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Incoherent feedforward loop dominates the robustness and tunability of necroptosis biphasic, emergent, and coexistent dynamics

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Fundamental Research 2024年

作者： Xu, Fei Li, Xiang Wu, Rui Qi, Hong Jin, Jun Liu, Zhilong Wu, Yuning Lin, Hai Shen, Chuansheng Shuai, Jianwei Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials Research Xiamen University Xiamen 361005 China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling Network School of Life Sciences Xiamen University Xiamen 361102 China Complex Systems Research Center Shanxi University Taiyuan 030006 China Department of Mathematics and Physics Fujian Jiangxia University Fuzhou 350108 China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) and Wenzhou Institute University of Chinese Academy of Sciences Wenzhou 325001 China School of Mathematics and Physics Anqing Normal University Anqing 246011 China Department of Physics Anhui Normal University Wuhu 241002 China

Biphasic dynamics, the variable-dependent ability to enhance or restrain biological function, is prevalent in natural systems. Accompanied by biphasic dynamics, necroptosis signaling also appears emergent and coexistent dynamics. However, it remains elusive how the properties of these dynamics are characterized by specific circuit structures and components. Starting with necroptosis circuit modeling, we systematically analyzed the network topology for achieving RIP1-dependent biphasic, emergent, and coexistent (BEC) dynamics. RIP1-RIP3-Caspase-8 (C8) incoherent feedforward loop embedded with positive feedback of RIP3 to RIP1 is identified as the core topology. The peak value of RIP3 phosphorylation is determined to present a scale-invariant feature, dictating BEC dynamics and the bell-shaped regulation of necroptosis biphasic dynamics. To quantitatively determine the uncertainty of necroptosis coexistent dynamics, potential landscape and Shannon entropy that measure entropy production during cell death are introduced for the first time. Further random necroptosis circuit analysis identifies the bell-shaped regulation of necroptosis biphasic dynamics by RIP3 auto-phosphorylation, which acts as a complementary process for robustly attaining BEC dynamics. Finally, we searched all possible two- and three-node circuit topologies to screen those that could perform BEC dynamics. A complete atlas of three-node circuit BEC dynamics is generated and only three minimal circuits emerge as robust solutions, confirming incoherent feedforward loop is the core topology. Analysis of the association between the minimal circuit structure and robustness proves that the identified optimal functional achievement structure is highly consistent with the experimental observed RIP1-RIP3-C8 topology. Overall, through highlighting a finite set of circuits, this study yields guiding principles that enable the mapping, modulation, and design of circuits for BEC dynamics in diverse synthetic biol

关键词： Biphasic dynamics Design principle Emergence Incoherent feedforward loop Necroptosis

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Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance

arXiv

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arXiv 2021年

作者： Minot, Joshua R. Cheney, Nicholas Maier, Marc Elbers, Danne C. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Neurobotics Lab University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States VA Cooperative Studies Program VA Boston Healthcare System BostonMA02130 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05401 United States

Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how word choices made by healthcare practitioners and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce the potential for bias in natural language processing pipelines. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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Why Are There Six Degrees of Separation in a Social Network?

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Physical Review X 2023年第2期13卷 021032-021032页

