91 research outputs found

    Modeling the Thermosphere as a Driven-dissipative Thermodynamic System

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    Thermospheric density impacts satellite position and lifetime through atmospheric drag. More accurate specification of thermospheric temperature, a key input to current models such as the High Accuracy Satellite Drag Model, can decrease model density errors. This paper improves the model of Burke et al. (2009) to model thermospheric temperatures using the magnetospheric convective electric field as a driver. In better alignment with Air Force satellite tracking operations, we model the arithmetic mean temperature, T 1/2, defined by the Jacchia (1977) model as the mean of the daytime maximum and nighttime minimum exospheric temperatures occurring in opposite hemispheres at a given time, instead of the exospheric temperature used by Burke et al. (2009). Two methods of treating the solar ultraviolet (UV) contribution to T 1/2 are tested. Two model parameters, the coupling and relaxation constants, are optimized for 38 storms from 2002 to 2008. Observed T 1/2 values are derived from densities and heights measured by the Gravity Recovery and Climate Experiment satellite. The coupling and relaxation constants were found to vary over the solar cycle and are fit as functions of F 10.7a, the 162 day average of the F 10.7 index. Model results show that allowing temporal UV variation decreased model T 1/2 errors for storms with decreasing UV over the storm period but increased T 1/2 errors for storms with increasing UV. Model accuracy was found to be improved by separating storms by type (coronal mass ejection or co‐rotating interaction region). The model parameter fits established will be useful for improving satellite drag forecasts

    Analysis of five-year trends in self-reported language preference and issues of item non-response among Hispanic persons in a large cross-sectional health survey: implications for the measurement of an ethnic minority population

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    <p>Abstract</p> <p>Background</p> <p>Significant differences in health outcomes have been documented among Hispanic persons, the fastest-growing demographic segment of the United States. The objective of this study was to examine trends in population growth and the collection of health data among Hispanic persons, including issues of language preference and survey completion using a national health survey to highlight issues of measurement of an increasingly important demographic segment of the United States.</p> <p>Design</p> <p>Data from the 2003-2007 United States Census and the Behavioral Risk Factor Surveillance System were used to compare trends in population growth and survey sample size as well as differences in survey response based on language preference among a Hispanic population. Percentages of item non-response on selected survey questions were compared for Hispanic respondents choosing to complete the survey in Spanish and those choosing to complete the survey in English. The mean number of attempts to complete the survey was also compared based on language preference among Hispanic respondents.</p> <p>Results</p> <p>The sample size of Hispanic persons in the Behavioral Risk Factor Surveillance System saw little growth compared to the actual growth of the Hispanic population in the United States. Significant differences in survey item non-response for nine of 15 survey questions were seen based on language preference. Hispanic respondents choosing to complete the survey in Spanish had a significantly fewer number of call attempts for survey completion compared to their Hispanic counterparts choosing to communicate in English.</p> <p>Conclusions</p> <p>Including additional measures of acculturation and increasing the sample size of Hispanic persons in a national health survey such as the Behavioral Risk Factor Surveillance System may result in more precise findings that could be used to better target prevention and health care needs for an ethnic minority population.</p

    The genetic architecture of the human cerebral cortex

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    The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder
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