Stroke is a leading global cause of mortality and disability. Less than 5% of patients are able to receive tissue plasminogen activator thrombolysis within the necessary timeframe. Focusing on the process of neuronal apoptosis in the penumbra, which lasts from hours to days after ischaemia, appears to be promising. Here we report that tumour necrosis factor receptor-associated factor 1 (TRAF1) expression is markedly induced in wild-type mice 6 h after stroke onset. Using genetic approaches, we demonstrate that increased neuronal TRAF1 leads to elevated neuronal death and enlarged ischaemic lesions, whereas TRAF1 deficiency is neuroprotective. In addition, TRAF1-mediated neuroapoptosis correlates with the activation of the JNK pro-death pathway and inhibition of the Akt cell survival pathway. Finally, TRAF1 is found to exert pro-apoptotic effects via direct interaction with ASK1. Thus, ASK1 positively and negatively regulates the JNK and Akt signalling pathways, respectively. Targeting the TRAF1/ASK1 pathway may provide feasible therapies for stroke long after onset.

Energy consumption structure optimum is gradually discussed in recent literatures. Based on hierarchical clustering of optimally close to content demand of data group mine and analysis, industrial sectors layout on carbon emission intensity is researched. Computed carbon emission drawn support from IPCC methodological framework, formed carbon emission intensities of emissions divided by sectors GDP respectively, and transformed calculated figures into CDF of the continuous uniform distribution to cultivate the standardized data. Resulting of the case presents that there are two categories with types of v and inversed v after mining and analyzing 37 industrial sectors data in 2006-2011. Findings are that 39% annual max paired difference of emission intensities is appeared, and the divergence of energy consumption structure is significantly obtained, which is conducive to the whole industrial distribution of low carbon policy-making.

A Cost-Benefit matrix linear programming model is proposed to optimally allocate industrial sectors layout. The integrated matrix of maximized benefit and minimized cost factors, comprising environmental and technological factors of output, carbon emission and context, is associated with fuzzy factor t to form multiple objective functions. Resulting of the illustrative model implies that carbon emissions less than 3% are appeared from base year to neighboring year after applying the C-B model. Significantly, the optimal method can yield benefits, reduce carbon emissions, and systematically arrange industrial distribution for policy-making.