dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow cytometry and fluorescence associated cell sorting Erythrocytes in cell suspensions were depleted using erythrocyte lysis buffer (9 mg/ml NH 4 Cl, 1 mg/ml KHCO 3 , 10 mM EDTA in ddH 2 O) for 5 min on ice. Cells were blocked with anti-CD16/CD32 antibody, followed by antibody staining in flow cytometry buffer (5% FBS, 2 mM EDTA in PBS) for 15 min at 4 C. Intracellular staining was performed with the Fix & Perm Cell Permeabilization Kit (ThermoFisher Scientific). Dead cells were detected with 7AAD (BioLegend). All cells were analyzed on an Aurora spectral cytometer (Cytek), or a FACSCanto II instrument (BD Bioscience). Data were further analyzed with FlowJo software  (v. 10, TreeStar). For single-cell RNA sequencing, $6x10 4 BECs (7AAD -CD45 -TER119 -PDPN -CD31 + ) were sorted into HBSS buffer (4 C) using a FACS Aria Fusion instrument (100 mm nozzle; $8x10 3 events/s flow rate).
Single-cell RNA sequencing The cell suspensions containing sorted BECs were processed for single-cell RNA sequencing (scRNA-seq) using the Chromium Single Cell 3 0 library and Gel Bead Kit v2 (10X Genomics) according to the manufacturer's guidelines. Libraries were sequenced on an Illumina NextSeq 500 using 150 cycles high output V2 kit. The Cell Ranger package (v. 3. 0. 2) was used to align high quality reads to the mm10 transcriptome. Single cells from tumor and control samples were integrated using the FindIntegrationAnchors function from the Seurat package with the default parameters, as implemented in the CellRanger (v2. 1. 0) pipeline. Data analysis and generation of representative plots was performed with the Seurat (v. 2. 1) package in R  (Stuart et al. , 2019). Supervised cell selection of scRNA-seq data was used to remove non-BECs according to subpopulation specific marker genes: BECs (Cdh5 + , Pecam1 + ), leukocytes (Ptprc -, Cd52 -), lymphatic endothelial cells (Prox1 -, Lyve1 -, Pdpn -), mesenchymal cells (Acta2 -, Pdpn -, Pdgfb -).
Briefly, genes were ranked according to their significance and cluster association by multiplying the log value of the adjusted p value from Seurat's Find-Markers function by the sign of the associated fold change for that gene. Gene sets from The Molecular Signatures Database hallmark gene set collection (curated gene sets and hallmark gene sets: HUMMEL-BURKITTS_LYMPHOMA_UP, SHIPP_DLBCL_VS_FOL LICULAR_LYMPHOMA_UP, KEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION, KEGG_CELL_ ADHESION_MOLECULES_ CAMS, SCHOEN_NFKB_SIGNALING, GRAHAM_CML_ DIVIDING_VS_NORMAL_QUIESCENT_UP) were assessed for enrichment in our clusters. GSEA was run with the R package fgsea with a minimum pathway size of 15, a maximum pathway size of 500 and 100 permutations. A significance threshold of padj < 0. 05 was applied to resultant pathway enrichments.
scRNA-seq datasets were deposited at the ArrayExpress depository (ID:E-MTAB-10389). Immunofluorescence staining of mouse tissue specimen Organs were fixed overnight at 4 C in 4% PFA under permanent agitation. For immunofluorescence staining, LNs were embedded in 5% ultrapure low melting point agarose (Carl-Roth) in PBS and cut in 50-200 mm thick slices using a vibratome (VT-1200, Leica). LN sections from Ubow mice were treated using the CUBIC tissue clearing protocol  (Nojima et al. , 2017). All sections were blocked and permeabilized in blocking buffer (10% normal goat or donkey serum, 0. 1% Triton X-100 in PBS) for 1 hour at room temperature (RT). Primary antibodies were diluted in blocking buffer and incubated on tissue slides at 4 C ON. Secondary Alexa Fluor-conjugated antibodies were added and incubated for another 2 hours at RT.
Microscopic recordings were performed using a LSM-710 laser scanning confocal microscope and a LSM-980 airyscan microscope with the ZEN blue edition software (all Carl Zeiss). Fiji image processing software and Imaris (v. 9. 7. 2, Bitplane) were used for rendering, reconstruction, morphometric and volumetric analysis of image z-stacks and tile scans. No irregular nonlinear adjustments were performed and adjustments were only applied to whole images.
