Genetic control of organ shape and tissue polarity.

The mechanisms by which genes control organ shape are poorly understood. In principle, genes may control shape by modifying local rates and/or orientations of deformation. Distinguishing between these possibilities has been difficult because of interactions between patterns, orientations, and mechanical constraints during growth.

Mutational spaces for leaf shape and size.

A key approach to understanding how genes control growth and form is to analyze mutants in which shape and size have been perturbed. Although many mutants of this kind have been described in plants and animals, a general quantitative framework for describing them has yet to be established. Here we describe an approach based on Principal Component Analysis of organ landmarks and outlines.

Evolutionary paths underlying flower color variation in Antirrhinum.

To understand evolutionary paths connecting diverse biological forms, we defined a three-dimensional genotypic space separating two flower color morphs of Antirrhinum. A hybrid zone between morphs showed a steep cline specifically at genes controlling flower color differences, indicating that these loci are under selection. Antirrhinum species with diverse floral phenotypes formed a U-shaped cloud within the genotypic space.

Evolution through genetically controlled allometry space.

Understanding evolutionary change requires phenotypic differences between organisms to be placed in a genetic context. However, there are few cases where it has been possible to define an appropriate genotypic space for a range of species. Here we address this problem by defining a genetically controlled space that captures variation in shape and size between closely related species of Antirrhinum.

A complex-valued nonlinear neural adaptive filter with a gradient adaptive amplitude of the activation function.

A complex-valued nonlinear gradient descent (CNGD) learning algorithm for a simple finite impulse response (FIR) nonlinear neural adaptive filter with an adaptive amplitude of the complex activation function is proposed. This way the amplitude of the complex-valued analytic nonlinear activation function of a neuron in the learning algorithm is made gradient adaptive to give the complex-valued adaptive amplitude nonlinear gradient descent (CAANGD). Such an algorithm is beneficial when dealing with signals that have rich dynamical behavior.