Describing and communicating uncertainty within the semantic web

The Semantic Web relies on carefully structured, well defined, data to allow machines to communicate and understand one another. In many domains (e.g. geospatial) the data being described contains some uncertainty, often due to incomplete knowledge; meaningful processing of this data requires these uncertainties to be carefully analysed and integrated into the process chain. Currently, within the SemanticWeb there is no standard mechanism for interoperable description and exchange of uncertain information, which renders the automated processing of such information implausible, particularly where error must be considered and captured as it propagates through a processing sequence. In particular we adopt a Bayesian perspective and focus on the case where the inputs / outputs are naturally treated as random variables. This paper discusses a solution to the problem in the form of the Uncertainty Markup Language (UncertML). UncertML is a conceptual model, realised as an XML schema, that allows uncertainty to be quantified in a variety of ways i.e. realisations, statistics and probability distributions. UncertML is based upon a soft-typed XML schema design that provides a generic framework from which any statistic or distribution may be created. Making extensive use of Geography Markup Language (GML) dictionaries, UncertML provides a collection of definitions for common uncertainty types. Containing both written descriptions and mathematical functions, encoded as MathML, the definitions within these dictionaries provide a robust mechanism for defining any statistic or distribution and can be easily extended. Universal Resource Identifiers (URIs) are used to introduce semantics to the soft-typed elements by linking to these dictionary definitions. The INTAMAP (INTeroperability and Automated MAPping) project provides a use case for UncertML. This paper demonstrates how observation errors can be quantified using UncertML and wrapped within an Observations & Measurements (O&M) Observation. The interpolation service uses the information within these observations to influence the prediction outcome. The output uncertainties may be encoded in a variety of UncertML types, e.g. a series of marginal Gaussian distributions, a set of statistics, such as the first three marginal moments, or a set of realisations from a Monte Carlo treatment. Quantifying and propagating uncertainty in this way allows such interpolation results to be consumed by other services. This could form part of a risk management chain or a decision support system, and ultimately paves the way for complex data processing chains in the Semantic Web.

Enhancement of the Cytotoxic Effect of Anticancer Agent by Cytochrome c Functionalised Hybrid Nanoparticles in Hepatocellular Cancer Cells

Treatment of hepatocellular cancer with chemotherapeutic agents has limited successin clinical practice and their efficient IC50 concentration would require extremely highdoses of drug administration which could not be tolerated due to systemic side effects.In order to potentiate the efficacy of anticancer agents we explored the potentialof co-treatment with pro-apoptotic Cytochrome c which activates the apoptoticpathway downstream of p53 that is frequently mutated in cancer. To this end weused hybrid iron oxide-gold nanoparticles as a drug delivery system to facilitate theinternalisation of Cytochrome c into cultured HepG2 hepatocellular carcinoma cells.Our results showed that Cytochrome c can be easily conjugated to the gold shell ofthe nanoparticles which are readily taken up by the cells. We used Cytochrome cin concentration (0.2μgmL-1) below the threshold required to induce apoptosis onits own. When the conjugate was administered to cells treated by doxorubicin, itsignificantly reduced its IC50 concentration from 9μgmL-1 to 3.5μgmL-1 as detectedby cell viability assay, and the efficiency of doxorubicin on decreasing viability ofHepG2 cells was significantly enhanced in the lower concentration range between0.01μgmL-1 to 5μgmL-1. The results demonstrate the potential of the application oftherapeutic proteins in activating the apoptotic pathway to complement conventionalchemotherapy to increase its efficacy. The application of hybrid iron oxide-goldnanoparticles can also augment the specificity of drug targeting and could serve as amodel drug delivery system for pro-apoptotic protein targeting and delivery.