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Suo X, Lan H, Zuo C, Chen L, Qin K, Li L, Kemp GJ, Wang S, Gong Q. Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder. Cereb Cortex 2024; 34:bhad436. [PMID: 37991275 DOI: 10.1093/cercor/bhad436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 11/23/2023] Open
Abstract
Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.
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Affiliation(s)
- Xueling Suo
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Huan Lan
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Chao Zuo
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Li Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Kun Qin
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, United States
| | - Lingjiang Li
- Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha 410008, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Song Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen 361000, China
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2
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Russell M, Aqi A, Saitou M, Gokcumen O, Masuda N. Gene communities in co-expression networks across different tissues. ArXiv 2023:arXiv:2305.12963v2. [PMID: 37292479 PMCID: PMC10246089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.
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Affiliation(s)
| | - Alber Aqi
- Department of Biological Sciences, University at Buffalo
| | - Marie Saitou
- Faculty of Biosciences, Norwegian University of Life Sciences
| | - Omer Gokcumen
- Department of Biological Sciences, University at Buffalo
| | - Naoki Masuda
- Department of Mathematics, University at Buffalo
- Institute for Artificial Intelligence and Data Science, University at Buffalo
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Fosch A, Aleta A, Moreno Y. Characterizing the role of human behavior in the effectiveness of contact-tracing applications. Front Public Health 2023; 11:1266989. [PMID: 38026393 PMCID: PMC10657191 DOI: 10.3389/fpubh.2023.1266989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Although numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behavior, like delays in adherence or heterogeneous compliance, are often disregarded. Methods To characterize the impact of human behavior on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialized to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioral features in peak incidence and maximal prevalence. Results The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers. Discussion The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesize that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.
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Affiliation(s)
- Ariadna Fosch
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- CENTAI Institute, Turin, Italy
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Alberto Aleta
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Yamir Moreno
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- CENTAI Institute, Turin, Italy
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
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Milano M, Agapito G, Cannataro M. An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics. Genes (Basel) 2023; 14:1915. [PMID: 37895264 PMCID: PMC10606656 DOI: 10.3390/genes14101915] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/26/2023] [Accepted: 10/05/2023] [Indexed: 10/29/2023] Open
Abstract
Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.
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Affiliation(s)
- Marianna Milano
- Department of Experimental and Clinical Medicine, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
| | - Giuseppe Agapito
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
- Department of Law, Economics and Social Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
- Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
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Fronhofer EA, Corenblit D, Deshpande JN, Govaert L, Huneman P, Viard F, Jarne P, Puijalon S. Eco-evolution from deep time to contemporary dynamics: The role of timescales and rate modulators. Ecol Lett 2023; 26 Suppl 1:S91-S108. [PMID: 37840024 DOI: 10.1111/ele.14222] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 10/17/2023]
Abstract
Eco-evolutionary dynamics, or eco-evolution for short, are often thought to involve rapid demography (ecology) and equally rapid heritable phenotypic changes (evolution) leading to novel, emergent system behaviours. We argue that this focus on contemporary dynamics is too narrow: Eco-evolution should be extended, first, beyond pure demography to include all environmental dimensions and, second, to include slow eco-evolution which unfolds over thousands or millions of years. This extension allows us to conceptualise biological systems as occupying a two-dimensional time space along axes that capture the speed of ecology and evolution. Using Hutchinson's analogy: Time is the 'theatre' in which ecology and evolution are two interacting 'players'. Eco-evolutionary systems are therefore dynamic: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can change the speed of ecology and evolution, and hence impact eco-evolution. Environmental change may synchronise the speed of ecology and evolution via these rate modulators, increasing the occurrence of eco-evolution and emergent system behaviours. This represents substantial challenges for prediction, especially in the context of global change. Our perspective attempts to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from today to deep time.
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Affiliation(s)
| | - Dov Corenblit
- GEOLAB, Université Clermont Auvergne, CNRS, Clermont-Ferrand, France
- Laboratoire écologie fonctionnelle et environnement, Université Paul Sabatier, CNRS, INPT, UPS, Toulouse, France
| | | | - Lynn Govaert
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Philippe Huneman
- Institut d'Histoire et de Philosophie des Sciences et des Techniques (CNRS/Université Paris I Sorbonne), Paris, France
| | - Frédérique Viard
- ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France
| | - Philippe Jarne
- CEFE, UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - IRD - EPHE, Montpellier Cedex 5, France
| | - Sara Puijalon
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, Villeurbanne, France
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Vitali A, Ruiz-Suarez S, Vázquez DP, Schleuning M, Rodríguez-Cabal MA, Sasal Y, Pilosof S. Invasive species modulate the structure and stability of a multilayer mutualistic network. Proc Biol Sci 2023; 290:20230132. [PMID: 37357855 DOI: 10.1098/rspb.2023.0132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023] Open
Abstract
Species interactions are critical for maintaining community structure and dynamics, but the effects of invasive species on multitrophic networks remain poorly understood. We leveraged an ongoing invasion scenario in Patagonia, Argentina, to explore how non-native ungulates affect multitrophic networks. Ungulates disrupt a hummingbird-mistletoe-marsupial keystone interaction, which alters community composition. We sampled pollination and seed dispersal interactions in intact and invaded sites. We constructed pollination and seed dispersal networks for each site, which we connected via shared plants. We calculated pollination-seed dispersal connectivity, identified clusters of highly connected species, and quantified species' roles in connecting species clusters. To link structural variation to stability, we quantified network tolerance to single random species removal (disturbance propagation) and sequential species removal (robustness) using a stochastic coextinction model. Ungulates reduced the connectivity between pollination and seed dispersal and produced fewer clusters with a skewed size distribution. Moreover, species shifted their structural role, fragmenting the network by reducing the 'bridges' among species clusters. These structural changes altered the dynamics of cascading effects, increasing disturbance propagation and reducing network robustness. Our results highlight invasive species' role in altering community structure and subsequent stability in multitrophic communities.
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Affiliation(s)
- Agustin Vitali
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Grupo de Ecología de Invasiones, INIBIOMA, Universidad Nacional del Comahue, CONICET. San Carlos de Bariloche, Río Negro, Argentina
| | - Sofía Ruiz-Suarez
- Grupo de Ecología de Invasiones, INIBIOMA, Universidad Nacional del Comahue, CONICET. San Carlos de Bariloche, Río Negro, Argentina
| | - Diego P Vázquez
- Instituto Argentino de Investigaciones de las Zonas Áridas, CONICET - Universidad Nacional de Cuyo, Mendoza, Argentina
- Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Matthias Schleuning
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
| | - Mariano A Rodríguez-Cabal
- Grupo de Ecología de Invasiones, INIBIOMA, Universidad Nacional del Comahue, CONICET. San Carlos de Bariloche, Río Negro, Argentina
- Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, Vermont 05405, USA
| | - Yamila Sasal
- Grupo de Ecología de Invasiones, INIBIOMA, Universidad Nacional del Comahue, CONICET. San Carlos de Bariloche, Río Negro, Argentina
| | - Shai Pilosof
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- The Goldman Sonnenfeldt School of Sustainability and Climate Change, Ben-Gurion University of the Negev, Be'er Sheva, Israel
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Sood M, Sridhar A, Eletreby R, Wu CW, Levin SA, Yağan O, Poor HV. Spreading processes with mutations over multilayer networks. Proc Natl Acad Sci U S A 2023; 120:e2302245120. [PMID: 37289806 DOI: 10.1073/pnas.2302245120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/22/2023] [Indexed: 06/10/2023] Open
Abstract
A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mutate in response to changing environments, especially caused by the increase in population immunity to existing strains, and the emergence of new pathogen strains poses a continued threat to public health. Further, in the light of differing transmission risks in different congregate settings (e.g., schools and offices), different mitigation strategies may need to be adopted to control the spread of infection. We analyze a multilayer multistrain model by simultaneously accounting for i) pathways for mutations in the pathogen leading to the emergence of new pathogen strains, and ii) differing transmission risks in different settings, modeled as network layers. Assuming complete cross-immunity among strains, namely, recovery from any infection prevents infection with any other (an assumption that will need to be relaxed to deal with COVID-19 or influenza), we derive the key epidemiological parameters for the multilayer multistrain framework. We demonstrate that reductions to existing models that discount heterogeneity in either the strain or the network layers may lead to incorrect predictions. Our results highlight that the impact of imposing/lifting mitigation measures concerning different contact network layers (e.g., school closures or work-from-home policies) should be evaluated in connection with their effect on the likelihood of the emergence of new strains.
