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Wildfire smoke fine particles (PM2.5) are a growing public health threat as wildfire events become more common and intense under climate change, especially in the Western United States. Studies assessing the association between wildfire PM2.5 exposure and health typically summarize the effects over the study area. However, health responses to wildfire PM2.5 may vary spatially. We evaluated spatially-varying respiratory acute care utilization risks associated with short-term exposure to wildfire PM2.5 and explored community characteristics possibly driving spatial heterogeneity. Using ensemble-modeled daily wildfire PM2.5, we defined a wildfire smoke day to have wildfire-specific PM2.5 concentration ≥15 μg/m3. We included daily respiratory emergency department visits and unplanned hospitalizations in 1,396 California ZIP Code Tabulation Areas (ZCTAs) and 15 census-derived community characteristics. Employing a case-crossover design and conditional logistic regression, we observed increased odds of respiratory acute care utilization on wildfire smoke days at the state level (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.05, 1.07). Across air basins, ORs ranged from 0.88 to 1.57, with the highest effect estimate in San Diego. A within-community matching design and spatial Bayesian hierarchical model also revealed spatial heterogeneity in ZCTA-level rate differences. For example, communities with a higher percentage of Black or Pacific Islander residents had stronger wildfire PM2.5-outcome relationships, while more air conditioning and tree canopy attenuated associations. We found an important heterogeneity in wildfire smoke-related health impacts across air basins, counties, and ZCTAs, and we identified characteristics of vulnerable communities, providing evidence to guide policy development and resource allocation.

Commuting flows refer to the regular movement of people from their homes to workplaces. The spatial heterogeneity of commuting flows indicates the uneven spatial distribution of places of residence, employment, or job-residence connections. Understanding the characteristics of spatial heterogeneity in commuting flows is vital for effective transportation planning. However, limited by the scarcity of flow data, traditional research on commuting flows predominantly focuses on the spatial distribution of employment, residence, and job-residence connections individually, which fails to unveil the spatial heterogeneity of commuting flows. In this study, we examined the spatial heterogeneity of commuting flows in Beijing using mobile phone data. We analyzed the degree of heterogeneity and the aggregation scale of commuting flows. The results showed that (1) the degree of spatial heterogeneity varies between different regional pairs, and (2) the aggregation scale of commuting flows varies with distance; moreover, the longer the distance, the larger the aggregation scale of commuting flow distribution, and dominant heterogeneous clusters expand from the center to the periphery. These findings enhance the strategic planning of public transit. In less densely populated areas such as those outside the Fifth Ring Road, efficiency can be boosted by new bus routes. In contrast, denser areas such as those within the Third Ring Road, may benefit from an integrated approach: establishing new rail systems alongside expanded bus services to optimize commuting. [ABSTRACT FROM AUTHOR]

AbstractSpatial interpolation is essential for handling sparsity and missing spatial data. Current machine learning-based spatial interpolation methods are subject to the statistical constraints of spatial stratified heterogeneity (SSH), normally involving separate modeling of each stratum and simple weighted averaging to integrate intra-stratum and inter-strata features. However, these models overlook the different contributions of inter-strata features to different locations within a stratum (heterogeneous inter-strata associations, HIA) and the explanation of spatial effects on the interpolation process, leading to suboptimal and unreliable interpolation outcomes. This article proposes a novel explainable spatial interpolation method considering SSH (X-SSHM). Spatial and environmental features are utilized to describe intra-stratum and inter-strata information, which are fed into random forest-based learners to achieve high-level semantic feature mapping. Geographically weighted regression is employed to integrate intra-stratum and inter-strata features to achieve a unified expression of SSH and HIA, obtaining the final interpolation result. Geographically weighted Shapley (GSHAP) is proposed to decompose the marginal contributions of intra-stratum and inter-strata features. Model performance is evaluated on simulated and soil organic matter datasets. X-SSHM outperformed five baselines regarding interpolation accuracy. Moreover, statistical methods validated X-SSHM’s ability to elucidate the mechanisms by which SSH, spatial autocorrelation and HIA affect the model interpolation process. [ABSTRACT FROM AUTHOR]

Dockless bike sharing (DBS) and metro integration offer innovative solutions for the first-mile and last-mile challenges of urban transportation. However, accurately identifying transfer passenger volume, analyzing DBS and metro integration characteristics, and understanding the spatial heterogeneity of factors affecting this volume remain areas to explore. This study, using Shenzhen as a case, constructs a framework to identify transfer passenger volume by considering built environment elements around metro stations and applying a Thiessen polygon. Transfer passenger volumes are classified into six categories, and their spatio-temporal characteristics are analyzed. A multiscale geographically weighted regression (MGWR) model investigates the impacts of socio-economic attributes, built environment, and transportation-related factors on transfer passenger volume, revealing spatial heterogeneity. Results show that the demand for DBS and metro integration is higher in the morning peak than in the evening peak, with access transfer passenger volume peaks occurring earlier than egress peaks. Spatially, transfer passenger volume shows heterogeneity, with higher demand in the urban core than in the suburbs. Commercial and residential and enterprises have the most significant impact on various types of transfer passenger volume, showing the highest spatial heterogeneity in enterprises. In contrast, science education and culture positively influence evening peak access transfer volume and exhibit the weakest spatial heterogeneity. Population density, sports and leisure, shopping and entertainment, and tourist attractions display localized positive or negative impacts. These findings can guide operators and planners in optimizing public transportation resources to enhance DBS and metro integration. [ABSTRACT FROM AUTHOR]

Bioprinted breast tumor microenvironment (TME) models with spatial heterogeneity recaptured a well-defined cancer cell-rich stroma structure. Heterogeneity in angiogenesis and extracellular matrix (ECM) stiffness was found in bioprinted TME models. Intercellular crosstalk was identified in bioprinted TME models, which was associated with tumor angiogenesis and ECM remodeling. Bioprinted TME models demonstrated spatially heterogeneous drug resistance in breast cancer. Cellular, extracellular matrix (ECM), and spatial heterogeneity of tumor microenvironments (TMEs) regulate disease progression and treatment efficacy. Developing in vitro models that recapitulate the TME promises to accelerate studies of tumor biology and identify new targets for therapy. Here, we used extrusion-based, multi-nozzle 3D bioprinting to spatially pattern triple-negative MDA-MB-231 breast cancer cells, endothelial cells (ECs), and human mammary cancer-associated fibroblasts (HMCAFs) with biomimetic ECM inks. Bioprinted models captured key features of the spatial architecture of human breast tumors, including varying-sized dense regions of cancer cells and surrounding microvessel-rich stroma. Angiogenesis and ECM stiffening occurred in the stromal area but not the cancer cell-rich (CCR) regions, mimicking pathological changes in patient samples. Transcriptomic analyses revealed upregulation of angiogenesis-related and ECM remodeling-related signatures in the stroma region and identified potential ligand–receptor (LR) mediators of these processes. Breast cancer cells in distinct parts of the bioprinted TME showed differing sensitivities to chemotherapy, highlighting environmentally mediated drug resistance. In summary, our 3D-bioprinted tumor model will act as a platform to discover integrated functions of the TME in cancer biology and therapy. Graphical abstract [Display omitted] This study demonstrates a proof of concept for an in vitro 3D-bioprinted tumor model that recapitulates the spatial heterogeneity of the breast tumor microenvironment. The model mimics key physiological features of patient samples, such as localized vasculature and mechanical stiffness variations, and identifies potential intercellular interactions involved in pathological processes, validating its biological relevance. It also exhibits spatially distinct chemotherapeutic sensitivities, highlighting its potential for studying microenvironment-mediated drug resistance and discovering novel therapies. To advance this technology, optimizing and standardizing the bioprinting process are essential for reproducibility and scalability. Functional assays and preclinical testing are required to validate predictive accuracy. Collaboration with clinical researchers and regulatory bodies is crucial for validation and scale-up, enhancing cancer research and improving therapeutic strategies. Applied bioprinted vascularized breast tumor models recapturing tumor–stroma structures of human breast tumors to study intratumoral heterogeneity and drug resistance. Bioprinted tumor models recapitulated spatially distinct pathological features and identified potential intercellular interactions regulating these pathological changes. Bioprinted tumor models also demonstrated microenvironmentally mediated drug resistance. [ABSTRACT FROM AUTHOR]

