Serogroups of the beer spoilage bacterium <i>Megasphaera cerevisiae</i> correlate with the molecular weight of the major EDTA-extractable surface protein
Bibliographic record
Abstract
Megasphaera cerevisiae is a Gram-negative obligate anaerobe that causes turbidity and off-flavour and aroma in beer. Seven isolates of M. cerevisiae were obtained worldwide, and their extractable surface antigens were focused upon to determine if there is more than one serogroup of this bacterium. Sodium dodecyl sulphate polyacrylamide gel electrophoresis of ethylenediaminetetraacetic acid (EDTA) bacterial extracts revealed a predominant protein with apparent molecular weights of 46,000, 45,000, and 43,000 for three, two, and two isolates, respectively. When mouse anti-serum generated against any of the EDTA extracts was reacted with denatured bacterial proteins in immunoblots, all bacterial isolates exhibited extensive cross-reactivity involving three antigens, one being the major EDTA-extractable protein. In contrast, when the sera were tested for surface reactivity with intact bacteria, three cross-reactivity groups were observed, with the groups individually comprised of bacteria having the same size major EDTA-extractable surface protein. When BALB/c mice immunized with a bacterium from each of the three serogroups were used for monoclonal antibody (Mab) hybridoma production, bacterial surface-reactive Mabs were obtained whose reactivities parallel the three polyclonal antibody-defined serogroups. Through combining these surface-reactive Mabs, it will be possible to rapidly detect and identify beer contamination by M. cerevisiae belonging to any serogroup.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".