Hwee Kuan Lee

Fast phase transitions induced by picosecond electrical pulses on phase change memory cells

The reversible and fast phase transitions induced by picosecond electrical pulses are observed in the nanostructured GeSbTe materials, which provide opportunities in the application of high speed nonvolatile random access memory devices. The mechanisms for fast phase transition are discussed based on the investigation of the correlation between phase transition speed and material size. With the shrinkage of material dimensions, the size effects play increasingly important roles in enabling the ultrafast phase transition under electrical activation. The understanding of how the size effects contribute to the phase transition speed is of great importance for ultrafast phenomena and applications.

Quantitative neurite outgrowth measurement based on image segmentation with topological dependence

The study of neuronal morphology and neurite outgrowth has been enhanced by the combination of imaging informatics and high content screening, in which thousands of images are acquired using robotic fluorescent microscopy. To understand the process of neurite outgrowth in the context of neuroregeneration, we used mouse neuroblastoma N1E115 as our model neuronal cell. Six‐thousand cellular images of four different culture conditions were acquired with two‐channel widefield fluorescent microscopy. We developed a software package called NeuronCyto. It is a fully automatic solution for neurite length measurement and complexity analysis. A novel approach based on topological analysis is presented to segment cells. The detected nuclei were used as references to initialize the level set function. Merging and splitting of cells segments were prevented using dynamic watershed lines based on the constraint of topological dependence. A tracing algorithm was developed to automatically trace neurites and measure their lengths quantitatively on a cell‐by‐cell basis. NeuronCyto analyzes three important biologically relevant features, which are the length, branching complexity, and number of neurites. The application of NeuronCyto on the experiments of Toca‐1 and serum starvation show that the transfection of Toca‐1 cDNA induces longer neurites with more complexities than serum starvation.

A Multiresolution Stochastic Level Set Method for Mumford–Shah Image Segmentation

The Mumford-Shah model is one of the most successful image segmentation models. However, existing algorithms for the model are often very sensitive to the choice of the initial guess. To make use of the model effectively, it is essential to develop an algorithm which can compute a global or near global optimal solution efficiently. While gradient descent based methods are well-known to find a local minimum only, even many stochastic methods do not provide a practical solution to this problem either. In this paper, we consider the computation of a global minimum of the multiphase piecewise constant Mumford-Shah model. We propose a hybrid approach which combines gradient based and stochastic optimization methods to resolve the problem of sensitivity to the initial guess. At the heart of our algorithm is a well-designed basin hopping scheme which uses global updates to escape from local traps in a way that is much more effective than standard stochastic methods. In our experiments, a very high-quality solution is obtained within a few stochastic hops whereas the solutions obtained with simulated annealing are incomparable even after thousands of steps. We also propose a multiresolution approach to reduce the computational cost and enhance the search for a global minimum. Furthermore, we derived a simple but useful theoretical result relating solutions at different spatial resolutions.

Anterior segment optical coherence tomography parameters in subtypes of primary angle closure.

PURPOSE To compare anterior segment parameters, assessed by anterior segment optical coherence tomography (ASOCT), in subjects categorized as primary angle closure suspect (PACS), primary angle closure (PAC), primary angle closure glaucoma (PACG), and previous acute PAC (APAC); and to identify factors associated with APAC. METHODS This was a prospective ASOCT study of 425 subjects with angle closure (176 PACS, 66 PAC, 125 PACG, and 58 APAC). Customized software was used to measure ASOCT parameters, including angle opening distance (AOD750), trabecular-iris space area (TISA750), anterior chamber depth, width, area and volume (ACD, ACW, ACA, ACV), iris thickness (IT750), iris area (IAREA), and lens vault (LV). Mean differences in anterior segment parameters were evaluated by analysis of covariance (ANCOVA) adjusted for age, sex, and pupil diameter (PD). RESULTS Comparison among the different subgroups showed that ACD, ACA, and ACV were smallest, and IT750 thickest in the APAC group compared with the other subgroups (P < 0.001). LV was greatest in the APAC group (1218 ± 34 μm) followed by PAC (860 ± 31 μm), PACG (845 ± 23 μm), and PACS (804 ± 19 μm), respectively (P = <0.001). While the APAC group had the narrowest angles, the PACS group had the widest (P < 0.001 for both AOD750 and TISA750). Logistic regression showed that greater LV (P = <0.001), narrower TISA750 (P = <0.001), and thicker IT750 (P = 0.007) were the major determinants of APAC. CONCLUSIONS Eyes with APAC had the narrowest angles, smallest anterior segment dimensions, thickest iris, and largest LV compared with PACS, PAC, and PACG. LV, TISA750, and IT750 were the major determinants of APAC.

