Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

When Are Investors Rational

Abstract: Taking our cue from certain recent advances in experimental psychology, the authors propose a plausible theory of conflict between rationality and inherent behavioral biases of investors. In this t...

Vibration of porous functionally graded plates with geometric discontinuities and partial supports

Abstract: Vibrational study of the porous functionally graded plate with geometric discontinuities and partial supports has been presented in the present paper. The kinematics of functionally graded plate is...

Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

Abstract: The high entropy alloys have become the most intensely researched materials in recent times. They offer the flexibility to choose a large array of metallic elements in the periodic table, a combination of which produces distinctive desirable properties that are not possible to be obtained by the pristine metals. Over the past decade, a myriad of publications has inundated the aspects of materials synthesis concerning HEA. Hitherto, the practice of HEA development has largely relied on a trial-and-error basis, and the hassles associate with this effort can be reduced by adopting a machine learning approach. This way, the “right first time” approach can be adopted to deterministically predict the right combination and composition of metallic elements to obtain the desired functional properties. This article reviews the latest advances in adopting machine learning approaches to predict and develop newer compositions of high entropy alloys. The review concludes by highlighting the newer applications areas that this accelerated development has enabled such that the HEA coatings can now potentially be used in several areas ranging from catalytic materials, electromagnetic shield protection and many other structural applications.

Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forests and Extra-trees regressors.

Abstract: Antimicrobial peptides are host defense peptides being viewed as replacement to broad-spectrum antibiotics due to varied advantages. Hepatitis is the commonest infectious disease of liver, affecting 500 million globally with reported adverse side effects in treatment therapy. Antimicrobial peptides active against hepatitis are called as anti-hepatitis peptides (AHP). In current work, we present Extratrees and Random Forests based Quantitative Structure Activity Relationship (QSAR) regression modeling using extracted sequence based descriptors for prediction of the anti-hepatitis activity. The Extra-trees regression model yielded a very high performance in terms coefficient of determination (R2) as 0.95 for test set and 0.7 for the independent dataset. We hypothesize that the developed model can further be used to identify potentially active anti-hepatitis peptides with a high level of reliability.

Development of novel anti-malarial from structurally diverse library of molecules, targeting plant-like CDPK1, a multistage growth regulator of P. falciparum

Abstract: Upon Plasmodium falciparum merozoites exposure to low [K+] environment in blood plasma, there is escalation of cytosolic [Ca2+] which activates Ca2+-Dependent Protein Kinase 1 (CDPK1), a signaling hub of intra-erythrocytic proliferative stages of parasite. Given its high abundance and multidimensional attributes in parasite life-cycle, this is a lucrative target for designing antimalarials. Towards this, we have virtually screened MyriaScreenII diversity collection of 10,000 drug-like molecules, which resulted in 18 compounds complementing ATP-binding pocket of CDPK1. In vitro screening for toxicity in mammalian cells revealed that these compounds are non-toxic in nature. Furthermore, SPR analysis demonstrated differential binding affinity of these compounds towards recombinantly purified CDPK1 protein. Selection of lead compound 1 was performed by evaluating their inhibitory effects on phosphorylation and ATP binding activities of CDPK1. Furthermore, in vitro biophysical evaluations by ITC and FS revealed that binding of compound 1 is driven by formation of energetically favorable non-covalent interactions, with different binding constants in presence and absence of Ca2+, and TSA authenticated stability of compound 1 bound CDPK1 complex. Finally, compound 1 strongly inhibited intra-erythrocytic growth of P. falciparum in vitro. Conceivably, we propose a novel CDPK1-selective inhibitor, step towards developing pan-CDPK kinase inhibitors, prerequisite for cross-stage anti-malarial protection.