With the rapid urbanization in China, urban land resources gradually become the core of urban development. This study spatially evaluated the urban land resource carrying capacity (LRCC) with a case study of the built‐up area in Wuhan from 2015 to 2020. Following an evaluation index system, five critical LRCC indicators, including population density, GDP per land area, plot ratio, building density, and road network density, were selected by an analytical hierarchical process. The synthesis of indicators, however, is usually challengeable due to homogeneous assumptions of traditional techniques. In this study, we adopted a local technique, geographically weighted principal component analysis, to calculate a comprehensive carrying pressure (CCP) concerning spatially varying contributions of each indicator on their synthesis across different geographic locations. On mapping these spatial outputs of the built‐up area in Wuhan, the highest CCP was found in the central areas, where population size tends to be influential and the dominant variable in 62.69% of subdistricts. Furthermore, increased construction over the 5 years has led to an increased CCP in some of the peripheries of the built‐up area, and 55.22% of subdistricts show rising changes. With the GWPCA technique, this framework works well in evaluating and analyzing urban LRCC from a new local perspective. [ABSTRACT FROM AUTHOR]

The forest floor is an integral component of forest soils, which constitutes a significant yet underaccounted terrestrial carbon pool. Comprehensive studies were conducted at a test site in the Kivach State Nature Reserve, which represents little disturbed middle-taiga ecosystems of Eastern Fennoscandia. Forest floor properties were studied following the methods guidelines of the project "National system for monitoring the dynamics of climate-active substances in terrestrial ecosystems of the Russian Federation". Organic carbon content (Сorg) was determined using Perkin Elmer 2400 Series II CHNS/O Analyzer (USA). The forest floor is a heterogeneous formation with quite variable composition and state. There predominantly (80% of all sampling points) forms a fermentation-type raw-humus forest floor with average thickness of 5.9 ± 0.2 cm and a stock of 49.8±1.9 t/ha in the study area. Organic matter is lost gradually in the course of dead plant biomass degradation, so Сorg content in the dwarf shrub–true moss habitats predominating at the test site was the highest in the top layer of the forest floor, OL, reaching 52.8 ± 0.6%. Carbon content in the lower floor subhorizons declined to 40.8 ± 2.0%. Where the proportion of forb species increased, average Сorg content significantly decreased, to 19%. Average estimated forest-floor Сorg stock in the studied ecosystems is 20.9 ± 0.9 t/ha. It exhibited high spatial variation of 1.5 to 45 t/ha. The predictors of forest-floor Сorg variation in the study area include land cover characteristics, dominant tree species, and location relative to the tree. Thorough estimation of the contribution of forest-floor carbon is necessary for accurate quantification of the stock of this pool within the new national system for monitoring carbon pools and greenhouse gas fluxes. [ABSTRACT FROM AUTHOR]

Traffic accident prediction is a crucial component of an intelligent traffic system, which is important to maintain citizen safety and decrease economic losses. Current methods for traffic accident prediction based on deep learning fail to consider the driving mechanisms of traffic accidents, so a novel traffic accident prediction method based on multi-view spatial-temporal learning is proposed, which represents the driving mechanism of traffic accidents from multiple views. Firstly, for the urban regions divided by grids, a new augmentation was designed to augment the spatial semantic information of regions through learnable semantic embedding, then deformable convolutional networks with non-fixed convolution kernels are used to learn dynamic spatial dependencies between regions and gated recurrent units are used to learn temporal dependencies, which can capture dynamic spatial-temporal evolution patterns of traffic accidents. Secondly, long short-term memory is employed to learn the traffic flow breakdown from the flow difference of adjacent time steps in each region to recognize the traffic accident precursor in the risk environment. Thirdly, accident patterns in different regions are learned from historical traffic flow to determine whether the flow is the dominant factor and capture the spatial heterogeneity of traffic accidents. Finally, the above features are fused for accident prediction at the regional level. Experiments are conducted on two real datasets, and the experimental results show that the proposed method outperforms eight benchmark methods. [ABSTRACT FROM AUTHOR]

One strand of modern coexistence theory (MCT) partitions invader growth rates (IGR) to quantify how different mechanisms contribute to species coexistence, highlighting fluctuation‐dependent mechanisms. A general conclusion from the classical analytic MCT theory is that coexistence mechanisms relying on temporal variation (such as the temporal storage effect) are generally less effective at promoting coexistence than mechanisms relying on spatial or spatiotemporal variation (primarily growth‐density covariance). However, the analytic theory assumes continuous population density, and IGRs are calculated for infinitesimally rare invaders that have infinite time to find their preferred habitat and regrow, without ever experiencing intraspecific competition. Here we ask if the disparity between spatial and temporal mechanisms persists when individuals are, instead, discrete and occupy finite amounts of space. We present a simulation‐based approach to quantifying IGRs in this situation, building on our previous approach for spatially non‐varying habitats. As expected, we found that spatial mechanisms are weakened; unexpectedly, the contribution to IGR from growth‐density covariance could even become negative, opposing coexistence. We also found shifts in which demographic parameters had the largest effect on the strength of spatial coexistence mechanisms. Our substantive conclusions are statements about one model, across parameter ranges that we subjectively considered realistic. Using the methods developed here, effects of individual discreteness should be explored theoretically across a broader range of conditions, and in models parameterized from empirical data on real communities. [ABSTRACT FROM AUTHOR]

Urban transportation significantly contributes to greenhouse gas emissions, with CO2 emissions from urban travelers varying across different regions. Identifying the determinants of residents' travel-related CO2 emissions (TCE) from a spatial perspective is significant for formulating effective carbon reduction strategies. However, limited research explores the spatial heterogeneous relationship between TCE and its determinants. In this study, we focus on Guiyang, China, as a representative city and adopt Traffic Analysis Zones (TAZ) as geospatial units of analysis to capture regional differences in CO2 emissions. By analyzing data from the 2021 Guiyang resident travel survey, we measure per capita CO2 emissions and reveal that in Guiyang, each person emits 599.42 g of CO2 during their travels. Furthermore, we apply a semiparametric geographically weighted regression model to investigate the spatial nonstationarity of the variables' impact on TCE. Our findings reveal that local variables have heterogeneous effects on TCE across different TAZs. For example, while the Huaxi district in southern Guiyang shows a negative correlation between TCE and the proportion of low-income individuals, most regions in Guiyang exhibit the opposite trend with a positive correlation. In the northern regions of Guiyang, such as Kaiyang and Xifeng, the proportion of car ownership negatively impacts TCE, contrasting with conclusions drawn from other regions. Moreover, the results of geographical variation tests indicate that sociodemographic attributes exhibit greater spatial heterogeneity in their influence on TCE, in contrast to built environment attributes. These results provide valuable theoretical support for policymakers in designing location-specific, low-carbon transportation policies, with significant implications for advancing sustainable transportation development. [ABSTRACT FROM AUTHOR]

