dataesr/scientific-paragraphs-categorization huggingface dataset:
Cranial Description of Tanystropheus hydroides The fossilised skull of PIMUZ T 2790 is heavily compressed, obscuring much of its anatomy (Figure  1A ). However, the compression caused individual bones to disarticulate rather than deform, so they largely maintain their three-dimensional morphology. Furthermore, the dorsal part of the skull has been displaced posteriorly, essentially folding over the rest of the skull bones. Consequently, many of the bones of the posterior part of the skull are well-preserved underneath the large frontals, which cover them. The digital models rendered from the SRµCT dataset allow the elements to be placed into their in-vivo position, thus 're-assembling' the skull (Figures  1G  and 2 ). The skull is virtually complete and only parts of the nasal and anterior palatal elements are missing.
Synchrotron micro Computer Tomography acquisition and image processing The specimen was scanned at the BM05 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using propagation phase contrast synchrotron radiation micro-computed tomography. The experimental setup consisted of: filtered white beam (bending magnet, filters: 18 rods of Al, 5 mm in diameter and 10 cm in length, Mo 0. 25 mm) with a total integrated detected energy of 115 keV, a sample-detector propagation distance of 4 m and an indirect detector (2 mm LuAG scintillator 0. 25x magnification, CCD FReLoN 2K camera) producing data with a measured isotropic voxel size of 46. 76 µm. To image the full sample, the centre or rotation was shifted to increase the lateral field of view by ~30%, and 77 acquisitions were necessary on the vertical axis (keeping a 50% overlap between consecutive scans).
Each acquisition consisted of 2999 projections of a total integration time of 0. 3 seconds (10 frames of 0. 03 second per projection in accumulation mode  [51] ) over a rotation of 360°. Tomographic reconstruction was achieved with PyHST2  [52] , using the single distance phase retrieval approach  [53]. Post processing included: modification of the bit depth from 32 bits to 16 bits as a stack of tiff, merging of the 77 datasets using a weighted average on overlapping parts, ring correction  [54] ; cropping of the volume. The data was segmented and reconstructed in Mimics Research v19. 0 (https://biomedical. materialise. com/mimics; Materialise NV, Leuven, Belgium). The models of the individual elements were imported as PLY files into Blender 2. 7 (https://blender. org; Stitching Blender Foundation, Amsterdam, the Netherlands), a 3D modelling and visualisation program in which the elements could be rotated and moved independently, and images could be rendered, applying colours and texture to the models.
Acknowledgements We are very thankful for the full-body skeletal reconstructions of the two Tanystropheus species by  Beat Scheffold (PIMUZ). Christian Klug (PIMUZ)  is thanked for specimen access and allowing us to remove the skull of PIMUZ T 2790 from the larger slab. Dylan Bastiaans ,  Feiko Miedema ,  Christian Klug ,  Roger Benson ,  Erin Saupe ,  Jonah Choiniere ,  Roland Sookias ,  Richard Butler ,  Fabio Dalla Vecchia ,  Pernille Troelsen ,  Oliver Rauhut ,  Gabriela Sobral , and  Dennis Voeten  are thanked for fruitful discussions. We thank the  European Synchrotron Radiation Facility (ESRF), Grenoble, France , for provision of synchrotron radiation beamtime at beamline  BM05. This study is part of the  SNSF  project granted to  TMS  (grant no. 2'5321-162775 ). JMN was funded by a  Leverhulme Trust   Early Career Fellowship  ( ECF-2017-360 ).
We present a new model of the geomagnetic field spanning the last 20 years and called Kalmag. Deriving from the assimilation of CHAMP and Swarm vector field measurements, it separates the different contributions to the observable field through parameterized prior covariance matrices. To make the inverse problem numerically feasible, it has been sequentialized in time through the combination of a Kalman filter and a smoothing algorithm. The model provides reliable estimates of past, present and future mean fields and associated uncertainties. The version presented here is an update of our IGRF candidates; the amount of assimilated data has been doubled and the considered time window has been extended from [2000. 5, 2019. 74] to [2000. 5, 2020. 33].
