dataesr/scientific-paragraphs-categorization huggingface dataset:
Despite a stronger crude association between low-dose aspirin and the risk of COVID-19 among people aged <70 years, multivariable associations showed no association for hospitalization or death/intubation (HR, 1. 05 [95% CI, 0. 99-1. 12] and HR, 0. 95 [95% CI, 0. 84-1. 07], respectively). The results were very similar using models restricted to patients without a known chronic respiratory disease history. In models restricted to patients with diabetes, models adjusted for age and sex were already closer to a null association, with no substantial change in fully adjusted associations. These results do not support using aspirin for primary cardiovascular prevention in patients with diabetes to prevent severe COVID-19. 15 ministrative health care databases help in the analysis of whether patients treated with a given drug had a lower probability to be infected or severely affected.
12, 16 The database does not guarantee that the subjects actually took the drug dispensed, but patients considered in the aspirin group had received at least three dispensations at different times, which reduces this drawback. Furthermore, little information is available on lifestyle, no information is available on ethnicity, and we know that other variables, such as obesity or smoking, are underestimated, which may result in residual confounding. However, we do not believe this strongly biased our results, especially after adjusting for the other variables, including a large number of chronic diseases and health conditions. Finally, it is difficult to disentangle the effect of low-dose aspirin from that of the disease for which it is used, but excluding people with a history of cardiovascular diseases likely limited the association between the risk of atherosclerotic cardiovascular disease and COVID-19.
After excluding reviews, technical publications, book chapters, preprint and duplicated studies (Table  S2 ), we retained 120 publications. Finally, as we were interested in studies using a DG-ABM approach, we checked whether part of this variation is heritable. With this method, we filtered out 45 additional publications that did not satisfy these five criteria, resulting in a total of 75 publications using DG-ABMs where interindividual interactions affect fitness. Using a nonexhaustive snowball approach, we found 14 additional references cited in or citing the 75 selected publications (see Table  S3  and  Lamarins et al. , 2022  for the final database). Note that the difficulties we encountered in selecting studies using DG-ABMs with interindividual interactions from the WOS illustrate the need for clearer referencing based on keywords better shared by the community.
, 2014). The exponential increase in genomic databases should help in the calibration/validation of DG-ABMs  (Rudman et al. , 2018). The use of description protocols such as Overview, Design concepts and Details protocol ensures the replicability and enhances the understanding of the models  (Grimm et al. , 2020). The TRACE framework  (Grimm et al. , 2014)  is also a powerful tool for planning, documenting and assessing model development, analysis and application. Software Intrinsically, DG-ABMs conception requires a multidisciplinary approach integrating multiple levels of knowledge and can be used in interdisciplinary research projects as a tool of interaction among disciplines. ABMs are also used as frontier objects in several contexts (Le  Page & Perrotton, 2017; Reilly et al. , 2021).
RealTime ready assays from Universal Probe Library (Roche) were used with forward and reverse primers (400 nM) and fluorescently labeled hydrolysis probes (200 nM) lyophilized in a Custom Panel 384 (configuration no. 100127094, Roche, Table  S2 , Supporting Information), performed according to manufacturer's instructions. Results were processed using the 2 -∆∆CT method for relative gene expression analysis. Changes in gene expression were normalized using the housekeeping genes RPL22, in the case of RT-qPCR, and GAPDH, B2M, and ACTB, in the case of RealTime Ready assay. Transcriptomics Analyses of the Genomic Modifications: Gene ontology enrichment analysis was performed for the identified differentially expressed genes using ShinyGO (http://bioinformatics. sdstate. edu/ go/). [69] Cellular pathways were explored using GeneTRial2 software (https://genetrail2.
bioinf. uni-sb. de). [70] Datasets were analyzed for KEGG and BioCarta pathway databases. Network of pathways, their interactions, and genes involved with each pathway were also searched using ClueGO Cytoscape (v 3. 7. 2) to create and visualize functionally grouped networks. [71] Protein-protein interactions (PPI) between the proteins that were coded by the differentially expressed genes, including their neighboring proteins, was conducted using STRING (v10. 5, (https://string-db. org/),  [72]  considering: confidence-text-mining, experiments, databases, coexpression; high confidence-0. 700; no more than 10 interactions in the first shell and no interactions in the second shell, without clustering. Genes identified in this PPI network were applied to pathway analysis using KEGG mapping analysis tool.
