Bibliographic record
Abstract
Plasmids, as extrachromosomal elements, bear the burden of ensuring their own faithful segregation at cell division. This chapter reviews partition systems, which are, in general, systems that actively dictate the specific localization of plasmids inside the bacterial cell and coordinate this localization with the bacterial cell cycle. Partition systems also exert incompatibility, which is distinct from the replication-mediated incompatibility that has been used to classify plasmids. Growth of the membrane between attachment sites was proposed to push plasmids apart. It was subsequently shown that membrane growth is dispersive and thus cannot solely account for plasmid movement. An appealing candidate for the plasmid road sign is the bacterial replication apparatus. Experiments in Escherichia coli and Bacillus subtilis indicate that the replication machinery exists as localized factories in the cell. The intracellular localization patterns of the ParA from the E. coli virulence plasmid pB171 provide an intriguing clue as to the mechanism of ParA function. RepA and RepB are not essential for replication but are essential for plasmid stability. They have been shown to influence copy number, but this may be due to effects on the expression of repC. Recent cell biology, biochemical, and structural data show that R1 ParM looks and behaves like actin and suggest a partition model in which ParM acts as a cytoskeletal element to drive the movement of plasmids during the cell cycle.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".