ABSTRACT
Powassan virus (POWV) is an emerging tick-borne virus and cause of lethal encephalitis in humans. The lack of treatment or prevention strategies for POWV disease underscores the need for an effective POWV vaccine. Here, we took two independent approaches to develop vaccine candidates. First, we recoded the POWV genome to increase the dinucleotide frequencies of CpG and UpA to potentially attenuate the virus by raising its susceptibility to host innate immune factors, such as the zinc-finger antiviral protein (ZAP). Secondly, we took advantage of the live-attenuated yellow fever virus vaccine 17D strain (YFV-17D) as a vector to express the structural genes pre-membrane (prM) and envelope (E) of POWV. The chimeric YFV-17D-POWV vaccine candidate was further attenuated for in vivo application by removing an N-linked glycosylation site within the nonstructural protein (NS)1 of YFV-17D. This live-attenuated chimeric vaccine candidate significantly protected mice from POWV disease, conferring a 70% survival rate after lethal challenge when administered in a homologous two-dose regimen. Importantly, when given in a heterologous prime-boost vaccination scheme, in which vaccination with the initial chimeric virus was followed by a protein boost with the envelope protein domain III (EDIII), 100% of the mice were protected without showing any signs of morbidity. Combinations of this live-attenuated chimeric YFV-17D-POWV vaccine candidate with an EDIII protein boost warrant further studies for the development of an effective vaccine strategy for the prevention of POWV disease.
ABSTRACT
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
ABSTRACT
High entropy alloys (HEA) are a new type of high-performance structural material. Their vast degrees of compositional freedom provide for extensive opportunities to design alloys with tailored properties. However, compositional complexities present challenges for alloy design. Current approaches have shown limited reliability in accounting for the compositional regions of single solid solution and composite phases. For the first time, a phenomenological method analysing binary phase diagrams to predict HEA phases is presented. The hypothesis is that the HEA structural stability is encoded within the phase diagrams. Accordingly, we introduce several phase-diagram inspired parameters and employ machine learning (ML) to classify 600+ reported HEAs based on these parameters. Compared to other large database statistical prediction models, this model gives more detailed and accurate phase predictions. Both the overall HEA prediction and specifically single-phase HEA prediction rate are above 80%. To validate our method, we demonstrated its capability in predicting HEA solid solution phases with or without intermetallics in 42 randomly selected complex compositions, with a success rate of 81%. The presented search approach with high predictive capability can be exploited to interact with and complement other computation-intense methods such as CALPHAD in providing an accelerated and precise HEA design.
