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Rapid determination of bacterial abundance, biovolume, morphology, and growth by neural network-based image analysis
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för molekylärbiologi (Teknisk-naturvetenskaplig fakultet). Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå marina forskningscentrum (UMF). (Åke Hagström)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för molekylärbiologi (Teknisk-naturvetenskaplig fakultet). Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå marina forskningscentrum (UMF).
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för molekylärbiologi (Teknisk-naturvetenskaplig fakultet). Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå marina forskningscentrum (UMF). (Johan Wikner ; UMFpub)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för molekylärbiologi (Teknisk-naturvetenskaplig fakultet). (Johan Wikner)
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1998 (Engelska)Ingår i: Applied and Environmental Microbiology, ISSN 0099-2240, E-ISSN 1098-5336, Vol. 64, nr 9, s. 3246-3255Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Annual bacterial plankton dynamics at several depths and locations in the Baltic Sea were studied by image analysis. Individual bacteria were classified by using an artificial neural network which also effectively identified nonbacterial objects, Cell counts and frequencies of dividing cells were determined, and the data obtained agreed well with visual observations and previously published values. Cell volumes were measured accurately by comparison with bead standards. The survey included 690 images from a total of 138 samples. Each image contained approximately 200 bacteria. The images were analyzed automatically at a rate of 100 images per h, Bacterial abundance exhibited coherent patterns with time and depth, and there were distinct subsurface peaks in the summer months. Four distinct morphological classes were resolved by the image analyzer, and the dynamics of each could be visualized. The bacterial growth rates estimated from frequencies of dividing cells were different from the bacterial growth rates estimated by the thymidine incorporation method. With minor modifications, the image analysis technique described here can be used to analyze other planktonic classes.

Ort, förlag, år, upplaga, sidor
American Society for Microbiology , 1998. Vol. 64, nr 9, s. 3246-3255
Nationell ämneskategori
Mikrobiologi
Identifikatorer
URN: urn:nbn:se:umu:diva-52550ISI: 000075892200019OAI: oai:DiVA.org:umu-52550DiVA, id: diva2:506347
Tillgänglig från: 2012-02-28 Skapad: 2012-02-24 Senast uppdaterad: 2018-06-08

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Wikner, Johan

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Wikner, Johan
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Institutionen för molekylärbiologi (Teknisk-naturvetenskaplig fakultet)Umeå marina forskningscentrum (UMF)
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Applied and Environmental Microbiology
Mikrobiologi

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