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Mastrandrea R, Antuofermo G, Ovadek M, Yeung TYC, Dyevre A, Caldarelli G. Coalitions in international litigation: a network perspective. Philos Trans A Math Phys Eng Sci 2024; 382:20230158. [PMID: 38403063 DOI: 10.1098/rsta.2023.0158] [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: 02/27/2024]
Abstract
We apply network science principles to analyse the coalitions formed by European Union nations and institutions during litigation proceedings at the European Court of Justice. By constructing Friends and Foes networks, we explore their characteristics and dynamics through the application of cluster detection, motif analysis and duplex analysis. Our findings demonstrate that the Friends and Foes networks exhibit disassortative behaviour, highlighting the inclination of nodes to connect with dissimilar nodes. Furthermore, there is a correlation among centrality measures, indicating that member states and institutions with a larger number of connections play a prominent role in bridging the network. An examination of the modularity of the networks reveals that coalitions tend to align along regional and institutional lines, rather than national government divisions. Additionally, an analysis of triadic binary motifs uncovers a greater level of reciprocity within the Foes network compared to the Friends network. This article is part of the theme issue 'A complexity science approach to law and governance'.
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Affiliation(s)
- R Mastrandrea
- IMT Alti Studi Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
| | - G Antuofermo
- Centro Studi Giuridici Francesco Carrara, Lucca, Italy
| | - M Ovadek
- Centre for Empirical Jurisprudence, University of Leuven, Tiensestraat 45, 3000 Leuven, Belgium
| | - T Y-C Yeung
- Centre for Empirical Jurisprudence, University of Leuven, Tiensestraat 45, 3000 Leuven, Belgium
| | - A Dyevre
- Centre for Empirical Jurisprudence, University of Leuven, Tiensestraat 45, 3000 Leuven, Belgium
| | - G Caldarelli
- DSMN Ca'Foscari, University of Venice, Via Torino 155, 30171 Venezia Mestre, Italy
- ECLT Ca'Foscari, University of Venice, Dorsoduro 3246, 30123 Venice, Italy
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2
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Hu J, Weber JN, Fuess LE, Steinel NC, Bolnick DI, Wang M. A spectral framework to map QTLs affecting joint differential networks of gene co-expression. bioRxiv 2024:2024.03.29.587398. [PMID: 38585912 PMCID: PMC10996691 DOI: 10.1101/2024.03.29.587398] [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] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype → expression → phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype → network → phenotype mechanism. Here, we develop a network-based method, called snQTL, to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.
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Affiliation(s)
- Jiaxin Hu
- Department of Statistics, University of Wisconsin-Madison
| | - Jesse N. Weber
- Department of Integrative Biology, University of Wisconsin-Madison
| | | | | | - Daniel I. Bolnick
- Department of Ecology and Evolutionary Biology, University of Connecticut
| | - Miaoyan Wang
- Department of Statistics, University of Wisconsin-Madison
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3
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Nabizadeh M, Nasirian F, Li X, Saraswat Y, Waheibi R, Hsiao LC, Bi D, Ravandi B, Jamali S. Network physics of attractive colloidal gels: Resilience, rigidity, and phase diagram. Proc Natl Acad Sci U S A 2024; 121:e2316394121. [PMID: 38194451 PMCID: PMC10801866 DOI: 10.1073/pnas.2316394121] [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: 09/26/2023] [Accepted: 12/03/2023] [Indexed: 01/11/2024] Open
Abstract
Colloidal gels exhibit solid-like behavior at vanishingly small fractions of solids, owing to ramified space-spanning networks that form due to particle-particle interactions. These networks give the gel its rigidity, and with stronger attractions the elasticity grows as well. The emergence of rigidity can be described through a mean field approach; nonetheless, fundamental understanding of how rigidity varies in gels of different attractions is lacking. Moreover, recovering an accurate gelation phase diagram based on the system's variables has been an extremely challenging task. Understanding the nature of colloidal clusters, and how rigidity emerges from their connections is key to controlling and designing gels with desirable properties. Here, we employ network analysis tools to interrogate and characterize the colloidal structures. We construct a particle-level network, having all the spatial coordinates of colloids with different attraction levels, and also identify polydisperse rigid fractal clusters using a Gaussian mixture model, to form a coarse-grained cluster network that distinctly shows main physical features of the colloidal gels. A simple mass-spring model then is used to recover quantitatively the elasticity of colloidal gels from these cluster networks. Interrogating the resilience of these gel networks shows that the elasticity of a gel (a dynamic property) is directly correlated to its cluster network's resilience (a static measure). Finally, we use the resilience investigations to devise [and experimentally validate] a fully resolved phase diagram for colloidal gelation, with a clear solid-liquid phase boundary using a single volume fraction of particles well beyond this phase boundary.
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Affiliation(s)
- Mohammad Nabizadeh
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA02215
| | - Farzaneh Nasirian
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA02215
| | - Xinzhi Li
- Department of Physics, Northeastern University, Boston, MA02215
| | - Yug Saraswat
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC27606
| | - Rony Waheibi
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC27606
| | - Lilian C. Hsiao
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC27606
| | - Dapeng Bi
- Department of Physics, Northeastern University, Boston, MA02215
| | - Babak Ravandi
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA02215
| | - Safa Jamali
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA02215
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Scagliarini T, Sparacino L, Faes L, Marinazzo D, Stramaglia S. Gradients of O-information highlight synergy and redundancy in physiological applications. Front Netw Physiol 2024; 3:1335808. [PMID: 38264338 PMCID: PMC10803408 DOI: 10.3389/fnetp.2023.1335808] [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: 11/09/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024]
Abstract
The study of high order dependencies in complex systems has recently led to the introduction of statistical synergy, a novel quantity corresponding to a form of emergence in which patterns at large scales are not traceable from lower scales. As a consequence, several works in the last years dealt with the synergy and its counterpart, the redundancy. In particular, the O-information is a signed metric that measures the balance between redundant and synergistic statistical dependencies. In spite of its growing use, this metric does not provide insight about the role played by low-order scales in the formation of high order effects. To fill this gap, the framework for the computation of the O-information has been recently expanded introducing the so-called gradients of this metric, which measure the irreducible contribution of a variable (or a group of variables) to the high order informational circuits of a system. Here, we review the theory behind the O-information and its gradients and present the potential of these concepts in the field of network physiology, showing two new applications relevant to brain functional connectivity probed via functional resonance imaging and physiological interactions among the variability of heart rate, arterial pressure, respiration and cerebral blood flow.
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Affiliation(s)
- Tomas Scagliarini
- Dipartimento di Fisica e Astronomia G. Galilei, Università degli Studi di Padova, Padova, Italy
| | - Laura Sparacino
- Dipartimento di Ingegneria, Università di Palermo, Palermo, Italy
| | - Luca Faes
- Dipartimento di Ingegneria, Università di Palermo, Palermo, Italy
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Center of Innovative Technologies for Signal Detection and Processing (TIRES), Università degli Studi di Bari Aldo Moro, Bari, Italy
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Vitevitch MS, Pisoni DB, Soehlke L, Foster TA. Using Complex Networks in the Hearing Sciences. Ear Hear 2024; 45:1-9. [PMID: 37316992 PMCID: PMC10721731 DOI: 10.1097/aud.0000000000001395] [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] [Indexed: 06/16/2023]
Abstract
In this Point of View, we review a number of recent discoveries from the emerging, interdisciplinary field of Network Science , which uses graph theoretic techniques to understand complex systems. In the network science approach, nodes represent entities in a system, and connections are placed between nodes that are related to each other to form a web-like network . We discuss several studies that demonstrate how the micro-, meso-, and macro-level structure of a network of phonological word-forms influence spoken word recognition in listeners with normal hearing and in listeners with hearing loss. Given the discoveries made possible by this new approach and the influence of several complex network measures on spoken word recognition performance we argue that speech recognition measures-originally developed in the late 1940s and routinely used in clinical audiometry-should be revised to reflect our current understanding of spoken word recognition. We also discuss other ways in which the tools of network science can be used in Speech and Hearing Sciences and Audiology more broadly.
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Klemann D, Winasti W, Tournois F, Mertens H, van Merode F. Quantifying the Resilience of a Healthcare System: Entropy and Network Science Perspectives. Entropy (Basel) 2023; 26:21. [PMID: 38248147 PMCID: PMC10814470 DOI: 10.3390/e26010021] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024]
Abstract
In this study, we consider the human body and the healthcare system as two complex networks and use theories regarding entropy, requisite variety, and network centrality metrics with resilience to assess and quantify the strengths and weaknesses of healthcare systems. Entropy is used to quantify the uncertainty and variety regarding a patient's health state. The extent of the entropy defines the requisite variety a healthcare system should contain to be able to treat a patient safely and correctly. We use network centrality metrics to visualize and quantify the healthcare system as a network and assign the strengths and weaknesses of the network and of individual agents in the network. We apply organization design theories to formulate improvements and explain how a healthcare system should adjust to create a more robust and resilient healthcare system that is able to continuously deal with variations and uncertainties regarding a patient's health, despite possible stressors and disturbances at the healthcare system. In this article, these concepts and theories are explained and applied to a fictive and a real-life example. We conclude that entropy and network science can be used as tools to quantify the resilience of healthcare systems.
