dataesr/scientific-paragraphs-categorization huggingface dataset:
For each subject, functional images were time-corrected, realigned, unwarped using the magnitude and phase images, and coregistered to the anatomical scan. The anatomical scan was then normalized to the MNI space using the ICBM152 template brain and the resulting transformation matrix was applied to the functional images. Finally, the normalized functional images were spatially smoothed with an 8 mm Gaussian kernel. Statistical analyses of fMRI signals were performed using a conventional two-level random-effects approach with SPM8 (http:/www. fil. ion. ucl. ac. uk/spm). At the first level, the general linear model (GLM) included the 6 motion parameters from the realignment step, in order to covary out potential movement-related artifacts in the BOLD signal, as well as a high-pass filter to remove low-frequency artifacts from the data (cut-off: 64 s) and a run-specific intercept.
Acknowledgements This research was funded by grants from the  Agence Nationale pour la Recherche (ANR 'Brain Choice '  n°14-CE13-0006 ) and ( ANR-NSF CRCNS 'SOCIAL_POMDP' n°16-NEUC ) to JCD. This work was also performed within the framework of the LABEX  ANR-11-LABEX-0042  of  Université de Lyon , within the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  to JCD.
1 % formic acid. The elution gradient (A : B, v/v) was as follow: hold initial conditions 98:2 for 2 min, followed by a linear gradient from 98:2 to 0:100 over a 15 min period, hold at 0:100 for 3 min, return to initial conditions 98:2 over 2. 5 min and then hold these conditions for a further 2 min. A constant flow rate of 0. 300 mL min 1 was used; the injection volume was optimized at 5 µl and samples injection order was randomized. A divert valve was used and the eluent was directed to waste at 22. 45 min. Between each injection, the system was equilibrated for 1. 5 min. High resolution MS was acquired in positive and negative ionization mode, respectively (a distinct run for each modality). Full scan mass spectra (Full MS) were acquired and data dependent MS/MS (ddMS2) experiments were performed at the start and the end of each sequence, on several QCs, and acquired in 'Top5' data dependent mode.
MS conditions can be found in Supporting Information. Xcalibur 2. 2 software (Thermo Fisher Scientific, San Jose, CA, USA) was used for data acquisition. Prior to each sequence acquisition, the mobile phase was run for 1h30, followed by injection of 3 solvent blanks and 5 QC samples to allow column equilibration and conditioning. Column pressures and isotope labeled standards monitoring were performed at every analytical run for verifying the current state of the whole system, as a quality control procedure. A Hydrophilic Interaction Chromatography (HILIC) -HRMS method was further implemented to ascertain the biomarkers identified by RPLC HRMS. The chromatographic separation was achieved with an Acquity® BEH HILIC 1. 7µm 150x2. 1mm column together with the corresponding pre column BEH HILIC 1,7Nm VanGuard (Waters, Guyancourt, France).
Here, selectivity was provided by detection of metabolites in the matrix, based on compounds' exact masses. One way analysis of variance (ANOVA) was applied, not to test significance between day difference, but to correctly estimate variances and generate assay precision coefficients of variation (CV). The within day precision, between day and intermediate precision were calculated as computed in equation 1, equation 2 and equation 3, in Supporting Information. Serial dilutions of QC extracts (1:2 and 1:4) were used to assess the linearity of the response for isotope labeled standards. experiments were initiated for RIPC rat cohort), comparison with the MSMLS™ library for database match and metabolites identification. Inter batch normalization was applied to data from day1, day2 and day3 of the validation process; each sampling day corresponding to one analytical batch (sequence).
The normalization followed the procedure previously described, assuming that the measurement errors in a single batch are randomly distributed and that different batches can be compared and corrected using the average or median value of the QC samples in a batch  16. MSMLS™ is a collection of high quality small biochemical molecules that span a broad range of primary metabolism. From the MSMLS™ metabolites library, an in house database (internal metabolites library) was created. Mass spectra were compared to those available in mzcloud and HMDB 1 mass spectral databases. Biomarkers identification was therefore facilitated using our in house library of 500 accurately identified and well characterized molecules. Accurate m/z, Full MS, MS/MS spectra, RT and isotopic pattern of metabolites were acquired on the same platform using the current chromatographic and mass spectrometry conditions.
