeability, the location of natural fractures is key to the successful development of buried hill reservoirs. Due to the high degree of rock fragmentation and strong absorption of seismic waves at the top of buried hill formations, it is hard to identify the distribution of natural fractures inside a buried hill using conventional seismic methods. To overcome this difficulty, this study proposes a natural fracture identification technology for buried hill reservoirs that combines a continuum damage mechanics model with finite element numerical simulation. A 3D numerical solution workflow is established to determine the natural fracture distribution in target buried hill reservoirs. By constructing a geological model of a block, reconstructing the orogenic history, developing a 3D finite element model, and performing numerical simulations, the multi-stage orogenic processes experienced by buried hill reservoirs and the resultant natural fracture formation are replicated. This approach yields 3D numerical results of natural fracture distribution. Using the G-Block in the Zhongyuan Oilfield as a case study, the natural fracture distribution in a buried hill reservoir composed of mixed lithologies, including marble and Carboniferous formations, within the faulted G6-well group is analyzed. The results include plane views of the contour of damage variable SDEG, which represents the fracture distribution within the subsurface layer at 600 m intervals below the buried hill surface, as well as a vertical sectional view of the contour of SDEG’s distribution along specified well trajectories. By comparison with the results of the fracture distribution obtained with logging data, a consistency of 87.5% is achieved. This indicates the reliability of the numerical results for natural fractures obtained using the technology presented here. Full article

The TIFY gene family participates in crucial processes including plant development, stress adaptation, and hormonal signaling cascades. While the TIFY gene family has been extensively characterized in model plant systems and agricultural crops, its functional role in Eucalyptus grandis, a commercially valuable tree species of significant ecological and economic importance, remains largely unexplored. In the present investigation, systematic identification and characterization of the TIFY gene family were performed in E. grandis using a combination of genome-wide bioinformatics approaches and RNA-seq-based expression profiling. Nineteen EgTIFY genes were identified in total and further grouped into four distinct subfamilies, TIFY, JAZ (subdivided into JAZ I and JAZ II), PPD, and ZML, based on phylogenetic relationships. These genes exhibited considerable variation in gene structure, chromosomal localization, and evolutionary divergence. Promoter analysis identified a multitude of cis-acting motifs involved in mediating hormone responsiveness and regulating abiotic stress responses. Transcriptomic profiling indicated that EgJAZ9 was strongly upregulated under methyl jasmonate (JA) treatment, suggesting its involvement in JA signaling pathways. Taken together, these results offer valuable perspectives on the evolutionary traits and putative functional roles of EgTIFY genes. Full article

Background: Perinatal research faces significant challenges in understanding placental biology and maternal–fetal interactions due to limited access to human tissues and the lack of reliable models. Emerging technologies, such as liquid biopsy and single-cell analysis, offer novel, non-invasive approaches to investigate these processes. This scoping review explores the current applications of these technologies in placental development and the diagnosis of pregnancy complications, identifying research gaps and providing recommendations for future studies. Methods: This review adhered to PRISMA-ScR guidelines. Studies were selected based on their focus on liquid biopsy or single-cell analysis in perinatal research, particularly related to placental development and pregnancy complications such as preeclampsia, preterm birth, and fetal growth restriction. A systematic search was conducted in PubMed, Scopus, and Web of Science for studies published in the last ten years. Data extraction and thematic synthesis were performed to identify diagnostic applications, monitoring strategies, and biomarker identification. Results: Twelve studies were included, highlighting the transformative potential of liquid biopsy and single-cell analysis in perinatal research. Liquid biopsy technologies, such as cfDNA and cfRNA analysis, provided non-invasive methods for real-time monitoring of placental function and early identification of complications. Extracellular vesicles (EVs) emerged as biomarkers for conditions like preeclampsia. Single-cell RNA sequencing (scRNA-seq) revealed cellular diversity and pathways critical to placental health, offering insights into processes such as vascular remodeling and trophoblast invasion. While promising, challenges such as high costs, technical complexity, and the need for standardization limit their clinical integration. Conclusion: Liquid biopsy and single-cell analysis are revolutionizing perinatal research, offering non-invasive tools to understand and manage complications like preeclampsia. Overcoming challenges in accessibility and standardization will be key to unlocking their potential for personalized care, enabling better outcomes for mothers and children worldwide. Full article

