A rapid and highly efficient sorghum transformation strategy using <scp>GRF4‐GIF1</scp>/ternary vector system
Bibliographic record
Abstract
Sorghum is an important crop for food, forage, wine and biofuel production. To enhance its transformation efficiency without negative developmental by-effects, we investigated the impact of GRF4-GIF1 chimaera and GRF5 on sorghum transformation. Both GRF4-GIF1 and GRF5 effectively improved the transformation efficiency of sorghum and accelerated the transformation process of sorghum to less than 2 months which was not observed when using BBM-WUS. As agrobacterium effectors increase the ability of T-DNA transfer into plant cells, we checked whether ternary vector system can additively enhance sorghum transformation. The combination of GRF4-GIF1 with helper plasmid pVS1-VIR2 achieved the highest transformation efficiency, reaching 38.28%, which is 7.71-fold of the original method. Compared with BBM-WUS, overexpressing GRF4-GIF1 caused no noticeable growth defects in sorghum. We further developed a sorghum CRISPR/Cas9 gene-editing tool based on this GRF4-GIF1/ternary vector system, which achieved an average gene mutation efficiency of 41.36%, and null mutants were created in the T0 generation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".