dataesr/scientific-paragraphs-categorization huggingface dataset:
To generate a quantitative description of ovule primordia with respect to cell number, size, and shape according to cell labels, layers, domains, ovule stage, and genotype, we developed an interactive, R-based interface named OvuleViz. The interface imports cell descriptors exported from segmented image files and enables multiple plots from a user-based selection of (sub)datasets (Figure  1 -figure supplement 1A-C, Materials and methods). This work generated a reference collection of annotated, 3D images capturing ovule primordium development at cellular resolution from emergence until the onset of meiosis. The collection of 92 segmented images, comprising a total of 7763 annotated cells and five morphological cell descriptors (volume, area, sphericity, prolate and oblate ellipticity), provides a unique resource for morphodynamic analyses of ovule primordium growth.
, 2001; Bouquin et al. , 2003; Brodersen et al. , 2008). We generated and analyzed a new dataset of 59 annotated 3D digital kat and corresponding wild-type ovule primordia at stages 0-III, 1-I, and 1-II (Figure  4 -source data 1). kat mutant primordia clearly showed an increased size and a more isotropic shape (Figure  4A ), being 1. 5 times bigger in volume than wild-type primordia (p=0. 007, stage 0-III, Figure  4B ) with a smaller aspect ratio (p<0. 01 stages 1-I, 1-II, Figure  4C ). Because the width-to-height ratio does not inform on the shape at the flanks, we derived an equation to estimate the 'plumpiness' of the primordia: primordia with rounder flanks will have a higher bounding box occupancy, that is, the volume fraction of a fitting, 3D parallelepiped (bounding box) effectively occupied by the primordium (Figure  4D , left), than straight digit-shaped ovules of the same aspect ratio.
Quantification of cell number, size and shape and interactive plotting using OvuleViz Several cell descriptors were retrieved using the ImarisCell' Statistics function and exported as. csv files: cell area, cell volume, cell sphericity, cell ellipticity (oblate and prolate). We developed an interactive R-based data plotting interface, OvuleViz, reading the Imaris-derived data within the exported files ordered by genotype then stages (Figure  1 -figure supplement 1). OvuleViz is freely available at https://github. com/barouxlab/OvuleViz (Pires, 2021) , and is based on a shiny interface for R. Ovu-leViz allows plotting selectively one or several of the cell descriptors for chosen stages and genotypes, along different visualization (scatter plots, box plots, histograms). In addition, OvuleViz retrieves the cell number from the number of objects in the.
3D quantification of ovule volume and shape using IMARIS Ovule volume and shape were quantified on 3D segmentations using IMARIS software, to compare katanin and wild-type genotypes. For each ovule, all segmented labeled cells were duplicated and fused as a single cell object. The ImarisCell' Statistics function was used to retrieve 'cell volume' and 'bounding box OO' (object-oriented 3D bounding rectangle, exporting the minimum (A), mid (B) and maximum (C) lengths of the object bounding rectangle). Width to Height ratio was calculated by dividing the maximum (C) length by the mean of mid (B) + minimum (A) lengths. Bounding box occupancy was calculated as the ratio of ovule volume by the bounding box volume.
Statistical analysis To identify the main cellular descriptors -cell area, cell volume, sphericity, ellipticity oblate, ellipticity prolate -explaining variance of each cell types, at each ovule primordium growth stages and between genotypes, we used principal component analysis (PCA) on a cells' descriptors matrix. PCA was executed using the R software version 3. 6. 3. In all different subsets of data, PCA was performed by singular value composition of the centred and scaled data matrix. Data entries with missing values were removed before analysis. All Cell data as exported by Imaris were uploaded but only cells with a 'Cell Label' as described in the method section '3D imaging' were plotted. For visualization of PCAs, only the first two principal components were represented in both score and loading plots.
To determine if the means between two datasets were significantly different we used two-tailed t-test when the data were normally distributed (n > 30). For all datasets with n < 30, we assume that normality was not possible to assess properly (as small samples most often pass normality tests), thus we used non-parametric tests. Wilcoxon Signed-Rank two-tailed test was used for paired quantifications, and Mann Whitney U two-tailed test was used for unpaired quantifications. Tests were performed in Excel or in R (wilcox. test function). To compare ovule proportions, we used two-tailed Fischer's Exact test, using https://www. langsrud. com/fisher. html, available online. Variability was assessed using the Standard Error of the mean (SE), indicated in the graphs and/or the supplemental data when applicable.
For some datasets, boxplots were used to improve visualization of data distribution. The number of samples and biological replicates for each experiment are indicated in the figure and/or figure legends and/or supplemental data. Appendix 1 1. 1 Mass spring-based ovule growth simulations at cellular resolution 1. 1. 1 Template preparation and initialization -Regular grid of cells Following observation of microscopy images of longitudinal sections of carpels showing the placenta, a regular grid of cells has been created with MorphoGraphX (Barbier de Reuille et al. , 2015, https://morphographx. org/, Cell Maker plugin). The grid is composed of 28 columns by 5 rows of square cells of side 5. 44 mm, the cell rows are staggered with respect to each other by 1. 36 mm in the longitudinal direction.
The mass spring mesh is constituted by interconnected segments outlining the cells boundaries and representing the cell walls. The segments have a varying length of 1. 36 mm and 2. 72 mm, the top margin of the grid of cells has a segment length of 5. 44 mm (this is to increase mass spring stability w. r. t. compressive forces generated during ovule growth). A dual mesh connecting the cell centers is associated to the mass-spring mesh. The template initialization as well as the proper growth simulations are run with an in-house built plugin for MorphoMechanX (https://morphographx. org/morphomechanx/) available at a git repository: github. com/GabriellaMosca/MassSpring_2DovuleGrowthModel (Mosca, 2021b). Once the template has been generated, it needs to be initialized with the parameters illustrated in Figure  3 -figure supplement 1B.
Confocal microscopy-based template preparation A confocal stack from an almost flat placenta (dataset 2404_275a) has been used. The raw stack has been loaded in MorphoGraphX and by means of the clipping planes, a longitudinal central section of it has been selected. Then, the same procedure described in the MorphoGraphX manual (available here: https://www. mpipz. mpg. de/4085950/MGXUserManual. pdf) for generating 2 1 2 D mesh has been followed, with the only difference that the surface mesh is generated with the CellMaker plugin as a rectangle with side length 0. 5 m. The signal projection depth has been calibrated so that only the walls of the midsection get projected (the longitudinal mid section has been as well selected accordingly to not display spurious walls from cells at different depth) and set to the value of 1.
The propagation rules for fixed high concentration are not equivalent to a proper canalization model (which was not the aim of these simulations) and sometimes manual curation during the simulation time evolution has been required to ensure an uninterrupted vertical line of cells with fixed high concentration. To assess the stability of the reference simulation (MS-Model 2), a robustness analysis has been performed by varying independently stiffness and growth signal intensity of 10% around the reference value. The results are qualitatively compatible with the reference model and available in the git repository: github. com/GabriellaMosca/MassSpring_2DovuleGrowthModel/tree/master/ robustnessAnalysis.
2D FEM based simulations for ovule growth -continuous limit The finite element method (FEM) simulations are handled in MorphoMechanX by an in-house developed tool available at the git repository github. com/GabriellaMosca/FEM_2DOvuleGrowthModel (Mosca, 2021c). They run on a 2D mesh tessellated with triangles which constitute linear membrane elements with a mathematically prescribed thickness (not relevant in this type of simulation though).
3 '. An additional stiffness, negatively proportional to the growth signal previously computed through diffusion, is assigned to the E1E3 plane by running Model/CCF/06b Material Properties based on Morphogens (for faces) in all the models as it creates a sharper delimitation of the ovule protrusion w. r. t to the placenta (for a comparison see the outcomes at the git repository github. com/GabriellaMosca/FEM_2DOvuleGrowthModel/tree/master/constantStiffness) Y E1E3 þ ¼ g MAX growth signal À growth signal MAX growth signal À MIN growth signal (1. 14) where Y E1E3 is the Young modulus in the isotropic plane, g stands for the proportionality factor (set to 400), and growth signal is the signal obtained from the diffusive process Model/CCF/Fem Diffusion Growth(make sure to put the proper field signal name in the dialog box, its name was specified in Model/CCF/Fem Diffusion Anisotropy/05 Morphogen Visualize).
The Young modulus distribution is the one shown in Figure  3 -figure supplement 1A. In the mass springs simulation the stiffness was instead assigned constant in the template, as a replication of what done with the FEM simulation produced a numerically unstable model (see github. com/GabriellaMosca/MassSpring_2Dovule-GrowthModel/tree/master/variableStiffness). We argue that for mass spring models, the effect of softer cell wall is already implicitly encoded in the combination of signal based growth and passive strain based growth, so that adding such an effect explicitly exaggerates the dishomogeneity between faster and more slowly growing cells. In the FEM simulation, lacking cellular resolution, intead, the passive strain based growth is mostly acting as a relaxation to accumulation of stresses due to tissue conflicts.
