Optimization of the expression of the main protease from SARS-CoV-2.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a vital role in viral replication. To study the function of Mpro and screen inhibitors targeting Mpro, it is necessary to prepare high-purity and high-activity Mpro. In this study, four types of SARS-CoV-2 Mpros containing different termini were prepared, and their activities were determined successfully. The results showed that the activity of wild-type (WT) Mpro was the highest, and the additional residues at the N-terminus but not at the C-terminus had a major effect on the enzyme activity. To explain this, the alignment of structures of different forms of Mpro was determined, and the additional residues at the N-terminus were found to interfere with the formation of the substrate binding pocket. This study confirms the importance of the natural N-terminus to the activity of Mpro and suggests that WT-GPH6 (Mpro with eight additional residues at the C-terminus) can be used as a substitute for authentic Mpro to screen inhibitors. In short, this study provides a reference for the expression and purification of new coronaviruses confronted in the future.

icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland.

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.

GCN-based stock relations analysis for stock market prediction.

Most stock price predictive models merely rely on the target stock's historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility.

Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.

Consistent weekly cycles of atmospheric NO2, CO, and CO2 in a North American megacity from ground-based, mountaintop, and satellite measurements

The weekday-weekend effect of anthropogenic emissions in cities, driven by the associated weekly changes in human activities, provides a unique opportunity to assess the sensitivity of observation networks (e.g., ground-based and space-borne instruments) on urban emissions. In this study, we focus on the weekly cycle amplitudes of nitrogen dioxide (NO2), carbon monoxide (CO), and carbon dioxide (CO2) in the Los Angeles (LA) megacity, where a significant weekly cycle of human activities exists. In addition, abundant observations are being produced continuously from existing ground-based, mountaintop, and satellite platforms to monitor carbon emissions and air quality in LA. From our analysis, significant agreement can be found in observations from different platforms. For NO2, a 30%–35% Sunday decline relative to mid-week mixing ratios can be observed from both ground-based and satellite observations. For CO, the Sunday drops from ground-based, mountain-top and satellite observations are 13%–20%. The TROPOMI instrument with its high spatial resolution provides detailed spatial information on the reduction of tropospheric NO2 and CO columns on Sundays. The spatial pattern is in good agreement with traffic density in LA. Impact due to the prevailing winds from the coast in the afternoon can also be observed. For anthropogenic CO2, we show that the weekly cycle of XCO2 enhancement above background from OCO-2 observations has a Sunday decline (15%–20%) consistent with ground-based observations and TCCON. This weekly pattern of CO2 in a megacity directly detected by OCO-2 is reported for the first time. In addition, we also investigate the weekly cycles in the stable carbon isotopic composition of CO2 (δ13C) from ground-based observations, which demonstrates the weekly variation in fossil fuel usage in LA. Finally, using the COVID-19 lockdown period as an example of a short-term perturbation on anthropogenic emissions, we found that the weekly cycle amplitude became larger during the lockdown period primarily because of the traffic volume changes in light-duty vehicles. This study highlights the consistencies and effectiveness of existing observing platforms in monitoring the anthropogenic emissions of the LA megacity.

Detecting the Responses of CO2 Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2

The continuing increase in atmospheric CO2 concentration caused by anthropogenic CO2 emissions significantly contributes to climate change driven by global warming. Satellite measurements of long-term CO2 data with global coverage improve our understanding of global carbon cycles. However, the sensitivity of the space-borne measurements to anthropogenic emissions on a regional scale is less explored because of data sparsity in space and time caused by impacts from geophysical factors such as aerosols and clouds. Here, we used global land mapping column averaged dry-air mole fractions of CO2 (XCO2) data (Mapping-XCO2), generated from a spatio-temporal geostatistical method using GOSAT and OCO-2 observations from April 2009 to December 2020, to investigate the responses of XCO2 to anthropogenic emissions at both global and regional scales. Our results show that the long-term trend of global XCO2 growth rate from Mapping-XCO2, which is consistent with that from ground observations, shows interannual variations caused by the El Niño Southern Oscillation (ENSO). The spatial distributions of XCO2 anomalies, derived from removing background from the Mapping-XCO2 data, reveal XCO2 enhancements of about 1.5–3.5 ppm due to anthropogenic emissions and seasonal biomass burning in the wintertime. Furthermore, a clustering analysis applied to seasonal XCO2 clearly reveals the spatial patterns of atmospheric transport and terrestrial biosphere CO2 fluxes, which help better understand and analyze regional XCO2 changes that are associated with atmospheric transport. To quantify regional anomalies of CO2 emissions, we selected three representative urban agglomerations as our study areas, including the Beijing-Tian-Hebei region (BTH), the Yangtze River Delta urban agglomerations (YRD), and the high-density urban areas in the eastern USA (EUSA). The results show that the XCO2 anomalies in winter well capture the several-ppm enhancement due to anthropogenic CO2 emissions. For BTH, YRD, and EUSA, regional positive anomalies of 2.47 ± 0.37 ppm, 2.20 ± 0.36 ppm, and 1.38 ± 0.33 ppm, respectively, can be detected during winter months from 2009 to 2020. These anomalies are slightly higher than model simulations from CarbonTracker-CO2. In addition, we compared the variations in regional XCO2 anomalies and NO2 columns during the lockdown of the COVID-19 pandemic from January to March 2020. Interestingly, the results demonstrate that the variations of XCO2 anomalies have a positive correlation with the decline of NO2 columns during this period. These correlations, moreover, are associated with the features of emitting sources. These results suggest that we can use simultaneously observed NO2, because of its high detectivity and co-emission with CO2, to assist the analysis and verification of CO2 emissions in future studies.

