dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We would like to thank the other members of the  Peri-Operative Prophylaxis in Neonatal and Pediatric Age (POP-NeoPed) Study Group  who participated in the consensus:  Alberto Argentiero ,  Giuseppe Maglietta ,  Matteo Puntoni ,  Cosimo Neglia ,  Isabella Cremonini , and  Mara Caramia-University Hospital of Parma, Parma, Italy) ;  Elena Carrara ,  Agnese Corbelli ,  Marilia Di Pietro ,  Marcella Gaffuri ,  Elena Gusson ,  Francesca Opri ,  Rosa Longo ,  Michele Piazza ,  Tessari Denis ,  Laura Venditto ,  Marcella Sibani , and  Evelina Tacconelli-Azienda Ospedaliera ,  Universitaria di Verona, Verona, Italy ;  Luigia Scudeller  and  Federico Pea-IRCCS Azienda Ospedaliera, Universitaria di Bologna, Bologna, Italy; Matilde Ciccia-AUSL Bologna, Bologna, Italy ;  Guido Castelli Gattinara ,  Andrzej Krizystofiak ,  Andrea Dotta ,  Sergio Picardo ,  Alessandro Inserra ,  Massimiliano Raponi ,  Paolo Rossi , and  Alberto Villani-IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy ;  Leonardo Bussolin  and  Carlotta Montagnani-Meyer Hospital ,  University of Florence, Florence, Italy; Alessandro Mugelli and Andrea Novelli-University of Florence, Florence, Italy; Fabio Mosca-Università degli Studi di Milano, Milan, Italy ;  Mario Giuffrè ,  Claudia Colomba ,  Giovanni Corsello  and  Marcello Cimador-University of Palermo, Italy ;  Elisabetta Bignamini-Regina Margherita Hospital, Torino, Italy; Alfonso Papparella-Università Vanvitelli, Napoli, Italy; Giangiacomo Nicolini-Treviso Hospital, Treviso, Italy; Rossella Garra-Catholic University, Roma, Italy; Laura Marchesini-Perugia Hospital, Perugia, Italy; Stefania Stefani-University of Catania, Catania, Italy ;  Valeria Caldarelli-AUSL Reggio Emilia ,  Reggio Emilia , Italy;  Gianni Sava  and  Gabriele Stocco-University of Trieste, Trieste, Italy.
Statistical analyses. Analyses were conducted using the SPSS 21. 0 software package (IBM, Armonk, USA). A chi-square test was used to compare the lifestyle composition of cases from Qinghai-Tibet Plateau and non-Qinghai-Tibet Plateau. Clustering analysis was used to group data objects as previously described  [16]. The kmeans algorithm was used to separate n objects into k clusters, each having a high similarity of data objects, whereas data objects inside the other clusters are less similar  [17]. The procedure was the following [18]: (1) Any k point was chosen as the initial cluster center. (2) The distance between the remaining points and the cluster center was calculated based on the minimum distance, as follows: dis(x i ,x j )= ∑ D d = 1 (x i , d , x j , d ) 2 (3) A new cluster center was calculated for each cluster.
MATERIALS AND METHODS Patients and samples We performed a retrospective search of the electronic patient database (ConSoRe) of Institut Curie (Paris, France), using the keywords "breast", "carcinoma" and "osteoclast(ic)" to identify patients whose medical records contained all three terms. Those patients who had undergone breast surgery between 2000 and 2021 and for whom pathology slides were available at Institut Curie were included. Slides were reviewed by study pathologists (J. C. , A. V. S. ) to confirm the diagnosis. Clinical data were obtained from electronic medical records and/or from treating oncologists. The study was approved by the Institutional Review Board (reference: DATA220058).
Pathology data All cases were further classified into three groups: (1) invasive carcinomas of no special type with OGC (group 1: OGC-IC NST); (2) "mixed" cases, i. e. , tumors composed of clearly distinct areas with and without OGC, or multifocal tumors with and without OGC (group 2: OGC-Mixed); and (3) metaplastic carcinomas (MC) with OGC (group 3: OGC-MC). Diagnoses of IC NST and MC were made in accordance with criteria of the 2019 WHO breast tumor classification  1. Surgical pathology data (size, grade, lymph node status, hormone receptors, HER2 and Ki67 status) were collected from pathology reports and/or during pathology review.
