Estimating Stress in Online Meetings by Remote Physiological Signal and Behavioral Features

Work stress impacts people's daily lives. Their well-being can be improved if the stress is monitored and addressed in time. Attaching physiological sensors are used for such stress monitoring and analysis. Such approach is feasible only when the person is physically presented. Due to the transfer of the life from offline to online, caused by the COVID-19 pandemic, remote stress measurement is of high importance. This study investigated the feasibility of estimating participants' stress levels based on remote physiological signal features (rPPG) and behavioral features (facial expression and motion) obtained from facial videos recorded during online video meetings. Remote physiological signal features provided higher accuracy of stress estimation (78.75%) as compared to those based on motion (70.00%) and facial expression (73.75%) features. Moreover, the fusion of behavioral and remote physiological signal features increased the accuracy of stress estimation up to 82.50%. © 2022 Owner/Author.

An Enhanced Convolution Neural Network Model based on Depthwise-separable Convolutions for Pneumonia Classification

This paper is an attempt to customize a lightweight model to classify pneumonia images by integrating depthwise-separable convolutions with typical CNN model, and focus on the performance of DSCNN in comparison with typical CNN model based on X-ray images. The experimental result shows that in our four-layer structure, DSCNN reduce around 50,000 parameters compared to CNN. But DSCNN had a relative low recall on COVID-19(89.23%). However, with proper means of optimization such as focal loss and data augmentation, there was a slight increase in test accuracy of DSCNN(from 95.25% to 96.14%), and a significant increase in recall on COVID-19(from 89.23% to 94.61%). And this model also performed well on the rest two labels. © 2022 IEEE.

Three-dimensional printing in cardiopulmonary disease

Three-dimensional (3D) printing has gained increasing interests and recognition in the medical domain with studies confirming its clinical value and applications in many areas. 3D-printed personalized models provide information that cannot be obtained by traditional visualization tools, and this is especially apparent in the cardiopulmonary disease due to the complexity of anatomical structures and pathologies that are seen in the cardiopulmonary system. This chapter provides an overview of the application and usefulness of 3D-printed models in cardiopulmonary disease, including 3D printing in heart and cardiovascular disease, and 3D printing in pulmonary disease. Emerging applications of 3D printing in coronavirus disease 2019 patients, in particular, the 3D-printed ventilators, and 3D printing in cardiopulmonary bypass are also presented, while limitations and future research of 3D printing in cardiopulmonary disease are highlighted. It is expected that this chapter presents an update of current research on 3D printing in cardiopulmonary disease and possible research directions along this pathway. © 2023 Elsevier Inc. All rights reserved.

Research on the Academic Early Warning Model of Distance Education based on Student Behavior Data in the Context of COVID-19 Early Warning Model of Distance Education

The COVID-19 epidemic has caused great impact on the entire society, and the spread of novel coronavirus has brought a lot of inconvenience to the education industry. To ensure the sustainability of education, distance education plays a significant role. During the process of distance education, it is necessary to examine the learning situation of students. This study proposes an academic early warning model based on long- and short-term memory (LSTM), which firstly extracts and classifies students' behavior data, and then uses the optimized LSTM to establish an academic early warning model. The precision rate of the optimized LSTM algorithm is 0.929, the recall rate is 0.917 and the F value is 0.923, showing a higher degree of convergence than the basic LSTM algorithm. In the actual case analysis, the accuracy rate of the academic early warning system is 92.5%. The LSTM neural network shows high performance after parameter optimization, and the academic early warning model based on LSTM also has high accuracy in the actual case analysis, which proves the feasibility of the established academic early warning model.

