dataesr/scientific-paragraphs-categorization huggingface dataset:
(A) Axial, sagittal, and coronal views of population percentage atlases of 42 tracts from 277 full-term dHCP neonates. The tract atlases were created by averaging binarized (at a threshold of 0. 1%) path density maps across subjects, obtained from probabilistic tractography. For ease of visualization, all tracts are displayed as maximum intensity projections with 30 to 100% population coverage. Tract names and abbreviations are provided in Table  1. (B) Tract atlases from the adult human, neonatal human, and macaque brain. Visualization is the same as in (A). (C) Left: Intersubject variability of tract delineations across three neonatal independent datasets: dHCP (300 diffusion volumes, 1. 5-mm isotropic resolution), Oxford (163 diffusion volumes, 1. 75-mm isotropic resolution), and Oxford-subsampled (65 diffusion volumes, 1.
75-mm isotropic resolution). Each violin plot is a distribution of 231 correlations between pairs of subjects, averaged across all tracts, within and across datasets. Right: Neonatal tract atlases from a subset of 22 age-and sex-matched subjects from independent datasets [dHCP (top row) versus Oxford-subsampled (bottom row)]. Downloaded from https://www. science. org on November 02, 2022 To assess the robustness of these findings, we performed a bootstrap analysis, repeatedly calculating the minimum KL divergence between randomly subsampled groups. We found a similar pattern in divergence (fig. S3 ) between all tested pairs, although with greater variance in phylogeny maps, perhaps due to the greater divergence observed in these comparisons or due to the limited macaque sample size. To explore whether areas with high connectivity divergence are the ones with high connectivity complexity, we explored the association between divergence maps and entropy in connectivity blueprints (i.
This avoids some of the major problems associated with tracking toward the surface  (8). A limitation of the current study is the use of a prespecified set of white matter fiber bundles to describe cortical connectivity patterns. Even if these protocols sample representative groups of white matter fibers and provide relatively high cortical coverage to contrast the effects of phylogeny and ontogeny, they do not fully represent connectivity patterns from all cortical areas. For instance, the insular cortex was excluded from our analyses. Further work could either define more standardized protocols for the extraction of additional fiber bundles or use data-driven approaches for the extraction of denser connectivity components  (11). Another limitation is that we did not replicate the ontogeny/phylogeny analyses using the independent neonate datasets (Oxford and Oxford-subsampled).
Data and preprocessing Neonatal data dMRI data were drawn from 438 neonates born at 24 to 43 and scanned at 29 to 45 weeks PMA, made publicly available by the second data release of the dHCP  (13). Briefly, data were acquired during natural sleep on a 3T Philips Achieva with a dedicated neonatal imaging system, including a neonatal 32-channel head coil  (13). dMRI data were acquired over a spherically optimized set of directions on three shells (b = 400, 1000, and 2600 s/mm 2 ). A total of 300 volumes were acquired per subject, including 20 with b = 0 s/mm 2 , across four acquisition subsets (two pairs with opposing phase encoding polarities). For each volume, 64 interleaved overlapping slices were acquired (in-plane resolution = 1. 5 mm, thickness = 3 mm, and overlap = 1. 5 mm). The data were then superresolved  (69)  along the slice direction to achieve isotropic resolution of 1.
Adult data We drew from the preprocessed, publicly released HCP dMRI data  (73). We randomly chose 50 unrelated subjects (age range, 22 to 35 years). Briefly, the HCP data were acquired using a bespoke 3T Connectom Skyra (Siemens, Erlangen) with a monopolar diffusionweighted (Stejskal-Tanner) spin-echo echo planar imaging (EPI) sequence, an isotropic spatial resolution of 1. 25 mm, three shells (b values = 1000, 2000, and 3000 s/mm 2 ), and 90 unique diffusion directions per shell plus 6 b = 0 s/mm 2 volumes, acquired twice with opposing phase encoding polarities. Protocol details are summarized in table  S2. Nonlinear transformations to the MNI152 standard space were obtained using T1-weighted images with FSL's FNIRT  (74). The distortion-corrected dMRI data were separately linearly aligned to the T1-weighted space, and the concatenation of the diffusion-to-T1 and T1-to-MNI transforms allowed diffusion-to-MNI warp fields to be obtained.
Macaque data We used six high-quality postmortem macaque (age range, 4 to 16 years) datasets in this study, as described previously  (12). These data were acquired at 7T using a Agilent DirectDrive console (Agilent Technologies, Santa Clara, CA, USA) using a 2D diffusion-weighted spin-echo protocol with single-line readout, with 16 b = 0 volume, 128 volumes acquired with b = 4000 s/mm 2 , and an isotropic spatial resolution of 0. 6 mm. Protocol details are summarized in table  S2. Nonlinear transformations to the macaque standard space (F99) (75) were estimated using FSL's FNIRT  (74)  based on the FA maps.
Robustness against neonatal data quality To explore the robustness of the protocols across data of varying quality and acquisition parameters, we use an additional dataset of 22 neonates born and scanned at 37 to 42 weeks PMA (full details in fig. S9 )  (78) , taking the dHCP dataset as the benchmark dataset. These data were collected on a 3T Siemens Prisma with a nonspecialized adult 32-channel receive coil. dMRI data were acquired with 163 volumes per subject, over three shells (b = 500, 1000, and 2000 s/mm 2 ), with 1. 75-mm isotropic voxels and an acquisition time of 8 min. This dataset was acquired at the Wellcome Centre for Integrative Neuroimaging (Oxford, UK) and is referred to as the Oxford dataset. As a further test, a third dataset was generated by removing the b = 2000 s/mm 2 shell from the Oxford dataset and reducing the overall number of volumes to 65 (which corresponds to an approximate Downloaded from https://www.
science. org on November 02, 2022 scan duration of 3 min). This corresponds to a more conventional lowb value acquisition and will be referred to as the Oxford-subsampled dataset. The acquisition parameters of the three datasets are summarized in table  S1. These datasets were analyzed following the dHCP data preprocessing as described above, including motion and distortion correction, the generation of diffusion-template warp fields, crossing-fiber modeling, and standardized tractography for each subject. We compared tract-atlases and intersubject variability across the three datasets. A group of dHCP neonates, matched for age and sex, was selected for comparison. Tract atlases were compared quantitatively by correlating each tract (with population threshold of 30% applied) from the dHCP dataset with the respective tracts from the comparison datasets.
Intersubject variability in the tractography results was assessed within and across the subject groups. Similarity was assessed using the Pearson's correlation coefficient between subjects' normalized tractography maps in template space, thresholded at 0. 1%. The correlation values were averaged across tracts for each subject pair. For within-group comparisons, the subjects were each compared with each of the other subjects in the group, yielding 231 pairs of subjects. For across-group comparisons, 231 pairs were randomly generated across the groups to give the same number of data points.
Microstructure of white matter tracts in early development We used the developed library of neonatal tractography protocols to explore early developmental trends of white matter microstructure, specifically, the rate of maturation at birth. We investigated changes in tract-averaged DTI parameters (FA and MD) with age. To reduce confounds caused by, e. g. , prematurity, we restricted our subject pool to a group of 277 full-term infants from the dHCP dataset, born at 37 to 42 weeks, and scanned at 37 to 45 weeks PMA (see fig. S9  for full details). The diffusion tensor model was fitted to the b = 1000 s/mm 2 shell for each subject, and the voxel-wise FA and MD maps were calculated. The tract atlases derived from the dHCP dataset were thresholded at 30% and binarized. These tract masks were then warped to each subjects' native diffusion space and used as ROIs to calculate tract-wise median FA and MD values.
Surface extraction Neonatal cortical surfaces were reconstructed from T2-weighted images, using a pipeline specifically adapted for neonatal structural MRI data  (79). These surfaces were registered to a representative template space before performing tractography, to ensure alignment between subjects. Subjects' WGB surfaces were first aligned to the dHCP's 40-week PMA surface template (80), using a specialized surface registration pipeline (https://github. com/ecr05/dHCP_ template_alignment), based on multimodal surface matching (MSM)  (81). This aligned vertices on the WGB to ensure consistent seed points for tractography across subjects. A previously computed nonlinear volumetric registration  (82)  was then applied to all MSMderived surfaces to register them to 40-week PMA volumetric template space  (68).
