Brain tumor-initiating cells （BTICs） have been enriched using antibodies against the cell surface protein CD133; however, the biological relevance and the regulatory mechanism of CD133 expression in human gliomas ar...Brain tumor-initiating cells （BTICs） have been enriched using antibodies against the cell surface protein CD133; however, the biological relevance and the regulatory mechanism of CD133 expression in human gliomas are not yet understood. In this study, we initially demonstrated that CD133 was overexpressed in high-grade human glioblastomas where CD133-positive cells were focally observed as a micro-cluster. In addition, CD133 transcripts with exon 1A, 1B, or 1C were predominantly expressed in glioblastomas. To elucidate the mechanism regulating this aberrant expression of CD133, three proximal promoters （P1, P2, and P3） containing a CpG island were isolated. In U251MG and T98G glioblastoma cells, the P1 region flanking exon 1A exhibited the highest activity among the three promoters, and this activity was significantly inactivated by in vitro methylation. After treatment with the demethylating agent 5-azacytidine and/or the histone deacetylase inhibitor valproic acid, the expression level of CD133 mRNA was significantly restored in glioma cells. Importantly, hypomethylation of CpG sites within the P1, P2, and P3 regions was observed by bisulfite sequencing in human glioblastoma tissues with abundant CD133 mRNA. Taken together, our results indicate that DNA hypomethylation is an important determinant of CD133 expression in glioblastomas, and this epigenetic event may be associated with the development of BTICs expressing CD133.展开更多
To improve the training efficiency and recommendation accuracy in cold-start interactive recommendation systems,a new graph structure called item similarity graph is proposed on the basis of real data from a public da...To improve the training efficiency and recommendation accuracy in cold-start interactive recommendation systems,a new graph structure called item similarity graph is proposed on the basis of real data from a public dataset.The proposed graph is built from collaborative interactions and a deep reinforcement learning-based graph-enhanced neural interactive collaborative filtering(GE-ICF)model.The GE-ICF framework is developed with a deep reinforcement learning framework and comprises an embedding propagation layer designed with graph neural networks.Extensive experiments are conducted to investigate the efficiency of the proposed graph structure and the superiority of the proposed GE-ICF framework.Results show that in cold-start interactive recommendation systems,the proposed item similarity graph performs well in data relationship modeling,with the training efficiency showing significant improvement.The proposed GE-ICF framework also demonstrates superiority in decision modeling,thereby increasing the recommendation accuracy remarkably.展开更多
A new system is developed to recognize promoter sequences from non promoter sequences based on position weight matrix and backpropagation neural network in this paper. The system performs significantly better on the t...A new system is developed to recognize promoter sequences from non promoter sequences based on position weight matrix and backpropagation neural network in this paper. The system performs significantly better on the training set and the test set, the mean recognition rate is as high as 99% on the training set and 97% on the testing set. Experimental results demonstrate the effectiveness of the system to recognize the promoter sequences that have been trained and the promoter sequences that have not been seen previously.展开更多
Although much has been known about how humans psychologically perform data-driven scientific discovery,less has been known about its brain mechanism.The number series completion is a typical data-driven scientific dis...Although much has been known about how humans psychologically perform data-driven scientific discovery,less has been known about its brain mechanism.The number series completion is a typical data-driven scientific discovery task,and has been demonstrated to possess the priming effect,which is attributed to the regularity identification and its subsequent extrapolation.In order to reduce the heterogeneities and make the experimental task proper for a brain imaging study,the number magnitude and arithmetic operation involved in number series completion tasks are further restricted.Behavioral performance in Experiment 1 shows the reliable priming effect for targets as expected.Then,a factorial design (the priming effect:prime vs.target;the period length:simple vs.complex) of event-related functional magnetic resonance imaging (fMRI) is used in Experiment 2 to examine the neural basis of data-driven scientific discovery.The fMRI results reveal a double dissociation of the left DLPFC (dorsolateral prefrontal cortex) and the left APFC (anterior prefrontal cortex) between the simple (period length=1) and the complex (period length=2) number series completion task.The priming effect in the left DLPFC is more significant for the simple task than for the complex task,while the priming effect in the left APFC is more significant for the complex task than for the simple task.The reliable double dissociation may suggest the different roles of the left DLPFC and left APFC in data-driven scientific discovery.The left DLPFC (BA 46) may play a crucial role in rule identification,while the left APFC (BA 10) may be related to mental set maintenance needed during rule identification and extrapolation.展开更多
文摘Brain tumor-initiating cells （BTICs） have been enriched using antibodies against the cell surface protein CD133; however, the biological relevance and the regulatory mechanism of CD133 expression in human gliomas are not yet understood. In this study, we initially demonstrated that CD133 was overexpressed in high-grade human glioblastomas where CD133-positive cells were focally observed as a micro-cluster. In addition, CD133 transcripts with exon 1A, 1B, or 1C were predominantly expressed in glioblastomas. To elucidate the mechanism regulating this aberrant expression of CD133, three proximal promoters （P1, P2, and P3） containing a CpG island were isolated. In U251MG and T98G glioblastoma cells, the P1 region flanking exon 1A exhibited the highest activity among the three promoters, and this activity was significantly inactivated by in vitro methylation. After treatment with the demethylating agent 5-azacytidine and/or the histone deacetylase inhibitor valproic acid, the expression level of CD133 mRNA was significantly restored in glioma cells. Importantly, hypomethylation of CpG sites within the P1, P2, and P3 regions was observed by bisulfite sequencing in human glioblastoma tissues with abundant CD133 mRNA. Taken together, our results indicate that DNA hypomethylation is an important determinant of CD133 expression in glioblastomas, and this epigenetic event may be associated with the development of BTICs expressing CD133.
