Can myocardial contrast echo provide incremental benefit to exercise echo? Implications of the intensity and duration of hyperaemia

Background. Stress myocardial contrast echo (MCE) is technically challenging with exercise (Ex) because of cardiacmovementandshort duration ofhyperemia.Vasodilators solve these limitations, but are less potent for inducing abnormal wall motion (WM). We sought whether a combined dipyridamole (DI; 0.56 mg/kg i.v. 4 min) and Ex stress protocol would enable MCE to provide incremental benefit toWManalysis for detection of CAD. Methods. Standard echo images were followed by real time MCE at rest and following stress in 85 pts, 70 undergoing quantitative coronary angiography and 15 low risk pts.WMAfrom standard and LVopacification images, and then myocardial perfusion were assessed sequentially in a blinded fashion. A subgroup of 13 pts also underwent Ex alone, to assess the contribution of DI to quantitative myocardial flow reserve (MFR). Results. Significant (>50%) stenoses were present in 43 pts, involving 69 territories. Addition of MCE improved SE sensitivity for detection of CAD (91% versus 74%, P = 0.02) and better appreciation of disease extent (87% versus 65%territories, P=0.003), with a non-significant reduction in specificity. In 55 territories subtended by a significant stenosis, but with no resting WM abnormality, ability to identify ischemia was also significantly increased by MCE (82% versus 60%, P = 0.002). MFR was less with Ex alone than with DIEx stress (2.4 ± 1.6 versus 4.0 ± 1.9, P = 0.05), suggesting prolongation of hyperaemia with DI may be essential to the results. Conclusions. Dipyridamole-exercise MCE adds significant incremental benefit to standard SE, with improved diagnostic sensitivity and more accurate estimation of extent of CAD.

Efficient spatial clustering algorithm using binary tree

In this paper we present an efficient k-Means clustering algorithm for two dimensional data. The proposed algorithm re-organizes dataset into a form of nested binary tree*. Data items are compared at each node with only two nearest means with respect to each dimension and assigned to the one that has the closer mean. The main intuition of our research is as follows: We build the nested binary tree. Then we scan the data in raster order by in-order traversal of the tree. Lastly we compare data item at each node to the only two nearest means to assign the value to the intendant cluster. In this way we are able to save the computational cost significantly by reducing the number of comparisons with means and also by the least use to Euclidian distance formula. Our results showed that our method can perform clustering operation much faster than the classical ones. © Springer-Verlag Berlin Heidelberg 2005

Incremental model transformation for the evolution of model-driven systems

Model transformations are an integral part of model-driven development. Incremental updates are a key execution scenario for transformations in model-based systems, and are especially important for the evolution of such systems. This paper presents a strategy for the incremental maintenance of declarative, rule-based transformation executions. The strategy involves recording dependencies of the transformation execution on information from source models and from the transformation definition. Changes to the source models or the transformation itself can then be directly mapped to their effects on transformation execution, allowing changes to target models to be computed efficiently. This particular approach has many benefits. It supports changes to both source models and transformation definitions, it can be applied to incomplete transformation executions, and a priori knowledge of volatility can be used to further increase the efficiency of change propagation.

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

Incremental derivation of abstraction relations for data refinements

Data refinements are refinement steps in which a program’s local data structures are changed. Data refinement proof obligations require the software designer to find an abstraction relation that relates the states of the original and new program. In this paper we describe an algorithm that helps a designer find an abstraction relation for a proposed refinement. Given sufficient time and space, the algorithm can find a minimal abstraction relation, and thus show that the refinement holds. As it executes, the algorithm displays mappings that cannot be in any abstraction relation. When the algorithm is not given sufficient resources to terminate, these mappings can help the designer find a suitable abstraction relation. The same algorithm can be used to test an abstraction relation supplied by the designer.