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Record W1999799926 · doi:10.1021/ja073305n

Guest-Mediated Access to a Single DNA Nanostructure from a Library of Multiple Assemblies

2007· article· en· W1999799926 on OpenAlexaff
Faisal A. Aldaye, Hanadi F. Sleiman

Bibliographic record

VenueJournal of the American Chemical Society · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsChemistryDNAVertex (graph theory)TemplateNanostructureMoleculeNanotechnologyCombinatorial chemistryCrystallographyTopology (electrical circuits)Computer scienceTheoretical computer scienceBiochemistryMaterials scienceCombinatoricsMathematics

Abstract

fetched live from OpenAlex

We present a method in which a small molecule template is used to access a single DNA nanostructure from a library of multiple assemblies. DNA building blocks 2 and 2 ‘, in which two identical DNA arms branch from a fully rigid organic vertex, were synthesized and their self-assembly was found to generate a small dynamic library of products. The small molecule Ru(bpy) 3 2+ was found to provide access to a single member from this library (i.e., square 5 ) and was also found to re-equilibrate each other already formed member into 5 quantitatively. The use of a small molecule to access a single DNA assembly from many possible combinations introduces an additional level of control in DNA construction. In addition, the ability of this template to transform the other members into the same single product provides a built-in correction mechanism that helps ensure the integrity of the construction process, even when “errors” are made. We apply this approach to predictably generate periodic 1D DNA fibers extending over tens of microns, using two trifunctional symmetrical DNA building blocks that otherwise assemble into ill-defined oligomeric networks.

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 score0.001
Threshold uncertainty score0.003

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.270
Teacher spread0.260 · 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

Citations59
Published2007
Admission routes1
Has abstractyes

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Same venueJournal of the American Chemical SocietySame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207