RNA sequencing Total RNA were extracted from fresh-frozen tumor tissues using the Qiagen RNAeasy kit (Qiagen, Venlo, the Netherlands). Libraries were prepared using the Illumina TruSeq Stranded mRNA Library preparation kit and sequencing was performed on an Illumina NovaSeq 6000 platform in paired-end 100 bp mode. Fusion transcript detection was done using Defuse (0. 6. 0), FusionMap (2015-01-09), FusionCatcher (0. 99. 7d) and STAR-Fusion (1. 0. 0). Expression data were generated with SALMON (v 0. 13. 1) on ensembl96 (hg19) and count matrices were normalized as Transcripts Per Million using tximport (R library). Molecular BC subtyping was performed using the subtype. cluster. predict () function (sbt. model = "pam50") from R package genefu (version 2. 16. 0), based on the intrinsic subtyping classifier that measures expression of 50 genes (PAM50) selected as characteristic of the five BC intrinsic subtypes  8.
Principal component analysis was performed on the top 5000 genes with the highest variance. Differential expression analysis was done with the R DESeq2 package, considering adjusted p value <0. 05 as significant. Gene set enrichment analysis (GSEA) was performed with the GSEA application for Mac (v4. 2. 3), using a pre-ranked list of a total of 14,630 genes. Gene Ontology overrepresentation analysis was performed using the enrichGO () function with ont = "BP" (for biological processes), "MF" (for molecular functions), "CC" (for cellular components) from R package clusterProfiler. Additional details of the statistical, genomic and transcriptomic analyses are provided in Supplementary Methods.
Clinicopathologic data Twenty-seven patients were identified between 2000 and 2021; this represents ~0. 05% of patients who underwent surgery for invasive BC at our institution during that period. Seventeen (63%) cases were classified as IC NST with OGC (OGC-IC NST), four (15%) as "mixed" cases (OGC-Mixed), and six (22%) as metaplastic carcinomas with OGC (OGC-MC). A summary of clinicopathologic findings for each group is presented in Table  1. Additional details are provided in Supplementary Tables  ST1  and ST2. A variety of morphologies was observed (Fig. 1 ). OGC-IC NST displayed tubular, cribriform, trabecular and/or solid architecture; two cases had a minor micropapillary component. Within the OGC-Mixed group, there were two cases of multifocal IC NST with and without OGC, one multifocal case including IC NST with OGC and multiple foci of invasive lobular carcinoma (ILC) without OGC, and one case of mixed NST and ILC with OGC present only in a part of the IC NST component.
Transcriptomic findings and evidence of RANK-L expression To our knowledge, this is the first study to report significantly deregulated gene expression programs between OGC-IC NST and IC NST without OGC. One limitation of our analysis is the fact that bulk RNA-seq does not allow one to determine which cell type is responsible for the significantly deregulated transcripts. Nevertheless, a few hypotheses can be proposed. First, our data suggest that OGC in BC do not merely resemble osteoclasts, but also share their functions, including matrix degradation, as exemplified by overexpression of genes such as ACP5, MMP9, CTSK, as well as by GSEA and Gene Ontology results. Accordingly, expression of selected osteoclastic enzymes has previously been evidenced in BC-associated OGC by IHC  16, 28, 36, 37  , and one functional in vitro study confirmed that these cells were capable of bone resorption  37.
ACKNOWLEDGEMENTS We would like to thank the Patients for participating in precision cancer care studies. We are grateful to  Dr Suzanne Chartier ,  Dr Guillaume Bataillon  and  Magalie Toutain  for their help with identifying cases. We thank the organizers of the EBAii  AVIESAN-Institut Français de Bioinformatique-Inserm bioinformatics course , and in particular Hugo Varet, for their insightful suggestions regarding data analysis.
