317 research outputs found

    Experiment of Leymus Chinensis in Raising Diary Cows

    Get PDF

    Disruption of Nrf2 Enhances the Upregulation of Nuclear Factor-kappaB Activity, Tumor Necrosis Factor-α, and Matrix Metalloproteinase-9 after Spinal Cord Injury in Mice

    Get PDF
    Matrix metalloproteinase-9 (MMP-9) plays an important role in the acute periods of spinal cord injury (SCI), and its expression is related to the inflammation which could cause the disruption of the blood-spinal barrier (BBB). Nuclear factor erythroid 2-related factor 2 (Nrf2) is a key transcription factor that plays a crucial role in cytoprotection against inflammation. The present study investigated the role of Nrf2 in upregulating of nuclear factor kappa B (NF-κB) activity, tumor necrosis factor-α (TNF-α), and MMP-9 after SCI. Wild-type Nrf2 (+/+) and Nrf2-deficient (Nrf (−/−)) mice were subjected to an SCI model induced by the application of vascular clips (force of 10 g) to the dura after a three-level T8-T10 laminectomy. We detected the wet/dry weight ratio of impaired spinal cord tissue, the activation of NF-κB, the mRNA and protein levels of TNF-α and MMP-9, and the enzyme activity of MMP-9. Nrf2 (−/−) mice were demonstrated to have more spinal cord edema, NF-κB activation, TNF-α production, and MMP-9 expression after SCI compared with the wild-type controls. The results suggest that Nrf2 may play an important role in limiting the upregulation of NF-κB activity, TNF-α, and MMP-9 in spinal cord after SCI

    A dynamic multi-objective evolutionary algorithm based on decision variable classification

    Get PDF
    The file attached to this record is the author's final peer reviewed version.In recent years, dynamic multi-objective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multi-objective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multi-objective evolutionary algorithms. Maintaining good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a dynamic multi-objective evolutionary algorithm based on decision variable classification (DMOEA-DVC) is proposed in this study. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and change response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. Experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms
    corecore