dataesr/scientific-paragraphs-categorization huggingface dataset:
Intra-cohort immune classifications Fibroblasts signature was removed from this analysis as all STS tumours exhibited high and homogeneous scores for this cell population, which is consistent with the mesenchymal origin of STS. The signature for CD8 T cells was removed from the analysis for GSE21050, GSE21122 and GSE30929 as it showed very small variation across all samples in these microarray-based cohorts. Unsupervised clustering of samples in each cohort was performed based on the metagene Z-score for the included populations of MCP-counter (Extended Data Fig. 9a-d ) using R software, with the Euclidian distance and Ward's linkage criterion, using the gplots package. The TCGA SARC, GSE21050, GSE21122 and GSE30929 cohorts were respectively separated into 6, 9, 7 and 6 groups. The number of clusters was chosen empirically following the dendrograms shown in Extended data Fig.
Prediction of the immune classes Centroids of SICs were computed on MCP-counter intra-series Z-scores for T cells, cytotoxic lymphocytes, B cell lineage, NK cells, monocytic lineage, myeloid dendritic cells, neutrophils and endothelial cells, on all cohorts. To predict de novo the immune classes of each of the cohorts, MCP-counter Z-scores were computed, and each sample was assigned to the closest immune class based on its Euclidian distance to the related centroids. The SICs labels used in the article are the ones predicted using this method. Principal component analysis of the 608 samples on the MCP-counter scores shows that the intra-SIC homogeneity was improved by this prediction step (Extended Data Fig. 9j-k ), as confirmed by supervised tests across SICs (Extended Data Fig. 9l-m ).
Prediction of the immune classes on new samples The predictor described above was adapted to analyse new and independent samples, from Nanostring-analysed FFPE samples. In a first step, SICs were estimated on the NTUH cohort by sorting samples on the B lineage signature, T cells signature then Endothelial cell signature and assigning each sample according to the SIC it resembled the most. Similarly to what was done above, centroids of each SIC on Nanostring data MCP-counter scores Z-scores were computed and samples were reassigned to the SIC they were closest to the centroid of. For new samples from the SARC028 cohort, MCP-counter scores for T cells, Cytotoxic lymphocytes, B lineage and Endothelial cells were computed and transformed as Z-scores. Distances with Nanostring-defined centroids presented above were computed with Euclidian metric, and samples were assigned to the SIC with the lowest distance.
Enzymatic immunohistochemistry The stainings were performed with an Autostainer Link 48 (Dako). Chromogenic detection was performed using 3,3'-diaminobenzidine (Dako, K3468) for CD8, CD20, CD21, PD-L1 and PNAd, 3-amino-9-ethylcarbazole substrate (Vector Laboratories, SK-4200) for DC-Lamp, Blue Alkaline Phosphatase Substrate (Vector Laboratories, SK5300) for CD3, HighDef red IHC chromogen (AP) (Enzo, ADI-950-140-0030) for CD20 and Permanent HRP Green (Zytomed Systems, ZUC070-100) for CD23 and CD34. The nuclei were counterstained with haematoxylin (Dako, S3301). After mounting with Glycergel Mounting Medium (Dako, C056330-2) or EcoMount (Biocare Medical, EM897L), the slides were scanned with a Nanozoomer (Hamamatsu). For CD3, CD8, CD20 and DC-Lamp markers, the density of positive cells/mm² was quantified with Calopix Software (Tribvn).
For CD34 marker, the density of positive vessels/mm² was quantified with Halo10 software (Indica labs). TLS were identified using the registration module to fit one slide on the other (Halo10 software, Indica labs). Tumours were considered TLS-positive when a CD3 aggregate with DC-Lamp staining was found juxtaposing a CD20 aggregate. Only aggregates with surface above 60,000 µm², containing at least 700 cells and at least 350 CD20 + cells were considered.
