Syndecan-4 proteoglycan cytoplasmic domain and phosphatidylinositol 4,5-bisphosphate coordinately regulate protein kinase C activity

Phosphatidylinositol 4,5-bisphosphate (PIP2) is involved in the organization of the actin cytoskeleton by regulating actin-associated proteins. The transmembrane heparan sulfate proteoglycan syndecan-4 also plays a critical role in protein kinase C (PKC) signaling in the formation of focal adhesions and actin stress fibers. The cytoplasmic domain of syndecan-4 core protein directly interacts with and potentiates PKCalpha activity, and it can directly interact with the phos- phoinositide PIP2. We, therefore, investigated whether the interaction of inositol phosphates and inositol phospholipids with syndecan-4 could regulate PKC activity. Data from in vitro kinase assays using purified PKCalpha beta gamma show that in the absence of phosphatidylserine and diolein, PIP2 increased the extent of autophosphorylation of PKCalpha beta gamma and partially activated it to phosphorylate both histone III-S and an epidermal growth factor receptor peptide. This activity was dose-dependent, and its calcium dependence varied with PKC isotype/source. Addition of the cytoplasmic syndecan-4 peptide, but not equivalent syndecan-1 or syndecan-2 peptides, potentiated the partial activation of PKCalpha beta gamma by PIP2, resulting in activity greater than that observed with phosphatidylserine, diolein, and calcium. This study indicates that syndecan-4 cytoplasmic domain may bind both PIP2 and PKCalpha, localize them to forming focal adhesions, and potentiate PKCalpha activity there.     

Kinematic Design of Modular Reconfigurable In-Parallel Robots

This paper describes the kinematic design issues of a modular reconfigurable parallel robot. Two types of robot modules, the fixed-dimension joint modules and the variable dimension link modules that can be custom-designed rapidly, are used to facilitate the complex design effort. Module selection and robot configuration enumeration are discussed. The kinematic analysis of modular parallel robots is based on a local frame representation of the Product-Of-Exponentials (POE) formula. Forward displacement analysis algorithms and a workspace visualization scheme are presented for a class of three-legged modular parallel robots. Two three-legged reconfigurable parallel robot configurations are actually built according to the proposed design procedure.     

Document retrieval from compressed images

With the emergence of digital libraries, more and more documents are stored and transmitted through the Internet in the format of compressed images. It is of significant meaning to develop a system which is capable of retrieving documents from these compressed document images. Aiming at the popular compression standard-CCITT Group 4 which is widely used for compressing document images, we present an approach to retrieve the documents from CCITT Group 4 compressed document images in this paper. The black and white changing elements are extracted directly from the compressed document images to act as the feature pixels, and the connected components are detected simultaneously. Then the word boxes are bounded based on the merging of the connected components. Weighted Hausdorff distance is proposed to assign all of the word objects from both the query document and the document from database to corresponding classes by an unsupervised classifier, whereas the possible stop words are excluded. Document vectors are built by the occurrence frequency of the word object classes, and the pair-wise similarity of two document images is represented by the scalar product of the document vectors. Nine groups of articles pertaining to different domains are used to test the validity of the presented approach. Preliminary experimental results with the document images captured from students’ theses show that the proposed approach has achieved a promising performance.     

A fast path planning by path graph optimization

A fast path planning method by optimization of a path graph for both efficiency and accuracy is proposed. A conventional quadtree-based path planning approach is simple, robust, and efficient. However, it has two limitations. We propose a path graph optimization technique employing a compact mesh representation. A world space is triangulated into a base mesh and the base mesh is simplified to a compact mesh. The compact mesh representation is object-dependent; the positions of vertexes of the mesh are optimized according to the curvatures of the obstacles. The compact mesh represents the obstacles as accurately as the quadtree even though using much fewer vertexes than the quadtree. The compact mesh distributes vertexes in a free space in a balanced way by ensuring that the lengths of edges are below an edge length threshold. An optimized path graph is extracted from the compact mesh. An iterative vertex pushing method is proposed to include important obstacle boundary edges in the path graph. Dijkstra's shortest path searching algorithm is used to search the shortest path in the path graph. Experimental results show that the path planning using the optimized path graph is an order of magnitude faster than the quadtree approach while the length of the path generated by the proposed method is almost the same as that of the path generated by the quadtree.     

Efficient Update of Indexes for Dynamically Changing Web Documents

Recent work on incremental crawling has enabled the indexed document collection of a search engine to be more synchronized with the changing World Wide Web. However, this synchronized collection is not immediately searchable, because the keyword index is rebuilt from scratch less frequently than the collection can be refreshed. An inverted index is usually used to index documents crawled from the web. Complete index rebuild at high frequency is expensive. Previous work on incremental inverted index updates have been restricted to adding and removing documents. Updating the inverted index for previously indexed documents that have changed has not been addressed. In this paper, we propose an efficient method to update the inverted index for previously indexed documents whose contents have changed. Our method uses the idea of landmarks together with the diff algorithm to significantly reduce the number of postings in the inverted index that need to be updated. Our experiments verify that our landmark-diff method results in significant savings in the number of update operations on the inverted index.     

Learning model order from labeled and unlabeled data for partially supervised classification, with application to word sense disambiguation

Previous partially supervised classification methods can partition unlabeled data into positive examples and negative examples for a given class by learning from positive labeled examples and unlabeled examples, but they cannot further group the negative examples into meaningful clusters even if there are many different classes in the negative examples. Here we proposed an automatic method to obtain a natural partitioning of mixed data (labeled data + unlabeled data) by maximizing a stability criterion defined on classification results from an extended label propagation algorithm over all the possible values of model order (or the number of classes) in mixed data. Our experimental results on benchmark corpora for word sense disambiguation task indicate that this model order identification algorithm with the extended label propagation algorithm as the base classifier outperforms SVM, a one-class partially supervised classification algorithm, and the model order identification algorithm with semi-supervised k-means clustering as the base classifier when labeled data is incomplete.