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Affiliation(s)
- Mansi Sood
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Anirudh Sridhar
- Department of Electrical Engineering, Princeton University, Princeton, NJ 08544
| | | | - Chai Wah Wu
- Thomas J. Watson Research Center, IBM, Yorktown Heights, NY 10598
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
| | - Osman Yağan
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
- Software and Societal Systems Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - H Vincent Poor
- Department of Electrical Engineering, Princeton University, Princeton, NJ 08544
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8
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Windsor FM. Expanding network ecology in freshwater ecosystems. J Anim Ecol 2023. [PMID: 37264534 DOI: 10.1111/1365-2656.13947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/11/2023] [Indexed: 06/03/2023]
Abstract
Research in freshwater ecosystems has always had a strong focus on ecological interactions. The vast majority of studies, however, have investigated trophic interactions and food webs, overlooking a wider suite of non-trophic interactions (e.g. facilitation, competition, symbiosis and parasitism) and the ecological networks they form. Without a complete understanding of all potential interactions, ranging from mutualistic through to antagonistic, we may be missing important ecological processes with consequences for ecosystem assembly, structure and function. Ecological networks can be constructed at different scales, from genes to ecosystems, but also local to global, and as such there is significant opportunity to put them to work in freshwater research. To expand beyond food webs, we need to leverage technological and methodological advances and look to recent research in marine and terrestrial systems-which are far more advanced in terms of detecting, measuring and contextualising ecological interactions. Future studies should look to emerging technologies to aid in merging the wide range of ecological interactions in freshwater ecosystems into networks to advance our understanding and ultimately increase the efficacy of conservation, management, restoration and other applications.
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Affiliation(s)
- F M Windsor
- School of Biosciences, Cardiff University, Cardiff, UK
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9
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Wątroba P, Bródka P. Influence of Information Blocking on the Spread of Virus in Multilayer Networks. Entropy (Basel) 2023; 25:231. [PMID: 36832598 PMCID: PMC9955474 DOI: 10.3390/e25020231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
In this paper, we present the model of the interaction between the spread of disease and the spread of information about the disease in multilayer networks. Next, based on the characteristics of the SARS-CoV-2 virus pandemic, we evaluated the influence of information blocking on the virus spread. Our results show that blocking the spread of information affects the speed at which the epidemic peak appears in our society, and affects the number of infected individuals.
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Affiliation(s)
| | - Piotr Bródka
- Department of Artificial Intelligence, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
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10
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Qiu L, Su R, Wang Z. Research on China's Risk of Housing Price Contagion Based on Multilayer Networks. Entropy (Basel) 2022; 24:1305. [PMID: 36141192 PMCID: PMC9498244 DOI: 10.3390/e24091305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/03/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
The major issue in the evolution of housing prices is risk of housing price contagion. To model this issue, we constructed housing multilayer networks using transfer entropy, generalized variance decomposition, directed minimum spanning trees, and directed planar maximally filtered graph methods, as well as China's comprehensive indices of housing price and urban real housing prices from 2012 to 2021. The results of our housing multilayer networks show that the topological indices (degree, PageRank, eigenvector, etc.) of new first-tier cities (Tianjin, Qingdao, and Shenyang) rank higher than those of conventional first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzheng).
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11
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Qiu Z, Espinoza B, Vasconcelos VV, Chen C, Constantino SM, Crabtree SA, Yang L, Vullikanti A, Chen J, Weibull J, Basu K, Dixit A, Levin SA, Marathe MV. Understanding the coevolution of mask wearing and epidemics: A network perspective. Proc Natl Acad Sci U S A 2022; 119:e2123355119. [PMID: 35733262 DOI: 10.1073/pnas.2123355119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Nonpharmaceutical interventions (NPIs) such as mask wearing can be effective in mitigating the spread of infectious diseases. Therefore, understanding the behavioral dynamics of NPIs is critical for characterizing the dynamics of disease spread. Nevertheless, standard infection models tend to focus only on disease states, overlooking the dynamics of "beneficial contagions," e.g., compliance with NPIs. In this work, we investigate the concurrent spread of disease and mask-wearing behavior over multiplex networks. Our proposed framework captures both the competing and complementary relationships between the dueling contagion processes. Further, the model accounts for various behavioral mechanisms that influence mask wearing, such as peer pressure and fear of infection. Our results reveal that under the coupled disease-behavior dynamics, the attack rate of a disease-as a function of transition probability-exhibits a critical transition. Specifically, as the transmission probability exceeds a critical threshold, the attack rate decreases abruptly due to sustained mask-wearing responses. We empirically explore the causes of the critical transition and demonstrate the robustness of the observed phenomena. Our results highlight that without proper enforcement of NPIs, reductions in the disease transmission probability via other interventions may not be sufficient to reduce the final epidemic size.
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12
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Li Q, Zhao G, Feng M. Prisoner's Dilemma Game with Cooperation-Defection Dominance Strategies on Correlational Multilayer Networks. Entropy (Basel) 2022; 24:e24060822. [PMID: 35741542 PMCID: PMC9222612 DOI: 10.3390/e24060822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/03/2022] [Accepted: 06/11/2022] [Indexed: 11/16/2022]
Abstract
As multilayer networks are widely applied in modern society, numerous studies have shown the impact of a multilayer network structure and the network nature on the proportion of cooperators in the network. In this paper, we use Barabási-Albert scale-free networks (BA) and Watts and Strogatz networks (WS) to build a multilayer network structure, and we propose a new strategy-updating rule called "cooperation-defection dominance", which can be likened to dominant and recessive traits in biogenetics. With the newly constructed multilayer network structure and the strategy-updating rules, based on the simulation results, we find that in the BA-BA network, the cooperation dominance strategy can make the networks with different rs show a cooperative trend, while the defection dominance strategy only has an obvious effect on the network cooperation with a larger r. When the BA network is connected to the WS network, we find that the effect of strategy on the proportion of cooperators in the network decreases, and the main influencing factor is the structure of the network. In the three-layer network, the cooperation dominance strategy has a greater impact on the BA network, and the proportion of the cooperators is enhanced more than under the natural evolution strategy, but the promotion effect is still smaller than that of the two-layer BA network because of the WS network. Under the defection dominance strategy, the WS layer appears different from the first two strategies, and we conclude through simulation that when the payoff parameter is at the middle level, its cooperator proportion will be suppressed, and we deduce that the proportion of cooperators and defectors, as well as the payoff, play an important role.
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Affiliation(s)
- Qin Li
- School of Public Policy and Administration, Chongqing University, Chongqing 400044, China;
| | - Guopeng Zhao
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China;
| | - Minyu Feng
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China;
- Correspondence:
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Naro A, Pignolo L, Calabrò RS. Brain Network Organization Following Post-Stroke Neurorehabilitation. Int J Neural Syst 2022; 32:2250009. [PMID: 35139774 DOI: 10.1142/s0129065722500095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection models based on multiplex-multilayer network analysis (MMN) to explore functional network connectivity changes induced by robot-aided gait training (RAGT) using the Ekso, a wearable exoskeleton, and compared it to conventional overground gait training (COGT) in chronic stroke patients. We extracted the coreness of individual nodes at multiple locations in the brain from EEG recordings obtained before and after gait training in a resting state. We found that patients provided with RAGT achieved a greater motor function recovery than those receiving COGT. This difference in clinical outcome was paralleled by greater changes in connectivity patterns among different brain areas central to motor programming and execution, as well as a recruitment of other areas beyond the sensorimotor cortices and at multiple frequency ranges, contemporarily. The magnitude of these changes correlated with motor function recovery chances. Our data suggest that the use of RAGT as an add-on treatment to COGT may provide post-stroke patients with a greater modification of the functional brain network impairment following a stroke. This might have potential clinical implications if confirmed in large clinical trials.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy. Via Palermo, SS 113, Ctr. Casazza, 98124, Messina, Italy
| | - Loris Pignolo
- Sant'Anna Institute, Via Siris, 11, 88900 Crotone, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy. Via Palermo, SS 113, Ctr. Casazza, 98124, Messina, Italy
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14
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Stožer A, Šterk M, Paradiž Leitgeb E, Markovič R, Skelin Klemen M, Ellis CE, Križančić Bombek L, Dolenšek J, MacDonald PE, Gosak M. From Isles of Königsberg to Islets of Langerhans: Examining the Function of the Endocrine Pancreas Through Network Science. Front Endocrinol (Lausanne) 2022; 13:922640. [PMID: 35784543 PMCID: PMC9240343 DOI: 10.3389/fendo.2022.922640] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 05/16/2022] [Indexed: 12/12/2022] Open
Abstract
Islets of Langerhans are multicellular microorgans located in the pancreas that play a central role in whole-body energy homeostasis. Through secretion of insulin and other hormones they regulate postprandial storage and interprandial usage of energy-rich nutrients. In these clusters of hormone-secreting endocrine cells, intricate cell-cell communication is essential for proper function. Electrical coupling between the insulin-secreting beta cells through gap junctions composed of connexin36 is particularly important, as it provides the required, most important, basis for coordinated responses of the beta cell population. The increasing evidence that gap-junctional communication and its modulation are vital to well-regulated secretion of insulin has stimulated immense interest in how subpopulations of heterogeneous beta cells are functionally arranged throughout the islets and how they mediate intercellular signals. In the last decade, several novel techniques have been proposed to assess cooperation between cells in islets, including the prosperous combination of multicellular imaging and network science. In the present contribution, we review recent advances related to the application of complex network approaches to uncover the functional connectivity patterns among cells within the islets. We first provide an accessible introduction to the basic principles of network theory, enumerating the measures characterizing the intercellular interactions and quantifying the functional integration and segregation of a multicellular system. Then we describe methodological approaches to construct functional beta cell networks, point out possible pitfalls, and specify the functional implications of beta cell network examinations. We continue by highlighting the recent findings obtained through advanced multicellular imaging techniques supported by network-based analyses, giving special emphasis to the current developments in both mouse and human islets, as well as outlining challenges offered by the multilayer network formalism in exploring the collective activity of islet cell populations. Finally, we emphasize that the combination of these imaging techniques and network-based analyses does not only represent an innovative concept that can be used to describe and interpret the physiology of islets, but also provides fertile ground for delineating normal from pathological function and for quantifying the changes in islet communication networks associated with the development of diabetes mellitus.
