dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). Overall, we observed similar cell type proportions between the different datasets and diagnosis groups (Supplementary Table  3 , Supplementary Fig. 2 ), with the exception of high AT2 cell numbers in the Northwestern dataset, as expected due to the protocol favoring isolation of AT2 and macrophages (personal communication). Despite the variation in sample collection and processing at different research institutes, the similarity in cell type composition per dataset indicated the compatibility of samples and that no major sampling bias would confound an integrated analysis. Expression profile of SARS-CoV-2 associated receptors and factors in the diseased lung. SARS-CoV-2 utilizes the host ACE2, and other putative factors such as BSG, NRP1 and HSPA5, as entry receptors and TMPRSS2, CTSL or FURIN as priming proteases to facilitate cellular entry  [33] [34] [35] [36] [37] [38] [39] [40].
6 ). TMPRSS2 expression is high in AT1, AT2, Transitional AT2, PNEC/ionocytes and club cells (Fig. 2b , Supplementary Fig. 5 ) and is slightly upregulated in the AT2 COPD samples (log2FC = 0. 28, q value 0. 04) (Supplementary Dataset 3), in contrast to a recent publication demonstrating decreased TMPRSS2 expression in severe COPD  42. Two alternative priming proteases (CTSL and FURIN) are expressed at low level and show no significant differences in expression between control and disease samples (Fig. 2b , Supplementary Fig. 5 ). However, the SARS-CoV-2 entry gene score (calculated on the average expression levels of all SARS-CoV-2 entry factors over a random control gene set) is significantly increased in the CLD samples in many epithelial cell types, including AT1, AT2, Basal, Club cells, and KRT5-/KRT17 + cells, an ECM-producing epithelial cell type enriched in the fibrotic lung  31, 32  (Fig.
03) (Supplementary Dataset 4). ACE2 correlated genes are involved in various cellular processes, including viral processes (Supplementary Table  5 ). Many ACE2-correlated genes in the disease samples are associated with the innate and antiviral immune response. In the COPD samples, genes with strong correlation coefficients with ACE2 include several interferon-induced genes (IFI6, IFIT1, IFIT2), a modulator of innate immune function (OAS1), the chemokine receptor ACKR4, a gene associated with West Nile viral infection (OASL), and the ECM regulated transcription factor SOX9. In the IPF samples, ACE2 expression is strongly correlated with the nuclear factor NXF3, the transcription factor SP4, the antiviral factor TRIM11, and the Forkhead Box Q1 (FOXQ1). In other ILD diseases (non-IPF related), the integrin ITGB8, a member of the TNF receptor family (TNFRSF11B), an important component of the immune response system (NOD2) and an innate immune pathway component (ITLN1) are among the genes with high correlation with ACE2.
Amphiregulin (AREG), a ligand for epidermal growth factor receptor, is known to have essential roles in wound repair and inflammation resolution; furthermore, upregulation of AREG is associated with viral infections of the lung  64. In COVID-19 patients, AREG is significantly upregulated in PBMCs  65  , monocytes, CD4 T Cells, NK cells, neutrophils, and DCs  61  , suggesting that upregulation of AREG may be an attempt to ameliorate the severe injury induced by SARS-CoV-2 infection. We observed reduced expression of AREG in the cDCs and macrophages, but not in the monocytes, in the CLD samples (Fig. 4c , Supplementary Dataset 3, Supplementary Fig. 12 ). SOCS1, a suppressor of cytokine signaling, was shown to reduce the type I IFN antiviral response in bronchial epithelial cells after influenza infection  66, 67.
Expression of the S100A8/A9, members of the S100 family, and the IL6 co-receptorIL6ST was elevated in COVID-19 patients  [68] [69] [70]. In our study, S100A8/A9 expression is lower in the disease samples in cDCs, macrophages and monocytes while SOCS1 expression is elevated in Other-ILD samples in NK Cells and pDCs (Fig. 4c , Supplementary Dataset 3). IL6ST expression level is elevated significantly in COPD and IPF but reduced dramatically in Other-ILD samples in macrophages (log2FC = -2. 75, q value = 1. 63e-61) (Fig. 4c , Supplementary Dataset 3). These basal differences in inflammatory gene expression programs highlight how chronic lung disease alters the inflammatory microenvironment encountered upon viral exposure to the peripheral lung.
