dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability: The UNAM and Alzomoni FTIR data used in this study are available from the correspondent author upon request (beatriz. herrera@mail. utoronto. ca). The meteorological data for both FTIR stations are available at https://www. ruoa. unam. mx/. The IASI ANNI-NH3-v3 L2 data can be access at https://iasi. aeris-data. fr/NH3/. MODIS active fire snapshots are available at https://wvs. earthdata. nasa. gov/. The code of the HYSPLIT model can be obtained from https://www. arl. noaa. gov/hysplit/getrun-hysplit/. Last access to all URLs: 11 March 2022.
Multicolor FISH (M-FISH) Chromosome spreads were prepared by arresting actively dividing MRC-5 cultures using 50 ng/ml colcemid (KaryoMAX, Invitrogen) for 2 hours at 37 ºC. Afterwards, trypsinized cells were incubated with hypotonic solution (75 mM KCl) for 15 minutes at 37 ºC, fixed with ice-cold methanol:glacial acetic acid (3:1), and spread on slides. mFISH was performed using the 24XCyte kit (MetaSystems Probes) according to manufacturer's instructions. Stained metaphase chromosomes were visualized on the Zeiss Axiovert Z2 epi-fluorescent microscope and analyzed using the metasystem ISIS software. To identify "cryptic" centromeric and PCH chromosomal rearrangements, we destained M-FISH chromosome preparations with multiple washes in PBS at 37 ºC. We consequently applied dual color FISH, using an alpha-satellite pancentromeric probe labelled with FITC and a Satellite III probe labelled with Cy3, on the same mitotic plates examined by M-FISH and Inverted-DAPI Banding, retrieved by pre-recording slide's co-ordinates.
Slot blotting The DNA obtained from native ChIP experiments was denatured (0. 5 M NaOH, 2 M NaCl and 25 mM EDTA) and blotted onto nylon membranes using a slot blot apparatus, crosslinked, and hybridized with radioactively labeled probes. The membranes were exposed onto phosphor imager screens and the signal intensity was quantified with ImageQuant software.
Acknowledgments The  IRCAN's Molecular and Cellular Core Imaging (PICMI)  is supported by  "le Cancéropole PACA , la  Région Provence Alpes-Côte d'Azur, le Conseil Départemental 06 ", and  INSERM. The IRCAN's GenoMed, the  Genomic Core Facility  at IRCAN is supported by le Conseil Départemental  06 , Aviesan and INSERM. This work was supported by the cross-cutting INSERM program on aging (AGEMED), '' Investments for the Future ''  LABEXSIGNALIFE  (reference  ANR-11-LABX-0028-01 ), the "Fondation ARC pour la recherche contre le cancer",  INCa  (project  REPLITOP ) and the  ANR  (project  TELOCHROM ). Work in the JY laboratories was supported by the  National Natural Science Foundation of China  (grant numbers  81971312 ,  91749126 ,  81911530241 ,  81871549  and  81671900 ), the  Program of Shanghai Academic/Technology Research Leader  (grant number  19XD1422500 ) and the  Shanghai Municipal Education Commission (Oriental Scholars Program ,  2019 ).
Each subcolony was introduced in the experimental setup between 15 to 18 days prior to the first experiment; the setup was composed of a one-chamber nest (56 x 41 x 2 mm) covered by a glass window. This duration was long enough to stabilise the task repartition among individuals. A single access route (4 x 3 x 2 mm) leads to the foraging area (61 x 49 x mm) containing a 0. 3 M sucrose solution and water ad libitum. The walls of the foraging area were covered in Fluon® to prevent the ants from escaping. The subcolonies were kept at 22 ±3°C and 60 ±5% relative humidity, with a 12:12 h constant photoperiod. After 4 days of starvation, we introduced 3 mL of a 1 M sucrose solution. The ants were filmed for 90 min, starting 30 min before food source introduction. Each colony was tested once. The video data were recorded using a Panasonic® Lumix DMC-GH4-R mounted with a 30 mm Olympus® ED lens capturing 25 frames/s at the definition of 4180*2160 p.
