Search Results (8,157)

Arid inland saline lakes are key components of basin ecosystems. As the largest saline lake in Xinjiang and a critical ecological barrier in northwest China, Ebinur Lake’s area dynamics are vital to regional sustainable development. This study integrates Landsat imagery (1992–2024) with meteorological and socio-economic data to investigate optimal water extraction methods, spatio-temporal lake area variations, and driving mechanisms. Multiple methods were employed, including water index comparison, Mann–Kendall test, Pearson correlation, and ridge regression. Results show that: (1) the Normalized Difference Water Index (NDWI) maintains high, stable classification accuracy across years and months, making it suitable for long-term monitoring; (2) from 1992 to 2024, lake area demonstrates a significant fluctuating downward trend without abrupt change points, indicating continuous degradation. During the growing season (April–October), it first decreases and then increases, with larger early-season areas, minima in August and September, coinciding with peak agricultural irrigation demand; (3) regarding driving mechanisms, socio-economic factors dominate (approximately 70%), while meteorological factors play a weakly regulatory role (about 30%). Population growth and increased water consumption are the primary drivers, with obvious seasonal differences. Meteorological changes, socio-economic development, and ecological measures jointly influence lake area. Although extreme events (e.g., anomalous precipitation) induce short-term fluctuations, they do not alter the long-term degradation trend dominated by human activities. This study provides methodological support for long-term monitoring of arid saline lakes and scientific evidence for ecological conservation and water resource management in the Ebinur Lake Basin. Full article

Coastal wetlands are important sinks for heavy metals, yet the linkage between vertical redistribution, quantitative source contributions, and ecological–health risk drivers remain insufficiently understood. This study collected the stratified sediment samples from three depth intervals (0–20, 20–40, and 40–60 cm) from 28 representative sites in the coastal wetlands of western Bohai Bay, and evaluated the spatial distribution, vertical variation, pollution status, potential sources, and ecological–human health risks of eight heavy metals (Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb). The mean concentrations were below the Class I limits of the Marine Sediment Quality Standard. Most metals were close to or slightly below regional background values, whereas Cu and As showed mild enrichment and Cr was higher than values reported for Bohai Bay and Hangzhou Bay. Vertical profiles were generally homogeneous, with weak enrichment of specific metals, probably due to sediment resuspension, tidal disturbance, and bioturbation. Pollution assessment based on the geoaccumulation index (Igeo), Nemerow pollution index (P_N), and pollution load index (PLI) consistently indicated low contamination levels, with Hg and Pb as the main contributors to the regional pollution load. Source apportionment indicated that heavy metals were jointly influenced by lithogenic background (38.9%), atmospheric deposition (31.1%), and local anthropogenic activities (30.0%). Ecological risk assessment showed that the integrated risk index (RI) remained within the low-risk category, although Hg consistently fell within the moderate-risk range and Cd approached the moderate-risk threshold. Health risk assessment showed that both non-carcinogenic and carcinogenic risks were within acceptable limits for all receptor groups. Children had higher risks in core residential areas, whereas adults showed higher risks in non-core industrial–agricultural zones because of increased exposure frequency. The source–risk analysis indicates that non-carcinogenic risk is mainly associated with background-derived Cr, whereas carcinogenic risk is primarily linked to anthropogenic As inputs. These findings indicate that source contributions and risk contributions are not necessarily consistent, highlighting the need for source-oriented risk management in industrialized coastal wetlands. Full article

The sustainable management of land resources and the formulation of land policies are closely linked to the stability and health of terrestrial ecological systems, which in turn underpin sustainable regional economic, social, and environmental development. However, land use change has a time effect on the environment and requires long-term observation to discover its impact on landscape patterns. The Yellow River Basin functions as a critical ecological barrier in northern China, where land use changes are particularly intense in the transitional zone between its middle and lower reaches. Using Landsat imagery as the data source, this study adopts the Random Forest (RF) algorithm to classify eight sets of sequential data covering a 35-year period from 1990 to 2025 in the study area. Landscape pattern metrics and transfer matrices are employed to conduct qualitative and quantitative analyses of the spatiotemporal dynamics of land use changes. Additionally, land expansion analysis strategies and the RF algorithm are applied to identify the relative importance of different driving factors. The results show that: (1) The classification accuracy based on the Google Earth Engine (GEE) cloud platform remains consistently high, exceeding 90% across all phases. (2) Patch density decreases significantly, while the largest patch index continues to decline; the Shannon diversity index shows a fluctuating upward trend, and the aggregation index exhibits a slight increase. (3) Mutual conversions among farmland, forest, and grassland are the dominant processes driving land use changes in the region. (4) The Digital Elevation Model (DEM), construction land area distribution, and distance to primary roads are the key factors influencing land use patterns, with human activities acting as the primary driver of land use type transformations in the area. Full article