Quantifying the effects of environmental factors on soil organic carbon and spatial distribution is fundamental to soil quality regulation, restoration, and response to climate change. The present study aims to explore the spatial distribution characteristics of the soil organic carbon (SOC) contents in Anhui Province, China, based on national soil data. In addition, we used the geographically weighted regression (GWR) model to quantify the influence degrees of environmental factors on the soil organic carbon density (SOCD). The results showed that the spatial distribution of SOCD in Anhui Province in both 1985 and 2018 was characterized by high in the south and low in the north. The GWR model prediction results of the 0–30 cm SOCD showed local coefficients of determination (local R2) ranging from 0.21 to 0.96 and 0.14 to 0.96 in 1985 and 2018, respectively. Therefore, the predicted results were effective in evaluating the overall spatial distribution of the SOCD in Anhui Province. The regression coefficients of the normalized difference vegetation index (NDVI) and air temperature ranged from − 0.39 to 5.67 and − 0.17 to 3.11, respectively, demonstrating their strong controlling effects on the spatiotemporal variations in the 0–30 cm SOCD in Anhui Province. [ABSTRACT FROM AUTHOR]

Landslides are pervasive geological hazards that pose significant risks to human life, property, and the environment. Understanding landslide susceptibility is crucial for predicting and mitigating these disasters. This article advocates for a comprehensive review by systematically compiling and analyzing 146 relevant studies up to 2024. It assesses current progress and limitations and offers guidance for future research. This paper provides a comprehensive overview of the diverse challenges encountered by machine learning models in landslide susceptibility assessment, encompassing aspects such as model selection, the formulation of evaluation index systems, model interpretability, and spatial heterogeneity. The construction of an evaluation index system, which serves as the foundational data for the model, profoundly influences its accuracy. This study extensively investigates the selection of evaluation factors and the identification of positive and negative samples, proposing valuable methodologies. Furthermore, this paper briefly deliberates and compares classical machine learning models, offering valuable insights for model selection. Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. These research findings promise to enhance the precision of landslide susceptibility assessments and furnish effective strategies for risk management. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth. [ABSTRACT FROM AUTHOR]

We propose a reaction-diffusion model to describe the interactive wild and sterile mosquito populations in spatially heterogeneous environments, where the vital dynamics of mosquito populations, e.g., offspring produced per mate, the death rates, the amount of releasing sterile mosquitoes, depend on its location habits and the distribution of wild mosquito populations. First, we show the model may present multiple positive steady states with either Neumann or Dirichlet boundary conditions. Then we investigate the interactive dynamics of mosquito populations with the different initial data. Our results show that wild mosquitoes can be eradicated or suppressed within a certain range with different releasing amounts. Finally, numerical simulations further show that the spatial variability environment within the habitat may play a crucial role in extirpating wild mosquito populations in the context of the sterile insect techniques (SIT). [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to construct models for predicting platinum resistance in high‐grade serous ovarian cancer (HGSOC) derived from quantitative spatial heterogeneity indicators obtained from 18F‐FDG PET/CT images. Methods: A retrospective study was conducted on patients diagnosed with HGSOC. Quantitative indicators of spatial heterogeneity were generated using conventional features and Haralick texture features from both CT and PET images. Three groups of predictive models (conventional, heterogeneity, and integrated) were built. Each group's optimal model was the one with the highest area under curve (AUC). Postoperative immunohistochemical staining for Ki‐67 and p53 was conducted. The correlation between the heterogeneity indicators and scores for Ki‐67 and p53 was assessed by Spearman's correlation coefficient (ρ). Results: A total of 286 patients (54.6 ± 9.3 years) were enrolled. And 107 spatial heterogeneity indicators were extracted. The optimal models for each group were obtained using the Gradient Boosting Machine (GBM) algorithm. There was an AUC of 0.790 (95% CI: 0.696, 0.885) in the conventional model for the validation set, and an AUC of 0.904 (95% CI: 0.842, 0.966) in the heterogeneity model for the validation set. The integrated model achieved the highest predictive performance, with an AUC value of 0.928 (95% CI: 0.872, 0.984) for the validation set. Spearman's correlation showed that HU_Kurtosis had the strongest correlation with p53 scores with ρ = 0.718, while cluster site entropy had the strongest correlation with Ki‐67 scores with ρ = 0.753. Conclusions: Adding quantitative spatial heterogeneity indicators derived from PET/CT images can improve the prediction of platinum resistance in patients with HGSOC. Spatial heterogeneity indicators were related to Ki‐67 and p53 scores. [ABSTRACT FROM AUTHOR]

Summary: Understanding the drivers of assemblages of arbuscular mycorrhizal fungi (AMF) is essential to leverage the benefits of AMF for plant growth and health. Arbuscular mycorrhizal fungi are heterogeneously distributed in space even at small scale. We review the role of plant distribution in driving AMF assemblages (the passenger hypothesis), using a transposition of the conceptual framework of landscape ecology. Because rooting systems correspond to habitat patches with limited carrying capacity that differ in quality due to host‐preference effects, we suggest considering plant communities as mosaics of AMF microhabitats. We review how predictions from landscape ecology apply to plant community effects on AMF, and the existing evidence that tests these predictions. Although many studies have been conducted on the effect of plant compositional heterogeneity on AMF assemblages, they mostly focused on the effect of plant richness, while only a few investigated the effect of configurational heterogeneity, plant connectivity or plant community temporal dynamics. We propose key predictions and future prospects to fill these gaps. Considering plant communities as landscapes extends the passenger hypothesis by including a spatially explicit dimension and its associated ecological processes and may help understand and manipulate AMF assemblages at small spatial scales. [ABSTRACT FROM AUTHOR]

Accurately understanding the travel demand of urban rail transit (URT) systems is crucial for effective operational management. Despite the recognition that the diversity in human activity patterns results in different travel demands, few studies have thoroughly investigated the heterogeneity among passengers and its impact on URT ridership. This study utilizes smart card data collected from the Beijing Subway to categorize passengers into four groups: tourist passengers, flexible commuters, regular commuters, and life‐oriented passengers, based on their spatiotemporal travel patterns. Furthermore, a Multiscale Geographically Weighted Regression (MGWR) model is employed to examine the relationship between station‐level ridership of URT and its determinants, including the built environment and station properties, for each passenger group. The results indicate that the influence of these determinants on station‐level ridership varies across passenger groups and spatial scales. For instance, regular commuters exhibit lower sensitivity to accessibility on workdays, whereas those unfamiliar with the URT network are more concerned about the bus accessibility in pedestrian‐ or bicycle‐unfriendly areas. Notably, for tourist and life‐oriented passengers, the stations significantly affected by population density are concentrated in areas with a higher proportion of elderly individuals. Conversely, for flexible and regular commuters, these stations are predominantly situated in areas associated with internet technology and scientific research. These findings are valuable for policymakers in designing strategies tailored to different passenger groups to balance trip demand and capacity, thereby improving URT services and promoting a sustainable urban environment. [ABSTRACT FROM AUTHOR]

Identifying the green efficiency of water resources and its driving factors is paramount for promoting sustainable development in China. The existing research has primarily focused on the spatial heterogeneity of individual factors that impact green efficiency of water resources. However, it has often overlooked the heterogeneity in the interactions between these factors. In this study, we utilized a multiscale geographically weighted regression (MGWR) model to discern the spatial heterogeneity of the individual factors influencing the green efficiency of water resources in China between 2002 and 2016. Subsequently, we demarcated several subregions based on the coefficients derived from the MGWR model. Employing a geographical detector (GD), we quantified the interactive impacts of different factors within these subregions. Our findings unveiled, for the first time, the diverse patterns in the temporal and spatial fluctuations in the factors impacting the eco-friendliness of water resources. The findings underscored that disregarding the spatial heterogeneity of these interactive effects may result in an underestimation of the interactions among factors. Significantly, in 2016, the impact of tertiary industry proportion and completed investment in pollution treatment displayed an enhanced non-linear effect across the entire sample and concurrently demonstrated a bivariate enhanced effect within subregions. These discoveries contribute to a deeper comprehension of the mechanisms influencing these factors, providing valuable insights for policymakers in crafting region-specific water resource policies tailored to the unique developmental requirements of different areas. [ABSTRACT FROM AUTHOR]

Ecosystem services (ES) are the foundation for the survival and development of human society. It is of great significance to clarify the trade-off and synergy between different types of ES in promoting regional sustainable development. This paper selects the western Jilin Province as a case, focusing on exploring the trade-offs and synergies of ES in ecologically fragile areas, enriching the geographical types of research on the relationships between ecosystem services. Utilizing the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, this study focused on quantifying four pivotal services—carbon storage, water yield, soil conservation, and habitat quality index—over a two-decade span encompassing the years 2000, 2010, and 2020, and analyzed the trade-offs/synergies between the four ecosystem service functions. The results show that from 2000 to 2020, among the four ecosystem services, water yield and soil conservation increased by 13.57 × 109 m3 and 220.61 × 106 t, respectively, while carbon storage and habitat quality decreased by 5.09 × 106 milligrams and 0.01 units, respectively. At the regional scale, there were only synergistic relationships between carbon storage and soil conservation, and carbon storage and habitat quality. This study provides scientific references to promote sustainable development in the western Jilin Province and offers new insights into the trade-off and synergy of ecosystem services in ecologically fragile areas. [ABSTRACT FROM AUTHOR]

Both the amount and spatial arrangement (configurational heterogeneity) of resources can affect population abundance and community diversity via influence on the growth, survival, reproduction, recruitment and movement of species. However, in most cases, it is difficult to separate the effects of resource amount from arrangement because these two attributes are often naturally correlated. In this study, we examined the configurational heterogeneity of resources (oviposition habitat—emergent rocks, ER) within rivers and decoupled the effects of resource amount from those due to the spatial arrangement on oviposition by eight species of aquatic insects (seven caddisflies and one mayfly). To capture the configurational heterogeneity of resources in 28 sites (riffles) across multiple streams in Australia and Scotland, we calculated fractal dimensions (DB) using the box‐counting technique. We then used simulated riffles to explore how numbers of ER, edginess (the proportion of ER along river margins) and patchiness (clustering of emergent rocks in the middle) separately and together affected the values of DB using asymptotic regression models. Finally, we used multiple regression to test whether the numbers of egg masses laid in natural riffles of each of the eight species were explained by the number of ER, fractal dimension or both. The distributions of ER in natural riffles were scale‐independent, self‐repeating patterns (i.e. they were fractal), and values of DB varied significantly among riffles. Variations in fractal dimensions among simulated riffles were significantly related to the number of ER, edginess and patchiness. However, in natural riffles, only the number of ER and patchiness affected DB. Egg mass abundances were related to the fractal dimensions of ER distributions in riffles in three species and to the number of ER in five species. The fractal dimensions of riffles are unlikely to be driven by large‐scale processes but instead may result from within‐riffle variability that influences rock movement, arrangement and emergence. Increased oviposition by aquatic insects in riffles with greater numbers of ER suggests that these species may be limited by the amount of oviposition resources. Of the species responding to fractal dimension, two species favoured tightly clustered ER whereas the third favoured ER in much looser clusters. It is feasible that aquatic insects can detect ER clusters from the air by responding to changes in the reflectivity of water (albedo) caused by turbulence around ER. That fractals can capture configurational heterogeneity of resources in streams suggests that this technique is useful to test for general ecological patterns across diverse stream systems. Whether configurational heterogeneity influences adults directly or indirectly, these results showed that the amount and arrangement of suitable oviposition habitat plays a role in determining egg mass densities, with potential consequences for larval densities. These patterns may have important community‐level and functional consequences and hence spatial arrangements should be considered when humans manipulate these resources in rivers. [ABSTRACT FROM AUTHOR]

We address the linear eigenvalue problem associated with reaction-diffusion models for patchy linear domains with any finite number of unique patches. Interface conditions between adjacent patches may be chosen independently to accurately reflect organism behavior. We show that there exists a real principal eigenvalue with a corresponding non-negative eigenfunction that is non-zero interior to the domain. We demonstrate that our model is a generalization of several existing two-patch models. We consider simple three- and four-patch landscapes representing fragmented reserves to illustrate how our model can be used to analyze population persistence, spatial distribution, and migration dynamics. Fragmented landscapes with increased heterogeneity capture migration dynamics not found in landscapes with only two patch types, such as favorable habitat patches that experience net immigration. Conditions for the existence and non-existence of zero-flux conditions interior to three-patch systems are shown, which provide insight to the location of sources and direction(s) of migration. • Principal eigenvalues are studied for patchy domain problems, and corresponding eigenfunctions are positive on the interior. • An implicit equation is used to obtain numerical solutions to eigenvalue problems on previously intractable patchy domains. • Conditions are derived for the existence of extrema for principal eigenfunctions. • Results are used to simulate different landscapes with fragmented reserves. • Predictions are made for migration across landscapes, including when favorable patches might receive net immigration. [ABSTRACT FROM AUTHOR]

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol' method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies. [ABSTRACT FROM AUTHOR]

Understanding the spatially varying effect mechanism of intermodal connection on metro ridership helps policymakers develop differentiated interventions to promote metro usage, especially for megacities with multiple city sub-centers and ring roads. Using multiple datasets in Shanghai, this study combines Light Gradient Boosting Machine (LightGBM) with Shapley additive explanations (SHAP) to explore these effects with the consideration of the built environment and metro network topology. Results show that the collective impacts of intermodal connection are positive, not only within the main city but also alongside the main commuting corridors, while negative effects occur in the peripheral area. Specifically, bike sharing trips increase metro ridership within the inner ring of the city, while bus services lower metro usage at stations alongside the elevated ring roads. Parking facilities enable metro usage at city sub-centers, and the small pedestrian catchment area increases metro riders alongside the main commuting corridors. Empirical findings help policymakers understand the effect mechanism of intermodal connection for stations in different regions and prioritize customized planning strategies. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a crucial vegetation parameter that characterizes leaf sparsity and canopy structure, and the study of the spatial distribution pattern of the forest LAI and its environmental response can help to reveal the adaptive capacity of forest vegetation to climate change in semiarid areas. In this paper, a remote sensing inversion model of the LAI, which pertains to the forest ecosystem of Xinglong Mountain in the transition zone between the Qinghai‒Tibet Plateau and Loess Plateau, was established by combining an optical instrumentation method, a remote sensing inversion method, and a generalized additive model (GAM). The results showed that (1) the Meris terrestrial chlorophyll index (MTCI) linear regression model provided the greatest explanatory power for the LAI in the Xinglong Mountain forest, with R2 = 0.88 and RMSE = 0.32. (2) The LAI was influenced mainly by the altitude, slope, profile curvature, aspect, planform curvature, temperature, precipitation, and evapotranspiration. According to the single-factor GAM, altitude (R2 = 0.43) explained most of the total variation in the LAI, followed by precipitation (R2 = 0.36). According to the multifactor GAM, the above influencing factors could explain 84.2% of the total variation in the LAI, which was significant (P < 0.001). (3) Interaction analysis revealed that the LAI was significantly influenced by the interaction between topographic and meteorological factors (P < 0.001). It was revealed that the topography of Xinglong Mountain is fragmented, the vertical band spectrum of vegetation is notable, and the forest LAI exhibits high spatial heterogeneity under the interaction between topographic and meteorological factors, reflecting the environmental response mechanism of vegetation growth in forest ecosystems in ecological transition zones. [ABSTRACT FROM AUTHOR]