(2013). Since groundbased observatories and satellite missions such as Oersted, SAC-C, CHAMP, or Swarm, have produced or are still generating a huge amount of data, a block inversion would be numerically impossible. This is why we decided to assimilate the data sequentially using a Kalman filter approach combined with a smoothing algorithm. The article is organized as follows: in the next section, the data selection criteria and the modeling strategy are detailed. We first present the different magnetic sources that are taken into account, we then show how they are a priori characterized, and how such prior information can be modeled through autoregressive processes. Based on these processes, the equations for the Kalman filter approach and the smoothing algorithm are then given. Finally, the methodology used to derive the different candidate models for IGRF-13 is explained.
Data For the moment, the Kalmag model only uses the vector field measurements of the CHAMP and Swarm loworbiting satellites. We sample CHAMP data between 2000. 59 and 2010. 63 at a rate of 1 datum every 5 s and only use measurements where the vector field magnetometer (VFM) and the star tracker (STR) instruments were functioning nominally. For the Swarm constellation, we consider from 2013. 8 the level 1B data from only the Alpha and Bravo satellites with baseline version 0505/0506, using simultaneous sampling every 10 s. For the construction of the IGRF-13 candidate models, which we will refer to as the Kalmag candidates, a two times lower sampling rate was used, impacting marginally the accuracy of the model. Furthermore, we only used data up to 2019. 74 for the Kalmag candidates, but now extended this to 2020.
33. Contrary to polar latitudes, where every vector field measurement is considered, for latitudes between 60 • north and south, only night-time data (when the sun is below the horizon), are kept. Furthermore, independently of the satellites locations, the following selection criteria are also applied: • The z-component of the interplanetary magnetic field (IMF) is positive in order to minimize the influence of the substorm auroral electrojet. • The index K p ≤ 2 0 to operate under conditions of low geomagnetic activity. All in all the dataset is composed of 2 985 442 vector field measurements for CHAMP and 4 606 159 for Swarm.
In our parametriza- tion of the problem, we therefore use four main parameters to characterize each source: (1) the amplitude A i ; (2) the (virtual) source radius a i ; (3) the time-scale amplitude M i and (4) the time-scale slope α i. In addition, because of the specific behavior of the dipole components, for the core field (see  Christensen and Tilgner 2004; Lhuillier et al. 2011 ) but also for the magnetospheric sources (see  Sugiuara 1963; Finlay et al. 2016) , the spatial and temporal properties of each source's dipole (except for the lithospheric field) is treated separately from the remaining SH coefficients. These parameters are directly estimated with a subsample of the dataset following the procedure described in "Parameter estimation" section.
Sequential assimilation The Kalmag model consists in a vector z containing the spherical harmonics coefficients of every magnetic source (including the SH expansion of the secular variation). With the decomposition detailed in "Magnetic sources" section, z contains 6624 SH coefficient entries. Its evaluation is performed with a Kalman filter algorithm which proceeds sequentially in two steps. In the first step, the forecast, the evolution of the mean model E[z] together with its associated covariance matrix z are predicted until observations become available. In the second step, namely the analysis, the model is corrected to better reflect the data through a Bayesian inversion. (10) ∂ t g i,ℓ,m (t) + 1 τ i (ℓ) g i,ℓ,m (t) = σ i 1 (ℓ) ωi 1 (t), (11) ∂ 2 t g i,ℓ,m (t) + 2 τ i (ℓ) ∂ t g i,ℓ,m (t) + 1 τ 2 i (ℓ) g i,ℓ,m (t) = σ i 2 (ℓ) ωi 2 (t), ( 12 ) z i (t + �t) = F i (�t)z i (t) + ξ i (t, �t), (13) F i (ℓ, �t) = exp [-|�t|/τ i (ℓ)].
