dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: Research reported in this publication was supported by the  National Institute On Aging of the National Institutes of Health  under Award Number  R01AG054073  (SEO),  R01AG058537  (SEO), R01AG046543 (KL),  R01AG056466  (GMB;CMR),  R03AG055482  (GMB),  R01AG054449 ,  DP5OD019833  (YTQ), HDR064993 (EH), P01HD035897 (NS),  U54 HD079123  (NS),  R01AG014673 (NS) ,  P50AG05142  (LSS),  R01AG057684  (LSS). Other support included:  UK National Institute for Health Research (NIHR)  ( grant  RP-DG-0611-10003-AS  ),  Florida Department of Health , Ed and  Ethel More Alzheimer 's  Disease Research Program  ( 6AZ01 ,  8AZ06 ),  Alzheimer Drug Discovery Fund (KL) ,  AARFD-16-439140  (GMB),  Canadian Institutes for Health Research  ( PJT153079 )(KL), Alzheimer's Association 2017 Part the Cloud (KL),  Carlos III Institute of Health, Spain  (grants  PI14/01126  and  PI17/01019  to JF), partly funded by  Fondo Europeo de Desarrollo Regional (FEDER) ,  Unión Europea , " Una manera de hacer Europa ", a  "Marató TV3" grant  ( 20141210  to JF),  Generalitat de Catalunya  ( 2014SGR-0235 ), the  Fundació Bancaria La Caixa  and  T21   Research Society Clinical Committee (AS).
Participants The ERP data of 25 individuals (15 women, 10 men; M = 24. 5 years-old, SD = 4. 8) participating in a preliminary behavioral study (see S1 File for details), were analyzed. Participants had normal or corrected-to-normal vision. All participants gave informed written consent before the experiment, in accordance with the ethical standards of the Declaration of Helsinki. The EA 4532 local Ethics Committee of Paris-Sud University approved this study for both experiments.
Dynamic stimuli were created with the help of realistic 3D synthetic facial movements, aimed at mobilizing specific Action Units (AUs) of pain, using 3ds Max 2010 1 software. A virtual character was imported from the Poser 8 1 software library. For this virtual character, a dynamic facial expression of pain was created, which recruited five principal AUs of pain, based on the FACS  [38]. In accordance with Oliveira et al. [40] , these AUs were: brow lowering (AU 4), cheek raise (AU 6), lid tightening (AU 7), nose wrinkling (AU 9) and upper raise (AU 10). We created a facial pain expression composed of all five AUs with an intensity of 100% (see  Fig 1) , which was arbitrarily defined and considered as a naturally high facial pain expression (according to Ekman & Friesen  [38]  model). Using 3ds Max 2010 1 software, we carried out morphing in equal stages to create the intermediary intensities between neutral expression (by default in the software) and 100% intensity.
EEG recording and processing All the experiments were conducted in the same lab and in exactly the same environmental conditions. EEG recordings were carried out using the BrainAmp system with active Ag-AgCl electrodes (ActiCap). Six of the twenty-five participants, (see 2. 1 Participants section for details) wore a 32-electrode cap, whereas the remaining 19 participants wore a 64-electrode cap. The change in cap type came after the upgrading of our EEG system from 32 to 64 channels a few weeks after the beginning of our experiment. Both 32-and 64-electrode positions conformed to the 10-20 system. In order to avoid mixing data from 32 electrodes with data from 64 electrodes, only the channels corresponding to the 32-electrode cap for all analyses were kept. Recordings were made using the Brainvision Recorder at a 1000 Hz sample rate; no filter was applied during data acquisition.
Trials were then baseline corrected, with a reference baseline taken between -1313 and -1213 ms pre-TestEI; that is to say, during the fixation point pre-video period. For each participant, data from the two video EIs were pooled in order to have enough data per congruity condition (i. e. , TestEIs judged as congruent or incongruent with respect to the final video intensity). Finally, trials were averaged for each condition and participant. Note that the ERPs' time-values were corrected in order to consider the 13 ms latency of the display.