This step was necessary to ensure that the tractography seeds were aligned to the target space. The adult surfaces were derived using the HCP pipelines  (83). For the macaque surface data, we follow the approach of (8). Briefly, a single set of macaque surfaces were derived using a set of highquality structural data from one of the macaque subjects. The remaining macaque data were then nonlinearly transformed to this space, and the surfaces were nonlinearly transformed to the F99 standard space  (75)  to allow group-level tractography. Before tractography, the surfaces were downsampled to approximately 10,000 vertices per hemisphere. We then carried out probabilistic tractography, seeding 1000 streamlines from each vertex on the WGB, and recording visitation counts between each seed point and each voxel in a whole-brain mask with the ventricles removed, down-sampled to 2 mm 3 for the neonatal and macaque brain and 3 mm 3 for adult brain.
Group-averaged blueprints Following subject-wise construction of connectivity blueprints, we derived group-averaged blueprints for each dataset. Average connectivity blueprints were generated using 33 full-term neonates born and scanned just after birth (at 40 weeks PMA) from the dHCP cohort, 50 adult subjects from the HCP cohort and 6 macaques. Further groupaveraged connectivity blueprints were derived for other subgroups of the neonatal dHCP cohort: a group of 25 very premature infants (<32 weeks of gestational age at birth) who were scanned at fullterm equivalence (37 to 45 weeks), a sex-and age (at scan)-matched group of 25 neonates born full-term and also generated for three groups of 73 full-term neonates scanned at three age ranges (36 to 40, 40 to 42, and 42 to 45 weeks). For full subgroup details, see fig.
Bootstrapping To explore the stability of the ontogeny-phylogeny KL divergence maps, we performed a bootstrapped analysis, repeatedly calculating the KL divergence between (i) the macaque and neonate, (ii) the neonate and adult, and (iii) the macaque and adult. We randomly subsampled (with replacement) each of the three datasets over 100 iterations, using 20, 20, and 4 subjects from the neonate (from the pool of 33 neonates in the 40-week group), adult, and macaque datasets, respectively. In each iteration, we averaged the connectivity blueprints for each subsample and calculated the KL divergence maps to report the mean and variance across iterations.
Acknowledgments Funding: This work was supported by  ERC   Consolidator grant  ( 101000969  S. W. and  MRC PhD Studentship UK  ( MR/N013913/1  to S. W. ),  Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) (ONBI CDT , grant number  EP/L016052/1  to E. T. ),  Wellcome Trust  award ( 217266/Z/19/Z  to S. N. S. and E. T. ),  IdEx Université de Paris  ( ANR-18-IDEX-0001 ), the  Médisite Foundation and the "Fondation de France " (to J. D. ). BLISS research grant (to L. B. ), Senior  Wellcome Research Fellowship  ( 207457/Z/17/Z  to R. S. ),  Wellcome   Collaborative Award  ( 215573/Z/19/Z  to S. J. ),  Wellcome   Senior Research Fellowship  ( 221933/Z/20/Z  to S. J. ), and  BBSRC   David Phillips Fellowship  ( BB/N019814/1  to R. B. M. ). WIN was supported by a  Wellcome Trust   Center grant  ( 203139/Z/16/Z ).
developingconnectome. org). For the "Oxford" neonatal data, see the original publication (78). All other data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Data processing and analysis were performed using FSL (v6. 0 onward, https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSL), Connectome Workbench  (v1. 5. 0, www. humanconnectome. org/software/connectome-workbench)and Python (v3. 8. 9), including nibabel (v3. 2. 1) and surfplot. Scripts and data for generating figures; tractography protocols for the neonate, adult, and macaque; and neonatal tract atlases are available via Zenodo (doi: 10. 5281/zenodo. 7014568 ). Neonatal tractography protocols will be made available via FSL in a future release. Tools for performing standardized tractography and building connectivity blueprints (XTRACT and xtract_blueprint) are available in FSL (v6.
Approach Tools producing k-mer sets essentially use similar storage techniques. In Jellyfish  (Marc¸ais and Kingsford, 2011)  and DSK, a k-mer is encoded in 2 bits per nucleotide and the entire set is stored as a succession of k-mers and associated data (e. g. abundances). In KMC, a more advanced format is used to reduce space and to allow fast, logarithmic time, queries (see 'KMC file format description' in the Supplementary Material for more details). Recent works  (B rinda et al. , 2021; Rahman et al. , 2021)  demonstrated space-efficient storage of genomic k-mers using their spectrum-like property  (Chikhi et al. , 2021) , i. e. assuming that most kmers originate from a set of long strings. In this spectrum-preserving string set representation (SPSS), what are stored are sequences longer than k, where each window of length k is a k-mer from the original set, and achieve a space of around 3 bits per k-mer [in  Rahman et al.
