dataesr/scientific-paragraphs-categorization huggingface dataset:
Internal transcribed spacer dataset assembly To produce a tree that would include as much diversity of Russulaceae as possible, including undescribed taxa and environmental samples, we used an approach based on operational taxonomic units (OTUs) similar to that of  Looney et al. (2016). We downloaded all 35 944 ITS sequence records annotated as Russulaceae (as of August 2021) on the International Nucleotide Sequence Database Collaboration or the fungal sequence database UNITE  (Kõljalg et al. , 2013)  using the PLUTOF web platform (https://plutof. ut. ee). Sequences annotated as 'chimeric' were excluded, and we added our 241 new ITS sequences. The ITS1-5. 8S-ITS2 region was extracted from this set using ITSX v. 1. 1. 3 (Bengtsson-Palme et al. , 2013) , keeping only matches at least 50 bp long.
Phylogenetics A 'supertree' approach was used, estimating first a backbone phylogeny using conserved markers and then inserting backboneconstrained, genus-level phylogenies estimated from the ITS sequences. For the backbone, we first assembled a set of nuclear LSU (nrLSU) (28S), rpb1 (RNA polymerase II gene, largest subunit), and rpb2 sequences from 437 accessions of Russulaceae and seven outgroup Russulales, based on previous studies  (Buyck et al. , 2008 (Buyck et al. , , 2018 (Buyck et al. , , 2020;; Verbeken et al. , 2014; Looney et al. , 2016; De Crop et al. , 2017; Wang et al. , 2018). We added 28 of the newly sequenced Russulaceae for which nrLSU and/or rbp2 were amplified successfully. In the backbone accessions, 324 matched accessions in the ITS dataset and were used to constrain the genus phylogenies (see later).
Sequences for each locus were aligned with MAFFT/E-INS-I v. 7. 407  (Katoh & Standley, 2013) , end columns with > 90% missing data were trimmed, and alignments then concatenated. A maximum likelihood phylogeny was estimated using RAXML v. 8  (Stamatakis, 2014) , a GTR+Γ substitution model for each of the LSU, rpb1, and rpb2 partitions, and 1000 rapid bootstrap searches. Transfer bootstrap expectation (TBE) was calculated in addition to traditional Felsenstein bootstrap support, which is sensitive to 'rogue taxa' in large datasets  (Lemoine et al. , 2018). We then estimated phylogenies from the ITS dataset of representative OTU sequences. To improve alignment and tree estimation, we divided them into three genus datasets (combining the small Multifurca with its sister Lactarius). The ITS sequences were searched via BLASTN  (Altschul et al.
, 1990)  against the ITS sequences associated with backbone tips to assign them to a genus dataset. Sequences with < 70% similarity to a backbone ITS sequence and only a single OTU member were discarded as likely chimeric or mislabelled as Russulaceae. Rapid alignments and trees were made with MAFFT (automatic option) and FASTTREE v. 2. 1. 10 (Price et al. , 2010) for visual inspection. We removed a further three sequences that appeared as conspicuously long branches. The final set of ITS sequences per genus was then aligned (with suitable outgroup sequences from the other genera) using MAFFT/E-INS-I. Alignments were trimmed and RAXML trees inferred as described for the backbone, specifying ITS1, 5. 8S, and ITS2 partitions; splits that had a TBE support of 0. 7 or higher in the backbone tree were constrained in the tree search.
1 ). The final supertree contained 3285 tips, corresponding to OTUs identified in the ITS dataset and representing 29 167 ITS sequences. For phylogenetic tree manipulation and plotting, we used the packages APE v. 5. 3  (Paradis & Schliep, 2019) , GEIGER v. 2. 0. 6. 1  (Harmon et al. , 2008) , PHYTOOLS v. 0. 6. 60  (Revell, 2011) , and PLOTRIX v. 3. 7. 5  (Lemon, 2006 ) in R v. 3. 6. 1.
Biogeographic modelling We estimated ancestral areas using a simple Markov model of trait evolution. The frequently used dispersal-extinction- cladogenesis model  (Ree et al. , 2005)  proved too computationally expensive for our phylogeny and also has various issues  (Ree & Sanmart ın, 2018). We fitted a one-parameter trait evolution model to the supertree and the tip areas assigned in the CORHMM R package v. 2. 7  (Beaulieu et al. , 2021) , which can handle polymorphic tips and missing values. With the estimated ancestral areas, we summarized lineages that have a relative likelihood of > 0. 5 to occur in one of the four focal areas. We recovered their stem ages (divergence from groups outside the focal areas) and, if applicable, crown ages (first divergence within the focal areas). We then assessed, under the two ages scenarios, whether these ages coincide with the following biogeographic events: the split between South America and Antarctica, 50 Ma To count the number of dispersal events (i.
