101
|
Kyogoku D, Kondoh M, Sota T. Does past evolutionary history under different mating regimes influence the demographic dynamics of interspecific competition? Ecol Evol 2019; 9:8616-8624. [PMID: 31410266 PMCID: PMC6686342 DOI: 10.1002/ece3.5397] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 05/28/2019] [Indexed: 11/08/2022] Open
Abstract
Interspecific interactions are contingent upon organism phenotypes, and thus phenotypic evolution can modify interspecific interactions and affect ecological dynamics. Recent studies have suggested that male-male competition within a species selects for capability to reproductively interfere with a closely related species. Here, we examine the effect of past evolutionary history under different mating regimes on the demographic dynamics of interspecific competition in Callosobruchus seed beetles. We used previously established experimental evolution lines of Callosobruchus chinensis that evolved under either forced lifelong monogamy or polygamy for 17 generations, and examined the demographic dynamics of competition between these C. chinensis lines and a congener, Callosobruchus maculatus. Callosobruchus chinensis was competitively excluded by C. maculatus in all trials. Time series data analyses suggested that reproductive interference from C. chinensis was relatively more important in the trials involving polygamous C. chinensis than those involving monogamous C. chinensis, in accordance with the potentially higher reproductive interference capability of polygamous C. chinensis. However, the estimated signs and magnitudes of interspecific interactions were not fully consistent with this explanation, implying the evolution of not only reproductive interference but also other interaction mechanisms. Our study thus suggests multifaceted effects of sexually selected traits on interspecific competitive dynamics.
Collapse
Affiliation(s)
- Daisuke Kyogoku
- Ecological Integration, Graduate School of Life SciencesTohoku UniversityAobaSendaiJapan
- Department of Science and TechnologyRyukoku UniversitySetaOtsuJapan
- Department of Zoology, Graduate School of ScienceKyoto UniversitySakyoKyotoJapan
- Research Institute for Food and AgricultureRyukoku UniversityOtsuShiga520‐2194Japan
| | - Michio Kondoh
- Ecological Integration, Graduate School of Life SciencesTohoku UniversityAobaSendaiJapan
| | - Teiji Sota
- Department of Zoology, Graduate School of ScienceKyoto UniversitySakyoKyotoJapan
| |
Collapse
|
102
|
Anneville O, Chang C, Dur G, Souissi S, Rimet F, Hsieh C. The paradox of re‐oligotrophication: the role of bottom–up versus top–down controls on the phytoplankton community. OIKOS 2019. [DOI: 10.1111/oik.06399] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Orlane Anneville
- INRA, UMR CARRTEL,75 bis avenue de Corzent FR‐74200 Thonon les Bains France
| | - Chun‐Wei Chang
- Research Center for Environmental Changes, Academia Sinica Taipei Taiwan
| | - Gaël Dur
- INRA, UMR CARRTEL France
- Creative Science Unit (Geosciences), Faculty of Science, Shizuoka Univ Japan
| | - Sami Souissi
- Univ. de Lille, CNRS, Université du Littoral Côte d'Opale, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences France
| | | | - Chih‐hao Hsieh
- Research Center for Environmental Changes, Academia Sinica Taipei Taiwan
- Inst. of Oceanography, National Taiwan Univ Taipei Taiwan
| |
Collapse
|
103
|
Laneri K, Cabella B, Prado PI, Mendes Coutinho R, Kraenkel RA. Climate drivers of malaria at its southern fringe in the Americas. PLoS One 2019; 14:e0219249. [PMID: 31291316 PMCID: PMC6619762 DOI: 10.1371/journal.pone.0219249] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 06/19/2019] [Indexed: 01/01/2023] Open
Abstract
In this work we analyze potential environmental drivers of malaria cases in Northwestern Argentina. We inspect causal links between malaria and climatic variables by means of the convergent cross mapping technique, which provides a causality criterion from the theory of dynamic systems. Analysis is based on 12 years of weekly malaria P. vivax cases in Tartagal, Salta, Argentina-at the southern fringe of malaria incidence in the Americas-together with humidity and temperature time-series spanning the same period. Our results show that there are causal links between malaria cases and both maximum temperature, with a delay of five weeks, and minimum temperature, with delays of zero and twenty two weeks. Humidity is also a driver of malaria cases, with thirteen weeks delay between cause and effect. Furthermore we also determined the sign and strength of the effects. Temperature has always a positive non-linear effect on cases, with maximum temperature effects more pronounced above 25°C and minimum above 17°C, while effects of humidity are more intricate: maximum humidity above 85% has a negative effect, whereas minimum humidity has a positive effect on cases. These results might be signaling processes operating at short (below 5 weeks) and long (over 12 weeks) time delays, corresponding to effects related to parasite cycle and mosquito population dynamics respectively. The non-linearities found for the strength of the effect of temperature on malaria cases make warmer areas more prone to higher increases in the disease incidence. Moreover, our results indicate that an increase of extreme weather events could enhance the risks of malaria spreading and re-emergence beyond the current distribution. Both situations, warmer climate and increase of extreme events, will be remarkably increased by the end of the century in this hot spot of climate change.
