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The design of nanoscale yet highly echogenic agents for imaging outside of the vasculature and for ultrasound-mediated drug delivery remains a formidable challenge. We have previously reported on formulation of echogenic perfluoropropane gas nanobubbles stabilized by a lipid-Pluronic surfactant shell. In the current work we describe the development of a new generation of these nanoparticles which consist of perfluoropropane gas stabilized by a surfactant and lipid membrane and a crosslinked network of N, N-diethylacrylamide. The resulting crosslinked nanobubbles (CL-PEG-NB) were 95.2 ± 25.2 nm in diameter and showed significant improvement in stability and retention of echogenic signal over 24 h. In vivo analysis via ultrasound and fluorescence mediated tomography showed greater tumor extravasation and accumulation with CL-PEG-NB compared to microbubbles. Together these results demonstrate the capabilities and advantages of a new, more stable, nanometer-scale ultrasound contrast agent that can be utilized in future work for diagnostic scans and molecular imaging.

Intraductal papillary mucinous neoplasms (IPMN) are pancreatic lesions with uncertain biologic behavior. This study sought objective, accurate prediction tools, through the use of quantitative histopathological signatures of nuclear images, for classifying lesions as chronic pancreatitis (CP), IPMN, or pancreatic carcinoma (PC). Forty‐four pancreatic resection patients were retrospectively identified for this study (12 CP; 16 IPMN; 16 PC). Regularized multinomial regression quantitatively classified each specimen as CP, IPMN, or PC in an automated, blinded fashion. Classification certainty was determined by subtracting the smallest classification probability from the largest probability (of the three groups). The certainty function varied from 1.0 (perfectly classified) to 0.0 (random). From each lesion, 180 ± 22 nuclei were imaged. Overall classification accuracy was 89.6% with six unique nuclear features. No CP cases were misclassified, 1/16 IPMN cases were misclassified, and 4/16 PC cases were misclassified. Certainty function was 0.75 ± 0.16 for correctly classified lesions and 0.47 ± 0.10 for incorrectly classified lesions (P = 0.0005). Uncertainty was identified in four of the five misclassified lesions. Quantitative histopathology provides a robust, novel method to distinguish among CP, IPMN, and PC with a quantitative measure of uncertainty. This may be useful when there is uncertainty in diagnosis.

Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covariate effects in a time-varying coefficient Cox model, i.e. covariates with null effect, constant effect and truly time-varying effect, and estimate the corresponding regression coefficients. Combining the ideas of local polynomial smoothing and group nonnegative garrote, we develop a new penalization approach to achieve such goals. Our method is able to identify the underlying true model structure with probability tending to one and simultaneously estimate the time-varying coefficients consistently. The asymptotic normalities of the resulting estimators are also established. We demonstrate the performance of our method using simulations and an application to the primary biliary cirrhosis data.

In analysis of longitudinal data, it is not uncommon that observation times of repeated measurements are subject-specific and correlated with underlying longitudinal outcomes. Taking account of the dependence between observation times and longitudinal outcomes is critical under these situations to assure the validity of statistical inference. In this article, we propose a flexible joint model for longitudinal data analysis in the presence of informative observation times. In particular, the new procedure considers the shared random-effect model and assume a time-varying coefficient for the latent variable, allowing a flexible way of modeling longitudinal outcomes while adjusting their association with observation times. Estimating equations are developed for parameter estimation. We show that the resulting estimators are consistent and asymptotically normal, with variance-covariance matrix that has a closed form and can be consistently estimated by the usual plug-in method. One additional advantage of the procedure is that, it provides a unified framework to test whether the effect of the latent variable is zero, constant, or time-varying. Simulation studies show that the proposed approach is appropriate for practical use. An application to a bladder cancer data is also given to illustrate the methodology.

Extensive baseline covariate information is routinely collected on participants in randomized clinical trials, and it is well recognized that a proper covariate-adjusted analysis can improve the efficiency of inference on the treatment effect. However, such covariate adjustment has engendered considerable controversy, as post hoc selection of covariates may involve subjectivity and may lead to biased inference, whereas prior specification of the adjustment may exclude important variables from consideration. Accordingly, how to select covariates objectively to gain maximal efficiency is of broad interest. We propose and study the use of modern variable selection methods for this purpose in the context of a semiparametric framework, under which variable selection in modeling the relationship between outcome and covariates is separated from estimation of the treatment effect, circumventing the potential for selection bias associated with standard analysis of covariance methods. We demonstrate that such objective variable selection techniques combined with this framework can identify key variables and lead to unbiased and efficient inference on the treatment effect. A critical issue in finite samples is validity of estimators of uncertainty, such as standard errors and confidence intervals for the treatment effect. We propose an approach to estimation of sampling variation of estimated treatment effect and show its superior performance relative to that of existing methods. Copyright © 2012 John Wiley & Sons, Ltd.

