Tzu-Yu Liu

Relationship between burden of premature ventricular complexes and left ventricular function

BACKGROUND Frequent idiopathic premature ventricular complexes (PVCs) can result in a reversible form of left ventricular dysfunction. The factors resulting in impaired left ventricular function are unclear. Whether a critical burden of PVCs can result in cardiomyopathy has not been determined. OBJECTIVE The objective of this study was to determine a cutoff PVC burden that can result in PVC-induced cardiomyopathy. METHODS In a consecutive group of 174 patients referred for ablation of frequent idiopathic PVCs, the PVC burden was determined by 24-hour Holter monitoring, and transthoracic echocardiograms were used to assess left ventricular function. Receiver-operator characteristic curves were constructed based on the PVC burden and on the presence or absence of reversible left ventricular dysfunction to determine a cutoff PVC burden that is associated with left ventricular dysfunction. RESULTS A reduced left ventricular ejection fraction (mean 0.37 +/- 0.10) was present in 57 of 174 patients (33%). Patients with a decreased ejection fraction had a mean PVC burden of 33% +/- 13% as compared with those with normal left ventricular function 13% +/- 12% (P <.0001). A PVC burden of >24% best separated the patient population with impaired as compared with preserved left ventricular function (sensitivity 79%, specificity 78%, area under curve 0.89) The lowest PVC burden resulting in a reversible cardiomyopathy was 10%. In multivariate analysis, PVC burden (hazard ratio 1.12, 95% confidence interval 1.08 to 1.16; P <.01) was independently associated with PVC-induced cardiomyopathy. CONCLUSION A PVC burden of >24% was independently associated with PVC-induced cardiomyopathy.

The Value of Defibrillator Electrograms for Recognition of Clinical Ventricular Tachycardias and for Pace Mapping of Post-Infarction Ventricular Tachycardia

OBJECTIVES The purpose of this study was to assess the value of implantable cardioverter-defibrillator (ICD) electrograms (EGMs) in identifying clinically documented ventricular tachycardias (VTs). BACKGROUND Twelve-lead electrocardiograms (ECG) of spontaneous VT often are not available in patients referred for catheter ablation of post-infarction VT. Many of these patients have ICDs, and the ability of ICD EGMs to identify a specific configuration of VT has not been described. METHODS In 21 consecutive patients referred for catheter ablation of post-infarction VT, 124 VTs (mean cycle length: 393 ± 103 ms) were induced, and ICD EGMs were recorded during VT. Clinical VT had been documented with 12-lead ECGs in 15 of 21 patients. The 12-lead ECGs of the clinical VTs were compared with 64 different inducible VTs (mean cycle length: 390 ± 91 ms) to assess how well the ICD EGMs differentiated the clinical VTs from the other induced VTs. The exit site of 62 VTs (mean cycle length: 408 ± 112 ms) was identified by pace mapping (10 to 12 of 12 matching leads). The spatial resolution of pace mapping to identify a VT exit site was determined for both the 12-lead ECGs and the ICD EGMs using a customized MATLAB program (version 7.5, The MathWorks, Inc., Natick, Massachusetts). RESULTS Analysis of stored EGMs by comparison of receiver-operating characteristic curve cutoff values accurately distinguished the clinical VTs from 98% of the other inducible VTs. The mean spatial resolution of a 12-lead ECG pace map for the VT exit site was 2.9 ± 4.0 cm(2) (range 0 to 17.5 cm(2)) compared with 8.9 ± 9.0 cm(2) (range 0 to 35 cm(2)) for ICD EGM pace maps. The spatial resolution of pace mapping varied greatly between patients and between VTs. The spatial resolution of ICD EGMs was < 1.0 cm(2) for ≥ 1 of the target VTs in 12 of 21 patients and 19 of 62 VTs. By visual inspection of the ICD EGMs, 96% of the clinical VTs were accurately differentiated from previously undocumented VTs. CONCLUSIONS Stored ICD EGMs usually are an accurate surrogate for 12-lead ECGs for differentiating clinical VTs from other VTs. Pace mapping based on ICD EGMs has variable resolution but may be useful for identifying a VT exit site.