Reconstruction of gene regulatory networks involved in cell specialization driven by retinoid treatment Our previous work has shown that ligand binding of retinoid receptors triggers a cascade of events which leads to the dynamic activation of other transcription factors (TF) which then regulate their cognate targets. This cascade of transcription regulatory events can be reconstructed by integration of transcription factor-target gene (TF-TG) databases in the temporal transcriptome analysis  (Mendoza-Parra et al, 2016a; Cahan et al, 2014). This way, gene regulatory networks (GRNs) can be reconstructed and master regulator genes deduced  (Cholley et al, 2018). Herein we have reconstructed a master GRN from the integration of the temporal transcriptomes assessed on WT P19 cells treated with the pan-agonist ATRA, covering the 10 days of cell treatment, with transcription factor-target gene (TF-TG) annotations (CellNet database:  (Cahan et al, 2014) ).
A close look at the "common" and "inhibited" programs revealed that, despite their distinct number of edges, most of the genes composing the "common" program are also part of the "inhibited" program (414 from 452 genes), and this observation is also conserved for the involved transcription factors (161 shared TFs; Figure  4B ). This observation suggests that gene expression for the "common" and "inhibited" programs are differentially controlled by other molecular factors in addition to TF regulation. To address this hypothesis, we have evaluated their promoter epigenetics status (defined by the repressive mark H3K27me3 and the active mark H3K4me3), their chromatin accessibility (revealed by FAIRE-sequencing assays), and their transcriptional response (revealed by the enrichment of the RNA Polymerase II (RNAPII)) after two days of ATRA treatment (GSE68291;  (Mendoza-Parra et al, 2016a) ).
With the aim of confirming the role of the RAR-alpha receptor on the predicted "inhibited" program, we have performed an enrichment analysis on their associated 219 FAIRE sites (Figure  4F ), by comparing them with ChIP-seq binding sites collected from the public domain. Specifically we have used a collection of more than 40 thousand public mouse ChIP-seq datasets, collected as part of our NGS-QC database (https://ngsqc. org/), among which 71 ChIP-seq public profiles correspond to RXR or RAR transcription factors  (Blum et al, 2020; Mendoza-Parra et al, 2016b , 2013). This analysis revealed that ~24% of the FAIRE sites associated to the "inhibited" program were enriched for RARα binding sites, ~20% to pan-RXR sites; further supported by a motif analysis revealing the enrichment of the RARα primary motif (Figure  4G  & H), confirming their transcriptional response driven by the RARα/RXR heterodimer.
Primary bioinformatics analyses Fastq files were qualified with the NGS-QC Generator tool  (Mendoza-Parra et al, 2016b , 2013). Reads from fastq files were mapped to the mouse mm9 reference genome using Bowtie 2. 1. 0 under by default parameters. Mapped reads were associated to known genes with featureCounts. RNA-Seq analyses were done with DESeq2 R-package. t-distributed stochastic neighbor embedding (t-SNE) analysis was performed with the R package Rtsne. Heatmap matrices display were generated with MeV 4. 9. 0. Gene Ontology analyses were performed with DAVID Bioinformatics Resources (https://david. ncifcrf. gov/).
Collection of gene markers associated to specialized cells Gene markers associated to neurons (1,352 genes), astrocytes (501 genes), oligodendrocyte precursors (OPC: 501 genes) were collected from the supplementary material (Supplementary dataset_S02) of  (Voskuhl et al, 2019). Gene markers associated to GABAergic (318 genes) and Glutamatergic (311 genes) neuronal subtypes were collected from the Supplementary Table  9  of  (Tasic et al, 2018). Gene markers for Dopaminergic (Th+) (513 genes) neuronal subtypes were collected from the Table  S2  of  (Hook et al, 2018). This assembled collection is available in our supplementary material (Suppl. Table  S1 ). Promoter epigenetic status analysis and RAR/RXR enrichment P19 epigenetic readouts assessed for the repressive mark H3K27me3 and the active mark H3K4me3, the chromatin accessibility -revealed by FAIRE-sequencing profiling-and the transcriptional response (RNA Polymerase II) after two days of ATRA treatment were collected from our previous published study (GSE68291;  (Mendoza-Parra et al, 2016a) ).