Human skin displays extensive spatial heterogeneity and maintains distinct positional identity. However, the impact of disease processes on these site-specific differences remains poorly understood, especially in keloid, a skin disorder characterized by pronounced spatial heterogeneity. This study aimed to assess whether the spatial heterogeneity and positional identity observed in different anatomic sites persist in keloids. Transcriptome sequencing was conducted on 139 keloid dermal tissues and 19 keloid fibroblast samples spanning seven distinct anatomic sites to identify the spatial transcriptomic heterogeneity. In addition, single-cell RNA sequencing data were utilized to elucidate the contributions of various cell types to the maintenance of positional identity. Keloid dermal tissues from diverse sites were categorized into three anatomic groupings: trunk and extremity, ear, and mandible regions. Enrichment analysis of differentially expressed genes unveiled that keloids across distinct regions retained unique anatomically-related gene expression profiles, reminiscent of those observed in normal skin. Notably, regional disparities consistently prevailed and surpassed inter-donor variations. Single-cell RNA sequencing further revealed that mesenchymal cells, particularly fibroblasts, made major contributions to positional identity in keloids. Moreover, gene expression profiles in primary keloid fibroblasts demonstrated a remarkable persistence of positional identity, enduring even after prolonged in vitro propagation. Taken together, these findings imply that keloids remain positional identity and developmental imprinting characteristic of normal skin. Fibroblasts predominantly contribute to the spatial heterogeneity observed in keloids. • Keloids across diverse regions retain unique gene profiles, exhibiting associations with underlying tissue development. • The site-specific discrepancy in keloid tissues prevails over the variations attributable to individual donors. • Fibroblasts predominantly contribute to the maintenance of positional identity, which persists after prolonged cultivation. [ABSTRACT FROM AUTHOR]

A series of empirical analytical tools have been adopted to investigate the driving mechanisms of surface urban heat islands (SUHI) on a global scale, among which spatial heterogeneity is yet to be fully elucidated. In this study, we investigated the spatial non-stationarity of the driving factors concerning surface properties, climate conditions, and urbanization processes for global long-term SUHI. First, the potential impact on SUHI was explored using global ordinary least squares regression. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) from local perspectives were employed for comparison. The results show that the MGWR has the highest goodness of fit at 0.87, 0.73, 0.90, 0.74, 0.85, and 0.76 for annual day/night (AD/AN), summer day/night (SD/SN), and winter day/night (WD/WN) scales, respectively. Although both global and local schemes exhibit similar influencing magnitudes and signs on the SUHI, the MGWR is better at capturing spatial non-stationarity. Globally, for AD, AN, SD, SN, WD, and WN, the coefficients of the urban-rural vegetation index difference (ΔEVI) and surface albedo difference (ΔWSA), urban mean precipitation (MAP), wind speed (WS), population density (PD), and urban area (UA) are −0.50, +0.30, +0.16, +1.31, −0.03, and +0.03, respectively, at daytime, and −0.38, −0.33, −0.39, −0.10, +0.18, and +0.08, respectively, at night-time. Given the spatial heterogeneity of multiple factors, ΔEVI exhibits a strong mitigation effect on the SD SUHI especially in arid zones. The negative influence of ΔWSA on night-time SUHI demonstrates a strong latitudinal disparity and greater sensitivity in the equatorial zone. The positive correlations between MAP and AD/SD SUHIs have evident latitudinal and longitudinal variations. The mitigation effect of WS displayed distinct coastal amplification, especially in WD. In contrast, the PD and UA presented prominent positive impacts on night-time SUHI with less seasonal contrast. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) can be regarded as a complex and dynamic microecosystem generated by the interactions of tumor cells, interstitial cells, the extracellular matrix, and their products and plays an important role in the occurrence, progression and metastasis of tumors. In a previous study, we constructed an IEO model (prI-, prE-, and pOst-metastatic niche) according to the chronological sequence of TME development. In this paper, to fill the theoretical gap in spatial heterogeneity in the TME, we defined an MCIB model (Metabolic, Circulatory, Immune, and microBial microenvironment). The MCIB model divides the TME into four subtypes that interact with each other in terms of mechanism, corresponding to the four major links of metabolic reprogramming, vascular remodeling, immune response, and microbial action, providing a new way to assess the TME. The combination of the MCIB model and IEO model comprehensively depicts the spatiotemporal evolution of the TME and can provide a theoretical basis for the combination of clinical targeted therapy, immunotherapy, and other comprehensive treatment modalities for tumors according to the combination and crosstalk of different subtypes in the MCIB model and provide a powerful research paradigm for tumor drug-resistance mechanisms and tumor biological behavior. [ABSTRACT FROM AUTHOR]

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents' spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran's I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments. [ABSTRACT FROM AUTHOR]

GeoAI has emerged as an exciting interdisciplinary research area that combines spatial theories and data with cutting-edge AI models to address geospatial problems in a novel, data-driven manner. Although GeoAI research has flourished in the GIScience literature, its reproducibility and replicability (R&R), fundamental principles that determine the reusability, reliability, and scientific rigor of research findings, have rarely been discussed. This article aims to provide an in-depth analysis of this topic from both computational and spatial perspectives. We first categorize the major goals for reproducing GeoAI research, namely, validation (repeatability), learning and adapting the method for solving a similar or new problem (reproducibility), and examining the generalizability of the research findings (replicability). Each of these goals requires different levels of understanding of GeoAI, as well as different methods to ensure its success. We then discuss the factors that might cause the lack of R&R in GeoAI research, with an emphasis on (1) the selection and use of training data; (2) the uncertainty that resides in the GeoAI model design, training, deployment, and inference processes; and more important (3) the inherent spatial heterogeneity of geospatial data and processes. We use a deep learning-based image analysis task as an example to demonstrate the results' uncertainty and spatial variance caused by different factors. The findings reiterate the importance of knowledge sharing, as well as the generation of a "replicability map" that incorporates spatial autocorrelation and spatial heterogeneity into consideration in quantifying the spatial replicability of GeoAI research. [ABSTRACT FROM AUTHOR]

Purpose: Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Methods: Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Results: Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Conclusion: Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future. [ABSTRACT FROM AUTHOR]

Ozone (O3) pollution has a profound impact on human health, vegetation development, and the ecological environment, making it a critical focus of global academic research. In recent years, O3 pollution in China has been on a steady rise, with ozone emerging as the sole conventional pollutant to consistently increase in concentration without any decline. This study conducted a quantitative analysis of O3 concentrations across 367 Chinese cities in 2019, examining spatial autocorrelation and local clustering of O3 levels, and investigated the diverse relationships between human activity factors and O3 concentration. The seasonal fluctuation of O3 exhibited the "M-type" pattern, with peak concentrations in winter and the lowest levels in summer. The center of O3 pollution migrated southeastward, with the area of highest concentration progressively shifting south along the eastern coast. Moreover, O3 concentration showed a strong positive correlation with population density, road freight volume, and industrial emissions, suggesting that human activities, vehicle emissions, and industrial operations are significant contributors to O3 production. The results provide comprehensive information on the characteristics, causes, and occurrence mechanism of O3 in Chinese cities that can be utilized by global government departments to formulate strategies to prevent and control O3 pollution. [ABSTRACT FROM AUTHOR]

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The abundance of an invasive species within an ecosystem after introduction can depend on multiple factors. Although large-scale abiotic data are typically used to model the distribution of invasive species, there may be other fine-scale environmental or biotic factors within the invaded range influencing changes in the species' distribution and abundance. Here, we examined the abundance of the invasive tropical/subtropical green porcelain crab, Petrolisthes armatus (Gibbes, 1850), at 33 oyster reef sites along 150 km of coastline within the northerly portion of its invaded range. We measured a suite of biological, physical, and landscape level metrics to examine which factors (other than temperature) were most associated with the abundance of this invasive species. P. armatus were present at 32 reefs across our spatial range and densities were highest at the northern sites in our domain. Our results revealed native mud crab density is positively correlated with P. armatus density, accounting for 23% of the spatial variation in abundance of P. armatus within its invaded range, more than 30 years after initial invasion. We hypothesize that the positive associations between native predatory mud crabs and this invasive crab may be a result of hydrodynamics aggregating both species within the same areas, or attraction by both crabs for the same fine-scale habitat structural attributes (i.e., interstitial space). These data emphasize the importance of collecting high-resolution survey data to understand the variables that are correlated with the abundance and distribution of invasive species at regional scales within its invaded range. [ABSTRACT FROM AUTHOR]

Assessing vegetation changes in alpine arid and fragile ecosystems is imperative for informed ecological restoration initiatives and adaptive ecosystem management. Previous studies primarily employed the Normalized Difference Vegetation Index (NDVI) to reveal vegetation dynamics, ignoring the spatial heterogeneity alterations caused by bare soil. In this study, we used a comprehensive analysis of NDVI and its spatial heterogeneity to examine the vegetation changes across the Three-River Headwaters Region (TRHR) over the past two decades. A random forest model was used to elucidate the underlying causes of these changes. We found that between 2000 and 2022, 9.4% of the regions exhibited significant changes in both NDVI and its spatial heterogeneity. These regions were categorized into six distinct types of vegetation change: improving conditions (62.1%), regrowing conditions (11.0%), slight degradation (16.2%), medium degradation (8.4%), severe degradation (2.0%), and desertification (0.3%). In comparison with steppe regions, meadows showed a greater proportion of improved conditions and medium degradation, whereas steppes had more instances of regrowth and slight degradation. Climate variables are the dominant factors that caused vegetation changes, with contributions to NDVI and spatial heterogeneity reaching 68.9% and 73.2%, respectively. Temperature is the primary driver of vegetation dynamics across the different types of change, with a more pronounced impact in meadows. In severely degraded steppe and meadow regions, grazing intensity emerged as the predominant driver of NDVI change, with an importance value exceeding 0.50. Notably, as degradation progressed from slight to severe, the significance of this factor correspondingly increased. Our findings can provide effective information for guiding the implementation of ecological restoration projects and the sustainable management of alpine arid ecosystems. [ABSTRACT FROM AUTHOR]

Clarifying the complex relationships among ecosystem services (ESs) and their driving mechanisms is essential for effective ecosystem management and enhancing human welfare. Nonetheless, the current research on these issues still remains limited; therefore, further theoretical exploration is required. This study aims to quantitatively illustrate the trade-off strength of ESs and investigate the spatiotemporal heterogeneity connections between these relationships and various anthropogenic and natural factors in Baiyangdian basin, China, integrating InVEST, RMSE, geographical detector and MGWR methods. From 2000 to 2020, the total water yield (WY) and nutrient export (NE) increased, while the total carbon storage (CS) and habitat quality (HQ) decreased slightly. The trade-offs of ESs showed spatiotemporal heterogeneity. The most serious trade-off occurred between regulating services (CS and NE) and supporting services (HQ) in 2000, which was mainly distributed in the densely forested and grassed western and northern regions of the basin. The trade-off intensities of half of the pairwise ESs in 2020 increased, with the strengthened areas mainly located in the southeast of the watershed where built-up lands are concentrated. Various factors dominated the trade-offs among ESs, with the interactive effects of multiple drivers being more significant than those of individual factors. Land use type, vegetation cover and precipitation have the most pronounced effect on the trade-offs among ESs. The findings of this study may suggest and advocate for spatial ecological strategies to enhance the integrated and holistic advancement of various ESs and also serve as a reference for regional ecosystem governance and the attainment of sustainable growth. [ABSTRACT FROM AUTHOR]

Due to its flexibility and door-to-door service, taxis are an integral part of the urban transportation system. They have become an essential solution to the first/last mile problem. Even though much research has been conducted on the effects of built environment variables on taxi passengers' travel behaviors, few have accounted for the spatial heterogeneity embedded in multiscale spatial processes. This study applies multiscale geographically weighted regression (MGWR) to investigate the associations between taxi ridership and spatial contexts to address the gaps. The MGWR considerably improves modeling fit compared to the global OLS model by capturing the spatially varying processes at different scales. The results demonstrate the existence of strong spatial non-stationarity in the various built environment factors affecting the spatial distribution of taxi pick-ups and drop-offs. Specifically, increased residential density induces more taxi demand in areas with less access to public transportation than their surrounding units. Increasing bus coverage where bus coverage is relatively low may attract more commuters to adopt taxi plus bus mode for commuting. Road network density has a more substantial effect on taxi ridership in the south end of the city than in the north. The former is characterized by lower road density. This study reveals the complex relationships between the built environment and the distribution of taxi ridership at different spatial scales and provides valuable insights for transport planning, taxi resource allocation and urban governance. [ABSTRACT FROM AUTHOR]

Regional socioeconomic development is intricately tied to reasonable land-use resources. Although many studies have analyzed land-use carbon emissions, there is a lack of analysis of the concept of intensity. Studying the land-use carbon emission intensity (LUCEI) is crucial for shaping effective land management strategies that support the integrated sustainable development of society, the economy, and the environment. This study examines land-use changes on the Loess Plateau (LP) from 2000 to 2020. The coefficient method, spatial autocorrelation analysis, and optimal parameters-based geographical detector model are used to identify and analyze the spatial clustering patterns and influencing factors affecting LUCEI, which provides more in-depth insights for the study of LUCEI. The results indicate: (1) Urban and Grassland areas showed the most significant growth, with Urban areas expanding by 10,845.21 km2 and Grasslands by 7848.91 km2, respectively. This Urban expansion was mainly caused by the conversion of Grassland and Cropland, while Grassland expansion was primarily attributed to the decline in Barren. (2) The average LUCEI on the LP climbed from 0.38 in 2000 to 0.73 in 2020, indicating a 190.70% growth rate. (3) The spatial pattern of LUCEI remained stable but unevenly distributed, with extensive High-High and Low-Low clusters. (4) Socioeconomic factors had a greater explanatory power for LUCEI in the LP than natural factors. The LUCEI is not driven by a single factor, but by the combined influence of multiple factors. The interaction between nighttime light and population density explained the spatial distribution of LUCEI most strongly, with a q-value of 0.928. The findings underscore the critical role of socioeconomic development in shaping carbon emission dynamics on the LP. By linking LUCEI growth to land-use changes, this study offers concrete scientific guidance for policymakers seeking to balance socioeconomic growth with sustainable land-use practices. Based on these results, we recommend developing appropriate urban development plans that optimize land-use structures, enhance regional carbon sequestration capacities, and fully implement green transition requirements. [ABSTRACT FROM AUTHOR]

Soil erosion has been recognized as a critical environmental issue worldwide. While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective, there is a notable gap in understanding the intricate interplay between natural and socioeconomic factors, especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions. To address this, our study evaluates the soil erosion vulnerability at a provincial scale, taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors. We developed an evaluation index system based on 15 indicators of soil erosion vulnerability: exposure, sensitivity, and adaptability. In addition, the combination weighting method was applied to determine index weights, and the spatial interaction was analyzed using spatial autocorrelation, geographical temporally weighted regression and geographical detector. The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020. The soil erosion vulnerability increased before 2000 and then. The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province (Xiantao, Tianmen, Qianjiang and Ezhou) with obvious spatial aggregation that intensified over time. Natural factors (habitat quality index) had negative impacts on soil erosion vulnerability, whereas socioeconomic factors (population density) showed substantial spatial variability in their influences. There was a positive correlation between soil erosion vulnerability and erosion intensity, with the correlation coefficients ranging from -0.41 and 0.93. The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity. [ABSTRACT FROM AUTHOR]

