Implementation and Application of Large-Scale Drug Virtual Screening

The molecular docking-based virtual screening technique evaluates the binding abilities between multiple ligand compounds and receptors to screen for the active compounds. In the context of the global spread of the COVID-19 pandemic, large-scale and rapid drug virtual screening is crucial for identifying potential drug molecules from massive datasets of ligand structures. The powerful computing power of supercomputer provides hardware guarantee for drug virtual screening, but the super large-scale drug virtual screening still faces many challenges that affects the effective execution of the calculation. Based on the analysis of the challenges, this paper proposes a centralized task distribution scheme with a central database, and designs a multi-level task distribution framework. The challenges are effectively solved through multi-level intelligent scheduling, multi-level compression processing of massive small molecule files, dynamic load balancing and high error tolerance management technology. An easy-touse"tree”multi-level task distribution system is implemented. A fast, efficient and stable drug virtual screening task distribution, calculation and result analysis function is realized, and the computing efficiency is nearly linear. Then, heterogeneous computing technology is used to complete the drug virtual screening of more than 2 billion compounds, for two different active sites for COVID-19, on the domestic super computing system, which provides a powerful computing guarantee for the super large-scale rapid virtual screening of explosive malignant infectious diseases. © 2023, Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

A selectivity-enhanced colloidal crystal array for ribavirin sensing based on the synergistic effect of covalent and non-covalent interactions

Ribavirin is an important antiviral with demonstrated activity against coronaviruses such as severe acute respiratory syndrome coronavirus and coronavirus disease 2019 virus. However, abuse of ribavirin will cause great environmental damage and threaten human health owing to its reproductive toxicity and teratogenicity. Therefore, an innovative detection method is demanded for simple and sensitive detection of ribavirin. This work reports an imprinted colloidal crystal array (ICCA) for ribavirin sensing. The building blocks of the ICCA are ribavirin imprinted spheres, which possess superior binding efficiency toward ribavirin. Benefiting from the highly ordered structure, the ICCA exhibits optical properties which change upon binding ribavirin. The changes in reflectance wavelength enable a fast and label-free detection of ribavirin between 21 and 245 μmol L−1. Moreover, the sensor shows excellent selectivity for ribavirin detection in river water. Overall, all the results reported in this work demonstrate that the ICCA should be a promising detection tool for antivirals. © 2023 Society of Industrial Chemistry. © 2023 Society of Industrial Chemistry.

Cutaneous manifestations of COVID-19.

COVID - 19 can be accompanied by a variety of cutaneous abnormalities, which mainly include vascular lesions chilblain - like lesions, livedo reticularis, purpura, ecchymosis, acral cyanosis, gangrene, etcand inflammatory lesionsdiffuse erythema, morbilliform exanthem, acute urticaria, varicella- like exanthem, etc. Some types of skin lesions may be the first symptom or the only clinical manifestation of COVID-19.Copyright © 2022 Chinese Journal of Dermatology. All rights reserved.

Assembly process based on supply chain management of prefabricated houses using BIM

The assembly of modular containers using building information modelling (BIM) technologies was studied. The purpose of this study was to analyse the literature on prefabricated (prefab) houses and explore the concept of creating a digital prototype of a building based on Huoshenshan hospital using Autodesk Revit software. This hospital was constructed to treat Covid-19 patients in early 2020. The article describes the methodology of installing modular containers and assembly structures using BIM technologies for rapid construction. The results of this study showed that building object implementation depends directly on a proper model with a step-by-step mechanism for installation. Due to the supply of prefab structures at the construction site, both initial project cost and project time can be reduced. Prefab house technology demonstrated the high efficiency of using BIM technology in the assembly of Huoshenshan hospital, which was constructed in 10 days. The need for information modelling data exchange with modern technology and systems, which allows the team to become acquainted with the project before installation work starts at the construction site, is investigated.

Cutaneous manifestations of COVID⁃19

COVID ⁃ 19 can be accompanied by a variety of cutaneous abnormalities, which mainly include vascular lesions （chilblain ⁃ like lesions, livedo reticularis, purpura, ecchymosis, acral cyanosis, gangrene, etc）and inflammatory lesions（diffuse erythema, morbilliform exanthem, acute urticaria, varicella⁃ like exanthem, etc）. Some types of skin lesions may be the first symptom or the only clinical manifestation of COVID⁃19. © 2022 Chinese Journal of Dermatology. All rights reserved.

Assembly process based on supply chain management of prefabricated houses using building information modeling

The paper analyzes the assembly process by the example of assembly-modular containers using building information modeling technologies. This paper simulates a 3D model of the Huoshenshan Hospital with a description of the assembly mechanism process based on information modeling of prefabricated buildings. The purpose of this paper is to analyze the sources on prefabricated houses and explore the concept of creating a digital prototype of a building based on Huoshenshan Hospital, using the Autodesk Revit software. The article describes the methodology of installing modular containers and assembly structures using building information modeling technologies to improve rapid construction technology. The study results showed that building object implementation directly depends on a proper model with a step-by-step mechanism for installation, which can reduce the initial project cost due to the supply of prefabricated structures on the construction site, as well as reduce the project time. The prefabricated house technology demonstrated the high efficiency of using information technology in the assembly of the Huoshenshan Hospital, with which the simulated facility was implemented in 10 days. The need for information modeling data exchange with modern gadgets and systems is investigated, which allows one to get acquainted with the object at the construction site before installation work start. © 2022 ICE Publishing: All rights reserved.

The efficacy and acceptance of online learning vs. offline learning in medical student education: a systematic review and meta-analysis

Background: To maintain the continuity of medical education during the COVID-19 epidemic, online learning has replaced traditional face-to-face learning. But the efficacy and acceptance of online learning for medical education remains unknown. This meta-analysis aimed to assess whether online learning improves learning outcomes and is more acceptable to medical students compared to offline learning. Methods: Four databases were searched for randomized controlled trials (RCTs) and comparative studies (non-RCTs) involving online learning published from January 1900 to October 2020. A total of twenty-seven studies comparing online and offline learning in medical students were included. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework and Newcastle-Ottawa Scale (NOS) were used to assess the methodological quality of RCTs and non-RCTs respectively. The data of knowledge and skills scores and course satisfaction were synthesized using a random effects model for the meta-analysis. Results: Twenty-one RCTs that were judged to be of high quality according to the GRADE framework and six non-RCTs studies which ranged from 6 to 8 (NOS) and can be considered high-quality were included in this meta-analysis. The revealed that the online learning group had significantly higher post-test scores (SMD =0.58, 95% CI: 0.25 to 0.91;P=0.0006) and pre-and post-test score gains than the offline group (SMD =1.12, 95% CI: 0.14 to 2.11, P=0.02). In addition, online education was more satisfactory to participants than the offline learning (OR: 2.02;95% CI: 1.16 to 3.52;P=0.01). Subgroup analysis was performed on knowledge and skill scores at the post-test level. The selected factors included study outcome, study design and type, participants, course type and country. No significant factors were observed in the subgroup analysis except for course type subgroup analysis. Discussion: Online learning in medical education could lead to higher post-test knowledge and skill scores than offline learning. It also has higher satisfaction ratings than offline education. In conclusion, online learning can be considered as a potential educational method during the COVID-19 pandemic. However, given the risk of bias of included studies such as the inclusion of non-randomized comparative studies, the conclusion should be made with cautions. Trial Registration: CRD42020220295. © Journal of Xiangya Medicine. All rights reserved.

Decreased Aviation Leads to Increased Ice Crystal Number and a Positive Radiative Effect in Cirrus Clouds

Travel restrictions in the wake of the COVID-19 pandemic resulted in an unprecedented decrease of 73% in global flight mileage in April-May 2020 compared to 2019. Here we examine the CALIPSO satellite observations and find a significant increase in ice crystal number concentrations (Ni) in cirrus clouds in the mid-latitudes of the Northern Hemisphere, which we attribute to an increase in homogeneous freezing when soot from aircraft emissions is reduced. A relatively small positive global average radiative effect of 21 mW m(-2) is estimated if a decrease in aircraft traffic continues, with an average of up to 64 mW m(-2) over the area where aviation is most active. We infer from this analysis that the worldwide adoption of biofuel blending in aircraft fuels that lead to smaller soot emissions could lead to a significant change in the microphysical properties of cirrus clouds but a rather small positive radiative effect.

Clinical characteristics of liver and kidney injuries and correlation with severity and mortality in patients with COVID-19

