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Tal O, Ostrovsky I, Gal G. A framework for identifying factors controlling cyanobacterium Microcystis flos-aquae blooms by coupled CCM-ECCM Bayesian networks. Ecol Evol 2024; 14:e11475. [PMID: 38932972 PMCID: PMC11199127 DOI: 10.1002/ece3.11475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 05/06/2024] [Accepted: 05/10/2024] [Indexed: 06/28/2024] Open
Abstract
Cyanobacterial blooms in freshwater sources are a global concern, and gaining insight into their causes is crucial for effective resource management and control. In this study, we present a novel computational framework for the causal analysis of cyanobacterial harmful algal blooms (cyanoHABs) in Lake Kinneret. Our framework integrates Convergent Cross Mapping (CCM) and Extended CCM (ECCM) causal networks with Bayesian Network (BN) models. The constructed CCM-ECCM causal networks and BN models unveil significant interactions among factors influencing cyanoHAB formation. These interactions have been validated by domain experts and supported by evidence from peer-reviewed publications. Our findings suggest that Microcystis flos-aquae levels are influenced not only by community structure but also by ammonium, phosphate, oxygen, and temperature levels in the weeks preceding bloom occurrences. We demonstrated a non-parametric computational framework for causal analysis of a multivariate ecosystem. Our framework offers a more comprehensive understanding of the underlying mechanisms driving M. flos-aquae blooms in Lake Kinneret. It captures complex interactions and provides an explainable prediction model. By considering causal relationships, temporal dynamics, and joint probabilities of environmental factors, the proposed framework enhances our understanding of cyanoHABs in Lake Kinneret.
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Affiliation(s)
- O. Tal
- Kinneret Limnological LaboratoryIsrael Oceanographic and Limnological ResearchMigdalIsrael
| | - I. Ostrovsky
- Kinneret Limnological LaboratoryIsrael Oceanographic and Limnological ResearchMigdalIsrael
| | - G. Gal
- Kinneret Limnological LaboratoryIsrael Oceanographic and Limnological ResearchMigdalIsrael
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2
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Zheng L, Gao Q, Yu S, Chen Y, Shi Y, Sun M, Liu Y, Wang Z, Li X. Using empirical dynamic modeling to identify the impact of meteorological factors on hemorrhagic fever with renal syndrome in Weifang, Northeastern China, from 2011 to 2020. PLoS Negl Trop Dis 2024; 18:e0012151. [PMID: 38843297 PMCID: PMC11185475 DOI: 10.1371/journal.pntd.0012151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/18/2024] [Accepted: 04/16/2024] [Indexed: 06/19/2024] Open
Abstract
BACKGROUND Hemorrhagic Fever with Renal Syndrome (HFRS) continues to pose a significant public health threat to the well-being of the population. Given that the spread of HFRS is susceptible to meteorological factors, we aim to probe into the meteorological drivers of HFRS. Thus, novel techniques that can discern time-delayed non-linear relationships from nonlinear dynamical systems are compulsory. METHODS We analyze the epidemiological features of HFRS in Weifang City, 2011-2020, via the employment of the Empirical Dynamic Modeling (EDM) method. Our analysis delves into the intricate web of time-delayed non-linear associations between meteorological factors and HFRS. Additionally, we investigate the repercussions of minor perturbations in meteorological variables on future HFRS incidence. RESULTS A total of 2515 HFRS cases were reported in Weifang from 2011 to 2020. The number of cases per week was 4.81, and the average weekly incidence was 0.52 per 1,000,000. The propagation of HFRS is significantly impacted by the mean weekly temperature, relative humidity, cumulative rainfall, and wind speed, and the ρCCM converges to 0.55,0.48,0.38 and 0.39, respectively. The graphical representation of the relationship between temperature (lagged by 2 weeks) and the incidence of HFRS exhibits an inverted U-shaped curve, whereby the incidence of HFRS culminates as the temperature reaches 10 °C. Moreover, temperature, relative humidity, cumulative rainfall, and wind speed exhibit a positive correlation with HFRS incidence, with a time lag of 4-6 months. CONCLUSIONS Our discoveries suggest that meteorological factors can drive the transmission of HFRS both at a macroscopic and microscopic scale. Prospective alterations in meteorological conditions, for instance, elevations in temperature, relative humidity, and precipitation will instigate an upsurge in the incidence of HFRS after 4-6 months, and thus, timely public health measures should be taken to mitigate these changes.
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Affiliation(s)
- Liang Zheng
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Qi Gao
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Shengnan Yu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Yijin Chen
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Yuan Shi
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Minghao Sun
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Ying Liu
- School of International Business, Xiamen University Tan Kah Kee College, Zhangzhou, Fujian, China
| | - Zhiqiang Wang
- Institute of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Xiujun Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
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3
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Saiki-Ishikawa A, Agrios M, Savya S, Forrest A, Sroussi H, Hsu S, Basrai D, Xu F, Miri A. Hierarchy between forelimb premotor and primary motor cortices and its manifestation in their firing patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.23.559136. [PMID: 38798685 PMCID: PMC11118350 DOI: 10.1101/2023.09.23.559136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Though hierarchy is commonly invoked in descriptions of motor cortical function, its presence and manifestation in firing patterns remain poorly resolved. Here we use optogenetic inactivation to demonstrate that short-latency influence between forelimb premotor and primary motor cortices is asymmetric during reaching in mice, demonstrating a partial hierarchy between the endogenous activity in each region. Multi-region recordings revealed that some activity is captured by similar but delayed patterns where either region's activity leads, with premotor activity leading more. Yet firing in each region is dominated by patterns shared between regions and is equally predictive of firing in the other region at the single-neuron level. In dual-region network models fit to data, regions differed in their dependence on across-region input, rather than the amount of such input they received. Our results indicate that motor cortical hierarchy, while present, may not be exposed when inferring interactions between populations from firing patterns alone.
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4
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Yuval, Levi Y, Broday DM. Revealing causality in the associations between meteorological variables and air pollutant concentrations. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 345:123526. [PMID: 38355085 DOI: 10.1016/j.envpol.2024.123526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 02/16/2024]
Abstract
Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO2) concentrations at multiple observation locations. Time-lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region-dependent. No causal association was found with relative humidity, temperature and precipitation.
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Affiliation(s)
- Yuval
- Department of Civil and Environmental Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel.
| | - Yoav Levi
- Israel Meteorological Service, P.O. Box 25, Bet Dagan 5025001, Israel
| | - David M Broday
- Department of Civil and Environmental Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel
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5
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Rybarczyk Y, Zalakeviciute R, Ortiz-Prado E. Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach. Heliyon 2024; 10:e25134. [PMID: 38322928 PMCID: PMC10844283 DOI: 10.1016/j.heliyon.2024.e25134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/15/2024] [Accepted: 01/22/2024] [Indexed: 02/08/2024] Open
Abstract
Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.
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Affiliation(s)
- Yves Rybarczyk
- School of Information and Engineering, Dalarna University, Falun, Sweden
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6
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Kollas N, Gewehr S, Kioutsioukis I. Empirical dynamic modelling and enhanced causal analysis of short-length Culex abundance timeseries with vector correlation metrics. Sci Rep 2024; 14:3597. [PMID: 38351267 PMCID: PMC10864305 DOI: 10.1038/s41598-024-54054-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
Employing Empirical Dynamic Modelling we investigate whether model free methods could be applied in the study of Culex mosquitoes in Northern Greece. Applying Simplex Projection and S-Map algorithms on yearly timeseries of maximum abundances from 2011 to 2020 we successfully predict the decreasing trend in the maximum number of mosquitoes which was observed in the rural area of Thessaloniki during 2021. Leveraging the use of vector correlation metrics we were able to deduce the main environmental factors driving mosquito abundance such as temperature, rain and wind during 2012 and study the causal interaction between neighbouring populations in the industrial area of Thessaloniki between 2019 and 2020. In all three cases a chaotic and non-linear behaviour of the underlying system was observed. Given the health risk associated with the presence of mosquitoes as vectors of viral diseases these results hint to the usefulness of EDM methods in entomological studies as guides for the construction of more accurate and realistic mechanistic models which are indispensable to public health authorities for the design of targeted control strategies and health prevention measures.
