dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of imaging mass cytometry data First, images were segmented by pixel classification into three regions: B cell follicles, T cell zone, and background/crypt. For this, we used the open-source software Ilastik (Sommer, C. , C. Strähle, U. Köthe, F. A. Hamprecht. 2011. Eighth IEEE International Symposium on Biomedical Imaging). We manually annotated pixels inside each of the three regions, and the software then learned to perform segmentation in the entire image. Second, we detected individual nuclei from the DNA staining with in-house-developed software. After removal of salt-andpepper noise and normalization of the image, we applied a LoG filter (sigma = 0. 004), thereby enhancing spot-like features, which appear as local minima. We then applied the h-minima transform  (Soille, 2003)  to detect local minima with a reasonable local contrast (h = 0.
This provided us with coordinates for each population studied and their distance distribution to the border of the corresponding regions. All scripts were written in Python, and made use of the open-source library scikit-image (van der  Walt et al. , 2014). Normalized images for each staining were deposited in Bio-Studies (https://www. ebi. ac. uk/biostudies/studies/S-BSST221). The software is publicly available at https://thomaswalter. github. io/ImageMassCytometry/. Single cell RNA-seq library preparation Cellular suspension (3,500 cells) was loaded on a 10× Chromium instrument (10× Genomics) according to the manufacturer's protocol based on the 10× GEMCode proprietary technology. For the APC analysis, tonsil HLA-DR + CD11c + CD14 -and tonsil HLA-DR + CD11c + CD14 + cells were sorted and loaded separately.
Single-cell RNA-seq data analysis Single-cell expression was analyzed using the Cell Ranger Single Cell Software Suite (v2. 0. 1) to perform quality control, sample de-multiplexing, barcode processing, and single-cell 39 gene counting  (Zheng et al. , 2017). Sequencing reads were aligned to the UCSC hg38 transcriptome using the Cell Ranger suite with default parameters. Samples for APC or CD4 + T cells were merged using the Cellranger aggregate function with default parameters. A total of 4,402 single cells were analyzed for the APC dataset, and 4,342 single cells were analyzed for the CD4 + T cell dataset. Mean raw reads per cell were 77,372 for the tonsil HLA-DR + CD11c + CD14 -sample, 119,822 for the tonsil HLA-DR + CD11c + CD14 + sample, 162,772 for the CXCR5 + PD-1 low T cell sample, 110,381 for the CXCR5 + PD-1 int T cell sample, and 64,291 for the CXCR5 + PD-1 high Tfh cell sample.
Further analysis was performed in R (v3. 5) using the Seurat package  (v2. 3. 3; Satija et al. , 2015). The gene-cell-barcode matrix of the samples was log-transformed and filtered based on the number of genes detected per cell (any cell with <400 genes or >5,000 genes per cell was filtered out). Regression in gene expression was performed based on the number of UMIs and the percentage of mitochondrial genes. Only genes detected in ≥3 cells were included. Cells were then scaled to a total of 1 e4 molecules. Any cell with >6% of mitochondrial UMI counts and >50% of ribosomal UMI was filtered out. Altogether, 4,330 cells for the APC dataset and 4,090 cells for the CD4 + T cell dataset were kept for statistical analysis. To reduce data dimensionality, 17,581 variable genes for the APC dataset and 15,535 variable genes for the CD4 + T cell dataset were selected based on their expression and dispersion (expression cutoff = 0, and dispersion cutoff = 0.
5). Principal component analysis was run on the normalized gene-barcode matrix. Barnes-hut approximation to t-distributed stochastic neighbor embedded (t-SNE; van der Maaten, L. 2013. Proceedings of the International Conference on Learning Representations) was then performed on the first 15 principal components to visualize cells in a two-dimensional space. The first 15 principal components were used for the t-SNE projection and clustering analysis using the Elbow Plot approach. Clusters were identified using the "FindClusters" function in Seurat with a resolution parameter of 0. 6 for the APC dataset and 0. 8 for the CD4 + T cell dataset. This graph-based clustering method relies on a clustering algorithm based on shared nearest neighbor modularity optimization. Unique cluster-specific genes were identified by running the Seurat "FindAllMarkers" function, and putative conserved markers running the "FindMarkers" function, both using the Model-based Analysis of Single-cell Transcriptomics (MAST) framework  (Finak et al.
