dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow cytometry for ILCs For cell surface staining of ILCs, freshly prepared human cells were stained with live/dead cell markers, Fixable Viability stain 520 (BD 564407), and Fc Block as for ILCs sorting. Cells were stained with surface antibodies against CD45, CD127, CD117, CRTH2, CD5, TIGIT and SLAMF1 and antibodies against same lineage markers for ILCs sorting for 30 min at room temperature. For each of the staining, paired samples from same patients were used. PBMC from more healthy donors was stained at same time. Cells were kept at 4 C and analyzed on a BD Symphony (BD Biosciences). Flow data was analyzed with FlowJo software (FlowJo LLC).
Single-cell RNA sequencing Purified ILCs were resuspended in PBS supplemented with 0. 04% BSA, and kept on ice. Cells were counted and cell density was adjusted to that recommended for 10x Genomics Chromium single-cell 3 0 v3 processing and library preparation. Sequencing was performed on an Illumina platform (NovaSeq 6000), by GENERGY BIO (Shanghai, China), at a sequencing depth of about 90,000 reads per single cell. Raw 10X read alignment, quality control and normalization Raw sequencing reads were subjected to quality control with FastQC software v0. 11. 9 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Sequencing data in a bcl file were converted to FASTQ format with Illumina bcl2fastq2 Conversion Software v2. 20 (https://support. illumina. com/downloads/bcl2fastq-conversion-software-v2-20.
html). We then used Cell Ranger Single Cell Software Suite v. 2. 2 to process, align, and summarize unique molecular identifier (UMI) counts, according to the standard pipeline and default parameters described at https://support. 10xgenomics. com/single-cell-gene-expression/software/pipelines/latest/ what-is-cell-ranger. Briefly, we used the standard Cell Ranger Count pipeline to align FASTQ reads with the GRch38 genome. We then filtered sequencing reads on the basis of base-calling quality scores, and assigned cell barcodes and UMIs to each read. The Cell Ranger aggr pipeline was used to normalize all scRNaseq data with default parameters, to obtain a uniform sequencing depth. The combined feature-barcode matrix was used for downstream analysis. During quality control with Seurat analysis, raw UMI count matrices were filtered to remove genes expressed in fewer than three cells, cells with fewer than 200 genes, cells with more than 4000 genes, and cells with high percentages of mitochondrial genes (more than 8%).
Filtering contaminated cells We used the R package SingleR 66 and default parameters to assign individual cells to cell types, with the Human Primary Cell Atlas Data as the reference dataset. Each single cell was annotated with a cell type in ''label. main'' of the dataset. As the dataset did not include the human ILC dataset, and, given the similarities between ILCs and T cells or NK cells, we retained cells labeled as both NK and T cells. We removed cells considered as doublets with an range from 0. 14% to 3. 69%. In addition, when analyzing the ILC3 in tumor tissue, two small clusters with percentage ranging from 0. 35% to 3. 59% that were not present in each donor were removed. Donor-specific clusters with a strong specific NK cell signature were considered to be true NK cell contaminants and were removed from downstream analysis.
5 (yellow). Squares identify specific transcriptomic signatures of ctILC subsets. (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.
5 (purple) to 2. 5 (yellow). Squares identify specific transcriptomic signatures of ctILC subsets. (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.
S7B ). This data suggests that dilp2 is required for the nutrient-dependent upregulation of IIS activity in females in a protein-rich context. To determine whether the inability to augment IIS activity on 2Y affects nutrientdependent body size plasticity in females, we measured body size in w 1118 and dilp2 mutant larvae cultured on either 1Y or 2Y. In w 1118 control females, adult weight was significantly higher in flies cultured on 2Y compared with flies raised on 1Y (Fig. 3C ); however, this nutrient-dependent increase in adult weight was not observed in dilp2 mutant females (Fig. 3C ; genotype:diet interaction p = 0. 0024; S1 Table ). In w 1118 control males and dilp2 mutant males, there was no significant increase in adult weight in flies raised on 2Y compared with genotype-matched flies cultured on 1Y (Fig.
