Design and Analysis of Cognitive Interviews for Comparative Multinational Testing

This article summarizes the work of the Comparative Cognitive Testing Workgroup, an international coalition of survey methodologists interested in developing an evidence-based methodology for examining the comparability of survey questions within cross-cultural or multinational contexts. To meet this objective, it was necessary to ensure that the cognitive interviewing (CI) method itself did not introduce method bias. Therefore, the workgroup first identified specific characteristics inherent in CI methodology that could undermine the comparability of CI evidence. The group then developed and implemented a protocol addressing those issues. In total, 135 cognitive interviews were conducted by participating countries. Through the process, the group identified various interpretive patterns resulting from sociocultural and language-related differences among countries as well as other patterns of error that would impede comparability of survey data

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Application of Conditional Random Fields and Sparse Polynomial Chaos Expansions to Geotechnical Problems

In geotechnical applications, mechanical properties of soil vary spatially within the soil mass and they are often represented by random fields. When data at certain locations of the soil mass are available, conditional random fields may be used to incorporate them. In this paper, we combine conditional random fields with sparse polynomial chaos expansions to analyze response quantities with otherwise too expensive Monte Carlo-based techniques, such as reliability and sensitivity analysis

Combining Polynomial Chaos Expansions and Kriging

Computer simulation has emerged as a key tool for designing and assessing engineeringsystems in the last two decades. Uncertainty quantification has becomepopular more recently as a way to model all the uncertainties affecting the systemand their impact onto its performance.In this respect meta-models (a.k.a. surrogate models) have gained interest. Indeeddealing with uncertainties requires running the computer model many times,which may not be affordable for complex models. Surrogate models mimic the behaviourof the original model while being cheap to evaluate.Polynomial chaos expansion (PCE) and Kriging are two popular techniques, whichhave been developed with very little interaction so far. In this report we present a newapproach, called PC-Kriging, that combines the two tools. The algorithm is based onthe universal Kriging model where the trend is represented by a set or orthonormalpolynomials.Various aspects of the new metamodelling technique are presented and investigatedin details. The discussion starts with a survey on methods for generating anoptimal design of experiments (DOE). The PC-Kriging algorithm inherits many parametersand sub-methods such as the number of polynomial terms and the choiceof the autocorrelation kernel. A variety of kernels are presented and discussed.The methods are compared on analytical benchmark functions. The conclusionof this report is that PC-Kriging performs better or at least as well as PCE or Krigingtaken separately in terms of relative generalized error (L2-error)

Combining Polynomial Chaos Expansions and Kriging

Computer simulation has emerged as a key tool for designing and assessing engineeringsystems in the last two decades. Uncertainty quantification has becomepopular more recently as a way to model all the uncertainties affecting the systemand their impact onto its performance.In this respect meta-models (a.k.a. surrogate models) have gained interest. Indeeddealing with uncertainties requires running the computer model many times,which may not be affordable for complex models. Surrogate models mimic the behaviourof the original model while being cheap to evaluate.Polynomial chaos expansion (PCE) and Kriging are two popular techniques, whichhave been developed with very little interaction so far. In this report we present a newapproach, called PC-Kriging, that combines the two tools. The algorithm is based onthe universal Kriging model where the trend is represented by a set or orthonormalpolynomials.Various aspects of the new metamodelling technique are presented and investigatedin details. The discussion starts with a survey on methods for generating anoptimal design of experiments (DOE). The PC-Kriging algorithm inherits many parametersand sub-methods such as the number of polynomial terms and the choiceof the autocorrelation kernel. A variety of kernels are presented and discussed.The methods are compared on analytical benchmark functions. The conclusionof this report is that PC-Kriging performs better or at least as well as PCE or Krigingtaken separately in terms of relative generalized error (L2-error)