Search Results (29,587)

We present a comprehensive study of host galaxies of radio sources within the 1.35$R_{200}$ of the Coma cluster by combining deep $144\mathrm{MHz}$ observations from the LOFAR Two-Metre Sky Survey (LoTSS-DR2) with optical spectroscopy and photometry from DESI and SDSS. We identify 79 spectroscopically confirmed cluster members with reliable radio emission and classify them into compact, extended, and tailed subsamples according to their radio morphologies. By combining their radio and optical properties, we find compact radio sources are predominantly associated with massive, quiescent galaxies driven by $\mathrm{AGN}$ activity, while tailed sources are largely hosted by star-forming galaxies, tracing ongoing ram pressure stripping ($\mathrm{RPS}$). Using phase-space analysis and a projected infall time proxy ($d_R$), we find that extended sources are preferentially located in the cluster outskirts ($d_R>1$), while tailed sources are concentrated in the intermediate infall region ($0.4<d_R<1.0$), highlighting the influence of the dense intracluster medium. Full article

Historic towns serve as vital carriers of both tangible and intangible cultural heritage, preserving unique historical memories. Quantitative analysis of their architectural facades is crucial for scientific conservation and cultural continuity. While existing studies predominantly employ qualitative descriptions or small-sample analyses, a systematic and replicable quantitative methodology remains elusive. To address this gap, this study innovatively proposes an integrated framework combining UAV oblique photogrammetric modeling, multivariate statistics, and spatial time series analysis. This framework aims to establish a methodological system for analyzing the morphological characteristics of building facades in historic districts. The study selected main streets from four ancient towns in the Chengdu Plain—Pingle, Anren, Xinchang, and Yuantong—and performed 3D reconstruction and morphological indicator extraction on 365 contiguous facade samples. Factor analysis was employed to reduce dimensionality, identifying three dimensions influencing facade morphology. Combined with cluster analysis for classification, the study systematically categorized four statistically significant and architecturally meaningful facade types. Furthermore, it quantified the sequential patterns and combination modes of street-facing distributions, providing crucial theoretical support and reference for the preservation, renewal, and sustainable development of ancient towns. Full article

Understanding public stigma against patients (also known as disease stigma)—negative attitudes or discriminatory responses toward individuals with a disease—is essential for improving health outcomes and fostering inclusive communities. In this study, 279 participants rated their responses toward eight disease groups (e.g., HIV/AIDS, COVID-19, and depression). Using multiple factor analysis, we identified three components of disease stigma: exclusionary (e.g., avoidance and harmful evaluation), prosocial (e.g., sympathy and helping), and attribution (blame/responsibility). Confirmatory factor analysis supported this three-component structure. Repeated-measures ANOVAs revealed systematic differences across diseases: COVID-19 and schizophrenia elicited stronger exclusionary responses, depression evoked the strongest prosocial responses, and HIV/AIDS was associated with the highest attribution of blame. Linear mixed-effects models further indicated that perceived cultural tightness was positively associated with the attribution component, self-control was positively associated with the prosocial component, and higher self-esteem was linked to greater exclusionary responses. Furthermore, network analysis showed dense within-component clustering (e.g., trust—negative evaluation; sympathy—helping) and a peripheral positioning of attribution within the stigma network. These findings provide insights into the psychological components of disease stigma and its cultural and personal correlates, providing targets for component-specific stigma reduction strategies. Full article

Nucleation remains one of the least understood steps during protein crystallization, although it strongly impacts product quality attributes, including total crystal numbers, final crystal size distributions, and thus downstream processing. In this work, the nucleation behavior of Lactobacillus brevis alcohol dehydrogenase (LbADH) wild type (WT) and five mutants (Q207D, Q126H, K32A, D54F, and T102E) is investigated in a stirred 7 mL crystallizer monitored by in situ multi-angle dynamic light scattering (MADLS). Nucleation was studied with highly pure homotetrameric LbADHs by establishing a crystallization, lyophilization, and re-solubilization protocol combined with size exclusion chromatography (SEC) and size exclusion high-performance liquid chromatography (SE-HPLC), yielding tetramer purities above 94% and removing low molecular weight impurities. During stirred batch crystallizations initiated by the addition of polyethyleneglycol 550 monomethyl ether (PEG 550 MME), SEC and SE-HPLC revealed decreasing tetramer peak areas but essentially constant peak apex positions, indicating that no long-lasting oligomeric intermediates accumulate at detectable levels. Time-resolved MADLS measurements using a custom-made flow-through cuvette in a bypass to the stirred crystallizer uncovered transient cluster populations. All protein variants exhibited an initial tetramer peak, followed by the formation of larger aggregates and a rapid rise in signal above a hydrodynamic diameter of 1000 nm, coinciding with the onset of macroscopic turbidity. A simple mesoscale nucleation model was formulated, yielding end-of-nucleation times, crystallized fractions, critical soluble concentrations, and apparent nucleation rate constants. The crystal contact mutations modulate both the timing and magnitude of the nucleation burst (rapid build-up of nuclei/cluster populations). The mutant Q207D showed strongly attenuated nucleation compared to the WT, whereas the other mutants (K32A, D54F, and particularly T102E) display markedly accelerated nucleation at nearly invariant critical concentrations. The combined workflow demonstrates how in situ MADLS, together with a tailored kinetic description, can provide mechanistic insight into protein nucleation in stirred batch crystallizers. Full article