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Affiliation(s)
- Andraž Stožer
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Marko Šterk
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Eva Paradiž Leitgeb
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Rene Markovič
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Institute of Mathematics and Physics, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Maša Skelin Klemen
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Cara E. Ellis
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | | | - Jurij Dolenšek
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Patrick E. MacDonald
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Marko Gosak
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- *Correspondence: Marko Gosak,
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15
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Anesiadis K, Provata A. Synchronization in Multiplex Leaky Integrate-and-Fire Networks With Nonlocal Interactions. Front Netw Physiol 2022; 2:910862. [PMID: 36926067 PMCID: PMC10013047 DOI: 10.3389/fnetp.2022.910862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022]
Abstract
We study synchronization phenomena in a multiplex network composed of two rings with identical Leaky Integrate-and-Fire (LIF) oscillators located on the nodes of the rings. Within each ring the LIF oscillators interact nonlocally, while between rings there are one-to-one inter-ring interactions. This structure is motivated by the observed connectivity between the two hemispheres of the brain: within each hemisphere the various brain regions interact with neighboring regions, while across hemispheres each region interacts, primarily, with the functionally homologous region. We consider both positive (excitatory) and negative (inhibitory) linking. We identify numerically various parameter regimes where the multiplex network develops coexistence of active and subthreshold domains, chimera states, solitary states, full coherence or incoherence. In particular, for weak inter-ring coupling (weak multiplexing) different synchronization patterns on the two rings are supported. These are stable and are obtained when the intra-ring coupling values are near the critical points separating qualitatively distinct synchronization regimes, e.g., between the travelling fronts regime and the chimera state one.
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Affiliation(s)
- K Anesiadis
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", Athens, Greece.,School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - A Provata
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", Athens, Greece
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16
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Felipe-Lucia MR, Guerrero AM, Alexander SM, Ashander J, Baggio JA, Barnes ML, Bodin Ö, Bonn A, Fortin MJ, Friedman RS, Gephart JA, Helmstedt KJ, Keyes AA, Kroetz K, Massol F, Pocock MJO, Sayles J, Thompson RM, Wood SA, Dee LE. Conceptualizing ecosystem services using social-ecological networks. Trends Ecol Evol 2021; 37:211-222. [PMID: 34969536 DOI: 10.1016/j.tree.2021.11.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/15/2021] [Accepted: 11/24/2021] [Indexed: 12/01/2022]
Abstract
Social-ecological networks (SENs) represent the complex relationships between ecological and social systems and are a useful tool for analyzing and managing ecosystem services. However, mainstreaming the application of SENs in ecosystem service research has been hindered by a lack of clarity about how to match research questions to ecosystem service conceptualizations in SEN (i.e., as nodes, links, attributes, or emergent properties). Building from different disciplines, we propose a typology to represent ecosystem service in SENs and identify opportunities and challenges of using SENs in ecosystem service research. Our typology provides guidance for this growing field to improve research design and increase the breadth of questions that can be addressed with SEN to understand human-nature interdependencies in a changing world.
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Affiliation(s)
- María R Felipe-Lucia
- Department Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany.
| | - Angela M Guerrero
- Stockholm Resilience Centre, Kräftriket 2B, 10691 Stockholm, Sweden; Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Steven M Alexander
- Environmental Change and Governance Group, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
| | - Jaime Ashander
- Resources for the Future, 1616 P St. NW, Washington, DC 20036, USA
| | - Jacopo A Baggio
- School of Politics, Security and International Affairs, National Center for Integrated Coastal Research, 4297 Andromeda Loop N, Orlando, FL 32816, USA
| | - Michele L Barnes
- ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, QLD, 4810, Australia
| | - Örjan Bodin
- Stockholm Resilience Centre, Kräftriket 2B, 10691 Stockholm, Sweden
| | - Aletta Bonn
- Department Ecosystem Services, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany; Institute of Biodiversity, Friedrich Schiller University Jena, Dornburgerstraße 159, 07743 Jena, Germany
| | - Marie-Josée Fortin
- Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada
| | - Rachel S Friedman
- Institute for Climate, Energy and Disaster Solutions, Australian National University College of Science, Building 141, Linnaeus Way, Acton, ACT, 2601, Australia
| | - Jessica A Gephart
- Department of Environmental Science, American University, 4400 Massachusetts Ave. NW, Washington, DC 20016, USA
| | - Kate J Helmstedt
- School of Mathematical Sciences, Queensland University of Technology, 2 George St., Brisbane, City, QLD, 4000, Australia
| | - Aislyn A Keyes
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Ramaley Biology, Boulder, CO 80302, USA
| | - Kailin Kroetz
- School of Sustainability, Arizona State University and Resources for the Future, PO Box 875502, Tempe, AZ 85287-5502, USA
| | - François Massol
- University of Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019, UMR 9017, Center for Infection and Immunity of Lille (CIIL), F-59000 Lille, France
| | | | - Jesse Sayles
- ORISE Postdoctoral Fellow Appointed with the US Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Atlantic Coastal Environmental Sciences Division, 27 Tarzwell Dr., Narragansett, RI 02882, USA
| | - Ross M Thompson
- Centre for Applied Water Science, Institute for Applied Ecology, University of Canberra, ACT, 2617, Australia
| | - Spencer A Wood
- College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA
| | - Laura E Dee
- School of Sustainability, Arizona State University and Resources for the Future, PO Box 875502, Tempe, AZ 85287-5502, USA
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17
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Alonso M, Turanzas J, Amaris H, Ledo AT. Cyber-Physical Vulnerability Assessment in Smart Grids Based on Multilayer Complex Networks. Sensors (Basel) 2021; 21:5826. [PMID: 34502722 DOI: 10.3390/s21175826] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/05/2021] [Accepted: 08/26/2021] [Indexed: 11/16/2022]
Abstract
In the last decade, the main attacks against smart grids have occurred in communication networks (ITs) causing the disconnection of physical equipment from power networks (OTs) and leading to electricity supply interruptions. To deal with the deficiencies presented in past studies, this paper addresses smart grids vulnerability assessment considering the smart grid as a cyber-physical heterogeneous interconnected system. The model of the cyber-physical system is composed of a physical power network model and the information and communication technology network model (ICT) both are interconnected and are interrelated by means of the communication and control equipment installed in the smart grid. This model highlights the hidden interdependencies between power and ICT networks and contains the interaction between both systems. To mimic the real nature of smart grids, the interconnected heterogeneous model is based on multilayer complex network theory and scale-free graph, where there is a one-to-many relationship between cyber and physical assets. Multilayer complex network theory centrality indexes are used to determine the interconnected heterogeneous system set of nodes criticality. The proposed methodology, which includes measurement, communication, and control equipment, has been tested on a standardized power network that is interconnected to the ICT network. Results demonstrate the model's effectiveness in detecting vulnerabilities in the interdependent cyber-physical system compared to traditional vulnerability assessments applied to power networks (OT).
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18
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Abstract
Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms, which do not account for the function of a protein in a particular phosphorylation state. Analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. We propose a method that combines shotgun phosphoproteomics data, protein-protein interactions, and functional annotations into a heterogeneous multilayer network. Phosphorylation sites are associated to potential functions using a random walk on the heterogeneous network (RWHN) algorithm. We validated our approach against a model of the MAPK/ERK pathway and functional annotations from PhosphoSitePlus and were able to associate differentially regulated sites on the same proteins to their previously described specific functions. We further tested the algorithm on three previously published datasets and were able to reproduce their experimentally validated conclusions and to associate phosphorylation sites with known functions based on their regulatory patterns. Our approach provides a refinement of commonly used analysis methods and accurately predicts context-specific functions for sites with similar phosphorylation profiles.
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Affiliation(s)
- Joanne Watson
- Division
of Evolution & Genomic Sciences, School of Biological Sciences,
Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, U.K.
- Division
of Molecular and Cellular Function, School of Biological Sciences,
Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, U.K.
| | - Jean-Marc Schwartz
- Division
of Evolution & Genomic Sciences, School of Biological Sciences,
Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, U.K.
| | - Chiara Francavilla
- Division
of Molecular and Cellular Function, School of Biological Sciences,
Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, U.K.
