dataesr/scientific-paragraphs-categorization huggingface dataset:
Data records All high-quality WGS data from this data descriptor are available for download at SRA/ENA public repository, including the sequences already available at the beginning of this study  43. Assembly and annotation files are available through Figshare  44. Complete metadata and quality check parameters are here reported in Figshare File 1  26. technical Validation redundant strains. The LISTADAPT dataset was analyzed by core-genome MLST (cgMLST) analysis, using BioNumerics (Table  5 ) according to a fixed cgMLST scheme consisting of 1748 Moura et al. loci  45. All strains with genomes presenting less than < 7 allele differences (AD), isolated in the same year, as well as sharing the same source of isolation and sharing identical geographic location (same region or country) were considered as redundant.
When the latter information was not available, the provider was used instead. Although year of isolation was unknown for four strains, they were marked as redundant because of similar cgMLST (<7 AD). Among the 1484 strains, 157 were identified as redundant. These strains were maintained in the dataset and marked accordingly (Figshare File 1  26  ) Consistency analysis. The present study includes 648 strains from existing collections and 108 strains isolated in the framework of this study. The strains from historical collections were provided from 19 different laboratories. The management of large strain collections may lead to storage issue such as the isolation of two strains in the same tube. Furthermore, the sequencing of the strains involved several handling that may lead to human error.
acknowledgements This work was supported by the One Health European Joint Programme, European Union's Horizon 2020 research and innovation programme (Grant Agreement No 773830). The authors would like to acknowledge Karol Romero and Karine Capitaine from ANSES, Laboratory for Food Safety, SEL Unit, for their technical help in the strain culture management. The authors also thank Thomas Berger, Jan-Erik Ingenhoff and René Imhof from Agroscope for collecting strains from soil and wild animals in Switzerland and for providing the associated genomes.
author contributions All authors read and approved the final manuscript. Benjamin Félix contributed substantially to the study design, soil sampling, acquisition of strains and the corresponding genomes, data analysis as well as writing and editing of the manuscript. Yann Sévellec contributed extensively to the acquisition of strains and the corresponding genomes, data analysis, quality control of the WGS dataset as well as writing and editing the manuscript. Federica Palma contributed to the acquisition of WGS data, data analysis and quality control of the WGS dataset and to writing and revision of the manuscript. Arnaud Felten, Nicolas Radomski and Ludovic Mallet performed the assembly centralized in a meaningful database. Pierre Emmanuel Douarre contributed substantially to the technical validation of the dataset.
Pascal Piveteau participated in the study design and collected strains from natural environment in France. Pascal Piveteau and Eliette Ascensio performed the detection of Lm for part of the soil samples collected by the partners. Christophe Soumet, Arnaud Bridier and Michel Hébraud contributed to the study design and the manuscript revision. Marina Torresi and Francesco Pomilio collected strains from natural environment and provided strains and genomes from animals and food in Italy. Cesare Cammà and Adriano di Pasquale were responsible for sequencing part of the strains. Taran Skjerdal collected strains from natural environment and provided strains from wild animals and food in Norway. Ariane Pietzka and Werner Ruppitsch performed soil sampling and provided WGS data of strains isolated from food in Austria.
Renáta Karpíšková collected and provided strains from soil, ruminants and food isolated in the Czech Republic. Tereza Gelbíčová contributed to the selection and characterization of strains, DNA extraction in the Czech Republic and partly to data sequencing. Monica Ricao collected strains from soil and provided strains from wild animals and food isolated in Sweden. Bojan Papić collected strains from soil, provided strains from ruminants and natural environment in Slovenia and partly contributed to data sequencing. Bart Wullings provided strains and genomes from ruminants previously isolated from the Netherlands. Hana Bulawova provided strains from wild animals and ruminants isolated in Czech Republic. Hanna Castro, Miia Lindström and Hannu Korkeala collected and provided strains from wild and farm animals from Finland.
Žanete Šteingolde and Toomas Kramarenko provided strains from farm animals isolated from Latvia and Estonia. Lenka Cabanova provided strains from ruminants and wild animals isolated from Slovakia. Barbara Szymczak provided strains from soil isolated from Poland. Verena Oswaldi provided strains from farm animals isolated from Germany. Manfred Gareis provided strains from wild animals isolated from Germany. Anne-Mette Seyfarth provided genomes from food strains isolated from Denmark. Ana Hurtado provided strains from farm animals isolated from Spain. Thomas Berger and the team from Agroscope collected strains from soil and wild animals, and provided the genomes of these strains isolated from Switzerland. Jean-Charles Leblanc contributed to the revision of the manuscript and the technical validation of the dataset.
It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features.