, 2015). Hierarchical clustering between meta-cells was performed running the Seurat "BuildCluster-Tree" function using default parameters. Clusters containing contaminating cells were removed from the analysis: one cluster of 72 cells from the APC dataset corresponding to natural killer T cells (top genes: KLRB1, CTSW, CD7, TRDC, XCL2, XCL1, AC092580. 4, TNFRSF18, TRBC1, CD247, KLRC1, and GNLY), and two clusters from the CD4 + T cell dataset of 262 cells corresponding to natural killer cells (top genes: MALAT1, N4BP2L2, PTPRC, POLR2J3, MBNL1, PLCG2, ATM, MT-ND4L, HSPA1B, NKTR, PCSK7, and ANKRD36C) and of 77 cells corresponding to B cells (top genes: ISG15, MX1, IFI6, LY6E, IFI44L, OAS1, IFIT1, MX2, EPSTI1, IFI16, IFI44, and EIF2AK2). Heatmaps and violin plots were plotted using Seurat. Data are available at GEO (accession no.
GSE115007 for tonsil HLA-DR + CD11c + CD14 -cells, GSE119506 for tonsil HLA-DR + CD11c + CD14 -cells, and GSE119507 for tonsil CD4 + T cells). Analysis of gene signatures at the single-cell level Signature scores were computed using the Seurat function "AddModuleScore" using the gene signature of interest. This function calculates for each individual cell the average expression of each gene signature, subtracted by the aggregated expression of control gene sets  (Tirosh et al. , 2016). All analyzed genes are binned into 25 bins based on averaged expression, and for each gene of the gene signature, 100 control genes are randomly selected from the same bin as the gene. We used published gene signatures for blood cDC1s, blood cDC2s  (Carpentier et al. , 2016) , ascites macrophages, and in vitro-generated mo-Macs  (Tang-Huau et al.
Software and statistical analysis Flow cytometry data were analyzed using FlowJo software (Tree Star). Statistical analyses were performed using the Prism software v7 (GraphPad). Paired Wilcoxon tests were applied in all analysis to compare two groups, except for Fig. 8 D , where a paired t test was applied. Significance was retained for P < 0. 05. The software designed for analysis of imaging mass cytometry data is publicly available at https://github. com/ThomasWalter/ ImageMassCytometry. Online supplemental material Fig. S1  shows Th cell polarization by DC subsets and macrophages. Fig. S2  shows that tonsil CD14 + cells do not contain a population of DCs. Fig. S3  shows that Tfh cells comprise two distinct effector states. Fig. S4  shows an analysis of IL-12, activin A, and TGFβ in Th cell polarization.
Bars, 200 μm (left); 40 μm (right). (D) Individual cells displaying similar staining patterns are detected using unsupervised hierarchical clustering. 60 clusters are extracted in order to obtain overclustering. Individual clusters for one dataset with number of cells in each cluster are shown. (E) After cell identity assignment and merging of clusters with the same identity, individual cells are projected on the segmentation map. Example images are shown. Bars, 200 μm.
Acknowledgments The authors wish to thank the  Flow Cytometry Platform of Institut Curie  for cell sorting,  L. Pattarini  and  C. Trichot  for helpful discussions, and  L. Ardouin-Bataille  and  A. Coillard  for critical reading of the manuscript. This work was supported by the  Institut National de la Santé et de la Recherche Médicale ,  Agence Nationale de la.
e. , hand sample to regional elevation models). Consequently, geoscientists are empowered to obtain their own spatial data, which once would have been unachievable without expensive equipment and high expertise levels. The result is the current rapid adoption of digital spatial data across the diverse arms of the geosciences  (Buckley et al. , 2017). Although data acquisition and processing have become more automated-embodied by the empowerment of field geoscientists who, armed with digital cameras and easy-to-use SfM software, are now able to capture and process image sets to make 3-D models, even during a field campaign-the exploitation of this data for many geoscience applications continues to lag behind. Applications commonly demand custom analysis solutions; however, interpretation, visualization, and dissemination needs are shared by many different geoscience subdisciplines.
