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Record W2550107908 · doi:10.2527/jam2016-1617

1617 Effect of dietary energy source and level on rumen bacteria community in lactating dairy cows

2016· article· en· W2550107908 on OpenAlexaff
Dengpan Bu, Sheng Li, Zhongtang Yu, Shuai Gao, Lei Ma, Xudong Zhou, J. Wang

Bibliographic record

VenueJournal of Animal Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRumenFood scienceDairy cattleAnimal scienceBacteriaEnergy sourceBiologyChemistryFermentationEcologyRenewable energy

Abstract

fetched live from OpenAlex

Increased dietary energy level and degradation rate are beneficial to rumen microbial protein synthesis and milk production by dairy cattle. This study aimed to examine the effects of dietary energy source and level on rumen bacterial community in lactating dairy cows. Eight primiparous Chinese Holstein cows were used in a replicated 4 × 4 Latin square design. Cows were allocated to four treatments arranged in a 2 × 2 factorial design with energy levels (NEL, 1.52 vs. 1.72 Mcal/kg of DM, referred to as LE vs. HE) and energy sources (steam-flaked corn or ground corn, SFC vs. GC). All cows were fed twice daily ad libitum. Each experimental period consisted of 14 d for adaptation and 7 d for sample collection. Rumen fluid was collected in the morning 3 h after feeding via stomach tubing at d 17. Total DNA was extracted from each rumen sample, and the V4 hypervariable region of 16S rRNA gene was amplified and subjected to paired-end Illumina sequencing. After merging of paired-end sequencing reads, the low-quality sequences were removed and the quality-checked sequences were clustered into operational units (OTU) using the UPARSE pipeline. The resulting OTU were taxonomically classified using the RDP classifier implemented in QIIME. The bacterial communities were profiled using α diversity measurements, whereas the effects of both energy sources and levels were evaluated based on UniFrac distance using a permutational ANOVA method. The differences in bacterial community structure were examined using DESeq2 in R. The LE and the GC treatments decreased bacterial richness as indicated by lowered number of species detected and estimates of both Chao1 and ACE (P < 0.05). The lower Shannon diversity index also suggests that LE and GC treatments decreased evenness of the rumen bacterial communities (P < 0.05). The composition of the bacterial communities was similar at the phylum level between two types of corn, but the high-energy diet altered bacterial communities by increasing Cyanobacteria while reducing Firmicutes and Proteobacteria (P < 0.05). At the genus level, the SFC diet had lower relative abundance of Papillibacter but higher relative abundance of Mitsuokella than the GC diet (P < 0.05). In contrast, dietary energy levels affected bacterial communities more extensively, with 51 genera being affected. The results indicate that increase in dietary energy level can affect rumen bacterial community to a greater extent than energy source when provided as steam-flaked vs. ground corn (P < 0.05)

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.267
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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes1
Has abstractyes

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