Dongping Du

Multi-scale modeling of glycosylation modulation dynamics in cardiac electrical signaling

The cardiac action potential (AP) is produced by the orchestrated functions of ion channel dynamics. The coordinated functions can be simulated by computational cardiac cell models, which could not only overcome the practical and ethical limitations in physical experiments but also provide predictive insights on the underlying mechanisms. This investigation is aimed at modeling the variations of cardiac electrical signaling due to changes in glycosylation of a voltage-gated K+ channel, hERG, responsible for late phase 2 and phase 3 of the human ventricular AP. The voltage-dependence of hERG channels steady-state activation and inactivation under four glycosylation conditions, i.e., full glycosylation, reduced sialylation, mannose-rich and N-Glycanase treated, demonstrated that reduced glycosylation modulates hERG channel gating. Here, the proposed multi-scale computer model incorporates the measured changes in hERG channel gating observed under conditions of reduced glycosylation, and further predicts the electrical behaviors of cardiac cells and tissues (cable/ring). The multi-scale modeling results show that reduced glycosylation would act to shorten the repolarization period of cardiac APs, and distort the AP propagation in cardiac tissues. This multi-scale modeling investigation reveals novel mechanisms of hERG channel modulation by regulated glycosylation that also impact cardiac myocyte and tissue functions. It can potentially lead to new pharmaceutical treatments and drug designs for long QT syndrome and cardiac arrhythmia.

Statistical Metamodeling and Sequential Design of Computer Experiments to Model Glyco-Altered Gating of Sodium Channels in Cardiac Myocytes

Glycan structures account for up to 35% of the mass of cardiac sodium (Nav) channels. To question whether and how reduced sialylation affects Nav activity and cardiac electrical signaling, we conducted a series of in vitro experiments on ventricular apex myocytes under two different glycosylation conditions, reduced protein sialylation (ST3Gal4-/-) and full glycosylation (control). Although aberrant electrical signaling is observed in reduced sialylation, realizing a better understanding of mechanistic details of pathological variations in INa and AP is difficult without performing in silico studies. However, computer model of Nav channels and cardiac myocytes involves greater levels of complexity, e.g., high-dimensional parameter space, nonlinear and nonconvex equations. Traditional linear and nonlinear optimization methods have encountered many difficulties for model calibration. This paper presents a new statistical metamodeling approach for efficient computer experiments and optimization of Nav models. First, we utilize a fractional factorial design to identify control variables from the large set of model parameters, thereby reducing the dimensionality of parametric space. Further, we develop the Gaussian process model as a surrogate of expensive and time-consuming computer models and then identify the next best design point that yields the maximal probability of improvement. This process iterates until convergence, and the performance is evaluated and validated with real-world experimental data. Experimental results show the proposed algorithm achieves superior performance in modeling the kinetics of Nav channels under a variety of glycosylation conditions. As a result, in silico models provide a better understanding of glyco-altered mechanistic details in state transitions and distributions of Nav channels. Notably, ST3Gal4-/- myocytes are shown to have higher probabilities accumulated in intermediate inactivation during the repolarization and yield a shorter refractory period than WTs. The proposed statistical design of computer experiments is generally extensible to many other disciplines that involve large-scale and computationally expensive models.

Parallel computing simulation of electrical excitation and conduction in the 3D human heart.

A correctly beating heart is important to ensure adequate circulation of blood throughout the body. Normal heart rhythm is produced by the orchestrated conduction of electrical signals throughout the heart. Cardiac electrical activity is the resulted function of a series of complex biochemical-mechanical reactions, which involves transportation and bio-distribution of ionic flows through a variety of biological ion channels. Cardiac arrhythmias are caused by the direct alteration of ion channel activity that results in changes in the AP waveform. In this work, we developed a whole-heart simulation model with the use of massive parallel computing with GPGPU and OpenGL. The simulation algorithm was implemented under several different versions for the purpose of comparisons, including one conventional CPU version and two GPU versions based on Nvidia CUDA platform. OpenGL was utilized for the visualization / interaction platform because it is open source, light weight and universally supported by various operating systems. The experimental results show that the GPU-based simulation outperforms the conventional CPU-based approach and significantly improves the speed of simulation. By adopting modern computer architecture, this present investigation enables real-time simulation and visualization of electrical excitation and conduction in the large and complicated 3D geometry of a real-world human heart.A correctly beating heart is important to ensure adequate circulation of blood throughout the body. Normal heart rhythm is produced by the orchestrated conduction of electrical signals throughout the heart. Cardiac electrical activity is the resulted function of a series of complex biochemical-mechanical reactions, which involves transportation and bio-distribution of ionic flows through a variety of biological ion channels. Cardiac arrhythmias are caused by the direct alteration of ion channel activity that results in changes in the AP waveform. In this work, we developed a whole-heart simulation model with the use of massive parallel computing with GPGPU and OpenGL. The simulation algorithm was implemented under several different versions for the purpose of comparisons, including one conventional CPU version and two GPU versions based on Nvidia CUDA platform. OpenGL was utilized for the visualization / interaction platform because it is open source, light weight and universally supported by various operating systems. The experimental results show that the GPU-based simulation outperforms the conventional CPU-based approach and significantly improves the speed of simulation. By adopting modern computer architecture, this present investigation enables real-time simulation and visualization of electrical excitation and conduction in the large and complicated 3D geometry of a real-world human heart.

