Search Results (112)

The study presents an exploratory design-space mapping approach for analysing knitted fabrics through the combined consideration of structural, comfort-related, and optical parameters. The methodology addresses the multi-parameter nature of knitted macrostructures, where functional behaviour emerges from the interaction of yarn composition, stitch architecture, and structural configuration rather than from isolated descriptors. Twelve knitted samples differing in stitch type and yarn linear density, and incorporating photoluminescent and reflective yarns, were analysed. Fabric thickness and air permeability were selected as representative structural and comfort-related parameters, while optical response was characterised using a dimensionless reflectance ratio under multiple illumination conditions. All parameters were normalised to enable comparative representation within a unified design space. The resulting maps reveal visual clusters, structurally isolated cases, and illumination-dependent optical equivalence between structurally different configurations. The findings demonstrate that similar optical performance can be achieved through alternative structural solutions, depending on the illumination context. The proposed approach provides a qualitative, design-oriented framework that supports engineering decision-making without implying optimisation or ranking, while revealing alternative design pathways and context-dependent equivalence. Full article

Traditional villages in ecologically fragile and multi-ethnic frontier regions are increasingly threatened by rapid urbanization and socio-economic transformation. Northwestern Yunnan, located in the longitudinal valleys of the Hengduan Mountains, represents a key cultural landscape of plateau agropastoral civilization and ethnic interaction, yet its spatial organization and clustering mechanisms remain insufficiently understood. This study develops a four-dimensional analytical framework integrating four dimensions—spatial morphology (village distribution patterns and density), geomorphological conditions (elevation, slope, and terrain features), cultural attributes (ethnic composition and historical-cultural corridors), and architectural typologies (dominant residential structure types) to examine 246 officially recognized traditional villages. Using GIS-based spatial statistics, kernel density estimation (KDE), spatial autocorrelation, and a hierarchical overlay model, the study identifies the spatial structure (distribution patterns and density gradients), environmental adaptability (relationships with elevation, slope, and hydrological conditions), and multidimensional clustering characteristics (integrated clustering intensity across four analytical dimensions) of settlements. The results reveal a highly uneven and a statistically significant clustered spatial pattern ($R$ = 0.606, Moran’s $I$ = 0.251, p < 0.05) characterized by a “two corridors–six clusters–multiple nodes” structure. Settlement distribution demonstrates strong coupling with mid-elevation plateau basins, river valley systems, and trade-cultural corridors shaped by the Ancient Tea Horse Road. Multidimensional integration further classifies villages into three typologies—comprehensive, specialized, and general clusters—reflecting different levels of coordination among spatial, environmental, cultural, and architectural dimensions. These findings reveal the spatial regularities and multidimensional clustering characteristics of officially recognized traditional villages in Northwestern Yunnan, and suggest that environmental setting, historical corridors, and cultural-architectural features jointly shape the current recognized heritage landscape. The proposed framework provides a context-sensitive basis for differentiated heritage conservation and rural management in mountainous multi-ethnic regions. Full article

We reported the first isolation and characterization of Neopestalotiopsis spp. from symptomatic strawberry plants in Nova Scotia, Canada. Morphological and multilocus sequence analyses confirmed that these isolates were closely related to previously identified aggressive Neopestalotiopsis spp. strains from strawberry and blueberry in the southeastern United States and other countries. Five representative isolates were evaluated for pathogenicity on detached leaves, whole plants, and fruits of multiple strawberry cultivars. The results revealed significant variation in virulence, with isolate NS-1 causing the most severe necrosis across all tissue types. Statistical analysis revealed significant effects of isolate, cultivar, and their interaction on disease severity, indicating differential cultivar responses to the tested isolates. Notably, tissue-specific differences were observed, with some isolates being aggressive on leaves but less virulent on fruit or whole plants, reinforcing the importance of multi-organ phenotyping in resistance screening. Phylogenetic analysis clustered the Nova Scotia isolates within the same clade as Neopestalotiopsis isolate 17–43 L from strawberry and isolates from blueberry, suggesting a potential epidemiological link. The shared nursery propagation system of strawberries and blueberries raises the risk of cross-infection, posing a substantial challenge to disease management strategies in both crops. Collectively, these findings underscore the urgent need for continued surveillance, population-level pathogen analysis, and the development of resistant cultivars to mitigate the spread of this emerging and rapidly evolving pathogen. Full article

