Resolving spatial response heterogeneity in glioblastoma.

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.

Using a Leroux-prior-based conditional autoregression-based strategy to map the short-term association between temperature and bacillary dysentery and its attributable burden in China.

Background: In China, bacillary dysentery (BD) is the third most frequently reported infectious disease, with the greatest annual incidence rate of 38.03 cases per 10,000 person-years. It is well acknowledged that temperature is associated with BD and the previous studies of temperature-BD association in different provinces of China present a considerable heterogeneity, which may lead to an inaccurate estimation for a region-specific association and incorrect attributable burdens. Meanwhile, the common methods for multi-city studies, such as stratified strategy and meta-analysis, have their own limitations in handling the heterogeneity. Therefore, it is necessary to adopt an appropriate method considering the spatial autocorrelation to accurately characterize the spatial distribution of temperature-BD association and obtain its attributable burden in 31 provinces of China. Methods: A novel three-stage strategy was adopted. In the first stage, we used the generalized additive model (GAM) model to independently estimate the province-specific association between monthly average temperature (MAT) and BD. In the second stage, the Leroux-prior-based conditional autoregression (LCAR) was used to spatially smooth the association and characterize its spatial distribution. In the third stage, we calculate the attribute BD cases based on a more accurate estimation of association. Results: The smoothed association curves generally show a higher relative risk with a higher MAT, but some of them have an inverted "V" shape. Meanwhile, the spatial distribution of association indicates that western provinces have a higher relative risk of MAT than eastern provinces with 0.695 and 0.645 on average, respectively. The maximum and minimum total attributable number of cases are 224,257 in Beijing and 88,906 in Hainan, respectively. The average values of each province in the eastern, western, and central areas are approximately 40,991, 42,025, and 26,947, respectively. Conclusion: Based on the LCAR-based three-stage strategy, we can obtain a more accurate spatial distribution of temperature-BD association and attributable BD cases. Furthermore, the results can help relevant institutions to prevent and control the epidemic of BD efficiently.

Assessment of spatial variation in lung cancer incidence and air pollutants: spatial regression modeling approach.

A notable finding is that Kerala's capital Thiruvananthapuram has shown an increasing trend in lung cancer (LC) incidence. Long-term exposure to air pollution is a significant environmental risk factor for LC. This study investigated the spatial association between LC and exposure to air pollutants in Thiruvananthapuram, using Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The results showed that overall LC incidence rate was 111 per 105 males (age >60 years), whereas spatial distribution map revealed that 48% of the area had an incidence rate greater than 150. The results revealed a significant association between PM2.5 and LC. SLM was identified as the best model that predicted 62% variation in LC. GWR model improved model performance and made better local predictions in the southeastern parts of the study area. This study explores the effectiveness of spatial regression techniques for dealing spatial effects and pinpointing high-risk areas.

Threshold dynamics of a reaction-advection-diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity.

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction-advection-diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R 0 and show that the disease-free periodic solution is globally attractive when R 0 < 1 whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case when R 0 > 1 . Moreover, we find that R 0 is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

Digital financial inclusion and upgrading of consumption structure: Evidence from rural China.

Based on the perspective of spatial economy, this paper focuses on the primary effects and spatial characteristics of Digital Financial Inclusion (DFI) on the upgrading of rural consumption structure (URCS) in China, conducting a literature review and theoretical analysis. It then uses statistical data collected over the years and the Digital Financial Inclusion Index (DFII) of Peking University to prepare panel data for 31 provinces in China (aside from Hong Kong, Macao, and Taiwan) from 2011 to 2020 for empirical testing. The results are as follows: DFI can considerably boost URCS, and there is a strong spatial neighbor impact, that is, it is affected by random shocks in surrounding provinces via its spatial effect; DFI has nonlinear characteristics in the process of fostering URCS, with the threshold variables of income level and family sizes; the impact of DFI on URCS is spatially heterogeneous, and the promotion of the eastern region is better than other zones. These results can inform policymakers about rural development and provide valuable references to push forward rural vitalization.

Transport accessibility and hospital attributes: A nonlinear analysis of their impact on Women's prenatal care seeking behavior.

