Databases Trends

Altschul, S. F.andKoonin, E. V.(1998), Iterated profile searches with PSI-LAST - a tool for discovery in protein databases, Trends Biochem. Set ( Computer Corner ), 23, 444-446. 

Oliveira, C. and Johnston, D.A. (2001) Mining the schistosome DNA sequence database. Trends in... 

Carugo, O., and S. Pongor. 2002. The evolution of structural databases. Trends Biotechnol. 20 498-501. 

Ginsburg, H. (2006). Progress in in silico functional genomics the malaria Metabolic Pathways database. Trends Parasitol. 22,238-240. 

Figure 12. Database trends for strengdi ratio vs PI for clays tested by CKqUC, DSS, and CKqUE modes. 

K. Y. Marcaccio, K. HiUstrom, C. Tomassini, and G. E. Turecki, eds., and M. E. WiUiams, founding ed.. Computer Readable Databases, A Directory ana Data Sourcebook, 8th ed.. Gale Research Inc., Detroit, Mich., 1992 Online Services 1992 Review, Trends oNForecast, SIMBA Information Inc., Wilton, Conn., 1992. 

Historical DataBase Subsystem We have discussed the use of on-hne databases. An historical database is built similar to an on-line database. Unlike their on-line counterparts, the information stored in a historical database is not normally accessed directly by other subsystems for process control and monitoring. Periodic reports and longterm trends are generated based on the archived data. The reports are often used for long-term planning and system performance evaluations such as statistical process (quality) control. The trends may be used to detect process drifts or to compare process variations at different times. 

The presence of errors within the underlying database fudher degrades the accuracy and precision of the parameter e.stimate. If the database contains bias, this will translate into bias in the parameter estimates. In the flash example referenced above, including reasonable database uncertainty in the phase equilibria increases me 95 percent confidence interval to 14. As the database uncertainty increases, the uncertainty in the resultant parameter estimate increases as shown by the trend line represented in Fig. 30-24. Failure to account for the database uncertainty results in poor extrapolations to other operating conditions. 

FIG. 30-24 Trend in parameter estimate uncertainty as the database uncertainty increases. 

The main function of an incident reporting system (IRS) is to identify recurring trends from large numbers of incidents with relatively minor outcomes, or from near misses. One of the important characteristics of an IRS is that the time and resources required to evaluate an incident and incorporate it into the database must be minimized. This means that the designers of an IRS have to carefully evaluate the benefits and costs of requiring more comprehensive information from each incident that is to be reported. A requirement for too much information will bring the system into disrepute, and too little information will mean that the results are too general to be of any real value. 

This review includes most of the published articles from the defined area and excludes only imidazoquinolines, which were reviewed in Weissberger-Taylor s series The Chemistry of Heterocyclic Compounds (81MI1). Comprehensive Heterocyclic Chemistry II (96MI1) mentioned only some of the azoloquinolines in the first edition the authors omitted citations about this type of compounds. The trend toward interest in these compounds can be illustrated by the number of citations in Chemical Abstract as shown in Table I. Besides Chemical Abstracts Substance/Subject (Collective) Indexes, the MDL database search has been used. 

This is especially true in applications that use microprocessor/computer-based systems. These systems require a database that specifies the monitoring parameters, measurement routes, analysis parameters, and a variety of other information. This input is needed to acquire, trend, store, and report what is referred to as conditioned vibration data. 

As a minimum the software program should provide automatic database management, automatic trending, automatic report generation and simplified diagnostics. As in... 

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.

Lake/coastal Herring gull Adult Egg Widespread disfi ibution and easily studied large existing database documenting trends in Great Lakes... 

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