dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection of a Hematopoietic Transcriptome Program Induced Pluripotent Stem Cell-Derived Kidney Organoids We performed, in duplicate, whole transcriptome analysis of iPSC-derived kidney organoids as compared with native iPSC with Clariom S human technology. After Robust Multi-array Average (RMA) normalization, we identified 3,546 differentially expressed genes (DEGs) with LIMMA algorithm (Figure  4A ) comprising 1,432 upregulated genes. This DEG profile allowed to discriminate experimental sample groups by unsupervised classification (Figure  4B ). After functional enrichment on WikiPathways database, we identified a hematopoietic program in iPSC-derived kidney organoids. Especially, we uncovered an upregulation of RUNX1 and CD34 corresponding to genes expressed in HSCs and that of FLI1, CXCR4, and MXI1 downstream at erythrocyte and megakaryocyte progenitor levels.
There was also a repression of MYB megakaryocytic repressor (Figure  4C ). These results suggest the implication of a hematopoietic transcriptional program in our iPSC-derived kidney organoids and especially induction of the primitive hematopoietic transcription factor RUNX1, which was also detectable at the single-cell level (Figure  2D ) in human ex vivo fetal kidney cortex. Transcriptome data of iPSCs and organoids have been submitted on National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) data repository under GEO accession number Series GSE172319.
DISCUSSION The involvement of kidney in hematopoiesis has been clearly demonstrated in zebrafish  (Ej and Li, 2010). In humans, the most primitive hematopoietic cells arise from mesodermal lineage in AGM through hemogenic endothelium  (Dzierzak and Speck, 2008). Kidney is also a tissue developed from mesoderm, but the presence of cells with HSC transcriptome has not been studied. Here, we first analyzed the HSC markers in fetal kidney through analysis of fetal kidney single-cell dataset analyses. In human fetal kidney cortex, we found some cells expressing RUNX1 in a cluster of cells that harbored expression hematopoietic markers (cluster 9 on Figures  1A, B ). In cluster 9 of human fetal kidney sc-RNAseq, a high expression of hematopoietic markers was confirmed by the presence of SRGN-positive cells (Figure  2A ) as well as cells expressing PTPRC alias CD45 Leukocyte Common Antigen and CD44 (receptor of hyaluronic acid) (Figure  2B ).
Human Fetal Single-Cell Transcriptome Analysis Dataset GSE112570 of single-cell RNA-sequencing allows to explore cellular heterogeneity of human kidney cortical nephrogenic niche  (Lindström et al. , 2018). Experiments were performed with technology 10× Genomics single-cell RNA sequencing on two human kidney samples (17 weeks) indexed in GEO database: GSM3073088 and GSM3073089. Molecular index was realized: Chromium Single Cell 3 v2 single-cell RNA-Seq of poly A selected mRNA kit (10× Genomics), and sequencing was processed on NextSeq 500 (Illumina, San Diego, CA, United States). Bioinformatics base call by bcl2fastq v. 2. 17 reads were mapped using STAR 2. 5. 1b (Genome: GRCh37), and count tables were generated using the Cell Ranger software version 1. 3. 1. Downstream bioinformatics single-cell transcriptome analyses were performed in R software version 3.
4. 3. Digital matrix was built with both 10× MTX files and merged in Seurat R-package version 2. 3. 0  (Butler et al. , 2018)  with package dependencies of matrix version 1. 2-12, cowplot 0. 9. 2, and ggplot2 version 2. 2. 1  (Wickham, 2009). Batch correction was performed with canonical correlation on 30 dimensions before mathematical dimension reduction with tSNE algorithm. Also, dplyr library version 0. 7. 4 was used to generate intermediate table of best genes by cluster. Bioinformatics code to perform these single-cell analyses was deposed at the following web address: https://github. com/ cdesterke/hsckidney/.
Kidney Organoid Microarray Analysis Microarray Clariom S human was done on process total RNA from human wild-type (WT) iPSC and its derived kidney organoids in duplicates  (Hwang et al. , 2019). Expression matrix was built with CEL files generated on Affymetrix Station and normalized by RMA method with TAC version 4. 0 software (Applied Biosystems, Foster City, CA, United States)  (Irizarry et al. , 2003). DEGs were estimated with linear models for microarray data (LIMMA) algorithm by using a false discovery rate threshold of less 5%  (Ritchie et al. , 2015). Functional enrichment analysis on DEGs was performed on WikiPathway database.
