Molecular characterization of seven genes encoding ethylene-responsive transcriptional factors during plum fruit development and ripening
Bibliographic record
Abstract
Seven ERF cDNAs were cloned from two Japanese plum (Prunus salicina L.) cultivars, 'Early Golden' (EG) and 'Shiro' (SH). Based on the sequence characterization, these Ps-ERFs could be classified into three of the four known ERF families. Their predicted amino acid sequences exhibited similarities to ERFs from other plant species. Functional nuclear localization signal analyses of two Ps-ERF proteins (Ps-ERF1a and -1b) were carried out using confocal microscopy. Expression analyses of Ps-ERF mRNAs were studied in the two plum cultivars in order to determine the role of this gene family in fruit development and ripening. The seven Ps-ERFs displayed differential expression pattern and levels throughout the various stages of flower and fruit development. The diversity in Ps-ERFs accumulation was largely due to the differences in their responses to the levels of ethylene production. However, other plant hormones such as cytokinin and auxin, which accumulate strongly throughout the various developmental stages, also influence the Ps-ERFs expression. The effect of the plant hormones, gibberellin, cytokinin, auxin, and ethylene in regulating the different Ps-ERF transcripts was investigated. A model was proposed in which the role played by the plant hormone auxin is as important as that of ethylene in initiating and determining the date and rate of ripening in Japanese plums.
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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 | 0.000 |
| 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".