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Affiliation(s)
- Désirée Klemann
- Department of Gynecology and Obstetrics, Maastricht University Medical Centre+, Maastricht University, 6229 HX Maastricht, The Netherlands
- Care and Public Health Research Institute, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Windi Winasti
- IQ Healthcare, Radboudumc, 6525 EP Nijmegen, The Netherlands
- Elisabeth-TweeSteden Ziekenhuis, 5022 GC Tilburg, The Netherlands
| | - Fleur Tournois
- Department of Gynecology and Obstetrics, Maastricht University Medical Centre+, Maastricht University, 6229 HX Maastricht, The Netherlands
| | - Helen Mertens
- Executive Board, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Frits van Merode
- Care and Public Health Research Institute, Maastricht University, 6200 MD Maastricht, The Netherlands
- Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
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Nguyen TAS, Castro N, Vitevitch MS, Harding A, Teng R, Arciuli J, Leyton CE, Piguet O, Ballard KJ. Do age and language impairment affect speed of recognition for words with high and low closeness centrality within the phonological network? Int J Speech Lang Pathol 2023; 25:915-928. [PMID: 36416187 DOI: 10.1080/17549507.2022.2141323] [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] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
PURPOSE Speed and accuracy of lexical access change with healthy ageing and neurodegeneration. While a word's immediate phonological neighbourhood density (i.e. words differing by a single phoneme) influences access, connectivity to all words in the phonological network (i.e. closeness centrality) may influence processing. This study aimed to investigate the effect of closeness centrality on speed and accuracy of lexical processing pre- and post- a single word-training session in healthy younger and older adults, and adults with logopenic primary progressive aphasia (lvPPA), which affects phonological processing. METHOD Participants included 29 young and 17 older healthy controls, and 10 adults with lvPPA. Participants received one session of word-training on words with high or low closeness centrality, using a picture-word verification task. Changes in lexical decision reaction times (RT) and accuracy were measured. RESULT Baseline RT was unaffected by age and accuracy was at ceiling for controls. Post-training, only young adults' RT were significantly faster. Adults with lvPPA were slower and less accurate than controls at baseline, with no training effect. Closeness centrality did not influence performance. CONCLUSION Absence of training effect for older adults suggests higher threshold to induce priming, possibly associated with insufficient dosage or fatigue. Implications for word-finding interventions with older adults are discussed.
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Affiliation(s)
| | - Nichol Castro
- Department of Communicative Disorders and Sciences, University at Buffalo, Buffalo, NY, USA
| | | | - Annabel Harding
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Renata Teng
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Joanne Arciuli
- College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia
| | - Cristian E Leyton
- School of Psychology and the Brain & Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Olivier Piguet
- School of Psychology and the Brain & Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Kirrie J Ballard
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- School of Psychology and the Brain & Mind Centre, The University of Sydney, Sydney, NSW, Australia
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Li Z, Liu J, Dong Y, Ren J, Li W. Modelling and Research on Intuitionistic Fuzzy Goal-Based Attack and Defence Game for Infrastructure Networks. Entropy (Basel) 2023; 25:1558. [PMID: 37998250 PMCID: PMC10670381 DOI: 10.3390/e25111558] [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: 10/19/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023]
Abstract
Network attack and defence games are gradually becoming a new approach through which to study the protection of infrastructure networks such as power grids and transportation networks. Uncertainty factors, such as the subjective decision preferences of attackers and defenders, are not considered in existing attack and defence game studies for infrastructure networks. In this paper, we introduce, respectively, the attacker's and defender's expectation value, rejection value, and hesitation degree of the target, as well as construct an intuitionistic fuzzy goal-based attack and defence game model for infrastructure networks that are based on the maximum connectivity slice size, which is a network performance index. The intuitionistic fuzzy two-player, zero-sum game model is converted into a linear programming problem for solving, and the results are analysed to verify the applicability and feasibility of the model proposed in this paper. Furthermore, different situations, such as single-round games and multi-round repeated games, are also considered. The experimental results show that, when attacking the network, the attacker rarely attacks the nodes with higher importance in the network, but instead pays more attention to the nodes that are not prominent in the network neutrality and median; meanwhile, the defender is more inclined to protect the more important nodes in the network to ensure the normal performance of the network.
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Affiliation(s)
| | | | | | | | - Weili Li
- Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China; (Z.L.); (J.L.); (Y.D.); (J.R.)
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Park CM, Donnelly HK, Rodriguez A, Esquivel L, Nardi C, Trunfio P, Oliver-Davila A, Howard KAS, Solberg VSH. Developing STEM Career Identities among Latinx Youths: Collaborative Design, Evaluations, and Adaptations during COVID-19. Behav Sci (Basel) 2023; 13:949. [PMID: 37998695 PMCID: PMC10669665 DOI: 10.3390/bs13110949] [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: 09/29/2023] [Revised: 11/04/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023] Open
Abstract
In response to the low representation of Latinx adults in STEM occupations, this community-based participatory action research study aims to increase the number of middle school youths developing STEM career identities and entering high school with the intention to pursue STEM careers. The students were provided with summer and after-school activities focusing on network science and career development curricula. Using a quasi-experimental pretest-posttest design and career narratives, this study examined the changes in STEM and career self-efficacy, as well as career identity. The results show improvements in self-efficacy, an increased number of youths with intentions of pursuing future STEM career opportunities, and deeper reflections on their talents and skills after program participation. This paper also describes the program development and implementation in detail, as well as the adaptations that resulted from COVID-19, for scholars and educators designing similar programs. This study provides promising evidence for the quality of STEM and career development lessons in supporting the emergence of a STEM career identity and self-efficacy.
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Affiliation(s)
- Chong Myung Park
- Wheelock College of Education and Human Development, Boston University, Boston, MA 02215, USA; (L.E.); (K.A.S.H.); (V.S.H.S.)
| | - Hayoung Kim Donnelly
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | | | - Luis Esquivel
- Wheelock College of Education and Human Development, Boston University, Boston, MA 02215, USA; (L.E.); (K.A.S.H.); (V.S.H.S.)
| | - Cecilia Nardi
- Office of Government & Community Affairs, Boston University, Boston, MA 02215, USA;
| | - Paul Trunfio
- Department of Physics, Boston University, Boston, MA 02215, USA;
| | | | - Kimberly A. S. Howard
- Wheelock College of Education and Human Development, Boston University, Boston, MA 02215, USA; (L.E.); (K.A.S.H.); (V.S.H.S.)
| | - V. Scott H. Solberg
- Wheelock College of Education and Human Development, Boston University, Boston, MA 02215, USA; (L.E.); (K.A.S.H.); (V.S.H.S.)
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Morselli Gysi D, Barabási AL. Noncoding RNAs improve the predictive power of network medicine. Proc Natl Acad Sci U S A 2023; 120:e2301342120. [PMID: 37906646 PMCID: PMC10636370 DOI: 10.1073/pnas.2301342120] [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: 01/24/2023] [Accepted: 09/09/2023] [Indexed: 11/02/2023] Open
Abstract
Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.
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Affiliation(s)
- Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA02115
- Department of Physics, Northeastern University, Boston, MA02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA02115
- US Department of Veteran Affairs, Boston, MA02130
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA02115
- Department of Physics, Northeastern University, Boston, MA02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA02115
- US Department of Veteran Affairs, Boston, MA02130
- Department of Network and Data Science, Central European University, Budapest1051, Hungary
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Poudyal B, Ghoshal G, Kirkley A. Characterizing network circuity among heterogeneous urban amenities. J R Soc Interface 2023; 20:20230296. [PMID: 37907093 PMCID: PMC10618061 DOI: 10.1098/rsif.2023.0296] [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: 05/23/2023] [Accepted: 10/13/2023] [Indexed: 11/02/2023] Open
Abstract
The spatial configuration of urban amenities and the streets connecting them collectively provide the structural backbone of a city, influencing its accessibility, vitality and ultimately the well-being of its residents. Most accessibility measures focus on the proximity of amenities in space or along transportation networks, resulting in metrics largely determined by urban density alone. These measures are unable to gauge how efficiently street networks can navigate between amenities, since they neglect the circuity component of accessibility. Existing measures also often require ad hoc modelling choices, making them less flexible for different applications and difficult to apply in cross-sectional analyses. Here, we develop a simple, principled and flexible measure to characterize the circuity of accessibility among heterogeneous amenities in a city, which we call the pairwise circuity (PC). The PC quantifies the excess travel distance incurred when using the street network to route between a pair of amenity types, summarizing both spatial and topological correlations among amenities. Measures developed using our framework exhibit significant statistical associations with a variety of urban prosperity and accessibility indicators when compared with an appropriate null model, and we find a clear separation in the PC values of cities according to development level and geographical region.
