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Since the COVID-19 outbreak in 2020, ICT-based technology application platforms have played a prominent role in promoting cooperative governance of community epidemic prevention, realizing cooperative supply of public services, and promoting resident participation. Starting from the definition, background and prospect of cooperative production, the study explores how public services can effectively promote collaborative governance through ICTs, combined with the popularization of ICT platforms and applications to promote citizens' ability to access information, participate in public affairs and participate in the development of ways. The practice of community cooperative governance during the COVID-19 pandemic in Guangzhou demonstrated how the city can ensure the development of community public management and services while coordinating the prevention and control of COVID-19 based on ICT-related information systems and technology platforms. Based on the application of ICT, the ability of citizens to participate in community public governance has been improved, and the mode of public service supply has been changed, and the pressure on community governance has been reduced through scientific and technological governance tools, so as to promote the cooperative production and participation of public governance to achieve the sharing of results and responsibilities, providing a new way for public governance in the future intelligent society. © 2022 ACM.
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Knowledge graphs capture the complex relationships among various entities, which can be found in various real world applications, e.g., Amazon product graph, Freebase, and COVID-19. To facilitate the knowledge graph analytical tasks, a system that supports interactive and efficient query processing is always in demand. In this demonstration, we develop a prototype system, CheetahKG, that embeds with our state-of-the-art query processing engine for the top-k frequent pattern discovery. Such discovered patterns can be used for two purposes, (i) identifying related patterns and (ii) guiding knowledge exploration. In the demonstration sessions, the attendees will be invited to test the efficiency and effectiveness of the query engine and use the discovered patterns to analyze knowledge graphs on CheetahKG. © 2022 IEEE.
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Background: To investigate the efficacy and safety of anti-PD-1 antibody plus regorafenib in refractory microsatellite stable (MSS) metastatic colorectal cancer (mCRC). Methods: We retrospectively analyzed the efficacy and safety of 103 MSS mCRC patients treated with anti -PD-1 antibodies(including nivolumab, pembrolizumab, camrelizumab, sintilimab, and toripalimab) combined with regorafenib(80 mg once daily for 21 days on/7 days off)between July 2019 and June 2021 in Hunan Cancer Hospital. Results: 103 patients had received at least one dose of regorafenib plus anti -PD-1 antibody. Due to COVID-19 pandemics, economic or other reasons, 48 patients (46.6%) did not return to hospital for second cycle of combination treatment .With a median follow-up of 5.30 months (range 0.50-22.50), the median overall survival (mOS) and progression-free survival (mPFS) were 8.40 months (95%CI:6.40-12.70) and 2.50 months (95%CI:2.27-3.47) in the entire cohort, respectively. The mOS and mPFS were significantly longer in patients who received more than 1 cycle (n = 55, 53.4%) compared to those who received just 1 cycle(n = 48, 46.6%). (16.07 vs. 4.37 months, HR 0.21;95%CI:[0.12-0.38];P<0.001;3.10 vs. 1.11 months, HR 0.12;95%CI: [0.05-0.31];P<0.001). Further analyses revealed that sintilimab (n = 66, 64.1%) significantly improved mPFS from 1.61 months to 3.30 months, compared to other anti-PD-1 antibodies (n = 37, 35.9%) (HR 0.55;95%CI:[0.31-0.99];P = 0.044). Cox multivariate regression analysis demonstrated that cycles of regorafenib plus PD-1 was a significant independent risk factor for the OS and PFS(P0.001).Patients who had previously undergone surgery were better than those who had not (P= 0.029).Sintilimab plus regorafenib had a better PFS benefit(P= 0.044.)Seven patients were diagnosed as partial response and another 16 cases were diagnosed as stable disease in the 55 patients who were evaluated, but no complete response. Thus the objective response rate was 12.7% and the disease control rate was 41.8%.Treatment-related adverse events of grade 3 or higher occurred in 13 (12.6%) patients. Conclusions: The combination of regorafenib plus anti-PD-1 antibody had a manageable safety profile and promising efficacy in MSS mCRC patients, especially in patients who received more than one cycle. Compared with the other anti-PD-1 antibodies, sintilimab may have more encouraging efficacy, which needs further prospective studies.
