Modeling geometric rules in object based models:an XML / GML approach

Most object-based approaches to Geographical Information Systems (GIS) have concentrated on the representation of geometric properties of objects in terms of fixed geometry. In our road traffic marking application domain we have a requirement to represent the static locations of the road markings but also enforce the associated regulations, which are typically geometric in nature. For example a give way line of a pedestrian crossing in the UK must be within 1100-3000 mm of the edge of the crossing pattern. In previous studies of the application of spatial rules (often called 'business logic') in GIS emphasis has been placed on the representation of topological constraints and data integrity checks. There is very little GIS literature that describes models for geometric rules, although there are some examples in the Computer Aided Design (CAD) literature. This paper introduces some of the ideas from so called variational CAD models to the GIS application domain, and extends these using a Geography Markup Language (GML) based representation. In our application we have an additional requirement; the geometric rules are often changed and vary from country to country so should be represented in a flexible manner. In this paper we describe an elegant solution to the representation of geometric rules, such as requiring lines to be offset from other objects. The method uses a feature-property model embraced in GML 3.1 and extends the possible relationships in feature collections to permit the application of parameterized geometric constraints to sub features. We show the parametric rule model we have developed and discuss the advantage of using simple parametric expressions in the rule base. We discuss the possibilities and limitations of our approach and relate our data model to GML 3.1. © 2006 Springer-Verlag Berlin Heidelberg.

The role of glucocorticoids in the induction of zinc-α2-glycoprotein expression in adipose tissue in cancer cachexia

Loss of adipose tissue in cancer cachexia in mice bearing the MAC16 tumour arises from an increased lipid mobilisation through increased expression of zinc-α2-glycoprotein (ZAG) in white (WAT) and brown (BAT) adipose tissue. Glucocorticoids have been suggested to increase ZAG expression, and this study examines their role in cachexia and the mechanisms involved. In mice bearing the MAC16 tumour, serum cortisol concentrations increased in parallel with weight loss, and the glucocorticoid receptor antagonist RU38486 (25 mg kg-1) attenuated both the loss of body weight and ZAG expression in WAT. Dexamethasone (66 μg kg-1) administration to normal mice produced a six-fold increase in ZAG expression in both WAT and BAT, which was also attenuated by RU38486. In vitro studies using 3T3-L1 adipocytes showed dexamethasone (1.68 μM) to stimulate lipolysis and increase ZAG expression, and both were attenuated by RU38486 (10 μM), anti-ZAG antibody (1 μ gml-1), and the β3-adrenoreceptor (β3-AR) antagonist SR59230A (10 μM). Zinc-α2-glycoprotein also increased its own expression and this was attenuated by SR59230A, suggesting that it was mediated through the β3-AR. This suggests that glucocorticoids stimulate lipolysis through an increase in ZAG expression, and that they are responsible for the increase in ZAG expression seen in adipose tissue of cachectic mice. © 2005 Cancer Research UK.

Exchanging uncertainty: interoperable geostatistics?

Traditionally, geostatistical algorithms are contained within specialist GIS and spatial statistics software. Such packages are often expensive, with relatively complex user interfaces and steep learning curves, and cannot be easily integrated into more complex process chains. In contrast, Service Oriented Architectures (SOAs) promote interoperability and loose coupling within distributed systems, typically using XML (eXtensible Markup Language) and Web services. Web services provide a mechanism for a user to discover and consume a particular process, often as part of a larger process chain, with minimal knowledge of how it works. Wrapping current geostatistical algorithms with a Web service layer would thus increase their accessibility, but raises several complex issues. This paper discusses a solution to providing interoperable, automatic geostatistical processing through the use of Web services, developed in the INTAMAP project (INTeroperability and Automated MAPping). The project builds upon Open Geospatial Consortium standards for describing observations, typically used within sensor webs, and employs Geography Markup Language (GML) to describe the spatial aspect of the problem domain. Thus the interpolation service is extremely flexible, being able to support a range of observation types, and can cope with issues such as change of support and differing error characteristics of sensors (by utilising descriptions of the observation process provided by SensorML). XML is accepted as the de facto standard for describing Web services, due to its expressive capabilities which allow automatic discovery and consumption by ‘naive’ users. Any XML schema employed must therefore be capable of describing every aspect of a service and its processes. However, no schema currently exists that can define the complex uncertainties and modelling choices that are often present within geostatistical analysis. We show a solution to this problem, developing a family of XML schemata to enable the description of a full range of uncertainty types. These types will range from simple statistics, such as the kriging mean and variances, through to a range of probability distributions and non-parametric models, such as realisations from a conditional simulation. By employing these schemata within a Web Processing Service (WPS) we show a prototype moving towards a truly interoperable geostatistical software architecture.

