The spatial dissimilarities and connections of the microbiota in the upper and lower respiratory tract of beef cattle
Bibliographic record
Abstract
Bovine respiratory disease (BRD) causes morbidity and mortality in cattle. The critical roles of the respiratory microbiota in BRD have been widely studied. The nasopharynx was the most popular sampling niche for BRD pathogen studies. The oral cavity and other niches within the respiratory tract, such as nostrils and lung, are less assessed. In this study, oropharyngeal swabs (OS), nasal swabs (NS), nasopharyngeal swabs (NP), and bronchoalveolar lavage (BAL) were collected from calves located in four countries and analyzed for investigation of the dissimilarities and connections of the respiratory microbiota. The results showed that the microbial diversity, structure, and composition in the upper and lower respiratory tract in beef cattle from China, the USA, Canada, and Italy were significantly different. The microbial taxa for each sampling niche were specific and associated with their local physiology and geography. The signature microbiota for OS, NS, NP, and BAL were identified using the LEfSe algorithm. Although the spatial dissimilarities among the respiratory niches existed, the microbial connections were observed in beef cattle regardless of geography. Notably, the nostril and nasopharynx had more similar microbiomes compared to lung communities. The major bacterial immigration patterns in the bovine respiratory tract were estimated and some of them were associated with geography. In addition, the contribution of oral microbiota to the nasal and lung ecosystems was confirmed. Lastly, microbial interactions were characterized to reveal the correlation between the commercial microbiota and BRD-associated pathogens. In conclusion, shared airway microbiota among niches and geography provides the possibility to investigate the common knowledge for bovine respiratory health and diseases. In spite of the dissimilarities of the respiratory microbiota in cattle, the spatial connections among these sampling niches not only allow us to deeply understand the airway ecosystem but also benefit the research and development of probiotics for BRD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".