By comparing gene expression in people before and after they received the influenza vaccine, researchers were able to identify genes that contribute to differences in individual responses to vaccination.
The combination of phenotypic measures of antigenic drift and genotypic measures of functional constraint improves the accuracy of long-term seasonal influenza A/H3N2 forecasts.
Mutants of influenza A virus with increased CpG dinucleotide frequencies show restricted replication and reduced or absent pathogenicity, and powerful host innate and adaptive responses to infection that confer immunity to re-infection.
Older individuals have impaired conventional dendritic cell and T follicular helper cell formation upon vaccination, which can be rescued by treatment with a TLR7 agonist.
Machine learning in conjunction with super-resolution imaging allows for the first time to quantitatively analyse large and heterogenous virus samples structure at a high throughput and specificity.
Temporal availability of antigen presentation by dendritic cells influences the differentiation of follicular helper T (Tfh) cells, which enhances germinal centre responses and induces protective immunity.
De novo protein nanoparticles were designed from scratch to present viral glycoprotein antigens, providing a systematic way to study the influence of antigen presentation geometry on immune response.
Machine learning and experimental tests of receiver bias identify signal components critical to correct species classification in guenons, linking face pattern diversity to selection for species discrimination.
