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Murari A, Gelfusa M, Craciunescu T, Gelfusa C, Gaudio P, Bovesecchi G, Rossi R. Effects of environmental conditions on COVID-19 morbidity as an example of multicausality: a multi-city case study in Italy. Front Public Health 2023; 11:1222389. [PMID: 37965519 PMCID: PMC10642182 DOI: 10.3389/fpubh.2023.1222389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/06/2023] [Indexed: 11/16/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), broke out in December 2019 in Wuhan city, in the Hubei province of China. Since then, it has spread practically all over the world, disrupting many human activities. In temperate climates overwhelming evidence indicates that its incidence increases significantly during the cold season. Italy was one of the first nations, in which COVID-19 reached epidemic proportions, already at the beginning of 2020. There is therefore enough data to perform a systematic investigation of the correlation between the spread of the virus and the environmental conditions. The objective of this study is the investigation of the relationship between the virus diffusion and the weather, including temperature, wind, humidity and air quality, before the rollout of any vaccine and including rapid variation of the pollutants (not only their long term effects as reported in the literature). Regarding them methodology, given the complexity of the problem and the sparse data, robust statistical tools based on ranking (Spearman and Kendall correlation coefficients) and innovative dynamical system analysis techniques (recurrence plots) have been deployed to disentangle the different influences. In terms of results, the evidence indicates that, even if temperature plays a fundamental role, the morbidity of COVID-19 depends also on other factors. At the aggregate level of major cities, air pollution and the environmental quantities affecting it, particularly the wind intensity, have no negligible effect. This evidence should motivate a rethinking of the public policies related to the containment of this type of airborne infectious diseases, particularly information gathering and traffic management.
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Affiliation(s)
- Andrea Murari
- Consorzio RFX (CNR, ENEA, INFN, Università di Padova, Acciaierie Venete SpA), Padua, Italy
- Istituto per la Scienza e la Tecnologia dei Plasmi, CNR, Padua, Italy
| | - Michela Gelfusa
- Department of Industrial Engineering, University of Rome “Tor Vergata”, Rome, Italy
| | - Teddy Craciunescu
- National Institute for Laser, Plasma and Radiation Physics, Măgurele, Romania
| | - Claudio Gelfusa
- Department of Industrial Engineering, University of Rome “Tor Vergata”, Rome, Italy
| | - Pasquale Gaudio
- Department of Industrial Engineering, University of Rome “Tor Vergata”, Rome, Italy
| | - Gianluigi Bovesecchi
- Department of Enterprise Engineering, University of Rome “Tor Vergata”, Rome, Italy
| | - Riccardo Rossi
- Department of Industrial Engineering, University of Rome “Tor Vergata”, Rome, Italy
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Liu S, Wang Q, Liu C, Sun Y, He L. Natural Exponential and Three-Dimensional Chaotic System. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2204269. [PMID: 36976542 DOI: 10.1002/advs.202204269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 02/08/2023] [Indexed: 05/27/2023]
Abstract
Existing chaotic system exhibits unpredictability and nonrepeatability in a deterministic nonlinear architecture, presented as a combination of definiteness and stochasticity. However, traditional two-dimensional chaotic systems cannot provide sufficient information in the dynamic motion and usually feature low sensitivity to initial system input, which makes them computationally prohibitive in accurate time series prediction and weak periodic component detection. Here, a natural exponential and three-dimensional chaotic system with higher sensitivity to initial system input conditions showing astonishing extensibility in time series prediction and image processing is proposed. The chaotic performance evaluated theoretically and experimentally by Poincare mapping, bifurcation diagram, phase space reconstruction, Lyapunov exponent, and correlation dimension provides a new perspective of nonlinear physical modeling and validation. The complexity, robustness, and consistency are studied by recursive and entropy analysis and comparison. The method improves the efficiency of time series prediction, nonlinear dynamics-related problem solving and expands the potential scope of multi-dimensional chaotic systems.
