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Automated identification and characterisation of microbial populations using flow cytometry: the AIMS Project

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Citation:
Jonker, R. , Groben, R. , Tarran, G. , Medlin, L. , Wilkins, M. , Garcia, L. , Zabala, L. and Boddy, L. (2000): Automated identification and characterisation of microbial populations using flow cytometry: the AIMS Project , Scientia marina, 64 , pp. 225-234 .
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Abstract:

The AIMS (Automatic Identification and characterisation of Microbial populationS) project is developing and integrating flowcytometric technology for the identification of microbial cell populations and the determination of their cellular characteristics.This involves applying neural network approaches and molecular probes to the identification of cell populations, and derivingand verifying algorithms for assessing the chemical, optical and morphometric characteristics of these populations. The productsof AIMS will be calibrated data, protocols, algorithms and software designed to turn flow cytometric observations into a datamatrix of the abundance and cellular characteristics of identifiable populations. This paper describes the general approach of theAIMS project, with details on the application of artificial neural nets and rRNA oligonucleotide probes.

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