201
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Li K, Abdelsattar MM, Gu M, Zhao W, Liu H, Li Y, Guo P, Huang C, Fang S, Gan Q. The Effects of Temperature and Humidity Index on Growth Performance, Colon Microbiota, and Serum Metabolome of Ira Rabbits. Animals (Basel) 2023; 13:1971. [PMID: 37370481 DOI: 10.3390/ani13121971] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/16/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
This study investigates the effects of different THI values on growth performance, intestinal microbes, and serum metabolism in meat rabbits. The results showed that there were significant differences in THI in different location regions of the rabbit house. The high-THI group (HG) could significantly reduce average daily gain and average daily feed intake in Ira rabbits (p < 0.05). The low-THI group (LG) significantly increased the relative abundance of Blautia (p < 0.05). The HG significantly increased the relative abundance of Lachnospiraceae NK4A136 group and reduced bacterial community interaction (p < 0.05). The cytokine-cytokine receptor interactions, nuclear factor kappa B signaling pathway, and toll-like receptor signaling pathway in each rabbit's gut were activated when the THI was 26.14 (p < 0.05). Metabolic pathways such as the phenylalanine, tyrosine, and tryptophan biosynthesis and phenylalanine metabolisms were activated when the THI was 27.25 (p < 0.05). Meanwhile, the TRPV3 and NGF genes that were associated with heat sensitivity were significantly upregulated (p < 0.05). In addition, five metabolites were found to be able to predict THI levels in the environment with an accuracy of 91.7%. In summary, a THI of 26.14 is more suitable for the growth of meat rabbits than a THI of 27.25, providing a reference for the efficient feeding of meat rabbits.
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Affiliation(s)
- Keyao Li
- College of Animal Science (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Mahmoud M Abdelsattar
- Department of Animal and Poultry Production, Faculty of Agriculture, South Valley University, Qena 83523, Egypt
| | - Mingming Gu
- College of Animal Science (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Wei Zhao
- Key Laboratory of Feed Biotechnology of the Ministry of Agriculture and Rural Affairs, Institute of Feed Research of Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Haoyu Liu
- College of Animal Science (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Yafei Li
- College of Animal Science (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Pingting Guo
- College of Animal Science (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Caiyun Huang
- College of Animal Science (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Shaoming Fang
- College of Animal Science (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Qianfu Gan
- College of Animal Science (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou 350002, China
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202
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Heer M, Giudice L, Mengoni C, Giugno R, Rico D. Esearch3D: propagating gene expression in chromatin networks to illuminate active enhancers. Nucleic Acids Res 2023; 51:e55. [PMID: 37021559 PMCID: PMC10250221 DOI: 10.1093/nar/gkad229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 03/06/2023] [Accepted: 04/03/2023] [Indexed: 04/07/2023] Open
Abstract
Most cell type-specific genes are regulated by the interaction of enhancers with their promoters. The identification of enhancers is not trivial as enhancers are diverse in their characteristics and dynamic in their interaction partners. We present Esearch3D, a new method that exploits network theory approaches to identify active enhancers. Our work is based on the fact that enhancers act as a source of regulatory information to increase the rate of transcription of their target genes and that the flow of this information is mediated by the folding of chromatin in the three-dimensional (3D) nuclear space between the enhancer and the target gene promoter. Esearch3D reverse engineers this flow of information to calculate the likelihood of enhancer activity in intergenic regions by propagating the transcription levels of genes across 3D genome networks. Regions predicted to have high enhancer activity are shown to be enriched in annotations indicative of enhancer activity. These include: enhancer-associated histone marks, bidirectional CAGE-seq, STARR-seq, P300, RNA polymerase II and expression quantitative trait loci (eQTLs). Esearch3D leverages the relationship between chromatin architecture and transcription, allowing the prediction of active enhancers and an understanding of the complex underpinnings of regulatory networks. The method is available at: https://github.com/InfOmics/Esearch3D and https://doi.org/10.5281/zenodo.7737123.
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Affiliation(s)
- Maninder Heer
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Luca Giudice
- Department of Computer Science, University of Verona, Strada le Grazie 15, 37134, Verona, Italy
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Claudia Mengoni
- Department of Computer Science, University of Verona, Strada le Grazie 15, 37134, Verona, Italy
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Strada le Grazie 15, 37134, Verona, Italy
| | - Daniel Rico
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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203
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Barnett GA, Calabrese C, Ruiz JB. A comparison of three methods to determine the subject matter in textual data. Front Res Metr Anal 2023; 8:1104691. [PMID: 37334104 PMCID: PMC10272525 DOI: 10.3389/frma.2023.1104691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/05/2023] [Indexed: 06/20/2023] Open
Abstract
This study compares three different methods commonly employed for the determination and interpretation of the subject matter of large corpuses of textual data. The methods reviewed are: (1) topic modeling, (2) community or group detection, and (3) cluster analysis of semantic networks. Two different datasets related to health topics were gathered from Twitter posts to compare the methods. The first dataset includes 16,138 original tweets concerning HIV pre-exposure prophylaxis (PrEP) from April 3, 2019 to April 3, 2020. The second dataset is comprised of 12,613 tweets about childhood vaccination from July 1, 2018 to October 15, 2018. Our findings suggest that the separate "topics" suggested by semantic networks (community detection) and/or cluster analysis (Ward's method) are more clearly identified than the topic modeling results. Topic modeling produced more subjects, but these tended to overlap. This study offers a better understanding of how results may vary based on method to determine subject matter chosen.
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Affiliation(s)
- George A. Barnett
- Department of Communication, University of California, Davis, Davis, CA, United States
| | | | - Jeanette B. Ruiz
- Department of Communication, University of California, Davis, Davis, CA, United States
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204
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Bröhl T, Lehnertz K. A perturbation-based approach to identifying potentially superfluous network constituents. CHAOS (WOODBURY, N.Y.) 2023; 33:2894464. [PMID: 37276550 DOI: 10.1063/5.0152030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/16/2023] [Indexed: 06/07/2023]
Abstract
Constructing networks from empirical time-series data is often faced with the as yet unsolved issue of how to avoid potentially superfluous network constituents. Such constituents can result, e.g., from spatial and temporal oversampling of the system's dynamics, and neglecting them can lead to severe misinterpretations of network characteristics ranging from global to local scale. We derive a perturbation-based method to identify potentially superfluous network constituents that makes use of vertex and edge centrality concepts. We investigate the suitability of our approach through analyses of weighted small-world, scale-free, random, and complete networks.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
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205
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Frąszczak D. Detecting rumor outbreaks in online social networks. SOCIAL NETWORK ANALYSIS AND MINING 2023; 13:91. [PMID: 37274600 PMCID: PMC10233536 DOI: 10.1007/s13278-023-01092-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 03/29/2023] [Accepted: 05/03/2023] [Indexed: 06/06/2023]
Abstract
Social media platforms are broadly used to exchange information by milliards of people worldwide. Each day people share a lot of their updates and opinions on various types of topics. Moreover, politicians also use it to share their postulates and programs, shops to advertise their products, etc. Social media are so popular nowadays because of critical factors, including quick and accessible Internet communication, always available. These conditions make it easy to spread information from one user to another in close neighborhoods and around the whole social network located on the given platform. Unfortunately, it has recently been increasingly used for malicious purposes, e.g., rumor propagation. In most cases, the process starts from multiple nodes (users). There are numerous papers about detecting the real source with only one initiator. There is a lack of solutions dedicated to problems with multiple sources. Most solutions that meet those criteria need an accurate number of origins to detect them correctly, which is impossible to obtain in real-life usage. This paper analyzes the methods to detect rumor outbreaks in online social networks that can be used as an initial guess for the number of real propagation initiators.
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206
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Huang Z, Xu W, Zhuo X. Community-CL: An Enhanced Community Detection Algorithm Based on Contrastive Learning. ENTROPY (BASEL, SWITZERLAND) 2023; 25:864. [PMID: 37372208 DOI: 10.3390/e25060864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/21/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023]
Abstract
Graph contrastive learning (GCL) has gained considerable attention as a self-supervised learning technique that has been successfully employed in various applications, such as node classification, node clustering, and link prediction. Despite its achievements, GCL has limited exploration of the community structure of graphs. This paper presents a novel online framework called Community Contrastive Learning (Community-CL) for simultaneously learning node representations and detecting communities in a network. The proposed method employs contrastive learning to minimize the difference in the latent representations of nodes and communities in different graph views. To achieve this, learnable graph augmentation views using a graph auto-encoder (GAE) are proposed, followed by a shared encoder that learns the feature matrix of the original graph and augmentation views. This joint contrastive framework enables more accurate representation learning of the network and results in more expressive embeddings than traditional community detection algorithms that solely optimize for community structure. Experimental results demonstrate that Community-CL achieves superior performance compared to state-of-the-art baselines in community detection. Specifically, the NMI of Community-CL is reported to be 0.714 (0.551) on the Amazon-Photo (Amazon-Computers) dataset, which represents a performance improvement of up to 16% compared with the best baseline.
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Affiliation(s)
- Zhaoci Huang
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Wenzhe Xu
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xinjian Zhuo
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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207
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Gao Z, Gu Z, Yang L. Effects of Community Connectivity on the Spreading Process of Epidemics. ENTROPY (BASEL, SWITZERLAND) 2023; 25:849. [PMID: 37372193 DOI: 10.3390/e25060849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/21/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023]
Abstract
Community structure exists widely in real social networks. To investigate the effect of community structure on the spreading of infectious diseases, this paper proposes a community network model that considers both the connection rate and the number of connected edges. Based on the presented community network, a new SIRS transmission model is constructed via the mean-field theory. Furthermore, the basic reproduction number of the model is calculated via the next-generation matrix method. The results reveal that the connection rate and the number of connected edges of the community nodes play crucial roles in the spreading process of infectious diseases. Specifically, it is demonstrated that the basic reproduction number of the model decreases as the community strength increases. However, the density of infected individuals within the community increases as the community strength increases. For community networks with weak strength, infectious diseases are likely not to be eradicated and eventually will become endemic. Therefore, controlling the frequency and range of intercommunity contact will be an effective initiative to curb outbreaks of infectious diseases throughout the network. Our results can provide a theoretical basis for preventing and controlling the spreading of infectious diseases.
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Affiliation(s)
- Zhongshe Gao
- School of Mathematics and Statistics, Tianshui Normal University, Tianshui 741000, China
| | - Ziyu Gu
- School of Mathematics and Data Science, Shaanxi University of Science & Technology, Xi'an 710021, China
| | - Lixin Yang
- School of Mathematics and Data Science, Shaanxi University of Science & Technology, Xi'an 710021, China
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208
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Liu P, Ewald J, Pang Z, Legrand E, Jeon YS, Sangiovanni J, Hacariz O, Zhou G, Head JA, Basu N, Xia J. ExpressAnalyst: A unified platform for RNA-sequencing analysis in non-model species. Nat Commun 2023; 14:2995. [PMID: 37225696 DOI: 10.1038/s41467-023-38785-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 05/16/2023] [Indexed: 05/26/2023] Open
Abstract
The increasing application of RNA sequencing to study non-model species demands easy-to-use and efficient bioinformatics tools to help researchers quickly uncover biological and functional insights. We developed ExpressAnalyst ( www.expressanalyst.ca ), a web-based platform for processing, analyzing, and interpreting RNA-sequencing data from any eukaryotic species. ExpressAnalyst contains a series of modules that cover from processing and annotation of FASTQ files to statistical and functional analysis of count tables or gene lists. All modules are integrated with EcoOmicsDB, an ortholog database that enables comprehensive analysis for species without a reference transcriptome. By coupling ultra-fast read mapping algorithms with high-resolution ortholog databases through a user-friendly web interface, ExpressAnalyst allows researchers to obtain global expression profiles and gene-level insights from raw RNA-sequencing reads within 24 h. Here, we present ExpressAnalyst and demonstrate its utility with a case study of RNA-sequencing data from multiple non-model salamander species, including two that do not have a reference transcriptome.
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Affiliation(s)
- Peng Liu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Jessica Ewald
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Zhiqiang Pang
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Elena Legrand
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Yeon Seon Jeon
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Jonathan Sangiovanni
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Orcun Hacariz
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Guangyan Zhou
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Jessica A Head
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Canada.
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209
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Alves CL, Toutain TGLDO, de Carvalho Aguiar P, Pineda AM, Roster K, Thielemann C, Porto JAM, Rodrigues FA. Diagnosis of autism spectrum disorder based on functional brain networks and machine learning. Sci Rep 2023; 13:8072. [PMID: 37202411 DOI: 10.1038/s41598-023-34650-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.
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Affiliation(s)
- Caroline L Alves
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil.
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany.
| | | | - Patricia de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil
| | - Aruane M Pineda
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | - Kirstin Roster
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | | | | | - Francisco A Rodrigues
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
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210
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Wang S, Yang J, Ding X, Zhao M. Detecting local communities in complex network via the optimization of interaction relationship between node and community. PeerJ Comput Sci 2023; 9:e1386. [PMID: 37346543 PMCID: PMC10280398 DOI: 10.7717/peerj-cs.1386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/17/2023] [Indexed: 06/23/2023]
Abstract
The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and community expansion. This study develops and tests a novel local community detection algorithm called OIRLCD that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.
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Affiliation(s)
- Shenglong Wang
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
| | - Jing Yang
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
| | - Xiaoyu Ding
- Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Meng Zhao
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
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211
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Yu J, Yan C, Dodd T, Tsai CL, Tainer JA, Tsutakawa SE, Ivanov I. Dynamic conformational switching underlies TFIIH function in transcription and DNA repair and impacts genetic diseases. Nat Commun 2023; 14:2758. [PMID: 37179334 PMCID: PMC10183003 DOI: 10.1038/s41467-023-38416-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Transcription factor IIH (TFIIH) is a protein assembly essential for transcription initiation and nucleotide excision repair (NER). Yet, understanding of the conformational switching underpinning these diverse TFIIH functions remains fragmentary. TFIIH mechanisms critically depend on two translocase subunits, XPB and XPD. To unravel their functions and regulation, we build cryo-EM based TFIIH models in transcription- and NER-competent states. Using simulations and graph-theoretical analysis methods, we reveal TFIIH's global motions, define TFIIH partitioning into dynamic communities and show how TFIIH reshapes itself and self-regulates depending on functional context. Our study uncovers an internal regulatory mechanism that switches XPB and XPD activities making them mutually exclusive between NER and transcription initiation. By sequentially coordinating the XPB and XPD DNA-unwinding activities, the switch ensures precise DNA incision in NER. Mapping TFIIH disease mutations onto network models reveals clustering into distinct mechanistic classes, affecting translocase functions, protein interactions and interface dynamics.
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Affiliation(s)
- Jina Yu
- Department of Chemistry, Georgia State University, Atlanta, GA, USA
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA
| | - Chunli Yan
- Department of Chemistry, Georgia State University, Atlanta, GA, USA
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA
| | - Thomas Dodd
- Department of Chemistry, Georgia State University, Atlanta, GA, USA
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA
| | - Chi-Lin Tsai
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John A Tainer
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Susan E Tsutakawa
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ivaylo Ivanov
- Department of Chemistry, Georgia State University, Atlanta, GA, USA.
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA.
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212
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Jiang S, Zhou J, Small M, Lu JA, Zhang Y. Searching for Key Cycles in a Complex Network. PHYSICAL REVIEW LETTERS 2023; 130:187402. [PMID: 37204881 DOI: 10.1103/physrevlett.130.187402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 04/07/2023] [Accepted: 04/13/2023] [Indexed: 05/21/2023]
Abstract
Searching for key nodes and edges in a network is a long-standing problem. Recently cycle structure in a network has received more attention. Is it possible to propose a ranking algorithm for cycle importance? We address the problem of identifying the key cycles of a network. First, we provide a more concrete definition of importance-in terms of Fiedler value (the second smallest Laplacian eigenvalue). Key cycles are those that contribute most substantially to the dynamical behavior of the network. Second, by comparing the sensitivity of Fiedler value to different cycles, a neat index for ranking cycles is provided. Numerical examples are given to show the effectiveness of this method.
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Affiliation(s)
- Siyang Jiang
- School of Mathematics and Statistics, Wuhan University, Hubei 430072, China
| | - Jin Zhou
- School of Mathematics and Statistics, Wuhan University, Hubei 430072, China
- Hubei Key Laboratory of Computational Science, Wuhan University, Hubei 430072, China
| | - Michael Small
- The Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Crawley, Western Australia 6009, Australia
- Mineral Resources, CSIRO, Kensington 6151, Western Australia
| | - Jun-An Lu
- School of Mathematics and Statistics, Wuhan University, Hubei 430072, China
| | - Yanqi Zhang
- School of Mathematics and Statistics, Wuhan University, Hubei 430072, China
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213
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Schmitt O, Eipert P, Wang Y, Kanoke A, Rabiller G, Liu J. Connectome-based prediction of functional impairment in experimental stroke models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.05.539601. [PMID: 37205373 PMCID: PMC10187266 DOI: 10.1101/2023.05.05.539601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.