Collapse
Affiliation(s)
- Karina Laneri
- Grupo de Física Estadística e Interdisciplinaria, CONICET, Centro Atómico Bariloche, Bariloche, Río Negro, Argentina
- * E-mail:
| | - Brenno Cabella
- Instituto de Física Teórica, Universidade Estadual Paulista - UNESP, São Paulo, SP, Brazil
| | - Paulo Inácio Prado
- LAGE do Departamento de Ecologia, Instituto de Biociências da Universidade de São Paulo, São Paulo, SP, Brazil
| | - Renato Mendes Coutinho
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC, Santo André, SP, Brazil
| | - Roberto André Kraenkel
- Instituto de Física Teórica, Universidade Estadual Paulista - UNESP, São Paulo, SP, Brazil
| |
Collapse
|
104
|
Bagh N, Reddy MR. Improving The Performance of Motor Imagery Based Brain-Computer Interface Using Phase Space Reconstruction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:3075-3078. [PMID: 31946537 DOI: 10.1109/embc.2019.8857066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In recent decades, motor imagery (MI) based brain-computer interface (BCI) is served as a control system or rehabilitation tool for motor disabled people. But it has limited applications because of its lower classification performance (classification accuracy, Cohen's kappa coefficient and etc.). The performance depends on the feature extraction techniques and extraction of relevant features from the brain is challenging task. The existing techniques have low classification performance and are computationally inefficient. This paper introduces phase space reconstruction (PSR) to detect various MI activities and improve the performance of the system. First, raw signals were decomposed into multiple frequency sub-bands using filter bank technique. Second, PSR was applied to each sub-band and dynamical behavior of the brain activities has been analyzed. The optimal parameters (time delay and embedding dimension) of PSR were calculated by average mutual information (AMI) and false nearest neighbors (FNN) methods. The time delay and embedding dimension extracted significant features related to MI activities. The significant features were fed into multi-class support vector machine (SVM) and performance of the classifier was evaluated. The performance of the system is based on classification accuracy (%CA) and Cohen's kappa coefficient (K). The proposed algorithm and classifier were tested on BCI competition-2005, MI dataset-III-a. The results show that the proposed technique increases the classification accuracy by 3.7% and achieved higher performance (%CA = 89.20% and K= 0.85).
Collapse
|
105
|
Temporal Effects of Groundwater on Physical and Biotic Components of a Karst Stream. WATER 2019. [DOI: 10.3390/w11061299] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Although most lotic ecosystems are groundwater dependent, our knowledge on the relatively long-term ecological effects of groundwater discharge on downstream reaches remains limited. We surveyed four connected reaches of a Chinese karst stream network for 72 consecutive months, with one reach, named Hong Shi Zi (HSZ), evidently affected by groundwater. We tested whether, compared with other reaches, HSZ had (1) milder water temperature and flow regimes, and (2) weaker influences of water temperature and flow on benthic algal biomass represented by chlorophyll a (Chl. a) concentrations. We found that the maximum monthly mean water temperature in HSZ was 0.6 °C lower than of the adjacent upstream reach, and the minimum monthly mean water temperature was 1.0 °C higher than of the adjacent downstream reach. HSZ had the smallest coefficient of variation (CV) for water temperature but the largest CV for discharge. Water temperature and discharge displayed a significant 12-month periodicity in all reaches not directly groundwater influenced. Only water temperature displayed such periodicity in HSZ. Water temperature was an important predictor of temporal variation in Chl. a in all reaches, but its influence was weakest in HSZ. Our findings demonstrate that longer survey data can provide insight into groundwater–surface water interactions.
Collapse
|
106
|
Runge J, Bathiany S, Bollt E, Camps-Valls G, Coumou D, Deyle E, Glymour C, Kretschmer M, Mahecha MD, Muñoz-Marí J, van Nes EH, Peters J, Quax R, Reichstein M, Scheffer M, Schölkopf B, Spirtes P, Sugihara G, Sun J, Zhang K, Zscheischler J. Inferring causation from time series in Earth system sciences. Nat Commun 2019; 10:2553. [PMID: 31201306 PMCID: PMC6572812 DOI: 10.1038/s41467-019-10105-3] [Citation(s) in RCA: 144] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 04/17/2019] [Indexed: 11/25/2022] Open
Abstract
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
Collapse
Affiliation(s)
- Jakob Runge
- German Aerospace Center, Institute of Data Science, Mälzer Str. 3, 07745, Jena, Germany.
- Grantham Institute, Imperial College, London, SW7 2AZ, UK.
| | - Sebastian Bathiany
- Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht, Fischertwiete 1, 20095, Hamburg, Germany
- Department of Environmental Sciences, Wageningen University, P.O. Box 47, NL-6700 AA, Wageningen, The Netherlands
| | - Erik Bollt
- Department of Mathematics, Clarkson Center for Complex Systems Science (C3S2), Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699-5815, USA
| | - Gustau Camps-Valls
- Image Processing Laboratory, Universitat de València, ES-46980, Paterna (València), Spain
| | - Dim Coumou
- Department of Water and Climate Risk, Institute for Environmental Studies (IVM), VU University Amsterdam, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands
- Potsdam Institute for Climate Impact Research, Earth System Analysis, Telegraphenberg A62, 14473, Potsdam, Germany
| | - Ethan Deyle
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Clark Glymour
- Department of Philosophy, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - Marlene Kretschmer
- Potsdam Institute for Climate Impact Research, Earth System Analysis, Telegraphenberg A62, 14473, Potsdam, Germany
| | - Miguel D Mahecha
- Max Planck Institute for Biogeochemistry, PO Box 100164, 07701, Jena, Germany
| | - Jordi Muñoz-Marí
- Image Processing Laboratory, Universitat de València, ES-46980, Paterna (València), Spain
| | - Egbert H van Nes
- Department of Environmental Sciences, Wageningen University, P.O. Box 47, NL-6700 AA, Wageningen, The Netherlands
| | - Jonas Peters
- Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100, København, Denmark
| | - Rick Quax
- Institute for Informatics, University of Amsterdam, PO Box 94323, 1090 GH, Amsterdam, The Netherlands
- Institute of Advanced Studies, University of Amsterdam, Oude Turfmarkt 147, 1012, GC, Amsterdam, The Netherlands
| | - Markus Reichstein
- Max Planck Institute for Biogeochemistry, PO Box 100164, 07701, Jena, Germany
| | - Marten Scheffer
- Department of Environmental Sciences, Wageningen University, P.O. Box 47, NL-6700 AA, Wageningen, The Netherlands
| | - Bernhard Schölkopf