[73] ranscription factors (TFs) potentially involved in the transcriptional regulation of the identified genes were identified using the TTRUST database (https://www. grnpedia. org/trrust/). [74] Potential interactions of the metabolites with TFs were assessed using SwissDock (http://www. swissdock. ch/docking) molecular modeling tool. The 3D structures of the proteins were obtained from Uniprot database and the chemical structures of the metabolites were obtained from PubChem database. Search for miRNAs potentially involved in post-transcriptional regulation of the differentially expressed genes was performed with MIcroRNA ENrichiment TURned NETwork (MIENTURNET), used for miRNA-target enrichment and network-based analysis (http://userver. bio. uniroma1. it/apps/mienturnet/). [75]  Intersections between differentially expressed genes with those identified as involved in neurodegenerative diseases and PD have been searched using Comparative Toxicogenomics Database (http://ctdbase.
Acknowledgements This work was supported by  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No. 804229 ;  iNOVA4Health -UIDB/04462/2020  and  UIDP/04462/2020 , a program financially supported by  Fundação para a Ciência e Tecnologia (FCT)/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) , through national funds. FCT/MCTES, through the project  PTDC/BTM-ORG/29580/2017. Authors would like to acknowledge  FCT  for financial support of  R. C. ( PD/BD/135492/2018 ) and J. G. -P. ( SFRH/BD/145522/2019 ). M. L. was supported by the  Land-BW (NAM-ACCEPT)  and funding by the  European Union 's  Horizon 2020 research and innovation program  under grant agreements No. 964537  ( RISK-HUNT3R ), No. 964518  (ToxFree), and No.
EDCs are no longer recognized as a "country-specific issue"; in fact, scientists have recently called for a multifaceted international program to collate surveillance data on EDCs worldwide to identify hazardous chemicals more expeditiously for subsequent regulation  (Kassotis et al. , 2020). Currently, there are several guidelines and specific programs designed to manage EDCs. The Organization for Economic Co-operation and Development (OECD) published a Revised Guidance Document 150 on Standardized Test Guidelines for Evaluating Chemicals for Endocrine Disruption (updated in 2018) to harmonize approaches for chemical testing for EDC modalities. Guidance for the identification of endocrine disruptors in the context of Regulations (EU)  (No 528/2012 and No 1107 /2009)  formulates the criteria for labelling a substance an "EDC", and proposes tiered testing strategies.
Why the need for validated assays to test non-EATS modalities? A disease perspective Global and national efforts, such as the high-throughput chemical screening initiatives of the United States Environmental Protection Agency (EPA) ToxCast/Tox21 programs, have revealed widespread promiscuity of chemical interaction with protein receptors and enzymes. Additional bioassays designed to test specific pathways should subsequently validate such data. For example, active hits in the database for dibutyl phthalate (Fig. 2 ) reveal that it may affecting many receptors, such as estrogen receptor, pregnane X receptor, and a number of cytochrome p450 enzymes. As a result, our current understanding for most chemicals is that their biological activity can occur via different molecular initiating events (MIEs) dependent upon the specific target organ (e.
Various in vitro assays have been developed to test disruption of the PPAR signaling and this is a focus of the EU program GOLIATH; however, currently there are no validated methods and PPAR assays are proposed candidates outlined in OECD DRP No. 178. As for other nuclear receptors, transactivation reporter assays demonstrate functional activation of PPARs when screening EDCs, allowing for the identification of molecular initiation events leading to adverse outcomes  (Seimandi et al. , 2005). When considering PPAR pathways related to obesity and metabolism, the OECD proposes to assess in vitro transactivation of different PPARs in reporter gene assays and adipocyte differentiation in models like 3T3-L1 cells and in vivo peroxisome proliferation, and lipid accumulation  (LeBlanc et al. , 2011).
