Pattern identification

Scheler, C., Muller, E. C., Stahl, J., Muller-Werdan, U, Salnikow J, and Jungblut, P. (1997) Identification and characterization of heat shock protein 27 protein species in human myocardial two-dimensional electrophoresis patterns. Identification and characterization of heat shock protein 27 protein species in human myocardial two-dimensional electrophoresis patterns. Electrophoresis 18, 2823-2831. 

The simultaneous input of elemental composition data (see following section) into the computer treatment of the X-ray data substantially facilitates the powder pattern identification. 

Three-dimensional alignment incorporates the problem of conformational flexibility. One possibility to address the problem is to pre-generate conformations (like in DISCO or Catalyst ), which makes the actual alignment less time-consuming, but the user has to ensure that all relevant conformations are included. On the other hand, there are in-process approaches that perform the pattern identification and conformational search simultaneously. 

This system s pattern identification fimction can automatically metisure the film thickness with very fine test patterns (as small as 40 square microns). When used with Si02 single layer films, the experimental prototype s measurements proved reliably reproducible (showing a deviation of less than 4 nm at 3 o over repeated trials), and required thirty seconds or less per wafer (including time for pre-alignment and five-point measurement). The system s theoretical basis and actual operations are described below. 

S. Bicciato M. Pandin G. Didone C. D. Bello, In Analysis of an Associative Memory Neural Network for Pattern Identification in Gene Expression Data, Proceedings of 1st Workshop on Data Mining in Bioinformatics, M. J. Zaki, H. T. T. Toivonen, J. T. L. Wang, Eds. San Francisco, CA, USA, pp 22-30. 

A comprehensive description of the databases and methods for domain, family, and pattern identification is available in chapter 2. Therefore, in this chapter, the discussion of the application of family and domain information to function assignment will be limited to Pfam, the virtual standard in protein domain/family classification, and to InterPro and CDD, two resources that integrate multiple domain databases. In addition, I discuss tools that can be used to scan those databases, namely HMMER,... 

Young, J. A., Fivelman, Q. L., Blair, P. L., de la Vega, P., Le Roch, K. G., Zhou, Y., Carucci, D. J., Baker, D. A., and Winzeler, E. A. (2005). The Plasmodium falciparum sexual development transcriptome A microarray analysis using ontology-based pattern identification. Mol. Biochem. Parasitol. 143,67-79. 

More intriguing are cases where the pattern identification itself is difficult. There are instances when, because of competing isoenergetic interactions in a... 

Table IV gives results for the two types of test and several choices of element patterns. Important dual pattern Identification conclusions follow (a) Discrimination power (Identifiability)...

Kaczmarek, K.A., Tyler, M.E., and Bach-y-Rita, P 1997. Pattern identification on a fingertip-scanned electrotactile display. Proceedings of the 19th Annual International Conference on IEEE Engineering Medical Biological Society, pp. 1694-1697. 

Although the exact position of the transition boundaries between the flow patterns are inherently related to the liquid-liquid system studied, the arrangement of the regions of the different flow patterns are similar to a number of systems. Models based on the Capillary, Reynolds and Weber numbers have been developed in order to allow an a priori prediction of the flow patterns using fluid properties and flow velocities. A general criterion for flow pattern identification in a given micro-channel was given in terms of dimensionless ratio of Reynolds to Capillary (ReJ Ca< ) numbers as a function of the product of Reynolds number and hydraulic diameter (Readh/Cd) by Kashid and Kiwi-Minsker (2011) and was applied to different literature data which are summarised in Fig. 2.5. 

The two items belonging to the remote patient monitoring classification are telemedicine and telehealth. Finally, the pattern identification (to learn from errors and systems influences on adverse events) classification includes items such as electronic medical records and workload and staffing data systems. Additional information on technology commonly used by the nursing profession is available in Ref. [4]. 

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