Impact of modified teaching strategies used in a gross anatomy course on the academic performance of dental students during the COVID-19 pandemic.

INTRODUCTION: Modified teaching strategies (MTS), asynchronous online teaching and smaller dissection groups, were applied to a gross anatomy course for dental students in the National Taiwan University in April 2020 in response to the COVID-19 pandemic. This study aimed to investigate the effects and perceptions of MTS on dental students. MATERIALS AND METHODS: Scores for anatomy examinations for 2018-2019 (without MTS) and 2019-2020 (with MTS) cohorts were compared to explore the effect on academic performance. Moreover, questionnaire from the 2019-2020 cohort was analysed to determine dental students' perceptions about MTS. RESULTS: The lecture performance in the final examination of the second semester for the 2019-2020 cohort was significantly higher than that of the first semester (pre-COVID-19) and that for the 2018-2019 cohort. However, the laboratory performance in the midterm examination of the second semester for the 2019-2020 cohort was significantly lower than that for the 2018-2019 cohort and showed no difference in the final examination of the first semester. The questionnaires revealed that the majority of students displayed positive attitudes towards MTS and agreed with the importance of peer discussion during laboratory dissection. CONCLUSIONS: Asynchronous online learning for anatomy lecture may be beneficial for dental students; however, a smaller dissection group accompanied by reduced peer discussion may temporarily exert negative effects on their laboratory performance at the beginning of the application. Furthermore, more dental students exhibited positive perceptions towards smaller dissection groups. These findings could illuminate the learning condition of dental students in anatomy education.

The borders are re-opening! Has virtual reality been a friend or a foe to the tourism industry so far?

Purpose Leveraging the technology acceptance model (TAM) and the stimulus-organism-response (S-O-R) theory, this paper aims to investigate how the utilitarian and hedonic factors in virtual reality (VR) technologies affect consumers' intention to travel in the endemic phase of COVID-19. At the same time, the study incorporated emotional engagement and two forms of trust as possible organisms for this model. Design/methodology/approach Through snowball sampling, data collected from 263 respondents were analysed using the partial least square structural equation modelling (PLS-SEM). Findings The findings revealed that among the different forms of hedonic and utilitarian factors, all but perceived entertainment has a significant positive relationship to emotional engagement. Additionally, emotional engagement positively influences trust in the product and seller. However, the results show that only trust in the seller has a significant relationship with travelling intention. Predictive analysis shows that the model displays a strong predictive power. Originality/value This study differentiates from the existing literature by investigating the effect of VR technologies on the two different forms of trust and emotional engagement on travelling intention. This study extends earlier studies by supplementing the explanatory perspective with a predictive focus, which is particularly important in making sound recommendations on managerial decision-making.

Data-Driven Building Energy Consumption Prediction Model Based on VMD-SA-DBN

Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency.

COVID-19 Vaccines in Older Adults: Challenges in Vaccine Development and Policy Making.

The coronavirus disease 2019 (COVID-19) pandemic has had strong adverse impacts on vulnerable populations, such as frail older adults. The success of COVID-19 vaccine development, together with extensive global public health efforts, has brought hope to the control of the COVID-19 pandemic. Nevertheless, challenges in COVID-19 vaccine development and vaccination strategies among older people remain. This article reviews vaccinations in older adults, compares COVID-19 vaccine platforms, the efficacy and safety of COVID-19 vaccines in frail older people in long-term care settings, and the challenges of COVID-19 vaccine development and policy making for vaccination strategies in older adults.

Genomic Epidemiology of Imported Cases of COVID-19 in Guangdong Province, China, October 2020 - May 2021.

