It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily ...It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.展开更多
Lean body mass (LBM) and age at menarche (AAM) are two important complex traits for human health. The aim of this study was to identify pleiotropic genes for both traits using a powerful bivariate genome-wide asso...Lean body mass (LBM) and age at menarche (AAM) are two important complex traits for human health. The aim of this study was to identify pleiotropic genes for both traits using a powerful bivariate genome-wide association study (GWAS). Two stud- ies, a discovery study and a replication study, were performed. In the discovery study, 909622 single nucleotide polymor- phisms (SNPs) were genotyped in 801 unrelated female Han Chinese subjects using the Affymetrix human genome-wide SNP array 6.0 platform. Then, a bivariate GWAS was performed to identify the SNPs that may be important for LBM and AAM. In the replication study, significant findings from the discovery study were validated in 1692 unrelated Caucasian female subjects One SNP rs3027009 that was bivafiately associated with left arm lean mass and AAM in the discovery samples (P=7.26x10-6) and in the replication samples (P=0.005) was identified. The SNP is located at the upstream of DARC (Duffy antigen receptor for chemokines) gene, suggesting that DARC may play an important role in regulating the metabolisms of both LBM and AAM.展开更多
基金Project(KJZD-M202000801) supported by the Major Project of Chongqing Municipal Education Commission,ChinaProject(2016YFE0205600) supported by the National Key Research&Development Program of China+1 种基金Project(CXQT19023) supported by the Chongqing University Innovation Group Project,ChinaProjects(KFJJ2018069,1853061,1856033) supported by the Key Platform Opening Project of Chongqing Technology and Business University,China。
文摘It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.
基金supported by the Shanghai Leading Academic Discipline Project(Grant No.S30501)a start-up fund from the Shanghai University of Science and Technology,China+1 种基金supported by grants from National Institutes of Health(Grant Nos.P50AR055081, R01AG026564,R01AR050496,RC2DE020756,R01AR057049,and R03TW008221)supported by the National Natural Science Foundation of China(Grant No.31100902)
文摘Lean body mass (LBM) and age at menarche (AAM) are two important complex traits for human health. The aim of this study was to identify pleiotropic genes for both traits using a powerful bivariate genome-wide association study (GWAS). Two stud- ies, a discovery study and a replication study, were performed. In the discovery study, 909622 single nucleotide polymor- phisms (SNPs) were genotyped in 801 unrelated female Han Chinese subjects using the Affymetrix human genome-wide SNP array 6.0 platform. Then, a bivariate GWAS was performed to identify the SNPs that may be important for LBM and AAM. In the replication study, significant findings from the discovery study were validated in 1692 unrelated Caucasian female subjects One SNP rs3027009 that was bivafiately associated with left arm lean mass and AAM in the discovery samples (P=7.26x10-6) and in the replication samples (P=0.005) was identified. The SNP is located at the upstream of DARC (Duffy antigen receptor for chemokines) gene, suggesting that DARC may play an important role in regulating the metabolisms of both LBM and AAM.