The mechanisms that specify and maintain the charac- teristics of germ cells during animal development are poorly understood. In this study, we demonstrated that loss of function of the zinc-finger gene Isy-2 results ...The mechanisms that specify and maintain the charac- teristics of germ cells during animal development are poorly understood. In this study, we demonstrated that loss of function of the zinc-finger gene Isy-2 results in various somatic cells adopting germ cells characteris- tics, including expression of germline-specific P gran- ules, enhanced RNAi activity and transgene silencing. The soma to germ transformation in Isy-2 mutants requires the activities of multiple chromatin remodeling complexes, including the MES-4 complex and the ISW-1 complex. The distinct germline-specific features in somatic cells and the gene expression profile indicate that LSY-2 acts in the Mec complex in this process. Our study demonstrated that Isy-2 functions in the mainte- nance of the soma-germ distinction.展开更多
Dorsal root ganglion (DRG) cells are primary sensory neurons and are important in pain. Recently, a distinct type of exocytosis, Ca2+ independent but voltage-dependent, is found
Diffusion magnetic resonance imaging(dMRI)is a noninvasive method to capture the anisotropic pattern of water displacement in the neuronal tissue.The soma and neurite density imaging(SANDI)model introduced soma size a...Diffusion magnetic resonance imaging(dMRI)is a noninvasive method to capture the anisotropic pattern of water displacement in the neuronal tissue.The soma and neurite density imaging(SANDI)model introduced soma size and density to biophysical model for the first time.In addition to neurite density,it can achieve their joint estimation non-invasively using dMRI.In the traditional method,parameters of the SANDI are estimated in a maximum likelihood frame-work,where the nonlinear model fitting is computationally intensive.Also,the present methods require a large number of diffusion gradients.Efficient and accurate algorithms for tissue microstructure estimation of SANDI is still a challenge currently.Consequently,we introduce deep learning method for tissue microstructure estimation of the SANDI model.The model comprises two functional components.The first component produces the sparse representation of diffusion sig-nals of input patches.The second component computes tissue microstructure from the sparse repre-sentation given by the first component.The deep network can produce not only tissue microstruc-ture estimates but also the uncertainty of the estimates with a reduced number of diffusion gradi-ents.Then,multiple deep networks are trained and their results are fused for the final prediction of tissue microstructure and uncertainty quantification.The deep network was evaluated on the MGH Connectome Diffusion Microstructure Dataset.Results indicate that our approach outperforms the traditional methods in terms of estimation accuracy.展开更多
近年来对某些衰老学说及相关问题有新的论述,我们考虑结合衰老定义探讨衰老学说的某些研究进展,将有助于从宏观方面研究衰老与衰老相关的增龄性疾病发病机制、抗衰老以及某些老年病的防治.首先想指出2014年Moskalev[1]在英国细胞周期杂...近年来对某些衰老学说及相关问题有新的论述,我们考虑结合衰老定义探讨衰老学说的某些研究进展,将有助于从宏观方面研究衰老与衰老相关的增龄性疾病发病机制、抗衰老以及某些老年病的防治.首先想指出2014年Moskalev[1]在英国细胞周期杂志上综述衰老及寿限的遗传学与表观遗传学（genetics and epigenetics）研究进展.提及衰老的进化性学说,指出寿限保险基因（废弃体细胞学说,disposable soma theory）.展开更多
基金We thank Dr. Bob Goldstein for gfp：：pgl-1 strain, Dr. Oliver Hobert for Isy-2（ot64） and Dr. Isabel Hanson for editing the manuscript. Some strains used in this work were received from the Caenorhabditis Genetics Center, which is supported by a grant from the NIH. This work was supported by the National Basic Research Program （973 Program） （Nos. 2013CB910100 and 2011CB910100） and the National Natural Science Foundation of China （Grant Nos. 31421002 and 31225018） to H.Z, The research of Hong Zhang was supported in part by an International Early Career Scientist grant from the Howard Hughes Medical Institute.
文摘The mechanisms that specify and maintain the charac- teristics of germ cells during animal development are poorly understood. In this study, we demonstrated that loss of function of the zinc-finger gene Isy-2 results in various somatic cells adopting germ cells characteris- tics, including expression of germline-specific P gran- ules, enhanced RNAi activity and transgene silencing. The soma to germ transformation in Isy-2 mutants requires the activities of multiple chromatin remodeling complexes, including the MES-4 complex and the ISW-1 complex. The distinct germline-specific features in somatic cells and the gene expression profile indicate that LSY-2 acts in the Mec complex in this process. Our study demonstrated that Isy-2 functions in the mainte- nance of the soma-germ distinction.
基金Supported by grant from Chinese NSFC "973" program
文摘Dorsal root ganglion (DRG) cells are primary sensory neurons and are important in pain. Recently, a distinct type of exocytosis, Ca2+ independent but voltage-dependent, is found
文摘Diffusion magnetic resonance imaging(dMRI)is a noninvasive method to capture the anisotropic pattern of water displacement in the neuronal tissue.The soma and neurite density imaging(SANDI)model introduced soma size and density to biophysical model for the first time.In addition to neurite density,it can achieve their joint estimation non-invasively using dMRI.In the traditional method,parameters of the SANDI are estimated in a maximum likelihood frame-work,where the nonlinear model fitting is computationally intensive.Also,the present methods require a large number of diffusion gradients.Efficient and accurate algorithms for tissue microstructure estimation of SANDI is still a challenge currently.Consequently,we introduce deep learning method for tissue microstructure estimation of the SANDI model.The model comprises two functional components.The first component produces the sparse representation of diffusion sig-nals of input patches.The second component computes tissue microstructure from the sparse repre-sentation given by the first component.The deep network can produce not only tissue microstruc-ture estimates but also the uncertainty of the estimates with a reduced number of diffusion gradi-ents.Then,multiple deep networks are trained and their results are fused for the final prediction of tissue microstructure and uncertainty quantification.The deep network was evaluated on the MGH Connectome Diffusion Microstructure Dataset.Results indicate that our approach outperforms the traditional methods in terms of estimation accuracy.
文摘近年来对某些衰老学说及相关问题有新的论述,我们考虑结合衰老定义探讨衰老学说的某些研究进展,将有助于从宏观方面研究衰老与衰老相关的增龄性疾病发病机制、抗衰老以及某些老年病的防治.首先想指出2014年Moskalev[1]在英国细胞周期杂志上综述衰老及寿限的遗传学与表观遗传学（genetics and epigenetics）研究进展.提及衰老的进化性学说,指出寿限保险基因（废弃体细胞学说,disposable soma theory）.