The limited availability of accessible and fit-for-purpose software tools has been a continued barrier to adoption of 3-D spatial data for new users (e. g. ,  Hodgetts, 2013). At the same time, quantitative and qualitative analysis is critical to maximizing the full value of the 3-D spatial data sets now achievable. Thus, there is still a need for cross-disciplinary software tools that allow geoscientists to achieve simple measurement, interpretation, and visualization tasks, as well as share their results with collaborators. To address this need, this paper presents LIME as a software resource for the geoscience community and beyond. LIME focuses on the high-performance rendering of meshes, with or without texture, and provides simple tools for measurement and interpretation. In addition, new co-visualization methods allow placement and integration of 3-D models and two-dimensional (2-D) image data.
BACKGROUND AND MOTIVATION LIME has its origins in the need for geologists to have simple tools for performing measurements and interpretation on 3-D terrestrial lidar models in research and in education. In the mid-2000s, procedures for acquiring and, especially, processing terrestrial lidar data were relatively cumbersome compared to today's possibilities. Large point clouds and photo-textured meshes frequently pushed standard computing hardware to the limits. In addition, software tools designed for geoscience purposes were not widely available, requiring users to utilize, e. g. , complex lidar post-processing or GIS software to perform relatively modest measurement or interpretation operations  (Hodgetts, 2013). This resulted in a significant barrier for researchers and students to take advantage of the benefits of including 3-D data sets in their studies.
Indeed, early work with lidar data required geoscience students with little background in geomatics or geospatial data handling to process data sets to point clouds or photo-textured models, a stage both inefficient and not central to their project outcomes, though not for their general development and understanding of spatial data sets and their potential limitations. The need for a high-performance, simple-to-use software for geoscience users to quickly start working with visual 3-D data sets was the initial motivation for creating LIME. Although data acquisition and processing have become easier, and computing power has been vastly improved with 64-bit processors and more powerful graphics processing units (GPUs), issues in these areas continue to exist (e. g. ,  Hodgetts, 2013). Initially, LIME provided measurement and digitization tools to aid quantitative interpretation, as well as level-of-detail rendering of large digital topographic 3-D models, where model resolution is adaptively loaded according to the user's current position and zoom when navigating the 3-D scene.
Over time, the software has additionally become increasingly focused on novel co-visualization of disparate geoscience data types (e. g. ,  Buckley et al. , 2013)  and visual presentation (dissemination) of 3-D environments. This has resulted in LIME as a software framework for compiling spatial and non-spatial data sets, to aid the interpretation process by integrating tools in a coherent implementation useful to geoscientists. Another important motivation for LIME has been to facilitate the dissemination of 3-D model data sets and resulting measurements, interpretations, and visualizations, either by sharing data sets with collaborators or as outputs for academic presentation. As a lightweight executable, the software can be shared easily with partners and in its most basic form functions as an uncluttered 3-D viewer.
The advanced graphical data fusion framework can be used to generate high-quality figures for use in journal articles or posters, images for use in slideshows, and input image sequences for generating movies. In addition, orthorectified images can be created based on a user-defined viewport, which can be exported to give new synthetic views of the 3-D environment, e. g. , where interpretations or additional images are overlain on loaded data objects. LIME is not a unique initiative for solving the lack of available analysis and visualization software within the geosciences. Indeed, parallel efforts have been presented by a number of groups. In natural hazards research,  Jaboyedoff et al. (2007)  developed Coltop3D for structural characterization of lidar point clouds. For exposed outcrops in geology and petroleum geology, software such as Virtual Reality Geological Studio  (Hodgetts, 2013)  allows extensive 3-D interpretation and modeling of sedimentary and structural features in point clouds and photo-textured models.
