Inroads through the bacterial cell envelope: seeing is believing
Bibliographic record
Abstract
A singular feature of all prokaryotic cells is the presence of a cell envelope composed of a cytoplasmic membrane and a cell wall. The introduction of bacterial cell fractionation techniques in the 1950s and 1960s along with developments in procedures for electron microscopy opened the window towards an understanding of the chemical composition and architecture of the cell envelope. This review traces the contribution of Terry Beveridge in these endeavours, beginning with his doctoral studies in the 1970s on the structure of paracrystalline surface arrays (S-layers), followed by an exploration of cryogenic methods for preserving bacteria for ultrastructural analyses. His insights are reflected in a current example of the contribution of cryo-electron microscopy to S-layer studies - the structure and assembly of the surface array of Caulobacter crescentus. The review then focuses on Terry's contributions to imaging the ultrastructure of bacterial cell envelopes and to the development of cryo-electron microscopy techniques, including the use of CEMOVIS (Cryo-electron Microscopy of Vitreous Sections) to "see" the ultrastructure of the Gram-positive cell envelope - his last scientific endeavour.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".