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Record W4394106342 · doi:10.6084/m9.figshare.825659

A histidine-kinase <i>cheA</i> gene of <i>Pseudomonas pseudoalcaligens</i> KF707 not only has a key role in chemotaxis but also affects biofilm formation and cell metabolism

2011· dataset· en· W4394106342 on OpenAlexaboutno aff
Valentina Tremaroli, Stefano Fedi, Sabrina Tamburini, Carlo Viti, Enrico Tatti, Howard Ceri, Raymond J. Turner, Davide Zannoni

Bibliographic record

VenueFigshare · 2011
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsnot available
Fundersnot available
KeywordsChemotaxisHistidine kinaseBiofilmMicrobiologyBiologyKinaseHistidineGeneCell biologyPseudomonasCellChemistryBacteriaBiochemistryGeneticsEnzyme

Abstract

fetched live from OpenAlex

A histidine-kinase cheA gene in Pseudomonas pseudoalcaligenes KF707 plays a central role in the regulation of metabolic responses as well as in chemotaxis. Non-chemotactic mutants harboring insertions into the cheA gene were screened for their ability to form biofilms in the Calgary biofilm device. Notably, ≥95% decrease in the number of cells attached to the polystyrene surface was observed in cheA mutants compared to the KF707 wild-type biofilm phenotype. The ability to form mature biofilms was restored to wild-type levels, providing functional copies of the KF707 cheA gene to the mutants. In addition, phenotype micro-arrays and proteomic analyses revealed that several basic metabolic activities and a few periplasmic binding proteins of cheA mutant cells differed compared to those of wild-type cells. These results are interpreted as evidence of a strong integration between chemotactic and metabolic pathways in the process of biofilm development by P. pseudoalcaligenes KF707.

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)0.001
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.019
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.011

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 head0.019
GPT teacher head0.217
Teacher spread0.198 · 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
GenreDataset

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

Citations0
Published2011
Admission routes1
Has abstractyes

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