Probabilistic uncertainty in an interoperable framework

This thesis provides an interoperable language for quantifying uncertainty using probability theory. A general introduction to interoperability and uncertainty is given, with particular emphasis on the geospatial domain. Existing interoperable standards used within the geospatial sciences are reviewed, including Geography Markup Language (GML), Observations and Measurements (O&M) and the Web Processing Service (WPS) specifications. The importance of uncertainty in geospatial data is identified and probability theory is examined as a mechanism for quantifying these uncertainties. The Uncertainty Markup Language (UncertML) is presented as a solution to the lack of an interoperable standard for quantifying uncertainty. UncertML is capable of describing uncertainty using statistics, probability distributions or a series of realisations. The capabilities of UncertML are demonstrated through a series of XML examples. This thesis then provides a series of example use cases where UncertML is integrated with existing standards in a variety of applications. The Sensor Observation Service - a service for querying and retrieving sensor-observed data - is extended to provide a standardised method for quantifying the inherent uncertainties in sensor observations. The INTAMAP project demonstrates how UncertML can be used to aid uncertainty propagation using a WPS by allowing UncertML as input and output data. The flexibility of UncertML is demonstrated with an extension to the GML geometry schemas to allow positional uncertainty to be quantified. Further applications and developments of UncertML are discussed.

Automated synthesis and evaluation of potential new anti-microbial agents

A series of N1-benzylideneheteroarylcarboxamidrazones was prepared in an automated fashion, and tested against Mycobacterium fortuitum in a rapid screen for antimycobacterial activity. Many of the compounds from this series were also tested against Mycobacterium tuberculosis, and the usefulness as M.fortuitum as a rapid, initial screen for anti-tubercular activity evaluated. Various deletions were made to the N1-benzylideneheteroarylcarboxamidrazone structure in order to establish the minimum structural requirements for activity. The N1-benzylideneheteroarylcarbox-amidrazones were then subjected to molecular modelling studies and their activities against M.fortuitum and M.tuberculosis were analysed using quantitative structure-analysis relationship (QSAR) techniques in the computational package TSAR (Oxford Molecular Ltd.). A set of equations predictive of antimycobacterial activity was hereby obtained. The series of N1-benzylidenehetero-arylcarboxamidrazones was also tested against a multidrug-resistant strain of Staphylococcus aureus (MRSA), followed by a panel of Gram-positive and Gram-negative bacteria, if activity was observed for MRSA. A set of antimycobacterial N1-benzylideneheteroarylcarboxamidrazones was hereby discovered, the best of which had MICs against m. fortuitum in the range 4-8μgml-1 and displayed 94% inhibition of M.tuberculosis at a concentration of 6.25μgml-1. The antimycobacterial activity of these compounds appeared to be specific, since the same compounds were shown to be inactive against other classes of organisms. Compounds which were found to be sufficiently active in any screen were also tested for their toxicity against human mononuclear leucocytes. Polyethylene glycol (PEG) was used as a soluble polymeric support for the synthesis of some fatty acid derivatives, containing an isoxazoline group, which may inhibit mycolic acid synthesis in mycobacteria. Both the PEG-bound products and the cleaved, isolated products themselves were tested against M.fortuitum and some low levels of antimycobacterial activity were observed, which may serve as lead compounds for further studies.

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.