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Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry. Research Abstract Details 

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  • Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry. Abstract Text:

    sirisha busamSirisha Busam,maia mcnabbMaia McNabb,anke wackwitzAnke Wackwitz,wasana senevirathnaWasana Senevirathna,siham beggahSiham Beggah,jan roelof van der meerJan Roelof van der Meer,mona wellsMona Wells,uta breuerUta Breuer,hauke harmsHauke Harms,sirisha busamSirisha Busam,maia mcnabbMaia McNabb,anke wackwitzAnke Wackwitz,wasana senevirathnaWasana Senevirathna,siham beggahSiham Beggah,jan roelof van der meerJan Roelof van der Meer,mona wellsMona Wells,uta breuerUta Breuer,hauke harmsHauke Harms,

    Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

    Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry. Publishing Authors By Initials

    s busamS Busam,m mcnabbM McNabb,a wackwitzA Wackwitz,w senevirathnaW Senevirathna,s beggahS Beggah,jr meerJR Meer,m wellsM Wells,u breuerU Breuer,h harmsH Harms,s busamS Busam,m mcnabbM McNabb,a wackwitzA Wackwitz,w senevirathnaW Senevirathna,s beggahS Beggah,jr meerJR Meer,m wellsM Wells,u breuerU Breuer,h harmsH Harms,

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    PUBMED ID PMID:

    MEDLINE DATE:

    Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry. Journal Published:

    PUBLICATION TYPE: Research Support, Non-U.S. Gov

    Journal: Analytical chemistry

    VOLUME: 79

    Page Numbers: 9107-14

    Journal Abbreviation: Anal. Chem.

    ISSN: 0003-2700

    DAY: 24

    MONTH: 10

    YEAR: 2007

    Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry. Information

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    LANGUAGE: eng

    NlmUniqueID: 370536

    Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry. Keywords Mesh Terms:

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    Grant and Affiliation Information for Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

    AFFILIATION: Department of Chemistry, Tennessee Technological University, Cookeville, Tennessee 38505, USA.

    Country: United States

    United States Research PublicationUnited States Research Publication

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    MEDLINETA: Anal Chem

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