The differentiation and maturation of oligodendrocyte precursor cells(OPCs) is essential for myelination and remyelination in the CNS. The failure of OPCs to achieve terminal differentiation in demyelinating lesions o...The differentiation and maturation of oligodendrocyte precursor cells(OPCs) is essential for myelination and remyelination in the CNS. The failure of OPCs to achieve terminal differentiation in demyelinating lesions often results in unsuccessful remyelination in a variety of human demyelinating diseases. However, the molecular mechanisms controlling OPC differentiation under pathological conditions remain largely unknown. Myt1 L(myelin transcription factor 1-like), mainly expressed in neurons,has been associated with intellectual disability, schizophrenia, and depression. In the present study, we found that Myt1 L was expressed in oligodendrocyte lineage cells during myelination and remyelination. The expression level of Myt1 L in neuron/glia antigen 2-positive(NG2+)OPCs was significantly higher than that in mature CC1+oligodendrocytes. In primary cultured OPCs,overexpression of Myt1 L promoted, while knockdown inhibited OPC differentiation. Moreover, Myt1 L was potently involved in promoting remyelination after lysolecithin-induced demyelination in vivo. Ch IP assays showed that Myt1 L bound to the promoter of Olig1 and transcriptionally regulated Olig1 expression. Taken together, our findings demonstrate that Myt1 L is an essential regulator of OPC differentiation, thereby supporting Myt1 L as a potential therapeutic target for demyelinating diseases.展开更多
Purpose-For the large-scale power grid monitoring system equipment,its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing.This paper pr...Purpose-For the large-scale power grid monitoring system equipment,its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing.This paper proposes a fault classification algorithm based on Gaussian mixture model(GMM),which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approach-The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy(MYT)decomposition under each normal distribution in the GMM.Then,the weight value of GMM is used to calculate weighted classification value of fault detection and separation,and by comparing the actual control limits with the classification result of GMM,the fault classification results are obtained.Findings-The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/value-The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.展开更多
基金supported by the International Cooperation and Exchange Program of the National Natural Science Foundation of China(81461138035)the National Natural Science Foundation of China(81371326,31571066,and 31371068)+2 种基金the National Basic Research Development Program of China(2016YFA0100802)the UK Medical Research Council(MR/M010503/1)the UK Multiple Sclerosis Society(33)
文摘The differentiation and maturation of oligodendrocyte precursor cells(OPCs) is essential for myelination and remyelination in the CNS. The failure of OPCs to achieve terminal differentiation in demyelinating lesions often results in unsuccessful remyelination in a variety of human demyelinating diseases. However, the molecular mechanisms controlling OPC differentiation under pathological conditions remain largely unknown. Myt1 L(myelin transcription factor 1-like), mainly expressed in neurons,has been associated with intellectual disability, schizophrenia, and depression. In the present study, we found that Myt1 L was expressed in oligodendrocyte lineage cells during myelination and remyelination. The expression level of Myt1 L in neuron/glia antigen 2-positive(NG2+)OPCs was significantly higher than that in mature CC1+oligodendrocytes. In primary cultured OPCs,overexpression of Myt1 L promoted, while knockdown inhibited OPC differentiation. Moreover, Myt1 L was potently involved in promoting remyelination after lysolecithin-induced demyelination in vivo. Ch IP assays showed that Myt1 L bound to the promoter of Olig1 and transcriptionally regulated Olig1 expression. Taken together, our findings demonstrate that Myt1 L is an essential regulator of OPC differentiation, thereby supporting Myt1 L as a potential therapeutic target for demyelinating diseases.
文摘Purpose-For the large-scale power grid monitoring system equipment,its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing.This paper proposes a fault classification algorithm based on Gaussian mixture model(GMM),which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approach-The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy(MYT)decomposition under each normal distribution in the GMM.Then,the weight value of GMM is used to calculate weighted classification value of fault detection and separation,and by comparing the actual control limits with the classification result of GMM,the fault classification results are obtained.Findings-The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/value-The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.