, 2011)  for such a large phylogeny and multiple areas, we partitioned per-branch diversification rates by area in an approach similar to  Chazot et al. (2021). We randomly paired the BAMM posterior samples with the area evolution stochastic maps. For each pair, the tree branches were divided into segments with the same range and diversification regime and no longer than 2% of the root height. We then calculated the mean and 95% credible interval of diversification rate per area, both overall and for each of 20 equal-sized time windows. The R code for this is available in the online repository.
Internal transcribed spacer sequences and occurrence in biogeographic areas We compiled a dataset of 29 167 global ITS sequences of the ectomycorrhizal Russulaceae clade. These were obtained through a series of filtering steps applied to 35 944 sequences retrieved from public databases, combined with 241 new sequences generated for neotropical taxa. These sequences clustered into 3285 OTUs at a 97% similarity threshold, represented as tips in the Russulaceae supertree (Table  1 ), with 2234 belonging to Russula, 565 to Lactarius, 472 to Lactifluus, and 14 to Multifurca. Of these, 879 sequences and 353 OTUs could be assigned to one of four focal areas of the Neotropics and adjacent regions, with Central America/Caribbean having the highest numbers. Most sequences and OTUs in the dataset were assigned to the Palaearctic and Nearctic areas, respectively.
Acknowledgements JH was funded by a  'Future Leaders in Plant and Fungal Science'  fellowship by the  Royal Botanic Gardens ,  Kew. JH  and MR received funding from the  CNRS Nouragues Research Station  ( Mycotin  project) and from the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 , DIADEMA and DIAMOND projects). TWH was supported by  National Science Foundation  grants  DEB-0918591 ,  DEB-1556338 , and  National Geographic Society's Committee for Research and Exploration  grants  6679-99  and  7435-03. EDC was supported by the  'Special Research Fund Ghent University' (BOF) , grants  B/13485/01  and  BOF-PDO-2017-001201. Computational resources used at the  UK's Crop New Phytologist  ( 2022 )  236 :  698-713  www. newphytologist. comÓ 2022 The Authors New Phytologist Ó 2022  New Phytologist Foundation.
S1b ). To determine whether CD73 expression is intrinsically necessary for tumor cell proliferation and survival, we examined genome-wide RNAi screening data from Project Achilles, a database of gene dependencies for 1,736 cell lines from 34 cancer types  42. Among all cancers, glioma cell lines exhibited the highest mean dependence on CD73 expression (Fig. 1b, c ). Glioblastoma cell lines without isocitrate dehydrogenase-1 and -2 mutations (IDH1,IDH2; IDH-wildtype glioblastoma) such as LN340 and U343 exhibited among the highest dependence on CD73 of all cancer lines, and gliomas were the only lines with a significant mean dependence on CD73 for survival (p<0. 05) (Fig. 1c ). To further characterize the significance of purine metabolism in glioblastoma, we used unbiased spatially resolved timsTOF mass spectrometry imaging (MSI) to quantify metabolite levels from multiple pathways.
While the 'T cell' category was predominantly composed of CD3G positive T cells (85. 1%), it also included sub-populations of NK cells (NCAM1/CD56+) (1. 1%), and B cells (MS4A1/CD20+) (3. 2%); we therefore classified this category as 'lymphoid' (Fig. 2a, b ). Prior studies of immune populations isolated from glioblastoma by fluorescence-activated cell sorting (FACS) suggested a role for CD73-expressing macrophages in the pathogenesis of glioma  33. Analysis of scRNA-seq data confirmed that subsets of myeloid cells do express CD73; however, we found that CD73 was much more prominently expressed by tumor cells (35. 9% of cells;1. 19 scaled mean expression) than by myeloid cells (2. 52%;0. 06), or other populations (3. 2% oligodendrocytes,0% lymphoid) in this dataset (Fig. 2a-c ). Conversely, CD39, the upstream ectoenzyme that hydrolyzes extracellular ATP and ADP to AMP, while weakly expressed by multiple populations including tumor cells (94.
Correlative analysis for all common sequence and chromosomal variants was notable only for significantly greater CD73 protein expression (by IHC) in EGFR amplified tumors (p=0. 029) (Fig. 4b ). To validate these results, we analyzed bulk mRNA sequencing data from TCGA and found a significant positive correlation between EGFR and CD73 mRNA (Fig. 4c ; r=0. 17; p=0. 03). There was a strong trend toward increased NT5E mRNA expression in EGFR amplified tumors, but this association did not reach statistical significance in this dataset (p=0. 1) (Fig. 4d ). There was no correlation between CD73 expression and CDK4, PDGFRA, or NF1 copy-number alterations or mRNA expression (Supplementary Fig. S4a-f ). In our cohort, CD73 expression was slightly lower in recurrent/residual tumors compared to primary lesions (Supplementary Fig.
Monoclonal antibody CD73 inhibitors are in phase 1-2 clinical trials in advanced solid malignancies (NCT02503774, NCT02754141) and recurrent ovarian cancer (NCT03267589), typically in combination with other checkpoint inhibitors, and have shown promising early results and limited toxicity  68. Given the high expression of CD73 in glioblastoma and association with clinical outcome, targeting this enzyme with single or combination therapy with checkpoint inhibitors likely represents an attractive therapeutic avenue. In conclusion, this study uses multiple complementary experimental methodologies (transcriptomics, metabolomic, and high-plex imaging) to demonstrate that purinergic signaling is highly enriched and biologically relevant in IDH-wildtype glioblastoma. Elevated CD73 expression is associated with EGFR amplification, astrocyte-like lineage differentiation, inflammatory transcriptional states, and poor clinical outcomes.
Single-Cell RNA-Seq Analysis Single-cell analysis was performed using the data from a previously published study (Neftel et  al, 2019)   43. The processed log TPM gene expression matrix, metadata file, and the hierarchy data were all downloaded from the Broad Single Cell portal (https://singlecell. broadinstitute. org/single_cell). The analysis was performed in R using the Seurat package (Butler et al. , 2018) and the figures were plotted using the ggplot package. Pathway enrichment analyses were performed using GSEA (Subramanian et al. , 2005).
MALDI FT-ICR mass spectrometry imaging The mass spectrometry imaging experiments were conducted using a 9. 4 Tesla SolariX XR FT-ICR MS (Bruker Daltonics, Billerica, MA) in negative ion mode. Using a tune mix solution (Agilent Technologies, Santa Clara, CA) the method was calibrated through the electrospray source. The instrumental parameters included a defined pixel step size of 100 µm that covered the m/z range of 58-1200. Each pixel consisted of 200 shots at a laser power of 12% (arbitrary scale) and frequency of 1,000 Hz. To bias the acquisition to improve the capture of the purine metabolites mass range, constant accumulation of selected ions (CASI) mode was used, in which the Q1 was set to m/z 350 with an isolation window of 415. Ion images and mass spectra were viewed and processed using SCiLS Lab software (version 2019c premium, Bruker Daltonics, Billerica, MA), in which the dataset was normalized to the total ion current (TIC).
t-CyCIF image processing and data quantification Image analysis was performed with the Docker-based NextFlow pipeline MCMICRO (Schapiro et al. , 2021) and with customized scripts in Python, ImageJ and MATLAB. All code is available on GitHub. Briefly, after raw images were acquired, the stitching and registration of different tiles and cycles were done with MCMICRO using the ASHLAR module. The assembled OME. TIFF files from each slide were then passed through quantification modules. For background subtraction, a rolling ball algorithm with 50-pixel radius was applied using ImageJ/Fiji. For segmentation and quantification, UNMICST2 was used (Schapiro et al. , 2021) supplemented by customized ImageJ scripts (Lin et al. , 2018) to generate single-cell data. More details and source code can be found at www.
Single-cell data QC for t-CyCIF Single-cell data was passed through several QC steps during generation of cell feature table. Initial QC was done simultaneously with segmentation and quantification, so that cells lost from the specimen in the later cycles would not be included in the output. Next, single-cell data was filtered based on mean Hoechst staining intensity across cycles; cells with coefficient of variation (CV) greater than three standard deviation from the mean were discarded as were any objected identified by segmentation as "cells" but having no DNA intensity. These steps are designed to eliminate cells in which the nuclei are not included as a result of sectioning. Highly auto-fluorescent (AF) cells (measured in cycle 1 or 2) were also removed from the analysis, using a customized MATLAB script that applied a Gaussian Mixture Model (GMM) to identify high-AF population.
