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Record W2821972515 · doi:10.5539/jps.v7n2p49

Transcriptomic Analysis on the Regulation of Tomato Ripening by the Ethylene Inhibitor 1-methylcyclopropene

2018· article· en· W2821972515 on OpenAlexvenueno aff
Jamshed Bobokalonov, Yanhong Liu, Tasnuva Shahrin, LinShu Liu

Bibliographic record

VenueJournal of Plant Studies · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsRipeningClimactericTranscriptomeEthylene1-MethylcyclopropeneRNA-SeqGeneBiologyPlant hormoneGene expressionBiochemistryHorticultureGenetics

Abstract

fetched live from OpenAlex

Tomato is a climacteric fruit whose ripening is regulated by the plant hormone ethylene. 1-methylcyclopropene (1-MCP) is a competitive ethylene inhibitor that can delay the fruit ripening process. To understand the molecular mechanism of how 1-MCP inhibits tomato fruit ripening, transcriptomics (RNA-Seq) was used to identify genes that were differentially expressed in 1-MCP-treated (Day 1) tomato fruits. Of the 35340 genes in the tomato genome, about 50% were expressed with 1-MCP treatment. There were 5683 genes identified as significantly differentially expressed. Quantitative reverse transcription PCR (qRT-PCR) assays were used to validate the RNA-Seq data. Our results showed that 1-MCP treatment resulted in the down-regulation of fruit ripening-related genes, including genes involved in ethylene synthesis, signal transduction and carotenoid biosynthesis. Our results provide insight at the whole genome level regarding gene regulation by 1-MCP during fruit ripening. Understanding the molecular basis of 1-MCP inhibition on tomato ripening may help farmers and food processors to better use 1-MCP in agriculture and food industry.

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)0.002
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 score0.732
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.055
GPT teacher head0.272
Teacher spread0.217 · 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
Published2018
Admission routes1
Has abstractyes

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