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Expression and functional identification of the hypothetical adhesin P32 from Mycoplasma genitalium
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作者 LIN BO LI YI MOU WU WEN BO ZHANG MIN JUN YU 《Journal of Microbiology and Immunology》 2006年第3期200-206,共7页
Mycoplasma genitalium is the main causative agent for non-gonococcal and non-chlamydial urethritis. P32 is the putative surface-exposed membrane protein of M. genitalium and it has substaintial identity in amino acid ... Mycoplasma genitalium is the main causative agent for non-gonococcal and non-chlamydial urethritis. P32 is the putative surface-exposed membrane protein of M. genitalium and it has substaintial identity in amino acid sequence with adhesin protein P30 from M. pneumoniae. Since M. pneumoniae mutants lacking P30 protein is defective in cytadherence, P32 protein has been proposed to be an essential adhesin implicated in the adherence of M. genitaliurn to host cells. The prokaryotic expression vector pET-30 ( + )/p32 was constructed in the present study, and the recombinant protein was expressed in E. coli and purified under denaturing condition. As demonstrated by the immuno- blotting analysis, the recombinant protein could react with rabbit antisera against M. genitalium, and adherence inhibition assays were performed with antisera against this recombinant protein. It was demonstrated that P32 protein apperared to be an adhesion protein of M. genitalium, thus providing the experimental basis for better understanding of the pathogenesis of M. genitalium infection and for the development of the related vaccines against the infection. 展开更多
关键词 Mycoplasma genitaliurn Adhesin P32 Recombinant protein Expression functional identification
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Identifying dynamical characteristics of directed functional epilepsy networks from eigenmodes
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作者 Songan HOU Haodong WANG +2 位作者 Ying YU Xiaotong LIU Qingyun WANG 《Science China(Technological Sciences)》 2025年第5期199-210,共12页
Epilepsy often accompanies cognitive impairments,which are featured by dynamics of EEG data.The eigenmode method,combined with functional networks derived from EEG data,provides a valid method to investigate dynamical... Epilepsy often accompanies cognitive impairments,which are featured by dynamics of EEG data.The eigenmode method,combined with functional networks derived from EEG data,provides a valid method to investigate dynamical characteristics of the brain’s integration and segregation while establishing connections with cognitive function.Based on the transfer entropy method,we utilize the eigenmode approach to analyze SEEG data from epilepsy patients,which extends the theory of eigenmode hierarchical modules to directed functional networks.This work mainly refines and employs the dynamical characteristics from the eigenmodes of the epilepsy directional functional networks,including integration and segregation theories and proposes the network’s functional recombination rate feature.Results indicate that directed functional networks constructed through transfer entropy can also manifest the phenomenon of hierarchical modules in brain functional modes.In addition,during epileptic seizures,higher layers of overall integration features and increased functional recombination rates are observed.Furthermore,alterations in the aggregation of prominent nodes within the eigenmodes of epilepsy patients are noted during seizure episodes.This paper provides an improved method for the analysis of dynamical features of directed epilepsy network,which may potentially provide help and new understanding for the analysis of functional features of epilepsy. 展开更多
关键词 EPILEPSY eigemode method integration and segregation functional recombination dynamical features
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