Effects of COVID-19 on Residential Planning and Design: A Scientometric Analysis

Coronavirus disease has caused city blockades, making people spend longer in residential areas than ever before. Human well-being and health are directly affected by the suppression of the epidemic through residential planning and design. In this regard, scholars from all over the world have made significant efforts to explore the links between COVID-19 and residential planning and design, trying to adjust the states in time to cope with the effects of COVID-19 in the long run. This study is based on Bibliometrix to conduct a scientometric analysis of the literature on "Effects of COVID-19 on residential planning and design (ECRPD)” published in Web of Science and Scopus from 2019 to October 2022. The aim of this study is to comprehensively present the scientific knowledge of ECRPD research through general characteristics' analysis, citation analysis, and horizontal conceptual structure analysis, and try to summarize how residential planning and design responds to COVID-19, so as to provide support and advice for urban planners, builders, and policy makers. According to the results, ECRPD research is growing significantly, and the scientific productivity of it has increased exponentially. The main effects and feedback are characterized by three aspects: residential environment, residential building space and planning space, and residential traffic and community management. Generally, ECRPD research has expanded beyond the disciplines of architecture and planning. Environmental and energy concerns have attracted the most attention, though practical research into residential building space is relatively limited. To fully deal with COVID-19's multiple negative facets, it is imperative to promote cross-disciplinary and multi-field collaboration, implement new technologies and methods for traditional disciplines, develop bioclimatic buildings to cope with environmental changes, and strengthen practical research in residential building and planning to ensure that a sustainable and resilient living environment is created in the post-pandemic era. © 2023 by the authors.

Analysis of Students' Online Learning Engagement during the COVID-19 Pandemic: A Case Study of a SPOC-Based Geography Education Undergraduate Course

With the long-lasting impact of the COVID-19 pandemic, online learning has gradually become one of the mainstream learning methods in Chinese universities. The effectiveness of online learning is significantly influenced by learning engagement, and studies into this topic can help learners by providing them with process-based learning support and focused teaching interventions. Based on the online learning environment, this research constructs an online learning engagement analysis model. Additionally, this study explores the relationship between students' online learning engagement and their online learning performance by taking the Secondary School Geography Curriculum Standards and Textbooks Research, a small-scale private online course (SPOC) of the geography education undergraduate course at Nanjing Normal University, as an example. The findings are as follows: In the cognitive engagement dimension, only "analyze” is significantly positively correlated with learning performance;in the behavioral engagement dimension, the "number of question and answer (Q&A) topic posts,” the "replies to others,” and the "teachers' replies” are all significantly positively correlated with learning performance. In terms of the emotional engagement dimension, "curiosity” and "pleasure” are positively correlated with learning performance;as for the social engagement dimension, "point centrality” and "intermediary centrality” are positively correlated with learning performance. The findings of this case study reveal that the student's engagement in higher-order cognitive learning is obviously insufficient. Students' online learning performance can be enhanced both by behavioral engagement in knowledge reprocessing and positive emotional engagement. Further research should be focused on finding ways to increase students' enthusiasm for social engagement. © 2023 by the authors.

How Teaching Presence Facilitate Rural Teachers' Learning Engagement in MOOC for Teacher Education: The Mediating Effect of Learning Motivation

Under the dual influence of the rapid development of education informatization and the severe impact of COVID-19, online training has become a normal way of professional learning for teachers. Teaching presence and learning engagement, as important factors affecting learning effect, have received extensive attention from educational researchers and practitioners. Based on a teacher education MOOC, this study conducted a questionnaire survey on 169 rural teachers who participated in this MOOC, and explored the relationship and mechanism of teaching presence, learning motivation and learning engagement perceived by rural teachers. This study found that there was a significant positive correlation between teaching presence, learning motivation, and learning engagement, and learning motivation played a partial mediating role in teaching presence and learning engagement. In the following MOOCs for teacher education, MOOC designers should enhance the sense of teaching presence from three aspects: learning design, learning organization, and learning intervention, and stimulate the learning motivation of rural teachers, so as to achieve the occurrence of deep online learning for rural teachers. © 2022 ACM.

Lipolysis is accompanied by immune microenvironment remodeling in adipose tissue of obesity with different exercise intensity.

