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Assembly of Bacterial Capsular Polysaccharides and Exopolysaccharides

2020· review· en· W3043527632 on OpenAlexafffund
Chris Whitfield, Samantha S. Wear, Caitlin Sande

Bibliographic record

VenueAnnual Review of Microbiology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health Research
KeywordsPolysaccharideBacteriaBiofilmComputational biologyBacterial capsuleBiologyBiochemical engineeringChemistryBiochemistryGeneticsGeneEngineering

Abstract

fetched live from OpenAlex

Polysaccharides are dominant features of most bacterial surfaces and are displayed in different formats. Many bacteria produce abundant long-chain capsular polysaccharides, which can maintain a strong association and form a capsule structure enveloping the cell and/or take the form of exopolysaccharides that are mostly secreted into the immediate environment. These polymers afford the producing bacteria protection from a wide range of physical, chemical, and biological stresses, support biofilms, and play critical roles in interactions between bacteria and their immediate environments. Their biological and physical properties also drive a variety of industrial and biomedical applications. Despite the immense variation in capsular polysaccharide and exopolysaccharide structures, patterns are evident in strategies used for their assembly and export. This review describes recent advances in understanding those strategies, based on a wealth of biochemical investigations of select prototypes, supported by complementary insight from expanding structural biology initiatives. This provides a framework to identify and distinguish new systems emanating from genomic studies.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.018
GPT teacher head0.320
Teacher spread0.302 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations305
Published2020
Admission routes2
Has abstractyes

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