dataesr/scientific-paragraphs-categorization huggingface dataset:
RAMSES For this work, we take advantage of the RAMSES code  (Teyssier 2002). This finite-volume Eulerian code solves the hydrodynamics equations using a second-order Godunov method  (Godunov 1959 ) on an adaptive-mesh-refinement grid  (Berger & Oliger 1984). With a proper refinement criteria, the AMR grid is a powerful tool to study multi-scale problems such as the protostellar collapse of a dense core. The RAMSES code is very proficient for problems that require a treatment of magnetohydrodynamics  (Teyssier et al. 2006; Fromang et al. 2006; Masson et al. 2012; Marchand et al. 2018; Marchand et al. 2019) , radiation hydrodynamics  (Commercon et al. 2011; Rosdahl et al. 2013; Commercon et al. 2014; Rosdahl & Teyssier 2015; González et al. 2015; Mignon-Risse et al. 2020)  or cosmic rays  (Dubois & Commercon 2016; Dubois et al.
Acknowledgements. First, we thank the referee for providing useful comments and advice that helped us to ameliorate our manuscript. We acknowledge financial support from " Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU ,  CEA  and  CNES, France. This work was granted access to the  HPC resources of CINES (Occigen)  under the allocation DARI A0020407247 made by  GENCI. Computations were also performed at the  Common Computing Facility (CCF)  of the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ). This work took part under the programs ISM3D and Core2disk of the  PSI2  project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. This project was partly supported by the  IDEXLyon  project (contract n  ANR-16-IDEX-0005 ) under University of Lyon auspices. The plots were generated using the very efficient Osiris library developed by  Neil Vaytet ,  Tommaso Grassi  and  Matthias González  whom we thank.
Study area The study area is located in the southwest of France, below the city of Toulouse (Figure  1 ). The S2 images were downloaded on the pole Theia for year 2017, 2018 and 2019 (http://www. theialand. fr/en/products/sentinel-2). These data are available directly at level 2A (i. e. Top-of-canopy reflectance products orthorectified) and they include a cloud and shadow mask  (Baetens et al. , 2019). These masks came from the MAJA algorithm which includes atmospheric correction by combining multi-temporal and multispectral criteria in order to estimate the optical thickness of aerosols  (Hagolle et al. , 2015). If the image was not computed by Theia, it means that the product was not valid (it can be due to too many clouds). Thus, 37 dates were acquired for 2017 and 2018, 32 for 2019. Each time series has different temporal resolution due to the clouds over our study area (Figure  2 ).
Ground references of tree species Four field surveys were conducted in the 72 main forests between November 2013 and January 2017 to identify the forest species in the study area. Only dominant broadleaf and coniferous species location were gathered. To ensure data consistency, references were acquired from a Garmin GPSMap 62st GPS receiver (3-5 m accuracy) in the centre of an area covering approximately 900 m 2 (i. e. nine contiguous pixels from the Sentinel-2 satellite). Only the pixels in the center of these areas were used for classification. A total 1626 field references was collected for 14 species, including 8 deciduous and 6 coniferous (Table  1 ). For some species, identification was limited to the genus because of the existence of many cultivars (as for poplar) or the difficulty in determining the exact species (oak, willow or eucalyptus).
Undersampling of the reference data As our set of reference samples reflect the reality of the forests, the effective per species is therefore very imbalanced. The two main species, Oak (Quercus robur/pubescens/petraea) and Corsican pine (Pinus nigra subsp. laricio) have more than 200 samples each, contrary to Willow (Salix spp. ) and Cypress (Cupressus) which have about 50 samples. Training samples for the two first cited species tend to be 5 to 6 times higher than the rarest species in our dataset. For each species, we decided to undersample our dataset to the size of the smallest class using their feature representations (i. e. spectral bands)  (Jain et al. , 1999). A simple approach is to downsize using a random selection by fixing the number of desired output samples. However, to keep the richness of our small dataset and not to lose crucial information that could be under-represented, we prefered to use a K-means to group samples from their similar features  (Hartigan, Wong).