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19
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Abrahão FS, Wehmuth K, Zenil H, Ziviani A. Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks. Entropy (Basel) 2021; 23:835. [PMID: 34210065 DOI: 10.3390/e23070835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 05/18/2021] [Accepted: 05/21/2021] [Indexed: 11/18/2022]
Abstract
In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.
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20
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Miller KE, Polaszek A, Evans DM. A dearth of data: fitting parasitoids into ecological networks. Trends Parasitol 2021:S1471-4922(21)00092-1. [PMID: 34030983 DOI: 10.1016/j.pt.2021.04.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 11/22/2022]
Abstract
Studying parasitoids can provide insights into global diversity estimates, climate change impacts, and agroecosystem service provision. However, this potential remains largely untapped due to a lack of data on how parasitoids interact with other organisms. Ecological networks are a useful tool for studying and exploiting the impacts of parasitoids, but their construction is hindered by the magnitude of undescribed parasitoid species, a sparse knowledge of host ranges, and an under-representation of parasitoids within DNA-barcode databases (we estimate <5% have a barcode). Here, we advocate the use of DNA metabarcoding to construct the host-parasitoid component of multilayer networks. While the incorporation of parasitoids into network-based analyses has far ranging applications, we focus on its potential for assessing ecosystem service provision within agroecosystems.
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21
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Abstract
Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.
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Affiliation(s)
- Yuanyuan Lv
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
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22
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Corsi MC, Chavez M, Schwartz D, George N, Hugueville L, Kahn AE, Dupont S, Bassett DS, De Vico Fallani F. BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks. J Neural Eng 2021; 18. [PMID: 33725682 DOI: 10.1088/1741-2552/abef39] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 03/16/2021] [Indexed: 11/11/2022]
Abstract
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.
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Affiliation(s)
| | - Mario Chavez
- UMR-7225, CNRS, 47, boulevard de l'Hôpital, Paris, 75013, FRANCE
| | - Denis Schwartz
- INSERM, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Nathalie George
- UMR-7225, CNRS, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Laurent Hugueville
- Institut du Cerveau et de la Moelle Epiniere, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Ari E Kahn
- Department of Neuroscience, University of Pennsylvania, 210 S. 33rd Street 240 Skirkanich Hall, Philadelphia, Pennsylvania, 19104-6321, UNITED STATES
| | - Sophie Dupont
- Institut du Cerveau et de la Moelle Epiniere, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, 210 S. 33rd Street 240 Skirkanich Hall, USA, Philadelphia, Pennsylvania, 19104-6321, UNITED STATES
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23
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Abstract
The formalization of multilayer networks allows for new ways to measure sociality in complex social systems, including groups of animals. The same mathematical representation and methods are widely applicable across fields and study systems, and a network can represent drastically different types of data. As such, in order to apply analyses and interpret the results in a meaningful way the researcher must have a deep understanding of what their network is representing and what parts of it are being measured by a given analysis. Multilayer social networks can represent social structure with more detail than is often present in single layer networks, including multiple "types" of individuals, interactions, or relationships, and the extent to which these types are interdependent. Multilayer networks can also encompass a wider range of social scales, which can help overcome complications that are inherent to measuring sociality. In this paper, I dissect multilayer networks into the parts that correspond to different components of social structures. I then discuss common pitfalls to avoid across different stages of multilayer network analyses-some novel and some that always exist in social network analysis but are magnified in multi-layer representations. This paper serves as a primer for building a customized toolkit of multilayer network analyses, to probe components of social structure in animal social systems.
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Affiliation(s)
- Kelly R Finn
- Neukom Institute, Dartmouth College, Hanover, NH 03755, USA
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24
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Abstract
Introduction: How components of the distributed brain networks that support cognition participate in typical functioning remains a largely unanswered question. An important subgroup of regions in the larger network are connector hubs, which are areas that are highly connected to several other functionally specialized sets of regions, and are likely important for sensorimotor integration. The present study attempts to characterize connector hubs involved in typical expressive language functioning using a data-driven, multimodal, full multilayer magnetoencephalography (MEG) connectivity-based pipeline. Methods: Twelve adolescents, 16-18 years of age (five males), participated in this study. Participants underwent MEG scanning during a verb generation task. MEG and structural connectivity were calculated at the whole-brain level. Amplitude/amplitude coupling (AAC) was used to compute functional connections both within and between discrete frequency bins. AAC values were then multiplied by a binary structural connectivity matrix, and then entered into full multilayer network analysis. Initially, hubs were defined based on multilayer versatility and subsequently reranked by a novel measure called delta centrality on interconnectedness (DCI). DCI is defined as the percent change in interfrequency interconnectedness after removal of a hub. Results: We resolved regions that are important for between-frequency communication among other areas during expressive language, with several potential theoretical and clinical applications that can be generalized to other cognitive domains. Conclusion: Our multilayer, data-driven framework captures nonlinear connections that span across scales that are often missed in conventional analyses. The present study suggests that crucial hubs may be conduits for interfrequency communication between action and perception systems that are crucial for typical functioning.
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Affiliation(s)
- Brady J Williamson
- Department of Radiology, University of Cincinnati, Cincinnati, Ohio, USA
| | - Manlio De Domenico
- Fondazione Bruno Kessler, Center for Information and Communication Technology, Trento, Italy
| | - Darren S Kadis
- Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Physiology, University of Toronto, Toronto, Ontario, Canada
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25
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Font-Clos F, Spelta B, D'Agostino A, Donati F, Sarasso S, Canevini MP, Zapperi S, La Porta CAM. Information Optimized Multilayer Network Representation of High Density Electroencephalogram Recordings. Front Netw Physiol 2021; 1:746118. [PMID: 36925574 PMCID: PMC10013144 DOI: 10.3389/fnetp.2021.746118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022]
Abstract
High-density electroencephalography (hd-EEG) provides an accessible indirect method to record spatio-temporal brain activity with potential for disease diagnosis and monitoring. Due to their highly multidimensional nature, extracting useful information from hd-EEG recordings is a complex task. Network representations have been shown to provide an intuitive picture of the spatial connectivity underlying an electroencephalogram recording, although some information is lost in the projection. Here, we propose a method to construct multilayer network representations of hd-EEG recordings that maximize their information content and test it on sleep data recorded in individuals with mental health issues. We perform a series of statistical measurements on the multilayer networks obtained from patients and control subjects and detect significant differences between the groups in clustering coefficient, betwenness centrality, average shortest path length and parieto occipital edge presence. In particular, patients with a mood disorder display a increased edge presence in the parieto-occipital region with respect to healthy control subjects, indicating a highly correlated electrical activity in that region of the brain. We also show that multilayer networks at constant edge density perform better, since most network properties are correlated with the edge density itself which can act as a confounding factor. Our results show that it is possible to stratify patients through statistical measurements on a multilayer network representation of hd-EEG recordings. The analysis reveals that individuals with mental health issues display strongly correlated signals in the parieto-occipital region. Our methodology could be useful as a visualization and analysis tool for hd-EEG recordings in a variety of pathological conditions.
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Affiliation(s)
- Francesc Font-Clos
- Center for Complexity and Biosystems, Department of Physics, University of Milan, Milano, Italy
| | - Benedetta Spelta
- Center for Complexity and Biosystems, Department of Physics, University of Milan, Milano, Italy
| | - Armando D'Agostino
- Department of Health Sciences, University of Milan, Milano, Italy.,Department of Mental Health and Addiction, ASST Santi Paolo e Carlo, Milano, Italy
| | - Francesco Donati
- Department of Health Sciences, University of Milan, Milano, Italy.,Department of Mental Health and Addiction, ASST Santi Paolo e Carlo, Milano, Italy
| | - Simone Sarasso
- Department of Biomedical and Clinical Sciences 'Luigi Sacco', Milano, Italy
| | - Maria Paola Canevini
- Department of Health Sciences, University of Milan, Milano, Italy.,Department of Mental Health and Addiction, ASST Santi Paolo e Carlo, Milano, Italy
| | - Stefano Zapperi
- Center for Complexity and Biosystems, Department of Physics, University of Milan, Milano, Italy.,CNR-Consiglio Nazionale delle Ricerche, Istituto di Chimica della Materia Condensata e di Tecnologie per l'Energia, Milano, Italy
| | - Caterina A M La Porta
- Center for Complexity and Biosystems, Department of Environmental Science and Policy, University of Milan, Milano, Italy.,CNR-Consiglio Nazionale delle Ricerche, Istituto di Biofisica, Milano, Italy
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26
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Hammoud Z, Kramer F. Multipath: An R Package to Generate Integrated Reproducible Pathway Models. Biology (Basel) 2020; 9:483. [PMID: 33371258 DOI: 10.3390/biology9120483] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 12/16/2020] [Accepted: 12/19/2020] [Indexed: 11/16/2022]
Abstract
Biological pathway data integration has become a topic of interest in the past years. This interest originates essentially from the continuously increasing size of existing prior knowledge as well as from the many challenges scientists face when studying biological pathways. Multipath is a framework that aims at helping re-trace the use of specific pathway knowledge in specific publications, and easing the data integration of multiple pathway types and further influencing knowledge sources. Multipath thus helps scientists to increase the reproducibility of their code and analysis by allowing the integration of numerous data sources and documentation of their integration steps while doing so. In this paper, we present the package Multipath, and we describe how it can be used for data integration and tracking pathway modifications. We present a multilayer model built from the Wnt Pathway as a demonstration.
