dataesr/scientific-paragraphs-categorization huggingface dataset:
3 of ixpeobssim. The software suite ixpeobssim is now publicly available on GitHub. 48 Before processing the event files, we corrected for position offsets and energy calibration offsets as recommended by the IXPE team using the standard ftools (as outlined in the README file associated with the data). For 4U 1626-67, the core region was filtered from the rest of the event list using a 60″ aperture around the centroid of the X-ray data. The background region was derived from an annulus region with an inner radius of 120″ and an outer radius of 240″. Information on the polarization in the core of 4U 1626-67 was obtained by running the xpbin tool on the selected event list with the pcube algorithm. The resulting data structure, hereafter the polarization cube, measures the polarization degree and angle with their uncertainties along with the minimum detectable polarization at the 99% level of significance of the region in a given energy band.
The data from ObsID 5661010103 are unusable, due to visibility constraints that are likely a result of glint off the solar panels on the ISS (K. Gendreau 2022, private communication). The observations were processed using version 9 of the NICER Data Analysis Software (NICERDAS) in HEASOFT 6. 30. 1. The following filtering criteria were imposed in the construction of good time intervals: undershoot (dark current) count rate per FPM underonly_range < 500 c s -1 ; overshoot (charged particle saturation) count rate per FPM overonly_range < 1. 5 c s -1 and 1. 52 × 1. 5 × COR_SAX -0. 633 c s -1 ; 38 operational FPMs; pointing offset of <0°. 015 from the nominal source position; and 20°for the source-Earth limb angle (30°for the bright Earth angle). Response files were generated for each ObsID using nicerrmf and nicerarf available as part of NICERDAS.
26250 ) and 86 ks (ObsIDs 26009 and 26086), respectively. All the observations were taken with the High Energy Transmission Grating Spectrometer (HETGS) in the timed exposure mode  (Canizares et al. 2005). We extracted the high-energy grating (HEG) and medium-energy grating (MEG) spectra using the CIAO X-ray data analysis package  (Fruscione et al. 2006) , along with the most recent calibration (CALDB) products, and we reduced them following the processing procedures from the Chandra Gratings Catalog and Archive (TGCat;  Huenemoerder et al. 2011 ). X-ray spectral analysis was performed using the SPEX fitting package  (Kaastra et al. 2020)  v3. 06. 01. The spectral shape of the continuum did not vary among the different observations and we combined the spectra using the combine_grating_spectra tool.
Timing In order to perform pulse-phase-resolved polarization analysis of 4U 1626-67, we generated a timing model using the NICER data, which bracketed the IXPE observation span (see Table  1 ). A simple Fourier power spectrum analysis of the NICER data showed pulsations at a period of ∼7. 668 s. With this initial estimate, we generated 64 pulse times of arrival (TOAs) using the photon_toa. py script in the NICERsoft 49  data analysis repository. They were constructed by using a Gaussian pulse template and imposing an integration time of 300 s (with a minimum exposure time of 200 s). The final timing parameters were then obtained through a weighted leastsquares fit in the PINT pulsar data analysis package, by computing model corrections for the residuals between the initial TOAs and the pulse phase model involving the first frequency derivative.
All these difficulties can be overcome today. We indeed now have access to decades of in-situ measurements from a fleet of spacecraft that have been or still are exploring the near-Earth environment. Many of these missions have been operating at a time when solar wind monitoring was available. Furthermore, handling such a massive, heterogeneous and complex dataset to build large statistics is now within reach with the help of statistical learning. In this study, statistical learning was key to automatically detect all time intervals during which Cluster, Double Star, THEMIS and MMS spacecraft have measured magnetosheath data, as explained in the method section. This allowed us to gather the unprecedented number of 45 million magnetosheath in-situ measurements at 5 second resolution over a period of two decades, offering an excellent spatial coverage of the 3D dayside magnetosheath, under all and statistically unbiased IMF orientations (Figures  1f  and g ).
This may marginally impact the draping close to the boundary. An effect that we neglect in the present discussion by considering all IMF clock angles together. In the following, the data will thus adequately Panel e shows in the orbit of the probe on a 5-day period with the dotted line. The bow shock  [11]  and magnetopause  [45]  are represented as solid gray lines. In a-c and e, the green, red, and blue color filling and line colors correspond to the time intervals automatically classified by the machine learning algorithm as magnetosphere, magnetosheath and solar wind respectively. The blue and green histograms in f and g correspond to the polar density distribution of the IMF clock and cone angle, respectively, for all times associated with magnetosheath measurements. The blue and green solid lines represent the same distributions but for the whole twenty-six years of OMNI data.
Selection of magnetosheath data The plasma moments and magnetic field were used to automatically identify  [30]  the magnetosheath dataset with a gradient boosting algorithm  [33]. A perfect forecast obtains an Heidke Skill Score (HSS) and Area Under Curve (AUC) score of 1. Our classification is highly effective considering that it obtains average values of 0. 980 and 0. 998 for each score respectively. The data with density lower than 4cm -3 has been removed to discard the majority of the magnetopause boundary layer crossings. More than 50 millions of magnetosheath in-situ measurements have been automatically selected. The aberration on the velocity due to the orbital motion of the Earth has been corrected.
Solar wind parameters Each magnetosheath data point is associated to solar wind and IMF properties (magnetic field, density, temperature, velocity, dynamic pressure, Mach number, plasma beta) from the OMNI dataset  [20]  measured at a previous time. This time delay is associated with the propagation of the IMF between the nose of the bow at which OMNI data is defined and the spacecraft position. The time delay is estimated using the propagation method of Safrankova et al. 2002  [42].
Spatial representation of the dataset First, it should be noticed that the same methodology has been used to build the spatial representation of the draping, regardless of the origin of the magnetic field value (model or data). The GSM positions have been normalized between the average models of  Shue et al. 1998 [45]  and Jelinek et al. 2012  [11]  using the relative positions to the machine learning boundaries models given the solar wind conditions of each point. The points located too far outside the magnetosheath as predicted by the boundaries models have been excluded, bringing the total number of data in this study to almost 45 million measurements. The solar wind interplanetary (SWI) magnetic field coordinate system  [53]  is used in this study. This system depends on the magnetic field and velocity of the solar wind.
Figure1: In-situ data and orbit from THEMIS B probe onMay 16 2008. Panels a to d show respectively the ion density, the magnetic field components, the velocity components, the omnidirectionnal energy fluxes of ions. Panel e shows in the orbit of the probe on a 5-day period with the dotted line. The bow shock [11]  and magnetopause [45]  are represented as solid gray lines. In a-c and e, the green, red, and blue color filling and line colors correspond to the time intervals automatically classified by the machine learning algorithm as magnetosphere, magnetosheath and solar wind respectively. The blue and green histograms in f and g correspond to the polar density distribution of the IMF clock and cone angle, respectively, for all times associated with magnetosheath measurements. The blue and green solid lines represent the same distributions but for the whole twenty-six years of OMNI data.
Partitions that include autism GWAS loci  11  are enriched among positive (z-score >0) S-pTDT scores in both SSC + SPARK and PGC trios: 29 of 46 (63%) partitions including an ASD GWAS locus are located in the top right quadrant, compared with expectation of 12 partitions (P = 1. 7 × 10 -8 ) (Fig. 1b , red points). This observation is consistent with the expectation that individuals with autism on average over-inherit alleles that increase the probability of autism diagnosis. Unexpectedly, partitions with large S-pTDT z-scores cluster on 16p (approximately 0-33 Mb; Fig. 1b , blue points): of the 12 partitions with the largest average S-pTDT z-score across SSC + SPARK and PGC, 5 are on 16p, whereas the other 7 localize to autism GWAS loci. The three partitions that are nominally S-pTDT enriched in both datasets (S-pTDT z-score >1.
2 CNV. Further linking the 16p11. 2 CNV with the broader p-arm of the chromosome, in vitro deletion of the 16p11. 2 locus was associated with decreased average expression of neuronally expressed genes on chromosome 16p. Similarly, an increased autism polygenic score (PGS) constructed exclusively with 16p variants was associated with decreased average expression of cortically expressed genes on 16p across multiple cohorts. These transcriptional effects of the 16p11. 2 deletion and 16p autism PGS were correlated at the level of individual genes on 16p, suggesting mechanistic convergence of common and rare variant influences on autism in the region. We observed chromatin contact patterns that we hypothesize explain this transcriptional convergence: uncommonly high within-16p chromatin contact in two independent Hi-C datasets and increased contact between the 16p11.
