dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA concentrations and quality were determined on a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Washington, USA) and on an Agilent 2100 Bioanalyzer (Agilent, Basel, Switzerland), respectively. Paired-end RNA-seq using an Illumina HiSeq 2500 v4 was conducted at the Functional Genomics Center Zurich (ETH Zurich, Switzerland). RNA libraries were prepared using TruSeq RNA stranded library preparation kit and standard protocols supplied by Illumina. For bioinformatics analysis, a pipeline consisting of SortMeRNA  (45)  for separation of rRNA and mRNA, and FLASH  (46)  for overlapping the paired-end sequences were used. rRNA sequences (100,000) were compared to the modified SILVA database provided by CREST  (47). Putative mRNA reads were compared to the NCBI RefSeq database using MALT (http://ab.
Data aquisition and analysis The images were scanned for all the blots using the digital systems for enhanced chemiluminescence and autoradiograph, using the Biorad Pharos FX Plus Molecular Imager. The linear range was determined through the system by detection of saturation. The exposures for westerns were kept constant for each blot. The image quantitation was performed using Image J software. Equal concentrations of proteins were used and normalized with an internal control. Replicates and repeats were performed to ensure the data did not suffer experimental bias. The statistical analysis was done using one-way ANOVA followed by Tukey's multiple comparisons. Multiple comparisons with pairwise comparisons was performed within all columns using Graphed Prism software. For autorads, the automatic detection system during scanning, minimized the background and saturation.
ACKNOWLEDGEMENTS B. S. and K. K. B. were supported by  Senior Research Fellowships from Council of Scientific and Industrial Research, New Delhi, India. We are grateful to  P. Nurse ,  J. Hurwitz ,  T. Wang  and  S. Kearsey  for gifts of strains and plasmids. We thank  L. Kaur  for editorial help. Help of students in the labs of  A. Kumar ,  V. Bhalla ,  C. Sharma ,  R. Suri ,  D. Sharma ,  P. Gupta ,  P. Sen ,  J. Agrewala  and  D. Dutta  is gratefully acknowledged.
Recruitment of participants Eighty three counselees belonging to HBOC families, aged between 18 and 30 and with a known address, were extracted from the database of the Oncogenetics Department of the Jean Perrin anticancer center. Invitations were sent to the 39 women meeting the inclusion criteria (Fig. 2 ). Because the workshop was cost-free, at a comfortable spa hotel, we thought that this number would be sufficient to fill two randomized groups of 8 to 12 women. Surprisingly we only could recruit seven participants, despite follow-up mails or phone calls. We therefore established one group with a minimum size to allow group sharing and role games. Among the reasons explaining the high refusal rate (74% overall) was the feeling that cancer did not concern the candidate at such a young age, and that they had time before acting on it.
Statistical analysis Quantitative analysis was used for scores obtained by questionnaire. Categorical variables are described using counts per category and quantitative variables using means ± standarddeviation  [range]. Longitudinal analyses of questionnaire scores were tested using one-way ANOVA or Kruskal-Wallis H-test depending on normality and/or homoscedasticity of distributions. Pearson's correlation test was performed in complement to check the significance of trends over time, when ANOVA or H-test was close to significance. Differences between sub-dimensions of questionnaires were tested using two-way ANOVA. All tests were two-sided and standard p-value < 0. 05 was used as significance threshold. Data gathered via the internet website was managed using Ennov Clinical software, version 7.
3 in human preimplantation embryos using public single-cell RNA sequencing datasets  30, [35] [36] [37]. Expression from both loci can be detected concomitantly between the four-and the eightcell stages, likely following the major wave of zygotic gene activation (Sup. Fig. 2C ). Furthermore, the expression detected from the XACT and T113. 3 loci is correlated at early (E3 to early E5) and late (E5 to E7) stages of development in both female and male embryos (Fig. 2C ). A weaker transcriptional correlation between XIST and T113. 3 is observed in early stages of pre-implantation development, but is mostly lost at later stages (Sup. Fig. 2D ). These observations confirm that the XACT and T113. 3 loci display similar transcriptional dynamics in different pluripotent contexts, in hESCs and in early embryos, suggesting that they may be co-regulated.
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. To corroborate this hypothesis, we analyzed Hi-C datasets  38  from H1 hESCs and from human foreskin fibroblasts (HFF), in which XACT and T113. 3 are expressed and silenced, respectively. In hESCs, the 5' region of XACT is embedded within a local compartment that includes T113. 3 and expands into the upstream LRCH2 gene, while in fibroblasts this local compartmentalization is absent (Fig. 2D ). This indicates that the regulatory sequences of XACT and T113. 3 belong to the same TAD in cells where both genes are expressed.
