dataesr/scientific-paragraphs-categorization huggingface dataset:
We thank the  ICGex NGS  platform of the  Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan   Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOSInserm_12554 ) for the high-throughput sequencing. We thank the Bioinformatics platform of the  Institut Curie  for data management, quality control and primary analysis, in particular  Dr. Nicolas Servant , Head of the platform, for advices on capture-HiC data analyses. This project was funded by  Agence Nationale pour la Recherche  [ ANR-14-CE10-0017  to C. R. and to P. S. ];  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  [ 101020423 ];  LabEx 'Who Am I ?' [ ANR-11-LABX-0071 ];  Université Paris-Cité IdEx  [ ANR-18IDEX-0001 ] funded by the  French Government  through its ' Investments for the Future' program.
ATAC-seq ATAC-seq was performed as previously described  86. Briefly, 50000 cells were resuspended in 50µL cold lysis buffer (10mM Tris-HCl; pH 7. ATAC-seq raw data were analyzed using the atacseq pipeline (1. 2. 1) (https://github. com/nf-core/atacseq/) from nf-core 87 using the default parameters. Briefly, reads were aligned to the macaque genome (rheMac10) using BWA (0. 7. 17). Duplicates were marked by picard and reads mapping to mitochondrial DNA and blacklisted regions were removed. BigWig files were generated using deeptools as described above.
Sequence analyses To date the HERVK insertion within the primate XIC, a custom BLAST database spanning CHIC1-XIST orthologous intervals was constructed from NCBI retrieved sequences. HERVK consensus sequence from Dfam was blasted against it. To date the HERVK invasion of the primate genomes, the consensus was blasted against the genome of the selected primates. All phylogenetic trees were created with www. timetree. org. All species silhouettes were taken from https://www. phylopic. org/. The phyloP scores for human and macaque were generated using the reference-free multi-way alignment of 241 mammalian species 2. The annotations of constrained and not constrained CTCF motifs in hESCs were generated using the resource referencing the natural selection over every human CTCF sites  16. hESC constrained enhancer list was built by overlapping the active enhancers from the present analysis with intergenic regions under selective constraint in humans, that is the zooUCE, UNICORNS and RoCCs loci identified by the Zoonomia consortium 2.
Acknowledgments We thank  R. Janik ,  J. Penedones  and  A. Sever  for the valuable discussions. We are especially grateful to  G. Korchemsky  for his contributions on the early stage of this work and for generously sharing with us his knowledge on the subject. E. S. thanks  Perimeter Institute in Waterloo Canada and Kavli IPMU in Tokyo for hospitality and UNIFY  grant for support. V. K. and E. S. thank e  ANR grants StrongInt  ( BLANCSIMI-4-2011 ) and  GATIS network  for the support. V. K. and E. S. also thank  Yukawa Institute  for Theoretical Physicsin Kyoto where the final part of the work was done, for the hospitality. The work of  Ian Balitsky  was supported by  DOE  grant  DE-AC05-06OR23177.
Datasets This study focuses on the initial part of the mission, the first 380 sols on Mars (a "Sol" is a solar day on Mars (88,775 s), and Perseverance landed on Sol 0). Geological context From Sol 0 to Sol 380, Perseverance mainly traversed the intersection between two Crater-Fill Units labelled Crater floor fractured rough (Cf-fr, named Máaz), and Crater floor fractured 1 (Cf-f-1, named Séítah)  [17]. Máaz is a crater-retaining unit which covers much of the Jezero crater floor. It has been described as a 'dark-toned soil' and mafic unit composed of erosion-resistant bedrock  [17, 18]. On the other hand, Séítah consists of massive light-toned units, classified as either part of the regionally extensive carbonate-bearing or the olivinebearing units. Based on SuperCam measurements, several chemical differences were found between the units  [19].
Fig. 3  a-e presents some examples of the different targets using the classification mentioned above (Annex 1 in the Supplementary data provides the links to the image sources). These panels illustrate the types of targets detailed in Fig. 2. Given the outlined particularities of the acoustic acquisition environment for the Ti plate and the Sample Collection Hole data, they cannot be compared to the rest of the observations and, therefore, will not be considered in this study. Targeting Position (red crosses in Fig. 3 ) refers to the number of positions analyzed considering all the targets, whereas sequence refers to a set of analyses performed at one or several positions in one target (each image in Fig. corresponds  to a sequence).
