RPW: a hybrid reverse prediction method for level of detail.

In this paper, we propose a way of using multiple domain analysis methodology to speed up the image rendering process. We first apply wavelet transform to the original image, and then compress the wavelets in the frequency domain using histogram equalization techniques. When rendering the image, we uncompress the wavelets and reverse predict the upper level images. This process continues until it reaches a certain criteria. We use two terms-total image size (TIS) and total loading time (TLT) to measure the performance of level of detail (LOD) in a network environment. We compare traditional image-based LOD methods with the new method we are proposing. Experiments show that the proposed method can reduce both TIS and TLT. The image rendering speed on a remote client is approximately 2.5 times faster than the common image compression methods. Applications such as remote diagnostic systems and online museums can use this technique to achieve better real-time animation effects.

The Protein Information Resource.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors. The superfamily curation defines signature domain architecture and categorizes memberships to improve automated classification. To increase the amount of experimental annotation, the PIR has developed a bibliography system for literature searching, mapping, and user submission, and has conducted retrospective attribution of citations for experimental features. PIR also maintains NREF, a non-redundant reference database, and iProClass, an integrated database of protein family, function, and structure information. PIR-NREF provides a timely and comprehensive collection of protein sequences, currently consisting of more than 1 000 000 entries from PIR-PSD, SWISS-PROT, TrEMBL, RefSeq, GenPept, and PDB. The PIR web site (http://pir.georgetown.edu) connects data analysis tools to underlying databases for information retrieval and knowledge discovery, with functionalities for interactive queries, combinations of sequence and text searches, and sorting and visual exploration of search results. The FTP site provides free download for PSD and NREF biweekly releases and auxiliary databases and files.

The Protein Information Resource: an integrated public resource of functional annotation of proteins.

The Protein Information Resource (PIR) serves as an integrated public resource of functional annotation of protein data to support genomic/proteomic research and scientific discovery. The PIR, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the PIR-International Protein Sequence Database (PSD), the major annotated protein sequence database in the public domain, containing about 250 000 proteins. To improve protein annotation and the coverage of experimentally validated data, a bibliography submission system is developed for scientists to submit, categorize and retrieve literature information. Comprehensive protein information is available from iProClass, which includes family classification at the superfamily, domain and motif levels, structural and functional features of proteins, as well as cross-references to over 40 biological databases. To provide timely and comprehensive protein data with source attribution, we have introduced a non-redundant reference protein database, PIR-NREF. The database consists of about 800 000 proteins collected from PIR-PSD, SWISS-PROT, TrEMBL, GenPept, RefSeq and PDB, with composite protein names and literature data. To promote database interoperability, we provide XML data distribution and open database schema, and adopt common ontologies. The PIR web site (http://pir.georgetown.edu/) features data mining and sequence analysis tools for information retrieval and functional identification of proteins based on both sequence and annotation information. The PIR databases and other files are also available by FTP (ftp://nbrfa.georgetown.edu/pir_databases).

PIRSF: family classification system at the Protein Information Resource.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics. To facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors, PIR has extended its superfamily concept and developed the SuperFamily (PIRSF) classification system. Based on the evolutionary relationships of whole proteins, this classification system allows annotation of both specific biological and generic biochemical functions. The system adopts a network structure for protein classification from superfamily to subfamily levels. Protein family members are homologous (sharing common ancestry) and homeomorphic (sharing full-length sequence similarity with common domain architecture). The PIRSF database consists of two data sets, preliminary clusters and curated families. The curated families include family name, protein membership, parent-child relationship, domain architecture, and optional description and bibliography. PIRSF is accessible from the website at http://pir.georgetown.edu/pirsf/ for report retrieval and sequence classification. The report presents family annotation, membership statistics, cross-references to other databases, graphical display of domain architecture, and links to multiple sequence alignments and phylogenetic trees for curated families. PIRSF can be utilized to analyze phylogenetic profiles, to reveal functional convergence and divergence, and to identify interesting relationships between homeomorphic families, domains and structural classes.

Processing X-ray images to eliminate irrelevant structures that mask important features.

In a plane radiographic image, there generally is an important area of interest (AOI). Too often, the AOI is partially masked by images of other overlapping and underlying structures that may be in front of or behind the AOI. An important adjunct to radiological diagnosis would be the capability of eliminating images of such masking structures to isolate the AOI for more detailed examination. We described a computerized method that utilizes a stereo pair of plane X-ray images to enable radiologists to interact with these images for first identifying for the computer the AOI and then directing the computer to eliminate all structures in front of and behind the AOI. The result is a plane X-ray image or a stereo X-ray image pair that includes only the AOI, but not any overlapping or underlying structures. The method uses a stereo pair of X-rays and the 3D perception of radiologists. 3D perception involves eye convergence and lens focus as well as cues, such as parallax and relative sizes. Convergence of the eyes is by far the strongest factor in 3D visualization. The horizontal separation or disparity between points in the left and right eye images on a screen or X-ray film produces convergence which determines an object's perceived depth in visual 3D space. All points in a given perceived depth plane have the same disparity on the screen. In theory, a given depth plane can be eliminated from the 3D image by shifting one image and then the other image of a stereo pair horizontally by the distance of the disparity of the depth plane, and subtracting. A new stereo image pair is thereby produced in which points only of the depth plane do not appear. However, in practical situations, certain artifacts arise that must be considered. The method has the potential for important applications in many areas of medical imaging processing.

DENTAL FORCES AND MASTICATION.

An engineering approach is presented to the evaluation of forces of mastication. An analysis of the force direction on the teeth is given. Applications of the results are shown to artificial teeth in their placement in a complete denture and in the design of their occlusal surfaces.