XACT/T113. 3 locus and wires it to the pluripotency network The analysis of H3K27ac distribution across the locus, a mark of active enhancers, pointed to a potential regulatory region approximately 2 kb upstream of the T113. 3 TSS (Sup. Fig. 3B ). Examination of ATAC-seq and ChIP-seq datasets from H1 hESCs  40  confirmed the existence of a broad acetylation domain, which is not found in fetal fibroblasts (Fig. 5A , Sup. Fig. 5A ), suggesting that this region may serve as a pluripotent specific enhancer. Further inspection of ChIP-seq profiles revealed that this domain encompasses a CTCF peak and binding sites for several transcription factors, including pluripotency factors OCT4, SOX2 and NANOG. In order to test the enhancer activity of this domain and identify which element(s) are involved in the regulation of XACT and T113.
To gain further insight into the evolution of the activity of this enhancer in the primate lineage, we explored available iPSC RNA-seq and ChIP-seq datasets from humans and rhesus macaques  42. As expected, in iPSCs of Rhesus, whose genome lacks the LTR elements defining the promoters of XACT and T113. 3, no expression of either gene is observed (Sup. Fig. 6A ). Nonetheless, the LTR48B element coincides with a peak of H3K27ac (Sup. Fig. 6A ), suggesting that this element harbors an active enhancer in Rhesus iPSCs, despite the absence of transcription at the syntenic positions of the XACT and T113. 3 genes. Information about the evolution of the LTR48B family in primates is scarce. We thus investigated whether this family of retroelements may have been co-opted into the pluripotency network of host cells.
We scanned the consensus sequence of human LTR48B for the presence of putative binding sites for core pluripotency transcription factors (Sup. Fig. 6B ). Strikingly, the consensus sequence of the LTR48B group lacks potential binding sites for OCT4, SOX2 or NANOG. Accordingly, the majority of LTR48B elements found in the human genome are not bound by OCT4 and NANOG in available ChIP-seq datasets from hESCs  43  (Sup. Fig. 6C, 6D ). Out of the 1026 LTR48B found in the human genome, only thirty-four are bound by NANOG, eight of which are also bound by OCT4 (Sup. Fig. 6E ). This is in contrast with LTR7-HERVH elements, which are bound by OCT4 and NANOG genome-wide (Sup. Fig. 6C, D  and E ), in agreement with this HERV family being essential for the maintenance of pluripotency in human cells  15, 17.
This file was converted to a PSSM using hmmlogo from hmmer tool (hmmer. org). From this matrix, the sequences corresponding to the identified binding sites in the XACT/T113. 3 enhancer were selected. Consensus and reference matrices were compared using RSAT compare-matrices. For ChIP-seq analysis, OCT4 and NANOG binding profiles on LTR48B and LTR7 were analysed using available ChIP-seq datasets: SRR6177948 for OCT4, SRR6177944 for NANOG and SRR6177932 for IgG  43. LTR7 and LTR48B sequences in the human genome were selected using the RepeatMasker database  58. ChIP-seq reads were aligned with hg38 using bowtie2 aligner 59 using the following parameters: "bowtie2 -p <threads> --endto-end --no-mixed --no-discordant --minins 100 --maxins 1000 -x hg38". This assigns reads with multiple hits of similar mapping quality to one of those locations randomly.
In order to select only uniquely mapped reads, we used a previously reported custom script  60. SAM files were sorted, compressed and indexed using samtools sort and samtools index  61. PCR duplicates were then removed using MarkDuplicates from the Picard tool (http://broadinstitute. github. io/picard/). bigWigs were obtained with bamCompare from deepTools  62  , which calculates the log2 ratio between the ChIP-seq and a ChIP-seq IgG control. A matrix for the relative-coverage of reads in the bigWig was then calculated for all the LTRs belonging to the LTR48B and LTR7 subfamilies using computeMatrix (deepTools). This matrix was finally plotted as a heatmap with plotHeatmap or as a binding profile around subfamilies of LTRs with plotProfile (deepTools). In order to calculate the number of uniquely mapped reads aligning to different classes of LTRs, we calculated the number of reads that aligned to each individual LTR using samtools bedcov.
B. Multiple alignment across 6 species (3 other hominoids: chimpanzee, gorilla, gibbon; rhesus macaque, lemur and mouse) for the human XACT promoter and T113. 3 gene. The multiple alignment was performed using Multiz online from the USCS browser. Positions on the X chr, are indicated for genome assembly hg38. C. Phylogenetic tree of primates, with evolutionary distance between species. The approximate evolutionary time for the appearance of the LTR/ERVs giving rise to the promoters of XACT and T113. 3 are represented. D. ENCODE strand-specific RNA-seq from male H1 hESCs  64  showing transcription over a large region encompassing the XACT and T113. 3 genes from the minus and plus strand, respectively. ChIP-seq data for H1 hESCs from the ENCODE project  64  showing distribution of H3K4me3 along the XACT/T113.
Acknowledgments: We are grateful to members of the  Rougeulle lab and members of the Epigenetics and Cell Fate Department  for helpful discussion and evaluation of the work leading to this publication. We thank  Anne-Valérie Gendrel ,  Céline Morey ,  Déborah Bourc 'his,  Sophie Polo ,  Miguel Branco  and  Andrea Cerase  for critical reading of the manuscript and valuable feedback. We also thank the  Microscopy and Vectorology Platforms at the Epigenetic and Cell Fate Department , and the ImagoSeine cell sorting facilities at the  Institut Jacques Monod  for access to instruments and technical advices. This study was supported by the  LabEx  " Who Am I?"  ( ANR-11-LABX-0071 ) and the  Université de Paris IdEx  ( ANR-18-IDEX-0001 ) funded by the  French Government  through its " Investments for the Future" program.
This work was also supported by the  European Commission Network of Excellence EpiGeneSys  ( HEALTH-F4-2010-257082 ),  Agence Nationale pour la Recherche  ( ANR-14-CE10-0017-01 ), and Ligue contre le cancer. M. C. has received financial support from the  European Commission Network of Excellence EpiGeneSys  ( HEALTH-F4-2010-257082 ),  Agence Nationale pour la Recherche  ( ANR-14-CE10-0017 ) and  Labex Who Am I  ( ANR-11-LABX-0071 ). M. M. is supported by  La Ligue  contre le cancer. L. E. C. is supported by a fellowship from the  French Ministry of Education and Research. O. R. was supported by a fellowship from the  French Ministry of Education and Research , from the  French Medical Research Foundation (FRM)  and from the  Labex Who Am I  ( ANR-11-LABX-0071 ).
Identifying and annotating TEs To minimize bias due to annotating TEs using sequences from different reference genomes, we built libraries of consensus sequences that are representative of repetitive elements identified in each assembly separately using the TEdenovo pipeline of the package REPET  [23]. The libraries were then pooled to form a "bundle" library. Each consensus sequence was classified into types of repeats and TE superfamilies using PASTEC  [45]. Finally, the bundle library was used to annotate TEs in each assembly in parallel. Overall, the bundle library was composed of 3821 families of repeats. This library was used to annotate 68,583; 85,835; 87,477 and 39,210 TEs in A. halleri halleri, A. halleri gemmifera, A. lyrata and A. thaliana, respectively (Table  1 ). Our deep repeatome annotation strategy thus confirmed the higher proportion of TEs in A.
2e -16 , Fig. 5g ). Indeed, they presented a mean percentage of methylation of only 3. 7% compared to 22. 8, 17. 9, 32. 4% for non-orthologous TEs within genes, orthologous TEs outside of genes, and non-orthologous TEs outside of genes, respectively. These results suggest that a low siRNA production and low DNA methylation levels are associated with the long-term maintenance of old TEs within genes. In contrast, these two factors may not be related to the long-term maintenance of TEs outside of genes. Finally, we used RNA-seq data from the same A. halleri halleri accession to compare the expression of genes containing old TE sequences that have been either retained or removed since the separation of the two species. We reasoned that if TEs are deleterious on average, removing them should be advantageous even in the face of the deleterious effect of local deletions.
pl perl script of SSPACE  [72]  (version 1. 1) and gap closing was achieved using the GapFiller. pl perl script of gapfiller  [73]  (version 1. 10). Gene annotation was based on Maker  [74]  (version 2. 31. 8). EST evidence, protein homology and repeat masking references were provided from A. thaliana. Gene prediction was allowed from EST inference and from protein homology and resulted in the prediction of 27. 992 genes. Genome metrics were obtained using QUAST  [75]  (version 4. 0) and genome assembly and annotation completeness was assessed with BUSCO  [41]  (version 3)  [41]  using the Embryphyta odb9 dataset composed of 1440 universal single-copy orthologs.
