There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss. The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness.

ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks.

Textbook of biochemistry with clinical correlations , 4th edn TM Devlin, ed pp xvii + 1186, illustrated Wiley-Liss, New York, 1997 £2995, hardback

This beautifully illustrated and well-written book, with an impressive array of authors, is aimed at both undergraduate and postgraduate level As the editor states in the preface, it is not intended to be a compendium of biochemistry but rather emphasises the biochemistry of mammalian cells The first 22 chapters cover …

Textbook of biochemistry with clinical correlations

Research issues

Introduction to circuit synthesis and design

Application of Euclidean distance measurement and principal component analysis for gene identification.

During the last several years, substantial progress has been made in developing high-throughput experimental techniques that produce large amounts of genomic data pertaining to molecular activities in cells. Consequently, a great deal of research is being focused on addressing important problems in molecular biology by analyzing these data using mathematical and computational approaches. Genomic signal processing has been an active area of research for the past two decades and have increasingly attracted the attention of researchers from digital signal processing area all over the world. An important step in genomic annotation is to identify protein coding regions of DNA sequence especially in the study of eukaryotic genomes. Due to lack of obvious sequence features among exons and introns, distinguishing protein coding regions from non-coding regions effectively is a challenging problem. A variety of computational algorithms have been developed to predict exons. Most of the exon finding algorithms are based on statistics methods. The signal processing approaches of recent years may identify some hidden periodicity and features which can not be revealed easily by conventional statistics methods. In this paper the authors have presented an algorithm to separate out coding regions from non-coding regions based on positional frequency distribution of nucleotides and the algorithm shows the results that exon regions exhibit more random behavior compared to intron regions. Such a behavior was also observed by FFT power spectrum analysis of DNA sequences. Case studies on genes from different organisms show that the algorithm is an effective approach towards exon prediction.

Identification and analysis of coding and non-coding regions of a DNA sequence by positional frequency distribution of nucleotides (PFDN) algorithm

Prediction of Prostate Cancer Cells based on Principal Component Analysis Technique

Modeling of cancerous and healthy Homo Sapiens colon gene using electrical network is proposed to study their behavior. In this paper, the individual amino acid models are designed using hydropathy index of amino acid side chain. The phase and magnitude responses of genes are examined to screen out cancer from healthy genes. The performance of proposed modeling technique is judged using various performance measurement metrics such as accuracy, sensitivity, specificity, etc. The network model performance is increased with frequency, which is analyzed using the receiver operating characteristic curve. The accuracy of the model is tested on colon genes and achieved maximum 97% at 10-MHz frequency.

Performance Analysis of Network Model to Identify Healthy and Cancerous Colon Genes

http://ieeexplore.ieee.org/iel5/5398767/5407062/05407254.pdf

Soma Barman

Papers

Application of Euclidean distance measurement and principal component analysis for gene identification.

Identification and analysis of coding and non-coding regions of a DNA sequence by positional frequency distribution of nucleotides (PFDN) algorithm

Prediction of Prostate Cancer Cells based on Principal Component Analysis Technique

Performance Analysis of Network Model to Identify Healthy and Cancerous Colon Genes

Identification and analysis of coding and non-coding regions of a DNA sequence by positional frequency distribution of nucleotides (PFDN) algorithm