Indian Institute of Technology Indore

About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Investigating Conformational Dynamics of Lewis Y Oligosaccharides and Elucidating Blood Group Dependency of Cholera Using Molecular Dynamics.

Abstract: Cholera is caused by Vibrio cholerae and is an example of a blood-group-dependent disease. Recent studies suggest that the receptor-binding B subunit of the cholera toxin (CT) binds histo–blood group antigens at a secondary binding site. Herein, we studied the conformational dynamics of Lewis Y (LeY) oligosaccharides, H-tetrasaccharides and A-pentasaccharides, in aqueous solution by conducting accelerated molecular dynamics (aMD) simulations. The flexible nature of both oligosaccharides was displayed in aMD simulations. Furthermore, aMD simulations revealed that for both oligosaccharides in the free form, 4C1 and 1C4 puckers were sampled for all but GalNAc monosaccharides, while either the 4C1 (GlcNAc, Gal, GalNAc) or 1C4 (Fuc2, Fuc3) pucker was sampled in the CT-bound forms. In aMD, the complete transition from the 4C1 to 1C4 pucker was sampled for GlcNAc and Gal in both oligosaccharides. Further, we have observed a transition from the open to closed conformer in the case of A-pentasaccharide, while H-te...

Highly Selective and Sensitive Methanol Sensor Using Rose-Like ZnO Microcube and MoO 3 Micrograss-Based Composite

Abstract: Rose-like ZnO microcube/MoO3 micrograss-based composite was synthesized via hydrothermal process followed by solution-based synthesis approach. The crystal structure, chemical state, morphology, and elemental analysis of the obtained rose-like composite were examined by X-ray diffraction, X-ray photoelectron spectroscopy, field emission scanning electron microscopy, and energy dispersive X-ray spectrometer, respectively. The results indicated that rose-like ZnO microcube/MoO3 micrograss composite was obtained where ZnO microcube pistil and MoO3 micrograss petal were formed. Furthermore, volatile organic compounds sensing performance of the rose-like composite was examined, where sensors presented outstanding sensing performance toward methanol including high selectivity and sensitivity, low-optimal operating temperature as well as very stable response-recovery characteristics, and long-term stability. Such sensing performance can be ascribed to a combined effect of the unique rose-like structures and band formation between ZnO/MoO3 n–n heterojunction.

EEG-Based Detection of Focal Seizure Area Using FBSE-EWT Rhythm and SAE-SVM Network

Abstract: The neurological pathology which occurs due to the disturbance of the nerve cell activity and causing recurrent seizures is called epilepsy. In medical practice, the localization of the epileptogenic area or region in the brain is the primary task for the effectiveness of the epilepsy surgery. The epileptogenic region is identified based on the presence of focal electroencephalogram (EEG) signals during recording. Therefore, the classification of focal (FL) and non-focal (NFL) classes of EEG channels is the prerequisite to identify the epileptogenic regions in the brain. In this paper, a hybrid approach based on the combination of the band or rhythm specific Fourier-Bessel series expansion domain empirical wavelet transform (FBSE-EWT) filter bank and sparse autoencoder (SAE) based support vector machine (SAE-SVM) network is proposed for the categorization of FL and NFL types of EEG channels. The rhythms such as $\delta $ , $\theta $ , $\alpha $ , $\beta $ , and $\gamma $ are obtained from the EEG signal of each channel using FBSE-EWT filter bank. The SAE-SVM network classifies the FL and NFL categories of EEG channels directly from the rhythms. The results demonstrate that the proposed hybrid approach has 100% accuracy for the classification of FL and NFL types using the $\delta $ -rhythms of EEG signals for both channels. The approach extracts learnable features in the SAE stage, and these features have higher performance as compared to the existing features for the categorization of FL and NFL types of EEG channels.