dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis All the statistical analyses were performed using the R-software (version 3. 2. 5) [29]  and associated Bioconductor packages. For mean body weights calculated at 74 dpf, the normality and homogeneity of residuals were tested with the Shapiro-Wilk's test and the Bartlett test, respectively. A two-way analysis of variance (ANOVA) was performed to assess effects of genotype, diet, and genotype×diet interaction, followed by a Tukey's range test. Data from the microarray analysis were transformed with a logarithmic transformation and scale-normalised. The quality of each sample was controlled using the ArrayQualityMetrics package  [30] , and two samples were excluded from the analysis, as they did not pass the quality control. Data were analysed using the package Limma  [31]  and for each comparison Limma ttests were performed.
In order to exclude these genes from our results, the two lists of differentially expressed genes obtained per line comparison were compared. Probes that were found to be differentially expressed when fed both the M diet and the V diet were considered as intrinsic differences between genotypes, irrespective of the dietary treatment and were excluded from the analysis. Probes found to be differentially expressed only with the V diet (V diet \ M diet C ) were identified as potential molecular markers linked to plant-based diet utilisation, and were thus kept for further analysis (Fig  1 ). To interpret results, gene ontology (GO) enrichment analyses were performed using the functional annotation chart tool from the DAVID (Database for Annotation, Visualization and Integrated Discovery) bioinformatics resource, version 6.
Bromeliads are flowering plants represented by 3,403 species native to the Neotropics  (Ulloa et al. , 2017) , some of which have rosettes of leaves that trap water, forming "freshwater islands" in a terrestrial matrix. Such tank bromeliads collect rainwater and detritus, providing a habitat for aquatic organisms. Detailed descriptions of the bromeliad biota, food web structure and ecosystem can be found in  Laessle (1961) ,  Frank and Lounibos (2009), Petermann et al. (2015) , among others. We examine the strategies and constraints underlying the realized niche of aquatic invertebrates, using data collected from tank bromeliads. Over the past 20 + years, the bromeliad invertebrate fauna has been sampled by our teams of researchers at 22 Neotropical locations covering the latitudinal range of tank bromeliads, and we documented 12 functional traits for 852 taxa recorded.
, 2009; Merritt & Cummins, 1996; Vinogradova, 2007). The CP scores were based on relevant life-history studies  (Dézerald et al. , 2017; Oliver, 1971). Scores for MD and BF were based on our own observations of specimens. Traits were coded at genus or family level, a resolution known to capture the functional trait diversity of freshwater invertebrates  (Dolédec et al. , 1998) , with subsequent analyses of phylogenetic constraint accounting for the level at which traits were coded (see Data analysis below). Information on the traits was structured using a fuzzy-coding technique  (Chevenet, Dolédec, & Chessel, 1994) : scores ranged from "0" indicating "no affinity," to "3" indicating "high affinity" of the taxon for a given trait modality (see  Céréghino et al. , 2011  for a detailed example).
Two successive PERMANOVAs were applied, first on the PCA scores of morphospecies coded at genus level to test taxonomic signal at family level, and second on the scores of morphospecies coded at family or genus level to test signal at order level. The analyses were conducted in multiv Software, which is available at http://ecoqua. ecologia. ufrgs. br/arquivos/software/ MULTIV/. The procedure, except bootstrapped ordination, is also implemented in rstudio 3. 4. 2. using the SYNCSA package. The testing of hypervolume concentration was adapted from  Díaz et al. (2016)  and the r script available at ftp://pbil. univ-lyon1. fr/pub/datasets/ dray/DiazNature/. PERMANOVAs was conducted using the adonis function in the r package Vegan. The r code and the morphospecies PCA scores are archived on Zenodo at https://doi.
It could be argued that in both our study and that of  González et al. (2017) , partial filling of potential hypervolumes represents incomplete sampling of the global pool. However, this is unlikely to be the full explanation. In a review of the bromeliad fauna,  Frank and Lounibos (2009)  listed 25 families of aquatic invertebrates, noting the dominance of Diptera with aquatic larvae (16 families reported), and to a lesser extent Coleoptera (3 families). With our geographically broader dataset, we found more than 70 invertebrate families, including 30 Diptera and 10 Coleoptera families. We are therefore confident that, even though we did not sample all Neotropical ecoregions for bromeliad invertebrates, the discovery of new taxa would not add extreme trait combinations that would further influence our estimate of the nonrandom trait space  (Brandl & Bellwood, 2014).
