dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation Results We used the Atlas open source simulator developed by Abu-Aisheh et al. [4]  to simulate 1800 exploration and mapping runs in total, including the three floorplans as well as three different fleet sizes. (As an online addition to this article, all the source code used is published under an open-source license at https://github. com/openwsn-berkeley/Atlas (accessed on 11 November 2022) We ran the CARA simulations with an upper threshold of 0. 9 and a lower threshold of 0. 8. The distance specified for the DBRA disk range was 7 m. The work of Brun et al. [21]  demonstrates how, due to multi-path fading, PDR can not be directly mapped to distance at all times. Therefore, the distance of 7 m was chosen based on trial and error to determine the optimal distance where robots did not lose connectivity completely while minimising the number of relays placed.
RNA sequencing and analysis Total mRNA was extracted from puri ed cortical MVs on P5 or P15, using the RNeasy Lipid Tissue Kit (Qiagen, Hilden, Germany). Ten ng of total RNA were ampli ed and converted into cDNA using a SMART-Seq v4 Ultra Low Input RNA Kit (Clontech). Next, an average of 150 pg of ampli ed cDNA per library were processed with a Nextera XT DNA Kit (Illumina). Libraries were multiplexed on two high-output ow cells and sequenced (using 75 bp reads) on a NextSeq 500 device (Illumina). The mean ± standard deviation (SD) number of reads meeting the Illumina quality criterion was 48 ± 5 million per sample. The RNA-seq gene expression data and raw fastq les are available on the GEO repository (www. ncbi. nlm. nih. gov/geo/) under the accession number GSE173844. The RNA-Seq data were analyzed using GenoSplice technology (www.
genosplice. com). Sequencing, data quality checks, read distribution checks (e. g. for potential ribosomal contamination), and insert size estimation were performed using FastQC, Picard-Tools, Samtools and rseqc. Reads were mapped onto the mm10 mouse genome assembly using STARv2. 4. 0  (Dobin et al. 2013). The procedures for the gene expression regulation study have been described elsewhere  (Noli et al. 2015). Brie y, for each gene present in the Mouse FAST DB v2018_1 annotation, we counted the reads aligning on constitutive regions (which are not prone to alternative splicing). Based on these counts, the level of differential gene expression was normalized using DESeq2 in R (v. 3. 2. 5). Genes were considered to be expressed when their RPKM value was greater than 99% of the value for the background (intergenic region).
Single-cell RNA-seq analysis Raw reads from GEO datasets GSE99058 and GSE98816 were downloaded  (He et al. 2016; Vanlandewijck et al. 2018 ). Seurat 3. 1. 1 was used to normalize unique molecular identi ers  (Butler et al. 2018 ), using a global-scaling method with a scale factor of 10000 and log-transformation of the data. This was followed by a linear transformation scaling step, so as to avoid highly expressed genes with an excessively high weight in the downstream analysis. With the exception of PCs, cell types were grouped by their level of identity: broblasts ( broblast-like type 1 and 2); ECs (types 1, 2 and 3 ECs, arterial ECs, venous ECs, and capillary ECs), and VSMCs (arteriolar SMCs, arterial SMCs, and venous SMCs). A transcript was considered to be speci c or preferentially expressed in a given cell type when it was detected in more than 60% of the corresponding single cells and in a small percentage of cells of other types (Table  S3 ) and had a higher expression level than in other cells (according to a Wilcoxon rank sum test, and with logFC > 1.
High-resolution uorescent in situ hybridization Fluorescent in situ hybridization (FISH) was performed on oating PBS/paraformaldehyde (PFA) 4% xed brain sections or puri ed MVs immobilized on a glass slide coated with Cell-Tak (Corning) and xed for 10 min in PBS/PFA 4%, according to the v2 Multiplex RNAscope technique (Advanced Cell Diagnostics, Inc. , Newark, CA, USA) described previously  (Oudart et al. 2020 ). Brain sections and MVs were treated with protease at room temperature. After the FISH, MVs were stained with isolectin B4 (1/100) in PBS/ normal goat serum (NGS) 5%/Triton 0. 5% overnight at 4°C. Nuclei were stained with Hoechst reagent  (1/2000). The brain sections and puri ed MVs were imaged using a Spinning Disk CSU-W1 microscope and Metamorph Premier 7. 8 software.
