Propfan test assessment testbed aircraft stability and control/performance 1/9-scale wind tunnel tests

One-ninth scale wind tunnel model tests of the Propfan Test Assessment (PTA) aircraft were performed in three different NASA facilities. Wing and propfan nacelle static pressures, model forces and moments, and flow field at the propfan plane were measured in these tests. Tests started in June 1985 and were completed in January 1987. These data were needed to assure PTA safety of flight, predict PTA performance, and validate analytical codes that will be used to predict flow fields in which the propfan will operate

Dilation of the Giant Vortex State in a Mesoscopic Superconducting Loop

We have experimentally investigated the magnetisation of a mesoscopic aluminum loop at temperatures well below the superconducting transition temperature $T_{c}$. The flux quantisation of the superconducting loop was investigated with a $\mu$-Hall magnetometer in magnetic field intensities between $\pm 100 {Gauss}$. The magnetic field intensity periodicity observed in the magnetization measurements is expected to take integer values of the superconducting flux quanta $\Phi_{0}=h/2e$. A closer inspection of the periodicity, however, reveal a sub flux quantum shift. This fine structure we interpret as a consequence of a so called giant vortex state nucleating towards either the inner or the outer side of the loop. These findings are in agreement with recent theoretical reports.Comment: 12 pages, 5 figures. Accepted for publication in Phys. Rev.

Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis : A Tutorial

Cost-effectiveness analyses (CEA) of randomised controlled trials are a key source of information for health care decision makers. Missing data are, however, a common issue that can seriously undermine their validity. A major concern is that the chance of data being missing may be directly linked to the unobserved value itself [missing not at random (MNAR)]. For example, patients with poorer health may be less likely to complete quality-of-life questionnaires. However, the extent to which this occurs cannot be ascertained from the data at hand. Guidelines recommend conducting sensitivity analyses to assess the robustness of conclusions to plausible MNAR assumptions, but this is rarely done in practice, possibly because of a lack of practical guidance. This tutorial aims to address this by presenting an accessible framework and practical guidance for conducting sensitivity analysis for MNAR data in trial-based CEA. We review some of the methods for conducting sensitivity analysis, but focus on one particularly accessible approach, where the data are multiply-imputed and then modified to reflect plausible MNAR scenarios. We illustrate the implementation of this approach on a weight-loss trial, providing the software code. We then explore further issues around its use in practice

Hygienic quality of dehydrated aromatic herbs marketed in Southern Portugal

Dehydrated aromatic herbs are highly valued ingredients, widely used at home level and by food processing industry, frequently added to a great number of recipes in the Mediterranean countries. Despite being considered low-moisture products and classified as GRAS, during pre and post-harvesting stages of production they are susceptible of microbial contamination. In Europe an increasing number of food recalls and disease outbreaks associated with dehydrated herbs have been reported in recent years. In this study the microbial quality of 99 samples of aromatic herbs (bay leaves, basil, coriander, oregano, parsley, Provence herbs, rosemary and thyme) collected from retails shops in the region of Algarve (Southern Portugal) was assessed. All the samples were tested by conventional methods and were assayed for the total count of aerobic mesophilic microorganisms, Salmonella spp., Escherichia coli, coagulase-positive staphylococci and filamentous fungi. Almost 50 % of the herbs did not exceed the aerobic mesophilic level of 104 CFU/g. The fungi count regarded as unacceptable (106 CFU/g) was not found in any of the tested herbs, while 84 % of the samples ranged from ≤102 to 104 CFU/g. No sample was positive for the presence of Salmonella spp., Escherichia coli and staphylococci. The results are in compliance with the European Commission criteria although they point out to the permanent need of surveillance on the good standards of handling/cooking practices as well as the importance of avoiding contamination at production, retailing and distribution. The microbiological hazards associated with the pathogenic and toxigenic microbiota of dried herbs remain as a relevant public health issue, due to the fact that they are added to foods not submitted to any following lethal procedure. Control measures should be adopted in order to ensure that all phases of their supply chain respect the food safety standards.FCT: UID/BIA/04325/2019.info:eu-repo/semantics/publishedVersio

MI-GWAS: a SAS platform for the analysis of inherited and maternal genetic effects in genome-wide association studies using log-linear models

<p>Abstract</p> <p>Background</p> <p>Several platforms for the analysis of genome-wide association data are available. However, these platforms focus on the evaluation of the genotype inherited by affected (i.e. case) individuals, whereas for some conditions (e.g. birth defects) the genotype of the mothers of affected individuals may also contribute to risk. For such conditions, it is critical to evaluate associations with both the maternal and the inherited (i.e. case) genotype. When genotype data are available for case-parent triads, a likelihood-based approach using log-linear modeling can be used to assess both the maternal and inherited genotypes. However, available software packages for log-linear analyses are not well suited to the analysis of typical genome-wide association data (e.g. including missing data).</p> <p>Results</p> <p>An integrated platform, Maternal and Inherited Analyses for Genome-wide Association Studies <b>(</b>MI-GWAS) for log-linear analyses of maternal and inherited genetic effects in large, genome-wide datasets, is described. MI-GWAS uses SAS and LEM software in combination to appropriately format data, perform the log-linear analyses and summarize the results. This platform was evaluated using existing genome-wide data and was shown to perform accurately and relatively efficiently.</p> <p>Conclusions</p> <p>The MI-GWAS platform provides a valuable tool for the analysis of association of a phenotype or condition with maternal and inherited genotypes using genome-wide data from case-parent triads. The source code for this platform is freely available at <url>http://www.sph.uth.tmc.edu/sbrr/mi-gwas.htm</url>.</p

A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials

The authors would like to thank Professor Adrian Grant and the team at the University of Aberdeen (Professor Craig Ramsay, Janice Cruden, Charles Boachie, Professor Marion Campbell and Seonaidh Cotton) who kindly allowed the REFLUX dataset to be used for this work, and Eldon Spackman for kindly sharing the Stata (R) code for calculating the probability that an intervention is cost effective following MI. The authors are grateful to the reviewers for their comments, which greatly improved this paper. M. G. is recipient of a Medical Research Council Early Career Fellowship in Economics of Health (grant number: MR/K02177X/1). I. R. W. was supported by the Medical Research Council [Unit Programme U105260558]. No specific funding was obtained to produce this paper. The authors declare no conflicts of interest.Missing data are a frequent problem in cost-effectiveness analysis (CEA) within a randomised controlled trial. Inappropriate methods to handle missing data can lead to misleading results and ultimately can affect the decision of whether an intervention is good value for money. This article provides practical guidance on how to handle missing data in within-trial CEAs following a principled approach: (i) the analysis should be based on a plausible assumption for the missing data mechanism, i.e. whether the probability that data are missing is independent of or dependent on the observed and/or unobserved values; (ii) the method chosen for the base-case should fit with the assumed mechanism; and (iii) sensitivity analysis should be conducted to explore to what extent the results change with the assumption made. This approach is implemented in three stages, which are described in detail: (1) descriptive analysis to inform the assumption on the missing data mechanism; (2) how to choose between alternative methods given their underlying assumptions; and (3) methods for sensitivity analysis. The case study illustrates how to apply this approach in practice, including software code. The article concludes with recommendations for practice and suggestions for future research.Medical Research Council Early Career Fellowship in Economics of Health MR/K02177X/1Medical Research Council UK (MRC) U105260558Medical Research Council UK (MRC) MC_U105260558 MR/K02177X/