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ABSTRACT Space is a critical and also challenging frontier in human microbiome research. It has been found that lack of consideration of scales beyond individual and ignoring of microbe dispersal are two crucial roadblocks in preventing deep understanding of the spatial heterogeneity of human microbiome. Assessing and interpreting the heterogeneity and dispersal in microbiomes explicitly are particularly challenging, but implicit approaches such as Taylor's power law (TPL) can be rather effective. Based on TPL, which achieved a rare status of ecological laws, we introduce a general methodology for characterizing the spatial heterogeneity of microbiome (i.e. characterization of microbial spatial distribution) and further apply it for investigating the heterogeneity–disease relationship (HDR) via analyzing a big dataset of 26 MAD (microbiome-associated disease) studies covering nearly all high-profile MADs including obesity, diabetes and gout. It was found that in majority of the MAD cases, the microbiome was sufficiently resilient to endure the disease disturbances. Specifically, in ∼10–16% cases, disease effects were significant—the healthy and diseased cohorts exhibited statistically significant differences in the TPL heterogeneity parameters. We further compared HDR with classic diversity–disease relationship (DDR) and explained their mechanistic differences. Both HDR and DDR cross-verified remarkable resilience of the human microbiomes against MADs. Since assessing and interpreting the heterogeneity and dispersal in microbiomes explicitly are particularly challenging, we introduce an implicit approach, i.e. Taylor's power law, to investigate the heterogeneity–disease relationship in the human microbiome-associated diseases.

The paper investigates the spatial heterogeneity of public’s preferences for the implementation of a new country-wide forest management and protection program in Poland. Spatial econometric methods and high resolution geographical information system data related to forest characteristics are used to explain the variation in individual-specific willingness to pay (WTP) values, derived from a discrete choice experiment study. We find that respondents’ WTP is higher the closer they live to their nearest forest, and the scarcer forests are in the area where they live. Interestingly, the higher the ecological value of forests in respondents’ area, the more people prefer extending areas of national forest protection. We also investigate spatial patterns in individual-specific WTP scores and in latent class membership probabilities, finding that preferences are indeed spatially clustered. We argue that this clustering should be taken into account in forest management and policy-making.

Woody encroachment has been a major land cover change in dryland ecosystems during the past century. While numerous studies have demonstrated strong effects of woody encroachment on soil carbon (C), nitrogen (N), and phosphorus (P) storage, far less is known about the plasticity of soil C:N:P stoichiometry in response to woody encroachment. We assessed landscape-scale patterns of spatial heterogeneity in soil C:N:P ratios throughout a 1.2 m soil profile in a region where grassland is being replaced by a diverse assemblage of subtropical woody plants dominated by Prosopis glandulosa, an N₂-fixing tree. Woody species had leaf and fine root C:N:P ratios significantly different from grasses. Variation in soil C:N ratios in both horizontal and vertical planes was remarkably smaller than that of soil N:P and C:P ratios. Spatial patterns of soil C:N ratio throughout the profile were not strongly related to vegetation cover. In contrast, spatial patterns of soil N:P and C:P ratios displayed a strong resemblance to that of vegetation cover throughout the soil profile. Within the uppermost soil layer (0–5 cm), soil N:P and C:P ratios were higher underneath woody patches while lower within the grassland; however, this pattern was reversed in subsurface soils (15–120 cm). These results indicate a complex response of soil C:N:P stoichiometry to vegetation change, which could have important implications for understanding C, N, and P interactions and nutrient limitations in dryland ecosystems.

The non-uniformity of the relationships between urban temperature and landscape has attracted board attention. The non-uniformity in urban areas is reflected in the spatial landscape’s heterogeneity and the difference of socio-economic functions. The former is shown as the spatial differentiation of land-cover, land-use, landscape composition, and configuration, while the latter leads to the difference of the intensity of human activities and population density, which are closely related with anthropogenic heat emission. Therefore, this study introduces urban functional zones (UFZs) to express urban spatial heterogeneity. This study also attempts to comprehend urban heat island (UHI) effects and discloses the variability of urban surface temperature (LST)–landscape relationships in different kinds of UFZs. There are two main technical difficulties—how to characterize the spatial heterogeneity of UFZs and how to quantify non-uniform LST effects. A three-level variable system is established from their attributes, inner structures, and interrelationships to characterize UFZs and their LST effects hierarchically. Considering the multi-collinearity among high-dimensional variables, the Elastic Net regression method is selected for quantitative analysis. The experimental results reveal the deficiency of uniform LST analysis for heterogeneous urban areas and verify the variable relationships of LST-landscaped with different kinds of UFZs.

