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Record W3149779697 · doi:10.3390/microorganisms9040701

Contribution of the Locus of Heat Resistance to Growth and Survival of Escherichia coli at Alkaline pH and at Alkaline pH in the Presence of Chlorine

2021· article· en· W3149779697 on OpenAlexafffund
Tongbo Zhu, Zhiying Wang, Lynn M. McMullen, Tracy Raivio, David J. Simpson, Michael G. Gänzle

Bibliographic record

VenueMicroorganisms · 2021
Typearticle
Languageen
FieldChemistry
Topicthermodynamics and calorimetric analyses
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates Bio SolutionsAlberta Agriculture and Forestry
KeywordsChlorineEscherichia coliCell envelopeEndogenyOxidizing agentOxidative stressBiologyChemistryLocus (genetics)BiochemistryGene

Abstract

fetched live from OpenAlex

The locus of heat resistance (LHR) confers resistance to extreme heat, chlorine and oxidative stress in Escherichia coli. This study aimed to determine the function of the LHR in maintaining bacterial cell envelope homeostasis, the regulation of the genes comprising the LHR and the contribution of the LHR to alkaline pH response. The presence of the LHR did not affect the activity of the Cpx two-component regulatory system in E. coli, which was measured to quantify cell envelope stress. The LHR did not alter E. coli MG1655 growth rate in the range of pH 6.9 to 9.2. However, RT-qPCR results indicated that the expression of the LHR was elevated at pH 8.0 when CpxR was absent. The LHR did not improve survival of E. coli MG1655 at extreme alkaline pH (pH = 11.0 to 11.2) but improved survival at pH 11.0 in the presence of chlorine. Therefore, we conclude that the LHR confers resistance to extreme alkaline pH in the presence of oxidizing agents. Resistance to alkaline pH is regulated by an endogenous mechanism, including the Cpx envelope stress response, whereas the LHR confers resistance to extreme alkaline pH only in the presence of additional stress such as chlorine.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.220
Teacher spread0.212 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2021
Admission routes2
Has abstractyes

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