, 2020; D'Amour et al. , 2020). In the following, we describe the functionalities of Anatomist, the pillar of our software world dedicated to the visualization tasks specific to the structural approach to brain mapping. While most kinds of neuroimaging visualization software exploit the possibility to bring all the subjects to the same space, Anatomist lets each individual data live in its own native space, preserving it from warping operations modifying its shape. Inter-subject correspondences can be achieved via coordinate transformations, if needed, but mainly by structure correspondence. In our opinion, this strategy is mandatory for the questions related to structural variability, which cover topological variations in the shape of a specific entity or in the relative topography of a set of entities.
A Few Words About Anatomist History and the Motivations to Develop It The ability to display, edit, and annotate structured data is one of Anatomist's most original features, and constitutes what we call a "structural viewer. " These unique features have been developed specifically for the needs of our lab to work on structural anatomy. The original need was to build supervised learning databases for our cortical sulci identification algorithms (Morphologist, 1 Riviere et al. , 2002). Each cortical sulcus corresponds to a group of cortical elementary folds and is difficult to draw in a 3D T1-weighted MRI volume. Therefore, a dedicated preprocessing is used first to extract the folds one by one. Each such fold is represented by a local negative cast of the cortex, usually corresponding to the cerebrospinal fluid, which fills up the fold  (Mangin et al.
Hence, all the folds belonging to a given sulcus are annotated with the same label. In order to train an automatic machine learning system, we had to annotate the folds of a set of subjects using a sulcus-based nomenclature. Hence, we had to browse each graph in 3D in order to annotate the folds (the graph nodes) according to their localization, shape, and topography. This was one of Anatomist's first use cases. The goal was to display the folds as colored meshes in 3D, load and display nomenclatures, and select and edit graph nodes (folds in this case) to perform the annotation: assign names 1 https://brainvisa. info from a nomenclature to graph nodes and of course, save the modified graphs. To build a consistent database, we also needed to display and annotate sulci from several subjects side to side, which led to the inter-subject atlas navigation features.
STRUCTURAL ATLAS BROWSING FEATURES Brief Presentation of Anatomist Features As a general usage viewer, Anatomist is not directly set up for every use case, but it is developed both as a user-oriented application and as a software library, so it can be used to easily build case-specific custom applications, and extension modules can be added. Anatomist differs from other visualization software commonly used in the field (some are part of popular software packages, such as Freesurfer, FSL, or SPM) in several ways. First, it does not impose a hard-coded views layout (typically 3 or 4 views for the axial, sagittal, and coronal views) but allows the user to open as many customizable views as needed. Second, it is designed to work with "structural objects, " which may have a complex graph structure, and allows the user to edit and annotate such objects.
Third, its coordinate transformation system does not impose a standard common coordinate system but allows free transformations between objects. Such flexibility makes it appear a bit more complex to handle at first than more classical software, but concepts are quite simple, and it is rapidly taken in hand. Moreover, dedicated applications can easily be developed and hide some of the complexity: as an example, a classical simplified 4-views application variant is proposed with Anatomist (under the name "AnaSimpleViewer"). Anatomist is free and open source, and its source code can be found on GitHub repositories 2 , 3  (separated for licensing reasons). It relies on several other open-source libraries; some also developed in our lab and available in GitHub repositories (the AIMS image manipulation library, for instance) and other general libraries (Qt for graphical interfaces, OpenGL for 3D rendering, PyQt for python bindings).
Binary distributions can be downloaded on: https://brainvisa. info. In addition to loaded objects, it is possible to create new objects, which correspond to interactions between several existing ones. For instance, to display a 3D or 4D functional activation volume onto a gray/white matters interface mesh, it is possible to load both the mesh and the functional volume, and then create a mixture object from them. This mechanism is called "fusion" in Anatomist and offers a large panel of display possibilities; about 25 types of "fusion objects" are handled. Anatomist features are not limited to visualization; several edition modules allow the user to interact with the displayed data, like regions of interest drawing, painting on a mesh surface, or structural data edition, which will be discussed later in this paper.
Multimodal Data Visualization Anatomist views may display any data and combine them. Complex scenes can be built to display multimodal data, acquired from different devices, each endowed with its coordinate system. Figure  3  illustrates such a use case on a single subject, a multimodal situation where several kinds of processing software have been used; T1 MRI data have been processed using FreeSurfer 4  for segmentation and brain meshing; Morphologist (see text footnote 1) has been used to extract and identify cortical sulci, and MRTrix 5  for diffusion MRI processing and tractography. Processed data have been combined in different ways (here, a skull-stripped brain MRI, a segmentation volume, gray/white interface surface meshes, a Desikan parcellation texture, cortical sulci, axonal fibers tracts).