Ensuring women receive vital prenatal care is crucial for maternal and newborn health. Limited research explores factors influencing prenatal care-seeking from a geospatial perspective. This study, based on a substantial Wuhan dataset (23,947 samples), investigates factors influencing prenatal care-seeking, focusing on transport accessibility and hospital attributes. Findings indicate a nuanced relationship: (1) A non-linear trend, resembling an inverted "U," reveals the complex interplay between transport accessibility, hospital attributes, and prenatal care visits. Hospital attributes have a more pronounced impact than transport accessibility. (2) Interaction analysis underscores that lower prenatal care visits relate to low-income and education levels, despite reasonable public transport accessibility. (3) Spatial disparities are significant, with suburban areas facing increased obstacles compared to urban areas, particularly for those in suburban rural areas. This study enhances understanding by emphasizing threshold effects and spatial heterogeneity, offering valuable perspectives for refining prenatal care policies and practices.

Shining a Light on How Soil Organic Carbon Behaves at Fine Scales under Long-Term Elevated CO2: An 8 Year Free-Air Carbon Dioxide Enrichment Study.

Building and protecting soil organic carbon (SOC) are critical to agricultural productivity, soil health, and climate change mitigation. We aim to understand how mechanisms at the organo-mineral interfaces influence SOC persistence in three contrasting soils (Luvisol, Vertisol, and Calcisol) under long-term free air CO2 enrichment conditions. A continuous wheat-field pea-canola rotation was maintained. For the first time, we provided evidence to a novel notion that persistent SOC is molecularly simple even under elevated CO2 conditions. We found that the elevated CO2 condition did not change the total SOC content or C forms compared with the soils under ambient CO2 as identified by synchrotron-based soft X-ray analyses. Furthermore, synchrotron-based infrared microspectroscopy confirmed a two-dimensional microscale distribution of similar and less diverse C forms in intact microaggregates under long-term elevated CO2 conditions. Strong correlations between the distribution of C forms and O-H groups of clays can explain the steady state of the total SOC content. However, the correlations between C forms and clay minerals were weakened in the coarse-textured Calcisol under long-term elevated CO2. Our findings suggested that we should emphasize identifying management practices that increase the physical protection of SOC instead of increasing complexity of C. Such information is valuable in developing more accurate C prediction models under elevated CO2 conditions and shift our thinking in developing management practices for maintaining and building SOC for better soil fertility and future environmental sustainability.

Evolution and spread of multiadapted pathogens in a spatially heterogeneous environment.

Pathogen adaptation to multiple selective pressures challenges our ability to control their spread. Here we analyze the evolutionary dynamics of pathogens spreading in a heterogeneous host population where selection varies periodically in space. We study both the transient dynamics taking place at the front of the epidemic and the long-term evolution far behind the front. We identify five types of epidemic profiles arising for different levels of spatial heterogeneity and different costs of adaptation. In particular, we identify the conditions where a generalist pathogen carrying multiple adaptations can outrace a coalition of specialist pathogens. We also show that finite host populations promote the spread of generalist pathogens because demographic stochasticity enhances the extinction of locally maladapted pathogens. But higher mutation rates between genotypes can rescue the coalition of specialists and speed up the spread of epidemics for intermediate levels of spatial heterogeneity. Our work provides a comprehensive analysis of the interplay between migration, local selection, mutation, and genetic drift on the spread and on the evolution of pathogens in heterogeneous environments. This work extends our fundamental understanding of the outcome of the competition between two specialists and a generalist strategy (single- vs. multiadapted pathogens). These results have practical implications for the design of more durable control strategies against multiadapted pathogens in agriculture and in public health.

Exploring the evolution of coupled natural-cultural ecosystem services and their geographically scaled driven modeling in a coastal city of Southeast China.

Rapid urban expansion and economic development challenges to the sustainability of ecosystem services (ESs), a solid understanding of the mechanisms that drive ESs helps policymakers to respond. However, few existing studies on ES-driven mechanisms emphasize the integration of natural and cultural services, with most neglecting spatial non-stationarity at the geographic scale. Here, we improved the ROS model to quantify cultural ecosystem services (CES) and developed a comprehensive ecosystem services index (CESI) by coupling CES with 6 typical natural ESs (carbon storage (CS), water yield (WY), nitrogen export (NE), soil conservation (SC), habitat quality (HQ), food supply (FS)), subsequently, Spearman's correlation and MGWR were employed to reveal the CESI-driven mechanism considering geographic scales. The results showed that: (1) From 2000 to 2020, CS, WY, SC, and HQ exhibited decline, which contrasts with the significant increase in CES. (2) The CESI showed a decreasing trend (3.28-3.70) while the coefficient of variation was increasing over time (0.11-0.15). The overall spatial distribution of CESI shows higher northwest than southeast, with strong spatial autocorrelation. (3) The CESI exhibits synergistic associations with CS, SC, HQ, and CES (0.54-0.83), and forms trade-offs with WY, NE, and FS. (4) Climate, vegetation, landscape, human, and topography have significant effects on CES and CESI with a significantly geographic scale differences, especially areas closer to the sea exhibit heightened sensitivity. Besides, the combined effects of multiple factors are stronger than any individual driver. The results emphasize the necessity of introducing ecological land in coastal cities and establishing natural reserves in high CESI areas to maintain diversity. The study improves the CES assessment methodology and proposes an integrated analytical framework that combines natural and cultural ESs with geographic-scale drivers, providing a new perspective on the analysis of ESs mechanisms.

