dataesr/scientific-paragraphs-categorization huggingface dataset:
Data preprocessing Genotypes for all datasets were converted to the forward strand, and converted into coordinates based on the hg19 reference sequence using UCSC liftOver  [10]. The genotypes were then merged and pre-processed according to platform. Determination of cryptic relatedness (pairwise proportion IBD, PI-HAT > 0. 2), sample missingness (> 0. 05), genotype missingness (> 0. 05), Hardy-Weinberg equilibrium p-value (< 10 -5 ), and sex-matching was performed in PLINK v1. 90b3. 28 [11]  and used to quality-control (QC) samples using standard parameters  [12]. Ancestry was predicted by multidimensional scaling based on raw Hamming distances, implemented in PLINK. Only samples of presumed European ancestry that clustered with known Europeans from the HapMap3 cohort  [13]  were included.
QQ and Manhattan plots were constructed using the R package "qqman"  [19]. Forest plots were constructed using the R package "metafor"  [20]. The genomic inflation factor λ was computed with PLINK. Correction of the genomic inflation factor to an equivalent sample size of 1000 cases and 1000 controls was performed as previously described  [21]. To control for the extended haplotype on chr17q21 and to identify independent association signals, we performed association as before, but including the haplotype (tagged by the SNP rs1560310)  [22]  as a covariate.
Credible set of causal variants at PSP GWAS loci A credible set (potential causal variants) was identified at each of total of seven genome-wide significant loci identified in this study using the CAusal Variants Identification in Associated Regions (CAVIAR) software package  [28]. Because of the extended linkage disequilibrium patterns in the chromosome 17q21. 31 haplotype region, causal variants were not identified at this associated locus. Within each of the selected loci, the SNP with the minimum joint association p-value was chosen as the index SNP, and variants with p-value < 10 -5 and in LD (r 2 > 0. 6) with the index SNP were input into CAVIAR. The CAVIAR-identified credible set contains potential causal variants (with a confidence level of 95% under the statistical model) that could explain the association at each locus.
9 × 10 -12 ) in the haplotype-regressed and H1/H1 only datasets, respectively (Additional file 2: Figure  S5 ). These SNPs did not appear to be in strong linkage disequilibrium with a previously reported SNP association, rs242557  [8]  (r 2 = 0. 008 and 0. 007 in the 1000 Genomes Project data -EUR super-population, respectively) that was filtered from this dataset in variant QC; however, they were highly correlated with each other (r 2 = 0. 996). Additionally, both variants and rs242557 are within the first intron of the MAPT gene. To further understand how variation at each of the loci contributes to disease risk, we assessed the functional consequences of significant SNPs. We first identified a set of potential causal SNPs using the CAVIAR method, which identifies a "credible set" of SNPs that encompasses those likely to be causal  [28].
To supplement the mapping information from Hi-C, we also identified the functional consequences of GWAS hits using the TWAS method to predict genes that may be affected by risk alleles  [27]. TWAS estimates gene expression values using paired reference transcriptome/ genotyping datasets (e. g. , for expression quantitative trait loci -eQTL studies) and genotype information from summary statistics, and predicts differential expression between cases and controls. Using reference data from the GTEx Consortium, TWAS predicted the effect of gene expression from the risk haplotypes in multiple tissues. At a threshold of P < 1 × 10 -5 , we identified a number of genes that were called as differentially expressed (Additional file 1: Table  S6 ). As expected due to the length and lack of recombination in the region, most of these genes  (17)  clustered around the chromosome 17 haplotype.
Notably, MAPT (within the associated 17q21. 31 locus) was among the genes predicted to be differentially expressed, as well as STX6 (within the associated 1q24 locus), SP1 (within the suggestive 12q13. 13 locus), SKIV2L (within 6p21. 33, nearby the associated 6p21. 1 locus), and RPSA (within the associated 3p22. 1 locus). Other genes that were pinpointed outside of association regions were CEP57 (in 11q21) and RPS6KL1 (in 14q24. 3). The strong neuropathological overlap of PSP with other tauopathies suggests that genetic overlap may exist. Using the LDSC software  [32] , we assessed genetic overlap of PSP with other neurodegenerative diseases, including AD, behavioral variant FTD (bvFTD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), by using GWAS summary statistics. As controls, we included summary statistics from GWAS for heritable, non-neurodegenerative diseases of brain (schizophrenia and bipolar disorder), a quantitative trait (height), and a non-brain disease (type 2 diabetes) (for further details, refer to the Supplementary Methods).
