NIR-Mediated Nanohybrids of Upconversion Nanophosphors and Fluorescent Conjugated Polymers for High-Efficiency Antibacterial Performance Based on Fluorescence Resonance Energy Transfer

2016 ◽  
Vol 5 (23) ◽  
pp. 2967-2971 ◽  
Author(s):  
Junting Li ◽  
Qi Zhao ◽  
Feng Shi ◽  
Chenghui Liu ◽  
Yanli Tang
Keyword(s):  
Energy Transfer ◽  
Conjugated Polymers ◽  
Fluorescence Resonance Energy Transfer ◽  
High Efficiency ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Antibacterial Performance ◽  
Fluorescence Resonance

scholarly journals Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning

2020 ◽  
Vol 13 (06) ◽  
pp. 2050021
Author(s):  
Lin Ge ◽  
Fei Liu ◽  
Jianwen Luo
Keyword(s):  
Energy Transfer ◽  
Deep Learning ◽  
Fluorescence Resonance Energy Transfer ◽  
High Efficiency ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Optical Diffraction ◽  
Fluorescence Resonance ◽  
Quantitative Fluorescence ◽  
Image Analyses

Intensity-based quantitative fluorescence resonance energy transfer (FRET) is a technique to measure the distance of molecules in scale of a few nanometers which is far beyond optical diffraction limit. This widely used technique needs complicated experimental process and manual image analyses to obtain precise results, which take a long time and restrict the application of quantitative FRET especially in living cells. In this paper, a simplified and automatic quantitative FRET (saqFRET) method with high efficiency is presented. In saqFRET, photoactivatable acceptor PA-mCherry and optimized excitation wavelength of donor enhanced green fluorescent protein (EGFP) are used to simplify FRET crosstalk elimination. Traditional manual image analyses are time consuming when the dataset is large. The proposed automatic image analyses based on deep learning can analyze 100 samples within 30[Formula: see text]s and demonstrate the same precision as manual image analyses.

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