Mid-density Gene Expression profiling of SARSCOV-2 infected samples using Applied BiosystemsTM TaqManTM Flexible Array Panels

Background/Objectives: The broad spectrum of clinical manifestations from SARS-COV-2 infection and observed risk factors for severe disease highlight the importance of understanding molecular mechanisms underlying SARS-CoV-2 associated disease pathogenesis. Research studies have identified a large number of host proteins that play roles in viral entry, innate immune response, or immune signalling during infection. The ability to interrogate subsets of these genes simultaneously within SARSCOV-2 infected samples is critical to understanding how their expression contribute to phenotypic variability of the disease caused by the pathogen. Method(s): 30 Nasopharyngeal swab were obtained and included SARS-CoV-2 infected and control samples. RNA was extracted, reverse transcribed and loaded onto flexible TaqMan array panels designed specifically for targeting the most cited genes related to SARS-COV-2 entry and restriction factors as well as cytokines, chemokines, and growth factors involved in the pathogenesis. Result(s): Our data indicated that not only were the levels of several of these host factors differentially modulated between the two study groups, but also that SARS-CoV-2 infected subjects presented with greater frequency of several important inflammatory cytokines and chemokines such as CCL2, CCL3, IFNG, entry receptors such as ACE2, TMRPS11A, and host restriction factors including LY6E and ZBP1. Conclusion(s): TaqMan array plates provide a fast, midthroughput solution to determine the levels of several virus and host-associated factors in various cell types and add to our understanding of how the pathogenesis may vary depending on gender, age, infection site etc.

Dr.'s Eye: The Design and Evaluation of a Video Conferencing System to Support Doctor Appointments in Home Settings

The spread of COVID-19 has encouraged the practice of using video conferencing for family doctor appointments. Existing applications and off-the-shelf devices face challenges in dealing with capturing the correct view of patients' bodies and supporting ease of use. We created Dr.'s Eye, a video conferencing prototype to support varying types of body exams in home settings. With our prototype, we conducted a study with participants using mock appointments to understand the simultaneous use of the camera and display and to get insights into the issues that might arise in real doctor appointments. Results show the benefits of providing more flexibility with a decoupled camera and display, and privacy protection by limiting the camera view. Yet, challenges remain in maneuvering two devices, presenting feedback for the camera view, coordinating camera work between the participant and the examiner, and reluctance towards showing private body regions. This inspires future research on how to design a video system for doctor appointments. © 2023 ACM.

YOLOv7 for Face Mask Identification Based on Deep Learning

The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good"class. © 2023 IEEE.

Racism, Risk, and Supply: U.S. Policies Toward COVID-19 and Asia, and Current and Future Impacts on Asian Americans

COVID-19 illuminates the contradictions of U.S. relations with Asia economically, culturally, and socially in relation to Asian immigrant labor, goods and manufacturing, and with Asian Americans. We explore the importance of Asia as a supplier of labor and goods to the U.S. health system in order to analyze how the U.S. navigates its interdependence with Asia while demonizing Asians/Americans and attempting to protect its borders metaphorically and materially. We analyze how Asian American nurses are fighting the battle against the pandemic on the frontlines while also fighting the stereotypes and stigma that some Americans may have against them because they associate Asian Americans with the spread of COVID-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Review of Motion Simulation of Particulate Matter in the Respiratory System and Further CFD Simulations on COVID-19

Respirable particulate matter (RSP) is currently very harmful to the human body, potentially causing pulmonary silicosis, allergic rhinitis, acute bronchitis, and pulmonary heart disease. Therefore, the study of the deposition pattern of RSP in the human respiratory system is key in the prevention, treatment, and research of related diseases, whereby the main methods are computer simulation, in vitro solid models, and theoretical analysis. This paper summarizes and analyzes past deposition of RSP in the respiratory tract and also describes them in specific case studies such as COPD and COVID-19 patients, based on the review of the evidence, direction, and focus of future research focusing on simulation, experimentation, and related applications of RSP deposition in the respiratory tract. © 2023 by the authors.

