Search Results (123)

Background: Mass spectrometry imaging (MSI) enables in situ mapping of metabolite distributions within tissues, and spatial clustering is a key step for delineating metabolically distinct regions. Nevertheless, spatial clustering methods have not been systematically benchmarked for spatial metabolomics data. Methods: Here, we evaluated the effects of ion filtering and clustering method selection on clustering performance and established a dual-metric framework that jointly assesses the spatial continuity of cluster labels and inter-cluster metabolic heterogeneity. We benchmarked 30 clustering algorithms across 12 heterogeneous MSI datasets spanning three major ion sources, four mass analyzers, and multiple spatial resolutions, covering approaches from non-spatial methods to advanced spatially aware models. Results: Noise filtering markedly improved the spatial continuity of results generated by non-spatial methods (mean improvement, approximately 28%) but provided limited benefit for spatially aware methods. Across the 12 datasets, a median of only 11 methods satisfied both evaluation criteria simultaneously, whereas SSC and DRSC met the dual-metric thresholds in at least nine datasets. In the mbrain2_pos50 dataset, the top-ranked method based on the composite dual-metric score achieved 22% higher concordance between cluster assignments and cell-type annotations than the lowest-ranked method. Conclusions: Together, the proposed evaluation framework and the online platform SMcluster provide a standardized resource for benchmarking and selecting MSI clustering methods. Our results highlight the critical roles of preprocessing and method selection in determining spatial clustering performance and offer practical guidance for spatial metabolomics studies. Full article

Background/Objectives: Population aging necessitates a shift from disease-focused paradigms to a holistic characterization of biological aging processes. While chronological age remains the primary metric, it poorly captures inter-individual variability in physiological resilience and health trajectories. This study aimed to identify robust, multidimensional aging phenotypes independent of chronological age and sex using integrative factor analysis of heterogeneous biomedical data from a Russian cohort—a population underrepresented in aging research. Methods: We analyzed data from 1201 conditionally healthy adults (aged 18–99 years) enrolled in the RUSS AGE study. A comprehensive dataset comprising 118 variables across 11 modalities—including biochemical markers, anthropometry, physical function, cognitive-emotional assessments, lifestyle factors, and psychosocial indicators—was integrated using Multi-Omics Factor Analysis v2 (MOFA2). Following the extraction of 16 latent factors and residualization for demographic confounders, consensus clustering was performed to identify distinct aging phenotypes. Phenotype stability was internally recapitulated using gradient-boosting classifiers (XGBoost, CatBoost) in a stratified five-fold cross-validation and on a held-out test set. Results: MOFA2 identified 16 stable latent factors, explaining 21.3% of the total variance and capturing coordinated variation across metabolic, inflammatory, cardiovascular, cognitive, and behavioral domains. Consensus clustering revealed five reproducible phenotypes—Anemic (n = 82), Metabolically Subcompensated (n = 99), Metabolically Decompensated (n = 304), Overloaded (n = 302), and Balanced (n = 414)—characterized by distinct multisystem profiles independent of age (p > 0.05 after FDR correction) and sex. Supervised classification achieved high discriminative performance (macro F1-score = 0.75, OvR ROC-AUC = 0.93 on the held-out test set), quantifying the internal reconstructability of the phenotype labels from the original feature space rather than external generalization to an independent cohort. Conclusions: This study demonstrates the feasibility of data-driven, biologically coherent phenotyping of healthy aging using integrative factor analysis. The identified phenotypes represent stable configurations of physiological, functional, and psychosocial characteristics that transcend chronological age, providing a foundation for the future development of risk-stratification tools, preventive interventions, and biological-age calculators, subject to subsequent validation in longitudinal and independent external cohorts. Full article

Federated learning enables multiple clients to collaboratively train a global model without exchanging raw data, thereby alleviating data silos and privacy concerns. However, federated learning model design is still challenged by complex architecture and hyperparameter optimization, especially under heterogeneous data distributions. To address these issues, this paper proposes a multi-objective federated learning framework based on particle swarm optimization (PSO). Specifically, neural architecture search and training-parameter optimization are formulated as a multi-objective evolutionary optimization problem, where predictive performance and model complexity are optimized jointly. Furthermore, to improve robustness under non-independent and identically distributed (non-IID) data, a clustered multi-population extension is developed. In this framework, each cluster is associated with a dedicated particle population. An inter-population evolutionary knowledge transfer mechanism is then introduced to enable effective sharing of search experience across clusters, thereby improving the optimization efficiency and adaptability of federated learning under heterogeneous environments. Experiments on MNIST and CIFAR10 under both IID and non-IID settings demonstrate that the proposed methods achieve a superior trade-off between predictive accuracy and model complexity and outperform baseline approaches in robustness, search efficiency, and adaptability to imbalanced distributed environments. Full article

