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Record W2992977612 · doi:10.1016/j.npe.2019.11.004

Simultaneous, hybrid single-molecule method by optical tweezers and fluorescence

2019· article· en· W2992977612 on OpenAlexaff
Guoteng Ma, Chunguang Hu, Shuai Li, Xiaoqin Gao, Hongbin Li, Xiaotang Hu

Bibliographic record

VenueNanotechnology and Precision Engineering · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversity of British Columbia
FundersNational Key Research and Development Program of China
KeywordsOptical tweezersFluorescenceTotal internal reflection fluorescence microscopeTweezersBiomoleculeNanotechnologyFluorescence microscopeSingle-molecule experimentConfocalComputer scienceMaterials scienceChemistryOpticsPhysics

Abstract

fetched live from OpenAlex

As studies on life sciences progress toward the single-molecule level, new experiments have put forward more requirements for simultaneously displaying the mechanical properties and conformational changes of biomolecules. Optical tweezers and fluorescence microscopy have been combined to solve this problem. The combination of instruments forms a new generation of hybrid single-molecule technology that breaks through the limitations of traditional biochemical analysis. Powerful manipulation and fluorescence visualization have been widely used, and these techniques provide new possibilities for studying complex biochemical reactions at the single-molecule level. This paper explains the features of this combined technique, including the application characteristics of single-trap and dual-traps, the anti-bleaching method, and optical tweezers combined with epi-fluorescence, confocal fluorescence, total internal reflection fluorescence, and other fluorescence methods. Using typical experiments, we analyze technical solutions and explain the factors and principles that instrument designers should consider. This review aims to give an introduction to this novel fusion technology process and describe important biological results.

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 score0.242
Threshold uncertainty score0.603

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 head0.003
GPT teacher head0.205
Teacher spread0.203 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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