Correlated evolution

Cooper W.E. Jr. (1997). Correlated evolution of prey chemical discrimination with foraging, lingual morphology and vomeronasal chemoreceptor abundance in lizards. Behav Ecol Sociobiol 41, 257-265. 

Cooper, W. E. (2003) Correlated evolution of herbivory and food chemical discrimination in igua-nian and ambush foraging lizards. Behav. Ecol. 14, 409-416. 

Tlie availability of over 20 fully sequenced genomes has forced the development of new methods to find protem functions and interactions. Proteins were grouped by correlated evolution, correlated mRNA expression patterns and patterns of domain fusion to deter-mme functional relationships among the 6217 proteins of the yeast S. cerevisiae. Using these methods, over 93,000 pair-wise finks between functionally related yeast proteins were discovered. Links between characterized and uncharacterized proteins allow a general function to be assigned to more than half of the 2557 previously uncharacterized yeast proteins. 

Figure 2.19 Left part of panel, (a-h) Qualitative representation of the IR spectroscopic features of weak, medium, and strong A-H- -B or A - -H-B H-bonded complexes. The half-width of the bending modes is (somewhat arbitrarily) assumed to slightly increase upon increasing the hydrogen bond strength. The shaded areas correspond to regions obscured by the skeletal modes of the zeolite framework. Right part of panel, (a -h ) Schematic representation of the correlated evolution of the proton potential as function of the A-H distance in A-H- - -B or A - - -H-B The separation barrier in e -f can be very low, and a potential curve characterized by an asymmetric single flat minimum may be used alternatively. Reproduced with permission from Ref. (11). Copyright 1997 American Chemicai Society.

Tests of correlated evolution of dwarf males with the ranaining morphological features and the epiphytic habitat were conducted using Discrete (Pagel, 1999). This was accomplished by first fitting a model to the data in which the two characters were allowed to evolve independently. The likelihood of this model was then compared to the likelihood of a more complicated model in which the characters evolve in a correlated fashion, and a likelihood ratio was calculated between the dependent and the independent model to test if the more complicated model was a better fit to the data than was the model assuming independent evolntion. 

The evolution of dwarf males was significantly correlated with most of the morphological characters except seta length (Character 11), gemmae (13), endostome cilia (16), exostome development (17), and inner perichaetial leaf plication (18). In addition, there is no statistical support for correlated evolution of dwarf males with the epiphytic habit. The statistics of the correlation tests are presented in Table 18.4. 

Pagel, M. (1994) Detecting correlated evolution on phytogenies A general method for the comparative analysis of discrete characters. Proceedings of the Royal Society of London. Series B, Biological Sciences, 255 37-15. 

Figure Al.6.14. Schematic diagram showing the promotion of the initial wavepacket to the excited electronic state, followed by free evolution. Cross-correlation fiinctions with the excited vibrational states of the ground-state surface (shown in the inset) detennine the resonance Raman amplitude to those final states (adapted from [14].

The common theme in the evolution of methods for property and parameter prediction is the development of equations, either theoretical or empirical, containing quantities that can be calculated from theoretical considerations or experimental data. Mathematical expressions for correlating thermodynamic data may take several forms. 

Another recent database, still in evolution, is the Linus Pauling File (covering both metals and other inorganics) and, like the Cambridge Crystallographic Database, it has a "smart software part which allows derivative information, such as the statistical distribution of structures between symmetry types, to be obtained. Such uses are described in an article about the file (Villars et al. 1998). The Linus Pauling File incorporates other data besides crystal structures, such as melting temperature, and this feature allows numerous correlations to be displayed. 

To describe an arbitrary nonequilibrium evolution of the adsorbate we need the whole hierarchy, or at least a suitably truncated subset. We can close the hierarchy at the level of 2-site correlators by a factorization of higher correlators with 1-site overlap [58,59]... 

The above is an example of how direct algorithms may be formulated for methods involving electron correlation. It illustrates that it is not as straightforward to apply direct methods at the correlated level as at the SCF level. However, the steady increase in CPU performance, and especially the evolution of multiprocessor machines, favours direct (and semi-direct where some intermediate results are stored on disk) algorithms. Recently direct methods have also been implemented at the coupled cluster level. 

The most important result of this work is that despite two different SRO patterns, we have found concentration independent EPI. The evolution of the diffuse intensity with composition is thus mainly due to the sensitivity of the equilibrium state (i.e. the correlation function) to the concentration. 

In the PPF, the first factor Pi describes the statistical average of non-correlated spin fiip events over entire lattice points, and the second factor P2 is the conventional thermal activation factor. Hence, the product of P and P2 corresponds to the Boltzmann factor in the free energy and gives the probability that on<= of the paths specified by a set of path variables occurs. The third factor P3 characterizes the PPM. One may see the similarity with the configurational entropy term of the CVM (see eq.(5)), which gives the multiplicity, i.e. the number of equivalent states. In a similar sense, P can be viewed as the number of equivalent paths, i.e. the degrees of freedom of the microscopic evolution from one state to another. As was pointed out in the Introduction section, mathematical representation of P3 depends on the mechanism of elementary kinetics. It is noted that eqs.(8)-(10) are valid only for a spin kinetics. 

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