Amy N. Rose

Aberrant miR-182 expression promotes melanoma metastasis by repressing FOXO3 and microphthalmia-associated transcription factor.

The highly aggressive character of melanoma makes it an excellent model for probing the mechanisms underlying metastasis, which remains one of the most difficult challenges in treating cancer. We find that miR-182, member of a miRNA cluster in a chromosomal locus (7q31–34) frequently amplified in melanoma, is commonly up-regulated in human melanoma cell lines and tissue samples; this up-regulation correlates with gene copy number in a subset of melanoma cell lines. Moreover, miR-182 ectopic expression stimulates migration of melanoma cells in vitro and their metastatic potential in vivo, whereas miR-182 down-regulation impedes invasion and triggers apoptosis. We further show that miR-182 over-expression promotes migration and survival by directly repressing microphthalmia-associated transcription factor-M and FOXO3, whereas enhanced expression of either microphthalmia-associated transcription factor-M or FOXO3 blocks miR-182s proinvasive effects. In human tissues, expression of miR-182 increases with progression from primary to metastatic melanoma and inversely correlates with FOXO3 and microphthalmia-associated transcription factor levels. Our data provide a mechanism for invasion and survival in melanoma that could prove applicable to metastasis of other cancers and suggest that miRNA silencing may be a worthwhile therapeutic strategy.

Locally adaptive, spatially explicit projection of US population for 2030 and 2050

Significance Oak Ridge National Laboratory (ORNL) is a leader in population distribution and dynamics research, particularly in developing gridded population datasets. For this study, ORNL researchers leverage their expertise in intelligent dasymetric modeling to construct large-scale, national level, spatially distributed population projections for the contiguous United States. The model presented here departs from other spatially explicit projection models by accounting for socioeconomic and cultural characteristics that influence spatial population growth at smaller scales, while still projecting population at a large scale. The resulting projected population distribution can be exploited for long-term urban and infrastructure planning, and scientific modeling for climate change. Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census’s projection methodology, with the US Census’s official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

Abstract Despite the increasing availability of current national censuses, these datasets are limited by their lack of small area demographic depth. At the same time, spatial microdata that include detailed demographic information are only available for limited geographies, thus limiting the complex analysis of population subgroups within and between small areas. Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internal validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.

Estimating traveler populations at airport and cruise terminals for population distribution and dynamics

In recent years, uses of high-resolution population distribution databases are increasing steadily for environmental, socioeconomic, public health, and disaster-related research and operations. With the development of daytime population distribution, temporal resolution of such databases has been improved. However, the lack of incorporation of transitional population, namely business and leisure travelers, leaves a significant population unaccounted for within the critical infrastructure networks, such as at transportation hubs. This paper presents two general methodologies for estimating passenger populations in airport and cruise port terminals at a high temporal resolution which can be incorporated into existing population distribution models. The methodologies are geographically scalable and are based on, and demonstrate how, two different transportation hubs with disparate temporal population dynamics can be modeled utilizing publicly available databases including novel data sources of flight activity from the Internet which are updated in near-real time. The airport population estimation model shows great potential for rapid implementation for a large collection of airports on a national scale, and the results suggest reasonable accuracy in the estimated passenger traffic. By incorporating population dynamics at high temporal resolutions into population distribution models, we hope to improve the estimates of populations exposed to or at risk to disasters, thereby improving emergency planning and response, and leading to more informed policy decisions.

Construction of Synthetic Populations with Key Attributes: Simulation Set-Up While Accommodating Multiple Approaches within a Flexible Simulation Platform

In this paper, we describe our concept for overcoming the data barriers of building credible synthetic populations to assist the transformation between social theories and mathematical models. We specifically developed a 31-million-agent model of Afghanistan’s population to demonstrate the ability to computationally control and analytically manipulate a system with the large number of agents (i.e., 108) necessary to model regions at the individual level using the LandScan Global population database. Afghanistan was selected for this case study because gathering data for Afghanistan was thought to be especially challenging. The LandScan Global population database is used by a majority of key U.S. and foreign agencies as their database system for worldwide geospatial distribution of populations. Assigning attributes to disaggregated population was achieved by fusing appropriate indicator databases using two forms of aggregation techniques – geographical and categorical. A new approach of matching attributes to theoretical constructs was illustrated. The other data sources used include data on military and peacekeeper forces’ loyalties, readiness, and deployment collected through a combination of UN and classified force projections; economic data collected at the national level and disaggregated using data fusion techniques; data on social attitudes, beliefs, and social cleavages through anthropological studies, worldwide polling, and classified sources; and data on infrastructure and information systems and networks.

