Highlights Asymptomatic 2019-coronavirus disease patient with normal radiography throughout appeared in this study.  Lopinavir has an positive effect on 2019-coronavirus disease patients.  Eosinophil counts presented potentiality as predictor on the progression of 2019-coronavirus disease. J o u r n a l P r e -p r o o f Abstract Objectives: To explore the epidemiological information, clinical characteristics, therapeutic outcomes and temporal progression of laboratory findings in 2019-coronavirus disease (COVID-19) patients exposed to lopinavir.

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To investigate the diagnostic value of serological testing and dynamic variance of serum antibody in coronavirus disease 2019 . Methods: This study retrospectively included 43 patients with a laboratory-confirmed infection and 33 patients with a suspected infection, in whom the disease was eventually excluded. The IgM/IgG titer of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was measured by chemiluminescence immunoassay analysis. Results: Compared to molecular detection, the sensitivities of serum IgM and IgG antibodies to diagnose COVID-19 were 48.1% and 88.9%, and the specificities were 100% and 90.9%, respectively.In the COVID-19 group, the IgM-positive rate increased slightly at first and then decreased over time; in contrast, the IgGpositive rate increased to 100% and was higher than IgM at all times. The IgM-positive rate and titer were not significantly different before and after conversion to virus-negative. The IgG-positive rate was up to 90% and not significantly different before and after conversion to virus-negative. However, the median IgG titer after conversion to virus-negative was double that before, and the difference was significant. Conclusions: Viral serological testing is an effective means of diagnosis for SARS-CoV-2 infection. The positive rate and titer variance of IgG are higher than those of IgM in COVID-19.

Severe fever with thrombocytopenia syndrome bunyavirus is a newly discovered bunyavirus with high pathogenicity to human. The transmission model has been largely uncharacterized. Investigation on a cluster of severe fever with thrombocytopenia syndrome cases provided evidence of person-to-person transmission through blood contact to the index patient with high serum virus load.

This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D) CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods.