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1.
Cell Discov ; 8, 2022.
Article in English | PMC | ID: covidwho-2008266

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs), especially the latest Omicron, have exhibited severe antibody evasion. Broadly neutralizing antibodies with high potency against Omicron are urgently needed for understanding the working mechanisms and developing therapeutic agents. In this study, we characterized the previously reported F61, which was isolated from convalescent patients infected with prototype SARS-CoV-2, as a broadly neutralizing antibody against all VOCs including Omicron BA.1, BA.1.1, BA.2, BA.3 and BA.4 sublineages by utilizing antigen binding and cell infection assays. We also identified and characterized another broadly neutralizing antibody D2 with epitope distinct from that of F61. More importantly, we showed that a combination of F61 with D2 exhibited synergy in neutralization and protecting mice from SARS-CoV-2 Delta and Omicron BA.1 variants. Cryo-Electron Microscopy (Cryo-EM) structures of the spike-F61 and spike-D2 binary complexes revealed the distinct epitopes of F61 and D2 at atomic level and the structural basis for neutralization. Cryo-EM structure of the Omicron-spike-F61-D2 ternary complex provides further structural insights into the synergy between F61 and D2. These results collectively indicated F61 and F61-D2 cocktail as promising therapeutic antibodies for combating SARS-CoV-2 variants including diverse Omicron sublineages.

2.
2021 International Conference on Computing in Civil Engineering, I3CE 2021 ; : 835-842, 2021.
Article in English | Scopus | ID: covidwho-1908371

ABSTRACT

Health and safety problems are essential for the construction industry, and such problems are more pronounced in small and medium enterprises (SMEs) due to the lack of financial resources and skilled personnel. Researchers have explored the feasibility and viability of addressing such constraints using artificial intelligence-enhanced, low-cost sensor systems. Our previous studies have investigated both conventional machine learning and deep neural network models for recognizing workers' postures from low-cost wearable sensors and assessing the ergonomics risks for injury prevention. In the next steps for this research, we are investigating adoption drivers and diffusion barriers for the scaled deployment of AI-enhanced sensor networks and other emerging digital technologies for construction health and safety in a real-work setting. Although the COVID-19 pandemic has brought unprecedented challenges, it has also sped up the digital technology adoption. The discussion in this paper is directed at building on this momentum to advance the use of emerging digital technologies at construction SMEs. The authors conducted a systematic review of literature on digital technologies at construction SMEs and how COVID-19 affected the digital transformation at SMEs. After an initial screening of a total of 170 articles, the key publications based on the research questions were selected for a more in-depth analysis. It emerged that although construction SMEs have embraced the use of several digital technologies during the current pandemic, there is still a large digital divide between these companies and larger companies. The research discussed in this paper contributes to efforts directed at addressing this problem through the design and deployment of SME-centric digital technologies for construction health and safety. © 2021 Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021. All rights reserved.

3.
Embase; 2020.
Preprint in English | EMBASE | ID: ppcovidwho-337377

ABSTRACT

Computational approaches for accurate prediction of drug interactions, such as drug-drug interactions (DDIs) and drug-target interactions (DTIs), are highly demanded for biochemical researchers due to the efficiency and cost-effectiveness. Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively, their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in the corresponding chemical structure. In this paper, we develop a deep learning framework, named DeepDrug, to overcome the above limitation by using residual graph convolutional networks (RGCNs) and convolutional networks (CNNs) to learn the comprehensive structural and sequential representations of drugs and proteins in order to boost the DDIs and DTIs prediction accuracy. We benchmark our methods in a series of systematic experiments, including binary-class DDIs, multi-class/multi-label DDIs, binary-class DTIs classification and DTIs regression tasks using several datasets. We then demonstrate that DeepDrug outperforms state-of-the-art methods in terms of both accuracy and robustness in predicting DDIs and DTIs with multiple experimental settings. Furthermore, we visualize the structural features learned by DeepDrug RGCN module, which displays compatible and accordant patterns in chemical properties and drug categories, providing additional evidence to support the strong predictive power of DeepDrug. Ultimately, we apply DeepDrug to perform drug repositioning on the whole DrugBank database to discover the potential drug candidates against SARS-CoV-2, where 3 out of 5 top-ranked drugs are reported to be repurposed to potentially treat COVID-19. To sum up, we believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising insight in understanding the underlying mechanism of these biochemical relations. The source code of the DeepDrug can be freely downloaded from https://github.com/wanwenzeng/deepdrug.

4.
Frontiers in Energy Research ; 9:13, 2022.
Article in English | Web of Science | ID: covidwho-1855338

ABSTRACT

Energy and other related sectors are changing in China. This study attempted to estimate the energy product price volatility with energy efficiency during COVID-19 with the role of green fiscal policies. For this, we applied unit-root tests, ADCC-GARCH, and CO-GARCH techniques to infer the study findings. The results showed that energy price volatility was significantly connected until 2018. More so, the green fiscal policies were significantly connected between energy product price volatility and energy efficiency during COVID-19 (2019-2020). From energy products, the crude oil price volatility was significant at 16.4%, heating oil volatility was significant at 18.2%, natural oil price volatility was 9.7%, gasoline price volatility was 28.7%, and diesel price volatility was 34.1% significant with energy efficiency, due to the intervening role of green fiscal policies. The findings of this study are robust in comparison to previous studies. Multiple stakeholders can take guidelines from the findings of the recent study. As per our best understanding and knowledge, if suggested recommendations are implemented effectively, these results will help to enhance energy efficiency through green fiscal policies in the post-COVID period.

