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Record W2966725506 · doi:10.1139/cjb-2018-0183

Molecular analysis of genes related to phenylpropanoid and ascorbate biosynthesis in salt and UV-B treated pak choi grown under LEDs

2019· article· en· W2966725506 on OpenAlexvenueno aff
Young Beom Yun, Hee‐Jeong Jung, Abdur Rahim, Jong‐In Park, Yong In Kuk

Bibliographic record

VenueBotany · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsnot available
Fundersnot available
KeywordsPhenylpropanoidBiosynthesisBiologyCarotenoidChlorophyllGene expressionGeneBotanyBiochemistryFood science

Abstract

fetched live from OpenAlex

In this study, we measured the expression of genes related to the phenylpropanoid pathway and ascorbate oxidation by qRT-PCR to explore the mechanism of nutraceutical compounds enhancement in pak choi during stress treatments. The expression of most phenylpropanoid biosynthesis related genes (PAL, C4H, CHS, CHI, F3H1, DFR, and ANS1) was up-regulated in plants treated with NaCl, solar salt, and UV-B radiation, compared with the untreated controls under both white light and light emitting diode illumination. Moreover, CHS and ANS1 were expressed at levels 15 and 95 times higher than the untreated control. The expression of the ascorbate oxidation related gene tAPX was also up-regulated in the NaCl and UV-B treated samples compared with the control under both light conditions. By comparison, the expression of APX1 was down-regulated during the stress treatment under both light conditions. Total chlorophyll and total carotenoid contents were unaffected by the stress treatments, and there was no significant difference in the maximum quantum yield (F v /F m ) between the stress treatments and the untreated controls. These results suggest that the expression of genes involved in phenylpropanoid biosynthesis and ascorbate oxidation correlates with the previously reported enhanced production of phenolic compounds in pak choi plants subjected to stress treatments.

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 score0.722
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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