Integrated transcriptomic and proteomic profiling of white spruce stems during the transition from active growth to dormancy
Bibliographic record
Abstract
In the autumn, stems of woody perennials such as forest trees undergo a transition from active growth to dormancy. We used microarray transcriptomic profiling in combination with a proteomics analysis to elucidate processes that occur during this growth-to-dormancy transition in a conifer, white spruce (Picea glauca[Moench] Voss). Several differentially expressed genes were likely associated with the developmental transition that occurs during growth cessation in the cambial zone and the concomitant completion of cell maturation in vascular tissues. Genes encoding for cell wall and membrane biosynthetic enzymes showed transcript abundance patterns consistent with completion of cell maturation, and also of cell wall and membrane modifications potentially enabling cells to withstand the harsh conditions of winter. Several differentially expressed genes were identified that encoded putative regulators of cambial activity, cell development and of the photoperiodic pathway. Reconfiguration of carbon allocation figured centrally in the tree's overwintering preparations. For example, genes associated with carbon-based defences such as terpenoids were down-regulated, while many genes associated with protein-based defences and other stress mitigation mechanisms were up-regulated. Several of these correspond to proteins that were accumulated during the growth-to-dormancy transition, emphasizing the importance of stress protection in the tree's adaptive response to overwintering.
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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".