At 3p22. 1, the gene closest to the GWAS lead SNP was reported as MOBP. This locus has previously been implicated in differential expression of the SLC25A38/ appoptosin gene, which may regulate tau cleavage  [42]. Using Hi-C, we have identified chromatin interactions with MYRIP and EIF1B that could also explain this association. Similarly, at 1q25. 3, the gene closest to the GWAS lead SNP was STX6; by Hi-C, we have also identified XPR1 as a possible candidate gene. Interestingly, our group has previously demonstrated XPR1 mutations in primary familial brain calcification  [43] , though any mechanistic overlap with PSP is unclear. Analysis of eQTL datasets (in GTEx) suggests that RPSA at 3p22. 1 and SKIV2L near 6p21. 1 may also be the causal genes but the tissue-relevant datasets were relatively underpowered.
Funding This work was funded by grants from the  Tau Consortium  (D. H. G. and G. C. ); the  National Institutes of Health ,  F31 NS084556 (J. A. C. ) UG3 NS104095  and  P30 NS062691  from the  National Institute of Neurological Disorders and Stroke (Informatics Center for Neurogenetics and Neurogenomics  to G. C. ); and the  Fu-Hsing  and  Jyu-Yuan Chen  family. Z. C. was supported by funding from the  NIHR Academic Clinical Fellowship. Three academic institutions ( Institute of Psychiatry, Psychology and Neuroscience, King's College London; Assistance Publique-Hôpitaux de Paris and University of Ulm ) were sponsors of the NNIPPS study in each country, and jointly own the data. The BBBIPPS study was supported by the  French Health Ministry ,  Programme Hospitalier de Recherche Clinique  ( AOM04035 ).
The  Assistance Publique -Hôpitaux de Paris (France)  was the sponsor of the study. The protocol and amendments were reviewed and approved by the  Comité de Protection des Personnes of Pitié-Salpêtrière Hospital (France). The NNIPPS genotyping and analysis was supported under the aegis of JPND -(www. jpnd. eu,United Kingdom),  Medical Research Council  ( MR/L501529/1 ;  MR/R024804/1 ) and  Economic and Social Research Council  ( ES/L008238/1 ). A. A. C. receives salary support from the  National Institute for Health Research (NIHR) Dementia Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The work leading up to this publication was funded by the  European Community 's  Health Seventh Framework Programme  ( FP7/2007-2013 ; grant agreement number  259867 ) and  Horizon 2020 Programme  ( H2020-PHC-2014 -two-stage; grant agreement number  633413 ).
Data Acquisition Images were acquired using a 3 T Siemens TRIO 32-channel TIM system (Siemens, Erlangen, Germany) with a standard 12-channel head coil for signal reception. The MRI protocol included the following acquisitions: sagittal 3-dimensional T1-weighted images (repetition time = 2200 milliseconds, echo time = 2. 9 milliseconds, flip angle = 10 , 1 mm isovoxel size); 3-dimensional T2-weighted images (repetition time = 3000 milliseconds, echo time = 300 milliseconds, flip angle = 120 , 1 mm isovoxel size); dMRI (48 axial spin echo echo planar slices, 60 directions with a b-value of 1000 s/ mm 2 and 3 b0 images; 1. 7 mm isovoxel size, anteroposterior phase-encoding direction), blood oxygen level dependent echo planar images for rsfMRI (axial plane, repetition time = 2900 milliseconds, echo time = 30 milliseconds; flip angle = 90 , 200 volumes; 3 mm isovoxel size, antero-posterior phase-encoding direction).
Diffusion MRI Analysis Data preprocessing for dMRI was performed by using standard pipeline (distortion correction with field MAP and motion and eddy-current with EDDY from FSL software; www. fsl. fmrib. ox. ac. uk/fsl/fslwiki/eddy). The tensor metrics included fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) and were computed by using dtifit tools from FSL. The dMRI analysis was performed in the native space. Thus we denormalized the group mean Ch1-2 and Ch3-4 masks to each individual's native space by using SPM. Within the denormalized Ch1-2 and Ch3-4 masks, we calculated the mean of each diffusion metric (FA, MD, AD, and RD) by averaging individual values of each voxels contained within the masks. We then studied basal forebrain structural connectivity using tractography.
