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Record W2783712708 · doi:10.3390/plants7010006

Deciphering the Evolution and Development of the Cuticle by Studying Lipid Transfer Proteins in Mosses and Liverworts

2018· review· en· W2783712708 on OpenAlexaff
Tiina A. Salminen, D. Magnus Eklund, Valentin Joly, Kristina Blomqvist, Daniel P. Matton, Johan Edqvist

Bibliographic record

VenuePlants · 2018
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsUniversité de Montréal
FundersBiocenter FinlandÅbo Akademi
KeywordsCutinCuticle (hair)Plant cuticleSuberinWaxEpidermis (zoology)Plant lipid transfer proteinsSporopolleninBiologyBotanyCell wallBiochemistryChemistryAnatomyPollenGene

Abstract

fetched live from OpenAlex

When plants conquered land, they developed specialized organs, tissues, and cells in order to survive in this new and harsh terrestrial environment. New cell polymers such as the hydrophobic lipid-based polyesters cutin, suberin, and sporopollenin were also developed for protection against water loss, radiation, and other potentially harmful abiotic factors. Cutin and waxes are the main components of the cuticle, which is the waterproof layer covering the epidermis of many aerial organs of land plants. Although the in vivo functions of the group of lipid binding proteins known as lipid transfer proteins (LTPs) are still rather unclear, there is accumulating evidence suggesting a role for LTPs in the transfer and deposition of monomers required for cuticle assembly. In this review, we first present an overview of the data connecting LTPs with cuticle synthesis. Furthermore, we propose liverworts and mosses as attractive model systems for revealing the specific function and activity of LTPs in the biosynthesis and evolution of the plant cuticle.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.064
GPT teacher head0.236
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2018
Admission routes1
Has abstractyes

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