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Record W3198275873 · doi:10.1016/j.crmicr.2021.100064

Identifying active rumen epithelial associated bacteria and archaea in beef cattle divergent in feed efficiency using total RNA-seq

2021· article· en· W3198275873 on OpenAlexafffund
Rebecca Tan, Mi Zhou, Fuyong Li, Le Luo Guan

Bibliographic record

VenueCurrent Research in Microbial Sciences · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Livestock and Meat AgencyAlberta Agriculture and Forestry
KeywordsRumenArchaeaBacteriaBiologyMicrobiologyFermentationMicroorganismEuryarchaeotaFood scienceGenetics

Abstract

fetched live from OpenAlex

To date, the role of ruminal epithelial attached microbiota in cattle feed efficiency is undefined. In this study, we aimed to characterize transcriptionally active bacteria and archaea attached to the rumen epithelial wall and to determine whether they differ in cattle with varied feed efficiency. RNA-sequencing was performed to obtain the rumen epithelial transcriptomes from 9 of the most efficient (low RFI) and 9 of the most inefficient (high RFI) animals. The bacteria and archaea 16S rRNA transcripts were identified using an in-house developed pipeline, enriched from filtered reads that did not map to the bovine genome. Archaea from unclassified genera belonging to the Euryarchaeota phylum showed the most activity on the rumen epithelium of low RFI (81.3 ± 1.9%) and high RFI (76.4 ± 3.0%) steers. Bacteria from the Succinivibrionaceae family showed the greatest activity of bacteria on the low RFI (28.7 ± 9.0%) and high RFI (33.9± 8.8%) epithelium. Of the bacterial families, Campylobacteraceae and Neisseriaceae had significantly greater activity on the low RFI epithelium (p < 0.05) and are known to play a role in oxygen scavenging. Greater activity of rumen epithelial attached oxygen scavenging bacteria may provide more optimal feed fermentation conditions, which contributes to high fermentation efficiency in the rumen.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.155
GPT teacher head0.379
Teacher spread0.224 · 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

Citations22
Published2021
Admission routes2
Has abstractyes

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