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COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients. © 2021 Neural information processing systems foundation. All rights reserved.
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The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science. Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. Here, we introduce a novel framework that can extract the COVID-19 public health evidence knowledge graph (CPHE-KG) from papers relating to a modelling study. We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process. We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset (CPHIE). We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++ based on the dataset. Leveraging the model on the new corpus, we construct CPHE-KG containing 60,967 entities and 51,140 relations. Finally, we seek to apply our KG to support evidence querying and evidence mapping visualization. Our SS-DYGIE++(SpanBERT) model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks. It has also shown high performance in the relation identification task. With evidence querying, our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions. The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic. Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health. © 2021
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The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the early stage of COVID-19. Our method performs a semantic-based quantitative analysis to unveil essential spatial-temporal characteristics of COVID-19 stigmatization for timely alerts and risk mitigation. Empirical evaluations are carried out to examine our method's predictive utilities. The visualization results of the co-occurrence network using Gephi indicate two distinct groups of stigmatized words that pertain to people in Wuhan and their dietary behaviors, respectively. Netizens' participations and stigmatizations in the Hubei region, where the COVID-19 broke out, are twice (p < 0.05) and four (p <0.01) times more frequent and intense than those in other parts of China, respectively. Also, the number of COVID-19 patients is correlated with COVID-19-related stigma significantly (correlation coefficient = 0.838, p <0.01). The responses to individual users' posts have the power law distribution, while posts by official media appear to attract more responses (e.g., likes, replies, and forward). Our method can help platforms and government agencies manage public health disasters through effective identification and detailed analyses of social stigma on social media.
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The 2019 Novel Coronavirus Disease (COVID-19) vaccines have been placed significant expectation to end the COVID-19 pandemic sooner. However, issues related to vaccines still need to be resolved urgently, including the vaccination number and range. In this paper, we proposed an epidemic spread model based on the hierarchical weighted network. This model fully considers the heterogeneity of the community social contact network and the epidemiological characteristics of COVID-19 in China, which enables to evaluate the potential impact of vaccine efficacy, vaccination schemes, and mixed interventions on the epidemic. The results show that a mass vaccination can effectively control the epidemic but cannot completely eliminate it. In the case of limited resources, giving vaccination priority to the individuals with high contact intensity in the community is necessary. Joint implementation with non-pharmacological interventions strengthening the control of virus transmission. The results provide insights for decision-makers with effective vaccination plans and prevention and control programs. © 2021 IEEE.
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COVID-19 pandemic continues to rampage in the world. Before the achievement of global herd immunity, non-pharmacological interventions(NPIs) are crucial to mitigate the pandemic. Although various NPIs have been put into practice, there are many concerns about the impacts and effectiveness of these NPIs. COVID-19 modelling study (CMS) in epidemiology can provide evidence to solve the aforementioned concerns. It is time-consuming to collect evidence manually when dealing with the vast amount of CMS papers. Accordingly, we seek to accelerate evidence collection by developing an information extraction model to automatically identify evidence from CMS papers. This work presents a novel COVID-19 Non-pharmacological Interventions Evidence (CNPIE) Corpus, which contains 597 s of COVID-19 modelling study with richly annotated entities and relations of the impacts of NPIs. We design a semi-supervised document-level information extraction model (SS-DYGIE++) which can jointly extract entities and relations. Our model outperforms previous baselines in both entity recognition and relation extraction tasks by a large margin. The proposed work can be applied towards automatic evidence extraction in the public health domain for assisting the public health decision-making of the government. © 2021 IEEE.