作者： I. Samoylenko D. Aleja E. Primo K. Alfaro-Bittner E. Vasilyeva K. Kovalenko D. Musatov A. M. Raigorodskii R. Criado M. Romance D. Papo M. Perc B. Barzel S. Boccaletti Moscow Institute of Physics and Technology 9 Institutskiy Pereulok Dolgoprudny Moscow Region 141701 Russia National Research University Higher School of Economics 6 Ulitsa Usacheva Moscow 119048 Russia Universidad Rey Juan Carlos Calle Tulipán s/n 28933 Móstoles Madrid Spain Department of Internal Medicine University of Michigan Ann Arbor Michigan 48109 USA Laboratory of Mathematical Computation on Complex Networks and their Applications Universidad Rey Juan Carlos Calle Tulipán s/n Móstoles 28933 Madrid Spain P.N. Lebedev Physical Institute of the Russian Academy of Sciences 53 Leninsky Prospekt Moscow 119991 Russia Scuola Superiore Meridionale Largo S. Marcellino 10 80138 Napoli NA Italy Russian Academy of National Economy and Public Administration 84 Prospekt Vernadskogo Moscow 119606 Russia Caucasus Mathematical Center at Adyghe State University 208 Pervomayskaya Ulitsa Maykop Adygea 385000 Russia Moscow State University 1 Leninskie Gory Moscow 119991 Russia Institute of Mathematics and Computer Science Buryat State University 5 Ulitsa Ranzhurova Ulan-Ude Buryatia 670000 Russia Data Complex Networks and Cybersecurity Research Institute Universidad Rey Juan Carlos Plaza Manuel Becerra 14 28028 Madrid Spain Department of Neuroscience and Rehabilitation University of Ferrara Ferrara Italy Center for Translational Neurophysiology of Speech and Communication Fondazione Istituto Italiano di Tecnologia Ferrara Italy Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 2000 Maribor Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung 404332 Taiwan Complexity Science Hub Vienna Josefstädterstraße 39 1080 Vienna Austria Alma Mater Europaea Slovenska ulica 17 2000 Maribor Slovenia Department of Physics Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul Republic of Korea Department of Mathematics Bar-Ilan University Ramat-Gan 52900 Israel The Gonda Multi

A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. We show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. We show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, our results show how simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.

关键词： Degree distributions Network evolution Real world networks Small-world networks

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Non-equilibrium time-relaxation kinetic model for compressible turbulence modeling

arXiv

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arXiv 2021年

作者： Cao, Guiyu Pan, Liang Xu, Kun Wan, Minping Chen, Shiyi Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Guangdong Shenzhen518055 China Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Laboratory of Mathematics and Complex Systems School of Mathematical Sciences Beijing Normal University Beijing100875 China Shenzhen Research Institute Hong Kong University of Science and Technology Guangdong Shenzhen518057 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Jt. Lab. for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Guangdong Shenzhen518055 China

For the first time, the non-equilibrium time-relaxation kinetic model (NTRKM) is proposed for compressible turbulence modeling on unresolved grids. Within the nonequilibrium time-relaxation framework, NTRKM is extended in the form of modified Bhatnagar-Gross-Krook model. Based on the first-order Chapman-Enskog expansion, NTRKM connects with the six-variable macroscopic governing equations. The first five governing equations correspond to the conservative laws in mass, momentum and total energy, while the sixth equation governs the evolution of unresolved turbulence kinetic energy Kutke. The unknowns in NTRKM, including turbulent relaxation time and source term, are determined by essential gradient-type assumption and standard dynamic modeling approach. Current generalized kinetic model on unresolved grids consequently offers a profound mesoscopic understanding for one-equation subgrid-scale turbulence kinetic energy Ksgs model in compressible large eddy simulation. To solve NTRKM accurately and robustly, a non-equilibrium gas-kinetic scheme is developed, which succeeds the well-established gas-kinetic scheme for simulating Navier-Stokes equations. Three-dimensional decaying compressible isotropic turbulence and temporal compressible plane mixing layer on unresolved grids are simulated to evaluate the generalized kinetic model and non-equilibrium gas-kinetic scheme. The performance of key turbulent quantities up to second-order statistics confirms that NTRKM is comparable with the widely-used eddy-viscosity Smagorinsky model (SM) and dynamic Smagorinsky model (DSM). Specifically, compared with the DNS solution in temporal compressible plane mixing layer, the performance of NTRKM is much closer with DSM and better than SM. This study provides a workable approach for compressible turbulence modeling on unresolved grids, enriching the understanding of turbulence modeling within the non-equilibrium time-relaxation framework. © 2021, CC BY.

