Search Results (567)

Ecosystem service assessment provides a critical basis for optimizing regional ecological management and promoting sustainable development. From the water–land–carbon coupling perspective, this study established a technical framework for quantifying individual services, coupling a composite index, and analyzing multidimensional driving mechanisms. The InVEST model was applied to quantify three core ecosystem services: water yield, habitat quality, and carbon storage. A Composite Ecosystem Service Index (CESI) was constructed through normalization and weighted summation. Multidimensional driving factors were identified using the Optimal Parameter-Based Geographical Detector. Taking Ningxia during 2004–2024 as the study area, the results showed that the CESI exhibited a fluctuating upward trend with significant spatial heterogeneity, characterized by a south–high and north–low pattern. Land use transitions were dominated by bidirectional conversions between cropland and grassland, while impervious area expanded rapidly and barren land decreased overall. The spatial differentiation of CESI was jointly controlled by natural and anthropogenic factors, with land use type, precipitation, and digital elevation model showing the strongest explanatory power, and all two-factor interactions displaying pronounced enhancement effects. This study provides a reproducible framework for ecosystem service assessment in arid and semi-arid regions, supporting ecological restoration, land use optimization, and the coordinated development of ecology and economy under water–land–carbon synergy. Full article

Tumor acidity is a hallmark of the tumor microenvironment (TME) and has been widely regarded as a promising therapeutic target due to its ubiquity, functional relevance, and apparent selectivity for malignant tissues. Extensive preclinical studies have demonstrated that targeting tumor acidity—through inhibition of lactate production, blockade of proton transport, systemic buffering, and pH-responsive drug delivery—can suppress tumor growth, reduce metastasis, and enhance antitumor immunity. However, despite strong mechanistic rationale and consistent preclinical efficacy, these strategies have failed to achieve meaningful and durable clinical success. In this review, we examine the underlying reasons for this translational discrepancy. We highlight key mechanistic and systemic barriers, including spatial heterogeneity of tumor pH, temporal dynamics and adaptive evolution, metabolic plasticity, redundancy of pH-regulating systems, systemic physiological constraints, and drug delivery limitations in hypoxic and acidic regions. We further argue that tumor acidity is not a sufficient standalone driver of tumor progression but rather a feature of a complex and adaptive system shaped by metabolic and microenvironmental interactions. Finally, we discuss emerging strategies that may overcome these limitations, including combination therapies integrating metabolic targeting with immunotherapy, pH-responsive drug delivery systems, microenvironment reprogramming, and biomarker-guided patient stratification. Overall, current evidence suggests that future therapeutic approaches may benefit more from exploiting tumor acidity as a feature of the tumor microenvironment rather than attempting to directly neutralize it. Full article

Ion diffusion, the precipitation of hydration products, and interactions between different reactive particles are critical for optimizing the design of low-carbon cementitious systems. However, at the sub-micron scale, the complex spatial and chemical interactions among diverse components at an early age remain challenging to quantify. In this study, a machine learning-assisted BSE-EDS analytical method was applied to quantify both the phase assemblage and the spatial element features of cement–fly ash–slag ternary systems. The equidistant strip delineation of single-particle and rectangular inter-particle path methods were employed to quantify ionic diffusion gradients in the ternary systems. Single-particle strip analysis quantified the hydration front of clinker, slag and fly ash, while inter-particle analysis identified a persistent calcium-starvation zone at slag–fly ash interfaces. This region is characterized by exceptionally high Si/Ca ratios and a lower average atomic number and material density due to ionic diffusion limitations. These findings identify the slag–fly ash interface as the primary microstructural weak link, providing a robust methodology for capturing the chemical heterogeneities and optimizing the design of sustainable cementitious materials. Full article

