An efficient shoot regeneration system and <i>Agrobacterium</i>-mediated transformation with <i>codA</i> gene in a doubled haploid line of broccoli
Bibliographic record
Abstract
Broccoli is an important vegetable crop belonging to the genus of Brassica. However, it is often affected by biotic and abiotic stresses that result in large losses in yield and quality. Genetic manipulation has opened the opportunity for germplasm improvement of broccoli. In this study, an efficient shoot regeneration and Agrobacterium-mediated transformation system was established. The optimum medium for shoot induction was selected for three types of explant including hypocotyl, petiole, and peduncle, and up to 90% regeneration frequency was obtained. The transformation procedure was developed with the EHA105 strain, harboring the PUC19 vector, along with the target gene of codA and a hygromycin-resistance gene. Several factors were optimized, including hygromycin concentration for selection and the Agrobacterium density yielding the highest transformation frequency. Among the three types of explants, peduncle explants showed the highest response to this procedure, and the highest frequency of transformation (3.4%) was obtained depending on the analysis of polymerase chain reaction and Southern blot. In conclusion, the present study introduced a reliable system for plant regeneration and genetic transformation with three types of explants for a DH line of broccoli, and peduncle explant resulted in the highest transformation frequency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".