dataesr/scientific-paragraphs-categorization huggingface dataset:
Evolution of life history traits. Three key traits (life form, breeding system and pollination mode) were studied. Stochastic mapping  (Huelsenbeck et al. 2003)  as described in  Bollback (2006)  and implemented in the R package phytools was utilized to estimate the ancestral state and the number of transitions for each trait. The transition matrix was first sampled from its posterior probability distribution conditioned on the substitution model (10,000 generations of MCMC, sampling every 100 generations). Then, 100 stochastic character histories were simulated conditioned on each sampled value of the transition matrix. Three Markov models were tested: equal rates model (ER) with a single parameter for all transition rates, symmetric model (SYM) in which forward and reverse transition have the same rate and all rates different model (ARD).
Molecular phylogenetic inference with outgroup rooting. As a reminder, classifications of the genus Ficus are provided in Table  1. Data sets are described in Table  2  and features of taxa are reported in Tables  S1-S3. An average of 2*416,834 paired reads; 2,520 ustacks loci and 2,308 cstacks loci were obtained for each sample included in the RAD library (Table  S1 ). In an attempt to solve the tree backbone, we kept only the 583 most conserved loci assembled from forward reads (75% complete matrix, mergeR1 data set), 91% of which (530) were retained in the final (mergeR1R2) data set. Mining of loci in outgroup genomes largely reduced the level of missing data (from ca 70-75% in the mergeR1 data set to ca 15-30% in the mergeR1R2 data set depending on the outgroup, Table  S1 ). Between 1 and 55 loci were flagged for each sample by Treeshrink (average 15, Table  S4 ).
The final data set used for phylogenetic inference (mergeR1R2) was composed of 70 species of Ficus and five outgroups. We did not obtain enough reads for Sparattosyce dioica to include it in our analysis. Alignment length was 419,945 bp (Table  2 ). RAxML and IQTREE produced identical topologies with high statistical support (Figure  1 , S1; Table  3 ). Neither gaps (K=0. 418, Pvalue=0. 342) nor missing data (K=0. 384, Pvalue=0. 555) were phylogenetically clustered in the ingroup (i. e. taxa with high percentages of missing data /gaps did not cluster together more often than expected by chance). All subgenera except Ficus and Pharmacosycea were recovered monophyletic with strong support and all non-monospecific sections of the data set except Ficus were monophyletic with strong support. Section Pharmacosycea was sister to all other Ficus species with strong support.
Aside from the breeding system, this clade is well defined by several non-ambiguous synapomorphies on the morphological tree (species have generally less than 10 lateral veins; figs are frequently stipitate; and bears more than three ostiolar bracts; the stigma in short-styled pistillate flowers is mostly cylindrical; and the fruits are compressed). Finally, and as observed in previous molecular work, the subgenus Ficus appeared polyphyletic in our molecular and morphological trees, though results differ between the two approaches as discussed in the next section. It is the first time that the non-monophyly of this subgenus is assessed through phylogenetic inference of morphological data as only two representatives of a single section of the subgenus Ficus (sect. Eriosycea) were included in previous analysis.
Topology 1: tree obtained from the mergeR1R2 data set with outgroup rooting. This topology is not supported by morphological data (Figures  3  & S9 ) and supposed to result from an LBA artefact of sect. Pharmacosycea to the outgroups. Topology 2 is obtained for a few data sets when alternative rooting methods are used (Figure  2 ). It is the most supported by morphological data and evolutionary history of pollinators. Topology 3 is obtained for a few data sets when alternative rooting methods are used. It is less supported by morphological data and evolutionary history of pollinators than topology 2. Topology 4 is obtained for a few data sets when alternative rooting methods are used. It is considered unlikely as the position of sect. Urostigma is not supported by morphological data. The position of the root could be driven by the GC-content bias exhibited by Mixtiflores.
highlighted and compared (LB scores: sect. Pharmacosycea versus all other fig trees; GC content: Mixtiflores versus all other fig trees and then sect. Pharmacosycea versus other fig trees, Mixtiflores excluded). An initial PCA was performed on all loci and the difference between the groups was statistically assessed by means of a Wilcoxon test applied to the score of the loci upon the first PCA axis. Then, the locus showing the highest correlation with the axis was removed and another PCA and Wilcoxon test were performed on the thinned dataset.