For Ch1-2 connections, we defined the fornix in each participant space. We performed probabilistic tractography based on the constrained spherical deconvolution with mrtrix software (http://www. mrtrix. org/). We generated the fornix tracks by seeding from Ch1-2 to hippocampus (target). We converted these tracks into a density map and kept voxels containing more than two tracks to define the fornix mask. We further excluded voxels belonging to the CSF by using the CSF map obtained by segmenting the anatomical scan with spm_segment. There was no between-group difference in the volumes of the fornix masks (HV = 1519 AE 623 mm 3 , PD = 1324 AE 709 mm 3 , t-test P =. 26). To reconstruct the tracts, we used exclusion masks that corresponded to the right hemisphere for tracks in the left hemisphere (eg, exclusion mask of the right hemisphere to reconstruct tracts between the left Ch1-2 and the left hippocampus and tracts between the left Ch3-4 and the left cortical areas) and vice versa.
Resting-State fMRI Analysis Data preprocessing for fMRI was performed by using the toolboxes DPARSF  34  and fMRI Data Analysis Toolkit REST,  35  which were based on Statistical Parametric Mapping (SPM8). Preprocessing included slice timing: the functional images were interpolated in time to correct phase advance during volume acquisition and realigned to the first image of each session. Nuisance covariates were regressed out individually (head movements, white matter, and cerebrospinal fluid signals). To evaluate head movements, we used the 6 translations/rotations parameters. We calculated the frame displacement as defined in ref. 36. There were no significant differences in frame displacement between the 2 groups (mean AE standard deviation, HV = 0. 25 AE 0. 12; PD = 0. 22 AE 0. 11; analysis of variance P =.
Funding agencies: This work was supported by grants from  ANR Nucleipark, DHOS-Inserm, France Parkinson, Ecole des Neuro-Sciences de Paris, Fondation pour la Recherche Médicale , and the  Investissements d'Avenir ,  IAIHU-06  ( Paris Institute of Neurosciences-IHU ),  ANR-11-INBS-0006 ,  Fondation d'Entreprise EDF , and the  Fondation Thérèse and René Planiol pour l'étude du Cerveau. We thank  Mr Alain Mallart  for his unrestricted support for research on Parkinson's disease. Relevant conflicts of interests/financial disclosures: F. G. received grants from the  PHC Utique 34899PE. C. G. received grants from the  Fondation pour la Recherche Médicale  (doctoral and postdoctoral grants). M. M. received a grant from the  Fondation pour la Recherche Médicale. M. V. received grants from  Fondation d'Entreprise EDF  and the  Fondation Thérèse and René Planiol pour l'étude du Cerveau  and unrestricted support for research on Parkinson's disease from  Mr Alain Mallart.
S. L. received grants from  Agence Nationale de la Recherche  ( ANRMNP 2009 , Nucleipark),  DHOS-Inserm  (2010, Nucleipark),  France Parkinson  ( 2008 ),  Ecole Neuroscience de Paris, 'Investissements d'avenir'  (Grant  ANR-10-IAIHU-06  and  ANR-11-INBS-0006 ) during the conduct of the study. R. V. , L. Y. -C. , M. S. , and N. P. have nothing to report. Acknowledgments: We thank  E. C. Hirsch ,  C. Duyckaerts , and  Y. Agid  for providing the histological material.
The excess of TPP was removed by washing several times with double-distilled water, and then the CS-HA-ArgD membranes were frozen and freeze dried again for 12 h. Using a similar procedure, a membrane containing N ω -nitro-L-arginine methyl ester (NO 2 -Arg-OMe) hydrochloride (CS-HA-ArgD), used as a reference membrane, has been developed. The presence of the arginine derivatives with thiazolidine-4-one scaffold into the polymer matrix was proved by FT-IR spectroscopy. The IR spectra were recorded using a ABB-MB 3000 FT-IR MIRacle TM Single Bounce ATR-crystal ZnSe Fourier transform spectrometer, Quebec, QC, Canada, in the range of 4000-500 cm -1 , at a resolution of 4 cm -1 using a total of 16 scans. The spectra were interpreted using Horizon program MB™ FT-IR.
