東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Fast and Long: Characterizing Biofil...

Peng, Lin Qian.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fast and Long: Characterizing Biofilm Growth Dynamics with Holographic Microscopy./
作者:	Peng, Lin Qian.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	115 p.
附註:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
標題:	Optics. -
電子資源:	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.
LDR:02479nmm a2200373 4500 001 2276910
005 20210510092438.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798662420410
035 $a (MiAaPQ)AAI28023156
035 $a AAI28023156
040 $a MiAaPQ $c MiAaPQ
100 1 $a Peng, Lin Qian. $3 3555212
245 1 0 $a Fast and Long: Characterizing Biofilm Growth Dynamics with Holographic Microscopy.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
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.
590 $a School code: 0779.
650 4 $a Optics. $3 517925
650 4 $a Microbiology. $3 536250
650 4 $a Engineering. $3 586835
653 $a 3D imaging
653 $a Convolution neural network
653 $a Holographic microscope
653 $a In vivo imaging
653 $a P.aeruginosa
690 $a 0752
690 $a 0410
690 $a 0537
710 2 $a University of Toronto (Canada). $b Chemical Engineering & Applied Chemistry. $3 3190728
773 0 $t Masters Abstracts International $g 82-01.
790 $a 0779
791 $a M.A.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28023156