ACKNOWLEDGEMENTS We thank  S. Santoni  ( INRAE ) for technical advices, MGX for sequencing, the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for computing resources and  C. C. Berg  and  E. J. H. Corner  for their eternal contributions. This work was partially funded by  UPD-OVCRD  ( 171715   PhDIA ) and  UPD-NSRI  ( BIO-18-1-02 ). AC thanks  C. J. Rivera-Rivera  for his help with running LS 3.
Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the present day modelling of aerosol optical properties has been assessed using simulated data representative for the year 2010, from 14 global aerosol models participating in the Phase III Control experiment. The model versions are close or equal to those used for CMIP6 and AerChemMIP and inform also on bias in state of the art ESMs. Modelled column optical depths (total, fine and coarse mode AOD) and Ångström Exponents (AE) were compared both with ground based observations from the Aerosol 5.
This study investigates modelled aerosol optical properties of the most recent models participating in the AeroCom 2019 control experiment (in the following denoted CTRL, https://wiki. met. no/aerocom/phase3-experiments) on a global scale. Making use of the increasing amount of data which have become available during the past decade, we are able to extend the assessment of modelled optical properties beyond what was originally presented in  Kinne et al. (2006). Here, we use observations of ground and space based observations of the above introduced columnar variables of total, fine and coarse AOD and AE as well as, for the first time, surface in-situ measurements of scattering and absorption coefficients, primarily from surface observatories contributing to Global Atmospheric Watch (GAW), obtained from the World Data Centre for Aerosols (GAW-WDCA) archive.
Surface in-situ data Surface in-situ measurements of the aerosol light scattering and absorption coefficients, were accessed through the GAW-WDCA database EBAS (http://ebas. nilu. no/). The EBAS database also includes various observations of atmospheric chemical composition and physical parameters, although those were not used here. For both scattering and absorption variables, only level 2 data from the EBAS database were used (i. e. , quality controlled, hourly averaged, reported at STP). All data in EBAS have version control, and a detailed description of the quality assurance and quality control procedures for GAW aerosol in-situ data are available in  Laj et al. (2020, submitted). Additionally, for this study, data was only considered if it was associated with the EBAS categories aerosol or pm10.
MODIS data Daily gridded level 3 AOD data data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used from both satellite platforms (Terra and Aqua) for evaluation of the models. The merged land and ocean global product (named Aerosol_Optical_Depth_Land_Ocean_Mean in the product files) of the recent collection 6. 1 was used. This is an updated and improved version of collection 6 (e. g. Levy et al. (2013) ,  Sayer et al. (2014). For changes between both data-sets, see Hubanks (2017).
Merged satellite AOD data The MERGED-FMI data-set , developed by the Finnish Meteorological Institute, includes gridded L3 monthly AOD products merged from 12 available satellite products  (Sogacheva et al. (2019) ). The merging method is based on the results of the evaluation of the individual satellite AOD products against AERONET. Those results were utilised to infer a regional ranking which was then used to calculate a weighted AOD mean. Because it is combined from the individual products of different spatial and temporal resolution, the AOD merged product is characterised by the best possible coverage, compared with other individual satellite products. The AOD merged product is at least as capable of representing monthly means as the individual products  (Sogacheva et al. (2019) ). Standard pixel-level uncertainties for the merged AOD product were estimated as the root mean squared sum of the deviations between that product and other eight merged AOD products calculated with different merging approaches applied for different aerosol types  (Sogacheva et al.
The focus was on the assessment of the modelled column integrated aerosol optical properties AOD, AOD < 1 µm , AOD > 1 µm , and AE, as well as, for the first time, surface (dry) scattering and absorption coefficients. The columnar data was compared to ground based observations from AERONET as well as to several space based observations. In addition to the model evaluation, the performance of the satellite products -in the resolution as aggregated and used for this study -was investigated by comparison with AERONET observations. This was done in order to establish potential relative biases when evaluating the models using satellite observations (Fig. 2 ). From this analysis, AATSR-SU and MERGED-FMI showed slight underestimations of AOD (ca. -5%) and MODIS Aqua and Terra showed overestimations of about 10% and 20%, respectively at AERONET sites.
Results from sensitivity studies related to spatio-temporal representation errors. AERONET* indicates that two different site selection schemes where used (see text and Fig. A5). See also Tab. A2 for an assessment of satellite resampling sensitivities. doi. org/10. 5194/acp-2019-1214 Preprint. Discussion started: 18 March 2020 c Author(s) 2020. CC BY 4. 0 License. TableA1. Model names and corresponding AeroCom database IDs. Also indicated is whether models diagnosed a clear-sky AOD (CS) or not and which models were included in the AeroCom mean and median ensemble.
Acknowledgements. Data providers from all observation networks and satellite products and all people involved in the production of these products are highly acknowledged for sharing and submitting their data and for support. JG wishes to thank  O. Landgren  and  H. Svennevik  for providing data and code used in this study. TT was supported by the supercomputer system of the  National Institute for Environmental Studies, Japan , and  JSPS KAKENHI  Grant Number  JP19H05669. TvN and PLS acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme project CRESCENDO  under grant agreement No  641816. DN acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme project FORCeS  under grant agreement No  821205. SEB and KT acknowledge funding from  NASA's Atmospheric Composition Modeling and Analysis Program (ACMAP) , contract number  NNX15AE36G.
Global analysis of gene expression across the four distinct developmental processes There were 22,743, 19,745, 21,144, and 21,102 expressed genes in NEC, EC, ICpEC, and GE stage. Among these, more than 75. 3% of the expressed genes were present in all four developmental stages, while 2645 genes were only expressed in NEC. However, only 366, 505 and 588 genes were unique present in EC, ICpEC, and GE stage, respectively (Fig. 3a ), which suggested that distinct spatial transcriptional patterns were present in the four developmental processes. To evaluate the differences of molecular response among four samples, gene expression were normalized to FPKM by RSEM software. After filtering with FPKM> 60, a total of 2961 (11. 40%), 3445 (13. 26%), 3445 (13. 26%), and 3442 (13. 25%) genes were highly expressed in NEC, EC, ICpEC, and GE, respectively (Table  2 ).
Among these four comparisons (Fig. 3b ), a total of 10,642, 4180, 5846 and 1785 DEGs were identified, respectively. Compared with NEC, EC had 4887 up-regulated and 5755 down-regulated genes. Compared with EC, ICpEC had 2689 up-regulated and 1491 downregulated genes, GE had 3451 up-regulated and 2395 down-regulated genes. Compared with ICpEC, GE had 832 up-regulated and 953 down-regulated genes. DEGs analysis revealed that longan transcriptome undergoes significantly dynamic changes during SE, particularly during the transition period from NEC to EC. Therefore, the longan SE transcriptome datasets given here may serve as a valuable molecular resource for future studies.
To investigate the biological pathways of the DEGs, we used the KEGG database to classify the DEGs function with emphasis on biological pathways (Additional file 2: Figure  S2 ). According to KEGG annotation, 6516 DEGs (NEC_vs_EC) were assigned to 128 pathways, 2514 DEGs (EC_vs_ICpEC) were assigned to 126 pathways, 3555 DEGs (EC_vs_GE) were assigned to 126 pathways, 1062 DEGs (ICpEC_vs_GE) were assigned to 111 pathways. The annotated changes in all comparisons were mainly enriched in 'metabolic pathway'  (21. 38, 22. 43, 23. 12 and 25. 52%, respectively), 'biosynthesis of secondary metabolites'  (11. 97, 11. 46, 11. 70 and 14. 52%, respectively), 'plant-pathogen interaction' (8. 01, 8. 23, 7. 59 and 6. 40%, respectively) and 'plant hormone signal transduction' (5. 22, 5. 41, 5. 40 and 8. 38%, respectively) pathway.
Conclusions In summary, our study generated a high resolution transcriptome datasets for longan SE. A comparative analysis of global gene expression patterns during early SE stages provided subsets of DEGs that regulated SE in longan. Our study revealed the expression profiles of genes involved in plant hormones such as auxin and cytokinin signaling pathway, flavonoid and fatty acid biosynthesis pathway, extracellular protein, as well as the representative molecular marker genes, indicating their possible roles in longan SE. This transcriptomic data provides new insights into future functional studies, as a means of studying the molecular mechanisms in SE.
