THE FEEDBACK BETWEEN CELL SHAPE AND CYTOSKELETAL DYNAMICS ON CELL DIVISION AND EXPANSION IN EPIDERMAL PAVEMENT CELLS OF ARABIDOPSIS
Bibliographic record
Abstract
The epidermal layer of Arabidopsis thaliana (L.) Heynh. leaves plays a critical role in tissue formation, particularly through the development of lobes in the anticlinal walls of pavement cells, though curvature is also noted in the periclinal walls. The epidermal pavement cells form unique lobed shapes over time, while stomata, composed of guard cells, are identical in size and shape. Both cell types emerge from the same precursor cells and are produced through highly regulated divisions based on the morphology of the parent cell. Specifically, asymmetric divisions within the epidermis initiate stomatal lineages, where all divisions, excluding the terminal division, are asymmetric. Mutant lines with altered gene expression were selected based on phenotypes exhibiting altered cell division, expansion, and generation of specialized cells were used to evaluate hypotheses regarding sidewall curvature, mother cell bisection, and cell-division frequency. The pre-prophase band (PPB) microtubule array predicts the future division sites. This study explored a potential feedback loop between cell division and shape in Arabidopsis pavement cells, where lobes in the anticlinal cell wall curve towards the cell center, forming narrow regions that are hotspots for new cell divisions. In these divisions, the other cell area and intercepted sidewall segments were often bisected asymmetrically. Persistent microtubule bands at these sites can develop into PPBs, driving new cell wall formation and enhancing existing sidewall curvature. My research aims to determine how these lobes influence division sites and contribute to my understanding of the cytoskeletal regulation of cell division and expansion within the epidermal layer. My thesis focused on the development of the epidermal layer in Arabidopsis thaliana, emphasizing division orientation and microtubule activity in the epidermis.
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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".