Search Results (4,439)

Global environmental changes significantly alter ecosystem services (ESs), particularly in fragile regions like the Tibetan Plateau. While methodological advances have improved spatial assessment capabilities, understanding of how multiple drivers interact to shape ecosystem service heterogeneity remains limited to regional scales, especially across complex alpine landscapes. This study aims to clarify whether multi-factor interactions produce nonlinear enhancements in ES explanatory power and how these driver–response relationships vary across heterogeneous terrains. We quantified spatiotemporal patterns of four key ecosystem services—water yield (WY), soil conservation (SC), carbon sequestration (CS), and habitat quality (HQ)—across the southeastern Tibetan Plateau from 2000 to 2020 using multi-source remote sensing data and spatial econometric modeling. Our analysis reveals that SC increased by 0.43 t·hm⁻²·yr⁻¹, CS rose by 1.67 g·m⁻²·yr⁻¹, and HQ improved by 0.09 over this period, while WY decreased by 3.70 mm·yr⁻¹. ES variations are predominantly shaped by potent synergies, where interactive explanatory power consistently surpasses individual drivers. Hydrothermal coupling (precipitation ∩ potential evapotranspiration) reached 0.52 for WY and SC, while climate–vegetation synergy (precipitation ∩ normalized difference vegetation index) achieved 0.76 for CS. Such climate–restoration synergies now fundamentally shape the region’s ESs. Geographically weighted regression (GWR) further revealed distinct spatial dependencies, with southeastern regions experiencing strong negative effects of land use type and elevation on WY, while northwestern areas showed a positive elevation associated with WY but negative effects on SC and HQ. These findings highlight the critical importance of accounting for spatial non-stationarity in driver–ecosystem service relationships when designing conservation strategies for vulnerable alpine ecosystems. Full article

Melasma management is challenged by heterogeneity in patient presentation, particularly among individuals with darker skin tones. This study applied k-means clustering, an unsupervised machine learning algorithm that partitions data into k distinct clusters based on feature similarity, to identify patient subgroups that could provide a hypothesis-generating framework for future precision strategies. We analysed clinical and demographic data from 150 South African women with melasma using k-means clustering. The optimal number of clusters was determined using the Elbow Method and Bayesian Information Criterion (BIC), with t-distributed stochastic neighbour embedding (t-SNE) visualization for assessment. The k-Means algorithm identified seven exploratory patient clusters explaining 52.6% of the data variability (R² = 0.526), with model evaluation metrics including BIC = 951.630 indicating optimal model fit and a Silhouette Score of 0.200 suggesting limited separation between clusters consistent with overlapping clinical phenotypes, while the Calinski-Harabasz index of 26.422 confirmed relatively well-defined clusters that were characterized by distinct profiles including “The Moderately Sun Exposed Young Women”, “Elderly Women with Long-Term Melasma”, and “Younger Women with Severe Melasma”, with key differentiators being age distribution and menopausal status, melasma severity and duration patterns, sun exposure behaviours, and quality of life impact profiles that collectively define the unique clinical characteristics of each subgroup. This study demonstrates how machine learning can identify clinically relevant patient subgroups in melasma. Aligning interventions with the characteristics of specific clusters can potentially improve treatment efficacy. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