Extended Data Tables titles and footnotes Extended Data Table  1 : Clinicopathological composition of the cohorts included in this study. For cohort GSE21050, sex information could not be retrieved for 14 patients. For cohort NTUH, SIC could be determined for 73 patients only. NA: Not Available Aurélien De Reynies <Aurelien. DeReynies@ligue-cancer. net> Pour : Florent Petitprez <Florent. Petitprez@ligue-cancer. net>, Keung,Emily <ekeung@mdanderson. org>, Wei-Wu (Tom) Chen <saxotomy@gmail. com>, Cheng-Ming Sun <cheng-ming. sun@crc. jussieu. fr>, Julien Calderaro <julien. calderaro@hmn. aphp. fr>, liippiil@yahoo. com. tw <liippiil@yahoo. com. tw>, mRNA0912@yahoo. com. tw <mRNA0912@yahoo. com. tw>, laetitia. lacroix@inserm. fr <laetitia. lacroix@inserm. fr>, antoine. bougouin@crc. jussieu. fr <antoine.
Upper whisker extends to whichever is minimal, the maximum of the data or the 3 rd quartile plus 1. 5*IQR. Lower whisker extends to whichever is maximal, the minimum of the data or the first quartile minus 1. 5*IQR. Extended Data Figure Legends Extended Data Fig. Diagram representation of analytic workflow Extended Data Fig. SICs in various STS histologies. Repartition of the SICs in various histologies of TCGA SARC and GSE21050 (leiomyosarcoma [LMS], undifferentiated pleomorphic sarcoma [UPS], dedifferentiated liposarcoma [DDLPS]), and FSG cohort (synovial sarcoma, myxoid liposarcoma, gastrointestinal stromal tumour [GIST]). Survival of patients from the FSG cohort (n=136) according to SIC classification. Patients with synovial sarcoma, myxoid liposarcoma and GIST were pooled. The analysis was performed with the Kaplan-Meier estimates and two-sided logrank tests.
Extended Data Fig. 3: The Sarcoma Immune Classes exhibit strongly different tumour microenvironments. This figure refers to the GSE21050 cohort (n=283). a Composition of the GSE21050 cohort by SIC, histology and site of disease. b Composition of the TME by sarcoma immune class (SIC) as defined by the MCP-counter Z-scores. c Expression of gene signatures related to the functional orientation of the immune TME by SIC. d Expression of genes related to immune checkpoints by SIC. Adjusted p-values are obtained from Benjamini-Hochberg correction of two-sided Kruskal-Wallis tests p-values. These observations stand for cohorts GSE21122 and GSE30929 (not shown). Extended Data Fig. Distribution of MCP-counter scores in TCGA SARC (n=213) (a,c,e) and GSE21050 (n=283) (b,d,e), for CD8 + T cells (a,b), cytotoxic lymphocytes (c,d) and B lineage (e,f).
The blue line indicates the density curve. The red dotted line indicates the cut-off chosen to segregate high or low values, set at the median for CD8 + T cells and at the third quartile for cytotoxic lymphocytes and B lineage, in each cohort. These values were chosen since the CD8 T cells scores present a normal distribution, while the cytotoxic lymphocytes and B lineage scores distribution exhibit a long right tail. Extended Data Fig. 5: B cell infiltration of STS is the key factor associated with overall survival This figure refers to TCGA SARC and GSE21050 pooled cohorts (n=496). a Overall survival of STS patients according to MCP-counter scores for cytotoxic lymphocytes. b Overall survival of patients based on the infiltration level of their tumours by B lineage cells and cytotoxic lymphocytes.