The 16p autism PGS associates with decreased 16p gene expression Our analysis of 16p11. 2 deletion lines suggests that this genetic event causes transcriptional perturbation across 16p. Given that our S-pTDT analysis identified excess polygenic influence on autism across 16p, we tested the hypothesis that this common variant factor would also associate with decreased mean expression across 16p. We analyzed paired genotype and expression data from three sources. First, we drew on data from ongoing single-nucleus RNA-sequencing (snRNA-seq) analysis of prefrontal cortex (Brodmann area 46) from 122 European ancestry donors from the Harvard Brain Tissue Resource Center/National Institutes of Health (NIH) NeuroBioBank (HBTRC) (Supplementary Fig. 18 ); we performed our analyses in glutamatergic neurons because they were the most abundant cell type and the most similar to the induced neurons from the in vitro deletion analysis  41.
Next, we analyzed paired genotype and bulk cortical RNA-seq from the CommonMind Consortium, split into two ancestry-specific subgroups (n = 193 individuals of African ancestry, n = 229 individuals of European ancestry) (Supplementary Fig. 19 )  42. Both the HBTRC and the CommonMind cohorts included donors with and without schizophrenia, and we controlled for this diagnostic status in our analyses. Within each cohort, we constructed regional PGSs for autism within the 33-Mb partitions described above and regressed average regional gene expression on the regional PGS (Methods). To increase power, and to be consistent across datasets, we restricted each of the three analyses to half the genes with the highest expression in glutamatergic neurons in the HBTRC data (n = 8,878 genes; Methods). Increased autism PGS within 16p was associated with decreased expression of genes throughout the 16p region (Fig.
Convergence of gene expression and chromatin contact at 16p Given that both the 16p11. 2 deletion and 16p autism PGS are associated with decreased average gene expression in 16p, we asked whether these effects converged at the level of individual genes. We found a positive association between the per-gene expression effects of the 16p11. 2 deletion and the 16p autism PGS across 168 glutamatergically expressed genes shared across both datasets (r = 0. 18, P = 0. 02; Fig. 4a  and Supplementary Fig. 22 ). This observation suggests that the common variant 16p autism PGS and the rare variant 16p11. 2 deletion share downstream functional impact on gene expression. We also note that genes with expression decreased in response to both the 16p autism PGS and the 16p11. 2 deletion are enriched at the end of 16p (Ch16: 0-5.
Article https://doi. org/10. 1038/s41588-022-01203-y chromosome 16 is very gene dense-with 182 genes, it is the second most gene dense of 526 5. 2-Mb regions in the genome. As with 16p more broadly, it is enriched in genes specifically expressed in adult cortex (n = 33, 83% more than expected by chance, P = 0. 0002). The correlation in transcriptional effects associated with the 16p PGS and 16p11. 2 deletion motivated us to explore genomic structural factors that could help to explain these coordinated effects across a large segment of the genome. We hypothesized that the 16p region may have increased within-region chromatin contact, which could explain the apparent nonindependence of genetic and expression variation on megabase scale. To examine chromatin contact within 16p, we used two published Hi-C datasets: a dataset of lymphoblastoid cell lines (LCLs)  43  and a dataset from the primarily neuronal midgestational cortical plate  44.
The i,jth entry of a Hi-C contact matrix estimates the degree of physical interaction between the ith and jth regions of the genome. We estimated contact within 33-Mb partitions as the mean of the off-diagonal values of the contact matrix. As segmental duplication content and gene density of the partition are associated with mean Hi-C estimates (Supplementary Fig. 23 ), we regressed them out to yield a per-partition z-score, which we interpreted as the Hi-C regional contact corrected for these genomic features. The 16p partition exhibits high levels of within-region contact in both cohorts: 4/74 highest partition in LCL (z-score: 1. 50) and 2/74 highest in the cortical plate dataset (z-score: 2. 05) (Fig. 4b ). We hypothesize axis, the association t-statistics from the all sample meta-analyses of 16p PGS and 16p gene expression; on the y axis, the association t-statistics from the 16p11.
