dataesr/scientific-paragraphs-categorization huggingface dataset:
Cellular Origin of the Cytokine Profiles: Single-Cell RNA Sequencing of Kidney Transplant Biopsies Based on the observed association between cytokines and HLA-DSA, we hypothesized that these cytokines originated from the transplanted kidneys as the allograft is the site where the DSAmediated injury occurs. To determine the cellular origin of the cytokine profiles in the allograft, we used publicly available singlecell data from kidney biopsies. Briefly, scRNASeq was performed on two renal allograft biopsies with DSA-mediated injury, namely, ABMR (one with class I and II anti-HLA-DSA (B8, DQA1) and one with class II HLA-DSA [DR1; DR53 and DQ2  (36) ], and five reference biopsies. After quality control and filtering (Supplementary Figure  S5 ), 33,216 cells were detected and clustering revealed 14 clusters corresponding to the main renal cell subtypes but also infiltrating immune cells (Figures  3A, B ).
After subclustering using well-established markers  (39) , we evaluated the expression of genes corresponding to the cytokines across the kidney structural cells and infiltrated immune cells (Figures  3A, B ). The cytokines of interest were expressed by activated endothelial cells (ECa), antigen-presenting cells (APCs), and lymphocytes. More specifically, ECa expressed CSF3, FGF2, IL15, CXCL9, and CCL2. APCs expressed IL1RN, IL8, CCL3, IL1B, CXCL10, IL10, TNF, and CCL3 whereas lymphocytes expressed CCL4, CSF2, IL2, IFNG, IL4, IL13, and CCL5 (Figure  3C ). Of note, IL9 and IL17A were not detected in this dataset. Given the heterogeneity of APC and lymphocyte populations, we subclustered these two populations. In APCs, we identified CD19+MS4A1+ B cells, the three main monocytic populations -classical CD14+CD16-, intermediate CD14+CD16+, and nonclassical CD14-CD16+ monocytes-and two dendritic cell (DC) populations (Figure  3D ).
Cell-Cell Communication As cytokines and chemokines are the mediators of cell-cell communication, we used the CellChat package to infer the intercellular communication from the scRNASeq data. Twenty signaling pathways showed significant communication between the allograft cells. Among these were the CXCL, CCL, CX3C, PDGF, VEGF, and CSF signaling pathways (Figure  4A ). ECa were important senders but also receivers of CXCL and CCL signaling (Figure  4B ). Lymphocytes were important senders of CCL signaling, whereas APCs were receiving CX3C and CSF signals from ECa in these biopsies. When looking at all signals received by ECa, sent by ECa, APCs, and lymphocytes, the atypical chemokine receptor (ACKR1), also known as DARC, was the identified receptor for all ligand-receptor pairs (Figure  4C ).
FUNDING EVL holds a fellowship grant ( 1143919N ) from The  Research Foundation Flanders  (F. W. O. ). MN is a senior clinical investigator of F. W. O. ( 1844019N ) and is supported by an  F. W. O TBM  grant (grant no. T004417N ), an  F. W. O. junior project grant  (grant no. G087620N ), an ERACoSysMed  H2020  grant (grant no. JTC2_29 ), and a C3 internal grant from the  KU Leuven  (grant no. C32/17/049 ).
Methods Participants. Behavioral and MRI data from 93 healthy participants (43 women, mean age = 25. 6 years, SD = 3. 83) were collected in total. Data from seven participants were excluded due to a brain abnormality revealed by the MRI acquisition (n = 6), and for falling asleep during the experiment (n = 1). The final sample consisted of 86 participants (43 women, mean age = 25. 5 years, SD = 3. 48), who took part in a larger MRI study on creative thinking  27. All participants were French native speakers, right-handed, with normal or corrected-to-normal vision and with no neurological disorder, cognitive disability or medication affecting the central nervous system. All gave informed written consent and received monetary compensation for their participation. The study was approved by the approved French ethics committee "CPP Sud Mediterranee IV" and we have complied with all relevant ethical regulations.