Objective: The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been engendering enormous hazards to the world. We obtained the complete genome sequences of SARS-CoV-2 from imported cases admitted to the Guangzhou Eighth People's Hospital, which was appointed by the Guangdong provincial government to treat coronavirus disease 2019 (COVID-19). The SARS-CoV-2 diversity was analyzed, and the mutation characteristics, time, and regional trend of variant emergence were evaluated. Methods: In total, 177 throat swab samples were obtained from COVID-19 patients (from October 2020 to May 2021). High-throughput sequencing technology was used to detect the viral sequences of patients infected with SARS-CoV-2. Phylogenetic and molecular evolutionary analyses were used to evaluate the mutation characteristics and the time and regional trends of variants. Results: We observed that the imported cases mainly occurred after January 2021, peaking in May 2021, with the highest proportion observed from cases originating from the United States. The main lineages were found in Europe, Africa, and North America, and B.1.1.7 and B.1.351 were the two major sublineages. Sublineage B.1.618 was the Asian lineage (Indian) found in this study, and B.1.1.228 was not included in the lineage list of the Pangolin web. A reasonably high homology was observed among all samples. The total frequency of mutations showed that the open reading frame 1a (ORF1a) protein had the highest mutation density at the nucleotide level, and the D614G mutation in the spike protein was the commonest at the amino acid level. Most importantly, we identified some amino acid mutations in positions S, ORF7b, and ORF9b, and they have neither been reported on the Global Initiative of Sharing All Influenza Data nor published in PubMed among all missense mutations. Conclusion: These results suggested the diversity of lineages and sublineages and the high homology at the amino acid level among imported cases infected with SARS-CoV-2 in Guangdong Province, China.

AI-based Prevention Embedded System Against COVID-19 in Daily Life

Since the prevalence of COVID-19, the virus has spread all over the world. A large number of people have been infected and died, and countries all over the world have experienced the most severe crisis. Vaccination can effectively resist the virus. However, it does not mean that vaccination can suppress virus spread completely. Hence, wearing a mask correctly and keeping the social distance become emergency methods for reducing the risk of infection. This paper proposes an AI-based prevention embedded system against COVID-19 in daily life by keeping the function of the emergency method. The system consists of two functions, mask-wearing-status detection, and social-distance measurement. Mask-wearing-status detection employs YOLO and realizes the detection and classification of three mask-wearing-status, corrected-wearing, non-corrected-wearing, and without-wearing. Social-distance measurement equips a depth camera for measuring the distance between humans. The system gives an alert when people do not wear a mask correctly or do not keep their social distance. The system has been implemented on Jetson-nano, a compact embedded board, and achieves 6 f ps. The experimental results also show that the mask-wearing-status detection accuracy archives at 93.21% and the error of social-distance measurement are within 3 cm, which have proved the effectiveness of the system.

COVID-19 Vaccines in Older Adults: Challenges in Vaccine Development and Policy-Making

Synopsis The COVID-19 pandemic has strong adverse impacts on vulnerable populations, such as frail older adults. The success of COVID-19 vaccine development, together with extensive global public health efforts (face masks, hand hygiene, lockdowns and many others), brought a glimmer of hope to the control of the COVID-19 pandemic. Nevertheless, challenges in COVID-19 vaccine development and vaccination strategies among older people remain still. In this article, some previous examples regarding vaccination in older adults, including influenza, herpes zoster, and pneumococcal pneumonia, have been reviewed for comparisons. This article further conducts comparisons between different COVID-19 vaccine platforms based on their technology, production requirement, immune response, advantages and disadvantages. Efficacy and safety of COVID-19 vaccines in frail older people in long-term care settings have been reviewed. Challenges of COVID-19 vaccine development and policy-making for vaccination strategies in older adults have been addressed.

The impact of asynchronous online anatomy teaching and smaller learning groups in the anatomy laboratory on medical students' performance during the Covid-19 pandemic.

Due to the Covid-19 pandemic, National Taiwan University anatomy teachers adopted asynchronous online video teaching and reduced the size of anatomy laboratory groups in April 2020. The aim of this study was to investigate the impact of these changes on medical students' learning. Before Covid-19, the performance of the 2019-2020 cohort was significantly better than that of the 2018-2019 cohort. However, the implementation of modified teaching strategies significantly lowered the laboratory midterm score of the 2019-2020 cohort in the second semester. Conversely, the final laboratory examination score of the 2019-2020 cohort was significantly higher than that of the 2018-2019 cohort. Through correlation analysis, lecture and laboratory examination scores were highly correlated. Additionally, the difference in lecture and laboratory z-scores between two cohorts, the Likert scale survey and free-text feedback of the 2019-2020 cohort, were conducted to show the impact of modified teaching strategies. There were several important findings in this study. First, the change in teaching strategies may temporarily negatively influence medical students to learn anatomy. Besides, analyzing the performance of laboratory assessments could be a complementary strategy to evaluate online assessments. Applying lecture examination scores to predict laboratory performance was a feasible way to identify students who may have difficulty in learning practical dissection. Finally, reducing group size together with reduced peer discussion may have a negative effect on learning cadaver dissection for students with low academic performance. These findings should be taken into consideration when anatomy teachers apply new teaching strategies in anatomy courses.

