Megataxonomy and global ecology of the virosphere

Nearly all organisms are hosts to multiple viruses that collectively appear to be the most abundant biological entities in the biosphere. With recent advances in metagenomics and metatranscriptomics, the known diversity of viruses substantially expanded. Comparative analysis of these viruses using advanced computational methods culminated in the reconstruction of the evolution of major groups of viruses and enabled the construction of a virus megataxonomy, which has been formally adopted by the International Committee on Taxonomy of Viruses. This comprehensive taxonomy consists of six virus realms, which are aspired to be monophyletic and assembled based on the conservation of hallmark proteins involved in capsid structure formation or genome replication. The viruses in different major taxa substantially differ in host range and accordingly in ecological niches. In this review article, we outline the latest developments in virus megataxonomy and the recent discoveries that will likely lead to reassessment of some major taxa, in particular, split of three of the current six realms into two or more independent realms. We then discuss the correspondence between virus taxonomy and the distribution of viruses among hosts and ecological niches, as well as the abundance of viruses versus cells in different habitats. The distribution of viruses across environments appears to be primarily determined by the host ranges, i.e. the virome is shaped by the composition of the biome in a given habitat, which itself is affected by abiotic factors.

Artificial Intelligence and Infectious Disease Imaging

The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.