The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level m...The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.展开更多
Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other...Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.展开更多
The exploration of the variability of spatial spillovers of ecosystem services(ESs)across scales is essential for sustainable regional development.Using advanced models such as In VEST,Geodetector,MGWR,and SLM/SEM/SDB...The exploration of the variability of spatial spillovers of ecosystem services(ESs)across scales is essential for sustainable regional development.Using advanced models such as In VEST,Geodetector,MGWR,and SLM/SEM/SDB,this study investigates spatial heterogeneity and cross-regional effects on ESs at the raster and county scales.Key findings from 2000 to 2020 include an upward trend in ESs,with pronounced regional variations.The southern Yellow River Basin(YRB)shows higher levels of water yield,carbon sequestration,soil retention,and habitat quality,while the northern areas score lower,except for food provisioning in central and lower regions.The overall ES index has risen,particularly in the southern part,aligning with China's ecological patterns and showing significant cross-regional benefits.At various scales,natural elements,landscape configurations,and human influences significantly impact ESI,with different cross-regional effects.While natural and landscape indices demonstrate substantial cross-regional impacts at the raster scale,human influence is more apparent at the county scale.The identified cross-regional impacts underscore the interconnectedness of regional ES and sustainability,extending to nearby areas.Spatial management and planning may be limited by zoning and regulations.This study underscores regional ecosystem spatial spillovers and cross-scale knowledge differences and linkages,introducing new perspectives and methods for spatial planning in watersheds to support sustainable ecosystem optimisation.展开更多
We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination unif...We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination uniformity in inertial confinement fusion(ICF)laser systems.The fundamental operating mechanism and key fabrication techniques for the SRPCP are systematically developed and experimentally validated.The SRPCP converts a linearly polarized 3ω incident laser beam into an output beam with a spatially randomized polarization distribution.When combined with a continuous phase plate,the SRPCP effectively suppresses high-intensity speckles at all spatial frequencies in the focal spot.The proposed PS technique is specifically designed for high-fluence large-aperture laser systems,enabling novel polarization control regimes in laser-driven ICF.展开更多
This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By e...This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.展开更多
Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analy...Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analysis of the spatial evolution pattern and influencing factors of urban green innovation,as well as revealing the development differences between regions.This study's research object includes 284 Chinese cities that are at the prefecture level or above,excluding Xizang,Hong Kong,Macao,and Taiwan of China due to incomplete data.The spatial evolution characteristics of urban green innovation in China between 2005 and 2021 are comprehensively described using the gravity center model and boxplot analysis.The factors that affect urban green innovation are examined using the spatial Durbin model(SDM).The findings indicate that:1)over the period of the study,the gravity center of urban green innovation in China has always been distributed in the Henan-Anhui border region,showing a migration characteristic of‘initially shifting northeast,subsequently southeast',and the migration speed has gradually increased.2)Although there are also noticeable disparities in east-west,the north-south gap is the main cause of the shift in China's urban green innovation gravity center.The primary areas of urban green innovation in China are the cities with green innovation levels higher than the median.3)The main influencing factor of urban green innovation is the industrial structure level.The effect of the financial development level,the government intervention level,and the openness to the outside world degree on urban green innovation is weakened in turn.The environmental regulation degree is not truly influencing urban green innovation.The impact of various factors on green innovation across cities of different sizes,exhibiting heterogeneity.This study is conducive to broadening the academic community's comprehension of the spatial evolution characteristics of urban green innovation and offering a theoretical framework for developing policies for the all-encompassing green transformation of social and economic growth.展开更多
The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination ...The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.展开更多
This study investigates the spatial courting between digital economic signs and local monetary overall performance throughout ten provinces in Sumatra,Indonesia,from 2019 to 2022.As digitalization hastens economic and...This study investigates the spatial courting between digital economic signs and local monetary overall performance throughout ten provinces in Sumatra,Indonesia,from 2019 to 2022.As digitalization hastens economic and business sports,devices together with fintech lending,e-cash,debit card usage,and e-commerce are increasingly more diagnosed as capability drivers of regional increase.But,the unequal distribution of digital infrastructure and monetary literacy across regions raises issues approximately the inclusivity of these benefits.constructing upon current findings by using Miranti et al.(2024),this research employs spatial econometric fashions-particularly the Spatial Lag model(SLM)and Spatial mistakes model(SEM)-to evaluate how digital variables influence provincial financial overall performance while accounting for spatial spillover consequences.The results reveal that fintech lending and debit card usage exert a positive and significant impact on economic growth,whereas the effect of e-money is negative,suggesting potential substitution effects or access constraints.Spatial dependency is also evident,as demonstrated by the significant lambda coefficient in the SEM model.These findings highlight the importance of spatially coordinated digital policies,particularly in addressing disparities and enhancing digital financial inclusion.The study concludes with policy recommendations aimed at fostering inclusive and spatially balanced digital economic development in Sumatra.展开更多
Smoking is a well-established risk factor for periodontitis,yet the precise mechanisms by which smoking contributes to periodontal disease remain poorly understood.Recent advances in spatial transcriptomics have enabl...Smoking is a well-established risk factor for periodontitis,yet the precise mechanisms by which smoking contributes to periodontal disease remain poorly understood.Recent advances in spatial transcriptomics have enabled a deeper exploration of the periodontal tissue microenvironment at single-cell resolution,offering new opportunities to investigate these mechanisms.In this study,we utilized Visium HD single-cell spatial transcriptomics to profile gingival tissues from 12 individuals,including those with periodontitis,those with smoking-associated periodontitis,and healthy controls.Our analysis revealed that smoking disrupts the epithelial barrier integrity,induces fibroblast alterations,and dysregulates fibroblast–epithelial cell communication,thereby exacerbating periodontitis.The spatial analysis showed that endothelial cells and macrophages are in close proximity and interact,which further promotes the progression of smoking-induced periodontal disease.Importantly,we found that targeting the endothelial CXCL12 signalling pathway in smoking-associated periodontitis reduced the proinflammatory macrophage phenotype,alleviated epithelial inflammation,and reduced alveolar bone resorption.These findings provide novel insights into the pathogenesis of smoking-associated periodontitis and highlight the potential of targeting the endothelial–macrophage interaction as a therapeutic strategy.Furthermore,this study establishes an essential information resource for investigating the effects of smoking on periodontitis,providing a foundation for future research and therapeutic development for this prevalent and debilitating disease.展开更多
While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states...While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states,the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation.Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues,gradually finding applications in musculoskeletal research.This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system.The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined,with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs,accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research.The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system,particularly during developmental processes,is thoroughly summarized.Furthermore,recent discoveries and achievements of this emerging omics tool in addressing inflammatory,traumatic,degenerative,and tumorous diseases of the musculoskeletal system are compiled.Finally,challenges and potential future directions for spatial transcriptomics,both as a field and in its applications in the musculoskeletal system,are discussed.展开更多
Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of a...Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of agricultural planting,unregulated aquaculture,and poultry farming.However,relevant esti-mations of the pollutant content in its sediment have not been carried out.This study analyzed the spatial patterns of heavy metal pollution and eutrophication at 36 water sampling sites in the Xiaonanhai Lake area,focusing on eight heavy metals:Cd,Cr,Cu,Ni,As,Pb,Hg,and Zn.The nutrient status of the lake area was evaluated using the nitrogen-phosphorus comprehensive pollution index,and heavy metal pollution status of the lake area was evaluated using geo-accumulation and the potential ecological risk index.Spatial autocorrelation analysis revealed the spatial correlation and aggregation of eutrophication levels in Xiaonanhai Lake.The results showed that the overall trophic state of the Xiaonanhai Lake area was moderate eutrophication,with a gradually decreasing eutrophication level from north to south.The Chengnan Wastewater Treatment Plant in the northern part of the lake area and surface source pollution from aquaculture were the main nitrogen and phosphorus sources.The overall eco-logical risk index of heavy metal pollution was medium and gradually weakened from north to south,consistent with the thickness of the bottom mud.The heavy metal pollution load was mainly precipitated from the bottom mud in the lake area.The eutrophication and heavy metal pollution levels in the lake area showed significant positive spatial autocorrelation,the influence range of the regional eutrophication level was small,and the spatial heterogeneity of the eutrophication and heavy metal pollution levels in Xiaonanhai Lake was relatively high.The northern part of the lake was a hotspot(high/high aggregation)of eutrophication(p<0.01)while the southern part was a cold spot(low/low concentration;p<0.05).The middle and northern part of the lake area was the hot spot(high/high concentration)of heavy metal pollution level(p<0.1)while the southern part was the cold spot(low/low concentration;p<0.1).Therefore,when carrying out water environment management in Xiaonanhai Lake,the northern area and the middle area should be prioritized for eutrophication prevention and control and dredging.展开更多