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Affiliation(s)
- Nikos Kollas
- Department of Physics, University of Patras, 26504, Patras, Greece
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7
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Du Z, Yu L, Chen X, Gao B, Yang J, Fu H, Gong P. Land use/cover and land degradation across the Eurasian steppe: Dynamics, patterns and driving factors. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 909:168593. [PMID: 37972781 DOI: 10.1016/j.scitotenv.2023.168593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/16/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
Despite the ecological and socio-economic importance of Eurasian steppe, the land use/cover change, land degradation and the threats facing this precious ecosystem still have not been comprehensively understood. Taking advantages of the land use/cover change monitoring platform (FROM-GLC Plus), this study developed the annual land use/cover maps during 2000-2022, and the land use/cover change, especially the change of grassland, was further analyzed. The grassland area exhibited a net increase, predominantly transformed from cropland, forest, and bareland, accounting for 17.64 %, 31.91 %, and 45.60 %, respectively. To monitor land degradation, we adopted the framework suggested by the United Nations Convention to Combat Desertification (UNCCD). According to the monitoring result, grassland constituted the highest proportion of degraded land (39.82 %). This may due to its dominance in the Eurasian steppe's land use/cover, as the extent of grassland degradation (1.92 %) was lower than the overall land degradation level (2.83 %) across the region. To offer tailored and sustainable development recommendations, we quantified the driving factors behind land dynamics using the geographical detector model and convergent cross mapping (CCM), considering both spatial and temporal dimensions. Environmental and socio-economic factors, such as precipitation, temperature, urbanization, mining and grazing intensity, etc., were integrated into the analysis. We found that urbanization, cropland and moisture distribution emerged as key drivers influencing land degradation's spatial distribution in the Eurasian steppe, while temperature variations between years impacted vegetation changes. This research thus provides a deeper understanding of the region's land dynamics, enhancing comprehensive monitoring of the Eurasian steppe's land dynamics. Moreover, it serves as a foundation for policymakers and land managers to devise conservation strategies and sustainable development initiatives for this critical ecosystem.
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Affiliation(s)
- Zhenrong Du
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China; School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
| | - Le Yu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China; Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China; Tsinghua University (Department of Earth System Science)- Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Beijing 100084, China.
| | - Xin Chen
- Institute of Loess Plateau, Shanxi University, Taiyuan 030006, China
| | - Bingbo Gao
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jianyu Yang
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Haohuan Fu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China; Tsinghua University (Department of Earth System Science)- Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Beijing 100084, China
| | - Peng Gong
- Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China; Department of Geography, Department of Earth Sciences, and Institute for Climate and Carbon Neutrality, University of Hong Kong, Hong Kong 999077, China
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8
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Wulczyn F, Kaligotla C, Hummel J, Wagner A, MacLeod A. Agent-based simulation and child protection systems: Rationale, implementation, and verification. CHILD ABUSE & NEGLECT 2024; 147:106578. [PMID: 38128373 DOI: 10.1016/j.chiabu.2023.106578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 10/16/2023] [Accepted: 11/23/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Simulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence. PARTICIPANTS AND SETTING In this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE. METHODS The governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement. RESULTS As an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE. CONCLUSIONS Management of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.
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Affiliation(s)
- Fred Wulczyn
- Center for State Child Welfare Data, Chapin Hall, University of Chicago, United States of America.
| | | | - John Hummel
- Argonne National Laboratory, University of Chicago, United States of America
| | - Amanda Wagner
- Argonne National Laboratory, University of Chicago, United States of America
| | - Alex MacLeod
- Beedie School of Business, Simon Fraser University, Canada
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9
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Deng J, Sun B, Scheel N, Renli AB, Zhu DC, Zhu D, Ren J, Li T, Zhang R. Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis. PNAS NEXUS 2024; 3:pgad422. [PMID: 38169910 PMCID: PMC10758925 DOI: 10.1093/pnasnexus/pgad422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
Abstract
Convergent cross-mapping (CCM) has attracted increased attention recently due to its capability to detect causality in nonseparable systems under deterministic settings, which may not be covered by the traditional Granger causality. From an information-theoretic perspective, causality is often characterized as the directed information (DI) flowing from one side to the other. As information is essentially nondeterministic, a natural question is: does CCM measure DI flow? Here, we first causalize CCM so that it aligns with the presumption in causality analysis-the future values of one process cannot influence the past of the other, and then establish and validate the approximate equivalence of causalized CCM (cCCM) and DI under Gaussian variables through both theoretical derivations and fMRI-based brain network causality analysis. Our simulation result indicates that, in general, cCCM tends to be more robust than DI in causality detection. The underlying argument is that DI relies heavily on probability estimation, which is sensitive to data size as well as digitization procedures; cCCM, on the other hand, gets around this problem through geometric cross-mapping between the manifolds involved. Overall, our analysis demonstrates that cross-mapping provides an alternative way to evaluate DI and is potentially an effective technique for identifying both linear and nonlinear causal coupling in brain neural networks and other settings, either random or deterministic, or both.
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Affiliation(s)
- Jinxian Deng
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Boxin Sun
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Norman Scheel
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA
| | - Alina B Renli
- Department of Neuroscience, Michigan State University, East Lansing, MI 48824, USA
| | - David C Zhu
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, University of Texas, Arlington, TX 76010, USA
| | - Jian Ren
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Tongtong Li
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Rong Zhang
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, TX 75231, USA
- Departments of Neurology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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10
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Laumann F, von Kügelgen J, Park J, Schölkopf B, Barahona M. Kernel-Based Independence Tests for Causal Structure Learning on Functional Data. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1597. [PMID: 38136477 PMCID: PMC10742995 DOI: 10.3390/e25121597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/11/2023] [Accepted: 11/15/2023] [Indexed: 12/24/2023]
Abstract
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert-Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.
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Affiliation(s)
- Felix Laumann
- Department of Mathematics, Imperial College London, London SW7 2BX, UK
| | - Julius von Kügelgen
- Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
- Department of Engineering, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Junhyung Park
- Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
| | | | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2BX, UK
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11
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Lee S, Lee B, Lee J, Song J, McCarty GW. Detecting causal relationship of non-floodplain wetland hydrologic connectivity using convergent cross mapping. Sci Rep 2023; 13:17220. [PMID: 37821495 PMCID: PMC10567775 DOI: 10.1038/s41598-023-44071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/03/2023] [Indexed: 10/13/2023] Open
Abstract
The hydrologic connectivity of non-floodplain wetlands (NFWs) with downstream water (DW) has gained increased importance, but connectivity via groundwater (GW) is largely unknown owing to the high complexity of hydrological processes and climatic seasonality. In this study, a causal inference method, convergent cross mapping (CCM), was applied to detect the hydrologic causality between upland NFW and DW through GW. CCM is a nonlinear inference method for detecting causal relationships among environmental variables with weak or moderate coupling in nonlinear dynamical systems. We assumed that causation would exist when the following conditions were observed: (1) the presence of two direct causal (NFW → GW and GW → DW) and one indirect causal (NFW → DW) relationship; (2) a nonexistent opposite causal relationship (DW → NFW); (3) the two direct causations with shorter lag times relative to indirect causation; and (4) similar patterns not observed with pseudo DW. The water levels monitored by a well and piezometer represented NFW and GW measurements, respectively, and the DW was indicated by the baseflow at the outlet of the drainage area, including NFW. To elucidate causality, the DW taken at the adjacent drainage area with similar climatic seasonality was also tested as pseudo DW. The CCM results showed that the water flow from NFW to GW and then DW was only present, and any opposite flows did not exist. In addition, direct causations had shorter lag time than indirect causation, and 3-day lag time was shown between NFW and DW. Interestingly, the results with pseudo DW did not show any lagged interactions, indicating non-causation. These results provide the signals for the hydrologic connectivity of NFW and DW with GW. Therefore, this study would support the importance of NFW protection and management.
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Affiliation(s)
- Sangchul Lee
- Department of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, 02504, Republic of Korea.
| | - Byeongwon Lee
- Department of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, 02504, Republic of Korea
| | - Junga Lee
- Division of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul, 02841, Republic of Korea
| | - Jihoon Song
- Ojeong Resilience Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Gregory W McCarty
- USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD, 20705, USA.
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12
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Sasaki T, Collins SL, Rudgers JA, Batdelger G, Baasandai E, Kinugasa T. Dryland sensitivity to climate change and variability using nonlinear dynamics. Proc Natl Acad Sci U S A 2023; 120:e2305050120. [PMID: 37603760 PMCID: PMC10587894 DOI: 10.1073/pnas.2305050120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/26/2023] [Indexed: 08/23/2023] Open
Abstract
Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation-free, nonlinear time-series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time-delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time-delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system.
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Affiliation(s)
- Takehiro Sasaki
- Graduate School of Environment and Information Sciences, Yokohama National University, Hodogaya, Yokohama240-8501, Japan
| | - Scott L. Collins
- Department of Biology, MSC03-2020, University of New Mexico, Albuquerque, NM87131
| | - Jennifer A. Rudgers
- Department of Biology, MSC03-2020, University of New Mexico, Albuquerque, NM87131
| | - Gantsetseg Batdelger
- Information and Research Institute of Meteorology, Hydrology and Environment of Mongolia, Ulaanbaatar15160, Mongolia
| | - Erdenetsetseg Baasandai
- Information and Research Institute of Meteorology, Hydrology and Environment of Mongolia, Ulaanbaatar15160, Mongolia
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13
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Park SH, Ha S, Kim JK. A general model-based causal inference method overcomes the curse of synchrony and indirect effect. Nat Commun 2023; 14:4287. [PMID: 37488136 PMCID: PMC10366229 DOI: 10.1038/s41467-023-39983-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/22/2023] [Indexed: 07/26/2023] Open
Abstract
To identify causation, model-free inference methods, such as Granger Causality, have been widely used due to their flexibility. However, they have difficulty distinguishing synchrony and indirect effects from direct causation, leading to false predictions. To overcome this, model-based inference methods that test the reproducibility of data with a specific mechanistic model to infer causality were developed. However, they can only be applied to systems described by a specific model, greatly limiting their applicability. Here, we address this limitation by deriving an easily testable condition for a general monotonic ODE model to reproduce time-series data. We built a user-friendly computational package, General ODE-Based Inference (GOBI), which is applicable to nearly any monotonic system with positive and negative regulations described by ODE. GOBI successfully inferred positive and negative regulations in various networks at both the molecular and population levels, unlike existing model-free methods. Thus, this accurate and broadly applicable inference method is a powerful tool for understanding complex dynamical systems.