This technique is recommended and performs very well with small scale datasets  (Lin et al. , 2017). In concrete terms, European ash (Fraxinus excelsior) with 130 samples has many more samples than cypress (n=42), which is the minority class. So, the undersampling algorithm generated a K-means with 42 clusters and used the coordinates of these cluster centroids as the new European ash samples. This processing was handled by the imbalanced-learn library  (Lemaître et al. , 2017).
Validation by spatial cross-validation Every model was learned and evaluated using cross-validation. Given the possible optimistic bias related to spatial autocorrelation in the datasets (Pugh, Congalton; Hammond, Verbyla;  Mannel et al. , 2011) , we opted for a spatialized leave-one-out cross-validation method  (Le Rest et al. , 2014; Karasiak et al. , 2019). Spatial cross-validation involves selecting validation samples that are spatially separated by a given distance from the training samples. To get the distance, Moran's Index  (Moran, 1950)  was computed on each spectral band by variing lineary the distance from 1 pixel (10-m) to 50 pixels (500-m). Pixels of non forests were masked using our forest/non forest mask made from 2018 very high resolution images (See section 2. 3). The more the variability of the defined neighborhood equals the total variability of the data set the more Moran's I will be close to zero.
Classification protocol For each year we built as many models as deciduous species to map. In combination with an undersampling and spatial crossvalidation strategy, the Support Vector Machine (SVM) algorithm  (Vapnik, 1998)  was used to train our models. This classifier is known to perform very well with few samples per class and a high number of features  (Melgani, Bruzzone; Mountrakis et al. , 2011). In this study, we selected the Radial Basis Function (RBF) kernel which is the most frequently used and has already been proven to be effective in the case of similar classification problems  (Kavzoglu, Colkesen; Ferreira et al. , 2016). The learning process was computed using the scikit-learn python library  Pedregosa et al. (2011) , and the vector of features were standardized (i. e. centering and scaling to unit variance) before the training process.
The parameters of SVM fitted were the regularization parameter (C) know as the penalty parameter and the kernel bandwidth (γ) were tuned by cross-validation. The grid search was constructed with C = {0. 01, 0. 1,. , 1 10 } and γ = {1 -9 , 1 -8 ,. , 1 3 }. This work is based on the python library we developped: Museo ToolBox  (Karasiak, 2020). This library contains the spatialized cross-validation, the Sequential Feature Selection approach and many more. Moreover, it is fully compatible with the scikitlearn library.
Optimal dates versus all features The classification performances of the three years are presented for each approach in Figure  3. Generally speaking, the performances between the three years were relatively similar. However, there is a significant difference in quality between the two methods. The average F1 of the seven deciduous species when using the full SITS is 0. 64 while with the SFS method the results are 0. 23 points higher, i. e. 0. 87. For both datasets, the F1 differences between the best year and the worst year is about 0. 05 F1. In 2019, when the algorithm was trained using all the dates, the best deciduous average F1 was 0. 69 while learning with a reduced dataset using SFS was 0. 86. Looking at the species for both datasets Red oak has, except once, the highest accuracy (F1 = 0.
Cell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: 1) global integration of cell-to-cell communication, 2) biological interpretation, and 3) application to individual cell population transcriptomic profiles. We developed ICELLNET, a transcriptomic-based framework integrating: 1) an original expertcurated database of ligand-receptor interactions accounting for multiple subunits expression, 2) quantification of communication scores, 3) the possibility to connect a cell population of interest with 31 reference human cell types (BioGPS), and 4) three visualization modes to facilitate biological interpretation. We applied ICELLNET to uncover different communication in breast cancer associated fibroblast (CAF) subsets.
