Shu-Kay Ng

The EM Algorithm

The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood estimates in a wide variety of incomplete-data problems. The EM algorithm has a number of desirable properties, such as its numerical stability, reliable global convergence, and simplicity of implementation. There are, however, two main drawbacks of the basic EM algorithm – lack of an in-built procedure to compute the covariance matrix of the parameter estimates and slow convergence. In addition, some complex problems lead to intractable Expectation-steps and Maximization-steps. The first edition of the book chapter published in 2004 covered the basic theoretical framework of the EM algorithm and discussed further extensions of the EM algorithm to handle complex problems. The second edition attempts to capture advanced developments in EM methodology in recent years, especially in its applications to the related fields of biomedical and health sciences.

Prolotherapy injections and eccentric loading exercises for painful Achilles tendinosis: a randomised trial

Objective To compare the effectiveness and cost-effectiveness of eccentric loading exercises (ELE) with prolotherapy injections used singly and in combination for painful Achilles tendinosis. Design A single-blinded randomised clinical trial. The primary outcome measure was the VISA-A questionnaire with a minimum clinically important change (MCIC) of 20 points. Setting Five Australian primary care centres. Participants 43 patients with painful mid-portion Achilles tendinosis commenced and 40 completed treatment protocols. Interventions Participants were randomised to a 12-week program of ELE (n=15), or prolotherapy injections of hypertonic glucose with lignocaine alongside the affected tendon (n=14) or combined treatment (n=14). Main outcome measurements VISA-A, pain, stiffness and limitation of activity scores; treatment costs. Results At 12 months, proportions achieving the MCIC for VISA-A were 73% for ELE, 79% for prolotherapy and 86% for combined treatment. Mean (95% CI) increases in VISA-A scores at 12 months were 23.7 (15.6 to 31.9) for ELE, 27.5 (12.8 to 42.2) for prolotherapy and 41.1 (29.3 to 52.9) for combined treatment. At 6 weeks and 12 months, these increases were significantly less for ELE than for combined treatment. Compared with ELE, reductions in stiffness and limitation of activity occurred earlier with prolotherapy and reductions in pain, stiffness and limitation of activity occurred earlier with combined treatment. Combined treatment had the lowest incremental cost per additional responder (

Selenium binding protein 1 in ovarian cancer

A1539) compared with ELE. Conclusions For Achilles tendinosis, prolotherapy and particularly ELE combined with prolotherapy give more rapid improvements in symptoms than ELE alone but long-term VISA-A scores are similar. Trial registration number ACTRN: 12606000179538

Environments for Healthy Living (EFHL) Griffith birth cohort study: background and methods.

Selenium binding protein 1 (SELENBP1) was identified to be the most significantly down‐regulated protein in ovarian cancer cells by a membrane proteome profiling analysis. SELENBP1 expression levels in 4 normal ovaries, 8 benign ovarian tumors, 12 borderline ovarian tumors and 141 invasive ovarian cancers were analyzed with immunohistochemical assay. SELENBP1 expression was reduced in 87% cases of invasive ovarian cancer (122/141) and was significantly reduced in borderline tumors and invasive cancers (p < 0.001). Cox multivariate analysis within the 141 invasive cancer tissues showed that SELENBP1 expression score was a potential prognostic indicator for unfavorable prognosis of ovarian cancer (hazard ratio [HR], 2.18; 95% CI = 1.22–3.90; p = 0.009). Selenium can disrupt the androgen pathway, which has been implicated in modulating SELENBP1 expression. We investigated the effects of selenium and androgen on normal human ovarian surface epithelial (HOSE) cells and cancer cells. Interestingly, SELENBP1 mRNA and protein levels were reduced by androgen and elevated by selenium treatment in the normal HOSE cells, whereas reversed responses were observed in the ovarian cancer cell lines. These results suggest that changes of SELENBP1 expression in malignant ovarian cancer are an indicator of aberration of selenium/androgen pathways and may reveal prognostic information of ovarian cancer.

