Xu Ding

Background: Opsoclonus-myoclonus syndrome (OMS) is a rare neurological disease. Some children with OMS also have neuroblastoma (NB). We and others have previously documented that serum IgG from children with OMS and NB induces neuronal cytolysis and activates several signaling pathways. However, the mechanisms underlying OMS remain unclear. Here, we investigated whether nitric oxide (NO) from activated microglias and its cascade contribute to neuronal cytolysis in pediatric OMS. Methods: The activation of cultured cerebral cortical and cerebellar microglias incubated with sera or IgG isolated from sera of children with OMS and NB was measured by the expression of the activation marker, cytokines, and NO. Neuronal cytolysis was determined after exposing to IgG-treated microglia-conditioned media. Using inhibitors and activators, the effects of NO synthesis and its intracellular cascade, namely soluble guanylyl cyclase (sGC) and protein kinase G (PKG), on neuronal cytolysis were evaluated. Results: Incubation with sera or IgG from children with OMS and NB increased the activation of cerebral cortical and cerebellar microglias, but not the activation of astrocytes or the cytolysis of glial cells. Moreover, the cytolysis of neurons was elevated by conditioned media from microglias incubated with IgG from children with OMS and NB. Furthermore, the expression of NO, sGC, and PKG was increased. Neuronal cytolysis was relieved by the inhibitors of NO signaling, while neuronal cytolysis was exacerbated by the activators of NO signaling but not proinflammatory cytokines. The cytolysis of neurons was suppressed by pretreatment with the microglial inhibitor minocycline, a clinically tested drug. Finally, increased microglial activation did not depend on the Fab fragment of serum IgG. Conclusions: Serum IgG from children with OMS and NB potentiates microglial activation, which induces neuronal cytolysis through the NO/sGC/PKG pathway, suggesting an applicability of microglial inhibitor as a therapeutic candidate.

A NOx emission forecasting model using multiresolution least squares support vector machine (LSSVM) based on data-driven and SF-KPCA is proposed. Firstly, the data-driven model based on real operation data contains a lot of redundant information. Therefore, a Kernel Principal Component Analysis based on Similarity Function (SF-KPCA) is proposed to reduce the redundant information and optimize the modeling complexity. Then, inspired by multiresolution analysis, a multiresolution LSSVM with online adaptive update ability is conducted, which employs the total forecast error as the threshold to update the forecasting model parameters in real time. Finally, the proposed method is applied to predict the NOx emission of a coal-fired boiler. Results reveal that the online multiresolution LSSVM model with SF-KPCA maintains the prediction accuracy at a high level even if the process characteristics vary. Meanwhile, the model only needs 0.15 seconds for each forecast, which is suitable for real time monitoring in field.