2 in vitro deletion analysis. The shaded region is the 95% CI. Genes are colored by their location on 16p; telomeric is defined as a gene midpoint <5. 2 Mb. A single outlying point has been truncated from the plot for visualization; the untruncated plot is shown in Supplementary Fig. 22. b, Hi-C analysis revealing elevated within-region chromatin interaction at 16p. Each point represents a 33-Mb partition, with 16p colored blue. Both axes are in units of residual z-score, where the residual is from a linear model regressing out segmental duplication content and gene count from the mean within-region Hi-C contact value (Methods). The x axis is a dataset of LCLs, whereas the y axis is a dataset of midgestational cortical plate. c, Hi-C analysis revealing elevated contact between the 16p11. 2 locus and the 5.
Supplementary analyses for 16p S-pTDT association First, we confirmed that autism-associated loci through GWAS were enriched in the S-pTDT distribution. We defined an autism-associated locus as the five loci from the most recently published autism GWAS reaching genome-wide significance from analysis of autism alone (index SNPs: rs910805, rs10099100, rs201910565, rs71190156 and rs111931861)  11. Next, we analyzed the distribution of autism-associated CNVs in the S-pTDT distribution. We identified autism-associated CNVs from the set on SFARI Gene (https://gene. sfari. org/database/cnv) and then identified the S-pTDT partitions with at least one of these CNVs within the boundary (16p11. 10 ). We also estimated the association between segmental duplication content and S-pTDT for the 33-Mb partitions: we annotated each partition for segmental duplication rate by calculating the fraction of nucleotides in each partition that overlapped at least one segmental duplication per the University of California, Santa Cruz (UCSC) Genome Browser  51.
Autism differential expression analysis We analyzed whether genes on 16p are overrepresented in differentially expressed genes between individuals diagnosed with autism and controls. We used a previously published differential expression dataset of human brain RNA-seq (n = 51 individuals with autism, n = 936 controls)  34. We defined genes as differentially expressed at Bonferroni's significant level correcting for the number of genes overlapping between the dataset and our consensus gene list described above (n = 15,288 genes, n = 83 differentially expressed genes, n = 383 genes on 16p). We performed a χ 2 test for enrichment of differentially expressed genes on 16p.
8%, astrocytes 13. 5%, oligodendrocytes 8. 0%, polydendrocytes 5. 2%, microglia 1. 5% and endothelia 1. 0%. All downstream analyses used expression data from glutamatergic neurons. The cell type-specific gene-by-donor expression matrices were processed with VST normalization  57. We performed a number of pre-association quality control (QC) steps. The majority of genotyped samples were European ancestry (1,707/1,770, 96%) and we identified these samples for downstream https://doi. org/10. 1038/s41588-022-01203-y analysis using PCA (Supplementary Fig. 18 ). Next, we identified any samples as expression outliers with mean expression >3 s. d. from the cohort mean (3/125 samples). This yielded a final EUR subset of 122 samples. We identified genes with expression above median across the 122 samples in the count matrix, with the only processing step of normalization of expression count sum equal for all samples.
We used these genes (n = 8,878) in all subsequent regional PGS-expression analyses. CommonMind (bulk cortical RNA-seq). We next analyzed paired genotype and bulk DLPFC expression data from donors in the Com-monMind consortium. Generation of expression count matrices is described in the CommonMind publication  42. Within CommonMind, we restricted analysis to donors from the National Institute of Mental Health (NIMH) Human Brain Collection Core (HBCC) and the University of Pittsburgh (PITT) biobanks due to previous analysis demonstrating increased concordance with the snRNA-seq resource described above. We performed variance stabilization on the count matrices separately in HBCC and PITT using the SCTransform package in Seurat with the goal of closely paralleling the approach of the single-nucleus resource (parameters: do.
We performed two classes of local PGS-gene expression association. The first class is a per-gene association, as in Fig. 3a. The second is average gene association, as in Fig. 3b, c. To be consistent across datasets, we restricted all analyses to half the genes most expressed in glutamatergic neurons in the HBTRC data (n = 8,878 genes). The association in Fig. 3a  is a per-gene association meta-analyzed across the three cohorts. We combined individual-level expression and genotype PGS across the three cohorts; before concatenating the PGSs and expression matrices used in the individual cohort analyses, we within-cohort scaled per-gene expression and per-partition PGS to mean = 0 and s. d. = 1. Per-gene association followed the linear model: gene expression ≈ regional PGS + schizophrenia diagnostic status + ancestry (binary for yes/no African ancestry) + single cell (binary yes/no).
