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Record W2806968799 · doi:10.4149/av_2018_213

Two agroinfection-compatible fluorescent protein-tagged infectious cDNA clones of papaya leaf distortion mosaic virus facilitate the tracking of virus infection

2018· article· en· W2806968799 on OpenAlexfundno aff
D. Tuo, Pu Yan, Guanghao Zhao, X. Y. Li, Peng Zhou, Wentao Shen

Bibliographic record

VenueActa Virologica · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaNatural Science Foundation of Hainan Province
KeywordsBiologyComplementary DNAVirologyPapaya ringspot virusCauliflower mosaic virusVirusmCherryGreen fluorescent proteinPotyvirusCucumber mosaic virusPlasmidcDNA libraryPlant virusGeneGenetically modified cropsGeneticsTransgene

Abstract

fetched live from OpenAlex

Papaya leaf distortion mosaic virus (PLDMV, the genus Potyvirus) is an emerging threat to papaya production. Here, agroinfection-compatible fluorescent protein-tagged PLDMV infectious cDNA clones driven by the Cauliflower mosaic virus 35S promoter were successfully constructed using one-step Gibson assembly. The clones were directly transformed into Agrobacterium tumefaciens to prevent potential problems such as plasmid instability during propagation in Escherichia coli. Ninety-five percent of papaya seedlings infected with PLDMV-GFP or PLDMV-mCherry developed systemic symptoms typical of those caused by wild-type PLDMV. Green and mCherry red fluorescence was observed in leaves, stems, and roots of infected papaya plants. The fluorescent protein-tagged agroinfectious PLDMV cDNA clones were stable in papaya for more than 90 days and during six serial passages at 30-day intervals. The availability of these infectious clones will contribute to research on PLDMV-host interactions and can be applied in the papaya breeding program for PLDMV resistance.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0010.001
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 head0.045
GPT teacher head0.266
Teacher spread0.221 · 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

Citations19
Published2018
Admission routes1
Has abstractyes

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