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: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Plant-derived natural polyphenols as potential antiviral drugs against SARS-CoV-2 via RNA-dependent RNA polymerase (RdRp) inhibition: an in-silico analysis.

Abstract: The sudden outburst of Coronavirus disease (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) poses a massive threat to global public health. Currently, no therapeutic drug or vaccine exists to treat COVID-19. Due to the time taking process of new drug development, drug repurposing might be the only viable solution to tackle COVID-19. RNA-dependent RNA polymerase (RdRp) catalyzes SARS-CoV-2 RNA replication and hence, is an obvious target for antiviral drug design. Interestingly, several plant-derived polyphenols effectively inhibit the RdRp of other RNA viruses. More importantly, polyphenols have been used as dietary supplementations for a long time and played beneficial roles in immune homeostasis. We were curious to study the binding of polyphenols with SARS-CoV-2 RdRp and assess their potential to treat COVID-19. Herein, we made a library of polyphenols that have shown substantial therapeutic effects against various diseases. They were successfully docked in the catalytic pocket of RdRp. The investigation reveals that EGCG, theaflavin (TF1), theaflavin-3'-O-gallate (TF2a), theaflavin-3'-gallate (TF2b), theaflavin 3,3'-digallate (TF3), hesperidin, quercetagetin, and myricetin strongly bind to the active site of RdRp. Further, a 150-ns molecular dynamic simulation revealed that EGCG, TF2a, TF2b, TF3 result in highly stable bound conformations with RdRp. The binding free energy components calculated by the MM-PBSA also confirm the stability of the complexes. We also performed a detailed analysis of ADME prediction, toxicity prediction, and target analysis for their druggability. Overall, our results suggest that EGCG, TF2a, TF2b, TF3 can inhibit RdRp and represent an effective therapy for COVID-19. Communicated by Ramaswamy H. Sarma.

UAV-Enabled Downlink Wireless System with Non-Orthogonal Multiple Access

Abstract: In this paper, we consider an unmanned aerial vehicle (UAV)-enabled downlink wireless system wherein a UAV serves as flying base station to communicate with two ground users using non- orthogonal multiple access (NOMA). Specifically, we deploy a fixed-wing type UAV which moves in a circular trajectory around the centre of a macrocell to provide ubiquitous coverage to the ground users located outside an offloaded BS coverage region within the cell. By adopting Rician fading model for the line-of-sight (LoS) UAV-to-ground links, we investigate outage probability (OP) of both ground users with NOMA. For comparison with NOMA, we also analyze the outage performance of ground users with orthogonal multiple access (OMA). We highlight the relative performance between NOMA and OMA schemes for the considered system. Further, to manage the fairness among ground users outage performance, we derive the dominant conditions which guarantee the outage performance of both ground users with NOMA transmission mode is better than that with OMA. Based on these conditions, at high signal-to-noise ratio (SNR), we observe that the power allocation factor for far ground user from UAV is independent of channel and trajectory parameters for better performance of NOMA. Whereas the dominant condition for near ground user strongly affected by the channel and trajectory parameters.

Measurement of an excess in the Yield of J/ψ at very low pT in Pb-Pb collisions at √sNN=2.76 TeV

Abstract: We report on the first measurement of an excess in the yield of J/ψ at very low transverse momentum (pT< 0.3 GeV/c) in peripheral hadronic Pb-Pb collisions at √sNN = 2.76 TeV, performed by ALICE at the CERN LHC. Remarkably, the measured nuclear modification factor of J/ψ in the rapidity range 2.5< y< 4 reaches about 7 (2) in the pT range 0- 0.3 GeV/c in the 70-90% (50-70%) centrality class. The J/ψ production cross section associated with the observed excess is obtained under the hypothesis that coherent photoproduction of J/ψ is the underlying physics mechanism. If confirmed, the observation of J/ψ coherent photoproduction in Pb-Pb collisions at impact parameters smaller than twice the nuclear radius opens new theoretical and experimental challenges and opportunities. In particular, coherent photoproduction accompanying hadronic collisions may provide insight into the dynamics of photoproduction and nuclear reactions, as well as become a novel probe of the Quark-Gluon Plasma.

An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals

Abstract: Classification of normal and CAD subjects is proposed using HRV signals.FAWT is used to decompose the HRV signal.K-NN entropy estimator and fuzzy entropy are used for feature extraction.Obtained classification accuracy of 100%. Coronary Artery Disease (CAD) causes maximum death among all types of heart disorders. An early detection of CAD can save many human lives. Therefore, we have developed a new technique which is capable of detecting CAD using the Heart Rate Variability (HRV) signals. These HRV signals are decomposed to sub-band signals using Flexible Analytic Wavelet Transform (FAWT). Then, two nonlinear parameters namely; K-Nearest Neighbour (K-NN) entropy estimator and Fuzzy Entropy (FzEn) are extracted from the decomposed sub-band signals. Ranking methods namely Wilcoxon, entropy, Receiver Operating Characteristic (ROC) and Bhattacharya space algorithm are implemented to optimize the performance of the designed system. The proposed methodology has shown better performance using entropy ranking technique. The Least Squares-Support Vector Machine (LS-SVM) with Morlet wavelet and Radial Basis Function (RBF) kernels obtained the highest classification accuracy of 100% for the diagnosis of CAD. The developed novel algorithm can be used to design an expert system for the diagnosis of CAD automatically using Heart Rate (HR) signals. Our system can be used in hospitals, polyclinics and community screening to aid the cardiologists in their regular diagnosis.