High nitrogen fertilization and stem leaning have overlapping effects on wood formation in poplar but invoke largely distinct molecular pathways
Bibliographic record
Abstract
Previous studies indicated that high nitrogen fertilization may impact secondary xylem development and alter fibre anatomy and composition. The resulting wood shares some resemblance with tension wood, which has much thicker cell walls than normal wood due to the deposition of an additional layer known as the G-layer. This report compares the short-term effects of high nitrogen fertilization and tree leaning to induce tension wood, either alone or in combination, upon wood formation in young trees of Populus trichocarpa (Torr. & Gray) × P. deltoides Bartr. ex Marsh. Fibre anatomy, chemical composition and transcript profiles were examined in newly formed secondary xylem. Each of the treatments resulted in thicker cell walls relative to the controls. High nitrogen and tree leaning had overlapping effects on chemical composition based on Fourier transform infrared analysis, specifically indicating that secondary cell wall composition was shifted in favour of cellulose and hemicelluloses relative to lignin content. In contrast, the high-nitrogen trees had shorter fibres, whilst the leaning trees had longer fibres that the controls. Microarray transcript profiling carried out after 28 days of treatment identified 180 transcripts that accumulated differentially in one or more treatments. Only 10% of differentially expressed transcripts were affected in all treatments relative to the controls. Several of the affected transcripts were related to carbohydrate metabolism, secondary cell wall formation, nitrogen metabolism and osmotic stress. RT-qPCR analyses at 1, 7 and 28 days showed that several transcripts followed very different accumulation profiles in terms of rate and level of accumulation, depending on the treatment. Our findings suggest that high nitrogen fertilization and tension wood induction elicit largely distinct and molecular pathways with partial overlap. When combined, the two types of environmental cue yielded additive effects.
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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 | 0.001 |
| 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".