Assessing the ecological and environmental quality of old industrial cities is crucial for understanding the spatial heterogeneity of ecological quality and its associated factors during regional transformation. Taking Tangshan, a typical old industrial city in China, as a case study, this study employed Landsat 8/9 remote sensing imagery and multi-source auxiliary data from 2015 to 2024 to calculate annual Remote Sensing Ecological Index (RSEI) values using a unified multi-year standardization and principal component analysis framework. Global and local Moran’s I analyses were conducted to examine spatial clustering patterns, and the Optimal-Parameter Geographical Detector (OPGD) was used to quantify the spatial correspondence between RSEI and selected natural and anthropogenic explanatory factors. The results indicate the following. (1) The mean RSEI in Tangshan fluctuated between 0.34 and 0.54 from 2015 to 2024, exhibiting significant interannual variability. (2) Higher RSEI values were primarily distributed in the northern mountainous and southern coastal ecological zones, while lower values were concentrated in the central and eastern industrial-mining zones. (3) The global Moran’s I was significantly positive in all years (0.702–0.778, p = 0.001), indicating the persistence of spatial clustering; the proportion of non-significant local spatial units decreased from 72.00% in 2015 to 69.46% in 2024. (4) Land use/land cover (LULC) exhibited the most consistently high explanatory power. Elevation (ELE), nighttime light (NTL), and built-up intensity (BUILT) also formed a leading group of spatially associated factors, although their relative ranking varied between the optimal-parameter results and the robustness analysis. Slope (SLOPE), annual precipitation (Pre), and annual mean temperature (Tmean) generally showed relatively lower explanatory power. Interaction detection showed that pairwise factor combinations generally had higher q values than individual factors, with LULC × ELE showing consistently high explanatory power in representative years. This study provides a scientific reference for ecological and environmental monitoring and differentiated management in old industrial cities. Full article

Reinforced concrete (RC) buildings in coastal seismic regions are exposed to coupled deterioration processes driven by chloride-induced corrosion and earthquake loading. This interaction is particularly critical along the Mexican Pacific coast, where persistent marine exposure coincides with high seismic hazard. Nevertheless, current models lack a consistent multi-physics framework that integrates chloride transport, electrochemical heterogeneity (including galvanic interactions), and seismic structural response. This study quantifies the influence of corrosion on seismic collapse probability by explicitly modeling the coupled mechanisms of moisture transport, chloride ingress, and electrochemical potential distribution in RC members. A three-dimensional mechanistic framework is adopted to capture the spatial variability in corrosion, including galvanic interactions between passive and active reinforcement regions. A representative scenario is examined in which a corner column remains in continuous contact with seawater, promoting localized chloride accumulation and sustained corrosion activity. The resulting nonuniform section loss is incorporated into nonlinear structural models subjected to mainshock–aftershock sequences. The results show that corrosion-induced heterogeneity, amplified by galvanic coupling between passive and active zones, accelerates strength and stiffness degradation. Compared to conventional uniform corrosion assumptions, this effect leads to a significant increase in early collapse probability, with values increasing from near-zero levels to approximately $0.6$–$0.9$ at moderate seismic intensity levels. These findings emphasize the need to account for coupled transport and electrochemical processes, as well as localized exposure conditions, in the seismic assessment of RC structures in aggressive coastal environments. Full article