OBJECTIVE: Obesity and overweight are risk factors for chronic disease worldwide. The purpose of this study was to compare the transcriptome of exercise-induced fat mobilization in obese people, and to explore the effect of different exercise intensity on the correlation of immune microenvironment remodeling and lipolysis in adipose tissue. MATERIALS AND METHODS: Microarray datasets of adipose tissue before and after exercise were downloaded from the Gene Expression Omnibus. Then, we used gene-enrichment analysis and PPI-network construction to elucidate the function and enrichment pathways of the differentially expressed genes (DEGs) and to identify the central genes. A network of protein-protein interactions was obtained using STRING and visualized with Cytoscape. RESULTS: A total of 929 DEGs were identified between 40 pre-exercise (BX) samples and 65 post-exercise (AX) samples from GSE58559, GSE116801, and GSE43471. Among these DEGs, adipose tissue-expressed genes were duly recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that DEGs were mostly enriched in lipid metabolism. Studies have found that mitogen-activated protein kinase (MAPK) signaling pathway and forkhead box O (FOXO) signaling pathway are up-regulated, while Ribosome, coronavirus disease (COVID-19) and IGF-1 gene are down-regulated. Although we found the up-regulated genes that noted IL-1 among others, and the down-regulated gene was IL-34. The increase of inflammatory factors leads to changes in cellular immune microenvironment, and high-intensity exercise leads to increased expression of inflammatory factors in adipose tissue, leading to inflammatory responses. CONCLUSIONS: Exercise at different intensities leads to the degradation of adipose and is accompanied by changes in the immune microenvironment within adipose tissue. High intensity exercise can cause the imbalance of immune microenvironment of adipose tissue while causing fat degradation. Therefore, moderate intensity and below exercise is the best way for the general population to reduce fat and weight.

Design and implementation of community express delivery system

The development of a community express delivery system in the context of COVID-19 reduces the risk of virus infection for people in the community who go out to pick up their deliveries, which highlights the benefits of "contactless delivery”. The system is developed using a separate development method for the front-end and back-end. The mobile end in the front end uses the WeChat applet, and the Web end uses the Vue framework for development. The back-end server is developed in Java and designed with a MySQL database. This system contributes to the community residents' courier delivery challenges. © 2022 SPIE.

Design and implementation of community express delivery system

The development of a community express delivery system in the context of COVID-19 reduces the risk of virus infection for people in the community who go out to pick up their deliveries, which highlights the benefits of "contactless delivery”. The system is developed using a separate development method for the front-end and back-end. The mobile end in the front end uses the WeChat applet, and the Web end uses the Vue framework for development. The back-end server is developed in Java and designed with a MySQL database. This system contributes to the community residents' courier delivery challenges. © 2022 SPIE.

A Deep Learning Based Method For COVID-19 Classification Using Chest CT Images

At the beginning of 2020, coronavirus disease 2019(COVID-19) infection spread in Wuhan, China and all over the world. Until April, it had affected millions of people. The computed tomography (CT) imaging is confirmed as one of the assessment method for COVID-19 patients. However distinguish the COVID-19 from those CT images is extremely challenging as it is very time-consuming, and lack of the experienced radiologists. So deep learning based approaches are proposed to triage the COVID-19 images from the normal or other pneumonia images. Here, we proposed a novel global average pooling (GAP) method for the deep neural network to improve the performance of the COVID-19 classification. The novel GAP method is using lung mask region as weighting factor for GAP, which reduce the influence of background region and highlight the classification features of interesting tissue region. The result of our method achieved the triage of COVID-19 with sensitivity 96.4 % and specificity 93.3 % on the independence validation dataset with 2062 CT scans. © 2022 IEEE.

Visual protest repertoires and protesters' health identity: a battlefield of the anti-new normal movement

This study explores the health identity of the anti-new normal protesters during the coronavirus pandemic through analyzing the visual protest repertoires that were employed by the protesters in the two waves of demonstrations occurring across the world between 2020 and 2021. The authors examine the protesters' interpretations of the coronavirus and its mandatory protective measures, their imaginations of the social milieu that has been forever transformed by COVID-19, and their expectations of life. The protesters prioritize the innate physical body, which is essentialized in that there is a static relationship between the physical body and the self as well as between the self and others. The current study enriches our understandings of the new normal opponents' health identity and provides insights into how it differs from that of the supporters. It helps health educators to effectively construct and deliver health promotion messages and foster healthy behaviors during the pandemic.

