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Buaban Sayan,Puangdee Somsook,Duangjinda Monchai,Boonkum Wuttigrai 아세아·태평양축산학회 2020 Animal Bioscience Vol.33 No.9
Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,-3-lactation random regression test-day model. Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.
Forecasting Government Bond Yields in Thailand: A Bayesian VAR Approach
Wantana BUABAN,Yuthana SETHAPRAMOTE 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.3
This paper seeks to investigate major macroeconomic factors and bond yield interactions in Thai bond markets, with the goal of forecasting future bond yields. This study examines the best predictive yields for future bond yields at different maturities of 1-, 3-, 5-, 7-, and 10-years using time series data of economic indicators covering the period from 1998 to 2020. The empirical findings support the hypothesis that macroeconomic factors influence bond yield fluctuations. In terms of forecasting future bond yields, static predictions reveal that in most cases, the BVAR model offers the best predictivity of bond rates at various maturities. Furthermore, the BVAR model has the best performance in dynamic rolling-window, forecasting bond yields with various maturities for 2-, 4-, and 8-quarters. The findings of this study imply that the BVAR model forecasts future yields more accurately and consistently than other competitive models. Our research could help policymakers and investors predict bond yield changes, which could be important in macroeconomic policy development.
Thermostable Xylanase from Marasmius sp.: Purification and Characterization
Ratanachomsri, Ukrit,Sriprang, Rutchadaporn,Sornlek, Warasirin,Buaban, Benchaporn,Champreda, Verawat,Tanapongpipat, Sutipa,Eurwilaichitr, Lily Korean Society for Biochemistry and Molecular Biol 2006 Journal of biochemistry and molecular biology Vol.39 No.1
We have screened 766 strains of fungi from the BIOTEC Culture Collection (BCC) for xylanases working in extreme pH and/or high temperature conditions, the so-called extreme xylanases. From a total number of 32 strains producing extreme xylanases, the strain BCC7928, identified by using the internal transcribed spacer (ITS) sequence of rRNA to be a Marasmius sp., was chosen for further characterization because of its high xylanolytic activity at temperature as high as $90^{\circ}C$. The crude enzyme possessed high thermostability and pH stability. Purification of this xylanase was carried out using an anion exchanger followed by hydrophobic interaction chromatography, yielding the enzyme with >90% homogeneity. The molecular mass of the enzyme was approximately 40 kDa. The purified enzyme retained broad working pH range of 4-8 and optimal temperature of $90^{\circ}C$. When using xylan from birchwood as substrate, it exhibits $K_m$ and $V_{max}$ values of $2.6{\pm}0.6\;mg/ml$ and $428{\pm}26\;U/mg$, respectively. The enzyme rapidly hydrolysed xylans from birchwood, beechwood, and exhibited lower activity on xylan from wheatbran, or celluloses from carboxymethylcellulose and Avicel. The purified enzyme was highly stable at temperature ranges from 50 to $70^{\circ}C$. It retained 84% of its maximal activity after incubation in standard buffer containing 1% xylan substrate at $70^{\circ}C$ for 3 h. This thermostable xylanase should therefore be useful for several industrial applications, such as agricultural, food and biofuel.