Karst regions face acute conflicts among production, living, and ecology (PLE) functions under the constraints of rugged terrain and rocky desertification. Existing studies have separately examined driving factors, interaction directions and intensities, and nonlinear thresholds, while how these dimensions co-evolve across space and time remains unclear. To address this gap, we used seven concentric buffers radiating from the built-up area as a spatial proxy for human activity intensity and constructed a framework within each buffer using Geodetector to identify core driving factors, Pearson correlation to quantify the direction and intensity of three pairwise interactions, and Pareto frontier analysis to capture nonlinear thresholds and tipping points. In the Guizhou-Guangxi karst region (2010–2019), the core drivers of production and ecology functions shifted from construction land through elevation to precipitation, while population consistently drove the living function. Along this gradient, the coupling relationships fluctuated in the core but improved in the periphery. Although trade-off intensities eased beyond 10 km, three Pareto frontier curves shifted from inverted-U patterns to monotonic trade-offs, indicating that this improvement was only quantitative while the interaction structure degraded. Tipping points disappeared in these curves, eroding the carrying-capacity buffers where win-win synergies remained attainable. Averaged correlation coefficients obscured this contrast between apparent improvement and structural degradation, and linear correlation analysis alone would not have detected it. These findings highlight the need for zone-specific management in karst regions, where monitoring based on Pareto-derived structural indicators can provide early warning of coupling degradation that averaged correlations mask. Full article

Injectable dermal fillers are widely used in aesthetic medicine for soft-tissue augmentation and facial rejuvenation; however, systematic comparative data on their physicochemical properties and biological activities remain limited. This study aimed to characterise five commercially available dermal filler products—Facetem^®, cCaHA, PDLLA, PLLA, and PCL—with respect to particle morphology and size distribution, in vitro cytotoxicity, collagen-stimulating gene expression, and macrophage cytokine secretion profiles. Particle size and distribution were determined by laser diffraction. Cytotoxicity was assessed in L929 mouse fibroblasts using the CCK-8 assay at concentrations of 0.1–5 mg/mL. Collagen-stimulating activity was evaluated by measuring COL1A1 and COL3A2 mRNA expression in primary human fibroblasts via quantitative RT-PCR. Macrophage immune responses were profiled by a multiplexed cytokine array (40 analytes) in lipopolysaccharide/interferon-γ-polarised M1 and interleukin-4/interleukin-13-polarised M2 macrophages. Scanning electron microscopy revealed distinct morphological differences among the five products. PDLLA exhibited the smallest median particle size (d(0.5) = 24.9 μm) and highest specific surface area (701.4 m²/kg), while PLLA showed the broadest size distribution (Span = 1.617). All products maintained cell viability above 85% at all tested concentrations, indicating acceptable biocompatibility. Facetem^®, PDLLA, and PLLA significantly upregulated COL1A1 expression in human fibroblasts; PDLLA and Facetem^® also significantly increased COL3A2 expression. Cytokine profiling demonstrated that the products did not substantially alter pro-inflammatory cytokine secretion in M1 macrophages, whereas selected products at high concentrations modulated several mediators in M2 macrophages, suggesting a tissue-remodelling rather than inflammatory response. These findings demonstrate product-specific physicochemical and biological profiles that may guide clinician selection and formulation development of injectable dermal fillers. Facetem^® exhibited a favourable combination of biocompatibility, collagen-stimulating activity, and immune-modulatory properties comparable or superior to established reference products. Full article

Artificial intelligence (AI) is increasingly reshaping production management by enabling data-driven optimization, predictive decision-making, and more adaptive operational systems across manufacturing. With research activity in this area growing rapidly, the literature is fragmented; thus, understanding the current reconfigurations of production management driven by AI has become difficult, though necessary. This study offers a comprehensive analysis of scientific publications (n = 439) on AI in production management published between 2016 and 2026. Research was conducted through an adapted selection process guided by Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to ensure transparency, while data were analyzed using VOSviewer and complementary statistics, combining keyword co-occurrence mapping, subject area classification, and country and author collaboration networks. Findings indicate an increase in research activity, with computer science, engineering and telecommunications emerging as the primary foundations of the field. The analysis identifies three interconnected thematic directions: AI-enabled smart manufacturing and operational optimization, including scheduling, production planning, predictive maintenance, and quality control; sustainability- and resilience-oriented production and supply chain transformation; and digital twin-based, human-centered, and knowledge-driven production systems. Moreover, international collaboration patterns show a highly globalized research landscape led by Germany, France, Italy, Sweden, Norway and South Korea. Overall, bibliometric patterns suggest an emerging framing of AI in production management research which needs more attention and further development. Full article

