Cytauxzoon sp. and Hepatozoon spp. in Domestic Cats: A Preliminary Study in North-Eastern Italy

Knowledge on the presence of Cytauxzoon sp. and Hepatozoon spp. in Italy is scant and mostly limited to a few areas of Northern and Southern regions, respectively. The present study updated the current epidemiological scenario by investigating the occurrence of these protozoa in domestic cats from three broad regions of North-Eastern Italy. Blood samples from cats at risk of vector-borne diseases were processed by PCR to detect Cytauxzoon and Hepatozoon DNA. Blood smears were observed for haemoparasite inclusions. The influence of cat individual data (e.g., provenance, management, indoor/outdoor lifestyle) on the prevalence of haemoprotozoan infections was statistically evaluated. Among 158 cats, Cytauxzoon and Hepatozoon DNA were detected in 6 (3.8%) and 26 (16.5%) animals, respectively. No Hepatozoon gamonts were detected in blood smears, whereas all Cytauxzoon PCR-positive samples were microscopically positive, though with low levels of parasitaemia. Two species of Hepatozoon were identified, Hepatozoon felis (n = 10) and Hepatozoon silvestris (n = 16). Hepatozoon silvestris prevalence values were significantly (p < 0.05) higher in the region Friuli Venezia Giulia and in stray cats. Cytauxzoon sp. was detected in 6/39 (15.4%) stray cats from Friuli Venezia Giulia (Trieste province). These data add new information on the occurrence of these neglected protozoa in domestic cats’ populations.

ESAMP: Event-Sourced Architecture for Materials Provenance Management and Application to Accelerated Materials Discovery

While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in data generation, ingestion, and materials state-aware machine learning. High-throughput experiments and automated computational workflows are addressing the challenge of data generation, and capitalizing on these emerging data resources requires ingestion of data into an architecture that captures the complex provenance of experiments and simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence and interrelationships among events occurring in a simulation or experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments performed using various modalities such as serial, parallel, multimodal experimentation. Our data architecture tracks the evolution of a material’s state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with statechanging processes in addition to the typically used composition data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in enabling several aspects of accelerated materials discovery such as dynamic workflow design, generation of knowledge graphs, and efficient integration of theory and experiment.

ESAMP: Event-Sourced Architecture for Materials Provenance Management and Application to Accelerated Materials Discovery

While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in data generation, ingestion, and materials state-aware machine learning. High-throughput experiments and automated computational workflows are addressing the challenge of data generation, and capitalizing on these emerging data resources requires ingestion of data into an architecture that captures the complex provenance of experiments and simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence and interrelationships among events occurring in a simulation or experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments performed using various modalities such as serial, parallel, multimodal experimentation. Our data architecture tracks the evolution of a material’s state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with statechanging processes in addition to the typically used composition data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in enabling several aspects of accelerated materials discovery such as dynamic workflow design, generation of knowledge graphs, and efficient integration of theory and experiment.

CoPS - Cooperative Provenance System with ZKP using Ethereum Blockchain Smart Contracts

The redesign of cloud storage with the amalgamation of cooperative cloud and an immutable and unhackable distributed database blockchain thrives towards a strong CIA triad and secured data provenance. The conspiracy ideology associated with the traditional cloud has economized with cooperative cloud storage like Storj and Sia, decentralized storage, which allows renting the unused hard drive space and getting monetary compensation in an exchange with cryptocurrency. In this article, the authors explain how confidentiality, integrity and availability can be progressed with cooperative cloud storage along with tamper-proof data provenance management with ethereum smart contracts using zero-knowledge proof (ZKP). A contemporary architecture is proposed with regards to storing data on the cooperative cloud and collecting and verifying the provenance data from the cloud and publishing the provenance data into blockchain network as transactions.