dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantifying the accuracy of the shell-scan and propagative scan How do the shell and propagative scans compare with a full scan on a quantitative basis? To address this, we performed a full scan image of a wing imaginal disc. From this, we determined a ground truth epithelial shell with a manually curated surface-fit, using a shell-thickness of 3μm as in previous acquisitions. We then emulated the shell-scan and propagative-scan from the raw data for comparison with the ground truth image on a voxel by voxel basis. Figures  4a-b  reports the 3D visualizations of emulated shell-scan and propagative-scan by projecting the 2D maximum intensity projections on the 3D surfaces. The two images are very similar on qualitative ground. It then seems that most of the bright cell contours visible in the shell-scan have been captured in the propagative scan.
9 s for each step of the propagative scan in case of the example of Fig. 4 , and respectively ~12 s and ~1. 3 s on the emulated image of Fig. S5  (computation times obtained on a MacBook-pro labtop, with 2. 8 GHz Intel Core i7 with 16 Go RAM). In comparison with the typical time required to scan an equivalent volume with a confocal (~50 s for 50 × 1000 × 1000 voxels with μs dwell time) and the similar requirements on our set-up, the overtime associated with the computation is not significant. Speeding up computation, which has not been fully optimized, may be as simple as using a computing workstation instead of the current laptop, or compiling our Matlab script. A natural extension of our work will be to address how temporal redundancy could be used to optimize the scanning strategy in a time sequence.
Acknowledgements We thank  Sophie Brasselet ,  Philippe Réfrégier ,  Vincent Bertrand  and  Cathie Ventalon  for fruitful discussions on the project. We thank  Cécile Leduc ,  Amaury Badon  and  Annafrancesca Rigato  for commenting and proofreading the manuscript. This work was funded by the following agencies:  Agence Nationale de la Recherche  ( ANR-18-CE13-028 ,  ANR-17-CE30-0007 );  Excellence Initiative of Aix-Marseille University -A*Midex  (capostromex), a  French Investissements d'Avenir programme ;  Centre National de la Recherche Scientifique, Mission pour l'Interdisciplinarite ;  Institut Carnot  star; SATT-SudEst; The project leading to this publication has received funding from the  "Investissements d'Avenir "  French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University  -A*MIDEX.
| IMC data analysis Pre-processing and single-cell segmentation were performed following the instructions on the Bodenmiller Github repository (https:// github. com/Boden mille rGrou p/ImcSe gment ation Pipeline). After single-cell generation, all subsequent analyses were performed using R bioconductor. For cell-type annotation, we manually gated major cell types of interest using the cytomapper R package. 20 The following markers were used to define cell types: CD8 + T cells (CD3 + , CD8 + ), CD4 + T cells (CD3 + , CD8 -, CD4 + ), keratinocytes I (E-cadherin + , Filaggrin -), keratinocytes II (Filaggrin + ), Langerhans cells (LC; E-cadherin + , Langerin + ), macrophages (CD163 + ), neutrophils (polymorphonuclear leukocytes (PMN); MPO + ), plasmacytoid dendritic cells (pDC; CD303 + ), vasculature (CD31 + ).
Roughly, half of all cells were manually gated using the markers from above. T cells were clustered with CD3, CD4, CD7, CD8, CD27, CD45RA, CD45RO, CD57, After prediction of a cell-type for all unlabeled cells the classification results were inspected on images and additional rounds of cell labelling performed if needed. We excluded 1 HC sample, since it contained highly increased numbers of CD3 + T cells and was obtained from an excision of peritumoral tissue. Cellular interactions were quantified using our published neighborhood algorithm  22  and a R implementation thereof (https:// github. com/Boden mille rGrou p/neigh bouRhood). All IMC data is available via a zonedo online repository (https://doi. org/10. 5281/ zenodo. 5036924), all R code used for IMC data analysis in this study is available via our github (https://github.
| RNA-seq and proximity extension assay data analysis A detailed description of the analysis is found in the Supplementary Materials and Methods. Genes with a false discovery rate (FDR) of less than 0. 05 were included in this study (Table  S2 ). All RNA-seq data performed in this paper can be found on the NCBI Gene Expression Omnibus (GEO) under accession number GSE161225. Enrichr, a gene list enrichment analysis tool, was utilized to search for enriched Gene ontology biological processes (GO BP). The data of a proteomic multiplex assay in Normalized protein expression (NPX) format were imported, processed by Olink-R Package (https://github. com/ge112 32002/ OlinkR). The statistical comparison of protein levels between groups was performed as previously described. 26 The age was 55 ± 7 years.