Similar to other coronaviruses, SARS-CoV-2 utilizes cellular receptors (ACE2 and putatively, BSG, NRP1 and HSPA5 gene products) and priming proteases (TMPRSS2, CTSL, FURIN), for viral entry. These factors are expressed predominantly in the upper and lower airways, with ACE2 being expressed highly in nasal goblet and ciliated cells and in a subset of AT2 cells and the absorptive enterocytes in the gut  33, 34, 41, 48, 51. We observed a similar expression pattern of ACE2 in our dataset, with AT2 cells having the highest number ACE2 + cells. To our knowledge, publications investigating baseline expression of these SARS-CoV-2 entry factors in lung disease have been limited to asthma and COPD with variable results. For example, studies in asthma patients showed elevated expression of ACE2, TMPRSS2, and FURIN in patients with severe but not mild-moderate asthma  75, 76.
Methods scRNA-seq samples. scRNA-seq data were obtained from published data with samples in the "VUMC/TGen" dataset from Habermann et al. 32  (GEO accession GSE135893), samples in the "Yale/BWH" dataset came from Adams et al. 31  (GEO accession number GSE136831), samples in the "Pittsburgh" dataset from Morse et al. 30  (GEO accession GSE128033) and samples in the "Northwestern" dataset from Reyfman et al. 29  (GEO accession GSE122960) (Supplementary Tables  1, 2 ). For specific IRB review of each dataset, please refer to the original paper cited here. In addition, there are 39 unpublished scRNA-seq samples in the "VUMC/TGen" dataset that were collected under Vanderbilt IRB #'s 060165, 171657 and Western IRB # 20181836. scRNA-seq data processing. Seurat v4. 0 package  83  was used to process the scRNA-seq data.
Specifically, for the Pittsburgh, Northwestern datasets and 39 unpublished samples from the VUMC/TGen, the CellRanger (10X Genomics) output files were read into Seurat using the function Read10X, the remaining datasets were already in Seurat format and were loaded using the function readRDS. To eliminate low-quality/dying cells or empty droplets, we removed any cells containing fewer than 1000 genes or more than 25% mitochondrial genes. Due to the large size of the joint dataset, we performed SCTransform 84 for normalization and scaling of each dataset separately, split into four major cell populations using known markers: EPCAM + (Epithelial), PECAM1 + PTPRC -(Endothelial), PTPRC + (Immune) and EPCAM-PECAM-PTPRC-(Mesenchymal). Each population from the four datasets was then merged together to generate four merged Seurat objects (Endothelial, Epithelial, Immune and Mesenchymal).
Next, each object was SCTransformed with "dataset" being used as batch_var to eliminate batch effects between datasets. Cell clustering was performed using the Seurat function FindNeighbors and FindClusters and cell type annotation was manually performed on each object using known cell-type specific markers (Supplementary Fig. 1 32  ). For each cell population, cell type annotation was performed at four levels, ranging from the most general to more granular annotation. Cells expressing more than one cell type specific marker were identified as doublets. After removing doublet cells, all four population datasets were merged to generate the final ILD object containing a total of 611,398 cells and 32 distinct cell types (Supplementary Table  3 , Supplementary Fig. 1 ).
Integrated analysis of joint dataset. To calculate the percentage of single positive or double positive cells for ACE2 and other cofactors, we counted the number of cells with >0 transcripts of corresponding genes. For double positive, cells have >0 transcripts of both genes of interest. To assess the expression profile of SARS-CoV-2 mediators (ACE2, BSG, NRP1, HSPA5), the corresponding proteases (TMPRSS2, CTSL, FURIN) and other candidate genes involved in SARS-CoV-2 infection in different chronic lung disease subset (COPD, IPF or Other-ILD), we ran the function FindMarkers in Seurat package using the negative binomial test. Using Seurat function CellCycleScoring, we calculated the cell cycle state across all epithelial cells to ensure the cell cycle is not a factor contributing to the differential expression analysis (Supplementary Fig.
7 ). To account for batch effects, we used the parameter "latent_vars" to incorporate the four variables (Age, Ethnicity, Smoking status and Dataset) into the negative binomial model. For the binary heatmap, the differential expression analysis was performed between the Disease (including all chronic disease subset) and Control samples. Then, log 2 fold-change was converted into 0 (downregulated in the disease samples) or 1 (upregulated in the disease samples) regardless of the Bonferroni adjusted p values. Heatmaps were generated from the adjusted log 2 FC values using the heatmap. 2 function of the gplots R package  85. For the boxplots, count numbers of selected genes were plotted using the geom_boxplot and geom_jitter function of the ggplot2 R package  86. Gene module score. To calculate the combined expression of genes, we used the AddModuleScore in Seurat v3.