Statistical Analysis The data are expressed as mean ± standard deviation (SD). The statistical software package StatView was used for the analysis of biological results. The experimental data were analyzed by repeated measures by 3 (groups) × 3 (time sample points), using Analysis of Variance (ANOVA) and Fisher's post hoc test to compare the burn surface area between groups at Day 5, 10 and 15. Statistical significance was set to p value ≤ 0. 05.
In cross-sectional analyses, reverse causation cannot be excluded. Participants with visual impairment might have changed their behaviours, especially diet and physical activity due to vision loss and people who are less health conscious may have a lower presenting visual acuity due to lower utilization of eye care services, or other behavioural or environmental factors that are associated with inadequate diet, decreased exercise or smoking. CONSTANCES is a prospective cohort study, so we expect to have prospective data in the future. Another limitation of this study is related to missing data and selection bias. We therefore used multiple imputations in order to assess the potential selection bias due to missing data. In the imputed dataset, the associations between the number of unhealthy behaviours and visual impairment remained in the same range.
In addition, participants in the CONSTANCES cohort might be more health conscious and have a healthier lifestyle and better ocular health than non-participants. This may have affected the frequency of visual impairment and frequency of unhealthy behaviours. However, data collection was performed in the same way in all participants. We can assume that the error was not differential and was unlikely to have biased the estimation of the associations of visual impairment with unhealthy behaviours. In our study, physical activity was assessed using a score which is less reproducible than using metabolic equivalent of task and does not allow us to compare physical activity levels with other studies. Nevertheless, physical activity was estimated using physical activity at work and outside work which is a better evaluation of total physical activity than leisure/sport activity only.
Our results suggest a linear effect of diet on visual function and using a dichotomous variable was questionable. To assess whether associations between the number of unhealthy behaviours and visual impairment may differ using a different cut-off for diet quality, we used the median value of the MedDiet score as an alternate cut-off and the results remained unchanged. This study's main strengths include a randomly selected and large nationwide sample providing high statistical power. All data were recorded using standardized examination and questionnaires. Compared to survey studies  33, 48  , our study has the advantage of having presenting visual acuity measured according to the international standards rather than self-reported visual impairment. Another major strength of our study is the exploration of the combined effect of unhealthy behaviours.
Methods Study population. The CONSTANCES cohort is a prospective cohort study of general adult population randomly selected among the French National Health Insurance Fund database  46  (http://www. constances. fr/ index_EN. php). A total of 200 000 participants are expected to be included over a 6-year period (2012-2018). At inclusion, the randomly selected subjects are invited to attend one of the 21 selected health screening centers 46 for a comprehensive health examination. During the examination, weight, height, blood pressure and vision were measured by trained nurses and laboratory tests were performed according to standardized operational procedures, included in an extensive quality control program  46, 56. Health events, including diabetes, hypercholesterolemia and hypertension, were recorded by a physician during the medical examination.
Sociodemographic (age, sex, education and monthly income), health events and behaviours (diet, physical activity, smoking and alcohol consumption) were collected using a self-administrated questionnaire completed at home. All the participants included in the CONSTANCES cohort have signed an informed consent form. This research follows the tenets of the Declaration of Helsinki and was approved by the National Data Protection Authority (Commission Nationale Informatique et Libertés) and the Institutional Review Board of the National Institute for Medical Research and the local Committee for Persons Protection (Comité de Protection des Personnes). The study reported here is based on available data in 2017 collected from February 2012 to January 2016 and age range for participants was 18 to 73 years.
Visual impairment. Presenting distance visual acuity (using current refractive correction, if any) was measured in each eye using the Snellen scale according to a standard operating procedure, by trained nurses in each health screening center at inclusion. Visual impairment was defined as a presenting visual acuity <20/40 in the better eye, as in other studies  57. Unhealthy behaviours. Diet assessment. Data on food consumption were collected at enrolment with a validated self-administered 40-items food frequency questionnaire (FFQ). Subjects were asked to report how often they consumed each food or beverage item. Consumption was classified into 6 categories from "never or almost never" to "one serving per day or more, if more than once a day indicate the number of serving per day". To assess diet quality, we used the MedDiet score developed by Panagiotakos et al.
