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Record W4405851982 · doi:10.5376/gab.2024.15.0030

Genetic Basis of Oil Content in Camellia Species

2024· article· en· W4405851982 on OpenAlexvenueno aff
Yuejun Wu

Bibliographic record

VenueGenomics and Applied Biology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsnot available
Fundersnot available
KeywordsCamelliaBasis (linear algebra)BiologyMathematicsFood scienceBotanyBiotechnology

Abstract

fetched live from OpenAlex

The primary objective of this study was to elucidate the genetic basis of oil content in various Camellia species, with a particular focus on identifying key genes and genetic markers associated with oil biosynthesis and fatty acid composition. The study identified several significant genetic markers and differentially expressed genes (DEGs) associated with oil content and quality in Camellia species. In Camellia oleifera , single nucleotide polymorphisms (SNPs) and insertion-deletion (InDel) markers within key fatty acid desaturase genes were found to be significantly associated with oil content and composition, explaining up to 17.93% of phenotypic variance. Transcriptomic analyses revealed critical genes involved in lipid metabolism and oil accumulation, such as stearoyl-ACP desaturases (SADs) and fatty acid desaturase 2 (FAD2), which were differentially expressed during seed development. Additionally, integrative proteomic and transcriptomic analyses identified key metabolites and co-expressed genes involved in oil quality during seed ripenin4. Comparative studies between high- and low-oil cultivars highlighted the coordinated regulation of upstream and downstream genes essential for high oleic acid accumulation. The findings from this study provide valuable genetic markers and insights into the molecular mechanisms underlying oil biosynthesis in Camellia species. These discoveries have significant implications for the genetic improvement of oil content and quality in Camellia cultivars through marker-assisted selection and genetic engineering.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.209
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
Published2024
Admission routes1
Has abstractyes

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