Novel Degree-Based Topological Descriptors of Fenofibrate Using M-Polynomial

Chemical graph theory is currently expanding the use of topological indices to numerically encode chemical structure. The prediction of the characteristics provided by the chemical structure of the molecule is a key feature of these topological indices. The concepts from graph theory are presented in a brief discussion of one of its many applications to chemistry, namely, the use of topological indices in quantitative structure-activity relationship (QSAR) studies and quantitative structure-property relationship (QSPR) studies. This study uses the M-polynomial approach, a newly discovered technique, to determine the topological indices of the medication fenofibrate. With the use of degree-based topological indices, we additionally construct a few novel degree based topological descriptors of fenofibrate structure using M-polynomial. When using M-polynomials in place of degree-based indices, the computation of the topological indices can be completed relatively quickly. The topological indices are also plotted. Using M-polynomial, we compute novel formulas for the modified first Zagreb index, modified second Zagreb index, first and second hyper Zagreb indices, SK index, SK1 index, SK2 index, modified Albertson index, redefined first Zagreb index, and degree-based topological indices.

Graph Neural Network Models for Chemical Compound Activeness Prediction For COVID-19 Drugs Discovery using Lipinski's Descriptors

In this study, we implemented graph neural network (GNN) methods to forecast in vitro inhibitory bioactivity or pharmacological concentration of chemical compounds against severe acute respiratory syndrome (SARS) coronaviruses from the graph representation amongst the compounds (i.e., nodes) and their respective features(i.e., node features) obtained by RDKit tool from their respectively SMILES (Simplified MolecularInput Line-Entry System), and we compared GNN models by experiments with our graph data of 375 nodes with 44,475 edges or links. This was done in response to the severe and significant consequences of the ongoing Coronavirus disease 2019 (COVID-19) disease. As a result, we discovered that implemented models, simple graph convolution (SGC), and graph convolution network (GCN) performed significantly well with comparable performance. © 2022 IEEE.

THE CONTAGION NUMBER: HOW FAST CAN A DISEASE SPREAD?

The overall purpose of this paper is to define a new metric on the spreadability of a disease. Herein, we define a variant of the well-known graph-theoretic burning number (BN) metric that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the BN as the CN concentrates on first time infections. This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a novel methodology to solve for the CN of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis). We then employ Monte Carlo simulation to determine the average contagion number (ACN) (i.e., a most-likely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are specifically designed to model human social networks (sociograms). We test our method on some randomly generated scale-free graphs and our findings indicate the CN to be a robust, tractable (the BN is NP-hard even for a tree), and effective disease spread metric for decision makers. The contributions herein advance disease spread understanding and reveal the importance of the underlying network structure. Understanding disease spreadability informs public policy and the associated managerial allocation decisions. © 2023 by the authors.

An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic.

Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of 'SARS CoV-2' RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-023-02468-3.

Evolution of coronavirus frameshifting elements: Competing stem networks explain conservation and variability.

The frameshifting RNA element (FSE) in coronaviruses (CoVs) regulates the programmed -1 ribosomal frameshift (-1 PRF) mechanism common to many viruses. The FSE is of particular interest as a promising drug candidate. Its associated pseudoknot or stem loop structure is thought to play a large role in frameshifting and thus viral protein production. To investigate the FSE structural evolution, we use our graph theory-based methods for representing RNA secondary structures in the RNA-As-Graphs (RAG) framework to calculate conformational landscapes of viral FSEs with increasing sequence lengths for representative 10 Alpha and 13 Beta-CoVs. By following length-dependent conformational changes, we show that FSE sequences encode many possible competing stems which in turn favor certain FSE topologies, including a variety of pseudoknots, stem loops, and junctions. We explain alternative competing stems and topological FSE changes by recurring patterns of mutations. At the same time, FSE topology robustness can be understood by shifted stems within different sequence contexts and base pair coevolution. We further propose that the topology changes reflected by length-dependent conformations contribute to tuning the frameshifting efficiency. Our work provides tools to analyze virus sequence/structure correlations, explains how sequence and FSE structure have evolved for CoVs, and provides insights into potential mutations for therapeutic applications against a broad spectrum of CoV FSEs by targeting key sequence/structural transitions.

