Search Results (270)

Drought disturbances are becoming more frequent with global warming. Accurately assessing the regulatory effect of drought on vegetation phenology is key to understanding terrestrial ecosystem response mechanisms in the context of climate change. Previous studies on cumulative and lagged effects of drought on vegetation growth have mostly focused on a single vegetation type or the overall vegetation NDVI, overlooking the possible influence of different adaptation strategies of different vegetation types and differences in drought effects on different phenological nodes. This study investigates the cumulative and lagged effects of drought on vegetation phenology across a region of East Asia from 2001 to 2020 using NDVI data and the Standardized Precipitation Evapotranspiration Index (SPEI). We analyzed the start of growing season (SOS) and end of growing season (EOS) responses to drought across four vegetation types: deciduous needleleaf forests (DNFs), deciduous broadleaf forests (DBFs), shrublands, and grasslands. Results reveal contrasting phenological responses: drought delayed SOS in grasslands through a “drought escape” strategy but advanced SOS in forests and shrublands. All vegetation types showed earlier EOS under drought stress. Cumulative drought effects were strongest on DNFs, SOS, and shrubland SOS, while lagged effects dominated DBFs and grassland SOS. Drought impacts varied with moisture conditions: they were stronger in dry regions for SOS but more pronounced in humid areas for EOS. By confirming that drought effects vary by vegetation type and phenology node, these findings enhance our understanding of vegetation adaptation strategies and ecosystem responses to climate stress. Full article

Diabetes mellitus (DM) is one of the leading causes of death and disability worldwide and its prevalence continues to rise. Chronic hyperglycemia exposes patients to severe complications. Among these, diabetic vascular lesions are the most destructive. Their primary driver is the synergistic interaction between hyperglycemia-induced oxidative stress and chronic inflammation. This review systematically elucidates how multiple pathological pathways—namely, metabolic dysregulation, mitochondrial dysfunction, endoplasmic reticulum stress, and epigenetic reprogramming—cooperate to drive oxidative stress and inflammatory cascades. Confronting this complex pathological network, natural products, unlike conventional single-target synthetic drugs, exert multi-target synergistic effects, simultaneously modulating several key pathogenic networks. This enables the restoration of redox homeostasis and the suppression of inflammatory responses, thereby improving vascular function and delaying both microvascular and macrovascular disease progression. However, the clinical translation of natural products still faces multiple challenges and requires comprehensive mechanistic studies and rigorous validation to fully realize their therapeutic potential. Full article

Dexamethasone, widely used as an exogenous glucocorticoid in clinical and animal practice, has recently been recognized as an environmental contaminant of concern. Existing evidence documents its ability to induce persistent dyslipidemia in adult offspring. In this study, plasma cholesterol levels in male rats exposed to dexamethasone prenatally (PDE) were increased. Meanwhile, developmental tracking revealed a reduction in hepatic low-density lipoprotein receptor (LDLR) promoter H3K27 acetylation (H3K27ac) and corresponding transcriptional activity across gestational-to-postnatal stages. Mechanistic investigations established glucocorticoid receptor/histone deacetylase2 (GR/HDAC2) axis-mediated epigenetic programming of LDLR through H3K27ac modulation in PDE offspring, potentiating susceptibility to hypercholesterolemia. Additionally, in peripheral blood mononuclear cells (PBMC) of PDE male adult offspring, LDLR H3K27ac level and expression were also decreased and positively correlated with those in the liver. Clinical studies further substantiated that male newborns prenatally treated with dexamethasone exhibited increased serum cholesterol levels and consistent reductions in LDLR H3K27ac levels and corresponding transcriptional activity in PBMC. This study establishes a complete evidence chain linking PDE with epigenetic programming and cholesterol metabolic dysfunction, proposing PBMC epigenetic biomarkers as a novel non-invasive monitoring tool for assessing the developmental toxicity of chemical exposures during pregnancy. This has significant implications for improving environmental health risk assessment systems. Full article