- Max Planck Institute for Intelligent Systems, Max Planck Ring 4, 72076, Tübingen, Germany
| | - Peter Spirtes
- Department of Philosophy, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - George Sugihara
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Jie Sun
- Department of Mathematics, Clarkson Center for Complex Systems Science (C3S2), Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699-5815, USA
- Department of Physics and Department of Computer Science, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699-5815, USA
| | - Kun Zhang
- Department of Philosophy, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - Jakob Zscheischler
- Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092, Zurich, Switzerland
- Climate and Environmental Physics, University of Bern, Sidlerstrasse 5, 3012, Bern, Switzerland
- Oeschger Centre for Climate Change Research, University of Bern, Bern, 3012, Switzerland
| |
Collapse
|
107
|
Eckhardt D, Koo J, Martin R, Holmes M, Hara K. Spatiotemporal data fusion and manifold reconstruction in Hall thrusters. ACTA ACUST UNITED AC 2019. [DOI: 10.1088/1361-6595/ab0b1f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
108
|
Benkő Z, Moldován K, Szádeczky-Kardoss K, Zalányi L, Borbély S, Világi I, Somogyvári Z. Causal relationship between local field potential and intrinsic optical signal in epileptiform activity in vitro. Sci Rep 2019; 9:5171. [PMID: 30914731 PMCID: PMC6435742 DOI: 10.1038/s41598-019-41554-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 03/11/2019] [Indexed: 11/10/2022] Open
Abstract
The directed causal relationship were examined between the local field potential (LFP) and the intrinsic optical signal (IOS) during induced epileptiform activity in in vitro cortical slices by the convergent cross-mapping causality analysis method. Two components of the IOS signal have been distinguished: a faster, activity dependent component (IOSh) which changes its sign between transmitted and reflected measurement, thus it is related to the reflectance or the scattering of the tissue and a slower component (IOSl), which is negative in both cases, thus it is resulted by the increase of the absorption of the tissue. We have found a strong, unidirectional, delayed causal effect from LFP to IOSh with 0.5-1s delay, without signs of feedback from the IOSh to the LFP, while the correlation was small and the peaks of the cross correlation function did not reflect the actual causal dependency. Based on these observations, a model has been set up to describe the dependency of the IOSh on the LFP power and IOSh was reconstructed, based on the LFP signal. This study demonstrates that causality analysis can lead to better understanding the physiological interactions, even in case of two data series with drastically different time scales.
Collapse
Affiliation(s)
- Zsigmond Benkő
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Center for Physics of the Hungarian Academy of Sciences, Budapest, H-1121, Hungary.,János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, H-1085, Hungary
| | - Kinga Moldován
- Department of Physiology and Neurobiology, Eötvös Loránd University, Budapest, H-1117, Hungary
| | | | - László Zalányi
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Center for Physics of the Hungarian Academy of Sciences, Budapest, H-1121, Hungary
| | - Sándor Borbély
- Department of Physiology and Neurobiology, Eötvös Loránd University, Budapest, H-1117, Hungary
| | - Ildikó Világi
- Department of Physiology and Neurobiology, Eötvös Loránd University, Budapest, H-1117, Hungary
| | - Zoltán Somogyvári
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Center for Physics of the Hungarian Academy of Sciences, Budapest, H-1121, Hungary. .,Neuromicrosystems ltd., Budapest, H-1113, Hungary.
| |
Collapse
|
109
|
Satake A, Kawatsu K, Chiba Y, Kitamura K, Han Q. Synchronized expression of FLOWERING LOCUS T
between branches underlies mass flowering in Fagus crenata. POPUL ECOL 2018. [DOI: 10.1002/1438-390x.1010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Akiko Satake
- Department of Biology; Faculty of Science, Kyushu University; Fukuoka Japan
| | - Kazutaka Kawatsu
- Graduate School of Life Sciences; Tohoku University; Sendai Japan
| | - Yukako Chiba
- Graduate School of Life Science; Hokkaido University; Sapporo Japan
| | - Keiko Kitamura
- Hokkaido Research Center; Forestry and Forest Products Research Institute; Sapporo Japan
| | - Qingmin Han
- Department of Plant Ecology; Forestry and Forest Products Research Institute; Tsukuba Japan
| |
Collapse
|
110
|
Vinci GV, Benzi R. Economic Complexity: Correlations between Gross Domestic Product and Fitness. ENTROPY 2018; 20:e20100766. [PMID: 33265854 PMCID: PMC7512328 DOI: 10.3390/e20100766] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 11/30/2022]
Abstract
In this paper we study the causal relation between country Economic Fitness Fc and its Gross Domestic Product per capita (GDP). Using the Takens’ theorem, as first suggested in (Sugihara, G. et al. 2012), we show that there exists a reasonable evidence of causal correlation between GDP and Fc for relatively rich countries. This is not the case for relatively poor countries where Fc and GDP do not show any significant causal relation. We also present some preliminary results to understand whether GDP or Fc are driving factor for economic growth.
Collapse
|
111
|
Yang AC, Peng CK, Huang NE. Causal decomposition in the mutual causation system. Nat Commun 2018; 9:3378. [PMID: 30140008 PMCID: PMC6107666 DOI: 10.1038/s41467-018-05845-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 07/20/2018] [Indexed: 11/09/2022] Open
Abstract
Inference of causality in time series has been principally based on the prediction paradigm. Nonetheless, the predictive causality approach may underestimate the simultaneous and reciprocal nature of causal interactions observed in real-world phenomena. Here, we present a causal-decomposition approach that is not based on prediction, but based on the covariation of cause and effect: cause is that which put, the effect follows; and removed, the effect is removed. Using empirical mode decomposition, we show that causal interaction is encoded in instantaneous phase dependency at a specific time scale, and this phase dependency is diminished when the causal-related intrinsic component is removed from the effect. Furthermore, we demonstrate the generic applicability of our method to both stochastic and deterministic systems, and show the consistency of causal-decomposition method compared to existing methods, and finally uncover the key mode of causal interactions in both modelled and actual predator–prey systems. Causality inference in time series analysis based on temporal precedence principle between cause and effect fails to detect mutual causal interactions. Here, Yang et al. introduce a causal decomposition approach based on the covariation principle of cause and effect that overcomes this limitation.
