dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This set of instruments is operational all year long thanks to the 90 volunteer astronomers: the "Observateurs associés", who share the tasks of image acquisition, instrument development and software, with the staff at  Observatoire Midi-Pyrénées (OMP)  and  Institut de recherche en astrophysique et planétologie (IRAP) , in scientific collaboration with the " Programme national Soleil-Terre " (PNST). The Observateurs associés are funded by  Fiducial  ( GEO Christian Latouche ); IRAP and OMP are funded jointly by  CNRS  and  Université de Toulouse. Special thanks to  Raphaël Jimenez ,  Martine Lafon ,  Philippe Saby ,  Franck Vaissière  (president of the Observateurs associés), and the  OMP  staff, whose work has been crucial to the good operation of these instruments, computers and networks.
Participants -AD The data used in the preparation of this article were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu). The ADNI was launched in 2003 as a publicprivate partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. For up-to-date information, see www. adni-info. org. We collected longitudinal measurements for all the available ADNI 1/GO/2 individuals with at least one 'quality control' flagged 3D-T1 MRI scan. Repeated T1-weighted structural MRI images were acquired at 3T machines across multiple centers according to a harmonized protocol.
Longitudinal FreeSurfer was used to align images from multiple time-points according to subject specific median templates, in order to avoid temporal bias. Subject with no available diagnosis were discarded, thus leaving us 1713 subjects, with 'HC', 'MCI' or 'AD' diagnosis. We collected longitudinal FreeSurfer 5. 1 vol data on all the GM and subcortical regions from ADNIMERGE. csv. For information on scanning protocols and segmentation algorithms see www. adni-info. org. The subjects included in our analysis are 1713; the mean (std) age of the cohort is 73. 9 (7. 2) years. The overall scans are 6670; the average (std) time between scans is 2. 4 (1. 8) years. More information is in Figure  3 -figure supplement 1 and Supplementary file 1-Table  S7.
Participants -PPMS This was a retrospective study of 64 participants, studied at the UCL Queen Square Institute of Neurology in London. The participants include 44 individuals with primary progressive multiple sclerosis (PPMS) and 20 healthy controls (HC). We collected longitudinal FreeSurfer 5. 1 volumetric data on all the GM and subcortical regions. For information on scanning protocol, segmentation algorithms, please refer to  Hofman et al. (2015). The longitudinal data set used for this study consisted in 64 individuals; the mean (std) age of the cohort is 41. 6 (10. 2) years. The overall scans are 244 scans; the average (std) time between scans is 4. 0 (1. 5) years. More information are in Figure  3 -figure supplement 1 and Supplementary file 1-Table  S7.
Participants -HA The data used in the preparation of this article were obtained from The Rotterdam Scan Study  (Ikram et al. , 2015; Veraart et al. , 2016). The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngology, locomotor, and respiratory diseases. Initially, in 1995 and 1999, random subsamples of participants from the Rotterdam Study underwent neuroimaging, whereas from 2005 onwards brain MRI has been implemented into the core protocol of the Rotterdam Study. We excluded individuals with prevalent dementia at study entry, and individuals presenting cortical infarcts on the MRI. The study contains 148 individuals that would develop dementia in the 2-4 years follow-up(s).
We leave them in the study as we are interested to model their individual topological profiles with respect to the topological profile of the cohort-level. We collected longitudinal FreeSurfer 5. 1 volumetric data on all the GM and subcortical regions. For information on the scanning protocols and segmentation algorithms see  Veraart et al. (2016). The longitudinal data set used for this study consisted in 5463 individuals; the mean (std) age of the cohort is 64. 8 (10. 8) years. The overall scans are 11627 scans; the average (std) time between scans is 5. 3 (1. 1) years. More information is in Figure  3 -figure supplement 1 and Supplementary file 1-Table  S9.
Data pre-processing to obtain model inputs On each data set of volumetric GM and subcortical regions separately, we performed three steps: 1. Adjustment for nuisance variables: we constructed a regression model for each region separately with volumes as dependent variable and total intracranial volume, gender, and age (age not included for the HA cohort) as independent variables. 2. Variable selection: from the original FreeSurfer Regions of Interest (ROIs) we discarded white matter, brain stem, ventricular and cerebellar regions, leaving 82 ROIs. Then, we averaged the (adjusted) volumes of each region from both hemispheres, obtaining 41 bilateral regions. 3. Z-scores computation: we computed z-scores against a control population. For the AD data set, the control population was formed by the 'HC'-diagnosed individuals; for the HA cohort it was formed by the 'Young' individuals (those whose age is more than one standard deviation less than the mean age); for the PPMS data set it was formed by the 'HC'-diagnosed individuals.
Connectome generation Structural connectomes were generated using tools provided in the MRtrix3 software package (http://mrtrix. org). The pipeline included  (Oxtoby et al. , 2018) : DWI denoising  (Andersson and Sotiropoulos, 2016) , pre-processing  (Tustison et al. , 2010)  and bias-field correction  (Modat et al. , 2014) ; inter-modal registration  (Jeurissen et al. , 2014) ; T1 tissue segmentation  (Braak and Braak, 1995) ; spherical deconvolution  (Smith et al. , 2013; Tournier et al. , 2010) ; probabilistic tractography  (Smith et al. , 2012)  utilizing anatomically-constrained tractography  (Yeh et al. , 2016) , dynamic seeding and SIFT  (Tournier et al. , 2010) ; T1 parcellation  (Braak and Braak, 1995) ; robust structural connectome construction  (Bullmore and Sporns, 2009). Our anatomical connectome for each participant is a weighted adjacency matrix that includes only inter-node connections across the 82 ROIs consisting of cortical and subcortical gray-matter regions, excluding the cerebellum and brain stem.
They are the hippocampus (AD), the caudate (PPMS) and the insula (HA). These epicenters are supported by literature in AD  (Zhou et al. , 2012; Abe et al. , 2008) , PPMS  (Eshaghi et al. , 2018a; Vinke et al. , 2018)  and HA  (Allen et al. , 2002; Good et al. , 2001; Kalpouzos et al. , 2009; Tisserand et al. , 2004; Rubinov and Sporns, 2010). 1. We computed the first seven network metrics using the Brain Connectivity Toolbox (Daube-Witherspoon and Muehllehner, 1986) in MATLAB, after appropriate normalization (weight_conversion function). To compute the cortical proximity metric, which does depend on the structural connections, we select the segmented structural MRIs in the HCP cohort, and measure the (average) pairwise cortical distances between all the macro-regions' barycenters (using the mris_pmake command in FreeSurfer).
Data used in the preparation of this work were obtained from the MGH-USC Human Connectome Project (HCP) database (ida. loni. usc. edu/login. jsp). The HCP project (Principal Investigators: Bruce Rosen, M. D. , Ph. D. , Martinos Center at Massachusetts General Hospital; Arthur W. Toga, Ph. D. , University of California, Los Angeles, Van J. Weeden, MD, Martinos Center at Massachusetts General Hospital) is supported by the National Institute of Dental and Cranio-facial Research (NIDCR), the National Institute of Mental Health (NIMH) and the National Institute of Neurological Disorders and Stroke (NINDS). Collectively, the HCP is the result of efforts of co-investigators from the University of California, Los Angeles, Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH), Washington University, and the University of Minnesota.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 666992. SG, NPO, EJV, OC, FB, JMS, MWV, DCA acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme. SG acknowledges financial support from the  French government managed by L'Agence Nationale de la Recherche  under Investissements d' Avenir UCA JEDI  ( ANR-15-IDEX-01 ) through the project ' AtroProDem: A data-driven model of mechanistic brain Atrophy Propagation in Dementia '. NPO acknowledges financial support from The  Michael J Fox Foundation for Parkinson's Research , the  Alzheimer's Association, Alzheimer's Research UK , and the  Weston Brain Institute  through ' NetMON: Network Models Of Neurodegeneration'  ( BAND-15-368107 ,  11042 ).
DCA and NPO were also funded from  EPSRC  grants  EP/M020533/1  and  EP/J020990/01. DCA, NPO, FB, and JMS are supported by the  NIHR UCLH Biomedical Research Centre. Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ). ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following:  AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  Araclon Biotech ;  BioClinica, Inc; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc; Eisai Inc; Elan Pharmaceuticals, Inc; Eli Lilly and Company; EuroImmun; F Hoffmann-La Roche Ltd  and its affiliated company  Genentech, Inc ; Fujirebio;  GE Healthcare ;  IXICO Ltd.
