automated identification and characterisation of microbial populations using flow cytometry: the aims project

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2000
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Abstract
The AIMS (Automatic Identification and characterisation of Microbial populationS) project is developing and integrating flow cytometric 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 deriving and verifying algorithms for assessing the chemical, optical and morphometric characteristics of these populations. The products of AIMS will be calibrated data, protocols, algorithms and software designed to turn flow cytometric observations into a data matrix of the abundance and cellular characteristics of identifiable populations. This paper describes the general approach of the AIMS project, with details on the application of artificial neural nets and rRNA oligonucleotide probes.
Reference Key
jonker2000scientiaautomated Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Richard Jonker;René Groben;Glen Tarran;Linda Medlin;Malcolm Wilkins;Laura García;Laura Zabala;Lynne Boddy
Journal Nutrients
Year 2000
DOI
10.3989/scimar.2000.64n2225
URL
http://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/756
Keywords
artificial neural networks flow cytometry phytoplankton plankton fisheries angling

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