through an Investissement d'Avenir grant (Labex CEBA, ANR-10-LABX-25-01) to C. L. and R. C. , by a BPE-FAPESP grant #2016/01209-9 to GQR, by CNPq-Brazil research grants to VDP (#307689/2014-0) and by grants from the Royal Society of Edinburgh, the Carnegie Trust for the Universities of Scotland, the US NSF (DEB-0218039, DEB-0620910), USDA IITF (#01-1G11120101-001) and the Saba Conservation Foundation to B. A. R. and M. J. R. We acknowledge postdoctoral fellowship support from a PNPD-CAPES grant #2014/04603-4 to P. M. deO. , a PNPD/CAPES grant #20130877 to N. A. C. M. and a CNPq grant (#401345/2014-9) under the Ciências sem Fronteiras program to V. J. D. We thank B. Starzomski for contributing data. AUTH O R S' CO NTR I B UTI O N S R. C. , V. D. P. and D. S. S. conceived the ideas; V. D. P.
Funding information CESAB-FRB; Labex  CEBA, Grant/Award Number :  ANR-10-LABX-25-01 ;  BPE-FAPESP , Grant/Award Number:  2016/01209-9 ;  CNPq-Brazil , Grant/Award Number:  307689/2014-0  and  401345/2014-9 ;  Royal Society of Edinburgh ;  Carnegie Trust for the Universities of Scotland ;  US NSF , Grant/ Award Number:  DEB-0218039  and  DEB-0620910 ;  USDA IITF , Grant/Award Number:  01-1G11120101-001 ;  Saba Conservation Foundation; PNPD-CAPES , Grant/Award Number:  2014/04603-4  and 20130877.
Reference Panels We assembled two panels for different types of analyses. Initial analyses were undertaken using the HUGO Pan-Asian SNP Database (30) (1,744 individuals; 50,796 SNPs) and Onge from the Simons Genome Diversity Panel (SGDP)  (64) , resulting in a panel maximising populations, at the expense of a lower SNP number, with 50,136 overlapping SNPs (hereafter the "Pan-Asia panel"). We assembled a second panel using whole genomes from the SGDP, limiting to the 2,240k capture SNPs from Yang et al. (45). We used the first panel for ADMIXTURE / fastNGSadmix analyses and PCA, as well as f3 statistics. We used the second panel for more parameter-rich modelling.
Acknowledgements: We thank the  National High-throughput DNA Sequencing Centre (Copenhagen Denmark)  for expert advice and sequencing of samples, the  Duckworth Laboratory, University of Cambridge , for permission to sample material in their care, and  Kristian Gregersen  for making casts of teeth before sampling. This work was supported by the  Lundbeck Foundation , the  Danish National Research Foundation , and the  KU2016 program. HM is supported by the  George Murray Scholarship (University of Adelaide ). RS thanks the  Thailand Research Fund (TRF)  for their support (Grants  RTA6080001  and  RDG55H0006 ). MML is supported by the  ERC  award  295907. DML was supported by  ARC  Grants  LP120200144 ,  LP150100583  and  DP170101313. EW thanks  St. John 's  College, University of Cambridge , for providing an inspiring environment for scientific thought.
M C is the carbon mass burnt and C wood represents the average carbon fraction of the wood fuel, ∼ 0. 46, measured in this study using an elemental analyzer. OC data were obtained from AMS measurements. Similarly, the organic aerosol (OA) emission factors (EF OA ) were calculated by replacing the PB-ROS concentration by OA. -PB-ROS fraction. In order to study the PB-ROS formation during aging, the secondary PB-ROS fraction (f ROS-SOA ) is introduced. It expresses the amount of secondary PB-ROS (ROS S = aged ROSprimary ROS) per amount of secondary organic aerosol (SOA) formed during aging and is calculated from Eq. (  3 ). f ROS-SOA = ROS S SOA (3) Secondary organic aerosol (SOA) and secondary PB-ROS (ROS S ) were calculated by subtracting primary organic aerosol (POA) and primary PB-ROS (ROS P ) from the total OA and aged PB-ROS, respectively, assuming ROS P and POA to only be lost to the chamber wall at the same rate as eBC but otherwise to remain constant during aging.
