BACKGROUND Focus groups, rapid assessment procedures, key informant interviews and institutional reviews of local health services provide valuable insights on health service resources and performance. A long-standing challenge of health planning is to combine this sort of qualitative evidence in a unified analysis with quantitative evidence from household surveys. A particular challenge in this...

The normal growth of mycobacteria attributes to the integrity of cell wall core which consists of peptidoglycan (PG), arabinogalactan (AG) and mycolic acids. N-acetyl glucosamine (GlcNAc) is an essential component in both PG and AG of mycobacterial cell wall. The biosynthetic pathway for UDP-N-acetylglucosamine (UDP-GlcNAc), as a sugar donor of GlcNAc, is different in prokaryotes and eukaryotes...

The ongoing Small Area Income and Poverty Estimates (SAIPE) project at the Census Bureau estimates numbers of poor school-age children by state, county, and ultimately school district, based upon Current Population Survey (CPS) and IRS data along with information from the latest decennial census. The current SAIPE county-level methodology relies on a Fay-Herriot (1979) model fitted to log-count...

BACKGROUND The low (LF) vs. high (HF) frequency energy ratio, computed from the spectral decomposition of heart beat intervals, has become a major tool in cardiac autonomic system control and sympatho-vagal balance studies. The (statistical) distributions of response variables designed from ratios of two quantities, such as the LF/HF ratio, are likely to non-normal, hence preventing e.g., from ...

In this work, we consider some computational issues related to the minimum mean-squared error (MMSE) prediction of non-Gaussian variables under a spatial generalized linear mixed model (GLMM). This model has been used to model spatial non-Gaussian variables by Diggle et al. (1998) and Zhang (2002), under which MMSE prediction of non-Gaussian variables can be computed. Since the MMSE prediction ...

We introduce a flexible marginal modelling approach for statistical inference for clustered/longitudinal data under minimal assumptions. This estimated estimating equations (EEE) approach is semiparametric and the proposed models are fitted by quasi-likelihood regression, where the unknown marginal means are a function of the fixed-effects linear predictor with unknown smooth link, and variance...

Phosphoglucosamine mutase (GlmM) catalyzes the formation of glucosamine-1-phosphate from glucosamine-6-phosphate, an essential step in the pathway for UDP-N-acetylglucosamine biosynthesis in bacteria. This enzyme must be phosphorylated to be active and acts according to a ping-pong mechanism involving glucosamine-1, 6-diphosphate as an intermediate (L. Jolly, P. Ferrari, D. Blanot, J. van Heije...

We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. ...

Abstract Generalized linear mixed models form a general class of random effects models for discrete and continuous response in the exponential family. Spatial GLMM are an extension of such models that allows us to fit spatial-dependent data. A popular model in this class is the probit-normal model. In this study we develop a novel exact algorithm to estimate a probit spatial generalized linear ...