Surface grinding of CFRP composites using rotary ultrasonic machining: design of experiment on cutting force, torque and surface roughness

Carbon fiber reinforced plastic (CFRP) composites provide excellent properties, which make them attractive in many industries. However, due to the properties of high stiffness, anisotropy, and high abrasiveness of carbon fiber in CFRPs, they are regarded as difficult-to-cut materials. To find an efficient surface grinding process for CFRP, this paper conducts an investigation using RUM. Design of experiment (DOE) is vital to evaluate effects of input variables on output variables, and it could be used to obtain the optimal values of variables in such a process. DOE could also be used to decrease the test numbers, the research cost etc. However, there are no investigations on DOE in such a process. This investigation firstly tests the effects of three input variables, including tool rotation speed, feedrate, and ultrasonic power, on output variables, including cutting force, torque, and surface roughness, at two levels. This investigation will provide guides for future research.

Propagation of parametric uncertainty for the K+ channel model in mouse ventricular myocytes

Cardiac potassium (K+) channel plays an important role in cardiac electrical signaling. Mathematical models have been widely used to investigate the effects of K+ channels on cardiac functions. However, the model of K+ channel involves parametric uncertainties, which can be induced by fitting the models parameters that best capture experimental data. Since the prediction of cardiac functions are highly parameter-dependent, it is critical to quantify the influence of parametric uncertainty on the model responses to provide the more reliable predictions. This paper presents a new method to efficiently propagate the uncertainty on the models parameters of K+ channel to the gating variables as well as the current density. In this way, we can estimate the model predictions and their corresponding confidence intervals simultaneously. A generalized polynomial chaos (gPC) expansion approximating the parametric uncertainty is used in combination with the physical models to quantify and propagate the parametric uncertainties onto the modeled predictions of steady state activation and steady state inactivation of the K+ channel. Using Galerkin projection, the variation (i.e., confidence interval) of the gating variables resulting from the uncertainty of model parameters can then be estimated in a computationally efficient fashion. As compared with the Monte Carlo (MC) simulations, the proposed methodology shows its advantageous in terms of computational efficiency and accuracy, thus demonstrating the potential for dealing with more complicated cardiac models.Cardiac potassium (K+) channel plays an important role in cardiac electrical signaling. Mathematical models have been widely used to investigate the effects of K+ channels on cardiac functions. However, the model of K+ channel involves parametric uncertainties, which can be induced by fitting the models parameters that best capture experimental data. Since the prediction of cardiac functions are highly parameter-dependent, it is critical to quantify the influence of parametric uncertainty on the model responses to provide the more reliable predictions. This paper presents a new method to efficiently propagate the uncertainty on the models parameters of K+ channel to the gating variables as well as the current density. In this way, we can estimate the model predictions and their corresponding confidence intervals simultaneously. A generalized polynomial chaos (gPC) expansion approximating the parametric uncertainty is used in combination with the physical models to quantify and propagate the parametric uncertainties onto the modeled predictions of steady state activation and steady state inactivation of the K+ channel. Using Galerkin projection, the variation (i.e., confidence interval) of the gating variables resulting from the uncertainty of model parameters can then be estimated in a computationally efficient fashion. As compared with the Monte Carlo (MC) simulations, the proposed methodology shows its advantageous in terms of computational efficiency and accuracy, thus demonstrating the potential for dealing with more complicated cardiac models.

Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention:

Massive open online courses (MOOCs) show great potential to transform traditional education through the Internet. However, the high attrition rates in MOOCs have often been cited as a scale-efficacy tradeoff. Traditional educational approaches are usually unable to identify such large-scale number of at-risk students in danger of dropping out in time to support effective intervention design. While building dropout prediction models using learning analytics are promising in informing intervention design for these at-risk students, results of the current prediction model construction methods do not enable personalized intervention for these students. In this study, we take an initial step to optimize the dropout prediction model performance toward intervention personalization for at-risk students in MOOCs. Specifically, based on a temporal prediction mechanism, this study proposes to use the deep learning algorithm to construct the dropout prediction model and further produce the predicted individual student dropout probability. By taking advantage of the power of deep learning, this approach not only constructs more accurate dropout prediction models compared with baseline algorithms but also comes up with an approach to personalize and prioritize intervention for at-risk students in MOOCs through using individual drop out probabilities. The findings from this study and implications are then discussed.