Large-scale classification of architectural styles in Chinese traditional settlements is important for heritage conservation and geospatial documentation, but scalable deployment remains constrained by the high cost of expert annotation because villages are widely distributed, the imagery is captured from heterogeneous viewpoints, and each architectural tradition exhibits substantial intra-class variation. To address this bottleneck, we propose CTSMatch, a label-efficient semi-supervised framework that combines an ImageNet-pretrained EfficientNetV2 backbone with SoftMatch-based adaptive pseudo-label weighting so that ambiguous but informative unlabeled samples can still contribute to training, thereby reducing reliance on costly expert annotation. We also construct SemiCTS, an extension of the original CTS dataset that adds 4360 unlabeled images. Using only 545 labeled samples, CTSMatch achieves 96.93% accuracy on SemiCTS, outperforming the strongest fully supervised baseline (Dense-TL-Aug) by 2.73 percentage points and two standard semi-supervised baselines (FixMatch and FreeMatch) by 3.06 percentage points. Beyond classification, we further analyze the feature space to examine stylistic lineage through intra-style heterogeneity, inter-style transitions, and outlier detection. The results reveal two broad regional groupings, a northern cluster (Jing, Jin, Su) and a southern cluster (Chuan, Min, Wan), connected by gradual transitions rather than rigid boundaries. Approximately 15% of the samples are identified as atypical cases, including 8.7% comprising regional variants and 6.3% comprising hybrid forms. These findings show that CTSMatch provides a practical label-efficient framework for architectural heritage classification while supporting the interpretable analysis of stylistic diversification and convergence in Chinese traditional settlements. Full article

The “Digital Countryside” initiative is profoundly reshaping rural China, transforming traditional villages into complex hybrids of physical realities and virtual networks. However, current research often treats rural space statically and overlooks the dynamic interplay between spatial dimensions in developing regions. Drawing on Henri Lefebvre’s spatial triad theory, this study proposes a novel framework to examine how the restructuring of physical, social, and digital spaces influences grassroots governance effectiveness. Empirically, this study is based on a dataset covering 108 villages across Jiangsu Province, with 210 valid questionnaires collected from village cadres and representatives. Each questionnaire is linked to a specific village, forming a village-referenced individual-level dataset. The analysis primarily focuses on Northern Jiangsu as a representative developing region, while retaining inter-regional variation for robustness. Using K-Means clustering and Partial Least Squares Structural Equation Modeling (PLS-SEM), the results reveal significant spatial heterogeneity, identifying distinct village configurations with uneven developmental paths. Crucially, structural analysis indicates a “saturation effect” where traditional physical infrastructure no longer directly drives governance improvements. Instead, Digital Space has emerged as the dominant engine. However, this digital impact is not automatic; it relies on a critical mediation pathway through “Technological Embedding” and the fostering of multi-actor “Subject Synergy.” Furthermore, avoiding governance “involution” ultimately depends on an institutional imperative: synergy alone cannot directly drive governance efficacy without flexible “Institutional Environment Adaptation.” Most critically, Spatial Justice Perception is identified as a decisive boundary condition; low perceived fairness acts as a “justice trap” that significantly dampens the positive returns of digital investment, underscoring that breaking this trap is essential for promoting sustainable rural development and long-term governance effectiveness in the digital era. Full article

Chronic low-grade inflammation is a key feature of obesity-associated dysglycemia, yet substantial heterogeneity exists in inflammatory responses among individuals with normoglycemia, prediabetes, and type 2 diabetes mellitus (T2DM). Whether circulating inflammatory protein profiles define distinct patient phenotypes beyond conventional glycemic classification remains incompletely understood. In this cross-sectional analysis of 142 individuals with severe obesity, plasma inflammatory proteins were quantified using Olink proximity extension assay technology. Subjects were stratified by glycemic status (noDM, normoglycemia; PreDM, prediabetes and T2DM) while maintaining comparable distributions of metabolic dysfunction-associated steatotic liver disease. Differential expression analyses were performed across glycemic groups, and unsupervised topological data analysis (TDA) was applied to identify inflammatory protein-based patient subgroups. Several inflammatory proteins were significantly upregulated in T2DM and PreDM compared with noDM, with interleukin-8 (IL-8), Fms-relatedlike tyrosine kinase 3 ligand (Flt3L), and CUB domain containing protein (CDCP1) showing the largest significant differences. NPX distributions of these proteins exhibited gradual increases across glycemic stages with substantial inter-individual variability. TDA identified seven clusters defined by distinct inflammatory protein signatures. One cluster was enriched for individuals with T2DM and characterized by coordinated upregulation of IL-8, Flt3L, CDCP1, and additional immune- and cytokine-related proteins, whereas other clusters displayed alternative inflammatory profiles that were not explained by glycemic status alone. Inflammatory proteomic profiling in severe obesity reveals both glycemia-associated protein changes and distinct inflammatory phenotypes that transcend conventional clinical classification. Integration of differential expression analysis with TDA highlights heterogeneity in inflammatory states, supporting a hypothesis-generating framework for future studies aimed at validating these proteomic patterns and clarifying their longitudinal relevance in obesity-related dysglycemia. Full article