Glioblastoma multiforme (GBM) remains the most aggressive and therapeutically intractable primary brain tumor, with many patients experiencing rapid relapse despite maximal surgical resection followed by standard chemoradiation. This persistent failure reflects the convergence of profound tumor-intrinsic genetic heterogeneity and a highly dynamic, spatially structured, and immunosuppressive tumor microenvironment (TME). Together, these forces create strong selective pressures that fuel tumor evolution, intratumoral diversity, phenotype plasticity, diffuse invasion, and robust resistance to therapy. The TME of GBM is orchestrated through a complex interplay between diverse cellular constituents, including tumor-associated macrophages, reactive astrocytes, endothelial cells, pericytes, and GBM stem cells, and non-cellular components such as extracellular matrix remodeling, hypoxia, metabolic and nutrient gradients, and spatially patterned cytokine and chemokine signaling networks. Additionally, heterogeneity in blood–brain barrier (BBB) and blood–tumor barrier (BTB) complicates drug delivery and immune surveillance, reinforcing therapeutic resistance and regional tumor adaptation. Conventional two-dimensional cell cultures and animal models fail to sufficiently capture these multiscale, patient-specific interactions, limiting their translational predictive power. In this narrative review, we synthesize recent advances in GBM organoid technologies as physiologically relevant, three-dimensional platforms that more faithfully recapitulate TME for driving tumor evolution and treatment resistance. We compare complementary organoid strategies, including patient-derived GBM organoids that preserve native cytoarchitecture, cerebral organoid co-culture systems that reconstruct tumor–brain interactions, and advanced platforms incorporating immune and vascular features such as air–liquid interface cultures, microglia-enriched systems, and BBB/BTB-integrated models. Finally, we highlight emerging innovations such as spatial transcriptomics, organoid-on-a-chip systems, live imaging coupled with lineage tracing, genome engineering, and artificial intelligence integration that collectively position GBM organoids at the forefront of precision neuro-oncology, reproducing TME, enabling dynamic mapping of tumor evolution, and accelerating patient-specific therapeutic discovery. Full article

Rapid urbanization and stringent ecological protection policies in China have reshaped spatial competition among urban, agricultural, and ecological spaces. However, existing studies often overlook how this competition evolves across different slope structures. To address this, this study establishes a fine-scale analytical framework using H3 hexagonal grids and slope spectrum analysis to investigate slope structure evolution and spatial competition patterns from 1990 to 2023. The results reveal a distinct topographic stratification: urban space dominates low-slope regions (<6°) but exhibits a pervasive “upslope expansion” trend, with its average slope increasing from $1.81°$ to $2.07°$, equivalent to an annualized increase of approximately $0.008°{\mathrm{yr}}^{-1}$; agricultural space characterizes the transition zones (6–$15°$), showing an “upslope migration” in the Southeastern Hills associated with urban expansion pressure in low-slope areas; and ecological space functions as a stable barrier in steep terrains (>15°) but faces encroachment in transition zones. Furthermore, cluster analysis identifies significant regional heterogeneity aligned with China’s macro-topography, including “low-slope agglomeration” in the Eastern Plains, “interwoven upslope” patterns in the Southern Hilly Regions, and ecological dominance in the Western Highlands. Association analysis using GeoDetector and Multiscale Geographically Weighted Regression (MGWR) indicates that competition intensity is most strongly associated with human activity factors, especially human footprint and nighttime lights ($q>0.29$), which show the highest explanatory power among the examined factor groups. The interaction between human activity and elevation further shows relatively high explanatory power ($q=0.41$), suggesting that spatial competition is more pronounced where intensive human activities overlap with topographic constraints. Crucially, this study challenges the traditional flat-projection planning model. We propose a transition to “three-dimensional topographic regulation,” advocating differentiated management strategies—such as strict “slope redlines” for urban-agricultural transition zones—to mitigate intensifying spatial conflicts in complex terrains and safeguard agricultural sustainability. Full article

As climate conditions become increasingly extreme, greater emphasis should be placed on environmental considerations in outward investment to achieve sustainable green development for Chinese enterprises. Therefore, based on panel data of Chinese listed enterprises from 2008 to 2023, this study examines the impact of Outward Foreign Direct Investment (OFDI) and climate policy uncertainty (CPU) on corporate green total factor productivity (GTFP). The findings indicate that OFDI significantly enhances GTFP, but CPU weakens this positive effect. Mechanism analysis reveals that OFDI improves corporate GTFP through promoting green management innovation, deepening digital transformation, and increasing green investment, while CPU exerts negative effects by undermining these mechanisms. Heterogeneity analysis shows that the effect of OFDI is more pronounced for enterprises in eastern regions, non-heavy-pollution enterprises, and low-carbon-intensity enterprises. Furthermore, spillover effect analysis demonstrates that OFDI’s impact on corporate GTFP exhibits significant spatial boundary characteristics and time-varying evolutionary patterns. Finally, external incentives (government environmental subsidies) and internal drivers (climate risk) can hedge against the negative effects of the interaction between CPU and OFDI. Full article

