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Record W2748144848 · doi:10.1021/acsomega.7b00366

Ultrasonic Characterization of Amyloid-Like Ovalbumin Aggregation

2017· article· en· W2748144848 on OpenAlexafffund
Koen J.A. Jansens, Kristof Brijs, Jörg Stetefeld, Jan A. Delcour, Martin G. Scanlon

Bibliographic record

VenueACS Omega · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaFonds Wetenschappelijk Onderzoek
KeywordsOvalbuminAttenuationChemistryCompressibilityUltrasonic sensorBiophysicsAmyloid (mycology)Analytical Chemistry (journal)Egg whiteMaterials scienceChromatographyThermodynamicsBiochemistryInorganic chemistryOptics

Abstract

fetched live from OpenAlex

Thermal processing conditions, pH, and salt content affect the formation of egg white ovalbumin amyloid, which was investigated using high-precision measurements of ultrasonic velocity and attenuation. These were related to fluorescence and particle size measurements. Fluorescence changes indicated the formation of amyloid-like aggregates that was enhanced by increasing time-temperature treatments. The ultrasonic velocity of ovalbumin after heating at neutral pH (60 min at 70 or 80 °C) was lower than that of unheated ovalbumin, whereas the attenuation was higher. The decrease in the velocity represents increased compressibility associated with a change in the compactness of the protein, whereas changes in attenuation are due to protein conformational changes. Heating ramp studies revealed transitions at approximately 58 and 73 °C. During heating at a constant temperature, the ultrasonic velocity decreased slowly with increasing heating time, indicating an increase in ovalbumin compressibility. It is suggested that the obtained amyloid-like ovalbumin aggregates contain a compact core surrounded by loosely packed protein segments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.184

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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.

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

Citations13
Published2017
Admission routes2
Has abstractyes

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