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Transcripts and tumors: regulatory and metabolic programming during biotrophic phytopathogenesis

2018· preprint· en· W2901020414 on OpenAlexafffund
Lara Schmitz, Sean McCotter, Matthias Kretschmer, James W. Kronstad, Kai Heimel

Bibliographic record

VenueF1000Research · 2018
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsBiologyTranscriptomeUstilagoHost (biology)EffectorAdaptation (eye)Computational biologyGeneGeneticsEvolutionary biologyCell biologyGene expressionNeuroscience

Abstract

fetched live from OpenAlex

<ns4:p>Biotrophic fungal pathogens of plants must sense and adapt to the host environment to complete their life cycles. Recent transcriptome studies of the infection of maize by the biotrophic pathogen <ns4:italic>Ustilago maydis</ns4:italic> are providing molecular insights into an ordered program of changes in gene expression and the deployment of effectors as well as key features of nutrient acquisition. In particular, the transcriptome data provide a deeper appreciation of the complexity of the transcription factor network that controls the biotrophic program of invasion, proliferation, and sporulation. Additionally, transcriptome analysis during tumor formation, a key late stage in the life cycle, revealed features of the remodeling of host and pathogen metabolism that may support the formation of tremendous numbers of spores. Transcriptome studies are also appearing for other smut species during interactions with their hosts, thereby providing opportunities for comparative approaches to understand biotrophic adaptation.</ns4:p>

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 categoriesMeta-epidemiology (narrow)
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 score0.136
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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
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
Published2018
Admission routes2
Has abstractyes

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