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Dynamics of biofilm structure.

Milferstedt, Kim.

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Dynamics of biofilm structure.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Dynamics of biofilm structure./
作者:	Milferstedt, Kim.
面頁冊數:	175 p.
附註:	Adviser: Eberhard Morgenroth.
Contained By:	Dissertation Abstracts International68-11B.
標題:	Biology, Microbiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3290321
ISBN:	9780549341116

Dynamics of biofilm structure.
Milferstedt, Kim.

Dynamics of biofilm structure. - 175 p.

Adviser: Eberhard Morgenroth.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.

In aqueous systems, surfaces are rapidly colonized by microorganisms. The surface-associated growth of microorganisms, frequently referred to as biofilms, are notoriously heterogeneous systems in many aspects. An obvious manifestation of heterogeneity is the morphology of a biofilm: Biofilms can be flat and homogenous layers or build spectacular 3-D structures composed of ripples, dunes and mushroom-shaped microcolonies. These architectural features of a biofilm may be microscopically small. In many instances, however, biofilm heterogeneity is visible with the unaided eye. Especially changes in biofilm morphology induced by detachment, the loss of biofilm-biornass to the bulk water, can occur on the order of millimeters to centimeters. Changes on that scale have drastic consequences for the microbial biofilm community, as the previously evolved spatial organization of the biofilm is overthrown. By characterizing heterogeneity and changes in biofilm structure on this large scale, we may be able to relate biofilm morphology to biofilm function, for example in biofilms degrading organic pollutants in wastewater treatment plants.

ISBN: 9780549341116Subjects--Topical Terms:

1017734
Biology, Microbiology.

Dynamics of biofilm structure.
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245 1 0 $a Dynamics of biofilm structure.
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500 $a Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7583.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
520 $a In aqueous systems, surfaces are rapidly colonized by microorganisms. The surface-associated growth of microorganisms, frequently referred to as biofilms, are notoriously heterogeneous systems in many aspects. An obvious manifestation of heterogeneity is the morphology of a biofilm: Biofilms can be flat and homogenous layers or build spectacular 3-D structures composed of ripples, dunes and mushroom-shaped microcolonies. These architectural features of a biofilm may be microscopically small. In many instances, however, biofilm heterogeneity is visible with the unaided eye. Especially changes in biofilm morphology induced by detachment, the loss of biofilm-biornass to the bulk water, can occur on the order of millimeters to centimeters. Changes on that scale have drastic consequences for the microbial biofilm community, as the previously evolved spatial organization of the biofilm is overthrown. By characterizing heterogeneity and changes in biofilm structure on this large scale, we may be able to relate biofilm morphology to biofilm function, for example in biofilms degrading organic pollutants in wastewater treatment plants.
520 $a The objective of this thesis is to develop and test methods to quantify large-scale biofilm structure by image analysis. With the methods, the long-term development (up to 10 weeks) of structure in mixed-culture biofilms is monitored. By exposing the biofihn to controlled disturbances caused by increased fluid shear, biofilm could structurally be re-set to earlier states during the development. Depending on the biofilm history, however, not every increase in shear led to the development of the same structure. When undisturbed, biofihn spontaneously detached after a climax structure was reached.
520 $a The image analysis approach taken for this work is based on Spatial Gray Level Dependence Matrices (SGLDM). SGLDDI capture the texture on an image in a histogram from which textural descriptors extract features of the spatial organization of the images. With tools from multivariate statistics, the large datasets of image analysis results could be translated into textural fingerprints. Each fingerprint is associated with a developmental state of the biofilm. By following the fingerprints of images over time, we visualize the dynamic developmental history of the biofilm.
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