Spatial heterogeneity plays a vital role in shaping the structure and function of estuarine carbon-fixing bacterial communities.

Carbon-fixing bacterial communities are essential drivers of carbon fixation in estuarine ecosystems that critically affect the global carbon cycle. This study compared the abundances of the Calvin cycle functional genes cbbL and cbbM and Reductive tricarboxylic acid cycle gene aclB, as well as compared carbon-fixing bacterial community features in the two estuaries, predicted potential ecological functions of carbon-fixation bacteria, and analyzed their symbiosis strategies in two estuaries having different geographical distributions. Gammaproteobacteria was the dominant carbon-fixing bacterial community in the two estuaries. However, a higher number of Alphaproteobacteria were noted in the Liaohe Estuary, and a higher number of Betaproteobacteria were found in the Yalujiang Estuary. The carbon-fixing functional gene levels exhibited the order of aclB > cbbL > cbbM, and significant effects of Cu, Pb, and petroleum were observed (p < 0.05). Nitrogen-associated nutrient levels are major environmental factors that affect carbon-fixing bacterial community distribution patterns. Spatial factors significantly affected cbbL carbon-fixing functional bacterial community structure more than environmental factors. With the increase in offshore distance, the microbial-led processes of methylotrophy and nitrogen fixation gradually weakened, but a gradual strengthening of methanotrophy and nitrification was observed. Symbiotic network analysis of the microorganisms mediating these ecological processes revealed that the carbon-fixing bacterial community in these two estuaries had a non-random symbiotic pattern, and microbial communities from the same module were strongly linked among the carbon, nitrogen, and sulfur cycle. These findings could advance the understanding of carbon fixation in estuarine ecosystems.

A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of COVID-19.

The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.

Exploring the spatial and seasonal heterogeneity of cooling effect of an urban river on a landscape scale.

Urban water bodies can effectively mitigate the urban heat island effect and thus enhance the climate resilience of urban areas. The cooling effect of different water bodies varies, however, the cooling heterogeneity of different sections of a single watercourse or river network is rarely considered. Based on various satellite images, geospatial approaches and statistical analyses, our study confirmed the cooling heterogeneity from spatial and seasonal perspectives of the Suzhou Outer-city River in detail in the urban area of Suzhou, China. The cooling effect of the river was observed in the daytime in four seasons, and it is strongest in summer, followed by spring and autumn, and weakest in winter. The combination of the width of the river reach, the width and the NDVI value of the adjacent green space can explain a significant part of the cooling heterogeneity of the different river sections in different seasons. Land surface temperature (LST) variations along the river are more related to the width of the river reach, but the variations of the cooling distance are more related to the adjacent green space. The cooling effect of a river reach could be enhanced if it is accompanied by green spaces. In addition, the cooling effect of a looping river is stronger on the inside area than on the outside. The methodology and results of this study could help orient scientific landscape strategies in urban planning for cooler cities.

Spatial Heterogeneity of the Respiratory Health Impacts of Wildfire Smoke PM2.5 in California.

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.

Source apportionment of soil PTE in a northern industrial county using PMF model: Partitioning strategies and uncertainty analysis.