Participants A total of 43 participants took part in the study in exchange for £7. 50. Participants also received an additional bonus of on average £3. 573 (range = 2. 3-3. 9, mean reward rate per trial in the test blocks =. 213). One participant with below chance accuracy was excluded from the analysis and the data of 42 participants were included (M age = 26. 595, SD = 5. 315, Range = 18-35, 15 women, 27 men). They were recruited via Prolific and completed the experiment online. All participants were right-handed, with a normal color perception, normal or corrected-tonormal vision, English as first language, and no reported history of diagnosed mental disorders. The study was performed in accordance with the principles expressed in the declaration of Helsinki and with the protocol of the local ethics review board (Faculty of Psychology and Educational Sciences at Ghent University).
Stimuli and procedure All the material can be found on the Open Science Framework. The experiment was programmed and presented using JavaScript language and jsPsych (version 6. 1. 0) libraries. The experiment was run on desktop computers or laptops and required Chrome or Mozilla Firefox as browsers. Each trial of the color-naming Stroop task consisted of a word presented in the center of a black screen (BLUE, GREEN, RED, or YELLOW, letter font: Calibri; letter size: 60px; font-weight: bold). The ink-color of the word could be congruent (same) or incongruent (different) with regard to the word meaning (blue: RGB = [0, 0, 255], green: RGB = [0, 128, 0], red: RGB = [255, 0, 0], or yellow: RGB = [255, 255, 0]). On each trial, participants were asked to indicate the ink color of the word by pushing one of four keys and ignore the word meaning.
In a pre-registered exploratory analysis, we also evaluated how performance was influenced by time-on-task. Considering that the congruency-reward contingencies were not instructed, and these changed between blocks, it is possible that the detection of new contingencies took time. This could dilute an effect of contingency when averaging all trials across a block. For this analysis, each block was divided in two sub-blocks (1 and 2) and Time-on-task was considered as a within-subject factor. Thus, RTs and error rates were submitted to an additional 3 � 2 � 2 repeated measures ANOVA, with Contingency, Congruency, and Time-on-task (1, 2) as within-subject factors. Raw data, scripts, and processed data can be found on the Open Science Framework, for all the three experiments. Data from first and second baseline blocks, not presented here, are also available.
Participants A total of 43 participants took part in the study in exchange for £7. 50. One participant with incomplete data was excluded and the data of 42 participants were included in the analysis (M age = 27. 286, SD = 5. 558, Range = 18-35, 28 women, 14 men). Participants received an additional bonus of on average £3. 65 (range = 2. 9-4, mean reward rate per trial in the test blocks =. 219).
Participants A total of 47 participants took part in the study in exchange for £7. 50. Five participants were excluded due to below chance accuracy or incomplete data, and the data of 42 participants were included in the analysis (M age = 25. 024, SD = 4. 906, Range = 18-35, 29 women, 13 men). Participants received an additional bonus of on average £5. 581 (range = 3. 9-7. 2, mean reward rate per trial in the test blocks =. 354).
A single balanced polymorphism expressed in pistil and colocalizing with a selfincompatibility QTL was identified thanks to Pooled Single-Molecule transcriptomics To carry out the transcriptomic scan, we sampled pistils in 8 self-incompatible individuals in the wild population. Even in a small dataset, diversity at SI genes is expected to be high because of negative frequency-dependent selection. A cDNA library was prepared from pooled pistil mRNAs and sequenced on 4 SMRT-cells on a Sequel sequencer, yielding 1,672,707 raw polymerase reads (Supplementary Table  1 ). After cleaning, 707,888 consensus circular sequences (CCS) were obtained (Supplementary Table  2 ). In our experimental design, CCS may represent isoforms or alleles. All CCS were mapped to the reference genome, and grouped based on their intersection with predicted mRNA.
