The integration of digital twin and serious game framework for new normal virtual urban exploration and social interaction

COVID-19 has disconnected humanity, reduced social interaction in physical urban areas, and led to the new normal, which describes the anticipated changes in human life and professionals due to the impact of the pandemic. In addition, as part of digital transformation, the post-pandemic New Normal includes accelerating digital solutions and new standards for virtual urban exploration and planning. This study applies the concept of digital twin and serious game design and uses Minecraft, a game-based platform that inspires creative, inclusive learning through play, for virtual urban exploration and the development of social interaction among participants. Dadaocheng, a historical area of Taipei city, is then studied as a case study with the Geoboxers application to develop the prototype for a co-creation experiment in Minecraft. The prototype development and the results of the experiment are then used in the Analytic Hierarchical Process (AHP) method to evaluate a set of influential criteria proposed in this study through pairwise comparisons by expert panelists. The results of the AHP analysis reveal users' simultaneous preferences for urban planning and social interaction with urban characteristics (22.14%), urban exploration (12.29%), and 3D models (11.97%). Subsequently, the research results showed a need to promote the integration of digital twins and serious game applications as digital tools for urban exploration and social interaction, increasing post-pandemic virtual urban planning and applying new urban design techniques. This study also contributes to the acceleration of digital transformation in urban planning and management. © 2023 The Authors

Stop-Virus Collaborative: A Qualitative Analysis to Forming a Virtual Landscape of Practice

INTRODUCTION: The Structured Team-based Optimal Patient-Centered Care for Virus COVID-19 (STOP-VIRUS) Collaborative was a virtual adaptation to healthcare collaboration and quality improvement during COVID-19 pandemic. The learning that happens in this space is complex, nuanced, and multi-dimensional, best explained using Wenger's social learning theory of the Landscape of Practice, where one needs knowledgeability and the ability to identify and cross the boundaries to achieve learning. METHOD(S): We conducted a qualitative study using thematic analysis to explore STOP-VIRUS participants' perspective on their experience. We used identified themes to inform the creation of the continuum of readiness for change to better characterize common challenges that institutions face at different QI readiness stages. We used a blended framework of the ADKAR model for changes start at the individual level, McKinsey's 7S framework to focus our efforts on different components necessary for change at the organizational level, and the overarching theory of landscape of practice to guide analysis and development of our conceptual framework. RESULT(S): We constructed a blended conceptual framework based on the ADKAR stages of change and the necessary components for successful change implementation based on the McKinsey's 7S framework. The 7S framework effectively demonstrates a systematic and comprehensive approach to change on an organizational level, including the 7 constructs: staff, style/culture, skills, strategy, systems, structure, with shared culture at the center of change. However, change starts at the individual level. Within the STOP-VIRUS Collaborative, the participants from each site are the catalyst for change. As a result, this is reflected through the stages of change embodied in the ADKAR model: awareness, desire, knowledge, ability, and reinforcement. Together, they provided a continuum that enable individual healthcare providers to impact change on an organization level. CONCLUSION(S): STOP-VIRUS collaborative was a multicenter, interprofessional, and diverse learning environment that re-emphasized best-practice guidelines. It provides valuable support to institutions at various stages of readiness for quality improvement initiatives, with important lessons that can be applied to future virtual collaboratives.

