Anatomical and transcriptomic characterization of the canola (Brassica napus) maternal seed subregions during ovule and seed development.
Bibliographic record
Abstract
Canola (Brassica napus) contributes $19.3 billion dollars to the Canadian economy each year as a result of its oil- and protein-rich seeds. These economically important seed products are produced in highest concentration in the embryo. Embryo development is supported nutritionally and structurally by the maternal subregions, which include the inner (ISC) and outer distal seed coat (OSC), the chalazal seed coat (CZSC), and the chalazal proliferating tissue (CPT). Research on the maternal seed subregions is limited to the SC as a result of its accessibility; the embedded CZSC and CPT subregions have yet to be characterized in canola. Using light and transmission electron microscopy, I found the CZSC and CPT to be anatomically distinct and experience profound changes throughout seed development. To understand these changes at the RNA level, laser microdissection and RNA sequencing were used to profile these subregions spatially and temporally from the ovule to mature green stage of seed development. Employing vigorous bioinformatics analyses, I found that the maternal subregions are transcriptomically distinct and possess unique RNA populations. From here I began to elucidate the biological processes operating within the maternal subregions. As a whole, the maternal subregions appear to have a critical role in transporting nutrients to the filial subregions as well as in coping with oxidative stress produced during these energy-rich processes. Additionally, using CanEnrich, I was able to generate predictive transcriptional circuits regulating the biological processes occurring within the maternal seed. This research has produced the most comprehensive dataset on the canola seed to date and will provide a valuable resource for research on seed development as well as seed improvement.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".