关键词： Kinetic theory

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Random walks on hypergraphs

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Physical Review E 2020年第2期101卷 022308-022308页

作者： Timoteo Carletti Federico Battiston Giulia Cencetti Duccio Fanelli Namur Institute for Complex Systems University of Namur 5000 Namur Belgium Department of Network and Data Science Central European University Budapest 1051 Hungary Mobile and Social Computing Lab Fondazione Bruno Kessler Via Sommarive 18 38123 Povo Trento Italy Dipartimento di Fisica e Astronomia Università di Firenze INFN and CSDC Via Sansone 1 50019 Sesto Fiorentino Firenze Italy

In the past 20 years network science has proven its strength in modeling many real-world interacting systems as generic agents, the nodes, connected by pairwise edges. Nevertheless, in many relevant cases, interactions are not pairwise but involve larger sets of nodes at a time. These systems are thus better described in the framework of hypergraphs, whose hyperedges effectively account for multibody interactions. Here we propose and study a class of random walks defined on such higher-order structures and grounded on a microscopic physical model where multibody proximity is associated with highly probable exchanges among agents belonging to the same hyperedge. We provide an analytical characterization of the process, deriving a general solution for the stationary distribution of the walkers. The dynamics is ultimately driven by a generalized random-walk Laplace operator that reduces to the standard random-walk Laplacian when all the hyperedges have size 2 and are thus meant to describe pairwise couplings. We illustrate our results on synthetic models for which we have full control of the high-order structures and on real-world networks where higher-order interactions are at play. As the first application of the method, we compare the behavior of random walkers on hypergraphs to that of traditional random walkers on the corresponding projected networks, drawing interesting conclusions on node rankings in collaboration networks. As the second application, we show how information derived from the random walk on hypergraphs can be successfully used for classification tasks involving objects with several features, each one represented by a hyperedge. Taken together, our work contributes to unraveling the effect of higher-order interactions on diffusive processes in higher-order networks, shedding light on mechanisms at the heart of biased information spreading in complex networked systems.

关键词： Collective behavior in networks Random walks

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Probability-turbulence divergence: A tunable allotaxonometric instrument for comparing heavy-tailed categorical distributions

arXiv

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05401 United States Vermont Advanced Computing Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States MassMutual Data Science AmherstMA01002 United States Howard Hughes Medical Institute University of California Berkeley BerkeleyCA94720 United States Data Visualization Lab Khoury College of Computer Sciences Northeastern University BostonMA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

Real-world complex systems often comprise many distinct types of elements as well as many more types of networked interactions between elements. When the relative abundances of types can be measured well, we further observe heavy-tailed categorical distributions for type frequencies. For the comparison of type frequency distributions of two systems or a system with itself at different time points in time—a facet of allotaxonometry—a great range of probability divergences are available. Here, we introduce and explore ‘probability-turbulence divergence’, a tunable, straightforward, and interpretable instrument for comparing normalizable categorical frequency distributions. We model probability-turbulence divergence (PTD) after rank-turbulence divergence (RTD). While probability-turbulence divergence is more limited in application than rank-turbulence divergence, it is more sensitive to changes in type frequency. We build allotaxonographs to display probability turbulence, incorporating a way to visually accommodate zero probabilities for ‘exclusive types’ which are types that appear in only one system. We explore comparisons of example distributions taken from literature, social media, and ecology. We show how probability-turbulence divergence either explicitly or functionally generalizes many existing kinds of distances and measures, including, as special cases, L(p) norms, the Sørensen-Dice coefficient (the F1 statistic), and the Hellinger distance. We discuss similarities with the generalized entropies of Rényi and Tsallis, and the diversity indices (or Hill numbers) from ecology. We close with thoughts on open problems concerning the optimization of the tuning of rank- and probability-turbulence divergence. Copyright © 2020, The Authors. All rights reserved.