Food festival research review in contemporary tourism

Purpose: Food festivals are prevalent for those passionate about food experience globally. More importantly, feedback from food reviewers on mass media platforms has been becoming a critical factor in facilitating the decision-making process of tourists in particular cities. Moreover, stimulating local tourism activities, thanks to food festivals, prove advantageous to the well-being of local habitants. The purpose of this paper is to provide readers with a general overview of food festival research trends in tourist cities, as tourism has the potential to contribute to targets in Goals 8, 12 and 14 on sustainable consumption and production and the sustainable use of resources, respectively, (UNWTO: World Tourism Organization). Design/methodology/approach: This study searched and filtered documents from the Scopus and Web of Science databases, as well as used bibliometric analysis and other mathematical and statistical methods, to better understand the food festival research context between 1970 and 2021. The carriers with mathematical and statistical methods. VOSviewer algorithm was used to identify critical input for visualizing bibliometric networks and to create a framework for this academic food festival research. Findings: The findings are primarily related to pre and post-COVID-19 research on food festivals worldwide. Furthermore, using an inductive approach, this paper reveals the impact of food festivals in cities and tourist behaviors. According to the findings, the food festival research trends are about “food festivals,” “slow food festivals” and “local food festivals.” Factor analysis is one of the most common analyses in this type of research. Other studies could use the findings and limitations to select appropriate themes and analysis approaches for their research topics. Research limitations/implications: Research data sets are mainly from articles that may not account for all actual trends during this pandemic. Originality/value: This review expects to provide insights into food festivals and help future researchers to recognize several research gaps such as the lack of research on food festival manufacturers and producers or the consistency in visitors' aspect research of quality service, visitors' loyal intentions, satisfaction and culinary experience. The tourism industry can find research trends of food festivals and issues following COVID-19 to find their management styles to fit the context of the post-COVID-19 pandemic, facilitating organizing a safe and effective food festival. © 2022, International Tourism Studies Association.

Potent universal beta-coronavirus therapeutic activity mediated by direct respiratory administration of a Spike S2 domain-specific human neutralizing monoclonal antibody.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) marks the third novel ß-coronavirus to cause significant human mortality in the last two decades. Although vaccines are available, too few have been administered worldwide to keep the virus in check and to prevent mutations leading to immune escape. To determine if antibodies could be identified with universal coronavirus activity, plasma from convalescent subjects was screened for IgG against a stabilized pre-fusion SARS-CoV-2 spike S2 domain, which is highly conserved between human ß-coronavirus. From these subjects, several S2-specific human monoclonal antibodies (hmAbs) were developed that neutralized SARS-CoV-2 with recognition of all variants of concern (VoC) tested (Beta, Gamma, Delta, Epsilon, and Omicron). The hmAb 1249A8 emerged as the most potent and broad hmAb, able to recognize all human ß-coronavirus and neutralize SARS-CoV and MERS-CoV. 1249A8 demonstrated significant prophylactic activity in K18 hACE2 mice infected with SARS-CoV-2 lineage A and lineage B Beta, and Omicron VoC. 1249A8 delivered as a single 4 mg/kg intranasal (i.n.) dose to hamsters 12 hours following infection with SARS-CoV-2 Delta protected them from weight loss, with therapeutic activity further enhanced when combined with 1213H7, an S1-specific neutralizing hmAb. As little as 2 mg/kg of 1249A8 i.n. dose 12 hours following infection with SARS-CoV Urbani strain, protected hamsters from weight loss and significantly reduced upper and lower respiratory viral burden. These results indicate in vivo cooperativity between S1 and S2 specific neutralizing hmAbs and that potent universal coronavirus neutralizing mAbs with therapeutic potential can be induced in humans and can guide universal coronavirus vaccine development.

Potent universal-coronavirus therapeutic activity mediated by direct respiratory administration of a Spike S2 domain-specific human neutralizing monoclonal antibody (preprint)

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) marks the third novel {beta}-coronavirus to cause significant human mortality in the last two decades. Although vaccines are available, too few have been administered worldwide to keep the virus in check and to prevent mutations leading to immune escape. To determine if antibodies could be identified with universal coronavirus activity, plasma from convalescent subjects was screened for IgG against a stabilized pre-fusion SARS-CoV-2 spike S2 domain, which is highly conserved between human {beta}-coronavirus. From these subjects, several S2-specific human monoclonal antibodies (hmAbs) were developed that neutralized SARS-CoV-2 with recognition of all variants of concern (VoC) tested (Beta, Gamma, Delta, Epsilon, and Omicron). The hmAb 1249A8 emerged as the most potent and broad hmAb, able to recognize all human {beta}-coronavirus and neutralize SARS-CoV and MERS-CoV. 1249A8 demonstrated significant prophylactic activity in K18 hACE2 mice infected with SARS-CoV-2 lineage A and lineage B Beta, and Omicron VoC. 1249A8 delivered as a single 4 mg/kg intranasal (i.n.) dose to hamsters 12 hours following infection with SARS-CoV-2 Delta protected them from weight loss, with therapeutic activity further enhanced when combined with 1213H7, an S1-specific neutralizing hmAb. As little as 2 mg/kg of 1249A8 i.n. dose 12 hours following infection with SARS-CoV Urbani strain, protected hamsters from weight loss and significantly reduced upper and lower respiratory viral burden. These results indicate in vivo cooperativity between S1 and S2 specific neutralizing hmAbs and that potent universal coronavirus neutralizing mAbs with therapeutic potential can be induced in humans and can guide universal coronavirus vaccine development.

