Is protein carbonylation a biomarker of seed priming and ageing?
Bibliographic record
Abstract
For a long time, it has been known that seed priming allows the improvement of plant production and tolerance to abiotic stresses. However, a negative effect on the longevity of the seeds thus primed was observed; these mechanisms are still poorly understood. In addition, it has been shown by several authors that seed ageing is associated with the oxidation and particularly with carbonylation of protein. Our work consisted in studying the AOPP and carbonyl protein at the different parts of the embryo from freshly primed seeds and from those that have been primed for 4 years (after storage). We subjected Vigna unguiculata (L.) Walp. seeds to a single or double hydropriming. Our study showed that hydropriming, and more particularly a double cycle of hydration-dehydration, makes it possible to attenuate the oxidation of the protein while it favours a certain threshold of carbonylation in the freshly dehydrated seeds in order to better trigger the germination process. On the other hand, after a storage period of 4 years, these dehydrated seeds are characterised by a strong accumulation of the products of oxidation and especially carbonylated protein, compared with the untreated seeds, which could explain the decrease of the longevity of these seeds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".