Expressed sequence tag analysis of genes expressed during development of the tropical abalone Haliotis asinina

The tropical abalone. Haliotis asinina. is,in ideal species to investigate the molecular mechanisms that control development. growth, reproduction and shell formation in all cultured haliotids. Here we describe the analysis of 232 expressed sequence tags (EST) obtained front a developmental H. asinina cDNA library intended for future microarray studies. From this data set we identified 183 unique gene Clusters. Of these, 90 clusters showed significant homology with sequences lodged in GenBank, ranging in function from general housekeeping to signal transduction, gene regulation and cell-cell communication. Seventy-one clusters possessed completely novel ORFs greater than 50 codons in length, highlighting the paucity of sequence data from molluscs and other lophotrochozoans. This study of developmental gene expression in H. asinina provides the foundation for further detailed analyses of abalone growth, development and reproduction.

Site-specific labelling of proteins with a rigid lanthanide-binding tag

This paper describes a generic method for the site-specific attachment of lathanide complexes to proteins through a disulfide bond. The method is demonstrated by the attachment of a lanthanide-binding peptide tag to the single cysteine residue present in the N-terminal DNA-binding domain of the Echerichia coli arginine repressor. Complexes with Y3+, Tb3+, Dy3+, Ho3+, Er3+, Tm3+ and Yb3+ ions were formed and analysed by NMR spectroscopy. Large pseudocontact shifts and residual dipolar couplings were induced by the lanthanide-binding tag in the protein NMR spectrum, a result indicating that the tag was rigidly attached to the protein. The axial components of the magnetic susceptibility anisostropy tensors determined for the different lanthanide ions were similarly but not identically oriented. A single tag with a single protein attachment site can provide different pseudocontact shifts from different magnetic susceptibility tensors and thus provide valuable nondegenerate long-range structure information in the determination of 3D protein structures by NMR spectroscopy.

A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.