Field transcriptome revealed critical developmental and physiological transitions involved in the expression of growth potential in japonicarice
Bibliographic record
Abstract
BACKGROUND: Plant growth depends on synergistic interactions between internal and external signals, and yield potential of crops is a manifestation of how these complex factors interact, particularly at critical stages of development. As an initial step towards developing a systems-level understanding of the biological processes underlying the expression of overall agronomic potential in cereal crops, a high-resolution transcriptome analysis of rice was conducted throughout life cycle of rice grown under natural field conditions. RESULTS: A wide range of gene expression profiles based on 48 organs and tissues at various developmental stages identified 731 organ/tissue specific genes as well as 215 growth stage-specific expressed genes universally in leaf blade, leaf sheath, and root. Continuous transcriptome profiling of leaf from transplanting until harvesting further elucidated the growth-stage specificity of gene expression and uncovered two major drastic changes in the leaf transcriptional program. The first major change occurred before the panicle differentiation, accompanied by the expression of RFT1, a putative florigen gene in long day conditions, and the downregulation of the precursors of two microRNAs. This transcriptome change was also associated with physiological alterations including phosphate-homeostasis state as evident from the behavior of several key regulators such as miR399. The second major transcriptome change occurred just after flowering, and based on analysis of sterile mutant lines, we further revealed that the formation of strong sink, i.e., a developing grain, is not the major cause but is rather a promoter of this change. CONCLUSIONS: Our study provides not only the genetic basis for functional genomics in rice but also new insight into understanding the critical physiological processes involved in flowering and seed development, that could lead to novel strategies for optimizing crop productivity.
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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".