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Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Data have become valuable assets for enterprises. Data governance aims to manage and reuse data assets, facilitating enterprise management and enabling product innovations. A data lineage graph (DLG) is an abstracted collection of data assets and their data lineages in data governance. Analyzing DLGs can provide rich data insights for data governance. However, the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs. This paper introduces an open dataset of DLGs, including the DLG model, the dataset construction process, and applied areas. This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited, which contains 18 DLGs with three types of data assets and two types of relations. To the best of our knowledge, this dataset is the first open dataset of DLGs for data governance. This dataset can also support the development of other application areas, such as graph analytics and visualization.

In this note we show that for each positive integer a⩾2 there exist infinitely many trees whose spectral radius is equal to 2a. Such trees are obtained by replacing the central edge of the double star S(a,2a−2) with suitable bidegreed caterpillars.

A convex hull of finitely many points in the Euclidean space Rd is known as a convex polytope. Graphically, they are planar graphs i.e. embeddable on R2. Minimum dominating sets possess diverse applications in computer science and engineering. Locating-dominating sets are a natural extension of dominating sets. Studying minimizing locating-dominating sets of convex polytopes reveal interesting distance-dominating related topological properties of these geometrical planar graphs. In this paper, exact value of the locating-dominating number is shown for one infinite family of convex polytopes. Moreover, tight upper bounds on γl−d are shown for two more infinite families. Tightness in the upper bounds is shown by employing an updated integer linear programming (ILP) model for the locating-dominating number γl−d of a fixed graph. Results are explained with help of some examples. The second part of the paper solves an open problem in Khan (2023) [28] which asks to find a domination-related parameter which delivers a correlation coefficient of ρ>0.9967 with the total π-electronic energy of lower benzenoid hydrocarbons. We show that the locating-dominating number γl−d delivers such a strong prediction potential. The paper is concluded with putting forward some open problems in this area.

Introduced by Gutman & Trinajstić in 1972, the two well-known Zagreb indices efficiently determine the total π-electron energy of alternant hydrocarbons. The hyper-Zagreb index is a variant of the classical first Zagreb index. A tree is an acyclic graphical structure. A segment in a tree is equivalent to a vertex of degree two. This paper derives sharp upper and lower bounds on the hyper-Zagreb index of trees with given number of segments. We also characterize the corresponding trees achieving those bounds. The second part of the paper studies the predictive potential of the hyper-Zagreb index for determining physicochemical properties such as the enthalpies of combustion, formation, sublimation, and vaporization of monocarboxylic acids. Our statistical analysis asserts that the hyper-Zagreb index correlates these physicochemical properties of monocarboxylic acids with correlation coefficient ρ>0.99. The strongest correlation by the hyper-Zagreb index was obtained with the enthalpy of combustion having correlation coefficient ρ=0.999991. This warrants the further employability of the hyper-Zagreb index in QSPR modeling. This potential applicability justifies the construction of the hyper-Zagreb graphical index whose motivation of defining was purely mathematical.

This paper investigates the free in-plane vibrations of rectangular sheets under various boundary conditions using an improved Riley-Ritz method. A novel approach involving graph theory and canonical forms is introduced, marking a first in the study of symmetric structures. The research presents an advanced version of Riley’s theory for accurately computing the natural frequencies of structures, showing significant improvements in efficiency and precision over traditional methods like Monte Carlo simulations. Key findings include the ability of the upgraded Riley theory and graphs to conduct comprehensive structural reliability and sensitivity analyses, particularly in evaluating changes in failure risk and aiding in precise design. Numerical analysis demonstrates the method’s rapid convergence and accuracy, proving its effectiveness in structural analysis. The study also explores the impact of geometric parameter variations on the free vibrations of rectangular sheets, offering crucial insights for various engineering applications. These findings have broad implications in mechanical, marine, aerospace, and civil engineering, particularly in the design and analysis of structural components such as fuselages, airplanes, missiles, and tank bottoms.

Arabic handwritten words segmentation is considered a challenging step in Arabic optical character recognition. In this work, we propose a convolutional neural network-based segmentation of handwritten Arabic words. After generating candidate segmentation points, we applied a graph theory-based technique to divide the word into sub-words. Our approach reached a 96% correct sub-word segmentation rate. Additionally, we develop a Segmentation Hypothesis Graph (SHG) to generate candidate characters for each sub-word. We employ both a manually designed convolutional neural network and a transfer learning-based CNN using the pretrained AlexNet model. The accuracy, 96.97%, of the pretrained model outperformed the manually and state of the art models trained on the same dataset. Finally, we used recognition confidence to validate the segmentation points. Hence, our approach correctly segments 88% of the words into characters and successfully resolves 100% of the overlapping cases.

