5 research outputs found
ELFI
Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as priors, simulators, summaries or distances, to a network called ELFI graph. The components can be implemented in a wide variety of languages. The stand-alone ELFI graph can be used with any of the available inference methods without modifications. A central method implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference (BOLFI), which has recently been shown to accelerate likelihood-free inference up to several orders of magnitude by surrogate-modelling the distance. ELFI also has an inbuilt support for output data storing for reuse and analysis, and supports parallelization of computation from multiple cores up to a cluster environment. ELFI is designed to be extensible and provides interfaces for widening its functionality. This makes the adding of new inference methods to ELFI straightforward andautomatically compatible with the inbuilt features.Peer reviewe
ELFI: Engine for likelihood-free inference
Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as priors, simulators, summaries or distances, to a network called ELFI graph. The components can be implemented in a wide variety of languages. The stand-alone ELFI graph can be used with any of the available inference methods without modifications. A central method implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference (BOLFI), which has recently been shown to accelerate likelihood-free inference up to several orders of magnitude by surrogate-modelling the distance. ELFI also has an inbuilt support for output data storing for reuse and analysis, and supports parallelization of computation from multiple cores up to a cluster environment. ELFI is designed to be extensible and provides interfaces for widening its functionality. This makes the adding of new inference methods to ELFI straightforward andautomatically compatible with the inbuilt features.Peer reviewe
Emergence and dissemination of antimicrobial resistance in Escherichia coli causing bloodstream infections in Norway in 2002-17: a nationwide, longitudinal, microbial population genomic study.
BACKGROUND: The clonal diversity underpinning trends in multidrug resistant Escherichia coli causing bloodstream infections remains uncertain. We aimed to determine the contribution of individual clones to resistance over time, using large-scale genomics-based molecular epidemiology. METHODS: This was a longitudinal, E coli population, genomic, cohort study that sampled isolates from 22 512 E coli bloodstream infections included in the Norwegian surveillance programme on resistant microbes (NORM) from 2002 to 2017. 15 of 22 laboratories were able to share their isolates, and the first 22·5% of isolates from each year were requested. We used whole genome sequencing to infer the population structure (PopPUNK), and we investigated the clade composition of the dominant multidrug resistant clonal complex (CC)131 using genetic markers previously reported for sequence type (ST)131, effective population size (BEAST), and presence of determinants of antimicrobial resistance (ARIBA, PointFinder, and ResFinder databases) over time. We compared these features between the 2002-10 and 2011-17 time periods. We also compared our results with those of a longitudinal study from the UK done between 2001 and 2011. FINDINGS: Of the 3500 isolates requested from the participating laboratories, 3397 (97·1%) were received, of which 3254 (95·8%) were successfully sequenced and included in the analysis. A significant increase in the number of multidrug resistant CC131 isolates from 71 (5·6%) of 1277 in 2002-10 to 207 (10·5%) of 1977 in 2011-17 (p<0·0001), was the largest clonal expansion. CC131 was the most common clone in extended-spectrum β-lactamase (ESBL)-positive isolates (75 [58·6%] of 128) and fluoroquinolone non-susceptible isolates (148 [39·2%] of 378). Within CC131, clade A increased in prevalence from 2002, whereas the global multidrug resistant clade C2 was not observed until 2007. Multiple de-novo acquisitions of both blaCTX-M ESBL-encoding genes in clades A and C1 and gain of phenotypic fluoroquinolone non-susceptibility across the clade A phylogeny were observed. We estimated that exponential increases in the effective population sizes of clades A, C1, and C2 occurred in the mid-2000s, and in clade B a decade earlier. The rate of increase in the estimated effective population size of clade A (Ne=3147) was nearly ten-times that of C2 (Ne=345), with clade A over-represented in Norwegian CC131 isolates (75 [27·0%] of 278) compared with the UK study (8 [5·4%] of 147 isolates). INTERPRETATION: The early and sustained establishment of predominantly antimicrobial susceptible CC131 clade A isolates, relative to multidrug resistant clade C2 isolates, suggests that resistance is not necessary for clonal success. However, even in the low antibiotic use setting of Norway, resistance to important antimicrobial classes has rapidly been selected for in CC131 clade A isolates. This study shows the importance of genomic surveillance in uncovering the complex ecology underlying multidrug resistance dissemination and competition, which have implications for the design of strategies and interventions to control the spread of high-risk multidrug resistant clones. FUNDING: Trond Mohn Foundation, European Research Council, Marie Skłodowska-Curie Actions, and the Wellcome Trust
DataLad: distributed system for joint management of code, data, and their relationship
DataLad is a Python-based tool for the joint management of code, data, and their relationship,built on top of a versatile system for data logistics (git-annex) and the most popular distributedversion control system (Git). It adapts principles of open-source software development anddistribution to address the technical challenges of data management, data sharing, and digitalprovenance collection across the life cycle of digital objects. DataLad aims to make datamanagement as easy as managing code. It streamlines procedures to consume, publish, andupdate data, for data of any size or type, and to link them as precisely versioned, lightweightdependencies. DataLad helps to make science more reproducible and FAIR (Wilkinson et al.,2016). It can capture complete and actionable process provenance of data transformations toenable automatic re-computation. The DataLad project (datalad.org) delivers a completelyopen, pioneering platform for flexible decentralized research data management (RDM) (Hanke,Pestilli, et al., 2021). It features a Python and a command-line interface, an extensiblearchitecture, and does not depend on any centralized services but facilitates interoperabilitywith a plurality of existing tools and services. In order to maximize its utility and target audience, DataLad is available for all major operating systems, and can be integrated intoestablished workflows and environments with minimal friction