Analysis of bulk mRNA expression in a broader cohort of IDH-wildtype glioblastoma cases (TCGA) validated these findings, showing a significant direct correlation between CD73 and EGFR expression (C). Cases with EGFR amplification showed a trend toward elevated CD73 expression, but this did not reach statistical significance in this dataset (p=0. 10) (D). Mantel-Cox Log-Rank survival analysis showed that elevated CD73 protein expression (by IHC) was associated with significantly shorter progression-free survival (PFS) (E). CD73 IHC was performed on 9 IDH-wildtype glioblastomas previously submitted for mass spectrometry (MSI) showed that 4 cases exhibited high and 5 cases with low CD73 expression. Analysis of purine metabolites in these cases revealed that those with elevated CD73 exhibited significantly higher levels of adenosine relative to cases with low expression (3.
To explore this association, spatial neighborhood analysis was performed on the t-CyCIF dataset of 172 IDH-wildtype glioblastoma tissue specimens. This analysis revealed significant spatial correlation between tumor CD73 expression and myeloid CD39 expression (D). Tumor CD73 expression does not correlate with the spatial distribution of myeloid cells agnostic for marker expression. Tumor CD73 expression was also significantly correlated with myeloid PD-L1 (D) and HIF1a (E). Analysis of myeloid differentiation markers showed that tumor CD73 correlated with cells expressing CD163 and CD11b (F). Sub-group analysis of microglial cells  51  showed that these markers correspond to a glioma-associated microglial state (cluster 14) which exhibits elevated inflammatory signaling (G). Mantel-Cox survival analysis showed that cases with higher levels of spatial interaction between tumor CD73 and myeloid CD39 showed particularly poor progression-free survival (PFS) (H).
ACKNOWLEDGEMENTS We thank  Dana-Farber / Harvard Cancer Center in Boston, MA , for the use of the Specialized Histopathology Core, which provided tissue processing services. Dana-Farber/Harvard Cancer Center  is supported in part by an  NCI Cancer Center Support  Grant #  NIH 5 P30 CA06516. The results shown here are in part based upon data generated by the  TCGA Research Network : https://www. cancer. gov/tcga. This work was supported by  NIH  grants  U54-CA225088  (PKS, SS),  R01-CA194005  (SS),  T32-GM008313  (SW), and  T32-GM007748  (SC), by the  Ludwig Center at Harvard  (PKS, SS) and by the  Specialized Histopathology Core Facility of the Dana-Farber/Harvard Cancer Center  ( P30-CA06516 ). KLL was supported by  R01CA188288 ,  P01CA163205 ,  R01CA219943.
This state-of-the-art RA-Atlas includes an updated version of the global RAmap covering relevant metabolic pathways and cell-specific molecular interaction maps for CD4+ Th1 cells, fibroblasts, and M1 and M2 macrophages. The molecular interaction maps were built using information extracted from published literature and pathway databases and enriched using omic data. The RA-Atlas is freely accessible on the webserver MINERVA (https://ramap. uni. lu/minerva/), allowing easy navigation using semantic zoom, cell-specific or experimental data overlay, gene set enrichment analysis, pathway export or drug query.
Summarizing this scattered and fragmented knowledge in a formal and standardized representation can significantly facilitate understanding the underlying disease mechanisms. In this direction, some efforts have already been made to assemble the available knowledge in molecular interaction maps. In 2010, a comprehensive RA-specific map  (Wu et al. , 2010)  was published based on high throughput experiments (mRNA, miRNA), literature, publicly available datasets and the pathway database KEGG  (Ogata et al. , 1999)  to establish the connections between different components of the map. A decade later, in 2020, a second effort to formalize RA knowledge was published using Wu et al. 's map as a basis, the RA-map  (Singh et al. , 2020). For this map, the researchers used the Systems Biology Graphical Notation (SBGN) standard and, more precisely, the Process Description (PD) language  (Le Novère, 2015)  to represent the molecular pathways implicated in the disease.
The map was built as a global map using information from human-specific studies related to RA, focusing on small-scale experiments in various cell and tissue types (e. g. , fibroblasts, macrophages, synovial tissue, and peripheral blood mononuclear cells (PBMC). The RAmap was manually curated for each component and reaction, with defined cellular pathways and molecular signatures under expert validation. In addition to manual curation of the scientific literature, other sources such as the Ingenuity Pathway Analysis (IPA)  (Krämer et al. , 2014) , DISNOR  (Lo Surdo et al. , 2018) , or KEGG databases were used to obtain RA related information regarding molecular pathways and phenotypic signatures. While the previously published RA-map offers a formalized representation of the major pathways implicated in the disease, it does not cover metabolic pathways.