, 2010). To extend our findings beyond dilp genes, we next examined fecundity in females with an RNAi-mediated reduction in sun. We found that the nutrient-dependent increase in egg production in r4>UAS-sun-RNAi females was eliminated, in contrast to the robust diet-induced increase in fecundity in r4>+ and +>UAS-sun-RNAi control females (Fig. 6E ). Together, this data suggests that dilp2 and fat body-derived sun play a role in maximizing IIS activity and body size to promote egg production in a protein-rich context. In males, which have a reduced ability to augment body size in response to a protein-rich diet, we also investigated the relationship between nutrient content, body size, and fertility. When we compared fertility in w 1118 males reared on 1Y compared with males raised on 2Y, we found no significant difference in the number of offspring produced (Fig.
Data Availability. Raw values for all data collected and displayed in this manuscript are available in S2 Table. Fly husbandry. Larvae were raised at a density of 50 animals per 10 ml food at 25°C (recipes in S3 Table ), collected as indicated in figure legends, and sexed by gonad size. When gonad size could not be used to determine sex (e. g. , tra mutants, da-GAL4>UAStra F ), chromosomal females were identified by the presence of an X-linked GFP. Adult flies were maintained at a density of 20 flies per vial in single-sex groups. Fly strains. The following fly strains from the Bloomington Drosophila Stock Center were used: Canton-S (#64349), w 1118 (#3605), tra 1 (#675), Df(3L)st-j7 (#5416), srl 1 (#14965), InR E19 (#9646), TRiP control (#36303) UAS-ilp2-RNAi (#32475), UAS-upd2-RNAi (#33949), UAS-tra F (#4590), da-GAL4 (ubiquitous), r4-GAL4 (fat body), cg-GAL4 (fat body).
ACKNOWLEDGEMENTS dFOXO targets and topology of the transcriptomic response to stress and insulinsignalling. Mol Syst Biol, 7, 502. https://doi. org/10. 1038/msb. 2011. 36 Almudi, I. , Poernbacher, I. , Hafen, E. , & Stocker, H. (2013). The Lnk/SH2B adaptor provides a fail-safe mechanism to establish the Insulin receptor-Chico interaction. Cell CommunSignal, 11(1), 26. https://doi. org/10. 1186/1478-811X-11-26 Alpatov, W. W. (1930). Phenotypical Variation in Body and Cell Size of Drosophila melanogaster. Biological Bulletin, 58(1), 85-103. JSTOR. https://doi. org/10. 2307/1537121 Anand, A. , Villella, A. , Ryner, L. C. , Carlo, T. , Goodwin, S. F. , Song, H. J. , Gailey, D. A. , Morales, A. , Hall, J. C. , Baker, B. S. , & Taylor, B. J. (2001). Molecular genetic dissection of the sexspecific and vital functions of the Drosophila melanogaster sex determination gene fruitless.
, 2000; Türkes et al. , 2002). Similar influences have been found on precipitation and temperature over the North Atlantic region  (Jézéquel et al. , 2018). Analogs of circulation were initially designed to provide "model-free" forecasts by assuming that similar situations in atmospheric circulation may lead to similar local weather conditions  (Lorenz, 1969). The potential to simulate large ensembles of forecast temperature with circulation analogs was explored by  Yiou and Déandréis (2019)  by considering random resamplings of K best analogs (rather than only considering the best analog). This has led to the development of an SWG in "predictive" mode, which uses updates of reanalysis datasets as input. Alternative systems of analogs to forecast precipitation have been proposed by  Atencia and Zawadzki (2014).
We then analyze the performance of this SWG for lead times of 5-20 d, with the forecast skill scores used by  Yiou and Déandréis (2019). We will evaluate the seasonal dependence of the forecast skills of precipitation and the conditional dependence on weather regimes. Finally, comparisons with medium-range precipitation forecasts from the ECMWF will be performed. The paper is divided as follows: Section 2 is dedicated to describing the data used for the experiments. Section 3 explains the methodology (analogs, stochastic weather generator, and forecast skill scores). Section 4 details the experimental setup and justifies the choice of parameters that we made for the forecast parameters. Section 5 details the results of simulations and the evaluation of the ensemble forecast. Section 6 contains the main conclusions of the analyses.
Data Daily precipitation data were obtained from the European Climate Assessment and Data (ECAD) project  (Klein Tank et al. , 2002)  for four locations in western Europe (Berlin, Madrid, Orly, and Toulouse), which are subject to contrasted meteorological influences (Fig. 1 ). The ECAD provides station data that are available at a daily time step from 1948 to 2019. The choice of those stations was based on the availability of a large and common period of observations with a low rate of missing data (less than 10 %). For verification purposes, we used also the E-Obs data  (Haylock et al. , 2008) , which are a daily gridded data available from 1979 to the present with a horizontal resolution of 0. 25 • × 0. 25 •. E-Obs data are spatial interpolations of ECAD data. We recovered the geopotential height at 500 hPa (Z500) and sea level pressure (SLP) fields from the reanalysis of the National Centers for Environmental Prediction (NCEP:  Kistler et al.
, 2001)  with a spatial resolution of 2. 5 • × 2. 5 • from 1 January 1948 to 31 December 2019. We also used the atmospheric reanalysis (version 5) of the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5;  Hersbach et al. , 2020). ERA5 data are available from 1950 to the present with a horizontal resolution of 0. 25 • × 0. 25 •. The two reanalyses have fundamental differences in terms of atmospheric models, assimilated data, and assimilation scheme. We considered the daily averages of Z500 from NCEP and ERA5, over the region covering 30 • W-20 • E and 40-60 • N, to compute circulation analogs. Daily averages of SLP were used over the region covering 80 • W-20 • E and 30-70 • N to define weather regimes. In order to assess the predictive skill of our precipitation forecast model, a comparison with another forecast was made.
Many available datasets can be used for deriving this information. We considered the ECMWF ensemble forecast dataset system 5  (Vitart et al. , 2017). It is a daily gridded dataset interpolated over Europe that provides information covering all the domains. Data are available through the Copernicus Climate Data Store. They include forecasts created in real time (since 2017) and hindcast forecasts from 1993 to 2019  (Vitart et al. , 2017). The data are provided at an hourly time step with a horizontal resolution of 0. 25 • × 0. 25 •. We considered the grid points that include Berlin, Madrid, Orly, and Toulouse, which were identified in the ECAD database.
Analogs The first step is to build a database of analogs of the atmospheric circulation. We outline the procedure of  Yiou and Déandréis (2019) , summarized in Fig. 1a. For a given day t, we determine the similarity of Z500 for all days t that are within 30 calendar days of t but in a different year from t. The similarity is quantified by a Euclidean distance (or root mean square error) between the daily Z500 maps. Other types of similarity measures are possible  (Blanchet et al. , 2018) , but the expected impact on the results is often marginal  (Toth, 1991). We believe that the simplicity of the Euclidean distance makes it more robust to changes in horizontal resolution (e. g. , from NCEP to ERA5), compared with more sophisticated distances that include local spatial gradients, which would require adjustments and additional tuning.
We consider different geographic domains as shown in Fig. 1  for the computation of analogs and weather regimes. The computation of circulation analogs was performed with the "blackswan" Web Processing Service (WPS;  Hempelmann et al. , 2018). The "blackswan" WPS is an online tool that helps compute circulation analogs on various datasets (e. g. , reanalyses and climate model simulations) with a userfriendly interface.
Therefore, we focused on SWG simulations with analogs from the NCEP reanalysis in the sequel as both  NCEP and ERA5 (1950-2019)  have the same skill, as shown in Table  4 , and because NCEP is easier to handle due to its lower horizontal resolution. The computations were made using observations of precipitation from the ECAD  (Klein Tank et al. , 2002 ) and E-Obs  (Haylock et al. , 2008)  databases. We found the same results because the ECAD and E-Obs are highly correlated (by the construction of E-Obs). In summary, we made the forecast of the precipitation using K = 20 analogs computed from Z500 over the [30 • W-20 • E; 40-60 • N] domain (red rectangle in Fig. 1b ). To compute analogs, we used NCEP reanalyses and an embedding of τ = 4 d. The computations were based on ECAD observations  (Klein Tank et al.