In the aerial images taken from the forests, the crowns of the trees are recorded. Using DN, texture and crown pattern, the type of trees can be predicted with a certain accuracy and used in forest studies. DN is the amount of light intensity that assigns a number to each component of digital images depending on the number of image bits. The presence of foliage causes the space under the canopy to be affected by shade and semi-shade, and these areas have close to zero DNs. The canopy of trees has less effect on aerial images in autumn and winter, but the need to study the forest throughout the year, in spring and summer will be a problem. On the other hand, some LIDAR and RADAR sensors have the ability to pass through the canopy of trees, but these sensors are not always available and their imaging cost is higher than UAVs that capture in the visible range. The need to use image processing methods is to be able to prevent the loss of information about the crown of trees in visible aerial images, and to extract information about the areas affected by the shadow and semi-shadow of the crown for interpretation. Due to the 8-bit image, in this research, 8 level of transfer have been done. At each step of the transition, the DN of each pixel is compared with the average DN of the image. DNs that are larger than the average are assigned a value of 1 and other DNs are assigned a value of 0. The output of this step is an image with an 8-digit binary number assigned to each pixel. By converting the binary number to decimal, the DNs of the image are reconstructed and the image is improved. Due to the increased structural similarity of the fused image (0.8762) compared to the original image (0.2783), we expect the image to improve. In the second step, the transferred pixels are multiplied by the new mean and then subtracted from the value of the old DNs. This creates an image where the tree trunks are dark and other parts of the image are light. In the next step, we open and close the image. This operation consists of two stages: Erosion and Dilation. Erosion is performed on a binary image and the remaining parts of the crown are shortened or thinned. This morphological operation works evenly on the whole image and makes the trunks of the trees thinner, so the Dilation operation is applied to remove the spots created in the image, creating large small gaps between the crown The trees are shown in the picture, which shows the trunks of the trees. Finally, by forming a graph between the trees, the distances between them are calculated in terms of pixels of the image.

In our modern digital era, social networks have seamlessly integrated into the fabric of our daily lives. These digital platforms serve as vital channels for communication, exchanging information, and cultivating valuable connections. The propagation of information within these social networks has emerged as a central focus for numerous sectors, including politics, marketing, research, education, and finance. Diverse models have been employed to depict the dynamics of information dissemination across these networks. Nevertheless, the notion of influence holds profound significance for both businesses and individuals. Influence maximization, particularly within the context of social networks, has garnered considerable attention owing to its potential to reach and impact a broad audience. This intricate challenge is commonly referred to as the “influence maximization problem,” a problem well-known for its NP-hard complexity. This paper proposes a cutting-edge technique that leverages the Moth-Flame Optimization Algorithm to enhance influence maximization. Influence maximization is an important issue in network analysis, which widely occurs in social networks. Influence can be seen as a cascading effect, where the actions of a few trigger a chain reaction, ultimately reaching a large portion of the network. Identifying these “influencers” is crucial for efficient resource allocation and information dissemination. One of the important issues in finding the maximum influence is choosing the best vertex among all the vertices in the graph. This research presents a new method to find the maximum influence in social networks based on the Moth-Flame Algorithm (MFA). The proposed method aims to find the maximum influence in the social network graph that has a good fitness degree. The algorithm can identify potential influencers. Our simulations across multiple networks have unequivocally showcased the superiority of this algorithm as the preeminent and scalable solution to the influence maximization problem. The experimental outcomes clearly delineate that the employment of the MFA (Maximal First Activation) approach effectively diminishes the execution time required to approximate the maximum influence. The proposed technique improved the accuracy and excucation time by 3.140 % and 12.2 % compared to other methods.

The complementary prism of G, denoted by GG¯, is the graph obtained from the disjoint union of G and G¯ by adding edges between the corresponding vertices of G and G¯. Up to date, the progress of experimental research around complementary prisms is limited by the unavailability of publicly available instances that could be used to run extensive experiments and to compare the performance on different topological index solutions and its bounds. For this reason, we decided to make publicly available 435 instances of type GG¯ randomly generated, with increasing network size (from 12 to 1948 nodes). The dataset presents instances of Complementary Prism Networks suitable to measure the Wiener Index and Generalized Wiener Index and the value of these indices for these instances. In addition, are presented the value of some lower and upper bounds proposed in the literature for these indices and their error with respect to the value of the index.

Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution.We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN.

