UDP-N-acetylglucosamine (UDP-GlcNAc) is a direct glycosyl donor of linker unit (L-Rhamnose-D-GlcNAc) and an essential precursor of peptidoglycan in mycobacteria. Phosphoglucosamine mutase (GlmM) is involved in the formation of glucosamine-1-phosphate from glucosamine-6-phosphate, the second step in UDP-GlcNAc biosynthetic pathway. We have demonstrated that GlmM protein is essential for the grow...

The generalized linear mixed model (GLMM) is a useful tool for modeling genetic correlation among family data in genetic association studies. However, when dealing with families of varied sizes and diverse genetic relatedness, the GLMM has a special correlation structure which often makes it difficult to be specified using standard statistical software. In this study, we propose a Cholesky deco...

Phosphoglucosamine mutase (GlmM; EC 5.4.2.10) catalyzes the interconversion of glucosamine-6-phosphate to glucosamine-1-phosphate, an essential step in the biosynthetic pathway leading to the formation of the peptidoglycan precursor uridine 5'-diphospho-N-acetylglucosamine. We have recently identified the gene (glmM) encoding the enzyme of Streptococcus gordonii, an early colonizer on the human...

In psychophysics, researchers usually apply a two-level model for the analysis of the behavior of the single subject and the population. This classical model has two main disadvantages. First, the second level of the analysis discards information on trial repetitions and subject-specific variability. Second, the model does not easily allow assessing the goodness of fit. As an alternative to th...

Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Four GLMM algorithms are described and simulations are performed to vet these algorithms before ap...

Generalized linear mixed models (GLMM) are useful in a variety of applications. With surrogate covariate data, existing methods of inference for GLMM are usually computationally intensive. We propose a two-step inference procedure for GLMM with missing covariate data. It is shown that the proposed estimator is consistent and asymptotically normal with covariance matrix that can be easily estima...