Bacterial Bio-indicators of Marcellus Shale Activities in Pennsylvania: A Molecular Ecology Survey

The practice of hydraulic fracking has increased over the years especially in Pennsylvania where most of the subterraneous gas-rich Marcellus Shale formations are located. Our previous work showed that headwater streams in proximity to hydraulic fracking operations have significantly different bacterial assemblages as compared to un-impacted streams in central PA. Aquatic bacterial communities are of great importance because they are often the ‘first-responders’ to environmental perturbations. We are interested in which bacteria become enriched, as this might serve as robust biomarkers of fracking, and can potentially biodegrade constituents of fracking fluids. In this study, we plan to expand upon our previous work to identify additional sentinel bacterial taxa in other areas in PA (Northeast and Southwest) heavily impacted by fracking. Water and sediment samples have been collected from Northern Pennsylvania (n=31) and Southwestern (n=11) regions upstream and downstream of fracking activities. Bacterial community profiles of these samples were generated via high-throughput sequencing of the 16S rRNA, a robust phylogenetic marker for bacterial identification. The data generated provide a snapshot of all bacteria taxa present and their relative abundance. Thus, differences in bacterial community structure between impacted and un-impacted environments can help glean which bacterial taxa are responding to environmental perturbations associated with fracking. This research can help us generate a list of potential bioindicators of nascent fracking activities and can be used to help track impacts and bioremediation potential within environmental scenarios

First-Principles Study on Structural Properties of GeO2_2 and SiO2_2 under Compression and Expansion Pressure

The detailed analysis of the structural variations of three GeO$_2$ and SiO$_2$ polymorphs ($\alpha$-quartz, $\alpha$-cristobalite, and rutile) under compression and expansion pressure is reported. First-principles total-energy calculations reveal that the rutile structure is the most stable phase among the phases of GeO$_2$, while SiO$_2$ preferentially forms quartz. GeO$_4$ tetrahedras of quartz and cristobalite GeO$_2$ phases at the equilibrium volume are more significantly distorted than those of SiO$_2$. Moreover, in the case of quartz GeO$_2$ and cristobalite GeO$_2$, all O-Ge-O bond angles vary when the volume of the GeO$_2$ bulk changes from the equilibrium point, which causes further deformation of tetrahedra. In contrast, the tilt angle formed by Si-O-Si in SiO$_2$ markedly changes. This flexibility of the O-Ge-O bonds reduces the stress at the Ge/GeO$_2$ interface due to the lattice-constant mismatch and results in the low defective interface observed in the experiments [Matsubara \textit{et al.}: Appl. Phys. Lett. \textbf{93} (2008) 032104; Hosoi \textit{et al.}: Appl. Phys. Lett. \textbf{94} (2009) 202112].Comment: 15 pages, 5 figures and 2 table

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Priorities for synthesis research in ecology and environmental science

Synthesis research in ecology and environmental science improves understanding, advances theory, identifies research priorities, and supports management strategies by linking data, ideas, and tools. Accelerating environmental challenges increases the need to focus synthesis science on the most pressing questions. To leverage input from the broader research community, we convened a virtual workshop with participants from many countries and disciplines to examine how and where synthesis can address key questions and themes in ecology and environmental science in the coming decade. Seven priority research topics emerged: (1) diversity, equity, inclusion, and justice (DEIJ), (2) human and natural systems, (3) actionable and use-inspired science, (4) scale, (5) generality, (6) complexity and resilience, and (7) predictability. Additionally, two issues regarding the general practice of synthesis emerged: the need for increased participant diversity and inclusive research practices; and increased and improved data flow, access, and skill-building. These topics and practices provide a strategic vision for future synthesis in ecology and environmental science