As can be depicted from Table  S  1, the method displays good extraction recoveries (comprised between 70 and 84%), within day and between day CV below 10%, and excellent instrumental precisions. The acceptance criteria for method repeatability were set at 15%, according to the FDA recommendations 7. Peak area and intensity repeatability for isotope labeled standards was set at 30%; it is common in non targeted metabolomics analysis by LC-MS to proceed with ions exhibiting CV below 30% in QCs, since it is considered that ions with higher CV would not be good candidates as biomarkers. Checking the linearity of metabolites in diluted QCs was evaluated during the validation study, as we were planning to use the criteria of linear trend to filter metabolites in the data matrix. We only performed ½ and ¼ dilutions as we considered that further dilutions might lead to exclusion of some potential metabolites of low abundance.
% The MSMLS™ library contains 619 unique metabolites, of a broad spectrum of key primary metabolites and intermediates including the following classes of compounds: carboxylic acids, amino acids, biogenic amines, polyamines, nucleotides, coenzymes, vitamins, mono and disaccharides, fatty acids, lipids, steroids, and hormones. Among the 619 metabolites of MSMLS™ library, we were able to reliably analyze 499 metabolites, in positive and negative ionization mode (Table  S  2). Urea, initially not contained in the MSMLS™ library was added to our database. Our in house database includes 500 metabolites of key pathways, for which full MS, MS/MS spectra, RT and isotopic pattern were acquired, under our current chromatographic and mass spectrometry conditions. Identification and confirmation of biomarkers from metabolomics investigations are essential for precision medicine.
e. , ornithine, glycine, and kynurenine. The use of the same sample cohort in the current research had several purposes: (1) to validate the developed workflow, (2) to test the in house database and (3) to see whether similar biomarkers can be obtained using different analytical platforms and technologies. $Instrument stability and the validity of the RIPC sequence before further processing was verified as described above. Chromatograms alignment illustrates system stability and chromatographic reproducibility as shown in Figure S 3. PCA was employed to provide an overview of the data structure and ensure clustering of the QCs (Figure S 4). As illustrated in Figure S 4, QC samples are clustered at the center of the PCA, guaranteeing that further discrimination observed would reflect metabolome modification upon RIPC and not analytical variability.
Ruxton, G. D. %. , -. 6, DOI:10. 1093/beheco/ark016. Research fundings from Université d'Angers, Angers University Hospital, Région Pays de la Loire and Angers Loire Métropole are gratefully acknowledged. The research was performed under the auspice of the PREMMi project in the MitoVasc Institute. We acknowledge fruitful discussions from the MitoLab team members. Further information on the experimental section, figures presenting system stability, PCA, OPLS DA models, Venn diagram of common metabolites between the in house library and the Biocrates kit, detailed process of biomarker selection, biomarker confirmation, permutation test for final model; and further tables illustrating method performances and list of in house database metabolites. Schematic diagram of the experimental design , % -: S plot for biomarkers selection.
The generated data matrix containing identified features was filtered out based on the following criteria: CV below 30%, accurate m/z measurement with delta ppm<5, isotopic pattern (masses and abundances of the isotopes, pass), expected retention time (RT, pass), linearity for dilutions 1, ½ and ¼, library search (pass) and MS/MS fragments (Top5 ddMS2 experiments) database matching. Estimation of RT drift was made from RT time of the metabolite in the internal library vs. experimental RT of metabolite during the sequence; X RT ± 10s was used. Metabolites identification required a manual checking of each feature in TraceFinder™, isomers selection by visualization of RT and MS2 fragmentations before exporting the generated data matrix for multivariate and univariate statistics. This was followed by an S plot which provided examination of the OPLS DA predictive component loading to facilitate model interpretation.
Selection of putative biomarkers from the S plot was combined to jack knife confidence intervals from a loading column plot, and looking at the Variable Importance in tool for assessing the reliability of OPLS models; the returned p value is indicative of the statistical significance of the investigated model. !RT and intensity CVs were computed in Microsoft Excel ® for each labeled isotope standard as for the ratio of abundance observed between C/T and T/C samples for each metabolite. ANOVA test was applied to the validation dataset in order to generate repeatability precision coefficients of variation. CV within QC samples and p value further served for metabolites selection in the RIPC cohort. Kolmogorov Smirnov and Shapiro Wilk tests were performed to test for normality, whilst Levene's test was used to assess the homogeneity of variance.
