Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict (preprint)

Drawing inspiration from the significant impact of the ongoing Russia-Ukraine conflict and the recent COVID-19 pandemic on global financial markets, this study conducts a thorough analysis of three key crude oil futures markets: WTI, Brent, and Shanghai (SC). Employing the visibility graph (VG) methodology, we examine both static and dynamic characteristics using daily and high-frequency data. We identified a clear power-law decay in most VG degree distributions and highlighted the pronounced clustering tendencies within crude oil futures VGs. Our results also confirm an inverse correlation between clustering coefficient and node degree and further reveal that all VGs not only adhere to the small-world property but also exhibit intricate assortative mixing. Through the time-varying characteristics of VGs, we found that WTI and Brent demonstrate aligned behavior, while the SC market, with its unique trading mechanics, deviates. The 5-minute VGs' assortativity coefficient provides a deeper understanding of these markets' reactions to the pandemic and geopolitical events. Furthermore, the differential responses during the COVID-19 and Russia-Ukraine conflict underline the unique sensitivities of each market to global disruptions. Overall, this research offers profound insights into the structure, dynamics, and adaptability of these essential commodities markets in the face of worldwide challenges.

Immune responses to inactivated COVID-19 vaccine were decreased in Chinese patients with chronic respiratory diseases.

Purpose: The effectiveness of inactivated vaccines against acute respiratory syndrome coronavirus 2 (SARS­CoV­2), the causative agent of coronavirus disease 2019 (COVID-19), has become a global concern. Hence, the aim of this study was to evaluate vaccine safety and to assess immune responses in individuals with chronic respiratory disease (CRD) following a two-dose vaccination. Methods: The study cohort included 191 participants (112 adult CRD patients and 79 healthy controls [HCs]) at least 21 (range, 21-159) days after a second vaccination. Frequencies of memory B cells (MBCs) subsets and titers of SARS-CoV-2 neutralizing antibodies (NAbs) and anti-receptor binding domain (RBD) IgG antibodies (Abs) were analyzed. Results: As compared to the HCs, CRD patients had lower seropositivity rates and titers of both anti-RBD IgG Abs and NAbs, in addition to lower frequencies of RBD-specific MBCs (all, p < 0.05). At 3 months, CRD patients had lower seropositivity rates and titers of anti-RBD IgG Abs than the HCs (p < 0.05). For CoronaVac, the seropositivity rates of both Abs were lower in patients with old pulmonary tuberculosis than HCs. For BBIBP-CorV, the seropositivity rates of CoV-2 NAbs were lower in patients with chronic obstructive pulmonary disease than HCs (all, p < 0.05). Meanwhile, there was no significant difference in overall adverse events between the CRD patients and HCs. Univariate and multivariate analyses identified the time interval following a second vaccination as a risk factor for the production of anti-RBD IgG Abs and CoV-2 NAbs, while the CoronaVac had a positive effect on the titers of both Abs. Female was identified as a protective factor for CoV-2 NAb levels. Conclusion: Inactivated COVID-19 vaccines were safe and well tolerated by CRD patients but resulted in lower Ab responses and the frequencies of RBD-specific MBCs. Therefore, CRD patients should be prioritized for booster vaccinations.

BENEFIT-RISK ASSESSMENT OF COVID-19 VACCINE, MRNA (MRNA-1273) FOR MALES AGE 18-64 YEARS (preprint)

Since authorization of the Moderna mRNA COVID-19 Vaccine, real-world evidence has indicated its effectiveness in preventing COVID-19 cases. However, increased cases of mRNA vaccine-associated myocarditis/pericarditis have been reported, predominantly in young adults and adolescents. The Food and Drug Administration conducted benefit-risk assessment to inform review of the Biologics License Application for use of the Moderna vaccine among individuals ages 18 years and older. We modeled benefit-risk per million individuals who receive two complete doses of the vaccine. Benefit endpoints were vaccine-preventable COVID-19 cases, hospitalizations, intensive care unit (ICU) admissions, and deaths. The risk endpoints were vaccine-related myocarditis/pericarditis cases, hospitalizations, ICU admissions and deaths. The analysis was conducted on the age-stratified male population, due to data signals and previous work showing males to be the main risk group. We constructed six scenarios to evaluate the impact of uncertainty associated with pandemic dynamics, vaccine effectiveness (VE) against novel variants, and rates of vaccine-associated myocarditis/pericarditis cases on the model results. For our most likely scenario, we assumed the US COVID-19 incidence was for the week of December 25, 2021, and a VE of 30% against cases and 72% against hospitalization with the Omicron-dominant strain. Our source for estimating vaccine-attributable myocarditis/pericarditis rates was FDA's CBER Biologics Effectiveness and Safety (BEST) System databases. Overall, our results supported the conclusion that the benefits of the vaccine outweigh its risks. Remarkably, we predicted vaccinating one million 18-25 year-old males would prevent 82,484 cases, 4,766 hospitalizations, 1,144 ICU admissions, and 51 deaths due to COVID-19, comparing to 128 vaccine-attributable myocarditis/pericarditis cases, 110 hospitalizations, zero ICU admissions, and zero deaths. Uncertainties in the pandemic trajectory, effectiveness of vaccine against novel variants, and vaccine-attributable myocarditis/pericarditis rate are important limitations of our analysis. Also, the model does not evaluate potential long-term adverse effects due to either COVID-19 or vaccine-attributable myocarditis/pericarditis.

