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Record W2989291826 · doi:10.1139/cjps-2019-0140

Influence of red light on the expression of genes on stomatal formation in maize seedlings

2019· article· en· W2989291826 on OpenAlexvenueno aff
Tie Dong Liu, Xi Wen Zhang, Yong Xu

Bibliographic record

VenueCanadian Journal of Plant Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsnot available
Fundersnot available
KeywordsSeedlingPhytochromeTranspirationBiologyPhotosynthesisStomatal conductanceBotanyArabidopsisPhotomorphogenesisBlue lightIntracellularPhototropismCryptochromeCell biologyRed lightGeneMutantGenetics

Abstract

fetched live from OpenAlex

Red light significantly affects the expression of plant photoreceptor genes and influences stomatal development through crosstalk of the constitutive photomorphogenic 1–cryptochrome–phytochrome signaling pathway. When blue light was replaced with red light, the expression levels of ZmCry1, ZmPhyB1, ZmEPF2, and ZmEPFL9 were enhanced, whereas that of ZmCOP1 was restricted. Moreover, the expression levels of ZmSPCH and ZmMUTE were also enhanced, but they were generally lower than those under white light. Consequently, stomatal formation, which was determined by net photosynthesis, stomatal conductance, intercellular CO 2 concentration, and transpiration rate, was inhibited through decreased stomatal index and stomatal density. We conclude that red light positively regulates EPFL9 in the intercellular signaling but reduces the positive regulation of blue light on COP1 and epidermal patterning factor 2 in the intracellular and intercellular signaling; therefore, though red light promotes the gene’s function on stomatal development of seedling maize, blue light maybe dominant to red light in seedling stage.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

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)0.0010.000

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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 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

Citations9
Published2019
Admission routes1
Has abstractyes

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