When confronting complexity science, complexity systems indicating evolution regularities are addressed due to their nonlinearity, uncertainty, emergence and self-organization. Among them, totally asymmetric simple ex...
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When confronting complexity science, complexity systems indicating evolution regularities are addressed due to their nonlinearity, uncertainty, emergence and self-organization. Among them, totally asymmetric simple exclusion process (TASEP) belonging to asymmetric simple exclusion process (ASEP) stands out as a paradigm nonlinear dynamical model depicting microscopic non-equilibrium dynamics of real active particles. Motivated by real multi-physics processes of protein motors, TASEP networks with Langmuir kinetics and finite resources are studied. Partially differential equations for boundary and bulk dynamics in stochastically regular and irregular networks are numerically calculated. Connectivity, effective interactions and concentration coefficient are found to govern system dynamics. Six kinds of density profiles are observed. Fruitful dynamical patterns of global density distributions, weight distributions of each phase, local currents, local densities and related phase diagrams are obtained, which lead to acquisitions of evolvement regularities of physical mechanisms of bulk dynamics and non-equilibrium phase transitions. Three kinds of domination mechanisms including three-phase, two-phase and single-phase dominations of global dynamics are found in irregular networks. Global dynamics gradually become high-density dominated with increasing concentration coefficient or effective attachment rate. While, they gradually become low-density dominated with increasing effective detachment rate or connectivity. However, global dynamics are found to be dominated by low and high coexistence phase in general cases of regular networks. Our work is conducive to modelling nonlinear dynamical behaviors and non-equilibrium phase transition mechanisms in mesoscopic self-driven particle systems. (c) 2021 Elsevier Ltd. All rights reserved.
Heart rate variability (HRV), systolic period variability (SPV), and diastolic period variability (DPV) have shown potential for assessing cardiac function. It is unknown whether the time delay between the myocardial ...
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Heart rate variability (HRV), systolic period variability (SPV), and diastolic period variability (DPV) have shown potential for assessing cardiac function. It is unknown whether the time delay between the myocardial electrical and mechanical activities (i.e., electromechanical delay, EMD) also possesses variability, and if it does, whether this EMD variability (EMDV) could render additional value for cardiac function assessment. In this paper, we extracted the beat-to-beat EMD from 5-min simultaneously recorded electrocardiogram and phonocardiogram signals in 30 patients with coronary artery disease (CAD) and 30 healthy control subjects, and studied its variability using the same methods as applied for HRV including time-domain measures [mean and standard deviation (SD)], frequency-domain measures [normalized low-and high-frequency (LFn, HFn) and LF/HF)], and nonlinear measures [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns]. In addition, we examined whether the addition of EMDV could offer improved performance for distinguishing between the two groups compared to using the HRV, SPV, and DPV features. Support vector machine with 10-fold cross-validation was used for classification. Results showed increased SD of SPV, increased mean, SD and decreased SampEn of EMDV in CAD patients. Besides, the dynamical pattern analysis showed that CAD patients had significantly increased fluctuated patterns and decreased monotonous patterns in EMDV. In particular, the addition of EMDV indices dramatically increased the classification accuracy from 0.729 based on HRV, SPV, and DPV features to 0.958. Our results suggest promising of the EMDV analysis that could potentially be helpful for detecting CAD noninvasively.
This study aimed to test how different QT interval variability (QTV) indices change in patients with coronary artery disease (CAD) and congestive heart failure (CHF). Twenty-nine healthy volunteers, 29 age-matched CAD...
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This study aimed to test how different QT interval variability (QTV) indices change in patients with coronary artery disease (CAD) and congestive heart failure (CHF). Twenty-nine healthy volunteers, 29 age-matched CAD patients, and 20 age-matched CHF patients were studied. QT time series were derived from 5-min resting lead-II electrocardiogram (ECG). Time domain indices [mean, SD, and QT variability index (QTVI)], frequency-domain indices (LF and HF), and nonlinear indices [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns] were calculated. In order to account for possible influence of heart rate (HR) on QTV, all the calculations except QTVI were repeated on HR-corrected QT time series (QTc) using three correction methods (i.e., Bazett, Fridericia, and Framingham method). Results showed that CHF patients exhibited increased mean, increased SD, increased LF and HF, decreased T-wave amplitude, increased QTVI, and decreased PE, while showed no significant changes in SampEn. Interestingly, CHF patients also showed significantly changed distribution of the dynamical patterns with less monotonously changing patterns while more fluctuated patterns. In CAD group, only QTVI was found significantly increased as compared with healthy controls. Results after HR correction were in common with those before HR correction except for QTc based on Bazett correctionFig. The framework of this paper. The arrows show the sequential analysis of the data.
We study the problem of how to assess the reliability of a statistical measurement on data set containing unknown quantity of noises, inconsistencies, and outliers. A practical approach that analyzes the dynamical pat...
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We study the problem of how to assess the reliability of a statistical measurement on data set containing unknown quantity of noises, inconsistencies, and outliers. A practical approach that analyzes the dynamical patterns (trends) of the statistical measurements through a sequential extreme-boundary-points (EBP) weed-out process is explored. We categorize the weed-out trend patterns (WOTP) and examine their relation to the reliability of the measurement. The approach is applied to the processes of extracting genes that are predictive to BCL2 translocations and to clinical survival outcomes of diffuse large B-cell lymphoma (DLBCL) from DNA Microarray gene expression profiling data sets. Fisher's Discriminate Criterion (FDC) is used as a statistical measurement in the processes. It is found that the weed-out trend analysis (WOTA) approach is effective for qualitatively assessing the statistics-based measurements in the experimentations conducted. (c) 2007 Elsevier B.V. All rights reserved.
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