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This paper describes a semi-automated process, framework and tools for harvesting, assessing, improving and maintaining high-quality linked-data. The framework, known as DaCura 1 , provides dataset curators, who may not be knowledge engineers, with tools to collect and curate evolving linked data datasets that maintain quality over time. The framework encompasses a novel process, workflow and architecture. A working implementation has been produced and applied firstly to the publication of an existing social-sciences dataset, then to the harvesting and curation of a related dataset from an unstructured data-source. The framework's performance is evaluated using data quality measures that have been developed to measure existing published datasets. An analysis of the framework against these dimensions demonstrates that it addresses a broad range of real-world data quality concerns. Experimental results quantify the impact of the DaCura process and tools on data quality through an assessment framework and methodology which combines automated and human data quality controls.

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Cancer is the second leading cause of death globally, responsible for an estimated 9.6 million deaths in 2018, and this burden continues to increase. Therefore, there is a clear and urgent need for novel drugs with increased efficacy for the treatment of different cancers. Previous research has demonstrated that brevilin A (BA) exerts anticancer activity in various cancers, including human multiple myeloma, breast cancer, lung cancer, and colon carcinoma, suggesting the anticancer potential present in the chemical scaffold of BA. Here, we designed and synthesized a small library of 12 novel BA derivatives and evaluated the biological anticancer effects of the compounds in various cancer cell lines. The results of this structure–activity relationship study demonstrated that BA derivatives BA-9 and BA-10 possessed significantly improved anticancer activity toward lung, colon, and breast cancer cell lines. BA-9 and BA-10 could more effectively reduce cancer cell viability and induce DNA damage, cell-cycle arrest, and apoptosis when compared with BA. Our findings represent a significant step forward in the development of novel anticancer entities.

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To increase the interoperability and accessibility of data in sensor-rich systems, there has been a recent proliferation of the use of Semantic Web technologies in sensor-rich systems. Quite a range of such applications have emerged, such as hazard monitoring and rescue, context-aware computing, environmental monitoring, field studies, internet of things, and so on. These systems often assume a centralized paradigm for data processing, which does not always hold in reality especially when the systems are deployed in a hostile environment. At runtime, the infrastructure of systems deployed in such an environment is also prone to interference or damage, causing part of the infrastructure to have limited network connection or even to be detached from the rest. A solution to such a problem would be to push the intelligence, such as semantic reasoning, down to the device layer. A key enabler for such a solution is to run semantic reasoning on resourceconstrained devices. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only a "well-suited" amount of reasoning for a given ontology) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms are introduced and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, potentially facilitating greater semantic reasoning on sensor devices.

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