Driving Sustainable Airport Business with Big Data Analytics Services: A Multichannel Approach

Business performance has increased dramatically owing to the increase use of multichannel services in global markets posed by COVID-19 pandemic new normal. The viability of commercial airports depends on strong business models that integrates multichannel services such as digital services, cloud services and big data analytics services to its building blocks. This paper examines how multichannel services and big data analytics services can be used to optimize and enhance airport business building blocks to its business models to increase airports' revenue and business value. A case study was conducted to analyze PNG's airport authority's (National Airports Corporation (NAC) existing business models in comparison to the Osterwalder's business model building blocks. A sustainable business model was proposed that integrates digital and web-based technologies to boost the model's commercial and operational viability. © 2022 ACM.

Research on the Academic Early Warning Model of Distance Education based on Student Behavior Data in the Context of COVID-19

The COVID-19 epidemic has caused great impact on the entire society, and the spread of novel coronavirus has brought a lot of inconvenience to the education industry. To ensure the sustainability of education, distance education plays a significant role. During the process of distance education, it is necessary to examine the learning situation of students. This study proposes an academic early warning model based on long and short-term memory (LSTM), which firstly extracts and classifies students’ behavior data, and then uses the optimized LSTM to establish an academic early warning model. The precision rate of the optimized LSTM algorithm is 0.929, the recall rate is 0.917 and the F value is 0.923, showing a higher degree of convergence than the basic LSTM algorithm. In the actual case analysis, the accuracy rate of the academic early warning system is 92.5%. The LSTM neural network shows high performance after parameter optimization, and the academic early warning model based on LSTM also has high accuracy in the actual case analysis, which proves the feasibility of the established academic early warning model. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Research on classification of COVID-19 and common pneumonia by X-ray images based on convolutional attention mechanism

As the COVID-19 pandemic spreads across the globe, it highlights the importance of using all available resources to mitigate this common human challenge. Therefore, this paper studies and evaluates the general convolutional neural network and a method based on lung X-ray image classification. Attention mechanism mechanism + DenesNet method for detecting infected patients from chest X-ray images. In order to improve the validity, the mixed data set is preprocessed in this paper. In order to reduce the problem of small number of samples, we adopt transfer learning to transfer the information extracted from the pre-trained model to the model to be trained. The experimental results show that the overall accuracy of the design experiment in this paper has been improved to a certain extent compared with the original network model, and the overall accuracy has reached 83.82%. © 2022 SPIE.

Screening potential Q-Marker from Huoxiang Zhengqi Shui based on UPLC-Q-TOF-MS fingerprints and molecular docking

Objective To screen the potential quality markers (Q-Marker) of anti-coronavirus of Huoxiang Zhengqi Shui (, HZS) based on the ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS) fingerprints and molecular docking. Methods UPLC-Q-TOF-MS fingerprints and chemometric methods were employed to establish fingerprints and find out the difference between the peaks for the 27 batches of HZS samples. The SARS-CoV-2 main protease (Mpro) inhibition potential of the differential compounds among the 27 batches of HZS were further predicted by molecular docking with remdesivir as positive control. Results The UPLC-Q-TOF-MS fingerprints of 27 batches of HZS samples were set up with 27 common peaks. Combined with hierarchical clustering analysis (HCA) and principal component analysis (PCA), 14 common peaks were determined as differential compounds, and nine of them were identified as hesperidin, oxypeucedanin, neobyakangelicol, sinensetin, glycyrrhizic acid, 3,5,6,7,8,3',4'-heptamethoxyflavone, tangeretin, imperatorin and phellopterin. Molecular docking results showed that a total of six differential compounds were proven to have a certain inhibitory effect on SARS-CoV-2 Mpro, which can be used as potential Q-Marker of HZS, including hesperidin, oxypeucedanin, neobyakangelicol, glycyrrhizic acid, imperatorin and phellopterin. Conclusion The potential Q-Marker of HZS was determined by UPLC-Q-TOF-MS fingerprints, chemometric analysis and molecular docking. This method may provide a certain reference for the identification of various drug components, analysis of the differences of the same type drug components and pharmaceutical activity evaluation. Copyright © 2022 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Blood Transcriptomes of Kidney Recipients with Covid-19 Demonstrate Deficient Adaptive Immune Activation