Freshwater scarcity is emerging as a critical constraint on industrial clusters, production networks, and urban service systems, where water functions simultaneously as an essential production input and a shared regional resource. This study investigates how post-allocation water-quota imbalances in large inter-basin diversion systems can be addressed through adaptive secondary water rights trading. Focusing on China’s South-to-North Water Diversion Project (SNWDP), the research aims to explain under what institutional and efficiency conditions water rights trading can enhance corporate social responsibility, environmental management, and sustainable supply chain resilience. The study’s main innovation lies in the development of a corporate-oriented evolutionary game model that links water governance with corporate production, urban–industrial demand, and responsible supply chain management. Unlike conventional models, it incorporates bounded rationality, heterogeneous water-use efficiency, information asymmetry, transaction costs, primary allocation water pricing, and the risk of unrecovered basic water fees. Using a case inspired by the Zhengzhou–Nanyang transaction along the Middle Route of the SNWDP, the model simulates the strategic interaction between a water-rich node with surplus quota and a water-scarce node facing deficit demand. The findings show that a socially desirable Trade–Trade equilibrium emerges only when efficiency expectations and institutional conditions are favorable. Lower transaction costs and basic water prices, higher sunk-fee risk, and clearer efficiency differentials significantly increase trading willingness. The study demonstrates the practical value of transparent secondary water markets in improving allocative flexibility, reducing governance rigidity, and promoting more responsible and environmentally efficient regional water management. Full article

Background/Objectives: Acinetobacter baumannii is a critical opportunistic pathogen causing severe healthcare-associated infections, particularly in intensive care units. The global dissemination of carbapenem-resistant A. baumannii (CRAB) and its environmental persistence necessitate continuous genomic surveillance to monitor high-risk clones. Methods: We conducted whole-genome sequencing (WGS), core genome multi-locus sequence typing (cgMLST), and phylogenomic analyses on 26 CRAB isolates collected at the National Institute for Infectious Diseases (INMI) “Lazzaro Spallanzani” IRCCS (September 2023–September 2024). Antimicrobial resistance determinants, virulence-related genes, and capsular (KL) and lipooligosaccharide outer core (OCL) loci were characterized by interrogation of comprehensive bioinformatic pipelines. Results: All CRAB isolates displayed an extensively drug-resistant (XDR) phenotype, with a shared resistance pattern to carbapenems, aminoglycosides, fluoroquinolones, fosfomycin, and sulfonamides, while being susceptible only to colistin and cefiderocol. The carbapenemase gene bla_OXA-23 was detected in all CRAB isolates, together with clone-specific bla_OXA-51-like variants. For all isolates, the resistome profile fully matched the observed resistance phenotype. All isolates belonged to the International Clonal Lineage II (ICL II), Pasteur Sequence Type (ST) 2, and Oxford ST369, ST208, and ST455. Integration of cgMLST data with phylogenomic analyses and genome-based classification of KL and OCL loci revealed five distinct clusters, each one including nearly identical isolates, indicating both intra-hospital dissemination and possible inter-hospital transmission. Virulome profiling revealed heterogeneous repertoires of virulence-associated genes, resulting in cluster-specific patterns, while patristic analysis identified phylogenetic clusters linking the study isolates to other Italian and other European lineages. Conclusions: This study underscores the complex genomic landscape of CRAB in our setting, driven by the circulation of different ICL II clonal types, and reinforces the urgency of integrated genomic surveillance and robust antimicrobial stewardship to mitigate the spread of high-risk XDR A. baumannii clones. Full article

