Bibliographic record
Abstract
Protein glycosylation is one of the most common and complex post-translational modifications. Many techniques have been developed to characterize the specific roles of glycans, the relationship between their structures and their impact on the functions of proteins. A common method for glycan analysis is to employ exoglycosidase cleavage to release N-linked glycans from glycoproteins or glycopeptides using Peptide-N-Glycosidase F (PNGase F). However, the glycan-protein linkages in bacteria are different and there is no enzyme available to release glycans from bacterial glycoproteins. In addition, free glycans have also been described in mammalian cells, bacteria, yeast, plants, and fish. In this article, we present a method that can characterize the N-linked glycosylation system in Campylobacter jejuni by detecting asparagine (Asn)-linked and free glycans that are not attached to their target proteins. In this method, total proteins from C. jejuni were digested by Pronase E with a higher enzyme to protein ratio (2:1−3:1) and a longer incubation time (48−72 h). The resulted Asn-glycans and free glycans were then purified using porous graphitic carbon cartridges, permethylated, and analyzed by mass spectrometry.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.348 | 0.275 |
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".