Browsing by Subject "beta"

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    Expression, Crystallization And Preliminary X-Ray Crystallographic Analysis Of Cystathionine Gamma-Synthase (Xometb) From Xanthomonas Oryzae Pv. Oryzae
    (2012-12) Ngo, Ho-Phuong-Thuy; Kim, Jin-Kwang; Kim, Seung-Hwan; Pham, Tan-Viet; Tran, Thi-Huyen; Nguyen, Dinh-Duc; Kim, Jeong-Gu; Chung, Sumi; Ahn, Yeh-Jin; Kang, Lin-Woo; Chung, Sumi
    Cystathionine gamma-synthase (CGS) catalyzes the first step in the transsulfuration pathway leading to the formation of cystathionine from O-succinylhomoserine and l-cysteine through a gamma-replacement reaction. As an antibacterial drug target against Xanthomonas oryzae pv. oryzae (Xoo), CGS from Xoo (XometB) was cloned, expressed, purified and crystallized. The XometB crystal diffracted to 2.4 angstrom resolution and belonged to the tetragonal space group I4(1), with unit-cell parameters a = b = 165.4, c = 241.7 angstrom. There were four protomers in the asymmetric unit, with a corresponding solvent content of 73.9%.
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    A Tractable State-Space Model for Symmetric Positive-Definite Matrices
    (2014-12) Windle, Jesse; Carvalho, Carlos M.; Carvalho, Carlos M.
    The Bayesian analysis of a state-space model includes computing the posterior distribution of the system's parameters as well as its latent states. When the latent states wander around R-n there are several well-known modeling components and computational tools that may be profitably combined to achieve this task. When the latent states are constrained to a strict subset of R-n these models and tools are either impaired or break down completely. State-space models whose latent states are covariance matrices arise in finance and exemplify the challenge of devising tractable models in the constrained setting. To that end, we present a state-space model whose observations and latent states take values on the manifold of symmetric positive-definite matrices and for which one may easily compute the posterior distribution of the latent states and the system's parameters as well as filtered distributions and one-step ahead predictions. Employing the model within the context of finance, we show how one can use realized covariance matrices as data to predict latent time-varying covariance matrices. This approach out-performs factor stochastic volatility.