Jeng-Min Chiou

Arabidopsis SUMO E3 ligase SIZ1 is involved in excess copper tolerance.

The reversible conjugation of the small ubiquitin-like modifier (SUMO) to protein substrates occurs as a posttranslational regulatory process in eukaryotic organisms. In Arabidopsis (Arabidopsis thaliana), several stress-responsive SUMO conjugations are mediated mainly by the SUMO E3 ligase SIZ1. In this study, we observed a phenotype of hypersensitivity to excess copper in the siz1-2 and siz1-3 mutants. Excess copper can stimulate the accumulation of SUMO1 conjugates in wild-type plants but not in the siz1 mutant. Copper accumulated to a higher level in the aerial parts of soil-grown plants in the siz1 mutant than in the wild type. A dramatic difference in copper distribution was also observed between siz1 and wild-type Arabidopsis treated with excess copper. As a result, the shoot-to-root ratio of copper concentration in siz1 is nearly twice as high as that in the wild type. We have found that copper-induced Sumoylation is involved in the gene regulation of metal transporters YELLOW STRIPE-LIKE 1 (YSL1) and YSL3, as the siz1 mutant is unable to down-regulate the expression of YSL1 and YSL3 under excess copper stress. The hypersensitivity to excess copper and anomalous distribution of copper observed in the siz1 mutant are greatly diminished in the siz1ysl3 double mutant and slightly in the siz1ysl1 double mutant. These data suggest that SIZ1-mediated sumoylation is involved specifically in copper homeostasis and tolerance in planta.

Diagnostics for functional regression via residual processes

Methods of regression diagnostics for functional regression models are developed which relate a functional response to predictor variables that can be multivariate vectors or random functions. For this purpose, a residual process is defined by subtracting the predicted from the observed response functions. This residual process is expanded into functional principal components (FPC), and the corresponding FPC scores are used as natural proxies for the residuals in functional regression models. For the case of a univariate covariate, a randomization test is proposed based on these scores to examine if the residual process depends on the covariate. If this is the case, it indicates lack of fit of the model. Graphical methods based on the FPC scores of observed and fitted functions can be used to complement more formal tests. The methods are illustrated with data from a recent study of Drosophila fruit flies regarding life-cycle gene expression trajectories as well as functional data from a dose-response experiment for Mediterranean fruit flies (Ceratitis capitata).

Quasi-Likelihood Regression with Unknown Link and Variance Functions

Abstract We consider the multiple regression model E(y) = μ, μ = g(x T β), var(y) — [sgrave]2(μ) with predictors x, link function g, and variance function [sgrave]2(·). The aim is to reduce the assumptions in a fully parametric generalized linear model or a quasi-likelihood model by allowing the link and the variance functions to be unknown but smooth. These functions are then estimated nonparametrically, and the estimates are substituted into the quasi-likelihood function. We propose a three-stage approach to identify this semiparametric model by estimating the link function, the variance function, and the vector of regression coefficients in the linear predictor of the model. Consistency results for the link and the variance function estimators, as well as the asymptotic limiting distribution of the regression coefficients, are obtained. We show that the resulting parameter estimates are asymptotically efficient, as compared to the quasi-likelihood parameter estimates obtained for the case where link an...

Functional quasi-likelihood regression models with smooth random effects

Summary. We propose a class of semiparametric functional regression models to describe the influence of vector-valued covariates on a sample of response curves. Each observed curve is viewed as the realization of a random process, composed of an overall mean function and random components. The finite dimensional covariates influence the random components of the eigenfunction expansion through single-index models that include unknown smooth link and variance functions. The parametric components of the single-index models are estimated via quasi-score estimating equations with link and variance functions being estimated nonparametrically. We obtain several basic asymptotic results. The functional regression models proposed are illustrated with the analysis of a data set consisting of egg laying curves for 1000 female Mediterranean fruit-flies (medflies).

A Randomised Placebo-Controlled Trial of a Traditional Chinese Herbal Formula in the Treatment of Primary Dysmenorrhoea

Background Most traditional Chinese herbal formulas consist of at least four herbs. Four-Agents-Decoction (Si Wu Tang) is a documented eight hundred year old formula containing four herbs and has been widely used to relieve menstrual discomfort in Taiwan. However, no specific effect had been systematically evaluated. We applied Western methodology to assess its effectiveness and safety for primary dysmenorrhoea and to evaluate the compliance and feasibility for a future trial. Methodology/Principal Findings A randomised, double-blind, placebo-controlled, pilot clinical trial was conducted in an ad hoc clinic setting at a teaching hospital in Taipei, Taiwan. Seventy-eight primary dysmenorrheic young women were enrolled after 326 women with self-reported menstrual discomfort in the Taipei metropolitan area of Taiwan were screened by a questionnaire and subsequently diagnosed by two gynaecologists concurrently with pelvic ultrasonography. A dosage of 15 odorless capsules daily for five days starting from the onset of bleeding or pain was administered. Participants were followed with two to four cycles for an initial washout interval, one to two baseline cycles, three to four treatment cycles, and three follow-up cycles. Study outcome was pain intensity measured by using unmarked horizontal visual analog pain scale in an online daily diary submitted directly by the participants for 5 days starting from the onset of bleeding or pain of each menstrual cycle. Overall-pain was the average pain intensity among days in pain and peak-pain was the maximal single-day pain intensity. At the end of treatment, both the overall-pain and peak-pain decreased in the Four-Agents-Decoction (Si Wu Tang) group and increased in the placebo group; however, the differences between the two groups were not statistically significant. The trends persisted to follow-up phase. Statistically significant differences in both peak-pain and overall-pain appeared in the first follow-up cycle, at which the reduced peak-pain in the Four-Agents-Decoction (Si Wu Tang) group did not differ significantly by treatment length. However, the reduced peak-pain did differ profoundly among women treated for four menstrual cycles (2.69 (2.06) cm, mean (standard deviation), for the 20 women with Four-Agents-Decoction and 4.68 (3.16) for the 22 women with placebo, p = .020.) There was no difference in adverse symptoms between the Four-Agents-Decoction (Si Wu Tang) and placebo groups. Conclusion/significance Four-Agents-Decoction (Si Wu Tang) therapy in this pilot post-market clinical trial, while meeting the standards of conventional medicine, showed no statistically significant difference in reducing menstrual pain intensity of primary dysmenorrhoea at the end of treatment. Its use, with our dosage regimen and treatment length, was not associated with adverse reactions. The finding of statistically significant pain-reducing effect in the first follow-up cycle was unexpected and warrants further study. A larger similar trial among primary dysmenorrheic young women with longer treatment phase and multiple batched study products can determine the definitive efficacy of this historically documented formula. Trial Registration Controlled-Trials.com ISRCTN23374750

