ABSTRACT
Disinfection robots have applications in promoting public health and reducing hospital acquired infections and have drawn considerable interest due to the COVID-19 pan-demic. To disinfect a room quickly, motion planning can be used to plan robot disinfection trajectories on a reconstructed 3D map of the room's surfaces. However, existing approaches discard semantic information of the room and, thus, take a long time to perform thorough disinfection. Human cleaners, on the other hand, disinfect rooms more efficiently by prioritizing the cleaning of high-touch surfaces. To address this gap, we present a novel GPU-based volumetric semantic TSDF (Truncated Signed Distance Function) integration system for semantic 3D reconstruction. Our system produces 3D reconstructions that distinguish high-touch surfaces from non-high-touch surfaces at approximately 50 frames per second on a consumer-grade GPU, which is approximately 5 times faster than existing CPU-based TSDF semantic reconstruction methods. In addition, we extend a UV disinfection motion planning algorithm to incorporate semantic awareness for optimizing coverage of disinfection tra-jectories. Experiments show that our semantic-aware planning outperforms geometry-only planning by disinfecting up to 20% more high-touch surfaces under the same time budget. Further, the real-time nature of our semantic reconstruction pipeline enables future work on simultaneous disinfection and mapping. Code is available at: https://github.com/uiuc-iml/RA-SLAM © 2022 IEEE.
ABSTRACT
Pollutants emitted from motor vehicles have become an important source of air pollution. Based on the traffic flow data of the expressways in Fujian Province, a high-resolution pollutant emission inventory of the expressways from January to July in 2020 was established by the bottom-up calculation method. The results show that under the influence of the COVID-19, the monthly average traffic flow and pollutant emissions of the expressways in Fujian province decreased firstly and then increased. Pollutant emissions reached the lowest in April, and quickly recovered to the pre-COVID-19 emission level in May. The pollutant emissions of the CO, HC, NOx, PM2.5 and PM10 in the middle stage of the COVID-19 decreased by 90.68%, 89.06%, 92.58%, 89.58% and 89.63%, respectively, compared with those in the post stage of the COVID-19. In the entire study period, different cities have different sharing rates of the pollutant emissions from motor vehicles, with Quanzhou, Fuzhou and Zhangzhou having higher motor vehicle emission sharing rates on the expressways. In terms of the vehicle types, the small passenger buses and the light trucks are the main contributors for CO and HC, and the heavy trucks and the light trucks are the main contributors for NOx and PM. In terms of the fuel types, the gasoline vehicle is the main source of CO and HC, and the diesel vehicle is the main source of NOx and PM. In terms of the emission standards, vehicles with China 3 and China 4 have the largest contribution rate to various pollutants. However, the spatial distribution of various pollutants is consistent, with the higher level emissions in the eastern coastal expressways, while the lower level emissions in the western inland expressways. From the spatial distribution of NOx emission intensity of Expressways in Fujian Province, the emission intensity of each section in March and April was at a very low level. Taking the main emission of the motor vehicle: NOx as a case, the Shenyang-Haikou Expressway section has the highest NOx emission intensity. Moreover, the Xiamen and Quanzhou sections of Shenyang-Haikou Expressway have relatively high exposure levels of motor vehicle pollution, while other sections have relatively low exposure levels. © 2022, Science Press. All right reserved.
ABSTRACT
Background: To examine the outcomes heterogeneity of clinical trial protocols of coronavirus disease 2019 (COVID-19) to prioritize the establishment of a core outcome set. Methods: Databases of the International Committee of Medical Journal Editors - accepted clinical trial registry platforms were searched on February 14, 2020 and May 31, 2020. Randomized controlled trials and non-randomized controlled trials of COVID-19 were considered. Patient condition was classified as common, severe, or critical. Interventions included traditional Chinese medicine and Western medicine. We excluded trials that involved discharged patients, psychological intervention, and complications of COVID-19. The general information and outcomes, outcome measurement instruments, and measurement times were extracted. The results were analyzed by descriptive analysis. Results: In all, 19 registry platforms were searched. A total of 97 protocols were selected from among 160 protocols for the first search. For protocols of traditional Chinese medicine clinical trials, 76 outcomes from 16 outcome domains were reported, and almost half (34/76, 44.74%) of the outcomes were reported only once;the most frequently reported outcome was time taken for severe acute respiratory syndrome coronavirus 2 RNA to become negative. Twenty-seven (27/76, 35.53%) outcomes provided one or more outcome measurement instruments. Ten outcomes provided one or more measurement time frame. For protocols of Western medicine clinical trials, 126 outcomes from 17 outcome domains were reported;almost half (62/126, 49.21%) of the outcomes were reported only once;the most frequently reported outcome was proportion of patients with negative severe acute respiratory syndrome coronavirus 2. Twenty-seven outcomes provided one or more outcome measurement instruments. Forty (40/126, 31.75%) outcomes provided one or more measurement time frame. There were > 40 duplicated outcomes between the clinical trials protocols of traditional Chinese medicine and western medicine protocols. All of them were included in the Delphi survey when developing core outcome set for COVID-19. A total of 1,027 protocols were selected from 2,741 protocols for the second search. Forty-two new outcomes and 47 new outcome measurement instruments were reported. Conclusion: Outcome reporting in clinical trial protocols of COVID-19 is inconsistent. Thus, establishing a core outcome set is necessary for diagnosis and management.
ABSTRACT
Since its establishment in 2018, the Young Generation Network (YGN) has been dedicated, with support of the International Radiation Protection Association (IRPA), to a variety of activities to promote communication, collaboration and professional development of students and young professionals in the area of radiation protection and its allied fields. This article reports our recent activities from the middle of 2018 to the beginning of 2021, with highlights on some important events: “Joint JHPS-SRP-KARP Workshop of Young Generation Network” (December 2019 in Japan);contribution to “Nuclear Energy Agency Workshop on Optimization: Rethinking the Art of Reasonable” (January 2020 in Portugal);survey on the impact of coronavirus disease 2019 (COVID-19) on radiation protection among IRPA YGN members (March 2020);and contribution to IRPA15 (15th International Congress of the IRPA;January–February 2021, online). The discussion and insight obtained from each activity are also summarized. The IRPA YGN will aim to achieve its on-going activities and continue to follow the ways paved in the Strategic Agenda and despite the challenges raised by the COVID-19 pandemic. Namely, running an international survey (for example, on the usage of social media in radiation protection, and on the long-term consequences of the COVID-19 pandemic), engaging national YGNs, extending the network, finding new relationships with networks with an interest in the young generation and participation in (remote) events will be aspired for. Copyright © 2021 The Korean Association for Radiation Protection.
ABSTRACT
This study shows how data-driven modeling can be applied to facilitating policymaking at the geographical hierarchy in terms of the administrative structure of regions and communities when a public health crisis arises. Specifically, rich data and machine learning based models are explored for public health policies, exploring the timing and restrictive levels of intervention measures, such as school/workplace closure and lifting, gathering ban, or travel restrictions, needed for a community, at the region and community level as time goes. This study articulates that rich data and machine learning work well in reducing policy discrepancies. Real world data of COVID-19 cases at the state level in the U.S. are used first in this study to show the consequence of different policy responses in 2020. To demonstrate what different policy responses could result, an agent-based simulation model using a small-scale school setting will be then presented. The simulation model could be further developed, scaled up, and customarily adopted across any geographical hierarchy, facilitating policymaking in public health. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference, June 2021.
ABSTRACT
To investigate the material basis and mechanism of Liupao tea on preventing COVID-19 by network pharmacology and molecular docking. The active ingredients and targets of Liupao tea were searched through the literature and the TCMSP databases and the network between the two was built by Cytoscape 3. 7. 1. Then using GenCards platform to predict the disease targets,mapping the common targets between Liupao tea and disease. The common targets were imported into the STRING database for exploring the protein-protein interaction. Core targets were enriched by gene ontology (GO) enrichment analysis and KEGG (kyoto encyclopedia of genes and genomes) pathway enrichment analysis using DAVID database etc.. Finally,the screened active components were docked with the receptor protein SARS-CoV-2 3CL hydrolase (Mpro). Six active ingredients of Liupao tea were screened,such as (-)-epigallocatechin gallate (EGCG),(+)-catechin,(-)-eatechin gallate, α-spinasterol,pelargonidin chloride and squalene,and 156 targets were identified. Among them, there were 112 common targets and 38 core targets with COVID-19. GO enrichment analysis (P<0. 01) involved lipopolysaccharide,cell response to hypoxia, etc.. And the KEGG pathway enrichment analysis (P<0. 01)was conducted to obtain the HIF-1,IL-17,T cell receptor and other signaling pathways associated with COVID-19. The results of molecular docking showed that the active ingredients of Liupao tea were well bound to the receptor protein Mpro. The active ingredients of Liupao tea may control HIF-1,IL-17,T cell receptors signaling pathways by binding Mprohydrolase and acting on inflammation and immune related targets such as MAPK1,TNF to prevent COVID-19. The EGCG of Mproactivity was determined,and the IC50was 3. 4 μmol/L,which confirmed that EGCG was a certain inhibition effect on Mpro © 2020 China Pharmaceutical University. All rights reserved.
ABSTRACT
A large number of traditional Chinese medicine (TCM) prescriptions have played an active role in the prevention and treatment of emerging epidemic diseases, which contain huge potential of research and development (R&D). However, in recent years, few TCM new drugs have been approved for market and the industry is in a downturn. With examples of the recent drug review policies and the "three Chinese patent medicines and three TCM prescriptions" for Corona Virus Disease 2019 (COVID-19), this article proposed a series of strategies from the perspectives of review and R&D of TCM new drugs as well as resource allocation. The strategies are improving the R&D layout of TCM new drugs, clarifying the evaluation criteria of human experience evidence, supervising the syndrome indications for TCM new drugs other than diseases, implementing differentiation competitive strategy, applying multi-disciplinary methods in prescription screening, applying integrated research design, increasing investment in high-quality clinical research, and integrating resources to break through international barriers. © 2020, Chinese Journal of New Drugs Co. Ltd. All right reserved.