CSPGs (chondroitin sulfate proteoglycans) are a family of extracellular matrix molecules with various functions in regulating tissue morphogenesis, cell division and axon guidance. A number of CSPGs are highly upregulated by reactive glial scar tissues after injuries and form a strong barrier for axonal regeneration in the adult vertebrate CNS. Although CSPGs may negatively regulate axonal growth via binding and altering activity of other growth-regulating factors, the molecular mechanisms by which CSPGs restrict axonal elongation are not well understood. Here, we identified a novel receptor mechanism whereby CSPGs inhibit axonal growth via interactions with neuronal transmembrane LAR (the leukocyte common antigen-related phosphatase). CSPGs bind LAR with high affinity in transfected COS-7 cells and co-immunoprecipitate with LAR expressed in various tissues including the brain and spinal cord. CSPG stimulation enhances activity of LAR phosphatase in vitro. Deletion of LAR in knockout mice or blockade of LAR with sequence-selective peptides significantly overcomes neurite growth restrictions of CSPGs in neuronal cultures. Intracellularly, CSPG-LAR interaction mediates axonal growth inhibition of neurons partially via inactivating Akt and activating RhoA signals. Systemic treatments with LAR-targeting peptides in mice with thoracic spinal cord transection injuries induce significant axon growth of descending serotonergic fibers in the vicinity of the lesion and beyond in the caudal spinal cord and promote locomotor functional recovery. Identification of LAR as a novel CSPG functional receptor provides a therapeutic basis for enhancing axonal regeneration and functional recovery after CNS injuries in adult mammals.

Axonal regeneration is minimal after CNS injuries in adult mammals and medical treatments to recover neurological deficits caused by axon disconnection are extremely limited. The failure of axonal elongation is principally attributed to the nonpermissive environment and reduced intrinsic growth capacity. In this report, we studied the role of glycogen synthase kinase-3 (GSK-3) inactivation on neurite and axon growth from adult neurons via combined in vitro and in vivo approaches. We found that the major CNS inhibiting substrates including chondroitin sulfate proteoglycans could inactivate protein kinase B (Akt) and activate GSK-3␤ signals in neurons. GSK-3 inactivation with pharmacologic inhibitors enhances neurite outgrowth of dorsal root ganglion neurons derived from adult mice or cerebellar granule neurons from postnatal rodents cultured on CNS inhibitors. Application of GSK-3 inhibitors stimulates axon formation and elongation of mature neurons whether in presence or absence of inhibitory substrates. Systemic application of the GSK-3 inhibitor lithium to spinal cord-lesioned rats suppresses the activity of this kinase around lesion. Treatments with GSK-3 inhibitors including a clinical dose of lithium to rats with thoracic spinal cord transection or contusion injuries induce significant descending corticospinal and serotonergic axon sprouting in caudal spinal cord and promote locomotor functional recovery. Our studies suggest that GSK-3 signal is an important therapeutic target for promoting functional recovery of adult CNS injuries and that administration of GSK-3 inhibitors may facilitate the development of an effective treatment to white matter injuries including spinal cord trauma given the wide use of lithium in humans.

prefer to view part or all of the network at varying levels of detail. Hierarchical clustering provides a framework for viewing the network at different levels of detail by superimposing a hierarchy on it. Nodes are grouped into clusters, and clusters are themselves placed into other clusters. Users can then navigate these clusters until an appropriate level of detail is reached. This article describes an experiment comparing two methods for viewing hierarchically clustered networks. Traditional full-zoom techniques provide details of only the current level of the hierarchy. In contrast, fisheye views, generated by the "variable-zoom" algorithm described in this article, provide information about higher levels as well. Subjects using both viewing methods were given problem-solving tasks requiring them to navigate a network, in this case, a simulated telephone system, and to reroute links in it. Results suggest that the greater context provided by fisheye views significantly improved user performance. Users were quicker to complete their task and made fewer unnecessary navigational steps through the hierarchy. This validation of fisheye views is important for designers of interfaces to complicated monitoring systems, such as control rooms for supervisory control and data acquisition systems, where efficient human performance is often critical. However, control room operators remained concerned about the size and visibility tradeoffs between the fine detail provided by full-zoom techniques and the global context supplied by fisheye views. Specific interface features are required to reconcile the differences.

Ibuprofen is a nonsteroidal anti-inflammatory drug widely used to relieve pain and inflammation in many disorders via inhibition of cyclooxygenases. Recently, we have demonstrated that ibuprofen inhibits intracellular signaling of RhoA and promotes significant axonal growth and functional recovery following spinal cord lesions in rodents. In addition, another study suggests that ibuprofen reduces generation of amyloid-β42 peptide via inactivation of RhoA signaling, although it may also regulate amyloid-β42 formation by direct inhibition of the γ-secretase complex. The molecular mechanisms by which ibuprofen inhibits the RhoA signal in neurons, however, remain unclear. Here, we report that the transcription factor peroxisome proliferator-activated receptorγ (PPARγ) is essential for coupling ibuprofen to RhoA inhibition and subsequent neurite growth promotion in neurons. Ibuprofen activates PPARγ in neuron-like PC12 and B104 cells. Activation of PPARγ with traditional agonists mimics the RhoA-inhibiting properties of ibuprofen in PC12 cells and, like ibuprofen, promotes neurite elongation in primary cultured neurons exposed to axonal growth inhibitors. Protein knockdown with small interfering RNA specific for PPARγ blocks RhoA suppression of PPARγ agonists in PC12 cells. Moreover, the effect of ibuprofen on RhoA activity and neurite growth in neuronal cultures is prevented by selective PPARγ inhibition. These findings support that PPARγ plays an essential role in mediating the RhoA-inhibiting effect of ibuprofen. Elucidation of the novel molecular mechanisms linking ibuprofen to RhoA inhibition may provide additional therapeutic targets to the disorders characterized by RhoA activation, including spinal cord injuries and Alzheimer’s disease.

Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw [24]). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw's approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses.