Browsing by Author "Zhang, Yafei"

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    Exploring spatial heterogeneity of CPUE year trend and nonstationarity in fisheries stock assessment, an example based on Atlantic Weakfish (Cynoscion regalis)
    Zhang, Yafei (Virginia Tech, 2016-02-03)
    Quantitative population dynamics modeling is needed to evaluate the stock status and fisheries management plans to provide robust model and management strategies. Atlantic Weakfish (Cynoscion regalis), one important commercial and recreational fish species along the west coast of Atlantic Ocean that was found to be declining in recent years, was selected as an example species. My study aimed to explore the possible spatial heterogeneity of CPUE (catch per unit effort) year trend based on three fishery independent surveys and explore the influence of nonstationary natural mortality on the fisheries management through a MSE (Management Strategy Evaluation) algorithm based on the Weakfish stock assessment results. Five models for catch rate standardization were constructed based on the NEAMAP (NorthEast Area Monitoring and Assessment Program) survey data and the ‘best' two models were selected based on the ability to capture nonlinearity and spatial autocorrelation. The selected models were then used to fit the other two survey data to compare the CPUE year trend of Weakfish. Obvious differences in distribution pattern of Weakfish along latitude and longitude were detected from these three surveys as well as the CPUE year trend. To test the influence of the model selection on the MSE, five stock-recruitment models and two forms of statistical catch-at-age models were used to evaluate the fishery management strategies. The current biomass-based reference point tends to be high if the true population dynamics have nonstationary natural mortality. A flexible biomass based reference point to match the nonstationary process is recommended for future fisheries management.
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    Nonlinearity and Spatial Autocorrelation in Species Distribution Modeling: An Example Based on Weakfish (Cynoscion regalis) in the Mid-Atlantic Bight
    Zhang, Yafei; Jiao, Yan; Latour, Robert J. (MDPI, 2022-12-31)
    Nonlinearity and spatial autocorrelation are common features observed in marine fish datasets but are often ignored or not considered simultaneously in modeling. Both features are often present within ecological data obtained across extensive spatial and temporal domains. A case study and a simulation were conducted to evaluate the necessity of considering both characteristics in marine species distribution modeling. We examined seven years of weakfish (Cynoscion regalis) survey catch rates along the Atlantic coast, and five types of statistical models were formulated using a delta model approach because of the high percentage of zero catches in the dataset. The delta spatial generalized additive model (GAM) confirmed the presence of nonlinear relationships with explanatory variables, and results from 3-fold cross-validation indicated that the delta spatial GAM yielded the smallest training and testing errors. Spatial maps of residuals also showed that the delta spatial GAM decreased the spatial autocorrelation in the data. The simulation study found that the spatial GAM over competes other models based on the mean squared error in all scenarios. That indicates that the recommended model not just works well for the NEAMAP survey but also for other cases as in the simulated scenarios.
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    Role of the Sh3 and Cysteine-Rich Domain 3 (STAC3) Gene in Proliferation and Differentiation of Bovine Satellite Cells
    Zhang, Yafei (Virginia Tech, 2013-08-15)
    The STAC3 gene is a functionally undefined gene predicted to encode a protein containing two SH3 domains and one cysteine-rich domain. In this study, we determined the potential role of the STAC3 gene in proliferation and differentiation of bovine satellite cells. We isolated satellite cells from skeletal muscle of adult cattle and transfected them with STAC3 small interfering RNA (siRNA) or scrambled siRNA. Cell proliferation assays revealed that STAC3 knockdown had no effect on the proliferation rate of bovine satellite cells. We assessed the differentiation status of bovine satellite cells by quantifying the expression levels of myogenin and myosin heavy chain genes, and by quantifying fusion index. STAC3 knockdown stimulated mRNA and protein expression of myogenin, and myosin heavy chain 3 and 7, and increased fusion index of bovine satellite cells. These data together suggest that STAC3 inhibits differentiation of bovine satellite cells into myotubes. To determine the underlying mechanism, we identified and validated AP1?1 as a STAC3-interacting protein by yeast two-hybrid screening and co-immunoprecipitation. In C2C12 cells, STAC3 knockdown decreased the expression level of AP1?1 protein. In bovine satellite cells, STAC3 knockdown increased the membrane localization of glucose transporter 4 (GLUT4) and glucose uptake. These data together suggest the following mechanism by which STAC3 inhibits differentiation of bovine satellite cells: STAC3 increases AP1?1 stability in cells; a high level of AP1?1 keeps GLUT4 from translocating to the plasma membrane; reduced membrane localization of GLUT4 impedes glucose uptake; and restricted glucose uptake inhibits differentiation of satellite cells into myotubes.
