1,900 research outputs found
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GraphTool : a tool for interactive design and manipulation of graphs and graph algorithms
GraphTool is an interactive tool for editing graphs and visualizing the execution and results of graph algorithms. It runs under both the SunView and X Windows environments and has a full window/mouse interface which is as similar as possible for the two windowing systems. In addition, there is a standalone program called the Wrapper which simulates the Graph-Tool interface without graphics for batch processing of graph algorithms. While the primary purpose of GraphTool is to provide a means for experimentally investigating the performance of graph algorithms, it has other useful features as well. It provides features for printing graphs in a visually appealing format, which makes it easier to prepare papers for publication. It also provides a facility for "animating" algorithms, which means that it can be used in computer assisted instruction (CAI) and for preparing video presentations of algorithms
Estimating the impact of structural directionality: How reliable are undirected connectomes?
Directionality is a fundamental feature of network connections. Most
structural brain networks are intrinsically directed because of the nature of
chemical synapses, which comprise most neuronal connections. Due to limitations
of non-invasive imaging techniques, the directionality of connections between
structurally connected regions of the human brain cannot be confirmed. Hence,
connections are represented as undirected, and it is still unknown how this
lack of directionality affects brain network topology. Using six directed brain
networks from different species and parcellations (cat, mouse, C. elegans, and
three macaque networks), we estimate the inaccuracies in network measures
(degree, betweenness, clustering coefficient, path length, global efficiency,
participation index, and small worldness) associated with the removal of the
directionality of connections. We employ three different methods to render
directed brain networks undirected: (i) remove uni-directional connections,
(ii) add reciprocal connections, and (iii) combine equal numbers of removed and
added uni-directional connections. We quantify the extent of inaccuracy in
network measures introduced through neglecting connection directionality for
individual nodes and across the network. We find that the coarse division
between core and peripheral nodes remains accurate for undirected networks.
However, hub nodes differ considerably when directionality is neglected.
Comparing the different methods to generate undirected networks from directed
ones, we generally find that the addition of reciprocal connections (false
positives) causes larger errors in graph-theoretic measures than the removal of
the same number of directed connections (false negatives). These findings
suggest that directionality plays an essential role in shaping brain networks
and highlight some limitations of undirected connectomes.Comment: 29 pages, 6 figures, 9 supplementary figures, 4 supplementary table
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Regenerating Corticospinal Axons Innervate Phenotypically Appropriate Neurons within Neural Stem Cell Grafts.
Neural progenitor cell grafts form new relays across sites of spinal cord injury (SCI). Using a panel of neuronal markers, we demonstrate that spinal neural progenitor grafts to sites of rodent SCI adopt diverse spinal motor and sensory interneuronal fates, representing most neuronal subtypes of the intact spinal cord, and spontaneously segregate into domains of distinct cell clusters. Host corticospinal motor axons regenerating into neural progenitor grafts innervate appropriate pre-motor interneurons, based on trans-synaptic tracing with herpes simplex virus. A human spinal neural progenitor cell graft to a non-human primate also received topographically appropriate corticospinal axon regeneration. Thus, grafted spinal neural progenitor cells give rise to a variety of neuronal progeny that are typical of the normal spinal cord; remarkably, regenerating injured adult corticospinal motor axons spontaneously locate appropriate motor domains in the heterogeneous, developing graft environment, without a need for additional exogenous guidance
A New Quartet Tree Heuristic for Hierarchical Clustering
We consider the problem of constructing an an optimal-weight tree from the
3*(n choose 4) weighted quartet topologies on n objects, where optimality means
that the summed weight of the embedded quartet topologiesis optimal (so it can
be the case that the optimal tree embeds all quartets as non-optimal
topologies). We present a heuristic for reconstructing the optimal-weight tree,
and a canonical manner to derive the quartet-topology weights from a given
distance matrix. The method repeatedly transforms a bifurcating tree, with all
objects involved as leaves, achieving a monotonic approximation to the exact
single globally optimal tree. This contrasts to other heuristic search methods
from biological phylogeny, like DNAML or quartet puzzling, which, repeatedly,
incrementally construct a solution from a random order of objects, and
subsequently add agreement values.Comment: 22 pages, 14 figure
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