Washington State University

About: Washington State University is a education organization based out in Pullman, Washington, United States. It is known for research contribution in the topics: Population & Gene. The organization has 26947 authors who have published 57736 publications receiving 2341509 citations. The organization is also known as: WSU & Wazzu.

Altered Titin Expression, Myocardial Stiffness, and Left Ventricular Function in Patients With Dilated Cardiomyopathy

Abstract: Background—The role of the giant protein titin in patients with heart failure is not well established. We investigated titin expression in patients with end-stage heart failure resulting from nonischemic dilated cardiomyopathy, in particular as it relates to left ventricular (LV) myocardial stiffness and LV function. Methods and Results—SDS-agarose gels revealed small N2B (stiff) and large N2BA (compliant) cardiac titin isoforms with a mean N2BA:N2B expression ratio that was significantly (P0.003) increased in 20 heart failure patients versus 6 controls. However, total titin was unchanged. The coexpression ratio was highest in a subsample of patients with an impaired LV relaxation pattern (n7), intermediate in those with pseudonormal filling (n6), and lowest in the group with restrictive filling (n7). Mechanical measurements on LV muscle strips dissected from these hearts (n8) revealed that passive muscle stiffness was significantly reduced in patients with a high N2BA:N2B expression ratio. Clinical correlations support the relevance of these changes for LV function (assessed by invasive hemodynamics and Doppler echocardiography). A positive correlation between the N2BA:N2B titin isoform ratio and deceleration time of mitral E velocity, A wave transit time, and end diastolic volume/pressure ratio was found. These changes affect exercise tolerance, as indicated by the positive correlation between the N2BA:N2B isoform ratio and peak O2 consumption (n10). Upregulated N2BA expression was accompanied by increased expression levels of titin-binding proteins (cardiac ankyrin repeat protein, ankrd2, and diabetes ankyrin repeat protein) that bind to the N2A element of N2BA titin (studied in 13 patients). Conclusions—Total titin content was unchanged in end-stage failing hearts and the more compliant N2BA isoform comprised a greater percentage of titin in these hearts. Changes in titin isoform expression in heart failure patients with dilated cardiomyopathy significantly impact diastolic filling by lowering myocardial stiffness. Upregulation of titin-binding proteins indicates that the importance of altered titin expression might extend to cell signaling and regulation of gene expression. (Circulation. 2004;110:155-162.)

Aquatic therapy: scientific foundations and clinical rehabilitation applications

Abstract: The aquatic environment has broad rehabilitative potential, extending from the treatment of acute injuries through health maintenance in the face of chronic diseases, yet it remains an underused modality. There is an extensive research base supporting aquatic therapy, both within the basic science literature and clinical literature. This article describes the many physiologic changes that occur during immersion as applied to a range of common rehabilitative issues and problems. Because of its wide margin of therapeutic safety and clinical adaptability, aquatic therapy is a very useful tool in the rehabilitative toolbox. Through a better understanding of the applied physiology, the practitioner may structure appropriate therapeutic programs for a diverse patient population.

Cyclization Enzymes in the Biosynthesis of Monoterpenes, Sesquiterpenes, and Diterpenes

Abstract: Terpene synthases catalyze the first committed steps in the biosynthesis of monoterpenes, sesquiterpenes, and diterpenes. An overview is presented of the enzymology and mechanism of these terpene synthases, and their molecular cloning, expression, and sequence analysis. Detailed structural and functional evaluation of four representative monoterpene, sesquiterpene, and diterpene synthases is also presented.

Large-Scale Learnable Graph Convolutional Networks

Abstract: Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches.