Finding a resveratrol analogue as potential anticancer agent with apoptosis and cycle arrest.

Resveratrol has been extensively studied as the anti-cancer agent. A variety of resveratrol analogues have been developed with structural modification to improve its bioactivity. In this work, resveratrol analogues, compound 1-4, were designed and synthesized with the Stille-Heck reaction. These results showed compound 1-4 had better anticancer effect than that of parent resveratrol. Especially compound 1 ((E)-4,4'-(ethene-1,2-diyl)bis(3-methylphenol)) displayed the excellent cytotoxicity and high selectivity. The mechanism research indicated compound 1 inhibited cell proliferation by binary paths of cell cycle arrest in S phase regulated by cyclin A1/A2 and apoptosis induction mediated by Bax/Bcl2 in a prooxidant manner.

Lasso adjustments of treatment effect estimates in randomized experiments.

We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting the difference of means between treatment and control groups. Their aim is to reduce the variance of the estimated treatment effect by adjusting for covariates. If there are a large number of covariates relative to the number of observations, regression may perform poorly because of overfitting. In such cases, the least absolute shrinkage and selection operator (Lasso) may be helpful. We study the resulting Lasso-based treatment effect estimator under the Neyman-Rubin model of randomized experiments. We present theoretical conditions that guarantee that the estimator is more efficient than the simple difference-of-means estimator, and we provide a conservative estimator of the asymptotic variance, which can yield tighter confidence intervals than the difference-of-means estimator. Simulation and data examples show that Lasso-based adjustment can be advantageous even when the number of covariates is less than the number of observations. Specifically, a variant using Lasso for selection and ordinary least squares (OLS) for estimation performs particularly well, and it chooses a smoothing parameter based on combined performance of Lasso and OLS.

ORACLE INEQUALITIES FOR THE LASSO IN THE COX MODEL.

We study the absolute penalized maximum partial likelihood estimator in sparse, high-dimensional Cox proportional hazards regression models where the number of time-dependent covariates can be larger than the sample size. We establish oracle inequalities based on natural extensions of the compatibility and cone invertibility factors of the Hessian matrix at the true regression coefficients. Similar results based on an extension of the restricted eigenvalue can be also proved by our method. However, the presented oracle inequalities are sharper since the compatibility and cone invertibility factors are always greater than the corresponding restricted eigenvalue. In the Cox regression model, the Hessian matrix is based on time-dependent covariates in censored risk sets, so that the compatibility and cone invertibility factors, and the restricted eigenvalue as well, are random variables even when they are evaluated for the Hessian at the true regression coefficients. Under mild conditions, we prove that these quantities are bounded from below by positive constants for time-dependent covariates, including cases where the number of covariates is of greater order than the sample size. Consequently, the compatibility and cone invertibility factors can be treated as positive constants in our oracle inequalities.

A comparison of bias-corrected covariance estimators for generalized estimating equations.

Although asymptotically the sandwich covariance estimator is consistent and robust with respect to the selection of the working correlation matrix, when the sample size is small, its bias may not be negligible. This article compares the small sample corrections for the sandwich covariance estimator as well as the inferential procedures proposed by Mancl and DeRouen ( 2001 ), Kauermann and Carroll ( 2001 ), Fay and Graubard ( 2001 ), and Fan et al. ( 2012 ). Simulation studies show that when using a maximum likelihood method to estimate the covariance parameters and using the between-within method for the denominator degrees of freedom when making inference, the Kauermann and Carroll method is preferred in the investigated balanced logistic regression and the Mancl and DeRouen and Fan et al. methods are preferred in the investigated proportional odds model. A collagen-induced arthritis study is employed to demonstrate the application of the methods.

Robust small-sample inference for fixed effects in general Gaussian linear models.

Although asymptotically, the empirical covariance estimator is consistent and robust with respect to the selection of the working correlation matrix, when the sample size is small, its bias may not be negligible. This article proposes a small sample correction for the empirical covariance estimator in general Gaussian linear models. Inference for the fixed effects based on the corrected covariance matrix is also derived. A two-way analysis of variance (ANOVA) model with repeated measures, which evaluates the effectiveness of a CB1 receptor antagonist, and a four-period crossover design, which assesses the treatment effect in subjects with intermittent claudication, serve as examples to illustrate the proposed and other investigated methods. Simulation studies show that the proposed method generally performs better than other bias-correction methods, including Mancl and DeRouen (2001), Kauermann and Carroll (2001), and Fay and Graubard (2001), in the investigated balanced designs.

On sample size of the kruskal-wallis test with application to a mouse peritoneal cavity study.

As the nonparametric generalization of the one-way analysis of variance model, the Kruskal-Wallis test applies when the goal is to test the difference between multiple samples and the underlying population distributions are nonnormal or unknown. Although the Kruskal-Wallis test has been widely used for data analysis, power and sample size methods for this test have been investigated to a much lesser extent. This article proposes new power and sample size calculation methods for the Kruskal-Wallis test based on the pilot study in either a completely nonparametric model or a semiparametric location model. No assumption is made on the shape of the underlying population distributions. Simulation results show that, in terms of sample size calculation for the Kruskal-Wallis test, the proposed methods are more reliable and preferable to some more traditional methods. A mouse peritoneal cavity study is used to demonstrate the application of the methods.

