A H_(2)O_(2)generation-detection-regulation integrated platform for boosting the efficiency of peroxygenase-catalysed C-H oxidative hydroxylation

The peroxygenases are ideal biocatalysts for the selective oxyfunctionalisation of stable C-H bonds.However,the catalytic efficiency of this approach is limited due to enzyme lability toward oxidant H_(2)O_(2).Although the reported in-situ H_(2)O_(2) generation system enables the stable biocatalytic process without deactivating the enzyme,the greatest catalytic potential of peroxygenases still cannot be fulfilled effectively.To address the above issue,a H_(2)O_(2) generation-detection-regulation platform that integrated an effective organocatalyst-driven H_(2)O_(2) generation system,a precise electrochemical H_(2)O_(2) real-time detection device,and a convenient H_(2)O_(2) regulation strategy was first developed.The suitable range of H_(2)O_(2) generation rate for maximizing the catalytic efficiency of peroxygenases while minimizing inactivation of the enzyme was firstly obtained by simply adjusting the amount of organocatalyst.According to the determined suitable range,the C-H oxyfunctionalisation efficiency of peroxygenases for each substrate was significantly boosted,achieving~3-fold of the reported highest turnover frequency.

ComR:a combined OWL reasoner for ontology classification

Ontology classification,the problem of computing the subsumption hierarchies for classes (atomic concepts),is a core reasoning service provided by Web Ontology Language (OWL)reasoners.Although general-purpose OWL 2 reasoners employ sophisticated optimizations for classification,they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient;however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL fragment.In this paper,we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive SROIQ.To optimize the workload,we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure completeness.During the ontology classification,the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non- EL subontology is handled by a less efficient OWL 2 reasoner.The proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology classification.For the wellknown ontology NCI,the classification time is reduced by 96.9%(resp.83.7%)compared against the standard reasoner Pellet (resp.the modular reasoner MORe).