Search Results (74)

Background/Objectives: Pygmy rabbits (Brachylagus idahoensis) are closely associated with sagebrush steppe habitat across the western United States, and loss and fragmentation of this habitat has contributed to the near extirpation of the Columbia Basin population in Washington state (CB). The CB pygmy rabbit was listed under the Endangered Species Act in 2003, and recovery efforts have included captive breeding, reintroduction, and genetic rescue with the translocation of rabbits from populations across the species range. Methods: We used restriction site-associated DNA sequencing (RADseq) on samples from across the species range, including CB pygmy rabbits captured prior to genetic rescue and admixture. We determined population genetic structure across the pygmy rabbit range, tested for genomic signatures of adaptive divergence among populations, assessed the genetic distinctiveness of the ancestral CB population, and identified loci useful for monitoring ancestry in the current admixed CB population. Results: Our dataset included 9794 single-nucleotide polymorphisms (SNPs) across 123 individuals. We identified four distinct genetic groups, including the central portion of the species range and three peripheral populations: CB, northern Utah/Wyoming, and southern Utah. The ancestral CB population showed the highest degree of genetic distinctiveness using multiple clustering, ordination, and genetic differentiation analyses. We identified evidence for putatively adaptive variation among populations, but no significant gene ontology associated with local adaptation. Conclusions: Our results highlight the long-term isolation of the ancestral CB population as well as historical isolation of other peripheral populations. Our results also provide SNP loci for monitoring the consequences of genetic rescue efforts in the current admixed CB population. Full article

The integration of intangible cultural heritage (ICH) and tourism development (TD) is regarded as a crucial national strategy for China’s sustainable development, as their synergistic relationship is considered pivotal for regional progress. A coupling coordination evaluation system was constructed. Kernel density estimation, entropy method, coupling coordination degree (CCD) and relative development degree (RDD) models, and a tobit model were employed to examine the spatiotemporal characteristics and influencing factors of ICH–TD integration in Sichuan Province. Key findings are as follows: (1) Sichuan is endowed with abundant ICH resources characterized by high heritage value and diverse typologies. However, the distribution is skewed toward traditional skills, exhibiting notable regional disparities. ICH demonstrates a “single-core, belt-shaped and multi-cluster” pattern, which is centered on Chengdu, extends along a north–south high-density belt, and forms several secondary high-density clusters. (2) Temporally, the CCD demonstrates a sustained upward trend, whereas the RDD transitions from ICH-lagged to TD-lagged. Spatially, the number of high coordinated cities increases annually, expanding radially from regional centers, while central-eastern regions consistently outperform the west. (3) Regarding influencing factors, comprehensive economic strength, distribution of industrial structure, overall level of urbanization, and transportation accessibility exert significant positive effects on the CCD, with comprehensive economic strength demonstrating the strongest influence. This study contributes to the theoretical understanding of ICH–TD synergy and provides policy-relevant guidance for integration. Full article

Background/Objectives: The Mediterranean diet (MeDiet) is widely recognised as one of the healthiest dietary patterns, associated with reduced risk of chronic diseases and increased longevity. Beyond its nutritional components, the Mediterranean lifestyle encompasses a broader set of culturally rooted behaviours that may contribute to its health benefits. This study aimed to assess adherence to the Mediterranean diet and lifestyle using the MEDLIFE index and to explore how dietary and lifestyle behaviours cluster into coherent behavioural patterns. Methods: We conducted an observational study among undergraduate students in health and sports sciences and a comparison group of older adults, using an anonymous questionnaire based on the MEDLIFE index. Data were analysed using a pattern-based approach combining network analysis and score-based enrichment to characterise behavioural profiles associated with different levels of Mediterranean lifestyle adherence. Results: Network-based analyses revealed a high degree of internal coherence among dietary and lifestyle behaviours traditionally associated with the Mediterranean lifestyle. In particular, dietary restraint behaviours (e.g., limitation of sugar, salt, and snack consumption) systematically co-occurred with recommended Mediterranean food choices, indicating that positive intake and self-regulation are part of a unified behavioural framework. Score-based stratification confirmed these patterns at the individual level, with low adherence characterised by the absence of key Mediterranean components and unhealthy lifestyle habits, and high adherence reflecting an integrated profile combining healthy food choices, moderation, and lifestyle practices. Conclusions: Adherence to the Mediterranean diet reflects a holistic lifestyle strategy rather than a collection of isolated dietary behaviours. These findings support public health approaches that target coherent behavioural patterns, integrating diet, self-regulation, and lifestyle habits, rather than focusing exclusively on individual dietary components. Full article

