Extracellular Disintegration of Viral Proteins as an Innovative Strategy for Developing Broad-Spectrum Antivirals Against Coronavirus

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has claimed millions of lives and caused innumerable economic losses worldwide. Unfortunately, state-of-the-art treatments still lag behind the continual emergence of new variants. Key to resolving this issue is developing antivirals to deactivate coronaviruses regardless of their structural evolution. Here, we report an innovative antiviral strategy involving extracellular disintegration of viral proteins with hyperanion-grafted enediyne (EDY) molecules. The core EDY generates reactive radical species and causes significant damage to the spike protein of coronavirus, while the hyperanion groups ensure negligible cytotoxicity of the molecules. The EDYs exhibit antiviral activity down to nanomolar concentrations, and the selectivity index of up to 20,000 against four kinds of human coronavirus, including the SARS-CoV-2 Omicron variant, suggesting the high potential of this new strategy in combating the COVID-19 pandemic and a future "disease X."

Predict Turnaround Time of Hospital Discharge

COVID-19 pandemics lead to further shortages of beds globally. Ningbo No.1 Hospital implemented an integrated digital management system to tackle inefficiency in the discharge process, however, this problem is not fully solved. To help the hospital fully address this problem, this article identifies the problems in the hospital's dataset and proposes a methodology for the machine learning model training in order to predict the patient's leaving time, which provides a space for the hospital to improve the discharge process when procedures simplify, integration and digitalization are done. © 2022 IEEE.

Investigation on the core competency of residents during Coronavirus disease 2019 outbreak.

Objective To investigate the impact of the outbreak of coronavirus disease 2019 (COVID-19) as an intervention factor on residency training at different stages, and look into the enhancement effect of post-graduation medical training program based on competency of residency training, so as to provide reference for the optimization of medical education at the postgraduate stage. Methods After the initial success of COVID-19 prevention and control, 169 clinical postdoctoral trainess(clinical postdocs) and 515 graduate students specializing in clinical medicine(professional postdocs) were surveyed by an anonymous online questionnaire. To analyze the differences of cognition and self- evaluation of core competence between the two groups. Results There were 141 valid questionnaires collected from clinical postdocs (83.43%, 141/169) and 264 valid questionnaires collected from professional postdocs (51.26%, 264/515). In both groups, more than 85% of the students agreed or strongly agreed that they had a deeper understanding of the profession of doctors during the epidemic. The results of competency self-evaluation showed that, except for the items of "self-improvement", the self-evaluation scores of clinical postdoctoral students on other items were significantly higher than those of professional postdoctoral students (all P <0.05). Conclusions COVID-19, as a factor of emergency intervention, can improve the competency cognition of residents. The core-competency based post-graduation medical education model can comprehensively improve the students' comprehensive ability, which is an effective training program for residents. It is suggested that the vocational planning education for residents should be paid attention to in the stage of college education, and a new mode of college education that is closely combined with the post-graduation education should be further explored.Copyright © 2021 Thomson Reuters and Contributors.

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data non-stationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and outperform baselines. © 2022 IEEE.

Research on COVID-19 document fine-grained classification based on graph neural network

Since the outbreak of COVID-19, academia has published tens of thousands of new papers. Facing so much literature knowledge, how to realize the fine-grained classification of covid-19 literature and help researchers carry out research? This is an urgent problem to be solved. This paper makes COVID-19 text classification graph data set, designs covid-19 scientific literature fine-grained classification model LC-GAT based on graph attention network, adds attention mechanism at word level, sentence level and graph level, effectively retains the classification information contained in article title and key words, and significantly improves the performance of covid-19 scientific literature fine-grained classification. This paper has positive significance for the classification of COVID-19 scientific literature. © 2022 IEEE.

Remote passive monitoring in people living with progressive multiple sclerosis during the COVID-19 pandemic shows a measurable reduction in daily activity

