An evaluation of the costs of making specific secondary metabolites: does the yield penalty incurred by host plant resistance to insects due to competition for resources?

The presumption that the synthesis of 'defence' compounds in plants must incur some 'trade-off' or penalty in terms of annual crop yields has been used to explain observed inverse correlations between resistance to herbivores and rates of growth or photosynthesis. An analysis of the cost of making secondary compounds suggests that this accounts for only a small part of the overall carbon budget of annual crop plants. Even the highest reported amounts of secondary metabolites found in different crop species (flavonoids, allylisothiocyanates, hydroxamic acids, 2-tridecanone) represent a carbon demand that can be satisfied by less than an hour's photosynthesis. Similar considerations apply to secondary compounds containing nitrogen or sulphur, which are unlikely to represent a major investment compared to the cost of making proteins, the major demand for these elements. Decreases in growth and photosynthesis in response to stress are more likely the result of programmed down-regulation. Observed correlations between yield and low contents of unpalatable or toxic compounds may be the result of parallel selection during the refinement of crop species by humans.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.