Greenhouses are high energy-consuming and anti-seasonal production facilities. In some cases, energy consumption in greenhouses accounts for 50% of the cost of greenhouse production. The high energy consumption has become a major factor hindering the development of greenhouses. In order to improve the energy efficiency of the greenhouse, it is important to predict its energy consumption. In this study, the energy consumption mathematical model of a Venlo greenhouse is established based on the principle of energy conservation. Three optimization algorithms are used to identify the parameters which are difficult to determine in the energy consumption model. In order to examine the accuracy of the model, some verifications are made. The goal of achieving high yield, high quality and high efficiency production is a problem in the study of greenhouse environment control. Combining the prediction model of greenhouse energy consumption with the relatively accurate weather forecast data for the next week, the energy consumption of greenhouse under different weather conditions is predicted. Taking the minimum energy consumption as the objective function, the indoor daily average temperatures of 7 days are optimized to provide the theoretical reference for the decision-making of heating in the greenhouse. The results show that the optimized average daily temperatures save 9% of the energy cost during a cold wave.

Deubiquitinases are deubiquitinating enzymes (DUBs), which remove ubiquitin from proteins, thus regulating their proteasomal degradation, localization and activity. Here, we discuss DUBs as anti-cancer drug targets.

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Objective— Angiogenesis is tightly controlled by growth factors and cytokines in pathophysiological settings. Interleukin 37 (IL-37) is a newly identified cytokine of the IL-1 family, some members of which are important in inflammation and angiogenesis. However, the function of IL-37 in angiogenesis remains unknown. We aimed to explore the regulatory role of IL-37 in pathological and physiological angiogenesis. Approach and Results— We found that IL-37 was expressed and secreted in endothelial cells and upregulated under hypoxic conditions. IL-37 enhanced endothelial cell proliferation, capillary formation, migration, and vessel sprouting from aortic rings with potency comparable with that of vascular endothelial growth factor. IL-37 activates survival signals including extracellular signal-regulated kinase 1/2 and AKT in endothelial cells. IL-37 promoted vessel growth in implanted Matrigel plug in vivo in a dose-dependent manner with potency comparable with that of basic fibroblast growth factor. In the mouse model of retinal vascular development, neonatal mice administrated with IL-37 displayed increased neovascularization. We demonstrated further that IL-37 promoted pathological angiogenesis in the mouse model of oxygen-induced retinopathy. Conclusions— Our findings suggest that IL-37 is a novel and potent proangiogenic cytokine with essential role in pathophy siological settings.

The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eyemovement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen's kappa coefficient of about 0.56 for the classification of Wake, REM and NREM.

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White sponge nevus (WSN) in the oral mucosa is a rare autosomal dominant genetic disease. The involved mucosa is white or greyish, thickened, folded and spongy. The genes associated with WSN include mutant cytokeratin keratin 4 (KRT4) and keratin 13 (KRT13). In recent years, new cases of WSN and associated mutations have been reported. Here, we summarise the recent progress in our understanding of WSN, including clinical reports, genetics, animal models, treatment, pathogenic mechanisms and future directions. Gene-based diagnosis and gene therapy for WSN may become available in the near future and could provide a reference and instruction for treating other KRT-associated diseases.