However, the secondary PB-ROS content of the aerosol in the PAM chamber was largely within the expected range, following consistent trends with high OH exposures and high f 44-SOA as in the SC (Fig. 4 ). We examined in more detail whether the regression model parameters obtained from the SC could faithfully represent the f ROS-SOA measured in the PAM chamber. Indeed, the model was capable of predicting, within uncertainties (2σ ), the f ROS-SOA measured in the PAM chamber for low organic aerosol concentrations (average 21 µg m -3 ), but considerably (factor of 3 on average) overestimated f ROS-SOA at higher concentrations (average 68 µg m -3 ). This is because such a range of concentrations at high OH exposures and high f 44-SOA was not included in the training dataset, and as a result the model slightly underestimated the effect of OA concentration on f ROS-SOA (e.
Acknowledgements. This study was financially supported by the  Swiss National Science Foundation  ( NRP 70  " Energy Turnaround "), the  European Union 's  Horizon 2020 research and innovation programme  through the  EUROCHAMP-2020 Infrastructure Activity  under grant agreement no. 730997 , the  Swiss National Science Foundation  starting grant  BSSGI0_155846 , and the  China Scholarship Council (CSC). Edited by:  Maria Cristina Facchini Reviewed  by:  Rodney Weber  and one anonymous referee.
Rainfall data The archives of climate and hydrological services of West African countries sometimes contain large amounts of sub-daily rainfall records. However, most of the time these records are stored in paper strip chart formats, requiring the tedious task of digitization in order to use them in numerical applications. The present study has been made possible thanks to the important work of analyzing and digitizing rain gauge charts carried out for the main synoptic stations of Senegal. This process was undertaken by the hydro-morphology laboratory of the Geography Department at the University Cheikh Anta Diop of Dakar (UCAD) in collaboration with the National Agency of Civil Aviation and Meteorology (ANACIM) that provided the rainfall paper charts. Senegalese synoptic stations are equipped with tipping bucket rain gauges; the receiving ring is 400 cm 2 and a bucket corresponds to 0.
5 mm of rain. The roll rotation is daily. The chart analysis has been performed with the software "Pluvio" developed by  Vauchel (1992)  allowing the computation of 5 min time step digitized rainfall series from the paper diagrams. It is a long and laborious task, which has the advantage of allowing a careful "chart by chart" checking of the quality of the records before digitization. For more information on the digitization process, the reader may refer to the publications of  Laaroubi (2007)  and  Bodian et al. (2016). A total of 23 tipping bucket rain gauges were analyzed, with data going back to 1955 for the oldest and to 2005 for the most recent. As the assessment of extreme rainfall distributions is known for being highly sensitive to sampling effect and erroneous data  (Blanchet et al.
Since missing years influence the mean interannual values, step (ii), (iii) and (iv) are repeated until all remaining years are classified as valid (note that, in fact, no station year had to be excluded after the initial step). All valid years for all series are plotted in Fig. 3. In order to keep the IDF fitting robust, only series with at least 10 years of valid data have been used. This led us to retain 14 stations with record length varying from 10 (Fatick station) to 44 years (Ziguinchor station) with a median of 28 years. This data set has the advantage of spatially representing the entire country, but as the length of the series varies, the quality of the IDF estimates might differ from one station to another. This effect will be analyzed more precisely in Sect. 5. 2. 1. 3 Theoretical background 3.
Model evaluation and selection With the aim of selecting the best IDF model from the two compared IDF formulations (IDF Koutso and IDF SiSca ), a process of model evaluation and comparison is proposed here by looking at both their flexibility (the models are fitted on a calibration sample) and their robustness (the models are fitted in a predictive mode on samples not used for calibration). The flexibility characterizes the capacity of a model to fit the observed data that are used to calibrate its parameters. To evaluate flexibility, the IDF models are fitted at each station, then different scores are computed to assess the fitting performances. The robustness, on the other hand, aims at evaluating whether or not the IDF model is too flexible due to the model having too many parameters with respect to the number of observations.