Hardebol and Bertotti (2013)  presented DigiFract for characterizing fracture data in images of geological outcrops. For point cloud analysis, CloudCompare (Girardeau-Montaut, 2017) is currently the software of choice for many working with photogrammetric and laser scanning data sets. This software is extended with plugins, such as qFacets  (Dewez et al. , 2016)  for structural orientation analysis needed, e. g. , in geology and natural hazards applications. Within this constellation of relevant software packages, LIME currently focuses on the importation of triangular surface meshes rather than point clouds. This was an early design principle motivated by the general need of geologists (though not exclusively) to see both geological overviews and close-up details at the pixel scale of the overlaid photographic texture, where Buckley et al.
| LIME virtual geoscience software GEOSPHERE | Volume 15 | Number 1 the discontinuous nature of point clouds can be problematic for viewing purposes  (Buckley et al. , 2008a; Hodgetts, 2013). Point clouds require a simpler data structure and can therefore be efficiently rendered, whereas triangular meshes can be more complex to create and require more graphical elements to be displayed. However, the continuous surface representation obtained using triangular meshes is highly beneficial if the rendering of the larger data sets is handled in an appropriate manner. To this end, LIME allows display of level-of-detail photo-textured models, such as the octree-based processing described by  Buckley et al. (2008b) , allowing scalable level-of-detail rendering of large-area terrain data. Combining measurement and interpretation tools with high-performance rendering and co-visualization of different data sets makes LIME a simple tool for analyzing and presenting digital geoscience data sets.
IMPLEMENTATION AND FRAMEWORK LIME is written in the C++ software language and makes use of the cross-platform libraries OpenSceneGraph (OSG) for 3-D rendering (e. g. ,  Wang and Qian, 2010)  and wxWidgets (wxWidgets, 2017) for the graphical user interface (GUI). Both are long-running and stable open-source projects widely employed in scientific and industry projects. OSG presents a highlevel interface to OpenGL, which is itself an industry-standard library for high-performance 2-D and 3-D graphics applications. While OpenGL provides advanced real-time rendering, it has few methods for loading and organizing 3-D objects in a common spatial environment. OSG encapsulates OpenGL and combines it with methods to create and interact with scene graphs-a concept used in 3-D graphics to allow organizing, positioning, transforming, and applying individual or grouped rendering settings to objects in a 3-D scene (e.
g. ,  Wang and Qian, 2010). In addition, OSG includes data loaders for many common file formats (e. g. ,. obj,. ply,. stl,. 3ds) and makes use of the OpenGL Shading Language (GLSL) to define flexible and advanced GPUbased rendering using shaders  (Rost, 2006). Currently, LIME is compiled for Microsoft Windows 64-bit platforms, though because of the software's reliance on cross-platform libraries, this may be extended to other operating systems in the future. The software features a GUI based around a 3-D viewer linked to a project database window, listing the available loaded data. The user can interact with the loaded data using standard operations (e. g. , show/hide, remove, rename, etc. ), create new elements, as well as change the visual properties of the elements contained within the 3-D scene (Fig.
Data Import and Large-Data Handling LIME can handle a number of industry-standard 3-D model formats commonly found in graphics software (examples are. obj,. ply, or. stl). In addition, OSG native binary formats allow for high-performance loading and rendering with graphics-card optimization such as texture compression; models can be exported to binary after import into LIME. Using the native graphics library support for out-of-core data loading, hierarchical terrain databases can be loaded and viewed without being restricted to physical hardware memory. LIME handles digital elevation models (DEMs), typically organized as quadtrees owing to the raster (2-D gridded) input format, and 3-D photo-textured models from lidar or photogrammetry, organized as octrees due to the additional high resolution in the vertical (z) direction (described in  Buckley et al.
Visualization of Surface and Texture Properties One of the main features of LIME is the possibility to perform advanced manipulation of the visual properties of individual or multiple objects in the 3-D scene. All visual properties of the model are dynamically editable, and rely on composition of shaders  (Rost, 2006) , which are compiled and run on the GPU to achieve flexible and high-performance rendering. The software uses a layer-based approach to visualization, where the terrain surface is the base layer, which can be manipulated in different ways to change the visual presentation of the 3-D mesh. Image textures are shown as additional layers on top of the terrain surface, and the transparency of these layers may be individually altered to build up complex and visually appealing combinations of topographic and image data.
