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Record W3000417649 · doi:10.1038/s41598-019-57365-z

Nano-indentation reveals a potential role for gradients of cell wall stiffness in directional movement of the resurrection plant Selaginella lepidophylla

2020· article· en· W3000417649 on OpenAlexafffund
Meisam Asgari, Véronique Brulé, Tamara L. Western, Damiano Pasini

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFern and Epiphyte Biology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsStiffnessIndentationMaterials scienceComposite material

Abstract

fetched live from OpenAlex

As a physical response to water loss during drought, inner Selaginella lepidophylla stems curl into a spiral shape to prevent photoirradiation damage to their photosynthetic surfaces. Curling is reversible and involves hierarchical deformation, making S. lepidophylla an attractive model with which to study water-responsive actuation. Investigation at the organ and tissue level has led to the understanding that the direction and extent of stem curling can be partially attributed to stiffness gradients between adaxial and abaxial stem sides at the nanoscale. Here, we examine cell wall elasticity to understand how it contributes to the overall stem curling. We compare the measured elastic moduli along the stem length and between adaxial and abaxial stem sides using atomic force microscopy nano-indentation testing. We show that changes in cortex secondary cell wall development lead to cell wall stiffness gradients from stem tip to base, and also between adaxial and abaxial stem sides. Changes in cortical cell wall morphology and secondary cell wall composition are suggested to contribute to the observed stiffness gradients.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.135

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)0.0000.000

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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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 designBench or experimental
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

Citations19
Published2020
Admission routes2
Has abstractyes

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