Alkaline electrolytic water hydrogen generation, a key driver in the growth of hydrogen energy, heavily relies on high-efficiency and high-purity ion exchange membranes. In this study, three-dimensional (3D) wrinkled reduced graphene oxide (WG) nanosheets obtained through a simple thermal reduction process and two-dimensional (2D) graphene oxide act as building blocks, with ethylenediamine as a crosslinking stabilizer, to construct a unique 3D/2D 2 nm-tunneling structure between the GO and WG sheets through via an amide connection at a WG/GO ratio of 1:1. Here, the wrinkled graphene (WG) undergoes a transition from two-dimensional (2D) graphene oxide (GO) into three-dimensional (3D) through the adjustment of surface energy. By increasing the interlayer spacing and the number of ion fluid channels within the membranes, the E-W/G membrane has achieved the rapid passage of hydroxide ions (OH⁻) and simultaneous isolation of produced gas molecules. Moreover, the dense 2 nm nano-tunneling structure in the electrolytic water process enables the E-W/G membrane to attain current densities >99.9% and an extremely low gas crossover rate of hydrogen and oxygen. This result suggests that the as-prepared membrane effectively restricts the unwanted crossover of gases between the anode and cathode compartments, leading to improved efficiency and reduced gas leakage during electrolysis. By enhancing the purity of the hydrogen production industry and facilitating the energy transition, our strategy holds great potential for realizing the widespread utilization of hydrogen energy. Full article

A yellow leaf mutant named ‘ZT00091’ was discovered during the cultivation of the melon variety ‘ZT091’. An analysis of the leaf ultrastructure revealed that the chloroplasts of ‘ZT00091’ were significantly smaller than those of ‘ZT091’, with irregular shapes, blurred contours, and no starch granules. Metabolomic analysis revealed 792 differentially abundant metabolites between ‘ZT00091’ and ‘ZT091’, with 273 upregulated and 519 downregulated. The Kyoto Encyclopedia of Genes and Genomes (KEGG) results indicated that the differentially abundant metabolites were enriched mainly in the carotenoid pathway. qRT-PCR was used to analyze key genes in the carotenoid pathway of melon. Compared with those in ‘ZT091’, the genes promoting carotenoids and lutein in ‘ZT00091’ were significantly upregulated, which may explain the yellow color of ‘ZT00091’ leaves. Significant differences in the chlorophyll contents (chlorophyll a, chlorophyll b, and total chlorophyll) and carotenoid contents were found between ‘ZT00091’ and ‘ZT091’, indicating that the yellowing of melon leaves is related to changes in the carotenoid and chlorophyll contents. This study provides a theoretical basis for research on the molecular mechanism of melon yellowing. Full article

With the widespread adoption of mobile devices and the increasing availability of user trajectory data, accurately predicting the next location a user will visit has become a pivotal task in location-based services. Despite recent progress, existing methods often fail to effectively disentangle the diverse and entangled behavioral signals, such as collaborative user preferences, global transition mobility patterns, and geographical influences, embedded in user trajectories. To address these challenges, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which explicitly captures and disentangles user preferences from three complementary perspectives. Specifically, MPHCL constructs a global transition flow graph and two specialized hypergraphs: a collective preference hypergraph to model collaborative check-in behavior and a geospatial-context hypergraph to reflect geographical proximity relationships. A unified hypergraph representation learning network is developed to preserve semantic independence across views through a dual propagation mechanism. Furthermore, we introduce a cross-view contrastive learning strategy that aligns multi-perspective embeddings by maximizing agreement between corresponding user and location representations across views while enhancing discriminability through negative sampling. Extensive experiments conducted on two real-world datasets demonstrate that MPHCL consistently outperforms state-of-the-art baselines. These results validate the effectiveness of our multi-perspective learning paradigm for next-location prediction. Full article