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With a text mining and bibliometrics approach, we review the literature on the evolution of deep learning in medical image literature from 2012 - 2020 in order to understand the current state of the research and to identify the major research themes in image analysis to answer our research questions: RQ1: What are the learning modes that are evident in the literature? RQ2: What are the emerging learning modes in the literature? RQ3: What are the major themes in medical imaging literature? The analysis of 8704 resulting from a data collection process from peer-reviewed databases, our analysis discovered the six major themes of image segmentation studies, studies with image classification, evaluation procedures such as sensitivity and specificity, optical coherence tomography studies, MRI imaging studies, and Chest imaging studies. Additionally, we assessed the number of articles published each year, the frequent keywords, the author networks, the trending topics, and connections to other topics. We discovered that segmenting and classifying the images are the most common tasks. Transfer learning is the most researched area and cancer is the highly targeted disease and Covid-19 is the most recent research trend. © WMSCI 2021.All right reserved.
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Covid-19 Diagnosis needs new Information Systems technologies as Deep learning methods, especially in medical image screening. We aim to review the applications of deep learning augmented systems in Covid-19 predictions with the help of a large literature collection from four major databases IEEE explore, ACM, Web of Science, and PubMed. We have identified three major research themes from the current literature, Image Classification, Image segmentation, and evaluation methods for DL models. Among the DL techniques, Transfer Learning is identified as the most popular method for different tasks on Chest X-rays and CT scans. Pre-trained models such as ResNet, VGG, DenseNet, and UNet are widely used in the covid-19 diagnosis. While these models are pre-trained on natural images, a Chest X-ray image pre-trained model CheXnet is gaining popularity in Covid-19 image tasks helping in improving accuracies of classifications. © AMCIS 2021.
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A war between individual investors and Wall Street institutions that occurred in January 2021 has attracted attention worldwide. There were many participants in this event, and the causality behind that was also complex. Due to the COVID-19, people work from home, so social media has become the main battlefield of GameStop events, especially Reddit's subreddit: r/WallStreetBets ('WSB' for short). Analyzing the causal interaction in social media helps people gain a more comprehensive and profound understanding of this online Wall Street movement. We use the WSB data set to construct causal networks and demonstrate the evolution of causality between GameStop stock price and WSB Redditors sentiment. We further analyzed the causality between GameStop stock price, WSB, and cryptocurrency. The discovery convinced us that Redditor in WSB did dominate and promote this movement and extended the battle to cryptocurrency market. © 2021 IEEE.
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Special economic zones (SEZs) have been used by many developing countries as a policy tool to promote industrialization and economic transformation. Since the initiation of the first modern zone in Shannon, Ireland, special economic zones have evolved in many ways, from an initial 'enclave' nature towards today's 'Economic Zone 5.0', which is built on emerging digital technologies and well integrated with urban development. The special economic zones represents a new unilateral compromise between the state and market, while still contributing to economic globalization, by presenting itself as a complementary or as an alternative approach to integrate with the global market in addition to the international economic law instruments. Despite the prevalence of special economic zones worldwide, their performance and impact on the economy and structural transformation are quite mixed. Among the many lessons learned from successful special economic zone programmes, the key elements include a strategic location, integration of zone strategy with the overall development strategy, understanding the market and leveraging comparative advantage, and, most importantly, ensuring that zones are 'special' in terms of a business-friendly environment-especially a sound legal and regulatory framework and an embodiment of sustainability and resiliency towards various external shocks like today's COVID-19 pandemic. © 2021 The Author(s). Published by Oxford University Press. All rights reserved.
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With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatio-temporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies.
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Purpose The environment in high-tech industries is highly dynamic, and after COVID-19, it has become even more unpredictable. Hence, it has become critical for firms to develop strategies to cope with a highly dynamic environment. This paper aims to analyze how the impact of the scientific collaboration networks with URIs (universities and research institutes) on firm innovation performance is contingent on technological and market dynamics. Design/methodology/approach Using a sample of 174 Chinese firms in the new-energy vehicle industry during 2004-2015, the authors applied a random-effects negative binomial modeling approach to model these relationships. Findings A broad and strong scientific collaboration network promotes firm innovation network effects are contingent on technological and market dynamics. While technological dynamics strengthen the effect market dynamics weaken it due to the different purposes of collaboration for firms and URIs. Practical implications Firms should adjust the structure of scientific collaboration networks with URIs when facing different environments. The government should encourage firms to jointly research with diverse URIs and play an active role in stabilizing market environments. Originality/value This study contributes to the academic debate on university-industry scientific collaborations. Applying the temporary competitive advantage (TCA) framework, we provide nuances to the literature that studies the factors that condition the effects of networks. This study also adds to the research on firm scientific collaboration networks by measuring networks based on the coauthorship between firms and URIs.