( 21 )%, % 5 : 215 Ruxton, G. D. %. , -. 6, DOI:10. 1093/beheco/ark016. Research fundings from Université d'Angers, Angers University Hospital, Région Pays de la Loire and Angers Loire Métropole are gratefully acknowledged. The research was performed under the auspice of the PREMMi project in the MitoVasc Institute. We acknowledge fruitful discussions from the MitoLab team members. Further information on the experimental section, figures presenting system stability, PCA, OPLS DA models, Venn diagram of common metabolites between the in house library and the Biocrates kit, detailed process of biomarker selection, biomarker confirmation, permutation test for final model; and further tables illustrating method performances and list of in house database metabolites. Schematic diagram of the experimental design , % -: S plot for biomarkers selection.
Unequal variance two tailed t test was then performed to assess the significance of biomarkers of interest in this experiment. The significance threshold was set at p < 0. 05. Metabolites of interest were confirmed by MS/MS spectral database, the Projection (VIP) values of those variables. Consequently, only metabolites with strong model contribution and highly statistical reliability were selected as discriminated metabolites (putative biomarkers). " & Data were exported to Microsoft Excel ® for inter batch normalization, then to Simca P+ v 14. 0 software (Umetrics, Umea, Sweden) for multivariate statistical analysis, and finally to SPSS Statistics v22 (IBM , Bois Colombes , France) for univariate statistics. Data were subjected to Log transformation and Pareto scaling prior to Principal Component Analysis (PCA) and Orthogonal Projection of Latent Structures Discriminant Analysis (OPLS DA).
⎜ ⎟ = = ⎛ ⎝ ⎞ ⎠ = ⎛ ⎝ ⎞ ⎠ = ⎛ ⎝ ⎞ ⎠ A D ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ = = ⎛ ⎝ ⎞ ⎠ = ⎛ ⎝ ⎞ ⎠ = ⎛ ⎝ ⎞ ⎠ A D cosh ρ a e ; B sinh λ ; C λ p a sinh ρ a e i i i i e a i p a i i i i ρ E i i i (7) E is the sample thermal effusivity, ρc the sample thermal capacity, e the sample thickness, λ i the polystyrene thermal conductivity, a i the polystyrene thermal diffusivity and e i the polystyrene thickness. Combining those five equations, the system leads to: = + θ(p) Φ (p) 0 D B D B 1 1 i i (9) The theoretical curve T mod (t) was calculated from relation (9) using the De Hoog algorithm  [30]. The Levenberg Marquert algorithm was integrated in the Matlab code to estimate the value of the parameter E and ρC p of the sample that minimize the sum of the quadratic error ∑ = - = ψ T t T t [ ( ) ( )] j N exp j mod j 0 2 between experimental (ΔT=T(t)-T a ) and theoretical curve.
Each JMF study site chose the optimal procedure for collection of two stool samples (about 8 g using provided kits) over a 4-day period depending on the proximity of the patient to the JMF study site, the practicality of patient travel to the study site and the availability of adequate cold storage of the stool sample (Table  S2  in Supplementary Material). After collection, the stool samples were kept refrigerated at a temperature of 2-8°C and shipped to the local or regional Global Polio Laboratory Network (GPLN) laboratory along with supporting documentation. The JMF study site provided an electronic copy of the CRF by encrypted means (using a secure FTP server at TFGH) to Centers for Disease Control and Prevention (CDC) for secure data management. Stool specimens were sent to the GPLN laboratory for isolation of poliovirus and NPEVs.
6% for Sabin type 2 in the VP1 coding region  (6). If testing results were positive for poliovirus or NPEV, the JMF site physician assured that family members were appropriately vaccinated against polio and educated about principles of good hygiene to prevent fecal-oral transmission. The physician also followed-up any patient found to be excreting poliovirus periodically (every 1-2 months) until the cessation of excretion and the procedures for testing and reporting above were followed each time. Patients were considered as having cleared an infection if two consecutive stool specimens in 2 months were negative according to the WHO-recommended surveillance standard of poliomyelitis. 2 he TFGH study manager ensured coordination of specimen shipment along with the JMF scientific program director, and laboratory results were communicated to CDC both by the GPLN laboratory through the GPLN global coordinator and by the TFGH study manager and entered into the secure database at CDC.