Serious game design for playful exploratory urban simulation

The year 2020 has stricken humanities with the ferocious COVID-19. During this period, regulation and restriction are active to mitigate the spread while simultaneously causing massive behavioral change globally. While the post-pandemic situation has created new habits, rules, and limitations to restrict traveling, it also offers opportunities to introduce the citizens an interactive technology solution to explore their urban space and neighborhood effortlessly and risk-free. This paper proposes the use of Minecraft, a sandbox game, and its gameplay to design an interactive serious game-based platform for collaborative urban exploration in a playful, immersive approach. The prototype within this paper applies the digital twin concept and its principles for the realistic re-creation of a virtual Dihua Street of Dadaocheng, an old town and a tourist hotspot in Taipei, Taiwan. In a serious-game environment that is both a playground and a working platform, users can cooperate with others, create dynamic user-generated content. As they explore while sharing their knowledge and experience in the virtual open space, users are also engaged in serious urban planning tasks. This paper’s main objective is to present the preliminary data of top priority users’ satisfaction with game elements or features to develop a serious game-based urban exploration platform while demonstrating the proof-of-concept of the urban digital twin. © Copyright owned by the author(s) under the terms of the Creative Commons

Microbiota as a potentially-modifiable factor influencing COVID-19.

Impacts of respiratory tract viruses have long been appreciated to highly heterogeneous both between and within various populations. The SARS-CoV-2 pandemic, which is the first time that a pathogen's spread across the globe has been extensively monitored by direct detection of the pathogen itself rather just than the morbidity left in its wake, indicates such heterogeneity is not limited to outcomes of infections but whether infection of a particular host occurs at all. This suggests an important role for yet to be discovered environmental (i.e. non-genetic) factors that influence whether an exposure to the virus initiates a productive infection and, moreover, the severity of disease that results. This article discusses the emerging hypothesis that the composition of a host's commensal microbial communities, that is, its 'microbiome', may be one such determinant that influences outcomes following encounters with respiratory viral pathogens in general and SARS-CoV-2 in particular. Specifically, we will review the rationales and evidence that supports this hypothesis and, moreover, speculate as to possible approaches to manipulate microbiota to ameliorate disease induced by respiratory viral pathogens.

An approach for recognizing COVID-19 cases using Convolutional Neural Networks applied to CT scan images

This study aims to investigate an automated approach using Convolutional Neural Network (CNN) to efficiently classify COVID-19 cases vs healthy cases using chest CT images. Convolutional Neural Network (CNN) is a class of deep neural networks, usually applied to analyzing image data, and can learn features effectively from images in comparison to the traditional method with image segmentation, feature extraction/selection and classification steps. Several models using pre-trained weights, including VGG16, VGG19, InceptionV3, InceptionResNetV2, Xception, DenseNet121, DenseNet169, and DenseNet201 were investigated. Overfitting was handled by randomly dropping nodes during training, augmenting training data, as well as using the validation set. We concluded that a CNN approach can detect COVID-19 using CT features, and DenseNet201is the highest performing model.

Therapeutic activity of an inhaled potent SARS-CoV-2 neutralizing human monoclonal antibody in hamsters.