It thus depends on the sensitivity of the IDF model parameters to sampling effects: the less the model parameters are sensitive to sampling effects, the more robust the model. As the two models tested here have a different number of parameters (4 for IDF SiSca , 5 for IDF Koutso ), there is particular interest in comparing how the goodness of fit for each model is degraded when shifting from the calibration mode to the predictive mode. The predictive capacity of the IDF models is assessed by using a classical calibration/validation process. At each station, a subset of data is used to fit the IDF model; a second independent subset is used to validate it. The same scores used in the calibration mode are computed for the validation subset. Rather than using two consecutive sub-periods, one for the calibration sample and one for the predictive sample, a year-to-year separation was used to build the two subsets.
are given for the two longest series of our data set (Dakar Yoff, 38 years; Ziguinchor, 44 years), which display two different behaviors. At Dakar Yoff, the GEV(24 h) uncertainty becomes distinctly larger than the scaling uncertainty from the 10-year return period onwards; at Ziguinchor, this occurs only from the 100-year return period onwards. Associated with this difference is the fact that the downscaled GEV model (dots in Fig. 8 ) diverges from the reference scaled model (continuous line in Fig. 8 ) for Dakar Yoff while they are almost identical for Ziguinchor. At Dakar, the width of the 90 % confidence interval associated with the estimation of GEV(24 h) reaches 130 mm h -1 for a return period of 500 years, compared to 30 mm h -1 for the confidence interval associated with the scaling uncertainty.
Points of discussion and perspectives In the perspective of extending this work to other tropical regions of the world where sub-daily rainfall data might be rare, it remains to explore the effect of using a fixed window to extract the daily rainfall annual maxima, whereas a moving window was used for all durations (including 24 h) in this study. As a matter of fact, daily records of rainfall are carried out at a given hour of the day (usually 06:00 GMT or local time), producing smaller totals than when a mobile window is used to extract the daily rainfall maximum of a given year. Since the scaling relationships that are used to deduce sub-daily statistics from these fixed-window 24 h maxima are tuned on multi-temporal maxima extracted using mobile windows, there is a potential underestimation bias of the sub-daily statistics inferred at 24 h stations that merits further study.
Acknowledgements. The research leading to these results received funding from the  UK's National Environment Research Council (NERC)/Department for International Development (DFID)   Future Climate For Africa programme , under the  AMMA-2050  project (grant numbers  NE/M020428/1 ,  NE/M019969/1 ,  NE/M019950/1 ,  NE/M020126/1  and  NE/M019934/1 ). Edited by: Thomas Glade Reviewed by:  Francesco De Paola  and one anonymous referee.
ASE-TIGAR uses a Bayesian approach to estimate allelespecific expression  21. This approach allowed us to use all sequenced reads even those without SNPs. After obtaining the estimated read counts from ASE-TIGAR, we used DESeq2 27 to normalize counts, for female and male transcripts, respectively (Fig. 1b , Supplementary Fig. 2  Supplementary Table. 4). From this large dataset, we first determined the contribution of each tissue (stigma vs pollen) in mixed samples harvested immediately after compatible pollination (C0). We found that among the 47. 0 million RNA reads, 69 % were estimated as derived from Col-0/SRK14 (stigma), and 31 % were from C24 (pollen) (Fig. 1d ) although 15 % of reads were assigned to the genes without using SNPs. This proportion is stable over time (0, 10 min, 60 min) and independent of the pollination type (compatible, incompatible), thus suggesting that these proportions are reflecting the relative abundance of stigmatic cells compared with pollen grains collected by our manipulation.
Post-validation of the SNP-based analysis To have a global view of expressed genes in stigma and pollen, we constructed three classes of genes from calculated nFPKM: expressed genes, sex-preferentially and sex-specifically expressed genes (see methods for precise criteria of gene selection) (Supplementary Table  5 ). Briefly, we defined genes that showed nFPKM >1 as expressed genes, those that were expressed at least a ten-fold higher in stigma than in pollen as stigma-preferentially expressed genes, and those that were at least one hundred-fold higher expressed in stigma than in pollen as stigmaspecific expressed genes (and vice versa for pollen preferentially and specifically expressed genes) (Fig. 2a ). To evaluate the accuracy of our SNP-based analysis, we first focused on sex-specific genes and analysed the top 20 highly expressed genes (Supplementary Table  5 ) using the ThaleMine database (https://apps.
Discussion: To decipher the mechanisms that control reproduction in flowering plants, we extracted the global transcriptomic signatures associated with both pollen and stigmatic cells following their interaction during compatible or incompatible pollination. Although transcriptomes of pollinated stigmas have been recently published  17, 18  , these analyses did not try to distinguish between pollen and stigma transcripts. In the present work, we provide a comprehensive transcriptomic dataset for stigma-pollen interactions using a novel SNP-based analysis. We took advantage of a recently developed statistical tool called ASE-TIGAR, which is based on a Bayesian approach to estimate allele-specific expression in diploid cells  21. Nariai et al. showed an accurate estimation of gene expression from RNA-seq with ASE-TIGAR and identified some autosomal genes as allelespecific genes in a human reference lymphoblastoid cell line.
