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Record W2895313173 · doi:10.1139/cjb-2018-0118

Decreased number of locules and pericarp cell layers underlie smaller and ovoid fruit in tomato <i>smaller fruit</i> (<i>sf</i>) mutant

2018· article· en· W2895313173 on OpenAlexvenueno aff
Yan Zhang, Yushun Li, Jie Zhang, Tayeb Muhammad, Yan Liang

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsnot available
Fundersnot available
KeywordsLoculeBiologyMutantBotanyHorticulturePhenotypeGeneGenetics

Abstract

fetched live from OpenAlex

Fruit size and shape are the primary criteria for domestication and improvement of tomato. The varying sizes and shapes of tomato fruit further signify their importance as agronomic traits. Here, we characterized a tomato mutant, smaller fruit (sf), which bears relatively small and ovoid fruits compared with the large and flat fruits of the wild-type (WT). Phenotypic measurements and histological analyses revealed that fruit diameter but not fruit length of the sf mutant decreased compared with that of the WT. This phenotypic change was attributed to significant decreases in locule number and pericarp cell layers in a transverse direction, which resulted in the transition of fruit shape from flat in the WT to ovoid in sf. Comparison of the transcriptomes of ovaries of sf with the WT using RNA-Seq identified 2596 differentially expressed genes, in which 1737 genes significantly were up-regulated and 859 genes were dramatically down-regulated in the sf ovary. Further analyses confirmed that some genes, such as CRCa, CNRs, CYCs, WUS, SUNs, OFRs, CDKs, participate in regulation of fruit size and shape of sf mutant. Thus, our study adds a new genetic resource regarding fruit size and shape of tomato, and provides a valuable basis for understanding molecular regulation of small and ovoid fruit of the sf mutant.

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 score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.242
Teacher spread0.222 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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