Collapse
Affiliation(s)
- Albert C Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, 02215, USA. .,Institute of Brain Science, National Yang-Ming University, 11221, Taipei, Taiwan. .,Department of Psychiatry, Taipei Veterans General Hospital, 11217, Taipei, Taiwan.
| | - Chung-Kang Peng
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, 02215, USA
| | - Norden E Huang
- Center for Dynamical Biomarkers and Translational Medicine, National Central University, 32001, Chungli, Taiwan.,Key Laboratory of Data Analysis and Applications, First Institute of Oceanography, SOA, 266061, Qingdao, China
| |
Collapse
|
112
|
Detecting the Causal Effect of Soil Moisture on Precipitation Using Convergent Cross Mapping. Sci Rep 2018; 8:12171. [PMID: 30111861 PMCID: PMC6093863 DOI: 10.1038/s41598-018-30669-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 07/18/2018] [Indexed: 11/08/2022] Open
Abstract
As a vital land surface parameter, soil moisture influences climate through its impact on water and energy cycles. However, the effect of soil moisture on precipitation has been strongly debated. In this study, a new causal detection method, convergent cross mapping (CCM), was applied to explore the causality between soil moisture and precipitation over low- and mid- latitude regions in the Northern Hemisphere. CCM method generally identified a strong effect of soil moisture on precipitation. Specifically, the optimal effect of soil moisture on precipitation occurred with a lag of one month and clearly decreased after four months, suggesting that soil moisture has potentials to improve the accuracy of precipitation forecast at a sub-seasonal scale. In addition, as climate (i.e., aridity index) changed from dry to wet, the effect of soil moisture on precipitation first increased and then decreased with peaks in semi-arid and semi-humid areas. These findings statistically support the hypothesis that soil moisture impacts precipitation and also provide a reference for the design of climate prediction systems.
Collapse
|
113
|
Forcing of late Pleistocene ice volume by spatially variable summer energy. Sci Rep 2018; 8:11520. [PMID: 30069038 PMCID: PMC6070525 DOI: 10.1038/s41598-018-29916-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 07/20/2018] [Indexed: 11/16/2022] Open
Abstract
Changes in Earth’s orbit set the pace of glacial cycles, but the role of spatial variability in the insolation forcing of global ice volume remains unknown. Here, we leverage the intrinsic dynamical information in empirical records to show that ice volume responded to summer energy at high northern latitudes, as predicted by Milankovitch theory. However, the external forcing of ice volume encompasses insolation signals with a wide range of orbital frequency content, and cannot be fully accounted for by a unique time series. Southern mid-latitude insolation forcing coincides with the position of the subtropical front and the westerlies, which have been implicated in Quaternary climate changes. Dominant forcing modes at northern mid-latitudes are anti-phased with the canonical Milankovitch forcing, consistent with ice volume sensitivity to latitudinal insolation gradients.
Collapse
|
114
|
Garland J, Berdahl AM, Sun J, Bollt EM. Anatomy of leadership in collective behaviour. CHAOS (WOODBURY, N.Y.) 2018; 28:075308. [PMID: 30070518 DOI: 10.1063/1.5024395] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/30/2018] [Indexed: 06/08/2023]
Abstract
Understanding the mechanics behind the coordinated movement of mobile animal groups (collective motion) provides key insights into their biology and ecology, while also yielding algorithms for bio-inspired technologies and autonomous systems. It is becoming increasingly clear that many mobile animal groups are composed of heterogeneous individuals with differential levels and types of influence over group behaviors. The ability to infer this differential influence, or leadership, is critical to understanding group functioning in these collective animal systems. Due to the broad interpretation of leadership, many different measures and mathematical tools are used to describe and infer "leadership," e.g., position, causality, influence, and information flow. But a key question remains: which, if any, of these concepts actually describes leadership? We argue that instead of asserting a single definition or notion of leadership, the complex interaction rules and dynamics typical of a group imply that leadership itself is not merely a binary classification (leader or follower), but rather, a complex combination of many different components. In this paper, we develop an anatomy of leadership, identify several principal components, and provide a general mathematical framework for discussing leadership. With the intricacies of this taxonomy in mind, we present a set of leadership-oriented toy models that should be used as a proving ground for leadership inference methods going forward. We believe this multifaceted approach to leadership will enable a broader understanding of leadership and its inference from data in mobile animal groups and beyond.
Collapse
Affiliation(s)
| | | | - Jie Sun
- Department of Mathematics, Clarkson University, Potsdam, New York 13699, USA
| | - Erik M Bollt
- Department of Mathematics, Clarkson University, Potsdam, New York 13699, USA
| |
Collapse
|
115
|
Nayak SK, Bit A, Dey A, Mohapatra B, Pal K. A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:6920420. [PMID: 29854361 PMCID: PMC5954865 DOI: 10.1155/2018/6920420] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 01/13/2018] [Accepted: 02/27/2018] [Indexed: 12/22/2022]
Abstract
Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis.
Collapse
Affiliation(s)
- Suraj K. Nayak
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Arindam Bit
- Department of Biomedical Engineering, National Institute of Technology, Raipur, Chhattisgarh 492010, India
| | - Anilesh Dey
- Department of Electronics and Communication Engineering, Kaziranga University, Jorhat, Assam 785006, India
| | | | - Kunal Pal
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, Odisha 769008, India
| |
Collapse
|
116
|
Krakovská A, Jakubík J, Chvosteková M, Coufal D, Jajcay N, Paluš M. Comparison of six methods for the detection of causality in a bivariate time series. Phys Rev E 2018; 97:042207. [PMID: 29758597 DOI: 10.1103/physreve.97.042207] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Indexed: 06/08/2023]
Abstract
In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.
