基于EVI2数据集提取青藏高原草地植被的物候信息,分析青藏高原草地返青期(Start of Growth Season,SOG)、枯黄期(End of Growth Season,EOG)和生长季长度(Length of Growth Season,LOG)的空间分布格局及近30 a来青藏高原草地物候的时空...基于EVI2数据集提取青藏高原草地植被的物候信息,分析青藏高原草地返青期(Start of Growth Season,SOG)、枯黄期(End of Growth Season,EOG)和生长季长度(Length of Growth Season,LOG)的空间分布格局及近30 a来青藏高原草地物候的时空动态变化特征。结果表明:青藏高原的草地物候由东南向西北呈现出明显的区域性差异。其中,高原东部和西北部地区的草地植被返青时间早于中部和西南部地区,而枯黄时间却晚于中部和西南部地区,生长季长度较中部和西南部地区长。同时,青藏高原物候变化趋势在东西部地区的差异十分明显。草地植被返青提前的区域主要集中在高原的东部,提前速率为0.49 d/a(R^(2)=0.54)。草地植被物候分布和变化趋势在不同海拔和坡向上的差异也十分显著。海拔每升高1000 m,草地SOG推迟4 d,EOG提前5 d,LOG缩短9 d。随海拔的升高,草地SOG的推迟速率逐渐增加,LOG变化速率呈现出逐渐减小的趋势。此外,南坡草地SOG较北坡晚,其LOG较北坡、东坡和西坡的短。北坡草地SOG平均推迟速率低于南坡。展开更多
EVI2A has emerged as a significant biomarker in various diseases;however,its biological role and mechanism in kidney renal clear cell carcinoma(KIRC)remains unexplored.We used TCGA and GEO databases to analyze EVI2A g...EVI2A has emerged as a significant biomarker in various diseases;however,its biological role and mechanism in kidney renal clear cell carcinoma(KIRC)remains unexplored.We used TCGA and GEO databases to analyze EVI2A gene expression comprehensively and performed pan-cancer assessments.Clinical relevance was evaluated through Kaplan-Meier analysis and ROC curves.The gene’s immune relevance was explored through analyses of the tumor microenvironment(TME),Tumor Immune Single-cell Hub(TISCH),immune checkpoints,and immunotherapy sensitivity.Our results indicate that EVI2A expression is upregulated in KIRC,showing correlations with tumor grade and T/N/M stage.EVI2A demonstrates high diagnostic accuracy(AUC=0.906)and predicts poor overall and progression-free survival in KIRC patients.Furthermore,EVI2A expression exhibits significant associations with immunity,including TME scores and specific immune cell types such as Tfh cells,CD4 memory T cells,and CD8+T cells.Elevated EVI2A expression suggests increased sensitivity to PD-1/CTLA-4 and tyrosine kinase inhibitors.In vitro assays confirmed the impact of EVI2A on KIRC behavior,with its knockdown resulting in reduced cell proliferation and migration.In conclusion,our comprehensive analysis identifies EVI2A as a promising biomarker and a novel therapeutic target for intervening in KIRC.These findings hold significant implications for further research and potential clinical applications.展开更多
Background Accurate measurements of aboveground biomass(AGB)are essential for understanding the planet's carbon balance.The Atlantic Forest of the Serra do Mar in southeastern Brazil contains large areas of well-p...Background Accurate measurements of aboveground biomass(AGB)are essential for understanding the planet's carbon balance.The Atlantic Forest of the Serra do Mar in southeastern Brazil contains large areas of well-preserved remnants,characterized by mountainous terrain with significant orographic contrasts along its elevation gradient.This diverse landscape creates a variety of biophysical factors that strongly influence the spatial distribution of AGB.This study aims to estimate AGB using a hybrid geostatistical methodology,regression kriging simulation(RKS),to analyze AGB spatial distribution at a local scale(84 plots,each 0.01 ha)across a small forest fragment covering the entire tree-covered area(8777 ha).Building on traditional regression kriging method,this study introduces an innovative approach by incorporating Gaussian simulation to interpolate residuals,allowing RKS to account for uncertainties in the estimation process and create new results.This allows us to clearly distinguish exogenous ecological processes from endogenous ones before reaching the model's final estimate.Results Four regression kriging models were created,and the best-performing model used the Enhanced Vegetation Index and direct solar radiation(DSR),achieving an R^(2) of 55%.A Gaussian simulation was performed to interpolate the residuals of this model.The final results indicate that RKS provides accurate AGB estimates(RMSE=1.333 Mg/0.01 ha and R^(2) of 77%).Additionally,the inclusion of DSR as a new predictor variable enhances the precision of AGB estimates.The analysis showed that 63%of the sample pairs exhibited measurable spatial dependence.Conclusions Regression kriging simulation is proposed using Gaussian simulation,altering the classical application of regression kriging.For this,a case study was conducted in the Atlantic Forest of Serra do Mar to estimate the spatial distribution of tree biomass in a forest fragment of this region.We demonstrate that the proposed method better captures the heterogeneity of the region and produces more comprehensive results than regression kriging.Regression kriging simulation estimates tree biomass by considering the actual fluctuations of the spatial distribution of tree biomass in the region,taking into account exogenous and endogenous ecological processes,addressing random noise,and allowing the creation of dynamic maps for use by environmental managers.展开更多
基金the Ethics Committee of the First Affiliated Hospital of Nanchang University(Approval:(2023)CDYFYYLK(03-013)).