Rural areas are currently facing a deepening “social-ecological divide,” where the fragmentation of natural, economic, and cultural data—often trapped in “data silos”—hinders effective systemic governance. To bridge this gap, in this study, the Rural Landscape Information Model (RLIM), an integrative framework designed to reconfigure rural connections through data fusion, process coordination, and performance feedback, is proposed. We validate the framework’s effectiveness through a comparative analysis of two distinct rural archetypes in China: the innovation-driven Yu Village and the heritage-conservation-oriented Hani Terraces. Our results reveal that digital technologies drive distinct empowerment pathways moderated by regional contexts: (1) In the data domain, heterogeneous resources were successfully integrated into the framework in both cases (achieving a Monitoring Coverage > 80%), yet served divergent strategic ends—comprehensive territorial management in Yu Village versus precision heritage monitoring in the Hani Terraces. (2) In the process domain, digital platforms restructured social interactions differently. Yu Village achieved high individual participation (Participation Rate ≈ 0.85) via mobile governance apps, whereas the Hani Terraces relied on cooperative-mediated engagement to bridge the digital divide for elderly farmers. (3) In the performance domain, the interventions yielded contrasting but positive economic-ecological outcomes. Yu Village realized a 25% growth in tourism revenue through “industrial transformation” (Ecology+), while the Hani Terraces achieved a 12% value enhancement by stabilizing traditional agricultural ecosystems (Culture+). This study contributes a verifiable theoretical model and a set of operational tools, demonstrating that digital technologies are not merely instrumental add-ons but catalysts for fostering resilient, collaborative, and context-specific rural socio-ecological systems, ultimately offering scalable governance strategies for sustainable rural revitalization in the digital era. Full article

In the context of China’s dual-carbon agenda and the Digital China initiative, elucidating the role of digital literacy in shaping consumption-based household carbon emissions (HCE) is essential for advancing low-carbon urban living and supporting a broader green transition. Existing research has rarely examined, at the individual level, how digital capability shapes household consumption decisions and the structure of carbon emissions. Accordingly, this study draws on matched household-individual microdata from the China Family Panel Studies (CFPS). We employ a two-way fixed effects model, kernel density analysis, and qualitative comparative analysis. We test the nonlinear effect of digital literacy on household consumption-related carbon emissions and examine its heterogeneity. We also examined the mediating role of perceived environmental pressure, social trust and income level. The research results show that: (1) The net impact of digital literacy on carbon emissions related to household consumption shows an inverted U-shaped curve, rising first and then falling. When digital literacy is low, it mainly increases emissions by expanding consumption channels, reducing transaction costs and improving convenience. Once digital literacy exceeds a certain threshold, the mechanism will gradually turn to optimize the consumption structure, so as to support the low-carbon transformation of individuals. (2) The impact of digital literacy on HCE is structurally different in different types of consumption. In terms of transportation and communication expenditure, the emission reduction effect is the most significant, and with the improvement in digital literacy, this effect will become more and more obvious. For housing-related consumption, the turning point appeared the earliest. With the improvement in digital literacy, its effect will enter the emission reduction stage faster. (3) Digital literacy can reduce carbon emissions related to household consumption by enhancing residents’ perception of environmental pressure and strengthening social trust. However, it may also increase emissions by increasing residents’ incomes, because it will expand the scale of consumption, which will lead to an increase in carbon emissions related to household consumption. (4) The heterogeneity analysis shows that as digital literacy improves, carbon emissions increase more strongly among rural residents, people with low human capital, low-income households, and women. However, the turning-point threshold for emission reduction is relatively lower for women and rural residents. (5) Low-carbon transitions in household consumption are shaped by dynamic interactions among multiple factors, and multiple pathways can coexist. Digital literacy can work with environmental responsibility to endogenously promote low-carbon consumption behavior. It can also, under well-developed infrastructure, empower households and amplify the emission-reduction effects of technology. Full article