Smoothing With the Kalman filter algorithm, one gets access to the distribution p(z k |d k ) , where d k corresponds to all the measurements up to iteration k. To obtain p(z k |d) the posterior distribution of the model at iteration k given the entire dataset d , one can apply a smoothing algorithm. In this study, we chose the formulation of  Rauch et al. (1965). Starting at the last iteration of the Kalman filter algorithm, the smoothing algorithm performs iteratively backward in time accordingly to the following steps: (16) E[z k|k-1 ] = F k-1 E[z k-1 ], (17) z k|k-1 = F k-1 z k-1 F T k-1 + ˜. ( 18 ) K k = z k|k-1 H T k H k z k|k-1 H T k -1 , ( 19 ) E[z k|d k ] = E[z k|k-1 ] + K k d k -H k E[z k|k-1 ] , (20) z k|d k = (I -K k H k ) z k|k-1 , (21) G k-1 = z k-1|d k-1 F T k -1 z k|k-1 , (22) E[z k-1|d ] = E[z k-1|d k-1 ] + G k E[z k|d ] -E[z k|k-1 ] , The combined Kalman filter-smoothing algorithm was also chosen by  Ropp et al.
Finally, the red dashed lines are the mean SV ± 2σ coming from 5 year forecast simulations. The first observation one can make is that whenever the secular variation derived from observatory data exhibits a smooth evolution, the latter is well reproduced by the Kalmag model. We can also notice that at least until 2019. 0, the CHAOS-6. 9 SV is always lying within 95. 4% confidence interval ( E[∂ t b c ] ± 2σ ) predicted by our model. The latter interval does not depend on the location where the SV is evaluated. Nonetheless, estimated uncertainties are globally larger for the radial component of the SV than for its azimuthal or longitudinal components. This is certainly due to the fact that the main field is dipole dominated. Because the Kalmag model is only derived from the CHAMP and Swarm measurements, data are missing between 2010.
These errors are displayed in Fig. 5  through their energy spectra evaluated at the Earth's surface. Whereas the error spectrum of the Kalmag candidate is drawn with a thick black line, the ones associated with the other candidates are shown with thin gray lines. Because the Kalmag model may exhibit a permanent bias, the difference between the model and the candidate may represent an erroneous evaluation of the error. Therefore the errors for each of the candidate models were also computed using another model taking recent data into account (up to March 2020), the CHAOS-7. 2 model of  Finlay et al. (2020)  which is also, in its first version, the parent model of the DTU candidate for IGRF-13. The spectra of these error evaluations are shown with dashed lines in Fig. 5. When compared to the Kalmag model, the Kalmag candidate appears to be the most accurate prediction of the main field in 2020.
Availability of data and materials CHAMP data can be downloaded at https ://isdc. gfz-potsd am. de/champ -isdc/ acces s-to-the-champ -data/. Swarm data can be downloaded at ftp://swarm -diss. eo. esa. int/Level 1b/Entir e_missi on_data/MAGx_LR/. The K p index can be downloaded at ftp://ftp. gfz-potsd am. de/pub/home/obs/kp-ap/. The IMF indices can be downloaded at https ://spdf. gsfc. nasa. gov/pub/data/omni/ low_res_omni/. The model presented here is available upon request.
Circulating Innate Immune Cells in Patients with Mild and Severe COVID-19 Exhibit Distinct Phenotypes To explore changes in circulating immune cell phenotype induced by SARS-CoV-2 infection, we first collected peripheral blood samples from a discovery cohort of 13 patients positive for SARS-CoV-2 (hereafter called COVID-19 patients) by RT-PCR and 12 patients suffering from flu-like symptoms but negative for SARS-CoV-2. The former group included 5 patients with mild disease and 8 patients with severe COVID-19 (Table  S2 ). After red blood cell lysis, we labeled peripheral blood cells with a panel of 25 antibodies recognizing immune cell surface markers  (Key Resources Table)  and analyzed them by spectral flow cytometry  (Figures 1A, S1A, and S1B). By pooling the data from the 25 control and COVID-19 patients and subjecting them to dimensionality reduction using the non-supervised Uniform Manifold Approximation and Projection (UMAP) for dimension reduction algorithm  (Becht et al.