The driving effects of climate change and human activities on vegetation change have always been a focal point of research. However, the coupling mechanisms of these driving factors across different temporal and spatial scales remain controversial. The Southwestern Alpine Canyon Region of China (SACR), as an ecologically fragile area, is highly sensitive to the impacts of climate change and human activities. This study constructed a vegetation cover dataset for the SACR based on the Enhanced Vegetation Index (EVI) from 2000 to 2020. Spatial autocorrelation, Theil-Sen trend, and Mann-Kendall tests were used to analyze the spatiotemporal characteristics of vegetation cover changes. The main drivers of spatial heterogeneity in vegetation cover were identified using the optimal parameter geographic detector, and an improved residual analysis model was employed to quantify the relative contributions of climate change and human activities to interannual vegetation cover changes. The main findings are as follows: Spatially, vegetation cover exceeds 60% in most areas, especially in the southern part of the study area. However, the border area between Linzhi and Changdu exhibits lower vegetation cover. Climate factors are the primary drivers of spatial heterogeneity in vegetation cover, with temperature having the most significant influence, as indicated by its q-value, which far exceeds that of other factors. Additionally, the interaction q-value between the two factors significantly increases, showing a relationship of bivariate enhancement and nonlinear enhancement. In terms of temporal changes, vegetation cover shows an overall improving trend from 2000 to 2020, with significant increases observed in 68.93% of the study area. Among these, human activities are the main factors driving vegetation cover change, with a relative contribution rate of 41.31%, while climate change and residual factors contribute 35.66% and 23.53%, respectively. By thoroughly exploring the coupled mechanisms of vegetation change, this study provides important references for the sustainable management and conservation of the vegetation ecosystem in the SACR. [ABSTRACT FROM AUTHOR]

The fluorescence intensities of the cellular respiratory cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD++ (oxidized flavin adenine dinucleotide) reflect energy metabolism in skin and other tissues and can be quantified in vivo by fluorescence spectroscopy (FS). However, the variability of physiological parameters largely determines the reproducibility of measurement results and the reliability of the diagnostic test. In this prospective study, we evaluated the interday reproducibility of NADH and FAD++ fluorescence intensity measurements in the skin of 51 healthy volunteers assessed by the FS at baseline, after local cooling (10°C) and heating of the skin (35°C). Results showed that the fluorescence amplitude of NADH (AFNADH) in forearm skin was the most reproducible of the FS parameters studied. Assessment of AFNADH in the dorsal forearm in combination with a thermal functional test is the most promising method for clinical use for assessing energy metabolism in the skin. [ABSTRACT FROM AUTHOR]

As one of the two main histologic subtypes of gastric cancer (GC), diffuse-type gastric cancer (DGC) containing poorly cohesive gastric carcinoma (PCC) components has a worse prognosis and does not respond well to typical therapies. Despite the large number of studies revealing the complex pathogenic network of DGC, the molecular heterogeneity of DGC is still not fully understood. We obtained single-cell RNA-seq data and bulk data from the tumor immune single cell hub, the public gene expression omnibus, and the cancer genome atlas databases. A series of bioinformatics analyses were performed using R software. Immunofluorescence staining, hematoxylin and eosin staining, western blot, and functional experiments were used for experimental validation. Caudin-3, -4 and -7 were lowly expressed in DGC and their expression levels were further reduced in PCC. The PCC components were mainly located in the deeper layers of the DGC and had a high level of hypoxic Wnt/β-catenin signaling and stemness. We further identified Insulin Like Growth Factor Binding Protein 7 (IGFBP7) as a marker for PCC components in the deep layer. IGFBP7 is stimulated by hypoxia and promotes cancer cell invasiveness and reduced claudin expression. In addition, programmed death-1 ligand (PD-L1) was specifically expressed in the deep layer, reflecting deep layer-specific immunosuppression. The PCC components are predominantly situated in the deeper layers of DGC. Initial molecular characterization of these PCC components revealed distinct features, including low expression of claudin-3, -4, and -7, high expression of IGFBP7, and the presence of PD-L1. These molecular traits may partially account for the pronounced tumor heterogeneity observed in GC. [ABSTRACT FROM AUTHOR]

The emergence of characteristic towns has created a new development mode for the development of small towns, promoted the extension of talent, technology, finance, information and other factors in rural areas, pulled the integration of rural industries and advanced development, and played an important role in promoting rural revitalization, new urbanization and common prosperity. To realize the high-quality development of characteristic towns, it is necessary to promote the synchronous development of high-quality employment and high-quality life for town residents. Taking Inner Mongolia as an example, the synergistic development relationship between the construction of characteristic towns and high-quality employment is systematically analyzed from the theoretical level and practical level. It is found that the synergistic effect between the construction of characteristic towns and high-quality employment in Inner Mongolia is not strong, and there is obvious spatial heterogeneity in the distribution. Meanwhile, the path of the synergistic development of characteristic towns and high-quality employment is explored with a view to providing certain references and lessons for the sustainable development of characteristic towns. [ABSTRACT FROM AUTHOR]

In this paper, some properties of the solutions for Nicholson’s blowflies system with nonlocal operator and delay are performed in heterogeneous environment. First of all, the existence and uniqueness of the positive steady-state solution are studied by Lyapunouv–Schmidt reduction method and implicit function theorem. Moreover, the asymptotic behaviors of trivial and nontrivial positive steady-state solutions to the system are also researched through the sub-supersolution method and iterative method. This paper ends with conclusion and discussion. [ABSTRACT FROM AUTHOR]

Background: Monitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat. Traditional ground-based sampling methods have been widely used and provide valuable information; however, they are time-consuming, labor-intensive, and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems. Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms. Our objectives were to (1) assess the accuracy of vegetation cover height in grasses using drones, (2) quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant (fall–winter) and growing seasons (spring–summer), and (3) evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite (Colinus virginianus) habitat in South Texas. We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration. Results: Estimated heights from drone imagery had a significant relationship with the field height measurements in September (r2 = 0.83; growing season) and February (r2 = 0.77; dormant season). Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022–2023 following grazing. Dormant season pasture had an increase in bare ground cover, and a shift from many large patches of tall herbaceous cover (40–120 cm) to few large patches of low herbaceous cover (5–30 cm) (p < 0.05). Conclusions: Drones provided high-resolution imagery that allowed us to assess the spatial and temporal changes of vertical herbaceous vegetation structure in a semi-arid rangeland subject to grazing. This study shows how drone imagery can be beneficial for wildlife conservation and management by providing insights into changes in fine-scale vegetation spatial and temporal heterogeneity from livestock grazing. [ABSTRACT FROM AUTHOR]

