The Protein Data Bank (PDB) is the central worldwide repository for three-dimensional (3D) structure data of biological macromolecules. The Research Collaboratory for Structural Bioinformatics (RCSB) has completely redesigned its resource for the distribution and query of 3D structure data. The re-engineered site is currently in public beta test at http://pdbbeta.rcsb.org. The new site expands the functionality of the existing site by providing structure data in greater detail and uniformity, improved query and enhanced analysis tools. A new key feature is the integration and searchability of data from over 20 other sources covering genomic, proteomic and disease relationships. The current capabilities of the re-engineered site, which will become the RCSB production site at http://www.pdb.org in late 2005, are described.

Biomedical text mining and other automated techniques are beginning to achieve performance which suggests that they could be applied to aid database curators. However, few studies have evaluated how these systems might work in practice. In this article we focus on the problem of annotating mutations in Protein Data Bank (PDB) entries, and evaluate the relationship between performance of two automated techniques, a text-mining-based approach (MutationFinder) and an alignment-based approach, in intrinsic versus extrinsic evaluations. We find that high performance on gold standard data (an intrinsic evaluation) does not necessarily translate to high performance for database annotation (an extrinsic evaluation). We show that this is in part a result of lack of access to the full text of journal articles, which appears to be critical for comprehensive database annotation by text mining. Additionally, we evaluate the accuracy and completeness of manually annotated mutation data in the PDB, and find that it is far from perfect. We conclude that currently the most cost-effective and reliable approach for database annotation might incorporate manual and automatic annotation methods.

Our main objective is to design a computer based system to monitor the crowding system so as to avoid crowding disasters.We have proposed a two-step mode. The first step is 'pre-disaster planning' including determination of sensitive locations and space management, evacuation paths using G.I.S. and management related arrangements. The second step is real time analysis of crowds to detect a possible emergency. It contains two modules, the first being a method to determine crowding situations and plan of action, and the second being the determination of the shortest evacuation path for the current area under surveillance. In the fuzzy inference system, used in determining crowding situation, crowd density is determined with the help of a number of pixels and shape of objects. For determining speed and cumulative displacement a new method is named 'determination of speed and displacement from images with help of object characterization'. The shortest path is determined with the help of G.I.S. and the overall crowding situation. We have considered two case studies:1. Open air theatre: this particular case study was considered to get better understanding of general crowd movement patterns in the absence of crowd management systems and possible sensitive locations. 2. Auditorium: this case study was used to check the applicability of the overall project and to determine an evacuation path network. While applying this case study we checked for results such as accuracy and usability of components of the project such as crowd density determination, fuzzy inference system, Determination of speed and displacement etc., and overall usability.

The Protein Data Bank (PDB; http://www.pdb.org/) is the international repository for three-dimensional structural data. The Nucleic Acid Database (NDB; http://ndbserver.rutgers.edu) specializes in nucleic acid-containing structures. These resources have developed several integrated tools for the deposition and query of structures in these databases.ADIT (AutoDep Input Tool) is used by the community for data validation and deposition, and internally by the PDB and NDB for processing and annotation. Because it is based on the data dictionary technology of mmCIF, ADIT is easily extended to accept and process information describing new science and technology. Examples of how this system is used for processing some of the most complex structures in the PDB and NDB will be described.The ADIT system has been designed to operate as a distributed network application or as a stand-alone tool. A workstation version of ADIT is available at http://deposit.pdb.org/software/ for researchers to prepare and validate entries in their home laboratories. This system also includes a variety of utility programs to assist in the extraction of information from several crystallographic applications, and utilities for merging the various program outputs into a single mmCIF data file ready for PDB deposition.The PDB is managed by three members of the RCSB: Rutgers, NIST, and SDSC. The PDB project is funded by the NSF, DOE, and two units of the NIH: NIGMS and NLM. The NDB is funded by the NSF and the DOE. The Protein Data Bank (PDB; http://www.pdb.org/) is the international repository for the processing and distribution of three-dimensional structural data. Its mission is to enable science by providing the most accurate and timely data for macromolecular structures. Data distribution and query functionality are replicated at six additional mirror sites, each of which maintains a Web site and an ftp archive. Weekly data updates are first tested on a local staging site, and then distributed to all production sites. All new functionality is first released on a public β test site (http://beta.rcsb.org/pdb/) prior to its distribution to all production sites. Examples of added query or display functionality include an enzyme classification browser, customized tabular reports, the pre-release of sequence information for some unreleased structures, and the STING Millennium Suite of graphical structure/sequence viewing tools (courtesy Goran Neshich and Barry Honig). Since the PDB holdings contain a considerable amount of redundancy, a sequence homology filter was implemented that provides the choice of displaying either a representative set of structures or the full search results. Progress on a re-engineering effort of the database, software, and Web interface will also be described. The PDB is managed by three members of the RCSB: Rutgers, NIST, and SDSC. The PDB project is funded by the NSF, DOE, and two units of the NIH: NIGMS and NLM. Keywords: DATABASES, STRUCTURAL BIOINFORMATICS, MMCIF Keywords: DATABASES, DATA DISTRIBUTION, QUERY FUNCTIONAL...