Individual bat viromes reveal the co-infection, spillover and emergence risk of potential zoonotic viruses (preprint)

Bats are reservoir hosts for many zoonotic viruses. Despite this, relatively little is known about the diversity and abundance of viruses within bats at the level of individual animals, and hence the frequency of virus co-infection and inter-species transmission. Using an unbiased meta-transcriptomics approach we characterised the mammalian associated viruses present in 149 individual bats sampled from Yunnan province, China. This revealed a high frequency of virus co-infection and species spillover among the animals studied, with 12 viruses shared among different bat species, which in turn facilitates virus recombination and reassortment. Of note, we identified five viral species that are likely to be pathogenic to humans or livestock, including a novel recombinant SARS-like coronavirus that is closely related to both SARS-CoV-2 and SARS-CoV, with only five amino acid differences between its receptor-binding domain sequence and that of the earliest sequences of SARS-CoV-2. Functional analysis predicts that this recombinant coronavirus can utilize the human ACE2 receptor such that it is likely to be of high zoonotic risk. Our study highlights the common occurrence of inter-species transmission and co-infection of bat viruses, as well as their implications for virus emergence.

Integrated network pharmacology analysis, molecular docking, LC-MS analysis and bioassays revealed the potential active ingredients and underlying mechanism of Scutellariae radix for COVID-19

Scutellariae radix (“Huang-Qin” in Chinese) is a well-known traditional herbal medicine and popular dietary supplement in the world, extensively used in prescriptions of TCMs as adjuvant treatments for coronavirus pneumonia 2019 (COVID-19) patients in China. According to the differences in its appearance, Scutellariae radix can be classified into two kinds: ZiQin (1∼3 year-old Scutellariae baicalensis with hard roots) and KuQin (more than 3 year-old S. baicalensis with withered pithy roots). In accordance with the clinical theory of TCM, KuQin is superior to ZiQin in cooling down the heat in the lung. However, the potential active ingredients and underlying mechanisms of Scutellariae radix for the treatment of COVID-19 remain largely unexplored. It is still not clear whether there is a difference in the curative effect of ZiQin and KuQin for the treatment of COVID-19. In this research, network pharmacology, LC-MS based plant metabolomics, and in vitro bioassays were integrated to explore both the potential active components and mechanism of Scutellariae radix for the treatment of COVID-19. As the results, network pharmacology combined with molecular docking analysis indicated that Scutellariae radix primarily regulates the MAPK and NF-κB signaling pathways via active components such as baicalein and scutellarin, and blocks SARS-CoV-2 spike binding to human ACE2 receptors. In vitro bioassays showed that baicalein and scutellarein exhibited more potent anti-inflammatory and anti-infectious effects than baicalin, the component with the highest content in Scutellariae radix. Moreover, baicalein inhibited SARS-CoV-2’s entry into Vero E6 cells with an IC50 value of 142.50 μM in a plaque formation assay. Taken together, baicalein was considered to be the most crucial active component of Scutellariae radix for the treatment of COVID-19 by integrative analysis. In addition, our bioassay study revealed that KuQin outperforms ZiQin in the treatment of COVID-19. Meanwhile, plant metabolomics revealed that baicalein was the compound with the most significant increase in KuQin compared to ZiQin, implying the primary reason for the superiority of KuQin over ZiQin in the treatment of COVID-19.