In brief, mice were cardially perfused with PBS and SHIELD fixation solution (including 4% PFA, LifeCanvas technologies), followed by post-fixation in SHIELD fixation solution at 4 C overnight. Subsequent tissue processing and clearing was performed following the manufacturer's instructions with Smart Clear Pro II (LifeCanvas technologies). Cleared LNs were imaged in Easy Index solution (refraction index 1. 45, LifeCanvas technologies) using a Z. 1 light sheet imager (Zeiss) with a 25x objective (immerged in Easy Index solution). Z-stacks were acquired in the multi-view tile scan mode with dual side illumination. Stitching, 3D reconstruction and resampling was performed using arivis Vision4D software (v. 2. 12). Visualization and quantification of the vessel network and HEVs was performed using the filament tracer module in Imaris (v.
(G) Expression of BEC subset signature genes displayed as mean gene expression per subset. (H) Proportions of BEC subsets from unsupervised clusters; numbers indicate the percentages of subsets among all BECs. (I) Frequencies in percent of BECs annotated to subsets along known markers: (left) scRNA-seq datasets and (right) analyzed by flow cytometry (FCM). (J and K) UMAP dimensional reduction plots of BECs from tumor LNs depicted in unsupervised clusters (J) or depicting indicated gene expression (K). Vectors represent predicted trajectories based on RNA-velocity computation. Expression intensities indicated by color scale (G and K). Scale bars, 100 mm (C and E). Mean and SEM are indicated. Significances calculated by t test (A) or Mann-Whitney U-test (B). **p < 0. 01.
in the integrated dataset of both conditions (Figures 3A, S3A, and S3B). Dimensional reduction (K) Immunhistochemical detection of PNAd expression in benign human LNs or with DLBCL or cHL. (L) PNA + vessels per section, as in (J). Benign LNs n = 7, cHL n = 51, DLBCL n = 79. Scale bars, 100 mm (A, D, I, and K), 20 mm (B), and 10 mm (C). Mean and SEM are indicated. Data points represent individual HEVs (B), mice (D, F, H, and J) or cases (L). Statistics were calculated using Mann-Whitney U-test (B, F, J, and L), Wilcoxon rank sum-test (H), and t test (D). *p < 0. 05, **p < 0. 01, ***p < 0. 005. demonstrated a minor overlap of HECs from both groups (Figure 3B, top), with several genes differentially expressed (Figure.
ACKNOWLEDGMENTS The authors thank  K. Gerlach ,  K. Kr€ uger , and  A. Golz  for expert technical assistance in immunohistology and mouse genotyping. We thank  C. Fischer  for help in scRNA-seq data acquisition and  H. -P. Rahn  and  K. Rautenberg  for assistance in flow cytometry. We acknowledge  A. Margineau ,  M. Richter ,  S. Raimundo , and  A. Sporbert  for expert technical expertise in microscopy and image analysis. This work was funded in part by a grant from  Deutsche Krebshilfe  (project  111918 ) awarded to A. R. and  U. E. H. , a grant from  Deutsche Forschungsgemeinschaft  ( TRR130 , project  17 ,  C01 ) awarded to A. H. , and  ERC StG LNEXPANDS  ( CRUK-A19763 ) awarded to S. E. A. A. R. and U. E. H received research funding from  Fate Therapeutics  ( San Diego, CA ) for work unrelated to the topic of the present manuscript.
DISCUSSION In this study, we dissect the dichotomy of angiogenesis in aggressive BCL, characterized by an increase in microvessel density on the one hand and loss of mature HEVs on the other. We provide functional evidence that not only is the multistep dedifferentiation program of HEVs tightly synchronized with the conduit-mediated lymph flow, but LN remodeling in lymphoma also leads to impaired T lymphocyte transmigration. These processes shape a disturbed LN microarchitecture in which spatially organized Considering the limitations of mouse models, future dissection of the lymphoma-stroma interface will profit from high-resolution scRNA-seq analysis in patients. Functional and even local interactions between tumor and stromal cells can be inferred from this type of analysis. However, we suggest that for deeper kinetic and mechanistic validation of scRNA-seq-derived datasets, mouse models remain essential.
Gene expression profiling and gene expression arrays Microarray data are available at the Gene Expression Omnibus database under the accession numbers GSE126033 in  Gloger et al. (2020), and GSE123593 in Scholz et al. (2020). The DLBCL heterogeneity signature was generated based on microarray data from publically available datasets of DLBCL patients  (Grau et al. , 2019). Accession number of DLBCL microarray datasets GSE31312.
As well as these direct approaches, ozone measurements can provide indirect evidence for halogen chemistry. Ozone destruction in tropospheric volcanic plumes, caused by volcanic halogen cycling, has been measured in a limited number of cases  (Vance et al. , 2010; Oppenheimer et al. , 2010; Kelly et al. , 2013; Surl et al. , 2015). In ash-rich explosive eruptions, it is possible that uptake of ozone on ash particles may also contribute to some ozone loss  (Maters et al. , 2017). Measuring ozone in volcanic plumes in the troposphere region downwind from volcanoes is challenging and typically only achieved using instrumented aircraft. Observations suggest a direct relation between bromine content and ozone depletion  (Roberts, 2018) , although this is based on only three available volcanic datasets: Mount Etna, Mount Redoubt, and Kīlauea.
This study The present study is devoted to a plume from Etna during July/August 2012, a period when this volcano was passively degassing. We present new airborne ozone and SO 2 measurements which were made during traverses of plumes at distances 7-21 km from the vents. Several other previously published plume measurements were also made around this time. Near-simultaneous near-vent (< 500 m) ozone measurements and DOAS observations of BrO / SO 2 ratios around 10 km downwind were reported by  Surl et al. (2015). Additionally in situ sampling of halogen emissions was undertaken that summer at the crater rim by  Wittmer et al. (2014) , and further DOAS measurements were also made of Etna's plume by  Gliß et al. (2015). Consequently, this period of Etna's activity may have the richest overall dataset to date for determining halogen activity in relation to ozone loss in the plume of a volcano.
The observational dataset is analysed using a 3D regional chemistry-transport model (WRF-Chem) modified with respect to its handling of volcanic emissions and with halogen chemistry added to a chemical mechanism. The goal of the modelling is to assess the ability of the 3D model to adequately reproduce the key chemistry features (ozone loss, BrO / SO 2 ratios) of the Etna plume given reasonable input parameters, such as the typical halogen emission fluxes for the volcano in a passive-degassing state. We then diagnose in-plume chemical processes in the model, exploiting the fact that a model can be interrogated in far greater detail than an observational dataset. The focus is on the chemical processes in the near-downwind plume, up to tens of kilometres from emissions sources for plume ages of up to tens of minutes.
, 2016a). Both the SO 2 and ozone instruments have a temporal resolution of 10 s (averaging time), and their response times are 80 and < 30 s, respectively. Three flights were conducted, one each on the mornings of 30 July 2012, 31 July, and 1 August, during daylight hours. These flights started and ended at Reggio Calabria Airport and attempted several transects of the plume. The flight paths are shown in Fig. 2. Since ambient concentrations of ozone vary both spatially and temporally, rather than assessing all of the observation data together, we undertook a systematic approach to identify and isolate separate "major plume encounters" from the dataset and separately evaluate the ozone variations within these. This approach was designed such that the majority of the variation in ozone within each major plume encounter could be ascribed to plume chemistry rather than variations in the background.
Using this value yields a rate with respect to time of -1. 3 × 10 -5 molec. molec. -  s -1. Interestingly, this rate is very close to a value of -1. 0 × 10 -5 molec. molec. -  s -1 derived from in situ measurements made within 500 m of the vents on 27-30 July 2012  (Surl et al. , 2015) , supporting the theory of a continuous process beginning from no ozone depletion at the source. We note that an analysis to evaluate the trend in ozone depletion with respect to distance within the dataset yielded a null result: ozone-depletion-to-SO 2 ratios were calculated for each in-encounter data point by using the y-axis intersect of each plume encounter as an estimate for background ozone for all data points within that encounter. The output from this analysis across the whole dataset was too noisy to discern an overall trend.
The observed depletion of ozone in the Mount Etna plume is consistent with ozone-destructive halogen chemistry in volcanic plumes  (Gutmann et al. , 2018 , and references therein). Halogens were not measured by the aircraft, but ground-based remote sensing confirms the presence of volcanic BrO in the plume during July-August 2012  (Surl et al. , 2015). The following section presents the results of 3D highresolution WCV model simulations, focusing on the ozonedestructive halogen chemistry in Mount Etna plumes during the 2012 aircraft campaign. The model is used to quantify the ozone depletion and attribute it to specific halogen reactions and to investigate additional impacts of volcanic plume halogen chemistry on atmospheric HO x , NO xy , and mercury. Observational data and the model outputs are compared in Section 4.