Several applications, including our software DIVA for image stack and our platform Genuage for point clouds, have leveraged Virtual Reality (VR) to visualize and interact with data in 3D. While the visualization aspects can be made compatible with different types of data, quantifications, on the other hand, are far from being standard. In addition, complex analysis can require significant computational resources, making the real-time VR experience uncomfortable. Moreover, visualization software is mainly designed to represent a set of data points but lacks flexibility in manipulating and analyzing the data. This paper introduces new libraries to enhance the interaction and human-in-theloop analysis of point cloud data in virtual reality and integrate them into the open-source platform Genuage.
, 2006; Rust et al. , 2006). Large-scale recordings of single molecules at the nanometer resolution reveal the complex interplay between 4D geometry (space and time) and biological activity. Understanding these complex time-evolving structures requires freely navigating within the data and analyzing portions of it. Another significant component associated with singlemolecule imaging is the complexity of the data. Positions and dynamics of biomolecules are accessed through multiple preliminary operations  (Chenouard et al. , 2014) , such as image deconvolution and tracking, which leads to noisy datasets. Furthermore, these datasets are usually highly heterogeneous in space, time, and other associated properties. Furthermore, organelles unseen in datasets influence the geometry of the recorded point cloud in the surrounding environment.
Hence, we are convinced that numerous scientific initiatives benefit from immersive visualization modalities such as virtual reality (VR) to enhance data comprehension. Data in this context may be uploaded to a VR environment and visualized using a custom shader program linked to relevant experimental parameters. Exploring the data in VR offers the possibility to grasp the global geometrical structures while allowing the possibility to dive within the data to explore more local structures and their relation in space. Besides, the interaction in VR facilitates the quantitative analysis of the data through the combination of user intervention and optimized analysis algorithms. The ability to easily and quickly isolate regions of interests in 3D for example is time consuming and impractical even with efficient desktop software such as ViSP  (Beheiry and Dahan, 2013) , while these sorts of interactions are faster and more precise using VR.
Several applications including our software platform Genuage are dedicated to visualize and analyze point cloud data in virtual reality  (Blanc et al. , 2020). Genuage is an open-source VR-compatible platform for visualization of n-dimensional point cloud data. It features a set of VR-assisted tools for data exploration, data selection, and built-in analysis. While VR visualization parameters can be adapted to the various types and dimensionality of the recorded point clouds, quantification, on the other hand, seems far from being standard. Point clouds generated in different acquisition modalities can vary in dimensionality and might underlay specific information that requires dedicated algorithms for analysis. Moreover, such algorithms are developed in different scripting platforms and remain case-dependent.
The standardization of the quantification process remains thus difficult. In addition, complex analysis can require large computational resources, making the real-time VR experience uncomfortable. Moreover, visualization software is mostly designed to represent a set of data points but lacks flexibility in manipulating and analyzing the data. Examples include generating new data properties to visualize, calculating physical properties from selected points, creating new point clouds derived from the one already uploaded and combining data sets. This paper presents the Genuage software for point cloud visualization and interaction and highlights recent developments. We introduce new approaches and processes implemented within the Genuage platform, in which enhanced flexibility and user experience are made possible by a set of communication tools and built-in functions.
, 2020). They range from optimized interfacing to easy interactions with the data to mixed interactions with various algorithms to prepare data or detect specific structures of interest. Some hybrid applications can be found in astronomy such as  (Gaia Sky, 2014). General visualization and interaction software include PointCloud XR and developments centered on compression to ensure visualization in VR (and AR)  (Pavez et al. , 2018). Other applications such as MegaMol and UnityMol are dedicated for the visualization of point-based molecular data sets  (Doutreligne et al. , 2014; Grottel et al. , 2015). Within the context of microscopy and biological data analysis, which is the focus of this paper, the open source vLUME software  (Spark et al. , 2020).
General Features of Genuage We recently introduced the open-source software platform Genuage  (Blanc et al. , 2020; Genuage GitHub, 2020)  to address visualization, interaction and analysis of complex multi-dimensional point cloud data. The platform can accommodate several types of point cloud data. Genuage is built on the Unity game engine and was initiated on the simple premise that extracting information from high dimensional point clouds is a non-trivial task. VR immersion provides the dual possibility of increasing the number of dimensions represented and allowing more direct interaction with data. Genuage has a dual interface: a computer desktop interface and a VR interface. The desktop interface derives its design principle from ViSP (Beheiry and Dahan, 2013): it allows users to load the data and set general representation parameters (Supplementary Figure  S2 ).
g. ,  (Hajj et al. , 2014; Nehme et al. , 2020) ], and, depending on the complexity of the detection microscope, several associated parameters such as the color, the intensity of the detected molecule, the time stamp, and the molecular orientation to name a few. Visualizing and analyzing such complex datasets is not trivial on a 2D screen given the large dimensionalities of the recorded point cloud. The complexity and the density of data points can conceal or intersect with other features within the data. In addition to the visualization aspects, analysis might require specific tasks to be performed within the complex dataset such as selection and counting. Users often face the need to analyze a specific trajectory or a selection of trajectories within a region of interest. Thus, the main requirements for exploiting single-molecule localization data are an efficient and intuitive perception of multidimensional data, ease of navigation through the data, and a facilitated interaction to accomplish specific tasks such as placing landmarks for measurements or counting and performing selections.
There is a clear advantage in using Genuage and VR to navigate and interact with such complex data. However, analyses of single-molecule datasets are far from being standard. It is common for different users to adopt different strategies for the quantification of the recorded cloud. Custommade algorithms are often required and can continuously improve with evolving functionalities. Moreover, different algorithms might impact the original point cloud differently, for instance by generating additional properties for the point cloud or by creating a completely new data set. There is thus a clear need to adapt Genuage for different custom-made algorithms with non-standardized outputs.
For example, we provide an analysis tool for creating a complex 3D mesh based on local point cloud density. In each region, the code infers the local diffusion behavior of dynamic molecules data and presents it as a variation in the color of the mesh or as surfaces with adequate color. The implementation will be further detailed in the following section. The Genuage platform is developed as a versatile package for point cloud data and is highly modular to allow easy repurposing of portions of the code. Besides visualization, the goal of Genuage is efficient, and simplified data analysis. As point cloud data is ubiquitous, developing a general analysis framework is challenging. Our first development ensured compatibility with Python with a dedicated communication protocol launchable from Genuage.
This development was motivated by ensuring that complex analysis could be performed outside the platform, especially if they required significant computational resources. We used a similar approach for volumetric data annotation in the DIVA-cloud platform (Voxel Learning in DIVA, 2020). Efficient uses of the pipeline consist of transmitting a subset of selected data to a python script for analysis and using the results to enhance the visualization of this subset or provide extra information in space and time. We also implemented the Matlab pipeline as it is a popular tool for biological data analysis. Similar pipelines will soon be added for R and Julia. An example of an application is shown using a custom Graph neural network to perform single-molecule trajectory analysis (see Figure  2 ; Supplementary Video S1).
Trajectory selection is performed using the VR controllers with a visual aid in the form of a sphere. Other shapes can be used to select points. In highly convoluted point distributions, the user can define landmarks generating a convex hull that will capture points of interest. Results are displayed as changes of the trajectory colors, with intensity depending on the value of the sub-diffusive exponent. The inference analysis is performed in real-time on the selected trajectories (inset shows real-time calculation performed). Various means to display results are available. The screenshot also shows the interface in the current version of the Genuage software. Most of the buttons and displays are self-explanatory [an introduction to the design can be found in the videos associated with  (Blanc et al.
, 2020) ]. Our newest development focused on numerical data analysis that either requires limited computational resources or requires a complex set of packages. We added a toolbox to import ad-hoc external DLLs libraries. While we use the classical development process of using addons, plugins, and DLLs to enhance the Genuage platform, our challenge lied in the end-user community willingness to engage in the challenges of developing VR software and in their limited computing facilities (usually computers with basic graphic cards and few CPU cores). We streamlined the development and use of a VR software for experimental scientist and biologists. Furthermore, the architecture is optimized to ensure usability on most computing platforms. We provide different templates for the required possible formats of the DLLs and structures.