TE annotation In order to produce a genome-wide annotation of repetitive sequences, the four genomes were annotated using the package REPET  [76, 77]  (version 2. 5), which is composed of two main pipelines, dedicated to de novo detection, annotation and analysis of repeats, in particular TEs, in genomic sequences (Additional file 5). Briefly, the first pipeline, TEdenovo, starts by comparing the genome with itself and clusters matches sharing at least 90% identity. Then, for each cluster, it builds a multiple alignment from which a consensus sequence is obtained. Finally, consensus sequences are classified according to TE features, and redundancy is collapsed by keeping the longest consensus from groups that share 95% of their length and 98% identity. The second pipeline dedicated to the annotation (TEannot) involves several steps, including TE detection by search for similarity between consensus and genomic sequences, the removal of hits that are included in regions corresponding to micro-and minisatellites and connection of distant fragments (up to 15 kb) using the long-join procedure  [78].
In our study, a library of classified, nonredundant consensus sequences was obtained by combining the TE de novo analysis performed on the four species. Then, the bundle library was used to annotate each of the four genomes separately using TE annot. In parallel, the proportion of TEs in each of the four genomes was estimated using an assembly-free approach. The raw sequencing reads that mapped onto the bundle library using Bowtie2  [79]  (version 4. 1. 2) were considered as representing TEs and the other reads as non-TEs sequences. The genomic Illumina reads were obtained from  [40]  (37,262,746 reads in total) for A. halleri halleri, or downloaded from the NCBI SRA database: DRR013376 (38,782,027 reads) for A. gemmifera, SRR2040788 and SRR2040789 (48,602,962 reads in total) for A. lyrata and ERR1399719 (38,425,727 reads) for A.
0. 0) alignments using a set of 7743 eukaryotic sequences obtained from NCBI database corresponding to these types of non-coding RNAs. sRNA reads were mapped on the A. lyrata MN47 genome  [31]  using Bowtie. Multiply mapping reads were discarded and only alignments presenting no mismatch were conserved. A TE was defined as producing substantial siRNAs when it presented an overlap with more than 5 reads per million and when it was covered on more than 10% of its length. Differences were tested using a χ 2 test with 1 degree of freedom. The DNA methylation matrix for A. lyrata MN47 of Seymour et al. [50]  was used to evaluate the methylation status of orthologous vs non-orthologous TEs. Following Seymour et al. , we considered a cytosine site as significantly methylated when its methylation rate was ≥20% in at least one of the four tissue-treatment combinations (shoot, root, 4 °C, 23 °C).
Proxies of essentiality of A. halleri genes Size of the gene family was estimated using an allagainst-all Blast approach performed from the CDS and removing hits with a query coverage inferior to 50% and/or an E-value superior to 1E -30. For Ka/Ks calculation, CDSs from each pair of orthologous genes between A. halleri and A. lyrata were aligned using Water from the EMBOSS package  [83]  and alignments were submitted to KaKs_Calculator2. 0  [84]  using the Goldman and Yang method  [85]. Essential genes were annotated using a dataset composed of 2400 Arabidopsis genes with a loss-of-function mutant phenotype  [86]. Statistical significance was tested using a χ 2 test for gene copy numbers and proportions of genes with a loss-of-function mutant phenotype, and using a Kruskal-Wallis test for Ka/Ks distributions.
Funding This work was funded by the  European Research Council  ( NOVEL  project, grant # 648321 ), by  Région Hauts-de-France  ( MICRO 2 project, Program  "projet emergent"), by  Agence Nationale de la Recherche  ( TE-MoMa  project, grant # ANR-18-CE02-0020-01 ) by the  Fonds de la Recherche Scientifique-FNRS  ( PDR-T. 0206. 13 )and by the  University of Liège  ( SFRD-12/03 ). The  ANR ELO-CANTH  (grant  ANR-12-JSV7-0010 ) provided resources for plant culture. The authors also thank the  Région Hauts-de-France , the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ) and the  European Fund for Regional Economic Development  for their financial support. M. H. is Research Associate of the FNRS.