Data processing Acoustic data in digital number (DN) are first converted to pressure units (Pa), then, the signals, 60 ms per laser shot, are filtered with a bandpass filter between 2 and 15 kHz, as earlier studies  [13]  have shown that this region contains >95% of the acoustic energy from the laserinduced shock wave. The peak-to-peak amplitude of the first compression-rarefaction in the acoustic wave is extracted as an analytical signal. Such a signal, hereinafter called "acoustic signal", corresponds to the amplitude of the acoustic wave between the first maximum and the first minimum (represented in the inset of Fig. 4b ). From Pa, signal values were converted in dB according to the expression: dB = 20log 10 P / P ′ Where P ′ is the reference pressure value for Earth, i. e. , P ′ = 20 μPa.
The impedance conditions of the medium strongly influence the variation of the acoustic signal over time. Fig. 4a  represents data with and without the impedance correction, showing that a large number of raw data are underestimated without this correction due to the impedance evolution during this period. In order to correct the influence of this variation on the acquired signals, the acoustic signal is corrected by the variation of the impedance value with respect to the first acoustic measurement (Sol 37). Distance correction for acoustic data. As mentioned above, targets analyzed by SuperCam were located at distances ranging from 2 m to m from the instrument, resulting in various atmospheric column densities along the acoustic path. In order to readily compare the different acoustic signals, a distance correction factor was applied.
Results and discussion From the considered dataset, we investigated to what extent the absolute intensity of the sound may be modulated by factors including 1/ the quality of the focus, 2/ the rock hardness, 3/ the size of grains in soils, 4/ variations between geologic units and their chemistry. Focal Influence. The laser-to-target distance may change significantly during the raster analysis, in most cases due to changes in target geometry. Usually, SuperCam adjusts the focus at the raster's center and at the extreme positions, so several points inherit the focusing of another position and, therefore, might fall out of focus. Fig. 5  presents a violin plot showing the relationship of acoustic variation to focal position. The acoustic variation (AV) refers to any variation between the acoustic signal from non-focused points (AcS) and the previous focal one (AcS f ) computed as AV = ( AcS -AcS f ).
Acknowledgment Many people helped with this project in addition to the co-authors, including hardware and operation teams, and we are most grateful for their support. This project was supported in the USA by  NASA 's  Mars Exploration Program  and in France is conducted under the authority of CNES. Research funded by projects  UMA18-FEDERJA-272  from  Junta de Andalucía  and  PID2020-119185GB-I00  from  Ministerio de Ciencia e Innovacion, of Spain. P. P. is grateful to the  European Union's Next Generation EU (NGEU)  plan and the  Spanish Ministerio de Universidades  for his  Margarita Salas  fellowship under the  program ′′ Ayudas para la Recualificacion del Sistema Universitario Español ′′. RCW was funded by  JPL  contract  1681089. A. U was funded by  NASA   Mars 2020 Participating Scientist program   80NSSC21K0330.
The observations and the source catalog are described in  Wright et al. (2014a). Guarcello et al. (2017)  have matched X-ray sources with available optical and infrared (IR) photometric catalogs, while  Kashyap et al. (2017)  have applied statistical methods to separate out approximately ∼6000 sources identified as association members from an additional ∼ 2000 sources assessed as foreground and background interlopers. Flaccomio et. al. (2018)  characterize the X-ray spectral properties of these populations, and discuss the results in the context of different emission models. This thorough assessment of the point source content, combined with the deep and wide nature of the Chandra survey itself, provides a valuable dataset for a thorough exploration of Xrays from diffuse gas in the region.