Evolving generalized Voronoi diagrams for accurate cellular image segmentation

Analyzing cellular morphologies on a cell‐by‐cell basis is vital for drug discovery, cell biology, and many other biological studies. Interactions between cells in their culture environments cause cells to touch each other in acquired microscopy images. Because of this phenomenon, cell segmentation is a challenging task, especially when the cells are of similar brightness and of highly variable shapes. The concept of topological dependence and the maximum common boundary (MCB) algorithm are presented in our previous work (Yu et al., Cytometry Part A 2009;75A:289–297). However, the MCB algorithm suffers a few shortcomings, such as low computational efficiency and difficulties in generalizing to higher dimensions. To overcome these limitations, we present the evolving generalized Voronoi diagram (EGVD) algorithm. Utilizing image intensity and geometric information, EGVD preserves topological dependence easily in both 2D and 3D images, such that touching cells can be segmented satisfactorily. A systematic comparison with other methods demonstrates that EGVD is accurate and much more efficient.

Subgrouping of Primary Angle-Closure Suspects Based on Anterior Segment Optical Coherence Tomography Parameters

PURPOSE To identify subgroups of primary angle-closure suspects (PACS) based on anterior segment optical coherence tomography (AS-OCT) and biometric parameters. DESIGN Cross-sectional study. PARTICIPANTS We evaluated 243 PACS subjects in the primary group and 165 subjects in the validation group. METHODS Participants underwent gonioscopy and AS-OCT (Carl Zeiss Meditec, Dublin, CA). Customized software (Zhongshan Angle Assessment Program, Guangzhou, China) was used to measure AS-OCT parameters. An agglomerative hierarchical clustering method was first used to determine the optimum number of parameters to be included in the determination of subgroups. The best number of subgroups was then determined using Akaike Information Criterion (AIC) and Gaussian Mixture Model (GMM) methods. MAIN OUTCOME MEASURES Subgroups of PACS. RESULTS The mean age of the subjects was 64.8 years, and 65.02% were female. After hierarchical clustering, 1 or 2 parameters from each cluster were chosen to ensure representativeness of the parameters and yet keep a minimum of redundancy. The parameters included were iris area, anterior chamber depth (ACD), anterior chamber width (ACW), and lens vault (LV). With the use of GMM, the optimal number of subgroups as given by AIC was 3. Subgroup 1 was characterized by a large iris area, subgroup 2 was characterized by a large LV and a shallow ACD, and subgroup 3 was characterized by elements of both subgroups 1 and 2. The results were replicated in a second independent group of 165 PACS subjects. CONCLUSIONS Clustering analysis identified 3 distinct subgroups of PACS subjects based on AS-OCT and biometric parameters. These findings may be relevant for understanding angle-closure pathogenesis and management.

Mapping the Monte Carlo scheme to Langevin dynamics: a Fokker-Planck approach.

We propose a general method of using the Fokker-Planck equation (FPE) to link the Monte Carlo (MC) and the Langevin micromagnetic schemes. We derive the drift and diffusion FPE terms corresponding to the MC method and show that it is analytically equivalent to the stochastic Landau-Lifshitz-Gilbert (LLG) equation of Langevin-based micromagnetics. Subsequent results such as the time-quantification factor for the Metropolis MC method can be rigorously derived from this mapping equivalence. The validity of the mapping is shown by the close numerical convergence between the MC method and the LLG equation for the case of a single magnetic particle as well as interacting arrays of particles. We also find that our Metropolis MC method is accurate for a large range of damping factors alpha, unlike previous time-quantified MC methods which break down at low alpha, where precessional motion dominates.

