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|a (TOE)ost1769086
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|a (TOE)1769086
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|a E 1.99:LLNL-JRNL-819709
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|a E 1.99:LLNL-JRNL-819709
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|a LLNL-JRNL-819709
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|a Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images
|h [electronic resource]
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|a Washington, D.C. :
|b United States. National Nuclear Security Administration ;
|a Oak Ridge, Tenn. :
|b Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
|c 2020.
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| 300 |
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|a Size: p. 8540-8548 :
|b digital, PDF file.
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| 336 |
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|a text
|b txt
|2 rdacontent.
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| 337 |
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|a computer
|b c
|2 rdamedia.
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|a online resource
|b cr
|2 rdacarrier.
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|a Published through Scitech Connect.
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|a 09/03/2020.
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|a "LLNL-JRNL-819709."
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|a "Journal ID: ISSN 1520-6106."
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|a "Other: 1030767."
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|a ": US2207209."
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|a Lohrer, Michael F. ; Liu, Yang ; Hanna, Darrin M. ; Wang, Kang-Hsin ; Liu, Fu-Tong ; Laurence, Ted A. ; Liu, Gang-yu ;
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|a The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high-resolution images acquired via scanning electron microscopy (SEM) or atomic force microscopy (AFM). While direct and visual, the determination has been made by directly looking at the images by researchers. Here we report a machine learning approach using pattern recognition in conjunction with cellular biophysical knowledge of dendritic cells to determine the maturation status of dendritic cells automatically. The determination from AFM images reaches 100% accuracy. The results from SEM images reaches 94.9%. The results demonstrate the accuracy of using machine learning for accelerating data analysis, extracting information, and drawing conclusions from high-resolution cellular images, paving the way for future applications requiring high-throughput and automation, such as cellular sorting and selection based on morphology, quantification of cellular structure, and DC-based immunotherapy.
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| 536 |
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|b AC52-07NA27344.
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| 650 |
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|a 37 inorganic, organic, physical, and analytical chemistry
|2 local.
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| 650 |
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|a Scanning electron microscopy
|2 local.
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| 650 |
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|a Morphology
|2 local.
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|a Algorithms
|2 local.
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|a Cells
|2 local.
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|a Structural characteristics
|2 local.
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|a Inorganic, organic, physical, and analytical chemistry
|2 local.
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| 710 |
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|a Lawrence Livermore National Laboratory.
|4 res.
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| 710 |
1 |
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|a United States.
|b National Nuclear Security Administration.
|4 spn.
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| 710 |
1 |
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|a United States.
|b Department of Energy.
|b Office of Scientific and Technical Information
|4 dst.
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| 856 |
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|u https://www.osti.gov/servlets/purl/1769086
|z Full Text (via OSTI)
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| 907 |
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|a .b128285722
|b 02-28-23
|c 12-08-22
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| 998 |
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|a web
|b 12-08-22
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| 952 |
f |
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|p Can circulate
|a University of Colorado Boulder
|b Online
|c Online
|d Online
|e E 1.99:LLNL-JRNL-819709
|h Superintendent of Documents classification
|i web
|n 1
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