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Affiliation(s)
- Se Ho Park
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Seokmin Ha
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, 34126, Republic of Korea
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea
| | - Jae Kyoung Kim
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea.
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14
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Bahamonde AD, Montes RM, Cornejo P. Usefulness and limitations of convergent cross sorting and continuity scaling methods for their application in simulated and real-world time series. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221590. [PMID: 37448474 PMCID: PMC10336384 DOI: 10.1098/rsos.221590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
Causality detection methods are valuable tools for detecting causal links in complex systems. The efficiency of continuity scaling (CS) and the convergent cross sorting (CSS) methods to detect causality was analysed. Usefulness and limitations of both methods in their application to simulated and real-world time series was explored under different scenarios. We find that CS is more robust and efficient than the CSS method for all simulated systems, even when increasing noise levels were considered. Both methods were not able to infer causality when time series with a marked difference in their main frequencies were analysed. Minimum time-series length required for the detection of a causal link depends on intrinsic system dynamics and on the method selected to detect it. Using simulated time series, only the CS method was capable to detect bidirectional causality. Causality detection, using the CS method, should at least include: (i) causality strength convergence analysis, (ii) statistical tests of significance, (iii) time-series standardization, and (iv) causality strength ratios as a strength indicator of relative causality between systems. Causality cannot be detected by either method in simulated time series that exhibit generalized synchronization.
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Affiliation(s)
- Adolfo D Bahamonde
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
| | - Rodrigo M Montes
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
| | - Pablo Cornejo
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
- Mechanical Engineering Department, University of Concepción, Concepción, Chile
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15
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Glidden CK, Karakoç C, Duan C, Jiang Y, Beechler B, Jabbar A, Jolles AE. Distinct life history strategies underpin clear patterns of succession in microparasite communities infecting a wild mammalian host. Mol Ecol 2023; 32:3733-3746. [PMID: 37009964 PMCID: PMC10389068 DOI: 10.1111/mec.16949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/20/2023] [Accepted: 03/28/2023] [Indexed: 04/04/2023]
Abstract
Individual animals in natural populations tend to host diverse parasite species concurrently over their lifetimes. In free-living ecological communities, organismal life histories shape interactions with their environment, which ultimately forms the basis of ecological succession. However, the structure and dynamics of mammalian parasite communities have not been contextualized in terms of primary ecological succession, in part because few datasets track occupancy and abundance of multiple parasites in wild hosts starting at birth. Here, we studied community dynamics of 12 subtypes of protozoan microparasites (Theileria spp.) in a herd of African buffalo. We show that Theileria communities followed predictable patterns of succession underpinned by four different parasite life history strategies. However, in contrast to many free-living communities, network complexity decreased with host age. Examining parasite communities through the lens of succession may better inform the effect of complex within host eco-evolutionary dynamics on infection outcomes, including parasite co-existence through the lifetime of the host.
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Affiliation(s)
- Caroline K. Glidden
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, USA
| | - Canan Karakoç
- Department of Biology, Indiana University, Bloomington, Indiana, USA
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Chenyang Duan
- Department of Statistics, Oregon State University, Corvallis, Oregon, USA
| | - Yuan Jiang
- Department of Statistics, Oregon State University, Corvallis, Oregon, USA
| | - Brianna Beechler
- College of Veterinary Medicine, Oregon State University, Corvallis, Oregon, USA
| | - Abdul Jabbar
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Victoria, Australia
| | - Anna E. Jolles
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, USA
- College of Veterinary Medicine, Oregon State University, Corvallis, Oregon, USA
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16
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Zhao Q, Van den Brink PJ, Xu C, Wang S, Clark AT, Karakoç C, Sugihara G, Widdicombe CE, Atkinson A, Matsuzaki SIS, Shinohara R, He S, Wang YXG, De Laender F. Relationships of temperature and biodiversity with stability of natural aquatic food webs. Nat Commun 2023; 14:3507. [PMID: 37316479 DOI: 10.1038/s41467-023-38977-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 05/22/2023] [Indexed: 06/16/2023] Open
Abstract
Temperature and biodiversity changes occur in concert, but their joint effects on ecological stability of natural food webs are unknown. Here, we assess these relationships in 19 planktonic food webs. We estimate stability as structural stability (using the volume contraction rate) and temporal stability (using the temporal variation of species abundances). Warmer temperatures were associated with lower structural and temporal stability, while biodiversity had no consistent effects on either stability property. While species richness was associated with lower structural stability and higher temporal stability, Simpson diversity was associated with higher temporal stability. The responses of structural stability were linked to disproportionate contributions from two trophic groups (predators and consumers), while the responses of temporal stability were linked both to synchrony of all species within the food web and distinctive contributions from three trophic groups (predators, consumers, and producers). Our results suggest that, in natural ecosystems, warmer temperatures can erode ecosystem stability, while biodiversity changes may not have consistent effects.
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Affiliation(s)
- Qinghua Zhao
- Aquatic Ecology and Water Quality Management Group, Wageningen University & Research, P.O. Box 47, 6700 AA, Wageningen, The Netherlands.
- Research Unit of Environmental and Evolutionary Biology (URBE), University of Namur, Namur, Belgium.
- Institute of Complex Systems (naXys), University of Namur, Namur, Belgium.
- Institute of Life, Earth and the Environment (ILEE), University of Namur, Namur, Belgium.
| | - Paul J Van den Brink
- Aquatic Ecology and Water Quality Management Group, Wageningen University & Research, P.O. Box 47, 6700 AA, Wageningen, The Netherlands
- Wageningen Environmental Research, P.O. Box 47, 6700 AA, Wageningen, The Netherlands
| | - Chi Xu
- School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Shaopeng Wang
- Institute of Ecology, College of Urban and Environmental Science, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, 100871, Beijing, China
| | - Adam T Clark
- Institute of Biology, University of Graz, Holteigasse 6, 8010, Graz, Austria
| | - Canan Karakoç
- Department of Biology, Indiana University, 1001 East Third Street, Bloomington, IN, 47405, USA
| | - George Sugihara
- Scripps Institution of Oceanography, University of California-San Diego, La Jolla, CA, USA
| | | | - Angus Atkinson
- Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL13DH, UK
| | | | | | - Shuiqing He
- Wildlife Ecology and Conservation Group, Wageningen University & Research, Wageningen, The Netherlands
| | - Yingying X G Wang
- Department of Biological and Environmental Science, University of Jyväskylä, FI-40014, Jyväskylä, Finland
| | - Frederik De Laender
- Research Unit of Environmental and Evolutionary Biology (URBE), University of Namur, Namur, Belgium
- Institute of Complex Systems (naXys), University of Namur, Namur, Belgium
- Institute of Life, Earth and the Environment (ILEE), University of Namur, Namur, Belgium
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17
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Chen D, Sun X, Cheke RA. Inferring a Causal Relationship between Environmental Factors and Respiratory Infections Using Convergent Cross-Mapping. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050807. [PMID: 37238562 DOI: 10.3390/e25050807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/12/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023]
Abstract
The incidence of respiratory infections in the population is related to many factors, among which environmental factors such as air quality, temperature, and humidity have attracted much attention. In particular, air pollution has caused widespread discomfort and concern in developing countries. Although the correlation between respiratory infections and air pollution is well known, establishing causality between them remains elusive. In this study, by conducting theoretical analysis, we updated the procedure of performing the extended convergent cross-mapping (CCM, a method of causal inference) to infer the causality between periodic variables. Consistently, we validated this new procedure on the synthetic data generated by a mathematical model. For real data in Shaanxi province of China in the period of 1 January 2010 to 15 November 2016, we first confirmed that the refined method is applicable by investigating the periodicity of influenza-like illness cases, an air quality index, temperature, and humidity through wavelet analysis. We next illustrated that air quality (quantified by AQI), temperature, and humidity affect the daily influenza-like illness cases, and, in particular, the respiratory infection cases increased progressively with increased AQI with a time delay of 11 days.
