One of the hallmarks of the human CNS disease subacute sclerosing panencephalitis (SSPE) is a high level of measles virus (MV) infection of oligodendrocytes. It is therefore surprising that there is only one previous report of MV infection of rat oligodendrocytes in culture and no reports of human oligodendrocyte infection in culture. In an attempt to develop a model system to study MV infection of oligodendrocytes, time-lapse confocal microscopy, immunocytochemistry, and electron microscopy (EM) were used to study infection of the human oligodendroglioma cell line, MO3.13. A rat oligodendrocyte cell line, OLN-93, was also studied as a control. MO3.13 cells were shown to be highly susceptible to MV infection and virus budding was observed from the surface of infected MO3.13 cells by EM. Analysis of the infection in real time and by immunocytochemistry revealed that virus spread occurred by cell-to-cell fusion and was also facilitated by virus transport in cell processes. MO3.13 cells were shown to express CD46, a MV receptor, but were negative for the recently discovered MV receptor, signaling leucocyte activation molecule (SLAM). Immunohistochemical studies on SSPE tissue sections demonstrated that CD46 was also expressed on populations of human oligodendrocytes. SLAM expression was not detected on oligodendrocytes. These studies, which are the first to show MV infection of human oligodendrocytes in culture, show that the cells are highly susceptible to MV infection and this model cell line has been used to further our understanding of MV spread in the CNS.
To address the global challenge of the climate change, more strict legislations worldwide on carbon emission reductions have put energy intensive industries under immense pressure to improve the energy efficiency. Due to the lack of technical support and financial incentives, a range of technical and economic barriers still exist for small-medium enterprises (SMEs). This paper first introduces a point energy technology, which is developed for SMEs to improve the insight of the energy usage in the manufacturing processes and installed in a local bakery. Statistical analysis of electricity consumption data over a sevenday period is conducted, including the identification of operational modes for individual processing units using an enhanced clustering method and the voltage unbalance conditions associated with these identified modes. Two technical strategies, namely electrical load allotment and voltage unbalance minimisation, are then proposed, which could attain more than 800 kwh energy saving during this period and the current unbalance could be reduced to less than 10%. In
Motivated by the global economy greatly shaped by the manufacturing technology, more research on the intelligent manufacturing is studied. This paper firstly introduces an energy monitoring and data acquisition system namely the Point Energy Technology, which has been developed by the team and installed in a local bakery. While there is always lack of data because of various reasons, such as measurement or transmission mistakes during data collection. To solve this problem, we introduce a generative adversarial framework which is based on a game theory for data augmentation. This framework consists of two multi-layer perceptron networks-generator and discriminator. The upgrade framework with Q-net that extracts the latent variables from real data is proposed. To control the number of parameters, Q-net shares the structure with discriminator except the last layer. In addition, the two optimization methods, mini-batch gradient descent and adaptive moment estimation are adopted to tune the parameters. To evaluate the performance of these algorithms, the collected data from baking process is used in the experiment. Considering the reality, the missing data is processed into the state of missing completely at random (non-time series missing data). The experimental results illustrate that the latent generative adversarial framework with adaptive moment estimation could generated samples of good quality for non-time series missing data.
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