Evolutionary specifications

I suggest that we take the acquisition of the total set of species-typical traits (however that is to be measured empirically) to be a maximum specification of the process of development. Anything more exhaustive than species-typical might entail that new species could not evolve. As a minimum bound, I suggest the following evolutionary specification - development is the acquisition of the capacity to reproduce. It will become clear in a moment why I bracket the process of development in this way. First, though, let us consider the sorts of questions about development at multiple levels of evolutionary transition that must be addressed if we are to understand units of evolutionary transition in terms of the propagation of developmental capacities. 

Next, there is a need for evolutionary specifications, where one deliberately omits some functionalities in a first specification, and then relies on replay facilities [Wile 83] when adding the missing functionalities in subsequent versions of the specification. This form of exploratory programming promises an answer to the endemic difficulty of providing partially correct and complete formal specifications. 

The planned use of our synthesis mechanism is for situations where only fragmentary information is available about the intended relation, or where the specifier is unwilling (if not unable) to come up with a more complete description of the intended relation. By the phrase incomplete specifications , we thus mean specifications where some information about the intended relation is deliberately withheld and where the synthesis is expected to extrapolate. We thus exclude incomplete specifications in the sense of evolutionary specifications, where one also deliberately withholds some information, but without expecting any extrapolation. The idea there is to gradually refine an algorithm by adding functionalities to its specification. 

Evolutionary. The evolutionary approach to network development starts with a good network developed by some alternative means and proceeds to change it into an optimal network from the standpoint of cost. The success of any evolutionary method depends gteatiy on the starting network. Evolutionary methods ate specific to starting network characteristics and ate usually matched with the method used to develop the initial network, eg, TI followed by ED. There ate two extremes for possible starting networks. 

Development of useful shortcut methods by intuitive and evolutionary mental analysis alone must surely be extremely fulfilling. We will see later how other devel-opmentally oriented individuals are able to build upon the original shortcut method ideas to create variations on the theme for specific applications. 

Substrate specificity is determined by high affinity for the cognate neurotransmitter substrate. However, low affinity uptake does also have a part in the clearance of transmitters from the interstitial space (e.g., in uptake mediated by the extraneuronal monoamine transporter, EMT) and in the intestinal absoiption of glycine and glutamate. It is obvious that there is an evolutionary relation of neurotransmitter transporters and amino acid and cation transporters in epithelia. 

Chemokines are small chemotactic cytokines that act as important messenger molecules between cells of the immune system. Chemokines produce their effects by activating a family of G-protein-coupled receptors. Chemokine receptors are all seven-transmembrane glycoproteins that are structurally related. They may be characterized into those that bind to specific ligands, or those that bind several chemokine ligands. There are also virally encoded (viral) chemokine receptors that represent shared receptors that have been transduced into the viral genome during evolutionary history (Premack and SchaU 1996). 

Basically, we make a distinction between methods which are carried out in the space defined by the original variables (Section 34.4) or in the space defined by the principal components. A second distinction we can make is between full-rank methods (Section 34.2), which consider the whole matrix X, and evolutionary methods (Section 34.3) which analyse successive sub-matrices of X, taking into account the fact that the rows of X follow a certain order. A third distinction we make is between general methods of factor analysis which are applicable to any data matrix X, and specific methods which make use of specific properties of the pure factors. 

With increases in group size and social complexity, and the consequent expansion of diversity in communication, there is presumably selection pressure for unambiguous signals. Over time, evolutionary change may reduce unwanted complexity and act to refine specificity. Whether this results in reliance on the properties of a single component rather than those of the pattern provided by a complex mixture is conjectural. A reasonable expectation is that emitted semiochemical(s) can be reliably produced, and that their contents are acted upon by selection. The improvements in control which can thus be attained are seen in the operation of stereospecificity (Miiller-Schwarze et al, 1976). 

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