, 2016; Janesick et al. , 2016). Several software tools are also available to predict endocrine activity related to both EATS and non-EATS modalities. Andersson and colleagues  (Andersson et al. , 2018)  Fig. 5. Generalized AOP for liver steatosis both via EATS and non-EATS (adapted from [  (Mellor et al. , 2016) , with information from  AOPwiki 36, 57, 60, 318] ). ER, estrogen receptor; LXR, liver X receptor; PXR, pregnane X receptor; PPARγ/β/α, peroxisome proliferator activated receptor gamma/beta/alpha, respectively; AhR, aryl hydrocarbon receptor; GR, glucocorticoid receptor; RAR, retinoic acid receptor. highlight useful software tools available for predictive toxicology regarding the non-EATS, which include MolCode Toolbox (AhR), Virtual Toxab (GRs, AhRs, PPARs), Endocrine Disruptome (GRs, PPAR, RXRs), and Danish (Q)SATR DB (PXR) to name but a few.
Funding LNM was supported by a  H2020-Marie Skłodowska-Curie Action MSCA-IF-RI-2017  awarded by the  European Commission  (ref. 797725-EpiSTOX). JK was funded by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement GOLIATH No. 825489. AS and LEC were supported by a  Grant-in-Aid  from the  Heart and Stroke Foundation of Canada.
Introduction Developmental and evolutionary effects on the brain and its organisation occur at vastly different timescales, yet these effects have been shown to be linked  [1] [2] [3]. For instance, allometric changes in cortical area expansion show striking correspondence across ontogeny and phylogeny. Brain regions that expand later in newborn humans are also those that differ the most in size between humans and monkeys 2. Despite these early markers, our understanding of how brains differ across species and ages and how this affects the behaviour they produce is still in its infancy, as mapping changes that are relevant to brain function across multiple domains is inherently challenging. Such mapping and understanding requires the integration and synthesis of diverse datasets, often lacking common references and terminologies, and must consider very different brain Having a unified framework and common vocabulary for mapping brain organisation across the ontogenic and phylogenic dimensions is key.
Neonatal Tract Protocols and Atlases We developed a novel library of standardised tractography protocols for 42 major white matter bundles of the neonatal brain, including commissural, association, projection and limbic tracts (Table  1  in Methods). Crucially, these protocols were defined in consistency with previous protocols for the same bundles in the adult human and macaque  (Supplementary Fig. 1 ). Figure  2b  shows qualitatively how the neonatal tract delineations compare against the ones defined before in the adult brain, and in the non-human primate brain. We tested the robustness of these tractography protocols using independent neonatal diffusion MRI data of varying quality. In addition to the high-quality dHCP dataset (acquisition time = 20 mins, high angular and spatial resolution, bespoke setup, subset of neonates scanned at 37-42 weeks PMA), we used two further datasets (from neonates also scanned at 37-42 weeks PMA): a) a good quality dataset from a conventional clinical scanner (no specialised acquisition hardware and software as in dHCP, reduced acquisition time of 8 mins, but still high angular resolution and contrast -"Oxford dataset"); and b) a lower quality (and lower b value) dataset with further reduced angular resolution and acquisition time (3 mins -"standard dataset") (see Methods for full acquisition protocol and cohort details).
We compared the tract atlases and the inter-subject variability across these three datasets (after age and sex-matching). Strong similarity was observed between the average tract atlases, shown in Fig. 2c  (right, and Supplementary Fig. 2 ), allowing good neonatal tract delineations even with a standard data acquisition protocol. Some differences can be observed between the high and lower quality datasets, for instance reduced tract extent in the second branch of the superior longitudinal fasciculus (SLF2) and lower population coverage in the acoustic radiation (AR). Yet, the average spatial correlation across all tracts in the atlases was 0. 89 (±0. 04) between the 20-minute and 8-minute acquisition dataset, and 0. 86 (±0. 08) between the 20-minute and 3-minute data. Inter-subject variability in the tractography results was assessed within and across the subject groups (relative to the 20-minute dHCP dataset).
As shown in Fig. 2c  (left), intersubject variability was relatively consistent across the datasets with, as expected, greater inter-subject similarity within than across groups, albeit with slightly greater variance in the lower quality datasets. We further investigated whether the developed neonatal tractography protocols could capture early developmental trends in tract maturation. Projection fibres (e. g. thalamocortical and cortico-thalamic) are expected to mature more quickly over this early life.
We performed comparisons across both the ontogeny and phylogeny dimensions using this common connectivity space. Connectivity blueprints were constructed for groups of neonate (33 subjects -born and scanned at 40 weeks PMA), adult (20 random HCP subjects) and macaque (6 high-quality post-mortem macaque datasets) brains and KL divergence was used to assess similarities and divergences. Figure  4a-c  shows the minimum KL divergence maps for pairs of groups. By finding the minimum KL divergence for each cortical location, i. e. by asking how different is the best matching connectivity profile of a given area across brains, we could assess predictability in connectivity patterns between groups. We found higher divergence when comparing across species (mean minimum KL divergence of 0. 62 (s. d.
103 , presumably because evolution exploits the variation across individuals. Our common space framework allows us to explicitly test such hypotheses in a unifying manner and identify connectivity patterns that are linked to these differences. distributions are then added together. This extra step was added in cases where robustness of tract reconstructions was significantly improved (see Table  1 ). Table  1. Description of the 42 tracts included in baby-XTRACT, along with their abbreviations used to refer to them in the text. Bilateral tracts have separate protocols for their left and right counterparts. The reverse seeding approach is used for some tracts, whereby the protocols are run twice with the seed and target masks reversed. the good-quality (Oxford) dataset and reducing the overall number of volumes to 65 (which corresponds to an approximate scan duration of 3 minutes).
This corresponds to a more conventional low-b value acquisition, and so will be referred to as the "standard" dataset. The acquisition parameters of the three datasets are summarised in Table  2  below. These datasets were analysed following the dHCP data pre-processing as described above, including motion and distortion correction, the generation of diffusion-template warp fields, crossing-fibre modelling and standardised tractography for each subject. We compared tract-atlases and inter-subject variability across the three datasets. This dataset was further subsampled to generate a third dataset (the "standard" dataset) without the b=2000 s/mm 2 shell, which could be acquired in more clinically feasible scan times.
17. Briefly, a single set of macaque surfaces were derived using a set of high-quality structural data from one of the macaque subjects. The remaining macaque data were then non-linearly transformed to this space and the surfaces were nonlinearly transformed to the F99 standard space 115 to allow group-level tractography. Prior to tractography, the surfaces were downsampled to approximately 10,000 vertices per hemisphere. We then carried out probabilistic tractography, seeding 1,000 streamlines from each vertex on the WGB, and recording visitation counts between each seed point and each voxel in a whole-brain mask with the ventricles removed, down-sampled to 2 mm.
We used this scoring system to exclude subjects with score > 3. This resulted in 351 datasets, that we considered for our study. Adult Data: We drew from the pre-processed 108 , publicly-released Human Connectome Project (HCP) dMRI data 109,We randomly chose 20 unrelated subjects (age range: 22-35 years). In brief, the HCP data was acquired using a bespoke 3T Connectom Skyra (Siemens, Erlangen) with a monopolar diffusion-weighted (Stejskal-Tanner) spin-echo EPI sequence, an isotropic spatial resolution of 1. 25 mm, three shells (b-values = 1000, 2000 and 3000 s/mm and 90 unique diffusion directions per shell, acquired twice. Non-linear transformations to the MNI152 standard space were obtained using T1-weighted images with FSL's FNIRT 111,The distortion-corrected diffusion MRI data were separately linearly aligned to the T1weighted space and the concatenation of the diffusion-to-T1 and T1-to-MNI transforms allowed diffusion-to-MNI warp fields to be obtained.
Acknowledgements S. W. was supported by an  MRCdPhD Studentship UK  [ MR/N013913/1 ] and is now supported by an  ERC   Consolidator grant  [ 101000969 ]. E. T. was supported by funding from the  Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [ONBI CDT , grant number  EP/L016052/1 ]. J. D. is supported by the  IdEx Université de Paris  ( ANR-18-IDEX-0001 ), the  Médisite Foundation and the "Fondation de France ". L. B. is funded by a  BLISS  research grant. R. S. is funded by a  Senior Wellcome Research Fellowship  [ 207457/Z/17/Z ]. S. J. is supported by a  Wellcome Collaborative Award  [ 215573/Z/19/Z ] and a  Wellcome Senior Research Fellowship  [ 221933/Z/20/Z ]. R. B. M. is supported by a  BBSRC   David Phillips Fellowship  [ BB/N019814/1 ] and WIN is supported by a  Wellcome Trust   Center grant  [ 203139/Z/16/Z ].
