Claim · model-written summary
The approach enabled fluorescence detection and quantification for three bacterial gene expression examples.
- Taxon
- Oryza sativa
Oryza sativae1 - Interactor
- A. oleariusAzoarcus oleariuse0symbiont
- Outcome
- fluorescence detection and quantificatione0
Not reportedComparatorValue
Verbatim
e0“The deep-learning-based approach can be applied for fluorescence detection and quantification, exemplified by a weakly expressed, a cell-density modulated and a nitrogen-fixation gene in A. olearius.”
e1“Azoarcus olearius BH72 colonizing Oryza sativa roots”
Title
Xun Jiang; Tobias Pees; Barbara Reinhold‐Hurek New Phytologist 2022. doi:10.1111/nph.18344
Other claims from this paper (3)
- Method / resource The approach greatly reduced or removed tdTomato-channel autofluorescence in rice-root images.
- Association nif genes were induced more strongly inside rice roots than outside.
- Method / resource The pretrained model can process other images of the same tissues under the same settings.