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Record W3113204369

Modification of the Stripe Rust Resistance Gene Yr10 in Triticum aestivum

2018· article· en· W3113204369 on OpenAlexaff
Kaden K. Fujita, Michele Frick, André Laroche

Bibliographic record

VenueURSCA Proceedings · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Lethbridge
Fundersnot available
KeywordsGeneBiologyGeneticsTransformation (genetics)MutagenesisPhakopsora pachyrhiziR geneConserved sequenceSequence (biology)Stripe rustPathogenPlant disease resistanceComputational biologyMutantPeptide sequenceBotany
DOInot available

Abstract

fetched live from OpenAlex

Stripe rust is a disease in Triticum aestivum (bread wheat) that is caused by the fungal pathogen Puccinia striiformis. The pathogen has evolved to defeat an R gene in T. aestivum known as the Yr10 gene. The Yr10 gene was found to encode an evolutionary-conserved sequence known as the CC-NBS-LRR. This conserved sequence was found to be involved in producing resistance to various pathogens. Within this sequence the coiled coil (CC) and leucine rich repeat (LRR) domains are thought to be important to the protein’s function. This project made use of PCR overlap-extension mutagenesis to mutagenize the CC and LRR domains in an attempt to create modified constructs of the Yr10 gene. PCR reactions gave fragments of the expected sizes which were then assembled into the pANIC6D vector to be used in transformation. Transformation of the vector with inserted sequences into Escherichia coli will be done to confirm the successful insertion of the fragments. There is hope that in the future the modified constructs could be transformed into wheat. *Indicates presenter

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.023
GPT teacher head0.227
Teacher spread0.203 · 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
Published2018
Admission routes1
Has abstractyes

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Same venueURSCA ProceedingsSame topicWheat and Barley Genetics and PathologyFrench-language works237,207