Objective: To evaluate a reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and compare it with RT-PCR. Methods: We designed primers specific to the orf1ab and S genes of SARS-CoV-2. Total viral RNA was extracted using the QIAamp Viral RNA Mini Kit. We optimized the RT-LAMP assay, and evaluated it for its sensitivity and specificity of detection using real-time turbidity monitoring and visual observation. Results: The primer sets orf1ab-4 and S-123 amplified the genes in the shortest times, the mean (±SD) times were 18 ± 1.32 min and 20 ± 1.80 min, respectively, and 63 C was the optimum reaction temperature. The sensitivities were 2 Â 10 1 copies and 2 Â 10 2 copies per reaction with primer sets orf1ab-4 and S-123, respectively. This assay showed no cross-reactivity with 60 other respiratory pathogens. To describe the availability of this method in clinical diagnosis, we collected 130 specimens from patients with clinically suspected SARS-CoV-2 infection. Among them, 58 were confirmed to be positive and 72 were negative by RT-LAMP. The sensitivity was 100% (95% CI 92.3%e100%), specificity 100% (95% CI 93.7% e100%). This assay detected SARS-CoV-2 in a mean (±SD) time of 26.28 ± 4.48 min and the results can be identified with visual observation. Conclusion: These results demonstrate that we developed a rapid, simple, specific and sensitive RT-LAMP assay for SARS-CoV-2 detection among clinical samples. It will be a powerful tool for SARS-CoV-2 identification, and for monitoring suspected patients, close contacts and high-risk groups. C.

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Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP) for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV), and the Levenberg-Marquardt algorithm (LMA) is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS), and the results indicated that the forecast accuracy is obviously improved under variable weather conditions. OPEN ACCESS

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ObjectivesPneumoconiosis remains a major global occupational health hazard and illness. Accurate data on the incidence of pneumoconiosis are critical for health resource planning and development of health policy.MethodsWe collected data for the period between 1990 and 2017 on the annual incident cases and the age-standardised incidence rates (ASIR) of pneumoconiosis aetiology from the Global Burden of Disease Study 2017. We calculated the average annual percentage changes of ASIR by sex, region and aetiology in order to determine the trends of pneumoconiosis.ResultsGlobally, the number of pneumoconiosis cases increased by a measure of 66.0%, from 36 186 in 1990 to 60 055 in 2017. The overall ASIR decreased by an average of 0.6% per year in the same period. The number of pneumoconiosis cases increased across the five sociodemographic index regions, and there was a decrease in the ASIR from 1990 to 2017. The ASIR of silicosis, coal workers’ pneumoconiosis and other pneumoconiosis decreased. In contrast, measures of the ASIR of asbestosis displayed an increasing trend. Patterns of the incidence of pneumoconiosis caused by different aetiologies were found to have been heterogeneous for analyses across regions and among countries.ConclusionIncidence patterns of pneumoconiosis which were caused by different aetiologies varied considerably across regions and countries of the world. The patterns of incidence and temporal trends should facilitate the establishment of more effective and increasingly targeted methods for prevention of pneumoconiosis and reduce associated disease burden.