Results We created the kff-tools software suite on top of the Cþþ KFF API. It is a collection of small programs that manipulate KFF files, such as merging/splitting, validation, bucketing. They are available at github. com/Kmer-File-Format/kff-tools. These tools complement the already existing KMC tools  (Kokot et al. , 2017)  that allow more complex operations on k-mer sets, e. g. union, intersection and complex joins. KMC tools have further been adapted to support KFF files where k-mers are ordered. To demonstrate that KFF provides significant space savings compared to other file formats, we downloaded short-read sequencing data from the chicken genome (2. 8 billion distinct 32-mers) and the Human genome (5. 7 billion distinct 32-mers), counted using KMC  (Deorowicz et al. , 2013). We evaluated several file formats: naive text representation, KMC format, KFF storing k-mers naively, KFF where k-mers are compacted as super-k-mers (i.
e. a group of overlapping k-mers that share the same minimizer) (see Supplementary Material 'Experimental setup relative to kmtricks' section) and KFF where k-mers are compacted using a spectrum-preserving string set  (Rahman et al. , 2021)  (see Supplementary Material 'Experimental setup relative to ESS-Compress' section). Full data processing details, as well as additional results using compression, are available in the Supplementary Materials. Table  1  shows that by recording compacted super-k-mers with KFF, it is possible to use roughly 3Â less space than with native KMC format for storing the same set of k-mers. In terms of running times, on the Gallus dataset using 8 threads, KMC took 9 min, KFFþsk 113 min and KFFþSPSS 900 single-threaded minutes (optimization pending). On average KFF with super-k-mers requires 17 bits per k-mer (omitting the data), while KMC uses 56 bits/k-mer.
Using SPSS improves storage further to 5 bits per k-mer. Furthermore, gzip compression adds an additional 2Â compression gain for KFF files and 1. 25Â gain for KMC files. In conclusion, we propose the k-mer set file format KFF, along with a versatile Cþþ and Rust API to read and write k-mers and a toolkit for file manipulations. We hope that KFF will boost interoperability between many software tools that use k-mer sets, and simultaneously improve their efficiency due to the compression features of KFF. Many suggestions and requests are emerging from discussions with the community and extensions of features to the format are currently being considered. The KFF format could for instance be used to store k-mer sketches, although current sketching tools store hashes on disk  (Pierce et al. , 2019) , discarding the originating k-mers.
Funding R. C. was funded by  ANR Inception  ( ANR-16-CONV-0005 ) and  PRAIRIE  [ ANR-19-P3IA-0001 ] grants. M. K. and S. D. were supported by the  National Science Centre, Poland  [ DEC-2019/33/B/ST6/02040 ]. Work supported in part by the  National Science Foundation  [1453527 and 1931531], and by the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie  grant agreement No  956229.
For this purpose, genes regulated by ESR1 were chosen, and we used the Chea 2022 database of ESR1 binding ChIP-seq data from Enrichr  [28]. Thus, we chose to analyse DNA methylation at the promoters of the ARID2, ESRRA, EGFR, PGR, ESR2, SUV39H1, VEGFA and TERT genes. The results showed a significant decrease in the DNA methylation of ESRRA (FC = 0. 6, p = 0. 0096) and EGFR (FC = 0. 5, p = 0. 038) and a decreasing trend in ARID2 (FC = 0. 7, p = 0. 15) and SUV39H1 (FC = 0. 63, p = 0. 13) in the high BPA group compared to the low BPA group (Fig. 1C ). We also analysed DNA methylation at the telomerase (TERT), since it is known that oestrogenic compounds affect telomere length (TL); which was corroborated by our results showing a significant decrease of TERT DNA methylation in the high BPA group (FC = 0.
2A ). For example, the enriched cluster 'H4 histone acetyltransferase activity' includes genes such as KAT2A, KAT8, and OG, and the enriched cluster 'telomeric DNA binding' contains 22 genes (Additional file 1: Table  S4 ) important for telomere function, such as PURA, RPA1, TEN1, TERT, TERF2IP and ZBTB48. An example of differential peaks for TERF2IP and ZBTB48 is illustrated in Fig. 2B. The list of genes identified in differential regions assigned by GREAT was then compared with the Enrichr protein-protein interaction (PPI) database  [28]. This database combines the data of protein-protein interactions previously described in experimental studies. This analysis revealed that targets of 9 proteins, including ESR1/ERa, DNA repair factors (BRCA1, TP53), transcriptional factors (MYC, FOXP3, POU5F1, NOTCH1) and chromatin regulators (EP300, TRIM28), were all enriched in our dataset.
