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Gielen J, Stessens L, Meeusen R, Aerts JM. Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests. Physiol Meas 2024; 45:065008. [PMID: 38861999 DOI: 10.1088/1361-6579/ad56f7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Objective.The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (V˙O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linearV˙O2responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR andV˙O2data of single ramp incremental running tests.Approach.A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate.Main results.As the work rate increases,τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s forV˙O2. Both increases are statistically significant (p< 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h-1)-1for HR, and from 218 to 144 ml·min-1forV˙O2(p< 0.01 for decrease parameters of HR andV˙O2). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures.Significance.We show that time-variant modeling is able to determine the time-varying dynamics HR andV˙O2responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.
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Affiliation(s)
- Jasper Gielen
- Biosystems Department, Research Group M3-BIORES, KU Leuven, 3001 Leuven, Belgium
| | - Loes Stessens
- Biosystems Department, Research Group M3-BIORES, KU Leuven, 3001 Leuven, Belgium
| | - Romain Meeusen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Jean-Marie Aerts
- Biosystems Department, Research Group M3-BIORES, KU Leuven, 3001 Leuven, Belgium
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Niu H, Chen Y, West BJ. Why Do Big Data and Machine Learning Entail the Fractional Dynamics? ENTROPY (BASEL, SWITZERLAND) 2021; 23:297. [PMID: 33671047 PMCID: PMC7997214 DOI: 10.3390/e23030297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 02/23/2021] [Accepted: 02/24/2021] [Indexed: 11/16/2022]
Abstract
Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: "is there a more optimal way to optimize?". Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.
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Affiliation(s)
- Haoyu Niu
- Electrical Engineering and Computer Science Department, University of California, Merced, CA 95340, USA;
| | - YangQuan Chen
- Mechanical Engineering Department, University of California, Merced, CA 95340, USA
| | - Bruce J. West
- Office of the Director, Army Research Office, Research Triangle Park, NC 27709, USA;
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Alderson DL, Doyle JC, Willinger W. Lessons from "a first-principles approach to understanding the internet's router-level topology". ACM SIGCOMM COMPUTER COMMUNICATION REVIEW 2019. [DOI: 10.1145/3371934.3371964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Our main purpose for this editorial is to reiterate the main message that we tried to convey in our SIGCOMM'04 paper but that got largely lost in all the hype surrounding the use of scale-free network models throughout the sciences in the last two decades. That message was that because of (1) the Internet's highly-engineered architecture, (2) a thorough understanding of its component technologies, and (3) the availability of extensive (but typically noisy) measurements, this complex man-made system affords unique opportunities to unambiguously resolve most claims about its properties, structure, and functionality. In the process, we point out the fallacy of popular approaches that consider complex systems such as the Internet from the perspective of
disorganized complexity
and argue for renewed efforts and increased focus on advancing an "architecture first" view with its emphasis on studying the
organized complexity
of systems such as the Internet.
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Bhat R, Pally D. Complexity: the organizing principle at the interface of biological (dis)order. J Genet 2018; 96:431-444. [PMID: 28761007 DOI: 10.1007/s12041-017-0793-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The term complexity means several things to biologists.When qualifying morphological phenotype, on the one hand, it is used to signify the sheer complicatedness of living systems, especially as a result of the multicomponent aspect of biological form. On the other hand, it has been used to represent the intricate nature of the connections between constituents that make up form: a more process-based explanation. In the context of evolutionary arguments, complexity has been defined, in a quantifiable fashion, as the amount of information, an informatic template such as a sequence of nucleotides or amino acids stores about its environment. In this perspective, we begin with a brief review of the history of complexity theory. We then introduce a developmental and an evolutionary understanding of what it means for biological systems to be complex.We propose that the complexity of living systems can be understood through two interdependent structural properties: multiscalarity of interconstituent mechanisms and excitability of the biological materials. The answer to whether a system becomes more or less complex over time depends on the potential for its constituents to interact in novel ways and combinations to give rise to new structures and functions, as well as on the evolution of excitable properties that would facilitate the exploration of interconstituent organization in the context of their microenvironments and macroenvironments.
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Affiliation(s)
- Ramray Bhat
- Department of Molecular Reproduction Development and Genetics, Indian Institute of Science, Bengaluru 560 012, India.
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Stone DJ, Csete M. Actuating critical care therapeutics. J Crit Care 2016; 35:90-5. [PMID: 27481741 DOI: 10.1016/j.jcrc.2016.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Revised: 04/30/2016] [Accepted: 05/04/2016] [Indexed: 10/21/2022]
Abstract
Viewing the intensive care unit (ICU) as a control system with inputs (patients) and outputs (outcomes), we focus on actuation (therapies) of the system and how to enhance our understanding of status of patients and their trajectory in the ICU. To incorporate the results of these analytics meaningfully, we feel that a reassessment of predictive scoring systems and of ways to optimally characterize and display the patient's "state space" to clinicians is important. Advances in sensing (diagnostics) and computation have not yet led to significantly better actuation, and so we focus on ways that data can be used to improve actuation in the ICU, in particular by following therapeutic burden along with disease severity. This article is meant to encourage discussion about how the critical care community can best deal with the data they see each day, and prepare for recommendations that will inevitably arise from application of major federal and state initiatives in big data analytics and precision medicine.
