dataesr/scientific-paragraphs-categorization huggingface dataset:
, and it contained codes recorded between 1/1/2002 and 12/31/2012 (note the same start date but a later end date in RA2). The two datasets likely have overlapping patients, and we wanted to know if we can link the data from these two studies. The 26,681 patients in the RA1 dataset together had 7,868 unique diagnosis codes from 2002 through 2007. Over the same time span as the RA1 dataset (2002 through 2007), 5,707 of the 6,394 patients in the RA2 dataset had at least one diagnosis recorded. These patients have 4,981 unique diagnosis codes through 2007, of which 4,936 also appear in the RA1 dataset. All patients in the RA2 dataset had at least one diagnosis when considering the full date range, from 2002 through 2012, with 6,086 unique diagnosis codes, of which 5,593 appear in the RA1 dataset.
Anonymized data usage notes The R package ludic also includes an anonymized version of the binarized diagnosis code data from the RA1 and RA2 datasets, for both 6-year and 11-year time span. In accordance with the IRB, the ICD-9 diagnosis codes have also been masked and randomly reordered, replaced by meaningless names. Finally, the silver standard matching pairs are also provided to allow the benchmarking of methods for probabilistic record linkage using diagnosis codes.
The effect of various numerical and physical parameters is investigated numerically on a two-dimensional flow configuration, confirming the reduced number of iterations over state-of-the-art steady-state and time-stepping-based preconditioners. A parallel implementation of the steady Navier-Stokes and eigenvalue solvers, developed in the FreeFem++ language, suitably interfaced with the PETSc/SLEPc libraries, is described and made openly available to tackle three-dimensional flow configurations. Its application on a small-scale three-dimensional problem shows the good performance of this iterative approach over a direct LU factorization strategy, in regards of memory and computational time. On a large-scale three-dimensional problem with 75 million unknowns, a 80% parallel efficiency on 256 up to 2,048 processes is obtained.
The full code is made available at https: //github. com/prj-/moulin2019al. The paper is organized as follows. The governing equations required to carry out the linear stability analysis of incompressible flows are introduced in section 2. The Newton method used to solve the steady nonlinear equations and the eigensolver based on the shiftand-invert strategy are also described. The preconditioning technique and the modified augmented Lagrangian preconditioner are introduced in section 3. The parallel implementation is detailed in section 4. Numerical results are given in section 5. First we examine, on a two-dimensional problem, the effect of various numerical and physical parameters on the performance of the mAL preconditioner for solving the complex shifted linearized Navier-Stokes problem.
Parallel implementation with FreeFem++ and its interface to PETSc/SLEPc For realistic three-dimensional geometries, the approach derived in the previous sections requires the solution of nonlinear systems and generalized eigenproblems of large dimensions. Thus, high-performance computing becomes necessary. The goal of this section is to show how this is done using a finite element domain specific language, FreeFem++  [34, 39] , interfaced with distributed linear algebra backends, PETSc  [6]  and SLEPc  [36]. A thorough introduction of these libraries may be found in their respective manuals 1 , 2 , 3. Our implementation is openly available at https://github. com/prj-/moulin2019al and the rest of this section follows the available source code.
The average number of mAL-preconditioned FGCRO-DR iterations (inner iterations) per linear solve (outer iteration) is presented in the fourth column whereas the last three columns contain the average number of innermost iterations for each velocity field, per application of P mAL. It was not possible to have the code run on 256 processes due to memory requirements significantly higher than for the nonlinear solver. Indeed, we switch from a real-valued to a complex-valued problem and the additional operators M and L p are assembled explicitly. In table 4, the scalability of our implementation is shown, using the run with 512 processes as the reference, and going up to 2,048 processes. The parallel efficiency of this approach is approximately the same as for the nonlinear solver, though on a narrower range of process counts, remaining above 82%.
Then, the mAL preconditioner was tested against some other widely used steady-state (PCD, SIMPLE) and time-stepping-based (Stokes) preconditioners, all of them used in a sequential version. The mAL preconditioner was shown to require lower numbers of GMRES iterations and to be faster than all its competitors. In order to perform large-scale three-dimensional stability analysis computations, a parallel implementation of the mAL preconditioner was presented and is made available online: https://github. com/prj-/moulin2019al. The FreeFem++ finite element language was used as a discretization kernel whereas PETSc and SLEPc were used as distributed linear algebra backends. First, a comparison with the parallel direct solver MUMPS was presented on a three-dimensional bluff-body flow configuration, using a coarse enough mesh to make the LU factorization possible.