DAPI staining is shown in white. DAPI-negative green dots correspond to fluorescent erythrocytes. f Distribution of TLS maturation steps in a subset of tumours. Each bar represents one tumour. Differences between the number of TLS observed here and in other figures can be explained by use of non-consecutive slides or a different tumour block for some samples. Extended Data Fig. 9: Pan-cohort immune classification This figure refers to the four discovery cohorts: TCGA SARC (n=213), GSE21050 (n=283), GSE21122 (n=72) and GSE30929 (n=40). a-d Heatmap and unsupervised hierarchical clustering of the MCP-counter scores describing the tumour microenvironment. Each of the population is represented by the Z-scores of the signature. a TCGA SARC. b GSE21050. c GSE21122. d GSE30929. e-h Evolution of the variance explained by the clusters as a function of the number of clusters.
The red dots indicate the number of clusters that was retained in this study. Each graph corresponds to the heatmap that is situated on its left. i Heatmap of the Pearson correlation of centroids from each SIC class of discovery cohorts (TCGA SARC, GSE21050, GSE21122, GSE30929, n=608), with 5 immune classes and 2 groups of unclassified samples. j-k Principal component analysis of samples from the four discovery cohorts (n=608), based on their normalized and merged MCP-counter scores. (j is coloured according to the original classes, k is coloured according to the predicted immune classes, showing a heightened homogeneity within each SIC class). l-m Composition of the TME with classes defined as in j and k for the four discovery cohorts (n=608), expressed in cohort-specific row Z-scores.
Acknowledgements This work was supported by the  Institut National de la Santé et de la Recherche Médicale , the  Université de Paris, Sorbonne University , the  Programme Cartes d'Identité des Tumeurs (CIT)  from the  Ligue Nationale Contre le Cancer , grants from  Institut National du Cancer (HTE-INSERM plan cancer ,  C16082DS ),  Association pour la Recherche sur le Cancer (ARC) ,  Cancer Research for Personalized Medecine programme  ( CARPEM T8 ),  French Sarcoma Group , the  European Connective Tissue Cancer Network (CONTICANET ,  FP6-018806 ), "FONCER contre le cancer" programme,  Labex Immuno-Oncology  ( LAXE62_9UMRS972 FRIDMAN ), the  National Institutes of Health  (EZK is supported by  T32CA0095999 ) and the  Moon Shot program  at  MD Anderson Cancer Center. Grants from the  Ministry of Education  ( NTU-107L9014 ) and  Ministry of Science and Technology (MOST   107-3017-F-002 -002 -), Taiwan and from the  National Taiwan University  ( YongLin Chair Grant   S-01  and  S-03 ) also supported this study.
SARC028 was jointly funded by  Merck, Inc ,  SARC, Sarcoma Foundation of America , and the  QuadW Foundation. FP is supported by  CARPEM  doctorate fellowship. CLR is recipient of the  Paul Calabresi Clinical Oncology Award  ( K12 CA088084 ). The slides stained for IF were scanned and analysed at the  "Centre d'Histologie, d'Imagerie et de Cytométrie" (CHIC), Centre de Recherche des Cordeliers UMRS1138 (Paris, France ). CHIC is a member of the  Sorbonne University Flow Cytometry Network (RECYF). We thank  Christophe Klein ,  Kevin Garbin  and  Estelle Devevre  from the  CHIC  for their support with the imaging. The nanostring analysis of the NTUH core cohort was performed by the " Plateforme Génomique " of the  Institut Curie (Paris, France). We thank  David Gentien  and  Emilie Henry  for their support.
Data and Code Availability statement The transcriptomic datasets analysed in this study can be accessed on the GDC Portal (portal. gdc. cancer. gov, cohort TCGA SARC) and the Gene Expression Omnibus repository (www. ncbi. nlm. nih. gov/geo, accession numbers GSE21050, GSE21122, GSE30929). FSG cohort data is publicly available from ArrayExpress for GIST (www. ebi. ac. uk/arrayexpress, accession code E-MTAB-373) and from Gene Expression Omnibus for synovial sarcomas (www. ncbi. nlm. nih. gov/geo, accession number GSE40021). Myxoid liposarcomas from the FSG cohort are available upon reasonable request to the authors. Immunohistochemistry and gene expression data related to NTUH cohort (Fig. 3 , Extended Data Figs. 7  and 8 ) are available upon reasonable request to WHF (herve. fridman@crc. jussieu.
