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Record W1941260291 · doi:10.1139/cjb-2015-0156

Effect of linoleic acid on reproduction and yeast–mycelium dimorphism in the Dutch elm disease pathogens

2015· article· en· W1941260291 on OpenAlexafffundvenue
Erika Sayuri Naruzawa, Fabienne Malagnac, Louis Bernier

Bibliographic record

VenueBotany · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCentre de Géomatique du Québec
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMyceliumBiologyLinoleic acidDutch elm diseaseSexual reproductionYeastBotanySexual dimorphismDimorphic fungusAscomycotaVirulenceMicrobiologyBiochemistryGeneZoologyFatty acid

Abstract

fetched live from OpenAlex

Elm populations from North America and Europe were devastated by Dutch elm disease (DED), which is a vascular disease caused by fungi from the genus Ophiostoma (Ascomycota). These pathogens feature a yeast–mycelium dimorphism that may be related to virulence by facilitating colonization of the host xylem. Cyclooxygenases (COX) have been proposed to modulate yeast–mycelium dimorphism of DED fungi, and homologs of cox genes have been found in the nuclear genome of O. novo-ulmi subsp. novo-ulmi. Linoleic acid, a substrate for COX, was reported to stimulate the formation of asexual and sexual reproduction structures in DED strains grown on complex media. We hypothesized that linoleic acid also induced mycelium production in liquid shake culture conditions. Linoleic acid was found to enhance the production of reproductive structures in sexual crosses conducted on a complex medium (elm sapwood agar), but was not sufficient for these structures to form on a minimal medium. In liquid shake cultures grown in a minimal medium, the addition of linoleic acid stimulated mycelial formation. Our results suggest that linoleic acid plays a role in reproduction and dimorphism in the DED pathogens.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.224
Teacher spread0.215 · 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 designObservational
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

Citations10
Published2015
Admission routes3
Has abstractyes

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