dataesr/scientific-paragraphs-categorization huggingface dataset:
Coregulation data harvester (CDH) analysis The identification of co-expressed genes for Tetrahymena Fer2 (TTHERM_00886960), Nd6 (TTHERM_00410160), NdP1 (TTHERM_ 01287970), and NdP2 (TTHERM_00498010) was performed by Coregulation Data Harvester (CDH) software (http://ciliate. org/ index. php/show/CDH) as previously described  (Tsypin & Turkewitz, 2017). A list of co-expressed genes, with homologs in T. gondii and P. falciparum, was obtained for each query. We then generated a cross-list by selecting genes shared at least by three queries, for which reciprocal BLAST toward ToxoDB and PlasmoDB databases retrieved Toxoplasma and Plasmodium homologous genes with an E-value of at least 10 À4 , respectively (Dataset EV1).
The primers used to test correct integration are listed in Table  EV1. Homology searching and phylogenetic tree construction for CRMP genes in Apicomplexa Toxoplasma gondii CRMPa (TGME49_261080) and CRMPb (TGME49_292020) protein sequences were used as queries against translated ORF coding sequences from the genomes of selected Apicomplexa (Dataset EV4) using the BLAST algorithm. Positive BLAST hits against CRMPa and CRMPb queries were those for which reciprocal BLAST against the genome containing the query sequence retrieved the same sequence with similar E-value or lower followed by the other TgCRMP. Once the homologs of TgCRMPa and b were identified by BLAST, the phylogenetic relationships of CRMPs within apicomplexans were determined as described earlier for ferlins. Apicomplexa sequences were retrieved from http://veupathdb.
4, equipped with the CMOS Orca Flash 4. 0 (Hammamatsu) camera, using Zen software (Zeiss, Intelligent Imaging Innovations), or Zeiss LSM880 confocal microscope equipped with Airyscan detector, with a 63× oil objective NA = 1. 4, using Zen Black software (Zeiss, Intelligent Imaging Innovations). Images of single focal planes and z-stacks were uniformly denoised, adjusted in brightness and contrast, and colored with the program Fiji  (Schindelin et al, 2012). Images were collected at the Montpellier Ressources Imagerie (MRI) facility of the University of Montpellier.
Raw spectra were processed using the MaxQuant  (Cox & Mann, 2008 ) using standard parameters with a match between runs  (Cox et al, 2011). MS/MS spectra were matched against the UniProt Reference proteomes of Toxoplasma gondii and humans (respectively, Proteome ID UP000001529, v2019_11 and UP000005640, v2020_01) and 250 frequently observed contaminants as well as reversed sequences of all entries (MaxQuant contaminant database). Statistical analyses were done using Perseus on intensity data  (Tyanova et al, 2016). Freeze-fracture and transmission electron microscopy of Toxoplasma and Tetrahymena strains Tetrahymena Dcrmp1 cells were grown overnight in 30 ml SPP to mid-log phase density, harvested by centrifugation at 1,800 g for 3 min, and washed once with 10 mM Tris pH 7. 4. Cells were resuspended in 5 ml 20 mM phosphate buffer (prepared from 0.
Fixed Tetrahymena and Toxoplasma samples were subjected to freeze-fracture as previously described  (Aquilini et al, 2021). Briefly, cells were quickly frozen in liquid nitrogen and fractured in a Bal-Tec BAF060 apparatus. The fracture surface replica was obtained by evaporating platinum at a 45°angle (~3. 2 nm thick) and carbon at a 90°angle (~25 nm thick), respectively. Replicas were washed in 6. 5% sodium hypochlorite, rinsed first in chloroform solution (2:1, v/v; this step was skipped for Tetrahymena replicas), and then in distilled water prior to mounting on copper grids. Images were acquired with a Jeol 1200 EXII transmission electron microscope FLAGat the Electron Microscopy Platform of the University of Montpellier, adjusted in brightness and contrast, with the program Fiji  (Schindelin et al, 2012).
