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Record W2420403436 · doi:10.1385/0-89603-302-3:37

Effect of Acetosyringone on Growth and Oncogenic Potential of Agrobacterium tumefaciens

2003· article· en· W2420403436 on OpenAlexafffund
Patrice Dion, Christian Bélanger, Dong Xu, Mahmood Mohammadi

Bibliographic record

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsUniversité LavalMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAcetosyringoneAgrobacterium tumefaciensBiologyMutantBacteriaMicrobiologyTransformation (genetics)AgrobacteriumBotanyGeneGenetics

Abstract

fetched live from OpenAlex

Expression of the virulence genes of Agrobacterium tumefaciens occurs in acidic media containing sugars and certain phenolics secreted at plant wounds. In some strains of A. tumefaciens, exposure to these inducing conditions leads to inhibition of bacterial growth, which can then be followed by the selection of avirulent mutants (). Observation of this effect of vir gene induction depends on the use of particular experimental conditions. This chapter first specifies experimental parameters allowing to reveal, in A. tumefaciens, the modifications that accompany vir gene induction. Other parts of the chapter then describe how the mutants that are generated in the presence of acetosyringone are recognized and characterized. These protocols may prove useful to those with an interest in Agrobacterium biology. In addition, acetosyringone enhances the ability of A. tumefaciens to transform recalcitrant host plants, and as such has been incorporated in medium used for co-cultivation of bacteria and plant tissue (, , , ) or alternatively has been used to precondition explants prior to addition of bacteria (). Hence, the possibility of side effects of acetosyringone on A. tumefaciens bears direct relevance to experiments dealing with transformation of crop plants.

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 score0.005
Threshold uncertainty score0.288

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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations4
Published2003
Admission routes2
Has abstractyes

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