Nervous and muscle system development in Phascolion strombus (Sipuncula)

Recent interpretations of developmental gene expression patterns propose that the last common metazoan ancestor was segmented, although most animal phyla show no obvious signs of segmentation. Developmental studies of non-model system trochozoan taxa may shed light on this hypothesis by assessing possible cryptic segmentation patterns. In this paper, we present the first immunocytochemical data on the ontogeny of the nervous system and the musculature in the sipunculan Phascolion strombus. Myogenesis of the first anlagen of the body wall ring muscles occurs synchronously and not subsequently from anterior to posterior as in segmented spiralian taxa (i.e. annelids). The number of ring muscles remains constant during the initial stages of body axis elongation. In the anterior-posteriorly elongated larva, newly formed ring muscles originate along the entire body axis between existing myocytes, indicating that repeated muscle bands do not form from a posterior growth zone. During neurogenesis, the Phascolion larva expresses a non-metameric, paired, ventral nerve cord that fuses in the mid-body region in the late-stage elongated larva. Contrary to other trochozoans, Phascolion lacks any larval serotonergic structures. However, two to three FMRFamide-positive cells are found in the apical organ. In addition, late larvae show commissure-like neurones interconnecting the two ventral nerve cords, while early juveniles exhibit a third, medially placed FMRFamidergic ventral nerve. Although we did not find any indications for cryptic segmentation, certain neuro-developmental traits in Phascolion resemble the conditions found in polychaetes (including echiurans) and myzostomids and support a close relationship of Sipuncula and Annelida.

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We de. ne a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states i and j in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.