Effects of storage conditions on the microbiota of fecal samples collected from dairy cattle
Bibliographic record
Abstract
Microbiota analyses are key to understanding the bacterial communities within dairy cattle, but the impact of different storage conditions on these analyses remains unclear. This study sought to examine the effects of freezing at -80°C immediately after collection, refrigeration at 4°C for three days and seven days and absolute ethanol preservation on the microbiota diversity of pooled fecal samples from dairy cattle. Examining 16S rRNA gene sequences, alpha (Shannon, Pielou evenness, observed features and Faith PD indices) and beta (Bray-Curtis, βw and Weighted UniFrac) diversity were assessed. The effects of storage conditions on these metrics were evaluated using linear mixed models and PERMANOVA, incorporating the farm as a random effect. Our findings reveal that 7d and E significantly altered the Shannon index, suggesting a change in community composition. Changes in Pielou evenness for 3d and 7d storage when compared to 0d were found, indicating a shift in species evenness. Ethanol preservation impacted both observed features and Faith PD indices. Storage conditions significantly influenced Bray-Curtis, βw, and Weighted UniFrac metrics, indicating changes in community structure. PERMANOVA analysis showed that these storage conditions significantly contributed to microbiota differences compared to immediate freezing. In conclusion, our results demonstrate that while refrigeration for three days had minimal impact, seven days of refrigeration and ethanol preservation significantly altered microbiota analyses. These findings highlight the importance of sample storage considerations in microbiota research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".