(2005). Adopting the "lightly absorbed optical sample" as representative of the low-mass ONC population, we estimate that the Cygnus OB2 population of stars with mass between 1. 0 and 10 M ⊙ is ∼10 times larger than the ONC one. We can then scale the total luminosity of the ONC sample by this factor, obtaining L LMS total x ∼ 2 × 10 34 erg s -1 for the whole association, which is comparable to what we expect for the diffuse emission. To deal successfully with the difficult task of extracting the diffuse emission signal, we made use of the specific data analysis software ACIS-Extract (AE)  (Broos et al. 2012) , which is able to remove the point source X-ray emission via the construction of "swiss-cheese" ACIS-I images with holes at the positions of detected point sources. It computes the local point spread function (PSF) at each source position and masks its local contribution to the observation (see section 2).
The survey was performed over a central 0. 5 deg 2  of the Cygnus OB2 association with an effective exposure of 120 ksec and an outer 0. 35 deg 2 area with an exposure of 60 ksec. The description of the survey design and observations, the data reduction and source detection, has been presented extensively in  Wright et al. (2014a). All 40 ObsIDs were uniformly reprocessed using version 4. 8 of the CIAO software  (Fruscione 2014) , combined with CALDB 4. 7. 1 calibration database files. In order to get consistency in the calibration procedure, we re-ran the Level 1 to Level 2 processing of event files using the CIAO chandra repro meta task. The check vf pha=yes option was set to flag bad events and then filter them. The new Level 2 event file was produced by filtering events to status==b0. The initial set of observation were processed with older gain files and were updated during the acis process event to the lastest available file version.
Similar treatment was applied to the background event files (see section 2. 1 for details). Since the available "stowed" background files (from which the energetic particle event background can be estimated) were made 1 See further details in http://cxc. harvard. edu/Acis/Cal prods/vfbkgrnd/ using only quiescent background periods, background flares needed to be excluded from our observations in the exactly same way for consistency. We extracted light-curves with the same time bin size as was performed for the background files (bin=259. 28 sec), and we made use of the deflare CIAO 4. 8 Python routine 2. We time-filtered all of the observations. The time reduction was found to be necessary, and in the worst case this amounted to less than 4% of the total exposure of an observation. In order to avoid intense instrumental emission lines (see section 2.
Masking X-ray point sources Here we give details of the procedure implemented to reduce the impact of X-ray point sources on the event and calibration (stowed) data files. We used the specific task ae mask stowed data from the AE code that masked event files, exposure maps, and both stowed events and stowed exposure files. We thus subtracted events from a total of 7924 Xray point sources listed in our catalogue  (Wright et al. 2014a ) covering all 40 of the ACIS-I observations. The first step in the point source subtraction is to build intensity models for sources, as well as for readout streaks, 3 http://cxc. cfa. harvard. edu/contrib/maxim/acisbg/data/ and draw masks around them to compute an image that models the signal from all these features. We use the task ae better masking from AE to adaptively compute source emission at the 99% enclosed energy aperture threshold.
LARGE-SCALE DIFFUSE X-RAY MAP The morphology and energetic large-scale appearance of diffuse X-ray emission in the Cygnus OB2 association is shown in Figure  10 -left. It is a mosaicked, point sourceremoved image in the [0. 5-7. 0] keV energy range. Similar images were also made in three discrete energy bands: Soft [0. 5-1. 2] keV, Medium [1. 2-2. 5] keV and Hard [2. 5-7. 0] keV. These images include the complete set of ACIS-I observations of our survey and cover an area of about 1 deg 2 (see Section 7). Mosaicking was performed using the Montage software to generate a list of images with corresponding WCS information. Before reprojection, we computed the individual emission levels required to match overlapping images. To do this, we determined the average emission level of two or multiple overlapped observations and scaled each image to match this average emission level.
SPECTRAL ANALYSIS OF DIFFUSE X-RAY EMISSION X-ray spectral fitting of diffuse emission is a difficult undertaking, and requires sufficient X-ray photons to provide meaningful constraints on the X-ray spectral model parameters. The most straightforward procedure is to increase the source extraction areas to gain larger signal-to-noise ratios. To this end, we tessellated the whole image mosaic in the [0. 5-7. 0] keV band to achieve surface brightness regions with S/N ratio greater than 60. Figure  11  shows the tessellate-generated image made using the Weighted Voronoi Tessellation (WVT) Binning code  (Diehl & Statler 2006). These tessellated regions can be readily translated into region files and used to extract events within CIAO. In order to avoid the contamination produced by the bright background X-ray scattered halo of Cyg X-3, all tessellated regions within a 8 arcmin radius of its position (RA ≈ 20:32' , DEC ≈ +40:57') were excluded from the spectral analysis.