S. N. S. is supported by an  ERC   Consolidator grant  [ 101000969 ] and a  Wellcome Trust  award [ 217266/Z/19/Z ]. Neonatal data were provided in part by the  developing Human Connectome Project , a  KCL-Imperial-Oxford Consortium  funded by the  European Research Council  under the  European Union Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement no.
with streamlines seeded from and constrained by the protocol masks, as described in the protocols. A curvature threshold of 80° was used, the maximum number of streamline steps was 2000, and subsidiary fibres were considered above a volume fraction threshold of 1%. A step size of 0. 5 mm was used for the neonatal and adult brain and a step size of 0. 2 mm for the macaque brain. Resultant path distributions were normalised by the total number of valid streamlines. macaque standard space (F99) 115 Robustness Against Neonatal Data Quality were estimated using FSL's FNIRT 111,112 based on the fractional anisotropy (FA) maps. To explore the robustness of the protocols across data of varying quality, we use an Fibre Orientation Estimation and Tractography: Fibre orientations were modelled for up to additional dataset of 22 neonates born and scanned at 37-42 weeks (mean = 38.
9, s. d. 1. 4) 3 orientations per voxel using FSL's BEDPOSTX PMA 121 , taking the dHCP dataset as the high-quality benchmark dataset. These data were 116 and used to inform tractography. For the neonatal brain, a model-based deconvolution against a zeppelin response kernel was used collected on a 3T Siemens Prisma with a non-specialised adult 32-channel receive coil. to accommodate for the low anisotropy inherent in data from this age group Diffusion MRI data were acquired with 163 volumes per subject, over three shells (b = 500, 26,117,118. 1000, 2000 s/mm 2 ), with 1. 75 mm isotropic voxels and an acquisition time of 8 minutes. This Probabilistic dataset was acquired at the Wellcome Centre for Integrative Neuroimaging (Oxford, UK), tractography was performed using FSL's XTRACT 20 , which uses FSL's 119,120 and is referred to as the Oxford dataset.
PROBTRACKX As a further test, a third dataset was generated by removing the b = 2000 s/mm 2 shell from Generation of Population WM Tract Atlases Tractography results from groups of subjects were used to obtain tract atlases, in the form of population percentage overlap. The normalised path distributions for each tract were binarized and then averaged across subjects. The resultant spatial maps describe the percentage of subjects for which a given tract is present at a given voxel. Tract atlases were generated for three different neonatal age-groups from the dHCP cohort, composed of 73 full-term and normally-appearing (i. e. no analysis-significant incidental findings) neonates each, with ages of 37-40 weeks (mean = 38. 9, s. d. 0. 70), 40-42 weeks (mean = 41. 0, s. d. 0. 48), and 42-45 weeks (mean = 43.
1, s. d. 0. 81) PMA at scan, as well as for a larger group of 277 full-term and normally appearing dHCP neonates (mean age = 41. 0, s. d. 1. 69, range = 37. 4-44. 7). These data were acquired at 7T, with 16 b=0 volumes and 128 volumes acquired with b = 4000 s/mm 2. These datasets are available as part of the Primate Data Exchange 114. Non-linear transformations to the ,.
114 ; and neonatal data are available through the developing Human Connectome Project (http://www. developingconnectome. org) 25. For the "Oxford" neonatal data, see the original publication 121. Data processing and analysis were performed using FSL (v6. 0 onwards, https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSL), Connectome Workbench (v1. 5. 0, https://www. humanconnectome. org/software/connectome-workbench) and Python (v3. 8. 9), including nibabel (v3. 2. 1) 127 and surfplot 128,129. Python scripts and the required data for generating figures are available via GitHub (https://github. com/SPMIC-UoN/baby- xtract). Neonatal tr acto graph y prot oco ls are curr ently available via Git Hub (https://github. com/SPMIC-UoN/baby_xtract_protocols) and will be made available via FSL in a future release.