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1.
7th IEEE International Conference on Collaboration and Internet Computing (CIC) ; : 96-104, 2021.
Article in English | English Web of Science | ID: covidwho-1883116

ABSTRACT

Since 2019, the world has been seriously impacted by the global pandemic, COVID-19, with millions of people adversely affected. This is coupled with a trend in which the intensity and frequency of natural disasters such as hurricanes, wildfires, and earthquakes have increased over the past decades. Larger and more diverse communities have been negatively influenced by these disasters and they might encounter crises socially and/or economically, further exacerbated when the natural disasters and pandemics co-occurred. However, conventional disaster response and management rely on human surveys and case studies to identify these in-crisis communities and their problems, which might not be effective and efficient due to the scale of the impacted population. In this paper, we propose to utilize the data-driven techniques and recent advances in artificial intelligence to automate the in-crisis community identification and improve its scalability and efficiency. Thus, immediate assistance to the in-crisis communities can be provided by society and timely disaster response and management can be achieved. A novel framework of the in-crisis community identification has been presented, which can be divided into three subtasks: (1) community detection, (2) in-crisis status detection, and (3) community demand and problem identification. Furthermore, the open issues and challenges toward automated in-crisis community identification are discussed to motivate future research and innovations in the area.

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.
2021 China Automation Congress, CAC 2021 ; : 4690-4695, 2021.
Article in English | Scopus | ID: covidwho-1806893

ABSTRACT

Owing to the global lockdown caused by the pandemic of COVID-19, the electricity demand is greatly affected, and the electricity market is also constantly fluctuating. During the pandemic period, the prediction of electricity demand is crucial to the economy and power dispatching. In this study, we combine the pandemic data and government anti-pandemic policies data to predict the electricity demand of the Contiguous United States by using the artificial neural network and recurrent neural network. In addition, the linear regression method is used to forecast the thermal generation with total generation data. Some experiments have developed to verify the effectiveness of the model. Then the model is used to forecast electricity demand and thermal generation under different policies and pandemic development, and the result were analyzed. © 2021 IEEE

5.
2nd Asia Conference on Computers and Communications, ACCC 2021 ; : 115-121, 2021.
Article in English | Scopus | ID: covidwho-1735774

ABSTRACT

At present, the world economy is in recession, especially under the impact of the Covid-19 epidemic, China's economy has also been greatly impacted. In this context, the disposable personal income of residents has also declined to varying degrees. More and more people choose economical life. If they can buy a used car in good condition at a good price, they are less likely to buy a brand new one. Under such a consumption concept, China's demand for second-hand cars is increasing. However, although China's second-hand car industry has developed for more than 30 years and the market scale has gradually expanded, there are still many problems behind the prosperity of the second-hand car market. These problems have existed for a long time, leading to a lot of disputes, unhappiness, disappointment, and even threats to the lives of consumers. These long-term problems also affect the virtuous circle of the second-hand car market, and hinder the healthy development of China's economy to a certain extent. In the past, research work mainly focused on the role of new policies, relevant laws and vehicle management and traffic management functions, this paper introduces the blockchain technology, which has the advantages of non tampering, transparency and traceability. This paper attempts to use blockchain technology as an auxiliary means to solve the long-standing problems in the used car market. This paper proposes a framework of used car trading based on blockchain in cloud service environment, and explains the working principle of the framework. Finally, the future research work is prospected. © 2021 IEEE.

6.
IEEE Transactions on Learning Technologies ; 2022.
Article in English | Scopus | ID: covidwho-1731043

ABSTRACT

During the COVID-19 pandemic, many students lost opportunities to explore science in labs due to school closures. Remote labs provide a possible solution to mitigate this loss. However, most remote labs to date are based on a somehow centralized model in which experts design and conduct certain types of experiments in well-equipped facilities, with a few options of manipulation provided to remote users. In this paper, we propose a distributed framework, dubbed remote labs 2.0, that offers the flexibility needed to build an open platform to support educators to create, operate, and share their own remote labs. Similar to the transformation of the Web from 1.0 to 2.0, remote labs 2.0 can greatly enrich experimental science on the Internet by allowing users to choose and contribute their subjects and topics. As a reference implementation, we developed a platform branded as Telelab. In collaboration with a high school chemistry teacher, we conducted remote chemical reaction experiments on the Telelab platform with two online classes. Pre/post-test results showed that these high school students attained significant gains (t(26)=8.76, p<0.00001) in evidence-based reasoning abilities. Student surveys revealed three key affordances of Telelab: live experiments, scientific instruments, and social interactions. All 31 respondents were engaged by one or more of these affordances. Students behaviors were characterized by analyzing their interaction data logged by the platform. These findings suggest that appropriate applications of remote labs 2.0 in distance education can, to some extent, reproduce critical effects of their local counterparts on promoting science learning. IEEE

7.
Frontiers in Materials ; 8:6, 2021.
Article in English | Web of Science | ID: covidwho-1560794

ABSTRACT

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has significantly affected the world, creating a global health emergency. For controlling the virus spread, effective and reliable diagnostic and therapeutic measures are highly expected. Using proper biomedical materials to produce detection kits/devices and personal protective equipment (PPE), such as swabs and masks, has become the focus since they play critical roles in virus diagnostics and prevention. Electrospun polymer composites have garnered substantial interest due to their potential to provide antiviral healthcare solutions. In this review, we summarized the recent efforts in developing advanced antiviral electrospun polymer composites for virus detection and prevention. We highlighted some novel strategies for developing effective antiviral personal protective equipment (PPE), including self-sterilization, reusability, and potential antiviral drug encapsulation. Besides, we discussed the current challenges and future perspectives for improving the materials' performance to achieve better virus detection, antiviral, prevention, and therapeutics.

8.
Chinese Journal of Applied Clinical Pediatrics ; 36(18):1368-1372, 2021.
Article in Chinese | Scopus | ID: covidwho-1481061

ABSTRACT

Severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)infection is still worldwide.As a vulnerable group, severe and dead pediatric cases are also reported.Under this severe epidemic situation, children should be well protected.With the widespread vaccination of SARS-CoV-2 vaccine in adults, the infection rate have decreased.Therefore, SARS-CoV-2 vaccine inoculation for children groups step by step is of great significance to the protection of children and the prevention and control of corona virus disease 2019(COVID-19) as a whole.But the safety of children vaccinated with SARS-CoV-2 vaccine is a main concern of parents.Therefore, in order to ensure the safety of vaccination and the implementation of vaccination work, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health and the Society of Pediatrics, Chinese Medical Association organized experts to interpret the main issue of parents about SARS-CoV-2 vaccine for children, in order to answer the doubts of parents. Copyright © 2021 by the Chinese Medical Association.

9.
Chinese Journal of Applied Clinical Pediatrics ; 36(18):1361-1367, 2021.
Article in Chinese | Scopus | ID: covidwho-1481060

ABSTRACT

At present, severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)infection is still rampant worldwide.As of September 10, 2021, there were about 222 million confirmed cases of corona virus disease 2019(COVID-19)and more than 4.6 million deaths worldwide.With the development of COVID-19 vaccines and the gradual vaccination worldwide, the increasing number of cases in children and unvaccinated young people has drawn attention.According to World Health Organization surveillance data, the proportion of COVID-19 infection cases in children gradually increased, and the proportion of cases in the age groups of under 5 years and 5-14 years increased from 1.0% and 2.5% in January 2020 to 2.0% and 8.7% in July 2021, respectively.At present, billions of adults have been vaccinated with various COVID-19 vaccines worldwide, and their protective effects including reducing infection and transmission, reducing severe disease and hospitalization, and reducing death, as well as high safety have been confirmed.Canada, the United States, Europe and other countries have approved the emergency COVID-19 vaccination in children and adolescents aged 12 to 17 years, and China has also approved the phased vaccination of COVID-19 vaccination in children and adolescents aged 3 to 17 years. For smooth advancement and implementation of COVID-19 vaccination in children, academic institutions, including National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, and The Society of Pediatrics, Chinese Medical Association organized relevant experts to reach this consensus on COVID-19 vaccination in children. Copyright © 2021 by the Chinese Medical Association.