Different DLL formats allow performing specific tasks. Currently, we provide 3 configurations of libraries that allow efficient integration within the Genuage platform (Figure  3 ; Supplementary Video S2). These configurations provide guidelines for a user to develop other possible integration architectures. We provide code templates to help user easily create DLLs from their analysis algorithms, and integrate their code into an analysis routine through Genuage. Users are able to run custom algorithms with no need for advanced knowledge in C# or software development. We favored three analysis configurations. First, the library generates new columns in the point cloud data represented within the VR environment and associated to a specific visual parameter (Figure  3B ). Second, the library generates numerical outputs shown in an extra visualization window (Figure  3C ).
Third, the library creates a new point-cloud dataset (Figure  3D ). To illustrate these procedures and provide development guidelines for new libraries, we provide three DLLs. The first one simplifies point density estimation. The second one computes the apparent diffusion coefficient on a selected trajectory. The third one combines and displays point clouds localizations generated by the same molecule due to the inherent molecular blinking commonly encountered in single-molecule super-resolution acquisitions (check Supplementary Information for more details). While VR is instrumental for data analysis, results are often communicated without the VR environment. Furthermore, VR and AR remain new technologies that are not widespread. Hence, we developed a toolbox to capture information within the VR environment and export it in an easy to share format.
We developed a dedicated toolbox that features an image capture module within the VR environment and a video capture mode where the user defines landmarks for the displacement of a virtual camera within the virtual point cloud. Furthermore, the user can adjust the timing at each waypoint and set visual parameters such as the color and thresholding. Then, the user can access in desktop mode the newly created external file. The file can be further adjusted manually to define the position of the waypoints and the animation using a dedicated scripting language. In addition, the desktop mode has an interface for timing adjustment. As a showcase, we present the video capture of dual color super-resolution localization microscopy data sets performed in budding yeast (Supplementary Video S3).
We refer the reader to  (Höfling and Franosch, 2013; Manzo and Garcia-Parajo, 2015; Miller et al. , 2017)  for a general introduction. Hence, we used simulation-based inference to analyze selected data within the virtual environment. We leverage our recently developed graph neural networks  (Fey and Lenssen, 2019; Verdier et al. , 2021)  to run efficient parameter inferences. We used the following architecture (Supplementary Figure  S1 , with explication in the caption). The inference model was trained on numerical datasets of various random walk using the "ANDI" package  (AnDi, 2020; Muñoz-Gil et al. , 2020). The model is pretrained, and the number of associated parameters is reasonable for a deep model (300,000-500,000). VR representation was done on a GPU card while the inference was run directly on the CPU, leveraging multi-threading to analyze sets of individual trajectories as they were selected by the user.
Trajectories are later generated using the Utrack algorithm  (Jaqaman et al. , 2008)  (Figure  2B  and Figure  6A ). Upon injection, the particles explore the heterogeneous volume of the nucleus, and the diffusive behavior is thought to be a direct indicator of the physical properties and compaction of the surrounding chromatin. In addition, and regardless of the dynamical aspects, the accumulation of all the localizations of the single particles reconstitutes a super-resolution image of the empty space that is surrounded by the chromatin fibers. In Figure  2  we show a capture of the analysis with the Genuage interface. Supplementary Video S1 shows a full demonstration of the inference being performed sequentially on subsets of selected trajectories. They also show the entire procedure from loading the data to performing a complete analysis of experimental data and procedures to import custom analysis software within Genuage.
Genuage is an optimized tool to facilitate data analysis of complex non-isotropic point clouds such as those obtained from biomolecule dynamics. We have shown a set of libraries and toolboxes implemented in the Genuage platform to perform human-in-the-loop point cloud analysis in VR. Our developments focused on allowing analysis without impacting the image rendering refresh rate and thus ensuring fluid interactions between the user and the data. We provided the required tools to allow efficient data extraction from the VR environment to be displayed onto regular 2D viewing devices. We ensured compatibility with popular analysis frameworks while keeping on adding new ones. Finally, we developed proof of concept analysis pipelines to demonstrate the analysis process in VR and provide guidelines to develop new ones.