New measures of influenza virus fitness could improve vaccine strain selection through more accurate forecasts of the evolution of the virus.

First influenza infections determine susceptibility to future seasonal influenza infection throughout life, and may impact vaccine effectiveness.

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.

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.

Controlled neo-glycosylation of antigens can influence intracellular routing of antigens and the nature and strength of immune response, and should therefore be considered as a major determinant in the design of vaccines against cancer and infectious diseases.

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.

Immune cells in the endometrium are targeted by HIV and re-programmed to allow them to survive and spread the virus throughout the body.

The first reports of CRISPR/Cas9 genome editing in flatworms could usher in a new era of research on these dangerous human parasites.

Each stage of the scientific life cycle stands to benefit from the introduction of data science techniques.