Box plots represent median (larger bar) and interquartile range (IQR). Upper whisker extends to whichever is minimal, the maximum of the data or the 3 rd quartile plus 1. 5*IQR. Lower whisker extends to whichever is maximal, the minimum of the data or the first quartile minus 1. 5*IQR. Extended Data Figure Legends Extended Data Fig. Diagram representation of analytic workflow Extended Data Fig. SICs in various STS histologies. Repartition of the SICs in various histologies of TCGA SARC and GSE21050 (leiomyosarcoma [LMS], undifferentiated pleomorphic sarcoma [UPS], dedifferentiated liposarcoma [DDLPS]), and FSG cohort (synovial sarcoma, myxoid liposarcoma, gastrointestinal stromal tumour [GIST]). Survival of patients from the FSG cohort (n=136) according to SIC classification. Patients with synovial sarcoma, myxoid liposarcoma and GIST were pooled.
The analysis was performed with the Kaplan-Meier estimates and two-sided logrank tests. Extended Data Fig. 3: The Sarcoma Immune Classes exhibit strongly different tumour microenvironments. This figure refers to the GSE21050 cohort (n=283). a Composition of the GSE21050 cohort by SIC, histology and site of disease. b Composition of the TME by sarcoma immune class (SIC) as defined by the MCP-counter Z-scores. c Expression of gene signatures related to the functional orientation of the immune TME by SIC. d Expression of genes related to immune checkpoints by SIC. Adjusted p-values are obtained from Benjamini-Hochberg correction of two-sided Kruskal-Wallis tests p-values. These observations stand for cohorts GSE21122 and GSE30929 (not shown). Extended Data Fig. Distribution of MCP-counter scores in TCGA SARC (n=213) (a,c,e) and GSE21050 (n=283) (b,d,e), for CD8 + T cells (a,b), cytotoxic lymphocytes (c,d) and B lineage (e,f).
For all reactions, real-time PCR was performed under the following cycling conditions: initial denaturation at 95°C for 5 minutes, followed by 50 cycles of 95°C for 10 s, 61°C for 20 s, 72°C for 20 s and fluorescence measurement. Following PCR, a thermal melting profile was used for amplicon identification. For both quantification methods, standard curves were generated from four-fold dilutions of the corresponding genomic DNA. Serial dilutions of plasmids containing the loci of interest were used as standards for cDNA quantification. Each reaction was performed at least in duplicate. The second-derivative maximum method was used to quantify sequences, as described in the Light Cycler Software. For BrdU-labeled NS quantification, we used primer pairs binding to mitochondrial DNA to normalize enrichment in each S-phase fraction (Mitochondrial DNA), an early-replicated region (Early timing control) for the control of cell sorting, a genomic position located 5 kb downstream from insertion site 2 (Both), amplifying both alleles, for the control of accurate qPCR measurements and the studied regions in the transgenes.
A/B compartments A and B compartments were assigned using an eigenvector decomposition procedure  (Imakaev et al, 2012)  implemented in the cooltools package (https://github. com/mirnylab/cooltools, http://doi. org/10. 5281/zenodo. 3553140). Eigenvector decomposition was performed on observed-overexpected cis contact matrices binned at 250 kb on each library and at 100kb on the pooled libraries for a better resolution of compartments. The first eigenvectors (EV1) positively correlated with gene density were used to assign A and B compartment status to each bin. EV1 calculated at 100kb was used for all quantitative analyses that involved compartment status, if not stated otherwise.
