Cardiac image segmentation using generalized polynomial chaos expansion and level set function.

Cardiovascular Magnetic Resonance (CMR) images involves a great amount of uncertainties. Such uncertainties may originate from either intrinsic measurement limitations or heterogeneities among patients. Without properly considering these uncertainties, image analysis may provide inaccurate estimations of cardiac functions, and ultimately lead to false diagnosis and inappropriate treatment strategy. In this work, a stochastic image segmentation algorithm is developed to separate cardiac chambers from the background of CMR images. To account for noise and uncertainties in pixel values, a generalized polynomial chaos (gPC) expansion is integrated with a level set function to dynamically evolve boundaries of cardiac chambers. Two consecutive steps are developed: a deterministic segmentation to identify an immediate neighborhood of boundary, of which pixel values are used to calibrate the gPC model; and a stochastic segmentation applied to the neighborhood region to evolve boundaries of cardiac chambers in a stochastic manner. The proposed method can provide a probabilistic description of the segmented heart boundary, which will greatly improve the reliability of image analysis, and potentially enhanced cardiac function evaluation.

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.

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.

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 model's 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 model's 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 it's advantageous in terms of computational efficiency and accuracy, thus demonstrating the potential for dealing with more complicated cardiac models.

Expression and characterization of a novel ω-transaminase from Burkholderia phytofirmans PsJN / 生物工程学报

Production of chiral amines and unnatural amino-acid using ω-transaminase can be achieved by kinetic resolution and asymmetric synthesis, thus ω-transaminase is of great importance in the synthesis of pharmaceutical intermediates. By genomic data mining, a putative ω-transaminase gene hbp was found in Burkholderia phytofirmans PsJN. The gene was cloned and over-expressed in Escherichia coli BL21 (DE3). The recombinant enzyme (HBP) was purified by Ni-NTA column and its catalytic properties and substrate profile were studied. HBP showed high relative activity (33.80 U/mg) and enantioselectivity toward β-phenylalanine (β-Phe). The optimal reaction temperature and pH were 40 ℃ and 8.0-8.5, respectively. We also established a simpler and more effective method to detect the deamination reaction of β-Phe by UV absorption method using microplate reader, and demonstrated the thermodynamic property of this reaction. The substrate profiling showed that HBP was specific to β-Phe and its derivatives as the amino donor. HBP catalyzed the resolution of rac-β-Phe and its derivatives, the products (R)-amino acids were obtained with about 50% conversions and 99% ee.