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Record W2976120737 · doi:10.1134/s0003683819050041

Pathogenicity and Lipid Composition of Mycelium of the Fungus Stagonospora cirsii VIZR 1.41 Produced on Liquid Media with Different Nitrogen Sources

2019· article· en· W2976120737 on OpenAlexaboutno aff
Г. М. Фролова, С. В. Сокорнова, Alexander Berestetskiy

Bibliographic record

VenueApplied Biochemistry and Microbiology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMyceliumFood scienceSucroseComposition (language)BotanyBiologyFungusChemistryHorticulture

Abstract

fetched live from OpenAlex

The effect of the nitrogen source (sodium nitrate and soybean meal) on the growth, pathogenicity, and lipid composition of mycelium of Stagonospora cirsii Davis VIZR 1.41, a potential mycoherbicide against Canada thistle (Cirsium arvens (L.) Scop.), was studied. The fungus grew significantly (two times) faster on sucrose-soybean meal medium (SSM) than on modified Czapek medium (CM). The fungal mycelium during the exponential growth phase demonstrated the maximal pathogenicity level on the third day on SSM and on the sixth day on CM. Canada thistle leaf tissues were more susceptible (25% higher development of necrotic lesion) to mycelium obtained on SSM than to mycelium obtained on CM. The nitrogen source strongly affected the lipid composition of S. cirsii mycelium. In S. cirsii mycelium obtained on SSM, the total lipid content was 1.7 times lower; the ratio of nonpolar lipids to polar lipids was three times lower than that for fungal mycelium grown on CM. Considerable differences were found in the composition of nonpolar (sterols and fatty acid) and polar (sphingolipids) lipids with respect to the nitrogen source in the culture medium. The higher contents of sterols, free fatty acids, and some glycoceramides in the mycelium of S. cirsii obtained on SSM may be related to its higher pathogenicity level and can serve as a marker of mycoherbicide quality.

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 score0.005
Threshold uncertainty score0.532

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 head0.003
GPT teacher head0.165
Teacher spread0.162 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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