Search Results (4,648)

Background/Objectives: This study aims to establish a systematic phytochemical characterization and quality evaluation method to systematically evaluate the influence of multiple factors on the chemical composition of Taxillus chinensis, thereby providing a scientific basis for its development, utilization, and quality control standards. Methods: To ensure a targeted and representative metabolic screening, six representative batches covering the major geographical origins and host plants were selected for initial metabolomic profiling. An integrated analytical approach combining UPLC-MS/MS-based widely targeted metabolomics, HPLC fingerprinting, and multi-component quantitative analysis with multivariate statistical analysis was employed. Results: Significant quality variations were identified across the samples. Metabolomics results indicated that while chemical component types were qualitatively consistent across growth conditions, their contents varied significantly. Unique differential metabolites clustered according to specific geographical origins or host plants. KEGG pathway analysis revealed that geographical origin primarily regulated phenylpropanoid biosynthesis, whereas host differences mainly influenced flavonoid and monoterpenoid biosynthesis. Furthermore, HPLC fingerprinting of 20 batches demonstrated similarities greater than 0.9, with 15 common peaks determined. Based on their high relative abundance, differential significance across samples, and documented pharmacological relevance to the herb’s traditional efficacy, six bioactive components—gallic acid, catechin, epicatechin, hyperoside, isoquercitrin, and quercitrin—were identified and quantified. Notably, samples originating from Wuzhou exhibited the highest total content of these components. Consistent with PCA and HCA results, gallic acid, hyperoside, isoquercitrin, and quercitrin were identified as potential markers driving quality differences. Conclusions: This integrated approach allows for a systematic analytical screening of Taxillus chinensis, clarifying chemical variations caused by environmental and biological factors, and supporting the standardization and comprehensive utilization of this medicinal plant. Full article

Background: Acne vulgaris affects up to 80% of individuals aged 11–30 years and frequently results in permanent scarring with significant psychosocial impact. This prospective single-arm case series evaluated the safety and high-frequency ultrasound-assessed morphological changes in a combined protocol integrating subcision, PEGDE-crosslinked hyaluronic acid supplemented with calcium hydroxyapatite (CaHA), and fractional 1470 nm diode laser therapy in patients with facial atrophic acne scars. Methods: Twenty patients (aged 18–42 years, Fitzpatrick phototypes I–II) with moderate-to-severe atrophic acne scars underwent subcision of fibrotic adhesions using a 22G cannula combined with a single subcutaneous injection of 2 mL PEGDE-crosslinked hyaluronic acid with CaHA microparticles on day 0, followed by two sessions of fractional 1470 nm diode laser therapy on days 7 and 28. Scar depth and diameter were assessed using high-frequency ultrasound (48 MHz) at baseline and on days 28, 49, 77, and 139. Results: All participants completed the protocol without serious adverse events. High-frequency ultrasound demonstrated progressive reductions in mean scar depth (from 0.35 to 0.05 mm; −86%) and scar diameter (from 4.27 to 1.06 mm; −75%) by day 139, with reductions continuing beyond the active treatment phase. In linear mixed-effects models accounting for within-patient clustering of the two lesions assessed per participant, the reductions in both depth and diameter were statistically significant at every follow-up timepoint relative to baseline (all p < 0.001). These ultrasound findings were not corroborated by a control group, blinded assessment, validated clinical grading, or patient-reported outcomes. Conclusions: In this single-arm case series, the combined subcision, PEGDE-crosslinked HA–CaHA filler, and fractional 1470 nm diode laser protocol was well tolerated and associated with progressive, sustained reductions in high-frequency ultrasound-measured scar depth and diameter. As an uncontrolled, unblinded study without validated clinical grading or patient-reported outcomes, these findings are preliminary and require confirmation in larger, controlled trials. Full article