Cryo-electron tomography (cryo-ET) and subtomogram averaging Cryo-ET and subtomogram averaging were performed as previously described  (Mageswaran et al, 2021). Briefly, projection images were recorded on a Thermo Fisher Titan Krios G3i 300 keV field-emission gun cryogenic electron microscope equipped with a K3 direct electron detector (Gatan Inc. , Pleasanton, CA, USA) using SerialEM software  (Mastronarde, 2005). The camera was operated in the electron-counted mode and images were dose fractionated at 10 frames per second. Images were motion corrected using the Align-Frames function in IMOD software package  (Kremer et al, 1996). Volta phase plate  (Danev et al, 2014; Fukuda et al, 2015)  and Gatan Imaging Filter (Gatan Inc. , Pleasanton, CA, USA;  Krivanek et al, 1995)  with a slit width of 20 eV were used to increase the contrast.
The imaging workflow is as follows: cells were initially assessed at lower magnifications for ice thickness and plasma membrane integrity, following which tilt series were collected with a span of 120°( À60°to +60°; bi-directional or dose-symmetric scheme) with 2°i ncrements accounting for a total dosage of 120-140 eÀ/ A 2 per tilt series. Tilt series were collected at 33,000× magnification with a corresponding pixel size of 2. 65 A (it is noteworthy that a part of the CRMPb_iKD dataset was collected on a replacement K3 camera that reported a slightly increased pixel size of 2. 72 A). Each tilt series had a fixed defocus value between 1 and 3 lm under focus. Our inhouse automated computation pipeline (built on functions from the IMOD software package) was used to align tilt series and reconstruct tomograms; the 10 nm colloidal gold served as fiducials for the alignment procedure.
IMOD's slicer program was used to visualize tomograms. After orienting the 3D volume and sectioning through the desired location, we generally averaged a few slices above and below to enhance contrast. Subtomogram averaging was performed on the RSA (including the AV) of CRMPb_iKD cells using PEET  (Nicastro et al, 2006). These features were first located in several parasite tomograms by manually inspecting their apical region, and subtomograms (a. k. a. particles) were extracted from each in a manually pre-oriented fashion. Subsequently, these particles were computationally aligned over four iterations, each one using reduced angular and translational search parameters compared to the previous. A template was generated for the first alignment iteration by computing an initial average from the manually pre-oriented particles; the template for each of the subsequent iterations was computed by averaging the aligned particles from the corresponding previous iteration.
Quantifications of Cryo-ET data, statistics, and reproducibility We obtained a total of 100 tomograms (over 7 days spanning two independent imaging sessions) for wild-type T. gondii and 59 tomograms (over 7 days spanning three independent imaging sessions) for CRMPb_iKD T. gondii, each dataset from multiple frozen grids. The wild-type dataset is the same as the one previously published  (Mageswaran et al, 2021). Each of the quantifications (described below) was performed on a randomly chosen subset of these tomograms that resolved the feature of interest. In the case of the wildtype, quantifications were performed again independent of the previous quantifications in  Mageswaran et al (2021)  to control for small discrepancies in measurements that could arise from different users or different attempts at the same analysis.
Eccentricity (or Ecc) is calculated as (1-b2/a2)1/2, where "a" is the semi-major axis and "b" is the semi-minor axis. Sample size: 22 tomograms each of wild-type and CRMPb_iKD cells were used for the above quantifications, except for Ψ', which used 37 tomograms for each sample. Quantifications were performed using models generated in IMOD slicer. Models were exported as text files, parsed, and analyzed using Python 3. 8 or 3. 9. Numpy, Matplotlib, and Seaborn libraries were used for plotting. Boxplots show the distribution of measurements; in each dataset, the lower and upper boundaries of the box represent the first and third quartiles (Q1 and Q3), whiskers extend to 1. 5 times the interquartile range (Q3-Q1) below and above Q1 and Q3, and points outside (diamonds) are regarded as outliers (NOTE: the whiskers on either side are shortened if there are no data points spanning the previously calculated whisker length).
The horizontal divider within the box represents the median. The boxplots are overlaid with swarmplots, each data point representing a measurement from a tomogram. Jointplots are a combination of a bivariate scatterplot and two marginal univariate kernel density estimate plots (a. k. a. probability density plots). Mann-Whitney U test, which is a non-parametric alternative for unpaired Student's t-test (available within Python's Scipy package), was used to calculate the P-values. Actual P-values are presented in the plots for values < 0. 1. For values > 0. 1, they are replaced with n. s (not significant).