Positive matrix factorization (PMF) has commonly been applied for source apportionment of potentially toxic elements (PTE) in agricultural soil, however, spatial heterogeneity of PTE significantly undermines the accuracy and reliability of PMF results. In this study, a representative industrial-agricultural hub in North China (Xuanhua district, Zhangjiakou City) was selected as the research subject, multiple partition processing (PP) strategies and uncertainty analyses were integrated to advance the PMF modeling and associated algorithm mechanisms were comparatively discussed. Specifically, we adopted three methods to split the research area into several subzones according to industrial density (PP-1), population density (PP-2), and the ecological risk index (PP-3) respectively, to rectify the spatial bias phenomenon of PTE concentrations and to achieve a more interpretable result. Our results indicated that the obvious enrichment of Cd, Pb, and Zn was found in the agricultural soil, with Hg and Cd accounted for 83.49% of the overall potential ecological risk. Combining proper PP with PMF can significantly improve the modelling accuracy. Uncertainty analysis showed that interval ratios of tracer species (Cd, Pb, Hg, and Zn) calculated by PP-3 were consistently lower than that of PP-1 and PP-2, indicating that PP-3 coupled PMF can afford the optimal modeling results. It suggested that natural sources, fertilizers and pesticides, atmosphere deposition, mining, and smelting were recognized as the major contributor for the soil PTE contamination. The contribution of anthropogenic activities, specifically fertilizers and pesticides, and atmosphere deposition, increased by 1.64% and 5.91% compared to PMF results. These findings demonstrate that integration of proper partitioning processing into PMF can effectively improve the accuracy of the model even at the case of soil PTE contamination with high heterogeneity, offering support to subsequently implement directional control strategies.

An inexpensive "do-it-yourself" device for rapid generation of uniform tumor spheroids.

Three-dimensional (3D) cancer cell culture models such as tumor spheroids better recapitulate in vivo tumors than conventional two-dimensional (2D) models. However, two major challenges limit the routine use of 3D tumor spheroids. Firstly, most existing methods of generating tumor spheroids are not high-throughput. Secondly, tumor spheroids generated using current methods are highly variable in dimension. Here, we describe a simple 'Do-It-Yourself (DIY)' device that can be assembled for less than $7 of parts and generate uniform tumor spheroids in a high-throughput manner. We used a simple phone coin vibrating motor to superimpose the vibration for breaking a laminar jet of cell-loaded alginate solution into equally sized spherical beads. We generated 3,970 tumor spheroids/min, which exhibited a hypoxic core recapitulating in vivo tumors and could be used to test the diffusion efficacy of anticancer drugs. Such low-cost, easy-to-fabricate, simple-to-operate systems with high-throughput outcomes are essential to democratize and standardize cancer research.

Comparative analysis of biofilm characterization of probiotic Escherichia coli.

Biofilms are thought to play a vital role in the beneficial effects of probiotic bacteria. However, the structure and function of probiotic biofilms are poorly understood. In this work, biofilms of Escherichia coli (E. coli) Nissle 1917 were investigated and compared with those of pathogenic and opportunistic strains (E. coli MG1655, O157:H7) using crystal violet assay, confocal laser scanning microscopy, scanning electron microscopy and FTIR microspectroscopy. The study revealed significant differences in the morphological structure, chemical composition, and spatial heterogeneity of the biofilm formed by the probiotic E. coli strain. In particular, the probiotic biofilm can secrete unique phospholipid components into the extracellular matrix. These findings provide new information on the morphology, architecture and chemical heterogeneity of probiotic biofilms. This information may help us to understand the beneficial effects of probiotics for various applications.

Evolution of rates, patterns, and driving forces of green eco-spaces in a subtropical hilly region.

Monitoring ecological resource change in mountainous and hilly areas (MHAs) is vital for theoretical and practical advancements of ecological resource utilization and management in complex ecosystems. The factors driving structural and functional changes in green eco-spaces (GES) in these areas are complex and uncertain, with notable spatial scale effects. However, analyzing the multi-scale driving mechanisms of ecological and socioeconomic factors at a fine spatiotemporal scale presents significant challenges. To address these challenges, we analyzed dynamic changes in GES and eco-socio-economic development in Shanghang County, a typical mountainous region in southern China. We used multiple linear regression and multi-scale geographically weighted regression model to identify key factors driving GES changes and their multi-scale effects at both global and local levels. Over the past two decades, the GES area in the study area has exhibited a consistent pattern of decline, characterized by phases of gradual decline (2000-2005), sharp decline (2005-2009), slow decline (2009-2019). Key global factors driving GES changes included elevation (ELE), slope (SLOPE), population density (PD), distance to settlements (SETTLE), and distance to administrative centers (ADMIN). These factors exhibited significant spatial heterogeneity and multi-scale effects on GES changes. Specifically, SETTLE, PD, SLOPE, and ELE consistently drove GES changes at the local level, while ADMIN only showed significant localized effects during 2005-2009. The synergy between SETTLE and SLOPE had a considerable impact on GES changes, increasing over time, whereas ELE and PD demonstrated a consistent trade-off effect. These findings provide detailed spatiotemporal insights into the driving mechanisms of natural ecological resources, offering crucial guidance for environmental management, land source management, regional economic development, and biodiversity conservation in Shanghang and analogous subtropical hilly regions worldwide.