We found that both NCEP and ERA5 reanalyses perform well for simulations. We evaluated the relation between the quality of the forecast and weather regimes over Europe. We found that low and high predictability were related to specific weather regimes. This dependence is more significant in winter than in summer. We found that good predictability is mainly related to blocking. A comparison with the ECMWF forecast system over western Europe confirmed quantitatively and qualitatively the skill forecast of the SWG , for lead times of T ≤ 10 d. Of course, the SWG model cannot replace a numerical weather prediction, as the SWG parameters (e. g. , region of analogs) need to be tuned to local variables and rely on the existence of a fairly large database to compute analogs. Here we used the same domain of circulation analogs for stations from Madrid to Berlin.
Acknowledgements. This work is part of the  EU International Training Network (ITN) Climate Advanced   Forecasting of subseasonal Extremes ( CAFE). We thank  Linus Magnusson  and  Florian Pappenberger  for helpful discussions on the ECMWF data. Financial support. This work is part of the  EU International Training Network (ITN)  " Climate Advanced Forecasting of subseasonal Extremes" (CAFE ). The project receives funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie Grant  (agreement no. 813844 ). Review statement. This paper was edited by  Chiel van Heerwaarden  and reviewed by two anonymous referees.
Normalized collision energy (NCE) was set at 27. For each scan, the AGC was set at 1 × 10 5 , the MIT was 60 ms, and the dynamic exclusion of 40 s. For identification, the data were searched against the UniProt Mus musculus canonical (house Mouse downloaded on 22/08/2017) database using Sequest-HT through proteome discoverer (version 2. 0). Enzyme specificity was set to trypsin, and a maximum of two missed cleavage sites was allowed. Oxidized methionine, Nterminal acetylation, and carbamidomethyl cysteine were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 02 Da for MS/MS peaks. The resulting files were further processed using myProMS 68 v3. 9. FDR calculation used Percolator and was set to 1% at the peptide level for the whole study.
1 × 100 mm, 1. 7 µm; Waters), lipids were measured in reversed-phase-LC mode with a gradient between 70% solvent A (50% methanol) and 99% solvent B (1% methanol, 99% isopropanol), both containing 0. 1% formic acid and 10 mM of ammonium formate as additives. Lipids were detected by multiple reaction monitoring (MRM). GlcCers were detected with the transition to their sphingoid base fragment, and GA1 was detected with the loss of the terminal disaccharide ion. Both, GlcCer, and GA1 with ceramide anchors containing a C18-sphingosine or C18 phytosphingosine in combination with either non-hydroxy or α-hydroxy fatty acids (C16:0, C18:0, C20:0, C22:0, C23:0, C24:1, C24:0) were monitored. Statistics and data reproducibility. All experiments were done with at least three biological replicates (except when indicated).
Acknowledgements We would like to thank the following people for help in experiments, providing materials, and/or expertise:  Marina Glukhova ,  Anna Zagryazhskaya-Masson ,  Danijela Vignjevic ,  Hakon Leffler ,  Florent Dingli ,  Guillaume Arras ,  Caspar Caspersen ,  Fu-Tong Liu ,  Denis Krndija ,  Fatima El Marjou ,  Institute Curie , and  Institut Jacques Monod  mouse facility members. We acknowledge support by grants from the  Agence Nationale pour la Recherche  ( ANR-14-CE14-0002-02 ,  ANR-16-CE23-0005-02 ,  ANR-19-CE13-0001-01 ),  Human Frontier Science Program  ( RGP0029-2014 ),  European Research Council  (advanced grant  340485 ), the  Swedish Research Council  ( K2015-99X-22877-01-6 ). The Johannes team is a member of  Labex Cell(n)Scale  ( 11-LBX-0038 ) and  Idex Paris Sciences et Lettres  ( ANR10-IDEX-0001-02 PSL ).