In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative features caused by occlusion and background interference in pedestri...In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative features caused by occlusion and background interference in pedestrian re-identification tasks,a person re-identification method combining spatial feature learning and multi-granularity feature fusion was proposed.First,an attention spatial transformation network(A-STN)is proposed to learn spatial features and solve the problem of misalignment of pedestrian spatial features.Then the network was divided into a global branch,a local coarse-grained fusion branch,and a local fine-grained fusion branch to extract pedestrian global features,coarse-grained fusion features,and fine-grained fusion features,respectively.Among them,the global branch enriches the global features by fusing different pooling features.The local coarse-grained fusion branch uses an overlay pooling to enhance each local feature while learning the correlation relationship between multi-granularity features.The local fine-grained fusion branch uses a differential pooling to obtain the differential features that were fused with global features to learn the relationship between pedestrian local features and pedestrian global features.Finally,the proposed method was compared on three public datasets:Market1501,DukeMTMC-ReID and CUHK03.The experimental results were better than those of the comparative methods,which verifies the effectiveness of the proposed method.展开更多
To reduce the spatial simulation error generated by the finite difference method,previous researchers compute the optimal finite-difference weights always by minimizing the error of spatial dispersion relation.However...To reduce the spatial simulation error generated by the finite difference method,previous researchers compute the optimal finite-difference weights always by minimizing the error of spatial dispersion relation.However,we prove that the spatial simulation error of the finite difference method is associated with the dot product of the spatial dispersion relation of the finite-difference weights and the spectrum of the seismic wavefield.Based on the dot product relation,we construct a L_(2) norm cost function to minimize spatial simulation error.For solving this optimization problem,the seismic wavefield infor-mation in wavenumber region is necessary.Nevertheless,the seismic wavefield is generally obtained by costly forward modeling techniques.To reduce the computational cost,we substitute the spectrum of the seismic wavelet for the spectrum of the seismic wavefield,as the seismic wavelet plays a key role in determining the seismic wavefield.In solving the optimization problem,we design an exhaustive search method to obtain the solution of the L_(2) norm optimization problem.After solving the optimization problem,we are able to achieve the finite-difference weights that minimize spatial simulation error.In theoretical error analyses,the finite-difference weights from the proposed method can output more accurate simulation results compared to those from previous optimization algorithms.Furthermore,we validate our method through numerical tests with synthetic models,which encompass homogenous/inhomogeneous media as well as isotropic and anisotropic media.展开更多
Latitudinal patterns of treeβ-diversity reveal important insights into the biogeographical processes that influence forest ecosystems.Although previous studies have extensively documentedβ-diversity within relativel...Latitudinal patterns of treeβ-diversity reveal important insights into the biogeographical processes that influence forest ecosystems.Although previous studies have extensively documentedβ-diversity within relatively small spatial extents,the potential drivers ofβ-diversity along latitudinal gradients are still not well understood at larger spatial extents.In this study,we determined whether treeβ-diversity is correlated with latitude in forests of southeastern China,and if so,what ecological processes contribute to these patterns of treeβ-diversity.We specifically aimed to disentangle the relative contributions from interspecific aggregation and environmental filtering across various spatial extents.We delineated regional communities comprising multiple nearby national forest inventory(NFI)plots around random focal plots.The number of NFI plots in a regional community served as a surrogate for spatial extent.We also used a null model to simulate randomly assembled communities and quantify the deviation(β-deviation)between observed and expectedβ-diversity.We found thatβ-diversity decreased along a latitudinal gradient and that this pattern was clearer at larger spatial extents.In addition,latitudinal patterns ofβ-deviation were explained by the degree of species spatial aggregation.We also identified environmental factors that driveβ-deviation in these forests,including precipitation,seasonality,and temperature variation.At larger spatial extents,these environmental variables explained up to 84%of theβ-deviation.Our results reinforce that ecological processes are scale-dependent and collectively contribute to theβ-gradient in subtropical forests.We recommend that conservation efforts maintain diverse forests and heterogeneous environments at multiple spatial extents to mitigate the adverse effects of climate change.展开更多
Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for...Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks.Currently,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain identification.To tackle this challenge,we propose a novel method,SpaGRA(https://github.com/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph augmentation.SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning.Additionally,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random selection.Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols.Using SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data.Overall,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.展开更多