Examining Disease Multimorbidity in U.S. Hospital Visits Before and During COVID-19 Pandemic: A Graph Analytics Approach

Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted by identifying patterns in diseases recorded in hospital visits over time. Disease multimorbidity or simultaneous occurrence of multiple diseases is a growing global public health challenge as populations age and long-term conditions become more prevalent. We propose a graph analytics framework for analyzing disease multimorbidity in hospital visits. Within the framework, we propose a graph model to explain multimorbidity as a function of prevalence, category, and chronic nature of the underlying disease. We apply our model to examine and compare multimorbidity patterns in public hospitals in Arizona, U.S., during five six-month time periods before and during the pandemic. We observe that while multimorbidity increased by 34.26% and 41.04% during peak pandemic for mental disorders and respiratory disorders respectively, the gradients for endocrine diseases and circulatory disorders were not significant. Multimorbidity for acute conditions is observed to be decreasing during the pandemic while multimorbidity for chronic conditions remains unchanged. Our graph analytics framework provides guidelines for empirical analysis of disease multimorbidity using electronic health records. The patterns identified using our proposed graph model informs future research and healthcare policy makers for pre-emptive decision making. © 2023 Association for Computing Machinery.

Micromodel and Analysis of COVID-19 Based on Hypergraph

In view of the fact that the existing propagation models ignore the influence of different fields and different virus variants on individual infection, and the classical propagation models only describe the macroscopic situation of virus transmission, which cannot be specific to individual cases, this paper proposes 67ya microscopic virus propagation model based on hypergraph (HC-SIRS). Firstly, the concept of hypergraph is used to divide different fields of individuals into corresponding hyperedges. Based on different contact probabilities of each hyperedge, the contact probability matrix is formed to relate the contact between individuals. The individual infection probability of micro-virus propagation model based on hypergraph is deduced, and the corresponding differential equation is established. Secondly, the basic regeneration number and its characteristics of the model are derived. The upper bound of the basic regeneration number of the model is less than or equal to that of the classical SIRS model, indicating that the virus is more difficult to spread in this model. In fact, the different fields people live in and the different personal constitutions have a certain impact on the spread of the virus. The model is more comprehensive, so it is more suitable for simulating the spread of the virus in theory. Finally, the COVID-19 data of Diamond Princess and two cities in China are used for simulation experiments, and the mean absolute error(MAE) is used as the evaluation standard. The results showed that HC-SIRS could well simulate the spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Discovering AI-enabled convergences based on BERT and topic network

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-Associated research s collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective. © 2023 Korean Society for Internet Information. All rights reserved.

A comparison of centrality measures and their role in controlling the spread in epidemic networks

The ranking of nodes in a network according to their centrality or "importance” is a classic problem that has attracted the interest of different scientific communities in the last decades. The current COVID-19 pandemic has recently rejuvenated the interest in this problem, as it informs the selection of which individuals should be tested in a population of asymptomatic individuals, or which individuals should be vaccinated first. Motivated by these issues, in this paper we review some popular methods for node ranking in undirected unweighted graphs, and compare their performance in a benchmark realistic network that takes into account the community-based structure of society. In particular, we use the information of the relevance of individuals in the network to take a control decision, i.e., which individuals should be tested, and possibly quarantined. Finally, we also review the extension of these ranking methods to weighted graphs, and explore the importance of weights in a contact network by exhibiting a toy model and comparing node rankings for this case in the context of disease spread. [ FROM AUTHOR] Copyright of International Journal of Control is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from "hydropower–gold” to "smart grid–zinc”, and the systematically influential markets correspondingly become smart grid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits. © 2023 Elsevier Ltd