, 2018) , we identified populations of CD4 + T cells, CD8 + T cells, CD19 + B cells, CD14 High monocytes, and CD15 + CD66b + neutrophils  (Figures 1B and 1C). We also identified HLA-DR High CD11b + and CD16 High monocytes as well as neutrophils expressing CD11b,  CD15, CD16, and CD64 (Figures 1B and 1C). Analysis and visualization, using UMAP dimensionality reduction to the cell surface marker expression datasets from control and mild and severe COVID-19 groups suggested differences in the repartition of cell populations (Figure  1D ). Patients with severe disease exhibited an expansion in the proportion of circulating neutrophils within the peripheral blood cell population (Figure  1E ) that was associated with an increase in their absolute numbers (Table  S2 ), as already reported  (Huang et al.
This would potentially identify individuals at greatest risk of rapid decline and highlight the need for pro-active management/intervention and intensive monitoring. This assay could be reinforced by analysis of HLA-DR Low classical monocyte and CD16 Low neutrophil fractions. Integration of Lung and Blood scRNA-Seq Reveals Abnormal Myeloid Cell Populations Discriminating Severe and Mild COVID-19 The lungs are major organs affected in patients with severe COVID-19. To better understand how the distinctive cell signatures found in the blood of patients with severe COVID-19 affect immune cell compartments in the lungs, particularly the presence of immature neutrophils and HLA-DR Low monocytes, we integrated our dataset using the Seurat V3 pipeline  (Stuart et al. , 2019)  with the published scRNA-seq dataset of cells from 12 bronchoalveolar lavage fluid samples (BALFs) of controls (n = 3) and patients with mild (n = 3) and severe (n = 6) COVID-19  (Liao et al.
, 2020; Database: GSE145926). This analysis provided an unbiased global map of immune cells in the blood and BALF of controls and patients with mild and severe COVID-19. Using dimensional reduction, we identified 5 regions based on DEGs across pooled data from all samples (Figures  7F  and S7I ), including T cells (characterized by expression the NKG7, CD8A, CST7, GZMB, and GZMA genes), B cells (IGLV3-19, IGHV4-34, IGHG1, IGHA1, and JCHAIN), neutrophils (G0S2, RSAD2, IL1R2, and IL1RN), alveolar macrophages (APOE, MSR1, MARCO, and FBP1), and monocytes/macrophages (FN1, CXCL10, CD68, and NUPR1). Validating this approach, the alveolar macrophage region was mainly present in BALF of control patients but decreased dramatically in patients with mild and severe COVID-19,and only one cell from our blood scRNA-seq matched in this region (Figures  7F  and 7G ).
Monitoring calprotectin plasma levels and non-classical monocyte in the blood of patients could be implemented routinely in the lab to discriminate patients with early immunological signs consistent with developing more severe disease, as suggested recently  (Chen et al. , 2020b). Finally, in addition to the network of potential drug targets depicted recently by analysis of SARS-CoV-2 interactions  (Gordon et al. , 2020) , our work provides a further rationale for testing several clinical strategies, including blocking emergency myelopoiesis with lenzilumab (NCT04351152), a recombinant anti-human granulocyte-macrophage colony-stimulating factor (GM-CSF) antibody  (Patnaik et al. , 2020) ; testing the oral quinoline-3-carboxamide tasquinimod  (Fizazi et al. , 2017)  and related molecules, such as ABR-215757 (paquinimod), which block binding of S100A9 to TLR4 and RAGE  (Kraakman et al.
Spectral flow Cytometry Cells were resuspended in 100 mL extra-cellular antibody cocktail and incubated at room temperature for 15 min. For intra-cellular labeling, a step of permeabilization was performed using 200 mL of BD Cytofix/Cytoperm Kit (BD); cells were then incubated for 40 min at +4 C, washed in Perm Buffer (BD) and resuspended in intra-cellular antibody cocktail. After incubation, cells were washed in Flow Cytometry Buffer (1% BSA, 0. 5% Na-Azide and 0. 5M EDTA in PBS) and resuspended to proceed to the acquisition. All antibodies are listed in the Key Resources Table. Samples were acquired on CyTEK Aurora flow cytometer (Cytek Biosciences). Fcs files were exported and analyzed using FlowJo software. 3 0 scRNaseq analysis of human blood cells To fully capture peripheral blood cell heterogeneity, we analyzed fresh samples without cell sorting or freezing and without Ficoll enrichment, minimizing time of incubation and processing.
We pooled all of the libraries for sequencing in a single SP Illumina flow cell. All of the libraries were sequenced with an 8-base index read, a 28-base Read1 containing cell-identifying barcodes and unique molecular identifiers (UMIs), and a 91-base Read2 containing transcript sequences on an Illumina NovaSeq 6000. Reads were aligned to the hg19 genome and were used for subsequent analysis. Analysis of scRNaseq and integration of dataset from bronchoalveolar lavage fluid of COVID-19 patients Using the package Seurat V3  (Stuart et al. , 2019) , we normalized and scaled scRNA sequencing data. We next applied a principle component analysis to the scRNA sequencing results yielding a number of significant PCs (Using Jackstraw plot analysis). In addition, the standard deviation differences from one PC to another was taken into account as described by the Seurat V3 manual  (Stuart et al.
The bronchoalveolar dataset was downloaded from the NIH GEO database  (Liao et al. , dataset GSE145926)  and integrated with our own blood scRNaseq data using the Seurat V3 anchoring method  (Stuart et al. , 2019). Briefly, the datasets were normalized independently and the highly variable genes were identified for each dataset using the Seurat pipeline. A corrected data matrix with both datasets was then generated using the Seurat v3 anchoring procedure to allow for joint analysis. The matrix was scaled and a Principal Component Analysis (PCA) was performed using the Seurat v3 pipeline. A UMAP was performed on the 30 first Principal Components (PCs)  (Becht et al. , 2018). These principle components and subsequent clustering and analysis of scRNA sequencing data was performed as previously described.
Cytokine and chemokine measurements Plasma samples (Table  S6 ) were centrifuged for 15 min at 1,000 g, diluted 1:4, then monitored using the Bio-Plex Pro TM Human Chemokine Panel 40-plex Assay (Bio-rad, ref: 171AK99MR2) according to the manufacturer's instructions. 40-plex cytokines and chemokines provided are: CCL1, CCL11, CCL13, CCL15, CCL17, CCL19, CCL2, CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL3, CCL7, CCL8, CX3CL1, CXCL1, CXCL10, CXCL11, CXCL12, CXCL13, CXCL16, CXCL2, CXCL5, CXCL6, CXCL8, CXCL9, GM-CSF, IFNa, IL-10, IL-16, IL-1b, IL-2, IL-4, IL-6, MIF, TNFa. Acquisitions and analyses were performed on a Bio-Plex 200 system (Bio-rad) and a Bio-Plex Manager 6. 1 Software (Bio-rad), respectively. Soluble Calprotectin (diluted 1:100) and IFNa2a were analyzed using a R-plex Human Calprotectin Antibody Set (Meso Scale Discovery, ref: F21YB-3) and the ultra-sensitive assay S-plex Human IFNa2a kit (Meso Scale Discovery, ref: K151P3S-1), respectively, following manufacturer's instructions.
Acquisitions and analyses of soluble Calprotectin and IFNa were performed on a MESO QuickPlex SQ120 reader and the MSD's Discovery Workbench 4. 0. Each plasma sample was assayed twice with the average value taken as the final result. Data representation was performed with software R v3. 3. 3 using tidyverse, dplyr, ggplot2, ggpubr, pheatmap, corrplot or Hmisc packages.