Exhaustion gene set: LAG3, TIGIT, PDCD1, CTLA4, HAVCR2, TOX  63  , and PRDM1, MAF 61. Significant differences between different groups were calculated using the Tukey_HSD statistic test in the R package rstatix with a confidence level of 0. 95 (Supplementary Dataset 2). Gene correlation analysis. To identify genes that correlate with ACE2 in the AT2 ACE2 + cells, we performed Spearman correlation coefficient analysis on the logtransformed and normalized data using the function cor. test in the R stats v3. 6. 1 package with Benjamini-Hochberg corrections for p-adjusted values. Gene ontology analysis for the significant correlated genes (p value ≤ 0. 03 and 99th percentile rho) was performed with the Bioconductor R package TopGO version 2. 42 and the Bioconductor annotation data package org.
The immunohistochemically stained slides were scanned using a ScanScope XT Slide Scanner (Leica Aperio Technologies, Vista, CA, USA) under 20× objective magnification (0. 5 µm resolution) using Pannoramic Viewer (3DHISTECH Ltd Budapest, Hungary) slide viewing software. Both the percentage of staining and staining intensity of ACE2 expression in lung sections were individually assessed. For ACE2 quantification, the following scoring system of five high-power fields at X40. per tissue section were used: The coding was performed prior to scoring/analysis as: 0-Negative; 1-0-≤10%; 2-11-≤25%; 3-≤26%. Statistical analyses were completed using GraphPad Prism 7. 0 (GraphPad Software, San Diego, CA, USA). One-way analysis of variance was used for comparison of more than two datasets and significant differences between diagnosis groups were calculated using the Tukey HSD test.
Visualization was performed with Clarity Max™ ECL Substrate (Biorad, UK) on a Licor C-DiGit. For more information on the anti-ACE2 antibody, please refer to the manufacturer's datasheet here: https://www. abcam. com/ ace2-antibody-epr44352-ab108252. html. Two replicates were performed for the western blot. Statistical analysis. Tukey Honest Significant Difference (Tukey_HSD) statistical test from the R package rstatix with a confidence level of 0. 95 was used to test statistical dependence of cells expressing the SARS-CoV-2 mediators among chronic disease subsets. Tukey_HSD test was also used to test significant difference in gene expression module score, quantification of cell types and the ACE2 protein expression quantification. Significant differences in gene expression were the Bonferroni adjusted p-values calculated from the FindMarkers function between Control and Disease groups (COPD, IPF, Other ILD) using the fitted negative binomial model and latent_vars parameters as described above.
Acknowledgements This study was supported by the  NIH/NHLBI   R01HL145372  ( NEB/JAK ), the  Department of Defense   W81XWH1910415  ( NEB/JAK ),  Doris Duke Charitable Foundation  (J. A. K. ),  T32HL094296  (NIW, JBB), the  Department of Veterans Affairs   IK2BX003841  (BWR),  DoD W81XWH-19-1-0131  (J. C. S. ),  R01HL127349  (N. K. ),  R01HL141852  (N. K. ),  U01HL145567  (N. K. ),  UH2 HL123886  (N. K. ), and a generous gift from  Three Lakes Partners  to N. K. and I. O. R. The integrated data sets were funded by various sponsors as indicated in the original publications. R. G. J. is funded by and  NIHR   Research Professorship  ( RP-2017-08-ST2-014 ).
Data availability The majority of the data used in this manuscript are publicly available from published papers: GEO accession "GSE135893" 32 , GEO accession "GSE136831" 31 , GEO accession "GSE128033"  30  and GEO accession "GSE122960"  29. The unpublished data from VUMC/ TGen (39 samples) are included in the supplementary data (Supplementary Dataset 1) as a count matrix format containing all the genes being used in the manuscript. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. Source data are provided with this paper. A reporting summary for this article is available as a Supplementary Information file. Source data are provided with this paper.
Control ACE2 + vs. ACE2-and ACE2 correlated genes in the AT2 cells. d Upregulation of two genes uniquely differentially expressed in the CLD ACE2 + vs. ACE2-. e Spearman gene correlation analysis identified genes correlated with ACE2 expression in AT2 ACE2 + cells in different diagnosis groups, p-value was adjusted using Benjamini-Hochberg corrections, dashed lines indicate the 99th percentile of Spearman rho values. 2 response genes in the AT2 cell types among different diagnosis groups, Boxes: interquartile range, lower and upper hinges correspond to the first and third quantiles, upper and lower whisker extends from the hinge to the largest values or smallest values of 1. 5 × interquartile range; **p value-adj ≤ 0. 05 (negative binomial test, corrected for Age, Ethnicity, Smoking_status and Dataset).