A graph-based edge attention gate medical image segmentation method

For the purpose of solving the problems of missing edges and low segmentation accuracy in medical image segmentation, a medical image segmentation network (EAGC_UNet++) based on residual graph convolution UNet++ with edge attention gate (EAG) is proposed in the study. With UNet++ as the backbone network, the idea of graph theory is introduced into the model. First, the dropout residual graph convolution block (DropRes_GCN Block) and the traditional convolution structure in UNet++ are used as encoders. Second, EAGs are adopted so that the model pays more attention to image edge features during decoding. Finally, aiming at the imbalance problem of positive and negative samples in medical image segmentation, a new weighted loss function is introduced to enhance segmentation accuracy. In the experimental part, three datasets (LiTS2017, ISIC2018, COVID-19 CT scans) were used to evaluate the performances of various models;multiple groups of ablation experiments were designed to verify the effectiveness of each part of the model. The experimental results showed that EAGC_UNet++ had better segmentation performance than the other models under three quantitative evaluation indicators and better solved the problem of missing edges in medical image segmentation. © 2023 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Disentangling the Information Flood on OSNs: Finding Notable Posts and Topics

Online Social Networks (OSN s) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer's fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events. © 2022 IEEE.

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

A decision support model for selecting unmanned aerial vehicle for medical supplies: context of COVID-19 pandemic

PurposeIn recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approachThe authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.FindingsThe findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implicationsThe methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implicationsThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implicationsThe proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/valueThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

Collaborative Filtering Model of Graph Neural Network Based on Random Walk

This paper proposes a novel graph neural network recommendation method to alleviate the user cold-start problem caused by too few relevant items in personalized recommendation collaborative filtering. A deep feedforward neural network is constructed to transform the bipartite graph of user–item interactions into the spectral domain, using a random wandering method to discover potential correlation information between users and items. Then, a finite-order polynomial is used to optimize the convolution process and accelerate the convergence of the convolutional network, so that deep connections between users and items in the spectral domain can be discovered quickly. We conducted experiments on the classic dataset MovieLens-1M. The recall and precision were improved, and the results show that the method can improve the accuracy of recommendation results, tap the association information between users and items more effectively, and significantly alleviate the user cold-start problem.

Unsupervised Identification of Abnormal Nodes and Edges in Graphs

Much of today's data are represented as graphs, ranging from social networks to bibliographic citations. Nodes in such graphs correspond to records that generally represent entities, while edges represent relationships between these entities. Both nodes and edges in a graph can have attributes that characterize the entities and their relationships. Relationships are either explicitly known (like friends in a social network), or they are inferred using link prediction (such as two babies are siblings because they have the same mother). Any graph representing real-world data likely contains nodes and edges that are abnormal, and identifying these can be important for outlier detection in applications ranging from crime and fraud detection to viral marketing. We propose a novel approach to the unsupervised detection of abnormal nodes and edges in graphs. We first characterize nodes and edges using a set of features, and then employ a one-class classifier to identify abnormal nodes and edges. We extract patterns of features from these abnormal nodes and edges, and apply clustering to identify groups of patterns with similar characteristics. We finally visualize these abnormal patterns to show co-occurrences of features and relationships between those features that mostly influence the abnormality of nodes and edges. We evaluate our approach on datasets from diverse domains, including historical birth certificates, COVID patient records, e-mails, books, and movies. This evaluation demonstrates that our approach is well suited to identify both abnormal nodes and edges in graphs in an unsupervised way, and it can outperform several baseline anomaly detection techniques. © 2022 Copyright held by the owner/author(s).

D155Y substitution of SARS-CoV-2 ORF3a weakens binding with Caveolin-1.