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Affiliation(s)
- Daipeng Chen
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
- Mathematical Institute, Leiden University, 2333 CA Leiden, The Netherlands
| | - Xiaodan Sun
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Robert A Cheke
- Natural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Chatham ME4 4TB, Kent, UK
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18
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Benincà E, Pinto S, Cazelles B, Fuentes S, Shetty S, Bogaards JA. Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome. Sci Rep 2023; 13:8042. [PMID: 37198426 DOI: 10.1038/s41598-023-34713-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/05/2023] [Indexed: 05/19/2023] Open
Abstract
Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.
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Affiliation(s)
- Elisa Benincà
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
| | - Susanne Pinto
- Biomedical Data Sciences, Leiden UMC, Leiden, The Netherlands
| | - Bernard Cazelles
- CNRS UMR-8197, IBENS, Ecole Normale Supérieure, Paris, France
- Sorbonne Université, UMMISCO, Paris, France
| | - Susana Fuentes
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Sudarshan Shetty
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Medical Microbiology and Infection Prevention, UMC Groningen, Groningen, The Netherlands
| | - Johannes A Bogaards
- Department of Epidemiology & Data Science, Amsterdam UMC location VUMC, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam UMC, Amsterdam, The Netherlands
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19
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Naviaux RK. Mitochondrial and metabolic features of salugenesis and the healing cycle. Mitochondrion 2023; 70:131-163. [PMID: 37120082 DOI: 10.1016/j.mito.2023.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/24/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Pathogenesis and salugenesis are the first and second stages of the two-stage problem of disease production and health recovery. Salugenesis is the automatic, evolutionarily conserved, ontogenetic sequence of molecular, cellular, organ system, and behavioral changes that is used by living systems to heal. It is a whole-body process that begins with mitochondria and the cell. The stages of salugenesis define a circle that is energy- and resource-consuming, genetically programmed, and environmentally responsive. Energy and metabolic resources are provided by mitochondrial and metabolic transformations that drive the cell danger response (CDR) and create the three phases of the healing cycle: Phase 1-Inflammation, Phase 2-Proliferation, and Phase 3-Differentiation. Each phase requires a different mitochondrial phenotype. Without different mitochondria there can be no healing. The rise and fall of extracellular ATP (eATP) signaling is a key driver of the mitochondrial and metabolic reprogramming required to progress through the healing cycle. Sphingolipid and cholesterol-enriched membrane lipid rafts act as rheostats for tuning cellular sensitivity to purinergic signaling. Abnormal persistence of any phase of the CDR inhibits the healing cycle, creates dysfunctional cellular mosaics, causes the symptoms of chronic disease, and accelerates the process of aging. New research reframes the rising tide of chronic disease around the world as a systems problem caused by the combined action of pathogenic triggers and anthropogenic factors that interfere with the mitochondrial functions needed for healing. Once chronic pain, disability, or disease is established, salugenesis-based therapies will start where pathogenesis-based therapies end.
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Affiliation(s)
- Robert K Naviaux
- The Mitochondrial and Metabolic Disease Center, Departments of Medicine, and Pediatrics, University of California, San Diego School of Medicine, 214 Dickinson St., Bldg CTF, Rm C107, MC#8467, San Diego, CA 92103.
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20
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Setty S, Cramwinckel MJ, van Nes EH, van de Leemput IA, Dijkstra HA, Lourens LJ, Scheffer M, Sluijs A. Loss of Earth system resilience during early Eocene transient global warming events. SCIENCE ADVANCES 2023. [PMID: 37027462 DOI: 10.5281/zenodo.7620884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Superimposed on long-term late Paleocene-early Eocene warming (~59 to 52 million years ago), Earth's climate experienced a series of abrupt perturbations, characterized by massive carbon input into the ocean-atmosphere system and global warming. Here, we examine the three most punctuated events of this period, the Paleocene-Eocene Thermal Maximum and Eocene Thermal Maximum 2 and 3, to probe whether they were initiated by climate-driven carbon cycle tipping points. Specifically, we analyze the dynamics of climate and carbon cycle indicators acquired from marine sediments to detect changes in Earth system resilience and to identify positive feedbacks. Our analyses suggest a loss of Earth system resilience toward all three events. Moreover, dynamic convergent cross mapping reveals intensifying coupling between the carbon cycle and climate during the long-term warming trend, supporting increasingly dominant climate forcing of carbon cycle dynamics during the Early Eocene Climatic Optimum when these recurrent global warming events became more frequent.
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Affiliation(s)
- Shruti Setty
- Department of Environmental Science, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - Margot J Cramwinckel
- Department of Earth Sciences, Faculty of Geoscience, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, Netherlands
| | - Egbert H van Nes
- Department of Environmental Science, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - Ingrid A van de Leemput
- Department of Environmental Science, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - Henk A Dijkstra
- Institute for Marine and Atmospheric research Utrecht, Department of Physics, Utrecht University, Princetonlaan 5, 3584 CC Utrecht, Netherlands
- Centre for Complex Systems Studies, Utrecht University, Princetonlaan 5, 3584 CC Utrecht, Netherlands
| | - Lucas J Lourens
- Department of Earth Sciences, Faculty of Geoscience, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, Netherlands
| | - Marten Scheffer
- Department of Environmental Science, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - Appy Sluijs
- Department of Earth Sciences, Faculty of Geoscience, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, Netherlands
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21
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Setty S, Cramwinckel MJ, van Nes EH, van de Leemput IA, Dijkstra HA, Lourens LJ, Scheffer M, Sluijs A. Loss of Earth system resilience during early Eocene transient global warming events. SCIENCE ADVANCES 2023; 9:eade5466. [PMID: 37027462 PMCID: PMC10081840 DOI: 10.1126/sciadv.ade5466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
Superimposed on long-term late Paleocene-early Eocene warming (~59 to 52 million years ago), Earth's climate experienced a series of abrupt perturbations, characterized by massive carbon input into the ocean-atmosphere system and global warming. Here, we examine the three most punctuated events of this period, the Paleocene-Eocene Thermal Maximum and Eocene Thermal Maximum 2 and 3, to probe whether they were initiated by climate-driven carbon cycle tipping points. Specifically, we analyze the dynamics of climate and carbon cycle indicators acquired from marine sediments to detect changes in Earth system resilience and to identify positive feedbacks. Our analyses suggest a loss of Earth system resilience toward all three events. Moreover, dynamic convergent cross mapping reveals intensifying coupling between the carbon cycle and climate during the long-term warming trend, supporting increasingly dominant climate forcing of carbon cycle dynamics during the Early Eocene Climatic Optimum when these recurrent global warming events became more frequent.
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Affiliation(s)
- Shruti Setty
- Department of Environmental Science, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - Margot J. Cramwinckel
- Department of Earth Sciences, Faculty of Geoscience, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, Netherlands
| | - Egbert H. van Nes
- Department of Environmental Science, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - Ingrid A. van de Leemput
- Department of Environmental Science, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - Henk A. Dijkstra
- Institute for Marine and Atmospheric research Utrecht, Department of Physics, Utrecht University, Princetonlaan 5, 3584 CC Utrecht, Netherlands
- Centre for Complex Systems Studies, Utrecht University, Princetonlaan 5, 3584 CC Utrecht, Netherlands
| | - Lucas J Lourens
- Department of Earth Sciences, Faculty of Geoscience, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, Netherlands
| | - Marten Scheffer
- Department of Environmental Science, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - Appy Sluijs
- Department of Earth Sciences, Faculty of Geoscience, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, Netherlands
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22
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Runge J. Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships. Nat Commun 2023; 14:1917. [PMID: 37024476 PMCID: PMC10079963 DOI: 10.1038/s41467-023-37546-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 03/17/2023] [Indexed: 04/08/2023] Open
Affiliation(s)
- Jakob Runge
- Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Datenwissenschaften, Mälzerstr. 3-5, Jena, 07745, Germany.
- Technische Universität Berlin, Institute of Computer Engineering and Microelectronics, Straße des 17. Juni 135, Berlin, 10623, Germany.
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23
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Brešar M, Boškoski P. Directional coupling detection through cross-distance vectors. Phys Rev E 2023; 107:044220. [PMID: 37198824 DOI: 10.1103/physreve.107.044220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/03/2023] [Indexed: 05/19/2023]
Abstract
Inferring the coupling direction from measured time series of complex systems is challenging. We propose a state-space-based causality measure obtained from cross-distance vectors for quantifying interaction strength. It is a model-free noise-robust approach that requires only a few parameters. The approach is applicable to bivariate time series and is resilient to artefacts and missing values. The result is two coupling indices that quantify coupling strength in each direction more accurately than the already established state-space measures. We test the proposed method on different dynamical systems and analyze numerical stability. As a result, a procedure for optimal parameter selection is proposed, circumventing the challenge of determining the optimal embedding parameters. We show it is robust to noise and reliable in shorter time series. Moreover, we show that it can detect cardiorespiratory interaction in measured data. A numerically efficient implementation is available at https://repo.ijs.si/e2pub/cd-vec.
