scholarly journals Oil type and temperature dependent biodegradation dynamics - Combining chemical and microbial community data through multivariate analysis

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Deni Ribicic ◽  
Kelly Marie McFarlin ◽  
Roman Netzer ◽  
Odd Gunnar Brakstad ◽  
Anika Winkler ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Microbial Community ◽  
Temperature Dependent ◽  
Community Data ◽  
Oil Type

scholarly journals Microbial Community Analysis Using MiSeq Sequencing and Pathway of Methane Production in Tehran WWTP: A Full-Scale Anaerobic Digester

2020 ◽  
Author(s):  
Reza Barati Rashvanlou ◽  
Mahdi Farzadkia ◽  
Abbas Ali Moserzadeh ◽  
Asghar Riazati ◽  
Chiang Wei ◽  
...  
Keyword(s):  
Microbial Community ◽  
Methane Production ◽  
Community Analysis ◽  
Full Scale ◽  
Microbial Community Analysis ◽  
Miseq Sequencing ◽  
Sequencing Method ◽  
Community Data ◽  
Rank Abundance ◽  
Made In

Introduction: One of biological wastewater treatment methods that utilizes to both digesting waste activated sludge and methane production is anaerobic digestion (AD). It is believed to be most effective solution in terms of energy crisis and environmental pollution issues. Materials and Methods: In this study the sludge was digested anaerobically sampled from a full-scale WWTP, located at south of Tehran, Iran for evaluation. To study the microbial community within the sludge the MiSeq Sequencing method utilized. Based on our field data (data not shown) and microbial community data, a schematic diagram of probable leading pathways was made in the studied digester. Results: At first, the community variety in the bulk sludge and richness were enhanced followed by loading increasing. Meanwhile, the loading change enhanced the community richness and variety of the sludge. By comparing the rank-abundance distributions, a shallow gradient would show high evenness since the abundances of diverse species are alike. The results showed all the communities were extremely diverse and 15 phyla were distinguished in the sludge sample. The dominant phyla of the community were Bacteroidetes and Firmicutes and quantity of the two phyla were 21% and 11%, respectively. Anaerobaculum, Acinetobacter, Syntrophomonas, and Coprothermobacter were the chief genera for the microbial communities and the sum of four genera were 7%, 3%, 3%, and 2%, respectively. Conclusion: It was shown that syntrophic acetate oxidizing bacterias (SAOBs) metabolized acetate through hydrogen trophic methanogenesis in the digester. Generally, the findings may be useful to help the wastewater operators to utilize an effective method that able to treat waste sludge plus methane production, simultaneously.

Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells

2019 ◽  
Vol 133 ◽  
pp. 64-71 ◽  
Author(s):  
Wenfang Cai ◽  
Keaton Larson Lesnik ◽  
Matthew J. Wade ◽  
Elizabeth S. Heidrich ◽  
Yunhai Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fuel Cells ◽  
Microbial Community ◽  
Microbial Fuel Cells ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Community Data

scholarly journals Towards a Better Understanding of Microbial Community Dynamics through High-Throughput Cultivation and Data Integration

mSystems ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Karoline Faust
Keyword(s):  
Microbial Community ◽  
High Throughput ◽  
Community Dynamics ◽  
Analysis Software ◽  
High Quality ◽  
Microbial Community Dynamics ◽  
Lack Of Control ◽  
Basic Parameters ◽  
Community Data ◽  
The Impact

ABSTRACT The investigation of microbial community dynamics is hampered by low resolution, a lack of control, and a small number of replicates. These deficiencies can be tackled with defined communities grown under well-controlled conditions in high-throughput automated cultivation devices. Besides delivering high-quality microbial community data, automated cultivation will also ease measurement of the basic parameters needed to parameterize mathematical models of microbial communities. Better experimental data will allow revisiting classical ecological questions, such as the impact of community structure on dynamics. In addition, such data will allow validation and comparison of community models and benchmarking of microbial data analysis software. In summary, high-throughput automated cultivation will lead to a deeper understanding of microbial community dynamics through better models and software.

scholarly journals Model-based quantification of metabolic interactions from dynamic microbial-community data

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173183 ◽  
Author(s):  
Mark Hanemaaijer ◽  
Brett G. Olivier ◽  
Wilfred F. M. Röling ◽  
Frank J. Bruggeman ◽  
Bas Teusink
Keyword(s):  
Microbial Community ◽  
Model Based ◽  
Metabolic Interactions ◽  
Community Data

Multivariate Analysis of Mixed Lipid Aggregate Phase Transitions Monitored Using Raman Spectroscopy

2017 ◽  
Vol 72 (1) ◽  
pp. 102-113 ◽  
Author(s):  
Sharon L. Neal
Keyword(s):  
Raman Spectroscopy ◽  
Multivariate Analysis ◽  
Minor Component ◽  
Data Matrix ◽  
Melting Transition ◽  
Spectral Features ◽  
Temperature Dependent ◽  
Spectral Parameters ◽  
Spectral Matrix ◽  
Lipid Response