Background: The human gut microbiota plays a central role in host physiology by influencing digestion, immune function, and metabolism. Characterizing age-associated differences in the organization of microbial metabolism may provide insights into functional variation in the gut microbiome across the human lifespan. Methods: Gut microbiota metabolic organization was analyzed in a cohort of 30 individuals spanning three age groups (infants, adults, and elderly individuals) and comprising 156 stool samples. Community metabolic networks were reconstructed using the metabolic Directed Acyclic Graph (m-DAG) framework derived from KEGG Ortholog annotations. Network topology was characterized to assess whether the resulting networks conform to previously described global structural patterns and to examine age-associated variability. Pairwise m-DAG dissimilarities were computed, and hierarchical clustering was applied to evaluate similarities among samples. Results: All samples revealed a conserved global network organization, alongside marked variability in specific structural features. Hierarchical clustering did not strictly reflect chronological age. A homogeneous cluster composed exclusively of adult samples was identified, whereas elderly samples were distributed across two clusters, one grouping with adults and the other with infants. Exploratory discriminative analyses identified functional reactions contributing to the separation between the adult cluster and the remaining samples, indicating age-associated differences in metabolic network organization. Conclusions: Gut microbiota metabolic networks in adults tend to exhibit lower redundancy and structural complexity, whereas those in infant and elderly samples display more heterogeneous network configurations. This network-based analysis provides a functional perspective on age-associated variation in gut microbiota metabolism and offers a framework for future integrative studies. Full article

This study investigates whether the moderate geomagnetic storm of 14 November 2012 was associated with measurable variability in selected power-quality parameters of the Polish National Power System, utilising anonymised, standardised hourly transmission data alongside solar-wind and geomagnetic drivers. Cross-correlation analysis reveals location-dependent, time-lagged couplings, with the strongest correlation, r = 0.74, between a current-harmonic component and the Dst index at a lag of −8 h. The most pronounced anticorrelation, with r = −0.66, occurs between current harmonics and the Ap index at lags of −9 to −11 h during a storm interval that reached $Ds{t}_{min}=-108$ nT. Principal Component Analysis and Hierarchical Agglomerative Clustering distinguish internally driven grid variability from externally driven storm-time signatures, demonstrating that seven principal components capture $89.54\%$ and $86.47\%$ of the variance at the two most responsive locations. These findings indicate that moderate storms can coincide with detectable changes in power-transfer and harmonic-related parameters at specific substations, supporting the need for multi-event studies and physics-based geoelectric or geomagnetically induced current (GIC) modelling to assess operational significance. Overall, this analysis demonstrates that space weather may contribute to observable variability in the Polish power grid. However, further research incorporating additional geomagnetic events, seasonal variability, and geophysical modelling is necessary to fully assess operational impacts and inform potential mitigation strategies. The findings highlight the importance of continued monitoring and interdisciplinary analysis to support long-term resilience planning. Full article

There has been substantial debate regarding the consequences of overtourism in cities. Scholars have also examined variables that are directly and indirectly related to tourism, including demography, urban rehabilitation and requalification, gentrification, speculation in the real estate market, the influence of digital booking platforms, and the expansion of short-term rental (STR) accommodation. This research seeks to develop a clearer spatial understanding of this last one. By analyzing their distribution, density (maximum occupancy), and clustering and by employing Geographic Information Systems (GIS), this article will propose methodologies to better visualize spatial patterns, providing different perspectives of the city of Lisbon and its most tourism-intensive parishes. The article finds that STRs in Lisbon have expanded rapidly, concentrating overwhelmingly in six historic parishes where STR supply and maximum occupancy now exceed resident populations and housing availability. GIS analysis reveals intense clustering in central neighborhoods—especially Alfama—indicating significant tourism pressure and signs of overtourism. These spatial patterns correlate with depopulation and rising housing costs. The study concludes that STR are now a decisive factor in urban imbalance and that detailed spatial analysis is essential for regulating tourism, defining carrying-capacity thresholds, and developing more sustainable, socially just urban planning policies. Full article