RNA-Seq reads mapping and differential expression The raw reads were cleaned by removing adapter reads, reads containing poly-N larger than 10%, and low quality reads (Q Phred < 20). Cleaned reads were then aligned to the longan reference genome using Bowtie software (http://bowtie-bio. sourceforge. net/ index. shtml) and TopHat2 (http://ccb. jhu. edu/software/tophat/index. shtml), read count for each gene was then obtained after mapping. Gene expression levels for each sample were estimated by RSEM (RNA-Seq by Expectation Maximization) software  [122]. The expression levels of matched genes in each cDNA library were derived and normalized to FPKM (Fragments Per Kilobase of exon per Million fragments mapped)  [123]. The differentially expression analysis of the pairwise comparison of RNA-Seq libraries were confirmed by using the Poisson Distribution analysis method  [124] , and the False Discovery Rate (FDR) was used to determine the P value threshold.
Expression annotation and functional analysis of DEGs Gene function was annotated based on the databases of Blast Nr (NCBI non-redundant protein sequences), GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes database). The GO and KEGG functional enrichment analysis of DEGs were performed to identify which DEGs were significantly enriched in GO terms or KEGG pathways. GO terms with corrected P-value ≤0. 05 were considered as significantly enriched terms. The KEGG enrichment was determined by Rich factor, Q-value, and the number of enriched genes in this pathway. Q-value ≤0. 05 was defined as those with genes that showed significant differential expression.
Quantitative real-time PCR analysis For qRT-PCR validation, 500 ng total RNA extracted from each stage of longan SE (NEC, EC, ICpEC, GE, TE and CE) were transcribed into cDNA with random primers and Oligo dT primer using the SYBR Ex Script™ kit (Takara, China), Sixteen unique transcripts with potential roles in longan SE were chosen and their specific primers were designed using DNAMAN 7. 0. qRT-PCR was performed on the LightCycler 480 instrument (Roche Applied Science, Switzerland) in a total volume of 20 μL in each well containing 10 μL of 2× SYBR Premix Ex Taq™; 0. 8 μL of each specific primer (100 nM); 1. 0 μL of cDNA template (in a 1:10 dilution); and 7. 4 μL of ddH 2 O. The PCR conditions were: denaturation for 60 s at 95 °C, and then 40 cycles of 10 s at 95 °C and 20 s between 58 °C and 61 °C in function of the Tm of the primers.
Primer annealing specificity was examined and verified by melting curve analysis. Four-point standard curves of a fivefold dilution series (1:5 to 1:625) from pooled cDNA were used to calculate PCR efficiency. The reactions were performed in 96-well PCR plates and each experiment consisted of three biological replicates. The expressive abundance of the sixteen selected genes were calculated relative to the expression of reference genes DlFSD, DlEF-1a, and Dlelf4a. Data were further processed in MS Excel. Gene names, primer sequences, product sizes, and annealing temperatures are given in Additional file 11: Table  S7.
01 in all available databases (Table  S2 ). The identified variant was a missense substitution located in the HSF2BP gene, rs200655253 (21:43630396 G>A, GRCh38). The variant lies within the sixth exon of the reference transcript ENST00000291560. 7 (NM_007031. 2:c. 500C>T) and changes a TCG codon into a TTG (NP_008962. 1:p. Ser167Leu). It is very rare (VAF 0. 0001845 in the GnomAD database and 0. 0005 in the GME Variome dedicated to Middle-East populations) and absent at the homozygous state in all databases. The variant was verified by Sanger sequencing and was found to segregate in a Mendelian fashion within the family: the affected twin III-1 was homozygous for the variant and both parents and fertile siblings were heterozygous carriers (Figure  S2 ). Serine 167 is a highly conserved position and the S167L variant is predicted to be pathogenic or deleterious by 11 out of the 18 pathogenicity predictors available in dbNSFP 3.
Whole Exome Sequencing Written informed consent was received from participants prior to inclusion in the study and the institutions involved. Genomic DNA was extracted from blood samples by standards protocols. For individuals III-3 and III-10, library preparation, exome capture, sequencing and initial data processing were performed by Beckman Coulter Genomics (Danvers, USA). Exon capture was performed using the hsV5UTR kit target enrichment kit. Libraries were sequenced on an Illumina HiSEQ instrument as paired-end 100bp reads. For individual III-2, library preparation, exome capture, sequencing and data processing were performed by IntegraGen SA (Evry, France) according to their in-house procedures. Target capture, enrichment and elution were performed according to manufacturer's instructions and protocols (SureSelect Human All Exon Kits Version CRE, Agilent).