Hence, when deciphering cell-to-cell communication, one should account for potential signals coming both from spatially proximal and distal cells. Most studies in the past decades have focused on a limited number of communication molecules in a given anatomical site or physiological process. The availability of large-scale transcriptomic datasets from the literature  4, 10  , without necessarily providing systematic combinatorial rules for the association of protein subunits into multimeric ligands or receptors. The last point relates to the granularity that is structuring the biological information into families and subfamilies of functionally and structurally related molecules. We only found one tool that provides a classification into four families of communication molecules  5  , while suffering from other limitations in particular the lack of manual curation.
In this study we developed ICELLNET, a novel and versatile computational framework to infer cellto-cell communication from a wide range of bulk and single cell transcriptomic datasets. Each family of communication molecules was expert curated and organized into biologically relevant subfamilies. ICELLNET offers an array of visualization tools in order to facilitate biological interpretation and discoveries. We provide applications to public datasets, and our own original transcriptomic datasets in non-immune (tumor fibroblasts) and immune cell types. Experimental validation of ICELLNET-derived predictions demonstrated IL-10 control of human dendritic cell communication.
Expert-curated database of ligand-receptor interactions In order to globally reconstruct cell communication networks, we curated a comprehensive database of ligand-receptor interactions from the literature  3, 11, 12  and public databases  10, 13. Rather than focusing on the breadth of the resource, we used a strategy that prioritizes expert manual curation and biological insight based on precise biochemical and functional classifications. This led to the integration of 380 ligand-receptor interactions into the ICELLNET database (Suppl. Table  S1 ). Whenever relevant, we took into account the multiple subunits of the ligands and the receptors (Figure  1A ). Interactions were classified into 6 major families of communication molecules, with a strong emphasis on inflammatory and immune processes: Growth factors, Cytokines, Chemokines, Immune Checkpoints, Notch signaling and Antigen binding (Figure  1B  and Suppl.
Development of a computational pipeline to dissect intercellular communication In the ICELLNET framework, we developed an automatized tool in R script to infer communication between multiple cell types by integrating; 1) prior knowledge on ligand-receptor interactions (Figure  1 ); 2) computation of a communication score between pairs of cells based on their transcriptomic profiles, and; 3) several visualization modes to guide results interpretation. Quantification of intercellular communication was achieved by scoring the intensity of each ligandreceptor interaction between two cell types from their expression profiles (Figure  2 ). From each transcriptomic profile, all genes or only differentially expressed genes could be used, and no filtering threshold was applied to gene expression. Taking advantage of the ICELLNET database, the genes coding for ligands/receptors were selected from all 380 interactions to compute the score, but it is also possible to restrict the database to specific families of molecules, depending on the biological question.
top-left). ICELLNET separately considers other cell types with known transcriptomic profiles that can connect to the central cell, called « peripheral cells ». These can be cell types coming from the same dataset as the central cell, or from any other transcriptomic dataset. The ICELLNET pipeline has integrated reference transcriptomic profiles by using the Human Primary Cell Atlas  17, 18. This public dataset includes transcriptomic profiles of 31 cell types including immune cells, stromal cells, neural cells, and tissue specific cell types, all generated with the same Affymetrix technology (Fig  2.
Establishment of a score to assess the communication between cells From the transcriptomic profiles, we selected the genes coding for the ligands and the receptors in our database. We designed the tool to enable the user to focus either on the ligands and/or receptors that are differentially expressed between conditions of study, or to use the entire ligand-receptor database to compute the communication score. Since cell-to-cell communication is directional, we considered ligand expression from the central cell, and receptor expression from the peripheral cells in order to assess outward communication. Conversely, we then selected receptor expression from the central cell, and ligand expression from peripheral cells in order to assess inward communication (Figure  2 middle ). Gene expression levels were scaled to avoid a communication score driven predominantly by highly expressed genes.