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Affiliation(s)
- Martin Brešar
- Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia and Jožef Stefan International Postgraduate School, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | - Pavle Boškoski
- Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
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24
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Koutsodendris A, Dakos V, Fletcher WJ, Knipping M, Kotthoff U, Milner AM, Müller UC, Kaboth-Bahr S, Kern OA, Kolb L, Vakhrameeva P, Wulf S, Christanis K, Schmiedl G, Pross J. Atmospheric CO 2 forcing on Mediterranean biomes during the past 500 kyrs. Nat Commun 2023; 14:1664. [PMID: 36966144 PMCID: PMC10039881 DOI: 10.1038/s41467-023-37388-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 03/14/2023] [Indexed: 03/27/2023] Open
Abstract
There is growing concern on the survival of Mediterranean forests under the projected near-future droughts as a result of anthropogenic climate change. Here we determine the resilience of Mediterranean forests across the entire range of climatic boundary conditions realized during the past 500 kyrs based on continuous pollen and geochemical records of (sub)centennial-scale resolution from drillcores from Tenaghi Philippon, Greece. Using convergent cross-mapping we provide empirical confirmation that global atmospheric carbon dioxide (CO2) may affect Mediterranean vegetation through forcing on moisture availability. Our analysis documents two stable vegetation regimes across the wide range of CO2 and moisture levels realized during the past four glacial-interglacial cycles, with abrupt shifts from forest to steppe biomes occurring when a threshold in precipitation is crossed. Our approach highlights that a CO2-driven moisture decrease in the near future may bear an impending risk for abrupt vegetation regime shifts prompting forest loss in the Mediterranean region.
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Affiliation(s)
| | - Vasilis Dakos
- Institute des Sciences de l'Évolution, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France
- Institut d'Écologie et des Sciences de l'Environnement de Paris (iEES Paris), Sorbonne Université, Paris, France
| | - William J Fletcher
- School of Environment, Education and Development, The University of Manchester, Manchester, UK
| | - Maria Knipping
- Department of Molecular Botany, Institute of Biology, University of Hohenheim, Stuttgart, Germany
| | - Ulrich Kotthoff
- Center for Earth System Research and Sustainability, Institute of Geology, Hamburg University, Hamburg, Germany
- Leibniz Institute for the Analysis of Biodiversity Change (LIB), Hamburg, Germany
| | - Alice M Milner
- Department of Geography, Royal Holloway University of London, London, UK
| | - Ulrich C Müller
- Parlamentsstraße 32, 60385, Frankfurt am Main, Germany
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberg Gesellschaft für Naturforschung, Frankfurt am Main, Germany
| | - Stefanie Kaboth-Bahr
- Institute of Earth Sciences, Heidelberg University, Heidelberg, Germany
- Institute of Geosciences, University of Potsdam, Potsdam-Golm, Germany
| | - Oliver A Kern
- Institute of Earth Sciences, Heidelberg University, Heidelberg, Germany
| | - Laurin Kolb
- Institute of Earth Sciences, Heidelberg University, Heidelberg, Germany
| | | | - Sabine Wulf
- School of the Environment, Geography and Geosciences, University of Portsmouth, Portsmouth, UK
| | | | - Gerhard Schmiedl
- Center for Earth System Research and Sustainability, Institute of Geology, Hamburg University, Hamburg, Germany
| | - Jörg Pross
- Institute of Earth Sciences, Heidelberg University, Heidelberg, Germany
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberg Gesellschaft für Naturforschung, Frankfurt am Main, Germany
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25
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Munch SB, Rogers TL, Sugihara G. Recent developments in empirical dynamic modelling. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.13983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Stephan B. Munch
- Southwest Fisheries Science Center, National Marine Fisheries Service National Oceanic and Atmospheric Administration Santa Cruz California USA
- Department of Applied Mathematics University of California Santa Cruz California USA
| | - Tanya L. Rogers
- Southwest Fisheries Science Center, National Marine Fisheries Service National Oceanic and Atmospheric Administration Santa Cruz California USA
| | - George Sugihara
- Scripps Institution of Oceanography University of California San Diego La Jolla California USA
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26
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Zhang Q, Yi GY, Chen LP, He W. Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines. PLoS One 2023; 18:e0277878. [PMID: 36827382 PMCID: PMC9955611 DOI: 10.1371/journal.pone.0277878] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 11/06/2022] [Indexed: 02/26/2023] Open
Abstract
While the impact of the COVID-19 pandemic has been widely studied, relatively fewer discussions about the sentimental reaction of the public are available. In this article, we scrape COVID-19 related tweets on the microblogging platform, Twitter, and examine the tweets from February 24, 2020 to October 14, 2020 in four Canadian cities (Toronto, Montreal, Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago, and Seattle). Applying the RoBERTa, Vader and NRC approaches, we evaluate sentiment intensity scores and visualize the results over different periods of the pandemic. Sentiment scores for the tweets concerning three anti-epidemic measures, "masks", "vaccine", and "lockdown", are computed for comparison. We explore possible causal relationships among the variables concerning tweet activities and sentiment scores of COVID-19 related tweets by integrating the echo state network method with convergent cross-mapping. Our analyses show that public sentiments about COVID-19 vary from time to time and from place to place, and are different with respect to anti-epidemic measures of "masks", "vaccines", and "lockdown". Evidence of the causal relationship is revealed for the examined variables, assuming the suggested model is feasible.
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Affiliation(s)
- Qihuang Zhang
- Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada
| | - Grace Y. Yi
- Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada
- Department of Computer Science, University of Western Ontario, London, Ontario, Canada
- * E-mail:
| | - Li-Pang Chen
- Department of Statistics, National Chengchi University, Taipei, Taiwan
| | - Wenqing He
- Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada
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27
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Tao P, Wang Q, Shi J, Hao X, Liu X, Min B, Zhang Y, Li C, Cui H, Chen L. Detecting dynamical causality by intersection cardinal concavity. FUNDAMENTAL RESEARCH 2023. [DOI: 10.1016/j.fmre.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
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O'Brien DA, Gal G, Thackeray SJ, Matsuzaki SS, Clements CF. Planktonic functional diversity changes in synchrony with lake ecosystem state. GLOBAL CHANGE BIOLOGY 2023; 29:686-701. [PMID: 36370051 PMCID: PMC10100413 DOI: 10.1111/gcb.16485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/23/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
Managing ecosystems to effectively preserve function and services requires reliable tools that can infer changes in the stability and dynamics of a system. Conceptually, functional diversity (FD) appears as a sensitive and viable monitoring metric stemming from suggestions that FD is a universally important measure of biodiversity and has a mechanistic influence on ecological processes. It is however unclear whether changes in FD consistently occur prior to state responses or vice versa, with no current work on the temporal relationship between FD and state to support a transition towards trait-based indicators. There is consequently a knowledge gap regarding when functioning changes relative to biodiversity change and where FD change falls in that sequence. We therefore examine the lagged relationship between planktonic FD and abundance-based metrics of system state (e.g. biomass) across five highly monitored lake communities using both correlation and cutting edge non-linear empirical dynamic modelling approaches. Overall, phytoplankton and zooplankton FD display synchrony with lake state but each lake is idiosyncratic in the strength of relationship. It is therefore unlikely that changes in plankton FD are identifiable before changes in more easily collected abundance metrics. These results highlight the power of empirical dynamic modelling in disentangling time lagged relationships in complex multivariate ecosystems, but suggest that FD cannot be generically viable as an early indicator. Individual lakes therefore require consideration of their specific context and any interpretation of FD across systems requires caution. However, FD still retains value as an alternative state measure or a trait representation of biodiversity when considered at the system level.
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Affiliation(s)
| | - Gideon Gal
- Kinneret Limnological LaboratoryIsrael Oceanographic and Limnological ResearchMigdalIsrael
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29
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Chew LY, Ong YS. Granger causality using Jacobian in neural networks. CHAOS (WOODBURY, N.Y.) 2023; 33:023126. [PMID: 36859223 DOI: 10.1063/5.0106666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here, we introduce JGC (Jacobian Granger causality), a neural network-based approach to Granger causality using the Jacobian as a measure of variable importance, and propose a variable selection procedure for inferring Granger causal variables with this measure, using criteria of significance and consistency. The resulting approach performs consistently well compared to other approaches in identifying Granger causal variables, the associated time lags, as well as interaction signs. In addition, we also discuss the need for contemporaneous variables in Granger causal modeling as well as how these neural network-based approaches reduce the impact of nonseparability in dynamical systems, a problem where predictive information on a target variable is not unique to its causes, but also contained in the history of the target variable itself.