We performed this analysis in the midgestational cortical plate data only because the 1-Mb bin resolution of the LCL resource did not have sufficient resolution. We defined the telomeric region based on the gene-dense segment at the start of chromosome 16, from 0 Mb to the closest 100-kb segment after the endpoint of the final brain-expressed gene in that window in the 16p11. 2 deletion dataset (5. 2 Mb). We defined the 16p11. 2 locus as Ch16: 29. 5-30. 2 Mb. To define control contact regions, we first calculated the minimum (24. 3 Mb) and maximum (30. 2 Mb) distances spanned by the contact matrix defined by 0-5. 2 Mb (telomeric region) and 29. 5-30. 2 Mb (16p11. 2 locus). We then defined control contacts on 16p as all contacts of distance >24. 3 or <30. 2 that were not located in the telomeric-CNV contact range described above.
Acknowledgements We thank the following for their generous support of this work: the  SFARI  (grant no. 704413  to E. B. R. and L. J. O. ), the  NIMH  (grant nos. F30MH129009  to D. J. W. ,  R01MH111813  to E. B. R. ,  R01MH115957  to M. E. T. and  1R01MH124851-01  to A. D. B. ), the  National Institute of General Medical Sciences  (grant nos. T32GM007753  and  T32GM144273  to A. N. and D. J. W. ), the  National Institute of Child Health and Development  (grant no. R01HD096326  to M. E. T. ), the  National Institute of Neurological Disorders and Stroke  (grant no. R01NS093200  to M. E. T. ), the  National Library of Medicine  (grant no. T15LM007092  to D. J. W. ), the  Lundbeck Foundation  (grant nos. R102-A9118 ,  R155-2014-1724 , and  R248-2017-2003  to iPSYCH), the  Novo Nordisk Foundation  (to the  Danish National Biobank)  and the universities and university hospitals of  Aarhus and Copenhagen.
Data availability Individual-level genotypes are available via request to SFARI (https:// www. sfari. org) and from the PGC (https://pgc. unc. edu) and its contributing data holders. GWAS summary statistics from iPSYCH are available by request from members of the consortium. RNA-seq data are available from collection 2304 at the NIMH data archive (https://nda. nih. gov/ edit_collection. html?id=2304) and the CommonMind Consortium (https://www. nimhgenetics. org/resources/commonmind). Hi-C data are available via the respective referenced publications. GTEx-specific expression data are available from the Price Lab repository (https:// alkesgroup. broadinstitute. org/LDSCORE/LDSC_SEG_ldscores/tstats). Additional gene information is available from the gnomAD browser (https://gnomad. broadinstitute.
The rapid emergence of large-scale atlas-level single-cell RNA-seq datasets presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integration approaches to be not only computationally scalable, but also capable of preserving a wide range of fine-grained cell populations. We created Portal, a unified framework of adversarial domain translation to learn harmonized representations of datasets. With innovation in model and algorithm designs, Portal achieves superior performance in preserving biological variation during integration, while achieving integration of millions of cells in minutes with low memory consumption. We show that Portal is widely applicable to integrating datasets across samples, platforms and data types (including.
Introduction Advances in single-cell sequencing have enabled identification of novel cell types  [1, 2] , investigation of gene regulation networks  [3, 4] , and understanding of cellular differentiation processes  [5, 6]. As single-cell technologies rapidly evolved over recent years, its experimental throughput substantially increased, allowing researchers to profile increasingly complex and diverse samples, and accelerating the accumulation of vast numbers of rich datasets over time  [7, 8, 9]. Integrative and comparative analyses of such large-scale datasets originating from various samples, different platforms and data modalities, as well as across multiple species, offer unprecedented opportunities to establish a comprehensive picture of diverse cellular behaviors. Integration is a critical step, to account for heterogeneity of different data sources when taking advantage of single-cell data from different studies  [10].