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Record W2105711978 · doi:10.1109/iembs.1997.758748

Frequency response of assemblies of biological cells exposed to electric fields

2002· article· en· W2105711978 on OpenAlexafffund
Elise Fear, M.A. Stuchly

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsGap junctionFilter (signal processing)StopbandFrequency responseLow-pass filterBand-stop filterElectric fieldBand gapMaterials scienceAcousticsPhysicsBiological systemOptoelectronicsIntracellularElectrical engineeringBiologyEngineeringCell biologyResonator

Abstract

fetched live from OpenAlex

For most cell types, gap junctions connect the interiors of neighbouring cells and provide the local intercellular communication that is essential for normal biological cell processes. In this work, we test the hypothesis that the frequency responses of gap-connected cells exposed to electric fields differ from those of isolated cells. The frequency responses of geometrically complex models of gap-connected cell configurations are evaluated using the finite element method. Results indicate that the inclusion of gap junctions changes the frequency behaviour of the cells. A group of gap-connected cells can be modeled as a lowpass filter cascaded with a bandstop filter. The lowpass filter corresponds to the frequency behaviour of a single cell with the same shape as the configuration. The bandstop filter represents the gap junctions. The characteristics of the stopband of this filter depend in a complex way on the properties of the gap junctions, such as area, conductivity and location.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.048
GPT teacher head0.300
Teacher spread0.252 · 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".

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Citations1
Published2002
Admission routes2
Has abstractyes

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