Association between delayed surgery and survival in early-stage cervical cancer in mainland China: Implications during the coronavirus pandemic (preprint)

Objective: The COVID-19 pandemic has had a significant impact on oncogynecologic patients worldwide, particularly with respect to delayed diagnosis and treatment. During the COVID-19 pandemic, few studies have examined the impact of delayed surgery on survival in early-stage cervical cancer patients. The purpose of this study was to determine the effect of delayed surgical time on survival in patients with early cervical cancer. Design A retrospective cohort study. Setting A single general hospital in Shaanxi, Northwest China. Population A total of 1207women with early cervical cancer were recruited between April 2013 and December 2018 in Mainland China and followed up until 29 Feb 2022. Methods This retrospective cohort study was conducted in a comprehensive tertiary hospital in Shaanxi, Xi’an, China. We used a Cox proportional hazard model with delay time in weeks as a categorical variable to analyse the effect of surgical delay time on survival. Main Outcome Measures The 5-year overall and disease-free survival were used as the primary outcome measures. Results A total of 800 participants were included in the final cohort. In the multivariate Cox regression analysis (median follow-up, 58 months), patients in the long delay time group had DFS (5-year rates, 91.5% versus 90.9%, HR 0.99, 95% CI 0.62~1.59, P=0.98) and OS (5-year rates of 92.9% versus 90.8%, HR 0.68, 95% CI 0.42~1.10, P=0.11) similar to those in the short delay time group. Conclusions Our findings indicate that a 12-week delay in surgery is not associated with long-term survival in women with early-stage cervical cancer.

Biocompatible Conductive Hydrogels: Applications in the Field of Biomedicine.

The impact of COVID-19 has rendered medical technology an important factor to maintain social stability and economic increase, where biomedicine has experienced rapid development and played a crucial part in fighting off the pandemic. Conductive hydrogels (CHs) are three-dimensional (3D) structured gels with excellent electrical conductivity and biocompatibility, which are very suitable for biomedical applications. CHs can mimic innate tissue's physical, chemical, and biological properties, which allows them to provide environmental conditions and structural stability for cell growth and serve as efficient delivery substrates for bioactive molecules. The customizability of CHs also allows additional functionality to be designed for different requirements in biomedical applications. This review introduces the basic functional characteristics and materials for preparing CHs and elaborates on their synthetic techniques. The development and applications of CHs in the field of biomedicine are highlighted, including regenerative medicine, artificial organs, biosensors, drug delivery systems, and some other application scenarios. Finally, this review discusses the future applications of CHs in the field of biomedicine. In summary, the current design and development of CHs extend their prospects for functioning as an intelligent and complex system in diverse biomedical applications.

Geographical and temporal encoding for improving the estimation of PM2.5 concentrations in China using end-to-end gradient boosting

• We present an estimation result of PM 2.5 concentration across China. • The proposed method captures the influence of spatial-temporal information. • The proposed method shows superior performance. • The interaction of prediction and observation is analyzed in detail. Fine particulate matter with aerodynamic diameters less than 2.5 μ m (PM 2.5) profoundly affects environmental systems and human health. To dynamically monitor fine particulate matter over large geographic areas, some machine learning methods have been utilized to estimate its concentration using satellite-based aerosol optical depth (AOD). To improve the estimation of PM 2.5 concentration across large areas, a geospatial-temporal joint code is proposed in this paper to characterize the influence of spatial-temporal information hidden in satellite-based aerosol products. This encoding method can reveal the relationship between the PM 2.5 concentration and its geospatial location and observation time. Instead of aggregating observation data over neighbors, the method directly encodes the spatial-temporal information as features of the end-to-end gradient boosting model for the estimation of PM 2.5. Experimental results of PM 2.5 concentration in 2019 across China show that the state-of-the-art method is outperformed by the proposed method by a large margin, with R2 from 0.89 to 0.92, RMSE from 10.35 to 7.89 μg/m3, and MAE from 6.71 to 5.17 μg/m3. In addition, overall partial dependence plots (PDPs) are used for the first time to visualize the complicated relationship between satellite-based aerosol products and PM 2.5 concentrations. [ FROM AUTHOR] Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19.

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

New insights into price drivers of crude oil futures markets: Evidence from quantile ARDL approach (preprint)

This paper investigates the cointegration between possible determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of WTI and newly-launched Shanghai crude oil futures (SC) via the Autoregressive Distributed Lag (ARDL) model and Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results confirm that economic policy uncertainty, stock markets, interest rates and coronavirus panic are important drivers of WTI futures prices. Our findings also suggest that the US and China's stock markets play vital roles in movements of SC futures prices. Meanwhile, CSI300 stock index has a significant positive short-run impact on SC futures prices while S\&P500 prices possess a positive nexus with SC futures prices both in long-run and short-run. Overall, these empirical evidences provide practical implications for investors and policymakers.

