dataesr/scientific-paragraphs-categorization huggingface dataset:
Paper Nanoscale Nanoscale This journal is © The Royal Society of Chemistry 2022 Lanes, NJ, USA) equipped with CXP software (Beckman Coulter Inc. , Pasadena, CA, USA). Data were analysed using Flowing Software 2. 5. 1 (https://www. flowingsoftware. com/). The CellEvent reagent was also used for imaging apoptotic cells using the same protocol followed by imaging via epifluorescence microscopy using an Axiovert 200 microscope (Zeiss).
Acknowledgements This work has received funding from the  Horizon 2020  research and innovation programme of the  EU  under grant agreement no. 665440. The authors would like to thank  F. Rivera  for providing INS-1E cells,  V. Collin-Faure  for FACS analyses and  C. Moriscot  for electron microscopy sample preparation and imaging. S. Auffret  is acknowledged for NiFe film deposition. This work used the EM facility and flow cytometry platform at the  Grenoble Instruct-ERIC Center (ISBG ;  UMS 3518 CNRS CEA-UGA-EMBL ) operated by  C. Moriscot , with support from the  French Infrastructure for Integrated Structural Biology (FRISBI ;  ANR-10-INSB-05-02 ) and GRAL, a project of the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ) within the  Grenoble Partnership for Structural Biology.
Stellar abundances We proceed for the abundance analysis with the extensive line list of  Meléndez et al. (2014)  because the lines of some important elements (e. g. Mg) in  Biazzo et al. (2012)  are not covered by our observations. A hyperfine structure was taken into account for Sc, V, Mn, Co, and Cu using atomic data from the Kurucz database 3 , while the Eu data were taken from  Ivans et al. (2006). 3 Available at http://kurucz. harvard. edu/linelists. html A classical curve-of-growth analysis making use of the EWs was performed for most species. However, the determination of some abundances relied on spectral synthesis. The oxygen abundance was based on [O I] λ630. 0, while the C abundance was also estimated from the C 2 lines at 508. 6 and 513. 5 nm. Readers can refer to  Morel et al.
To model the photometry, we used the JKT Eclipsing Binary Orbit Program  (Southworth 2008 ) with an oversampling factor of 30 to account for the long integration time of Kepler (Kipping 2010). The star was modelled with the PARSEC evolution tracks  (Bressan et al. 2012) , taking the asterodensity profiling into account  (Kipping 2014) , and with the limb darkening coefficients taken from  Claret & Bloemen (2011). We ran 80 Markov chain Monte Carlo (MCMC) chains with 10 6 iterations for the two different effective temperatures to explore the posterior distributions of the parameters. The convergence was assessed with a Kolmogorov-Smirnov test  (Brooks et al. 2003). The burn-in phase was then removed  (Díaz et al. 2014 ) and the remaining iterations of the different chains having converged were merged.
Acknowledgements. We would like to thank  Maria Bergemann  and  Matthew Raymond Gent  for a preliminary analysis of the stellar spectrum. This research has made use of the services of the  ESO Science Archive Facility. This research was made possible through the use of the  AAVSO Photometric All-Sky Survey (APASS) , funded by the  Robert Martin Ayers Sciences Fund. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation. This publication makes use of data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration.
Data collection To gather the required data, we carried out a face-to-face survey with a sample of farmers. Based on the list of farmers provided by the ADG, we selected those who owned irrigable area outside the PIP and who bought water during the 2013 cropping year to irrigate crops inside and/or outside the PIP. The sample size reached 130 farmers, representing more than 50% of landholders of the PIP. We conducted the survey during the spring of 2014. The interview guide involves two sections. The first section was devoted to gathering all technical and economic details regarding the farming activities during the 2013 cropping year. The second section addressed the exposition degree of the family to ZCL disease and their eventual strategies to avoid it with regard to irrigation practices.
