Single Molecule, Real-Time (SMRT®) Sequencing (Pacific Biosciences, Menlo Park, CA, USA) provides the longest continuous DNA sequencing reads currently available. However, the relatively high error rate in the raw read data requires novel analysis methods to deconvolute sequences derived from complex samples. Here, we present a workflow of novel computer algorithms able to reconstruct viral variant genomes present in mixtures with an accuracy of >QV50. This approach relies exclusively on Continuous Long Reads (CLR), which are the raw reads generated during SMRT Sequencing. We successfully implement this workflow for simultaneous sequencing of mixtures containing up to forty different >9 kb HIV-1 full genomes. This was achieved using a single SMRT Cell for each mixture and desktop computing power. This novel approach opens the possibility of solving complex sequencing tasks that currently lack a solution.

An application pull has occurred in biomedicine with the move to in silico studies, which augment in vivo and in vitro studies by simulating more details of biomedical processes. Using these simulated processes helps medical doctors make decisions by exploring different scenarios. Preoperative simulation and visualization of vascular surgery 3 and expert systems for drug ranking 4 are two examples of such processes. At the same time, a technology push is occurring in computing resources and data availability. 5 In the field of high-performance computing, as computing advanced from sequential to parallel to distributed, killer applications moved from mathematics to physics, chemistry, biology, and now to medicine. In addition, advances in Internet technology and grid computing 6 have made huge amounts of data available from sensors, experiments, and simulations.Still, significant computational, integration, collaboration, and interaction gaps exist between the observed application pull and the technology push. Bridging the gapsClosing the computational gap in systems biology requires constructing, integrating, and managing a plethora of models. A bottom-up, data-driven approachComputer science provides the language needed to study and understand complex multiscale, multiscience systems.ViroLab, a grid-based decision-support system, demonstrates how researchers can now study diseases from the DNA level all the way up to medical responses to treatment.

We discuss the performance of direct summation codes used in the simulation of astrophysical stellar systems on highly distributed architectures. These codes compute the gravitational interaction among stars in an exact way and have an O(N^2) scaling with the number of particles. They can be applied to a variety of astrophysical problems, like the evolution of star clusters, the dynamics of black holes, the formation of planetary systems, and cosmological simulations. The simulation of realistic star clusters with sufficiently high accuracy cannot be performed on a single workstation but may be possible on parallel computers or grids. We have implemented two parallel schemes for a direct N-body code and we study their performance on general purpose parallel computers and large computational grids. We present the results of timing analyzes conducted on the different architectures and compare them with the predictions from theoretical models. We conclude that the simulation of star clusters with up to a million particles will be possible on large distributed computers in the next decade. Simulating entire galaxies however will in addition require new hybrid methods to speedup the calculation

The PCORnet Antibiotics and Childhood Growth Study is a large national longitudinal observational study in a diverse population that will examine the relationship between early antibiotic use and subsequent growth patterns in children.

Supplement 23 to DICOM (Digital Imaging and Communications for Medicine), Structured Reporting, is a specification that supports a semantically rich representation of image and waveform content, enabling experts to share image and related patient information. DICOM SR supports the representation of textual and coded data linked to images and waveforms. Nevertheless, the medical information technology community needs models that work as bridges between the DICOM relational model and open object-oriented technologies. The authors assert that representations of the DICOM Structured Reporting standard, using object-oriented modeling languages such as the Unified Modeling Language, can provide a high-level reference view of the semantically rich framework of DICOM and its complex structures. They have produced an object-oriented model to represent the DICOM SR standard and have derived XML-exchangeable representations of this model using World Wide Web Consortium specifications. They expect the model to benefit developers and system architects who are interested in developing applications that are compliant with the DICOM SR specification.