The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

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BackgroundAedes albopictus is among the 100 most invasive species worldwide and poses a major risk to public health. Photoperiodic diapause provides a crucial ecological basis for the adaptation of this species to adverse environments. Ae. albopictus is the vital vector transmitting dengue virus in Guangzhou, but its diapause activities herein remain obscure.MethodsIn the laboratory, yeast powder and food slurry were compared for a proper diapause determination method, and the critical photoperiod (CPP) was tested at illumination times of 11, 11.5, 12, 12.5, 13, and 13.5 h. A 4-parameter logistic (4PL) regression model was selected to estimate the CPP. In the field, the seasonal dynamics of the Ae. albopictus population, egg diapause, and hatching of overwintering eggs were investigated monthly, weekly, and daily, respectively. A distributed lag non-linear model (DLNM) was used to assess the associations of diapause with meteorological factors.ResultsIn the laboratory, both the wild population and the Foshan strain of Ae. albopictus were induced to diapause at an incidence greater than 80%, and no significant difference (P > 0.1) was observed between the two methods for identifying diapause. The CPP of this population was estimated to be 12.312 h of light. In the field, all of the indexes of the wild population were at the lowest levels from December to February, and the Route Index was the first to increase in March. Diapause incidence displayed pronounced seasonal dynamics. It was estimated that the day lengths of 12.111 h at week2016, 43 and 12.373 h at week2017, 41 contributed to diapause in 50% of the eggs. Day length was estimated to be the main meteorological factor related to diapause.ConclusionsPhotoperiodic diapause of Ae. albopictus in Guangzhou of China was confirmed and comprehensively elucidated in both the laboratory and the field. Diapause eggs are the main form for overwintering and begin to hatch in large quantities in March in Guangzhou. Furthermore, this study also established an optimized investigation system and statistical models for the study of Ae. albopictus diapause. These findings will contribute to the prevention and control of Ae. albopictus and mosquito-borne diseases.Electronic supplementary materialThe online version of this article (10.1186/s40249-018-0466-8) contains supplementary material, which is available to authorized users.

This study provides a possible new mechanism for CoQ10 in the treatment of AS and may bring a new hope for the prevention and treatment of AS in the future.

Field-orthogonal temporal mode analysis of optical fields has recently been developed for a new framework of quantum information science. However, so far, the exact profiles of the temporal modes are not known, which makes it difficult to achieve mode selection and demultiplexing. Here, we report a novel method that measures directly the exact form of the temporal modes. This, in turn, enables us to make mode-orthogonal homodyne detection with mode-matched local oscillators. We apply the method to a pulse-pumped, specially engineered fiber parametric amplifier and demonstrate temporally multiplexed multidimensional quantum entanglement of continuous variables in telecom wavelength. The temporal mode characterization technique can be generalized to other pulse-excited systems to find their eigenmodes for multiplexing in the temporal domain.