Interpretation and Measurement Provided interpretation tools within LIME allow digitization of line features and orientation planes based on intersection of cursor positions with the visible 3-D scene. Lines can be closed to form polygon outlines, and visual properties can be set to change the color, thickness, stipple pattern, etc. A line can be modified, allowing vertices to be changed or removed and new vertices inserted. Three-point planes can be plotted to measure and represent the orientation of surfaces, with visual display properties available as for 3-D meshes. Objects can be added into hierarchies of folders to group common feature types. LIME exports ASCII and XML project data to facilitate interchange with GIS, reservoir modeling, or other 3-D viewing software. 4C); as long as the image size and/or aspect ratio does not change, then the modified image retains the spatial properties of the LIME 3-D scene.
Panels The second usage scenario relies on using panels for the creation of orthorectified images of the 3-D scene. Because orthorectified images can be used for direct measurement, and creation through the panel process preserves all visual properties of the captured scene, they are a useful output independent of further analysis or modification. However, edits to the exported orthorectified imagery can be again applied in image processing software, similar to in the first scenario, to create visual products for interpretation or communication purposes. In image processing software or a drawing package, it is possible to add new layers, which can be used as the basis for sketching on or annotating the orthorectified image. Saving only the modified image layer, containing the annotations on an otherwise transparent background, provides the input for adding a new panel texture layer in LIME.
Placement, Co-Visualization, and Multi-Texture Overlay of Other Geoscience Data In addition to using panels for aiding the interpretation process and presentation of analysis, LIME facilitates the introduction of additional spatial and non-spatial data into the 3-D scene. Surfaces or horizons created in reservoir modeling or GIS software  (Enge et al. , 2007)  can be imported and viewed simultaneously with the 3-D photo-textured models (Fig. 1 ). This can be useful in outcrop analogue studies in geology, e. g. , for quality controlling the veracity of interpolated or extrapolated 3-D data derived from input sparse interpretations. Georeferenced imagery-such as topographic or geological maps, classifications, and orthoimages from UAV, aerial, or satellite imagery-can be loaded and draped onto models, surfaces, and panels in the 3-D scene (Fig.
7 ). Panels can also be used to integrate spatial and geophysical data. In this case, geophysical sections of the subsurface, acquired using techniques such as ground-penetrating radar, can be placed as 2-D images into the 3-D scene relative to 3-D models or elevation data. Where geophysical acquisition is georeferenced, such as using GNSS data, this can be used to correctly position the resulting sections. The exposed topography can thus be co-visualized with the subsurface information. Furthermore, geophysical sections can be projected onto the 3-D models as additional texture layers, as for all panels (Fig. 8 ). Panels in LIME provide a flexible and powerful approach to linking accurate and high-resolution digital spatial data with ancillary field, laboratory, GIS, and multidisciplinary data sets.
Multi-sensor image data, acquired from multispectral, hyperspectral, or thermal cameras, are increasingly being integrated into geoscience studies, requiring new tools for co-visualization and data fusion. In LIME, the multiple-texture-layer approach allows registered additional sensor imagery to be overlaid on loaded 3-D data sets, as well as simultaneous display of models textured using multiple image sources. Buckley et al. (2013)  described the integration of close-range hyperspectral imagery with 3-D models using the multilayer display framework in LIME, and Figure  9  illustrates a further example where thermal image results from a UAV acquisition are co-visualized with a photogrammetric 3-D model generated from conventional camera images. A current limitation of LIME is that the co-registration of such additional image types must be performed externally, prior to overlay within the software.
CONCLUDING REMARKS This paper presents LIME as an available software resource for the geoscientific community, motivated by the ongoing need to ease barriers to adoption of 3-D spatial data sets in all branches of the discipline. We described the background of the software, and outlined the current scope of LIME as a lightweight viewer focusing on visual inspection and interpretation, integration, and co-visualization of disparate spatial and non-spatial data sources, and highquality presentation of results. The software is applicable for quantitative and qualitative analysis, as a planning tool for fieldwork, for educational purposes, as well as for dissemination. Although written primarily for geoscientists, LIME is applicable to other relevant fields, such as archaeology, cultural heritage, and environmental studies, and for supporting infrastructure projects.