Effects of Chemistry on Blunt-Body Wake Structure

Results of a numerical study are presented for hypersonic low-density flow about a 70-deg blunt cone using direct simulation Monte Carlo (DSMC) and Navier-Stokes calculations. Particular emphasis is given to the effects of chemistry on the near-wake structure and on the surface quantities and the comparison of the DSMC results with the Navier-Stokes calculations. The flow conditions simulated are those experienced by a space vehicle at an altitude of 85 km and a velocity of 7 km/s during Earth entry. A steady vortex forms in the near wake for these freestream conditions for both chemically reactive and nonreactive air gas models. The size (axial length) of the vortex for the reactive air calculations is 25% larger than that of the nonreactive air calculations. The forebody surface quantities are less sensitive to the chemistry than the base surface quantities. The presence of the afterbody has no effect on the forebody flow structure or the surface quantities. The comparisons of DSMC and Navier-Stokes calculations show good agreement for the wake structure and the forebody surface quantities

Relationship Difficulties Among U.K. Military Personnel: Impact of Sociodemographic, Military, and Deployment-Related Factors

© 2015, Taylor & Francis Group, LLC. Military work, especially operational deployments, may impact the romantic relationships of military personnel. Using a subsample of 7,581 participants from a cohort study of U.K. military personnel (data collected between 2007 and 2009), the prevalence of relationship difficulties and associations with sociodemographic, military-, and deployment-related characteristics was examined. Most participants did not report experiencing relationship difficulties. Adjusted regression analyses indicate that childhood adversity, limited support for and from partners, being in unmarried relationships, financial problems, deploying for more than 13 months in 3 years, and work being above trade, ability, and experience were key factors associated with relationship difficulties. The likelihood of U.K. military personnel experiencing relationship difficulties is increased because of personal vulnerabilities that may be exacerbated in the military context

Monitoring international migration flows in Europe. Towards a statistical data base combining data from different sources

The paper reviews techniques developed in demography, geography and statistics that are useful for bridging the gap between available data on international migration flows and the information required for policy making and research. The basic idea of the paper is as follows: to establish a coherent and consistent data base that contains sufficiently detailed, up-to-date and accurate information, data from several sources should be combined. That raises issues of definition and measurement, and of how to combine data from different origins properly. The issues may be tackled more easily if the statistics that are being compiled are viewed as different outcomes or manifestations of underlying stochastic processes governing migration. The link between the processes and their outcomes is described by models, the parameters of which must be estimated from the available data. That may be done within the context of socio-demographic accounting. The paper discusses the experience of the U.S. Bureau of the Census in combining migration data from several sources. It also summarizes the many efforts in Europe to establish a coherent and consistent data base on international migration. The paper was written at IIASA. It is part of the Migration Estimation Study, which is a collaborative IIASA-University of Groningen project, funded by the Netherlands Organization for Scientific Research (NWO). The project aims at developing techniques to obtain improved estimates of international migration flows by country of origin and country of destination

Characterization of the Tomato ARF Gene Family Uncovers a Multi-Levels Post-Transcriptional Regulation Including Alternative Splicing

Background: The phytohormone auxin is involved in a wide range of developmental processes and auxin signaling is known to modulate the expression of target genes via two types of transcriptional regulators, namely, Aux/IAA and Auxin Response Factors (ARF). ARFs play a major role in transcriptional activation or repression through direct binding to the promoter of auxin-responsive genes. The present study aims at gaining better insight on distinctive structural and functional features among ARF proteins. Results: Building on the most updated tomato (Solanum lycopersicon) reference genome sequence, a comprehensive set of ARF genes was identified, extending the total number of family members to 22. Upon correction of structural annotation inconsistencies, renaming the tomato ARF family members provided a consensus nomenclature for all ARF genes across plant species. In silico search predicted the presence of putative target site for small interfering RNAs within twelve Sl-ARFs while sequence analysis of the 59-leader sequences revealed the presence of potential small uORF regulatory elements. Functional characterization carried out by transactivation assay partitioned tomato ARFs into repressors and activators of auxin-dependent gene transcription. Expression studies identified tomato ARFs potentially involved in the fruit set process. Genome-wide expression profiling using RNA-seq revealed that at least one third of the gene family members display alternative splicing mode of regulation during the flower to fruit transition. Moreover, the regulation of several tomato ARF genes by both ethylene and auxin, suggests their potential contribution to the convergence mechanism between the signaling pathways of these two hormones. Conclusion: All together, the data bring new insight on the complexity of the expression control of Sl-ARF genes at the transcriptional and post-transcriptional levels supporting the hypothesis that these transcriptional mediators might represent one of the main components that enable auxin to regulate a wide range of physiological processes in a highly specific and coordinated manner

Political and social determinants of life expectancy in less developed countries: a longitudinal study

<p>Abstract</p> <p>Background</p> <p>This study aimed to examine the longitudinal contributions of four political and socioeconomic factors to the increase in life expectancy in less developed countries (LDCs) between 1970 and 2004.</p> <p>Methods</p> <p>We collected 35 years of annual data for 119 LDCs on life expectancy at birth and on four key socioeconomic indicators: economy, measured by log10 gross domestic product per capita at purchasing power parity; educational environment, measured by the literacy rate of the adult population aged 15 years and over; nutritional status, measured by the proportion of undernourished people in the population; and political regime, measured by the regime score from the Polity IV database. Using linear mixed models, we analyzed the longitudinal effects of these multiple factors on life expectancy at birth with a lag of 0-10 years, adjusting for both time and regional correlations.</p> <p>Results</p> <p>The LDCs' increases in life expectancy over time were associated with all four factors. Political regime had the least influence on increased life expectancy to begin with, but became significant starting in the 3rd year and continued to increase, while the impact of the other socioeconomic factors began strong but continually decreased over time. The combined effects of these four socioeconomic and political determinants contributed 54.74% - 98.16% of the life expectancy gains throughout the lag periods of 0-10 years.</p> <p>Conclusions</p> <p>Though the effect of democratic politics on increasing life expectancy was relatively small in the short term when compared to the effects of the other socioeconomic factors, the long-term impact of democracy should not be underestimated.</p

New Directions in the Development of Population Estimates in the United States?

The advent of a continuously updated Master Area File (MAF) following the 2000 census represents an information resource that can be tapped for purposes of developing timely, cost-effective, and precise population estimates for even the smallest of geographical units (e.g., census blocks). We argue that the MAF can be enhanced (EMAF) for these purposes. In support of our argument we describe a set of activities needed to develop EMAF, each of which is well within the current capabilities of the U.S. Census Bureau and discuss various costs and benefits of each. We also describe how EMAF would provide population estimates containing a wide range of demographic (e.g., age, race, and sex) and socio-economic characteristics (e.g., educational attainment, income, and employment). As such, it could largely negate and eliminate the need for many of the traditional demographic methods of population estimation and possibly reduce the number of sample surveys. We identify important challenges that must be surmounted in order to realize EMAF and make suggestions for doing so. We conclude by noting that the idea of the EMAF could be of interest to other countries with MAF files and strong administrative records systems that, like the United States, are facing the challenge of producing good population information in the face of increasing census costs

Modeling causes of death: an integrated approach using CODEm

Background: Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting.Methods: We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance.Results: Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers.Conclusions: CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death