Genetic analyses Details of the UK Biobank genotyping, imputation, and quality control procedures is available elsewhere  9. Genetic association was conducted using Plink 2. 0 10 (https://www. cog-genomics. org/plink/2. 0/). We included sex, year of birth, genotyping batch and the first 40 genetic principal components as covariates. We excluded SNPs that failed Hardy-Weinberg Equilibrium (P < 1x10 -6 ), had a minor allele frequency < 0. 01, and had a per-SNP genotyping rate < 95%, and imputation INFO < 0. 1. We further excluded SNPs not in the Haplotype Reference Consortium (http://www. haplotype-reference-consortium. org/)  11. We excluded individuals who were ancestry outliers, and had per-individual genotyping rate < 90%. To increase the effective sample size, we leveraged the high genetic correlation between the two phenotypes to conduct MTAG  12  (or multi-trait analysis of GWAS) (https://github.
com/omeed-maghzian/mtag/). MTAG is an extension of the standard inverse variance weighted meta-analysis that considers the genetic correlation between phenotypes to estimate phenotype-specific effects. Our phenotypes are excellent for MTAG given both the high genetic correlation between the two phenotypes and the similar mean chi-square. We do not expect the estimates to be biased. Clumping of the independent SNPs was conducted using Plink 10 , using an r 2 of 0. 6. Winner's curse correction was conducted using FIQT, which is an FDR based inverse quantile transformation  13. Genetic correlations were conducted using LD score regression (LDSR)  14, 15  (https://github. com/bulik/ldsc/wiki). We used north-west European population LD scores, did not constrain the intercept and used the pre-MTAG summary statistics to obtain unbiased genetic correlations.
We ran correlations with psychiatric and psychological phenotypes (Supplementary Table  1 ). Summary scores were obtained from the PGC (http://www. med. unc. edu/pgc/results-and-downloads), the SSGAC (https://www. thessgac. org/), and the CTG (https://ctg. cncr. nl/software/summary_statistics). In addition, summary statistics for systemizing, self-reported empathy  16  , and cognitive empathy  17  were obtained from 23andMe, Inc. Summary statistics for the iPsych autism replication dataset were obtained from the iPsych autism team. Given the correlation between the phenotypes, we identified significant genetic correlations using a Benjamini-Hochberg FDR < 0. 05, taking into account all the tests conducted for both the phenotypes combined. Heritability analyses were conducted using both LDSR (all participants)  14  and GCTA-GREML  18  (for computational efficiency, this was conducted on one-fifth of the total participants, N = 26,000).
To keep the methods as identical as possible, GCTA heritability was calculated after including sex, age, batch and the first 40 genetic principal components as covariates. Bivariate genetic correlations between the two phenotypes were conducted using both LDSR and GCTA. Functional annotation was performed on the MTAG-GWAS datasets. We used FUMA  19  (http://fuma. ctglab. nl/) to identify eQTLs and chromatin interactions for the genome-wide significant results. eQTLS were identified using data from BRAINEAC and GTEx 20 brain tissues. Chromatin interactions were identified using Hi-C data from the dorsolateral prefrontal cortex, hippocampus, and neural progenitor cells. Significant mapping was identified at a Benjamini-Hochberg FDR < 0. 05. Gene-based association analyses was conducted using MAGMA  21  within FUMA, and significant genes were identified using a Bonferroni-corrected threshold < 0.
05 for each phenotype, as provided within FUMA. We conducted partitioned heritability  22  analyses using the MTAG-GWAS dataset using the baseline categories and additional categories: genes that are loss-of-function intolerant  23  , and genes with brain specific annotation. Significant enrichments were identified using a Benjamini-Hochberg FDR < 0. 05 applied to both the phenotypes combined. To identify phenotype relevant tissues based on gene expression, we conducted partitioned heritability analyses applied to tissue-specific genes  24. We focussed on GTEx consortium based gene expression from 13 brain regions (Cortex, Anterior cingulate cortex, frontal cortex, cerebellum, cerebellar hemisphere, putamen, nucleus accumbens, caudate, substantia nigra, hippocampus, spinal cord, amygdala, and hypothalamus).
