The digital transformation of critical infrastructures, such as energy or water distribution systems, is essential for their smart management. Faster issue identification and smoother services enable better adaptation to consumers' evolving demands. However, these large-scale infrastructures are often outdated. Their digital transformation is crucial to enable them supporting societies. This process must be carefully planned, providing guidance that ensures that the data they rely on is dependable and that the system remains fully operational during the transition. This paper presents a formal model that supports reliable data acquisition in legacy critical infrastructures, facilitating their evolution towards a data-driven smart system. Our model provides the foundation for a flexible transformation process while generating dependable data for system management. We demonstrate the model's applicability in a use case within the water distribution domain and discuss its benefits.

Escherichia coli is the main pathogen responsible for extraintestinal infections. A total of 196 clinical E. coli consecutively isolated during 2016 in Spain (100 from Lucus Augusti hospital in Lugo) and France (96 from Beaujon hospital in Clichy) were characterized. Phylogroups, clonotypes, sequence types (STs), O:H serotypes, virulence factor (VF)-encoding genes and antibiotic resistance were determined. Approximately 10% of the infections were caused by ST131 isolates in both hospitals and approximately 60% of these infections were caused by isolates belonging to only 10 STs (ST10, ST12, ST58, ST69, ST73, ST88, ST95, ST127, ST131, ST141). ST88 isolates were frequent especially in Spain while ST141 isolates significantly predominated in France. The 23 ST131 isolates displayed four clonotypes: CH40-30, CH40-41, CH40-22 and CH40-298. Only 13 (6.6%) isolates were carriers of ESBL enzymes. However, 37.2% of the isolates were multidrug-resistant (MDR). Approximately 40% of the MDR isolates belonged to only four of the dominant clones (B2-CH40-30-ST131, B2-CH40-41-ST131, C-CH4-39-ST88 and D-CH35-27-ST69). Among the remaining MDR isolates two isolates belonged to B2-CH14-64-ST1193 i.e the new global emergent MDR clone. To our knowledge, it is the first identification of this emergent clone in Spain. Moreover, a hybrid ExPEC/enteroaggregative isolate belonging to A-CH11-54-ST10 clone was identified.

Under one-health perspective and the worldwide antimicrobial resistance concern, we investigate extraintestinal pathogenic Escherichia coli (ExPEC), uropathogenic E. coli (UPEC), and multidrug resistant (MDR) E. coli from 197 isolates recovered from healthy dogs in Spain between 2013 and 2017. Ninety-one (46.2%) isolates were classified as ExPEC and/or UPEC including 50 clones, among which (i) four clones were dominant (B2-CH14-180-ST127, B2-CH52-14-ST141, B2-CH103-9-ST372 and F-CH4-58-ST64815) and (ii) 15 had been shown to be displayed by previously published isolates causing extraintestinal infections in humans. Twenty-eight (14.2%) isolates were classified as MDR, associated with B1, D and E phylogroups and included 24 clones, of which eight had also been identified among human isolates causing infections. We selected 23 ST372 strains, 21 healthy dogs faecal isolates and two human clinical isolates for whole genome sequencing and built a SNP-tree with these 23 genomes and 174 genomes (128 from canine strains and 46 from human strains) obtained from public databases. The analysis of these 197 genomes allowed to identify six clusters. Cluster 1 comprised 74.6% of the strain genomes that were mostly composed of canine strain genomes (P < 0.00001). Clusters 4 and 6 also included canine strain genomes, while clusters 2, 3 and 5 were significantly associated with human strain genomes. All these findings suggest that dogs are reservoirs of ExPEC, UPEC and MDR E. coli isolates with zoonotic potential.

Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide. In recent years, the field of radiological imaging has experienced an explosion that may reach unprecedented heights with the advent of artificial intelligence. One of the latest innovations in artificial intelligence is radiomics, which is based on the fact that a large amount of quantitative data can be extracted from radiological images, from which patterns can be identified and associated with specific pathologies. Since its inception, radiomics has been particularly associated with the field of oncology, with promising results in a wide range of clinical situations. The performance of radiomics in non-tumour pathologies has been increasingly explored in recent years and the results continue to be promising. The aim of this review is to explore the potential applications of radiomics in AIS patients and to theorise how radiomics may change the paradigm for these patients in the coming years.