Bibliographic record
Abstract
Antibodies to many Epstein-Barr virus (EBV) gene products are present in sera from infected human carriers. Historically, the ready demonstration of antibodies to lytic cycle proteins in particular allowed the seroepidemiological studies, which implicated EBV as a factor in various diseases ( 1 – 4 ). However, human sera also contain antibodies to EBV latent gene products, as first demonstrated in an immunofluorescence test that detected predominantly EBNA1 ( 5 ). Subsequently, human sera proved to be remarkably useful reagents for detecting and characterizing various latent EBV proteins in Western blot assays (e.g., refs. 6 – 8 ). Indeed, even now that monoclonal antibodies (MAbs) are available to many of the EBV latent proteins, human sera continue to be useful for “EBNotyping” assays in which EBV isolates can be distinguished by virtue of the characteristic fingerprint of variable-sized EBNA proteins (e.g., refs. 9 – 12 ). An example of EBNotyping is shown in Fig. 1. EBNotyping of EBV-transformed B cell lines. Proteins from 10 6 cells were separated by Laemmli SDS-PAGE on a 7.5% acrylamide resolving gel in a large-format slab gel apparatus giving 16×16 cm gels. Lane 1 contains protein from an EBV-negative B cell line, and lanes 2 through 7 contain proteins from 6 lymphoblastoid cell lines, each transformed with a different EBV isolate. The upper blot was probed with a serum from a juvenile rheumatoid arthritis patient which showed reactivity with EBNAs 1, 2, 3A, 3B, and 3C, and that was unusual in showing little crossreactivity with cellular proteins. Variations in the size of the EBNA proteins between different EBV isolates produces a characteristic fingerprint, or EBNotype, that allows different EBV isolates to be distinguished. The lower blot of a second gel run in parallel with the first, was probed with a MAb, PE2, reactive with EBNA2. Comparison of the two blots enables the EBNA1 and EBNA2 bands to be identified in the upper blot, and in this panel of cell lines it is apparent that the EBNA1 proteins show considerable size variation. EBNA1 usually has a lower MWt than EBNA2, but these samples include two EBV isolates (lanes 2 and lane 5) where the EBNA1 protein clearly migrates more slowly than the EBNA2 band. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".