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Affiliation(s)
- Oliver Schmitt
- Medical School Hamburg - University of Applied Sciences, Department of Anatomy; University of Rostock, Institute of Anatomy
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
| | - Peter Eipert
- Medical School Hamburg - University of Applied Sciences, Department of Anatomy; University of Rostock, Institute of Anatomy
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
| | - Yonggang Wang
- Department of Neurological Surgery, UCSF
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
- Department of Neurological Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China, 100050
| | - Atsushi Kanoke
- Department of Neurological Surgery, UCSF
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan
| | - Gratianne Rabiller
- Department of Neurological Surgery, UCSF
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
| | - Jialing Liu
- Department of Neurological Surgery, UCSF
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
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214
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Zoelzer F, Schneider S, Dierkes PW. Time series cluster analysis reveals individual assignment of microbiota in captive tiger ( Panthera tigris) and wildebeest ( Connochaetes taurinus). Ecol Evol 2023; 13:e10066. [PMID: 37168984 PMCID: PMC10166651 DOI: 10.1002/ece3.10066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/18/2023] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
Fecal microbiota variability and individuality are well studied in humans and also in farm animals (related to diet- or disease-specific influences), but very little is known for exotic zoo-housed animals. This includes a wide range of species that differ greatly in microbiota composition and variation. For example, herbivorous species show a very similar and constant fecal microbiota over time, whereas carnivorous species appear to be highly variable in fecal microbial diversity and composition. Our objective was to determine whether species-specific and individual-specific clustering patterns were observed in the fecal microbiota of wildebeest (Connochaetes taurinus) and tigers (Panthera tigris). We collected 95 fecal samples of 11 animal individuals that were each sampled over eight consecutive days and analyzed those with Illumina MiSeq sequencing of the V3-V4 region of the 16SrRNA gene. In order to identify species or individual clusters, we applied two different agglomerative hierarchical clustering algorithms - a community detection algorithm and Ward's linkage. Our results showed that both, species-specific and individual-specific clustering is possible, but more reliable results were achieved when applying dynamic time warping which finds the optimal alignment between different time series. Furthermore, the bacterial families that distinguish individuals from each other in both species included daily occurring core bacteria (e.g., Acidaminococcaceae in wildebeests or Clostridiaceae in tigers) as well as individual dependent and more fluctuating bacterial families. Our results suggest that while it is necessary to consider multiple consecutive samples per individual, it is then possible to characterize individual abundance patterns in fecal microbiota in both herbivorous and carnivorous species. This would allow establishing individual microbiota profiles of animals housed in zoos, which is a basic prerequisite to quickly detect deviations and use microbiome analysis as a non-invasive and cost-effective tool in animal welfare.
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Affiliation(s)
- Franziska Zoelzer
- Bioscience Education and Zoo BiologyGoethe University FrankfurtFrankfurt am MainGermany
| | - Sebastian Schneider
- Bioscience Education and Zoo BiologyGoethe University FrankfurtFrankfurt am MainGermany
| | - Paul Wilhelm Dierkes
- Bioscience Education and Zoo BiologyGoethe University FrankfurtFrankfurt am MainGermany
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215
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Désy B, Desrosiers P, Allard A. Dimension matters when modeling network communities in hyperbolic spaces. PNAS NEXUS 2023; 2:pgad136. [PMID: 37181048 PMCID: PMC10167553 DOI: 10.1093/pnasnexus/pgad136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/17/2023] [Accepted: 03/30/2023] [Indexed: 05/16/2023]
Abstract
Over the last decade, random hyperbolic graphs have proved successful in providing geometric explanations for many key properties of real-world networks, including strong clustering, high navigability, and heterogeneous degree distributions. These properties are ubiquitous in systems as varied as the internet, transportation, brain or epidemic networks, which are thus unified under the hyperbolic network interpretation on a surface of constant negative curvature. Although a few studies have shown that hyperbolic models can generate community structures, another salient feature observed in real networks, we argue that the current models are overlooking the choice of the latent space dimensionality that is required to adequately represent clustered networked data. We show that there is an important qualitative difference between the lowest-dimensional model and its higher-dimensional counterparts with respect to how similarity between nodes restricts connection probabilities. Since more dimensions also increase the number of nearest neighbors for angular clusters representing communities, considering only one more dimension allows us to generate more realistic and diverse community structures.
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Affiliation(s)
- Béatrice Désy
- School of Information Management, Victoria University of Wellington, Wellington 6140, New Zealand
- Antarctic Research Centre, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Patrick Desrosiers
- Département de physique, de génie physique et d’optique, Université Laval, Québec, QC, Canada G1V 0A6
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, Canada G1V 0A6
- Centre de recherche CERVO, Québec, QC, Canada G1J 2G3
| | - Antoine Allard
- Département de physique, de génie physique et d’optique, Université Laval, Québec, QC, Canada G1V 0A6
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, Canada G1V 0A6
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216
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Vegué M, Thibeault V, Desrosiers P, Allard A. Dimension reduction of dynamics on modular and heterogeneous directed networks. PNAS NEXUS 2023; 2:pgad150. [PMID: 37215634 PMCID: PMC10198746 DOI: 10.1093/pnasnexus/pgad150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/17/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023]
Abstract
Dimension reduction is a common strategy to study nonlinear dynamical systems composed by a large number of variables. The goal is to find a smaller version of the system whose time evolution is easier to predict while preserving some of the key dynamical features of the original system. Finding such a reduced representation for complex systems is, however, a difficult task. We address this problem for dynamics on weighted directed networks, with special emphasis on modular and heterogeneous networks. We propose a two-step dimension-reduction method that takes into account the properties of the adjacency matrix. First, units are partitioned into groups of similar connectivity profiles. Each group is associated to an observable that is a weighted average of the nodes' activities within the group. Second, we derive a set of equations that must be fulfilled for these observables to properly represent the original system's behavior, together with a method for approximately solving them. The result is a reduced adjacency matrix and an approximate system of ODEs for the observables' evolution. We show that the reduced system can be used to predict some characteristic features of the complete dynamics for different types of connectivity structures, both synthetic and derived from real data, including neuronal, ecological, and social networks. Our formalism opens a way to a systematic comparison of the effect of various structural properties on the overall network dynamics. It can thus help to identify the main structural driving forces guiding the evolution of dynamical processes on networks.
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Affiliation(s)
- Marina Vegué
- Département de physique, de génie physique et d'optique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
| | - Vincent Thibeault
- Département de physique, de génie physique et d'optique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
| | - Patrick Desrosiers
- Département de physique, de génie physique et d'optique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- CERVO Brain Research Center, 2301 avenue d'Estimauville, G1E 1T2 Québec, Canada
| | - Antoine Allard
- Département de physique, de génie physique et d'optique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
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217
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Wang W, Meng J, Li H, Fan J. Non-negative matrix factorization for overlapping community detection in directed weighted networks with sparse constraints. CHAOS (WOODBURY, N.Y.) 2023; 33:2890081. [PMID: 37163995 DOI: 10.1063/5.0152280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023]
Abstract
Detecting overlapping communities is essential for analyzing the structure and function of complex networks. However, most existing approaches only consider network topology and overlook the benefits of attribute information. In this paper, we propose a novel attribute-information non-negative matrix factorization approach that integrates sparse constraints and optimizes an objective function for detecting communities in directed weighted networks. Our algorithm updates the basic non-negative matrix adaptively, incorporating both network topology and attribute information. We also add a sparsity constraint term of graph regularization to maintain the intrinsic geometric structure between nodes. Importantly, we provide strict proof of convergence for the multiplication update rule used in our algorithm. We apply our proposed algorithm to various artificial and real-world networks and show that it is more effective for detecting overlapping communities. Furthermore, our study uncovers the intricate iterative process of system evolution toward convergence and investigates the impact of various variables on network detection. These findings provide insights into building more robust and operable complex systems.
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Affiliation(s)
- Wenxuan Wang
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Jun Meng
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Huijia Li
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Jingfang Fan
- School of Systems Science/Institute of Nonequilibrium Systems, Beijing Normal University, Beijing 100875, China
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218
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Xiao W, Zhou L, Yang P, Yan N, Wei C. An international comparative study of rare earth research from the perspective of bibliometrics. Heliyon 2023; 9:e16075. [PMID: 37206007 PMCID: PMC10189500 DOI: 10.1016/j.heliyon.2023.e16075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/01/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023] Open
Abstract
Rare earth refers to a type of strategic resource. Countries worldwide have invested considerable money in relevant research. This bibliometric study was to evaluate the global situation of published rare earth research to discover rare earth research strategies in a wide range of countries. In this study, 50,149 SCI papers related to rare earth were collected. In addition, we divided the above papers into 11 main research fields according to discipline and keyword clustering, and divided the above theoretical cultures into different industry fields according to the keywords of the above papers. After that, the research directions, research institutions, funding, and other aspects of rare earth research in numerous countries were compared. The result of this study suggests that China's rare earth research has been generally in the leading position worldwide, whereas there are still some problems in the discipline layout, strategic strategies, green development, and fund support. Other countries place a greater focus on areas regarding national security strategies (e.g., mineral exploration, smelting, and permanent magnetism).
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219
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He S, Chen D, Black KC, Chong P, Marzouk S, Yoon BJ, Davis K, Lee J. Network Analysis of Academic Medical Center Websites in the United States. Sci Data 2023; 10:245. [PMID: 37117246 PMCID: PMC10147938 DOI: 10.1038/s41597-023-02104-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/24/2023] [Indexed: 04/30/2023] Open
Abstract
Healthcare resources are published annually in repositories such as the AHA Annual Survey DatabaseTM. However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.