As a strategic node of the Silk Road Economic Belt and a prototypical valley-type city, Lanzhou is subject to the dual constraints of rapid urbanization and an inherently fragile ecological foundation, making the coordination between urban expansion and ecosystem services a critical issue for regional sustainability. Drawing upon multi-temporal land use remote sensing datasets provided by the Chinese Academy of Sciences Resource and Environment Science Data Center, in conjunction with soil, meteorological, and socio-economic data, this study integrates a land use transition matrix, the InVEST model, a modified coupling coordination degree model, and the geographic detector to comprehensively examine land use dynamics, the spatiotemporal evolution of urban expansion, and the spatial heterogeneity of ecosystem services (i.e., carbon storage, water yield, habitat quality, and soil conservation) in Lanzhou. In addition, the coupling coordination relationship and its underlying driving mechanisms are systematically explored. The results demonstrate the following: (1) Between 1980 and 2020, urban land area in Lanzhou increased from 103.87 km² to 286.83 km², accounting for 2.17% of the total area, with cropland constituting the dominant source of expansion and exhibiting a fluctuating “high–low–high” conversion trajectory. (2) Ecosystem services exhibit pronounced spatial heterogeneity, with carbon storage and habitat quality displaying a pattern of “low in the southeast and high in the northwest”, water yield showing an increasing gradient from southeast to northwest, and soil conservation characterized by “lower values in central areas and higher values in peripheral regions”; (3) Urban expansion has accelerated significantly, with Yongdeng County and Gaolan County emerging as principal expansion hotspots during 2010–2020. (4) The dominant driving mechanism gradually shifted from natural factors to the synergistic interaction between natural and socioeconomic factors, and the interaction among driving factors markedly enhanced the explanatory power for ecosystem service evolution. (5) The coupling coordination degree has transitioned from widespread imbalance to a spatially differentiated pattern, characterized by relatively coordinated conditions in peripheral areas and persistent imbalance within the central urban core. These findings provide a robust scientific basis for territorial spatial optimization and the synergistic development of ecological and economic systems in valley-type cities, and offer important implications for sustainable development in arid and semi-arid regions. Full article

Satellite-observed greening in arid regions is often interpreted as ecological restoration success, yet this assessment may conflate natural recovery with agricultural expansion. We developed an Arid Remote Sensing Ecological Index (ARSEI) incorporating a Comprehensive Salinity Index (CSI) to address systematic biases in the traditional RSEI when applied to irrigated drylands. ARSEI scores were validated against MODIS Net Primary Production (NPP) ($R^2>0.75$ at the regional scale), confirming its reliability in capturing ecosystem productivity, while CSI effectively maps the upper-bound of surface salinization potential dictated by intrinsic soil properties. Applied to China’s Mu Us Sandy Land (2000–2024), the ARSEI reveals that 2327 km² of sandy land—54% of current cropland—was converted to agriculture, creating “assessment-induced false greening” signals. While the traditional RSEI increased monotonically (+135%), the ARSEI shows a nuanced pattern with plateau (2010–2015) and decline (2015–2020) phases, reflecting salinization risks masked by high crop NDVI. Optimal Parameters-Based Geographical Detector analysis demonstrates that Land Cover × Precipitation interactions (q = 0.28) drive spatial heterogeneity through irrigation-mediated water redistribution. The ARSEI provides a dialectical evaluation framework: acknowledging agricultural greening’s economic benefits while monitoring subsurface degradation risks. This study offers a critical methodological advance for sustainable land assessment in global drylands undergoing agricultural intensification. Full article

