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Record W2950354533 · doi:10.1186/s13068-019-1496-6

Molecular and biochemical analysis of the castor caruncle reveals a set of unique genes involved in oil accumulation in non-seed tissues

2019· article· en· W2950354533 on OpenAlexaff
Xia Wan, Qing Liu, Bei Dong, Sapna Vibhakaran Pillai, Fenghong Huang, Surinder Singh, Xue‐Rong Zhou

Bibliographic record

VenueBiotechnology for Biofuels · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsMinistry of Agriculture
FundersChinese Academy of Agricultural SciencesAgricultural Science and Technology Innovation ProgramMinistry of Science and Technology of the People's Republic of China
KeywordsCastor oilGeneBiologySet (abstract data type)BiotechnologyComputational biologyCastor beansBotanyGeneticsBiochemistryComputer scienceRicinus

Abstract

fetched live from OpenAlex

BACKGROUND: ) is one of the crops for vegetable oil for industrial applications with yield around 1.4 ton oil per hectare produced in seed. The castor caruncle is a non-seed tissue attached to seed. RESULTS: Caruncle accumulates up to 40% oil by weight in the form of triacylglycerol (TAG), with a highly contrasting fatty acid composition when compared to the seed oil. Biochemical analysis indicated that the caruncle synthesizes TAGs independent of the seed. Such non-seed tissue has provided an excellent resource for understanding the mechanism of oil accumulation in tissues other than seeds. Transcriptome analysis revealed the key members of gene families involved in fatty acid synthesis and TAG assembly in the caruncle. A transient expression assay of these selected genes resulted in a 20-fold increased TAG accumulation in leaves. CONCLUSIONS: Castor caruncle utilizes an independent system to synthesize TAGs. Results provide the possibility of exploiting caruncle gene set to engineer oil production in non-seed tissues or microbes.

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 score0.044
Threshold uncertainty score0.461

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 integrity0.0010.000
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 head0.012
GPT teacher head0.263
Teacher spread0.251 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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