Purpose: Kidney transplant recipients (KTRs) have poor outcomes compared to non-KTRs with acute COVID-19. To provide insight into management of immunosuppression (IS) during COVID-19, we studied immune signatures from the peripheral blood during and after COVID-19 infection from a multicenter KTR cohort. Method(s): Clinical data were collected by chart review. Paxgene blood RNA was polyA-selected and sequenced at enrollment Results: A total of 64 KTRs affected with COVID-19 were enrolled (31 Early cases (<4weeks from a positive SARS-CoV-2 PCR test) and 33 late cases). Out of the 64 patients, eight died and three encountered graft losses during follow-up. Among 31 early cases, we detected differentially expressed genes (nominal p-value < 0.01) in the blood transcriptome that were positively or negatively associated with the COVID-19 severity score (scale of 1 to 7 with increasing severity;Fig 1A). Enrichment analyses showed upregulation of neutrophil and innate immune pathways and downregulation of adaptive immune activation pathways with increasing severity score (Fig 1B). This observation was independent of lymphocyte count, despite reduction in immunosuppression (IS) in 75% of KTRs. Interestingly, compared with early cases, the blood transcriptome in late cases showed "normalization" of these enriched pathways after 4 weeks, suggesting return of adaptive immune system activation despite re-initiation of immunosuppression (Fig 1C). The latter analyses were adjusted for the severity score. Interestingly, similar pathway enrichment with worsening severity of COVID-19 was identifiable from a public dataset of non-KTRs (GSE152418), showing overlapped signatures for acute COVID-19 between KTRs and non-KTRs (overlap P<0.05) (Fig 1D). Conclusion(s): Blood transcriptome of COVID-KTRs shows marked decrease in adaptive immune system activation during acute COVID-19, even during IS reduction, which show recovery after acute illness. (Figure Presented).

Blood Transcriptomes of Covid-19 Infected Kidney Recipients Demonstrate Frequent and Persistent SARS-CoV-2 Virome Gene Expression

Purpose: Kidney transplant recipients (KTRs) have poor outcomes vs non-KTRs with acute COVID-19. To provide insight into management of immunosuppression during acute COVID-19, we studied peripheral blood transcriptomes during and after COVID-19 from a multicenter KTR cohort. Method(s): Clinical data were collected by chart review. Paxgene blood RNA was polyA-selected and sequenced at enrollment. Result(s): A total of 64 KTRs with COVID-19 were enrolled (31 Early cases (<4weeks from a positive SARS-CoV-2 PCR test) and 33 late cases). Out of the 64 patients, eight died and three encountered graft losses during follow-up. Due to presence of mRNA reads in the blood transcriptome unmapped to the human genome, we aligned the mRNA short reads to the SARS-CoV-2 genome. Surprisingly, our strategy detected the SARS-Cov2 mRNA, especially Spike mRNA in 27 (87%) early cases, and 18 (54%) of late cases (Fig 1A and B). We then analyzed the raw reads from a public dataset of non-KTRs with Paxgene RNA (GSE172114). The SARS-CoV-2 Spike mRNA was detected in 2/47 (4.2%) critically ill COVID-19 cases and 0/25 noncritically ill cases in this non-KTR dataset (compared to KTRs, Chi-square P<0.001;Fig 1B). Among our KTRs, the amount of Spike mRNA was associated positively with the COVID-19 severity score (scale of 1 to 7 of increasing severity;Fig 1C) and inversely with time from initial positive PCR (Fig 1D). More interestingly, 7/64 patients had detectable Spike RNA-emia beyond 60 days after COVID-19 diagnosis. Of the 3 graft losses in our cohort, 2 occurred among these 7 patients. Conclusion(s): Blood transcriptome of KTRs with COVID-19 demonstrated a risk for persistent viremia with implications for pathogenesis of COVID-19 disease. This finding also supports using passive immune strategies in COVID-KTRs. (Figure Presented).

