dataesr/scientific-paragraphs-categorization huggingface dataset:
Temporal signal The strength of temporal signals in these genomes was evaluated by a Bayesian Evaluation of Temporal Signal (BETS) analysis  [39]  using BEAST v1. 10. 5pre [40]. We performed BETS runs for each HC20 cluster in Table  2. For each dataset, we compared relaxed substitution clock and strict substitution clock models with the correct sampling times (heterochronous) and with (artificial) uniform sampling times (isochronous). Marginal likelihoods were generated using generalized stepping-stone sampling (GSS) for each clock model  [62] , and Log Bayes factors were calculated as the difference between the marginal likelihoods for heterochronous and isochronous data for that model. Models with a Log Bayes factor of 5 or more were accepted as having strong significant evidence for a temporal signal.
Acknowledgments The ATCC14028s Study Group consisted of  Steven Huynh a , Lisa Gorski a , Anita S. Liang a , Ohannes Guerbidjian f , Chien-Shun Chiou g , Niall Delappe h , Heike Claus i , Francois-Xavier Weill j , Derek Brown k , Eva Litrup l and Mia Torpdahl l ,  Anthony M. Smith  m and Jake David Turnball n. We also gratefully acknowledge the receipt of bacterial strains and/or DNA as well as historical information from  David Holden ,  Michael Hensel  and  Roman Gerlich. Addresses of the members of the ATCC14028s Study Group: a  USDA, Agricultural Research Service, Albany, CA 94710, U. S. A. , f Dept. of Biology, California State University-Northridge, Northridge, CA 91330, U. S. A. , g Central Regional Laboratory, Center for Diagnostics and Vaccine Development, Centers for Disease Control, Taichung, Taiwan, h National Salmonella, Shigella and Listeria Reference Laboratory, Galway, Ireland, i Institut fu ¨r Hygiene und Mikrobiologie, Universita ¨t Wu ¨rzburg, 97080 Wu ¨rzburg, Germany, j Institut Pasteur, Unitedes Bacte  ´ries pathogènes enteriques, 75724 Paris cedex 15, France, k  Scottish Microbiology Reference Laboratory, Glasgow, UK, l SSI, Copenhagen, Denmark, m Centre for Enteric Diseases, National Institute for Communicable Diseases, Johannesburg, Gauteng 2131 ,  South Africa , n The  National Collection of Type Cultures, Colindale, London, UK.
Data Availability Statement Computational resources were provided by the ARCHER UK National Supercomputing Service and the MONSooN system. The latter is a collaborative facility supplied under the Joint Weather and Climate Research Programme (JWCRP), a strategic partnership between the Met Office and NERC. The WED025 simulation output created for this study is available on the CEDA Archieve: https://doi. org/10. 5285/5F9E37B3-AF39-4190-99A8-8AF84EA51D88 (Bull, 2020). The scripts used to prepare the regional model configuration, and model components are available on https://doi. org/10. 5281/zenodo. 3909234. The WED025 configuration is also the basis of a new NEMO trunk reference configuration: https://zenodo. org/record/3767939. B-SOSE as used here is evolved from  Verdy and Mazloff (2017).
Study design, setting and patient identification This study was conducted as part of the "Molecular Diagnosis and Risk Stratification of Sepsis" (MARS) project, a prospective observational cohort study in the ICUs of two tertiary teaching hospitals (Academic Medical Center in Amsterdam and University Medical Center in Utrecht) in the Netherlands (ClinicalTrials. gov identifier NCT01905033)  [12, 13]. Between January 2011 and January 2014, all patients above 18 years of age admitted with an expected length of longer than 24 h were included via an opt-out method approved by the medical ethical committees of the participating hospitals. CAP was diagnosed by the clinical team and subsequently reassessed and categorized in a four-point scale (ascending from none, possible, probable to definite) by dedicated research nurses, as described before  [12, 14].
Blood gene expression microarrays Whole blood was drawn in PAXgene ™ tubes (Becton-Dickinson, Breda, the Netherlands) within 24 h after ICU admission. PAXgene ™ blood samples were also collected from 42 healthy controls [median age 35 years (interquartile range 30-63); 57% male] after obtaining written informed consent. Total RNA was extracted using the PAXgene blood mRNA kit (Qiagen, Venlo, the Netherlands), according to manufacturer's instructions. Total RNA (RNA integrity number > 6. 0) was processed and hybridized to the Affymetrix Human Genome U219 96-array and scanned by using the GeneTitan instrument at the Cologne Center for Genomics (CCG, Cologne, Germany), as described by the manufacturer (Affymetrix). Raw data scans (. CEL files) were read into the R language and environment for statistical computing (version 2.