Gross primary productivity (GPP) and terrestrial ecosystem respiration (TER) estimates from the Joint UK Land Environment Simulator (JULES) and Data Assimilation Linked Ecosystem Carbon (DALEC) models are used as prior flux constraints. JULES is a state-of-the-art physically based, process-driven model that estimates the energy, water and carbon fluxes at the land-atmosphere boundary and uses a variety of observation-derived products describing physical parameters as inputs  (Best et al. , 2011; Clark et al. , 2011). DALEC, on the other hand, is a simplified terrestrial C-cycle model which is calibrated independently at each location retrieving both process parameters and initial conditions using the carbon data model framework (CARDAMOM) modeldata fusion system. CARDAMOM ingests satellite-based remotely sensed estimates of the state of terrestrial ecosystems  (Bloom and Williams, 2015; Bloom et al.
g. l. inlet (see  Vardag et al. , 2014 , for a full site description). All of the UK sites are tall-tower stations (with inlets ranging from 42 to 248 m a. g. l. ), designed to measure elevated GHG mole fractions as air is transported over the surface in the UK and Europe. Continuous CO 2 measurements are made at all stations using cavity ring-down spectrometers (CRDS: Picarro G2301 or G2401). CRDS data are corrected for daily linear instrumental drift using standard gases and for instrumental nonlinearity using calibration gases, spanning a range of above and below ambient mole fractions, on a monthly basis  (Stanley et al. , 2018)   Centre for CO 2 at Empa, linking them to the World Meteorological Organisation (WMO) X2007 scale  (Stanley et al. , 2018; Stavert et al. , 2018). At sites with multiple inlets, measurements are taken for the same length of time at each inlet, each hour.
This means that measurements at each height at Bilsdale and Tacolneston (with three inlets) are taken continuously for roughly 20 min every hour, and at Heathfield and Ridge Hill (with two inlets) measurements are taken continuously for roughly 30 min at each inlet every hour. For the purposes of the inverse modelling carried out in this study, the continuous CRDS data are used from the highest inlets and averaged to a 2 h time resolution. Further information about the instruments, measurement protocol and uncertainty estimates can be found in  Stanley et al. (2018)  and  Stavert et al. (2018).
S1  in the Supplement). Since most CO 2 flux to the atmosphere occurs at the surface, we record the instances where the particles are in the lowest 40m of the atmosphere and assume that this represents the sensitivity of observed mole fractions to surface fluxes in the inversion domain. The domain used to calculate atmospheric transport covers most of Europe, the east coast of North and Central America, and North Africa (10. 729-79. 057 • N and 97. 9 • W-39. 38 • E). The spatial resolution of the meteorological analysis dataset used to drive the model, from the Met Office Unified Model  (Cullen, 1993) , was 0. 233 • by 0. 352 • (roughly 25 km by 25 km over the UK). In many previous inverse modelling studies using LPDMs (e. g. Manning et al. , 2011; Thompson and Stohl, 2014; Steinkamp et al.
Data selection and model uncertainty LPDMs are known to perform poorly under certain meteorological conditions. In particular, it is often assumed that model-data mismatch should be smallest during periods when the boundary layer is relatively well mixed. A common approach is to only include daytime data in the inversion (e. g. Meesters et al. , 2012; Steinkamp et al. , 2017; Kountouris et al. , 2018a)  or separate morning and afternoon averages (e. g. Matross et al. , 2006). To make use of as much highfrequency-measurement information as possible, we use a filter based on two metrics to remove times of high atmospheric stability and/or stagnant conditions. The first metric is based on calculating the ratio of the NAME footprint magnitude in the 25 grid boxes in the immediate vicinity of the measurement station to the total for all of the grid boxes in the domain.
Boundary conditions The footprints from the LPDM only take into consideration the influence on the observations of sources intercepted within the model domain. Therefore, an estimate of the mole fraction at the boundary must be made and incorporated into the simulated mole fractions. To estimate spatial and temporal gradients in these boundary conditions we use the global Eulerian Model for OZone And Related chemical Tracers (MOZART,  Emmons et al. , 2010). The model was run using GEOS-5 meteorology  (Rienecker et al. , 2011)  and global biospheric fluxes from the NASA-CASA biosphere model  (Potter, 1999) , global ocean fluxes from  Takahashi et al. (2009)  and global anthropogenic fluxes from the Emission Database for Global Atmospheric Research (EDGAR, EC-JRC/PBL, 2011). When particles leave the NAME model domain, we record the time and location of the exit point.