Application of ICELLNET to study communication between specific immune cells After using TNBC-infiltrating CAFs dataset to test the connectivity map reconstruction, we wanted to test the hypothesis that the ICELLNET tool would allow us to characterize cellular communication using the immune system as a model. Particularly, we were interested in studying communication of resting and perturbed immune cells. To explore the role of autocrine loops, we cultured LPS-activated human monocyte-derived dendritic cells (DCs) in the presence or absence of blocking antibodies (Abs) to the TNF and IL-10 receptors (aTNFR and aIL-10R). No effect on cell viability was observed (Suppl. Figure  S3A ). The most prominent effect of LPS on DC hallmark maturation markers was observed at the mRNA level in the time frame of 4 to 8 hours following activation  20.
IL-10 controls an intercellular communication module in LPS-activated dendritic cells We compared the transcriptomic profiles of each condition (aTNFR and aIL-10R) to the LPS-alone condition to extract the differentially expressed genes (DEG) (Suppl. Table  4F ). We then screened the IL-10 and TNF DEG to identify ligands and receptors included in the database. We were able to extract 27 ligands and 23 receptors which were differentially regulated from the aIL-10R condition, while there were only 12 ligands and 10 receptors differentially regulated from the aTNFR condition (Figure  4C ). ICELLNET barplots suggested that cytokines were driving the increase in the communication score when blocking IL-10R. We looked at the subfamilies of cytokines to precisely identify the key communication channels (Suppl.
Discussion The majority of studies which aim to reconstruct intercellular communication from transcriptomic datasets integrate prior knowledge in the form of a ligand-receptor interaction database. This provides a straightforward manner to infer communication when a match is identified between a ligand and a cognate receptor for two respective cell types. The largest of such databases 9 integrated over 2500 ligand-receptor pairs through literature mining and computational analysis, and has been exploited in multiple computational tools for predicting cell-to-cell communication  5, 8, 33, 34. However, this approach lacks experimental validation of predicted ligand-receptor interactions, and it does not take into account the different subunits of ligands or receptors. With ICELLNET, we have developed a fully manually curated database, combining biological relevance, ease of use, and experimental validation.
Except for one study  5  , ICELLNET is the only database offering a classification of predicted interactions into biological families. Similar to CellPhoneDB 10 , logical rules for co-expression of protein subunits. A systematic comparison of cytokine interactions revealed 14 interactions included in ICELLNET but not in CellPhoneDB, such as MIF/CXCR2 and MIF/CXCR4  35. Although ICELLNET includes a relatively small number of interactions compared to other existing databases, it is very specific and exhaustive for cytokine interactions, and will in time be extended to all chemokine and checkpoint interactions, thus providing a unique resource to study intercellular communication within the immune system. To make ICELLNET a valuable resource, we have established a strategy to keep the database updated and integrate missing knowledge.
A significant number of interactions have been established in the past 20 years, but there are still receptors without known ligands, such as TNFRSF21 (DR6), RELT, TROY and NGFR from TNF receptor family  36  , and ligands without known receptors such as IL17D  16. New receptors for existing ligand-receptor pairs can also be uncovered using this approach. For example, even though it was already known that IL34 and M-CSF could separately activate M-CSFR  37  it was then described that IL34/M-CSF heterodimer was also capable of activating M-CSFR  38. We will apply a PubMed alert strategy to cover all new interactions that could be described on these pre-identified ligand and/or receptor partners. Existing tools infer communication between cells from scRNAseq datasets  4, 5, 7. We have designed ICELLNET as a versatile tool, which can be applied to bulk cell profiles (Affymetrix or RNAseq) widely available in public databases, but also to fully documented scRNAseq datasets to infer communication between clusters or groups of cells.
This can be easily adapted to other types of data such as flow cytometry data. By using the Human Primary Cell Atlas as a reference for transcriptomic profiles  17, 18  , ICELLNET allows us to integrate cell communication partners (sender or receiver) not included in a given original experimental dataset. We identified the Human Primary Cell Atlas as a particularly suitable resource, as it integrates transcriptional profiles of over thirty human primary cell types generated with the same Affymetrix platform  18. While previous applications of this atlas enabled the identification of specific tissue-related genes  39, 40  , we developed an original use for this resource to simulate cell cross-talks in diverse microenvironments. In addition, ICELLNET can accommodate other original RNAseq datasets of cell populations  41, 42  as reference profiles to infer intercellular communication.