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Affiliation(s)
- Shuhan He
- Massachusetts General Hospital, Boston, USA
| | - David Chen
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | | | - Paul Chong
- Campbell University School of Osteopathic Medicine, Lillington, USA
| | - Sammer Marzouk
- Harvard Department of Chemistry and Chemical Biology, Cambridge, USA
| | - Byung-Jun Yoon
- Texas A&M University, College Station, USA
- Brookhaven National Laboratory, Upton, USA
| | | | - Jarone Lee
- Massachusetts General Hospital, Boston, USA
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220
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Maschietto F, Morzan UN, Tofoleanu F, Gheeraert A, Chaudhuri A, Kyro GW, Nekrasov P, Brooks B, Loria JP, Rivalta I, Batista VS. Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase. Nat Commun 2023; 14:2239. [PMID: 37076500 PMCID: PMC10115891 DOI: 10.1038/s41467-023-37956-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
Allosteric drugs have the potential to revolutionize biomedicine due to their enhanced selectivity and protection against overdosage. However, we need to better understand allosteric mechanisms in order to fully harness their potential in drug discovery. In this study, molecular dynamics simulations and nuclear magnetic resonance spectroscopy are used to investigate how increases in temperature affect allostery in imidazole glycerol phosphate synthase. Results demonstrate that temperature increase triggers a cascade of local amino acid-to-amino acid dynamics that remarkably resembles the allosteric activation that takes place upon effector binding. The differences in the allosteric response elicited by temperature increase as opposed to effector binding are conditional to the alterations of collective motions induced by either mode of activation. This work provides an atomistic picture of temperature-dependent allostery, which could be harnessed to more precisely control enzyme function.
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Affiliation(s)
- Federica Maschietto
- Department of Chemistry, Yale University, P.O. Box 208107, New Haven, CT, 06520-8107, USA.
| | - Uriel N Morzan
- International Center for Theoretical Physics, Strada Costiera 11, 34151, Trieste, Italy.
| | - Florentina Tofoleanu
- Department of Chemistry, Yale University, P.O. Box 208107, New Haven, CT, 06520-8107, USA
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
- Treeline Biosciences, 500 Arsenal Street, Watertown, MA, 02472, USA
| | - Aria Gheeraert
- ENSL, CNRS, Laboratoire de Chimie UMR 5182, 46 allée d'Italie, 69364, Lyon, France
- Dipartimento di Chimica Industriale "Toso Montanari", Alma Mater Studiorum, Università di Bologna, Bologna, Italy
| | - Apala Chaudhuri
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Gregory W Kyro
- Department of Chemistry, Yale University, P.O. Box 208107, New Haven, CT, 06520-8107, USA
| | - Peter Nekrasov
- Department of Chemistry, Yale University, P.O. Box 208107, New Haven, CT, 06520-8107, USA
| | - Bernard Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - J Patrick Loria
- Department of Chemistry, Yale University, P.O. Box 208107, New Haven, CT, 06520-8107, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA.
| | - Ivan Rivalta
- ENSL, CNRS, Laboratoire de Chimie UMR 5182, 46 allée d'Italie, 69364, Lyon, France.
- Dipartimento di Chimica Industriale "Toso Montanari", Alma Mater Studiorum, Università di Bologna, Bologna, Italy.
| | - Victor S Batista
- Department of Chemistry, Yale University, P.O. Box 208107, New Haven, CT, 06520-8107, USA.
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221
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Moosavi-Movahedi Z, Salehi N, Habibi-Rezaei M, Qassemi F, Karimi-Jafari MH. Intermediate-aided allostery mechanism for α-glucosidase by Xanthene-11v as an inhibitor using residue interaction network analysis. J Mol Graph Model 2023; 122:108495. [PMID: 37116337 DOI: 10.1016/j.jmgm.2023.108495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/04/2023] [Accepted: 04/12/2023] [Indexed: 04/30/2023]
Abstract
Exploring allosteric inhibition and the discovery of new inhibitor binding sites are important studies in protein regulation mechanisms and drug discovery. Structural and network-based analyses of trajectories resulting from molecular dynamics (MD) simulations have been developed to discover protein dynamics, landscape, functions, and allosteric regions. Here, an experimentally suggested non-competitive inhibitor, xanthene-11v, was considered to explore its allosteric inhibition mechanism in α-glucosidase MAL12. Comparative structural and network analyses were applied to eight 250 ns independent MD simulations, four of which were performed in the free state and four of which were performed in ligand-bound forms. Projected two-dimensional free energy landscapes (FEL) were constructed from the probabilistic distribution of conformations along the first two principal components. The post-simulation analyses of the coordinates, side-chain torsion angles, non-covalent interaction networks, network communities, and their centralities were performed on α-glucosidase conformations and the intermediate sub-states. Important communities of residues have been found that connect the allosteric site to the active site. Some of these residues like Thr307, Arg312, TYR344, ILE345, Phe357, Asp406, Val407, Asp408, and Leu436 are the key messengers in the transition pathway between allosteric and active sites. Evaluating the probability distribution of distances between gate residues including Val407 in one community and Phe158, and Pro65 in another community depicted the closure of this gate due to the inhibitor binding. Six macro states of protein were deduced from the topology of FEL and analysis of conformational preference of free and ligand-bound systems to these macro states shows a combination of lock-and-key, conformational selection, and induced fit mechanisms are effective in ligand binding. All these results reveal structural states, allosteric mechanisms, and key players in the inhibition pathway of α-glucosidase by xanthene-11v.
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Affiliation(s)
- Zahra Moosavi-Movahedi
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Najmeh Salehi
- School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | | | | | - Mohammad Hossein Karimi-Jafari
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran; School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
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222
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Alassad M, Hussain MN, Agarwal N. Developing an agent-based model to minimize spreading of malicious information in dynamic social networks. COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY 2023:1-16. [PMID: 37360911 PMCID: PMC10090746 DOI: 10.1007/s10588-023-09375-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/06/2023] [Indexed: 06/28/2023]
Abstract
This research introduces a systematic and multidisciplinary agent-based model to interpret and simplify the dynamic actions of the users and communities in an evolutionary online (offline) social network. The organizational cybernetics approach is used to control/monitor the malicious information spread between communities. The stochastic one-median problem minimizes the agent response time and eliminates the information spread across the online (offline) environment. The performance of these methods was measured against a Twitter network related to an armed protest demonstration against the COVID-19 lockdown in Michigan state in May 2020. The proposed model demonstrated the dynamicity of the network, enhanced the agent level performance, minimized the malicious information spread, and measured the response to the second stochastic information spread in the network.
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Affiliation(s)
| | | | - Nitin Agarwal
- COSMOS Research Center, UA-Little Rock, Little Rock, AR USA
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223
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Wang HJ, Hu ZL, Tao L, Shao S, Wang SZ. The locatability of Pearson algorithm for multi-source location in complex networks. Sci Rep 2023; 13:5692. [PMID: 37029261 PMCID: PMC10082217 DOI: 10.1038/s41598-023-32832-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/03/2023] [Indexed: 04/09/2023] Open
Abstract
We study locating propagation sources in complex networks. We proposed an multi-source location algorithm for different propagation dynamics by using sparse observations. Without knowing the propagation dynamics and any dynamic parameters, we can calculate node centrality based on the character that positive correlation between inform time of nodes and geodesic distance between nodes and sources. The algorithm is robust and have high location accuracy for any number of sources. We study locatability of the proposed source location algorithm and present a corresponding strategy to select observer nodes based on greedy algorithm. All simulations on both model and real-world networks proved the feasibility and validity of this algorithm.
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Affiliation(s)
- Hong-Jue Wang
- School of Information, Beijing Wuzi University, Beijing, 101149, People's Republic of China
| | - Zhao-Long Hu
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, 321004, People's Republic of China
| | - Li Tao
- School of Information, Beijing Wuzi University, Beijing, 101149, People's Republic of China.
| | - Shuyu Shao
- School of Information, Beijing Wuzi University, Beijing, 101149, People's Republic of China
| | - Shi-Zhe Wang
- School of Economics, Liaoning University, Shenyang, 110000, People's Republic of China
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224
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Benati S, Puerto J, Rodríguez-Chía AM, Temprano F. Overlapping communities detection through weighted graph community games. PLoS One 2023; 18:e0283857. [PMID: 37014883 PMCID: PMC10072486 DOI: 10.1371/journal.pone.0283857] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 03/19/2023] [Indexed: 04/05/2023] Open
Abstract
We propose a new model to detect the overlapping communities of a network that is based on cooperative games and mathematical programming. More specifically, communities are defined as stable coalitions of a weighted graph community game and they are revealed as the optimal solution of a mixed-integer linear programming problem. Exact optimal solutions are obtained for small and medium sized instances and it is shown that they provide useful information about the network structure, improving on previous contributions. Next, a heuristic algorithm is developed to solve the largest instances and used to compare two variations of the objective function.
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Affiliation(s)
- Stefano Benati
- Dipartimento di Sociologia e Ricerca Sociale, Università di Trento, Trento, Italy
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225
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Spampinato AG, Scollo RA, Cutello V, Pavone M. Random search immune algorithm for community detection. Soft comput 2023. [DOI: 10.1007/s00500-023-07999-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
AbstractCommunity detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in turn is combined with purely stochastic operators. According to the obtained outcomes, opt-IA shows strictly better performances than almost all heuristics and metaheuristics to which it was compared; whilst it turns out to be comparable with the Hyper-Heuristic method. Overall, it can be claimed that opt-IA, even if driven by a purely random process, proves to be reliable and with efficient performance. Furthermore, to prove the latter claim, a sensitivity analysis of the functionality was conducted, using the classic metrics NMI, ARI and NVI.
