Search Results (19,586)

Evapotranspiration (ET) is a key component of the terrestrial water and energy cycle, and its long-term dynamics are essential for regional hydrological assessment in subtropical China. In this study, two widely used satellite-based ET products, MOD16 and PML-V2, were selected for intercomparison because they provide consistent spatial (500 m) and temporal (8-day) resolutions. Validation against flux observations showed that PML-V2 performed better than MOD16 and was therefore used for subsequent analysis. Based on the 500 m, 8-day PML-V2 dataset, the spatiotemporal variation in ET in subtropical China during 2001–2020 was examined using the Theil–Sen slope estimator, Mann–Kendall test, and Hurst exponent. To identify the most relevant controls on ET variation, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to screen environmental factors and rank their relative importance. Multiple linear regression (MLR) was then applied only to the selected dominant factors to quantify their contributions. Residual analysis was used to distinguish climate–vegetation effects from residual influences, which could arise from human activities and unmodeled natural processes. The results showed that annual ET averaged 669 mm and increased significantly at a rate of 2.03 mm yr⁻¹ from 2001 to 2020, with an accelerated increase after 2010. Spatially, ET exhibited clear gradients from south to north and from coastal to inland regions. Downward shortwave radiation (SWDown) and leaf area index (LAI) were the dominant drivers over most of the study area, although their controls varied geographically, with northern subregions being more energy-limited and southern subregions being jointly influenced by vegetation and temperature. Residual ET trends largely coincide with cropland and urbanising areas, indicating a partial influence of human activities, while in subregions such as XM, complex terrain and hydrological heterogeneity suggest that unmodeled natural processes may dominate. These findings enhance understanding of ET dynamics in subtropical China and demonstrate the value of high-resolution remote sensing products for regional hydrological monitoring and driver attribution. Full article

Atopic dermatitis (AD) is among the most common chronic inflammatory diseases. Due to the heterogeneous presentation of AD, patient response to treatment may differ considerably. Therefore, there is a pressing need for biomarkers associated with response to biological therapies. Thus, we aimed to identify blood-based candidate biomarkers associated with response in patients treated with dupilumab. The present study applied a multi-stage integrative analytical framework combining transcriptomic profiling, functional enrichment, co-expression network analysis, and genomic variant analysis to identify potential biomarkers. Eighteen dupilumab-naïve patients were enrolled in the transcriptomic analysis, with blood samples collected at baseline and after 16–18 weeks of therapy; five patients were identified as non-responders. Additionally, genotyping was performed in 34 patients. We identified a set of candidate genes (RPL18A, RPS28, FAU, MASTL, AURKA, TAF2, BUB1B, and RNF135) and genomic variants that may reflect underlying biological mechanisms influencing therapeutic response. However, given the limited sample size, these findings should be considered exploratory and hypothesis-generating. Finally, our study identified exploratory candidate genes potentially associated with variability in dupilumab treatment response. Moreover, our study represents an incremental contribution to existing knowledge, opening avenues for research that may ultimately lead to personalized medicine. Full article

Background: Preseason screening is widely used in handball to identify athletes at increased risk of injury, yet the prognostic value of different screening approaches remains unclear. The aim of this study was to systematically review the evidence on preseason screening tests and physical assessments in relation to subsequent injury outcomes in handball players. Methods: A systematic review was conducted according to PRISMA guidelines. PubMed, MEDLINE, CINAHL, and Scopus were searched on 14 March 2026. The first 100 results from Google Scholar were also screened, and backward citation searching was performed. Eligible studies included handball players and examined preseason or baseline screening, functional, musculoskeletal, or physical performance assessments in relation to prospectively recorded injury outcomes. Two independent reviewers performed study selection, data extraction, and risk-of-bias assessment using the QUIPS tool. Due to substantial heterogeneity in screening tools, injury outcomes, and follow-up procedures, meta-analysis was not performed. Results: Eight studies were included. Most were prospective cohorts involving adolescent, youth elite, or elite adult handball players. Shoulder-specific screening variables, particularly external rotation strength, strength imbalances, total rotational motion, and selected rotational adaptations, showed more consistent associations with subsequent shoulder-related outcomes. In contrast, broader movement-screening tools, including the Functional Movement Screen, the 9+ screening battery, and the upper quarter Y-Balance Test, generally showed limited associations with overall injury outcomes. Conclusions: Shoulder-specific preseason assessments may be more closely associated with subsequent shoulder-related outcomes than broader movement-screening tools, although the available evidence remains limited, heterogeneous, and derived exclusively from observational studies. Full article