10.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 ; 12978 LNAI:319-334, 2021.
Article in English | Scopus | ID: covidwho-1446044

ABSTRACT

Modeling and predicting human mobility are of great significance to various application scenarios such as intelligent transportation system, crowd management, and disaster response. In particular, in a severe pandemic situation like COVID-19, human movements among different regions are taken as the most important point for understanding and forecasting the epidemic spread in a country. Thus, in this study, we collect big human GPS trajectory data covering the total 47 prefectures of Japan and model the daily human movements between each pair of prefectures with time-series Origin-Destination (OD) matrix. Then, given the historical observations from past days, we predict the countrywide OD matrices for the future one or more weeks by proposing a novel deep learning model called Origin-Destination Convolutional Recurrent Network (ODCRN). It integrates the recurrent and 2-dimensional graph convolutional components to deal with the highly complex spatiotemporal dependencies in sequential OD matrices. Experiment results over the entire COVID-19 period demonstrate the superiority of our proposed methodology over existing OD prediction models. Last, we apply the predicted countrywide OD matrices to the SEIR model, one of the most classic and widely used epidemic simulation model, to forecast the COVID-19 infection numbers for the entire Japan. The simulation results also demonstrate the high reliability and applicability of our countrywide OD prediction model for a pandemic scenario like COVID-19. © 2021, Springer Nature Switzerland AG.

11.
Letters in Drug Design & Discovery ; 18(5):429-435, 2021.
Article in English | Web of Science | ID: covidwho-1332066

ABSTRACT

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a novel member of the genus betacoronavirus in the Coronaviridae family. It has been identified as the causative agent of coronavirus disease 2019 (COVID-19), spreading rapidly in Asia, America and Europe. Like some other RNA viruses, RNA replication and transcription of SARS-CoV-2 rely on its RNA-dependent RNA polymerase (RdRP), which is a therapeutic target of clinical importance. Crystal structure of SARS-CoV-2 was solved recently (PDB ID 6M71) with some missing residues. Objective: We used SARS-CoV-2 RdRP as a target protein to screen for possible chemical molecules with potential anti-viral effects. Methods: Here we modelled the missing residues 896-905 via homology modelling and then analysed the interactions of Hepatitis C virus allosteric non-nucleoside inhibitors (NNIs) in the reported NNIs binding sites in SARS-CoV-2 RdRP. Results: We found that MK-3281, filibuvir, setrobuvir and dasabuvir might be able to inhibit SARS-CoV-2 RdRP based on their binding affinities in the respective binding sites. Conclusion: Further in vitro and in vivo experimental research will be carried out to evaluate their effectiveness in COVID-19 treatment in the near future.

12.
2021 International Conference on Management of Data, SIGMOD 2021 ; : 2614-2627, 2021.
Article in English | Scopus | ID: covidwho-1299241

