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Record W4285226285 · doi:10.5376/tgg.2022.13.0002

Molecular Mechanism of Silicon Response to Oat Root under Drought Stress

2022· article· en· W4285226285 on OpenAlexvenueaboutno aff
Jie Zhang, Qiang Yin, Zhijian Yan, Yongqing Wan, Yuqing Wang

Bibliographic record

VenueTriticeae Genomics and Genetics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsSiliconTranscriptomeAbiotic stressAbiotic componentGeneDrought stressPEG ratioPolyethylene glycolMechanism (biology)BiologyStress (linguistics)Gene expressionBotanyChemistryBiochemistryEcology

Abstract

fetched live from OpenAlex

There are many reports that silicon has beneficial effects on plant growth and development under abiotic stress, the molecular mechanism of silicon affecting oats under drought stress is unclear. This test variety was introduced from Canada, Sweety. Na 2 SiO 3 ·9H 2 O was used as the silicon source, and polyethylene glycol (PEG-6000) was used to simulate the drought stress environment. Normal growth as control, drought stress (25% PEG-6000), and silicon + drought stress (10 mmol/L Na 2 SiO 3 ·9H 2 O + 25% PEG-6000) on the root of oats were analyzed by transcriptome analysis (176 555 single genes) in order to compare the differential genes of plants under the three treatments. Comparative transcriptomics analysis showed that silicon plays an important role in changing the expression levels of 234 genes under drought stress. GO enrichment analysis showed that the enrichment of related genes such as oxidoreductase activity, cellular nitrogen compound metabolism, and cellular macromolecular metabolism significantly increased, and these genes are inextricably linked to abiotic resistance, indicating that silicon may induce drought tolerance in oat seedlings. By studying the mechanism of silicon-mediated drought resistance, it provides a theoretical basis for the application of silicon in crop production in arid regions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.304

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 head0.010
GPT teacher head0.216
Teacher spread0.205 · 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 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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

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