Greenspace exposure is conducive to the resilience of public sentiment during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic significantly impacts people's sentiment and mental health, threatening their health and lives. We gathered 4.17 million geotagged social media posts from Weibo and scrutinized the nuances of the collective sentiments of netizens in four megacities in China during the first pandemic wave (from 1 December 2019 to 30 April 2020). Our findings suggest that the COVID-19 outbreak significantly reduced the Sentiment Index (SI) in China's cities, and the collective sentiments expressed in Wuhan were even more negative than those in the other three megacities. We explored the uncharted impacts of exposure to three geographical environment factors (GEFs) on SIs. Public exposure to greenspaces increased, while exposure to indoor built spaces decreased during the lockdown period. The exposure to sidewalks increased in rural areas but decreased in the main urban areas. The contributions of various GEFs to the SIs were the lowest during the lockdown period, and SIs were strongly affected by the pandemic. However, greenspace had the most potent effect on SIs, improving public sentiment resilience and mitigating mental health risks.

A Novel Method for Constructing EEG Large-Scale Cortical Dynamical Functional Network Connectivity (dFNC): WTCS.

As a kind of biological network, the brain network conduces to understanding the mystery of high-efficiency information processing in the brain, which will provide instructions to develop efficient brain-like neural networks. Large-scale dynamical functional network connectivity (dFNC) provides a more context-sensitive, dynamical, and straightforward sight at a higher network level. Nevertheless, dFNC analysis needs good enough resolution in both temporal and spatial domains, and the construction of dFNC needs to capture the time-varying correlations between two multivariate time series with unmatched spatial dimensions. Effective methods still lack. With well-developed source imaging techniques, electroencephalogram (EEG) has the potential to possess both high temporal and spatial resolutions. Therefore, we proposed to construct the EEG large-scale cortical dFNC based on brain atlas to probe the subtle dynamic activities in the brain and developed a novel method, that is, wavelet coherence-S estimator (WTCS), to assess the dynamic couplings among functional subnetworks with different spatial dimensions. The simulation study demonstrated its robustness and availability of applying to dFNC. The application in real EEG data revealed the appealing "Primary peak" and "P3-like peak" in dFNC network properties and meaningful evolutions in dFNC network topology for P300. Our study brings new insights for probing brain activities at a more dynamical and higher hierarchical level and pushing forward the development of brain-inspired artificial neural networks. The proposed WTCS not only benefits the dFNC studies but also gives a new solution to capture the time-varying couplings between the multivariate time series that is often encountered in signal processing disciplines.

Early forecasting of the potential risk zones of COVID-19 in China's megacities.

Recently, the coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Early and quick identification of the potential risk zones of COVID-19 infection is increasingly vital for the megacities implementing targeted infection prevention and control measures. In this study, the communities with confirmed cases during January 21-February 27 were collected and considered as the specific epidemic data for Beijing, Guangzhou, and Shenzhen. We evaluated the spatiotemporal variations of the epidemics before utilizing the ecological niche models (ENM) to assemble the epidemic data and nine socioeconomic variables for identifying the potential risk zones of this infection in these megacities. Three megacities were differentiated by the spatial patterns and quantities of infected communities, average cases per community, the percentages of imported cases, as well as the potential risks, although their COVID-19 infection situations have been preliminarily contained to date. With higher risks that were predominated by various influencing factors in each megacity, the potential risk zones coverd about 75% to 100% of currently infected communities. Our results demonstrate that the ENM method was capable of being employed as an early forecasting tool for identifying the potential COVID-19 infection risk zones on a fine scale. We suggest that local hygienic authorities should keep their eyes on the epidemic in each megacity for sufficiently implementing and adjusting their interventions in the zones with more residents or probably crowded places. This study would provide useful clues for relevant hygienic departments making quick responses to increasingly severe epidemics in similar megacities in the world.