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27
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Nascimento DC, Pinto-Orellana MA, Leite JP, Edwards DJ, Louzada F, Santos TEG. BrainWave Nets: Are Sparse Dynamic Models Susceptible to Brain Manipulation Experimentation? Front Syst Neurosci 2020; 14:527757. [PMID: 33324178 PMCID: PMC7726475 DOI: 10.3389/fnsys.2020.527757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 08/19/2020] [Indexed: 11/15/2022] Open
Abstract
Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. This paper was motivated by a neuroscience experiment using EEG signals as the outcome of our established interventional protocol, a new method in neurorehabilitation toward developing a treatment for visual verticality disorder in post-stroke patients. To analyze the [complex outcome measure (EEG)] that reflects neural-network functioning and processing in more specific ways regarding traditional analyses, we make a comparison among sparse time series models (classic VAR, GLASSO, TSCGM, and TSCGM-modified with non-linear and iterative optimizations) combined with a graphical approach, such as a Dynamic Chain Graph Model (DCGM). These dynamic graphical models were useful in assessing the role of estimating the brain network structure and describing its causal relationship. In addition, the class of DCGM was able to visualize and compare experimental conditions and brain frequency domains [using finite impulse response (FIR) filter]. Moreover, using multilayer networks, the results corroborate with the susceptibility of sparse dynamic models, bypassing the false positives problem in estimation algorithms. We conclude that applying sparse dynamic models to EEG data may be useful for describing intervention-relocated changes in brain connectivity.
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Affiliation(s)
- Diego C Nascimento
- Institute of Mathematical Science and Computing, University of São Paulo, Sao Carlos, Brazil.,Departamento de Matemática, Universidad de Atacama de Chile, Copiapo, Chile
| | | | - Joao P Leite
- Ribeirao Preto Medical School, University of São Paulo, Ribeirao Preto, Brazil
| | - Dylan J Edwards
- Moss Rehabilitation Research Institute, Elkins Park, PA, United States.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Francisco Louzada
- Institute of Mathematical Science and Computing, University of São Paulo, Sao Carlos, Brazil
| | - Taiza E G Santos
- Ribeirao Preto Medical School, University of São Paulo, Ribeirao Preto, Brazil
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28
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Hervías-Parejo S, Tur C, Heleno R, Nogales M, Timóteo S, Traveset A. Species functional traits and abundance as drivers of multiplex ecological networks: first empirical quantification of inter-layer edge weights. Proc Biol Sci 2020; 287:20202127. [PMID: 33234084 DOI: 10.1098/rspb.2020.2127] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Many vertebrate species act as both plant pollinators and seed-dispersers, thus interconnecting these processes, particularly on islands. Ecological multilayer networks are a powerful tool to explore interdependencies between processes; however, quantifying the links between species engaging in different types of interactions (i.e. inter-layer edges) remains a great challenge. Here, we empirically measured inter-layer edge weights by quantifying the role of individually marked birds as both pollinators and seed-dispersers of Galápagos plant species over an entire year. Although most species (80%) engaged in both functions, we show that only a small proportion of individuals actually linked the two processes, highlighting the need to further consider intra-specific variability in individuals' functional roles. Furthermore, we found a high variation among species in linking both processes, i.e. some species contribute more than others to the modular organization of the multilayer network. Small and abundant species are particularly important for the cohesion of pollinator seed-dispersal networks, demonstrating the interplay between species traits and neutral processes structuring natural communities.
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Affiliation(s)
- S Hervías-Parejo
- Oceanography and Global Change Department. C/ Miquel Marqués 21, Institut Mediterrani d'Estudis Avançats IMEDEA (CSIC-UIB), E07190-Esporles, Mallorca, Balearic Islands, Spain
| | - C Tur
- Oceanography and Global Change Department. C/ Miquel Marqués 21, Institut Mediterrani d'Estudis Avançats IMEDEA (CSIC-UIB), E07190-Esporles, Mallorca, Balearic Islands, Spain
| | - R Heleno
- Department of Life Sciences, University of Coimbra, Centre for Functional Ecology, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - M Nogales
- Instituto de Productos Naturales y Agrobiologia (IPNA-CSIC), Island Ecology and Evolution Research Group. C/Astrofísico Fco. Sánchez 3, 38206 La Laguna, Tenerife, Canaries, Spain
| | - S Timóteo
- Department of Life Sciences, University of Coimbra, Centre for Functional Ecology, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - A Traveset
- Oceanography and Global Change Department. C/ Miquel Marqués 21, Institut Mediterrani d'Estudis Avançats IMEDEA (CSIC-UIB), E07190-Esporles, Mallorca, Balearic Islands, Spain
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29
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Kinsley AC, Rossi G, Silk MJ, VanderWaal K. Multilayer and Multiplex Networks: An Introduction to Their Use in Veterinary Epidemiology. Front Vet Sci 2020; 7:596. [PMID: 33088828 PMCID: PMC7500177 DOI: 10.3389/fvets.2020.00596] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 07/27/2020] [Indexed: 11/13/2022] Open
Abstract
Contact network analysis has become a vital tool for conceptualizing the spread of pathogens in animal populations and is particularly useful for understanding the implications of heterogeneity in contact patterns for transmission. However, the transmission of most pathogens cannot be simplified to a single mode of transmission and, thus, a single definition of contact. In addition, host-pathogen interactions occur in a community context, with many pathogens infecting multiple host species and most hosts being infected by multiple pathogens. Multilayer networks provide a formal framework for researching host-pathogen systems in which multiple types of transmission-relevant interactions, defined as network layers, can be analyzed jointly. Here, we provide an overview of multilayer network analysis and review applications of this novel method to epidemiological research questions. We then demonstrate the use of this technique to analyze heterogeneity in direct and indirect contact patterns amongst swine farms in the United States. When contact among nodes can be defined in multiple ways, a multilayer approach can advance our ability to use networks in epidemiological research by providing an improved approach for defining epidemiologically relevant groups of interacting nodes and changing the way we identify epidemiologically important individuals such as superspreaders.
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Affiliation(s)
- Amy C Kinsley
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| | - Gianluigi Rossi
- Roslin Institute and Royal (Dick) School of Veterinary Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Matthew J Silk
- Centre for Ecology and Conservation, University of Exeter Penryn Campus, Penryn, United Kingdom.,Environment and Sustainability Institute, University of Exeter, Penryn, United Kingdom
| | - Kimberly VanderWaal
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
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30
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Bonnell TR, Vilette C, Young C, Henzi SP, Barrett L. Formidable females redux: male social integration into female networks and the value of dynamic multilayer networks. Curr Zool 2020; 67:49-57. [PMID: 33654490 PMCID: PMC7901752 DOI: 10.1093/cz/zoaa041] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 07/23/2020] [Indexed: 12/21/2022] Open
Abstract
The development of multilayer network techniques is a boon for researchers who wish to understand how different interaction layers might influence each other, and how these in turn might influence group dynamics. Here, we investigate how integration between male and female grooming and aggression interaction networks influences male power trajectories in vervet monkeys Chlorocebus pygerythrus. Our previous analyses of this phenomenon used a monolayer approach, and our aim here is to extend these analyses using a dynamic multilayer approach. To do so, we constructed a temporal series of male and female interaction layers. We then used a multivariate multilevel autoregression model to compare cross-lagged associations between a male’s centrality in the female grooming layer and changes in male Elo ratings. Our results confirmed our original findings: changes in male centrality within the female grooming network were weakly but positively tied to changes in their Elo ratings. However, the multilayer network approach offered additional insights into this social process, identifying how changes in a male’s centrality cascade through the other network layers. This dynamic view indicates that the changes in Elo ratings are likely to be short-lived, but that male centrality within the female network had a much stronger impact throughout the multilayer network as a whole, especially on reducing intermale aggression (i.e., aggression directed by males toward other males). We suggest that multilayer social network approaches can take advantage of increased amounts of social data that are more commonly collected these days, using a variety of methods. Such data are inherently multilevel and multilayered, and thus offer the ability to quantify more precisely the dynamics of animal social behaviors.
