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Record W4394201568 · doi:10.6084/m9.figshare.21744245

Concentration and gene expression analyses of dragon′s blood flavonoids in different tissues of Dracaena cochinchinensi

2022· dataset· en· W4394201568 on OpenAlexaff
Yanqian Wang, Shuang Li, Chunyong Yang, Yanfang Wang, Jianming Peng, Ge Li, Zhen Yan, Yan Mou, Er Li, Jianhe Wei, Jianjun Qi, Lixia Zhang

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhytochemical Studies and Bioactivities
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGeneBiologyExpression (computer science)Traditional medicineChemistryGeneticsMedicineComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Background: Dragon′s blood is a well-known traditional Chinese medicine, isolated mainly from the trunks of Dracaena trees. Overexploitation of the Dracaena resource has resulted in this plant becoming endangered. This work aimed to assess the potential to use various tissues of the Dracaena cochinchinensis tree, such as leaves and roots, to produce dragon′s blood. Results: We found that many dragon′s blood components accumulated in the bark of trunk and roots of D. cochinchinensis under natural conditions. In addition, small amounts of loureirin A were detected in leaves, suggesting that the leaves could be a potential source of dragon′s blood. Real-time quantitative PCR analysis showed that most of the genes tested in this study, which encoded enzymes involved in the biosynthesis of dragon′s blood flavonoids, were highly expressed in the bark of roots and trunk. Conclusion: We confirmed that the bark of roots and trunk of D. cochinchinensis tree were the main tissues for the synthesis and storage of dragon′s blood under natural conditions. This study demonstrated the potential to extract dragon′s blood from the roots that have been abandoned due to mining difficulties without destroying the tree, a process which would be beneficial to the protection of the endangered wild D. cochinchinensis tree populations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.399
Threshold uncertainty score0.991

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)0.0100.000

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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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.

Study designBench or experimental
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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