This tool was needed to overcome segmentation imperfections, or anatomical specificities. It is generally used when two sulci are erroneously merged into a single fold node by the Morphologist segmentation pipeline. After the splitting operation, the graph structure is updated; new nodes and edges are added to the graph. Figure  4  illustrates such a use case. The structural edition tools have allowed our team to build and maintain structural cortical sulci atlases, which are used to train supervised Machine Learning algorithms. The resulting sulcus recognition tools are integrated in Morphologist and have been applied to more than 50,000 brains. This application of Anatomist is still useful, as training databases continue to evolve and grow, in order to feed several generations of pattern recognition algorithms.
This structural edition tool is also useful outside of our team and the training database application; for studies where identification is crucial, users familiar with sulcal anatomy are using Anatomist to check and correct the automatic labeling before proceeding further with downstream operations. Ease of use and clarity of the interface is essential for this kind of application. Such an application can be, for instance, a morphological study, which statistically compares measurements on identified sulci between two population groups (healthy/pathological, or right handers/left handers, etc. ). To do so, the accuracy of sulci identification is important. For large studies (about 50 subjects and more), manual corrections are generally not needed to reach statistical robustness, but for smaller studies, performing manual corrections on sulci labelings helps obtain cleaner, sharper, and more significant statistics.
When sulcal graphs are obtained, they can be browsed, visualized, and edited manually using Anatomist, which can be triggered from the BrainVisa/Morphologist application using a single button click. It shall be noted too that if Morphologist is the only tool designed to build sulcal graphs from segmented brain images, it is not the only brain segmentation tool. Several kinds of software may be used for segmentation and preprocessing, and then segmented images may be imported in Morphologist to build sulcal graphs. This importation may require some adaptations in some cases, and links have been written with the Freesurfer software (see text footnote 4), for instance. A sulcal morphometry tool allows the user to get measurements on identified sulci, since sulcal graphs embed a series of preprocessed measurements, such as sulci sizes, lengths, depths, average cortical thickness, sulcal opening, etc.
On standard computers, it is possible to load, for instance, at least a hundred cortical sulci graphs. But display screens are actually a limit, because showing hundreds of brains on the same screen will display each one too small, and we may also reach 3D hardware memory and computing limits. Loading time for the data may also be a burden. In practice, display will significantly slow down when more than 20-30 brains are displayed on a standard computer (depending on the machine and GPU capabilities). To navigate structural atlases, it is thus alternatively possible to display brains by a subset, in a kind of a paginated view. Dedicated tools may be easily developed from Anatomist, which is usable as a programming and scripting library to build custom applications. The Morphologist toolbox of BrainVisa software suite provides this kind of display applications, and Figure  5  illustrates such a use case for cortical sulci comparison.
Screen Walls For large structural atlases, it is also possible to make use of screen walls, which offer a very large display space where hundreds of brains can be displayed at the same time. Using a screen wall generally requires adapted tools to efficiently operate computer and 3D hardware. Some derivations of Anatomist scripts have been written for a wall of screens application. The Maison de la Simulation (MDLS, Paris Saclay, France) has developed a tiling web application "TiledViz"  (Mancip et al. , 2018) , which allows us to display on its wall of screens a rather large number of views, each in a display "tile. " Tiles are rendered on the large screen wall (or alternately on smaller ones), and each one is a containerized application FIGURE 5 | Structural atlas browsing, displaying the same entities on many subjects; here, 11 left hemispheres are displayed, each with 3 sulci: central sulcus (selected, in red), superior frontal (green), and inferior frontal.
, 2020). Our databases sizes were 60-80 subjects, each having two separate views for left and right hemispheres. All the subjects could be seen simultaneously by a group of experts, which was very important to achieve a consistent consensus across the human expert annotations of sulci (see Figure  7 ). Several experts could discuss while looking at the entire dataset, which prevented drifts occurring with the paginated strategy. The wall-based approach overcame the weaknesses of the earlier training atlas  (Perrot et al. , 2011) , which contained contradictory identifications that could not be detected when all the brains could not be displayed side to side. Indeed, the sulcal patterns are very complex, and require a good expertise to be identified; and even experts do hesitate or propose conflicting opinions.
Library and Languages Anatomist has been developed as a C++ library, with a user application. On top of this C++ library, Python language bindings have been added, and extensions are developed in either language. Of course, Python, as an interpreted language, is more flexible, does not require to deal with compiler and system compatibility issues, and allows simpler and faster development. To summarize these technical aspects, the C++ core ensures an efficient and fast rendering engine, based on OpenGL with hardware 3D acceleration, and Python allows very easy handling and extensibility of the library.