1% Triton X-100/PBS, and incubated with 10% FBS/PBS blocking solution for 1 h. Parasites staining was performed with rat anti-HA 3F10 (1:1,000; Roche; 11867460001) and AlexaFluor488-conjugated goat anti-rat (1:2,000; Invitrogen) antibodies. DNA was stained with 16 lM Hoechst, and coverslips were mounted onto microscope slides using Immunomount (Calbiochem). Imaging was performed with a Leica Thunder microscope, with a 100× oil objective NA = 1. 4, equipped with the sCMOS 4. 2MP camera, using Leica Application Suite X (LAS X) software (Leica Biosystems). Z-stacks were denoised, adjusted in brightness and contrast, and colored with the program Fiji  (Schindelin et al, 2012).
Secondary antibody staining was performed with AlexaFluor647 goat anti-rabbit (1:1,000), AlexaFluor594 goat anti-mouse (1:2,000), and AlexaFluo-r488 goat anti-guinea pig HCA (1:1,500) secondary antibodies (Invitrogen). Gels were incubated with primary and secondary antibodies for 3 h at 37°C each time. Antibodies were diluted in 2% BSA/PBS and washed after each antibody incubation was performed with PBScontaining 0. 1% Tween20. Gels were subjected to the second round of expansion in ddH 2 O overnight prior to microscopy imaging. Expanded parasites were imaged with Zeiss LSM880 confocal microscope equipped with Airyscan detector, with a 63× oil objective NA = 1. 4, using Zen Black software (Zeiss, Intelligent Imaging Innovations). Z-stacks were denoised, adjusted in brightness and contrast, colored, and processed to obtain maximum intensity projections, with the program Fiji  (Schindelin et al, 2012).
Acknowledgments We thank  Sebastian Lourido  for the pU6-Universal plasmid,  Dominique Soldati-Favre  for providing the anti-ARM (ARO) antibodies and pLinker-2xTy-DHFR plasmid,  Nicolas Dos Santos Pacheco  for helping in setting up the  Ultrastructure Expansion Microscopy ,  Anita Koshy  for the toxofilin-Cre plasmid, and  Helen Blau 's lab for the Cre reporter DSred cell line. We thank  Veronique Richard  and  Frank Godiard  of the  MEA platform, University of Montpellier , for their assistance with electron microscopy and  Pilar Ruga Fahy  of the  Pôle Facultaire de Microscopie Ultrastructurale, in Geneva  for the preparation of freeze-fracture replicas. We are also grateful to  Elodie Jublanc ,  Vicky Diakou , and the imaging facility MRI at the  University of Montpellier , part of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»), and  Christophe Duperray  of the  MRI-Cytometry at the Institute for Regenerative Medicine and Biotherapy  for their assistance and technical support.
Data availability The mass spectrometry proteomics data have been deposited to the Pro-teomeXchange Consortium via the PRIDE (Perez-Riverol et al, 2019) partner repository with the dataset identifiers PXD031161 (http://www. ebi. ac. uk/pride/archive/projects/PXD031161) and PXD031164 (http:// www. ebi. ac. uk/pride/archive/projects/PXD031164). Expanded View for this article is available online.
Introduction We are in the middle of a new era where stellar atmospheric parameters (APs) and abundances are produced on massive scales by spectroscopic surveys. The iron abundance [Fe/H] is an essential stellar property that has to be known for the determination of other parameters through stellar models, such as the mass and the age. Iron abundances are also needed in galactic archeology in order to understand how the different stellar populations have formed and evolved. The pioneer of spectroscopic surveys, the Radial Velocity Experiment (RAVE), produced its first data release (DR) 15 years ago  (Steinmetz et al. 2006 ) and its final one, DR6, last year  (Steinmetz et al. 2020a,b). In the meantime, other surveys have been operated, and survey designers have developed new methodologies, learning progressively from their own and one another's experience on how to reduce biases and uncertainties in the automated determination of APs and abundances from massive datasets of stellar spectra with various resolutions and spectral coverages.
In classical spectroscopy, the assessment of uncertainties usually evaluates the random errors due to the characteristics of the input spectra and to the line selection, as well as the systematic errors due to the adopted assumptions, for example local thermodynamic equilibrium (LTE), or to the method itself, for example equivalent width versus synthetic spectrum fitting. Comparisons to independent reference datasets and inter-comparisons of surveys are mandatory for tracking systematic differences, although these comparisons are limited by the number of stars in common and their range of properties. The strategies adopted by the ongoing surveys for calibrations and validations are reviewed in  Jofré et al. (2019). The validation of the Gaia's APs is challenging due to the size of the dataset, the large magnitude range, and the observing mode.
