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Record W4251726390 · doi:10.1121/1.4800298

Sound speed estimation in single cells using the ultrasound backscatter power spectrum

2013· article· en· W4251726390 on OpenAlexaff
Eric M. Strohm, Michael C. Kolios

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSpeed of soundAcousticsBackscatter (email)UltrasoundUltrasonic sensorTransducerAttenuationSound powerMaterials scienceWavelengthOpticsSpectral densityAcoustic attenuationScatteringSuspension (topology)PhysicsSound (geography)Computer scienceMathematicsTelecommunications

Abstract

fetched live from OpenAlex

The acoustic properties of single cells such as their size, sound speed and attenuation are known to change depending on the type, state or disease progression of a cell. Typically ultrasonic pulse echo methods on adherent cells are used. We propose using the ultrasound backscatter power spectrum on cells in suspension to extract the acoustic parameters. When the ultrasound wavelength is on the same order as the dimensions of the cell, periodically varying minima and maxima occur throughout the power spectrum that depend on the size and sound speed of the object. The ultrasound parameters can be determined by comparing the measured spectrum to a theoretical scattering model. We measured the backscattered ultrasound signals from single MCF7 breast cancer cells in suspension using a 375 MHz transducer. The cell diameter was determined through simultaneous optical imaging. The sound speed was calculated by adjusting the parameters in the scattering model until a good fit of the spectral features between the model and measured agreed. The average sound speed from 16 cells found to vary between 1577 to 1611 m/s when the density was fixed at 1050 kg/m3.

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)0.001
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 score0.251
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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