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Record W2060977217 · doi:10.1021/jf0507691

Increasing Expression of an Anti-Picloram Single-Chain Variable Fragment (ScFv) Antibody and Resistance to Picloram in Transgenic Tobacco (<i>Nicotiana tabacum</i>)

2005· article· en· W2060977217 on OpenAlexafffund
Fernando C. Olea-Popelka, Michael D. McLean, Jonathan Horsman, Kurt C. Almquist, Jim Brandle, J. Christopher Hall

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTransgenic Plants and Applications
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
FundersUniversity of Guelph
KeywordsPicloramNicotiana tabacumTransgeneSingle-chain variable fragmentHerbicide resistanceGenetically modified cropsBiologyChemistryAntibodyGeneBotanyGeneticsMonoclonal antibody

Abstract

fetched live from OpenAlex

Systematic research involving four chimeric gene constructions designed to express the same anti-picloram single-chain variable fragment (scFv) antibody is described. Agrobacterium-mediated transformation produced at least 25 transgenic tobacco plants with each of these, and the number of T-DNA loci in each plant was determined using kanamycin-resistance segregation assays. The relative amounts of active and total scFv in each plant were evaluated using quantitative enzyme-linked immunosorbent assay and immunoblot technologies, respectively. No significant differences in scFv activity were found among the four groups of single-locus plants, although the 35S/M construct was found to produce significantly more total anti-picloram scFv than the other three constructs. A dose-response bioassay involving T(1) seedlings from several of the highest expressers of active scFv demonstrated resistance to a constant exposure of picloram at 5 x 10(-)(8) M. Other approaches for increasing antibody-based herbicide resistance are discussed, as further improvements are needed before practical application of this technology.

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 score0.036
Threshold uncertainty score0.437

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations21
Published2005
Admission routes2
Has abstractyes

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