Although molecular data have revealed the vast scope of microbial diversity, two fundamental questions remain unanswered even for well-defined natural microbial communities: how many bacterial types co-exist, and are such types naturally organized into phylogenetically discrete units of potential ecological significance? It has been argued that without such information, the environmental function, population biology and biogeography of microorganisms cannot be rigorously explored. Here we address these questions by comprehensive sampling of two large 16S ribosomal RNA clone libraries from a coastal bacterioplankton community. We show that compensation for artefacts generated by common library construction techniques reveals fine-scale patterns of community composition. At least 516 ribotypes (unique rRNA sequences) were detected in the sample and, by statistical extrapolation, at least 1,633 co-existing ribotypes in the sampled population. More than 50% of the ribotypes fall into discrete clusters containing less than 1% sequence divergence. This pattern cannot be accounted for by interoperon variation, indicating a large predominance of closely related taxa in this community. We propose that such microdiverse clusters arise by selective sweeps and persist because competitive mechanisms are too weak to purge diversity from within them.

The advancement of computer technology and the increasing complexity of research problems are creating the need to teach parallel programming in higher education more effectively. In this paper we present StarHPC, a system solution that supports teaching parallel programming in courses at the Massachusetts Institute of Techology. StarHPC prepackages a virtual machine image used by students, the scripts used by an administrator, and a virtual image of the Amazon Elastic Computing Cloud (EC2) machine used to build the cluster shared by the class. This architecture coupled with the no-cost availability of StarHPC allows it to be deployed at other institutions interested in teaching parallel programming with a dedicated compute cluster without incurring large upfront or ongoing costs.

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