Developing interest management techniques in distributed interactive simulation using Java

Bandwidth consumption in distributed real time simulation, or networked real time simulation, is a major problem as the number of participants and the sophistication of joint simulation exercises grow in size. The paper briefly reviews distributed real time simulation and bandwidth reduction techniques and introduces the Generic Runtime Infrastructure for Distributed Simulation (GRIDS) as a research architecture for studying such problems. GRIDS uses Java abstract classes to promote distributed services called thin agents, a novel approach to implementing distributed simulation services, such as user defined bandwidth reduction mechanisms, and to distributing the executable code across the simulation. Thin agents offer the advantages of traditional agents without the overhead imposed by mobility or continuous state, which are unnecessary in this context. We present our implementation and some predicted results from message reduction studies using thin agent

Flow and thermal effects in continuous flow electrophoresis

In continuous flow electrophoresis the axial flow structure changes from a fully developed rectilinear form to one characterized by meandering as power levels are increased. The origin of this meandering is postulated to lie in a hydrodynamic instability driven by axial (and possibly lateral) temperature gradients. Experiments done at MSFC show agreement with the theory

Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment

Covariate-adjusted sensitivity analyses is proposed for missing time-to-event outcomes. The method invokes multiple imputation (MI) for the missing failure times under a variety of specifications regarding the post-withdrawal tendency for having the event of interest. With a clinical trial example, we compared methods of covariance analyses for time-to-event data, i.e., the multivariable Cox proportional hazards model and non-parametric ANCOVA, and then illustrated how to incorporate these methods into the proposed sensitivity analysis for covariate adjustment. The MI methods considered are Kaplan-Meier Multiple Imputation (KMMI), covariate-adjusted and unadjusted proportional hazards multiple imputation (PHMI). The assumptions, statistical issues, and features for these methods are discussed

<i>In vitro</i> Characterization of Phenylacetate Decarboxylase, a Novel Enzyme Catalyzing Toluene Biosynthesis in an Anaerobic Microbial Community

Anaerobic bacterial biosynthesis of toluene from phenylacetate was reported more than two decades ago, but the biochemistry underlying this novel metabolism has never been elucidated. Here we report results of in vitro characterization studies of a novel phenylacetate decarboxylase from an anaerobic, sewage-derived enrichment culture that quantitatively produces toluene from phenylacetate; complementary metagenomic and metaproteomic analyses are also presented. Among the noteworthy findings is that this enzyme is not the well-characterized clostridial p-hydroxyphenylacetate decarboxylase (CsdBC). However, the toluene synthase under study appears to be able to catalyze both phenylacetate and p-hydroxyphenylacetate decarboxylation. Observations suggesting that phenylacetate and p-hydroxyphenylacetate decarboxylation in complex cell-free extracts were catalyzed by the same enzyme include the following: (i) the specific activity for both substrates was comparable in cell-free extracts, (ii) the two activities displayed identical behavior during chromatographic separation of cell-free extracts, (iii) both activities were irreversibly inactivated upon exposure to O2, and (iv) both activities were similarly inhibited by an amide analog of p-hydroxyphenylacetate. Based upon these and other data, we hypothesize that the toluene synthase reaction involves a glycyl radical decarboxylase. This first-time study of the phenylacetate decarboxylase reaction constitutes an important step in understanding and ultimately harnessing it for making bio-based toluene

Using DHS and MICS data to complement or replace NGO baseline health data: An exploratory study

