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Urotensin II (UII) is a vasoactive peptide composed of 11 amino acids that has been implicated to contribute to the development of cardiovascular disease. The purpose of this study was to investigate whether UII affects the development of atherosclerosis in cholesterol-fed rabbits. UII was infused for 16 weeks through an osmotic mini-pump into male Japanese White rabbits fed on a high-cholesterol diet. Plasma lipids and body weight were measured every 4 weeks. Aortic atherosclerotic lesions along with cellular components, collagen fibers, matrix metalloproteinase-1 and -9 were examined. Moreover, vulnerability index of atherosclerotic plaques was evaluated. UII infusion significantly increased atherosclerotic lesions within the entire aorta by 21% over the control (P = 0.013). Atherosclerotic lesions were increased by 24% in the aortic arch (P = 0.005), 11% in the thoracic aorta (P = 0.054) and 18% in the abdominal aorta (P = 0.035). These increases occurred without changes in plasma levels of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides or body weight. Immunohistochemical staining revealed that macrophages and matrix metalloproteinase-9 were significantly enhanced by 2.2-fold and 1.6-fold in UII group. In vitro studies demonstrated that UII up-regulated the expression of vascular cell adhesion protein-1 and intercellular adhesion molecule-1 in human umbilical vein endothelial cells, which was inhibited by the UII receptor antagonist urantide. In conclusion, our results showed that UII promotes the development of atherosclerotic lesions and destabilizes atherosclerotic plaques in cholesterol-fed rabbits.

The production of construction projects is carbon-intensive and interrelated to multiple other industries that provide related materials and services. Thus, the calculations of carbon emissions are relatively complex, and the consideration of other factors becomes necessary, especially in China, which has a massive land area and regions with greatly uneven development. To improve the accuracy of the calculations and illustrate the impacts of the various factors at the provincial level in the construction industry, this study separated carbon emissions into two categories, the direct category and the indirect category. The features of carbon emissions in this industry across 30 provinces in China were analysed, and the logarithmic mean Divisia index (LMDI) model was employed to decompose the major factors, including direct energy proportion, unit value energy consumption, value creation effect, indirect carbon intensity, and scale effect of output. It was concluded that carbon emissions increased, whereas carbon intensity decreased dramatically, and indirect emissions accounted for 90% to 95% of the total emissions from the majority of the provinces between 2005 and 2014. The carbon intensities were high in the underdeveloped western and central regions, especially in Shanxi, Inner-Mongolia and Qinghai, whereas they were low in the well-developed eastern and southern regions, represented by Beijing, Shanghai, Zhejiang and Guangdong. The value creation effect and indirect carbon intensity had significant negative effects on carbon emissions, whereas the scale effect of output was the primary factor creating emissions. The factors of direct energy proportion and unit value energy consumption had relatively limited, albeit varying, effects. Accordingly, this study reveals that the evolving trends of these factors vary in different provinces; therefore, overall, our research results and insights support government policy and decision maker’s decisions to minimize the carbon emissions in the construction industry.

In recent years, emotional recognition based on Electrophysiological (EEG) signals has become more and more popular. But the researchers ignored the fact that peripheral physiological signals can also reflect changes in mood. We propose an Ensemble Convolutional Neural Network (ECNN) model, which is used to automatically mine the correlation between multi-channel EEG signals and peripheral physiological signals in order to improve the emotion recognition accuracy. First, we design five convolution networks and use global average pooling (GAP) layers instead of fully connected layers; and then the plurality voting strategy is adopted to establish the ensemble model; eventually this model divides emotions into four categories. Based on the simulations on DEAP dataset, the experimental results demonstrate the superiority of the ECNN compared with other methods.

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