Publications by authors named "E Donini"

Background: Moonmilk represents complex secondary structures and model systems to investigate the interaction between microorganisms and carbonatic rocks. Grotta Nera is characterized by numerous moonmilk speleothems of exceptional size hanging from the ceiling, reaching over two meters in length. In this work we combined microbiological analyses with analytical pyrolysis and carbon stable isotope data to determine the molecular composition of these complex moonmilk structures as well as the composition of the associated microbiota.

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The data economy is based on data and information sharing and tremendously impacts society as it facilitates innovative collaborations and decision-making strategies. Nonetheless, most dataset-sharing solutions rely on a centralized authority that rules data ownership, availability, and accessibility. Recent works have explored the integration of distributed storage and blockchain to enhance decentralization, data access, and smart contracts for automating the interactions between actors and data.

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Bacterial strains belonging to the genus are able to degrade various toxic organic compounds and tolerate high concentrations of metal(loid)s. We have previously shown that Rhodococcus aetherivorans BCP1 is resistant to various levels of the two arsenic inorganic species, arsenite [As(III)] and arsenate [As(V)]. However, while arsenite showed toxic effects at concentrations as low as 5 mM, arsenate at 30 mM boosted the growth rate of BCP1 cells and was toxic only at concentrations of >100 mM.

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Rhodococcus spp. strains are widespread in diverse natural and anthropized environments thanks to their high metabolic versatility, biodegradation activities, and unique adaptation capacities to several stress conditions such as the presence of toxic compounds and environmental fluctuations. Additionally, the capability of Rhodococcus spp.

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Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic.

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