Normalized enrichment levels at gene promoter regions, relative to EtOH control profiles, were used for predicting their epigenetic combinatorial status. Enrichment heat maps and mean density plots for FAIRE readouts at gene promoter regions (+/-500 bp) were obtained with seqMINER 1. 3. 4. FAIRE sites motif analysis has been performed with the MEME Suite 5. 4. 1. RXR/RAR enrichment on FAIRE sites were inferred by comparing them with > 40 thousand mouse ChIP-seq binding sites collected from the public domain, as part of our NGS-QC database (https://ngsqc. org/). Among all mouse ChIP-seq collected data, 71 ChIP-seq public profiles correspond to RXR or RAR transcription factors  (Blum et al, 2020; Mendoza-Parra et al, 2016b , 2013).
Acknowledgements We thank all the members of our laboratory and the Genoscope sequencing platform for discussion. This work was supported by the institutional bodies  CEA ,  CNRS , and  Université d'Evry-Val d'Essonne. E. M. was supported by  Genopole Thematic Incentive Actions funding  ( ATIGE-2017 ); A. K. by the '' Fondation pour la Recherche Medicale'' (FRM ; funding ALZ-.
Balance data analysis The transpiration per leaf area (E in mmol m -2 s -1 ) was calculated as E ¼ D w =AL=MW w ; (1) where D w is the rate of change in weight within the considered period (g s -1 ), AL the leaf area (m 2 ), and MW w the molecular weight of water (18 g mol -1 ). The leaf area was estimated through the relationship obtained between the leaf midrib length and the leaf area, measured with a leaf area meter (LI-3000, LI-COR, Lincoln, Neb. ) for each genotype using $100 leaves per genotype. The leaf midrib length was measured weekly on all the leaves of each plant and the respective total leaf area per plant was then calculated using the equations obtained. The canopy stomatal conductance G c (mmol m -2 s -1 ) was calculated using the simplification suggested by  Monteith and Unsworth (1990) : G c ¼ K G T ð Þ: E D ; (2) where K G (T) is the conductance coefficient in kPa m 3 kg -1  (Ewers et al.
Brightness and contrast as well as leaf scanned area were adjusted to optimize visualization of embolisms and provide images not exceeding 9 Mb. Each leaf was automatically scanned every 5 min throughout plant dehydration using the AutoIt 3 computer automation software. Stem water potential (W stem ) was simultaneously measured using psychrometers (ICT International, Armidale, NSW, Australia) that were installed on the main stem of each plant located $10 cm above the scanned leaf to avoid inducing artifactual leaf embolism. The W stem values were automatically recorded every 30 min and the accuracy of the readings was confirmed by W leaf measurements on basal leaves previously bagged with aluminum foil (2 h at least) using a Scholander pressure bomb (DG Meca). The stack of images captured at the end of the experiment comprised between 1,800 and 2,000 scans per leaf and was analyzed using ImageJ software and following the protocol from http://www.
When independent data types do not converge towards the same results, molecular trees should certainly be acknowledged as valuable contributions, but considered only as hypotheses, instead of being hailed as "the resolved tree of life". Chalcidoidea (jewel wasps, chalcidoid wasps, hereafter called chalcid wasps) are among the most species rich, ecologically important, biologically diverse and morphologically disparate groups of terrestrial organisms. These minute wasps (mostly 0. 5-2 mm in size) are numerically abundant and ubiquitous in almost every terrestrial habitat on Earth. Their diversity is staggering, with an estimated 500,000 species placed in 27 families (among which two are extinct) and 87 subfamilies (among which 3 are extinct) and several incertae sedis taxa (Table  S1a ). 2,731 genera and 27,021 species have been described so far [Universal Chalcidoidea Database  (Noyes 2019 ) currently being migrated to TaxonWorks  (TaxonWorks Community, 2022).
LIBRARY PREPARATION AND SEQUENCING Exons -Exons were obtained for 520 taxa following the protocol in  Zhang et al. (2022) , of which 51 taxa were retrieved from previously published transcriptomes or genomes  (Peters et al. 2018; Zhang et al. 2020) , and 469 taxa (363 used in combined dataset) were enriched using the anchored hybrid enrichment (AHE) probe sets (Hym_Ich set or Hym_Cha set). See supplemental methods for details on library preparation and sequencing. UCEs -UCEs for 116 taxa were retrieved from previous studies  (Cruaud et al. 2019; Rasplus et al. 2020; Rodriguez et al. 2021). For the remaining 291 taxa, library preparation followed  Cruaud et al. (2019). Specimens were enriched in 1432 UCEs using the 2749 probes designed by  Faircloth et al. (2015)  (myBaits UCE Hymenoptera 1.
ASSEMBLY OF DATA SETS Exons -Assembly of loci, orthology assessment and contamination checking for the AHE520 data set followed  Zhang et al. (2022)  (see also supplementary methods). Exons were selected of 414 taxa that were compatible for data combination with the UCE taxa, either as the same extraction, same species, same genus, or in 3 cases as closely related genera (Table  S1b ). However, the assembly protocol used in  Zhang et al. (2022)  was overly stringent, resulting in 28. 5% of missing or ambiguous nucleotide calls. Therefore, raw sequence data for these 414 taxa were re-processed with HybPiper to increase completeness  (Johnson et al. 2016). All exons of the AHE520 data set were used as a reference database (final % of ambiguous/missing nucleotides = 19. 1%; see supplementary methods for details).
Loci were re-aligned with MAFFT after each round of TreeShrink. Gene trees were inferred with IQ-TREE v 2. 0. 6  (Nguyen et al. 2015; Minh et al. 2020)  with the best fit model selected by ModelFinder  (Kalyaanamoorthy et al. 2017). Positions with > 50% gaps and sequences with > 25% gaps were removed from the alignments of UCEs using SEQTOOLS (package PASTA;  Mirarab et al. 2014)  to speed up inference of individual gene trees. Contaminations -A BLAST search of all DNA sequences on themselves (i. e. , by using sequences of all exons/UCEs for all samples as query and target sequences) was performed (blastn with -evalue 1e-20 and -max_target_seqs 2). Only hits for which the same locus was identified as both target and query sequence and for which samples were different for target and query sequences were kept for downstream analysis.
Putative contaminations were identified using a script that scored hits according to four criteria appropriate for our data (cf. https://github. com/mjy/cgq for details): 1) Taxon Difference: either subfamily or family was different between target and query sequences; 2) Proportional Difference: the percentage of similarity between target and query was > 99. 95; 3) Proportional Length Difference: length of match divided by the smaller of length of the target and query was > 0. 95; 4) Plate Similarity: target and query sequences were obtained from specimens processed on the same plate for DNA extraction/library preparation. When a criterion was met, a score of 1 was attributed, otherwise the score was 0. A composite score was calculated as the sum of criteria 1 to 4. When the composite score equaled 4 both target and query loci were considered as potentially contaminated.
PHYLOGENETIC INFERENCE Data sets were analyzed with concatenation (using IQ-TREE 2. 0. 6) and tree reconciliation (using ASTRAL-III,  Zhang et al. 2018)  approaches. For the concatenation approach, loci were merged and the resulting data set was analyzed 1) without partitioning, 2) with one partition for each locus and 3) with one partition for each data type (combined data set only; 1 partition for the exons another for the UCEs). Best fit models were selected with the Bayesian Information Criterion (BIC) as implemented in ModelFinder. FreeRate models with up to ten categories of rates were included in tests for the unpartitioned exon and UCE data sets, but only common substitution models were tested when data sets were partitioned by locus. The candidate tree set for all tree searches was composed of 98 parsimony trees + 1 BIONJ tree and only the 20 best initial trees were retained for NNI search.