The genomic sequence AlSCR14, a 5'mock sequence and a 3'mock sequence were inserted in the pB7m34G. GenBank accession numbers for pgAlSCR14 (promoter and gene of AlSCR14) and pSLR1-gAlSRK14 (SLR1 promoter and AlSRK14 gene) are MH680584 and MH680585, respectively.
RNA sequencing and expression analysis Library preparation and RNA sequencing were performed by HELIXIO with TruSeq® Standard mRNA sample Preparation kit (Illumina) and NextSeq500 platform (Illumina) applying pairedend sequencing (2x75 bp). We then proceeded to the analysis of the sequencing data using ASE-TIGAR after quality check by FastQC, trimming and pairing of the resulting reads using custom Perl scripts. First, we derived mRNA reference sequences from new reference genomes and merged them in one FASTA file, then mapped the clean reads on the mRNA reference with Bowtie2  70. We then run ASE-TIGAR with SAM files produced by mapping and the mRNA reference  21  (http://nagasakilab. csml. org/ase-tigar/). Raw sequencing data is available at NCBI database (https://www. ncbi. nlm. nih. gov/sra/SRP154565) under SRA accession: SRP154565.
Comparison with published datasets To check the consistency of our RNA-seq data with previously published microarray data  [32] [33] [34]  , for each pair of condition, we computed a Pearson correlation coefficient (r) over all genes with SNPs from our variant analysis, using mean of estimated read counts from three or four biological replicates for RNA-seq and mean absolute intensity for multiple microarray replicates. We also computed Pearson correlation coefficients (r) between previously published RNA-seq data  20  and the microarray data sets.
geneontology. org/) and PANTHER13. 1 software  71, 72. We used "GO biological process complete" for the classification. GO terms with False Discovery Rate (FDR) < 0. 05 were considered as significantly enriched. Pathway analysis was performed using and KEGG PATHWAY Database using KEGG Mapper software (http://www. kegg. jp/kegg/tool/map_pathway2. html)  37.
Protein extraction and western blotting Proteins from around 50 pollinated stigmas at C10 and C60 were extracted in SDG buffer (Tris-HCl 62. 5 mM pH 6. 8, Glycerol 10%, DTT 2%, SDS 2. 5 %). Proteins were separated on a 10 % SDS-PAGE gel and immunodetected with Anti-AtMPK3 (Sigma, M8318), anti-phospho p44/p42 MAPK (Cell Signaling, #9101S), or anti-alpha-Tubulin (Sigma, T5168). Band intensities were normalized to tubulin signal using ImageJ software.
Environmental Scanning Electron Microscopy (SEM). Late stage 12 stigmas were pollinated with mature Col-0 pollen and 60 min after pollination, stigmas were observed under Hirox SEM (SH-3000 table-top) at -20 °C with an accelerating voltage of 10 kV. The width of each pollen tube-stigma contact site was measured using ImageJ sofware. We applied a Student test to the measurements. Author Contributions: CK, JJ, MDR developed the data analysis pipeline and performed the bioinformatics analyses. AL, JL supported the bioinformatics analyses. AL helped with interpretation of the transcriptomic data. CK is responsible for all experiments and analysis performed in the study except described below. FR produced and characterized Col-0/SRK14 and C24/SCR14 lines. CK and LR performed the image acquisition by scanning electron microscope.
Acknowledgments: We thank  Yvon Jaillais ,  Olivier Hamant ,  Gwyneth Ingram  for critical reading of the manuscript, SiCE team members for fruitful discussions and comments,  Fabrice Besnard  for critical reading of the manuscript and for specific advice and script sharing relative to SNP calling,  Naoki Nariai  for the help to perform analysis with ASE-TIGAR, Alexander R. Leydon for the information for SNP-based RNAseq analysis,  Marcel Wiermer  and  Yuelin Zhang  for mpk3-DG line,  Agnès Attard  for the discussion of the bioinformatics data analysis. We gratefully acknowledge support from the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon  for the computing resources. This work was supported by Grant  ANR-14-CE11-0021.