Returning to Normal?: Reimagining Study Abroad and Language Learning for a Sustainable and Equitable Future

Due to health and travel restrictions, COVID-19 has presented unusual challenges to international education. Meanwhile, the pandemic has also become a historical juncture overlapping with other political and cultural moments (e.g., renewed Black Lives Matter movement, resurgence of anti-Asian racism, extreme weather phenomena). These events have propelled a reconsideration of the complex relationship between access to and participation in study abroad, language learning, and social and environmental justice. In this paper, we draw on our collective experiences as practitioners and researchers across three languages (Arabic, Mandarin, Spanish) to argue that study abroad must be a part of equitable and sustainable world language education curricula. We begin by reflecting on existing issues related to access and participation in U.S.-based study abroad and the underlying ideologies that reinforce them. We then provide possibilities – within our spheres of influence – to reconceptualize study abroad from critical and translingual perspectives in an effort to contest ideologies and shift towards a more diverse and inclusive study abroad programming. Lastly, we suggest possible ways to better integrate at home, virtual, and study abroad opportunities in language learning curricula, some of which may serve as alternatives to study abroad, especially in an environmentally and politically volatile world where social privilege shapes access to international education. © 2023, eScholarship Publishing. All rights reserved.

Towards De Novo Drug Design for the Coronavirus: A Drug-Target Interaction Prediction Approach using Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism

For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus. © 2022 IEEE.

The COVID-19 Pandemic Impact and Responses in Emerging Economies: Evidence from Vietnamese Firms

A nationwide survey of 162,738 firms in Vietnam asked firms to report the impact of the COVID-19 pandemic on the health of the business, coping strategies used, and various firm and situational characteristics. More than 80% of firms reported negative impacts from the pandemic with fewer than 4% reporting positive effects;63% of the firms adopted at least one coping strategy. The coping strategies were categorized into seven groups as follows: (1) Non-adoption, (2) promoting e-commerce, (3) transforming key products/services, (4) training employees to improve professional qualifications, (5) finding new markets for input materials, (6) finding markets for products outside of the traditional market, (7) producing new products/services according to market demand during the epidemic period, and (8) other strategies. A multinomial logit regression model showed statistically significant associations between a firm's selected coping strategy and several independent variables, as follows: (1) Firm size, (2) impact of the pandemic on firm health, firm access to inputs, and firm access to domestic markets, (3) decrease in firm revenue, and (4) receipt of government support. However, many businesses have not implemented coping strategies, leading to concerns regarding their resilience to upcoming threats and uncertainties.

BeCaked(+): An Explainable AI Model to Forecast Delta-Spreading Covid-19 Situations for Ho Chi Minh City

Covid-19 is a global disaster that needs computing power to analyze, predict and interpret. So far, there have been several models doing the job. With a huge amount of daily data, deep learning models can be trained to achieve highly accurate forecasts but theirmechanism lacks explainability. Epidemiological models, e.g. SIR, on the other hand, can provide insightful analyses, but they require appropriate parameter values, which might be complicated in certain locations. The fourth wave of the pandemic in Ho Chi Minh City (HCMC), Vietnam in 2021, brought valuable lessons along with accurate and specific data. Hence, we introduce an explainableAI model, known as BeCaked(+), to predict and analyze the pandemic situation efficiently from the collected data. BeCaked(+) combined deep learning and epidemiological models enhanced by specific parameters related to the policies endorsed by the government. Such a combination makes BeCaked(+) so accurate and a tool that provides information for policymakers to respond appropriately. One take a try BeCaked(+) at http://www.cse.hcmut.edu.vn/BeCaked.

Computational Screening of Repurposed Drugs Targeting Sars-Cov-2 Main Protease By Molecular Docking