Nuclear import of a lipid-modified transcription factor: mobilization of NFAT5 isoform a by osmotic stress.

Lipid-modified transcription factors (TFs) are biomolecular oddities since their reduced mobility and membrane attachment appear to contradict nuclear import required for their gene-regulatory function. NFAT5 isoform a (selected from an in silico screen for predicted lipid-modified TFs) is shown to contribute about half of all endogenous expression of human NFAT5 isoforms in the isotonic state. Wild-type NFAT5a protein is indeed myristoylated and palmitoylated on its transport to the plasmalemma via the endoplasmic reticulum and the Golgi. In contrast, its lipid anchor-deficient mutants as well as isoforms NFAT5b/c are diffusely localized in the cytoplasm without preference to vesicular structures. Quantitative/live microscopy shows the plasmamembrane-bound fraction of NFAT5a moving into the nucleus upon osmotic stress despite the lipid anchoring. The mobilization mechanism is not based on proteolytic processing of the lipid-anchored N-terminus but appears to involve reversible palmitoylation. Thus, NFAT5a is an example of TFs immobilized with lipid anchors at cyotoplasmic membranes in the resting state and that, nevertheless, can translocate into the nucleus upon signal induction.

Detection of meibomian glands and classification of meibography images.

Computational methods are presented that can automatically detect the length and width of meibomian glands imaged by infrared meibography without requiring any input from the user. The images are then automatically classified. The length of the glands are detected by first normalizing the pixel intensity, extracting stationary points, and then applying morphological operations. Gland widths are detected using scale invariant feature transform and analyzed using Shannon entropy. Features based on the gland lengths and widths are then used to train a linear classifier to accurately differentiate between healthy (specificity 96.1%) and unhealthy (sensitivity 97.9%) meibography images. The user-free computational method is fast, does not suffer from inter-observer variability, and can be useful in clinical studies where large number of images needs to be analyzed efficiently.

How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health.

AbstractThe currently hyped expectation of personalized medicine is often associated with just achieving the information technology led integration of biomolecular sequencing, expression and histopathological bioimaging data with clinical records at the individual patients’ level as if the significant biomedical conclusions would be its more or less mandatory result. It remains a sad fact that many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known and, thus, most of the molecular and cellular data cannot be interpreted in terms of biomedically relevant conclusions. Whereas the historical trend will certainly be into the general direction of personalized diagnostics and cures, the temperate view suggests that biomedical applications that rely either on the comparison of biomolecular sequences and/or on the already known biomolecular mechanisms have much greater chances to enter clinical practice soon. In addition to considering the general trends, we exemplarily review advances in the area of cancer biomarker discovery, in the clinically relevant characterization of patient-specific viral and bacterial pathogens (with emphasis on drug selection for influenza and enterohemorrhagic E. coli) as well as progress in the automated assessment of histopathological images. As molecular and cellular data analysis will become instrumental for achieving desirable clinical outcomes, the role of bioinformatics and computational biology approaches will dramatically grow.Author summaryWith DNA sequencing and computers becoming increasingly cheap and accessible to the layman, the idea of integrating biomolecular and clinical patient data seems to become a realistic, short-term option that will lead to patient-specific diagnostics and treatment design for many diseases such as cancer, metabolic disorders, inherited conditions, etc. These hyped expectations will fail since many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known yet and, thus, most of the molecular and cellular data collected will not lead to biomedically relevant conclusions. At the same time, less spectacular biomedical applications based on biomolecular sequence comparison and/or known biomolecular mechanisms have the potential to unfold enormous potential for healthcare and public health. Since the analysis of heterogeneous biomolecular data in context with clinical data will be increasingly critical, the role of bioinformatics and computational biology will grow correspondingly in this process.