Co-occurring nitrifying symbiont lineages are vertically inherited and widespread in marine sponges.

Ammonia-oxidizing archaea and nitrite-oxidizing bacteria are common members of marine sponge microbiomes. They derive energy for carbon fixation and growth from nitrification-the aerobic oxidation of ammonia to nitrite and further to nitrate-and are proposed to play essential roles in the carbon and nitrogen cycling of sponge holobionts. In this study, we characterize two novel nitrifying symbiont lineages, Candidatus Nitrosokoinonia and Candidatus Nitrosymbion in the marine sponge Coscinoderma matthewsi using a combination of molecular tools, in situ visualization, and physiological rate measurements.

Anaerobic oxidation of ammonium and short-chain gaseous alkanes coupled to nitrate reduction by a bacterial consortium.

The bacterial species "Candidatus Alkanivorans nitratireducens" was recently demonstrated to mediate nitrate-dependent anaerobic oxidation of short-chain gaseous alkanes (SCGAs). In previous bioreactor enrichment studies, the species appeared to reduce nitrate in two phases, switching from denitrification to dissimilatory nitrate reduction to ammonium (DNRA) in response to nitrite accumulation. The regulation of this switch or the nature of potential syntrophic partnerships with other microorganisms remains unclear.

Future ocean conditions induce necrosis, microbial dysbiosis and nutrient cycling imbalance in the reef sponge Stylissa flabelliformis.

Oceans are rapidly warming and acidifying in the context of climate change, threatening sensitive marine biota including coral reef sponges. Ocean warming (OW) and ocean acidification (OA) can impact host health and associated microbiome, but few studies have investigated these effects, which are generally studied in isolation, on a specific component of the holobiont. Here we present a comprehensive view of the consequences of simultaneous OW and OA for the tropical sponge Stylissa flabelliformis.

Performance of an artificial intelligence automated system for diabetic eye screening in a large English population.

Aims: A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep-learning artificial intelligence software in a large English population.