Rapid urbanization, climate change, and uneven regional development have increasingly intensified spatial heterogeneity in food security. As one of China’s major commercial grain-producing areas, the Main Grain-Producing Region in the Middle Reaches of the Yangtze River (MGPR-MRYR) plays a critical role in ensuring national food security. However, existing studies have paid limited attention to spatial heterogeneity and driving mechanisms at the urban agglomeration scale. Taking the Wuhan (WUA), Changsha–Zhuzhou–Xiangtan (CZXUA), and Poyang Lake (PYLUA) urban agglomerations as analytical units, this study constructs a multidimensional food security evaluation framework covering supply security, production resource security, and circulation–consumption security. Based on panel data from 2013 to 2023, the entropy weight method, kernel density estimation (KDE), Theil index decomposition, spatial autocorrelation analysis, and the optimal-parameter geographical detector (OPGD) model were employed. Food security levels in the MGPR-MRYR exhibited an overall upward trend, particularly after 2020, although significant spatial heterogeneity persisted among urban agglomerations. A spatial pattern of “higher in the west than east, and inland over lakeside” emerged, with significant positive clustering gradually expanding westward. Intra-agglomeration disparities—especially within the WUA—contributed more to regional inequality than inter-agglomeration differences. Agricultural machinery power and rural population remained the dominant driving factors, while the influence of urbanization and annual precipitation increased over time. All factor interactions showed enhancement effects, indicating that food security is shaped by the synergistic interplay of natural, socioeconomic, and agricultural production factors. This study reveals the transition of driving mechanisms from traditional factor dependence to multi-factor system synergy. These findings suggest that food security governance in rapidly urbanizing grain-producing regions should shift from uniform policies to differentiated, synergy-oriented strategies tailored to each urban agglomeration’s development stage and resource constraints. Full article

This study examines the multi-scale relationships between extreme climate indices and maize yield from a hydrothermal perspective, across both temporal (long-term trends, interannual anomalies, and abrupt changes) and spatial (regional and grid) scales in the Chengdu–Chongqing region, using long-term meteorological (1985–2025) and crop yield (1982–2015) datasets. Results reveal pronounced warming and drying trends, characterized by increasing warm-related temperature extremes and consecutive dry days, along with a decline in cold extremes. A shift toward drier conditions occurred around 2005, while temperature extremes have exhibited stepwise changes since the late 1990s. Maize yield shows a significant upward trend with an abrupt increase around 1997, closely linked to reduced cold stress. Scale-dependent analyses reveal that climate-yield relationships are primarily expressed through long-term hydrothermal changes rather than short-term variability, with maize yield showing positive responses to warm conditions and prolonged dry spell duration, and negative responses to cold extremes and excessive precipitation. In contrast, relationships based on interannual anomalies are weak and spatially inconsistent, suggesting limited sensitivity of yield to short-term climate variability due to system buffering and agricultural adaptation. Spatially, climate–yield relationships exhibit marked heterogeneity, with temperature constraints dominating in the western region and moisture-related effects being more pronounced in the central–eastern basin. Mechanistically, abrupt change analysis indicates two distinct controls: cold extremes act as threshold constraints associated with rapid yield shifts, whereas warming and drying exert gradual cumulative effects on productivity. Overall, maize yield dynamics are more strongly associated with long-term hydrothermal evolution than interannual variability, highlighting the importance of distinguishing temporal scales, hydrothermal regimes and long-term agricultural system evolution in climate–crop assessments under ongoing climate change. Full article

