Sharda University

About: Sharda University is a education organization based out in Greater Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Medicine. The organization has 1276 authors who have published 2012 publications receiving 16188 citations.

Preparation and characterization of zinc ferrite—Polyaniline nanocomposite for removal of rhodamine B dye from aqueous solution

Abstract: Zinc Ferrite (ZnFe2O4), a magnetic ferrite of nanodimension was synthesized and characterized by XRD and SEM techniques. In situ technique was used to prepare Zinc Ferrite-Polyaniline nanocomposite. Electrical conductivity and dielectric constant of nanocomposite were determined at different frequency and temperature. Rhodamine B dye (RHB) from aqueous solution was removed by adsorbent Zinc Ferrite-Polyaniline nanocomposite. Effect of contact time, concentration of nanocomposite and dye, pH of the dye solution and temperature on the removal of dye were studied. Different isotherm and kinetic models were tested. Thermodynamic parameters were calculated. Role of NaCl on the removal of dye was investigated. Nanocomposite was desorbed again and again and used for adsorption.

miRNAs in SARS-CoV 2: A Spoke in the Wheel of Pathogenesis

Abstract: Introduction The rapid emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an increased mortality rate across the globe. However, the underlying mechanism of SARS-CoV-2 altering human immune response is still elusive. The existing literature on miRNA mediated pathogenesis of RNA virus viz. Dengue virus, West Nile virus, etc. raises a suspicion that miRNA encoded by SARS-CoV-2 might facilitate virus replication and regulate the host's gene expression at the post-transcriptional level. Methods We investigated this possibility via computational prediction of putative miRNAs encoded by the SARS-CoV-2 genome using a novel systematic pipeline that predicts putative mature-miRNA and their targeted genes transcripts. To trace down if viral-miRNAs targeted the genes critical to the immune pathway, we assessed whether mature miRNA transcripts exhibit effective hybridization with the 3'UTR region of human gene transcripts. Conversely, we also tried to study human miRNA-mediated viral gene regulation to get insight into the miRNA mediated offense and defense mechanism of viruses and their host organisms in too. Results Our analysis led us to shortlist six putative miRNAs that target, majorly, genes related to cell proliferation/differentiation/signaling, and senescence. Nonetheless, they also target immune-related genes that directly/indirectly orchestrate immune pathways like TNF (Tumor Necrosis Factor) signaling and Chemokine signaling pathways putatively serving as the nucleus to cytokine storms. Conclusion Besides, these six miRNAs were found to conserved so far across 80 complete genomes of SARS-CoV-2 (NCBI Virus, last assessed 12 April 2020) including Indian strains that are also targeted by 7 human miRNAs and can, therefore, be exploited to develop MicroRNA-Attenuated Vaccines.

Intrusion Detection System using Machine Learning Techniques: A Review

Abstract: The rapid growth in the use of computer networks results in the issues of maintaining the network availability, integrity, and confidentiality. This necessitates the network administrators to adopt various types of intrusion detection systems (IDS) that help in monitoring the network traffics for unauthorized and malicious activities. Intrusion is the breach of security policy with malicious intent. Therefore, intrusion detection system monitors traffic flowing on a network through computer systems to search for malicious activities and known threats, sending up alerts when it finds those threats. The detection of malicious activities is of two types, the misuse or signature-based detection in which the IDS collects information, analyzes it and then compares it to the attack signatures stored in a large database. The second detection is the anomaly detection which assumes malicious activity as any action that deviates from normal behavior. The proposed paper presents an overview of various works being done on building an efficient IDS using single, hybrid and ensemble machine learning (ML) classifiers, evaluated using seven different datasets. The results obtained by various works were discussed and compared which gives a clear path and guide for future work.