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Biology subjects

Sharma, M. K.

Publications and source records attributed to Sharma, M. K..

2 recordsLinked to original sources

Genetic and antigenic variation of foot-and-mouth disease virus during persistent infection in naturally infected cattle and Asian buffalo in India

The role of foot-and-mouth disease virus (FMDV) persistently infected ruminants in initiating new outbreaks remains controversial, and the perceived threat posed by such animals hinders international trade in FMD-endemic countries. In this study we report longitudinal analyses of genetic and antigenic variations of FMDV serotype O/ME-SA/Ind2001d sublineage during naturally occurring, persistent infection in cattle and buffalo at an organised dairy farm in India. The proportion of animals from which FMDV RNA was recovered was not significantly different between convalescent (post-clinical) and sub-clinically infected animals or between cattle and buffalo across the sampling period. However, infectious virus was isolated from a higher proportion of buffalo samples and for a longer duration compared to cattle. Analysis of the P1 sequences from recovered viruses indicated fixation of mutations at the rate of 1.816 x 10-2 substitution/site/year (s/s/y) (95% CI 1.362-2.31 x 10-2 s/s/y). However, the majority of point mutations were transitional substitutions. Within individual animals, the mean dN/dS ({omega}) value for the P1 region varied from 0.076 to 0.357, suggesting the selection pressure acting on viral genomes differed substantially across individual animals. Statistical parsimony analysis indicated that all of the virus isolates from carrier animals originated from the outbreak virus. The antigenic relationship value as determined by 2D-VNT assay revealed fluctuation of antigenic variants within and between carrier animals during the carrier state which suggested that some carrier viruses had diverged substantially from the protection provided by the vaccine strain. This study contributes to understanding the extent of within-host and within-herd evolution that occurs during the carrier state of FMDV.

evolutionary biology

Mammogram Segmentation using Multi-atlas Deformable Registration

Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. Existing segmentation algorithms underperform due to variations in image quality and shape of the breast region. In this paper, we propose to segment breast region by combining data-driven clustering with deformable image registration. In the first phase of the approach, we identify atlas images from a dataset of mammograms using data-driven clustering. Then, we segment these atlas images and use in the next phase of the algorithm. The second phase is atlas-based registration. For a candidate image, we find the most similar atlas image from the set of atlases identified in phase one. We deform the selected atlas image to match the given test image using the Demon's registration algorithm. Then, the segmentation mask of the deformed atlas is transferred to the mammogram in consideration. Finally, we refine the segmentation mask with some morphological operations in order to obtain accurate breast region boundary. We evaluated the performance of our method using ground-truth segmentation masks verified by an expert radiologist. We compared the proposed method with three existing state-of-the-art algorithms for breast region segmentation and the proposed approach outperformed all three in most of the cases.

bioinformatics