The increasing demand for high-throughput, real-time, and single-molecule protein analysis in precision medicine has propelled the development of novel sensing technologies. Among these, nanopore-based methods have garnered significant attention for their unique capabilities, including label-free detection, ultra-sensitivity, and the potential for miniaturization and portability. Originally designed for nucleic acid sequencing, nanopore technology is now being adapted for peptide and protein analysis, offering promising applications in biomarker discovery and disease diagnostics. This review examines the latest advances in biological, solid-state, and hybrid nanopores for protein sensing, focusing on their ability to detect amino acid sequences, structural variants, post-translational modifications, and dynamic protein–protein or protein–drug interactions. We critically compare these systems to conventional proteomic techniques, such as mass spectrometry and immunoassays, discussing advantages and persistent technical challenges, including translocation control and signal deconvolution. Particular emphasis is placed on recent advances in protein sequencing using biological and solid-state nanopores and the integration of machine learning and signal-processing algorithms that enhance the resolution and accuracy of protein identification. Nanopore protein sensing represents a disruptive innovation in biosensing, with the potential to revolutionize clinical diagnostics, therapeutic monitoring, and personalized healthcare. Full article

Metabolic syndrome (MetS) is defined by a combination of metabolic abnormalities, such as central obesity, insulin resistance, hypertension, and dyslipidemia, and significantly increases the risk of cardiovascular diseases and type 2 diabetes. The high prevalence of MetS is a public health concern, necessitating rapid identification and intervention strategies to prevent this emerging epidemic. Diagnosing MetS requires the presence of three or more of these abnormalities, underscoring the need for effective management approaches. Despite a growing body of literature, limited reviews have critically evaluated the complex interplay between metabolic dysfunction, inflammation, and coagulation, particularly in the context of dietary interventions. Therefore, this article reviews the relationship between metabolic syndrome, inflammation, and thrombotic diseases, with an emphasis on their impacts on hematological health. Furthermore, this review explores the potential role of vegetarian and vegan dietary patterns in controlling these processes and improving hematological outcomes. This narrative review aims to critically evaluate current research on the inflammatory and thrombotic implications of MetS and assess the potential modulating role of vegan and plant-based diets within this context. Full article

The advent of next-generation sequencing technologies has given rise to considerably diverse techniques. However, integrating data from these technologies to generate high-quality genomes remains challenging, particularly when starting from metagenomic data. To provide further insight into this process, the genome of the lichenized fungus Solorina crocea was sequenced using DNA extracted from the thallus, which contains the genome of the mycobiont, along with those of the photobionts (a green alga and a cyanobacterium), and other associated microorganisms. Three different strategies were assessed for the assembly of a de novo genome, employing data obtained from Illumina and PacBio HiFi technologies: (1) hybrid assembly based on metagenomic data; (2) assembly based on metagenomic long reads and scaffolded with filtered mycobiont long and short reads; (3) hybrid assembly based on filtered mycobiont short and long reads. Assemblies were compared according to contiguity and completeness criteria. Strategy 2 achieved the most continuous and complete genome, with a size of 55.5 Mb, an N50 of 148.5 kb, and 519 scaffolds. Genome annotation and functional prediction were performed, including identification of secondary metabolite biosynthetic gene clusters. Genome annotation predicted 6151 genes, revealing a high number of genes associated with transport, carbohydrate metabolism, and stress response. Full article

Background: China has the largest number of patients with type 2 diabetes (T2D) worldwide, and the chronic complications and economic burden associated with T2D are becoming increasingly severe. Developing accurate and widely applicable risk prediction models is of great significance for the early identification of and intervention in high-risk populations. However, current Chinese models still have many shortcomings in terms of methodological design and clinical application. Objective: This study conducts a systematic review and narrative synthesis of existing risk prediction models for type 2 diabetes in China, aiming to identify issues with existing models and provide references with which Chinese scholars can develop higher-quality risk prediction models. Methods: This study followed the PRISMA guidelines to conduct a systematic search of the literature related to T2D risk prediction models in China published in English journals from October 2019 to October 2024. The databases included PubMed, CNKI and Web of Science. Included studies had to meet criteria such as clear modeling objectives, detailed model development and validation processes, and a focus on non-diabetic populations in China. A total of 20 studies were ultimately selected and comprehensively analyzed based on model type, variable selection, validation methods, and performance metrics. Results: The 20 included studies employed various modeling methods, including statistical and machine learning approaches. The AUC values of the models ranged from 0.728 to 0.977, indicating overall good predictive capability. However, only one study conducted external validation, and 45% (9/20) of the studies binned continuous variables, which may have reduced the models’ generalization ability and predictive performance. Additionally, most models did not include key variables such as lifestyle, socioeconomic factors, and cultural background, resulting in limited data representativeness and adaptability. Conclusions: Chinese T2DM risk prediction models remain in the developmental stage, with issues such as insufficient validation, inconsistent variable handling, and incomplete coverage of key influencing factors. Future research should focus on strengthening multicenter external validation, standardizing modeling processes, and incorporating multidimensional social and behavioral variables to enhance the clinical utility and cross-population applicability of these models. Registration ID: CRD420251072143. Full article

