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Record W2969582514

Bioinformatic and morphological characterization of Catharanthus roseus mutants

2019· other· en· W2969582514 on OpenAlexaff
Graham Jones

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2019
Typeother
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAlkaloids: synthesis and pharmacology
Canadian institutionsBrock University
Fundersnot available
KeywordsCatharanthus roseusMutantBiologyBotanyBiotechnologyComputational biologyGeneticsGene
DOInot available

Abstract

fetched live from OpenAlex

Catharanthus roseus, a member of the Apocynaceae family, has been studied extensively for its valuable chemotherapeutic monoterpenoid indole alkaloids (MIAs). Ethyl methanesuphonate (EMS) mutagenesis is a screening tool that has been used to look for altered MIA profiles in hope of discovering mutations of crucial MIA biosynthetic genes. Without a high-throughput mutation detection screen for C. roseus sequencing data, a range of techniques must be used to discover the EMS-induced changes within the plant. Bioinformatic and morphological analysis revealed the likely alterations leading to unique MIA profiles in two C. roseus EMS mutants: the high-ajmalicine accumulating line M2-0754 and the low-MIA accumulating line M2-1582. Expression of geissoschizine synthase (GS) was downregulated almost seven-fold in the leaves of M2-0754, leading to the accumulation of an alternate pathway MIA from the labile intermediate. The low-MIA profile and increased auxin sensitivity of M2-1582 is likely due to the expression of a dysfunctional auxin influx transport protein homologue.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.268
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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