Statistical support of nodes was assessed with ultrafast bootstrap (UFBoot)  (Minh et al. 2013 ) with a minimum correlation coefficient set to 0. 99 and 1,000 replicates of SH-aLRT tests  (Guindon et al. 2010). Gene (gCF) and site (sCF) concordance factors  (Minh et al. 2020)  were also calculated in IQ-TREE. For ASTRAL analyses, nodes in gene trees with UFBoot support lower than 90 were collapsed (perl script AfterPhylo. pl, Zhu 2014) before reconciliation. Statistical support of nodes was assessed with local posterior probabilities (local PP) as implemented in ASTRAL-III. Distance between a node and its parent node was calculated with the R package ape  (Paradis and Schliep 2018). RF distances  (Robinson and Foulds 1981)  among recovered trees were calculated with RAxML-NG_v0. 9. 0  (Kozlov et al.
DIVERGENCE TIME ESTIMATES Time calibrated trees were generated with MCMCtree  (Yang and Rannala 2006). Twenty-one fossils were used as calibration priors and uniform distributions were used as calibration densities (Table  S3a ; Appendix S1). Analyses were run with uncorrelated relaxed clock models. The combined IQ-TREE tree (partitioning by data type) was used as the input tree. Five data sets, each composed of 10,000 amino acid sites randomly selected (custom script in  Rougerie et al. 2022 ) from the exonsAA partition + 10,000 nucleotide sites randomly selected from the UCEs90-25 partition, were used as sequence data to make computation tractable. Each data set was partitioned into two partitions exonsAA (WAG+G model) and UCEs (GTR+G model). Four chains were run for each data set; 20,000 generations were discarded as burnin and chains were run for 2M generation with sampling every 200 generations.
HISTORICAL BIOGEOGRAPHY Distributions of species in each genus were mined from the Universal Chalcidoidea Database  (Noyes 2019). Occurrences were double checked by experts of each taxonomic group and modified if needed. Species used as biocontrol agents or accidentally introduced with their host (plant or insect)  (Rasplus et al. 2010; Noyes 2019)  were removed from the list before compiling genus-level distribution data. Genera were scored as present/absent in the six following biogeographical areas: Neotropical, Nearctic, Afrotropical, Palaearctic, Oriental, Australasian (Table  S4a ). Any genus for which a single species occurs at the boundary of the transition zone between two areas while all other species occur in only one area was coded as present only in the latter area. Ancestral area estimations were performed using the R package BioGeoBEARS 1.
Indeed, monophyletic groups with different GC content as well as polyphyletic groups with similar LB scores were recovered in the different trees. Interestingly, with decreasing saturation, groupings of taxa that were not expected based on morphological features but that are concordant with biological data appeared (i. e. , a group clustering gall associates hereafter called "Gall clade" and a group of "Tiny wasps" hereafter called "Tiny Wasp clade" Figures  2, S1 ). Topological changes that may be attributed to reduction of mutational saturation are listed in Table  S6. S2c ) showed that the exonsAA and UCEs90-25 data sets (i. e. , the least saturated) produced IQ-TREE and ASTRAL trees that were the most similar, and that mostly agreed with morphological or biological expectations and prior taxonomic classifications.
MUTATIONAL SATURATION DISTURBS PHYLOGENOMIC INFERENCES This study expands upon earlier investigations  (Munro et al. 2011; Heraty et al. 2013; Peters et al. 2018; Zhang et al. 2020)  to yield a more comprehensive phylogenetic framework for higher relationships within Chalcidoidea. We used a comparison of phylogenomic inferences from molecular data sets (exons for 414 taxa and UCEs for 407 taxa) and morphological/biological/ecological knowledge to enable us to detect systematic bias attributable to mutational saturation, and, conversely, morphological convergence. With decreasing saturation, exons and UCEs topologies tended to become more similar (Figures 1-2, S1, Appendix S2) and relationships of groups for which placement could be evaluated based on morphological data tended to become more concordant with morphology or biology (Table  S6 ), though not always.
Mymaridae and Rotoitidae Agaonidae -Sycophaginae and all other subfamilies of Agaonidae are both associated with Ficus (Moraceae). They have been considered as part of the same family based on morphological data  (Heraty et al. 2013) , but this result was not supported by earlier molecular data  (Munro et al. 2011 ). In our results, Sycophaginae consistently clustered away from other Agaonidae. With decreasing saturation in the exon data set and with UCEs, Sycophaginae were consistently recovered in the same group of pteromalids. While several morphological characters group Sycophaginae and other Agaonidae, several others are shared with lineages of Pteromalidae with which Sycophaginae clustered on our trees. These include the separated postgenae with an interceding lower tentorial bridge impressed relative to the postgena, the structure of the antenna 14-segmented with a terminal button, and presence of an axillular sulcus in all but a few highly derived species  (Heraty et al.
Chalcididae and Eurytomidae -The polyphyly of Eurytomidae as well as the placement of Cratocentrinae away from other Chalcididae in the most saturated exon and UCE data sets can be also attributable to mutational saturation. Indeed, Eurytomidae is a well-supported family  (Heraty et al. 2013). Cratocentrinae is considered as the sister group of other Chalcididae  (Cruaud et al. 2021 ). However, a monophyletic Chalcididae is only recovered in the ASTRAL trees (exonsAA, UCEs90-25, combined), while Cratocentrinae is sister to Eurytomidae+other Chalcididae in the IQTREE trees. A mesothoracic spiracle that is hidden in Eurytomidae and all Chalcididae except for Cratocentrinae gives support to Cratocentrinae being sister to the two other taxa, but 15 synapomorphies support Chalcididae as monophyletic  (Cruaud et al.
2013). We propose that Aphelinidae should be restricted to four subfamilies: Aphelininae, Coccophaginae, Eretmocerinae, Eriaphytinae, while Calesinae should be upgraded to family rank  (Burks et al. submitted). Agaonidae -Following discussion in the previous section, Sycophaginae should be removed from Agaonidae. Chalcididae -Until proven otherwise and following the discussion in the previous section, Cratocentrinae is maintained within Chalcididae. Eulophidae -As previously reported with molecular data  (Burks et al. 2011; Munro et al. 2011; Heraty et al. 2013; Rasplus et al. 2020) , Trisecodes (incertae sedis within Eulophidae) never clustered with other eulophids, but its placement remains ambiguous. Trisecodes is either recovered as sister to Systasini (exonsAA 433/520, combined) or Trichogrammatidae (UCEs90-25).
, while Cynipencyrtus is a parasitoid of either Cynipidae or their inquilines  (Ito and Hijii 2000). "Tiny Wasp clade" (Aphelinidae + Azotidae + Calesidae + Neodiparidae + Encyrtidae + Eulophidae + Eunotidae + Idioporidae + Signiphoridae + Trichogrammatidae) -This higher-level grouping is highlighted for the first time but recovered only in the IQ-TREE combined tree with moderate support (100/74/0/30. 9). However, there is hidden support for this clade in the exonsAA (sCF=32) and UCEs90-25 (sCF=31. 4) data sets (Figure  S1 ). In addition, the backbone node (#3) that splits Mymaridae/Baeomorphidae/"Tiny Wasp clade" from the other Chalcidoidea is moderately supported (100/73/0/37. 9) and receives hidden support from the exonsAA (sCF=38. 6) and the UCEs90-25 (sCF=40. 4) data sets (Figure  S1 ).