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Record W2901886448 · doi:10.22215/etd/2018-12906

The role of transcription factor MYB53 from Arabidopsis thaliana in the regulated production of suberin

2018· dissertation· en· W2901886448 on OpenAlexaff
Hefeng Hu

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsCarleton University
Fundersnot available
KeywordsSuberinEndodermisArabidopsisArabidopsis thalianaTranscription factorMutantBiologyBotanyCell wallGeneCell biologyBiochemistry

Abstract

fetched live from OpenAlex

Suberin is a cell wall-associated polymer that is deposited in diverse plant tissues including root exodermis and endodermis, aerial and underground periderms, and seed coats under both normal and stressful conditions.Suberin plays important roles in protecting plants against various stressors but the molecular mechanisms governing the regulated deposition of suberin are currently unclear.I provide evidence here that AtMYB53, AtMYB92, and AtMYB93 of the MYB-type transcription factor family are important regulators of suberin in root endodermis under non-stress conditions.I first characterized an Arabidopsis steroid-inducible line and found that suberin can be rapidly induced in both roots and leaves upon overexpression of MYB53.A suite of suberin biosynthetic genes was positively regulated at the transcriptional level after MYB53 overexpression.I also generated a collection of loss-of-function mutants of MYB53/MYB92/MYB93, which exhibited major reductions of suberin in the endodermis of young roots in comparison to wild-type.The transcripts of all suberin biosynthetic genes tested were down-regulated in the mutants.The identification of master regulators of suberin may provide the means to generate crops that are more stress resistant via enhancement of their suberized cell walls.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.016
GPT teacher head0.202
Teacher spread0.186 · 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 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

Citations5
Published2018
Admission routes1
Has abstractyes

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