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226
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Hugenschmidt CE, Ip EH, Laurita-Spanglet J, Babcock P, Morgan AR, Fanning JT, King K, Thomas JT, Soriano CT. IMOVE: Protocol for a randomized, controlled 2x2 factorial trial of improvisational movement and social engagement interventions in older adults with early Alzheimer's disease. Contemp Clin Trials Commun 2023; 32:101073. [PMID: 36949846 PMCID: PMC10025420 DOI: 10.1016/j.conctc.2023.101073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 01/02/2023] [Accepted: 01/14/2023] [Indexed: 01/25/2023] Open
Abstract
Background In addition to cognitive impairment, people with Alzheimer's disease (PWAD) experience neuropsychiatric symptoms (e.g., apathy, depression), altered gait, and poor balance that further diminish their quality of life (QoL). Here, we describe a unique, randomized, controlled trial to test the hypothesis that both movement and social engagement aspects of a group dance intervention alter the connectivity of key brain networks involved in motor and social-emotional functioning and lead to improved QoL in PWAD. Methods IMOVE (NCT03333837) was a single-center, randomized, controlled 2x2 factorial trial that assigned PWAD/caregiver dyads to one of 4 study conditions (Movement Group, Movement Alone, Social Group, or Usual Care control). The Movement Group participated in twice-weekly group improvisational dance (IMPROVment® Method) classes for 12 weeks. The Movement Alone intervention captured the same dance movement and auditory stimuli as the group class without social interaction, and the Social Group used improvisational party games to recapitulate the fun and playfulness of the Movement Group without the movement. The primary outcome was change in QoL among PWAD. Key secondary outcomes were functional brain network measures assessed using graph-theory analysis of resting-state functional magnetic resonance imaging scans, as well as neuropsychiatric symptoms, gait, and balance. Results A total of 111 dyads were randomized; 89 completed the study, despite interruption and modification of the protocol due to COVID-19 restrictions (see companion paper by Fanning et al.). The data are being analyzed and will be submitted for publication in 2023.
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Affiliation(s)
- Christina E. Hugenschmidt
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Corresponding author. Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
| | - Edward H. Ip
- Department of Biostatistics and Data Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | | | - Phyllis Babcock
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ashley R. Morgan
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jason T. Fanning
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, NC, USA
| | - Kamryn King
- Department of Theatre and Dance, Wake Forest University, Winston-Salem, NC, USA
| | - Jantira T. Thomas
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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227
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Wu H, Liang B, Chen Z, Zhang H. MultiSimNeNc: A network representation learning-based module identification method by network embedding and clustering. Comput Biol Med 2023; 156:106703. [PMID: 36889026 DOI: 10.1016/j.compbiomed.2023.106703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/05/2023] [Accepted: 02/19/2023] [Indexed: 02/26/2023]
Abstract
Accurate identification of gene modules based on biological networks is an effective approach to understanding gene patterns of cancer from a module-level perspective. However, most graph clustering algorithms just consider low-order topological connectivity, which limits their accuracy in gene module identification. In this study, we propose a novel network-based method, MultiSimNeNc, to identify modules in various types of networks by integrating network representation learning (NRL) and clustering algorithms. In this method, we first obtain the multi-order similarity of the network using graph convolution (GC). Then, we aggregate the multi-order similarity to characterize the network structure and use non-negative matrix factorization (NMF) to achieve low-dimensional node characterization. Finally, we predict the number of modules based on the bayesian information criterion (BIC) and use the gaussian mixture model (GMM) to identify modules. To testify to the efficacy of MultiSimeNc in module identification, we apply this method to two types of biological networks and six benchmark networks, where the biological networks are constructed based on the fusion of multi-omics data from glioblastoma (GBM). The analysis shows that MultiSimNeNc outperforms several state-of-the-art module identification algorithms in identification accuracy, which is an effective method for understanding biomolecular mechanisms of pathogenesis from a module-level perspective.
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Affiliation(s)
- Hao Wu
- College of Information Engineering, Northwest A&F University, 712100, Yangling, China; School of Software, Shandong University, 250100, Jinan, China.
| | - Biting Liang
- College of Information Engineering, Northwest A&F University, 712100, Yangling, China
| | - Zhongli Chen
- Tibet Center for Disease Control and Prevention, the People's Government of Tibet Autonomous Region, 850000, Lhasa, China
| | - Hongming Zhang
- College of Information Engineering, Northwest A&F University, 712100, Yangling, China.
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228
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Jia W, Ma X. Clustering of multi-layer networks with structural relations and conservation of features. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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229
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Dantzer B. Frank Beach Award Winner: The centrality of the hypothalamic-pituitary-adrenal axis in dealing with environmental change across temporal scales. Horm Behav 2023; 150:105311. [PMID: 36707334 DOI: 10.1016/j.yhbeh.2023.105311] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 01/02/2023] [Accepted: 01/06/2023] [Indexed: 01/26/2023]
Abstract
Understanding if and how individuals and populations cope with environmental change is an enduring question in evolutionary ecology that has renewed importance given the pace of change in the Anthropocene. Two evolutionary strategies of coping with environmental change may be particularly important in rapidly changing environments: adaptive phenotypic plasticity and/or bet hedging. Adaptive plasticity could enable individuals to match their phenotypes to the expected environment if there is an accurate cue predicting the selective environment. Diversifying bet hedging involves the production of seemingly random phenotypes in an unpredictable environment, some of which may be adaptive. Here, I review the central role of the hypothalamic-pituitary-adrenal (HPA) axis and glucocorticoids (GCs) in enabling vertebrates to cope with environmental change through adaptive plasticity and bet hedging. I first describe how the HPA axis mediates three types of adaptive plasticity to cope with environmental change (evasion, tolerance, recovery) over short timescales (e.g., 1-3 generations) before discussing how the implications of GCs on phenotype integration may depend upon the timescale under consideration. GCs can promote adaptive phenotypic integration, but their effects on phenotypic co-variation could also limit the dimensions of phenotypic space explored by animals over longer timescales. Finally, I discuss how organismal responses to environmental stressors can act as a bet hedging mechanism and therefore enhance evolvability by increasing genetic or phenotypic variability or reducing patterns of genetic and phenotypic co-variance. Together, this emphasizes the crucial role of the HPA axis in understanding fundamental questions in evolutionary ecology.
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Affiliation(s)
- Ben Dantzer
- Department of Psychology, University of Michigan, MI 48109 Ann Arbor, MI, USA; Department of Ecology and Evolutionary Biology, University of Michigan, MI 48109, Ann Arbor, MI, USA.
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230
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Sobkowicz P. Social Depolarization and Diversity of Opinions-Unified ABM Framework. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040568. [PMID: 37190355 PMCID: PMC10137433 DOI: 10.3390/e25040568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 03/18/2023] [Accepted: 03/22/2023] [Indexed: 05/17/2023]
Abstract
Most sociophysics opinion dynamics simulations assume that contacts between agents lead to greater similarity of opinions, and that there is a tendency for agents having similar opinions to group together. These mechanisms result, in many types of models, in significant polarization, understood as separation between groups of agents having conflicting opinions. The addition of inflexible agents (zealots) or mechanisms, which drive conflicting opinions even further apart, only exacerbates these polarizing processes. Using a universal mathematical framework, formulated in the language of utility functions, we present novel simulation results. They combine polarizing tendencies with mechanisms potentially favoring diverse, non-polarized environments. The simulations are aimed at answering the following question: How can non-polarized systems exist in stable configurations? The framework enables easy introduction, and study, of the effects of external "pro-diversity", and its contribution to the utility function. Specific examples presented in this paper include an extension of the classic square geometry Ising-like model, in which agents modify their opinions, and a dynamic scale-free network system with two different mechanisms promoting local diversity, where agents modify the structure of the connecting network while keeping their opinions stable. Despite the differences between these models, they show fundamental similarities in results in terms of the existence of low temperature, stable, locally and globally diverse states, i.e., states in which agents with differing opinions remain closely linked. While these results do not answer the socially relevant question of how to combat the growing polarization observed in many modern democratic societies, they open a path towards modeling polarization diminishing activities. These, in turn, could act as guidance for implementing actual depolarization social strategies.
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Affiliation(s)
- Paweł Sobkowicz
- Nomaten Centre of Excellence, National Centre for Nuclear Research, A Soltana 7, 05-400 Otwock, Poland
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231
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Zheng Z. Tracing production carbon emission transfer through global value chains: Towards a top gainer principle. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 866:161316. [PMID: 36599379 DOI: 10.1016/j.scitotenv.2022.161316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/01/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
This study proposes the top gainer principle (TGP) and builds a calculation model based on the TGP to measure production carbon emissions transfer (PCET) in the context of global value chains. Compared with embodied carbon research, the innovative TGP model establishes a traceability mechanism based on the difference between responsibility and actual emissions from the perspective of the value chain, avoiding the endless debate between producer and consumer responsibility, which makes the TGP model more reasonable and fairer. In addition, using long-term input-output data, this study measures spatiotemporal patterns and the network evolution of global PCET. The results show that the total amount of global PCET has increased, and the regions with high outflows of PCET mainly include East Asia, North America, Central and Western Europe, and Russia. Among these regions, the United States and China accounted for the largest proportion of PCET outflow. By contrast, South America and Africa are typical low-outflow regions. From North America via central Europe, Turkey, Iran, South Asia to China, is a "W"-shaped high net outflow belt. The overall concentration of the global PCET network first decreased and then increased, and the network structure evolved into a bipolar network group with China and the United States as the core. Under the shock of the COVID-19 pandemic, the network structure showed a trend towards decentralization. This study suggests that efforts should be made to strengthen the responsibility of major countries, enhance the supervision of lead firms, establish a carbon emission transfer compensation system within value chains, and promote the development and spread of carbon emission reduction technologies to facilitate the reduction of global carbon emissions.
