Are government bonds still safe havens in the context of COVID-19?

This study analyzes whether government bonds can act as safe havens in the context of COVID-19. Using a panel fixed effect model, data were collected for both advanced and emerging market economies from March 11, 2020, to June 30, 2021. Robustness tests were used to add to the credibility of the findings. Our evidence supports that government bonds maintained their safe haven status during the COVID-19 pandemic. Hence, investors can still use government bonds to hedge financial market risks in the uncertain environment associated with this pandemic. Additionally, the negative effects of the COVID-19 pandemic on government bond yields in emerging economies are larger than in advanced economies. Therefore, policymakers' measures should focus on reducing COVID-19 cases to alleviate panic and diminish economic fluctuations, especially for emerging economies. Regulators can also use short-term interest rates to guide market capital flow to avoid a liquidity crisis, reducing financial stress and market uncertainty. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Upcycling disposable face masks into fuel range iso-alkanes through hydropyrolysis coupled with vapor-phase hydrocracking

The COVID-19 pandemic has resulted in an alarming accumulation of plastic waste. Herein, an integrated hydropyrolysis and hydrocracking process was performed to upcycle disposable masks into fuel-range iso-alkanes over carbon supported ruthenium (Ru/C). Experimental results indicated that catalyst type significantly affected product distribution during the hydropyrolysis and vapor-phase hydrocracking of disposable masks. Compared with zeolites-induced catalytic cascade process where up to ∼25.9 wt% yield of aromatic hydrocarbons such as toluene and xylenes were generated, a ∼82.7 wt% yield of desirable iso-alkanes with a high C5–C12 gasoline selectivity of 95.5% was obtained over Ru/C under 550 °C hydropyrolysis temperature and 300 °C hydrocracking temperature at 0.2 MPa H2. The cascade hydropyrolysis and hydrocracking process also exhibited high adaptability and flexibility in upcycling single-use syringes, food packaging, and plastic bags, generating 79.1, 81.6, and 80.3 wt% yields of fuel range iso/n-alkanes, respectively. This catalytic cascade hydrotreating process provides an efficient and effective approach to convert pandemic-derived plastic waste into gasoline-range fuel products. © 2022 Elsevier Ltd

The relationship between student motivation and academic performance: the mediating role of online learning behavior

Purpose: This paper aims to use a quantitative approach to explore the role of online learning behavior in students' academic performance during the COVID-19 pandemic. Specifically, the authors probe its mediating effect in the relationship between student motivation (extrinsic and intrinsic) and academic performance in a blended learning context. Design/methodology/approach: Survey data were collected from 148 students taking an organizational behavior course at one Chinese university. The data were paired and analyzed through regression analysis. Findings: The results show that students should actively engage in online learning behavior to maximize the effects of blended learning. Extrinsic motivation was found to positively influence academic performance both directly and indirectly through online learning behavior, while intrinsic motivation affected academic performance only indirectly. Originality/value: Through paired data on extrinsic and intrinsic motivation, online learning behavior and academic performance, this study provides a more nuanced understanding of how online learning behavior affects the focal relationship, and it advances research on the mechanisms underlying the focal relationship. Practitioners should enhance students' online learning behavior to boost blended learning effects during the COVID-19 pandemic. © 2022, Emerald Publishing Limited.

Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media

When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order. © 2022

Present status of microfluidic PCR chip in nucleic acid detection and future perspective

Polymerase chain reaction (PCR) amplifies specific fragment of DNA molecules and has been extensively applied in fields of pathogens and gene mutation detection, food safety and clinical diagnosis which on the other hand, holds the drawbacks of large size instrument, high heat dissipation etc. It has been demonstrated that microfluidics technique coupling with PCR reaction exhibits characteristics of integration, automatization, miniaturization, and portability. Meanwhile, various designed fabrication of microchip could contribute to diverse applications. In this review, we summarized major works about a variety of microfluidic chips equipped with several kinds of PCR techniques (PCR, RT-PCR, mPCR, dPCR) and detection methods like fluorescence, electrochemistry, and electrophoresis detection. The development and application of PCR-based microfluidic chip in pathogen and gene mutation detection, diseases prevention and diagnosis, DNA hybridization and low-volume sample treatment were also discussed. Copyright © 2022 Elsevier B.V.

Nirmatrelvir increases blood tacrolimus concentration in COVID-19 patients as determined by UHPLC-MS/MS method.