The shale oil reservoir is characterized by ultra-low porosity and permeability and multi-scale strong heterogeneity. During the sampling process of downhole cores, the rocks can easily be affected by drilling fluid contamination, mechanical stress damage, and other factors, altering the original distribution of oil–water and the characteristics of pore structures. Oil removal and oil saturation are critical steps in core pre-treatment, yet the mechanism of its impact on cores has not been systematically studied. This research focuses on oil removal in six cores from the Jimsar shale oil reservoir with different oil saturations. The necessity and effectiveness of the oil removal saturation and its impact on the microstructure of the cores were systematically evaluated by employing nuclear magnetic resonance (NMR), CT scanning, and permeability testing methods. The results indicate that there are significant differences in fluid composition, pore structure, and wettability among downhole cores, making oil removal saturation treatment a necessary prerequisite for subsequent experiments. High-temperature and high-pressure oil removal shows significant effectiveness, with an average core weight reduction of 2.46% and average reduction in T₂ peak area of 73.75%. The efficacy of oil saturation is influenced by the initial pore-throat distribution in the cores. The oil removal process significantly alters petrophysical parameters, with an average increase in porosity of 3.21 times and permeability rising by an average of 2.16 times, although individual variations exist. Microstructural analysis demonstrates that the oil removal process preferentially removes crude oil from larger pores, while residual oil is mainly distributed in smaller pores, indicated by a left shift in T₂ peak values. Meanwhile, high-temperature and high-pressure conditions induce microfracture development, promoting the migration of crude oil into smaller pores. This research reveals the complex impact mechanism of the oil removal saturation process on shale cores, providing a theoretical basis for accurately evaluating shale reservoir characteristics and optimizing experimental design. Full article

Background: Metabolic syndrome (MetS) is a multifactorial condition characterized by insulin resistance, dyslipidemia, hypertension, and central obesity, and is strongly influenced by lifestyle factors. Growing evidence highlights the gut microbiota as a key mediator linking diet and physical exercise to cardiometabolic health. Objective: This narrative review aims to qualitatively synthesize current evidence on the effects of physical exercise and major dietary patterns including the Mediterranean diet (MedDiet), Dietary Approaches to Stop Hypertension (DASH), and ketogenic/very-low-calorie ketogenic diets (KD/VLCKD) on gut microbiota composition and function, and their implications for metabolic health in MetS. Methods: A qualitative narrative synthesis of experimental, observational, and interventional human and animal studies was performed. The reviewed literature examined associations between structured physical exercise or dietary interventions and changes in gut microbiota diversity, key bacterial taxa, microbial metabolites, and cardiometabolic outcomes. Considerable heterogeneity across studies was noted, including differences in populations, intervention duration and intensity, dietary composition, and microbiota assessment methodologies. Results: Across human interventional studies, moderate-intensity physical exercise was most consistently associated with increased gut microbial diversity and enrichment of short-chain fatty acid (SCFA)-producing taxa, contributing to improved insulin sensitivity and reduced inflammation. MedDiet and DASH were generally linked to favorable microbiota profiles, including increased abundance of Faecalibacterium prausnitzii, Akkermansia muciniphila, and Bifidobacterium, alongside reductions in pro-inflammatory metabolites such as lipopolysaccharides and trimethylamine N-oxide. In contrast, KD and VLCKD were associated with rapid weight loss and glycemic improvements but frequently accompanied by reductions in SCFA-producing bacteria, depletion of Bifidobacterium, and markers of impaired gut barrier integrity, raising concerns regarding long-term microbiota resilience. Conclusions: Lifestyle-based interventions exert diet- and exercise-specific effects on the gut microbiota–metabolism axis. While MedDiet, DASH, and regular moderate physical activity appear to promote sustainable microbiota-mediated cardiometabolic benefits, ketogenic approaches require careful personalization, limited duration, and medical supervision. These findings support the integration of dietary quality, exercise prescription, and individual microbiota responsiveness into translational lifestyle strategies for MetS prevention and management. Full article

