Image_1_Ruminal bacterial communities differ in early-lactation dairy cows with differing risk of ruminal acidosis.pdf
Notice bibliographique
Résumé
Introduction<p>Early-lactation Holstein cows (n= 261) from 32 herds in three regions (Australia, California, and Canada) were previously categorized using a discriminant analysis model as being at a high (26.1% of cows), medium (26.8% of cows), or low risk (47.1% of cows) of ruminal acidosis. We aimed to investigate if (1) risk of acidosis would be associated with ruminal bacterial taxa and dietary nutrient components, (2) there would be individual or combinations of bacterial taxa associated with acidosis-risk groups, and (3) the abundance of bacterial taxa would be associated with the intake of dietary nutrient components.</p>Methods<p>Diets ranged from pasture supplemented with concentrates to total mixed rations. Bacteria 16S ribosomal DNA sequences from rumen samples collected < 3 hours after feeding via stomach tube were analyzed to determine bacterial presence. The relative abundance of each bacterial phylum and family was center log transformed and the transformed family data were subjected to two redundancy analysis biplots, one for acidosis risk group and one for region, to identify the 20 best-fit bacterial families from each respective redundancy analysis. A total of 29 unique families were identified when the lists of 20 families were combined from each redundancy analysis, and these 29 families were termed "influential" families." The association of acidosis-risk groups with the abundance of individual influential families was assessed by mixed models. Backward stepwise elimination mixed models were used to determine the bacterial taxa associated with each acidosis-risk group and the dietary nutrients associated with the abundance of the bacterial taxa.</p>Results and discussion<p>High-risk acidosis cows were associated with increased abundances of Anaerocella_f and Veillonellaceae and decreased abundances of several bacterial families with different characteristics. Five phyla: Firmicutes [odds ratio (OR) = 7.47 ± 7.43], Spirochaetes (OR = 1.28 ± 0.14), Lentisphaerae (OR = 0.70 ± 0.07), Planctomycetes (OR = 0.70 ± 0.09), and Tenericutes (OR = 0.44 ± 0.15), and nine families were associated with a higher risk of acidosis. Of the nine phyla identified to be of interest based on abundance and strength of association with acidosis-risk groups, all had one or more dietary nutrient that predicted their abundance. Sugar was the most frequently associated nutrient with the nine phyla, and was present in 78% (seven out of nine phyla) of the models; crude protein was present in 56% of models and crude fat was present in 44% of the models. Sugar and crude protein were most associated with the influential families and all but three families had one or more nutrient predictive of their abundance. Ruminal bacterial taxa are associated with ruminal acidosis; dietary sugar and crude protein are vital predictors of these and, thus, of ruminal acidosis risk.</p>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,367 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».