This review examines the state-of-the-art in spatial manipulation of ultrafast laser processing using dynamic light modulators,with a particular focus on liquid crystal-based systems.We discuss phase modulation strate...This review examines the state-of-the-art in spatial manipulation of ultrafast laser processing using dynamic light modulators,with a particular focus on liquid crystal-based systems.We discuss phase modulation strategies and highlight the current limitations and challenges in surface and bulk processing.Specifically,we emphasize the delicate balance between high-fidelity beam shaping and energy efficiency,both critical for surface and bulk processing applications.Given the inherent physical limitations of spatial light modulators such as spatial resolution,fill factor,and phase modulation range.We explore techniques developed to bridge the gap between desired intensity distributions and actual experimental beam profiles.We present various laser light modulation technologies and the main algorithmic strategies for obtaining modulation patterns.The paper includes application examples across a wide range of fields,from surgery to surface structuring,cutting,bulk photo-inscription of optical functions,and additive manufacturing,highlighting the significant enhancements in processing speed and precision due to spatial beam shaping.The diverse applications and the technological limitations underscore the need for adapted modulation pattern calculation methods.We discuss several advancements addressing these challenges,involving both experimental and algorithmic developments,including the recent incorporation of artificial intelligence.Additionally,we cover recent progress in phase and pulse front control based on spatial modulators,which introduces an extra control parameter for light excitation with high potential for achieving more controlled processing outcomes.展开更多
Research on human motion prediction has made significant progress due to its importance in the development of various artificial intelligence applications.However,effectively capturing spatio-temporal features for smo...Research on human motion prediction has made significant progress due to its importance in the development of various artificial intelligence applications.However,effectively capturing spatio-temporal features for smoother and more precise human motion prediction remains a challenge.To address these issues,a robust human motion prediction method via integration of spatial and temporal cues(RISTC)has been proposed.This method captures sufficient spatio-temporal correlation of the observable sequence of human poses by utilizing the spatio-temporal mixed feature extractor(MFE).In multi-layer MFEs,the channel-graph united attention blocks extract the augmented spatial features of the human poses in the channel and spatial dimension.Additionally,multi-scale temporal blocks have been designed to effectively capture complicated and highly dynamic temporal information.Our experiments on the Human3.6M and Carnegie Mellon University motion capture(CMU Mocap)datasets show that the proposed network yields higher prediction accuracy than the state-of-the-art methods.展开更多
Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still...Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China.展开更多
Microwave thermochemotherapy(MTC)has been applied to treat lip squamous cell carcinoma(LSCC),but a deeper understanding of its therapeutic mechanisms and molecular biology is needed.To address this,we used single-cell...Microwave thermochemotherapy(MTC)has been applied to treat lip squamous cell carcinoma(LSCC),but a deeper understanding of its therapeutic mechanisms and molecular biology is needed.To address this,we used single-cell transcriptomics(scRNA-seq)and spatial transcriptomics(ST)to highlight the pivotal role of tumor-associated neutrophils(TANs)among tumor-infiltrating immune cells and their therapeutic response to MTC.MNDA+TANs with anti-tumor activity(N1-phenotype)are found to be abundantly infiltrated by MTC with benefit of increased blood perfusion,and these TANs are characterized by enhanced cytotoxicity,ameliorated hypoxia,and upregulated IL1B,activating T&NK cells and fibroblasts via IL1B-IL1R.In this highly anti-tumor immunogenic and hypoxia-reversed microenvironment under MTC,fibroblasts accumulated in the tumor front(TF)can recruit N1-TANs via CXCL2-CXCR2 and clear N2-TANs(pro-tumor phenotype)via CXCL12-CXCR4,which results in the aggregation of N1-TANs and extracellular matrix(ECM)deposition.In addition,we construct an N1-TANs marker,MX2,which positively correlates with better prognosis in LSCC patients,and employ deep learning techniques to predict expression of MX2 from hematoxylin-eosin(H&E)-stained images so as to conveniently guide decision making in clinical practice.Collectively,our findings demonstrate that the N1-TANs/fibroblasts defense wall formed in response to MTC effectively combat LSCC.展开更多
This article analyzes The Kite Runner with the help of spactial narrative. The novel will be analyzed from three angles—the function of spatial narrative, the basic unit of spatial narrative, the periphery of spatial...This article analyzes The Kite Runner with the help of spactial narrative. The novel will be analyzed from three angles—the function of spatial narrative, the basic unit of spatial narrative, the periphery of spatial narrative and the overall effect presented in the novel by using these narrative techniques.展开更多
文摘The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.
文摘Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.
