CIVIL ENGINEERING 365 ALL ABOUT CIVIL ENGINEERING


  • 1.

    Bik, E. M. et al. Marine mammals harbor unique microbiotas shaped by and yet distinct from the sea. Nat. Commun. 7, 10516 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 2.

    Apprill, A. et al. Extensive core microbiome in drone-captured whale blow supports a framework for health monitoring. MSystems 2, e00119-e117 (2017).


    Google Scholar
     

  • 3.

    Pirotta, V. et al. An economical custom-built drone for assessing whale health. Front. Mar. Sci. 4, 425 (2017).


    Google Scholar
     

  • 4.

    Raverty, S. A. et al. Respiratory microbiome of endangered southern resident killer whales and microbiota of surrounding sea surface microlayer in the Eastern North Pacific. Sci. Rep. 7, 394 (2017).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 5.

    Lima, N., Rogers, T., Acevedo-Whitehouse, K. & Brown, M. V. Temporal stability and species specificity in bacteria associated with the bottlenose dolphins respiratory system. Environ. Microbiol. Rep. 4, 89–96 (2012).

    PubMed 

    Google Scholar
     

  • 6.

    Johnson, W. R. et al. Novel diversity of bacterial communities associated with bottlenose dolphin upper respiratory tracts. Environ. Microbiol. Rep. 1, 555–562 (2009).

    CAS 
    PubMed 

    Google Scholar
     

  • 7.

    Acevedo-Whitehouse, K., Rocha-Gosselin, A. & Gendron, D. A novel non-invasive tool for disease surveillance of free-ranging whales and its relevance to conservation programs. Anim. Conserv. 13, 217–225 (2010).


    Google Scholar
     

  • 8.

    Bassis, C. M., Tang, A. L., Young, V. B. & Pynnonen, M. A. The nasal cavity microbiota of healthy adults. Microbiome 2, 27 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 9.

    Dickson, R. P. et al. Bacterial topography of the healthy human lower respiratory tract. MBio 8, e02287-e2216 (2017).


    Google Scholar
     

  • 10.

    Bond, S. L., Timsit, E., Workentine, M., Alexander, T. & Léguillette, R. Upper and lower respiratory tract microbiota in horses: bacterial communities associated with health and mild asthma (inflammatory airway disease) and effects of dexamethasone. BMC Microbiol. 17, 184 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 11.

    Ericsson, A. C., Personett, A. R., Grobman, M. E., Rindt, H. & Reinero, C. R. Composition and predicted metabolic capacity of upper and lower airway microbiota of healthy dogs in relation to the fecal microbiota. PLoS ONE 11, e0154646 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 12.

    Vientos-Plotts, A. I. et al. Dynamic changes of the respiratory microbiota and its relationship to fecal and blood microbiota in healthy young cats. PLoS ONE 12, e0173818 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 13.

    Dickson, R. P. et al. The lung microbiota of healthy mice are highly variable, cluster by environment, and reflect variation in baseline lung innate immunity. Am. J. Respir. Crit. Care Med. 198, 497–508 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 14.

    Segal, L. N. et al. Enrichment of the lung microbiome with oral taxa is associated with lung inflammation of a th17 phenotype. Nat. Microbiol. 1, 16031 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 15.

    Shenoy, M. K. et al. Immune response and mortality risk relate to distinct lung microbiomes in patients with HIV and pneumonia. Am. J. Respir. Crit. Care Med. 195, 104–114 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 16.

    Biswas, K., Hoggard, M., Jain, R., Taylor, M. W. & Douglas, R. G. The nasal microbiota in health and disease: variation within and between subjects. Front. Microbiol. 6, 134 (2015).

    PubMed Central 

    Google Scholar
     

  • 17.

    Dickson, R. P., Erb-Downward, J. R., Martinez, F. J. & Huffnagle, G. B. The microbiome and the respiratory tract. Annu. Rev. Physiol. 78, 481–504 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 18.