This study explores the impact of asking middle school students to generate drawings of their ideas about chemical reactions on integrated understanding. Students explored atomic interactions during hydrogen combustion using a dynamic visualization. The generation group drew their ideas about how the reaction takes place at the molecular level. The interaction group conducted multiple experiments with the visualization by varying the amount of energy provided to ignite the reaction. The generation group integrated more ideas about chemical reactions and made more precise interpretations of the visualization than the interaction group. Embedded assessments show that generation motivated students to interpret the visualization carefully and led to more productive explanations about ideas represented in the dynamic visualization. In contrast, the interaction group was less successful in linking the visualization to underlying concepts and observable phenomena and wrote less detailed explanations. The study suggests that drawing is a promising way to help students interpret complex visualizations and integrate information. 2011 Wiley Periodicals, Inc. J Res Sci Teach 48: 1177-1198, 2011

SummaryEfficient memory formation relies on the establishment of functional hippocampal circuits. It has been proposed that synaptic connections are refined by neural activity to form functional brain circuitry. However, it is not known whether and how hippocampal connections are refined by neural activity in vivo. Using a mouse genetic system in which restricted populations of neurons in the hippocampal circuit are inactivated, we show that inactive axons are eliminated after they develop through a competition with active axons. Remarkably, in the dentate gyrus, which undergoes neurogenesis throughout life, axon refinement is achieved by a competition between mature and young neurons. These results demonstrate that activity-dependent competition plays multiple roles in the establishment of functional memory circuits in vivo.

Margin-based classifiers have been popular in both machine learning and statistics for classification problems. Among numerous classifiers, some are hard classifiers while some are soft ones. Soft classifiers explicitly estimate the class conditional probabilities and then perform classification based on estimated probabilities. In contrast, hard classifiers directly target on the classification decision boundary without producing the probability estimation. These two types of classifiers are based on different philosophies and each has its own merits. In this paper, we propose a novel family of large-margin classifiers, namely large-margin unified machines (LUMs), which covers a broad range of margin-based classifiers including both hard and soft ones. By offering a natural bridge from soft to hard classification, the LUM provides a unified algorithm to fit various classifiers and hence a convenient platform to compare hard and soft classification. Both theoretical consistency and numerical performance of LUMs are explored. Our numerical study sheds some light on the choice between hard and soft classifiers in various classification problems.

We propose a fully Bayesian model with a non-informative prior for analyzing misclassified binary data with a validation substudy. In addition, we derive a closed-form algorithm for drawing all parameters from the posterior distribution and making statistical inference on odds ratios. Our algorithm draws each parameter from a beta distribution, avoids the specification of initial values, and does not have convergence issues. We apply the algorithm to a data set and compare the results with those obtained by other methods. Finally, the performance of our algorithm is assessed using simulation studies.

We study a general class of partially linear transformation models, which extend linear transformation models by incorporating nonlinear covariate effects in survival data analysis. A new martingale-based estimating equation approach, consisting of both global and kernel-weighted local estimation equations, is developed for estimating the parametric and nonparametric covariate effects in a unified manner. We show that with a proper choice of the kernel bandwidth parameter, one can obtain the consistent and asymptotically normal parameter estimates for the linear effects. Asymptotic properties of the estimated nonlinear effects are established as well. We further suggest a simple resampling method to estimate the asymptotic variance of the linear estimates and show its effectiveness. To facilitate the implementation of the new procedure, an iterative algorithm is developed. Numerical examples are given to illustrate the finite-sample performance of the procedure. Supplementary materials are available online.

Dentate granule cell (DGC) neurogenesis persists throughout life in the hippocampal dentate gyrus. In rodent temporal lobe epilepsy models, status epilepticus (SE) stimulates neurogenesis, but many newborn DGCs integrate aberrantly and are hyperexcitable, whereas others may integrate normally and restore inhibition. The overall influence of altered neurogenesis on epileptogenesis is therefore unclear. To better understand the role DGC neurogenesis plays in seizure-induced plasticity, we injected retroviral (RV) reporters to label dividing DGC progenitors at specific times before or after SE, or used x-irradiation to suppress neurogenesis. RV injections 7 weeks before SE to mark DGCs that had matured by the time of SE labeled cells with normal placement and morphology 4 weeks after SE. RV injections 2 or 4 weeks before seizure induction to label cells still developing during SE revealed normally located DGCs exhibiting hilar basal dendrites and mossy fiber sprouting (MFS) when observed 4 weeks after SE. Cells labeled by injecting RV after SE displayed hilar basal dendrites and ectopic migration, but not sprouting, at 28 d after SE; when examined 10 weeks after SE, however, these cells showed robust MFS. Eliminating cohorts of newborn DGCs by focal brain irradiation at specific times before or after SE decreased MFS or hilar ectopic DGCs, supporting the RV labeling results. These findings indicate that developing DGCs exhibit maturation-dependent vulnerability to SE, indicating that abnormal DGC plasticity derives exclusively from aberrantly developing DGCs. Treatments that restore normal DGC development after epileptogenic insults may therefore ameliorate epileptogenic network dysfunction and associated morbidities.