基金Jiangsu Funding Program for Excellent Postdoctoral Talent,No.2024ZB454National Natural Science Foundation of China,No.42071229,No.41671174Priority Academic Program Development of Jiangsu Higher Education Institutions,No.164320H116。
文摘The exploration of the variability of spatial spillovers of ecosystem services(ESs)across scales is essential for sustainable regional development.Using advanced models such as In VEST,Geodetector,MGWR,and SLM/SEM/SDB,this study investigates spatial heterogeneity and cross-regional effects on ESs at the raster and county scales.Key findings from 2000 to 2020 include an upward trend in ESs,with pronounced regional variations.The southern Yellow River Basin(YRB)shows higher levels of water yield,carbon sequestration,soil retention,and habitat quality,while the northern areas score lower,except for food provisioning in central and lower regions.The overall ES index has risen,particularly in the southern part,aligning with China's ecological patterns and showing significant cross-regional benefits.At various scales,natural elements,landscape configurations,and human influences significantly impact ESI,with different cross-regional effects.While natural and landscape indices demonstrate substantial cross-regional impacts at the raster scale,human influence is more apparent at the county scale.The identified cross-regional impacts underscore the interconnectedness of regional ES and sustainability,extending to nearby areas.Spatial management and planning may be limited by zoning and regulations.This study underscores regional ecosystem spatial spillovers and cross-scale knowledge differences and linkages,introducing new perspectives and methods for spatial planning in watersheds to support sustainable ecosystem optimisation.
基金supported by the National Natural Science Foundation of China(Grant No.62275235).
文摘We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination uniformity in inertial confinement fusion(ICF)laser systems.The fundamental operating mechanism and key fabrication techniques for the SRPCP are systematically developed and experimentally validated.The SRPCP converts a linearly polarized 3ω incident laser beam into an output beam with a spatially randomized polarization distribution.When combined with a continuous phase plate,the SRPCP effectively suppresses high-intensity speckles at all spatial frequencies in the focal spot.The proposed PS technique is specifically designed for high-fluence large-aperture laser systems,enabling novel polarization control regimes in laser-driven ICF.
文摘This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.
基金Under the auspices of National Natural Science Foundation of China(No.42371192)Natural Science Foundation of Hunan Province(No.2023JJ30100)Social Science Foundation of Hunan Province(No.23ZDAJ023,23YBA133)。
文摘Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analysis of the spatial evolution pattern and influencing factors of urban green innovation,as well as revealing the development differences between regions.This study's research object includes 284 Chinese cities that are at the prefecture level or above,excluding Xizang,Hong Kong,Macao,and Taiwan of China due to incomplete data.The spatial evolution characteristics of urban green innovation in China between 2005 and 2021 are comprehensively described using the gravity center model and boxplot analysis.The factors that affect urban green innovation are examined using the spatial Durbin model(SDM).The findings indicate that:1)over the period of the study,the gravity center of urban green innovation in China has always been distributed in the Henan-Anhui border region,showing a migration characteristic of‘initially shifting northeast,subsequently southeast',and the migration speed has gradually increased.2)Although there are also noticeable disparities in east-west,the north-south gap is the main cause of the shift in China's urban green innovation gravity center.The primary areas of urban green innovation in China are the cities with green innovation levels higher than the median.3)The main influencing factor of urban green innovation is the industrial structure level.The effect of the financial development level,the government intervention level,and the openness to the outside world degree on urban green innovation is weakened in turn.The environmental regulation degree is not truly influencing urban green innovation.The impact of various factors on green innovation across cities of different sizes,exhibiting heterogeneity.This study is conducive to broadening the academic community's comprehension of the spatial evolution characteristics of urban green innovation and offering a theoretical framework for developing policies for the all-encompassing green transformation of social and economic growth.
文摘The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.
文摘This study investigates the spatial courting between digital economic signs and local monetary overall performance throughout ten provinces in Sumatra,Indonesia,from 2019 to 2022.As digitalization hastens economic and business sports,devices together with fintech lending,e-cash,debit card usage,and e-commerce are increasingly more diagnosed as capability drivers of regional increase.But,the unequal distribution of digital infrastructure and monetary literacy across regions raises issues approximately the inclusivity of these benefits.constructing upon current findings by using Miranti et al.(2024),this research employs spatial econometric fashions-particularly the Spatial Lag model(SLM)and Spatial mistakes model(SEM)-to evaluate how digital variables influence provincial financial overall performance while accounting for spatial spillover consequences.The results reveal that fintech lending and debit card usage exert a positive and significant impact on economic growth,whereas the effect of e-money is negative,suggesting potential substitution effects or access constraints.Spatial dependency is also evident,as demonstrated by the significant lambda coefficient in the SEM model.These findings highlight the importance of spatially coordinated digital policies,particularly in addressing disparities and enhancing digital financial inclusion.The study concludes with policy recommendations aimed at fostering inclusive and spatially balanced digital economic development in Sumatra.
