Ecological security is fundamentally interconnected with territorial spatial conflict (TSC); however, current assessment frameworks frequently neglect ecological vulnerability. To address this gap, based on landscape pattern theory, this study proposes an enhanced TSC assessment framework that integrates ecological sensitivity and ecological risk into ecological vulnerability and further incorporates landscape complexity and fragmentation to construct a conflict risk index based on a composite landscape index. Applying this framework to the Yellow River Basin, this study examines the spatiotemporal evolution of TSC from 1990 to 2020 using multi-source data on land use, terrain, climate, vegetation, and socioeconomic conditions. Spatial autocorrelation reveals distinct clustering patterns, and a Multiscale Geographically Weighted Regression (MGWR) model quantifies the spatially heterogeneous effects of key drivers. Results indicate a 5.40% reduction in severe conflict areas over the study period. Conflicts are predominantly concentrated in rapidly urbanizing and densely populated midstream and downstream cities, whereas plateau regions remain largely low-conflict. TSC exhibits significant positive spatial autocorrelation, with high-high clusters progressively expanding southeastward. MGWR identifies SHDI and NDVI as generally positive influences on TSC, while distance from urban centers exerts a mitigating effect. Socioeconomic factors such as land-use intensity, secondary industry value-added, and local fiscal revenue demonstrate broad, scale-dependent impacts. This framework provides a transferable methodology for embedding ecological security considerations into territorial spatial planning and conflict management.
Quantifying the relative roles of climate change and human activities in vegetation change is essential for sustainable restoration planning, yet the impacts of extreme climate events and their time-lagged effects are often overlooked, biasing assessments of climatic controls. Here, we developed an integrated pattern-process-attribution framework to evaluate vegetation dynamics across China's four major climatic zones using a long-term, high-resolution kernel normalized difference vegetation index (kNDVI) dataset for 2000-2024. Theil-Sen trend estimation and the coefficient of variation (CV) were used to characterize long-term changes and interannual stability. Partial correlation analysis was applied to isolate the independent associations between kNDVI and extreme climate indices while controlling for background mean temperature and precipitation, and lagged correlation analysis with 0-3-month lags was used to quantify delayed responses. A regression-based residual attribution was further used to decompose observed kNDVI changes into a climate-driven component and a human-activity-related component (approximated by the residual not explained by temperature and precipitation). Results show widespread greening with pronounced spatial heterogeneity, with the most extensive improvement in the Tropical and Subtropical Humid Region and the Temperate Humid and Semi-humid Region. Vegetation stability exhibits a southeast-northwest contrast, and the highest variability occurs in the Temperate Arid and Semi-arid Region and the western Qinghai-Tibet Plateau. Responses to climate extremes are region-dependent and generally short-lagged (mean 0.35-1.05 months), with drought constraints dominating in arid regions and thermal extremes (TXx) most relevant on the plateau. Nationally, human activities contribute 70.8% of vegetation change, exceeding the climate-driven contribution (29.2%).
The establishment of an efficient symbiotic relationship between soybean and rhizobia requires precise regulation of root nodule development. While sugar transporters are known to participate in nutrient allocation during symbiosis, their molecular functions and regulatory mechanisms in this process remain poorly characterized. This study aims to functionally characterize the sugar transporter GmSWEET17 and elucidate its role in soybean-rhizobia symbiosis. Building upon initial yeast library screening that identified GmSWEET17 as a novel interacting partner of the Nod factor receptor GmNFR1α, this study further confirmed their physical interaction through yeast two-hybrid assays and luciferase complementation imaging (LCI) in tobacco leaves. The two proteins were shown to co-localize at the plasma membrane in Arabidopsis mesophyll protoplasts. Rhizobial infection induced GmSWEET17 expression in roots, with dynamic expression patterns showing an initial increase followed by a decrease during nodule development. Promoter activity assays demonstrated specific expression of GmSWEET17 in the vascular tissues of roots and nodules. Heterologous expression in yeast revealed that GmSWEET17 primarily transports glucose. Overexpression of GmSWEET17 in transgenic hairy roots led to reduced nodule number but increased nodule size and enhanced nitrogen fixation activity. Furthermore, GmSWEET17 overexpression up-regulated the expression of key nodulation marker genes. Our results demonstrate that GmSWEET17, a sugar transporter interacting with GmNFR1α, positively regulates soybean nodule development and nitrogen fixation efficiency. This study provides new molecular evidence for the biological function of GmSWEET17 in nodulation and nitrogen fixation, and offers novel insights into the regulatory mechanisms of GmNFR1α in rhizobial symbiosis.
To clarify the relative contributions of meteorological conditions and anthropogenic activities to ozone (O3) pollution in the Chengdu–Chongqing urban agglomeration, this study systematically quantified their impacts on the daily maximum 8-h average ozone concentration (O3-8 h) during 2015–2024. Using ground-based observations and ERA5-Land reanalysis data, we combined the Light Gradient Boosting Machine (LightGBM) machine learning model, the SHAP (SHapley Additive exPlanations) interpretability framework, a meteorological normalization approach, and controlled-variable perturbation experiments to investigate the drivers of O₃ pollution and compare the differences among interpretative methods. The results showed that the LightGBM model reproduced O₃-8 h well, with a cross-validated R2 of 0.88. Meteorological normalization analysis indicated that meteorological factors dominated the interannual variability of O3-8 h, with an average contribution of 70.5
Despite the growing emphasis on labor education, the latent structure and key predictors of undergraduates’ labor literacy remain poorly understood, particularly with respect to how psychological resources interact with or compensate for contextual constraints. To address this gap, this study identifies latent profiles of labor literacy, examines demographic and psychological predictors of profile membership, and tests the compensatory role of psychological resources within the bioecological systems framework. A sample of 2,500 undergraduates completed measures of labor literacy, meaning in life (MIL), and general self-efficacy (GSE), and latent profile analysis (LPA) revealed four distinct profiles: an Integrated Excellence profile (12.6
