Advances in eggplant tissue culture and genetic engineering
Bibliographic record
Abstract
This paper summarized the development of tissue culture and genetic engineering of eggplant, and also discussed the prospect on application of genetic engineering in eggplant breeding. The most studied regeneration system has been established through the culture of cotyledon, hypocotyl, leave, embryo and stalk. The regeneration is influenced by plant genotype, different explants, type of medium etc. Different genotype has different regeneration ability and approach. Because of its excellence as the object of genetic transformation, protoplasts has been studied a lot on its sources, component of the culture liquid and growing method. Haploid acquired through the culture of pollen and microspore can been used by the production of F_(1) hybrid. So far, the regeneraiton system has been established through the culture of seeding explants, pollens, protoplasts and microspores. Genetic enginering of eggplant plays an important role in enhancement of existing quality and creation of new germplasm. The Agrobacterium-mediated method is the usual way in eggplant genetic engineering, and has been optimized in the select and regeneration of the explants, select of the antibiotic concentration and bacterium concentration, days of advance culture and corporate culture. Genetic engineering of eggplant is mainly used in insect resistance and parthenocarpy, but it is rarely used in other field such as resistance of disease and adversity. So, the author think that the future research should center on: the mark and clone of the destination gene, the explore of efficient regeneration system, the application of transgene methods and scale the use of it.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".