Casein kinase I epsilon interacts with mitochondrial proteins for the growth and survival of human ovarian cancer cells

The health of an individual is determined by the interaction of genetic and individual factors with wider social and environmental elements. Public health approaches to improving the health of disadvantaged populations will be most effective if they optimise influences at each of these levels, particularly in the early part of the life course. In order to better ascertain the relative contribution of these multi-level determinants there is a need for robust studies, longitudinal and prospective in nature, that examine individual, familial, social and environmental exposures. This paper describes the study background and methods, as it has been implemented in an Australian birth cohort study, Environments for Healthy Living (EFHL): The Griffith Study of Population Health. EFHL is a prospective, multi-level, multi-year longitudinal birth cohort study, designed to collect information from before birth through to adulthood across a spectrum of eco-epidemiological factors, including genetic material from cord-blood samples at birth, individual and familial factors, to spatial data on the living environment. EFHL commenced the pilot phase of recruitment in 2006 and open recruitment in 2007, with a target sample size of 4000 mother/infant dyads. Detailed information on each participant is obtained at birth, 12-months, 3-years, 5-years and subsequent three to five yearly intervals. The findings of this research will provide detailed evidence on the relative contribution of multi-level determinants of health, which can be used to inform social policy and intervention strategies that will facilitate healthy behaviours and choices across sub-populations.

Characterization of aldehyde dehydrogenase isozymes in ovarian cancer tissues and sphere cultures

Epithelial ovarian cancer is the leading cause of death among gynaecologic cancers in Western countries. Our studies have shown that casein kinase I‐epsilon (CKIε), a Wnt pathway protein, is significantly overexpressed in ovarian cancer tissues and is associated with poor survival. Ectopic expression of CKIε in normal human ovarian surface epithelial cells and inhibition of CKIε in ovarian cancer cells and in xenografts demonstrated the importance of CKIε in regulating cell proliferation and migration. Interestingly, CKIε function did not seem to involve β‐catenin activity. Instead, CKIε was found to interact with several mitochondrial proteins including adenine nucleotide translocase 2 (ANT2). Inhibition of CKIε in ovarian cancer cells resulted in suppression of ANT2, downregulation of cellular ATP and the resulting cancer cells were more susceptible to chemotherapy. Our studies indicate that, in the context of ovarian cancer, the interaction between CKIε and ANT2 mediates pathogenic signalling that is distinct from the canonical Wnt/β‐catenin pathway and is essential for cell proliferation and is clinically associated with poor survival.

On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures

BackgroundAldehyde dehydrogenases belong to a superfamily of detoxifying enzymes that protect cells from carcinogenic aldehydes. Of the superfamily, ALDH1A1 has gained most attention because current studies have shown that its expression is associated with human cancer stem cells. However, ALDH1A1 is only one of the 19 human ALDH subfamilies currently known. The purpose of the present study was to determine if the expression and activities of other major ALDH isozymes are associated with human ovarian cancer and ovarian cancer sphere cultures.MethodsImmunohistochemistry was used to delineate ALDH isozyme localization in clinical ovarian tissues. Western Blot analyses were performed on lysates prepared from cancer cell lines and ovarian cancer spheres to confirm the immunohistochemistry findings. Quantitative reverse transcription-polymerase chain reactions were used to measure the mRNA expression levels. The Aldefluor® assay was used to measure ALDH activity in cancer cells from the four tumor subtypes.ResultsImmunohistochemical staining showed significant overexpression of ALDH1A3, ALDH3A2, and ALDH7A1 isozymes in ovarian tumors relative to normal ovarian tissues. The expression and activity of ALDH1A1 is tumor type-dependent, as seen from immunohistochemisty, Western blot analysis, and the Aldefluor® assay. The expression was elevated in the mucinous and endometrioid ovarian epithelial tumors than in serous and clear cell tumors. In some serous and most clear cell tumors, ALDH1A1 expression was found in the stromal fibroblasts. RNA expression of all studied ALDH isozymes also showed higher expression in endometrioid and mucinous tumors than in the serous and clear cell subtypes. The expression of ALDH enzymes showed tumor type-dependent induction in ovarian cancer cells growing as sphere suspensions in serum-free medium.ConclusionsThe results of our study indicate that ALDH enzyme expression and activity may be associated with specific cell types in ovarian tumor tissues and vary according to cell states. Elucidating the function of the ALDH isozymes in lineage differentiation and pathogenesis may have significant implications for ovarian cancer pathophysiology.

Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data.

The EM algorithm is a popular method for parameter estimation in situations where the data can be viewed as being incomplete. As each E-step visits each data point on a given iteration, the EM algorithm requires considerable computation time in its application to large data sets. Two versions, the incremental EM (IEM) algorithm and a sparse version of the EM algorithm, were proposed recently by Neal R.M. and Hinton G.E. in Jordan M.I. (Ed.), Learning in Graphical Models, Kluwer, Dordrecht, 1998, pp. 355–368 to reduce the computational cost of applying the EM algorithm. With the IEM algorithm, the available n observations are divided into B (B ≤ n) blocks and the E-step is implemented for only a block of observations at a time before the next M-step is performed. With the sparse version of the EM algorithm for the fitting of mixture models, only those posterior probabilities of component membership of the mixture that are above a specified threshold are updated; the remaining component-posterior probabilities are held fixed. In this paper, simulations are performed to assess the relative performances of the IEM algorithm with various number of blocks and the standard EM algorithm. In particular, we propose a simple rule for choosing the number of blocks with the IEM algorithm. For the IEM algorithm in the extreme case of one observation per block, we provide efficient updating formulas, which avoid the direct calculation of the inverses and determinants of the component-covariance matrices. Moreover, a sparse version of the IEM algorithm (SPIEM) is formulated by combining the sparse E-step of the EM algorithm and the partial E-step of the IEM algorithm. This SPIEM algorithm can further reduce the computation time of the IEM algorithm.

Environments For Healthy Living (EFHL) Griffith birth cohort study: characteristics of sample and profile of antenatal exposures

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

C-terminal binding protein-2 regulates response of epithelial ovarian cancer cells to histone deacetylase inhibitors

BackgroundThe Environments for Healthy Living (EFHL) study is a repeated sample, longitudinal birth cohort in South East Queensland, Australia. We describe the sample characteristics and profile of maternal, household, and antenatal exposures. Variation and data stability over recruitment years were examined.MethodsFour months each year from 2006, pregnant women were recruited to EFHL at routine antenatal visits on or after 24 weeks gestation, from three public maternity hospitals. Participating mothers completed a baseline questionnaire on individual, familial, social and community exposure factors. Perinatal data were extracted from hospital birth records. Descriptive statistics and measures of association were calculated comparing the EFHL birth sample with regional and national reference populations. Data stability of antenatal exposure factors was assessed across five recruitment years (2006–2010 inclusive) using the Gamma statistic for ordinal data and chi-squared for nominal data.ResultsAcross five recruitment years 2,879 pregnant women were recruited which resulted in 2904 live births with 29 sets of twins. EFHL has a lower representation of early gestational babies, fewer still births and a lower percentage of low birth weight babies, when compared to regional data. The majority of women (65%) took a multivitamin supplement during pregnancy, 47% consumed alcohol, and 26% reported having smoked cigarettes. There were no differences in rates of a range of antenatal exposures across five years of recruitment, with the exception of increasing maternal pre-pregnancy weight (p=0.0349), decreasing rates of high maternal distress (p=0.0191) and decreasing alcohol consumption (p<0.0001).ConclusionsThe study sample is broadly representative of births in the region and almost all factors showed data stability over time. This study, with repeated sampling of birth cohorts over multiple years, has the potential to make important contributions to population health through evaluating longitudinal follow-up and within cohort temporal effects.Trial registrationAustralian and New Zealand Clinical Trials Registry ACTRN12610000931077