Objective:SOX11 is expressed in numerous malignancies,including hepatocellular carcinomas(HCC),but its oncogenic function has not been elucidated.Here,we performed a comprehensive bioinformatics analysis of the Liver ...Objective:SOX11 is expressed in numerous malignancies,including hepatocellular carcinomas(HCC),but its oncogenic function has not been elucidated.Here,we performed a comprehensive bioinformatics analysis of the Liver Hepatocellular Carcinoma(LIHC)dataset to investigate the function of SOX11 in tumorgenesis.Methods:SOX11 expression data from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)were validated by immunohistochemistry(IHC).Co-expression,differential expression,and functional analyses utilized TCGA-LIHC,Timer 2.0,Metascape,GTEx,and LinkedOmics databases.Associations with immune infiltration,ferroptosis,and immune checkpoint genes were assessed.Genetic changes were explored via CBioPortal.Logistic regression,receiver operating characteristic curve(ROC),Kaplan-Meier analysis,and nomogram modeling evaluated associations with HCC clinicopathological features.SOX11’s impact on proliferation and migration was studied in HepG2 and HuH7 cell lines.Results:SOX11 was significantly elevated in HCC tumors compared to controls.SOX11-associated genes exhibited differential expression in pathways involving extracellular membrane ion channels.Significant associations were found between SOX11 levels,immune infiltration,ferroptosis,and immune checkpoint genes in HCC tissue.SOX11 levels correlated with HCC stage,histologic grade,and tumor status,and independently predicted overall and disease-specific survival.SOX11 expression effectively distinguished between tumor and normal liver tissue.Spearman correlations highlighted a significant relationship between SOX11 and ferroptosis-associated genes.Decreased SOX11 levels in HepG2 and HuH7 cells resulted in reduced proliferation and migration.Conclusions:SOX11 was found to represent a promising biomarker within HCC diagnosis and prognosis together with being a possible drug-target.展开更多
Water quality in surface bodies remains a pressing issue worldwide.While some regions have rich water quality data,less attention is given to areas that lack sufficient data.Therefore,it is crucial to explore novel wa...Water quality in surface bodies remains a pressing issue worldwide.While some regions have rich water quality data,less attention is given to areas that lack sufficient data.Therefore,it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly.Here we showed sparse-dataset-based prediction of water pollution using machine learning.We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory(LSTM)models,integrated with the Load Estimator(LOADEST).The research was conducted at a river-lake confluence,an area with intricate hydrological patterns.We found that the Self-Attentive LSTM(SA-LSTM)model outperformed the other three machine learning models in predicting water quality,achieving Nash-Sutcliffe Efficiency(NSE)scores of 0.71 for COD_(Mn)and 0.57 for NH_(3)N when utilizing LOADEST-augmented water quality data(referred to as the SA-LSTMLOADEST model).The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error(RMSE)by 24.6%for COD_(Mn)and 21.3%for NH_(3)N.Furthermore,the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly.Additionally,the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance.This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.展开更多
In this study, an approach integrating digital land use/cover change （LUCC） analysis, hydraulic model- ing and statistical methods was applied to quantify the effect of LUCC on floods in terms of inundation extent, ...In this study, an approach integrating digital land use/cover change （LUCC） analysis, hydraulic model- ing and statistical methods was applied to quantify the effect of LUCC on floods in terms of inundation extent, flood arrival time and maximum water depth. The study took Beijing as an example and analyzed five specific floods with return periods of 20-year, 50-year, 100-year, 1000-year and 10000-year on the basis of LUCC over a nine-year period from 1996 to 2004. The analysis reveals that 1） during the period of analysis Beijing experienced unprecedented LUCC; 2） LUCC can affect inundation extent and flood arrival time, and floods with longer return periods are more influenced; 3） LUCC can affect maximum water depth and floods with shorter return periods are more influenced; and 4） LUCC is a major flood security stressor for Beijing. It warns that those cities having experienced rapid expansion during recent decades in China are in danger of more serious floods and recommends that their actual land use patterns should be carefully assessed considering flood security. This inte- grated approach is demonstrated to he a useful tool for joint assessment, planning and management of land and water.展开更多
基金supported by grants from Guizhou Nursing Vocational College Foundation(No.gzhlyj2023-04)Guizhou Nursing Vocational College Foundation(No.gzhlyj2021-02)+1 种基金Science and Technology Foundation of Guizhou Provincial Health Committee(No.gzwkj2022-518)Nature Science Foundation of Beijing,China(No.7214253).
