Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes

The Growing Neural Gas model is used widely in artificial neural networks. However, its application is limited in some contexts by the proliferation of nodes in dense areas of the input space. In this study, we introduce some modifications to address this problem by imposing three restrictions on the insertion of new nodes. Each restriction aims to maintain the homogeneous values of selected criteria. One criterion is related to the square error of classification and an alternative approach is proposed for avoiding additional computational costs. Three parameters are added that allow the regulation of the restriction criteria. The resulting algorithm allows models to be obtained that suit specific needs by specifying meaningful parameters.     

Posterior parietal cortex is critical for the encoding,consolidation, and retrieval of a memory that guides attention for learning

Within most contemporary learning theories, reinforcement prediction error, the difference between the obtained and expected reinforcer value, critically influences associative learning. In some theories, this prediction error determines the momentary effectiveness of the reinforcer itself, such that the same physical event produces more learning when its presentation is surprising than when it is expected. In other theories, prediction error enhances attention to potential cues for that reinforcer by adjusting cue‐specific associability parameters, biasing the processing of those stimuli so that they more readily enter into new associations in the future. A unique feature of these latter theories is that such alterations in stimulus associability must be represented in memory in an enduring fashion. Indeed, considerable data indicate that altered associability may be expressed days after its induction. Previous research from our laboratory identified brain circuit elements critical to the enhancement of stimulus associability by the omission of an expected event, and to the subsequent expression of that altered associability in more rapid learning. Here, for the first time, we identified a brain region, the posterior parietal cortex, as a potential site for a memorial representation of altered stimulus associability. In three experiments using rats and a serial prediction task, we found that intact posterior parietal cortex function was essential during the encoding, consolidation, and retrieval of an associability memory enhanced by surprising omissions. We discuss these new results in the context of our previous findings and additional plausible frontoparietal and subcortical networks.     

A new method for muscle fatigue assessment: Online model identification techniques

Introduction: The purpose of this study was to propose a method that allows extraction of the current muscle state under electrically induced fatigue. Methods: The triceps surae muscle of 5 subjects paralyzed by spinal cord injury was fatigued by intermittent electrical stimulation (5 × 5 trains at 30 Hz ). Classical fatigue indices representing muscle contractile properties [peak twitch (Pt) and half‐relaxation time (HRT)] were assessed before and after each 5‐train series and were used to identify 2 relevant parameters (F_m, U_r) of a previously developed mathematical model using the Sigma‐Point Kalman Filter. Results: Pt declined significantly during the protocol, whereas HRT remained unchanged. Identification of the model parameters with experimental data yielded a model‐based fatigue assessment that gave a more stable evaluation of fatigue than classical parameters. Conclusions: This work reinforces clinical research by providing a tool that clinicians can use to monitor fatigue development during stimulation. Muscle Nerve 50: 556–563, 2014     

长春瑞滨化疗性静脉炎动物模型的建立

目的探讨建立长春瑞滨化疗性静脉炎动物模型适宜的给药浓度,为化疗性静脉炎的发生机制研究提供实验基础。方法选择实验用健康家兔28只,采用随机数字表法分为四组各7只。均于一侧耳缘静脉注药建模。第1组注射生理盐水10mL作为阴性对照,其余3组将长春瑞滨用10mL生理盐水溶解,分别按12.5mg/m2、25mg/m2和50mg/m2剂量注射。结果长春瑞滨组静脉炎症反应出现于注射后24h,48h左右表现最显著,约于1周恢复。25mg/m2组、50mg/m2组注射后48h有典型的静脉炎病理变化,50mg/m2组4只家兔分别于药物注射后第7～10天死亡,25mg/m2组家兔无死亡。结论参照人体用量采用长春瑞滨25mg/m2于家兔耳缘静脉化疗,能较好建立化疗性静脉炎动物模型。     

Level‐adjusted funnel plots based on predicted marginal expectations: an application to prophylactic antibiotics in gallstone surgery

Funnel plots are widely used to visualize grouped data, for example, in institutional comparison. This paper extends the concept to a multi‐level setting, displaying one level at a time, adjusted for the other levels, as well as for covariates at all levels. These level‐adjusted funnel plots are based on a Markov chain Monte Carlo fit of a random effects model, translating the estimated model parameters to predicted marginal expectations. Working within the estimation framework, we accommodate outlying institutions using heavy‐tailed random effects distributions. We also develop computer‐efficient methods to compute predicted probabilities in the case of dichotomous outcome data and various random effect distributions. We apply the method to a data set on prophylactic antibiotics in gallstone surgery. Copyright © 2014 John Wiley & Sons, Ltd.     

Covariance adjustment for batch effect in gene expression data

Batch bias has been found in many microarray gene expression studies that involve multiple batches of samples. A serious batch effect can alter not only the distribution of individual genes but also the inter‐gene relationships. Even though some efforts have been made to remove such bias, there has been relatively less development on a multivariate approach, mainly because of the analytical difficulty due to the high‐dimensional nature of gene expression data. We propose a multivariate batch adjustment method that effectively eliminates inter‐gene batch effects. The proposed method utilizes high‐dimensional sparse covariance estimation based on a factor model and a hard thresholding. Another important aspect of the proposed method is that if it is known that one of the batches is produced in a superior condition, the other batches can be adjusted so that they resemble the target batch. We study high‐dimensional asymptotic properties of the proposed estimator and compare the performance of the proposed method with some popular existing methods with simulated data and gene expression data sets. Copyright © 2014 John Wiley & Sons, Ltd.