Introduction: Government-led restrictions during the coronavirus disease 2019 (COVID-19) pandemic provided an opportunity to assess whether digital remote monitoring tools could effectively measure changes in daily activity in people with progressive multiple sclerosis (PwPMS). Objective(s): To assess the impact of COVID-19 restrictions on daily activity and clinical change in PwPMS using smartphone sensor data. Method(s): Using a precursor to FloodlightTM MS, we collected sensor data on daily activity and functional performance from June 2018 to August 2020 in a cohort of 427 PwPMS treated with ocrelizumab in CONSONANCE (NCT03523858). We developed a method for identifying individuals with an abrupt change in daily activity corresponding to local restrictions using life-space (the spatial range of mobility in everyday life [km]). Clinical measurements of the Timed 25-Foot Walk (T25FW) and Nine- Hole Peg Test (9HPT) were taken at baseline, Weeks 24, 48 and 72. Changes in digital (daily steps) and clinical outcome measures were correlated to changes in life-space. Result(s): Patients with at least 50 days of life-space data with no more than 28 days between measurements in 2020 (n=122/427) were selected. Of these, 54 (44%) patients experienced a measurable reduction in life-space, with a median occurrence on 11 March 2020 (+/-19 days);68 (56%) had no detectable change, of whom 13 (11%) had a consistently low life-space (mean=<1 km). Patients with a measurable reduction in life-space experienced a 28% reduction in daily step count in the first 4 weeks following restrictions (p<0.0001, Wilcoxon paired test). Longer duration of life-space restriction was correlated with greater reduction in step count (Spearman's r=-0.86, p<0.00001;n=31). A significant worsening (16% increase, p=0.011) in T25FW from Week 48 to 72 was observed in 30 eligible PwPMS with detected reduction in life-space between clinical visits, whereas a nonsignificant change (7% increase, p=0.23) was observed for 37 eligible PwPMS with a mean life-space consistently >1 km and no detectable reduction. No significant change was found for 9HPT in either group. There was no significant difference in baseline demographics and clinical measures between groups. Conclusion(s): Prolonged reduction in mobility due to COVID-19 restrictions may impact physical activity and clinical disability in PwPMS. Remote digital monitoring using Floodlight MS may be useful for assessing activity and functional performance in PwPMS.

Changes in Perceived Stress of Pharmacy Students Pre- and Mid-COVID-19 Pandemic.

Objective: The objective of this study was to examine (1) the difference in perceived stress in first-year pharmacy students before and during the COVID-19 pandemic and (2) the difference in perceived stress among pharmacy students working different numbers of hours. Methods: Perceived Stress Scale (PSS), via an electronic survey, was administered throughout 2016-2021 using Qualtrics. End-of-year PSS scores were compared between the pre-pandemic group (2016-2018) and mid-pandemic group (2019-2021) using independent t-test and ANCOVA. All analyses were conducted using IBM SPSS Statistic Version 28.0. Results: A total of 209 first-year pharmacy students participated (response rate of 88%). No significant difference in mean PSS score was detected in the mid-pandemic cohort when compared to pre-pandemic. The mean PSS score was greater in those who worked greater than 10 h weekly compared to those who worked less. Those who did not work had an even greater mean PSS score than those who worked. Conclusions: No significant difference was observed in perceived stress between the pre-pandemic and mid-pandemic cohorts, and an increased perceived stress score was observed in pharmacy students who did not work in comparison to students who worked 1-9 h and 10-29 h.

Clinical application of clot waveform analysis

Bleeding and thrombotic diseases are closely related to various clinical departments. Laboratory‐related tests play an important role in disease diagnosis and differential diagnosis, risk assessment, cause finding, and efficacy monitoring. Clot waveform analysis (CWA), as an automated coagulation detection technology, can provide more valuable information about the entire coagulation process of a plasma sample. A large number of studies have showed that CWA has certain value in the evaluation of coagulation status of COVID‐19 patients, the judgment of clinical phenotype of hemophilia A (HA) patients, and the monitoring of direct oral anticoagulant drugs (DOAC). In‐depth interpretation and application of CWA in different clinical settings can provide more laboratory information for diagnosis and treatment of bleeding and thrombotic diseases. © 2021 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.

A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning offer new promising ways to mine such rich datasets (e.g., satellite imagery, COVID statistics), spatial heterogeneity - an intrinsic characteristic embedded in spatial data - poses a major challenge as data distributions or generative processes often vary across space at different scales, with their spatial extents unknown. Recent studies (e.g., SVANN, spatial ensemble) targeting this difficult problem either require a known space-partitioning as the input, or can only support very limited number of partitions or classes (e.g., two) due to the decrease in training data size and the complexity of analysis. To address these limitations, we propose a model-agnostic framework to automatically transform a deep learning model into a spatial-heterogeneity-aware architecture, where the learning of arbitrary space partitionings is guided by a learning-engaged generalization of multivariate scan statistic and parameters are shared based on spatial relationships. We also propose a spatial moderator to generalize learned space partitionings to new test regions. Experiment results on real-world datasets show that the spatial transformation and moderation framework can effectively capture flexibly-shaped heterogeneous footprints and substantially improve prediction performances.