In turn, CDC6 overexpression aims at enhancing origin activity directly, with fork slowdown being a likely consequence of reduced dNTP availability (Rodriguez-Acebes et al, 2018). In the experimental conditions used, both APH treatment and CDC6 overexpression slowed down forks without blocking overall DNA synthesis or inducing significant DNA damage ( Supp Figure  1B  ). The quality of SNS preparations was monitored by controlling the completeness of lambda-exonuclease digestion, a necessary step to eliminate contaminant DNA  (Foulk et al, 2015 ; Supp Figure  1C  ), as well as confirming the enrichment of SNS at a known origin relative to its flanking region (Sequeira-Mendes et al, 2009; Supp Figure  1D  ). Two separate algorithms were used to identify peaks from SNS-Seq aligned reads: MACS, a ChIP-Seq tool that has been applied to map origins from SNS-Seq data  (Comoglio et al, 2015 )  , and a dedicated algorithm optimized for SNS-Seq by Picard et   al (2014) that takes into account local coverage heterogeneities ( Figure  1A  ).
Responsive origins are active in control conditions The intersection of WT, APH and CDC6 datasets ( Figure  1D  ) revealed a large group of origins active in every condition, which we hence termed "common" (COMM). Additionally, subsets of origins were apparently responsive to aphidicolin (APH-R), CDC6 (CDC6-R) or both stimuli (APH+CDC6-R; Figure  1E  ), while a smaller subset of WT origins were not identified upon stress (WT-nonCOMM; Figure  1D  and Supp. The identification of APH-R and CDC6-R origins at new positions seems consistent with a deterministic model of DNA replication, in which every cell makes use of a defined set of origins along S phase, while extra origins activated by stress are located at new genomic positions ( Figure  2A , left ). However, the majority of genomic positions corresponding to responsive origins also displayed a moderate enrichment of SNS-seq reads in WT cells and many of them were identified as origins by one of the two peak-calling algorithms in at least one of the replicates ( Figure  1E  ; see Supp Figure  2B  for more examples).
This effect was also observed when the analysis was extended to all origins in the datasets ( Figure  2E  ). Of note, no differences in efficiency were observed in equivalent sets of randomized genomic positions mimicking origins ( Figure  2D-E  ). As anticipated from the genome browser images, origins located at APH-R or CDC6-R positions displayed lower efficiency in WT cells than upon APH or CDC6 stimuli ( Figure  2F  ). We conclude that the APH-R, CDC6-R and APH+CDC6-R origin subsets represent low-efficiency initiation sites whose activity remained below the stringent threshold set by the combination of MACS and Picard algorithms in WT cells, but not upon stress ( Figure  2G  ).
Origin efficiency correlates with TSS proximity In a stochastic model of origin activation, one of the key questions becomes the nature of the molecular determinants that regulate origin efficiency in the population. To gain insights into this issue, origins in the three datasets (WT, APH and CDC6) were subdivided into four quartiles according to their relative efficiencies. Origin strength directly correlated with their presence at CGIs, TSS, exons, enhancers, active and poised promoters ( Figure  3A-C  ). In addition, stronger origins displayed a more marked association with individual epigenetic marks ( Supp Figure  2C  ). In every dataset, origin efficiency was inversely proportional to their distance to the nearest TSS, i. e. most efficient origins were located very close to TSS; Figure  3D.
Integration of origins into 3D chromatin contact maps To provide a three-dimensional context for the replication start sites identified in this study, origin datasets were integrated with chromatin contact maps available for mESCs. Chromatin contact maps can be represented as networks in which chromatin fragments are located at the 'nodes' and experimentally-determined interactions between them are represented as 'edges'  (Sandhu et al, 2012; Norton et al, 2018). Given the enrichment of origins at promoters, we focused our analysis on a Promoter-Capture HiC (PCHiC) map that identifies long-range contacts (>10 Kb, mean >1 Mb) between two promoters (P-P) or a promoter and a non-promoter region (P-O, "other end";  Schoenfelder et al, 2015). A large chromatin contact network can be derived from PCHiC data, involving >55,000 fragment nodes (mean length 5 Kb, range 0.
For instance, Polycomb Group (PcG) proteins, master regulators of mESCs genome architecture  (Schoenfelder et al, 2015) , and their associated histone marks display assortativity values ranging 0. 2-0. 35 in the PCHiC network  (Pancaldi et al, 2016). Replication timing (RT) also displayed high assortativity in the P-P network (RTAs=0. 61), as expected by the excellent alignment between RT domains and chromatin interaction maps  (Ryba et al, 2010; De and Michor, 2011; Boulos et al, 2015 ; for a visual representation of this phenomenon, see Supp Figure  3B  ). Assortativity of origin presence (OriAs) was p ositive in the P-P subnetwork for the WT, APH and CDC6 datasets, as well as for a collection of all combined origins (ALL-ORI) and the subset of COMM origins ( Figure  4C  ), and to a much lesser extent for the responsive subsets.
Origin connectivity correlates with efficiency and RT An origin subnetwork termed ori-net (7,611 nodes and 7,791 edges) was defined by the PCHiC fragments that contained at least one origin in the WT, APH or CDC6 datasets, since virtually all of them displayed some activity in WT cells. All across ori-net, clusters of connecting origins formed hubs in 3D that resemble the type of organization expected at replication factories ( Figure  5A ). The majority of origin-origin contacts spanned 100 Kb to 1 Mb (mean 550 Kb), with 10% of them spanning >1 Mb and 2% of them spanning >10 Mb ( Figure  5B ). Origin-origin interactions were mainly intra-chromosomal, albeit some contacts were detected between different chromosomes ( Supp Figure  4D  ). Approximately 80% of ori-net interactions were established within the same topologically associated domain (TAD), while the rest reflected inter-TAD links.
The median distance between contacting origin fragments within a TAD was 190 Kb. A positive correlation between origin efficiency and node degree was observed, indicating that origins located in more connected nodes are activated more frequently in the population ( Figure  5C  ). Besides ori-net, this effect was also detected when the individual WT, APH and CDC6 datasets were superimposed with the PCHiC network ( Supp Figure  5A  ). In human and Drosophila cells, higher origin density and/or efficiency has been reported in early-replicating chromosomal domains  (Cadoret et al, 2008; Besnard et al, 2012; Lubelsky et al, 2014; Cayrou et al, 2015). A cross-analysis of our origin datasets stratified by efficiency with published RT data for mESCs  (Hiratani et al, 2010 ) also revealed a direct correlation between efficiency and RT, in which more efficient origins tend to be early-replicating ( Figure  5D  ).
Short-and long-range interacting origins display similar efficiency To further test the link between origin connectivity and frequency of activation, the assortativity of origin efficiency (OriEfAs) was calculated. Positive OriEfAs was observed for WT, CDC6, APH and COMM origins in the P-P subnetwork, indicating that origins that interact with each other have a tendency to be activated with similar efficiency. The highest OriEfAs value was obtained with a combined dataset of origins from the WT, APH and CDC6 sets (ALL-ORI), in which their efficiency was taken from WT conditions. In contrast, OriEfAs values in responsive origins were close to those obtained in random permutations of origin efficiency ( Figure  6A  and Supp Figure  5C  ). A visual representation of these phenomena is presented in the chromosomal domain corresponding to TAD856, highlighted in Figure  6B.
Origin integration in alternative chromatin contact networks Finally, origin assortativity was measured in alternative chromatin contact networks generated from RNA polymerase II and SMC1 ChIA-PET datasets (Zhang et al, 2013; Dowen et al, 2014) as well as a haploid mESC HiC dataset (Stevens et al, 2017). Despite the fact that these networks displayed very different levels of origin coverage, long-range interactions and connectivity, WT, APH, CDC6 and COMM datasets showed significantly higher assortativity than expected at random in all of them ( Supp Figure  6  ). In these cases, assortativity was also observed in some of the responsive origin subsets, probably reflecting the fact that these networks include many contacts between non-promoter regions.