基金The National Natural Science Foundation of China(No.62173251)the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control,the Fundamental Research Funds for the Central Universities.
文摘To improve the training efficiency and recommendation accuracy in cold-start interactive recommendation systems,a new graph structure called item similarity graph is proposed on the basis of real data from a public dataset.The proposed graph is built from collaborative interactions and a deep reinforcement learning-based graph-enhanced neural interactive collaborative filtering(GE-ICF)model.The GE-ICF framework is developed with a deep reinforcement learning framework and comprises an embedding propagation layer designed with graph neural networks.Extensive experiments are conducted to investigate the efficiency of the proposed graph structure and the superiority of the proposed GE-ICF framework.Results show that in cold-start interactive recommendation systems,the proposed item similarity graph performs well in data relationship modeling,with the training efficiency showing significant improvement.The proposed GE-ICF framework also demonstrates superiority in decision modeling,thereby increasing the recommendation accuracy remarkably.
文摘A new system is developed to recognize promoter sequences from non promoter sequences based on position weight matrix and backpropagation neural network in this paper. The system performs significantly better on the training set and the test set, the mean recognition rate is as high as 99% on the training set and 97% on the testing set. Experimental results demonstrate the effectiveness of the system to recognize the promoter sequences that have been trained and the promoter sequences that have not been seen previously.
基金supported by the National Natural Science Foundation of China (Grant Nos.60775039 and 60875075)supported by the Grant-in-aid for Scientific Research (Grant No.18300053) from the Japanese Society for the Promotion of Science+2 种基金Support Center for Advanced Telecommunications Technology Research,Foundationthe Open Foundation of Key Laboratory of Multimedia and Intelligent Software Technology (Beijing University of Technology) Beijingthe Doctoral Research Fund of Beijing University of Technology (Grant No.00243)
文摘Although much has been known about how humans psychologically perform data-driven scientific discovery,less has been known about its brain mechanism.The number series completion is a typical data-driven scientific discovery task,and has been demonstrated to possess the priming effect,which is attributed to the regularity identification and its subsequent extrapolation.In order to reduce the heterogeneities and make the experimental task proper for a brain imaging study,the number magnitude and arithmetic operation involved in number series completion tasks are further restricted.Behavioral performance in Experiment 1 shows the reliable priming effect for targets as expected.Then,a factorial design (the priming effect:prime vs.target;the period length:simple vs.complex) of event-related functional magnetic resonance imaging (fMRI) is used in Experiment 2 to examine the neural basis of data-driven scientific discovery.The fMRI results reveal a double dissociation of the left DLPFC (dorsolateral prefrontal cortex) and the left APFC (anterior prefrontal cortex) between the simple (period length=1) and the complex (period length=2) number series completion task.The priming effect in the left DLPFC is more significant for the simple task than for the complex task,while the priming effect in the left APFC is more significant for the complex task than for the simple task.The reliable double dissociation may suggest the different roles of the left DLPFC and left APFC in data-driven scientific discovery.The left DLPFC (BA 46) may play a crucial role in rule identification,while the left APFC (BA 10) may be related to mental set maintenance needed during rule identification and extrapolation.