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Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different modalities. Existing deep learning based methods have made remarkable progress in NIR-VIS face recognition, while it encounters certain newly-emerged difficulties during the pandemic of COVID-19, since people are supposed to wear facial masks to cut off the spread of the virus. We define this task as NIR-VIS masked face recognition, and find it problematic with the masked face in the NIR probe image. First, the lack of masked face data is a challenging issue for the network training. Second, most of the facial parts (cheeks, mouth, nose) are fully occluded by the mask, which leads to a large amount of loss of information. Third, the domain gap still exists in the remaining facial parts. In such scenario, the existing methods suffer from significant performance degradation caused by the above issues. In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method. Specifically, we propose a novel heterogeneous training method to maximize the mutual information shared by the face representation of two domains with the help of semi-siamese networks. In addition, a 3D face reconstruction based approach is employed to synthesize masked face from the existing NIR image. Resorting to these practices, our solution provides the domain-invariant face representation which is also robust to the mask occlusion. Extensive experiments on three NIR-VIS face datasets demonstrate the effectiveness and cross-dataset-generalization capacity of our method. IEEE
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Based on big data, this paper studies the influence of new type of filling pneumonia on the development of sports industry. When selecting the typical economic indicators that reflect the development trend of sports industry, it is found that the data is huge according to the big industrial data, but the information that can be reflected is poor and complex. Therefore, it is necessary to process these big economic data in order to obtain the impact of new coronary pneumonia on the development of sports industry. This paper studies the feature selection algorithm of big data samples, so as to select typical economic indicators from many economic indicators of sports industry to reflect the development trend of sports industry. A deep learning algorithm based on feature selection of big data is proposed. Firstly, a feature selection framework for big data is constructed, and then data fusion and deep learning are carried out. Experiments show that the algorithm can solve the contradiction between large data and poor information. This method has a certain forward-looking, and has a certain reference value for the information discrimination of the development trend of sports industry. © 2020 - IOS Press and the authors. All rights reserved.
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BACKGROUND: Nearly 20% novel coronavirus disease 2019 (COVID-19) patients have abnormal coagulation function. Padua prediction score (PPS) is a validated tools for venous thromboembolism (VTE) risk assessment. However, its clinical value in COVID-19 patients' evaluation was unclear. METHODS: We prospectively evaluated the VTE risk of COVID-19 patients using PPS. Demographic and clinical data were collected. Association of PPS with 28-day mortality was analyzed by multivariate logistic regression and Kaplan-Meier analysis. RESULTS: Two hundred and seventy-four continuous patients were enrolled, with total mortality of 17.2%. Patients in high PPS group, with significantly abnormal coagulation, have a higher levels of interleukin 6 (25.27 vs. 2.55 pg/ml, P < 0.001), prophylactic anticoagulation rate (60.7% vs. 6.5%, P < 0.001) and mortality (40.5% vs. 5.9%, P < 0.001) when compared with that in low PPS group. Critical patients showed higher PPS (6 vs. 2 score, P < 0.001) than that in severe patients. Multivariate logistic regression revealed the independent risk factors of in-hospital mortality included high PPS [odds ratio (OR): 7.35, 95% confidence interval (CI): 3.08-16.01], increased interleukin-6 (OR: 11.79, 95% CI: 5.45-26.20) and elevated d-dimer (OR: 4.65, 95% CI: 1.15-12.15). Kaplan-Meier analysis indicated patients with higher PPS had a significant survival disadvantage. Prophylactic anticoagulation in higher PPS patients shows a mild advantage of mortality but without statistical significance (37.1% vs. 45.7%, P = 0.42). CONCLUSION: Higher PPS associated with in-hospital poor prognosis in COVID-19 patients. Prophylactic anticoagulation showed a mild advantage of mortality in COVID-19 patients with higher PPS, but it remain to need further investigation.