Background/Objectives: Southeastern Europe and Croatia have served as a genetic crossroads between the Near East and Europe since prehistoric times, shaped by numerous and repeated migrations. By integrating 19 newly generated ancient genomes with 285 previously published ancient genomes from Croatia, we investigated patterns of maternal and paternal landscapes from the Neolithic, Bronze, and Iron Ages through to the Antiquity and medieval periods, as well as the modern Croatian population. Methods: Ancient DNA extraction from human remains and library preparation were conducted in dedicated clean-room facilities, followed by high-throughput sequencing on the Illumina platform. Sequencing data were analyzed with established pipelines to determine mitochondrial and Y-chromosomal haplogroups and the genetic sex of individuals. Results: New ancient data reveal a predominantly European maternal profile, dominated by haplogroups H, U, and HV0, whereas Y-chromosomal lineages are characterized by J subclades and R1a, with limited representation of R1b and the absence of I2a. When combined with published ancient Croatian genomes, the results reveal similar haplogroup diversity and patterns, as well as the expansion of mtDNA haplogroup H over time and a substantial increase in Y-chromosome R1a and I2a haplogroup frequency from the prehistoric to the modern period. Conclusions: Although the analyzed samples are heterogeneous and originate from different historical periods, their genetic signatures conform to the broader patterns expected for the region. In a wider context, the ancient Croatian mitochondrial data reveal stronger genetic persistence from prehistory to modern times, unlike paternal lineages, which show significantly higher divergence. Full article

Background: Parotid gland tumors pose diagnostic and surgical challenges due to their histological heterogeneity and proximity to the facial nerve. This study aimed to evaluate clinicopathological features and postoperative outcomes with a specific focus on facial nerve function in patients undergoing parotidectomy. Methods: This retrospective study included 314 patients who underwent parotidectomy between 2008 and 2024 at a tertiary center. Demographic data, tumor histology, and postoperative complications—particularly facial nerve paralysis within the first three months—were analyzed. Histopathological features including capsular, perineural, and lymphovascular invasion were also assessed. Results: Of all cases, 79% were benign, 14.6% malignant, and 6.4% non-neoplastic. Pleomorphic adenoma and Warthin tumor were the most common benign entities, while mucoepidermoid carcinoma was the most frequent malignancy. Malignant tumors were associated with higher rates of positive surgical margins (44.2% vs. 12.5%, p < 0.001), capsular invasion (25% vs. 7%, p < 0.001), and tumor necrosis (22% vs. <1%, p < 0.001). Facial paralysis occurred in 4.4% of patients, largely transient and significantly associated with malignant tumors (p < 0.001) and extensive lymph node dissection (p < 0.001). Capsular invasion and necrosis were rare in benign lesions but still observed, especially in pleomorphic adenoma. Conclusions: Histopathologic aggressiveness markers were associated with malignant disease and postoperative facial nerve dysfunction. These findings support a risk-stratified approach to follow-up: all patients undergo universal early assessment at two weeks and three months, after which surveillance intensity may be individualized according to histopathologic features—such as necrosis, perineural invasion, capsular invasion, or positive margins. Full article

Background/Objectives: Gambling disorder (GD) is characterized by a high prevalence of co-occurring psychiatric disorders, including personality disorders (PDs), which may negatively influence clinical presentation, treatment outcomes, and relapse rates. The aim of this systematic review was to synthesize recent evidence regarding the association between GD and formally diagnosed PD and/or diagnostically anchored PD symptomatology, and to describe the main personality dimension most frequently reported in affected individuals. Methods: A systematic search was conducted in the PubMed and Dialnet databases for articles published between 30 November 2015 and 30 November 2025, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. PubMed was selected as the primary database because it is the most comprehensive source for peer-reviewed biomedical and psychiatric research, while Dialnet was included to complement PubMed by ensuring coverage of peer-reviewed psychiatric and psychological research published in other Romance-language journals, which are often underrepresented in international databases. The methodological quality and risk of bias of the included studies were evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for cross-sectional studies and the Newcastle–Ottawa Scale (NOS) for observational studies. Data extraction and synthesis were performed manually by two independent reviewers. Eight studies, predominantly cross-sectional in nature, assessing exclusively formally diagnosed personality disorders in adult individuals (≥18 years) diagnosed with GD were included. Results: Eight studies met the inclusion criteria, including a total of 4607 patients with GD. Across studies, personality pathology was highly prevalent among individuals with GD, with antisocial and borderline personality disorders most consistently reported. Elevated levels of impulsivity, emotional dysregulation, and narcissistic traits were frequently observed and were additionally associated with greater gambling severity, earlier onset, and poorer clinical outcomes. Antisocial personality symptoms were strongly linked to high-risk gambling subtypes, while obsessive–compulsive personality traits showed a more heterogeneous relationship with gambling severity. Conclusions: These results underscore the importance of personality assessment in individuals with GD and highlight the need for longitudinal studies using standardized diagnostic frameworks to inform tailored prevention and treatment strategies. Full article

Acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease where current clinical practice guidelines remain focused on traditional cytogenetic markers. Despite recent advances demonstrating excellent diagnostic accuracy for gene expression signatures, a discontinuity exists between biomarker validation and clinical implementation. This study aimed to develop and validate a multiparametric gene expression signature using digital PCR (dPCR) to accurately diagnose pediatric ALL, with potential utility for monitoring measurable residual disease (MRD). We analyzed 130 bone marrow aspirates from pediatric patients from four clinical groups: non-leukemia, MRD-negative, MRD-positive and leukemia characterized by immunophenotype. Gene expression of an 8-gene panel (JUP, MYC, NT5C3B, GATA3, PTK7, CNP, ICOSLG, and SNAI1) was quantified by dPCR. The diagnostic performance of individual markers was assessed, and a Random Forest machine learning model was trained to classify active disease. The model was validated using a 5-fold stratified cross-validation approach. Individual markers, particularly JUP, MYC, and NT5C3B, showed good diagnostic accuracy for distinguishing leukemia from non-leukemia. However, integrating all eight markers into a multivariate Random Forest model significantly enhanced performance. The model achieved a mean cross-validated area under the curve (AUC) of 0.908 (±0.041) on receiver operator characteristic (ROC) analysis and 0.961 (±0.019) on Precision–Recall (PR) analysis, demonstrating high reliability and a favorable balance between sensitivity and precision. The integrated model achieved high sensitivity (88.9%) for detecting active disease, particularly at initial diagnosis. Although specificity was moderate (65.0%), the high positive predictive value (PPV 85.1%) and accuracy (81.5%) confirm the clinical utility of a positive result. While the panel showed promising performance for distinguishing MRD-positive from MRD-negative samples, the limited MRD-positive cohort size (n = 11) indicates that validation in larger MRD-focused studies is required before clinical implementation for treatment monitoring. This dPCR-based platform provides accessible, quantitative detection without requiring knowledge of clonal shifts or specific genomic landscape, offering potential advantages for resource-limited settings such as those represented in our Mexican pediatric cohort. Full article

Sudden cardiac death (SCD) remains a major challenge in forensic medicine, representing a leading cause of natural mortality and frequently occurring in individuals without antecedent symptoms. Although conventional autopsy and histology remain the cornerstones of investigation, up to 10–15% of cases are classified as “autopsy-negative sudden unexplained death,” underscoring the need for complementary diagnostic tools. In recent years, post-mortem biochemistry and molecular approaches have become essential to narrowing this gap. Classical protein markers of myocardial necrosis (cardiac troponins, CK-MB, H-FABP, GPBB) continue to play a fundamental role, though their interpretation is influenced by post-mortem interval and sampling site. Peptide biomarkers reflecting hemodynamic stress (BNP, NT-proBNP, copeptin, sST2) offer additional insight into cardiac dysfunction and ischemic burden, while inflammatory and immunohistochemical markers (CRP, IL-6, fibronectin, desmin, C5b-9, S100A1) assist in detecting early ischemia and myocarditis when routine histology is inconclusive. Beyond these traditional markers, molecular signatures—including cardiac-specific microRNAs, exosomal RNA, proteomic alterations, and metabolomic fingerprints—provide innovative perspectives on metabolic collapse and arrhythmic mechanisms. Molecular autopsy through next-generation sequencing has further expanded diagnostic capability by identifying pathogenic variants associated with channelopathies and cardiomyopathies, enabling both cause-of-death clarification and cascade screening in families. Emerging multi-omics and artificial intelligence frameworks promise to integrate these heterogeneous data into standardized and robust interpretive models. Pre- and post-analytical considerations, together with medico-legal implications ranging from malpractice evaluation to the management of genetic information, remain essential components of this evolving field. Overall, the incorporation of validated biomarkers into harmonized international protocols, increasingly supported by AI, represents the next frontier in forensic cardiology. Full article