Personalized exercise recommendation method based on causal deep learning: Experiments and implications

The COVID-19 pandemic has accelerated innovations for supporting learning and teaching online. However, online learning also means a reduction of opportunities in direct communication between teachers and students. Given the inevitable diversity in learning progress and achievements for individual online learners, it is difficult for teachers to give personalized guidance to a large number of students. The personalized guidance may cover many aspects, including recommending tailored exercises to a specific student according to the student's knowledge gaps on a subject. In this paper, we propose a personalized exercise recommendation method named causal deep learning (CDL) based on the combination of causal inference and deep learning. Deep learning is used to train and generate initial feature representations for the students and the exercises, and intervention algorithms based on causal inference are then applied to further tune these feature representations. Afterwards, deep learning is again used to predict individual students' score ratings on exercises, from which the Top-N ranked exercises are recommended to similar students who likely need enhancing of skills and understanding of the subject areas indicated by the chosen exercises. Experiments of CDL and four baseline methods on two real-world datasets demonstrate that CDL is superior to the existing methods in terms of capturing students' knowledge gaps in learning and more accurately recommending appropriate exercises to individual students to help bridge their knowledge gaps. © 2022 The Author(s).

Workshops in TEI: Development, Evaluation, Exploration, and Implementation

Workshops are frequently used in human-computer interaction research, in a diverse range of research projects. However, the COVID-19 pandemic has made this research activity difficult to conduct since they often involve group work, physical interaction with tangibles and/or bodily activity. Motivated by this, the authors conducted a review of papers from the International Conference on Tangible, Embedded and Embodied Interaction (TEI) to develop a better understanding of workshops as a research method in TEI. The meta-review led to the development of a preliminary classification for workshops in research. Four categories of workshops were identified: Design development, Evaluation, Exploration, and Implementation. This work is intended to spark discussion and further research around the value and the challenges of conducting research workshops.

Face Mask Recognition System for Epidemic Prevention and Control Based on Multi-algorithm Fusion

Against the backdrop of the ongoing COVID-19 pandemic, We propose FMRS-CFR (Face mask recognition system-Centerface Resnet), a mask recognition system for epidemic prevention and control based on multi-algorithm fusion to adapt to multi-scenario applications. In this work, Centerface face key point detection and Resnet50 classification model were used. Built a system that maintains multi-adaptation with the dynamics of external scenarios and ported the system to the Atlas 200 Developer Kit, And quantitative evaluation of videos in more than a dozen different scenarios. Experimental results show that the FMRS-CFR system can achieve a recognition accuracy rate of 99.88%, which greatly improves the recognition rate of not wearing a mask or wearing the correct one to a certain extent, and achieves the purpose of effectively assisting epidemic prevention and control. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

Nirsevimab binding-site conservation in respiratory syncytial virus fusion glycoprotein worldwide between 1956 and 2021: an analysis of observational study sequencing data

BACKGROUND: Nirsevimab is an extended half-life monoclonal antibody to the respiratory syncytial virus (RSV) fusion protein that has been developed to protect infants for an entire RSV season. Previous studies have shown that the nirsevimab binding site is highly conserved. However, investigations of the geotemporal evolution of potential escape variants in recent (ie, 2015-2021) RSV seasons have been minimal. Here, we examine prospective RSV surveillance data to assess the geotemporal prevalence of RSV A and B, and functionally characterise the effect of the nirsevimab binding-site substitutions identified between 2015 and 2021. METHOD(S): We assessed the geotemporal prevalence of RSV A and B and nirsevimab binding-site conservation between 2015 and 2021 from three prospective RSV molecular surveillance studies (the US-based OUTSMART-RSV, the global INFORM-RSV, and a pilot study in South Africa). Nirsevimab binding-site substitutions were assessed in an RSV microneutralisation susceptibility assay. We contextualised our findings by assessing fusion-protein sequence diversity from 1956 to 2021 relative to other respiratory-virus envelope glycoproteins using RSV fusion protein sequences published in NCBI GenBank. FINDINGS: We identified 5675 RSV A and RSV B fusion protein sequences (2875 RSV A and 2800 RSV B) from the three surveillance studies (2015-2021). Nearly all (25 [100%] of 25 positions of RSV A fusion proteins and 22 [88%] of 25 positions of RSV B fusion proteins) amino acids within the nirsevimab binding site remained highly conserved between 2015 and 2021. A highly prevalent (ie, >40.0% of all sequences) nirsevimab binding-site Ile206Met:Gln209Arg RSV B polymorphism arose between 2016 and 2021. Nirsevimab neutralised a diverse set of recombinant RSV viruses, including new variants containing binding-site substitutions. RSV B variants with reduced susceptibility to nirsevimab neutralisation were detected at low frequencies (ie, prevalence <1.0%) between 2015 and 2021. We used 3626 RSV fusion-protein sequences published in NCBI GenBank between 1956 and 2021 (2024 RSV and 1602 RSV B) to show that the RSV fusion protein had lower genetic diversity than influenza haemagglutinin and SARS-CoV-2 spike proteins. INTERPRETATION: The nirsevimab binding site was highly conserved between 1956 and 2021. Nirsevimab escape variants were rare and have not increased over time. FUNDING: AstraZeneca and Sanofi.Copyright © 2023 Elsevier Ltd. All rights reserved.

