Lack of SARS-CoV-2 in environmental samples collected from September 2020-February 2021 in a university that followed CDC reopening guidance.

This study aimed to provide environmental surveillance data for evaluating the risk of acquiring SARS-CoV-2 in public areas with high foot traffic in a university. Air and surface samples were collected at a university that had the second highest number of COVID-19 cases among public higher education institutions in the U.S. during Fall 2020. A total of 60 samples were collected in 16 sampling events performed during Fall 2020 and Spring 2021. Nearly 9800 students traversed the sites during the study period. SARS-CoV-2 was not detected in any air or surface samples. The university followed CDC guidance, including COVID-19 testing, case investigations, and contact tracing. Students, faculty, and staff were asked to maintain physical distancing and wear face coverings. Although COVID-19 cases were relatively high at the university, the possibility of acquiring SARS-CoV-2 infections at the sites tested was low.

Intelligent optimization design of electron barrier layer for AlGaN-based deep-ultraviolet light-emitting diodes

AlGaN-based deep-ultraviolet light-emitting diodes (DUV LEDs) are widely used in sterilization, sensing,water purification, medical treatment, non-line of sight (NLOS) communication and many other fields.Especially it has been reported that the global novel coronavirus (COVID-19) can be effectively inactivated bythe DUV light with a wavelength below 280 nm (UVC) within a few seconds, which has also attracted greatattention. However, the external quantum efficiency (EQE) of UVC LED is still at a low level, generally notmore than 10%. As an important component of EQE, internal quantum efficiency (IQE) plays a crucial role inrealizing high-performance DUV-LED. In order to improve the IQE of AlGaN-based DUV-LED, this workadopts an electron blocking layer (EBL) structure based on InAlGaN/AlGaN superlattice. The results showthat the superlattice EBL structure can effectively improve the IQE compared with the traditional single-layerand double-layer EBL structure for the DUV-LED. On this basis, the optimization method based on JAYAintelligent algorithm for LED structure design is proposed in this work. Using the proposed design method, theInAlGaN/AlGaN superlattice EBL structure is further optimized to maximize the LED' s IQE. It isdemonstrated that the optimized superlattice EBL structure is beneficial to not only the suppression of electronleakage but also the improvement of hole injection, leading to the increase of carrier recombination in the activeregion. As a result, the IQE of the DUV-LED at 200 mA injection current is 41.2% higher than that of thesingle-layer EBL structure. In addition, the optimized structure reduces IQE at high current from 25% to 4%.The optimization method based on intelligent algorithm can break through the limitation of the current LEDstructure design and provide a new method to improve the efficiency of AlGaN-based DUV-LED.

Mask detection algorithm based on YOLOv5 integrating attention mechanism

Wearing masks correctly during the COVID-19 pandemic can effectively prevent the spread of the virus. In response to the detection challenge posed by dense crowds and small detection targets in public places, a mask wearing detection algorithm based on the YOLOv5s model and integrating an attention mechanism was proposed. Four attention mechanisms were introduced into the backbone network of the YOLOv5s model to respectively suppress irrelevant information, enhance the ability of the feature map to express information, and improve the modelʹs detection ability for small-scale targets. Experimental results show that the introduction of the convolutional block attention module could increase the mAP value by 6.9 percentage points compared with the original network, with the greatest improvement among the four attention mechanisms. The normalization-based attention module also showed excellent performance, with the least quantity of parameters while losing a small amount of mAP. Through comparative experiments, the GIoU loss function was selected to calculate the bounding box regression loss, resulting in further improvements to positioning accuracy, resulting in an mAP value that was improved by 8.5 percentage points compared to the original network. The detection results of the improved model in different scenarios prove the accuracy and practicability of the algorithm for small target detection. © 2023, Editorial of Board of Journal of Graphics. All rights reserved.

Human Mobility-based Individual-level Epidemic Simulation Platform

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on real-world human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers. © 2022 held by the owner/author(s). Publication rights licensed to ACM.

Empowerment of Cross-Border E-Commerce Platforms for Small and Medium-Sized Enterprises: Evidence from China

Purpose: Although the important role of cross-border e-commerce platforms in supporting small and medium-sized enterprises (SMEs) has continuously attracted scholarly attention, existing research overlooks the perspective of value chain processes, which have become more crucial when facing a dual shock from resurgent protectionist policies and the COVID-19 pandemic. This paper deconstructs strategic flexibility from the perspective of value chain processes and further explores the mechanism of cross-border e-commerce platform empowerment on SME export performance. Methodology/approach: A total of 425 SMEs participating in cross-border e-commerce platforms were used as the research sample for this empirical study, subsequently analyzing the multiple mediating effects. Findings: The results show that strategic flexibility plays a partial mediating role between the empowerment of e-commerce platforms and SMEs' export performance. Specifically, market flexibility shows a relatively weak significance unlike delivery flexibility, which shows the strongest significance. Originality/Value: Following the overall perspective of the platform ecosystem, this study expands and integrates traditional empowerment theory and value chain theory into one analytical framework. It investigates the mechanism through which e-commerce platform empowerment influences a firm's export performance. Practical implications: Managerial suggestions for collaborative innovation of cross-border e-commerce platforms and SMEs in China are proposed. SMEs should actively integrate into platforms according to their business characteristics, fully exploit the platform resources, and focus on improving their responsiveness to export markets. © 2023 Taylor & Francis Group, LLC.

MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks

Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimating the time/region-varying epidemiological parameters, while most deep learning models lack the guidance of epidemiological domain knowledge and interpretability of prediction results. In this study, we propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting by incorporating Graph Neural Networks (GNNs) and graph learning mechanisms into Metapopulation SIR model. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters and the underlying epidemic propagation graph from heterogeneous data in an end-to-end manner. Experiment results demonstrate our model outperforms the existing mechanistic models and deep learning models by a large margin. Furthermore, the analysis on the learned parameters demonstrates the high reliability and interpretability of our model and helps better understanding of epidemic spread. Our model and data have already been public on GitHub https://github.com/deepkashiwa20/MepoGNN.git. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

INTEGRATION OF MINIVALVES WITH RNA AMPLIFICATION DEVICE FOR SIMULTANEOUS DETECTION OF SARS-COV-2 AND INFLUENZA VIRUSES

We report a point-of-care (POC) device for simultaneous detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A viruses. The device carries out sample preparation using ball-based valves for sequential delivery of reagents. A microfluidic paper-based analytical device (µPAD) in the detection unit enables RNA isolation and enrichment, followed by reverse transcription loop-mediated isothermal amplification (RT-LAMP) and colorimetric detection. The device integrates all the necessary steps for the sample preparation, including virus lysis, RNA enrichment and purification of two virus samples. The device enabled simultaneous detection of SARS-CoV-2 and Influenza A N1H1 viruses in 50 min., with limit of detection of 2 and 6 genome equivalents (GEs), respectively. The device was also capable of detecting environmental sample of the two viruses. Copyright © 2022 by ASME.

Stimuli-responsive hydrogels for cancer immunotherapy

Immunotherapy plays an important role in cancer treatment by activating or suppressing the immune system. However, there are still a series of challenges to overcome regarding the delivery vehicles of immunotherapeutic agents and their effective activation at tumor sites. Meanwhile, owing to their well-hydrated environment and capability of immobilizing biological cargos, hydrogels in combination with immunotherapies provide a chance to enhance the antitumor immune response with reduced side effects. In addition, stimuli-responsiveness has been also widely applied to optimize the pharmacokinetics with an improved therapeutic outcome. In this review, we discuss the opportunities for the combination of immunotherapy and stimuli-responsive hydrogels, such as light, temperature, ultrasound and magnetically responsive hydrogels, for effective cancer treatment. Finally, we explore the potential of stimuli-responsive hydrogels as vaccine implants against cancer and Covid-19.

Volatility in US dairy futures markets

US dairy futures markets of Class III milk, butter, cheese, and dry whey exhibit unique volatility patterns under the Federal Milk Marketing Order pricing system. We find that dairy volatilities have a relatively low connectedness among themselves and the overall commodity market. We develop a price information uncertainty measure to investigate dairy markets' response to government-released information. Dairy futures markets respond to government-released information with increased trading activity. The price information uncertainty measure has a strong positive impact on price volatility across all dairy commodities. We provide evidence that the COVID-19 pandemic increases volatility in dairy commodities. The pandemic also significantly reduces the impact of information uncertainty on volatility. © 2022 Elsevier B.V.

Household income is associated with functional outcomes in a multi-institutional cohort of patients with ischemic stroke and COVID-19.

OBJECTIVES: The COVID-19 pandemic has heightened awareness of health disparities associated with socioeconomic status (SES) across the United States. We examined whether household income is associated with functional outcomes after stroke and COVID-19. MATERIALS AND METHODS: This was a multi-institutional, retrospective cohort study of consecutively hospitalized patients with SARS-CoV-2 and radiographically confirmed stroke presenting from March through November 2020 to any of five comprehensive stroke centers in metropolitan Chicago, Illinois, USA. Zip-code-derived household income was dichotomized at the Chicago median. Logistic regression was used to examine the relationship between household income and good functional outcome (modified Rankin Scale 0-3 at discharge, after ischemic stroke). RESULTS: Across five hospitals, 159 patients were included. Black patients comprised 48.1%, White patients 38.6%, and Hispanic patients 27.7%. Median household income was $46,938 [IQR: $32,460-63,219]. Ischemic stroke occurred in 115 (72.3%) patients (median NIHSS 7, IQR: 0.5-18.5) and hemorrhagic stroke in 37 (23.7%). When controlling for age, sex, severe COVID-19, and NIHSS, patients with ischemic stroke and household income above the Chicago median were more likely to have a good functional outcome at discharge (OR 7.53, 95% CI 1.61 - 45.73; P=0.016). Race/ethnicity were not included in final adjusted models given collinearity with income. CONCLUSIONS: In this multi-institutional study of hospitalized patients with stroke, those residing in higher SES zip codes were more likely to have better functional outcomes, despite controlling for stroke severity and COVID-19 severity. This suggests that area-based SES factors may play a role in outcomes from stroke and COVID-19.

A Survey on Data-driven COVID-19 and Future Pandemic Management

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.

Human Mobility-based Individual-level Epidemic Simulation Platform

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on realworld human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers.

Transcriptomic Profiling of Ganoderic Acid Me-Mediated Prevention of Sendai Virus Infection

Objectives: Ganoderic acid Me [GA-Me], a major bioactive triterpene extracted from Ganoderma lucidum, is often used to treat immune system diseases caused by viral infections. Although triterpenes have been widely employed in traditional medicine, the comprehensive mechanisms by which GA-Me acts against viral infections have not been reported. Sendai virus [SeV]-infected host cells have been widely employed as an RNA viral model to elucidate the mechanisms of viral infection. Method(s): In this study, SeV-and mock-infected [Control] cells were treated with or without 54.3 muM GA-Me. RNA-Seq was performed to identify differentially expressed mRNAs, followed by qRT-PCR validation for selected genes. GO and KEGG analyses were applied to investigate potential mechanisms and critical pathways associated with these genes. Result(s): GA-Me altered the levels of certain genes' mRNA, these genes revealed are associated pathways related to immune processes, including antigen processing and presentation in SeV-infected cells. Multiple signaling pathways, such as the mTOR pathway, chemokine signaling pathway, and the p53 pathways, significantly correlate with GA-Me activity against the SeV infection process. qRT-PCR results were consistent with the trend of RNA-Seq findings. Moreover, PPI network analysis identified 20 crucial target proteins, including MTOR, CDKN2A, MDM2, RPL4, RPS6, CREBBP, UBC, UBB, and NEDD8. GA-Me significantly changed transcriptome-wide mRNA profiles of RNA polymerase II/III, protein posttranslational and immune signaling pathways. Conclusion(s): These results should be further assessed to determine the innate immune response against SeV infection, which might help in elucidating the functions of these genes affected by GA-Me treatment in virus-infected cells, including cells infected with SARS-CoV-2. Copyright © 2022 Bentham Science Publishers.

Exit and transition: Exploring the survival status of Airbnb listings in a time of professionalization

This study analyzes the survival status of shared and non-shared listings in the peer-to-peer accommodation market. Using a large data set from Airbnb in Beijing, we identify 8640 shared listings and 50,741 non-shared listings. We then investigate the exit event and the identity transition event for both types of listings by applying a discrete-time hazard model. Our results suggest that, for the exit event, the two types of listings show significant differences in terms of survival determinants, including response time, tourism specialization, market volume, professionalization, and Covid-19. For the identity transition event, we find that internal flow exists in the market, mainly from shared listings to non-shared listings, and this flow is influenced by certain factors (i.e., capacity, facility, rating, reviews, minimum stay, service quality, tourism specialization, market volume, plat-form professionalization, and Covid-19).

Analyzing Medical Semantic Association with Complex Network

[Objective] This paper aims to study medical semantic association with the help of complex network. [Methods] First, we constructed a medical semantic association network using the medical semantic concepts as nodes and semantic associations as edges. Then, we analyzed the network characteristics and semantic community. Finally, we created vectors for the semantic concepts and conducted semantic clustering analysis with the neural network. [Results] We retrieved relevant literature on“coronavirus”from MEDLINE of PubMed and built a semantic association network with 43 nodes and 877 edges. Then, we visualized the network characteristics, semantic community and semantic clusters. [Limitations] The experimental data size needs to be expanded. [Conclusions] The proposed network effectively describes the semantic association among medical concepts and benefits medical knowledge discovery services. © 2022, Chinese Academy of Sciences. All rights reserved.

VALVE-ENABLED SEQUENTIAL REAGENT DELIVERY AND PAPER-BASED ENRICHMENT FOR SIMULTANEOUS DETECTION OF SARS-COV-2 AND INFLUENZA VIRUSES

We report a point-of-care (POC) testing platform for simultaneous detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A virus. The POC device integrates sample preparation using ball-based valves for sequential delivery of reagents, viral RNA isolation and enrichment by paper-based filtration, with reverse transcription loop-mediated isothermal amplification (RT-LAMP) and colorimetric detection. The device is capable of detecting both viruses, showing high sensitivity and specificity. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

CRISPR/Cas12a-mediated electrochemiluminescence platform for environmental and human serum SARS-CoV-2 RNA monitoring using a self-enhanced ruthenium complex linked to zeolitic imidazole framework-8

Since the emergence of coronavirus disease 2019 (COVID-19), this highly contagious disease has ravaged the world, cumulatively causing millions of deaths and huge economic losses. As the culprit of COVID-19, severe acute respiratory syndrome beta-coronavirus 2 (SARS-CoV-2) is highly infectious and pathogenic, which has caused extreme panic worldwide. Early and rapid monitoring of SARS-CoV-2 has a significant role in suppressing the pandemic and reducing the virus's lethality. In our work, we prepared a self-enhanced ruthenium complex linked to zeolitic imidazole framework-8 (ZIF-8) and used it as an electrochemiluminescence (ECL) emitter. Additionally, a double-stranded specific nuclease (DSN)-assisted target RNA cycling with catalytic hairpin assembly (CHA) signal amplification technology was used to achieve the conversion of target RNA concentration to double-stranded DNA (dsDNA) output which significantly improved the detection sensitivity of target RNA under environmental conditions and in real human serum samples. In addition, we also combined the trans-cleavage property of CRISPR-Cas12a with the adsorption property of C3N4 on a ferrocene (Fc)-labeled DNA probe and obtained target RNA-dependent ECL signals. The reliable detection protocol achieved the transformation of SARS-CoV-2 RNA concentration to ECL responses, obtaining a limit of detection (LOD) of 0.67 fM with high specificity and reproducibility, which was of guiding significance for current detection methods of mutant SARS-CoV-2 and universal RNA.

Viable SARS-CoV-2 Delta variant detected in aerosols in a residential setting with a self-isolating college student with COVID-19.

The B.1.617.2 (Delta) variant of SARS-CoV-2 emerged in India in October of 2020 and spread widely to over 145 countries, comprising over 99% of genome sequence-confirmed virus in COVID-19 cases of the United States (US) by September 2021. The rise in COVID-19 cases due to the Delta variant coincided with a return to in-person school attendance, straining COVID-19 mitigation plans implemented by educational institutions. Some plans required sick students to self-isolate off-campus, resulting in an unintended consequence: exposure of co-inhabitants of dwellings used by the sick person during isolation. We assessed air and surface samples collected from the bedroom of a self-isolating university student with mild COVID-19 for the presence of SARS-CoV-2. That virus' RNA was detected by real-time reverse-transcription quantitative polymerase chain reaction (rRT-qPCR) in air samples from both an isolation bedroom and a distal, non-isolation room of the same dwelling. SARS-CoV-2 was detected and viable virus was isolated in cell cultures from aerosol samples as well as from the surface of a mobile phone. Genomic sequencing revealed that the virus was a Delta variant SARS-CoV-2 strain. Taken together, the results of this work confirm the presence of viable SARS-CoV-2 within a residential living space of a person with COVID-19 and show potential for transportation of virus-laden aerosols beyond a designated isolation suite to other areas of a single-family home.

Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence (Nature Machine Intelligence, (2021), 3, 12, (1081-1089), 10.1038/s42256-021-00421-z)

In the version of this article initially published, the first name of Chuansheng Zheng was misspelled as Chuangsheng. The error has been corrected in the HTML and PDF versions of the article. © The Author(s) 2022.