Having calibration targets in common between surveys is very useful for tracking systematic differences. For this purpose, the OC M 67 and the GC NGC 104 are excellent targets. It is useful to observe the Hyades, Berkeley 81, and NGC 6791 to constrain the high metallicity regime, while the most metal-poor OCs (Berkeley 32, NGC 2243, Trumpler 5) can constrain metallicities around [Fe/H] = -0. 4 dex. Many GCs are available to anchor the lowest metallicities. The recent revision of memberships in clusters based on Gaia data provides reliable lists of targets. We want to mention the sample of Gaia FGK benchmark stars  (Heiter et al. 2015; Jofré et al. 2015 Jofré et al. , 2018)) , which was built for the calibration of the Gaia APs that will be delivered in DR3 and to serve as a common reference between Gaia and the groundbased surveys.
Acknowledgements. We thank the anonymous referee for the useful comments and suggestions that helped clarify the paper. C. S. and N. B. warmly thank  Philippe Prugniel  who made a significant contribution in the 2020 version of pastel. The preparation of this work has made extensive use of Topcat (Taylor 2011), of the Simbad and VizieR databases at  CDS, Strasbourg, France , and of  NASA's Astrophysics Data System Bibliographic Services.
Samples from patients with COVID-19 and healthy donors Clinical routine data and blood samples for PBMC isolation and cryopreservation were collected at the University Hospital Tuebingen in Germany, the Toulouse University Hospital in France (within the framework of the COVID-BioToul biobank [ClinicalTrials. gov identifier NCT04385108]), and the Nantes University Hospital in France (see Table  E1 , A in the Online Repository at www. jacionline. org), as previously reported. 13 In brief, longitudinal collection of PBMC samples of patients with COVID-19 was performed (see Tables E1, A and E1, B in the Online Repository at www. jacionline. org). The patients included in this study were recruited between April 2020 and November 2020 (first and second waves; Wuhan and Alpha clades); in other words, they were recruited.
Spectral flow cytometry data Spectral flow cytometry data used for this study are available from Mendeley Data at https://doi. org/10. 17632/ffkvft27ds. 2. 13  In brief, PBMC samples were acquired on a Cytek Aurora spectral flow cytometer (Cytek Biosciences, Fremont, Calif) by using a surface panel, a panel focused on cytokine production in lymphoid cells (phorbol myristate acetate/ionomycin-stimulated), and a panel for cytokines in myeloid cells (R848-stimulated). Quality control, transformation, normalization, and downstream analysis were performed by using FlowJo (TreeStar) and R environment, as previously reported. 13.
Statistical analysis and visualization Effect sizes were determined by using a nonparametric Wilcoxon signed rank test combined with a rank biserial correlation. These tests were performed by using the wilcox. effsize function from the rstatix package in R. Effect sizes greater than 0. 3 (for severe vs mild COVID-19 comparison) or 0. 2 (for comorbidity vs other samples) were considered relevant for further analysis. The value 0. 3 is considered to be a medium strength effect. Frequencies of immune populations and median expression were compared by using the nonparametric Mann-Whitney-Wilcoxon test from the ggpubr package in R, including Benjamini-Hochberg correction using the stats package. The upset plots were made by using the Complex UpSet package in R. The principal component analysis (PCA) plots were made by using the fviz_pca function from the factoextra package, and the forest plots were created by using the forest plot package in R.
Patients with COVID-19 display severity-and comorbidity-specific immune profiles Because several comorbidities are associated with dysregulation of the immune system, 7,18-25 we questioned whether the immune dysregulation seen in comorbidities influences the immune response to SARS-CoV-2 infection and whether severity of COVID-19 can be linked to the immune signature of preexisting medical conditions. To do so, we made use of the detailed immune profiles that we had generated from PBMC samples (collected on day 0-96 after hospital admission due to COVID-19) by using high-parametric single-cell spectral flow cytometry. 13 The data set provides information about the differentiation and activation status of lymphocyte and myeloid subpopulations (T cells, NK  To gain an initial overview of the global immune profiles of patients with COVID-19 with specific comorbidities, we performed PCA.
