Development of a monoclonal antibody binding okadaic acid and dinophysistoxins-1, -2 in proportion to their toxicity equivalence factors

Okadaic acid (OA) and structurally related toxins dinophysistoxin-1 (DTX-1), and DTX-2, are lipophilic marine biotoxins. The current reference method for the analysis of these toxins is the mouse bioassay (MBA). This method is under increasing criticism both from an ethical point of view and because of its limited sensitivity and specificity. Alternative replacement methods must be rapid, robust, cost effective, specific and sensitive. Although published immuno-based detection techniques have good sensitivities, they are restricted in their use because of their inability to: (i) detect all of the OA toxins that contribute to contamination; and (ii) factor in the relative toxicities of each contaminant. Monoclonal antibodies (MAbs) were produced to OA and an automated biosensor screening assay developed and compared with ELISA techniques. The screening assay was designed to increase the probability of identifying a MAb capable of detecting all OA toxins. The result was the generation of a unique MAb which not only cross-reacted with both DTX-1 and DTX-2 but had a cross-reactivity profile in buffer that reflected exactly the intrinsic toxic potency of the OA group of toxins. Preliminary matrix studies reflected these results. This antibody is an excellent candidate for the development of a range of functional immunochemical-based detection assays for this group of toxins.

Development of a Java-based Unified and Flexible Natural Language Discourse System

This paper outlines the design and development of a Java-based, unified and flexible natural language dialogue system that enables users to interact using natural language, e.g. speech. A number of software development issues are considered with the aim of designing an architecture that enables different discourse components to be readily and flexibly combined in a manner that permits information to be easily shared. Use of XML schemas assists this component interaction. The paper describes how a range of Java language features were employed to support the development of the architecture, providing an illustration of how a modern programming language makes tractable the development of a complex dialogue system.

Hidden Conditional Random Fields for Visual Speech Recognition

In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.