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Currently, people all over the world have been affected by coronavirus disease 2019 (COVID-19). Fighting against COVID-19 is the top priority for all the countries and nations. The development of a safe and effective COVID-19 vaccine is considered the optimal way of ending the pandemic. Three hundred and 44 vaccines were in development, with 149 undergoing clinical research and 35 authorized for emergency use as to March 15 of 2022. Many studies have shown the effective role of COVID-19 vaccines in preventing SARS-CoV-2 infections as well as serious and fatal COVID-19 cases. However, tough challenges have arisen regarding COVID-19 vaccines, including long-term immunity, emerging COVID-19 variants, and vaccine inequalities. A systematic review was performed of recent COVID-19 vaccine studies, with a focus on vaccine type, efficacy and effectiveness, and protection against SARS-CoV-2 variants, breakthrough infections, safety, deployment and vaccine strategies used in the real-world. Ultimately, there is a need to establish a unified evaluation standard of vaccine effectiveness, monitor vaccine safety and effectiveness, along with the virological characteristics of SARS-CoV-2 variants; and determine the most useful booster schedule. These aspects must be coordinated to ensure timely responses to beneficial or detrimental situations. In the future, global efforts should be directed toward effective and immediate vaccine allocations, improving vaccine coverage, SARS-CoV-2 new variants tracking, and vaccine booster development.

Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the game experience. Therefore, some studies have emerged to address the problem of RMT detection. However, they cannot well handle the label uncertainty problem in practice, where there are only labeled RMT samples (positive samples) and unlabeled samples, which could either be RMT samples or normal transactions (negative samples). Meanwhile, the trading relationship between RMTers is modeled in a simple way, leading to some normal transactions being falsely classified as RMT. In this article, we propose PU-Detector, a novel framework based on PU learning (learning from positive and unlabeled data) for RMT detection, considering the fact that there are only labeled RMT samples and other unlabeled transactions. We first automatically estimate the likelihood of one transaction being RMT by developing an improved PU learning method and proposing an assessment rule. Sequentially, we use the estimated likelihood as edge weight to construct a trading graph to learn trader representation. Then, with the trader representations and basic trading features, we detect RMT samples by the improved PU learning method. PU-Detector is evaluated on a large-scale real world dataset consisting of 33,809,956 transaction logs generated by 43,217 unique players. Compared with other approaches, it achieves the state-of-the-art performance and demonstrates its advantages in detecting underlying RMT samples. [ABSTRACT FROM AUTHOR]

The knowledge of Technological Pedagogical Content Knowledge (TPACK) is a new definition of the knowledge structure of higher vocational college teachers in the era of information society. This study investigated the prominent problems in TPACK training of teachers in higher vocational college of arts in our city by sampling. Firstly, in the professional training of provincial or municipal teachers, emphasis was placed on “theory or method propaganda” rather than “guidance of technology integration training”; Secondly, focus on “discipline knowledge”, “teaching knowledge” and “technical knowledge” of a single module of training, but lack of “integration and deep integration of the three”; In view of the above problems, it is suggested that educational administrative departments or universities should pay attention to the training of “subject teaching with technology integration” in the professional training of teachers, and pay attention to the deep integration of “Content knowledge”, “Pedagogical knowledge” and “ Technological knowledge” in the training process. In order to effectively improve the TPACK knowledge structure of the teachers in higher vocational college of arts, promote the high-quality development of vocational education in our city.

Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with reasonable structures, rich detail, and sharpness at the same time. In this paper, we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation. Our network is built on a variational autoencoder (VAE) backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images. The prior assists in reconstructing reasonable structures when inpainting. We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables. To avoid the usual incompatibility of requiring both reasonable structures and rich detail, we propose a novel cross-layer latent variable transfer module. This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information. We further use adversarial training to select the most reasonable results and to improve the sharpness of the images. Extensive experimental results on multiple datasets demonstrate the superiority of our method. Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN. [ABSTRACT FROM AUTHOR]

Nowadays, interactive recommendation systems (IRS) play a significant role in our daily life. Recently, reinforcement learning has shown great potential in solving challenging tasks in IRS, since it can focus on long-term profit and can capture the dynamic preference of users. However, existing RL methods for IRS have two typical deficiencies. First, most state representation models use left-to-right recurrent neural networks to capture the user dynamics, which usually fail to handle the long and noisy sequential data in real life. Second, an IRS always needs to handle millions of items, leading to a large discrete action space in RL settings, which has not been fully addressed by the inefficient existing works. To bridge these deficiencies, in this paper, we propose attention-based tree recommendation (ATRec), an efficient tree-structured policy with attention-based state representation for IRS. ATRec uses an attention-based state representation model to effectively capture the user's dynamic preference hidden in the long and noisy sequence of behaviors. Moreover, to improve the learning efficiency, we propose an efficient tree-structured policy representation method, in which a complete tree is devised to represent the policy, and a novel parameter-sharing strategy is introduced. Extensive experiments are conducted on three real-world datasets and the results show the proposed ATRec obtains 42.3% improvement over some of the state of the arts methods in the hit rate and 21.4% improvement in the mean reciprocal rank of the top 30 ranked items. Additionally, the learning and decision efficiency can also be improved at an average of 35.5%. [ABSTRACT FROM AUTHOR]

A cluster of Chlamydia psittaci (C. psittaci) cases was reported in Zhejiang Province, China, 2019. This study evaluates the extent of the outbreak and determines the source of infection. Real‐time PCR and sequencing of the ompA gene of C. psittaci were performed to identify the cases, the domesticated poultry and close contacts. The index patient was a 76‐year‐old woman with chronic vertigo, and Case 2 was a 64‐year‐old female farmer with herpes zoster. Both women bought psittaci‐infected chickens or ducks from the same mobile street vendor and raised them for 10 days and 23 days before fever onset. There were no direct contact between the two women. C. psittaci test was positive for the two patients, one sick chicken, three healthy ducks and the vendor's chicken cage. Phylogenetic analysis showed that all seven C. psittaci positive samples carried identical ompA genotype A of C. psittaci. Of all of the patients' 148 close contacts, none tested positive for C. psittaci, or developed acute respiratory symptoms. Both patients were discharged after a 4‐week hospital stay. In conclusion, the source of this cluster was the poultry infected with C. psittaci, which occasionally cause infections in farmers, but inter‐human transmission seems unlikely. [ABSTRACT FROM AUTHOR]