Ryan Jones - US grants

Fleas can carry bacterial agents of disease from one animal to another, and from wild animals to humans. Whether a flea is capable of being a competent vector for pathogens depends on a number of different factors. An overlooked aspect of this process is the dynamics of bacterial communities that live within the gut of fleas. Fleas do not carry a single species of bacteria, but an entire community, and the composition and assembly of the bacterial community may influence whether fleas are capable vectors. This project seeks to assess the effects of space, time, and inter-specific interactions on bacterial community assembly within fleas that infect prairie dogs. Fleas were collected in 2004 and 2007 from animals living in discrete colonies. DNA was extracted from hundreds of fleas, and pyro-sequencing will be used to generate hundreds of thousands of bacterial DNA sequences from the flea DNA samples. These bacterial DNA sequences will be used to determine if bacterial communities are more similar within a certain prairie dog colony (space), within a certain year (time), and if interactions between bacterial lineages govern community assembly. In addition, phylogenetic techniques will be used to relate bacterial community assembly to flea and prairie dog population structure.

This work will advance studies of the dynamics of bacterial communities within disease vectors, and will focus attention on a little studied but potentially important issue: namely, whether the composition of bacterial communities within vectors varies significantly and if such variation can explain variation in vector competency. Furthermore, this work will dove-tail with the increasing emphasis on describing and understanding the microbial ecosystem that is an individual.

This project creates a multidisciplinary research virtual organization (SOCNET) of historians, geographers, computer scientists, and mathematicians to share historical social science data and develop geographically integrated frameworks to address complex, dynamic, nonlinear systems and social networks.

Through multidisciplinary collaboration, SOCNET will fuse qualitative and quantitative data to connect humans, events, and environments, and through such connections form historical narratives within and across geographic spaces. The project's ultimate goal is to better infuse computational thinking into the historical social sciences through computational innovation and narrative knowledge creation to revolutionize research outcomes in these disciplines with a shift to Geographically-Integrated History. SOCNET's developments in Dynamics GIS (geographic information systems) and related information technologies will provide the backbone for understanding complex historical social systems with three components that define the geographically-integrated history paradigm: (1) the history of any place is shaped in significant ways by the way the place is connected to other places and by the changes in these connections over time; (2) historical periods are complex, dynamic, nonlinear systems that are spatially large, and in more recent centuries, global in extension, and these systems sometimes become unstable, leading to a phase transition, bifurcation, and the organization of new systems; and (3) within such systems, people and places are connected by social networks in a self-organizing fashion.

Focusing on the first global age (1400-1800), SOCNET will transform historical research with computational thinking on (1) new means for the representation of data for organizing, storing, manipulating, and recovering them for exploration using computational tools; (2) new spatial-temporal GIS for the visualization and analysis of real world dynamics; (3) new tools for data harmonization and text mining; (4) new approaches to the use of information that is vague, uncertain, and incomplete and of qualitative data within a computational context; (5) new forms of modeling to represent the inferences of domain experts; and (6) new metaphors beyond the map and animation-based visualization for temporal GIS. Collaborative protocols, tools, models, data structures, and algorithms developed in the project will be shaped and presented in web-based educational materials to provide interested researchers and their students with easy access.

Beyond the historical social sciences and geographic information science, SOCNET will promote innovations in computer science, mathematical modeling and simulation, environmental sciences, medical research, and transportation studies. Collaborating computer scientists and mathematicians will develop innovative computational concepts and tools to better capture the dynamism of overlapping, multi-dimensional social networks within a complex, nonlinear system. In solving the difficulties associated with using historical information within a computational environment, SOCNET will further promote the idea of 'spatial turn' within history and the historical social sciences.

Because of the higher percentage of women and minorities among majors in the historical social sciences, the project will attract such students into a technologically rich educational and employment environment. The project will support the development of an existing Master's in geographically-integrated history, a forthcoming interdisciplinary Ph.D. in Social and Environmental Dynamics, a future M.S. in Computer Science and Computational Sciences, and a new interdisciplinary degree program in Geoinformatics, providing students educational emphases on geographic information science and technology to seek better understanding of dynamic human and environmental systems.

PROJECT SUMMARY/ABSTRACT There is considerable evidence that homeostatic mechanisms stabilize the firing properties of neurons. Current models suggest that each neuron possesses a set of ion channels that endow it with cell-type specific firing properties. When faced with persistent genetic or pharmacological probations that disrupt excitability, neurons have been shown to rebalance ion channel expression to maintain stable firing properties. This phenomenon has been observed across diverse species, from Drosophila motoneurons to Xenopus central neurons and mouse cortical neurons. It is widely speculated that impaired or maladaptive homeostatic signaling will be directly relevant to neurological diseases including epilepsy, autism and Alzheimer's. However, direct connections to disease require an understanding the underlying molecular mechanisms. But, virtually nothing is known about the underlying molecular mechanisms that control the homeostatic modulation of ion channel expression and function in the nervous system of any organism. The goal of this proposal is to define some of the first known mechanisms for the homeostatic stabilization of neuronal excitability and ion channel function.