Hydrocarbons Physical Properties and their Relevance to Utilisation
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The ZigBee request is service for many book assistance. Water Drainage download hydrocarbons is elected for your dicke. Raw sequence data were processed using the series of commands in mothur version 1. The following initial steps were performed: trimming of pyrosequencing tag sequences and primers, and removal of sequences with more than eight homopolymer nucleotides. After chimera checking, low quality sequences were removed.
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The sequences passing these checks were then used to compute the sequence distance matrix. Rarefaction curve was generated by using the R package vegan v. Permutational multivariate analysis of variance using distance matrices was performed with the adonis function in the vegan package, which partitions sums of squares using semi-metric and metric distance matrices and fit linear models e.
Furthermore, the multinomial species classification method CLAM test 58 in the vegan R package was applied to statistically classify the bacterial OTUs at the genus level based on habitat into the following categories: 1 generalists, 2 specialists for permafrost upper layer, 3 specialists for permafrost deeper layer, 4 too rare to classify with confidence, and also based on crude oil contamination into the following categories: 1 generalists, 2 specialists for uncontaminated permafrost soil, 3 specialists for crude oil-contaminated permafrost soil and 4 too rare to classify with confidence.
The CLAM test was performed on the dataset retrieved in this study and compiled with datasets from previous works on the same permafrost habitat 15 , The CLAM test was applied on subsampled datasets using a default alpha value, a coverage limit of 30 and a specialisation threshold of 0. These two filtering steps were applied to remove poorly represented OTUs and reduce network complexity, which assisted in determining the bacterial core community of the samples under investigation.
Subsequently, the affiliation network was generated by using the R package of igraph, version 1. An affiliation network is defined as a network where the members are affiliated with one another based on co-membership in a group or co-participation in some type of event. The nodes in the reconstructed network represented variable genera and the edges that connect the nodes represented correlations between genera. In addition, to bring into focus potential keystone taxa and their interactions within the network, subsets of representative specialist genera from both contaminated and uncontaminated samples were selected.
Sub-networks were then generated from the meta-community networks by using subgraph functions in igraph package. Information on the target network was further organised in matrices and visualised in chord diagrams using R package circlize, version 0. How to cite this article : Yang, S. Hydrocarbon degraders establish at the costs of microbial richness, abundance and keystone taxa after crude oil contamination in permafrost environments.
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Jin, H. Cold Reg. Yang, S. Filler, D. Bioremediation of petroleum hydrocarbons in cold regions. Cambridge University Press, Yergeau, E. Metagenomic analysis of the bioremediation of diesel-contaminated Canadian high arctic soils. Bell, T. Predictable bacterial composition and hydrocarbon degradation in Arctic soils following diesel and nutrient disturbance.
ISME J. Akbari, A. Effects of diurnal temperature variation on microbial community and petroleum hydrocarbon biodegradation in contaminated soils from a sub-Arctic site. Aislabie, J. Bioremediation of hydrocarbon-contaminated polar soils.
Extremophiles 10 , —, doi: Vazquez, S. Bacterial community dynamics during bioremediation of diesel oil-contaminated Antarctic soil.
Polyhydroxyalkanoates Produced by Hydrocarbon-Degrading Bacteria
Dias, R. Ma, Y. Pseudomonas , the dominant polycyclic aromatic hydrocarbon-degrading bacteria isolated from Antarctic soils and the role of large plasmids in horizontal gene transfer. Margesin, R. Low-temperature biodegradation of petroleum hydrocarbons n-alkanes, phenol, anthracene, pyrene by four actinobacterial strains.
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Zhao, B. Insights into environmental bioremediation by microorganisms through functional genomics and proteomics. Proteomics 8 , —, doi: Desai, C. Identification of nitrogen-incorporating bacteria in petroleum-contaminated Arctic soils by using [ 15 N] DNA-based stable isotope probing and pyrosequencing. Crude oil treatment leads to shift of bacterial communities in soils from the deep active layer and upper permafrost along the China-Russia Crude Oil Pipeline Route.
Faust, K. Microbial co-occurrence relationships in the Human microbiome.
PLoS Comp. Barberan, A. Using network analysis to explore co-occurrence patterns in soil microbial communities. Ma, B. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. Fowler, S. Community structure in methanogenic enrichments provides insight into syntrophic interactions in hydrocarbon-impacted environments. Jiao, S. Bacterial communities in oil contaminated soils: Biogeography and co-occurrence patterns.
Soil Biol. Chang, W. Petroleum hydrocarbon biodegradation under seasonal freeze-thaw soil temperature regimes in contaminated soils from a sub-Arctic site. Sutton, N. Impact of long-term diesel contamination on soil microbial community structure. Jung, J. Metagenomic and functional analyses of the consequences of reduction of bacterial diversity on soil functions and bioremediation in diesel-contaminated microcosms. Alteration of microbial community structure affects diesel biodegradation in an Arctic soil.
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USA , —, doi: Kertesz, M. Timmis — Springer, Stolz, A. Degradative plasmids from sphingomonads. Hydrocarbon removal and bacterial community structure in on-site biostimulated biopile systems designed for bioremediation of diesel-contaminated Antarctic soil. Polar Biol. Labbe, D.