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Articles by M. Srinivas
Total Records ( 2 ) for M. Srinivas
  A.S. Vickram , V. Devi Rajeswari , M. Srinivas , G. Jayaraman , R.A. Kamini , M. Ramesh Pathy , S. Ventat Kumar and T.B. Sridharan
  Purpose of this research was to elucidate the protein profiles of the seminal plasma in various categories of male infertility, to scrutinize their correlation with seminal parameters. Oligoasthenospermia (N = 15), asthenospermaia (N = 17), azoospermia (N = 12), normospermia (N = 27), oligospermia (N = 12) and fertile (control subjects, N = 10) were collected. The samples were diluted by tris-egg yolk extender and were frozen. Plasma was separated from semen by centrifugation, underwent SDS Polyacrylamide Gel Electrophoresis (SDS-PAGE). The mean values with its standard error of semen parameters of fresh sample were shown significant difference (p<0.0001) when compared to the post-thaw samples. Of the various fractionations, the protein with molecular weight 44.6 kDa shows high significant and positive correlation (p<0.01) with sperm concentration of freshly evaluated semen samples and low level significant (p<0.00001) with the frozen samples. Sperm motility was positively correlated (p<0.029) with the protein molecular weight 56.6 kDa in the freeze thawed semen samples. This reality could sustain the implication that seminal plasma proteins act on the sperm physiology and morphology and it found to act on strange ways. Supplementary studies are essential to define the mechanism of different proteins involved in the fertilization and their correlation.
  M. Srinivas , G. Rama Krishna and K. Rajasekhara Rao
  Over the last two decades, many business organizations had noticed that a generous amount of nontrivial legacy software frame works fail due to unstructured architectural design. Moreover, refactoring is professional procedure for managing the software systems. Indeed, programmers practice regularly with refactoring tools in two different occasions-normal program development phase whenever and wherever design problems arise. Secondly these toolsare needed at the time of code duplication, specifically when adding a new feature, the programmer need to remove the duplication using the re-factor tool. Based on level of automation, refactoring can be classified into three categories-fully manual refactoring, semi-automatic refactoring and automatic refactoring. However, fully manual refactoring and semi-automatic refactoring tools are underused, because sometimes fails to recognize the legacy code and chasing the error messages that leads to more error-prone. This study proposed a novel refactoring tool called GA factor. The GA factor system detects a developer’s legacy code, reminds to the programmer that the automatic refactoring is available and if the programmer accepts then GA factor complete the refactoring automatically. GA factor automatically performs static analysis for analyzing the flowof knowledgeof the code that saves the software engineer from doing erring.
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