CMOS-MEMS technologies for the applications of environment sensors and environment sensing hubs

The booming growth in environmental conditions sensing and monitoring pushes the need of inexpensive environment sensors with small size and low power consumption. The outbreak of COVID-19 further increases the need for fast monitoring of environment conditions. The micro-electrical-mechanical-systems (MEMS) technologies are considered as promising solutions to realize the required environment sensors. The mature complementary metal-oxide-semiconductor (CMOS) process platforms available in many foundries can be extended to fabricate MEMS sensors to offer the advantage of relatively easier commercialization. Moreover, by leveraging the characteristics of CMOS process platforms, the integration of multiple sensors and sensing circuits to form a compact sensing system can also be achieved. This review paper will focus on introducing the miniaturized environmental sensing devices implemented and integrated using the CMOS-MEMS technologies. In general, the CMOS chips for environment sensing are firstly fabricated using the foundry-available CMOS processes, and then the post-CMOS micromachining processes are performed to implement the CMOS-MEMS environment sensors. This paper respectively reviews five different environment sensors (including the infrared, pressure (barometer), humidity/temperature, and gas sensors) using the CMOS-based MEMS technologies. The advantages and design concerns of sensors fabricated by different CMOS and post-CMOS processes are introduced and discussed. Moreover, the CMOS-MEMS environment sensing hub implemented through the monolithic integration of multiple environment sensors is also introduced. [ABSTRACT FROM AUTHOR] Copyright of Journal of Micromechanics & Microengineering is the property of IOP Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SUMOylation restrains ACE2 degradation through TOLLIP-mediated selective autophagy to facilitate the host susceptibility to SARS-CoV-2 infection (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Alongside investigations into the virology of SARS-CoV-2, understanding the host–virus dependencies are vital for the identification and rational design of effective antiviral therapy. Here, we report the dominant SARS-CoV-2 entry receptor, ACE2, conjugates with small ubiquitin-like modifier 3 (SUMO3) through a proteome-wide protein interaction analysis. We further demonstrate that E3 SUMO ligase PIAS4 prompts the SUMOylation and stabilization of ACE2, whereas deSUMOylation enzyme SENP3 reverses this process. Conjugation of SUMO3 with ACE2 at lysine (K) 187 hampers the K48-linked ubiquitination of ACE2, thus suppressing its subsequent cargo receptor TOLLIP-dependent autophagic degradation. Pharmacological intervention of ACE2 SUMOylation blocks the entry of SARS-CoV-2 and viral infection-triggered immune responses. Collectively, our findings suggest selective autophagic degradation of ACE2 orchestrated by SUMOylation and ubiquitination can be targeted to future antiviral therapy of SARS-CoV-2.

ACE2 expression by colonic epithelial cells is associated with viral infection, immunity and energy metabolism (preprint)

Respiratory disease caused by the 2019 novel coronavirus (2019-nCoV) pneumonia first emerged in Wuhan, Hubei Province, China, in December 2019 and spread rapidly to other provinces and other countries. Angiotensin-converting enzyme 2 (ACE2) is the receptor for SARS-CoV and has been suggested to be also the receptor for 2019-nCoV. Paradoxically, ACE2 expression in the lung protects mice from SARS-CoV spike protein induced lung injury by attenuating the renin-angiotensin system. In the intestine, ACE2 also suppresses intestinal inflammation by maintaining amino acid homeostasis, antimicrobial peptide expression and ecology of the gut microbiome. Upon analysis of single cell-RNA sequencing data from control subjects and those with colitis or inflammatory bowel disease (IBD), we found that ACE2 expression in the colonocytes was positively associated with genes regulating viral infection, innate and cellular immunity, but was negatively associated with viral transcription, protein translation, humoral immunity, phagocytosis and complement activation. In summary, we suggest that ACE2 may play dual roles in mediating the susceptibility and immunity of 2019-nCoV infection.