Accurate landslide susceptibility mapping (LSM) is important for hazard prevention and land use planning in mountainous regions. Existing machine learning and deep learning methods mainly use raster-based conditioning factors. They often ignore landslide-related attribute information and spatial context. To address this issue, this study proposes an image–tabular joint deep learning framework for regional-scale LSM. The framework is based on a FiLM-conditioned U-Net. The model combines raster patches with an estimated soft attribute-prior vector and uses FiLM to guide condition-aware spatial feature learning. The proposed framework was tested in the Tacheng region, Xinjiang, China. The dataset includes a landslide inventory and conditioning factors related to terrain, hydrology, vegetation, geology, land cover, and human activities. Model performance was evaluated using stratified five-fold cross-validation, an independent test set, buffer-radius sensitivity tests, and spatial hold-out validation. FiLM-U-Net achieved the best performance among the tested models. It obtained an accuracy of 89.73%, an F1-score of 89.41%, and an AUC of 0.953 on the independent test set. In the spatial hold-out validation area, the model achieved an AUC of 0.921. Feature importance analysis showed that distance to roads, rainfall, NDVI, and terrain factors provided important predictive information. These results suggest that the proposed image–tabular joint framework can improve condition-aware feature learning and support regional landslide susceptibility assessment. Full article

Methylation of proteins is a critical post-translational modification that regulates diverse cellular processes, including signal transduction, protein stability, and enzymatic activity. The methyltransferase enzymes that catalyse the addition of such methyl groups onto target molecules fall into a wide variety of categories and as such are classified into numerous families. Among them, the methyltransferase-like (METTL) family represents a unique cluster of enzymes with structural similarity to arginine methyltransferases. This family comprises 27 members, many of which methylate lysine residues on proteins, while others target various forms of RNA. Although discovered just over a decade ago, the protein-methylating METTLs remain incompletely characterised. Notably, most identified protein substrates are non-histone proteins, underscoring the distinctive functional roles of these enzymes. This review focuses exclusively on the protein-methylating METTL family members, summarising current knowledge of their structural features, enzymatic targets, sub-cellular localisation, and expression patterns. Their emerging relevance to disease, particularly cancer, is also highlighted, alongside areas where mechanistic understanding remains limited. By consolidating recent advances, this review aims to provide a comprehensive overview of protein-methylating METTLs in humans and to identify the critical knowledge gaps that will guide future research into their biological roles and therapeutic potential. Full article

Climate and human activity shape cropland dynamics, while their cascading interactions remain an intricate black box blocking mechanistic understanding of land system shifts. Taking Jilin Province as a case, we developed an integrated framework combining OPGD (optimal parameter geographic detector), SEM (structural equation modeling) and GWR (geographically weighted regression) to quantify multi-scale cascading mechanisms of 13 environmental factors and cropland. Cropland conversion showed distinct spatial disparities: western plains saw mainly grassland reclamation, central urban plains experienced cropland occupation by construction, and eastern mountain areas had forest-to-cropland expansion. Over 2010–2019, cropland decreased by 1.39% (1257.7 ha) for construction, compensated by conversions from 4.99% grassland (4518.8 ha) and 3.01% forestland (2720.2 ha). Climate dominated cropland variations through indirect human-mediated pathways, with path coefficients of 0.664 for Climate → Human and 0.571 for Human → Cropland; climate exerted direct driving effects on grass–cropland transition in western ecologically fragile plains. Greenhouse gases, evapotranspiration, humidity, temperature and leaf area index controlled overall cropland variations. Central and western croplands were dominated by topography, whereas precipitation, population and GDP determined eastern cropland dynamics. This framework differentiated direct and indirect driving pathways of cropland evolution, guiding policy formulation for regional grain security. Full article

Landslide susceptibility mapping (LSM) serves as a fundamental technical support for geohazard prevention and mitigation across mountainous terrains. This research constructs a multi-scale terrain unit integrated modeling framework targeting complex mountainous geomorphic settings, taking Zhenping County as the research object. Multi-resolution digital elevation model (DEM) datasets, multi-source satellite remote sensing imagery (GF-2), geological vector datasets and hydrological survey data are jointly adopted as the basic data source. The r.slopeunits module embedded in GRASS GIS is utilized to automatically segment slope units, and a comprehensive composite index S, coupling slope partition quality indicator F and model prediction accuracy metric R, is proposed to adaptively optimize two critical slope-unit hyperparameters: circular variance (c) and minimum unit area (a). Four DEM spatial resolutions (15 m, 25 m, 50 m, 100 m) are systematically calibrated with 42 groups of c–a parameter combinations to screen out the optimal slope-unit segmentation scheme (c = 0.1, a = 200,000 m²). Twelve landslide predisposing covariates covering topography, hydrology, lithology, human engineering activities and land cover are selected after multicollinearity diagnosis via Variance Inflation Factor and mean utility factor contribution evaluation. Logistic regression tree (LMT), LMT-Adaboost and LMT-Random Subspace are compared by random cross-validation and spatial block cross-validation. Parameter sensitivity analysis is further carried out to quantify the stability of model outputs against DEM resolution and slope-unit parameter perturbations. The LMT-RSM ensemble achieved the highest spatial cross-validation AUC (0.954 ± 0.019), outperforming LMT (0.925 ± 0.023) and AdaBoost-LMT (0.934 ± 0.021). The DeLong test confirmed that LMT-RSM’s superiority over LMT is statistically significant (p < 0.0001). The proportion of landslides in the very high and high susceptibility zones under the LMT-RSM model reached 95.98%, demonstrating relatively excellent spatial discrimination. This study provides an operational framework combining optimized slope units, ensemble learning, and spatially explicit validation for robust LSM in complex terrain, and offers a reproducible technical pathway for landslide risk prevention in mountainous regions. Full article

Disaster risk reduction, both natural and man-made, is a global priority and is in line with the goals of the 2030 Agenda, defined on the basis of what happened at the beginning of the millennium, starting with the disaster in human lives caused by Katrina in New Orleans. However, there is a scientific gap between the level of risk and the planned risk mitigation measures, with the related training and exercise actions implemented before real-life emergencies happen. In the context of Transport Risk Analysis (TRA), the size of exposure (E), in particular its reduction through evacuation, has been little studied, unlike the dimensions of occurrence (O) and vulnerability (V). This study aims to provide a framework of the scientific literature on the risk exposure component (graphical abstract) in urban transport systems, focusing on four macro areas: planning process; training and exercises; advanced Transport System Models (TSM); and use of emerging ICT (e-ICT). The study has been carried out using forward snowball techniques on a selected sample of the literature. The final set of references comprises 165 publications: 149 retrieved from Scopus (including records assessed at abstract level) and 16 retrieved via Google Scholar and forward-citation snowballing. The publications are distributed across the four macro-areas as follows: 68 concern the planning process, 24 training and exercises, 61 advanced Transport System Models, and 12 the use of e-ICT. The four macro-areas were examined both individually and in their mutual intersections, since several publications address more than one area simultaneously. Each work was classified under its predominant area for tabulation purposes. The analysis shows that the literature examined provides unequal coverage of the four broad areas. Whilst contributions relating to the planning process and modeling are relatively well-established, the integration of ICT with advanced transport system models in the context of transport risk analysis remains extremely limited. Approaches based on forecasts for outdoor evacuation are largely absent, and research into training and drill activities lags behind that on planning models. The framework developed in this work is useful to researchers, policy makers and public sector technicians involved in emergency management, as it offers a clear framework to guide future work in both research and operational action. Full article

Background: Remote patient monitoring (RPM) for chronic heart failure (CHF) management has demonstrated clinical and economic benefits. However, data on its organisational impact remain limited, particularly in the French healthcare context following the integration of RPM into standard care pathways. The objective of this study was to describe the organisational impact of RPM for CHF management from the perspective of healthcare professionals across diverse practice settings in France. Methods: A cross-sectional survey was conducted among 28 French cardiology departments and community practices (14 CHG, 9 CHU, 4 community practices, 1 private clinic) actively using RPM for CHF management. The questionnaire assessed organisational changes across five domains: care process modifications, human resources allocation, professional training and task delegation, coordination with community-based physicians, and perceived impact on patient quality of life and working conditions. Results: Nearly all structures (96.4%) had established a dedicated RPM team (mean size: 4.8 ± 2.3 professionals). A reduction in time between cardiac decompensation and medical care was reported by 96.4% of respondents. Three-quarters (75.0%) had implemented specific consultations for emergency alerts, and 57.1% had established direct admission protocols bypassing emergency departments. Task delegation was widespread (89.3%), primarily involving alert monitoring (100%), patient inclusion (92.0%), and patient contact (84.0%). However, only 53.6% of professionals had received specific training. The perceived impact on patient quality of life was unanimously positive (64.3% very positive, 35.7% positive). Working conditions were perceived as stable by 64.3% and improved by 32.1%. The main advantages cited were reduction in hospitalisations (89.3%) and improved patient communication (85.7%). The primary area for improvement was interprofessional coordination (53.6%). Conclusions: RPM implementation has driven substantial organisational restructuring across French cardiology departments and community practices. While dedicated teams and task delegation are now standard, challenges remain in professional training, coordination with community physicians, and dedicated time allocation. These findings provide an updated organisational assessment that complements prior clinical and economic evaluations. Full article

