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- 저자 : Zerehdaran, S. (검색결과 2 건)
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Ghorbani, B.,Ghoorchi, T.,Amanlou, H.,Zerehdaran, S. Asian Australasian Association of Animal Productio 2011 Animal Bioscience Vol.24 No.1
Twenty-four Holstein dairy cows were used to evaluate the single and combined effects of different levels of crude protein (CP) and monensin treatment during early lactation on blood metabolites, milk yield and digestion of dairy cows. The experiment was designed as a completely randomized block with a $3{\times}2$ factorial arrangement of treatments. The factors were three concentrations of CP supplement (19.5, 21.4, and 23.4% of dry matter) and two levels of monensin (0 and 350 mg per cow per day). The experiment consisted of three phases and each phase was 3 wk in length. Monensin did not affect milk yield, lactose, solids-non-fat (SNF), blood glucose, triglyceride and DMI, but increased blood cholesterol, blood urea nitrogen (BUN), insulin and reduced blood ${\beta}$-hydroxybutyrate (BHBA), milk fat and protein percentage. Monensin premix significantly decreased rumen ammonia, but rumen pH and microbial protein synthesis were not affected by monensin treatment. Increasing dietary CP improved milk and protein production, but did not alter the other components of milk. Digestibility of NDF, ADF, CP were improved by increasing dietary CP. Increasing dietary CP from 19.5 to 21.4% had no significant effect on ruminal ammonia, but increasing CP to 23.4% significantly increased ruminal ammonia. There was a linear relationship between level of crude protein in the diet and volume of urine excretion. Microbial protein synthesis was affected by increasing CP level; in this way maximum protein synthesis was achieved at 23.4% CP.
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( Abbas Mikhchi ),( Mahmood Honarvar ),( Nasser Emam Jomeh Kashan ),( Saeed Zerehdaran ),( Mehdi Aminafshar ) 한국동물자원과학회(구 한국축산학회) 2016 한국축산학회지 Vol.58 No.1
Background: Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker. Methods: In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel. Results: Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB. Conclusions: The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.
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