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Record W2300872164 · doi:10.1002/cbic.201600136

Nanostructures from Synthetic Genetic Polymers

2016· article· en· W2300872164 on OpenAlexaff
Alexander I. Taylor, Fabienne Beuron, Sew‐Yeu Peak‐Chew, Edward P. Morris, Piet Herdewijn, Philipp Holliger

Bibliographic record

VenueChemBioChem · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsConcordia University
FundersEuropean Social FundBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesMedical Research CouncilEuropean Science FoundationUniversity of OxfordCancer Research UK
KeywordsNucleic acidRNADNAPolymerChemistryNanotechnologyPolynucleotideComputational biologyBiochemistryBiologyMaterials scienceGeneOrganic chemistry

Abstract

fetched live from OpenAlex

Nanoscale objects of increasing complexity can be constructed from DNA or RNA. However, the scope of potential applications could be enhanced by expanding beyond the moderate chemical diversity of natural nucleic acids. Here, we explore the construction of nano-objects made entirely from alternative building blocks: synthetic genetic polymers not found in nature, also called xeno nucleic acids (XNAs). Specifically, we describe assembly of 70 kDa tetrahedra elaborated in four different XNA chemistries (2'-fluro-2'-deoxy-ribofuranose nucleic acid (2'F-RNA), 2'-fluoroarabino nucleic acids (FANA), hexitol nucleic acids (HNA), and cyclohexene nucleic acids (CeNA)), as well as mixed designs, and a ∼600 kDa all-FANA octahedron, visualised by electron microscopy. Our results extend the chemical scope for programmable nanostructure assembly, with implications for the design of nano-objects and materials with an expanded range of structural and physicochemical properties, including enhanced biostability.

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 score0.004

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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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

Citations75
Published2016
Admission routes1
Has abstractyes

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