文摘EVI2A has emerged as a significant biomarker in various diseases;however,its biological role and mechanism in kidney renal clear cell carcinoma(KIRC)remains unexplored.We used TCGA and GEO databases to analyze EVI2A gene expression comprehensively and performed pan-cancer assessments.Clinical relevance was evaluated through Kaplan-Meier analysis and ROC curves.The gene’s immune relevance was explored through analyses of the tumor microenvironment(TME),Tumor Immune Single-cell Hub(TISCH),immune checkpoints,and immunotherapy sensitivity.Our results indicate that EVI2A expression is upregulated in KIRC,showing correlations with tumor grade and T/N/M stage.EVI2A demonstrates high diagnostic accuracy(AUC=0.906)and predicts poor overall and progression-free survival in KIRC patients.Furthermore,EVI2A expression exhibits significant associations with immunity,including TME scores and specific immune cell types such as Tfh cells,CD4 memory T cells,and CD8+T cells.Elevated EVI2A expression suggests increased sensitivity to PD-1/CTLA-4 and tyrosine kinase inhibitors.In vitro assays confirmed the impact of EVI2A on KIRC behavior,with its knockdown resulting in reduced cell proliferation and migration.In conclusion,our comprehensive analysis identifies EVI2A as a promising biomarker and a novel therapeutic target for intervening in KIRC.These findings hold significant implications for further research and potential clinical applications.
文摘Background Accurate measurements of aboveground biomass(AGB)are essential for understanding the planet's carbon balance.The Atlantic Forest of the Serra do Mar in southeastern Brazil contains large areas of well-preserved remnants,characterized by mountainous terrain with significant orographic contrasts along its elevation gradient.This diverse landscape creates a variety of biophysical factors that strongly influence the spatial distribution of AGB.This study aims to estimate AGB using a hybrid geostatistical methodology,regression kriging simulation(RKS),to analyze AGB spatial distribution at a local scale(84 plots,each 0.01 ha)across a small forest fragment covering the entire tree-covered area(8777 ha).Building on traditional regression kriging method,this study introduces an innovative approach by incorporating Gaussian simulation to interpolate residuals,allowing RKS to account for uncertainties in the estimation process and create new results.This allows us to clearly distinguish exogenous ecological processes from endogenous ones before reaching the model's final estimate.Results Four regression kriging models were created,and the best-performing model used the Enhanced Vegetation Index and direct solar radiation(DSR),achieving an R^(2) of 55%.A Gaussian simulation was performed to interpolate the residuals of this model.The final results indicate that RKS provides accurate AGB estimates(RMSE=1.333 Mg/0.01 ha and R^(2) of 77%).Additionally,the inclusion of DSR as a new predictor variable enhances the precision of AGB estimates.The analysis showed that 63%of the sample pairs exhibited measurable spatial dependence.Conclusions Regression kriging simulation is proposed using Gaussian simulation,altering the classical application of regression kriging.For this,a case study was conducted in the Atlantic Forest of Serra do Mar to estimate the spatial distribution of tree biomass in a forest fragment of this region.We demonstrate that the proposed method better captures the heterogeneity of the region and produces more comprehensive results than regression kriging.Regression kriging simulation estimates tree biomass by considering the actual fluctuations of the spatial distribution of tree biomass in the region,taking into account exogenous and endogenous ecological processes,addressing random noise,and allowing the creation of dynamic maps for use by environmental managers.