15. 1; R Foundation for Statistical Computing, Vienna, Austria; http:// www. R-proje ct. org/). Pre-processing and quality control were performed by using the Affy package (version 1. 36. 1) [18]. Array data were background corrected by robust multi-array average, quantile-normalized and summarized by median polish using the expresso function. The resultant 49,386 log-transformed probe intensities were filtered by means of a 0. 5 variance cutoff using the genefilter method  [19]  to recover 24,646 expressed probes in at least one sample. The occurrence of non-experimental chip-effects was evaluated by means of the Surrogate Variable Analysis (R package version 3. 4. 0) and corrected by the empirical Bayes Method ComBat  [20, 21]. The non-normalized and normalized MARS gene expression data sets are available at the Gene Expression Omnibus public repository of NCBI under the accession number GSE65682.
Statistical analysis All categorical variables are presented as numbers (percentages), parametric continuous variables are presented as means ± standard deviation (SD) and nonparametric continuous variables are presented as median and interquartile ranges (IQR, 25th and 75th percentiles). Data distribution was assessed by the Shapiro-Wilk test. A Mann-Whitney U or a Kruskal-Wallis test was used to analyse continuous nonparametric data, whereas continuous parametric data were analysed using a Student's t-test or analysis of variance (two-sided analysis of variance). All categorical data were analysed using a Chi-square or Fisher exact test. Cox proportional hazard analysis of hospital mortality was performed using the Survival R package (version 3. 2-7). Aside from CRP, platelets, PT and APTT, all plasma protein levels were log10 transformed for plotting purposes; all biomarkers were analysed using a Mann-Whitney U test.
ICU admission Direct Delayed * Fig. 2  Host response plasma biomarkers in patients with community-acquired pneumonia with direct or delayed admission to the intensive care unit. Plasma biomarkers were measured on intensive care unit admission. Data are expressed as box-and-whisker plots depicting the median with the lower and upper quartiles. Upper and lower whiskers are defined as measurements that are within 1. deferred ICU admission defined "delayed" as 6-24 h after ED presentation  [29] [30] [31]. In our study, we sought to investigate delays in ICU admission that were more likely related to less severe disease on ED presentation with later deterioration, rather than by organizational factors. Therefore, we defined delayed ICU admissions as those involving patients that were first admitted to the ward and subsequently transferred to the ICU within a relative wide time window of 72 h; we chose not to study patients admitted to the ICU after more than 72 h in order to maintain a reasonable connection between the primary reason for ED presentation and the subsequent need for intensive care.
Acknowledgements The authors acknowledge all members of the  Molecular Diagnosis and Risk Stratification of Sepsis consortium  for the participation in data collection and especially acknowledge:  F. M. de Beer ,  L. D. Bos ,  G. J. Glas ,  J. Horn ,  A. J. Hoogendijk ,  R. T. van Hooijdonk ,  M. A. Huson ,  T. van der Poll ,  B. Scicluna ,  L. R. Schouten ,  M. J. Schultz ,  M. Straat , L. A. van Vught,  L. Wieske ,  M. A. Wiewel , and  E. Witteveen ;  Amsterdam University Medical Centers , location  AMC, Amsterdam , the  Netherlands ,  M. J. Bonten ,  O. L. Cremer ,  J. F. Frencken ,  K. van de Groep ,  P. M. Klein Klouwenberg ,  M. E. Koster-Brouwer ,  D. S. Ong , and  D. M. Verboom ;  University Medical Center Utrecht, Utrecht , the  Netherlands.
Large-scale comparative neuroscience requires data from many species and, ideally, at multiple levels of description. Here, we contribute to this endeavor by presenting diffusion and structural MRI data from eight primate species that have not or rarely been described in the literature. The selected samples from the Primate Brain Bank cover a prosimian, New and Old World monkeys, and a great ape. We present preliminary labelling of the cortical sulci and tractography of the optic radiation, dorsal part of the cingulum bundle, and dorsal parietal-frontal and ventral temporal-frontal longitudinal white matter tracts. Both dorsal and ventral association fiber systems could be observed in all samples, with the dorsal tracts occupying much less relative volume in the prosimian than in other species.
Introduction The comparative method is one of the primary ways to gain insight into brain evolution. In the study of humans and other primates, this paradigm has been very successful in elucidating the evolution of the size of the brain and some its constituent parts [e. g. ,  Miller et al. (2019) ;  Smaers and Vanier (2019) ]. Although these methods are able to identify specializations in different branches of the primate tree, including instances of mosaic evolution  (Barton and Harvey 2000; Smaers and Soligo 2013) , a poverty of data in this field has also meant that few datasets are available and definition of constituent parts is often difficult to determine, giving rise to strong controversies in the field. A prominent example of such a debate is whether the size of human prefrontal cortex follows trends expected based on other primates  (Barton and Venditti 2013; Passingham and Smaers 2014).
2019). This means that tissue that has been scanned remains available for further analyses, whether using neuroimaging or more traditional techniques. The digital nature of neuroimaging data makes data sharing easier, facilitating direct comparisons across various datasets. As neuroimaging and traditional techniques are often the expertise of different labs, this type of data sharing is crucial to reach a full understanding of brain diversity. Neuroimaging thus has potential beyond producing new data; it allows a fusion of data types describing brain organization at different levels of resolution  (McKavanagh et al. 2019). This, in turn, enhances understanding of brain diversity at multiple scales. Diffusion MRI in particular has recently proven a popular tool in comparative neuroimaging. Tractography algorithms allow one to use these data to reconstruct the major fiber bundles of the brain, which are an excellent starting point for quantitative comparisons of brain organization.
Samples and scanning We acquired eight brains from the Primate Brain Bank (PBB)-one prosimian primate (bushbaby), four New World monkeys (night monkey, woolly monkey, white-faced saki, and tufted capuchin), two Old World monkeys (black-andwhite colobus and grey-cheeked mangabey), and one great ape (chimpanzee). Demographics are listed in Table  1. All samples were returned to the PBB after scanning and remain available for future research. In addition, we used data from one rhesus macaque (Macaca mulatta, male, 4. 03 years of age at death) from a previous study  (Folloni et al. 2019 ) for comparison with this most commonly studied primate. Phylogenetic relationships between all species in this study and the human are displayed in Fig. 1. Prosimian and monkey data were acquired locally on a 7 T magnet with an Agilent DirectDrive console (Agilent Technologies, Santa Clara, CA, USA) using a 2D diffusionweighted spin-echo protocol with single line readout (DW-SEMS, TE/TR: 25 ms/10 s; Matrix = 128 × 128; number of slices: 128; Resolution: 0.
3 × 0. 3 × 0. 3 mm for the galago, 0. 4 × 0. 4 × 0. 4 mm for the night monkey, 0. 5 × 0. 5 × 0. 5 mm for the saki monkey, 0. 6 × 0. 6 × 0. 6 mm for the woolly, capuchin, colobus, and mangabey; diffusion data were acquired over the course of ~ 52. 5 h). 16 non-diffusion-weighted (b = 0 s/mm 2 ) and 128 diffusion-weighted (b = 4000 s/mm 2 ) volumes were acquired with diffusion directions distributed over the whole sphere. The brains were soaked in PBS before scanning and placed in fluorinert during the scan. Great ape data were acquired on a 7 T whole-body scanner with an 28 channel knee coil (QED) using a 3D diffusion-weighted steady-state free precession  (McNab et al. 2009 ) protocol (DW-SSFP, TE/TR: 18 ms/22 ms; flip angle: 50°; resolution: 0. 6 × 0. 6 mm 2 ; number of slices: 176; slice thickness: 0.
6 mm; EPI factor: 1; diffusion data were acquired over the course of ~ 50 h). 12 non-diffusionweighted (q-value = 20 cm -1 ) and 240 diffusion-weighted (q-value = 300 cm -1 ) volumes were acquired with diffusion directions distributed over the whole sphere. Accurate modelling of DW-SSFP data requires the estimation of DW-SSFP dependencies (T1, T2 and B1)  (Buxton 1993). To account for this, T 1 -weighted data were obtained using a turbo inversion recovery  (TIR, TIs: 30, 60, 120, 240, 480  and 940 ms; TE/TR: 17 ms/1020 ms; resolution: 0. 8 × 0. 8 mm 2 ; number of slices: 112; slice thickness: 0. 8 mm; TIR data acquired over the course of ~ 2 h), T 2 -weighted data were obtained using a turbo spin-echo (TSE, TEs: 8. 7, 17, 26, 35, 52 and 70 ms; TR: 1000 ms; resolution: 0. 8 × 0. 8 mm 2 ; number of slices: 112; slice thickness: 0.
06 ms/10. 12 ms; flip angle: 39°; resolution = 0. 4 × 0. 4 mm 2 ; number of slices: 256; slice thickness: 0. 4 mm; TRUFI data acquired over the course of ~ 22 min). 4 TRUFI datasets were acquired (phase increment: 0°, 90°, 180°, and 270°) and averaged via root sum of squares.
