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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 OpenAlexaff
Hans-Peter Höpp, Jian‐Young Wu, Chun X. Falk, Jill A. London, Dejan Zečević, Lawrence B. Cohen

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 ). Structures of several dyes that have been used to monitor membrane potential. The merocyanine (XVII) was the dye used in the experiments illustrated in Figs. 3 and 4. Dye XVII and the oxonol XXV are available from Dr. A. S. Waggoner, Center for Fluorescence, Carnegie Mellon University, 4400 Fifth Ave., Pittsburgh, PA, as WW375 and WW781. Dye XVII is available commercially as NK 2495 from Nippon Kankoh-Shikiso Kenkyusho Co. Ltd. The oxonol, RH155, and styryl, RH414, are available from Amiram Grinvald, Department of Neurobiology, Weizmann Institute, Rehovot, Israel. RH414is available commercially as dye 1112 from Molecular Probes, Junction City, OR. RH155 is available as NK3041 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.054
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

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

Citations45
Published2003
Admission routes1
Has abstractyes

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