Assessment of regeneration and transient expression factors for <i>Agrobacterium</i> -mediated transformation of <i>Prunus salicina</i> Lindl
Bibliographic record
Abstract
Summary High levels of regeneration and optimized transient expression factors are critical for increasing transformation rates in plant species. An efficient regeneration protocol was developed and transient expression experiments were carried out to provide information for the improvement of Agrobacterium tumefaciens mediated transformation system in Japanese plum ( Prunus salicina Lindl.). Five different varieties of Japanese plum ('Shiro', 'Early Golden', 'Gladstone', 'Red Heart' and 'Bruce'), four different basal salt mixtures (QL, WPM, B5 and MS), light regimes and TDZ pretreatments were evaluated to optimize adventitious shoot regeneration. Regeneration ability was highly variety dependent. Basal salt composition significantly affected the regeneration frequency. Best regeneration frequencies were obtained from 'Shiro' (56.4%) on QL salts and 'Early Golden' (42.8%) on B5 medium. Subsequently transient expression experiments were carried out using three Agrobacterium tumefaciens strains; LBA4404, EHA105 and GV3101, each containing binary vector pCAMBIA2301. There were significant differences observed among Agrobacterium strains tested. Agrobacterium strains LBA4404 and EHA105 were much more effective than GV3101 strain and the presence of acetosyringone in the co-cultivation medium resulted in increased GUS expression.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".