Time-lapse microscopy Cells were analyzed by time-lapse microscopy within the CLiC microfluidic device as described  (Fehrmann et al. , 2013)  in SC-based media at 30˚C essentially as described  (Ferrezuelo et al. , 2012)  using a fully-motorized Leica AF7000 microscope with a 63X/1. 3NA oil-immersion objective. The media was pumped into the microfluidic device at a rate of 20 mL/min. Time-lapse images were taken every 10 min. Time-lapse images were analyzed with the aid of BudJ  (Ferrezuelo et al. , 2012) , an ImageJ (Wayne Rasband, NIH) plugin that can be obtained from ibmb. csic. es/groups/spatial-control-of-cell-cycle-entry to obtain cell dimensions and fluorescence data as described  (Ferrezuelo et al. , 2012) ; budding events were identified visually. Wide-field microscopy is able to collect the total fluorescence emitted by yeast cells and, consequently, cellular concentration of fluorescent fusion proteins was obtained by dividing the integrated fluorescence signal within the projected area of the cell by its volume.
Chaperone mobility analysis by FLIP and RICS We used fluorescence loss in photobleaching (FLIP) to analyze chaperone mobility in a Zeiss LSM780 confocal microscope with a 40X/1. 2NA water-immersion objective at room temperature. FLIP was used as a qualitative assay to determine Ssa1-GFP and Ydj1-GFP mobility in the whole cell. A small circular region of the cytoplasm (3. 6 mm 2 ) was repetitively photobleached at full laser power while the cell was imaged at low intensity every 0. 5 s to record fluorescence loss. After background subtraction, fluorescence data from an unbleached cell region were made relative to the initial time point, and a mobility index was calculated as the inverse of the fluorescence half-life obtained by fitting an exponential function. We noticed a clear dependency of this mobility index on cell size.
Using a dataset of cells with very wide size range and expressing free GFP, we obtained an expression to correct the mobility index for cell size with MI c = MI. r 1. 49 where MI is the raw mobility index, r is the cell radius and MI c is the corrected mobility index (Figure  2 -figure supplement 2). Raster Image Correlation Spectroscopy (RICS) analysis was performed in a Zeiss LSM780 confocal microscope with a 63X/1. 3NA water-immersion objective; specifically, we used a 35 nm pixel size and 12. 6ms dwell time, tacking a stack of 100 frames (2 s/frame) in photon-counting detection mode, at room temperature. To obtain the coefficient of diffusion, 64 Â 64 pixel stacks were used to remove the immobile fraction with a five frame moving average and analyzed using a set of plugins written by Jay Unruh (Stowers Institute) for ImageJ.
The resulting autocorrelation function (ACF) in the scanning direction, that is G RICS (x,0), for each cell was obtained and averaged for each group of cells to determine the diffusion coefficient (D) and the number of molecules in the focus (N) by fitting the data to a simple diffusion model. In order to assess the uncertainty of the predicted D values from pooled RICS data by non-linear regression methods we used a Monte Carlo approximation. Coincidence analysis with RICS data was carried out with CoinRICSJ  (Moreno and Aldea, 2019). Briefly, after removal of the immobile fraction as described above, the ACF of each pixel was obtained using a 16 pixel range only in the raster direction. The intercept obtained by linear regression of the ACF (no specific model of diffusion assumed) was used as an approximation of the inverse of the number of moving particles (N).
Statistical analysis Sample size is always indicated in the figure legend and, unless stated otherwise, median and quartile (Q) values are shown in all plots with single-cell data. Pairwise comparisons were performed with a Mann-Whitney U test; and the resulting p values are shown in the corresponding figure panels. For percentages, 95% confidence limits (CL) are always shown. Time-lapse data recorded from single cells in the CLiC microfluidics chamber are represented as the mean value of the population along time (with cells aligned at last budding event), while the shadowed area represent the 95% confidence limits of the mean. Logistic regression analysis was performed with the aid of a Java applet developed by J. C. Pezzullo (statpages. info/logistic. html).
Acknowledgements We thank  E Rebollo ,  J Comas  and  M Kerexeta  for technical assistance, and  DE Gottschling ,  J Skotheim  and  KA Morano  for providing strains. We also thank  C Rose  for editing the manuscript, and  F Antequera ,  Y Barral , and  C Gallego  for helpful comments. This work was funded by the  Ministry of Economy and Competitiveness of Spain , Consolider-Ingenio 2010, and the  European Union (FEDER)  to MA. KJ was supported by the  EPSRC Centre for Doctoral Training in Cross-Disciplinary Approaches to Non-Equilibrium Systems  (CANES,  EP/L015854/1 ). DFM received an FI fellow of  Generalitat de Catalunya.