Energy system resilience is essential for maintaining energy security and system stability under growing global uncertainty. Based on panel data for 30 Chinese provinces over the period 2012–2023, this paper investigates the relationship between digital technology and energy system resilience. Digital technology and energy system resilience are measured with entropy-weighted composite indices, and the empirical tests are conducted using a two-way fixed-effects model, mediation-effect models, and a panel threshold model. The results show that digital technology significantly improves energy system resilience, and this finding remains stable after endogeneity treatment and several robustness checks. The mechanism analysis further shows that industrial structure upgrading, digital industrial agglomeration, and green innovation serve as important channels linking digital technology to energy system resilience. The threshold results further show that the effect of digital technology is stage-dependent. Digital technology has a positive effect in all three stages, with the strongest effect occurring in the medium digital development stage, followed by slower marginal improvement in the high digital development stage. The heterogeneity results show that the effect is more pronounced in provinces with high resource dependence and in the central and western regions. By contrast, the eastern region presents a weaker marginal effect, while the northeastern region faces stronger constraints in transforming digital technology into resilience improvement. These findings suggest that digital technology is an important driver of energy system resilience and can support a more stable and sustainable energy transition, although its effect varies across development stages and regional conditions. Full article

Background/Objectives: Gastrointestinal dysmotility, abdominal discomfort, and feeding-related respiratory instability are common in preterm infants. Although locust bean gum (LBG)-containing formulas are used for regurgitation, their short-term effects on gastrointestinal tolerance in neonatal intensive care settings are not well defined. We evaluated short-term changes in gastrointestinal tolerance and oxygenation after initiation of an LBG-containing formula and explored whether postmenstrual age (PMA) modified the response. Methods: This retrospective paired cohort study included 26 infants who received an LBG-containing anti-regurgitation formula, either alone or combined with human milk. Standardized ordinal scores (0–2) for stool consistency, straining, abdominal distension, gas passage, abdominal tenderness, mean oxygen saturation (SpO₂) category, and desaturation frequency were recorded at baseline (Day 0), Day 3, and Day 7. Paired comparisons were performed using the Wilcoxon signed-rank test, and PMA-stratified differences were explored using Kruskal–Wallis analysis. Results: By Day 7, soft/normal stools were observed in 96.2% of infants (p = 0.00017), severe straining resolved in 87.5% (p = 1.6 × 10⁻⁵), abdominal distension improved in 96.2% (p = 2.4 × 10⁻⁶), and gas passage normalized in all infants (p = 0.00025). Mean SpO₂ category improved significantly (p = 0.0023), and the proportion of infants with rare or no desaturation increased from 61.5% to 96.2%. Growth velocity remained clinically acceptable. Infants with PMA < 34 weeks showed the largest improvements across outcomes. Conclusions: In this retrospective paired cohort, initiation of an LBG-containing formula was associated with short-term improvement in gastrointestinal tolerance and oxygenation indices in preterm infants. Exploratory subgroup analyses suggested possible heterogeneity of response across postmenstrual age strata; these observations require confirmation in adequately powered prospective studies. Full article

Background: Rare neurological diseases are challenging to diagnose from brain MRI because of their low prevalence, heterogeneous imaging patterns, and limited annotated datasets. Deep learning may support image-level recognition, but results from curated datasets without complete patient-level identifiers require cautious interpretation. Objectives: This study proposes RareNeuroXNet, a frequency-aware multi-branch attention framework for image-level classification of rare neurological diseases from brain MRI. The objective was to assess whether combining global anatomical, local fine-grained, and frequency-domain representations improves benchmark performance, calibration, and interpretability. Methods: RareNeuroXNet uses three complementary branches: a global branch for whole-image representation, a local branch for regional feature extraction, and an FFT magnitude-based frequency branch. Features are refined using CBAM attention, fused, and classified through a fully connected head. The model was evaluated on a balanced curated dataset with five rare neurological disease classes using five-fold cross-validation, ablation analysis, calibration metrics, internal baseline comparison, paired testing against DenseNet121 local-only, and Grad-CAM visualization. MCND was also used as a complementary cross-dataset neurological MRI benchmark, not as same-task external validation. Results: RareNeuroXNet achieved strong image-level internal benchmark performance, with accuracy of $0.9924\pm 0.0061$, macro F1-score of $0.9924\pm 0.0061$, macro AUROC of $0.9998\pm 0.0002$, and macro AUPR of $0.9992\pm 0.0007$. Calibration was favorable, with ECE of $0.0052\pm 0.0029$ and NLL of $0.0276\pm 0.0159$. Ablation results showed that the local branch was the dominant contributor, while FFT and CBAM provided supportive refinement. Compared with DenseNet121 local-only, RareNeuroXNet showed modest classification gains and clearer calibration improvements. Conclusion: RareNeuroXNet demonstrated strong controlled image-level benchmark performance with high discrimination, stable cross-validation behavior, favorable calibration, and Grad-CAM interpretability. However, possible correlated slices, duplicate images, or subject overlap cannot be excluded. Future work should use patient-level, same-task, multi-center external validation and 3D multimodal MRI analysis. Full article

Honey is a natural food product which in traditional production represents a clear example of the “farm-to-table” principle, as it excludes any processing of the original product. This study proposes an analytical approach for determining 30 most frequently determined chemical elements (Ag, Al, As, B, Ba, Bi, Ca, Cd, Co, Cr, Cs, Cu, Ga, In, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, Rb, S, Se, Sr, Te, V, and Zn) in honey, emphasizing the use of a relatively large sample mass to overcome sample heterogeneity and ensure accurate and reliable results. About 31 linden and 16 rapeseed honey samples from different Bulgarian regions were analyzed. Pollen analysis data showed that pollen content ranged from 30 to 78% for linden and 30 to 93% for rapeseed honey. The results identify a group of elements—K, Ca, Mg, Sr, and Rb—whose concentrations show statistically significant dependence on the floral origin and purity of the honey. Based on these findings, these elements are proposed as potential markers for identifying the botanical origin of honey. Furthermore, macronutrients and micronutrients (P, S, B, Cu, Fe, Mn, and Zn), which are generally subject to homeostatic regulation, as well as micro-elements (Al, As, Cd, Co, Cr, and Pb), which are more strongly influenced by environmental factors, showed limited discriminatory potential and no clear correlation with floral purity and botanical origin. Therefore, they should not be used as criteria when assessing the botanical origin of honey, but rather as indicators of environmental pollution and potential quality or safety concerns. Overall, the research contributes to improving the reliability of botanical classification of honey by combining robust analytical methodology with statistically validated elemental markers, while also distinguishing between natural compositional features and contamination-related signals. Full article

Implementing low-carbon city pilot policies is a crucial strategic initiative for achieving global carbon neutrality goals and serves as a key lever for promoting a comprehensive green transformation of the economy and society. This study selects panel data from 272 Chinese cities between 2006 and 2023 to construct a staggered difference-in-differences (DID) model. It systematically investigates the impact of low-carbon pilot policies on environmental quality and economic growth, as well as their underlying mechanisms. The findings reveal that: (1) The implementation of low-carbon pilot policies not only reduces environmental pollution but also fosters economic growth, achieving a dual benefit of environmental improvement and economic advancement; (2) These policies promote environmental and economic gains by increasing investment in technological innovation and optimizing industrial structures; (3) Heterogeneity analysis indicates that the environmental improvement effect is stronger in non-resource-based cities and those with lower fiscal pressure, whereas the economic growth effect is more pronounced in resource-based cities and those facing higher fiscal pressure; (4) Low-carbon pilot policies generate significant spatial spillover effects. The implementation of policies in one locality positively influences the environmental quality and economic development of neighboring cities. The conclusions provide empirical evidence and policy references for promoting coordinated regional green development globally and achieving the synergy between ecological preservation and economic prosperity. Full article

