Whole genome sequencing of Shigella sonnei through PulseNet Latin America and Caribbean: advancing global surveillance of foodborne illnesses

Objectives Shigella sonnei is a globally important diarrhoeal pathogen tracked through the surveillance network PulseNet Latin America and Caribbean (PNLA&C), which participates in PulseNet International. PNLA&C laboratories use common molecular techniques to track pathogens causing foodborne illness. We aimed to demonstrate the possibility and advantages of transitioning to whole genome sequencing (WGS) for surveillance within existing networks across a continent where S. sonnei is endemic. Methods We applied WGS to representative archive isolates of S. sonnei (n = 323) from laboratories in nine PNLA&C countries to generate a regional phylogenomic reference for S. sonnei and put this in the global context. We used this reference to contextualise 16 S. sonnei from three Argentinian outbreaks, using locally generated sequence data. Assembled genome sequences were used to predict antimicrobial resistance (AMR) phenotypes and identify AMR determinants. Results S. sonnei isolates clustered in five Latin American sublineages in the global phylogeny, with many (46%, 149 of 323) belonging to previously undescribed sublineages. Predicted multidrug resistance was common (77%, 249 of 323), and clinically relevant differences in AMR were found among sublineages. The regional overview showed that Argentinian outbreak isolates belonged to distinct sublineages and had different epidemiologic origins. Conclusions Latin America contains novel genetic diversity of S. sonnei that is relevant on a global scale and commonly exhibits multidrug resistance. Retrospective passive surveillance with WGS has utility for informing treatment, identifying regionally epidemic sublineages and providing a framework for interpretation of prospective, locally sequenced outbreaks

Genetic analyses of diverse populations improves discovery for complex traits

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1–3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4–10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions13—the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. © 2019, The Author(s), under exclusive licence to Springer Nature Limited

Towards a self-consistent set of defect parameters for KCl

On a analysé des nouvelles données expérimentales sur la conductivité ionique du KCl pur et du KCl dopé par des ions Sr2+ ou SO2-4 en employant quatre modèles de défauts. Ils sont : (1) le modèle conventionnel des défauts de Schottky avec résidus d'impuretés M2+ dans les cristaux purs et dopés par SO2-4 ; (2) une modification du modèle (1) dans laquelle on admet que dans les cristaux purs il y a des impuretés qui sont aussi bien des anions bivalents que des cations bivalents ; (3) une modification du modèle (1) dans laquelle on admet que les enthalpies et les entropies des défauts peuvent dépendre de la température ; (4) un modèle qui admet que dans le KCl il y a des défauts de Frenkel dans les sous-réseaux aussi bien des cations que des anions outre les défauts de Schottky qui prédominent. On a tiré avantage des résultats des derniers calculs théoriques sur les énergies des défauts dans l'analyse avec l'ordinateur des données expérimentales. Seulement le modèle (4) conduit à une analyse qui s'accorde avec les données expérimentales sur la conductivité.New experimental data for the ionic conductivity of pure KCl and for KCl doped with Sr2+ or SO2-4 ions have been analysed on the basis of four defect models. These are : (1) the conventional Schottky defect model with residual M2+ impurity in the pure and SO2-4 doped crystals ; (2) a variant of model (1) in which pure crystals are supposed to contain both divalent-anion and divalent-cation impurities ; (3) a variant of model (1) in which the defect enthalpies and entropies are allowed to be temperature-dependent ; (4) a model which supposes that in KCl there are Frenkel defects on both the cation and anion sublattices in addition to the predominant Schottky disorder. Full advantage has been taken of the results of recent theoretical calculations of defect energies in the computer analysis of the experimental data. Only model (4) leads to a consistent analysis of the experimental conductivity data

Triangular Neutrosophic Cognitive Map for Multistage Sequential Decision-Making Problems

Nowadays, fuzzy cognitive maps (FCMs) are one of the most efficient artificial intelligence techniques for modeling large and complex systems. However, traditional FCMs have the limitation of not representing the indeterminacy situations presented in many decision-making problems. To overcome this limitation, neutrosophic cognitive maps (NCMs) were proposed as a new extension of traditional FCMs. Nevertheless, the way that NCMs reported in the bibliography handle the indeterminacy is still insufficient since they cannot quantify the degree of indeterminacy. Moreover, there are decision-making problems in which decisions should be considered as a sequence of decisions hardly interconnected in sequential order. This situation is presented in scenarios such as projects evaluation characterized by the existence of multiple interconnected processes (diagnosis, decision, and prediction). The lack of a suitable FCMs topology for modeling this kind of decision-making problems constitutes another challenging issue of FCMs. This paper presents a new neutrosophic cognitive map based on triangular neutrosophic numbers for multistage sequential decision-making problems (MS-TrNCM). In the proposed model, all the map’s connections are represented by triangular neutrosophic numbers, making it possible for decision makers to express their preferences considering the truth, indeterminacy, and falsity degrees. Furthermore, a new topology for representing multistage sequential processes is introduced. The suggested MS-TrNCM is applied to make diagnoses, decisions, and predictions during the evaluation of 1011 projects records from project evaluation database ”uci-gp-eval-201903051137” provided by the University of Informatics Sciences. In validation process, the superiority of the proposed MS-TrNCM over the NCMs and traditional FCMs has been demonstrated. © 2021, Taiwan Fuzzy Systems Association