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Affiliation(s)
- Bibandhan Poudyal
- Department of Physics and Astronomy, University of Rochester, Rochester, NY 14607, USA
| | - Gourab Ghoshal
- Department of Physics and Astronomy, University of Rochester, Rochester, NY 14607, USA
| | - Alec Kirkley
- Institute of Data Science, University of Hong Kong, Hong Kong
- Department of Urban Planning and Design, University of Hong Kong, Hong Kong
- Urban Systems Institute, University of Hong Kong, Hong Kong
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Jenness SM, Wallrafen-Sam K, Schneider I, Kennedy S, Akiyama MJ, Spaulding AC. Dynamic Contact Networks of Residents of an Urban Jail in the Era of SARS-CoV-2. medRxiv 2023:2023.09.29.23296359. [PMID: 37873313 PMCID: PMC10593002 DOI: 10.1101/2023.09.29.23296359] [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] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background In custodial settings such as jails and prisons, infectious disease transmission is heightened by factors such as overcrowding and limited healthcare access. Specific features of social contact networks within these settings have not been sufficiently characterized, especially in the context of a large-scale respiratory infectious disease outbreak. The study aims to quantify contact network dynamics within the Fulton County Jail in Atlanta, Georgia, to improve our understanding respiratory disease spread to informs public health interventions. Methods As part of the Surveillance by Wastewater and Nasal Self-collection of Specimens (SWANSS) study, jail roster data were utilized to construct social contact networks. Rosters included resident details, cell locations, and demographic information. This analysis involved 6,702 residents over 140,901 person days. Network statistics, including degree, mixing, and turnover rates, were assessed across age groups, race/ethnicities, and jail floors. We compared outcomes for two distinct periods (January 2022 and April 2022) to understand potential responses in network structures during and after the SARS-CoV-2 Omicron variant peak. Results We found high cross-sectional network degree at both cell and block levels, indicative of substantial daily contacts. While mean degree increased with age, older residents exhibited lower degree during the Omicron peak, suggesting potential quarantine measures. Block-level networks demonstrated higher mean degrees than cell-level networks. Cumulative degree distributions for both levels increased from January to April, indicating heightened contacts after the outbreak. Assortative age mixing was strong, especially for residents aged 20-29. Dynamic network statistics illustrated increased degrees over time, emphasizing the potential for disease spread, albeit with a lower growth rate during the Omicron peak. Conclusions The contact networks within the Fulton County Jail presented ideal conditions for infectious disease transmission. Despite some reduction in network characteristics during the Omicron peak, the potential for disease spread remained high. Age-specific mixing patterns suggested unintentional age segregation, potentially limiting disease spread to older residents. The study underscores the need for ongoing monitoring of contact networks in carceral settings and provides valuable insights for epidemic modeling and intervention strategies, including quarantine, depopulation, and vaccination. This network analysis offers a foundation for understanding disease dynamics in carceral environments.
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Affiliation(s)
- Samuel M. Jenness
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Karina Wallrafen-Sam
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Isaac Schneider
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Shanika Kennedy
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Matthew J. Akiyama
- Divisions of General Internal Medicine & Infectious Diseases, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Anne C. Spaulding
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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Song J, Okano JT, Ponce J, Busang L, Seipone K, Valdano E, Blower S. The role of migration networks in the development of Botswana's generalized HIV epidemic. eLife 2023; 12:e85435. [PMID: 37665629 PMCID: PMC10476964 DOI: 10.7554/elife.85435] [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: 12/08/2022] [Accepted: 08/02/2023] [Indexed: 09/05/2023] Open
Abstract
The majority of people with HIV live in sub-Saharan Africa, where epidemics are generalized. For these epidemics to develop, populations need to be mobile. However, the role of population-level mobility in the development of generalized HIV epidemics has not been studied. Here we do so by studying historical migration data from Botswana, which has one of the most severe generalized HIV epidemics worldwide; HIV prevalence was 21% in 2021. The country reported its first AIDS case in 1985 when it began to rapidly urbanize. We hypothesize that, during the development of Botswana's epidemic, the population was extremely mobile and the country was highly connected by substantial migratory flows. We test this mobility hypothesis by conducting a network analysis using a historical time series (1981-2011) of micro-census data from Botswana. Our results support our hypothesis. We found complex migration networks with very high rates of rural-to-urban, and urban-to-rural, migration: 10% of the population moved annually. Mining towns (where AIDS cases were first reported, and risk behavior was high) were important in-flow and out-flow migration hubs, suggesting that they functioned as 'core groups' for HIV transmission and dissemination. Migration networks could have dispersed HIV throughout Botswana and generated the current hyperendemic epidemic.
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Affiliation(s)
- Janet Song
- Center for Biomedical Modeling, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Justin T Okano
- Center for Biomedical Modeling, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Joan Ponce
- Center for Biomedical Modeling, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Lesego Busang
- The African Comprehensive HIV/AIDS Partnerships (ACHAP)GaboroneBotswana
| | - Khumo Seipone
- The African Comprehensive HIV/AIDS Partnerships (ACHAP)GaboroneBotswana
| | - Eugenio Valdano
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé PubliqueParisFrance
| | - Sally Blower
- Center for Biomedical Modeling, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
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14
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Hadjiabadi D, Soltesz I. From single-neuron dynamics to higher-order circuit motifs in control and pathological brain networks. J Physiol 2023; 601:3011-3024. [PMID: 35815823 PMCID: PMC10655857 DOI: 10.1113/jp282749] [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: 04/15/2022] [Accepted: 06/27/2022] [Indexed: 11/08/2022] Open
Abstract
The convergence of advanced single-cell in vivo functional imaging techniques, computational modelling tools and graph-based network analytics has heralded new opportunities to study single-cell dynamics across large-scale networks, providing novel insights into principles of brain communication and pointing towards potential new strategies for treating neurological disorders. A major recent finding has been the identification of unusually richly connected hub cells that have capacity to synchronize networks and may also be critical in network dysfunction. While hub neurons are traditionally defined by measures that consider solely the number and strength of connections, novel higher-order graph analytics now enables the mining of massive networks for repeating subgraph patterns called motifs. As an illustration of the power offered by higher-order analysis of neuronal networks, we highlight how recent methodological advances uncovered a new functional cell type, the superhub, that is predicted to play a major role in regulating network dynamics. Finally, we discuss open questions that will be critical for assessing the importance of higher-order cellular-scale network analytics in understanding brain function in health and disease.
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Affiliation(s)
- Darian Hadjiabadi
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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15
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Astle DE, Johnson MH, Akarca D. Toward computational neuroconstructivism: a framework for developmental systems neuroscience. Trends Cogn Sci 2023; 27:726-744. [PMID: 37263856 DOI: 10.1016/j.tics.2023.04.009] [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: 07/11/2022] [Revised: 01/05/2023] [Accepted: 04/19/2023] [Indexed: 06/03/2023]
Abstract
Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.
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Affiliation(s)
- Duncan E Astle
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 2QQ, UK; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK.
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, WC1E 7JL, UK
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK
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16
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Kaiser M. Connectomes: from a sparsity of networks to large-scale databases. Front Neuroinform 2023; 17:1170337. [PMID: 37377946 PMCID: PMC10291062 DOI: 10.3389/fninf.2023.1170337] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
The analysis of whole brain networks started in the 1980s when only a handful of connectomes were available. In these early days, information about the human connectome was absent and one could only dream about having information about connectivity in a single human subject. Thanks to non-invasive methods such as diffusion imaging, we now know about connectivity in many species and, for some species, in many individuals. To illustrate the rapid change in availability of connectome data, the UK Biobank is on track to record structural and functional connectivity in 100,000 human subjects. Moreover, connectome data from a range of species is now available: from Caenorhabditis elegans and the fruit fly to pigeons, rodents, cats, non-human primates, and humans. This review will give a brief overview of what structural connectivity data is now available, how connectomes are organized, and how their organization shows common features across species. Finally, I will outline some of the current challenges and potential future work in making use of connectome information.
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Affiliation(s)
- Marcus Kaiser
- NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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17
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Scattergood J, Bishop S. A Network Model Approach to International Aid. Entropy (Basel) 2023; 25:e25040641. [PMID: 37190429 PMCID: PMC10137545 DOI: 10.3390/e25040641] [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: 03/01/2023] [Revised: 04/02/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023]
Abstract
Decisions made by international aid donors regarding the allocation of their aid budgets to recipients can be mathematically modelled using network theory. The many countries and multilateral organisations providing developmental aid, mostly to developing countries, have numerous competing or conflicting interests, biases and motivations, often obscured by a lack of transparency and confused messaging. Using network theory, combined with other mathematical methods, these inter-connecting and inter-dependent variables are identified, revealing the complicated properties and dynamics of the international aid system. Statistical techniques are applied to the vast amount of available, open data to first understand the complexities and then identify the key variables, focusing principally on bilateral aid flows. These results are used to create a weighted network model which is subsequently adapted for use by a hypothetical aid recipient. By incorporating modern portfolio theory into this weighted network model and taking advantage of a donor's reasons for allocating their aid budgets to that recipient, a simulation is carried out treating the problem as an optimal investment portfolio of aid determinant 'assets' which illustrates how a recipient can maximise their aid receipts. Suggestions are also made for further uses and adaptations of this weighted network model.
