Chlamydia-related bacteria in respiratory samples in Finland.

Chlamydia-related bacteria, new members of the order Chlamydiales, are suggested to be associated with respiratory disease. We used real-time PCR to investigate the prevalence of Parachlamydia acanthamoebae, Protochlamydia spp., Rhabdochlamydia spp., Simkania negevensis and Waddlia chondrophila in samples taken from patients with suspected respiratory tract infections. Of the 531 samples analyzed, the subset of 136 samples contained 16 (11.8%) samples positive for Rhabdochlamydia spp. DNA. P. acanthamoebae, Protochlamydia spp., S. negevensis and W. chondrophila DNA were not detected among the respiratory samples investigated. These results suggest an association of Rhabdochlamydia spp. with respiratory disease.

PFAPA syndrome is not a sporadic disease.

OBJECTIVES: To determine whether PFAPA (periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis) patients have a positive family history (FH) for recurrent fever syndromes. METHOD: For all patients with PFAPA seen in two paediatric rheumatology centres (Romandy, Switzerland and Bordeaux, France), parents were interviewed to record the FH for periodic fever. As controls, we interviewed a group of children without history of recurrent fever. RESULTS: We recruited 84 patients with PFAPA and 47 healthy children. The FH for recurrent fever (without an infectious cause and recurring for at least half a year) was positive in 38/84 (45%), and was positive for PFAPA (diagnosis confirmed by a physician) in 10/84 (12%) of the PFAPA patients. For 29 of the 38 patients with positive FH, the affected person was a sibling or a parent. None of the healthy children had a positive FH for recurrent fever or PFAPA. A positive FH for rheumatological diseases was seen in both groups of children. CONCLUSION: These data show that a significant percentage of PFAPA patients present a positive FH of recurrent fever and PFAPA. This familial susceptibility suggests a potential genetic origin for this syndrome.

Memory-based scheduling of scientific computing clusters

This study looks at how increased memory utilisation affects throughput and energy consumption in scientific computing, especially in high-energy physics. Our aim is to minimise energy consumed by a set of jobs without increasing the processing time. The earlier tests indicated that, especially in data analysis, throughput can increase over 100% and energy consumption decrease 50% by processing multiple jobs in parallel per CPU core. Since jobs are heterogeneous, it is not possible to find an optimum value for the number of parallel jobs. A better solution is based on memory utilisation, but finding an optimum memory threshold is not straightforward. Therefore, a fuzzy logic-based algorithm was developed that can dynamically adapt the memory threshold based on the overall load. In this way, it is possible to keep memory consumption stable with different workloads while achieving significantly higher throughput and energy-efficiency than using a traditional fixed number of jobs or fixed memory threshold approaches.