Our extensive cross-analyses of WT, APH and CDC6 origins with a large collection of chromatin marks underscore this connection. Accordingly, a recent genome-wide mapping of origins in chicken DT40 cells has uncovered that the fraction of mammalian origins functionally conserved in avian cells is 2-3-fold higher among those associated with CGI and TSS than in the rest  (Massip et al, 2019). The intersection of origin datasets, as defined by a strict combination of two distinct peak-calling algorithms, hinted at the existence of many stress-responsive origins in the mouse genome. Interestingly, the distribution of WT SNS-Seq reads around these origins revealed that they were also active in a fraction of the cell population undergoing an unchallenged S-phase. This pattern is better reconciled with a stochastic rather than deterministic model of origin usage.
The proteins that recognize replication origins, ORC and CDC6, also serve as molecular chaperones capable of attracting and assembling many additional proteins to the factory core, including CDT1, MCM, CDC45, GINS, PCNA, and DNA polymerases that establish multiple contacts between them. We have noticed that human ORC1 is predicted to contain a large intrinsically disordered region (IDR; as analyzed by PONDR software, www. pondr. com ). The presence of IDRs is a common feature in proteins that facilitate multivalent interactions and higher-order signaling assemblies (reviewed by  Wright and Dyson, 2015). The bursts of DNA synthesis created by the activation of several adjacent origins, classically visualized as discrete nuclear BrdU or PCNA foci, would parallel the bursts of transcription driven by superenhancers.
Control λ-exo reactions were analysed in 1% agarose gels to confirm full digestion of the plasmid. To control for SNS enrichment at the Mecp2 origin, qPCR reactions were performed in duplicates using ABI Prism 7900HT Detection System (Applied Biosystems) and HotStarTaq DNA polymerase (Qiagen) according to manufacturer's instructions. Data was analysed in Applied Biosystems Software SDS v2. 4. Primer sequences for Mecp2 origin and Mecp2 flanking region are indicated in Supp Table  3.
Epigenomic features and chromatin states analyses Genomic coordinates of origins were converted from mm10 to mm9 genome assembly with LiftOver ( https://genome. ucsc. edu/util. html ). Origins were intersected with the genomic features depicted in Supp Table  5  or with a set of previously compiled epigenomic features (Juan et al, 2016). Each epigenomic feature dataset was 'discretized' in 200 bp windows: the presence of a given mark within a 200 bp window was scored as 1, and its absence as 0. The overlap between origin fragments and the genomic windows was calculated using findOverlaps in the genomicRanges R package. The number of origins overlapping with each feature was calculated for the experimental sets of origins and for 1,000 sets of origins randomly shuffled along the genome (excluding low-mappability regions).
Analysis of origin efficiency In each sample, the efficiency of each individual origin was determined following three steps: (1) The sum of reads covering each nucleotide (i. e. the sum of per base read depths) of the origin was calculated using samtools function bedcov with default parameters. This analysis was done after downsampling every dataset to the lowest coverage obtained (APH-I). Down-samplings were performed a total of 10 times, and the median value was determined for each origin; (2) Background was calculated for each sample as the sum of per base reads in random genomic fragments that are not origins but have the same size. After 100 randomized relocations, a function of median background noise by size of the fragment was calculated. The estimated background noise for each origin was subtracted from the initial values; (3) Background-corrected values were normalized dividing by the size of the origin.
Low-mappability regions The scanquantile peak-calling algorithm  (Picard et al , 2014)  was used on a sequenced genomic DNA input, with the same parameters set for the SNS-seq samples. The resulting peaks, as well as the small gaps present in the genome segmentation needed for peak-calling, were marked as non-mappable regions (ShadeAreas files at https://github. com/VeraPancaldi/RepOri3D ). Subtelomeric and pericentromeric regions, extended from the UCSC database telomere annotation by visual inspection in the browser, were also added.
Network analysis All network analyses including correlations between degree, efficiency and replication timing (RT) were performed using the igraph package in R and standard R functions. TAD definitions were taken from Dixon et al (2012). Networks were visualised using Cytoscape v3. 6. 1. Replication timing data for three mESC cell lines was downloaded from Hiratani et al (2010) and combined in our analysis. The median value for probes overlapping each origin was taken and individual origins were ranked based on RT in the range 0 to 1 (early to late). When indicated, the probes within each chromatin fragment in the 3D chromatin network were combined to give an average RT value for the entire fragments.