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Affiliation(s)
- Joe Scattergood
- Department of Mathematics, University College London, London WC1E 6BT, UK
| | - Steven Bishop
- Department of Mathematics, University College London, London WC1E 6BT, UK
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18
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Abstract
Network science was used to examine different dimensions of phonological similarity in English. Data from a phonological associate task and an identification of words in noise task were used to create a phonological association network and a misperception network. These networks were compared to a network formed by a computational metric widely used to assess phonological similarity (i.e., one-phoneme metric). The phonological association network and the misperception network were topographically more similar to each other than either were to the one-phoneme metric network, but there were several network features in common between the one-phoneme metric network and the phonological association network. To assess the influence of network structure on processing, we compared the influence of degree (i.e., neighborhood density) from each of the networks on visual and auditory lexical decision reaction times obtained from two psycholinguistic megastudies. The effect of degree differed across network types and tasks. We discuss the use of each approach to determine phonological similarity and a possible direction forward for language research through the use of multiplex networks.
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Affiliation(s)
- Nichol Castro
- Department of Psychology, The University of Kansas, USA; Department of Communicative Disorders and Sciences, University at Buffalo, USA
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Secor EA, Hamilton D, D'Huyvetter C, Salottolo K, Bar-Or D. Network Analysis Examining Intrahospital Traffic of Patients With Traumatic Hip Fracture. J Healthc Qual 2023; 45:83-90. [PMID: 36409627 DOI: 10.1097/JHQ.0000000000000367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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] [Received: 05/18/2022] [Accepted: 09/14/2022] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Increased intrahospital traffic (IHT) is associated with adverse events and infections in hospitalized patients. Network science has been used to study patient flow in hospitals but not specifically for patients with traumatic injuries. METHODS This retrospective analysis included 103 patients with traumatic hip fractures admitted to a level I trauma center between April 2021 and September 2021. Associations with IHTs (moves within the hospital) were analyzed using R (4.1.2) as a weighted directed graph. RESULTS The median (interquartile range) number of moves was 8 (7-9). The network consisted of 16 distinct units and showed mild disassortativity (-0.35), similar to other IHT networks. The floor and intensive care unit (ICU) were central units in the flow of patients, with the highest degree and betweenness. Patients spent a median of 20-28 hours in the ICU, intermediate care unit, or floor. The number of moves per patient was mildly correlated with hospital length of stay (ρ = 0.26, p = .008). Intrahospital traffic volume was higher on weekdays and during daytime hours. Intrahospital traffic volume was highest in patients aged <65 years ( p = .04), but there was no difference in IHT volume by dependent status, complications, or readmissions. CONCLUSIONS Network science is a useful tool for trauma patients to plan IHT, flow, and staffing.
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Chang HCH, Harrington B, Fu F, Rockmore DN. Complex systems of secrecy: the offshore networks of oligarchs. PNAS Nexus 2023; 2:pgad051. [PMID: 36909828 PMCID: PMC9998034 DOI: 10.1093/pnasnexus/pgad051] [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: 09/06/2022] [Revised: 12/16/2022] [Accepted: 02/02/2023] [Indexed: 03/05/2023]
Abstract
Following the invasion of Ukraine, the USA, UK, and EU governments-among others-sanctioned oligarchs close to Putin. This approach has come under scrutiny, as evidence has emerged of the oligarchs' successful evasion of these punishments. To address this problem, we analyze the role of an overlooked but highly influential group: the secretive professional intermediaries who create and administer the oligarchs' offshore financial empires. Drawing on the Offshore Leaks Database provided by the International Consortium of Investigative Journalists (ICIJ), we examine the ties linking offshore expert advisors (lawyers, accountants, and other wealth management professionals) to ultra-high-net-worth individuals from four countries: Russia, China, the USA, and Hong Kong. We find that resulting nation-level "oligarch networks" share a scale-free structure characterized by a heterogeneity of heavy-tailed degree distributions of wealth managers; however, network topologies diverge across clients from democratic versus autocratic regimes. While generally robust, scale-free networks are fragile when targeted by attacks on highly connected nodes. Our "knock-out" experiments pinpoint this vulnerability to the small group of wealth managers themselves, suggesting that sanctioning these professional intermediaries may be more effective and efficient in disrupting dark finance flows than sanctions on their wealthy clients. This vulnerability is especially pronounced amongst Russian oligarchs, who concentrate their offshore business in a handful of boutique wealth management firms. The distinctive patterns we identify suggest a new approach to sanctions, focused on expert intermediaries to disrupt the finances and alliances of their wealthy clients. More generally, our research contributes to the larger body of work on complexity science and the structures of secrecy.
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Affiliation(s)
| | | | - Feng Fu
- Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA
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21
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Liu YY. Controlling the human microbiome. Cell Syst 2023; 14:135-159. [PMID: 36796332 PMCID: PMC9942095 DOI: 10.1016/j.cels.2022.12.010] [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: 06/06/2022] [Revised: 10/18/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.
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Affiliation(s)
- Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
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22
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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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23
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Vitevitch MS, Castro N, Mullin GJD, Kulphongpatana Z. The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders. Brain Sci 2023; 13:brainsci13020188. [PMID: 36831731 PMCID: PMC9954478 DOI: 10.3390/brainsci13020188] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/12/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
A central tenet of network science states that the structure of the network influences processing. In this study of a phonological network of English words we asked: how does damage alter the network structure (Study 1)? How does the damaged structure influence lexical processing (Study 2)? How does the structure of the intact network "protect" processing with a less efficient algorithm (Study 3)? In Study 1, connections in the network were randomly removed to increasingly damage the network. Various measures showed the network remained well-connected (i.e., it is resilient to damage) until ~90% of the connections were removed. In Study 2, computer simulations examined the retrieval of a set of words. The performance of the model was positively correlated with naming accuracy by people with aphasia (PWA) on the Philadelphia Naming Test (PNT) across four types of aphasia. In Study 3, we demonstrated another way to model developmental or acquired disorders by manipulating how efficiently activation spread through the network. We found that the structure of the network "protects" word retrieval despite decreases in processing efficiency; words that are relatively easy to retrieve with efficient transmission of priming remain relatively easy to retrieve with less efficient transmission of priming. Cognitive network science and computer simulations may provide insight to a wide range of speech, language, hearing, and cognitive disorders.
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Affiliation(s)
- Michael S. Vitevitch
- Department of Psychology, University of Kansas, Lawrence, KS 66045, USA
- Correspondence: ; Tel.: +1-785-864-9312
| | - Nichol Castro
- Department of Communicative Disorders and Sciences, University at Buffalo, Buffalo, NY 14260, USA
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24
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Jing F, Cheng M, Li J, He C, Ren H, Zhou J, Zhou H, Xu Z, Chen W, Cheng W. Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis. Front Psychiatry 2023; 14:1119421. [PMID: 37124263 PMCID: PMC10140400 DOI: 10.3389/fpsyt.2023.1119421] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/13/2023] [Indexed: 05/02/2023] Open
Abstract
Background Occupational burnout is a type of psychological syndrome. It can lead to serious mental and physical disorders if not treated in time. However, individuals tend to conceal their genuine feelings of occupational burnout because such disclosures may elicit bias from superiors. This study aims to explore a novel method for estimating occupational burnout by elucidating its links with social, lifestyle, and health status factors. Methods In this study 5,794 participants were included. Associations between occupational burnout and a set of features from a survey was analyzed using Chi-squared test and Wilcoxon rank sum test. Variables that are significantly related to occupational burnout were grouped into four categories: demographic, work-related, health status, and lifestyle. Then, from a network science perspective, we inferred the colleague's social network of all participants based on these variables. In this inferred social network, an exponential random graph model (ERGM) was used to analyze how occupational burnout may affect the edge in the network. Results For demographic variables, age (p < 0.01) and educational background (p < 0.01) were significantly associated with occupational burnout. For work-related variables, type of position (p < 0.01) was a significant factor as well. For health and chronic diseases variables, self-rated health status, hospitalization history in the last 3 years, arthritis, cardiovascular diseases, high blood lipid, breast diseases, and other chronic diseases were all associated with occupational burnout significantly (p < 0.01). Breakfast frequency, dairy consumption, salt-limiting tool usage, oil-limiting tool usage, vegetable consumption, pedometer (step counter) usage, consuming various types of food (in the previous year), fresh fruit and vegetable consumption (in the previous year), physical exercise participation (in the previous year), limit salt consumption, limit oil consumption, and maintain weight were also significant factors (p < 0.01). Based on the inferred social network among all airport workers, ERGM showed that if two employees were both in the same occupational burnout status, they were more likely to share an edge (p < 0.0001). Limitation The major limitation of this work is that the social network for occupational burnout ERGM analysis was inferred based on associated factors, such as demographics, work-related conditions, health and chronic diseases, and behaviors. Though these factors have been proven to be associated with occupational burnout, the results inferred by this social network cannot be warranted for accuracy. Conclusion This work demonstrated the feasibility of identifying people at risk of occupational burnout through an inferred colleague's social network. Encouraging staff with lower occupational burnout status to communicate with others may reduce the risk of burnout for other staff in the network.
