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1.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-329416

ABSTRACT

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has high transmissibility and recently swept the globe. Due to the extensive number of mutations, this variant has high level of immune evasion, which drastically reduced the efficacy of existing antibodies and vaccines. Thus, it is important to test an Omicron-specific vaccine, evaluate its immune response against Omicron and other variants, and compare its immunogenicity as boosters with existing vaccine designed against the reference wildtype virus (WT). Here, we generated an Omicron-specific lipid nanoparticle (LNP) mRNA vaccine candidate, and tested its activity in animals, both alone and as a heterologous booster to existing WT mRNA vaccine. Our Omicron-specific LNP-mRNA vaccine elicited strong and specific antibody response in vaccination-naive mice. Mice that received two-dose WT LNP-mRNA, the one mimicking the commonly used Pfizer/Moderna mRNA vaccine, showed a >40-fold reduction in neutralization potency against Omicron variant than that against WT two weeks post second dose, which further reduced to background level >3 months post second dose. As a booster shot for two-dose WT mRNA vaccinated mice, a single dose of either a homologous booster with WT LNP-mRNA or a heterologous booster with Omicron LNP-mRNA restored the waning antibody response against Omicron, with over 40-fold increase at two weeks post injection as compared to right before booster. Interestingly, the heterologous Omicron LNP-mRNA booster elicited neutralizing titers 10-20 fold higher than the homologous WT booster against the Omicron variant, with comparable titers against the Delta variant. All three types of vaccination, including Omicron mRNA alone, WT mRNA homologous booster, and Omicron heterologous booster, elicited broad binding antibody responses against SARS-CoV-2 WA-1, Beta, and Delta variants, as well as other Betacoronavirus species such as SARS-CoV, but not Middle East respiratory syndrome coronavirus (MERS-CoV). These data provided direct proof-of-concept assessments of an Omicron-specific mRNA vaccination in vivo, both alone and as a heterologous booster to the existing widely-used WT mRNA vaccine form.

2.
35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence ; 35:7754-7761, 2021.
Article in English | Web of Science | ID: covidwho-1381686

ABSTRACT

In the fight against the COVID-19 pandemic, many social activities have moved online;society's overwhelming reliance on the complex cyberspace makes its security more important than ever. In this paper, we propose and develop an intelligent system named Dr.HIN to protect users against the evolving Android malware attacks in the COVID-19 era and beyond. In Dr.HIN, besides app content, we propose to consider higher-level semantics and social relations among apps, developers and mobile devices to comprehensively depict Android apps;and then we introduce a structured heterogeneous information network (HIN) to model the complex relations and exploit meta-path guided strategy to learn node (i.e., app) representations from HIN. As the representations of malware could be highly entangled with benign apps in the complex ecosystem of development, it poses a new challenge of learning the latent explanatory factors hidden in the HIN embeddings to detect the evolving malware. To address this challenge, we propose to integrate domain priors generated from different views (i.e., app content, app authorship, app installation) to devise an adversarial disentangler to separate the distinct, informative factors of variations hidden in the HIN embeddings for large-scale Android malware detection. This is the first attempt of disentangled representation learning in HIN data. Promising experimental results based on real sample collections from security industry demonstrate the performance of Dr.HIN in evolving Android malware detection, by comparison with baselines and popular mobile security products.

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