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Record W2015122106 · doi:10.1002/elps.201400488

Interfacing solid‐state nanopores with gel media to slow DNA translocations

2015· article· en· W2015122106 on OpenAlexafffund
Matthew Waugh, Autumn Carlsen, David Sean, Gary W. Slater, Kyle Briggs, Harold Kwok, Vincent Tabard‐Cossa

Bibliographic record

VenueElectrophoresis · 2015
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoporeDNABiomoleculeNanotechnologyChromosomal translocationInterfacingMaterials scienceChemical physicsBiophysicsChemistryComputer scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

We demonstrate the ability to slow DNA translocations through solid-state nanopores by interfacing the trans side of the membrane with gel media. In this work, we focus on two reptation regimes: when the DNA molecule is flexible on the length scale of a gel pore, and when the DNA behaves as persistent segments in tight gel pores. The first regime is investigated using agarose gels, which produce a very wide distribution of translocation times for 5 kbp dsDNA fragments, spanning over three orders of magnitude. The second regime is attained with polyacrylamide gels, which can maintain a tight spread and produce a shift in the distribution of the translocation times by an order of magnitude for 100 bp dsDNA fragments, if intermolecular crowding on the trans side is avoided. While previous approaches have proven successful at slowing DNA passage, they have generally been detrimental to the S/N, capture rate, or experimental simplicity. These results establish that by controlling the regime of DNA movement exiting a nanopore interfaced with a gel medium, it is possible to address the issue of rapid biomolecule translocations through nanopores-presently one of the largest hurdles facing nanopore-based analysis-without affecting the signal quality or capture efficiency.

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 score0.241
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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

Citations41
Published2015
Admission routes2
Has abstractyes

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