OBJECTIVE: Transplant recipients have a higher risk of SARS-CoV-2 infection owing to the use of immunosuppressive drugs like tacrolimus (FK506). FK506 and nirmatrelvir (NMV) (an anti-SARS-CoV-2 drug) are metabolized by cytochrome P450 3A4 and may have potential drug-drug interactions. It is important to determine the effect of NMV on FK506 concentrations. PATIENTS AND METHODS: Following protein precipitation from blood, FK506 and its internal standard (FK506-13C,2d4) were detected by ultra-high performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). Total 22 blood samples (valley concentrations) from two coronavirus disease 2019 (COVID-19) patients were collected and analyzed for FK506 concentrations. RESULTS: Blood levels of FK506 (0.5-100 ng/mL) showed good linearity. The UHPLC-MS/MS method was validated with intra- and inter-batch accuracies of 104.55-107.85%, and 99.52-108.01%, respectively, and precisions of < 15%. Mean blood FK506 concentration was 12.01 ng/mL (range, 3.15-33.1 ng/mL). Five-day co-administration with NMV increased the FK506 concentrations from 3.15 ng/mL to 33.1 ng/mL, returning to 3.36 ng/mL after a 9-day-washout. CONCLUSIONS: We developed a simple quantification method for therapeutic drug monitoring of FK506 in patients with COVID-19 using UHPLC-MS/MS with protein precipitation. We found that NMV increased FK506 blood concentration 10-fold. Therefore, it is necessary to re-consider co-administration of FK506 with NMV.

A class of anomalous diffusion epidemic models based on CTRW and distributed delay

In recent years, the epidemic model with anomalous diffusion has gained popularity in the literature. However, when introducing anomalous diffusion into epidemic models, they frequently lack physical explanation, in contrast to the traditional reaction-diffusion epidemic models. The point of this paper is to guarantee that anomalous diffusion systems on infectious disease spreading remain physically reasonable. Specifically, based on the continuous-time random walk (CTRW), starting from two stochastic processes of the waiting time and the step length, time-fractional space-fractional diffusion, time-fractional reaction-diffusion and fractional-order diffusion can all be naturally introduced into the SIR (S: susceptible, I: infectious and R: recovered) epidemic models, respectively. The three models mentioned above can also be applied to create SIR epidemic models with generalized distributed time delays. Distributed time delay systems can also be reduced to existing models, such as the standard SIR model, the fractional infectivity model and others, within the proper bounds. Meanwhile, as an application of the above stochastic modeling method, the physical meaning of anomalous diffusion is also considered by taking the SEIR (E: exposed) epidemic model as an example. Similar methods can be used to build other types of epidemic models, including SIVRS (V: vaccine), SIQRS (Q: quarantined) and others. Finally, this paper describes the transmission of infectious disease in space using the real data of COVID-19.

Role of vitamin D and ACE2 in COVID-19

The spread of COVID-19 has greatly threatened human health and economic growth. Angiotensin-converting enzyme 2 (ACE2) is a receptor for severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). By attaching to ACE2, SARS-COV-2 reduces its expression and induces lung injury. Vitamin D can inhibit the progression of COVID-19 by inhibiting the activity of ROCK pathway, up-regulating ACE2 expression and bio-availability, and slowing down the adverse reactions caused by Ang II accumulation. This study explored a novel mechanism, i.e., vitamin D protects against COVID-19-induced injury by upregulating ACE2 expression. It provides theoretical guidance for the role of Vitamin D in the prevention and treatment of COVID-19. Copyright © 2022 Chinese Journal of Clinical Pharmacology and Therapeutics. All rights reserved.

Humoral immune response to COVID-19 mRNA vaccines in patients with relapsing multiple sclerosis treated with ofatumumab