CFD simulations The numerical simulations are performed on OpenFOAM software. The PIMPLE algorithm is used, which is a combination of the PISO (pressure-implicit with splitting of operators)  [25, 26]  and SIMPLE (semi-implicit method for the pressure linked equations)  [27]  algorithms. The PISO algorithm is pressure implicit with splitting of operators  [25]  and allows the use of the sliding mesh method. The SIMPLE algorithm is the semi-implicit method for pressure linked equations  [28]. The pimpleFoam solver assumes incompressible, unsteady and viscous flows. The simulations are launched in parallel on 16 processors. The simulation time, for which we get a steady state, is a few seconds. The calculation time is about a few days.
For the pancake-wound option designed by CEA, a new approach that combines the magnet design calculation and the refrigeration load calculation using CEA tools (an adapted version of MADMACS-TF code and the simulation library Simcryogenics) was implemented. An optimization problem that considers both magnet and cryogenic merits was assessed. This problem consists in optimizing the superconducting amount and the refrigeration loads while ensuring that the temperature margin of the superconductor remains at 1. 5 K. This article presents the method for cost minimization and the information workflow between the different codes, as well as preliminary results. This method is relevant to optimize simultaneously the design of the magnets and the cryogenic system and to make comparisons between the different conductor designs during the next phase of EU DEMO, moving forwards to the conceptual design.
Iterative process for the CICC design As shown in Fig. 1 , a method involving CEA tools, the MADMACS-TF code and the Simcryogenics library, was developed in order to calculate the cryogenic cost and the cost of superconductor. The MADMACS-TF code is a fast pre-design tool for TF magnets that has been developed by CEA/IRFM for general TF design build before detailed analyses  [3]. A specific version of it was issued for the purpose of automatic studies, extracted from CEA SYCOMORE system code  [7]. The Simcryogenics library has been developed by CEA/DSBT for dynamic simulations for control with model-based design and for design optimization through parametric studies  [4]. As shown in Fig. 1 , the design of the CICC is determined by an iterative process using the MADMACS-TF code and a thermal-hydraulic model of a CICC, which allows the temperature profile in the CICC to be obtained.
Model of the CICC The design of the CICC obtained from the MADMACS-TF code is based on the TF WP#3 reference from 2018  [3]. This design is a pancake-wound conductor with 9 turns, 9 double-pancakes and a central channel. A simplified sketch of the CICC is shown in Fig. 2. The bundle contains copper and superconducting strands, which respective number (and therefore the cost of superconductor) depends on the operating conditions. During the iterative process previously described, the CICC is modelled using either THEA or a steady-state model from Simcryogenics. In the dynamic model of the supercritical helium loop described in 2. 4, the CICC is modelled using the Simcryogenics library. The THEA model comprises two 1-D hydraulic channels (bundle and central channel) modelling the supercritical helium flow with heat and mass transfer coupling, as well as three solid components (superconducting strands, copper strands and jacket).
Optimization of the cost Using the previously described Simcryogenics model, the cost of the cryomagnetic system can be studied for different values of the CICC inlet temperature and pressure difference across the CICC with a fixed inlet pressure of 6 bar. The studied values ranged from 3. 7 K to 4. 7 K for the CICC inlet temperature and from 0. 1 to 1 bar for the pressure difference. A gain in calculation time was investigated by performing an optimization using a dedicated integrated tool rather than sweeping systematically the range of variables. Hence, an optimization of the cost was performed using the MATLAB/Simulink Design Optimization toolbox with the CICC inlet temperature and the pressure difference across the CICC (with an inlet pressure of 6 bar) as optimization variables. The cost was optimized so that the sum of the cost of the superconductor and the cryogenic cost was minimized.
Multi-variable optimization Instead of drawing maps of costs, the MATLAB/Simulink Design Optimization toolbox was used for optimizing the cost in a more timeefficient manner using an appropriate algorithm. Under the same previous assumptions using the Simcryogenics steady-state model for the iterative process, the minimum was found to be 3. 7 K for the CICC inlet temperature and 0. 50 bar for the pressure difference, which is consistent with the results presented in section 3. 1. This optimization took about 2 hours on an i7-laptop computer, which was significantly lower than the duration required for the method consisting in sweeping the range of optimization variables. This method can be applied for a larger number of optimization variables including also the inlet pressure of the CICC. As this method shows promising results, it will be relevant to perform the same optimization for the reference design with accurate heat loads considering Fig.
Conclusion and future work A method for optimizing the cost of the cryomagnetic system was established using THEA and CEA tools: the MADMACS-TF code and the Simcryogenics library. The CICC inlet temperature and the pressure difference across CICC (with a fixed inlet pressure of 6 bar) are key parameters for the optimization of the superconductor amount, the cost of the cryoplant and the energy consumption. Over the ranges studied for these parameters, a minimum global cost was found at 3. 7 K for the CICC inlet temperature and 0. 50 bar for the pressure difference. Under these operating conditions, the cost can be reduced by 20 % compared to the cost in nominal operating conditions (T IN =4. 5 K, ΔP=1 bar). These preliminary results show the relevance of considering the cryomagnetic system as a whole.