Then, 400 mL of PBS containing 1. 6% PFA + Iridium (1:4000) were added for 35 min at room temperature. Finally, cells were washed in Cell Staining Buffer, resuspended in 50 mL and stored at +4 C until acquisition. Cells were counted, washed and resuspended in Maxpar Cell Acquisition Solution at 0. 5x 10 6 / mL and mixed with 10% EQ Beads immediately before acquisition on Helios mass cytometer using noise reduction, event length limits of 10-150 pushes. An average of 500,000 events were acquired per sample at a flow rate of 0. 03mL/min. Mass cytometry standard files were normalized to a global standard determined for each log of EQ beads using CyTOF Software v. 6. 7. 1014 (Fluidigm). Fcs files were exported and analyzed using FlowJo software. UMAP was performed with n_neighbors of 15 and a min_distance of 0.
Data analysis Calculations and statistical tests were performed using R v3. 3. 3. Unless stated, p values are two-sided with 95% confidence intervals for the reported statistic of interest. Individual data points representing the measurement from one patient are systematically calculated from the corresponding distribution. Wilcoxon rank-sum test was applied to assess differences in concentration between two different groups. When indicated, the false discovery rate (FDR, p > 0. 05) was controlled using the Benjamini-Hochberg procedure. Spearman correlations were computed using Hmisc R package and cytokine results were shown using R package Pheatmap. Soluble factor fold ratios were calculated as log2 transformation of values of mild and severe patients versus median value of all control patients, and were converted to z scores.
Similar approach was performed to model IL-6. Backward selection was applied to obtain a parsimonious model. To identify the most discriminant markers, we used a logistic regression adjusted for the scaled log2-transformed markers. Parameters were penalized using the least absolute shrinkage and selection operator (lasso) to limit overfitting due to the high number of markers. The regularization parameter was selected from 10-folds cross-validation using the glmnet R package  (Friedman et al. , 2010). The final AUC estimate was corrected for optimism using the Harrell's method  (Harrell et al. , 1996) , and its confidence interval was computed using the two-stage approach proposed by Noma and colleagues  (Noma et al. , 2020)  with 2000 bootstrap samples for each stage. In this analysis, we included age, sex and comorbidities together with biological parameters.
ACKNOWLEDGMENTS Our teams are supported by grants from  Ligue Nationale Contre le Cancer (Equipes labellisees) ,  Agence Nationale de la Recherche  ( IHU PRISM ),  Institut National du Cancer (SIRIC CARPEM and SOCRATE) ,  Fondation ARC, Fondation pour la Recherche Medicale , and private donations ( Dassault Systems ,  Agnes B, Izipizi ,  Ralph Lauren , and  Malakoff Humanis ) as well as  Fondation Gustave Roussy  (including the Paediatric Campaign). F. G. is supported by  Singapore Immunology Network (SIgN)  core funding and  Singapore National Research Foundation Senior Investigatorship (NRFI)   NRF2016NRF-NRFI001-02  and is a  European Molecular Biology Organization  (EMBO) YIP awardee. L. G. N. is supported by SIgN core funding. We thank  Emmanuelle Gallois ,  Jean-Franc  ¸ois Meritet, and  Flore Rosenberg  for SARS-CoV-2 PCR tests and the following individuals for technical support:  Jean-Daniel Chiche  and  Jean-Paul Mira  ( Medical Intensive Care Unit, Cochin Hospital );  Maela Francillette ,  Betty Leite , and  Valerie Camara-Clayette  ( AMMICa platforms, CRB, Gustave Roussy );  Audrey Naimo  ( AMMICa platforms, Gustave Roussy );  Karine Bailly  ( CYBIO platform, Institut Cochin ); and  Jerome Duchemin ,  Catherine Gicquel ,  Amandine Houvert ,  Georges Jourdi ,  Francoise Levavasseur ,  Laurence Marnet ,  Bruno Montout ,  Loetitia Rhino ,  Isabelle Souville , and  Christine Scamps.