A new method for obtaining explicit estimators in unbalanced mixed linear models

The data were collected by researchers at the Road Research Institute, in a study investigating the impact of differentfactors on road surface strength. In this statistical analysis, we apply linear mixed models (LMMs) to clustered longitudinal data, inwhich the units of analysis (points in the road) are nested within clusters (sample of four different road segments), and repeatedmeasures of road strength in these different points are collected over time with unequally spaced time intervals. The data arebalanced – each cluster has the same number of units, which are measured at the same number of time points. Because of correlateddata and different clusters in which data could be correlated, linear regression models are not appropriate here, and therefore linearmixed models are applied.

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Stool and ruminal microbiome components associated with methane emission and feed efficiency in Nelore beef cattle.

10.21203/rs.3.rs-74103/v2 ◽

2021 ◽

Author(s):

Bruno G.N. Andrade ◽

Haithem Afli ◽

Flavia A. Bressani ◽

Rafael R. C. Cuadrat ◽

Priscila S. N. de Oliveira ◽

...

Keyword(s):

Methane Emission ◽

Feed Efficiency ◽

Linear Models ◽

Anthropogenic Activities ◽

Mixed Linear Models ◽

Human Welfare ◽

Feed Composition ◽

Fecal Microbiome ◽

Treatment Groups ◽

The Impact

Abstract Background: The impact of extreme changes in weather patterns in the economy and human welfare are some of the biggest challenges that our civilization is facing. From the anthropogenic activities that contribute to climate change, reducing the impact of farming activities is a priority, since it is responsible for up to 18% of greenhouse gases linked to such activities. To this end, we tested if the ruminal and fecal microbiome components of 52 Brazilian Nelore bulls, belonging to two treatment groups based on the feed intervention, conventional and by-products based diet, could be used in the future as biomarkers for methane emission and feed efficiency in bovine.Results: We identified a total of 5,693 Amplicon Sequence Variants (ASVs) in the Nelore bulls microbiomes. Differential abundance (DA) analysis with the ANCOM approach identified 30 bacterial and 15 archaea ASVs as DA among treatment groups. Association analysis using Maaslin2 and Mixed Linear Models indicated that bacterial ASVs are linked to the residual methane emission (RCH4) and Residual Feed Intake (RFI) phenotypes, contributing to the host’s phenotypic variation, suggesting their potential as targets for interventions and/or biomarkers.Conclusion: Feed composition induced significant differences in abundance and richness of ruminal and fecal microbial populations. The diet based on industrial byproducts applied to our treatment groups influenced the microbiome diversity of bacteria and archaea, but not of protozoa. Different ASVs were associated with RCH4 emission and RFI in both ruminal and fecal microbiomes. While ruminal ASVs are expected to directly influence RCH4 emission and RFI, the relation of fecal taxa, such as Alistipes and Rikenellaceae (gut group RC9), with these traits might also be associated with host health due to their link to anti-inflammatory compounds, and these have the potential to be used as accessible biomarkers for these complex phenotypes.

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