Collapse
Affiliation(s)
- Anna Krakovská
- Institute of Measurement Science, Slovak Academy of Sciences, Dúbravská cesta 9, 842 19 Bratislava, Slovak Republic
| | - Jozef Jakubík
- Institute of Measurement Science, Slovak Academy of Sciences, Dúbravská cesta 9, 842 19 Bratislava, Slovak Republic
| | - Martina Chvosteková
- Institute of Measurement Science, Slovak Academy of Sciences, Dúbravská cesta 9, 842 19 Bratislava, Slovak Republic
| | - David Coufal
- Institute of Computer Science, Czech Academy of Sciences, Pod Vodárenskou věží 2, 182 07 Praha 8, Czech Republic
| | - Nikola Jajcay
- Institute of Computer Science, Czech Academy of Sciences, Pod Vodárenskou věží 2, 182 07 Praha 8, Czech Republic
| | - Milan Paluš
- Institute of Computer Science, Czech Academy of Sciences, Pod Vodárenskou věží 2, 182 07 Praha 8, Czech Republic
| |
Collapse
|
117
|
Hannisdal B, Haaga KA, Reitan T, Diego D, Liow LH. Common species link global ecosystems to climate change: dynamical evidence in the planktonic fossil record. Proc Biol Sci 2018; 284:rspb.2017.0722. [PMID: 28701561 PMCID: PMC5524498 DOI: 10.1098/rspb.2017.0722] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 06/05/2017] [Indexed: 12/02/2022] Open
Abstract
Common species shape the world around us, and changes in their commonness signify large-scale shifts in ecosystem structure and function. However, our understanding of long-term ecosystem response to environmental forcing in the deep past is centred on species richness, neglecting the disproportional impact of common species. Here, we use common and widespread species of planktonic foraminifera in deep-sea sediments to track changes in observed global occupancy (proportion of sampled sites at which a species is present and observed) through the turbulent climatic history of the last 65 Myr. Our approach is sensitive to relative changes in global abundance of the species set and robust to factors that bias richness estimators. Using three independent methods for detecting causality, we show that the observed global occupancy of planktonic foraminifera has been dynamically coupled to past oceanographic changes captured in deep-ocean temperature reconstructions. The causal inference does not imply a direct mechanism, but is consistent with an indirect, time-delayed causal linkage. Given the strong quantitative evidence that a dynamical coupling exists, we hypothesize that mixotrophy (symbiont hosting) may be an ecological factor linking the global abundance of planktonic foraminifera to long-term climate changes via the relative extent of oligotrophic oceans.
Collapse
Affiliation(s)
- Bjarte Hannisdal
- Centre for Geobiology, Department of Earth Science, University of Bergen, PO Box 7803, 5020 Bergen, Norway .,Bjerknes Centre for Climate Research, University of Bergen, PO Box 7803, 5020 Bergen, Norway
| | - Kristian Agasøster Haaga
- Centre for Geobiology, Department of Earth Science, University of Bergen, PO Box 7803, 5020 Bergen, Norway.,Bjerknes Centre for Climate Research, University of Bergen, PO Box 7803, 5020 Bergen, Norway
| | - Trond Reitan
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, PO Box 1066, Blindern, 0316 Oslo, Norway
| | - David Diego
- Centre for Geobiology, Department of Earth Science, University of Bergen, PO Box 7803, 5020 Bergen, Norway
| | - Lee Hsiang Liow
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, PO Box 1066, Blindern, 0316 Oslo, Norway.,Natural History Museum, University of Oslo, PO Box 1172 Blindern, 0318 Oslo, Norway
| |
Collapse
|
118
|
Frank MR, Obradovich N, Sun L, Woon WL, LeVeck BL, Rahwan I. Detecting reciprocity at a global scale. SCIENCE ADVANCES 2018; 4:eaao5348. [PMID: 29326983 PMCID: PMC5756659 DOI: 10.1126/sciadv.aao5348] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 11/29/2017] [Indexed: 06/07/2023]
Abstract
Reciprocity stabilizes cooperation from the level of microbes all the way up to humans interacting in small groups, but does reciprocity also underlie stable cooperation between larger human agglomerations, such as nation states? Famously, evolutionary models show that reciprocity could emerge as a widespread strategy for achieving international cooperation. However, existing studies have only detected reciprocity-driven cooperation in a small number of country pairs. We apply a new method for detecting mutual influence in dynamical systems to a new large-scale data set that records state interactions with high temporal resolution. Doing so, we detect reciprocity between many country pairs in the international system and find that these reciprocating country pairs exhibit qualitatively different cooperative dynamics when compared to nonreciprocating pairs. Consistent with evolutionary theories of cooperation, reciprocating country pairs exhibit higher levels of stable cooperation and are more likely to punish instances of noncooperation. However, countries in reciprocity-based relationships are also quicker to forgive single acts of noncooperation by eventually returning to previous levels of mutual cooperation. By contrast, nonreciprocating pairs are more likely to exploit each other's cooperation via higher rates of defection. Together, these findings provide the strongest evidence to date that reciprocity is a widespread mechanism for achieving international cooperation.