The Sparse Laplacian Shrinkage Estimator for High-Dimensional Regression.

We propose a new penalized method for variable selection and estimation that explicitly incorporates the correlation patterns among predictors. This method is based on a combination of the minimax concave penalty and Laplacian quadratic associated with a graph as the penalty function. We call it the sparse Laplacian shrinkage (SLS) method. The SLS uses the minimax concave penalty for encouraging sparsity and Laplacian quadratic penalty for promoting smoothness among coefficients associated with the correlated predictors. The SLS has a generalized grouping property with respect to the graph represented by the Laplacian quadratic. We show that the SLS possesses an oracle property in the sense that it is selection consistent and equal to the oracle Laplacian shrinkage estimator with high probability. This result holds in sparse, high-dimensional settings with p ≫ n under reasonable conditions. We derive a coordinate descent algorithm for computing the SLS estimates. Simulation studies are conducted to evaluate the performance of the SLS method and a real data example is used to illustrate its application.

Finding an efficient tetramethylated hydroxydiethylene of resveratrol analogue for potential anticancer agent.

With the improvement and advance in cancer diagnosis and treatment, the cancer is still a major cause of morbidity and mortality throughout the world. Obviously, new breakthroughs in therapies remain be urgent needed. In this work, we designed and synthesized the compound 1-4, namely resveratrol analogues with methylation of hydroxy distyrene, to further explore its new anti-cancer potential. Encouragingly, compound 1 ((E)-4,4'-(ethene-1,2-diyl)bis(3,5-dimethylphenol)) exhibited cytotoxicity superior to resveratrol in MCF 7 cells. More importantly, the compound 1 showed greater toxicity to tumor cells than that to normal cells, which proved that it could selectively kill tumor cells. The favorable results encouraged us to explore the inhibitory mechanism of compound 1 on MCF 7 cells. The research finding indicated the compound 1 inhibited tumor cell proliferation by both arresting cell cycle in S phase and apoptosis via a prooxidant manner. In addition, the results further verified compound 1 caused cell cycle arrest in S phase and apoptosis by down-regulation of the cycling A1/cycling A2 expression and the rise of Bax/Bcl-2 ratio in a p21-dependant pathway in MCF 7 cells. Therefore, these results are helpful for the effective design of anticancer reagents and the better understanding of their mechanism of action.

Rapid three-dimensional functional magnetic resonance imaging of the initial negative BOLD response.

Functional MRI is most commonly used to study the local changes in blood flow that accompanies neuronal activity. In this work we introduce a new approach towards acquiring and analyzing fMRI data that instead provides the potential to study the initial oxygen consumption in the brain that accompanies activation. As the oxygen consumption is closer in timing to the underlying neuronal activity than the subsequent blood flow, this approach promises to provide more precise information about the location and timing of activity. Our approach is based on using a new single shot 3D echo-volumar imaging sequence which samples a small central region of 3D k-space every 100ms, thereby giving a low spatial resolution snapshot of the brain with extremely high temporal resolution. Explicit and simple rules for implementing the trajectory are provided, together with a straightforward reconstruction algorithm. Using our approach allows us to effectively study the behavior of the brain in the time immediately following activation through the initial negative BOLD response, and we discuss new techniques for detecting the presence of the negative response across the brain. The feasibility and efficiency of the approach is confirmed using data from a visual-motor task and an auditory-motor-visual task. The results of these experiments provide a proof of concept of our methodology, and indicate that rapid imaging of the initial negative BOLD response can serve an important role in studying cognition tasks involving rapid mental processing in more than one region.

A near-infrared fluorescent probe for direct and selective detection of cysteine over homocysteine and glutathione.

In this work, we have designed and synthesized the fluorescent probe 1, which showed a highly selective and sensitive response to Cys over Hcy/GSH in the test. Moreover, the color of probe solution has changed dramatically from colorless to pink with the addition of Cys within 10 min. Meanwhile, the fluorescence intensity exhibited perfectly positive correlation with concentration of Cys from 0 to 200 µM, which offered the important condition for quantitative analysis. Finally, the bioimaging and fluorescence response of probe 1 for fetal calf serum are a powerful safeguard for practical detection of Cys. Therefore, this near-infrared probe will be of great benefit for detecting Cys in the biological systems.

What is the real size of a sampled network? The case of the Internet.

