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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.

Key Points Question Can machine-learning approaches predict opioid overdose risk among fee-for-service Medicare beneficiaries? Findings In this prognostic study of the administrative claims data of 560 057 Medicare beneficiaries, the deep neural network and gradient boosting machine models outperformed other methods for identifying risk, although positive predictive values were low given the low prevalence of overdose episodes. Meaning Machine-learning algorithms using administrative data appear to be a valuable and feasible tool for more accurate identification of opioid overdose risk., Importance Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. Objective To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. Design, Setting, and Participants A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. Exposures Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. Main Outcomes and Measures Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator–type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. Results Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. Conclusions and Relevance Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose., This prognostic study evaluates the use of machine-learning methods, with prescription drug and claims data, in detecting opioid overdose risk in Medicare beneficiaries with at least 1 opioid prescription.

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.

Sickle cell disease (SCD) is caused by a mutation in the β-globin gene resulting in a disease that affects more than 100,000 Americans and millions worldwide. Though pain is the hallmark of SCD, patients also suffer damage to most organ systems. Sickle cell hemoglobin (HbS) polymerization occurs when deoxygenated, rendering red blood cells rigid and fragile. Production of excessive reactive oxygen species (ROS) and intracellular hypoxia in RBCs further accelerates the pathology associated with SCD. Recently, vaso-occlusive crisis (VOC) and organ damage were established to be strongly associated with oxidative stress in RBCs. This occurs when there is an increase in oxidants that exceeds the cellular anti-oxidant defenses. Excessive ROS can trigger a cascade of oxidative reactions that damage membrane lipids, and essential enzymatic antioxidants such as GPx-1, ultimately leading to hemolysis and multi-organ dysfunction. ROS generation in RBCs of SCD patients is due to factors such as HbS auto-oxidation and potentially aberrant mitochondrial function. We recently determined that red blood cells obtained from SCD mice and SCD patients retain their mitochondria compared to control subjects. Mitochondria retained SCD RBCs are also associated with elevated levels of ROS and hemolysis. Oxidative stress in the RBCs of SCD patients may be elevated by lower levels of antioxidant proteins such as the selenium-dependent enzyme GPX1. GPX1 was first described as an enzyme capable of protecting hemoglobin from ROS and has been reported to be lower in the RBCs in SCD. Selenium levels are lower among African Americans in the Chicago area and elsewhere. In this regard, it is notable that in the United States, African Americans represent the majority of those with SCD. To investigate the relationship between selenium levels and SCD, we have utilized a mouse model of SCD to examine the impact of a reduced intake of selenium on parameters associated with SCD pathology. SCD mice on a selenium-deficient diet ( Disclosures Lavelle: Global Blood therapeutics: Research Funding.

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 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.

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.

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.

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].

Ablation of microRNA synthesis by deletion of the microRNA-processing enzyme Dicer has demonstrated that microRNAs are necessary for normal hematopoietic differentiation and function. However, it is still unclear which specific microRNAs are required for hematopoiesis and at what developmental stages they are necessary. This is especially true for immune cell development. We previously observed that overexpression of the products of the mirn23a gene (microRNA-23a, -24-2, and 27a) in hematopoietic progenitors increased myelopoiesis with a reciprocal decrease in B lymphopoiesis, both in vivo and in vitro. In this study, we generated a microRNA-23a, -24-2, and 27a germline knockout mouse to determine whether microRNA-23a, -24-2, and 27a expression was essential for immune cell development. Characterization of hematopoiesis in microRNA-23a, -24-2, and 27a−/− mice revealed a significant increase in B lymphocytes in both the bone marrow and the spleen, with a concomitant decrease in myeloid cells (monocytes/granulocytes). Analysis of the bone marrow progenitor populations revealed a significant increase in common lymphoid progenitors and a significant decrease in both bone marrow common myeloid progenitors and granulocyte monocyte progenitors. Gene-expression analysis of primary hematopoietic progenitors and multipotent erythroid myeloid lymphoid cells showed that microRNA-23a, -24-2, and 27a regulates essential B cell gene-expression networks. Overexpression of microRNA-24-2 target Tribbles homolog 3 can recapitulate the microRNA-23a, -24-2, and 27a−/− phenotype in vitro, suggesting that increased B cell development in microRNA-23a, -24-2, and 27a null mice can be partially explained by a Tribbles homolog 3-dependent mechanism. Data from microRNA-23a, -24-2, and 27a−/− mice support a critical role for this microRNA cluster in regulating immune cell populations through repression of B lymphopoiesis.

CAPriCORN, the Chicago Area Patient Centered Outcomes Research Network, is one of the eleven PCORI-funded Clinical Data Research Networks. A collaboration of six academic medical centers, a Chicago public hospital, two VA hospitals and a network of federally qualified health centers, CAPriCORN addresses the needs of a diverse community and overlapping populations. To capture complete medical records without compromising patient privacy and confidentiality, the network created policies and mechanisms for patient consultation, central IRB approval, de-identification, de-duplication, and integration of patient data by study cohort, randomization and sampling, re-identification for consent by providers and patients, and communication with patients to elicit patient-reported outcomes through validated instruments. The paper describes these policies and mechanisms and discusses two case studies to prove the feasibility and effectiveness of the network.

We propose a novel model selection method for a nonparametric extension of the Cox proportional hazard model, in the framework of smoothing splines ANOVA models. The method automates the model building and model selection processes simultaneously by penalizing the reproducing kernel Hilbert space norms. On the basis of a reformulation of the penalized partial likelihood, we propose an efficient algorithm to compute the estimate. The solution demonstrates great flexibility and easy interpretability in modeling relative risk functions for censored data. Adaptive choice of the smoothing parameter is discussed. Both simulations and a real example suggest that our proposal is a useful tool for multivariate function estimation and model selection in survival analysis.

This article proposes the multiclass proximal support vector machine (MPSVM) classifier, which extends the binary PSVM to the multiclass case. Unlike the one-versus-rest approach that constructs the decision rule based on multiple binary classification tasks, the proposed method considers all classes simultaneously and has better theoretical properties and empirical performance. We formulate the MPSVM as a regularization problem in the reproducing kernel Hilbert space and show that it implements the Bayes rule for classification. In addition, the MPSVM can handle equal and unequal misclassification costs in a unified framework. We suggest an efficient algorithm to implement the MPSVM by solving a system of linear equations. This algorithm requires much less computational effort than solving the standard SVM, which often requires quadratic programming and can be slow for large problems. We also provide an alternative and more robust algorithm for ill-posed problems. The effectiveness of the MPSVM is demons...

This article presents a nonparametric penalized likelihood approach for variable selection and model building, called likelihood basis pursuit (LBP). In the setting of a tensor product reproducing kernel Hilbert space, we decompose the log-likelihood into the sum of different functional components such as main effects and interactions, with each component represented by appropriate basis functions. Basis functions are chosen to be compatible with variable selection and model building in the context of a smoothing spline ANOVA model. Basis pursuit is applied to obtain the optimal decomposition in terms of having the smallest l1 norm on the coefficients. We use the functional L1 norm to measure the importance of each component and determine the “threshold” value by a sequential Monte Carlo bootstrap test algorithm. As a generalized LASSO-type method, LBP produces shrinkage estimates for the coefficients, which greatly facilitates the variable selection process and provides highly interpretable multivariate ...