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MCMQCT

Multi-functional Computational Microscopy for Quantitative Cell Tracking

Total Cost €

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EC-Contrib. €

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Partnership

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Project "MCMQCT" data sheet

The following table provides information about the project.

Coordinator
BAR ILAN UNIVERSITY 

Organization address
address: BAR ILAN UNIVERSITY CAMPUS
city: RAMAT GAN
postcode: 52900
website: www.biu.ac.il

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Israel [IL]
 Project website https://mcmqctblog.wordpress.com/
 Total cost 263˙385 €
 EC max contribution 263˙385 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-GF
 Starting year 2015
 Duration (year-month-day) from 2015-08-01   to  2018-07-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    BAR ILAN UNIVERSITY IL (RAMAT GAN) coordinator 263˙385.00
2    THE REGENTS OF THE UNIVERSITY OF CALIFORNIA US (OAKLAND CA) partner 0.00

Map

 Project objective

Quantitative assessment of cellular phenotypes can result from various processes such as genetic manipulation, gene silencing, drug application or environmental changes. In many biological processes evident development of phenotypes often requires a long-term study of the sample, which can capture events at high-resolution and high frame rates. The essential working tool for this kind of studies is the optical microscope, which is essentially limited by a small field of view of the sample in two-dimensional (2D) environment, unsuitable for the quantitative assessment of cells in a more natural environment, in which they develop and evolve within complex communities. For this task, I purpose a novel platform based on a holographic on-chip microscopy which represents a new generation of computational microscopes, by integrating transformative technologies, which will ultimately enable five-dimensional, photodamage optimized at diffraction limited resolution, quantitative cell tracking. We believe that these functionalities and the platform's cost effectiveness will enable next generation high-throughput scientific research on the influence of drugs, genome modification and environmental changes, developing new models for cell locomotion, such as viral plague growth assays performed in hospitals and clinics, detection of parasites and bacteria in food/water and for education purposes, including in developing countries.

 Publications

year authors and title journal last update
List of publications.
2017 Yair Rivenson, Zoltán Göröcs, Harun Günaydin, Yibo Zhang, Hongda Wang, Aydogan Ozcan
Deep learning microscopy
published pages: 1437, ISSN: 2334-2536, DOI: 10.1364/OPTICA.4.001437
Optica 4/11 2019-07-24
2016 Yair Rivenson, Yichen Wu, Hongda Wang, Yibo Zhang, Alborz Feizi, Aydogan Ozcan
Sparsity-based multi-height phase recovery in holographic microscopy
published pages: , ISSN: 2045-2322, DOI: 10.1038/srep37862
Scientific Reports 6/1 2019-07-24
2018 Yair Rivenson, Hatice Ceylan Koydemir, Hongda Wang, Zhensong Wei, Zhengshuang Ren, Harun Günaydın, Yibo Zhang, Zoltán Göröcs, Kyle Liang, Derek Tseng, Aydogan Ozcan
Deep Learning Enhanced Mobile-Phone Microscopy
published pages: 2354-2364, ISSN: 2330-4022, DOI: 10.1021/acsphotonics.8b00146
ACS Photonics 5/6 2019-07-24
2017 Yair Rivenson, Yibo Zhang, Harun Günaydın, Da Teng, Aydogan Ozcan
Phase recovery and holographic image reconstruction using deep learning in neural networks
published pages: 17141, ISSN: 2047-7538, DOI: 10.1038/lsa.2017.141
Light: Science & Applications 7/2 2019-07-24
2017 Yair Rivenson, Chris Wu, Hongda Wang, Yibo Zhang, Aydogan Ozcan
High resolution computational on-chip imaging of biological samples using sparsity constraint (Conference Presentation)
published pages: 100550L, ISSN: , DOI: 10.1117/12.2251765
Optics and Biophotonics in Low-Resource Settings III 2019-07-24

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