Background: COVID-19 (Coronavirus disease 2019) is caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses significant global health and economic crisis that urges effective treatment.Methods: A total of 11 molecules (baricitinib, danoprevir, dexamethasone, hydrox-ychloroquine, ivermectin, lopinavir, methylprednisolone, remdesivir, ritonavir and saridegib, ascorbic acid, and cepharanthine) were selected for molecular docking studies using AutoDock VINA to study their antiviral activities via targeting SARS-CoV's main protease (Mpro), a cysteine protease that mediates the maturation cleavage of polyproteins during virus replication.Results: Three drugs showed stronger binding affinity toward Mpro than N3 (active Mpro inhibitor as control): danoprevir (-7.7 kcal/mol), remdesivir (-8.1 kcal/mol), and saridegib (-7.8 kcal/mol). Two primary conventional hydrogen bonds were identified in the danoprevir-Mpro complex at GlyA:143 and GlnA:189, whereas the residue GluA:166 formed a carbon-hydrogen bond. Seven main conventional hydrogen bonds were identified in the remdesivir at AsnA:142, SerA:144, CysA:145, HisA:163, GluA:166, and GlnA:189, whereas two carbon-hydrogen bonds were formed by the residues HisA:41 and MetA:165. Cepharanthine showed a better binding affinity toward Mpro (-7.9 kcal/mol) than ascorbic acid (-5.4 kcal/mol). Four carbon-hydrogen bonds were formed in the cepharanthine-Mpro complex at HisA:164, ProA;168, GlnA;189, and ThrA:190.Conclusion: The findings of this study propose that these drugs are potentially inhibiting the SAR-CoV-2 virus by targeting the Mpro protein.

Redesigning Biomedical Science Practicum towards a New Frontier

Due to the COVID-19 pandemic, majority of the Biomedical Science students were not able to undergo their clinical internship at diagnostic laboratories and this has created an impact on students’ skills and the future of the Malaysian healthcare system. Hence, our objective was to implement arevolutionized Biomedical Science practicum completely in a virtual environment, without compromising the learning outcomes during the pandemic in 2021. To achieve the intended learning outcomes, various online teaching-learning and assessment activities were carefully curated in accordance to standard program guidelines, learning outcomes, student learning time and thorough analysis of actual student logbooks. Learning materials were reinforced with various initiatives such as actual engagements with real-life scenarios via synchronous meetings with external panelists from hospitals. Online video-log (Vlog) and a logbook of daily activities were used as part of the assessment to ensure that students were able to learn and reflect on the activities performed. The study showed that all students displayed increased confidence levels in medical laboratory skills. They were also able to apply them in real-life situations due to the clear instructions and realistic experience via the virtual learning activities. Therefore, students who participated in the virtual practicum demonstrated almost similar levels of performance when compared to the students who went for physical practicums in the year 2020. Our virtual practicum has achieved its intended outcomes of empowering students with similar skills as those who underwent physical clinical placements in diagnostic laboratories. Those skills include successful acquisition of discipline-specific knowledge, collaborative and communication skills, as well as solid experimental methods and good laboratory practices. © 2022 by the authors.

XPGAN: X-RAY PROJECTED GENERATIVE ADVERSARIAL NETWORK FOR IMPROVING COVID-19 IMAGE CLASSIFICATION

This work aims to fight against the current outbreak pandemic by developing a method to classify suspected infected COVID-19 cases. Driven by the urgency, due to the vastly increased number of patients and deaths worldwide, we rely on situationally pragmatic chest X-ray scans and state-of-the-art deep learning techniques to build a robust diagnosis for massive screening, early detection, and in-time isolation decision making. The proposed solution, X-ray Projected Generative Adversarial Network (XPGAN), addresses the most fundamental issue in training such a deep neural network on limited human-annotated datasets. By leveraging the generative adversarial network, we can synthesize a large amount of chest X-ray images with prior categories from more accurate 3D Computed Tomography data, including COVID-19, and jointly train a model with a few hundreds of positive samples. As a result, XPGAN outperforms the vanilla DenseNet121 models and other competing baselines trained on the same frontal chest X-ray images.

A Community-Based Model of Care During the Fourth Wave of the COVID-19 Outbreak in Ho Chi Minh City, Vietnam.

In response to a call for help during a surge in coronavirus disease-19 (COVID-19) cases in Ho Chi Minh City in July 2021, the University of Medicine and Pharmacy at Ho Chi Minh City developed and implemented a community care model for the management of patients with COVID-19. This was based on three main principles: home care; providing monitoring and care at a distance; and providing timely emergency care if needed. One team supported patients at home with frequent contacts and remote monitoring, while a second team transferred and cared for patients requiring treatment at field emergency care facilities. COVID-19-related mortality rates at the two districts where this approach was implemented (0.43% and 0.57%) were substantially lower than the overall rate in Ho Chi Minh City over the same period (4.95%). Thus, utilization of a community care model can increase the number of patients with COVID-19 who can be effectively managed from home, and use of field emergency care facilities limited the number of patients that had to be referred for tertiary care. Importantly, the community care model also markedly reduced the mortality rate compared with traditional methods of COVID-19 patient management.