Background/Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) causes hospital- and community-acquired infections. MRSA is a highly diverse strain that includes several epidemic clones, including CC45. A previous study conducted among MRSA isolates in Kuwait identified CC45 in two isolates in the early 2000s. This study provides an update on the prevalence and molecular characteristics of CC45 among MRSA isolates in Kuwait hospitals, during 2016–2022. Methods: A total of 13,276 MRSA isolates were collected during 2016–2022 and typed using antibiogram, DNA microarray, Staphylococcal protein A (spa) typing, pulsed-field gel electrophoresis (PFGE), and multi-locus sequence typing (MLST). Results: CC45 was detected in 87 (0.65%) of the 13,276 MRSA isolates. The isolates were resistant to fusidic acid (n = 71), erythromycin (n = 16), and inducible clindamycin resistance (n = 15). Twenty-one isolates were resistant to multiple antibiotics. Spa typing identified 19 types, with t362 (n = 35) and t132 (n = 27) as the dominant types. DNA microarray identified seven genotypes with CC45-MRSA-[IV + fus] (n = 36) and CC45-MRSA-[VI + fus] (n = 30) as the dominant types. MLST identified six sequence types (STs): ST7119, ST508, ST45, ST46, ST9548, and ST10699. PFGE clustered the isolates into two major types, A and B, with type A being the major type (n = 83), mostly consisting of CC45-MRSA-[IV + fus] isolates. The CC45-MRSA-[IV + fus] and CC45-MRSA-[VI + fus] genotypes were detected throughout the study period (2016–2022), whereas the other genotypes were detected less frequently. Conclusions: The CC45-MRSA circulating in Kuwait hospitals comprises genetically diverse isolates that may have originated from different sources. The emergence of multidrug resistance among the isolates poses challenges for therapy and infection prevention. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

Large-scale agricultural remote sensing monitoring is challenged by pronounced spatial heterogeneity arising from fragmented terrain, complex climatic backgrounds, and diverse cropping structures. However, existing agricultural zoning schemes generally lack an integrated consideration of remote sensing imaging mechanisms and key variable conditions such as atmospheric interference and crop phenology, limiting their direct utility in guiding region-specific sensor selection and classification algorithm calibration. To address this limitation, this study integrates multi-source earth observation data and agricultural statistical information to construct an Agricultural Remote-sensing Classification Difficulty Index (ARCDI) from multiple dimensions, including image availability, cropping structure, cropland fragmentation, and topographic environment. On this basis, a graph theory-based spatially constrained Skater clustering algorithm is introduced to establish a two-tier “cropland–major cereal crops” zoning framework oriented toward remote sensing applications. The results indicate that the proposed framework delineates five distinct first-tier cropland classification difficulty zones across China. This zoning scheme effectively quantifies the regional heterogeneities in monitoring challenges. Building upon this first-tier zoning, the framework is further refined into 50 second-tier major cereal crop classification difficulty zones, including 13 winter wheat zones, 21 maize zones, and 16 rice zones. Statistical tests and spatial analyses demonstrate that the proposed zoning scheme significantly outperforms conventional clustering approaches in balancing within-zone homogeneity and spatial continuity. This advantage is quantitatively reflected by consistently lower residual spatial autocorrelation (residual Moran’s I ≈ 0.10–0.11) and an approximately 20% reduction in within-zone variance compared with other spatially constrained methods. Extensive field-sample validation provides preliminary evidence of an inverse relationship between crop-type classification difficulty and accuracy. These results confirm the framework’s reliability in identifying regional difficulty and its decision-support value for selecting remote sensing strategies. Overall, this study systematically elucidates the spatial differentiation patterns of remote sensing classification difficulty for cropland and major cereal crops across China. The proposed framework provides robust scientific support for data selection, algorithm optimization, and differentiated strategy formulation in national-scale agricultural monitoring, thereby facilitating the operationalization of regional agricultural remote sensing applications. Full article

