Regulatory Role of OsMADS34 in the Determination of Glumes Fate, Grain Yield, and Quality in Rice
Bibliographic record
Abstract
Grasses produce seeds on spikelets, a unique type of inflorescence. Despite the importance of grass crops for food, the genetic mechanisms that control spikelet development remain poorly understood. In this study, we used m34-z, a new mutant allele of the rice (Oryza sativa) E-class gene OsMADS34, to examine OsMADS34 function in determining the identities of glumes (rudimentary glume and sterile lemma) and grain size. In the m34-z mutant, both the rudimentary glume and sterile lemma were homeotically converted to the lemma-like organs and acquired the lemma identity, suggesting that OsMADS34 plays important roles in the development of glumes. In the m34-z mutant, most of the grains from the secondary panicle branches were decreased in size, compared with grains from wild type, but no differences were observed in the grains from the primary panicle branches. The amylose content and gel consistency, and a seed-setting rate from the secondary panicle branches were reduced in the m34-z mutant. Interesting, transcriptional activity analysis revealed that OsMADS34 protein was a transcription repressor and it may influence grain yield by suppressing the expressions of BG1, GW8, GW2 and GL7 in the m34-z mutant. These findings revealed that OsMADS34 largely affects grain yield by affecting the size of grains from the secondary branches.
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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) | 0.000 | 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".