Acknowledgments This research received financial support from the  International Development Research Center (IDRC)  within the frame of the project " Community control of emerging leishmaniasis in the context of climate change in Tunisia " led by the  National Observatory of New and Emerging Diseases (ONMNE). The project is positioned to contribute to national policy guidelines for leishmaniasis control in Tunisia. Its lessons may apply across North Africa and beyond, where agriculture, outmoded irrigation practices, and climate change similarly favor the spread of ZCL.
Three-dimensional octa- Recently, [BTP * 19] has introduced HexaLab to the meshing community. HexaLab is a visualization tool enabling the evaluation of hexahedral meshes by filtering elements and showing various quality metrics (corner scaled Jacobian, edge ratio, etc. ). In addition, HexaLab collects the ouput hexahedral meshes of various state-of-the-art methods. However, there is no common dataset of input models, complicating the comparison of different methods. Most of the algorithms tackling the hex meshing challenge use tetrahedral meshes as input or during some intermediate steps. However, there is no suitable tetrahedral dataset for objective analysis and meaningful comparison of hexahedral mesh generators. Our goal is to provide such a dataset to reveal common robustness issues and guide future research toward practical relevance.
The result of our endeavour is the HexMe dataset, a collection of tetrahedral meshes with tagged feature entities. The feature entities are special points, curves and/or surfaces, which need to be accurately captured by a hexahedral mesh, c. f. Fig. 3. Please note that such feature points/curves/surfaces are common in all mesh generation scenarios, where they impose corresponding constraints on the local structure of the desired mesh. All meshes have been generated from computer-aided design (CAD) models, following a workflow defined with Snakemake [MJL * 21], using the Gmsh  [GR09]  API with custom parameters defined in yaml metadata files. CAD models are classified into three categories (simple, nasty, industrial), in order to grade their difficulty and consistency. For each model, three meshes are provided: two resolutions (curvature-adapted, uniform) to analyze the mesh dependency of algorithms, and an embedding of the object into a box resulting in interior feature structures.
The meshes including the feature tags are exported as. vtk datafiles (version 2, ASCII mode), a mesh format which is broadly used and easily accessible. HexMe and HexaLab share the goal of guiding future research on hexahedral meshing algorithms -HexMe through a suitable dataset of input models, HexaLab through the analysis and comparison of output hexahedral meshes. The HexMe dataset has been designed to meet the following goals: (G0) Unambiguous & Ready-to-use -The dataset offers volumetric tetrahedral meshes, ready to be used by any method relying on a tet mesh. Different methods can be compared without any bias introduced by ambiguous conversion procedures as for instance from surface or CAD representations. (G1) Challenging -The dataset specifically includes nasty geometric configurations that are often avoided, as well as realworld industrial models containing an assembly of such difficulties.
(G2) Discriminative -The dataset allows to diagnose and grade the limitations of hexahedral algorithms. There are simple models enabling a sanity check, whereas each nasty model is designed to reveal robustness w. r. t. a specific type of difficulty. The tessellation dependence of a method is evaluated by including two different tetrahedral meshes for each model. (G3) Realistic -The dataset mimics the workflow of a numerical practitioner, where all feature entities have to be preserved through all stages of the mesh generation pipeline. Explicitly defining such constraints is essential for comparison of methods since often it is possible to significantly improve the mesh quality or simplify the meshing task by violating some feature constraints. Consequently, output statistics are only comparable and meaningful if identical constraints have been enforced in the mesh generation.
(G6) Mutable -Simultaneously to the algorithms, the set of challenging and meaningful geometries will change over time. HexMe is designed in a way that new models and even meshing definitions can be added conveniently (Snakemake workflow available on a public GitHub). We envision that the entire mesh generation community will actively contribute to future versions of HexMe. In the following, we first describe in Section 2 datasets related to HexMe and discuss their differences. Afterwards, Section 3 introduces the pipeline to produce the tetrahedral meshes from CAD models, and Section 4 summarizes the content of HexMe. Finally, in Section 5, a state-of-the-art frame-field-based hexahedral meshing pipeline is applied to the dataset to verify that HexMe is suitable to benchmark robustness of hex meshing algorithms.