We then calculated the p-value for the observed value of 'difference allele 1+3 / 2 A -B'. In order to test if allele 1+3 and allele 2 demonstrate any preferential interactions with other chromosomes, i. e. trans-interactions, we performed the following: (1) Due to the sparsity and lack of distance decay for trans-data, we averaged interactions on the scale of individual chromosomes and normalized them by corresponding chromosome lengths; we refer to this measure as density of interactions. (  2 ) We compared the density of interactions between allele 1+3 and allele 2 with chromosomes larger than 5,000,000 bp (Figure  3E  and Appendix Figure  S4  C,F and Appendix Figure  S9D ). Code is available on github: https://github. com/dekkerlab/allelic-rt-supplement. git Red arrows represent the loci that show preferred interactions with allele 1+3 and that coincide with A compartment.
replication timing data was performed by the GENOM'IC core facility at Institut Cochin-Paris. We thank Ankita Nand for doing the initial mapping of the Hi-C data. This work was supported by grants from the Association pour la Recherche sur le Cancer (Equipe Labellisée) and the Agence Nationale pour la Recherche (ANR-15-CE12-0004-01), and from the National Human Genome Research Institute (HG003143 to JD). M-N. P and B. D are supported by Inserm. JD is an investigator of the Howard Hughes Medical Institute.
Acknowledgments The authors thank the members of the laboratory of M-N. P for useful insights and discussions. We thank  G. Felsenfeld  and  R. Veitia  for critical reading of the manuscript. We thank  J-F Bercher  for his help in defining the most accurate quantitative method for measuring RT shifts. We thank  Griselda Wentzinger  and  Magali Fradet  for performing cell sorting at  ImagoSeine Institut Jacques Monod platform. Sequencing of.
, 1997). L5 bands are derived using per-pixel solar illumination angles and generated at a 30 m spatial resolution on a Universal Transverse Mercator (UTM) mapping grid (USGS, 2017). LEDAPS in the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor (L7 hereinafter) was also used to corroborate our predictions (described below). Though L5 and L7 use the same wavelength bands, they are different sensors and differences in surface reflectance may exist, especially in tropical forests, due to large atmospheric effects  (Claverie et al. , 2015). Landsat 8 was not used since comparison between Landsat 8 and both L5 and L7 is not straightforward due to differences in the spectral bandwidth of these sensors. We used LEDAPS since a long time series of data is available with high spectral performance  (Claverie et al.
In order to assess the forest regrowth to spectral levels similar to old-growth forests (control), we applied a gap-filling method  (Gerber, 2018)  for time series to obtain estimates for missing years using the R package "zoo"  (Zeileis et al. , 2018). The gap-filled datasets were analyzed using the smoothing-spline technique (R package, 2017). To determine whether L5 bands were sensitive to regrowth, we analyzed changes in the slope (β) of the bands across our chronosequence. A t test on the slope coefficient was used to test the null hypothesis that β is zero (H 0 , β = 0) against the alternative hypothesis (H 1 , β = 0) at a 5 % significance level (α = 0. 05). If the computed test statistic (t stat) was inside the critical values, then H 0 was not rejected. The critical values (±t 1-α/2 , n -2; n is the number of data points) were obtained from statistical tables  (Neter et al.
Calculations were conducted on the R 3. 5. 2 software platform (R package, 2017) using the package "gss" (General Smoothing Splines)  (Gu, 2018). We calculated the smooth spline (using the cubic fit algorithm) of observed data and the associated standard errors, from which we calculated Bayesian 95 % confidence intervals. Predictions of the time after disturbance needed to reach old-growth forests values are based on these data using the function "ssanova" (Fitting Smoothing Spline ANOVA Models) of the R package gss (General Smoothing Splines), version 2. 1-9. The predictions were compared with published field observations (Sect. 2. 1) where data were available, and L7 images were used to assess the reliability of our predictions.