Against the backdrop of global energy transition and multiple uncertainties, enhancing energy resilience has become a core priority for China’s pursuit of secure and sustainable development. Using Chinese provincial panel data from 2011 to 2019, this study applies a two-way fixed effects model, mediation effect tests, and interaction term analysis to empirically investigate the relationship between industrial structure, green finance, and energy resilience. The main findings are as follows. First, the increases in gross regional product (GRP) and the added value of the secondary and tertiary sectors significantly enhance energy resilience. Second, heterogeneity analysis indicates that in regions with a high level of green finance, both GRP and the secondary sector’s added value exhibit stronger positive effects on energy resilience, whereas in regions with lower levels of green finance, the tertiary sector’s added value contributes more significantly to energy resilience improvement. In areas with high coal dependency, the secondary sector’s added value shows a significantly positive effect on energy resilience. Increases in industrial and construction industry added value significantly enhance energy resilience, suggesting that the expansion of the secondary industry contributes positively to the stability and resilience of the energy system. Third, the mechanism analysis shows that green finance contributes to energy resilience partly through the optimization of the energy consumption structure. Specifically, by effectively curbing coal consumption and, to a lesser extent, fuel oil production, green finance reduces the structural dependence of the economy on high-carbon energy. By contrast, channels such as electricity generation yield weaker and less robust evidence. These findings suggest that energy resilience is fundamentally shaped by the interplay of industrial structure, financial intermediation, and energy structure adjustment. Therefore, policy should shift from single instruments to integrated governance, synergizing industrial policy, green finance, and energy optimization to bolster energy resilience. Full article

Background: Ischemic cardiomyopathy (ICM) is characterized by heterogeneous structural remodeling that is not fully captured by conventional systolic metrics. How multidimensional structural echocardiographic information can improve pre-revascularization risk stratification remains unclear. Methods: In this retrospective study, 989 patients with ICM undergoing coronary angiography and revascularization were included in the derivation cohort, and 482 patients from an independent campus served as the validation cohort, with a median follow-up duration of 6.5 years. The primary endpoint was cardiovascular mortality. Eight routinely acquired pre-revascularization echocardiographic structural variables were analyzed. Unsupervised clustering identified structural clusters, and principal component analysis (PCA) was used to derive a structural risk score. Associations with cardiovascular mortality were assessed using the Cox proportional hazards model, and prognostic performance was evaluated by comparing individual echocardiographic predictors using Harrell’s C-index and time-dependent AUC analyses. Results: Three distinct structural clusters emerged, differing in chamber size, systolic function, pulmonary pressures, mitral regurgitation severity, and long-term cardiovascular mortality. The PCA-derived structural risk score, reflecting the dominant axis of remodeling and volume overload, showed association with cardiovascular mortality in the derivation cohort and remained independently predictive after multivariable adjustment. Compared with single echocardiographic parameters, both the structural clusters and the risk score demonstrated superior discriminative performance. In the validation cohort, the structural score again showed a consistent and independent association with cardiovascular mortality. Conclusions: Multidimensional structural echocardiographic assessment reveals clinically meaningful remodeling patterns and enables construction of a robust PCA-derived structural risk score. Both approaches provide prognostic information beyond individual echocardiographic measures and support more precise pre-revascularization risk stratification in patients with ICM. Full article