Objective To describe the clinical characteristics of liver and kidney injuries and investigate its effect on the severity and mortality in the COVID-19 patients.Methods A total of 3 548 patients diagnosed with COVID-19 hut without liver and kidney diseases admitted in the Huoshenshan Hospital, Jinyintan Hospital and Taikang Tongji Hospital from February 4, 2020 to April 16, 2020 were recruited in this study.Their clinical data were extracted from medical database, including general information, clinical features, laboratory results and outcomes such as death were collected and analyzed.SPSS statistics 23.0 was used to perform the statistical description and analysis.Results Among the 3 548 patients with COYID-19, 875 (24.7%) cases were severe illness and above and 91 (2.6%) died during hospitalization.The proportions of the patients with higher alanine amiotransferase ( ALT) , aspartate aminotransferase ( AST) and creatinine (Cr) were 14.6% (513/3 548) , 3.4% ( 1 19/3 548) and 2.8% ( 101/3 548), respectively.Compared with the patients with normal ALT, AST and Cr, the patients with elevated ALT did not have a significantly increased risk of severe illness or death ( /-∗>().05) , and the risk of severe illness and death was significantly increased in those with elevated AST and Cr ( P<0.05).The risk of severe disease was 2.32 times (95%CI: 1.73-3.10) and 1 1.40 times ( 95% CI: 2.36-54.98 ) for those with single or both liver and kidney injuries, and the risk of death was 5.21 times (95% CI: 3.10-8.75 ) and 13.53 times (95% CI: 2.76-66.32) for those with normal liver and kidney function, respectively.Logistic regression analysis indicated that after independent factors related to severe illness and death screened out as correction factors, the risk of severe illness and death was 1.612 times (95% CI: 1.17-2.22) and 2.907 times (95% CI: 1.61-5.24) of patients with liver or kidney injuries when compared with those with normal function, respectively.Conclusion The COYID-19 patients with liver and renal injuries have a significantly increased tendency to become severity and mortality, and should undergo early intervention. © 2021 Editorial Office of Journal of Third Military Medical University. All rights reserved.

An Interpretable Multi-Level Enhanced Graph Attention Network for Disease Diagnosis with Gene Expression Data

Clinical omics, especially gene expression data, have been widely studied and successfully applied for disease diagnosis using machine learning techniques. As genes often work interactively rather than individually, investigating co-functional gene modules can improve our understanding of disease mechanisms and facilitate disease state prediction. To this end, we in this paper propose a novel Multi-Level Enhanced Graph ATtention (MLE-GAT) network to explore the gene modules and intergene relational information contained in the omics data. In specific, we first format the omics data of each patient into co-expression graphs using weighted correlation network analysis (WGCNA) and then feed them to a well-designed multi-level graph feature fully fusion (MGFFF) module for disease diagnosis. For model interpretation, we develop a novel full-gradient graph saliency (FGS) mechanism to identify the disease-relevant genes. Comprehensive experiments show that our proposed MLE-GAT achieves state-of-the-art performance on transcriptomics data from TCGA-LGG/TCGA-GBM and proteomics data from COVID-19/non-COVID-19 patient sera. © 2021 IEEE.

Clinical characteristics and prediction analysis of the recovered COVID-19 patients with re-detectable positive RNA test

Background: Previous studies have unveiled the occurrence of re-detectable positive (RP) RNA test result after hospital discharge among recovered COVID-19 patients, but the clinical characteristics of RP patients (RP patients) and the potential features affecting RP RNA test outcome remain unclear. Methods: A total of 742 COVID-19 patients discharged between March 1st, 2020 and March 20th, 2020 were enrolled. All patients were followed-up for SARS-CoV-2 RNA test and RP patents were identified. The clinical characteristics between RP patients and NRP patients were compared, and the potential features affecting re-detectable RNA test outcome were further evaluated. Results: Up to April 9th, 2020, 60 recovered patients (8.09%) had been re-detected to be SARS-CoV-2 RNA positive. Among those 60 RP patients, the median RP time was 12 days from the last negative result of SARS-CoV-2 RNA test or 10 days from hospital discharge. RP patients were prone to be older, having mild/moderate conditions, unilateral lung involvement and fatigue, chills, stuffy or runny nose, with high lymphocyte count. Multivariate logistic analysis and COX regression analysis demonstrated that age, lymphocyte count, urea nitrogen, stuffy or runny nose as well as lung involvement were independently associated with RP RNA test (P<0.05). Conclusions: Older patients accompanied with stuffy or runny nose, low urea nitrogen as well as unilateral lung involvement were more likely to develop RP RNA test result after hospital discharge. Therefore, we strongly suggest using broncho-alveolar lavage fluid for RNA detection, extending quarantine time, and conducting continual follow-up medical examination for those discharged patients.

Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals

With the advance in automatic speech recognition, voice user interface has gained popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online communication due to its non-contact. Additionally, various ambient noise impedes the public applications of voice user interfaces due to the requirement of audio-only speech recognition methods for a high signal-to-noise ratio. In this paper, we present Wavoice, the first noise-resistant multi-modal speech recognition system that fuses two distinct voice sensing modalities, i.e., millimeter-wave (mmWave) signals and audio signals from a microphone, together. One key contribution is that we model the inherent correlation between mmWave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Furthermore, we elaborate on two novel modules into the neural attention mechanism for multi-modal signals fusion, and result in accurate speech recognition. Extensive experiments verify Wavoice's effectiveness under various conditions with the character recognition error rate below 1% in a range of 7 meters. Wavoice outperforms existing audio-only speech recognition methods with lower character error rate and word error rate. The evaluation in complex scenes validates the robustness of Wavoice. © 2021 ACM.

How does leader conscientiousness influence frontline staff under conditions of serious anxiety? An empirical study from integrated resorts (IRs)

Purpose - The objective of this study was to improve understanding of frontline staff's subjective happiness and anxiety during the COVID-19 pandemic by investigating the roles of employees' busy mindset and leader conscientiousness. Design/methodology/approach - The link between employee anxiety and subjective happiness was also explored, and the cross-level mediating effect of employee anxiety was tested using a multilevel design. A survey of 373 frontline staffers and 74 team leaders in the integrated resorts (IRs) was conducted in three waves: April (Time 1), May (Time 2) and June (Time 3) in 2020. The data were analysed with SPSS and Mplus using a hierarchical linear modelling (HLM) method. Findings - The results indicated that during the COVID-19 pandemic, a busy mindset increased frontline staff's anxiety and thus decreased their subjective happiness, and leader conscientiousness remedied the effect of anxiety on subjective happiness. Practical implications - The findings are relevant to frontline staffers, team leaders in the hospitality industry and corporate service departments. Against the background of COVID-19, conscientious leaders can significantly help employees to overcome their anxiety and insecurity and improve their subjective happiness, answering the urgent call to deal with the challenges of the new work-life environment. Originality/value - The study differs from previous other studies in two dimensions: First, the authors explored the interactions of the affective events from the cross-level perspectives, i.e. both team level and individual level. Second, the authors conducted this research on the mental issues of the hospitality frontline staffers in the context of the COVID-19 pandemic, which remains a black box to be explored.

Variational Disentanglement for Rare Event Modeling

Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence events, and develop a robust prediction arm that joins the merits of the generalized additive model and isotonic neural nets. Results on synthetic studies and diverse real-world datasets, including mortality prediction on a COVID-19 cohort, demonstrate that the proposed approach outperforms existing alternatives.

Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges

With the rapid increase in access to the internet and the subsequent growth in the population of social media users, the quality of information posted, disseminated, and consumed via these platforms is an issue of growing concern. A large fraction of the common public turn to social media platforms and, in general, the internet for news and even information regarding highly concerning issues such as COVID-19 symptoms and treatments. Given that the online information ecosystem is extremely noisy, fraught with misinformation and disinformation, and often contaminated by malicious agents spreading propaganda, identifying genuine and good quality information from disinformation is a challenging task for humans. In this regard, there is a significant amount of ongoing research in the directions of disinformation detection and mitigation. In this survey, we discuss the online disinformation problem, focusing on the recent "infodemic" in the wake of the coronavirus pandemic. We then proceed to discuss the inherent challenges in disinformation research, including data collection, early detection and effective mitigation, fact-checking based approaches, multi-modality approaches, and policy issues and fairness, and elaborate on the interdisciplinary approaches towards the detection and mitigation of disinformation, after a short overview of the various directions explored in computational detection and mitigation efforts. © 2021, Science Press. All right reserved.

COVID-19 outbreak represents an opportunity for the development of internet healthcare

The COVID-19 epidemic has presented a severe test for the medical systems of all countries in the world. Regardless of developed or developing countries, the traditional medical service model has been dramatically affected. The traditional face-to-face treatment model has increased doctors’ risk of infection. In contrast, for patients with chronic diseases, going to the hospital for treatment raises the risk of cross-infection, which seriously affects their standard medical treatment. The Internet Healthcare’s non-contact treatment model, the ultra-large crowd processing capacity of computers, responsiveness that is unrestricted by time and place, multi-channel information collection, and precise diagnosis and treatment are supported by a considerable knowledge base have unleashed their full potential in the COVID-19 epidemic. Thus, these are generally considered to be reliable medical tools that can effectively perform chronic disease management amid the epidemic. COVID-19 has left us with a huge scar. Also, human society will likely face similar natural disasters in the future. So, in the face of the COVID-19 epidemic, we medical workers need to duly think about and explore medical models that align with future social development. Internet Healthcare, as an inevitable outcome of social and technological developments, needs to inherit the advantages and essence of the traditional medical model and progress on its basis. Instead of blindly relying on new technologies, it should reexamine the future of Internet Healthcare on the foundation of a traditional model. Therefore, for Internet Healthcare, this novel coronavirus pandemic is perhaps an opportunity and a challenge. © 2021 by Nova Science Publishers, Inc.

Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation

Air quality is strongly influenced by both local emissions and regional transport. Atmospheric chemical transport models can distinguish between emissions and regional transport sources in air pollutant concentrations. However, quantifying model inventories is challenging due to emission changes caused by the recent strict control measures taken by the Chinese government. In this study, we use NO2 column observations from the Tropospheric Monitoring Instrument to retrieve top-down nitrogen oxide (NOX) emissions and quantify the contributions of local emissions and regional transport to NOx in Beijing (BJ), from 1 November 2018 to 28 February 2019 (W_2018) and 1 November 2019 to 29 February 2020 (W_2019). In W_2018 and W_2019, the BJ bottom-up NOX emissions from the multi-resolution emission inventory for China in 2017 were overestimated by 11.8% and 40.5%, respectively, and the input of NOX from other cities to BJ was overestimated by 10.9% and 51.6%, respectively. The simulation using our adjusted inventory exhibited a much higher spatial agreement (slope = 1.0, R-2 = 0.79) and reduced a mean relative error by 45% compared to those of bottom-up NOX emissions. The top-down inventory indicated that (1) city boundary transport contributes approximately 40% of the NOX concentration in BJ;(2) in W_2019, NOX emissions and transport in BJ decreased by 20.4% and 17.2%, respectively, compared to those of W_2018;(3) in W_2019, NOX influx substantially decreased (-699 g/s) in BJ compared to that of W_2018 despite negative meteorological conditions that should have increased NOx influx by +503 g/s. Overall, the contribution of intercity input to NOx in BJ has declined with decreasing emissions in the surrounding cities due to regional cooperative control measures, and the role of local emissions in BJ NOx levels was more prominent. Our findings indicate that local emissions may play vital roles in regional center city air quality.

An agile discovery method of drugs for infectious diseases and tentative exploration of its application in drug discovery for COVID-19

OBJECTIVE To establish an agile discovery method of drugs or natural products for epidemics (aCODE) for the development of anti-infectious disease drugs. METHODS Five infectious diseases (HIV infection, human influenza, Paramyxoviridae infections, bacterial infections and whooping cough) involving more than 40 drugs approved by the United States Food and Drug Administration (FDA) were selected. An experimental group and two negative control groups (A and B) for each disease were set up. The experimental group randomly selected (500 times) M FDA-approved indications as seed drugs for the disease, while negative control group A used all FDA-approved infectious drugs for non-current diseases instead of seed drugs, and negative control group B used all non-infectious disease drugs for non-infectious diseases instead of seed drugs. M ranged from 2 to 20, the target gene infor mation of the seed drug was input, and the feature vector of the seed drug set was calculated. Candi date compounds were predicted through similarity search of drug feature vectors. The size of the inter section between the predicted drug and the positive set of drugs approved by the FDA for the disease, and the significance of the intersection were calculated. After the establishment of the aCODE method, four drugs (lopinavir, ribavirin, ritonavir and chloroquine) were selected as seed drugs for COVID-19 to predict the composition of natural products. Using natural products with known anti-coronavirus activi ties as the verification set, the significance of the prediction results was calculated. RESULTS In the case of the five infectious diseases, the proportion of positive drugs in the results of prediction in the experimental group increased with the number of seed drugs, while the positive rate of the two negative control groups remained basically unchanged or somewhat trended down. The aCODE method, when applied to COVID-19 drug screening, could effectively predict drugs with potential anti-SARS-Cov-2 activity (P=0.0046). CONCLUSION With the aCODE method, the more the seed drugs, the more accu rate the characteristics of the disease-related gene modules calculated from this group of seed drugs, and the higher the proportion of positive drugs in the prediction result. This method may contribute to the discovery of drugs for COVID-19. © 2020 Chinese Journal of Pharmacology and Toxicology. All rights reserved.

COVID-19 and political polarization: Notes on Australia’s Chinese communities

In this article I draw from my personal observations as a participant in two WeChat groups;a group with my university classmates most of whom are now residents of Australia, and the other a skilled migrant group based in South Australia. I explore the main narrative threads of these two groups in relation to their responses to the COVID-19 pandemic during the first half of 2020. I argue that the COVID-19 pandemic sharpened the political polarization that exists between denouncers of the People’s Republic of China [PRC] and their detractors, and also underlined particular moral dilemmas. © 2021 by the author(s).