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Record W2627062987 · doi:10.1021/acs.jproteome.7b00273

Impact of Low-Intensity Pulsed Ultrasound on Transcript and Metabolite Abundance in <i>Saccharomyces cerevisiae</i>

2017· article· en· W2627062987 on OpenAlexafffund
Michael C. Schultz, Jian Zhang, Xian Luo, Oleksandra Savchenko, Liang Li, Michael K. Deyholos, Jie Chen

Bibliographic record

VenueJournal of Proteome Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsAlberta InnovatesUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsSaccharomyces cerevisiaeMetaboliteAbundance (ecology)ChemistryIntensity (physics)BiologyComputational biologyBiochemistryYeastPhysicsOpticsEcology

Abstract

fetched live from OpenAlex

The interactions of ultrasound with biological materials are exploited for diagnostic, interventional, and therapeutic applications in humans and can improve productivity in industrial-scale generation of organic molecules such as biofuels, vaccines, and antibodies. Accordingly, there is great interest in better understanding the biological effects of ultrasound. We studied the impact of low-intensity pulsed ultrasound (LIPUS) on RNA expression and metabolism of S. cerevisiae. Although the transcript expression signature of LIPUS-treated cells does not differ significantly from that of untreated cells after 5 days, metabolomic profiling by chemical-isotopic-labeling-liquid-chromatography-mass-spectrometry suggests that LIPUS has an impact on the pathways of pyrimidine, proline, alanine, aspartate, glutamate, and arginine metabolism. Therefore, LIPUS triggers metabolic effects beyond reprogramming of the core pathways of carbon metabolism. Further characterization of metabolism will likely be important for elucidation of the biological effects of LIPUS.

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)0.002
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.046
GPT teacher head0.398
Teacher spread0.352 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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