Urban vitality is a critical metric for measuring the quality of sustainable development and overall competitiveness, serving as the core kinetic energy for urban survival and growth. As a key link for land–sea resource coordination and internal–external economic circulation, the urban vitality of China’s coastal regions is of great significance for promoting regional coordinated development. Focusing on 130 cities in China’s coastal regions, this study constructs an evaluation system encompassing five dimensions: economy, society, culture, environment, and population. Utilizing the AHP–entropy combined weighting method, the urban vitality index (UVI) for 2023 is calculated based on a scientific measurement of each dimension’s vitality level. Additionally, spatial autocorrelation and the multiscale geographically weighted regression (MGWR) model are employed to examine the spatial evolution patterns and multidimensional driving mechanisms in depth. The results indicate the following: (1) Coastal regions exhibit significant spatial heterogeneity in vitality, characterized by a distinct south–north gradient (high in the south and low in the north). Geographically, the distribution of overall vitality is highly uneven: high-value clusters are concentrated in southern coastal urban agglomerations—notably the Pearl River Delta and the Yangtze River Delta—whereas northern coastal areas, with the exception of the Shandong Peninsula, generally demonstrate relatively low vitality levels. Administrative rank has a significant effect on vitality agglomeration; the average vitality of provincial capitals and above is approximately four times that of other cities. (2) Environmental vitality performs best but shows significant spatial polarization. High-value areas for economic and population vitality are concentrated in the Yangtze River Delta, Pearl River Delta, and Shandong Peninsula urban agglomerations, while social and cultural vitality only stand out in megacities such as Shenzhen, Guangzhou, and Shanghai. (3) Urban vitality exhibits strong spatial correlation and path dependence. Coastal urban vitality shows a significant positive spatial autocorrelation, with H-H (high–high) clusters primarily concentrated in the Yangtze River Delta and Pearl River Delta, indicating a high degree of spatial aggregation and regional synergy in urban vitality. Conversely, L-L (low–low) “depressed cities” are distributed in contiguous blocks in the north and peripheral areas, indicating that regional collaborative driving forces need to be further strengthened. (4) Multifactor driving mechanisms show obvious spatial heterogeneity and scale effects. The MGWR model results reveal that the medical insurance coverage rate, human capital level, and annual average PM 2.5 concentration are the dominant factors driving coastal urban vitality. Their influence intensity shows significant north–south differences across geographical locations, and the contribution of nonspatial factors is overall higher than that of traditional built environment factors. These findings provide a scientific reference for formulating precise and differentiated regional vitality enhancement strategies, optimizing coastal resource allocation, and promoting high-quality land–sea coordinated development. Full article