Related Datatsets There is a shift among the scientific community. Open science that is readily reproducible and shared is becoming popular. Among other things, this is mainly possible thanks to the free access to datasets supporting this open research. Consequently, an increased number of datasets has been published recently, where in the area of computer graphics ShapeNet [CFG * 15], ModelNet [WSK * 15], and Fusion360 [WPL * 21] are popular resources. In the following, we present the five datasets in more detail that are most related to HexMe and discuss their similarities and differences. Tetwild Even though Tetwild [HZG * 18] is a tetrahedral meshing technique, it is also a tetrahedral dataset, since the authors provide the output of their algorithm applied to Thingi10k  [ZJ16] , a triangular dataset.
ABC The ABC [KMJ * 19] dataset is a collection of one million computer-aided design models for geometric deep learning. All CAD files are from Onshape, and the original information related to those models is recorded within a metadata file meta. yml. Some processing tasks are done in order to filter the duplicate and broken models. The filtered models para. zip are afterwards converted into. step and. stl files using Parasolid. Gmsh  [GR09]  is then used to provide either uniform or curvature-adapted triangular meshes, which are exported as. obj meshes from the. step files. Differential quantities are stored in those. obj files, while the vertices and triangles of the mesh are respectively matched to the feature curves and patches, through another metadata file feat. yml. Further files may be provided, depending on the success of the processing.
The dataset is downloadable by chunks containing 7z archives of above files. Thingi10k Historically, Thingi10k  [ZJ16]  is the first dataset providing ten thousand diverse, complex and quality. stl triangulations of 3D (printing) models. All models come from Thingiverse, and have been selected only if they were tagged featured by the Thingiverse staff. An online query interface is provided, which returns all the contextual and original information related to a. stl triangulation. It is also possible to download the whole dataset as an archive tar. gz from Google Drive (∼9GB). SimJEB The recent SimJEB  [WBM21]  dataset provides 381 tetrahedral meshes from CAD models, by following a semi-automated pipeline. The CAD models come from a challenge organized by GrabCAD. Those former 700 models have been filtered (mostly based on the filename), manually repaired, and then meshed using the commercial software HyperMesh.
A structural simulation was performed using the commercial software OptiStruct. The 381. vtk tetrahedral meshes surviving this pipeline are hosted through the Harvard Dataverse (∼1. 6GB), along with the corresponding clean CAD. stp file, triangular surface. obj meshes, finite element. fem models, and simulation. csv results. The final models are identified by an integer, specified by readme files. A web page allows to browse the designs, and to explore the data. [GSP19] Recent grid-based hexahedral meshing papers [GLYL20, PLC * 21, LPC21] have benchmarked their method on a common dataset gathered by  [GSP19]. This dataset consists of 202. obj triangular meshes from  [FBL16]  and Drexel CAD repository (2018), 109 of them are equipped with feature curves. The feature curves.
Simple Nasty Industrial The available datasets differ by (i) their selection of models, (ii) whether triangular or tetrahedral meshes are supplied, (iii) potential specification of feature entities (0D/1D/2D/3D), and (iv) their infrastucture to access models and to potentially contribute new models. Table  1  summarizes the comparison between HexMe and the related datasets. Please note that for datasets solely providing triangular surface meshes, we interpret these as a 2D feature specification, even if no explicit tags are available. SimJEB is by-design the closest dataset to HexMe: small number of tetrahedral meshes from CAD models, semi-automated meshing pipeline, web catalog and. vtk mesh format. However, all Sim-JEB geometries describe a jet engine bracket, while HexMe supplies more diverse and challenging types of geometries.
ABC is the only related dataset providing meshes with feature tags corresponding to the CAD geometry. However, the ABC dataset consists only of triangular surface meshes and does not define any volumetric discretization. The dataset of  [GSP19]  supplies 109 triangular surface meshes with annotated feature points and curves. But neither tetrahedral meshes, nor tags relating feature entities to the corresponding CAD primitives, or a sustainable and extensible workflow are available. The Thingi10k and Tetwild meshes have not been generated from CAD files and thus do not specify feature entities.