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Affiliation(s)
- Fengshi Jing
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- UNC Project-China, UNC Global, School of Medicine, The University of North Carolina, Chapel Hill, NC, United States
| | - Mengyuan Cheng
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- UNC Project-China, UNC Global, School of Medicine, The University of North Carolina, Chapel Hill, NC, United States
| | - Jing Li
- Guangzhou Baiyun International Airport Co., Ltd, Guangzhou, China
| | - Chaocheng He
- School of Information Management, Wuhan University, Wuhan, China
| | - Hao Ren
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Jiandong Zhou
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Hanchu Zhou
- School of Traffic and Transportation Engineering, Central South University, Changsha, China
- School of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Zhongzhi Xu
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Weiming Chen
- Health Medicine Department, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Weibin Cheng
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- School of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China
- *Correspondence: Weibin Cheng,
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25
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Briola A, Aste T. Dependency Structures in Cryptocurrency Market from High to Low Frequency. Entropy (Basel) 2022; 24:1548. [PMID: 36359637 PMCID: PMC9689460 DOI: 10.3390/e24111548] [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: 09/21/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones.
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Affiliation(s)
- Antonio Briola
- Department of Computer Science, University College London, London WC1E 6BT, UK
- Center for Blockchain Technologies, University College London, London WC1E 6BT, UK
| | - Tomaso Aste
- Department of Computer Science, University College London, London WC1E 6BT, UK
- Center for Blockchain Technologies, University College London, London WC1E 6BT, UK
- Systemic Risk Center, London School of Economics, London WC2A 2AE, UK
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26
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Amin F, Abbasi R, Mateen A, Ali Abid M, Khan S. A Step toward Next-Generation Advancements in the Internet of Things Technologies. Sensors (Basel) 2022; 22:s22208072. [PMID: 36298422 PMCID: PMC9609757 DOI: 10.3390/s22208072] [Citation(s) in RCA: 2] [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] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/18/2022] [Accepted: 10/19/2022] [Indexed: 06/01/2023]
Abstract
The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT's next-generation developments and tell how they apply to the real world.
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Affiliation(s)
- Farhan Amin
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Rashid Abbasi
- School of Electrical Engineering, Anhui Polytechnic University, Jiujiang District, Wuhu 241000, China
| | - Abdul Mateen
- Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 45570, Pakistan
| | - Muhammad Ali Abid
- Faculty of Smart Engineering, the University of Agriculture, Dera Ismail Khan 29220, Pakistan
| | - Salabat Khan
- The College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
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27
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DeGruttola V, Goyal R, Martin NK, Wang R. Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters. Clin Trials 2022; 19:363-374. [PMID: 35894099 DOI: 10.1177/17407745221111818] [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: 11/15/2022]
Abstract
Network science methods can be useful in design, monitoring, and analysis of randomized trials for control of spread of infections. Their usefulness arises from the role of statistical network models in molecular epidemiology and in study design. Computational models, such as agent-based models that propagate disease on simulated contact networks, can be used to investigate the properties of different study designs and analysis plans. Particularly valuable is the use of these methods to assess how magnitude and detectability of intervention effects depend on both individual-level and network-level characteristics of the enrolled populations. Such investigation also provides an important approach to assessing consequences of study data being incomplete or measured with error. To address these goals, we consider two statistical network models: exponential random graph models and the more flexible congruence class models. We focus first on an historical use of these methods in design and monitoring of a cluster randomized trial in Botswana to evaluate the effect of combination HIV prevention modalities compared to standard of care on HIV incidence. We then present a framework for the design of a study of booster vaccine effects on infection with, and forward transmission of, SARS-CoV-2 variants. Motivation for the study is driven in part by guidance from the United Kingdom to base approval of booster vaccines with "strain changes" that target variants on results of neutralizing antibody tests and information about safety, but without requiring evidence of clinical efficacy. Using designs informed by our agent-based network models, we show it may be feasible to conduct a trial of novel SARS-CoV-2 vaccines in a single large campus to obtain useful information regarding vaccine efficacy against susceptibility and infectiousness. If needed, the sample size could be increased by extending the study to a small number of campuses. Novel network methods may be useful in developing pragmatic SARS-CoV-2 vaccine trials that can leverage existing infrastructure to reduce costs and hasten the development of results.
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Affiliation(s)
- Victor DeGruttola
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Division of Infectious Diseases & Global Public Health, Department of Medicine, University of California San Diego La Jolla, CA, USA
| | - Ravi Goyal
- Division of Infectious Diseases & Global Public Health, Department of Medicine, University of California San Diego La Jolla, CA, USA
| | - Natasha K Martin
- Division of Infectious Diseases & Global Public Health, Department of Medicine, University of California San Diego La Jolla, CA, USA
| | - Rui Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
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28
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Chen M, Ohm DT, Phillips JS, McMillan CT, Capp N, Peterson C, Xie E, Wolk DA, Trojanowski JQ, Lee EB, Gee J, Grossman M, Irwin DJ. Divergent Histopathological Networks of Frontotemporal Degeneration Proteinopathy Subytpes. J Neurosci 2022; 42:3868-3877. [PMID: 35318284 PMCID: PMC9087810 DOI: 10.1523/jneurosci.2061-21.2022] [Citation(s) in RCA: 4] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/22/2022] [Accepted: 03/01/2022] [Indexed: 11/21/2022] Open
Abstract
Network analyses inform complex systems such as human brain connectivity, but this approach is seldom applied to gold-standard histopathology. Here, we use two complimentary computational approaches to model microscopic progression of the main subtypes of tauopathy versus TDP-43 proteinopathy in the human brain. Digital histopathology measures were obtained in up to 13 gray matter (GM) and adjacent white matter (WM) cortical brain regions sampled from 53 tauopathy and 66 TDP-43 proteinopathy autopsy patients. First, we constructed a weighted non-directed graph for each group, where nodes are defined as GM and WM regions sampled and edges in the graph are weighted using the group-level Pearson's correlation coefficient for each pairwise node comparison. Additionally, we performed mediation analyses to test mediation effects of WM pathology between anterior frontotemporal and posterior parietal GM nodes. We find greater correlation (i.e., edges) between GM and WM node pairs in tauopathies compared with TDP-43 proteinopathies. Moreover, WM pathology strongly correlated with a graph metric of pathology spread (i.e., node-strength) in tauopathies (r = 0.60, p < 0.03) but not in TDP-43 proteinopathies (r = 0.03, p = 0.9). Finally, we found mediation effects for WM pathology on the association between anterior and posterior GM pathology in FTLD-Tau but not in FTLD-TDP. These data suggest distinct tau and TDP-43 proteinopathies may have divergent patterns of cellular propagation in GM and WM. More specifically, axonal spread may be more influential in FTLD-Tau progression. Network analyses of digital histopathological measurements can inform models of disease progression of cellular degeneration in the human brain.SIGNIFICANCE STATEMENT In this study, we uniquely perform two complimentary computational approaches to model and contrast microscopic disease progression between common frontotemporal lobar degeneration (FTLD) proteinopathy subtypes with similar clinical syndromes during life. Our models suggest white matter (WM) pathology influences cortical spread of disease in tauopathies that is less evident in TDP-43 proteinopathies. These data support the hypothesis that there are neuropathologic signatures of cellular degeneration within neurocognitive networks for specific protienopathies. These distinctive patterns of cellular pathology can guide future efforts to develop tissue-sensitive imaging and biological markers with diagnostic and prognostic utility for FTLD. Moreover, our novel computational approach can be used in future work to model various neurodegenerative disorders with mixed proteinopathy within the human brain connectome.
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Affiliation(s)
- Min Chen
- Penn Image Computing and Science Lab, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Daniel T Ohm
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Jeffrey S Phillips
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Corey T McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Noah Capp
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Claire Peterson
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Emily Xie
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - David A Wolk
- Alzheimer's Disease Research Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - James Gee
- Penn Image Computing and Science Lab, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - David J Irwin
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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29
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Abstract
In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.