Background/Objectives: Autism spectrum disorder (ASD) exhibits remarkable genetic heterogeneity involving hundreds of risk genes; however, the mechanism by which these genes organize within biological networks to contribute to disease pathogenesis remains incompletely understood. This study aims to elucidate these organizational principles and identify critical network bottlenecks using a novel integrative computational framework. Methods: We analyzed 893 SFARI genes using a three-pronged computational approach: (1) a Machine Learning Dynamic Perturbation Propagation algorithm; (2) a hypergraph construction method explicitly modeling multi-gene complexes by integrating protein–protein interactions, co-expression modules, and curated pathways; and (3) Hypergraph Neural Network embeddings for gene clustering. Validation was performed using hub-independent features to address potential circularity, followed by a druggability assessment to prioritize therapeutic targets. Results: The hypergraph construction captured 3847 multi-way relationships, representing a 45% increase in biological relationships compared to pairwise networks. The perturbation algorithm achieved a 51% higher correlation with TADA genetic evidence than random walk methods. Analysis revealed a hierarchical organization where 179 hub genes exhibited a 3.22-fold increase in degree centrality and a 4.71-fold increase in perturbation scores relative to non-hub genes. Hypergraph Neural Network clustering identified five distinct gene clusters, including a “super-hub” cluster of 10 genes enriched in synaptic signaling (4.2-fold) and chromatin remodeling (3.9-fold). Validation confirmed that 8 of these 10 genes co-cluster even without topological information. Finally, we identified high-priority therapeutic targets, including ARID1A, POLR2A, and CACNB1. Conclusions: These findings establish hierarchical network organization principles in ASD, demonstrating that hub genes maintain substantially elevated perturbation states. The identification of critical network bottlenecks and pharmacologically tractable targets provides a foundation for understanding autism pathogenesis and developing precision medicine approaches. Full article

This study presents an integrated geological and environmental radiological analysis of basaltic volcanic rocks, which have been characterized by their suitability and potential for risk when used as construction materials. A total of thirty-five representative basaltic samples from the environment of studied area, located in the Northern Eastern Desert of Egypt, were utilized for this study. The rocks were then analyzed by means of HPGe high-resolution gamma-ray spectrometry methods. The petrographic studies show that the basalt samples were composed mostly of three main minerals: plagioclase, olivine, and pyroxene. In addition, these rocks have a significant degree of secondary alteration products, including sericite, epidote, and zoethite. For uranium-238 (²³⁸U), thorium-232 (²³²Th), and potassium-40 (⁴⁰K), the average activity concentration measured 53 ± 20 Bq kg⁻¹, 54 ± 14 Bq kg⁻¹, and 1178 ± 269 Bq kg⁻¹, respectively. Using the current global reference limits, all the measured values are above acceptable levels for the radionuclides ²³⁸U, ²³²Th, and ⁴⁰K. The radiological indices calculated for each of the basalt volcanic samples measured radium equivalent activity (Ra_eq = 221 Bq kg⁻¹), external hazard index (H_ex = 0.60), internal hazard index (H_in = 0.74), gamma index (Iγ = 0.84), and annual effective dose (AED = 0.52 mSv y⁻¹) indicate that the radiological hazard values of these samples are acceptable, unlike several samples, where values are near or exceed the accepted standards for indoor hazards. The most significant finding of this study reveals that the major contributions in the environment from radiological risk can be attributed to radionuclides ²³⁸U and ⁴⁰K based on correlation analysis, hierarchical clustering, and PCA analyses, and this study establishes the first multivariate perspective of how radiogenic materials controlled by the environment can affect basaltic rocks. Therefore, this study creates an important baseline for future environmental monitoring and states that caution is warranted when using basalt as a finished material for constructed environments, and for using basaltic products as raw materials in indoor environments. Full article

In practical application, the gathered multi-view data typically misses samples, known as incomplete multi-view data. Most existing incomplete multi-view clustering methods obtain consensus information in multi-view data by completing incomplete data using zero, mean values, etc. These approaches often ignore the higher-order relationship and structural information between different views. To alleviate the above problems, we propose enhanced tensor incomplete multi-view clustering with dual adaptive weight (ETIMC), which can acquire the higher-order relationship, and structural information between multiple perspectives, adaptively recover the missing samples and distinguish the contribution degree of different views. Specifically, the embedded representations obtained from incomplete multi-view data are stacked into a third-order tensor to capture the higher-order relationship. Then, a consensus matrix can be drawn from these potential representations via a self-weighting mechanism. Additionally, we adaptively reconstruct the missing samples while capturing structural information by the hypergraph Laplacian item. Moreover, we integrate the embedded representation of each view, tensor constraints, hypergraph Laplacian regularization, and dual adaptive weighted mechanisms into a unified framework. Experimental results on natural and synthetic incomplete datasets show the superiority of ETIMC. Full article

