We study the discrete-time recurrent neural network that derived from the Leaky-integrator model and its application to compression of infra-red spectrum. Our results show that the discrete-time Leaky-integrator recurrent neural network (RNN) model can be used to approximate the continuous-time model and inherit its dynamical characters if a proper step size is chosen. Moreover, the discrete-time Leaky-integrator RNN model is absolutely stable. By developing the double discrete integral method and employing the state space search algorithm for the discrete-time recurrent neural network model, we demonstrate with quality spectra regenerated from the compressed data how to compress the infra-red spectrum effectively. The information we stored is the parameters of the system and its initial states. The method offers an ideal setting to carry out the recurrent neural network approach to chaotic cases of data compression.
Efficient degradation of damaged D1 during the repair of PSII is carried out by a set of dedicated FtsH proteases in the thylakoid membrane. Here we investigated whether the evolution of FtsH could hold clues to the origin of oxygenic photosynthesis. A phylogenetic analysis of over 6000 FtsH protease sequences revealed that there are three major groups of FtsH proteases originating from gene duplication events in the last common ancestor of bacteria, and that the FtsH proteases involved in PSII repair form a distinct clade branching out before the divergence of FtsH proteases found in all groups of anoxygenic phototrophic bacteria. Furthermore, we showed that the phylogenetic tree of FtsH proteases in phototrophic bacteria is similar to that for Type I and Type II reaction centre proteins. We conclude that the phylogeny of FtsH proteases is consistent with an early origin of photosynthetic water oxidation chemistry., S. Shao, T. Cardona, P. J. Nixon., and Obsahuje bibliografické odkazy