Evolução molecular Darwiniana nas proteínas relacionadas à patogênese (PRs) em plantas

Um total de 201 seqüências de DNA, de 50 espécies pertencentes a 32 gêneros e 12 famílias, foi investigado através do método da máxima verossimilhança para identificar, nas proteínas respectivas, possíveis códons nos quais estivesse ocorrendo seleção positiva. Foram considerados 15 tipos de proteínas relacionadas à patogênese (PR1-PR15), quanto a 14 modelos diferentes de seleção. Tanto quanto se possa avaliar, não há qualquer estudo disponível na literatura que tenha examinado de maneira homogênea tal número de seqüências de forma tão abrangente.

Graphical models and point set matching

Point pattern matching in Euclidean Spaces is one of the fundamental problems in Pattern Recognition, having applications ranging from Computer Vision to Computational Chemistry. Whenever two complex patterns are encoded by two sets of points identifying their key features, their comparison can be seen as a point pattern matching problem. This work proposes a single approach to both exact and inexact point set matching in Euclidean Spaces of arbitrary dimension. In the case of exact matching, it is assured to find an optimal solution. For inexact matching (when noise is involved), experimental results confirm the validity of the approach. We start by regarding point pattern matching as a weighted graph matching problem. We then formulate the weighted graph matching problem as one of Bayesian inference in a probabilistic graphical model. By exploiting the existence of fundamental constraints in patterns embedded in Euclidean Spaces, we prove that for exact point set matching a simple graphical model is equivalent to the full model. It is possible to show that exact probabilistic inference in this simple model has polynomial time complexity with respect to the number of elements in the patterns to be matched. This gives rise to a technique that for exact matching provably finds a global optimum in polynomial time for any dimensionality of the underlying Euclidean Space. Computational experiments comparing this technique with well-known probabilistic relaxation labeling show significant performance improvement for inexact matching. The proposed approach is significantly more robust under augmentation of the sizes of the involved patterns. In the absence of noise, the results are always perfect.