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ISPRS International Journal of Geo-Information ; 11(4):215, 2022.
Article in English | ProQuest Central | ID: covidwho-1809933

ABSTRACT

Population spatialization data is crucial to conducting scientific studies of coupled human–environment systems. Although significant progress has been made in population spatialization, the spatialization of different age populations is still weak. POI data with rich information have great potential to simulate the spatial distribution of different age populations, but the relationship between spatial distributions of POI and different age populations is still unclear, and whether it can be used as an auxiliary variable for the different age population spatialization remains to be explored. Therefore, this study collected and sorted out the number of different age populations and POIs in 2846 county-level administrative units of the Chinese mainland in 2010, divided the research data by region and city size, and explored the relationship between the different age populations and POIs. We found that there is a complex relationship between POI and different age populations. Firstly, there are positive, moderate-to-strong linear correlations between POI and population indicators. Secondly, POI has a different explanatory power for different age populations, and it has a higher explanatory power for the young and middle-aged population than the child and old population. Thirdly, the explanatory power of POI to different age populations is positively correlated with the urban economic development level. Finally, a small number of a certain kinds of POIs can be used to effectively simulate the spatial distributions of different age populations, which can improve the efficiency of obtaining spatialization data of different age populations and greatly save on costs. The study can provide data support for the precise spatialization of different age populations and inspire the spatialization of the other population attributes by POI in the future.

3.
Signal Transduct Target Ther ; 5(1): 157, 2020 10 19.
Article in English | MEDLINE | ID: covidwho-724972

ABSTRACT

Identification of a suitable nonhuman primate (NHP) model of COVID-19 remains challenging. Here, we characterized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in three NHP species: Old World monkeys Macaca mulatta (M. mulatta) and Macaca fascicularis (M. fascicularis) and New World monkey Callithrix jacchus (C. jacchus). Infected M. mulatta and M. fascicularis showed abnormal chest radiographs, an increased body temperature and a decreased body weight. Viral genomes were detected in swab and blood samples from all animals. Viral load was detected in the pulmonary tissues of M. mulatta and M. fascicularis but not C. jacchus. Furthermore, among the three animal species, M. mulatta showed the strongest response to SARS-CoV-2, including increased inflammatory cytokine expression and pathological changes in the pulmonary tissues. Collectively, these data revealed the different susceptibilities of Old World and New World monkeys to SARS-CoV-2 and identified M. mulatta as the most suitable for modeling COVID-19.


Subject(s)
Betacoronavirus/pathogenicity , Callithrix/virology , Coronavirus Infections/epidemiology , Disease Models, Animal , Macaca fascicularis/virology , Macaca mulatta/virology , Pandemics , Pneumonia, Viral/epidemiology , Animals , Antibodies, Viral/biosynthesis , Betacoronavirus/immunology , Body Temperature , Body Weight , COVID-19 , Callithrix/immunology , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/immunology , Coronavirus Infections/pathology , Cytokines/biosynthesis , Cytokines/classification , Cytokines/immunology , Disease Susceptibility , Female , Humans , Lung/diagnostic imaging , Lung/immunology , Lung/pathology , Lung/virology , Macaca fascicularis/immunology , Macaca mulatta/immunology , Male , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/immunology , Pneumonia, Viral/pathology , SARS-CoV-2 , Species Specificity , Tomography, X-Ray Computed , Viral Load , Virus Replication
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