Collapse
Affiliation(s)
- Morgan R. Frank
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Nick Obradovich
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Belfer Center for Science and International Affairs, Kennedy School of Government, Harvard University, Cambridge, MA 02138, USA
| | - Lijun Sun
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Wei Lee Woon
- Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, Abu Dhabi, UAE
| | - Brad L. LeVeck
- Department of Political Science, University of California, Merced, CA 95340, USA
| | - Iyad Rahwan
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| |
Collapse
|
119
|
Frossard V, Rimet F, Perga ME. Causal networks reveal the dominance of bottom-up interactions in large, deep lakes. Ecol Modell 2018. [DOI: 10.1016/j.ecolmodel.2017.11.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
120
|
Kawatsu K, Kishi S. Identifying critical interactions in complex competition dynamics between bean beetles. OIKOS 2017. [DOI: 10.1111/oik.04103] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Kazutaka Kawatsu
- Dept of Environmental Solution Technology; Faculty of Science and Technology, Ryukoku Univ., 1-5, Yokotani, Seta-Oe cho, Otsu; JP-520-2194 Shiga Japan
| | - Shigeki Kishi
- Center for Environmental Biology and Ecosystem Studies, National Inst. for Environmental Studies, Tsukuba; Ibaraki Japan
| |
Collapse
|
121
|
Luo L, Cheng F, Qiu T, Zhao J. Refined convergent cross-mapping for disturbance propagation analysis of chemical processes. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2017.03.026] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
122
|
Verma AK, Xu D, Garg A, Cote AT, Goswami N, Blaber AP, Tavakolian K. Non-linear Heart Rate and Blood Pressure Interaction in Response to Lower-Body Negative Pressure. Front Physiol 2017; 8:767. [PMID: 29114227 PMCID: PMC5660688 DOI: 10.3389/fphys.2017.00767] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 09/20/2017] [Indexed: 12/14/2022] Open
Abstract
Early detection of hemorrhage remains an open problem. In this regard, blood pressure has been an ineffective measure of blood loss due to numerous compensatory mechanisms sustaining arterial blood pressure homeostasis. Here, we investigate the feasibility of causality detection in the heart rate and blood pressure interaction, a closed-loop control system, for early detection of hemorrhage. The hemorrhage was simulated via graded lower-body negative pressure (LBNP) from 0 to -40 mmHg. The research hypothesis was that a significant elevation of causal control in the direction of blood pressure to heart rate (i.e., baroreflex response) is an early indicator of central hypovolemia. Five minutes of continuous blood pressure and electrocardiogram (ECG) signals were acquired simultaneously from young, healthy participants (27 ± 1 years, N = 27) during each LBNP stage, from which heart rate (represented by RR interval), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were derived. The heart rate and blood pressure causal interaction (RR↔SBP and RR↔MAP) was studied during the last 3 min of each LBNP stage. At supine rest, the non-baroreflex arm (RR→SBP and RR→MAP) showed a significantly (p < 0.001) higher causal drive toward blood pressure regulation compared to the baroreflex arm (SBP→RR and MAP→RR). In response to moderate category hemorrhage (-30 mmHg LBNP), no change was observed in the traditional marker of blood loss i.e., pulse pressure (p = 0.10) along with the RR→SBP (p = 0.76), RR→MAP (p = 0.60), and SBP→RR (p = 0.07) causality compared to the resting stage. Contrarily, a significant elevation in the MAP→RR (p = 0.004) causality was observed. In accordance with our hypothesis, the outcomes of the research underscored the potential of compensatory baroreflex arm (MAP→RR) of the heart rate and blood pressure interaction toward differentiating a simulated moderate category hemorrhage from the resting stage. Therefore, monitoring baroreflex causality can have a clinical utility in making triage decisions to impede hemorrhage progression.
Collapse
Affiliation(s)
- Ajay K Verma
- Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, United States
| | - Da Xu
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Amanmeet Garg
- Department of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
| | - Anita T Cote
- School of Human Kinetics, Trinity Western University, Langley, BC, Canada
| | - Nandu Goswami
- Institute of Physiology, Medical University of Graz, Graz, Austria
| | - Andrew P Blaber
- Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, United States.,Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Kouhyar Tavakolian
- Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, United States.,Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| |
Collapse
|
123
|
Tajima S, Mita T, Bakkum DJ, Takahashi H, Toyoizumi T. Locally embedded presages of global network bursts. Proc Natl Acad Sci U S A 2017; 114:9517-9522. [PMID: 28827362 PMCID: PMC5594667 DOI: 10.1073/pnas.1705981114] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially nonbursting network state is not fully understood. In this study, we develop a state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in "local" dynamics of individual neurons during nonbursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean-field activity for predicting future global bursts. Moreover, the intercell variability in the burst predictability is found to reflect the network structure realized in the nonbursting periods. These findings suggest that deterministic local dynamics can predict seemingly stochastic global events in self-organized networks, implying the potential applications of the present methodology to detecting locally concentrated early warnings of spontaneous seizure occurrence in the brain.
Collapse
Affiliation(s)
- Satohiro Tajima
- Department of Basic Neuroscience, University of Geneva, Centre Médical Universitaire, Genève 1211, Switzerland;
- Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Takeshi Mita
- Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Douglas J Bakkum
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Hirokazu Takahashi
- Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Meguro-ku, Tokyo 153-8904, Japan
| | - Taro Toyoizumi
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| |
Collapse
|
124
|
Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series. Sci Rep 2017; 7:10486. [PMID: 28874713 PMCID: PMC5585247 DOI: 10.1038/s41598-017-10759-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 08/15/2017] [Indexed: 11/08/2022] Open
Abstract
In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data. The results of the analysis reveal some statistical evidences that the causalities between time series is in a dynamic process. It implicates that stationary long-term causalities are not suitable for some special situations. Some short-term causalities that our model recognized can be referenced to the dynamic adjustment of the decisions. The results also show that weighted degree of the nodes obeys power law distribution. This implies that a few types of causality patterns play a major role in the process of the transition and that international crude oil market is statistically significantly not random. The clustering effect appears in the transition process and different clusters have different transition characteristics which provide probability information for predicting the evolution of the causality. The approach presents a potential to analyze multivariate time series and provides important information for investors and decision makers.
Collapse
|
125
|
Coufal D, Jakubík J, Jajcay N, Hlinka J, Krakovská A, Paluš M. Detection of coupling delay: A problem not yet solved. CHAOS (WOODBURY, N.Y.) 2017; 27:083109. [PMID: 28863488 DOI: 10.1063/1.4997757] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Nonparametric detection of coupling delay in unidirectionally and bidirectionally coupled nonlinear dynamical systems is examined. Both continuous and discrete-time systems are considered. Two methods of detection are assessed-the method based on conditional mutual information-the CMI method (also known as the transfer entropy method) and the method of convergent cross mapping-the CCM method. Computer simulations show that neither method is generally reliable in the detection of coupling delays. For continuous-time chaotic systems, the CMI method appears to be more sensitive and applicable in a broader range of coupling parameters than the CCM method. In the case of tested discrete-time dynamical systems, the CCM method has been found to be more sensitive, while the CMI method required much stronger coupling strength in order to bring correct results. However, when studied systems contain a strong oscillatory component in their dynamics, results of both methods become ambiguous. The presented study suggests that results of the tested algorithms should be interpreted with utmost care and the nonparametric detection of coupling delay, in general, is a problem not yet solved.
