Identification and expression analysis of <i>LATERAL ORGAN BOUNDARIES DOMAIN</i> (<i>LBD</i>) transcription factor genes in <i>Fragaria vesca </i>
Bibliographic record
Abstract
The LATERAL ORGAN BOUNDARIES DOMAIN (LBD) gene family encodes plant-specific transcription factors that play crucial roles in the growth and development in many plant species. However, no systematic study of LBD genes has been conducted in strawberry. In this study, 35 LBD (FvLBD) genes were identified in the diploid woodland strawberry genome (Fragaria vesca L.). These LBD proteins could be classified into two groups based on the structure of their lateral organ boundaries domain. The promoters of FvLBD genes contain different regulatory elements associated with potential response to different environmental stimuli and plant developmental signals. Furthermore, we analysed the expression patterns of the LBD genes during the callus formation in strawberry and the results suggested that FvLBD16 might play a prominent role in the regulation of callus formation. In addition, we investigated the expression profiles of FvLBD genes during early fruit development based on transcriptome data. We found that some FvLBD genes show a specific expression pattern. These results indicate that FvLBD genes may have a function in early fruit development. Together, the present study provides insights into possible functions of FvLBD genes and provides a basis for further functional research of FvLBD proteins.
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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".