Nonparametric identification and maximum likelihood estimation for hidden Markov models.

Nonparametric identification and maximum likelihood estimation for finite-state hidden Markov models are investigated. We obtain identification of the parameters as well as the order of the Markov chain if the transition probability matrices have full-rank and are ergodic, and if the state-dependent distributions are all distinct, but not necessarily linearly independent. Based on this identification result, we develop a nonparametric maximum likelihood estimation theory.

Inhibition of NR2B phosphorylation restores alterations in NMDA receptor expression and improves functional recovery following traumatic brain injury in mice.

Traumatic brain injury (TBI) triggers a massive glutamate efflux, hyperactivation of N-methyl-D-aspartate receptors (NMDARs) and neuronal cell death. Previously it was demonstrated that, 15 min following experimentally induced closed head injury (CHI), the density of activated NMDARs increases in the hippocampus, and decreases in the cortex at the impact site. Here we show that CHI-induced alterations in activated NMDARs correlate with changes in the expression levels of the major NMDARs subunits.