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Record W2799472885 · doi:10.1094/asbcj-2016-4895-01

Monoclonal Antibodies Binding to Lipopolysaccharide from the Beer-Spoilage Bacterium <i>Megasphaera Cerevisiae</i> Exhibit Panreactivity with the Strictly Anaerobic Gram-Negative Brewing-Related Bacteria

2016· article· en· W2799472885 on OpenAlexaff
Barry Ziola

Bibliographic record

VenueJournal of the American Society of Brewing Chemists · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsUniversity of SaskatchewanRoyal University Hospital
Fundersnot available
KeywordsBacteriaMonoclonal antibodyMicrobiologyBrewingLipopolysaccharideAnaerobic bacteriaAntigenGram-negative bacteriaEscherichia coliBiologyImmunoassayChemistryAntibodyFermentationBiochemistryGeneImmunologyGenetics

Abstract

fetched live from OpenAlex

Four mouse hybridomas were isolated that secrete monoclonal antibodies (Mabs) showing surface reactivity for isolates across the three defined serogroups of the strictly anaerobic Gram-negative beer-spoilage bacterium Megasphaera cerevisiae. These Mabs also were found to react with surface-accessible antigens on the other strictly anaerobic Gram-negative brewing-related bacteria in the genera Pectinatus, Propionispira, and Selenomonas. In each case, the target antigen was the bacterial lipopolysaccharide (LPS), with the four Mabs recognizing at least two different antigenic binding sites. Owing to their panreactivity with surface-accessible LPS on the strictly anaerobic Gram-negative brewing-associated bacteria, such Mabs can prove useful in fluorescent immunoassay monitoring of these bacteria in the brewery setting.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0020.000

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

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