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Record W2608869078 · doi:10.2527/asasann.2017.283

283 Effects of condensed tannins on bacterial and fungal core microbiomes involved in the ensiling and aerobic spoilage of purple prairie clover (Dalea purpurea Vent.) silage

2017· article· en· W2608869078 on OpenAlexaff
Kai Peng, Qianqian Huang, Long Jin, Dongze Niu, Tim A. McAllister, H. Denis, Hee Eun Yang, S. N. Acharya, Z. Xu, S. Wang, Yafang Wang

Bibliographic record

VenueJournal of Animal Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSilageBiologyLactococcusLeuconostocFood spoilageLactobacillusFood scienceMicrobial inoculantPediococcusMicrobiologyBotanyHorticultureBacteriaLactococcus lactisFermentationLactic acid

Abstract

fetched live from OpenAlex

Effects of condensed tannins (CT) on rumen microbes have been well documented, whereas little information exists about their effects on the microbial communities involved in ensiling. The objective of this study was to determine the effects of CT on the composition and diversity of bacterial and fungal core microbiomes associated with ensiling and aerobic spoilage of purple prairie clover (PPC). Purple prairie clover (Dalea purpurea Vent.; 60 g CT/kg DM) was harvested at full flower and ensiled in polyvinyl chloride laboratory silos with and without polyethylene glycol (PEG) for 76 d. Silage was then subjected to aerobic exposure for 14 d. Bacterial and fungal core microbiomes in the silage and in the aerobically exposed silage were examined using real-time qPCR and high-throughput sequencing. Real-time qPCR analysis revealed that PPC ensiled without PEG exhibited less (P < 0.01 to approximately 0.001) gene copy numbers of total bacteria, Lactobacillus, yeasts, and fungi than PEG-treated silage. This trend was also observed for d-7 aerobically exposed silage with the exception of Lactobacillus, which had greater (P < 0.05) gene copy numbers for non-PEG than for PEG-treated silage. Metagenome analyses generated a total of 4,273,668 bacterial sequences and 3,455,929 fungal sequences, which were assigned to 225 bacterial and 142 fungal genera, respectively. Addition of PEG increased (P < 0.001) the abundance of Lactobacillus and Pediococcus but decreased (P < 0.01) that of Lactococcus, Leuconostoc, Agrobacterium, Erwinia, Methylobacterium, Pseudomonas, and Sphingomonas. The abundance of the fungal genera Colletotrichum, Xylogone, Galactomyces, Penicillium, Fusarium, and Cryptococcus in silage were also increased (P < 0.05) by PEG. Diversity measurements of bacterial and fungal communities indicated that addition of PEG decreased (P < 0.01) the number of microbial core genome operational taxonomic units (OTU), ACE, Chao 1, and Shannon indexes. It did not affect the diversity of fungal communities. The PEG-treated silage had a higher (P < 0.001) abundance of Pediococcus but less (P < 0.001) Lactococcus and Leuconostoc than non-PEG silage after aerobic exposure. The PEG-treated silage also had a greater (P < 0.05 to approximately 0.01) abundance of Colletotrichum, Penicillium, and Fusarium at d 7 and greater (P < 0.05 to approximately 0.001) abundance of Candida, Colletotrichum, Wickerhamomyces, Penicillium, and Pterula after aerobic exposure. The observed bacterial and fungal OTU, ACE and Chao 1 were lower (P < 0.05) for PEG-treated than for non-PEG treated silage after aerobic exposure. The results indicated that CT decreased population of majority bacteria, fungus, and yeast but increased bacterial diversity during ensiling and aerobic deterioration but increased fungal diversity only after aerobic exposure.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.265
Teacher spread0.237 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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