Blocking Effects of Traffic Control Measures on COVID-19 Transmission in City Territories

With the outbreak of the novel coronavirus pneumonia (COVID-19), urban traffic control is an important measure to block the spread and control the development of the epidemic. In this study, to elucidate the blocking effects of different traffic control measures on the transmission of COVID-19 within city territories, the four administrative cities of Jingzhou, Xiaogan, Jingmen, and Shijiazhuang were selected as the study areas. The situation of the epidemic before and after the implementation of three typical traffic control measures-road closure, passenger traffic stoppage, and community population flow control-was analyzed. Based on the classical dynamics theory of infectious diseases, an optimized epidemic transmission susceptible, infectious, removed, exposed (SEIR) model was established, considering the infectivity of the exposed, population migration, and population flow in urban communities. Thereafter, the model was used to study the blocking effects based on whether traffic control measures were implemented in each city and the various traffic control measures on the development of the epidemic. The results indicate that the daily incidence rate of each city is related to the immigration index of approximately 14 days ago;the traffic control measures adopted by each city are also highly effective in blocking the spread of the epidemic. Compared with no traffic control measures, the peak numbers of infected persons in Jingzhou, Xiaogan, Jingmen, and Shijiazhuang decreased by 61.29%, 53.71%, 66.16%, and 66.33%, respectively. The blocking effects of the three control measures exhibit a few differences. The blocking effect of road closure measures is the most prominent;the numbers of latent and infected persons can be reduced by 40.54%-70.88% and 49.11%-64.34%, respectively, using different implementation nodes. Furthermore, a timely and comprehensive implementation of the three traffic control measures can effectively block the spread of the epidemic and control the development of the epidemic in cities. This improved SEIR model, which considers the characteristics of the incubation period, can accurately predict the development trends of the epidemic, thus serving as a technical reference for the formulation of urban traffic control plans over the duration of the epidemic. © 2022, Editorial Department of China Journal of Highway and Transport. All right reserved.

Twenty-Four Years of PRES Conferences: Recent Past, Present and Future-Process Integration Towards Sustainability

This paper reviews the Process Integration (PI) development related to the recent Conferences on Process Integration for Energy Saving and Pollution Reduction (PRES conferences) and makes suggestions for the future growth of PI branching out in the future. The conference history is now close to a quarter of the century-from 1998 to 2021, and has been flourishing despite the difficult COVID-19 period. The paper overviews the progresses in Process Integration with Pinch Analysis, heat exchangers and Process Integration, extensions of Process Integration for wider process system engineering, integration of renewable energy sources, Circular Economy, extended environmental footprints, extended water-energy nexus contribution to environmental assessment, COVID-19 pandemics environmental consequences, and ecosystem remediation and waste stream clean-up. Considerable progress in Process Integration has also been achieved thanks to PRES conferences. This overview is an attempt to demonstrate the contribution delivered and make suggestions for the future growth of the PI branching out during the next years. It has become apparent that further improvements of the PI technologies are necessary and possible for achieving sufficient reductions of resource demands and pollution so that available renewables and end-of-pipe cleaning can serve them, minimising the environmental impacts. The key methodology developments enabling this are multi-constraint Pinch Analysis and the joint use of several PI methods for delivering comprehensive macro-analyses. © 2021, AIDIC Servizi S.r.l.

The Impact of COVID-19 on the Water Sector: A Review

This paper provides a literature review of the impact of the COVID-19 spread-prevention measures on the water sectors, including general water network management, water quality and quantity, and wastewater treatment. Fifty-four papers are selected for the analytical review, and the results showed that the pandemic poses both positive and potential long term negative impacts to the water sector. In the short term, the limitations in mobility and industrial activities lead to the water use patterns shifts between especially the industrial and residential sector, and reduction in aquatic pollution discharge. But in the long run, the changes in the industrial development patterns and people's lifestyle caused by the pandemic might also require further adaption and update of the current water networks. Understanding the interactions between the pandemic and water-related aspects is essential to ensure the urban water supply system is resilient in pandemic situations. As a response, this resilience can help to facilitate controlling and mitigating the spreading of the virus. © 2021, AIDIC Servizi S.r.l.

