Optimization of the Genetic Transformation System of Lettuce
Bibliographic record
Abstract
Lettuce (Lactuca sativa L.) is an annual vegetable crop of the family Asteraceae. It is the most consumed leaf vegetable in the world and is highly valued for its edible and medicinal value. Using transgenic technology, introducing functional genes into plants can shorten the breeding time and improve the quality of lettuce. However, in the genetic transformation of lettuce, the application of transgenic technology is limited by the low conversion rate. In this experiment, using ‘S39’ cotyledons as the test material, to establish a stable genetic transformation system. The results showed that when the leaf regeneration medium was 0.01 mg/L 6-BA and 0.4 mg/L NAA, the highest regeneration rate reached 97.9%, which was the best leaf regeneration hormone concentration. When the infection fluid was used in the OD600 value of 0.2, the infected leaves were in good condition and controllable Agrobacterium was the most suitable infection fluid concentration. When the infection time was 15 min, the infection effect was the best, the leaves grew well, and the resistant plants could grow. The screening in a medium containing 30 mg/L of Kana concentration and 250 mg/L of Cef was the suitable medium formulation. NAA 0.1 mg /L and 6-BA 0.2 mg /L were selected as the optimal concentrations in the rooting medium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".