They used a Likert scale from 0 (not creative) to 4 (highly creative). The inter-rater reliability showed an intraclass correlation coefficient equal to 0. 74. The database to compute the AUT-uniqueness and the AUT-commonness included eight additional subjects who performed the set of creativity tasks but not the PolyFT. Creativity assessment with the combination of associates task (CAT). The CAT is an adaptation of the Remote Associates Test 9 , a classical task assessing convergent thinking in creativity. In the CAT, participants completed 100 trials composed of triplets of cue words that at first glance seem to be unrelated. On each trial, they were asked to provide a solution word that relates to each of the cue words. In developing the CAT, we varied the semantic distance between the cue words and the solution, as described in previous articles  15, 96.
In SemNets, these communities can represent different semantic categories. The computation of the SemNet metrics were performed in Matlab, via the Brain Connectivity Toolbox 100. Executive control assessment with the digit span test. The digit span test of the Wechsler Adult Intelligence Scale (WAIS)  144  was used to assess working memory ability. We used the criteria of performance of this task based on the WAIS manual. This task consisted of two parts in which participants repeated a string of numbers that increased in size. In the first part (forward-span), participants were instructed to repeat 16 different strings of numbers going from 2 to 9 digits in the same order. In the second part (backward-span), they were instructed to repeat 16 different strings from 2 to 8 digits, but reversely.
py pipeline from the Analysis of Functional Neuroimages software (AFNI; https://afni. nimh. nih. gov)  149. In a second step, the data were denoised using the TE-dependent analysis of multi-echo fMRI data (TEDANA; https://tedana. readthedocs. io/en/stable/), version 0. 0. 9  150, 151. The TEDANA pipeline consisted of an optimal combination of the echo time series followed by the reduction of the data using PCA and independent component analysis (ICA) to decompose the multi-echo BOLD data, and classify the BOLD components as BOLD or non-BOLD. The removal of the latter eliminates the thermal and physiological noise including the artifacts generated by the movements, respiration, and cardiac activity. The advantage of acquiring multi-echo EPI sequences is the possibility of assessing the BOLD and non-BOLD signal through the ICA-based denoising method, improving the reliability of the functional connectivity-based measurement  152.
In the last step of the preprocessing, the data were co-registered on the T1-weighted structural image using the Statistical Parametric Mapping (SPM) 12 package running in Matlab (Matlab R2017b, The MathWorks, Inc. , USA) and normalized to the Montreal Neurological Institute (MNI) template. To spatially normalize the fMRI data, we used the transformation matrix computed from the normalization of the T1-weighted structural image, using with the default settings of the computational anatomy toolbox (CAT 12; http://dbm. neuro. unijena. de/cat/)  153 implemented in SPM 12. No participant was removed due to excessive head motion within a single run, defined a priori as >2 mm translation or >3°rotation, and no participant had mean FD > 0. 5 mm. To covary out the task-related signal from each run, the denoised and normalized fMRI data were entered in a general linear model in SPM.
The functional connectivity matrices for the task and resting-state data of each participant were computed using Nilearn v0. 3 154 in Python 2. 7  155. We defined regions of interest (ROIs) based on the Schaefer brain atlas  103  which includes 200 ROIs of 2-mm dimensions distributed into 17 functional subnetworks distributed on eight main functional networks. We extracted and averaged the BOLD signal for each ROI and performed Pearson pairwise correlations. As a result, we obtained a 200 × 200 matrix for each participant that were Z-Fisher transformed and rescaled by the maximal weight to be in the range of -1 to 1 for the subsequent analyses. The CPM analysis 102 consisted of five steps, and a validation process. Since we used a leave-one-out validation, the first three steps were performed in N-1 participants, and the fourth step was performed in the left-out individual.
Hence, we ran a separate analysis for the PolyFT components. Since we used within-data set cross-validation, when Spearman correlations were significant, it was necessary to evaluate the predictive power of the predictions using permutation testing. To this end, we randomly shuffled the observed values 1000 times, and we ran the pipeline of our predictive model using the new random data. Thus, we generated an empirical null distribution and estimated the distribution of the test statistic given by the correlation between predicted and observed values. The CPM analyses were performed using Matlab Statistical Toolbox (Matlab R2017b, The MathWorks, Inc. , USA). We explored the functional anatomy of the positive and negative model networks of each score by localizing and describing the main nodes and links of the significant model networks.
The data visualization and plots were performed using BioImage Suite Web 1. 0 (http:// bisweb. yale. edu/connviewer), BrainNet viewer  156  (http://www. nitrc. org/projects/ bnv/) in Matlab, and custom scripts in RStudio version 1. 3. 1056. Internal validation: prediction of PolyFT components from resting-state functional connectivity. As an internal validation test, the predictive models built based on the task-based functional connectivity were applied to the participant's resting-state data (i. e. , the strength of functional connectivity within each participant's positive and negative model networks during resting-state acquisition). Similar as for the task-based analyses, the mean FD was included in the model, and the Spearman correlations between the predicted and the observed PolyFT components were computed to evaluate the prediction.
Acknowledgements The research was supported by  "Agence Nationale de la Recherche " [grant numbers  ANR-19-CE37-0001-01 ] (E. V. , M. B. , and Y. N. K. ), the " Fondation pour la recherche medicale " [grant number:  DEQ20150331725 ] (E. V. ), and received infrastructure funding from the  French program  " Investissements d'avenir "  ANR-10-IAIHU-06  (E. V. ). This work was also funded by  Becas-Chile of ANID-CONICYT (MOT). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was carried out in the PRISME and CENIR facilities of  ICM. We gratefully acknowledge  Karim N'diaye ,  Benoit Beranger , and  Romain Valabregue  for their help in the data collection. We thank the participants for making this work possible.
Code availability Analyses were conducted using open software and toolboxes available online as described in "Methods" (SPM 12: https://www. fil. ion. ucl. ac. uk/spm/software/spm12/; AFNI: https://afni. nimh. nih. gov); Nilearn: https://nilearn. github. io/stable/index. html; TEDANA: https://tedana. readthedocs. io/en/stable/; CPM: https://www. nitrc. org/ projects/bioimagesuite/; Network metrics computation: https://sites. google. com/site/ bctnet/Home/functions). Any custom scripts written for this paper are available upon request from the corresponding author.
, 2018; Isson et al. , 2020)  and have fundamental influences on, and relationships with, life on our planet  (Sleep & Zahnle, 2001). Although imperfect, particularly in deeper time (such as in pre-Pangea times), the geological record provides key constraints on Earth's past. By synthesizing geological and geophysical data, plate tectonic reconstructions can be constructed, which can often be used to fill spatiotemporal gaps in the geological record and in our understanding of key planetary processes. The open-source and cross-platform GPlates (www. gplat es. org) software  (Müller et al. , 2018)  has been used to create a range of community plate tectonic reconstructions  (Domeier & Torsvik, 2014; Matthews et al. , 2016; Müller et al. , 2019; Zahirovic et al. , 2016). These digital plate motion models can be interrogated with tectonic parameters (e.
| GPlates plate tectonic reconstruction models GPlates is a cross-platform and open-source desktop software for the interactive construction, modification, and visualization of plate tectonics, paleogeography, and mantle structure  (Müller et al. , 2018). GPlates is a deep-time geographic information system (GIS), that can handle vector, raster, and three-dimensional (3D) volume visualizations. The pyGPlates Python application programming interface (API) can be used to automate many of the vector-based functionalities of GPlates, such as interrogating plate reconstructions (also known as plate motion models) and spatiotemporal analyses of the plate kinematics. For the time of the Pangea supercontinent dispersal (~250 Ma to present), seafloor spreading histories from the preserved oceanic crust provide a relatively high level of confidence for relative plate motions where they are available.
2). Sediment thickness estimates were computed only back to 200 Ma (Figure  6 ) as this is the period where pelagic marine calcifiers contributed significant amounts of sediments to the seafloor  (Hull, 2017). The sediment thicknesses arriving at subduction zones were combined with the orthogonal plate convergence rates using an updated variant of the East et al. (  2020 ) approach using pyGPlates rev. 28 and Python 3, which now also enables the interrogation of plate reconstructions that include deformation (e. g. ,  Müller et al. , 2019). This enabled the comparison of subduction zone (and implied volcanic arc) lengths (Figure  7 ) with the subducting plate area and predicted subducting volumes of sediments (Figure  8 ).
| Data and model reproducibility The most recent version of the toolkit, and related data, described here can be accessed from https://github. com/ sabin z/DCO-Model ling-of-Deep-Time-Atmos pheri c-Carbo n-Flux-from-Subdu ction -Zone-Inter actions. The workflows presented here work best in Unix-like environments such as Linux (including in virtual machines), macOS, or the Windows Subsystem for Linux available since Windows 10. Currently, pyGPlates (revision 28 or more recent; www. gplat es. org) requires Python 3. The open-source Generic Mapping Tools (GMT 6. 2, www. gener ic-mappi ng-tools. org) are used for geospatial data processing and visualization  (Wessel et al. , 2019). Full digital supplement (~15 Gb), including the workflows, input grids, plate motion model, time series, and animation, is available from Zenodo repository accessible from https:// zenodo.
