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The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Introductory paragraphThe pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance.

A serial of Co3O4 catalysts were prepared through a facile method of calcining of ZIF-67 at different temperatures. Physicochemical properties of the samples were characterized by the powder X-ray diffraction (XRD), thermogravimetric analysis-differential thermal analysis(TGA-DTA), scanning electron microscopy(SEM), the Brunauer-Emmett-Teller(BET) equation, H2 temperature-programmed reduction (H2-TPR) and The X-ray photoelectron spectroscopy (XPS) techniques, and their catalytic performance were investigated for the oxidation of toluene. The structural characterization certified that the calcination temperature has an important effect to catalytic activities. With the increase of the calcination temperature, the BET surface area reduces, pore size increases and the ratio of Co3+/Co2+ and Oads/Olatt firstly increase then decrease. The results showed that the conversion of toluene on the Co3O4-400 catalyst could reach 100 % when the reaction temperature was 240 oC. The Co3O4-400 catalyst presented better performance and excellent resistance to H2O, which are very important to Industrial application.