Bioinformatic analysis For the 3 individuals, sequence reads were mapped onto the human genome build (hg38 / GRCh38) using the Burrows-Wheeler Aligner (BWA) tool. Duplicated reads were removed using sambamba tools. WES metrics are provided in Table  S1. Variant calling, allowing the identification of SNV (Single Nucleotide Variations) and small insertions/deletions (up to 20bp) was performed via the Broad Institute GATK Haplotype Caller GVCF tool (3. 7). Ensembl's VEP (Variant Effect Predictor, release 87) program was used for initial variant annotation. This tool considers data available in dbSNP (dbSNP147), the 1000 Genomes Project (1000G_phase3), the Exome Variant Server (ESP6500SI-V2-SSA137), the Exome Aggregation Consortium (ExAC r3. 0), and IntegraGen in-house databases. Additional annotation data was retrieved using dbNSFP (version 3.
5, https://sites. google. com/site/jpopgen/dbNSFP) and Varsome (https://varsome. com/). Minor allele frequencies were manually verified on GnomAD (http://gnomad. broadinstitute. org), ISB Kaviar (http://db. systemsbiology. net/kaviar/), and Great Middle Eastern variant database GME Variome (http://igm. ucsd. edu/gme/). Variant filtering was performed on the following criteria: -minimum depth at variant position of 10, -correct segregation in the family, on the basis of homozygosity by descent: variants should be homozygous in both affected sisters III-2 and III-3, and heterozygous or homozygous for Reference allele in the fertile sister III-10, -absence in unrelated in-house fertile controls, -Minor Allele Frequency (MAF) below 1% in global and in each population in the GnomAD database, -presence in the coding sequence (i.
, 2013)  MS/MS data analysis. Raw MS data were analyzed using MaxQuant v 1. 6. 2. 6  (Cox and Mann, 2008)  against SwissProt Mouse database (UP000000589, Oct, 2019) and MaxQuant contaminants. All FDRs were of 1%. Variable modifications taken into account were oxidation of M and acetylation of the N-term, while fixed modifications included considered only carbamidomethylation of C. The maximum number of modifications allowed per peptide was of 5. Proteins were quantified using iBAQ  (Schwanhausser et al. , 2011). Potential contaminants, reverse decoy sequences and proteins identified by site were removed. Proteins with less than two unique peptides in the Ab1 and Ab2 groups were not considered for ulterior analysis. Proteins with less than two unique peptides in the control group and more than two in both groups Ab1 and Ab2 were selected as high-confidence candidates (group Ab1 and Ab2 only).
Acknowledgements We thank  Dr. Barbero  and  Dr. Edyta Marcon , for providing antibodies against SYCP3 and RPA, respectively,  Dr. Emmanuelle Martini  for helpful advice with the DNA binding assay and  Dr. Fabien Fauchereau  for the genetic analysis of the family. This study was supported by  Université Paris Diderot  and the  Fondation pour la Recherche Médicale  (Labelisation Equipes  DEQ20150331757 , S. C. , A-L. T and R. A. V. ). This work was supported by  MINECO  ( BFU2017-89408-R ) and by  Junta de Castilla y León  ( CSI239P18 ). NFM, FSS and LGH are supported by  European Social Fund/JCyLe  grants ( EDU/310/2015 ,  EDU/556/2019  and  EDU/1083/2013 ). YBC is funded by a grant from  MINECO  ( BS-2015-073993 ). The proteomic analysis was performed in the  Proteomics Facility of Centro de Investigación del Cáncer, Salamanca , Grant  PRB3 ( IPT17/0019 -ISCIII-SGEFI / ERDF ).
The genomic signature of rock-sand divergence We compared the genomes of 8 rock dwellers and 14 sand dwellers to uncover the genomic signature of rock-versus sand-evolutionary diversification. We aligned sequence data to a reference genome of nearly 1 gigabase  (Conte, Joshi et al. 2019)  and identified approximately 22 million Single Nucleotide Polymorphisms (SNPs) and 200,000 Insertion-Deletions (InDels). We calculated FST per variant, and averaged across 10kb windows, to quantify divergence between rock and sand species. We found that 0. 06% of SNPs and 0. 44% of InDels are alternately fixed between rock-and sand-groups. When these divergent variants and genome regions (2. 5% FDR) are mapped to linkage groups (chromosomes), it is apparent that the signature of rock-vs. sand-divergence is distributed relatively evenly across the chromosomes (Figure  1B ).