Expression of the counter-regulatory peptide intermedin is augmented in the presence of oxidative stress in hypertrophied cardiomyocytes.

Background: Intermedin (IMD), a novel cardiac peptide related to adrenomedullin (AM), protects against myocardial ischemia-reperfusion injury and attenuates ventricular remodelling. IMD’s actions are mediated by a calcitonin receptor-like receptor in association with receptor activity modifying proteins (RAMPs 1-3). Aim/method: using the spontaneously hypertensive rat (SHR) and normotensive Wistar Kyoto (WKY) rat at 20 weeks of age, to examine (i) the presence of myocardial oxidative stress and concentric hypertrophy; (ii) expression of IMD, AM and receptor components. Results: In left and right ventricular cardiomyocytes from SHR vs. WKY cell width (26% left, 15% right) and mRNA expression of hypertrophic markers ANP (2.7 fold left, 2.7 fold right) and BNP (2.2 fold left, 2.0 fold right) were enhanced. In left ventricular cardiomyocytes only (i) oxidative stress was indicated by increased membrane protein carbonyl content (71%) and augmented production of O2- anion (64%); (ii) IMD (6.8 fold), RAMP1 (2.5 fold) and RAMP3 (2.0 fold) mRNA was increased while AM and RAMP2 mRNA was not altered; (iii) abundance of RAMP1 (by 48%), RAMP2 (by 41%) and RAMP3 (by 90%) monomers in cell membranes was decreased. Conclusion: robust augmentation of IMD expression in hypertrophied left ventricular cardiomyocytes indicates a prominent role for this counter-regulatory peptide in the adaptation of the SHR myocardium to the stresses imposed by chronic hypertension. The local concentration and action of IMD may be further enhanced by down-regulation of NEP within the left ventricle.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

A cross-sectional study demonstrating increased serum amyloid A-related inflammation in high density lipoproteins from subjects with type 1 diabetes mellitus and how this association was augmented by poor glycaemic control

Inflammatory atherosclerosis is increased in subjects with type 1 diabetes mellitus (T1DM). Normally high-density lipoproteins(HDL) protect against atherosclerosis; however, in the presence of serum amyloid-A- (SAA-) related inflammation this propertymay be reduced. Fasting blood was obtained from fifty subjects with T1DM, together with fifty age, gender and BMI matchedcontrol subjects. HDL was subfractionated into HDL2 and HDL3 by rapid ultracentrifugation. Serum-hsCRP and serum-, HDL2-,and HDL3-SAA were measured by ELISAs. Compared to control subjects, SAA was increased in T1DM subjects, nonsignificantly inserum (P = 0.088), and significantly in HDL2 (P  = 0.003) and HDL3 (P  = 0.005). When the T1DM group were separated accordingto mean HbA1c (8.34%), serum-SAA and HDL3-SAA levels were higher in the T1DM subjects with HbA1c ≥ 8.34%, compared towhen HbA1c was <8.34% (P  < 0.05). Furthermore, regression analysis illustrated, that for every 1%-unit increase in HbA1c, SAAincreased by 20% and 23% in HDL2 and HDL3, respectively, independent of BMI. HsCRP did not differ between groups (P  > 0.05).This cross-sectional study demonstrated increased SAA-related inflammation in subjects with T1DM that was augmented by poorglycaemic control. We suggest that SAA is a useful inflammatory biomarker in T1DM, which may contribute to their increasedatherosclerosis risk.

Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.

Sonification as Concurrent Augmented Feedback for Motor Skill Learning and the Importance of Mapping Design

In recent years, sonification of movement has emerged as a viable method for the provision of feedback in motor learning. Despite some experimental validation of its utility, controlled trials to test the usefulness of sonification in a motor learning context are still rare. As such, there are no accepted conventions for dealing with its implementation. This article addresses the question of how continuous movement information should be best presented as sound to be fed back to the learner. It is proposed that to establish effective approaches to using sonification in this context, consideration must be given to the processes that underlie motor learning, in particular the nature of the perceptual information available to the learner for performing the task at hand. Although sonification has much potential in movement performance enhancement, this potential is largely unrealised as of yet, in part due to the lack of a clear framework for sonification mapping: the relationship between movement and sound. By grounding mapping decisions in a firmer understanding of how perceptual information guides learning, and an embodied cognition stance in general, it is hoped that greater advances in use of sonification to enhance motor learning can be achieved.

Averaged Extended Tree Augmented Naive Classifier

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.

Learning Extended Tree Augmented Naive Structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.