16% Triton X-100/6% donkey serum/10% DMSO/PBS. Brains were incubated for 3 days at 37°C with primary antibody diluted in a 0. 2 Tween/1% heparin/3% donkey serum/5% DMSO/PBS solution, washed 5 times during 24h at 37°C in 0. 2% Tween20/1% heparin/PBS solution, incubated for 3 days at 37°C with secondary antibody diluted in a 0. 2 Tween/1% heparin/3% donkey serum/PBS solution, and another washed ve times. The brain samples were then dehydrated again with a MetOH/H 2 O series (20%, 40%, 60%, 80% and 100% for 1h each, and then 100% overnight) at RT. On the following day, brains were incubated for 3h in 66% DCM/33% MetOH and then twice for 15 min at RT in 100% DCM and lastly cleared overnight in dibenzyl ether. The cleared tissue was imaged using a light sheet microscope and Inspector pro software (Lavision Biotec GmbH, Bielefeld, Germany).
A macroscopic analysis enabled the samples to be selected and processed (para n embedding, preparation of 7-micron slices, and staining with hematein reagent) for histological analysis. Coronal slices (including the temporal telencephalic parenchyma and the hippocampus) were depara nized and unmasked in citrate buffer (pH 6. 0). Expression of MYH11 and SMA was detected using the Bond Polymer Re ne Detection kit (Leica) and processed on automated immunostaining systems (the Bond RX Leica for MYH11, and the LEICA BOND III for SMA). Pictures were acquired using a slide scanner (Lamina, Perkin Elmer). Stained samples were analyzed using the QuPath software  (Bankhead et al. 2017 ). For each sample, a QuPath "pixel classi er" was trained to discriminate between DAB-positive spots and the background.
Characterization of postnatal transcriptional maturation in brain microvessel cells We next sought to characterize the maturation of each vascular cell type of MVs. By taking advantage of recently published single cell RNASeq datasets for adult brain vascular cells, we started by identifying the genes preferentially or speci cally expressed in ECs, PCs, VSMCs and FBs  (He et al. 2016; Vanlandewijck et al. 2018 ) and we determined their expression level in MVs on P5 and P15 (Fig. 2A ). A transcript was considered to be speci c or preferentially expressed in a cell type when it was (i) detected in more than 60% of the single cells of that type and in a small percentage of other cells and (ii) expressed at a higher level than in other cells (LogFC ³ 1. 5). This approach led to the identi cation of 73 EC-, 14 PC-, 38 FB-and 21 VSMC-transcripts (Table  S3 ).
Our comparison of P5 and P15 MVs revealed that the transcriptome was stable overall: changes were observed for only 8% of the genes. Nevertheless, our GO analysis highlighted the upregulation of pathways speci cally related to ECM composition, cell junctions and ion transport and thus evidence the functional maturation of MVs between P5 and P15. In contrast, we observed the downregulation of pathways related to DNA replication, cell division and the cell cycle -suggesting that the putative angiogenic program involved in the postnatal densi cation of the capillary network was less active on P15. We next focused on transcripts that were preferentially or speci cally expressed in each vascular cell type. We identi ed these transcripts by checking brain vessel single-cell RNAseq databases  (He et al.
2016; Vanlandewijck et al. 2018). Our analysis therefore focused on the transcriptional signature of each vascular cell type, rather than overlaps in molecular repertoires between cell types. Of note, the single-cell RNAseq database used as a reference in our stuy has been generated from mice aged 10-19 weeks (adult stage) and we assumed here that the cellular pro le of preselected transcripts is stable during development, which might not be the case for all genes. Several transcripts encoding ECM components were speci cally expressed by FBs, con rming that these cells actively contribute to the BL  (Manberg et al. 2021 ) and (possibly) its postnatal development because some genes (Ctgf, Serping1, Lum, Col6a1, Col5a1 and Lama1) were more strongly expressed on P15 than on P5. The recruitment of perivascular FBs and their differentiation has not previously been fully described.
Declarations Figures Graphical comparison of the RNAseq data for MV transcripts on P5 vs. P15. Transcripts with no changes (in white): -1 < log2FC < 1 or padj > 0. 05; transcripts with changes (in black): log2FC ≤ -1 or log2FC ³ 1, padj ≤ 0. 05; transcripts upregulated on P15 (in red): log2FC ³ 1, padj ≤ 0. 05; transcripts downregulated on P15 (in blue): log2FC ≤ -1, padj ≤ 0. 05. n=3 libraries for each stage. D. E. A gene ontology (GO) analysis of transcriptional changes in cortical MVs between P5 and P15 for "biological processes" (D) and "cellular components" (E). A REViGO representation of the results of GO analysis. Data are given in Table  S2. Black dots correspond to transcripts that did not change between P5 and P15 (-1 < log2FC <1 or padj > 0. 05). Lines incate the mean for each category.
Acknowledgments This work was funded by grants from the  Association Européenne contre les Leucodystrophies (ELA)  ( ELA2012-014C2B ), the  Fondation pour la Recherche Médicale (FRM)  ( AJE20171039094 ) and the  Fondation Maladies Rares  (20170603). A. Gilbert 's PhD fellowship was funded by the  FRM (PLP20170939025p60)  and  ELA  ( ELA2012-014C2B ). L. Slaoui's fellowship PhD was funded by the  Ecole Normale Supérieure. A. -C. Boulay 's work was funded by the  FRM  ( AJE20171039094 ) and the Foundation pour la recherche sur la sclérose en plaques (ARSEP). Despite our efforts, our work has not received any support from the  French National Agency for Research (ANR).
Animal sampling and genotyping For MAY individuals (Zebus from Mayotte), we selected a group of 32 presumably nonrelated individuals (based on the newly created French National Registration Database) representative of the phenotypic diversity of the local population. They each originated from 32 different farms located in 17 townships spread over the Grande and Petite terres of the Mayotte Island (Supplementary Fig. 1 , Supplementary Table  1 ). Blood samples were collected during year 2016 from the tail vein of the individuals using 10 ml EDTA vacutainer tubes, strictly following the recommendations of the directive 2010/63/EU for animal care. Genomic DNA was further extracted using the Wizard Genomic DNA purification kit (Promega, France) and stored at À20 C. MAY DNA samples were genotyped (Supplementary Table  1 ) on the Illumina BovineHD genotyping beadchip at Labogena plateform (Jouy-en-Josas) using standard procedures (www.
illumina. com) together with 24 Zebus of Madagascar (ZMA) and 113 West-African cattle DNA samples (i. e. 44 Lagune, 44 Somba, and 25 Zebus Fulani), sampled in the 1990s and previously genetically characterized on the Illumina BovineSNP50 beadchip  (Gautier et al. 2009). The genotyping data were then added to the WIDDE database and combined with other publicly available ones, using WIDDE utilities  (Semp er e et al. 2015). To obtain the position of the 777,962 SNPs included in the BovineHD genotyping assay onto the latest ARS-UCD1. 2 (aka bosTau9) bovine genome assembly  (Rosen et al. 2020) , we first retrieved the sequences of each SNP (length ranging from 61 to 121 bp with only 227 sequences <121 bp long) from the Illumina manifest file bovinehd_15013478_b. csv. These sequences were further realigned onto the ARS-UCD1.
The complete genotyping dataset consisted of 532 animals including 363 individuals from eight other bovine populations  (Bahbahani et al. 2017, www. illumina. com) representative of the bovine worldwide genetic diversity (Table  1 ). The minimal individual genotyping call rate was set to 90% and the minimal SNP genotyping call rate to 90% overall populations and 75% within each population (i. e. SNPs genotyped for less than 75% of the animals from at least one population were discarded). SNPs with a MAF < 0. 01 or departing from Hardy-Weinberg equilibrium expectation (exact test P-value < 10 À3 in at least one breed) were also discarded. A total of 680,338 SNPs distributed throughout the 29 autosomes of the bosTau9 bovine genome assembly passed finally all our filtering criteria.
Characterization of the structuring of genetic diversity Principal component analysis (PCA) based on individual SNP genotyping data was performed with smartpca  (Patterson et al. 2006)  and visualized with the R package ggplot2  (Wickham 2016). Unsupervised genotype-based hierarchical clustering of the individual animal samples was carried out using the maximumlikelihood method implemented in ADMIXTURE 1. 06  (Alexander et al. 2009). Results were visualized with custom functions in R environment (www. r-project. org). Finally, the overall and pairwise-population F ST were computed with version 2. 0 of the R package poolfstat  (Gautier et al. 2021 ) using the computeFST ran with default settings (i. e. method¼Anova) and option nsnp. per. bjack. block ¼ 5000 to estimate standard errors (and 95% CI as 61.
f-statistics-based demographic inference f-statistics based demographic inference  (Patterson et al. 2012)  were carried out with the new functionalities of the version 2. 0 of the R package poolfstat  (Gautier et al. 2021). We used the compute. fstats function to estimate the different f-statistics (including F 3 and F 4 for all the population triplets and quadruplets respectively) and within-population heterozygosities. As for F ST , standard-errors of the estimated statistics (and their corresponding Z-scores for f 3 and f 4 ) were estimated using block-jackknife defining blocks of 5,000 consecutive SNPs (i. e. option nsnp. per. bjack. block ¼ 5,000). In addition, to mitigate SNP ascertainment bias by favoring SNPs of most remote ancestry (i. e. discard SNPs of exclusive European ancestry), we only kept for fstatistics based demographic inference the 497,949 polymorphic SNPs that were polymorphic (MAF>0.
Estimation of the timing of admixture events We estimated the timing of admixture events (in generations) with the program mALDER  (Pickrell et al. 2014 ) that implements a modified version of ALDER method originally described by  Loh et al. (2013). This approach relies on the modeling of the exponential decay of admixture-induced LD in a target admixed population (here based on two-reference weighted LD curves, using a LD measure weighted by allele frequencies in two source population proxies) as a function of genetic distance. Genetic distances between pairs of SNPs were derived from physical distances assuming a cM to Mb ratio of 1  (Kadri et al. 2016 ). As commonly done in studies focusing on the population genetics of local cattle breeds, we here assumed a 6-year generation time to convert the timing from generations to years (e.
Age-based partitioning of individual inbreeding in MAY and ZMA breeds The characterization of individual levels of inbreeding at a global and local scale in MAY and ZMA breeds was performed with the model-based approach implemented in the RZooRoH package  (Druet and Gautier 2017, 2021; Bertrand et al. 2019). This method models each individual genomes as a mosaic of Homozygous-by-Descent (HBD) and non-HBD segments using a multiple HBDclasses Hidden Markov Model  (Druet and Gautier 2017). Each HBD class is specified by a rate R k related to the expected length (equal to 1 R k Morgans) of the associated HBD segments and that is approximately equal to twice the number of generations to the common ancestor that transmitted the DNA segment. Given the density of the HD chip, we considered a model with 11 HBD classes (with R k ¼ 2 k for k ¼ 1;.
Computation of iHS and Rsb statistics The genome-wide scan for footprints of positive selection within and between MAY and ZMA breeds was performed using extended haplotype homozygosity (EHH)-based tests. To that end we first jointly phased the genotyping data for MAY, ZMA, EAZ, GIR, and NEL individuals with version 1. 4 of the fastPHASE software  (Scheet and Stephens 2006)  for each chromosome in turn using population label information and options -Ku40 -Kl10 -Ki10. Genetic distances between consecutive SNPs were obtained from physical distances assuming a cM to Mb ratio equal to 1 (see above). Based on haplotype information, we further computed the iHS  (Voight et al. 2006)  and Rsb  (Tang et al. 2007)  statistics using the version 3. 1. 2 of the R package rehh  (Gautier et al. 2017; Klassmann and Gautier 2020).
These statistics are designed to detect regions with high level of haplotype homozygosity over an unexpected long distance (relative to neutral expectations), either within population (iHS) or across populations (Rsb). Note that for the computation of iHS within MAY and ZMA populations, we chose to not polarize the SNP alleles (i. e. scan_hh function was run with option polarized¼FALSE) as discussed in section 7. 6 of the online rehh package vignette (https://cran. r-project. org/web/ packages/rehh/vignettes/rehh. html). The different SNP iHS and Rsb statistics were further transformed into p iHS ¼ À log 10 1 À 2jUðiHSÞ À 0:5j and p Rsb ¼ À log 10 UðRsbÞ where UðxÞ represents the Gaussian cumulative function. Assuming iHS and Rsb scores are normally distributed under neutrality, p iHS and p Rsb might thus be a 2-sided À log 10 ðP À valueÞ associated with the neutral hypothesis.
Identification of candidate genes We used the approach previously described in  Flori et al. (2019)  to identify candidate genes under positive selection from iHS and Rsb estimates. Briefly, all the genotyped SNPs were annotated using as a gene set reference a list of 14,562 RefSeqGenes anchored on the Btau9 bovine genome assembly (refGene. txt. gz, 2019-06-07; https://hgdownload. soe. ucsc. edu/goldenPath/bosTau9/ database/). A SNP was then considered as representative of one of these RefSeq genes if localized within its boundary positions extended by 15 kb upstream and downstream to account for persistence of LD (e. g. Gautier et al. 2007 ). Among the 680,338 SNP of HD-dataset participating to the analysis, 293,527 SNPs mapped to 13,696 different RefSeq Genes, corresponding to 13,536 gene symbols.
