Tree-Based Overlay Networks for Scalable Applications

The increasing availability of high-performance computing systems with thousands, tens of thousands, and even hundreds of thousands of computational nodes is driving the demand for programming models and infrastructures that allow effective use of such large-scale environments. Tree-based Overlay Networks (TBŌNs) have proven to provide such a model for distributed tools like performance profilers, parallel debuggers, system monitors and system administration tools. We demonstrate that the extensibility and flexibility of the TBŌN distributed computing model, along with its performance characteristics, make it surprisingly general, particularly for applications outside the tool domain. We describe many interesting applications and commonly-used algorithms for which TBŌNs are well-suited and provide a new (non-tool) case study, a distributed implementation of the mean-shift algorithm commonly used in computer vision to delineate arbitrarily shaped clusters in complex, multi-modal feature spaces. 1

CPAP, weight loss, or both for obstructive sleep apnea

BACKGROUNd: Obesity and obstructive sleep apnea tend to coexist and are associated with inflammation, insulin resistance, dyslipidemia, and high blood pressure, but their causal relation to these abnormalities is unclear. METHODS: We randomly assigned 181 patients with obesity, moderate-to-severe obstructive sleep apnea, and serum levels of C-reactive protein (CRP) greater than 1.0 mg per liter to receive treatment with continuous positive airway pressure (CPAP), a weight-loss intervention, or CPAP plus a weight-loss intervention for 24 weeks. We assessed the incremental effect of the combined interventions over each one alone on the CRP level (the primary end point), insulin sensitivity, lipid levels, and blood pressure. RESULTS: Among the 146 participants for whom there were follow-up data, those assigned to weight loss only and those assigned to the combined interventions had reductions in CRP levels, insulin resistance, and serum triglyceride levels. None of these changes were observed in the group receiving CPAP alone. Blood pressure was reduced in all three groups. No significant incremental effect on CRP levels was found for the combined interventions as compared with either weight loss or CPAP alone. Reductions in insulin resistance and serum triglyceride levels were greater in the combined-intervention group than in the group receiving CPAP only, but there were no significant differences in these values between the combined-intervention group and the weight-loss group. In per-protocol analyses, which included 90 participants who met prespecified criteria for adherence, the combined interventions resulted in a larger reduction in systolic blood pressure and mean arterial pressure than did either CPAP or weight loss alone. CONCLUSIONS: In adults with obesity and obstructive sleep apnea, CPAP combined with a weight-loss intervention did not reduce CRP levels more than either intervention alone. In secondary analyses, weight loss provided an incremental reduction in insulin resistance and serum triglyceride levels when combined with CPAP. In addition, adherence to a regimen of weight loss and CPAP may result in incremental reductions in blood pressure as compared with either intervention alone. (Funded by the National Heart, Lung, and Blood Institute; ClinicalTrials.gov number, NCT0371293 .)

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Habitual physical activity patterns in a nationally representative sample of U.S. adults

Physical inactivity is a leading determinant of noncommunicable diseases. Yet, many adults remain physically inactive. Physical activity guidelines do not account for the multidimensionality of physical activity, such as the type or variety of physical activity behaviors. This study identified patterns of physical activity across multiple dimensions (e.g., frequency, duration, and variety) using a nationally representative sample of adults. Sociodemographic characteristics, health behaviors, and clinical characteristics associated with each physical activity pattern were defined. Multivariate finite mixture modeling was used to identify patterns of physical activity among 2003–2004 and 2005–2006 adult National Health and Nutrition Examination Survey participants. Chi-square tests were used to identify sociodemographic differences within each physical activity cluster and test associations between the physical activity clusters with health behaviors and clinical characteristics. Five clusters of physical activity patterns were identified: (a) low frequency, short duration (n = 730, 13%); (b) low frequency, long duration (n = 392, 7%); (c) daily frequency, short duration (n = 3,011, 55%); (d) daily frequency, long duration (n = 373, 7%); and (e) high frequency, average duration (n = 964, 18%). Walking was the most common form of activity; highly active adults engaged in more varied types of activity. High-activity clusters were comprised of a greater proportion of younger, White, nonsmoking adult men reporting moderate alcohol use without mobility problems or chronic health conditions. Active females engaged in frequent short bouts of activity. Data-driven approaches are useful for identifying clusters of physical activity that encompass multiple dimensions of activity. These activity clusters vary across sociodemographic and clinical subgroups