Diffusion MRI data preprocessing All prosimian and monkey datasets were converted to NIFTI format, reoriented to approximate AC/PC orientation with the origin set at the middle of the anterior commissure, and preprocessed using tools from FSL (www. fmrib. ox. ac. uk/ fsl). These steps are implemented in the 'phoenix' module of the MR Comparative Anatomy Toolbox (Mr Cat; www. neuro ecolo gylab. org). We used BedpostX to fit a crossing fiber model to each dataset  (Behrens et al. 2007 ), allowing up to three fiber orientations per voxel. This tool produces voxel-wise posterior distributions of fiber orientations that are subsequently used for probabilistic tractography. For the great ape DW-SSFP data, prior to analysis a Gibbs-ringing correction  (Kellner et al. 2016 ) was performed on both the diffusion-weighted and non-diffusionweighted datasets.
To account for the DW-SSFP dependencies (T 1 , T 2 and B 1 ), T 1 and T 2 maps were estimated from the TIR and TSE datasets assuming mono-exponential signal evolution (the Gibbs correction was also performed on the TIR and TSE data). A B 1 map was generated from the AFI data following the methodology described in  Yarnykh (2007). All co-registrations between and within modalities were performed using FSL FLIRT using a 6 degrees-of-freedom (rotation and translation) transformation. These datasets were subsequently fit using custom diffusion tensor and ball and two stick models which incorporates the full Buxton DW-SSFP signal equations  (Buxton 1993 ) using cuDIMOT  (Hernandez-Fernandez et al. 2019).
Surface reconstruction Cortical surfaces were reconstructed from the structural scans using FreeSurfer v6. 0  (Fischl 2012). Processing steps included tissue segmentation of grey and white matter, identification of subcortical structures, and surface reconstruction. To obtain successful reconstructions of the non-human primate brains, the FreeSurfer pipeline was complemented with tools from FSL v6. 0. 1  (Jenkinson et al. 2012) , ANTs  (Avants et al. 2011), and MATLAB (2017a, The Mathworks, Inc. , Natick, Massachussets, United States). A brain mask was used to separate brain tissue from any other structures present in the scans such as fiducial markers. Bias field correction was then applied to the images with ANTs. Voxel intensities from the original MGE and TRUFI images were inverted to obtain a T1-like contrast with low voxel intensities in gray matter voxels and high intensities in white matter voxels.
For the mangabey, an average b0 volume was used as the original image instead. Next, tissue segmentation was performed using FreeSurfer. Manual corrections were made where necessary to correct segmentations of white matter, cerebellum, and subcortical structures. FreeSurfer's default Talairach registration step was complemented by a step-by-step registration process, starting with a registration from the subject at hand to a macaque template brain  (Seidlitz et al. 2018) , followed by a registration from the macaque template to a chimpanzee template brain (based on data from chimpanzeebrain. org), and from the chimpanzee template to human Talairach space. The chimpanzee brain was first registered to the chimpanzee template brain and then to human Talairach space, skipping the macaque template step.
This step-by-step registration process ensured that major brain structures such as the cerebellum and thalamus were correctly segmented in the smaller brains. Next, surface reconstruction was started on the high-resolution volumes. The pial surface was refined by alignment to a downsampled version of the original volumes using the high-resolution scripts from the HCP FreeSurfer pipeline (https:// github. com/ Washi ngton-Unive rsity/ HCPpi pelin es/ tree/ master/ FreeS urfer/ custom). Tissue segmentations and surface reconstructions were visually checked for accuracy and consistency across datasets. It should be noted that the reliance on manual interference does produce some artifacts, especially in the territory underneath the corpus callosum. This subcortical area, however, is usually masked out when working with cortical surface data.
Results We collected high-quality diffusion and structural MRI data from eight primate species that are not or little described in the comparative neuroimaging literature, as well as presenting data from the well-studied rhesus macaque. Samples were selected for their high quality and the absence of many large cuts due to the brain extraction process. Nevertheless, some cuts were present and evident in the imaging data around the occipitotemporal cortex in the night monkey, capuchin monkey, woolly monkey, and colobus monkey (Fig. 2 ). In addition, the saki monkey does not have a cerebellum attached anymore.
Discussion Gaining a full understanding of the similarities and differences across different species' brains requires multiple types of data that are available in multiple species, allowing comparisons across different scales within a species followed by comparisons between species using the same data types. We have termed these comparisons vertical translations and horizontal translations  (Mars et al. 2021) , respectively, and demonstrated that both are necessary to gain a full understanding of brain diversity  (Eichert et al. 2020). Here, we present data on sulcal morphology and white matter anatomy from eight primate species that are not or hardly described in the literature. The data are freely available to allow quantitative comparisons across species. Importantly, the brain samples are still available at the Primate Brain Bank, opening the door for acquisition of more, including potentially more destructive, data types from these brains to increase the number of possible comparisons.