Objectives: Local positioning systems (LPSs) used in indoor team sports generate a large number of external load variables, often exceeding practical monitoring capacity. The redundancy and overlap among these variables make it difficult to identify the most informative metrics for performance analysis and load management. This study aimed to reduce the dimensionality of external load variables derived from LPS data and to identify data-driven external-load observation profiles using principal component analysis and clustering techniques. Methods: A total of 188 observations from indoor team sports (basketball, handball, and futsal) were analyzed. Continuous external load variables were standardized and subjected to principal component analysis (PCA), with component retention based on a ≥90% cumulative explained variance threshold. K-means clustering was applied in both the full standardized feature space and the PCA-reduced space. The optimal number of clusters was determined using silhouette analysis and the elbow method. Agreement between clustering solutions was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Cluster characteristics were further examined using descriptive statistics and variable separation analysis. Results: The first two principal components explained 53.7% of the total variance, representing high-intensity external load and neuromuscular load dimensions, while 12 components were required to exceed 90% cumulative explained variance. Clustering analysis consistently identified three moderately separated clusters in both the full and PCA-reduced spaces. The PCA-based solution demonstrated improved separation (silhouette = 0.362) compared to the full-space solution (silhouette = 0.319). Agreement between clustering approaches was high (ARI = 0.981; NMI = 0.971), indicating that dimensionality reduction largely preserved the main clustering structure within the analyzed dataset. The most discriminative variables included jump load, acceleration load, metabolic power, and anaerobic activity distance. Conclusions: A large set of external load variables can be reduced into interpretable latent dimensions that support exploratory external-load profile identification. The combination of PCA and clustering provides an exploratory and structure-preserving framework for summarizing complex external-load datasets and identifying latent load dimensions. These findings may assist future monitoring strategies; however, the practical utility of the identified profiles requires prospective validation before implementation in training-load management. Full article

This study investigates nitrogen sources and biogeochemical pathways in a highly urbanized shallow aquifer in Shinagawa Ward, Tokyo, using an integrated approach combining hydrochemical analysis, multivariate statistics (PCA and K-means cluster analysis), and stable nitrogen isotopes (δ¹⁵N-NH₄⁺, δ¹⁵N-NO₃⁻, δ¹⁵N-DON, and dual δ¹⁵N–δ¹⁸O-NO₃⁻). K-means clustering (K = 2, silhouette = 0.54) partitioned all 41 samples into a background group (n = 34) and an ion-enriched group (n = 7; wells sbi 1, 2, 3, 4, 5, 13, and 19), with the latter exhibiting hydrochemical signatures consistent with localized sewage leakage. The convergence of hydrochemical, multivariate, and isotopic evidence suggests that soil organic matter may represent the dominant diffuse background source of nitrogen across the study area. DON constitutes the dominant fraction of total dissolved nitrogen (TDN), while the linear correlations between TDN and DON concentrations (r = 0.77, p < 0.001) and between δ¹⁵N-TDN and δ¹⁵N-DON (r = 0.88, p < 0.001) indicate a common primary source. The dominance of DON combined with the theoretical inverse relationship between δ¹⁵N-DON and DON concentration is consistent with active soil DON mineralization, supported by an isotope fractionation factor (ε = −4.4 ± 0.78‰). Dual isotope analysis of NO₃⁻ (δ¹⁵N–N–δ¹⁸O slope = 0.51) points towards denitrification as an ongoing process in the aquifer. Taken together, the isotopic variations among nitrogen species suggest a transformation sequence from soil organic nitrogen → DON → NH₄⁺/NO₃⁻ → N₂, though each step in this sequence is supported to varying degrees of confidence. These findings highlight the value of δ¹⁵N-DON as a tracer for nitrogen source attribution and cycling in urban groundwater systems, and underscore the importance of considering all dissolved nitrogen fractions in contamination assessments. Full article