The Yellow River Delta, a young alluvial plain in China, is experiencing severe land subsidence that threatens its ecological security and sustainable development. However, the driving mechanisms of this subsidence exhibit strong spatial heterogeneity, which traditional global models fail to capture. This study integrates high-precision subsidence measurements from Sentinel-1A imagery and SBAS-InSAR technology (2017–2023) with multi-source environmental factors (topography, geology, land use, precipitation) to propose a Spatially Adaptive Ensemble Learning Model with feature selection (SA-GSE). The model concatenates predictions from base learners (CatBoost, XGBoost, Random Forest) with spatial features (e.g., distance to salt pans, local topographic variance) to form meta-features, which are then input into a multilayer perceptron meta-learner. Through 5-fold spatial cross-validation, SA-GSE learns spatially dynamic base-model weights, implicitly adapting to regional variations in subsidence drivers. The model achieves an R² of 0.7810 and RMSE of 40.55 mm/yr on the test set, outperforming individual base models and ordinary stacking. Residual spatial autocorrelation is substantially reduced, with SA-GSE yielding the lowest Moran’s I (0.0334, p = 0.206) among all evaluated models, confirming effective capture of spatial heterogeneity. Driving force analysis reveals that distance to salt pans is the most important predictor (permutation importance: 0.4456), underscoring the dominant role of brine extraction-induced aquifer compaction. Lagged precipitation importance (0.3191) exceeds that of current precipitation (0.2453), indicating a recharge lag effect. SHAP interaction analysis uncovers a nonlinear “precipitation decoupling” mechanism in salt pan areas, where high precipitation paradoxically exacerbates subsidence. The resultant map of predicted subsidence rates highlights elevated rate zones in the northern salt pans and along the Guangli River. While the map does not represent a full risk assessment—as it does not include exposure or vulnerability—it provides a spatially explicit estimate of hazard likelihood. This ensemble framework yields novel perspectives on subsidence drivers in heterogeneous regions and can support land subsidence prevention and groundwater management planning. Full article

To investigate the heterogeneous interactions between various agricultural business entities (abbreviated as ABEs, including farmers, cooperatives and enterprises) and agricultural land transfer (abbreviated as ALT) in China, this study constructs a spatial simultaneous equation model based on the GS3SLS method and applied to data from 30 provinces in 2012–2020. The results show the following: (1) ABEs and ALT demonstrate significant bidirectional positive correlations at the intra-regional level, especially among farmers, while cooperatives and enterprises exhibit more pronounced spatial spillover effects. (2) Despite overall positive correlations, negative interactions emerge in specific entities of some regions (e.g., central China’s ALT among farmers vs. central China’s ABEs among farmers, and eastern China’s ABEs among enterprises vs. neighboring ABEs among enterprises). Conversely, cooperatives maintain universally positive ABE-ALT interactions, peaking in central/western regions. (3) The co-development of ABEs and ALT exhibits temporal heterogeneity: the growth in the number of farmer ABEs lags behind their agricultural land transfer (ALT), whereas for cooperatives and agricultural enterprises, ALT lags behind their growth in numbers. This indicates that the relationship between agricultural operators (“human”) and land transfer (“land”) needs to be reconfigured. The heterogeneous interactive relationships revealed in this study provide a solid theoretical basis for formulating differentiated and precise policies on the transfer of agricultural land and the coordination of various operating entities, so as to efficiently promote agricultural modernization. Full article

Coordinated development between new-type urbanization and rural revitalization is important for sustainable urban–rural transformation and balanced regional development in China. Using panel data for 30 provincial-level units from 2014 to 2023, this study examines the spatiotemporal evolution, dynamic transitions, and external drivers of the coupling coordination degree between the two systems. Spatial Markov chains and an interpretable machine-learning framework are used to identify neighborhood effects, nonlinear relationships, and interaction patterns. The results show four main findings. First, the coupling coordination degree increased over the study period, but clear spatial differences and clustering remained. This suggests that coordinated urban–rural development did not advance evenly across regions. Second, the evolution of coordination shows strong state dependence, and neighborhood context is closely related to transition probabilities. Provinces located in high-coordination neighborhoods were more likely to move to higher levels, while provinces in low-coordination neighborhoods were more likely to remain trapped at lower levels. Third, digital inclusive finance and fiscal self-sufficiency were the most important external factors. Both showed clear nonlinear patterns. Per capita electricity consumption and aging rate also showed heterogeneous relationships at different value ranges. Fourth, the interaction results suggest that higher coordination is more likely to emerge when digital finance, fiscal capacity, openness, human capital, and infrastructure improve together, rather than when only one factor expands on its own. The findings indicate that sustainable urban–rural transformation is shaped by spatial dependence, nonlinear changes, and context-specific factor combinations. Beyond their relevance for more targeted urban–rural coordination and place-based sustainability governance in China, these findings also provide a useful reference for other developing countries seeking to address similar urban–rural development challenges. Full article

Carbon use efficiency (CUE) is a critical indicator of ecosystem carbon sink capacity. However, the nonlinear response of CUE to complex environmental drivers remains poorly understood in subtropical humid regions. This study analyzed the spatiotemporal dynamics of vegetation CUE in the Poyang Lake Basin (PYLB) from 2001 to 2020 and quantified the influence of natural and anthropogenic factors using XGBoost-SHAP and GeoDetector models. The results showed that: (1) The average annual CUE in the PYLB was 0.455, exhibiting a declining trend, with a linear rate of −0.001524 yr⁻¹. (2) SHAP analysis revealed that the association between LAI and CUE exhibited a non-monotonic transition at a threshold of approximately 1.80. Specifically, while lower LAI levels were positively correlated with CUE, this relationship shifted to a negative trend as LAI exceeded the threshold, demonstrating a phase-specific coupling pattern across the canopy density gradient. For hydrothermal drivers, CUE exhibited localized downward fluctuations when precipitation was between 1700 and 2700 mm and temperature ranged from 15.0 to 18.0 °C. (3) GeoDetector analysis indicated that LAI was the dominant individual factor controlling the spatial heterogeneity of CUE (q = 0.471), and its interaction with TEM exerted the strongest synergistic effect (q = 0.493). These results emphasize the necessity of considering nonlinear thresholds and factor interactions when evaluating ecosystem carbon budgets in changing climates. Full article

Despite the ecological importance of desert ecosystems in Qatar, quantitative analyses integrating species diversity, phytogeographical regionalisation, and environmental drivers remain limited. This study applied species distribution models (SDMs) to delineate phytogeographical regions of Qatar, followed by the identification of indicator species and associated environmental drivers of each region using indicator species analysis and Relative Environmental Turnover (RET). Species distributions were developed for 112 perennial species to address sampling incompleteness, and converted into a presence–absence matrix, which was subjected to UPGMA clustering to identify phytogeographical regions. The analysis delineated three distinct phytogeographical regions: Shrubland–Gravel, Coastal Halophytic, and Inland Sandy Desert. Species richness exhibited a clear spatial gradient, with high richness (>60 species per site) concentrated in northeastern Qatar and declining towards the south. Indicator species analysis identified nine species as strong regional indicators, reflecting pronounced habitat specialisation. RET analysis revealed that soil nitrogen and organic carbon were strongly associated with the Coastal Halophytic region, indicating enhanced nutrient availability in these saline environments. Although the other regions did not exhibit statistically significant environmental clustering, descriptive patterns suggested tendencies toward higher precipitation in the Shrubland–Gravel region and elevated sand content and temperature in the Inland Sandy Desert region. Collectively, these findings demonstrate that vegetation patterns in Qatar are structured by the interaction of soil properties, precipitation variability, and landform heterogeneity. The integration of SDMs with clustering and environmental analyses provides a robust framework for phytogeographical analysis and supports biodiversity conservation and sustainable land management in hyper-arid environments. Full article