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Record W2117435310 · doi:10.1109/iscas.2007.378030

A CMOS Contact Imager for Cell Detection in Bio-Sensing Applications

2007· article· en· W2117435310 on OpenAlex
Timmy S. L. Tam, G.A. Jullien, Orly Yadid-Pecht

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCMOSBiosensorReset (finance)Sensitivity (control systems)Computer sciencePixelNoise (video)CMOS sensorImage sensorElectronic engineeringComputer hardwareNanotechnologyMaterials scienceEngineeringArtificial intelligenceImage (mathematics)

Abstract

fetched live from OpenAlex

Many experimental procedures in cell biology rely on the use of biochemical light-emitting reporters to enhance structures or processes of interest in a cell sample. The ability to integrate these widely-used protocols on the surface of a CMOS biosensor array, combined with the capacity to perform cell actuation/stimulation, would provide researchers with a valuable tool to conduct high-throughput, high-density, accurate analyses. To this end, a low noise, high-sensitivity CMOS contact imager is presented for the detection of cell cultures coupled directly to the sensor surface. Design considerations are discussed for the application of neural activity recording of neuronal networks cultured <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> from dissociated neuron cells. A 128 × 128 CMOS imager implemented in 0.18-μm CMOS technology is presented featuring the implementation of the active reset technique with the use of the active column sensor pixel architecture.

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.

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: none
Teacher disagreement score0.745
Threshold uncertainty score0.278

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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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

Quick stats

Citations9
Published2007
Admission routes1
Has abstractyes

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