Fault detection and diagnosis using empirical mode decomposition based principal component analysis

Abstract This paper presents a new algorithm to identify and diagnose stochastic faults in Tennessee Eastman (TE) process. The algorithm combines Ensemble Empirical Mode Decomposition (EEMD) with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) to diagnose a group of faults that could not be properly detected and/or diagnosed with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, fault detection, and fault diagnosis. Measured variables are first decomposed into different scales using the EEMD-based PCA, from which fault signatures can be extracted for fault detection and diagnosis (FDD). The T2 and Q statistics-based CUSUMs are further applied to improve fault detection, where a set of PCA models are developed from historical data to characterize anomalous fingerprints that are correlated with each fault for accurate fault diagnosis. The algorithm developed in this paper can successfully identify and diagnose both individual and simultaneous occurrences of stochastic faults.

Global sensitivity analysis for developing biological models: Application to K+ channel model in mouse ventricular myocytes

Mathematical models of cardiac myocytes are highly nonlinear and involve a large number of model parameters. The parameters are estimated using experimental data, which are often corrupted by noise and uncertainty. Such uncertainty can be propagated onto model parameters during model calibration, which further affects model reliability and credibility. In order to improve model accuracy, it is important to quantify and reduce the uncertainty in model response resulting from parametric uncertainty. Sensitivity analysis is a key technique to investigate the significance of parametric uncertainty and its effect on model responses. This can identify and rank most sensitive parameters, and evaluate the effect of uncertainty on model outputs. In this work, a global sensitivity analysis is developed to determine the significance of parametric uncertainty on model responses using Sobol indices. This method is applied to nonlinear K+ channel models of mouse ventricular myocytes to demonstrate the efficacy of the developed algorithm.Mathematical models of cardiac myocytes are highly nonlinear and involve a large number of model parameters. The parameters are estimated using experimental data, which are often corrupted by noise and uncertainty. Such uncertainty can be propagated onto model parameters during model calibration, which further affects model reliability and credibility. In order to improve model accuracy, it is important to quantify and reduce the uncertainty in model response resulting from parametric uncertainty. Sensitivity analysis is a key technique to investigate the significance of parametric uncertainty and its effect on model responses. This can identify and rank most sensitive parameters, and evaluate the effect of uncertainty on model outputs. In this work, a global sensitivity analysis is developed to determine the significance of parametric uncertainty on model responses using Sobol indices. This method is applied to nonlinear K+ channel models of mouse ventricular myocytes to demonstrate the efficacy of the developed algorithm.

Detection of the propagating direction of electrical wavefront in atrial fibrillation

Atrial Fibrillation (AF) is one of the most common sustained arrhythmia, which can increase the risk of heart failure and stroke. Understanding the complex electrical dynamics of AF and correctly targeting AF sources for ablation therapies remain challenging in clinical practice. This is due to the incapability to reconstruct the electrical dynamic of AF, and lack of efficient approach for AF source identification. This paper builds a multi-sale framework for modeling of the abnormal electrical propagation in AF initiated by triggers from Pulmonary Veins (PVs). A new algorithm is developed to detect the propagating direction of electrical wavefronts. The detection algorithm is further validated using modeling results. The developed multi-scale framework and the detection algorithm will contribute to AF diagnosis and potentially improve the treatment outcomes of AF ablations.Atrial Fibrillation (AF) is one of the most common sustained arrhythmia, which can increase the risk of heart failure and stroke. Understanding the complex electrical dynamics of AF and correctly targeting AF sources for ablation therapies remain challenging in clinical practice. This is due to the incapability to reconstruct the electrical dynamic of AF, and lack of efficient approach for AF source identification. This paper builds a multi-sale framework for modeling of the abnormal electrical propagation in AF initiated by triggers from Pulmonary Veins (PVs). A new algorithm is developed to detect the propagating direction of electrical wavefronts. The detection algorithm is further validated using modeling results. The developed multi-scale framework and the detection algorithm will contribute to AF diagnosis and potentially improve the treatment outcomes of AF ablations.

Nonperiodic cycle detection and application in gas liquid two-phase flow

Nonperiodic cycle detection methods for gas/liquid two phase flow system were discussed. Cycle detection methods, such as Hurst analysis, the V statistic and the P statistic were briefly reviewed; in addition, a modified P statistic method was introduced. Two types of time series, i.e. mathematically sine wave time series and experimental electrical capacitance tomography time series of plug flow and slug flow under different flow conditions were investigated to verify the effectiveness of different cycle detection methods. The sine wave time series and sine wave time series under gauss noise were used to validate different cycle detection methods. For the electrical capacitance tomography time series of different flow regime, discrete wavelet transform (DWT) was adopted to decompose the original signal into approximate signal and detail signals, and then different cycle detection methods were mainly applied to realize cyclic characterization analysis of detail signals for plug flow and slug flow. The results show that the nonperiodic cycle characteristic analysis can reflect the property of different flow regimes.