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Affiliation(s)
- David J Stone
- Departments of Anesthesiology and Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA; Center for Wireless Health, University of Virginia School of Engineering and Applied Science, Charlottesville, VA.
| | - Marie Csete
- Huntington Medical Research Institutes, Pasadena, CA.
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The application of information theory for the research of aging and aging-related diseases. Prog Neurobiol 2016; 157:158-173. [PMID: 27004830 DOI: 10.1016/j.pneurobio.2016.03.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 03/13/2016] [Accepted: 03/19/2016] [Indexed: 11/23/2022]
Abstract
This article reviews the application of information-theoretical analysis, employing measures of entropy and mutual information, for the study of aging and aging-related diseases. The research of aging and aging-related diseases is particularly suitable for the application of information theory methods, as aging processes and related diseases are multi-parametric, with continuous parameters coexisting alongside discrete parameters, and with the relations between the parameters being as a rule non-linear. Information theory provides unique analytical capabilities for the solution of such problems, with unique advantages over common linear biostatistics. Among the age-related diseases, information theory has been used in the study of neurodegenerative diseases (particularly using EEG time series for diagnosis and prediction), cancer (particularly for establishing individual and combined cancer biomarkers), diabetes (mainly utilizing mutual information to characterize the diseased and aging states), and heart disease (mainly for the analysis of heart rate variability). Few works have employed information theory for the analysis of general aging processes and frailty, as underlying determinants and possible early preclinical diagnostic measures for aging-related diseases. Generally, the use of information-theoretical analysis permits not only establishing the (non-linear) correlations between diagnostic or therapeutic parameters of interest, but may also provide a theoretical insight into the nature of aging and related diseases by establishing the measures of variability, adaptation, regulation or homeostasis, within a system of interest. It may be hoped that the increased use of such measures in research may considerably increase diagnostic and therapeutic capabilities and the fundamental theoretical mathematical understanding of aging and disease.
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Abstract
Integral feedback for perfect adaptation is a ubiquitous strategy in engineering and biology. Long studied in deterministic settings, it can now be understood in the context of the fully stochastic systems that are prevalent in biology.
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Affiliation(s)
- John C Doyle
- Computing and Mathematical Sciences, Electrical Engineering, and Bioengineering, California Institute of Technology, Pasadena, CA 91125, USA.
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Glass L. Dynamical disease: Challenges for nonlinear dynamics and medicine. CHAOS (WOODBURY, N.Y.) 2015; 25:097603. [PMID: 26428556 DOI: 10.1063/1.4915529] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Dynamical disease refers to illnesses that are associated with striking changes in the dynamics of some bodily function. There is a large literature in mathematics and physics which proposes mathematical models for the physiological systems and carries out analyses of the properties of these models using nonlinear dynamics concepts involving analyses of the stability and bifurcations of attractors. This paper discusses how these concepts can be applied to medicine.
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Affiliation(s)
- Leon Glass
- Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
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Stone DJ, Celi LA, Csete M. Engineering control into medicine. J Crit Care 2015; 30:652.e1-7. [PMID: 25680579 DOI: 10.1016/j.jcrc.2015.01.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Revised: 01/23/2015] [Accepted: 01/26/2015] [Indexed: 02/07/2023]
Abstract
The human body is a tightly controlled engineering miracle. However, medical training generally does not cover "control" (in the engineering sense) in physiology, pathophysiology, and therapeutics. A better understanding of how evolved controls maintain normal homeostasis is critical for understanding the failure mode of controlled systems, that is, disease. We believe that teaching and research must incorporate an understanding of the control systems in physiology and take advantage of the quantitative tools used by engineering to understand complex systems. Control systems are ubiquitous in physiology, although often unrecognized. Here we provide selected examples of the role of control in physiology (heart rate variability, immunity), pathophysiology (inflammation in sepsis), and therapeutic devices (diabetes and the artificial pancreas). We also present a high-level background to the concept of robustly controlled systems and examples of clinical insights using the controls framework.
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Affiliation(s)
- David J Stone
- Departments of Anesthesiology and Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA; Center for Wireless Health, University of Virginia School of Engineering and Applied Science, Charlottesville, VA.
| | - Leo Anthony Celi
- Laboratory of Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA.
| | - Marie Csete
- Huntington Medical Research Institutes, Pasadena, CA.
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