Sonic hedgehog signaling regulates mode of cell division of early cerebral cortex progenitors and increases

The morphogen Sonic Hedgehog (SHH) plays a critical role in the development of different tissues. In the central nervous system, SHH is well known to contribute to the patterning of the spinal cord and separation of the brain hemispheres. In addition, it has recently been shown that SHH signaling also contributes to the patterning of the telencephalon and establishment of adult neurogenic niches. In this work, we investigated whether SHH signaling influences the behavior of neural progenitors isolated from the dorsal telencephalon, which generate excitatory neurons and macroglial cells in vitro. We observed that SHH increases proliferation of cortical progenitors and generation of astrocytes, whereas blocking SHH signaling with cyclopamine has opposite effects. In both cases, generation of neurons did not seem to be affected. However, cell survival was broadly affected by blockade of SHH signaling. SHH effects were related to three different cell phenomena: mode of cell division, cell cycle length and cell growth. Together, our data in vitro demonstrate that SHH signaling controls cell behaviors that are important for proliferation of cerebral cortex progenitors, as well as differentiation and survival of neurons and astroglial cells.

Identificação em tempo real de modelo dinâmico de robô móvel com acionamento diferencial e zona morta

Several mobile robots show non-linear behavior, mainly due friction phenomena between the mechanical parts of the robot or between the robot and the ground. Linear models are efficient in some cases, but it is necessary take the robot non-linearity in consideration when precise displacement and positioning are desired. In this work a parametric model identification procedure for a mobile robot with differential drive that considers the dead-zone in the robot actuators is proposed. The method consists in dividing the system into Hammerstein systems and then uses the key-term separation principle to present the input-output relations which shows the parameters from both linear and non-linear blocks. The parameters are then simultaneously estimated through a recursive least squares algorithm. The results shows that is possible to identify the dead-zone thresholds together with the linear parameters