RESUMO
Measuring and predicting atmospheric visibility is important scientific research that has practical significance for urban air pollution control and public transport safety. We propose a deep learning model that uses principal component analysis and a deep belief network (DBN) to effectively predict atmospheric visibility in short- and long-term sequences. First, using a visibility meter, particle spectrometer, and ground meteorological station data from 2016 to 2019, the principal component analysis method was adopted to determine the influence of atmospheric meteorological and environmental parameters on atmospheric visibility, and an input dataset applicable to atmospheric visibility prediction was constructed. On the basis of deep belief network theory, network structure parameters, including data preprocessing, the number of hidden layers, the number of nodes, and activation and weight functions, are simulated and analyzed. A deep belief network model suitable for atmospheric visibility prediction is established, where a double hidden layer is adopted with the node numbers 70 and 50, and the Z-score method is used for normalization processing with the tanh activation function and Adam optimizer. The average accuracy of atmospheric visibility prediction by the deep belief network reached 0.84, and the coefficient of determination reached 0.96; these results are significantly superior to those of the back propagation (BP) neural network and convolutional neural network (CNN), thus verifying the feasibility and effectiveness of the established deep belief network for predicting atmospheric visibility. Finally, a deep belief network model based on time series is used to predict the short- and long-term trends of atmospheric visibility. The results show that the model has good visibility prediction results within 3 days and has an accuracy rate of 0.79. Covering the visibility change evaluations of different weather conditions, the model demonstrates good practicability. The established deep learning network model provides an effective and feasible technical solution for the prediction of atmospheric meteorology and environmental parameters, which enjoys a wide range of application prospects in highway transportation, navigation, sea and air, meteorology, and environmental research.
RESUMO
As a leading complication of sepsis, sepsis-induced cardiac dysfunction (SICD) contributed to the high mortality of patients with sepsis. Long non-coding RNA (LncRNA) LINC00472 has been reported to be in sepsis-induced disease. Nonetheless, its biological function and underlying molecular in SICD remain largely unknown. In this study, in vivo and in vitro SICD models were established via LPS treatment. H&E staining was employed for the evaluation of myocardial injury. ELISA assay was performed to detect cardiac Troponin I (cTnI), creatine kinase-MB (CK-MB), interleukin (IL)-1ß, and tumor necrosis factor-α (TNF-α) levels. Cardiomyocyte viability and apoptosis were assessed via CCK-8 and flow cytometry assays. The transcriptional regulation of YY1 on LINC00472 was demonstrated via ChIP assay. Besides, the interaction between YY1 and LINC00472, as well as the association between miR-335-3p and LINC00472 or MAOA were verified via luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Herein, highly expressed LINC00472 was observed in both in vivo and in vitro SICD models. LINC00472 knockdown substantially attenuated LPS-induced inhibition on cardiomyocyte viability and reversed cardiomyocyte apoptosis and inflammatory response mediated by LPS treatment. YY1 induced LINC00472 upregulation, thereby promoting cardiomyocyte dysfunction induced by LPS. In addition, MAOA upregulation or miR-335-3p inhibition could partly reverse the suppressive effect on LPS-induced cardiomyocyte dysfunction mediated by LINC00472 knockdown. Based on our results, it seemed that YY1-activated LINC00472 might contribute to SICD progression via the miR-335-3p/MAOA pathway.