Molecular phenotyping of cell wall development in the Arabidopsis thaliana primary stem
Bibliographic record
Abstract
Plant cell walls are sophisticated, dynamic structures that play a vital role in coordinating the directional growth of plant tissues. The rapid elongation of the inflorescence stem in the model plant Arabidopsis thaliana is accompanied by radical changes in cell wall structure and chemistry, but the study of the underlying mechanisms has been hampered by difficulties in sampling discrete developmental states along the developing stem. I have created a novel sampling approach that allows me to sample stem tissues representing specific and distinct developmental phases (elongation rate increase, maximum growth rate, and growth cessation) from individual plants, by use of time-lapse imagery and computational analysis of growth kinematic profiles. This high-resolution growth context enables the harvest of pooled, developmentally-matched samples that I then used for transcriptome profiling of growth-characterized stem segments, and for immunohistochemical analysis of growth-associated cell wall epitopes in specific cell types within stem crosssections. The resulting transcript profiles have identified dozens of genes, both known and novel, whose expression is coupled to these growth transitions. Unique cell typespecific epitope patterns were observed during the most rapid phase of elongation, providing evidence for structural divergence of cell walls among these cell types despite a common developmental environment of diffuse elongation. This study has thus produced two global perspectives of the cellular events accompanying the transition from early differentiation through maximum anisotropic cell expansion to growth cessation and secondary cell wall maturation, in a single organ. The results collectively provide important hypothesis-generating insights anchored to a precise developmental scaffold of cell wall differentiation established through growth kinematic profiling.
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".