Service supply chain resilience: a social-ecological perspective on last-mile delivery operations

Purpose: Considering the last-mile delivery service supply chain as a social-ecological system rather than just a firm-based service system, this research exploit the COVID-19 pandemic disruption to investigate how the supply chain develops resilience from a viewpoint that integrates a social-ecological perspective with the traditional engineering one. Design/methodology/approach: This research adopt a multi-case study approach using qualitative data collected via semi-structured interviews with executive-level managers from nine leading UK last-mile delivery companies. Data analysis is guided by a research framework which is developed by combining the social-ecological perspective with the structure–conduct–performance paradigm. This framework aids the investigation of the impacts of external challenges on companies' resilience strategies and practices, as well as performance, in response to disruptions. Findings: The research identifies three distinct pathways to resilience development: stabilization, focussing on bouncing back to the original normal;adaptation, involving evolutionary changes to a new normal;transformation, involving revolutionary changes in pursuit of a new normal-plus. Three strategic orientations are identified as operating across these pathways: people orientation, digital orientation, and learning orientation. Originality/value: In contrast to the manufacturing supply chain focus of most current research, this research concentrates on the service supply chain, investigating its resilience with a social-ecological perspective alongside the traditional engineering one. © 2022, Emerald Publishing Limited.

Pandemic Nationalism: Use of Government Social Media for Political Information and Belief in COVID-19 Conspiracy Theories in China

The COVID-19 pandemic unleashed a torrent of conspiracy theories across different social media platforms. Parallel to this conspiracy wave was a heightened sense of nationalism, which manifested through both in-group solidarity and perceived out-group threats. In this study, we examine how individuals' use of government social media to gather political information correlated with nation-related conspiracy beliefs during the pandemic. Data were collected from 745 subjects in China and analyzed through path analyses, which allowed us to examine the direct association with political information consumption from government social media and the indirect association with nationalism on conspiracy beliefs. The results indicated that the use of government social media to gather political information was associated with greater beliefs in nation-variant COVID-19 conspiracies, both directly and through different mediations of nationalism. Our findings highlight the importance of examining government social media use and how nationalism can have differentiated mediation effects on beliefs in conspiracy theories. © The Author(s) 2023.

Working Conditions and Burnout of Special Educators of Students With EBD: Longitudinal Outcomes

Special education teacher (SET) burnout is a significant concern, especially for SETs serving students with emotional–behavioral disorders (EBD), as they tend to experience higher burnout than other teachers. Working conditions, especially social support, have the potential to ameliorate burnout, but prior research has not articulated the sources and types of social support that are most important. The authors conducted a longitudinal study, surveying 230 SETs serving students with EBD at three time points across one school year. Data revealed administrative support, adequacy of planning time, and autonomy in fall predicted emotional exhaustion and personal accomplishment in winter and spring. Associations between working conditions and burnout components were partially mediated by SETs' perceptions of workload manageability. SET change in well-being due to COVID-19 during the early months of the pandemic was not associated with burnout. The authors discuss implications, limitations, and directions for future inquiry. © 2022 Teacher Education Division of the Council for Exceptional Children.