Consume Machine Learning Integration Algorithms to Predict COVID-19 Infection

In the context of the emerging coronavirus pneumonia epidemic becoming a global epidemic, nucleic acid testing as a as a precise prevention and control method has been universally recognized, but because the scope of the test is too big and the production process is complicated, the kits produced by biological companies are difficult to use widely, for this reason I develop some machine learning integrated algorithms which can forecast whether a man is infected with COVID-19 based on three highly accessible features. This method can predict whether a person has been infected with COVID-19 based only on three indicators: heart rate, blood oxygen level, and body surface temperature, and we use several tree integration. We used several tree integration algorithms such as Random Forest, XGBoost, and GBM, and its accuracy, recall, and F1 score obtained 100% accuracy on the test set, which has been better than the current nucleic acid detection methods, proving that this method can be theoretically used as an accurate, convenient, and efficient self-detection method. © 2022 IEEE.

Factors Impacting Time from Biopsy to Initiation of Treatment for Advanced NSCLC at an Academic Hospital and Affiliate Hospitals

Introduction: Delays in initiation of treatment for advanced cancers are associated with poorer outcomes. In advanced NSCLC, one factor impacting time of treatment initiation is next-generation sequencing (NGS) testing. In our hospital system, Northwestern Medicine, the standard for NGS testing at Northwestern Memorial, the 894-bed academic hospital, is in-house reflex testing on all histologies and stages for a 50-gene panel of mutations and fusions. At affiliate hospitals, there is no set protocol so testing is sent to private vendors. The purpose of this study was to compare time from biopsy to treatment between the academic center and two affiliate hospitals evaluating for impact of NGS testing, radiation therapy, repeat biopsies, and the COVID-19 pandemic. Methods: We queried the Northwestern Medicine Enterprise Data Warehouse for patients with a new diagnosis of lung cancer between January 1, 2019 and December 31, 2020 at Northwestern Memorial Hospital (NMH), Central DuPage Hospital (CDH), and Delnor Hospital. This yielded a total of 864 patients - 623 (72.1%) diagnosed in 2019 and 241 (27.9%) diagnosed in 2020. Inclusion criteria for analysis: new diagnosis of stage IV NSCLC with diagnostic evaluation conducted at one of the three aforementioned hospitals. Results: 191 patients with stage IV NSCLC met inclusion criteria, 68.6% (131/191) diagnosed in 2019 and 31.4% (60/191) diagnosed in 2020. 148/191 patients received systemic therapy, 102 diagnosed in 2019 (44 NMH / 58 CDH and Delnor), and 46 diagnosed in 2020 (27 NMH / 19 CDH + Delnor). 59/148 patients had radiation prior to systemic therapy (29 NMH, 30 CDH + Delnor), and 20/148 required repeat biopsy (10 NMH, 10 CDH + Delnor). Median time from first biopsy to treatment was 30 days at Northwestern and 37 days at CDH + Delnor overall;in 2019, these times were 35 days at Northwestern and 38 days at CDH + Delnor, and in 2020, these times were 26 days at Northwestern and 37 days at CDH + Delnor (Figure 1). [Formula presented] Conclusions: Time from biopsy to treatment decreased between 2019 and 2020 at Northwestern but not at CDH + Delnor, and was shorter overall at Northwestern compared with CDH + Delnor. Radiation therapy and need for repeat biopsy did not differ between the two sites, suggesting that reflex NGS may be associated with faster turnaround times. Fewer patients presented with lung cancer in 2020 than 2019, highlighting the impact of the COVID pandemic on cancer care. Keywords: NGS, Time to treatment