The acquisition of large-scale 3D urban scene by photogrammetry and remote sensing is becoming faster and easier in recent years. As one of the important steps to help machines understand scenarios, mesh semantic segmentation has received extensive attention. Aiming at the 3D urban scene, a surface graph based deep learning framework is proposed, which combines the merits of simple representation of point cloud and expresses complex surface topography and texture of mesh. The proposed model employs COG graph to represent surface topography of the mesh. Then novel mesh abstraction and neighborhood definition are conducted on the COG graph. In addition, we propose a texture convolution to extract textual features for individual facets. A hierarchical network architecture is adopted on the prebuilt abstraction and neighborhood data. The experiments on the self-made Wuhan dataset verify the effectiveness of the introduction of surface topography and texture convolution. Additionally, our model increases performance to 94.1% (OA), 71.5% (mIoU) and 79.4% (mF1) in comparative experiments on the SUM dataset that proves its strong competitiveness in semantic segmentation of urban scenes.

In this manuscript, new fixed point theorems for multivalued maps, particularly α−ϕ contractions, in complete multiplicative metric spaces are proved. We further prove a fixed point theorem for multivalued mappings on a graph-equipped multiplicative metric space. Our results extend the result of Dosenovic et al. [4], and generalize the result of Ali et al. [2]. We included an example to validate our result.

Social networks are electronically information sharing systems, due to this case, there are many studies on social networks. When studying social networks, text-based solution methods can be used; this type of study is outside the scope of this paper. Some studies have used mathematical models such as graphs, and graphs are mathematical models to represent many things, and social networks are one of them. However, graphs are static models whose structure cannot match the behaviors of social networks. To get rid of this case, Petri nets have been used in some recent studies , however, they have some deficiencies (obtained models are not complete and sound). Because of this case, we modeled social networks by using Petri nets. The resulting model is called Marked Social Network. The marked social network has two types such as Concurrent Marked Social Network and Parallel Marked Social Network. The obtained models were analyzed in case of behavioral and structural properties, and the major properties of the model were determined. All these properties are described in this study.

Urban centrality is critical for comprehending cities’ most significant locations. Conventionally, urbanists analyzed urban centrality by transforming urban spatial networks into graph objects, with nodes and edges reflecting network intersections and connections. However, the conversion techniques neglected the weights based on the physical qualities of urban buildings and plots. The preceding statement presents the following question: “Does the buildings' weight impact the spatial layout centrality?”. Accordingly, this article seeks to quantify the buildings' weights by establishing an urban centrality model that includes the buildings' physical characteristics and positions. The research methodology adopts the GIS analysis and consists of three steps: first, determine the buildings' weights; second, calculate the buildings' betweenness centrality using the R packages for calculating anti-betweenness centrality; and third, calculate the buildings' critical centrality or weighted anti-betweenness (ABtw). The research demonstrated that buildings' architectural traits influence urban centrality; the effect is negative when enhanced by implicit spatial network anti-betweenness, explaining that such sites are less accessible to the public and unfit for future growth.

Graph visualization plays an important role in several fields, such as social media networks, protein–protein interaction networks, and traffic networks. A number of visualization design tools and programming toolkits have been widely used in graph-related applications. However, a key challenge remains in the high-efficiency visualization of large-scale graph data. In this study, we present NetV.js, an open-source and WebGL-based JavaScript library that supports the fast visualization of large-scale graph data (up to 50 thousand nodes and 1 million edges) at an interactive frame rate with a commodity computer. Experimental results demonstrate that our library outperforms existing toolkits (Sigma.js, D3.js, Cytoscape.js, and Stardust.js) in terms of performance.

The paper suggests a self-organizing multi-component computational algorithm as a solution to the problem of optimizing the structure of distribution electrical networks to minimize the loss of power. The suggested algorithm is consistent with the method of branches and borders and uses the apparatus of the Petri computer networks (PCN) apparatus. The PCN apparatus has a universal computational capability to process symbolic-numeric data, which along with the solution of calculating problems, provides for the structural and logical analysis of the systems and processes under study. The structure of the PCN based algorithm is similar to the studied system, which provides for better visualization and convenience of interpretation, modification, and implementation of this algorithm on one or more computers by paralleling computational processes for better system performance. Computing modules within the general text of the algorithm can be arranged in any given order and solve the problem by organizing themselves in the process of functioning.