Cross-Platform Source Code and Container Distribution Anatomist and its underlying libraries have been developed as cross-platform code; the source code can compile and run on Linux, Windows, or MacOS systems. However, since version 5. 0, the binary distributions are Linux-based containers (Singularity images, or virtual machines built for VirtualBox). (Labra et al. , 2019). The other views show instances of the sulcus and bundle structural atlases in 3 individual subjects, each in its individual native space. Sulcal and bundles data are displayed with nomenclatures, which are used to identify the same structures across subjects, and assign colors to them. Hemisphere brain meshes are displayed in a wireframe mode, here, to allow a better visualization of the fiber trajectories, which circumvent the folds.
Nomenclatures are simpler objects; they are hierarchical tree structures where each element represents a brain structure, with a name, and, possibly, a color to be assigned to objects with this name. Nomenclatures also have file formats and manipulation libraries. A simple interaction between graphs and nomenclatures allows us to display colored objects sets and to navigate between them across subjects in Anatomist. For the programmer, a C++ library is available, and Python bindings allow us to manipulate graphs and nomenclatures just like python dictionaries objects, with additional convenience functions to ease storing graphical objects in graphs, insert or delete nodes or edges, etc. Once saved, a new kind of graph will be used in Anatomist the same way sulci graphs are used.
However, they can be handled in a custom application; Anatomist event system can react to mouse clicks or other user interactions. The custom application can non-linearly transform the click position coordinates, and then adapt other displays to visualize the corresponding location in other atlases and subjects, possibly aligning them in the "closest" affine orientation, without non-linearly resampling all objects. This preserves data specificity (no warping) but keeps, however, a precise local correspondence, without the cost of data resampling. This use case has been implemented in a little side application named "ana_atlas_nonlin. py" (Figure  9 ) and shipped with the version 5. 0. 1 (and later) of BrainVisa/Anatomist. The script can load non-linear transformation graphs and use them to work as the non-linear point-to-point correspondence.
It is also able to select regions (objects) by name, select the corresponding structure in every view, and focus the view on it, should it be in an individual subject or in an atlas. It, therefore, works as a hybrid iconic/structural viewer. The user may use it with the deformation fields between the MNI ICBM152 2009c template atlas, the MNI Colin27, the BigBrain atlas, and an infant template atlas. Those may be found on the Human Brain Project (HBP) knowledge graph at the following URL: https://search. kg. ebrains. eu/instances/Dataset/ 7a9aa738-a5b2-4601-818e-05db2627ba5c. The actual atlases can be downloaded by following the links on the HBP URL above. The deformation fields between the atlases have been obtained using the DISCO/DARTEL registration methods, with sulcal alignment constraints  (Lebenberg et al.
Comparisons of Anatomist With Existing Software and Libraries Anatomist is not the only visualization software available in the field of neuroimaging. Several software packages, such as SPM, FSL, or Freesurfer, provide their user-oriented viewer application. Most of them are good display solutions for classical, iconic visualization, but in our opinion, none of them offers the full features required for structural atlas browsing and edition. Namely, apart for displaying 3D or 4D volumes, and surface meshes, the features we expect are (1) the ability to display as many subjects as the user wants (or the display screen limitations impose), (2) structure selection and a link between subjects from a nomenclature, (3) using complex transformations graphs between several coordinate systems, (4) structural edition, (5) scripting and programming possibilities to design custom focused applications.
SPM 12  is one of the most popular brain image analysis software (and one of the first historically available), developed in Matlab language. Its visualization module is limited to classical 3 orthogonal views, 2D rendering of volume, and it assumes all coordinates' transformations going to the same central space. FSL 13 is also a widely spread analysis software and features FSLeyes, which is a 3D viewer enabling the display of 3D/4D images and meshes, and has some flexibility on views and layout, but is more oriented to pre-selectable layouts rather than to FIGURE 8 | Inter-species sulci comparison structural atlas; the first two lines of the figure display sulci of 6 different species (from left to right, and then, top to bottom: a macaque, a pongo, a chimpanzee, a baboon, a gorilla, a human).
Sulci have been manually identified on these brains, using a common sulci nomenclature, in order to get structural correspondence between subjects/species. The last line extends the comparative study to extinct species using skull endocasts, using another Anatomist feature, allowing to draw sulcal lines  (Le Troter et al. , 2012)  [from left to right: Sinanthrope XII (an Homo erectus), a recent Homo sapiens, La Ferrassie 1 (an Homo neanderthalensis]  (Balzeau and Mangin, 2021). full customization. Moreover, it also assumes a central common coordinate system to get from an image space to another. It has a Python language application programming interface (API) for customization. Freesurfer (see text footnote 4) is another very popular neuroimaging analysis software and proposes FreeView and TkSurfer for visualization.