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Affiliation(s)
- Thomas F. Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
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30
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He H, Deng H, Wang Q, Gao J. Percolation of temporal hierarchical mobility networks during COVID-19. Philos Trans A Math Phys Eng Sci 2022; 380:20210116. [PMID: 34802268 PMCID: PMC8607142 DOI: 10.1098/rsta.2021.0116] [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] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/08/2021] [Indexed: 05/03/2023]
Abstract
Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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Affiliation(s)
- Haoyu He
- Department of Computer Science and Center for Network Science and Technology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Hengfang Deng
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
| | - Qi Wang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
| | - Jianxi Gao
- Department of Computer Science and Center for Network Science and Technology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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31
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Abstract
Accelerating materials discovery is the cornerstone of modern technological competitiveness. Yet, the inorganic synthesis of new compounds is often an important bottleneck in this quest. Well-established quantum chemistry and experimental synthesis methods combined with consolidated network science approaches might provide revolutionary knowledge to tackle this challenge. Recent pioneering studies in this direction have shown that the topological analysis of material networks hold great potential to effectively explore the synthesizability of inorganic compounds. In this Perspective we discuss the most exciting work in this area, in particular emerging new physicochemical insights and general concepts on how network science can significantly help reduce the timescales required to discover new materials and find synthetic routes for their fabrication. We also provide a perspective on outstanding problems, challenges and open questions.
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Affiliation(s)
- Alex Aziz
- Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Vitoria-Gasteiz, Spain
| | - Javier Carrasco
- Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Vitoria-Gasteiz, Spain
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32
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Vitevitch MS, Mullin GJD. What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach. Brain Sci 2021; 11:brainsci11121628. [PMID: 34942930 PMCID: PMC8699506 DOI: 10.3390/brainsci11121628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 11/15/2021] [Revised: 12/05/2021] [Accepted: 12/09/2021] [Indexed: 11/16/2022] Open
Abstract
Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.
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33
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St Luce S, Sayama H. Network-Based Phase Space Analysis of the El Farol Bar Problem. Artif Life 2021; 27:113-130. [PMID: 34727159 DOI: 10.1162/artl_a_00347] [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] [Indexed: 06/13/2023]
Abstract
The El Farol Bar problem highlights the issue of bounded rationality through a coordination problem where agents must decide individually whether or not to attend a bar without prior communication. Each agent is provided a set of attendance predictors (or decision-making strategies) and uses the previous bar attendances to guess bar attendance for a given week to determine if the bar is worth attending. We previously showed how the distribution of used strategies among the population settles into an attractor by using a spatial phase space. However, this approach was limited as it required N - 1 dimensions to fully visualize the phase space of the problem, where N is the number of strategies available. Here we propose a new approach to phase space visualization and analysis by converting the strategy dynamics into a state transition network centered on strategy distributions. The resulting weighted, directed network gives a clearer representation of the strategy dynamics once we define an attractor of the strategy phase space as a sink-strongly connected component. This enables us to study the resulting network to draw conclusions about the performance of the different strategies. We find that this approach not only is applicable to the El Farol Bar problem, but also addresses the dimensionality issue and is theoretically applicable to a wide variety of discretized complex systems.
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Affiliation(s)
- Shane St Luce
- Binghamton University, State University of New York.
| | - Hiroki Sayama
- Binghamton University, State University of New York.
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34
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Gal E, Amsalem O, Schindel A, London M, Schürmann F, Markram H, Segev I. The Role of Hub Neurons in Modulating Cortical Dynamics. Front Neural Circuits 2021; 15:718270. [PMID: 34630046 PMCID: PMC8500625 DOI: 10.3389/fncir.2021.718270] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 05/31/2021] [Accepted: 08/24/2021] [Indexed: 12/03/2022] Open
Abstract
Many neurodegenerative diseases are associated with the death of specific neuron types in particular brain regions. What makes the death of specific neuron types particularly harmful for the integrity and dynamics of the respective network is not well understood. To start addressing this question we used the most up-to-date biologically realistic dense neocortical microcircuit (NMC) of the rodent, which has reconstructed a volume of 0.3 mm3 and containing 31,000 neurons, ∼37 million synapses, and 55 morphological cell types arranged in six cortical layers. Using modern network science tools, we identified hub neurons in the NMC, that are connected synaptically to a large number of their neighbors and systematically examined the impact of abolishing these cells. In general, the structural integrity of the network is robust to cells’ attack; yet, attacking hub neurons strongly impacted the small-world topology of the network, whereas similar attacks on random neurons have a negligible effect. Such hub-specific attacks are also impactful on the network dynamics, both when the network is at its spontaneous synchronous state and when it was presented with synchronized thalamo-cortical visual-like input. We found that attacking layer 5 hub neurons is most harmful to the structural and functional integrity of the NMC. The significance of our results for understanding the role of specific neuron types and cortical layers for disease manifestation is discussed.
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Affiliation(s)
- Eyal Gal
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Oren Amsalem
- Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alon Schindel
- Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michael London
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Idan Segev
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
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35
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Xu Q, Markowska M, Chodorow M, Li P. Modeling Bilingual Lexical Processing Through Code-Switching Speech: A Network Science Approach. Front Psychol 2021; 12:662409. [PMID: 34512435 PMCID: PMC8423917 DOI: 10.3389/fpsyg.2021.662409] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/05/2021] [Indexed: 11/28/2022] Open
Abstract
The study of code-switching (CS) speech has produced a wealth of knowledge in the understanding of bilingual language processing and representation. Here, we approach this issue by using a novel network science approach to map bilingual spontaneous CS speech. In Study 1, we constructed semantic networks on CS speech corpora and conducted community detections to depict the semantic organizations of the bilingual lexicon. The results suggest that the semantic organizations of the two lexicons in CS speech are largely distinct, with a small portion of overlap such that the semantic network community dominated by each language still contains words from the other language. In Study 2, we explored the effect of clustering coefficients on language choice during CS speech, by comparing clustering coefficients of words that were code-switched with their translation equivalents (TEs) in the other language. The results indicate that words where the language is switched have lower clustering coefficients than their TEs in the other language. Taken together, we show that network science is a valuable tool for understanding the overall map of bilingual lexicons as well as the detailed interconnections and organizations between the two languages.
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Affiliation(s)
- Qihui Xu
- Department of Psychology, Graduate Center, The City University of New York, New York, NY, United States
| | - Magdalena Markowska
- Department of Linguistics, Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, United States
| | - Martin Chodorow
- Department of Psychology, Graduate Center, The City University of New York, New York, NY, United States.,Department of Psychology, Hunter College, The City University of New York, New York, NY, United States
| | - Ping Li
- Department of Chinese and Bilingual Studies, Faculty of Humanities, The Hong Kong Polytechnic University, Hong Kong, China
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36
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Rincón-López J, Almanza-Arjona YC, Riascos AP, Rojas-Aguirre Y. When Cyclodextrins Met Data Science: Unveiling Their Pharmaceutical Applications through Network Science and Text-Mining. Pharmaceutics 2021; 13:1297. [PMID: 34452258 PMCID: PMC8399453 DOI: 10.3390/pharmaceutics13081297] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/14/2021] [Accepted: 08/16/2021] [Indexed: 12/21/2022] Open
Abstract
We present a data-driven approach to unveil the pharmaceutical technologies of cyclodextrins (CDs) by analyzing a dataset of CD pharmaceutical patents. First, we implemented network science techniques to represent CD patents as a single structure and provide a framework for unsupervised detection of keywords in the patent dataset. Guided by those keywords, we further mined the dataset to examine the patenting trends according to CD-based dosage forms. CD patents formed complex networks, evidencing the supremacy of CDs for solubility enhancement and how this has triggered cutting-edge applications based on or beyond the solubility improvement. The networks exposed the significance of CDs to formulate aqueous solutions, tablets, and powders. Additionally, they highlighted the role of CDs in formulations of anti-inflammatory drugs, cancer therapies, and antiviral strategies. Text-mining showed that the trends in CDs for aqueous solutions, tablets, and powders are going upward. Gels seem to be promising, while patches and fibers are emerging. Cyclodextrins' potential in suspensions and emulsions is yet to be recognized and can become an opportunity area. This is the first unsupervised/supervised data-mining approach aimed at depicting a landscape of CDs to identify trending and emerging technologies and uncover opportunity areas in CD pharmaceutical research.
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Affiliation(s)
- Juliana Rincón-López
- Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico;
| | - Yara C. Almanza-Arjona
- Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico;
| | - Alejandro P. Riascos
- Instituto de Física, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico
| | - Yareli Rojas-Aguirre
- Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico;
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37
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Hadjiabadi D, Lovett-Barron M, Raikov IG, Sparks FT, Liao Z, Baraban SC, Leskovec J, Losonczy A, Deisseroth K, Soltesz I. Maximally selective single-cell target for circuit control in epilepsy models. Neuron 2021; 109:2556-2572.e6. [PMID: 34197732 PMCID: PMC8448204 DOI: 10.1016/j.neuron.2021.06.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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: 12/03/2020] [Revised: 04/19/2021] [Accepted: 06/04/2021] [Indexed: 12/21/2022]
Abstract
Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.
