Search Results (1,338)

Arid and semi-arid areas serve a critical regulatory function within the global carbon cycle. Understanding the response mechanisms of vegetation productivity to variations in moisture availability represents a fundamental scientific challenge in elucidating terrestrial carbon dynamics. This study systematically disentangled the respective influences of summer surface soil moisture (RSM) and precipitation (PRE) on gross primary productivity (GPP) across arid and semi-arid regions of China from 2000 to 2022. Utilizing GPP datasets alongside correlation analysis, ridge regression, and data binning techniques, the investigation yielded several key findings: (1) Both GPP and RSM exhibited significant upward trends within the study area, whereas precipitation showed no statistically significant trend; notably, GPP demonstrated the highest rate of increase at 0.455 Cg m⁻² a⁻¹. (2) Decoupling analysis indicated a coupled relationship between RSM and PRE; however, their individual effects on GPP were not merely a consequence of this coupling. Controlling for evapotranspiration and root-zone soil moisture interference, the analysis revealed that under conditions of elevated RSM, the average increase in summer–autumn GPP (SA_GPP) was 0.249, significantly surpassing the increase observed under high-PRE conditions (−0.088). Areas dominated by RSM accounted for 62.13% of the total study region. Furthermore, examination of the aridity gradient demonstrated that the predominance of RSM intensified with increasing aridity, reaching its peak influence in extremely arid zones. This research provides a quantitative assessment of the differential impacts of RSM and PRE on vegetation productivity in China’s arid and semi-arid areas, thereby offering a vital theoretical foundation for improving predictions of terrestrial carbon sink dynamics under future climate change scenarios. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

Carassius auratus exhibits significant physiological and behavioral alterations under the combined stress of temperature and dissolved oxygen (DO) fluctuations, which are common challenges in aquaculture. In this investigation, we employed controlled thermal and DO gradients to characterize the multidimensional response profile of this species. The key findings revealed that thermal elevation profoundly influenced blood glucose and cortisol concentrations. Notably, exposure to hyperoxic conditions markedly attenuated stress responses relative to hypoxia at equivalent temperatures: cortisol levels were significantly suppressed (reductions of 60.11%, 118.06%, and 34.72%), while blood glucose levels exhibited concurrent increases (16.42%, 26.43%, and 26.34%). Distinctive behavioral patterns, including floating head behavior, surface swimming behavior, and rollover behavior, were identified as indicative behaviors of thermal–oxygen stress. Molecular analysis demonstrated the upregulated expression of stress-associated genes (HSP70, HSP90, HIF-1α, and Prdx3), which correlated temporally with elevated cortisol and glucose concentrations and the manifestation of stress behaviors. Furthermore, a muscle texture assessment indicated that increased DO availability mitigated the textural deterioration induced by heat stress. Collectively, this work establishes an authentic biomarker framework, providing crucial threshold parameters essential for the development of intelligent, real-time environmental monitoring and dynamic regulation systems to enhance climate-resilient aquaculture management. Full article

Background: Aortic stenosis (AS) is the most common valvular heart disease, associated with poor outcomes if left untreated. Current guidelines recommend that transcatheter aortic valve implantation (TAVI) procedures be performed in hospitals with an on-site cardiac surgery unit due to potential complications requiring surgical intervention. Objective: Based on our experience, we evaluated the feasibility and outcomes of implementing a TAVI program in a cardiology department without an on-site cardiac surgery unit, in collaboration with a remote hospital for surgical backup. Methods: The TAVI program involved pre- and post-procedural evaluations conducted at Meir Medical Center (Kfar Saba, Israel) with a remote surgical team available. The study population included 149 consecutive patients with severe aortic stenosis treated at the Meir valve clinic between November 2019 and December 2023. Procedures were performed by the center’s interventional cardiology team. Results: The mean age of the 149 patients was 80 ± 6 years, and 75 (50%) were female. The average STS score was 4.3, and the EuroSCORE II was 3.1. Among the patients, 68 (45%) were classified as New York Heart Association (NYHA) class III-IV. The valve types used included ACURATE neo2 (57 patients, 38%), Edwards SAPIEN 3 (43 patients, 28%), Evolut-PRO (41 patients, 27%), and Navitor (7 patients, 4%). There were no cases of moderate to severe paravalvular leak and no elevated post-implantation gradients, and there was no need for urgent cardiac surgery. One case of valve embolization was successfully managed percutaneously during the procedure. In-hospital follow-up revealed no deaths and only one major vascular complication. At one-year follow-up, six patients had died, with only one death attributed to cardiac causes. Conclusions: Our findings support the safe and effective performance of transfemoral TAVI in cardiology departments without on-site cardiac surgery, in collaboration with a remote surgical team. Further prospective, multicenter studies are warranted to confirm these results and guide broader clinical implementation of this practice. Full article

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects. Full article

The Qinghai-Tibetan Plateau is one of the world’s most prominent biodiversity hotspots. Understanding the spatial patterns of ecological uniqueness in its plant communities is essential for uncovering the mechanisms of community assembly and informing effective conservation strategies. In this study, we analyzed data from 758 plots across 338 sites on the Qinghai-Tibetan Plateau. For each plot, the vegetation type was classified, and all plant species present, along with their respective abundance or coverage, were recorded in the database. To assess overall compositional variation, community β-diversity was quantified, while a plot-level approach was applied to determine the influence of local environmental conditions and community characteristics on ecological uniqueness. We used stepwise multiple regressions, variation partitioning, and structural equation modeling to identify the key drivers of spatial variation in ecological uniqueness. Our results show that (1) local contributions to β-diversity (LCBD) exhibit significant geographic variation—increasing with longitude, decreasing with latitude, and showing a unimodal trend along the elevational gradient; (2) shrubs and trees contribute more to β-diversity than herbaceous species, and LCBD is strongly linked to the proportion of rare species; and (3) community characteristics, including species richness and vegetation coverage, are the main direct drivers of ecological uniqueness, explaining 36.9% of the variance, whereas climate and soil properties exert indirect effects through their interactions. Structural equation modeling further reveals a coordinated influence of soil, climate, and community attributes on LCBD, primarily mediated through soil nutrient availability. These findings provide a theoretical basis for adaptive biodiversity management on the Qinghai-Tibetan Plateau and underscore the conservation value of regions with high ecological uniqueness. Full article

Background: Transcatheter aortic valve implantation (TAVI) has revolutionized the treatment of severe aortic valve stenosis (AS). Balloon post-dilatation (PD) remains an important procedural step to optimize valve function by resolving incomplete valve expansion, which may lead to paravalvular regurgitation and other potentially adverse effects. There are only limited data on the predictors, incidence, and clinical impact of PD during TAVI. Methods: This retrospective, single-center study analyzed 585 patients who underwent TAVI (2016–2022). Pre-procedural evaluations included transthoracic echocardiography and CT angiography to assess key parameters, including the aortic valve calcium score (AVCS); aortic valve calcium density (AVCd); aortic valve maximal systolic transvalvular flow velocity (AV Vmax); and aortic valve mean systolic pressure gradient (AV MPG). We identified imaging predictors of PD and evaluated associated clinical outcomes by analyzing procedural endpoints (according to VARC-3 criteria) and long-term survival. Results: PD was performed on 67 out of 585 patients, with elevated AV Vmax (OR: 1.424, 95% CI: 1.039–1.950; p = 0.028) and AVCd (OR: 1.618, 95% CI: 1.227–2.132; p = 0.001) emerging as a significant independent predictor for PD in TAVI. Kaplan–Meier survival analysis revealed no significant differences in short- and mid-term survival between patients who underwent PD and those who did not. Interestingly, patients requiring PD exhibited a lower incidence of adverse events regarding major vascular complications, permanent pacemaker implantations and stroke. Conclusions: The study highlights AV Vmax and AVCd as key predictors of PD. Importantly, PD was not associated with increased procedural adverse events and did not predict adverse events in this contemporary cohort. Full article

As global climate change accelerates, the urban heat island (UHI) phenomenon has become increasingly pronounced, posing significant challenges to urban energy balance, atmospheric processes, and public health. This study used the Weather Research and Forecasting (WRF) model to dynamically downscale two CMIP6 scenarios—moderate forcing (SSP245) and high forcing (SSP585)—focusing on Zhengzhou, a rapidly urbanizing city in central China. High-resolution simulations captured fine-scale intra-urban temperature patterns and analyze the spatial and seasonal variations in UHI intensity in 2030 and 2060. The results demonstrated significant seasonal variations in UHI effects in Zhengzhou for both 2030 and 2060 under SSP245 and SSP585 scenarios, with the most pronounced warming in summer. Notably, under the SSP245 scenario, elevated autumn temperatures in suburban areas reduced the urban–rural temperature gradient, while intensified rural cooling during winter enhanced the UHI effect. These findings underscore the importance of integrating high-resolution climate modeling into urban planning and developing targeted adaptation strategies based on future UHI patterns to address climate challenges. Full article