Data availability The authors declare that all data supporting the findings of this study are available within the paper and its supplementary information files. All MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  70  with the dataset identifier PXD016499 (username: reviewer18055@ebi. ac. uk, password: ARHPfQ1I). All source data underlying the graphs presented in the main and Supplementary Figs. are available via the following Figshare repository link  71  : https:// ndownloader. figshare. com/files/25713287 (login: massiullah. shafaq-zadah@curie. fr, password: UMR3666-InstitutCurie). All other data generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Shell Proteome Proteomic analyses were conducted on the bulk ASM and AIM matrices (obtained after two and three bleaching treatments) after an in-gel digestion with trypsin, as previously published  (Immel et al. , 2016). For MS and MS/MS ORBITRAP, analyses were performed using an Ultimate 3000 Rapid Separation Liquid Chromatographic (RSLC) system (Thermo Fisher Scientific) online with a hybrid LTQ-Orbitrap-Velos mass spectrometer (Thermo Fisher Scientific). All technical details are provided in earlier work  (Immel et al. , 2016). Database searches were carried out using Mascot version 2. 4 and 2. 5 (Matrix Science, London, United Kingdom) on the transcriptome of T. crocea. The false discovery rate was set to 0. 05. Proteins supported by more than one peptide sequence were identified as shell matrix proteins (SMPs) in this study.
Characterization of Protein Sequences Protein sequences identified through proteomic analysis were analyzed using the InterProscan (ver. 5. 14-53. 0) platform  (Jones et al. , 2014)  in order to search for functional domains. Signal peptide prediction was conducted with SignalP 4. 0  (Petersen et al. , 2011). Transmembrane domains were assessed with TMHMM version 2. 0  (Krogh et al. , 2001). Protein sequences were also Blastp-searched against nr or UniProt databases. Theoretical molecular weights and isoelectric points of the proteins were calculated based on their amino acid sequences using IPC 1. 0  (Kozlowski, 2016). Phosphorylation sites in protein sequences were predicted using NetPhos 3. 1  (Blom et al. , 1999). In order to identify LCRs in protein sequences, we searched polypeptide sequences where majority of the amino acids were composed of one or two amino acids.
Only four LCR-CPs showed partial sequence similarity to known proteins in the public database: Tcr_393634 to serine protease inhibitor (although this SMP did not have the functional domain), Tcr_589629 to uncharacterized shell matrix protein of Lottia gigantea (Table  1 ). Other two proteins (Tcr_529389 and Tcr_564223) were hit to hypothetical proteins. In addition, LCRs were found in 4 ChBD-containing proteins (Tcr_714405, Tcr_356908, Tcr_684209, and Tcr_684124) and in 2 enzymatic domain-containing SMPs (Tcr_824966 and Tcr_531040) (Table  1  and Supplementary Table  1 ). Among the LCR-CPs, the most common are those with hydrophobic aliphatic amino (Table  1 ), since they represent 16 hits out of 19. Paired hydrophobic amino acids residues compose LCR in Tcr_325918 (AP-rich), Tcr_402398 (AGrich), Tcr_393634 (AP-rich), and Tcr_459507 (GP-rich).
Proteins of the Shell Matrix Our proteomic data give a snapshot of the protein composition of the crocus clam's shell matrix. Thirty-nine proteins were identified, they divide in three groups: those with one or more well identified domain (like enzymatic domains, protease inhibitors or domains interacting with chitin), those with Low Complexity Regions (LCRs), and finally, uncharacterized proteins, i. e. , proteins that cannot be affiliated to any of these two categories. Our data give a glimpse on the macroevolution of shell mineralizing matrices in a poorly investigated bivalve clade.
4%) with a D-rich LCR in its C-terminus (Table  1  and Supplementary Table 2 ), which does not show any significant sequence similarity to known proteins in public databases. This suggests its independent origin from pteriomorph D-rich proteins (MSP-1, Aspein) and possibly, a clade-specific recruitment within heterodont bivalves. In addition to the presence of acidic residues, phosphorylation may contribute to the acidic nature of some SMPs  (Marin and Luquet, 2007). Phosphate groups are detected in matrices associated to calcium carbonate biominerals of diverse metazoan animals such as the sea urchin Arbacia lixula  (Kanold et al. , 2015) , the zebra mussel Dreissena polymorpha  (Immel et al. , 2016) , or the scleractinian coral Porites australiensis  (Takeuchi et al. , 2018). Our FT-IR result showed that phosphate groups are retained in the matrices (AIMs) after intense bleach, suggesting their strong affinity with the biomineral (Figure  2 ).