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Affiliation(s)
- Darian Hadjiabadi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
| | - Matthew Lovett-Barron
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Fraser T Sparks
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Scott C Baraban
- Department of Neurological Surgery and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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38
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Rüdiger S, Konigorski S, Rakowski A, Edelman JA, Zernick D, Thieme A, Lippert C. Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones. Proc Natl Acad Sci U S A 2021; 118:e2026731118. [PMID: 34261775 DOI: 10.1073/pnas.2026731118] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 01/12/2023] Open
Abstract
Numerous COVID-19 studies used mobile phone data but with limited power in accounting for infection numbers. We have evaluated deidentified Global Positioning System (GPS) data from over 1 million devices in Germany and inferred contacts from coproximity of devices. Through calculating the contact graphs, we derived a contact index (CX) that exhibits a high correlation with the incidence-based reproduction number R so that changes in CX precede those in R by more than 2 wk. CX thus is an early indicator for outbreaks and can be used to guide social-distancing policies. Further questions, including those for the efficacy of vaccination, can be addressed with our method. We discuss limitations, e.g., transmission on international travel, and the relation to superspreading. Over the last months, cases of SARS-CoV-2 surged repeatedly in many countries but could often be controlled with nonpharmaceutical interventions including social distancing. We analyzed deidentified Global Positioning System (GPS) tracking data from 1.15 to 1.4 million cell phones in Germany per day between March and November 2020 to identify encounters between individuals and statistically evaluate contact behavior. Using graph sampling theory, we estimated the contact index (CX), a metric for number and heterogeneity of contacts. We found that CX, and not the total number of contacts, is an accurate predictor for the effective reproduction number R derived from case numbers. A high correlation between CX and R recorded more than 2 wk later allows assessment of social behavior well before changes in case numbers become detectable. By construction, the CX quantifies the role of superspreading and permits assigning risks to specific contact behavior. We provide a critical CX value beyond which R is expected to rise above 1 and propose to use that value to leverage the social-distancing interventions for the coming months.
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39
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Tompson SH, Falk EB, O'Donnell MB, Cascio CN, Bayer JB, Vettel JM, Bassett DS. Response inhibition in adolescents is moderated by brain connectivity and social network structure. Soc Cogn Affect Neurosci 2021; 15:827-837. [PMID: 32761131 PMCID: PMC7543938 DOI: 10.1093/scan/nsaa109] [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: 01/24/2020] [Revised: 04/28/2020] [Accepted: 07/15/2020] [Indexed: 11/12/2022] Open
Abstract
The social environment an individual is embedded in influences their ability and motivation to engage self-control processes, but little is known about the neural mechanisms underlying this effect. Many individuals successfully regulate their behavior even when they do not show strong activation in canonical self-control brain regions. Thus, individuals may rely on other resources to compensate, including daily experiences navigating and managing complex social relationships that likely bolster self-control processes. Here, we employed a network neuroscience approach to investigate the role of social context and social brain systems in facilitating self-control in adolescents. We measured brain activation using functional magnetic resonance imaging (fMRI) as 62 adolescents completed a Go/No-Go response inhibition task. We found that self-referential brain systems compensate for weaker activation in executive function brain systems, especially for adolescents with more friends and more communities in their social networks. Collectively, our results indicate a critical role for self-referential brain systems during the developmental trajectory of self-control throughout adolescence.
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Affiliation(s)
- Steven H Tompson
- US Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
| | - Emily B Falk
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA.,Wharton Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Christopher N Cascio
- School of Journalism and Mass Communication, University of Wisconsin, Madison, WI 53706, USA
| | - Joseph B Bayer
- School of Communication, The Ohio State University, Columbus, OH 43210, USA.,Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Jean M Vettel
- US Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.,Santa Fe Institute, Santa Fe, NM 87501, USA
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40
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Lin G, Siddiqui S, Bernstein J, Martinez DA, Gardner L, Albright T, Igusa T. Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals. J Am Med Inform Assoc 2021; 28:62-70. [PMID: 33164100 DOI: 10.1093/jamia/ocaa243] [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] [Received: 05/21/2020] [Revised: 08/19/2020] [Accepted: 09/17/2020] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors. MATERIALS AND METHODS We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors' collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics' relationship with success. RESULTS We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric (P < .01). Our results also showed a negative effect of the local clustering coefficient (P < .01) on the success of clinical trials. Large pharmaceutical companies have the lowest local clustering coefficient and more diversity in partnerships across biomedical specializations. CONCLUSIONS Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.
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Affiliation(s)
- Gary Lin
- Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.,Center for Data Science in Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sauleh Siddiqui
- Department of Environmental Science, American University, Washington, DC, USA
| | - Jen Bernstein
- Center for Leadership Education, Johns Hopkins University, Baltimore, Maryland, USA
| | - Diego A Martinez
- Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.,Center for Data Science in Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tenley Albright
- MIT Collaborative Initiatives, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Takeru Igusa
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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41
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Emmert-Streib F, Dehmer M. Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries. Front Big Data 2021; 4:591749. [PMID: 33969290 PMCID: PMC8100320 DOI: 10.3389/fdata.2021.591749] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 02/18/2021] [Indexed: 12/13/2022] Open
Abstract
The ultimate goal of the social sciences is to find a general social theory encompassing all aspects of social and collective phenomena. The traditional approach to this is very stringent by trying to find causal explanations and models. However, this approach has been recently criticized for preventing progress due to neglecting prediction abilities of models that support more problem-oriented approaches. The latter models would be enabled by the surge of big Web-data currently available. Interestingly, this problem cannot be overcome with methods from computational social science (CSS) alone because this field is dominated by simulation-based approaches and descriptive models. In this article, we address this issue and argue that the combination of big social data with social networks is needed for creating prediction models. We will argue that this alliance has the potential for gradually establishing a causal social theory. In order to emphasize the importance of integrating big social data with social networks, we call this approach data-driven computational social network science (DD-CSNS).
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Affiliation(s)
- Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.,Institute of Biosciences and Medical Technology, Tampere, Finland
| | - Matthias Dehmer
- Department of Computer Science, Swiss Distance University of Applied Sciences, Brig, Switzerland.,School of Science, Xian Technological University, Xian, China.,College of Artificial Intelligence, Nankai University, Tianjin, China.,Department of Biomedical Computer Science and Mechatronics, The Health and Life Science University, UMIT, Hall in Tyrol, Austria
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Galis ZS, Herr BW, Daptardar SS, Sydykanova M, Börner K. " Then and Now," Mapping the 25 Year Evolution and Impact of North American Vascular Biology Organization Science Through Publications of its Founding and Current Members. Front Res Metr Anal 2021; 5:591090. [PMID: 33870053 PMCID: PMC8025969 DOI: 10.3389/frma.2020.591090] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/14/2020] [Indexed: 11/29/2022] Open
Abstract
Scholarly organizations bring together experts to move forward specific areas of scientific endeavor. More than 5,000 scholarly societies exist world-wide yet little is known about the composition, evolution, and collaboration of experts within these organizations. This study presents general methods to study the evolution of a biomedical organization and its impact over time. Methods are illustrated in a case study that aims to capture the “Then and Now” science of the North American Vascular Biology Organization (NAVBO). Publications authored by the founding members who came together to create NAVBO in 1994 are compared with publications by those representing the society 25 years later. Google Scholar data was compiled for NAVBO members registered in 1994 (n = 70) and in 2019 (n = 465), some members being present in both data sets (n = 22). The 501 unique NAVBO members had more than 76,000 papers cited over 4,400,000 times. Several characteristics associated with the NAVBO members’ output were revealed, including their high productivity, the high impact of their publications, and the predominant contribution of relatively small laboratories, as suggested by a low average number of authors per publication. To understand how NAVBO fostered scientific collaborations and exchanges of expertize, data was analyzed and visualized to show the evolution of member co-author networks. The UCSD Map of Science was used to communicate the evolution of scientific topics covered by NAVBO members helping to create a global picture of NAVBO’s science “then and now.” All workflows are available online in case other scholarly organizations would want to use them to map their own evolution and impact; and meta studies that communicate the inner workings of these important scholarly efforts can be conducted.
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Affiliation(s)
- Zorina S Galis
- National Heart Lung and Blood Institute (NHLBI), Bethesda, MD, United States
| | - Bruce W Herr
- Cyberinfrastructure for Network Science Center, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
| | - Santoshmurti S Daptardar
- Cyberinfrastructure for Network Science Center, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
| | - Medina Sydykanova
- Cyberinfrastructure for Network Science Center, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
| | - Katy Börner
- Cyberinfrastructure for Network Science Center, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
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Abstract
Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well understood. We introduce a generative model for the dynamics of hierarchies using time-varying networks, in which new links are formed based on the preferences of nodes in the current network and old links are forgotten over time. The model produces a range of hierarchical structures, ranging from egalitarianism to bistable hierarchies, and we derive critical points that separate these regimes in the limit of long system memory. Importantly, our model supports statistical inference, allowing for a principled comparison of generative mechanisms using data. We apply the model to study hierarchical structures in empirical data on hiring patterns among mathematicians, dominance relations among parakeets, and friendships among members of a fraternity, observing several persistent patterns as well as interpretable differences in the generative mechanisms favored by each. Our work contributes to the growing literature on statistically grounded models of time-varying networks.