关键词： Normal distribution

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Evolving ab initio trading strategies in heterogeneous environments

arXiv

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arXiv 2019年

作者： Dewhurst, David Rushing Li, Yi Bogdan, Alexander Geng, Jasmine MassMutual Center of Excellence in Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center Computational Story Lab Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States MassMutual Data Science BostonMA02210 United States

Securities markets are quintessential complex adaptive systems in which heterogeneous agents compete in an attempt to maximize returns. Species of trading agents are also subject to evolutionary pressure as entire classes of strategies become obsolete and new classes emerge. Using an agent-based model of interacting heterogeneous agents as a flexible environment that can endogenously model many diverse market conditions, we subject deep neural networks to evolutionary pressure to create dominant trading agents. After analyzing the performance of these agents and noting the emergence of anomalous superdiffusion through the evolutionary process, we construct a method to turn high-fitness agents into trading algorithms. We backtest these trading algorithms on real high-frequency foreign exchange data, demonstrating that elite trading algorithms are consistently profitable in a variety of market conditions-even though these algorithms had never before been exposed to real financial data. These results provide evidence to suggest that developing ab initio trading strategies by repeated simulation and evolution in a mechanistic market model may be a practical alternative to explicitly training models with past observed market data. Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous agents

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Future-proofing cities against negative city mobility and public health impacts of impending natural hazards: a system dynamics modelling study

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The Lancet Planetary Health 2025年第3期9卷 e207-e218页

作者： Garcia, Leandro Hafezi, Mehdi Lima, Larissa Millett, Christopher Thompson, Jason Wang, Ruoyu Akaraci, Selin Goel, Rahul Reis, Rodrigo Nice, Kerry A Zapata-Diomedi, Belen Hallal, Pedro C Moro, Esteban Amoako, Clifford Hunter, Ruth F Centre for Public Health Queen's University Belfast Belfast United Kingdom Physical Activity Epidemiology Group University of São Paulo São Paulo Brazil Complex Systems Governance Group Faculty of Engineering and Applied Sciences Cranfield University Cranfield United Kingdom Public Health Policy Evaluation Unit School of Public Health Imperial College London London United Kingdom NOVA National School of Public Health Public Health Research Centre Comprehensive Health Research Center NOVA University Lisbon Lisbon Portugal Instituto de Estudos para Políticas de Saúde São Paulo Brazil Transport Health and Urban Systems Research Lab Faculty of Architecture Building and Planning University of Melbourne Melbourne VIC Australia Institute of Public Health and Wellbeing University of Essex Essex United Kingdom Transportation Research and Injury Prevention Centre Indian Institute of Technology Delhi New Delhi India People Health & Place Research Unit Prevention Research Center School of Public Health Washington University in St. Louis St Louis MO United States Healthy Liveable Cities Lab Centre for Urban Research RMIT University Melbourne VIC Australia Department of Health and Kinesiology University of Illinois Urbana–Champaign IL United States Connection Science Institute for Data Science and Society MIT Cambridge MA United States Department of Mathematics and GISC Universidad Carlos III de Madrid Leganes Spain Department of Planning College of Art and Built Environment Kwame Nkrumah University of Science and Technology Kumasi Ghana

Background: The world faces increasing risk from more frequent and larger scale natural hazards, including infectious disease outbreaks (IDOs) and climate change-related extreme weather events (EWEs). These natural hazards are expected to have adverse mobility and public health impacts, with people living in cities especially vulnerable. Little is known about how transport systems can be optimally designed to make cities more resilient to these hazards. Our aim was to investigate how cities’ transport systems, and their resulting mobility patterns, affect their capabilities to mitigate mobility and health impacts of future large-scale IDOs and EWEs. Methods: System dynamics modelling was used to investigate how different city mobility scenarios can affect the health and mobility impacts of four plausible future IDO and EWE (flooding) shocks in three cities: Belfast, UK;Belo Horizonte, Brazil;and Delhi, India. Three city mobility scenarios with incremental degrees of modal shift towards active travel (private motor vehicle volume reduced to 50% and 20% of total road trip volume in vision 1 and 2, and motor vehicle volume [including buses] reduced to 20% of total road trip volume in vision 3) were tested. For each city and each IDO and EWE shock, we estimated the percentage of deaths prevented in visions 1, 2, and 3, relative to the reference scenario, as well as changes in mode share over time. Findings: In all scenarios, all cities showed reduced susceptibility to flooding, with 4–50% of deaths potentially prevented, depending on case city, city mobility, and EWE scenario. The more ambitious the transition towards healthier city mobility patterns, the greater the resilience against flooding. Only vision 3 (the most ambitious transition) showed reduced vulnerability to IDOs, with 6–19% of deaths potentially prevented. Evolution of mode shares varied greatly across cities and mobility scenarios under the IDO shocks. Interpretation: Our results emphasise the importance