Collapse
Affiliation(s)
- David Coufal
- Institute of Computer Science, Czech Academy of Sciences, Pod Vodárenskou věží 2, 182 07 Praha 8, Czech Republic
| | - Jozef Jakubík
- Institute of Measurement Science, Slovak Academy of Sciences, Dúbravská cesta 9, 841 04 Bratislava, Slovak Republic
| | - Nikola Jajcay
- Institute of Computer Science, Czech Academy of Sciences, Pod Vodárenskou věží 2, 182 07 Praha 8, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science, Czech Academy of Sciences, Pod Vodárenskou věží 2, 182 07 Praha 8, Czech Republic
| | - Anna Krakovská
- Institute of Measurement Science, Slovak Academy of Sciences, Dúbravská cesta 9, 841 04 Bratislava, Slovak Republic
| | - Milan Paluš
- Institute of Computer Science, Czech Academy of Sciences, Pod Vodárenskou věží 2, 182 07 Praha 8, Czech Republic
| |
Collapse
|
126
|
Ma H, Leng S, Tao C, Ying X, Kurths J, Lai YC, Lin W. Detection of time delays and directional interactions based on time series from complex dynamical systems. Phys Rev E 2017; 96:012221. [PMID: 29347206 DOI: 10.1103/physreve.96.012221] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Indexed: 06/07/2023]
Abstract
Data-based and model-free accurate identification of intrinsic time delays and directional interactions is an extremely challenging problem in complex dynamical systems and their networks reconstruction. A model-free method with new scores is proposed to be generally capable of detecting single, multiple, and distributed time delays. The method is applicable not only to mutually interacting dynamical variables but also to self-interacting variables in a time-delayed feedback loop. Validation of the method is carried out using physical, biological, and ecological models and real data sets. Especially, applying the method to air pollution data and hospital admission records of cardiovascular diseases in Hong Kong reveals the major air pollutants as a cause of the diseases and, more importantly, it uncovers a hidden time delay (about 30-40 days) in the causal influence that previous studies failed to detect. The proposed method is expected to be universally applicable to ascertaining and quantifying subtle interactions (e.g., causation) in complex systems arising from a broad range of disciplines.
Collapse
Affiliation(s)
- Huanfei Ma
- School of Mathematical Sciences, Soochow University, Suzhou 215006, China
- Centre for Computational Systems Biology of ISTBI, Fudan University, Shanghai 200433, China
| | - Siyang Leng
- Centre for Computational Systems Biology of ISTBI, Fudan University, Shanghai 200433, China
- School of Mathematical Sciences and SCMS, Fudan University, Shanghai 200433, China
| | - Chenyang Tao
- Centre for Computational Systems Biology of ISTBI, Fudan University, Shanghai 200433, China
- School of Mathematical Sciences and SCMS, Fudan University, Shanghai 200433, China
| | - Xiong Ying
- Centre for Computational Systems Biology of ISTBI, Fudan University, Shanghai 200433, China
- School of Mathematical Sciences and SCMS, Fudan University, Shanghai 200433, China
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, D-14412 Potsdam, and Department of Physics, Humboldt University of Berlin, D-12489 Berlin, Germany
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom
| | - Ying-Cheng Lai
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom
- School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, Arizona 85287-5706, USA
| | - Wei Lin
- Centre for Computational Systems Biology of ISTBI, Fudan University, Shanghai 200433, China
- School of Mathematical Sciences and SCMS, Fudan University, Shanghai 200433, China
| |
Collapse
|
127
|
|
128
|
|
129
|
McGowan JA, Deyle ER, Ye H, Carter ML, Perretti CT, Seger KD, Verneil A, Sugihara G. Predicting coastal algal blooms in southern California. Ecology 2017; 98:1419-1433. [DOI: 10.1002/ecy.1804] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Revised: 02/16/2017] [Accepted: 02/06/2017] [Indexed: 11/10/2022]
Affiliation(s)
- John A. McGowan
- Scripps Institution of Oceanography University of California San Diego La Jolla California 92093 USA
| | - Ethan R. Deyle
- Scripps Institution of Oceanography University of California San Diego La Jolla California 92093 USA
| | - Hao Ye
- Scripps Institution of Oceanography University of California San Diego La Jolla California 92093 USA
| | - Melissa L. Carter
- Scripps Institution of Oceanography University of California San Diego La Jolla California 92093 USA
| | - Charles T. Perretti
- Scripps Institution of Oceanography University of California San Diego La Jolla California 92093 USA
- National Marine Fisheries Service Northeast Fisheries Science Center Woods Hole Massachusetts 02543 USA
| | - Kerri D. Seger
- Scripps Institution of Oceanography University of California San Diego La Jolla California 92093 USA
- School of Marine Science and Ocean Engineering University of New Hampshire Durham New Hampshire 03823 USA
| | - Alain Verneil
- Scripps Institution of Oceanography University of California San Diego La Jolla California 92093 USA
- Institut Méditerranéen d'Océanologie Campus de Luminy Case 901 13288 Marseille France
| | - George Sugihara
- Scripps Institution of Oceanography University of California San Diego La Jolla California 92093 USA
| |
Collapse
|
130
|
Skeletal Muscle Pump Drives Control of Cardiovascular and Postural Systems. Sci Rep 2017; 7:45301. [PMID: 28345674 PMCID: PMC5366896 DOI: 10.1038/srep45301] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 02/23/2017] [Indexed: 12/14/2022] Open
Abstract
The causal interaction between cardio-postural-musculoskeletal systems is critical in maintaining postural stability under orthostatic challenge. The absence or reduction of such interactions could lead to fainting and falls often experienced by elderly individuals. The causal relationship between systolic blood pressure (SBP), calf electromyography (EMG), and resultant center of pressure (COPr) can quantify the behavior of cardio-postural control loop. Convergent cross mapping (CCM) is a non-linear approach to establish causality, thus, expected to decipher nonlinear causal cardio-postural-musculoskeletal interactions. Data were acquired simultaneously from young participants (25 ± 2 years, n = 18) during a 10-minute sit-to-stand test. In the young population, skeletal muscle pump was found to drive blood pressure control (EMG → SBP) as well as control the postural sway (EMG → COPr) through the significantly higher causal drive in the direction towards SBP and COPr. Furthermore, the effect of aging on muscle pump activation associated with blood pressure regulation was explored. Simultaneous EMG and SBP were acquired from elderly group (69 ± 4 years, n = 14). A significant (p = 0.002) decline in EMG → SBP causality was observed in the elderly group, compared to the young group. The results highlight the potential of causality to detect alteration in blood pressure regulation with age, thus, a potential clinical utility towards detection of fall proneness.
