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Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Planning course study is critical to facilitate strategic intervention in education. As a significant basis of planning course study, student performance prediction aims to utilize students existing relevant information to predict their future learning performance including course grades, course failure, grade point average, etc. We target course grade prediction for course study planning, provide reminders for students' learning and teachers' teaching. A student's performance is usually influenced by a variety of factors, such as the difficulty of the test, personal performance, and learning conditions. Recent studies focus on the various factors that influence student performance. However, most of them ignore the differences in the impact of these factors on different students. Moreover, existing prediction models pay less attention to the impact of test complexity on students' performance. To tackle these problems, we propose a novel Complexity-based Attentive Interactive Student Performance prediction model (CAISP) for personalized course study planning. In CAISP, the individual differences among students can be considered by assigning dynamically different importance using attention network to facilitate personalized planning. Especially, to effectively alleviate the student performance bias caused by test complexity, we fuse test complexity features to enhance the ability of feature representation via deep neural networks. Meanwhile, we combine factorization machines to develop a deep joint representation based on interaction learning and further boost the correlation between features, leading to optimal prediction results. Extensive experiments on two real-world datasets show that CAISP outperforms the state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the pandemic of the coronavirus induced disease 2019 (COVID-19) with evolving variants of concern. It remains urgent to identify novel approaches against broad strains of SARS-CoV-2, which infect host cells via the entry receptor angiotensin-converting enzyme 2 (ACE2). Herein, we report an increase in circulating extracellular vesicles (EVs) that express ACE2 (evACE2) in plasma of COVID-19 patients, which levels are associated with severe pathogenesis. Importantly, evACE2 isolated from human plasma or cells neutralizes SARS-CoV-2 infection by competing with cellular ACE2. Compared to vesicle-free recombinant human ACE2 (rhACE2), evACE2 shows a 135-fold higher potency in blocking the binding of the viral spike protein RBD, and a 60- to 80-fold higher efficacy in preventing infections by both pseudotyped and authentic SARS-CoV-2. Consistently, evACE2 protects the hACE2 transgenic mice from SARS-CoV-2-induced lung injury and mortality. Furthermore, evACE2 inhibits the infection of SARS-CoV-2 variants (α, β, and δ) with equal or higher potency than for the wildtype strain, supporting a broad-spectrum antiviral mechanism of evACE2 for therapeutic development to block the infection of existing and future coronaviruses that use the ACE2 receptor. El-Shennawy et al. report that ACE2+ circulating extracellular vesicles (evACE2) are associated with COVID-19 severity and that evACE2 inhibits the infection of SARS-CoV-2 variants of concern at a higher efficacy than soluble ACE2. [ABSTRACT FROM AUTHOR]