Human milk oligosaccharide composition and associations with growth: results from an observational study in the US

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.

Human milk oligosaccharides (HMO) constitute the third most abundant class of molecules in breast milk. Since infants lack the enzymes required for milk glycan digestion, this group of carbohydrates passes undigested to the lower part of the intestinal tract, where they can be consumed by specific members of the infant gut microbiota. We review proposed mechanisms for the depletion and metabolism of HMO by two major bacterial genera within the infant intestinal microbiota, Bifidobacterium and Bacteroides. © 2012 The Authors. Clinical Microbiology and Infection © 2012 European Society of Clinical Microbiology and Infectious Diseases.