Asparagus (Asparagus officinalis L.) is a highly nutritious vegetable rich in various bioactive compounds. Ensuring both yield improvement and quality preservation is a shared goal for producers and researchers. As novel green yield-enhancing technologies in facility agriculture, electrostatic fields and elevated CO₂ application hold significant potential. This study investigated the effects of the interaction between electrostatic fields and elevated CO₂ on the growth and physiological characteristics of asparagus. The results demonstrated that the combined treatment of electrostatic fields and elevated CO₂ significantly increased total yield, tender stem number, and single tender stem weight of asparagus, while also shortening the harvesting period and promoting rapid shoot growth. Additionally, the treatment markedly enhanced the total chlorophyll content in asparagus leaves, improving photosynthetic capacity. By boosting antioxidant enzyme activities (e.g., SOD, APX) and reducing malondialdehyde (MDA) levels, the treatment maintained the redox homeostasis of asparagus shoots, effectively mitigating oxidative damage. In terms of nutrient accumulation, the interaction between electrostatic fields and elevated CO₂ significantly promoted the synthesis and accumulation of key nutrients, including soluble sugars, reducing sugars, soluble proteins, total phenolics, total flavonoids, and ascorbic acid, thereby substantially improving the nutritional quality of asparagus. Comprehensive analysis using fuzzy membership functions revealed that the combined treatment of electrostatic fields and elevated CO₂ outperformed individual treatments in enhancing asparagus growth and physiological characteristics. This study provides important theoretical insights and technical support for the efficient and sustainable cultivation of asparagus in facility agriculture. Full article

In distribution systems, limited measurement configurations and communication constraints often result in a low success rate of data acquisition, posing challenges to both system observability and the real-time performance required by state estimation (SE). Consequently, the distribution system SE (DSSE) relies heavily on pseudo-measurements to supplement real-time data. However, existing pseudo-measurement models generally fail to adequately account for topology changes and may lead to numerical instability issues. To resolve these challenges, this paper presents a graph computing-based DSSE method with enhanced numerical stability for low-observability distribution systems. Specifically, a graph neural network (GNN) is employed to dynamically learn the coupling relationships between bus and branch electrical quantities to improve the credibility of pseudo-measurements. Additionally, a loop belief propagation (LBP) algorithm based on factor graphs is designed to capture the statistical discrepancies between real-time and pseudo-measurements, thus avoiding the impact of pseudo-measurement modeling on numerical stability. Numerical results on IEEE 33-bus and 95-bus test systems demonstrate that the proposed method effectively adapts to topology variations and significantly improves the accuracy of both pseudo-measurement modeling and DSSE. Full article

The potato is the fourth largest cultivated crop worldwide. Soil cadmium (Cd) pollution poses a significant threat to crop growth. Brassinosteroids (BRs) play a significant part in enhancing plant resistance against abiotic stresses. The DWF4 (dwarf4) gene is one of the rate-limiting enzyme genes involved in the synthesis of BRs. This study employed seedlings of transgenic potatoes overexpressing the StDWF4 gene (OE) and wild-type (WT) potatoes to clarify their alleviating effect on Cd stresses. The differences in phenotype, ultrastructure, physiological indicators, and plant hormone levels of Cd²⁺-treated potatoes were analyzed. The molecular mechanism of potatoes’ response to Cd²⁺ stress was revealed by transcriptomics. Results showed that the dry weight, fresh weight, plant height, root length, and stem diameter of OE potatoes under Cd stress were significantly higher than those of WT potatoes. Ultrastructural analysis revealed that the mitochondria, cell walls, and cell membranes of WT were more fragile than those of OE under Cd stress. The Cd²⁺ concentration in OE was always lower than that in WT, and both concentrations increased gradually as the duration of Cd²⁺ treatment was prolonged. The 24-epibrassionlide (EBL) content in OE was higher than that in WT. RNA-seq analysis manifested that the gene expression levels of OE and WT plants changed significantly under Cd²⁺ treatment. The differentially expressed genes (DEGs) were primarily connected to the moderation of the metabolic pathways, biosynthesis of secondary metabolites, phenylpropanoid biosynthesis, and plant hormone signal transduction. These findings indicated that overexpression of the StDWF4 gene in potatoes enhanced their alleviating effect on Cd stresses. Full article

