CIVIL ENGINEERING 365 ALL ABOUT CIVIL ENGINEERING


  • 1.

    Mbow, H.-O.P., Reisinger, A., Canadell, J. & O’Brien, P. Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (SR2) (IPCC, Ginevra, 2017).


    Google Scholar
     

  • 2.

    Horowitz, C. A. Paris agreement. Int. Legal Mater. 55, 740–755 (2016).


    Google Scholar
     

  • 3.

    Mbow, C. et al. Food security. In Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security and Greenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2019).

  • 4.

    Finneran, E. et al. Simulation modelling of the cost of producing and utilising feeds for ruminants on Irish farms. J. Farm Manag. 14, 95–116 (2010).


    Google Scholar
     

  • 5.

    Opio, C. et al. Greenhouse Gas Emissions from Ruminant Supply Chains–A Global Life Cycle Assessment 1–214 (Food and agriculture organization of the United Nations (FAO), Rome, 2013).


    Google Scholar
     

  • 6.

    Tubiello, F. N. et al. The FAOSTAT database of greenhouse gas emissions from agriculture. Environ. Res. Lett. 8, 015009 (2013).

    ADS 

    Google Scholar
     

  • 7.

    Herd, R. & Arthur, P. Physiological basis for residual feed intake. J. Anim. Sci. 87, E64–E71 (2009).

    CAS 
    PubMed 

    Google Scholar
     

  • 8.

    Kelly, A. et al. Repeatability of feed efficiency, carcass ultrasound, feeding behavior, and blood metabolic variables in finishing heifers divergently selected for residual feed intake. J. Anim. Sci. 88, 3214–3225 (2010).

    CAS 
    PubMed 

    Google Scholar
     

  • 9.

    Fitzsimons, C., Kenny, D. & McGee, M. Visceral organ weights, digestion and carcass characteristics of beef bulls differing in residual feed intake offered a high concentrate diet. Animal 8, 949–959 (2014).

    CAS 
    PubMed 

    Google Scholar
     

  • 10.

    Coyle, S., Fitzsimons, C., Kenny, D., Kelly, A. & McGee, M. Feed efficiency correlations in beef cattle offered zero-grazed grass and a high-concentrate diet. Adv. Anim. Biosci. 8, 121 (2017).


    Google Scholar
     

  • 11.

    Janssen, P. H. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160, 1–22 (2010).

    CAS 

    Google Scholar
     

  • 12.

    Van Houtert, M. Challenging the rational for altering VFA ratios in growing ruminants. Feed Mix 4, 8–11 (1996).


    Google Scholar
     

  • 13.

    Bannink, A. et al. Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall. Anim. Feed Sci. Technol. 143, 3–26 (2008).


    Google Scholar
     

  • 14.

    Shabat, S. K. B. et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10, 2958–2972 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 15.

    Roehe, R. et al. Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane emitting and efficiently feed converting hosts based on metagenomic gene abundance. PLoS Genet. 12, e1005846 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 16.

    Li, F. Metatranscriptomic profiling reveals linkages between the active rumen microbiome and feed efficiency in beef cattle. Appl. Environ. Microbiol. 83, e00061-e117 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 17.

    Pickering, N. et al. Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9, 1431–1440 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 18.

    Tubiello, F. et al. Agriculture, Forestry and Other Land Use Emissions by Sources and Removals by Sinks (Statistics Division, Food and Agriculture Organization, Rome, 2014).


    Google Scholar
     

  • 19.

    Nkrumah, J. D. et al. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84, 145–153 (2006).

    CAS 
    PubMed 

    Google Scholar
     

  • 20.

    Fitzsimons, C., Kenny, D., Deighton, M., Fahey, A. & McGee, M. Methane emissions, body composition, and rumen fermentation traits of beef heifers differing in residual feed intake. J. Anim. Sci. 91, 5789–5800 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • 21.

    Kenny, D., Fitzsimons, C., Waters, S. & McGee, M. Invited review: improving feed efficiency of beef cattle—the current state of the art and future challenges. Animal 12, 1815–1826 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 22.

