Diurnal dynamics of different circadian transcription modules in Chinese pine needles and roots during dormancy induction
Bibliographic record
Abstract
Winter dormancy ensures that trees in temperate zones respond appropriately to environmental variations, thereby enhancing their adaptability and resilience. In the northern hemisphere, the dormancy of conifers is induced by short-day and cold temperature. Previous studies have revealed that TFL2 is a key regulator involved in conifers' bud set and growth cessation during the dormancy-induced phase. Based on the annual expression profile analysis of PtTFL2 in Chinese pine (Pinus tabuliformis Carr.), we identified key time nodes for dormancy initiation in autumn. To provide insight of the diurnal transcriptome dynamic in needles and roots during dormancy introduction, RNA-seq was performed at 12 consecutive time points in 24 h under natural environment in P. tabuliformis. Interestingly, we found that both needles and roots have rhythmic oscillatory genes, even though the roots could not receive light signals directly. We applied weighted gene co-expression network analysis (WGCNA) to integrate differentially expressed genes between needles and roots at different time points into highly correlated gene modules. Although the two modules are subject to different transcriptional controls during dormancy, both contain 35 identical transcriptional regulators. Some transcriptional factors with functional similarities and synergistic effects were found to play a role in the regulatory pathway, which provided some data support for mining gene functions and analyzing related regulatory pathways. Our results provide new insights into the molecular regulatory mechanisms involved in pine dormancy.
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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".