Fibroblasts are important structural stromal cells at steady state and inflammation. Yet, how they communicate with neighboring cells is not well described. Applying ICELLNET to breast cancer fibroblasts' bulk cell transcripts revealed potentially novel interactions between CAF subsets and tumor microenvironment components. The CXCL12/CXCR4 interaction that we found within CAF-S1-to-Tregs (Fig  3D ) was also described in other studies  19, 45  , and contributes to the immunosuppressive phenotype displayed by CAFS1. ICELLNET also highlighted interactions specific to CAF-S4 subset such as JAG1 with Notch receptors (NOTCH1, NOTCH2), and expression of PDGF proteins interacting with their cognate receptor. These proteins have never been associated specifically to CAF-S4 subset at the transcriptomic level and warrant further experimental validation studies.
Other studies have shown that IL-10 regulates DC-derived inflammatory cytokines and chemokines, in particular IL-12  46  , and that IL-10 secreted by LPS-activated DCs controls a communication channel in an autocrine manner  47. Through our systems approach, we could demonstrate that endogenous DC-derived IL-10 governs the global connectivity of DCs with multiple cell types. This original in vitro dataset also provides an experimental validation of our intercellular communication hypotheses, making ICELLNET the first experimentally validated tool to assess intercellular communication. Thus, ICELLNET is an adaptable tool that allows us to gain insight into communication channels between cells from one bulk transcriptomic profile of a cell population. By focusing on specific cell types or families of molecules, ICELLNET provides several representation modes to help the interpretation of the results.
CAFs RNA-seq data processing The dataset contains 77 samples from Luminal (Lum) and Triple Negative Breast Cancers (TNBC) from 16 patients (10 Lum, 6 TNBC)  19. The samples correspond either to tumor tissue or juxtatumoral tissue. Cells corresponding to CAF-S1 and CAF-S4 have been isolated, collected, and sequenced. Average sequencing depth was 30 million for paired-end reads, with a read length of 100bp. Reads were mapped on the reference genome (hg19/GRCh37 from UCSC genome release) using Tophat_2. 0. 6 algorithm. Duplicates were removed and gene expression quantification was performed using HTSeq-Count and featuresCount. Only genes with five reads in at least 25% of all samples were kept for further analyses. Normalization was done using the method implemented in DESeq2 R package. In this study, only 6 samples of CAF-S1 and 3 samples of CAF-S4 from TNBC were considered in the analyses.
Curation of the ligand/receptor database Surveying the literature for any potential interactions, we manually curated a ligand-receptor database using STRING (http://string-db. org/), Ingenuity (http://www. ingenuity. com/) and BioGRID (https://thebiogrid. org) online tools to verify protein-protein interactions, as well as Reactome and CellPhoneDB databases, already dedicated to ligand-receptor interactions. The interactions were classified into families of molecules based on the known biological function of the ligand and the receptor. The subfamilies of cytokines were defined based on molecular structures, as defined in the literature  3, [14] [15] [16]. The database of ligand-receptor interactions is contained in the supplementary table  1.
Statistical analysis of gene expression data Expression data were normalized with Plier. Transcriptomics analysis was performed in Matlab. For independent filtering, we used the function geneverfilter, which calculates the variance of each probe across the samples and identifies the ones with low variance. Probes with variance less than the 40 th percentile were filtered out. Differential analysis was performed using an ANOVA test (function anova1) at 4 hours and 8 hours. p-values were adjusted for multiple testing using the Benjamini-Hochberg correction using the function mafdr. Adjusted p-values <5% were considered significant (see Suppl. Table  4F ).
The gene expression profiles generated for this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE89342 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE89342). Birkbak, N. J. , Gyorffy, B. , Szallasi, Z. & Eklund, A. C. Jetset: selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics 12, 474 (2011). 49. Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B Methodol. 57,.