Our analysis revealed that genes encoding ESR1-interacting proteins were strongly enriched for H3K4me3 in the high BPA group (Fig. 2C ). According to the Enrichr PPI database, ESR1 could interact with 871 proteins, of which 510 were encoded by genes located within differential H3K4me3 peaks (adj. p value = 6. 95E-53). Among these genes were the AHR, ESRRA, NCOR1, NCOR2, RARA and RXRA receptors (Additional file 1: Table  S5 ). Furthermore, a PPI database search showed that 236 genes were located in differential peaks corresponding to genes encoding proteins that interact with BRCA1 (BRCA1 could interact with up to 418 proteins). Many BRCA1-interacting proteins encoded by genes located in differential peaks were DNA repair proteins. The AMIGO database includes 519 "DNA repair" genes. Of them, 39 genes were detected in our differential peaks, including ATM, ATR, BLM, FANCA, MDC1, MLH1, MSH2, MSH3 and RAD51 (Additional file 1: Table  S6 ).
In agreement with our immunofluorescence data (Fig. 3C, D ), the analysis of individual genes showed a global tendency towards a decrease in H3K9me3 occupancy following BPA treatment. This H3K9me3 decrease was especially significant for the STAT2 gene and the 5'UTR of LINE1 and slightly less significant for the SAT2 and SATA loci and PGR loci (Fig. 5D ). We also detected decreases in H3K9me3 at the promoters of genes encoding factors involved in telomere function or maintenance, with SMG7 and XRCC5 being the most significantly affected (Fig. 5E ). Thus, our data show that BPA treatment leads to decreased H3K9me3 occupancy at a large number of loci, suggesting that modifications of H3K9me3 occupancy could play a pivotal role in gene expression dysregulation following BPA exposure. To reveal whether there was a change in DNA methylation, we extracted DNA from exposed and unexposed HeLa cells to BPA and performed MEDIP analysis at targets analysed in human blood.
Limitations of this study The availability of human material was the main limiting factor, as well as the small number of subjects included, which reduced our ability to detect other possible effects of BPA. For example, we were not able to acquire sufficient material for the analysis of gene expression or protein levels in vivo. In addition, classification of subjects was performed on the basis of urinary BPA levels assessed in a single urine sample, which may lead to misclassification of exposure given the nonpersistent nature and short-term viability of this chemical compound. Moreover, BPA exposure was measured in samples collected before (when boys were between 9 and 10 yrs. ) and the genetic markers were assessed, when the boys were between 15 and 17 years old. Notwithstanding these limitations, this dataset was sufficient to reveal some interrelated associations consistent with the hypothesized toxicological pathway.
qPCR analysis of repeat number variation experiments The extracted genomic DNA was diluted to obtain a 0. 2 ng/μL concentration, and equal amounts of extracted genomic DNA were used for qPCR. Quantitative PCR was performed using a 384-well plate. Each well contained 5 μL of SYBR ® Green Master Mix (Bio-Rad), 0. 05 μL for each primer pair (100 µM stock solution), 0. 9 μL of H 2 O, and 4 μL (0. 8 ng) of DNA template. qPCR was performed using a CFX 384 Real-Time System (Bio-Rad) with a two-step protocol: initial denaturation at 98 °C for 30 s followed by a 40-cycle amplification program of 98 °C for 15 s and 65 for 1 min. The melting program was applied after the PCR program. Normalized expression values were calculated with the CFX Manager program using RLP0 as the reference gene. The primers used in this work are listed in Additional file 1: Table  S7.
ChIP-qPCR A total of 0. 4 ng of precipitated DNA or input sample was used for qPCR analysis (0. 1 ng/μL). Quantitative PCR was performed as described before. Normalized expression values were calculated with the CFX Manager program using a region located far from the promoter as a reference gene, and we used a region in RPLP0 for ChIP normalization. Enrichment of each target in the precipitated DNA was evaluated by calculating the ratio between the average of the normalized ChIP DNA copies and the average of the normalized DNA copies in the inputs. The primers used in this work are listed in Additional file 1: Table  S7.