基金supported by grants from the National Natural Science Foundation of China(Grant nos.82201011,82370958 and 81870770).
文摘Smoking is a well-established risk factor for periodontitis,yet the precise mechanisms by which smoking contributes to periodontal disease remain poorly understood.Recent advances in spatial transcriptomics have enabled a deeper exploration of the periodontal tissue microenvironment at single-cell resolution,offering new opportunities to investigate these mechanisms.In this study,we utilized Visium HD single-cell spatial transcriptomics to profile gingival tissues from 12 individuals,including those with periodontitis,those with smoking-associated periodontitis,and healthy controls.Our analysis revealed that smoking disrupts the epithelial barrier integrity,induces fibroblast alterations,and dysregulates fibroblast–epithelial cell communication,thereby exacerbating periodontitis.The spatial analysis showed that endothelial cells and macrophages are in close proximity and interact,which further promotes the progression of smoking-induced periodontal disease.Importantly,we found that targeting the endothelial CXCL12 signalling pathway in smoking-associated periodontitis reduced the proinflammatory macrophage phenotype,alleviated epithelial inflammation,and reduced alveolar bone resorption.These findings provide novel insights into the pathogenesis of smoking-associated periodontitis and highlight the potential of targeting the endothelial–macrophage interaction as a therapeutic strategy.Furthermore,this study establishes an essential information resource for investigating the effects of smoking on periodontitis,providing a foundation for future research and therapeutic development for this prevalent and debilitating disease.
基金supported by The National Natural Science Youth Foundation of China(Grant No.82102584).
文摘While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states,the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation.Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues,gradually finding applications in musculoskeletal research.This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system.The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined,with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs,accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research.The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system,particularly during developmental processes,is thoroughly summarized.Furthermore,recent discoveries and achievements of this emerging omics tool in addressing inflammatory,traumatic,degenerative,and tumorous diseases of the musculoskeletal system are compiled.Finally,challenges and potential future directions for spatial transcriptomics,both as a field and in its applications in the musculoskeletal system,are discussed.
基金China Institute of Water Resources and HydropowerResearch(IWHR)Innovative Team for Theoreticaland Technological Research on EcologicalLandscape Construction of Urban and Rural WaterSystems,Grant/Award Number:WE0145B042021。
文摘Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of agricultural planting,unregulated aquaculture,and poultry farming.However,relevant esti-mations of the pollutant content in its sediment have not been carried out.This study analyzed the spatial patterns of heavy metal pollution and eutrophication at 36 water sampling sites in the Xiaonanhai Lake area,focusing on eight heavy metals:Cd,Cr,Cu,Ni,As,Pb,Hg,and Zn.The nutrient status of the lake area was evaluated using the nitrogen-phosphorus comprehensive pollution index,and heavy metal pollution status of the lake area was evaluated using geo-accumulation and the potential ecological risk index.Spatial autocorrelation analysis revealed the spatial correlation and aggregation of eutrophication levels in Xiaonanhai Lake.The results showed that the overall trophic state of the Xiaonanhai Lake area was moderate eutrophication,with a gradually decreasing eutrophication level from north to south.The Chengnan Wastewater Treatment Plant in the northern part of the lake area and surface source pollution from aquaculture were the main nitrogen and phosphorus sources.The overall eco-logical risk index of heavy metal pollution was medium and gradually weakened from north to south,consistent with the thickness of the bottom mud.The heavy metal pollution load was mainly precipitated from the bottom mud in the lake area.The eutrophication and heavy metal pollution levels in the lake area showed significant positive spatial autocorrelation,the influence range of the regional eutrophication level was small,and the spatial heterogeneity of the eutrophication and heavy metal pollution levels in Xiaonanhai Lake was relatively high.The northern part of the lake was a hotspot(high/high aggregation)of eutrophication(p<0.01)while the southern part was a cold spot(low/low concentration;p<0.05).The middle and northern part of the lake area was the hot spot(high/high concentration)of heavy metal pollution level(p<0.1)while the southern part was the cold spot(low/low concentration;p<0.1).Therefore,when carrying out water environment management in Xiaonanhai Lake,the northern area and the middle area should be prioritized for eutrophication prevention and control and dredging.
