MétaCan
Menu
Back to cohort
Record W2394965243 · doi:10.1021/acsnano.5b04333

Topological Linkage of DNA Tiles Bonded by Paranemic Cohesion

2015· article· en· W2394965243 on OpenAlexaff
Yoel P. Ohayon, Ruojie Sha, Ortho Flint, Arun Richard Chandrasekaran, Hatem Abdallah, Tong Wang, Xing Wang, Xiaoping Zhang, Nadrian C. Seeman

Bibliographic record

VenueACS Nano · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsWestern University
FundersArmy Research OfficeDivision of Computing and Communication FoundationsNational Institute of General Medical SciencesDivision of Emerging Frontiers in Research and InnovationDivision of Civil, Mechanical and Manufacturing InnovationOffice of Naval ResearchGordon and Betty Moore Foundation
KeywordsCatenationTopoisomeraseDNATopology (electrical circuits)CrystallographyBiophysicsChemistryBiologyBiochemistryMathematicsCombinatorics

Abstract

fetched live from OpenAlex

Catenation is the process by which cyclic strands are combined like the links of a chain, whereas knotting changes the linking properties of a single strand. In the cell, topoisomerases catalyzing strand passage operations enable the knotting and catenation of DNA so that single- or double-stranded segments can be passed through each other. Here, we use a system of closed DNA structures involving a paranemic motif, called PX-DNA, to bind double strands of DNA together. These PX-cohesive closed molecules contain complementary loops whose linking by Escherichia coli topoisomerase 1 (Topo 1) leads to various types of catenated and knotted structures. We were able to obtain specific DNA topological constructs by varying the lengths of the complementary tracts between the complementary loops. The formation of the structures was analyzed by denaturing gel electrophoresis, and the various topologies of the constructs were characterized using the program Knotilus.

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 score0.013
Threshold uncertainty score0.323

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)0.0000.000

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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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

Citations32
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueACS NanoSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207