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.

Identification and Characterization of a Novel Palmitoyl Acyltransferase as a Druggable Rheostat of Dynamic Palmitoylome in L. donovani .

Abstract: Palmitoylation has been recently identified as an important post-translational rheostat for controlling protein function in eukaryotes. However, the molecular machinery underlying palmitoylation remains unclear in the neglected tropical parasite, Leishmania donovani. Herein, we have identified a catalog of 20 novel palmitoyl acyltransferases (PATs) and characterized the promastigote-specific PAT (LdPAT4) containing the canonical Asp-His-His-Cys (DHHC) domain. Immunofluorescence analysis using in-house generated LdPAT4-specific antibody demonstrated distinct expression of LdPAT4 in the flagellar pocket of promastigotes. Using metabolic labeling-coupled click chemistry method, the functionality of this recombinant enzyme could be authenticated in E. coli strain expressing LdPAT4-DHHC domain. This was evident by the cellular uptake of palmitic acid analogs, which could be successfully inhibited by 2-BMP, a PAT-specific inhibitor. Using CSS-Palm based in-silico proteomic analysis, we could predict up to 23 palmitoylated sites per protein in the promastigotes, and further identify distinctive palmitoylated protein clusters involved in microtubule assembly, flagella motility and vesicular trafficking. To highlight, proteins such as Flagellar Member proteins (FLAM1, FLAM5), Intraflagellar Transport proteins (IFT88), and flagellar motor assembly proteins including the Dynein family were found to be enriched. Furthermore, analysis of global palmitoylation in promastigotes using Acyl-biotin exchange purification identified a set of S-palmitoylated proteins overlapping with the in-silico proteomics data. The attenuation of palmitoylation using 2-BMP demonstrated several phenotypic alterations in the promastigotes including distorted morphology, reduced motility (flagellar loss or slow flagellar beating), and inefficient invasion of promastigotes to host macrophages. These analyses confirm the essential role of palmitoylation in promastigotes. In summary, the findings suggest that LdPAT4 acts as a functional acyltransferase that can regulate palmitoylation of proteins involved in parasite motility and invasion, thus, can serve as a potential target for designing chemotherapeutics in Visceral Leishmaniasis.

Joint reduction of baseline wander, PLI and its harmonics in ECG signal using Ramanujan Periodic Transform

Abstract: Ramanujan Periodic Transform (RPT) is a newly emerging transformation technique in the field of signal processing. It uses an integer bases (obtained from Ramanujan sum) for transformation. A recorded ECG signal often contains artifacts (bioelectric signals) namely, baseline wander, muscle artifacts (EMG-Electromyogram), motion artifacts, powerline interference (PLI) and its harmonics. With certain precautions during signal recording we can avoid both muscle and motion artifacts. The other noises can be reduced by preprocessing of the recorded ECG signal. In this paper, RPT is used for preprocessing, to reduce baseline wander noise, PLI and its harmonics. The proposed methodology is tested on a record from MIT-BIH Arrhythmia database for different block sizes. A sum (E) of Euclidean errors per block (e i -ith block), is used as a measure to compare the results of RPT with notch filter technique. From the results, the RPT is reducing the noise with minimum error (E), when compared with notch filter technique.

Novel hybrid method to additively manufacture denser graphite structures using Binder Jetting.

Abstract: This study introduces two hybrid processes integrating an additive manufacturing technique with post-processing treatments namely (i) Binder Jetting Printing (BJP) + Cold Isostatic Pressing (CIP) + cycle and (ii) BJP + cycle where cycle refers to a sequence of Impregnation—Drying—Pyrolysis. These two new processes yielded additively manufactured parts with higher density and reduced defects/porosities. As a testbed, we used these new processes to fabricate graphite structures. The samples produced by both methods were compared with each other and benchmarked to the samples produced by (a) BJP alone and (b) Traditional uniaxial pressing like compaction moulding. Various characterisation methods were used to investigate the microstructure and mechanical properties which showed that the porosity of hybrid manufactured samples reduces from 55% to a record 7%. This technological pathway is expected to create a new avalanche of industrial applications that are hitherto unexplored in the arena of hybrid additive manufacturing with BJP method.

Economic Consequences of 'Regulation on Corporate Governance': Evidence from India

Abstract: Regulations dealing with investor protection are essential for economic development of any country. India, with more than 20 million shareholders, is one of the largest emerging markets in terms of market capitalization. In order to protect the large investor base, the Securities and Exchange Board of India (SEBI) has enforced a regulation requiring mandatory disclosure of information and change in the corporate governance mechanisms of Indian listed companies. The aim of this study is to empirically examine the economic impact of this Regulation on Indian stock market. The results provide evidence of significant reduction in beta of the experimental group, where beta is used as surrogate for cost of equity capital. The result is consistent with the notion that increased information and better corporate governance mechanism reduces the cost of equity capital of these companies.

Ranking and Discovering Anomalous Neighborhoods in Attributed Multiplex Networks

Abstract: The attributed multiplex network is a set of attributed networks in which each network represents a different type of interaction between the same set of nodes. Individual networks are termed as layers or dimensions and network nodes are characterized by attribute vectors. Neighborhood, in general, refers to any dense connected subgraph. We refer neighborhood1 as subgraph induced on graph node and its neighbors. It is usually observed that majority of the nodes in multilayer networks are active only on small number of layers except some outliers [18]. However, node activity is not strictly correlated to the edges incident in a node. A node might be active at few layers with relatively large number of incident edges and at the same time, multi-active node might not have many links even on single layer. Moreover, each layer has distinct importance in the multiplex networks2 and the structure and size of neighborhood formed by these multiplex nodes are different on each layer. Nodes with different attributes come together on different layers in the attributed multiplex networks. This node and layer heterogeneity should be considered while identifying anomalous neighborhoods in the attributed multiplex networks. Thus, a measure is required to quantify the quality of neighborhoods formed by active nodes on different layers. Existing approaches do not consider heterogeneity among network layers and do quantify the structure of networks either separately for each layer or its aggregated network and ignore the attributes of nodes. In this work, we define a novel quality measure Multi-Normality which utilizes the structure and attributes together of each layer and detect attribute coherence in neighborhoods between layers. We also propose an algorithm exhausting multi-normality to identify anomalous neighborhoods in multiplex networks and is named as Anomaly Detection of Entity Neighborhoods in Multiplex Networks (ADENMN). We evaluate the effectiveness of the proposed algorithm in anomaly detection by comparing its performance with three existing baseline approaches including ADOMS, AMM and AGG+AD on five real-world attributed multiplex networks including Amazon, YouTube, Noordin top terrorist network, DBLP_C, and Aarhus. The results of experiments demonstrate that multi-normality outperforms baseline algorithms.