Morinda citrifolia (Noni) is well known as a plant with therapeutic potential and is also attractive to the food and cosmetic industries. Traditional medicine supports its use, mainly for the treatment of metabolic disorders and chronic inflammation but also for certain types of cancer. Noni has been the subject of considerable interest within the scientific community due to its purported health benefits. However, despite its growing popularity and extensive traditional use, significant gaps remain in the empirical understanding of its properties, mechanisms, and potential applications. To understand its biological activity, particular attention has been given to several chemical compounds present in its leaves and fruits, as they have been identified as bioactive agents. Moreover, several studies support the idea that specific flavonoids and anthraquinones from noni act on enzymes and transporters associated with glucose and lipid metabolism in humans. Its involvement in cardiovascular, neurological, metabolic, and inflammatory regulation across several high-burden diseases expands the potential medical applications of noni. This narrative review presents the current state of knowledge, highlighting preclinical studies that suggest the mechanisms of action underlying the observed effects, including theoretical approaches proposing specific interactions between noni compounds and proteins associated with human diseases as potential therapeutic targets. It also identifies areas where information remains insufficient and proposes future research directions for pharmacological applications, including the need for additional clinical studies and more comprehensive pharmacokinetic and toxicological evaluations. Full article

Climate adaptation in high-density cities requires equitable access to cooling and habitat benefits from blue–green infrastructure (BGI). However, the critical thresholds where human activity overwhelms these services remain unknown. Taking Shenzhen as a case study, we quantified habitat quality using the InVEST model and divided the urban landscape into low-, medium-, and high-quality zones. After screening significant drivers via multiple linear regression, we compared six machine learning algorithms and selected XGBoost (for low- and medium-quality zones) and LightGBM (for high-quality zones) with Optuna-tuned hyperparameters, then applied SHAP dependence analysis to identify nonlinear, threshold-driven responses. We identify a systemic thermal threshold of 23.7 °C. Above this temperature, BGI cooling efficiency decouples from habitat quality. We also quantify actionable intervention windows. Low-quality habitats—dominated by dense residential areas—collapse when building density exceeds 0.7 or road density exceeds 0.009. Medium-quality habitats offer a balance window (core area > 2844 m², building density < 0.361) that enables incidental nature contact during daily travel. High-quality refugia require a core area > 8600 m² with near-zero disturbance. These thresholds expose a stark green inequity: socioeconomically vulnerable groups in low-quality zones are systematically disconnected from cooling services. We translate these findings into a tiered spatial intervention framework—restoration, accessibility enhancement, and strict protection—to advance climate-resilient and socially equitable urban planning. Full article

Burned-area totals are useful for national monitoring, but they do not reveal how, when or under what social and ecological conditions a landscape burns. We developed an event-based fire-regime and stewardship framework for Eswatini, a topographically compressed southern African country where protected areas, communal rangelands, cropland margins, plantation landscapes and peri-urban interfaces occur in close proximity. Global Fire Atlas event histories for 2001–2025 were organised by fire year and intersected with approximately 10 km² hexagonal units. The burned-area rate, event frequency, recurrence, seasonality, large-fire dominance, pyrodiversity and trend were used to classify fire-regime types independently of socio-ecological predictors. An XGBoost regression model, evaluated on a 20% held-out test set, was interpreted using exact TreeSHAP diagnostics. Fire activity was strongly seasonal: July–September accounted for 78.2% of the burned area, with August alone accounting for 34.2%. Eight fire-regime types were identified, ranging from low-information and episodic units to frequent small-fire mosaics, large-fire-dominated areas and emerging burned-area intensification regimes. The burned-area-rate model performed well on held-out data (R2 = 0.71; Spearman rho = 0.75). Human modification, goat density, elevation, forest probability, fuelwood dependence and precipitation seasonality ranked among the most influential predictors, but their fitted effects were non-linear and often bidirectional. The combined diagnostics supported six adaptive management zones covering protected-area stewardship, conservation-sensitive management, settlement–livelihood interfaces, late-season risk reduction, monitoring and integrated landscape management. Although the Eswatini results are context-specific, the workflow offers a transferable way to connect fire histories, socio-ecological contexts and place-based stewardship in African mosaic landscapes. Full article