1. Recent global trends of increasing woody plant abundance in grass-dominated ecosystems may substantially enhance soil organic carbon (SOC) storage and could represent a strong carbon (C) sink in the terrestrial environment. However, few studies have quantitatively addressed the influence of spatial heterogeneity of vegetation and soil properties on SOC storage at the landscape scale. In addition, most studies assessing SOC response to woody encroachment consider only surface soils, and have not explicitly assessed the extent to which deeper portions of the soil profile may be sequestering C. 2. We quantified the direction, magnitude and pattern of spatial heterogeneity of SOC in the upper 1-2 m of the profile following woody encroachment via spatially specific intensive soil sampling across a landscape in a subtropical savanna in the Rio Grande Plains, USA, that has undergone woody proliferation during the past century. 3. Increased SOC accumulation following woody encroachment was observed to considerable depth, albeit at reduced magnitudes in deeper portions of the profile. Overall, woody clusters and groves accumulated 12-87 and 18-67 Mg ha⁻¹ more SOC compared to grasslands to a depth of 1·2 m. 4. Woody encroachment significantly altered the pattern of spatial heterogeneity of SOC to a depth of 5 cm, with marginal effect at 5-15 cm, and no significant impact on soils below 15 cm. Fine root density explained greater variability of SOC in the upper 15 cm, while a combination of fine root density and soil clay content accounted for more of the variation in SOC in soils below 15 cm across this landscape. 5. Synthesis. Substantial soil organic carbon sequestration can occur in deeper portions of the soil profile following woody encroachment. Furthermore, vegetation patterns and soil properties influenced the spatial heterogeneity and uncertainty of soil organic carbon in this landscape, highlighting the need for spatially specific sampling that can characterize this variability and enable scaling and modelling. Given the geographic extent of woody encroachment on a global scale, this undocumented deep soil carbon sequestration suggests this vegetation change may play a more significant role in regional and global carbon sequestration than previously thought.

Fractal geometry has provided a new perspective for urban road network morphology research. This study systematically verifies and analyzes the spatial heterogeneity of fractal characteristics and influencing factors of urban road networks using spatial analysis. Here, Tokyo Metropolis was selected as a case, and the fractal dimensions of road networks were calculated. To determine the spatial heterogeneity in the relationship between fractal dimensions and influencing factors, we examined the spatial distribution characteristics of fractal dimensions using spatial autocorrelation analysis, selected population, build-up area density, and road network density as the explanatory variables, and established the global regression model and local regression model using ordinary least squares (OLS) and geographically weighted regression (GWR), respectively. The results indicated that the spatial distribution of fractal dimensions of the urban road network exhibited an obvious tendency toward geographical dependency. Considering the spatial heterogeneity in the relationship between the fractal characteristics of the road network and the influencing factors not only improves the reliability of analysis but also helps planners and decision-makers grasp the morphological characteristics of the urban road network and estimate the evolution of the road network, thereby promoting the development of urban road networks in a more orderly, efficient, and sustainable direction.

Data from the Idealized Planar-Array experiment for Quantifying Spatial heterogeneity are used to perform a control volume analysis (400 × 400 × 2 m3) on the total derivative of the temperature tendency equation. Analysis of the heat-flux imbalance, which is defined as the ratio of the sum of advective, dispersive, and turbulence-flux terms to the turbulence-flux term, are presented. Results are divided amongst free-convective and forced-convective days, as well as high-wind-speed and quiescent nocturnal periods. Findings show that the median flux imbalance is greater on forced-convective days (a 168% turbulence-flux overestimation, or relative importance of the advection to dispersive flux to the turbulence flux) when compared to free-convective periods (79% turbulence-flux overestimation). During nocturnal periods, a median turbulence-flux underestimation of 146% exists for quiescent nights and a 43% underestimation of the flux for high-wind-speed nights. These results support the existing literature, suggesting that mean air-temperature heterogeneities lead to strong bulk advection and dispersive fluxes. A discussion of the impact of the flux imbalance on the surface energy balance and numerical-weather-prediction modelling is presented.

