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Record W2116985181 · doi:10.1128/aem.00986-12

Chromosomal Complementation Using Tn <i>7</i> Transposon Vectors in Enterobacteriaceae

2012· article· en· W2116985181 on OpenAlexaff
Sébastien Crépin, Josée Harel, Charles M. Dozois

Bibliographic record

VenueApplied and Environmental Microbiology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversité de MontréalArmand Frappier MuseumInstitut National de la Recherche Scientifique
FundersColorado State University
KeywordsTransposaseComplementationBiologyTransposable elementPlasmidGeneticsTransposition (logic)Vector (molecular biology)GeneComputational biologyShuttle vectorTransposon mutagenesisRecombinant DNAPhenotypeGenome

Abstract

fetched live from OpenAlex

Genetic complementation in many bacteria is commonly achieved by reintroducing functional copies of the mutated or deleted genes on a recombinant plasmid. Chromosomal integration systems using the Tn7 transposon have the advantage of providing a stable single-copy integration that does not require selective pressure. Previous Tn7 systems have been developed, although none have been shown to work effectively in a variety of enterobacteria. We have developed several mini-Tn7 and transposase vectors to provide a more versatile system. Transposition of Tn7 at the chromosomal attTn7 site was achieved by a classical conjugation approach, wherein the donor strain harbored the mini-Tn7 vector and the recipient strain possessed the transposase vector. This approach was efficient for five different pathogenic enterobacterial species. Thus, this system provides a useful tool for single-copy complementation at an episomal site for research in bacterial genetics and microbial pathogenesis. Furthermore, these vectors could also be used for the introduction of foreign genes for use in biotechnology applications, vaccine development, or gene expression and gene fusion constructs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.998

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)0.0030.000

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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.

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

Citations65
Published2012
Admission routes1
Has abstractyes

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