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Affiliation(s)
- Tyler R Bonnell
- Department of Psychology, University of Lethbridge, 4401 University Drive, Lethbridge, T1K 3M4, Canada.,Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Florida, Gauteng, South Africa
| | - Chloé Vilette
- Department of Psychology, University of Lethbridge, 4401 University Drive, Lethbridge, T1K 3M4, Canada.,Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Florida, Gauteng, South Africa
| | - Christopher Young
- Department of Psychology, University of Lethbridge, 4401 University Drive, Lethbridge, T1K 3M4, Canada.,Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Florida, Gauteng, South Africa.,Endocrine Research Laboratory, Mammal Research Institute, Faculty of Natural and Agricultural Science, University of Pretoria, Pretoria, South Africa
| | - Stephanus Peter Henzi
- Department of Psychology, University of Lethbridge, 4401 University Drive, Lethbridge, T1K 3M4, Canada.,Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Florida, Gauteng, South Africa
| | - Louise Barrett
- Department of Psychology, University of Lethbridge, 4401 University Drive, Lethbridge, T1K 3M4, Canada.,Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Florida, Gauteng, South Africa
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31
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Evans JC, Lindholm AK, König B. Long-term overlap of social and genetic structure in free-ranging house mice reveals dynamic seasonal and group size effects. Curr Zool 2020; 67:59-69. [PMID: 33654491 PMCID: PMC7901755 DOI: 10.1093/cz/zoaa030] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/10/2020] [Indexed: 12/02/2022] Open
Abstract
Associating with relatives in social groups can bring benefits such as reduced risk of aggression and increased likelihood of cooperation. Competition among relatives over limited resources, on the other hand, can induce individuals to alter their patterns of association. Population density might further affect the costs and benefits of associating with relatives by altering resource competition or by changing the structure of social groups; preventing easy association with relatives. Consequently, the overlap between genetic and social structure is expected to decrease with increasing population size, as well as during times of increased breeding activity. Here, we use multi-layer network techniques to quantify the similarity between long-term, high resolution genetic, and behavioral data from a large population of free-ranging house mice (Mus musculus domesticus), studied over 10 years. We infer how the benefit of associating with genetically similar individuals might fluctuate in relation to breeding behavior and environmental conditions. We found a clear seasonal effect, with decreased overlap between social and genetic structure during summer months, characterized by high temperatures and high breeding activity. Though the effect of overall population size was relatively weak, we found a clear decrease in the overlap between genetic similarity and social associations within larger groups. As well as longer-term within-group changes, these results reveal population-wide short-term shifts in how individuals associate with relatives. Our study suggests that resource competition modifies the trade-off between the costs and benefits of interacting with relatives.
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Affiliation(s)
- Julian C Evans
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
- Address correspondence to Julian C. Evans. E-mail:
| | - Anna K Lindholm
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
| | - Barbara König
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
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32
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Hernández-Lemus E. On a Class of Tensor Markov Fields. Entropy (Basel) 2020; 22:E451. [PMID: 33286225 DOI: 10.3390/e22040451] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 12/18/2022]
Abstract
Here, we introduce a class of Tensor Markov Fields intended as probabilistic graphical models from random variables spanned over multiplexed contexts. These fields are an extension of Markov Random Fields for tensor-valued random variables. By extending the results of Dobruschin, Hammersley and Clifford to such tensor valued fields, we proved that tensor Markov fields are indeed Gibbs fields, whenever strictly positive probability measures are considered. Hence, there is a direct relationship with many results from theoretical statistical mechanics. We showed how this class of Markov fields it can be built based on a statistical dependency structures inferred on information theoretical grounds over empirical data. Thus, aside from purely theoretical interest, the Tensor Markov Fields described here may be useful for mathematical modeling and data analysis due to their intrinsic simplicity and generality.
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33
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Montiglio PO, Gotanda KM, Kratochwil CF, Laskowski KL, Farine DR. Hierarchically embedded interaction networks represent a missing link in the study of behavioral and community ecology. Behav Ecol 2020; 31:279-286. [PMID: 32210523 PMCID: PMC7083094 DOI: 10.1093/beheco/arz168] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 08/05/2019] [Accepted: 08/29/2019] [Indexed: 12/19/2022] Open
Abstract
Because genes and phenotypes are embedded within individuals, and individuals within populations, interactions within one level of biological organization are inherently linked to interactors at others. Here, we expand the network paradigm to consider that nodes can be embedded within other nodes, and connections (edges) between nodes at one level of organization form "bridges" for connections between nodes embedded within them. Such hierarchically embedded networks highlight two central properties of biological systems: 1) processes occurring across multiple levels of organization shape connections among biological units at any given level of organization and 2) ecological effects occurring at a given level of organization can propagate up or down to additional levels. Explicitly considering the embedded structure of evolutionary and ecological networks can capture otherwise hidden feedbacks and generate new insights into key biological phenomena, ultimately promoting a broader understanding of interactions in evolutionary theory.
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Affiliation(s)
- P O Montiglio
- Département des Sciences Biologiques, Université du Québec à Montréal, Succursale Centre-ville, Montréal, Québec, Canada
| | - K M Gotanda
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - C F Kratochwil
- Chair in Zoology and Evolutionary Biology, Department of Biology, University of Konstanz, Konstanz, Germany
- Zukunftskolleg, University of Konstanz, Konstanz, Konstanz, Germany
| | - K L Laskowski
- Department of Biology, & Ecology of Fishes, Leibniz-Institute of Freshwater Ecology & Inland Fisheries, Berlin, Germany
- Department of Evolution and Ecology, University of California, Davis, Davis, CA, USA
| | - D R Farine
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, Universitätsstraße 10, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Edward Grey Institute of Ornithology, Department of Zoology, University of Oxford, Oxford, UK
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34
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Labib NS, Danoy G, Musial J, Brust MR, Bouvry P. Internet of Unmanned Aerial Vehicles-A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management. Sensors (Basel) 2019; 19:E4779. [PMID: 31684133 DOI: 10.3390/s19214779] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 10/27/2019] [Accepted: 10/31/2019] [Indexed: 12/24/2022]
Abstract
The rapid adoption of Internet of Things (IoT) has encouraged the integration of new connected devices such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous network. UAVs promise a pragmatic solution to the limitations of existing terrestrial IoT infrastructure as well as bring new means of delivering IoT services through a wide range of applications. Owning to their potential, UAVs are expected to soon dominate the low-altitude airspace over populated cities. This introduces new research challenges such as the safe management of UAVs operation under high traffic demands. This paper proposes a novel way of structuring the uncontrolled, low-altitude airspace, with the aim of addressing the complex problem of UAV traffic management at an abstract level. The work, hence, introduces a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulation results using three UAV traffic management heuristics.
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35
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Wu M, He S, Zhang Y, Chen J, Sun Y, Liu YY, Zhang J, Poor HV. A tensor-based framework for studying eigenvector multicentrality in multilayer networks. Proc Natl Acad Sci U S A 2019; 116:15407-15413. [PMID: 31315978 PMCID: PMC6681706 DOI: 10.1073/pnas.1801378116] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Centrality is widely recognized as one of the most critical measures to provide insight into the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.
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Affiliation(s)
- Mincheng Wu
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Shibo He
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China;
| | - Yongtao Zhang
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Jiming Chen
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Youxian Sun
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
- Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115
| | - Junshan Zhang
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287
| | - H Vincent Poor
- Department of Electrical Engineering, Princeton University, Princeton, NJ 08544
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36
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Hackett TD, Sauve AMC, Davies N, Montoya D, Tylianakis JM, Memmott J. Reshaping our understanding of species' roles in landscape-scale networks. Ecol Lett 2019; 22:1367-1377. [PMID: 31207056 DOI: 10.1111/ele.13292] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/15/2018] [Accepted: 05/03/2019] [Indexed: 01/13/2023]
Abstract
In network ecology, landscape-scale processes are often overlooked, yet there is increasing evidence that species and interactions spill over between habitats, calling for further study of interhabitat dependencies. Here, we investigate how species connect a mosaic of habitats based on the spatial variation of their mutualistic and antagonistic interactions using two multilayer networks, combining pollination, herbivory and parasitism in the UK and New Zealand. Developing novel methods of network analysis for landscape-scale ecological networks, we discovered that few plant and pollinator species acted as connectors or hubs, both within and among habitats, whereas herbivores and parasitoids typically have more peripheral network roles. Insect species' roles depend on factors other than just the abundance of taxa in the lower trophic level, exemplified by larger Hymenoptera connecting networks of different habitats and insects relying on different resources across different habitats. Our findings provide a broader perspective for landscape-scale management and ecological community conservation.