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Affiliation(s)
- Zhi Zheng
- Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China.
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232
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Dadashkhan S, Mirmotalebisohi SA, Poursheykhi H, Sameni M, Ghani S, Abbasi M, Kalantari S, Zali H. Deciphering crucial genes in multiple sclerosis pathogenesis and drug repurposing: A systems biology approach. J Proteomics 2023; 280:104890. [PMID: 36966969 DOI: 10.1016/j.jprot.2023.104890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 04/10/2023]
Abstract
This study employed systems biology and high-throughput technologies to analyze complex molecular components of MS pathophysiology, combining data from multiple omics sources to identify potential biomarkers and propose therapeutic targets and repurposed drugs for MS treatment. This study analyzed GEO microarray datasets and MS proteomics data using geWorkbench, CTD, and COREMINE to identify differentially expressed genes associated with MS disease. Protein-protein interaction networks were constructed using Cytoscape and its plugins, and functional enrichment analysis was performed to identify crucial molecules. A drug-gene interaction network was also created using DGIdb to propose medications. This study identified 592 differentially expressed genes (DEGs) associated with MS disease using GEO, proteomics, and text-mining datasets. 37 DEGs were found to be important by topographical network studies, and 6 were identified as the most significant for MS pathophysiology. Additionally, we proposed six drugs that target these key genes. Crucial molecules identified in this study were dysregulated in MS and likely play a key role in the disease mechanism, warranting further research. Additionally, we proposed repurposing certain FDA-approved drugs for MS treatment. Our in silico results were supported by previous experimental research on some of the target genes and drugs. SIGNIFICANCE: As the long-lasting investigations continue to discover new pathological territories in neurodegeneration, here we apply a systems biology approach to determine multiple sclerosis's molecular and pathophysiological origin and identify multiple sclerosis crucial genes that contribute to candidating new biomarkers and proposing new medications.
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Affiliation(s)
- Sadaf Dadashkhan
- Molecular Medicine Research Centre, Universitätsklinikum Jena, Jena, Germany; Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Seyed Amir Mirmotalebisohi
- Student Research Committee, Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hossein Poursheykhi
- Department of New Scientist, Faculty of Medical Sciences, Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Marzieh Sameni
- Student Research Committee, Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sepideh Ghani
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Sima Kalantari
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Regenerative Medicine Group (REMED), Universal Scientific Education & Research Network (USERN), Tehran, Iran
| | - Hakimeh Zali
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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233
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Qing H. Estimating the Number of Communities in Weighted Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040551. [PMID: 37190339 PMCID: PMC10137563 DOI: 10.3390/e25040551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/13/2023] [Accepted: 03/22/2023] [Indexed: 05/17/2023]
Abstract
Community detection in weighted networks has been a popular topic in recent years. However, while there exist several flexible methods for estimating communities in weighted networks, these methods usually assume that the number of communities is known. It is usually unclear how to determine the exact number of communities one should use. Here, to estimate the number of communities for weighted networks generated from arbitrary distribution under the degree-corrected distribution-free model, we propose one approach that combines weighted modularity with spectral clustering. This approach allows a weighted network to have negative edge weights and it also works for signed networks. We compare the proposed method to several existing methods and show that our method is more accurate for estimating the number of communities both numerically and empirically.
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Affiliation(s)
- Huan Qing
- School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China
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234
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Decoding the Conformational Selective Mechanism of FGFR Isoforms: A Comparative Molecular Dynamics Simulation. Molecules 2023; 28:molecules28062709. [PMID: 36985681 PMCID: PMC10052029 DOI: 10.3390/molecules28062709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/09/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Fibroblast growth factor receptors (FGFRs) play critical roles in the regulation of cell growth, differentiation, and proliferation. Specifically, FGFR2 gene amplification has been implicated in gastric and breast cancer. Pan-FGFR inhibitors often cause large toxic side effects, and the highly conserved ATP-binding pocket in the FGFR1/2/3 isoforms poses an immense challenge in designing selective FGFR2 inhibitors. Recently, an indazole-based inhibitor has been discovered that can selectively target FGFR2. However, the detailed mechanism involved in selective inhibition remains to be clarified. To this end, we performed extensive molecular dynamics simulations of the apo and inhibitor-bound systems along with multiple analyses, including Markov state models, principal component analysis, a cross-correlation matrix, binding free energy calculation, and community network analysis. Our results indicated that inhibitor binding induced the phosphate-binding loop (P-loop) of FGFR2 to switch from the open to the closed conformation. This effect enhanced extensive hydrophobic FGFR2-inhibitor contacts, contributing to inhibitor selectivity. Moreover, the key conformational intermediate states, dynamics, and driving forces of this transformation were uncovered. Overall, these findings not only provided a structural basis for understanding the closed P-loop conformation for therapeutic potential but also shed light on the design of selective inhibitors for treating specific types of cancer.
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235
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Cao R, Guan C, Gan Z, Leng S. Reviving the Dynamics of Attacked Reservoir Computers. ENTROPY (BASEL, SWITZERLAND) 2023; 25:515. [PMID: 36981403 PMCID: PMC10048059 DOI: 10.3390/e25030515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Physically implemented neural networks are subject to external perturbations and internal variations. Existing works focus on the adversarial attacks but seldom consider attack on the network structure and the corresponding recovery method. Inspired by the biological neural compensation mechanism and the neuromodulation technique in clinical practice, we propose a novel framework of reviving attacked reservoir computers, consisting of several strategies direct at different types of attacks on structure by adjusting only a minor fraction of edges in the reservoir. Numerical experiments demonstrate the efficacy and broad applicability of the framework and reveal inspiring insights into the mechanisms. This work provides a vehicle to improve the robustness of reservoir computers and can be generalized to broader types of neural networks.
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Affiliation(s)
- Ruizhi Cao
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Chun Guan
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Zhongxue Gan
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Siyang Leng
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
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236
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Koskin V, Kells A, Clayton J, Hartmann AK, Annibale A, Rosta E. Variational kinetic clustering of complex networks. J Chem Phys 2023; 158:104112. [PMID: 36922127 DOI: 10.1063/5.0105099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Efficiently identifying the most important communities and key transition nodes in weighted and unweighted networks is a prevalent problem in a wide range of disciplines. Here, we focus on the optimal clustering using variational kinetic parameters, linked to Markov processes defined on the underlying networks, namely, the slowest relaxation time and the Kemeny constant. We derive novel relations in terms of mean first passage times for optimizing clustering via the Kemeny constant and show that the optimal clustering boundaries have equal round-trip times to the clusters they separate. We also propose an efficient method that first projects the network nodes onto a 1D reaction coordinate and subsequently performs a variational boundary search using a parallel tempering algorithm, where the variational kinetic parameters act as an energy function to be extremized. We find that maximization of the Kemeny constant is effective in detecting communities, while the slowest relaxation time allows for detection of transition nodes. We demonstrate the validity of our method on several test systems, including synthetic networks generated from the stochastic block model and real world networks (Santa Fe Institute collaboration network, a network of co-purchased political books, and a street network of multiple cities in Luxembourg). Our approach is compared with existing clustering algorithms based on modularity and the robust Perron cluster analysis, and the identified transition nodes are compared with different notions of node centrality.
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Affiliation(s)
- Vladimir Koskin
- Department of Chemistry, King's College London, SE1 1DB London, United Kingdom
| | - Adam Kells
- Department of Chemistry, King's College London, SE1 1DB London, United Kingdom
| | - Joe Clayton
- Department of Physics and Astronomy, University College London, WC1E 6BT London, United Kingdom
| | | | - Alessia Annibale
- Department of Mathematics, King's College London, SE11 6NJ London, United Kingdom
| | - Edina Rosta
- Department of Physics and Astronomy, University College London, WC1E 6BT London, United Kingdom
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237
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Ferraz de Arruda G, Petri G, Rodriguez PM, Moreno Y. Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs. Nat Commun 2023; 14:1375. [PMID: 36914645 PMCID: PMC10011415 DOI: 10.1038/s41467-023-37118-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
Although ubiquitous, interactions in groups of individuals are not yet thoroughly studied. Frequently, single groups are modeled as critical-mass dynamics, which is a widespread concept used not only by academics but also by politicians and the media. However, less explored questions are how a collection of groups will behave and how their intersection might change the dynamics. Here, we formulate this process as binary-state dynamics on hypergraphs. We showed that our model has a rich behavior beyond discontinuous transitions. Notably, we have multistability and intermittency. We demonstrated that this phenomenology could be associated with community structures, where we might have multistability or intermittency by controlling the number or size of bridges between communities. Furthermore, we provided evidence that the observed transitions are hybrid. Our findings open new paths for research, ranging from physics, on the formal calculation of quantities of interest, to social sciences, where new experiments can be designed.
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Affiliation(s)
| | | | | | - Yamir Moreno
- CENTAI Institute, Turin, Italy
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain
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238
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Wei X, Dong S, Su Z, Tang L, Zhao P, Pan C, Wang F, Tang Y, Zhang W, Zhang X. NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers. ARXIV 2023:arXiv:2302.00104v2. [PMID: 36776814 PMCID: PMC9915719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST's capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches.