Ericksonian hypnotherapy (EH), a client-centered hypnotic approach characterized by indirect suggestion, individualized flexibility, collaboration, and the principle of Utilization, has seen increased interest as a therapeutic modality across diverse clinical settings. This systematic review and meta-analysis aimed to evaluate the efficacy of EH by synthesizing evidence from randomized controlled trials (RCTs) published between 2015 and 2025. Eight eligible RCTs (N = 676) were identified, spanning conditions such as acute pain, depression, grief, irritable bowel syndrome, disordered eating, and alcohol use. EH interventions consistently produced significant symptom reductions compared to waitlists or standard care, with a pooled standardized mean difference of 1.17 (95% CI: 0.70–1.64), indicating a large effect. Moreover, trials comparing EH to active treatments (e.g., CBT, motivational interviewing) revealed comparable efficacy, with pooled estimates supporting non-inferiority. Sensitivity analyses confirmed the robustness of these findings. Notably, some trials suggested that the indirect and personalized nature of EH may confer advantages in domains like grief and hypervigilance. Although evidence remains limited by sample size and heterogeneity, this review provides initial empirical support for EH and supports its inclusion in the evidence-based repertoire for both physical and psychological conditions. Future research should examine mechanisms of change and individual predictors of response to optimize the use of this distinctive hypnotic style. Full article

Transposable elements (TEs) are ubiquitous components of plant genomes that profoundly influence plant diversity, adaptation, and genome structure. Transposition of TEs is primarily suppressed by distinct DNA methylation systems. However, the distribution of DNA methylation at the level of individual TEs in plants remains poorly understood. Here, we address this question by generating per-base cytosine methylation maps of individual long terminal repeat retrotransposons (LTR-RTEs) for the large sunflower (Helianthus annuus L.) and the small Arabidopsis thaliana genomes. A. thaliana was selected as the model species, for which genome-wide DNA methylation profiles have been extensively characterized in prior studies. Our analysis revealed significant heterogeneity in methylation patterns both between and within individual LTR-RTE lineages. We also found that the sunflower genes harboring intact or fragmented LTR-RTE insertions exhibit altered DNA methylation and expression profiles, with intact LTR-RTE insertions enriched in stress-response and regulatory pathways. Our interspecies comparison of DNA methylation patterns indicates that methylation patterns are intrinsic features of LTR-RTE lineages, conserved across diverse plant species but influenced by factors such as insertion age, element length, and proximity to genes. Furthermore, we identified epigenetically distinct clusters of Tork and Athila sunflower elements corresponding to separate phylogenetic clades, suggesting a link between epigenetic regulation and the genetic diversity of plant LTR-RTEs. Full article

This paper addresses the Multi-Target Reconnaissance (MTR) problem for heterogeneous Fixed-Wing Unmanned Aerial Vehicles (FW-UAVs), focusing on synchronized and time-optimal mission execution under stringent constraints. A two-stage coordinated assignment and planning framework is proposed. First, a time-balanced clustering algorithm is designed to minimize the overall mission duration while balancing individual UAV workloads by jointly employing a target reallocation strategy and an improved Genetic Algorithm (GA). Subsequently, an online trajectory planning method based on differential flatness is developed, integrating a robust replanning and flight-time synchronization strategy to ensure coordinated execution. Simulation results unequivocally demonstrate that the proposed approach enhances time optimality and temporal coordination in complex scenarios. Full article