The complex propagation behavior of hydraulic fractures (HFs) in strongly heterogeneous conglomerate reservoirs poses significant challenges for effective reservoir stimulation. In particular, the interaction between fractures and gravel-induced heterogeneity often leads to highly tortuous fracture networks and uneven stimulation efficiency. To address this issue, a series of laboratory true triaxial hydraulic fracturing experiments were conducted on artificially prepared conglomerate specimens with controlled gravel size and distribution. A quantitative evaluation index, termed the Fracture Complexity Index (FCI), was proposed to characterize the tortuosity and complexity of fracture networks by integrating multiple geological and engineering factors. The effects of cluster spacing and fracturing fluid viscosity on multi-fracture propagation behavior were systematically investigated. The results show that increasing cluster spacing enhances inter-fracture interaction and promotes fracture tortuosity, while lower fluid viscosity facilitates fracture branching but may limit effective propagation distance due to energy dissipation. To further quantify the trade-off between fracture complexity and propagation extent, a dimensionless fracture length was introduced and combined with FCI to establish a fracture morphology evaluation framework. This framework enables the classification of fracture patterns and reveals the coupling relationship between engineering parameters and fracture geometry. The findings provide new insights into the mechanisms of fracture propagation in conglomerate reservoirs and offer a quantitative basis for optimizing fracturing design, particularly in balancing fracture complexity and effective stimulation range in strongly heterogeneous formations. Full article

Amid escalating global crises, tourism sustainability is threatened by heightened industry vulnerability, yet the intrinsic coupling of tourism industry vulnerability (TIV) and resilience (TIR) remains underexplored via systemic theoretical frameworks. This study aimed to define TIV/TIR as industry-specific constructs and develop an integrated analytical model grounded in dissipative structure theory to characterize tourism systems’ crisis responses. We selected Southwest China’s ethnic minority regions (Guizhou, Guangxi, Yunnan) as cases, using 2015–2024 prefecture-level panel data to explores the spatio-temporal differentiation characteristics of TIV/TIR. Results revealed severe COVID-19-induced TIV surges in 2020–2021, followed by rapid TIR rebounds; TIV and TIR exhibited a significant negative correlation with regional heterogeneity. Most cities showed high TIV–low TIR, with Guizhou displaying negative TIV-TIR spatial autocorrelation and Guangxi–Yunnan showing TIR clustering; inter-city TIV disparities widened while TIR levels converged, leading to a low-vulnerability, balanced-resilience tourism system by 2024. This research introduces the novel sensitivity-adaptive capacity-recovery (SACR) framework, advancing understanding of TIV-TIR dynamics and providing targeted empirical insights for tourism resilience building and sustainable development in resource-dependent destinations. Full article

Objectives: This study evaluates the research landscape of the cyanobacterium Spirulina (recently reclassified as Limnospira), a strategic resource in the nutraceutical, pharmaceutical, and functional food industries. The central objective is to transition from the traditional “superfood” narrative to a structured analysis of its modern therapeutic potential as reflected in current scientific literature. This study employs bibliometric analysis to highlight research trends and thematic directions in Spirulina-related studies, rather than to experimentally validate therapeutic effects. Methods: The investigation employed an exploratory bibliometric analysis of 996 peer-reviewed articles indexed in the Web of Science (2016–2025). Using VOSviewer software, we mapped keyword co-occurrence networks, international collaborations, and institutional clusters to identify dominant thematic directions and emerging research frontiers in biotechnology and medicine. Results: Bibliometric mapping illustrates research trends and thematic associations reported in the scientific literature centered on pathophysiological mechanisms, particularly oxidative stress, inflammation, and hepatoprotection. While often referred to as “microalgae”, Spirulina is biologically a photosynthetic prokaryote with a unique lipid profile characterized by high gamma-linolenic acid (GLA) content, although clinical evidence remains heterogeneous. The analysis highlights a robust regional research hub in the Middle East and North Africa, led by Egypt and Saudi Arabia, in contrast to fragmented inter-continental collaboration. Conclusions: The steady upward trend in publications confirms expanding academic interest in Spirulina as a functional ingredient. However, this study underscores a persistent gap between in vitro bioactivity and standardized clinical validation. These findings provide a roadmap for future biotechnological developments, emphasizing the need for more rigorous, multi-center clinical trials to bridge the “superfood” perception with evidence-based therapeutic applications. Full article