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Affiliation(s)
- Lock Yue Chew
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371
| | - Yew-Soon Ong
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798
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30
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Nemat H, Khadem H, Elliott J, Benaissa M. Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction. Comput Biol Med 2023; 153:106535. [PMID: 36640530 DOI: 10.1016/j.compbiomed.2022.106535] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 12/05/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023]
Abstract
Effective control of blood glucose level (BGL) is the key factor in the management of type 1 diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is in the target range and thus minimise both acute and chronic diabetes-related complications. To predict future BGL, histories of variables known to affect BGL, such as carbohydrate intake, injected bolus insulin, and physical activity, are utilised. Due to these identified cause and effect relationships, T1D management can be examined via the causality context. In this respect, this work initially investigates these relations and quantifies the causality strengths of each variable with BGL using the convergent cross mapping method (CCM). Then, considering the extended CCM, the causality strengths of each variable for different lags are quantified. After that, the optimal time lag for each variable is determined according to the quantified causality effects. Subsequently, the feasibility of leveraging causality information as prior knowledge for BGL prediction is investigated by proposing two approaches. In the first approach, causality strengths are used as weights for relevant affecting variables. In the second approach, the optimal causal lags and the corresponding causality strengths are considered the shifts and weights for the variables, respectively. Overall, the evaluation criteria and statistical analysis used for comparing results show the effectiveness of using causality analysis in T1D management.
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Affiliation(s)
- Hoda Nemat
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, S1 4DE, UK.
| | - Heydar Khadem
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, S1 4DE, UK.
| | - Jackie Elliott
- Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2RX, UK; Sheffield Teaching Hospitals, Diabetes and Endocrine Centre, Northern General Hospital, Sheffield S5 7AU, UK.
| | - Mohammed Benaissa
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, S1 4DE, UK.
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31
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Feng G, Heiselman C, Quirk JG, Djurić PM. Cardiotocography analysis by empirical dynamic modeling and Gaussian processes. Front Bioeng Biotechnol 2023; 10:1057807. [PMID: 36714626 PMCID: PMC9877465 DOI: 10.3389/fbioe.2022.1057807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023] Open
Abstract
Introduction: During labor, fetal heart rate (FHR) and uterine activity (UA) can be continuously monitored using Cardiotocography (CTG). This is the most widely adopted approach for electronic fetal monitoring in hospitals. Both FHR and UA recordings are evaluated by obstetricians for assessing fetal well-being. Due to the complex and noisy nature of these recordings, the evaluation by obstetricians suffers from high interobserver and intraobserver variability. Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. Methods: Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. In this paper, we propose to model intrapartum CTG recordings from a dynamical system perspective using empirical dynamic modeling with Gaussian processes, which is a Bayesian nonparametric approach for estimation of functions. Results and Discussion: In the context of our paper, Gaussian processes are capable for simultaneous estimation of the dimensionality of attractor manifolds and reconstructing of attractor manifolds from time series data. This capacity of Gaussian processes allows for revealing causal relationships between the studied time series. Experimental results on real CTG recordings show that FHR and UA signals are causally related. More importantly, this causal relationship and estimated attractor manifolds can be exploited for several important applications in computerized analysis of CTG recordings including estimating missing FHR samples, recovering burst errors in FHR tracings and characterizing the interactions between FHR and UA signals.
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Affiliation(s)
- Guanchao Feng
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, United States,*Correspondence: Guanchao Feng, ; Petar M. Djurić,
| | - Cassandra Heiselman
- Department of Obstetrics and Gynecology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - J. Gerald Quirk
- Department of Obstetrics and Gynecology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Petar M. Djurić
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, United States,*Correspondence: Guanchao Feng, ; Petar M. Djurić,
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Gibert JP, Wieczynski DJ, Han Z, Yammine A. Rapid eco-phenotypic feedback and the temperature response of biomass dynamics. Ecol Evol 2023; 13:e9685. [PMID: 36644704 PMCID: PMC9831973 DOI: 10.1002/ece3.9685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 01/13/2023] Open
Abstract
Biomass dynamics capture information on population dynamics and ecosystem-level processes (e.g., changes in production over time). Understanding how rising temperatures associated with global climate change influence biomass dynamics is thus a pressing issue in ecology. The total biomass of a species depends on its density and its average mass. Consequently, disentangling how biomass dynamics responds to increasingly warm and variable temperatures ultimately depends on understanding how temperature influences both density and mass dynamics. Here, we address this issue by keeping track of experimental microbial populations growing to carrying capacity for 15 days at two different temperatures, and in the presence and absence of temperature variability. We develop a simple mathematical expression to partition the contribution of changes in density and mass to changes in biomass and assess how temperature responses in either one influence biomass shifts. Moreover, we use time-series analysis (Convergent Cross Mapping) to address how temperature and temperature variability influence reciprocal effects of density on mass and vice versa. We show that temperature influences biomass through its effects on density and mass dynamics, which have opposite effects on biomass and can offset each other. We also show that temperature variability influences biomass, but that effect is independent of any effects on density or mass dynamics. Last, we show that reciprocal effects of density and mass shift significantly across temperature regimes, suggesting that rapid and environment-dependent eco-phenotypic dynamics underlie biomass responses. Overall, our results connect temperature effects on population and phenotypic dynamics to explain how biomass responds to temperature regimes, thus shedding light on processes at play in cosmopolitan and abundant microbes as the world experiences increasingly warm and variable temperatures.
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Affiliation(s)
- Jean P. Gibert
- Department of BiologyDuke UniversityDurhamNorth CarolinaUSA
| | | | - Ze‐Yi Han
- Department of BiologyDuke UniversityDurhamNorth CarolinaUSA
| | - Andrea Yammine
- Department of BiologyDuke UniversityDurhamNorth CarolinaUSA
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33
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Rineau V, Smyčka J, Storch D. Diversity dependence is a ubiquitous phenomenon across Phanerozoic oceans. SCIENCE ADVANCES 2022; 8:eadd9620. [PMID: 36306361 PMCID: PMC9616491 DOI: 10.1126/sciadv.add9620] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/12/2022] [Indexed: 06/16/2023]
Abstract
Biodiversity on Earth is shaped by abiotic perturbations and rapid diversifications. At the same time, there are arguments that biodiversity is bounded and regulated via biotic interactions. Evaluating the role and relative strength of diversity regulation is crucial for interpreting the ongoing biodiversity changes. We have analyzed Phanerozoic fossil record using public databases and new approaches for identifying the causal dependence of origination and extinction rates on environmental variables and standing diversity. While the effect of environmental factors on origination and extinction rates is variable and taxon specific, the diversity dependence of the rates is almost universal across the studied taxa. Origination rates are dependent on instantaneous diversity levels, while extinction rates reveal delayed diversity dependence. Although precise mechanisms of diversity dependence may be complex and difficult to recover, global regulation of diversity via negative diversity dependence of lineage diversification seems to be a common feature of the biosphere, with profound consequences for understanding current biodiversity crisis.
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Affiliation(s)
- Valentin Rineau
- Center for Theoretical Study, Charles University and the Academy of Sciences of the Czech Republic, Jilská 1, 110 00 Prague, Czech Republic
| | - Jan Smyčka
- Center for Theoretical Study, Charles University and the Academy of Sciences of the Czech Republic, Jilská 1, 110 00 Prague, Czech Republic
| | - David Storch
- Center for Theoretical Study, Charles University and the Academy of Sciences of the Czech Republic, Jilská 1, 110 00 Prague, Czech Republic
- Department of Ecology, Faculty of Science, Charles University, Viničná 7, 128 44 Prague, Czech Republic
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34
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Decomposing predictability to identify dominant causal drivers in complex ecosystems. Proc Natl Acad Sci U S A 2022; 119:e2204405119. [PMID: 36215500 PMCID: PMC9586263 DOI: 10.1073/pnas.2204405119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.
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35
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Muraoka WT, Cramer KL, O’Dea A, Zhao JX, Leonard ND, Norris RD. Historical declines in parrotfish on Belizean coral reefs linked to shifts in reef exploitation following European colonization. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.972172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Humans have utilized the Mesoamerican Reef (MAR) for millennia but the effects of prehistorical and historical fishing on this ecosystem remain understudied. To assess the long-term coupling of reef ecosystem and human dynamics in this region, we tracked trends in the structure and functioning of lagoonal reefs within the Belizean portion of the MAR using fish teeth fossils and sediment accumulation rates within reef sediment cores. We then paired this with a timeline of demographic and cultural changes in this region’s human populations. The ∼1,300-year timeline encompassed in the core record shows that declines in the relative abundance and accumulation rate of teeth from parrotfish, a key reef herbivore, occurred at all three reef sites and began between ∼1500 and 1800 AD depending on site and metric of abundance. A causality analysis showed that parrotfish relative abundance had a positive causal effect on reef accretion rates, a proxy of coral growth, reconfirming the important role of these fish in reef ecosystem functioning. The timing of initial declines in parrotfish teeth occurred during a time of relatively low human population density in Belize. However, declines were synchronous with cultural and demographic upheaval resulting from European colonization of the New World. The more recent declines at these sites (∼1800 AD) occurred in tandem with increased subsistence fishing on reefs by multiple immigrant groups, a pattern that was likely necessitated by the establishment of an import economy controlled by a small group of land-owning European elites. These long-term trends from the paleoecological record reveal that current parrotfish abundances in central Belize are well below their pre-European contact peaks and that increased fishing pressure on parrotfish post-contact has likely caused a decline in reef accretion rates. The origins of reef degradation in the Belizean portion of the MAR began hundreds of years before the onset of modern declines resulting from the combined effects of local human disturbances and climate change.
