Proteome analysis of early somatic embryogenesis in <b><i>Picea glauca</i></b>
Bibliographic record
Abstract
Forestry is a valuable natural resource for many countries. Rapid production of large quantities of genetically improved and uniform seedlings for restocking harvested lands is a key component of sustainable forest management programs. Clonal propagation through somatic embryogenesis has the potential to meet this need in conifers and can offer the added benefit of ensuring consistent seedling quality. Although in commercial use, mass production of conifers through somatic embryogenesis is relatively new and there are numerous biological unknowns regarding this complex developmental pathway. To aid in unravelling the embryo developmental process, two-dimensional electrophoresis was employed to quantitatively assess the expression levels of proteins across four stages of somatic embryo maturation in white spruce (0, 7, 21 and 35 days post abscisic acid treatment). Forty-eight differentially expressed proteins have been identified, which display a significant change in abundance as early as day 7 of embryo development. These proteins are involved in a variety of cellular processes, many of which have not previously been associated with embryo development. The identification of these proteins was greatly assisted by the availability of a substantial expressed sequence tag (EST) resource developed for white, sitka and interior spruce. The combined use of these spruce ESTs in conjunction with GenBank accessions for other plants improved the rate of protein identification from 38% to 62% when compared with GenBank alone using automated, high-throughput techniques. This underscores the utility of EST resources in a proteomic study of any species for which a genome sequence is unavailable.
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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".