Abstract. Protein secondary structure prediction is the method of extracting locally defined protein structures from the sequence of amino acids. It is a challenging and elucidating part of the field of bioinformatics. Several methods are attempting to meet these challenges. But the Artificial Neural Network (ANN) technique is turning out to be the most successful. In this work, an ANN based multi level classifier is designed for predicting secondary structure of the proteins. In this method ANNs are trained to make them capable of recognizing amino acids in a sequence following which from these amino acids secondary structures are derived. Then based on the majority of the secondary structure final structure is derived. This work shows the prediction of secondary structure of proteins employing ANNs though it is restricted initially to four structures only.

Elecrooculogram (EOG) signal extraction is critical in the working of any electrooculography aided system based upon the tracking of the ocular movement of the eye dipole. In this chapter the signals captured using sensors (electrodes), are first amplified, then the noise is removed and then digitized, before being transferred to controller for movement of the wheelchair. Finally, from the muscle sensor, the output is directly being given to the controller to reach the target and complete the control of the movement of the wheelchair. Initially, a potentiometer is used instead of the Ag-Agcl electrodes to test the strength of signal obtained due to the movement of the eyes. Using this wheelchair is quite an advantage because this chair helps a physically handicapped person to move freely without being dependent on anyone else. The research provides a new method for human-machine interface system.

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Esophageal cancer involves multiple genetic alternations. A systematic codon usage bias analysis was completed to investigate the bias among the esophageal cancer responsive genes. GC-rich genes were low (average effective number of codon value was 49.28). CAG and GTA are over-represented and under-represented codons, respectively. Correspondence analysis, neutrality plot, and parity rule 2 plot analysis confirmed the dominance over mutation pressure in modulating the codon usage pattern of genes linked with esophageal cancer.