Yung-Kyun Noh

Impact of low dose atorvastatin on development of new-onset diabetes mellitus in Asian population: Three-year clinical outcomes

BACKGROUND High dose atorvastatin is known to be associated with new onset diabetes mellitus (NODM) in patients with high risk for developing diabetes mellitus (DM). However, low dose atorvastatin is more commonly used as compared with high dose atorvastatin. The aim of this study is to investigate the impact of low dose atorvastatin (LDA, 10mg or 20mg) on the development of NODM up to three years in Asian patients. METHODS From January 2004 to September 2009, we investigated a total of 3566 patients who did not have DM. To adjust for potential confounders, a propensity score matching (PSM) analysis was performed using the logistic regression model. After PSM (C-statistics: 0.851), a total of 818 patients (LDA group, n=409 patients and control group, n=409 patients) were enrolled for analysis. RESULTS Before PSM, the cumulative incidence of NODM (5.8% vs. 2.1%, p<0.001), myocardial infarction (0.5% vs. 0.1%, p-value=0.007), and major adverse cardio-cerebral event (MACCE, 1.8% vs. 0.7%, p-value=0.012) at three-years were higher in the LAD group. However, after PSM, there was a trend toward higher incidence of NODM (5.9% vs. 3.2%, p=0.064) in the LDA group, but the incidence of MACCE (1.2% vs. 1.5%, p-value=1.000) was similar between the two groups. In multivariable analysis, the LDA administration was tended to be an independent predictor of NODM (OR: 1.99, 95% CI: 1.00-3.98, p-value 0.050). CONCLUSIONS In this study, the use of LDA tended to be a risk factor for NODM in Asian patients and reduced clinical events similar to the control group. However, large-scale randomized controlled trials will be needed to get the final conclusion.

Association of inflammation, myocardial fibrosis and cardiac remodelling in patients with mild aortic stenosis as assessed by biomarkers and echocardiography

The aim of the present study was to investigate the relationships of inflammation, myocardial fibrosis and cardiac remodelling in patients with mild aortic stenosis (AS), as assessed by biomarkers and echocardiography. We consecutively evaluated 32 patients with mild AS, as well as 30 age‐ and gender‐matched healthy individuals with normal aortic valves as control subjects. Baseline echocardiography showed that the left ventricular (LV) mass index (111.3 ± 26.9 vs 94.5 ± 18.2 g/m2; P = 0.006) and left atrial (LA) volume index (LAVI 27.5 ± 9.0 vs 21.9 ± 5.2 mm3/mm2; P = 0.005) were significantly higher in patients with mild AS. Furthermore, LA enlargement (LAVI > 33 mm3/mm2; 32.4% vs 3.3%;P = 0.003) and elevated LV filling pressure (E/e′ > 15; 50.0% vs 23.3%; P = 0.036) were higher in patients with mild AS. In patients with mild AS, stepwise, multivariate linear regression analysis revealed that the LV end‐diastolic volume index was independently associated with matrix metalloproteinase (MMP)‐1 (β = 0.371; P = 0.015), that the aortic valve mean pressure gradient was independently associated with MMP‐2 (β = 0.19; P = 0.019), that MMP‐2 was independently associated with transforming growth factor‐β (β = 0.95; P < 0.001) and interleukin (IL)‐1 (β = 0.17; P = 0.019) and that IL‐1 was independently associated with tissue inhibitor of matrix metalloproteinase‐1 (β = 0.68; P = 0.001). Myocardial fibrosis in mild AS is independently associated with three factors: LV volume overload, aortic valve pressure overload and inflammation.

An evolutionary Monte Carlo algorithm for predicting DNA hybridization

Many DNA-based technologies, such as DNA computing, DNA nanoassembly and DNA biochips, rely on DNA hybridization reactions. Previous hybridization models have focused on macroscopic reactions between two DNA strands at the sequence level. Here, we propose a novel population-based Monte Carlo algorithm that simulates a microscopic model of reacting DNA molecules. The algorithm uses two essential thermodynamic quantities of DNA molecules: the binding energy of bound DNA strands and the entropy of unbound strands. Using this evolutionary Monte Carlo method, we obtain a minimum free energy configuration in the equilibrium state. We applied this method to a logical reasoning problem and compared the simulation results with the experimental results of the wet-lab DNA experiments performed subsequently. Our simulation predicted the experimental results quantitatively.

Decoding of top-down cognitive processing for SSVEP-controlled BMI

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.

Impact of Angiotensin Converting Enzyme Inhibitor versus Angiotensin Receptor Blocker on Incidence of New-Onset Diabetes Mellitus in Asians

Purpose Angiotensin converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) are associated with a decreased incidence of new-onset diabetes mellitus (NODM). The aim of this study was to compare the protective effect of ACEI versus ARBs on NODM in an Asian population. Materials and Methods We investigated a total of 2817 patients who did not have diabetes mellitus from January 2004 to September 2009. To adjust for potential confounders, a propensity score matched (PSM) analysis was performed using a logistic regression model. The primary end-point was the cumulative incidence of NODM, which was defined as having a fasting blood glucose ≥126 mg/dL or HbA1c ≥6.5%. Multivariable cox-regression analysis was performed to determine the impact of ACEI versus ARB on the incidence of NODM. Results Mean follow-up duration was 1839±1019 days in all groups before baseline adjustment and 1864±1034 days in the PSM group. After PSM (C-statistics=0.731), a total 1024 patients (ACEI group, n=512 and ARB group, n=512) were enrolled for analysis and baseline characteristics were well balanced. After PSM, the cumulative incidence of NODM at 3 years was lower in the ACEI group than the ARB group (2.1% vs. 5.0%, p=0.012). In multivariate analysis, ACEI vs. ARB was an independent predictor of the lower incidence for NODM (odd ratio 0.37, confidence interval 0.17-0.79, p=0.010). Conclusion In the present study, compared with ARB, chronic ACEI administration appeared to be associated with a lower incidence of NODM in a series of Asian cardiovascular patients.