| Cell-cell contacts in COVID-MDR, DRESS and MDR are comparable to HC skin We investigated the cellular contacts amount the cell types classified in this dataset and compared them to HC samples (Figure  3E ). Overall, the patterns of interaction between the indications were very similar. Despite the large differences in interaction counts, we did not observe interactions that were absent in any of the indications while present in another. We also applied our previously published neighborhood algorithm and could not detect recurrent significant changes (data not shown). We conclude that the overall cellular interactions in drug rashes stay intact given that larger amounts of immune cells are present.
CD69, CD134, CLA, FoxP3, GrzB, Ki-67 and macrophages were clustered with CD1c, CD11b, CD11c, CD14, CD16, CD40, CD68, CD163, CD206, CD370, CLA, HLA-DR, DC-Lamp, STING. For the remaining cells in the dataset, we used a random forest classifier to assign cell types based on uniquely labelled cells. 21 Therefore, all labelled cells were spit in training and test data (70:30). A random forest model was trained on the training-set (10 fold cross-validation, mtry parameter optimization) and the model performance validated on the test-set.
IMC mapping of COVID-MDR, MDR and DRESSTo explore the phenotype and topographical distribution of the T celland pan-leukocyte-infiltrates in COVID-MDR, we designed and applied a 36 antibody-IMC panel to COVID-MDR, MDR, DRESS and HC skin sections (Table2). After single-cell segmentation, we used a manual gating strategy to label cell types of interest followed by classification of the remaining, unlabeled cells in the dataset (Methods). The analysis of these cell types (Figure2A-D) showed many overlaps between the indications, but also some differences. In a quantitative comparison (Figure2B), monocyte/macrophages (Mo/Mac) were increased in number in all three conditions (COVID-MDR, DRESS, MDR) compared to HC. CD8 + but not CD4 + T cells were more prominent in COVID-MDR than in DRESS, MDR, and HC.
3. 3 | 3 IMC mapping of COVID-MDR, MDR and DRESSTo explore the phenotype and topographical distribution of the T celland pan-leukocyte-infiltrates in COVID-MDR, we designed and applied a 36 antibody-IMC panel to COVID-MDR, MDR, DRESS and HC skin sections (Table2). After single-cell segmentation, we used a manual gating strategy to label cell types of interest followed by classification of the remaining, unlabeled cells in the dataset (Methods). The analysis of these cell types (Figure2A-D) showed many overlaps between the indications, but also some differences. In a quantitative comparison (Figure2B), monocyte/macrophages (Mo/Mac) were increased in number in all three conditions (COVID-MDR, DRESS, MDR) compared to HC. CD8 + but not CD4 + T cells were more prominent in COVID-MDR than in DRESS, MDR, and HC.
MucilAir TM stained samples were visualized primarily with a STELLARIS 8 inverted microscope (Leica Microsystems, Mannheim, Germany). A super continuum white light laser tunable between 440 and 790 nm was used for excitation and focused through an HC PL APO CS2 ×40 NA 1. 1 water immersion or a HC PL APO CS2 ×63 NA 1,3 glycerol immersion objective. Emission signals were captured with Power HyD Detectors. The system was controlled with Leica Application Suite (LAS) X v4. 1 software. 3D images were directly processed using LAS X 3D module. The excitation wavelength and detection window are given in parentheses for each fluorophore: Hoeschst (405 nm; 410-470 nm); AlexaFluor488 (499 nm; 504-533 nm); AlexaFluor555 (553 nm; 558-600 nm); AlexaFluor647 (653 nm; 658-700 nm). Samples of hamster trachea were imaged with an inverted Zeiss LSM 710 confocal microscope controlled by the ZEN pro 2.
Image analysis Quantitative image analysis of the ZO-1-associated tight junction pattern. The surface of the epithelium contained in each microscopy z-stack was extracted using the Zellige program (C. Trébeau, R. Etournay, manuscript in preparation): the tissue 3D-manifold is determined based on both the detection of intensity maxima along the z direction and the spatial correlation of adjacent maxima along the x, y directions, with a maximum z difference Δz = ±1 between adjacent maxima. The 3D-manifold is then projected onto a plane. The TissueMiner software was then used to segment and quantify the cell apical surface area and the cell neighbor number  68. The Wilcoxon test was used to compare the median cell area between conditions. The Welch corrected t-test was used to compare the average neighbor number between conditions.
