Swalpa Kumar Roy

Plasmodium falciparum: Genetic polymorphism in apical membrane antigen-1 gene from Indian isolates ☆

A number of stage-specific antigens have been characterized for vaccine development against Plasmodium falciparum malaria. This study presents a comprehensive analysis of the sequence polymorphism in Plasmodium falciparum apical membrane antigen-1 (PfAMA-1) in population samples from the eastern and western parts of India. This is the first study of its kind for the nearly full length PfAMA-1 gene from these regions in India. Our observations confirmed that sequence diversity of PfAMA-1 confines only to point mutations and shows 4-8% variation as compared to the prototypes. As opposed to the previous studies on PfAMA-1, our study revealed a greater degree of polymorphism in the Domain II region of PfAMA-1 protein, though signature for diversifying selection is seen throughout the gene. Our present investigation also indicates a very high degree of variation in the reported T- and B-cell epitopes of PfAMA-1. Few noteworthy and unique observations made in this study are the substitution of Cysteine residues responsible for the disulfide bond structure of the protein and the presence of premature termination after 595 amino acids in 3 of the 13 isolates under consideration. These crucial findings add new perspectives to the future of AMA-1 research and could have major implications in establishing AMA-1 as a vaccine candidate.

Fractal Image Compression using Hierarchical Classification of Sub-images

In fractal image compression (FIC) an image is divided into sub-images (domains and ranges), and a range is compared with all possible domains for similarity matching. However this process is extremely timeconsuming. In this paper, a novel sub-image classification scheme is proposed to speed up the compression process. The proposed scheme partitions the domain pool hierarchically, and a range is compared to only those domains which belong to the same hierarchical group as the range. Experiments on standard images show that the proposed scheme exponentially reduces the compression time when compared to baseline fractal image compression (BFIC), and is comparable to other sub-image classification schemes proposed till date. The proposed scheme can compress Lenna (512x512x8) in 1.371 seconds, with 30.6 dB PSNR decoding quality (140x faster than BFIC), without compromising compression ratio and decoded image quality.

Image Denoising Using Fractal Hierarchical Classification

This paper proposes an efficient yet simple fractal-based image denoising technique. Denoising is carried out during fractal coding process. Hierarchical classification is used to increase encoding speed, and avoid a lot of futile mean-square-error (MSE) computations. Quadtree-based image partitioning using dynamic range and domain sizes is used to increase the degree of noise removal. Further denoising is achieved using pyramidal decoding, using non-arbitrary seed image, and additional post processing. Results from experiments show that our proposed scheme improves the structural similarity (SSIM) index of the Lenna image from 44% to 78% for low noise cases, and from 9% to 35% for high noise cases.