This occurs because BrO is more rapidly converted to HOBr in the middle of the day, when atmospheric HO 2 is at a maximum. Although moderate in magnitude, this phenomenon may be significant when comparing spectroscopic columns at different times, including datasets from low-earth-orbit satellites with overpasses at different local times. Our model study identifies how variations in time of day and plume intensity (which is controlled by wind speed in this constant-emission case) impact BrO / SO 2. These may present additional complications for the interpretation of BrO / SO 2 ratios observed in passively degassing volcanic plumes. Variations in BrO / SO 2 are often evaluated with respect to distance from a volcano. We note that the wind speed has a "double effect". First, faster winds give the impression that the chemistry evolves more slowly as a function of distance downwind because the plume moves further from the source in a given time.
Comparison of model and aircraft data on ozone loss Here we compare the aircraft observations to model outputs. Because the model plume does not precisely trace the same path as the observed plume, using the exact coordinates of each in-plume observation to identify the plume in the model domain would certainly often result in missing the modelled plume. Instead, for each of the plume encounters discussed in Sect. 3, we identify the equivalent of the observations in the model by the following method. Model data are considered "equivalent" for a plume encounter if they satisfy the following criteria: -Time is closest to the median time of the observed encounter. -Grid box is wholly or in part within the altitude range of the observed plume encounter. -Grid box centre is within the range of distances from Etna for the observed plume encounter.
Conclusions Volcanoes emit halogens that are converted into active chemical radicals in plumes and whose chemistry, notably bromine, causes ozone destruction. However, the plume processes driving this halogen conversion, the so-called halogen activation, and the ensuing ozone depletion within the volcanic plumes are not yet well constrained by the limited observational datasets and numerical model studies. It remains difficult to assess accurately the large-scale impact of volcanic halogens without a quantitative understanding of halogen plume chemistry. This study presents a new dataset of airborne measurements made during the summer 2012 in the degassing plume from the Mount Etna volcano, up to a few tens of kilometres from the source. This chemically reactive plume is simulated using a new numerical 3D model "WRF-Chem Volcano" (WCV), a version of WRF-Chem we have modified to incorporate volcanic emissions (including HBr and HCl) and multi-phase halogen chemistry.
Code availability. The code of WCV is available on GitHub  (Surl, 2020 , https://github. com/LukeSurl/WCV). This repository is being actively maintained. The version of the code used to generate the results of this study is included in this repository as a static branch (etna2012). The modifications to the PREP-CHEM-SRC utility have been submitted to the maintainers of this software for consideration and are available from the authors on request. Data availability. WRF-Chem generates NetCDF files as output. The output relating to the innermost domain (d04 in Fig. 3 ) is uploaded to a Zenodo online repository (Surl, 2021,  https://doi. org/10. 5281/zenodo. 4415788). This repository also contains the input setting files used for the runs.
Statistical and Microbiota Analysis Statistical analysis was performed using GraphPad Prism software (v7; San Diego, CA). All data were analyzed using Kruskal-Wallis test followed by a Dunn's multiple comparison test. 16S rDNA sequencing data were analyzed using the Phyloseq package in R and custom scripts as described previously  (48). Briefly, 16S sequencing data were analyzed using the Phyloseq (49), ggplot2 (50), DESeq2  (51) , and custom scripts. Samples were rarefied to even sampling depths before a-diversities (observed richness and Inverse Simpson) and b-diversity (Bray-Curtis) analysis. Principal coordinates analysis (PCoA) was performed on Bray-Curtis dissimilarities. a-diversity data were analyzed using Kruskal-Wallis test. Permutational multivariate analysis of variance (PERMANOVA) test was performed on the Bray-Curtis matrices using 999 random permutations and at a significance level of 0.
01. Phylum and family abundances data were compared using Kruskal-Wallis test. Raw, unrarefied OTU (also named clusters) counts were used to produce relative abundance graphs and to find taxa with significantly different abundances between two groups. DESeq2 was used to estimate abundance log-fold changes (logfc) between glucose/ control, fructose/control, or glucose/fructose groups. Clusters were selected based on effect size (fold change (FC) > 1. 5 or FC < 1/1. 5) and adjusted p-value (<0. 05). Pro-inflammatory cytokine gene expression levels (TNFa, IL13, Il22, and IFNg) and normalized bacterial abundance  (52)  were integrated and analyzed using Data Integration Analysis for Biomarker discovery using a Latent cOmponents (DIABLO) method implemented in R mixOmics package  (53). Correlation analysis between cytokines gene expression and bacterial species abondance was performed using the multiblock analysis DIABLO (block.