The phase behavior of aqueous 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC)/1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) mixtures between 8.0 ℃ and 41.0 ℃ were monitored using Raman spectroscopy. Temperature-dependent Raman matrices were assembled from series of spectra and subjected to multivariate analysis. The consensus of pseudo-rank estimation results is that seven to eight components account for the temperature-dependent changes observed in the spectra. The spectra and temperature response profiles of the mixture components were resolved by applying a variant of the non-negative matrix factorization (NMF) algorithm described by Lee and Seung (1999). The rotational ambiguity of the data matrix was reduced by augmenting the original temperature-dependent spectral matrix with its cumulative counterpart, i.e., the matrix formed by successive integration of the spectra across the temperature index (columns). Successive rounds of constrained NMF were used to isolate component spectra from a significant fluorescence background. Five major components exhibiting varying degrees of gel and liquid crystalline lipid character were resolved. Hydrogen-bonded water networks exhibiting varying degrees of organization are associated with the lipid components. Spectral parameters were computed to compare the chain conformation, packing, and hydration indicated by the resolved spectra. Based on spectral features and relative amounts of the components observed, four components reflect long chain lipid response. The fifth component could reflect the response of the short chain lipid, DHPC, but there were no definitive spectral features confirming this assignment. A minor component of uncertain assignment that exhibits a striking response to the DMPC pre-transition and chain melting transition also was recovered. While none of the spectra resolved exhibit features unequivocally attributable to a specific aggregate morphology or step in the gelation process, the results are consistent with the evolution of mixed phase bicelles (nanodisks) and small amounts of worm-like DMPC/DHPC aggregates, and perhaps DHPC micelles, at low temperature to suspensions of branched and entangled worm-like aggregates above the DMPC gel phase transition and perforated multi-lamellar aggregates at high temperature.

scholarly journals theseus - An R package for the analysis and visualization of microbial community data

2018 ◽  
Author(s):  
Jacob R. Price ◽  
Stephen Woloszynek ◽  
Gail Rosen ◽  
Christopher M. Sales
Keyword(s):  
Microbial Community ◽  
Community Composition ◽  
Microbial Community Composition ◽  
R Package ◽  
Composition Data ◽  
Programming Framework ◽  
Community Data ◽  
R Programming

Abstracttheseus is a collection of functions within the R programming framework [1] to assist microbiologists and molecular biologists in the interpretation of microbial community composition data.

scholarly journals Analysis of Cultivable Microbial Community during Kimchi Fermentation Using MALDI-TOF MS

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1068
Author(s):  
Eiseul Kim ◽  
Seung-Min Yang ◽  
Hae-Yeong Kim
Keyword(s):  
Microbial Community ◽  
Temperature Dependent ◽  
Factors Affecting ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Flight Mass Spectrometry ◽  
Different Temperatures ◽  
Health Promoting ◽  
Tof Ms

Kimchi, a traditional Korean fermented vegetable, has received considerable attention for its health-promoting effects. This study analyzes the cultivable microbial community in kimchi fermented at different temperatures using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to comprehensively understand the factors affecting the quality of kimchi. Of the 5204 strains isolated from kimchi, aligned with the in-house database, 4467 (85.8%) were correctly identified at the species level. The fermentation temperature affected the microbial community by varying the pH and acidity, which was mainly caused by temperature-dependent competition between the different lactic acid bacteria (LAB) species in kimchi. LAB, such as Levilactobacillus (Lb.) brevis and Lactiplantibacillus (Lpb.) plantarum associated with rancidity and tissue softening, proliferated faster at higher temperatures than at low temperature. In addition, LAB, such as Latilactobacillus (Lat.) sakei and Leuconostoc (Leu.) mesenteroides, which produce beneficial substances and flavor, were mainly distributed in kimchi fermented at 4 °C. This study shows as a novelty that MALDI-TOF MS is a robust and economically affordable method for investigating viable microbial communities in kimchi.

scholarly journals How sample heterogeneity can obscure the signal of microbial interactions

2019 ◽  
Author(s):  
David W. Armitage ◽  
Stuart E. Jones
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Species Interactions ◽  
Dynamic Models ◽  
Spatial Scales ◽  
Microbial Interactions ◽  
Vital Rates ◽  
Ecological Processes ◽  
Correlation Networks ◽  
Community Data

ABSTRACTMicrobial community data are commonly subjected to computational tools such as correlation networks, null models, and dynamic models, with the goal of identifying the ecological processes structuring microbial communities. Researchers applying these methods assume that the signs and magnitudes of species interactions and vital rates can be reliably parsed from observational data on species’ (relative) abundances. However, we contend that this assumption is violated when sample units contain any underlying spatial structure. Here, we show how three phenomena — Simpson’s paradox, context-dependence, and nonlinear averaging — can lead to erroneous conclusions about population parameters and species interactions when samples contain heterogeneous mixtures of populations or communities. At the root of this issue is the fundamental mismatch between the spatial scales of species interactions (micrometres) and those of typical microbial community samples (millimetres to centimetres). These issues can be overcome by measuring and accounting for spatial heterogeneity at very small scales, which will lead to more reliable inference of the ecological mechanisms structuring natural microbial communities.

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