This article frames the virtual museum as a meaning-modeling system within digital heritage and proposes an operational semiotic method for analysis. Grounded in Modeling Systems Theory and informed by Adorno’s non-identity, we construct a twelve-category coding matrix that combines three modeling levels with four organizational forms. Applying this matrix to five heterogeneous cases (web, VR, and 3D environments), we derive three quantitative ratios that summarize each system’s profile: the Abstraction Ratio (degree of conceptual mediation), the Connectivity Ratio (degree of interlinking and systematic organization), and the Object Primacy Score (degree of object-centered representation). Exploratory clustering on these ratios reveals three recurrent patterns of virtual-heritage mediation: Network-Symbolic, Concept-Dominant, and Object-Preserving. The results articulate how different curatorial and technical choices redistribute attention between objects, contexts, and concepts, and how these redistributions affect the subject–object balance in digital settings. The contribution is twofold: a transparent, reproducible coding protocol that enables cross-case comparison, and an interpretive lens that relates quantitative patterns to critical concerns in heritage, including authenticity, legibility, and over-standardization. We conclude with implications for curators and designers seeking to align immersive interfaces with heritage values while preserving the irreducible remainder of the object. Full article

This study takes 22 megacities and super-large cities in China as research subjects, systematically exploring the coupled system relationship between digital maturity and resilient cities. Using methods such as information entropy theory, coupling coordination degree model, and grey relational coefficient measurement, it conducts comprehensive evaluation analysis, coupling coordination analysis, and factor contribution analysis. The results indicate that digital maturity and resilient cities exhibit a mutually reinforcing relationship. The positive interaction and coupling between digital maturity and resilient cities development have further promoted innovation in China’s urban governance and contributed to the long-term sustainable development of cities. Although the digital maturity and resilience levels of these cities show a fluctuating upward trend, they have not yet reached an ideal state, and significant differences exist among different urban clusters and regions. Megacities and super-large cities demonstrate high coupling characteristics among their internal subsystems, but their coordination level remains relatively low. Key factors such as digital governance policies, digital economy, and disaster-resistant infrastructure play a crucial role in advancing the sustainable development of resilient cities. Therefore, it is urgent to improve the coupling coordination mechanism to enhance sustainable development capacity. Full article

Oases in arid regions are crucial for sustaining agricultural production and ecological stability, yet few studies have simultaneously examined the coupled dynamics of land use/cover change (LUCC), carbon emissions, and ecosystem service value (ESV) at the oasis–agricultural scale. This gap limits our understanding of how different land use trajectories shape trade-offs between carbon processes and ecosystem services in fragile arid ecosystems. This study examines the spatiotemporal interactions between land use carbon emissions and ESV from 1990 to 2020 in the Wensu Oasis, Northwest China, and predicts their future trajectories under four development scenarios. Multi-period remote sensing data, combined with the carbon emission coefficient method, modified equivalent factor method, spatial autocorrelation analysis, the coupling coordination degree model, and the PLUS model, were employed to quantify LUCC patterns, carbon emission intensity, ESV, and its coupling relationships. The results indicated that (1) cultivated land, construction land, and unused land expanded continuously (by 974.56, 66.77, and 1899.36 km²), while grassland, forests, and water bodies declined (by 1363.93, 77.92, and 1498.83 km²), with the most pronounced changes occurring between 2000 and 2010; (2) carbon emission intensity increased steadily—from 23.90 × 10⁴ t in 1990 to 169.17 × 10⁴ t in 2020—primarily driven by construction land expansion—whereas total ESV declined by 46.37%, with water and grassland losses contributing substantially; (3) carbon emission intensity and ESV exhibited a significant negative spatial correlation, and the coupling coordination degree remained low, following a “high in the north, low in the south” distribution; and (4) scenario simulations for 2030–2050 suggested that this negative correlation and low coordination will persist, with only the ecological protection scenario (EPS) showing potential to enhance both carbon sequestration and ESV. Based on spatial clustering patterns and scenario outcomes, we recommend spatially differentiated land use regulation and prioritizing EPS measures, including glacier and wetland conservation, adoption of water-saving irrigation technologies, development of agroforestry systems, and renewable energy utilization on unused land. By explicitly linking LUCC-driven carbon–ESV interactions with scenario-based prediction and evaluation, this study provides new insights into oasis sustainability, offers a scientific basis for balancing agricultural production with ecological protection in the oasis of the arid region, and informs China’s dual-carbon strategy, as well as the Sustainable Development Goals. Full article

This work is framed as an application of static and small exponential random graph models for complex networks in multiple layers. This document revisits the small network and exhibits its potential. Examining the bibliography reveals considerable interest in large and dynamic complex networks. This research examines the application of small networks (50,000 population) for analyzing global commerce, conducting a comparative graph structure of the tariffs, and importing multilayer networks. The authors created and described the scenario where the readers can compare the graph models visually, at a glance. The proposed methodology represents a significant contribution, providing detailed descriptions and instructions, thereby ensuring the operational effectiveness of the application. The method is organized into five distinct blocks (Bn) and an accompanying appendix containing reproduction notes. Each block encompasses a primary task and associated sub-tasks, articulated through a hierarchical series of steps. The most challenging mathematical aspects of a small network analysis pertain to modeling and sample selection (sel_p). This document describes several modeling tasks that confirm that sel_p = 10 is the best option, including modeling the edges and the convergence and covariance model parameters, modeling the node factor by vertex names, Pearson residual distributions, goodness of fit, and more. This method establishes a foundation for addressing the intricate questions derived from the established hypotheses. It provides eight model specifications and a detailed description. Given the scope of this investigation, a historical examination of the relationships between different network actors is deemed essential, providing context for the study of actors engaged in global trade. Various analytical perspectives (six), encompassing degree analyses, diameter and edges, hubs and authority, co-citation and cliques in mutual and collapse approaches, k-core, and clustering, facilitate the identification of the specific roles played by actors within the importation network in comparison to the tariff network. This study focuses on the Latin American and Caribbean region. Full article

Background/Objectives: Breast cancer is the most commonly diagnosed cancer worldwide, with high rates of distant metastasis. While circulating tumor cells (CTCs) are the disseminatory units of metastasis and are indicative of a poor prognosis, CTC heterogeneity within individual patients, among breast cancer subtypes, and between primary and metastatic tumors within a patient obscures the relationship between CTCs and disease progression. EpCAM, its homolog Trop2, and a pan-Cytokeratin marker were evaluated to determine their contributions to CTC presence and clustering over the study period. We conducted a systematic longitudinal analysis of 51 breast cancer patients during the course of their treatment to deepen our understanding of CTC contributions to breast cancer progression. Methods: 272 total blood samples from 51 metastatic breast cancer (mBC) patients were included in the study. Patients received diverse treatment schedules based on discretion of the practicing oncologist. Patients were monitored from July 2020 to March 2023, with blood samples collected at scheduled care appointments. Nucleated cells were isolated, imaged, and analyzed using Rarecyte^® technology, and statistical analysis was performed in R using the lmerTest and lme4 packages, as well as in Graphpad Prism version 10.4.1. Results: Both classical CTCs (DAPI+, EpCAM+, CK+, CD45– cells) and Trop2+ CTCs were detected in the blood of breast cancer patients. A high degree of correlation was found between CTC biomarkers, and CTC expression of EpCAM, Trop2, and the presence of CD45+ cells, all predicted cluster size, while Pan-CK did not. Furthermore, while analyses of biomarkers by receptor status revealed no significant differences among HR+, HER2+, and TNBC patients, longitudinal analysis found evidence for discrete trajectories of EpCAM, Trop2, and clustering between HR+ and HER2+ cancers after diagnosis of metastasis. Conclusions: Correlation and longitudinal analysis revealed that EpCAM+, Trop2+, and CD45+ cells were predictive of CTC cluster presence and size, and highlighted distinct trajectories of biomarker change over time between HR+ and HER2+ cancers following metastatic diagnosis. Full article

