12 Search Results

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removalmore » efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t – 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.« less

Modifying asphalt is a potentially high-value application for reusing waste plastics because of the high-volume usage of asphalt in highway construction. However, simply blending hot plastics and asphalt encounters difficulties related to the poor solubility of polymers, which limits the formation of a swollen network with asphalt molecules. The polymer phases also tend to coalesce and separate from asphalt during high-temperature storage in static conditions. The present study developed an innovative process to stabilize waste plastics in asphalt and improve binder storage stability by using lightly pyrolyzed crumb rubber together with a chemical compatibilizer. Both polymers were extruded to producemore » a thermoplastic elastomer (TPE) for asphalt modification. The mechanical performance and chemical reactions of TPEs were characterized via tension test and Fourier transform infrared spectroscopy. The storage stability and rheological properties of modified binder blends were evaluated through laboratory asphalt stability test and dynamic shear rheometer test. Polymer phases and network structures were characterized through optical microscopy. It was found that the pyrolyzed and reactive extrusion process improved the rubber solubility and polymer interaction, and therefore the storage stability of modified binder blends. The co-existence of rigid plastic and soft rubbery regimes in an entangled network provided a promising pathway to improve the mechanical performance of asphalt binders in both high- and low-temperature domains.« less

Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer–resource model with a neutral model in stochastic differential equations. Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer–resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantiallymore » as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology.« less

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoreticalmore » bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis. Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions.« less

An amendment to this paper has been published and can be accessed via a link at the top of the paper. Correction to: Nature Microbiology https://doi.org/10.1038/s41564-019-0426-5, published online 13 May 2019. In the version of this Article originally published, the name of the author ‘Mathew Robert Brown’ was incorrectly written as ‘Mathew Brown’ in the main author list and as ‘Matthew Brown’ in the Global Water Microbiome Consortium list. In addition, in the Global Water Microbiome Consortium list, the names of the authors ‘Kevin F. Boehnke’, ‘Janeth Sanabria’ and ‘Adalberto Noyola’ were incorrectly written as ‘Kevin Boehnke’, ‘Janeth Sanabria Gómez’more » and ‘Adalberto Noyola Robles’, respectively. The names have now been corrected and the author initials in the author contributions section updated accordingly.« less

Although biotic interactions among members of microbial communities have been conceived to be crucial for community assembly, it remains unclear how changes in environmental conditions affect microbial interaction and consequently system performance. Here, we adopted a random matrix theory-based network analysis to explore microbial interactions in triplicate anaerobic digestion (AD) systems, which is widely applied for organic pollutant treatments. The digesters were operated with incremental organic loading rates (OLRs) from 1.0 g volatile solids (VS)/liter/day to 1.3 g VS/liter/day and then to 1.5 g VS/liter/day, which increased VS removal and methane production proportionally. Higher resource availability led to networks withmore » higher connectivity and shorter harmonic geodesic distance, suggestive of more intense microbial interactions and quicker responses to environmental changes. Strikingly, a number of topological properties of microbial network showed significant (P< 0.05) correlation with AD performance (i.e., methane production, biogas production, and VS removal). When controlling for environmental parameters (e.g., total ammonia, pH, and the VS load), node connectivity, especially that of the methanogenic archaeal network, still correlated with AD performance. Last, we identified theMethanothermus,Methanobacterium,Chlorobium, andHaloarculataxa and an unclassifiedThaumarchaeotataxon as keystone nodes of the network.« less

Microorganisms in wastewater treatment plants (WWTPs) are essential for water purification to protect public and environmental health. However, the diversity of microorganisms and the factors that control it are poorly understood. Using a systematic global-sampling effort, we analysed the 16S ribosomal RNA gene sequences from ~1,200 activated sludge samples taken from 269 WWTPs in 23 countries on 6 continents. Our analyses revealed that the global activated sludge bacterial communities contain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution. Despite this high diversity, activated sludge has a small, global core bacterial community (n = 28 operational taxonomic units) thatmore » is strongly linked to activated sludge performance. Meta-analyses with global datasets associate the activated sludge microbiomes most closely to freshwater populations. In contrast to macroorganism diversity, activated sludge bacterial communities show no latitudinal gradient. Furthermore, their spatial turnover is scale-dependent and appears to be largely driven by stochastic processes (dispersal and drift), although deterministic factors (temperature and organic input) are also important. Finally, our findings enhance our mechanistic understanding of the global diversity and biogeography of activated sludge bacterial communities within a theoretical ecology framework and have important implications for microbial ecology and wastewater treatment processes.« less

The thermal equation of state of a natural kyanite has been investigated with a DIA-type, cubic-anvil apparatus (SAM85) combined with an energy-dispersive synchrotron X-ray radiation technique up to 8.55 GPa and 1273 K. No phase transition was observed in the studied pressure-temperature (P-T) range. The Le Bail full profile refinement technique was used to derive the unit-cell parameters. By fixing the bulk modulus K0 as 196 GPa and its pressure derivative K0′ as 4, our P-V (volume)-T data were fitted to the high temperature Birch–Murnaghan equation of state. The obtained parameters for the kyanite are: V0 = 294.05(9) Å3, αmore » = 2.53(11) × 10−5 K−1 and (∂K/∂T)P = −0.021(8) GPa∙K−1. These parameters have been combined with other experimentally-measured thermodynamic data for the relevant phases to calculate the P-T locus of the reaction kyanite = stishovite + corundum. With this thermodynamically constrained phase boundary, previous high-pressure phase equilibrium experimental studies with the multi-anvil press have been evaluated.« less

Acetoclastic methanogenesis is a key metabolic process in anaerobic digestion, a technology with broad applications in biogas production and waste treatment. Acetoclastic methanogenesis is known to be performed by two archaeal genera, Methanosaeta and Methanosarcina. The conventional model posits that Methanosaeta populations are more competitive at low acetate levels (<1 mM) than Methanosarcina and vice versa at higher acetate concentrations. While this model is supported by an extensive body of studies, reports of inconsistency have grown that Methanosaeta were observed to outnumber Methanosarcina at elevated acetate levels. In this study, monitoring of anaerobic digesters treating animal wastewater unexpectedly identified Methanosaetamore » as the dominant acetoclastic methanogen population at both low and high acetate levels during organic overloading. The surprising competitiveness of Methanosaeta at elevated acetate was further supported by the enrichment of Methanosaeta with high concentrations of acetate (20 mM). The dominance of Methanosaeta in the methanogen community could be reproduced in anaerobic digesters with the direct addition of acetate to above 20 mM, again supporting the competitiveness of Methanosaeta over Methanosarcina at elevated acetate levels. This study for the first time systematically demonstrated that the dominance of Methanosaeta populations in anaerobic digestion could be linked to the competitiveness of Methanosaeta at elevated acetate concentrations. Given the importance of acetoclastic methanogenesis in biological methane production, findings from this study could have major implications for developing strategies for more effective control of methanogenic treatment processes.« less