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Affiliation(s)
- Shiwei Liu
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qiaohua Wang
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chengkang Liu
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yanhua Sun
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Lingsong He
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
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Kang Y, Zhang Y, Huang K, Wang Z. Recurrence quantification analysis of periodic dynamics in the default mode network in first-episode drug-naïve schizophrenia. Psychiatry Res Neuroimaging 2023; 329:111583. [PMID: 36577311 DOI: 10.1016/j.pscychresns.2022.111583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Abnormal functional connectivity (FC) within the default model network (DMN) in schizophrenia has been frequently reported in previous studies. However, traditional FC analysis was mostly linear correlations based, with the information on nonlinear or temporally lagged brain signals largely overlooked. Fifty-five first-episode drug-naïve schizophrenia (FES) patients and 53 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scanning. The DMN was extracted using independent component analysis. Recurrence quantification analysis was used to measure the duration, predictability, and complexity of the periodic processes of the nonlinear DMN time series. The Mann‒Whitney U test was conducted to compare these features between FES patients and HCs. The support vector machine was applied to discriminate FES from HCs based on these features. Determinism, which means predictability of periodic process activity, between the ventromedial prefrontal cortex (vMPFC) and posterior cingulate and between the vMPFC and precuneus, was significantly decreased in FES compared with HCs. Determinism between the vMPFC and precuneus was positively correlated with category fluency scores in FES. The classifier achieved 77% accuracy. Our results suggest that synchronized periodicity among DMN brain regions is dysregulated in FES, and the periodicity in BOLD signals may be a promising indicator of brain functional connectivity.
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Affiliation(s)
- Yafei Kang
- Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, School of Psychology, Shaanxi Normal University, Xi'an, China
| | - Youming Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Kexin Huang
- West China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Zhenhong Wang
- Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, School of Psychology, Shaanxi Normal University, Xi'an, China.
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Zhang H, Liu C, Tang F, Li M, Zhang D, Xia L, Zhao N, Li S, Crozier S, Xu W, Liu F. Cardiac Arrhythmia classification based on 3D recurrence plot analysis and deep learning. Front Physiol 2022; 13:956320. [PMID: 35936913 PMCID: PMC9352947 DOI: 10.3389/fphys.2022.956320] [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: 05/30/2022] [Accepted: 06/27/2022] [Indexed: 11/29/2022] Open
Abstract
Artificial intelligence (AI) aided cardiac arrhythmia (CA) classification has been an emerging research topic. Existing AI-based classification methods commonly analyze electrocardiogram (ECG) signals in lower dimensions, using one-dimensional (1D) temporal signals or two-dimensional (2D) images, which, however, may have limited capability in characterizing lead-wise spatiotemporal correlations, which are critical to the classification accuracy. In addition, existing methods mostly assume that the ECG data are linear temporal signals. This assumption may not accurately represent the nonlinear, nonstationary nature of the cardiac electrophysiological process. In this work, we have developed a three-dimensional (3D) recurrence plot (RP)-based deep learning algorithm to explore the nonlinear recurrent features of ECG and Vectorcardiography (VCG) signals, aiming to improve the arrhythmia classification performance. The 3D ECG/VCG images are generated from standard 12 lead ECG and 3 lead VCG signals for neural network training, validation, and testing. The superiority and effectiveness of the proposed method are validated by various experiments. Based on the PTB-XL dataset, the proposed method achieved an average F1 score of 0.9254 for the 3D ECG-based case and 0.9350 for the 3D VCG-based case. In contrast, recently published 1D and 2D ECG-based CA classification methods yielded lower average F1 scores of 0.843 and 0.9015, respectively. Thus, the improved performance and visual interpretability make the proposed 3D RP-based method appealing for practical CA classification.
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Affiliation(s)
- Hua Zhang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Fangfang Tang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Mingyan Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Dongxia Zhang
- Zhejiang Provincial Centre for Disease Control and Prevention CN, Hangzhou, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Nan Zhao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Sheng Li
- The College of Science, Xijing University, Xi’an, China
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Wenlong Xu
- Department of Biomedical Engineering, China Jiliang University, Hangzhou, China
- *Correspondence: Wenlong Xu, ; Feng Liu,
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Wenlong Xu, ; Feng Liu,
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