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A practical method for estimating coupling functions in complex dynamical systems

arXiv

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arXiv 2019年

作者： Tokuda, Isao T. Levnajic, Zoran Ishimura, Kazuyoshi Department of Mechanical Engineering Ritsumeikan University Kusatsu Japan Complex Systems and Data Science Lab Faculty of Information Studies in Novo Mesto Novo Mesto Slovenia

A foremost challenge in modern network science is the inverse problem of reconstruction (inference) of coupling equations and network topology from the measurements of the network dynamics. Of particular interest are the methods that can operate on real (empirical) data without interfering with the system. One of such earlier attempts [Tokuda et al., PRL, 2007] was a method suited for general limit-cycle oscillators, yielding both oscillators’ natural frequencies and coupling functions between them (phase equations) from empirically measured time series. The present paper reviews the above method in a way comprehensive to domain-scientists other than physics. It also presents applications of the method to (i) detection of the network connectivity, (ii) inference of the phase sensitivity function, (iii) approximation of the interaction among phase-coherent chaotic oscillators, (iv) experimental data from a forced Van der Pol electric circuit. This reaffirms the range of applicability of the method for reconstructing coupling functions and makes it accessible to a much wider scientific community. Copyright © 2019, The Authors. All rights reserved.

关键词： Circuit oscillations

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Fame and Ultrafame: Measuring and comparing daily levels of 'being talked about' for United States' presidents, their rivals, God, countries, and K-pop

arXiv

引用

arXiv 2019年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States

When building a global brand of any kind-a political actor, clothing style, or belief system-developing widespread awareness is a primary goal. Short of knowing any of the stories or products of a brand, being talked about in whatever fashion-raw fame-is, as Oscar Wilde would have it, better than not being talked about at all. Here, we measure, examine, and contrast the day-to-day raw fame dynamics on Twitter for US Presidents and major US Presidential candidates from 2008 to 2020: Barack Obama, John McCain, Mitt Romney, Hillary Clinton, Donald Trump, and Joe Biden. We assign "lexical fame" to be the number and (Zipfian) rank of the (lowercased) mentions made for each individual across all languages. We show that all five political figures have at some point reached extraordinary volume levels of what we define to be "lexical ultrafame": An overall rank of approximately 300 or less which is largely the realm of function words and demarcated by the highly stable rank of 'god'. By this measure, 'trump' has become enduringly ultrafamous, from the 2016 election on. We use typical ranks for country names and function words as standards to improve perception of scale. We quantify relative fame rates and find that in the eight weeks leading up the 2008 and 2012 elections, 'obama' held a 1000:757 volume ratio over 'mccain' and 1000:892 over 'romney', well short of the 1000:544 and 1000:504 volumes favoring 'trump' over 'hillary' and 'biden' in the 8 weeks leading up to the 2016 and 2020 elections. Finally, we track how only one other entity has more sustained ultrafame than 'trump' on Twitter: The K-pop (Korean pop) band BTS. We chart the dramatic rise of BTS, finding their Twitter handle '@bts twt' has been able to compete with 'a' and 'the', reaching a rank of three at the day scale and a rank of one at the quarter-hour scale. Our findings for BTS more generally point to K-pop's growing economic, social, and political power. Copyright © 2019, The Authors. All rights reser

关键词： Social networking (online)

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