<i>cis</i>-Cytokinins that predominate in<i>Pisum sativum</i>during early embryogenesis will accelerate embryo growth in vitro
Bibliographic record
Abstract
Correlative data from monocots suggest that cytokinin (CK) regulates seed development. The involvement of CKs in seed growth was investigated using pea, a eudicot with an unknown CK profile, as a model system. CK profiles were measured by liquid chromatography – tandem mass spectrometry against major stages of embryogenesis, which were documented histologically. Like other grain legumes, CK levels of developing pea seeds fluctuated through development and had mainly nucleotide and riboside forms. Among the 11 CKs detected, cis-isomers (cis-[9R]Z (zeatin riboside), and cis-[9RMP]Z (zeatin riboside 5′ monophosphate)), along with their isopentenyl precursors, were the major forms during pea embryogenesis, whereas corresponding trans-isomers appeared as minor constituents. Highest CK concentrations occurred at the heart-shape stage, when there are high rates of cell division and sugar metabolism. To assess the significance of high CK concentrations observed at the heart-shape stage, a bioassay was developed wherein heart-shaped embryos were excised and cultured on medium containing either cis-[9R]Z, trans-[9R]Z, or kinetin. Growth of cultured heart-shaped embryo explants was significantly augmented by all exogenous CKs relative to controls that were not supplemented with CK. Moreover, at concentrations equivalent to those experienced by an embryo in vivo, cis-[9R]Z was active in enhancing the growth of cultured pea embryos to an extent equal to that of trans-[9R]Z. Overall, the results endorse a growth-promoting role for cis-CKs during seed development.
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 | 0.000 | 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".