Automated analysis of the 12-lead electrocardiogram to identify the exit site of postinfarction ventricular tachycardia

BACKGROUND The value of the 12-lead electrocardiogram (ECG) to identify the exit site of postinfarction ventricular tachycardia (VT) has been questioned. The purpose of this study was to assess the accuracy of a computerized algorithm for identifying a VT exit site on the basis of the 12-lead ECG. METHODS AND RESULTS In 34 postinfarction patients, pace mapping was performed from within scar tissue. A computerized algorithm that used a supervised learning method (support vector machine) received the digitized pace-map morphologies combined with the pacing sites as training data. No other information (ie, infarct localization, bundle branch block morphology, axis, or R-wave pattern) was used in the algorithm. The training data were validated in 58 VTs in 33 patients. The sizes of 10 different anatomic sections within the heart were determined by using the pace maps as the determining factor. Accuracy was found to be 69% for pace maps, and when 2 adjacent regions were combined, accuracy improved to 88%. Validation of the data in 33 patients showed an accuracy of 71% for localizing a VT exit site to 1 of the 10 regions within the left ventricle. If combined with the best adjacent region, accuracy improved to 88%. The median anatomic size of each section was 21 cm(2). The median spatial resolution of the 12-lead ECG pattern of the pace maps for a particular region was 15 cm(2). CONCLUSION The 12-lead ECG of postinfarction VT contains localizing information that enables determination of a region of interest in the 10-20 cm(2) range for more than 70% of VT exit sites in a given sector.

Signal processing challenges in quantitative 3-D cell morphology: More than meets the eye

Modern developments in light microscopy have allowed the observation of cell deformation with remarkable spatiotemporal resolution and reproducibility. Analyzing such phenomena is of particular interest for the signal processing and computer vision communities due to the numerous computational challenges involved, from image acquisition all the way to shape analysis and pattern recognition and interpretation. This article aims at providing an up-to-date overview of the problems, solutions, and remaining challenges in deciphering the morphology of living cells via computerized approaches, with a particular focus on shape description frameworks and their exploitation using machine-learning techniques. As a concrete illustration, we use our recently acquired data on amoeboid cell deformation, motivated by its direct implication in immune responses, bacterial invasion, and cancer metastasis.

An individualized predictor of health and disease using paired reference and target samples

BackgroundConsider the problem of designing a panel of complex biomarkers to predict a patient’s health or disease state when one can pair his or her current test sample, called a target sample, with the patient’s previously acquired healthy sample, called a reference sample. As contrasted to a population averaged reference this reference sample is individualized. Automated predictor algorithms that compare and contrast the paired samples to each other could result in a new generation of test panels that compare to a person’s healthy reference to enhance predictive accuracy. This paper develops such an individualized predictor and illustrates the added value of including the healthy reference for design of predictive gene expression panels.ResultsThe objective is to predict each subject’s state of infection, e.g., neither exposed nor infected, exposed but not infected, pre-acute phase of infection, acute phase of infection, post-acute phase of infection. Using gene microarray data collected in a large scale serially sampled respiratory virus challenge study we quantify the diagnostic advantage of pairing a person’s baseline reference with his or her target sample. The full study consists of 2886 microarray chips assaying 12,023 genes of 151 human volunteer subjects under 4 different inoculation regimes (HRV, RSV, H1N1, H3N2). We train (with cross-validation) reference-aided sparse multi-class classifier algorithms on this data to show that inclusion of a subject’s reference sample can improve prediction accuracy by as much as 14 %, for the H3N2 cohort, and by at least 6 %, for the H1N1 cohort. Remarkably, these gains in accuracy are achieved by using smaller panels of genes, e.g., 39 % fewer for H3N2 and 31 % fewer for H1N1. The biomarkers selected by the predictors fall into two categories: 1) contrasting genes that tend to differentially express between target and reference samples over the population; 2) reinforcement genes that remain constant over the two samples, which function as housekeeping normalization genes. Many of these genes are common to all 4 viruses and their roles in the predictor elucidate the function that they play in differentiating the different states of host immune response.ConclusionsIf one uses a suitable mathematical prediction algorithm, inclusion of a healthy reference in biomarker diagnostic testing can potentially improve accuracy of disease prediction with fewer biomarkers.