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36
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Zhu Y, Lu X, Hong J, Wang F. Joint dynamic topic model for recognition of lead-lag relationship in two text corpora. Data Min Knowl Discov 2022. [DOI: 10.1007/s10618-022-00873-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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37
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Liu OR, Gaines SD. Environmental context dependency in species interactions. Proc Natl Acad Sci U S A 2022; 119:e2118539119. [PMID: 36037344 PMCID: PMC9457591 DOI: 10.1073/pnas.2118539119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 07/17/2022] [Indexed: 11/28/2022] Open
Abstract
Ecological interactions are not uniform across time and can vary with environmental conditions. Yet, interactions among species are often measured with short-term controlled experiments whose outcomes can depend greatly on the particular environmental conditions under which they are performed. As an alternative, we use empirical dynamic modeling to estimate species interactions across a wide range of environmental conditions directly from existing long-term monitoring data. In our case study from a southern California kelp forest, we test whether interactions between multiple kelp and sea urchin species can be reliably reconstructed from time-series data and whether those interactions vary predictably in strength and direction across observed fluctuations in temperature, disturbance, and low-frequency oceanographic regimes. We show that environmental context greatly alters the strength and direction of species interactions. In particular, the state of the North Pacific Gyre Oscillation seems to drive the competitive balance between kelp species, asserting bottom-up control on kelp ecosystem dynamics. We show the importance of specifically studying variation in interaction strength, rather than mean interaction outcomes, when trying to understand the dynamics of complex ecosystems. The significant context dependency in species interactions found in this study argues for a greater utilization of long-term data and empirical dynamic modeling in studies of the dynamics of other ecosystems.
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Affiliation(s)
- Owen R. Liu
- Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, 93106
| | - Steven D. Gaines
- Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, 93106
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Yuan AE, Shou W. Data-driven causal analysis of observational biological time series. eLife 2022; 11:72518. [PMID: 35983746 PMCID: PMC9391047 DOI: 10.7554/elife.72518] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 01/23/2022] [Indexed: 11/28/2022] Open
Abstract
Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called ‘model-free’ causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AlV0ttQrjK8).
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Affiliation(s)
- Alex Eric Yuan
- Molecular and Cellular Biology PhD program, University of Washington, Seattle, United States.,Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Wenying Shou
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
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Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003–2018 in China Using Convergent Cross Mapping Method. REMOTE SENSING 2022. [DOI: 10.3390/rs14143280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The relationship between land surface temperature (LST) and environmental factors is complex and nonlinear. To determine these relationships for China, this study analyzed the driving effects of air temperature, vegetation index, soil moisture, net surface radiation, precipitation, aerosols, evapotranspiration, and water vapor on LST based on remote-sensing and reanalysis data from 2003–2018, using a convergent cross-mapping method. During the study period, air temperature and net surface radiation were the dominant drivers of LST with a cross-mapping skill above 0.9. Vegetation index and evapotranspiration were the secondary drivers of LST with a cross-mapping skill that was higher than 0.5. Except for air temperature and net surface radiation, the direction and strength of the effects of the driving factors on LST were related to the climate type. The effects of air temperature and net radiation on LST diminished from north to south, indicating that LST was more sensitive to air temperature and net radiation in energy-limited regions. However, the effects of vegetation index and evapotranspiration on LST varied significantly across climate zones; that is, positive effects were mostly in non-monsoonal zones and negative effects were primarily in monsoonal zones. Our results quantified the driving role of environmental factors on LST and provided a comprehensive understanding of LST dynamics.
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40
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A hybrid empirical and parametric approach for managing ecosystem complexity: Water quality in Lake Geneva under nonstationary futures. Proc Natl Acad Sci U S A 2022; 119:e2102466119. [PMID: 35733249 PMCID: PMC9245694 DOI: 10.1073/pnas.2102466119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
This paper develops a hybrid approach to account for the complex interactions affecting lake water quality and its management in a nonlinear, changing world. The approach uses data to leverage our first-principles understanding of the mechanisms operating on dissolved oxygen in the lake. This yields a manageable, but more complete systems perspective for environmental management of the lake under climate change, where our analysis suggests that multiple modes of intervention may be necessary to achieve a healthy lake. Severe deterioration of water quality in lakes, characterized by overabundance of algae and declining dissolved oxygen in the deep lake (DOB), was one of the ecological crises of the 20th century. Even with large reductions in phosphorus loading, termed “reoligotrophication,” DOB and chlorophyll (CHL) have often not returned to their expected pre–20th-century levels. Concurrently, management of lake health has been confounded by possible consequences of climate change, particularly since the effects of climate are not neatly separable from the effects of eutrophication. Here, using Lake Geneva as an iconic example, we demonstrate a complementary alternative to parametric models for understanding and managing lake systems. This involves establishing an empirically-driven baseline that uses supervised machine learning to capture the changing interdependencies among biogeochemical variables and then combining the empirical model with a more conventional equation-based model of lake physics to predict DOB over decadal time-scales. The hybrid model not only leads to substantially better forecasts, but also to a more actionable description of the emergent rates and processes (biogeochemical, ecological, etc.) that drive water quality. Notably, the hybrid model suggests that the impact of a moderate 3°C air temperature increase on water quality would be on the same order as the eutrophication of the previous century. The study provides a template and a practical path forward to cope with shifts in ecology to manage environmental systems for non-analogue futures.
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Zhang R, Lai KY, Liu W, Liu Y, Lu J, Tian L, Webster C, Luo L, Sarkar C. Community-level ambient fine particulate matter and seasonal influenza among children in Guangzhou, China: A Bayesian spatiotemporal analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 826:154135. [PMID: 35227720 DOI: 10.1016/j.scitotenv.2022.154135] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/21/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Influenza is a major preventable infectious respiratory disease. However, there is little detailed long-term evidence of its associations with PM2.5 among children. We examined the community-level associations between exposure to ambient PM2.5 and incident influenza in Guangzhou, China. METHODS We used data from the city-wide influenza surveillance system collected by Guangzhou Centre for Disease Control and Prevention (GZCDC) over the period 2013 and 2019. Incident influenza was defined as daily new influenza (both clinically diagnosed and laboratory confirmed) cases as per standard diagnostic criteria. A 200-meter city-wide grid of daily ambient PM2.5 exposure was generated using a random forest model. We developed spatiotemporal Bayesian hierarchical models to examine the community-level associations between PM2.5 and the influenza adjusting for meteorological and socioeconomic variables and accounting for spatial autocorrelation. We also calculated community-wide influenza cases attributable to PM2.5 levels exceeding the China Grade 1 and World Health Organization (WHO) regulatory thresholds. RESULTS Our study comprised N = 191,846 children from Guangzhou aged ≤19 years and diagnosed with influenza between January 1, 2013 and December 31, 2019. Each 10 μg/m3 increment in community-level PM2.5 measured on the day of case confirmation (lag 0) and over a 6-day moving average (lag 0-5 days) was associated with higher risks of influenza (RR = 1.05, 95% CI: 1.05-1.06 for lag 0 and RR = 1.15, 95% CI: 1.14-1.16 for lag 05). We estimated that 8.10% (95%CI: 7.23%-8.57%) and 20.11% (95%CI: 17.64%-21.48%) influenza cases respectively were attributable to daily PM2.5 exposure exceeding the China Grade I (35 μg/m3) and the WHO limits (25 μg/m3). The risks associated with PM2.5 exposures were more pronounced among children of the age-group 10-14 compared to other age groups. CONCLUSIONS More targeted non-pharmaceutical interventions aimed at reducing PM2.5 exposures at home, school and during commutes among children may constitute additional influenza prevention and control polices.
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Affiliation(s)
- Rong Zhang
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Ka Yan Lai
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Wenhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Yanhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Jianyun Lu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Linwei Tian
- School of Public Health, The University of Hong Kong, Patrick Mason Building, Sassoon Road, Pokfulam, Hong Kong, China
| | - Chris Webster
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China.
| | - Chinmoy Sarkar
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China.
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Comments on identifying causal relationships in nonlinear dynamical systems via empirical mode decomposition. Nat Commun 2022; 13:2860. [PMID: 35606366 PMCID: PMC9126924 DOI: 10.1038/s41467-022-30359-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 02/11/2022] [Indexed: 11/08/2022] Open
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De Castro Martins C, Chaminade T, Cavazza M. Causal Analysis of Activity in Social Brain Areas During Human-Agent Conversation. FRONTIERS IN NEUROERGONOMICS 2022; 3:843005. [PMID: 38235459 PMCID: PMC10790851 DOI: 10.3389/fnrgo.2022.843005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 04/11/2022] [Indexed: 01/19/2024]
Abstract
This article investigates the differences in cognitive and neural mechanisms between human-human and human-virtual agent interaction using a dataset recorded in an ecologically realistic environment. We use Convergent Cross Mapping (CCM) to investigate functional connectivity between pairs of regions involved in the framework of social cognitive neuroscience, namely the fusiform gyrus, superior temporal sulcus (STS), temporoparietal junction (TPJ), and the dorsolateral prefrontal cortex (DLPFC)-taken as prefrontal asymmetry. Our approach is a compromise between investigating local activation in specific regions and investigating connectivity networks that may form part of larger networks. In addition to concording with previous studies, our results suggest that the right TPJ is one of the most reliable areas for assessing processes occurring during human-virtual agent interactions, both in a static and dynamic sense.