This paper proposes a harmonic source localization method for power systems, combining voltage difference features with a random forest classifier. The method captures harmonic propagation patterns and optimizes network topology handling to ensure accurate and efficient identification across various configurations. Validated on IEEE standard transmission networks, it achieves high accuracy and scalability. While effective in transmission systems, distribution networks pose challenges due to complex topologies and high impedance. Future enhancements will focus on advanced feature engineering, data augmentation, and real-time processing to improve adaptability in diverse power system environments. Full article

The identification of influential nodes in complex networks modeling manufacturing environments is a critical aspect, especially when considering both structure and nodal attributes. This becomes particularly relevant given that conventional weighted centrality measures typically only consider edge weights while ignoring node heterogeneity. We present SL-WLEN (Semi-Local centrality with Weighted Lexicographic Extended Neighborhood), a novel centrality metric designed to overcome these limitations. Based on LRASP (Local Relative Average Shortest Path) and lexicographic ordering, SL-WLEN integrates topological structure and nodal attributes by combining local components (degree and nodal values). The incorporation of lexicographic ordering preserves the relative importance of nodes at each neighborhood level, ensuring that those with high values maintain their influence in the final metric without distortions from statistical aggregations. This method is applied and its robustness evaluated in a quality control network for chip manufacturing, comprising 1555 nodes representing critical process characteristics, with weighted connections indicating their degree of correlation. Finally, the metric was evaluated against other established methods using the SIR propagation model and Kendall’s τ coefficient, demonstrating that SL-WLEN maintains consistent values across all analyzed test networks. Full article

As a typical cold region, Northeast China is characterized by its unique climate, hydrological conditions, and land systems, which collectively shape the diversity and complexity of regional ecosystem services (ESs). This review systematically examines research on ESs in Northeast China from 1997 to 2025, with particular emphasis on recent advances in service classification and spatiotemporal patterns, trade-offs and synergies among ESs, the identification of driving mechanisms, regulatory pathways, and policy effectiveness. The findings reveal obvious spatial heterogeneity and distinct stage-wise changing patterns in ESs across the region, with particularly pronounced trade-offs between food production and regulating services. The primary driving factors are concentrated in natural and human activities dimensions, whereas region-specific variables and policy-related drivers remain underexplored. Current research predominantly employs methods such as correlation analysis and geographically weighted regression; however, the capacity to uncover causal mechanisms and nonlinear interactions remains limited. Future research should strengthen the simulation of ecological processes in cold regions, improve the balance between ES supply and demand, improve policy scenario assessments, and develop dynamic feedback mechanisms. Compared with previous studies focusing on single services or regions, this review provides a multidimensional perspective by synthesizing multiple ES categories, integrating spatiotemporal comparative analysis, and incorporating modeling strategies specific to cold-region dynamics. These efforts will help shift ES research beyond static description toward more systematic regulation and management, providing both theoretical support and practical guidance for sustainable development and ecological governance in Northeast China. Full article

Computer vision recognition technology, due to its non-invasive and convenient nature, can effectively avoid damage to fragile wild plants during recognition. However, balancing model complexity, recognition accuracy, and data processing difficulty on resource-constrained hardware is a critical issue that needs to be addressed. To tackle these challenges, we propose an improved lightweight Faster R-CNN architecture named ULS-FRCN. This architecture includes three key improvements: a Light Bottleneck module based on depthwise separable convolution to reduce model complexity; a Split SAM lightweight spatial attention mechanism to improve recognition accuracy without increasing model complexity; and unsharp masking preprocessing to enhance model performance while reducing data processing difficulty and training costs. We validated the effectiveness of ULS-FRCN using five representative wild plants from the PlantCLEF 2015 dataset. Ablation experiments and multi-dataset generalization tests show that ULS-FRCN significantly outperforms the baseline model in terms of mAP, mean F1 score, and mean recall, with improvements of 12.77%, 0.01, and 9.07%, respectively. Compared to the original Faster R-CNN, our lightweight design and attention mechanism reduce training parameters, improve inference speed, and enhance computational efficiency. This approach is suitable for deployment on resource-constrained forestry devices, enabling efficient plant identification and management without the need for high-performance servers. Full article