ABSTRACT

Recently, there has been a pressing need to manage high-dimensional vector data in data science and AI applications. This trend is fueled by the proliferation of unstructured data and machine learning (ML), where ML models usually transform unstructured data into feature vectors for data analytics, e.g., product recommendation. Existing systems and algorithms for managing vector data have two limitations: (1) They incur serious performance issue when handling large-scale and dynamic vector data;and (2) They provide limited functionalities that cannot meet the requirements of versatile applications. This paper presents Milvus, a purpose-built data management system to efficiently manage large-scale vector data. Milvus supports easy-to-use application interfaces (including SDKs and RESTful APIs);optimizes for the heterogeneous computing platform with modern CPUs and GPUs;enables advanced query processing beyond simple vector similarity search;handles dynamic data for fast updates while ensuring efficient query processing;and distributes data across multiple nodes to achieve scalability and availability. We first describe the design and implementation of Milvus. Then we demonstrate the real-world use cases supported by Milvus. In particular, we build a series of 10 applications (e.g., image/video search, chemical structure analysis, COVID-19 dataset search, personalized recommendation, biological multi-factor authentication, intelligent question answering) on top of Milvus. Finally, we experimentally evaluate Milvus with a wide range of systems including two open source systems (Vearch and Microsoft SPTAG) and three commercial systems. Experiments show that Milvus is up to two orders of magnitude faster than the competitors while providing more functionalities. Now Milvus is deployed by hundreds of organizations worldwide and it is also recognized as an incubation-stage project of the LF AI & Data Foundation. Milvus is open-sourced at https://github.com/milvus-io/milvus. © 2021 Owner/Author.

13.
Chinese Journal of Applied Clinical Pediatrics ; 36(10):721-732, 2021.
Article in Chinese | Scopus | ID: covidwho-1278526

ABSTRACT

2019 novel coronavirus(2019-nCoV) outbreak is one of the public health emergency of international concern.Since the 2019-nCoV outbreak, China has been adopting strict prevention and control measures, and has achieved remarkable results in the initial stage of prevention and control.However, some imported cases and sporadic regional cases have been found, and even short-term regional epidemics have occurred, indicating that the preventing and control against the epidemic remains grim.With the change of the incidence proportion and the number of cases in children under 18 years old, some new special symptoms and complications have appeared in children patients.In addition, with the occurrence of virus mutation, it has not only attracted attention from all parties, but also proposed a new topic for the prevention and treatment of 2019-nCoV infection in children of China.Based on the second edition, the present consensus further summarizes the clinical characteristics and experience of children's cases, and puts forward recommendations on the diagnostic criteria, laboratory examination, treatment, prevention and control of children's cases for providing reference for further guidance of treatment of 2019-nCoV infection in children. © 2021 Chinese Medical Association

14.
Topics in Antiviral Medicine ; 29(1):50-51, 2021.
Article in English | EMBASE | ID: covidwho-1250722

ABSTRACT

Background: One third of COVID-19 patients develop significant neurological symptoms, yet SARS-CoV-2 is rarely detected in central nervous system (CNS) tissue, suggesting a potential role for parainfectious processes, including neuroimmune responses. Methods: We examined immune parameters in CSF and blood samples from a cohort of hospitalized patients with COVID-19 and significant neurological complications (n=6), compared to SARS-CoV-2 uninfected controls (Fig1A). Immune cells were characterized by single cell RNA and repertoire sequencing. Intrathecal antibodies were assessed for anti-viral and auto-reactivity by ELISA, mouse brain immunostaining, phage display, and IP-MS. Results: Through single cell and parallel cytokine analyses of CSF and paired plasma, we found divergent T cell responses in the CNS compartment, including increased levels of IL-1B and IL-12-associated innate and adaptive immune cell activation (Fig1B). We found evidence of clonal expansion of B cells in the CSF, with B cell receptor sequences that were unique from those observed in peripheral blood B cells (Fig1C), suggesting a divergent intrathecal humoral response to SARS-CoV-2. Indeed, all COVID-19 cases examined had anti-SARS-antibodies. Next, we directly examined whether CSF resident antibodies targeted self-antigens and found a significant burden of CNS autoimmunity, with the CSF from most patients recognizing neural self-antigens. COVID-19 CSF produced immunoreactive staining of specific anatomic regions of the brain including cortical neurons, olfactory bulb, thalamus, and cerebral vasculature. Finally, we produced a panel of monoclonal antibodies from patients' CSF and peripheral blood, and show that these target both anti-viral and anti-neural antigens-including one CSF-derived mAb specific for the spike protein that also recognizes neural tissue (Fig1D). Conclusion: This immune survey reveals evidence of a compartmentalized and self-reactive immune response in the CNS in COVID-19 patients with neurologic symptoms. We identified both innate and adaptive anti-viral immune responses, as well as humoral autoimmunity that appears to be unique to the CNS during SARS-CoV-2 infection. These data suggest a potential role for autoimmunity in contributing to neurological symptoms, and merit further investigation to the potential role of autoantibodies in post-acute COVID-19 neurological symptoms.

