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Comparison of Disc Diffusion and Sensititre® Broth Microdilution Methods for Antimicrobial Susceptibility Testing of Bacteroides fragilis Group clinical isolates following the French Guidelines

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Anaerobe 2025年 102969页

作者： Gauthier Delvallez Laure Diancourt Julie Germond Pascal Campagne Bruno Dupuy National Reference Center for Anaerobic bacteria and Botulism Institut Pasteur Université Paris Cité Paris France Laboratory Pathogenesis of Bacterial Anaerobes Institut Pasteur Université Paris Cité Paris France. Electronic address: delvallez.gauthier@***. National Reference Center for Anaerobic bacteria and Botulism Institut Pasteur Université Paris Cité Paris France. Electronic address: laure.diancourt@pasteur.fr. National Reference Center for Anaerobic bacteria and Botulism Institut Pasteur Université Paris Cité Paris France. Electronic address: julie.germond@pasteur.fr. Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France. Electronic address: pascal.campagne@pasteur.fr. Laboratory Pathogenesis of Bacterial Anaerobes Institut Pasteur Université Paris Cité Paris France. Electronic address: bruno.dupuy@pasteur.fr.

OBJECTIVES:This study aims to evaluate the concordance between the disc diffusion test (DDT) and custom-designed Sensititre® Broth Microdilution plates and to estimate their respective sensitivity (Se) and specificity (Sp) for antimicrobial susceptibility testing (AST) of Bacteroides fragilis group (BFG) clinical isolates. METHODS:AST was performed on 126 BFG isolates using both DDT and Sensititre® methods according to the 2024 CA-SFM guidelines. Cohen's kappa coefficients were used to assess concordance, while a Bayesian approach estimated Se and Sp with 95% confidence intervals (95% CIs) for amoxicillin-clavulanate (AMC), piperacillin-tazobactam (PTZ), imipenem (IPM), clindamycin (CLI), metronidazole (MTR), and moxifloxacin (MXF). RESULTS:Cohen's kappa coefficients with 95% CIs for AMC, PTZ, IPM, CLI, MTR, and MXF were 0.92 (0.85-1.00), 0.70 (0.56-0.83), 0.96 (0.92-1.00), 0.98 (0.94-1.00), 0.96 (0.89-1.00), and 0.70 (0.58-0.82), respectively. DDT demonstrated Se > 0.8 for all antibiotics, and Sp > 0.8 except for PTZ (0.51; 0.37-0.65) and CLI (0.71; 0.58-0.82). Sensititre® exhibited Se > 0.9 for all antibiotics except for PTZ (0.86; 0.70-0.99), and Sp > 0.9 except for MXF (0.88; 0.75-0.99). CONCLUSIONS:DDT and Sensititre® Broth Microdilution demonstrated strong concordance for most antibiotics. Predictive values estimated using Bayesian model showed that Sensititre® offered the highest overall Se and Sp.

关键词： Antimicrobial Susceptibility Testing Bacteroides fragilis group Bayesian analysis CA-SFM Sensititre® disc diffusion test

Using the Empirical Attainment Function for Analyzing Single-Objective Black-Box Optimization Algorithms

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IEEE Transactions on Evolutionary Computation 2024年

作者： Lopez-Ibanez, Manuel Vermetten, Diederick Dreo, Johann Doerr, Carola University of Manchester Alliance Manchester Business School ManchesterM13 9PL United Kingdom Leiden Institute for Advanced Computer Science Leiden Netherlands Systems Biology Group Pasteur Institute Université Paris Cité Bioinformatics and Biostatistics Hub Department of Computational Biology Paris France Cnrs LIP6 Sorbonne Université Paris France

A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime. In this work, we consider an alternative approach, based on the empirical attainment function (EAF) and we show that the target-based ECDF is an approximation of the EAF. We argue that the EAF has several advantages over the target-based ECDF. In particular, it does not require defining a priori quality targets per function, captures performance differences more precisely, and enables the use of additional summary statistics that enrich the analysis. We also show that the average area over the convergence curves is a simpler-to-calculate, but equivalent, measure of anytime performance. To facilitate the accessibility of the EAF, we integrate a module to compute it into the IOHanalyzer platform. Finally, we illustrate the use of the EAF via synthetic examples and via the data available for the BBOB suite. © 1997-2012 IEEE.

关键词： Iterative methods

Supervised multiple kernel learning approaches for multi-omics data integration

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arXiv 2024年

作者： Briscik, Mitja Tazza, Gabriele Vidács, László Dillies, Marie-Agnès Déjean, Sébastien Institut de Mathématiques de Toulouse UMR5219 CNRS UPS Université de Toulouse Cedex 9 Toulouse31062 France Department of Computer Science Applied Artificial Intelligence Group University of Szeged Szeged6720 Hungary Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub ParisF‑75015 France

Background: Advances in high‑throughput technologies have originated an ever‑ increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learn‑ ing (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi‑omics inputs, despite being an underused tool in genomic data mining. Results: We provide novel MKL approaches based on different kernel fusion strategies To learn from the meta‑kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL‑ based models can outperform more complex, state‑of‑the‑art, supervised multi‑omics integrative approaches. Conclusion: Multiple kernel learning offers a natural framework for predictive models in multi‑omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio‑ data mining research, biomarker discovery and further development of methods for heterogeneous data integration. © 2024, CC BY.

关键词： Self-supervised learning

MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with Known Pareto Front

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arXiv 2023年

作者： Quemy, Alexandre Schoenauer, Marc Dreo, Johann YData Lab Inc. Seattle United States Poznan University of Technology Poznan Poland Inria Saclay Orsay France Universite Paris-Saclay Orsay France Computational Systems Biomedicine laboratory Department of Computational Biology Institut Pasteur Paris France Bioinformatics and Biostatistics hub Université Paris Cité Institut Pasteur Paris France

Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In this work, we propose a tunable benchmark generator, together with a dedicated solver that provably computes the true Pareto front of the resulting instances. First, we prove a proposition allowing us to characterize the optimal plans for a constrained version of the problem, and then show how to reduce the general problem to the constrained one. Second, we provide a constructive way to find all the Pareto-optimal plans and discuss the complexity of the algorithm. We provide an implementation that allows the solver to handle realistic instances in a reasonable time. Finally, as a practical demonstration, we used this solver to find all Pareto-optimal plans between the two largest airports in the world, considering the routes between the 50 largest airports, spherical distances between airports and a made-up risk. © 2023, CC BY-SA.

关键词： Computational complexity

Reproducible Mapping of Tabular Data into Semantic Knowledge Graphs with OntoWeaver and BioCypher

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Reproducible Mapping of Tabular Data into Semantic Knowledge...

International Conference on Information Fusion

作者： Johann Dreo Claire Laudy Sebastian Lobentanzer Marko Baric Ekaterina Gaydukova Benno Schwikowski Computational Biology Dept. Bioinformatics and Biostatistics Hub. Computational Systems Biomedicine Institut Pasteur Université Paris Cité Paris France Thales Palaiseau France Institute for Computational Biomedicine Heidelberg University Faculty of Medicine and Heidelberg University Hospital Heidelberg Germany Computational Biology Dept. Computational Systems Biomedicine Institut Pasteur Université Paris Cité Paris France

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Large-scale high-level information fusion and data integration is a pressing need in several scientific domains. Recently, the biomedical community established BioCypher, a tool to help create large Semantic Knowledge Graphs (SKGs) in a simple and reproducible way. In this article, we introduce OntoWeaver, a companion tool to BioCypher that allows to easily extract tabular data into SKGs by using a simple declarative mapping. OntoWeaver allows implementing reproducible mappings from tabular data to SKGs with a simple declarative configuration. The use of OntoWeaver and BioCypher is demonstrated in two different use cases: cancer database integration and invasive species monitoring. We believe that OntoWeaver and BioCypher, both free and open-source software, can help several scientific communities working on SKGs and high-level information fusion problems.

关键词： Invasive species Databases Semantics Knowledge graphs Pressing Ontologies Data mining Software tools Open source software Monitoring

Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms

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arXiv 2024年

作者： López-Ibáñez, Manuel Vermetten, Diederick Dreo, Johann Doerr, Carola Alliance Manchester Business School University of Manchester ManchesterM13 9PL United Kingdom Leiden Institute for Advanced Computer Science Leiden Netherlands Pasteur Institute Université Paris Cité Bioinformatics and Biostatistics Hub Department of Computational Biology Systems Biology Group Paris France Sorbonne Université CNRS LIP6 Paris France

关键词： Optimization algorithms

Genetic identification and reiterated captures suggest that the Astyanax mexicanus El Pachón cavefish population is closed and declining

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Zoological Research 2023年第4期44卷 701-711页