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Affiliation(s)
- Mari Kawakatsu
- Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544;
| | - Philip S Chodrow
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139;
- Department of Mathematics, University of California, Los Angeles, CA 90095
| | - Nicole Eikmeier
- Department of Computer Science, Grinnell College, Grinnell, IA 50112;
| | - Daniel B Larremore
- Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309;
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303
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44
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Qian Y, Expert P, Panzarasa P, Barahona M. Geometric graphs from data to aid classification tasks with Graph Convolutional Networks. Patterns (N Y) 2021; 2:100237. [PMID: 33982027 PMCID: PMC8085612 DOI: 10.1016/j.patter.2021.100237] [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: 10/12/2020] [Revised: 01/11/2021] [Accepted: 03/12/2021] [Indexed: 12/02/2022]
Abstract
Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here, we show that, even if additional relational information is not available in the dataset, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world datasets from various scientific domains. Geometric graphs from data can be used in deep learning to improve classification Optimized graphs align the data to the class labels and enhance class separability Sparsifying the optimized graph can potentially improve classification performance Extensive experiments are performed on datasets from various scientific domains
Supervised classification assigns unseen samples to classes based on their features by learning from examples with known class labels. We show that classification can be improved by using the sample features not only as the basis for classification, but also as a means to construct geometric graphs that encapsulate the closeness between samples. Such feature-derived graphs can be used within graph-based deep-learning models to improve classification. To understand the benefits of these graphs, we show that they align the data to the class labels and enhance class separability. We also demonstrate how to make the graphs sparser, and hence more efficient, while still potentially improving their performance. Our findings are timely given the increasing interest in combining graphs with classification and learning tasks.
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Affiliation(s)
- Yifan Qian
- School of Business and Management, Queen Mary University of London, London, UK
| | - Paul Expert
- Global Digital Health Unit, School of Public Health, Imperial College London, London, UK.,World Research Hub Initiative, Tokyo Institute of Technology, Tokyo, Japan
| | - Pietro Panzarasa
- School of Business and Management, Queen Mary University of London, London, UK
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Abstract
We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local social interactions, as well as to idiosyncratic factors preventing their population from reaching consensus. We model the latter to account for both scenarios where noise is entirely exogenous to peer influence and cases where it is instead endogenous, arising from the agents' desire to maintain some uniqueness in their opinions. We derive a general analytical expression for opinion diversity, which holds for any network and depends on the network's topology through its spectral properties alone. Using this expression, we find that opinion diversity decreases as communities and clusters are broken down. We test our predictions against data describing empirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by such sources on a variety of topics yields a notable improvement in terms of explanatory power.
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Affiliation(s)
- Samuel Stern
- Department of Computer Science, University College London, Gower Street, London WC1E 6EA, UK
| | - Giacomo Livan
- Department of Computer Science, University College London, Gower Street, London WC1E 6EA, UK
- Systemic Risk Centre, London School of Economics and Political Sciences, Houghton Street, London WC2A 2AE, UK
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46
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Kumar Das J, Tradigo G, Veltri P, H Guzzi P, Roy S. Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing. Brief Bioinform 2021; 22:855-872. [PMID: 33592108 PMCID: PMC7929414 DOI: 10.1093/bib/bbaa420] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/09/2020] [Accepted: 12/19/2020] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data. RESULTS Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions. CONTACT hguzzi@unicz.it, sroy01@cus.ac.in.
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Affiliation(s)
- Jayanta Kumar Das
- Department of Pediatrics, School of Medicine, Johns Hopkins University, Maryland, USA
| | - Giuseppe Tradigo
- eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy
| | - Pierangelo Veltri
- Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, 88100, Italy
| | - Pietro H Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, 88100, Italy
| | - Swarup Roy
- Network Reconstruction & Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, Gangtok, India
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Roy S, Cherevko A, Chakraborty S, Ghosh N, Ghosh P. Leveraging Network Science for Social Distancing to Curb Pandemic Spread. IEEE Access 2021; 9:26196-26207. [PMID: 34812379 PMCID: PMC8545212 DOI: 10.1109/access.2021.3058206] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 02/05/2021] [Indexed: 05/20/2023]
Abstract
COVID-19 has irreversibly upended the course of human life and compelled countries to invoke national emergencies and strict public guidelines. As the scientific community is in the early stages of rigorous clinical testing to come up with effective vaccination measures, the world is still heavily reliant on social distancing to curb the rapid spread and mortality rates. In this work, we present three optimization strategies to guide human mobility and restrict contact of susceptible and infective individuals. The proposed strategies rely on well-studied concepts of network science, such as clustering and homophily, as well as two different scenarios of the SEIRD epidemic model. We also propose a new metric, called contagion potential, to gauge the infectivity of individuals in a social setting. Our extensive simulation experiments show that the recommended mobility approaches slow down spread considerably when compared against several standard human mobility models. Finally, as a case study of the mobility strategies, we introduce a mobile application, MyCovid, that provides periodic location recommendations to the registered app users.
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Affiliation(s)
- Satyaki Roy
- Department of GeneticsUniversity of North CarolinaChapel HillNC27515USA
| | - Andrii Cherevko
- Department of Computer ScienceVirginia Commonwealth UniversityRichmondVA23284-3019USA
| | - Sayak Chakraborty
- Department of CSTIndian Institute of Engineering Science and TechnologyShibpur711103India
| | - Nirnay Ghosh
- Department of CSTIndian Institute of Engineering Science and TechnologyShibpur711103India
| | - Preetam Ghosh
- Department of Computer ScienceVirginia Commonwealth UniversityRichmondVA23284-3019USA
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48
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Karapiperis C, Chasapi A, Angelis L, Scouras ZG, Mastroberardino PG, Tapio S, Atkinson MJ, Ouzounis CA. The Coming of Age for Big Data in Systems Radiobiology, an Engineering Perspective. Big Data 2021; 9:63-71. [PMID: 32991205 DOI: 10.1089/big.2019.0144] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
As high-throughput approaches in biological and biomedical research are transforming the life sciences into information-driven disciplines, modern analytics platforms for big data have started to address the needs for efficient and systematic data analysis and interpretation. We observe that radiobiology is following this general trend, with -omics information providing unparalleled depth into the biomolecular mechanisms of radiation response-defined as systems radiobiology. We outline the design of computational frameworks and discuss the analysis of big data in low-dose ionizing radiation (LDIR) responses of the mammalian brain. Following successful examples and best practices of approaches for the analysis of big data in life sciences and health care, we present the needs and requirements for radiation research. Our goal is to raise awareness for the radiobiology community about the new technological possibilities that can capture complex information and execute data analytics on a large scale. The production of large data sets from genome-wide experiments (quantity) and the complexity of radiation research with multidimensional experimental designs (quality) will necessitate the adoption of latest information technologies. The main objective was to translate research results into applied clinical and epidemiological practice and understand the responses of biological tissues to LDIR to define new radiation protection policies. We envisage a future where multidisciplinary teams include data scientists, artificial intelligence experts, DevOps engineers, and of course radiation experts to fulfill the augmented needs of the radiobiology community, accelerate research, and devise new strategies.
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Affiliation(s)
- Christos Karapiperis
- School of Informatics, Aristotle University of Thessalonica (AUTH), Thessalonica, Greece
| | - Anastasia Chasapi
- Biological Computation & Process Laboratory (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), Thessalonica, Greece
| | - Lefteris Angelis
- School of Informatics, Aristotle University of Thessalonica (AUTH), Thessalonica, Greece
| | - Zacharias G Scouras
- School of Biology, Aristotle University of Thessalonica (AUTH), Thessalonica, Greece
| | | | - Soile Tapio
- Institute of Radiation Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (HMGU), Neuherberg, Germany
| | - Michael J Atkinson
- Institute of Radiation Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (HMGU), Neuherberg, Germany
| | - Christos A Ouzounis
- School of Informatics, Aristotle University of Thessalonica (AUTH), Thessalonica, Greece
- Biological Computation & Process Laboratory (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), Thessalonica, Greece
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Dusad V, Thiel D, Barahona M, Keun HC, Oyarzún DA. Opportunities at the Interface of Network Science and Metabolic Modeling. Front Bioeng Biotechnol 2021; 8:591049. [PMID: 33569373 PMCID: PMC7868444 DOI: 10.3389/fbioe.2020.591049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 08/03/2020] [Accepted: 12/22/2020] [Indexed: 12/17/2022] Open
Abstract
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.
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Affiliation(s)
- Varshit Dusad
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Denise Thiel
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Hector C Keun
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom.,Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.,School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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50
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