N. S. acknowledges support by the french  ANR  and the  german DFG  through the project " GENESIS " ( ANR-16-CE92-0035-01/DFG1591/2-1 ). We kindly thanks  Dr. Patrick Broos  of the  Penn State Universitys astrophysics group  who share some private AE scripts for the diffuse X-ray analysis of the Chandra ACIS-I observations. This research made use of Montage, funded by the  National Aeronautics and Space Administration's Earth Science Technology Office, Computation Technologies Project , under Cooperative Agreement Number  NCC5-626  between  NASA  and the  California Institute of Technology. Montage is maintained by the  NASA/IPAC Infrared Science Archive.
Clinical ndings of individuals with SMARCA1 variants Affected individuals included 31 males and 9 females, aged 2 to 48 years at their last clinical assessment. The affected cohort showed a broad range of clinical features that are summarized in Table  1. Most individuals for whom clinical data were available presented with ID/DD (80%; 31/39) and speech delay or speech regression (85%; 34/40). Increased head circumference (OFC 2 SD) and/or reported macrocephaly/brain overgrowth (45%; 17/38) was identi ed in the index family and reported in eight individuals with LOF variants (Figure  1B ; Table  1 ). However, three individuals with LOF variants without brain overgrowth and ve individuals with missense variants with brain overgrowth phenotype suggest that this feature was more complex and not restricted to LOF variants.
Cohort ascertainment Pathogenic variants were identi ed in SMARCA1 using a combinationof trio-based exome sequencing (ES; 23 families), short read whole genome sequencing (WGS; 3 families) in both clinical diagnostic and research settings. Families were identi ed across several institutions with data shared via nodes of the MatchMaker Exchange (MME) network, including MyGene2, Gene-Matcher, PhenomeCentral, and by querying investigators with large cohorts of patients with intellectual disability, macrocephaly, overgrowth and/or other neurodevelopmental features  (47). All available clinical data from affected individuals were reviewed by the investigators. The study was approved by Seattle Children's Hospital Institutional Review Board (IRB# 13921). All participants or their legal guardians signed an informed consent for publication of clinical and genotype data according to the Declaration of Helsinki or were de-identi ed and included in the study under an IRB approved 'waiver of consent'.
Methylation data analysis The SMARCA1 cohort for methylation analysis consists of 19 individuals from 9 families: 7 females and 12 males for whom samples were available. Among the cohort, four individuals are unaffected female carriers of SMARCA1 variants, 13 carry truncating variants and 6 have missense variants (See Table  1 ). DNA samples derived from peripheral blood were collected and anonymized. Replicate samples were also obtained from 6 individuals, 5 using LB-line and 1 using peripheral blood and were only used for investigating unsupervised clustering using the identi ed probes for the methylation pro les. The DNA obtained from the samples collected were subjected to bisul te conversion using Illumina In nium MethylationEPIC BeadChip kit. The manufacturer's protocols were followed and quality control of the generated methylation data was done using the min R package (56).
Standard data preprocessing for Illumina arrays were implemented, including background correction and normalization. Density plots of beta values were checked, as well as recorded and predicted gender and age differences. Analysis was done on the pre-processed methylation data as described in previous research  (57) (58) (59). Initial ltering of probes includes removing those with failed detected p-values, are located in the X or Y chromosome, cross-reactive, or known to target CpG sites overlapping SNPs. This resulted to 772557 probes before the main analysis. For differential methylation analysis, matched unaffected controls were rst selected from the London Health Sciences Centre EpiSign Knowledge Database (EKD)  (57) (58) (59). Batches in the EKD known to cause batch effects and samples with more than 5% failed probes were excluded.