Introduction: Ofatumumab (OMB), a fully-human anti-CD20 monoclonal antibody (Ab), is indicated for the treatment of adults with relapsing multiple sclerosis (RMS). As OMB induces B-cell depletion, it is important to understand if OMB-treated patients (pts) can mount a protective immune response to the COVID-19 vaccine. Objective(s): Assess humoral immune response (HIR) to mRNA COVID-19 vaccines in OMB-treated pts with RMS. Method(s): This was an open-label, single-arm, multicentre, prospective pilot study (NCT04847596) of pts with RMS aged 18-55y receiving 2 doses of an mRNA COVID-19 vaccine after treatment with OMB 20mg for >=1mo. Pts who received a 3rd/ booster vaccine dose were also eligible. Exclusion criteria included prior COVID-19 diagnosis, recent major infections and prior sphingosine 1-phosphate receptor modulator or natalizumab treatment. The 1st post-vaccination immune assay was performed >=14d after full vaccination course (2 or 3 doses), with the 2nd assay conducted 90d after the 1st assessment (assays conducted by local laboratories). Primary endpoint was proportion of pts achieving an HIR, defined as a positive response on the SARS-CoV-2 qualitative IgG Ab assay. Secondary endpoints were adverse events (AEs) and serious AEs. Result(s): 26 pts (median [range] age: 42 [27-54]y) were included;81% were female, 96% were White and 35% were Hispanic/Latino. Median (range) OMB treatment duration at screening was 237d (50-364). 15 pts (58%) received 2 vaccine doses;11 (42%) received a 3rd/booster dose. HIR to COVID-19 vaccines was achieved by 14/26 pts (54% [95%CI: 33%-73%]) at the 1st post-vaccination assay. In pts who received a booster;7/10 achieved an HIR and 6/7 aged <50y achieved HIR. Prior ocrelizumab use or age >=50y led to a decreased HIR while length of OMB treatment and COVID-19 mRNA vaccine type did not impact HIR. At the 2nd assay, 13/26 pts (50% [95%CI: 30%-70%]) achieved an HIR (10 pts maintained and 3 additional pts achieved HIR;2 pts who achieved HIR at the 1st assay were negative at the 2nd assay;2 pts had missing assays). Overall, 5/26 pts (19%) reported >=1 AEs, including COVID-19 infection (n=4), herpes zoster infection (n=1), S. pharyngitis (n=1) and headache (n=1). No serious AEs were reported. Conclusion(s): These findings suggest that most OMB-treated pts with RMS mount an HIR after COVID-19 mRNA vaccination and may help inform the coordination of vaccination and treatment of RMS pts with OMB.

THE EFFECTIVENESS OF HUMAN INTERVENTIONS AGAINST COVID-19 BASED ON EVOLUTIONARY GAME THEORY

Social distancing strategy (including Six-Foot Rule, wearing masks, and other easy-to-operate measures) and quarantine measures have played a critical role in the early stage of the COVID-19 epidemic. In order to explore the mechanisms of these two human interventions accurately, we develop a coupling epidemiological-behavioral model based on evolutionary game the-ory. Individuals decide whether to take strategy measures based on rational consideration of payoffs. Moreover, authorities also balance the costs and effectiveness of the interventions at the public level. Our simulation shows that social distancing strategy can suppress every single outbreak effectively. In the early stage of an epidemic, the implementation of the quarantine measures determines the scale of the epidemic. Timely and effective quarantine measures can control recurrent outbreaks without social lockdown. Support policy for individual-level intervention or high diagnosis rates are beneficial to control the epidemic but require long-term social lockdown. © 2022, Wilmington Scientific Publisher. All rights reserved.

Hydrophobic RuO2/Graphene/N-doped porous carbon hybrid catalyst for Li-air batteries operating in ambient air

Li–air batteries have received significant attention for their ultrahigh theoretical energy density. However, the byproducts induced by attacking air hinder the conversion of Li–O2 batteries to Li–air batteries. Humidity is one of the main obstacles, not only causing side reactions with the discharge products but also leading to rapid corrosion of the lithium anode. Here, we fabricated a novel composite hydrophobic catalyst by loading RuO2 and graphene on N-doped porous carbon. The catalyst was endowed with hydrophobicity and showed superior catalytic performance and low affinity to water in the air. A Li–air battery equipped with this novel composite catalyst exhibited eminent cycling performance in pure oxygen (over 470 h), humid oxygen [∼40% relative humidity (RH), over 310 h], and ambient air (∼42% RH, over 330 h) at a current density of 500 mA g−1, and the discharge specific capacity increased from 13122.1 to 19358.6 mAh g−1. © 2022