Genetic polymorphisms contribute to inter-individual variability in cardiometabolic risk and quality-of-life outcomes, yet their clinical relevance often remains unclear due to population heterogeneity and reliance on single-variant analyses. Integrative approaches combining genetic and phenotypic data may improve the characterization of complex disease profiles, particularly in orthopedic populations burdened by cardiometabolic comorbidities. This study included 289 patients scheduled for orthopedic surgery. Polymorphisms in the vitamin D receptor (VDR; ApaI, FokI, BsmI), catechol-O-methyltransferase (COMT rs4680), and opioid receptor mu 1 (OPRM1 rs510769) genes were genotyped. Clinical, anthropometric, hematological, biochemical, and quality-of-life (SF-36) data were collected. Unsupervised k-means clustering was applied to standardized phenotypic variables to identify homogeneous patient subgroups. Inter-cluster differences were assessed using analysis of variance and chi-squared tests. Three distinct patient clusters were identified, characterized by specific combinations of cardiometabolic, inflammatory, and quality-of-life features. VDR polymorphisms were differentially distributed across clusters associated with differences in body mass index, hypertension prevalence, and inflammatory status. COMT and OPRM1 variants were primarily associated with variability in physical and mental quality-of-life dimensions. The cluster-based approach revealed multidimensional clinical heterogeneity not captured by conventional univariate analyses. Integrating genetic polymorphisms with clinical and quality-of-life data may support the identification and interpretation of distinct cardiometabolic profiles among orthopedic patients. Cluster-based stratification represents a valuable framework for capturing complex patient heterogeneity and supports future precision-oriented research in orthopedic and cardiometabolic populations. Full article

Scandium (Sc), when added together with magnesium (Mg), forms a highly effective synergistic pair in aluminum (Al) alloys, enhancing their performance in various applications. While the thermomechanical processing and heat treatment of such Al-Mg-Sc alloys have been well investigated, the behavior and features of their as-cast state remain less understood. In particular, the evolution of cellular/dendritic microstructures and the formation of phases at submicrometric and nanometric scales, especially those developing during solid-state cooling, require further elucidation. The present study employs a combination of conventional and advanced characterization techniques in the Al-5 wt.%Mg-0.4 wt.% Sc alloy, including CALPHAD, optical microscopy, scanning electron microscopy (SEM), transmission and scanning transmission electron microscopy (TEM/STEM) with energy-dispersive spectroscopy (EDS), x-ray diffractometry (XRD), tensile testing, and fractographic analysis. Al-rich dendrites surrounded by Al₃Sc, AlFe, and β-Al₃Mg₂ phases and the formation of primary submicrometric clusters containing AlFe and Al₃Sc have been identified, revealing important microstructural features that depend strongly on the solidification conditions. Moreover, nanometric Al₃Sc precipitates mainly in the form of rod-like structures with sizes in the order of 50–200 nm have been observed within the α-Al matrix during solid-state cooling stage. At higher solidification rates, such as 15.3 °C/s, these precipitates remain predominantly in solid solution, indicating strong solidification rate dependence in the precipitation behavior. Comparisons between alloys containing 0.1 Sc and 0.4 Sc have demonstrated that the morphology, size, and distribution of Sc-rich phases significantly affect the stress–strain tensile response and underlying strengthening mechanisms. Distinct Portevin–Le Chatelier (PLC) effects have been observed, corresponding to very different serration activities in the stress–strain curves comparing both Al-5%Mg-0.4%Sc and Al-5%Mg-0.1%Sc alloy samples. Among the compositions and conditions studied, the Al–5Mg–0.4Sc alloy samples solidified under the fast-cooling condition (11.2 °C/s) exhibited the most improved mechanical performance, attaining a strength of 306 MPa and an elongation of 22.6%, underscoring the pivotal role of Sc content and solidification rate in achieving optimized mechanical properties. Full article