Functional annotation of candidate genes under selection Following the approach outlined in  Flori et al. (2019) , candidate genes were functionally annotated and submitted to gene network analysis using Ingenuity Pathway Analysis software (IPA, QIAGEN Inc. , https://www. qiagenbioinformatics. com/products/in genuity-pathway-analysis). Among the 13,535 genes symbols taken into account in the analysis, 12,324 were mapped in the Ingenuity Pathway Knowledge Base (IPKB) and were considered as the reference data set. Among the candidate genes identified for MAY (N ¼ 27) and ZMA (N ¼ 47) breeds, 24 and 45 were respectively mapped to IPKB. The top significant functions and diseases (P-value < 0. 05) were obtained by comparing functions associated with the candidate genes under selection against functions associated with all genes in the reference set, using the right-tailed Fisher exact test.
Genetic relationships of Mayotte and Madagascar cattle breeds with other world cattle breeds We first carried out an individual-based PCA of the SNP genotyping dataset consisting of 680,338 autosomal SNPs genotyped on individuals from MAY (n ¼ 32) and ZMA (n ¼ 23) populations together with 473 individuals belonging to 11 cattle breeds representative of the bovine genetic diversity (Table  1 ). The first factorial plan of the PCA is represented in Fig. 1a. The first two components accounting for 21. 09% and 7. 85% of the total variation, respectively. In agreement with previous studies (e. g. Gautier et al. 2010) , the first two PCs revealed a clear structuring of individual genetic diversity according to their population of origins and highlighted a triangle-like 2-dimensional global structure of the cattle populations with apexes corresponding to three main groups: (1) EUT represented by ANG, HOL, JER, and LMS; (2) AFT represented by LAG, NDA, and SOM; and (3) zebus (ZEB) represented by NEL and GIR.
Timing of admixture events We estimated the timing of the two aforementioned admixture event t afz a (leading to African Zebus, closely related to EAZ) and t ioz a (leading to Indian Ocean Zebus, the direct ancestor of MAY and ZMA) with the program mALDER  Pickrell et al. (2014). Based on the inferred admixture graph (Fig. 2 ), we estimated t afz a by using EAZ as a population target and a pair of source population proxies consisting of one AFT (either NDA or LAG) and one ZEB (either GIR or NEL). In agreement with admixture graph fitting, the highest amplitude (i. e. y-intercept) estimate of the fitted weighted LD curve was obtained with NDA and NEL as source proxies which suggests that these populations are the closest (among the sampled ones) to the actual source population  (Loh et al. 2013).
MAY and ZMA individuals levels of inbreeding In order to characterize individual levels of recent inbreeding at both a global (genome-wide) and local (locus-specific) scale, we estimated the individual inbreeding coefficients in MAY and ZMA populations by applying the method implemented in RZooRoH software  (Druet and Gautier 2017; Bertrand et al. 2019). The distribution of the genome-wide coefficients of individual inbreeding estimated with the R package RZooRoH are detailed in Fig. 4a  for the MAY and ZMA populations together with their age-based partitioning to assess the contribution of various class of ancestors Fig. 3. Population size history (N e ) of MAY and ZMA populations estimated with GONE  (Santiago et al. 2020). The average N e trajectories (dashed line) and 95% confidence envelope estimated from block-jackknife samples are plotted in green for ZMA and red for MAY.
e. GBE1, GPC5, SLC6A2, and ZMAT4) were detected in MAY population and four (i. e. CPS1, KCND2, KCTD8, and KCNH1) in ZMA population by two Rsb tests. Overall, a total of 27 and 47 candidate genes under selection were detected in at least one of the above tests for MAY and ZMA, respectively. To obtain a global view of the main gene functions under selection, these 2 lists of candidate genes were annotated using IPA software. The main functional categories, in which these genes are involved, are listed in Supplementary Table  3  (see Supplementary Tables  4  and 5  for an exhaustive list of their functional annotation). For MAY, the top 5 significant functions belonging to the 3 IPA main categories (i. e. Diseases and Disorders, Molecular and Cellular Functions and Physiological System Development and Function) were related to inflammation ("Inflammatory Disease"), cancer and cell death ("Cancer" and "Cell Death and Survival"), gastrointestinal and hepatic diseases ("Hepatic Disease," "Gastrointestinal disease"), nervous system ("Nervous System Development and Function"), organ and tissue development and morphology ("Organismal Injury and Abnormalities," "Cell Morphology," "Cellular development," "Embryonic Development," "Organ development," "Organ morphology," and "Organismal development") and inflammation ("Inflammatory Disease").