Investigation on the core competency of residents during Coronavirus disease 2019 outbreak

Objective To investigate the impact of the outbreak of coronavirus disease 2019 (COVID-19) as an intervention factor on residency training at different stages, and look into the enhancement effect of post-graduation medical training program based on competency of residency training, so as to provide reference for the optimization of medical education at the postgraduate stage. Methods After the initial success of COVID-19 prevention and control, 169 clinical postdoctoral trainess(clinical postdocs) and 515 graduate students specializing in clinical medicine(professional postdocs) were surveyed by an anonymous online questionnaire. To analyze the differences of cognition and self- evaluation of core competence between the two groups. Results There were 141 valid questionnaires collected from clinical postdocs (83.43%, 141/169) and 264 valid questionnaires collected from professional postdocs (51.26%, 264/515). In both groups, more than 85% of the students agreed or strongly agreed that they had a deeper understanding of the profession of doctors during the epidemic. The results of competency self-evaluation showed that, except for the items of "self-improvement", the self-evaluation scores of clinical postdoctoral students on other items were significantly higher than those of professional postdoctoral students (all P <0.05). Conclusions COVID-19, as a factor of emergency intervention, can improve the competency cognition of residents. The core-competency based post-graduation medical education model can comprehensively improve the students' comprehensive ability, which is an effective training program for residents. It is suggested that the vocational planning education for residents should be paid attention to in the stage of college education, and a new mode of college education that is closely combined with the post-graduation education should be further explored. © 2021 Thomson Reuters and Contributors.

Detection System of Wearing Face Masks Normatively Based on Deep Learning

The novel coronavirus COVID-19 can be transmitted, for example, by contacting with oral and nasal's secretions, and traditional mask detection algorithms often fail to distinguish whether masks are worn regularly. In this article, we improve the algorithm network structure based on the improved YOLOV3-tiny algorithm and use the combination of nose detection and mask detection for feature fusion based on the training of massive data sets, which perfectly solves the problem of detecting whether the mask is worn in a normative way. The experiment shows that this system can detect the target of wearing face masks in different scenes with an accuracy rate of over 99%, laying a solid foundation for the detection of wearing face masks normatively. © 2021 IEEE.

The impact of COVID-19 on economic globalization: Based on the perspective of factor flow

When economic globalization develops vigorously, there is also the phenomenon of anti-globalization. The global epidemic of COVID-19 broke out in early 2020 in this economic backdrop. How does COVID-19 affect economic globalization? Will COVID-19 further exacerbate the tide of anti-economic globalization? International trade gradually turns from commodity trade to factor flow after economic globalization. Therefore, this paper discusses the impact of COVID-19 on economic globalization from the perspective of factor flow. This paper finds that COVID-19 causes the deterioration of the market environment. The deterioration of the market environment leads to the restriction of production and the decline of purchasing power, which may temporarily restrain the economic development within countries. Strict import and export controls have reduced or even temporarily disrupted the flow of factors between countries with close trade links. The consequence of the disruption is that International direct investment projects may be delayed by the emergence of COVID-19, that factor resources of the investing country may not be integrated with host country resources, and that economic globalization is temporarily hindered. © 2021 ACM.

Collaborative platform for remote manufacturing systems using industrial internet and digital twin in the COVID-19 era

In the wake of COVID-19, significant influence on the manufacturing industries has been observed in the past year due to the restrictions of in-person communications and interactions. As a consequence, manufacturing efficiency has reduced remarkably all over the world. Despite the great harm to the industrial operations under the pandemic, the opportunities for remote collaborative manufacturing system also arise. Effective and efficient remote manufacturing systems for the real industries have been highly demanded. Through the integration of industrial internet and digital twin systems, the remote manufacturing system can be largely facilitated. This paper proposes a general framework for the remote manufacturing system during the COVID-19 era. The concept of the intelligent collaborative remote manufacturing system is firstly reviewed, as well as discussions of the current pandemic situation and its influence on the industries. The current commercial platforms of the systems are also presented. A case study on the lighthouse factories at the Foxconn Technology Group is finally presented for understanding the implementation of the proposed strategy. The effectiveness of the framework has been validated in the real industrial scenarios, and great economic and operational benefits have been obtained. The proposed framework offers a promising solution for the remote manufacturing system under the current pandemic. Copyright © 2021 by ASME