Analysing human neural stem cell ontogeny by consecutive isolation of Notch active neural progenitors

Decoding heterogeneity of pluripotent stem cell (PSC)-derived neural progeny is fundamental for revealing the origin of diverse progenitors, for defining their lineages, and for identifying fate determinants driving transition through distinct potencies. Here we have prospectively isolated consecutively appearing PSC-derived primary progenitors based on their Notch activation state. We first isolate early neuroepithelial cells and show their broad Notch-dependent developmental and proliferative potential. Neuroepithelial cells further yield successive Notch-dependent functional primary progenitors, from early and midneurogenic radial glia and their derived basal progenitors, to gliogenic radial glia and adult-like neural progenitors, together recapitulating hallmarks of neural stem cell (NSC) ontogeny. Gene expression profiling reveals dynamic stage-specific transcriptional patterns that may link development of distinct progenitor identities through Notch activation. Our observations provide a platform for characterization and manipulation of distinct progenitor cell types amenable for developing streamlined neural lineage specification paradigms for modelling development in health and disease

History and Actuality of Galician Emigrants: A Galicia (Spain) Shared between Latin America and Europe

Despite the significant advances in path planning methods, problems involving highly constrained spaces are still challenging. In particular, in many situations the configuration space is a non-parametrizable variety implicitly defined by constraints, which complicates the successful generalization of sampling-based path planners. In this paper, we present a new path planning algorithm specially tailored for highly constrained systems. It builds on recently developed tools for Higher-dimensional Continuation, which provide numerical procedures to describe an implicitly defined variety using a set of local charts. We propose to extend these methods to obtain an efficient path planner on varieties, handling highly constrained problems. The advantage of this planner comes from that it directly operates into the configuration space and not into the higher-dimensional ambient space, as most of the existing methods do.Postprint (author’s final draft

Motion Planning for Highly Constrained Spaces

We introduce a sampling-based motion planning method that automatically adapts to the difficulties caused by thin regions in the free space (not necessarily narrow corridors). These problems arise frequently in settings such as closed-chain manipulators, humanoid motion planning, and generally any time bodies are in contact or maintain close proximity with each other. Our method combines the aggressive exploration properties of RRTs with the intrinsic dimensionality-reduction properties of kd-trees to focus the sampling and searching in the appropriate subspaces.We handle closed-chains and other kinds of constraints in a general way that avoids inverse kinematics computations, if desired. We have implemented the method and show its computational advantages on a variety of challenging examples