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Affiliation(s)
- Xinru Wei
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Shuai Dong
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Zhao Su
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Lili Tang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Pengfei Zhao
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Chunyu Pan
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
- Department of Gerontology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Weixiong Zhang
- Department of Health Technology and Informatics, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
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239
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Quantum computing reduces systemic risk in financial networks. Sci Rep 2023; 13:3990. [PMID: 36894579 PMCID: PMC9998608 DOI: 10.1038/s41598-023-30710-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/28/2023] [Indexed: 03/11/2023] Open
Abstract
In highly connected financial networks, the failure of a single institution can cascade into additional bank failures. This systemic risk can be mitigated by adjusting the loans, holding shares, and other liabilities connecting institutions in a way that prevents cascading of failures. We are approaching the systemic risk problem by attempting to optimize the connections between the institutions. In order to provide a more realistic simulation environment, we have incorporated nonlinear/discontinuous losses in the value of the banks. To address scalability challenges, we have developed a two-stage algorithm where the networks are partitioned into modules of highly interconnected banks and then the modules are individually optimized. We developed a new algorithms for classical and quantum partitioning for directed and weighed graphs (first stage) and a new methodology for solving Mixed Integer Linear Programming problems with constraints for the systemic risk context (second stage). We compare classical and quantum algorithms for the partitioning problem. Experimental results demonstrate that our two-stage optimization with quantum partitioning is more resilient to financial shocks, delays the cascade failure phase transition, and reduces the total number of failures at convergence under systemic risks with reduced time complexity.
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240
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Ouedraogo D, Souffrant M, Yao XQ, Hamelberg D, Gadda G. Non-active Site Residue in Loop L4 Alters Substrate Capture and Product Release in d-Arginine Dehydrogenase. Biochemistry 2023; 62:1070-1081. [PMID: 36795942 PMCID: PMC9996824 DOI: 10.1021/acs.biochem.2c00697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Numerous studies demonstrate that enzymes undergo multiple conformational changes during catalysis. The malleability of enzymes forms the basis for allosteric regulation: residues located far from the active site can exert long-range dynamical effects on the active site residues to modulate catalysis. The structure of Pseudomonas aeruginosa d-arginine dehydrogenase (PaDADH) shows four loops (L1, L2, L3, and L4) that span the substrate and the FAD-binding domains. Loop L4 comprises residues 329-336, spanning over the flavin cofactor. The I335 residue on loop L4 is ∼10 Å away from the active site and ∼3.8 Å from N(1)-C(2)═O atoms of the flavin. In this study, we used molecular dynamics and biochemical techniques to investigate the effect of the mutation of I335 to histidine on the catalytic function of PaDADH. Molecular dynamics showed that the conformational dynamics of PaDADH are shifted to a more closed conformation in the I335H variant. In agreement with an enzyme that samples more in a closed conformation, the kinetic data of the I335H variant showed a 40-fold decrease in the rate constant of substrate association (k1), a 340-fold reduction in the rate constant of substrate dissociation from the enzyme-substrate complex (k2), and a 24-fold decrease in the rate constant of product release (k5), compared to that of the wild-type. Surprisingly, the kinetic data are consistent with the mutation having a negligible effect on the reactivity of the flavin. Altogether, the data indicate that the residue at position 335 has a long-range dynamical effect on the catalytic function in PaDADH.
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Affiliation(s)
- Daniel Ouedraogo
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302, United States
| | - Michael Souffrant
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302, United States
| | - Xin-Qiu Yao
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302, United States
| | - Donald Hamelberg
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302, United States.,Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, Georgia 30302, United States.,Center for Biotechnology and Drug Design, Georgia State University, Atlanta, Georgia 30302, United States
| | - Giovanni Gadda
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302, United States.,Department of Biology, Georgia State University, Atlanta, Georgia 30302, United States.,Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, Georgia 30302, United States.,Center for Biotechnology and Drug Design, Georgia State University, Atlanta, Georgia 30302, United States
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241
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Jang YH, Han J, Kim J, Kim W, Woo KS, Kim J, Hwang CS. Graph Analysis with Multifunctional Self-Rectifying Memristive Crossbar Array. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2209503. [PMID: 36495559 DOI: 10.1002/adma.202209503] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Many big data have interconnected and dynamic graph structures growing over time. Analyzing these graphical data requires the hidden relationship between the nodes in the graphs to be identified, which has conventionally been achieved by finding the effective similarity. However, graphs are generally non-Euclidean, which does not allow finding it. In this study, the non-Euclidean graphs are mapped to a specific crossbar array (CBA) composed of self-rectifying memristors and metal cells at the diagonal positions. The sneak current, an intrinsic physical property in the CBA, allows for the identification of the similarity function. The sneak-current-based similarity function indicates the distance between the nodes, which can be used to predict the probability that unconnected nodes will be connected in the future, connectivity between communities, and neural connections in a brain. When all bit lines of the CBA are connected to the ground, the sneak current is suppressed, and the CBA can be used to search for adjacent nodes. This work demonstrates the physical calculation methods applied to various graphical problems using the CBA composed of the self-rectifying memristor based on the HfO2 switching layer. Moreover, such applications suffer less from the memristors' inherent issues related to their stochastic nature.
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Affiliation(s)
- Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jihun Kim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Woohyun Kim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Kyung Seok Woo
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jaehyun Kim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
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242
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Cervia LD, Shibue T, Borah AA, Gaeta B, He L, Leung L, Li N, Moyer SM, Shim BH, Dumont N, Gonzalez A, Bick NR, Kazachkova M, Dempster JM, Krill-Burger JM, Piccioni F, Udeshi ND, Olive ME, Carr SA, Root DE, McFarland JM, Vazquez F, Hahn WC. A Ubiquitination Cascade Regulating the Integrated Stress Response and Survival in Carcinomas. Cancer Discov 2023; 13:766-795. [PMID: 36576405 PMCID: PMC9975667 DOI: 10.1158/2159-8290.cd-22-1230] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/12/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
Systematic identification of signaling pathways required for the fitness of cancer cells will facilitate the development of new cancer therapies. We used gene essentiality measurements in 1,086 cancer cell lines to identify selective coessentiality modules and found that a ubiquitin ligase complex composed of UBA6, BIRC6, KCMF1, and UBR4 is required for the survival of a subset of epithelial tumors that exhibit a high degree of aneuploidy. Suppressing BIRC6 in cell lines that are dependent on this complex led to a substantial reduction in cell fitness in vitro and potent tumor regression in vivo. Mechanistically, BIRC6 suppression resulted in selective activation of the integrated stress response (ISR) by stabilization of the heme-regulated inhibitor, a direct ubiquitination target of the UBA6/BIRC6/KCMF1/UBR4 complex. These observations uncover a novel ubiquitination cascade that regulates ISR and highlight the potential of ISR activation as a new therapeutic strategy. SIGNIFICANCE We describe the identification of a heretofore unrecognized ubiquitin ligase complex that prevents the aberrant activation of the ISR in a subset of cancer cells. This provides a novel insight on the regulation of ISR and exposes a therapeutic opportunity to selectively eliminate these cancer cells. See related commentary Leli and Koumenis, p. 535. This article is highlighted in the In This Issue feature, p. 517.
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Affiliation(s)
- Lisa D. Cervia
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Tsukasa Shibue
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Ashir A. Borah
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Benjamin Gaeta
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Linh He
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Lisa Leung
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Naomi Li
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Sydney M. Moyer
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Brian H. Shim
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Nancy Dumont
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | | | - Nolan R. Bick
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | | | | | | | | | | | - Meagan E. Olive
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Steven A. Carr
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - David E. Root
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | | | | | - William C. Hahn
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
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243
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Zhou Z, Lu Y, Gu Z, Sun Q, Fang W, Yan W, Ku X, Liang Z, Hu G. HNRNPA2B1 as a potential therapeutic target for thymic epithelial tumor recurrence: An integrative network analysis. Comput Biol Med 2023; 155:106665. [PMID: 36791552 DOI: 10.1016/j.compbiomed.2023.106665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/31/2023] [Accepted: 02/10/2023] [Indexed: 02/13/2023]
Abstract
Thymic epithelial tumors (TETs) are rare malignant tumors, and the molecular mechanisms of both primary and recurrent TETs are poorly understood. Here we established comprehensive proteomic signatures of 15 tumors (5 recurrent and 10 non-recurrent) and 15 pair wised tumor adjacent normal tissues. We then proposed an integrative network approach for studying the proteomics data by constructing protein-protein interaction networks based on differentially expressed proteins and a machine learning-based score, followed by network modular analysis, functional enrichment annotation and shortest path inference analysis. Network modular analysis revealed that primary and recurrent TETs shared certain common molecular mechanisms, including a spliceosome module consisting of RNA splicing and RNA processing, but the recurrent TET was specifically related to the ribosome pathway. Applying the shortest path inference to the collected seed gene module identified that the ribonucleoprotein hnRNPA2B1 probably serves as a potential target for recurrent TET therapy. The drug repositioning combined molecular dynamics simulations suggested that the compound ergotamine could potentially act as a repurposing drug to treat recurrent TETs by targeting hnRNPA2B1. Our study demonstrates the value of integrative network analysis to understand proteotype robustness and its relationships with genotype, and provides hits for further research on cancer therapeutics.
