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Record W3202106315 · doi:10.1101/2021.10.07.463410

A sensitive and specific genetically encodable biosensor for potassium ions

2021· preprint· en· W3202106315 on OpenAlexafffund
Sheng-Yi Wu, Yurong Wen, Nelson Bernard Calixte Serre, Cathrine Charlotte Heiede Laursen, Andrea Dietz, Brian Taylor, Abhi Aggarwal, Vladimir Rančić, Michael E. Becker, Klaus Ballanyi, Kaspar Podgorski, Hajime Hirase, Maiken Nedergaard, Matyáš Fendrych, M. Joanne Lemieux, Daniel F. Eberl, Alan R. Kay, Robert E. Campbell, Yi Shen

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Alberta
FundersNational Cancer InstituteOffice of ScienceNational Institutes of HealthNovo Nordisk FondenNational Institute of General Medical SciencesNovo NordiskNational Natural Science Foundation of ChinaAlberta Innovates - Technology FuturesUniverzita Karlova v PrazeAlberta InnovatesArgonne National LaboratoryU.S. Department of EnergyH. Lundbeck A/SUniversity of AlbertaUniversity of SaskatchewanNatural Sciences and Engineering Research Council of CanadaCanadian Light SourceLundbeckfondenCanadian Institutes of Health ResearchNational Science Foundation
KeywordsBiosensorPotassiumFluorescenceElectrolyteGenetically engineeredIonChemistryBiophysicsNanotechnologyBiologyMaterials scienceBiochemistryPhysicsElectrodeGenePhysical chemistryOptics

Abstract

fetched live from OpenAlex

Abstract Potassium ions (K + ) play a critical role as an essential electrolyte in all biological systems. Here we report the crystal structure-guided optimization and directed evolution of an improved genetically encoded fluorescent K + biosensor, GINKO2. GINKO2 is highly sensitive and specific for K + and enables in vivo detection of K + dynamics in multiple species.

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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicAdvanced biosensing and bioanalysis techniques→French-language works237,207→