Toxoplasma gondii infection results in an infiltration of immune cells. The mechanisms responsible for triggering inflammatory cell infiltration in T. gondii infection are not fully understood. We report that T. gondii-infected HeLa cells induced nuclear factor-kappa B (NF-kappaB) activation and increased the expression of interleukin-8 (IL-8) and monocyte chemotactic protein-1 (MCP-1) mRNA. An inhibitor of NF-kappaB activation, calpain-1 inhibitor, blocked the chemokine secretion induced by live T. gondii. Activation of the IL-8 and NF-kappaB transcriptional reporters was suppressed in cells co-transfected with IkappaB kinase beta and the IkappaBalpha super-repressor plasmids. Moreover, the addition of IL-1alpha increased NF-kappaB activation and IL-8 mRNA expression in T. gondii-infected HeLa cells. These results suggest that NF-kappaB is a central regulator of the chemokine response in T. gondii-infected human epithelial cells and that chemokine IL-8 and MCP-1 secretion might be involved in the pathogenesis of T. gondii, via the recruitment of neutrophils, monocytes, and lymphocytes.

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Abstract:The construction industry has made considerable energy-saving efforts in buildings, and studies of energy-savings are ongoing. Shading is used to control the solar radiation transferred through windows. Many studies have examined the position and type of shading in different countries, but few have investigated the effects of shading installation in Korea. In this study, the case of the shading installation according to the standard of Korea, and variations of the heating and cooling load in the unit area on the performance of the windows were examined. This study compared the variations of the heating and cooling load in the case of horizontal shading and the changing position of venetian blinds. This study confirmed that horizontal shading longer than the standard length in Korea saved a maximum of 13% energy consumption. This study confirmed the point of change of energy consumption by the Solar Heat Gain Coefficient (SHGC) variations. The exterior venetian blinds and those between glazing were unaffected by the SHGC. On the other hand, in the case of a south façade, the interior venetian blinds

Abstract:Window performance in buildings is very important for energy saving. Many efforts have been made towards saving energy in buildings, and research has focused attention on enhancing the thermal performance of windows. Vacuum glazing has attracted much interest as a means of enhancing the thermal performance of windows by strengthening insulation performance. However, the performance of vacuum glazing differs based on various component combinations, therefore, further study on vacuum glazing is needed. In this paper, through simulations, the authors confirmed the heat transfer value (U-value) of the vacuum glazing composed of various combinations (glass type, number of layers, interval of pillar, etc.). A physical test of vacuum glazing was also performed using standard test methods of windows and the U-value of the vacuum glazing by various intervals of the pillar position was confirmed. The simulation revealed a U-value for vacuum glazing of 0.682-1.466 W/m 2 ·K as per the interval of the pillar position, the performance of solar heat gain, and visible light transmission. The U-value of the double vacuum glazing was calculated as 0.607-1.154 W/m 2 ·K and was similar regardless of the interval of pillar position, the performance of solar heat gain, and visible light transmission. Based on the results of the energy simulation, in the case of a used low U-value of vacuum glazing, the heating and cooling energy consumption in buildings decreased by 2.46%, than when low-e glass and argon gas filled layers were used in windows. Furthermore, in double vacuum glazing, the heating and cooling energy consumption in buildings decreased by 3.91%.

Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN model showed the highest prediction accuracy (R2 = 0.961) when the number of hidden layers was five and the number of neurons was 22. When the optimal DNN model was applied for the standard model of low-income detached houses, the prediction accuracy (Cv(RMSE)) of the optimal DNN model, compared to the EnergyPlus calculation result, was 8.74%, which satisfied the ASHRAE standard sufficiently.