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    Stac3 Inhibits Myoblast Differentiation into Myotubes
    Ge, Xiaomei; Zhang, Yafei; Park, Sungwon; Cong, Xiaofei; Gerrard, David E.; Jiang, Honglin (PLOS, 2014-04-30)
    The functionally undefined Stac3 gene, predicted to encode a SH3 domain- and C1 domain-containing protein, was recently found to be specifically expressed in skeletal muscle and essential to normal skeletal muscle development and contraction. In this study we determined the potential role of Stac3 in myoblast proliferation and differentiation, two important steps of muscle development. Neither siRNA-mediated Stac3 knockdown nor plasmid-mediated Stac3 overexpression affected the proliferation of C2C12 myoblasts. Stac3 knockdown promoted the differentiation of C2C12 myoblasts into myotubes as evidenced by increased fusion index, increased number of nuclei per myotube, and increased mRNA and protein expression of myogenic markers including myogenin and myosin heavy chain. In contrast, Stac3 overexpression inhibited the differentiation of C2C12 myoblasts into myotubes as evidenced by decreased fusion index, decreased number of nuclei per myotube, and decreased mRNA and protein expression of myogenic markers. Compared to wild-type myoblasts, myoblasts from Stac3 knockout mouse embryos showed accelerated differentiation into myotubes in culture as evidenced by increased fusion index, increased number of nuclei per myotube, and increased mRNA expression of myogenic markers. Collectively, these data suggest an inhibitory role of endogenous Stac3 in myoblast differentiation. Myogenesis is a tightlycontrolled program; myofibers formed from prematurely differentiated myoblasts are dysfunctional. Thus, Stac3 may play a role in preventing precocious myoblast differentiation during skeletal muscle development.
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    Variable screening and graphical modeling for ultra-high dimensional longitudinal data
    Zhang, Yafei (Virginia Tech, 2019-07-02)
    Ultrahigh-dimensional variable selection is of great importance in the statistical research. And independence screening is a powerful tool to select important variable when there are massive variables. Some commonly used independence screening procedures are based on single replicate data and are not applicable to longitudinal data. This motivates us to propose a new Sure Independence Screening (SIS) procedure to bring the dimension from ultra-high down to a relatively large scale which is similar to or smaller than the sample size. In chapter 2, we provide two types of SIS, and their iterative extensions (iterative SIS) to enhance the finite sample performance. An upper bound on the number of variables to be included is derived and assumptions are given under which sure screening is applicable. The proposed procedures are assessed by simulations and an application of them to a study on systemic lupus erythematosus illustrates the practical use of these procedures. After the variables screening process, we then explore the relationship among the variables. Graphical models are commonly used to explore the association network for a set of variables, which could be genes or other objects under study. However, graphical modes currently used are only designed for single replicate data, rather than longitudinal data. In chapter 3, we propose a penalized likelihood approach to identify the edges in a conditional independence graph for longitudinal data. We used pairwise coordinate descent combined with second order cone programming to optimize the penalized likelihood and estimate the parameters. Furthermore, we extended the nodewise regression method the for longitudinal data case. Simulation and real data analysis exhibit the competitive performance of the penalized likelihood method.