Traditional Chinese villages are important carriers of rural heritage, collective memory, vernacular landscapes, and living cultural traditions. However, rapid urbanization, agricultural modernization, climate change, and tourism development have increasingly threatened their spatial integrity and cultural continuity, highlighting the need for evidence-based conservation and adaptive management. This study examines the spatial distribution patterns and associated factors of 8155 national-level traditional villages in China. An integrated spatial analytical framework was developed by combining kernel density estimation, spatial autocorrelation analysis, Geodetector, and multiscale geographically weighted regression (MGWR). The results show that: (1) traditional villages are unevenly distributed across China and form a distinct “three-core and multi-node” spatial pattern, with major high-density clusters concentrated in several cross-provincial regions and secondary clusters distributed in other heritage-rich areas; (2) the spatial differentiation of traditional village density is statistically associated with natural, cultural, and socioeconomic factors, among which temperature and precipitation show the strongest explanatory power, while cultural endowment, ecological quality, and socioeconomic variables show more context-dependent associations; and (3) compared with OLS and conventional GWR, MGWR improves model performance by capturing spatially heterogeneous and scale-dependent relationships through variable-specific bandwidths. These findings provide national-scale empirical evidence for differentiated conservation planning and support the integration of traditional village protection with rural revitalization, cultural heritage conservation, and sustainable regional development. Full article

This study examines motivational and capability barriers to internet non-adoption in Thailand’s near-saturated digital economy. Using the 2025 Q4 ICT Household Survey conducted by Thailand’s National Statistical Office, the analysis focuses on 20,633 adult non-adopters who report either motivational or capability-related barriers. The dependent variable distinguishes capability non-adoption, defined as lack of skill or awareness, from motivational non-adoption, defined as lack of perceived need or privacy/security concerns. Weighted logistic regression with normalised population weights, PSU-clustered robust standard errors, and average marginal effects is used to estimate associations between household ICT access, age, education, employment, smartphone access, and barrier type. Motivational barriers account for 56.2% of the two-category non-adopter population and capability barriers for 43.8%. Although motivational reasons are the more common, household ICT access is positively—if modestly—associated with capability rather than motivational barriers (average marginal effect +1.7 percentage points): capability-constrained non-adopters are concentrated in connected households, the compositional signature predicted by the second-level digital divide. Age does not significantly moderate this association. Among older non-adopters, education, employment, and smartphone access are negatively associated with capability barriers, while household ICT access is not. The findings suggest that in post-access digital economies, household connectivity is insufficient for digital market inclusion; individual-level skills and device access become central to expanding effective digital market reach. Full article

Hyperspectral remote sensing enables precise identification of alteration mineral through spectral–image integration and high-resolution capabilities. However, vegetation interference significantly hinders the extraction of alteration information in vegetated areas, thereby posing challenges to the reliable identification of alteration minerals. This study employs GF-5B satellite AHSI imagery acquired in the Huzhou region of Zhejiang Province, China, to address this challenge via a novel Zonal Adaptive Vegetation Suppression Technique (ZAVST). By constructing segmented statistical models that links reflectance characteristics across multiple spectral bands to NDVI values, ZAVST demonstrates an enhanced capability to mitigate vegetation obscuration effects on subsurface lithological features while substantially improving the identification of subtle spectral signatures characteristic of mineralization. Results reveal distinct spatial patterns: Fe-bearing alteration minerals (hematite, pyrite) align along NE-trending faults and volcanic basin margins; Al-OH alterations (montmorillonite, kaolinite) cluster near intrusive contacts; Mg-OH alterations (chlorite, epidote) occur at interfaces between carbonate sequences and concealed intrusions. Composite alteration anomalies exhibiting stacked mineral signatures (up to four distinct types) were identified across the region, demonstrating a strong spatial correlation with known mineralization centers. By integrating alteration zonation, structural lineaments, stratigraphy, geochemical anomalies, and orebody records, this study delineated four priority targets: Lijiaxiang Town, eastern Meixi Town, Miaoxi Town, and the central Moganshan Volcanic Basin. Full article

High-throughput technologies such as RNA-seq and single-cell transcriptomics generate increasingly large and high-dimensional gene expression datasets in which nonlinear dependence structures are common. Because classical methods primarily capture linear associations, they may fail to characterize many biologically relevant patterns of dependence. To address this limitation, diverse nonlinear dependence measures—including information-theoretic, rank-based, kernel-based, distance-based, copula-based, and clustering-based approaches—have been developed. However, the field remains fragmented, and comparative evaluations are often inconsistent. This review organizes nonlinear methods into major methodological families and critically compares their statistical behavior, strengths, limitations, and characteristic modes of failure. We emphasize that method selection depends on matching inferential objectives to estimator assumptions, analytical constraints, and characteristic failure modes. By identifying recurring trade-offs among flexibility, robustness, interpretability, and computational scalability, we provide scenario-based guidance for method selection in transcriptomics, network inference, and functional genomics. In doing so, we aim to align inferential objectives with analytical requirements, supporting principled and application-specific use of nonlinear dependence methods in modern omics research. Full article