Quantifications of β-tubulin+ area and spike+ cells in epithelial cultures. Confocal images of samples stained with anti-β-IV-tubulin and anti-spike antibodies were first imported and visualized by Z-stack projection in the ImarisViewer 9. 5. 1 software. Images were then thresholded on the tubulin signal (0. 06) using the FIJI software v2. 1. 0, and the tubulin+ area was measured with the area measurement tool. The area was normalized to the total area of the picture. To quantify the percentage of spike+ cells, the numbers of Hoechst-stained nuclei and of spike+ cells were manually counted on maximal intensity projections, and the percentage was computed as (spike+ cell number/nuclei number) × 100. Quantification of β-tubulin depth intensity profile in cilia. Samples were imaged at ×63 magnification using the STELLARIS 8 microscope (Leica Microsystems), and image analysis was carried out using customized scripts written in Python 3.
Cell segmentation An ImageJ user-guided tool was developed to extract single cells in the epithelium. Three cell categories were defined: cell from a mock-infected sample (mock), cell expressing the spike antigen in an infected sample (spike+), and bystander cell not expressing the spike antigen in an infected sample (spike-). A dataset of 79 mock, 85 spike+, and 71 spikecells was extracted from 2 mock samples and 4 SARS-CoV-2-infected samples.
Acknowledgements We thank  Florence Guivel-Benhassine  for help with virus titration,  Charlotte Calvet  for help with sample preparation for scanning electron microscopy,  Sylvie Van der Werf  for the SARS-CoV-2 isolate used in this study, and  Nicolas Escriou  for the gift of a SARS-CoV-2 spike antibody. This work was supported by:  Institut Pasteur TASK FORCE   SARS COV2  ( TROPICORO  project), the  Urgence COVID-19 Fundraising Campaign of Institut Pasteur  ( COROCHIP  project),  DIM ELICIT Region Ile-de-France , and  Agence Nationale de Recherche sur le SIDA et les Maladies Infectieuses Emergentes (ANRS ; project  19052 ) (L. A. C. ); the  Vaccine Research Institute  ( ANR-10-LABX-77 ),  ANRS ,  Labex IBEID  ( ANR-10-LABX-62-IBEID ), the  French National Research Agency (ANR ; projects " TIMTAMDEN "  ANR-14-CE14-0029 , " CHIKV-Viro-Immuno "  ANR-14-CE14-0015-01 ), the  Gilead   HIV cure program ,  ANR/Fondation pour la Recherche Médicale (FRM)   Flash Covid PROTEO-SARS-CoV-2  and IDISCOVR (O.
S. );  Institut Pasteur TASK FORCE SARS COV2  and  ANR Flash Covid CoVarImm  (D. D. );  Institut Pasteur TASK FORCE SARS COV2  ( Neuro-Covid  project) (H. B. ). The Lledo lab is supported by the life insurance company " AG2R-La-Mondiale ". The  UtechS Photonic BioImaging (Imagopole)  and the  UtechS Ultrastructural BioImaging (UBI)  are supported by the  ANR  ( France BioImaging ;  ANR-10-INSB-04 ; Investments for the Future). R. R. is the recipient of a Sidaction fellowship, N. S. of a  Pasteur-Roux-Cantarini fellowship , and St. G. of a MESR/Ecole Doctorale  B3MI ,  Université de Paris  fellowship. S. L. is supported by  FRM  (fellowship  ECO201906009119 ) and by " Ecole Doctorale FIRE-Programme Bettencourt ".
Code availability The image analysis scripts used for computing β-tubulin IV, cytokeratin-5, and Foxj1 distributions are available from S. R. upon reasonable request. The Zellige script used to extract the surface of the epithelium in ZO-1 labeled samples is available from R. E. upon reasonable request. The TissueMiner toolkit used to analyze perturbations in epithelial packing is available at: GitHub https://github. com/mpicbg-scicomp/tissue_miner#about and is referenced in ref. 68  at https://doi. org/10. 7554/eLife. 14334.
2004; Dawson & Tregoning 2007; Remy et al. 2014; Peltier et al. 2017). When different types of data are used, such as GNSS and InSAR, in which the number of measurements and the uncertainties are also different, the question is raised of the data weighting required for an accurate determination of the sources of displacement. Given that the number of sample points of GNSS is less than that of InSAR, GNSS data have little weight on the inversion result when only the intrinsic data uncertainties are taken into consideration, as advocated by  Tarantola (2005)  and applied for instance by  Árnadóttir et al. (2004). Recognizing that this approach might not be sufficient to counterbalance uneven numbers of points in data sets, several weighting strategies have been developed. Fukuda & Johnson (2008 , 2010)  estimated the optimal data weighting simultaneously with the model parameters.