Transmission lines are one of the most important infrastructures of energy Internet in China, and the effective detection of fittings is a necessary prerequisite to ensure their safety and stability. The fitting detection method based on classic deep learning only focuses on a single region and extracts the visual features of the region proposal into the classifier to identify the target, but never considers the connection between fittings. There is a certain regularity between fittings when constructing a complete and intact transmission line, interdependent and interact with each other. Therefore, we propose a knowledge-guided aerial transmission line image fitting detection model (KGFD) which uses two modules to learn the regularity of the fittings. The first module is an implicit module, starting from the spatial layout of the fittings on the image and taking the relative geometric features of the region proposal as the input knowledge of the spatial position of the fittings on the image. The second module is an explicit one. By introducing the prior knowledge of the fittings expressed in co-occurrence mode and using the region proposal as the node while the prior knowledge as the edge, a knowledge graph is constructed. The information spreads on the knowledge graph via a gated graph neural network, which realizes a combination of the prior knowledge of the fitting with the features of the region proposal. These two modules are added to the deep learning framework to detect the fitting targets, and the typical fitting data set of aerial transmission lines are used for testing. The AP value increased by 4.4% when IoU={0.5:0.95} and the AP value by 3.9% when IoU=0.5 compared with those of Faster R-CNN classification.

Network analysis particularly graph clustering has become a useful and important technique in data mining applications. It provides a global view of data structure where densely connected objects are grouped based on their common properties. Over the past decade, various simple graph clustering and biclustering techniques have been widely used but the implementation of those algorithms remains limited. In this work, we present a new integrated software implementing the DPClusO and BiClusO algorithms using Java to be utilized for simple and bipartite graph clustering. Our aim is to provide an open-source tool with lots of user-friendly options to delve into network data. This tool will provide the user with GUI based facilities for simple and bipartite graph clustering along with filtering and amalgamation of clusters, hierarchical node analysis, node distribution among cluster set and visualization of all or partial portion of a big cluster set. We name this tool DPClusSBO because this can be used to perform Density Periphery based Clustering of Simple and Bipartite graphs with Overlapping property.

Non-semantic word graphs obtained from oral reports are useful to describe cognitive decline in psychiatric conditions such as Schizophrenia, as well as education-related gains in discourse structure during typical development. Here we provide non-semantic word graph attributes of texts spanning approximately 4500 years of history, and pre-literate Amerindian oral narratives. The dataset assessed comprises 707 literary texts representative of 9 different Afro-Eurasian traditions (Syro-Mesopotamian, Egyptian, Hinduist, Persian, Judeo-Christian, Greek-Roman, Medieval, Modern and Contemporary), and Amerindian narratives (N = 39) obtained from a single ethnic group from South America (Kalapalo, N = 18), or from a mixed ethnic group from South, Central and North America (non-Kalapalo, N = 21). The present article provides detailed information about each text or narrative, including measurements of four graph attributes of interest: number of nodes (lexical diversity), repeated edges (short-range recurrence), largest strongly connected component (long-range recurrence), and average shortest path (graph length).

Loss to follow-up occurs in randomized controlled trials. Missing data methods, including multiple imputation (MI), can be used but often rely upon untestable assumptions. Sensitivity analysis can quantify violations of these assumptions. Since an adequate sensitivity analysis requires evaluation of multiple scenarios, presenting this information in an easily interpretable manner is challenging.We propose to graphically represent a thorough sensitivity analysis displaying all possible outcomes for loss to follow-up in randomized controlled trial data relating a completely observed binary exposure to a binary outcome. We describe plausible results under different missingness mechanisms using data from the EAGeR Trial (n = 1228) on low-dose aspirin versus placebo on pregnancy and live birth, in which 140 participants had early withdrawal. For the effect of aspirin on live birth, sensitivity analysis risk ratios (RR) for all potential outcome scenarios ranged from 0.88 to 1.34, applicable to any possible missingness mechanism. MI produced RR = 1.10; 95% confidence interval: (0.98, 1.22). RRs from individual imputations ranged from 1.04 to 1.16, the range of results that could have been observed if data were missing at random. Under this mechanism, the conclusions about the efficacy of low-dose aspirin could have been sensitive to the missing outcome data. Rather than limiting sensitivity analysis for loss to follow-up to a few scenarios that can be presented tabularly, results of a complete sensitivity analysis can be presented in a single plot, which should be implemented in all studies with missing outcome data to convey certainty or uncertainty, confidence or caution.

All graphs of order n with partition dimension 2, n−2, n−1, or n have been characterized. However, finding all graphs on n vertices with partition dimension other than these above numbers is still open. In this paper, we characterize all graphs of order n≥11 and diameter 2 with partition dimension n−3.