Forest regrowth simulation in ELM-FATES Time series of L5 bands sensitive to disturbances and the pathways of forest regrowth were compared with modeling results from ELM-FATES  (Fisher et al. , 2010 (Fisher et al. , , 2015;; Holm et al. , 2020). The underlying model structure and concepts in FATES are based on the Ecosystem Demography (ED) concept  (Moorcroft et al. , 2001)  and are described in detail at https://github. com/NGEET/fates, last access: 1 July 2019. A major development is the modularization of the model structure in FATES so that boundary conditions and vegetation can be coupled with ESM land models. FATES is integrated into the E3SM Land Model (ELM)  (Riley et al. , 2018; Zhu et al. , 2019)  and the Community Land Model (CLM)  (Fisher et al. , 2019; Lawrence et al. , 2019)  coupled to the Community Earth System Model  (Hurrell et al.
org/10. 5194/bg-17-6185-2020 Biogeosciences, 17, 6185-6205, 2020 adds to the site-level seed pool for recruitment of new cohorts. Field data were not used to simulate or calibrate the modeled forest regrowth post-disturbance. To account for uncertainty in the representation of plant physiology within tropical evergreen forests, we analyzed an ensemble of 20 simulations varying in targeted plant functional traits. We prescribed each ensemble with a single tropical evergreen plant functional type (PFT) that varied in wood density (0. 44 to 1. 06 g cm -3 ) and maximum rate of carboxylation (V cmax ; 42 to 55 µmol m 2 s -1 ) (Table  1 ), via random sampling. To evaluate changes in canopy coverage of the forest stand, each PFT additionally varied by an allometric coefficient (1. 35 to 1. 65), determining the crown-area-todiameter ratio, and a leaf-clumping index (0.
About 1 year after the disturbance the bands that experienced increases in surface reflectance showed a decrease in surface reflectance (the opposite to the NIR band) due to the increases in vegetation cover. A similar response is expected for the clear-cut that occurred in 1982 and therefore before the beginning of our available data (L5 imagery is available from 1984; Fig. 3e ). The simi-Table  1. The range (minimum to maximum) of four key model input parameters used in the 20-ensemble ELM-FATES simulations for both windthrow and clear-cut simulations, to account for uncertainty in the representation of plant traits, along with the default value used in the ELM-FATES model. Wood density value from  Moorcroft et al. (2001) ; V cmax based on  Oleson et al. (2013)  and  Walker et al. (2014) ; crown area : DBH derived from  Farrior et al.
The average control NIR value was 28 ± 1 %. For the windthrow site the NIR values became similar to control levels after about 39 years (range 32 to 57 years). For the clear-cut and cut + burn sites, this period was estimated to be 36 years (range 31 to 42 years) and 56 years (range 42 to 93 years), respectively. From Figs. 4-6 it is evident that the type of disturbance has a clear effect on the pathways of NIR recovery. L7 data, in general, are within the 95 % CI of predictions. During the first 12 years following the windthrow, the spline curve fitted to the NIR data decreased by ∼ 0. 13 % yr -1 after which the rate of decrease doubled (0. 26 % yr -1 ; Fig. 4 ). For clear-cutting, NIR values decreased faster, i. e. , ∼ 0. 4 % yr -1. The decrease in NIR values for the clear-cut site appears to be independent of the distance from the edge of the disturbance since the changes in NIR values of all selected areas (A 1 , A 2 , A 3 , and A T ) are similar (Fig.
9 ). Low stem density could be attributed to multiple model assumptions, such as high density-dependent mortality and self-thinning due to the marginal carbon economics of understory trees, low branch-fall turnover, a need for greater limitation of maximum crown area than currently modeled, and increases in mortality rates with tree size  (Johnson et al. , 2018). Our findings here will guide future ELM-FATES and ecosystem modeling development efforts towards improving the representation of forests comprising dense canopies and how they shift during regrowth. Land surface models do not typically simulate spectral leaf reflectance, but there is potential to include such output within radiative transfer schemes as is currently done in the CLM. That addition would greatly assist our ability to compare with Earth observation datasets.