作者： Laurent Legendre Julie Rode Isabelle Germon Marie Pavie Carla Quiviger Maxime Policarpo Julien Leclercq Stéphane Père Julien Fumey Carole Hyacinthe Patricia Ornelas-García Luis Espinasa Sylvie Rétaux Didier Casane UniversitéParis-Saclay CNRSIRDUMRÉvolutionGénomesComportement etÉcologieGif-sur-Yvette 91190France Objectif Sciences International NGO OSI-Panthera ProgramGeneva 99140Switzerland Institut de Neuroscience Paris-Saclay UniversitéParis-Saclay and CNRSSaclay 91400France Institut Pasteur UniversitéParis CitéCNRS UMR 2000Microbial Paleogenomics UnitParis 75015France Institut Pasteur UniversitéParis CitéBioinformatics and Biostatistics HubParis 75015France Department of Genetics Harvard Medical SchoolBlavatnik InstituteBoston MA 02115USA Colección Nacional de Peces Departamento de ZoologíaInstituto de BiologíaUniversidad Nacional Autónoma de MéxicoMéxico DF CP 04510México School of Science Marist CollegePoughkeepsieNew York 12601USA UniversitéParis Cité UFR Sciences du VivantParis 75013France Zoological Institute Department of Environmental SciencesUniversity of BaselBaselSwitzerland

The sizes of Astyanax mexicanus blind cavefish populations of North-East Mexico are demographic parameters of great importance for investigating a variety of ecological,evolutionary,and conservation ***,few estimates have been *** these mobile animals living in an environment difficult to explore as a whole,methods based on capture-mark-recapture are appropriate,but their feasibility and interpretation of results depend on several assumptions that must be carefully ***,we provide evidence that minimally invasive genetic identification from captures at different time intervals(three days and three years)can give insights into cavefish population size dynamics as well as other important demographic parameters of *** also provide tools to calibrate sampling and genotyping efforts necessary to reach a given level of *** results suggest that the El Pachón cave population is currently very small,of an order of magnitude of a few hundreds of individuals,and is distributed in a relatively isolated *** probable decline in population size in the El Pachón cave since the last census in 1971 raises serious conservation issues.

关键词： Cavefish Population size Conservation Swabbing Genetic identification

Automated Algorithm Selection for Radar Network Configuration

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arXiv 2022年

作者： Renau, Quentin Dreo, Johann Peres, Alain Semet, Yann Doerr, Carola Doerr, Benjamin Thales Research & Technology École Polytechnique Institut Polytechnique de Paris Palaiseau France CSB Lab. Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub Department of Computational Biology Computational Systems Biomedicine Laboratory ParisF-75015 France Thales Land & Air Systems Rungis France Thales Research & Technology Paris France Sorbonne Université CNRS LIP6 Paris France CNRS École Polytechnique Institut Polytechnique de Paris Palaiseau France

The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed. Classic optimization algorithms can therefore not be applied to this problem, and we rely on "trial-and-error" black-box approaches. In this paper, we study the performances of 13 black-box optimization algorithms on 153 radar network configuration problem instances. The algorithms perform considerably better than human experts. Their ranking, however, depends on the budget of configurations that can be evaluated and on the elevation profile of the location. We therefore also investigate automated algorithm selection approaches. Our results demonstrate that a pipeline that extracts instance features from the elevation of the terrain performs on par with the classical, far more expensive approach that extracts features from the objective function. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

covSTATIS: a multi-table technique for network neuroscience

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arXiv 2024年

作者： Baracchini, Giulia Yu, Ju-Chi Rieck, Jenny Beaton, Derek Guillemot, Vincent Grady, Cheryl Abdi, Herve Spreng, R. Nathan Montreal Neurological Institute Department of Neurology and Neurosurgery McGill University Montreal Canada Campbell Family Mental Health Institute Centre for Addiction and Mental Health Toronto Canada Rotman Research Institute at Baycrest Toronto Canada Data Science & Advanced Analytics Unity Health Toronto Toronto Canada Institut Pasteur Universite Paris Cite Bioinformatics and Biostatistics Hub Paris France Departments of Psychiatry and Psychology University of Toronto Toronto Canada School of Behavioral and Brain Sciences The University of Texas at Dallas Richardson United States

Similarity analyses between multiple correlation or covariance tables constitute the cornerstone of network neuroscience. Here, we introduce covSTATIS, a versatile, linear, unsupervised multi-table method designed to identify structured patterns in multi-table data, and allow for the simultaneous extraction and interpretation of both individual and group-level features. With covSTATIS, multiple similarity tables can now be easily integrated, without requiring a priori data simplification, complex black-box implementations, user-dependent specifications, or supervised frameworks. Applications of covSTATIS, a tutorial with Open Data and source code are provided. CovSTATIS offers a promising avenue for advancing the theoretical and analytic landscape of network neuroscience. © 2024, CC BY-NC-ND.

关键词： Open Data