Graph-Based Text Summarization and Its Application on COVID-19 Twitter Data

Large volumes of structured and semi-structured data are being generated every day. Processing this large amount of data and extracting important information is a challenging task. The goal of an automatic text summarization is to preserve the key information and the overall meaning of the article to be summarized. In this paper, a graph-based approach is followed to generate an extractive summary, where sentences of the article are considered as vertices, and weighted edges are introduced based on the cosine similarities among the vertices. A possible subset of maximal independent sets of vertices of the graph is identified with the assumption that adjacent vertices provide sentences with similar information. The degree centrality and clustering coefficient of the vertices are used to compute the score of each of the maximal independent sets. The set with the highest score provides the final summary of the article. The proposed method is evaluated using the benchmark BBC News data to demonstrate its effectiveness and is applied to the COVID-19 Twitter data to express its applicability in topic modeling. Both the application and comparative study with other methods illustrate the efficacy of the proposed methodology.

Computing Basis and Dimension of Chloroquine and Hydroxychloroquine by Using Chemical Graph Theory

Discovering a vaccine with reliable and effective treatment for the new corona virus disease 2019 (COVID-19) is indeed a long way off, and there seems to be a critical need to research additional viable medications that could save countless lives in the event of the COVID-19 pandemic. Scientists from all over the world are actively developing medical or anti-virus medications, which are safe and effective for COVID-19. The anti-viral drugs of chloroquine (CQ) and hydroxychloroquine (HCQ) have proved to be an efficient inhibitory effect by preventing the binding of spike covid protein. The determination of a pharmaceutical structure using a topological index allows researchers to have a better understanding of the physiological as well as bio-organic properties of drugs. The goal of this study is to employ molecular graph theory to determine some graph-theoretic parameters related to the molecular graph of CQ and HCQ. In this paper, we present some resolvability parameters such as metric dimension (MD), edge metric dimension (EMD), fault-tolerant metric dimension (FTMD), and fault-tolerant edge metric dimension (FTEMD) of CQ and HCQ. We prove that these resolvability parameters for CQ and HCQ are bounded and constant. © 2022 Taylor & Francis Group, LLC.

Accurate Identification of Transcription Regulatory Sequences and Genes in Coronaviruses.

Transcription regulatory sequences (TRSs), which occur upstream of structural and accessory genes as well as the 5' end of a coronavirus genome, play a critical role in discontinuous transcription in coronaviruses. We introduce two problems collectively aimed at identifying these regulatory sequences as well as their associated genes. First, we formulate the TRS Identification problem of identifying TRS sites in a coronavirus genome sequence with prescribed gene locations. We introduce CORSID-A, an algorithm that solves this problem to optimality in polynomial time. We demonstrate that CORSID-A outperforms existing motif-based methods in identifying TRS sites in coronaviruses. Second, we demonstrate for the first time how TRS sites can be leveraged to identify gene locations in the coronavirus genome. To that end, we formulate the TRS and Gene Identification problem of simultaneously identifying TRS sites and gene locations in unannotated coronavirus genomes. We introduce CORSID to solve this problem, which includes a web-based visualization tool to explore the space of near-optimal solutions. We show that CORSID outperforms state-of-the-art gene finding methods in coronavirus genomes. Furthermore, we demonstrate that CORSID enables de novo identification of TRS sites and genes in previously unannotated coronavirus genomes. CORSID is the first method to perform accurate and simultaneous identification of TRS sites and genes in coronavirus genomes without the use of any prior information.

An automated tool for optimal classroom seating assignment with social distancing constraints

Social distancing has become a necessity due to COVID-19, requiring schools to reduce classroom capacities to host in-person students. In doing so, schools seek to maximize the number of seats that can be used within a classroom, while ensuring that no pair of usable seats violates social distancing guidelines. We model this problem as a graph-theoretic maximum independent set problem and develop a user-friendly tool that solves real instances of the problem. We then use that tool to create optimal classroom seating plans for a major university. Our core model considers the case of a classroom with fixed seats. This problem can be expressed as a graph, identifying seats as nodes and inserting edges between seats that are closer than some prescribed threshold. A maximum independent set in this graph corresponds to an optimal seating plan. Our seat assignment tool allows any user to solve this problem by uploading an architect's drawing of a classroom. Then, computer vision aids the user by locating seats, and the tool finds and prints an optimal plan. Our tool also allows users to easily incorporate additional requirements, such as designated teacher spaces and the inclusion of movable chairs. Our tool helped automate the classroom planning process at Cornell University where its ease of use allowed it to be run on hundreds of classrooms. Compared to initial reduced-capacity classroom estimates determined by the Office of the University Architect, it helped identify over 400 additional seats that could be used. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.