MétaCan
Menu
Back to cohort
Record W4281771012 · doi:10.1002/leg3.154

A fast and efficient method to introduce apple latent spherical virus to legume plants via <scp><i>Agrobacterium rhizogenes</i></scp>‐mediated transformation of hairy roots

2022· article· en· W4281771012 on OpenAlexafffund
Ruyi Xiong, Aiming Wang, Frédéric Marsolais, Christopher D. Todd

Bibliographic record

VenueLegume Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food Canada
FundersSaskatchewan Pulse Growers
KeywordsAgrobacteriumBiologySativumInoculationLegumePisumclone (Java method)Transformation (genetics)Gene silencingGeneFunctional genomicsBotanyVirusPlant virusVirologyGeneticsHorticultureGenomicsGenome

Abstract

fetched live from OpenAlex

Abstract Virus‐induced gene silencing (VIGS) is a functional genomics tool used to determine the function of unknown genes or assess the impact of gene silencing on plant phenotype. However, VIGS methods for analyzing gene function are not equally efficient across species, and virus inoculation is difficult for some legume species. We describe a fast and efficient inoculation method using Agrobacterium rhizogenes K599 harboring an apple latent spherical virus (ALSV) full‐length cDNA clone. Pisum sativum cv. AAC Lacombe and Lens culinaris cv. CDC Viceroy showed silencing rates of 100% and 47.7%, respectively, within 30 days starting from seed. To our knowledge, this is the first report of virus inoculation by A. rhizogenes ‐mediated introduction to legume plants. This work paves the way for high throughput gene function screening in Pisum sativum and other closely related legume crops using ALSV as a symptomless VIGS vector.

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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.253
Teacher spread0.235 · 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
GenreMethods

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
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueLegume ScienceSame topicPlant Virus Research StudiesFrench-language works237,207