Peak fraction of infected in epidemic spreading for multi-community networks

One of the most effective strategies to mitigate the global spreading of a pandemic (e.g. coronavirus disease 2019) is to shut down international airports. From a network theory perspective, this is since international airports and flights, essentially playing the roles of bridge nodes and bridge links between countries as individual communities, dominate the epidemic spreading characteristics in the whole multi-community system. Among all epidemic characteristics, the peak fraction of infected, I-ma(x), is a decisive factor in evaluating an epidemic strategy given limited capacity of medical resources but is seldom considered in multi-community models. In this article, we study a general two-community system interconnected by a fraction r of bridge nodes and its dynamic properties, especially I-max, under the evolution of the susceptibleinfected-recovered model. Comparing the characteristic time scales of different parts of the system allows us to analytically derive the asymptotic behaviour of I-max with r, as r -> 0, which follows different power-law relations in each regime of the phase diagram. We also detect crossovers when I-max changes from one power law to another, crossing different power-law regimes as driven by r. Our results enable a better prediction of the effectiveness of strategies acting on bridge nodes, denoted by the power-law exponent epsilon(I) as in I-max proportional to r(1/epsilon I).

An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors

Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion of survivors has respiratory complications, but currently, experienced radiologists and state-of-the-art artificial intelligence systems are not able to detect many abnormalities from follow-up computerized tomography (CT) scans of COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), a computer-aided detection (CAD) method for detecting and quantifying pulmonary parenchyma lesions on chest CT. Through proposing a number of deep-learning-based segmentation models and assembling them in an interpretable manner, DLPE removes irrelevant tissues from the perspective of pulmonary parenchyma, and calculates the scan-level optimal window, which considerably enhances parenchyma lesions relative to the lung window. Aided by DLPE, radiologists discovered novel and interpretable lesions from COVID-19 inpatients and survivors, which were previously invisible under the lung window. Based on DLPE, we removed the scan-level bias of CT scans, and then extracted precise radiomics from such novel lesions. We further demonstrated that these radiomics have strong predictive power for key COVID-19 clinical metrics on an inpatient cohort of 1,193 CT scans and for sequelae on a survivor cohort of 219 CT scans. Our work sheds light on the development of interpretable medical artificial intelligence and showcases how artificial intelligence can discover medical findings that are beyond sight. Respiratory complications after a COVID infection are a growing concern, but follow-up chest CT scans of COVID-19 survivors hardly present any recognizable lesions. A deep learning-based method was developed that calculates a scan-specific optimal window and removes irrelevant tissues such as airways and blood vessels from images with segmentation models, so that subvisual abnormalities in lung scans become visible.

The relationship between student motivation and academic performance: the mediating role of online learning behavior

Purpose: This paper aims to use a quantitative approach to explore the role of online learning behavior in students’ academic performance during the COVID-19 pandemic. Specifically, the authors probe its mediating effect in the relationship between student motivation (extrinsic and intrinsic) and academic performance in a blended learning context. Design/methodology/approach: Survey data were collected from 148 students taking an organizational behavior course at one Chinese university. The data were paired and analyzed through regression analysis. Findings: The results show that students should actively engage in online learning behavior to maximize the effects of blended learning. Extrinsic motivation was found to positively influence academic performance both directly and indirectly through online learning behavior, while intrinsic motivation affected academic performance only indirectly. Originality/value: Through paired data on extrinsic and intrinsic motivation, online learning behavior and academic performance, this study provides a more nuanced understanding of how online learning behavior affects the focal relationship, and it advances research on the mechanisms underlying the focal relationship. Practitioners should enhance students’ online learning behavior to boost blended learning effects during the COVID-19 pandemic. © 2022, Emerald Publishing Limited.

Questionnaires assessing the anxiety alleviation benefits of indoor plants for self-isolated population during COVID-19

Indoor plants have great benefits to humans, including physical health, cognition and emotion through their repair and purification capabilities, but most of these positiv e effects have not been quantified and valued. In this study, the Corona Virus Disease 2019 (COVID-19), when people must be self-isolated at home and avoid outdoor activities in China, was utilized adequately and the influence of indoor plants was analyzed via the 2031 valid questionnaires, in which indoor plant status, interest degree, interaction frequency and anxiety alleviation were surveyed. Results showed that indoor plants were widely cultivated especially in the living room. Compared to before the COVID-19, the interest degree with indoor plants increased by similar to 33% and their overall interaction frequency increased by similar to 78% during the COVID-19. More than 70% of the surveyed people exhibited anxiety during the COVID-19, and the overall anxiety level was 1.17 (between 'Slight anxiety' and 'Anxiety'). And similar to 61% of the surveyed people supported that indoor plants could alleviate self-isolation anxiety, and the anxiety alleviation degree was 0.79 (tend to 'Releasing the certain anxiety'), which showed that indoor plants had also shown to have an indirect psychological effect on anxiety alleviation.