Robustness analysis As done with mass springs, a robustness analysis around the reference model (FEM-Model 1) has been performed by varying independently stiffness and intensity of growth signal of 10% around the reference values. As can be observed by examining the results in the git repository github. com/ GabriellaMosca/FEM_2DOvuleGrowthModel/tree/master/robustnessAnalysis such variations do not affect significantly the outcome, confirming the stability of the reference model. 1. 4 3D analysis of ovule morphology and connectivity 1. 4. 1 Ovule segmentation export from IMARIS A in-house tool (ExportImarisCells, git repo github. com/barouxlab/ExportImarisCells, Mosca, 2021a) has been developed to export segmentations performed in the proprietary software  IMARIS (imaris. oxinst. com). This tool, to be launch within IMARIS, creates a cell-mask with a different integer value for each segmented cell and the value zero for everything else.
Connectivity analysis for the ovule layers The 3D Cell Tissue mesh saved as described in the previous section can be loaded in MorphoMe-chanX to perform a semi-automated cell layer classification and connectivity analysis. An in-house tool to be run within MorphoMechanX has been developed to be able to detect semi-authomatically cell layers, after the user selects manually the L1 cells. If the user selects as well explicitly the pSMC/ MMC, this will be marked as so in the analysis ad the companion cells will be identified (both cell types are considered sub-cases on L2 layer cell classification). The tool is available at the git repository: github. com/GabriellaMosca/3DAutoLabeling-ShapeQuant (Mosca, 2021d). A cell is identified to be a L2 cells if it shares a portion of its wall with the L1 layer.
Acknowledgements We thank  S Strauss  ( Max Planck Institute for Plant Breeding Research, Cologne, Germany ) and  B Lane  ( John Innes Centre, Norwich, UK ) for providing code and assessment for mesh generation and simulations;  S Guyer  ( Bitplane, Switzerland ) and  M Lartaud  ( CIRAD, France ) for their help with IMA-RIS analyses;  O Leblanc  ( IRD Montpellier, France ) for his help with data visualization in R; the Montpellier imaging facility MRI, member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ) and the  Center for Microscopy and Image Analysis of the University of Zurich (ZMB)  for microscopy imaging infrastructures;  M Ueda  ( Nagoya University, Japan ),  I Siddiqi  ( Center for Cellular and Molecular Biology, India) ,  N de Storme  ( Katholieke Universiteit Leuven, Belgium ),  S Matsunaga  ( Tokyo Science University, Japan ),  M Tucker  ( University of Adelaide, Australia ),  R Dixit  ( Washington University in St.
Louis, USA ),  DW Ehrhardt  ( Carnegie Institution for Science, Stanford, USA ),  WC Yang  ( Chinese Academy of Sciences University ) and the  NASC  stock center for seeds. This work was funded by the  University of Zu ¨rich , the  IRD , a  PRCI grant  from the  Agence Nationale de la Recherche/Swiss National Science Foundation  (# ANR-16-CE93-0002/SNF-310030L_170167 ) to DA and CB; grants from the  Swiss National Science Foundation  (#  310030B_160336  to UG, #  IZCOZ0_182949  to CB), from the  Commission for Technology and Innovation  ( CTI grant  # 16997 ) to CB, from the  Baugarten Stiftung Zu  ¨rich to CB, a  CONACYT  fellowship (#  438277 ) to EHL, and a fellowship from the  Forschungskredit of the University of Zurich  (# FK-74502-04-01 ) to GM.