Data analysis Behavioral data. We conducted two behavioral data analyses, with two complementary objectives: a) determined participants' individual PSE; and b) determined congruent and incongruent TestEI trials with respect to the participants' PSE, on an individual basis (see Fig  3  for an example). First, we computed the PSE values per VideoEI condition, for each participant, using a logistic probability density function fitted to the proportion of "equal" answers as a function of the TestEIs. The PSE provided an estimate of the TestEI stimuli, for which participants would answer "equal" with a probability of 1. The PSE was an estimation of the memorized intensity of the last frame of the video. With regard to statistical analysis, we conducted Student's t tests on the individuals' memory bias (see section Results for the results).
The second aim of this behavioral data analysis was to identify the trials which corresponded to TestEIs perceived as congruent or incongruent with respect to the memorized final EI (PSE) of each participant. This data would be used for the subsequent ERP analysis. In fact, our objective was to compare ERPs in response to the TestEIs which were perceived as equal to the memorized final EI (congruent TestEI) with ERPs in response to TestEIs that were perceived as different from the memorized final EI (incongruent TestEI). Therefore, the proportion of "equal" responses very seldom reached 1 for the TestEIs that were close to the PSE2, and symmetrically, did not always reach 0 for the TestEIs that were furthest from the PSE2. For this reason, on the basis of individual data, we identified the TestEIs that led to a proportion of "equal" responses superior to 60%, as "PSE congruent TestEIs" (see Fig  3 : continuous line square).
Event-related potentials (ERPs). Data from EEG electrodes were grouped into four functional anatomical regions of interest (ROIs): frontal (F), centro-parietal (CP), temporal (T) and occipital (O), as illustrated in Fig 4. The occipital and temporal regions are known to be associated with the P100 and N170 ERPs, concomitant with visual (and especially face) processing  [44]. With regard to the centro-parietal and frontal regions, they are supposed to be sensitive to higher level processes such as context integration (reflected by the N400 ERP  [45] ). ERP post-onset TestEI data were analyzed using the averaged signal amplitude of three subsequent but non-overlapping time windows: [50-140 [ms for the P100 ERP; [140-330 [ms for the N170 ERP; and [330-700] ms for the LPP ERP. Indeed, instead of performing a point by point analysis in the time domain or analyzing ERP peaks, we performed a time-window analysis which consisted in dividing waveforms into a series of time windows (see  [46] ).
Behavioral data We found a significant negative memory bias (i. e. , a bias significantly different from 0) in the VideoEI50% condition (-8. 6%, SD = 17. 2%, ranging from -45. 4% to +18. 9%), t(24) = 2. 5, p =. 02, and in the VideoEI90% condition (-20. 4%, SD = 21. 3%, ranging from -75. 2% to +3. 7%), t(24) = 4. 7, p =. 00009. Memory displacement (bias values) varied with VideoEI conditions (50% and 90%), t(24) = 5. 1, p =. 00003. Variances of bias values among both VideoEI conditions were not significantly different (Fisher's test, F(1, 24) =. 65, Var VideoEI50% = 307. 2, Var VideoEI90% = 471. 8, p =. 15). This analysis allowed us to average the two VideoEI conditions in the ERP analysis of the mismatch effect. Further analysis of the behavioral data was conducted using reaction time data in order to test our hypothesis that incongruent stimuli are more easily detected than congruent stimuli (see the end of the Behavioral data subsection, in the Data analysis section).
Figs 5 and 6 illustrate the ERP signal data for the entire trial sequence, from the baseline to the post-onset TestEI. Statistical analyses were conducted only on ERPs for the TestEI stimuli. Three ERP components were evoked by TestEI onsets. We identified the first component as a P100 wave, peaking in the O ROI, around 100 ms after the stimulus onset. We only found a marginal mismatch effect on this wave (details are presented hereafter). The second component was a N170 wave, peaking in the O ROI around 230 ms after face onset. This wave presented a greater deflection in the right hemisphere than in the left. Lastly, we identified an LPP wave, peaking around 500 ms post TestEI onset. The amplitude of this wave was left lateralized in the T ROI. The LPP deflection was also reduced for incongruent trials in all ROIs.
Figs 5 and 6 6 Figs 5 and 6 illustrate the ERP signal data for the entire trial sequence, from the baseline to the post-onset TestEI. Statistical analyses were conducted only on ERPs for the TestEI stimuli. Three ERP components were evoked by TestEI onsets. We identified the first component as a P100 wave, peaking in the O ROI, around 100 ms after the stimulus onset. We only found a marginal mismatch effect on this wave (details are presented hereafter). The second component was a N170 wave, peaking in the O ROI around 230 ms after face onset. This wave presented a greater deflection in the right hemisphere than in the left. Lastly, we identified an LPP wave, peaking around 500 ms post TestEI onset. The amplitude of this wave was left lateralized in the T ROI. The LPP deflection was also reduced for incongruent trials in all ROIs.
WM connectivity and rate of change in WM connectivity over 24 months were normalized for preHD relative to control participants for each participant. Connectivity measures were then transformed to give atrophy and rate of atrophy measures. The resulting atrophy score was used in the cross-sectional analysis, while the rate of atrophy score was used in the longitudinal analysis. To compare regional WM loss in preHD with regional gene expression in the healthy brain, the 70 cortical ROIs  (23)  were matched to the closest AIBS ROI, and gene expression data were averaged across RNA probes corresponding to the same gene. ROIs with gene expression values. 2 SD above the mean or range were excluded this resulted in the inclusion of 20,737 genes across 68 cortical ROIs. Partial least squares (PLS) regression was used to investigate the relationship between regional gene expression and regional WM loss.
Imaging Cohort The cohort included preHD and control participants from the Track-On HD study  (27) , followed up at 3 time points over 24 months at four sites (London, United Kingdom; Leiden, the Netherlands; Paris, France; and Vancouver, British Columbia, Canada). Baseline participants included 72 preHD and 85 control participants. For the longitudinal analysis only preHD participants with diffusion data from all 3 time points were included (56 preHD and 65 control participants; see Supplemental Methods).
However, studies from postmortem manifest HD brains show that the transcription in the striatum is most affected, with limited abnormalities in the cortex  (31). Indeed, the transcription of only 25 genes in the cortex is abnormal in both human and animal studies, compared with 515 in the striatum  (30). Therefore, we mitigated for the likely transcription abnormalities in preHD by using only cortical gene expression data from the AIBS transcriptome atlas  (45). We mapped the anatomical location of ROIs to corresponding regions in the AIBS atlas. However, the resolution of these atlases are different, and thus we acknowledge that the correspondence may not be exact and may be a limitation of our methodology. There are other human brain transcriptome atlas such as Braineac  (46)  and the Human Brain Transcriptome project  (47) ; however, these atlases offer low resolution compared with the AIBS atlas, in which only a small number of cortical regions have been sampled, so the analysis carried out in this study could not be reproduced using Braineac or the Human Brain Transcriptome project atlas.
The utility of using information from the healthy human brain to inform us about the patterns and mechanisms of neurodegeneration has been demonstrated many times in neuroimaging. Functional connectivity and WM networks from healthy participants can predict atrophy in Alzheimer's disease, corticobasal syndrome, frontotemporal dementia, and Parkinson's disease  (41, (48) (49) (50). More recently, transcriptome data from the healthy brains of the AIBS atlas has been used to investigate the association between the expression of schizophrenia risk genes and WM disconnectivity (51). The regional expression of the tau gene MAPT from the AIBS atlas has also been linked to the selective vulnerability of highly connected brain regions in Parkinson's disease and progressive supranuclear palsy  (52).
ACKNOWLEDGMENTS AND DISCLOSURES This work was supported by  Wellcome Trust  Grant Nos. 091593/Z/10/Z  and  103437/Z/13/Z  (to GR and PMC) and the  National Institute for Health Research, University College London Hospitals, Biomedical Research Centre. The  Track-On HD study  is funded by the  CHDI Foundation , a notfor-profit organization dedicated to finding treatments for  Huntington's disease. National Hospital for Neurology and Neurosurgery (SJT) ,  Queen Square , London; and the  MRC Centre for Neuropsychiatric Genetics and Genomics (PH) ,  School of Medicine, Cardiff University, Cardiff ,  United Kingdom ;  Department of Electronic Engineering (AR) ,  NED University of Engineering and Technology, Karachi, Pakistan ;  APHP Department of Genetics (AD), University Hospital Pitié-Salpêtrière ; and  ICM (Brain and Spine Institute)   INSERM U1127  (AD),  CNRS UMR7225 ,  Sorbonne Universités -UPMC  Paris  VI UMR_S1127 , Paris, France;  Department of Neurology (RACR) ,  Leiden University Medical Centre, Leiden , the  Netherlands ; and the  Centre for Molecular Medicine and Therapeutics (BRL) ,  Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada.