    Garcia-Nuñez, M. et al. Severity-related changes of bronchial microbiome in chronic obstructive pulmonary disease. J. Clin. Microbiol. 52, 4217–4223 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 19.

    Dickson, R. P. et al. Cell-associated bacteria in the human lung microbiome. Microbiome 2, 28 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 20.

    Huang, Y. J. et al. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J. Allergy Clin. Immunol. 127, 372–381 (2011).

    PubMed 

    Google Scholar
     

  • 21.

    Dickson, R. P., Martinez, F. J. & Huffnagle, G. B. The role of the microbiome in exacerbations of chronic lung diseases. Lancet 384, 691–702 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 22.

    Cardona, C. et al. Environmental sources of bacteria differentially influence host-associated microbial dynamics. MSystems 3, e00052-00018 (2018).


    Google Scholar
     

  • 23.

    Hunt, K. E. et al. Overcoming the challenges of studying conservation physiology in large whales: a review of available methods. Conserv. Physiol. 1, 1–24 (2013).


    Google Scholar
     

  • 24.

    International Whaling Commission. Annexe. Report of the sub-committee on other great whales. J. Cetacean Res. Manag. 1, 117–155 (1999).


    Google Scholar
     

  • 25.

    Rasmussen, K. et al. Southern hemisphere humpback whales wintering off Central America: insights from water temperature into the longest mammalian migration. Biol. Lett. 3, 302–305 (2007).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 26.

    Stevick, P. T. et al. A quarter of a world away: female humpback whale moves 10 000 km between breeding areas. Biol. Lett. 7, 299–302 (2010).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 27.

    Riekkola, L. et al. Application of a multi-disciplinary approach to reveal population structure and Southern Ocean feeding grounds of humpback whales. Ecol. Indic. 89, 455–465 (2018).


    Google Scholar
     

  • 28.

    Zerbini, A. N. et al. Satellite-monitored movements of humpback whales Megaptera novaeangliae in the southwest Atlantic Ocean. Mar. Ecol. Prog. Ser. 313, 295–304 (2006).

    ADS 

    Google Scholar
     

  • 29.

    Chittleborough, R. G. Dynamics of two populations of the humpback whale, Megaptera novaeangliae (borowski). Mar. Freshw. Res. 16, 33–128 (1965).


    Google Scholar
     

  • 30.

    Chittleborough, R. G. The breeding cycle of the female humpback whale, Megaptera nodosa (bonnaterre). Mar. Freshw. Res. 9, 1–18 (1958).


    Google Scholar
     

  • 31.

    Clapham, P. J. The humpback whale: seasonal feeding and breeding in a baleen whale. In Cetacean societies: field studies of dolphins and whales (eds Mann, J. et al.) 173–196 (University of Chicago Press, Chicago, 2000).


    Google Scholar
     

  • 32.

    Franklin, T. The social and ecological significance of Hervey bay Queensland for Eastern Australian humpback whales (Megaptera novaeangliae). Ph.D. thesis, Southern Cross University, Lismore, NSW (2012).

  • 33.

    Franklin, T. et al. Seasonal changes in pod characteristics of Eastern Australian humpback whales (Megaptera novaeangliae). Mar. Mammal Sci. 27, E134–E152 (2011).


    Google Scholar
     

  • 34.

    Dawbin, W. H. The seasonal migratory cycle of humpback whales. In Whales, dolphins and porpoises (ed. Norris, K. S.) (University of California Press, Berkeley, 1996).


    Google Scholar
     

  • 35.

    Seymour, J. R. et al. Contrasting microbial assemblages in adjacent water masses associated with the East Australian current. Environ. Microbiol. 4, 548–555 (2012).

    CAS 

    Google Scholar
     

  • 36.

    Chao, A. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).


    Google Scholar
     

  • 37.

    Chao, A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 43, 783–791 (1987).

    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar
     

  • 38.

    Chao, A. & Yang, M. C. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80, 193–201 (1993).