To overcome this, we used the MACSE2 software  22  to remove non-homologous fragments, built refined alignments, computed nucleotide diversity and pairwise difference rates. We drew dendrogramms and used the same distance threshold to estimate the number of divergent haplotypes in the 789 most diverse clusters. Only 6 outliers displayed both a very large diversity and at least three highly divergent haplotypes (Figure  2b ); five of them encoding proteins with putative functions (Supplementary Table  3 ). Among the six outliers, one colocalized with a major QTL responsible for selfincompatibility  10  , located at the top of chromosome 17 (176-191 Mb, Figure  2c , Supplementary Table  4 ). After manual re-alignment and cleaning, we identified 6 distinct alleles for this gene (with 2 to 7 isoforms per allele, Supplementary Table  5 ), displaying 2.
Generation of pooled Single-Molecule, Real-Time transcriptomics datasets Plant material. In 2017, four populations of wild H. annuus maintained in the sunflower gene bank (CRB) at the Laboratory of Plants-Microorganisms Interactions (LIPM), INRAE, Toulouse, were grown in an insect-free greenhouse (8 to 12 individuals per population). Selfincompatibility was phenotyped qualitatively by checking the absence of viable seeds in inflorescences. In one of the populations for which no seeds were observed (PI 413066 from New Mexico), pistils from diverse developmental stage (from central immature disc florets to well-opened peripheral disc florets) were dissected, frozen in liquid nitrogen and stored at -80°C. mRNA extractions and Single-Molecule, Real-Time sequencing. Total RNA were extracted using a QIAZOL lysis reagent (QIAGEN, USA) following manufacturer's protocol.
Scan of diversity using Single-Molecule, Real-Time transcriptomics Obtention and mapping of consensus circular sequences (CCS) We applied the css (default parameters) and lima (--isoseq, primer_5p: AAGCAGTGGTATCAACGCAGAGTACATGGGG; primer_3p:AAGCAGTGGTATCAACGCAGAGTAC) software of SMRT Link 6. 0. 0 (https://www. pacb. com/support/software-downloads/) to extract, and clean up circular subsequences (CCS). We obtained 188,154 and 519,734 high-quality CCS for batches 1 and 2 respectively. The 707,888 high-quality CCS were mapped against the sunflower XRQ reference genome v1 (www. heliagene. org, Genbank Accession MNCJ00000000) with gmap  34  (version 2018-07-04, indexing: gmap_build -q 2; mapping: --allow-close-indels=1 --max-intronlength-middle=500000 --cross-species. Hits were found for all but 6,425 CCS.
Diversity scan In our experimental setting, a single gene can be represented by several CCS that represent either isoforms, transcriptional variants, alleles, or paralogues in cases of hidden paralogy (i. e. , if a transcript procuded by a gene wrongly map to another gene). Hereafter we refer to CCS that may be alleles or paralogues as haplotypes. In order to remove introns and long UTR that may cause alignment errors, non-homologous fragments were removed with the sub-program trimNonHomologousFragments of MACSE v2. 03  22  (-min_cov 2 -min_trim_ext 20 -min_trim_in 40). After trimming, 18,696 CCS clusters with at least 3 CCS remained. CCS clusters were aligned with mafft v7. 307  36  (default parameters). Nucleotide diversity (π) was computed with the eggstats  37  (parameters groups=no outgroup=no multiple=yes minimum=0.
Refined alignment and error correction. Alignement errors, presence of low-quality CCS, remaining introns or untranscribed transcript regions (UTR), can inflate estimations of diversity and haplotype number. Thus clusters were re-aligned with mafft, using recommanded parameters for sequences with structural variations, and reduced penalties for gap opening (-ep 0 --genafpair --maxiterate 1000 --op 0. 5 --lop -0. 5). Low-quality CCS display high rates in insertions and deletions (INDEL). Single-nucleotide gaps caused by a single CCS in multiple alignments are most likely artefacts of Single-Molecule, Real-Time sequencing. We computed individual rates of 1-bp insertions or deletions (INDEL) from multiple alignments. Visual examination of several multiple alignments helped defining a threshold above which poorquality CCS caused alignment issues (e.
At that stage, one cluster was excluded because it consisted in non-coding sequences that mostly mapped in an intronic region. Coding and non-coding diversity were computed on complete alignements and by sliding windows (18-nucleotides width, 9-nucleotides step). Overlap with QTL for self-incompatibility. A major self-incompatibility QTL had been identified in Gandhi et al. (2005)   10. The two closest Sequenced Tag Site markers (ORS735 and HT925) were retrieved respectively from NCBI (accession number BV006734. 1) and from the sunflower transcriptome database (https://www. sunflowergenome. org, Ha412T4l22256C0S1). Both sequences were mapped with the blastn programme of the blast+ suite  38  (default parameters) against the sunflower genome. Coordinates of the best hits were used to define the physical limits of the QTL.
Sanger sequences were aligned with Ha7650b alleles to validate long-reads sequences and identify the amplified allele. Detection of genes specifically expressed in pistils or anthers. We used index of organspecificity computed in Badouin et al (2017)  21  from RNAseq data in 11 vegetative and floral organs. An index of expression specificity ( 39 ) had been computed for each gene. Organspecific genes were defined by a  higher than 0. 8, the organ in which the expression was highest was retrieved.
Fine-scale diversity analysis of Ha7650b Assessment of completeness of the CCS dataset. To identify potential false negative (i. e. , Ha7650b CCS that may not have mapped to Ha7650b genomic locus), we blasted with blastn or blastp nucleotide or inferred protein sequences of two CCS (S6i1 and S4i1, Supplementary Table  5 ) against the database of all CCS. We identified three additional CCS that were highly similar to other two CCS of Ha7650b (S1i1 and S1i2). This yielded a final dataset of 25 CCS (including two CCS that displayed high INDEL rates, Supplementary Table  5 ). Manual curation of the alignment. We performed again manual curation and INDEL correction, as described above for the study of the top diversity outliers. Despite this curation, translation of CCS showed that frameshifts remained in several sequences.
The largest estimation was retained for the limits of the different domains (signal peptide, LRR array, malectin, transmembrane and kinase domains). Subsequent analyses were performed on the complete alignment of sub-alignments corresponding to CDS regions encoding the respective functional domains. Estimation of the number of alleles. Matrices of pairwise differences were computed with a custom python script and displayed as heatmaps. We inferred the presence of six putative alleles in the dataset, hereafter referred to as S1 to S6. Subsequent analyses were performed either on the alignment of all 25 Ha7650b CCS, or on an alignment of one representative CCS per allele. A representative CCS of each allele was retained for further analysis for S1, S4, S5 and S6. For S2 and S3 respectively, two CCS displaying complementary splicing variations were used to build a composite sequence covering the whole transcript.
Measure of total, synonymous, non-synonymous diversity. The total, synonymous and nonsynonymous diversity in the alignment were measured with egglib v3  37. This was done on the complete alignment and by sliding windows (width 36 bp, step 18 bp). The number of bi-allelic, tri-allelic and quadri-allelic position was computed with a custom python script. Sequencing and assembly of the Ha7650b locus of the PI413066 wild sunflower from New Mexico DNA was extracted from youngest leaves from 6-weeks old plants grown in an insect-free greenhouse using the Mayjonade et al. protocol  41. At the end of flowering, the number of viable seeds per square cm was measured in all inflorescences to ensure the selection of a selfincompatible plant, and the plant with the lower mean seed number was chosen for sequencing.
Two HiFi libraries were prepared and sequenced on Sequel 2 by the gentyane platform according the Pacific Biosciences specifications. The ccs software of SMRT Link v8. 0. 0 (--min-passes 4 --min-length 5000) was used to extract 2,367,365 circular consensus sequences (N50: 14. 2kb) corresponding to a total of 32,937,296,594 nucleotides. Two assemblies were first performed with CANU 42 version 1. 9 or FALCON  43  , with a filtering step of falcon replaced by til-r software (N50: 533,277 and 1,348,425 bp respectively). Then, to assemble the Ha7650b locus, was performed with Minimus2 (default parameters, version amos-3. 1. 0). Minimus2 found an overlap of 60,821 nucleotides at 99. 82 percent identity and generated a consensus sequence spanning the whole locus in a single contiguous sequence. The mRNA sequence data were mapped using gmap (version 2019-09-12, same parameters as above) to annotate a gene spanning 385,712 nucleotides in the PI413066 wild sunflower.
Phylogeny of the Ha7650b in Asterids Dataset building. The homeologue of Ha7650b was annotated  21  as the orthologue of the At1g07650 gene. This was confirmed with blastp of the amino acid-translation of Ha7650b alleles. The amino-acid and CDS sequences of members of the At1g07650 orthogroup in Asterids were retrieved from OrthoDB  44  , and Populus trichocarpa was used as an outgroup. In addition, we performed blastp against the predicted proteomes of recently published Asterids genome (Artemisia annua  45  , Chrysanthemum nankingense  46  , Panax notoginseng  47  , Calotropis gigantea  48  , Gelsenium sempervirens  49  and Mikania micrantha) in order to retrieve additionnal orthologues. Since this orthogroup mostly counts single-copy genes, and malectins receptors have been found to be highly conserved on a structural point of view  27  , we retained orthologues only when they were full copy, in order to avoid annotation issues.
The exception was the genome of Mikania micrantha, a member of the Heliantheae tribe that shares a whole-genome duplication with sunflower. We performed a manual annotation by tblastn of the predicted amino-acid sequences of S1-S6 and Augustus-online  50. Two full-length homeologues were retrieved in the two syntenic regions resulting from the whole-genome duplication. The final dataset included 28 Asterid and 1 outgroup sequences ( Supplementary Table  8 ). Codon and amino-acid alignment. We aligned the predicted CDS sequences with mafft v7. 307 36 (default parameters). We extracted sub-alignments corresponding to the main functional domains (LRR array, malectin, transmembrane and kinase domains), and for the whole extra-cellular domain. This was done with or without the sunflower tandem repeats that lack the kinase domain.
Acknowledgements This work was supported by starting grants from  Université Claude Bernard Lyon1 (BQR)  and  Université de Lyon  ( IDEX/IMP/2017/17  and  IDEX/ELAN/2018/07 ) to HB. We thank the  Lausanne GTF  and the  INRAE Gentyane platforms  for producing the Single-Molecule transcriptome and genome data, respectively. We thank the  GenoToul bioinformatics platform  for computing resources. We thank  Patrick Vincourt  for discussions, advices and for feedback on the manuscript.
Data The primary data source for this cross-sectional study was deidentified administrative claims data from a large commercial insurer. The data included medical claims and monthly enrollment details for more than 60 million individuals from January 1, 2008, to December 31, 2019, and each individual's exact date of birth. Billed medical claims and encounter data were used to identify services provided before and during childbirth. This study of deidentified data was deemed exempt by the Harvard University Longwood Campus institutional review board. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Sample The sample included all pregnant people with a delivery covered by the insurer who were within 120 days of turning age 35 years on the expected date of delivery (eTable 1 in the Supplement). As information regarding race and ethnicity was not available in the claims data, it was not analyzed in this study. However, we included variables that were percentage Hispanic individuals and percentage White individuals in the zip code based on publicly available summary data from the American Community Survey. No other race or ethnicity was included as a zip-level variable because of the high rates of missing data in the American Community Survey due to suppression in small zip codes with low diversity. The age on the expected date of delivery was calculated using the patient's exact birth date and the estimated expected date of delivery based on the infant's gestational age on the actual date of delivery (eTable 2 in the Supplement).
confidence in the derived trend. In the case of highly gregarious species, which are common in the animal kingdom, the estimation of group size is an important potential bias, which is characterized by high variance among observers. In this context, it is crucial to quantify the impact of observer changes, inherent to population monitoring, on i) the minimum length of population time series required to detect significant trends and ii) the accuracy (bias and precision) of the trend estimate. 2. We acquired group size estimation error data by an experimental protocol where 24 experienced observers conducted counting simulation tests on group sizes. We used this empirical data to simulate observations over 25 years of a declining population distributed over 100 sites. Five scenarios of changes in observer identity over time and sites were tested for each of three simulated trends (true population size evolving according to deterministic models parameterized with declines of 1.
| Group size estimation error Group size estimation error data were obtained by an experimental protocol using animal counting simulation software Wildlife Counts (version 2. 0. ;  Hodges, 1993). In this study, 24 experienced observers from the Camargue, Southern France, conducted counting simulation tests on group sizes ranging from 2 to 1,098 individuals with various spatial configurations. The 24 experienced observers sampled in our study are part of several institutions with conservation professionals involved in the counts in Camargue. These professionals have counted in several countries under different field conditions and have to estimate groups with very large numbers of individuals in a relatively short period of time (especially during airplane counts and during ground counts when birds are taking off).