GWAS summary statistics for the 42 disorders and phenotypes were centralized and underwent uniform quality control and processing(25)(83). We used European-only meta-analyses for each disorder to avoid potential bias arising from ancestry differences, generating new metaanalyses for those datasets where the original sample sets had diverse ancestries. Clinically relevant subtypes from three disorders (epilepsy, migraine, and ischemic stroke) were also included; in these cases, the subtype datasets are parts of the top-level dataset (Table  1 ). We have developed a heritability estimation method, linkage disequilibrium score regression (LDSC)  (24) , which was used to calculate heritability estimates and correlations, as well as to estimate their statistical significance from block jack-knife-based standard errors.
Heritability estimates and their error sources We observed a similar range of heritability estimates among the disorders and the behavioral-cognitive phenotypes (Fig. S1A-B  and Table  S1 , S2), roughly in line with previously reported estimates from smaller datasets (Table  S3 ). Three ischemic stroke subtypes (cardioembolic, large-vessel disease, and small-vessel disease) as well as the "agreeableness" personality measure from NEO Five-Factor Inventory  (27)  had insufficient evidence of additive heritability for robust analysis and thus were excluded from further analysis  (25). The only observed correlation between heritability estimates and factors relating to study makeup (Table  S4 ; Fig. S1C-F ) was a modest correlation between age of onset of the disorder and heritability, suggesting that early-onset brain disorders tend to be more heritable.
Even so, some of the pairs of phenotypes included here lack sufficient power for robust estimation of genetic correlations. Moreover, our analyses only examine the properties of common variant contributions and extending these analyses to include the effects of rare variants may further inform the extent of genetic overlap. For example, epilepsy and ASD show substantial overlap in genetic risk from de novo loss of functional mutations  (31) , in contrast to the limited common variant sharing observed in this study. This may suggest that the rare and common variant contributions to genetic overlap may behave differently and that incorporating the two variant classes into a single analysis may provide further insight into brain disorder pathogenesis. To address the possibility of methodological differences contributing to the differences in the estimates and although LDSC and REML have previously been shown to yield similar estimates from the same data  (24) , we performed our own comparison in Alzheimer's disease{ ADDIN EN.
CITE { ADDIN EN. CITE. DATA }} (selected based on data availability). In Alzheimer's disease, the previously published heritability estimate (0. 24 [SE 0. 03]) is significantly different from the estimate in the current study (0. 13 [SE 0. 02]). These differences may reflect implicit heterogeneity in a much larger case collection used in the current study (effective sample size 10,494 vs. 46, 669)  and the potential reasons listed above, but they could also be due to methodological variability (most of the previous estimated listed in Table  S3  are estimated with a different methodology). To evaluate this, we applied the same analytical process used in this paper to the summary statistics of the GERAD cohort (3,941 cases and 7,848 controls) from the Alzheimer's disease metaanalysis, where the previous heritability estimate was calculated.
The highest degree of genetic correlation among the neurological disorders was observed with focal epilepsy (average rg =0. 46, excluding the other epilepsy datasets), though none were significant, reflecting the relatively modest power of the current focal epilepsy meta-analysis (Fig. S2C ). However, the modest heritability and the broad pattern of sharing observed for focal epilepsy may be consistent with heterogeneity and potentially even diagnostic misclassification across a range of neurological conditions. In the cross-category correlation analysis, the observed pattern is consistent with limited sharing across the included neurological and psychiatric disorders (Fig. 3 ; average rg=0. 03). The only significant cross-category correlations were with migraine, suggesting it may share some of its genetic architecture with psychiatric disorders; migraine-ADHD (rg=0.
For smoking-related measures, the only significant genetic correlations were between never/ever smoked and MDD (rg=0. 33, p=3. 10 x 10 - 11 ) as well as ADHD (rg=0. 37, p=3. 15 x 10 -6 ). Among the additional phenotypes, the two examples of disorders with well-defined etiologies had different results. Crohn's disease, representing immunological pathophysiology, showed no correlation with any of the study phenotypes, while the phenotype representing vascular pathophysiology (coronary artery disease) showed significant correlation to MDD (rg=0. 19, p=8. 71 x 10 -5 ) as well as the two stroke-related phenotypes (rg=0. 69, p=2. 47 x 10 -6 to ischemic stroke and rg=0. 86, p=2. 26 x 10 -5 to early-onset stroke), suggesting shared genetic effects across these phenotypes. Significant correlations were also observed for BMI, which was positively correlated with ADHD and MDD, and negatively correlated with anorexia nervosa (as previously reported with a different dataset  (24) ) and schizophrenia.