Prosocial behaviour and CCC were reverse coded to indicate difficulties in prosocial behaviour and communication. Polygenic score regression was conducted using a negative binomial model (MASS package in R: https://cran. r-project. org/web/packages/MASS/MASS. pdf) as this provided the best fit. Sex and the first two genetic principal components were included as covariates. Incremental variance explained due to polygenic scores was calculated using Nagelkerke's pseudo-R  2. Further details of the ALSPAC cohort are provided in the Supplementary Note.
We replicated the negative genetic correlations with autism using a separate autism GWAS dataset (autism-iPsych). Underscoring the contribution to the two phenotypes to most psychiatric conditions, we also identified significant negative genetic correlation for both phenotypes and cross-disorder psychiatric GWAS. For cognitive and psychological phenotypes, we identified negative genetic correlations for both phenotypes and educational attainment, cognitive aptitude, depressive symptoms and neuroticism. In contrast, we identified significant positive genetic correlations between both phenotypes and conscientiousness, empathy, extraversion, and subjective wellbeing. Genetic correlations are provided in Figure  1  and Supplementary Table  1. Insert Figure  1  here.
13 for family relationship satisfaction and 1. 14 for friendship satisfaction) and the high genetic correlation, we conducted a modified inverse variance weighted meta-analysis (MTAG). This increased the effective sample size to 164,112 for family relationship satisfaction and over 158,166 for friendship satisfaction. There was a high genetic correlation between the pre-MTAG and post-MTAG GWAS datasets (Family: 0. 98±0. 005, P < 2. 2x10 -16 ; Friendship: r g = 0. 98±0. 004; P < 2. 2x10  -16  ). We identified two significant loci associated with both the phenotypes (Supplementary Table  4 ). On Chromosome 3, the lead SNP was rs1483617 for both phenotypes (P = 1. 6x10 -8 for family relationship satisfaction and 4. 55x10 -8 for friendship satisfaction). In addition, on chromosome 6, we identified another locus with rs2189373 as the lead SNP for both phenotypes (P = 1.
Discussion We present the results of a large-scale genome-wide association study of social relationship satisfaction in the UK Biobank measured using family relationship satisfaction and friendship satisfaction. Despite the modest phenotypic correlations, there was a significant and high genetic correlation between the two phenotypes, suggesting a similar genetic architecture between the two phenotypes. We first investigated if the two phenotypes were genetically correlated with psychiatric conditions. As predicted, most if not all psychiatric conditions had a significant negative correlation for the two phenotypes. We replicated the correlation with autism using a second dataset. We observed significant negative genetic correlation between the two phenotypes and a large cross-condition psychiatric GWAS  38.
Acknowledgements We thank  Patrick Turley  and  Beate St. Pourcain  for their helpful discussion on the methods. We thank  Anders Børglum ,  Jakob Grove , and the  iPSYCH  team for generously sharing the data with us. We thank  23andMe  for giving us access to summary statistics for this study, and the research volunteers at  23andMe  for making this research possible. The 23andMe Research Team are:  Michelle Agee ,  Babak Alipanahi ,  Adam Auton ,  Robert K. Bell ,  Katarzyna Bryc ,  Sarah L. Elson ,  Pierre Fontanillas ,  Nicholas A. Furlotte ,  David A. Hinds ,  Karen E. Huber ,  Aaron Kleinman ,  Nadia K. Litterman ,  Jennifer C. McCreight ,  Matthew H. McIntyre ,  Joanna L. Mountain ,  Elizabeth S. Noblin ,  Carrie A. M. Northover ,  Steven J. Pitts ,  J. Fah Sathirapongsasuti ,  Olga V.
Sazonova ,  Janie F. Shelton ,  Suyash Shringarpure ,  Chao Tian ,  Joyce Y. Tung ,  Vladimir Vacic , and  Catherine H. Wilson. We are grateful to all the families who took part in  ALSPAC , the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. VW is funded by  St. John's College, Cambridge , and the  Cambridge Commonwealth Trust. This study was funded by grants from the  Medical Research Council , the  Wellcome Trust , the  Autism Research Trust , the  Templeton World Charity Foundation , the  Institut Pasteur , the  CNRS  and the  University Paris Diderot. The research was funded and supported by the  National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East of England at Cambridgeshire and Peterborough NHS Foundation Trust.