Prior information In this work, we used model analyses to provide prior information about biospheric fluxes. Two models (DALEC and JULES) were used to assess how much influence the choice of biospheric prior has on the outcome of the inversion. The NAME model was used to simulate the contribution of anthropogenic and oceanic fluxes to the data, and this contribution was removed from the observations prior to the inversion. The fluxes used for this calculation are described below. The spatial and temporal resolution of the prior information and fixed fluxes are summarised in Table  2  and emissions from each source over the UK are shown in Fig. 2. In a synthetic data study in which biospheric CO 2 was inferred,  Tolk et al. (2011)  found that separately solving for positive fluxes (autotrophic and heterotrophic respiration combined, TER) and negative fluxes (GPP) in atmospheric inversions provided a better fit to the atmospheric mole fraction data than inversions that scaled NEE only.
(2016)  and DALEC in  Smallman et al. (2017). DALEC estimates carbon fluxes at a weekly time step and 25 km × 25 km spatial resolution. The weekly time step information was downscaled to 2-hourly intervals, assuming that each day repeated throughout each week. Downscaling of GPP fluxes was assumed to be distributed through the daylight period based on intensity of incoming shortwave radiation. Respiration fluxes were downscaled across the full diurnal cycle assuming exponential temperature sensitivity (code for downscaling is available from the authors on request). Observation-derived information used in the current analysis comes from satellite-based remotely sensed time series of leaf area index (LAI) (MODIS; MOD15A2 LAI-8 day version 5, http://lpdaac. usgs. gov/, last access: 2 April 2019), a prior estimate of above-ground biomass  (Thurner et al.
, 2014)  and a prior estimate of soil organic matter  (Hiederer and Köchy, 2012). Meteorological drivers were taken from the ERA-Interim reanalysis. Ecosystem disturbance due to forest clearances was imposed using Global Forest Watch information  (Hansen et al. , 2013). CARDAMOM-DALEC differs from typical land surface models in using these data to generate probabilistic model parameterisations and initial conditions estimates for each pixel, with no a priori assumptions about plant functional types, nor steady states.
JULES biospheric fluxes JULES is a process-driven land surface model that estimates the energy, water and carbon fluxes at the landatmosphere boundary  (Best et al. , 2011; Clark et al. , 2011). We used JULES version 4. 6 driven with the WATCH Forcing Data methodology applied to ERA-Interim reanalysis data (WFDEI) meteorology  (Weedon et al. , 2014) , which were interpolated to a 0. 25   , 2017). We prescribed the land cover for nine surface types and the vegetation phenology for five plant functional types (PFTs) using MODIS monthly LAI climatology and fixed MODIS land cover and canopy height data  (Berry, et al. , 2009). The soil thermal and hydrology physics are described using the JULES implementation of the Brooks and Corey formulation  (Marthews et al. , 2015)  with the soil properties sourced from the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009).
Anthropogenic fluxes Estimates of fluxes due to anthropogenic activity within the UK were obtained from the National Atmospheric Emissions Inventory (NAEI, http://naei. beis. gov. uk, last access: 2 April 2019). The NAEI provides a yearly estimate of emissions, which we have disaggregated into a 2-hourly product, based on temporal patterns in activity data, varying on diurnal, weekly and seasonal scales. The inventory emissions were disaggregated according to the UNECE/CORINAIR Selected Nomenclature for sources of Air Pollution (SNAP) sectors (UNECE/EMEP, 2001). Figure  2d  shows the seasonal and diurnal cycle for this inventory, summed over the UK, for 2014. Outside the UK, anthropogenic emissions come from EDGAR v4. 2 FT2010 inventory data for 2010 (EC-JRC/PBL, 2011). This is a fixed 2-D map that is used throughout the inversion period.
Acknowledgements. Emily D. White  acknowledges the support of a  NERC GW4+ Doctoral Training Partnership studentship  from the  Natural Environment Research Council  ( NE/L002434/1 ) and  NERC  grant  NE/M014851/1. Observations from BSD and HFD were supported under  NERC  grant  NE/K002236/1. DECC network data are maintained by grant  TRN1028/06/2015  from the  UK Department of Business, Energy and Industrial Strategy. We are grateful to  Gerard Spain ,  Duncan Brown ,  Stephen Humphrey  and  Andy MacDonald ,  Carole Helfter  and  Neil Mullinger  for their work maintaining the measurements at the  Mace Head, Tacolneston and Bilsdale measurement sites. Anita L. Ganesan  was funded under a  UK Natural Environment Research Council (NERC)   Independent Research Fellowship  ( NE/L010992/1 ). T.
Luke Smallman  and  Mathew Williams  were supported by  NERC  GHG programme GREENHOUSE, grant  NE/K002619/1 , and this study was funded as part of  NERC 's support of the  National Centre for Earth Observation. Paul Palmer  gratefully acknowledges funding from  NERC  under grant reference  NE/K002449/1  and gratefully acknowledges his  Royal Society Wolfson Research Merit Award.
These and all other assumptions were proven valid on all our datasets by the Belief and Decision Networks tool provided by AIspace  [38] ): Following the initialization of a new profile according to the PGM and any given information about the deaf woman, the recommendations for her will include contents corresponding to the categories for which her presumed level of knowledge falls under an experts-defined threshold (denoted by µ). Thereafter, her profile will be updated following her interactions with the platform, in order to maintain more precise levels of knowledge, one per category of content (remember Section 3. 2). At that point, the recommended contents will also include materials tagged with the categories about which the user has the lowest knowledge levels, regardless of the thresholds.
A total of 1,030,430 SNPs (including InDels) were genotyped or imputed. SNPs were excluded if: they failed the Hardy-Weinberg equilibrium test at P < 10 -20 ; had a genotype rate of <90%; they failed the parentoffspring transmission test using trio data in the larger 23andMe research participant database; or if allele frequencies were significantly different from the European 1000 Genomes reference data (χ 2 test, P < 10 -20 ). Phasing was conducted using Beagle (version 3. 3. 1)  85 in batches of 8000-9000 individuals. This was followed by imputation against all-ethnicity 1000 Genomes haplotypes (excluding monomorphic and singleton sites) using Minimac2  86. Genetic association analyses were restricted to SNPs with a minor-allele frequency > 1%. After quality control, 9,955,952 SNPs (imputed and genotyped) were included in the GWAS.
Further details of this formula are provided in the Supplementary Note. Genotyping, imputation, and quality control and genetic association in the ALSPAC. The SCDC 61 scores were calculated from children of the 90 s (ALSPAC cohort)  71  , in children aged 8. In total, SCDC scores were available on N = 7,825 children. From this, we removed individuals for whom complete SCDC scores were not available. After excluding related individuals and individuals with no genetic data, data was available on a total of N = 5,421 unrelated individuals. Participants were genotyped using the Illumina® HumanHap550 quad chip by Sample Logistics and Genotyping Facilities at the Wellcome Sanger Institute and LabCorp (Laboratory Corportation of America) using support from 23andMe. Individuals were excluded based on gender mismatches, high missingness (>3%), and disproportionate heterozygosity.
We restricted subsequent analyses to individuals of European descent (CEU), which were identified by multi-dimensional scaling analysis and compared with Hapmap II (release 22). Individuals were also removed if cryptic relatedness, assessed using identity by descent, was >0. 1. Genotyped SNPs were filtered out if they had >5% missingness, violated Hardy-Weinberg equilibrium (P < 1 × 10 -6 ), and had a minor-allele frequency < 1%, resulting in a total of 526,688 genotyped SNPs. Haplotypes were estimated using data from mothers and children using ShapeIT (v2. r644)  88. Imputation was performed using Impute2 V2. 2. 2  89  against the 1000 genomes reference panel (Phase 1, Version 3). Imputed SNPs were excluded from all further analyses if they had a minor-allele frequency < 1% and info < 0. 8.
After quality control, there were 8,282,911 genotyped and imputed SNPs that were included in subsequent analyses. Dosage data from BGEN files were converted using hard-calls, with calls with uncertainty > 0. 1 treated as missing data. Post-imputation, we excluded SNPs that deviated from Hardy-Weinberg equilibrium (P < 1 × 10 -6 ), with minor-allele frequency < 0. 01 and missing call rates > 2%. We further excluded individuals with genotype missing rates > 5%. The SCDC score was not normally distributed so we log-transformed the scores and ran regression analyses using the first two ancestry principal components and sex as the covariates using Plink 2. 0 (ref. 90 ). The log-transformed SCDC scores (henceforth, SCDC scores) had a modest but significant h 2 SNP as quantified using LDSR h 2 SNP ¼ 0:12 ± 0:05 À Á.
