Filling in the gaps: a road map to establish a model system to study developmental programmed cell death
Bibliographic record
Abstract
Only a handful of model systems for studying programmed cell death (PCD) exist. The model Arabidopsis thaliana has generated a plethora of knowledge, but it is essential to introduce new models to broaden our understanding of the commonalities of PCD. This review focuses on Aponogeton madagascariensis (the lace plant) as a choice model to study PCD in vivo. PCD plays a key role in plant development and defence. Thus, identifying key regulators across plants is a priority in the field. The formation of perforations in lace plant leaves in areas called areoles is a striking example of PCD. Cells undergoing PCD within areoles can be easily identified from a loss of their anthocyanin pigmentation. In contrast, cells adjacent to veins, non-PCD cells, retain anthocyanins, creating a gradient of cell death. The spatiotemporal pattern of perforation formation, a gradient of cell death within areoles, and the availability of axenic cultures provide an excellent in vivo system to study mechanisms of developmental PCD. The priorities to further develop this model involve sequencing the genome, establishing transformation protocols, and identifying anthocyanin species to determine their medicinal properties. We discuss practical methodologies and challenges associated with developing the lace plant as a model to study PCD.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".