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Record W2412082333 · doi:10.1385/0-89603-160-8:193

Multisite Optical Measurement of Membrane Potential

2003· book-chapter· en· W2412082333 on OpenAlex
Hans-Peter Höpp, Jian‐Young Wu, Chun X. Falk, Jill A. London, Dejan Zečević, Lawrence B. Cohen

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

VenueHumana Press eBooks · 2003
Typebook-chapter
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsMembraneMembrane potentialBirefringenceTransducerChemistryNanotechnologyBiological systemOpticsMaterials scienceBiophysicsPhysicsAcousticsBiologyBiochemistry

Abstract

fetched live from OpenAlex

An optical measurement of membrane potential using a molecular probe might be beneficial in a variety of circumstances. “Such a probe could, we believe, provide a powerful new technique for measuring membrane potential in systems where, for reasons of scale, topology, or complexity, the use of electrodes is inconvenient or impossible” (B. M. Salzberg, personal sentence). The possibility of using optical methods was first suggested in 1968 by the discovery of potential-dependent changes in intrinsic optical properties of squid giant axons (Cohen et al., 1968). Shortly thereafter, (1968) found stimulus-dependent changes in fluorescence of stained axons, and in 1971 a search was begun (Cohen et al., 1971) for dyes that would give signals large enough to be useful for monitoring membrane potential. By now more than 1000 dyes have been tested for their ability to act as molecular transducers of changes in membrane potential into changes in three types of optical signals: absorption, birefringence, and fluorescence. This screening effort has resulted in the discovery of dyes with a signal-to-noise ratio 100 times larger than was available from any signal in 1971. Several of these dyes (see, e.g., Fig. 1) have been used to monitor changes in potential in a variety of preparations. For reviews, see (1978), (1979), (1983), (1988), and (1988). An earlier discussion of methods was published (Cohen and Lesher, 1986).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.756
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.054
GPT teacher head0.231
Teacher spread0.177 · 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