Fukushima et al. (2005)  have shown that InSAR noise correlation decreases exponentially with distance. We follow their formulation of the covariance matrix defined as: C d (d ) = σ 2 × e -d /a (4) where d is the distance between two points, σ 2 is the noise variance, and a is the correlation length. As shown by  Tarantola (2005) , the variance σ 2 results from (1) errors in the data and (2) errors of the model (C d = C i nt er f er og r am + C mod el ). This variance is estimated through the residuals of preliminary inversions. For Piton de la Fournaise interferograms covering five eruptions,  Fukushima et al. (2010)  use best-fitting model residuals to determine that a variance of 5 × 10 -4 m 2 and a correlation length of 850 m are representative of both data and modelling errors. These values are the one taken for this study.
Criteria for models comparisons Because our objective is to have the inversions fit the observed InSAR data and to accurately determine the geometry of an intrusion, we base the comparison of each final model on three criteria. The first is the RMS of the residuals. However, such absolute criterion does not give information on the goodness of the fit relative to the intrinsic magnitude of the signal but rather indicates the magnitude of residual. Indeed, a RMS of 1 cm on a signal of 40 cm, is indicative of a much better model than an RMS of 1 cm for a signal of 10 cm (regardless of any noise). Consequently, to estimate residuals relative to the data set average magnitude and to allow for the cross comparison of results between inversions having different cost criteria, we assess the goodness of fit of the best model by using the percentage of explained data (%E D), defined as: %E D =      1 - U r e f -U mod T U r e f -U mod U T r e f U r e f      × 100 (8) where U r e f and U mod are the reference and modelled displacements on the full interferograms (without any subsampling), respectively.
Biased inversions arising from unbalanced data weights To test whether the relative magnitudes of data sets bias the inverted sources, we carry out Conventional inversions on three synthetic dykes: N140  2 ). These dykes are characterized by a high relative weight (W D sc ≈1), a low relative weight (W D sc ≈0. 1) and a well-balanced relative weight (W D sc ≈0. 5), respectively (see Table  1  and Fig. 1 ). LOS displacements were computed, then inverted following the Conventional inversion method described in Section 2. 1. Here, we only discuss how well the data are explained. Detailed tests on the way a dyke is biased will be conducted in the following section. • E-60 • E, N140 • E-60 • W and N90 • E-60 • S (see Fig. The inversion of a N140 • E-60 • E dyke, in which the weight of the descending data dominates (W D sc ≈1), better explains the descending than the ascending one (94% versus 80%).
2010; Richter & Froger 2020 ). This characteristic is shown both on the cumulated displacements from October 2010 to April 2020 (Supporting Information Fig. S8 ), as well as on the displacement patterns of individual rift zone eruptions, whether they took place in the north, middle or south rift zones (Supporting Information Fig. S9 ). Such an asymmetrical displacement produces significantly different patterns for InSAR data acquired in ascending and descending orbital passes. Typically, descending data commonly show one main displacement pattern to the east of the eruptive fissure, corresponding to a ground motion towards the satellite, whereas ascending data show a displacement pattern exhibiting two lobes with one corresponding to a ground motion towards the satellite and one away from it.
The better a model, the lower the criteria. We found that the AIC is lower when considering the curvature parameters, indicating that models with curvatures are preferable (see Supporting Information Fig. S12 ). Moreover, thanks to the curvature parameters, the near surface dip of the intrusion is sub-vertical which is consistent with field data showing mostly vertical dykes  (Letourneur et al. 2008). Whereas, without curvatures, the intrusion has a much lower dip (≈50 • ). The curvature leads to a transition from a sill at depth to a dyke shallower in the edifice. Such transition was already found at Piton de la Fournaise for the May 2016 eruption  (Smittarello et al. 2019b). It was also observed at Fernandina Volcano in Galápagos in the case of circumferential intrusion  (Chadwick et al.
