Diurnal dynamics of different circadian transcription modules in Chinese Pine needles and roots during dormancy introduction
Bibliographic record
Abstract
Abstract Winter dormancy ensures temperate zone’s trees proper response to environmental changes and enhances their adaptability. In northern hemisphere, conifers’ dormancy 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 hours under natural environment in Chinese pine. Interestingly, we found that both needles and roots have rhythmic oscillatory genes, even though the roots could not receive light signals directly. We constructed the transcription factor (TF) co-expression networks of needles and roots and in the core TF modules, we identified 10 hub genes in each. Revealed a network associated with the putative core clock genes (PtCCA1, PtPRR4, PtGI, and PtTCP42), which were also isolated, suggesting that they are important in the circadian regulation of the transcriptome. Regulatory network analysis identified key TFs associated with dormancy and cold acclimation in two tissues. 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".