Our enrichment analysis (Fig. S7 , Tables  S8-12 ) demonstrated significant heritability enrichments between central nervous system (CNS) and generalized epilepsy, MDD, TS, college attainment, intelligence, neuroticism, never/ever smoked); depressive symptoms and adrenal/pancreatic cells and tissues, as well as between hematopoetic cells (category which includes immune system cells) and multiple sclerosis (Figs. S7A  and S7B , Tables  S8  and S9 ). We replicate the reported (CNS) enrichment for schizophrenia, bipolar disorder, and years of education (Tables  S8, S9 ), and observe the reported enrichments for BMI (CNS), years of education (CNS), height (connective tissues and bone, cardiovascular system and other), and Crohn's disease (hematopoietic cells) from the same datasets (Fig. S7C, D )  (26).
Materials and Methods We collected GWAS meta-analysis summary statistics for 25 brain disorders and 17 other phenotypes from various consortia, and where necessary generated new, non-sex-stratified European-cohorts-only versions of the meta-analyses  (25). All datasets underwent uniform quality control (83). For each trait, using the linkage disequilibrium score (LDSC) framework  (24) , the total additive common SNP heritability present in the summary statistics (h 2 g) was estimated by regressing the association χ 2 statistic of a SNP against the total amount of common genetic variation tagged by that SNP, for all SNPs. Genetic correlations (rg; i. e. , the genome-wide average shared genetic risk) for pairs of phenotypes were estimated by regressing the product of Z-score for each phenotype and for each SNP, instead of the χ 2 statistic.
Significance was assessed by Bonferroni multiple testing correction via estimating the number of independent brain disorder phenotypes via matrix decomposition (83). Functional and partitioning analyses for the GWAS datasets were also performed using LDSC. Power analyses and simulation work to aid in interpretation of the results were conducted using genotype data from the UK Biobank Resource (83). Author Information Correspondence and requests for materials should be addressed to V. A. (verneri. anttila@gmail. com), A. C. (acorvin@tcd. ie), or B. M. N. (bneale@broadinstitute. org).
Acknowledgments We thank the members of the  Neale and Daly labs  for helpful discussions, and to acknowledge  R. Hoskins ,  J. Wessman  and  J. Martin  for their comments on the manuscript,  M. Whittall  for inspiration,  S. Knemeyer  for help with the summary figure,  C. Hammond  for organizational assistance, and the patients and participants of the respective consortia for their invaluable participation. Data on coronary artery disease has been contributed by CARDIoGRAMplusC4D investigators and have been downloaded from www. CARDIOGRAMPLUSC4D. ORG. matSpD is available at neurogenetics. qimrberghofer. edu. au/matSpD/. This research has been conducted using the  UK Biobank Resource  (application # 18597 ). Funding: This work was supported by grants  1R01MH10764901  and  5U01MH09443203  as well as the  Orion Farmos Research Foundation (VA)  and the  Fannie and John Hertz Foundation  (HKF); disorder-specific funding is detailed in the Supplementary Materials ("Study-specific acknowledgments").
nih. gov) gel analysis function or the ChemiDoc Imaging Systems Software (Bio-Rad). For immunodetection on cortical section, mouse embryonic brains were dissected and fixed in 4% paraformaldehyde (PFA) at 4 C for 2 hr in agitation, then washed in PBS 13 and dehydrated in 30% sucrose overnight at 4 C. Brains were then embedded in optimal cutting temperature compound (OCT) and stored at À80 C. Cryostat tissue sections (14 mm) were processed for immunodetection, as described for in vitro ICD, with the only difference that antigen-retrieval unmasking was performed at the beginning of the staining by boiling slides twice in sodium citrate 85 mM (pH 6). For RNA in situ hybridization (ISH), miRNA-21 antisense and control scramble probes (Exiqon) were used following the manufacturer's instruction.