Inferring gene expression dynamics via functional regression analysis

BackgroundTemporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other.ResultsWe demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles.ConclusionOur findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

Inhibition of hepatitis C virus replication by arsenic trioxide.

ABSTRACT Hepatitis C virus (HCV) is a serious global problem, and present therapeutics are inadequate to cure HCV infection. In the present study, various antiviral assays show that As2O3 at submicromolar concentrations is capable of inhibiting HCV replication. The 50% effective concentration (EC50) of As2O3 required to inhibit HCV replication was 0.35 μM when it was determined by a reporter-based HCV replication assay, and the EC50 was below 0.2 μM when it was determined by quantitative reverse transcription-PCR analysis. As2O3 did not cause cellular toxicity at this concentration, as revealed by an MTS [3-(4,5-dimethylthiozol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt] assay. A combination of As2O3 and alpha interferon exerted synergistic effects against HCV, as revealed by a multiple linear logistic model and isobologram analysis. Furthermore, in an alternative HCV antiviral system that may recapitulate additional steps involved in HCV infection and replication, As2O3 at 0.3 μM totally abolished the HCV signal, whereas alpha interferon at a high dose (5,000 IU/ml) only partially suppressed the HCV signal. The study highlights the indications for use of a novel class of anti-HCV agent. Further elucidation of the exact antiviral mechanism of As2O3 may lead to the development of agents with potent activities against HCV or related viruses.

Dynamical functional prediction and classification, with application to traffic flow prediction

Motivated by the need for accurate traffic flow prediction in transportation management, we propose a functional data method to analyze traffic flow patterns and predict future traffic flow. In this study we approach the problem by sampling traffic flow trajectories from a mixture of stochastic processes. The proposed functional mixture prediction approach combines functional prediction with probabilistic functional classification to take distinct traffic flow patterns into account. The probabilistic classification procedure, which incorporates functional clustering and discrimination, hinges on subspace projection. The proposed methods not only assist in predicting traffic flow trajectories, but also identify distinct patterns in daily traffic flow of typical temporal trends and variabilities. The proposed methodology is widely applicable in analysis and prediction of longitudinally recorded functional data.

Modeling Hazard Rates as Functional Data for the Analysis of Cohort Lifetables and Mortality Forecasting

As world populations age, the analysis of demographic mortality data and demographic predictions of future mortality have met with increasing interest. The study of mortality patterns and the forecasting of future mortality with its associated impacts on social welfare, health care, and societal planning has become a more pressing issue. An ideal set of data to study patterns of change in long-term mortality is the well-known historical Swedish cohort mortality data, because of its high quality and long span of more than two centuries. We explore the use of functional data analysis to model these data and to derive mortality forecasts. Specifically, we address the challenge of flexibly modeling these data while including the effect of the birth year by regarding log-hazard functions, derived from observed cohort lifetables, as random functions. A functional model for the analysis of these cohort log-hazard functions, extending functional principal component approaches by introducing time-varying eigenfunctions, is found to adequately address these challenges. The associated analysis of the dependency structure of the cohort log-hazard functions leads to the concept of time-varying principal components of mortality. We then extend this analysis to mortality forecasting, by combining prediction of incompletely observed log-hazard functions with functional local extrapolation, and demonstrate these functional approaches for the Swedish cohort mortality data.

Correlation-Based Functional Clustering via Subspace Projection

A correlation-based functional clustering method is proposed for grouping curves with similar shapes. A correlation between two random functions defined through the functional inner product is used as a similarity measure. Curves with similar shapes are embedded in the cluster subspace spanned by a mean shape function and eigenfunctions of the covariance kernel. The cluster membership prediction for each curve attempts to maximize the functional correlation between the observed and predicted curves via shape standardization and subspace projection among all possible clusters. The proposed method accounts for shape differentials through the functional multiplicative random-effects shape function model for each cluster, which regards random scales and intercept shifts as a nuisance. A consistent estimate is proposed for the random scale effect, whose sample variance estimate is also consistent. The derived identifiability conditions for the clustering procedure unravel the predictability of cluster memberships. Simulation studies and a real data example illustrate the proposed method.