Software, algorithm IMARIS http://www. bitplane. com/ imaris/imaris RRID:SCR_007370 3D image processing software Bitplane AG, Switzerland Software, algorithm ExportImarisCells, This paper. plugin for IMARIS to export segmented cells in meshes for MorphographX. https://github. com/barouxlab/ ExportImarisCells (copy archived at swh:1:rev:50badce519f07cf529 c3abef765b58972fff70e6); Mosca, 2021a. Software, algorithm MorphoGraphX https://morphographx. org/ Software to perform 2D/3D segmentation and image analysis Software, algorithm MorphoMechanX https://morphographx. org/ morphomechanx/ Software to perform 2D/3D biomechanical simulations Software, algorithm MassSpring Models_ovuleGrowth2D This paper. DOI:10. 5281/zenodo. 4681169 plugin for MorphoMechanX, 2D MS-models simulation tool. https://github.
com/GabriellaMosca/ MassSpring_2DovuleGrowthModel (copy archived at swh:1:rev:a66b0496ba 51ca7674f0020ace723aa0b850470f); Mosca, 2021b. Additional information Software, algorithm FEM_2DOvule GrowthModel This paper. DOI:10. 5281/zenodo. 4681167 plugin for MorphoMechanX, 2D FEM-models simulation tool. https://github. com/GabriellaMosca/FEM _2DOvuleGrowthModel (copy archived at swh:1:rev:6b6c09f6039ce3e9d556 86d6b6e9dadf28b53e2f); Mosca, 2021c. Software, algorithm 3DAutoLabeling-ShapeQuant This paper. DOI:10. 5281/zenodo. 4681165 plugin for MorphoMechanX, automatic labeling of L1, L2, L3 layers and cell shape quantifier. https://github. com/Gabriella Mosca/3DAutoLabeling-ShapeQuant (copy archived at swh:1:rev:3138c3fb10891afbc1d48 d35395dc00ee58ce199); Mosca, 2021d. Software, algorithm OvuleViz This paper.
R-based, shiny interface for interactive plotting of Imaris data. https://github. com/ barouxlab/OvuleViz (copy archived at swh:1:rev:fd614aa1e80258928e e036191f26c3dd703d3141); Data availability The data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for all Figures. Segmented Image data are deposited in Dryad Digital Repository, available at: https://doi. org/10. 5061/dryad. 02v6wwq2c. Cell Statistics from these images are deposited as 'Segmented Dataset. csv' at https://github. com/barouxlab/Ovu-leViz (copy archived at https://archive. softwareheritage. org/swh:1:rev: fd614aa1e80258928ee036191f26c3dd703d3141). The following datasets were generated:.
Once a cell was selected, a second, infinity corrected setup, composed of a 5µm Gadolinium (Gd2O2S:Tb/ 6 LiF) scintillator and a combination of a 50 mm f. /1. 2 Canon lens and a 55 mm Heliflex lens was employed to achieve a 7. 4 µm pixel size over a 14 mm field of view. A series of 2600 projections, each 25 seconds long were acquired over a 360˚ rotation, leading to a total acquisition time of 18 h. In the 32 bits reconstructed images via the RX Solutions software which employs a Feldkamp (FDK) filtered back projection algorithm, the grayscale value represents the attenuation coefficient, so more attenuating regions are brighter. The volume obtained was 2575 x 2538 x 3397 µm 3. Further image analysis was performed using the Fiji software  (Schindelin et al. 2012 ).
Synchrotron X-ray tomography Synchrotron X-ray computed tomography (XRCT) using phase contrast was performed at the PSICHE beamline at the Soleil Synchrotron (Gif-sur-Yvette, France)  (King et al. 2016). At the beamline, the cell was mounted on a rotating plate and illuminated at room temperature with a pink Xray beam set at an average energy of 25 keV. A series of 1500 projections along a 180° rotation with an exposure time of 40 ms per projection was acquired. The field of view was restricted to 1. 3 x 1. 3 mm with a 0. 64 µm voxel size. The collected radiographs were reconstructed using the PyHST2 software  (Mirone et al. 2014). Given that the Li cell height comprising the 2 mm long PFA tube is larger than the field of view, two tomographic acquisitions were performed on the cell; one focusing on the top of the active cell to image the natural Li electrode and another one focusing on the lower half of the active cell to image the 6 Li electrode.
70 frames per second, optimized to ensure sufficient X-ray flux to have good signal to noise ratio. The volume obtained was 3422 x 3422 x 2614 µm 3 with an isotropic voxel size of 2 µm. The collected radiographs were reconstructed to produce a 3-dimensional (3D) stack via the RX Solutions software without correction of beam hardening and Fiji software was used for image analysis.
We present examples of diffuse emission in the LMC (H regions, supernova remnants, bubbles) and distant galaxies showcasing spectacular interaction between jets and intracluster medium. Among 14,333 infrared counterparts of the predominantly background radio source population we find that star-forming galaxies become more prominent below 3 mJy compared to active galactic nuclei. We combine the new 888 MHz data with archival Australia Telescope Compact Array data at 1. 4 GHz to determine spectral indices; the vast majority display synchrotron emission but flatter spectra occur too. We argue that the most extreme spectral index values are due to variability.
