Abstract Some organs in animals and human being such as ; liver , intestine and lung contain specific chemicals and active enzymes responsible for biochemical detoxification mechanisms against intoxicating agents. Glutathione is the famous chemical compound which is the substrate for glutathione peroxidase and glutathione – S – transeferase for the phase (II) of the general xenobiotic detoxificating mechanisms . This glutathione is consumed during these reactions and could be restablished by mainly sulfur containing compounds (total thiols). Aflatoxicosis is the most threating environmental condition for health community and animal wealth which is one of the highly interesting field of investigation . This submitted work has been designed in order to deduce the relationship between the level of GSH in these organs during the consecutive exposure to aflatoxicosis and the activity of these enzymes. The exposed rats day after day (for five weeks) to digestive doses of AFB1 (60μg /kg b.w.) showed that the tissue organs were increasingly enriched by accumulated lipid peroxide as a result of intoxication. This cellular peroxidation reactions ( by tissue cytochrome p450) simultaneously showed characteristic histological collaps especially in the lung tissue. The glutathione peroxidase and transeferase were concomitantly diminished by further intoxication . Conclusively; chronic intoxication by aflatoxinB1 consumed the biological reducing glutathione and total thiols as well as the acting peroxidase and transeferase.

Abstract Some organs in animals and human being such as ; liver , intestine and lung contain specific chemicals and active enzymes responsible for biochemical detoxification mechanisms against intoxicating agents. Glutathione is the famous chemical compound which is the substrate for glutathione peroxidase and glutathione – S – transeferase for the phase (II) of the general xenobiotic detoxificating mechanisms . This glutathione is consumed during these reactions and could be restablished by mainly sulfur containing compounds (total thiols). Aflatoxicosis is the most threating environmental condition for health community and animal wealth which is one of the highly interesting field of investigation . This submitted work has been designed in order to deduce the relationship between the level of GSH in these organs during the consecutive exposure to aflatoxicosis and the activity of these enzymes. The exposed rats day after day (for five weeks) to digestive doses of AFB1 (60μg /kg b.w.) showed that the tissue organs were increasingly enriched by accumulated lipid peroxide as a result of intoxication. This cellular peroxidation reactions ( by tissue cytochrome p450) simultaneously showed characteristic histological collaps especially in the lung tissue. The glutathione peroxidase and transeferase were concomitantly diminished by further intoxication . Conclusively; chronic intoxication by aflatoxinB1 consumed the biological reducing glutathione and total thiols as well as the acting peroxidase and transeferase.

Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) is an extremely fast and simple algorithm for generating linear and nonlinear classifiers. Kernel functions are essential in fitting GEPSVM. Usually a single kernel is used by most researchers in their studies, but the real world applications may require a combination of multiple kernel functions. There are two kind of kernels which known as global and local kernels. Global kernel functions have good generalization ability, but low learning ability. Local kernel functions have good learning ability with weak generalization. The presented approach constructs a mixed kernel function with better performance by fully combining local kernel function for strong learning ability and global kernel function for strong generalization. The Backtracking Search Algorithm (BSA) is used for determining the best value of the weight parameter between the two kernels. To evaluate the performance of the proposed approach, we applied it to public datasets from UCI repository.

Most Read Research Articles Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Adaptivity and Adaptability of Learning Object’s Interface Enhanced TCP Westwood Congestion Avoidance Mechanism (TCP WestwoodNew) Migration of Legacy Information System based on Business Process Theory HomeArchivesVolume 34Number 6An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Call for Paper - July 2015 Edition IJCA solicits original research papers for the July 2015 Edition. Last date of manuscript submission is June 20, 2015. Read More An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study E-mail Print inShare Download Full Text International Journal of Computer Applications © 2011 by IJCA Journal Volume 34 - Number 6 Year of Publication: 2011 Authors: M. H. Marghny Rasha M. Abd El-Aziz Ahmed I. Taloba 10.5120/4092-5420 M H Marghny, Rasha Abd M El-Aziz and Ahmed I Taloba. Article: An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study. International Journal of Computer Applications 34(6):1-6, November 2011. Full text available. BibTeX Abstract Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms (GAs), reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multi-sampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.

Most Read Research Articles Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Adaptivity and Adaptability of Learning Object’s Interface Enhanced TCP Westwood Congestion Avoidance Mechanism (TCP WestwoodNew) Migration of Legacy Information System based on Business Process Theory HomeArchivesVolume 34Number 6An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Call for Paper - July 2015 Edition IJCA solicits original research papers for the July 2015 Edition. Last date of manuscript submission is June 20, 2015. Read More An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study E-mail Print inShare Download Full Text International Journal of Computer Applications © 2011 by IJCA Journal Volume 34 - Number 6 Year of Publication: 2011 Authors: M. H. Marghny Rasha M. Abd El-Aziz Ahmed I. Taloba 10.5120/4092-5420 M H Marghny, Rasha Abd M El-Aziz and Ahmed I Taloba. Article: An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study. International Journal of Computer Applications 34(6):1-6, November 2011. Full text available. BibTeX Abstract Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms (GAs), reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multi-sampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.