Changes in the expression of DNA double strand break repair genes in primordial follicles from immature and aged rats

Oocytes present at birth undergo a progressive process of apoptosis in humans and other mammals as they age. Accepted opinion is that no fresh oocytes are produced other than those present at the time of birth. Studies have shown that DNA repair genes in oocytes of mice and women decline with age, and lack of these genes show higher DNA breaks and increased oocyte death rates. In contrast to the ethical problems associated with monitoring the changes in DNA double-strand breaks in oocytes from young and old humans, it is relatively easy to carry out such a study using a rodent model. In this study, the mRNA levels of DNA repair genes are compared with protein products of some of the genes in the primordial follicles isolated from immature (18-20 days) and aged (400-450 days) female rats. Results revealed a significant decline in mRNA levels of BRAC1 (P < 0.01), RAD51 (P < 0.05), ERCC2 (P < 0.05), and H2AX (P < 0.01) of DNA repair genes and phospho-protein levels of BRAC1 (P < 0.01) and H2AX (P < 0.05) in primordial follicles of aged rats. Impaired DNA repair is confirmed as a mechanism of oocyte ageing. (C) 2014 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Improved recognition of aged Kannada documents by effective segmentation of merged characters

In optical character recognition of very old books, the recognition accuracy drops mainly due to the merging or breaking of characters. In this paper, we propose the first algorithm to segment merged Kannada characters by using a hypothesis to select the positions to be cut. This method searches for the best possible positions to segment, by taking into account the support vector machine classifier's recognition score and the validity of the aspect ratio (width to height ratio) of the segments between every pair of cut positions. The hypothesis to select the cut position is based on the fact that a concave surface exists above and below the touching portion. These concave surfaces are noted down by tracing the valleys in the top contour of the image and similarly doing it for the image rotated upside-down. The cut positions are then derived as closely matching valleys of the original and the rotated images. Our proposed segmentation algorithm works well for different font styles, shapes and sizes better than the existing vertical projection profile based segmentation. The proposed algorithm has been tested on 1125 different word images, each containing multiple merged characters, from an old Kannada book and 89.6% correct segmentation is achieved and the character recognition accuracy of merged words is 91.2%. A few points of merge are still missed due to the absence of a matched valley due to the specific shapes of the particular characters meeting at the merges.