Accurate spatial information on individual tree locations is essential for precision forestry, the integration of field and remote sensing data, and tree-level forest analyses. This study compared the positional accuracy and tree identification performance of four tree-mapping approaches: legacy paper maps, a pseudolite-based field positioning system (TerraHärp), drone-based laser scanning, and mobile laser scanning (MLS). The analysis was conducted in five long-term experimental forest sites in Lithuania, comprising pine- and spruce-dominated stands with varying stand densities. Tree locations derived from legacy maps and the TerraHärp system were compared to assess systematic and random positional discrepancies. TerraHärp-derived tree positions were subsequently used as a reference to evaluate the laser scanning-based methods. Positional accuracy was assessed using Hotelling’s T² test, root-mean-square error, and the National Standard for Spatial Data Accuracy (NSSDA), while spatial autocorrelation of deviations was examined using Moran’s I. The results indicated that discrepancies between TerraHärp and legacy maps were dominated by systematic horizontal shifts in the historical maps, whereas random positional variability was relatively small and consistent across stand types. Drone-based laser scanning showed a strong dependence of tree identification accuracy on stand density and mean tree diameter. Overall, CHM-based segmentation yielded more accurate tree identification than 3D point cloud segmentation, with mean F1-scores of 0.78 and 0.72, respectively. Positional accuracy varied by method, with the largest errors from CHM apexes and highest 3D point cloud points (mean NSSDA ≈ 1.8–2.0 m), improved accuracy using the lowest 3D cluster points (1.45–1.72 m), and the highest accuracy achieved using mobile laser scanning (mean NSSDA 0.76–0.90 m; >95% of trees within 1 m). These results demonstrate that pseudolite-based field mapping provides a reliable reference for high-precision tree location and for integrating field and laser scanning data in managed conifer stands. Full article

To address the rapidly growing demand for charging stations (CSs) and the associated challenges posed by the expansion of electric vehicles (EVs), this study proposes a collaborative planning method integrates user demand considerations with operational constraints at the grid level. Based on graph theoretical principles, static topology models of the road network and distribution grid were constructed. A dynamic origin–destination (OD) prediction framework was then formulated by jointly considering traffic flow variations, battery energy consumption, user charging behavior, and ambient temperature, in which an enhanced gravity model is coupled with the Floyd algorithm. Charging load characteristics were quantified through Monte Carlo simulation, and K-means++ clustering was further applied to identify spatial charging demand hotspots. On this basis, a multi-objective optimization model was established to simultaneously balance the annualized cost of charging stations, user costs, and voltage deviation in the distribution network. To solve the resulting high dimensional problem, a collaborative optimization mechanism was designed by integrating a weighted Voronoi diagram with a multi-objective particle swarm optimization (MOPSO) algorithm, enabling dynamic service area partitioning and global capacity optimization. Case analysis demonstrates that the proposed method reduces user time costs by 15.8%, optimizes queue delay by 42.2%, and improves voltage stability, maintaining fluctuations within 5%. It also balances the interests of charging station operators, users, and distribution networks, with only a slight increase in construction costs. These results offer valuable theoretical and practical insights for charging infrastructure planning. Full article

Background: The aim of this study was to identify patient clusters based on acute symptom profiles and individual characteristics most likely to develop pediatric post-acute sequelae of SARS-CoV-2 infection (PASC), as well as clusters among patients with PASC based on post-acute sequelae and associated characteristics. Methods: This multicenter cohort study in 12 Italian pediatric units enrolled patients aged 0–17 years within three months of a laboratory-confirmed SARS-CoV-2 infection. Participants who completed at least two surveys developed by the ISARIC over one year were analyzed. PASC was defined per WHO criteria. Multiple Correspondence Analysis and Hierarchical Clustering were performed. Results: Of 1137 children enrolled, 850 (76%) completed at least two surveys. The most prevalent age group was older children (6–11 years) (46%); adolescents (12–17) and young children (0–5) were numerically similar. Males were more represented (51.9%), except for the adolescent group (45.1%). PASC occurred in 32.8% of participants, with the distribution of sequelae types varying by age. Clustering in COVID-19 cases identified three clusters: young children mainly presented with respiratory symptoms and with a higher risk of hospitalization, while older children were spared in both acute and post-acute phases. Adolescents, particularly females, reported more pronounced acute symptoms and developed PASC more frequently. Clustering analysis of cases with PASC identified three clusters, confirming these age-related patterns. Young children still exhibited respiratory sequelae, and older children confirmed good recovery with minimal complications, while adolescents, especially females, remained the most affected subgroup, reporting persistent neuropsychological sequelae such as fatigue and insomnia. Conclusions: Findings support age-tailored follow-up, emphasizing respiratory monitoring for young children and targeted neuropsychological care for adolescents, particularly girls. Full article