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Affiliation(s)
- Ziyun Zhou
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou, 215123, China
| | - Yu Lu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, China
| | - Zhitao Gu
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Qiangling Sun
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China; Thoracic Cancer Institute, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Wentao Fang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Wei Yan
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xin Ku
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou, 215123, China; Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou, 215123, China.
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244
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Tahabi FM, Storey S, Luo X. SymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes using Graph Clustering. PROCEEDINGS OF THE ... SYMPOSIUM ON APPLIED COMPUTING. SYMPOSIUM ON APPLIED COMPUTING 2023; 2023:518-527. [PMID: 37720922 PMCID: PMC10504685 DOI: 10.1145/3555776.3577685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Patients with cancer or other chronic diseases often experience different symptoms before or after treatments. The symptoms could be physical, gastrointestinal, psychological, or cognitive (memory loss), or other types. Previous research focuses on understanding the individual symptoms or symptom correlations by collecting data through symptom surveys and using traditional statistical methods to analyze the symptoms, such as principal component analysis or factor analysis. This research proposes a computational system, SymptomGraph, to identify the symptom clusters in the narrative text of written clinical notes in electronic health records (EHR). SymptomGraph is developed to use a set of natural language processing (NLP) and artificial intelligence (AI) methods to first extract the clinician-documented symptoms from clinical notes. Then, a semantic symptom expression clustering method is used to discover a set of typical symptoms. A symptom graph is built based on the co-occurrences of the symptoms. Finally, a graph clustering algorithm is developed to discover the symptom clusters. Although SymptomGraph is applied to the narrative clinical notes, it can be adapted to analyze symptom survey data. We applied Symptom-Graph on a colorectal cancer patient with and without diabetes (Type 2) data set to detect the patient symptom clusters one year after the chemotherapy. Our results show that SymptomGraph can identify the typical symptom clusters of colorectal cancer patients' post-chemotherapy. The results also show that colorectal cancer patients with diabetes often show more symptoms of peripheral neuropathy, younger patients have mental dysfunctions of alcohol or tobacco abuse, and patients at later cancer stages show more memory loss symptoms. Our system can be generalized to extract and analyze symptom clusters of other chronic diseases or acute diseases like COVID-19.
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245
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Large-scale community detection based on core node and layer-by-layer label propagation. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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246
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Samanta R, Sanghvi N, Beckett D, Matysiak S. Emergence of allostery through reorganization of protein residue network architecture. J Chem Phys 2023; 158:085104. [PMID: 36859102 PMCID: PMC9974213 DOI: 10.1063/5.0136010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/03/2023] [Indexed: 02/09/2023] Open
Abstract
Despite more than a century of study, consensus on the molecular basis of allostery remains elusive. A comparison of allosteric and non-allosteric members of a protein family can shed light on this important regulatory mechanism, and the bacterial biotin protein ligases, which catalyze post-translational biotin addition, provide an ideal system for such comparison. While the Class I bacterial ligases only function as enzymes, the bifunctional Class II ligases use the same structural architecture for an additional transcription repression function. This additional function depends on allosterically activated homodimerization followed by DNA binding. In this work, we used experimental, computational network, and bioinformatics analyses to uncover distinguishing features that enable allostery in the Class II biotin protein ligases. Experimental studies of the Class II Escherichia coli protein indicate that catalytic site residues are critical for both catalysis and allostery. However, allostery also depends on amino acids that are more broadly distributed throughout the protein structure. Energy-based community network analysis of representative Class I and Class II proteins reveals distinct residue community architectures, interactions among the communities, and responses of the network to allosteric effector binding. Bioinformatics mutual information analyses of multiple sequence alignments indicate distinct networks of coevolving residues in the two protein families. The results support the role of divergent local residue community network structures both inside and outside of the conserved enzyme active site combined with distinct inter-community interactions as keys to the emergence of allostery in the Class II biotin protein ligases.
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Affiliation(s)
- Riya Samanta
- Fischell Department of Bioengineering, University of Maryland, College Park, Maryland 20742, USA
| | - Neel Sanghvi
- Fischell Department of Bioengineering, University of Maryland, College Park, Maryland 20742, USA
| | - Dorothy Beckett
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
| | - Silvina Matysiak
- Fischell Department of Bioengineering, University of Maryland, College Park, Maryland 20742, USA
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247
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Madan LK, Welsh CL, Kornev AP, Taylor SS. The "violin model": Looking at community networks for dynamic allostery. J Chem Phys 2023; 158:081001. [PMID: 36859094 PMCID: PMC9957607 DOI: 10.1063/5.0138175] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/03/2023] [Indexed: 02/09/2023] Open
Abstract
Allosteric regulation of proteins continues to be an engaging research topic for the scientific community. Models describing allosteric communication have evolved from focusing on conformation-based descriptors of protein structural changes to appreciating the role of internal protein dynamics as a mediator of allostery. Here, we explain a "violin model" for allostery as a contemporary method for approaching the Cooper-Dryden model based on redistribution of protein thermal fluctuations. Based on graph theory, the violin model makes use of community network analysis to functionally cluster correlated protein motions obtained from molecular dynamics simulations. This Review provides the theory and workflow of the methodology and explains the application of violin model to unravel the workings of protein kinase A.
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Affiliation(s)
- Lalima K. Madan
- Author to whom correspondence should be addressed: and . Telephone: 843.792.4525. Fax: 843.792.0481
| | - Colin L. Welsh
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, 173 Ashley Ave., Charleston, South Carolina 29425, USA
| | - Alexandr P. Kornev
- Department of Pharmacology, University of California San Diego, 9500 Gilman Drive, San Diego, California, 92093, USA
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248
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Mandal AS, Brem S, Suckling J. Brain network mapping and glioma pathophysiology. Brain Commun 2023; 5:fcad040. [PMID: 36895956 PMCID: PMC9989143 DOI: 10.1093/braincomms/fcad040] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/23/2022] [Accepted: 02/18/2023] [Indexed: 02/25/2023] Open
Abstract
Adult diffuse gliomas are among the most difficult brain disorders to treat in part due to a lack of clarity regarding the anatomical origins and mechanisms of migration of the tumours. While the importance of studying networks of glioma spread has been recognized for at least 80 years, the ability to carry out such investigations in humans has emerged only recently. Here, we comprehensively review the fields of brain network mapping and glioma biology to provide a primer for investigators interested in merging these areas of inquiry for the purposes of translational research. Specifically, we trace the historical development of ideas in both brain network mapping and glioma biology, highlighting studies that explore clinical applications of network neuroscience, cells-of-origin of diffuse glioma and glioma-neuronal interactions. We discuss recent research that has merged neuro-oncology and network neuroscience, finding that the spatial distribution patterns of gliomas follow intrinsic functional and structural brain networks. Ultimately, we call for more contributions from network neuroimaging to realize the translational potential of cancer neuroscience.
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Affiliation(s)
- Ayan S Mandal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Steven Brem
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, Philadelphia, PA 19104, USA
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
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249
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Pfaff ER, Madlock-Brown C, Baratta JM, Bhatia A, Davis H, Girvin A, Hill E, Kelly E, Kostka K, Loomba J, McMurry JA, Wong R, Bennett TD, Moffitt R, Chute CG, Haendel M. Coding long COVID: characterizing a new disease through an ICD-10 lens. BMC Med 2023; 21:58. [PMID: 36793086 PMCID: PMC9931566 DOI: 10.1186/s12916-023-02737-6] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 01/13/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.
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Affiliation(s)
- Emily R Pfaff
- University of North Carolina at Chapel Hill, Chapel Hill, USA.
| | | | - John M Baratta
- University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Abhishek Bhatia
- University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Hannah Davis
- Patient-Led Research Collaborative, New York, USA
| | | | | | - Elizabeth Kelly
- University of North Carolina at Chapel Hill, Chapel Hill, USA
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250
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Lubashevskiy V, Ozaydin SY, Ozaydin F. Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:365. [PMID: 36832730 PMCID: PMC9954822 DOI: 10.3390/e25020365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved version of the Link Entropy method. Using Louvain, Leiden and Walktrap methods, our proposal detects the number of communities in each iteration on discovering the communities. Running experiments on various benchmark networks, we show that our proposed method outperforms the Link Entropy method in quantifying edge significance. Considering also the computational complexities and possible defects, we conclude that Leiden or Louvain algorithms are the best choice for community number detection in quantifying edge significance. We also discuss designing a new algorithm for not only discovering the number of communities, but also computing the community membership uncertainties.
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Affiliation(s)
- Vasily Lubashevskiy
- Institute for International Strategy, Tokyo International University, 1-13-1 Matoba-kita, Kawagoe 350-1197, Saitama, Japan
| | - Seval Yurtcicek Ozaydin
- Department of Social and Human Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan
| | - Fatih Ozaydin
- Institute for International Strategy, Tokyo International University, 1-13-1 Matoba-kita, Kawagoe 350-1197, Saitama, Japan
- CERN, CH-1211 Geneva, Switzerland
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