Background: Metabolic enzymes catalyze biochemical pathways that sustain cellular metabolism. Their activity, stability, and molecular interactions are extensively regulated by post-translational modifications (PTMs). However, an integrated systems-level understanding of how diverse PTMs are organized across the human metabolic network remains poorly defined. Methods: We integrated experimentally reported PTM annotations from PhosphoSitePlus, dbPTM, and the quantitative PTM database (qPTM), and identified 29 distinct PTM types present across the 771 human metabolic enzymes. PTM features were quantitatively characterized at multiple levels, including sequence- and composition-based metrics (modification density and PTM potentiality rate), recurrence- and co-occurrence-based features (predominant sites, hotspot regions and PTM crosstalk), and functional-context annotations (protein-region localization and mutation overlap). These integrated features were subsequently used for unsupervised clustering to evaluate higher-order organizational patterns. Results: The analysis revealed that PTMs are unevenly distributed across metabolic enzymes, with phosphorylation, acetylation, ubiquitination, and methylation representing the most prevalent and recurrent regulatory modifications. Clustering segregated enzymes into two regulatory groups: (i) a PTM-enriched regulatory group characterized by high PTM density, frequent hotspot and crosstalk regions, and enrichment of rate-limiting enzymes, and (ii) a broad metabolic group with comparatively sparse PTM regulation. This non-uniform organization reflects the preferential accumulation of multiple regulatory PTMs on enzymes occupying key control points in central metabolic pathways, thereby forming a discrete regulatory subnetwork within metabolism. Conclusions: This study presents a systems-level, multi-PTM atlas of human metabolic enzymes and provides a quantitative framework for prioritizing PTM-regulated enzymes and pathways relevant to signaling–metabolism integration and disease-associated metabolic regulation. Full article

Background: Metabolic reprogramming is a hallmark of cancer and supports tumor growth and adaptation within the tumor microenvironment (TME). The complexity of this reprogramming manifests as both distinct variations across cancer types and spatial heterogeneity within individual tumors. The specificity of these metabolic alterations, whether to cancer type, spatial niche, or as shared features, remains unclear, highlighting a critical gap in our systematic, pan-cancer understanding of metabolic reprogramming. Methods: We integrated bulk metabolomics and spatial metabolomics to investigate pan-cancer metabolic features and used blood-based metabolomics and spatial transcriptomics data to validate key findings. Metabolic differences were compared between tumor and normal tissues across multiple cancer types at the bulk level to identify metabolic modules shared across cancers or specific to individual cancer types. A two-step clustering framework was applied to identify both local and global TME-associated spatial metabolic modules of spatial metabolomics data from various tumor tissue slices. Results: We have identified a spectrum of metabolic features, including those specific to individual cancer types or spatial architectures and others shared across cancers, with some features emerging only at bulk-level and others uniquely discernible through spatial metabolomics. Integrative analyses also identified 19 metabolites consistently altered in both bulk and spatial data, especially carnitine species, which also showed concordant changes in blood samples and spatial associations with genes involved in fatty acid metabolism. Conclusions: This pan-cancer, multi-scale integrative analysis highlights substantial metabolic heterogeneity within the TME and across cancer types and identifies metabolites with consistent alterations across analytical layers, providing candidate features for future studies of tumor metabolism and potential metabolic biomarkers. Full article