Several risk assessments of comorbidities and severe COVID-19 have been performed, but there is a striking lack of investigative studies examining the underlying immunologic mechanisms. Our study uncovers novel associations between specific comorbidities and the severe COVID-19-associated immune response, presenting characteristic immune landscapes depending on the underlying medical condition of a patient. These results enrich the database of COVID-19 immunopathology and provide a source for more individualized treatments of patients with COVID-19 based on their comorbidities. Using therapies targeting the immunologic changes specific to severe COVID-19 and even a patient's comorbidities could be a promising tool to alleviate the severity of this disease.
We thank the patients and blood donors who contributed to this study, as well as the hospitals in Nantes, Toulouse, and T€ ubingen for sample and data collection. Regarding the COVID-BioToul biobank (ClinicalTrials. gov identifier NCT04385108) specifically, we thank the Centre de Ressources Biologiques ''Toulouse -Bio Ressources'' (CRB TBR), the Clinical Research Center 1436, and the Delegation for Clinical Research and Innovation of the Toulouse University Hospital for their highly valuable implication. We thank the Biological Resource Centre for Biobanking (CHU Nantes, Nantes Universit e, Centre de ressources biologiques (BB-0033-00040), F-44000 Nantes, France). The graphical abstract was created with Biorender. com.
Induced Polarization (IP) is a non-intrusive geophysical method to monitor Dense Non-Aqueous Phase Liquid (DNAPL) contamination and remediation processes underground. In this study, an advanced numerical code simulating DNAPL flow and complex electrical resistivity is presented. The model was validated against existing IP results and image measurements that were carried out previously in a series of 2D tank experiment. Multiphase flow modeling in porous media is coupled with electrical current modeling to simulate the process of DNAPL migration and the associated IP response. This brings a broader view of the contamination in space and time compared to surface and borehole measurements, especially when the results are supported by field measurements or laboratory experiments. The simulations are developed in 3D and are performed in COMSOL Multiphysics®.
The numerical modeling challenge is augmented when the set of partial differential equations associated with the electric current (the Poisson-Nernst-Planck equations) is added to the governing equations  (Pimenta and Alves, 2019). Therefore, coupling simulation of multiphase flow and electrical current in porous media is a challenging task and the existing tool for handling such objective efficiently is limited. It is indeed more appreciable, when possible, to apply and examine the applicability of the existing simulation software packages for the coupling of multiphase flow and electrical current in porous media. COMSOL is a finite element method that has gained the attention of geoscientists in the past years. The main advantages of COMSOL are the multiple choices of different physical modules and the facility of coupling these modules with different physics and geometry involved  (Colombano et al.
The electrical current is simulated until the potential difference in the desired points is obtained. The results (debit and complex resistivity) are then compared against the experimental results. If the desired tolerance is not reached the petrophysical exponents are optimized using the parameter estimation module of COMSOL and based on the new parameters, new simulation results are obtained. The same process is repeated until the desired tolerance is reached. Fig. 1  is a flowchart showing schematically the process and the approaching algorithm used in our study.
Numerical simulation tool and scenarios The system of partial differential equations (Eqs. (  1 ) and (2) and Eqs. (10) to (  13 )) is discretized using the finite element method and solved in COMSOL software package. The models are constructed as an abstraction of the experimental set-ups in three dimensions with identical geometry and boundary conditions (i. e. a cuboid of 50 cm × 30 cm × 7 cm in respectively x-, z-and y-directions). Three modular physics (Phase transport in porous media, Darcy's law, and Electric current) are included and coupled to simulate the experiments. The phase transport equations and Darcy's law are coupled and discretized with pressuresaturation formulation; the pressure from one phase (e. g. , water) and the saturation from the other phase (e. g. , DNAPL) are chosen as the primary variables throughout the solution.
Acknowledgments This work was supported by the  BRGM-Carnot Institute. The postdoctoral grant allocated to  Behshad Koohbor  was supported by the  BRGM-Carnot Institute. The authors also gratefully acknowledge the financial support provided to the  PIVOTS  project by the  Centre -Val de Loire region  ( ARD 2020 program  and CPER) and  LE STUDIUM Loire Valley Institute for Advanced Studies. This research was carried out as part of the  SILPHES  project supported by  ADEME (French Environment and Energy Management Agency)  in the framework of the  Future Investments ("Investissements d'Avenir ") funding scheme and the  BRGM   MULTISCALEXPER  project. The authors acknowledge ADEME and the  BRGM/DRP  and  BRGM/DEPA division  for its financial support.