African ground squirrels (Xerus spp.), the inhabitants of African arid zones, face extreme heat and water scarcity driving selection for metabolic optimization. We assembled and annotated the first mitogenomes of Xerus inauris and Xerus rutilus (16,525–16,517 bp), revealing conserved vertebrate architecture with genus-specific traits. Key features include Xerus rutilus’s elongated ATP6 (680 vs. 605 bp), truncated ATP8–ATP6 spacers (4 vs. 43 bp), and tRNA-Pro control regions with 78.1–78.3% AT content. Their nucleotide composition diverged from that of related sciurids, marked by reduced T (25.78–26.9%) and extreme GC skew (−0.361 to −0.376). Codon usage showed strong Arg-CGA bias (RSCU = 3.78–3.88) and species-specific elevations in Xerus rutilus’s UGC-Cys (RSCU = 1.83 vs. 1.17). Phylogenetics positioned Xerus as sister to Ratufa bicolor (Bayesian PP = 0.928; ML = 1.0), aligning with African biogeographic isolation. Critically, we identified significant signatures of positive selection in key mitochondrial genes linked to arid adaptation. Positive selection signals in ND4 (ω = 1.8 × background), ND1, and ATP6 (p < 0.0033) correspond to enhanced proton gradient efficiency and ATP synthesis–molecular adaptations likely crucial for optimizing energy metabolism under chronic water scarcity and thermoregulatory stress in desert environments. Distinct evolutionary rates were observed across mitochondrial genes and complexes: Genes encoding Complex I subunits (ND2, ND6) and Complex III (Cytb) exhibited accelerated evolution in arid-adapted lineages, while genes encoding Complex IV subunits (COXI) and Complex V (ATP8) remained highly conserved. These findings resolve the Xerus mitogenomic diversity, demonstrating adaptive plasticity balancing arid-energy optimization and historical diversification while filling critical genomic gaps for this xeric-adapted lineage. Full article

Understanding how habitat conditions drive morphological adaptations in animals is critical in ecology, yet amphibian studies remain limited. This study investigated intraspecific variation in ecomorphological traits of three montane stream-dwelling frogs (Quasipaa boulengeri, Amolops sinensis, and Odorrana margaratae) across elevation gradients in Tianping Mountain, China. Using morphological measurements and environmental variables collected from ten transects, we analyzed functional traits related to feeding and locomotion and assessed their associations with microhabitat variables. Significant trait differences between low- and high-elevation groups were detected only in Q. boulengeri, with high-elevation individuals exhibiting greater body mass and shorter hindlimbs. Redundancy analysis demonstrated that microhabitat variables, particularly air humidity, flow rate, and rock coverage, were linked to trait variations. For example, air humidity and flow rate significantly influenced Q. boulengeri’s body and limb proportions, while flow rate affected A. sinensis’s snout and limb morphology. In addition, sex and seasonal effects were also associated with trait variations. These results underscore amphibians’ phenotypic plasticity in response to the environment and highlight the role of microhabitat complexity in shaping traits. By linking habitat heterogeneity to eco-morphology, this study advocates for conservation strategies that preserve varied stream environments to support amphibian resilience amid environmental changes. Full article

The Mw 7.8 Gorkha Earthquake of 25 April 2015 triggered over 25,000 landslides across central Nepal, with 4775 events concentrated in Gorkha District alone. Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. Using a balanced dataset of 4775 landslide and non-landslide instances, we evaluated the performance of Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models through spatial cross-validation, SHapley Additive exPlanations (SHAP) explainability, and ablation analysis. The RF model outperformed all others, achieving an accuracy of 87.9% and a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) value of 0.94, while XGBoost closely followed (AUC = 0.93). Ensemble models collectively classified over 95% of observed landslides into High and Very High susceptibility zones, demonstrating strong spatial reliability. SHAP analysis identified elevation, proximity to fault, peak ground acceleration (PGA), slope, and rainfall as dominant predictors. Notably, the inclusion of post-seismic rainfall substantially improved recall and F1 scores in ablation experiments. Spatial cross-validation revealed the superior generalizability of ensemble models under heterogeneous terrain conditions. The findings underscore the value of integrating post-seismic hydrometeorological factors and spatial validation into susceptibility assessments. We recommend adopting ensemble models, particularly RF, for operational hazard mapping in earthquake-prone mountainous regions. Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience. Full article

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

Anthropogenic disturbance alters macro- and microclimatic conditions, often increasing ambient temperatures. These changes can strongly affect insects, particularly those experiencing high thermal stress (i.e., large differences between body and environmental temperature), as prolonged exposure to elevated temperatures can reduce their energetic reserves due to increased metabolic demands and physiological stress. We evaluated thermal stress in 16 insect dragonfly species during two sampling periods (2019 and 2022) in preserved and disturbed sites within a tropical dry forest in western Mexico. Also, we compared energetic condition (lipid and protein content) and thoracic mass for the seven most abundant species between both habitat types. In preserved sites, insects showed higher thermal stress at lower maximum temperatures, which decreased as temperatures increased. Dragonflies in disturbed sites maintained consistent levels of thermal stress across the temperature gradient. Thermal stress was linked to lower lipid and protein content, and individuals from disturbed sites had reduced energy reserves. We also found a weak but consistent positive relationship between mean ambient temperature and protein content. In preserved sites, thoracic mass increased with thermal stress, but only at high mean temperatures. These findings suggest that although species can persist in disturbed environments, their energetic condition may be compromised, potentially affecting their performance and fitness. Preserving suitable habitats is essential for preserving both biodiversity and ecological function. Full article