Collapse
|
131
|
|
132
|
Reply to Baskerville and Cobey: Misconceptions about causation with synchrony and seasonal drivers. Proc Natl Acad Sci U S A 2017; 114:E2272-E2274. [PMID: 28298533 DOI: 10.1073/pnas.1700998114] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
133
|
Ghodsi Z, Huang X, Hassani H. Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo. GENOMICS DATA 2017; 11:20-38. [PMID: 27924281 PMCID: PMC5129166 DOI: 10.1016/j.gdata.2016.11.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 10/28/2016] [Accepted: 11/10/2016] [Indexed: 11/28/2022]
Abstract
In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.
Collapse
Affiliation(s)
- Zara Ghodsi
- Statistical Research Centre, Bournemouth University, 89 Holdenhurst Road, Bournemouth BH8 8EB, UK; Translational Genetics Group, Bournemouth University, Fern Barrow, Poole BH125BB, UK
| | - Xu Huang
- Statistical Research Centre, Bournemouth University, 89 Holdenhurst Road, Bournemouth BH8 8EB, UK
| | - Hossein Hassani
- Institute for International Energy Studies (IIES), Tehran 1967743 711, Iran
| |
Collapse
|
134
|
Cramer KL, O'Dea A, Clark TR, Zhao JX, Norris RD. Prehistorical and historical declines in Caribbean coral reef accretion rates driven by loss of parrotfish. Nat Commun 2017; 8:14160. [PMID: 28112169 PMCID: PMC5267576 DOI: 10.1038/ncomms14160] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 12/05/2016] [Indexed: 11/09/2022] Open
Abstract
Caribbean coral reefs have transformed into algal-dominated habitats over recent decades, but the mechanisms of change are unresolved due to a lack of quantitative ecological data before large-scale human impacts. To understand the role of reduced herbivory in recent coral declines, we produce a high-resolution 3,000 year record of reef accretion rate and herbivore (parrotfish and urchin) abundance from the analysis of sediments and fish, coral and urchin subfossils within cores from Caribbean Panama. At each site, declines in accretion rates and parrotfish abundance were initiated in the prehistorical or historical period. Statistical tests of direct cause and effect relationships using convergent cross mapping reveal that accretion rates are driven by parrotfish abundance (but not vice versa) but are not affected by total urchin abundance. These results confirm the critical role of parrotfish in maintaining coral-dominated reef habitat and the urgent need for restoration of parrotfish populations to enable reef persistence.
Collapse
Affiliation(s)
- Katie L Cramer
- Center for Marine Biodiversity and Conservation, Scripps Institution of Oceanography, UC San Diego, La Jolla, California 92093, USA.,Smithsonian Tropical Research Institute, Box 0843-03092 Balboa, Republic of Panama
| | - Aaron O'Dea
- Smithsonian Tropical Research Institute, Box 0843-03092 Balboa, Republic of Panama
| | - Tara R Clark
- Radiogenic Isotope Facility, School of Earth Sciences, The University of Queensland, Brisbane, Queensland QLD 4072, Australia
| | - Jian-Xin Zhao
- Radiogenic Isotope Facility, School of Earth Sciences, The University of Queensland, Brisbane, Queensland QLD 4072, Australia
| | - Richard D Norris
- Center for Marine Biodiversity and Conservation, Scripps Institution of Oceanography, UC San Diego, La Jolla, California 92093, USA
| |
Collapse
|
135
|
Cobey S, Baskerville EB. Limits to Causal Inference with State-Space Reconstruction for Infectious Disease. PLoS One 2016; 11:e0169050. [PMID: 28030639 PMCID: PMC5193453 DOI: 10.1371/journal.pone.0169050] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 12/09/2016] [Indexed: 01/17/2023] Open
Abstract
Infectious diseases are notorious for their complex dynamics, which make it difficult to fit models to test hypotheses. Methods based on state-space reconstruction have been proposed to infer causal interactions in noisy, nonlinear dynamical systems. These “model-free” methods are collectively known as convergent cross-mapping (CCM). Although CCM has theoretical support, natural systems routinely violate its assumptions. To identify the practical limits of causal inference under CCM, we simulated the dynamics of two pathogen strains with varying interaction strengths. The original method of CCM is extremely sensitive to periodic fluctuations, inferring interactions between independent strains that oscillate with similar frequencies. This sensitivity vanishes with alternative criteria for inferring causality. However, CCM remains sensitive to high levels of process noise and changes to the deterministic attractor. This sensitivity is problematic because it remains challenging to gauge noise and dynamical changes in natural systems, including the quality of reconstructed attractors that underlie cross-mapping. We illustrate these challenges by analyzing time series of reportable childhood infections in New York City and Chicago during the pre-vaccine era. We comment on the statistical and conceptual challenges that currently limit the use of state-space reconstruction in causal inference.
Collapse
Affiliation(s)
- Sarah Cobey
- Ecology & Evolution, University of Chicago, Chicago, IL, United States of America
- * E-mail:
| | | |
Collapse
|
136
|
Hefley TJ, Hooten MB, Drake JM, Russell RE, Walsh DP. When can the cause of a population decline be determined? Ecol Lett 2016; 19:1353-1362. [DOI: 10.1111/ele.12671] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 07/19/2016] [Accepted: 08/03/2016] [Indexed: 11/30/2022]
Affiliation(s)
- Trevor J. Hefley
- Department of Statistics and Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins CO80523 USA
| | - Mevin B. Hooten
- Department of Statistics and Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins CO80523 USA
- U.S. Geological Survey Colorado Cooperative Fish and Wildlife Unit Fort Collins CO 80523 USA
| | - John M. Drake
- Odum School of Ecology, University of Georgia Athens GA30602
| | - Robin E. Russell
- U.S. Geological Survey, National Wildlife Health Center Madison WI 80523 USA
| | - Daniel P. Walsh
- U.S. Geological Survey, National Wildlife Health Center Madison WI 80523 USA
| |
Collapse
|