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Record W2653214071 · doi:10.1134/s1021443717040045

Constitutive down-regulation of SiSGR gene is related to green millet in Setaria italica

2017· article· en· W2653214071 on OpenAlexaff
Lu Cheng, B. Zhang, Lu He, F. F., X. C. Wang, H. Y. Li, Yuanhuai Han

Bibliographic record

VenueRussian Journal of Plant Physiology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsMinistry of Agriculture
FundersNational Natural Science Foundation of China
KeywordsSetariaPlant physiologyBiologyGeneBotanyFoxtailAgronomyGenetics

Abstract

fetched live from OpenAlex

Millet colour is an important index to evaluate the quality of dehulled foxtail millet ( Setaria italica (L.) Beauv.). Most varieties are yellow, due to the accumulation of carotenoids. However, there are some foxtail millet germplasms producing dark green millet. To elucidate the molecular mechanism of the chlorophyll retention phenotype, Daqinggu with green millet colour and Jingu 21 with yellow millet colour were selected as research material in this study. The total carotenoid level in dehulled millet of Daqinggu was about 0.024 mg/g, and 0.038 mg/g in Jingu 21. The transcript levels of carotenoid structural genes were investigated at three stages of grain development in both millet varieties. The expression levels of carotenoid biosysnthesis-related genes SiPSY3, SiPDS, SiZ-ISO, SiLCYB and SiCYP97C were significantly higher in Daqinggu than in Jingu 21, which was not consistent with the difference in the carotenoid levels between these two varieties. Interestingly, SiSGR , a homologue to the STAY-GREEN gene in Arabidopsis, tomato, and rice, was constitutively down-regulated during maturation in Daqinggu. In addition, the total chlorophyll content was consistently higher in Daqinggu than in Jingu21 during grain maturation. These evidences suggest that SiSGR is a key gene in regulating chlorophyll retention for dark green foxtail millet.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.263

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 head0.014
GPT teacher head0.220
Teacher spread0.207 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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