    MathSciNet 
    MATH 

    Google Scholar
     

  • 39.

    Chao, A., Hwang, W. H., Chen, Y. C. & Kuo, C. Y. Estimating the number of shared species in two communities. Stat. Sin. 10, 227–246 (2000).

    MathSciNet 
    MATH 

    Google Scholar
     

  • 40.

    Dunn, O. J. Multiple comparisons using rank sums. Technometrics 6, 241–252 (1964).


    Google Scholar
     

  • 41.

    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).

    MathSciNet 
    MATH 

    Google Scholar
     

  • 42.

    Shade, A. & Handelsman, J. Beyond the Venn diagram: the hunt for a core microbiome. Environ. Microbiol. 14, 4–12 (2012).

    CAS 
    PubMed 

    Google Scholar
     

  • 43.

    Magurran, A. E. & Henderson, P. A. Explaining the excess of rare species in natural species abundance distributions. Nature 422, 714 (2003).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 44.

    Hernandez-Agreda, A., Gates, R. D. & Ainsworth, T. D. Defining the core microbiome in corals’ microbial soup. Trends Microbiol. 25, 125–140 (2017).

    CAS 
    PubMed 

    Google Scholar
     

  • 45.

    Astudillo-García, C. et al. Evaluating the core microbiota in complex communities: a systematic investigation. Environ. Microbiol. 19, 1450–1462 (2017).

    PubMed 

    Google Scholar
     

  • 46.

    Einarsson, G. G. et al. Community dynamics and the lower airway microbiota in stable chronic obstructive pulmonary disease, smokers and healthy non-smokers. Thorax 79, 795–803 (2016).


    Google Scholar
     

  • 47.

    Venn-Watson, S., Daniels, R. & Smith, C. Thirty year retrospective evaluation of pneumonia in a bottlenose dolphin Tursiops truncatus population. Dis. Aquat. Organ. 99, 237–242 (2012).

    PubMed 

    Google Scholar
     

  • 48.

    Venn-Watson, S., Smith, C. R. & Jensen, E. D. Primary bacterial pathogens in bottlenose dolphins Tursiops truncatus: needles in haystacks of commensal and environmental microbes. Dis. Aquat. Organ. 79, 87–93 (2008).

    PubMed 

    Google Scholar
     

  • 49.

    Waltzek, T., Cortés-Hinojosa, G., Wellehan, J. Jr. & Gray, G. C. Marine mammal zoonoses: a review of disease manifestations. Zoonoses Public Health 59, 521–535 (2012).

    CAS 
    PubMed 

    Google Scholar
     

  • 50.

    Cusick, P. & Bullock, B. Ulcerative dermatitis and pneumonia associated with Aeromonas hydrophila infection in the bottle-nosed dolphin. J. Am. Vet. Med. Assoc. 163, 578–579 (1973).

    CAS 
    PubMed 

    Google Scholar
     

  • 51.

    Owen, K. et al. Effect of prey type on the fine-scale feeding behaviour of migrating East Australian humpback whales. Mar. Ecol. Prog. Ser. 541, 231–244 (2015).

    ADS 

    Google Scholar
     

  • 52.

    Lockyer, C. Body weights of some species of large whales. ICES J. Mar. Sci. 36, 259–273 (1976).


    Google Scholar
     

  • 53.

    Bengtson Nash, S. M., Waugh, C. A. & Schlabach, M. Metabolic concentration of lipid soluble organochlorine burdens in the blubber of Southern Hemisphere humpback whales through migration and fasting. Environ. Sci. Technol. 47, 9004–9413 (2013).


    Google Scholar
     

  • 54.

    Johnson, K. L. Capital and income breeding as alternative tactics of resource use in reproduction. Oikos 78, 57–66 (1997).


    Google Scholar
     

  • 55.

    Noad, M. J., Dunlop, R. A., Paton, D. & Cato, D. H. Absolute and relative abundance estimates of Australian east coast humpback whales (Megaptera novaeangliae). J. Cetacean Res. Manag. 243, 252 (2011).