(F) Driving genes for each cell subset, accounting for 20% of the total information in each principal component (PC) from (E). (G) Top ten expressed genes from the total gene set, and top ten expressed genes encoding transcription factors, secreted proteins, and cell membrane markers significantly differentiating among the groups of ILCs. Gene symbols and annotations were retrieved from public databases. Transcription factor genes are colorcoded in red, secreted protein genes in orange, cell membrane protein genes in blue, and other protein-encoding genes in black. Genes encoding both secreted and cell membrane proteins are color-coded in violet. Genes are ranked by p value. (H) Module score for tonsil ILC gene expression programs for each of the three groups of ILCs, at the single-cell level.
(E) Principal-component analysis (PCA) of the four ctILC subsets of each sample on the basis of the mean level of expression of genes with variable expression. (F) Driving genes for each cell subset accounting for 20% of the total information in each PC from (E). (G) Top ten expressed genes from the total gene set and top ten expressed genes encoding transcription factors, secreted proteins, and cell membrane markers significantly differentiating among the groups of ctILCs. Gene symbols and annotations were retrieved from public databases. Transcription factor genes are color-coded in red, secreted protein genes in orange, cell membrane protein genes in blue, and other protein-encoding genes in black. Genes encoding both secreted and cell membrane proteins are color-coded in violet. Genes are ranked by p value.
SLAMF1 is a biomarker of CRC We confirmed, using flow cytometry, the expansion of the ILC1 subset accompanied by reduction of the ILC3 subset in tumor tis-sues from CRC patients relative to adjacent normal mucosa (Figures 5A and 5B). Consistent with the scRNA-seq analysis revealing higher levels of TIGIT in an ILC1-like subset and the presence of ctILC2s (Figure  2G ), we observed by flow cytometry a population of TIGIT + ctILC1-like cells and ctILC2 in tumors, but not in normal tissue (Figures  5A  and 5B ). Higher levels of expression of the ILC2-activating cytokine IL33 in tumors were correlated with longer survival in CRC patients from The Cancer Genome Atlas (TCGA) dataset, suggesting that ctILC2 might be indicative of a good prognosis in CRC patients (Figure  5C ). In contrast to the findings for gut helper-like ILCs, the frequency of each helper-like ILC subset among total helper-like ILCs in blood was similar in CRC patients and healthy donors (Figure  5D ).
We then investigated the potential role of SLAMF1 in CRC disease development and progression further, by studying the clinical outcome of cancer patients from TCGA database. Survival was much higher in patients with SLAMF1-high colon and rectal cancer than in those with SLAMF1-low tumors (Figures  5I  and 5J ), strongly suggesting that SLAMF1 is an anti-tumor biomarker in CRC.
Indeed, and in contrast to what has been reported for mice, ILC2s are almost entirely absent from healthy human tissues, with the exception of the lungs, adipose tissue, and the blood connective tissue. 41 In our study, we detected tumor-infiltrating ctILC2s in CRC patients. ctILC2s were also observed in breast,  42  gastric,  42  and pancreatic  19  tumors and in urine from bladder cancer patients. 15 Several data support a model in which ILC2s infiltrate tumors via an IL-33-dependent pathway  15, 16, 19  and mediate tumor immune surveillance by promoting cytolytic CD8 + T cell responses. IL-33 expression showed different survival prognosis in different cancer types, with better prognosis in melanoma patients but not in those with lung squamous cell carcinoma and pancreatic adenocarcinoma.
ILC1-like cells known as ''intermediate ILC1s'' (intILC1s) have also been described in a mouse model of methylcholanthrene (MCA)-induced tumors. 47 In humans, CD56 + CD16 À ILC1-like cells have been found in solid tumors and in peritoneal and pleural fluids from cancer patients,  48  and the cytotoxic functions of these cells are altered in the peripheral blood of donors with acute myeloid leukemia. 49 Intratumoral intILC1 may emerge from NK cell differentiation driven by TGF-b signaling, a phenomenon known as ILC plasticity. 47, 50 The conversion of ILC3s into ILC1s upon TGF-b signaling has been demonstrated in humanized mice, and a transitional ILC3-ILC1 population has been identified in the human intestine. 40 We observed no such phenomenon in our gut ILC dataset, and none of the algorithms tested was able to establish a relatedness between ctILC1-like TIGIT + and another gut ILC subset reflecting possible differentiation (data not shown).