Browsing by Author "Zhao, Xiaofeng"

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    ADAMTS4 Enhances Oligodendrocyte Differentiation and Remyelination by Cleaving NG2 Proteoglycan and Attenuating PDGFRα Signaling
    (Society for Neuroscience, 2023) Jiang, Chunxia; Qiu, Wanwan; Yang, Yingying; Huang, Hao; Dai, Zhong-min; Yang, Aifen; Tang, Tao; Zhao, Xiaofeng; Qiu, Mengsheng; Anatomy, Cell Biology and Physiology, School of Medicine
    Although NG2 is known to be selectively expressed in oligodendrocyte precursor cells (OPCs) for many years, its expressional regulation and functional involvement in oligodendrocyte differentiation have remained elusive. Here, we report that the surface-bound NG2 proteoglycan can physically bind to PDGF-AA and enhances PDGF receptor alpha (PDGFRα) activation of downstream signaling. During differentiation stage, NG2 protein is cleaved by A disintegrin and metalloproteinase with thrombospondin motifs type 4 (Adamts4), which is highly upregulated in differentiating OPCs but gradually downregulated in mature myelinating oligodendrocytes. Genetic ablation of Adamts4 gene impedes NG2 proteolysis, leading to elevated PDGFRα signaling but impaired oligodendrocyte differentiation and axonal myelination in both sexes of mice. Moreover, Adamts4 deficiency also lessens myelin repair in adult brain tissue following Lysophosphatidylcholine-induced demyelination. Thus, Adamts4 could be a potential therapeutic target for enhancing oligodendrocyte differentiation and axonal remyelination in demyelinating diseases. SIGNIFICANCE STATEMENT: NG2 is selectively expressed in OPCs and downregulated during differentiation stage. To date, the molecular mechanism underlying the progressive removal of NG2 surface proteoglycan in differentiating OPCs has been unknown. In this study, we demonstrate that ADAMTS4 released by differentiating OPCs cleaves surface NG2 proteoglycan, attenuates PDGFRα signaling, and accelerates oligodendrocyte differentiation. In addition, our study also suggests ADAMTS4 as a potential therapeutic target for promoting myelin recovery in demyelinating diseases.
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    Regression analysis of big count data via a-optimal subsampling
    (2018-07-19) Zhao, Xiaofeng; Tan, Fei; Peng, Hanxiang
    There are two computational bottlenecks for Big Data analysis: (1) the data is too large for a desktop to store, and (2) the computing task takes too long waiting time to finish. While the Divide-and-Conquer approach easily breaks the first bottleneck, the Subsampling approach simultaneously beat both of them. The uniform sampling and the nonuniform sampling--the Leverage Scores sampling-- are frequently used in the recent development of fast randomized algorithms. However, both approaches, as Peng and Tan (2018) have demonstrated, are not effective in extracting important information from data. In this thesis, we conduct regression analysis for big count data via A-optimal subsampling. We derive A-optimal sampling distributions by minimizing the trace of certain dispersion matrices in general estimating equations (GEE). We point out that the A-optimal distributions have the same running times as the full data M-estimator. To fast compute the distributions, we propose the A-optimal Scoring Algorithm, which is implementable by parallel computing and sequentially updatable for stream data, and has faster running time than that of the full data M-estimator. We present asymptotic normality for the estimates in GEE's and in generalized count regression. A data truncation method is introduced. We conduct extensive simulations to evaluate the numerical performance of the proposed sampling distributions. We apply the proposed A-optimal subsampling method to analyze two real count data sets, the Bike Sharing data and the Blog Feedback data. Our results in both simulations and real data sets indicated that the A-optimal distributions substantially outperformed the uniform distribution, and have faster running times than the full data M-estimators.