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Record W2802781915 · doi:10.22215/etd/2018-12721

Augmenting the Detection of Verotoxin-Producing Escherichia coli Through use of Fluorescently-labeled DNA Oligonucleotides

2018· dissertation· en· W2802781915 on OpenAlexafffund
Dylan Fox-Altherr

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsCarleton University
FundersHealth Canada
KeywordsAptamerVTECEscherichia coliOligonucleotideDNAFluorophoreFluorescenceBiologyOligomer restrictionChemistryMolecular biologyComputational biologyBiochemistryGene

Abstract

fetched live from OpenAlex

Verotoxin-producing Escherichia coli (VTEC) present a significant risk of foodborne illness and severe patient outcomes.Isolation is laborious and time consuming due to the diversity of strains.Development of a differential agent would enhance rate and speed of isolation.Production of verotoxin (VT) is the only phenotypic trait exclusive to VTEC.Aptamers are single-stranded oligonucleotides with high selectivity and affinity for their targets.A VT-targeting aptamer beacon could aid in isolation of VTEC.Proof of concept was demonstrated using an existing aptamer sequence, showing increased fluorescence in the presence of VT1a.Further characterization showed target specificity, but weaker signal contrast in complex media.Comparison of fluorophore-quencher pairs showed Texas Red and Black Hole Quenchers as optimal choices.Finally, a DNA substrate developed to detect VT enzyme activity produced weak fluorescent signal making it unlikely to aid in detection.

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 score0.003
Threshold uncertainty score0.009

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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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Same topicBacterial Genetics and BiotechnologyFrench-language works237,207