15.
2020 5th International Conference on Computational Intelligence and Applications ; : 98-102, 2020.
Article in English | Web of Science | ID: covidwho-1186098

ABSTRACT

Microscopic blood cell analysis is an important methodology for medical diagnosis, and complete blood cell counts (CBCs) are one of the routine tests operated in hospitals. Results of the CBCs include amounts of red blood cells, white blood cells and platelets in a unit blood sample. It is possible to diagnose diseases such as anemia when the numbers or shapes of red blood cells become abnormal. The percentage of white blood cells is one of the important indicators of many severe illnesses such as infection and cancer. The amounts of platelets are decreased when the patient suffers hemophilia. Doctors often use these as criteria to monitor the general health conditions and recovery stages of the patients in the hospital. However, many hospitals are relying on expensive hematology analyzers to perform these tests, and these procedures are often time consuming. There is a huge demand for an automated, fast and easily used CBCs method in order to avoid redundant procedures and minimize patients' burden on costs of healthcare. In this research, we investigate a new CBC detection method by using deep neural networks, and discuss state of the art machine learning methods in order to meet the medical usage requirements. The approach we applied in this work is based on YOLOv3 algorithm, and our experimental results show the applied deep learning algorithms have a great potential for CBCs tests, promising for deployment of deep learning methods into microfluidic point-of-care medical devices. As a case of study, we applied our blood cell detector to the blood samples of COVID-19 patients, where blood cell clots are a typical symptom of COVID-19.

16.
Wool Textile Journal ; 48(12):98-102, 2020.
Article in Chinese | Scopus | ID: covidwho-1168307
17.
IEEE Access ; 2021.
Article in English | Scopus | ID: covidwho-1101972

ABSTRACT

In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQIS-Net model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N-connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state-of-the-art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening. CCBY

18.
Chinese Journal of New Drugs ; 29(18):2041-2048, 2020.
Article in Chinese | EMBASE | ID: covidwho-984968

ABSTRACT

Under the outbreak of COVID-19, the unapproved experimental drug remdesivir was used by the US in order to treat the first diagnosed COVID-19 patient, following the principle of compassionate use. The application of compassionate use also provides a new way of drug accessibility for patients with rare diseases. By analyzing the relevant official websites, laws and regulations, guidelines and domestic and foreign literature of the US, the European Union, France and the UK, this paper focused on the analysis of the use of compassionate use system in the US, the UK and France, so as to provide reference for the application of compassionate use of drugs under clinical trials for patients with rare diseases in China. Both the Expanded Access (EA) and the Right To Try system in the US provide patients with access to experimental drugs in advance. French Autorisation Temporaire D'utilisation (ATU) shortens the waiting time of patients to obtain drugs and accelerates the treatment of more than 20 000 patients (rare and non-rare diseases) within three years. The UK's Early Access to Medicines Scheme (EAMS) combined with subsequent drug marketing and reimbursement, shortens the corresponding approval time for drugs. It is suggested that when implementing the compassionate use system for rare diseases in line with the national conditions of China, such important aspects be fully considered as simple and flexible methods, procedures for quick approval, reasonable cost-sharing mechanism, and system to ensure drug safety. Meanwhile, ensuring patients' right to know about drug use, opening and sharing information platform, effective incentive policy and other important aspects should be noted.

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