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Record W2950834454 · doi:10.3389/fpls.2019.00872

ABA Alleviates Uptake and Accumulation of Zinc in Grapevine (Vitis vinifera L.) by Inducing Expression of ZIP and Detoxification-Related Genes

2019· article· en· W2950834454 on OpenAlexafffund
Changzheng Song, Yifan Yan, Abel Rosado, Zhenwen Zhang, Simone D. Castellarin

Bibliographic record

VenueFrontiers in Plant Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsAbscisic acidDetoxification (alternative medicine)Vitis viniferaJasmonic acidPhotosynthesisBotanyChemistryGene expressionGeneHorticultureBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abscisic acid (ABA) is a stress phytohormone which can mitigate heavy metal toxicity. Exogenous ABA and ABA mimic 1 (AM1) were applied to study the influence on Zn uptake and accumulation in Vitis vinifera L. cv. ‘Merlot’ seedlings exposed to excess Zn. The seedlings were treated with either normal or excess levels of Zn in combination with ABA and AM1 applications. The excess Zn exposure resulted in decreased lateral root length, decreased photosynthesis, elevated uptake and accumulation of Zn in roots, trunks, and stems, increased content of jasmonic acid in roots and leaves, and induced the expression of Zn transportation and detoxification related genes. Exogenous applications of ABA, but not AM1, alleviated the over-uptake and accumulation of Zn in roots and trunks at 10 days after Zn exposure and induced higher expression of both ZIP genes and detoxification-related genes. These results indicate that exogenous ABA enhances the tolerance of grape seedlings to excess Zn, and that AM1 is not a suitable ABA mimic compound for Zn stress alleviation in grapes.

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)0.000
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 score0.577
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.019
GPT teacher head0.234
Teacher spread0.215 · 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

Citations65
Published2019
Admission routes2
Has abstractyes

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