Space-specific flux estimation of atmospheric chemicals from point sources to soil.

Predicting chemical flux to soil from industrial point sources accurately at a regional scale has been a significant challenge due to high uncertainty in spatial heterogeneity and quantification. To address this challenge, we developed an innovative approach by combining California Air Resources Board Puff (CALPUFF) and mass balance models, leveraging their complementary strengths in quantitative accuracy and spatial precision. Specifically, CALPUFF was used to predict the polycyclic aromatic hydrocarbons (PAHs) flux to soil due to industrial sources. Additionally, the spatial distribution coefficient of PAHs flux (e.g., si for spatial unit i) was calculated by neural network and combined with the mass balance model to obtain the results of total PAHs fluxes, which were then combined with the results predicted by CALPUFF to effectively estimate the contribution of industrial sources to soil PAHs flux. Taking a petrochemical industry region located in Zhejiang province, China as a case study, results showed the input Phenanthrene (Phe) and Benzo(a)pyrene (BaP) fluxes predicted by CALPUFF were generally lower than those by the mass balance model, with slightly different distribution patterns. CALPUFF results, based on 36 industrial sources, partially represent those of the mass balance model, which includes all sources and pathways. It was suggested that industrial sources contributed 49%-89% and 65%-100% of soil Phe and BaP, respectively across the study area. The average Phe flux from point sources by deposition averaged 2.68 mg m-2∙a-1 in 2021, accounting for approximately 60% of the total Phe flux to soil. The average BaP flux from point sources by deposition averaged 0.0755 mg m-2∙a-1, accounting for only 0.1%-3.65% of the total BaP flux to soil. Thereby, our approach fills up a gap between the relevance to point sources and the accuracy of deposition quantification in estimating chemical flux from specific point sources to soil at a regional scale.

The stromal microenvironment endows pancreatic neuroendocrine tumors with spatially specific invasive and metastatic phenotypes.

Cancer-associated fibroblasts (CAFs) play an important role in a variety of cancers. However, the role of tumor stroma in nonfunctional pancreatic neuroendocrine tumors (NF-PanNETs) is often neglected. Profiling the heterogeneity of CAFs can reveal the causes of malignant phenotypes in NF-PanNETs. Here, we found that patients with high stromal proportion had poor prognosis, especially for that with infiltrating stroma (stroma and tumor cells that presented an infiltrative growth pattern and no regular boundary). In addition, myofibroblastic CAFs (myCAFs), characterized by FAP+ and α-SMAhigh, were spatially closer to tumor cells and promoted the EMT and tumor growth. Intriguingly, only tumor cells which were spatially closer to myCAFs underwent EMT. We further elucidated that myCAFs stimulate TGF-ß expression in nearby tumor cells. Then, TGF-ß promoted the EMT in adjacent tumor cells and promoted the expression of myCAFs marker genes in tumor cells, resulting in distant metastasis. Our results indicate that myCAFs cause spatial heterogeneity of EMT, which accounts for liver metastasis of NF-PanNETs. The findings of this study might provide possible targets for the prevention of liver metastasis.

Characterization of road-deposited sediment wash-off and accurate splitting of initial runoff pollution in heterogeneous urban spaces.

Particulate materials arising from road-deposited sediments (RDS) are an essential target for the control and management of surface runoff pollution. However, the heterogeneity of urban spaces hinders the identification and quantification of particulate pollution, which is challenging when formulating precise control measures. To elucidate the factors that drive particulate pollution in heterogeneous urban spaces, the accumulation of RDS on dry days and the total suspended solids during six natural rainfall events were investigated across three urban-rural spatial units (central urban, central suburban, and remote suburban). The underlying surface type (asphalt or cement roads) and particle size composition jointly determined the spatial heterogeneity in the static accumulation and dynamic output loads of RDS during rainfall. These two factors explained 59.6% and 18.9% of the spatial heterogeneity, respectively, according to principal component analysis. A novel CPSI exponential wash-off equation that incorporates particle size composition and underlying surface type was applied. It precisely described the spatial heterogeneity of RDS wash-off loads, the estimated values exhibiting event mean concentration errors of 10.8-18.2%. When coupled with the M(V) curve, this CPSI exponential wash-off equation more precisely split the initial volume of runoff: a lower total volume (17.6-38.0%) was shown to carry a higher proportion of the load (70.0-93.7%) compared to the traditional coupled exponential wash-off equation (volume: 31.6-49.0%, load: 37-90%). This study provides a new approach to characterizing RDS wash-off processes and splitting initial runoff in heterogeneous spaces.