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Record W4288069550 · doi:10.1242/dev.200783

Cell type-specific dynamics underlie cellular growth variability in plants

2022· article· en· W4288069550 on OpenAlexafffund
Constance Le Gloanec, Loann Collet, Sylvia Rodrigues da Silveira, Binghan Wang, Anne‐Lise Routier‐Kierzkowska, Daniel Kierzkowski

Bibliographic record

VenueDevelopment · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsBiologyMulticellular organismTrichomePetiole (insect anatomy)ArabidopsisCell biologyArabidopsis thalianaBotanyMutantEvolutionary biologyCellGeneticsGene

Abstract

fetched live from OpenAlex

Coordination of growth, patterning and differentiation is required for shaping organs in multicellular organisms. In plants, cell growth is controlled by positional information, yet the behavior of individual cells is often highly heterogeneous. The origin of this variability is still unclear. Using time-lapse imaging, we determined the source and relevance of cellular growth variability in developing organs of Arabidopsis thaliana. We show that growth is more heterogeneous in the leaf blade than in the midrib and petiole, correlating with higher local differences in growth rates between neighboring cells in the blade. This local growth variability coincides with developing stomata. Stomatal lineages follow a specific, time-dependent growth program that is different from that of their surroundings. Quantification of cellular dynamics in the leaves of a mutant lacking stomata, as well as analysis of floral organs, supports the idea that growth variability is mainly driven by stomata differentiation. Thus, the cell-autonomous behavior of specialized cells is the main source of local growth variability in otherwise homogeneously growing tissue. Those growth differences are buffered by the immediate neighbors of stomata and trichomes to achieve robust organ shapes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.203
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2022
Admission routes2
Has abstractyes

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