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Record W3117161833 · doi:10.5376/mpb.2020.11.0024

Transciptome and Metabolomics Analysis of Pepper (Yin Chuan Cavel ) Male Sterility Lind

2020· article· en· W3117161833 on OpenAlexvenueno aff
Xiujuan Yan, Xin He, Yunxia Zhao, Jingxia Gao, Xuemei Wang

Bibliographic record

VenueMolecular Plant Breeding · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyMetabolomicsKEGGMetabolismBiochemistryMetabolic pathwayGeneSterilityGlycosyltransferasePepperGene expressionLipid metabolismBotanyTranscriptomeFood scienceBioinformatics

Abstract

fetched live from OpenAlex

In order to study the relation of different gene expression and Metabolomics difference of pepper (Yin Chuan Cavel) male sterility lind pollen abortion. The transciptiome was sequenced using RNA-seq technology, the metabolomics was tested by UPLC-MS/Ms. The result was analyzed by GO categories and KEGG enrichment analysis. A total of 3 319 different expression genes were successfully obained, include up genes 800, down genes 2 519. Of the 536 metabolites identified, a total of 102 metabolites were significantly differentiated. The differential metabolites involved up regulation metabolites 68, regulation metabolites 34. Included 17 amino acid derivatives, 16 Nucleotides and derivatives, 13 glyceryl ester, 10 sphingolipids, 8 flavonoids and phenolic acids, 6 in other classes (mainly related to carbohydrate metabolism). Differential metabolites and significant differential expression genes are mainly concentrated in protein related amino acid metabolism pathway and flavonoid metabolism pathway. The results showed that the significant differential expression of transcription genes, through the regulation of metabolism related enzymes, resulted in the differential metabolism of lipid, Phenolic acids and carbohydrate metabolites, resulted in pollen abortion of the Pepper (Yin Chuan Cavel) male sterile.  

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 score0.625
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.235
Teacher spread0.194 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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