The ongoing coronavirus disease (COVID-19) pandemic is caused by a new coronavirus (severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)) first reported in Wuhan City, China. From there, it has been rapidly spreading to many cities inside and outside China. Nowadays, more than 110 million cases with deaths surpassing 2 million have been recorded worldwide, thus representing a major health and economic issues. Rapid development of a protective vaccine against COVID-19 is therefore of paramount importance. Here, we demonstrated that the recombinantly expressed receptor-binding domain (RBD) of the spike protein can be coupled to immunologically optimized virus-like particles derived from cucumber mosaic virus (CuMVTT). The RBD displayed CuMVTT bound to ACE2, the viral receptor, demonstrating proper folding of RBD. Furthermore, a highly repetitive display of the RBD on CuMVTT resulted in a vaccine candidate that induced high levels of specific antibodies in mice, which were able to block binding of the spike protein to ACE2 and potently neutralize SARS-CoV-2 virus in vitro.

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We consider the problem of template-independent news extraction. The state-of-the-art news extraction method is based on template-level wrapper induction, which has two serious limitations. 1) It cannot correctly extract pages belonging to an unseen template until the wrapper for that template has been generated. 2) It is costly to maintain up-to-date wrappers for hundreds of websites, because any change of a template may lead to the invalidation of the corresponding wrapper. In this paper we formalize news extraction as a machine learning problem and learn a template-independent wrapper using a very small number of labeled news pages from a single site. Novel features dedicated to news titles and bodies are developed respectively. Correlations between the news title and the news body are exploited. Our template-independent wrapper can extract news pages from different sites regardless of templates. In experiments, a wrapper is learned from 40 pages from a single news site. It achieved 98.1% accuracy over 3,973 news pages from 12 news sites.

With the gradual improvement of the telecommunication infrastructure in China, the Internet and other new technologies have been frequently used. The Internet technology also brings many network security threats, for example, botnet, while bringing convenience. Botnet is a network formed between hosts controlled by malicious code. One of the most serious threat to network security faced by the Internet is a variety of malicious network attacks on the carrier of botnet. Back propagation (BP) neural network is proposed to detect botnet threat transmission. In this study, a botnet detection model was established using BP neural network system. BP neural network classifier could identify the botnet traffic and normal traffic. Moreover a test was carried out to detect botnet traffic using BP neural network; the performance of the BP neural network classifier was evaluated by the detection rate and false positive rate. The results showed that it had high detection rate and low false positive rate, which indicated that the BP neural network had a good performance in detecting the traffic of botnet threat transmission.