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Harris R.B.S.,GRU
Biochimica et Biophysica Acta - Molecular Basis of Disease | Year: 2013

Parabiosis is a chronic preparation that allows exchange of whole blood between two animals. It has been used extensively to test for involvement of circulating factors in feedback regulation of physiological systems. The total blood volume of each animal exchanges approximately ten times each day, therefore, factors that are rapidly cleared from the circulation do not reach equilibrium across the parabiotic union whereas those with a long half-life achieve a uniform concentration and bioactivity in both members of a pair. Involvement of a circulating factor in the regulation of energy balance was first demonstrated when one member of a pair of parabiosed rats became hyperphagic and obese following bilateral lesioning of the ventromedial hypothalamus. The non-lesioned partner stopped eating, lost a large amount of weight and appeared to be responding to a circulating "satiety" factor released by the obese rat. These results were confirmed using different techniques to induce obesity in one member of a pair. Studies with phenotypically similar ob/ob obese and db/db diabetic mice indicated that the obese mouse lacked a circulating signal that regulated energy balance, whereas the diabetic mouse appeared insensitive to such a signal. Positional cloning studies identified leptin as the circulating factor and subsequent parabiosis studies confirmed leptin's ability to exchange effectively between parabionts. These studies also suggest the presence of additional unidentified factors that influence body composition. © 2013 Elsevier B.V.

Kannan S.R.,National Cheng Kung University | Kannan S.R.,Pondicherry University | Ramathilagam S.,National Cheng Kung University | Devi R.,GRU | Sathya A.,GRU
Expert Systems with Applications | Year: 2011

This paper presents an automatic effective fuzzy c-means segmentation method for segmenting breast cancer MRI based on standard fuzzy c-means. To introduce a new effective segmentation method, this paper introduced a novel objective function by replacing original Euclidean distance on feature space using new hyper tangent function. This paper obtains the new hyper tangent function from exited hyper tangent function to perform effectively with large number of data from more noised medical images and to have strong clusters. It derives an effective method to construct the membership matrix for objects, and it derives a robust method for updating centers from proposed novel objective function. Experiments will be done with an artificially generated data set to show how effectively the new fuzzy c-means obtain clusters, and then this work implements the proposed methods to segment the breast medical images into different regions, each corresponding to a different tissue, based on the signal enhancement-time information. This paper compares the results with results of standard fuzzy c-means algorithm. The correct classification rate of proposed fuzzy c-means segmentation method is obtained using silhouette method. © 2010 Elsevier Ltd. All rights reserved.

Kannan S.R.,National Cheng Kung University | Ramathilagam S.,National Cheng Kung University | Pandiyarajan R.,GRU | Lian S.,Orange S.A. | Sathya A.,GRU
Neural Network World | Year: 2010

The purpose of this paper is to develop some effective robust fuzzy c-means methods for segmentation of Brain Medical Images and Dynamic ContrastEnhanced Breast Magnetic Images (DCE-BMRI). Segmentation is a difficult task and challenging problem in the brain and breast medical images for diagnosing Breast and Brain cancer related diseases before the image goes for treatment plan. This paper presents three new effective fuzzy clustering techniques: Robust KFCM (Kernel Fuzzy C-Means) with spatial information, Effective Robust FCM based Kernel function, Modified fuzzy c-means algorithm with weight Bias Estimation. In experiments, the presented methods are compared with other reported methods. Experimental results on both breast and brain MR images show that the proposed algorithms have better performance than the standard algorithms. Thus, the proposed method is capable of dealing with the intensity in-homogcneities and noised image effectively. © ICS AS CR 2010.

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