Smoking status was defined as follow: never smoker, moderate smokers (<20 PY) and heavy smokers (≥20 PY)  59. We considered heavy smoking to be an unhealthy behaviour. Alcohol consumption: Data on alcohol consumption were collected at enrolment with a validated self-administered questionnaire. Alcohol consumption was defined as never/light (0-3 glass/week (0-30 g/week) for men and 0-2 (0-20 g/week) for women), moderate (4-21 (40-210 g/week) glass/week for men and 3-14 (30-140 g/week) for women) and heavy drinkers (>21 glass/week (>210 g/week) for men and >14 (>140 g/week) for women)  60. We considered heavy drinking to be an unhealthy behaviour. Covariates. Sociodemographic measures: age, sex, education and monthly income/household were collected with a self-reported questionnaire. Body mass index (BMI, kg/m²) was measured at the examination.
Participants. Among participants included between February 2012 and January 2016, 55 230 had available data for visual acuity and questionnaires. Subjects who did not have available data for smoking (5 497), physical activity (1 011) and MedDiet score/alcohol (5 865) were excluded from our analyses (Fig. 3 ). We also excluded 2 483 subjects with extreme dietary consumptions (>99 th percentile of the distribution for at least one item of the MedDiet score). We thus included 38 903 (70. 4%) participants with available data for visual acuity, MedDiet score, smoking, physical activity, sociodemographic and medical data. Statistical methods. Each characteristic was compared between subjects with and without visual impairment using mixed logistic regressions adjusted for age and sex with random intercept for health screening center.
Acknowledgements We thank the  UMS 11 Inserm-Versailles Saint Quentin en Yvelines University  "Cohortes épidémiologiques en population" who designed and is in charge of the  CONSTANCES Cohort Study. They also thank the "Caisse nationale d'assurance maladie des travailleurs salaries" (CNAMTS)  and the  "Centres d'examens de santé" of the French Social Security  which are collecting a large part of the data, as well as ClinSearch, Asqualab and Eurocell in charge of the data quality control. The  CONSTANCES Cohort Study  was funded by the  Caisse nationale d'assurance maladie des travailleurs salaries (CNAMTS) , the  Ministry of Health , the  Council of the Ile de France Region , and by the  Cohorts TGIR IReSP-ISP INSERM (Ministère de la santé et des sports, Ministère délégué à la recherche, Institut national de la santé et de la recherche médicale, Institut national du cancer et Caisse nationale de solidarité pour l'autonomie).
CONSTANCES benefits from a grant from the  French Commissariat général à l'investissement  (contrat  ANR-11-INBS-0002 ). CONSTANCES also receives funding from  MSD ,  AstraZeneca  and  Lundbeck  managed by  INSERM-Transfert. The PréVis (Prévalence et facteurs associés aux déficiences visuelles) project is supported by  Iresp (Institut de recherche en santé publique)  grant number  AAP-2014-01.
The software allows to create a perfusion cartography with a color code representing the slope of the time-to-peak, pixel by pixel. Superimposing perfusion cartography onto real-time laparoscopic images then creates the so-called fluorescence-based enhanced reality (FLER) (Figure  1 ). The accuracy of the FLER analysis has been demonstrated in a series of experimental studies  (17, (38) (39) (40) (41)  and it is currently confirmed in the clinical setting (NCT02626091). Wada et al. have used a similar algorithm to obtain a virtual perfusion cartography and evaluate the fluorescence signal during rectal surgery. In their series, including 52 low and 13 high anterior resections, the authors used perfusion cartography but without a direct overlay on realtime images, in a side-by-side observation.
Patients and study design A convenient sample was recruited from January 20, 2015 to April 20, 2015, from the Medicine Campus, University of Lorraine, Nancy (north-eastern France), by posting an advertisement on campus and by e-mailing students and staff. Participants had to be 18 years old, working or studying at the Medicine Campus, able to read and understand French, and willing to participate in the study. The study protocol was approved by the Legal representative of the French data protection authority (Commission Nationale Informatique et Libert es) of the University of Lorraine, France. All participants were asked to read and sign a consent form. A ratio of 5 subjects per item was used to determine the number of participants to include. 20 Because the GPAQ contained 16 items, a minimum number of 80 participants was required.