Background/Objectives: Age-related macular degeneration (AMD) is a multifactorial retinal disease involving inflammatory, metabolic, and genetic factors. Increasing evidence suggests that the gut microbiome may contribute to systemic pathways involved in retinal homeostasis. This exploratory pilot study investigated gut microbiome alterations in AMD patients and controls using long-read whole-genome sequencing. Methods: Bacterial DNA was extracted from fecal samples and analyzed using Oxford Nanopore sequencing, followed by taxonomic profiling, alpha and beta diversity analyses, and differential abundance testing. Results: AMD patients showed significantly reduced microbial diversity, reflected by lower richness, Shannon and Simpson indices. Species-level beta diversity analyses revealed significant differences in microbial community composition, particularly with Bray-Curtis metrics, alongside increased inter-individual microbial heterogeneity in AMD samples. Differential abundance analyses identified the depletion of several potentially beneficial commensal taxa, including Faecalibacterium prausnitzii and Parabacteriodes distasonis, whereas Staphylococcus aureus was enriched in AMD patients. Comparisons between wet and dry subtypes showed no significant differences in alpha or beta diversity. Conclusions: Overall, the findings support the presence of gut microbial dysbiosis in AMD characterized by reduced diversity, abundance-driven community shifts, and increased microbiome heterogeneity. Given the small cohort size, cross-sectional design and lack of functional analysis, these results should be considered preliminary and hypothesis-generating. Full article

Corporate debt crises constitute a critical source of instability in modern financial distress, rendering their early prediction essential for market regulators and investors. However, corporate debt crisis prediction is severely hindered by extreme class imbalance, as actual crisis samples are far fewer than normal ones. This issue greatly undermines the robustness and generalization ability of conventional forecasting models. To address this issue, we propose an adaptive meta weighting learning (named AMetaW) for corporate debt crisis prediction. Specifically, the model incorporates an adaptive meta weighting mechanism to alleviate class imbalance, ensuring that rare crisis samples receive sufficient attention during training. Moreover, AMetaW integrates multiple financial characteristics into a unified framework, while employing explainable machine learning techniques to reveal the heterogeneous importance of indicators across regions. Empirical analysis using firm-level data across multiple provinces in China demonstrates that: (1) AMetaW achieves superior predictive performance compared with state-of-the-art baselines under imbalanced conditions; (2) our analysis reveals that short-term benchmark interest rate, equity concentration degree, and operating profit margin are consistently the strongest predictors of debt crises; and (3) the relative importance of indicators varies across regions, with eastern firms more sensitive to equity concentration degree and cash ratio, while western firms are more exposed to risks from short-term benchmark interest rate and operating profit margin. These findings provide both methodological contributions to Corporate Debt Crises forecast model and practical insights for region-specific debt crisis prevention and offering practical guidance for group enterprises and regulators. Full article

Slopes transitioning from open-pit to underground mining typically exhibit heterogeneous and nonlinear deformation characteristics. Under complex environmental disturbances, monitoring data are often affected by high noise and outliers, making it difficult to accurately capture critical deformation characteristics and posing challenges for landslide early warning and safety assessment. Therefore, it is necessary to develop a high-precision data optimization technique suitable for complex, high-noise monitoring time series data to improve slope stability evaluation and the robustness of prediction algorithms. Based on slope deformation monitoring data from the Hainan Shilu Iron Mine, the multi-type, nonlinear, and alternating acceleration-deceleration patterns of deformation time series data were analyzed, and the performances of multiple anomaly detection and interpolation compensation algorithms were compared. The results show that the Local Outlier Factor (LOF) and K-Nearest Neighbors (KNN) algorithms achieve better performance in processing noisy and dynamically varying time series data based on comparative evaluations of detection accuracy and interpolation error. Furthermore, a Bessel function-based denoising technique was proposed for landslide monitoring systems. This technique effectively filters high-frequency noise while preserving the main characteristics of the data and outperforms conventional methods, including the Moving Average Method (MAM), Triple Exponential Smoothing (TES), and Least Squares Method (LSM). The proposed technique, integrating LOF-based anomaly detection, KNN-based interpolation compensation, and Bessel function denoising, can effectively process slope deformation monitoring data characterized by multi-type, nonlinear, and alternating acceleration-deceleration patterns. Engineering application at the Hainan Shilu Iron Mine demonstrated that the proposed technique improves data quality and model prediction performance, providing valuable support for slope stability analysis and disaster early warning systems in slopes transitioning from open-pit to underground mining. Full article