This study aims to explore the pathogenic mechanism of H-type hypertension. A rat model of H-type hypertension was established through high-methionine dietary intervention, with subsequent folic acid administration. Untargeted serum metabolomic profiling identified a significant reduction in arachidonic acid (AA) levels in the methionine-enriched group, which were effectively normalized following folic acid supplementation. Transcriptomic analysis revealed methionine-induced upregulation of AA pathway-associated genes Cyp1a1 and Gpr75. In contrast, after the intervention with folic acid, a downregulation of these genes was observed. These findings were corroborated through Western blotting and RT-qPCR validation. In vitro studies using EA.hy926 endothelial cells demonstrated that methionine exposure significantly elevated CYP1A1 expression. Furthermore, methionine stimulation induced marked upregulation of GPR75 and its downstream signaling components (NRAS, MEK1, and ERK1). Population-level evidence from the U.S. NHANES database substantiated significant correlations between essential fatty acids (AA, LA, and GLA) and H-type hypertension prevalence. Our research findings suggest that the CYP1A1/20-HETE/GPR75 axis-mediated dysregulation of AA metabolism may be one of the key pathological mechanisms of H-type hypertension. The research results provide clues for the discovery of new therapeutic targets for H-type hypertension. Full article

A self-centering prefabricated concrete frame structure has good seismic performance, and its seismic capacity is mainly provided by the recovery force of the unbonded prestressing tendons and the energy-dissipation deformation capacity of embedded steel reinforcement. Relocating embedded reinforcement to external positions enables replaceability of energy dissipation components. And the configuration of external energy dissipation components is the primary factor influencing their energy dissipation capacity. Based on the existing external “Plug & Play” configuration, the internal steel bar size and material properties such as those of steel bar and filling material were varied in this study, and then, cyclic tension-compression experimental studies and numerical simulations were conducted to investigate the energy dissipation performance index and key influencing factors of this type of external composite steel bar. The research results showed that the composite steel bars designed in the experiments exhibited superior overall energy dissipation performance. Specimens utilizing Q345B steel as the core material outperformed those with Grade 30 steel. Moreover, the slenderness ratio of the composite steel bars and the diameter ratio between the end region and weakened segment of the internal steel bars were identified as critical parameters governing energy dissipation performance, and recommendations for optimal parameter ranges were discussed. This study provides a theoretical foundation for implementing external composite steel bars in self-centering structural systems. Full article

Accurate prediction of the Normalized Difference Vegetation Index (NDVI) is crucial for regional ecological management and precision decision-making. Existing methodologies often rely on smoothed NDVI data as ground truth, overlooking uncertainties inherent in data acquisition and processing. Fuzzy time series (FTS) prediction models based on the Fuzzy C-Means (FCM) clustering algorithm address some of these uncertainties by enabling soft partitioning through membership functions. However, the method remains limited by its reliance on expert experience in setting fuzzy parameters, which introduces uncertainty in the definition of fuzzy intervals and negatively affects prediction performance. To overcome these limitations, this study enhances the interval type-2 fuzzy clustering time series (IT2-FCM-FTS) model by developing a pixel-level time series forecasting framework, optimizing fuzzy interval divisions, and extending the model from unidimensional to spatial time series forecasting. Experimental results from 2021 to 2023 demonstrate that the proposed model outperforms both the Autoregressive Integrated Moving Average (ARIMA) and conventional FCM-FTS models, achieving the lowest RMSE (0.0624), MAE (0.0437), and SEM (0.000209) in 2021. Predictive analysis indicates a general ecological improvement in the Aksu region (Xinjiang, China), with persistent growth areas comprising 61.12% of the total and persistent decline areas accounting for 2.6%. In conclusion, this study presents an improved fuzzy model for NDVI time series prediction, providing valuable insights into regional desertification prevention and ecological strategy formulation. Full article

