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Record W2119060525 · doi:10.1186/s12284-014-0028-z

A fertility restorer gene, Rf4, widely used for hybrid rice breeding encodes a pentatricopeptide repeat protein

2014· article· en· W2119060525 on OpenAlexfundno aff
Tomohiko Kazama, Kinya Toriyama

Bibliographic record

VenueRice · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceInstitute of GeneticsMinistry of Agriculture, Forestry and FisheriesMinistry of Education, Culture, Sports, Science and Technology
KeywordsPentatricopeptide repeatCytoplasmic male sterilityGeneGeneticsBiologySterilityCloning (programming)FertilityGene expressionMedicineComputer sciencePopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Uncontrolled expression of a certain mitochondrial gene often causes cytoplasmic male sterility (CMS) in plants. This phenotype is prevented by the presence of a fertility restorer (Rf) gene in the nuclear genome. Such CMS/Rf systems have been successfully used for breedings of F1 hybrid cultivars. In rice, approximately 99% of F1 hybrid cultivars have been developed using a wild abortive type of CMS (WA-CMS) and its Rf genes. Recently, a newly identified mitochondrial gene, orf352, was reported as a WA-CMS-causing gene. FINDINGS: We cloned and functionally characterized Rf4, a major Rf gene for WA-CMS. We revealed that Rf4 encoded a pentatricopeptide repeat-containing protein and reduced the orf352-containing transcripts, thereby restoring pollen fertility. CONCLUSIONS: Through a map-based cloning, we have independently identified an allele of a recently reported Rf4 gene and demonstrated that the fertility restoration is controlled sporophytically.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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

Citations86
Published2014
Admission routes1
Has abstractyes

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