(B) Overlay of 1 H-15 N-HSQC spectra of 15 N-Rab11a in its GDP (black) and GTP (red) bound forms. The two pairs of nucleotide-specific peaks monitored during nucleotide exchange are highlighted in black boxes. (C) Comparison of nucleotide exchange rates of Rab11a between its intrinsic activity and with addition of recombinant full-length SH3BP5 or SH3BP5L. The GEF:GTPase molar ratio is 1:6,000. Data represent mean ± SD of three independent experiments. (D) Protein domain maps of SH3BP5 and SH3BP5L. Green boxes represent the conserved four α-helices predicted by the PSIPRED server. (E) Left: GST-tagged full-length and deletions of SH3BP5 and SH3BP5L coexpressed with GFP-Rab11a S25N in HEK293T cells. Lysates were immunoprecipitated with GFP antibody and immune complexes were analyzed by Western blotting and probed with GST and GFP antibodies.
Antibodies and reagents The following antibodies were used for immunoblotting: Flag (clone M2, 1:2,000; F1804; Sigma-Aldrich), Myc (clone 9E10, 1:250; sc40; Santa Cruz Biotechnology), HA (1:1,000; #3724; Cell Signaling Technologies), α-tubulin (1:1,000; sc69969; Santa Cruz Biotechnology), streptavidin-HRP (1:2,000; #3999; Cell Signaling Technologies), ubiquitin Lys48 (clone Apu2, 1:1,000; Millipore), GAPDH (1:1,000; #2118; Cell Signaling Technologies), GFP (1:2,000; ab290; Abcam), GST (1:1,000; sc138; Santa Cruz Biotechnology), SH3BP5 (1:300; HPA036445; Sigma-Aldrich), SH3BP5L (1:500; NBP2-38385; Novus Biologics), Tankyrase-1/2 (1:500; sc365897; Santa Cruz Biotechnology), RNF146 (1:1,000; SAB1408054; Sigma-Aldrich), and Rab11a (1:500; 71-5300; Thermo Fisher Scientific). For immunofluorescence: β-Catenin (1:500; #9562; Cell Signaling Technologies), podocalyxin (clone 3F2, 1:1,000; MABS1327; Millipore), Rab11a (1:250; 71-5300; Thermo Fisher Scientific), Alexa Fluor 488 (1:500; A28175/ A11008; Thermo Fisher Scientific), and Alexa Fluor 647 (1:500; A27040; Thermo Fisher Scientific).
4 NA) oil objective at RT. Images were captured with a Hamamatsu C9100-13 EM-CCD camera and Volocity software (PerkinElmer). Image contrast and brightness were adjusted using ImageJ. For Rab11 colocalization, cysts were imaged with a Leica SP8 laser scanning confocal 63× plan apo (1. 4 NA) oil objective lens at RT and images captured with PMT detectors and LAS X software (Leica). Z-stacks were taken in 1-µm increments and colocalization between Tag-RFPT-SH3BP5/SH3BP5L and Rab11a was calculated using Imaris (Oxford Instruments). The percent colocalized 3D volume (± SD) of Tag-RFP-T-SH3BP5/SH3BP5L with Rab11a above threshold intensity was calculated and modeled using the Coloc function. The same absolute threshold value for positive staining for each channel was applied for all images within an Protein was purified by size exclusion on a Superdex S75 26/60 column in dialysis buffer and elution peak fractions were pooled and concentrated to 6.
7-mm cryogenic TCI MicroCryoProbe. Data were analyzed using Bruker TopSpin. The half-life of the exchange reaction was determined as the point at which GDP-and GTPspecific peaks exhibited equal heights, and nucleotide exchange rates were calculated from the half-life based on single-phase exponential decay (rate = ln2/half-life). To determine whether SH3BP5 and SH3BP5L possessed GEF activity and to investigate which parts of the protein were required to mediate nucleotide exchange, the experiment was repeated with the addition of a series of purified recombinant fragments as indicated. BioID assay and mass spectrometry analysis BioID was performed as previously described  (Roux et al. , 2012; Coyaud et al. , 2015). HEK293 Flp-In T-REx cell lines stably expressing Flag-BirA-RNF146, Flag-BirA-RNF146 R163A, and Flag-BirA-SH3BP5 were generated using the FlpIn system (Invitrogen).