The Comprehensive Analysis of the Network of Superstructure Based on Territorial Characteristics of Accommodation and Food and Beverage Service Providers Considering the Financial Crisis and COVID-19: The Case of Bihor County, Romania

This study presents the territorial differences in structure of functioning accommodation and food-and-beverage service providers by their distribution based on registered field of economic activity and financial data on the level of local governments within Bihor County that were classified into four potential destination types. Furthermore, the aim of the research is to identify the concentration of business activities based on their turnover in proportion to the population on the level of local governments using the Hoover-index method for the period of 2005–2020. Lastly, another aim is to examine the efficiency of these hospitality service providers using the Hoover index on their sales revenue in proportion to their labor force between 2005 and 2020. The time frame enabled the examination of the effects of two critical incidents: the financial crisis of 2008 and COVID-19, which impacted the activity of the examined firms in hospitality. As a result, both group of service providers showed a significant increase in number of entities, turnover, and average annual number of employees after 2014. That trend was intensively interrupted by COVID-19, which was not the case for the period of financial crisis. The significance of Oradea and Sânmartin were present as central areas regarding business activities of the examined fields, thus crucially influencing the trends of the county. In the case of territorial inequalities, the two categories showed differing trends, as in the case of efficiency, although lodging services proved to show higher territorial inequalities but better performance.

Taxi Utilization Rate Maximization by Dynamic Demand Prediction: A Case Study in the City of Chicago

The explosive popularity of transportation network companies (TNCs) in the last decade has imposed dramatic disruptions on the taxi industry, but not all the impacts are beneficial. For instance, studies have shown taxi capacity utilization rate is lower than 50% in five major U.S. cities. With the availability of taxi data, this study finds the taxi utilization rate is around 40% in June 2019 (normal scenario) and 35% in June 2020 (COVID 19 scenario) in the city of Chicago, U.S. Powered by recent advances in the deep learning of capturing non-linear relationships and the availability of datasets, a real-time taxi trip optimization strategy with dynamic demand prediction was designed using long short-term memory (LSTM) architecture to maximize the taxi utilization rate. The algorithms are tested in both scenarios?normal time and COVID 19 time?and promising results have been shown by implementing the strategy, with around 19% improvement in mileage utilization rate in June 2019 and 74% in June 2020 compared with the baseline without any optimizations. Additionally, this study investigated the impacts of COVID 19 on the taxi service in Chicago.

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses.

BACKGROUND: During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. OBJECTIVE: We propose an elaboration likelihood model-based theoretical model to understand the persuasion process of COVID-19-related misinformation on social media. METHODS: The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19-related misinformation feature includes five topics: medical information, social issues and people's livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic-related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. RESULTS: Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination. CONCLUSIONS: Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.

High Expression of HERV-K (HML-2) Might Stimulate Interferon in COVID-19 Patients.

Background. Interferon is a marker of host antiviral immunity, which is disordered in COVID-19 patients. ERV can affect the secretion of interferon through the cGAS-STING pathway. In this study, we explored whether IFN-I and HERV-K (HML-2) were activated in COVID-19 patients and whether there was an interaction between them. Methods. We collected blood samples from COVID-19 patients and healthy controls. We first detected the expression of HERV-K (HML-2) gag, env, and pol genes and IFN-I-related genes between patients and healthy people by qPCR, synchronously detected VERO cells infected with SARS-CoV-2. Then, the chromosome distributions of highly expressed HERV-K (HML-2) gag, env, and pol genes were mapped by the next-generation sequencing results, and GO analysis was performed on the related genes. Results. We found that the HERV-K (HML-2) gag, env, and pol genes were highly expressed in COVID-19 patients and VERO cells infected with SARS-CoV-2. The interferon-related genes IFNB1, ISG15, and IFIT1 were also activated in COVID-19 patients, and GO analysis showed that HERV-K (HML-2) can regulate the secretion of interferon. Conclusions. The high expression of HERV-K (HML-2) might activate the increase of interferon in COVID-19 patients, proving that HERV-K does not only play a negative role in the human body.

Establishment of a rapid diagnosis assay for SARS-CoV-2 infection based on MALDI-TOF MS

Objective: To establish a rapid, high-throughput detection assay of serum peptidome profiling for the diagnosis of SARS-CoV-2 infection based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS).

Highly Sensitive Immunoresistive Sensor for Point-Of-Care Screening for COVID-19.

Current point-of-care (POC) screening of Coronavirus disease 2019 (COVID-19) requires further improvements to achieve highly sensitive, rapid, and inexpensive detection. Here we describe an immunoresistive sensor on a polyethylene terephthalate (PET) film for simple, inexpensive, and highly sensitive COVID-19 screening. The sensor is composed of single-walled carbon nanotubes (SWCNTs) functionalized with monoclonal antibodies that bind to the spike protein of SARS-CoV-2. Silver electrodes are silkscreen-printed on SWCNTs to reduce contact resistance. We determine the SARS-CoV-2 status via the resistance ratio of control- and SARS-CoV-2 sensor electrodes. A combined measurement of two adjacent sensors enhances the sensitivity and specificity of the detection protocol. The lower limit of detection (LLD) of the SWCNT assay is 350 genome equivalents/mL. The developed SWCNT sensor shows 100% sensitivity and 90% specificity in clinical sample testing. Further, our device adds benefits of a small form factor, simple operation, low power requirement, and low assay cost. This highly sensitive film sensor will facilitate rapid COVID-19 screening and expedite the development of POC screening platforms.

A Novel Strategy for the Detection of SARS-CoV-2 Variants Based on Multiplex PCR-Mass Spectrometry Minisequencing Technology.

The objective of this study was to construct a novel strategy for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants using multiplex PCR-mass spectrometry minisequencing technique (mPCR-MS minisequencing). Using the nucleic acid sequence of a SARS-CoV-2 nonvariant and a synthetic SARS-CoV-2 variant-carrying plasmid, a matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) method based on the single-base mass probe extension of multiplex PCR amplification products was established to detect 9 mutation types in 7 mutated sites (HV6970del, N501Y, K417N, P681H, D614G, E484K, L452R, E484Q, and P681R) in the receptor-binding domain of the spike protein of SARS-CoV-2 variants. Twenty-one respiratory tract pathogens (9 bacteria and 12 respiratory viruses) and nucleic acid samples from non-COVID-19 patients were selected for specific validation. Twenty samples from COVID-19 patients were used to verify the accuracy of this method. The 9 mutation types could be detected simultaneously by triple PCR amplification coupled with MALDI-TOF MS. SARS-CoV-2 and six variants, B.1.1.7 (Alpha), B.1.351 (Beta), B.1.429 (Epsilon), B.1.526 (Iota), P.1 (Gamma) and B.1.617.2 (Delta), could be identified. The detection limit for all 9 sites was 1.5 × 103 copies. The specificity of this method was 100%, and the accuracy of real-time PCR cycle threshold (CT) values less than 27 among positive samples was 100%. This method is open and extensible, and can be used in a high-throughput manner, easily allowing the addition of new mutation sites as needed to identify and track new SARS-CoV-2 variants as they emerge. mPCR-MS minisequencing provides a new detection option with practical application value for SARS-CoV-2 and its variant infection. IMPORTANCE The emergence of SARS-CoV-2 variants is the key factor in the second wave of the COVID-19 pandemic. An all-in-one SARS-CoV-2 variant identification method based on a multiplex PCR-mass spectrometry minisequencing system was developed in this study. Six SARS-CoV-2 variants (Alpha, Beta, Epsilon, Iota, Gamma, and Delta) can be identified simultaneously. This method can not only achieve the multisite simultaneous detection that cannot be realized by PCR coupled with first-generation sequencing technology and quantitative PCR (qPCR) technology but also avoid the shortcomings of time-consuming, high-cost, and high technical requirements of whole-genome sequencing technology. As a simple screening assay for monitoring the emergence and spread of SARS-CoV-2 and variants, mPCR-MS minisequencing is expected to play an important role in the detection and monitoring of SARS-CoV-2 infection as a supplementary technology.

Different Profiles of Antibodies and Cytokines Were Found Between Severe and Moderate COVID-19 Patients.

Objectives: Our objective was to determine the antibody and cytokine profiles in different COVID-19 patients. Methods: COVID-19 patients with different clinical classifications were enrolled in this study. The level of IgG antibodies, IgA, IgM, IgE, and IgG subclasses targeting N and S proteins were tested using ELISA. Neutralizing antibody titers were determined by using a toxin neutralization assay (TNA) with live SARS-CoV-2. The concentrations of 8 cytokines, including IL-2, IL-4, IL-6, IL-10, CCL2, CXCL10, IFN-γ, and TNF-α, were measured using the Protein Sample Ella-Simple ELISA system. The differences in antibodies and cytokines between severe and moderate patients were compared by t-tests or Mann-Whitney tests. Results: A total of 79 COVID-19 patients, including 49 moderate patients and 30 severe patients, were enrolled. Compared with those in moderate patients, neutralizing antibody and IgG-S antibody titers in severe patients were significantly higher. The concentration of IgG-N antibody was significantly higher than that of IgG-S antibody in COVID-19 patients. There was a significant difference in the distribution of IgG subclass antibodies between moderate patients and severe patients. The positive ratio of anti-S protein IgG3 is significantly more than anti-N protein IgG3, while the anti-S protein IgG4 positive rate is significantly less than the anti-N protein IgG4 positive rate. IL-2 was lower in COVID-19 patients than in healthy individuals, while IL-4, IL-6, CCL2, IFN-γ, and TNF-α were higher in COVID-19 patients than in healthy individuals. IL-6 was significantly higher in severe patients than in moderate patients. The antibody level of anti-S protein was positively correlated with the titer of neutralizing antibody, but there was no relationship between cytokines and neutralizing antibody. Conclusions: Our findings show the severe COVID-19 patients' antibody levels were stronger than those of moderate patients, and a cytokine storm is associated with COVID-19 severity. There was a difference in immunoglobulin type between anti-S protein antibodies and anti-N protein antibodies in COVID-19 patients. And clarified the value of the profile in critical prevention.

Understanding small Chinese cities as COVID-19 hotspots with an urban epidemic hazard index.

Multiple small- to middle-scale cities, mostly located in northern China, became epidemic hotspots during the second wave of the spread of COVID-19 in early 2021. Despite qualitative discussions of potential social-economic causes, it remains unclear how this unordinary pattern could be substantiated with quantitative explanations. Through the development of an urban epidemic hazard index (EpiRank) for Chinese prefectural districts, we came up with a mathematical explanation for this phenomenon. The index is constructed via epidemic simulations on a multi-layer transportation network interconnecting local SEIR transmission dynamics, which characterizes intra- and inter-city population flow with a granular mathematical description. Essentially, we argue that these highlighted small towns possess greater epidemic hazards due to the combined effect of large local population and small inter-city transportation. The ratio of total population to population outflow could serve as an alternative city-specific indicator of such hazards, but its effectiveness is not as good as EpiRank, where contributions from other cities in determining a specific city's epidemic hazard are captured via the network approach. Population alone and city GDP are not valid signals for this indication. The proposed index is applicable to different epidemic settings and can be useful for the risk assessment and response planning of urban epidemic hazards in China. The model framework is modularized and the analysis can be extended to other nations.

Joint Learning of 3D Lesion Segmentation and Classification for Explainable COVID-19 Diagnosis.

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy.

Progress in research in diagnosis of SARS-CoV-2 infection based on proteomics technique

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has swept the world in 2020, resulting in unprecedented pandemic of coronavirus disease 2019 (COVID-19). The number of infected persons and deaths increase every day at a frightening speed, threatening the health and life of people in the world and causing heavy burden to the global public health system. So far, nucleic acid detection is the main diagnostic method and gold standard for COVID-19. Meanwhile, other techniques and methods are also in developing for the diagnosis of SARS-CoV-2 infection. Proteomics technique is one of them. Proteomics technique has been widely used in the research of disease-related mechanism, development of diagnostic methods and pathogen identification. Up to now, there are mainly two applications of proteomics in the diagnosis of SARS-CoV-2 infection. First, proteomics based on virus particles has great potential in early diagnosis. Second, proteomics based on body fluids can be used not only for early diagnosis, but also for good monitoring the progress of infection, predicting the trend of disease, and evaluating the prognoses. In this paper, the research and application of proteomics technique in the diagnosis of SARS-CoV-2 infection in the world are summarized and prospected.

Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion.

Coronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided diagnosis technology can reduce the burden on doctors, which is conducive to rapid and large-scale diagnostic screening. In this paper, we proposed an automatic detection method for COVID-19 based on spatiotemporal information fusion. Using the segmentation network in the deep learning method to segment the lung area and the lesion area, the spatiotemporal information features of multiple CT scans are extracted to perform auxiliary diagnosis analysis. The performance of this method was verified on the collected dataset. We achieved the classification of COVID-19 CT scans and non-COVID-19 CT scans and analyzed the development of the patients' condition through the CT scans. The average accuracy rate is 96.7%, sensitivity is 95.2%, and F1 score is 95.9%. Each scan takes about 30 seconds for detection.

Simulating the spread of epidemics in China on multi-layer transportation networks: Beyond COVID-19 in Wuhan

A general-purpose simulator for the spread of epidemics in China is built. The Chinese public transportation system between over 340 prefectural-level cities is modeled as a multi-layer network having block structure, with layers representing different means of transportation (airlines, railways, sails, buses), and nodes representing two categories of cities (central, peripheral). At each city, an open-system SEIR model tracks the local spread of the disease, with population in- and out-flow exchanging with the overlying transportation network. The model accounts for 1) different transmissivities on different transportation media, 2) the transit of inbound flow, 3) cross-infection in public transportation due to path overlap, and that 4) infected population not entering public transportation, 5) recovered population not subject to repeated infections. The model is used to simulate the spread of COVID-19 in China;it shows that the framework is robust and reliable, and best-fit inversion results match public datasets to an extraordinary extent.