文摘Objective:SOX11 is expressed in numerous malignancies,including hepatocellular carcinomas(HCC),but its oncogenic function has not been elucidated.Here,we performed a comprehensive bioinformatics analysis of the Liver Hepatocellular Carcinoma(LIHC)dataset to investigate the function of SOX11 in tumorgenesis.Methods:SOX11 expression data from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)were validated by immunohistochemistry(IHC).Co-expression,differential expression,and functional analyses utilized TCGA-LIHC,Timer 2.0,Metascape,GTEx,and LinkedOmics databases.Associations with immune infiltration,ferroptosis,and immune checkpoint genes were assessed.Genetic changes were explored via CBioPortal.Logistic regression,receiver operating characteristic curve(ROC),Kaplan-Meier analysis,and nomogram modeling evaluated associations with HCC clinicopathological features.SOX11’s impact on proliferation and migration was studied in HepG2 and HuH7 cell lines.Results:SOX11 was significantly elevated in HCC tumors compared to controls.SOX11-associated genes exhibited differential expression in pathways involving extracellular membrane ion channels.Significant associations were found between SOX11 levels,immune infiltration,ferroptosis,and immune checkpoint genes in HCC tissue.SOX11 levels correlated with HCC stage,histologic grade,and tumor status,and independently predicted overall and disease-specific survival.SOX11 expression effectively distinguished between tumor and normal liver tissue.Spearman correlations highlighted a significant relationship between SOX11 and ferroptosis-associated genes.Decreased SOX11 levels in HepG2 and HuH7 cells resulted in reduced proliferation and migration.Conclusions:SOX11 was found to represent a promising biomarker within HCC diagnosis and prognosis together with being a possible drug-target.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23040502)National Natural Science Foundation of China(41890823)Key Laboratory of Water Cycle and Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences(No.WL2019003).
文摘Water quality in surface bodies remains a pressing issue worldwide.While some regions have rich water quality data,less attention is given to areas that lack sufficient data.Therefore,it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly.Here we showed sparse-dataset-based prediction of water pollution using machine learning.We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory(LSTM)models,integrated with the Load Estimator(LOADEST).The research was conducted at a river-lake confluence,an area with intricate hydrological patterns.We found that the Self-Attentive LSTM(SA-LSTM)model outperformed the other three machine learning models in predicting water quality,achieving Nash-Sutcliffe Efficiency(NSE)scores of 0.71 for COD_(Mn)and 0.57 for NH_(3)N when utilizing LOADEST-augmented water quality data(referred to as the SA-LSTMLOADEST model).The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error(RMSE)by 24.6%for COD_(Mn)and 21.3%for NH_(3)N.Furthermore,the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly.Additionally,the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance.This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.
文摘In this study, an approach integrating digital land use/cover change （LUCC） analysis, hydraulic model- ing and statistical methods was applied to quantify the effect of LUCC on floods in terms of inundation extent, flood arrival time and maximum water depth. The study took Beijing as an example and analyzed five specific floods with return periods of 20-year, 50-year, 100-year, 1000-year and 10000-year on the basis of LUCC over a nine-year period from 1996 to 2004. The analysis reveals that 1） during the period of analysis Beijing experienced unprecedented LUCC; 2） LUCC can affect inundation extent and flood arrival time, and floods with longer return periods are more influenced; 3） LUCC can affect maximum water depth and floods with shorter return periods are more influenced; and 4） LUCC is a major flood security stressor for Beijing. It warns that those cities having experienced rapid expansion during recent decades in China are in danger of more serious floods and recommends that their actual land use patterns should be carefully assessed considering flood security. This inte- grated approach is demonstrated to he a useful tool for joint assessment, planning and management of land and water.