ACKNOWLEDGEMENTS We thank  Nicky Wright  for helpful discussions on the deeptime estimation of sediment thicknesses. We thank  Maria Seton  for publicly releasing the Matthews et al. (2016) and the Müller et al. (2016) models that included fixes to Pacific plate motions flagged by Torsvik et al. (2019). We thank  Douwe van Hinsbergen ,  Richard Palin , and an anonymous reviewer for feedback that improved the manuscript. SZ was supported by the  Australian Research Council  grant  DE210100084  and a  University of Sydney Robinson Fellowship. JP, SD, and SZ were supported by Alfred P Sloan grants  G-2017-9997  and  G-2018-11296. MP was supported by  Ambizione Fellowship  (grant  PZ00P2_168166 ) of the  Swiss National Science Foundation  and  UGA Presidential Funds. MGT was supported by the  AUGURY European Research Council (ERC)  Project  617588.
Funding information Alfred Sloan Foundation, Grant/ Award Number: G-2017-9997 and G-2018-11296;  Australian Research Council , Grant/Award Number:  DE210100084 ;  Ambizione Fellowship of the Swiss National Science Foundation  and  UGA Presidential Funds , Grant/Award Number:  PZ00P2_168166 ; Australian National Collaborative Research Infrastructure System;  H2020   European Research Council , Grant/Award Number: 617588.
Statistical analysis The dataset was split into a training (80% of individuals) and a validation (20%) set. Samples for the validation set were selected randomly from population studies of cases unselected for family history of breast cancer and controls, in countries contributing a total of > 5000 samples (Denmark, Germany, Singapore (Chinese), Sweden, UK, USA). All remaining samples were included in the training set. The training set included 37,211 cases from population studies, 11,409 cases from familial studies, and 42,334 controls. Of these, 3818 individuals were carriers of PTVs in one or more of the five genes under consideration and were excluded from all analyses except the mixture models (see below). The validation set included 11,019 cases and 10,831 controls from population studies and did not include any carriers of PTVs.
Oversampling of cases with a family history increases power but leads to biased effect sizes, so we chose this approach to maximize the power to discriminate between models in the training set, which could then be refit and tested on a dataset unselected for family history. All analyses were adjusted for country as a covariate; in addition, for Malaysia and Singapore, the three distinct ethnic groups (Chinese, Indian, Malay) were treated as different strata, and the UK was treated as three strata (SEARCH from East Anglia, GENSCOT from Scotland, and PROCAS and FHRISK from north-west England).
Training dataset analysis An analysis flow diagram is presented in Fig. S1  (see Additional File 1). Analyses were performed in R version 4. 0. 3 (R: A Language and Environment for Statistical Computing; http:// www. r-proje ct. org). We first used logistic regression (LR) to explore which of the five in silico scores (Align-GVGD, BayesDel, CADD, Helix, and REVEL-all analyzed as continuous variables) were most strongly associated with risk of breast cancer. In addition, to assess the utility of gene-specific in silico tools, we analyzed the Hart et al. RF and NVM in silico predictions for BRCA1 and BRCA2. To evaluate the usefulness of functional predictions, we also analyzed the BRCA1 SGE score; the Richardson et al. BRCA2 HDR score; and, for PALB2, five functional assay scores. These analyses were restricted to carriers of a rare (frequency < 0.
We considered two types of mixture model: a constrained model in which the missense OR was equal to that of PTVs, and an unconstrained model in which the missense OR could differ from the PTV OR. Carriers of PTVs in the gene under consideration were re-included in the mixture models (to allow the risk associated missense OR to be constrained to the PTV OR). The mixture models were fitted using an expectation-maximization (EM) algorithm  [28]. In the expectation step, the (posterior) probability that each variant was risk associated, given the case control data on that variant in the training set and the current parameter values was calculated. These probabilities were then used as weights in a logistic regression analysis in the maximization step. In a case-control dataset, the naïve proportions, α, will be biased because risk associated variants are more likely to be found in cases.
