Efficient genetic transformation is essential for the delivery of the CRISPR/Cas9 genome editing system and thus represents an important technology for breeding-oriented research in barley (
Hordeum vulgare
L.). However, transformation and plant regeneration from tissue culture remain challenging in non-model barley genotypes. Previous studies demonstrated that expression of a chimeric fusion between two interacting transcription factors, GROWTH-REGULATING FACTOR 4 (GRF4) and GRF-INTERACTING FACTOR 1 (GIF1), enhances regeneration capacity in wheat and other species.
In this study, we evaluated the effect of the wheat-derived
GRF4-GIF1
morphogenic regulators on biolistic transformation and genome editing efficiency in three commercial barley cultivars: Tselinniy 5, Aley, and G-23035.
The JD633 construct carrying GRF4-GIF1 enabled recovery of stable transformants in all three genotypes, with efficiencies ranging from 2.5% to 5%, whereas the control construct lacking morphogenic regulators resulted in no transgenic events in any of the tested varieties. Among transformed T
0
plantlets, genome editing efficiency reached 64.3%, with predominantly biallelic mutations that were stably inherited in the T
1
generation. Molecular screening revealed the presence of plasmid-free edited plants in the T
0
generation, likely arising from transient
Cas9
expression, and provided evidence of tissue chimerism.
These results demonstrate that the
GRF-GIF
system facilitates genome editing, providing a practical framework for accelerating precision breeding in barley.
E. M. Timonova, A. Kiseleva, M. A. Nesterov et al.· Frontiers in Plant Science· 0 citations
Effective flood monitoring is critical for minimizing the impacts of flood disasters on populations and infrastructure. Yet reliable remote sensing across extensive and environmentally diverse regions remains challenging, as most segmentation algorithms lack the generalisation capacity required for large-scale application, while annotated flood data are scarce and unevenly distributed. This study presents an end-to-end multimodal framework for Russian Federation territories sustainable flood monitoring and damage assessment based on synthetic aperture radar data, multispectral imagery, and digital elevation models with their derivatives, forming a 21-channel input. Using a self-collected multimodal dataset covering seven Russian regions, two strategies for water surface detection under limited data conditions were compared: a supervised U-Net++ model and the self-supervised AnySat architecture pre-trained and fine-tuned for the segmentation task. Under the data conditions of this study, supervised learning proved more effective, while the AnySat-based approach offered greater stability and retains advantages for settings where larger unlabelled data or missing modalities at inference are expected. The best flood area predictions were used to estimate flood impact in urban areas in terms of the area affected, material damage, casualties, and ecological and agricultural impact. The estimations were conducted following the official methodology of the Russian Ministry of Emergency Situations. Applied to the 2019 Tulun flood, the obtained results closely matched official assessments, except for material damage, due to the open-source databases usage. The results demonstrate the potential of deep learning and multimodal satellite data integration for scalable, reliable flood monitoring across diverse environmental and data-limited conditions.
I. Novikov, S. Illarionova, Ruslan B. Dzharkinov et al.· arXiv.org· 0 citations
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