Employing spaceborne multispectral stereo pairs and pedestrian flow modeling to support disaster response activities in urban environments

Pedestrian flow modeling applies geocomputational techniques to understand the patterns of human movement within an environment. A key principle is that the “cost” (time, distance, energy, etc.) of travel along different routes is affected by environmental factors, such as terrain variation or land cover. High-probability travel routes can therefore be estimated by performing a least-cost analysis on base geographic data layers. The primary data input to these methods are 3D structural information related to the Earth topography and the objects upon its surface. However, generating and analyzing such data at high resolution (1 m) has only recently become viable at scale. We present a data fusion approach that combines recent advances in dense stereo reconstruction, in conjunction with multispectral-derived land cover, towards exploring the travel routes of pedestrians in urban environments. Most previous studies focus on rural areas; this analysis provides a novel entry into understanding the flow of pedestrians in more densely populated areas, with applications to humanitarian assistance and disaster response (HADR).

Spatializing Global Urban Extent: A Source Driven Approach

GIScience 2016 Short Paper Proceedings Spatializing Global Urban Extent: A Source Driven Approach J. J. Moehl 1 , A. N. Rose 1 , E. A. Bright 1 Oak Ridge National Laboratory, Oak Ridge, TN 37831 Email: moehljj;rosean;brightea@ornl.gov Abstract “Urban” is something that intuitively feels very well defined; however, when it comes time to express this idea on a map, things get complicated. Definitions of “urban” vary globally, and as such there is not universal understanding of what makes a given place “urban”. The common approach to spatially defining urban extents is through remotely sensed imagery. The alternative approach presented in this paper uses the percent of population in urban areas, which is a common macroeconomic (country-level) variable with the definition for urban generally defined by each country’s statistical office, along with temporally-aligned high- resolution population data to spatially define urban extents for each country. Because the percent urban number is defined by the same producer as other urban/rural defined statistical data, such as household characteristics or birth rate, understanding the spatial aspect of urban from the same perspective is ideal for high resolution spatial modeling of these other phenomena. 1. Introduction At Oak Ridge National Laboratory, we’ve been modeling population for nearly two decades. Whether using a top down approach such as the LandScan Global model (Dobson et al. 2000), or a hybrid top down/bottom up approach as is used to develop LandScan USA (Bhaduri et al. 2007), spatial data that aligns with tabular datasets is essential. Ensuring that national and subnational boundaries match the data from statistical organizations is extremely important for dasymetric mapping, as these boundaries play a large role in appropriately disaggregating zonal summaries. Many summary data values, such as population counts, birth rates, average household size, and age distributions are listed not only by administrative zone, but often times they are further listed by whether they occur in urban or rural areas. Spatial data for administrative boundaries are widely available, although of varying quality and level of detail. However, very rarely are spatial data available with urban area delineation from the perspective of the statistical organization. Summary data designated as urban or rural for each administrative zone within the country is common. The idea of what is “urban” is defined and implemented by each country’s statistical office. The UN’s World Urbanization Prospects: The 2014 Revision provides an exhaustive comparison of these definitions which vary widely in their considerations (UN 2014). This may be an extremely detailed formal definition like the one provided by the US Census Bureau (US Census 2011) complete with spatial data freely available in multiple formats, or very generally defined with no publicly accessible spatial data. Administrative data and population density are the most common criteria used by countries to fit the urban definition, with 125 and 137 countries respectively using each criterion in part or whole (UN 2014). The HYDE datasets, which provide historical population and land use estimates, combine historical urban population estimates with population density in their models; however, these datasets are historical and at a coarser ~10 sq km resolution (Klein Goldewijk 2001, 2005).

Data driven approach for high resolution population distribution and dynamics models

High resolution population distribution data are vital for successfully addressing critical issues ranging from energy and socio-environmental research to public health to human security. Commonly available population data from Census is constrained both in space and time and does not capture population dynamics as functions of space and time. This imposes a significant limitation on the fidelity of event-based simulation models with sensitive space-time resolution. This paper describes ongoing development of high-resolution population distribution and dynamics models, at Oak Ridge National Laboratory, through spatial data integration and modeling with behavioral or activity-based mobility datasets for representing temporal dynamics of population. The model is resolved at 1 km resolution globally and describes the U.S. population for nighttime and daytime at 90m. Integration of such population data provides the opportunity to develop simulations and applications in critical infrastructure management from local to global scales.