Hydrogen-compatible elastomeric seals are critical for the reliability and safety of high-pressure hydrogen infrastructure. However, hydrogen exposure can alter the mechanical response and surface condition of elastomeric materials through coupled transport–mechanical interactions. This study presents a comparative experimental and data-driven investigation of the pressure-dependent degradation behavior of ethylene propylene diene monomer (EPDM), nitrile butadiene rubber (NBR), and fluorocarbon elastomer (FKM) O-ring seals following 192 h exposure to hydrogen pressures ranging from 800 to 7000 psi at room temperature. Tensile testing was performed directly on complete O-ring geometries, and descriptor-based analysis was used to quantify peak-response behavior, energy absorption, stiffness evolution, and normalized deformation characteristics. Multivariate statistical methods, principal component analysis (PCA), clustering analysis, and Random Forest regression were applied to identify material-specific degradation patterns. NBR maintained the highest overall load-bearing capability and stiffness-related response across the investigated pressure range, whereas EPDM exhibited more compliant and non-monotonic deformation behavior. FKM showed the strongest pressure sensitivity, with substantial increases in force- and stiffness-related descriptors at elevated hydrogen pressures. Optical image analysis revealed pronounced increases in defect density and defect area fraction for NBR, while FKM exhibited comparatively stable surface-state behavior. PCA and clustering analyses identified distinct material-dependent degradation trajectories, and Random Forest regression achieved an R² value of 0.888 for energy-absorption prediction. The results demonstrate that hydrogen-induced degradation emerges through coupled interactions among stiffness evolution, deformation progression, energy absorption, and surface-state changes, providing a comparative framework for assessing elastomer performance in hydrogen environments. Full article

Stilbocrea banihashemiana Bolboli, Tavakolian & Mostowf. is an emerging pathogen causing canker and dieback in a broad range of fruit and ornamental trees in Iran, and its distribution is expanding across the country. Extensive surveys conducted over five consecutive years (2019–2023) yielded 88 isolates of S. banihashemiana from multiple hosts, including different fig (Ficus caricae L.) cultivars, as well as loquat (Eryobotria japonica (Thunb.) Lindl.), pomegranate (Punica granatum L.), and walnut (Juglans regia L.) trees, across eight distinct regions of southern Iran. Species identification was performed morphologically and molecularly by employing the S. banihashemiana-specific primer pair TEF-Sb1 and TEF-Sb3. The genetic diversity of the S. banihashemiana population of isolates was assessed using eight inter-simple sequence repeats (ISSRs) markers. The UPGMA dendrogram demonstrated broad genetic variability among the isolates, with similarity coefficient values spanning from 0.46 to 1.00. This wide range indicates the presence of multiple divergent genotypes within the population, rather than a single dominant lineage. Principal coordinate analysis (PCoA) grouped the 88 isolates into three distinct genetic clusters that partially corresponded to geographic origin and host species. Pathogenicity assessment of 53 selected isolates from various hosts and geographic origins on detached fig shoots demonstrated highly significant variability in aggressiveness among isolates originating from different host species and geographically distinct regions. Multivariate analysis using principal component analysis (PCA) combined with heatmap-based clustering of the aggressiveness dataset clearly separated the isolates into four distinct groups, ranging from highly to less aggressive. A susceptibility assessment of 10 fig cultivars using the ex-type-isolate of S. banihashemiana revealed that the pathogen caused internal lesions and wood discoloration in all cultivars. Based on statistical analysis, the cultivars were classified into three groups: susceptible (cv. ‘Siah’), moderately susceptible (‘Brown Turkey’, ‘C8-M’, ‘C8-F’, ‘Dehdez’, ‘Gilasi’, ‘Payves’, ‘Shah-Anjeer’ and ‘Sabz’), and less susceptible (‘Matti’). High genetic variability, multiple-host association, and partial geographic structure indicate that in Fars Province S. banihashemiana’s population structure and epidemiology are complex, with high adaptive potential. This complexity may influence disease spread, management strategies, and long-term evolutionary trajectories. Full article

