Spatial distribution differences of 25-hydroxyvitamin D in healthy elderly people under the influence of geographical environmental factors.

The main targets of this were to screen the factors that may influence the distribution of 25-hydroxyvitamin D[25(OH)D] reference value in healthy elderly people in China, and further explored the geographical distribution differences of 25(OH)D reference value in China. In this study, we collected the 25(OH)D of 25,470 healthy elderly from 58 cities in China to analyze the correlation between 25(OH)D and 22 geography secondary indexes through spearman regression analysis. Six indexes with significant correlation were extracted, and a ridge regression model was built, and the country's urban healthy elderly'25(OH)D reference value was predicted. By using the disjunctive Kriging method, we obtained the geographical distribution of 25(OH)D reference values for healthy elderly people in China. The reference value of 25(OH)D for healthy elderly in China was significantly correlated with the 6 secondary indexes, namely, latitude (°), annual temperature range (°C), annual sunshine hours (h), annual mean temperature (°C), annual mean relative humidity (%), and annual precipitation (mm). The geographical distribution of 25(OH)D values of healthy elderly in China showed a trend of being higher in South China and lower in North China, and higher in coastal areas and lower in inland areas. This study lays a foundation for further research on the mechanism of different influencing factors on the reference value of 25(OH)D index. A ridge regression model composed of significant influencing factors has been established to provide the basis for formulating reference criteria for the treatment factors of the vitamin D deficiency and prognostic factors of the COVID-19 using 25(OH)D reference value in different regions.

Technical framework for wastewater-based epidemiology of SARS-CoV-2.

Wastewater-based epidemiology (WBE) is expected to become a powerful tool to monitor the dissemination of SARS-CoV-2 at the community level, which has attracted the attention of scholars all over the world. However, there is not yet a standard protocol to guide its implementation. In this paper, we proposed a comprehensive technical and theoretical framework of relative quantification via qPCR for determining the virus abundance in wastewater and estimating the infection ratio in corresponding communities, which is expected to achieve horizontal and vertical comparability of the data using a human-specific biomarker as the internal reference. Critical factors affecting the virus detectability and the estimation of infection ratio include virus concentration methods, lag-period, per capita virus shedding amount, sewage generation rate, temperature-related decay kinetics of virus/biomarker in wastewater, and hydraulic retention time (HRT), etc. Theoretical simulation shows that the main factors affecting the detectability of virus in sewage are per capita virus shedding amount and sewage generation rate. While the decay of SARS-CoV-2 RNA in sewage is a relatively slow process, which may have limited impact on its detection. Under the ideal condition of high per capita virus shedding amount and low sewage generation rate, it is expected to detect a single infected person within 400,000 people.

Detecting fake news by exploring the consistency of multimodal data.

During the outbreak of the new Coronavirus (2019-nCoV) in 2020, the spread of fake news has caused serious social panic. Fake news often uses multimedia information such as text and image to mislead readers, spreading and expanding its influence. One of the most important problems in fake news detection based on multimodal data is to extract the general features as well as to fuse the intrinsic characteristics of the fake news, such as mismatch of image and text and image tampering. This paper proposes a Multimodal Consistency Neural Network (MCNN) that considers the consistency of multimodal data and captures the overall characteristics of social media information. Our method consists of five subnetworks: the text feature extraction module, the visual semantic feature extraction module, the visual tampering feature extraction module, the similarity measurement module, and the multimodal fusion module. The text feature extraction module and the visual semantic feature extraction module are responsible for extracting the semantic features of text and vision and mapping them to the same space for a common representation of cross-modal features. The visual tampering feature extraction module is responsible for extracting visual physical and tamper features. The similarity measurement module can directly measure the similarity of multimodal data for the problem of mismatching of image and text. We assess the constructed method in terms of four datasets commonly used for fake news detection. The accuracy of the detection is improved clearly compared to the best available methods.