The global climate crisis has driven unprecedented agreements among nations on carbon mitigation. With China’s commitment to carbon peaking and carbon neutrality targets, the building sector has emerged as a critical focus for emission reduction, particularly because office buildings account for over 30% of building energy consumption. However, a systematic and regionally adaptive low-carbon technology evaluation framework is lacking. To address this gap, this study develops a multidimensional decision-making system to quantify and rank low-carbon technologies for office buildings in Beijing. The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. The results indicate distinct priority patterns across the building types: low-rise buildings prioritize roof-mounted photovoltaic (PV) systems, LED lighting, and thermal-break aluminum frames with low-E double-glazed laminated glass. Mid- and high-rise buildings emphasize integrated PV-LED-T8 lighting solutions and optimized building envelope structures. Ranking analysis further highlights LED lighting, T8 high-efficiency fluorescent lamps, and rooftop PV systems as the top-recommended technologies for Beijing. Additionally, four policy recommendations are proposed to facilitate the large-scale implementation of the program. This study presents a holistic technical integration strategy that simultaneously enhances the technological performance, economic viability, and carbon reduction outcomes of architectural design and renovation. It also establishes a replicable decision-support framework for decarbonizing office and public buildings in cities, thereby supporting China’s “dual carbon” goals and contributing to global carbon mitigation efforts in the building sector. Full article

With the rapid development of urbanization in rural areas of China, various environmental issues have become increasingly prominent, particularly the water pollution problems in small rural watersheds, which have garnered considerable attention. Comprehensive management of small watersheds requires an initial analysis of the sources and characteristics of water pollution. This study focuses on small rural watersheds in the Pearl River Delta. Based on the characteristics of the watersheds, 35 water quality monitoring stations were set up to collect water quality data. Cluster analysis was used to study the spatial distribution characteristics of water quality indicators at each monitoring point. Further, factor analysis methods (PCA/FA) and Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) models were employed to identify water quality influencing factors and quantify pollution source contributions. Finally, the comprehensive index method for eutrophication assessment was used to evaluate and analyze the potential eutrophication pollution risk in the watersheds. The results indicate significant pollution in the water quality of rural small watersheds in the study area, with varying degrees of pollution over time and space. During the wet season, water quality is mainly influenced by agricultural nutrients, followed by biochemical factors. In the normal and dry seasons, water quality is primarily affected by oxygen-consuming organic pollutants, followed by eutrophication factors. The comprehensive eutrophication evaluation shows that the overall water quality in the watershed is better during the wet season, with a lower risk of eutrophication; during the normal season, the overall water quality is poorer, with the highest eutrophication risk in the midstream; during the dry season, the upstream and midstream water quality is better, while the downstream water quality is poorer. In contrast, the pond water exhibits a higher risk of eutrophication during the wet season compared to the normal and dry seasons. This is mainly due to the peak of fish farming during the wet season, which results in a heavier load on the water body. This study provides effective data support for the water environment management of rapidly developing rural small watersheds. Full article

Additives are compounds used for material to increase specific properties. When used for polymers, they extend their life and contribute to environmental sustainability. This article presents the study findings related to 24 additives—antioxidants, UV stabilizers, and quenchers—using cheminformatics methods. The compounds’ characteristics (e.g., number of atoms, functional groups) were emphasized, followed by some descriptors. The Tanimoto coefficient, computed based on the maximum common structure algorithm, and the overlap coefficient indicated the degree of similarity between the molecules. The molecules were grouped by binning and hierarchical clustering (HC) based on the extracted results. In the last case, two scenarios were considered—with four (CL1–CL4) and six clusters (CL1.1, CL1.2, CL2, CL3, CL4.1, CL4.2) being built. Considering the mechanical properties of the compounds and the standard deviation and amplitude of their values, the most homogenous class was CL2 (respectively CL4.2). Considering the toxicity of additives, the highest possible negative impact on the environment is that of the compounds in CL1 and CL3. The clustering results guide the selection of additives with reduced environmental impact, thereby supporting the development of sustainable polymer formulations aligned with circular economy principles. Full article