Combating the COVID-19 Pandemic: The Role of the SARS Imprint

We provide evidence of delayed attention and inaction in response to COVID-19 in countries that did not experience SARS in 2003. Using cross-country data, we find that individuals in countries that had SARS infections in 2003 searched more intensively for COVID-19-related information on Google in late January 2020, the time of the first known outbreak in Wuhan, China. Early attention to the novel virus, as measured by Google searches, is associated with deeper stock market drops in countries with SARS experience. In contrast, people in countries without SARS experience started to pay more attention much later, in March. Moreover, governments in these countries responded significantly more slowly in implementing social distancing policies to combat domestic COVID-19 outbreaks than governments in countries with SARS experience. Moreover, such early responses of individuals and governments in countries with SARS experience are prevalent within continent, even in non-Asian countries. Furthermore, people in countries with SARS experience are more compliant with social distancing rules. These timely attention and proactive responses of individuals and governments are more pronounced in countries that reported deaths caused by SARS, which left deeper imprints. Our findings suggest that the imprint of similar viruses' experience is a fundamental mechanism underlying timely responses to COVID-19.

Air quality in Nanjing during COVID-19 lockdown period

In order to investigate the impact of COVID-19 lockdown on air quality in Nanjing, the air pollutants observed from January 25 to February 10, in 2020（COVID-19 lockdown period） in Nanjing and its surrounding cities was analyzed. During the lockdown period with poor atmospheric diffusion conditions, the concentrations of PM2.5, PM10, NO2, SO2, and CO decreased obviously, with the value of 36, 44, 5, 22μg/m3 and 1.1 mg/m3, whereasO3 increased by 4%. The net effectiveness of the emission reduction measures was calculated through comparisons of concentrations of air pollutants between and before COVID in the similar meteorological conditions. Concentrations of PM2.5, PM10, SO2, NO2 and CO decreased by 41.7%, 45.3%, 14.3%, 43.5% and 18.2%, respectively, whereasO3 increased by 4.8%. Compared to capital cities of the Yangtze River Delta in the same period, the largest decline of SO2 and the medium decline of the other pollutions were appeared in Nanjing. The diurnal variation concentration of PM2.5 and PM10 changed from double peak to single peak, due to the disappearance of nighttime sub-peak of particle. The concentration ofO3 increased significantly at night, which was resulted from that sharp reduction of traffic sources weaken the titration reaction of NO toO3. The peak ofO3 during the daytime depended on the variation of the ratio of VOCs to NOx due to the emission control.

Discovery of potential small molecular SARS-CoV-2 entry blockers targeting the spike protein.

An epidemic of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. SARS-CoV-2 relies on its spike protein to invade host cells by interacting with the human receptor protein Angiotensin-Converting Enzymes 2 (ACE2). Therefore, designing an antibody or small-molecular entry blockers is of great significance for virus prevention and treatment. This study identified five potential small molecular anti-virus blockers via targeting SARS-CoV-2 spike protein by combining in silico technologies with in vitro experimental methods. The five molecules were natural products that binding to the RBD domain of SARS-CoV-2 was qualitatively and quantitively validated by both native Mass Spectrometry (MS) and Surface Plasmon Resonance (SPR). Anti-viral activity assays showed that the optimal molecule, H69C2, had a strong binding affinity (dissociation constant KD) of 0.0947 µM and anti-virus IC50 of 85.75 µM.

Temporal risk of coronavirus disease 2019 in Gansu Province

Objective: To describe the temporal risk characteristics of coronavirus disease 2019 (COVID-19) in Gansu province.

A Quantitative Evaluation of COVID-19 Epidemiological Models (preprint)

Quantifying how accurate epidemiological models of COVID-19 forecast the number of future cases and deaths can help frame how to incorporate mathematical models to inform public health decisions. Here we analyze and score the predictive ability of publicly available COVID-19 epidemiological models on the COVID-19 Forecast Hub. Our score uses the posted forecast cumulative distributions to compute the log-likelihood for held-out COVID-19 positive cases and deaths. Scores are updated continuously as new data become available, and model performance is tracked over time. We use model scores to construct ensemble models based on past performance. Our publicly available quantitative framework may aid in improving modeling frameworks, and assist policy makers in selecting modeling paradigms to balance the delicate trade-offs between the economy and public health.