We propose and study a unified procedure for variable selection in partially linear models. A new type of double-penalized least squares is formulated, using the smoothing spline to estimate the nonparametric part and applying a shrinkage penalty on parametric components to achieve model parsimony. Theoretically we show that, with proper choices of the smoothing and regularization parameters, the proposed procedure can be as efficient as the oracle estimator [J. Fan, R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of American Statistical Association 96 (2001) 1348–1360]. We also study the asymptotic properties of the estimator when the number of parametric effects diverges with the sample size. Frequentist and Bayesian estimates of the covariance and confidence intervals are derived for the estimators. One great advantage of this procedure is its linear mixed model (LMM) representation, which greatly facilitates its implementation by using standard statistical software. Furthermore, the LMM framework enables one to treat the smoothing parameter as a variance component and hence conveniently estimate it together with other regression coefficients. Extensive numerical studies are conducted to demonstrate the effective performance of the proposed procedure.

This note proposes a new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables. The unknown parameters are determined by the first four cumulants of the quadratic forms. The proposed method is compared with Pearson's three-moment central @g^2 approximation approach, by means of numerical examples. Our method yields a better approximation to the distribution of the non-central quadratic forms than Pearson's method, particularly in the upper tail of the quadratic form, the tail most often needed in practical work.

We propose a new class of variable selection techniques for regression in high dimensional linear models based on a forward selection version of the LASSO, adaptive LASSO or elastic net, respectively to be called as forward iterative regression and shrinkage technique (FIRST), adaptive FIRST and elastic FIRST. These methods seem to work effectively for extremely sparse high dimensional linear models. We exploit the fact that the LASSO, adaptive LASSO and elastic net have closed form solutions when the predictor is onedimensional. The explicit formula is then repeatedly used in an iterative fashion to build the model until convergence occurs. By carefully considering the relationship between estimators at successive stages, we develop fast algorithms to compute our estimators. The performance of our new estimators are compared with commonly used estimators in terms of predictive accuracy and errors in variable selection. AMS 2000 subject classifications: Primary 62J05, 62J05; secondary 62J07.

Lysophosphatidic acid (LPA) is a ligand of multiple G protein–coupled receptors. The LPA1–3receptors are members of the endothelial cell differentiation gene (Edg) family. LPA4/p2y9/GPR23, a member of the purinergic receptor family, and recently identified LPA5/GPR92 and p2y5 are structurally distant from the canonical Edg LPA receptors. Here we report targeted disruption of lpa4in mice. Although LPA4-deficient mice displayed no apparent abnormalities, LPA4-deficient mouse embryonic fibroblasts (MEFs) were hypersensitive to LPA-induced cell migration. Consistent with negative modulation of the phosphatidylinositol 3 kinase pathway by LPA4, LPA4deficiency potentiated Akt and Rac but decreased Rho activation induced by LPA. Reconstitution of LPA4converted LPA4-negative cells into a less motile phenotype. In support of the biological relevance of these observations, ectopic expression of LPA4strongly inhibited migration and invasion of human cancer cells. When coexpressed with LPA1in B103 neuroblastoma cells devoid of endogenous LPA receptors, LPA4attenuated LPA1-driven migration and invasion, indicating functional antagonism between the two subtypes of LPA receptors. These results provide genetic and biochemical evidence that LPA4is a suppressor of LPA-dependent cell migration and invasion in contrast to the motility-stimulating Edg LPA receptors.

We investigate the variable selection problem for Cox's proportional hazards model, and propose a unified model selection and estimation procedure with desired theoretical properties and computational convenience. The new method is based on a penalized log partial likelihood with the adaptively weighted L 1 penalty on regression coefficients, providing what we call the adaptive Lasso estimator. The method incorporates different penalties for different coefficients: unimportant variables receive larger penalties than important ones, so that important variables tend to be retained in the selection process, whereas unimportant variables are more likely to be dropped. Theoretical properties, such as consistency and rate of convergence of the estimator, are studied. We also show that, with proper choice of regularization parameters, the proposed estimator has the oracle properties. The convex optimization nature of the method leads to an efficient algorithm. Both simulated and real examples show that the method performs competitively. Copyright 2007, Oxford University Press.

