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Record W7132982857

Characterization of Abscisic Acid Transport in Arabidopsis thaliana and Brassica napus

2022· dissertation· W7132982857 on OpenAlexfundno aff
Frederik Duy-Anh Nguyen

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicCalcium signaling and nucleotide metabolism
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsAbscisic acidArabidopsisBrassicaArabidopsis thalianaMutantShootPhytochemical
DOInot available

Abstract

fetched live from OpenAlex

The phytohormone abscisic acid (ABA) plays an important role in regulating numerous plant processes such as seed maturation, seed dormancy, and stress responses. These regulations require the long-distance movement of ABA in plants, which has been documented and requires a proper network of ABA transporters. Positron Emission Tomography (PET) in planta allows a real-time and non-destructive method for studying the movement of small molecules using a radiotracer. We show that 3’-F-ABA displayed similar bioactivity as ABA via ABA-responsive β-glucuronidase (GUS) reporter expression in Arabidopsis thaliana. In addition, the destructive phytochemical analysis of Brassica napus plants co-applied 3’-F-ABA and deuterium-labeled ABA (d6-ABA) showed a similar distribution pattern. Arabidopsis transporter mutants (abcg25, abcg40, ait1) showed delayed GUS expression in shoots which suggests that ABCG25, ABCG40, and AIT1 are required for the quick transport of ABA from root to shoot. Lastly, several ABA analogs were found to be candidates for ABA transport inhibitors.

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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.279
Teacher spread0.269 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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