In this direction, we selected three cell types of interest: fibroblast, macrophage (including the pro-inflammatory M1 and the antiinflammatory M2), and CD4+ T helper cell 1 (Th1). These cell types are abundant in RA patients' joints  (Huber et al. , 2006; Li et al. , 2017) , and produce high amounts of cytokines  (Bartok and Firestein, 2010; Schulze-Koops and Kalden, 2001) , resist apoptosis and proliferate at high rates  (Krämer et al. , 2014; Yang, Chang and Wei, 2020). The cell type selection was also based on literature availability, including already existing cell-specific maps, specific signalling pathways, and gene expression datasets. This paper introduces an attempt to create a multicellular atlas of the rheumatic joint to recapitulate existing knowledge related to the disease's onset, progression, and pathogenesis.
Map Construction The original RA-map paper thoroughly describes the general methodology for constructing the different RA-Atlas maps  (Singh et al. , 2020). Briefly, we used the CellDesigner software  (Funahashi et al. , 2003)  to construct the molecular interaction maps in the PD format, one of the three standard languages of SBGN  (Le Novère, 2015). The various components of the maps (i. e. , phenotype, protein, gene, RNA, simple molecule, ion, complex) were distinguished in the form of specific glyphs and notations according to the said standards. Cellular compartments featured in each map differ according to their specificities. They are designed to reflect the molecular architecture of each map (e. g. , extracellular space, plasma membrane, cytoplasm, nucleus, mitochondrion, endoplasmic reticulum, phenotypes, secreted compartment, and transmembrane domain).
Curation Criteria All the RA-Atlas maps underwent the first step of manual curation based on a broad study of literature and pathway databases to search for components and interactions involved in RA's pathogenesis. We focused on small-scale experimental studies and added animal-based studies where the human-related information was not adequate to validate the role of components or interactions. However, the information based on animal studies remains limited and completely traceable (four molecules are involved in the Th1 map, four in macrophage maps and two in the fibroblast map). Including a component or a reaction in a map followed precise curation criteria: first, we included all components experimentally proven to be expressed in RA's pathogenesis (and in a cell-specific manner for the cell-specific maps).
Visualization and Accessibility All RA-Atlas maps presented in this work are available as online interactive maps on the standalone web server MINERVA (Molecular Interaction NEtwoRks VisuAlization)  (Gawron et al. , 2016). MINERVA platform allows for visual exploration, analysis and management of molecular networks encoded in systems biology formats, including CellDesigner, SBML and SBGN. MINERVA also provides automated content annotation and verification and overlaying experimental data. Our maps integrate data from various sources. We provide cell-specific overlays (cf. the overlay section) extracted from the literature and publicly available datasets to visualize cellular signatures on the different RA-Atlas maps.
Metabolic Pathways Addition Metabolic pathways of interest (i. e. , glycolysis, pentose phosphate pathway, citric acid cycle and oxidative phosphorylation) were extracted from the PANTHER pathway database  (Mi and Thomas, 2009) , adapted to SBGN PD standards when required and added to the original RA-map. Metabolites and metabolic enzymes were named following BiGG IDs  (King et al. , 2016). In addition, considerable bibliographical work was conducted to find evidence linking the newly added metabolic pathways to the already present signalling and gene regulatory pathways.
Cell-specific Maps To build cell-specific maps for M1 and M2 macrophages, fibroblasts, and Th1, we screened literature (small scale experiments, GWAS) for signalling pathways, genes or transcription factors associated with RA. If available, we also integrated pre-existing maps. We used cell/disease-specific markers or gene expression datasets to retrieve signalling pathways involved in RA pathogenesis. To define gene signatures and primary RA cellular outcomes, we used databases such as the MSig database  (Liberzon et al. , 2015) , KEGG and biocuration. The retrieved gene signatures were used to connect secreted molecules to the cellular phenotypes included in the map. The cellular phenotypes include angiogenesis, apoptosis, bone erosion, cell chemotaxis, recruitment and infiltration, cell growth, survival and proliferation, hypoxia, inflammation, matrix degradation and osteoclastogenesis.
Two proprietary software were used to enrich the maps: IPA and MetaCore (Clarivate) version 22. 1 build 70800, both displaying vast map databases. In MetaCore, we mapped the list of DEG to the internal map database using the "Pathway maps" option through the "One-click analysis" tab. The number of overlapping DEGs is displayed next to each identified map. The same analysis was done using IPA's "canonical pathways" analysis. Highlighted DEG and their associated pathways were identified, added to the corresponding RA-Atlas map and further annotated with PMIDs or GEO accession numbers.