In the context of intensifying climate change, understanding the spatial heterogeneity of urban climate risk is critical to effective climate governance in the United States. This study takes 251 major Metropolitan Statistical Areas (MSAs) in the United States as the analytical unit and establishes a multidimensional urban climate risk assessment framework covering hazard risk, exposure vulnerability, and adaptive capacity. Principal Component Analysis (PCA) is adopted for dimensionality reduction to extract key factors, and K-means clustering is used to identify the spatial differentiation characteristics of climate risk across these MSAs. The results show that climate risk in U.S. MSAs presents significant spatial disparities and can be categorized into four types: high resource and adaptive capacity, high exposure with insufficient adaptive support, complex socio-environmental vulnerability, and low current vulnerability with latent cumulative risk. Based on these findings, this study proposes targeted policy recommendations, including promoting inter-MSA coordination and adaptive capacity spillover, implementing gray–green integrated infrastructure development and enhancing social resilience in the southeastern coastal regions, strengthening equity orientation in climate governance, and advancing proactive governance of cumulative and chronic risks. These conclusions provide a reference for relevant authorities to formulate climate policies. Full article

Time-series clustering is a prominent research area with extensive practical applications. Given the complexity and diversity of modern time-series data, this study proposes a novel time-series clustering method based on inter-network heterogeneity. First, each time-series is converted into a network by using two types of time-series segmentation techniques. Second, an inter-network clustering approach based on graph spectral theory is introduced: we calculate the total variation (TV) distance between the empirical spectral distributions of each network and identify distinct clusters using a hierarchical clustering algorithm. From the perspective of symmetry, networks constructed from similar time-series tend to exhibit comparable spectral structures, which reflect the underlying structural symmetries of their dynamics. Differences in spectral distributions correspond to symmetry breaking among networks, providing an effective mechanism for distinguishing heterogeneous time-series patterns. Our method effectively preserves more distinctive features inherent in the original time-series. To evaluate the performance of the proposed method, simulation studies are conducted, including the recognition of both stationary and non-stationary sequences. The method also performs well on real-world datasets, such as stock closing prices. These results demonstrate that our approach can handle non-stationary sequences and identify the intrinsic correlations in time-series. Full article

Advancing environmental monitoring through coordinated autonomous systems is central to sustainable smart region governance and data-driven territorial management. The article presents an engineering-oriented architecture and deployment methodology for an integrated wildfire monitoring and response system that combines unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and stationary sensor networks (SNs). We formalise hub-and-spoke infrastructure placement as a mixed-integer optimisation problem that accounts for platform types, endurance, travel times and logistical constraints, and propose a practical pre-processing pipeline (confidence scoring, resampling, Kalman/median filtering, strategy fusion) for heterogeneous telemetry and imagery. The system couples multimodal neural network processing (image backbones, clustering and time-series models) with online resource-allocation and mission-planning mechanisms to prioritise UAV/UGV sorties and dynamically select launch sites. The article describes scenario-driven operational modes (early warning, alarm verification, autonomous local extinguishing, post-fire recovery, sensor-gap compensation, and inter-hub reinforcement), defines validation protocols (synthetic experiments, precision/recall/F1, and hardware-in-the-loop testing), and proposes KPIs to assess environmental, social, and economic impacts for smart regions. The contribution is a reproducible, deployment-focused blueprint that bridges conceptual UAV–UGV–SN research and practical implementation, highlighting trade-offs in reliability, communication redundancy, and sustainability, and outlining directions for simulation, field pilots and algorithmic refinement. Full article

Marine fungi are pivotal components of coastal ecosystems, facilitating essential biogeochemical cycling and trophic dynamics. However, the complex mechanisms governing their spatiotemporal community patterns in tropical–subtropical coasts remain largely unexplored. In this study, we characterized marine fungal diversity across a comprehensive seasonal cycle (spring (March), summer (June), autumn (August), and winter (December)) at 21 representative sites along the Leizhou Peninsula, China. These sites were strategically selected to encompass a broad range of dissolved inorganic nitrogen (DIN) gradients. Fungal community composition was characterized via high-throughput sequencing of the internal transcribed spacer 2 (ITS2) region, followed by functional guild profiling using the FUNGuild database. A total of 8777 amplicon sequence variants (ASVs) were identified, encompassing a broad taxonomic breadth of 10 phyla and 358 genera. Ascomycota, Basidiomycota, and Chytridiomycota emerged as the predominant phyla across all samples. Our results revealed significant spatiotemporal heterogeneities: seasonal succession fundamentally reshaped community composition, with DIN exerting its most pronounced influence during the winter. Furthermore, fungal functional structures exhibited distinctive clustering across regions defined by DIN enrichment levels. Co-occurrence network analysis revealed a highly modular and robust architecture, characterized by predominantly positive interactions and dense inter-taxon connectivity. These findings underscore the synergistic influence of temporal dynamics and DIN enrichment in shaping marine fungal community assembly and functional compositions. Our study provides critical baseline insights into the ecological resilience of coastal mycobiota in the South China Sea. Full article