Most data concerning the topology of complex networks are the result of mapping projects which bear intrinsic limitations and cannot give access to complete, unbiased datasets. A particularly interesting case is represented by the physical Internet. Router-level Internet mapping projects generally consist of sampling the network from a limited set of sources by using traceroute probes. This methodology, akin to the merging of spanning trees from the different sources to a set of destinations, leads necessarily to a partial, incomplete map of the Internet. The determination of the real Internet topology characteristics from such sampled maps is therefore, in part, a problem of statistical inference. In this paper we present a twofold contribution in order to address this problem. First, we argue that inference of some of the standard topological quantities is, in fact, a version of the so-called "species" problem in statistics, which is important in categorizing the problem and providing some indication of its inherent difficulties. Second, we tackle the issue of estimating arguably the most basic of network characteristics-its number of nodes-and propose two estimators for this quantity, based on subsampling principles. Numerical simulations, as well as an experiment based on probing the Internet, suggest the feasibility of accounting for measurement bias in reporting Internet topology characteristics.

A generalization of the two-dimensional prolate spheroidal wave function method for nonrectilinear MRI data acquisition methods.

The two-dimensional (2-D) prolate spheroidal wave function (2-D PSWF) method was previously introduced as an efficient method for trading off between spatial and temporal resolution in magnetic resonance imaging (MRI), with minimal penalty due to truncation and partial volume effects. In the 2-D PSWF method, the k-space sampling area and a matching 2-D PSWF filter, with optimal signal concentration and minimal truncation artifacts, are determined by the shape and size of a given convex region of interest (ROI). The spatial information in the reduced k-space data is used to calculate the total image intensity over a nonsquare ROI instead of producing a low-resolution image. This method can be used for tracking dynamic signals from non-square ROIs using a reduced k-space sampling area, while achieving minimal signal leakage. However, the previous theory is limited to the case of rectilinear sampling. In order to make the 2-D PSWF method more suitable for dynamic studies, this paper presents a generalized version of the 2-D PSWF theory that can be applied to nonrectilinear data acquisition methods. The method is applied to an fMRI study using a spiral trajectory, which illustrates the methods efficiency at tracking hemodynamic signals with high temporal resolution.

Two dimensional prolate spheroidal wave functions for MRI.

The tradeoff between spatial and temporal resolution is often used to increase data acquisition speed for dynamic MR imaging. Reduction of the k-space sampling area, however, leads to stronger partial volume and truncation effects. A two dimensional prolate spheroidal wave function (2D-PSWF) method is developed to address these problems. Utilizing prior knowledge of a given region of interest (ROI) and the spatial resolution requirement as constraints, this method tailors the k-space sampling area with a matching 2D-PSWF filter so that optimal signal concentration and minimal truncation artifacts are achieved. The k-space sampling area is reduced because the shape and size of the sampling area match the resolution posed by the non-rectangular shape of a convex ROI. The 2D-PSWF method offers an efficient way for spatial and temporal tradeoff with minimal penalty due to truncation, and thus, it promises a wide range of applications in MRI research.

Mixed group ranks: preference and confidence in classifier combination.

Classifier combination holds the potential of improving performance by combining the results of multiple classifers. For domains with very large numbers of classes, such as biometrics, we present an axiomatic framework of desirable mathematical properties for combination functions of rank-based classifiers. This framework represents a continuum of combination rules, including the Borda Count, Logistic Regression, and Highest Rank combination methods as extreme cases [11], [23], [4], [13]. Intuitively, this framework captures how the two complementary concepts of general preference for specific classifiers and the confidence it has in any specific result (as indicated by ranks) can be balanced while maintaining consistent rank interpretation. Mixed Group Ranks (MGR) is a new combination function that balances preference and confidence by generalizing these other functions. We demonstrate that MGR is an effective combination approach by performing multiple experiments on data sets with large numbers of classes and classifiers from the FERET face recognition study.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications.

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results.

A group bridge approach for variable selection.

In multiple regression problems when covariates can be naturally grouped, it is important to carry out feature selection at the group and within-group individual variable levels simultaneously. The existing methods, including the lasso and group lasso, are designed for either variable selection or group selection, but not for both. We propose a group bridge approach that is capable of simultaneous selection at both the group and within-group individual variable levels. The proposed approach is a penalized regularization method that uses a specially designed group bridge penalty. It has the oracle group selection property, in that it can correctly select important groups with probability converging to one. In contrast, the group lasso and group least angle regression methods in general do not possess such an oracle property in group selection. Simulation studies indicate that the group bridge has superior performance in group and individual variable selection relative to several existing methods.

Nonparametric methods for measurements below detection limit.

Analytical data are often subject to left-censoring when the actual values to be quantified fall below the limit of detection. The primary interest of this paper is statistical inference for the two-sample problem. Most of the current publications are centered around naive approaches or the parametric Tobit model approach. These methods may not be suitable for data with high censoring rates and relatively small sample sizes. In this paper, we establish the theoretical equivalence of three nonparametric methods: the Wilcoxon rank sum, the Gehan, and the Peto-Peto tests, under fixed left-censoring and other mild conditions. We then develop a nonparametric point and interval estimation procedure for the location shift model. A large set of simulations compares 14 methods including naive, parametric, and nonparametric methods. The results clearly favor the nonparametric methods for a range of sample sizes and censoring rates. Simulations also demonstrate satisfactory point and interval estimation results. Finally, a real data example is given followed by discussion.