SARS-CoV-2 infection results in viral burden in the respiratory tract, enabling transmission and leading to substantial lung pathology. The 1212C2 fully human monoclonal antibody was derived from an IgM memory B cell of a COVID-19 patient, has high affinity for the Spike protein receptor binding domain, neutralizes SARS-CoV-2, and exhibits in vivo prophylactic and therapeutic activity in hamsters when delivered intraperitoneally, reducing upper and lower respiratory viral burden and lung pathology. Inhalation of nebulized 1212C2 at levels as low as 0.6 mg/kg, corresponding to 0.03 mg/kg lung-deposited dose, reduced the viral burden below the detection limit and mitigated lung pathology. The therapeutic efficacy of an exceedingly low dose of inhaled 1212C2 supports the rationale for local lung delivery for dose-sparing benefits, as compared to the conventional parenteral route of administration. These results suggest that the clinical development of 1212C2 formulated and delivered via inhalation for the treatment of SARS-CoV-2 infection should be considered.

Very large multimodal multi-architecture convolutional neural network with bundled transfer learning for automated Coronavirus diagnosis

We propose the so-called Bundled Transfer Learning approach with multiple large architectures for automated detection of Coronavirus using X-Ray and CT images. Extracted features from each pretrained model in the bundle are gathered for training new deep layers. The dataset includes CT scan images of COVID-19 and normal classes and X-Ray images of COVID-19, Pneumonia, and normal classes. © 2020 SPIE

Application of deep learning model (DeepCOVID-19) for detecting COVID-19 cases using chest x-ray images

By the time of the abstract, the coronavirus COVID-19 is affecting 213 countries with more than 9.4 million cases, and more than 480 thousand deaths. In the US it has been confirmed that there are more than 2.4 million COVID-19 cases and approximately 123 thousand related deaths, according to Worldometer. The Wuhan originated COVID-19 has become a global challenge since late December 2019. As control measures lift, life and businesses start going to open, the coronavirus pandemic continues to grow. In this research, we propose a Deep Learning model that recognizes COVID-19 cases using X-ray images (DeepCOVID-19). The model would help physicians to be more confident by having a second opinion in assessing patients. An implementation of the model would help countries where there is shortage of test kits, while X-ray devices are widely available. © 2020 COPYRIGHT SPIE.

Therapeutic activity of an inhaled potent SARS-CoV-2 neutralizing human monoclonal antibody in hamsters (preprint)

SARS-CoV-2 infection results in viral burden in the upper and lower respiratory tract, enabling transmission and often leading to substantial lung pathology. Delivering the antiviral treatment directly to the lungs has the potential to improve lung bioavailability and dosing efficiency. As the SARS-CoV-2 Receptor Binding Domain (RBD) of the Spike (S) is increasingly deemed to be a clinically validated target, RBD-specific B cells were isolated from patients following SARS-CoV-2 infection to derive a panel of fully human monoclonal antibodies (hmAbs) that potently neutralize SARS-CoV-2. The most potent hmAb, 1212C2 was derived from an IgM memory B cell, has high affinity for SARS-CoV-2 RBD which enables its direct inhibition of RBD binding to ACE2. The 1212C2 hmAb exhibits in vivo prophylactic and therapeutic activity against SARS-CoV-2 in hamsters when delivered intraperitoneally, achieving a meaningful reduction in upper and lower respiratory viral burden and lung pathology. Furthermore, liquid nebulized inhale treatment of SARS-CoV-2 infected hamsters with as low as 0.6 mg/kg of inhaled dose, corresponding to approximately 0.03 mg/kg of lung deposited dose, mediated a reduction in respiratory viral burden that is below the detection limit, and mitigated lung pathology. The therapeutic efficacy achieved at an exceedingly low-dose of inhaled 1212C2 supports the rationale for local lung delivery and achieving dose-sparing benefits as compared to the conventional parenteral route of administration. Taken together, these results warrant an accelerated clinical development of 1212C2 hmAb formulated and delivered via inhalation for the prevention and treatment of SARS-CoV-2 infection.