Databases automation

Supporting IT systems Finally, the accuracy and speed of an integrated process is heavily influenced by the availability of IT systems, which provide a common database, automated functions like Materials Requirements Planning (MRP), and opportunities for simulation and optimization (for instance, real time finite capacity production scheduling, or on-line customer order confirmation based on existing overall supply chain inventory or capacity). 

The quahty of an analytical result also depends on the vaUdity of the sample utilized and the method chosen for data analysis. There are articles describiag Sampling and automated sample preparation (see Automated instrumentation) as well as articles emphasizing data treatment (see Chemometrics Computer technology), data iaterpretation (see Databases Imaging technology), and the communication of data within the laboratory or process system (see Expert systems Laboratory information managet nt systems). 

As computing capabiUty has improved, the need for automated methods of determining connectivity indexes, as well as group compositions and other stmctural parameters, for existing databases of chemical species has increased in importance. New naming techniques, such as SMILES, have been proposed which can be easily translated to these indexes and parameters by computer algorithms. Discussions of the more recent work in this area are available (281,282). SMILES has been used to input Contaminant stmctures into an expert system for aquatic toxicity prediction by generating LSER parameter values (243,258). 

TJA Ewing, ID Kuntz. Critical evaluation of search algorithms for automated molecular docking and database screening. J Comput Chem 18 1175-1189, 1997. 

Computer Automated Laboratory System/ Environmental Waste Database System... 

The Superfund database containing information on all aspects of hazardous waste sites from initial discover) to listing on the National Priorities List. Magnetic tapes are available quarterly from NTIS. Summaiy data under the Freedom of Information Act is available free by calling the Superfund Automated Phone System +1 800 775-5037. 

While providing many advantages, simplified data acquisition and analysis also can be a liability. If the database is improperly configured, the automated capabilities of these analyzers will yield faulty diagnostics that can allow catastrophic failure of critical plant machinery. 

The steps in developing such a database are (1) collection of machine and process data and (2) database setup. Input requirements of the software are machine and process specifications, analysis parameters, data filters, alert/alarm limits, and a variety of other parameters used to automate the data-acquisition process. 

Automated data acquisition The object of using microprocessor-based systems is to remove any potential for human error, reduce manpower and to automate as much as possible the acquisition of vibration, process and other data that will provide a viable predictive maintenance database. Therefore the system must be able to automatically select and set monitoring parameters without user input. The ideal system would limit user input to a single operation. However this is not totally possible with today s technology. 

In order to make as much data on the structure and its determination available in the databases, approaches for automated data harvesting are being developed. Structure classification schemes, as implemented for example in the SCOP, CATH, andFSSP databases, elucidate the relationship between protein folds and function and shed light on the evolution of protein domains. 

While other programs require modification of the actual code in changing the polymer, spectra, or model, only changes in the user database is required here. Changes in the program since a brief report (22) in 1985 include improvement of the menu structure, added utilities for spectral manipulations, institution of demo spectra and database. Inclusion of Markov statistics, and automation for generation of the coefficients in Equation 1. Current limitations are that only three models (Bernoul llan, and first- and second-order Markov) can be applied, and manual input Is required for the N. A. S. L.. 

Figure 4.4 The general protocol for information extraction from an herbal text (A-E) is paired with case examples from our work with the Ambonese Herbal by Rumphius. (A) Text is digitized. (B) Through either manual reading or automated extraction the plant name(s), plant part(s), and symptoms or disorders are identified. (C) These extracted data are then updated (as necessary) to reflect current names of the plants, using the International Plant Names Index (IPNI), and the pharmacological function(s) of the described medicinal plants are extrapolated from the mentioned symptoms and disorders. (D) The current botanical names are queried against a natural products database such as the NAPRALERT database to determine whether the plant has been previously examined. (E) Differential tables are generated that separate the plants examined in the literature from plants that may warrant further examination for bioactivity. (Adapted from Trends in Pharmacological Sciences, with permission.) See color plate.

Web in the life of the medicinal chemist. One may see the development of alerting services for the primary medicinal chemistry journals. The Web-based information search process could be replaced by a much more structured one based on metadata, derived by automated processing of the original full-text article. To discover new and potentially interesting articles, the user subscribes to the RSS feeds of relevant publishers and can simply search the latest items that appear automatically for keywords of interest. The article download is still necessary, but it may be possible for the client software to automatically invoke bibliographic tools to store the found references. Another application of the Chemical Semantic Web may be as alerting services for new additions to chemical databases where users get alerts for the new additions of structures or reactions. 

Ewing TJ, Makino S, Skillman AG, Kuntz ID. DOCK 4.0 search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Dei 2001 15 411-28. 

Web-based data collection and management systems provide a mechanism for remote data entry, where entered data are added to a centralized database once the submit button is pressed. They can be designed to automate the various aspects of clinical trials such as eligibility evaluation, data collection, and tracking specimens. They also serve as a resource site for participating sites to access trial-specific information, facilitate communication, track data queries and their resolutions, and allow administrative management of trials [28, 29]. For these reasons, they play an important role in facilitating the conduct of international clinical trials. 

Zhang, S., Golbraikh, A., Oloff, S., Kohn, H., Tropsha, A. A novel automated lazy learning QSAR (ALL-QSAR) approach method development, applications, and virmal screening of chemical databases using validated ALL-QSAR models. 

Rapid and automated. The large size of the databases to be processed requires the conversion program to run in batch mode and to work with acceptable speed. 

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