Background: Non-government organizations (NGOs) spend substantial time and resources collecting baseline data in order to plan and implement health interventions with marginalized populations. Typically interviews with households, often mothers, take over an hour, placing a burden on the respondents. Meanwhile, estimates of numerous health and social indicators in many countries already exist in publicly available datasets, such as the Demographic and Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS), and it is worth considering whether these could serve as estimates of baseline conditions. The objective of this study was to compare indicator estimates from non-governmental organizations (NGO) health projects' baseline reports with estimates calculated using the Demographic and Health Surveys (DHS) or the Multiple Indicator Cluster Surveys (MICS), matching for location, year, and season of data collection. / Methods: We extracted estimates of 129 indicators from 46 NGO baseline reports, 25 DHS datasets and three MICS datasets, generating 1,996 pairs of matched DHS/MICS and NGO indicators. We subtracted NGO from DHS/MICS estimates to yield difference and absolute difference, exploring differences by indicator. We partitioned variance of the differences by geographical level, year, and season using ANOVA. / Results: Differences between NGO and DHS/MICS estimates were large for many indicators but 33% fell within 5% of one another. Differences were smaller for indicators with prevalence 85%. Difference between estimates increased with increasing year and geographical level differences. However, <1% of the variance of the differences was explained by year, geographical level, and season. / Conclusions: There are situations where publicly available data could complement NGO baseline survey data, most importantly when the NGO has tolerance for estimates of low or unknown accuracy

Participatory women’s groups and counseling through home visits to improve child growth in rural eastern India: protocol for a cluster randomised controlled trial

Background: Childhood stunting (low height-for-age) is a marker of chronic undernutrition and predicts children’s subsequent physical and cognitive development. An estimated 52 million children in India are stunted. There is a broad consensus on determinants of child undernutrition and interventions to address it, but a lack of operational research testing strategies to increase the coverage of these interventions in high burden areas. Our study aims to assess the impact, costeffectiveness, and scalability of a community intervention involving a government-proposed community-based worker to improve growth in children under two

Testing Random Effects in the Linear Mixed Model Using Approximate Bayes Factors

Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance and introduce a parameter that controls the relative contribution of each random effect free of the scale of the data. We integrate out the random effects and the variance components using closed form solutions. The resulting integrals needed to calculate the Bayes factor are low-dimensional integrals lacking variance components and can be efficiently approximated with Laplace’s method. We propose a default prior distribution on the parameter controlling the contribution of each random effect and conduct simulations to show that our method has good properties for model selection problems. Finally, we illustrate our methods on data from a clinical trial of patients with bipolar disorder and on data from an environmental study of water disinfection by-products and male reproductive outcomes

Validity and reliability of the Patient-Reported Arthralgia Inventory; validation of a newly-developed survey instrument to measure arthralgia

BACKGROUND: There is a need for a survey instrument to measure arthralgia (joint pain) that has been psychometrically validated in the context of existing reference instruments. We developed the 16-item Patient-Reported Arthralgia Inventory (PRAI) to measure arthralgia severity in 16 joints, in the context of a longitudinal cohort study to assess aromatase inhibitor-associated arthralgia in breast cancer survivors and arthralgia in postmenopausal women without breast cancer. We sought to evaluate the reliability and validity of the PRAI instrument in these populations, as well as to examine the relationship of patient-reported morning stiffness and arthralgia. METHODS: We administered the PRAI on paper in 294 women (94 initiating aromatase inhibitor therapy and 200 postmenopausal women without breast cancer) at weeks 0, 2, 4, 6, 8, 12, 16, and 52, as well as once in 36 women who had taken but were no longer taking aromatase inhibitor therapy. RESULTS: Cronbach’s alpha was 0.9 for internal consistency of the PRAI. Intraclass correlation coefficients of test-retest reliability were in the range of 0.87–0.96 over repeated PRAI administrations; arthralgia severity was higher in the non-cancer group at baseline than at subsequent assessments. Women with joint comorbidities tended to have higher PRAI scores than those without (estimated difference in mean scores: −0.3, 95% confidence interval [CI] −0.5, −0.2; P<0.001). The PRAI was highly correlated with the Functional Assessment of Cancer Therapy-Endocrine Subscale item “I have pain in my joints” (reference instrument; Spearman r range: 0.76–0.82). Greater arthralgia severity on the PRAI was also related to decreased physical function (r=−0.47, 95% CI −0.55, −0.37; P<0.001), higher pain interference (r=0.65, 95% CI 0.57–0.72; P<0.001), less active performance status (estimated difference in location (−0.6, 95% CI −0.9, −0.4; P<0.001), and increased morning stiffness duration (r=0.62, 95% CI 0.54–0.69; P<0.0001). CONCLUSION: We conclude that the psychometric properties of the PRAI are satisfactory for measuring arthralgia severity