Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2.

The ongoing COVID-19 outbreak have posed a significant threat to public health worldwide. Recently Toll-like receptor (TLR) has been proposed to be the drug target of SARS-CoV-2 treatment, the specificity and efficacy of such treatments remain unknown. In the present study we performed the investigation of repurposed drugs via a framework comprising of Search Tool for Interacting Chemicals (STITCH), Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular docking, and virus-host-drug interactome mapping. Chloroquine (CQ) and hydroxychloroquine (HCQ) were utilized as probes to explore the interaction network that is linked to SARS-CoV-2. 47 drug targets were shown to be overlapped with SARS-CoV-2 network and were enriched in TLR signaling pathway. Molecular docking analysis and molecular dynamics simulation determined the direct binding affinity of TLR9 to CQ and HCQ. Furthermore, we established SARS-CoV-2-human-drug protein interaction map and identified the axis of TLR9-ERC1-Nsp13 and TLR9-RIPK1-Nsp12. Therefore, the elucidation of the interactions of SARS-CoV-2 with TLR9 axis will not only provide pivotal insights into SARS-CoV-2 infection and pathogenesis but also improve the treatment against COVID-19.

Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning

We investigate the predictability of 12 exchange rates with machine learning, Deep Learning and interpretable machine learning (IML) models, based on a daily dataset from December 2019 to August 2021. We find that the appreciation and depreciation of exchange rates can be partly captured by Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory, especially for the developed currencies. Inconsistent with general perception, the LightGBM model performs the best in exchange rates forecasting since its short-term information extracting mode and great robustness on small datasets. Furthermore, by employing a representative global IML method, the Accumulated Local Effect algorithm, we find that the 1 similar to 3 lags of exchange rates provide more useful information for forecasting, which can help investors improve their models' predictive ability.

Education and Quality of Life: Does the Internet Matter in China?

The internet has influenced human wellbeing through social networking, time-saving, diffusion of knowledge, and access to health information. Health is a key component of human quality of life. This study examines the nexus between education, the internet, and quality of life using data from China spanning the period from 1991 to 2020. The study used ARDL to examine the short and long-term, exploring education and the impact of the internet on quality of life. Education status plays a significant role in promoting quality of life in the short and long term. The empirical findings show the significant positive impact of the internet and ICT on quality of life in the short and long-run. Financial development and FDI improve the quality of life in the long-term in China. Based on these results, policymakers in China should develop the ICT infrastructure and human capital to support increased quality of life.

Pandemic-induced fear and stock market returns: Evidence from China

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.