    Google Scholar
     

  • 56.

    Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142 (2019).

    ADS 

    Google Scholar
     

  • 57.

    Katona, S. K. & Beard, J. A. Population size, migrations and feeding aggregations of the humpback whale (Megaptera novaeangliae) in the western North Atlantic Ocean. Rep. Int. Whal. Comm. Spec. Issue 12, 295–306 (1990).


    Google Scholar
     

  • 58.

    Christiansen, F. et al. Maternal body size and condition determine calf growth rates in southern right whales. Mar. Ecol. Prog. Ser. 592, 267–281 (2018).

    ADS 

    Google Scholar
     

  • 59.

    Durban, J. W., Fearnbach, H., Barrett-Lennard, L., Perryman, W. & Leroi, D. Photogrammetry of killer whales using a small hexacopter launched at sea. J. Unmanned Veh. Syst. 3, 131–135 (2015).


    Google Scholar
     

  • 60.

    Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. & Bejder, L. Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7, e01468 (2016).


    Google Scholar
     

  • 61.

    Mingramm, F., Dunlop, R., Blyde, D., Whitworth, D. & Keeley, T. Evaluation of respiratory vapour and blubber samples for use in endocrine assessments of bottlenose dolphins (Tursiops spp.). Gen. Comp. Endocrinol. 274, 37–49 (2019).

    CAS 
    PubMed 

    Google Scholar
     

  • 62.

    Mingramm, F. M., Keeley, T., Whitworth, D. J. & Dunlop, R. A. Relationships between blubber and respiratory vapour steroid hormone concentrations in humpback whales (Megaptera novaeangliae). Aquat. Mamm. 45, 465–477 (2019).


    Google Scholar
     

  • 63.

    Castrillon, J., Huston, W. & Bengtson Nash, S. The blubber adipocyte index: a nondestructive biomarker of adiposity in humpback whales (Megaptera novaeangliae). Ecol. Evol. 7, 5131–5139 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 64.

    Hogg, C. J. et al. Determination of steroid hormones in whale blow: it is possible. Mar. Mammal Sci. 25, 605–618 (2009).

    CAS 

    Google Scholar
     

  • 65.

    Lane, D. J. et al. Rapid determination of 16s ribosomal RNA sequences for phylogenetic analyses. Proc. Natl. Acad. Sci. 82, 6955–6959 (1985).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 66.

    Lane, D. J. 16s/23s rRNA sequencing. In Nucleic acid techniques in bacterial systematics (eds Stackebrandt, E. & Goodfellow, M.) (Wiley, New York, 1991).


    Google Scholar
     

  • 67.

    Bioinformatics.babraham.ac.uk (2019) Babraham bioinformatics—fastqc a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc. 20 Jan 2019.

  • 68.

    Edgar, R. C. Uparse: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 69.

    Edgar, R. C. Unoise2: improved error-correction for illumina 16s and its amplicon sequencing. BioRxiv 081257 (2016).

  • 70.

    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
     

  • 71.

    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. Uchime improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 72.

    Quast, C. et al. The silva ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 73.

    Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2013).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 74.

    Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H. & Wagner, H. (2010) Vegan: community ecology package. R package version 1.17-4. https://cran.r-project.org. 30 Jan 2019.

  • 75.

    Lozupone, C. & Knight, R. Unifrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 76.

    Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1–15 (2014).

  • 77.

    Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).


    Google Scholar
     

  • 78.

    Wang, Y. I., Naumann, U., Wright, S. T. & Warton, D. I. Mvabund—an r package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).


    Google Scholar
     

  • 79.

    Warton, D. I., Thibaut, L. & Wang, Y. A. The pit-trap—a “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses. PLoS ONE 12, e0181790 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 80.

    Lokmer, A. et al. Spatial and temporal dynamics of Pacific oyster hemolymph microbiota across multiple scales. Front. Microbiol. 7, 1367 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     



  • Source link

    Leave a Reply

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