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Affiliation(s)
- Talya D Hackett
- Life Sciences Building, University of Bristol, Bristol, BS81TQ, UK.,Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK
| | - Alix M C Sauve
- Life Sciences Building, University of Bristol, Bristol, BS81TQ, UK.,Department of Computer Science, University of Bristol, Bristol, BS8 1UB, UK.,Integrative and Theoretical Ecology Group, LabEx COTE, University of Bordeaux, 33615, Pessac, France
| | - Nancy Davies
- Life Sciences Building, University of Bristol, Bristol, BS81TQ, UK
| | - Daniel Montoya
- Life Sciences Building, University of Bristol, Bristol, BS81TQ, UK.,Centre for Biodiversity Theory and Modelling, Theoretical and Experimental Ecology Station, CNRS, 2 route du CNRS, 09200, Moulis, France
| | - Jason M Tylianakis
- Bioprotection Centre and Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Private bag 4800, Christchurch, New Zealand
| | - Jane Memmott
- Life Sciences Building, University of Bristol, Bristol, BS81TQ, UK
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37
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Formichini M, Cimini G, Pugliese E, Gabrielli A. Influence of Technological Innovations on Industrial Production: A Motif Analysis on the Multilayer Network. Entropy (Basel) 2019; 21:e21020126. [PMID: 33266842 PMCID: PMC7514614 DOI: 10.3390/e21020126] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 01/14/2019] [Accepted: 01/28/2019] [Indexed: 11/16/2022]
Abstract
In this work we aim at identifying combinations of technological advancements that reveal the presence of local capabilities for a given industrial production. To this end, we generated a multilayer network using country-level patent and trade data, and performed motif-based analysis on this network using a statistical-validation approach derived from maximum-entropy arguments. We show that in many cases the signal far exceeds the noise, providing robust evidence of synergies between different technologies that can lead to a competitive advantage in specific markets. Our results can be highly useful for policymakers to inform industrial and innovation policies.
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Affiliation(s)
- Martina Formichini
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale A. Moro 2, 00185 Rome, Italy
| | - Giulio Cimini
- IMT School for Advanced Studies, Piazza S. Ponziano 6, 55100 Lucca, Italy
- Istituto dei Sistemi Complessi (ISC), Consiglio Nazionale delle Ricerche (CNR), c/o Dipartimento di Fisica, Sapienza Università di Roma, Piazzale A. Moro 2, 00185 Rome, Italy
| | - Emanuele Pugliese
- Joint Research Centre (JRC), European Commission (EC), Edificio Expo, Calle Inca Garcilaso 3, 41092 Seville, Spain
| | - Andrea Gabrielli
- Istituto dei Sistemi Complessi (ISC), Consiglio Nazionale delle Ricerche (CNR), c/o Dipartimento di Fisica, Sapienza Università di Roma, Piazzale A. Moro 2, 00185 Rome, Italy
- Correspondence:
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38
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Yu L, Yao S, Gao L, Zha Y. Conserved Disease Modules Extracted From Multilayer Heterogeneous Disease and Gene Networks for Understanding Disease Mechanisms and Predicting Disease Treatments. Front Genet 2019; 9:745. [PMID: 30713550 PMCID: PMC6346701 DOI: 10.3389/fgene.2018.00745] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 12/27/2018] [Indexed: 12/29/2022] Open
Abstract
Disease relationship studies for understanding the pathogenesis of complex diseases, diagnosis, prognosis, and drug development are important. Traditional approaches consider one type of disease data or aggregating multiple types of disease data into a single network, which results in important temporal- or context-related information loss and may distort the actual organization. Therefore, it is necessary to apply multilayer network model to consider multiple types of relationships between diseases and the important interplays between different relationships. Further, modules extracted from multilayer networks are smaller and have more overlap that better capture the actual organization. Here, we constructed a weighted four-layer disease-disease similarity network to characterize the associations at different levels between diseases. Then, a tensor-based computational framework was used to extract Conserved Disease Modules (CDMs) from the four-layer disease network. After filtering, nine significant CDMs were reserved. The statistical significance test proved the significance of the nine CDMs. Comparing with modules got from four single layer networks, CMDs are smaller, better represent the actual relationships, and contain potential disease-disease relationships. KEGG pathways enrichment analysis and literature mining further contributed to confirm that these CDMs are highly reliable. Furthermore, the CDMs can be applied to predict potential drugs for diseases. The molecular docking techniques were used to provide the direct evidence for drugs to treat related disease. Taking Rheumatoid Arthritis (RA) as a case, we found its three potential drugs Carvedilol, Metoprolol, and Ramipril. And many studies have pointed out that Carvedilol and Ramipril have an effect on RA. Overall, the CMDs extracted from multilayer networks provide us with an impressive understanding disease mechanisms from the perspective of multi-layer network and also provide an effective way to predict potential drugs for diseases based on its neighbors in a same CDM.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Shunyu Yao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yunhong Zha
- Department of Neurology, Institute of Neural Regeneration and Repair, Three Gorges University College of Medicine, The First Hospital of Yichang, Yichang, China
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Sahneh FD, Vajdi A, Melander J, Scoglio CM. Contact Adaption During Epidemics: A Multilayer Network Formulation Approach. IEEE Trans Netw Sci Eng 2019; 6:16-30. [PMID: 34192124 PMCID: PMC7309295 DOI: 10.1109/tnse.2017.2770091] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 10/18/2017] [Accepted: 10/28/2017] [Indexed: 05/29/2023]
Abstract
People change their physical contacts as a preventive response to infectious disease propagations. Yet, only a few mathematical models consider the coupled dynamics of the disease propagation and the contact adaptation process. This paper presents a model where each agent has a default contact neighborhood set, and switches to a different contact set once she becomes alert about infection among her default contacts. Since each agent can adopt either of two possible neighborhood sets, the overall contact network switches among [Formula: see text] possible configurations. Notably, a two-layer network representation can fully model the underlying adaptive, state-dependent contact network. Contact adaptation influences the size of the disease prevalence and the epidemic threshold-a characteristic measure of a contact network robustness against epidemics-in a nonlinear fashion. Particularly, the epidemic threshold for the presented adaptive contact network belongs to the solution of a nonlinear Perron-Frobenius (NPF) problem, which does not depend on the contact adaptation rate monotonically. Furthermore, the network adaptation model predicts a counter-intuitive scenario where adaptively changing contacts may adversely lead to lower network robustness against epidemic spreading if the contact adaptation is not fast enough. An original result for a class of NPF problems facilitate the analytical developments in this paper.
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Affiliation(s)
- Faryad Darabi Sahneh
- Department of Electrical and Computer EngineeringKansas State UniversityManhattanKS66506
| | - Aram Vajdi
- Department of Electrical and Computer EngineeringKansas State UniversityManhattanKS66506
| | - Joshua Melander
- Department of Electrical and Computer EngineeringKansas State UniversityManhattanKS66506
| | - Caterina M. Scoglio
- Department of Electrical and Computer EngineeringKansas State UniversityManhattanKS66506
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Abstract
The human brain comprises multiple distinct and highly complex networks responsible for specific functions. Most of our current understanding about functional brain networks comes from studies treating the brain as a static entity, and the spatiotemporal configuration of brain networks remains poorly understood. Using a multilayer network model, we show that brain regions, particularly the lateral frontal and parietal brain areas, transit between different network configurations at a high rate (i.e., have high network switching). This network switching rate predicts performance of higher-order cognitive functions including working memory, planning, and reasoning. In other words, efficient brain network switching appears to be an important aspect of optimal brain function. Large-scale brain dynamics are characterized by repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood, and whether switching between these states is important for behavior has been little studied. Our aim was to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved fMRI connectivity in 1,003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data were used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching, which we define as the rate at which each brain region transits between different networks. We found (i) an inverse relationship between network switching and connectivity dynamics, where the latter was defined in terms of time-resolved fMRI connections with variance in time that significantly exceeded phase-randomized surrogate data; (ii) brain connectivity was lower during intervals of network switching; (iii) brain areas with frequent network switching had greater temporal complexity; (iv) brain areas with high network switching were located in association cortices; and (v) using cross-validated elastic net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning, and amount of sleep. Our findings shed light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.
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Buldú JM, Busquets J, Martínez JH, Herrera-Diestra JL, Echegoyen I, Galeano J, Luque J. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Front Psychol 2018; 9:1900. [PMID: 30349500 PMCID: PMC6186964 DOI: 10.3389/fpsyg.2018.01900] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 09/18/2018] [Indexed: 11/21/2022] Open
Affiliation(s)
- Javier M. Buldú
- Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
- Complex Systems Group and GISC, Universidad Rey Juan Carlos, Móstoles, Spain
- Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain
| | | | - Johann H. Martínez
- Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain
- INSERM-UM1127, Institute du Cerveau et de la Moelle Épinière. H. Salpêtrière, Paris, France
| | | | - Ignacio Echegoyen
- Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
- Complex Systems Group and GISC, Universidad Rey Juan Carlos, Móstoles, Spain
- Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain
| | - Javier Galeano
- Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Madrid, Spain
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Battiston F, Guillon J, Chavez M, Latora V, De Vico Fallani F. Multiplex core-periphery organization of the human connectome. J R Soc Interface 2018; 15:20180514. [PMID: 30209045 PMCID: PMC6170773 DOI: 10.1098/rsif.2018.0514] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 08/16/2018] [Indexed: 01/16/2023] Open
Abstract
What is the core of the human brain is a fundamental question that has been mainly addressed by studying the anatomical connections between differently specialized areas, thus neglecting the possible contributions from their functional interactions. While many methods are available to identify the core of a network when connections between nodes are all of the same type, a principled approach to define the core when multiple types of connectivity are allowed is still lacking. Here, we introduce a general framework to define and extract the core-periphery structure of multi-layer networks by explicitly taking into account the connectivity patterns at each layer. We first validate our algorithm on synthetic networks of different size and density, and with tunable overlap between the cores at different layers. We then use our method to merge information from structural and functional brain networks, obtaining in this way an integrated description of the core of the human connectome. Results confirm the role of the main known cortical and subcortical hubs, but also suggest the presence of new areas in the sensori-motor cortex that are crucial for intrinsic brain functioning. Taken together these findings provide fresh evidence on a fundamental question in modern neuroscience and offer new opportunities to explore the mesoscale properties of multimodal brain networks.
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Affiliation(s)
- Federico Battiston
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
| | - Jeremy Guillon
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
| | - Mario Chavez
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
- Dipartimento di Fisica ed Astronomia, Università di Catania and INFN, 95123 Catania, Italy
| | - Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
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Silk MJ, Finn KR, Porter MA, Pinter-Wollman N. Can Multilayer Networks Advance Animal Behavior Research? Trends Ecol Evol 2018; 33:376-378. [PMID: 29685580 DOI: 10.1016/j.tree.2018.03.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 03/23/2018] [Accepted: 03/27/2018] [Indexed: 10/17/2022]
Abstract
Interactions among individual animals - and between these individuals and their environment - yield complex, multifaceted systems. The development of multilayer network analysis offers a promising new approach for studying animal social behavior and its relation to eco-evolutionary dynamics.
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Affiliation(s)
- Matthew J Silk
- Environment and Sustainability Institute, University of Exeter, Exeter, UK
| | - Kelly R Finn
- Animal Behavior Graduate Group, University of California, Davis, Davis, CA, USA
| | - Mason A Porter
- Department of Mathematics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noa Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA.
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Braun U, Schaefer A, Betzel RF, Tost H, Meyer-Lindenberg A, Bassett DS. From Maps to Multi-dimensional Network Mechanisms of Mental Disorders. Neuron 2018; 97:14-31. [PMID: 29301099 PMCID: PMC5757246 DOI: 10.1016/j.neuron.2017.11.007] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 10/31/2017] [Accepted: 11/01/2017] [Indexed: 12/31/2022]
Abstract
The development of advanced neuroimaging techniques and their deployment in large cohorts has enabled an assessment of functional and structural brain network architecture at an unprecedented level of detail. Across many temporal and spatial scales, network neuroscience has emerged as a central focus of intellectual efforts, seeking meaningful descriptions of brain networks and explanatory sets of network features that underlie circuit function in health and dysfunction in disease. However, the tools of network science commonly deployed provide insight into brain function at a fundamentally descriptive level, often failing to identify (patho-)physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Here we describe recently developed techniques stemming from advances in complex systems and network science that have the potential to overcome this limitation, thereby contributing mechanistic insights into neuroanatomy, functional dynamics, and pathology. Finally, we build on the Research Domain Criteria framework, highlighting the notion that mental illnesses can be conceptualized as dysfunctions of neural circuitry present across conventional diagnostic boundaries, to sketch how network-based methods can be combined with pharmacological, intermediate phenotype, genetic, and magnetic stimulation studies to probe mechanisms of psychopathology.
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Affiliation(s)
- Urs Braun
- Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, 68159 Mannheim, Germany
| | - Axel Schaefer
- Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, 68159 Mannheim, Germany
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Heike Tost
- Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, 68159 Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, 68159 Mannheim, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Abstract
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition—i.e., the parameter-space domain where it has the largest modularity relative to the input set—discarding partitions with empty domains to obtain the subset of partitions that are “admissible” candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.
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Affiliation(s)
- William H. Weir
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA
- Correspondence:
| | - Scott Emmons
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ryan Gibson
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Peter J. Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA
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Abstract
Understanding how the human brain is structured, and how its architecture is related to function, is of paramount importance for a variety of applications, including but not limited to new ways to prevent, deal with, and cure brain diseases, such as Alzheimer's or Parkinson's, and psychiatric disorders, such as schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude toward interdisciplinary approaches involving computer science, mathematics, and physics, are fostering interesting results from computational neuroscience that are quite often based on the analysis of complex network representation of the human brain. In recent years, this representation experienced a theoretical and computational revolution that is breaching neuroscience, allowing us to cope with the increasing complexity of the human brain across multiple scales and in multiple dimensions and to model structural and functional connectivity from new perspectives, often combined with each other. In this work, we will review the main achievements obtained from interdisciplinary research based on magnetic resonance imaging and establish de facto, the birth of multilayer network analysis and modeling of the human brain.
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Affiliation(s)
- Manlio De Domenico
- Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Av.da Països Catalans, 26, 43004 Tarragona, Spain
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Castellani GC, Menichetti G, Garagnani P, Giulia Bacalini M, Pirazzini C, Franceschi C, Collino S, Sala C, Remondini D, Giampieri E, Mosca E, Bersanelli M, Vitali S, Valle IFD, Liò P, Milanesi L. Systems medicine of inflammaging. Brief Bioinform 2016; 17:527-40. [PMID: 26307062 PMCID: PMC4870395 DOI: 10.1093/bib/bbv062] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Revised: 06/29/2015] [Indexed: 12/30/2022] Open
Abstract
Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.
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Hristova D, Noulas A, Brown C, Musolesi M, Mascolo C. A multilayer approach to multiplexity and link prediction in online geo-social networks. EPJ Data Sci 2016; 5:24. [PMID: 32355599 PMCID: PMC7175673 DOI: 10.1140/epjds/s13688-016-0087-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Accepted: 07/13/2016] [Indexed: 05/09/2023]
Abstract
Online social systems are multiplex in nature as multiple links may exist between the same two users across different social media. In this work, we study the geo-social properties of multiplex links, spanning more than one social network and apply their structural and interaction features to the problem of link prediction across social networking services. Exploring the intersection of two popular online platforms - Twitter and location-based social network Foursquare - we represent the two together as a composite multilayer online social network, where each platform represents a layer in the network. We find that pairs of users connected on both services, have greater neighbourhood similarity and are more similar in terms of their social and spatial properties on both platforms in comparison with pairs who are connected on just one of the social networks. Our evaluation, which aims to shed light on the implications of multiplexity for the link generation process, shows that we can successfully predict links across social networking services. In addition, we also show how combining information from multiple heterogeneous networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction systems with valuable applications to social bootstrapping and friend recommendations.
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Affiliation(s)
- Desislava Hristova
- Computer Lab, University of Cambridge, 15 JJ Thompson Ave, Cambridge, CB3 0FD UK
| | - Anastasios Noulas
- Data Science Institute, University of Lancaster, South Drive, Lancaster, LA1 4YW UK
| | - Chloë Brown
- Computer Lab, University of Cambridge, 15 JJ Thompson Ave, Cambridge, CB3 0FD UK
| | - Mirco Musolesi
- Department of Geography, University College London, Gower Street, London, WC1E 6BT UK
| | - Cecilia Mascolo
- Computer Lab, University of Cambridge, 15 JJ Thompson Ave, Cambridge, CB3 0FD UK
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Strano E, Shai S, Dobson S, Barthelemy M. Multiplex networks in metropolitan areas: generic features and local effects. J R Soc Interface 2015; 12:20150651. [PMID: 26400198 PMCID: PMC4614501 DOI: 10.1098/rsif.2015.0651] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 08/28/2015] [Indexed: 11/12/2022] Open
Abstract
Most large cities are spanned by more than one transportation system. These different modes of transport have usually been studied separately: it is however important to understand the impact on urban systems of coupling different modes and we report in this paper an empirical analysis of the coupling between the street network and the subway for the two large metropolitan areas of London and New York. We observe a similar behaviour for network quantities related to quickest paths suggesting the existence of generic mechanisms operating beyond the local peculiarities of the specific cities studied. An analysis of the betweenness centrality distribution shows that the introduction of underground networks operate as a decentralizing force creating congestion in places located at the end of underground lines. Also, we find that increasing the speed of subways is not always beneficial and may lead to unwanted uneven spatial distributions of accessibility. In fact, for London—but not for New York—there is an optimal subway speed in terms of global congestion. These results show that it is crucial to consider the full, multimodal, multilayer network aspects of transportation systems in order to understand the behaviour of cities and to avoid possible negative side-effects of urban planning decisions.
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Affiliation(s)
- Emanuele Strano
- Laboratory of Geography Information Systems (LaSig), Polytechnic School of Lausanne (EPFL), Lausanne, CH, Switzerland
| | - Saray Shai
- School of Computer Science, University of St Andrews, St Andrews, Scotland, UK Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA
| | - Simon Dobson
- School of Computer Science, University of St Andrews, St Andrews, Scotland, UK
| | - Marc Barthelemy
- CEA, Institut de Physique Theorique, Gif-sur-Yvette, France EHESS, Centre d'Analyse et de Mathématique Sociales, Paris, France
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