The clinical manifestation of the recent pandemic COVID-19, caused by the novel SARS-CoV-2 virus, varies from mild to severe respiratory illness. Although environmental, demographic and co-morbidity factors have an impact on the severity of the disease, contribution of the mutations in each of the viral genes towards the degree of severity needs a deeper understanding for designing a better therapeutic approach against COVID-19. Open Reading Frame-3a (ORF3a) protein has been found to be mutated at several positions. In this work, we have studied the effect of one of the most frequently occurring mutants, D155Y of ORF3a protein, found in Indian COVID-19 patients. Using computational simulations we demonstrated that the substitution at 155th changed the amino acids involved in salt bridge formation, hydrogen-bond occupancy, interactome clusters, and the stability of the protein compared with the other substitutions found in Indian patients. Protein-protein docking using HADDOCK analysis revealed that substitution D155Y weakened the binding affinity of ORF3a with caveolin-1 compared with the other substitutions, suggesting its importance in the overall stability of ORF3a-caveolin-1 complex, which may modulate the virulence property of SARS-CoV-2.

Forearm multimodal recognition based on IAHP-entropy weight combination

Biometrics are the among most popular authentication methods due to their advantages over traditional methods, such as higher security, better accuracy and more convenience. The recent COVID-19 pandemic has led to the wide use of face masks, which greatly affects the traditional face recognition technology. The pandemic has also increased the focus on hygienic and contactless identity verification methods. The forearm is a new biometric that contains discriminative information. In this paper, we proposed a multimodal recognition method that combines the veins and geometry of a forearm. Five features are extracted from a forearm Near-Infrared (Near-Infrared) image: SURF, local line structures, global graph representations, forearm width feature and forearm boundary feature. These features are matched individually and then fused at the score level based on the Improved Analytic Hierarchy Process-entropy weight combination. Comprehensive experiments were carried out to evaluate the proposed recognition method and the fusion rule. The matching results showed that the proposed method can achieve a satisfactory performance. © 2022 The Authors. IET Biometrics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Inter-and Intrastate Network Analysis of COVID-19 Spread Using the Social Connectedness Index

Since 2020, the outbreak of the coronavirus disease 2019 (COVID-19) pandemic has affected the entire world, and networks of human connections were identified as a factor that had potentially impacted the geographical spread of COVID-19. With the help of social media platforms, these networks have connected populations across the word and allowed people to view each other in close virtual proximity. Consequently, the Social Connectedness Index (SCI) is used to measure the strength of social connectivity across geographical regions through friendship ties on Facebook. The importance of social networks—and their relation to human connections—may correlate with the spread of COVID-19. Since these networks can have a potential effect on the spread of COVID-19, it is crucial to identify the factors that were associated with its spread during the pandemic. In order to analyze SCI data, a social network analysis was conducted to define the network parameters and perform calculations using graph theory. A correlation analysis was also performed to identify factors that correlated with the spread of COVID-19 cases using the data in the United States (US). Finally, the machine learning model was used to create a case prediction paradigm from the network parameters. The results showed that SCI can be used as a parameter to create a pandemic prediction model. Multiple linear regression also yielded satisfactory results that predicted the total number of positive cases measured by adjusted R2. In terms of the time frame, this study suggested that the parameters from the previous week can be used to predict the number of weekly infections. The findings showed that social networks had a greater impact on the prediction of current active cases than total positive cases. The social networks between counties within a state also held more importance than those across states. © Fuji Technology Press Ltd.

Using Graph Theory to Optimize Career Transitions

Grounded in graph theory, this paper proposes and demonstrates a novel methodology to analyze career transitioning. We collect and integrate official U.S. Government data on 35 general job skills and the annual wage data for over 900 standard occupations. Our research can help people move from unemployment, or a current job, to their desired occupation. We use graph theory to determine the most efficient way to hop between intermediate jobs to gain the necessary set of skills required by the targetted occupation. Our analysis assumes that working in a job proffers the skills from that job to the employee. A potential application involves an employee who wishes to transition to a different occupation, perhaps even in a different industry. The employee does not have the necessary skills to transition directly to the desired career because the skill levels are too different between the jobs. Instead, the employee must make a series of smaller job hops to acquire the skills. This type of analysis can provide valuable insights into the most efficient way to change careers. Our study may be especially relevant and helpful because some employees may need to move from languishing careers or industries to ones less impacted by COVID 19 or less threatened by automation. © 2022 James Otto et al., published by Sciendo.