Atlas of Anchorage Community Indicators

The Anchorage Community Indicators (ACI) project is designed to make information (extracted from data) accessible so that conversations about the health and well-being of Anchorage may become more completely informed. Policy makers, social commentators, service delivery systems, and scholars often stake out positions based on anecdotal evidence or hunches when, in many instances, solid, empirical evidence could be compiled to support or challenge these opinions.The Atlas of Anchorage Community Indicators makes empirical information about neighborhoods widely accessible to many different audiences. The initial selection of indicators for presentation in the Atlas was inspired by Peter Blau and his interest in measures of heterogeneity (diversity) and inequality and by the work of the Project on Human Development in Chicago Neighborhoods. In both cases the measures they developed were well-conceptualized and validated. The Atlas presents community indicators at the census block group level derived from data captured in the 2000 U.S. Census and the 2005 Anchorage Community Survey. All maps in the Atlas are overlaid by Community Council boundaries to facilitate comparisons across maps.Introduction / COMMUNITY COUNCIL BOUNDARY MAPS / Eagle River Community Councils / North Anchorage Community Councils / South Anchorage Community Councils / Girdwood Community Councils / CENSUS-DERIVES INDICATORS AT BLOCK GROUP LEVEL / 1. Concentrated Affluence / 2. Concentrated Disadvantage / 3. Housing Density / 4. Immigrant Concentration / 5. Index of Concentration at Extremes / 6. Industrial Heterogeneity / 7. Multiform Disadvantage / 8. Occupational Heterogeneity / 9. Population Density / 10. Racial Heterogeneity / 11. Ratio of Adults to Children / 12. Residential Stability / 13. Income Inequality // APPENDIX: ACI Technical Report: Initial Measures Derived from Censu

Weather Variability, Tides, and Barmah Forest Virus Disease in the Gladstone Region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland

Transfection of Neuroprogenitor Cells with Iron Nanoparticles for Magnetic Resonance Imaging Tracking: Cell Viability, Differentiation, and Intracellular Localization

Magnetic resonance imaging (MRI) can track labeled cells in the brain. The use of hemagglutinating virus of Japan envelopes (HVJ-Es) to effectively introduce the contrast agent to neural progenitor cells (NPCs) is limited to date despite their high NPC affinity.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41579/1/11307_2005_Article_8.pd