5.
2021 4th International Conference on Computer Information Science and Application Technology, CISAT 2021 ; 2010, 2021.
Article in English | Scopus | ID: covidwho-1437803

ABSTRACT

The emergence of the novel coronavirus(COVID-19) has left disastrous effect on global health and individuals. Even though in most areas, the RT-PCR test used as the dominant approach for diagnosis of COVID-19 has shown good accuracy, the test requires equipment, personnel and it is time-consuming. Researches have shown the effectiveness of X-ray images for predicting COVID-19. In this study, we applied a transformer-like deep-learning model on this problem with transfer learning technique, to diagnose X-ray images as COVID-19 or normal. The model outperformed the CNN SOTA. The model achieved a classification accuracy of 99.7% on the targeting dataset. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

6.
Physical Review X ; 11(3):9, 2021.
Article in English | Web of Science | ID: covidwho-1398211

ABSTRACT

The combination of nontrivial band topology and symmetry-breaking phases gives rise to novel quantum states and phenomena such as topological superconductivity, quantum anomalous Hall effect, and axion electrodynamics. Evidence of intertwined charge density wave (CDW) and superconducting order parameters has recently been observed in a novel kagome material AV(3)Sb(5) (A = K, Rb, Cs) that features a Z(2) topological invariant in the electronic structure. However, the origin of the CDW and its intricate interplay with the topological state has yet to be determined. Here, using hard-x-ray scattering, we demonstrate a three-dimensional CDW with 2 x 2 x 2 superstructure in (Rb, Cs)V3Sb5. Unexpectedly, we find that the CDW fails to induce acoustic phonon anomalies at the CDW wave vector but yields a novel Raman mode that quickly damps into a broad continuum below the CDW transition temperature. Our observations exclude strong electron-phonon-coupling-driven CDW in AV(3)Sb(5) and support an unconventional CDW that was proposed in the kagome lattice at van Hove filling.

7.
Medical Journal of Wuhan University ; 42(5):714-717, 2021.
Article in Chinese | Scopus | ID: covidwho-1350554

ABSTRACT

Objective: To analyze the clinical characteristics and prognosis of the coronavirus disease 2019 (COVID‑19) in the elderly(aged 60 or above), and to explore the high risk factors of severe disease progression for early identification and prevention. Methods: Novel coronavirus pneumonia patients aged 60 or above diagnosed in Hubei Veterans Hospital from January 20 to February 29 in year 2020 were collected. According to the degrees of disease, the patients were divided into mild and severe groups, and their clinical features, laboratory examination, chest CT features, treatment, and outcome were compared. Results: A total of 108 patients were included, including 69 in mild group and 39 in severe group. The average age of the severe group was higher than that of the mild group ( P <0.001). The clinical symptoms of fever, expectoration, dyspnea, fatigue and diarrhea in the severe group were severer and more common than those in the mild group (all P <0.001). The proportion of hypertension ( P <0.05), respiratory system diseases (such as chronic bronchitis and COPD) ( P <0.05), and hypoproteinemia ( P <0.001) combined with COVID⁃19 were higher in severe groupthe severe group. Leukocyte count (WBC), neutrophil count (NEUT), CRP and SAA in the severe group were significantly higher ( P <0.05), while lymphocyte count (LY) and eosinophil count (EOS) were lower than those in the mild group ( P <0.05). Lung CT images showed that patients in the severe group had more bilateral lung involvements and pleural effusion than those in the mild group ( P <0.05). Among the 108 cases, 96 (88.9%) were cured and improved, 12 (11.1%) died. Conclusion: Age, basic comorbidities, decreasing in lymphocytes and acidophilic granulocytes, and multiple bacterial infections are risk factors for severe COVID‑19. Hypoalbuminemia may be a potential and independent adverse prognostic indicator for the elderly COVID‑19. Symptoms of dyspnea and diarrhea, bilateral lung involvements, the pleural effusion are high risk signs for the elderly COVID‑19 patients progressing to severe. These findings are valuable for the early recognition, early diagnosis and treatment for COVID⁃19. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.

8.
Academic Journal of Second Military Medical University ; 41(9):953-957, 2020.
Article in Chinese | EMBASE | ID: covidwho-994686

ABSTRACT

Objective To explore the risk perception characteristics and influencing factors of frontline medical staff during the coronavirus disease 2019 (COVID-19) epidemic, so as to provide effective reference for correctly perceiving the risk, improving stress-coping skills and maintaining mental health during the high-risk and high-intensity combat against the COVID-19 epidemic. Methods A risk perception questionnaire based on the context of COVID-19 epidemic was used to investigate the risk perception level of 181 frontline medical staff fighting against COVID-19 epidemic. Nonparametric test was used to compare the demographic factors and risk perception dimensions. Logistic regression analysis was used to predict the effect of demographic factors on the risk perception level of frontline medical staff. Results During the COVID-19 epidemic, the overall risk perception score of the frontline medical staff was 36.39±8.59, and the scoring rate was 60.65%. The top three dimensions with the highest scoring rate were physical function risk, organization risk and personal safety risk. The score of frontline medical staff in Hubei province was higher than that outside Hubei province (Z=-2.180, P<0.05) and the score of medical technicians (doctors and technicians) was higher than that of nurses (Z=-3.039, P<0.01). The location of frontline medical staff could significantly predict the overall risk perception (P<0.05). Conclusion During the COVID-19 epidemic, the risk perception of frontline medical staff has been found at the medium level, with the risk perception degree of frontline medical staff in Hubei province being higher than that outside Hubei province and the risk perception degree of medical technicians being higher than that of nurses. The location of frontline medical staff can predict their risk perception.

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