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Record W2066751882 · doi:10.1109/tbcas.2012.2230172

CMOS Spectrally-Multiplexed FRET-on-a-Chip for DNA Analysis

2013· article· en· W2066751882 on OpenAlexaff
Derek Ho, M. Omair Noor, Ulrich J. Krull, Glenn Gulak, Roman Genov

Bibliographic record

VenueIEEE Transactions on Biomedical Circuits and Systems · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrosystemMultiplexingCMOSMaterials scienceOptoelectronicsFörster resonance energy transferOptical filterElectronic engineeringPhysicsOpticsFluorescenceNanotechnologyEngineering

Abstract

fetched live from OpenAlex

A spectral-multiplexed fluorescence contact imaging microsystem for DNA analysis is presented. The microsystem integrates a filterless CMOS Color PhotoGate (CPG) sensor that exploits the polysilicon gate as an optical filter, and therefore does not require an external color filter. The CPG is applied to fluorescence-based transduction in a spectrally multiplexed format by differentiating among multiple emission bands, hence replacing the functionality of a bank of emission filters. A microsystem has been quantitatively modeled and prototyped based on the CPG fabricated in a standard 0.35 μm CMOS technology. The multi-color imaging capability of the microsystem in analyzing DNA targets has been validated in the detection of marker gene sequences for spinal muscular atropy disease and Escherichia coli (E. coli). Spectral-multiplexing enables the two DNA targets to be simultaneously detected with a measured detection limits of 240 nM and 210 nM for the two target concentrations at a sample volume of 10 μL for the green and red transduction channels, respectively.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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

Citations24
Published2013
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Biomedical Circuits and SystemsSame topicAdvanced Fluorescence Microscopy TechniquesFrench-language works237,207