Direct density derivative estimation

Estimating the derivatives of probability density functions is an essential step in statistical data analysis. A naive approach to estimate the derivatives is to first perform density estimation and then compute its derivatives. However, this approach can be unreliable because a good density estimator does not necessarily mean a good density derivative estimator. To cope with this problem, in this letter, we propose a novel method that directly estimates density derivatives without going through density estimation. The proposed method provides computationally efficient estimation for the derivatives of any order on multidimensional data with a hyperparameter tuning method and achieves the optimal parametric convergence rate. We further discuss an extension of the proposed method by applying regularized multitask learning and a general framework for density derivative estimation based on Bregman divergences. Applications of the proposed method to nonparametric Kullback-Leibler divergence approximation and bandwidth matrix selection in kernel density estimation are also explored.

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence

Nearest-neighbor estimators for the Kullback-Leiber (KL) divergence that are asymptotically unbiased have recently been proposed and demonstrated in a number of applications. However, with a small number of samples, nonparametric methods typically suffer from large estimation bias due to the nonlocality of information derived from nearest-neighbor statistics. In this letter, we show that this estimation bias can be mitigated by modifying the metric function, and we propose a novel method for learning a locally optimal Mahalanobis distance function from parametric generative models of the underlying density distributions. Using both simulations and experiments on a variety of data sets, we demonstrate that this interplay between approximate generative models and nonparametric techniques can significantly improve the accuracy of nearest-neighbor-based estimation of the KL divergence.

Hyperuricaemia and development of type 2 diabetes mellitus in Asian population

Recently, meta‐analysis studies reported that hyperuricaemia is associated with higher incidence of type 2 diabetes mellitus (T2DM), however, there are limited data on the Asian population. The aim of this observational study is to estimate the long‐term impact of hyperuricaemia on the new‐onset T2DM and cardiovascular events. This study is based on a single‐centre, all‐comers, and large retrospective cohort. Subjects that visited from January 2004 to February 2014 were enrolled using the electronic database of Korea University Guro Hospital. A total of 10 505 patients without a history of T2DM were analyzed for uric acid, fasting glucose and haemoglobin (Hb) A1c level. Inclusion criteria included both Hb A1c <5.7% and fasting glucose level <100 mg/dL without T2DM. Hyperuricaemia was defined as a uric acid level ≥7.0 mg/dL in men, and ≥6.5 mg/dL in women. To adjust baseline confounders, a propensity score matching (PSM) analysis was performed. The impact of hyperuricaemia on the new‐onset T2DM and cardiovascular events were compared with the non‐hyperuricaemia during the 5‐year clinical follow‐up. After PSM, baseline characteristics of both groups were balanced. In a 5‐year follow‐up, the hyperuricaemia itself was a strong independent predictor of the incidence of new‐onset T2DM (HR, 1.78; 95% CI, 1.12 to 2.8). Hyperuricaemia was a strong independent predictor of new‐onset T2DM, which suggests a substantial implication for a correlation between uric acid concentration and insulin resistance (or insulin sensitivity). Also, hyperuricaemia is substantially implicated in cardiovascular risks and the further long‐term cardiovascular events in the crude population, but it is not an independent predictor of long‐term cardiovascular mortality in the matched population.

Motion planning with movement primitives for cooperative aerial transportation in obstacle environment

This paper presents a motion planning approach for cooperative transportation using aerial robots. We describe a framework based on Parametric Dynamic Movement Primitives (PDMPs) for coordinating multiple aerial robots and their manipulators quickly in an environment cluttered with obstacles. In order to emulate the optimal motion, we combine PDMPs and Rapidly Exploring Randomized Trees star (RRT∗) by using the results of RRT∗ as demonstrations for PDMPs. For efficient description of the motions corresponding to the environment, we utilize Gaussian Process Regression (GPR) to acquire of the explicit relationship between environmental parameters and style parameters of PDMPs which decide the motions. Simulation and experiment results are attached to validate the proposed framework.

Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis

Classical discriminant analysis attempts to discover a low-dimensional subspace where class label information is maximally preserved under projection. Canonical methods for estimating the subspace optimize an information-theoretic criterion that measures the separation between the class-conditional distributions. Unfortunately, direct optimization of the information-theoretic criteria is generally non-convex and intractable in high-dimensional spaces. In this work, we propose a novel, tractable algorithm for discriminant analysis that considers the class-conditional densities as interacting fluids in the high-dimensional embedding space. We use the Bhattacharyya criterion as a potential function that generates forces between the interacting fluids, and derive a computationally tractable method for finding the low-dimensional subspace that optimally constrains the resulting fluid flow. We show that this model properly reduces to the optimal solution for homoscedastic data as well as for heteroscedastic Gaussian distributions with equal means. We also extend this model to discover optimal filters for discriminating Gaussian processes and provide experimental results and comparisons on a number of datasets.