Discussion on Etiology and Pathogenesis of Corona Virus Disease 2019 from “Cold-dampness and Insidious Dryness”

Based on the characteristics of the epidemic situation and the authors’ understanding of the related ancient books and documents, this paper explores the etiology and pathogenesis of Corona Virus Disease 2019 (COVID-19) from 5 aspects: abnormal climate in “warm winter”, unique geographical location, pathogenesis evolution of cold and dampness mixed with insidious dryness, transmission and change of “triple energizer” of toxic pathogens, and game between healthy Qi and toxic pathogens. Combined with the special treatment of traditional Chinese medicine (TCM), the purpose is to make a modest contribution to curbing the epidemic situation with TCM.

Both Boceprevir and GC376 efficaciously inhibit SARS-CoV-2 by targeting its main protease.

COVID-19 was declared a pandemic on March 11 by WHO, due to its great threat to global public health. The coronavirus main protease (Mpro, also called 3CLpro) is essential for processing and maturation of the viral polyprotein, therefore recognized as an attractive drug target. Here we show that a clinically approved anti-HCV drug, Boceprevir, and a pre-clinical inhibitor against feline infectious peritonitis (corona) virus (FIPV), GC376, both efficaciously inhibit SARS-CoV-2 in Vero cells by targeting Mpro. Moreover, combined application of GC376 with Remdesivir, a nucleotide analogue that inhibits viral RNA dependent RNA polymerase (RdRp), results in sterilizing additive effect. Further structural analysis reveals binding of both inhibitors to the catalytically active side of SARS-CoV-2 protease Mpro as main mechanism of inhibition. Our findings may provide critical information for the optimization and design of more potent inhibitors against the emerging SARS-CoV-2 virus.

Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state (preprint)

Contact tracing is increasingly being used to combat COVID-19, and digital implementations are now being deployed, many of them based on Apple and Google's Exposure Notification System. These systems are new and are based on smartphone technology that has not traditionally been used for this purpose, presenting challenges in understanding possible outcomes. In this work, we use individual-based computational models to explore how digital exposure notifications can be used in conjunction with non-pharmaceutical interventions, such as traditional contact tracing and social distancing, to influence COVID-19 disease spread in a population. Specifically, we use a representative model of the household and occupational structure of three counties in the state of Washington together with a proposed digital exposure notifications deployment to quantify impacts under a range of scenarios of adoption, compliance, and mobility. In a model in which 15% of the population participated, we found that digital exposure notification systems could reduce infections and deaths by approximately 8% and 6%, effectively complementing traditional contact tracing. We believe this can serve as guidance to health authorities in Washington state and beyond on how exposure notification systems can complement traditional public health interventions to suppress the spread of COVID-19.

A noncompeting pair of human neutralizing antibodies block COVID-19 virus binding to its receptor ACE2.

Neutralizing antibodies could potentially be used as antivirals against the coronavirus disease 2019 (COVID-19) pandemic. Here, we report isolation of four human-origin monoclonal antibodies from a convalescent patient, all of which display neutralization abilities. The antibodies B38 and H4 block binding between the spike glycoprotein receptor binding domain (RBD) of the virus and the cellular receptor angiotensin-converting enzyme 2 (ACE2). A competition assay indicated different epitopes on the RBD for these two antibodies, making them a potentially promising virus-targeting monoclonal antibody pair for avoiding immune escape in future clinical applications. Moreover, a therapeutic study in a mouse model validated that these antibodies can reduce virus titers in infected lungs. The RBD-B38 complex structure revealed that most residues on the epitope overlap with the RBD-ACE2 binding interface, explaining the blocking effect and neutralizing capacity. Our results highlight the promise of antibody-based therapeutics and provide a structural basis for rational vaccine design.