Based on the survey data of nine primitive broad-leaved Korean pine forest plots ranging from 1 to 10.4 ha in Heilongjiang Province, this study used the moving window method and GIS technology to analyze the variation characteristics of the spatial distribution pattern of forest biomass in each plot. We explored the minimum area that can reflect the structural and functional characteristics of the primitive broad-leaved Korean pine forest, and used computer simulation random sampling method to verify the accuracy of the minimum area. The results showed that: (1) Through the analysis of the spatial distribution raster map of biomass deviation in the plots at various scales of 10 − 100 m, there is a minimum area (0.64 ha) for the critical range of biomass density variation in the primitive broad-leaved Korean pine forest. This minimum area based on biomass density can indirectly reflect the comprehensive characteristics of productivity level per unit area, structure, function, and environmental quality of the primitive broad-leaved Korean pine forest community. (2) Using computer simulation random sampling, it was found that only by sampling in a specific plot larger than or equal to the minimum area can equivalent or similar results be achieved as random sampling within the plot, indicating that the minimum area determined by the moving window method is accurate. (3) The minimum area determined in this paper is an excellent indicator reflecting the complexity of community structure, which can be used for comparing changes in community structure and function before and after external disturbances, and has a good evaluation effect. This minimum area can also be used as a basis for scientific and reasonable setting of plot size in the investigation and monitoring work of broad-leaved Korean pine forests in this region, thereby achieving the goals of improving work efficiency and saving work costs. [ABSTRACT FROM AUTHOR]

Objective: To investigate the role of protein kinase C (PKC) in action potential duration (APD) restitution and ventricular tachyarrhythmias (VAs). Methods and results: Rabbits hearts were isolated and prepared for Langendorff perfusion technique. The stimuli–extra‐stimulus (S1–S2) method and dynamic S1 pacing protocol were performed to construct APD restitution and to induce APD alternans or VA, respectively, at 10 sites throughout the ventricular chamber. Administration of phorbol‐12‐myristate‐13‐acetate (PMA) (100 nM) (n = 15) greatly steepened the restitution curves (Smax > 1) (p <.01) at each site compared to the control group (n = 15). Furthermore, treatment with PMA also induced larger spatial dispersions of Smax (p <.05) and decreased the thresholds of the VA and APD alternans (p <.01). However, perfused with the PKC inhibitor, bisindolylmaleimide (BIM) (500 nM) (n = 10), reversibly flattened the APD restitution curves at each site (Smax < 1), decreased the spatial dispersions of Smax, and increased the thresholds of APD alternans and VA. According to the results of patch‐clamp, peak amplitude of L‐type Ca2+ current was significantly increased by addition of PMA compared with control (CTL) group (p <.05). Antagonize this current with verapamil (n = 10) can fully inhibited the PMA induced increasing of Smax and inducibility of VA and alternans. Conclusion: PKC activation increased the dispersion of APD restitution and thus led to occurrence of VA, which possibly related to the increased Ca2+ influx. [ABSTRACT FROM AUTHOR]

Urban vibrancy emerges from the complex interplay between individuals and public spaces. Recognizing the importance of capturing people's experience and perception of streetscape and network configurations, this study investigates the relationship between human-scale street design and urban vibrancy in four Chinese metropolises: Nanjing, Wuhan, Xi'an, and Shenyang. Using a bottom-up metric framework with street view images and spatial design network analysis, this study assesses human-scale street design across five dimensions: reachability, betweenness, detours, aesthetics, and disorder. Multiscale geographically weighted regression is employed to examine how these design metrics spatially affect urban vibrancy during daytime and nighttime. The findings reveal the efficacy of enhancing street closeness and optimizing green spaces in promoting urban vibrancy. It further identifies mitigating strategies for reversing declines in urban vibrancy, such as addressing physical disorder and reducing street network severance. Additional analysis using a geographical detector explores the interactive effects of these elements, emphasizing the necessity of a comprehensive approach to urban planning. The comparative analysis across multiple cities amplifies the generalizability of the findings, offering actionable insights for adaptable, vibrancy-oriented urban planning. [ABSTRACT FROM AUTHOR]

2D Ruddlesden‐Popper perovskites (RPP) with formamidinium‐cesium (FAC) alloyed cations possess powerful thermal‐moisture stability. However, their photovoltaic performance is hindered by the elusive spatial heterogeneity at multiscale lengths, highly associated with their coupled charge selective contacts. Herein, it reports on how the rational formation of self‐assembly molecules (SAMs) govern the orderliness of the spatial heterogeneity and crystal growth model in FACs‐RPP films, which is proposed to dictate the charge‐carrier dynamics. Unlike the disordered RPP film driven by the amorphous polymeric contacts, the laterally ordered crystallized SAM contacts facilitate a spatially ordered 2D/3D heterogeneity of FACs‐RPP film in long range, where distinct minor 2D phases are tightly coupled in short range. Such ordered heterogeneity can improve the energy transfer efficiency, facilitating the charge‐carrier dynamics in a well‐aligned 2D/3D landscape is discovered. Finally, the champion solar cells using selective SAM‐based FACs‐RPP films (n = 5) yield an efficiency of 18.85% (certified 18.19%) and excellent damp‐heat/operational stability, ranking in the top league of reported 2D RPP solar cells. More importantly, this SAM‐based principle can be adapted to diverse 2D structures, active areas, and substrate types. This work provides design directions for leveraging bottom‐selective contacts to tune spatial heterogeneity in state‐of‐the‐art 2D RPP optoelectronics. [ABSTRACT FROM AUTHOR]

The impacts of large‐scale disturbance events on the species diversity of rocky intertidal sessile assemblages across multiple spatial scales are not well understood. To evaluate the influence of the 2011 Tohoku Earthquake on alpha and beta diversities of rocky intertidal sessile assemblages, we surveyed sessile assemblages in the mid‐shore zone from 2011 to 2019 and compared the data with those collected from 2003 to 2010 before the earthquake at the same region. The census was conducted across 22 study plots on five rocky shores along 30 km of the Sanriku Coast of Japan, which is located 150–160 km north–northwest of the earthquake epicenter. Alpha diversity was measured with three Hill numbers (H0, H1, and H2), which represent the number of equally common species that would exist in a community with the same diversity as the sampled community, with higher values of the subscript indicating more weight placed on abundant species. Beta diversity was measured with two metrics (BDtotal at two spatial scales). Values were compared between the post‐earthquake period (2011–2019) and the pre‐earthquake period (2003–2010). The results show that the Tohoku Earthquake significantly altered the species diversity of intertidal sessile assemblages across multiple spatial scales. All diversity metrics obtained at multiple spatial scales (i.e., alpha diversities: H0, H1, and H2; beta diversities: BDtotal at the shore and regional scales) decreased immediately after the earthquake and then increased in subsequent years. At 2 years after the earthquake, H0 recovered to within the range of pre‐earthquake values and H1 and H2 became significantly higher than pre‐earthquake values. Most metrics of alpha and beta diversities recovered to pre‐earthquake levels after several years, but regional BDtotal remained low for a longer period. [ABSTRACT FROM AUTHOR]

The spatially resolved transcriptomic study is a recently developed biological experiment that can measure gene expressions and retain spatial information simultaneously, opening a new avenue to characterize fine-grained tissue structures. In this article, we propose a nonparametric Bayesian method named BINRES to carry out the region segmentation for a tissue section by integrating all the three types of data generated during the study—gene expressions, spatial coordinates, and the histology image. BINRES is able to capture more subtle regions than existing statistical partitioning models that only partially make use of the three data modes and is more interpretable than neural-network-based region segmentation approaches. Specifically, due to a nonparametric spatial prior, BINRES does not require a prespecified region number and can learn it automatically. BINRES also combines the image and the gene expressions in the Bayesian consensus clustering framework and thus flexibly adjusts their label alignment contribution weights in a data-adaptive manner. A computationally scalable extension is developed for large-scale studies. Both simulation studies and the real application to three mouse spatial transcriptomic datasets demonstrate that BINRES outperforms the competing methods and easily achieves the uncertainty quantification of the integrative partition. The R package of the proposed method is publicly available at . for this article are available online. [ABSTRACT FROM AUTHOR]