Enantioselective transformation of alpha-hexachlorocyclohexane by the dehydrochlorinases LinA1 and LinA2 from the soil bacterium Sphingomonas paucimobilis B90A.

Sphingomonas paucimobilis B90A contains two variants, LinA1 and LinA2, of a dehydrochlorinase that catalyzes the first and second steps in the metabolism of hexachlorocyclohexanes (R. Kumari, S. Subudhi, M. Suar, G. Dhingra, V. Raina, C. Dogra, S. Lal, J. R. van der Meer, C. Holliger, and R. Lal, Appl. Environ. Microbiol. 68:6021-6028, 2002). On the amino acid level, LinA1 and LinA2 were 88% identical to each other, and LinA2 was 100% identical to LinA of S. paucimobilis UT26. Incubation of chiral alpha-hexachlorocyclohexane (alpha-HCH) with Escherichia coli BL21 expressing functional LinA1 and LinA2 S-glutathione transferase fusion proteins showed that LinA1 preferentially converted the (+) enantiomer, whereas LinA2 preferred the (-) enantiomer. Concurrent formation and subsequent dissipation of beta-pentachlorocyclohexene enantiomers was also observed in these experiments, indicating that there was enantioselective formation and/or dissipation of these enantiomers. LinA1 preferentially formed (3S,4S,5R,6R)-1,3,4,5,6-pentachlorocyclohexene, and LinA2 preferentially formed (3R,4R,5S,6S)-1,3,4,5,6-pentachlorocyclohexene. Because enantioselectivity was not observed in incubations with whole cells of S. paucimobilis B90A, we concluded that LinA1 and LinA2 are equally active in this organism. The enantioselective transformation of chiral alpha-HCH by LinA1 and LinA2 provides the first evidence of the molecular basis for the changed enantiomer composition of alpha-HCH in many natural environments. Enantioselective degradation may be one of the key processes determining enantiomer composition, especially when strains that contain only one of the linA genes, such as S. paucimobilis UT26, prevail.

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.