The interindividual phenotypic heterogeneity in Alpha-1 Antitrypsin Deficiency (AATD), despite a shared genetic etiology (the Z-allele of SERPINA1), is explained by the interaction of dual pathogenic mechanisms (gain-of-function vs. loss-of-function), additional genetic modifiers, and environmental or metabolic factors. Building on recent evidence suggesting divergent disease trajectories, we investigated whether pulmonary and hepatic impairments represent coupled manifestations or independent clinical dimensions within a large European cohort. Methods: This international multicenter study utilized the European Alpha-1 Research Collaboration (EARCO) registry (n = 1217). Pulmonary and hepatic severities were quantified using concurrent 0.0–10.0 composite indices. Independence was evaluated via partial Spearman correlations, multivariable multinomial regression, and geometric mapping across a continuous phenotypic space. Results: Cross-domain correlations between respiratory metrics and liver stiffness were near zero (r = −0.03), demonstrating statistical independence. Phenotypic dominance classification isolated distinct profiles; the lung-dominant group exhibited a higher age (57.0 vs. 54.0 years; p < 0.001) and tobacco exposure, while the liver-dominant group registered a higher body mass index (25.8 vs. 24.4 kg/m²; p < 0.001). Multivariable models identified age (OR 1.03; 95% CI 1.02–1.05) and smoking as independent predictors of lung dominance, whereas body mass index was independently associated with liver dominance (OR 1.04; 95% CI 1.01–1.07). Geometric mapping revealed advanced disease clusters at orthogonal margins rather than forming a systemic continuum. Conclusions: Hepatic and pulmonary impairments in AATD operate as independent clinical dimensions modulated by distinct metabolic and environmental factors. Risk stratification must transition toward organ-specific prognostic models. Full article

Urban freight externalities are increasingly addressed through regulation policies targeting both vehicle access and loading zones management. While urban vehicle access regulations and loading and unloading zone management are widely applied, existing research has largely regarded them as separate policy domains, overlooking their potential interdependence within urban freight governance. This study develops an exploratory comparative typology of UVARs and loading zone management across selected global cities. A hierarchical clustering method was applied to a harmonized set of indicators to identify distinct urban freight governance typologies. The UVAR clustering analysis was conducted on 39 cities with freight-related UVARs, while the loading zone clustering analysis was conducted on 39 cities with formal loading management zones. The cross-analysis suggests some co-occurrence patterns between UVARs and loading zone typologies. But the chi-square test does not provide statistical evidence of dependence. Therefore, this study can be interpreted as an exploratory mapping of regulatory configurations. The findings provide a comparative basis for future research linking urban freight regulatory typologies with environmental, operational, economic, and social performance indicators. Full article

During startup, ransomware is associated with abnormal fluctuations in underlying hardware resources. Hardware Performance Counters (HPC) can characterize this ultra-early behavior without interference from software-based countermeasures. However, existing studies lack a cross-platform hardware-layer analysis paradigm and typically neglect the first 10 s post-execution. This study selects two platforms—Windows 7 (homogeneous x86) and Windows 10 (Intel performance hybrid architecture with P-core (performance core) and E-core (efficiency core))—and constructs a large-scale dataset (1721 ransomware and 1039 benign samples on Windows 7; 1562 ransomware and 718 benign on Windows 10). On Windows 7, 25 HPC events are monitored. On Windows 10, each event yields two instance-level metrics (P-core and E-core), resulting in 42 instance-level metrics. Using statistical analysis (Pearson correlation, fold change) and feature selection (Random Forest + clustering), four core metrics are independently selected per platform. Windows 7 favors LLC and branch events (increasing trends, fold change ≥ 1.5, e.g., LLC-store_std), while Windows 10 favors P/E-core branch and cache events (decreasing trends, fold change ≤ 0.667, e.g., cpu_atom_branch-load-misses_max). The 10 s window is divided into startup (0–2 s), key generation (2–5 s), and encryption (5–10 s) phases. Results indicate opposite correlation patterns: resource-enhanced disturbance (positive correlation, fold change ≥ 1.5) on Windows 7 versus resource-suppressed disturbance (negative correlation, fold change ≤ 0.667) on Windows 10. Critically, startup-phase HPC events exhibit substantially stronger correlation on Windows 10 (S-level, >85%) compared to Windows 7 (A-level, 70–84%). This difference may be associated with the fine-grained P/E-core separation, which preserves core-type behavioral information that is aggregated and lost on homogeneous platforms. This study contributes a cross-platform correlation framework, observes an architecture-dependent inversion pattern of HPC responses, and suggests that core-type granularity—rather than event quantity—is associated with stronger feature–behavior correlations on heterogeneous architectures, providing preliminary empirical insights for future lightweight detection system design. Full article