Two-tailed P-values were calculated and reported as significant if P < 0. 05. For the primary GWAS (non-stratified analyses), we conducted functional annotation using FUMA  93. We restricted our analyses to the non-stratified analyses due to the high genetic correlation between the sexes and the low statistical power of the sex-stratified GWAS. We conducted gene-based association analyses using MAGMA  94  within FUMA and report significant genes after using a stringent Bonferroni corrected P < 0. 05. In addition, we conducted enrichment for tissue specific expression and pathway analyses within FUMA. For the significant SNPs, we investigated enrichment for eQTLs using brain tissues in the BRAINEAC and GTEx 95 database within FUMA. We further conducted partitioned heritability for tissue-specific active chromatin marks and baseline functional categories using extended methods in LDSR  96.
To understand the correlation between systemising and various phenotypes that have been genetically correlated with autism, we used GWAS data from 15 phenotypes including autism. 10 of these phenotypes (cognitive aptitude  59  , educational attainment  58  , tiredness  113  , neuroticism  103  , subjective wellbeing  103  , schizophrenia  114  , major depression  102  , depressive symptoms  103  , ADHD  63  , and chronotype  115  ), have been previously reported to be significantly genetically correlated with autism out of 234 phenotypes tested using LDHub 62 (P < 2. 1 × 10 -4 ). We excluded college degree from this list, as previous work has identified near perfect genetic correlation between educational attainment and college degree  58. In addition, we included data from friendship satisfaction  55  , family relationship satisfaction  55  , systemising, and self-reported empathy  46  , all of which are also significantly genetically correlated with autism with P < 2.
Further, there is a high genetic correlation between cognitive aptitude and educational attainment. In addition to GWIS, to validate the findings, we conducted GSEM  123  , a complementary but independent method. GSEM uses the genetic correlations and covariances calculated using LDSR after accounting for sample overlap. Polygenic scores in the SSC, AGRE, EU-AIMS LEAP, and Paris cohorts. We generated polygenic scores for SQ-R (mean weighted score of all the alleles that contribute to higher systemising) in 2221 probands from the SSC (Discovery dataset). We downloaded genotype data from the SSC from SFARI base (https:// www. sfari. org/resource/sfari-base/). Individuals were genotyped on three different platforms: Illumina Omni2. 5, Illumina 1Mv3, or Illumina 1Mv1. Informed consent or assent was obtained from all participants.
In addition, the research team obtained ethical approval from the Cambridge Human Biology Research Ethics Committee to access and analyse the de-identified data from the SSC. We conducted stringent quality control and imputation separately for each platform. The full pipeline is available here: https://github. com/autism-research-centre/ SSC_liftover_imputation. Briefly, individuals were excluded if they had: a genotyping rate < 95%, excessive or low heterozygosity (less or more than 3 SD from the mean), mismatched reported and genetic sex, and families with Mendelian errors > 5%. We further removed SNPs that significantly deviated from Hardy-Weinberg equilibrium (P < 1 × 10 -6 ), had Mendelian errors in >10% of the families, and SNPs that were not genotyped in >10% of the families. We then conducted multidimensional scaling using the HapMap3 phase 3 population using the unrelated individuals CEU and TSI populations as representatives of the European population.
github. io/ PRSice/) for the SQ-R using the non-stratified GWAS data. We calculated the mean polygenic score for each of the 2221 probands in the SSC, after clumping SNPs using an R 2 threshold of 0. 1. Prior to generating polygenic scores, we confirmed that the probands were not related to each other using identity by descent PI-HAT > 0. 15 as a relatedness cut-off. We used a P-value threshold of 1 as previous research on educational attainment, subjective wellbeing and social relationship satisfaction, all suggest that the maximum variance explained is at a threshold of 1 (refs. 58, 103 ). This is expected for highly polygenic traits where many SNPs incrementally contribute to the variance explained  124. Polygenic scoring was done using standardised scores on two different phenotypes as the dependent variable (RBS-R and the social and communication domain of the ADOS-G).