ACKNOWLEDGMENTS This research was financed by the  French Government Laboratory of Excellence  initiative  n°ANR-10-LABX-0006 , the  Region Auvergne  and the  European Regional Development Fund. This is Laboratory of Excellence ClerVolc contribution number 473. All interferograms were produced with the Diapason software and inversions have been performed on the supercomputer facilities of the  Mésocentre Clermont-Auvergne. We are thankful to  Delphine Smittarello  for constructive discussion on data weighting and for proofreading this manuscript. Finally, we thank  Frances van Wyk de Vries  for proofreading the English of this manuscript.
DATA AVAILABILITY InSAR data were acquired within the framework of the Indian Ocean InSAR Observatory (OI 2 /OPGC/SNOV/INSU), where the interferograms used in this study are available on the CASOAR link (https://wwwobs. univ-bpclermont. fr/casoar). Otherwise, unwrapped interferograms, meshes and results of simulations can be downloaded via the Zenodo website (https://doi. org/10. 5281/zenodo. 4629171). To launch inversions presented in this study, the DEFVOLC software is available online on the OPGC website (http://opgc. fr/defvolc).
Heterogeneity Between Groups In order to study the possible differences in the language values of the agents belonging to different communities, we first detect the structural communities within the network using the Louvain community detection algorithm  (Blondel et al. , 2008)  as implemented in NetLogo's nw extension package, which detects communities by maximizing modularity based on the connections agents share with each other, and not on the agents' language values (see Figure  4 ). Since the network is static, we then use the detected communities to compute the language value of each community for each iteration. Our measure of heterogeneity between groups is the standard deviation of these mean language values across communities. Thus, a low number indicates that all communities have approximately the same mean language value, whereas a high number indicates that the communities have rather different language values.
t(e g ) = (e g+w -e g ) ω * 10, 000 On the rounded t(e g ) values, we find the first value of g, g stabilization , when the rounded value of t(e g ) = 0, and we stop if for 50 consecutive steps (i. e. , g ∈ [g stabilization. (g stabilization + 50)]), there is no change, t(e g ) = 0; in this case, the stabilization time is the first moment where there was no change, namely g stabilization. Our framework is implemented in NetLogo 6. 1. 1 (see here), the experiments were run on an Intel(R) Xeon(R) W-2255, 64 Gb RAM system under Ubuntu 18. 04, and the results analyzed using R 3. 6. 3/Rstudio 1. 4 on machines running Ubuntu 18. 04 and macOS 10. 15 (Catalina); the full source code and results are available at Github (mathjoss/bayes-in-network). The runtimes were between 6 h (scale-free networks) and 3 days (random networks) for the main analysis.
FUNDING This project was funded by an  IDEXLYON  ( 16-IDEX-0005 ) Fellowship grant ( 2018-2021 ), the  NSCO doctoral school of Lyon , and the  LabEx ASLAN  ( ANR-10-LABX-0081 ) of the  University of Lyon  within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) of the  French National Research Agency (ANR).
Introduction Systems biology can predict and explain how a whole organism responds to a stimulus or a perturbation and how different components of the organism are connected. In particular, one way to unravel physiological mechanisms is the use of constraint-based metabolic modeling. Constraint-based metabolic modeling relies on genome-scale metabolic networks, which gather all the identified metabolic reactions of an organism according to its genomic sequences and current metabolic knowledge summarized in databases  (Gu, Kim, Kim, Kim, & Lee, 2019). It predicts and quantifies the metabolic pathways used thanks to mathematical constraints that encompass physiological states, such as the uptake rate of nutrients present in the environment. Initially developed for unicellular organisms, constraint-based metabolic modeling has been extended to diverse plant species such as Arabidopsis thaliana (Gomes de Oliveira Dal  'Molin, Quek, Saa, & Nielsen, 2015; Mintz-Oron et al.
The converted model was also manually curated to achieve mass balance for carbon, nitrogen, oxygen, phosphate and sulfur for each reaction. We name this novel tomato metabolic model Sl2183; it includes 2183 reactions, 2097 metabolites and 3433 genes. It is available in different formats: table format (Supplementary File 1), SBML format (Supplementary File 2), in the database BioModels  (Glont et al. , 2018)  under the ID XXX [once the paper is accepted], and in the MetExplore database to enable pathway visualization and omics mapping  (Cottret et al. , 2018)  https://metexplore. toulouse. inrae. fr/metexplore2/?idBioSource=6237. To transpose this genome-scale metabolic model into a multi-organ model, we considered the whole plant as three main organs (Figure  1 ). Each organ was modeled with a replicate of the Sl2183 genome-scale metabolic model with an organ-specific biomass equation.