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Record W7063935968

Anatoxin-producing and non-toxic strains of Microcoleus sp. coexist in benthic cyanobacterial mats in the Wolastoq (Saint John River, Canada)

2022· article· en· W7063935968 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks@BGSU (Bowling Green State University) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCyanobacteriaMicrobial matBenthic zoneMetagenomicsGenomeBiodiversityBacteria
DOInot available

Abstract

fetched live from OpenAlex

The presence of toxigenic benthic cyanobacteria in riverine ecosystems is an increasing concern around the world. In 2018, the death of three dogs along the Wolastoq in New Brunswick, Canada, was attributed to anatoxin exposure after they ingested benthic microbial mats found along the shore. Four samples of the material ingested by the dogs and from the vicinity were collected. Then, 15 non-axenic cyanobacterial isolates were obtained from the same material. Total DNA of the 19 samples was sequenced using Illumina technology. Metagenomic assemblies recovered near-complete Microcoleus genomes from 12 of the sequenced samples. The high average genomic sequence similarity (>95% identity at the nucleotide level) suggests that the 12 genomes are representatives of the same “genomic” Microcoleus species. The genetic repertoire to produce anatoxin-a and dihydroanatoxin-a was identified in 9 of these genomes. The capacity for anatoxin production was confirmed by LC-MS. The overall comparison revealed that genomes of the 9 toxigenic Microcoleus isolates contain a higher number of accessory genes than their 3 non-toxigenic relatives. These differences suggest that toxigenic Microcoleus variants from the Wolastoq would be more responsive to changing environments, nutrient limitation and/or bacteriophage infection. Our results suggest that the two Microcoleus strains inhabited the original benthic mats in similar relative abundances.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.196
Teacher spread0.187 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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