In the future, the dataset will evolve together with the algorithms with the goal of providing a minimal set of test cases, which is maximally meaningful. In contrast to the comparable datasets (Section 2), the CAD models come from several databases: ABC dataset (originally from Onshape), GrabCAD and MAMBO. Some of the CAD models were created specifically for HexMe, using Gmsh and Siemens NX software. Those latter models are released to the public domain, while the other ones are regulated by licenses, which are respectively: Onshape Terms 1(g)(i), Grab-CAD Terms (c. f. related FAQ) and Apache 2. 0. The above information is reported within a metadata file (with the fol-  For each CAD model, three tetrahedral meshes are provided, c. f. Figure  4:  • curvature-adapted: the mesh element size is adapted to the curvature, and upper bounded such that the CAD geometry is sufficiently preserved (e.
g. i05c_m5. vtk). • uniform: the mesh element size is constant, even in the neighborhood of the tiniest geometrical features (e. g. i05u_m5. vtk). • box-embedded: the initial model is embedded in a box that is twice as large as the original bounding box, and the corresponding mesh is generated such that the smallest gap between the box limits and the initial model is meshed by one layer of tetrahedra (e. g. i05b_m5. vtk). The pipeline handling the mesh generation is orchestrated by Snakemake [MJL * 21], a popular (currently ∼ 7 citations per week) scalable workflow management system. In a few words, Snakemake is a modern version of GNU Make, whose syntax is close to Python. The workflow Snakemesh consists of two rules. The first rule meshes defines which meshes should be produced. The second rule cad2vtk generates a mesh from a CAD model and a metadata file (s|n|i)(\d{2})(c|u|b)_{extra}.
yaml containing the custom mesh options (for curvature-adapted, uniform, or boxembedded). To do so, this second rule runs a python script using Gmsh API, with a maximum of 8 threads. For each mesh, a log file (s|n|i)(\d{2})(c|b|u)_{extra}. txt is written with the corresponding console output, in order to record the history of the meshing task. Snakemake scans the workflow in a backward fashion, meaning that the input files are determined from the output ones. In other words, the purpose of the first rule is to state all meshes that should be produced. Afterwards, the second rule provides those meshes by identifying the corresponding input files accordingly, which are the CAD model and the metadata file. This backward identification is the key of the workflow definition, since the rules are mostly written with wildcards.
The use of Snakemake easily yields a sustainable dataset, since a rule is applied only if an output is either missing or older than the corresponding input. Gmsh  [GR09]  does not only mesh the volume, but also the feature entities as defined by the CAD model. In addition to the tetrahedral elements, there are triangle, edge and vertex elements (lower dimensional elements are conforming to the higher ones) to respectively discretize feature surfaces, feature curves and feature points. Those features are identified by the CAD with a tag (i. e. a positive integer), which corresponds to a physical group within Gmsh. Doing so, the corresponding mesh elements are created accordingly with the inherited CAD tag. Meshes are exported as vtk Datafile Version 2. 0, in ASCII mode. The used Gmsh git-version is written within the file header.
HexMe Anatomy The HexMe tetrahedral dataset is downloadable in a single file: hexme. zip (∼1. 5GB). Alternatively, it is possible to download meshes one-by-one from the catalog (the catalog is mostly generated by Snakemake, using the report feature) The catalog is split into three categories (i, n, s), that correspond to the model categories (respectively: industrial, nasty, simple). Within each category, there are three subcategories (b, c, u), that correspond to the mesh types (respectively: box-embedded, curvature-adapted, uniform). An entry of the catalog is described by two pictures (a cut view and a quality histogram), a. pdf file, a. vtk mesh, the corresponding log file (s|n|i)(\d{2})(c|u|b)_{extra}. txt and the metadata file (s|n|i)(\d{2})o_{extra}. yaml related to the CAD model. A summary of the mesh is also available in a.
The meshing community is invited to actively contribute to the HexMe dataset, by creating pullrequests for proposing new models and/or filtering of existing ones. There will be releases with appropriate git-tag, whenever the dataset has been significantly updated. How to contribute. The workflow supports CAD models with extensions. geo,. step,. stp, or. brep. For each CAD model, four metadata files need to be defined. The first one specifies general information about the model # meta/(i|n|s){\d{2}}o_{extra}. yaml description:. license:. name: (i|n|s){\d{2}}o_{extra} original: original/(i|n|s){\d{2}}o_{extra}. (geo|stp|brep|step) references:. The other three files define the desired meshing parameters for each mesh type (c|u|b), respectively: # meta/(i|n|s){\d{2}}c_{extra}. yaml gmsh. option.
Example Evaluation To demonstrate the value of HexMe, we challenge a state-of-the-art pipeline of frame-field based hexahedral meshing with our dataset and evaluate its robustness. Robustness issues regularly stem from hard constraints induced by feature points, curves and surfaces. Consequently, our primary concern here is to quantify how faithful the tagged input features are reproduced in the generated hexahedral meshes. Note that this is a novel way to assess robustness, which is more fine-grained than simply counting passed or failed per model. It is also more meaningful in our setting than other straightforward choices (percentage of hexahedral elements, or distortion of the integer-grid map for instance) where excellent numbers could be reported despite some feature constraints are violated.
Conclusion The contributions of HexMe are twofold. On the one hand, it is a tetrahedral dataset with tagged feature entities and on the other hand, it is a transparent workflow. The main objective of HexMe is to provide tetrahedral meshes for the meaningful assessment of hexahedral meshers and associated auxiliary tools such as 3D frame-fields. In the future, the choice of meshes will evolve together with the progress of hexahedral meshing techniques. Therefore, we publish the full workflow to ensure that the HexMe dataset can be updated easily and does not become outdated in the future. The selected 63 CAD models come from several databases. Their origin and license are recorded within a metadata file. There are three categories of CAD models, and three types of meshes per CAD model. The 189 meshes are produced thanks to a workflow that is defined with Snakemake.
The CAD features are reproduced by Gmsh as lower dimensional elements (vertices, edges, triangles), with corresponding tags. The meshes are expressed as. vtk Datafile Version 2. 0 in ASCII mode. There are two ways to access the HexMe tetrahedral mesheseither by downloading all of them in a 1. 5GB. zip file, or by picking individual ones from the catalog. In addition to the meshes and log files, the catalog yields the metadata related to the CAD model, a summary about the mesh, and information related to the workflow. The files that are involved in the workflow, are available on a GitHub repository. From this git repository, it is possible to raise issues and/or pull requests to improve the dataset or the workflow. The commit corresponding to the version used for this paper has been tagged HexMe-1.
Background Strategies to modulate the tumor microenvironment (TME) have opened new therapeutic avenues with dramatic yet heterogeneous intertumoral e cacy in multiple cancers, including glioblastomas. Therefore, investigating molecular actors of TME may help understand the interactions between tumor cells and TME. Immune checkpoint proteins such as a Cluster of Differentiation 80 (CD80) and CD86 are expressed on the surface of tumor cells and in ltrative tumor lymphocytes. However, their expression and prognostic value in glioblastoma (GBM) microenvironment is still unclear. Methods In this study, we investigated, in a retrospective local discovery cohort and a validation TCGA dataset, expression of CD80 and CD86 at mRNA level and their prognostic signi cance in response to standard of care.
Furthermore, CD80 and CD86 at the protein level were investigated in the discovery cohort. Results Both CD80 and CD86 are expressed heterogeneously in the TME at mRNA and protein levels. In a univariate analysis, the mRNA expression of CD80 and CD86 was not signi cantly correlated with OS in both local OncoNeuroTech dataset (ONT) and TCGA datasets. CD80 and CD86 mRNA high expression was signi cantly associated with shorter PFS (p<0. 05). These ndings were validated using the TCGA cohort; higher CD80 and CD86 expressions were correlated with shorter PFS (p<0. 05). In multivariate analysis, CD86 mRNA expression was an independent prognostic factor for PFS in the TCGA dataset only (p<0. 05). Conclusion CD86 could be used as a potential biomarker for the prognosis of GBM patients treated with immunotherapy however additional studies are needed to validate these ndings.