The recent emergence of highly virulent human adenoviruses (HAdVs) with new tissue tropisms underscores the need to determine their ontogeny. Here we report complete high quality genome sequences and analyses for all the previously unsequenced HAdV serotypes (n = 20) within HAdV species D. Analysis of nucleotide sequence variability for these in conjunction with another 40 HAdV prototypes, comprising all seven HAdV species, confirmed the uniquely hypervariable regions within species. The mutation rate among HAdV-Ds was low when compared to other HAdV species. Homologous recombination was identified in at least two of five examined hypervariable regions for every virus, suggesting the evolution of HAdV-Ds has been highly dependent on homologous recombination. Patterns of alternating GC and AT rich motifs correlated well with hypervariable region recombination sites across the HAdV-D genomes, suggesting foci of DNA instability lead to formulaic patterns of homologous recombination and confer agility to adenovirus evolution.

Colony-stimulating factor 1 receptor (CSF1R) inhibition has been proposed as a method for microglia depletion, with the assumption that it does not affect peripheral immune cells. Here, we show that CSF1R inhibition by PLX5622 indeed affects the myeloid and lymphoid compartments, causes long-term changes in bone marrow-derived macrophages by suppressing interleukin 1β, CD68, and phagocytosis but not CD208, following exposure to endotoxin, and also reduces the population of resident and interstitial macrophages of peritoneum, lung, and liver but not spleen. Thus, small-molecule CSF1R inhibition is not restricted to microglia, causing strong effects on circulating and tissue macrophages that perdure long after cessation of the treatment. Given that peripheral monocytes repopulate the central nervous system after CSF1R inhibition, these changes have practical implications for relevant experimental data.

With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for ‘intelligent’ healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.

Structure-based immunogen design: Vaccine design for highly mutable pathogens is hindered by a paucity of conserved immunogenic epitopes. Gaiha et al. employed a structure-based technique using network theory to assign scores to protein structure in order to infer mutational constraints (see the Perspective by McMichael and Carrington). The authors validated the method on proteins with published functional outcomes and then assessed mutational constraints within the HIV proteome. Highly networked residues strongly associated with immune control of HIV infection and may lead to protective immunogens for pathogens for which there is currently no efficient vaccine. Science , this issue p. 480 ; see also p. 438