This study was conducted in Al-Salam station for Dairy cattle/private sector, for the period from 1-11-2016 to 1-11-2017, to determine the association between BTN1A1 gene polymorphism and reproductive efficiency indicator and heat tolerance in 50 Holstein cows. The results of BTN1A1 gene analysis showed a highly significant Different (P<0.01) between genotypes of BTN1A1 gene’s genotypes AA, AB the percentage were 72.00, 28.00 % respectively. Results showed that services per conception and days open was significantly (P<0.05) affected by polymorphism of BTN1A1 gene and for cows with AA genotype, there was also a significant difference (P<0.05) between the genotypes of BTN1A1 gene for IgG concentration in calves blood who belong to mother’s with AA genotypes compared with AB genotype, for the heat coefficient tolerance trait the results showed a significant different (P<0.05) with BTN1A1 polymorphism and for cows with AB genotype in third month of lactation, while there are no significant differences in other months of lactation with different genotypes of BTN1A1 gene. It was possible to conclude from this study the possibility of BTN1A1 gen’s polymorphism in the development of genetic improvement strategies and breeding programs that achieved the best productive performance in dairy cows.
It is estimated that over the next few decades, EOR will be used for the more than 50% of oil production in the US and worldwide. From these, in reservoir with viscositites ranging between 10 – 150 mPa.s, polymer flooding is suggsted as the EOR method. Therefore, there is an upper limit to the recommended range of reservoir oil viscosities for polymer flooding. To address the issue of this limitation of polymer injectivity and pumping efficiency, we propose a novel method. The method involves the use of Supramolecular Systems, which are composed of long-chain aminoacids and maleic acids post complexation. Their unique feature of resersible viscosities allows the operator to overcome
This paper is concerned with preliminary test double stage shrinkage estimators to estimate the variance (s2) of normal distribution when a prior estimate of the actual value (s2) is a available when the mean is unknown , using specifying shrinkage weight factors y(×) in addition to pre-test region (R).
Expressions for the Bias, Mean squared error [MSE (×)], Relative Efficiency [R.EFF (×)], Expected sample size [E(n/s2)] and percentage of overall sample saved of proposed estimator were derived. Numerical results (using MathCAD program) and conclusions are drawn about selection of different constants including in the me
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