In recent years, accurate estimation and spatial mapping of above-ground carbon (AGC) storage in forests have been crucial for formulating carbon trading policies and promoting sustainable development strategies. Forest structure complexities mean that during their growth, trees may be affected by the surrounding environment, giving rise to spatial autocorrelation and heterogeneity in nearby forest segments. When estimating forest AGC through remote sensing, data saturation can arise in dense forest stands, adding to the uncertainties in AGC estimation. Our study used field-measured stand factors data from 138 forest fire risk plots located in Fenglin County in the Northeastern region, set within a series of temperate forest environments in 2021 and Sentinel-2 remote sensing image data with a spatial resolution of 10 m. Using ordinary least squares (OLS) as a baseline, we constructed and compared it against four spatial regression models, spatial lag model (SLM), spatial error model (SEM), spatial Durbin model (SDM), and geographically weighted regression (GWR), to better understand forest AGC spatial distribution. The results of local spatial analysis reveal significant spatial effects among plot data. The GWR model outperformed others with an R2 value of 0.695 and the lowest rRMSE at 0.273, considering spatial heterogeneity and extending the threshold range for AGC estimation. To address the challenge of light saturation during AGC estimation, we deployed traditional linear functions, the generalized additive model (GAM), and the quantile generalized additive model (QGAM). AGC light saturation values derived from QGAM most accurately reflect the actual conditions, with the forests in Fenglin County exhibiting a light saturation range of 108.832 to 129.894 Mg/ha. The GWR effectively alleviated the impact of data saturation, thereby reducing the uncertainty of AGC spatial distribution in Fenglin County. Overall, accurate predictions of large-scale forest carbon storage provide valuable guidance for forest management, forest conservation, and the promotion of sustainable development strategies.

Tumor oxygen spatial heterogeneity is a critical challenge for the photodynamic inhibition of solid tumors. Development of an intelligent nanoagent to initiate optimal therapeutics according to the localized oxygen levels is an effective settlement. Herein, we report an activatable nanoagent (BDP-Oxide nanoparticles (NPs)) to enable the oxygen auto-adaptive photodynamic/photothermal complementary treatment. Upon the nanoagent accumulated in the tumor region, the low extracellular pH could trigger the disassociation of the nanoagent to release the phototheranostic agent, BDP-Oxide, which will subsequently afford the fluorescence imaging-guided photodynamic oxidation after it gets into the outer oxygen-rich tumors. Along with the penetration deepening in the solid tumor, furthermore, BDP-Oxide could be reduced into BDP by the cytochrome P450 (CYP450) enzymes activated in the low oxygen tension regions of inner hypoxic tumors, which will switch on the photothermal and photoacoustic effects. Overall, the BDP-Oxide NPs-enabled photodynamic/photothermal complementary therapy significantly suppressed the solid tumor growth (inhibition rate of 94.8%). This work proposes an intelligent platform to address the oxygen partial pressure for the optimization of cancer phototherapeutics.

Pancreatic cancer is lethal due to lack of perceptible symptoms and effective treatment methods. Immunotherapy may provide promising therapeutic choices for malignant tumors like pancreatic cancer. Tumor‐infiltrating lymphocytes (TIL) in tumor mesenchyme could recognize peptide antigens presented on the surface of tumor cells. The present study aimed to test the relationship between the T cell receptor (TCR) β repertoire of the tumor and peripheral blood, and also to investigate the intra‐tumor spatial heterogeneity of the TCR β repertoire in pancreatic cancer. To the best of our knowledge, this is the first study to evaluate the clonal composition of TCR β repertoire in TIL across the spatial extent of pancreatic cancer. In this study, we studied 5 patients who were diagnosed with primary pancreatic cancer. Ultra‐deep sequencing was used to assess the rearrangement of the TCR β‐chain (TCR β) gene. HE staining and immunohistochemistry of CD3, CD4, CD8 and HLA class I were used to show histopathology and immune conditions macroscopically. TIL repertoire showed that different regions of the same tumor showed a greater number of repertoire overlaps between each other than between peripheral blood, which suggested that T cell clones in pancreatic cancer might be quite different from those in peripheral blood. In contrast, intra‐tumoral TCR β repertoires were spatially homogeneous between different regions of a single tumor tissue. Based on these results, we speculated that the cellular adaptive immune response in pancreatic cancer was spatially homogeneous; this may pave the way for immunotherapy for the treatment of pancreatic cancer patients. In this study, rearrangement genes in the TCR β‐chain (TCR β) of 5 pancreatic cancer patients were assessed by ultra‐deep sequencing. HE staining and immunohistochemistry of CD3, CD4, CD8 and HLA class I were used to show histopathology and immune conditions macroscopically. TIL repertoire showed that T cell clones within pancreatic cancer were quite different from those in peripheral blood, while intratumoral TCR β repertoires were spatially homogeneous in different regions of 1 tumor tissue.