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Record W2604416670 · doi:10.1002/anie.201701097

In‐Phase Ultra High‐Resolution In Vivo NMR

2017· article· en· W2604416670 on OpenAlexafffund
Ioana Fugariu, Wolfgang Bermel, Daniel Lane, Ronald Soong, André J. Simpson

Bibliographic record

VenueAngewandte Chemie International Edition · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaKrembil Foundation
KeywordsNuclear magnetic resonanceNMR spectra databaseIsotropyChemical shiftPhase (matter)In vivoNuclear magnetic resonance spectroscopyChemical physicsSpectral lineRelaxation (psychology)Intermolecular forceResolution (logic)Materials scienceChemistryPhysicsMoleculeOpticsComputer scienceBiology

Abstract

fetched live from OpenAlex

Although current NMR techniques allow organisms to be studied in vivo, magnetic susceptibility distortions, which arise from inhomogeneous distributions of chemical moieties, prevent the acquisition of high-resolution NMR spectra. Intermolecular single quantum coherence (iSQC) is a technique that breaks the sample's spatial isotropy to form long range dipolar couplings, which can be exploited to extract chemical shift information free of perturbations. While this approach holds vast potential, present practical limitations include radiation damping, relaxation losses, and non-phase sensitive data. Herein, these drawbacks are addressed, and a new technique termed in-phase iSQC (IP-iSQC) is introduced. When applied to a living system, high-resolution NMR spectra, nearly identical to a buffer extract, are obtained. The ability to look inside an organism and extract a high-resolution metabolic profile is profound and should find applications in fields in which metabolism or in vivo processes are of interest.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.216
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.337
Teacher spread0.324 · 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.

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

Citations40
Published2017
Admission routes2
Has abstractyes

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