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.

A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.