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Record W2952740098 · doi:10.1038/s41467-019-10667-2

SLR1 inhibits MOC1 degradation to coordinate tiller number and plant height in rice

2019· article· en· W2952740098 on OpenAlexfundno aff
Zhigang Liao, Hong Yu, Jingbo Duan, Kun Yuan, Chaoji Yu, Xiangbing Meng, Liquan Kou, Mingjiang Chen, Yanhui Jing, Guifu Liu, Steven M. Smith, Jiayang Li

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsnot available
FundersChinese Academy of Agricultural SciencesInstitute of Genetics and Developmental Biology, Chinese Academy of SciencesChinese Academy of SciencesInstitute of GeneticsNational Natural Science Foundation of China
KeywordsTiller (botany)Degradation (telecommunications)AgronomyRice plantChemistryBiologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

The breeding of cereals with altered gibberellin (GA) signaling propelled the 'Green Revolution' by generating semidwarf plants with increased tiller number. The mechanism by which GAs promote shoot height has been studied extensively, but it is not known what causes the inverse relationship between plant height and tiller number. Here we show that rice tiller number regulator MONOCULM 1 (MOC1) is protected from degradation by binding to the DELLA protein SLENDER RICE 1 (SLR1). GAs trigger the degradation of SLR1, leading to stem elongation and also to the degradation of MOC1, and hence a decrease in tiller number. This discovery provides a molecular explanation for the coordinated control of plant height and tiller number in rice by GAs, SLR1 and MOC1.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.001

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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 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

Citations234
Published2019
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicPlant Parasitism and ResistanceFrench-language works237,207