    Coyle, S., Fitzsimons, C., Kenny, D., Kelly, A. & McGee, M. 1482 Repeatability of feed efficiency in steers offered a high-concentrate diet. J. Anim. Sci. 94, 719–719 (2016).


    Google Scholar
     

  • 23.

    Coyle, S., Fitzsimons, C., Kenny, D., Kelly, A. & McGee, M. 1481 Repeatability of feed efficiency in beef cattle offered grass silage and zero-grazed grass. J. Anim. Sci. 94, 719–719 (2016).


    Google Scholar
     

  • 24.

    Fitzsimons, C., McGee, M., Keogh, K., Waters, S. M. & Kenny, D. A. Molecular physiology of feed efficiency in beef cattle. In Biology of Domestic Animals (eds Scanes, C. G. & Hill, R. A.) 122–165 (CRC Press, Boca Raton, 2017).


    Google Scholar
     

  • 25.

    Paz, H. A. et al. Rumen bacterial community structure impacts feed efficiency in beef cattle. J. Anim. Sci. 96, 1045–1058 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 26.

    Carberry, C. A., Kenny, D. A., Han, S., McCabe, M. S. & Waters, S. M. Effect of phenotypic residual feed intake and dietary forage content on the rumen microbial community of beef cattle. Appl. Environ. Microbiol. 78, 4949–4958 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 27.

    Brockman, R. Glucose and short-chain fatty acid metabolism. In Quantitative Aspects of Ruminant Digestion and Metabolism (eds Dijkstra, J. et al.) 291–310 (CAB International, Wallingford, 2005).


    Google Scholar
     

  • 28.

    Borrel, G. et al. Genomics and metagenomics of trimethylamine-utilizing Archaea in the human gut microbiome. ISME J. 11, 2059–2074 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 29.

    McDonnell, R. et al. Effect of divergence in phenotypic residual feed intake on methane emissions, ruminal fermentation, and apparent whole-tract digestibility of beef heifers across three contrasting diets. J. Anim. Sci. 94, 1179–1193 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 30.

    Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 31.

    Guan, L. L., Nkrumah, J. D., Basarab, J. A. & Moore, S. S. Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle’s feed efficiency. FEMS Microbiol. Lett. 288, 85–91 (2008).

    CAS 
    PubMed 

    Google Scholar
     

  • 32.

    Myer, P. R., Smith, T. P., Wells, J. E., Kuehn, L. A. & Freetly, H. C. Rumen microbiome from steers differing in feed efficiency. PLoS ONE 10, e0129174 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 33.

    McGovern, E. et al. Characterisation of the rumen archaeal and bacterial populations in bulls offered a high concentrate diet phenotypically divergent for residual feed intake (in review).

  • 34.

    Hegarty, R., Goopy, J., Herd, R. & McCorkell, B. Cattle selected for lower residual feed intake have reduced daily methane production. J. Anim. Sci. 85, 1479–1486 (2007).

    CAS 
    PubMed 

    Google Scholar
     

  • 35.

    Carberry, C. A., Waters, S. M., Kenny, D. A. & Creevey, C. J. Rumen methanogenic genotypes differ in abundance according to host residual feed intake phenotype and diet type. Appl. Environ. Microbiol. 80, 586–594 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 36.

    Martin, C., Morgavi, D. P. & Doreau, M. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4, 351–365 (2009).


    Google Scholar
     

  • 37.

    Nkamga, V. D. & Drancourt, M. Methanomassiliicoccus. Bergey’s Manual of Systematics of Archaea and Bacteria (Wiley, Hoboken, 2016).


    Google Scholar
     

  • 38.

    McGovern, E. et al. Plane of nutrition affects the phylogenetic diversity and relative abundance of transcriptionally active methanogens in the bovine rumen. Sci. Rep. 7, 13047 (2017).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 39.

    Danielsson, R. et al. Methane production in dairy cows correlates with rumen methanogenic and bacterial community structure. Front. Microbiol. 8, 226 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 40.

    Shi, W. et al. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. 24, 1517–1525 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 41.

    Kittelmann, S. et al. Simultaneous amplicon sequencing to explore co-occurrence patterns of bacterial, archaeal and eukaryotic microorganisms in rumen microbial communities. PLoS ONE 8, e47879 (2013).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 42.

    Leahy, S. C. et al. The genome sequence of the rumen methanogen Methanobrevibacter ruminantium reveals new possibilities for controlling ruminant methane emissions. PLoS ONE 5, e8926 (2010).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 43.

    Bonacker, L. G., Baudner, S., Mörschel, E., Böcher, R. & Thauer, R. K. Properties of the two isoenzymes of methyl-coenzyme M reductase in Methanobacterium thermoautotrophicum. Eur. J. Biochem. 217, 587–595 (1993).

    CAS 
    PubMed 

    Google Scholar
     

  • 44.

    Saleem, F. et al. A metabolomics approach to uncover the effects of grain diets on rumen health in dairy cows. J. Dairy Sci. 95, 6606–6623 (2012).

    CAS 
    PubMed 

    Google Scholar
     

  • 45.

    Ametaj, B. N. et al. Metabolomics reveals unhealthy alterations in rumen metabolism with increased proportion of cereal grain in the diet of dairy cows. Metabolomics 6, 583–594 (2010).

    CAS 

    Google Scholar
     

  • 46.

    Poulsen, M. et al. Methylotrophic methanogenic Thermoplasmata implicated in reduced methane emissions from bovine rumen. Nat. Commun. 4, 1428 (2013).

    ADS 
    PubMed 

    Google Scholar
     

  • 47.

    Nakazawa, F. et al. Description of Mogibacterium pumilum gen. nov., sp. nov. and Mogibacterium vescum gen. nov., sp. nov., and reclassification of Eubacterium timidum (Holdeman et al. 1980) as Mogibacterium timidum gen. nov., comb. nov. Int. J. Syst. Evol. Microbiol. 50 Pt 2, 679–688 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • 48.

    Li, M., Zhou, M., Adamowicz, E., Basarab, J. A. & Guan, L. L. Characterization of bovine ruminal epithelial bacterial communities using 16S rRNA sequencing, PCR-DGGE, and qRT-PCR analysis. Vet. Microbiol. 155, 72–80 (2012).

    CAS 
    PubMed 

    Google Scholar
     

  • 49.

    Rius, A. G. et al. Nitrogen metabolism and rumen microbial enumeration in lactating cows with divergent residual feed intake fed high-digestibility pasture. J. Dairy Sci. 95, 5024–5034 (2012).

    CAS 
    PubMed 

    Google Scholar
     

  • 50.

    Oki, K. et al. Comprehensive analysis of the fecal microbiota of healthy Japanese adults reveals a new bacterial lineage associated with a phenotype characterized by a high frequency of bowel movements and a lean body type. BMC Microbiol. 16, 284 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 51.

    Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 52.

    Richardson, E. C. et al. Body composition and implications for heat production of Angus steer progeny of parents selected for and against residual feed intake. Aust. J. Exp. Agric. 41, 1065–1072 (2001).


    Google Scholar
     

  • 53.

    Li, F., Hitch, T. C. A., Chen, Y., Creevey, C. J. & Guan, L. L. Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle. Microbiome 7, 6 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 54.

    Yu, Z. & Morrison, M. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques 36, 808–812 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • 55.

    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 56.

    Bolyen, E. et al. QIIME 2: reproducible, interactive, scalable, and extensible microbiome data science (PeerJ Preprints, 2018).

  • 57.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 58.

    Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 59.

    O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 60.

    Wickham, H. ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).


    Google Scholar
     

  • 61.

    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).


    Google Scholar
     

  • 62.

    Mallick, H. et al. Multivariable association in population-scale meta’omic surveys (2019) (in submission).



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *