Impacts of COVID-19 on tourism and management response from Banff National Park, Canada.

The COVID-19 pandemic posed challenges to the tourism sector globally. We investigated changes in visitor demographics, satisfaction level, and its determinants pre- and peri-COVID-19. Data were collected using questionnaire surveys in 2019 and 2021 within Banff National Park (BNP). The data analyses were based on a sample size of 1183 respondents by conducting factor analysis, correlation analysis and stepwise regression analysis. Results highlight that there were fewer international visitors and more local and domestic visitors during the pandemic. Park attributes were evaluated at a higher satisfaction level peri-COVID-19. The quality of the Park facilities and services were the most important satisfaction determinants pre- and peri-COVID-19, and all the Park COVID-19 measures and actions received positive experience from visitors. This research fills this knowledge gap by developing a better understanding in the change of visitor demographics and satisfaction level in BNP under the context of the pandemic. It also provides implication for both scholars and practitioners to understand the impacts of the pandemic on Park visitation. The study can provide insights for utilizing the pandemic as a transformative strength and for mitigating its negative impact on tourism industry.

Impacts of COVID-19 on tourism and management response from Banff National Park, Canada

The COVID-19 pandemic posed challenges to the tourism sector globally. We investigated changes in visitor demographics, satisfaction level, and its determinants pre- and peri-COVID-19. Data were collected using questionnaire surveys in 2019 and 2021 within Banff National Park (BNP). The data analyses were based on a sample size of 1183 respondents by conducting factor analysis, correlation analysis and stepwise regression analysis. Results highlight that there were fewer international visitors and more local and domestic visitors during the pandemic. Park attributes were evaluated at a higher satisfaction level peri-COVID-19. The quality of the Park facilities and services were the most important satisfaction determinants pre- and peri-COVID-19, and all the Park COVID-19 measures and actions received positive experience from visitors. This research fills this knowledge gap by developing a better understanding in the change of visitor demographics and satisfaction level in BNP under the context of the pandemic. It also provides implication for both scholars and practitioners to understand the impacts of the pandemic on Park visitation. The study can provide insights for utilizing the pandemic as a transformative strength and for mitigating its negative impact on tourism industry.

Delivering forestry courses online: experiences, lessons learned, and future of forestry online education in the Asia Pacific.

Innovation in forestry education is needed to address changing contexts of the positionality of forests. This is particularly significant in the Asia-Pacific region, where deforestation and degradation are high. However, the accessibility of high-quality forestry education to address changing regional and global contexts is lacking. A series of innovative sustainable forest management (SFM) open education resource (OER) courses were developed and implemented to improve the accessibility of SFM education to enhance teaching quality, curriculum, and research capacity of universities in the Asia-Pacific Region. To evaluate the SFM-OER program in terms of student experiences, this study investigated student achievement, perceived success of the pedagogical approach and instructional design, and perceived effectiveness of the learning activities in promoting active and transformative learning through the assessment of a 1,191-course feedback survey between 2018 and 2020, including the global pandemic. This study revealed that the program attracted diverse student demographics, including a higher proportion of female students majoring in forestry, ecology, and other environmental studies. Their primary motivation to participate in the courses was to gain international experience, followed by the flexibility of online learning, mandatory course requirements, and earning course credits. Students were satisfied with the Canvas learning management system. Most students spent less than 5 to 10 h of their weekly time in the course and agreed or strongly agreed that the workloads were manageable. Students reflected positively on various learning activities and assignments, such as watching lecture videos, taking quizzes, reading and summarizing, having discussions, and peer review writing. However, they did not clearly prefer specific learning activities, signifying the importance of using diverse learning activities to satisfy diverse individual learning styles in online settings. This analysis contributes to the further development of student-centered pedagogical development for online learning and provides insight into the ways forward for online higher forestry education, while repurposing existing OER courses in a post-Covid-19 era.

Recombinase Polymerase Amplification Combined with Fluorescence Immunochromatography Assay for On-Site and Ultrasensitive Detection of SARS-CoV-2.

This study established a portable and ultrasensitive detection method based on recombinase polymerase amplification (RPA) combined with high-sensitivity multilayer quantum dot (MQD)-based immunochromatographic assay (ICA) to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The RPA-MQD-based ICA method is reported for the first time and has the following advantages: (i) RPA is free from the constraints of instruments and can be promoted in point-of-care testing (POCT) scenarios, (ii) fluorescence ICA enhances the portability of detection operation so that the entire operation time is controlled within 1 h, and (iii) compared with common colorimetric-based RPA-ICA, the proposed assay used MQD to provide strong and quantifiable fluorescence signal, thus enhancing the detection sensitivity. With this strategy, the proposed RPA-MQD-based ICA can amplify and detect the SARS-CoV-2 nucleic acid on-site with a sensitivity of 2 copies/reaction, which is comparable to the sensitivity of commercial reverse transcription quantitative polymerase chain reaction (RT-qPCR) kits. Moreover, the designed primers did not cross-react with other common respiratory viruses, including adenovirus, influenza virus A, and influenza virus B, suggesting high specificity. Thus, the established portable method can sensitively detect SARS-CoV-2 nucleic acid without relying on equipment, having good application prospects in SARS-CoV-2 detection scenarios under non-lab conditions.

CapsNet-COVID19: Lung CT image classification method based on CapsNet model.

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.

Visitor satisfaction and behavioral intentions in nature-based tourism during the COVID-19 pandemic: A case study from Zhangjiajie National Forest Park, China

Nature-based tourism (NBT) has become a popular tool for developing countries to achieve economic growth by the non-destructive use of their natural resources. COVID-19 has caused severe financial impacts on tourism-dependent areas. Revitalizing NBT is needed for economic recovery in those regions and can also help deal with mental health issues worldwide. Zhangjiajie National Forest Park (ZNFP), the first national park created in China, was selected to examine the important factors that influence visitor satisfaction during the COVID-19 pandemic and the relationship between satisfaction and visitors' environmentally responsible behavior (ERB) intention. The authors collected 788 onsite and online questionnaires from visitors to ZNFP during June–September 2020. This paper reveals previously underestimated factors and offers practical applications for park development at ZNFP and other NBT destinations. Visitors had a high level of satisfaction with the natural scenery of the park but were relatively dissatisfied with price reasonableness, park services, activities and events, and artificial attractions. Younger visitors, especially students, and well-educated visitors looking for environmental education opportunities tended to have lower satisfaction rates. Visitor satisfaction may have a positive but limited influence on promoting visitors' ERB intentions. We propose group-specific strategies for national park managers to attract more visitors and increase their length of stay.

T-cell receptor repertoires as potential diagnostic markers for patients with COVID-19.

OBJECTIVE: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health emergency. T-cell receptors (TCRs) are crucial mediators of antiviral adaptive immunity. This study sought to comprehensively characterize the TCR repertoire changes in patients with COVID-19. METHODS: A large sample size multi-center randomized controlled trial was implemented to study the features of the TCR repertoire and identify COVID-19 disease-related TCR sequences. RESULTS: It was found that some T-cell receptor beta chain (TCRß) features differed markedly between COVID-19 patients and healthy controls, including decreased repertoire diversity, longer complementarity-determining region 3 (CDR3) length, skewed utilization of the TCRß variable gene/joining gene (TRBV/J), and a high degree of TCRß sharing in COVID-19 patients. Moreover, this analysis showed that TCR repertoire diversity declines with aging, which may be a cause of the higher infection and mortality rates in elderly patients. Importantly, a set of TCRß clones that can distinguish COVID-19 patients from healthy controls with high accuracy was identified. Notably, this diagnostic model demonstrates 100% specificity and 82.68% sensitivity at 0-3 days post diagnosis. CONCLUSIONS: This study lays the foundation for immunodiagnosis and the development of medicines and vaccines for COVID-19 patients.

Author Correction: Seroprevalence of immunoglobulin M and G antibodies against SARS-CoV-2 in China.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Personnel protection strategy for healthcare workers in Wuhan during the COVID-19 epidemic.

Objective: To identify the effectiveness of a personnel protection strategy in protection of healthcare workers from SARS-CoV-2 infection. Design: During the COVID-19 pandemic, 943 healthcare staff sent from Guangzhou to Wuhan to care for patients with suspected/confirmed COVID-19 received infection precaution training before their mission and were equipped with Level 2/3 personal protective equipment (PPE), in accordance with guidelines from the National Health Commission of China. We conducted a serological survey on the cumulative attack rate of SARS-CoV-2 among the healthcare workers sent to Wuhan and compared the seropositive rate to that in local healthcare workers from Wuhan and Jingzhou. Results: Serial tests for SARS-CoV-2 RNA and tests for SARS-CoV-2 immunoglobulin M and G after the 6-8 week mission revealed a zero cumulative attack rate. Among the local healthcare workers in Wuhan and Jingzhou of Hubei Province, 2.5% (113 out of 4495) and 0.32% (10 out of 3091) had RT-PCR confirmed COVID-19, respectively. The seropositivity for SARS-CoV-2 antibodies (IgG, IgM, or both IgG/IgM positive) was 3.4% (53 out of 1571) in local healthcare workers from Wuhan with Level 2/3 PPE working in isolation areas and 5.4% (126 out of 2336) in healthcare staff with Level 1 PPE working in non-isolation medical areas, respectively. Conclusions and relevance: Our study confirmed that adequate training/PPE can protect medical personnel against SARS-CoV-2.

Clinical longitudinal evaluation of COVID-19 patients and prediction of organ-specific recovery using artificial intelligence.

Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems. To greatly enhance the speed and utility of our model, we applied an artificial intelligence method to segment and classify regions on CT imaging, from which interpretable data could be directly fed into the predictive machine learning model for overall recovery. Across all organ systems we achieved validation set area under the receiver operator characteristic curve (AUC) values for organ-specific recovery ranging from 0.80 to 0.89, and significant overall recovery prediction in Kaplan-Meier analyses. This demonstrates that the synergistic use of an artificial intelligence (AI) framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.

COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation.

COVID-19 is a new type of respiratory infectious disease that poses a serious threat to the survival of human beings all over the world. Using artificial intelligence technology to analyze lung images of COVID-19 patients can achieve rapid and effective detection. This study proposes a COVSeg-NET model that can accurately segment ground glass opaque lesions in COVID-19 lung CT images. The COVSeg-NET model is based on the fully convolutional neural network model structure, which mainly includes convolutional layer, nonlinear unit activation function, maximum pooling layer, batch normalization layer, merge layer, flattening layer, sigmoid layer, and so forth. Through experiments and evaluation results, it can be seen that the dice coefficient, sensitivity, and specificity of the COVSeg-NET model are 0.561, 0.447, and 0.996 respectively, which are more advanced than other deep learning methods. The COVSeg-NET model can use a smaller training set and shorter test time to obtain better segmentation results.

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.

COVID-19 in early 2021: current status and looking forward.

Since the first description of a coronavirus-related pneumonia outbreak in December 2019, the virus SARS-CoV-2 that causes the infection/disease (COVID-19) has evolved into a pandemic, and as of today, >100 million people globally in over 210 countries have been confirmed to have been infected and two million people have died of COVID-19. This brief review summarized what we have hitherto learned in the following areas: epidemiology, virology, and pathogenesis, diagnosis, use of artificial intelligence in assisting diagnosis, treatment, and vaccine development. As there are a number of parallel developments in each of these areas and some of the development and deployment were at unprecedented speed, we also provided some specific dates for certain development and milestones so that the readers can appreciate the timing of some of these critical events. Of note is the fact that there are diagnostics, antiviral drugs, and vaccines developed and approved by a regulatory within 1 year after the virus was discovered. As a number of developments were conducted in parallel, we also provided the specific dates of a number of critical events so that readers can appreciate the evolution of these research data and our understanding. The world is working together to combat this pandemic. This review also highlights the research and development directions in these areas that will evolve rapidly in the near future.

Impacts of COVID-19 pandemic on urban park visitation: a global analysis.

The COVID-19 pandemic has resulted in over 33 million confirmed cases and over 1 million deaths globally, as of 1 October 2020. During the lockdown and restrictions placed on public activities and gatherings, green spaces have become one of the only sources of resilience amidst the coronavirus pandemic, in part because of their positive effects on psychological, physical and social cohesion and spiritual wellness. This study analyzes the impacts of COVID-19 and government response policies to the pandemic on park visitation at global, regional and national levels and assesses the importance of parks during this global pandemic. The data we collected primarily from Google's Community Mobility Reports and the Oxford Coronavirus Government Response Tracker. The results for most countries included in the analysis show that park visitation has increased since February 16th, 2020 compared to visitor numbers prior to the COVID-19 pandemic. Restrictions on social gathering, movement, and the closure of workplace and indoor recreational places, are correlated with more visits to parks. Stay-at-home restrictions and government stringency index are negatively associated with park visits at a global scale. Demand from residents for parks and outdoor green spaces has increased since the outbreak began, and highlights the important role and benefits provided by parks, especially urban and community parks, under the COVID-19 pandemic. We provide recommendations for park managers and other decision-makers in terms of park management and planning during health crises, as well as for park design and development. In particular, parks could be utilized during pandemics to increase the physical and mental health and social well-being of individuals.

Seroprevalence of immunoglobulin M and G antibodies against SARS-CoV-2 in China.

Detection of asymptomatic or subclinical novel human coronavirus SARS-CoV-2 infection is critical for understanding the overall prevalence and infection potential of COVID-19. To estimate the cumulative prevalence of SARS-CoV-2 infection in China, we evaluated the host serologic response, measured by the levels of immunoglobulins M and G in 17,368 individuals, in the city of Wuhan, the epicenter of the COVID-19 pandemic in China, and geographic regions in the country, during the period from 9 March 2020 to 10 April 2020. In our cohorts, the seropositivity in Wuhan varied between 3.2% and 3.8% in different subcohorts. Seroposivity progressively decreased in other cities as the distance to the epicenter increased. Patients who visited a hospital for maintenance hemodialysis and healthcare workers also had a higher seroprevalence of 3.3% (51 of 1,542, 2.5-4.3%, 95% confidence interval (CI)) and 1.8% (81 of 4,384, 1.5-2.3%, 95% CI), respectively. More studies are needed to determine whether these results are generalizable to other populations and geographic locations, as well as to determine at what rate seroprevalence is increasing with the progress of the COVID-19 pandemic. Serologic surveillance has the potential to provide a more faithful cumulative viral attack rate for the first season of this novel SARS-CoV-2 infection.