How rumors diffuse in the infodemic: Evidence from the healthy online social change in China

Infodemic is defined as 'an overabundance of information-some accurate and some not-that makes it hard for people to find trustworthy sources and reliable guidance when they need it' by the World Health Organization. As unverified information, rumors can widely spread in online society, further diffusing infodemic. Existed studies mainly focused on rumor detection and prediction from the statement itself and give the probability that it will evolve into a rumor in the future. However, the detection and prediction from rumors production perspective is lack. This research explores the production mechanism from the uncertainty perspective using the data from Weibo and public rumor data set. Specifically, we identify the public uncertainty through usergenerated content on social media based on systemic functional linguistics theory. Then we empirically verify the promoting effect of uncertainty on rumor production and constructed a model for rumor prediction. The fitting effect of the empirical model with the public uncertainty is significantly better than that with only control variables, indicating that our framework identifies public uncertainty well and uncertainty has a significantly predictive effect on rumors. Our study contributes to the research of rumor prediction and uncertainty identification, providing implications for healthy online social change in the post-epidemic era.

Weight Gain in Young Adults During the Covid-19 Pandemic

Introduction: During the COVID-19 pandemic, measures taken to prevent the spread of the coronavirus have led to significant changes in the lifestyle and habits of young people. The pandemic led to poor dietary patterns, reduced physical activities, and increased mental stress, which are all risk factors for weight gain. The goal of this study was to investigate the pattern of weight gain among young adults between age 18-50 years during the pandemic, and to identify factors associated with significant weight gain of 20 pounds of more. Method(s): We included young adults between ages 18 to 50 years with at least one documented weight in their electronic health records before the start of the COVID-19 pandemic shelter-in-place orders (3/19/2019 to 3/19/2020) and at least one documented weight after COVID-19 vaccines became available (12/14/2020 to 12/14/2021). Multivariable logistic regression analysis was used to identify factors associated with greater than 20 pounds of weight gain. Odds ratios (OR) and 95% confidence intervals (CI) were calculated. Result(s): The study cohort included 133,750 young adults aged 18-50 years (median age 43 years. 39.7% men). The cohort is racially and ethnically diverse, with 22.6% self-identified as White, 7.2% Black, 48.1% Hispanic, and 17.3% Asian. During the pandemic, 53866 (40.3%) lost weight or had no weight gain, 50662 (37.9%) gained 0-9 pounds (lbs), 19422 (14.5%) gained 10-19 lbs, and 9800 (7.3%) gained 20 lbs or more. Individuals who gained 20 lbs or more were younger and more likely to reside in low-income neighborhoods. Multivariable logistic regression demonstrated the following factors to be associated with significant weight gain: male sex (OR 1.10, 95% CI 1.05-1.15), Black race (OR 1.14, 95% CI 1.05-1.23), low income (OR 1.16, 95% CI 1.16-1.35), and history of depression (OR 1.64, 95% CI 1.56-1.73). Conclusion(s): In this cohort of young adults, 59.7% experienced weight gain during the pandemic, with 7.5% gaining 20 lbs or more. Factors associated with significant weight gain included male sex, black race, low income, and a history of depression. Intervention strategies to promote healthy lifestyle may be particularly important for patients with depression, and young adults from lowincome neighborhoods.

Providing preventative care during the SARS-CoV-2 pandemic: Street medicine interventions amongst those experiencing unsheltered homelessness in the City of Detroit

Background. In 2021, there were approximately 1300 people on a given day who have experienced homelessness within the city of Detroit, Michigan. Sheltered beds within the 24 homeless shelters in the city were drastically cut in half during the COVID-19 pandemic due to concerns of overcrowding perpetuating SARS-CoV-2 outbreaks. We aimed to describe the outreach efforts made by Street Medicine Organizations (SMO) of Detroit during the SARS-CoV-2 pandemic, highlighting infection prevention and control strategies, and promotion of COVID-19 vaccinations amongst the unsheltered homeless. Methods. Health promotion interventions were directed at individuals who were unsheltered (defined as those living on the streets of Detroit, encampment sites and abandoned buildings). Education, which was provided through in-person sessions, as well as targeted COVID-19 informational pamphlets were distributed with every street-based run. Hygiene kits, which included masks, hand sanitizer and gloves were distributed thrice weekly at shelters and encampment sites. Since access to hand hygiene was drastically limited, the SMO constructed 10 hand washing stations throughout the city. COVID-19 vaccination in people experiencing homelessness started in April 2021. Results. SMO prioritized a 60 square mile range within the city of Detroit, providing care to approximately 500 persons over the months of April 2020 to April 2021. Demographics for this population varied;age ranged from 23 to 76 years old, sex was 70% males, race were 67% Black, 29% White and 4% Hispanic. More than 2000 hygiene kits were distributed throughout this period. Ninety-one individuals experiencing unsheltered homelessness were provided the COVID-19 vaccine in April 2021. Conclusion. Individuals experiencing unsheltered homelessness face unique challenges to accessing timely medical care, which has been further exacerbated during the pandemic. These individuals have limited or no access to necessary measures needed to prevent the spread and severity of diseases of SARS-CoV-2. We describe a focused and effective approach to preventing infection among these individuals as a model for organizations nationally.

Evaluation of Vaccination Rates and Factors associated with Vaccine Uptake among People Living with HIV in Detroit, MI

Background. Determination of vaccination rates for people living with HIV (PLWH) and factors that affect adherence to vaccination is important to ensure these vulnerable patients are optimally protected against vaccine-preventable diseases. We analyzed the rates of vaccination and associated factors in PLWH receiving care in the Henry Ford Health Infectious Diseases (HFH ID) Clinic in Detroit, MI. Methods. We implemented a retrospective, observational study. Inclusion criteria were all PLWH who had at minimum two clinic visits at HFH ID clinic within 12 months from 2015-2021. Charts were reviewed for demographic data. We analyzed the rates of all eligible vaccines including the hepatitis A and B, HPV, influenza, pneumococcal, tetanus, zoster, and COVID-19 vaccines. Results. A total of 661 met the inclusion criteria. Average age of the patients was 50 years. 78.6% were male, 74.3% black, and 57.6% patients were from Detroit. On average, patients had 1 clinic visit in the past year at HFH ID Clinic. Rates of influenza, pneumococcal, and tetanus vaccinations were above 90%. Rates of hepatitis A and B vaccinations were above 80%. Rates of zoster and HPV vaccinations were above 50%. COVID-19 vaccination had the lowest rate at 42.1%. Patients who had received all recommended vaccines were more likely to be male, have a HFH PCP, men who have sex with men (MSM), younger, more HFH ID clinic visits, and have a higher CD4 count on entry into care. Factors associated with increased vaccine uptake include having a HFH primary care physician (PCP), more HFH ID clinic visits, and a CD4 count above 200 on entry. Having 2 clinic visits in the previous year was associated with a higher likelihood of vaccine adherence [OR 5.85 (95% CI 0.360 - 0.723)]. Conclusion. Our study shows that even in this highly vulnerable, vaccinehesitant population, programs that integrate vaccines and promote adherence to clinic care into the routine care of PLWH results in high rates of vaccine uptake. (Table Presented).

When national identity meets conspiracies: the contagion of national identity language in public engagement and discourse about COVID-19 conspiracy theories

There are growing concerns about the role of identity narratives in spreading misinformation on social media, which threatens informed citizenship. Drawing on the social identity model of deindividualization effects (SIDE) and social identity theory, we investigate how the use of national identity language is associated with the diffusion and discourse of COVID-19 conspiracy theories on Weibo, a popular social media platform in China. Our results reveal a pattern of identity communication contagion in public conversations about conspiracies: national identity language usage in original posts is associated with more frequent use of such language in all subsequent conversations. Users who engaged in discussions about COVID-19 conspiracies used more national identity expressions in everyday social media conversations. By extending the SIDE model and social identity theory to misinformation studies, our article offers theoretical and empirical insight into how identity-contagious communication might exacerbate public engagement with misinformation on social media in non-Western contexts. Lay Summary This article examined the use and consequences of national identity language in public discourse related to COVID-19 conspiracy theories on Weibo, one of the largest social media platforms in China. We investigated how social media users discussed conspiracy theories about the origins of COVID-19 to understand how national identity expressions on Weibo affected public engagement with these conspiracy theories. Our findings reveal a contagion of national identity language between the original posts and all subsequent replies. We also discovered that users who employed national identity language during discussions about COVID-19 conspiracy theories subsequently used more of this language, even in everyday posts that were unrelated to COVID-19. Our findings uncover how social media platforms are used as public spheres for identity-contagious communication that challenges misinformation correction and public understanding of other social groups.