Receptor for Fc on the surfaces of schistosomes

Schistosoma mansoni masks its surface with adsorbed host proteins including erythrocyte antigens, immunoglobulins, major histocompatibility complex class I, and beta (2)-microglobulin (beta (2)m), presumably as a means of avoiding host immune responses, How this is accomplished has not been explained. To identify surface receptors for host proteins, we biotinylated the tegument of live S, mansoni adults and mechanically transformed schistosomula and then removed the parasite surface with detergent, Incubation of biotinylated schistosome surface extracts witt l human immunoglobulin G (IgG) Fc-Sepharose resulted in purification of a 97-kDa protein that was subsequently identified as paramyosin (Pmy), using antiserum specific for recombinant Pmy, Fc also bound recombinant S. mansoni Pmy and native S. japonicum Pmy, Antiserum to Pmy decreased the binding of Pmy to Fc-Sepharose, and no proteins bound after removal of Pmy from extracts. Fluoresceinated human Fe bound to the surface, vestigial penetration glands, and nascent oral cavity of mechanically transformed schistosomula, and rabbit anti-Pmy Fab fragments ablated the binding of Fc to the schistosome surface, Pmy coprecipitated with host IgG from parasite surface extracts, indicating that complexes formed on the parasite surface as well as in vitro. Binding of Pmy to Fe was not inhibited by soluble protein A, suggesting that Pmy does not bind to the region between the CH2 and CH3 domains used by many other Fc-binding proteins. beta (2)m did not bind to the schistosome Fc receptor (Pmy), a finding that contradicts reports from earlier workers but did bind to a heteromultimer of labeled schistosomula surface proteins, This is the first report of the molecular identity of a schistosome Fc receptor; moreover it demonstrates an additional aspect of the unusual and multifunctional properties of Pmy from schistosomes and other parasitic flatworms.

Empirical evidence for ultrametric structure in multi-layer perceptron error surfaces

Combinatorial optimization problems share an interesting property with spin glass systems in that their state spaces can exhibit ultrametric structure. We use sampling methods to analyse the error surfaces of feedforward multi-layer perceptron neural networks learning encoder problems. The third order statistics of these points of attraction are examined and found to be arranged in a highly ultrametric way. This is a unique result for a finite, continuous parameter space. The implications of this result are discussed.

Exploring complex fitness surfaces: Multiple ornamentation and polymorphism in male guppies

The sexual ornamentation used by male guppies to attract females comprises many components, each of which varies considerably among males. Although natural and sexual selection have been shown to contribute to divergence among populations in male sexual ornaments, the role of sexual selection in maintaining polymorphism within populations is less clear. We used both parametric quadratic regression and nonparametric projection pursuit regression techniques to reveal the major axes of non-linear sexual selection on male ornaments. We visualized the fitness surfaces defined by these axes using thin-plate splines to allow a direct comparison of the two methodologies. Identification of the major axes of selection and their visualization was critical in determining the form and strength of nonlinear selection. Both types of analysis revealed fitness surfaces comprising three peaks, suggesting that there is more than one way to make an attractive guppy. Disruptive selection may be an important process underlying the presence of multiple sexual ornaments and may contribute to the maintenance of the high levels of polymorphism in male sexual ornaments found in guppy populations.

Mapping the royal road and other hierarchical functions

In this paper we present a technique for visualising hierarchical and symmetric, multimodal fitness functions that have been investigated in the evolutionary computation literature. The focus of this technique is on landscapes in moderate-dimensional, binary spaces (i.e., fitness functions defined over {0, 1}(n), for n less than or equal to 16). The visualisation approach involves an unfolding of the hyperspace into a two-dimensional graph, whose layout represents the topology of the space using a recursive relationship, and whose shading defines the shape of the cost surface defined on the space. Using this technique we present case-study explorations of three fitness functions: royal road, hierarchical-if-and-only-if (H-IFF), and hierarchically decomposable functions (HDF). The visualisation approach provides an insight into the properties of these functions, particularly with respect to the size and shape of the basins of attraction around each of the local optima.