The standard support vector machine (SVM) minimizes the hinge loss function subject to the L"2 penalty or the roughness penalty. Recently, the L"1 SVM was suggested for variable selection by producing sparse solutions [Bradley, P., Mangasarian, O., 1998. Feature selection via concave minimization and support vector machines. In: Shavlik, J. (Ed.), ICML'98. Morgan Kaufmann, Los Altos, CA; Zhu, J., Hastie, T., Rosset, S., Tibshirani, R., 2003. 1-norm support vector machines. Neural Inform. Process. Systems 16]. These learning methods are non-adaptive since their penalty forms are pre-determined before looking at data, and they often perform well only in a certain type of situation. For instance, the L"2 SVM generally works well except when there are too many noise inputs, while the L"1 SVM is more preferred in the presence of many noise variables. In this article we propose and explore an adaptive learning procedure called the L"q SVM, where the best q>0 is automatically chosen by data. Both two- and multi-class classification problems are considered. We show that the new adaptive approach combines the benefit of a class of non-adaptive procedures and gives the best performance of this class across a variety of situations. Moreover, we observe that the proposed L"q penalty is more robust to noise variables than the L"1 and L"2 penalties. An iterative algorithm is suggested to solve the L"q SVM efficiently. Simulations and real data applications support the effectiveness of the proposed procedure.

In this paper we study the problem of variable selection for the proportional odds model, which is a useful alternative to the proportional hazards model and might be appropriate when the proportional hazards assumption is not satisfied. We propose to fit the proportional odds model by maximizing the marginal likelihood subject to a shrinkage-type penalty, which encourages sparse solutions and hence facilitates the process of variable selection. Two types of shrinkage penalties are considered: the LASSO and the adaptive-LASSO (ALASSO) penalty. In the ALASSO penalty, different weights are imposed on different coefficients such that important variables are more protectively retained in the final model while unimportant ones are more likely to be shrunk to zeros. We further provide an efficient computation algorithm to implement the proposed methods, and demonstrate their performance through simulation studies and an application to real data. Numerical results indicate that both methods can produce accurate and interpretable models, and the ALASSO tends to work better than the usual LASSO.

For a variety of reasons, journal articles may be republished in translation (although this cannot be detected by computerized text-matching tools such as CrossCheck). It may be, for example, that the original language of publication is unlikely to reach one part of the author’s intended readership. This raises the difficult question of whether or not this constitutes duplicate publication.

As an author in order to avoid plagiarism, the best way is learning and compliance with the basic principles of good academic from your academic career’s starting. And as signaled by the University of Oxford’s website ‘avoiding plagiarism is not simply a matter of making sure your references are all correct, or changing enough words so the examiner will not notice your paraphrase; it is about deploying your academic skills to make your work as good as it can be’.

As comments by Nature in 2010, ‘Major science publishers are gearing up to fight plagiarism… including Elsevier and Springer, are set to roll out software across their journals that will scan submitted papers for identical or paraphrased chunks of text that appear in previously published articles [1]’.

As this book has made clear, plagiarism is an offence against research integrity itself. So, in looking forward, let us consider how the whole culture of research integrity might be improved.

In our first survey (see Chap. 2), we noticed some significant differences in the responses to some of the questions between the subject areas of Biomedicine and Biotechnology, and Computers and Electronics. We therefore decided to conduct a further investigation focusing on these key differences in approach in these disciplinary fields.

Plagiarism is a form of academic dishonesty, and the importance of honesty in human society is not a new topic!

As mentioned in the Preface, in 2011 the author and her colleagues received a grant from the Committee on Publication Ethics (COPE) in support of a research study intended to develop evidence-based guidance for journal editors on how to deal with different kinds of plagiarism detected through the use of CrossCheck (COPE Research Grant in CrossCheck guidance: an analysis of typical cases of plagiarism in different disciplines, 2010). The first part of the research was a global survey of journal editors and publishers, the results of which were published in an article entitled ‘A survey on the use of Cross Check in detecting plagiarism in academic journals’, which appeared in Learned Publishing in 2012 (Zhang and Jia in Learned Publishing 25, 2012). The article is reproduced in its entirety (with the publisher’s permission) below.

There are now a variety of online tools available which can compare a submitted text with texts already in its database (or freely available online). The most widely used are CrossCheck, which is mainly used for texts in English, and AMLC (Academic Manuscript Literature Checking) from CNKI (China National Knowledge Infrastructure) that almost covers all contents of Chinese Integrated Knowledge Resources Database and is mainly used for texts in Chinese.

The republication of conference proceedings as journal articles seems to be particularly prevalent in the areas of Computing and Electrical and Electronic Engineering. Our own use of CrossCheck turned up a disproportionate amount of duplication of this type in these particular disciplines; we also noted in our previous survey (see Chap. 2) that the attitudes to this question of authors in this area were markedly different from those in other fields. We therefore felt that it would be useful to carry out a survey of the attitudes of the editors of relevant journals to this issue; we surveyed over 300 journals in the field, and the results were published in an article entitled ‘Republication of conference papers in journals?’, which is reproduced in full (with the publisher’s permission) below [1].