Disease burden due to Streptococcus dysgalactiae subsp equisimilis (group G and C streptococcus) is higher than that due to Streptococcus pyogenes among Mumbai school children

Streptococcus pyogenes [group A streptococcus (GAS)], a human pathogen, and Streptococcus dysgalactiae subsp. equisimilis [human group G and C streptococcus (GGS/GCS)] are evolutionarily related, share the same tissue niche in humans, exchange genetic material, share up to half of their virulence-associated genes and cause a similar spectrum of diseases. Yet, GGS/GCS is often considered as a commensal bacterium and its role in streptococcal disease burden is under-recognized. While reports of the recovery of GGS/GCS from normally sterile sites are increasing, studies describing GGS/GCS throat colonization rates relative to GAS in the same population are very few. This study was carried out in India where the burden of streptococcal diseases, including rheumatic fever and rheumatic heart disease, is high. As part of a surveillance study, throat swabs were taken from 1504 children attending 7 municipal schools in Mumbai, India, during 2006-2008. GAS and GGS/GCS were identified on the basis of beta-haemolytic activity, carbohydrate group and PYR test, and were subsequently typed. The GGS/GCS carriage rate (1166/1504, 11%) was eightfold higher than the GAS carriage (22/1504, 1.5%) rate in this population. The 166 GGS/GCS isolates collected represented 21 different emm types (molecular types), and the 22 GAS isolates represented 15 different emm types. Although the rate of pharyngitis associated with GGS/GCS is marginally lower than with GAS, high rates of throat colonization by GGS/GCS underscore its importance in the pathogenesis of pharyngitis.

Compiler/Runtime Framework for Dynamic Dataflow Parallelization of Tiled Programs

Task-parallel languages are increasingly popular. Many of them provide expressive mechanisms for intertask synchronization. For example, OpenMP 4.0 will integrate data-driven execution semantics derived from the StarSs research language. Compared to the more restrictive data-parallel and fork-join concurrency models, the advanced features being introduced into task-parallelmodels in turn enable improved scalability through load balancing, memory latency hiding, mitigation of the pressure on memory bandwidth, and, as a side effect, reduced power consumption. In this article, we develop a systematic approach to compile loop nests into concurrent, dynamically constructed graphs of dependent tasks. We propose a simple and effective heuristic that selects the most profitable parallelization idiom for every dependence type and communication pattern. This heuristic enables the extraction of interband parallelism (cross-barrier parallelism) in a number of numerical computations that range from linear algebra to structured grids and image processing. The proposed static analysis and code generation alleviates the burden of a full-blown dependence resolver to track the readiness of tasks at runtime. We evaluate our approach and algorithms in the PPCG compiler, targeting OpenStream, a representative dataflow task-parallel language with explicit intertask dependences and a lightweight runtime. Experimental results demonstrate the effectiveness of the approach.