Innovative Methods for Integrating Knowledge for Long-Term Monitoring of Contaminated Groundwater Sites: Understanding Microorganism Communities and their Associated Hydrochemical Environment
Abstract: This interdisciplinary study integrates hydrochemical and genome-based data to estimate the redox processes occurring at long-term monitoring sites. Groundwater samples have been collected from a well-characterized landfill-leachate contaminated aquifer in northeastern New York. Primers from the 16S rDNA gene were used to amplify Bacteria and Archaea in groundwater taken from monitoring wells located in clean, fringe, and contaminated locations within the aquifer. PCR-amplified rDNA were digested with restriction enzymes to evaluate terminal restriction fragment length polymorphism (T-RFLP) community profiles. The rDNA was cloned, sequenced, and partial sequences were matched against known organisms using the NCBI Blast database. Phylogenetic trees and bootstrapping were used to identify classifications of organisms and compare the communities from clean, fringe, and contaminated locations. We used Artificial Neural Network (ANN) models to incorporate microbial data with hydrochemical information for improving our understanding of subsurface processes.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).