Background: Several studies have attempted to identify the initiating drivers of small intestinal neuroendocrine tumor (SI-NET) development and the molecular mechanisms underlying their progression and metastatic spread. Previous gene expression studies have used bulk microarrays or RNA sequencing to compare tumor tissue with normal intestinal mucosa. However, the intestine comprises multiple distinct cell types, and bulk analyses are limited by this cellular heterogeneity, which can confound tumor-specific signals. Methods: We performed single-cell RNA sequencing on primary SI-NETs and paired normal mucosa from two patients to directly compare tumor cells with their cells of origin, the enterochromaffin (EC) cells. To minimize type I errors, we applied a two-step validation strategy by overlapping differentially expressed genes with an external single-cell dataset and cross-referencing candidate genes for enteroendocrine expression in the Human Protein Atlas. Results: For further distinction and characterization, ECs were subdivided into serotonergic and non-serotonergic clusters. This analysis revealed that the SI-NET cells are transcriptionally more similar to serotonergic ECs, consistent with serum metabolite profiles derived from clinical parameters. Our analyses uncovered a loss-of-expression program characterized by regulators of epithelial differentiation and in parallel, a gain-of-expression program displayed neuronal signaling gene induction, implicating functional reprogramming toward neuronal-like properties. Together, these specific losses and gains suggest that our patient-derived SI-NETs undergo adaptation through both loss of enteroendocrine functions and acquisition of neurobiological-promoting signaling pathways. Conclusions: These findings nominate candidate drivers for further functional validation and highlight potential therapeutic strategies in our patient cohort, including restoring suppressed Notch signaling and targeting aberrant neuronal signaling networks. However, even with a two-step validation procedure, the modest cohort size limits statistical power and generalizability, particularly for the proposed association to a serotonergic phenotype. Larger, multi-patient single-cell studies are required to confirm these mechanisms and establish their clinical relevance. Full article

Genome-wide association studies (GWAS) have transformed the study of chronic kidney disease (CKD) by identifying hundreds of genetic loci associated with multiple aspects of kidney function, including albuminuria and CKD risk factors, in diverse populations. A major challenge is translating statistically significant signals into causal genes and mechanisms, as most CKD-associated variants lie in non-coding regulatory regions and often act in a cell type- and context-specific manner. In this review, we provide an overview of the current strategies for moving from GWAS signals toward the identification of causal genes for CKD. We discuss advances in four areas: statistical and functional fine-mapping, molecular quantitative trait locus (QTL) mapping, colocalization, and transcriptome-wide associations, highlighting the advantages and disadvantages of each. We further examined how emerging kidney-specific single-cell, single-nucleus, and spatial transcriptomic atlases have enabled the mapping of genetic risk to specific renal cell types and microanatomical niches. By combining these approaches with chromatin interaction data, multi-omics analytics, and clustered regularly interspaced short palindromic repeats (CRISPR)-based studies, the process of generating causal relationships and mechanistic understanding has been further refined. Importantly, this review provides a unifying framework that synthesizes cross-sectional and longitudinal GWAS with kidney-specific functional genomics to distinguish genetic determinants of CKD susceptibility from modifiers of disease progression, thereby highlighting how regulatory variation and disease trajectories inform precision nephrology. As a result, we can provide insights into the role of genetically informed gene prioritization for experimentation, therapeutic target discovery, and the development of a framework for precision nephrology. Together, these advancements highlight how human genetics, in conjunction with functional genomics and experimental biology, can link an association signal to a clinically relevant interpretation of CKD. Full article

Transferring cell type annotations from reference dataset to query dataset is a fundamental problem in AI-based single-cell data analysis. However, single-cell measurement techniques lead to domain gaps between multiple batches or datasets. The existing deep learning methods lack consideration on batch integration when learning reference annotations, which is a challenge for cell type annotation on multiple query batches. For cell representation, batch integration can not only eliminate the gaps between batches or datasets but also improve the heterogeneity of cell clusters. In this study, we proposed PLKD, a cell type annotation method based on pattern learning and knowledge distillation. PLKD consists of Teacher (Transformer) and Student (MLP). Teacher groups all input genes (features) into different gene sets (patterns), and each pattern represents a specific biological function. This design enables model to focus on biologically relevant functions interaction rather than gene-level expression that is susceptible to gaps of batches. In addition, knowledge distillation makes lightweight Student resistant to noise, allowing Student to infer quickly and robustly. Furthermore, PLKD supports multi-modal cell type annotation, multi-modal integration and other tasks. Benchmark experiments demonstrate that PLKD is able to achieve accurate and robust cell type annotation. Full article

Background/Objectives: Constructing a comprehensive spatiotemporal map of tumor heterogeneity is essential for understanding tumor evolution, with copy number variation (CNV) being a significant feature. Existing studies often rely on tools originally developed for single-cell data, which fail to utilize spatial information, often leading to an incomplete map of clonal architecture. Our study aims to develop a model that fully leverages spatial omics data to elucidate spatio-temporal changes in tumor evolution. Methods: Here, we introduce SCOIGET (Spatial COpy number Inference by Graph on Evolution of Tumor), a novel framework using graph neural networks with graph attention layers to learn spatial neighborhood features of gene expression and infer copy number variations. This approach integrates spatial multi-omics features to create a comprehensive spatial map of tumor heterogeneity. Results: Notably, SCOIGET achieves a substantial reduction in error metrics (e.g., mean squared error, cosine similarity, and distance measures) and produces superior clustering performance, as indicated by higher Silhouette Scores compared to existing methods, validated by both simulated data with spot-level ground truth and patient cohorts. Our model significantly enhances the accuracy of tumor evolution depiction, capturing detailed spatial and temporal changes within the tumor microenvironment. It is versatile and applicable to various downstream tasks, demonstrating strong generalizability across different spatial omics platforms, including 10× Visium and Visium HD and various cancer types, including colorectal cancer and prostate cancer. This robust performance improves research efficiency and provides valuable insights into tumor progression. Conclusions: SCOIGET offers an innovative solution by integrating multiple features and advanced algorithms, providing a detailed and accurate representation of tumor heterogeneity and evolution, aiding in the development of personalized cancer treatment strategies. Full article

Background: Diabetic foot ulcers (DFUs) are chronic wounds with high morbidity and mortality. Secretory proteins coordinate intercellular communication and may regulate inflammation, tissue repair and regeneration, but their contributions to DFU pathogenesis remain unclear. Aim: To discover and validate secretory protein–linked biomarkers that distinguish non-healing DFUs and to explore their potential utility for diagnosis and therapy. Methods: We integrated bulk RNA-sequencing datasets (GSE199939 training set; GSE80178 and GSE143735 validation sets) and a single-cell RNA-sequencing dataset (GSE223964). Differentially expressed genes, secretory protein lists, and weighted gene co-expression networks were intersected to select candidates. Functional enrichment, protein interaction networks and support vector machine–recursive feature elimination identified key markers. We visualized their cell-type distribution at single-cell resolution and validated their expression in external cohorts. Pathway enrichment, gene co-expression networks, ceRNA regulatory analysis and qRT-PCR in patient samples were used for further characterization. Results: Among 4803 differentially expressed genes, 743 overlapped with known secretory proteins. WGCNA highlighted modules strongly associated with DFUs, yielding 386 candidates. SVM-RFE combined with protein interaction analysis pinpointed four secretory proteins—LYZ, MPO, SLCO2B1 and TREM2—as putative biomarkers. Single-cell analyses showed that MPO, LYZ, SLCO2B1 and TREM2 transcripts are detectable in multiple skin-resident and immune cell populations, and that the DFU-associated upregulation of MPO and LYZ is most pronounced within keratinocyte clusters, while MPO and TREM2 remained consistently dysregulated in independent bulk cohorts. MPO-associated genes were enriched for immune and inflammatory pathways, whereas TREM2-linked genes implicated cell cycle and cytoskeletal regulation. GeneMANIA and ceRNA analyses revealed extensive interaction networks. qRT-PCR confirmed differential expression of MPO and TREM2 in clinical DFU tissues. Conclusions: Integrated multi-modal analysis identifies secretory proteins, particularly MPO and TREM2, as central determinants of impaired healing in DFUs. These molecules and their regulatory networks represent promising biomarkers and therapeutic targets for precision management of diabetic wounds. Full article

The ecological strategies of understory shrubs are critical for maintaining the structure and function of forest understory vegetation. Understanding the assembly mechanisms of these shrub communities is a central issue in modern ecology. To address this, our study was conducted in the typical red soil regions of Jiangxi, China, focusing on secondary forests (including both broadleaved and coniferous types) of similar stand age. We aimed to assess the effects of various environmental factors—such as soil pH, total nitrogen content, bulk density, and understory temperature—along with tree-layer characteristics—including canopy closure, tree species richness, and diameter at breast height (DBH)—on the species composition, functional traits, and phylogenetic structure of the shrub layer. Results showed: One-way ANOVA revealed significant differences in functional traits between the two forest types. Specifically, leaf thickness, specific leaf area, and chlorophyll content were significantly higher in the coniferous forest, whereas leaf dry matter content was significantly lower compared to the broadleaved forest (p < 0.05). These results suggest that understory shrubs in the coniferous forest primarily adopt a resource-conservative strategy, while those in the broadleaved forest exhibit a resource-acquisitive strategy. Phylogenetic analysis further revealed that the phylogenetic diversity (PD) of coniferous forests was significantly lower than that of broadleaved forests (p < 0.05). The phylogenetic structure in coniferous forests showed a more clustered pattern (NTI > 0, NRI > 0), suggesting stronger environmental filtering. Diversity index analysis showed that the Chao1 index indicated a richer potential species pool in broadleaved forests (p < 0.05), while species distribution was more even in coniferous forests (p < 0.05). Random Forest model analysis identified the diameter at breast height (DBH) of trees as the most critical negative driver, while soil pH was the primary positive driver. Redundancy Analysis (RDA) confirmed that the community structure in coniferous forests was mainly driven by biotic competition pressure represented by DBH, whereas the structure in broadleaved forests was more closely associated with abiotic factors like soil total nitrogen and pH (R² = 0.29, p < 0.05). These environmental drivers, through strong environmental filtering, collectively resulted in a phylogenetically clustered pattern of shrub communities in both forest types. This study demonstrates that the assembly of understory shrub communities is a complex, multi-level process co-regulated by multiple factors, shaped by both the biotic pressure from the overstory structure and abiotic filtering from the soil environment. This finding deepens our understanding of the rules governing community assembly in forest ecosystems. Full article

Multi-agent inverse reinforcement learning (MA-IRL) infers the underlying reward functions or objectives of multiple agents by observing their behavioral data, thereby providing insights into collaboration, competition, or mixed interaction strategies among agents, and addressing the symmetrical ambiguity problem where multiple rewards may correspond to the same strategy. However, most existing algorithms mainly focus on solving cooperative and non-cooperative tasks among homogeneous multi-agent systems, making it difficult to adapt to the dynamic topologies and heterogeneous behavioral strategies of multi-agent systems in real-world applications. This makes it difficult for the algorithm to adapt to scenarios with locally sparse interactions and dynamic heterogeneity, such as autonomous driving, drone swarms, and robot clusters. To address this problem, this study proposes a dynamic heterogeneous multi-agent inverse reinforcement learning framework (GAMF-DHIRL) based on a graph attention mean field (GAMF) to infer the potential reward functions of agents. In GAMF-DHIRL, we introduce a graph attention mean field theory based on adversarial maximum entropy inverse reinforcement learning to dynamically model dependencies between agents and adaptively adjust the influence weights of neighboring nodes through attention mechanisms. Specifically, the GAMF module uses a dynamic adjacency matrix to capture the time-varying characteristics of the interactions among agents. Meanwhile, the typed mean-field approximation reduces computational complexity. Experiments demonstrate that the proposed method can efficiently recover reward functions of heterogeneous agents in collaborative tasks and adversarial environments, and it outperforms traditional MA-IRL methods. Full article