Background/Objective: High-frequency micro-ultrasound (micro-US) offers real-time, high-resolution imaging for prostate cancer. Although artificial intelligence (AI) has shown potential in enhancing micro-US interpretation, a comprehensive review of this emerging field is currently missing. This review synthesizes current evidence on AI applied to ExactVu 29 MHz micro-US for prostate cancer. Methods: PubMed/MEDLINE, Embase, Scopus, Web of Science and the Cochrane Library were searched up to December 2025. Studies were included if they applied machine learning or deep learning directly to 29 MHz micro-US data and reported quantitative performance metrics. Results: Ten studies met the inclusion criteria: six on prostate cancer detection, three on prostate segmentation and one on micro-US–histopathology registration. Detection models ranged from classical quantitative ultrasound machine learning to deep architectures using self-supervision, transformers, multiple-instance learning, ensemble calibration and 3D segmentation-based pipelines. Among core-level models for clinically significant cancer, area under the receiver operating characteristic curve (AUROC) values clustered around 0.76–0.81; one lesion-level framework reported an AUROC of 0.92, though at a non-comparable analytical unit. Segmentation studies achieved accurate prostate delineation (Dice similarity coefficient ≈ 0.94), and a single study demonstrated high-precision 3D registration to whole-mount histopathology (Dice similarity coefficient 0.97 and landmark error < 3 mm). All studies evaluated AI on previously acquired data, without real-time clinical implementation. Conclusions: AI for micro-US shows promising and reproducible early results across detection, segmentation and registration, but evidence is still limited. In view of the potential of AI to optimize micro-US utilization and its related advantages, additional efforts are warranted to achieve clinical adoption. Full article

To address the disconnect between macro-quantity planning and micro-spatial allocation at the township level during rapid urbanization, this study developed a coupled model framework based on Multi-Objective Planning (MOP) and the Future Land-Use Simulation (FLUS) model, using Longhu Town as a case study. First, economic and ecological benefit coefficients were calibrated via the Grey Prediction Model and equivalent factor method to define three scenarios: Economic Priority (EPS), Ecological Protection (EcPS), and Balanced Development (BDS). Second, an Artificial Neural Network (ANN) was employed to quantify driving factors, coupled with self-adaptive Cellular Automata (CA) for spatial allocation in 2030. The results indicate that: (1) The model exhibits high reliability for small-scale simulation, with a Kappa coefficient of 0.95 and a Figure of Merit (FoM) of 0.29. (2) Strategic orientations lead to distinct spatial differentiation: under the EPS, urban–industrial land expands significantly northwestward (+16.60%), causing fragmented erosion of cropland; the EcPS achieves a 5.27% increase in forest land and ecological restoration through strict quantitative constraints; the BDS realizes the synergy of urban clustering and ecological enhancement with a marginal urban increase (0.72%). (3) The eastern urban sectors and northeastern cropland belts are identified as future land-use conflict hotspots. The “quantity-space” collaborative optimization path proposed in this study provides a scientific basis and dynamic simulation tool for refined territorial spatial management at the township scale. Full article

Under the influence of China’s rapid urbanization, traditional villages and intangible cultural heritage (ICH), as an organically coupled cultural ecosystem, are faced with severe spatial imbalance. Quantitative analysis and mechanism interpretation of their spatial relationship are crucial for the integrated conservation and collaborative development of cultural heritage. This study takes Zhejiang Province as the research scope, integrates GIS spatial analysis, bivariate spatial autocorrelation, spatial mismatch index, and optimal parameter geographic detector (OPGD) to systematically reveal the spatial distribution characteristics, spatial associations, spatial mismatch patterns, and driving mechanisms of traditional villages and ICH. The results show that: (1) In terms of spatial distribution, traditional villages are highly concentrated in the hilly and mountainous southwest, with a hierarchical pattern featured by “two main cores and two secondary cores”, while ICH is abundant in the flat and coastal northeast and southeast, presenting a multi–center equilibrium pattern. (2) In terms of spatial relationships, there exists a weak but statistically significant negative correlation, which is embodied in typical clusters: “high–low” clusters mainly in the southwest and “low–high” clusters in the northeast and southeast, corresponding to negative spatial mismatch zones dominated by traditional villages and positive spatial mismatch zones dominated by ICH, respectively. (3) As for driving mechanisms, the spatial mismatch pattern is influenced by the tripartite interaction of “natural geographical constraints, socioeconomic drivers, and cultural policy adjustments,” with resident population, GDP, and public budget expenditure as the core driving factors. This study offers scientific recommendations for the conservation and governance of traditional villages and ICH in Zhejiang Province, while providing methodological guidance for cultural heritage preservation in comparable regions. Full article