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Nuclear fusion is entering the era of power plant-scale devices, which are now undergoing extensive studies to support the design phase. Plasma disruptions pose a high risk to these classes of devices because of the large stored thermal and magnetic energy which might jeopardize machine integrity and availability. Therefore, disruptions within these devices must be virtually eliminated, and any disruptions which do happen must be highly mitigated. However, the characterisation, prediction and technology used to mitigate disruptions is still an area of active development. In this paper, the authors investigate the disruptions within ST40, with particular attention at the identification of causes and effects associated with disruptions, both from a physics basis and an engineering standpoint. This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021-2022 experimental campaign. The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. The classification was validated by benchmarking the numerical reconstruction of the plasma dynamics with experimental data recovered from the plasma diagnostics. Subsequent Machine Learning analyses allowed the extrapolation of new disrupted plasma configurations for preliminary predictive simulations of the plasma column displacement. Thanks to the numerical simulations performed in MAXFEA environment, it is possible to investigate the plasma vertical displacement both during disrupted and regularly terminated plasma scenarios and to provide lessons to be learnt for the next ST40 experimental campaign and for the design of future ST devices., (© 2024. The Author(s).)

The aim of this study was to identify virulence and antimicrobial resistance profiles and determine the sequence type (ST) by multilocus sequence typing (MLST) of Salmonella enterica isolates from bovine carcasses from slaughterhouse located in Minas Gerais state, Brazil, and its relationship with bovine isolates obtained on the American continent based on sequence type profile. The MLST results were compared with all Salmonella STs associated with cattle on American continent, and a multi-locus sequence tree (MS tree) was built. Among the 17 S. enterica isolates, five ST profiles identified, and ST10 were the most frequent, grouping seven (41.2%) isolates. The isolates presented 11 different profiles of virulence genes, and six different antibiotics resistance profiles. The survey on Enterobase platform showed 333 Salmonella STs from American continent, grouped into four different clusters. Most of the isolates in the present study (13/17), were concentrated in a single cluster (L4) composed by 74 STs. As a conclusion, five different STs were identified, with ST10 being the most common. The isolates showed great diversity of virulence genes and antibiotics resistance profiles. Most of the isolates of this study were grouped into a single cluster composed by 74 STs formed by bovine isolates obtained on the American continent., (© 2024. The Author(s) under exclusive licence to Sociedade Brasileira de Microbiologia.)

Salmonella enterica serovar Derby ( S . Derby) ranks fifth among nontyphoidal Salmonella serovars causing human infections in the European Union. S . Derby isolates (36) collected between 2006 and 2018 in a Spanish region (Asturias) from human clinical samples (20) as well as from pig carcasses, pork- or pork and beef-derived products, or wild boar (16) were phenotypically characterized with regard to resistance, and 22 (12 derived from humans and 10 from food-related samples) were also subjected to whole genome sequence analysis. The sequenced isolates belonged to ST40, a common S . Derby sequence type, and were positive for SPI-23, a Salmonella pathogenicity island involved in adherence and invasion of the porcine jejune enterocytes. Isolates were either susceptible (30.6%), or resistant to one or more of the 19 antibiotics tested for (69.4%). Resistances to tetracycline [ tet (A), tet (B) and tet (C)], streptomycin ( aadA2 ), sulfonamides ( sul1 ), nalidixic acid [ gyrA (Asp87 to Asn)] and ampicillin ( bla TEM-1 -like) were detected, with frequencies ranging from 8.3% to 66.7%, and were higher in clinical than in food-borne isolates. The fosA7.3 gene was present in all sequenced isolates. The most common phenotype was that conferred by the tet (A), aadA2 and sul1 genes, located within identical or closely related variants of Salmonella Genomic Island 1 (SGI1), where mercury resistance genes were also present. Diverse IncI1-I(α) plasmids belonging to distinct STs provided antibiotic [ bla TEM-1 , tet (A) and/or tet (B)] and heavy metal resistance genes (copper and silver), while small pSC101-like plasmids carried tet (C). Regardless of their location, most resistance genes were associated with genetic elements involved in DNA mobility, including a class one integron, multiple insertion sequences and several intact or truncated transposons. By phylogenetic analysis, the isolates were distributed into two distinct clades, both including food-borne and clinical isolates. One of these clades included all SGI1-like positive isolates, which were found in both kinds of samples throughout the entire period of study. Although the frequency of S . Derby in Asturias was very low (0.5% and 3.1% of the total clinical and food isolates of S. enterica recovered along the period of study), it still represents a burden to human health linked to transmission across the food chain. The information generated in the present study can support further epidemiological surveillance aimed to control this zoonotic pathogen.

This study aimed to investigate the prevalence of fosfomycin fosA7 in Salmonella enterica isolates from food animals and retail meat products in China and the impact of fosA7 on bacterial fitness. A total of 360 Salmonella isolates collected from 11 provinces and cities in China were detected for fosA7 . All fosA7 -positive Salmonella isolates were determined minimum inhibitory concentrations (MICs) and sequenced by Illumina Hiseq. The fosA7 gene of S . Derby isolate HA2-WA5 was knocked out. The full length of fosA7 was cloned into vector pBR322 and then transformed into various hosts. MICs of fosfomycin, growth curves, stability, and fitness of fosA7 were evaluated. The fosA7 gene was identified in S . Derby (ST40, n = 30) and S . Reading (ST1628, n = 5). MICs to fosfomycin of 35 fosA7 -positive isolates were 1 to 32 mg/L. All fosA7 were located on chromosomes of Salmonella . The deletion of fosA7 in HA2-WA5 decreased fosfomycin MIC by 16-fold and slightly affected its fitness. The acquisition of plasmid-borne fosA7 enhanced MICs of fosfomycin in Salmonella (1,024-fold) and Escherichia coli (16-fold). The recombinant plasmid pBR322- fosA7 was stable in Salmonella Typhimurium, S . Pullorum, S . Derby, and E. coli , except for Salmonella Enteritidis, and barely affected on the growth of them but significantly increased biological fitness in Salmonella . The spread of specific Salmonella serovars such as S . Derby ST40 will facilitate the dissemination of fosA7 . fosA7 can confer high-level fosfomycin resistance and enhance bacterial fitness in Salmonella if transferred on plasmids; thus, it has the potential to be a reservoir of the mobilized fosfomycin resistance gene., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wang, Wang, Wang, Wu, Mei, Shen, Pan and Jiao.)