基金the Foshan Science and technology Innovation Team Project(No.FS0AA-KJ919-4402-0060)the National Natural Science Foundation of China(No.62263018)。
文摘In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative features caused by occlusion and background interference in pedestrian re-identification tasks,a person re-identification method combining spatial feature learning and multi-granularity feature fusion was proposed.First,an attention spatial transformation network(A-STN)is proposed to learn spatial features and solve the problem of misalignment of pedestrian spatial features.Then the network was divided into a global branch,a local coarse-grained fusion branch,and a local fine-grained fusion branch to extract pedestrian global features,coarse-grained fusion features,and fine-grained fusion features,respectively.Among them,the global branch enriches the global features by fusing different pooling features.The local coarse-grained fusion branch uses an overlay pooling to enhance each local feature while learning the correlation relationship between multi-granularity features.The local fine-grained fusion branch uses a differential pooling to obtain the differential features that were fused with global features to learn the relationship between pedestrian local features and pedestrian global features.Finally,the proposed method was compared on three public datasets:Market1501,DukeMTMC-ReID and CUHK03.The experimental results were better than those of the comparative methods,which verifies the effectiveness of the proposed method.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(No.2021QNLM020001)the Major Scientific and Technological Projects of Shandong Energy Group(No.SNKJ2022A06-R23)+1 种基金the Funds of Creative Research Groups of China(No.41821002)the Major Scientific and Technological Projects of CNPC(No.ZD2019-183-003).
文摘To reduce the spatial simulation error generated by the finite difference method,previous researchers compute the optimal finite-difference weights always by minimizing the error of spatial dispersion relation.However,we prove that the spatial simulation error of the finite difference method is associated with the dot product of the spatial dispersion relation of the finite-difference weights and the spectrum of the seismic wavefield.Based on the dot product relation,we construct a L_(2) norm cost function to minimize spatial simulation error.For solving this optimization problem,the seismic wavefield infor-mation in wavenumber region is necessary.Nevertheless,the seismic wavefield is generally obtained by costly forward modeling techniques.To reduce the computational cost,we substitute the spectrum of the seismic wavelet for the spectrum of the seismic wavefield,as the seismic wavelet plays a key role in determining the seismic wavefield.In solving the optimization problem,we design an exhaustive search method to obtain the solution of the L_(2) norm optimization problem.After solving the optimization problem,we are able to achieve the finite-difference weights that minimize spatial simulation error.In theoretical error analyses,the finite-difference weights from the proposed method can output more accurate simulation results compared to those from previous optimization algorithms.Furthermore,we validate our method through numerical tests with synthetic models,which encompass homogenous/inhomogeneous media as well as isotropic and anisotropic media.
基金supported by the National Natural Science Foundation of China(42271317)the Innovation Research Team Project of the Natural Science Foundation of Hainan Province(422CXTD515)。
文摘Latitudinal patterns of treeβ-diversity reveal important insights into the biogeographical processes that influence forest ecosystems.Although previous studies have extensively documentedβ-diversity within relatively small spatial extents,the potential drivers ofβ-diversity along latitudinal gradients are still not well understood at larger spatial extents.In this study,we determined whether treeβ-diversity is correlated with latitude in forests of southeastern China,and if so,what ecological processes contribute to these patterns of treeβ-diversity.We specifically aimed to disentangle the relative contributions from interspecific aggregation and environmental filtering across various spatial extents.We delineated regional communities comprising multiple nearby national forest inventory(NFI)plots around random focal plots.The number of NFI plots in a regional community served as a surrogate for spatial extent.We also used a null model to simulate randomly assembled communities and quantify the deviation(β-deviation)between observed and expectedβ-diversity.We found thatβ-diversity decreased along a latitudinal gradient and that this pattern was clearer at larger spatial extents.In addition,latitudinal patterns ofβ-deviation were explained by the degree of species spatial aggregation.We also identified environmental factors that driveβ-deviation in these forests,including precipitation,seasonality,and temperature variation.At larger spatial extents,these environmental variables explained up to 84%of theβ-deviation.Our results reinforce that ecological processes are scale-dependent and collectively contribute to theβ-gradient in subtropical forests.We recommend that conservation efforts maintain diverse forests and heterogeneous environments at multiple spatial extents to mitigate the adverse effects of climate change.
基金supported by the National Natural Science Foundation of China(Nos.62303271,U1806202,62103397)the Natural Science Foundation of Shandong Province(ZR2023QF081)Funding for open access charge:the National Natural Science Foundation of China(Nos.62303271,U1806202).
文摘Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks.Currently,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain identification.To tackle this challenge,we propose a novel method,SpaGRA(https://github.com/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph augmentation.SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning.Additionally,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random selection.Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols.Using SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data.Overall,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.
基金supported by the French ANRT agence nationale de la recherche technologique under the CIFRE conventions industrielles de formation par la recherche framework.
文摘This review examines the state-of-the-art in spatial manipulation of ultrafast laser processing using dynamic light modulators,with a particular focus on liquid crystal-based systems.We discuss phase modulation strategies and highlight the current limitations and challenges in surface and bulk processing.Specifically,we emphasize the delicate balance between high-fidelity beam shaping and energy efficiency,both critical for surface and bulk processing applications.Given the inherent physical limitations of spatial light modulators such as spatial resolution,fill factor,and phase modulation range.We explore techniques developed to bridge the gap between desired intensity distributions and actual experimental beam profiles.We present various laser light modulation technologies and the main algorithmic strategies for obtaining modulation patterns.The paper includes application examples across a wide range of fields,from surgery to surface structuring,cutting,bulk photo-inscription of optical functions,and additive manufacturing,highlighting the significant enhancements in processing speed and precision due to spatial beam shaping.The diverse applications and the technological limitations underscore the need for adapted modulation pattern calculation methods.We discuss several advancements addressing these challenges,involving both experimental and algorithmic developments,including the recent incorporation of artificial intelligence.Additionally,we cover recent progress in phase and pulse front control based on spatial modulators,which introduces an extra control parameter for light excitation with high potential for achieving more controlled processing outcomes.
基金supported by the National Key R&D Program of China(No.2018YFB1305200)the Natural Science Foundation of Zhejiang Province(No.LGG21F030011)。
文摘Research on human motion prediction has made significant progress due to its importance in the development of various artificial intelligence applications.However,effectively capturing spatio-temporal features for smoother and more precise human motion prediction remains a challenge.To address these issues,a robust human motion prediction method via integration of spatial and temporal cues(RISTC)has been proposed.This method captures sufficient spatio-temporal correlation of the observable sequence of human poses by utilizing the spatio-temporal mixed feature extractor(MFE).In multi-layer MFEs,the channel-graph united attention blocks extract the augmented spatial features of the human poses in the channel and spatial dimension.Additionally,multi-scale temporal blocks have been designed to effectively capture complicated and highly dynamic temporal information.Our experiments on the Human3.6M and Carnegie Mellon University motion capture(CMU Mocap)datasets show that the proposed network yields higher prediction accuracy than the state-of-the-art methods.
基金Under the auspices of the National Natural Science Foundation of China(No.42571228,42401212)National Natural Science Foundation of Shandong(No.ZR2024MD022)。
文摘Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China.
基金supported by National Natural Science Foundation of China grants(Nos.82173326 and 82473058)Key Research and Development Project of Sichuan Province(Nos.2024YFFK0374 and 2024YFFK0198)Interdisciplinary Innovation Project of West China College of Stomatology,Sichuan University(RD-03-202004).
文摘Microwave thermochemotherapy(MTC)has been applied to treat lip squamous cell carcinoma(LSCC),but a deeper understanding of its therapeutic mechanisms and molecular biology is needed.To address this,we used single-cell transcriptomics(scRNA-seq)and spatial transcriptomics(ST)to highlight the pivotal role of tumor-associated neutrophils(TANs)among tumor-infiltrating immune cells and their therapeutic response to MTC.MNDA+TANs with anti-tumor activity(N1-phenotype)are found to be abundantly infiltrated by MTC with benefit of increased blood perfusion,and these TANs are characterized by enhanced cytotoxicity,ameliorated hypoxia,and upregulated IL1B,activating T&NK cells and fibroblasts via IL1B-IL1R.In this highly anti-tumor immunogenic and hypoxia-reversed microenvironment under MTC,fibroblasts accumulated in the tumor front(TF)can recruit N1-TANs via CXCL2-CXCR2 and clear N2-TANs(pro-tumor phenotype)via CXCL12-CXCR4,which results in the aggregation of N1-TANs and extracellular matrix(ECM)deposition.In addition,we construct an N1-TANs marker,MX2,which positively correlates with better prognosis in LSCC patients,and employ deep learning techniques to predict expression of MX2 from hematoxylin-eosin(H&E)-stained images so as to conveniently guide decision making in clinical practice.Collectively,our findings demonstrate that the N1-TANs/fibroblasts defense wall formed in response to MTC effectively combat LSCC.
文摘This article analyzes The Kite Runner with the help of spactial narrative. The novel will be analyzed from three angles—the function of spatial narrative, the basic unit of spatial narrative, the periphery of spatial narrative and the overall effect presented in the novel by using these narrative techniques.