Such analyses require quite extensive datasets. The current dataset adds to the growing number of initiatives that make data from a variety of species openly available to the scientific community  (Thiebaut De Schotten et al. 2019). Such initiatives include the Primate Data Exchange  (Milham et al. (2018) ; fcon_1000. projects. nitrc. org/indi/indiPRIME. html); the National Chimpanzee Brain Resource (www. chimp anzee brain. org), the Japan Monkey Centre Primates Brain Imaging Repository  (Sakai et al. (2018) ; j-monkey. jp/BIR/about_e. html); resources focused specifically on the marmoset (e. g. ,  Liu et al. (2021) , marmosetbrainmapping. org/data. html; Woodward et al. (2018) , https:// www. brain minds. riken. jp/ atlas-packa ge-downl oad-main-page); and the brains provided in  Heuer et al.
(2019)  and  Navarrete et al. (2018). The latter of these contains structural images from the Primate Brain Bank, the same sample source as used in the present manuscript. In a parallel effort, resources for analysing data from non-human primate are also increasingly shared openly and support fora are emerging, including the Primate Resource Exchange  (Messinger et al. 2021 ). These resources provide great opportunities for comparative science, and have already inspired and enabled projects of larger scale than possible by any one laboratory on its own.
Data availability The pipeline for processing the spin-echo data uses FSL (www. fmrib. ox. ac. uk/ fsl) and is implemented in the MR Comparative Anatomy Toolbox (Mr Cat; www. neuro ecolo gylab. org) as the 'phoenix' module. The surface reconstruction pipeline is available at https:// github. com/ ardes ch/ nhp-frees urfer. All data are made available for scientific purposes on the WIN Digital Brain Bank (https:// open. win. ox. ac. uk/ Digit alBra inBank/) subject to a Material Transfer Agreement. Tractography and results are available as a Data Sharing Collection link for reviewers: (https:// data. donde rs. ru. nl/ colle ctions/ di/ dcc/ DSC_ 2020. 00034_ 408).
A summary of the sequencing data for each sample is provided in Supplementary Table  S1. Differentially methylated region (DMR) calling. DMRs were detected with the DeNovoDMR algorithm included in the Specific Methylation Analysis and Report Tool (SMART2) (29) using all the default parameters except for the number of CpGs per segment, which was set to 4, the absolute mean methylation difference, which was set to 0. 2, and a threshold value of P of 0. 01. Only those CpGs with a coverage of ≥5 in all samples were considered in the construct of the SMART input matrix. DMR calling was performed on all possible comparisons between naïve and memory B cells for both control and HIGM2 patients. Bisulfite pyrosequencing. 500 ng of genomic DNA was converted with an EZ DNA Methylation-Gold kit (Zymo Research), following the manufacturer's instructions.
Processing of chromatin immunoprecipitation (ChIP) assays with sequencing (ChIP-Seq) datasets Sequencing reads from ChIP-seq experiments from the BLUEPRINT consortium  (21)  were mapped to the hg19 assembly of human reference genome using Burrows-Wheeler Aligner (BWA) v0. 7. 13 (with parameters -q 5, -l 32, -k 2). After removing reads with MAPQ <30 with Sequence Alignment/Map (SAMtools) v1. 2, PCR duplicates were eliminated using the Picard function available in MarkDuplicates software v1. 126. Peak calling was performed using macs2 (with parameters -p 1e-2 -nomodel -shift 0 -B -SPMR). Only peaks with an overlap of ≥0. 5 between replicates were considered. Histone mark signals around DMR sets were extracted with the annotatePeaks. pl algorithm available in Hypergeometric Optimization of Motif EnRichment (HOMER) software v4.
10. 3 (with parameters: size = 10 000, hist = 10). Super-enhancer identification. H3K27ac ChIP-seq data from BLUEPRINT database were used to identify the super-enhancer regions, as described previously (30) using Rank-Ordering of Super-Enhancers (ROSE) software. An enhancer stitching distance of 15 kb was used along with a 2. 5 kb transcriptional start site (TSS)-exclusion window. DNA methylation data analysis. Hierarchical clustering was carried out based on Pearson correlation distance metrics and average linkage criteria. For low-dimensional analysis, we used the t-distributed stochastic neighbor embedding (t-SNE) method implemented in the Rtsne v0. 15 package. Transcription factor motifs were enriched for each set of DMRs using HOMER software v4. 10. 3. Specifically, we used findMotifsGenome.