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein-protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This paper presents a systematic pathway for the floor plan design when given the shape of required floor plan, the list of spaces, the dimensions of each space and the weighted matrix of required adjacencies between the spaces. The first step is to partition the given shape into say k possible rectangles. Then using the given adjacencies, divide the given spaces into k groups. Next is to construct a rectangular block for each group and at last adjoin all rectangular blocks to have the required floor plan. The obtained rectangular blocks are one of the best arrangement of spaces inside a rectangle from the point of view of connectivity.

Objectives: To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission., Methods: a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases., Results: Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common., Conclusion: We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To design interdialytic and daily weight gain graphs for patients on maintenance haemodialysis and to evaluate their effect on patient adherence to restricted fluid intake. Methods: Forty-five patients on maintenance haemodialysis were recruited from August to October 2012. The graphs were applied for 12 weeks based on Bandura's self-efficacy theory. Adherence to restricted fluid intake, dialysis adequacy, and satisfaction were compared before and after the graphs were applied. Results: Adherence to restricted fluid intake increased from 53.3% to 91.1%; the mean rate of urea clearance (Kt/V) decreased from 1.197 to 1.311, and the qualified rate increased from 42.5% to 70%. The rate of adherence was 86.77%; acceptance and satisfaction rates were 100%. Conclusion: It is acceptable to apply the graphs clinically for subsequent effective improvement of adherence to restricted fluid intake, promoting dialysis adequacy, and increasing patient satisfaction. Therefore, clinical application of the graphs is worthwhile.

The prediction of molecules toxicity properties plays an crucial role in the realm of the drug discovery, since it can swiftly screen out the expected drug moleculars. The conventional method for predicting toxicity is to use some in vivo or in vitro biological experiments in the laboratory, which can easily pose a threat significant time and financial waste and even ethical issues. Therefore, using computational approaches to predict molecular toxicity has become a common strategy in modern drug discovery. In this article, we propose a novel model named MTBG, which primarily makes use of both SMILES (Simplified molecular input line entry system) strings and graph structures of molecules to extract drug molecular feature in the field of drug molecular toxicity prediction. To verify the performance of the MTBG model, we opt the Tox21 dataset and several widely used baseline models. Experimental results demonstrate that our model can perform better than these baseline models., Competing Interests: Declaration of competing interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “The prediction of molecular toxicity based on BiGRU and GraphSAGE”., (Copyright © 2022. Published by Elsevier Ltd.)

The virus that causes Ebola is fatal. Although many researchers have attempted to contain this deadly infection, the fatality rate remains high. The atom-pair fingerprint technique was used to compare drugs suggested for the treatment of Ebola or those that are currently being tested in clinical settings. Subsequently, using scaffold network graph (SNG) methods, the molecular and structural scaffolds of the drugs chosen based on these similar results were created, and the drug structures were examined. Public databases (PubChem and DrugBank) and literature regarding Ebola treatment were used in the analysis. Graphical representations of the molecular architecture and core structures of the drugs with the highest similarity to Food and Drug Administration (FDA)-approved drugs were produced using the SNG method. The combination of molnupiravir, the first licensed oral medication candidate for COVID-19, and favipiravir, employed in other viral outbreaks, should be further researched for treating Ebola, as observed in our study. We also believe that chemists will benefit from understanding the core structure(s) of medication molecules effective against the Ebola virus, their inhibitors, and the chemical structure similarities of existing pharmaceuticals utilized to build alternative drugs or drug combinations., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Autism spectrum disorder (ASD) is a heterogeneous disorder with a rapidly growing prevalence. In recent years, the dynamic functional connectivity (DFC) technique has been used to reveal the transient connectivity behavior of ASDs' brains by clustering connectivity matrices in different states. However, the states of DFC have not been yet studied from a topological point of view. In this paper, this study was performed using global metrics of the graph and persistent homology (PH) and resting-state functional magnetic resonance imaging (fMRI) data. The PH has been recently developed in topological data analysis and deals with persistent structures of data. The structural connectivity (SC) and static FC (SFC) were also studied to know which one of the SC, SFC, and DFC could provide more discriminative topological features when comparing ASDs with typical controls (TCs). Significant discriminative features were only found in states of DFC. Moreover, the best classification performance was offered by persistent homology-based metrics and in two out of four states. In these two states, some networks of ASDs compared to TCs were more segregated and isolated (showing the disruption of network integration in ASDs). The results of this study demonstrated that topological analysis of DFC states could offer discriminative features which were not discriminative in SFC and SC. Also, PH metrics can provide a promising perspective for studying ASD and finding candidate biomarkers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)