A Genomic Signature of Influenza Infection Shows Potential for Presymptomatic Detection, Guiding Early Therapy, and Monitoring Clinical Responses

Early, presymptomatic intervention with oseltamivir (corresponding to the onset of a published host-based genomic signature of influenza infection) resulted in decreased overall influenza symptoms (aggregate symptom scores of 23.5 vs 46.3), more rapid resolution of clinical disease (20 hours earlier), reduced viral shedding (total median tissue culture infectious dose [TCID50] 7.4 vs 9.7), and significantly reduced expression of several inflammatory cytokines (interferon-γ, tumor necrosis factor-α, interleukin-6, and others). The host genomic response to influenza infection is robust and may provide the means for early detection, more timely therapeutic interventions, a meaningful reduction in clinical disease, and an effective molecular means to track response to therapy.

Globally Sparse PLS Regression

Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principal component analysis by taking into account both the independent and response variables in the dimension reduction procedure. However, PLS suffers from over-fitting problems for few samples but many variables. We formulate a new criterion for sparse PLS by adding a structured sparsity constraint to the global SIMPLS optimization. The constraint is a sparsity-inducing norm, which is useful for selecting the important variables shared among all the components. The optimization is solved by an augmented Lagrangian method to obtain the PLS components and to perform variable selection simultaneously. We propose a novel greedy algorithm to overcome the computation difficulties. Experiments demonstrate that our approach to PLS regression attains better performance with fewer selected predictors.

Riches of phenotype computationally extracted from microbial colonies

Significance The genome and physiology of a cell can undergo complex changes among the many cells that make up a growing microbial colony. Genetic and physiological dynamics can be revealed by measuring reporter-gene expression, but rigorous quantitative analysis of colony-wide patterns has been underexplored. Here, we developed a suite of automated image processing, feature extraction, visualization, and classification algorithms to facilitate the analysis of sectoring patterns in Saccharomyces colonies. Classification results for various mutants and for colonies grown under different environmental conditions revealed significant differences in sectoring that were not apparent by visual inspection. The genetic, epigenetic, and physiological differences among cells in clonal microbial colonies are underexplored opportunities for discovery. A recently developed genetic assay reveals that transient losses of heterochromatic repression, a heritable form of gene silencing, occur throughout the growth of Saccharomyces colonies. This assay requires analyzing two-color fluorescence patterns in yeast colonies, which is qualitatively appealing but quantitatively challenging. In this paper, we developed a suite of automated image processing, visualization, and classification algorithms (MORPHE) that facilitated the analysis of heterochromatin dynamics in the context of colonial growth and that can be broadly adapted to many colony-based assays in Saccharomyces and other microbes. Using the features that were automatically extracted from fluorescence images, our classification method distinguished loss-of-silencing patterns between mutants and wild type with unprecedented precision. Application of MORPHE revealed subtle but significant differences in the stability of heterochromatic repression between various environmental conditions, revealed that haploid cells experienced higher rates of silencing loss than diploids, and uncovered the unexpected contribution of a sirtuin to heterochromatin dynamics.

Prediction of ribosome footprint profile shapes from transcript sequences

Motivation: Ribosome profiling is a useful technique for studying translational dynamics and quantifying protein synthesis. Applications of this technique have shown that ribosomes are not uniformly distributed along mRNA transcripts. Understanding how each transcript-specific distribution arises is important for unraveling the translation mechanism. Results: Here, we apply kernel smoothing to construct predictive features and build a sparse model to predict the shape of ribosome footprint profiles from transcript sequences alone. Our results on Saccharomyces cerevisiae data show that the marginal ribosome densities can be predicted with high accuracy. The proposed novel method has a wide range of applications, including inferring isoform-specific ribosome footprints, designing transcripts with fast translation speeds and discovering unknown modulation during translation. Availability and implementation: A software package called riboShape is freely available at https://sourceforge.net/projects/riboshape Contact: yss@berkeley.edu

A sparse multi-class classifier for biomarker screening

We introduce an approach to sparsity penalized multi-class classifier design that accounts for multi-block structure of the data. The unified multi-class classifier is parameterized by a set of weights defined over the classes and over the blocks. The proposed sparse multi-block multi-class classifier imposes structured sparsity on the weights so that the same variables are selected for all classes and all blocks. The classifier is trained to minimize an objective function that captures the unified miss-classification probabilities of error over the classes in addition to the sparsity of the weights. The optimization of the objective function is implemented by a convex augmented Lagrangian and variable splitting method. This results in a classifier that automatically selects biomarkers for inclusion or exclusion in the classifier and results in significantly improved classifier performance. The approach is illustrated on publicly available longitudinal gene microarray data.