Inward rectifier potassium (Kir) channels play important roles in the maintenance and control of cell excitability. Both intracellular trafficking and modulation of Kir channel activity are regulated by protein-protein interactions. We adopted a proteomics approach to identify proteins associated with Kir2 channels via the channel C-terminal PDZ binding motif. Detergent-solubilized rat brain an...

Multidimensional Scaling analysis (MDS) and Multivariate of variance (MANOVA) are among the commonly used multivariate statistical methods. While MANOVA is to evaluate whether there statistically significant differences between mean vectors experimental groups in terms more than one independent variable; MDS both for dimension reduction classify individuals/variables according their differences...

A maximum likelihood estimation method is proposed to fit an asymmetric multidimensional scaling model to a set of asymmetric data. This method is based on successive categories scaling, and enables us to analyze asymmetric proximity data measured, at least, at an ordinal scale level. It enables us to examine not only the appropriate scaling level of the data, but also the appropriate dimension...

Geodesic distance matrices can reveal shape properties that are largely invariant to non-rigid deformations, and thus are often used to analyze and represent 3-D shapes. However, these matrices grow quadratically with the number of points. Thus for large point sets it is common to use a low-rank approximation to the distance matrix, which fits in memory and can be efficiently analyzed using met...

In the past decade there has been a resurgence of interest in nonlinear dimension reduction. Among new proposals are “Local Linear Embedding” (LLE, Roweis and Saul 2000), “Isomap” (Tenenbaum et al. 2000) and Kernel PCA (KPCA, Schölkopf, Smola and Müller 1998), which all construct global lowdimensional embeddings from local affine or metric information. We introduce a competing method called “Lo...

Asymmetric cluster analysis is one of the most useful methods together with asymmetric multidimensional scaling (MDS) to analyze asymmetric (dis)similarity data. In both methods, visualization of the result of the analysis plays an important role in the analysis. Some methods for visualizing the result of the asymmetric clustering and MDS have been proposed (Saito and Yadohisa, Data Analysis of...

Adults consistently make errors in solving simple multiplication problems. These errors have been explained with reference to the interference between similar problems. In this paper, we apply multidimensional scaling (MDS) to the domain of multiplication problems, to uncover their underlying similarity structure. A tree-sorting task was used to obtain perceived dissimilarity ratings. The deriv...

We present an online system for automatic smart face morphing, which can be accessed at http://facewarping.com. This system morphs the user-uploaded image to “beautify” it by embedding features from a user-selected celebrity. This system consists of three major modules: facial feature point detection, geometry embedding, and image warping. To embed the features of a celebrity face and at the sa...

Many previous studies have examined the ease with which two spatially adjacent textures can be segmented. Our goal is to examine the representational system that determines the appearance of isolated patches of visual texture. To this end, similarity judgments from three subjects were obtained for 20 artificial textures comprising filtered noise. Multidimensional scaling (MDS) revealed that thr...

In this paper we present and evaluate two semantic music mood models relying on metadata extracted from over 180,000 production music tracks sourced from I Like Music (ILM)’s collection. We performed non-metric multidimensional scaling (MDS) analyses of mood stem dissimilarity matrices (1 to 13 dimensions) and devised five different mood tag summarisation methods to map tracks in the dimensiona...