Faculty Publications
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Item Health Fear Mongering Make People More Sicker: Twitter Analysis in the Context of Corona Virus Infection(Springer Science and Business Media Deutschland GmbH, 2020) Jayan, J.; Alathur, S.The purpose of this study is to assess the fear factor in Social media data in the context of Coronavirus Disease - 2019(COVID-19) across the globe. The fear generated from social media content will adversely affect the mental health of the public. Design/methodology/approach: The study is followed by a literature survey during the emergence of social media and Internet technologies since the year 2006 where the people commonly started to use the internet across the world. The Twitter data collected on COVID-19 during the infection period and the analysis. Findings: The social media contents adversely affect the mental health of the common public and also the healthcare programs run by the government organizations to some extent. The findings show that the social media are the major source of fear-mongering information and the people behind the fear-mongering are making use of the disaster situation to set their agenda. The strict enactment of law and the efforts by the social media platforms can reduce the fake news and misinformation. Research limitations/implications: The research focuses only on the Twitter data for the analysis during the COVID-19 distress. The detailed study needs to be done in similar distress situations across the globe. The data retrieval became limited from different social media platforms because of privacy issues. © 2020, IFIP International Federation for Information Processing.Item 2019-nCoV disease control and rehabilitation: Insights from twitter analytics(Institute of Electrical and Electronics Engineers Inc., 2020) Chetty, N.; Alathur, S.; Kumar, V.Coronaviruses are the large family of viruses and life threatening with the capabilities to cause respiratory related diseases. The current outbreak of 2019-nCoV (novel Coronavirus) is challenging governance authorities and health care systems around the globe. The epidemic of 2019-nCoV is affecting people globally. The purpose of this paper is to examine the current status of disease control and rehabilitation in relation to outbreak of 2019-nCoV. In this regard, the Twitter social media contents are collected, analyzed and interpreted. Using a set of appropriate keywords, 110000 tweets are extracted from Twitter social media. The collected tweets are first pre-processed and then analyzed with a software developed in R language. The discussions on social media in relation to the outbreak of 2019-nCoV involves disease control, rehabilitation and anti-rehabilitation. Expressions involving specific locations revealed that the discussions are more oriented towards antirehabilitation than rehabilitation and disease control. The content analysis also revealed that the outbreak epidemic victimizes those who possess weaker immune system. © 2020 IEEE.Item A Scientometric Analysis and Visualization Mapping of Convalescent Plasma Therapy(University of Idaho Library, 2021) Shettar, I.M.; Hadagali, G.S.This paper deals with the scientometric analysis of the scholarly literature on Convalescent Plasma Therapy, or simply Plasma Therapy, as indexed in the SCOPUS database from its first publication to 2020. In this study, 1,722 bibliographic records were analysed which are published in 545 journals by 9491 authors from 6046 organizations located in nearly 175 countries. The results showed a sudden increase in the number of publications in 2020 because of the clinical trials due to the COVID-19 pandemic. During the period an inconsistent trend of publications and the annual growth rate is observed. The average Degree of Collaboration calculated for the overall period was 0.89 and Collaboration Index was 6.83. Pediatric Nephrology and Transfusion were the most preferred journals; Chantal Loirat was the most productive author in the field. The AP-HP Assistance Publique - Hopitaux de Paris, France, was the top productive institution and the USA was the most productive country in terms of the number of publications. © 2021,Library Philosophy and Practice.All Rights ReservedItem Understanding the role of water on temperature-dependent structural modifications of SARS CoV-2 main protease binding sites(Elsevier B.V., 2022) Venugopal, P.P.; Singh, O.; Chakraborty, D.Thermally stable and labile proteases are found in microorganisms. Protease mediates the cleavage of polyproteins in the virus replication and transcription process. 6 µs MD simulations were performed for monomer/dimer SARS CoV-2 main protease system in both SPC/E and mTIP3P water model to analyse the temperature-dependent behaviour of the protein. It is found that maximum conformational changes are observed at 348 K which is near the melting temperature. Network distribution of evolved conformations shows an increase in the number of communities with the rise in the temperature. The global conformation of the protein was found to be intact whereas a local conformational space evolved due to thermal fluctuations. The global conformational change in the free energy ΔΔG value for the monomer and the dimer between 278 K and 383 K is found to be 2.51 and 2.10 kJ/mol respectively. A detailed analysis was carried out on the effect of water on the temperature-dependent structural modifications of four binding pockets of SARS CoV-2 main protease namely, catalytic dyad, substrate-binding site, dimerization site and allosteric site. It is found that the water structure around the binding sites is altered with temperature. The water around the dimer sites is more ordered than the monomer sites regardless of the rise in temperature due to structural rigidity. The energy expense of binding the small molecules at substrate binding is less compared to the allosteric site. The water-water hydrogen bond lifetime is found to be more near the cavity of His41. Also, it is observed that mTIP3P water molecules have a similar effect to that of SPC/E water molecules on the main protease. © 2022 Elsevier B.V.Item Exploring the multiple conformational states of RNA genome through interhelical dynamics and network analysis(Elsevier Inc., 2022) Singh, O.; Venugopal, P.P.; Mathur, A.; Chakraborty, D.The structural variation of RNA is often very transient and can be easily missed in experiments. Molecular dynamics simulation studies along with network analysis can be an effective tool to identify prominent conformations of such dynamic biomolecular systems. Here we describe a method to effectively sample different RNA conformations at six different temperatures based on the changes in the interhelical orientations. This method gives the information about prominent states of the RNA as well as the probability of the existence of different conformations and their interconnections during the process of evolution. In the case of the SARS-CoV-2 genome, the change of prominent structures was found to be faster at 333 K as compared to higher temperatures due to the formation of the non-native base pairs. ΔΔG calculated between 288 K and 363 K are found to be 10.31 kcal/mol (88 nt) considering the contribution from the multiple states of the RNA which agrees well with the experimentally reported denaturation energy for E. coli α mRNA pseudoknot (∼16 kcal/mol, 112 nt) determined by calorimetry/UV hyperchromicity and human telomerase RNA telomerase (4.5–6.6 kcal/mol, 54 nt) determined by FRET analysis. © 2022 Elsevier Inc.Item Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm(Springer Science and Business Media Deutschland GmbH, 2023) Chakraborty, S.The worldwide spread of the novel coronavirus originating from Wuhan, China led to an ongoing pandemic as COVID-19. The disease being a contagion transmitted rapidly in India through the people having travel histories to the affected countries, and their contacts that tested positive. Millions of people across all states and union territories (UT) were affected leading to serious respiratory illness and deaths. In the present study, two unsupervised clustering algorithms namely k-means clustering and hierarchical agglomerative clustering are applied on the COVID-19 dataset in order to group the Indian states/UTs based on the pandemic effect and the vaccination program from the period of March, 2020 to early June, 2021. The aim of the study is to observe the plight of each state and UT of India combating the novel coronavirus infection and to monitor their vaccination status. The research study will be helpful to the government and to the frontline workers coping to restrict the transmission of the virus in India. Also, the results of the study will provide a source of information for future research regarding the COVID-19 pandemic in India. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Bhattacharjee, S.; Dutta, A.; Namtirtha, A.The growth of COVID-19, caused by the SARS-CoV-2 virus, has turned into an unprecedented pandemic in the last century. It is crucial to identify superspreading nodes to prevent the pandemic's progress. Most available superspreader identification techniques consider only a single or few network metrics related to the complex network's topological structure. Furthermore, it is more challenging to determine influential spreaders from heterogeneous structures of networks. In a disease transmission network, the degree of heterogeneity is essential to locate the path of the infection spread. Therefore, it is required to have an extended degree of centrality to collect information from various neighborhood levels. This article presents an approach, namely, global structure influence (GSI), which considers network nodes' local and global influence. This method can gather information from multiple levels of the neighborhood. Evaluation of our proposed method is done by considering different types of networks, i.e., social networks, highly heterogeneous human contact networks, and epidemiological networks, and also by using the benchmark susceptible-infected-recovered (SIR) epidemic model. The GSI technique provides real-spreading dynamics across various network structures and has outperformed the baseline techniques with an average Kendall's τ improvement range from 0.017 to 0.278. This study will help to identify the superspeaders in real applications, where pathogens spread quickly because of close contact, such as the recently witnessed COVID-19 pandemic. © 2014 IEEE.