Observational and statistical studies of chemically peculiar (CP) stars show that the origin of heavy radioactive element overabundances in their atmospheres remains unclear. We investigate whether such anomalies can be explained by supernova explosions in binary or multiple systems by searching for CP star–pulsar pairs via Galactic traceback analysis. We examine 529 CP stars and 28 pulsars (ages ≤ 100 Myr) and identify several candidate pairs that may have shared a common origin. Comparison with young stellar clusters suggests possible parent groups. Further work is needed to constrain CP star ages and assess the statistical significance of the proposed supernova scenarios. Full article

Background: Recovery after major head-and-neck reconstruction extends beyond flap survival and wound closure, involving swallowing, psychological adaptation, body image, and overall quality of life. Integrated multidimensional assessments remain limited in routine reconstructive outcomes research. Aim: The aim of this study was to characterize and compare six-month multidimensional recovery—clinical, functional, nutritional, psychological, and body-image outcomes—between microvascular free-flap and regional pedicled-flap reconstruction and to identify factors that stratify risk for persistent functional and psychosocial impairment. Methods: We conducted a single-center prospective cohort study at the “Victor Babeș” University of Medicine and Pharmacy, Timișoara, Romania, enrolling 87 adults undergoing major reconstructive surgery after ablative treatment of head-and-neck defects (52 microvascular free flaps; 35 regional pedicled flaps). Patients were assessed at baseline and 6 months using the SF-36, WHOQOL-BREF, Body Image Scale (BIS), HADS, PHQ-9, GAD-7, Functional Oral Intake Scale (FOIS), speech intelligibility, and PEG/tracheostomy dependence. Results: At 6 months, most SF-36 and WHOQOL-BREF domains improved with moderate effect sizes (d = 0.3–0.7; all p ≤ 0.009), and body image distress decreased significantly (ΔBIS −2.9 ± 4.6; p < 0.001), whereas social functioning showed no robust gain (p = 0.098; not surviving false-discovery-rate correction). Pedicled reconstruction was associated with higher PEG dependence (37.1% vs. 9.6%; p = 0.005) and worse FOIS (4.7 ± 1.4 vs. 5.6 ± 1.2; p = 0.003). Major complications were linked to blunted or worsening psychological trajectories and a threefold higher rate of clinically significant depression (HADS-D ≥ 11: 66.7% vs. 18.7%; p = 0.001). In a reduced four-predictor multivariable model, pedicled flap (aOR 4.6), adjuvant radiotherapy (aOR 2.8), major complication (aOR 3.3), and lower baseline FOIS (aOR 0.5 per point) were independently associated with PEG dependence (optimism-corrected AUC 0.79). Clustering identified three recovery phenotypes—functional/emotional responders, psychological/body-image responders, and global slow recovery—with significantly different PEG rates (5.9%, 21.4%, 40.0%; p = 0.006). Exploratory mediation analysis suggested that the association between reconstruction technique and mental quality-of-life recovery was partly statistically accounted for by swallowing and body-image improvement. Conclusions: Recovery after major head-and-neck reconstruction is multidimensional and heterogeneous. Baseline swallowing function, reconstruction technique, radiotherapy, and major complications jointly stratify risk for persistent functional and psychosocial impairment, supporting risk-adapted multidisciplinary rehabilitation and early psycho-oncologic screening. Full article