2006) , HERITAGE (HERschel Inventory of the Agents of Galaxy Evolution;  Meixner et al. 2010) , VMC (VISTA Magellanic Cloud;  Cioni et al. 2011 Cioni et al. , 2017)) , SMASH (Survey of the Magellanic Stellar History;  Nidever et al. 2017 ) and sections of the LMC surveyed by XMM-Newton  (Haberl 2014). At radio frequencies AGN and galaxies dominate the source counts. Hence, multi-wavelength surveys from X-ray to far-infrared (far-IR) combined with the new EMU ASKAP data provide a prime opportunity to study a large population of high redshift AGN in the direction of the LMC. These AGN, once found, while interesting in their own right, can be used as a reference frame for proper-motion studies of the LMC (e. g. ,  Kallivayalil et al. 2006; Costa et al. 2009; Pedreros 2011; van der Marel & Kallivayalil 2014; Vieira et al.
This paper is laid out as follows: Section 2 describes the data used to create the source lists. In section 3 we describe the creation of the source catalogue and compare our work to previous catalogues of point sources towards the LMC and SMC. In section 4 we discuss the Magellanic and Galactic foreground sources. Lastly, in section 5 we discuss the sources seen beyond the LMC and in section 6 we determine and discuss the spectral indices of sources in the new catalogue. The source catalogue and 888 MHz image are made available publicly.
Source detection We created the 888 MHz LMC ASKAP source catalog using the A source finder algorithm  (Hancock et al. 2012; Hancock et al. 2018 ). Variable noise is present across the image, from the combination of the multiple beams and artefacts from bright sources. To ensure accurate source thresholds the variable noise must be parameterised before source finding. The B (Background and Noise Estimation) routine in the AegeanTools software was used, with default parameters, to create root mean squared noise (RMS) and background level maps  4. The maps were then used, alongside the LMC image, with A , using default parameters to create initial source lists. A cursory visual inspection of the sources was carried out to verify detections from the initial source list. To ascertain the best A parameters, the algorithm was used on subsamples of the image with different values of (the number of  above the local RMS for sources to be deemed detected) to find the best value that obtains the least false positive detections without sacrificing too many true positive detections.
6 (where   ∝   ). This corroborates that the majority of sources are extragalactic synchrotron emitters. Extreme spectral indices (|| > 3) can be explained by variability. Future work will include more comprehensive analyses of the planetary nebulae and supernova remnants, as well as the radio variability. We are employing machine learning to classify a combined multi-wavelength data set and study the radio-IR relation for galaxies and quasars. The background population seen in the ASKAP image shows the potential for large-scale Faraday rotation and H absorption measurements throughout the LMC.
ACKNOWLEDGEMENTS We thank the anonymous referee for their feedback, which helped improve the paper. The Australian SKA Pathfinder is part of the  Australia Telescope National Facility , which is managed by  CSIRO. Operation of ASKAP is funded by the  Australian Government  with support from the  National Collaborative Research Infrastructure Strategy. ASKAP uses the resources of the  Pawsey Supercomputing Centre. Establishment of ASKAP , the  Murchison Radio-astronomy Observatory  and the  Pawsey Supercomputing Centre are initiatives of the Australian Government , with support from the  Government of Western Australia  and the  Science and Industry Endowment Fund. We acknowledge the  Wajarri Yamatji people  as the traditional owners of the Observatory site. CMP is supported by an  STFC  studentship awarded to Keele University.
M. J. M. acknowledges the support of the  National Science Centre, Poland  through  SONATA BIS  grant  2018/30/E/ST9/00208. Partial support for L. R. comes from  US National Science Foundation  grant  AST17-14205  to the  University of Minnesota. S. A. acknowledges financial support from the  Centre for Research & Development in Mathematics and Applications (CIDMA)  strategic project  UID/MAT/04106/2019  and from  Enabling Green Escience for the Square Kilometre Array Research Infrastructure (ENGAGESKA) ,  POCI-01-0145-FEDER-022217 , funded by the  Programa Operacional Competitividade e Internacionalização  ( COMPETE 2020 ) and FCT, Portugal. This research made use of the SIMBAD database, operated at  CDS, Strasbourg, France , and the  NASA/IPAC  Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.