Ecophysiology and niche differentiation of Nitrospira-like bacteria, the key nitrite oxidizers in wastewater treatment plants.

Nitrite-oxidizing bacteria of the genus Nitrospira are key nitrifiers in wastewater treatment plants. Pure cultures of these organisms are unavailable, but cultivation-independent molecular methods make it possible to detect Nitrospira-like bacteria in environmental samples and to investigate their ecophysiology. Comprehensive screening of natural and engineered habitats and of public databases for 16S rRNA sequences of Nitrospira-like bacteria revealed a surprisingly high biodiversity in the genus Nitrospira, which comprises at least four phylogenetic sublineages. All Nitrospira-like bacteria detected in wastewater treatment plants belonged to the sublineages I and II. Subsequently, the population dynamics of different Nitrospira-like bacteria were monitored, by quantitative fluorescence in situ hybridization with rRNA-targeted probes, confocal laser scanning microscopy and digital image analysis, during incubation of nitrifying activated sludge in media containing different nitrite concentrations. These experiments showed that Nitrospira-like bacteria, which were affiliated with the phylogenetic sublineages I or II of the genus Nitrospira, responded differently to nitrite concentration shifts. Previously unknown properties of Nitrospira-like bacteria were discovered in the course of an environmental genomics project. Implications of the obtained results for fundamental understanding of the microbial ecology of nitrite oxidizers as well as for future improvement of nutrient removal in wastewater treatment plants are discussed.     

Fast maximum intensity projections of large medical data sets by exploiting hierarchical memory architectures.

Maximum intensity projections (MIPs) are an important visualization technique for angiographic data sets. Efficient data inspection requires frame rates of at least five frames per second at preserved image quality. Despite the advances in computer technology, this task remains a challenge. On the one hand, the sizes of computed tomography and magnetic resonance images are increasing rapidly. On the other hand, rendering algorithms do not automatically benefit from the advances in processor technology, especially for large data sets. This is due to the faster evolving processing power and the slower evolving memory access speed, which is bridged by hierarchical cache memory architectures. In this paper, we investigate memory access optimization methods and use them for generating MIPs on general-purpose central processing units (CPUs) and graphics processing units (GPUs), respectively. These methods can work on any level of the memory hierarchy, and we show that properly combined methods can optimize memory access on multiple levels of the hierarchy at the same time. We present performance measurements to compare different algorithm variants and illustrate the influence of the respective techniques. On current hardware, the efficient handling of the memory hierarchy for CPUs improves the rendering performance by a factor of 3 to 4. On GPUs, we observed that the effect is even larger, especially for large data sets. The methods can easily be adjusted to different hardware specifics, although their impact can vary considerably. They can also be used for other rendering techniques than MIPs, and their use for more general image processing task could be investigated in the future.