Chrysophanol is an anthraquinone compound, which exhibits anticancer effects on certain types of cancer cells. However, the effects of chrysophanol on human breast cancer remain to be elucidated. The aim of the present study was to clarify the role of chrysophanol on breast cancer cell lines MCF-7 and MDA-MB-231, and to identify the signal transduction pathways regulated by chrysophanol. MTT assay and flow cytometric analysis demonstrated that chrysophanol inhibited cell proliferation, and cell cycle progression in a dose-dependent manner. The expression of cell cycle-associated cyclin D1 and cyclin E were downregulated while p27 expression was upregulated following chrysophanol treatment at the mRNA, and protein levels. The Annexin V/propidium iodide staining assay results revealed that apoptosis levels increased following chrysophanol treatment. Chrysophanol upregulated caspase 3 and poly (ADP-ribose) polymerase cleavage in both cell lines. Furthermore, chrysophanol enhanced the effect of paclitaxel on breast cancer cell apoptosis. In addition, chrysophanol downregulated apoptosis regulator Bcl-2 protein, and transcription factor p65 and IκB phosphorylation. Inhbition of nuclear factor (NF)-κB by ammonium pyrrolidine dithiocarbamate diminished the effect of chrysophanol on apoptosis and associated proteins. In conclusion, the results of the current study demonstrated that chrysophanol effectively suppresses breast cancer cell proliferation and facilitates chemosentivity through modulation of the NF-κB signaling pathway.

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The paper is concerned with empirical Bayes shrinkage estimators for the heteroscedastic hierarchical normal model based on Stein's unbiased estimate of risk (SURE).Recently, Xie, Kou and Brown (2012) proposed a class of estimators for this type of problems and established their asymptotic optimality properties under the assumption of known but unequal variances. In this paper, we consider this problem with unequal and unknown variances, which may be more appropriate in real situations. Specifically, by putting priors for both means and variances, we propose novel double-shrinkage SURE type estimators that shrink both the means and the variances. Optimal properties for these estimators are derived under certain regularity conditions. We also conduct extensive simulation studies to compare the newly developed methodologies with other shrinkage techniques. Finally, the methods are applied to the well known baseball data set and a gene expression data set.

In the present paper, we consider the high dimensional classification problem, which has become much important in many modern statistical studies and applications. We develop new classifiers based on Fisher's linear classification rule and empirical Bayes. In particular, we propose to employ the Stein's unbiased risk estimate (SURE) to estimate the sparse or non‐sparse mean difference, which could be plugged into the linear classification rules. Using simulation studies under a variety of settings, we demonstrate that our classifiers perform well especially when the features are non‐sparse. We also illustrate the use of the new proposal to classification problems in some real data examples.

In many applications, the parameters of interest are estimated by solving non‐smooth estimating functions with U‐statistic structure. Because the asymptotic covariances matrix of the estimator generally involves the underlying density function, resampling methods are often used to bypass the difficulty of non‐parametric density estimation. Despite its simplicity, the resultant‐covariance matrix estimator depends on the nature of resampling, and the method can be time‐consuming when the number of replications is large. Furthermore, the inferences are based on the normal approximation that may not be accurate for practical sample sizes. In this paper, we propose a jackknife empirical likelihood‐based inferential procedure for non‐smooth estimating functions. Standard chi‐square distributions are used to calculate the p‐value and to construct confidence intervals. Extensive simulation studies and two real examples are provided to illustrate its practical utilities.