Validation dataset analysis To evaluate the calibration of the in silico training models, we performed case-control analyses using the validation dataset. In these analyses, OR estimates were fixed according to the population estimates from the training models (Table  1 ), but the other parameters (intercept and country covariates) were reestimated, since the case-control proportions might differ between the training and validation datasets. From the validation model, we extracted the predicted probability that each individual was a case and hence derived expected numbers of cases and controls in each risk group. These were used to plot observed versus expected OR estimates and perform a goodness of fit chi-squared test. The mixture models were assessed similarly, with the exception that both the OR parameter and the proportion of risk associated variants, α, were fixed.
64 (1. 33-2. 02), p = 3. 1 × 10 -6 ; Table  1 , Figs. 1a  and 2a ). In the most parsimonious mixture model, risk associated variants conferred an equivalent risk to PTVs (OR 2. 16 (1. 78-2. 63)); an estimated 54% (95% CI (41-68%)) of variants in the FAT/PIK + CADD5 risk group were risk associated, compared to less than 6% of variants in other risk categories (Table  1 , Figs. 1a  and 2a ). There was no evidence that missense variants were associated with a different risk compared with PTVs (p = 0. 48). The mixture model was a slightly better fit to the data than the LR model (2 × log-likelihood difference = 0. 67). There was no association between age-at-diagnosis and risk category (see Additional File 1: Table  S6 ). Thirteen ATM missense variants were classified as (likely) pathogenic on the ClinVar database (see Additional File 1: Table  S7 ).
One of these (c. 8546G > C) is located in the PIK domain, the other (c. 3848 T > C) is not within any domain; however, both have a Q5 CADD score. The pathogenic variants listed on ClinVar include c. 7271 T > G (p. Val2424Gly), previously reported as associated with high risk of breast cancer  [29, 30]. In the training dataset, c. 7271 T > G was identified in 12 cases (6 population-based) and 6 controls and was not associated with risk (p = 0. 37, p ALL = 0. 081); its population-based OR estimate of 1. 63 (0. 56-4. 73) was lower than previous estimates (for example  [31] ). Another variant previously reported as risk associated, c. 6919C > T (p. Leu2307Phe)  [32] , was associated with an increased population risk (OR = 3. 71 (1. 87-7. 38), p = 0. 00018). Both variants are located in the FAT domain and have a CADD score in Q5, but after excluding them from the model, there remained a significantly increased risk for carriers in the FAT/PIK + CADD5 risk group (OR 1.
According to the ENIGMA guidelines and/or ClinVar classifications  [13, 14] , 13 of the BRCA1 missense variants in the dataset (four in the RING domain and nine in the BRCT domains) would be classified as (likely) pathogenic (see Additional File 1: Table  S7 ). In total, the 13 variants were carried by 60 cases and 6 controls and were strongly associated with risk in the subset of population samples (OR 16. 68 (5. 16-53. 94), p = 2. 6 × 10 -6 ). In our dataset, the most frequent of these variants was c. 181 T > G (p. Cys61Gly), carried by 29 cases and 2 controls (OR 15. 06 (3. 58-63. 36)). After excluding all (likely) pathogenic variants, there also remained an increased risk associated with variants in the RING/BRCT + Helixhigh category (OR 2. 39 (1. 19-4. 78), p = 0. 014)). RF and NVM predictions from the analysis of Hart et al.
027). Under the best fitting model, the OR for missense variants was 1. 75 (1. 47-2. 08), with 95% (86-98%) of Helix-high variants and 33% (25-43%) of Helix-low variants being risk associated (see Table  1 , Figs. 1d  and 2d ). The mixture model was a similar fit to the LR model (2 × log-likelihood difference = 0. 52). We also explored mixture models with two levels of risk variant: one with an OR equal to that of PTVs and another conferring a lower risk compared to that of PTVs. The two-level model fitted slightly better in the full training dataset (2 × log-likelihood difference = 1. 10) but not in the population-based studies (two-level model converged to the one-level model). The OR associated with Helix-high variants decreased as age increased (per year OR 0. 99 (0. 98-1. 00), p = 0. 017; see Additional File 1: Table  S6 ).