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Record W2516793494 · doi:10.1098/rsif.2016.0210

Putting life on ice: bacteria that bind to frozen water

2016· article· en· W2516793494 on OpenAlexafffund
Maya Bar Dolev, Reut Bernheim, Shuaiqi Guo, Peter L. Davies, Ido Braslavsky

Bibliographic record

VenueJournal of The Royal Society Interface · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsQueen's University
FundersLady Davis Fellowship Trust, Hebrew University of JerusalemEuropean Research CouncilCanadian Institutes of Health ResearchIsrael Science Foundation
KeywordsBacterial adhesinBacteriaChemistryIce nucleusIce crystalsBiophysicsCell biologyBiologyMicrobiologyBiochemistryEscherichia coliGenetics

Abstract

fetched live from OpenAlex

Ice-binding proteins (IBPs) are typically small, soluble proteins produced by cold-adapted organisms to help them avoid ice damage by either resisting or tolerating freezing. By contrast, the IBP of the Antarctic bacterium Marinomonas primoryensis is an extremely long, 1.5 MDa protein consisting of five different regions. The fourth region, a 34 kDa domain, is the only part that confers ice binding. Bioinformatic studies suggest that this IBP serves as an adhesin that attaches the bacteria to ice to keep it near the top of the water column, where oxygen and nutrients are available. Using temperature-controlled cells and a microfluidic apparatus, we show that M. primoryensis adheres to ice and is only released when melting occurs. Binding is dependent on the mobility of the bacterium and the functionality of the IBP domain. A polyclonal antibody raised against the IBP region blocks bacterial ice adhesion. This concept may be the basis for blocking biofilm formation in other bacteria, including pathogens. Currently, this IBP is the only known example of an adhesin that has evolved to bind ice.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.237
Teacher spread0.225 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations68
Published2016
Admission routes2
Has abstractyes

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