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Affiliation(s)
| | - Thierry Chaminade
- Institut de Neurosciences de la Timone (INT, UMR7289), Aix-Marseille University-CNRS, Marseille, France
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Ying X, Leng SY, Ma HF, Nie Q, Lai YC, Lin W. Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately. RESEARCH 2022; 2022:9870149. [PMID: 35600089 PMCID: PMC9101326 DOI: 10.34133/2022/9870149] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/24/2022] [Indexed: 11/06/2022]
Abstract
Data-based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering, and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross-map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.
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Affiliation(s)
- Xiong Ying
- School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China
- Research Institute for Intelligent Complex Systems, CCSB, and LCNBI, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China
| | - Si-Yang Leng
- Research Institute for Intelligent Complex Systems, CCSB, and LCNBI, Fudan University, Shanghai 200433, China
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Huan-Fei Ma
- School of Mathematical Sciences, Soochow University, Suzhou 215006, China
| | - Qing Nie
- Department of Mathematics, Department of Developmental and Cell Biology, And NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697-3875, USA
| | - Ying-Cheng Lai
- School of Electrical, Computer, And Energy Engineering, Arizona State University, Tempe, Arizona 85287-5706, USA
| | - Wei Lin
- School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China
- Research Institute for Intelligent Complex Systems, CCSB, and LCNBI, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
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46
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Buxton RB, Prisk GK, Hopkins SR. A novel nonlinear analysis of blood flow dynamics applied to the human lung. J Appl Physiol (1985) 2022; 132:1546-1559. [PMID: 35421317 DOI: 10.1152/japplphysiol.00715.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The spatial/temporal dynamics of blood flow in the human lung can be measured noninvasively with magnetic resonance imaging (MRI) using arterial spin labeling (ASL). We report a novel data analysis method using nonlinear prediction to identify dynamic interactions between blood flow units (image voxels), potentially providing a probe of underlying vascular control mechanisms. The approach first estimates the linear relationship (predictability) of one voxel time series with another using correlation analysis, and after removing the linear component estimates the nonlinear relationship with a numerical mutual information approach. Dimensionless global metrics for linear prediction (FL) and nonlinear prediction (FNL) represent the average amplitude of fluctuations in one voxel estimated by another voxel, as a percentage of the global average voxel flow. A proof-of-principle test of this approach analyzed experimental data from a study of high-altitude pulmonary edema (HAPE), providing two groups exhibiting known differences in vascular reactivity. Subjects were mountaineers divided into HAPE-susceptible (S, n=4) and HAPE-resistant (R, n=5) groups based on prior history at high altitude. Dynamic ASL measurements in the lung in normoxia (N, FIO2=0.21) and hypoxia (H, FIO2=0.13±0.01) were compared. The nonlinear prediction metric FNL decreased with hypoxia (7.4±1.3(N) vs. 6.3±0.7(H), P=0.03) and was significantly different between groups (7.4±1.2 (R) vs. 6.2±14.1 (S), P=0.03). This proof-of-principle test demonstrates that this nonlinear analysis approach applied to ASL data is sensitive to physiological effects even in small subject cohorts, and potentially can be used in a wide range of studies in health and disease in the lung and other organs.
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Affiliation(s)
| | | | - Susan Roberta Hopkins
- Department of Radiology, University of California San Diego.,Department of Medicine, University of California San Diego
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Causality in Control Systems Based on Data-Driven Oscillation Identification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
This paper addresses the subject of causality analysis using simulation data and data collected from a real control system. Simulated data includes Gaussian and Cauchy noise signals. Real-time series include various, mostly unknown distortions, like trends, oscillations, and noises. Presented research focuses on the oscillatory component in data and its propagation in multi-loop control systems. Oscillation identification is based on a deep decomposition process for control error time series. Identified periodic signals are used for further causality processing. The analysis uses the Transfer Entropy approach. This method belongs to the group of model-free methods. The determination of information pathways is conducted without any model or a priori process knowledge. The research investigates the impact of the oscillation time-series component on the Transfer Entropy causality analysis. The summary shows the observations obtained for given simulated datasets and those collected from real processes. The obtained results show that simulated analysis works properly. On the contrary, the direct application of the oscillation decomposition in real industrial cases may be misleading. Large datasets demand modification in the methodology. Different variants are tested. They show that oscillation propagation is biased in real systems and, therefore, the decomposition should be applied with caution. Furthermore, it is important to remember that the algorithm transition from simulated data to real industrial ones is demanding and should be done with the utmost care.
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Li L, Zhang Y, Huang L, Zhao J, Wang J, Liu T. Robot Assisted Treatment of Hand Functional Rehabilitation Based on Visual Motor Imagination. Front Aging Neurosci 2022; 14:870871. [PMID: 35462690 PMCID: PMC9029075 DOI: 10.3389/fnagi.2022.870871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/07/2022] [Indexed: 11/17/2022] Open
Abstract
This pilot study implements a hybrid brain computer interface paradigm based on motor imagery (MI) and steady-state visual evoked potential (SSVEP), in order to explore the neural mechanism and clinical effect of MI-SSVEP intervention paradigm on upper limb functional rehabilitation. In this study, EEG data of 12 healthy participants were collected, and the activation regions of MI-SSVEP paradigm were identified by power spectral density (PSD). By analyzing the inter trial phase consistency (ITPC) of characteristic regions and the causal relationship of brain network, the motor cognitive process including high-level somatosensory joint cortex in the intervention process of MI-SSVEP was studied. Subsequently, this study verified the clinical effect of MI-SSVEP intervention paradigm for 61 stroke patients. The results show that the robot assisted therapy using MI-SSVEP intervention paradigm can more effectively improve the rehabilitation effect of patients.
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Affiliation(s)
- Long Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
| | - Yanlong Zhang
- Key Laboratory for Artificial Intelligence and Cognitive Neuroscience of Language, Xi’an International Studies University, Xi’an, China
| | - Liang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
| | - Jie Zhao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
- *Correspondence: Jue Wang,
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
- *Correspondence: Jue Wang,
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Shi J, Chen L, Aihara K. Embedding entropy: a nonlinear measure of dynamical causality. J R Soc Interface 2022; 19:20210766. [PMID: 35350881 PMCID: PMC8965400 DOI: 10.1098/rsif.2021.0766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Research on concepts and computational methods of causality has a long history, and there are various concepts of causality as well as corresponding computing algorithms based on measured data. Here, by considering causes and effects from a dynamical perspective, we present a unified mathematical framework for the so-called dynamical causality (DC), which can detect causal interactions over time; notably, this framework covers Granger causality, transfer entropy, embedding causality and their conditional versions. Based on this framework, we further propose a causality criterion called embedding entropy (EE) to measure the DC between two variables. Moreover, its conditional version, conditional embedding entropy (cEE), is also derived for detecting conditional/direct causality. The significant advantages of EE and cEE are that they can be employed for solving not only nonlinear causal inference but also the non-separability problem, and they can reduce the scale bias in numerical calculation. The performance and robustness of EE and cEE were demonstrated through numerical simulations, and causal inference on various real-world datasets validated their effectiveness.
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Affiliation(s)
- Jifan Shi
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China.,Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, People's Republic of China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, People's Republic of China.,Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong 519031, People's Republic of China
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
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Abstract
It is often claimed that only experiments can support strong causal inferences and therefore they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments results in their overuse both by researchers and decision makers and in an underappreciation of their shortcomings. Neglect of other methods often follows. Experiments can suggest whether X causes Y in a specific experimental setting; however, they often fail to elucidate either the mechanisms responsible for an effect or the strength of an effect in everyday natural settings. In this article, we consider two overarching issues. First, experiments have important limitations. We highlight problems with external, construct, statistical-conclusion, and internal validity; replicability; and conceptual issues associated with simple X causes Y thinking. Second, quasi-experimental and nonexperimental methods are absolutely essential. As well as themselves estimating causal effects, these other methods can provide information and understanding that goes beyond that provided by experiments. A research program progresses best when experiments are not treated as privileged but instead are combined with these other methods.
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Affiliation(s)
- Ed Diener
- Department of Psychology, University of Utah.,Department of Psychology, University of Virginia.,Gallup, Washington, D.C
| | - Robert Northcott
- Department of Philosophy, Birkbeck College, University of London
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