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Record W2800773985 · doi:10.3389/fmicb.2018.01031

Arginine Catabolic Mobile Elements in Livestock-Associated Methicillin-Resistant Staphylococcal Isolates From Bovine Mastitic Milk in China

2018· article· en· W2800773985 on OpenAlexafffund
Chao Tong, Zhaowei Wu, Xin Zhao, Huping Xue

Bibliographic record

VenueFrontiers in Microbiology · 2018
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsMcGill University
FundersRecruitment Program of Global ExpertsNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMicrobiologyBiologyLivestockBovine milkFood scienceEcology

Abstract

fetched live from OpenAlex

The arginine catabolic mobile element (ACME) facilitates colonization of staphylococci on skin and mucous membranes by improving their tolerances to polyamines and acidic conditions. ACME is inserted in tandem with the SCCmec element and S. epidermidis has been proposed to be a reservoir of ACME for other staphylococci. In this study, we investigated the existence of ACME in 146 staphylococcal isolates from mastitic milk and found 21 of them carried ACME. Almost half of the investigated S. epidermidis isolates contained the element. The whole genome of a S. epidermidis strain Y24 with ACME was further sequenced and the ACME-SCCmec composite island was assembled. This composite island is 81.3 kb long and consisted of 77 ORFs including a methicillin resistance gene mecA, a type II’ ACME gene cluster, a virulence gene pls and eight heavy metal tolerance genes. Wide existence of ACME in livestock-associated staphylococci from this study and a potential risk of spreading ACME among different staphylococcal species warrant close monitoring and further 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
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 head0.009
GPT teacher head0.260
Teacher spread0.252 · 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 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

Citations7
Published2018
Admission routes2
Has abstractyes

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