This study systematically investigates the electrical characteristics of the vertical NPN bipolar junction transistor (VNPN BJT) in the strong magnetic field environment, focusing on analyzing the effects of magnetic field direction and intensity on key parameters such as terminal current and current gain (β). The simulation results show that the magnetic field induces changes in the carrier distribution, thereby affecting the current transport path. Through the in-depth analysis of electron motion trajectories, potential distribution, and Hall voltage, this paper reveals the physical mechanisms behind the device’s characteristic changes under the magnetic field and discovers that the inherent asymmetry of the BJT structure induces significant magnetic anisotropy effects. On this basis, a design for interference-resistant structures in strong magnetic field environments is proposed, effectively suppressing the adverse effects of magnetic-field-sensitive directions on BJT performance and significantly improving the device’s stability in complex magnetic field environments. Full article

Smoke tree (Cotinus coggygria) is an important component of the urban landscape and represents red-leaf scenery in Beijing; however, Verticillium wilt, caused by Verticillium dahliae, has caused high mortality of smoke trees. Traditional control methods, such as chemical root irrigation and trunk injection, are problematic due to environmental pollution and potential plant damage. This study aimed to explore effective prevention and control methods for Verticillium wilt of smoke tree across different regions of red-leaf scenery in Beijing. In 2023, 240 smoke trees from the Pofengling Park of Beijing were selected for the study. Four different fungicides, a plant growth regulator and a biocontrol agent were tested. Three application methods (root irrigation, trunk spraying, and a combination of both) were used in the different trials. Based on the results of the 2023 trial, control trials were conducted under the disease classification in 2024 at key red-leaf scenery regions, such as Xiangshan Park, Xishan Park, and Pofengling Park. The bioagents of Bacillus subtilis root irrigation combined with the trunk spraying treatment group showed the best disease control effects. Calculated by the change in disease index in the treatment and blank groups, the corrective control effect in the treatment group reached 104.55%, and 60% of the plants remained healthy, indicating that this method of disease control was the most effective. Propiconazole root irrigation also had a significant effect on diseased smoke trees. Furthermore, validation experiments conducted in 2024 confirmed that various combinations of root irrigation and trunk spraying provided strong preventive and therapeutic effects on Verticillium wilt. In conclusion, the graded control measures demonstrated effective control of wilt at different disease index grades. This study offers an effective and practical solution for controlling Verticillium wilt, benefiting both environmental sustainability and landscape health. Full article

In the processing of industry front-end waste, the reactor plays a critical role as a key piece of equipment, making its operational status monitoring essential. However, in practical applications, issues such as equipment aging, data transmission failures, and storage faults often lead to data loss, which affects monitoring accuracy. Traditional methods for handling missing data, such as ignoring, deleting, or interpolation, have various shortcomings and struggle to meet the demand for accurate data under complex operating conditions. In recent years, although artificial intelligence-based machine learning techniques have made progress in data imputation, existing methods still face limitations in capturing the coupling relationships between the sequential and channel dimensions of time series data. To address this issue, this paper proposes a time series decoupling-based data imputation model, referred to as the Decomposite-based Transformer Model (DTM). This model utilizes a time series decoupling method to decompose time series data for separate sequential modeling and employs the proposed MixTransformer module to capture channel-wise information and sequence-wise information, enabling deep modeling. To validate the performance of the proposed model, we designed data imputation experiments under two fault scenarios: random data loss and single-channel data loss. Experimental results demonstrate that the DTM model consistently performs well across multiple data imputation tasks, achieving leading performance in several tasks. Full article