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Fast and Long: Characterizing Biofil...

Peng, Lin Qian.

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Fast and Long: Characterizing Biofilm Growth Dynamics with Holographic Microscopy.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fast and Long: Characterizing Biofilm Growth Dynamics with Holographic Microscopy./
Author:	Peng, Lin Qian.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	115 p.
Notes:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
Subject:	Optics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28023156
ISBN:	9798662420410

Fast and Long: Characterizing Biofilm Growth Dynamics with Holographic Microscopy.
Peng, Lin Qian.

Fast and Long: Characterizing Biofilm Growth Dynamics with Holographic Microscopy. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 115 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.A.S.)--University of Toronto (Canada), 2020.

This item must not be sold to any third party vendors.

In this thesis, we applied a 4D imaging pipeline based on digital holographic microscopy (DHM) to characterize the development of Pseudomonas aerugi-nosa biofilms. A technique that can recover 3D information from a 2D image, DHM has unique advantages for three-dimensional imaging and is particularly well suited for tracking the dynamics of microscopic biological and physical phenomena. We characterized the growth dynamics of Pseudomonas aeruginosa biofilms to reveal how individual colonies aggregated to form a dense final network. Itwas found that inhibition of a specific hydrolase production encouraged the formation of a denser biofilm structure. This finding assists in guiding directions for developing drugs that can alter specific molecular pathways responsible for biofilm formation and structure. While DHM proved to be a powerful strategy for tracking biofilm formation, it is a computationally intensive approach. Accordingly, we explored the potential of convolutional neural networks (CNN) for reducing the reconstruction and image processing time. The integration of advanced computational approaches with improved hardware strategies, along with the fact that it is a label-less imaging approach, establishes DHM has a powerful new biophysical imaging platform for studying developmental dynamics.

ISBN: 9798662420410Subjects--Topical Terms:

517925
Optics.
Subjects--Index Terms:

3D imaging

Fast and Long: Characterizing Biofilm Growth Dynamics with Holographic Microscopy.
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300 $a 115 p.
500 $a Source: Masters Abstracts International, Volume: 82-01.
500 $a Advisor: Yip, Christopher.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2020.
506 $a This item must not be sold to any third party vendors.
520 $a In this thesis, we applied a 4D imaging pipeline based on digital holographic microscopy (DHM) to characterize the development of Pseudomonas aerugi-nosa biofilms. A technique that can recover 3D information from a 2D image, DHM has unique advantages for three-dimensional imaging and is particularly well suited for tracking the dynamics of microscopic biological and physical phenomena. We characterized the growth dynamics of Pseudomonas aeruginosa biofilms to reveal how individual colonies aggregated to form a dense final network. Itwas found that inhibition of a specific hydrolase production encouraged the formation of a denser biofilm structure. This finding assists in guiding directions for developing drugs that can alter specific molecular pathways responsible for biofilm formation and structure. While DHM proved to be a powerful strategy for tracking biofilm formation, it is a computationally intensive approach. Accordingly, we explored the potential of convolutional neural networks (CNN) for reducing the reconstruction and image processing time. The integration of advanced computational approaches with improved hardware strategies, along with the fact that it is a label-less imaging approach, establishes DHM has a powerful new biophysical imaging platform for studying developmental dynamics.
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650 4 $a Microbiology. $3 536250
650 4 $a Engineering. $3 586835
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653 $a Convolution neural network
653 $a Holographic microscope
653 $a In vivo imaging
653 $a P.aeruginosa
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