Mollusc shells serve as valuable biogeochemical archives of natural or anthropogenic processes occurring in the aquatic environment throughout the life of the molluscs. One such process is trace element pollution, which can be assessed by analyzing the elemental composition of mollusc shells. However, different mollusc species accumulate elements in their shells from the aquatic environment at varying concentrations, and specific patterns of this accumulation remain largely unknown. In the present study, we measured the concentrations of 33 elements in the shells of five commercially important Black Sea molluscs, all collected from the same site, using inductively coupled plasma mass spectrometry. The species were ranked according to the number of elements with the highest concentrations in their shells as follows: Crassostrea gigas (9) = Rapana venosa (9) = Anadara kagoshimensis (9) > Flexopecten glaber ponticus (4) > Mytilus galloprovincialis (2). Cluster analysis of Pearson’s coefficients of correlation of elemental concentrations in the molluscan shells revealed significant separation of C. gigas, F. glaber ponticus, and M. galloprovincialis. Multivariate ordination analyses allowed the accurate classification of >92.3% of shell samples using as few as four elements (Fe, As, Sr, and I). Linear discriminant analysis revealed the probability of separation of all species based on the concentrations of these elements in their shells being not lower than 79%. The applied multivariate approach based on the analysis of four base elements in shells can help not only in the taxonomic identification of molluscs, but also, upon appropriate calibration, in monitoring medium-term dynamics of trace elements in the aquatic environment. Full article

Lycium barbarum is a traditional medicinal and edible plant species in China, exhibiting notable salt tolerance that enables cultivation in salt-affected soils. However, intensifying soil salinization has rendered severe salt stress a critical limiting factor for its fruit yield and quality. Universal stress proteins (USPs) serve as crucial regulators for plant abiotic stress responses through developmental process modulation. Nevertheless, the characteristics and functional divergence of USP gene family members remain unexplored in L. barbarum. Here, we performed genome-wide identification and characterization of the USP gene family in L. barbarum, revealing 52 members unevenly distributed across all 12 chromosomes. Phylogenetic analysis classified these LbUSP members into four distinct groups, demonstrating the integration of the conserved USP domain and diverse motifs within each group. Collinearity analysis indicated a stronger synteny of LbUSPs with orthologs in Solanum lycopersicum than with other species (Arabidopsis thaliana, Vitis vinifera, and Oryza sativa), demonstrating that gene duplication coupled with functional conservation represented the primary mechanism underlying USP family expansion in L. barbarum. In silico promoter screening detected abundant cis-acting elements associated with abiotic/biotic stress responses (MYB and MYC binding sites), phytohormone regulation (ABRE motif), and growth/development processes (Box-4 and G-box). Transcriptome sequencing and RT-qPCR validation revealed tissue-specific differential expression patterns of LbUSP8, LbUSP11, LbUSP12, LbUSP23, and LbUSP25 in roots and stems under salt stress, identifying them as prime candidates for mediating salt resistance in L. barbarum. Our findings establish a foundation for the functional characterization of LbUSPs and molecular breeding of salt-tolerant L. barbarum cultivars. Full article

The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible to deceptive practices, including counterfeit reviews, dubious transactions, and anomalous usage behaviors. This research introduces a framework for anomaly detection powered by artificial intelligence, integrating deep learning and natural language processing (NLP) with the isolation forest algorithm tree to enhance the identification of unusual activities on e-commerce platforms. We leveraged customer feedback, transaction logs, and user interaction data obtained from Kaggle. Textual reviews were interpreted using natural language processing (NLP), while deep learning was utilized to discern behavioral patterns. The isolation forest algorithm tree was employed to detect statistical anomalies in multidimensional data. The hybrid model surpassed conventional techniques in terms of detection accuracy, recall, and interpretability. It successfully detects suspicious actions and clarifies anomalies in their relevant context. The application of AI techniques, particularly natural language processing, deep learning, and isolation forest algorithm trees, establishes a solid foundation for anomaly detection in the realm of e-commerce. This approach fosters a more secure and trustworthy experience for online consumers. Full article