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Plasmid Localization and Partition in <i>Enterobacteriaceae</i>

2019· review· en· W2953010236 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEcoSal Plus · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsUniversity of Toronto
FundersAgence Nationale de la Recherche
KeywordsPlasmidRepliconBiologyPartition (number theory)EnterobacteriaceaeNucleoidGeneticsComputational biologyEscherichia coliGeneMathematics

Abstract

fetched live from OpenAlex

Plasmids are ubiquitous in the microbial world and have been identified in almost all species of bacteria that have been examined. Their localization inside the bacterial cell has been examined for about two decades; typically, they are not randomly distributed, and their positioning depends on copy number and their mode of segregation. Low-copy-number plasmids promote their own stable inheritance in their bacterial hosts by encoding active partition systems, which ensure that copies are positioned in both halves of a dividing cell. High-copy plasmids rely on passive diffusion of some copies, but many remain clustered together in the nucleoid-free regions of the cell. Here we review plasmid localization and partition (Par) systems, with particular emphasis on plasmids from Enterobacteriaceae and on recent results describing the in vivo localization properties and molecular mechanisms of each system. Partition systems also cause plasmid incompatibility such that distinct plasmids (with different replicons) with the same Par system cannot be stably maintained in the same cells. We discuss how partition-mediated incompatibility is a consequence of the partition mechanism.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.277
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it