| |  Online-Ressource |
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| Verfasst von: | Constantinou, Iordania [VerfasserIn]  |
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| | Jendrusch, Michael [VerfasserIn] |
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| | Aspert, Théo [VerfasserIn] |
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| | Görlitz, Frederik [VerfasserIn] |
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| | Schulze, André [VerfasserIn] |
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| | Charvin, Gilles [VerfasserIn] |
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| | Knop, Michael [VerfasserIn] |
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| Titel: | Self-learning microfluidic platform for single-cell imaging and classification in flow |
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| Verf.angabe: | Iordania Constantinou, Michael Jendrusch, Théo Aspert, Frederik Görlitz, André Schulze, Gilles Charvin and Michael Knop |
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| E-Jahr: | 2019 |
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| Jahr: | 9 May 2019 |
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| Umfang: | 21 S. |
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| Fussnoten: | Gesehen am 29.10.2019 |
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| Titel Quelle: | Enthalten in: Micromachines |
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| Ort Quelle: | Basel : MDPI, 2010 |
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| Jahr Quelle: | 2019 |
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| Band/Heft Quelle: | 10(2019,5) Artikel-Nummer 311, 21 Seiten |
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| ISSN Quelle: | 2072-666X |
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| Abstract: | Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy. |
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| DOI: | doi:10.3390/mi10050311 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext ; Verlag ; Resolving-System: https://doi.org/10.3390/mi10050311 |
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| | Volltext: https://www.mdpi.com/2072-666X/10/5/311 |
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| | DOI: https://doi.org/10.3390/mi10050311 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | 3D flow focusing |
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| | 3D particle focusing |
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| | bioMEMS |
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| | microfluidics |
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| | neural networks |
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| | particle/cell imaging |
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| | unsupervised learning |
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| | variational inference |
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| K10plus-PPN: | 1680651226 |
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| Verknüpfungen: | → Zeitschrift |
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Self-learning microfluidic platform for single-cell imaging and classification in flow / Constantinou, Iordania [VerfasserIn]; 9 May 2019 (Online-Ressource)