Selection was done by matching age, sex and array type of the cases using the R MatchIt package (60) with case-control ratios ranging from 1:3 to 1:8, depending on the number of cases in the analysis. Batch effects and outliers in the original cohort as well as the case-control training data used for discovery of differential pro les were inspected using principal component analysis (PCA). Differential analysis of methylation signals between cases and matched controls was implemented for de ning methylation pro les. A linear regression model was tted using the limma R package (61), with the methylation M values as predictors and case/control labels as response. Covariates were also included using the estimated blood cell proportions of the samples. Using empirical Bayes method, moderated t-statistics and corresponding p-values were computed for each probe, and p-values were adjusted using the Benjamini-Hochberg method to control for false discovery rates.
ACKNOWLEDGMENTS We thank the families and referring physicians for their participation in this study. The authors would like to thank  Dr. Pierre Mattar  for critical reading of the manuscript. Special gratitude to  Barbara  and  Eric Mann  whose visionary generosity made this research and discovery possible. We are grateful to all donors to  Seattle Children's Research Institute  who invest in breakthrough discoveries that help prevent, treat, and eliminate childhood disease. This work was supported by operating grants from the  Canadian Institutes of Health Research  ( PJT180577  and  MOP142398 ; DJP); the  Qatar National Research Fund  ( NPRP 5-175-3-051 ;  TB-O ); the  National Human Genome Research Institute  grant  R01 HG009141  and  National Institute of Mental Health of the National Institutes of Health  award  U01MH119689  (EO'H),  SFARI, United States  ( 704413 ; EBR) and from the  Stanley Center for Psychiatric Research at the Broad Institute (EO'H) ; the  Research Foundation -Flanders  ( 1861419N ,  G041821N , and  G056122N ; SW); the  Queen Elisabeth Medical Foundation (SW) ; the  Italian Ministry for Education, University and Research (Ministero dell'Istruzione, dell'Università e della Ricerca -MIUR)   PRIN2020  code  20203P8C3X  (AB); and.
M3 and M5 include similar or larger sample sizes compared to M2 and M4 due to the inclusion of males fledging early (prior to December 15) or helper (nonbreeding) males with known survival, respectively. The red circle labelled TL denotes approximately day 7 when a blood sample was collected for telomere measurement. Grey bars represent life stages when nestlings or juveniles are dependent on parental care and provisioning, whilst black refers to independent life stages. The dotted line represents adulthood and when they were followed until death. For survival models M1-M3 we used general linear models (GLMs; middle panels; Tables  S1-S3 ) and M4-M6 Cox proportional hazards models (CoxPH; bottom panels; Tables  S4-S6 ). The middle panels show raw data (circles) and quartile mean telomere length vs.
Relative telomere length (rTL) was calculated following  Eastwood et al. (2018) , which accounts for variations in well efficiency (mean [SD] efficiency telomere 94. 13 [3. 37]%, GAPDH 97. 34 [2. 72]%), DNA concentration, and the between-plate control. To calculate assay repeatability, we ran extracted DNA of n = 39 samples two to three times on separate plates (n = 110 rTL measures). Using the rptr package  (Stoffel et al. , 2017) , we confirmed that the repeatability of rTL was high (repeatability = 0. 85, SE = 0. 07). When measuring telomere length using qPCR, this yields a combined measurement of terminal telomeres and interstitial telomeric sequences (ITS). To assess the effect of ITS on TL estimates we compared TL measurements using telomere restriction fragment (TRF) between nondenatured and denatured DNA.
| Statistical analysis Statistical analyses were conducted in R version 4. 0. 5 (R Core Team, 2016), and results presented using the ggplot2 package  (Wickham, 2016). TL measurement using qPCR is prone to methodological sources of error which could reduce the power to detect a relationship between survival and TL  (Lindrose et al. , 2021). Here, we address the issue by calculating the TL residuals from a linear regression controlling for nestling age (factor: 6-8 days) and the number of days between blood sample collection and TL qPCR (mean 7289. 21, SD 676. 53 days standardized). qPCR plate ID was included as a random term in a linear mixed effects model but was excluded in favour of the simpler regression above as it explained zero variance. In this model, 7-day-old (estimate [SE] = -0. 41 [0.